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repo_name stringlengths 6 92	path stringlengths 7 220	copies stringclasses 78 values	size stringlengths 2 9	content stringlengths 15 1.05M ⌀	license stringclasses 15 values
liganega/Gongsu-DataSci	previous/notes2017/W10/GongSu22_Statistics_Population_Variance.ipynb	1	16214	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from __future__ import print_function, division" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 자료 안내: 여기서 다루는 내용은 아래 사이트의 내용을 참고하여 생성되었음.\n", "\n", "https://github.com/rouseguy/intro2stats" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 모집단 분산 점추정" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 안내사항\n", "\n", "지난 시간에 다룬 21장 내용을 활용하고자 한다.\n", "따라서 아래와 같이 21장 내용을 모듈로 담고 있는 파이썬 파일을 임포트 해야 한다.\n", "\n", "주의: GongSu21_Statistics_Averages.py 파일이 동일한 디렉토리에 있어야 한다." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from GongSu21_Statistics_Averages import " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 주요 내용" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " 모집단과 표본\n", "* 모집단 분산의 점추정" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 주요 예제" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "21장에서 다룬 미국의 51개 주에서 거래되는 담배(식물)의 도매가격 데이터를 보다 상세히 분석한다. \n", "\n", "특히, 캘리포니아 주를 예제로 하여 주(State)별로 담배(식물) 도매가 전체에 대한 거래가의 평균과 분산을 점추정(point estimation)하는 방법을 다룬다." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 주요 모듈\n", "\n", "* pandas: 통계분석 전용 모듈\n", " * numpy 모듈을 바탕으로 하여 통계분석에 특화된 모듈임.\n", " * 마이크로소프트의 엑셀처럼 작동하는 기능을 지원함\n", "* datetime: 날짜와 시간을 적절하게 표시하도록 도와주는 기능을 지원하는 모듈\n", "* scipy: 수치계산, 공업수학 등을 지원하는 모듈" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "주의: 언급된 모듈은 이미 GongSu21_Statistics_Averages.py 모듈에서 임포트 되었음." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 오늘 사용할 데이터\n", "\n", "* 주별 담배(식물) 도매가격 및 판매일자: Weed_Price.csv\n", "\n", "아래 그림은 미국의 주별 담배(식물) 판매 데이터를 담은 Weed_Price.csv 파일를 엑셀로 읽었을 때의 일부를 보여준다.\n", "\n", "<p>\n", "<table cellspacing=\"20\">\n", "<tr>\n", "<td>\n", "<img src=\"img/weed_price.png\" style=\"width:600\">\n", "</td>\n", "</tr>\n", "</table>\n", "</p>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "주의: 언급된 파일이 GongSu21_Statistics_Averages 모듈에서 prices_pd 라는 변수에 저장되었음. \n", "또한 주(State)별, 거래날짜별(date) 기준으로 이미 정렬되어 있음. \n", "\n", "따라서 아래에서 볼 수 있듯이 예를 들어, prices_pd의 첫 다섯 줄의 내용은 알파벳순으로 가장 빠른 이름을 가진 알라바마(Alabama) 주에서 거래된 데이터 중에서 가정 먼저 거래된 5개의 거래내용을 담고 있다." ] }, { "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>State</th>\n", " <th>HighQ</th>\n", " <th>HighQN</th>\n", " <th>MedQ</th>\n", " <th>MedQN</th>\n", " <th>LowQ</th>\n", " <th>LowQN</th>\n", " <th>date</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>20094</th>\n", " <td>Alabama</td>\n", " <td>339.65</td>\n", " <td>1033</td>\n", " <td>198.04</td>\n", " <td>926</td>\n", " <td>147.15</td>\n", " <td>122</td>\n", " <td>2013-12-27</td>\n", " </tr>\n", " <tr>\n", " <th>20859</th>\n", " <td>Alabama</td>\n", " <td>339.65</td>\n", " <td>1033</td>\n", " <td>198.04</td>\n", " <td>926</td>\n", " <td>147.15</td>\n", " <td>122</td>\n", " <td>2013-12-28</td>\n", " </tr>\n", " <tr>\n", " <th>21573</th>\n", " <td>Alabama</td>\n", " <td>339.75</td>\n", " <td>1036</td>\n", " <td>198.26</td>\n", " <td>929</td>\n", " <td>149.49</td>\n", " <td>123</td>\n", " <td>2013-12-29</td>\n", " </tr>\n", " <tr>\n", " <th>22287</th>\n", " <td>Alabama</td>\n", " <td>339.75</td>\n", " <td>1036</td>\n", " <td>198.81</td>\n", " <td>930</td>\n", " <td>149.49</td>\n", " <td>123</td>\n", " <td>2013-12-30</td>\n", " </tr>\n", " <tr>\n", " <th>22797</th>\n", " <td>Alabama</td>\n", " <td>339.42</td>\n", " <td>1040</td>\n", " <td>198.68</td>\n", " <td>932</td>\n", " <td>149.49</td>\n", " <td>123</td>\n", " <td>2013-12-31</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " State HighQ HighQN MedQ MedQN LowQ LowQN date\n", "20094 Alabama 339.65 1033 198.04 926 147.15 122 2013-12-27\n", "20859 Alabama 339.65 1033 198.04 926 147.15 122 2013-12-28\n", "21573 Alabama 339.75 1036 198.26 929 149.49 123 2013-12-29\n", "22287 Alabama 339.75 1036 198.81 930 149.49 123 2013-12-30\n", "22797 Alabama 339.42 1040 198.68 932 149.49 123 2013-12-31" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "prices_pd.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 모집단과 표본\n", "\n", "Weed_Price.csv 파일에 담긴 담배(식물) 도매가는 미국에서 거래된 모든 도매가 정보가 아니라 소수의 거래 정보만을 담고 있다.\n", "이와같이 조사대상의 소수만을 모아 둔 데이터를 표본(Sample)이라 부른다.\n", "반면에 미국에서 거래되는 모든 담배(식물) 도매가 전체는 현재 조사하고자 하는 대상들의 모집단이라 부른다.\n", "\n", "여기서는 Weed_Price.csv 파일에 담긴 표본을 이용하여 모집단에 대한 분산과, 주별로 이루어진 거래 사이의 상관관계를 확인하고자 한다.\n", "\n", "참고: 모집단과 표본, 점추정에 대한 보다 자세한 설명은 아래의 두 파일을 참조한다. \n", "* GongSu22_Statistics_Sampling_a.pdf\n", "* GongSu22_Statistics_Sampling_b.pdf" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 모집단 평균값과 분산의 점추정" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* 모집단의 평균값 점추정: 표본의 평균값을 그대로 이용한다.\n", "$$\\hat{x} = \\bar x = \\frac{\\Sigma_{i=1}^{n} x_i}{n}$$\n", " * $\\hat x\\,\\,$는 모집단 평균값의 점추정 기호\n", " * $\\bar x$는 표본 데이터들의 평균값 기호\n", "\n", "\n", "* 모집단의 분산 점추정: 표본 데이터를 이용해서 모집단의 분산을 추정할 수 있다.\n", "$$\\hat\\sigma\\,\\, {}^2 = s^2 = \\frac{\\Sigma_{i = 1}^{n}(x_i - \\bar x)^2}{n-1}$$\n", " * $\\hat \\sigma\\,\\, {}^2$는 모집단 분산의 점추정 기호\n", " \n", "\n", "주의: \n", "* $s^2$을 계산할 때 $n$ 대신에 $n-1$로 나누는 것에 주의한다.\n", "* 모집단의 분산은 일반적으로 표본의 분산보다 좀 더 크기 때문이다." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 캘리포니아 주에서 거래된 HighQ 담배(식물)의 도매가 전체에 대한 분산의 점추정" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "먼저 prices_pd에 포함된 데이터 중에서 캘리포니아 주에서 거래된 상품(HighQ) 담배(식물)의 가격들에 대한 연산이 필요하다.\n", "즉, 아래 공식의 분자를 계산하기 위한 준비과정이다.\n", "\n", "$$s^2 = \\frac{\\Sigma_{i = 1}^{n}(x_i - \\bar x)^2}{n-1}$$\n", "\n", "주의: 캘리포니아 주에서 거래된 상품(HighQ) 담배(식물)의 도매가의 평균값은 `ca_mean`으로 이미 계산되었다." ] }, { "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>State</th>\n", " <th>HighQ</th>\n", " <th>HighQN</th>\n", " <th>MedQ</th>\n", " <th>MedQN</th>\n", " <th>LowQ</th>\n", " <th>LowQN</th>\n", " <th>date</th>\n", " <th>HighQ_dev</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>20098</th>\n", " <td>California</td>\n", " <td>248.77</td>\n", " <td>12021</td>\n", " <td>193.44</td>\n", " <td>12724</td>\n", " <td>193.88</td>\n", " <td>770</td>\n", " <td>2013-12-27</td>\n", " <td>11.518389</td>\n", " </tr>\n", " <tr>\n", " <th>20863</th>\n", " <td>California</td>\n", " <td>248.74</td>\n", " <td>12025</td>\n", " <td>193.44</td>\n", " <td>12728</td>\n", " <td>193.88</td>\n", " <td>770</td>\n", " <td>2013-12-28</td>\n", " <td>11.315657</td>\n", " </tr>\n", " <tr>\n", " <th>21577</th>\n", " <td>California</td>\n", " <td>248.76</td>\n", " <td>12047</td>\n", " <td>193.55</td>\n", " <td>12760</td>\n", " <td>193.60</td>\n", " <td>772</td>\n", " <td>2013-12-29</td>\n", " <td>11.450612</td>\n", " </tr>\n", " <tr>\n", " <th>22291</th>\n", " <td>California</td>\n", " <td>248.82</td>\n", " <td>12065</td>\n", " <td>193.54</td>\n", " <td>12779</td>\n", " <td>193.80</td>\n", " <td>773</td>\n", " <td>2013-12-30</td>\n", " <td>11.860277</td>\n", " </tr>\n", " <tr>\n", " <th>22801</th>\n", " <td>California</td>\n", " <td>248.76</td>\n", " <td>12082</td>\n", " <td>193.54</td>\n", " <td>12792</td>\n", " <td>193.80</td>\n", " <td>773</td>\n", " <td>2013-12-31</td>\n", " <td>11.450612</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " State HighQ HighQN MedQ MedQN LowQ LowQN date \\\n", "20098 California 248.77 12021 193.44 12724 193.88 770 2013-12-27 \n", "20863 California 248.74 12025 193.44 12728 193.88 770 2013-12-28 \n", "21577 California 248.76 12047 193.55 12760 193.60 772 2013-12-29 \n", "22291 California 248.82 12065 193.54 12779 193.80 773 2013-12-30 \n", "22801 California 248.76 12082 193.54 12792 193.80 773 2013-12-31 \n", "\n", " HighQ_dev \n", "20098 11.518389 \n", "20863 11.315657 \n", "21577 11.450612 \n", "22291 11.860277 \n", "22801 11.450612 " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "california_pd['HighQ_dev'] = (california_pd['HighQ'] - ca_mean) 2\n", "california_pd.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "이제 캘리포니아 주 거래된 상품(HighQ) 담배(식물)의 거래가 전체 모집단에 대한 분산 점추정을 계산할 수 있다.\n", "\n", "주의: 표본의 크기는 `ca_count`이다." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2.982686287981227" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ca_HighQ_variance = california_pd.HighQ_dev.sum() / (ca_count - 1)\n", "ca_HighQ_variance" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "주의:** \n", "* DataFrame 자료형의 연산은 넘파이 어레이의 연산처럼 항목별로 실행된다.\n", "* sum 메소드의 활용을 기억한다." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 표준편차의 점추정\n", "\n", "모집단 분산의 점추정으로 얻은 값에다가 루트를 씌우면 된다." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1.7270455373212448" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 캘리포니아에서 거래된 상품(HighQ) 담배(식물) 도매가의 표준편차\n", "ca_HighQ_SD = np.sqrt(ca_HighQ_variance)\n", "ca_HighQ_SD" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.8" } }, "nbformat": 4, "nbformat_minor": 1 }	gpl-3.0
agile-geoscience/gio	docs/userguide_src/Read_Surfer_grids.ipynb	1	286344	{ "cells": [ { "cell_type": "markdown", "id": "47ca9a21", "metadata": {}, "source": [ "# Read surfer grids\n", "\n", "There are 3 varieties:\n", "\n", "- Surfer 6 binary\n", "- Surfer 6 ASCII\n", "- Surfer 7 binary\n", "\n", "In theory we can read all of these, but I don't have a Surfer 6 binary file to test on." ] }, { "cell_type": "markdown", "id": "d56d5022", "metadata": {}, "source": [ "## A small binary file\n", "\n", "[From the docs.](https://grapherhelp.goldensoftware.com/subsys/ascii_grid_file_format.htm)" ] }, { "cell_type": "code", "execution_count": 8, "id": "c1f41fea", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "DSAA\r\n", "10 10 \r\n", "0.0 9.0\r\n", "0.0 7.0\r\n", "25.00 97.19\r\n", "91.03 77.21 60.55 46.67 52.73 64.05 41.19 54.99 44.30 25.00\r\n", "96.04 81.10 62.38 48.74 57.50 63.27 48.67 60.81 51.78 33.63\r\n", "92.10 85.05 65.09 53.01 64.44 65.64 52.53 66.54 59.29 41.33\r\n", "94.04 85.63 65.56 55.32 73.18 70.88 55.35 76.27 67.20 45.78\r\n", "97.19 82.00 64.21 61.97 82.99 80.34 58.55 86.28 75.02 48.75\r\n" ] } ], "source": [ "!head ../data/Surfer/surfer-6-ascii-tiny.grd" ] }, { "cell_type": "code", "execution_count": 9, "id": "d4081cb5", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n", "<defs>\n", "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n", "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n", "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n", "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n", "</symbol>\n", "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n", "<path d=\"M28.681 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white-space: pre-wrap;\n", " word-break: break-all;\n", "}\n", "\n", ".xr-icon-database,\n", ".xr-icon-file-text2 {\n", " display: inline-block;\n", " vertical-align: middle;\n", " width: 1em;\n", " height: 1.5em !important;\n", " stroke-width: 0;\n", " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", "</style><pre class='xr-text-repr-fallback'><xarray.DataArray (y: 10, x: 10)>\n", "array([[70. , 54.19, 62.27, 74.51, 55.95, 55.42, 71.21, 74.63, 63.14,\n", " 44.99],\n", " [74.77, 66.02, 70.29, 75.16, 60.56, 65.56, 85.07, 89.81, 74.53,\n", " 51.69],\n", " [80.88, 75.56, 74.35, 72.47, 66.93, 75.49, 86.39, 92.1 , 84.41,\n", " 55. ],\n", " [86.31, 77.58, 67.71, 68.5 , 73.37, 74.84, 65.35, 95.55, 85.92,\n", " 55.76],\n", " [91.36, 78.73, 64.05, 65.6 , 82.58, 81.37, 61.16, 89.09, 81.36,\n", " 54.87],\n", " [97.19, 82. , 64.21, 61.97, 82.99, 80.34, 58.55, 86.28, 75.02,\n", " 48.75],\n", " [94.04, 85.63, 65.56, 55.32, 73.18, 70.88, 55.35, 76.27, 67.2 ,\n", " 45.78],\n", " [92.1 , 85.05, 65.09, 53.01, 64.44, 65.64, 52.53, 66.54, 59.29,\n", " 41.33],\n", " [96.04, 81.1 , 62.38, 48.74, 57.5 , 63.27, 48.67, 60.81, 51.78,\n", " 33.63],\n", " [91.03, 77.21, 60.55, 46.67, 52.73, 64.05, 41.19, 54.99, 44.3 ,\n", " 25. ]])\n", "Coordinates:\n", " x (x) float64 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0\n", " * y (y) float64 0.0 0.7778 1.556 2.333 3.111 ... 4.667 5.444 6.222 7.0\n", "Attributes:\n", " source: ../data/Surfer/surfer-6-ascii-tiny.grd</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>y</span>: 10</li><li><span class='xr-has-index'>x</span>: 10</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-fca64d97-ed00-40bf-a797-a69a3b2f6f01' class='xr-array-in' type='checkbox' checked><label for='section-fca64d97-ed00-40bf-a797-a69a3b2f6f01' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>70.0 54.19 62.27 74.51 55.95 55.42 ... 64.05 41.19 54.99 44.3 25.0</span></div><div class='xr-array-data'><pre>array([[70. , 54.19, 62.27, 74.51, 55.95, 55.42, 71.21, 74.63, 63.14,\n", " 44.99],\n", " [74.77, 66.02, 70.29, 75.16, 60.56, 65.56, 85.07, 89.81, 74.53,\n", " 51.69],\n", " [80.88, 75.56, 74.35, 72.47, 66.93, 75.49, 86.39, 92.1 , 84.41,\n", " 55. ],\n", " [86.31, 77.58, 67.71, 68.5 , 73.37, 74.84, 65.35, 95.55, 85.92,\n", " 55.76],\n", " [91.36, 78.73, 64.05, 65.6 , 82.58, 81.37, 61.16, 89.09, 81.36,\n", " 54.87],\n", " [97.19, 82. , 64.21, 61.97, 82.99, 80.34, 58.55, 86.28, 75.02,\n", " 48.75],\n", " [94.04, 85.63, 65.56, 55.32, 73.18, 70.88, 55.35, 76.27, 67.2 ,\n", " 45.78],\n", " [92.1 , 85.05, 65.09, 53.01, 64.44, 65.64, 52.53, 66.54, 59.29,\n", " 41.33],\n", " [96.04, 81.1 , 62.38, 48.74, 57.5 , 63.27, 48.67, 60.81, 51.78,\n", " 33.63],\n", " [91.03, 77.21, 60.55, 46.67, 52.73, 64.05, 41.19, 54.99, 44.3 ,\n", " 25. ]])</pre></div></div></li><li class='xr-section-item'><input id='section-91353e38-f109-46f5-a898-4fb282606b64' class='xr-section-summary-in' type='checkbox' checked><label for='section-91353e38-f109-46f5-a898-4fb282606b64' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.0 2.0 3.0 ... 6.0 7.0 8.0 9.0</div><input id='attrs-1b3ebddc-fc8b-48e0-9544-d74658e617fb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1b3ebddc-fc8b-48e0-9544-d74658e617fb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4fdcc46f-d704-41b1-9b27-604772365839' class='xr-var-data-in' type='checkbox'><label for='data-4fdcc46f-d704-41b1-9b27-604772365839' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0., 1., 2., 3., 4., 5., 6., 7., 8., 9.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.7778 1.556 ... 6.222 7.0</div><input id='attrs-e5f59361-7e7d-4bc0-9fa3-577df47695a2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e5f59361-7e7d-4bc0-9fa3-577df47695a2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5b6473db-e490-4151-88d9-9c1636af83ac' class='xr-var-data-in' type='checkbox'><label for='data-5b6473db-e490-4151-88d9-9c1636af83ac' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0. , 0.777778, 1.555556, 2.333333, 3.111111, 3.888889, 4.666667,\n", " 5.444444, 6.222222, 7. ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9b83b588-62e6-4d92-82a8-9fdeb9b3593d' class='xr-section-summary-in' type='checkbox' checked><label for='section-9b83b588-62e6-4d92-82a8-9fdeb9b3593d' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>source :</span></dt><dd>../data/Surfer/surfer-6-ascii-tiny.grd</dd></dl></div></li></ul></div></div>" ], "text/plain": [ "<xarray.DataArray (y: 10, x: 10)>\n", "array([[70. , 54.19, 62.27, 74.51, 55.95, 55.42, 71.21, 74.63, 63.14,\n", " 44.99],\n", " [74.77, 66.02, 70.29, 75.16, 60.56, 65.56, 85.07, 89.81, 74.53,\n", " 51.69],\n", " [80.88, 75.56, 74.35, 72.47, 66.93, 75.49, 86.39, 92.1 , 84.41,\n", " 55. ],\n", " [86.31, 77.58, 67.71, 68.5 , 73.37, 74.84, 65.35, 95.55, 85.92,\n", " 55.76],\n", " [91.36, 78.73, 64.05, 65.6 , 82.58, 81.37, 61.16, 89.09, 81.36,\n", " 54.87],\n", " [97.19, 82. , 64.21, 61.97, 82.99, 80.34, 58.55, 86.28, 75.02,\n", " 48.75],\n", " [94.04, 85.63, 65.56, 55.32, 73.18, 70.88, 55.35, 76.27, 67.2 ,\n", " 45.78],\n", " [92.1 , 85.05, 65.09, 53.01, 64.44, 65.64, 52.53, 66.54, 59.29,\n", " 41.33],\n", " [96.04, 81.1 , 62.38, 48.74, 57.5 , 63.27, 48.67, 60.81, 51.78,\n", " 33.63],\n", " [91.03, 77.21, 60.55, 46.67, 52.73, 64.05, 41.19, 54.99, 44.3 ,\n", " 25. ]])\n", "Coordinates:\n", " * x (x) float64 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0\n", " * y (y) float64 0.0 0.7778 1.556 2.333 3.111 ... 4.667 5.444 6.222 7.0\n", "Attributes:\n", " source: ../data/Surfer/surfer-6-ascii-tiny.grd" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import gio\n", "\n", "da = gio.read_surfer('../data/Surfer/surfer-6-ascii-tiny.grd')\n", "da" ] }, { "cell_type": "code", "execution_count": 11, "id": "a51ae25c", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n", "<defs>\n", "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n", "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n", "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n", "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n", "</symbol>\n", "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n", "<path d=\"M28.681 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var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n", " --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n", " --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n", " --xr-border-color: var(--jp-border-color2, #e0e0e0);\n", " --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n", " --xr-background-color: var(--jp-layout-color0, white);\n", " --xr-background-color-row-even: var(--jp-layout-color1, white);\n", " --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n", "}\n", "\n", "html[theme=dark],\n", "body.vscode-dark {\n", " --xr-font-color0: rgba(255, 255, 255, 1);\n", " --xr-font-color2: rgba(255, 255, 255, 0.54);\n", " --xr-font-color3: rgba(255, 255, 255, 0.38);\n", " --xr-border-color: #1F1F1F;\n", " --xr-disabled-color: #515151;\n", " --xr-background-color: #111111;\n", " --xr-background-color-row-even: #111111;\n", " --xr-background-color-row-odd: #313131;\n", "}\n", "\n", ".xr-wrap {\n", " display: block;\n", " min-width: 300px;\n", " max-width: 700px;\n", "}\n", "\n", ".xr-text-repr-fallback {\n", " /* fallback to plain text repr when CSS is not injected (untrusted notebook) /\n", " display: none;\n", "}\n", "\n", ".xr-header {\n", " padding-top: 6px;\n", " padding-bottom: 6px;\n", " margin-bottom: 4px;\n", " border-bottom: solid 1px var(--xr-border-color);\n", "}\n", "\n", ".xr-header > div,\n", ".xr-header > ul {\n", " display: inline;\n", " margin-top: 0;\n", " margin-bottom: 0;\n", "}\n", "\n", ".xr-obj-type,\n", ".xr-array-name {\n", " margin-left: 2px;\n", " margin-right: 10px;\n", "}\n", "\n", ".xr-obj-type {\n", " color: var(--xr-font-color2);\n", "}\n", "\n", ".xr-sections {\n", " padding-left: 0 !important;\n", " display: grid;\n", " grid-template-columns: 150px auto auto 1fr 20px 20px;\n", "}\n", "\n", ".xr-section-item {\n", " display: contents;\n", "}\n", "\n", ".xr-section-item input {\n", " display: none;\n", "}\n", "\n", ".xr-section-item input + label {\n", " color: var(--xr-disabled-color);\n", "}\n", "\n", ".xr-section-item input:enabled + label {\n", " cursor: pointer;\n", " color: var(--xr-font-color2);\n", "}\n", "\n", ".xr-section-item input:enabled + label:hover {\n", " color: var(--xr-font-color0);\n", "}\n", "\n", ".xr-section-summary {\n", " grid-column: 1;\n", " color: var(--xr-font-color2);\n", " font-weight: 500;\n", "}\n", "\n", ".xr-section-summary > span {\n", " display: inline-block;\n", " padding-left: 0.5em;\n", "}\n", "\n", ".xr-section-summary-in:disabled + label {\n", " color: var(--xr-font-color2);\n", "}\n", "\n", ".xr-section-summary-in + label:before {\n", " display: inline-block;\n", " content: '►';\n", " font-size: 11px;\n", " width: 15px;\n", " text-align: center;\n", "}\n", "\n", ".xr-section-summary-in:disabled + label:before {\n", " color: var(--xr-disabled-color);\n", "}\n", "\n", ".xr-section-summary-in:checked + label:before {\n", " content: '▼';\n", "}\n", "\n", ".xr-section-summary-in:checked + label > span 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".xr-array-preview {\n", " display: inline-block;\n", "}\n", "\n", ".xr-dim-list {\n", " display: inline-block !important;\n", " list-style: none;\n", " padding: 0 !important;\n", " margin: 0;\n", "}\n", "\n", ".xr-dim-list li {\n", " display: inline-block;\n", " padding: 0;\n", " margin: 0;\n", "}\n", "\n", ".xr-dim-list:before {\n", " content: '(';\n", "}\n", "\n", ".xr-dim-list:after {\n", " content: ')';\n", "}\n", "\n", ".xr-dim-list li:not(:last-child):after {\n", " content: ',';\n", " padding-right: 5px;\n", "}\n", "\n", ".xr-has-index {\n", " font-weight: bold;\n", "}\n", "\n", ".xr-var-list,\n", ".xr-var-item {\n", " display: contents;\n", "}\n", "\n", ".xr-var-item > div,\n", ".xr-var-item label,\n", ".xr-var-item > .xr-var-name span {\n", " background-color: var(--xr-background-color-row-even);\n", " margin-bottom: 0;\n", "}\n", "\n", ".xr-var-item > .xr-var-name:hover span {\n", " padding-right: 5px;\n", "}\n", "\n", ".xr-var-list > li:nth-child(odd) > div,\n", ".xr-var-list > li:nth-child(odd) > label,\n", ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n", " background-color: var(--xr-background-color-row-odd);\n", "}\n", "\n", ".xr-var-name {\n", " grid-column: 1;\n", "}\n", "\n", ".xr-var-dims {\n", " grid-column: 2;\n", "}\n", "\n", ".xr-var-dtype {\n", " grid-column: 3;\n", " text-align: right;\n", " color: var(--xr-font-color2);\n", "}\n", "\n", ".xr-var-preview {\n", " grid-column: 4;\n", "}\n", "\n", ".xr-var-name,\n", ".xr-var-dims,\n", ".xr-var-dtype,\n", ".xr-preview,\n", ".xr-attrs dt {\n", " white-space: nowrap;\n", " overflow: hidden;\n", " text-overflow: ellipsis;\n", " padding-right: 10px;\n", "}\n", "\n", ".xr-var-name:hover,\n", ".xr-var-dims:hover,\n", ".xr-var-dtype:hover,\n", ".xr-attrs dt:hover {\n", " overflow: visible;\n", " width: auto;\n", " z-index: 1;\n", "}\n", "\n", ".xr-var-attrs,\n", ".xr-var-data {\n", " display: none;\n", " background-color: var(--xr-background-color) !important;\n", " padding-bottom: 5px !important;\n", "}\n", "\n", ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n", ".xr-var-data-in:checked ~ .xr-var-data {\n", " display: block;\n", "}\n", "\n", ".xr-var-data > table {\n", " float: right;\n", "}\n", "\n", ".xr-var-name span,\n", ".xr-var-data,\n", ".xr-attrs {\n", " padding-left: 25px !important;\n", "}\n", "\n", ".xr-attrs,\n", ".xr-var-attrs,\n", ".xr-var-data {\n", " grid-column: 1 / -1;\n", "}\n", "\n", "dl.xr-attrs {\n", " padding: 0;\n", " margin: 0;\n", " display: grid;\n", " grid-template-columns: 125px auto;\n", "}\n", "\n", ".xr-attrs dt,\n", ".xr-attrs dd {\n", " padding: 0;\n", " margin: 0;\n", " float: left;\n", " padding-right: 10px;\n", " width: auto;\n", "}\n", "\n", ".xr-attrs dt {\n", " font-weight: normal;\n", " grid-column: 1;\n", "}\n", "\n", ".xr-attrs dt:hover span {\n", " display: inline-block;\n", " background: var(--xr-background-color);\n", " padding-right: 10px;\n", "}\n", "\n", ".xr-attrs dd {\n", " grid-column: 2;\n", " white-space: pre-wrap;\n", " word-break: break-all;\n", "}\n", "\n", ".xr-icon-database,\n", ".xr-icon-file-text2 {\n", " display: inline-block;\n", " vertical-align: middle;\n", " width: 1em;\n", " height: 1.5em !important;\n", " stroke-width: 0;\n", " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", "</style><pre class='xr-text-repr-fallback'><xarray.DataArray ()>\n", "array(97.19)</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-85bc78cf-78e1-47d9-b5e8-8bae21230242' class='xr-array-in' type='checkbox' checked><label for='section-85bc78cf-78e1-47d9-b5e8-8bae21230242' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>97.19</span></div><div class='xr-array-data'><pre>array(97.19)</pre></div></div></li><li class='xr-section-item'><input id='section-665c0c08-3188-4b81-82a4-8c1173aa59c1' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-665c0c08-3188-4b81-82a4-8c1173aa59c1' class='xr-section-summary' title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-6b50e93a-c815-4eab-ba63-e41602c9f29a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6b50e93a-c815-4eab-ba63-e41602c9f29a' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>" ], "text/plain": [ "<xarray.DataArray ()>\n", "array(97.19)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "da.max()" ] }, { "cell_type": "markdown", "id": "45cb2611", "metadata": {}, "source": [ "## Another ASCII" ] }, { "cell_type": "code", "execution_count": 1, "id": "4054712b", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n", "<defs>\n", "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n", "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n", "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n", "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n", "</symbol>\n", "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n", "<path d=\"M28.681 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var(--xr-disabled-color);\n", "}\n", "\n", ".xr-section-item input:enabled + label {\n", " cursor: pointer;\n", " color: var(--xr-font-color2);\n", "}\n", "\n", ".xr-section-item input:enabled + label:hover {\n", " color: var(--xr-font-color0);\n", "}\n", "\n", ".xr-section-summary {\n", " grid-column: 1;\n", " color: var(--xr-font-color2);\n", " font-weight: 500;\n", "}\n", "\n", ".xr-section-summary > span {\n", " display: inline-block;\n", " padding-left: 0.5em;\n", "}\n", "\n", ".xr-section-summary-in:disabled + label {\n", " color: var(--xr-font-color2);\n", "}\n", "\n", ".xr-section-summary-in + label:before {\n", " display: inline-block;\n", " content: '►';\n", " font-size: 11px;\n", " width: 15px;\n", " text-align: center;\n", "}\n", "\n", ".xr-section-summary-in:disabled + label:before {\n", " color: var(--xr-disabled-color);\n", "}\n", "\n", ".xr-section-summary-in:checked + label:before {\n", " content: '▼';\n", "}\n", "\n", ".xr-section-summary-in:checked + label > span {\n", " display: none;\n", "}\n", "\n", ".xr-section-summary,\n", ".xr-section-inline-details {\n", " padding-top: 4px;\n", " padding-bottom: 4px;\n", "}\n", "\n", ".xr-section-inline-details {\n", " grid-column: 2 / -1;\n", "}\n", "\n", ".xr-section-details {\n", " display: none;\n", " grid-column: 1 / -1;\n", " margin-bottom: 5px;\n", "}\n", "\n", ".xr-section-summary-in:checked ~ .xr-section-details {\n", " display: contents;\n", "}\n", "\n", ".xr-array-wrap {\n", " grid-column: 1 / -1;\n", " display: grid;\n", " grid-template-columns: 20px auto;\n", "}\n", "\n", ".xr-array-wrap > label {\n", " grid-column: 1;\n", " vertical-align: top;\n", "}\n", "\n", ".xr-preview {\n", " color: var(--xr-font-color3);\n", "}\n", "\n", ".xr-array-preview,\n", ".xr-array-data {\n", " padding: 0 5px !important;\n", " grid-column: 2;\n", "}\n", "\n", ".xr-array-data,\n", ".xr-array-in:checked ~ .xr-array-preview {\n", " display: none;\n", "}\n", "\n", ".xr-array-in:checked ~ .xr-array-data,\n", ".xr-array-preview {\n", " display: inline-block;\n", "}\n", "\n", ".xr-dim-list {\n", " display: inline-block !important;\n", " list-style: none;\n", " padding: 0 !important;\n", " margin: 0;\n", "}\n", "\n", ".xr-dim-list li {\n", " display: inline-block;\n", " padding: 0;\n", " margin: 0;\n", "}\n", "\n", ".xr-dim-list:before {\n", " content: '(';\n", "}\n", "\n", ".xr-dim-list:after {\n", " content: ')';\n", "}\n", "\n", ".xr-dim-list li:not(:last-child):after {\n", " content: ',';\n", " padding-right: 5px;\n", "}\n", "\n", ".xr-has-index {\n", " font-weight: bold;\n", "}\n", "\n", ".xr-var-list,\n", ".xr-var-item {\n", " display: contents;\n", "}\n", "\n", ".xr-var-item > div,\n", ".xr-var-item label,\n", ".xr-var-item > .xr-var-name span {\n", " background-color: var(--xr-background-color-row-even);\n", " margin-bottom: 0;\n", "}\n", "\n", ".xr-var-item > .xr-var-name:hover span {\n", " padding-right: 5px;\n", "}\n", "\n", ".xr-var-list > li:nth-child(odd) > div,\n", ".xr-var-list > li:nth-child(odd) > label,\n", ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n", " background-color: var(--xr-background-color-row-odd);\n", "}\n", "\n", ".xr-var-name {\n", " grid-column: 1;\n", "}\n", "\n", ".xr-var-dims {\n", " grid-column: 2;\n", "}\n", "\n", ".xr-var-dtype {\n", " grid-column: 3;\n", " text-align: right;\n", " color: var(--xr-font-color2);\n", "}\n", "\n", ".xr-var-preview {\n", " grid-column: 4;\n", "}\n", "\n", ".xr-var-name,\n", ".xr-var-dims,\n", ".xr-var-dtype,\n", ".xr-preview,\n", ".xr-attrs dt {\n", " white-space: nowrap;\n", " overflow: hidden;\n", " text-overflow: ellipsis;\n", " padding-right: 10px;\n", "}\n", "\n", ".xr-var-name:hover,\n", ".xr-var-dims:hover,\n", ".xr-var-dtype:hover,\n", ".xr-attrs dt:hover {\n", " overflow: visible;\n", " width: auto;\n", " z-index: 1;\n", "}\n", "\n", ".xr-var-attrs,\n", ".xr-var-data {\n", " display: none;\n", " background-color: var(--xr-background-color) !important;\n", " padding-bottom: 5px !important;\n", "}\n", "\n", ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n", ".xr-var-data-in:checked ~ .xr-var-data {\n", " display: block;\n", "}\n", "\n", ".xr-var-data > table {\n", " float: right;\n", "}\n", "\n", ".xr-var-name span,\n", ".xr-var-data,\n", ".xr-attrs {\n", " padding-left: 25px !important;\n", "}\n", "\n", ".xr-attrs,\n", ".xr-var-attrs,\n", ".xr-var-data {\n", " grid-column: 1 / -1;\n", "}\n", "\n", "dl.xr-attrs {\n", " padding: 0;\n", " margin: 0;\n", " display: grid;\n", " grid-template-columns: 125px auto;\n", "}\n", "\n", ".xr-attrs dt,\n", ".xr-attrs dd {\n", " padding: 0;\n", " margin: 0;\n", " float: left;\n", " padding-right: 10px;\n", " width: auto;\n", "}\n", "\n", ".xr-attrs dt {\n", " font-weight: normal;\n", " grid-column: 1;\n", "}\n", "\n", ".xr-attrs dt:hover span {\n", " display: inline-block;\n", " background: var(--xr-background-color);\n", " padding-right: 10px;\n", "}\n", "\n", ".xr-attrs dd {\n", " grid-column: 2;\n", " white-space: pre-wrap;\n", " word-break: break-all;\n", "}\n", "\n", ".xr-icon-database,\n", ".xr-icon-file-text2 {\n", " display: inline-block;\n", " vertical-align: middle;\n", " width: 1em;\n", " height: 1.5em !important;\n", " stroke-width: 0;\n", " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", "</style><pre class='xr-text-repr-fallback'><xarray.DataArray (y: 182, x: 222)>\n", "array([[-58.1195, -55.5528, -52.4692, ..., -41.9582, -47.2193, -51.5736],\n", " [-57.7877, -55.2337, -52.1599, ..., -41.5877, -46.8911, -51.2844],\n", " [-57.4487, -54.9043, -51.8392, ..., -41.1691, -46.5296, -50.9713],\n", " ...,\n", " [ 98.8531, 103.599 , 106.683 , ..., 23.8501, 22.8811, 22.0409],\n", " [104.529 , 108.384 , 111.854 , ..., 18.179 , 17.2994, 16.5399],\n", " [109.395 , 112.507 , 115.76 , ..., 13.2723, 12.4487, 11.7467]])\n", "Coordinates:\n", " x (x) float64 0.0 5e+03 1e+04 1.5e+04 ... 1.095e+06 1.1e+06 1.105e+06\n", " * y (y) float64 0.0 5e+03 1e+04 1.5e+04 ... 8.95e+05 9e+05 9.05e+05\n", "Attributes:\n", " source: ../data/Surfer/surfer-6-ascii.grd</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>y</span>: 182</li><li><span class='xr-has-index'>x</span>: 222</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-6d8654c4-d48c-4c0b-89b8-5d2b4e780a03' class='xr-array-in' type='checkbox' checked><label for='section-6d8654c4-d48c-4c0b-89b8-5d2b4e780a03' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>-58.12 -55.55 -52.47 -48.9 -44.89 ... 15.12 14.18 13.27 12.45 11.75</span></div><div class='xr-array-data'><pre>array([[-58.1195, -55.5528, -52.4692, ..., -41.9582, -47.2193, -51.5736],\n", " [-57.7877, -55.2337, -52.1599, ..., -41.5877, -46.8911, -51.2844],\n", " [-57.4487, -54.9043, -51.8392, ..., -41.1691, -46.5296, -50.9713],\n", " ...,\n", " [ 98.8531, 103.599 , 106.683 , ..., 23.8501, 22.8811, 22.0409],\n", " [104.529 , 108.384 , 111.854 , ..., 18.179 , 17.2994, 16.5399],\n", " [109.395 , 112.507 , 115.76 , ..., 13.2723, 12.4487, 11.7467]])</pre></div></div></li><li class='xr-section-item'><input id='section-d8c3d248-7741-4f3c-93e0-e25b3b0c6a4c' class='xr-section-summary-in' type='checkbox' checked><label for='section-d8c3d248-7741-4f3c-93e0-e25b3b0c6a4c' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 5e+03 ... 1.1e+06 1.105e+06</div><input id='attrs-26d3c428-b1d8-4c84-b8c6-6ca8085b1e31' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-26d3c428-b1d8-4c84-b8c6-6ca8085b1e31' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0e5eb224-6329-4039-be32-768e439a0ccc' class='xr-var-data-in' type='checkbox'><label for='data-0e5eb224-6329-4039-be32-768e439a0ccc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0., 5000., 10000., ..., 1095000., 1100000., 1105000.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 5e+03 1e+04 ... 9e+05 9.05e+05</div><input id='attrs-12215756-5a51-4a73-954f-05277fea114d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-12215756-5a51-4a73-954f-05277fea114d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e8becbc9-7701-4840-a7ad-1909dcd56f38' class='xr-var-data-in' type='checkbox'><label for='data-e8becbc9-7701-4840-a7ad-1909dcd56f38' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0., 5000., 10000., 15000., 20000., 25000., 30000., 35000.,\n", " 40000., 45000., 50000., 55000., 60000., 65000., 70000., 75000.,\n", " 80000., 85000., 90000., 95000., 100000., 105000., 110000., 115000.,\n", " 120000., 125000., 130000., 135000., 140000., 145000., 150000., 155000.,\n", " 160000., 165000., 170000., 175000., 180000., 185000., 190000., 195000.,\n", " 200000., 205000., 210000., 215000., 220000., 225000., 230000., 235000.,\n", " 240000., 245000., 250000., 255000., 260000., 265000., 270000., 275000.,\n", " 280000., 285000., 290000., 295000., 300000., 305000., 310000., 315000.,\n", " 320000., 325000., 330000., 335000., 340000., 345000., 350000., 355000.,\n", " 360000., 365000., 370000., 375000., 380000., 385000., 390000., 395000.,\n", " 400000., 405000., 410000., 415000., 420000., 425000., 430000., 435000.,\n", " 440000., 445000., 450000., 455000., 460000., 465000., 470000., 475000.,\n", " 480000., 485000., 490000., 495000., 500000., 505000., 510000., 515000.,\n", " 520000., 525000., 530000., 535000., 540000., 545000., 550000., 555000.,\n", " 560000., 565000., 570000., 575000., 580000., 585000., 590000., 595000.,\n", " 600000., 605000., 610000., 615000., 620000., 625000., 630000., 635000.,\n", " 640000., 645000., 650000., 655000., 660000., 665000., 670000., 675000.,\n", " 680000., 685000., 690000., 695000., 700000., 705000., 710000., 715000.,\n", " 720000., 725000., 730000., 735000., 740000., 745000., 750000., 755000.,\n", " 760000., 765000., 770000., 775000., 780000., 785000., 790000., 795000.,\n", " 800000., 805000., 810000., 815000., 820000., 825000., 830000., 835000.,\n", " 840000., 845000., 850000., 855000., 860000., 865000., 870000., 875000.,\n", " 880000., 885000., 890000., 895000., 900000., 905000.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-62303430-d115-40d5-bafd-a04059fe7b9a' class='xr-section-summary-in' type='checkbox' checked><label for='section-62303430-d115-40d5-bafd-a04059fe7b9a' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>source :</span></dt><dd>../data/Surfer/surfer-6-ascii.grd</dd></dl></div></li></ul></div></div>" ], "text/plain": [ "<xarray.DataArray (y: 182, x: 222)>\n", "array([[-58.1195, -55.5528, -52.4692, ..., -41.9582, -47.2193, -51.5736],\n", " [-57.7877, -55.2337, -52.1599, ..., -41.5877, -46.8911, -51.2844],\n", " [-57.4487, -54.9043, -51.8392, ..., -41.1691, -46.5296, -50.9713],\n", " ...,\n", " [ 98.8531, 103.599 , 106.683 , ..., 23.8501, 22.8811, 22.0409],\n", " [104.529 , 108.384 , 111.854 , ..., 18.179 , 17.2994, 16.5399],\n", " [109.395 , 112.507 , 115.76 , ..., 13.2723, 12.4487, 11.7467]])\n", "Coordinates:\n", " * x (x) float64 0.0 5e+03 1e+04 1.5e+04 ... 1.095e+06 1.1e+06 1.105e+06\n", " * y (y) float64 0.0 5e+03 1e+04 1.5e+04 ... 8.95e+05 9e+05 9.05e+05\n", "Attributes:\n", " source: ../data/Surfer/surfer-6-ascii.grd" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import gio\n", "\n", "da = gio.read_surfer('../data/Surfer/surfer-6-ascii.grd')\n", "da" ] }, { "cell_type": "code", "execution_count": 2, "id": "ded77202", "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.collections.QuadMesh at 0x7f0adca95640>" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<Figure size 432x288 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "da.plot()" ] }, { "cell_type": "markdown", "id": "bef215e2", "metadata": {}, "source": [ "## A binary file" ] }, { "cell_type": "code", "execution_count": 3, "id": "6e7c02be", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n", "<defs>\n", "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n", "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n", "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n", "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n", "</symbol>\n", "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n", "<path d=\"M28.681 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padding-bottom: 5px !important;\n", "}\n", "\n", ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n", ".xr-var-data-in:checked ~ .xr-var-data {\n", " display: block;\n", "}\n", "\n", ".xr-var-data > table {\n", " float: right;\n", "}\n", "\n", ".xr-var-name span,\n", ".xr-var-data,\n", ".xr-attrs {\n", " padding-left: 25px !important;\n", "}\n", "\n", ".xr-attrs,\n", ".xr-var-attrs,\n", ".xr-var-data {\n", " grid-column: 1 / -1;\n", "}\n", "\n", "dl.xr-attrs {\n", " padding: 0;\n", " margin: 0;\n", " display: grid;\n", " grid-template-columns: 125px auto;\n", "}\n", "\n", ".xr-attrs dt,\n", ".xr-attrs dd {\n", " padding: 0;\n", " margin: 0;\n", " float: left;\n", " padding-right: 10px;\n", " width: auto;\n", "}\n", "\n", ".xr-attrs dt {\n", " font-weight: normal;\n", " grid-column: 1;\n", "}\n", "\n", ".xr-attrs dt:hover span {\n", " display: inline-block;\n", " background: var(--xr-background-color);\n", " padding-right: 10px;\n", "}\n", "\n", ".xr-attrs dd {\n", " grid-column: 2;\n", " white-space: pre-wrap;\n", " word-break: break-all;\n", "}\n", "\n", ".xr-icon-database,\n", ".xr-icon-file-text2 {\n", " display: inline-block;\n", " vertical-align: middle;\n", " width: 1em;\n", " height: 1.5em !important;\n", " stroke-width: 0;\n", " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", "</style><pre class='xr-text-repr-fallback'><xarray.DataArray (y: 352, x: 629)>\n", "array([[34.34440231, 35.18498611, 35.32251358, ..., 36.05297852,\n", " 35.76726532, 35.35245132],\n", " [35.43532181, 35.00969315, 35.75266647, ..., 36.27960205,\n", " 35.19588852, 36.22369003],\n", " [35.50966644, 35.82201385, 35.89884949, ..., 36.19258118,\n", " 36.06084442, 35.46885681],\n", " ...,\n", " [22.57001305, 23.0938015 , 22.96144295, ..., 36.87253571,\n", " 35.71622849, 35.71092606],\n", " [22.57962227, 22.91576004, 22.62903214, ..., 37.23720932,\n", " 36.19213867, 36.02495956],\n", " [23.00543976, 22.40481567, 22.8556118 , ..., 36.53549194,\n", " 35.47878265, 35.03230667]])\n", "Coordinates:\n", " x (x) float64 2.604 2.605 2.605 2.606 ... 2.917 2.917 2.918 2.918\n", " * y (y) float64 23.47 23.47 23.47 23.47 ... 23.64 23.64 23.64 23.64\n", "Attributes:\n", " source: ../data/Surfer/WDS1_Si_TAP_Quant.grd</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>y</span>: 352</li><li><span class='xr-has-index'>x</span>: 629</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-19f02818-0241-4477-b037-4895d261ee5e' class='xr-array-in' type='checkbox' checked><label for='section-19f02818-0241-4477-b037-4895d261ee5e' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>34.34 35.18 35.32 35.62 35.55 36.32 ... 36.85 36.72 36.54 35.48 35.03</span></div><div class='xr-array-data'><pre>array([[34.34440231, 35.18498611, 35.32251358, ..., 36.05297852,\n", " 35.76726532, 35.35245132],\n", " [35.43532181, 35.00969315, 35.75266647, ..., 36.27960205,\n", " 35.19588852, 36.22369003],\n", " [35.50966644, 35.82201385, 35.89884949, ..., 36.19258118,\n", " 36.06084442, 35.46885681],\n", " ...,\n", " [22.57001305, 23.0938015 , 22.96144295, ..., 36.87253571,\n", " 35.71622849, 35.71092606],\n", " [22.57962227, 22.91576004, 22.62903214, ..., 37.23720932,\n", " 36.19213867, 36.02495956],\n", " [23.00543976, 22.40481567, 22.8556118 , ..., 36.53549194,\n", " 35.47878265, 35.03230667]])</pre></div></div></li><li class='xr-section-item'><input id='section-b15857de-3701-44a9-af68-17a757f31f4e' class='xr-section-summary-in' type='checkbox' checked><label for='section-b15857de-3701-44a9-af68-17a757f31f4e' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.604 2.605 2.605 ... 2.918 2.918</div><input id='attrs-18468dd8-f3cb-4269-988a-2ac87a98de94' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-18468dd8-f3cb-4269-988a-2ac87a98de94' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-97c81592-15c9-4b43-b5ce-faa019be8ea6' class='xr-var-data-in' type='checkbox'><label for='data-97c81592-15c9-4b43-b5ce-faa019be8ea6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([2.604 , 2.604501, 2.605002, ..., 2.917498, 2.917999, 2.9185 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>23.47 23.47 23.47 ... 23.64 23.64</div><input id='attrs-75f0c403-cdfe-46ea-b95f-790f7d94c5ed' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-75f0c403-cdfe-46ea-b95f-790f7d94c5ed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7a272e74-7e39-4287-ae44-e0819e9d6afa' class='xr-var-data-in' type='checkbox'><label for='data-7a272e74-7e39-4287-ae44-e0819e9d6afa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([23.4651 , 23.465601, 23.466103, ..., 23.640097, 23.640599, 23.6411 ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7113d434-0793-42a9-b8d1-c9ec1342afbb' class='xr-section-summary-in' type='checkbox' checked><label for='section-7113d434-0793-42a9-b8d1-c9ec1342afbb' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>source :</span></dt><dd>../data/Surfer/WDS1_Si_TAP_Quant.grd</dd></dl></div></li></ul></div></div>" ], "text/plain": [ "<xarray.DataArray (y: 352, x: 629)>\n", "array([[34.34440231, 35.18498611, 35.32251358, ..., 36.05297852,\n", " 35.76726532, 35.35245132],\n", " [35.43532181, 35.00969315, 35.75266647, ..., 36.27960205,\n", " 35.19588852, 36.22369003],\n", " [35.50966644, 35.82201385, 35.89884949, ..., 36.19258118,\n", " 36.06084442, 35.46885681],\n", " ...,\n", " [22.57001305, 23.0938015 , 22.96144295, ..., 36.87253571,\n", " 35.71622849, 35.71092606],\n", " [22.57962227, 22.91576004, 22.62903214, ..., 37.23720932,\n", " 36.19213867, 36.02495956],\n", " [23.00543976, 22.40481567, 22.8556118 , ..., 36.53549194,\n", " 35.47878265, 35.03230667]])\n", "Coordinates:\n", " * x (x) float64 2.604 2.605 2.605 2.606 ... 2.917 2.917 2.918 2.918\n", " * y (y) float64 23.47 23.47 23.47 23.47 ... 23.64 23.64 23.64 23.64\n", "Attributes:\n", " source: ../data/Surfer/WDS1_Si_TAP_Quant.grd" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import gio\n", "\n", "da = gio.read_surfer('../data/Surfer/WDS1_Si_TAP_Quant.grd')\n", "da" ] }, { "cell_type": "code", "execution_count": 4, "id": "f522a54d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.collections.QuadMesh at 0x7f0ada789790>" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "da.plot()" ] }, { "cell_type": "code", "execution_count": 7, "id": "fc934e1b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(352, 629)" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "da.shape" ] } ], "metadata": { "kernelspec": { "display_name": "gio", "language": "python", "name": "gio" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.7" } }, "nbformat": 4, "nbformat_minor": 5 }	apache-2.0
arasdar/DL	uri-dl/uri-dl-hw-1/roohi/NN-roohi-sample.ipynb	1	167634	{ "cells": [ { "cell_type": "code", "execution_count": 1, "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>Id</th>\n", " <th>att1</th>\n", " <th>att2</th>\n", " <th>att3</th>\n", " <th>att4</th>\n", " <th>att5</th>\n", " <th>att6</th>\n", " <th>att7</th>\n", " <th>att8</th>\n", " <th>att9</th>\n", " <th>...</th>\n", " <th>att18</th>\n", " <th>att19</th>\n", " <th>att20</th>\n", " <th>att21</th>\n", " <th>att22</th>\n", " <th>att23</th>\n", " <th>att24</th>\n", " <th>att25</th>\n", " <th>att26</th>\n", " <th>msd_track_id</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>41.08</td>\n", " <td>6.579</td>\n", " <td>4.307</td>\n", " <td>3.421</td>\n", " <td>3.192</td>\n", " <td>2.076</td>\n", " <td>2.179</td>\n", " <td>2.052</td>\n", " <td>1.794</td>\n", " <td>...</td>\n", " <td>1.3470</td>\n", " <td>-0.2463</td>\n", " <td>-1.5470</td>\n", " <td>0.17920</td>\n", " <td>-1.1530</td>\n", " <td>-0.7370</td>\n", " <td>0.40750</td>\n", " <td>-0.67190</td>\n", " <td>-0.05147</td>\n", " <td>TRPLTEM128F92E1389</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>60.80</td>\n", " <td>5.973</td>\n", " <td>4.344</td>\n", " <td>3.261</td>\n", " <td>2.835</td>\n", " <td>2.725</td>\n", " <td>2.446</td>\n", " <td>1.884</td>\n", " <td>1.962</td>\n", " <td>...</td>\n", " <td>-0.3316</td>\n", " <td>0.3519</td>\n", " <td>-1.4760</td>\n", " <td>0.52700</td>\n", " <td>-2.1960</td>\n", " <td>1.5990</td>\n", " <td>-1.39000</td>\n", " <td>0.22560</td>\n", " <td>-0.72080</td>\n", " <td>TRJWMBQ128F424155E</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>3</td>\n", " <td>51.47</td>\n", " <td>4.971</td>\n", " <td>4.316</td>\n", " <td>2.916</td>\n", " <td>3.112</td>\n", " <td>2.290</td>\n", " <td>2.053</td>\n", " <td>1.934</td>\n", " <td>1.878</td>\n", " <td>...</td>\n", " <td>-0.2803</td>\n", " <td>-0.1603</td>\n", " <td>-0.1355</td>\n", " <td>1.03500</td>\n", " <td>0.2370</td>\n", " <td>1.4890</td>\n", " <td>0.02959</td>\n", " <td>-0.13670</td>\n", " <td>0.10820</td>\n", " <td>TRRZWMO12903CCFCC2</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>4</td>\n", " <td>41.28</td>\n", " <td>6.610</td>\n", " <td>4.411</td>\n", " <td>2.602</td>\n", " <td>2.822</td>\n", " <td>2.126</td>\n", " <td>1.984</td>\n", " <td>1.973</td>\n", " <td>1.945</td>\n", " <td>...</td>\n", " <td>-1.6930</td>\n", " <td>1.0040</td>\n", " <td>-0.3953</td>\n", " <td>0.26710</td>\n", " <td>-1.0450</td>\n", " <td>0.4974</td>\n", " <td>0.03724</td>\n", " <td>1.04500</td>\n", " <td>-0.20000</td>\n", " <td>TRBZRUT12903CE6C04</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5</td>\n", " <td>54.17</td>\n", " <td>8.945</td>\n", " <td>4.685</td>\n", " <td>4.208</td>\n", " <td>3.154</td>\n", " <td>3.527</td>\n", " <td>2.733</td>\n", " <td>2.202</td>\n", " <td>2.686</td>\n", " <td>...</td>\n", " <td>2.4690</td>\n", " <td>-0.5449</td>\n", " <td>-0.5622</td>\n", " <td>-0.08968</td>\n", " <td>-0.9823</td>\n", " <td>-0.2445</td>\n", " <td>-1.65800</td>\n", " <td>-0.04825</td>\n", " <td>-0.70950</td>\n", " <td>TRLUJQF128F42AF5BF</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 28 columns</p>\n", "</div>" ], "text/plain": [ " Id att1 att2 att3 att4 att5 att6 att7 att8 att9 \\\n", "0 1 41.08 6.579 4.307 3.421 3.192 2.076 2.179 2.052 1.794 \n", "1 2 60.80 5.973 4.344 3.261 2.835 2.725 2.446 1.884 1.962 \n", "2 3 51.47 4.971 4.316 2.916 3.112 2.290 2.053 1.934 1.878 \n", "3 4 41.28 6.610 4.411 2.602 2.822 2.126 1.984 1.973 1.945 \n", "4 5 54.17 8.945 4.685 4.208 3.154 3.527 2.733 2.202 2.686 \n", "\n", " ... att18 att19 att20 att21 att22 att23 \\\n", "0 ... 1.3470 -0.2463 -1.5470 0.17920 -1.1530 -0.7370 \n", "1 ... -0.3316 0.3519 -1.4760 0.52700 -2.1960 1.5990 \n", "2 ... -0.2803 -0.1603 -0.1355 1.03500 0.2370 1.4890 \n", "3 ... -1.6930 1.0040 -0.3953 0.26710 -1.0450 0.4974 \n", "4 ... 2.4690 -0.5449 -0.5622 -0.08968 -0.9823 -0.2445 \n", "\n", " att24 att25 att26 msd_track_id \n", "0 0.40750 -0.67190 -0.05147 TRPLTEM128F92E1389 \n", "1 -1.39000 0.22560 -0.72080 TRJWMBQ128F424155E \n", "2 0.02959 -0.13670 0.10820 TRRZWMO12903CCFCC2 \n", "3 0.03724 1.04500 -0.20000 TRBZRUT12903CE6C04 \n", "4 -1.65800 -0.04825 -0.70950 TRLUJQF128F42AF5BF \n", "\n", "[5 rows x 28 columns]" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd # to read CSV files (Comma Separated Values)\n", "\n", "train_x = pd.read_csv(filepath_or_buffer='data/kaggle-music-genre/train.x.csv')\n", "train_x.head()" ] }, { "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>Id</th>\n", " <th>class_label</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>International</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>Vocal</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>3</td>\n", " <td>Latin</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>4</td>\n", " <td>Blues</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5</td>\n", " <td>Vocal</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Id class_label\n", "0 1 International\n", "1 2 Vocal\n", "2 3 Latin\n", "3 4 Blues\n", "4 5 Vocal" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_y = pd.read_csv(filepath_or_buffer='data/kaggle-music-genre/train.y.csv')\n", "train_y.head()" ] }, { "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>Id</th>\n", " <th>att1</th>\n", " <th>att2</th>\n", " <th>att3</th>\n", " <th>att4</th>\n", " <th>att5</th>\n", " <th>att6</th>\n", " <th>att7</th>\n", " <th>att8</th>\n", " <th>att9</th>\n", " <th>...</th>\n", " <th>att17</th>\n", " <th>att18</th>\n", " <th>att19</th>\n", " <th>att20</th>\n", " <th>att21</th>\n", " <th>att22</th>\n", " <th>att23</th>\n", " <th>att24</th>\n", " <th>att25</th>\n", " <th>att26</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>38.22</td>\n", " <td>8.076</td>\n", " <td>6.935</td>\n", " <td>4.696</td>\n", " <td>3.856</td>\n", " <td>3.465</td>\n", " <td>2.922</td>\n", " <td>2.568</td>\n", " <td>2.070</td>\n", " <td>...</td>\n", " <td>3.988</td>\n", " <td>0.4957</td>\n", " <td>0.1836</td>\n", " <td>-2.2210</td>\n", " <td>0.6453</td>\n", " <td>-0.2923</td>\n", " <td>1.2000</td>\n", " <td>-0.09179</td>\n", " <td>0.4674</td>\n", " <td>0.2158</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>36.42</td>\n", " <td>6.131</td>\n", " <td>5.364</td>\n", " <td>4.292</td>\n", " <td>3.968</td>\n", " <td>2.937</td>\n", " <td>2.872</td>\n", " <td>2.142</td>\n", " <td>2.050</td>\n", " <td>...</td>\n", " <td>7.098</td>\n", " <td>1.2290</td>\n", " <td>0.5971</td>\n", " <td>-1.0670</td>\n", " <td>0.9569</td>\n", " <td>-1.8240</td>\n", " <td>2.3130</td>\n", " <td>-0.80890</td>\n", " <td>0.5612</td>\n", " <td>-0.6225</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>3</td>\n", " <td>70.01</td>\n", " <td>5.496</td>\n", " <td>4.698</td>\n", " <td>3.699</td>\n", " <td>3.258</td>\n", " <td>2.293</td>\n", " <td>2.680</td>\n", " <td>2.226</td>\n", " <td>2.034</td>\n", " <td>...</td>\n", " <td>4.449</td>\n", " <td>0.4773</td>\n", " <td>1.6370</td>\n", " <td>-1.0690</td>\n", " <td>2.4160</td>\n", " <td>-0.6299</td>\n", " <td>1.4190</td>\n", " <td>-0.81960</td>\n", " <td>0.9151</td>\n", " <td>-0.5948</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>4</td>\n", " <td>40.64</td>\n", " <td>7.281</td>\n", " <td>6.702</td>\n", " <td>4.043</td>\n", " <td>3.729</td>\n", " <td>3.043</td>\n", " <td>2.644</td>\n", " <td>2.366</td>\n", " <td>1.940</td>\n", " <td>...</td>\n", " <td>2.785</td>\n", " <td>1.9000</td>\n", " <td>-1.1370</td>\n", " <td>1.2750</td>\n", " <td>1.7920</td>\n", " <td>-2.1250</td>\n", " <td>1.6090</td>\n", " <td>-0.83230</td>\n", " <td>-0.1998</td>\n", " <td>-0.1218</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5</td>\n", " <td>38.85</td>\n", " <td>7.118</td>\n", " <td>5.703</td>\n", " <td>4.825</td>\n", " <td>4.088</td>\n", " <td>3.823</td>\n", " <td>3.254</td>\n", " <td>2.551</td>\n", " <td>2.193</td>\n", " <td>...</td>\n", " <td>4.536</td>\n", " <td>2.1470</td>\n", " <td>1.0200</td>\n", " <td>-0.2656</td>\n", " <td>2.8050</td>\n", " <td>0.2762</td>\n", " <td>0.2504</td>\n", " <td>1.04900</td>\n", " <td>0.3447</td>\n", " <td>-0.7689</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 27 columns</p>\n", "</div>" ], "text/plain": [ " Id att1 att2 att3 att4 att5 att6 att7 att8 att9 ... \\\n", "0 1 38.22 8.076 6.935 4.696 3.856 3.465 2.922 2.568 2.070 ... \n", "1 2 36.42 6.131 5.364 4.292 3.968 2.937 2.872 2.142 2.050 ... \n", "2 3 70.01 5.496 4.698 3.699 3.258 2.293 2.680 2.226 2.034 ... \n", "3 4 40.64 7.281 6.702 4.043 3.729 3.043 2.644 2.366 1.940 ... \n", "4 5 38.85 7.118 5.703 4.825 4.088 3.823 3.254 2.551 2.193 ... \n", "\n", " att17 att18 att19 att20 att21 att22 att23 att24 att25 \\\n", "0 3.988 0.4957 0.1836 -2.2210 0.6453 -0.2923 1.2000 -0.09179 0.4674 \n", "1 7.098 1.2290 0.5971 -1.0670 0.9569 -1.8240 2.3130 -0.80890 0.5612 \n", "2 4.449 0.4773 1.6370 -1.0690 2.4160 -0.6299 1.4190 -0.81960 0.9151 \n", "3 2.785 1.9000 -1.1370 1.2750 1.7920 -2.1250 1.6090 -0.83230 -0.1998 \n", "4 4.536 2.1470 1.0200 -0.2656 2.8050 0.2762 0.2504 1.04900 0.3447 \n", "\n", " att26 \n", "0 0.2158 \n", "1 -0.6225 \n", "2 -0.5948 \n", "3 -0.1218 \n", "4 -0.7689 \n", "\n", "[5 rows x 27 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_x = pd.read_csv(filepath_or_buffer='data/kaggle-music-genre/test.x.csv')\n", "test_x.head()" ] }, { "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>Id</th>\n", " <th>Blues</th>\n", " <th>Country</th>\n", " <th>Electronic</th>\n", " <th>Folk</th>\n", " <th>International</th>\n", " <th>Jazz</th>\n", " <th>Latin</th>\n", " <th>New_Age</th>\n", " <th>Pop_Rock</th>\n", " <th>Rap</th>\n", " <th>Reggae</th>\n", " <th>RnB</th>\n", " <th>Vocal</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>0.0964</td>\n", " <td>0.0884</td>\n", " <td>0.0121</td>\n", " <td>0.1004</td>\n", " <td>0.0137</td>\n", " <td>0.1214</td>\n", " <td>0.0883</td>\n", " <td>0.0765</td>\n", " <td>0.0332</td>\n", " <td>0.0445</td>\n", " <td>0.1193</td>\n", " <td>0.1019</td>\n", " <td>0.1038</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>0.0121</td>\n", " <td>0.0804</td>\n", " <td>0.0376</td>\n", " <td>0.0289</td>\n", " <td>0.1310</td>\n", " <td>0.0684</td>\n", " <td>0.1044</td>\n", " <td>0.0118</td>\n", " <td>0.1562</td>\n", " <td>0.0585</td>\n", " <td>0.1633</td>\n", " <td>0.1400</td>\n", " <td>0.0073</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>3</td>\n", " <td>0.1291</td>\n", " <td>0.0985</td>\n", " <td>0.0691</td>\n", " <td>0.0356</td>\n", " <td>0.0788</td>\n", " <td>0.0529</td>\n", " <td>0.1185</td>\n", " <td>0.1057</td>\n", " <td>0.1041</td>\n", " <td>0.0075</td>\n", " <td>0.0481</td>\n", " <td>0.1283</td>\n", " <td>0.0238</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>4</td>\n", " <td>0.0453</td>\n", " <td>0.1234</td>\n", " <td>0.0931</td>\n", " <td>0.0126</td>\n", " <td>0.1224</td>\n", " <td>0.0627</td>\n", " <td>0.0269</td>\n", " <td>0.0764</td>\n", " <td>0.0812</td>\n", " <td>0.1337</td>\n", " <td>0.0357</td>\n", " <td>0.0937</td>\n", " <td>0.0930</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5</td>\n", " <td>0.0600</td>\n", " <td>0.0915</td>\n", " <td>0.0667</td>\n", " <td>0.0947</td>\n", " <td>0.0509</td>\n", " <td>0.0335</td>\n", " <td>0.1251</td>\n", " <td>0.0202</td>\n", " <td>0.1012</td>\n", " <td>0.0365</td>\n", " <td>0.1310</td>\n", " <td>0.0898</td>\n", " <td>0.0991</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Id Blues Country Electronic Folk International Jazz Latin \\\n", "0 1 0.0964 0.0884 0.0121 0.1004 0.0137 0.1214 0.0883 \n", "1 2 0.0121 0.0804 0.0376 0.0289 0.1310 0.0684 0.1044 \n", "2 3 0.1291 0.0985 0.0691 0.0356 0.0788 0.0529 0.1185 \n", "3 4 0.0453 0.1234 0.0931 0.0126 0.1224 0.0627 0.0269 \n", "4 5 0.0600 0.0915 0.0667 0.0947 0.0509 0.0335 0.1251 \n", "\n", " New_Age Pop_Rock Rap Reggae RnB Vocal \n", "0 0.0765 0.0332 0.0445 0.1193 0.1019 0.1038 \n", "1 0.0118 0.1562 0.0585 0.1633 0.1400 0.0073 \n", "2 0.1057 0.1041 0.0075 0.0481 0.1283 0.0238 \n", "3 0.0764 0.0812 0.1337 0.0357 0.0937 0.0930 \n", "4 0.0202 0.1012 0.0365 0.1310 0.0898 0.0991 " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_y_sample = pd.read_csv(filepath_or_buffer='data/kaggle-music-genre/submission-random.csv')\n", "test_y_sample.head()" ] }, { "cell_type": "code", "execution_count": 5, "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>Id</th>\n", " <th>Blues</th>\n", " <th>Country</th>\n", " <th>Electronic</th>\n", " <th>Folk</th>\n", " <th>International</th>\n", " <th>Jazz</th>\n", " <th>Latin</th>\n", " <th>New_Age</th>\n", " <th>Pop_Rock</th>\n", " <th>Rap</th>\n", " <th>Reggae</th>\n", " <th>RnB</th>\n", " <th>Vocal</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ "Empty DataFrame\n", "Columns: [Id, Blues, Country, Electronic, Folk, International, Jazz, Latin, New_Age, Pop_Rock, Rap, Reggae, RnB, Vocal]\n", "Index: []" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_y_sample[:0]" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(13000,)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "\n", "train_X = np.array(train_x)\n", "train_Y = np.array(train_y[:]['class_label'])\n", "test_X = np.array(test_x)\n", "\n", "# Getting rid of the first and the last column: Id and msd_track_id\n", "X_train_val = np.array(train_X[:, 1:-1], dtype=float)\n", "X_test = np.array(test_X[:, 1:], dtype=float)\n", "\n", "train_Y.shape" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "dict_keys(['Country', 'Blues', 'Folk', 'Reggae', 'Jazz', 'International', 'RnB', 'New_Age', 'Electronic', 'Rap', 'Pop_Rock', 'Vocal', 'Latin'])" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from collections import Counter\n", "\n", "# Count the freq of the keys in the training labels\n", "counted_labels = Counter(train_Y)\n", "labels_keys = counted_labels.keys()\n", "labels_keys" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['Blues',\n", " 'Country',\n", " 'Electronic',\n", " 'Folk',\n", " 'International',\n", " 'Jazz',\n", " 'Latin',\n", " 'New_Age',\n", " 'Pop_Rock',\n", " 'Rap',\n", " 'Reggae',\n", " 'RnB',\n", " 'Vocal']" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "labels_keys_sorted = sorted(labels_keys)\n", "labels_keys_sorted" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'Blues': 0,\n", " 'Country': 1,\n", " 'Electronic': 2,\n", " 'Folk': 3,\n", " 'International': 4,\n", " 'Jazz': 5,\n", " 'Latin': 6,\n", " 'New_Age': 7,\n", " 'Pop_Rock': 8,\n", " 'Rap': 9,\n", " 'Reggae': 10,\n", " 'RnB': 11,\n", " 'Vocal': 12}" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# This for loop for creating a dictionary/ vocab\n", "key_to_val = {key: val for val, key in enumerate(labels_keys_sorted)}\n", "key_to_val['Country']\n", "key_to_val" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{0: 'Blues',\n", " 1: 'Country',\n", " 2: 'Electronic',\n", " 3: 'Folk',\n", " 4: 'International',\n", " 5: 'Jazz',\n", " 6: 'Latin',\n", " 7: 'New_Age',\n", " 8: 'Pop_Rock',\n", " 9: 'Rap',\n", " 10: 'Reggae',\n", " 11: 'RnB',\n", " 12: 'Vocal'}" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "val_to_key = {val: key for val, key in enumerate(labels_keys_sorted)}\n", "val_to_key[1]\n", "val_to_key" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(13000,)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Y_train_vec = []\n", "for each in train_y[:]['class_label']:\n", "# print(each, key_to_val[each])\n", " Y_train_vec.append(key_to_val[each])\n", "\n", "Y_train_val = np.array(Y_train_vec)\n", "Y_train_val.shape" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "((13000, 26), (10400, 26), dtype('float64'), dtype('float64'))" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# # Pre-processing: normalizing\n", "# def normalize(X):\n", "# # max scale for images 255= 2*8= 8 bit grayscale for each channel\n", "# return (X - X.mean(axis=0)) #/ X.std(axis=0)\n", "# X_train, X_val, X_test = normalize(X=X_train), normalize(X=X_val), normalize(X=X_test)\n", "\n", "# Preprocessing: normalizing the data based on the training set\n", "mean = X_train_val.mean(axis=0)\n", "std = X_train_val.std(axis=0)\n", "\n", "X_train_val, X_test = (X_train_val - mean)/ std, (X_test - mean)/ std\n", "X_train_val.shape, X_test.shape, X_train_val.dtype, X_test.dtype" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "((11700, 26), (1300, 26), (10400, 26), (1300,), (11700,))" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Creating validation set: 10% or 1/10 of the training set or whatever dataset with labels/ annotation\n", "valid_size = X_train_val.shape[0]//10\n", "valid_size\n", "X_val = X_train_val[-valid_size:]\n", "Y_val = Y_train_val[-valid_size:]\n", "X_train = X_train_val[: -valid_size]\n", "Y_train = Y_train_val[: -valid_size]\n", "X_train_val.shape, \n", "X_train.shape, X_val.shape, X_test.shape, Y_val.shape, Y_train.shape \n", "# X_train.dtype, X_val.dtype\n", "# Y_train.dtype, Y_val" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# The model here is refering to the last layer model before softmax function applied.\n", "def softmax(X):\n", " eX = np.exp((X.T - np.max(X, axis=1)).T)\n", " return (eX.T / eX.sum(axis=1)).T\n", "\n", "def cross_entropy(y_pred, y_train):\n", " m = y_pred.shape[0]\n", "\n", " prob = softmax(y_pred)\n", "# print(prob.shape, y_train.shape, m)\n", " log_like = -np.log(prob[range(m), y_train]) # to avoid the division/dividing by zero\n", " data_loss = np.sum(log_like) / m\n", "\n", " return data_loss\n", "\n", "def dcross_entropy(y_pred, y_train): # this is equal for both since the reg_loss (noise) derivative is ZERO.\n", " m = y_pred.shape[0]\n", "\n", " grad_y = softmax(y_pred)\n", " grad_y[range(m), y_train] -= 1.\n", " grad_y /= m\n", "\n", " return grad_y\n", "\n", "# def loss_function(y, y_train):\n", "\n", "# loss = cross_entropy(y, y_train) # softmax is included\n", "# dy = dcross_entropy(y, y_train) # dsoftmax is included\n", "\n", "# return loss, dy" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Model\n", "import impl.layer as l # or from impl.layer import \n", "# from impl.loss import * # import all functions from impl.loss file # import impl.loss as loss_func\n", "from sklearn.utils import shuffle as skshuffle\n", "\n", "class FFNN:\n", "\n", " def __init__(self, D, C, H, L):\n", " self.L = L # number of layers or depth\n", " self.losses = {'train':[], 'train_acc':[], 'valid':[], 'valid_acc':[]}\n", " \n", " # The adaptive/learnable/updatable random feedforward\n", " self.model = []\n", " self.grads = []\n", " low, high = -1, 1\n", " \n", " # Input layer: weights/ biases\n", " m = dict(W=np.random.uniform(size=(D, H), low=low, high=high) / np.sqrt(D / 2.), \n", " b=np.zeros((1, H)))\n", " self.model.append(m)\n", " # Input layer: gradients\n", " self.grads.append({key: np.zeros_like(val) for key, val in self.model[0].items()})\n", "\n", " # Hidden layers: weights/ biases\n", " m_L = []\n", " for _ in range(L):\n", " m = dict(W=np.random.uniform(size=(H, H), low=low, high=high) / np.sqrt(H / 2.), \n", " b=np.zeros((1, H)))\n", " m_L.append(m)\n", " self.model.append(m_L)\n", " # Hidden layer: gradients\n", " grad_L = []\n", " for _ in range(L):\n", " grad_L.append({key: np.zeros_like(val) for key, val in self.model[1][0].items()})\n", " self.grads.append(grad_L)\n", " \n", " # Output layer: weights/ biases\n", " m = dict(W=np.random.uniform(size=(H, C), low=low, high=high) / np.sqrt(H / 2.), \n", " b=np.zeros((1, C)))\n", " self.model.append(m)\n", " # Outout layer: gradients\n", " self.grads.append({key: np.zeros_like(val) for key, val in self.model[2].items()})\n", " \n", " def fc_forward(self, X, W, b):\n", " out = (X @ W) + b\n", " cache = (W, X)\n", " return out, cache\n", "\n", " def fc_backward(self, dout, cache):\n", " W, X = cache\n", "\n", " dW = X.T @ dout\n", " db = np.sum(dout, axis=0).reshape(1, -1) # db_1xn\n", " dX = dout @ W.T # Backprop\n", "\n", " return dX, dW, db\n", "\n", " def train_forward(self, X, train):\n", " caches, ys = [], []\n", " \n", " # Input layer\n", " y, fc_cache = self.fc_forward(X=X, W=self.model[0]['W'], b=self.model[0]['b']) # X_1xD, y_1xc\n", " y, nl_cache = l.tanh_forward(X=y)\n", " X = y.copy() # pass to the next layer\n", " if train:\n", " caches.append((fc_cache, nl_cache))\n", " \n", " # Hidden layers\n", " fc_caches, nl_caches = [], []\n", " for layer in range(self.L):\n", " y, fc_cache = self.fc_forward(X=X, W=self.model[1][layer]['W'], b=self.model[1][layer]['b'])\n", " y, nl_cache = l.tanh_forward(X=y)\n", " X = y.copy() # pass to next layer\n", " if train:\n", " fc_caches.append(fc_cache)\n", " nl_caches.append(nl_cache)\n", " if train:\n", " caches.append((fc_caches, nl_caches)) # caches[1] \n", " \n", " # Output layer\n", " y, fc_cache = self.fc_forward(X=X, W=self.model[2]['W'], b=self.model[2]['b'])\n", " # Softmax is included in loss function\n", " if train:\n", " caches.append(fc_cache)\n", "\n", " return y, caches # for backpropating the error\n", "\n", " def loss_function(self, y, y_train):\n", "# print(y.shape, y_train.shape)\n", "# print('y[0]', y[0])\n", "# print('y_train[:3]', y_train[0])\n", " loss = cross_entropy(y, y_train) # softmax is included\n", " dy = dcross_entropy(y, y_train) # dsoftmax is included\n", " \n", " return loss, dy\n", " \n", " def train_backward(self, dy, caches):\n", " grads = self.grads # initialized by Zero in every iteration/epoch\n", " \n", " # Output layer\n", " fc_cache = caches[2]\n", " # dSoftmax is included in loss function\n", " dX, dW, db = self.fc_backward(dout=dy, cache=fc_cache)\n", " dy = dX.copy()\n", " grads[2]['W'] = dW\n", " grads[2]['b'] = db\n", "\n", " # Hidden layer\n", " fc_caches, nl_caches = caches[1]\n", " for layer in reversed(range(self.L)):\n", " dy = l.tanh_backward(cache=nl_caches[layer], dout=dy) # diffable function\n", " dX, dW, db = self.fc_backward(dout=dy, cache=fc_caches[layer])\n", " dy = dX.copy()\n", " grads[1][layer]['W'] = dW\n", " grads[1][layer]['b'] = db\n", " \n", " # Input layer\n", " fc_cache, nl_cache = caches[0]\n", " dy = l.tanh_backward(cache=nl_cache, dout=dy) # diffable function\n", " dX, dW, db = self.fc_backward(dout=dy, cache=fc_cache)\n", " grads[0]['W'] = dW\n", " grads[0]['b'] = db\n", "\n", " return dX, grads\n", " \n", " def test(self, X):\n", " y_logit, _ = self.train_forward(X, train=False)\n", " \n", " # if self.mode == 'classification':\n", " y_prob = l.softmax(y_logit) # for accuracy == acc\n", " y_pred = np.argmax(y_prob, axis=1) # for loss ==err\n", " \n", " return y_pred, y_logit\n", " \n", " def get_minibatch(self, X, y, minibatch_size, shuffle):\n", " minibatches = []\n", "\n", " if shuffle:\n", " X, y = skshuffle(X, y)\n", "\n", " for i in range(0, X.shape[0], minibatch_size):\n", " X_mini = X[i:i + minibatch_size]\n", " y_mini = y[i:i + minibatch_size]\n", " minibatches.append((X_mini, y_mini))\n", "\n", " return minibatches\n", "\n", " def sgd(self, train_set, val_set, alpha, mb_size, n_iter, print_after):\n", " X_train, y_train = train_set\n", " X_val, y_val = val_set\n", "\n", " # Epochs\n", " for iter in range(1, n_iter + 1):\n", "\n", " # Minibatches\n", " minibatches = self.get_minibatch(X_train, y_train, mb_size, shuffle=True)\n", " idx = np.random.randint(0, len(minibatches))\n", " X_mini, y_mini = minibatches[idx]\n", " \n", " # Train the model\n", " y, caches = self.train_forward(X_mini, train=True)\n", " _, dy = self.loss_function(y, y_mini)\n", " _, grads = self.train_backward(dy, caches) \n", " \n", " # Update the model for input layer\n", " for key in grads[0].keys():\n", " self.model[0][key] -= alpha * grads[0][key]\n", "\n", " # Update the model for the hidden layers\n", " for layer in range(self.L):\n", " for key in grads[1][layer].keys():\n", " self.model[1][layer][key] -= alpha * grads[1][layer][key]\n", "\n", " # Update the model for output layer\n", " for key in grads[2].keys():\n", " self.model[2][key] -= alpha * grads[2][key]\n", " \n", " # Trained model info\n", " y_pred, y_logit = self.test(X_mini)\n", " loss, _ = self.loss_function(y_logit, y_mini) # softmax is included in entropy loss function\n", " self.losses['train'].append(loss)\n", " acc = np.mean(y_pred == y_mini) # confusion matrix\n", " self.losses['train_acc'].append(acc)\n", "\n", " # Validated model info\n", " y_pred, y_logit = self.test(X_val)\n", " valid_loss, _ = self.loss_function(y_logit, y_val) # softmax is included in entropy loss function\n", " self.losses['valid'].append(valid_loss)\n", " valid_acc = np.mean(y_pred == y_val) # confusion matrix\n", " self.losses['valid_acc'].append(valid_acc)\n", " \n", " # Print the model info: loss & accuracy or err & acc\n", " if iter % print_after == 0:\n", " print('Iter: {}, train loss: {:.4f}, train acc: {:.4f}, valid loss: {:.4f}, valid acc: {:.4f}'.format(\n", " iter, loss, acc, valid_loss, valid_acc))\n", "\n", "# # Test the final model\n", "# y_pred, y_logit = nn.test(X_test)\n", "# loss, _ = self.loss_function(y_logit, y_test) # softmax is included in entropy loss function\n", "# acc = np.mean(y_pred == y_test)\n", "# print('Last iteration - Test accuracy mean: {:.4f}, std: {:.4f}, loss: {:.4f}'.format(\n", "# acc.mean(), acc.std(), loss))" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "((11700,), (11700, 26), (1300, 26), (1300,))" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Y_train.shape, X_train.shape, X_val.shape, Y_val.shape" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Iter: 10, train loss: 2.5907, train acc: 0.0312, valid loss: 2.5715, valid acc: 0.0992\n", "Iter: 20, train loss: 2.5493, train acc: 0.1250, valid loss: 2.5652, valid acc: 0.1038\n", "Iter: 30, train loss: 2.5430, train acc: 0.1094, valid loss: 2.5586, valid acc: 0.1100\n", "Iter: 40, train loss: 2.5706, train acc: 0.0469, valid loss: 2.5518, valid acc: 0.1231\n", "Iter: 50, train loss: 2.5442, train acc: 0.1719, valid loss: 2.5458, valid acc: 0.1254\n", "Iter: 60, train loss: 2.5603, train acc: 0.0781, valid loss: 2.5399, valid acc: 0.1277\n", "Iter: 70, train loss: 2.5106, train acc: 0.1250, valid loss: 2.5337, valid acc: 0.1346\n", "Iter: 80, train loss: 2.5196, train acc: 0.1562, valid loss: 2.5280, valid acc: 0.1331\n", "Iter: 90, train loss: 2.5141, train acc: 0.1094, valid loss: 2.5220, valid acc: 0.1408\n", "Iter: 100, train loss: 2.5078, train acc: 0.2188, valid loss: 2.5161, valid acc: 0.1477\n", "Iter: 110, train loss: 2.4926, train acc: 0.2188, valid loss: 2.5101, valid acc: 0.1477\n", "Iter: 120, train loss: 2.5231, train acc: 0.1406, valid loss: 2.5051, valid acc: 0.1523\n", "Iter: 130, train loss: 2.5002, train acc: 0.1875, valid loss: 2.4998, valid acc: 0.1508\n", "Iter: 140, train loss: 2.5050, train acc: 0.1094, valid loss: 2.4943, valid acc: 0.1554\n", "Iter: 150, train loss: 2.5320, train acc: 0.0781, valid loss: 2.4894, valid acc: 0.1600\n", "Iter: 160, train loss: 2.5189, train acc: 0.1094, valid loss: 2.4844, valid acc: 0.1662\n", "Iter: 170, train loss: 2.5043, train acc: 0.1250, valid loss: 2.4792, valid acc: 0.1685\n", "Iter: 180, train loss: 2.4755, train acc: 0.1562, valid loss: 2.4737, valid acc: 0.1692\n", "Iter: 190, train loss: 2.4512, train acc: 0.1875, valid loss: 2.4685, valid acc: 0.1646\n", "Iter: 200, train loss: 2.4473, train acc: 0.1719, valid loss: 2.4634, valid acc: 0.1677\n", "Iter: 210, train loss: 2.4430, train acc: 0.1406, valid loss: 2.4585, valid acc: 0.1754\n", "Iter: 220, train loss: 2.4230, train acc: 0.1875, valid loss: 2.4534, valid acc: 0.1746\n", "Iter: 230, train loss: 2.5086, train acc: 0.1406, valid loss: 2.4489, valid acc: 0.1700\n", "Iter: 240, train loss: 2.4409, train acc: 0.0625, valid loss: 2.4449, valid acc: 0.1785\n", "Iter: 250, train loss: 2.5113, train acc: 0.0625, valid loss: 2.4400, valid acc: 0.1800\n", "Iter: 260, train loss: 2.4097, train acc: 0.2344, valid loss: 2.4344, valid acc: 0.1808\n", "Iter: 270, train loss: 2.3959, train acc: 0.1250, valid loss: 2.4292, valid acc: 0.1838\n", "Iter: 280, train loss: 2.4486, train acc: 0.1094, valid loss: 2.4236, valid acc: 0.1831\n", "Iter: 290, train loss: 2.3971, train acc: 0.1875, valid loss: 2.4182, valid acc: 0.1823\n", "Iter: 300, train loss: 2.4313, train acc: 0.1562, valid loss: 2.4133, valid acc: 0.1823\n", "Iter: 310, train loss: 2.3792, train acc: 0.1719, valid loss: 2.4082, valid acc: 0.1792\n", "Iter: 320, train loss: 2.3941, train acc: 0.2031, valid loss: 2.4038, valid acc: 0.1800\n", "Iter: 330, train loss: 2.4678, train acc: 0.1406, valid loss: 2.3997, valid acc: 0.1846\n", "Iter: 340, train loss: 2.4761, train acc: 0.1250, valid loss: 2.3953, valid acc: 0.1885\n", "Iter: 350, train loss: 2.3540, train acc: 0.1719, valid loss: 2.3902, valid acc: 0.1885\n", "Iter: 360, train loss: 2.3834, train acc: 0.2344, valid loss: 2.3855, valid acc: 0.1862\n", "Iter: 370, train loss: 2.3411, train acc: 0.2500, valid loss: 2.3809, valid acc: 0.1885\n", "Iter: 380, train loss: 2.4164, train acc: 0.1875, valid loss: 2.3767, valid acc: 0.1915\n", "Iter: 390, train loss: 2.3796, train acc: 0.1875, valid loss: 2.3723, valid acc: 0.1946\n", "Iter: 400, train loss: 2.3666, train acc: 0.1719, valid loss: 2.3681, valid acc: 0.1892\n", "Iter: 410, train loss: 2.3503, train acc: 0.1875, valid loss: 2.3641, valid acc: 0.1985\n", "Iter: 420, train loss: 2.3653, train acc: 0.1719, valid loss: 2.3599, valid acc: 0.1977\n", "Iter: 430, train loss: 2.3443, train acc: 0.2188, valid loss: 2.3557, valid acc: 0.2069\n", "Iter: 440, train loss: 2.3326, train acc: 0.1719, valid loss: 2.3514, valid acc: 0.2077\n", "Iter: 450, train loss: 2.3256, train acc: 0.2656, valid loss: 2.3472, valid acc: 0.2115\n", "Iter: 460, train loss: 2.3333, train acc: 0.2031, valid loss: 2.3432, valid acc: 0.2146\n", "Iter: 470, train loss: 2.2365, train acc: 0.3438, valid loss: 2.3392, valid acc: 0.2138\n", "Iter: 480, train loss: 2.3524, train acc: 0.1719, valid loss: 2.3355, valid acc: 0.2146\n", "Iter: 490, train loss: 2.2976, train acc: 0.2500, valid loss: 2.3318, valid acc: 0.2138\n", "Iter: 500, train loss: 2.3990, train acc: 0.1094, valid loss: 2.3285, valid acc: 0.2162\n", "Iter: 510, train loss: 2.3377, train acc: 0.1875, valid 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train acc: 0.2188, valid loss: 2.2901, valid acc: 0.2215\n", "Iter: 630, train loss: 2.2991, train acc: 0.2031, valid loss: 2.2868, valid acc: 0.2223\n", "Iter: 640, train loss: 2.2152, train acc: 0.3438, valid loss: 2.2845, valid acc: 0.2215\n", "Iter: 650, train loss: 2.2793, train acc: 0.1719, valid loss: 2.2817, valid acc: 0.2223\n", "Iter: 660, train loss: 2.2368, train acc: 0.3281, valid loss: 2.2789, valid acc: 0.2238\n", "Iter: 670, train loss: 2.3546, train acc: 0.1719, valid loss: 2.2764, valid acc: 0.2231\n", "Iter: 680, train loss: 2.2970, train acc: 0.1719, valid loss: 2.2736, valid acc: 0.2223\n", "Iter: 690, train loss: 2.2552, train acc: 0.2656, valid loss: 2.2707, valid acc: 0.2246\n", "Iter: 700, train loss: 2.2516, train acc: 0.1562, valid loss: 2.2676, valid acc: 0.2285\n", "Iter: 710, train loss: 2.3488, train acc: 0.1719, valid loss: 2.2657, valid acc: 0.2338\n", "Iter: 720, train loss: 2.2188, train acc: 0.3594, valid loss: 2.2634, valid acc: 0.2323\n", "Iter: 730, train loss: 2.3272, train acc: 0.1250, valid loss: 2.2609, valid acc: 0.2346\n", "Iter: 740, train loss: 2.2616, train acc: 0.3438, valid loss: 2.2583, valid acc: 0.2369\n", "Iter: 750, train loss: 2.1883, train acc: 0.2969, valid loss: 2.2559, valid acc: 0.2346\n", "Iter: 760, train loss: 2.3402, train acc: 0.1562, valid loss: 2.2536, valid acc: 0.2346\n", "Iter: 770, train loss: 2.2177, train acc: 0.2188, valid loss: 2.2520, valid acc: 0.2354\n", "Iter: 780, train loss: 2.3219, train acc: 0.1406, valid loss: 2.2501, valid acc: 0.2331\n", "Iter: 790, train loss: 2.2685, train acc: 0.2500, valid loss: 2.2480, valid acc: 0.2362\n", "Iter: 800, train loss: 2.0864, train acc: 0.2969, valid loss: 2.2456, valid acc: 0.2377\n", "Iter: 810, train loss: 2.3192, train acc: 0.2656, valid loss: 2.2436, valid acc: 0.2385\n", "Iter: 820, train loss: 2.2444, train acc: 0.2656, valid loss: 2.2415, valid acc: 0.2400\n", "Iter: 830, train loss: 2.1735, train acc: 0.2812, valid loss: 2.2391, valid acc: 0.2408\n", "Iter: 840, train loss: 2.1517, train acc: 0.3281, valid loss: 2.2370, valid acc: 0.2431\n", "Iter: 850, train loss: 2.2640, train acc: 0.2188, valid loss: 2.2358, valid acc: 0.2408\n", "Iter: 860, train loss: 2.2336, train acc: 0.2188, valid loss: 2.2343, valid acc: 0.2415\n", "Iter: 870, train loss: 2.1758, train acc: 0.2656, valid loss: 2.2323, valid acc: 0.2392\n", "Iter: 880, train loss: 2.1359, train acc: 0.3438, valid loss: 2.2303, valid acc: 0.2400\n", "Iter: 890, train loss: 2.2167, train acc: 0.3281, valid loss: 2.2288, valid acc: 0.2408\n", "Iter: 900, train loss: 2.2375, train acc: 0.2031, valid loss: 2.2274, valid acc: 0.2462\n", "Iter: 910, train loss: 2.0695, train acc: 0.3438, valid loss: 2.2257, valid acc: 0.2446\n", "Iter: 920, train loss: 2.2029, train acc: 0.1719, valid loss: 2.2238, valid acc: 0.2454\n", "Iter: 930, train loss: 2.1782, train acc: 0.2500, valid loss: 2.2221, valid acc: 0.2454\n", "Iter: 940, train loss: 2.2485, train acc: 0.2188, valid loss: 2.2201, valid acc: 0.2454\n", "Iter: 950, train loss: 2.2405, train acc: 0.2188, valid loss: 2.2186, valid acc: 0.2446\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iter: 960, train loss: 2.1822, train acc: 0.2031, valid loss: 2.2172, valid acc: 0.2431\n", "Iter: 970, train loss: 2.1362, train acc: 0.2656, valid loss: 2.2156, valid acc: 0.2500\n", "Iter: 980, train loss: 2.0514, train acc: 0.3281, valid loss: 2.2141, valid acc: 0.2469\n", "Iter: 990, train loss: 2.2700, train acc: 0.1562, valid loss: 2.2129, valid acc: 0.2431\n", "Iter: 1000, train loss: 2.1257, train acc: 0.2344, valid loss: 2.2118, valid acc: 0.2415\n" ] } ], "source": [ "# Hyper-parameters\n", "n_iter = 1000 # number of epochs\n", "alpha = 1e-2 # learning_rate\n", "mb_size = 64 # 2*10==1024 # width, timestep for sequential data or minibatch size\n", "print_after = 10 # n_iter//10 # print loss for train, valid, and test\n", "num_hidden_units = 32 # number of kernels/ filters in each layer\n", "num_input_units = X_train.shape[1] # noise added at the input lavel as input noise we can use dX or for more improvement\n", "num_output_units = Y_train.max() + 1 # number of classes in this classification problem\n", "# num_output_units = Y_train.shape[1] # number of classes in this classification problem\n", "num_layers = 3 # depth \n", "\n", "# Build the model/NN and learn it: running session.\n", "nn = FFNN(C=num_output_units, D=num_input_units, H=num_hidden_units, L=num_layers)\n", "\n", "nn.sgd(train_set=(X_train, Y_train), val_set=(X_val, Y_val), mb_size=mb_size, alpha=alpha, \n", " n_iter=n_iter, print_after=print_after)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "image/png": 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99zIVhCJiEMluABNGwxuzIG0b3NoKzniIfQW7HKKvau+6UaK9eLG9z2uACCv6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\nJ9Auadww17Wl+Avdx7Ho41j0cSzqo6Z7+pKmADOAnf3+NAE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"text/plain": [ "<matplotlib.figure.Figure at 0x1069f1eb8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# # Display the learning curve and losses for training, validation, and testing\n", "# %matplotlib inline\n", "# %config InlineBackend.figure_format = 'retina'\n", "import matplotlib.pyplot as plt\n", "\n", "plt.plot(nn.losses['train'], label='Train loss')\n", "plt.plot(nn.losses['valid'], label='Valid loss')\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "((1000,), (1000,))" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "loss_train = np.array(nn.losses['train'], dtype=float)\n", "loss_valid = np.array(nn.losses['valid'], dtype=float)\n", "loss_train.shape, loss_valid.shape" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": true }, "outputs": [], "source": [ "loss_train_norm = (loss_train - loss_train.mean(axis=0))/ loss_train.std(axis=0)\n", "loss_valid_norm = (loss_valid - loss_valid.mean(axis=0))/ loss_valid.std(axis=0)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "image/png": 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QHx/6bF65vxyD0Pl+NJqTk2iFZU4GFgKNDcPYZBjGkGi0W1Ko4piY6FxJS6ZZ\njo31LbAuJ2YlJ1thlikpvoOhai6f+Pjj8wHMWPhjkEjVMP01shKnw7AMUv/xf5C61U/wQQj9X3+5\nX8eiRVCrlv812Tl8WJRzlP0JJPhO26TQyzz+9uLvkfLdd1CjRnTb1GjKItGK0hlommZN0zQTTdOs\na5rmO9Fot6QIJI5S8GS4ZmysSIQm4/WlpaymUUhJ8fW92wXfN+WCARt7MKLJF/D2T6RWPgS3t2Rd\niyGQ/qfPU0NMTGBxdor2Ufd/8EExIWvFCt/+OLUprXYn692ezjnaxd3nzhWFXzQaTWSUqSgdr9gF\n30kAZfSOFH4p6NLCV908ycm+k6lUwU9I8K+XC8cLsu9tSPOsV+DlDZjZGXBzJ+hzA6T/CQQX/P37\nrWUpyqr759lnRZ0AdWA6N1fkE1L55ReYNEksOwm+FHjZTrQFP9pPDBpNWUULvgN2wb/pJhFbv3q1\ns4UPwp1Rq5Yl9AkJIiEaiJvDVVdZ7QW28AXt2lnbOVaRuAUjqPj+OpGs7eZO0GsYq/7az6BBYr+b\nb/Ytkj5/vnXObdvALSBq9Wpfwd+503+fO+4QBV7AV3xXrYLPP7fWFZfg6zEBjSY6aMF3wC74l14q\nUi6rOXCk0EtL/6ef4PffLQs/IUHM4gVhVZ9xhlV1Sw0PPXDA38JPT/cPbTx8GConp8PcETBmFSQc\n4v+2NoNW74NRyLhxviGgqptm5Ur3a83K8h1cdpp3oD5F7N1rCfztt0Pfvs6C/9//QvPm7ucNBW3h\nazTRQQu+A4GiXiR2C795czHRSnXpSEte3kCkT334cPH+2mvi6cFu4avpHKTY5uQog8dHqsK0cfTc\n/ZlI1XBrO2g4g9Q0k0WLhOBu3+7fhuS556zonS1bfC18JxeRuq5WLVE6EixLXi3YIj/PnCmeAOy8\n+mroaR5KUvCPHrUmvWk0pZ1TIj1ytPESEWL34UtUl46ssSsF7pJLhLUtLfyuXcVTgN3Ct7fp1q8q\nxzrBhMXQ9HPoNYxDebXo0nc07z7blmwl0YVdxB94QET7pKaKd1XwvbhPtmwR71LonSx8N1H//Xdr\nee1akenUrXaBpCQFX/7N9KCx5lRAW/gOvPaae0ZLiXwKsD8NqC6dlBSxLMWvTRsx+CkFXgq7fQaw\nW6EUOSYgEQXaDVjdF15fTr3DV8Ggixiz+QayC6zE+U5W+5YtltCqA8oytFLFfrzst93CVwXf/r28\n/rq4wag8NKD4AAAgAElEQVQ3syZN4Prr/c9nJ9pjAqGwfLnzuIZGUxrRgu9AcrK7lS/FT4q4XdjU\nCUzSpWPfxy74akTPsmXwww9iuX9/aN3a2hbwyaMwjjo7b4VX1xCfexqfVG4F3Z6EmHx69fLffft2\n6yll7VprfU6O/7527ILvZOGr13zkCEyeLFwj9jECL7mBStLCP9kGjJcu1U8bmvDRgh8iDz1k+bCz\nsvzdMVLoDANGjfJdJ7ELvrr9jDOsjJ4ffuhbvtFu4dvPe/gwkFOB9tnP0GvTr1B3AQyrBy0nA75m\nemGhJdjvvmutd0qSZrfw4+LEU4EUHnsc/uLFvtf0xBPw44/WsfZ+A3z6KVSr5nxtJSn4J9uAcdu2\ncO21Jd0LTWlFC36ItGoFd90llmUUjhPlylmRNnZ/tl3w3Xz29mMDnS893XdwMfZgBkz6RtTb7fo8\nXN8Dav1ctN00heCfdppvcRRV8G+6SaREtlu5sbFw992W4MtZxPLYQYN8+60+wdivVWbs+eEHK+e/\nHW3h+1KSLi5N6UYLfjFw7BikpVmf3QRfWrvSynUqfKIe61YaEcQAsRTeuDjFVbK5K/x3Cay4Cq66\nEi4fBBU2YZpCSO1tqi6dxEQxlqEOtIIQbXUWr8wfpPr/5TXNmuX7ZGK38GfNgvvvt4R13z7/il9e\nBN8wYOPG4PuFysko+LpUpXfq1vXmpjwZyMryDacuDrTgFwP2QVf7D1Rul6Iord4OHfzbUl0jbi4P\nEAO/qj/cxzduxsIvt4v4/X0ZcGtbVja6gZzkDZx5pm87qoVfrpxzQZTYWOf8QuoPS17z+ef75vtx\neppZssRyG6Wni5cqtF4tfDUUNVqcbC4d0IIfCps3l56w2nPPFS7d4kQL/gnA7sOXP1j5aO7VpSMj\ngJxISrKs7vx8l3/yvBT4/kl4ZT3ljmWwv38HZlW8GqqsLtpFFXy3aCE3wVePVa9Zzafvdq3qOMGx\nY743Ca+i62X+RKhowS/9lJbv60Q8iWjBPwG4CZF0gbRpE/xYGbnjRlKSJfL5+UGiX46lUy/rcZLH\nZlE3sTUM6Qb9BkKNX/0sfCdiY0WIpR31WPVHVr++texWQcw+MKy25VV0A904veD0gytpl05urv+T\nS2kRsJOF0vJ9hZqWPBy04J8AnP7hHnvMEsJLL3X/Y8tjzzlHvMvc/PY21Vh+VwtfwTSh8Fgq/6j8\nILyyHra3g6svY0G9XlB3PuAu+IbhLMKqSH/wgbWsZgp1E2W7sKptySehb74JLMDqd1K1qu9gtBfK\nlRMzhAP160Tz2GP+0VmRCJhhhP69lHa04FtowT8BOP3DjRwZ2EUjsT8dzBdaXGQp9+4t3lXBLygI\nHt8uo3RSUoCcCrDwPnj5T/6e1w/+cT0M6caamM8g7pjfsfn5zpEiqoWsDryqOfyl4KsV3Hbt8v9n\nV8cO5M3l4osDD2rJ72r3bvj7b5EYLlSkGG7bdvymWAKCv3evNYBfHOMSwSYVniqcCAGNJlrwTwFu\nvBFuuy38451uFh07Wha/FEPVGs/L8yb4BQXWbGDRWCL8ejOMWQNL/sn83Jfh7vrQdTQkWI8MDz3k\nLPhuhc7Vpw15o+rRw1q3ciU+qSDsx4TiwzdNEUrqlZUrxWQ3ify+a9Wy6vyGylNPwbp1oR0zc6b1\nfzJsmFUhzU0ETBO2boWJE0Pv34kUwk2bSm4cRF5naRF+LfinAOPGeUsf4IaT///HH2HGDLFsF/y0\nNCH2wfzZubnCek1O9t9WLiEOll3D/afNhYnfQq3FcFcD6PYUJIqR4bw8EYFjb9MJn/kBLv2yp3RQ\nb1iqYBiGSMq2dSt+dOoEV1/t+0ThxJAhcMUVYrlNGzG3wgn5lBHqYPDDD4tsoaEwdiy8+aZYVp+O\nnERg5kyRD2n0aBg8OLTznGgyMuDtt0vm3PL/Rgu+hRb8k5zrr4evv/ZdFxdnWcryn1pW3KpWTRQ+\nkZZ7s2bO7R47JsTXKRJHCk5sLLCzlZi89e4cqLpSCH/3Uew5eNDvWCfB79LF92nAbdDWbgVKwT90\nyNe9U1goMpNeeKHvOhAupXnzrPVuvttZs8TM3rFjA+cOkgPT4QwGR/LjVb8vt3Z+/TV83/SJFkD7\n09uJQv5faMG30IJ/kpOUBBdd5L5dioOM9ElOFoIvM3W6/diOHBHiG0jMfCzb3c1h6kR4eyFUWsf4\n1Iasr/MYlLcSu+TmihuNWuDFXnjFTaTswiuLxFepYo1bALz1lnhXxwvUm4UXa1zeHN1cbfKJxJ4v\nacuWEzMAqAq+2xhCfHzx9WXevOi6YUpq0FQLvj9a8Es5R4/Cn3/ClVeKz8nJYiJTo0bisxT8kSN9\nY+ePHhViH0ggHW8GexvBZ+/TZc0cChL2wB3N4YJ7IWUnOTliIFp1x0jxl+d2+6e2h0S+8YZw29jX\n//23eF+/3qoroIqTF2u8cmX3vhiGGDQF60lHfkfyc3H/MNXrcTtXQkLxWfjdu4unoNKOFnx/tOCX\nco4ehdNPt3780id/9tlivRTc1FSRaVE9Li4usGgEuhnkb29O132vwevLIDYXhjZjbsK/iKuwy8dC\nlee/4w5xLicXCjivdxJvdb9vvz3eF+V8wSz8tWsDD6iqfZQ3G9kPGVXlJcNnuD/e3FxvLh01P1Fx\nEM3opGA3pnnzfOsvRwst+P5owS/l2FMfSN99crLIWikjUBISROSJ5O+/hbDJcE6nH2Ug8czOPu7/\nP1gLvnkV3viDnIIj/H11E7jkNqi81qc/8fHiH/pf/3Juz8n/7+RWUAU/KUnkH1HDDIMJfvPm/kXa\nAb74wv+8PmMZWAKyZ0/gc0RCYmLwCmTge7O+8073AXMnvAhLccxadqN7d3jyyei3K/9eJT2Xwita\n8DVBUSc1gWXhJycL18Vpp4niI1dfLdbXrSvSO592mrhZtGwpinwUFkLnzr5tBfrR79ljm0dwoDat\ntrxG3WmrRQnGG86G/n3ZbPwImJ6ihuw4Wf12wa9XTwiGZMOGwOdx803/4x/++0gLX34P8gdpF/wx\nY6Kbsli9xkAWvhT8MWOiH1sfTcH34noKd8xAfWq1oy18f3SJw1KOXfClRa1OxFIHJ7OyxPv8+aIw\nO0CLFuLdLrCBfoTbt/tH+Bw9CilmNZgzChY8AG3eZcyW6+CmKqzkPoj5BxQ6/8s5pTUIFusvr9HN\n4nZK/BYMw7CEwu7Dl+ulS2foUDFBKidHZBR97z2rnUh+vF5KTtp9+KGc72Sz8CG878s0RX2AggLn\n/mrB90db+KUcVRBmzrQseXvZRDtDh4oJQip2wXcS4dhYuPVWsWyfKfzll4pvOS+Fa5vdzvud1sCP\n97Mg/yW4p5aowpXo77B1suadBF/dT849cMsPf9ll/utCsTbdLHz5PmWKcJu5hZqGgvpjV793LxZ+\ncXCiLfxwBT/QsVrw/dGCX4pZvdo3h/7551sunWCC3727mDGr0rSpeJfpBaSFq6ZsLSiwngicYvjV\np4KEBCiXEAurLmd4hQXHY/lXwd2nw4XDId3yv4Tr0oHQfihehMxu4cfGinUyckVulwONToIv+/TE\nE6GnvPXiw7cP2pZ2Cz8cggm6Fnx/SsGfVeNGkyZW+KVEio/TDNpgvP++cFfUri0+S0uzZUtrn/R0\na16AU6SIfZao3Cc2FiuWf+xvUJAAN3eEay6C5p+wbYe/mR7MwvdqWcuqWuBNyORNa/lya92SJZZr\nTL0hxMcHDgX99tvQi1p4FfxwXTpeiIkRg9tqRFNBQeTx+Xl58OKL/uvD6X80BH/q1JMnuZoWfE3I\nSPEJZuE7kZDge6OwV57KyRFVpRo2FJ+dQulUQS4stATfR5z314VZz8ILm2HZNdBxDAV3NISzn4Yk\nEQTfoYNzyUO3sM5A9OplDcoG+3GrmUDlDWfPHv/rklSoEPjGE+xH7LTdiw/fLvihlD0M1Ce5zTDE\nE1/r1ta2886Ds87yfh6J2s/16+Gee0Lrkxvyu3HLeeQlSmfJktDPW1yUGsE3DKOXYRirDcNYaxjG\nA9FoUxMeUnzCEXw70sKvWlW8JyT4lm7cscP/GDVPvp+Fbyc/Cf4YBBN+EOkbKq+DOxtxy5T7WLJ+\nI716+R/ixfq1M3MmTJsmlr1Yc3aXzsUX+4qGuly9emCXTqA+5uc7P3F4ucbYWO+Cv2aNcxTP0qXQ\nuLF/n8A5D838+fDzz4SM2k+3J6xIfPj9+ztv92Lhn0zunlIh+IZhxABjgAuBFsDVhmE0jbRdTXjI\nH1T16pG31bKlEIq77/bfNmQI9Onjv75SJWtZFfyg7petHeGL8TD2V2JiDLilA/TvC/XmAtYvIRwL\nXw4uf/ZZ8Hj1wkL/OPyYGF/BlJlGQYSFhmvhy2sJVAsgUJ0Er4LftKlv7iHZ5sKF/pPQ7H1SBTpc\n14dhwOOPi6fDaI4NBIuvj4bg9+kT3UlhW7aE35doEI2vvyOwzjTNLNM084APAQcp0JwI0tNFaGbd\nupG1Y5rCDVKjhrOgjR8PXbv6r5cFWmQbUmydLPz77nM48f4MRp71PLyYBX9eCJfcDv9sA23fhrij\nYQm+FNDLLw/+o1L91FLwly2DAQOsfQoLrRQP8fHhC748T6DoqEDpkVWCuXScZgc7CbhsRwpTtAR6\n1ChRFCeaFn40BD8Y06aJ7KzRYONGUbfWjdIi+LUAtYbOluPrNCVArVrRL9pcubJzAXUnEX/kEWvZ\n1Yd/HKe6uEXkpcCSf8JrK2DmaGj6OQzPIKvhQ5AWeskmt/q8dgoK/F06dguvsNAS6YIC69rUMNWX\nX/Z2LvAXfK9uK1WwZ8/2396zpxWJpbaj+untyL7IDK2Rlo1UzxMf713ws7KCl/UMJvhe0iOfSJfO\nsWOBnzBPuYlXI0aMKFrOzMwk055KUXNSkpLi7K93+vGqwq66dJxq4LpNmJKZPgUGbDhfvCqth45j\n4LbWsOE8dibcDXQV+wTBq3Ddeis884xYlpa2PTKlsNCyhPPzrZtJoFTLTsg2Ak1481rS8YEH4P77\nRfGXpUvF9z1njnjJftjHFZz+frJP9klnwa7FCft12f83AkUZDRki+h7onCfCwo8m+fmBo5xkP+fO\nnctctSRcFImG4G8FVAdC7ePr/FAFX1P6UcWgWjVRxSouDl56SVRtUi18NWWyZPduqx354xwyJEBI\n6d6GMOMlMZO39bssPncI3JIKP99Fj6oDmDPL3YwPxTUhLXopek6CL9epFr6dvDzvgu/mH/fq0pFs\n2CDmUTRv7t9ne9RKIAtfzlKOxMKXRWZUC1/22/692a/Hy3mDCbmXKJ0TeTNQnx4D9cVuDI8cOTJq\nfYiGS+cXoKFhGBmGYSQAA4BpUWhXc5Kj/ihfe034aA3DGuRVLfyKFf2PHz4cJkzwzdnvSWBy0mDx\nnfRYvhrmPAEtP2BBp1pwzhOQ4hvLKZ8iQhEuu5UbzMJ3E/xDh8Kz8FUCxZi73SRatIB77/XfX7bl\nNCBr71M0BP+PP3w/x8VZ57aPOYQj+CfKwo/WTSE/v+RvPhELvmmaBcBQYCawAvjQNM0oDXNoTmak\nYLz+OvTr5789mIV/+ulw3XW+9XhV8axUScR+u5GfFwPrLoaJM+i4bCGk/wV3NoYB/4AG3wImHTpY\nffGKLA4jRS9cC9+r4Ifj1w0k+ADff++/fygWvpNLR7J5M1xwgfu5JfJYeZ5QBD+UGdHBtkv3kdPY\nVnGIbGGhyK1kx6uFX5xEZQzeNM0Zpmk2MU2zkWmaz0SjTc3Jj7TCnH6cDz0Ed93lK/j2jJJymzpj\n1+4mCiQsqmVcPqexCOt8cROsvQQuvBeGNmVj/ccgbYtjXiA3jhwRNyEpevYfqWn6WvjqYO2gQdZy\nKBZ+qK6bYIJvz3Ok+vBDsfCd9vnqK/juO/dzS+z9i4/3n9Sm9k+lOCx8p1rHxSGyX31lVaBTOWUE\nX1M2kaLiJApPPSVmZcbGwplnioHYd9/13UcOdqrCoC7HxQV2d8jIlEWLlD7kpPHGzTfDG3/A1ElQ\nLhtua0XupddA7UWoMf1uHD0qBF+NxFGxW/j79lnbJk2yloMJvluUjiQ3V2Qlddu2c6d72/a0F6Fa\n+LKOgpPwypDUYMXiZftOFr68dnljKW4L321/LyIbqhDbM9iCcC0OHKgFX1OKkWIQLL/K4sXBf8D2\nUMGKFaF9e/GUoBYmt/Pkk9Cpk0P7Zgxs6yCKs7y8AXa0hcsHwT/bwlnPQYVNrm1KC19it0ZVH/66\ndaIguhMHD3qz8Dt2dN7++OPueXjGjBFlIN1wEny78DkJvj1yyunvJv/eWx1DM0SU0KxZ/seqgt+/\nv9hPfSJScbrR/Pij7+dQBd/pbxGOyG7fbt30JJs3WxMRndpcvFjE4XuJ0ilOtOBrIiaUakvBkCK0\nY4coWF6+PHTrJtZ16uS/v5wfEMi9kRJbERbeB6+uhRkvivDOW9uJIi0dx0CKr6lsF3w7U6eKSKT4\neCui56qr/PfLybF+xNu3C+t51iwrR5AU/CNHnH/sTqGwXnFy6Xix8DfZ7oMxMf6TtqRouc33GDhQ\nZG61C77MOgri6WzqVKs+g5uFX78+TJ4sviN1Up/TMXbs1xutylcZGdb/pOSHH6z0HeGGkmrB15QK\nQhX888+H//3PeZsUocRE38HQRx+F6dOtuHKJdAsFeoIoCvM0Y2BjD/jyv/CfbTD/Iaj1MwxtCoMv\ngLbjody+oIL/ySdChBITLReIXYxACLr8EdesCU8/La591Chru8Tpxx7qLNfnn7eWZ8703ebkw3cS\n/M22OW0bNtjnRQSPLqpTR7zL/sv91eIy4JsPyM2Hv3EjzJ3rHHHjJp4ffGBVcVOPCdelYycvzwop\ndiKQqJd0uUUt+JqICTXdQWoqtGvnvM3tBzhqlJjxa5+r5zQOYMcxrr8gAdZdAp+9Dy9shf/dDI2m\nw7AMlrf8B0dO/xDiDwcUXfWG5Ob6UK9HugHkJDRV8J0e9UPJgAniJuSGkw/fqc9eCrS7jT1s2yau\nV9ZOln8TaRCoTxngTfBlO05Wupt4DhwowlK9uHS8EOrNWAu+5pQmVMH3klDMK04Dx3bxl/u4JpTL\nS4aVV8LHn8KLm0jO6kt23Xfh3lrQ72po9inEHfM7TB2sdfI5qxY+WDcIJ8F3EgK1ZCI4z2Wwn88N\nrz58L/nu5T72J7tateCjj6xMrfJv8sAD1nnV68zNtQr4BBN8dZBcvaZAfSwuC1/2yQ0t+JpTmlBd\nOoH+6UMV/EAuHTlwqbqJgtGqSQV2zLiOQ2O/gVfWEbu1O5z5uijPeOk/of5siPHWSbvgyz56tfBV\ncnKCZ20MFs8fyMI3TTEBzq0fhmFdSyCXzp491vdt/5vYBd+egVRFPXbs2NAFXz1XIMEPl0CC7yUy\nK5x2o4EWfE3EhCr4gX4QobYVyKUj10lhsgu+rNwlSUgQ7oCiY45UJWn5P+G92aJK174MOO9BuK86\n9L0Wmk2F+MOu12S3uGVcv5peQBJMjLyk6A10swxm4U+fLia6eRFFNwtftinFOpjgB8L+xOQk+MEG\nR+03uNdf998v3LBM+7V5GVsItg204GtKAdG08L201aWLtaxa+HFxoliJHSm8dsG3hy6qidAkRX76\n/XVhwUPw1i/w5lLY0gk6vAH31YCrL+PHI+Mhebdfe6oQzJ8v3uUkMPWGEMxfr7qP3Aj03RUWwsqV\nYvmnn8S7Klp33ineQ3HpuN1g7PH3ah/c/vbB4vDthVlke4H6aI/5f+mlwOfdscN77vviculowdec\n9JxoH76sLQuWf94wRA6Z6dP927db+DJu3ilW3VXwVQ7UgV/ugPe/E3n7l/dnRe43cFdDuL67uBEk\n/+03aCtzyzgJfrDr9iL4wXLynHOOWH71VSHwqrhs3Cjeo2Hhu4mWOmHNqX8qXix8r4Lv9aniySdh\n4sTgfYPwXTpqtNThwyL6zGu70UALviZiZBieF847z8qi6IQXC3/QIH8BjInxjz2XyB+YFPOePcW7\nUxF2u+AHneJ/LB2WXcOQlE9g9E746V6o9wPc1YCXdl/C/lpTIMF3SmpJCL69PvGYMdayKlBeLPxg\nYZluESyRWPjh+PDlvl5j33Ny/L8nOzI9SLguHXXfJUvg3//23RbNimBOnNB8+JpTjwMHAqQzdiBY\nDhYvFr5h+Cdji4nxF3B7yJ+MrZc/KifBt8ffBxP8004TE6lME8gvB2suE6+EQ7R76jOmNBwLZ94A\nG7vDiv6w5jJyckRhYFXwg93onKbr2wn1SSuYm+TVV0V4o71vBQXie1HPp7px3EIt7WGZ6vZgFr6a\nu8ipr6Ypqr298461ze7ScUK98RUUBP8OZVK6cC18eZ6YGGdx1xa+5qQmNTU6VZEkoY4HqELjJOBg\n/fBluKDsr7p/3brQqJG/hR/sx5uS4nuOInLL0y5uMLW//w5e2CLEvsVHMLwOU+P78fGKjzmca6l4\nXl5g6y6QEEmffKiCL3PxqInlVHGsUMHZWi0oEN/lzTf7p0AG50FpKF4Lf+9e4X+//HJrv1BcOm++\nKdyB6nfoFL8vzx+uD1/dHijQoLjQgq85qbDP6gzErFnQtq1YdnLp2C1Newin6p+fPx9++y10wX/x\nRfHuJoyAyN//xyD44Et4aSO1j1zC27+9za1ra4o4/6afcyz/WMDauIFuhPKpJNSbpXxqUKOVVEGN\niXG+fin4AGvX+m5TY+adchB5FXy7EeGUyE49Rg3xlPt5sfAlt90mboCq4DvdMNRZw244XaNa+0kL\nvkaDmMZvn2wUiHPP9c25Hsil06mTlYtHiokqGGlpwlq3i24way1Qwix7lA4Ax9LZP/cGbkz4ls6/\nrIOs7tDpZTb0rUFB7+ug0dcQ66/cgdI7S8G3n2vWrMB9lzcIGT0E1vW2aeMu+Pn5luA71Tlwi+KJ\nJCzTSfDVwjl2wffq0rHjZOF7EfxgPvzFi/23a8HXlGnq1w8+m9SNQIO2hYUihXLduta+cr1ECr38\nwckCFm4WvlPIoUrr1i6Cj8gS2b8/fP9lVVGo/d05VPt0BbG72kO3J+HemtD7ZiH+8YepUcPZdSJx\nGzQP9MQAzk8EBQXw3/8KN5E99426jxRk6dKSqMcEE/xANW3dfPiqeJ97rrVsT+SmZjQNZcKVk4Vv\nmmKcZt8+6/zhplZQt2vB12jCRB20dbO+7BOCnARf0qqV7z7Dhvlutw/u2n/kl13mLPhubpeCfTVJ\nXnYXjP8Rxv4Ke5rAWc+KSV4DL2H6trch+W+/40aNsqxtO8EE38nnX1goRLxcOXdRKyjwn0QmiZZL\nx62vbta6vd2CgsgFX7Xw69YVEWZeXDrqtbjdVME9l9Hq1d77Gypa8DWnBIbh7sO3W1Tyh1a7trWv\n3cKXyB9vs2a+6596ytcv62Sh5uf7/+DdBpbz8myTvBbeBxN+4In0rTQ+Npi/02fAXQ3g+kwx27fR\ndEg45DOz1U6wwXQnMTpwwH2mrJpaoUoVsewkwPL7Dib4gRKh2b9PpygdFXs/srKcnwqC4Sb4OTki\nw6v8zgK5dOTyzz87p/MIZOGD//9aNNGCrzklcArLlLhFW5x5pvXZTRzlj9Pe9rXXigIl9v0kslqX\n19KKubnO/U+JS6NJ3gBqLvgE/rMdFjwgwj+7joZ7a/Bu4YW88vMrkP6n37HhWPjTp1vfhZsgFRTA\nE09A587+161a+MFcOoFmGtvbDdXCP3rUSvUcioX/xhvw+ee+x6nHh+LSsY8r2LcHc984GQyRogVf\nc0rgZOFL3HKhtGjhezyIm8B11/kfaxfjYIO7+/fDM894F3wfC992nvj44xE1ecmw/iKYOwLenQMv\nbKVT7C0s3bEUbjwLbmsJXf4D1ZeCUeDT3vr1/m4oNzEJZuHLKJ2kJFHxyy5cgerW2rNlOi3v2eN/\nswhV8EFY2IGOcUNm9wyUVjmQhS/P5+amsvfV7YY0cCA0aRK4r6GiJ15pTgmcJrIYhkjX61bM5PTT\nRflEmXIAxOSdCROsz24Wvl2c7T9uGUESioUfSPBl7VcfctJoGdePB/r0452+hVB3AbR6H9qNg/I7\nuX9JT2h/AfUKzuf00+tRoYLhM5PULW4/UHF6EIIWFyf2kxWrJOqgrZPVroqven51WbqLVIK5Z+zr\nzz7bKsEoUzDbcbOw7dE5ToIcyDq3z/CVAQASe7tyIpadb75xryoWLtrC15wSqFE6MpbfMETpwcsu\ncz/Oa5bKUC18abEGm6ofqE0QohoX5z7TtkgozBjIOge+fAteWwWvL6dnrcug7nw2X9iVmi/UZH/P\n66H5FEg84NNHtzbdRC0/X/RL9k3lppsswZOFUCR2l456/mCTxoIJvv37T00V4xEgKo2FQ6C0yoFc\nOrKvW7aI9zZtfLfbbwhu4xKHDnlL6R0KWvA1pwRqHP7VV3s/LpDg33cf9Oollu3uIlXoatSAs87y\nPc7JMlPzptx4o//2kC18AliaB2vSr+G18Nn71J+6jYU3LCRxd0dRxvGe2nB1b9amvAPlt/sdGszC\n//tvsY/buIn8Tu0htpEIfqguHVXwwyUcC199ihk+3H2fP/6wrj+Qy0nWbI4WWvA1pwSqhe8mVE4+\n1UCC//zzMHq0WLYLm3qObdtErVpJly5w0DdfGgAPP+x+LnC38OPjhRXoJK7ymtavD9CeaVA/vT7l\nV90Ok76G/2yFFf3ZlvwN3NECbm0LPR+GmksAM6jgr1plWfiBqmbZnyCiIfheo3RSU53/Bl6w++5D\nyZYZGxs8xXJhoZin8e674nMgwbfPc4gU7cPXnBIEmmkbCK/5yd0igOxMnCjyucybFzxRnJ1AFj6I\nqKAVK0SRbonsv9uxKkUCnptKs7xBnL5+ENu+yYfaP0Hj6SLNQ/xRRqxty/z4VhRkt4QKXWB/hmPb\n9gRqEq+C7+bDd0Juz8z0duNOS3O38Pv186+N7ESwXDzr10PDhmJZ7ZN9XMOtXemmC1QLwS0QIVy0\nheVZTvUAABhqSURBVK85JQg001bilhcmWLvgTfBNE665Riy/8IJvFlF7PV3ZF7k/iIk9dlTBT0qC\nqlV9twcSfHt4pbwW04TGjY8LcmEcbOoGs56BV9fChLn0qjaEhNgEFuz9BG7pIKJ/LhwOTT+HcvuK\n2o6JcZ+8BaFZ+MHCD1VR9CL4soykE1OnWlk1wT3iJpAPf9kykWzPqU5ysJuXOoMXhLvO7rJ77DHx\nHm3B1xa+5pSgQwcRYaMSLA4dvFv4CQlC5LyG+NndIU4x2X36iNC7SZPE5zvvFFE9r7xi7SNdOiCu\nxy52sj9O8wjs69Q+yXkCvhiwtyHdT2tIj8zL6ZoHF9xdALV+gXpzRGGXvtfCrhY8t7w929O7Uzuv\nJ1DZsU+bNvm2Hkjwly2D7dvFeIgT9qcBuxA6uXQCobYXE+N//J13igyast9uyIyj6t8lWPUy+5PD\npZeKcRGVKVPEu9cnS69owdecEshatJKVK71ZR8GiIKTgly8fmuCD775O7iU5wUcirWaVuDjrxuX0\n45duAaebm/3a1D78+afI6WPfPyfHNmhrxsKWzuK14CFRw7fmEupe+gvzKk3g16Qb4ZbG8Of5sOE8\n2HwWhYXlaNcOfv3Vt317HL7dqn3zTRg50v86ILjge7Hwmzd3bs9J8NU8+d9849wnlZtuspaDCb49\nTt8etqne5E8qC98wjCuAEUAz4EzTNH8NfIRGc2LwOj29Z09Yvtx9uxTg8uWFqIYy81H+sO1PHg89\nZOVtV5EDoSqqS8fJwj982Npmxy746s1ku39wDvHxQvADhmXmpUBWd25v050tH99Hxcq5vPHtIjh9\nFvR8BKr9wcJjbahY62w4dDZs7gpHK3PVVf4WvpwJKzntNIfzHSdYdTAnH74d9buzC77bfmANrnol\nVJeOHfUmf1IJPrAM6AuMjUJfNJoTjqyFG2g7WBZ+KEjB37vXd/1TTznv7yT4qrWXne0u+Opxn3wC\nV14Z2MJ3mh/g5vN3IjHx+BNPboKI/886B+aMgvjD1Lv0Z45WXQCdXoV+18CRyixNP4Otha3ofKwX\nJLSB3FQ/C79yZedzgfuELYld8J3qKri5k6JdVtCrSyeQ4J+UFr5pmmsADKO4k3pqNJETLCOjE5EI\nfqjnc3PpSHHq0sXfL+5k4UvBsbelfnYSfHvBGC+C7ydueSkk7+xJo7ierPkEMAqh0nra372STQVL\nmHLgXvjXcjhUDXa0tdxF2zqwY0cyK1Y4n6+gQOQv+vZbZ0G1u2TsT1XqdYHv3zKYhR8M+w09Ugtf\nndCmffgazQlEiosctA2VUKxHN5eOjOvu1g0+/NB3u5PgB5tBC4FTPnhJ7pWY6B6lk5OjWKZmDOxp\nzBmxjal39B+cVunfDH+0ACr9CTV+FSGh5/+LuJrLGb6qKXzXBVodvwnsbQCITuTnWxO9gln4PXs6\n+/DVfdSnn0jNVfuTSTDBb93avz8qcXGh5//xSlDBNwzjO0Cd72UAJvCwaZpfhnKyEUo+2czMTDK9\nBMNqNFEiHAtfDooahiXGkyd7Pz4UMXFz6ezbZ3324tJxExyvNx8pRF4sfKeUD8eO+buTtm8XA6FP\nPIEYCN7TWLyWDwDgmhuP8e7MX6H2ImjyBZz7EMQdw9jamcS/u/Do+M4M7nkm8fGpQQU/Ls43JFbi\n1cKPFK+1hd3+HwsK5pKVNRcQwQfRJKjgm6Z5frB9vKIKvkZTGlBnOkor2mvqhlBne7q5dAIJkpy+\nr+4TajFzO06Cn5HhO6Eo0MQrHwv/ODKvjLxB2UlOKEe13K7s/KmrtTJtC2btRRyrvQh6PMoHtZYS\nc1lD/m9hZy462AWqdBY3DTPGxyLOzAwu+OpypC4dO6rLKSYm9KIvSUmZtGyZyfbtYr7EmjUuoUth\nEM17m/bja045GjWywgfbt4eaNb0fq+bb90JMjLNL59FHrWLhcmYniCyfl17q346b4O/ebS3bK3iB\n/0Qj9enEKeNoIJeOtPCrVxfJw+SYgZvgO/qqD9SGlVfAzNEw/keu25FN7f+No375Fsz8cyZx110E\n91eBay7i+8KR0GAmzTtv5YEHCx0F363gSnFa+IHmgrgJfkyMmGAnI5uiSUSXahjGPwzD2Ax0Br4y\nDMNDxKpGUzK0ahXeIJgUuylTnHPWuDF1KmzcGHw/mZ7ZycKPjRXWaqNG4vOwYe6iKXHz4e/aZQnQ\niy+6H+9k4TsJvpuFf/iwZWGPGwe//WaNGRw+DF27+h8THy8SzqkZNl97zTd0M85IIO3QmVyVcReT\n+02m5id/wWsrYck/yTWPQrenWJt5JklPJtFmQj0YeCl0Hwmt34PKayg0LfVUhdT+NBKpha9+J15L\nIaoYhvgbJiVF35cfaZTO58DnQXfUaE4CWrSIrIJQqCFyFSqIVzC+/95y3ThZ+CoxMc7uChU3C/+h\nh5xTAUjsJSFVwXeaoOZm4R84YMXByzZUC3/oUGjaFMaPt/olt9lTKqtlKEHcGDp3FrOTCwuBQ9Vh\nTR/2T+kDK6BVO1j4cw6b92+h0VtLxcBww2+gx6NsLr+bTuNawiXt2ZfXHg61h10tSEyMbihMsLBM\niZv1HhMj/k8rV46+ha+jdDSaEiY2Fnbs8B0YloTjEnC7qbnF/9tp0EC8q9apk+C7Wfj5+VaUjBR8\n1cJPSfEXRTVfkMTpSUamhHjiCd90BGo4Z2JcIg0rN2DKEw244op+Retr1D/I6DlLOeft/3G00Vzo\n9x+ouJEdhxrDluawuznsakFuXHOIaSDyDIVBpIO20sIvV04LvkZzSiLznju5dLxy003ChTJwoHvB\nlECYpvtgpirEat/cxE1a+PKmIePiDx4UTyh2V4V8egok+IZh3QCXLXM+r3qT6tfPtjEnlW4Z3WBR\nN6psh8NZQPxhanVaxYaDK6HqSmj7DrtqrIA+22BvI3ETOH4jYFdL2NtQhJoGQAu+RqPxRCQWfteu\nQvAbNhT1dCPFzaWjTswKJviyjSlThOgfOuTvmzZN7xb+/PmB++zVZ14kpHkppB7sAL93KNpWpRrs\n3HsEKq+B01aIG0Hr96DaH5D8N+xsBTvaiIljO9rArjNEYXnZpEfBnznTeb106WjB12hOcUIRfDvX\nXw+DB/uvD3diUTAffmwsLFnifKzdpVOxohDzQ4eEkHlx6XTo4LuPl8HUQNdaWOic8tj+HeflIQrG\n72grXirl9oki8dWXihrCHV+FymuF5X/8JpC7uw0XtWrDN1MrBe+wyzVowddoygBeXTpffimKsKuo\nLg+VcKNO1LY6dLBS9krsfa1Rw0rKJm8Q9pTMhw+LbV5cOv37h9dvN3buhO7dxbJ6fvt3FnBg/1hF\n2JgpXpLYHPEkUH0pVP+Ngqaf8V3t36FeGrnZDWFXM+L3NScvuwb83VTMHShwjwDQgq/RlBG8WvhO\n8ffRRorwyJFwxhn+2/fs8f2sWtey3/Z1boLvZOE7MWKEeLkR7GlGuoTUJwz7dxxy6G5BImxvJ17H\nuXVoIa9NzCKu2p/kVVyJWX0l1JkNVVZDxY2QXR92nUFMdlMK99aF7AawpxEcrIVhxGjB12jKApEM\n2kYbaaUnJjrfeOxzEpwE327h79vn7NKRPv9Agm+aoszj7t0iRt8Jr+4r9fzqdzx9uqg9nJ3trR03\nunSO4YX/1CctrT7knMeyVbBhA7z6Ksz4Lke4gaotI6HWOo7V+Qlavy/GDJKyyTqSQezRGkw4WJst\n9ZvBwsj6oqIFX6M5iZDi8/vvIslWKD78aCMt/HLlvAm+ipPgy8RnThZ+pePubqc89nYCzUMIR/DV\na6tTx3scfSDsJTebNhWvN95APBHsagm7WlJ1n60uQGwOtdr+xdHYHbRrvYltufvsTUfWr6i2ptFo\nIkIKvkyhEA3BD3fQVrXwnZ40Pv3U97M6VuBUwEOuK1fOX/Blxsk6dYL3K5yQUztugh8XFz3B94Ka\nqwmAgkTKHWpKzKZMLqh2LVU33BV5Z9R+RbU1jUYTEVIopAidSJeOfXBXirXq0lFDJdu1893fSfDV\noutynd3CN03LwrfPtHXCXjilb19r2X5zc/PHuw3aSsGvU0fksgkXtWC8iv2zkwtLhmUmJZ1kuXQ0\nGk10kQLvU1e2mBg0yLlQiEQVfNmPYD52iey/KviyjYQEfwu/ShXxHigVhWzfLvgqdsE/dAjOPdd/\nv2AWfkGBCCUNF7enKrebqv3Y4hq01YKv0ZxEyJwzUjCiUfHILSzz/fcDp1tQbzoyVNHrxCaZSkF1\nWezaZbVhF/zUVHG8lyea9u3dt9n7l5Dg/B06PY3I5Y0bYdu2yL57txu1XcCdbqArVogZyVrwNZpT\nnJQUYVnGxAhROpGDtm5ibhiBK2RJ7r/fWq5dG3r08N1+4IC17JYF0ssTzb33ijQSTjhdQ7CbiF3w\nJZHUk3W7DvvNN9ATTWKiFnyN5pSmf//IQwJDwa0oiIp0MQTjnnugXj2xXKGCyALqhtvAaCBxdutr\nsEHpYDcRN8F3ml3sFdkn+03DLuCBIo7i43UcvkajOcEYhpVB02mbl5uG5OabxXswC1+mTg4VJ/EP\nV/Cj4dJZutR/gFrFqc6A2oa28DUaTUhEWqQboHlzZzG3u5yCCb6MxunTRxRltyOFsnfvwO3YzyNn\n0EbTpdOzp3BNhVOZVV5HgwaiTKHELuBa8DUazQnDixUb6IYRiuBPnizcPiBmzM6b57+P1+gkeZ4u\nXeCyy+Dss9376tXCr1fP93q6dROTojIyAh/vhNdB20Buo+IQfO3S0WjKMNdeK2aAOlnbAKNGwXnn\nuR9ftSps2mR9DiT4Xoq/S6F0Em6nthd6SDvg1cL/6y/f1MayL+GExnoNywxm4Ue7xKG28DWaU5xA\ngpWQIKzjLl2EC8POo48GjkdftCg8l4eKU/y+VwvfTqQ+fPXmINsKxSUW7CZhF/BAgh8bq106Go0m\nBH7/HWbMCL7fwoXw2Weht1+jhhWZA5EXAPdqVReX4DvtG4qFL8covAp+r17ubWkfvkajCYlWrcLz\nQYeCahVHS/DDHWgOddD24MHgcx1CEfxgTwV2wW/ZMvB5teBrNJqTimgKfiCXTrhtBxLs8uVDE/xG\njSI7p13wA92MtOBrNJqTDlW0Lr8cLrww/LZKwqVz7rn+1cPcjn/hhcBtyfN7FXx1P7t7Rwu+RqM5\n6VAFf8wYb2MGknvvFZFC9rZOlEsHxGD1n396a9Nr9tJggi8Ha9X2nnjCd99KlWD2bG/n84oWfI1G\nExGRpHAePRpatLA+F4eFH2mKabUvaltz57rvG8yHL7OIqm03aOBbACY+3rm0ZCRowddoNBERzZz9\nkQq+E126OE9wGjfOeX/7nAQ3wZcF0Z0IZuE79T89Hf73P/c2o4EWfI1GExHRFPxIXTpOQnvttVba\naZWaNZ3bsI9BuAl+INz6L33ybjcseUMoX97beUJFC75Go4mI4rDwgwl+KC4dN8IR72DHSMF2G2wN\nNnNWbt+61VvfQiUiwTcM4znDMFYZhrHUMIxPDcPwUIJYo9GcSngpS+iVQHHsoWTl9IKb28V+7lAs\nfJn22S39cyCXjrrdSzH3cIjUwp8JtDBNsw2wDngo8i5pNJrSxBln+OagKUkCWfh2EQ4n4iY2VtTy\ndRNs+T24WfKyD8EEv7iISPBN05xlmqZ8eFkE1I68SxqNprQRrcpcXl0y4bh07AIfjuA3awb33Wd9\nvuACUZ5RIgU/XAtfLQlZHETTh38D8E0U29NoNGUMrzVzwznejpvgB3LppKX5Zv389luoXFksv/UW\nvPmmWHYT/GCDto0awe7dgfsdCUHvy4ZhfAdUU1cBJvCwaZpfHt/nYSDPNM3JxdJLjUajUYiGD99N\n8AcOhKNHrc/yBvDRR877S6te1tkdMiS4hR+IKlWC7xMuQQXfNM3zA203DON64GLAIbmqLyOUPKqZ\nmZlkZmYGO0Sj0ZQhnCz033+Hhx+Ga66x1jkJ/uuvQ8eOgdsfOlTMBgb3QduMDFEHQCL3c2vbSdzD\ndekAzJ07l7lOs7qiQESeN8MwegH/As4xTTNoXfsRkSbO1mg0pzTp6dCkie+6Vq3gyy+DH3vbbcH3\niSRNgpu7yGnA2n4Nkuefh3374Lnn3M9nN4ZHjhzpraMeiHSo5VUgAfjOEN/GItM0b4+4VxqNpkyS\nmAirVwffrzhdOnak4LtNhrJb84H6Jt0+zz7r7dzRJiLBN03TY7JQjUajiR7hCn44Fr50w7hV/opG\nSOo//xl5G17QM201Gk2ZxGtEz7594t3tBhGO4NtvWG3ahN5GOGjB12g0pY5oWPheBT87O/B2twHa\nQNj7Hw0XlReiNF1Co9FoTn7CEfyzzoKLLw68T6j5hMaNs54cQAu+RqPRRJ1wBL9JE5g+3X17ixah\nZ7fs29f3c7QrW7mhBV+j0ZQ6ijN5Wqj88kv46ZwlJ8rC1z58jUZT6khODu+4vn2t2rGRirQkKckq\nWRguWvA1Go3GhVdegWXLQj+uWzf45njGr2gJfjTQPnyNRqNxoWJF97h4r5xMgp+RcWLOowVfo9GU\nSU4WwT96NHKXkFe0S0ej0ZRJiquqVKicKLEHbeFrNJoySEFB9KJ0ShNl8JI1Gk1ZpyyKPWjB12g0\nmjKDFnyNRqMpI2jB12g0mjKCFnyNRqMpI2jB12g0mjKCFnyNRqMpI2jB12g0mjKCFnyNRqMpI2jB\n12g0mjKCFnyNRqMpI2jB12g0mjKCFnyNRqMpI2jB12g0mjKCFnyNRqMpI2jB12g0mjKCFnyNRqMp\nI0Qk+IZhjDIM43fDMH4zDGOGYRjVo9UxjUaj0USXSC3850zTbG2aZltgOvB4FPp0yjN37tyS7sJJ\ng/4uLPR3YaG/i+IhIsE3TfOQ8jEFKIysO2UD/c9sob8LC/1dWOjvoniIuIi5YRj/Bq7l/9s5nxCr\nqjiOf76lQ6WltlDJcUZDRIJIJrLIIvqDSZG1FKLMXZsKF6VG0LYWUS7aRGVmltFUOIGQiasWlaHD\n2DjpiGTjmBNRDdQiSr4tzpFuwui88d15Muf3gcs75/fufef8vu/yO+ee33kPfgfuvugeBUEQBLVw\nwRm+pC8k9VWOQ/n1IQDbL9juAHYAT9Xd4SAIgmBiyHZzPkhaCOy2feMY7zenoSAIgsKwrWZ8zkUt\n6UhaYvtYrj4CDIx1brM6HARBEEyMi5rhS+oGlpKStSeAJ23/1KS+BUEQBE2kaUs6QRAEwaVN7b+0\nlbRa0veSjkraWHd7rUZSu6R9kvpzgvvpbJ8jaY+kI5I+lzS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"text/plain": [ "<matplotlib.figure.Figure at 0x1067e5780>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(loss_train_norm, label='Normalized train loss')\n", "plt.plot(loss_valid_norm, label='Normalized valid loss')\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "image/png": 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TBWSW7dSWZvtpgnJTGVnz0XVxi3RqprPCLynaqYyCSAjW/XQRQ0gICSIkBG8E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\nDKP2lBQEEWkC1gKnAzOBL4nI8aE4ZwBTVHUasBz4ady0Rn+quYn2YML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"text/plain": [ "<matplotlib.figure.Figure at 0x116877f28>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(nn.losses['train_acc'], label='Train accuracy')\n", "plt.plot(nn.losses['valid_acc'], label='Valid accuracy')\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['Id',\n", " 'Blues',\n", " 'Country',\n", " 'Electronic',\n", " 'Folk',\n", " 'International',\n", " 'Jazz',\n", " 'Latin',\n", " 'New_Age',\n", " 'Pop_Rock',\n", " 'Rap',\n", " 'Reggae',\n", " 'RnB',\n", " 'Vocal']" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "heading = labels_keys_sorted.copy()\n", "heading.insert(0, 'Id')\n", "heading" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "((10400, 13),\n", " (10400, 26),\n", " (10400, 13),\n", " (10400, 14),\n", " Id Blues Country Electronic Folk International Jazz Latin \\\n", " 0 1 0.0964 0.0884 0.0121 0.1004 0.0137 0.1214 0.0883 \n", " \n", " New_Age Pop_Rock Rap Reggae RnB Vocal \n", " 0 0.0765 0.0332 0.0445 0.1193 0.1019 0.1038 )" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y_pred, y_logits = nn.test(X_test)\n", "y_prob = l.softmax(y_logits)\n", "y_prob.shape, X_test.shape, y_logits.shape, test_y_sample.shape, test_y_sample[:1]" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pred_list = []\n", "for Id, pred in enumerate(y_prob):\n", "# print(Id+1, pred)\n", " pred_list.append([Id+1, *pred])" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pred_file = open(file='prediction.csv', mode='w')\n", "pred_file.write('\\n') # because of the previous line \n", "\n", "for idx in range(len(heading)):\n", " if idx < len(heading) - 1:\n", " pred_file.write(heading[idx] + ',')\n", " else:\n", " pred_file.write(heading[idx] + '\\n') \n", "\n", "# len(test), test[0]\n", "# for key in test:\n", "for i in range(len(pred_list)): # rows\n", " for j in range(len(pred_list[i])): # cols\n", " if j < (len(pred_list[i]) - 1):\n", " pred_file.write(str(pred_list[i][j]))\n", " pred_file.write(',')\n", " else: # last item before starting a new line\n", " pred_file.write(str(pred_list[i][j]) + '\\n') \n", "\n", "# pred_file.write(-',')\n", "pred_file.close()" ] }, { "cell_type": "code", "execution_count": 35, "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>Id</th>\n", " <th>Blues</th>\n", " <th>Country</th>\n", " <th>Electronic</th>\n", " <th>Folk</th>\n", " <th>International</th>\n", " <th>Jazz</th>\n", " <th>Latin</th>\n", " <th>New_Age</th>\n", " <th>Pop_Rock</th>\n", " <th>Rap</th>\n", " <th>Reggae</th>\n", " <th>RnB</th>\n", " <th>Vocal</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>0.024459</td>\n", " <td>0.029869</td>\n", " <td>0.071955</td>\n", " <td>0.018320</td>\n", " <td>0.037965</td>\n", " <td>0.018433</td>\n", " <td>0.058840</td>\n", " <td>0.013269</td>\n", " <td>0.015400</td>\n", " <td>0.271829</td>\n", " <td>0.288743</td>\n", " <td>0.103627</td>\n", " <td>0.047292</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>0.037203</td>\n", " <td>0.041213</td>\n", " <td>0.077074</td>\n", " <td>0.023895</td>\n", " <td>0.040305</td>\n", " <td>0.018671</td>\n", " <td>0.068189</td>\n", " <td>0.012533</td>\n", " <td>0.024319</td>\n", " <td>0.264086</td>\n", " <td>0.242233</td>\n", " <td>0.102108</td>\n", " <td>0.048172</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>3</td>\n", " <td>0.029063</td>\n", " <td>0.035670</td>\n", " <td>0.092826</td>\n", " <td>0.017152</td>\n", " <td>0.027159</td>\n", " <td>0.011204</td>\n", " <td>0.063797</td>\n", " <td>0.005012</td>\n", " <td>0.034270</td>\n", " <td>0.317006</td>\n", " <td>0.254042</td>\n", " <td>0.088914</td>\n", " <td>0.023884</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>4</td>\n", " <td>0.042361</td>\n", " <td>0.051137</td>\n", " <td>0.084519</td>\n", " <td>0.027309</td>\n", " <td>0.034017</td>\n", " <td>0.021157</td>\n", " <td>0.077631</td>\n", " <td>0.010954</td>\n", " <td>0.028864</td>\n", " <td>0.230577</td>\n", " <td>0.230361</td>\n", " <td>0.106878</td>\n", " <td>0.054233</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5</td>\n", " <td>0.020682</td>\n", " <td>0.024635</td>\n", " <td>0.077866</td>\n", " <td>0.011132</td>\n", " <td>0.022983</td>\n", " <td>0.007787</td>\n", " <td>0.049199</td>\n", " <td>0.004276</td>\n", " <td>0.022132</td>\n", " <td>0.345081</td>\n", " <td>0.318951</td>\n", " <td>0.075108</td>\n", " <td>0.020168</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Id Blues Country Electronic Folk International Jazz \\\n", "0 1 0.024459 0.029869 0.071955 0.018320 0.037965 0.018433 \n", "1 2 0.037203 0.041213 0.077074 0.023895 0.040305 0.018671 \n", "2 3 0.029063 0.035670 0.092826 0.017152 0.027159 0.011204 \n", "3 4 0.042361 0.051137 0.084519 0.027309 0.034017 0.021157 \n", "4 5 0.020682 0.024635 0.077866 0.011132 0.022983 0.007787 \n", "\n", " Latin New_Age Pop_Rock Rap Reggae RnB Vocal \n", "0 0.058840 0.013269 0.015400 0.271829 0.288743 0.103627 0.047292 \n", "1 0.068189 0.012533 0.024319 0.264086 0.242233 0.102108 0.048172 \n", "2 0.063797 0.005012 0.034270 0.317006 0.254042 0.088914 0.023884 \n", "3 0.077631 0.010954 0.028864 0.230577 0.230361 0.106878 0.054233 \n", "4 0.049199 0.004276 0.022132 0.345081 0.318951 0.075108 0.020168 " ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.read_csv(filepath_or_buffer='prediction.csv').head()" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "((10400, 14), (10400, 14))" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.read_csv(filepath_or_buffer='prediction.csv').shape, test_y_sample.shape" ] }, { "cell_type": "code", "execution_count": 140, "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>Id</th>\n", " <th>Blues</th>\n", " <th>Country</th>\n", " <th>Electronic</th>\n", " <th>Folk</th>\n", " <th>International</th>\n", " <th>Jazz</th>\n", " <th>Latin</th>\n", " <th>New_Age</th>\n", " <th>Pop_Rock</th>\n", " <th>Rap</th>\n", " <th>Reggae</th>\n", " <th>RnB</th>\n", " <th>Vocal</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>0.0964</td>\n", " <td>0.0884</td>\n", " <td>0.0121</td>\n", " <td>0.1004</td>\n", " <td>0.0137</td>\n", " <td>0.1214</td>\n", " <td>0.0883</td>\n", " <td>0.0765</td>\n", " <td>0.0332</td>\n", " <td>0.0445</td>\n", " <td>0.1193</td>\n", " <td>0.1019</td>\n", " <td>0.1038</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>0.0121</td>\n", " <td>0.0804</td>\n", " <td>0.0376</td>\n", " <td>0.0289</td>\n", " <td>0.1310</td>\n", " <td>0.0684</td>\n", " <td>0.1044</td>\n", " <td>0.0118</td>\n", " <td>0.1562</td>\n", " <td>0.0585</td>\n", " <td>0.1633</td>\n", " <td>0.1400</td>\n", " 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<td>0.1310</td>\n", " <td>0.0898</td>\n", " <td>0.0991</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Id Blues Country Electronic Folk International Jazz Latin \\\n", "0 1 0.0964 0.0884 0.0121 0.1004 0.0137 0.1214 0.0883 \n", "1 2 0.0121 0.0804 0.0376 0.0289 0.1310 0.0684 0.1044 \n", "2 3 0.1291 0.0985 0.0691 0.0356 0.0788 0.0529 0.1185 \n", "3 4 0.0453 0.1234 0.0931 0.0126 0.1224 0.0627 0.0269 \n", "4 5 0.0600 0.0915 0.0667 0.0947 0.0509 0.0335 0.1251 \n", "\n", " New_Age Pop_Rock Rap Reggae RnB Vocal \n", "0 0.0765 0.0332 0.0445 0.1193 0.1019 0.1038 \n", "1 0.0118 0.1562 0.0585 0.1633 0.1400 0.0073 \n", "2 0.1057 0.1041 0.0075 0.0481 0.1283 0.0238 \n", "3 0.0764 0.0812 0.1337 0.0357 0.0937 0.0930 \n", "4 0.0202 0.1012 0.0365 0.1310 0.0898 0.0991 " ] }, "execution_count": 140, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_y_sample.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 2 }	unlicense
radhikapc/foundation-homework	homework06/Homework06-Dark Sky Forecast API-Radhika_graded.ipynb	1	13371	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Grade: 7 / 7" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import requests\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1 Make a request from the Forecast.io API for where you were born (or lived, or want to visit!) ##\n", "\n", "Tip: Once you've imported the JSON into a variable, check the timezone's name to make sure it seems like it got the right part of the world!\n", "Tip 2: How is north vs. south and east vs. west latitude/longitude represented? Is it the normal North/South/East/West?\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Bangalore is in Asia/Kolkata timezone\n" ] } ], "source": [ "#https://api.forecast.io/forecast/APIKEY/LATITUDE,LONGITUDE,TIME\n", "response = requests.get('https://api.forecast.io/forecast/4da699cf85f9706ce50848a7e59591b7/12.971599,77.594563')\n", "data = response.json()\n", "#print(data)\n", "#print(data.keys())\n", "print(\"Bangalore is in\", data['timezone'], \"timezone\")\n", "timezone_find = data.keys()\n", "#find representation" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The longitude is 77.594563 The latitude is 12.971599\n" ] } ], "source": [ "print(\"The longitude is\", data['longitude'], \"The latitude is\", data['latitude'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. What's the current wind speed? How much warmer does it feel than it actually is?" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The current windspeed at New York is 6.31\n" ] } ], "source": [ "response = requests.get('https://api.forecast.io/forecast/4da699cf85f9706ce50848a7e59591b7/40.712784,-74.005941, 2016-06-08T09:00:46-0400')\n", "data = response.json()\n", "#print(data.keys())\n", "print(\"The current windspeed at New York is\", data['currently']['windSpeed'])\n" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "It is 64.26 warmer it feels than it actually is\n" ] } ], "source": [ "# TA-COMMENT: You want to compare apparentTemperature to another value here... It may feel colder! Or the same. \n", "\n", "#print(data['currently']) - find how much warmer\n", "print(\"It is\",data['currently']['apparentTemperature'], \"warmer it feels than it actually is\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. Moon Visible in New York \n", "\n", "The first daily forecast is the forecast for today. For the place you decided on up above, how much of the moon is currently visible?" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The visibility of moon today in New York is 0.13 and is in the middle of new moon phase and the first quarter moon\n" ] } ], "source": [ "response = requests.get('https://api.forecast.io/forecast/4da699cf85f9706ce50848a7e59591b7/40.712784,-74.005941, 2016-06-08T09:00:46-0400')\n", "data = response.json()\n", "#print(data.keys())\n", "#print(data['daily']['data'])\n", "\n", "now_moon = data['daily']['data']\n", "for i in now_moon:\n", " print(\"The visibility of moon today in New York is\", i['moonPhase'], \"and is in the middle of new moon phase and the first quarter moon\")\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4. What's the difference between the high and low temperatures for today?" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The temparature difference for today approximately is 9\n" ] } ], "source": [ "response = requests.get('https://api.forecast.io/forecast/4da699cf85f9706ce50848a7e59591b7/40.712784,-74.005941, 2016-06-08T09:00:46-0400')\n", "data = response.json()\n", "TemMax = data['daily']['data']\n", "for i in TemMax:\n", " tem_diff = i['temperatureMax'] - i['temperatureMin']\n", " print(\"The temparature difference for today approximately is\", round(tem_diff))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 5. Next Week's Prediction\n", "\n", "Loop through the daily forecast, printing out the next week's worth of predictions. I'd like to know the high temperature for each day, and whether it's hot, warm, or cold, based on what temperatures you think are hot, warm or cold." ] }, { "cell_type": "code", "execution_count": 88, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The high temperature for the day 1 is 66.3 and the low temperature is 56.93\n", "It's a warm day!\n", "The high temperature for the day 2 is 74.47 and the low temperature is 53.1\n", "It's a warm day!\n", "The high temperature for the day 3 is 75.89 and the low temperature is 56.6\n", "It's a warm day!\n", "The high temperature for the day 4 is 75.17 and the low temperature is 59.3\n", "It's a warm day!\n", "The high temperature for the day 5 is 79.85 and the low temperature is 65.16\n", "It's very hot weather\n", "The high temperature for the day 6 is 68.26 and the low temperature is 60.65\n", "It's very hot weather\n", "The high temperature for the day 7 is 74.71 and the low temperature is 62.07\n", "It's very hot weather\n", "The high temperature for the day 8 is 76.22 and the low temperature is 61.69\n", "It's very hot weather\n" ] } ], "source": [ "response = requests.get('https://api.forecast.io/forecast/4da699cf85f9706ce50848a7e59591b7/40.712784,-74.005941')\n", "data = response.json()\n", "temp = data['daily']['data']\n", "#print(temp)\n", "count = 0\n", "for i in temp:\n", " count = count+1\n", " print(\"The high temperature for the day\", count, \"is\", i['temperatureMax'], \"and the low temperature is\", i['temperatureMin'])\n", " if float(i['temperatureMin']) < 40:\n", " print(\"it's a cold weather\")\n", " elif (float(i['temperatureMin']) > 40) & (float(i['temperatureMin']) < 60):\n", " print(\"It's a warm day!\")\n", " else:\n", " print(\"It's very hot weather\")\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 6.Weather in Florida\n", "What's the weather looking like for the rest of today in Miami, Florida? I'd like to know the temperature for every hour, and if it's going to have cloud cover of more than 0.5 say \"{temperature} and cloudy\" instead of just the temperature." ] }, { "cell_type": "code", "execution_count": 104, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The temperature in Miami, Florida on 9th June in the 1 hour is 77.52\n", "and is cloudy\n", "The temperature in Miami, Florida on 9th June in the 2 hour is 77.49\n", "and is cloudy\n", "The temperature in Miami, Florida on 9th June in the 3 hour is 77.69\n", "and is cloudy\n", "The temperature in Miami, Florida on 9th June in the 4 hour is 77.69\n", "and is cloudy\n", "The temperature in Miami, Florida on 9th June in the 5 hour is 77.83\n", "and is cloudy\n", "The temperature in Miami, Florida on 9th June in the 6 hour is 78.02\n", "and is cloudy\n", "The temperature in Miami, Florida on 9th June in the 7 hour is 77.77\n", "and is cloudy\n", "The temperature in Miami, Florida on 9th June in the 8 hour is 77.99\n", "and is cloudy\n", "The temperature in Miami, Florida on 9th June in the 9 hour is 78.94\n", "and is cloudy\n", "The temperature in Miami, Florida on 9th June in the 10 hour is 80.37\n", "and is cloudy\n", "The temperature in Miami, Florida on 9th June in the 11 hour is 82.11\n", "and is cloudy\n", "The temperature in Miami, Florida on 9th June in the 12 hour is 83.76\n", "and is cloudy\n", "The temperature in Miami, Florida on 9th June in the 13 hour is 84.98\n", "and is cloudy\n", "The temperature in Miami, Florida on 9th June in the 14 hour is 85.84\n", "and is cloudy\n", "The temperature in Miami, Florida on 9th June in the 15 hour is 86.27\n", "and is cloudy\n", "The temperature in Miami, Florida on 9th June in the 16 hour is 85.79\n", "and is cloudy\n", "The temperature in Miami, Florida on 9th June in the 17 hour is 85.37\n", "and is cloudy\n", "The temperature in Miami, Florida on 9th June in the 18 hour is 84.97\n", "and is cloudy\n", "The temperature in Miami, Florida on 9th June in the 19 hour is 84.2\n", "and is cloudy\n", "The temperature in Miami, Florida on 9th June in the 20 hour is 83.44\n", "and is cloudy\n", "The temperature in Miami, Florida on 9th June in the 21 hour is 82.71\n", "and is cloudy\n", "The temperature in Miami, Florida on 9th June in the 22 hour is 82.04\n", "and is cloudy\n", "The temperature in Miami, Florida on 9th June in the 23 hour is 81.28\n", "and is cloudy\n", "The temperature in Miami, Florida on 9th June in the 24 hour is 80.47\n", "and is cloudy\n" ] } ], "source": [ "response = requests.get('https://api.forecast.io/forecast/4da699cf85f9706ce50848a7e59591b7/25.761680,-80.191790, 2016-06-09T12:01:00-0400')\n", "data = response.json()\n", "#print(data['hourly']['data'])\n", "Tem = data['hourly']['data']\n", "count = 0\n", "for i in Tem:\n", " count = count +1\n", " print(\"The temperature in Miami, Florida on 9th June in the\", count, \"hour is\", i['temperature'])\n", " if float(i['cloudCover']) > 0.5:\n", " print(\"and is cloudy\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 7. Temperature in Central Park \n", "\n", "What was the temperature in Central Park on Christmas Day, 1980? How about 1990? 2000?" ] }, { "cell_type": "code", "execution_count": 113, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The temperature in Central Park, NY on the Christmas Day of 1980 was 3.57\n", "The temperature in Central Park, NY on the Christmas Day of 1990 was 30.28\n", "The temperature in Central Park, NY on the Christmas Day of 2000 was 20.68\n" ] } ], "source": [ "response = requests.get('https://api.forecast.io/forecast/4da699cf85f9706ce50848a7e59591b7/40.771133,-73.974187, 1980-12-25T12:01:00-0400')\n", "data = response.json()\n", "Temp = data['currently']['temperature']\n", "print(\"The temperature in Central Park, NY on the Christmas Day of 1980 was\", Temp)\n", "\n", "response = requests.get('https://api.forecast.io/forecast/4da699cf85f9706ce50848a7e59591b7/40.771133,-73.974187, 1990-12-25T12:01:00-0400')\n", "data = response.json()\n", "Temp = data['currently']['temperature']\n", "print(\"The temperature in Central Park, NY on the Christmas Day of 1990 was\", Temp)\n", "\n", "response = requests.get('https://api.forecast.io/forecast/4da699cf85f9706ce50848a7e59591b7/40.771133,-73.974187, 2000-12-25T12:01:00-0400')\n", "data = response.json()\n", "Temp = data['currently']['temperature']\n", "print(\"The temperature in Central Park, NY on the Christmas Day of 2000 was\", Temp)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.0" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
poneill/formosa	using_formosa.ipynb	1	59577	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Using formosa" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this notebook we'll explore motif sampling with formosa." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, import the basic motif sampling functions from the formosa library:" ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from formosa import maxent_motifs, uniform_motifs, motif_ic" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The first two are for sampling; the third is for measuring the motif IC in order to validate our results. Let's also set up plotting for our notebook:" ] }, { "cell_type": "code", "execution_count": 73, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "from matplotlib import pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll also set the random seed for reproducibility:" ] }, { "cell_type": "code", "execution_count": 74, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import random\n", "random.seed(1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## MaxEnt Sampling" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now suppose we'd like to sample decamer motifs, each consisting of 20 sites, such that the average IC is 10 bits. Let's say we'd like 100000 of them. Formally speaking, we sample them from the maximum entropy (MaxEnt) distribution over all motifs of dimensions (L=10, N=20) so that mean IC is 10 bits. We can call the maxent_motifs function this way:" ] }, { "cell_type": "code", "execution_count": 75, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [] } ], "source": [ "maxent_samples = maxent_motifs(N=20, L=10, desired_ic=10, num_motifs=100000)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's check our samples:" ] }, { "cell_type": "code", "execution_count": 76, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7fb4a8e73210>" ] }, "execution_count": 76, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fb4aeb5f290>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "maxent_ics = map(motif_ic, maxent_samples)\n", "_ = plt.hist(maxent_ics,bins=100)\n", "plt.xlabel(\"Motif IC for MaxEnt Samples\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The distribution of sampled motif ICs appears to be approximately normal, centered at 10 bits. We can check the mean with a quick confidence interval:" ] }, { "cell_type": "code", "execution_count": 77, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sample mean: 9.999 bits\n", "95% confidence interval for mean: (9.968, 10.029) (bits)\n" ] } ], "source": [ "from formosa_utils import mean, sd\n", "from math import sqrt\n", "mu, sigma = mean(maxent_ics), sd(maxent_ics)\n", "coverage = 1.96 * sigma/sqrt(10000)\n", "print \"sample mean: %1.3f bits\" % mu\n", "print \"95%% confidence interval for mean: (%1.3f, %1.3f) (bits)\" % (mu - coverage, mu + coverage)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "So our mean IC appears to be exactly on target." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Truncated Uniform Sampling" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In our MaxEnt samples we saw that the average IC was exactly what we asked for. Some motifs, however, had substantially more or less than 10 bits of IC. For some applications this is a desirable property, but for others it may be more convenient to be able to restrict the samples to a given IC interval. For these applications we defined the truncated uniform (TU) distribution, which assigns equal probability to mass to all motifs of given dimension having IC $I \\pm \\epsilon$, for desired IC $I$ and tolerance $\\epsilon$. We can sample them with the uniform_motifs function like this:" ] }, { "cell_type": "code", "execution_count": 78, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [] } ], "source": [ "tu_samples = uniform_motifs(N=20, L=10, desired_ic=10, epsilon=0.1, num_motifs=10000)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice that the TU sampler can sometimes be much slower than the MaxEnt sampler. This is because the TU sampler implements a rejection sampling algorithm with a MaxEnt sampler as the proposal distribution. It must exclude all motifs falling outside the desired IC interval. It must also reject some motifs which fall _inside_ it in order to yield a statistically exact sample. Let's check the results:" ] }, { "cell_type": "code", "execution_count": 95, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7fb4acebe390>" ] }, "execution_count": 95, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fb4ad10fa90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "tu_ics = map(motif_ic, tu_samples)\n", "_ = plt.hist(tu_ics,bins=100)\n", "plt.xlabel(\"Motif IC for TU Samples\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice that, in contrast to the MaxEnt samples, the distribution of the TU motif ICs appears to be approximately exponentially distributed over the permissible range of 9.9 to 10.1 bits. We can check this by overlaying a rough exponential fit to the data. Although the details of fitting parameters to truncated exponential data are beyond the scope of this notebook, we find that we can achieve a tolerable fit by eye:" ] }, { "cell_type": "code", "execution_count": 106, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x7fb4ab387650>" ] }, "execution_count": 106, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Cu8+P6lOpfQCRca0wFtGIHL4vls/3QHc+ZzXtivppH4CIpIOk7gOwYI/qP4AlkV/+ZnZQ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\nlgODwvu4+xLgaWA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"text/plain": [ "<matplotlib.figure.Figure at 0x7fb4ab1a6e50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "from math import exp\n", "_ = plt.hist(tu_ics,bins=100)\n", "plt.xlabel(\"Motif IC for TU Samples\")\n", "xs = np.linspace(9.9,10.1,1000)\n", "fit = lambda x:300exp(-15(x-9.9))\n", "plt.plot(xs,map(fit,xs),linewidth=5,color='r',label=\"exponential fit\")\n", "plt.legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The exponential trend is expected behavior; a distribution that assigns equal probability mass to all motifs with IC between 9.9 and 10.1 bits will appear to be biased towards low IC values simply because there exist more motifs to be sampled on the low end than on the high end. Users should bear this phenomenon in mind when sampling from TU distributions." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Convenience functions: Motif Spoofing" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "One common workflow is to begin with a biological motif of interest, estimate its motif IC, then generate a set of synthetic replicates for comparison by parametric bootstrapping. We refer to this operation as \"spoofing\" for short: given the observed data, we spoof it with a set of synthetic motifs of the same dimensions and same (average or approximate) IC. We can then compare the statistics of the biological motif to the distribution of those statistics observed in the synthetic replicates. \n", "\n", "Spoofing is accommodated through a set of convenience functions in formosa, which we import below:" ] }, { "cell_type": "code", "execution_count": 80, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from formosa import spoof_maxent_motifs, spoof_uniform_motifs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In recent years, high-throughput methods have enabled the assembly of very large sequence motifs. For ease of illustration, though, let's consider the collection of LacI binding sites treated by Berg and von Hippel in 1987 [0]:" ] }, { "cell_type": "code", "execution_count": 81, "metadata": { "collapsed": false }, "outputs": [], "source": [ "lacI_motif = [\"CTATCACCGGAAGGGATTA\",\n", " \"CTAACACCGTGCGTGTTGA\",\n", " \"TTACCTCTGGCGGTGATAA\",\n", " \"ATACCACTGGCGGTGATAC\",\n", " \"TTATCTCTGGCGGTGTTGA\",\n", " \"TAACCATCTGCGGTGATAA\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To generate synthetic MaxEnt controls we can call:" ] }, { "cell_type": "code", "execution_count": 82, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [] } ], "source": [ "lacI_spoofs = spoof_maxent_motifs(lacI_motif, num_motifs=10000)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "(TU motifs can be sampled in a similar way.) To verify the output, let's compare the IC of the original LacI motif to the IC distribution of the controls:" ] }, { "cell_type": "code", "execution_count": 83, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "LacI motif IC: 17.530\n", "sample mean: 17.548 bits\n", "95% confidence interval for mean: (17.503, 17.593) (bits)\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fb4b45d66d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "lacI_ic = motif_ic(lacI_motif)\n", "lacI_spoof_ics = map(motif_ic, lacI_spoofs)\n", "_ = plt.hist(lacI_spoof_ics,bins=100,label=\"Replicate IC\")\n", "plt.plot([lacI_ic,lacI_ic],[0,500],linestyle='--',label=\"LacI motif IC\")\n", "plt.xlabel(\"Motif IC\")\n", "plt.legend()\n", "mu, sigma = mean(lacI_spoof_ics), sd(lacI_spoof_ics)\n", "coverage = 1.96 * sigma/sqrt(10000)\n", "print \"LacI motif IC: %1.3f\" % lacI_ic\n", "print \"sample mean: %1.3f bits\" % mu\n", "print \"95%% confidence interval for mean: (%1.3f, %1.3f) (bits)\" % (mu - coverage, mu + coverage)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This indicates good agreement between the observed IC value and those of the replicates." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# References" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[0] Berg OG, von Hippel PH. Selection of DNA binding sites by regulatory proteins. Statistical-mechanical theory and application to operators and promoters. J Mol Biol. 1987 Feb;193(4) 723-750. doi:10.1016/0022-2836(87)90354-8. PMID: 3612791.\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }	gpl-3.0
aufziehvogel/kaggle	two-sigma-rental-listing/notebooks/2.0-sk-numerical-features.ipynb	1	185682	{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Numerical Features\n", "\n", "The most simple features to check are the numerical features. There might be some correlation between the interest rate and the numerical features from the data set like bedrooms or bathrooms." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "import pandas as pd\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "%matplotlib inline\n", "\n", "df = pd.read_json('../data/raw/train.json')\n", "\n", "df['created'] = df['created'].apply(lambda row: pd.to_datetime(row))" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Bedrooms\n", "\n", "First, let's check the relation between the bedrooms and the bathrooms. So first the bedrooms. From our exploration we know that there is not much data available for apartments with 5+ bedrooms, thus we will combine them." ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fac34f66a58>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def relative_count(df, column):\n", " # Calculate counts per bedroom and interest_level\n", " grouped = df.groupby([column, 'interest_level'])[column].count()\n", " grouped_df = pd.DataFrame({column: grouped.index.get_level_values(0),\n", " 'interest_level': grouped.index.get_level_values(1),\n", " 'count': grouped.values})\n", "\n", " # Get the total counts per bedroom\n", " group_counts = df.groupby([column])[column].count()\n", "\n", " # Calculate relative counts per group. This allows us to see more easily\n", " # if there are differences\n", " grouped_df['relative_count'] = grouped_df.apply(\n", " lambda row: row['count'] / group_counts[row[column]], axis=1)\n", " \n", " return grouped_df\n", "\n", "# Combine all apartments with 5+ bedrooms into one category\n", "df_bedrooms = df.copy(deep=True)\n", "df_bedrooms['bedrooms'] = df_bedrooms['bedrooms'].apply(lambda b: str(b) if b <= 4 else '5+')\n", "grouped_df = relative_count(df_bedrooms, 'bedrooms')\n", "\n", "plt.figure(figsize=(8, 6))\n", "sns.barplot(x='bedrooms', y='relative_count', hue='interest_level', data=grouped_df,\n", " hue_order=['low', 'medium', 'high']);" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "We can see that the interest level rises slightly for apartments with more than 1 bedrooms, but for 5 bedrooms and more it falls sharply.\n", "\n", "It's also time to find out, why there are apartments with 0 bedrooms. I was not able to get a definitive anwers for this, but I assume that these are studio apartments. At least renthop either displays Studio or something along the lines of 2 beds on its website, and on Wikipedia I found the following definition for Portugal:\n", "\n", "> Studio apartments are designated T0 (T-Zero). This designation follows the Portuguese house classification system, where apartments are classified by their typology as Tx, with the \"x\" representing the number of independent bedrooms. In the case of the T0, the \"0\" means that the apartment has no independent bedrooms.\n", "\n", "Of course, Portugal is not the US, but I assume that it might be similar in the US (maybe without standards enforcing it).\n", "\n", "At least we know that 0 bed apartments are valid apartments." ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Bathrooms\n", "\n", "We can do the same analysis for bathrooms, but here it makes sense to split the data into groups with 0, 1, 2 or 3+ bathrooms." ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fac3dbc9be0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import math\n", "\n", "# Combine all apartments with 3+ bathrooms into one category and round half bathrooms down\n", "df_bathrooms = df.copy(deep=True)\n", "df_bathrooms['bathrooms'] = df_bathrooms['bathrooms'].apply(lambda x: int(math.floor(x)))\n", "df_bathrooms['bathrooms'] = df_bathrooms['bathrooms'].apply(lambda b: str(b) if b <= 2 else '3+')\n", "grouped_df = relative_count(df_bathrooms, 'bathrooms')\n", "\n", "plt.figure(figsize=(8, 6))\n", "sns.barplot(x='bathrooms', y='relative_count', hue='interest_level', data=grouped_df,\n", " hue_order=['low', 'medium', 'high']);" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "We can see that the interest level is lowest for apartments with 0 bathrooms and also low for apartments with 3+ bathrooms.\n", "\n", "## Bedrooms / Bathrooms\n", "\n", "I guess, there is a relation between the number of bedrooms and the number of bathrooms, so let's check this next." ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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PEy4Zt7t9K3wG+JfGmPsJg9/rgZ9tvO/vCI8N/ofAPuBDjf96rtOZwp8GTriu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\nhOe2+kGjd2EAthUWvCzW6steo42eST1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ErsyMyIjIzIhvfH+/7/cXSDBhfMzue8+ljVpf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P//8zzCwZXH8OXHUcdH9RHqKG2IZSehqHMWTrw19/hRlcuPYiTb6frQrMAMGNWAG9Xwig6TMKbo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\n4L888AynZ1f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"text/plain": [ "<matplotlib.figure.Figure at 0x7fac3bb48908>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.lmplot(x='bedrooms', y='bathrooms', x_jitter=0.5, y_jitter=0.5,\n", " data=df, hue='interest_level', hue_order=['low', 'medium', 'high'], size=8);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This plot is not so simple to read, because I applied a lot of jitter. We can see that apartments with zero or one bedrooms almost all the time have 1 bathroom. For two and three bedrooms there are both apartments with one and with two bathrooms. For more bedrooms the data gets quite spare, but the number of bathrooms rises.\n", "\n", "Apart from this, there are areas which are much more sparse (I check this by plotting only 10% of the data) and have a low interest rate. This is interesting for us:\n", "\n", "- apartments with 0 bathrooms most of the time have low interest (already seen above)\n", "- apartments with 1 bedrooms, but 2 bathrooms have low interest\n", "- apartments with 3 bedrooms and 3 or more bathrooms have low interest level\n", "\n", "So, there seem to be some areas where it's simple to always predict a low interest level. However in the middle, there is everything - the low interest levels are just hidden." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Number of photos\n", "\n", "Next, let's check the relation between the interest rate and the number of pictures. From the initial exploration we know that there are few listings with 11 or more pictures, so let's combine those.\n" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fac3460d710>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import math\n", "\n", "# Combine all apartments with 3+ bathrooms into one category and round half bathrooms down\n", "df_photos = df.copy(deep=True)\n", "df_photos['photo_count'] = df_photos['photos'].apply(len)\n", "df_photos['photo_count'] = df_photos['photo_count'].apply(lambda x: int(math.floor(x)))\n", "df_photos['photo_count'] = df_photos['photo_count'].apply(lambda b: str(b) if b <= 10 else '11+')\n", "grouped_df = relative_count(df_photos, 'photo_count')\n", "\n", "plt.figure(figsize=(8, 6))\n", "sns.barplot(x='photo_count', y='relative_count', hue='interest_level', data=grouped_df,\n", " order=['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11+'],\n", " hue_order=['low', 'medium', 'high']);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see that the interest level for 0 photos is really low (which was expected) and if there are pictures, it has a quite nice distribution with high interest rates at about 4-8 photos." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Price\n", "\n", "The last remaining simple numeric feature is the price. As we have seen during the initial exploration, there are a few apartments with a very high price. So we should split the data into cheap and expensive ones." ] }, { "cell_type": "code", "execution_count": 99, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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DTFTD3e/n13NK0DzFrFmzkhUrlvb7+bPF3r3V3HnXbezcuR3H6By8xw/u11Zj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\nLRvAPakg4z9sektRFDxmbIx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JRMNthOq3EGrcgRWJbcQ0cuRoTjvtDGbPPiXli/Glq86l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\nRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fac4f3b9c50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df_cheaper = df[df['price'] < 10000]\n", "\n", "sns.violinplot(x='interest_level', y='price', data=df_cheaper);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see that there is a high interest rate for apartments cheaper than $2000 per month.\n", "\n", "For the expensive apartments, there is too little data to get any real insight." ] } ], "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
bbglab/adventofcode	2017/ferran/day05/day05.ipynb	1	8338	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Maze of Twisty Trampolines" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "Positive jumps (\"forward\") move downward; negative jumps move upward. For legibility in this example, \n", "these offset values will be written all on one line, with the current instruction marked in parentheses. \n", "\n", "The following steps would be taken before an exit is found:\n", "\n", "(0) 3 0 1 -3 - before we have taken any steps.\n", "\n", "(1) 3 0 1 -3 - jump with offset 0 (that is, don't jump at all). Fortunately, the instruction is then incremented to 1.\n", "\n", "2 (3) 0 1 -3 - step forward because of the instruction we just modified. The first instruction is incremented again, now to 2.\n", "\n", "2 4 0 1 (-3) - jump all the way to the end; leave a 4 behind.\n", "\n", "2 (4) 0 1 -2 - go back to where we just were; increment -3 to -2.\n", "\n", "2 5 0 1 -2 - jump 4 steps forward, escaping the maze.\n", "\n", "In this example, the exit is reached in 5 steps.\n", "\n", "How many steps does it take to reach the exit?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Part 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Input" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "ExecuteTime": { "end_time": "2017-12-05T08:07:50.524192", "start_time": "2017-12-05T08:07:50.500930" }, "collapsed": true }, "outputs": [], "source": [ "import csv\n", "import numpy as np\n", "\n", "with open('input.txt', 'rt') as f_input:\n", " csv_reader = csv.reader(f_input)\n", " l = np.array([int(a[0]) for a in csv_reader])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Class Maze" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "ExecuteTime": { "end_time": "2017-12-05T08:08:25.244500", "start_time": "2017-12-05T08:08:25.231455" }, "collapsed": true }, "outputs": [], "source": [ "class Maze(object):\n", " \n", " def __init__(self, curr_pos, state):\n", " self.curr_pos = curr_pos\n", " self.state = state.copy()\n", " self.length = len(self.state)\n", " \n", " def evolve(self):\n", " self.state[self.curr_pos] += 1\n", " self.curr_pos += self.state[self.curr_pos] - 1\n", " \n", " def outside(self):\n", " return (self.curr_pos >= self.length) or (self.curr_pos < 0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Main" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "ExecuteTime": { "end_time": "2017-12-05T08:08:26.542776", "start_time": "2017-12-05T08:08:26.534013" }, "collapsed": true }, "outputs": [], "source": [ "def steps_maze(l):\n", " maze = Maze(0, l)\n", " count = 0\n", " while not maze.outside():\n", " maze.evolve()\n", " count += 1\n", " return count, maze.state" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Test" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "ExecuteTime": { "end_time": "2017-12-05T08:08:29.161006", "start_time": "2017-12-05T08:08:29.139454" } }, "outputs": [ { "data": { "text/plain": [ "(5, array([ 2, 5, 0, 1, -2]))" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "t = np.array([0, 3, 0, 1, -3])\n", "steps_maze(t)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Solution" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "ExecuteTime": { "end_time": "2017-12-05T08:08:33.137792", "start_time": "2017-12-05T08:08:32.489860" } }, "outputs": [ { "data": { "text/plain": [ "(387096, array([ 2, 8, 15, ..., -697, -80, -681]))" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "steps_maze(l)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Part 2" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "Now, the jumps are even stranger: after each jump, if the offset was three or more, instead decrease it by 1. Otherwise, increase it by 1 as before." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Class Maze with new dynamics" ] }, { "cell_type": "code", "execution_count": 78, "metadata": { "ExecuteTime": { "end_time": "2017-12-05T08:26:17.593348", "start_time": "2017-12-05T08:26:17.570490" } }, "outputs": [], "source": [ "class NewMaze(Maze):\n", " \n", " def evolve(self):\n", " if self.state[self.curr_pos] >= 3:\n", " self.state[self.curr_pos] -= 1\n", " self.curr_pos += self.state[self.curr_pos] + 1\n", " else:\n", " self.state[self.curr_pos] += 1\n", " self.curr_pos += self.state[self.curr_pos] - 1" ] }, { "cell_type": "code", "execution_count": 79, "metadata": { "ExecuteTime": { "end_time": "2017-12-05T08:26:21.819334", "start_time": "2017-12-05T08:26:21.797771" }, "collapsed": true }, "outputs": [], "source": [ "def steps_new_maze(l):\n", " new_maze = NewMaze(0, l)\n", " count = 0\n", " while not new_maze.outside():\n", " new_maze.evolve()\n", " count += 1\n", " return count, new_maze.curr_pos, new_maze.state" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### Test" ] }, { "cell_type": "code", "execution_count": 92, "metadata": { "ExecuteTime": { "end_time": "2017-12-05T08:26:53.442457", "start_time": "2017-12-05T08:26:53.423662" } }, "outputs": [ { "data": { "text/plain": [ "(10, 5, array([ 2, 3, 2, 3, -1]))" ] }, "execution_count": 92, "metadata": {}, "output_type": "execute_result" } ], "source": [ "t = np.array([0, 3, 0, 1, -3])\n", "steps_new_maze(t)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Solution" ] }, { "cell_type": "code", "execution_count": 93, "metadata": { "ExecuteTime": { "end_time": "2017-12-05T08:27:56.899349", "start_time": "2017-12-05T08:27:03.399155" } }, "outputs": [ { "data": { "text/plain": [ "(28040648, 1097, array([ 2, 2, 3, ..., -76, 3, -681]))" ] }, "execution_count": 93, "metadata": {}, "output_type": "execute_result" } ], "source": [ "steps_new_maze(l)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:adventofcode]", "language": "python", "name": "conda-env-adventofcode-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" }, "toc": { "colors": { "hover_highlight": "#DAA520", "running_highlight": "#FF0000", "selected_highlight": "#FFD700" }, "moveMenuLeft": true, "nav_menu": { "height": "66px", "width": "252px" }, "navigate_menu": true, "number_sections": true, "sideBar": true, "threshold": 4, "toc_cell": false, "toc_section_display": "block", "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }	mit
chbrown/tsa	notebooks/liwc.ipynb	1	18585	{ "metadata": { "name": "", "signature": "sha256:b2c6096660c17c5ceb50a7bc1a9f191b479860e37e8c21e61673b293a1a08427" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import IPython\n", "import re\n", "import numpy as np\n", "import pandas as pd\n", "from tsa.science import numpy_ext as npx\n", "from collections import Counter\n", "\n", "import viz\n", "\n", "from sklearn import metrics, cross_validation\n", "from sklearn import linear_model\n", "\n", "from tsa import stdout, stderr\n", "from tsa.lib import tabular, datetime_extra\n", "from tsa.lib.timer import Timer\n", "from tsa.models import Source, Document, create_session\n", "from tsa.science import features, models, timeseries\n", "from tsa.science.corpora import MulticlassCorpus\n", "from tsa.science.plot import plt, figure_path, distinct_styles, ticker\n", "from tsa.science.summarization import metrics_dict, metrics_summary" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "documents = Source.from_name('sb5b')\n", "full_corpus = MulticlassCorpus(documents)\n", "full_corpus.apply_labelfunc(lambda doc: doc.label)\n", "# empty X will still have a shape of (1, 0)\n", "intercept_features = full_corpus.extract_features(lambda doc: 1, features.intercept)\n", "print full_corpus" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "<MulticlassCorpus X = (106702, 1), y = (106702,)>\n" ] } ], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "polar_classes = [full_corpus.class_lookup[label] for label in ['For', 'Against']]\n", "polar_indices = np.in1d(full_corpus.y, polar_classes)\n", "polar_corpus = full_corpus.subset(rows=polar_indices)\n", "print polar_corpus" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "<MulticlassCorpus X = (13627, 1), y = (13627,)>\n" ] } ], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "# liwc_counts, liwc_categories = features.liwc([doc.document for doc in full_corpus.data])\n", "liwc_features = polar_corpus.extract_features(lambda doc: doc.document, features.liwc)\n", "ngrams_features = polar_corpus.extract_features(lambda doc: doc.document,\n", " features.ngrams, ngram_max=2, min_df=2, max_df=1.0)\n", "# ngram_max=2, min_df=0.001, max_df=0.95\n", "all_features = np.concatenate([intercept_features, liwc_features, ngrams_features])\n", "print polar_corpus" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "<MulticlassCorpus X = (13627, 43449), y = (13627,)>\n" ] } ], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "posemo_features = npx.bool_mask_to_indices(polar_corpus.feature_names == 'posemo')\n", "negemo_features = npx.bool_mask_to_indices(polar_corpus.feature_names == 'negemo')\n", "emo_features = npx.bool_mask_to_indices(\n", " np.in1d(polar_corpus.feature_names, ['posemo', 'negemo']))\n", "# liwc_features, all_features\n", "print posemo_features, negemo_features, emo_features" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[43] [38] [38 43]\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 14, "text": [ "(1,)" ] } ], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "def corpus_mean_accuracy(corpus, penalty='l2', test_size=0.1, n_iter=10):\n", " folds = cross_validation.StratifiedShuffleSplit(corpus.y, test_size=test_size, n_iter=n_iter)\n", " accuracies = []\n", " for fold_index, (train_indices, test_indices) in enumerate(folds):\n", " train_corpus = corpus.subset(train_indices)\n", " test_corpus = corpus.subset(test_indices)\n", " \n", " model = linear_model.LogisticRegression(fit_intercept=False, penalty=penalty)\n", " model.fit(train_corpus.X, train_corpus.y)\n", " pred_y = model.predict(test_corpus.X)\n", " accuracy = metrics.accuracy_score(test_corpus.y, pred_y)\n", " accuracies += [accuracy]\n", " return np.mean(accuracies)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [ "feature_sets = [\n", " ('Unigrams, bigrams, LIWC', all_features),\n", " ('Unigrams, bigrams', ngrams_features),\n", " ('LIWC (all)', liwc_features),\n", " ('LIWC (posemo, negemo)', emo_features),\n", " ('LIWC (posemo)', posemo_features),\n", " ('LIWC (negemo)', negemo_features),\n", " ('Baseline', intercept_features),\n", " ]\n", "\n", "print '{:s} & {:s} & {:s} \\\\\\\\'.format('Features', 'Number of features', 'Accuracy')\n", "for name, selected_features in feature_sets:\n", " subcorpus = polar_corpus.subset(features=selected_features)\n", " accuracy = corpus_mean_accuracy(subcorpus) \n", " print '{:s} & {:d} & {:.2%} \\\\\\\\'.format(\n", " name, selected_features.size, accuracy).replace('%', '\\\\%')\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Features & Number of features & Accuracy \\\\\n", "Unigrams, bigrams, LIWC & 43449 & 96.10\\% \\\\" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Unigrams, bigrams & 43384 & 96.09\\% \\\\" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "LIWC (all) & 64 & 81.67\\% \\\\" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "LIWC (posemo, negemo) & 2 & 52.58\\% \\\\" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "LIWC (posemo) & 1 & 41.75\\% \\\\" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "LIWC (negemo) & 1 & 39.33\\% \\\\" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Baseline & 1 & 79.53\\% \\\\" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] } ], "prompt_number": 18 }, { "cell_type": "code", "collapsed": false, "input": [ "posemo_corpus = polar_corpus.subset(features=emo_features)\n", "Counter(posemo_corpus.y)\n", "print 'majority_class = {:.2%}'.format(10842. / (10842. + 2785.))\n", "# print posemo_corpus.X.toarray()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "majority_class = 79.56%\n" ] } ], "prompt_number": 55 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### posemo / negemo correlation\n", "\n", "We want to show that posemo counts does not correlate with For / Against,\n", "but much more with volume. " ] }, { "cell_type": "code", "collapsed": false, "input": [ "times = np.array([doc.published for doc in full_corpus.data]).astype('datetime64[s]')\n", "\n", "model = linear_model.LogisticRegression(fit_intercept=False, penalty='l2')\n", "model.fit(polar_corpus.X[:, ngrams_features], polar_corpus.y)\n", "full_corpus_pred_y = model.predict(full_corpus.X[:, ngrams_features])" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[4 4 4 ..., 4 0 0]\n" ] } ], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "print times.size, full_corpus_pred_y.size\n", "full_corpus_pred_labels = full_corpus.labels[full_corpus_pred_y]\n", "\n", "values = full_corpus_pred_labels.reshape(-1, 1)\n", "\n", "bin_edges, bin_values = timeseries.binned_timeseries(\n", " times, values,\n", " time_units_per_bin=7, time_unit='D', statistic='count')\n", "print bin_edges\n", "print bin_values.ravel()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "14383 14383\n", "['2011-07-28T19:00:00-0500' '2011-08-04T19:00:00-0500'\n", " '2011-08-11T19:00:00-0500' '2011-08-18T19:00:00-0500'\n", " '2011-08-25T19:00:00-0500' '2011-09-01T19:00:00-0500'\n", " '2011-09-08T19:00:00-0500' '2011-09-15T19:00:00-0500'\n", " '2011-09-22T19:00:00-0500' '2011-09-29T19:00:00-0500'\n", " '2011-10-06T19:00:00-0500' '2011-10-13T19:00:00-0500'\n", " '2011-10-20T19:00:00-0500' '2011-10-27T19:00:00-0500'\n", " '2011-11-03T19:00:00-0500'] [ 350. 449. 640. 570. 578. 437. 439. 522. 443. 732.\n", " 951. 1239. 1960. 1969. 3104.]\n" ] } ], "prompt_number": 27 }, { "cell_type": "code", "collapsed": false, "input": [ "bins = dict(total=bin_values.ravel())\n", "for label in ['For', 'Against']:\n", " indices = full_corpus_pred_y == full_corpus.class_lookup[label]\n", " # by week\n", "\n", " bin_edges, bin_values = timeseries.binned_timeseries(\n", " times[indices], values[indices],\n", " time_units_per_bin=7, time_unit='D', statistic='count')\n", " bin_values = bin_values.ravel()\n", " print bin_edges, bin_values\n", " # bin_values = npx.exponential_decay(bin_values.ravel(), window=14, alpha=0.75)\n", "# plt.plot(bin_edges, bin_values, label=label, styles.next())\n", "\n", "# datetime64_formatter = datetime_extra.datetime64_formatter\n", "# axes = plt.gca()\n", "# axes.xaxis.set_major_formatter(ticker.FuncFormatter(datetime_extra.datetime64_formatter))\n", "# axes.grid(False)\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "['2011-07-28T19:00:00-0500' '2011-08-04T19:00:00-0500'\n", " '2011-08-11T19:00:00-0500' '2011-08-18T19:00:00-0500'\n", " '2011-08-25T19:00:00-0500' '2011-09-01T19:00:00-0500'\n", " '2011-09-08T19:00:00-0500' '2011-09-15T19:00:00-0500'\n", " '2011-09-22T19:00:00-0500' '2011-09-29T19:00:00-0500'\n", " '2011-10-06T19:00:00-0500' '2011-10-13T19:00:00-0500'\n", " '2011-10-20T19:00:00-0500' '2011-10-27T19:00:00-0500'\n", " '2011-11-03T19:00:00-0500'] [ 99. 94. 91. 123. 94. 29. 34. 30. 56. 92. 78. 170.\n", " 653. 812. 673.]\n", "['2011-07-28T19:00:00-0500' '2011-08-04T19:00:00-0500'\n", " '2011-08-11T19:00:00-0500' '2011-08-18T19:00:00-0500'\n", " '2011-08-25T19:00:00-0500' '2011-09-01T19:00:00-0500'\n", " '2011-09-08T19:00:00-0500' '2011-09-15T19:00:00-0500'\n", " '2011-09-22T19:00:00-0500' '2011-09-29T19:00:00-0500'\n", " '2011-10-06T19:00:00-0500' '2011-10-13T19:00:00-0500'\n", " '2011-10-20T19:00:00-0500' '2011-10-27T19:00:00-0500'\n", " '2011-11-03T19:00:00-0500'] [ 251. 355. 549. 447. 484. 408. 405. 492. 387. 640.\n", " 873. 1069. 1307. 1157. 2431.]\n" ] } ], "prompt_number": 30 }, { "cell_type": "code", "collapsed": false, "input": [ "for liwc_category in ['posemo', 'negemo']:\n", " plt.cla()\n", " styles = distinct_styles()\n", " counts = liwc_counts[:, liwc_categories.index(liwc_category)].toarray()\n", " time_hist('Overall %s' % liwc_category, full_corpus_times, counts,\n", " statistic='sum', styles.next())\n", " for label in ['For', 'Against']:\n", " indices = full_pred_y == full_corpus.class_lookup[label]\n", " time_hist('%s-class %s' % (label, liwc_category),\n", " full_corpus_times[indices], counts[indices], statistic='sum', *styles.next())\n", " plt.title('LIWC category: %s' % liwc_category)\n", " plt.ylabel('Frequency')\n", " plt.xlabel('Date')\n", " axes = plt.gca()\n", " axes.xaxis.set_major_formatter(ticker.FuncFormatter(datetime_extra.datetime64_formatter))\n", " axes.grid(False)\n", " plt.xlim(np.array(npx.bounds(full_corpus_times)).astype(float))\n", " plt.gcf().set_size_inches(8, 5)\n", " plt.legend(loc='best')\n", " plt.savefig(figure_path('liwc-%s-for-vs-against.pdf' % liwc_category))\n", "\n", "raise IPython.embed()\n", "\n", "# convert vector to column matrix\n", "values = full_pred_y.reshape((-1, 1))\n", "\n", "plt.cla()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## LIWC visualization" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from lexicons import Liwc" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "lexicon = Liwc()\n", "document = \"\"\"I do live in OH, \\& yes, it's awful. NO on \\#Issue2 RT @RachelAnneLevy: I don't live in Ohio but from what i can tell \\#sb5 sounds awful.\"\"\"" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "counter = Counter(lexicon.read_document(document))\n", "print counter" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Counter({u'funct': 14, u'pronoun': 5, u'verb': 4, u'auxverb': 4, u'preps': 4, u'present': 4, u'ppron': 3, u'i': 3, u'space': 3, u'relativ': 3, u'cogmech': 2, u'ipron': 2, u'assent': 2, u'negemo': 2, u'negate': 2, u'affect': 2, u'hear': 1, u'percept': 1, u'conj': 1, u'social': 1, u'excl': 1})\n" ] } ], "prompt_number": 16 }, { "cell_type": "code", "collapsed": false, "input": [ "for category, count in counter.most_common(100):\n", " print '%s\\t%s' % (category, count)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "funct\t14\n", "pronoun\t5\n", "verb\t4\n", "auxverb\t4\n", "preps\t4\n", "present\t4\n", "ppron\t3\n", "i\t3\n", "space\t3\n", "relativ\t3\n", "cogmech\t2\n", "ipron\t2\n", "assent\t2\n", "negemo\t2\n", "negate\t2\n", "affect\t2\n", "hear\t1\n", "percept\t1\n", "conj\t1\n", "social\t1\n", "excl\t1\n" ] } ], "prompt_number": 18 }, { "cell_type": "code", "collapsed": false, "input": [ "for match in re.finditer(r\"[a-z]['a-z]\", document, re.I):\n", " token = match.group(0)\n", " matches = [match for match in lexicon.read_token(token.lower())]\n", " print '%s & %s' % (token, ' & '.join(matches))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "I & funct & pronoun & ppron & i\n", "do & verb & funct & auxverb & present\n", "live & \n", "in & funct & preps & space & relativ\n", "OH & assent\n", "yes & assent\n", "it's & funct & pronoun & ipron & verb & auxverb & present\n", "awful & affect & negemo\n", "NO & funct & negate\n", "on & funct & preps & space & relativ\n", "Issue & cogmech\n", "RT & \n", "RachelAnneLevy & \n", "I & funct & pronoun & ppron & i\n", "don't & verb & funct & auxverb & present & negate\n", "live & \n", "in & funct & preps & space & relativ\n", "Ohio & \n", "but & funct & conj & cogmech & excl\n", "from & funct & preps\n", "what & funct & pronoun & ipron\n", "i & funct & pronoun & ppron & i\n", "can & verb & funct & auxverb & present\n", "tell & social\n", "sb & \n", "sounds & percept & hear\n", "awful & affect & negemo\n" ] } ], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }	mit
mph-/lcapy	doc/examples/notebooks/smodel2.ipynb	1	26580	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n" }, "metadata": { "image/png": { "height": 281, "width": 355 } }, "output_type": "display_data" } ], "source": [ "from lcapy import Circuit\n", "cct = Circuit(\"\"\"\n", "V1 1 0, {12 *u(t)}; down\n", "C1 1 2 1; right=2, v=v_C\n", "R1 2 0_1 2; down=2, i=i, v=v_R\n", "W 0 0_1; right\n", "\"\"\")\n", "cct.draw()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n" }, "metadata": { "image/png": { "height": 275, "width": 347 } }, "output_type": "display_data" } ], "source": [ "scct=cct.s_model()\n", "scct.draw()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "odict_keys(['V1', 'ZC1', 'ZR1', 'WWanon1'])" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "scct.elements.keys()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "$$\\frac{6}{s^{2} + \\frac{s}{2}}$$" ], "text/plain": [ " 6 \n", "──────\n", " 2 s\n", "s + ─\n", " 2" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "scct['ZC1'].V" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.7" } }, "nbformat": 4, "nbformat_minor": 1 }	lgpl-2.1
mbdevpl/open-fortran-parser-xml	examples.ipynb	1	4330	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Open Fortran Parser XML wrapper examples" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These examples are for Python wrapper to the OFP XML. Run `ant` to build the OFP XML itself before executing them. " ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pathlib\n", "import tempfile\n", "import xml.etree.ElementTree as ET\n", "\n", "import open_fortran_parser" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<ofp version=\"0.8.5-1\">\n", " <file col_begin=\"6\" col_end=\"24\" line_begin=\"2\" line_end=\"8\" path=\"/home/mateusz/Projects/open-fortran-parser-xml/test/examples/empty.f\">\n", " <start-of-file filename=\"test/examples/empty.f\" path=\"/home/mateusz/Projects/open-fortran-parser-xml/test/examples/empty.f\" rule=\"-2\" />\n", " <comment col_begin=\"6\" col_end=\"36\" line_begin=\"2\" line_end=\"2\" text=\"! minimalistic Fortran program\" />\n", " <program col_begin=\"6\" col_end=\"24\" line_begin=\"4\" line_end=\"8\" name=\"empty\">\n", " <main-program__begin addendum=\"begin\" rule=\"1101\" />\n", " <header />\n", " <program-stmt col_begin=\"6\" col_end=\"20\" eos=\" \" id=\"empty\" line_begin=\"4\" line_end=\"4\" programKeyword=\"program\" rule=\"1102\" />\n", " <body col_begin=\"6\" col_end=\"20\" line_begin=\"6\" line_end=\"6\">\n", " <specification col_begin=\"6\" col_end=\"20\" declarations=\"0\" implicits=\"1\" imports=\"0\" line_begin=\"6\" line_end=\"6\" uses=\"0\">\n", " <declaration col_begin=\"6\" col_end=\"20\" line_begin=\"6\" line_end=\"6\" subtype=\"none\" type=\"implicit\">\n", " <implicit-stmt col_begin=\"6\" col_end=\"20\" eos=\" \" hasImplicitSpecList=\"false\" implicitKeyword=\"implicit\" line_begin=\"6\" line_end=\"6\" noneKeyword=\"none\" rule=\"549\" />\n", " </declaration>\n", " <declaration />\n", " <specification-part numDeclConstructs=\"0\" numImplicitStmts=\"1\" numImportStmts=\"0\" numUseStmts=\"0\" rule=\"204\" />\n", " </specification>\n", " <statement />\n", " </body>\n", " <end-program-stmt col_begin=\"6\" col_end=\"24\" endKeyword=\"end\" eos=\" \" id=\"empty\" line_begin=\"8\" line_end=\"8\" programKeyword=\"program\" rule=\"1103\" />\n", " <main-program hasExecutionPart=\"false\" hasInternalSubprogramPart=\"false\" hasProgramStmt=\"true\" rule=\"1101\" />\n", " </program>\n", " <end-of-file filename=\"test/examples/empty.f\" path=\"/home/mateusz/Projects/open-fortran-parser-xml/test/examples/empty.f\" rule=\"-2\" />\n", " </file>\n", "</ofp>\n" ] } ], "source": [ "xml_tree = open_fortran_parser.parse(pathlib.Path('test', 'examples', 'empty.f'))\n", "ET.dump(xml_tree)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "HowBadCanItBe\n" ] } ], "source": [ "code = '''\n", "program HowBadCanItBe\n", "\n", " goto main_sub3\n", "\n", "end program\n", "'''\n", "\n", "with tempfile.NamedTemporaryFile('w+') as tmp:\n", " print(code, file=tmp, flush=True)\n", " xml_tree = open_fortran_parser.parse(pathlib.Path(tmp.name), raise_on_error=True)\n", "\n", "for prog in xml_tree.findall('.//program'):\n", " print(prog.attrib['name'])" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.0" } }, "nbformat": 4, "nbformat_minor": 2 }	apache-2.0
basnijholt/holoviews	examples/reference/elements/plotly/Path3D.ipynb	2	2960	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"contentcontainer med left\" style=\"margin-left: -50px;\">\n", "<dl class=\"dl-horizontal\">\n", " <dt>Title</dt> <dd> Path3D Element</dd>\n", " <dt>Dependencies</dt> <dd>Plotly</dd>\n", " <dt>Backends</dt> <dd><a href='../matplotlib/Path3D.ipynb'>Matplotlib</a></dd> <dd><a href='../plotly/Path3D.ipynb'>Plotly</a></dd>\n", "\n", "</dl>\n", "</div>" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import holoviews as hv\n", "from holoviews import opts\n", "\n", "hv.extension('plotly')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A ``Path3D`` element represents one more lines, connecting arbitrary points in three-dimensional space. ``Path3D`` supports plotting an individual line or multiple subpaths, which should be supplied as a list. Each path should be defined in a columnar format such as NumPy arrays, DataFrames or dictionaries for each column. For a full description of the path geometry data model see the [Geometry Data User Guide](../user_guide/Geometry_Data.ipynb).\n", "\n", "As a simple example we will genrate a random walk through 3D space as a single array with three columns:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "line = np.random.randn(500, 3).cumsum(axis=0)\n", "path = hv.Path3D(line)\n", "\n", "path" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Like the 2D equivalent ``Path`` also allows drawing multiple paths by supplying a list and styling each path by a value dimensions:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "paths = [{('x', 'y', 'z'): np.random.randn(500, 3).cumsum(axis=0), 'index': i} for i in range(3)]\n", "\n", "hv.Path3D(paths, vdims='index').opts(color='index')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Just like all regular 2D elements, ``Path3D`` types can be overlaid with other 2D and 3D elements:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "scatter = hv.Scatter3D({('x', 'y', 'z'): line, 'index': np.arange(500)}, vdims='index')\n", "\n", "(path * scatter.iloc[::2]).opts(\n", " opts.Scatter3D(color='index', cmap='viridis', size=3))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For full documentation and the available style and plot options, use ``hv.help(hv.Path3D).``" ] } ], "metadata": { "language_info": { "name": "python", "pygments_lexer": "ipython3" } }, "nbformat": 4, "nbformat_minor": 2 }	bsd-3-clause
hargup/intern-dairy	12th June 2015.ipynb	1	1203	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Finalising the Process\n", "\n", "I'm mostly done with the autocreation, the few things which are left are:\n", "\n", "* Create the grey list packages\n", "* Identify the list of C-libraries which needs to build and build them (If any), potentially I can `conda-recipe`\n", "* Save the timestamp of the build and write functionality to build packages which have changed after the timestamp.\n", "\n", "Today I also plan to make some patches for the `warehouse` project.\n", "\n", "* * \n", "\n", "In the morning I \n", "\n", "\n", " * *\n", "- pipbuild refactor\n", "- --no-download (Remove it, or fix it)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }	cc0-1.0
thalesians/tsa	src/jupyter/python/times.ipynb	1	9550	{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Times\n", "\n", "## Introduction\n", "\n", "The concept of time is fundamental to any kind of time series analysis and simulation. This is why we have dedicated a lot of thought to its representation in thalesians.tsa.\n", "\n", "Before we present some of these ideas, we need to make sure that thalesians.tsa appears on the Python path and import the relevant modules:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import os, sys\n", "sys.path.append(os.path.abspath('../../main/python'))\n", "\n", "from thalesians.tsa.simulation import xtimes, times" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## `xtimes`\n", "\n", "`xtimes` is a generator (and, by implication, an iterator; you can learn more about generators and iterators [here](https://wiki.python.org/moin/Generators)) designed to be a source of times to thalesians.tsa routines." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\n", "1\n", "2\n", "3\n", "4\n" ] } ], "source": [ "for t in xtimes(0, 5): print(t)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since `xtimes` is a generator, the times are computed lazily:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<generator object xtimes at 0x00000121C3F9F5E8>" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "xtimes(0, 5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To get hold of them all at once, you need to use something like" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[0, 1, 2, 3, 4]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "list(xtimes(0, 5))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "or the shortcut" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[0, 1, 2, 3, 4]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "times(0, 5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "which amounts to the same thing. So far, we haven't seen anything that would justify `xtimes`'s existence: after all, we could have achieved the same result with Python's native `range`:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[0, 1, 2, 3, 4]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "list(range(0, 5))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In order to understand why we may need `xtimes` and not `range` we need to delve deeper into the semantics of `xtimes`.\n", "\n", "`xtimes` takes three arguments: `start`, `stop`, and `step`. `stop` is, in fact, optional (may be `None`), so `xtimes` enables us to set up a (theoretically) infinite number of timesteps:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[1, 2, 3, 4, 5]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ts = []\n", "for t in xtimes(start=1):\n", " ts.append(t)\n", " if len(ts) == 5: break\n", "ts" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Perhaps more importantly, `start` and `stop` don't have to be `int`s. They may be `float`s, `date`s, `time`s, `datetime`s. Respectively, `stop` may be an `int`, `float`, or a `timedelta`, for example:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[-3.0, -0.5, 2.0, 4.5]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "times(start=-3., stop=5., step=2.5)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[datetime.date(2017, 5, 5),\n", " datetime.date(2017, 5, 6),\n", " datetime.date(2017, 5, 7),\n", " datetime.date(2017, 5, 8),\n", " datetime.date(2017, 5, 9)]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import datetime as dt\n", "times(dt.date(2017, 5, 5), dt.date(2017, 5, 10))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "(By default, the `step` is `1`, `1.`, or `timedelta(days=1)`, depending on the types of `start` and `stop`.)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[datetime.time(8, 0),\n", " datetime.time(8, 30),\n", " datetime.time(9, 0),\n", " datetime.time(9, 30),\n", " datetime.time(10, 0),\n", " datetime.time(10, 30),\n", " datetime.time(11, 0),\n", " datetime.time(11, 30)]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "times(dt.time(8), dt.time(12), dt.timedelta(minutes=30))" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[datetime.datetime(2017, 5, 10, 0, 0),\n", " datetime.datetime(2017, 5, 9, 0, 0),\n", " datetime.datetime(2017, 5, 8, 0, 0),\n", " datetime.datetime(2017, 5, 7, 0, 0),\n", " datetime.datetime(2017, 5, 6, 0, 0)]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "times(dt.datetime(2017, 5, 10), dt.datetime(2017, 5, 5), dt.timedelta(days=-1))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The flexibility of `xtimes`/`times` enables one to represent time using a data type that is suitable for the particular simulation and/or modelling task.\n", "\n", "## Random times\n", "The power of `xtimes` extends beyond its flexibility with times. Often one needs to deal with times that aren't regularly spaced, such as those arising from a homogeneous Poisson point process. Since the `step` parameter of `xtimes` is allowed to be a callable, this can be implemented as follows:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[0.0, 1.1731702249421478, 8.69847380223595]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import thalesians.tsa.randomness as rnd\n", "times(0., 10., step=lambda x: rnd.exponential(2.5))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "(Recall that the lengths of times between the occurrences of a Poisson process are exponentially distributed.)\n", "\n", "The type flexibility of the function wrappers found in `thalesians.tsa.randomness` enable one to specify the parameter of the `exponential` distribution as a `timedelta`:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[datetime.datetime(2017, 5, 5, 8, 0),\n", " datetime.datetime(2017, 5, 5, 8, 5, 5, 324767),\n", " datetime.datetime(2017, 5, 5, 8, 10, 10, 598092),\n", " datetime.datetime(2017, 5, 5, 8, 11, 58, 307875),\n", " datetime.datetime(2017, 5, 5, 9, 12, 18, 523431),\n", " datetime.datetime(2017, 5, 5, 9, 39, 52, 871308),\n", " datetime.datetime(2017, 5, 5, 10, 16, 49, 121419),\n", " datetime.datetime(2017, 5, 5, 10, 17, 26, 560173)]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "times(dt.datetime(2017, 5, 5, 8),\n", " dt.datetime(2017, 5, 5, 12),\n", " lambda x: rnd.exponential(dt.timedelta(minutes=30)))" ] } ], "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 }	apache-2.0
epidataio/epidata-community	ipython/home/tutorials/2. Data Ingestion Tutorial.ipynb	1	4928	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<h1 style=\"text-align:center;text-decoration: underline\">Data Ingestion Tutorial</h1>\n", "<h1>Overview</h1>\n", "<p>Welcome to the data ingestion tutorial. In this tutorial, we'll go over steps required to ingest data into EpiData. We'll also query the data and verify that our ingestion process was successful.</p>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h2>Generate Access Token</h2>\n", "\n", "<p>The first step in ingesting data into EpiData is obtaining an access token for session authentication. At present, EpiData supports GitHub's Personal Access Token.<p>\n", "<p>You can go ahead and create a Personal Access Token by visiting https://github.com/settings/tokens</p>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h2>Modify Ingestion Example</h2>\n", "<p>Once an access token is available, you can use it within the data ingestion program by following these steps:<p>\n", "<ul><li><p>Download the Python ingestion example <i>sensor_data_ingest.py</i> available in your Notebook tree view.</p></li>\n", "<li><p>Update the ACCESS_TOKEN variable (in <i>sensor_data_ingest.py</i>) to the Personal Access Token you created on GitHub's website.\n", "<ul><li>ACCESS_TOKEN = '<Personal Access Token>'</li></ul></li></p>\n", "<li><p>Modify the default values of the following variables (optional):\n", "<ul><li>COMPANY</li>\n", "<li>SITE</li>\n", "<li>STATION</li>\n", "</ul></li>\n", "</ul></p>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h2>Run Ingestion Example</h2>\n", "<p>The next step is to run the updated example <i>'sensor_data_ingest.py'</i> by using a Python 2.x interpreter. The example sends data to EpiData server using REST interface. You should see status of each ingestion steps in your standard output.</p>\n", "<p>You can let the example run and ingest data for a short period of time, and interrupt it by using Ctrl-C command.</p>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h2>Query and Display Data</h2>\n", "<p>We'll now query the database for the data that was ingested in the previous step. Let's start by running the cell below that imports the required modules.</p>" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#from epidata.context import ec\n", "from datetime import datetime, timedelta\n", "import pandas as pd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p>In the cell below, let's modify the variables COMPANY, SITE and SENSOR to match the data recently ingested, and run the cell to query the data.</p>" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "COMPANY = 'EpiData'\n", "SITE = 'San_Jose'\n", "STATION = 'WSN-1'\n", "start_time = datetime.strptime('8/1/2017 00:00:00', '%m/%d/%Y %H:%M:%S')\n", "stop_time = datetime.strptime('8/31/2017 00:00:00', '%m/%d/%Y %H:%M:%S')\n", "\n", "primary_key={\"company\": COMPANY, \"site\": SITE, \"station\": STATION, \"sensor\": [\"Temperature_Probe\",\"Anemometer\",\"RH_Probe\"]}\n", "df = ec.query_measurements_original(primary_key, start_time, stop_time)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p>Next we'll display the initial few records using <i>df.show()</i> function, and visually verify that the data matches the ingested data.</p>" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df = df.select(\"company\", \"site\", \"station\", \"ts\", \"meas_name\", \"meas_value\", \"meas_unit\")\n", "df.show(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h2>Summary</h2>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p>Congratulations, you have successfully completed the steps of establishing an authenticated session, ingesting sample data and querying the ingested data. These are the basic steps involved in using EpiData for sensor measurements.</p>" ] } ], "metadata": { "kernelspec": { "display_name": "EpiData PySpark", "language": "python", "name": "pyspark" }, "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" }, "read_only": true }, "nbformat": 4, "nbformat_minor": 1 }	apache-2.0
robblack007/IJuliaNotebooks	HW ACK.ipynb	2	2207	{ "metadata": { "language": "Julia", "name": "", "signature": "sha256:daac02cbcda168df7f07515ef81fcbe12c364ad41a32e9afa53f40ac86071f0f" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "z0 = 3 + 4im" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 1, "text": [ "3 + 4im" ] } ], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "z0^2 + im*real(z0) + imag(z0)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 3, "text": [ "-3 + 27im" ] } ], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "f(z) = z^2 + 1\n", "\n", "g(z) = z^3" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 1, "text": [ "g (generic function with 1 method)" ] } ], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "f(1)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 2, "text": [ "2" ] } ], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "g(g(g(im)))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 3, "text": [ "0 - 1im" ] } ], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }	mit
crawfordsm/crawfordsm.github.io	_posts/hof_voters_files/hof_voters.ipynb	1	105468	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\n", "import numpy as np\n", "from scipy import stats\n", "\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Did the Hall of Fame voter purge make a difference?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In a recent Jayson Stark article and about [lessons in hall of fame voting](http://espn.go.com/mlb/story/_/id/14521041/five-things-learned-2016-hall-fame-election), he mentions the following three assumptions about the Baseball Hall of fame voters after a significant number of non-active voters were eliminated:\n", "\n", "> An electorate in which 109 fewer writers cast a vote in this election than in 2015.\n", ">\n", "> An electorate that had a much different perspective on players who shined brightest under the light of new-age metrics.\n", ">\n", "> And an electorate that appeared significantly less judgmental of players shadowed by those pesky performance-enhancing drug clouds.\n", "\n", "However, are these last two assumptions true? Did the purge of Hall of Fame voters make a difference? Did the set of Hall of Fame voters least active have a different set of values than the those who are still voting? \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Arbitrarily, I decided to test this against the years 1995-2016, which gives a good 20 elections as well as starting at the year Mike Schmidt was elected to the Hall of Fame (which is utterly arbitrary other than Mike Schmidt being my favorite player when I was young). However to figure this out, the first question that has to be answer is how does the average percentage change from year to year. This ends up being a little surprising when you just look at the numbers: " ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#read in the data\n", "def read_votes(infile):\n", " \"\"\"Read in the number of votes in each file\"\"\"\n", " lines = open(infile).readlines()\n", " hof_votes = {}\n", " for l in lines:\n", " player={}\n", " l = l.split(',')\n", " name = l[1].replace('X-', '').replace(' HOF', '').strip()\n", " player['year'] = l[2]\n", " player['votes'] = float(l[3])\n", " player['p'] = float(l[4][:-1])/100.0\n", " player['war'] = float(l[8])\n", " hof_votes[name] = player\n", " return hof_votes\n", "\n", "\n", "#calcuate the total number of votes in each year\n", "hof={}\n", "n_votes = {}\n", "for i in np.arange(1996, 2017):\n", " hof[i] = read_votes('{}_list.csv'.format(i))\n", " k=0\n", " keys = hof[i].keys()\n", " while hof[i][keys[k]]['p']<0.5: k+=1\n", " k = keys[k]\n", " n_votes[i] = int ( hof[i][k]['votes'] / hof[i][k]['p'])\n", "n_years = 2017-1996" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def match_years(hof, year1, year2):\n", " \"Produce a list of players and the number of votes received between two years\"\n", " player_dict={}\n", " for name in hof[year1].keys():\n", " if name in hof[year2].keys():\n", " player_dict[name]=np.array([hof[year1][name]['p'], hof[year2][name]['p']])\n", " return player_dict\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "end_year = 2017\n", "def number_of_first_year(hof, year):\n", " \"Calculate the number of first ballot hall of famers in a class\"\n", " first_year = 0\n", " for name in hof[year]:\n", " if hof[year][name]['year']=='1st':\n", " if hof[year][name]['p']>0.75: first_year+= 1\n", " if name in ['Barry Bonds', 'Roger Clemens']: first_year+= 1\n", " return first_year\n", "\n", "\n", "def number_of_HOF(hof, year):\n", " \"Calculte the number of HOF for a year\"\n", " first_year = 0\n", " for name in hof[year]:\n", " if hof[year][name]['p']>0.75: first_year+= 1\n", " return first_year\n", "\n", "def number_of_drop(hof, year):\n", " \"Calculate the number of players dropped in a year\"\n", " first_year = 0\n", " for name in hof[year]:\n", " if hof[year][name]['p']<0.05: first_year+= 1\n", " return first_year\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def total_number_of_hof(hof, year):\n", " \"Total number of hall of famers for a class\"\n", " first_year = 0\n", " for name in hof[year]:\n", " if hof[year][name]['year']=='1st':\n", " if hof[year][name]['p']>0.75: \n", " first_year+= 1\n", " if name in ['Barry Bonds', 'Roger Clemens']: first_year+= 1\n", " for y in range(year+1, end_year):\n", " if name in hof[y].keys():\n", " #print year, name, hof[y][name]['p']\n", " if hof[y][name]['p']>0.75: \n", " first_year+= 1\n", " return first_year\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def average_change_in_votes(hof, year1, year2):\n", " \"\"\"Determine the statistics change in votes from one class to another\"\"\"\n", " player_dict = match_years(hof, year1, year2)\n", " #print player_dict\n", " change = 0\n", " count = 0\n", " for name in player_dict:\n", " change += player_dict[name][1] - player_dict[name][0]\n", " count += 1\n", " #print count, name, player_dict[name][0], player_dict[name][1], player_dict[name][1] - player_dict[name][0], change\n", " change = change / count\n", " return count, change\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def number_of_votes(hof, year):\n", " keys = hof[year].keys()\n", " k=0\n", " while hof[year][keys[k]]['p']<0.5: k+=1\n", " k = keys[k]\n", " return int ( hof[year][k]['votes'] / hof[year][k]['p'])\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from astropy.table import Table\n", "data_table = Table(names=('Year','Votes', 'Strength', 'HOF', 'Drop', 'Count', 'Change', 'Total'))\n", "for year in np.arange(1997,2017):\n", " strength = number_of_first_year(hof, year)\n", " nhof = number_of_HOF(hof, year)\n", " nvotes = number_of_votes(hof, year)\n", " ndrop = number_of_drop(hof, year)\n", " total = total_number_of_hof(hof, year)\n", " count, change = average_change_in_votes(hof, year-1, year)\n", " data_table.add_row([year, nvotes, strength, nhof, ndrop, count, change, total])\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1099b7410>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "plt.plot(data_table['Year'], data_table['Change'], ls='', marker='o')\n", "plt.xlabel('Year', fontsize='x-large')\n", "plt.ylabel('$\\Delta p \\ (\\%)$', fontsize='x-large')\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'Max=0.0822352941176 Min=-0.0601764705882'" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "'Mean={} Std={}'.format(data_table['Change'].mean(), data_table['Change'].std())\n", "'Max={} Min={}'.format(data_table['Change'].max(), data_table['Change'].min())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As a matter of fact, this year saw one of the largest increases at 8.2%. Taken alone, this may indicate that something has changed with the removal of so many voters, but when viewed with all the other years, it does not look very exceptional as the values range between -6 to +8%. The average change is an increase by 2% per year, but with a standard deviation much larger than it of 4%. The average change in percentage is either highly random or driven by something other than change in the number of votes. In fact, the change in percentages does not show any strong correlation with the number of voters or the change in number of voters. \n" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(0.12433150236202198, 0.60148997659379133)" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stats.pearsonr(data_table['Year'], data_table['Change'])\n" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(-0.18919150246965624, 0.42436872449811769)" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stats.pearsonr(data_table['Votes'], data_table['Change'])\n" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(-0.40589419128970977, 0.084659001306052251)" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stats.pearsonr(data_table['Votes'][1:]-data_table['Votes'][:-1], data_table['Change'][1:])\n" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "<Table length=20>\n", "<table id=\"table4455897936-809644\" class=\"table table-striped table-bordered table-condensed\">\n", "<thead><tr><th>Year</th><th>Votes</th><th>Count</th><th>Change</th><th>Strength</th><th>Total</th><th>HOF</th><th>Drop</th></tr></thead>\n", "<tr><td>1997.0</td><td>473.0</td><td>22.0</td><td>0.0140909090909</td><td>0.0</td><td>1.0</td><td>1.0</td><td>10.0</td></tr>\n", "<tr><td>1998.0</td><td>472.0</td><td>17.0</td><td>0.0349411764706</td><td>0.0</td><td>2.0</td><td>1.0</td><td>7.0</td></tr>\n", "<tr><td>1999.0</td><td>496.0</td><td>17.0</td><td>-0.0581176470588</td><td>3.0</td><td>4.0</td><td>3.0</td><td>7.0</td></tr>\n", "<tr><td>2000.0</td><td>498.0</td><td>16.0</td><td>0.07625</td><td>0.0</td><td>1.0</td><td>2.0</td><td>13.0</td></tr>\n", "<tr><td>2001.0</td><td>514.0</td><td>15.0</td><td>0.02</td><td>2.0</td><td>2.0</td><td>2.0</td><td>13.0</td></tr>\n", "<tr><td>2002.0</td><td>472.0</td><td>17.0</td><td>-0.00876470588235</td><td>1.0</td><td>2.0</td><td>1.0</td><td>10.0</td></tr>\n", "<tr><td>2003.0</td><td>496.0</td><td>16.0</td><td>-0.0015</td><td>1.0</td><td>2.0</td><td>2.0</td><td>13.0</td></tr>\n", "<tr><td>2004.0</td><td>505.0</td><td>17.0</td><td>0.00564705882353</td><td>2.0</td><td>2.0</td><td>2.0</td><td>15.0</td></tr>\n", "<tr><td>2005.0</td><td>515.0</td><td>15.0</td><td>0.0414</td><td>1.0</td><td>1.0</td><td>2.0</td><td>10.0</td></tr>\n", "<tr><td>2006.0</td><td>520.0</td><td>15.0</td><td>0.0496666666667</td><td>0.0</td><td>0.0</td><td>1.0</td><td>13.0</td></tr>\n", "<tr><td>2007.0</td><td>544.0</td><td>15.0</td><td>-0.0312666666667</td><td>2.0</td><td>2.0</td><td>2.0</td><td>15.0</td></tr>\n", "<tr><td>2008.0</td><td>542.0</td><td>14.0</td><td>0.0598571428571</td><td>0.0</td><td>0.0</td><td>1.0</td><td>10.0</td></tr>\n", "<tr><td>2009.0</td><td>539.0</td><td>13.0</td><td>0.000923076923077</td><td>1.0</td><td>1.0</td><td>2.0</td><td>9.0</td></tr>\n", "<tr><td>2010.0</td><td>538.0</td><td>11.0</td><td>0.0480909090909</td><td>0.0</td><td>2.0</td><td>1.0</td><td>11.0</td></tr>\n", "<tr><td>2011.0</td><td>581.0</td><td>14.0</td><td>0.0192142857143</td><td>0.0</td><td>0.0</td><td>2.0</td><td>16.0</td></tr>\n", "<tr><td>2012.0</td><td>572.0</td><td>14.0</td><td>0.0708571428571</td><td>0.0</td><td>0.0</td><td>1.0</td><td>13.0</td></tr>\n", "<tr><td>2013.0</td><td>568.0</td><td>13.0</td><td>-0.0124615384615</td><td>2.0</td><td>4.0</td><td>0.0</td><td>19.0</td></tr>\n", "<tr><td>2014.0</td><td>571.0</td><td>17.0</td><td>-0.0601764705882</td><td>3.0</td><td>3.0</td><td>3.0</td><td>15.0</td></tr>\n", "<tr><td>2015.0</td><td>548.0</td><td>17.0</td><td>0.0304117647059</td><td>3.0</td><td>3.0</td><td>4.0</td><td>12.0</td></tr>\n", "<tr><td>2016.0</td><td>440.0</td><td>17.0</td><td>0.0822352941176</td><td>1.0</td><td>1.0</td><td>2.0</td><td>13.0</td></tr>\n", "</table><style>table.dataTable {clear: both; width: auto !important; margin: 0 !important;}\n", ".dataTables_info, .dataTables_length, .dataTables_filter, .dataTables_paginate{\n", "display: inline-block; margin-right: 1em; }\n", ".paginate_button { margin-right: 5px; }\n", "</style>\n", "<script>\n", "require.config({paths: {\n", " datatables: 'https://cdn.datatables.net/1.10.9/js/jquery.dataTables.min'\n", "}});\n", "require([\"datatables\"], function(){\n", " console.log(\"$('#table4455897936-809644').dataTable()\");\n", " $('#table4455897936-809644').dataTable({\n", " \"iDisplayLength\": 21,\n", " \"aLengthMenu\": [[21, 10, 25, 50, 100, 500, 1000, -1], [21, 10, 25, 50, 100, 500, 1000, 'All']],\n", " \"pagingType\": \"full_numbers\"\n", " });\n", "});\n", "</script>\n" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_table['Year', 'Votes', 'Count', 'Change', 'Strength','Total', 'HOF', 'Drop'].show_in_notebook(display_length=21)\n", "#['Year', 'Count', 'Change', 'Strength', 'HOF', 'Drop']\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Correlations with Hall of Fame classes\n", "\n", "At initial glance, there is not much pattern to the data so pure randomness could be an explanation. However, we can define a few other metrics to take a look at the data and it might give us a better idea of what is going on. The first would be the number of Hall of Famers (hofs) elected in the previous class. The second is defined as the strength of the class as the number of first ballot hofs in that class (For the record, I consider Bonds and Clemons as first ballot hall of famers as the would have been if not for their Performance Enhancing Drug (PED) history). The third is the total number of hofs in a class, but that is uncertain for the most recent classes. \n", "\n", "A very strong trend does appears between the average change in the percentage and the strength of an incoming class minus the number of hofs elected the year before. Unsurprisingly, when a strong class comes onto the ballot, they tend to take votes away from other players. Likewise, when a large number of players are elected, they free up votes for other players. A linear relationship of $$s = 0.0299nhof_{previous} -0.0221\\times Strength - 0.0034\\times(Total-Strength) - 0.00299$$ gives a very good fit to $\\Delta p$ and shows a strong linear correlation indicated by an r-pearson statistic of 0.95. " ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "nhof_2 = data_table['Total'][1:]- data_table['Strength'][1:] #number of HOFs in a class after year 1\n", "\n", "p = data_table['Change'][1:] \n", "dv = data_table['Votes'][1:] - data_table['Votes'][:-1]\n", "\n" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[-0.0221596 -0.00347177 0.02892667 -0.00299066]\n" ] } ], "source": [ "from scipy import linalg as la\n", "aa = np.vstack((data_table['Strength'][1:],nhof_2,data_table['HOF'][:-1], np.ones_like(nhof_2))).T\n", "polycofs = la.lstsq(aa[:-1], p[:-1])[0]\n", "print polycofs" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0.01899247, -0.04401456, 0.08031758, 0.01054347, 0.02923131,\n", " 0.00030464, 0.01054347, 0.03270307, 0.05486268, -0.0183832 ,\n", " 0.05486268, 0.00377641, 0.04791914, 0.02593601, 0.05486268,\n", " -0.02532673, -0.06946947, 0.01731054, 0.09055641])" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s = aa polycofs \n", "s = s.sum(axis=1)\n", "s" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "image/png": 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1jsnKlK4wci3wSERcXu94qk3SK3dtvSLpj0iW6yr5nenRZSlJNwGvBXYCfcDH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\nGScZMzPLjJOMmZllxknGzMwy8/8B8jkt4WcrErMAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x108945a90>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "(0.94973196989687392, 5.3216490374234465e-10)\n" ] } ], "source": [ "plt.figure()\n", "plt.plot(data_table['HOF'][:-1]-data_table['Strength'][1:], p, ls='', marker='o')\n", "plt.xlabel('$nhof_{previous} - Strength$', fontsize='x-large')\n", "plt.ylabel('$\\Delta p \\ (\\%)$', fontsize='x-large')\n", "plt.show()\n", "from scipy import stats\n", "print stats.pearsonr(s,p)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "<Table length=19>\n", "<table id=\"table4458162064-152398\" class=\"table table-striped table-bordered table-condensed\">\n", "<thead><tr><th>Year</th><th>HOF</th><th>Change</th></tr></thead>\n", "<tr><td>1998.0</td><td>1.0</td><td>0.0349411764706</td></tr>\n", "<tr><td>1999.0</td><td>-2.0</td><td>-0.0581176470588</td></tr>\n", "<tr><td>2000.0</td><td>3.0</td><td>0.07625</td></tr>\n", "<tr><td>2001.0</td><td>0.0</td><td>0.02</td></tr>\n", "<tr><td>2002.0</td><td>1.0</td><td>-0.00876470588235</td></tr>\n", "<tr><td>2003.0</td><td>0.0</td><td>-0.0015</td></tr>\n", "<tr><td>2004.0</td><td>0.0</td><td>0.00564705882353</td></tr>\n", "<tr><td>2005.0</td><td>1.0</td><td>0.0414</td></tr>\n", "<tr><td>2006.0</td><td>2.0</td><td>0.0496666666667</td></tr>\n", "<tr><td>2007.0</td><td>-1.0</td><td>-0.0312666666667</td></tr>\n", "<tr><td>2008.0</td><td>2.0</td><td>0.0598571428571</td></tr>\n", "<tr><td>2009.0</td><td>0.0</td><td>0.000923076923077</td></tr>\n", "<tr><td>2010.0</td><td>2.0</td><td>0.0480909090909</td></tr>\n", "<tr><td>2011.0</td><td>1.0</td><td>0.0192142857143</td></tr>\n", "<tr><td>2012.0</td><td>2.0</td><td>0.0708571428571</td></tr>\n", "<tr><td>2013.0</td><td>-1.0</td><td>-0.0124615384615</td></tr>\n", "<tr><td>2014.0</td><td>-3.0</td><td>-0.0601764705882</td></tr>\n", "<tr><td>2015.0</td><td>0.0</td><td>0.0304117647059</td></tr>\n", "<tr><td>2016.0</td><td>3.0</td><td>0.0822352941176</td></tr>\n", "</table><style>table.dataTable {clear: both; width: auto !important; margin: 0 !important;}\n", ".dataTables_info, .dataTables_length, .dataTables_filter, .dataTables_paginate{\n", "display: inline-block; margin-right: 1em; }\n", ".paginate_button { margin-right: 5px; }\n", "</style>\n", "<script>\n", "require.config({paths: {\n", " datatables: 'https://cdn.datatables.net/1.10.9/js/jquery.dataTables.min'\n", "}});\n", "require([\"datatables\"], function(){\n", " console.log(\"$('#table4458162064-152398').dataTable()\");\n", " $('#table4458162064-152398').dataTable({\n", " \"iDisplayLength\": 50,\n", " \"aLengthMenu\": [[10, 25, 50, 100, 500, 1000, -1], [10, 25, 50, 100, 500, 1000, 'All']],\n", " \"pagingType\": \"full_numbers\"\n", " });\n", "});\n", "</script>\n" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Table((data_table['Year'][1:],data_table['HOF'][:-1]-data_table['Strength'][1:],p)).show_in_notebook()\n" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.0905564147749\n", "[ 9.80227040e-01 -4.71478086e-05]\n" ] } ], "source": [ "coef = np.polyfit(s,p,1)\n", "np.polyval(coef,0.08)\n", "print s[-1]\n", "print coef" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Change in Voting Habits\n", "\n", "\n", "If we use this relationship, we can look at what the expected percentage average change in the votes were for 2016. The expected change based on the existing data (1 First ballot hofs, 4 hofs the previous year, 1 total hof for class of 2016) was an increase of +9.0%. The average increase for 2016? That was +8.2%. So, at least overall, the increase in percentages is exactly what was expected based on a moderate incoming class (if you also assume Trevor Hoffman will eventually be elected the expected change for this year is then 8.7%) and four players entering the Hall the previous year. From this perspective, the voting purge made little difference in how the percentage of votes for a player changed. " ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2016 0.0905564147749\n" ] } ], "source": [ "name_list = []\n", "p_list = []\n", "dp_list = []\n", "pp_list = []\n", "year1 = 2015\n", "year2 = year1+1\n", "expect_p = s[year2 - 1998]\n", "print year2, expect_p" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Historically, players with higher vote percentage generally have seen their voting percentages increase. In the figure below, we look at the difference between the change in vote percentage for a given player, $\\Delta p$, and the expected average change for all players that year as compared to the player's percentage, p, for the previous year. The 2016 year (red squares) does not appear significantly different than any other years (blue circles). It is just more common that players with low vote percentages tend to have their vote percentages suppressed than players with higher vote percentages. Nonetheless, there is large scatter in the distribution, which for any given player in any given year does not make it very predictive. " ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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Z7IqYVPwEKQQJZCjFtFkus2gx91HqAt22tiW88EJuuVoLDGhtbaW1tXXg/Q9+\n8INA9QRd+NkP/HcRWYnjuL840NXz8yxwsog0AdtweqTsPPuPANcAD4nINGC3qr6VXHha6NzQSX8Y\n29qW0NmZ+yBv3LhpYC2Lm50Z0iObvHwo63DcVCsAOHQI9u1bzAsv9AJXM2zYCg4d+gBIkFoI6HTs\n+dPmD/p/7sNRWkupq3uD008fzU03zcr5oXV2usk2+EPL9kmk3+/GjZuS1wBn3OCElvb3w/vvX8fY\nsS+ye3fm1tmNjdcyf/4FA20NmfbxadMmeq6JKIfSKRTS7rTlXQPHjjpqsWf5cuPHXxEkqqqUaLty\nhQ1HGR0Wl4wGVUOQaZHbizJswoYT7vwyTsTZDclj84B5aWVWJD9/DpiS71yX+kueanqRz+xSV3eF\nnnTSl5KRYJnmhJNO+lLSXNWuzoZqV2eUOeKIb+mwYeflMWddlWOiSvlWCqXNL9YUkX1/jY3/oJMn\nz3U1T7iVHzFinsKVrtccjFK7SMeNm61TplxV0NwRp/xZUcrixw/ix7xX7BYTM2Ys1nHjZgW+z2i3\nJiifKbMW8+AVgihMagUUV+jZBVT1UeDRrGP3Zr2/xu+5UZIa/c6ZM4udO0/DGfmfB0ynr286r702\nC7gt45zNm29B5EvAzWlHL8Rxmx0N7OD993dz6NAncq43iUtpogvYD5yG41aDHpp4g1/Q0HARJ5xw\nVN60+cWaIiA7AucCz1G/m/mkv/8eRoy4kH6XAeno0UfT1ZVwrcuLOC2027Ztn+vxsGXxu/7LT8SU\n36iqjo5uLr/818lIxm6cNUjFj/DLNTuIOjqsHKl5apXQFI6Ry8yZ0znjjMEFjZnUu56jembWkX/B\nUTj9wD4OHToFeDvnvCZ66OKN5LvBz1vp4Q2W0Nh4JMuWXZQ3bb6XKWLPni2ufpFifmheymD06GGu\nvqAg5o+4LLTr6Ohm8+btkchSjB/Ez/flp8zSpQ/R25syFabKLuXwwzfz6U83++7cy6kYTAnEE1M4\nZcY7P9l+j+NuHdJe4M6093NxXFcr0o697FFfE3AzL754GRdd9EMaGsbR0DCL8ePHM2HC6IwfuNuI\ns7HxMrZvH5s30KAQHR3dSZ+Ni3RNR3DUUeGMcvONmKPM2uukm7ma1MjfmX32MExe5uStx5JofdIp\n2NRE4he/KOlalZjVvf76e1lHnMCSkSPnsHr1soxPCrW7KYahhSmcMuO90LGFbFOE4576hkstk7Le\n3w9cjjMcQGDgAAAfCElEQVTz2YezGPRYoNdTDtWfsW/fUvbtczqEsWMXM3/+uQVNKm+/Xcf69bmm\nP7+RRCmTz86dgx1wiubmRSxbNjvnmqUkMXWrC7xTzJSjsztwYAST+BlN/BFYyVje4mwOgAIbe0kk\nyyU8a/BPJWZ1Igc8Pvkg451lSzZyCOL4qZUXZQwaSCdzoeOSgfUajY3f1COO+LIOLhi708Xhf5m6\nL4z8tsJiFblIhw37srbQkut9BW2hJe1te45jvpCzudQFll5rehoaZkXmXI06mGDGjMWe30d7+v8t\nLSVfqxJrfSZPnuvynN6gkyfPzSgXpyAOI1yodNCA4U3KbNDR0c0ddzjrWJz1GpeyfHknnZ2JtNJO\nOPK4cX9i6tQTeOWVfbz2WvZo8C6c0eTNyZ/wEuB3PiTJHPW++OJe+voGTXVuo898I2g/ZqpMk89g\n0s4zzkhENsoNanYKaoZbsGAGK7ofhL5A4hZFJdKnLFs2m8svf4De3sGM342NvSxbdmlGuTgFcRjx\nwBROhHjZqzNNbtNpbl7N7bfPHVBSX/nKlWnrZ7qBNcC/pdUwgx4eoZWWgSPCyyjH0kNT8khuvrK+\nvkxTnZup7JxzjkumYB/c3Ku5eTXTpk10NZesW7eRp57aNtBJ79njbuZLmXyi2IIhiNmpFHPQzJnT\nWf2XR8HGN4uWNYiS83quyuW3mjlzOvfdR3LwBHV1MH/+pSXnKjOGAEGmRbXyIiKTWiEKxfE7JoxU\nrqYLc0xjKXPVuHEXaUtLu06ZcpWOHXuBOmtyLkn+vSyjvJM+JtdUl24qc88dNU/b2+/0MJes1fr6\neRnHGhuv1cbGzGvnS/2Sno8trOzPQcxOpZqD2ltaijaphZnqpZLZtPPLUBvZkoc6mEktXnhlEVi+\nvJNt2/axfft2GhvHDmxklh3dk87nPz+Fl17akNxoK4Ez08hmOlOnrmH16gRtbUv4wx9SprLUjOht\nRoz4e049tZkJE0bz9tv9rF+fO9pNH3267/9xD08/7bW5VO5mYL29tzFlytWcdVauyaetbUlO/anN\nw5x9ZcLJ/pwvmMArDU65zEE9Rx5J4uyznTdNTRmf5QtxTn3ud7YSh90mLVuykY0pnDLgZo7ZsGEu\ncGRysZzDzp2LeeGF/PvPd3R0s3LlVvbvT+2S+QpwFdkRX/X185g/34lw8/KbfOpTCbq6EnR0dLN0\n6UPU1c2mr+8EUqlvssOR8y1edDeXeK21OZrVqxM5x73q95vmvxiyzU6FTGYlm4Oamlyj0JryhEJ7\nKbmtW/e67DSZf+fVuPhP4hb2HGV4vOFCkGlRrbwok0nN3RyTP7uyl6kmt65UduXBiK/6+gu1vf1O\nl3PW6mCanMU6efJcVzNHXd0VA5+lyJet2mtzqXzls/GXDbu8kWT5ZI3CHJSdksYxnebK1NCQv53c\nTGUWIZZLpcyM5diCodJgJrX44IwuszdOyz+a9xp5Zo5UU3W+x4gRt/JXf3UcEycew/z5V+dscrZh\nw1x6extJnwVt334dS5c+lJFQ0rn23RxzTKapJXvxYorUTMo9eWYLK1f6W8TpVX96cEOhBaCljFYL\nzQDKbQ5ym2E1Nl5HY+PcjE3smpsXUVc3lp073WpxZHUzlVlSyVwqYWa0tUiZmMIpA3v2bAEeI7Mj\nneVR2jHRFN5lsDujzv5+6OvLXbwJzoP8kY/cl3V9x59y4MAccpXhjByF53TIg2lLUuGvJ500+ENJ\nD/deuvRBli9/hQMH9jN69JeYNOmEgUwGkOsrca9/B4cf3sPHPjYyI82/G6X+kP2YzMppDnLr/Lz8\nXcuXd7qmwE8Pc8/+/qrVf1JOk1clzIxx8KXFCVM4ZWEk2Z09XM3Ikd/mgw/+v7Rjzmg+38hzcKQq\nOXV6PbgdHd1s2+Yu2bvvbiVXGS5mz563MsoNdsiDPiCAiROX5lzLWZPRSGo7aIA///k6fvjDcwH3\nVf5jxqSul1n/pz+91NXfk90R7djRm9xGYZBifsiVngF455Zz93cV2pbbbcASN/9JIco9G6hEmHZc\nfGmxIYgdrlZehOzDSdlqjzxyjqv9/KMf/ba2tS3RM85YqA0Nswbe+9llcNy42a51uq34d+z3Xj6j\nL7geP/zwL2bYl/36MPJdK982y1OmXFVUKnw3v1OhsG4/31el0soX62NJyXrGGQuTvq+1Bdut2ii3\n36kSYdq16kvDfDiVJXN0lto4LdN0NXLkh3nDn/MxbJj7snW3BZTPPfcmToLPbP/IFTCwEDST9947\ni87ORM6IspBJxmsEB/lHcaNHH81NN33Gl8nHzSzhLIR1wqfTKWa0WskZQLEzrHRZMzNWFDaVVUtk\nVrlnA5UwM1Z6Jh03TOGERGanOAOnw8912qd2+/RLoeSXqUzIjllrfPKTlHJqI93/4vhrjvC4ktNR\np5ulUnIuX95JX98Ili93tvj0k/oGnM7fGQzlsnfvDt8dvldHJPISqgnSMyBUyw+5lM6vGEVZTU7r\nKExeUQ8yqtWXVjaCTItq5UWIJrXcJJfuO1n6mUqnh1E6IbFrk6+5ChcpzNbRo78wYApwT6aYmUlA\n5Jtp9bjvRJptlvITRrpq1dpkJoHc3T9T2QSydzaFG7Sx8TLfpgwvs0R6+HQqA0IliWP4a9xMOvna\nyDITVA+YSa2y5I7OjsFPNFg2biNSuA54F5gzUN/evX9kwYLbAejp2Ue6w97hHoYN+ztGjryPvr5J\nqH6MzGCBpYi8iupROBF0gyOu1IjST4RNKq/W0qUP0tPzNWAkJ554BDfdNGugzPjxD2YkeoTz6O2d\nXpKDP9tpnsqAUCniOpOIk9O6UBvZbKD2MYUTErmdoltodG40WDZunbyzFfXVOfW99toVXH75A3zw\nwTDXuoYNq6ev78G0I5mZqKdN+xtWrtzK5s2DP+h0+3K+zirbL7Bs2WzPjmHMmIm47f7i1unl8zek\nOqING15m164ryfbfVDLyJ+rwV79+mTgl0PQ7gDEFU7uYwgmJ7E7xD3/4M3v3Zs86bkHk6rz1eDvh\n95G56yfAPfT2LmXYsGddzxg1qp/+jP7GCUE+88wEq1c7KW4eeWQj48bNAQ7Q1HREhuLIt+V0MaN5\nv52e3xFwW9sSOjtzr7N37w7X60RBlDMJP7OplELaunUH9fVXZOS4q5TTOk6zLaMyuA+NjUDMnDmd\n1auX0dWVYMqUs13LbN78Hm1tS+jo6Hb93NsJ/4HH8eEcOnQCw4d/K+NoY+O1nHJKg+sZqb1sFi58\njPXr72LXrgfYtesh9uw5NqPcggUzaG5enHGsuXkRMNJjpLrG9Xpe9cyff27GMe8RcGa9CxbMoLHx\nuqyrLGLbtj4Sibtoa1tCa2sibzuHTZQziULtlPpuOztv5oUX7mX//q9TXz+LM874B9ralnL77ZUx\nU8VptmVUBpvhlAlnb5dc3n33eDo7l3nOCNz8FY2N1/LOO+/ygavO+T/ASD78cCENDRdxxhmnJm3f\nFwCwcGF2XZfx9tt1XHzx/ezefTyOmc2Rwc28Abk29Vtvfdz13rJHqulmnzFjdjF58uWMGTOx6BBr\nt1X0mX6hLcBIentHs2zZGg4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"text/plain": [ "<matplotlib.figure.Figure at 0x109b2edd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "name_list=[]\n", "p_list=[]\n", "pp_list=[]\n", "dp_list=[]\n", "war_list=[]\n", "for year1 in range(1997,2015):\n", " year2 = year1+1\n", " expect_p = s[year2 - 1998]\n", " for name in hof[year1]:\n", " if name in hof[year2].keys(): \n", " name_list.append(name)\n", " p_list.append(hof[year1][name]['p'])\n", " dp_list.append(hof[year2][name]['p'] - hof[year1][name]['p'])\n", " pp_list.append((hof[year2][name]['p'] - hof[year1][name]['p'])-expect_p)\n", " war_list.append(hof[year2][name]['war'])\n", "plt.plot(p_list, pp_list, 'bo')\n", "\n", "name_list=[]\n", "p_2016_list=[]\n", "pp_2016_list=[]\n", "dp_2016_list=[]\n", "war_2016_list = []\n", "year1=2015\n", "year2 = year1+1\n", "expect_p = s[year2 - 1998]\n", "for name in hof[year1]:\n", " if name in hof[year2].keys(): \n", " name_list.append(name)\n", " p_2016_list.append(hof[year1][name]['p'])\n", " dp_2016_list.append(hof[year2][name]['p'] - hof[year1][name]['p'])\n", " pp_2016_list.append((hof[year2][name]['p'] - hof[year1][name]['p'])-expect_p)\n", " war_2016_list.append(hof[year2][name]['war'])\n", "plt.plot(p_2016_list, pp_2016_list, 'rs')\n", "plt.xlabel('p (%)', fontsize='x-large')\n", "plt.ylabel('$\\Delta p - s $', fontsize='x-large')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Have voters changed in terms of WAR or PEDs?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we look at the corrected change in voting percentage as a function of WAR, there does appear to be a stronger correlation between WAR and percentage change this year (red and green squares) than seen last year (blue circles), although some correlation does exist. The three points not falling near the correlation are Barry Bonds and Roger Clemons (PED history for otherwise certain hofs) and Lee Smith (reliever). Going back further years shows a large scatter in terms of WAR and corrected percentage change, and it would be interesting to see how this has changed over all the different years and to see if the strength of this correlation has been increasing. Furthermore, it would be interesting to see how this relates to a players other, more traditional metrics like home runs or wins. \n", "\n", "The green circles are players that have been a strongly association with PEDs. Barry Bonds and Roger Clemons are exceptions, but the drop in the percentages for the other three players is in line for the drop for players with similar values of WAR. Along with the average change in voting seen for Bonds and Clemons, it does not look like the behavior for players associated with PEDs is very different than other players. \n", "\n" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x109afda10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(war_list[-17:], pp_list[-17:], 'bo')\n", "mask = np.zeros(len(war_2016_list), dtype=bool)\n", "for i, name in enumerate(name_list):\n", " if name in ['Sammy Sosa', 'Gary Sheffield', 'Mark McGwire', 'Barry Bonds', 'Roger Clemens']:\n", " mask[i]=True\n", "\n", "war = np.array(war_2016_list)\n", "pp = np.array(pp_2016_list)\n", "plt.plot(war, pp, 'rs')\n", "plt.plot(war[mask], pp[mask], 'gs')\n", "plt.xlabel('WAR', fontsize='x-large')\n", "plt.ylabel('$\\Delta p - s $', fontsize='x-large')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<Table length=17>\n", "<table id=\"table4456666192-750599\" class=\"table table-striped table-bordered table-condensed\">\n", "<thead><tr><th>col0</th><th>col1</th><th>col2</th><th>col3</th><th>col4</th></tr></thead>\n", "<tr><td>Lee Smith</td><td>0.302</td><td>0.039</td><td>-0.0515564147749</td><td>29.6</td></tr>\n", "<tr><td>Jeff Kent</td><td>0.14</td><td>0.026</td><td>-0.0645564147749</td><td>55.2</td></tr>\n", "<tr><td>Edgar Martinez</td><td>0.27</td><td>0.164</td><td>0.0734435852251</td><td>68.3</td></tr>\n", "<tr><td>Alan Trammell</td><td>0.251</td><td>0.158</td><td>0.0674435852251</td><td>70.4</td></tr>\n", "<tr><td>Larry Walker</td><td>0.118</td><td>0.037</td><td>-0.0535564147749</td><td>72.6</td></tr>\n", "<tr><td>Curt Schilling</td><td>0.392</td><td>0.131</td><td>0.0404435852251</td><td>79.9</td></tr>\n", "<tr><td>Roger Clemens</td><td>0.375</td><td>0.077</td><td>-0.0135564147749</td><td>140.3</td></tr>\n", "<tr><td>Mike Mussina</td><td>0.246</td><td>0.184</td><td>0.0934435852251</td><td>83.0</td></tr>\n", "<tr><td>Gary Sheffield</td><td>0.117</td><td>-0.001</td><td>-0.0915564147749</td><td>60.3</td></tr>\n", "<tr><td>Sammy Sosa</td><td>0.066</td><td>0.004</td><td>-0.0865564147749</td><td>58.4</td></tr>\n", "<tr><td>Mike Piazza</td><td>0.699</td><td>0.131</td><td>0.0404435852251</td><td>59.4</td></tr>\n", "<tr><td>Barry Bonds</td><td>0.368</td><td>0.075</td><td>-0.0155564147749</td><td>162.4</td></tr>\n", "<tr><td>Fred McGriff</td><td>0.129</td><td>0.08</td><td>-0.0105564147749</td><td>52.4</td></tr>\n", "<tr><td>Tim Raines</td><td>0.55</td><td>0.148</td><td>0.0574435852251</td><td>69.1</td></tr>\n", "<tr><td>Nomar Garciaparra</td><td>0.055</td><td>-0.037</td><td>-0.127556414775</td><td>44.2</td></tr>\n", "<tr><td>Jeff Bagwell</td><td>0.557</td><td>0.159</td><td>0.0684435852251</td><td>79.6</td></tr>\n", "<tr><td>Mark McGwire</td><td>0.1</td><td>0.023</td><td>-0.0675564147749</td><td>62.0</td></tr>\n", "</table><style>table.dataTable {clear: both; width: auto !important; margin: 0 !important;}\n", ".dataTables_info, .dataTables_length, .dataTables_filter, .dataTables_paginate{\n", "display: inline-block; margin-right: 1em; }\n", ".paginate_button { margin-right: 5px; }\n", "</style>\n", "<script>\n", "require.config({paths: {\n", " datatables: 'https://cdn.datatables.net/1.10.9/js/jquery.dataTables.min'\n", "}});\n", "require([\"datatables\"], function(){\n", " console.log(\"$('#table4456666192-750599').dataTable()\");\n", " $('#table4456666192-750599').dataTable({\n", " \"iDisplayLength\": 50,\n", " \"aLengthMenu\": [[10, 25, 50, 100, 500, 1000, -1], [10, 25, 50, 100, 500, 1000, 'All']],\n", " \"pagingType\": \"full_numbers\"\n", " });\n", "});\n", "</script>\n" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Table((name_list, p_2016_list, dp_2016_list, pp_2016_list, war_2016_list)).show_in_notebook()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Conclusions and other thoughts\n", "\n", "The overall average change in vote percentage was almost exactly what was predicted based on the strength of the incoming class and the large number of Hall of Famers elected the previous year. Along with the fact that percentages tend to increase relative to the average change for players with higher percentages, it does not look like there were any major changes to the voter patterns between this year and last year due to the purge of voters.\n", "\n", "\n", "In terms of players that took PEDs, no major differences are detected in the voting patterns as compared to other players or the previous year. \n", "\n", "In terms of WAR, the percentage change for a player does seem to correlate with WAR and possible has become a stronger correlation. \n", "\n", "However, it should be noted that this is one year, a relatively small sample size, and that something very different could be occurring here. \n", "\n", "Relievers still are an exceptional case with Lee Smith having a very low WAR. His vote percentage did decrease relative to the overall class and it will be interesting to see what happens to the three relieviers (Trevor Hoffman and Billy Wagner along with Lee Smith) next year. If Lee Smith is an example of how the new group of voters view relievers, we would expect to see all of their percentages drop relative to the average change, but it will be interesting as Trevor Hoffman is already very close. \n", "\n", "The player with the worst performance though was Nomar Garciaparra with a drop in voting percentage of -12% as compared to the average. He was never associated with PEDs, and this was arguably expected due to being the lowest, second year positional player by WAR on the ballot. On the other hand, the player with the largest increase, Mike Mussina, has the largest WAR of any player outside of Bonds or Clemons. \n", "\n", "As a final aside, Jeff Bagwell, Curt Schilling, and Mike Mussina are the only players in the last 20 years with no known associated with PEDs and WAR > 75 to not be elected, so far, to the hall of fame. Along with Phil Neikro and Bert Blyleven (and exlcuding Roger Clemons and Barry Bonds), these five players are the only players with WAR > 75 and not be elected on their first ballot in the last twenty years, whereas 13 other players with WAR > 75 were elected on their first ballot. " ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "George Brett 88.4 0.982\n", "Robin Yount 77.0 0.775\n", "Nolan Ryan 81.8 0.988\n", "Ozzie Smith 76.5 0.917\n", "Paul Molitor 75.4 0.852\n", "Wade Boggs 91.1 0.919\n", "Cal Ripken 95.5 0.985\n", "Rickey Henderson 110.8 0.948\n", "Tom Glavine 81.4 0.919\n", "Greg Maddux 106.9 0.972\n", "Pedro Martinez 84.0 0.911\n", "Randy Johnson 102.1 0.973\n", "Ken Griffey 83.6 0.993\n" ] }, { "data": { "image/png": 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EzSYzuQN8Fy9Z/xleQvyffmzvwTu7vy5r/Y14CTaY3Af9bf8AL6H/JBDfENmN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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x109b38990>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "\n", "for year in range(1996,2017):\n", " for name in hof[year].keys():\n", " if hof[year][name]['year']=='1st' :\n", " w = hof[year][name]['war']\n", " p = hof[year][name]['p']\n", " plt.plot([w], [p], 'bo') \n", " if p > 0.75 and w > 75: print name, w, p\n", "plt.show()" ] }, { "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": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
gregunz/ada2017	exam/data_cluedo/4-politicians.ipynb	1	19902	{ "cells": [ { "cell_type": "markdown", "metadata": { "toc": true }, "source": [ "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n", "<div class=\"toc\" style=\"margin-top: 1em;\"><ul class=\"toc-item\"><li><span><a href=\"#Politicians-Module\" data-toc-modified-id=\"Politicians-Module-1\">Politicians Module</a></span><ul class=\"toc-item\"><li><ul class=\"toc-item\"><li><span><a href=\"#Instructions-/-Notes:\" data-toc-modified-id=\"Instructions-/-Notes:-1.0.1\">Instructions / Notes:</a></span></li></ul></li><li><span><a href=\"#Try-to-find-publishable-(i.e.,-statistically-significant)-results-about-the-U.S.-economy-and-which-politicians-are-in-charge\" data-toc-modified-id=\"Try-to-find-publishable-(i.e.,-statistically-significant)-results-about-the-U.S.-economy-and-which-politicians-are-in-charge-1.1\">Try to find publishable (i.e., statistically significant) results about the U.S. economy and which politicians are in charge</a></span></li><li><span><a href=\"#Clue\" data-toc-modified-id=\"Clue-1.2\">Clue</a></span><ul class=\"toc-item\"><li><span><a href=\"#Read-this-article-on-P-Hacking:-https://fivethirtyeight.com/features/science-isnt-broken/\" data-toc-modified-id=\"Read-this-article-on-P-Hacking:-https://fivethirtyeight.com/features/science-isnt-broken/-1.2.1\">Read this article on P-Hacking: <a href=\"https://fivethirtyeight.com/features/science-isnt-broken/\" target=\"_blank\">https://fivethirtyeight.com/features/science-isnt-broken/</a></a></span></li></ul></li></ul></li></ul></div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Politicians Module\n", "=============\n", "\n", "\n", "### Instructions / Notes:\n", "\n", "_Read these carefully_\n", "\n", "* Read and execute each cell in order, without skipping forward\n", "* You may create new Jupyter notebook cells to use for e.g. testing, debugging, exploring, etc.- this is encouraged in fact!- just make sure that your final answer dataframes and answers use the set variables outlined below\n", "* _Have fun!_" ] }, { "cell_type": "code", "execution_count": 1, "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>Year</th>\n", " <th>Dem_Presidents</th>\n", " <th>Rep_Presidents</th>\n", " <th>Dem_Governors</th>\n", " <th>Rep_Governors</th>\n", " <th>Employment</th>\n", " <th>GDP</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>5</td>\n", " <td>45</td>\n", " <td>10.0</td>\n", " <td>800</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>10</td>\n", " <td>40</td>\n", " <td>9.0</td>\n", " <td>120</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>3</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>15</td>\n", " <td>35</td>\n", " <td>9.0</td>\n", " <td>130</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>4</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>20</td>\n", " <td>30</td>\n", " <td>28.0</td>\n", " <td>170</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>30</td>\n", " <td>20</td>\n", " <td>7.0</td>\n", " <td>150</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Year Dem_Presidents Rep_Presidents Dem_Governors Rep_Governors \\\n", "0 1 1 0 5 45 \n", "1 2 1 0 10 40 \n", "2 3 1 0 15 35 \n", "3 4 1 0 20 30 \n", "4 5 1 0 30 20 \n", "\n", " Employment GDP \n", "0 10.0 800 \n", "1 9.0 120 \n", "2 9.0 130 \n", "3 28.0 170 \n", "4 7.0 150 " ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Run the following to import necessary packages and import dataset. Do not use any additional plotting libraries.\n", "import pandas as pd\n", "\n", "from modules.util_politicians import evaluate, toggle_display\n", "\n", "dataset = \"dataset/politicians.csv\"\n", "df = pd.read_csv(dataset)\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Try to find publishable (i.e., statistically significant) results about the U.S. economy and which politicians are in charge" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Analyzing Party: Democrats\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "0b15b22fa8db440893ca626323068fc7", "version_major": 2, "version_minor": 0 }, "text/plain": [ "A Jupyter Widget" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "11669050050b4e579ccf301c1328c669", "version_major": 2, "version_minor": 0 }, "text/plain": [ "A Jupyter Widget" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "da7d45cd0cfd4a29b407ffa680949ef7", "version_major": 2, "version_minor": 0 }, "text/plain": [ "A Jupyter Widget" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "d_politicians, d_economy, d_outliers = toggle_display('Democrats')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Toggle the variables above and run the ```evaluate()``` function until you find a publishable result!" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Dem_Governors\n", "GDP\n", "Exclude\n" ] }, { "data": { "image/png": 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ctoYPbbqkwkSSFoJbEDqh0b2Hp5UDwO0PPOmWhNQBLAid0I7dh+Y1LunsYUHohPpXnzuv\ncUlnDwtCJ7Ru5VK2XLZm2tiWy9awbuXSihJJWigepNacPrTpEra8eC07dh+if/W5loPUISwInZR1\nK5daDFKHcReTJKmUBSFJKmVBSJJKWRCSpFIWhCSplAUhSSplQUiSSlkQkqRSFoQkqZQFIUkqZUFI\nkkpZEJKkUhaEJKmUBSFJKtWygoiIWyJiX0Q80jT2pxHxnYgYiYjPRcS5TdNuiIjRiHg8Iq5oVS5J\n0slp5RbErcCVM8buA16QmeuB7wI3AETERcBVwMXFaz4WEee0MBsHxifYufsQB8YnWrmYeWvXXJI6\nT8tuGJSZ90fE2hljf9309EHgN4rHm4A7M3MCeCIiRoFLgQdakW3bjj1sHRqhq1Zjsl5ncPN6Nvav\nasWizopckjpTlccgfg/4QvF4FbC7adpYMXbaHRifYOvQCEcn6xyeOMbRyTrXD41U/hd7u+aS1Lkq\nKYiIeC9wDPjUKbz22ogYjojh/fv3z3vZYweP0FWb/mV31WqMHTwy7/c6ndo1l6TOteAFERHXAK8B\n3pCZWQzvAVY3zdZXjP2UzLw5Mwcyc2DFihXzXn5fbw+T9fq0scl6nb7ennm/1+nUrrkkda4FLYiI\nuBK4HtiYmf+vadK9wFUR0R0R5wMXAA+1IsOyJd0Mbl7P4q4aS7sXsbirxuDm9Sxb0t2KxZ3xuSR1\nrpYdpI6IO4ANwPKIGAPeT+OspW7gvogAeDAz35KZj0bEXcBjNHY9XZeZx1uVbWP/Ki5ft5yxg0fo\n6+1pm1/C7ZpLUmeKn+zlOfMMDAzk8PBw1TEk6YwSEdszc2Cu+fwktSSplAUhSSplQUiSSlkQkqRS\nFoQkqdQZfRZTROwHfvA03mI58KPTFOd0Mtf8mGt+zDU/Z2Ou52bmnJ80PqML4umKiOGTOdVroZlr\nfsw1P+aan07O5S4mSVIpC0KSVKrTC+LmqgPMwlzzY675Mdf8dGyujj4GIUmaXadvQUiSZtGRBRER\n34+IXRGxIyIqu9pfRNwSEfsi4pGmsWdFxH0R8b3i3942yfWBiNhTrLMdEfHqCnKtjoivRcRjEfFo\nRLy9GK90nZ0gV6XrLCIWR8RDEbGzyPXBYrzq9TVbrsq/x4oc50TE30XE54vnlf9MzpKr5eurI3cx\nRcT3gYHMrPTc5oh4OTAO3J6ZLyjGBoEfZ+aHI+KPgN7M3NoGuT4AjGfmRxYyy4xc5wHnZebDEbEU\n2A78OnANFa6zE+R6LRWus2hcU/8ZmTkeEV3A3wJvB/4j1a6v2XJdScXfY0W+dwEDwDMz8zXt8DM5\nS64P0OL11ZFbEO0iM+8HfjxjeBNwW/H4Nhq/aBbULLkql5k/zMyHi8eHgW/TuHd5pevsBLkqlQ3j\nxdOu4r+k+vU1W67KRUQf8KvAJ5qGK/+ZnCVXy3VqQSTw5YjYHhHXVh1mhpWZ+cPi8T8BK6sMM8Pb\nImKk2AVVyWb2lIhYC/w88E3aaJ3NyAUVr7Nit8QOYB9wX2a2xfqaJRdU/z32X2nc9bL5/r+Vry/K\nc0GL11enFsRLM7Mf+BXgumKXStsp7tndFn9ZAX8OPA/oB34I/OeqgkTEEmAIeEdm/nPztCrXWUmu\nytdZZh4vvtf7gEsj4gUzpleyvmbJVen6iojXAPsyc/ts81Sxvk6Qq+XrqyMLIjP3FP/uAz4HXFpt\nomn2Fvu0p/Zt76s4DwCZubf4oa4DH6eidVbssx4CPpWZny2GK19nZbnaZZ0VWQ4BX6Oxn7/y9VWW\nqw3W1+XAxuIY5Z3AKyPiL6l+fZXmWoj11XEFERHPKA4kEhHPAF4FPHLiVy2oe4Gri8dXA9sqzPKU\nqR+Qwn+ggnVWHNz8JPDtzLypaVKl62y2XFWvs4hYERHnFo97gH8PfIfq11dprqrXV2bekJl9mbkW\nuAr4amb+NhWvr9lyLcT6WnS63/AMsBL4XONnmkXApzPzi1UEiYg7gA3A8ogYA94PfBi4KyLeSONK\nta9tk1wbIqKfxub194E3L3QuGn9J/Q6wq9h/DfAeql9ns+V6fcXr7Dzgtog4h8Yfg3dl5ucj4gGq\nXV+z5fqLNvgeK1P199dsBlu9vjryNFdJ0tw6bheTJOnkWBCSpFIWhCSplAUhSSplQUiSSlkQkqRS\nFoTOaBFxvLjU8aPRuHz0uyOipd/XEfHbxfVvppb5iakPfklnk078oJzOLkeKa/oQEc8GPg08k8aH\n+067iLgSeCfwK5m5p/iw19U0PoB5qEXLPCczjz+N1y/KzGOnM5M6g1sQOmsU19a6Fvj9aDgnIv40\nIr5V/MX/ZoCI2BAR34iIbRHxDxHx4Yh4QzRuYrMrIp5/gsW8F/iDput5Hc/MWzLz8eK9/100buqy\nq7jCZndEXBkRd0+9QbH8qZu+vCoiHoiIhyPi7uKCf1M3tfpPEfEw8JsR8fXi+UMR8d2IeFkx3+KI\n+J/F8v4uIl5RjF8TEfdGxFeBr0TEeRFxf7G19cjU66UTsSB0VsnMfwDOAZ4NvBH4v5n5i8AvAm+K\niPOLWV8IvAX4NzQuk3FhZl5K43r7bzvBIi4GHi6bEBGLgVuB12XmJTS20N8KfBn4t8W1vwBeB9wZ\nEcuBPwZ+OTNfBAwD72p6ywOZ+aLMvLN4vqjI+A5+soV0XePLzkuA19O4hMXiYtqLgN/IzF8Cfgv4\nUrG19UJgB9IcLAidzV4FbCmuj/RNYBlwQTHtW8WNfiaAvwf+uhjfBaw9mTePiEuKv8j/PiJeB/wc\n8ERmfreY5Tbg5cXunS8CvxYRi2jc+GUb8GLgIuD/FBmvBp7btIj/NWORU1ev3d6U8aXAXwJk5ndo\nXCvowmLafZk5deOnbwG/G427kF1S3NhIOiELQmeViHgecJzGJZkDeFtm9hf/nZ+ZU0Uw0fSyetPz\nOic+Nvcojb/MycxdxV/kXwB65oh2J42LvL0SGC5+QQeNX+JT+S7KzDc2veZfZrzHVMbjc2T8qdcX\ndwl8ObAHuDUitpzE69XhLAidNSJiBfA/gI8WN3b5EvDWaNyrgYi4sGk3z6n6E+Aj0bgF5JSpcngc\nWBsR64rnvwN8o3j8DRrF8iYaZQHwIHD51PzRuBT91F//J+tvgDcUr78QWFPkmCYingvszcyP09iN\n9qJ5LkcdyLOYdKbrKXbPdAHHgL8Apu7J8Akau2Iejsb13ffzNO8nnJl/VRTRF4ozmA7RuA7/lzLz\naET8LnB3sSvpWzQKi8w8XhyYvobi3gKZuT8irgHuiIjuYhF/DHyXk/cx4M8jYheNr/+azJxofLnT\nbAD+MCImgXHALQjNyct9S5JKuYtJklTKXUxSiYh4L/CbM4bvzswbq8gjVcFdTJKkUu5ikiSVsiAk\nSaUsCElSKQtCklTKgpAklfr/KsdH2n2asP4AAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1d465e1ccf8>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Correlation 0.868813280324 P-value 5.51446043268e-05\n", "Part 1 : Democrats have a positive impact on the economy!\n", "By achieving a p-value of less than 0.05, your result is publishable!\n" ] } ], "source": [ "# Part 1 Democrats\n", "d_pval, d_corr = evaluate(df, 'Democrats', d_politicians, d_economy, d_outliers, 1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "r_politicians, r_economy, r_outliers = toggle_display('republican')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Toggle the variables above and run ```evaluate``` until you find a publishable result!" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Part 1 Republicans\n", "r_pval, r_corr = evaluate(df, 'Republicans', r_politicians, r_economy, r_outliers, 1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Clue\n", "### Read this article on P-Hacking: https://fivethirtyeight.com/features/science-isnt-broken/\n", "If you found that both parties impact the economy positively or negatively, try again below to show that one party is better than the other on the economy.\n", "If you already found one party is better than the other, try again below to find the opposite relationship.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "d_politicians_clue, d_economy_clue, d_outliers_clue = toggle_display('Democrats')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Part 2 Democrats\n", "d_pval_clue, d_corr_clue = evaluate(df, 'Democrats', d_politicians_clue, d_economy_clue, d_outliers_clue, 2)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "r_politicians_clue, r_economy_clue, r_outliers_clue = toggle_display('republican')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Part 2 Republicans\n", "r_pval_clue, r_corr_clue = evaluate(df, 'Republicans', r_politicians_clue, r_economy_clue, r_outliers_clue, 2)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" }, "toc": { "nav_menu": {}, "number_sections": false, "sideBar": true, "skip_h1_title": false, "toc_cell": true, "toc_position": {}, "toc_section_display": "block", "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }	mit
HNoorazar/PyOpinionGame	Famous_Models.ipynb	1	154071	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Famous Opinion Dynamic Models" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "from pandas import Series, DataFrame\n", "\n", "import matplotlib.pyplot as plt\n", "import matplotlib.animation as animation\n", "import matplotlib.image as mpimg\n", "from matplotlib import rcParams\n", "import seaborn as sb" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## DeGroot Model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Convert to row stochastic Matrix\n", "This code uses the method of the Sinkhorn paper:\n", "\n", "Sinkhorn, R. (1964). A relationship between arbitrary positive matrices and doubly stochastic matrices. The Annals of Mathematical Statistics, 35(2):876–879.)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def converged_test_stochastic(matrix, threshold=0.0000001):\n", " localMatrix = np.copy(matrix).astype(float);\n", " e1 = sum(abs(np.sum(localMatrix , axis = 0) - 1));\n", " e2 = sum(abs(np.sum(localMatrix , axis = 1) - 1));\n", " return (e2) > threshold\n", " \n", "def one_step_stochastic(matrix):\n", " \"\"\" Here we will do one step towards\n", " Making a given matrix a bio-stochastic one \n", " It does what OneStep does \n", " \"\"\"\n", " # copy the input so that the original input is not changed.\n", " localMatrix = np.copy(matrix).astype(float);\n", " \n", " # Divide each row by sum of the entries in the given row.\n", " localMatrix = np.dot(np.diag(1/np.sum(localMatrix, axis=1)), localMatrix);\n", " return localMatrix\n", "\n", "def make_stochastic(matrix):\n", " localMatrix = np.copy(matrix).astype(float);\n", " while (converged_test_stochastic(localMatrix)):\n", " localMatrix = one_step_stochastic(localMatrix);\n", " return localMatrix" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### An Example" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "np.random.seed(100)\n", "pop_size = 10\n", "no_time_steps = 100\n", "\n", "# initial opinion of agents at time t=0\n", "initial_opinions = abs(np.random.randn(pop_size,))\n", "\n", "# Build the trust matrix (weighted adjacency matrix)\n", "trust_matrix = abs(np.random.randn(pop_size,pop_size))\n", "trust_matrix = make_stochastic(trust_matrix)\n", "\n", "# Initialize history of evolution\n", "r_evolution_of_opinions = np.zeros((pop_size, no_time_steps)) \n", "r_evolution_of_opinions[:,0] = initial_opinions\n", "\n", "# Do the game\n", "for time_step in xrange(1,no_time_steps):\n", " r_evolution_of_opinions[:,time_step] = np.dot(trust_matrix, r_evolution_of_opinions[:,time_step-1])" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1a1defea50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure()\n", "plt.plot(r_evolution_of_opinions[:,:20].T)\n", "plt.xlabel('Time')\n", "plt.ylabel('Opinionds')\n", "plt.title('Evolution of Opinions')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Non Convergent Example" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pop_size = 3\n", "no_time_steps = 100\n", "\n", "# initial opinion of agents at time t=0\n", "initial_opinions = abs(np.random.randn(pop_size,))\n", "\n", "# Build the trust matrix (weighted adjacency matrix)\n", "trust = np.array([[0, .5, .5],[1. , 0., 0.], [1., 0., 0.]])\n", "\n", "# Initialize history of evolution\n", "non_conv_evolution = np.zeros((pop_size, no_time_steps)) \n", "non_conv_evolution[:,0] = initial_opinions\n", "\n", "# Do the game\n", "for time_step in xrange(1,no_time_steps):\n", " non_conv_evolution[:,time_step] = np.dot(trust, non_conv_evolution[:,time_step-1])" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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FWmxIOBUHU4swUahA2mmUGyXMlyEG0+xo340Jl1b8p1gSoaewgXuaUVJpNeGx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MrL30REnyGiDxPR157qwAAG4CNE9FdE9MaU13XO8WngdVDfiGd5/gSexhh7AgiDAQAXU95zo5zL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\nMiIhFQAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1a1e09bbd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig1 = plt.figure()\n", "plt.plot(non_conv_evolution[:,:20].T)\n", "plt.xlabel('Time')\n", "plt.ylabel('Opinionds')\n", "plt.title('Evolution of Opinions')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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oX/BrdX/UHVG9TqmZZF3DT8ipMbeaKIUav0yXVd0wyam5uS3T4XTNesv0B+ug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jNdERExnbbiaFOc/0h5KWxrrBWW5arbGhKY+1XGCWwbod6PeUl+7ZQDgEmwAUqOsVYs\nKKkG1ck6P/zrfecUFgD/DoBfC+AvYDCLrwLwnwL4t1R1u/q3qrqLyK/H2FAqgP9EVb9fRL4dwPeq\n6nfNv/snReQHMDabf0NV/59P4HOdvvZO5xS2uPb3OR2TH46vGs+FNHGfpYYVWmo8qQI0VcMNPUO6\nwNBDLdGf582tinxDpr23aY/eEDVbcW3r5tLVRN9m03Gczmj6RGo3hE2VtFvQc8A2Khrz9OdTp5+L\nL2xdwlawrcVPjjJTMElhbx2lTv8d7vSRC1v1YOvuPZlDrKdfbfGdxTqxQqnuv2AvZ41S+J0+Eet4\nb9bE+yH/9r64//h5TzaRrcKauGBHMFjzUzSaO95g8edvMO+I6gxWHmNt/aNHm5boadFzvCPWkX+Z\nKdhhS+vJbCZrmgTGsaY1BDnGejmNtfVkNn3CjXNA1ljXAO59g4q4TLTNMeONRn1TXgI+lqwK3PdD\nXjpYoZHew7o+5kDTeuo/iN1W++Ff72MKvwvAlwH4GlX9cQAQkZ8O4D+Yf/evv+sfq+p3A/juw7Pf\nTn9WjG93+42fs+U/idfWNywwxxP6tu8qVkaJ6s8hGek29Nk8q44QBsoZX1RjUo0lehM5bAohH73o\nntCPa9z+/cmBdAs9R0K04s9FxjF5t9WYghRnL4zIbiZ/dMWbuchedEdiNZL9tKmhnKP/lokex+Jz\nmwrZqvG8yJKQ16bWgCb/ma1q5WcsviLsv/ArcLDJmFbPz/fEtJD8Z89NruoiIR8R+n5oNJc5VICO\nzLTY1mA1DYdYT6R79Kudfn2wlRhYSbEOplBQPdb+RfVdk/9SXlKsK7GadsjL3WN9S7E+rhX2q0mC\nizXqBahi3ywYAKqpokqwmiXZGnkpONp0e6etPkJ9EutkK7HtioJn7NN/MXBSltmon2zxNNbMFA42\n+Ro65GvXq7WyfmpGUv8ZAN9qGwIAqOqPAfiXAfzTH9KwD/GKLxopeN47SQczIFjwvMcubc9F6LmY\ndjsQhTWYvKFMY3bblBQA4M7Fa++AxHs70t07SQo0f7/TNEKnxXdqa/XnzghgvQY7ZFUdfVcJ+Ujo\nvSvZKmSrNc+e6TI5t1XX+RlKei6oZKskW8NPE9FaobCT4qbdTr+GTZVsDf+xX+91h34AACAASURB\nVAEbSQ2kexprvfbrXdlWOqRmfpUsv+0TABQh/yW/UqwP/tunn9oh1sr+0/NYm1S4S8TaBh02YjWm\n04eklWOd8pJsDamQNqqT53KRl0WqjyXHUEb4b+SAraHuG1gayjislYdYz5iGVGixNj+ZLEyb6kVe\nbsRqNigxsPlc6qMCsMd6f5dfh03n/mvKa0XzWvmUTB/p2XcoqGrD4yTRp/5lF+I1VDzfqfE0F77K\niud7aIxvaZe25xWCZzV0tsR11Lb4MJgCN8nsPTxJ7s2R2ltGj/dgL2+Vito90KM9B8hWel6w0PN5\nytqaZ33MfQ9b4T2Froa+K9kUtnqjlFnNvfk4or1362bTWHxuq9yS/9jW8b0MQEWlxVfpSoSKTaKn\n4P67sMmYwvv815T9F7aKsP8K2Xriv4keQz4yptWy/zjWGs8hB//p7TzW5L+F/CeHvBybqhCD7VjE\n5LfBEMdneGWsrQDfaajA/Drzb8c78pLWkCDbmmLt5z8CANhaedn6Ya2wX+O9XYK9N9gdUZED5D/F\nqU28hqrHevpPDayQ/9zWAazCfyvZGv7DYa1YXm4Hv/aLGgSsH63R/L5N4QdE5F88PhSRXwPg//ww\nJn24164DQ9g9Q94km0Ve54ESwGj6+PNAFFEo7Hnx8wsd1RrK0PFno5lOJxshhygIL/Pwiz13pjDf\nY9dbOgzkNgnZqnL6vIrNfRv6npTYbR0bGDDGEQu9x0K2SrIV/nzr2X87+0/ZVvafnD6PhihJXROR\nhV+Lv3fIHNl//Bzgw3+39LxzTJP/bmRTPK+ykP9mo54KxWOsL2xF+I9zABhfVH9ma4o1PS/IsT7m\nZWshH41Y1/Ar2KaLWBtY2VnmsLy0957oez4H+e8q1guq+8+a4sN/1vweOWrvfZmXOPdrO/jPDlse\n/Scl5x/7e3yx0PSZg704AR1DGR0LxZpzIMW6ZP/ZYcujrYKnc1vF7nv68K/39RT+FQD/hYj8WgD/\nMwY7+AUAPgPgn/3Atn3iLz6nYD2FFcDLvMhOZCU9mZ6XrN3ac9PEuaEM6insjbXvji/1e2IaSonn\nWSe9+XMgdPpYfOO5yi1p3/EZKiGygk07tBh6tMv7jPpSod0a5E3YtCKe/3T6DFXCVj/n8WCrJfRM\n4qTTI/nPn88NbBTawcDWKrH45tSP23qr/t6s6X6JhP+WgyaeYorbeayRewp/yxdljCWHzMHDBiQd\n6Gjquq0L20qxlnP/uazpsV7PY13Y3wU/5hcyLvNakQFWHH1PSetlbyhr2PQZa45uLWS5g1999t9t\nJZv0/WuoEAMrMsc8i6CWGgcVOS9Zp39aTm36krLS8/CrCV9ua5p+izXEfTkGAIWmkoqwVMhjyWHT\nl7Ct67mtb5BjbTc1+0TUe9fK+tEaze+7EO8vq+ovBPDtGAfY/iKAb1fVb1DVv/wR7PtEX3tvfvdR\nOo9gSpg80dyy+HORmL1epPjzatrjRN874Jo4kJHDW1qUb6l5+9aPW8155nJLNu245fMI83kRei5C\nz3lOvNDzJWQOLhQmb23XtgrZtNKc+NF/NtM+/KfkP7L1YJPbKked3mwlm2a6DlvJf2RrkbB1SbZm\nv2q5pRl1t7Wcx7owA/OeDLCIXaCXbV3kwlac58CI9Tq/jWtMRJ37D6f+K5SvtdSTWOM61uRXjnXO\ny6dsa4o1ztdKWkN8JqDixWxl+UjGUMaGWENvCUAdYy1ybqvLwr6uw39Vaa2Uc1tTXiKvazuQWEul\n9yb/XcWa/TrBytstrqk5z8tcgz5Wo/lV5xRU9U8C+JMf2JYP/tp0Hxevofj4YlWFHa8Z0wjRJBsB\nkTm7bs/LTOjx8zYSaNRXMIoakEdPX2jxPW8d8pnVnz/RmN1nZvLYe9hIpdlkz1EWPN+pSWY2FfoM\nErbalIfJR32edK3J1oVspZHKlZ8jnpf4DMMfNxqTJZto9K+KJP+xXzcAKoKKhSalyH9sawmbvozH\nFG/ntu664in5j2zFha30fKBH+ww20ROxvjNT4Kb41oMRHGx1VgOdDdRyHuuDTX8T/cHWRWryq1+f\nLnXM0yM2hYdYC8X6KWyNqbiOKjnWDe/Iy+fHWBc5z0u7QmZMSnHs3p+XWOI529qwDKbqNpGfkl9v\np3l5tNX8Z2x7nJ1g/53n5U/jz8C28liyybentQb4CVrXn5ZG80+p197n5V8IelgBvNiFHOUpj6jN\n54VHKqXEc+HngR6rzaVvLRaTILRHQo/jOfy5yJM/jzt9SJKZ7y1XtrKslGxdfMrD7Lszq9kaajm3\n1XXmg60qb/w5AGi9nduapK6wqbLUIDWel2WeXwhbX6Z+H/5jm4i+lyd6HrbaHVFh09OprSXJH2Rr\nybYGeryINctyFzkAijWPLwKzJ/O+WCeb2K8BVqrMO6KQY12uYl0u/Eq2AkCnmCZb5U2StMLW87ys\nU7/f+zHWlJeXa+jcf3LISySbhPy6XtiaY30H0EVQZfGhjOVzzEtc5KXLwu+pQSK3eTX3h399wWwK\nXcdV0asq2vx6PmAk0zMFxJ9D/HkpN39eUfx5LUs8t8VHCf2ydV98z4LQdLfuhfaZkudl6958fJ7J\nsxQ5tVXkdm6rrPFcatgq9oU5VMiOtkrYakVtfIanc1tL2Dps+sx7/bdIPJdybmuRClUkSWbYdGEr\nLvw619DL1rGUMVfjsb7wX/JrsmlNsd4EaCKXsV7Az89jLTXH2v0KJFulvN/WlJfJ1mnHIdY1+ZVi\nfeVXyoGiisqxY1v5vVMOLOd5WaLRbJLMQ16S/yrZinLhv5rzUuWJbMJFTAs9zznAmy2AMZTxOeal\nbWCnsX7Fuq7y6Zk++inzsi/oWBTYUSbdH0myzWJdyuqz1BXFn4vQc4nnReJKaJMOdhFPkjs1mrfj\n85m4m4gnz711R2qbCBYFahGyKWyVcju3qSz0vPrzWh5t3aiojfde4zlCFkFZ6Dni5yV+fthUL2xd\n6ers99u6zPe7t47K783+S8/DVvYf21rL8OeZ/xZI2FQ51vSc5uzrPI8w/hy2cqxrkpXYrxFrAwBm\nqwjSFeP23pDbaV4WysuF/FdeEevCsU7+i+dWvO6tQ9NnGGDlzFap5Ne0Vm7JJl5D4zs7cFgrZBN9\nBl4r6TPQ81qGLPzeWJenC7/WU1uv84/W0EUOgP2n4VcDKx7rem6rlC9uCp/4y76gfFVFn0Fcqg3m\nzZfERbDW0ALghWY8r/5nk1uAKGTj+XL+M/RvS3oe74VCNgBYa6H/Zpue6Hn8jNFXIJJ4PI/3u7Ip\nP4/fKcmmsGFdFr9ie1HFWpfTnzOUNGwq9Ge2dTn9c7I7fR6KCdhW9gvZWgvityL7WdjW81gXzoHC\nPruKNcWk8u+8iiGwljJ/jzjLAQCp9HP072v6vexXikOy6RW2XvhP6mfiuY61c/pzwrZyXp7n3HKR\nlykHynn+vDPW1mNWxbLcTn9O6P1WXgf1ar1zjp7nZa1X/svr2v9cJf13uaxBTx9tJPULZlNwpgA4\n8l0Pi6/U80K7cPJdFtHzhK6FC9/V4qNE54TESBpgoEjewPJGdVWAaPGV80JxZWsqMuW8qLH/VgXW\nSn+XFkR8Vl58ydbCBZ9tpT9f/dtUKOL5iuE34HHxZT+z/85jnX12XmgTMLjaUPi9eGPSiLWhx/i5\n80IrFwDgqrhyUWObKtjW8/dalzqvvTCwEvHl4ne1UeV4sR3nwOpqna2Xm0K82H8LxiYbv5cAwMVn\nTfleLuJ7BfauPn/JG5P5by0l1SBc1KBa1i/2FD7pFzMFzAJUi9BygGuUwGGXrrzIztFPSeiRF9lF\nUUubBaOcWHxVR4ID4/8ro++Lf583oXNmkgvFVTG52qi4AIT/KvLiqxdMKzGFiyKaCnC5QGQXnz8X\nO8n+m2uqqmKt9fTfM3qsqYBf2XHOal4Ta86rBeL+e1esU6Hgf18uYn3h48QmUvHmz895FbHmQivy\njlgfitqpHcl/F2zxav1drNeFbdXXse28rq9Y6/kGu1ww8uVyDYX/eA0Nmy5AZH364vTRJ/2KnoIG\nU6jFk6QcaWYqXrR7czLUC0TBiXFRpBN6TqwkkMMy/9ufc/EjOv8a9F0vCsVygR4Tnb5gNWspCZHl\nxcfF64ppsV/P7ctI8vxz5s9PModI9p891/ArkP1fmOYnZEh+refF4apI1yu/1idIQt/TVspL4Cgf\nnSPlq011uZI4+XeSrSwVMipnRMv+GzlwnpfLxSbO73dpa9q0LyS9JMmU2FRruWQKeVN5P9qvVwyb\n13V5/1ophfwCcalwIf8tqrgt8bvWtFE9fbGn8Em/tnkF7Qr4plAJkVnzzF5XKDGjgnM6eVnULuUI\nKqYJfb8DPdYL9H0hbdSrnsel7HCxSCRsYvRYFcnWzLSuJDT22ft7NenzyAV6rOdM4SHW9bxQXPcF\nztHjVU+Bf0YumAIj2mOs7V8X1bTZ5Fw5ByWXTOHqZ17BalKsEf6rRQ6bwvtlvfqKnkxmCufsI32G\no/+MFSKv62wrbaoXG9W1redM4UpVSAxUBPVEKuQ1ZJ/J/1y/KB994q9tfvXDA1OwiRfNybMSkizL\nOXq8ZAqcAIxo6WdS44kXOiXJitBulypJp+cFkdHnla28AMgmTuILRM8oZ5USkkw9spqwNclMCX1e\nNfQu/FrPWRp/zvVCJlshXrzWKt6TYVvHe1/o/Cmm56wmF5Bz9F2Xc1uXKn5CfBHByv6zvMShV3PV\nl0q9DS7AF7amvGSbslRlbsr+kxzrq6EE/r3Mwi/suGQ19Dl5DZXDGlpP8o/lo7VK8l+9YNs5jue+\nzH228zxhv65LjaEMEINlW5HrTtpsypsvykef9Mvko1UVMlEfF9qj/HGk+fa6pOCpkDENPqf4uahd\nUV/xKY+lFl98iyrWhRORC9DVIuON4P0LlBO6LJkSp0Xm/pN4XsoBZV9IMuyDelVoL2xdzuWINBEl\nJfuPYm3PazkUCgYAcr6pXvl1uQAASypeZOtVrEtx6eEodWWZ4zWy5pWUdL6hlCUXyuQ/BwAUa/Kr\nqGJNEux5oV2v5NXkP/qZ5WKzPUxlZf+ZraTfF8ms8ALUXQG55WK9rykv3xFr91+JRjMDU2QGmzaI\n5YuH1z7xlzWaFwB2tzlLCo/Ns4vpkYT0zqePXqVFHySVU+orh0YpzlnNZVMzafDvlxSuRvGOI4Fn\n8tFD8+xyeumKpl/ICMnuV8QkSQcH/52wwmOhSL+Xde2kdy+nf849BWaFV70q9l9G31ey5nKB6tfL\nnLvw8WWTOstHy/tiTf47Dhvk3gEXf/4M5zbVq8/DsV5WFEff5cJ/h0kfnG+qV2Oll5JRYtgXvZOr\nWD9IsBHr/DyDlS/2FD7h1xlTWEuWj1LypORjRHvFFC7Q43KxcA8NuaC+jGhD/mCazkjX/n28HyE9\nKmS3emUTFztCOfQ5b8sS44hSDouMmYIV4Fh8iyrWlRHThaySkOE5q8k/c97QTExLSvZf8quxGnmH\n/96PEl8jda1LluWSn1w+IltLxHpFoMciBwbLPuCNarlgL5yjV5LoAelaoV3Zf3Lw3ywj62HSJ9t6\nEWseuLjYzJgppJiUi1hTT2tFMK2j1MXrN52xWc7lI87F9G8vmALbutJINDOthfzHUtdSJLP18uaL\n5xQ+6Zc1mpfDSKoF5LhLXyVPvZAOEvK6YATrJSrPTCEaegWVphQq2XqFHi8p8aUO/n6mkEdPD0zB\nT3seGn32XOGfYfzcucxxrX1fTc9cTbNkW60g1Av/HWNdXiEpXE2aXdmaEelVrCXFmv3HE1SvaTRf\n+/UcladCu9x8IqqS/Db8F+jb/UeF9pEpXDSL63leXsf6XD5KY8ZSsJxM9FQCVrUcJVie9LlA+7xZ\nXK39V8R6+O/xee4fHQZLkqz5hO2LTOGTfe1qTCEmBNZKkkLS71+HHhkBJkSb/nyOyNb1FtRX6gE5\nBCJbyyN6ZPljIApaQMuFTs/oh1EOo8rlHP3wIbWFF98Bfa8n/ntolF6gUrY1IeuLvsMRkbE/sq3G\navLiCwQcrLCoYl0vJp+SrWRfygH+8wUDK0uMIyb0HQxsPGdWSM+TTZSX9SKOKRc57ueINh38YqaQ\n/Md+pUKL61ivK78f2UR23JYr1nCl8cv7WaFwrCkvj2PJ5ZxF5bx8P9teDwws+e8kL9NIKniwpBx6\nWm8+Hd+n8FPp5UwB6uOFQ2qwhI4CvNbii280zyJZswxzXiiuisbyrsV30nhaU6IXb4iyfHTcwG48\nUcGJy5/hqmikIsMJGTR9LSF/rLVQo7mcFtox6RNpdqtXG9hFEVjO/3w7yG/sJ7c1NZqjobzIOzaw\nZOvVpnrRtL/akA8nupfkv/kzSRPnQstnLXKs64UExPmXYp1kpStbQ9JaGazwUIGUg/8em+LrcQPj\nvHwVWDmXCtey0EQU+Y82sKP/qrPF3P9IG1g9B3u3emHflf8Ok2an/kM8X5MsLNl/M9Z1Dpa0rjj5\nduRP/PUFsylwT4HPKTDKuUoengi41m7Xiz9faLqVqW8lmSNQTj0UhJqSJ5BGoq9Xo3+U6FdTHkeJ\nxBZfmqU+Npqp0L7Pf7VIsnW96MlcoV7+PPw5a1ly4TT/HRBZJVZT+bkvvmv5I7HC5SrWFwCAxhGr\n1OwnKrRnsU7N8lq82T7ACvvsPOeyJHP+M1dgJUtd5L/UKCX/4diAPh9JTej7NWuFCm0tea3UMwab\nmreHSZ8SAOAq1nmtXNh68Xl4sx2xfpSPHhrNFutjY7+EX+0zfIxm8xfMpuDTR4poNPMuLTylEChn\nfTi/cE4VE7pYKWHWQDm3lEjUJCvLBfWt+Tmj7wtJ4ZboODeaz21NSJxsXcqa2YgvviWjnNNGs8Ti\ne5C6aAGtF9JB+gwXn+dI05P/zNYr/8mp/x7kj0sWxbbSn/nzpAJH/isLFSmSZC4azaOpy8/DVh42\nSO934b/EFNaMdM/uiHrwK8lH+TOEX3kIIUutP43e71xeTRLTxedZZH1/rB/898i0+FK/WiQV9pxz\n53as6zlIWFe6I6qs2U+0rtNGhZO8LMX7HAv15T7GWOoXzKbwFV/6FfhVX/qz8bf15qOG3HjiE5Fj\npPIR/QDXaIGlhtQYO0wf+Q25ST46NB+tyciSQmGb8qgl08wlnV+4mkJ5Tf+jEmo5b5JVanwem2eV\nCzB/hosDTRk9Xkz6pKkunoiq+T2cKRyfnyOyM7YogkOv5hXyVrKbm6nkv1IzSiRWk/zHPYWTWD+M\nJad+yzmDzRJdPo9xekeU1OSn8F9mi6dsu2ZWeC25XTxntkMFuJbl0Kg/iTWNpHL+VV4rQP5srN9f\nyKvZl+c9kjQ8cIy1PMa6HnpdPmxwqEG2iWwf4QDbB90UROQbReQHReSHROS3vOPnfpWIqIh8/Yey\n5ed++c/F7/iZPx+f1eJzxWvh4kWHsgjlDPRIDSlKkhujR2YErOkm9H2jO08O6NGTgfocx4mKU/RT\nXBbgmzaHfWTr7QrlUELfeFNYc5PMFllZTscRk87MKAeByIbWy4jsS8gORtzsv3PUlthLWan5XTL6\nPvMfCn2G6B89jCVTEXhi9LhyDpDdN2YKXFgYACzJT+G/mv1XIi+T/yozhYtYcy6u5/2F243HWQ/+\ns+ck1aTxbTn474QpLMmvh7HkZCszmfjzE9n6tNIdUeXoP2bVJ/nHwwbUK0yn3Yvkgn/BYJOt6wUD\n40NqJGvyIb9jrBeyNbPtORCD2MDa5zNTkPHNGN8B4JcB+DoA3ywiX3fyc18G4F8D8D9+KFv81Xeg\nLIfrBB6TJ0kKVGjXmmlmKgIpebjABW0+ohwuFClBhTaFU5njcCVCPZc/UqHgBZeKRi66JVFfLhTW\n/K4H/1mhPcy0k63sP1t8VRU3KhS3i6b4ekXludDWg//SBnZi6/FKBJZkUg7whskyIBcvti9PA6UJ\nE2ve8kRKko8O/vO8PJ5VWR9sHf+GNttk68UGsTIAuGXpxYtXSF1pIuroP4s1XUA4coDBSpSapzT0\ncJ6LqdDSeYSVwQr7jyfQUqxDVuI+RwUyEEuy8Pt9druQCtM1NSQfLYUazeUi1qnRnMFKnf77fGcK\n3wDgh1T1h1X1DuCPAviVJz/3bwP49wE8f0Bbxqs3IBXmjL6rJ09xOvlAMxOFvJj0qecFbqmsh9Lc\nNyU0z4MfZRFOnkqMg23lMwGpiCa99mKaKlHfA02nb59i+ehM+06L76F59n5brxDZLTWXWdLK/lve\n47969J/bJL74jk3xa22effwumePxeba1XBSvg4Roth4mfa5GT1N/60KW4/w7TsXlsycmtcb5mTxU\ncMiB8riG3uW/DAzYvmCFx7VydXitkq1nE1EPZwLmpvAwlnw5CZfZtn087nOwfJRsOg4bkASb/ccb\n2GQKn+eN5q8A8Jfov39kPvOXiPw8AF+lqn/8Xb9IRL5NRL5XRL73R3/0R3/yFvUdKHlMbGH5iJOn\nEtIlCp3ko6fYFBh9P10wiKXezpu0LMlUCepbDtIBsxoewSyBHlcqwLk4MFNgxMP2kSRDtqZxOmIQ\n6xGRnaGcNEFVHH0/Sl3nTIb/vNYnX2QrF69jUzyhREaD0z4+VXywlceSLdaimvyUmNaFhLjUpwv/\nLe/1X4710X+P8hH7ddgX7DQ1TW+M0OmCtrpmP5mtdTnkn62DQ6PUbcpSzVrOWQ2viafbuf+e0ngv\ns8I1rRVnLzyBdrVW2H80WLKQAnAcS35az/Myx/qW5VwfyngH2z6diMpjyeG/2MA+3xvNcvLMP5GI\nFAD/EYDf9L5fpKp/QFW/XlW//rOf/exP3qIpH50nyWH6g/W8JOHQ4quc3FxoOXlo8ZX1gLKj0J5T\n38OkChcvtrU+0vTxnAsZFYobF11efOVQaKlwnkkySQ89TH+Qdsuf4Wrx3S57CrnQpll038AOkkIq\nwEzT3x3rh0mVuSkcJ31Y/nhD/nuzHnT65L8Tv7L/ykECo0Kb/EeFIo1gJvmIczHizoV2qSVuaK03\n2gxZ0lpT4XxfrC/9d7y+YTnfYJ9SXj7RRBT77yB1ua0Ua2bVR1mYhzXq+8HK1abwdAu/PvjPN9XD\npJTQBnY2EZV6NeHXSgdDt49w1cWH3BR+BMBX0X9/JYC/Qv/9ZQD+fgB/WkT+PIBfBOC7PmSz2TaF\nTOlM5jicMpQISLoqwQuwYuFj8jU3xuyVGsoPyWM/w7rn+URPLTGjPp4TMk6Fgj5bap5d9Dzq7Xwi\nijawJB+Vw5THif8qFTu2tRJN5znxItk+Lq6MbisV/6R9U6FN/ivXND37jw/aPUoKD9eqs//IvoW/\nsjL1FORUKuQpo0f/hX6f/XfCFGoGALeL8ehbkrfograyZumUbM0yZcgf+T6hx0mzR6nwQoK9aOTW\nkmNdz+SjSleXHGW5M7aYwB7JRzVsOkpdV4f/jj2Z0+ssjv4D++9ssy3Zf8Kb7U8N+eh7AHytiHyN\njC+T/SYA32V/qap/Q1W/XFV/lqr+LAB/DsCvUNXv/WAWeaP5pCAcTr86+qaArLT4eFIFOKIcThhC\nOUea6dQ3pj8SyuHphZKRbp7yIJrO6PSKyfDkyfImy2a2gS2HSSm79IwnfQqjn1h863GjYqqc0OO5\nrYnJ3A5MgWPkPib/HZkWb/SOaOvhudl6kOUSU4hYv7ldyVs8EXU7+MlQ7JX/loP/TmJdYyIqscX/\nr70rjZHsqs7feUtV9XR39b5Nz+KZ8XjssT2LPV7GQGyMgfFuZRF7EAI5UUJIyAIkkRKDFIUsCiFK\nFMUCskhRCAGUWBEJRARlkTDYYILBDsFxYnuwYwxhc2xsz/TNj/fevd+5dcttj7umx13nkyxPva7u\nd9652znf+e595L9K6RNqIew/HQyI6peqDxAlo/zEmULSr5lua5qAdR+gAKAVFGjcR4uio/3k7W5F\nYyJNwRZEwarDJvPUGApZTVl/D6iCFVVobpOtJZ3QGlGFIVhpRf5jqusZ+C8n/9Xff+r5TB85544B\neDOAjwO4G8CHnHNfFpF3ich1g7rv02LlOJDl+q1HiYKejnKi6IciCt6/0OoTiVfRIOrfb+m/lSo0\n57xLWOvEiyzYqotkVChNZAqZc30LuUVe9olySh1JUaaQ9F+sac9SGUSIvtl/PChj+1p0PDJnCn39\nRxFZkcWFUspe8rT/UplWFT2GodIvYlT+y1vaf95WzmqoeBv7r8lU4z0sjf9oBzlTiHmU1aiifd7R\n9/DRN/cBOg8sVstxBqaKusHWfmNF7djP+rQ1LbA5Z1rsvzzyH3onWt3/cqRFGfoAQl3ADxN2rhgA\nXfwObZ3OqpX/8qitk6KMeAz1shUn40U7xepfOXE45z4G4GPRtV/p893LBmkLgIQkNUOxUnPfSiZG\nHCNr2vPM00Tx/oVOm5VIIygywbEVp+mjogWVdfjIIXT0Umms40JpHTlIprOdJqKNzvRp5JxxpDui\n+NAOivxR4KkmImt809LZiJcjltp/Te2gp9DM0Tc9g4p0aVIjvnukQ/bVUeKTqOSpRT2ThTOiXJ2B\nsf8ywDV+7S0+VrLaBP0WHfZXUlaoZMlUXB5Rezso+i60/0rmmbmfNTbFmnYXsppUH+iVJYd+2S+r\nKYoOirzql6p+xBlY4z9U/iv7+G9F9b+G0uI+EBRRPbLkuq1jWbKiXfM2ivxJ8l/j41bkv6atdaFZ\nUvUP9l8kqy0T/quejWpGLd6VXdW0nhKgKChTUG3dUtdDob4ImXcuKI5TWyulVBjXzSJsx1ysNXyh\nOdGhY+6R6CN1UmlioqiKZ72DD+gt3jI/7FNfppVU6httk8/Y1t50PD4TSVFdrKhodcJmoKJDaphM\nLQpJRVQeK316C6W6eBsGX5nx4IsphWrwxWf6KDVH3o4oBfHXk4qyuNDsbYomClpUVRGY6CP1onV1\n3MGmoIjqN9H2BAC8IBFVkyze5pH/QrCiziViqovautOmBZaoQuU/WsAUEjGK8QAAHLVJREFUfURR\nuep/av+HnmhTiqhqDLH/eAyx2CC8Na8KABKUTM9Y4QUs+EnZmvX6Ne8JVngMkY8okxkh+kgFAHlb\n+0nZmqgdxCKOFFWYZSTKCEHg8119dOqhWRRUqtekvlHxJyEHq1LiqqP3FM/UIVph8sp7Ji+eQFIc\nI6W+8Tb5rJdSyHkAcIfOI1spelS6+aKt/5b/TlQ88/RRH1t7/Bc6un6GQH8UCb/GqiRFv/EgY42/\nKjJSQTS+Tn5NTRQFsj7+E02XRG2t/KdqCjRpM/2h+l/jP7L16fyXk/8SSrN4X416HWzeoueIg5WU\n/+LrwVblP1Z1Kf819FH/McRUl9qPEG3+8/2vx3+JtiYBRG+w1yxU0f4PspUVbo2t4hyKgs6IimqF\nyeMs4mK59Ol/ZKs+JoTbuskUnt/qo1MPdU1BRzMcPVI0w52HI+l6kJUQKs6FSBcAirKjI4TmetGJ\nOkNzPypKssZaadq1rSotbSKvWGpZR7S8TT4TIMvohfF5Oy1HpAVMRznRxKyix7T/1DMUwX+KLqkj\nssJV9jVzRaXxT/iPo8Sirf+WyrTYf6mskCPayH++rRFRDZr7TsoRi6gPNNRBLDbwGRgfHxIVxdUz\n1AXRuABdpDNYybIgiS5G1KStghXlP4q+uV9mnBVyWycyMOU/LUsumNakRUHTb/EYan6/nfYft3VG\nx9T07EkJFGzK1vgAwuaMo7BLmfznaeGO9h9nWmpcJ7JFnoNiWTJlhY2fLFNYa3hJKkUOfaLHsErH\nxbPe6DuvB1+QI/Lkz1FiSw1KH+XEBVSviNKRbpHIFJSt8ZHaja1R8ayxv7KjrZ6DI9qUHFFzt09X\naA4ZWDLSjaJHpmrYziLLQkE014oeVbxNRLpxBuFlxvFO07y3rZX/lK2ZzxQK56p253ujsTXyn5dO\nxntSeqNvXWhe3X9qUovoDwDheGne58ERbRzpkiouKSqIZcnULwv2E/lVt3Xg71mWnCn/xW3d+I/E\nGly8jbMaP64jCXpqDFFWw/QbZ4VhR3WqrdOZQqEyM/ZfJEtOijIyP645MLVMYa2RKjT7yCE6ZtlP\nUszfc/GWM4X691xVPJNMp9EcfeuNS8134gJ0HeXk0Zubmog2i86kyTlTSBeaeQA01wCgKEfSUU7R\nRlKOGJ/p0xSaOcrpw98rSW/8opu6JhNScoGIprRKFT2y/yJO12cvrage1ESPsf96s8LKfxw9hrbO\n8gKZc+E8o1RNpuik5Yh9/Rfx93mIHtO2RjvzE6IC/yyNT8sR7b9EVqMEEEXkPyFb+brvl1qWzPUj\nnS2GrFBqGtHb2ixg5YiicfwYKqjQTP2yjOsfQv5LijKiAwi9/yJZcqJf+ufwmVYnXZOJ+x+3tQpM\nOVOgQnNOi8JJzBQGqj465RAVmtXkFW+Tp4lWUzV154mKkkDtTB+VUcdtUt+igyJ/yv+cJ1p9QmYz\neUXqj5wWMJ7Ii940s+DBB4HU2/r9z33qSxMFU1rRRFvyoqAKpY3/IqohQR/1G3xlRoPPc/JUnFST\nFxdKafJS6hkqijumRWr/xUofmiiU/5J7AsKiGk8Ulf8EgKttfZz8l1UqmZ4CdLBV+c81ftUnlZYc\nAPDkSG0d1DnB1iZY0Xr6DMBxqEJ9Vp/Qerya1DR1GqhCl1KUqcMS9RhSARfZ2lwLr8VFfb+RcO+M\n3prXV1TQUv5zZGtSlBEfQMjBCmfz9Turg10ZBSt1W5fcL+v+51DRmuw/HkMgP2W8gAWflGyrrynY\norC2qA/ES9McUfGMVmmdZgZKgf9OdS2c46GKfYo+Olb/XHOMOsXtTX1jSkb9/T6ceO6Lj5y6B/pI\nXPVq0tQL4wtK3+Pi42pFce0/oj9yQfNS9LjQnNHu1+r+GfI6KsrrwZerYy6E5Igt7T9ZxX9Z7L+U\neob9p5U+QEXJhB3BaVFB0n9xodTbFNMc5L884T8qiitaU6oAoHre0C/D/pN4AavaOs/c09qqKa0C\n4L+T9bZ1wTaB6aNAv7H/6h+HsVK09b1pDCXbOtf00YqErLAfBZv0H/QeFu8/oo98+zX2p+gj158W\nzrOCRBJaaaYUZYrCbugjqymsLVaOAfFhcnmgZJJyRNa0Z1y8DYOvTEaP9Ds+HY8LpU3k0NY2Nalv\nHklSla2cZnL0yLbW0bffTUuZBIiWUQU9spUjrKbjxmf6UKaQlCNS+l5mEgqiav9HKIiqwdL8PU8d\nEKWgKBkuirOtkdTSL1SRLFRFj+S/MmSF3Acq+0FS2WqiEEkXmtV+hLitmepSBc7G1th/iQBA0W88\n2QRKhjMw7z+mupStwX9JSjCWJSf6pS7eZvoZ8t5MwUf/qDZbZnkke238V/TxX9TW/oyonMQa3P9i\nWS0zAFEAULowrkv2K/XLMtHWelwzrdnff3rvTm9Wc+wknH00ZJlCtE+BIwdK01VGQLtfVZQjFGUw\nJdNEPJlQ8YwyBY5EKcpJRY9FVuK4j2bCB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"text/plain": [ "<matplotlib.figure.Figure at 0x1a1e1a6510>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig2 = plt.figure()\n", "plt.plot(non_conv_evolution.T)\n", "plt.xlabel('Time')\n", "plt.ylabel('Opinionds')\n", "plt.title('Evolution of Opinions (DeGroot)')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 2 }	gpl-3.0
Sidon/Sidon.github.io	_posts/dtree-w1.ipynb	1	165915	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# -- coding: utf-8 --\n", "\"\"\"\n", "Created on Sun august 21 14:35:15 2016\n", "@author: Sidon\n", "\"\"\"\n", "%matplotlib inline\n", "import pandas as pd\n", "import numpy as np\n", "from collections import OrderedDict\n", "from tabulate import tabulate, tabulate_formats\n", "import matplotlib.pyplot as plt\n", "from sklearn.cross_validation import train_test_split\n", "from sklearn.tree import DecisionTreeClassifier\n", "from sklearn.metrics import classification_report\n", "import sklearn.metrics\n", "\n", "from sklearn import tree\n", "from io import StringIO\n", "from IPython.display import Image\n", "import pydotplus\n", "import itertools\n", "\n", "# Variables Descriptions\n", "INCOME = \"2010 Gross Domestic Product per capita in constant 2000 US$\"\n", "ALCOHOL = \"2008 alcohol consumption (liters, age 15+)\"\n", "LIFE = \"2011 life expectancy at birth (years)\"\n", "\n", "# os.chdir('/home/sidon/dev/coursera')\n", "out = StringIO()\n", "\n", "# bug fix for display formats to avoid run time errors\n", "pd.set_option('display.float_format', lambda x:'%f'%x)\n", "\n", "# Load from CSV\n", "data0 = pd.read_csv('~/dev/coursera/gapminder.csv', skip_blank_lines=True,\n", " usecols=['country','incomeperperson',\n", " 'alcconsumption','lifeexpectancy'])\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def to_num(list, data):\n", " for dt in list :\n", " data[dt] = pd.to_numeric(data[dt], 'errors=coerce')\n", " return data \n", " " ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def plot_confusion_matrix(cm, classes,\n", " normalize=False,\n", " title='Confusion matrix',\n", " cmap=plt.cm.Blues):\n", " \"\"\"\n", " This function prints and plots the confusion matrix.\n", " Normalization can be applied by setting `normalize=True`.\n", " \"\"\"\n", " plt.imshow(cm, interpolation='nearest', cmap=cmap)\n", " plt.title(title)\n", " plt.colorbar()\n", " tick_marks = np.arange(len(classes))\n", " plt.xticks(tick_marks, classes, rotation=45)\n", " plt.yticks(tick_marks, classes)\n", "\n", " if normalize:\n", " cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n", " print(\"Normalized confusion matrix\")\n", " else:\n", " print('Confusion matrix, without normalization')\n", "\n", " print(cm)\n", "\n", " thresh = cm.max() / 2.\n", " for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n", " plt.text(j, i, cm[i, j],\n", " horizontalalignment=\"center\",\n", " color=\"white\" if cm[i, j] > thresh else \"black\")\n", "\n", " plt.tight_layout()\n", " plt.ylabel('True')\n", " plt.xlabel('Predicted')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Rename columns for clarity \n", "data0.columns = ['country','income','alcohol','life']\n", "\n", "# converting to numeric values and parsing (numeric invalids=NaN)\n", "data0 = to_num( ('income','alcohol','life'), data0 )\n", "\n", "# Remove rows with nan values\n", "data0 = data0.dropna(axis=0, how='any')\n", "\n", "# Copy dataframe for preserve original\n", "data1 = data0.copy()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Mean, Min and Max of life expectancy\n", "meal = data1.life.mean()\n", "minl = data1.life.min() \n", "maxl = data1.life.max()\n", "\n", "# Create categorical response variable life (Two levels based on mean)\n", "data1['life'] = pd.cut(data0.life,[np.floor(minl),meal,np.ceil(maxl)], labels=['<=69','>69'])\n", "data1['life'] = data1['life'].astype('category')" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Mean, Min and Max of alcohol\n", "meaa = data1.alcohol.mean()\n", "mina = data1.alcohol.min() \n", "maxa = data1.alcohol.max()\n", "\n", "# Categoriacal explanatory variable (Two levels based on mean) \n", "data1['alcohol'] = pd.cut(data0.alcohol,[np.floor(mina),meaa,np.ceil(maxa)], \n", " labels=[0,1])\n", "\n", "cat1 = pd.qcut(data0.alcohol,5).cat.categories\n", "data1[\"alcohol\"] = pd.qcut(data0.alcohol,5,labels=['0','1','2','3','4'])\n", "data1[\"alcohol\"] = data1[\"alcohol\"].astype('category')\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Mean, Min and Max of income\n", "meai = data1.income.mean()\n", "mini = data1.income.min() \n", "maxi = data1.income.max()\n", "\n", "# Categoriacal explanatory variable (Two levels based on mean) \n", "data1['income'] = pd.cut(data0.income,[np.floor(mini),meai,np.ceil(maxi)], \n", " labels=[0,1])\n", "data1[\"income\"] = data1[\"income\"].astype('category')" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data1 = to_num( ('alcohol', 'income'), data1 )\n", "#print (predictors)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "predictors = data1[['alcohol', 'income']]\n", "targets = data1.life\n", "pred_train, pred_test, tar_train, tar_test = train_test_split(predictors, targets, test_size=.4)\n", "\n", "#Build model on training data\n", "clf = DecisionTreeClassifier()\n", "clf = clf.fit(pred_train,tar_train)\n", "\n", "predictions=clf.predict(pred_test)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Confusion matrix, without normalization\n", "[[18 12]\n", " [ 6 33]]\n" ] }, { "data": { "image/png": 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ASccDRwDfr1yZRjYAd0dEPCKpD7Ad2dHdP/J7m5H0Y7IBuz0qV6Y1xYfk1asb\nsJOk1YF/At8BLgV+IWlZSd3JDsv3i4gWPWjAWs1/gdn57+QWst/LEGCMpO6SugBdgb3zQSBLlC8r\nqlKSVgR+kb99JyIuzB8wcDlwdt7z7BQR8ypXpQFIWossKB8BJkXEFfnyvwDPRsQw/66qg3uYVUZS\n7e/sPeB1YBNgfUlLRMRE4DGgd77N/Pav0EpJqomI/wA/I7tsqHTyrf8A8wAcltXBPcwqUtsLyXuX\nawBPAz8mO38ZZM/4+znwrdrBBauMPCjn11m2A3AF2VHAcmSnUQ6IiBZNyGXtzz3MKlESln3Jbnvs\nHhHzIuIyYBgwgex/wt0dlpVR+sCTiJifT+26saSrJO0REf8G9gOmAl8C+zssq4tHyatASVj2AUYC\n5wGvSboWODkiXgZe9nmwyqp94ImkfmRXMVwI3AXsCYzKt5kITKxUjVYeB2biJKmkZ/l34A9kd/Dc\nCpwREe/l24TDsvIknQkMBN4EziV7uvcQHJIdgs9hJqg2AGvPg+UXON8MXAY8Q3Z3yG8i4vbabSta\nsC0gaTPgc2BqREyX9Hvg1Yi4ssKlWStwDzNBJQHYH3iB7PbGU4BpZNdcnh4Rt9fZ1hIQEU/X/iyp\nM9ng3K2Vq8hakwd9EiXpCGC4pO4R8RZZz/InwKm1YWnJu4Ds77Snm9zSqoIPyRNTchh+KvB8RNxW\nsm6JiPjMh+HVQdI6QK+IeKK+y4ys+riHmZg8LPsBO5NdfgIsGHmdnW/jsKwC+X3io/OfHZYdgAMz\nIcp0AU4GrgEmSNpA0m1kd4r40V9VxkHZsXjQJyF5z3GOpKXIwvEBYAzZJSnnko2+mlmFODATI2ld\nssexCTgfuC8i5lS2KjMDD/okSVIPYG5EzCpZ5oEeswpzYJqZFeRBHzOzghyYZmYFOTDNzApyYJqZ\nFeTANDMryIFpZlaQA9NaTNI8SeMkPSdppKRuZbS1naTb85/3lPTLRrbtKemYFnzGUEkntbRGMwem\nleOziBgUERsBc8gmZFtI6Tw3BQRARNweEec3st0ywLHNqtSsFTgwrbU8CqwlaTVJL0q6VtJzQB9J\nO0t6QtLYvCfaHUDSrpImSxoL7FvbkKRDJV2a/7yCpH9ImiBpvKQtgN8Da+a92/Py7X4h6el8u6El\nbf1a0hRJjwDrtt8fh3VEvpfcyiFY8GTx3cgm/AJYGzg4Isbk02ucBuwUEZ/nh9onSfoDMBzYPiJe\nlTSyTtt+uqW1AAABgklEQVS1t6BdAjwUEfvmvdUlyZ4+v0FEDMo/f2dg7YjYLN/mNklbA7OAHwDf\nABYDxgFj2+DPwRYRDkwrx+KSxuU/PwpcDawCvB4RY/LlWwDrA4/nYdYFeBJYj2yum1fz7f4G/LCe\nz9gROBgWPM1ppqRl62yzC7BzXouAJchCuwdwa0TMBmbnj8kzazEHppVjVm0vr1Z+yvKz0kXAvRFx\nUJ3tBuTrmlLkYQcCfl93ojFJJxTY16wwn8O0cjQUeKXLRwPflLQmgKTuktYGXgRWk7RGvt0BDbT1\nAPkAj6Sa/ElOM4GlSra5BzhC0hL5ditLWh54BNhbUtf8GaN7NvsbmpVwYFo5Gur9LVgeER8AhwE3\nSJoIPAGsmx8m/wgYlQ/6TGugrROBHSQ9S3b+sX9ETAeekPSspPMi4j7gBuDJfLubgCUjYjzZXO7P\nAncCnozMyuLHu5mZFeQepplZQQ5MM7OCHJhmZgU5MM3MCnJgmpkV5MA0MyvIgWlmVpAD08ysoP8H\nLM/pIv5vLdIAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f7c2bedeac8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "confusion_matrix = sklearn.metrics.confusion_matrix(tar_test,predictions)\n", "plot_confusion_matrix(confusion_matrix,['<=69','>69'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This graph represents the confusion matrix, that shows the correct and incorrect classifications of the decision tree, the labels means the life expectancy greater and less than 69 years, the horizontal labels are the predictions and the vertical are the reference (true).\n", "the main diagonal: 16 and 34 represent the number of true negative and true positive for life expectancy and the other diagonal (values 7 and 12) represents the numbers of false negative and false positive respectively." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy Score: 0.739130434783 \n", "\n" ] } ], "source": [ "accuracy = sklearn.metrics.accuracy_score(tar_test, predictions)\n", "print ('Accuracy Score: ', accuracy,'\\n')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The accuracy score is approximately 0.72, this suggests that the decision tree model has classified 72% of the sample as either countrie with life expectancy greater or less than the mean (69 years)." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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QyaQzh3dp6hlrT51BK2z4WkMidQTsXr/KxU1WSa00P+eC374Xzx6dvf+SX0ik774AABpI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4+ab13p/97T/72Xxrymr+AT1GbSniYYrjDWVxkSHviFdvzFvesdXtuq2zanWFiI+87YGqww7sww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\nqa/JSoysLM4vL8wpyRtmNCn5ARMzc99EQeinqdUlX4jmTiOFger56gpPDMP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"text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tree.export_graphviz(clf, out_file=out, feature_names=['alcohol', 'income'],class_names=['0','1'], \n", " filled=True, rounded=True, special_characters=True)\n", "\n", "graph=pydotplus.graph_from_dot_data(out.getvalue())\n", "Image(graph.create_png())" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "income 7006.359798\n", "alcohol 6.784094\n", "life 69.385737\n", "dtype: float64\n" ] } ], "source": [ "print (data0.mean())" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Index(['[0.05, 1.87]', '(1.87, 4.72]', '(4.72, 7.32]', '(7.32, 11.1]',\n", " '(11.1, 23.01]'],\n", " dtype='object')\n" ] } ], "source": [ "print (cat1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here, the target is the life (my binary categorical variable) and alcohol consumption and income levels as the predictor or explanatory variables. The result tree starts with the split on income variable, my second explanatory variable. This binary variable has values of zero (0) representing income level less than or equals the mean (US$ 7.006,00) and value one (1) representing income greater than the mean. The resulting tree starts with the split on income variable. \n", "In the first split (1) we can see that 26 countries have the life expectancy and income levels greater than the mean (A) and the other 76 countries have the life expectancy less than the mean. The second split (2), splits in the other 2 internal nodes according to consumption alcohol levels and so on. \n", "\n", "Through internal nodes 2, 4 and 5 and the leaves D, E, and F, we can see that the majority of countries with the life expectancy greater than the mean (Class 1) has the alcohol consumption between 2.5 and 3.5 liters per year (11 countries in leaf D)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### Conclusion\n", "It is easy to note the significance of the variable income. In the first split, we see that all countries that have income level greater than 0.5 (The mean) have the life expectancy greater than the mean (A - Blue leaf), independently of the consumption alcohol level. In the other hand, although the differences are subtle we can see than the among the countries that have income levels less than the mean, those that have alcohol consumption between 2.5 and 3.5 have the life expectancy greater than the mean." ] } ], "metadata": { "anaconda-cloud": { "attach-environment": true, "environment": "Root", "summary": "Data Analysis Tools - Week3 \| Pearson Correlation \| Sidon \| 2016", "url": "https://anaconda.org/sidon/submitw4" }, "kernelspec": { "display_name": "Python [Root]", "language": "python", "name": "Python [Root]" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
ctroupin/OceanData_NoteBooks	PythonNotebooks/PlatformPlots/plot_CMEMS_vessel.ipynb	2	316689	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "The objective of this notebook is to show how to read and plot the data obtained with a vessel." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "import netCDF4\n", "from netCDF4 import num2date\n", "import numpy as np\n", "import matplotlib as mpl\n", "import matplotlib.pyplot as plt\n", "from matplotlib import colors\n", "from mpl_toolkits.basemap import Basemap" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Data reading" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The data file is located in the datafiles directory." ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": true }, "outputs": [], "source": [ "datadir = './datafiles/'\n", "datafile = 'GL_PR_ML_EXRE0065_2010.nc'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We extract only the spatial coordinates:" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(63,)\n" ] } ], "source": [ "with netCDF4.Dataset(datadir + datafile) as nc:\n", " lon = nc.variables['LONGITUDE'][:]\n", " lat = nc.variables['LATITUDE'][:]\n", "print lon.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Location of the profiles" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this first plot we want to see the location of the profiles obtained with the profiler.<br/>\n", "We create a Mercator projection using the coordinates we just read." ] }, { "cell_type": "code", "execution_count": 100, "metadata": { "collapsed": false }, "outputs": [], "source": [ "m = Basemap(projection='merc', llcrnrlat=lat.min()-0.5, urcrnrlat=lat.max()+0.5,\n", " llcrnrlon=lon.min()-0.5, urcrnrlon=lon.max()+0.5, lat_ts=0.5(lon.min()+lon.max()), resolution='h')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once we have the projection, the coordinates have to be changed into this projection:" ] }, { "cell_type": "code", "execution_count": 101, "metadata": { "collapsed": true }, "outputs": [], "source": [ "lon2, lat2 = m(lon, lat)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The locations of the vessel stations are added on a map with the coastline and the land mask." ] }, { "cell_type": "code", "execution_count": 103, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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sUMHTaDRYsmQJli5dCgcHB63ft3XrVty8eRNvvfVWiZ+/f/8+du7cCVdXV9ja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\nYMmSJQgKCsKhQ4fkbpZOqNARSaiPTjzURyemwj4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"text/plain": [ "<matplotlib.figure.Figure at 0x45a9f90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mpl.rcParams.update({'font.size': 16})\n", "fig = plt.figure(figsize=(8,8))\n", "m.plot(lon2, lat2, 'ko', ms=2)\n", "\n", "m.drawcoastlines(linewidth=0.5, zorder=3)\n", "m.fillcontinents(zorder=2)\n", "\n", "m.drawparallels(np.arange(-90.,91.,0.5), labels=[1,0,0,0], zorder=1)\n", "m.drawmeridians(np.arange(-180.,181.,0.5), labels=[0,0,1,0], zorder=1)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Profile plot" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "We read the temperature, salinity and depth variables." ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [], "source": [ "with netCDF4.Dataset(datadir + datafile) as nc:\n", " depth = nc.variables['DEPH'][:]\n", " temperature = nc.variables['TEMP'][:]\n", " temperature_name = nc.variables['TEMP'].long_name\n", " temperature_units = nc.variables['TEMP'].units\n", " salinity = nc.variables['PSAL'][:]\n", " salinity_name = nc.variables['PSAL'].long_name\n", " salinity_units = nc.variables['PSAL'].units\n", " time = nc.variables['TIME'][:]\n", " time_units = nc.variables['TIME'].units" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(63, 215)\n", "(63, 215)\n" ] } ], "source": [ "print depth.shape\n", "print temperature.shape" ] }, { "cell_type": "code", "execution_count": 75, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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ct20QMAAoBG6JoljR06YKsbKy4rvvvuPChQt89913FBYWYmRkxBtvvEFcXBwf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1e2tfJelLklZIeljSD83sr6r5mRF1MjldmLYs1WHklOqImZkSZ09/GXYtQUl2/njK3V8Ou5aQ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\ndXAe572WGI8AAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7b6f190>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nprofiles, ndepths = depth.shape\n", "\n", "fig = plt.figure(figsize=(8,8))\n", "ax = plt.subplot(111)\n", "for nn in range(0, nprofiles):\n", " plt.plot(temperature[nn,:], depth[nn,:], 'k-', linewidth=0.5)\n", "plt.gca().invert_yaxis()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We observe different types of profiles. As the covered region is rather small, this may be because the measurements were done at different time of the year. The time variable will tell us.<br/>\n", "We create a plot of time versus temperature (first measurement of each profile)." ] }, { "cell_type": "code", "execution_count": 99, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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Wff8R5G0CAzvG/R+h1H9H3N6IUFuwBBERERlgRIuzmNlkd188gvOPIrSVO3C2u98U09ch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\n4xtlbHT8O18Gut39Hl9T03MFsDmwsYf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"text/plain": [ "<matplotlib.figure.Figure at 0x1da63950>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dates = num2date(time, units=time_units)\n", "fig = plt.figure(figsize=(8,8))\n", "ax = plt.subplot(111)\n", "plt.plot(dates, temperature[:,0], 'ko')\n", "fig.autofmt_xdate()\n", "plt.ylabel(\"%s (%s)\" % (temperature_name, temperature_units))\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The graph confirms that we have data obtained during different periods:\n", " July-August 2010,\n", "* December 2010." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# T-S diagram" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The x and y labels for the plot are directly taken from the netCDF variable attributes." ] }, { "cell_type": "code", "execution_count": 95, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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Lz3/+83j33XcRCoWwatUqjI+P45lnnsHtt9+OVatWYWxsDJcuXQIAbNq0CV6v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P6hBJ0OPUpwdrqU4Afh9eHyi8PyfDycHkfw47CZICFyHsFl7Q+GcW4M931ixvtyJkFy2+MUyPgP\n34etgGXMnwVsCLd9afieXcMrd2/ke48Hh3E/Gn62O8PvwgaCKy/nhdM/BKaV+H3YjeCuga3he/WP\nFXwnTw6/I38MY7kRmJWxTs4yAK8iuPNlJ0HS7Qjg+8AT4ed3VWzdIwm+/33AMeG8oeH6zxE0R7wL\n+CXwz+H79WK4v4sIvn+vzijXjwjv6tFDD3OveYdzIlIBC/ogcHf/aNJlqRczu4ugAvbTGmxrAvD/\n3P3s2LwRwKeB37r7ymr3kSZmNpzgNtY3ufvLxdaX9qcmBhFppH8BFtdoWxcR/KD186CZ4AmCfAIp\nzwKCO1JUORBAFQSRZtJBdpZ/W/FgrIUVZja/BpubQNCm/l4zG29me5rZPwH7ekZXx1KYmb0V2Onu\nVY0MKe1FTQwiTcCCLm+/RNBO/C/tfqAObyXc4e6PFl05/zbGAhcA/0iQ0/EYQT8Ry2pSyIH7upFX\nOtfK5Qp3/2Gt99sIZrYHcIi7/yTpskhzUQVBREREsqiJQURERLKogiAiIiJZVEEQERGRLKogiIiI\nSBZVEERERCTL/wfu4jEoLoyZagAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x752c6d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(8,8))\n", "ax = plt.subplot(111)\n", "plt.plot(temperature, salinity, 'ko', markersize=2)\n", "plt.xlabel(\"%s (%s)\" % (temperature_name, temperature_units))\n", "plt.ylabel(\"%s (%s)\" % (salinity_name, salinity_units))\n", "plt.ylim(32, 36)\n", "plt.grid()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 3-D plot" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We illustrate with a simple example how to have a 3-dimensional representation of the profiles.<br/>\n", "First we import the required modules." ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from mpl_toolkits.mplot3d import Axes3D" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then the plot is easily obtained by specifying the coordinates (x, y, z) and the variables (salinity) to be plotted." ] }, { "cell_type": "code", "execution_count": 97, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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3kGw1j3TjFC8RPUvhgT8nuuzv9JROGyeCvu/vmeIEeTcT1svjOFg44cuhvxqL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"text/plain": [ "<matplotlib.figure.Figure at 0x4f28510>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "cmap = plt.cm.Spectral_r\n", "norm = colors.Normalize(vmin=32, vmax=36)\n", "\n", "fig = plt.figure(figsize=(8,8))\n", "ax = fig.add_subplot(111, projection='3d')\n", "for ntime in range(0, nprofiles):\n", " plt.scatter(lon[ntime]np.ones(ndepths), lat[ntime]np.ones(ndepths), zs=-depth[ntime,:], zdir='z', \n", " s=20, c=salinity[ntime,:], edgecolor='None', cmap=cmap, norm=norm)\n", "plt.colorbar(cmap=cmap, norm=norm)\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": "2.7.5+" } }, "nbformat": 4, "nbformat_minor": 0 }	gpl-3.0
ericmjl/hiv-resistance-prediction	old_notebooks/Predict HIV Genotype from Phenotype - Poster Figures.ipynb	1	575752	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using gpu device 0: Quadro 2000\n" ] } ], "source": [ "import numpy as np\n", "import nolearn\n", "import sklearn.linear_model as lm\n", "import scipy.stats as sps\n", "import math\n", "import pandas as pd\n", "\n", "from decimal import Decimal\n", "from lasagne import layers, nonlinearities\n", "from lasagne.updates import nesterov_momentum\n", "from lasagne import layers\n", "from nolearn.lasagne import NeuralNet\n", "from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor, GradientBoostingRegressor, ExtraTreesRegressor, BaggingRegressor\n", "from sklearn.cross_validation import train_test_split\n", "from sklearn.metrics import r2_score, mean_squared_error\n", "from sklearn.svm import SVR\n", "from sklearn.externals import joblib\n", "\n", "%matplotlib inline\n", "%load_ext autoreload\n", "%autoreload 2" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import custom_funcs as cf\n", "cf.init_seaborn('white', 'poster')\n", "from isoelectric_point import isoelectric_points\n", "from molecular_weight import molecular_weights" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1808\n" ] }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>FPV</th>\n", " <th>ATV</th>\n", " <th>IDV</th>\n", " <th>LPV</th>\n", " <th>NFV</th>\n", " <th>SQV</th>\n", " <th>TPV</th>\n", " <th>DRV</th>\n", " <th>P1</th>\n", " <th>P2</th>\n", " <th>...</th>\n", " <th>P90</th>\n", " <th>P91</th>\n", " 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<td>P</td>\n", " <td>Q</td>\n", " <td>...</td>\n", " <td>L</td>\n", " <td>T</td>\n", " <td>Q</td>\n", " <td>I</td>\n", " <td>G</td>\n", " <td>C</td>\n", " <td>T</td>\n", " <td>L</td>\n", " <td>N</td>\n", " <td>F</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>235719</th>\n", " <td>0.6</td>\n", " <td>0.6</td>\n", " <td>0.4</td>\n", " <td>0.6</td>\n", " <td>0.7</td>\n", " <td>0.5</td>\n", " <td>0.8</td>\n", " <td>0.5</td>\n", " <td>P</td>\n", " <td>Q</td>\n", " <td>...</td>\n", " <td>L</td>\n", " <td>T</td>\n", " <td>Q</td>\n", " <td>I</td>\n", " <td>G</td>\n", " <td>C</td>\n", " <td>T</td>\n", " <td>L</td>\n", " <td>N</td>\n", " <td>F</td>\n", " </tr>\n", " <tr>\n", " <th>235721</th>\n", " <td>4.7</td>\n", " <td>2.8</td>\n", " <td>3.4</td>\n", " <td>4.4</td>\n", " <td>5.1</td>\n", " <td>5.2</td>\n", " <td>3.2</td>\n", " <td>1.7</td>\n", " <td>P</td>\n", " <td>Q</td>\n", " <td>...</td>\n", " <td>L</td>\n", " <td>T</td>\n", " <td>K</td>\n", " <td>I</td>\n", " <td>G</td>\n", " <td>C</td>\n", " <td>T</td>\n", " <td>L</td>\n", " <td>N</td>\n", " <td>F</td>\n", " </tr>\n", " <tr>\n", " <th>235725</th>\n", " <td>0.5</td>\n", " <td>0.8</td>\n", " <td>0.6</td>\n", " <td>0.6</td>\n", " <td>0.7</td>\n", " <td>0.6</td>\n", " <td>0.9</td>\n", " <td>0.5</td>\n", " <td>P</td>\n", " <td>Q</td>\n", " <td>...</td>\n", " <td>L</td>\n", " <td>T</td>\n", " <td>Q</td>\n", " <td>I</td>\n", " <td>G</td>\n", " <td>C</td>\n", " <td>T</td>\n", " <td>L</td>\n", " <td>N</td>\n", " <td>F</td>\n", " </tr>\n", " <tr>\n", " <th>235729</th>\n", " <td>1.1</td>\n", " <td>1.1</td>\n", " <td>1.0</td>\n", " <td>1.2</td>\n", " <td>1.2</td>\n", " <td>1.1</td>\n", " <td>0.8</td>\n", " <td>1.0</td>\n", " <td>P</td>\n", " <td>Q</td>\n", " <td>...</td>\n", " <td>L</td>\n", " <td>T</td>\n", " <td>Q</td>\n", " <td>I</td>\n", " <td>G</td>\n", " <td>C</td>\n", " <td>T</td>\n", " <td>L</td>\n", " <td>N</td>\n", " <td>F</td>\n", " </tr>\n", " <tr>\n", " <th>235733</th>\n", " <td>23.0</td>\n", " <td>115.0</td>\n", " <td>35.0</td>\n", " <td>102.0</td>\n", " <td>68.0</td>\n", " <td>184.0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>P</td>\n", " <td>Q</td>\n", " <td>...</td>\n", " <td>M</td>\n", " <td>K</td>\n", " <td>Q</td>\n", " <td>L</td>\n", " <td>G</td>\n", " <td>C</td>\n", " <td>T</td>\n", " <td>L</td>\n", " <td>N</td>\n", " <td>F</td>\n", " </tr>\n", " <tr>\n", " <th>235739</th>\n", " <td>0.5</td>\n", " <td>0.7</td>\n", " <td>0.8</td>\n", " <td>0.7</td>\n", " <td>0.8</td>\n", " <td>0.8</td>\n", " <td>0.8</td>\n", " <td>0.7</td>\n", " <td>P</td>\n", " <td>Q</td>\n", " <td>...</td>\n", " <td>L</td>\n", " <td>T</td>\n", " <td>Q</td>\n", " <td>I</td>\n", " <td>G</td>\n", " <td>C</td>\n", " <td>T</td>\n", " <td>L</td>\n", " <td>N</td>\n", " <td>F</td>\n", " </tr>\n", " <tr>\n", " <th>257923</th>\n", " <td>1.1</td>\n", " <td>0.8</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>0.9</td>\n", " <td>1.0</td>\n", " <td>1.1</td>\n", " <td>1.6</td>\n", " <td>P</td>\n", " <td>Q</td>\n", " <td>...</td>\n", " <td>L</td>\n", " <td>T</td>\n", " <td>Q</td>\n", " <td>I</td>\n", " <td>G</td>\n", " <td>C</td>\n", " <td>T</td>\n", " <td>L</td>\n", " <td>N</td>\n", " <td>F</td>\n", " </tr>\n", " <tr>\n", " <th>257927</th>\n", " <td>0.4</td>\n", " <td>0.6</td>\n", " <td>0.4</td>\n", " <td>0.4</td>\n", " <td>0.8</td>\n", " <td>0.4</td>\n", " <td>0.5</td>\n", " <td>0.3</td>\n", " <td>P</td>\n", " <td>Q</td>\n", " <td>...</td>\n", " <td>L</td>\n", " <td>T</td>\n", " <td>Q</td>\n", " <td>I</td>\n", " <td>G</td>\n", " <td>C</td>\n", " <td>T</td>\n", " <td>L</td>\n", " <td>N</td>\n", " <td>F</td>\n", " </tr>\n", " <tr>\n", " <th>257929</th>\n", " <td>0.1</td>\n", " <td>0.4</td>\n", " <td>0.3</td>\n", " <td>0.3</td>\n", " <td>0.3</td>\n", " <td>0.4</td>\n", " <td>0.4</td>\n", " <td>0.4</td>\n", " <td>P</td>\n", " <td>Q</td>\n", " <td>...</td>\n", " <td>L</td>\n", " <td>T</td>\n", " <td>Q</td>\n", " <td>I</td>\n", " <td>G</td>\n", " <td>C</td>\n", " <td>T</td>\n", " <td>L</td>\n", " <td>N</td>\n", " <td>F</td>\n", " </tr>\n", " <tr>\n", " <th>257933</th>\n", " <td>0.6</td>\n", " <td>0.9</td>\n", " <td>1.0</td>\n", " <td>0.7</td>\n", " <td>1.0</td>\n", " <td>0.6</td>\n", " <td>0.6</td>\n", " <td>0.6</td>\n", " <td>P</td>\n", " <td>Q</td>\n", " <td>...</td>\n", " <td>L</td>\n", " <td>T</td>\n", " <td>Q</td>\n", " <td>L</td>\n", " <td>G</td>\n", " <td>C</td>\n", " <td>T</td>\n", " <td>L</td>\n", " <td>N</td>\n", " <td>F</td>\n", " </tr>\n", " <tr>\n", " <th>257935</th>\n", " <td>0.8</td>\n", " <td>0.9</td>\n", " <td>0.8</td>\n", " <td>0.8</td>\n", " <td>0.8</td>\n", " <td>0.8</td>\n", " <td>0.8</td>\n", " <td>0.7</td>\n", " <td>P</td>\n", " <td>Q</td>\n", " <td>...</td>\n", " <td>L</td>\n", " <td>T</td>\n", " <td>Q</td>\n", " <td>I</td>\n", " <td>G</td>\n", " <td>C</td>\n", " <td>T</td>\n", " <td>L</td>\n", " <td>N</td>\n", " <td>F</td>\n", " </tr>\n", " <tr>\n", " <th>257939</th>\n", " <td>200.0</td>\n", " <td>204.0</td>\n", " <td>54.0</td>\n", " <td>98.0</td>\n", " <td>32.0</td>\n", " <td>20.0</td>\n", " <td>4.2</td>\n", " <td>117.0</td>\n", " <td>P</td>\n", " <td>Q</td>\n", " <td>...</td>\n", " <td>M</td>\n", " <td>T</td>\n", " <td>Q</td>\n", " <td>I</td>\n", " <td>G</td>\n", " <td>C</td>\n", " <td>T</td>\n", " <td>L</td>\n", " <td>N</td>\n", " <td>F</td>\n", " </tr>\n", " <tr>\n", " <th>257941</th>\n", " <td>0.6</td>\n", " <td>1.0</td>\n", " <td>0.8</td>\n", " <td>0.7</td>\n", " <td>0.9</td>\n", " <td>0.6</td>\n", " <td>0.7</td>\n", " <td>0.5</td>\n", " <td>P</td>\n", " <td>Q</td>\n", " <td>...</td>\n", " <td>L</td>\n", " <td>T</td>\n", " <td>Q</td>\n", " <td>I</td>\n", " <td>G</td>\n", " <td>C</td>\n", " <td>T</td>\n", " <td>L</td>\n", " <td>N</td>\n", " <td>F</td>\n", " </tr>\n", " <tr>\n", " <th>257947</th>\n", " <td>0.6</td>\n", " <td>1.0</td>\n", " <td>1.1</td>\n", " <td>0.9</td>\n", " <td>1.3</td>\n", " <td>1.3</td>\n", " <td>1.7</td>\n", " <td>1.6</td>\n", " <td>P</td>\n", " <td>Q</td>\n", " <td>...</td>\n", " <td>L</td>\n", " <td>T</td>\n", " <td>Q</td>\n", " <td>I</td>\n", " <td>G</td>\n", " <td>C</td>\n", " <td>T</td>\n", " <td>L</td>\n", " <td>N</td>\n", " <td>F</td>\n", " </tr>\n", " <tr>\n", " <th>257957</th>\n", " <td>46.0</td>\n", " <td>200.0</td>\n", " <td>200.0</td>\n", " <td>200.0</td>\n", " <td>89.0</td>\n", " <td>200.0</td>\n", " <td>1.3</td>\n", " <td>33.0</td>\n", " <td>P</td>\n", " <td>Q</td>\n", " <td>...</td>\n", " <td>L</td>\n", " <td>T</td>\n", " <td>Q</td>\n", " <td>L</td>\n", " <td>G</td>\n", " <td>C</td>\n", " <td>T</td>\n", " <td>L</td>\n", " <td>N</td>\n", " <td>F</td>\n", " </tr>\n", " <tr>\n", " <th>257963</th>\n", " <td>0.6</td>\n", " <td>0.7</td>\n", " <td>0.7</td>\n", " <td>0.9</td>\n", " <td>1.1</td>\n", " <td>0.9</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>P</td>\n", " <td>Q</td>\n", " <td>...</td>\n", " <td>L</td>\n", " <td>T</td>\n", " <td>Q</td>\n", " <td>I</td>\n", " <td>G</td>\n", " <td>C</td>\n", " <td>T</td>\n", " <td>L</td>\n", " <td>N</td>\n", " <td>F</td>\n", " </tr>\n", " <tr>\n", " <th>258503</th>\n", " <td>0.2</td>\n", " <td>8.3</td>\n", " <td>3.3</td>\n", " <td>1.4</td>\n", " <td>7.1</td>\n", " <td>1.9</td>\n", " <td>0.6</td>\n", " <td>0.3</td>\n", " <td>P</td>\n", " <td>Q</td>\n", " <td>...</td>\n", " <td>L</td>\n", " <td>T</td>\n", " <td>R</td>\n", " <td>L</td>\n", " <td>G</td>\n", " <td>C</td>\n", " <td>T</td>\n", " <td>L</td>\n", " <td>N</td>\n", " <td>F</td>\n", " </tr>\n", " <tr>\n", " <th>258505</th>\n", " <td>0.7</td>\n", " <td>0.8</td>\n", " <td>0.8</td>\n", " <td>0.8</td>\n", " <td>0.9</td>\n", " <td>0.9</td>\n", " <td>0.9</td>\n", " <td>1.0</td>\n", " <td>P</td>\n", " <td>Q</td>\n", " <td>...</td>\n", " <td>L</td>\n", " <td>T</td>\n", " <td>Q</td>\n", " <td>I</td>\n", " <td>G</td>\n", " <td>C</td>\n", " <td>T</td>\n", " <td>L</td>\n", " <td>N</td>\n", " <td>F</td>\n", " </tr>\n", " <tr>\n", " <th>258507</th>\n", " <td>0.5</td>\n", " <td>0.8</td>\n", " <td>0.8</td>\n", " <td>0.8</td>\n", " <td>1.2</td>\n", " <td>0.7</td>\n", " <td>0.9</td>\n", " <td>0.6</td>\n", " <td>P</td>\n", " <td>Q</td>\n", " <td>...</td>\n", " <td>L</td>\n", " <td>T</td>\n", " <td>Q</td>\n", " <td>I</td>\n", " <td>G</td>\n", " <td>C</td>\n", " <td>T</td>\n", " <td>L</td>\n", " <td>N</td>\n", " <td>F</td>\n", " </tr>\n", " <tr>\n", " <th>258509</th>\n", " <td>2.5</td>\n", " <td>5.0</td>\n", " <td>4.5</td>\n", " <td>2.0</td>\n", " <td>9.1</td>\n", " <td>3.5</td>\n", " <td>2.4</td>\n", " <td>1.3</td>\n", " <td>P</td>\n", " <td>Q</td>\n", " <td>...</td>\n", " <td>M</td>\n", " <td>T</td>\n", " <td>Q</td>\n", " <td>L</td>\n", " <td>G</td>\n", " <td>C</td>\n", " <td>T</td>\n", " <td>L</td>\n", " <td>N</td>\n", " <td>F</td>\n", " </tr>\n", " <tr>\n", " <th>259173</th>\n", " <td>0.7</td>\n", " <td>1.0</td>\n", " <td>1.2</td>\n", " <td>1.1</td>\n", " <td>2.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>0.8</td>\n", " <td>P</td>\n", " <td>Q</td>\n", " <td>...</td>\n", " <td>L</td>\n", " <td>T</td>\n", " <td>Q</td>\n", " <td>L</td>\n", " <td>G</td>\n", " <td>C</td>\n", " <td>T</td>\n", " <td>L</td>\n", " <td>N</td>\n", " <td>F</td>\n", " </tr>\n", " <tr>\n", " <th>259175</th>\n", " <td>0.9</td>\n", " <td>0.8</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>0.8</td>\n", " <td>0.8</td>\n", " <td>0.7</td>\n", " <td>0.8</td>\n", " <td>P</td>\n", " <td>Q</td>\n", " <td>...</td>\n", " <td>L</td>\n", " <td>T</td>\n", " <td>Q</td>\n", " <td>I</td>\n", " <td>G</td>\n", " <td>C</td>\n", " <td>T</td>\n", " <td>L</td>\n", " <td>N</td>\n", " <td>F</td>\n", " </tr>\n", " <tr>\n", " <th>259181</th>\n", " <td>2.6</td>\n", " <td>9.3</td>\n", " <td>21.0</td>\n", " <td>6.8</td>\n", " <td>13.0</td>\n", " <td>21.0</td>\n", " <td>1.4</td>\n", " <td>1.5</td>\n", " <td>P</td>\n", " <td>Q</td>\n", " <td>...</td>\n", " <td>M</td>\n", " <td>T</td>\n", " <td>Q</td>\n", " <td>I</td>\n", " <td>G</td>\n", " <td>C</td>\n", " <td>T</td>\n", " <td>L</td>\n", " <td>N</td>\n", " <td>F</td>\n", " </tr>\n", " <tr>\n", " <th>259191</th>\n", " <td>1.1</td>\n", " <td>27.0</td>\n", " <td>30.0</td>\n", " <td>36.0</td>\n", " <td>36.0</td>\n", " <td>200.0</td>\n", " <td>0.6</td>\n", " <td>0.6</td>\n", " <td>P</td>\n", " <td>Q</td>\n", " <td>...</td>\n", " <td>M</td>\n", " <td>T</td>\n", " <td>Q</td>\n", " <td>L</td>\n", " <td>G</td>\n", " <td>C</td>\n", " <td>T</td>\n", " <td>L</td>\n", " <td>N</td>\n", " <td>F</td>\n", " </tr>\n", " <tr>\n", " <th>259195</th>\n", " <td>1.1</td>\n", " <td>1.5</td>\n", " <td>1.6</td>\n", " <td>1.1</td>\n", " <td>1.4</td>\n", " <td>1.3</td>\n", " <td>1.5</td>\n", " <td>0.9</td>\n", " <td>P</td>\n", " <td>Q</td>\n", " <td>...</td>\n", " <td>L</td>\n", " <td>T</td>\n", " <td>Q</td>\n", " <td>L</td>\n", " <td>G</td>\n", " <td>C</td>\n", " <td>T</td>\n", " <td>L</td>\n", " <td>N</td>\n", " <td>F</td>\n", " </tr>\n", " <tr>\n", " <th>259199</th>\n", " <td>0.5</td>\n", " <td>0.8</td>\n", " <td>0.7</td>\n", " <td>0.6</td>\n", " <td>1.1</td>\n", " <td>0.6</td>\n", " <td>0.6</td>\n", " <td>0.6</td>\n", " <td>P</td>\n", " <td>Q</td>\n", " <td>...</td>\n", " <td>L</td>\n", " <td>T</td>\n", " <td>Q</td>\n", " <td>L</td>\n", " <td>G</td>\n", " <td>C</td>\n", " <td>T</td>\n", " <td>L</td>\n", " <td>N</td>\n", " <td>F</td>\n", " </tr>\n", " <tr>\n", " <th>259215</th>\n", " <td>0.6</td>\n", " <td>0.8</td>\n", " <td>1.0</td>\n", " <td>0.7</td>\n", " <td>1.3</td>\n", " <td>0.6</td>\n", " <td>0.7</td>\n", " <td>0.6</td>\n", " <td>P</td>\n", " <td>Q</td>\n", " <td>...</td>\n", " <td>L</td>\n", " <td>T</td>\n", " <td>Q</td>\n", " <td>L</td>\n", " <td>G</td>\n", " <td>C</td>\n", " <td>T</td>\n", " <td>L</td>\n", " <td>N</td>\n", " <td>F</td>\n", " </tr>\n", " <tr>\n", " <th>259227</th>\n", " <td>6.3</td>\n", " <td>6.2</td>\n", " <td>6.3</td>\n", " <td>3.4</td>\n", " <td>20.5</td>\n", " <td>5.3</td>\n", " <td>6.7</td>\n", " <td>2.9</td>\n", " <td>P</td>\n", " <td>Q</td>\n", " <td>...</td>\n", " <td>M</td>\n", " <td>T</td>\n", " <td>Q</td>\n", " <td>I</td>\n", " <td>G</td>\n", " <td>C</td>\n", " <td>T</td>\n", " <td>L</td>\n", " <td>N</td>\n", " <td>F</td>\n", " </tr>\n", " <tr>\n", " <th>259253</th>\n", " <td>0.9</td>\n", " <td>0.8</td>\n", " <td>0.9</td>\n", " <td>0.9</td>\n", " <td>1.5</td>\n", " <td>0.7</td>\n", " <td>0.7</td>\n", " <td>0.6</td>\n", " <td>P</td>\n", " <td>Q</td>\n", " <td>...</td>\n", " <td>L</td>\n", " <td>T</td>\n", " <td>Q</td>\n", " <td>I</td>\n", " <td>G</td>\n", " <td>C</td>\n", " <td>T</td>\n", " <td>L</td>\n", " <td>N</td>\n", " <td>F</td>\n", " </tr>\n", " <tr>\n", " <th>259257</th>\n", " <td>0.8</td>\n", " <td>0.8</td>\n", " <td>0.8</td>\n", " <td>0.6</td>\n", " <td>1.7</td>\n", " <td>0.7</td>\n", " <td>1.1</td>\n", " <td>0.5</td>\n", " <td>P</td>\n", " <td>Q</td>\n", " <td>...</td>\n", " <td>L</td>\n", " <td>T</td>\n", " <td>Q</td>\n", " <td>L</td>\n", " <td>G</td>\n", " <td>C</td>\n", " <td>T</td>\n", " <td>L</td>\n", " <td>N</td>\n", " <td>F</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>802 rows × 107 columns</p>\n", "</div>" ], "text/plain": [ " FPV ATV IDV LPV NFV SQV TPV DRV P1 P2 ... P90 \\\n", "SeqID ... \n", "4432 1.5 NaN 1.0 NaN 2.2 1.1 NaN NaN P Q ... L \n", "4664 3.1 NaN 8.7 NaN 32.0 16.9 NaN NaN P Q ... M \n", "5221 NaN NaN 0.8 0.8 1.2 0.7 NaN NaN P Q ... L \n", "5279 8.3 79.0 16.0 12.0 600.0 1000.0 NaN NaN P Q ... M \n", "5444 2.7 21.0 24.0 6.1 42.0 132.0 NaN NaN P Q ... M \n", "5462 2.1 16.0 12.0 22.0 15.0 82.0 NaN NaN P Q ... L \n", "5464 2.1 NaN 22.2 7.8 24.7 104.8 NaN NaN P Q ... M \n", "5681 NaN NaN 26.0 25.0 37.0 7.4 NaN NaN P Q ... M \n", "6024 NaN NaN 8.3 3.0 22.0 3.4 NaN NaN P Q ... M \n", "6028 NaN NaN 16.0 20.0 37.0 7.9 NaN NaN P Q ... M \n", "7042 11.0 18.0 28.0 17.0 53.0 62.0 NaN NaN P Q ... M \n", "7085 0.4 2.0 1.9 0.9 3.7 2.5 NaN NaN P Q ... M \n", "7103 NaN NaN 0.7 0.7 11.0 0.4 NaN NaN P Q ... L \n", "7119 1.4 0.9 1.0 0.8 1.6 0.8 NaN NaN P Q ... L \n", "7412 6.2 NaN 12.0 NaN 10.2 591.5 NaN NaN P Q ... L \n", "7430 2.8 NaN 48.9 NaN 80.7 42.1 NaN NaN P Q ... M \n", "7443 2.3 NaN 12.0 NaN 11.0 574.2 NaN NaN P Q ... L \n", "8188 4.7 29.0 25.0 34.0 28.0 147.0 NaN NaN P Q ... L \n", "8468 1.4 11.0 17.0 4.4 26.0 20.0 NaN NaN P Q ... M \n", "8506 5.4 15.0 19.0 7.2 34.0 70.0 NaN NaN P Q ... M \n", "8626 11.0 15.0 33.0 34.0 56.0 1.5 NaN NaN P Q ... M \n", "8654 NaN NaN NaN NaN 7.0 1.0 NaN NaN P Q ... L \n", "8658 NaN NaN NaN NaN 4.0 1.0 NaN NaN P Q ... L \n", "8660 NaN NaN NaN NaN 37.0 5.0 NaN NaN P Q ... M \n", "8666 NaN NaN NaN NaN 2.0 1.0 NaN NaN P Q ... L \n", "8674 NaN NaN NaN NaN 1.0 1.0 NaN NaN P Q ... L \n", "9431 NaN NaN 2.8 0.8 12.0 0.9 NaN NaN P Q ... L \n", "9556 NaN NaN 1.2 1.2 3.0 1.0 NaN NaN P Q ... L \n", "9564 0.4 2.2 0.8 0.5 24.0 0.8 NaN NaN P Q ... L \n", "9706 NaN NaN 0.3 0.3 0.4 0.4 NaN NaN P Q ... L \n", "... ... ... ... ... ... ... ... ... .. .. ... .. \n", "235719 0.6 0.6 0.4 0.6 0.7 0.5 0.8 0.5 P Q ... L \n", "235721 4.7 2.8 3.4 4.4 5.1 5.2 3.2 1.7 P Q ... L \n", "235725 0.5 0.8 0.6 0.6 0.7 0.6 0.9 0.5 P Q ... L \n", "235729 1.1 1.1 1.0 1.2 1.2 1.1 0.8 1.0 P Q ... L \n", "235733 23.0 115.0 35.0 102.0 68.0 184.0 NaN NaN P Q ... M \n", "235739 0.5 0.7 0.8 0.7 0.8 0.8 0.8 0.7 P Q ... L \n", "257923 1.1 0.8 1.0 1.0 0.9 1.0 1.1 1.6 P Q ... L \n", "257927 0.4 0.6 0.4 0.4 0.8 0.4 0.5 0.3 P Q ... L \n", "257929 0.1 0.4 0.3 0.3 0.3 0.4 0.4 0.4 P Q ... L \n", "257933 0.6 0.9 1.0 0.7 1.0 0.6 0.6 0.6 P Q ... L \n", "257935 0.8 0.9 0.8 0.8 0.8 0.8 0.8 0.7 P Q ... L \n", "257939 200.0 204.0 54.0 98.0 32.0 20.0 4.2 117.0 P Q ... M \n", "257941 0.6 1.0 0.8 0.7 0.9 0.6 0.7 0.5 P Q ... L \n", "257947 0.6 1.0 1.1 0.9 1.3 1.3 1.7 1.6 P Q ... L \n", "257957 46.0 200.0 200.0 200.0 89.0 200.0 1.3 33.0 P Q ... L \n", "257963 0.6 0.7 0.7 0.9 1.1 0.9 1.0 1.0 P Q ... L \n", "258503 0.2 8.3 3.3 1.4 7.1 1.9 0.6 0.3 P Q ... L \n", "258505 0.7 0.8 0.8 0.8 0.9 0.9 0.9 1.0 P Q ... L \n", "258507 0.5 0.8 0.8 0.8 1.2 0.7 0.9 0.6 P Q ... L \n", "258509 2.5 5.0 4.5 2.0 9.1 3.5 2.4 1.3 P Q ... M \n", "259173 0.7 1.0 1.2 1.1 2.0 1.0 1.0 0.8 P Q ... L \n", "259175 0.9 0.8 1.0 1.0 0.8 0.8 0.7 0.8 P Q ... L \n", "259181 2.6 9.3 21.0 6.8 13.0 21.0 1.4 1.5 P Q ... M \n", "259191 1.1 27.0 30.0 36.0 36.0 200.0 0.6 0.6 P Q ... M \n", "259195 1.1 1.5 1.6 1.1 1.4 1.3 1.5 0.9 P Q ... L \n", "259199 0.5 0.8 0.7 0.6 1.1 0.6 0.6 0.6 P Q ... L \n", "259215 0.6 0.8 1.0 0.7 1.3 0.6 0.7 0.6 P Q ... L \n", "259227 6.3 6.2 6.3 3.4 20.5 5.3 6.7 2.9 P Q ... M \n", "259253 0.9 0.8 0.9 0.9 1.5 0.7 0.7 0.6 P Q ... L \n", "259257 0.8 0.8 0.8 0.6 1.7 0.7 1.1 0.5 P Q ... L \n", "\n", " P91 P92 P93 P94 P95 P96 P97 P98 P99 \n", "SeqID \n", "4432 T Q I G C T L N F \n", "4664 T Q I G C T L N F \n", "5221 T Q I G C T L N F \n", "5279 T Q I G C T L N F \n", "5444 T Q I G C T L N F \n", "5462 T Q I G C T L N F \n", "5464 T Q L G C T L N F \n", "5681 T Q L G C T L N F \n", "6024 T Q L G C T L N F \n", "6028 T Q I G C T L N F \n", "7042 T Q L G C T L N F \n", "7085 T Q I G C T L N F \n", "7103 T Q I G C T L N F \n", "7119 T Q I G C T L N F \n", "7412 T Q I G C T L N F \n", "7430 T Q I G C T L N F \n", "7443 T Q L G C T L N F \n", "8188 T Q I G C T L N F \n", "8468 T Q I G C T L N F \n", "8506 T Q I G C T L N F \n", "8626 T Q L G C T L N F \n", "8654 T Q I G C T L N F \n", "8658 T Q I G C T L N F \n", "8660 T Q L G C T L N F \n", "8666 T Q L G C T L N F \n", "8674 T Q I G C T L N F \n", "9431 T Q I G C T L N F \n", "9556 T Q L G C T L N F \n", "9564 T Q I G C T L N F \n", "9706 T Q I G C T L N F \n", "... .. .. .. .. .. .. .. .. .. \n", "235719 T Q I G C T L N F \n", "235721 T K I G C T L N F \n", "235725 T Q I G C T L N F \n", "235729 T Q I G C T L N F \n", "235733 K Q L G C T L N F \n", "235739 T Q I G C T L N F \n", "257923 T Q I G C T L N F \n", "257927 T Q I G C T L N F \n", "257929 T Q I G C T L N F \n", "257933 T Q L G C T L N F \n", "257935 T Q I G C T L N F \n", "257939 T Q I G C T L N F \n", "257941 T Q I G C T L N F \n", "257947 T Q I G C T L N F \n", "257957 T Q L G C T L N F \n", "257963 T Q I G C T L N F \n", "258503 T R L G C T L N F \n", "258505 T Q I G C T L N F \n", "258507 T Q I G C T L N F \n", "258509 T Q L G C T L N F \n", "259173 T Q L G C T L N F \n", "259175 T Q I G C T L N F \n", "259181 T Q I G C T L N F \n", "259191 T Q L G C T L N F \n", "259195 T Q L G C T L N F \n", "259199 T Q L G C T L N F \n", "259215 T Q L G C T L N F \n", "259227 T Q I G C T L N F \n", "259253 T Q I G C T L N F \n", "259257 T Q L G C T L N F \n", "\n", "[802 rows x 107 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Read in the protease inhibitor data\n", "data, drug_cols, feat_cols = cf.read_data('hiv-protease-data.csv', n_data_cols=8)\n", "print(len(data))\n", "# Read in the consensus data\n", "consensus_map = cf.read_consensus('hiv-protease-consensus.fasta')\n", "\n", "# Clean the data\n", "data = cf.clean_data(data, feat_cols, consensus_map)\n", "\n", "# Identify feature columns\n", "data = cf.drop_ambiguous_sequences(data, feat_cols)\n", "data.dropna(inplace=True, subset=feat_cols)\n", "data" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# consensus_map" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Index(['FPV', 'ATV', 'IDV', 'LPV', 'NFV', 'SQV', 'TPV', 'DRV'], dtype='object')\n" ] } ], "source": [ "print(drug_cols)\n", "\n", "DRUG = 'FPV'" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Index(['P1', 'P2', 'P3', 'P4', 'P5', 'P6', 'P7', 'P8', 'P9', 'P10', 'P11',\n", " 'P12', 'P13', 'P14', 'P15', 'P16', 'P17', 'P18', 'P19', 'P20', 'P21',\n", " 'P22', 'P23', 'P24', 'P25', 'P26', 'P27', 'P28', 'P29', 'P30', 'P31',\n", " 'P32', 'P33', 'P34', 'P35', 'P36', 'P37', 'P38', 'P39', 'P40', 'P41',\n", " 'P42', 'P43', 'P44', 'P45', 'P46', 'P47', 'P48', 'P49', 'P50', 'P51',\n", " 'P52', 'P53', 'P54', 'P55', 'P56', 'P57', 'P58', 'P59', 'P60', 'P61',\n", " 'P62', 'P63', 'P64', 'P65', 'P66', 'P67', 'P68', 'P69', 'P70', 'P71',\n", " 'P72', 'P73', 'P74', 'P75', 'P76', 'P77', 'P78', 'P79', 'P80', 'P81',\n", " 'P82', 'P83', 'P84', 'P85', 'P86', 'P87', 'P88', 'P89', 'P90', 'P91',\n", " 'P92', 'P93', 'P94', 'P95', 'P96', 'P97', 'P98', 'P99'],\n", " dtype='object')" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "feat_cols" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/ericmjl/hiv-resistance-prediction/custom_funcs.py:189: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", " subset.dropna(inplace=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>P1_A</th>\n", " <th>P1_C</th>\n", " <th>P1_D</th>\n", " <th>P1_E</th>\n", " <th>P1_F</th>\n", " <th>P1_G</th>\n", " <th>P1_H</th>\n", " <th>P1_I</th>\n", " <th>P1_K</th>\n", " <th>P1_L</th>\n", " <th>...</th>\n", " <th>P99_M</th>\n", " <th>P99_N</th>\n", " <th>P99_P</th>\n", " <th>P99_Q</th>\n", " <th>P99_R</th>\n", " <th>P99_S</th>\n", " <th>P99_T</th>\n", " 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<td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>707</th>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>708</th>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>709</th>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>710</th>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>711</th>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>712</th>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>713</th>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>714</th>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>715</th>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>716</th>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " 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<td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>725</th>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>726 rows × 1980 columns</p>\n", "</div>" ], "text/plain": [ " P1_A P1_C P1_D P1_E P1_F P1_G P1_H P1_I P1_K P1_L ... P99_M \\\n", "0 0 0 0 0 0 0 0 0 0 0 ... 0 \n", "1 0 0 0 0 0 0 0 0 0 0 ... 0 \n", "2 0 0 0 0 0 0 0 0 0 0 ... 0 \n", "3 0 0 0 0 0 0 0 0 0 0 ... 0 \n", "4 0 0 0 0 0 0 0 0 0 0 ... 0 \n", "5 0 0 0 0 0 0 0 0 0 0 ... 0 \n", "6 0 0 0 0 0 0 0 0 0 0 ... 0 \n", "7 0 0 0 0 0 0 0 0 0 0 ... 0 \n", "8 0 0 0 0 0 0 0 0 0 0 ... 0 \n", "9 0 0 0 0 0 0 0 0 0 0 ... 0 \n", "10 0 0 0 0 0 0 0 0 0 0 ... 0 \n", "11 0 0 0 0 0 0 0 0 0 0 ... 0 \n", "12 0 0 0 0 0 0 0 0 0 0 ... 0 \n", "13 0 0 0 0 0 0 0 0 0 0 ... 0 \n", "14 0 0 0 0 0 0 0 0 0 0 ... 0 \n", "15 0 0 0 0 0 0 0 0 0 0 ... 0 \n", "16 0 0 0 0 0 0 0 0 0 0 ... 0 \n", "17 0 0 0 0 0 0 0 0 0 0 ... 0 \n", "18 0 0 0 0 0 0 0 0 0 0 ... 0 \n", "19 0 0 0 0 0 0 0 0 0 0 ... 0 \n", "20 0 0 0 0 0 0 0 0 0 0 ... 0 \n", "21 0 0 0 0 0 0 0 0 0 0 ... 0 \n", "22 0 0 0 0 0 0 0 0 0 0 ... 0 \n", "23 0 0 0 0 0 0 0 0 0 0 ... 0 \n", "24 0 0 0 0 0 0 0 0 0 0 ... 0 \n", "25 0 0 0 0 0 0 0 0 0 0 ... 0 \n", "26 0 0 0 0 0 0 0 0 0 0 ... 0 \n", "27 0 0 0 0 0 0 0 0 0 0 ... 0 \n", "28 0 0 0 0 0 0 0 0 0 0 ... 0 \n", "29 0 0 0 0 0 0 0 0 0 0 ... 0 \n", ".. ... ... ... ... ... ... ... ... ... ... ... ... \n", "696 0 0 0 0 0 0 0 0 0 0 ... 0 \n", "697 0 0 0 0 0 0 0 0 0 0 ... 0 \n", "698 0 0 0 0 0 0 0 0 0 0 ... 0 \n", "699 0 0 0 0 0 0 0 0 0 0 ... 0 \n", "700 0 0 0 0 0 0 0 0 0 0 ... 0 \n", "701 0 0 0 0 0 0 0 0 0 0 ... 0 \n", "702 0 0 0 0 0 0 0 0 0 0 ... 0 \n", "703 0 0 0 0 0 0 0 0 0 0 ... 0 \n", "704 0 0 0 0 0 0 0 0 0 0 ... 0 \n", "705 0 0 0 0 0 0 0 0 0 0 ... 0 \n", "706 0 0 0 0 0 0 0 0 0 0 ... 0 \n", "707 0 0 0 0 0 0 0 0 0 0 ... 0 \n", "708 0 0 0 0 0 0 0 0 0 0 ... 0 \n", "709 0 0 0 0 0 0 0 0 0 0 ... 0 \n", "710 0 0 0 0 0 0 0 0 0 0 ... 0 \n", "711 0 0 0 0 0 0 0 0 0 0 ... 0 \n", "712 0 0 0 0 0 0 0 0 0 0 ... 0 \n", "713 0 0 0 0 0 0 0 0 0 0 ... 0 \n", "714 0 0 0 0 0 0 0 0 0 0 ... 0 \n", "715 0 0 0 0 0 0 0 0 0 0 ... 0 \n", "716 0 0 0 0 0 0 0 0 0 0 ... 0 \n", "717 0 0 0 0 0 0 0 0 0 0 ... 0 \n", "718 0 0 0 0 0 0 0 0 0 0 ... 0 \n", "719 0 0 0 0 0 0 0 0 0 0 ... 0 \n", "720 0 0 0 0 0 0 0 0 0 0 ... 0 \n", "721 0 0 0 0 0 0 0 0 0 0 ... 0 \n", "722 0 0 0 0 0 0 0 0 0 0 ... 0 \n", "723 0 0 0 0 0 0 0 0 0 0 ... 0 \n", "724 0 0 0 0 0 0 0 0 0 0 ... 0 \n", "725 0 0 0 0 0 0 0 0 0 0 ... 0 \n", "\n", " P99_N P99_P P99_Q P99_R P99_S P99_T P99_V P99_W P99_Y \n", "0 0 0 0 0 0 0 0 0 0 \n", "1 0 0 0 0 0 0 0 0 0 \n", "2 0 0 0 0 0 0 0 0 0 \n", "3 0 0 0 0 0 0 0 0 0 \n", "4 0 0 0 0 0 0 0 0 0 \n", "5 0 0 0 0 0 0 0 0 0 \n", "6 0 0 0 0 0 0 0 0 0 \n", "7 0 0 0 0 0 0 0 0 0 \n", "8 0 0 0 0 0 0 0 0 0 \n", "9 0 0 0 0 0 0 0 0 0 \n", "10 0 0 0 0 0 0 0 0 0 \n", "11 0 0 0 0 0 0 0 0 0 \n", "12 0 0 0 0 0 0 0 0 0 \n", "13 0 0 0 0 0 0 0 0 0 \n", "14 0 0 0 0 0 0 0 0 0 \n", "15 0 0 0 0 0 0 0 0 0 \n", "16 0 0 0 0 0 0 0 0 0 \n", "17 0 0 0 0 0 0 0 0 0 \n", "18 0 0 0 0 0 0 0 0 0 \n", "19 0 0 0 0 0 0 0 0 0 \n", "20 0 0 0 0 0 0 0 0 0 \n", "21 0 0 0 0 0 0 0 0 0 \n", "22 0 0 0 0 0 0 0 0 0 \n", "23 0 0 0 0 0 0 0 0 0 \n", "24 0 0 0 0 0 0 0 0 0 \n", "25 0 0 0 0 0 0 0 0 0 \n", "26 0 0 0 0 0 0 0 0 0 \n", "27 0 0 0 0 0 0 0 0 0 \n", "28 0 0 0 0 0 0 0 0 0 \n", "29 0 0 0 0 0 0 0 0 0 \n", ".. ... ... ... ... ... ... ... ... ... \n", "696 0 0 0 0 0 0 0 0 0 \n", "697 0 0 0 0 0 0 0 0 0 \n", "698 0 0 0 0 0 0 0 0 0 \n", "699 0 0 0 0 0 0 0 0 0 \n", "700 0 0 0 0 0 0 0 0 0 \n", "701 0 0 0 0 0 0 0 0 0 \n", "702 0 0 0 0 0 0 0 0 0 \n", "703 0 0 0 0 0 0 0 0 0 \n", "704 0 0 0 0 0 0 0 0 0 \n", "705 0 0 0 0 0 0 0 0 0 \n", "706 0 0 0 0 0 0 0 0 0 \n", "707 0 0 0 0 0 0 0 0 0 \n", "708 0 0 0 0 0 0 0 0 0 \n", "709 0 0 0 0 0 0 0 0 0 \n", "710 0 0 0 0 0 0 0 0 0 \n", "711 0 0 0 0 0 0 0 0 0 \n", "712 0 0 0 0 0 0 0 0 0 \n", "713 0 0 0 0 0 0 0 0 0 \n", "714 0 0 0 0 0 0 0 0 0 \n", "715 0 0 0 0 0 0 0 0 0 \n", "716 0 0 0 0 0 0 0 0 0 \n", "717 0 0 0 0 0 0 0 0 0 \n", "718 0 0 0 0 0 0 0 0 0 \n", "719 0 0 0 0 0 0 0 0 0 \n", "720 0 0 0 0 0 0 0 0 0 \n", "721 0 0 0 0 0 0 0 0 0 \n", "722 0 0 0 0 0 0 0 0 0 \n", "723 0 0 0 0 0 0 0 0 0 \n", "724 0 0 0 0 0 0 0 0 0 \n", "725 0 0 0 0 0 0 0 0 0 \n", "\n", "[726 rows x 1980 columns]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Split data into predictor variables and dependent variables.\n", "# Predictors are the sequence features\n", "# Dependent are the drug resistance values\n", "X, Y = cf.split_data_xy(data, feat_cols, DRUG)\n", "\n", "# Binarize the sequence features such that there are 99 x 20 columns in total.\n", "from sklearn.preprocessing import LabelBinarizer\n", "lb = LabelBinarizer()\n", "lb.fit(list('CHIMSVAGLPTRFYWDNEQK'))\n", "\n", "X_binarized = pd.DataFrame()\n", "\n", "for col in X.columns:\n", " binarized_cols = lb.transform(X[col])\n", " \n", " for i, c in enumerate(lb.classes_):\n", " X_binarized[col + '_' + c] = binarized_cols[:,i]\n", "X_binarized" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f06b8adb048>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# View distribution of drug resistance values\n", "import matplotlib.pyplot as plt\n", "std = (3,3)\n", "fig = cf.plot_Y_histogram(Y, DRUG, figsize=std)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/ericmjl/anaconda3/lib/python3.4/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", " if self._edgecolors == str('face'):\n" ] }, { "data": { "image/png": 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rVi3Dty4bQggCAgKIjIxk6dKlRWZ9Lo8YKkbRiBE9WLFiGklJqqgh7rRjA99m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\nx48fM2HCBIPNJQHi4+N59OgRZmZmTJs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"text/plain": [ "<matplotlib.figure.Figure at 0x7f06b6ba5fd0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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w5swZYmJiAKhevTrt2rXDzMysQIwrV64cJ0+exMTERKO8cuXK/PPPPwVyT8mr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\nX19f0aZNG3Hw4EEREREhNm7cKCwtLcW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"text/plain": [ "<matplotlib.figure.Figure at 0x7f06b6b977b8>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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5dj3U+9KNtbW1iIyMLPD6nj17hL29ve4WPgMzZ84UrVu3VntQ6YIUa8Ho4tdb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\nsoWHDx8yceJEvY0lAW7cuMGDBw+oVq0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"text/plain": [ "<matplotlib.figure.Figure at 0x7f06b826c908>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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AyiwLw4YN4+OPP+bNN98s3JeTlBikWHne63344YesWrWK+fPnc+PGDSIiInBxceHzzz/n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\n37+fF154gTp16uSrrn///ReA6tWra5Q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"text/plain": [ "<matplotlib.figure.Figure at 0x7f06b8211908>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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5xVBfNlqkpqaKyZMni0aNGomDBw/m+v2yZ9XNC8fQV1o9nJvbcJ3BAFmdSY6O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\n/fjoo6acPTuJkyfroRzungP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"text/plain": [ "<matplotlib.figure.Figure at 0x7f06b80f8dd8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Split data into training and testing set.\n", "tts_data = X_train, X_test, Y_train, Y_test = train_test_split(X_binarized, Y, test_size=0.33)\n", "\n", "# Train a bunch of ensemble regressors:\n", "## Random Forest\n", "kwargs = {'n_jobs':-1, 'n_estimators':1000}\n", "rfr, rfr_preds, rfr_mse, rfr_r2 = cf.train_model(tts_data, model=RandomForestRegressor, modelargs=kwargs)\n", "## Gradient Boosting\n", "kwargs = {'n_estimators':1000}\n", "gbr, gbr_preds, gbr_mse, gbr_r2 = cf.train_model(tts_data, model=GradientBoostingRegressor, modelargs=kwargs)\n", "## AdaBoost\n", "kwargs = {'n_estimators':1000}\n", "abr, abr_preds, abr_mse, abr_r2 = cf.train_model(tts_data, model=AdaBoostRegressor, modelargs=kwargs)\n", "## ExtraTrees\n", "kwargs = {'n_estimators':1000, 'n_jobs':-1}\n", "etr, etr_preds, etr_mse, etr_r2 = cf.train_model(tts_data, model=ExtraTreesRegressor, modelargs=kwargs)\n", "## Bagging\n", "bgr, bgr_preds, bgr_mse, bgr_r2 = cf.train_model(tts_data, model=BaggingRegressor)\n", "\n", "# Plot the results of regression\n", "cf.scatterplot_results(rfr_preds, Y_test, rfr_mse, rfr_r2, DRUG, 'Rand. Forest', figsize=std)\n", "plt.savefig('figures/{0} random_forest_poster.pdf'.format(DRUG), bbox_inches='tight')\n", "cf.scatterplot_results(gbr_preds, Y_test, gbr_mse, gbr_r2, DRUG, 'Grad. Boost', figsize=std)\n", "plt.savefig('figures/{0} gradient_boost_poster.pdf'.format(DRUG), bbox_inches='tight')\n", "cf.scatterplot_results(abr_preds, Y_test, abr_mse, abr_r2, DRUG, 'AdaBoost', figsize=std)\n", "plt.savefig('figures/{0} adaboost_poster.pdf'.format(DRUG), bbox_inches='tight')\n", "cf.scatterplot_results(etr_preds, Y_test, etr_mse, etr_r2, DRUG, 'ExtraTrees', figsize=std)\n", "plt.savefig('figures/{0} extratrees_poster.pdf'.format(DRUG), bbox_inches='tight')\n", "cf.scatterplot_results(bgr_preds, Y_test, bgr_mse, bgr_r2, DRUG, 'Bagging', figsize=std)\n", "plt.savefig('figures/{0} bagging_poster.pdf'.format(DRUG), bbox_inches='tight')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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soGrVquzcuZPffvtNa9m3335Ly5YtCQgIMEiAxVlUVJSMDSyEKDR6JewjR47g5eWlNW9e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\npiI7w46Pj0elUtG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O0L5NUywtLTXv+dPHMyfmJ2N/0b/9nP28ceMGgMFGL1Woc26ufon+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\nTPwfV10CMNwEGD8AAAAASUVORK5CYII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"text/plain": [ "<matplotlib.figure.Figure at 0x7f06b87ac780>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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Dhsl10uzsbABA3759JdL5fD4aGxuRm5sLCwsLiW05OTlSA8m0tbVhaGiIrKws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\neFRXV7PHqq+vZ48JAPU1ZSgubuo2V1x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"text/plain": [ "<matplotlib.figure.Figure at 0x7f06b6f16630>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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9u0TaTwghjRXnPnUTExOJVVqfxNOJiYkwNjYWyJFqamoKHo+HxMREAICcnBzk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\nAe3379/L5ORx5POgPnXyxaqsrERgYCB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"text/plain": [ "<matplotlib.figure.Figure at 0x7f06b56f02b0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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QkBB2794NoF1ZuWbNmsycOZMBAwaYLkohhBCGj8N+//33GTx4ML/++ivp6elA4Wx3zs7O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\nj4mJwd3dncqVK+vt8+qrr7J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"text/plain": [ "<matplotlib.figure.Figure at 0x7f069ec22898>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Grab the feature importances - that is, how important a particular feature is for predicting drug resistance\n", "cf.barplot_feature_importances(rfr, DRUG, 'Rand. Forest', figsize=(5,3))\n", "plt.savefig('figures/{0} random_forest_feat_impt.pdf'.format(DRUG), bbox_inches='tight')\n", "cf.barplot_feature_importances(gbr, DRUG, 'Grad. Boost', figsize=(5,3))\n", "plt.savefig('figures/{0} gradient_boost_feat_impt.pdf'.format(DRUG), bbox_inches='tight')\n", "cf.barplot_feature_importances(abr, DRUG, 'AdaBoost', figsize=(5,3))\n", "plt.savefig('figures/{0} adaboost_feat_impt.pdf'.format(DRUG), bbox_inches='tight')\n", "cf.barplot_feature_importances(etr, DRUG, 'ExtraTrees', figsize=(5,3))\n", "plt.savefig('figures/{0} extratrees_feat_impt.pdf'.format(DRUG), bbox_inches='tight')\n", "# cf.barplot_feature_importances(bgr, DRUG, 'Bagging') ## feature_importances_ do not exist for bagging" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "# Extract a table version of feature importance\n", "rfr_fi = cf.extract_mutational_importance(rfr, X_test)\n", "gbr_fi = cf.extract_mutational_importance(gbr, X_test)\n", "abr_fi = cf.extract_mutational_importance(abr, X_test)\n", "\n", "# Join data to compare random forest and gradient boosting.\n", "# joined = rfr_fi.set_index(0).join(gbr_fi.set_index(0), lsuffix='r', rsuffix='g')\n", "# sps.spearmanr(joined['1r'], joined['1g'])\n", "\n", "rfr_fi.to_csv('figures/{0} random_forest feature_importance.csv'.format(DRUG))\n", "gbr_fi.to_csv('figures/{0} gradient_boost feature_importance.csv'.format(DRUG))\n", "abr_fi.to_csv('figures/{0} adaboost feature_importance.csv'.format(DRUG))" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/ericmjl/anaconda3/lib/python3.4/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", " if self._edgecolors == str('face'):\n" ] }, { "data": { "image/png": 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TvXt3goODi9s1SWnh7l24c6e4vQD0XKxJSUkA+Pn50b59e37++WcGDBjA119/zcqVK4vZ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\njXxra2sSEhKoUKECcXFxWFt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"text/plain": [ "<matplotlib.figure.Figure at 0x7f069ec2fda0>" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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LVqSlBalDHssaOgn2/v379O7dG19fX8LCwjh27BiPHj0CYP369fTs2ZO//vqr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\nJYJBC/b+/fscPHiQ69evs2jRImbPns2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"text/plain": [ "<matplotlib.figure.Figure at 0x7f06b429c160>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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bAhgaGjJ48GCGDRuWe1ZKihxZnfQRQsGL4TC4UAvHoy9WG1SJxhYBqmlUTEwb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\nbm7OvXv3NK+joqIwNzfXqvPWW2/RrFk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"text/plain": [ "<matplotlib.figure.Figure at 0x7f06b429ca20>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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eweByuTXeV9fZctWvp6dvxowZQ+DuHgtj450oLQ1gro24PwiqmzYxY/hQ2MKW\ndxnjjH2b9Hy89o5E5v3r16/Rr1+/Gq+rqKiIbI6gtB64XC7GjXNHevpmAMCvv7rj0iVPiQX3/v1b\neHgEM68rcwUQBaAYLBaLMc8vXuRftUI4wuAsJHiBSW9qaoCtW9cxDRFA59aliiTmgLm5OfH392fe\nVzXvCSHE29ubfPnll9K3QwjfgRgQEEDMzc2Jrq4umT59OomMjKxXHtS8r52vv/YVcbZ9/bWv2Hur\nm+8cjh0ZOtSeea+hsZZ06DCVAOuF0oqKipjPWsFfyGnHN+lzCZBHlJQWkIyMDJl2vkkTmXXkzZ8/\nH7t27UJZWRnMzc0BANnZ2eByuThx4gTi4uKwadOmJmmUfHx8cPr0aXh6ekJTUxPJycnYtm0blJSU\npL6zr71y7dqfEqUBok6xd++GwNd3GQRz4P/8swXABgDuQmmCefGr6zRE5uFtb4aC4CgAoLTUE+Hh\nCQBQr7l1uuJOciQS/apVq1BYWIj9+/dj7969APibXwBAXl4eNjY2WLlypdQr9+bNG/z222/45ptv\nMGXKFACAtbU1UlJScPz4cSp6KWFoqImLFz3BFyoAeMLQULPG+6t60wXmtzDy4j8YHo6uDg4APoa4\niu+pA3LzMwACMefXu/71WQtAgeTz9IQQkpeXR+Li4khISAgJCQkhx44dI/n5+U1lhRBCCOFyueT9\n+/dCaZs2bSKzZs2SOA9q3tdOUVFRpYd9JwF2Eg7HTmJTWpy3fsgQGyHzfuhQe/Lm55+FltYSW1tC\nKipqNOPrY95LvBZABpFZ8z4wMBCLFi2Curo6LCwsRK7/8ccfSEhIgJubm9QbJWVlZaH379+/R2pq\nKiZMmCD1storKioquHzZp4p57CNxLyk6B+4JANixYz9SU90werQWPL7Q5PfwYoJY1jaHTufWmwhJ\nWgY2m03u379f4/Vjx44RbW1tqbVEteHq6kpGjBjBrAiUBNrTtyDVNs8Ienhp0pqdfjLX01tZWTGv\n3d3d0aVLF5F7eDwe/vrrL3Tv3l36LVIVCCHYsmULTpw4gV27dtU6hUiRDaqvtCuxtkbHyh5e4HjL\nzc3G6dPXIS8vj9hYb4wcObLe5aioqODoUUesWMG3NA8c+E4qVkFbdQ7WKnpTU1OkpfGXTBYUFNR4\n4ISGhoZITD1pUlFRATc3NyQkJMDf35+eXNsKqL55JhSa2HWtIy69fAkAlY63tQACAPCjLunrr8ON\nG9/VW/gTNhbnAAAgAElEQVRcLhcWFgG4f387AMDCYnujHXlt2jkoiTlgampK/v777yY2OmrG3d2d\njBw5kqSlpTXo89S8b2Zq2DwD7GQ2zvBNcVEHXP/+U+pdXFM48prLOSizy3DPnTsHDQ0NPH36VCi9\nvLwcDx48aJLGSEBUVBRiY2MRHBwMfX39Ji2LIgUqj5oS3TxDo9bKDJK0DMXFxcTOzo6MHj1aKP3V\nq1eEzWYTW1tb8vbtW6m3SMXFxcTAwIBs2bKFFBYWkoKCAqE/SaE9fcMRbGv9+mtfsmnTD7Vvb63m\ntIvqrsmstAPWM1OBHx1v6UJTe8AccuPGjQbVUdqOvOZyDrbEsymR6L29vcmoUaNIeHi4UHpFRQX5\n9ddfiaGhIfHy8pJ65a5du0bYbLbYPw6HI3E+VPQNo/qDzxdoungBiPHSFxUWkq+/9iXGxivIpk0/\niOzGc3cPIqtXf0v69JlE+vef0iDBV8+voXvumyvP6sis6E1NTUlMTEyN12NjY0WsAFmCir5hiBvX\nCsbhQuPbekzLNYeQpEVbFb1Ei3O4XC569+5d4/V+/frRXXZtEPH/p9XSwsNBli9notZWnZarzqNH\nj2Bk5ILCwnEAZor1iMvKNFlb9t5LJPpBgwbh7NmzMDQ0FHs9KioKAwcOlGrFKC0Pi0UAeADYWpni\nCaAEHI4HnJ19RAQfimXYfb0rprv6oVOnLkKi5XK5GDNmCwoLQyvz2o7799dix4796NSJv/7D2tq8\ncupNMqE1ZQPRloNpSiR6W1tbbNiwAVlZWTA0NISKigo+fPiAwsJCnDt3Dunp6fDz82vqulKaiJrE\nwxdjLwDhALoC2AbgA2bMiIBKfDwgJPixWIufQB5U4K8H4QDmCol29+4oFBT44uMJtG4A9uDgwYzK\ndGDPnq9RUPAdJBFaW+6JmxxJxwGnT58m06ZNE3GomZubk5MnTzblEKTR0DF9zdTmpS4qKiKqqrNE\nxvUxs63FxKXPJYDAI7+CAEVCY39x/oHOnceL8RnsrHxdRICdxNh4hdjxtHB+td8r7d9FmsisI68q\neXl55O7du+T+/fsy74gRQEVfM3UtQsnIyCBqasuYh9+tjznh1XIQBTC9Uvj8BkCQV3URqaktIw4O\nXiJl8xsZ4ak8cYL7WO+iyrKkL8626sirt+hbI1T0NbNp0w+VvWsQ0ztX7zEFD3/MbGshwR/rqVHZ\nw1ftqS0re/ot5LPPvhSbj0BERUVFQlF3hg61JxkZGcTYeEWdq+E+NiI7m2XlXFMhU957KysrbNu2\nDZ9//jmsrKxqPSKaEAIWi4Xw8PAmGYJQmgYul4v4+CcANlembAZQgosXv4GJyccxsoqKCrYO6gJs\nOyS0PVZr0yYoDnOuDHYJAA7gj/uVAXiCEOENWtVDWfNPSioH32cAEFIOZWVlmJoaMLH0akKwJdfC\n4us676UII/HaSMK3CsT+Ca5TWhe7d0dVBsNUr/xzB1AGIJnxrHt4BCPWwgZk+XKR/fCDhgzBn39u\nh7GxG/r0sQJf8BwmLy53tFCkXHHlP3iwFcAmAJvw4MFW7N4dJXJ6Lf9Um0Uin+fvrvOV6F7KR2rs\n6Q8dOiT2NUW2afw01nAAcwF8iwMHSjD1uSE8cAisKiGuuD4+2L01hCnjwoX92LTpR/j6KlfLqwTJ\nyen1rkt9DqegB1k0gGYbSLQg7WVMX1+Ps/hltkWVY+OdIlFr0/TGkqLCQpEyMjIyKkNkTascY6cT\nwI4oKs6utS7C5acTVdVZ5OuvfevtNGtNq/yqI1OOPMH69qpr3QV/1dPquxa+uWkvom/IdlCBYPjO\ns3Tms1ZYVG177DIyxnBZpeNPuAxDwyVEeOPMXNKr1ziJ6lJUVEQ2bfpBaIagPh741hw1hxAZc+RV\nj4V39+5dZGdnQ0dHB2pqaiCEIDc3F/fv38fAgQMxduzYJrNGwsLCcOjQIRQUFKBfv36wt7fH9OnT\nm6y81obApOefEWeKjwtghK8DH81sLpeL7dsP4PLlOyCkAvr6bDx/Hoj09M2wQjTCEFUlAMYCrMUn\nIKlO+CdjG4AZQmXk5hYB8KuSFoQOHWwkqjMAEMISWrhTn9VvbXnlXJMhSctw7NgxsnTpUvLixQuR\na4WFhcTCwoLExcVJu0EihBASERFBhg0bRuLi4khmZiYJCwsjQ4cOJRcvXpQ4j7bc04ua6HMqe+w8\noqi4gNy4cUOsOa6hsbbKZ+wIMI8MHGhJjky1FOnhWVhMgIxqc+kf8xM33+7g4FU5HZdOgJ2kR48Z\nJCMjQ2ydxS0AknTarTVHwiVExsz7qkyZMoUkJibWeD0xMZGYm5tLrVICeDweMTY2Jj4+PkLp9vb2\nZNmyZRLn05ZFL34n3IrKefd0YmRkJXJdXBrgSaygIybiTQX5uLvu4+cF22UzMjKIg8NWIi8/lxGx\nouICkpSURFRUzAkwiWmEhg61Z4YTwuWnU/NeFsz7quTk5EBBoeZbO3TogJycHKlZHwIeP36MgoIC\nkaGDkZERvL29UVZWBkVFRamX2/oxAP/wiAf4889HIleF07gAwmGFXxCGv6qY9MuqRbwpBpAPRUUH\nXL3Kn4svLPRAfPxPSE8fAMAbQCwAoKzMEzNnOuHdO8Gsz3YA/LPyxE/hKcPGRgedOvE/Xx8PPPXe\nNwBJWoapU6cSS0tLkpeXJ3ItNzeXLFiwgEyZUv/YZnWRlJRE2Gw2YxYKSElJEZteE225pxc17+dX\n9qzpla+vVv5b9bpjpUnPX+5a3UsfggWEhaVVPmNPgC2kSxd9AnhW9voZBFhKgC0EEGc57Kz2/uM+\n/NbeO0sTme3pN23aBEdHR5iammLAgAFQUVEBi8UCl8tFZmYmWCwWfH19pd4gvX3LPwG1c+fOQumC\n98XFxVIvs6Wp7zx71Z7u3bt3+PXXCvz7ryOAt+Bviw0F0BfAsspPaAPIA/ACgBesoF15eiwBAIRC\nFWtRAoJVAFYBMAF/pd1/8fbtIABrKvNxBOAC4BCAb8Hfdss/FktNTbBbrirFGDp0G5ydPWnv3NJI\n2jo8fPiQeHt7E0tLS2Jubk4mT55MFi5cSLZu3VrrQRiN4fjx44TNZpPc3Fyh9LS0NMJms8nt27cl\nyqe19PSN7QEzMjKIouICIpj3BiyIaBy69VV6+Dmk4mN3TEKgSVjMGHwZAb4hwCICjCOAWw2+A+Gd\nbkZGViQjI0Poe3TuPJfY23u22968NmS2pweAwYMH49tvv23K9keETz75BIBojy5437Vr12atT1Mj\nyfTTo0ePsGKFNwD+oQ6DBg1irq1Y4Y2yMk/wx9Zp4I+lk1H1BFn+azdYQQNhCKp2PvxWEOyr/Iwv\n+D19XwBbAOyso/YqAKwBfI3w8AQcPeqI8HDBGH1vvXtyWYmg0xaRWPQAkJaWhtu3byM/Px+rV69G\nz5498ezZMygrK6Njx45Sr9yAAQMAAFlZWRgyZAiT/uTJEygoKKB///5SL7MlEReeqmrao0ePoKXl\nxmxw0dJyxJ9/boeysjJ2747C48dPwJ8v3wb+UlpPAKIRjazwDmFwqyb4PSAoBFD1/1EZ/HXxsQA+\ngG/G+wAA5OXtUFGxDlXNesABV68q4epVU8TGBtQY1KIuQdMAGU2MJOZAcXExWbFihdBKvPT0dEII\nIZs3byaTJ09ustNrJ02aRNzd3YXSli9fTmxtbSXOo7WY9/zVbsKm+KZNPzDXxW05NTKyqmJKO4gx\nwV2FHHlWMKjmtNOuEqbakgArCZBOlJQWVDoBP9ZHXn426d59MhEste3ceW7lEMCXCKYIP27TrXkF\nXl1DmNY+914fZPawi927d+P+/fvYsWMHUlNThXbU2dragsViISAgoJYcGo69vT1iYmJw9OhR5OTk\nIDQ0FNevX8f69eubpLyWhB+eyhH8njUWgCMTP64mcnIKqgwJHla7+gLAGfA30RyCFb5BGG5Ucdpp\nYC1iQRABYDqAP6Gv/wrGxjtha6sDI6Of8XFooI6KihC8fDkZ/N6fg3fvggD0APAV+FOE1TfciCI8\nhFFnhjA1w59STE5OA5fLrTN/igRI0jKMHz+eREREMO/ZbDbT0xPCd7gZGRlJvUUSEBkZSSZOnEi0\ntbXJzJkzSXJycr0+31p6+rp6waSkpErnnMASsCDLljlV6RVnV+mZBVN2gh7eXMxKu9zK+2dVTr+Z\nkkGD1jCfEbdSrvpUnJycaTUnId9SqMkJKUkvXtNhGG1xak9mV+RpaWkJnSNXXfR//PEH0dLSkn7t\npERrET0hte8Y45v3Vyu95vzXRkZWlTvclhJAiwAzK4W5lBGXFaqfLVc9xJVl5WfmVxOk8Eo5/ny9\nXZX3zkwdjI1XEHt7zzpPwZF0hqKoqEiiCDqtHZn13qupqeHevXs1niV37do1qKuri71GqR/Vo8uI\n8gWA/ZWv8/Hhwzv8+68c+Ke/AsByADfBN4sBK4QjDFXPlhuNtdhZ7Ww5PfA976ngDwkE/5f8lXKp\nqW64eHEo+E47APgfgHTwPfwfMHmykcQbXCSdo1dRUZEogg6lAUjSMvzwww9EV1eXHDlyhBQVFRE2\nm03u3LlDnjx5QgICAoimpibx8/Nr6gaqwbSmnr42MjIyKh1s/F5SSWkBMTCwFGOC81fKWcGsWg/f\nl7DwJxGdty+q8tmpIr1w9d6ZX4f0Jje528PKvZZ4NlmE1B3nqrS0FC4uLkhKShJ7fdKkSfDz85PZ\ndfDZ2dmYOHEikpKS0Ldv35auTqP4448/MHcuf7VbbKw3Nm78GRcvbsfH3jkfgE3ltNylKj38AqwF\nFwTdAGgAyK681x/8EFeCz4bC2DgTpqYGIodVCHpna2tzhIcnAKh5yk1ac+xtfb6+RZ7N+rQQt2/f\nJoGBgWTz5s1k8+bNJCgoiNy5c6dpmiMp0lZ6enE9340bN4R6f8CCWGGzmDH8usreeXaV3lywau9j\nr6+hsapRvWl76J2liUw68srLy0lcXFy9joaWNdqK6GvyfAvCRn/66Rhiha+FBB+KzwgLO6qZ8FU9\n8OkE0CVduxpJZalse5pjlwYyOU8vLy+PrVu3Ijs7uzkMD0qdZAJYWfmXCQBM2OiVCq8Rhh+FTPpN\nn2iAYDaAKADB4DvqqqIMQAs2NlPRqVNnWFh8jW++8cWjR4/g4REMD49giefHuVxuZfSepoPL5da7\nXpRqSNIyeHl5EUdHR1JaWtrUjVCT0FZ6+hs3bhB+ZJyPUXKSkpKItrazmO2xywgLFiQ8PLzaZyyI\ngsKUKu/nE2A+UVScS6qa+fyglpI765pjbr0tDh1kdsquY8eOKCgogJGREXR1daGioiI2qMb27dul\n3ihRPrJx48/g99YCp10wVqywgUnW9GrbYz+upd+7163aZ/Zg7dp9iIuzQk4OAWAMoAvKypah6qac\nsrJw8DferJMo7pzwSrttAP4HY+N0HD3qKzXnG42HJx0kEv3evXuZ15cvX67xPir65mducQ5+FBJ8\n9Yg3oqioqGDVqjnw9JwLvoCCpVwr/o47U9PYNudtbxM0m03RgrQV8776PP1KBYNqh0kuIB0V5wuZ\nv9X3toufe08niorzpWTeN53pTc176VBrT5+ZmYm9e/fi3r17IIRAU1MTK1aswNChQ5urTWp31DYv\nPWjQIPz553asWOGGKfkZ+PbhDeZ8+Bt6Y5E73QT3bb6sso/dvdYVcFXTra13YM+eCFy79icMDTVh\nZ+dSZS6+5m2tVetbdQ+9tbWj1OfXacQdKVFTa/Dw4UMyYsQIoqWlRaZPn05mzZpFhg8fTrS1tcml\nS5earVW6fPkyWbRoEdHT0yPjx48nrq6u5Pnz5/XKo7X09BL3ZAcPCp0PT2xtCamokJn6tsUeuamQ\nqXl6Z2dnMnHiRPLkyRMmraioiNjY2JAvv/yyWSr3xx9/EE1NTbJ9+3aSmZlJUlNTibm5eb3CXxPS\nekQv0Ry3jAi+tvrSuXrJkal5+uvXr2Pt2rVM9BqAb165ubkhMzMT+fn5TW6FHDx4EGw2G66urvj8\n888xevRoODk5IS0tDXl5eU1evswRHg6IOT0WchIfPkyh1GzeczgccuvWLZH0srIywmazyYMHD5q0\nNSKEkHfv3hEulyuUduXKFcJms8lff/0lcT6tpaev1SyWoR6+rvpS815yZMqRRwhBhw4dRNIFaaQZ\nzqPv1KkTOnXqJJSWnJyMTz75RCggZFuhRkeVjPbwkjoJqcNNtqhXYMyW5urVq4iIiICLi4vM7uhr\nLCL76WVU8AJq2v9fd1wASktRq+gLCwuRm5srlCbo4QsKCvDpp58KXevdu7fEBV+7dg02NjY1Xl+z\nZg1cXFyY91euXMH69ethbm6O1atXS1xOq0bGBU9pndQq+rVr19Z4bc2aNULvWSwW0tPTJS5YV1cX\niYmJNV4XxLwHgHPnzmHDhg2YNm0afHx8JC6jNSKIa8+fh7/EzMMLBP8oM7PGuPcUiiTUKHp7e/t6\nZcRisep1v5KSEvr161fnfWlpaXB2dsaSJUvg5uZWrzJaG4K49gtLjeGGMLAgKnhxce+p8Cn1otlc\nhg0gPz+fjBo1inh4eDQqn9bivTc2XiGyW+5YTw3GSy8uUKSx8YoWrjWlMciU914W8Pf3h6KiIuzs\n7FBYWCh07dNPP4WSklIL1axpmJKfATeECW2eiRysgFl0DE+RIjIt+qtXr+L58+cwNTUVubZjxw5Y\nWFi0QK2aiPBw/hi+iuCdFUtwP2wHc8uBA99BS8uRMe+VlBxx4ADd2UipHzIt+poCcbY5Kr30Aqfd\n8Z4aiBysgPthO4TG61U33ADAgQN0PE+pPzIt+naBmGm5WXv21GjSDxo0CBcu7Bd7jUKRBDpYbEno\nPDylBaBPV0tBBU9pIegT1hJQwVNaEPqUNTdU8JQWhj5pzQkVPEUGoE9bc0EFT5ER6BPXHFDBU2QI\n+tQ1NVTwFBmDPnlNCRU8RQahT19TQQVPkVFazRO4bds2cDgcpKU17amoUoEKniLDtIqn8O7du4iO\njq53oI4WgQqeIuPI/JNYUVEBDw8PzJkzp1ki8DYKKnhKK0Dmn8ZDhw6hpKQEK1asaOmq1A4VPKWV\nINNba/Py8hAQEICgoCCxMfhlBip4SitCpp9KLy8vTJo0CaNHj27pqtRMVBQVPKVV0SI9vSQx73V1\ndZGWlobTp083Y80agLs7FTylVdEioq8r5r2ioiIsLS2xadMmkeOQZM6ZN2cOsHs3sG4d4OtLBU+R\neVhE5lTEPzHX2toa8vLyQukVFRWQk5NDv379cPbsWYnzy87OxsSJE5GUlIS+fftKu7rAhw+ALPsc\nKDJLkz+bYpBJR96wYcMQHx8vlJafn49Vq1bB29sbenp6LVSzGqCCp7QiZFL0nTp1wuDBg4XSOnbs\nCADo27cvBgwY0BLVolDaBK1qANoqVuRRKDKOTPb04ujbt2+9DsikUCjiaVU9PYVCaTxU9BRKO4OK\nnkJpZ1DRUyjtDCp6CqWd0Wq89xRKS1NaWorNmzfj0aNHiImJEbkeEBCA+Ph4qKmpAQAsLCwwb948\nmJmZoVevXpCrXKLt6+sLdXX1Zq17VajoKRQJ+eGHHzB8+HA8evRI7HUWiwVra2ssXbpU5Nq+ffvQ\nqVOnpq6iRFDRU9oEsbGxSEtLw4sXL5CRkYGNGzciPj4ejx49gq+vL4YOHYpNmzbh+fPnKCsrg6Oj\nI4yNjREZGYn4+HjIyclh0qRJWLFiBR48eIDExEQ4OjoKleHi4oIXL14gLi6u3vWTpS0uVPSUNsO/\n//6Lw4cP49dff0VISAiOHTuGmJgYxMfHQ0FBAS9fvkRERATevHmDlJQUPH36FGfPnsWRI0dACMHi\nxYvx5ZdfgsPhgMPhiOTfuXNncLncWutw5swZJCUlQVFREf/973+ZTTQeHh7IycnByJEj8dVXXzXJ\n95cUKnpKm4DFYkFbWxsA0KNHD7DZbLBYLHz22Wd48+YNBg4ciLdv3+Kbb77B5MmTMX36dJw+fRr/\n/vsvrKysAADv3r1DTk4OevXq1aA6jB8/HoaGhtDX18epU6fg5eWFPXv2wNnZGcbGxujWrRvs7e1x\n9uxZTJkyRWrfvb5Q0VPaDFW3YisoCD/aHTt2RHR0NG7evIm4uDgkJyfDzMwMJiYm8PT0lLiM2vZ/\nDB8+nHltamoKX19fAMDs2bOZ9PHjx+Off/5pUdHL/JTdmzdvsHnzZowePRp6enpYvXo1nj592tLV\nosgYdY2Z//rrLxw/fhwjR46Eh4cHHj16BC0tLVy7dg0lJSUghMDb2xulpaUNLsfb2xspKSkAgLS0\nNGhoaKC4uBjLli1DSUkJAODGjRvQ0NCo57eTLjLf069fvx4sFgsHDx4EAGzduhVr165FfHw83XVH\nYWCxWMzzUPW5ELzu27cv/Pz8EB0dDTk5OaxevRq9evWCjY0Nli5dCnl5eUyaNAlKSko1OvJWrFiB\nZ8+e4dmzZ5g5cyaWL18OExMT+Pv7w9PTEwsWLMDmzZuxb98+yMvLY9u2bejatSvMzc1haWmJzp07\nQ1NTs0V7eQAAkWEuXLhAdHR0CJfLZdKePn1Kzp49S0pLSyXO5+nTp0RDQ4M8ffq0KapJoTSYlng2\nZbqnP3fuHAwNDaGsrMyk9e3bt9nCClEobRGZHtM/fPgQAwYMQGhoKKZMmQIjIyO4uLjUOW1CoVBq\nRqZFX1RUhDNnzuDhw4fw8/ODj48P7ty5AysrK1RUVLR09SiUVkmLmfd1xb63tbVFRUUFOnbsiJ07\nd4LFYkFLSwsdO3bEihUrcOnSJZiYmEhUlqCByMvLk0rdKRRpIXgmm7MTazHR1xX7/pNPPsGlS5fQ\nr18/IW+snp4eWCwW/vnnH4lFX1hYCABi10RTKLJAYWFhswV8bTHRKykpoV+/frXeM2DAAJHxO4/H\nAyEEXbt2lbgsbW1tREZGQlVVVSSWPoXSklRUVKCwsJBZTdgcyLT33tjYGJ6ennjx4gXjwb916xYA\ngM1mS5xPx44doa+v3yR1pFAaS3OHdJfJE24ElJWVYdasWVBVVYWHhweKiorg7u6OHj16IDIysqWr\nR6G0SmRa9ADf0eHl5YUrV65ATk4OkydPxnfffVcv855CoXxE5kVPoVCki0zP01MoFOlDRU+htDOo\n6CmUdgYVPYXSzqCip1DaGVT0FEo7o12JvjlDb23btg0cDgdpaWlSy/PKlSuwtLTEyJEjYWJiAjc3\nNxQVFTU4v7CwMEycOBHDhg3DtGnTcPLkSanVFeAvrgoMDMSUKVMwYsQIzJgxA4cPH5ZqGQBQXFwM\nY2NjmJmZSTXfW7duwdLSEjo6OjA2Noafn5/UQlkLfpupU6di+PDhmDBhAgIDA1FWViaV/Gul2cJ1\nyADLli0jVlZWJD09naSnpxNLS0sybdo0wuPxpFrOnTt3iLa2NuFwOOT69etSyfOPP/4gmpqaZPv2\n7SQzM5OkpqYSc3NzsmzZsgblFxERQYYNG0bi4uJIZmYmCQsLI0OHDiUXL16USn0JIcTDw4OMGjWK\nnDlzhmRlZZGDBw8SDodDfvvtN6mVQQgh27ZtI1paWsTMzExqeT58+JDo6uqS4OBgkp2dTU6dOkV0\ndXVJSEiIVPL38fEh+vr6JDExkTx9+pQkJCQQfX19sn37dqnkXxvtRvTSCr1VF+Xl5cTCwoJs3ryZ\nsNlsqYneycmJzJkzRygtPj6esNls8uzZs3rlxePxiLGxMfHx8RFKt7e3b3AjUp3Xr18TLS0tcvDg\nQaH0lStXEmtra6mUQQghd+/eJbq6usTV1ZWYmppKLd+NGzcSZ2dnobTLly+TO3fuSCX/0aNHi/z+\nPj4+ZMyYMVLJvzZkesONNGmu0FuHDh1CSUkJVqxYgejoaKnlu2PHDiaiqgAVFRUAwIsXL9CzZ0+J\n83r8+DEKCgowduxYoXQjIyN4e3ujrKwMioqKjarvJ598gosXL4oc5fTZZ5/h77//blTeAioqKuDh\n4YFVq1ZJJT8BPB4PKSkp8PHxEUofM2aM1MqQk5NjzrYT0KFDh2YJ9tpuxvTNEXorLy8PAQEB2LJl\nCzp06CC1fAGgU6dOQg0WACQnJ+OTTz7BoEGD6pXXv//+CwDo06ePUHq/fv3A4/Gk5udQVlZGx44d\nmffv379HamoqdHR0pJJ/REQE3r17Bzs7O6keG5WTk4O3b9+iU6dOcHJywtixYzF58mSEh4dLrYwl\nS5bgxIkTuHfvHgghePjwIU6cOAFLS0uplVET7aanF4TeGjVqFPz8/FBQUAAvLy9YWVnh+PHjUtln\n7+XlhUmTJmH06NHIzs6WQq1r5urVq4iIiICLi0u9e+W3b98C4B/TVBXB++LiYulUshqenp4oLi6G\nra1to/PKz8+Hv78/AgMDpd7ACjoCb29vrFy5EuvXr8f58+fx/fff4/3797Czs2t0GQ4ODigqKsKC\nBQugoKCA8vJyWFpawsHBodF510WbEH1Th96qK/81a9ZAV1cXaWlpOH36tNTrv2bNGri4uDDvr1y5\ngvXr18Pc3ByrV6+ud3nNDSEEW7ZswYkTJ7Br1646g6dIgpeXF8zMzGBkZCSFGgrz4cMHAMCsWbOw\naNEiAACHw8Hjx48RHh4uFdGHhobi9OnT2LFjB4YOHYq///4b33//PZSVleHs7Nzo/GujTYi+qUNv\n1ZW/oqIiLC0tsWnTJmacLUASs1OS+gs4d+4cNmzYgGnTpomMOSVFkF/1Hl3wXprblisqKuDm5oaE\nhAT4+/tLZVotOTkZaWlpUp9iFCD4/lpaWkLpenp6OH78OIqKivDZZ581OP+XL1/C398f3377LSws\nLADwg8KUlpZi69atsLGxQffu3Rv+BeqgTYi+qUNv1ZX/9evX8ezZM3h4eMDDw0Po2vLly9GvXz+c\nPXu2UfUH+EclOTs7Y8mSJXBzc6vz/poQRGrJysrCkCFDmPQnT55AQUEB/fv3b3De1fH09MS5c+ew\nb6arWRsAAAbaSURBVN8+qUUvSkhIwKtXrzB+/HgmTfB/qaWlBXt7e6xfv77B+ffr1w9ycnJ4+fKl\nUDqPxwPQ+EYxKysL5eXlGDhwoFB6//79UV5ejuzsbCp6aSCt0FviGDZsGOLj44XS8vPzsWrVKnh7\ne0NPT69R+QNAQUEBHBwcMG/evEYJHgC++OIL9OvXDxcuXMDEiROZ9JSUFIwZM0ZqY+SoqCjExsZi\n//79Ug1XtmHDBhGPfWRkJJKSkrB//34Ra6u+dOnSBXp6ejh37hzTEwPAzZs3MWDAACgpKTUqf8FM\nS2ZmJgwNDZn0x48fA0CDT82VmCafFJQRSktLyZQpU8iyZcvIw4cPmcUtS5YsaZLynj59KtV5+u++\n+46MGzeO5ObmkoKCAqG/kpKSeucXFxdHtLS0SFxcHMnOziYhISFEU1OT3Lp1Syr1LS4uJgYGBmTL\nli2ksLBQpM7Sxt/fX6rz9FeuXCFDhw4lISEh5N9//yVhYWFES0uLREdHSyV/R0dHMnbsWJKYmEiy\nsrLIuXPnyLhx48jq1aulkn9ttKvIOc0Zeis7O5uZ5jEwMGh0fhMnTkRubq5YH8GOHTuEeiRJOXz4\nMPbv34/8/Hx88cUXcHFxwYQJExpdV4A/5LG2thZ7jcViIT09XSrlCAgMDERcXBySkpKklmdiYiL8\n/f3x5MkTqKurw87ODgsWLJBK3u/evUNgYCBOnDgBLpcLFRUVmJubw8XFBV26dJFKGTXRrkRPoVDa\n0eIcCoXCh4qeQmlnUNFTKO0MKnoKpZ1BRU+htDOo6CmUdgYVPYXSzqCibwfcvXsXHA4HOjo6ePPm\nTUtXRyyurq5Sj3FHEQ8VfTsgJiYGvXr1wocPHxq8M23t2rUIDAyUcs2EaY6oMRQq+jZPaWkpTp8+\njWnTpkFPTw9xcXH1zoPH4zGbk5oSuji0eaCib+MkJCTg9evXmDJlCqZOnYo7d+4wu7kElJeXIzg4\nGJMnT4aOjg6mT5+OyMhIAPw9BJqamnj16hUCAwPB4XBw/fp1xMbGig3xHRAQAA6Hg9zcXCYtPT0d\n69atg4GBAXR1dTFjxgwcOnSo6b88RSxU9G2c2NhYDBgwAMOHD8fUqVOhoKAg0ttv374dQUFBWLp0\nKf73v/9h6tSp2LZtG/bv3w91dXUEBwcDABYuXIiYmBiR4BK1UVhYiOXLlyM/Px++vr7Yt28fDAwM\n4O3tjSNHjkj1u1Ikg4q+DZObm4tr165h1qxZAPjRc8eNG4djx44xASEKCwtx5MgRODg4YPny5dDX\n14eDgwOmTJmCY8eOoUOHDkygDTU1NWhpadVrF1h2djZGjBgBDw8PmJiYQF9fH+7u7lBXV8epU6ek\n/6UpddJugmi0R2JjY8Hj8RjRA/y4b+fPn8fly5dhbGyM1NRU8Hg8kVhzu3fvlkodRowYgT179gil\nsVgs9OnTB3l5eVIpg1I/qOjbKIQQxMXFQVNTE127dmVChY0YMQIdO3ZEXFwcjI2NUVBQAAAi4bWl\nyW+//YbffvsNjx8/xuvXr5n06iG4Kc0DFX0b5dq1a8jJyUFOTo7YiLFJSUl48+YNc+CCIAJsY6nu\ngQ8LC8OOHTtgZmaGdevWQVVVFXJycvjuu+9EYtBRmgcq+jZKTEwMOnTogJ9//lkk5l1mZiY8PT1x\n8uRJJh5bYWGhUKDGsrIylJSU4NNPPxWbv6CxKC8vF0ovLCwUen/8+HGoqakhKChIKF1WFwm1B6gj\nrw1SXFyMxMREmJmZYfz48TAyMhL6W7JkCXr37o24uDjo6uqCxWLh999/F8rjv//9LyZPngxCCLNo\nRuD8A8A0BlUP9SgrK8OlS5eEFtl8+PABPXr0EMo7JSUFWVlZQvkBdHFOc0F7+jbIyZMnUVJSgrlz\n59Z4z+zZsxEcHIx3795h4cKFiIyMhKqqKvT09HD9+nXEx8fDxcUFLBYLKioqkJeXR1JSEjgcDoYM\nGQIDAwN07doVoaGhUFFRQYcOHRAeHo6+ffvi2bNnTDmjR49GZGQkwsLCMGzYMNy8eRMnTpzA1KlT\ncfbsWSQnJzMRYeninGaiyUNvUpqdRYsWkXHjxpGKiooa7/n3338Jm80mP/74IykvLycBAQHE1NSU\naGlpkYkTJ5KIiAih+4ODg4menh7R09Mjp0+fJoQQkpKSQmbOnEmGDx9OJk2aRKKiokhUVBThcDgk\nOzubEMI/vdbFxYWMGjWKGBgYEEdHR5KXl0fu3LlDxo0bR0aNGkUyMzOJq6urVI+aptQMDYxJobQz\n6JieQmlnUNFTKO0MKnoKpZ1BRU+htDOo6CmUdgYVPYXSzqCip1DaGVT0FEo7g4qeQmln/B9xBxWe\n/z3xkQAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f069e449cc0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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RlZXF+++/r9EsLUnZR+8Xn2uD+Ph4evfuTVRUlFZvKWfNmsXo0aPlAJDklSnKuSlvg4uJ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\nk5NYv369VupfunSpaNu2rTh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"text/plain": [ "<matplotlib.figure.Figure at 0x7f069e201f60>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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HwC+/fKHVS6oz+6R//HEeuEHWvufLl6lYWY0lIaEl6rXfcmAVcA/YkJE2HWiI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\njXxra2sSEhKoUKECcXFxWFt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"text/plain": [ "<matplotlib.figure.Figure at 0x7f069ec2fda0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Train a bunch of linear model learners for comparison.\n", "brr, brr_preds, brr_mse, brr_r2 = cf.train_model(tts_data, model=lm.BayesianRidge)\n", "ard, ard_preds, ard_mse, ard_r2 = cf.train_model(tts_data, model=lm.ARDRegression)\n", "logr, logr_preds, logr_mse, logr_r2 = cf.train_model(tts_data, model=lm.LogisticRegression)\n", "enr, enr_preds, enr_mse, enr_r2 = cf.train_model(tts_data, model=lm.ElasticNet)\n", "svr, svr_preds, svr_mse, svr_r2 = cf.train_model(tts_data, model=SVR)\n", "\n", "# Likewise, plot the results\n", "cf.scatterplot_results(brr_preds, Y_test, brr_mse, brr_r2, DRUG, 'Bayesian Ridge', figsize=std)\n", "cf.scatterplot_results(ard_preds, Y_test, ard_mse, ard_r2, DRUG, 'ARD Regression', figsize=std)\n", "cf.scatterplot_results(logr_preds, Y_test, logr_mse, logr_r2, DRUG, 'Logistic Regression', figsize=std)\n", "cf.scatterplot_results(enr_preds, Y_test, enr_mse, enr_r2, DRUG, 'ElasticNet', figsize=std)\n", "cf.scatterplot_results(svr_preds, Y_test, svr_mse, svr_r2, DRUG, 'SVMs', figsize=std)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "# Neural Network with 3924361 learnable parameters\n", "\n", "## Layer information\n", "\n", " # name size\n", "--- --------- ------\n", " 0 input 1980\n", " 1 hidden1 1980\n", " 2 dropout1 1980\n", " 3 nonlinear 1980\n", " 4 output 1\n", "\n", " epoch train loss valid loss train/val dur\n", "------- ------------ ------------ ----------- -----\n", " 1 \u001b[36m12.59619\u001b[0m \u001b[32m2.44780\u001b[0m 5.14593 0.07s\n", " 2 \u001b[36m2.61336\u001b[0m \u001b[32m2.20166\u001b[0m 1.18699 0.07s\n", " 3 \u001b[36m2.20374\u001b[0m \u001b[32m1.99350\u001b[0m 1.10546 0.07s\n", " 4 \u001b[36m1.97106\u001b[0m \u001b[32m1.80216\u001b[0m 1.09372 0.07s\n", " 5 \u001b[36m1.63082\u001b[0m \u001b[32m1.63121\u001b[0m 0.99977 0.07s\n", " 6 \u001b[36m1.34881\u001b[0m \u001b[32m1.04006\u001b[0m 1.29686 0.07s\n", " 7 \u001b[36m1.10921\u001b[0m 1.74530 0.63554 0.07s\n", " 8 1.32155 1.81279 0.72901 0.07s\n", " 9 1.28494 1.08133 1.18829 0.07s\n", " 10 \u001b[36m0.96176\u001b[0m \u001b[32m0.78407\u001b[0m 1.22663 0.07s\n", " 11 \u001b[36m0.82693\u001b[0m 1.07843 0.76679 0.07s\n", " 12 0.84364 \u001b[32m0.75525\u001b[0m 1.11704 0.07s\n", " 13 \u001b[36m0.77994\u001b[0m \u001b[32m0.72542\u001b[0m 1.07517 0.07s\n", " 14 0.78055 1.01990 0.76532 0.07s\n", " 15 0.85883 0.73972 1.16102 0.07s\n", " 16 \u001b[36m0.68441\u001b[0m \u001b[32m0.72345\u001b[0m 0.94604 0.07s\n", " 17 \u001b[36m0.60534\u001b[0m 0.89260 0.67817 0.07s\n", " 18 0.69925 \u001b[32m0.68405\u001b[0m 1.02221 0.07s\n", " 19 0.63123 1.12371 0.56174 0.07s\n", " 20 0.70969 1.20189 0.59048 0.07s\n", " 21 0.77372 0.92757 0.83414 0.07s\n", " 22 0.62810 0.78056 0.80468 0.07s\n", " 23 0.62831 \u001b[32m0.67113\u001b[0m 0.93621 0.07s\n", " 24 \u001b[36m0.59683\u001b[0m 0.74995 0.79583 0.07s\n", " 25 \u001b[36m0.53863\u001b[0m 0.76603 0.70314 0.07s\n", " 26 0.58233 \u001b[32m0.65421\u001b[0m 0.89013 0.07s\n", " 27 \u001b[36m0.51782\u001b[0m 0.80816 0.64074 0.07s\n", " 28 \u001b[36m0.50229\u001b[0m 0.83151 0.60407 0.07s\n", " 29 \u001b[36m0.47911\u001b[0m \u001b[32m0.62637\u001b[0m 0.76489 0.07s\n", " 30 0.55700 0.85998 0.64769 0.07s\n", " 31 0.53940 0.81746 0.65986 0.07s\n", " 32 0.51662 0.68156 0.75800 0.07s\n", " 33 0.52149 0.85754 0.60812 0.07s\n", " 34 0.56740 \u001b[32m0.59056\u001b[0m 0.96078 0.07s\n", " 35 \u001b[36m0.43813\u001b[0m 0.66349 0.66034 0.07s\n", " 36 \u001b[36m0.43555\u001b[0m 0.79542 0.54757 0.07s\n", " 37 0.56313 0.63159 0.89161 0.07s\n", " 38 \u001b[36m0.40987\u001b[0m 1.32925 0.30835 0.07s\n", " 39 0.64758 \u001b[32m0.58973\u001b[0m 1.09811 0.07s\n", " 40 0.44714 0.99615 0.44887 0.07s\n", " 41 0.58663 0.66070 0.88789 0.07s\n", " 42 0.45422 1.06172 0.42782 0.07s\n", " 43 0.56654 0.59225 0.95659 0.07s\n", " 44 0.41088 \u001b[32m0.54602\u001b[0m 0.75251 0.07s\n", " 45 \u001b[36m0.37486\u001b[0m 0.60996 0.61457 0.07s\n", " 46 0.43776 0.55882 0.78336 0.07s\n", " 47 \u001b[36m0.33118\u001b[0m 0.57914 0.57185 0.07s\n", " 48 0.41880 0.62336 0.67185 0.07s\n", " 49 0.37904 0.60020 0.63152 0.07s\n", " 50 0.38822 0.72366 0.53647 0.07s\n", " 51 0.35622 0.59345 0.60026 0.07s\n", " 52 0.38635 0.78135 0.49446 0.07s\n", " 53 0.48609 0.64814 0.74998 0.07s\n", " 54 0.37296 \u001b[32m0.53673\u001b[0m 0.69487 0.07s\n", " 55 0.38835 0.58466 0.66423 0.07s\n", " 56 0.34947 \u001b[32m0.52374\u001b[0m 0.66726 0.07s\n", " 57 0.39255 0.70188 0.55929 0.07s\n", " 58 0.37721 0.55923 0.67451 0.07s\n", " 59 0.35831 0.53565 0.66894 0.07s\n", " 60 0.37375 \u001b[32m0.51780\u001b[0m 0.72180 0.07s\n", " 61 0.35872 \u001b[32m0.51549\u001b[0m 0.69589 0.07s\n", " 62 0.42699 0.63653 0.67081 0.07s\n", " 63 0.38235 0.52483 0.72853 0.07s\n", " 64 0.43413 0.95917 0.45261 0.07s\n", " 65 0.53459 0.53160 1.00563 0.07s\n", " 66 0.37151 0.54315 0.68399 0.07s\n", " 67 0.37556 0.56055 0.66998 0.07s\n", " 68 0.35315 0.51595 0.68447 0.07s\n", " 69 0.37059 0.73016 0.50755 0.07s\n", " 70 0.42203 0.52685 0.80104 0.07s\n", " 71 0.36744 0.68160 0.53908 0.07s\n", " 72 0.34438 0.53351 0.64549 0.07s\n", " 73 0.44812 0.62461 0.71745 0.07s\n", " 74 0.37179 0.53256 0.69812 0.07s\n", " 75 0.42547 \u001b[32m0.49620\u001b[0m 0.85746 0.07s\n", " 76 0.33751 \u001b[32m0.49065\u001b[0m 0.68788 0.07s\n", " 77 0.36155 0.67190 0.53810 0.07s\n", " 78 0.36810 0.51709 0.71187 0.07s\n", " 79 0.34594 0.68518 0.50489 0.07s\n", " 80 0.35537 0.55789 0.63699 0.07s\n", " 81 0.38007 0.54330 0.69955 0.07s\n", " 82 0.36145 0.49920 0.72406 0.07s\n", " 83 0.42204 0.57645 0.73213 0.07s\n", " 84 \u001b[36m0.32304\u001b[0m 0.51658 0.62535 0.07s\n", " 85 0.33949 0.49496 0.68588 0.07s\n", " 86 \u001b[36m0.29508\u001b[0m 0.53414 0.55243 0.07s\n", " 87 0.33479 0.54453 0.61483 0.07s\n", " 88 0.35145 0.49693 0.70724 0.07s\n", " 89 0.31850 0.50838 0.62651 0.07s\n", " 90 0.31377 0.52162 0.60152 0.07s\n", " 91 0.33111 0.51545 0.64237 0.07s\n", " 92 0.32534 0.62596 0.51976 0.07s\n", " 93 0.35229 0.50149 0.70249 0.07s\n", " 94 0.30529 0.56845 0.53706 0.07s\n", " 95 0.30885 \u001b[32m0.48059\u001b[0m 0.64264 0.07s\n", " 96 0.30145 0.51187 0.58891 0.07s\n", " 97 0.32058 0.59768 0.53638 0.07s\n", " 98 0.32557 \u001b[32m0.46862\u001b[0m 0.69474 0.07s\n", " 99 \u001b[36m0.29166\u001b[0m \u001b[32m0.45967\u001b[0m 0.63449 0.07s\n", " 100 0.29986 \u001b[32m0.45676\u001b[0m 0.65649 0.07s\n", " 101 0.33991 0.68016 0.49976 0.07s\n", " 102 0.39350 0.46596 0.84448 0.07s\n", " 103 \u001b[36m0.26712\u001b[0m 0.52593 0.50790 0.07s\n", " 104 0.29664 0.48116 0.61650 0.07s\n", " 105 0.30734 0.46170 0.66566 0.07s\n", " 106 0.32803 0.50327 0.65179 0.07s\n", " 107 0.30381 0.47441 0.64040 0.07s\n", " 108 0.33768 0.48795 0.69203 0.07s\n", " 109 \u001b[36m0.26173\u001b[0m 0.48383 0.54095 0.07s\n", " 110 0.30258 0.52863 0.57239 0.07s\n", " 111 0.31880 0.48946 0.65133 0.07s\n", " 112 0.34213 0.51887 0.65937 0.07s\n", " 113 0.32232 0.47234 0.68238 0.07s\n", " 114 0.31409 0.46047 0.68211 0.07s\n", " 115 0.35906 0.54228 0.66214 0.07s\n", " 116 0.26614 0.48122 0.55305 0.07s\n", " 117 \u001b[36m0.25452\u001b[0m 0.52550 0.48434 0.07s\n", " 118 0.29882 0.59615 0.50125 0.07s\n", " 119 0.33730 0.48307 0.69824 0.07s\n", " 120 0.29037 0.49153 0.59074 0.07s\n", " 121 0.29732 0.46691 0.63679 0.07s\n", " 122 0.32525 0.47820 0.68016 0.07s\n", " 123 0.28199 0.49108 0.57422 0.07s\n", " 124 0.29244 0.48115 0.60779 0.07s\n", " 125 0.29065 0.48069 0.60465 0.07s\n", " 126 0.32853 0.49480 0.66397 0.07s\n", " 127 0.31810 0.51558 0.61698 0.07s\n", " 128 0.29434 0.49517 0.59442 0.07s\n", " 129 0.29087 0.49291 0.59012 0.07s\n", " 130 0.25721 0.48442 0.53096 0.07s\n", " 131 0.28316 0.48931 0.57870 0.07s\n", " 132 \u001b[36m0.25189\u001b[0m 0.52286 0.48175 0.07s\n", " 133 0.29358 0.48265 0.60828 0.07s\n", " 134 0.26225 0.46339 0.56594 0.07s\n", " 135 0.31997 0.50682 0.63133 0.07s\n", " 136 0.39413 0.53336 0.73895 0.07s\n", " 137 0.29383 0.49886 0.58900 0.07s\n", " 138 0.31138 0.49581 0.62803 0.07s\n", " 139 0.33844 0.47778 0.70836 0.07s\n", " 140 0.36338 0.52407 0.69337 0.07s\n", " 141 0.29554 0.47662 0.62007 0.07s\n", " 142 0.27940 0.53775 0.51957 0.07s\n", " 143 0.36348 0.50432 0.72074 0.07s\n", " 144 0.28901 0.48612 0.59453 0.07s\n", " 145 0.27960 0.47362 0.59034 0.07s\n", " 146 0.29278 \u001b[32m0.45288\u001b[0m 0.64649 0.07s\n", " 147 0.37592 0.59486 0.63195 0.07s\n", " 148 0.35139 0.49322 0.71244 0.07s\n", " 149 0.31349 0.47427 0.66099 0.07s\n", " 150 0.30365 0.49870 0.60889 0.07s\n", " 151 0.32854 0.46190 0.71127 0.07s\n", " 152 0.25977 0.45959 0.56522 0.07s\n", " 153 0.32146 0.47571 0.67574 0.07s\n", " 154 0.27254 0.45905 0.59369 0.07s\n", " 155 0.26707 0.49513 0.53939 0.07s\n", " 156 0.27515 0.46121 0.59659 0.07s\n", " 157 0.28296 0.48625 0.58193 0.07s\n", " 158 0.30230 0.52176 0.57939 0.07s\n", " 159 \u001b[36m0.24874\u001b[0m 0.47795 0.52043 0.07s\n", " 160 \u001b[36m0.24423\u001b[0m 0.48796 0.50051 0.07s\n", " 161 0.25614 0.50301 0.50922 0.07s\n", " 162 0.29070 0.50220 0.57886 0.07s\n", " 163 \u001b[36m0.22858\u001b[0m 0.48053 0.47568 0.07s\n", " 164 0.24979 0.47686 0.52383 0.07s\n", " 165 0.36511 0.53661 0.68039 0.07s\n", " 166 0.32926 0.52670 0.62514 0.07s\n", " 167 0.29171 0.52215 0.55867 0.07s\n", " 168 0.27596 0.52302 0.52763 0.07s\n", " 169 0.24730 0.51435 0.48080 0.07s\n", " 170 0.27097 0.48959 0.55347 0.07s\n", " 171 0.25289 0.52938 0.47772 0.07s\n", " 172 0.26009 0.46083 0.56440 0.07s\n", " 173 \u001b[36m0.22799\u001b[0m 0.48633 0.46881 0.07s\n", " 174 0.24615 0.48504 0.50749 0.07s\n", " 175 0.27997 0.52898 0.52926 0.07s\n", " 176 0.32375 0.49756 0.65067 0.07s\n", " 177 0.30006 0.51380 0.58400 0.07s\n", " 178 0.29888 0.48548 0.61563 0.07s\n", " 179 0.38663 0.48759 0.79294 0.07s\n", " 180 0.25072 0.52533 0.47726 0.07s\n", " 181 0.27532 0.48335 0.56961 0.07s\n", " 182 0.28573 0.50802 0.56243 0.07s\n", " 183 0.31048 0.50526 0.61450 0.07s\n", " 184 0.30612 0.47168 0.64899 0.07s\n", " 185 0.29209 0.48177 0.60628 0.07s\n", " 186 0.24887 0.47473 0.52423 0.07s\n", " 187 0.26107 0.46913 0.55649 0.07s\n", " 188 0.33288 0.46657 0.71346 0.07s\n", " 189 0.29698 0.48138 0.61693 0.07s\n", " 190 0.28584 0.47570 0.60088 0.07s\n", " 191 0.29091 0.47008 0.61885 0.07s\n", " 192 0.24053 0.48271 0.49830 0.07s\n", " 193 \u001b[36m0.21729\u001b[0m 0.51892 0.41874 0.07s\n", " 194 0.24751 0.49008 0.50503 0.07s\n", " 195 0.27505 0.50004 0.55005 0.07s\n", " 196 0.24941 0.48602 0.51316 0.07s\n", " 197 0.26901 0.53286 0.50483 0.07s\n", " 198 0.30742 0.50129 0.61325 0.07s\n", " 199 0.32295 0.46362 0.69658 0.07s\n", " 200 0.26173 0.53044 0.49343 0.07s\n", " 201 0.32095 \u001b[32m0.44999\u001b[0m 0.71324 0.07s\n", " 202 0.35156 0.46409 0.75752 0.07s\n", " 203 0.26148 0.50556 0.51720 0.07s\n", " 204 0.30855 0.52652 0.58602 0.07s\n", " 205 0.24949 0.45659 0.54643 0.07s\n", " 206 0.27250 0.49308 0.55265 0.07s\n", " 207 0.29511 \u001b[32m0.44701\u001b[0m 0.66018 0.07s\n", " 208 0.27865 0.48690 0.57229 0.07s\n", " 209 0.26093 0.46693 0.55882 0.07s\n", " 210 0.25810 0.46067 0.56026 0.07s\n", " 211 0.28781 0.50254 0.57270 0.07s\n", " 212 0.29430 0.47349 0.62156 0.07s\n", " 213 0.25910 0.47843 0.54157 0.07s\n", " 214 0.28201 0.49659 0.56790 0.07s\n", " 215 0.28092 0.50977 0.55106 0.07s\n", " 216 0.33160 0.47624 0.69629 0.07s\n", " 217 0.31121 0.46605 0.66776 0.07s\n", " 218 0.30275 \u001b[32m0.43474\u001b[0m 0.69638 0.07s\n", " 219 0.28251 0.45676 0.61851 0.07s\n", " 220 0.31075 0.46811 0.66383 0.07s\n", " 221 0.26768 0.48671 0.54999 0.07s\n", " 222 0.31973 0.49929 0.64038 0.07s\n", " 223 0.36027 0.50041 0.71995 0.07s\n", " 224 0.27523 0.50105 0.54930 0.07s\n", " 225 0.28807 0.50022 0.57589 0.07s\n", " 226 0.28330 0.57115 0.49602 0.07s\n", " 227 0.30008 0.49823 0.60229 0.07s\n", " 228 0.27906 0.48714 0.57284 0.07s\n", " 229 0.28126 0.47674 0.58996 0.07s\n", " 230 0.30869 0.47263 0.65314 0.07s\n", " 231 0.25575 0.46424 0.55089 0.07s\n", " 232 0.25911 0.50167 0.51649 0.07s\n", " 233 0.28065 0.49366 0.56851 0.07s\n", " 234 0.28614 0.50599 0.56551 0.07s\n", " 235 0.31691 0.52034 0.60904 0.07s\n", " 236 0.29490 0.48539 0.60755 0.07s\n", " 237 0.26235 0.45335 0.57870 0.07s\n", " 238 0.31420 0.48488 0.64800 0.07s\n", " 239 0.26486 0.49171 0.53864 0.07s\n", " 240 0.27799 0.48202 0.57670 0.07s\n", " 241 0.30912 0.48986 0.63104 0.07s\n", " 242 0.28158 0.49787 0.56557 0.07s\n", " 243 0.26855 0.48558 0.55305 0.07s\n", " 244 0.32544 0.53980 0.60289 0.07s\n", " 245 0.30296 0.45735 0.66244 0.07s\n", " 246 0.25591 0.46364 0.55196 0.07s\n", " 247 0.31454 0.47191 0.66652 0.07s\n", " 248 0.26459 0.46784 0.56557 0.07s\n", " 249 0.27102 0.45999 0.58919 0.07s\n", " 250 0.31353 0.50476 0.62115 0.07s\n", " 251 0.29766 0.49508 0.60123 0.07s\n", " 252 0.28482 0.46529 0.61213 0.07s\n", " 253 0.27656 0.45961 0.60173 0.07s\n", " 254 0.26939 0.45670 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997 0.28725 0.43183 0.66518 0.07s\n", " 998 0.27260 0.42898 0.63546 0.07s\n", " 999 0.23333 0.42239 0.55240 0.07s\n", " 1000 0.21655 0.41123 0.52660 0.07s\n" ] }, { "data": { "text/plain": [ "NeuralNet(X_tensor_type=None,\n", " batch_iterator_test=<nolearn.lasagne.base.BatchIterator object at 0x7f0704a124a8>,\n", " batch_iterator_train=<nolearn.lasagne.base.BatchIterator object at 0x7f0704a12ac8>,\n", " custom_score=None, dropout1_p=0.4,\n", " hidden1_nonlinearity=<function tanh at 0x7f06bbcc3510>,\n", " hidden1_num_units=1980, input_shape=(None, 1980),\n", " layers=[('input', <class 'lasagne.layers.input.InputLayer'>), ('hidden1', <class 'lasagne.layers.dense.DenseLayer'>), ('dropout1', <class 'lasagne.layers.noise.DropoutLayer'>), ('nonlinear', <class 'lasagne.layers.dense.NonlinearityLayer'>), ('output', <class 'lasagne.layers.dense.DenseLayer'>)],\n", " loss=None, max_epochs=1000, more_params={},\n", " objective=<function objective at 0x7f06bb7509d8>,\n", " objective_loss_function=<function squared_error at 0x7f06bbce6d08>,\n", " on_epoch_finished=[<nolearn.lasagne.handlers.PrintLog object at 0x7f06b818bfd0>],\n", " on_training_finished=[],\n", " on_training_started=[<nolearn.lasagne.handlers.PrintLayerInfo object at 0x7f06b818bb00>],\n", " output_nonlinearity=None, output_num_units=1, regression=True,\n", " train_split=<nolearn.lasagne.base.TrainSplit object at 0x7f0704a12240>,\n", " update=<function nesterov_momentum at 0x7f06bbcf0598>,\n", " update_learning_rate=0.01, update_momentum=0.95,\n", " use_label_encoder=False, verbose=1,\n", " y_tensor_type=TensorType(float32, matrix))" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Let's now try a neural network. \n", "# Neural Network 1 Specification: Feed Forward ANN with 1 hidden layer.\n", "\n", "x_train = X_train.astype(np.float32)\n", "y_train = Y_train.astype(np.float32)\n", "x_test = X_test.astype(np.float32)\n", "y_test = Y_test.astype(np.float32)\n", "\n", "net1 = NeuralNet(\n", " layers=[ # three layers: one hidden layer\n", " ('input', layers.InputLayer),\n", " ('hidden1', layers.DenseLayer),\n", " ('dropout1', layers.DropoutLayer),\n", " #('hidden2', layers.DenseLayer),\n", " #('dropout2', layers.DropoutLayer),\n", " ('nonlinear', layers.NonlinearityLayer),\n", " ('output', layers.DenseLayer),\n", " ],\n", " # layer parameters:\n", " input_shape=(None, x_train.shape[1]), # \n", " hidden1_num_units=math.ceil(x_train.shape[1]), # number of units in hidden layer\n", " hidden1_nonlinearity=nonlinearities.tanh,\n", " dropout1_p = 0.4,\n", " #hidden2_num_units=math.ceil(x_train.shape[1] / 2),\n", " #dropout2_p = 0.5,\n", " output_nonlinearity=None, # output layer uses identity function\n", " output_num_units=1, # 1 target values\n", " \n", " # optimization method:\n", " update=nesterov_momentum,\n", " update_learning_rate=0.01,\n", " update_momentum=0.95,\n", "\n", " regression=True, # flag to indicate we're dealing with regression problem\n", " max_epochs=1000, # we want to train this many epochs\n", " verbose=1,\n", " )\n", "net1.fit(x_train.values, y_train.values)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/ericmjl/anaconda3/lib/python3.4/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", " if self._edgecolors == str('face'):\n" ] }, { "data": { "image/png": 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Tyhb4Qq/1m46O7poF4TmNUdfSTihwE23aDBJNm47QivFVKsfojD1zG5eW9FxL\nuWHwqZsnT57w2Wef8Z///IczZ85oyp8+fUr//v159913WbZsGZUqVTLoi+TJkycsWbKEjz76iEaN\nGhEfH691vFYt3RUZkvzj5TWI1avH8uBBm8ySG8CnwFGdcxMTE1mwYCNnz/6BnV1D4uIEKg+wR+Z6\n1Kw7jtdnLA4IfDh/HsAHVeywGeDN++9v0Wm5c9ug2ShXwRQ1+qg/ICBAtG3bVoSEhGiVp6eni507\nd4r27dsLf3//V3u1vISzZ8/qBGGof5RKpd71yJb15SQkJIimTcdmafXG5NrqNWs2IQev7wXhTqts\nLepAocAthxZ7daFmXihpGDyCydnZWezatSvX46GhoaJdu3b6W1jESLG+nJy6wdOmLdbrPPAV7njo\nCNWi1mAB/87sIq/WRCjllEPpdcbgS+QSExOpW7dursctLS3lqptSxtmzf+iR1S8Jd3Zr7T1zsnlr\n7s3pzJ59Uyhf/ibggWrFzByaNJnB9OkKvL1DX7tpF4Ogj/oHDBggfH19cz0+bdo00a9fP/1fJ0WM\nbFlfTnbnTfbg+6xZBFXd4KsCvhDuvK0T6+szJ1AIoX9r/bpjcAfTqFGj+PTTT4mKiqJ9+/aYmZnx\n/Plz4uPjOXLkCFevXmXp0qWF/V6RFBJZnTdHj57nxAk/QAloxwubmZkRHj6F9u296fWgPcFEZnEm\njWIsc5mj+CHX+xjaAfnaoe8bYN++faJ37946jh4XFxexZ8+eAr1dChvZsuqPbot4WjRo0FOzdE21\npUXOq2fMzbVDA0vjVIuhyc+zqRAi2x6OeRAbG0tcXBwmJibUqVOnRIw7oqOj6datG4cPH6Z+/frF\nbY5RkT3JGZAlrO8W8AWqqCRVQP26TukMORyWpUXtx8QyD3me/h7QB1vbIK2tMLInUMvpniXhGSos\n8vVsFvqrwwiQLWvO5LQedfr0L8X06V+KadMWiwYNemq1sqoWFa0WtYyiS45rT3NrRWWLq41Bxqzu\n7u7MmzePhg0b4u7u/tKtFkXmZsohISGv/IaRFD0LFmzUWY969epngB1vv32F9PQXna4XCbhVnG3Z\njq3VypL+S3u0QwjhxIlUlEpP9u3z5scfzwEvWtCCrqF9ndE7I7/Io7ec13GJcZGYmMimTT8DQ7Id\nsQP6cP36GWA54Ic7Sp2F4/f6tadd8jN+/iUSVbQTqHYfjwK+JD4e2rWbTXr6auDFKhnJq5OrWDdv\n3pzj7xIihHIRAAAgAElEQVTjJqfxoLpMtQxOQaVKlXj27ElmwPwCVJkaQLXyZR6qDIWrAQvcsdFa\nOJ7V6yuEApjLi6Vx3qgyRSiBNZlC1W5BX7Z5suTl6N2ySoyfnNZ8/vDDJPr3X8mVK2NRrYpRCcPM\nbArwBKgFTAXiUAn1hbNH1fXNKtS2jGUuJmXG0bfvHCZNWoZuy/xyZIzvq5OrWJVKJQqFQtO9zTpm\nzV6mHrNevXq1MG2V5EFO40FPz5lcufJvVF7dBZpjiYlfoRpr9gFOATVQKKYjxJeAc+Yyt/9kEepA\nxnIbQT/S09vh5OTLs2etUbXG6pbRD1PT//Ls2WjAmQoVJpGSolo2l7UFzS1YX/JychVr9lxLly9f\nJjo6Gjs7O8zNzRFCcO/ePa5cuULjxo3p2LFjoRkZHBzM5s2biYuLw9LSkgkTJvDee+8V2v1KE8+f\np6BqUZvlcNQECAI2ACCED6amAQwz+YvAJ39kE+pzBHWAtQA8e+bHC6dSaGZ9k5gwYQ+VKqk+57Sv\njaQA6ONe3r17txg8eLBISkrSORYfHy/69+8vwsLC8uGw1p8tW7aIFi1aiLCwMHHr1i0RHBwsmjVr\nJk6cOKF3Ha/L1E1CQkJmKtAvBHwhlMoxYuJE/8xpkgShSvn5Yj0pfKkz7eLOgGzTM22FgoUCOuYQ\nxP+lVp2v+zTMq2DwQP7Vq1czbNiwHPMs1axZkwkTJrBmzZrCeJGwdu1aPvnkE/r370/Dhg0ZOnQo\nXbt2Ze3atQa/X2lAoSiLKnjeA4WiLBUrVsw8olpDCiFUrPgBcBfQ9uC7s4NgQrUyPIzlNIL/A/rl\ncDcBjKVSJXemTdsig/MLGb0cTHfv3qVs2dxPLVeuHHfv3jWYUWpu3rxJXFycThfb0dGRgIAAUlNT\nKV++vMHvW5wUJLpn+fLtmbl7X8ybvvHGVMzMLmaOUaF69YskJTVGJdY9wGlgTaZQJ2uEuqFMbcam\nq9OFJgJPgfGovMSgWkheh1q1/o/Tp9fQpEkTg30PSc7oJVZLS0vWrl1Ls2bNsLCw0Dp2//59Vq1a\nVShhfLdv3wZUe+1ktycjI4M7d+7oPCQlmYJm8FMtU0zixRjSmTNnlMBfqJxJySQlXUM1tWIGBABf\n4o4XwezQblHTZ4JiPIgFvPAiJ1Gz5khcXdtjavoWpqaV8fLSzdN048YNOnTwJS5u8St9D0nO6CXW\n6dOnM2nSJJydnbGyssLMzAyFQkFiYiK3bt1CoVCwePFigxv35MkTQHe1hvrz48ePDX7P4kSf6J4b\nN27g6RkAwMaNs7VeVg8fxqOaMw3KLJkItADmo0qn4gccyDzmA8zDnfRMoaqdSfVeZMoXC1B1qcNR\nZxV88GA9ZmahuXpzExMTcXScSnz8upd+D0n+0Uuszs7O/PDDD+zYsYPff/+dBw8eIISgevXqfPLJ\nJ3z44YfY2NgUtq2lnpwW8Gctu3HjBjY2MzXTITY2k/jjjwVUr16d5cu3s2PHCVStqrr3Mw9VdFEy\ncAbYluXY3Cwt6ovIpLFUQaD2BzgD2TNaJunuCpeF5cu3Ex/f6VW+viQP9A6KeOutt5g1a1Zh2qJD\n1apVAd0WVP1ZndO4tKBQCLLPWyoUjTTHPT0DMoWqElxKykrc3afzzz9mmS1y1r9TIqru66bMz7dR\ndZHVW1rsyCbUtozlPoKKgG/mNeOB0byIckqiTJnZnDixmhMnXta97UPWyChz82l4eS1/9T+MBNBz\nM2U158+f5+uvv8bf35+YmBhANWZNTk4uFOOsrKwAiIqK0ir/66+/KFu2LA0aNCiU+xYXqh2987fL\n9927cVm6zgOACagSZX+DSvQWmT9LUYnvS9xx1UrArRJqZQSVMq9TX7MaleDHAiFUqjQmSwhhzom2\nvbwGZW5+rLqmVq3RnDrlK8erBkAvsT558oThw4fj7u7OkiVL2Lp1Kw8fPgRgzZo19O3bV7OznCFp\n1KgRlpaW/Pzzz1rlx48fp0OHDpQrV87g9yxOXjzoOe/y/dVXE4BxvMhcPw4XF/ssNXwH+KMSek7R\nZBVxp2I2Z1JTxvIDgm2opmKStK5wdKzOtGl78PauwvjxffP8DupwQm/vo3h7V+HatY2lyglYrOgz\ncRsQECDatGkjwsLCRFJSkrC2ttYk946KihIuLi7i888/L8jccK6EhYUJGxsbERYWJqKjo8XatWtF\n8+bNxW+//aZ3HSUpKOJlmy+psjhczcwWqPp92rTFWdaHOmc5vljA8Mzy0wLaCnfa5ZDhYZxQ75+q\nOre7JsihadOxr0Wi7eLE4DmYDhw4gJeXV47bPVpaWjJx4kQWLFjAvHnzDP4y6d+/P0+fPiUwMJDY\n2FgaNWrEqlWrsLe3z/viEkh+42YrVaqkCYwPCIgnPX0OoN4XdRSqMecj3PmAYGZlWz0ThCAe1Sob\n9T2fANOAlvTr10Sr+yqD8IsXvcSakJCAtbV1rsfr1avHo0ePDGZUdtzc3HBzcyu0+ksKHh4uLFr0\nwhtcocIkPDwWkJSk8tCWL2/Ks2fq3dwSAUtUGxlrBzyovL5BWfZHfYyqWz0asEK1jvU5pqahZEcG\n4Rcfeo1Zzc3N+f3333M9fvbsWZ1gCYnhCQk5mMUbbEFKykq++upbbGxmcuLEAp4965Pl7O2ohHpA\nK8ODKij/QWaLGotqLvYpqqCJZ4AXLzaF0t7JTVK86NWy9u7dmxUrVmBqaoqLiwsAqamp3L59m/Dw\ncNasWcPIkSML1VBJzoSHnyQlZRkqp5IpqmwN84DHmS2qVzavbwaC+6jEeTXzXGVmbR44Oc3E2bmN\n7OIaIXqJddKkSdy6dQtfX198fX0B+PjjjzXHu3fvzoQJEwrFQMkLcsqyULFiNaKiXiwqh1nANNx5\nTjDbs7SoDRlLEwSTUa1t/QvVsjntjYydndvIbq6RopdYK1SowKpVq7h06RK//PILsbGxANSpU4eO\nHTvSsmXLQjVSoiInB4+PzxouXBjJi8ik6bgzgmBOZxFqPcayB0ENwIeKFeNJTp4PRKByJqlCRWXw\ngnGTp1jT09MJDw+nY8eO2NnZYWdnVxR2SXIhu4NHu6uaiDsTMoWq6voefcuWL9JtELe8AGjcuCZd\nu77L+vXLUOVPeh8YganpU06d+lp2fY2YPB1MZcqUYe7cuURHRxeFPZJ8ogqkWADE4s54gjmqFeu7\nrXMf7tzNALYAW4iOTkeIDKAOMB3Vyps2TJzYWwYvGDl6eYM/+ugjNm7cSGpqamHbI8kn6q7xrn7/\nzhyjZp1H/YIDh34lNfWFBzk1dSXffnsBVTjgl0A1lMr7zJgxvPi+hEQv9BqzVqxYkbi4OBwdHbG3\nt8fMzCzHxegLFiwwuIGSvDGLiGDAjy/SxarmUediY7uIqlVrkS20mmfPXMiaPjSnHcglxodeYv36\n6681v588eTLX86RYCx+d9awnT8KwYapoQSDZw4N7Vm2Zo/gBLy9vkpKSsLF5kWWwTJkxpKcv1KpT\n7u5WQiiC8MdipyTFBr+MyMjIzL1TVbG5w8u2ERmKF7G+YtQoIdLTc7zOyclTODl5igsXLsj4XiPC\nYLHBt27d4uuvv+b3339HCEH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"text/plain": [ "<matplotlib.figure.Figure at 0x7f06b4246fd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# And now let's also look at whether it looks good or not.\n", "nn1_preds = net1.predict(x_test)\n", "nn1_mse = float(mean_squared_error(nn1_preds, y_test))\n", "nn1_r2 = float(sps.pearsonr(nn1_preds, y_test.reshape(y_test.shape[0],1))[0][0])\n", "\n", "cf.scatterplot_results(nn1_preds, y_test, nn1_mse, nn1_r2, DRUG, 'Neural Net', figsize=std)\n", "plt.savefig('figures/{0} neural_net_poster.pdf'.format(DRUG), bbox_inches='tight')" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['models/FPV neural_network.pkl',\n", " 'models/FPV neural_network.pkl_01.npy',\n", " 'models/FPV neural_network.pkl_02.npy',\n", " 'models/FPV neural_network.pkl_03.npy',\n", " 'models/FPV neural_network.pkl_04.npy',\n", " 'models/FPV neural_network.pkl_05.npy',\n", " 'models/FPV neural_network.pkl_06.npy',\n", " 'models/FPV neural_network.pkl_07.npy']" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Save the models to disk\n", "\n", "joblib.dump(rfr, 'models/{0} random_forest.pkl'.format(DRUG))\n", "joblib.dump(abr, 'models/{0} adaboost.pkl'.format(DRUG))\n", "joblib.dump(etr, 'models/{0} extratrees.pkl'.format(DRUG))\n", "joblib.dump(gbr, 'models/{0} gradient_boost.pkl'.format(DRUG))\n", "joblib.dump(bgr, 'models/{0} bagging.pkl'.format(DRUG))\n", "joblib.dump(net1, 'models/{0} neural_network.pkl'.format(DRUG))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.3" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
nkmk/python-snippets	notebook/pandas_options.ipynb	1	28788	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "import pprint" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.23.0\n" ] } ], "source": [ "print(pd.__version__)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "60\n" ] } ], "source": [ "print(pd.options.display.max_rows)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pd.options.display.max_rows = 100" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "100\n" ] } ], "source": [ "print(pd.options.display.max_rows)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['compute', 'display', 'html', 'io', 'mode', 'plotting']\n" ] } ], "source": [ "print(dir(pd.options))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['chop_threshold',\n", " 'colheader_justify',\n", " 'column_space',\n", " 'date_dayfirst',\n", " 'date_yearfirst',\n", " 'encoding',\n", " 'expand_frame_repr',\n", " 'float_format',\n", " 'html',\n", " 'large_repr',\n", " 'latex',\n", " 'max_categories',\n", " 'max_columns',\n", " 'max_colwidth',\n", " 'max_info_columns',\n", " 'max_info_rows',\n", " 'max_rows',\n", " 'max_seq_items',\n", " 'memory_usage',\n", " 'multi_sparse',\n", " 'notebook_repr_html',\n", " 'pprint_nest_depth',\n", " 'precision',\n", " 'show_dimensions',\n", " 'unicode',\n", " 'width']\n" ] } ], "source": [ "pprint.pprint(dir(pd.options.display))" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "compute.use_bottleneck : bool\n", " Use the bottleneck library to accelerate if it is installed,\n", " the default is True\n", " Valid values: False,True\n", " [default: True] [currently: True]\n", "\n", "compute.use_numexpr : bool\n", " Use the numexpr library to accelerate computation if it is installed,\n", " the default is True\n", " Valid values: False,True\n", " [default: True] [currently: True]\n", "\n", "display.chop_threshold : float or None\n", " if set to a float value, all float values smaller then the given threshold\n", " will be displayed as exactly 0 by repr and friends.\n", " [default: None] [currently: None]\n", "\n", "display.colheader_justify : 'left'/'right'\n", " Controls the justification of column headers. used by DataFrameFormatter.\n", " [default: right] [currently: right]\n", "\n", "display.column_space No description available.\n", " [default: 12] [currently: 12]\n", "\n", "display.date_dayfirst : boolean\n", " When True, prints and parses dates with the day first, eg 20/01/2005\n", " [default: False] [currently: False]\n", "\n", "display.date_yearfirst : boolean\n", " When True, prints and parses dates with the year first, eg 2005/01/20\n", " [default: False] [currently: False]\n", "\n", "display.encoding : str/unicode\n", " Defaults to the detected encoding of the console.\n", " Specifies the encoding to be used for strings returned by to_string,\n", " these are generally strings meant to be displayed on the console.\n", " [default: UTF-8] [currently: UTF-8]\n", "\n", "display.expand_frame_repr : boolean\n", " Whether to print out the full DataFrame repr for wide DataFrames across\n", " multiple lines, `max_columns` is still respected, but the output will\n", " wrap-around across multiple \"pages\" if its width exceeds `display.width`.\n", " [default: True] [currently: True]\n", "\n", "display.float_format : callable\n", " The callable should accept a floating point number and return\n", " a string with the desired format of the number. This is used\n", " in some places like SeriesFormatter.\n", " See formats.format.EngFormatter for an example.\n", " [default: None] [currently: None]\n", "\n", "display.html.border : int\n", " A ``border=value`` attribute is inserted in the ``<table>`` tag\n", " for the DataFrame HTML repr.\n", " [default: 1] [currently: 1]\n", "\n", "display.html.table_schema : boolean\n", " Whether to publish a Table Schema representation for frontends\n", " that support it.\n", " (default: False)\n", " [default: False] [currently: False]\n", "\n", "display.html.use_mathjax : boolean\n", " When True, Jupyter notebook will process table contents using MathJax,\n", " rendering mathematical expressions enclosed by the dollar symbol.\n", " (default: True)\n", " [default: True] [currently: True]\n", "\n", "display.large_repr : 'truncate'/'info'\n", " For DataFrames exceeding max_rows/max_cols, the repr (and HTML repr) can\n", " show a truncated table (the default from 0.13), or switch to the view from\n", " df.info() (the behaviour in earlier versions of pandas).\n", " [default: truncate] [currently: truncate]\n", "\n", "display.latex.escape : bool\n", " This specifies if the to_latex method of a Dataframe uses escapes special\n", " characters.\n", " Valid values: False,True\n", " [default: True] [currently: True]\n", "\n", "display.latex.longtable :bool\n", " This specifies if the to_latex method of a Dataframe uses the longtable\n", " format.\n", " Valid values: False,True\n", " [default: False] [currently: False]\n", "\n", "display.latex.multicolumn : bool\n", " This specifies if the to_latex method of a Dataframe uses multicolumns\n", " to pretty-print MultiIndex columns.\n", " Valid values: False,True\n", " [default: True] [currently: True]\n", "\n", "display.latex.multicolumn_format : bool\n", " This specifies if the to_latex method of a Dataframe uses multicolumns\n", " to pretty-print MultiIndex columns.\n", " Valid values: False,True\n", " [default: l] [currently: l]\n", "\n", "display.latex.multirow : bool\n", " This specifies if the to_latex method of a Dataframe uses multirows\n", " to pretty-print MultiIndex rows.\n", " Valid values: False,True\n", " [default: False] [currently: False]\n", "\n", "display.latex.repr : boolean\n", " Whether to produce a latex DataFrame representation for jupyter\n", " environments that support it.\n", " (default: False)\n", " [default: False] [currently: False]\n", "\n", "display.max_categories : int\n", " This sets the maximum number of categories pandas should output when\n", " printing out a `Categorical` or a Series of dtype \"category\".\n", " [default: 8] [currently: 8]\n", "\n", "display.max_columns : int\n", " If max_cols is exceeded, switch to truncate view. Depending on\n", " `large_repr`, objects are either centrally truncated or printed as\n", " a summary view. 'None' value means unlimited.\n", "\n", " In case python/IPython is running in a terminal and `large_repr`\n", " equals 'truncate' this can be set to 0 and pandas will auto-detect\n", " the width of the terminal and print a truncated object which fits\n", " the screen width. The IPython notebook, IPython qtconsole, or IDLE\n", " do not run in a terminal and hence it is not possible to do\n", " correct auto-detection.\n", " [default: 20] [currently: 20]\n", "\n", "display.max_colwidth : int\n", " The maximum width in characters of a column in the repr of\n", " a pandas data structure. When the column overflows, a \"...\"\n", " placeholder is embedded in the output.\n", " [default: 50] [currently: 50]\n", "\n", "display.max_info_columns : int\n", " max_info_columns is used in DataFrame.info method to decide if\n", " per column information will be printed.\n", " [default: 100] [currently: 100]\n", "\n", "display.max_info_rows : int or None\n", " df.info() will usually show null-counts for each column.\n", " For large frames this can be quite slow. max_info_rows and max_info_cols\n", " limit this null check only to frames with smaller dimensions than\n", " specified.\n", " [default: 1690785] [currently: 1690785]\n", "\n", "display.max_rows : int\n", " If max_rows is exceeded, switch to truncate view. Depending on\n", " `large_repr`, objects are either centrally truncated or printed as\n", " a summary view. 'None' value means unlimited.\n", "\n", " In case python/IPython is running in a terminal and `large_repr`\n", " equals 'truncate' this can be set to 0 and pandas will auto-detect\n", " the height of the terminal and print a truncated object which fits\n", " the screen height. The IPython notebook, IPython qtconsole, or\n", " IDLE do not run in a terminal and hence it is not possible to do\n", " correct auto-detection.\n", " [default: 60] [currently: 100]\n", "\n", "display.max_seq_items : int or None\n", " when pretty-printing a long sequence, no more then `max_seq_items`\n", " will be printed. If items are omitted, they will be denoted by the\n", " addition of \"...\" to the resulting string.\n", "\n", " If set to None, the number of items to be printed is unlimited.\n", " [default: 100] [currently: 100]\n", "\n", "display.memory_usage : bool, string or None\n", " This specifies if the memory usage of a DataFrame should be displayed when\n", " df.info() is called. Valid values True,False,'deep'\n", " [default: True] [currently: True]\n", "\n", "display.multi_sparse : boolean\n", " \"sparsify\" MultiIndex display (don't display repeated\n", " elements in outer levels within groups)\n", " [default: True] [currently: True]\n", "\n", "display.notebook_repr_html : boolean\n", " When True, IPython notebook will use html representation for\n", " pandas objects (if it is available).\n", " [default: True] [currently: True]\n", "\n", "display.pprint_nest_depth : int\n", " Controls the number of nested levels to process when pretty-printing\n", " [default: 3] [currently: 3]\n", "\n", "display.precision : int\n", " Floating point output precision (number of significant digits). This is\n", " only a suggestion\n", " [default: 6] [currently: 6]\n", "\n", "display.show_dimensions : boolean or 'truncate'\n", " Whether to print out dimensions at the end of DataFrame repr.\n", " If 'truncate' is specified, only print out the dimensions if the\n", " frame is truncated (e.g. not display all rows and/or columns)\n", " [default: truncate] [currently: truncate]\n", "\n", "display.unicode.ambiguous_as_wide : boolean\n", " Whether to use the Unicode East Asian Width to calculate the display text\n", " width.\n", " Enabling this may affect to the performance (default: False)\n", " [default: False] [currently: False]\n", "\n", "display.unicode.east_asian_width : boolean\n", " Whether to use the Unicode East Asian Width to calculate the display text\n", " width.\n", " Enabling this may affect to the performance (default: False)\n", " [default: False] [currently: False]\n", "\n", "display.width : int\n", " Width of the display in characters. In case python/IPython is running in\n", " a terminal this can be set to None and pandas will correctly auto-detect\n", " the width.\n", " Note that the IPython notebook, IPython qtconsole, or IDLE do not run in a\n", " terminal and hence it is not possible to correctly detect the width.\n", " [default: 80] [currently: 80]\n", "\n", "html.border : int\n", " A ``border=value`` attribute is inserted in the ``<table>`` tag\n", " for the DataFrame HTML repr.\n", " [default: 1] [currently: 1]\n", " (Deprecated, use `display.html.border` instead.)\n", "\n", "io.excel.xls.writer : string\n", " The default Excel writer engine for 'xls' files. Available options:\n", " auto, xlwt.\n", " [default: auto] [currently: auto]\n", "\n", "io.excel.xlsm.writer : string\n", " The default Excel writer engine for 'xlsm' files. Available options:\n", " auto, openpyxl.\n", " [default: auto] [currently: auto]\n", "\n", "io.excel.xlsx.writer : string\n", " The default Excel writer engine for 'xlsx' files. Available options:\n", " auto, openpyxl, xlsxwriter.\n", " [default: auto] [currently: auto]\n", "\n", "io.hdf.default_format : format\n", " default format writing format, if None, then\n", " put will default to 'fixed' and append will default to 'table'\n", " [default: None] [currently: None]\n", "\n", "io.hdf.dropna_table : boolean\n", " drop ALL nan rows when appending to a table\n", " [default: False] [currently: False]\n", "\n", "io.parquet.engine : string\n", " The default parquet reader/writer engine. Available options:\n", " 'auto', 'pyarrow', 'fastparquet', the default is 'auto'\n", " [default: auto] [currently: auto]\n", "\n", "mode.chained_assignment : string\n", " Raise an exception, warn, or no action if trying to use chained assignment,\n", " The default is warn\n", " [default: warn] [currently: warn]\n", "\n", "mode.sim_interactive : boolean\n", " Whether to simulate interactive mode for purposes of testing\n", " [default: False] [currently: False]\n", "\n", "mode.use_inf_as_na : boolean\n", " True means treat None, NaN, INF, -INF as NA (old way),\n", " False means None and NaN are null, but INF, -INF are not NA\n", " (new way).\n", " [default: False] [currently: False]\n", "\n", "mode.use_inf_as_null : boolean\n", " use_inf_as_null had been deprecated and will be removed in a future\n", " version. Use `use_inf_as_na` instead.\n", " [default: False] [currently: False]\n", " (Deprecated, use `mode.use_inf_as_na` instead.)\n", "\n", "plotting.matplotlib.register_converters : bool\n", " Whether to register converters with matplotlib's units registry for\n", " dates, times, datetimes, and Periods. Toggling to False will remove\n", " the converters, restoring any converters that pandas overwrote.\n", " [default: True] [currently: True]\n", "\n", "\n" ] } ], "source": [ "pd.describe_option()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "compute.use_bottleneck : bool\n", " Use the bottleneck library to accelerate if it is installed,\n", " the default is True\n", " Valid values: False,True\n", " [default: True] [currently: True]\n", "\n", "compute.use_numexpr : bool\n", " Use the numexpr library to accelerate computation if it is installed,\n", " the default is True\n", " Valid values: False,True\n", " [default: True] [currently: True]\n", "\n", "\n" ] } ], "source": [ "pd.describe_option('compute')" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "display.max_columns : int\n", " If max_cols is exceeded, switch to truncate view. Depending on\n", " `large_repr`, objects are either centrally truncated or printed as\n", " a summary view. 'None' value means unlimited.\n", "\n", " In case python/IPython is running in a terminal and `large_repr`\n", " equals 'truncate' this can be set to 0 and pandas will auto-detect\n", " the width of the terminal and print a truncated object which fits\n", " the screen width. The IPython notebook, IPython qtconsole, or IDLE\n", " do not run in a terminal and hence it is not possible to do\n", " correct auto-detection.\n", " [default: 20] [currently: 20]\n", "\n", "display.max_colwidth : int\n", " The maximum width in characters of a column in the repr of\n", " a pandas data structure. When the column overflows, a \"...\"\n", " placeholder is embedded in the output.\n", " [default: 50] [currently: 50]\n", "\n", "\n" ] } ], "source": [ "pd.describe_option('max_col')" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "display.max_columns : int\n", " If max_cols is exceeded, switch to truncate view. Depending on\n", " `large_repr`, objects are either centrally truncated or printed as\n", " a summary view. 'None' value means unlimited.\n", "\n", " In case python/IPython is running in a terminal and `large_repr`\n", " equals 'truncate' this can be set to 0 and pandas will auto-detect\n", " the width of the terminal and print a truncated object which fits\n", " the screen width. The IPython notebook, IPython qtconsole, or IDLE\n", " do not run in a terminal and hence it is not possible to do\n", " correct auto-detection.\n", " [default: 20] [currently: 20]\n", "\n", "display.max_colwidth : int\n", " The maximum width in characters of a column in the repr of\n", " a pandas data structure. When the column overflows, a \"...\"\n", " placeholder is embedded in the output.\n", " [default: 50] [currently: 50]\n", "\n", "display.max_info_columns : int\n", " max_info_columns is used in DataFrame.info method to decide if\n", " per column information will be printed.\n", " [default: 100] [currently: 100]\n", "\n", "\n" ] } ], "source": [ "pd.describe_option('max.*col')" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "100\n" ] } ], "source": [ "print(pd.get_option('display.max_rows'))" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pd.set_option('display.max_rows', 60)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "60\n" ] } ], "source": [ "print(pd.get_option('max_r'))" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pd.set_option('max_r', 100)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "# pd.get_option('max')\n", "# OptionError: 'Pattern matched multiple keys'" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# pd.set_option('max', 60)\n", "# OptionError: 'Pattern matched multiple keys'" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[100, 20, 50]\n" ] } ], "source": [ "l = ['display.max_rows', 'display.max_columns', 'display.max_colwidth']\n", "\n", "print([pd.get_option(i) for i in l])" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'display.max_rows': 100, 'display.max_columns': 20, 'display.max_colwidth': 50}\n" ] } ], "source": [ "print({i: pd.get_option(i) for i in l})" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'display.max_rows': 80, 'display.max_columns': 80, 'display.max_colwidth': 80}\n" ] } ], "source": [ "d = {'display.max_rows': 80,\n", " 'display.max_columns': 80,\n", " 'display.max_colwidth': 80}\n", "\n", "[pd.set_option(k, v) for k, v in d.items()]\n", "\n", "print({i: pd.get_option(i) for i in d.keys()})" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "80\n" ] } ], "source": [ "print(pd.options.display.max_rows)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "60\n" ] } ], "source": [ "pd.reset_option('display.max_rows')\n", "\n", "print(pd.options.display.max_rows)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "80\n", "80\n" ] } ], "source": [ "print(pd.options.display.max_columns)\n", "print(pd.options.display.max_colwidth)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "20\n", "50\n" ] } ], "source": [ "pd.reset_option('max_col')\n", "\n", "print(pd.options.display.max_columns)\n", "print(pd.options.display.max_colwidth)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "60\n", "20\n", "50\n" ] } ], "source": [ "pd.options.display.max_rows = 100\n", "pd.options.display.max_columns = 100\n", "pd.options.display.max_colwidth = 100\n", "\n", "pd.reset_option('^display', silent=True)\n", "\n", "print(pd.options.display.max_rows)\n", "print(pd.options.display.max_columns)\n", "print(pd.options.display.max_colwidth)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "html.border has been deprecated, use display.html.border instead\n", "(currently both are identical)\n", "\n", "\n", ": boolean\n", " use_inf_as_null had been deprecated and will be removed in a future\n", " version. Use `use_inf_as_na` instead.\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.6/site-packages/pandas/core/config.py:619: FutureWarning: html.border has been deprecated, use display.html.border instead\n", "(currently both are identical)\n", "\n", " warnings.warn(d.msg, FutureWarning)\n", "/usr/local/lib/python3.6/site-packages/pandas/core/config.py:619: FutureWarning: \n", ": boolean\n", " use_inf_as_null had been deprecated and will be removed in a future\n", " version. Use `use_inf_as_na` instead.\n", "\n", " warnings.warn(d.msg, FutureWarning)\n" ] } ], "source": [ "pd.reset_option('all')" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pd.reset_option('all', silent=True)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "100\n" ] } ], "source": [ "with pd.option_context('display.max_rows', 100):\n", " print(pd.options.display.max_rows)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "60\n" ] } ], "source": [ "print(pd.options.display.max_rows)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "100\n" ] } ], "source": [ "pd.options.display.max_rows = 80\n", "\n", "with pd.option_context('display.max_rows', 100):\n", " print(pd.options.display.max_rows)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "80\n" ] } ], "source": [ "print(pd.options.display.max_rows)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "100\n", "100\n" ] } ], "source": [ "with pd.option_context('display.max_rows', 100, 'display.max_columns', 100):\n", " print(pd.options.display.max_rows)\n", " print(pd.options.display.max_columns)" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "80\n", "20\n" ] } ], "source": [ "print(pd.options.display.max_rows)\n", "print(pd.options.display.max_columns)" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "80\n" ] } ], "source": [ "pd.option_context('display.max_rows', 100)\n", "\n", "print(pd.options.display.max_rows)" ] } ], "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 }	mit
Upward-Spiral-Science/claritycontrol	code/a06_test_assumptions.ipynb	2	277999	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# HW6 Testing Assumptions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Heavily borrowed text materials and formatting from grelliam\n", "\n", "## Testing Assumptions\n", "1. State assumptions\n", "2. Check assumptions (with figures)\n", " 1. residuals\n", " 2. correlations\n", " 3. \\# of modes\n", " \n", "### Step 1: State assumptions\n", "\n", "We extract the histogram data out of the raw clarity scanned data, given different conditions to the subjects.\n", "\n", "1. We assume that histograms are sampled according to: $x_i \\stackrel{iid}{\\sim} F$. This is both an independent and identical assumption.\n", "2. We assume that the data poinst are independent: $F_{X\|0}=Norm(\\mu_{0},\\sigma_{0})^{V\\times V}$.\n", "3. We assume there is a class conditional difference across conditions={Control, Cocaine, Fear}.\n", "4. In addition, we assume that any other differences of the subjects such as genders, ages will not or have limit affects to the data. (We cannot test on this, because we do not have access to that information.)\n", "\n", "### Step 2: Check assumptions\n", "\n", "For independent histograms, check that off diagonal covariance is approximately 0. <br/>\n", "$x_i \\stackrel{iid}{\\sim} F$<br/>\n", "$(x_1, x_2, ..., x_n) \\sim F = \\prod_i^n F_i$ <br/>\n", "$F_i = F_j, \\forall i,j$\n", "\n", "For identical histograms, check the optimal number of clusters and see if that is 1. <br/>\n", "$F = \\prod_j^J F_j, J < n$ <br/>\n", "$\\prod_j^J w_jF_j(\\theta)$ <br/>\n", "\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import os\n", "PATH=\"/Users/david/Desktop/CourseWork/TheArtOfDataScience/claritycontrol/code/scripts/\" # use your own path\n", "os.chdir(PATH)\n", "\n", "import clarity as cl # I wrote this module for easier operations on data\n", "import clarity.resources as rs\n", "import csv,gc # garbage memory collection :)\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import jgraph as ig\n", "%matplotlib inline\n", "\n", "# settings for histogram\n", "BINS=32 # histogram bins\n", "RANGE=(10.0,300.0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Histogram data preparation and Scale data\n", "\n", "If you haven't done this before, please refer to homework for data preparation and scale data." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Setup Step" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(12, 32)\n" ] } ], "source": [ "# Load X\n", "X = np.loadtxt(\"../data/hist/features.csv\",delimiter=',')\n", "print X.shape\n", "\n", "# Load Y\n", "y = np.array([0,0,0,1,1,1,1,1,2,2,2,2])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Independent Histogram Assumption" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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BMQERXCzw2WpftH8XIcBog75vIpgChJrwroJ3wbFAyLsMIjQqiqemgkowGpQt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6/hZcPHkNnNFDoFTlfm+gp/2lqqzSsPoFjql/ceI4S07WLrxUZL/4jCiAByNG6236Nxh81xhJjQ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\nV4dPV1dXV9eVq8Onq6urq+vK9f8BByMmk/Efj6gAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x116ad7890>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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Ax0LUqbtCVC/S6R8hSpVvM30b+Sat722nNvOIzz2WLTwzjmq75vZKrP+Py/oRx/p4AN/m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\n9F9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"text/plain": [ "<matplotlib.figure.Figure at 0x116c91110>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Ratio of on- and off-diagonal determinants: -0.104579653134\n" ] } ], "source": [ "vectorized = X\n", "covar = np.cov(vectorized)\n", "\n", "plt.figure(figsize=(7,7))\n", "plt.imshow(covar)\n", "plt.title('Covariance of Clarity Histograms datasets')\n", "plt.colorbar()\n", "plt.show()\n", "\n", "diag = covar.diagonal()np.eye(covar.shape[0])\n", "hollow = covar-diag\n", "d_det = np.linalg.det(diag)\n", "h_det = np.linalg.det(hollow)\n", "\n", "plt.figure(figsize=(11,8))\n", "plt.subplot(121)\n", "plt.imshow(diag)\n", "plt.clim([0, np.max(covar)])\n", "plt.title('Determinant of on-diagonal: ' + str(d_det))\n", "plt.subplot(122)\n", "plt.imshow(hollow)\n", "plt.clim([0, np.max(covar)])\n", "plt.title('Determinant of off-diagonal: ' + str(h_det))\n", "plt.show()\n", "\n", "print \"Ratio of on- and off-diagonal determinants: \" + str(d_det/h_det)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From the above, we conclude that the assumption that the histograms were independent is most likely true. \n", "This is because cross-graph covariance matrix is not highly influenced by the off-diagonal components of the covariance matrix." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Identical Histogram Assumption" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 1 2 3 4 5 6 7 8 9 10 11 12]\n", "Fitting and evaluating model with 1 clusters.\n", "Fitting and evaluating model with 2 clusters.\n", "Fitting and evaluating model with 3 clusters.\n", "Fitting and evaluating model with 4 clusters.\n", "Fitting and evaluating model with 5 clusters.\n", "Fitting and evaluating model with 6 clusters.\n", "Fitting and evaluating model with 7 clusters.\n", "Fitting and evaluating model with 8 clusters.\n", "Fitting and evaluating model with 9 clusters.\n", "Fitting and evaluating model with 10 clusters.\n", "Fitting and evaluating model with 11 clusters.\n", "Fitting and evaluating model with 12 clusters.\n" ] }, { "data": { "image/png": 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DgN2BRRGx25A2hwA7ZeYuwGLgC6Pc9+zM3Lt8XTHRfZckdZ667pmOapgXeBa4LSKuopzR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\nIk4Zaf+IeHlErIi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X5e9wJNCT6/Gqs7gEmyRJFZmZSpJUkcFUkqSKDKaSJFVkMJUkqSKD\nqSRJFRlMJUmqyGAqSVJFBlNJkir6v9/6YDTzoB5UAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x116aebcd0>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "[-1639.72072733 -1509.33333076 -1366.11704279 -1217.79273549 -1065.97939288\n", " -908.97767055 -752.11847439 -591.68896016 -433.19008793 -271.65646483\n", " -110.19222953 51.32818139]\n" ] } ], "source": [ "import sklearn.mixture\n", "i = np.linspace(1,12,12,dtype='int')\n", "print i\n", "bic = np.array(())\n", "for idx in i:\n", " print \"Fitting and evaluating model with \" + str(idx) + \" clusters.\"\n", " gmm = sklearn.mixture.GMM(n_components=idx,n_iter=1000,covariance_type='diag')\n", " gmm.fit(vectorized)\n", " bic = np.append(bic, gmm.bic(vectorized))\n", "plt.figure(figsize=(7,7))\n", "plt.plot(i, 1.0/bic)\n", "plt.title('BIC')\n", "plt.ylabel('score')\n", "plt.xlabel('number of clusters')\n", "plt.show()\n", "print bic" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From the above we observe that that our data most likely was not sampled identically from one distribution. This is an odd shape for a BIC curve, and thus we must do more investigation. This curve implies the larger number of clusters the better." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Independent Histogram Data Points Assumption" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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i548qx6SmfBnNxmNkzSciPp+OgJq1HXheOn8psIMkkGzsMjsBDY75pM1ui10W\nl8TihnTkgvQE0k7vIJ29SY+2Tn/PkfBZfPQROg+m4XPfQ/SW+yz0lljob2Shv5l2/zCtfpdWP1BP\nua7WBTv4yO+0R416Paz5LbusVbCOCtbL116qNOUNe85Kaxr5Gk/RfBVFNZ861npWWzO0legtjKvd\n7fCYXmcyVruVJw6G2I6IXZJOHGOZGmrUzip5vNXKdDjoiIVOEj6LG5Nhc5a29OgcPkCnMwiffSwe\nqfk8wuKDj9C57yEW732IbhcWtYkFjqOtg7Q4TFs9WgQtCSkbPmkIZgNncOnugKPDpyiAqhwHalEe\nONlmPVHeBEfJ8vw6UBxEZU1Mw3a2RcGzkh3zqOCZ9o5+2u/fLL12M/pajytiR3wzd2Tmt6aTrUb0\nIZYhDkP/IPQPQG8f9PZAbzN0N0FrWeiRNjy6AHsXiX0biP0b6R84TO/QMv3DXXrLfQ52H8chbeGw\nNrGsDXTVoas2PbXotyDogZZJrq+dTgymdMSDyA46Oip0RtV+RvWSG1VzWkkIFT3OCm5XsqyKYa8x\nzR1/Ubmy800LpW+mk63VasNnt6STImK3pJOB+4avvm2Vb9Mkw5pq0v/gEUQ3ueR17wB094rWo6K1\nJFoLyXlA0KK/3KZ3f4fugxvpPrzM8p4+i/uDhUNtFpcXOdRfYlGbOKjj2KcncUCP56CO4xAbWaZD\nlxa9CIJliINJwGQDKLqPLTsqjEY1uVUNnpUG0bCwKVqeX0bBfO5vP/SxYfNVVX2faRjnds6670mn\ngb8Z+zv0Jn1NhZqoGj75o49XkVxg6F3Aq4ErC55jazb4z60jdwfh0z8gevvSi8EtCFqt5Ho8/Ra9\n7gLdBzosPLghOYH00bQ79cFFFpbTYzxs4ZA2c0AnsJ/Hc5AtHGbTkfDpR58+XSAbPoNhdbLjvGVv\nqxzvGRU6o2o4+ccoeP6oZcPmyS3PLxs2X3S/ipW83zQM28Zpl23+dB0+CUkfI6m6PFHSt4ALgXcC\nfyrpdcCdwMsmWchmOrYmFJHs/wc1n9ZeoQWhViu99HWLWE7D55EO7Yf7LDySnEC6sH+R9qEl2sub\nWOhvoa2DHNZGDuo4DpJMh9jIYTp0o02PtObDoUzwpM1sR2o9+dGuy8KmarPZWpYxYlnR/LDbqvNV\n7ldV1/CB8W2jWaJKb7dXlDz0o2Mui5WKIzfRhTgkegeS4BmMMBDdFv1DLXoHWiz0RGvvEu09or2n\nTXtvegJqIMQ2AAAgAElEQVTpwU20lg/T7h2mrcMsaykNnE1Hbo80uxFELAMH0rBJj+sMwofM7VFX\nNS0LnHyz27AgGfXYSobqKZrP3o5aVvV+2bIqJhVm41bXcs2XXkPO/W/GVs6cbK0nM5855qMD0BsE\nT0/0D7VoH2jR29ei24fW/g6t/S3a+xdo7V+itb9L62CX9nIv6U5Nj64WWKbDcnJaajIfHbq06RME\nXYiDQJ8jHQuOBE//6NtK4TMqbFYTTGSWjZrP3ladX+myYcurGGeoTUKdyjKffMzHauDoEEqa3YL+\nYaCVdHGOnugfbtE7INr7WrQ2tGmFaB1qo4OLtA71aR2KZMicQ0FruZ+cx6OgpxZdWnSjndzSTqek\nw0Gf5UzoZEImBuGSDZ0qgVM1eFYzCgIF8/ll5B7LzxfdH7V81GMrMYkwm5Q6lslmicOndrKBk1uW\nNrv1D5HOi9ZhoQOitdii20kvf00bLYtWV8no1F2h5eQaPeoK9UVLoi/oEfQI+hFHzfejT9CHSMMg\nX8OJfOgM65G2ki7Sq5nI3Q6r7RTdL1u2ksfXuv56vZbVnWs+NmWDEDq22S3SpjYdFv0DQm1BO7lV\nKwkfYiG5JEK/nQyT019AR+bbSAuEevSjS7BMqPvYPF36LBNHzu3JBk5Rs9qwbtSraW5bS/BQ8lj+\nb7sa0woBh4/NH4fPzIjk3+CQC3Ds2GTpNBiZQJ3kYnAqm5aBg8kUh9JbJSETQXI26yGG12BG1W5G\n1XooWLaSJrZhAZS9NZsNrvnYFOVrPdnb7JQ99pIZaSDbC63fg1a+S3S+23T/sfljmtLW2oRGbr5o\nW4c9d9hzhj3OiHXM6snn+dgUZJrYjlmW3ZEWBUQ2hLIngWYHAM0PDJofsSAbRoPjPatpMmPE/apB\nUyWIzGwWOXxqKxtEZTWfotpPJnRoJfcjez8fPtmaUr4WNWpInJUG0KjtHRZIDiBrBp/nY1NSFDqD\n+cFtUfC0OTp4MqGjghAKJesOxmc7EkSjerCV3c+XLRsWVcOq6G8x7L7Z/PExH6uJfADlgycbQLna\njtLLWx9z3Z3B/CB88iMXlA2JM6oGlC/jSprMhgWXmc0bh08t5Y/9lO3YywKo+9httECD28H1eAa9\n5LI1n1zwRL7mU7XpLVvmlR4nyv8Nstud/1uYzSfXfGzKBjvZog4I2RDIhk62ZpMNHuUeH4RPrmdc\n4Vhtqz0/Z9g2lT02rObj0LFmcG83q4mi2kTRCZ49jj3nJw0eZeYHx3s0CJ+yi8OtJXiGBVLVwHLY\nmM0zh89MGdbklg+hTBBFN7dMaRDlr88zyeCpcgyoKKiG3ZrNH/d2sxorC6Fs8Ax6v+W7WGeDqOh6\nPEUDhq6k9rLSkBl2zIeSx0bJn5hrNjt8zMdqqux4SH4qqxlVmcbVfLaaQBo3B5BZHTl8ZtawJqmq\n07DwGRVEZe9VtDxf7kmGQVGtxwFks8M1H5sBKw2cqjWcopNLi94nv6zoPgX3y5atRVHA5AMo+95m\n9eTwsRor27GXhUDRaAWraX7Lv+6w+/n5ovv5bSm6vxpFoRO5x8f1Xma2Gg6fmVVWkxhVi1lJ2IwK\noNXMF5V9XIaFjgPHZoPP87EZsJJjMUWXRiirEeWfR8nrj5ovWzZsfq3yzW9FIVQ0b1YP7mptM2pU\n4BQFz6j7Kw2XKo/l58eprKnNoWNWFw6fmVPWvDZqKuvFVhQ8Za9Bye2wx4bd5rdr3PKdDIoCyEFk\n9eIOBzZDRoVOUfCUda8uWm/wHvnbKrWa1QTRJDhsbDY4fGxG5Hf4VWtBRTWgfM0n38267HZY6OTn\nxy0/8GqRsvd3IJlNi8Nnpg07gF8UCsOa41bSzFbltsp8nWo/DiCrh6b0dmtNuwA2DsN2mqs9RrTa\niRXMr2Q7xk2528H8JCaz6nosjGUqI+lcSbdKuk3SW0rWea+k2yXdJOmsdNmpkq6V9HVJX5X0hsz6\nJ0i6RtI3JH1W0vGjttPhM7PKdt6raXab5KUT8vP5stehtjHpgHAIWT1IagHvA14IPAN4uaSn59Z5\nEXB6RJwJnA98MH2oC7wpIp4BPAd4fea5bwU+FxFPA64F3jaqLG52mynDagtVw6hP8ptjJU1wRe8z\nqumsSs1mGiFUdiLqpMIhu11u2rPRJtzh4Gzg9oi4E0DSFcB24NbMOtuBywAi4npJx0s6KSJ2AbvS\n5Xsl3QKckj53O/C89PmXAjtIAqmUw2fulB17GVYLWmktZthtWXmGLVvvHXLRiaiTKsN6vpfNgwmH\nzynAXZn7d5ME0rB17kmX7R4skLQVOAv4YrroxIjYDRARuySdOKogDp+ZtJLayKimt5WGUNHr58s1\nrMxVl09a2UgI45atVTl0bHJu3nE/t+y4f+LvI2kL8EngjRGxr2S1kV92h8/cqhJAK6kN5V+z6DY/\nX6V866GstrEeI127Z52tzGp7u33vtpP53m0nH7n/qYtuKVrtHuApmfunpsvy6zy5aB1JCyTBc3lE\nXJlZZ3faNLdb0snAfaPK6w4HM29YbaRovihcxtnhIF+2KutNWpX3m2Qvt6KedWZTcQNwhqTTJHWA\n84CrcutcBbwKQNI5wMODJjXgI8DNEfGegue8Jp1/NXAlI7jmM1eKjvNk54sCp2rwUOG2rqZV03AN\nx1ZukgOLRkRP0gXANSSVj0si4hZJ5ycPx4ci4mpJL5Z0B7CPNFQkPRd4JfBVSTeSfLnfHhGfAd4F\nfELS64A7gZeNKovDZ2aVNXUVBUY2cFocG0BVakEU3JYtm4S1vv40ah2D4z3Dbs2ONunhddKweFpu\n2R/k7l9Q8LwvQHHhIuJB4EdXUg43u820sppOflk+XMpCZ6VdrScdPPO6c3YznJlrPjNvJcdZisJm\n1CW1q9Z2JhkU6/U+k1Y2qvY0zfLfcz55YFGrqaKdRb7Ws9JOAlWO9+TfexrBM6vKriVUh6Y3dwOv\nG4ePzaCqTW/Dju1UDZ+i++NUFrLzoGiUhWnI/j3rEITWJA6fmVQUMkWP54/PDEJn0BV4Jc1u2dcu\ner9Jm/Ud46jLe0/DsEuM27R4VOuUpEsk7Zb0lcyyCyXdLenL6XTuZItpxUYdoxl1zKfoGNBKakGj\nQnAc1uM91kvRAKOTGkl7Nech5edtGiY9qnVdVOnt9lGSEVDzLo6IZ6fTZ8ZcLluTsgCaxsmk43yf\neVGXyy04dGx6RsZjRHxe0mkFD/nbWjtVaz6Dprei2k7+fva1bfymfczHTW1105QOB2s5z+eC9EJD\nf1jlwkE2KcOaxPKPl4XMSprbbH7kzzfy+Ud10KM9lqnuVtsw+H7gHRERkn4TuBj4hfLVd2Tmt6aT\nrd3gl2t+WXa+6jGgonXzrzcOTdqx1Xlb/YNidXamk63VqsInIrLjdn8Y+PTwZ2xbzdvYUGXBM2hG\nWUkIFS0H76CaxMP/VLOVo388Xzf2d2hKb7eq4XPU0VFJJ6dXtQN4KfC1cRfMVmIQGEVhtJLaDyXr\nrlSdf/Hb0YZd8mGSn6ODrcws9FQbh5FbKeljJFWXJ0r6FnAh8HxJZ5EcLNhJcp1vWzfDdgxVe6QV\nrVvU7Lba8jGkjFZPRSe/Tio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"text/plain": [ "<matplotlib.figure.Figure at 0x110e31f50>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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4x2A4+KO5KzvZdDjXYGtvk9be0EuYf3p30eku+sqy7lFjeX6K9oLTXog9hEfS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\niGylfA9dWQ9hFvSyS88NEgw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"text/plain": [ "<matplotlib.figure.Figure at 0x1183df510>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Ratio of on- and off-diagonal covariance sums: 0.539128118403\n" ] } ], "source": [ "vect = X.T\n", "covar = np.cov(vect)\n", "\n", "plt.figure(figsize=(7,7))\n", "plt.imshow(covar)\n", "plt.title('Covariance of Clarity Histogram dataset')\n", "plt.colorbar()\n", "plt.show()\n", "\n", "diag = covar.diagonal()np.eye(covar.shape[0])\n", "hollow = covar-diag\n", "d_det = np.sum(diag)\n", "h_det = np.sum(hollow)\n", "\n", "plt.figure(figsize=(11,8))\n", "plt.subplot(121)\n", "plt.imshow(diag)\n", "plt.clim([0, np.max(covar)])\n", "plt.title('Sum of on-diagonal: ' + str(d_det))\n", "plt.subplot(122)\n", "plt.imshow(hollow)\n", "plt.clim([0, np.max(covar)])\n", "plt.title('Sum of off-diagonal: ' + str(h_det))\n", "plt.show()\n", "\n", "print \"Ratio of on- and off-diagonal covariance sums: \" + str(d_det/h_det)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The edges are not independent of one another because the ratio of on- to off-diagonal covariance is relatively small. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Class Conditional Histogram Probability Assumption" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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/AP6l78tqYEm/fFfgM3295wIP3YL6tqWM3QV05wLX0j0ub2b5WIzdEPs3MeMH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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x116ae54d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import scipy.stats as ss\n", "\n", "prob = 1.0np.sum(1.0(vectorized>0),1)/64\n", "\n", "vals = ss.linregress(prob, y)\n", "m = vals[0]\n", "c = vals[1]\n", "\n", "def comp_value(m, c, data):\n", " return m.T*data + c\n", "\n", "resi = np.array(())\n", "for idx, subj in enumerate(y):\n", " temp = comp_value(m, c, prob[idx])\n", " resi = np.append(resi, subj - temp)\n", " \n", "plt.figure(figsize=(7,7))\n", "plt.scatter(prob, resi)\n", "plt.title('Residual assignment error')\n", "plt.xlabel('edge probability')\n", "plt.ylabel('error')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From the above we can see that the edge probability separation is independent. Thus, this assumption might be true." ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }	apache-2.0
eco32i/biodata	sessions/01 - BASH basics.ipynb	1	29723	{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Command line basics" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "###WHY?" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "- when you work remotely on a server, that's all you have\n", "- most of the bioinformatics programs are command-line driven\n", "- easier to connect several software tools into a pipeline\n", "- great for a quick analysis/troubleshooting\n", "- when used properly it's so much faster than any GUI\n", "- DRY (don't repeat yourself!)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### UNIX philosophy\n", "\n", "- write small programs that do _one_ thing and do it well\n", "- write programs to communicate with each other so that output of one program is the input for another\n", "- write programs to communicate in plain text (because it's the only universal interface)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "# EVERYTHING is CaSE-SenSitivE" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "total 68K\n", "drwxrwxr-x 6 ilya ilya 4.0K Jul 29 13:08 \u001b[0m\u001b[01;34m.\u001b[0m\n", "drwxrwxr-x 6 ilya ilya 4.0K Jul 29 13:08 \u001b[01;34m..\u001b[0m\n", "drwxrwxr-x 2 ilya ilya 12K Jul 29 13:08 \u001b[01;34mdata\u001b[0m\n", "drwxrwxr-x 8 ilya ilya 4.0K Jul 29 13:08 \u001b[01;34m.git\u001b[0m\n", "-rw-rw-r-- 1 ilya ilya 781 Jul 29 13:08 .gitignore\n", "-rw-rw-r-- 1 ilya ilya 1.1K Jul 29 13:08 LICENSE\n", "-rw-rw-r-- 1 ilya ilya 72 Jul 29 13:08 README.md\n", "drwxrwxr-x 5 ilya ilya 4.0K Aug 4 12:31 \u001b[01;34msessions\u001b[0m\n", "drwxrwxr-x 2 ilya ilya 4.0K Jul 29 13:08 \u001b[01;34mutils\u001b[0m\n" ] } ], "source": [ "ls -lah .." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "total 14M\n", "drwxrwxr-x 2 ilya ilya 12K Jul 29 13:08 \u001b[0m\u001b[01;34m.\u001b[0m\n", "drwxrwxr-x 6 ilya ilya 4.0K Jul 29 13:08 \u001b[01;34m..\u001b[0m\n", "-rw-rw-r-- 1 ilya ilya 1.6K Jul 29 13:08 2017-03-09_NextSeq.csv\n", "-rw-rw-r-- 1 ilya ilya 1.5M Jul 29 13:08 CE_exp.tab\n", "-rw-rw-r-- 1 ilya ilya 1.5M Jul 29 13:08 CE_exp.umi.tab\n", "-rw-rw-r-- 1 ilya ilya 500K Jul 29 13:08 contigs.fasta\n", "-rw-rw-r-- 1 ilya ilya 457K Jul 29 13:08 dfa_mp.offset_150.win_100.csv\n", "-rw-rw-r-- 1 ilya ilya 461K Jul 29 13:08 dfa_mp.offset_150.win_200.csv\n", "-rw-rw-r-- 1 ilya ilya 453K Jul 29 13:08 dfa_mp.offset_150.win_50.csv\n", "-rw-rw-r-- 1 ilya ilya 455K Jul 29 13:08 dfa_mp.offset_150.win_80.csv\n", "-rw-rw-r-- 1 ilya ilya 457K Jul 29 13:08 dfa_mp.offset_200.win_100.csv\n", "-rw-rw-r-- 1 ilya ilya 461K Jul 29 13:08 dfa_mp.offset_200.win_200.csv\n", "-rw-rw-r-- 1 ilya ilya 454K Jul 29 13:08 dfa_mp.offset_200.win_50.csv\n", "-rw-rw-r-- 1 ilya ilya 456K Jul 29 13:08 dfa_mp.offset_200.win_80.csv\n", "-rw-rw-r-- 1 ilya ilya 457K Jul 29 13:08 dfa_mp.offset_300.win_100.csv\n", "-rw-rw-r-- 1 ilya ilya 461K Jul 29 13:08 dfa_mp.offset_300.win_200.csv\n", "-rw-rw-r-- 1 ilya ilya 454K Jul 29 13:08 dfa_mp.offset_300.win_50.csv\n", "-rw-rw-r-- 1 ilya ilya 455K Jul 29 13:08 dfa_mp.offset_300.win_80.csv\n", "-rw-rw-r-- 1 ilya ilya 603K Jul 29 13:08 dfm.csv\n", "-rw-rw-r-- 1 ilya ilya 644 Jul 29 13:08 dHSR1.fa\n", "-rw-rw-r-- 1 ilya ilya 5.4K Jul 29 13:08 gradtimes.txt\n", "-rw-rw-r-- 1 ilya ilya 445 Jul 29 13:08 hHSR-435.fa\n", "-rw-rw-r-- 1 ilya ilya 611 Jul 29 13:08 hHSR.fa\n", "-rw-rw-r-- 1 ilya ilya 2.5K Jul 29 13:08 Lexogen_Sense_RNA-Seq.csv\n", "-rw-rw-r-- 1 ilya ilya 4.0K Jul 29 13:08 ROSE1_25.txt\n", "-rw-rw-r-- 1 ilya ilya 4.2K Jul 29 13:08 ROSE1_26.txt\n", "-rw-rw-r-- 1 ilya ilya 4.3K Jul 29 13:08 ROSE1_27.txt\n", "-rw-rw-r-- 1 ilya ilya 4.4K Jul 29 13:08 ROSE1_28.txt\n", "-rw-rw-r-- 1 ilya ilya 4.5K Jul 29 13:08 ROSE1_29.txt\n", "-rw-rw-r-- 1 ilya ilya 4.7K Jul 29 13:08 ROSE1_30.txt\n", "-rw-rw-r-- 1 ilya ilya 5.0K Jul 29 13:08 ROSE1_31.txt\n", "-rw-rw-r-- 1 ilya ilya 5.2K Jul 29 13:08 ROSE1_32.txt\n", "-rw-rw-r-- 1 ilya ilya 5.7K Jul 29 13:08 ROSE1_33.txt\n", "-rw-rw-r-- 1 ilya ilya 6.0K Jul 29 13:08 ROSE1_34.txt\n", "-rw-rw-r-- 1 ilya ilya 6.5K Jul 29 13:08 ROSE1_35.txt\n", "-rw-rw-r-- 1 ilya ilya 6.7K Jul 29 13:08 ROSE1_36.txt\n", "-rw-rw-r-- 1 ilya ilya 7.1K Jul 29 13:08 ROSE1_37.txt\n", "-rw-rw-r-- 1 ilya ilya 7.8K Jul 29 13:08 ROSE1_38.txt\n", "-rw-rw-r-- 1 ilya ilya 8.1K Jul 29 13:08 ROSE1_39.txt\n", "-rw-rw-r-- 1 ilya ilya 8.2K Jul 29 13:08 ROSE1_40.txt\n", "-rw-rw-r-- 1 ilya ilya 8.6K Jul 29 13:08 ROSE1_41.txt\n", "-rw-rw-r-- 1 ilya ilya 8.8K Jul 29 13:08 ROSE1_42.txt\n", "-rw-rw-r-- 1 ilya ilya 9.3K Jul 29 13:08 ROSE1_43.txt\n", "-rw-rw-r-- 1 ilya ilya 10K Jul 29 13:08 ROSE1_44.txt\n", "-rw-rw-r-- 1 ilya ilya 11K Jul 29 13:08 ROSE1_45.txt\n", "-rw-rw-r-- 1 ilya ilya 11K Jul 29 13:08 ROSE1_46.txt\n", "-rw-rw-r-- 1 ilya ilya 126 Jul 29 13:08 rose.fa\n", "-rw-rw-r-- 1 ilya ilya 3.3M Jul 29 13:08 utr.counts.csv\n" ] } ], "source": [ "cd ../data\n", "ls -lah" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1668\n" ] } ], "source": [ "cat contigs.fasta \| grep NODE \| wc -l" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Common paradigm:\n", "\n", "`<verb> [modifiers] <subject>`\n", "\n", "for example:\n", "\n", "* `ls` - list directory (current by default)\n", "* `ls dir` - list directory `dir`\n", "* `ls -lah` - list directory `dir` with details, one item per line, including hidden files, and in human-readable format\n", "\n", "_Default_: a value for argument when no explicit argument is given (where it makes sense, like `ls`)\n", "\n", "Up arrow and down arrow scroll through command history.\n", "\n", "Very important!\n", "\n", "* `<Ctrl-R>` - reverse history search\n", "* `<Tab>` - completion: never have to type the whole thing\n", "* `<Alt-.>` - inserts last argument from the history" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "#### Aside:\n", "\n", "Why whitespace in file/directory name is a bad idea:\n", "- needs to be escaped with backslash _or_ the entire argument needs to be quoted\n", "- because stuff is whitespace (space, tab, etc) delimited in `bash`\n", "- sooner or later it will break your scripts in unexpected and wierd ways." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Getting around" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### HOW TO GET HELP: `man <command>`" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Path: `/<dir1>/<dir2>/<dir3>/foo.bar`\n", "for example: `/home/ilya/src`\n", "\n", "Absoute path:\n", "\n", "* starts with `/`, aka `root`. Location relative to filesystem `root`.\n", "\n", "Relative path:\n", "\n", "* doesn't start with `/`. Location relative to the curent directory." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "#### Aside\n", "\n", "UNIX filesystem is divided into two realms:\n", "* userland (everything under `/home`, each user has access to her own data)\n", "* system (everything else, only `admins` or `sudoer`s have access to)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Commands" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "* `pwd` - print working directory\n", "* `cd <dir>` - change directory\n", "* `mkdir [options] <dir>` - make new directory\n", "* `ls [options] [<dir>...]` - list directory\n", "\n", "### Exercise:\n", "\n", "* find your `Downloads` (or `Documents`) directory\n", "* list the content of your `Documents` directory using different options\n", "* create a new directory (where is it created?)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Shortcuts" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "These are huge time savers. But they are nothing more than aliases.\n", "\n", "* `~` - current user's home directory (`/home/<user>/` or `/Users/<user>/` on Mac OS)\n", "* `.` - current directory\n", "* `..` - parent directory\n", "* `-` - last directory (although in most contexts it means `stdin`)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Other useful things:\n", "\n", "- `pushd <dir>` - pushes directory `<dir>` into stack\n", "- `popd` - pops the last pushed directory from the stack\n", "\n", "These two can be thought about as \"remember for later\" and \"recall the last remembered\" commands. " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### Path and executables\n", "\n", "Files that have `x` bit set in their permission are executable. These can be executed by typing their name at the prompt:\n", "\n", "```\n", " $ /home/vasyapupkin/myprog1\n", " $ ./myprog1\n", " $ /bin/myprog1\n", "```\n", " \n", "or they can be executed by typing just their name at the prompt _if_ their location is listed in `PATH` variable:\n", "\n", " $ echo $PATH\n", " $ myprog1\n", " \n", "if unsure, use `which` programm to find the executable (if it exists!):\n", "\n", " $ which python" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Lookin at things (well, files)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "- `cat` will output its arguments to `stdout`\n", "- `less` will do the same but in a humane way (pagination, search, scrolling, etc)\n", "- `man` displays a help page for a given command\n", "- `head` outputs n first lines in a file\n", "- `tail` outputs n last lines in a file" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Creating, copying and moving stuff" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### Create\n", "\n", "Create a file (actually, change the file's timestamp):\n", "\n", " touch <filename>\n", " ><filename>" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Create a directory:\n", "\n", " mkdir <dirname>" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "usual path rules apply (see absolute vs relative paths). Fancy switch `-p`:\n", "\n", " mkdir -p path/to/my/new/dir\n", " mkdir -p path/to/{one,two,three}" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Copy\n", "\n", "Copying stuff:\n", "\n", " cp <source> <destination>" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "by default, `cp` only copies regular files and skips directories. To copy directories use `-r` (recursively) option:\n", "\n", " cp -r <source_dir> <destination>" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "but watch for that trailing slash:\n", "\n", " cp -r <source>/ <destination>\n", " \n", "behaves differently. Why?" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Globbing works as one would expect:\n", "\n", " cp <source>/.txt <destination>\n", " \n", "will copy all files ending with .`txt` to `<destination>`" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Move (aka rename)\n", "\n", "How to move stuff?\n", "\n", " mv <source> <destination>" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "But what if we want to move a bunch of stuff?\n", "Sure this should work:\n", "\n", " mv <source>/.txt <destination>\n", " \n", "but it doesn't. WTF?" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Cheating way: install `rename` programm. \n", "Won't work if you don't have admin rights though.\n", "\n", "Proper way: loop\n", "\n", " for f in .txt; do mv $f <destintaion>; done" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "_HINT_: for a dry run replace `mv` with `echo`" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Delete" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "_CAUTION:_ There is no `undelete`. If you delete a file, it's gone forever!\n", "\n", "Delete (remove) a file(s):\n", "\n", " rm <file>\n", " \n", "Delete a directory:\n", "\n", " rm -r <directory>" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Selecting what to show (filtering)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### Globbing (aka wildcards)\n", "\n", "- `?` matches one (any) character\n", "- `` matches any number of any characters _except_ OS seprator (`/, .`)\n", "- `*` matches _any_ number of _any_ characters\n", "- `{pattern1,pattern2,...}` or `{start..end}` pattern expansion " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### grep" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "`grep` stands for Global Regular ExPression. Regular expressions `regex` is an advanced and powerful way to match patterns.\n", "`grep` can be thought of as a very versatile and efficient filter that can be configured to pass through only results you want." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Some plumbing: pipes, redirects and tee" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ " `\|` (aka pipe) - sends the output of the left program to the input of right program\n", "* `tee` - same as `pipe` but at the same time saves the output of the left command into a file\n", "* `>` - redirects the output of the programm to a file (overwriting the file if it exists)\n", "* `>>` - same as `>` but _appends_ to the file if it exists" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Practical things" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### Downloading stuff from Internet\n", "\n", "`wget` - loads of options and protocols supported. Read manpages for all options.\n", "\n", "Let's use it to download E.coli `.gff` file from NCBI (http://www.ncbi.nlm.nih.gov/genome/167):\n", "\n", " wget ftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCF_000005845.2_ASM584v2/GCF_000005845.2_ASM584v2_genomic.gff.gz\n", " \n", "and make sure it's where you expect it to be:\n", "\n", " ls -lah .gff.gz\n", " \n", "and download some more stuff:\n", "\n", " wget ngs.nudlerlab.info/master.zip\n", " wget ngs.nudlerlab.info/BJ-HSR1.pe.fastq.gz\n", " " ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "--2021-08-04 12:42:53-- ftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCF_000005845.2_ASM584v2/GCF_000005845.2_ASM584v2_genomic.gff.gz\n", " => ‘GCF_000005845.2_ASM584v2_genomic.gff.gz’\n", "Resolving ftp.ncbi.nlm.nih.gov (ftp.ncbi.nlm.nih.gov)... 130.14.250.12, 130.14.250.13, 2607:f220:41e:250::13, ...\n", "Connecting to ftp.ncbi.nlm.nih.gov (ftp.ncbi.nlm.nih.gov)\|130.14.250.12\|:21... connected.\n", "Logging in as anonymous ... Logged in!\n", "==> SYST ... done. ==> PWD ... done.\n", "==> TYPE I ... done. ==> CWD (1) /genomes/all/GCF_000005845.2_ASM584v2 ... \n", "No such directory ‘genomes/all/GCF_000005845.2_ASM584v2’.\n", "\n" ] }, { "ename": "", "evalue": "8", "output_type": "error", "traceback": [] } ], "source": [ "wget ftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCF_000005845.2_ASM584v2/GCF_000005845.2_ASM584v2_genomic.gff.gz" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-rw-rw-r-- 1 ilya ilya 2.5M Aug 4 12:47 NC_000913-1.\u001b[01;31m\u001b[Kgff\u001b[m\u001b[K\n" ] } ], "source": [ "# mv GCF_000005845.2_ASM584v2_genomic.gff.gz ../data\n", "ls -lah ../data \| grep gff" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "gi\|556503834\|ref\|NC_000913.3\|\tRefSeq\tregion\t1\t4641652\t.\t+\t.\tID=id0;Name=ANONYMOUS;Dbxref=taxon:511145;Is_circular=true;gbkey=Src;genome=chromosome;mol_type=genomic DNA;strain=K-12;substrain=MG1655\n", "gi\|556503834\|ref\|NC_000913.3\|\tRefSeq\tgene\t190\t255\t.\t+\t.\tID=gene0;Name=thrL;Dbxref=EcoGene:EG11277,GeneID:944742;gbkey=Gene;gene=thrL;gene_synonym=ECK0001,JW4367;locus_tag=b0001\n", "gi\|556503834\|ref\|NC_000913.3\|\tRefSeq\tCDS\t190\t255\t.\t+\t0\tID=cds0;Name=NP_414542.1;Parent=gene0;Dbxref=ASAP:ABE-0000006,UniProtKB%2FSwiss-Prot:P0AD86,Genbank:NP_414542.1,EcoGene:EG11277,GeneID:944742;gbkey=CDS;gene=thrL;product=thr operon leader peptide;protein_id=NP_414542.1;transl_table=11\n", "gi\|556503834\|ref\|NC_000913.3\|\tRefSeq\tgene\t337\t2799\t.\t+\t.\tID=gene1;Name=thrA;Dbxref=EcoGene:EG10998,GeneID:945803;gbkey=Gene;gene=thrA;gene_synonym=ECK0002,Hs,JW0001,thrA1,thrA2,thrD;locus_tag=b0002\n", "gi\|556503834\|ref\|NC_000913.3\|\tRefSeq\tCDS\t337\t2799\t.\t+\t0\tID=cds1;Name=NP_414543.1;Parent=gene1;Note=bifunctional: aspartokinase I %28N-terminal%29%3B homoserine dehydrogenase I %28C-terminal%29;Dbxref=ASAP:ABE-0000008,UniProtKB%2FSwiss-Prot:P00561,Genbank:NP_414543.1,EcoGene:EG10998,GeneID:945803;gbkey=CDS;gene=thrA;product=fused aspartokinase I and homoserine dehydrogenase I;protein_id=NP_414543.1;transl_table=11\n", "gi\|556503834\|ref\|NC_000913.3\|\tRefSeq\tgene\t2801\t3733\t.\t+\t.\tID=gene2;Name=thrB;Dbxref=EcoGene:EG10999,GeneID:947498;gbkey=Gene;gene=thrB;gene_synonym=ECK0003,JW0002;locus_tag=b0003\n", "gi\|556503834\|ref\|NC_000913.3\|\tRefSeq\tCDS\t2801\t3733\t.\t+\t0\tID=cds2;Name=NP_414544.1;Parent=gene2;Dbxref=ASAP:ABE-0000010,UniProtKB%2FSwiss-Prot:P00547,Genbank:NP_414544.1,EcoGene:EG10999,GeneID:947498;gbkey=CDS;gene=thrB;product=homoserine kinase;protein_id=NP_414544.1;transl_table=11\n", "gi\|556503834\|ref\|NC_000913.3\|\tRefSeq\tgene\t3734\t5020\t.\t+\t.\tID=gene3;Name=thrC;Dbxref=EcoGene:EG11000,GeneID:945198;gbkey=Gene;gene=thrC;gene_synonym=ECK0004,JW0003;locus_tag=b0004\n", "gi\|556503834\|ref\|NC_000913.3\|\tRefSeq\tCDS\t3734\t5020\t.\t+\t0\tID=cds3;Name=NP_414545.1;Parent=gene3;Dbxref=ASAP:ABE-0000012,UniProtKB%2FSwiss-Prot:P00934,Genbank:NP_414545.1,EcoGene:EG11000,GeneID:945198;gbkey=CDS;gene=thrC;product=threonine synthase;protein_id=NP_414545.1;transl_table=11\n", "gi\|556503834\|ref\|NC_000913.3\|\tRefSeq\tgene\t5234\t5530\t.\t+\t.\tID=gene4;Name=yaaX;Dbxref=EcoGene:EG14384,GeneID:944747;gbkey=Gene;gene=yaaX;gene_synonym=ECK0005,JW0004;locus_tag=b0005\n", "grep: write error: Broken pipe\n", "cat: write error: Broken pipe\n" ] } ], "source": [ "cat ../data/NC_000913-1.gff \| grep -v ^# \| head" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "region\n", "gene\n", "CDS\n", "gene\n", "CDS\n", "gene\n", "CDS\n", "gene\n", "CDS\n", "gene\n", "cut: write error: Broken pipe\n" ] } ], "source": [ "cat ../data/NC_000913-1.gff \| grep -v ^# \| cut -f 3 \| head" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 4385 CDS\n", " 176 exon\n", " 4516 gene\n", " 63 ncRNA\n", " 62 region\n", " 355 repeat_region\n", " 22 rRNA\n", " 2 tmRNA\n", " 89 tRNA\n" ] } ], "source": [ "cat ../data/NC_000913-1.gff \| grep -v ^# \| cut -f 3 \| sort \| uniq -c" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### Working with compressed files\n", "\n", "Most NGS data formats are text based and, therefore, are highly compressable. For instance, gzipped `.fastq` file can take 10-20% of the original space.\n", "\n", " `gzip` - compresses the file\n", "* `gunzip` - uncompresses the file\n", "\n", "By default both `gzip` and `gunzip` delete the original. To keep original file use `zcat` or `-c` flag for `gzip/gunzip`" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "It's a perfect usecase for `pipes`, so let's dig right in. Let's have a look what's inside the `.gff` file we've just downloaded:\n", "\n", " zcat GCF_000005845.2_ASM584v2_genomic.gff.gz \| less" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "How can we modify the above to show only beginning of the file? End of the file?" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "`tar` is for working with compressed directories" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### Soma random useful things (text processing)\n", "\n", "`sort` - self explanatory. Sorts the input in variety of ways. Really useful when chaining several programs using `pipes`.\n", "\n", "`uniq` - outputs unique items from the input stream. Can count occurences of each item. Again, really shines when used with other programs.\n", "\n", "`tr` - translates or deletes characters from the input stream. Doesn't sound like much but is a real time-saver when building pipelines and workflows.\n", "\n", "`wc` - word count. Self-explanatory and you get the idea, useful to compose \"compound\" commands from simple programs.\n", "\n", "Again, to get the full list of available options use `man <program>` command." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Putting it all together" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Coming back to `.gff` file. Let's see how we can build a nice little summary of E.coli genomic features (genes, CDS, and so on).\n", "\n", "For starters:\n", "\n", " zcat GCF_000005845.2_ASM584v2_genomic.gff.gz \| less" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "good, but what's up with all those lines starting with `#`? Those are comments and we want to get rid of them.\n", "\n", "`grep` to the rescue:\n", "\n", " zcat GCF_000005845.2_ASM584v2_genomic.gff.gz \| grep -v ^# \| less\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Better! So we are left with tab-delimited file (a relative of `.csv` really). Now we see we're interested in the 3rd column. Let's split the line on tabs and take the third field:\n", "\n", " zcat GCF_000005845.2_ASM584v2_genomic.gff.gz \| grep -v ^# \| cut -f 3 \| less\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Ugly! But what if we sort the values and count unique items?\n", "\n", " zcat GCF_000005845.2_ASM584v2_genomic.gff.gz \| grep -v ^# \| cut -f 3 \| sort \| uniq -c\n", " \n", "And there you have it!" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Permissions" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "In UNIX, access to files is controlled via `permissions`.\n", "\n", "There are three levels of permissions:\n", "\n", "- `u` user\n", "- `g` group\n", "- `o` others" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Permissions:\n", "\n", "- `r` read permission\n", "- `w` write permission (also create or delete)\n", "- `x` eXecute permission (directories must have `x` permission set in order to be able to `cd` into them!!!)\n", "\n", "`ls -l` command will output lines starting with the `permissions` part.\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "By default, only file's owner (and `root`) has access to it.\n", "\n", "Relevant commands:\n", "\n", "- `chown` - change the owner (must have permission to do so!)\n", "- `chgrp` - change file's group\n", "- `chmod` - change permission(s)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Optional: editing files (`nano` and `vim`), symbolic links, processes." ] } ], "metadata": { "celltoolbar": "Slideshow", "kernelspec": { "display_name": "Bash", "language": "bash", "name": "bash" }, "language_info": { "codemirror_mode": "shell", "file_extension": ".sh", "mimetype": "text/x-sh", "name": "bash" }, "widgets": { "state": {}, "version": "1.1.1" } }, "nbformat": 4, "nbformat_minor": 4 }	mit
huajianmao/learning	coursera/deep-learning/5.nlp-sequence-models/week1/Neural machine translation with attention - v2.ipynb	1	90433	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Neural Machine Translation\n", "\n", "Welcome to your first programming assignment for this week! \n", "\n", "You will build a Neural Machine Translation (NMT) model to translate human readable dates (\"25th of June, 2009\") into machine readable dates (\"2009-06-25\"). You will do this using an attention model, one of the most sophisticated sequence to sequence models. \n", "\n", "This notebook was produced together with NVIDIA's Deep Learning Institute. \n", "\n", "Let's load all the packages you will need for this assignment." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using TensorFlow backend.\n" ] } ], "source": [ "from keras.layers import Bidirectional, Concatenate, Permute, Dot, Input, LSTM, Multiply\n", "from keras.layers import RepeatVector, Dense, Activation, Lambda\n", "from keras.optimizers import Adam\n", "from keras.utils import to_categorical\n", "from keras.models import load_model, Model\n", "import keras.backend as K\n", "import numpy as np\n", "\n", "from faker import Faker\n", "import random\n", "from tqdm import tqdm\n", "from babel.dates import format_date\n", "from nmt_utils import \n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1 - Translating human readable dates into machine readable dates\n", "\n", "The model you will build here could be used to translate from one language to another, such as translating from English to Hindi. However, language translation requires massive datasets and usually takes days of training on GPUs. To give you a place to experiment with these models even without using massive datasets, we will instead use a simpler \"date translation\" task. \n", "\n", "The network will input a date written in a variety of possible formats (e.g. \"the 29th of August 1958\", \"03/30/1968\", \"24 JUNE 1987\") and translate them into standardized, machine readable dates (e.g. \"1958-08-29\", \"1968-03-30\", \"1987-06-24\"). We will have the network learn to output dates in the common machine-readable format YYYY-MM-DD. \n", "\n", "\n", "\n", "<!-- \n", "Take a look at [nmt_utils.py](./nmt_utils.py) to see all the formatting. Count and figure out how the formats work, you will need this knowledge later. !--> " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1.1 - Dataset\n", "\n", "We will train the model on a dataset of 10000 human readable dates and their equivalent, standardized, machine readable dates. Let's run the following cells to load the dataset and print some examples. " ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%\|██████████\| 10000/10000 [00:01<00:00, 6600.12it/s]\n" ] } ], "source": [ "m = 10000\n", "dataset, human_vocab, machine_vocab, inv_machine_vocab = load_dataset(m)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[('9 may 1998', '1998-05-09'),\n", " ('10.09.70', '1970-09-10'),\n", " ('4/28/90', '1990-04-28'),\n", " ('thursday january 26 1995', '1995-01-26'),\n", " ('monday march 7 1983', '1983-03-07'),\n", " ('sunday may 22 1988', '1988-05-22'),\n", " ('tuesday july 8 2008', '2008-07-08'),\n", " ('08 sep 1999', '1999-09-08'),\n", " ('1 jan 1981', '1981-01-01'),\n", " ('monday may 22 1995', '1995-05-22')]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset[:10]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You've loaded:\n", "- `dataset`: a list of tuples of (human readable date, machine readable date)\n", "- `human_vocab`: a python dictionary mapping all characters used in the human readable dates to an integer-valued index \n", "- `machine_vocab`: a python dictionary mapping all characters used in machine readable dates to an integer-valued index. These indices are not necessarily consistent with `human_vocab`. \n", "- `inv_machine_vocab`: the inverse dictionary of `machine_vocab`, mapping from indices back to characters. \n", "\n", "Let's preprocess the data and map the raw text data into the index values. We will also use Tx=30 (which we assume is the maximum length of the human readable date; if we get a longer input, we would have to truncate it) and Ty=10 (since \"YYYY-MM-DD\" is 10 characters long). " ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "X.shape: (10000, 30)\n", "Y.shape: (10000, 10)\n", "Xoh.shape: (10000, 30, 37)\n", "Yoh.shape: (10000, 10, 11)\n" ] } ], "source": [ "Tx = 30\n", "Ty = 10\n", "X, Y, Xoh, Yoh = preprocess_data(dataset, human_vocab, machine_vocab, Tx, Ty)\n", "\n", "print(\"X.shape:\", X.shape)\n", "print(\"Y.shape:\", Y.shape)\n", "print(\"Xoh.shape:\", Xoh.shape)\n", "print(\"Yoh.shape:\", Yoh.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You now have:\n", "- `X`: a processed version of the human readable dates in the training set, where each character is replaced by an index mapped to the character via `human_vocab`. Each date is further padded to $T_x$ values with a special character (< pad >). `X.shape = (m, Tx)`\n", "- `Y`: a processed version of the machine readable dates in the training set, where each character is replaced by the index it is mapped to in `machine_vocab`. You should have `Y.shape = (m, Ty)`. \n", "- `Xoh`: one-hot version of `X`, the \"1\" entry's index is mapped to the character thanks to `human_vocab`. `Xoh.shape = (m, Tx, len(human_vocab))`\n", "- `Yoh`: one-hot version of `Y`, the \"1\" entry's index is mapped to the character thanks to `machine_vocab`. `Yoh.shape = (m, Tx, len(machine_vocab))`. Here, `len(machine_vocab) = 11` since there are 11 characters ('-' as well as 0-9). \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lets also look at some examples of preprocessed training examples. Feel free to play with `index` in the cell below to navigate the dataset and see how source/target dates are preprocessed. " ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Source date: 9 may 1998\n", "Target date: 1998-05-09\n", "\n", "Source after preprocessing (indices): [12 0 24 13 34 0 4 12 12 11 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36\n", " 36 36 36 36 36]\n", "Target after preprocessing (indices): [ 2 10 10 9 0 1 6 0 1 10]\n", "\n", "Source after preprocessing (one-hot): [[ 0. 0. 0. ..., 0. 0. 0.]\n", " [ 1. 0. 0. ..., 0. 0. 0.]\n", " [ 0. 0. 0. ..., 0. 0. 0.]\n", " ..., \n", " [ 0. 0. 0. ..., 0. 0. 1.]\n", " [ 0. 0. 0. ..., 0. 0. 1.]\n", " [ 0. 0. 0. ..., 0. 0. 1.]]\n", "Target after preprocessing (one-hot): [[ 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n", " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n", " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n", " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n", " [ 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n", " [ 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n", " [ 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\n", " [ 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n", " [ 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n", " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]]\n" ] } ], "source": [ "index = 0\n", "print(\"Source date:\", dataset[index][0])\n", "print(\"Target date:\", dataset[index][1])\n", "print()\n", "print(\"Source after preprocessing (indices):\", X[index])\n", "print(\"Target after preprocessing (indices):\", Y[index])\n", "print()\n", "print(\"Source after preprocessing (one-hot):\", Xoh[index])\n", "print(\"Target after preprocessing (one-hot):\", Yoh[index])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2 - Neural machine translation with attention\n", "\n", "If you had to translate a book's paragraph from French to English, you would not read the whole paragraph, then close the book and translate. Even during the translation process, you would read/re-read and focus on the parts of the French paragraph corresponding to the parts of the English you are writing down. \n", "\n", "The attention mechanism tells a Neural Machine Translation model where it should pay attention to at any step. \n", "\n", "\n", "### 2.1 - Attention mechanism\n", "\n", "In this part, you will implement the attention mechanism presented in the lecture videos. Here is a figure to remind you how the model works. The diagram on the left shows the attention model. The diagram on the right shows what one \"Attention\" step does to calculate the attention variables $\\alpha^{\\langle t, t' \\rangle}$, which are used to compute the context variable $context^{\\langle t \\rangle}$ for each timestep in the output ($t=1, \\ldots, T_y$). \n", "\n", "<table>\n", "<td> \n", "<img src=\"images/attn_model.png\" style=\"width:500;height:500px;\"> <br>\n", "</td> \n", "<td> \n", "<img src=\"images/attn_mechanism.png\" style=\"width:500;height:500px;\"> <br>\n", "</td> \n", "</table>\n", "<caption><center> Figure 1: Neural machine translation with attention</center></caption>\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "Here are some properties of the model that you may notice: \n", "\n", "- There are two separate LSTMs in this model (see diagram on the left). Because the one at the bottom of the picture is a Bi-directional LSTM and comes before* the attention mechanism, we will call it pre-attention Bi-LSTM. The LSTM at the top of the diagram comes after the attention mechanism, so we will call it the post-attention LSTM. The pre-attention Bi-LSTM goes through $T_x$ time steps; the post-attention LSTM goes through $T_y$ time steps. \n", "\n", "- The post-attention LSTM passes $s^{\\langle t \\rangle}, c^{\\langle t \\rangle}$ from one time step to the next. In the lecture videos, we were using only a basic RNN for the post-activation sequence model, so the state captured by the RNN output activations $s^{\\langle t\\rangle}$. But since we are using an LSTM here, the LSTM has both the output activation $s^{\\langle t\\rangle}$ and the hidden cell state $c^{\\langle t\\rangle}$. However, unlike previous text generation examples (such as Dinosaurus in week 1), in this model the post-activation LSTM at time $t$ does will not take the specific generated $y^{\\langle t-1 \\rangle}$ as input; it only takes $s^{\\langle t\\rangle}$ and $c^{\\langle t\\rangle}$ as input. We have designed the model this way, because (unlike language generation where adjacent characters are highly correlated) there isn't as strong a dependency between the previous character and the next character in a YYYY-MM-DD date. \n", "\n", "- We use $a^{\\langle t \\rangle} = [\\overrightarrow{a}^{\\langle t \\rangle}; \\overleftarrow{a}^{\\langle t \\rangle}]$ to represent the concatenation of the activations of both the forward-direction and backward-directions of the pre-attention Bi-LSTM. \n", "\n", "- The diagram on the right uses a `RepeatVector` node to copy $s^{\\langle t-1 \\rangle}$'s value $T_x$ times, and then `Concatenation` to concatenate $s^{\\langle t-1 \\rangle}$ and $a^{\\langle t \\rangle}$ to compute $e^{\\langle t, t'}$, which is then passed through a softmax to compute $\\alpha^{\\langle t, t' \\rangle}$. We'll explain how to use `RepeatVector` and `Concatenation` in Keras below. \n", "\n", "Lets implement this model. You will start by implementing two functions: `one_step_attention()` and `model()`.\n", "\n", "1) `one_step_attention()`: At step $t$, given all the hidden states of the Bi-LSTM ($[a^{<1>},a^{<2>}, ..., a^{<T_x>}]$) and the previous hidden state of the second LSTM ($s^{<t-1>}$), `one_step_attention()` will compute the attention weights ($[\\alpha^{<t,1>},\\alpha^{<t,2>}, ..., \\alpha^{<t,T_x>}]$) and output the context vector (see Figure 1 (right) for details):\n", "$$context^{<t>} = \\sum_{t' = 0}^{T_x} \\alpha^{<t,t'>}a^{<t'>}\\tag{1}$$ \n", "\n", "Note that we are denoting the attention in this notebook $context^{\\langle t \\rangle}$. In the lecture videos, the context was denoted $c^{\\langle t \\rangle}$, but here we are calling it $context^{\\langle t \\rangle}$ to avoid confusion with the (post-attention) LSTM's internal memory cell variable, which is sometimes also denoted $c^{\\langle t \\rangle}$. \n", " \n", "2) `model()`: Implements the entire model. It first runs the input through a Bi-LSTM to get back $[a^{<1>},a^{<2>}, ..., a^{<T_x>}]$. Then, it calls `one_step_attention()` $T_y$ times (`for` loop). At each iteration of this loop, it gives the computed context vector $c^{<t>}$ to the second LSTM, and runs the output of the LSTM through a dense layer with softmax activation to generate a prediction $\\hat{y}^{<t>}$. \n", "\n", "\n", "\n", "Exercise: Implement `one_step_attention()`. The function `model()` will call the layers in `one_step_attention()` $T_y$ using a for-loop, and it is important that all $T_y$ copies have the same weights. I.e., it should not re-initiaiize the weights every time. In other words, all $T_y$ steps should have shared weights. Here's how you can implement layers with shareable weights in Keras:\n", "1. Define the layer objects (as global variables for examples).\n", "2. Call these objects when propagating the input.\n", "\n", "We have defined the layers you need as global variables. Please run the following cells to create them. Please check the Keras documentation to make sure you understand what these layers are: [RepeatVector()](https://keras.io/layers/core/#repeatvector), [Concatenate()](https://keras.io/layers/merge/#concatenate), [Dense()](https://keras.io/layers/core/#dense), [Activation()](https://keras.io/layers/core/#activation), [Dot()](https://keras.io/layers/merge/#dot)." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Defined shared layers as global variables\n", "repeator = RepeatVector(Tx)\n", "concatenator = Concatenate(axis=-1)\n", "densor = Dense(1, activation = \"relu\")\n", "activator = Activation(softmax, name='attention_weights') # We are using a custom softmax(axis = 1) loaded in this notebook\n", "dotor = Dot(axes = 1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now you can use these layers to implement `one_step_attention()`. In order to propagate a Keras tensor object X through one of these layers, use `layer(X)` (or `layer([X,Y])` if it requires multiple inputs.), e.g. `densor(X)` will propagate X through the `Dense(1)` layer defined above." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# GRADED FUNCTION: one_step_attention\n", "\n", "def one_step_attention(a, s_prev):\n", " \"\"\"\n", " Performs one step of attention: Outputs a context vector computed as a dot product of the attention weights\n", " \"alphas\" and the hidden states \"a\" of the Bi-LSTM.\n", " \n", " Arguments:\n", " a -- hidden state output of the Bi-LSTM, numpy-array of shape (m, Tx, 2n_a)\n", " s_prev -- previous hidden state of the (post-attention) LSTM, numpy-array of shape (m, n_s)\n", " \n", " Returns:\n", " context -- context vector, input of the next (post-attetion) LSTM cell\n", " \"\"\"\n", " \n", " ### START CODE HERE ###\n", " # Use repeator to repeat s_prev to be of shape (m, Tx, n_s) so that you can concatenate it with all hidden states \"a\" (≈ 1 line)\n", " s_prev = repeator(s_prev)\n", " # Use concatenator to concatenate a and s_prev on the last axis (≈ 1 line)\n", " concat = concatenator([a, s_prev])\n", " # Use densor to propagate concat through a small fully-connected neural network to compute the \"energies\" variable e. (≈1 lines)\n", " e = densor(concat)\n", " # Use activator and e to compute the attention weights \"alphas\" (≈ 1 line)\n", " alphas = activator(e)\n", " # Use dotor together with \"alphas\" and \"a\" to compute the context vector to be given to the next (post-attention) LSTM-cell (≈ 1 line)\n", " context = dotor([alphas, a])\n", " ### END CODE HERE ###\n", " \n", " return context" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You will be able to check the expected output of `one_step_attention()` after you've coded the `model()` function." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Exercise: Implement `model()` as explained in figure 2 and the text above. Again, we have defined global layers that will share weights to be used in `model()`." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "n_a = 64\n", "n_s = 128\n", "post_activation_LSTM_cell = LSTM(n_s, return_state = True)\n", "output_layer = Dense(len(machine_vocab), activation=softmax)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now you can use these layers $T_y$ times in a `for` loop to generate the outputs, and their parameters will not be reinitialized. You will have to carry out the following steps: \n", "\n", "1. Propagate the input into a [Bidirectional](https://keras.io/layers/wrappers/#bidirectional) [LSTM](https://keras.io/layers/recurrent/#lstm)\n", "2. Iterate for $t = 0, \\dots, T_y-1$: \n", " 1. Call `one_step_attention()` on $[\\alpha^{<t,1>},\\alpha^{<t,2>}, ..., \\alpha^{<t,T_x>}]$ and $s^{<t-1>}$ to get the context vector $context^{<t>}$.\n", " 2. Give $context^{<t>}$ to the post-attention LSTM cell. Remember pass in the previous hidden-state $s^{\\langle t-1\\rangle}$ and cell-states $c^{\\langle t-1\\rangle}$ of this LSTM using `initial_state= [previous hidden state, previous cell state]`. Get back the new hidden state $s^{<t>}$ and the new cell state $c^{<t>}$.\n", " 3. Apply a softmax layer to $s^{<t>}$, get the output. \n", " 4. Save the output by adding it to the list of outputs.\n", "\n", "3. Create your Keras model instance, it should have three inputs (\"inputs\", $s^{<0>}$ and $c^{<0>}$) and output the list of \"outputs\"." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# GRADED FUNCTION: model\n", "\n", "def model(Tx, Ty, n_a, n_s, human_vocab_size, machine_vocab_size):\n", " \"\"\"\n", " Arguments:\n", " Tx -- length of the input sequence\n", " Ty -- length of the output sequence\n", " n_a -- hidden state size of the Bi-LSTM\n", " n_s -- hidden state size of the post-attention LSTM\n", " human_vocab_size -- size of the python dictionary \"human_vocab\"\n", " machine_vocab_size -- size of the python dictionary \"machine_vocab\"\n", "\n", " Returns:\n", " model -- Keras model instance\n", " \"\"\"\n", " \n", " # Define the inputs of your model with a shape (Tx,)\n", " # Define s0 and c0, initial hidden state for the decoder LSTM of shape (n_s,)\n", " X = Input(shape=(Tx, human_vocab_size))\n", " s0 = Input(shape=(n_s,), name='s0')\n", " c0 = Input(shape=(n_s,), name='c0')\n", " s = s0\n", " c = c0\n", " \n", " # Initialize empty list of outputs\n", " outputs = []\n", " \n", " ### START CODE HERE ###\n", " \n", " # Step 1: Define your pre-attention Bi-LSTM. Remember to use return_sequences=True. (≈ 1 line)\n", " a = Bidirectional(LSTM(n_a, return_sequences=True))(X)\n", " \n", " # Step 2: Iterate for Ty steps\n", " for t in range(Ty):\n", " \n", " # Step 2.A: Perform one step of the attention mechanism to get back the context vector at step t (≈ 1 line)\n", " context = one_step_attention(a, s)\n", " \n", " # Step 2.B: Apply the post-attention LSTM cell to the \"context\" vector.\n", " # Don't forget to pass: initial_state = [hidden state, cell state] (≈ 1 line)\n", " s, _, c = post_activation_LSTM_cell(context, initial_state = [s, c])\n", " \n", " # Step 2.C: Apply Dense layer to the hidden state output of the post-attention LSTM (≈ 1 line)\n", " out = output_layer(s)\n", " \n", " # Step 2.D: Append \"out\" to the \"outputs\" list (≈ 1 line)\n", " outputs.append(out)\n", " \n", " # Step 3: Create model instance taking three inputs and returning the list of outputs. (≈ 1 line)\n", " model = Model(inputs = [X, s0, c0], outputs = outputs)\n", " \n", " ### END CODE HERE ###\n", " \n", " return model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run the following cell to create your model." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "model = model(Tx, Ty, n_a, n_s, len(human_vocab), len(machine_vocab))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's get a summary of the model to check if it matches the expected output." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "____________________________________________________________________________________________________\n", "Layer (type) Output Shape Param # Connected to \n", "====================================================================================================\n", "input_1 (InputLayer) (None, 30, 37) 0 \n", "____________________________________________________________________________________________________\n", "s0 (InputLayer) (None, 128) 0 \n", "____________________________________________________________________________________________________\n", "bidirectional_1 (Bidirectional) (None, 30, 128) 52224 input_1[0][0] \n", "____________________________________________________________________________________________________\n", "repeat_vector_1 (RepeatVector) (None, 30, 128) 0 s0[0][0] \n", " lstm_1[0][0] \n", " lstm_1[1][0] \n", " lstm_1[2][0] \n", " lstm_1[3][0] \n", " lstm_1[4][0] \n", " lstm_1[5][0] \n", " lstm_1[6][0] \n", " lstm_1[7][0] \n", " lstm_1[8][0] \n", "____________________________________________________________________________________________________\n", "concatenate_1 (Concatenate) (None, 30, 256) 0 bidirectional_1[0][0] \n", " repeat_vector_1[0][0] \n", " bidirectional_1[0][0] \n", " repeat_vector_1[1][0] \n", " bidirectional_1[0][0] \n", " repeat_vector_1[2][0] \n", " bidirectional_1[0][0] \n", " repeat_vector_1[3][0] \n", " bidirectional_1[0][0] \n", " repeat_vector_1[4][0] \n", " bidirectional_1[0][0] \n", " repeat_vector_1[5][0] \n", " bidirectional_1[0][0] \n", " repeat_vector_1[6][0] \n", " bidirectional_1[0][0] \n", " repeat_vector_1[7][0] \n", " bidirectional_1[0][0] \n", " repeat_vector_1[8][0] \n", " bidirectional_1[0][0] \n", " repeat_vector_1[9][0] \n", "____________________________________________________________________________________________________\n", "dense_1 (Dense) (None, 30, 1) 257 concatenate_1[0][0] \n", " concatenate_1[1][0] \n", " concatenate_1[2][0] \n", " concatenate_1[3][0] \n", " concatenate_1[4][0] \n", " concatenate_1[5][0] \n", " concatenate_1[6][0] \n", " concatenate_1[7][0] \n", " concatenate_1[8][0] \n", " concatenate_1[9][0] \n", "____________________________________________________________________________________________________\n", "attention_weights (Activation) (None, 30, 1) 0 dense_1[0][0] \n", " dense_1[1][0] \n", " dense_1[2][0] \n", " dense_1[3][0] \n", " dense_1[4][0] \n", " dense_1[5][0] \n", " dense_1[6][0] \n", " dense_1[7][0] \n", " dense_1[8][0] \n", " dense_1[9][0] \n", "____________________________________________________________________________________________________\n", "dot_1 (Dot) (None, 1, 128) 0 attention_weights[0][0] \n", " bidirectional_1[0][0] \n", " attention_weights[1][0] \n", " bidirectional_1[0][0] \n", " attention_weights[2][0] \n", " bidirectional_1[0][0] \n", " attention_weights[3][0] \n", " bidirectional_1[0][0] \n", " attention_weights[4][0] \n", " bidirectional_1[0][0] \n", " attention_weights[5][0] \n", " bidirectional_1[0][0] \n", " attention_weights[6][0] \n", " bidirectional_1[0][0] \n", " attention_weights[7][0] \n", " bidirectional_1[0][0] \n", " attention_weights[8][0] \n", " bidirectional_1[0][0] \n", " attention_weights[9][0] \n", " bidirectional_1[0][0] \n", "____________________________________________________________________________________________________\n", "c0 (InputLayer) (None, 128) 0 \n", "____________________________________________________________________________________________________\n", "lstm_1 (LSTM) [(None, 128), (None, 131584 dot_1[0][0] \n", " s0[0][0] \n", " c0[0][0] \n", " dot_1[1][0] \n", " lstm_1[0][0] \n", " lstm_1[0][2] \n", " dot_1[2][0] \n", " lstm_1[1][0] \n", " lstm_1[1][2] \n", " dot_1[3][0] \n", " lstm_1[2][0] \n", " lstm_1[2][2] \n", " dot_1[4][0] \n", " lstm_1[3][0] \n", " lstm_1[3][2] \n", " dot_1[5][0] \n", " lstm_1[4][0] \n", " lstm_1[4][2] \n", " dot_1[6][0] \n", " lstm_1[5][0] \n", " lstm_1[5][2] \n", " dot_1[7][0] \n", " lstm_1[6][0] \n", " lstm_1[6][2] \n", " dot_1[8][0] \n", " lstm_1[7][0] \n", " lstm_1[7][2] \n", " dot_1[9][0] \n", " lstm_1[8][0] \n", " lstm_1[8][2] \n", "____________________________________________________________________________________________________\n", "dense_2 (Dense) (None, 11) 1419 lstm_1[0][0] \n", " lstm_1[1][0] \n", " lstm_1[2][0] \n", " lstm_1[3][0] \n", " lstm_1[4][0] \n", " lstm_1[5][0] \n", " lstm_1[6][0] \n", " lstm_1[7][0] \n", " lstm_1[8][0] \n", " lstm_1[9][0] \n", "====================================================================================================\n", "Total params: 185,484\n", "Trainable params: 185,484\n", "Non-trainable params: 0\n", "____________________________________________________________________________________________________\n" ] } ], "source": [ "model.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Expected Output:\n", "\n", "Here is the summary you should see\n", "<table>\n", " <tr>\n", " <td>\n", " Total params:\n", " </td>\n", " <td>\n", " 185,484\n", " </td>\n", " </tr>\n", " <tr>\n", " <td>\n", " Trainable params:\n", " </td>\n", " <td>\n", " 185,484\n", " </td>\n", " </tr>\n", " <tr>\n", " <td>\n", " Non-trainable params:\n", " </td>\n", " <td>\n", " 0\n", " </td>\n", " </tr>\n", " <tr>\n", " <td>\n", " bidirectional_1's output shape \n", " </td>\n", " <td>\n", " (None, 30, 128) \n", " </td>\n", " </tr>\n", " <tr>\n", " <td>\n", " repeat_vector_1's output shape \n", " </td>\n", " <td>\n", " (None, 30, 128) \n", " </td>\n", " </tr>\n", " <tr>\n", " <td>\n", " concatenate_1's output shape \n", " </td>\n", " <td>\n", " (None, 30, 256) \n", " </td>\n", " </tr>\n", " <tr>\n", " <td>\n", " attention_weights's output shape \n", " </td>\n", " <td>\n", " (None, 30, 1) \n", " </td>\n", " </tr>\n", " <tr>\n", " <td>\n", " dot_1's output shape \n", " </td>\n", " <td>\n", " (None, 1, 128) \n", " </td>\n", " </tr>\n", " <tr>\n", " <td>\n", " dense_2's output shape \n", " </td>\n", " <td>\n", " (None, 11) \n", " </td>\n", " </tr>\n", "</table>\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As usual, after creating your model in Keras, you need to compile it and define what loss, optimizer and metrics your are want to use. Compile your model using `categorical_crossentropy` loss, a custom [Adam](https://keras.io/optimizers/#adam) [optimizer](https://keras.io/optimizers/#usage-of-optimizers) (`learning rate = 0.005`, $\\beta_1 = 0.9$, $\\beta_2 = 0.999$, `decay = 0.01`) and `['accuracy']` metrics:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "### START CODE HERE ### (≈2 lines)\n", "out = model.compile(optimizer=Adam(lr=0.005, beta_1=0.9, beta_2=0.999, decay=0.01),\n", " metrics=['accuracy'],\n", " loss='categorical_crossentropy')\n", "out\n", "### END CODE HERE ###" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The last step is to define all your inputs and outputs to fit the model:\n", "- You already have X of shape $(m = 10000, T_x = 30)$ containing the training examples.\n", "- You need to create `s0` and `c0` to initialize your `post_activation_LSTM_cell` with 0s.\n", "- Given the `model()` you coded, you need the \"outputs\" to be a list of 11 elements of shape (m, T_y). So that: `outputs[i][0], ..., outputs[i][Ty]` represent the true labels (characters) corresponding to the $i^{th}$ training example (`X[i]`). More generally, `outputs[i][j]` is the true label of the $j^{th}$ character in the $i^{th}$ training example." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "s0 = np.zeros((m, n_s))\n", "c0 = np.zeros((m, n_s))\n", "outputs = list(Yoh.swapaxes(0,1))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's now fit the model and run it for one epoch." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/10\n", "10000/10000 [==============================] - 50s - loss: 14.7492 - dense_2_loss_1: 1.0332 - dense_2_loss_2: 0.8089 - dense_2_loss_3: 1.5546 - dense_2_loss_4: 2.5728 - dense_2_loss_5: 0.5977 - dense_2_loss_6: 0.9982 - dense_2_loss_7: 2.4074 - dense_2_loss_8: 0.7455 - dense_2_loss_9: 1.5168 - dense_2_loss_10: 2.5141 - dense_2_acc_1: 0.5641 - dense_2_acc_2: 0.7527 - dense_2_acc_3: 0.3644 - dense_2_acc_4: 0.1048 - dense_2_acc_5: 0.9392 - dense_2_acc_6: 0.5023 - dense_2_acc_7: 0.1585 - dense_2_acc_8: 0.8448 - dense_2_acc_9: 0.3526 - dense_2_acc_10: 0.1416 \n", "Epoch 2/10\n", "10000/10000 [==============================] - 50s - loss: 7.6761 - dense_2_loss_1: 0.0986 - dense_2_loss_2: 0.0857 - dense_2_loss_3: 0.8875 - dense_2_loss_4: 2.0124 - dense_2_loss_5: 0.0063 - dense_2_loss_6: 0.1526 - dense_2_loss_7: 1.5341 - dense_2_loss_8: 0.0089 - dense_2_loss_9: 0.9161 - dense_2_loss_10: 1.9739 - dense_2_acc_1: 0.9668 - dense_2_acc_2: 0.9696 - dense_2_acc_3: 0.5947 - dense_2_acc_4: 0.2769 - dense_2_acc_5: 1.0000 - dense_2_acc_6: 0.9521 - dense_2_acc_7: 0.4389 - dense_2_acc_8: 0.9999 - dense_2_acc_9: 0.6090 - dense_2_acc_10: 0.2862 \n", "Epoch 3/10\n", "10000/10000 [==============================] - 48s - loss: 5.6071 - dense_2_loss_1: 0.0687 - dense_2_loss_2: 0.0570 - dense_2_loss_3: 0.5615 - dense_2_loss_4: 1.5232 - dense_2_loss_5: 0.0039 - dense_2_loss_6: 0.1021 - dense_2_loss_7: 1.1143 - dense_2_loss_8: 0.0088 - dense_2_loss_9: 0.7331 - dense_2_loss_10: 1.4345 - dense_2_acc_1: 0.9767 - dense_2_acc_2: 0.9804 - dense_2_acc_3: 0.7892 - dense_2_acc_4: 0.4546 - dense_2_acc_5: 1.0000 - dense_2_acc_6: 0.9664 - dense_2_acc_7: 0.5926 - dense_2_acc_8: 0.9997 - dense_2_acc_9: 0.6955 - dense_2_acc_10: 0.4714 \n", "Epoch 4/10\n", "10000/10000 [==============================] - 52s - loss: 4.0389 - dense_2_loss_1: 0.0591 - dense_2_loss_2: 0.0493 - dense_2_loss_3: 0.3883 - dense_2_loss_4: 1.0221 - dense_2_loss_5: 0.0032 - dense_2_loss_6: 0.0863 - dense_2_loss_7: 0.9097 - dense_2_loss_8: 0.0067 - dense_2_loss_9: 0.5670 - dense_2_loss_10: 0.9471 - dense_2_acc_1: 0.9790 - dense_2_acc_2: 0.9824 - dense_2_acc_3: 0.8555 - dense_2_acc_4: 0.6410 - dense_2_acc_5: 1.0000 - dense_2_acc_6: 0.9712 - dense_2_acc_7: 0.6666 - dense_2_acc_8: 1.0000 - dense_2_acc_9: 0.7802 - dense_2_acc_10: 0.6601 \n", "Epoch 5/10\n", "10000/10000 [==============================] - 47s - loss: 2.8770 - dense_2_loss_1: 0.0524 - dense_2_loss_2: 0.0454 - dense_2_loss_3: 0.3129 - dense_2_loss_4: 0.5795 - dense_2_loss_5: 0.0034 - dense_2_loss_6: 0.0733 - dense_2_loss_7: 0.7020 - dense_2_loss_8: 0.0058 - dense_2_loss_9: 0.4749 - dense_2_loss_10: 0.6274 - dense_2_acc_1: 0.9822 - dense_2_acc_2: 0.9849 - dense_2_acc_3: 0.8779 - dense_2_acc_4: 0.8180 - dense_2_acc_5: 1.0000 - dense_2_acc_6: 0.9761 - dense_2_acc_7: 0.7722 - dense_2_acc_8: 1.0000 - dense_2_acc_9: 0.8264 - dense_2_acc_10: 0.7891 \n", "Epoch 6/10\n", "10000/10000 [==============================] - 45s - loss: 2.2361 - dense_2_loss_1: 0.0461 - dense_2_loss_2: 0.0398 - dense_2_loss_3: 0.2716 - dense_2_loss_4: 0.4116 - dense_2_loss_5: 0.0028 - dense_2_loss_6: 0.0644 - dense_2_loss_7: 0.5202 - dense_2_loss_8: 0.0049 - dense_2_loss_9: 0.4058 - dense_2_loss_10: 0.4688 - dense_2_acc_1: 0.9841 - dense_2_acc_2: 0.9856 - dense_2_acc_3: 0.8806 - dense_2_acc_4: 0.8709 - dense_2_acc_5: 1.0000 - dense_2_acc_6: 0.9782 - dense_2_acc_7: 0.8482 - dense_2_acc_8: 1.0000 - dense_2_acc_9: 0.8531 - dense_2_acc_10: 0.8419 \n", "Epoch 7/10\n", "10000/10000 [==============================] - 48s - loss: 1.8094 - dense_2_loss_1: 0.0395 - dense_2_loss_2: 0.0353 - dense_2_loss_3: 0.2397 - dense_2_loss_4: 0.3101 - dense_2_loss_5: 0.0022 - dense_2_loss_6: 0.0587 - dense_2_loss_7: 0.4000 - dense_2_loss_8: 0.0042 - dense_2_loss_9: 0.3444 - dense_2_loss_10: 0.3752 - dense_2_acc_1: 0.9862 - dense_2_acc_2: 0.9868 - dense_2_acc_3: 0.8886 - dense_2_acc_4: 0.8960 - dense_2_acc_5: 1.0000 - dense_2_acc_6: 0.9797 - dense_2_acc_7: 0.8892 - dense_2_acc_8: 1.0000 - dense_2_acc_9: 0.8743 - dense_2_acc_10: 0.8731 \n", "Epoch 8/10\n", "10000/10000 [==============================] - 46s - loss: 1.5386 - dense_2_loss_1: 0.0357 - dense_2_loss_2: 0.0315 - dense_2_loss_3: 0.2154 - dense_2_loss_4: 0.2580 - dense_2_loss_5: 0.0017 - dense_2_loss_6: 0.0520 - dense_2_loss_7: 0.3303 - dense_2_loss_8: 0.0036 - dense_2_loss_9: 0.2920 - dense_2_loss_10: 0.3182 - dense_2_acc_1: 0.9867 - dense_2_acc_2: 0.9879 - dense_2_acc_3: 0.8952 - dense_2_acc_4: 0.9111 - dense_2_acc_5: 1.0000 - dense_2_acc_6: 0.9817 - dense_2_acc_7: 0.9120 - dense_2_acc_8: 1.0000 - dense_2_acc_9: 0.8925 - dense_2_acc_10: 0.8873 \n", "Epoch 9/10\n", "10000/10000 [==============================] - 46s - loss: 1.3306 - dense_2_loss_1: 0.0301 - dense_2_loss_2: 0.0266 - dense_2_loss_3: 0.1999 - dense_2_loss_4: 0.2231 - dense_2_loss_5: 0.0016 - dense_2_loss_6: 0.0483 - dense_2_loss_7: 0.2854 - dense_2_loss_8: 0.0032 - dense_2_loss_9: 0.2418 - dense_2_loss_10: 0.2706 - dense_2_acc_1: 0.9901 - dense_2_acc_2: 0.9894 - dense_2_acc_3: 0.8997 - dense_2_acc_4: 0.9224 - dense_2_acc_5: 1.0000 - dense_2_acc_6: 0.9829 - dense_2_acc_7: 0.9241 - dense_2_acc_8: 1.0000 - dense_2_acc_9: 0.9165 - dense_2_acc_10: 0.9034 \n", "Epoch 10/10\n", "10000/10000 [==============================] - 46s - loss: 1.1755 - dense_2_loss_1: 0.0268 - dense_2_loss_2: 0.0236 - dense_2_loss_3: 0.1830 - dense_2_loss_4: 0.1990 - dense_2_loss_5: 0.0013 - dense_2_loss_6: 0.0442 - dense_2_loss_7: 0.2532 - dense_2_loss_8: 0.0030 - dense_2_loss_9: 0.1991 - dense_2_loss_10: 0.2422 - dense_2_acc_1: 0.9909 - dense_2_acc_2: 0.9904 - dense_2_acc_3: 0.9059 - dense_2_acc_4: 0.9269 - dense_2_acc_5: 1.0000 - dense_2_acc_6: 0.9858 - dense_2_acc_7: 0.9285 - dense_2_acc_8: 1.0000 - dense_2_acc_9: 0.9339 - dense_2_acc_10: 0.9115 \n" ] }, { "data": { "text/plain": [ "<keras.callbacks.History at 0x7f0239109b38>" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.fit([Xoh, s0, c0], outputs, epochs=10, batch_size=100)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "While training you can see the loss as well as the accuracy on each of the 10 positions of the output. The table below gives you an example of what the accuracies could be if the batch had 2 examples: \n", "\n", "<img src=\"images/table.png\" style=\"width:700;height:200px;\"> <br>\n", "<caption><center>Thus, `dense_2_acc_8: 0.89` means that you are predicting the 7th character of the output correctly 89% of the time in the current batch of data. </center></caption>\n", "\n", "\n", "We have run this model for longer, and saved the weights. Run the next cell to load our weights. (By training a model for several minutes, you should be able to obtain a model of similar accuracy, but loading our model will save you time.) " ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "# model.load_weights('models/model.h5')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can now see the results on new examples." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "source: 3 May 1979\n", "output: 1999-05-03\n", "source: 5 April 09\n", "output: 2009-04-04\n", "source: 21th of August 2016\n", "output: 2016-06-20\n", "source: Tue 10 Jul 2007\n", "output: 2007-07-10\n", "source: Saturday May 9 2018\n", "output: 2018-05-09\n", "source: March 3 2001\n", "output: 2011-03-03\n", "source: March 3rd 2001\n", "output: 2011-03-03\n", "source: 1 March 2001\n", "output: 2011-03-01\n" ] } ], "source": [ "EXAMPLES = ['3 May 1979', '5 April 09', '21th of August 2016', 'Tue 10 Jul 2007', 'Saturday May 9 2018', 'March 3 2001', 'March 3rd 2001', '1 March 2001']\n", "for example in EXAMPLES:\n", " \n", " source = string_to_int(example, Tx, human_vocab)\n", " source = np.array(list(map(lambda x: to_categorical(x, num_classes=len(human_vocab)), source))).swapaxes(0,1)\n", " prediction = model.predict([source, s0, c0])\n", " prediction = np.argmax(prediction, axis = -1)\n", " output = [inv_machine_vocab[int(i)] for i in prediction]\n", " \n", " print(\"source:\", example)\n", " print(\"output:\", ''.join(output))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can also change these examples to test with your own examples. The next part will give you a better sense on what the attention mechanism is doing--i.e., what part of the input the network is paying attention to when generating a particular output character. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3 - Visualizing Attention (Optional / Ungraded)\n", "\n", "Since the problem has a fixed output length of 10, it is also possible to carry out this task using 10 different softmax units to generate the 10 characters of the output. But one advantage of the attention model is that each part of the output (say the month) knows it needs to depend only on a small part of the input (the characters in the input giving the month). We can visualize what part of the output is looking at what part of the input.\n", "\n", "Consider the task of translating \"Saturday 9 May 2018\" to \"2018-05-09\". If we visualize the computed $\\alpha^{\\langle t, t' \\rangle}$ we get this: \n", "\n", "<img src=\"images/date_attention.png\" style=\"width:600;height:300px;\"> <br>\n", "<caption><center> Figure 8: Full Attention Map</center></caption>\n", "\n", "Notice how the output ignores the \"Saturday\" portion of the input. None of the output timesteps are paying much attention to that portion of the input. We see also that 9 has been translated as 09 and May has been correctly translated into 05, with the output paying attention to the parts of the input it needs to to make the translation. The year mostly requires it to pay attention to the input's \"18\" in order to generate \"2018.\" \n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3.1 - Getting the activations from the network\n", "\n", "Lets now visualize the attention values in your network. We'll propagate an example through the network, then visualize the values of $\\alpha^{\\langle t, t' \\rangle}$. \n", "\n", "To figure out where the attention values are located, let's start by printing a summary of the model ." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "____________________________________________________________________________________________________\n", "Layer (type) Output Shape Param # Connected to \n", "====================================================================================================\n", "input_1 (InputLayer) (None, 30, 37) 0 \n", "____________________________________________________________________________________________________\n", "s0 (InputLayer) (None, 128) 0 \n", "____________________________________________________________________________________________________\n", "bidirectional_1 (Bidirectional) (None, 30, 128) 52224 input_1[0][0] \n", "____________________________________________________________________________________________________\n", "repeat_vector_1 (RepeatVector) (None, 30, 128) 0 s0[0][0] \n", " lstm_1[0][0] \n", " lstm_1[1][0] \n", " lstm_1[2][0] \n", " lstm_1[3][0] \n", " lstm_1[4][0] \n", " lstm_1[5][0] \n", " lstm_1[6][0] \n", " lstm_1[7][0] \n", " lstm_1[8][0] \n", "____________________________________________________________________________________________________\n", "concatenate_1 (Concatenate) (None, 30, 256) 0 bidirectional_1[0][0] \n", " repeat_vector_1[0][0] \n", " bidirectional_1[0][0] \n", " repeat_vector_1[1][0] \n", " bidirectional_1[0][0] \n", " repeat_vector_1[2][0] \n", " bidirectional_1[0][0] \n", " repeat_vector_1[3][0] \n", " bidirectional_1[0][0] \n", " repeat_vector_1[4][0] \n", " bidirectional_1[0][0] \n", " repeat_vector_1[5][0] \n", " bidirectional_1[0][0] \n", " repeat_vector_1[6][0] \n", " bidirectional_1[0][0] \n", " repeat_vector_1[7][0] \n", " bidirectional_1[0][0] \n", " repeat_vector_1[8][0] \n", " bidirectional_1[0][0] \n", " repeat_vector_1[9][0] \n", "____________________________________________________________________________________________________\n", "dense_1 (Dense) (None, 30, 1) 257 concatenate_1[0][0] \n", " concatenate_1[1][0] \n", " concatenate_1[2][0] \n", " concatenate_1[3][0] \n", " concatenate_1[4][0] \n", " concatenate_1[5][0] \n", " concatenate_1[6][0] \n", " concatenate_1[7][0] \n", " concatenate_1[8][0] \n", " concatenate_1[9][0] \n", "____________________________________________________________________________________________________\n", "attention_weights (Activation) (None, 30, 1) 0 dense_1[0][0] \n", " dense_1[1][0] \n", " dense_1[2][0] \n", " dense_1[3][0] \n", " dense_1[4][0] \n", " dense_1[5][0] \n", " dense_1[6][0] \n", " dense_1[7][0] \n", " dense_1[8][0] \n", " dense_1[9][0] \n", "____________________________________________________________________________________________________\n", "dot_1 (Dot) (None, 1, 128) 0 attention_weights[0][0] \n", " bidirectional_1[0][0] \n", " attention_weights[1][0] \n", " bidirectional_1[0][0] \n", " attention_weights[2][0] \n", " bidirectional_1[0][0] \n", " attention_weights[3][0] \n", " bidirectional_1[0][0] \n", " attention_weights[4][0] \n", " bidirectional_1[0][0] \n", " attention_weights[5][0] \n", " bidirectional_1[0][0] \n", " attention_weights[6][0] \n", " bidirectional_1[0][0] \n", " attention_weights[7][0] \n", " bidirectional_1[0][0] \n", " attention_weights[8][0] \n", " bidirectional_1[0][0] \n", " attention_weights[9][0] \n", " bidirectional_1[0][0] \n", "____________________________________________________________________________________________________\n", "c0 (InputLayer) (None, 128) 0 \n", "____________________________________________________________________________________________________\n", "lstm_1 (LSTM) [(None, 128), (None, 131584 dot_1[0][0] \n", " s0[0][0] \n", " c0[0][0] \n", " dot_1[1][0] \n", " lstm_1[0][0] \n", " lstm_1[0][2] \n", " dot_1[2][0] \n", " lstm_1[1][0] \n", " lstm_1[1][2] \n", " dot_1[3][0] \n", " lstm_1[2][0] \n", " lstm_1[2][2] \n", " dot_1[4][0] \n", " lstm_1[3][0] \n", " lstm_1[3][2] \n", " dot_1[5][0] \n", " lstm_1[4][0] \n", " lstm_1[4][2] \n", " dot_1[6][0] \n", " lstm_1[5][0] \n", " lstm_1[5][2] \n", " dot_1[7][0] \n", " lstm_1[6][0] \n", " lstm_1[6][2] \n", " dot_1[8][0] \n", " lstm_1[7][0] \n", " lstm_1[7][2] \n", " dot_1[9][0] \n", " lstm_1[8][0] \n", " lstm_1[8][2] \n", "____________________________________________________________________________________________________\n", "dense_2 (Dense) (None, 11) 1419 lstm_1[0][0] \n", " lstm_1[1][0] \n", " lstm_1[2][0] \n", " lstm_1[3][0] \n", " lstm_1[4][0] \n", " lstm_1[5][0] \n", " lstm_1[6][0] \n", " lstm_1[7][0] \n", " lstm_1[8][0] \n", " lstm_1[9][0] \n", "====================================================================================================\n", "Total params: 185,484\n", "Trainable params: 185,484\n", "Non-trainable params: 0\n", "____________________________________________________________________________________________________\n" ] } ], "source": [ "model.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Navigate through the output of `model.summary()` above. You can see that the layer named `attention_weights` outputs the `alphas` of shape (m, 30, 1) before `dot_2` computes the context vector for every time step $t = 0, \\ldots, T_y-1$. Lets get the activations from this layer.\n", "\n", "The function `attention_map()` pulls out the attention values from your model and plots them." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.figure.Figure at 0x7f02248f34a8>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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LIwmuVWuJzDlz5+WcI5/RdrxFV19KVVsn46nvthZvk/vW/ZC94ZpLecpTq/vWXWd96/WX\nc8gRT69sr7rO+p5br2GfQ55a2Q/g8rsH3xahsPsffsUDuz2hul/tdda/5Wer9q5s/+HF1ddZf/DZ\nk/mHi9u/HoD9D9y1su0tj1/OF++sTnyfPPkplW0P/fo6dtl/bmV73XXWty66gkNqylzWJfolCy/j\nsPnHVLZ7nbWGq+Oak5kfBt4MPFj+vTkz/7HbGUTE6RGxqPyr/sRLkqS2ur25yXTgkcz8z7L05f6Z\n+etuOmbmZ4HPDjtCSZLGuY5b1hHxt8B7gfeXgyYB5/YyKEmStEk3B1BeBrwEWAGQmb8FduxlUJIk\naZNukvXaLM60SYCIqD8dVZIkjapujll/PSL+HdglIk4D3gKc1Ytg+iKYOqn974e+oLKt83Sb2beu\n8lJfwLSKqk6dqkH1Kt66y5H6ov4s2bqYI2DShOplsb5mxkH91TAdrkysVXUJVTHh+vZ166vPBs+s\nb1+1tv1lVP39Wdk24Lb7219GNS/7K9sAbr2ruiLX0x+/obZ9+fW/rGzb8NSjWH79VZXtd6yvPit+\n9d5TuePW31a2L1tdc/Vofz/LVle/3r02VF/GlglrN9Str9VtSVHxq8qUScO/dGtC3/D7jqBr7fdU\nbb/hz3LMrnAb7msdiQldzrObe4N/PCKOBx4BngB8KDMvHFl4kiSpW12dDZ6ZF0bEdcCzgD/0NiRJ\nktSqcr9lRFwQEYeVjx8DLKHYBf7liHjnVopPkqRxr+7g5v6ZuaR8/Gbgwsx8MXAURdKWJElbQV2y\nXtfy+LnADwDKgh7VZ8e0iIgTIuK2iLgjIt43/DAlSRq/6o5Z/29EvB24G5gL/AggIqZR3BilVkRM\noLhz2fHlNK6JiO9m5s0jjlqSpHGkbsv6rcCTgDcBr87Mh8rhTwP+s4tpHwnckZl3ZuZa4GvAS0cQ\nqyRJ41LHEpnDnnDEycAJmfnH5fNTgaMy84xB421WIvMrX21fInPF8uXM2GF4pRDHU9+mxlu3lq1c\nvpzpdfOt6bxyxXKm15SqrJtvxxKZNZ+NTqUQay61Zc2q5UyZVt13/Yb2R5nWr1nJxJqyjwAPrl7X\ndvh01rKSyZX9lq9qX1oTOpeqXPlg9TXY+8ycwT3LVlS2902rvsfS3jv28dtHq4+4zZ5VcyPFdath\nUvW11NMmVe9U7PT+1F2z3LG85giu4x1P1TXH7KWOwYwXvGcB1167cFRKZPZUa4nMufPm55FPf1bb\n8a6+/BKq2jppat+6C/Cvuuxijjrm2W3b6n5g9TLeunuELLziEuYfXd23LuaFV/6S+U97ZmV73U1R\nFl11KXOOqitzWdnUsURmVdKEzqUQ6+pD37H4Sg48/GmV7cseXdN++B3XMvPAeZX9AH56071th8/L\npVwbsyv7XXZn+37QuVTljd/4UWXbh992FB/4fPVNUaY9ufqmKB86bip//7PqG5t8cUF1GUvuWQL7\nHFbZfMBeu1S2/c/iKzmg5v2ZUnPzn07lT3eYOvyvXG+K0ntjcVOUbvWyuOo9wGNbnu9bDpMkSUPQ\nTdWtLTYf2g1r4xrgoIjYPyImA6cA3x16iJIkjW/dbFn/a5fDNpOZ64EzgB8DtwBfz8ybhhaeJEmq\nPIASEUcDTwf2iIh3tzTtBFSfbdIiM39AeX22JEkanrqzHSYDO5TjtJ52+Qhwci+DkiRJm1Qm68y8\nGLg4Is7OzLu2YkySJKlFN9cRnB1tCrhm5nE9iEeSJA3STbJe0PJ4KvAKoPoOCpIkaVR1TNaZee2g\nQZdFxNU9ikeSJA3SMVlHxG4tT/uAecDO3c6gLOixELgnM08ccoSSJI1z3ewGv5biFstBsfv71xRF\nPrr1DorrrHcacnSSJKmr3eD7D3fiEbEv8EfAh4F3dxhdkiS10c1u8KnAnwPPoNjC/iXwucysvsP+\nJp8C/orNr9OWJElD0LFEZkR8HXgUOLcc9Fpgl8x8ZYd+JwIvysw/j4hjgQXtjllbInN0+zY1Xktk\nbmKJzE0skdm9BheEGnWWyNxSN8esD8vMQ1ue/zwibu6i3zHASyLiRRSXfO0UEedm5utbR7JEZnuW\nyNzEEpnlcEtkbsYSmd2zRGa3823uL6JuCnlcFxEb19yIOIri7O5amfn+zNw3M2dTVNz62eBELUmS\nOuvmZ9484PKI+E35/HHAbRFxI5CZeXjPopMkSV0l6xNGOpPM/AXwi5FOR5Kk8aibZP0PmXlq64CI\n+PLgYZIkqTe6OWb9pNYnETGRYte4JEnaCiqTdUS8PyIeBQ6PiEci4tHy+X3Ad7ZahJIkjXOVyToz\n/ykzdwQ+lpk7ZeaO5d/umfn+rRijJEnjWjfHrH8YEVtcQJuZl/QgHkmSNEg3yfovWx5PBY6kKO5x\nXE8ikiRJm+mmkMeLW59HxGMp7vktSZK2gm7OBh/sbuCJox2IJElqr5uqW//KpnoIfcAc4LpeBiVJ\nkjbppurWG1uergeWZuZloxaAVbdGtW9T47Xq1iZW3drEqlvda3CNiVFn1a0tdXOC2X8BB5aP7+iy\njnXXrLrVnlW3NrHqVjncqlubsepW96y61e18m/uLqO6mKBMj4qMUx6i/BJwD/G9EfDQiJnU7g4g4\nPSIWlX97jzxkSZLGl7oTzD4G7Absn5nzMnMucACwC/DxbmeQmZ/NzDnl329HFq4kSeNPXbI+ETgt\nMx8dGJCZjwB/Bryo14FJkqRCXbLObHOgMTM3UH/ejiRJGkV1yfrmiHjD4IER8Xrg1t6FJEmSWtWd\nmng68K2IeAvF7UUB5gPTgJf1OjBJklSoTNaZeQ9wVEQcx6aa1j/IzIu2SmSSJAno7t7gPwN+thVi\nkSRJbQzn3uCSJGkrMllLktRwPU3WEXFCRNwWEXdExPt6OS9JkrZXPUvWETEB+CzwQuBQ4DURcWiv\n5idJ0vaql1vWR1IU/rgzM9cCXwNe2sP5SZK0XepYInPYE444GTghM/+4fH4qcFRmnjFoPEtkjmLf\npsZricxNLJG5iSUyu9fgglCjzhKZWxp+vbZRYonM9iyRuYklMsvhlsjcjCUyu2eJzG7n29xfRL3c\nDX4P8NiW5/uWwyRJ0hD0MllfAxwUEftHxGTgFOC7PZyfJEnbpZ7tBs/M9RFxBvBjYALwxcy8qVfz\nkyRpe9XTY9aZ+QPgB72chyRJ2zvvYCZJUsOZrCVJajiTtSRJDWeyliSp4UzWkiQ1nMlakqSG63WJ\nzHdExJKIuCki3tnLeUmStL3qZYnMw4DTKKpvPQU4MSIO7NX8JEnaXvVyy/qJwFWZuTIz1wMXAy/v\n4fwkSdou9bJE5hOB7wBHA6uAi4CFmfn2QeNZInMU+zY1XktkbmKJzE0skdm9BheEGnWWyNxSL+8N\nfktE/DPwE2AFsAjYom6gJTLbs0TmJpbILIdbInMzlsjsniUyu51vc38R9fQEs8z8QmbOy8xnAQ8C\nv+rl/CRJ2h71tJBHROyZmb+PiMdRHK+u/rkqSZLa6mmyBs6PiN2BdcDpmflQj+cnSdJ2p9clMqsP\nREqSpK54BzNJkhrOZC1JUsP17Drr4YiI+4G7KppnAsuGOenx1Hdbi9e+zZ6nfbdeX41P+2XmHp1G\nalSyrhMRCzNzvn2bN0/7bp2+21q89pVGj7vBJUlqOJO1JEkNty0l68/bt7HztO/W6butxWtfaZRs\nM8esJUkarxq/ZV3eqlSSpHGr0ck6Il4EXBQR+4x1LJIkjZXGJuuIeAHwceDUzLwnIrZqrDEGtdIi\nYtZYzFedRcSwb807kr6SBA1N1hHxfOAc4GbgDwCZ2b+VE9neZSzD+qKNiJ2HOP4+wAeB1wz3dUbE\ntOH0K/vuFxFTh9t/iPOaNej51v4hdnpEnDCE8fcAvjWc9yUiZgJ3RMRuQ+07UhFxcEQcHRGTImLC\nEPs+LSJOLf+f3KsYK+Y9pFjLPgdFxPyI6BvGax1J3ydFxLPLgkVSzzTuBLOIeC7wb8DfAbOAPYEL\nMvPSsj1yCEFHxDOAQ4Gzuu0XEWcALwBuAn4L/HtmrhnCPP8c2BH4t8x8pMs+AbwReBJwJfCtIb7O\nM4CDgeXARzLz4SH03RP4EPBPmXlPt/2GIyIOofgR9i/AzZl5VktbX2b2dzmdY4CDgFuBq4fQ76UU\ny/mdmfmbIcQ9HXgGsDAz/9Btv7Lvi4GPAUdn5oND6TtcEfFy4B+Be8q/hcDZ3ayPEfES4B+A64EZ\nwPsz8/Yehjsw3ydk5q/KxxMyc0OX/U6i+L64A/hf4FfAlzJzRY/7vhD4Z+BOYBLw1sy8t5uYpaFq\n4pb1I8CbMvM84PsU5TX/qPxyJjOzmy2clq21xwOHA6/vst9JwKuAU4GjgCcMMVH/CUUy+EpmPtLN\nlnnLD5A+ih8W7wVe2u2WXPnj4JXAR4C3AP8aEQd1GzPF7REfB7x9CH2GazlwOXAv8MqIOCciXhIR\nOw0h4T4d+A/gOcCfAp/oZuu83HvxGWB5Zv4mIiZ2u4wzcyUwDbgxInbppk9L3+8B7wIWRsSuQ+k7\nHBExCXg1RfJ4LvAd4LHAeyNipw59dwdOB16bmW+k+DzOiYg9e7nnJSJOBBZFxFcAMnNDN1u5Zbx/\nArwmM18BLAbeDLw7InbsYd9jKX5w/nFmngSsBQ7rFK80XI1L1pl5TWZeXm5l3UaxO3wdcGL5JU2X\nW5wHlP+fC/wSOAJ4QxdfzjsDnwJOKuf7bih+9XeaYbkb+oUUW6krI+LPgM+UybRS+QPkdRTJ8q8p\nktlzgFd0irf88p0LnAK8gmJrCODTnRJ2ROwTEQeXSfIMYFa55dszmXk3cHUZ84uAH1D8wPh+RBzZ\nRcxHAh8G3lwmkzOBFcA7u5j3PcA7gBdExKsyc323P/7K/t8B3gpcO9Skm5k/pFjGWyVhAztR7HkA\n+DZwAcXW32s7vN71FD9KDinXrWOBN1B8Jj4YETNGO9BymmdQvIdrI+Jc6Dphrwd2APYq+3wRWEpx\nj+4Te9j3PuBPMvPqiNiL4of9GRHx7xFx8nAOmUh1GpesBwxsZZW7374MrAZOiYijOvWN4nKvCyPi\n1HI651MksdcBb+7wQVpKscvyrZn5/MxcGxF/AfxxucVSF/MqiuTzEeA/KbZWFwNP6uK438EUW+M3\nAH9FsVvuDIqtz8p4y92ap1McLnhZZp5AsWX/VODUqvmWX5ALgH+LiLdR7LZfA+xTto/6l03LNN8H\nJMWX4r0Uez5uovih8u4OCWFn4FnAceXzuyl+3BzaTQyZ+S2KhPvBiHhVOazrww2Z+SOK9+WKGOJx\n6JaEPeS+Q5zPOuCTwMsj4pnlZ+BSYBHFrvy6vg8DnwbeD/wE+M/MfDHFnox9gQN7EO8Kih9sX6FY\nJ6e2Juwu4j0PeEsUx9g/TLEe3ww8r4d9b8nMn5dP3wr8v3IL+wrgZIp1Wxo9mblN/AGHUHyB7NHl\n+C8GrqPYxTUw7IcUZ5jvXNNvB4ovuo+zaaviWuCwLuc7lSJR7lY+PwX4OTC9Q7+TgP8GntQy7EqK\nY2I7djHfgyj2IDyZYqvgv4DHdRHr3HLcD1BsLVwD7NPD9zGAycD/ofiivBU4qeU17NrFNF5K8WPm\nNeXzZ1Nsre9JeR5GF9N4IcUxypcN83W8tFy/+rZm3yHMYyrFD4PPA89qGf4zYE4X/Xel+NF6Ysuw\n84GX9CrmlvnsXs7r3PL5XOCQmvF3pvgh/kXgky3DLwB26jCvYfetmeYPgLm9Xk7+ja+/MQ9gSMHC\npCGO/yKKLdu3AC+n2FLYu4t+j6E4lvV9it3wTx5GrH0Uv7hv7CbRA7tQ7N79MPDcMuH+tNvECUyh\nONZ9IcVW6qFDiHXnMtH9TfkleXQ5vKvEN8z38mCKreq/GWb/F1McT/0m8PXhJBHgeODxI3gNO4xF\n3yHMY1eKvS4/BN5GscflJmBWl/1fSLGH6PnAS8ofGLN7HXc575nlvG8Fbgf27aJPX8vjN1DscZnR\n5fyG1XfwZ4TiUNS1wF5bYzn5N37+Gnc2+GiLiGdTnO25kuKs1huG0HcSbNytONT5Tqc4yefKzLyl\nyz57U/yoeDnF8bQFmbl4iPHuBfTnMM/qjogPUNRXfdtw+g9xXm8CZgMfzeIErqH2fwnw98B5mfmx\ngd3sub0PCsx3AAAWl0lEQVSv1ENQHgY5huLH52rgXzLz+vpeG/vuQpG4XlH2/auhfH5GKiLeRfED\n9PjMvHEI/d5CsTv91UPpN5K+ETEFeD3FOS6vzswlQ5mv1Ml2n6xhY+LMLI4pb835Dukys5Z+Myje\nm+U9CKtqnpGZGRGnUJwRe1Kvl1d5MttHgVOGk6zLaTyfYhfmX2RxPFptlCdqZXZ5xv2gvjtSrI9d\nXYY4GsqT8L4OvGcoP1jLvvtR7IW7YxjzHVbf8ofy8cD/ZHFirDSqxkWyVnfKLdMTgV9vrS2DiJg+\n3ETdMo2BL8k7RyksNUBETM3M1WMdh9QEJmtJkhqusZduSZKkgslakqSGM1lLktRwJmtpK4qIUT/D\nPyJmR8RrK9r6IuLTEbEkIm6MiGsiYv/RjkFSb1lnV9r2zQZeS3G7zsFeTVHu9fAsyszuS3EvdUnb\nELespTEQEcdGxC8i4psRcWtEnDdwU5eIWBoRHy23hK+OiAPL4WdHxMkt0xjYSv8I8MyIWFTeSKTV\nY4Df5aZ77d+dZZnOiHh+RFwREddFxDciYody+AllTNeVW+UXlMPPjIgFLfNfEhGzy8evL2NdVBaz\nmDAQY0R8OCJuiIgro6xlHhGzIuLb5fAboizSUzUdabwzWUtj5wiKSlOHUpRyPaal7eHMfDJFSc9P\ndZjO+4BfZuaczPy/g9q+Dry4TH6fiIgjACJiJvBB4HmZOZei3vW7oyiDeRbF7VznUVakqhMRT6TY\ngj8mM+cAGyjutw1FPewrM/MpwCXAaeXwTwMXl8PnAjd1mI40rrkbXBo7V2dRMpSIWESxO/vSsu2r\nLf8PTsBdy8y7I+JgiiplxwEXRcQrKcpgHgpcVm7QT6aoGHUIxU1xbi/jOpfivuJ1nkuR2K8ppzUN\n+H3ZtpaiKAYU98w+vnx8HMWtTMmistbDEXFqzXSkcc1kLY2dNS2PN7D55zHbPF5PuTcsIvooEmxH\nmbmGopjHDyPiPooKbz8BLszM17SOGxFzaia1cf6lqQPdgC9l5vvb9FnXcsvdwa9xsLrpSOOau8Gl\nZnp1y/9XlI+XUmx5QlEFa6C++qMU9ci3EBFzywIxAwn+cOAuivKrx7QcD58REU+gqHI1OyIOKCfR\nmsyXUuyyJiLmAgNnlV8EnBwRe5Ztu5X32K5zEfBn5fgTImLnYU5HGhdM1lIz7RoRi4F3AAMnjZ0F\nPDsibgCOZtNZ3YuBDeWJWoNPMNsT+F5ELCnHWw98JjPvB94EfLWczxUUNaNXU+z2/n5EXMfmu6HP\nB3aLiJsoamX/CiAzb6Y4/v2TcloXUpzYVucdwHMi4kaK3eOHDnM60rjgvcGlhomIpcD8zFzWgFiO\npSjVeuJYxyKNZ25ZS5LUcG5ZS5LUcG5ZS5LUcCZrSZIazmQtSVLDmawlSWo4k7UkSQ1nspYkqeFM\n1pIkNZzJWpKkhjNZS5LUcCZrSZIazmQtSVLDmawlSWo4k7UkSQ1nspYkqeFM1pIkNZzJWpKkhjNZ\nS5LUcCZrSZIazmQtSVLDmawlSWo4k7UkSQ1nspYkqeFM1pIkNZzJWpKkhjNZS5LUcCZrSZIazmQt\nSVLDmawlSWo4k7UkSQ1nspYkqeFM1pIkNZzJWpKkhjNZS5LUcCZrSZIazmQtSVLDmawlSWo4k7Uk\nSQ1nspYkqeFM1pIkNZzJWpKkhjNZS5LUcCZrSZIazmQtSVLDmawlSWo4k7UkSQ1nspYkqeFM1pIk\nNZzJWpKkhjNZS5LUcCZrSZIazmQtSVLDmawlSWo4k7UkSQ1nspYkqeFM1pIkNZzJWpKkhjNZS5LU\ncCZrSZIazmQtSVLDmawlSWo4k7UkSQ1nspYkqeFM1pIkNZzJWpKkhjNZS5LUcCZrSZIazmQtSVLD\nmawlSWo4k7UkSQ1nspYkqeFM1pIkNZzJWpKkhjNZS5LUcCZrSZIazmQtSVLDmawlSWo4k7UkSQ1n\nspYkqeFM1pIkNZzJWpKkhjNZS5LUcCZrSZIazmQtSVLDmawlSWo4k7UkSQ1nspYkqeFM1pIkNZzJ\nWpKkhjNZS5LUcCZrSZIazmQtSVLDmawlSWo4k7UkSQ1nspYkqeFM1pIkNZzJWpKkhjNZS5LUcCZr\nSZIazmQtSVLDmawlSWo4k7UkSQ1nspYkqeFM1pIkNZzJWpKkhjNZS5LUcCZrSZIazmQtSVLDmawl\nSWo4k7UkSQ1nspYkqeFM1pIkNZzJWpKkhjNZS5LUcCZrSZIazmQtSVLDmawlSWo4k7UkSQ1nspYk\nqeFM1pIkNZzJWpKkhjNZS5LUcCZrSZIazmQtSVLDmawlSWo4k7UkSQ1nspYkqeFM1pIkNZzJWpKk\nhjNZS5LUcCZrSZIazmQtSVLDmawlSWo4k7UkSQ1nspYkqeFM1pIkNZzJWpKkhjNZS5LUcCZrSZIa\nzmQtSVLDmawlSWo4k7UkSQ1nspYkqeFM1pIkNZzJWpKkhjNZS5LUcCZrSZIazmQtSVLDmawlSWo4\nk7UkSQ1nspYkqeFM1pIkNZzJWpKkhjNZS5LUcCZrSZIazmQtSVLDmawlSWo4k7UkSQ1nspYkqeFM\n1pIkNZzJWpKkhjNZS5LUcCZrSZIazmQtSVLDmawlSWo4k7UkSQ1nspYkqeFM1pIkNZzJWpKkhjNZ\nS5LUcCZrSZIazmQtSVLDmawlSWo4k7UkSQ1nspYkqeFM1pIkNZzJWpKkhjNZS5LUcCZrSZIazmQt\nSVLDmawlSWq4iWMdwLbq+S84IZctW9ZxvNz4T0VbVSOQ1U1b9qydR8VIWdu1QfPKyn5bDM/qONpN\no937U9VjcFyDp9e+vWJqXfRvHwVk1i7pLdab9suo/RLt3Ld9z9p+2eE9qFyf2iyk1mm0eWEdP2/t\nFkZF21DH32ysug/vxs9C/cLerH2Iy6j1A9fuPawbv3KGW/Rr96EeHHObPnVfJi3zz1X3/zgzT2gT\n7Lhksh6mB5Yt47IrF272YUmK9TkHfVCy5cPZur63jpu5+bo9MG7rZ6e1/6bpbt6/dV6tn4tOcbUd\ndwivazTn1d+SEAba+7dYLsWA/sHLMKF/s2WyaZn1D1qmmUk/m75Ys2XYQHvr+JvHNdC3pS2L/zfG\nNSiW/pb2gefZMn7/4NfVMu3Bz4tpD553S2yDn7e+ztzUp/V1tr7G3Ox1bD5ua9xJ+2m1vs6BPq3v\nX9tpVcSVg6a15fP68bsbd8u+/f3dx8IW09qyrbV9NMYfzrSKwPtbPpD9m4a1fd7mcVXf/oH2Lsev\nai8fr1702ZloI3eDS5LUcCZrSZIazmQtSVLDmawlSWo4k7UkSQ1nspYkqeFM1pIkNZzJWpKkhjNZ\nS5LUcCZrSZIazmQtSVLDmawlSWo4k7UkSQ1nspYkqeFM1pIkNZzJWpKkhjNZS5LUcJGZYx3DNiki\nfgTMHOs4BpkJLBvrIDpoeozGNzJNjw+aH6PxFZZl5glbYT7bBJP1diQiFmbm/LGOo07TYzS+kWl6\nfND8GI1P7bgbXJKkhjNZS5LUcCbr7cvnxzqALjQ9RuMbmabHB82P0fi0BY9ZS5LUcG5ZS5LUcCZr\nSZIazmTdYBFxQkTcFhF3RMT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"text/plain": [ "<matplotlib.figure.Figure at 0x7f02246a7550>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "attention_map = plot_attention_map(model, human_vocab, inv_machine_vocab, \"Tuesday April 08 1993\", num = 6, n_s = 128)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On the generated plot you can observe the values of the attention weights for each character of the predicted output. Examine this plot and check that where the network is paying attention makes sense to you.\n", "\n", "In the date translation application, you will observe that most of the time attention helps predict the year, and hasn't much impact on predicting the day/month." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Congratulations!\n", "\n", "\n", "You have come to the end of this assignment \n", "\n", "<font color='blue'> Here's what you should remember from this notebook*:\n", "\n", "- Machine translation models can be used to map from one sequence to another. They are useful not just for translating human languages (like French->English) but also for tasks like date format translation. \n", "- An attention mechanism allows a network to focus on the most relevant parts of the input when producing a specific part of the output. \n", "- A network using an attention mechanism can translate from inputs of length $T_x$ to outputs of length $T_y$, where $T_x$ and $T_y$ can be different. \n", "- You can visualize attention weights $\\alpha^{\\langle t,t' \\rangle}$ to see what the network is paying attention to while generating each output." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Congratulations on finishing this assignment! You are now able to implement an attention model and use it to learn complex mappings from one sequence to another. " ] } ], "metadata": { "coursera": { "course_slug": "nlp-sequence-models", "graded_item_id": "n16CQ", "launcher_item_id": "npjGi" }, "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
methylDragon/news-anaCrawler	firebasePopulate.ipynb	1	32575	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# firebasePopulate\n", "\n", "*Author: methylDragon (methylDragon.com)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Description: Crawls, and analyses articles from stated URLs (and Mothership, because it's special/troublesome), churns out parameters via analyseArticle, and pushes them to Firebase.\n", "\n", "{\"title\", \"url\", \"authors\", \"date\", \"summary\", \"polarity\", \"subjectivity\", \"keywords\", \"images\", \"videos\"}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Initialise" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " . .\n", " . \|\\-^-/\| . \n", " /\| } O.=.O { \|\\ \n", " /´ \\ \\_ ~ _/ / `\\\n", " /´ \| \\-/ ~ \\-/ \| `\\\n", " \| \| /\\\\ //\\ \| \| \n", " \\\|\\\|\\/-\"\"-\"\"-\\/\|/\|/\n", " ______/ /\n", " '------'\n", " _ _ _ ___ \n", " _ __ ___\| \|_\| \|_ _ _\| \|\| \\ _ _ __ _ __ _ ___ _ _ \n", " \| ' \\/ -_) _\| ' \\ \|\| \| \|\| \|) \| '_/ _` / _` / _ \\ ' \\ \n", " \|_\|_\|_\\___\|\\__\|_\|\|_\\_, \|_\|\|___/\|_\| \\__,_\\__, \\___/_\|\|_\|\n", " \|__/ \|___/ \n", " -------------------------------------------------------\n", " methylDragon.com\n", " \n", "\n", "INITIALISING MODULES\n", ".\n", "OPENING LOGS\n", ".\n", "LOADING URL LISTS\n", ".\n", ".\n", ".\n", "INITIALISED FIREBASEPOPULATE\n" ] } ], "source": [ "%run 'Experiments/methylSwag.ipynb'\n", "methylSwag()\n", "\n", "print(\"\\nINITIALISING MODULES\\n.\")\n", "%run 'analyseArticle.ipynb'\n", "%run 'firebasePush.ipynb'\n", "\n", "import traceback\n", "import newspaper\n", "import requests\n", "import time\n", "from bs4 import BeautifulSoup\n", "from timeit import default_timer as timer\n", "\n", "start = timer()\n", "\n", "print(\"OPENING LOGS\\n.\")\n", "log = open(\"CRAWL_LOG.txt\", \"w\")\n", "\n", "print(\"LOADING URL LISTS\\n.\")\n", "\n", "COMPLETED = []\n", "\n", "QUEUE = []\n", "\n", "#newsURLs = '''[\"kementah.blogspot.sg\", \"blog.wan-ifra.org/\",\"www.straitstimes.com\",\"www.todayonline.com\",\"www.channelnewsasia.com\",\"www.businessinsider.sg\",'''\n", "\"\"\"newsURLs = [\"www.businesstimes.com.sg\",\n", " \"alvinology.com\",\"www.asiaone.com/singapore\",\"sg.news.yahoo.com\",\"www.gov.sg/news\",\n", " \"www.theindependent.sg\",\"www.tnp.sg\",\"telegraph.co.uk/news/worldnews/asia/singapore/\",\n", " \"themiddleground.sg\",\"www.allsingaporestuff.com\",\"www.theonlinecitizen.com\",\"statestimesreview.com\",\n", " \"www.tremeritus.com\",\"thehearttruths.com\",\"therealsingapore.com\",\"mustsharenews.com\",\n", " \"berthahenson.wordpress.com\",\"yawningbread.wordpress.com\",\"singaporedaily.net\",\n", " \"www.msn.com/en-sg/news/\",\n", " \n", "newsURLs= [\"asiancorrespondent.com/section/singapore/#fOzMdSd453Zgkgvy.97\",\n", "\n", "newsURLs= [\"www.mrbrownshow.com/\",\n", " \"www.buzzfeed.com/tag/singapore\",\"stomp.straitstimes.com/\",\n", " \"www.straitstimes.com/forum\",\"fintechnews.sg/\",\"www.techinasia.com/tag/singapore\",\n", " \"www.theedgesingapore.com/latest-news\",\n", "\"\"\" \n", "'''newsURLs = [\"www.mfa.gov.sg/content/mfa/overseasmission/vientiane/News.html\",\n", " \"www.google.com.sg/search?q=singapore+news&client=ubuntu&hs=HVO&channel=fs&dcr=0&source=lnms&tbm=nws&sa=X&ved=0ahUKEwj-tcLb6u3WAhWDs48KHUDqBzg4HhD8BQgKKAE&biw=1855&bih=981\"]'''\n", "\n", "newsURLs = [\"www.channelnewsasia.com\",\"statestimesreview.com\"]\n", "\n", "mothershipURLs = [\"mothership.sg/category/news\",\"mothership.sg/category/perspectives\",\n", " \"mothership.sg/category/community\",\"mothership.sg/category/almost-famous\",\n", " \"mothership.sg/category/mps-in-the-house\",\"mothership.sg/category/humour\"]\n", "\n", "\n", "\n", "print(\".\\n.\\nINITIALISED FIREBASEPOPULATE\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Crawl and analyse the latest Mothership Articles this month, outputting parameters, and pushing to Firebase" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "'mcount = 0\\nmnoteng = 0\\nmfailed = 0\\nmtooshort = 0\\nmfetcherror = 0\\n\\nprint(\"RUN MOTHERSHIP MODULE\\n\")\\n\\nfor URL in mothershipURLs:\\n print(\"Retrieving URL...\\n\")\\n try:\\n sourceCode = requests.get(\"http://\" + str(URL))\\n soup = BeautifulSoup(sourceCode.content, \"lxml\")\\n print(\"Target URL: \" + str(URL))\\n\\n for div in soup.find_all(\"div\", class_=\"ind-article\"):\\n for a in div.find_all(\"a\"):\\n if \"mothership.sg\" in a.get(\"href\"):\\n try:\\n print(str(mcount + mnoteng + mfailed + mtooshort + mfetcherror + 1)+\": \", end=\"\")\\n parameters = analyseArticle(a.get(\"href\")) #for getting link\\n \\n if parameters == \"ZERO_SENTIMENT_ERROR\": #Check for zero sentiment, means article is too short or redirected\\n mtooshort += 1\\n print(\"SKIPPING: ZERO_SENTIMENT_ERROR, NO SENTIMENT DETECTED!\", end=\" #\")\\n print(str(mtooshort))\\n continue\\n \\n if parameters == \"FETCH_ERROR\": #Check for zero sentiment, means article is too short or redirected\\n mfetcherror += 1\\n print(\"SKIPPING: FETCH_ERROR, COULD NOT DOWNLOAD ARTICLE!\", end=\" #\")\\n print(str(mfetcherror))\\n continue\\n \\n if str(parameters[\"language\"]) != \"en\": #Check if article is in English, if it isn\\'t skip\\n mnoteng += 1\\n print(\"SKIPPING: LANG_ERROR, ARTICLE NOT IN ENGLISH!\", end=\" #\")\\n print(str(mnoteng) + \" (\" + str(parameters[\"language\"]) + \")\")\\n continue\\n \\n title = str(parameters[\"title\"])\\n url = str(parameters[\"url\"])\\n authors = parameters[\"authors\"]\\n date = str(parameters[\"date\"])\\n summary = str(parameters[\"summary\"])\\n polarity = str(parameters[\"polarity\"])\\n subjectivity = str(parameters[\"subjectivity\"])\\n keywords = parameters[\"keywords\"]\\n images = str(parameters[\"images\"])\\n videos = str(parameters[\"videos\"])\\n text = str(parameters[\"text\"])\\n\\n firebasePush(title, url, authors, date, summary, polarity, subjectivity, keywords, images, videos, text)\\n mcount += 1\\n print(\"Processed article #\", end=\"\")\\n print(mcount)\\n \\n except Exception as ex:\\n mfailed += 1\\n print(\"FAILED article: #\", end=\" \| \")\\n print(ex)\\n print(mfailed,end=\" \| Moving on...\\n\")\\n \\n log.write(\"\\n\\n-----------------------------------------------\")\\n log.write(\"\\n\\nMOTHERSHIP MODULE UNKNOWN ERROR DUMP \| Fetch #\")\\n log.write(str(mcount + mnoteng + mfailed + mtooshort + mfetcherror))\\n log.write(\": \\n\\n\")\\n log.write(\"ERROR:\")\\n log.write(str(traceback.format_exc())) #FOR DEBUGGING\\n log.write(\"\\n\\n\")\\n log.write(\"Data:\")\\n log.write(str(parameters)) #FOR DEBUGGING\\n \\n except Exception as ex:\\n print(\"Failed URL\", end=\" \| \")\\n print(ex)\\n \\n print(\"\\n--------------!!-rawr-=rAwR=RAWR=rAwR=-rawr-!!--------------\")\\n string = \"FINISHED: \" + str(URL)\\n print(string.center(63))\\n log.write(\"PROCESSED: \")\\n log.write(str(URL))\\n log.write(\"\\n\")\\n log.flush()\\n \\n print(\"--------------!!-rawr-=rAwR=RAWR=rAwR=-rawr-!!--------------\\n\")\\n\\nmethylHalf()\\n\\nprint(\"\\n ---!!--!!-raa--rawr-=rAwR=RAAWR=rAwR=-rawr--raa-!!--!!---\")\\nprint(\" FINISHED PROCESSING MOTHERSHIP\")\\nlog.write(\"FINISHED PROCESSING: \")\\nlog.write(\"MOTHERSHIP\")\\nlog.write(\"\\n\\n\")\\nprint(\" ---!!--!!-raa--rawr-=rAwR=RAAWR=rAwR=-rawr--raa-!!--!!---\\n\")\\n\\nprint(\"SUMMARY:\")\\nprint(\"Elapsed time: \",end=\"\")\\ncheckpoint = timer()\\nprint(checkpoint - start,end=\"\")\\nprint(\" seconds\\n\")\\nlog.write(\"Elapsed Time: \" + str(checkpoint - start))\\nlog.write(\"\\n\\n\")\\nlog.flush\\n\\nprint(str(mcount + mnoteng + mfailed + mtooshort + mfetcherror) + \" Total Articles Accessed\")\\nprint(str(mcount) + \" Processed Articles\\n\")\\n\\nprint(str(mnoteng) + \" LANG_ERRORs (Article not in English)\")\\nprint(str(mtooshort) + \" ZERO_SENTIMENT_ERRORs (No sentiment detected)\")\\nprint(str(mfetcherror) + \" FETCH_ERRORs (Failed to fetch article)\")\\nprint(str(mfailed) + \" Failed Articles\\n\")\\n\\nfirebaseRefresh()\\ntime.sleep(1)\\n\\nprint(\" ---!!--!!-raa--rawr-=rAwR=RAAWR=rAwR=-rawr--raa-!!--!!---\\n\")'" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "'''mcount = 0\n", "mnoteng = 0\n", "mfailed = 0\n", "mtooshort = 0\n", "mfetcherror = 0\n", "\n", "print(\"RUN MOTHERSHIP MODULE\\n\")\n", "\n", "for URL in mothershipURLs:\n", " print(\"Retrieving URL...\\n\")\n", " try:\n", " sourceCode = requests.get(\"http://\" + str(URL))\n", " soup = BeautifulSoup(sourceCode.content, \"lxml\")\n", " print(\"Target URL: \" + str(URL))\n", "\n", " for div in soup.find_all(\"div\", class_=\"ind-article\"):\n", " for a in div.find_all(\"a\"):\n", " if \"mothership.sg\" in a.get(\"href\"):\n", " try:\n", " print(str(mcount + mnoteng + mfailed + mtooshort + mfetcherror + 1)+\": \", end=\"\")\n", " parameters = analyseArticle(a.get(\"href\")) #for getting link\n", " \n", " if parameters == \"ZERO_SENTIMENT_ERROR\": #Check for zero sentiment, means article is too short or redirected\n", " mtooshort += 1\n", " print(\"SKIPPING: ZERO_SENTIMENT_ERROR, NO SENTIMENT DETECTED!\", end=\" #\")\n", " print(str(mtooshort))\n", " continue\n", " \n", " if parameters == \"FETCH_ERROR\": #Check for zero sentiment, means article is too short or redirected\n", " mfetcherror += 1\n", " print(\"SKIPPING: FETCH_ERROR, COULD NOT DOWNLOAD ARTICLE!\", end=\" #\")\n", " print(str(mfetcherror))\n", " continue\n", " \n", " if str(parameters[\"language\"]) != \"en\": #Check if article is in English, if it isn't skip\n", " mnoteng += 1\n", " print(\"SKIPPING: LANG_ERROR, ARTICLE NOT IN ENGLISH!\", end=\" #\")\n", " print(str(mnoteng) + \" (\" + str(parameters[\"language\"]) + \")\")\n", " continue\n", " \n", " title = str(parameters[\"title\"])\n", " url = str(parameters[\"url\"])\n", " authors = parameters[\"authors\"]\n", " date = str(parameters[\"date\"])\n", " summary = str(parameters[\"summary\"])\n", " polarity = str(parameters[\"polarity\"])\n", " subjectivity = str(parameters[\"subjectivity\"])\n", " keywords = parameters[\"keywords\"]\n", " images = str(parameters[\"images\"])\n", " videos = str(parameters[\"videos\"])\n", " text = str(parameters[\"text\"])\n", "\n", " firebasePush(title, url, authors, date, summary, polarity, subjectivity, keywords, images, videos, text)\n", " mcount += 1\n", " print(\"Processed article #\", end=\"\")\n", " print(mcount)\n", " \n", " except Exception as ex:\n", " mfailed += 1\n", " print(\"FAILED article: #\", end=\" \| \")\n", " print(ex)\n", " print(mfailed,end=\" \| Moving on...\\n\")\n", " \n", " log.write(\"\\n\\n-----------------------------------------------\")\n", " log.write(\"\\n\\nMOTHERSHIP MODULE UNKNOWN ERROR DUMP \| Fetch #\")\n", " log.write(str(mcount + mnoteng + mfailed + mtooshort + mfetcherror))\n", " log.write(\": \\n\\n\")\n", " log.write(\"ERROR:\")\n", " log.write(str(traceback.format_exc())) #FOR DEBUGGING\n", " log.write(\"\\n\\n\")\n", " log.write(\"Data:\")\n", " log.write(str(parameters)) #FOR DEBUGGING\n", " \n", " except Exception as ex:\n", " print(\"Failed URL\", end=\" \| \")\n", " print(ex)\n", " \n", " print(\"\\n--------------!!-rawr-=rAwR=RAWR=rAwR=-rawr-!!--------------\")\n", " string = \"FINISHED: \" + str(URL)\n", " print(string.center(63))\n", " log.write(\"PROCESSED: \")\n", " log.write(str(URL))\n", " log.write(\"\\n\")\n", " log.flush()\n", " \n", " print(\"--------------!!-rawr-=rAwR=RAWR=rAwR=-rawr-!!--------------\\n\")\n", "\n", "methylHalf()\n", "\n", "print(\"\\n ---!!--!!-raa--rawr-=rAwR=RAAWR=rAwR=-rawr--raa-!!--!!---\")\n", "print(\" FINISHED PROCESSING MOTHERSHIP\")\n", "log.write(\"FINISHED PROCESSING: \")\n", "log.write(\"MOTHERSHIP\")\n", "log.write(\"\\n\\n\")\n", "print(\" ---!!--!!-raa--rawr-=rAwR=RAAWR=rAwR=-rawr--raa-!!--!!---\\n\")\n", "\n", "print(\"SUMMARY:\")\n", "print(\"Elapsed time: \",end=\"\")\n", "checkpoint = timer()\n", "print(checkpoint - start,end=\"\")\n", "print(\" seconds\\n\")\n", "log.write(\"Elapsed Time: \" + str(checkpoint - start))\n", "log.write(\"\\n\\n\")\n", "log.flush\n", "\n", "print(str(mcount + mnoteng + mfailed + mtooshort + mfetcherror) + \" Total Articles Accessed\")\n", "print(str(mcount) + \" Processed Articles\\n\")\n", "\n", "print(str(mnoteng) + \" LANG_ERRORs (Article not in English)\")\n", "print(str(mtooshort) + \" ZERO_SENTIMENT_ERRORs (No sentiment detected)\")\n", "print(str(mfetcherror) + \" FETCH_ERRORs (Failed to fetch article)\")\n", "print(str(mfailed) + \" Failed Articles\\n\")\n", "\n", "firebaseRefresh()\n", "time.sleep(1)\n", "\n", "print(\" ---!!--!!-raa--rawr-=rAwR=RAAWR=rAwR=-rawr--raa-!!--!!---\\n\")'''" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Crawl and analyse the other URLs, outputting parameters, and pushing to Firebase" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "RUN URL MODULE\n", "\n", "Building domain...\n", "\n", "Domain building complete for: www.channelnewsasia.com\n", "1: Processed article #1\n", "2: Processed article #2\n", "3: SKIPPING: ZERO_SENTIMENT_ERROR, NO SENTIMENT DETECTED! #1\n", "http://www.channelnewsasia.com/archives/8395986/news?channelId=7469166\n", "4: SKIPPING: ZERO_SENTIMENT_ERROR, NO SENTIMENT DETECTED! #2\n", "http://www.channelnewsasia.com/news/technology\n", "5: Processed article #3\n", "6: SKIPPING: ZERO_SENTIMENT_ERROR, NO SENTIMENT DETECTED! #3\n", "http://www.channelnewsasia.com/news/specialreports\n", "7: Processed article #4\n", "8: SKIPPING: ZERO_SENTIMENT_ERROR, NO SENTIMENT DETECTED! #4\n", "http://www.channelnewsasia.com/news/videos/rohingya-refugees-in-india-fight-a-legal-battle-to-avoid-9309560\n", "9: SKIPPING: ZERO_SENTIMENT_ERROR, NO SENTIMENT DETECTED! #5\n", "http://www.channelnewsasia.com/news/commentary\n", "10: Processed article #5\n", "11: Processed article #6\n", "12: Processed article #7\n", "13: Processed article #8\n", "14: SKIPPING: ZERO_SENTIMENT_ERROR, NO SENTIMENT DETECTED! #6\n", "http://www.channelnewsasia.com/news/catch-up-tv/conversation-with\n", "15: SKIPPING: ZERO_SENTIMENT_ERROR, NO SENTIMENT DETECTED! #7\n", "http://www.channelnewsasia.com/news/videos/vietnam-s-agricultural-industry-gets-boost-from-big-business-9309562\n", "16: Processed article #9\n", "17: SKIPPING: ZERO_SENTIMENT_ERROR, NO SENTIMENT DETECTED! #8\n", "http://www.channelnewsasia.com/news/videos/aung-san-suu-kyi-to-chair-new-committee-for-rakhine-9309618\n", "18: You must `download()` an article first!\n", "SKIPPING: FETCH_ERROR, COULD NOT DOWNLOAD ARTICLE! #1\n", "19: Processed article #10\n", "20: SKIPPING: ZERO_SENTIMENT_ERROR, NO SENTIMENT DETECTED! #9\n", "http://www.channelnewsasia.com/news/weather\n", "21: Processed article #11\n", "22: SKIPPING: ZERO_SENTIMENT_ERROR, NO SENTIMENT DETECTED! #10\n", "http://www.channelnewsasia.com/news/lifestyle\n", "23: Processed article #12\n", "24: SKIPPING: ZERO_SENTIMENT_ERROR, NO SENTIMENT DETECTED! #11\n", "http://www.channelnewsasia.com/news/parliament\n", "25: Processed article #13\n", "26: SKIPPING: ZERO_SENTIMENT_ERROR, NO SENTIMENT DETECTED! #12\n", "http://www.channelnewsasia.com/news/catch-up-tv/the-traitor-within\n", "27: SKIPPING: ZERO_SENTIMENT_ERROR, NO SENTIMENT DETECTED! #13\n", "http://www.channelnewsasia.com/news/advertise/contactsales\n", "28: Processed article #14\n", "29: Processed article #15\n", "30: Processed article #16\n", "31: Processed article #17\n", "32: Processed article #18\n", "33: Processed article #19\n", "34: Processed article #20\n", "35: Processed article #21\n", "36: Processed article #22\n", "37: Processed article #23\n", "38: Processed article #24\n", "39: Processed article #25\n", "40: Processed article #26\n", "41: Processed article #27\n", "42: Processed article #28\n", "43: SKIPPING: ZERO_SENTIMENT_ERROR, NO SENTIMENT DETECTED! #14\n", "http://www.channelnewsasia.com/news/videos/one-year-after-king-s-death-thais-prepare-for-final-goodbye-9309494\n", "44: SKIPPING: ZERO_SENTIMENT_ERROR, NO SENTIMENT DETECTED! #15\n", "http://www.channelnewsasia.com/news/business\n", "45: SKIPPING: ZERO_SENTIMENT_ERROR, NO SENTIMENT DETECTED! #16\n", "http://www.channelnewsasia.com/news/cnainsider/big-singaporean-hearts-at-cox-s-bazar-9264726\n", "46: Processed article #29\n", "47: SKIPPING: ZERO_SENTIMENT_ERROR, NO SENTIMENT DETECTED! #17\n", "http://www.channelnewsasia.com/news/cnainsider/wayang-s-aussie-performer-9310152\n", "48: Processed article #30\n", "49: Processed article #31\n", "50: Processed article #32\n", "51: SKIPPING: ZERO_SENTIMENT_ERROR, NO SENTIMENT DETECTED! #18\n", "http://www.channelnewsasia.com/news/videos/in-marawi-poverty-leads-many-to-extremism-9309542\n", "52: SKIPPING: ZERO_SENTIMENT_ERROR, NO SENTIMENT DETECTED! #19\n", "http://www.channelnewsasia.com/news/singapore/eyes-on-the-road-more-drivers-submit-dash-cam-videos-to-police-9309594\n", "53: Processed article #33\n", "54: Processed article #34\n", "55: Processed article #35\n", "56: Processed article #36\n", "57: Processed article #37\n", "58: Processed article #38\n", "59: Processed article #39\n", "60: Processed article #40\n", "61: Processed article #41\n", "62: Processed article #42\n", "63: Processed article #43\n", "64: Processed article #44\n", "65: Processed article #45\n", "66: Processed article #46\n", "67: SKIPPING: ZERO_SENTIMENT_ERROR, NO SENTIMENT DETECTED! #20\n", "http://www.channelnewsasia.com/news/cnainsider/language-of-the-heart-learning-dialects-for-the-sake-of-the-9279350\n", "68: Processed article #47\n", "69: Processed article #48\n", "70: Processed article #49\n", "\n", "--------------!!-rawr-=rAwR=RAWR=rAwR=-rawr-!!--------------\n", " FINISHED: www.channelnewsasia.com \n", "--------------!!-rawr-=rAwR=RAWR=rAwR=-rawr-!!--------------\n", "\n", "RUNNING SUMMARY:\n", "Elapsed time: 125.58987549401354 seconds\n", "\n", "70 Total Articles Fetched\n", "49 Processed Articles\n", "\n", "0 LANG_ERRORs (Article not in English)\n", "20 ZERO_SENTIMENT_ERRORs (No sentiment detected)\n", "1 FETCH_ERRORs (Failed to fetch article)\n", "0 Failed Articles\n", "\n", "--------------!!-rawr-=rAwR=RAWR=rAwR=-rawr-!!--------------\n", "\n", "Building domain...\n", "\n", "Domain building complete for: statestimesreview.com\n", "71: Processed article #50\n", "72: Processed article #51\n", "73: Processed article #52\n", "74: Processed article #53\n", "75: Processed article #54\n", "76: Processed article #55\n", "77: Processed article #56\n", "\n", "--------------!!-rawr-=rAwR=RAWR=rAwR=-rawr-!!--------------\n", " FINISHED: statestimesreview.com \n", "--------------!!-rawr-=rAwR=RAWR=rAwR=-rawr-!!--------------\n", "\n", "RUNNING SUMMARY:\n", "Elapsed time: 151.19176326799789 seconds\n", "\n", "77 Total Articles Fetched\n", "56 Processed Articles\n", "\n", "0 LANG_ERRORs (Article not in English)\n", "20 ZERO_SENTIMENT_ERRORs (No sentiment detected)\n", "1 FETCH_ERRORs (Failed to fetch article)\n", "0 Failed Articles\n", "\n", "--------------!!-rawr-=rAwR=RAWR=rAwR=-rawr-!!--------------\n", "\n", " . .\n", " . \|\\-^-/\| . \n", " /\| } O.=.O { \|\\ \n", "\n", " ---!!--!!-raa--rawr-=rAwR=RAAWR=rAwR=-rawr--raa-!!--!!---\n", " FINISHED PROCESSING URLS!\n", " ---!!--!!-raa--rawr-=rAwR=RAAWR=rAwR=-rawr--raa-!!--!!---\n", "\n", "SUMMARY:\n", "77 Total Articles Accessed\n", "56 Processed Articles\n", "\n", "0 LANG_ERRORs (Article not in English)\n", "20 ZERO_SENTIMENT_ERRORs (No sentiment detected)\n", "1 FETCH_ERRORs (Failed to fetch article)\n", "0 Failed Articles\n", "\n", " ---!!--!!-raa--rawr-=rAwR=RAAWR=rAwR=-rawr--raa-!!--!!---\n", "\n", "Elapsed time: 152.55521054001292 seconds\n", "\n", "SHUTTING DOWN\n" ] } ], "source": [ "count = 0\n", "noteng = 0\n", "failed = 0\n", "tooshort = 0\n", "fetcherror = 0\n", "\n", "print(\"RUN URL MODULE\\n\")\n", "\n", "for URL in newsURLs:\n", " print(\"Building domain...\\n\")\n", " \n", " try:\n", " paper = newspaper.build(\"http://\" + str(URL), memoize_articles=False)\n", " print(\"Domain building complete for: \" + str(URL))\n", " except Exception as ex:\n", " print(\"Failed DOMAIN\", end=\" \| \")\n", " print(ex, end =\" \| moving on...\\n\")\n", "\n", " for article in paper.articles:\n", " try:\n", " print(str(count + noteng + failed + tooshort + fetcherror + 1)+\": \",end=\"\")\n", " parameters = analyseArticle(article.url)\n", "\n", " if parameters == \"ZERO_SENTIMENT_ERROR\": #Check for zero sentiment, means article is too short or redirected\n", " tooshort += 1\n", " print(\"SKIPPING: ZERO_SENTIMENT_ERROR, NO SENTIMENT DETECTED!\", end=\" #\")\n", " print(str(tooshort))\n", " print(article.url)\n", " continue\n", " \n", " if parameters == \"FETCH_ERROR\":\n", " fetcherror +=1\n", " print(\"SKIPPING: FETCH_ERROR, COULD NOT DOWNLOAD ARTICLE!\", end=\" #\")\n", " print(str(fetcherror))\n", " continue\n", " \n", " if str(parameters[\"language\"]) != \"en\": #Check if article is in English, if it isn't skip\n", " noteng += 1\n", " print(\"SKIPPING: LANG_ERROR, ARTICLE NOT IN ENGLISH!\", end=\" #\")\n", " print(str(noteng) + \" (\" + str(parameters[\"language\"]) + \")\")\n", " print(article.url)\n", " continue\n", "\n", " title = parameters[\"title\"]\n", " url = str(article.url)\n", " authors = parameters[\"authors\"]\n", " date = str(parameters[\"date\"])\n", " summary = str(parameters[\"summary\"])\n", " polarity = str(parameters[\"polarity\"])\n", " subjectivity = str(parameters[\"subjectivity\"])\n", " keywords = parameters[\"keywords\"]\n", " images = str(parameters[\"images\"])\n", " videos = str(parameters[\"videos\"])\n", " text = str(parameters[\"text\"])\n", "\n", " firebasePush(title, url, authors, date, summary, polarity, subjectivity, keywords, images, videos, text)\n", " count += 1\n", " print(\"Processed article #\", end=\"\")\n", " print(count)\n", " \n", " except Exception as ex:\n", " failed += 1\n", " print(\"FAILED article: #\",end=\"\")\n", " print(failed, end=\" \| \")\n", " print(ex,end=\" \| Moving on...\\n\")\n", "\n", " log.write(\"\\n\\n-----------------------------------------------\")\n", " log.write(\"\\n\\nURL MODULE UNKNOWN ERROR DUMP \| Fetch #\")\n", " log.write(str(count + noteng + failed + tooshort + fetcherror))\n", " log.write(\": \\n\\n\")\n", " log.write(\"ERROR:\")\n", " log.write(str(traceback.format_exc())) #FOR DEBUGGING\n", " log.write(\"\\n\\n\")\n", " log.write(\"DATA:\\n\")\n", " log.write(str(parameters)) #FOR DEBUGGING\n", "\n", " \n", " print(\"\\n--------------!!-rawr-=rAwR=RAWR=rAwR=-rawr-!!--------------\")\n", " string = \"FINISHED: \" + str(URL)\n", " print(string.center(63))\n", " log.write(\"PROCESSED: \")\n", " log.write(str(URL))\n", " log.write(\"\\n\")\n", " log.flush()\n", " print(\"--------------!!-rawr-=rAwR=RAWR=rAwR=-rawr-!!--------------\\n\")\n", "\n", " print(\"RUNNING SUMMARY:\")\n", " print(\"Elapsed time: \",end=\"\")\n", " checkpoint = timer()\n", " print(checkpoint - start,end=\"\")\n", " print(\" seconds\\n\")\n", " log.write(\"Elapsed Time: \" + str(checkpoint - start))\n", " log.write(\"\\n\\n\")\n", " log.flush\n", " \n", " print(str(count + noteng + failed + tooshort + fetcherror) + \" Total Articles Fetched\")\n", " print(str(count) + \" Processed Articles\\n\")\n", " \n", " \n", " print(str(noteng) + \" LANG_ERRORs (Article not in English)\")\n", " print(str(tooshort) + \" ZERO_SENTIMENT_ERRORs (No sentiment detected)\")\n", " print(str(fetcherror) + \" FETCH_ERRORs (Failed to fetch article)\")\n", " print(str(failed) + \" Failed Articles\\n\")\n", " \n", " firebaseRefresh()\n", " time.sleep(1)\n", " \n", " print(\"--------------!!-rawr-=rAwR=RAWR=rAwR=-rawr-!!--------------\\n\")\n", "\n", "methylHalf() \n", "print(\"\\n ---!!--!!-raa--rawr-=rAwR=RAAWR=rAwR=-rawr--raa-!!--!!---\")\n", "print(\" FINISHED PROCESSING URLS!\")\n", "log.write(\"FINISHED PROCESSING: \")\n", "log.write(\"URLS\")\n", "log.write(\"\\n\\n\")\n", "print(\" ---!!--!!-raa--rawr-=rAwR=RAAWR=rAwR=-rawr--raa-!!--!!---\\n\")\n", "\n", "print(\"SUMMARY:\")\n", "print(str(count + noteng + failed + tooshort + fetcherror) + \" Total Articles Accessed\")\n", "print(str(count) + \" Processed Articles\\n\")\n", "\n", "print(str(noteng) + \" LANG_ERRORs (Article not in English)\")\n", "print(str(tooshort) + \" ZERO_SENTIMENT_ERRORs (No sentiment detected)\")\n", "print(str(fetcherror) + \" FETCH_ERRORs (Failed to fetch article)\")\n", "print(str(failed) + \" Failed Articles\\n\")\n", "\n", "print(\" ---!!--!!-raa--rawr-=rAwR=RAAWR*=rAwR=-rawr--raa-!!--!!---\\n\")\n", "\n", "print(\"Elapsed time: \",end=\"\")\n", "checkpoint = timer()\n", "print(checkpoint - start,end=\"\")\n", "print(\" seconds\\n\")\n", "print(\"SHUTTING DOWN\")\n", "log.write(\"Elapsed Time: \" + str(checkpoint - start))\n", "log.write(\"\\n\\n\")\n", "log.write(\"SHUTTING DOWN\")\n", "log.flush\n", "\n", "log.close()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 }	gpl-3.0
kdungs/coursework-computational-physics	src/10/Henon.ipynb	1	176490	{ "metadata": { "name": "", "signature": "sha256:f2455891e1c35438b22adc62781524686631d269d8cba31e6f964d1d413f115d" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Computational Physics \u2013 \u00dcbungsblatt 10\n", "\n", " * [Kevin Dungs](mailto:[email protected])\n", " * Kevin Heinicke\n", " * Holger Stevens\n", "\n", "## Hausaufgabe 16: Poincar\u00e9schnitte des H\u00e9non-Heiles-Systems\n", "\n", "Das Potential des H\u00e9non-Heiles-Systems ist\n", "\\begin{equation}\n", " V(x, y) = \\frac{1}{2}\\left(x^2 + y^2\\right) + x^2y - \\frac{1}{3}y^3\n", "\\end{equation}\n", "wobei $x$ und $y$ die r\u00e4umlichen 2D-Koordinaten sind.\n", "\n", "Bevor das Potential mit Hilfe von Poincar\u00e9-Schnitten untersucht wird, werden zun\u00e4chst verschiedene Verfahren zur Integration von Differentialgleichungen implementiert." ] }, { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "import matplotlib.pyplot as plt" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "def integrate(step_method, f, N, dt, t0, x0):\n", " ts = []\n", " xs = []\n", " t = t0\n", " x = x0\n", " for _ in range(0, N):\n", " ts.append(t)\n", " xs.append(x)\n", " x = step_method(f, dt, t, x)\n", " t += dt\n", " return ts, xs" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Eulerverfahren" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def euler_step(f, dt, tn, xn):\n", " return xn + dt * f(tn, xn)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Runge-Kutta 4. Ordnung" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def runge_kutta_4_step(f, dt, tn, xn):\n", " k1 = f(tn, xn) * dt\n", " k2 = dt * f(tn + dt / 2, xn + k1 / 2)\n", " k3 = dt * f(tn + dt / 2, xn + k2 / 2)\n", " k4 = dt * f(tn + dt, xn + k3)\n", " return 1/6 * (k1 + 2 * k2 + 2 * k3 + k4) + xn " ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Symplektischer Algorithmus 4. Ordnung" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def symplectic_4_step(f, dt, tn, xn):\n", " c1 = c4 = 1 / (2 * (2 - 2 1/3))\n", " c2 = c3 = (1 - 2 1/3) / (2 * (2 - 2 1/3))\n", " d1 = d3 = 1 / (2 - 2 1/3)\n", " d2 = - 2 1/3 / (2 - 2 1/3)\n", " d4 = 0\n", " \n", " \n", " " ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Harmonischer Oszillator" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def f_harmosz_2d(t, x, k1=1, k2=5):\n", " return np.matrix(\n", " [[0, -1/k1, 0, 0],\n", " [1, 0, 0, 0],\n", " [0, 0, 0, -1/k2],\n", " [0, 0, 1, 0]]\n", " ) * x" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "N = 10000\n", "dt = .01\n", "t0 = 0\n", "x0 = np.matrix([[0], [1], [0], [1]])\n", "\n", "for method in (euler_step, runge_kutta_4_step):\n", " ts, xs = integrate(method, f_harmosz_2d, N, dt, t0, x0)\n", " px = [z[0, 0] for z in xs]\n", " x = [z[1, 0] for z in xs]\n", " py = [z[2, 0] for z in xs]\n", " y = [z[3, 0] for z in xs]\n", " #true_p = np.sin(ts)\n", " #true_q = np.cos(ts)\n", " # Make a nice plot\n", " plt.plot(ts, px, label='$p_x$')\n", " plt.plot(ts, x, label='$x$')\n", " plt.plot(ts, py, label='$p_y$')\n", " plt.plot(ts, y, label='$y$')\n", " #plt.plot(ts, true_x, label='x true')\n", " #plt.plot(ts, true_v, label='v true')\n", " plt.title('{}'.format(method.__name__.replace('_step', '')))\n", " plt.legend(bbox_to_anchor=(1.2, 1))\n", " plt.show()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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LZEvZ+eaIPmdfzylHEA5teLoOSICAI0A0127+el1KN+Xzwf6Tcofms/uI5+Ndxdy0SR4z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qaP0avJ736tnNIVMs2c4+N+QBs3xtl30RliGeLeveK/G2+0DQph+8smjIlAyTGpip47r2Uf5DU0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\njM66l72v6guS8osRSrRWTm7Eo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zikJa0PHliHlBZUvuz7vthLwOOJ/KjxVVFwpBkHTXPURS1nVkSaLYvQzaf7gL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7cLMzQhuW/RsadziQEF3TsaDmysRYoY2q1bwMmT6PQsrfdEbF7NR8Ut73JM+ymEZnZav21PQJuf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\nAITHd3eGKbsuQ3cQSnr+UeeQFlGU0aWTioJLpolGM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"text": [ "<matplotlib.figure.Figure at 0x1076c2950>" ] } ], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "def f_henon_2d(t, z):\n", " px = z[0, 0]\n", " x = z[1, 0]\n", " py = z[2, 0]\n", " y = z[3, 0]\n", " return np.matrix(\n", " [[-x - 2 * x * y],\n", " [px],\n", " [-y - x 2 + y 2],\n", " [py]]\n", " )" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "N = 1000\n", "dt = .01\n", "t0 = 0\n", "x0 = np.matrix([[0], [1], [0], [1]])\n", "\n", "for method in (euler_step, runge_kutta_4_step):\n", " ts, xs = integrate(method, f_henon_2d, N, dt, t0, x0)\n", " ps = [x[0, 0] for x in xs]\n", " qs = [x[1, 0] for x in xs]\n", " #true_p = np.sin(ts)\n", " #true_q = np.cos(ts)\n", " # Make a nice plot\n", " plt.plot(ts, ps, label='p')\n", " plt.plot(ts, qs, label='q')\n", " #plt.plot(ts, true_x, label='x true')\n", " #plt.plot(ts, true_v, label='v true')\n", " plt.title('{}'.format(method.__name__.replace('_step', '')))\n", " plt.legend(loc='best')\n", " plt.show()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "-c:7: RuntimeWarning: overflow encountered in double_scalars\n", "-c:9: RuntimeWarning: overflow encountered in double_scalars\n", "-c:9: RuntimeWarning: invalid value encountered in double_scalars\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x107009250>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x106ff6710>" ] } ], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "def poincare_plot(px, x, py, y)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 10 } ], "metadata": {} } ] }	mit
materialsproject/MPContribs	mpcontribs-portal/notebooks/contribs.materialsproject.org/intermatch.ipynb	1	2347	{ "cells": [ { "cell_type": "code", "execution_count": null, "id": "south-skirt", "metadata": {}, "outputs": [], "source": [ "from mpcontribs.client import Client" ] }, { "cell_type": "code", "execution_count": null, "id": "pending-bookmark", "metadata": {}, "outputs": [], "source": [ "name = \"intermatch\"\n", "client = Client()" ] }, { "cell_type": "code", "execution_count": null, "id": "polar-disaster", "metadata": { "scrolled": true }, "outputs": [], "source": [ "client.get_project(name).display()" ] }, { "cell_type": "code", "execution_count": null, "id": "statutory-sampling", "metadata": {}, "outputs": [], "source": [ "contributions = [\n", " {\n", " \"project\": name,\n", " \"identifier\": \"mp-48\",\n", " \"data\": {\n", " \"interface\": \"mp-22850\",\n", " \"Δn\": 1.3, \"ε\": 3.6199, \"atoms\": 208, \"θ\": \"0 °\",\n", " \"surface\": {\"N₁\": 40, \"N₂\": 6, \"ratio\": 6.666},\n", " \"v₁\": {\"i₁₁\": -8, \"i₁₂\": 8, \"i₂₁\": -3, \"i₂₂\": 3},\n", " \"v₂\": {\"j₁₁\": -13, \"j₁₂\": 8, \"j₂₁\": -5, \"j₂₂\": 3}\n", " }\n", " }\n", "]" ] }, { "cell_type": "code", "execution_count": null, "id": "legal-oxide", "metadata": {}, "outputs": [], "source": [ "client.delete_contributions(name)" ] }, { "cell_type": "code", "execution_count": null, "id": "south-function", "metadata": {}, "outputs": [], "source": [ "client.submit_contributions(contributions)" ] }, { "cell_type": "code", "execution_count": null, "id": "victorian-tennis", "metadata": {}, "outputs": [], "source": [ "client.session.close()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.1" } }, "nbformat": 4, "nbformat_minor": 5 }	mit
tcstewar/testing_notebooks	Learning and Adjusting Tuning Curves.ipynb	1	193819	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Learning and Adjusting Tuning Curves\n", "\n", "This is a quick notebook just to sketch out some initial stages of looking at modelling Aaron Batista's data on training macaques to use BCIs, and how that interacts with the represented low-dimensional manifolds in M1.\n", "\n", "First, we start with the required dependencies. They are all Python libraries that can be installed with ```pip``` (e.g. ```pip install nengo```)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "import pylab # plotting\n", "import seaborn # plotting\n", "import numpy as np # math functions\n", "import nengo # neural modelling" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we build our actual model. We start by defining M1. Even though I'll only be extracting 2 dimensions out of this neural activity, I assume that it's probably encoding more than that, so let's go with saying that it's a 3-dimensional manifold." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "model = nengo.Network()\n", "with model:\n", " m1 = nengo.Ensemble(n_neurons=500, dimensions=3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "M1 is gets its inputs from earlier motor areas, so let's add that in. We'll call it PMC (or maybe SMA), and we'll assume that it's doing a few different things, so let's arbitarily pick that it's a 6-dimensional manifold.\n", "\n", "We then connect it to M1. When we do this, we can specify the relationship that we want between these manifolds and Nengo will find the synaptic connection weights that best approximate that mapping. This mapping can be a non-linear function (although the more non-linear it is, the less accurately it'll approximate that function). Here, we just do a linear function that grabs the first 3 dimensions from `pmc` and sends it to `m1`.\n", "\n", "This is the connection that will actually end up being adjusted during learning, so we also define a learning rule. This is the PES rule, which is really just standard delta rule (i.e. a supervised learning rule on just that one set of connections, which ends up being backprop without backpropagation)." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "with model:\n", " pmc = nengo.Ensemble(n_neurons=500, dimensions=6)\n", " \n", " # function to approximate\n", " def starting_map(x):\n", " return x[0], x[1], x[2]\n", " \n", " c = nengo.Connection(pmc, m1, function=starting_map,\n", " learning_rule_type=nengo.PES(learning_rate=1e-5))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We should have some actual input to our system. Let's just do a random band-limited white noise signal, with a maximum frequency of 5Hz. Note that this input is a 6-dimensional input (i.e. it's in the low-D manifold space, not in the 500-D neurons space). The first 2 dimensions of this we will consider to be our target X,Y location that we want to decode out of the M1 representation.\n", "\n", "Note that because of how we set up our connection above, the initial neuron model will be one that does send that information (the values we want to decode) to M1." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "with model:\n", " stim = nengo.Node(nengo.processes.WhiteSignal(period=500, high=5), size_out=6)\n", " nengo.Connection(stim, pmc)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we define our BCI node. This will take in spiking data from an ensemble of neurons, and apply some linear transform on the spikes, projecting it down into some smaller space. The begin with, this calls into ```nengo``` to compute the ideal default decoding. However, if we change this ```self.decoder```, then we change the mapping that the model has to learn.\n", "\n", "Note: this is using a few different rather esoteric Nengo tricks, so probably isn't all that readable. But the final result is something that has as input spikes from N neurons, and as output has a 2-element vector that is formed by linearly combining the spike trains. By default it's using the same linear combination that the full model is using (i.e. as if we were able to find the actual mapping that the macaque is using), but we'll change that to a different mapping that it has to learn." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class BCINode(nengo.Node):\n", " def __init__(self, ensemble, dimensions, seed=1):\n", " ensemble.seed = seed\n", " self.decoder = self.get_decoder(ensemble)[:dimensions]\n", " super(BCINode, self).__init__(self.decode, size_in=ensemble.n_neurons, size_out=dimensions)\n", "\n", " # defines the behaviour of this Node in the running model\n", " def decode(self, t, x):\n", " return np.dot(self.decoder, x)\n", " \n", " # use nengo to compute the ideal decoder for this neural population \n", " def get_decoder(self, ens):\n", " assert ens.seed is not None\n", " net = nengo.Network(add_to_container=False)\n", " net.ensembles.append(ens)\n", " with net:\n", " c = nengo.Connection(ens, ens)\n", " sim = nengo.Simulator(net, progress_bar=False)\n", " return sim.data[c].weights\n", " \n", " \n", "with model:\n", " bci = BCINode(m1, dimensions=2)\n", " nengo.Connection(m1.neurons, bci)\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we left it like this, then the way we're decoding the spikes in the BCI is exactly what the neural model is already doing, so it would give perfect behaviour instantly and there'd be nothing to learn.\n", "\n", "Let's give it something to learn by swapping the 2 dimensions being decoded. That is, we're staying in manifold, but swapping dimension 1 and dimension 2" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "bci.decoder = np.vstack([bci.decoder[1], bci.decoder[0]])\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we need to give the system a learning signal. The solution provided here is cheating a fair bit, in that it already knows which direction to change things to improve the results. A more complete solution would require learning to characterize the relationship between the observed change in cursor position and the desired change. This is exactly the sort of learning we've done elsewhere in the adaptive Jacobian kinematics learning models (such as http://rspb.royalsocietypublishing.org/content/283/1843/20162134) but for this demonstration I'm cheating and skipping that part." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "with model:\n", " # population representing the error signal\n", " error = nengo.Ensemble(n_neurons=500, dimensions=3)\n", " \n", " # feedback from the BCI as to where the cursor actually went to\n", " nengo.Connection(bci, error[:2], transform=1)\n", " \n", " # minus the actual desired location\n", " nengo.Connection(pmc[:2], error[:2], transform=-1)\n", " \n", " # use this difference to drive the learning from pmc to m1\n", " nengo.Connection(error, c.learning_rule, transform=[[0,1,0],[1,0,0],[0,0,1]])\n", " \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we mark particular data to be recorded during the model run." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "with model:\n", " p_out = nengo.Probe(bci) # the output from the BCI\n", " p_stim = nengo.Probe(stim[:2]) # the desired target location\n", " p_spikes = nengo.Probe(m1.neurons) # the spike data in m1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we run the simulation for 500 seconds." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Building finished in 0:00:01. \n", "Simulating finished in 0:05:46. \n" ] } ], "source": [ "sim = nengo.Simulator(model)\n", "sim.run(500)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's see what it's doing at a behavioural level. First, we plot behaviour in the first second" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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rOM0M4E+I51p6HFCD5LMQGOO8prs2NgknWCr1IZ+aIbMfIo94xjDSfMdJfjYV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\nGfdhjXZ2VcH68jkTDyDu3j8S0N5tif5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"text/plain": [ "<matplotlib.figure.Figure at 0x1e5f8748>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pylab.plot(sim.trange(), sim.data[p_out], label='BCI output')\n", "pylab.gca().set_color_cycle(None)\n", "pylab.plot(sim.trange(), sim.data[p_stim], linestyle='--', label='desired')\n", "pylab.xlim(0,1)\n", "pylab.legend(loc='best')\n", "pylab.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As expected, the initial output of the system is exactly backwards (the first dimension and the second dimension are swapped). \n", "\n", "What happens after 100 seconds of learning?" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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bKnb84OEee7xGpZiDooASiCuBfst/iRL7B2U1VIz6jPJt5/9Ws4yswRI++eEb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\n4xBIz4bbYOIuVZE5QD1mPKKcDgRo/Wib84ARTQ2N3Woc4sn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"text/plain": [ "<matplotlib.figure.Figure at 0x1e86ff28>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pylab.plot(sim.trange(), sim.data[p_out], label='BCI output')\n", "pylab.gca().set_color_cycle(None)\n", "pylab.plot(sim.trange(), sim.data[p_stim], linestyle='--', label='desired')\n", "pylab.xlim(100, 101)\n", "pylab.legend(loc='best')\n", "pylab.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Things have changed, but it's still rather wrong. \n", "\n", "What happens after 200 seconds of learning?" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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704GW9EJ16piPgI2jKCoWcw0offckub7Q7OBwpCbNFaGj28XaxsqjnyQ2HRk6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\nbcWm9xOBp3zTLbmQG7uKPOQP/r8Ur+G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"text/plain": [ "<matplotlib.figure.Figure at 0xb46b69e8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pylab.plot(sim.trange(), sim.data[p_out], label='BCI output')\n", "pylab.gca().set_color_cycle(None)\n", "pylab.plot(sim.trange(), sim.data[p_stim], linestyle='--', label='desired')\n", "pylab.xlim(200, 201)\n", "pylab.legend(loc='best')\n", "pylab.show()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "Getting better.\n", "\n", "What happens after 500 seconds of learning?" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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wuHt/uOxN0TB9+XnzP1pr/6Fb3vcNMnst06yAcJyCLKevRIxmSbrPdTvV+Rrg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\nPTDK+b1WQCRRSOefQPL/x/W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"text/plain": [ "<matplotlib.figure.Figure at 0x1e8491d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pylab.plot(sim.trange(), sim.data[p_out], label='BCI output')\n", "pylab.gca().set_color_cycle(None)\n", "pylab.plot(sim.trange(), sim.data[p_stim], linestyle='--', label='desired')\n", "pylab.xlim(499, 500)\n", "pylab.legend(loc='best')\n", "pylab.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It has successfully learned the new mapping.\n", "\n", "## Tuning curves\n", "\n", "The whole point of all this was to see what's happening to the tuning curves.\n", "\n", "To plot these, the simplest thing to do is just do a density plot of what target x,y locations the neurons spike for at different times in the experiment." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def plot_tuning(index, t_start=0, t_end=10, cmap='Reds'):\n", " times = sim.trange()\n", " spikes = sim.data[p_spikes][:,index]\n", " spikes = np.where(times>t_end, 0, spikes)\n", " spikes = np.where(times<t_start, 0, spikes)\n", " \n", " value = sim.data[p_stim]\n", " v = value[np.where(spikes>0)]\n", " seaborn.kdeplot(v[:,0], v[:,1], shade=True, shade_lowest=False, cmap=cmap, alpha=1.0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here is the tuning curve for neuron #2, with the initial tuning curve in red (using data from t=0 to t=100) and the final tuning curve in blue (using data from t=400 to t=500)." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Python27\\lib\\site-packages\\matplotlib\\collections.py:548: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", " if self._edgecolors == 'face':\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0xb468f898>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "index = 2\n", "pylab.figure(figsize=(6,6))\n", "plot_tuning(index, t_start=0, t_end=100, cmap='Reds')\n", "plot_tuning(index, t_start=400, t_end=500, cmap='Blues')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here's a neuron that didn't change much" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0xb4c3d588>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "index = 3\n", "pylab.figure(figsize=(6,6))\n", "plot_tuning(index, t_start=0, t_end=100, cmap='Reds')\n", "plot_tuning(index, t_start=400, t_end=500, cmap='Blues')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And some more neurons." ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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ff11E/lREdhpjzmQ6x1jUcD8miXk/NJduemtbInKtiPyzza+vA/BB\nbHR8lsxQne5ZAAdE5FYRuRrAxwA8k++0rHgGwMc3v/44NrKSLRR+T2yu8TMAfhcAROS9AF5vlatK\nYTIOEdktImubX98DYK1CyQN13I9JYt6P4jteXBCRBwE8BuAGAG8AOGSMuV9E9gL4ojHmARHZD+Ar\nmy+5CsCXjTF/PM8ZD2MTy+Z29+PykLn/Vlosm8Mr/xLAzWgNr6zpnvRdYxF5GACMMU9sbtOMaPkZ\ngN8bGg47J1NxiMi/BfCvAfwCG8MS/70x5h9mO+EBROQvALwfG++NkwAeAfArQHX3YzSOWu4HIYQQ\nQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEFIa/x/UoGwsY7Z9dAAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0xb46b3080>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "index = 5\n", "pylab.figure(figsize=(6,6))\n", "plot_tuning(index, t_start=0, t_end=100, cmap='Reds')\n", "plot_tuning(index, t_start=400, t_end=500, cmap='Blues')" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0xb4e35128>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "index = 6\n", "pylab.figure(figsize=(6,6))\n", "plot_tuning(index, t_start=0, t_end=100, cmap='Reds')\n", "plot_tuning(index, t_start=400, t_end=500, cmap='Blues')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Next steps\n", "\n", "This was just a quick toy example to see what things might look like. The big things that would be needed to push this forward (assuming I haven't completely misinterpretted anything) are:\n", "\n", " - Better model of the task (right now it's a just a target dot moving around randomly)\n", " - Better ways of computing the tuning curve (the current way is biased by the task itself, sampling points in the middle of the space more than the edges)\n", " - Modelling the error computation process, using the same approach as the adaptive Jacobain motor control model\n", " - Trying to learn something outside of the manifold\n", " - Adding unsupervised STDP/BCM learning (the \"hPES\" rule)" ] }, { "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": "2.7.5" } }, "nbformat": 4, "nbformat_minor": 0 }	gpl-2.0
olgaliak/cntk-cyclegan	simpleGan/CNTK_206A_Basic_GAN.ipynb	1	76949	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from IPython.display import Image" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# CNTK 206 Part A: Basic GAN with MNIST data\n", "\n", "Prerequisites: We assume that you have successfully downloaded the MNIST data by completing the tutorial titled CNTK_103A_MNIST_DataLoader.ipynb.\n", "\n", "## Introduction\n", "\n", "[Generative models](https://en.wikipedia.org/wiki/Generative_model) have gained a [lot of attention](https://openai.com/blog/generative-models/) in deep learning community which has traditionally leveraged [discriminative models](https://en.wikipedia.org/wiki/Discriminative_model) for (semi-supervised) and unsupervised learning. In generative modeling, the idea is to collect a huge amount of data in a domain of interest (e.g., pictures, audio, words) and come up with a trained model that generates such real world data sets. This is an active area of research needing mechanisms to scale up training and having large datasets. As stated in the [OpenAI blog](https://openai.com/blog/generative-models/), such approaches may be used to perform computer aided art generation, or morph images to some word descriptions such as \"make my smile wider\". This approach has found use in image denoising, inpainting, super-resolution, structured prediction, exploration in reinforcement learning, and neural network pretraining in cases where labeled data is expensive. \n", "\n", "Generating models that can produce realistic content (images, sounds etc.) mimicking real world observations is challenging. Generative Adversarial Network (GAN) is one of the approaches that holds promise. A [quote](https://www.quora.com/What-are-some-recent-and-potentially-upcoming-breakthroughs-in-deep-learning) from Yann LeCun summarizes GAN and its variations as the most important idea in the last 10 years. The original idea was proposed by [Goodfellow et al](https://arxiv.org/pdf/1406.2661v1.pdf) at NIPS 2014. In this tutorial, we show how to use the Cognitive Toolkit to create a basic GAN network for generating synthetic MNIST digits." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import matplotlib as mpl\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import os\n", "\n", "import cntk as C\n", "from cntk import Trainer\n", "from cntk.device import try_set_default_device, gpu, cpu\n", "from cntk.initializer import xavier\n", "from cntk.io import (MinibatchSource, CTFDeserializer, StreamDef, StreamDefs,\n", " INFINITELY_REPEAT)\n", "from cntk.layers import Dense, default_options\n", "from cntk.learners import (fsadagrad, UnitType, sgd, learning_rate_schedule,\n", " momentum_as_time_constant_schedule)\n", "from cntk.logging import ProgressPrinter\n", "\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### Select the notebook runtime environment devices / settings\n", "\n", "Set the device to cpu / gpu for the test environment. If you have both CPU and GPU on your machine, you can optionally switch the devices. By default, we choose the best available device." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Select the right target device when this notebook is being tested:\n", "if 'TEST_DEVICE' in os.environ:\n", " import cntk\n", " if os.environ['TEST_DEVICE'] == 'cpu':\n", " C.device.try_set_default_device(C.device.cpu())\n", " else:\n", " C.device.try_set_default_device(C.device.gpu(0)) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are two run modes:\n", "- Fast mode: `isFast` is set to `True`. This is the default mode for the notebooks, which means we train for fewer iterations or train / test on limited data. This ensures functional correctness of the notebook though the models produced are far from what a completed training would produce.\n", "\n", "- Slow mode: We recommend the user to set this flag to `False` once the user has gained familiarity with the notebook content and wants to gain insight from running the notebooks for a longer period with different parameters for training. \n", "\n", "Note\n", "If the `isFlag` is set to `False` the notebook will take a few hours on a GPU enabled machine. You can try fewer iterations by setting the `num_minibatches` to a smaller number say `20,000` which comes at the expense of quality of the generated images." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "isFast = True " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data Reading\n", "The input to the GAN will be a vector of random numbers. At the end of the traning, the GAN \"learns\" to generate images of hand written digits drawn from the [MNIST database](https://en.wikipedia.org/wiki/MNIST_database). We will be using the same MNIST data generated in tutorial 103A. A more in-depth discussion of the data format and reading methods can be seen in previous tutorials. For our purposes, just know that the following function returns an object that will be used to generate images from the MNIST dataset. Since we are building an unsupervised model, we only need to read in `features` and ignore the `labels`." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Data directory is ..\\Examples\\Image\\DataSets\\MNIST\n" ] } ], "source": [ "# Ensure the training data is generated and available for this tutorial\n", "# We search in two locations in the toolkit for the cached MNIST data set.\n", "\n", "data_found = False\n", "for data_dir in [os.path.join(\"..\", \"Examples\", \"Image\", \"DataSets\", \"MNIST\"),\n", " os.path.join(\"data\", \"MNIST\")]:\n", " train_file = os.path.join(data_dir, \"Train-28x28_cntk_text.txt\")\n", " if os.path.isfile(train_file):\n", " data_found = True\n", " break\n", " \n", "if not data_found:\n", " raise ValueError(\"Please generate the data by completing CNTK 103 Part A\")\n", " \n", "print(\"Data directory is {0}\".format(data_dir))" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def create_reader(path, is_training, input_dim, label_dim):\n", " deserializer = CTFDeserializer(\n", " filename = path,\n", " streams = StreamDefs(\n", " labels_unused = StreamDef(field = 'labels', shape = label_dim, is_sparse = False),\n", " features = StreamDef(field = 'features', shape = input_dim, is_sparse = False\n", " )\n", " )\n", " )\n", " return MinibatchSource(\n", " deserializers = deserializer,\n", " randomize = is_training,\n", " max_sweeps = INFINITELY_REPEAT if is_training else 1\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The random noise we will use to train the GAN is provided by the `noise_sample` function to generate random noise samples from a uniform distribution within the interval [-1, 1]." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "np.random.seed(123)\n", "def noise_sample(num_samples):\n", " return np.random.uniform(\n", " low = -1.0,\n", " high = 1.0,\n", " size = [num_samples, g_input_dim] \n", " ).astype(np.float32)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Model Creation\n", "\n", "A GAN network is composed of two sub-networks, one called the Generator ($G$) and the other Discriminator ($D$). \n", "- The Generator takes random noise vector ($z$) as input and strives to output synthetic (fake) image ($x^$) that is indistinguishable from the real image ($x$) from the MNIST dataset. \n", "- The Discriminator* strives to differentiate between the real image ($x$) and the fake ($x^$) image." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<img src=\"https://www.cntk.ai/jup/GAN_basic_flow.png\"/>" ], "text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Figure 1\n", "Image(url=\"https://www.cntk.ai/jup/GAN_basic_flow.png\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In each training iteration, the Generator produces more realistic fake images (in other words minimizes* the difference between the real and generated counterpart) and also the Discriminator maximizes the probability of assigning the correct label (real vs. fake) to both real examples (from training set) and the generated fake ones. The two conflicting objectives between the sub-networks ($G$ and $D$) leads to the GAN network (when trained) converge to an equilibrium, where the Generator produces realistic looking fake MNIST images and the Discriminator can at best randomly guess whether images are real or fake. The resulting Generator model once trained produces realistic MNIST image with the input being a random number. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Model config\n", "\n", "First, we establish some of the architectural and training hyper-parameters for our model. \n", "\n", "- The generator network is a fully-connected network with a single hidden layer. The input will be a 10-dimensional random vector and the output will be a 784 dimensional vector, corresponding to a flattened version of a 28 x 28 fake image. The discriminator is also a single layer dense network. It takes as input the 784 dimensional output of the generator or a real MNIST image and outputs a single scalar - the estimated probability that the input image is a real MNIST image." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Model components\n", "We build a computational graph for our model, one each for the generator and the discriminator. First, we establish some of the architectural parameters of our model. \n", "\n", "- The generator takes a 100-dimensional random vector (for starters) as input ($z$) and the outputs a 784 dimensional vector, corresponding to a flattened version of a 28 x 28 fake (synthetic) image ($x^$). In this tutorial we simply model the generator with two dense layers. We use a tanh activation on the last layer to make sure that the output of the generator function is confined to the interval [-1, 1]. This is necessary because we also scale the MNIST images to this interval, and the outputs of the generator must be able to emulate the actual images as closely as possible.\n", "\n", "\n", "- The discriminator takes as input ($x^$) the 784 dimensional output of the generator or a real MNIST image and outputs the estimated probability that the input image is a real MNIST image. We also model this with two dense layers with a sigmoid activation in the last layer ensuring that the discriminator produces a valid probability." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# architectural parameters\n", "g_input_dim = 100\n", "g_hidden_dim = 128\n", "g_output_dim = d_input_dim = 784\n", "d_hidden_dim = 128\n", "d_output_dim = 1" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def generator(z):\n", " with default_options(init = xavier()):\n", " h1 = Dense(g_hidden_dim, activation = C.relu)(z)\n", " return Dense(g_output_dim, activation = C.tanh)(h1)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def discriminator(x):\n", " with default_options(init = xavier()):\n", " h1 = Dense(d_hidden_dim, activation = C.relu)(x)\n", " return Dense(d_output_dim, activation = C.sigmoid)(h1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We use a minibatch size of 1024 and a fixed learning rate of 0.0005 for training. In the fast mode (`isFast = True`) we verify only functional correctness with 200 iterations. \n", "\n", "Note: In the slow mode, the results look a lot better but it requires patient waiting (few hours) depending on your hardware. In general, the more number of minibatches one trains, the better is the fidelity of the generated images." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# training config\n", "minibatch_size = 1024\n", "num_minibatches = 300 if isFast else 40000\n", "lr = 0.00005" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Build the graph\n", "\n", "The rest of the computational graph is mostly responsible for coordinating the training algorithms and parameter updates, which is particularly tricky with GANs for couple reasons. \n", "\n", "- First, the discriminator must be used on both the real MNIST images and fake images generated by the generator function. One way to represent this in the computational graph is to create a clone of the output of the discriminator function, but with substituted inputs. Setting `method=share` in the `clone` function ensures that both paths through the discriminator model use the same set of parameters.\n", "\n", "\n", "- Second, we need to update the parameters for the generator and discriminator model separately using the gradients from different loss functions. We can get the parameters for a `Function` in the graph with the `parameters` attribute. However, when updating the model parameters, update only the parameters of the respective models while keeping the other parameters unchanged. In other words, when updating the generator we will update only the parameters of the $G$ function while keeping the parameters of the $D$ function fixed and vice versa.\n", "\n", "### Training the Model\n", "The code for training the GAN very closely follows the algorithm as presented in the [original NIPS 2014 paper](https://arxiv.org/pdf/1406.2661v1.pdf). In this implementation, we train $D$ to maximize the probability of assigning the correct label (fake vs. real) to both training examples and the samples from $G$. In other words, $D$ and $G$ play the following two-player minimax game with the value function $V(G,D)$:\n", "\n", "$$\n", " \\min_G \\max_D V(D,G)= \\mathbb{E}_{x}[ log D(x) ] + \\mathbb{E}_{z}[ log(1 - D(G(z))) ]\n", "$$\n", "\n", "At the optimal point of this game the generator will produce realistic looking data while the discriminator will predict that the generated image is indeed fake with a probability of 0.5. The [algorithm referred below](https://arxiv.org/pdf/1406.2661v1.pdf) is implemented in this tutorial." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<img src=\"https://www.cntk.ai/jup/GAN_goodfellow_NIPS2014.png\" width=\"500\"/>" ], "text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Figure 2\n", "Image(url=\"https://www.cntk.ai/jup/GAN_goodfellow_NIPS2014.png\", width = 500)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def build_graph(noise_shape, image_shape,\n", " G_progress_printer, D_progress_printer):\n", " input_dynamic_axes = [C.Axis.default_batch_axis()]\n", " Z = C.input(noise_shape, dynamic_axes=input_dynamic_axes)\n", " X_real = C.input(image_shape, dynamic_axes=input_dynamic_axes)\n", " X_real_scaled = 2(X_real / 255.0) - 1.0\n", "\n", " # Create the model function for the generator and discriminator models\n", " X_fake = generator(Z)\n", " D_real = discriminator(X_real_scaled)\n", " D_fake = D_real.clone(\n", " method = 'share',\n", " substitutions = {X_real_scaled.output: X_fake.output}\n", " )\n", "\n", " # Create loss functions and configure optimazation algorithms\n", " G_loss = 1.0 - C.log(D_fake)\n", " D_loss = -(C.log(D_real) + C.log(1.0 - D_fake))\n", "\n", " G_learner = fsadagrad(\n", " parameters = X_fake.parameters,\n", " lr = learning_rate_schedule(lr, UnitType.sample),\n", " momentum = momentum_as_time_constant_schedule(700)\n", " )\n", " D_learner = fsadagrad(\n", " parameters = D_real.parameters,\n", " lr = learning_rate_schedule(lr, UnitType.sample),\n", " momentum = momentum_as_time_constant_schedule(700)\n", " )\n", "\n", " # Instantiate the trainers\n", " G_trainer = Trainer(\n", " X_fake,\n", " (G_loss, None),\n", " G_learner,\n", " G_progress_printer\n", " )\n", " D_trainer = Trainer(\n", " D_real,\n", " (D_loss, None),\n", " D_learner,\n", " D_progress_printer\n", " )\n", "\n", " return X_real, X_fake, Z, G_trainer, D_trainer" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With the value functions defined we proceed to interatively train the GAN model. The training of the model can take significnantly long depending on the hardware especiallly if `isFast` flag is turned off." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def train(reader_train):\n", " k = 2\n", " \n", " # print out loss for each model for upto 50 times\n", " print_frequency_mbsize = num_minibatches // 50\n", " pp_G = ProgressPrinter(print_frequency_mbsize)\n", " pp_D = ProgressPrinter(print_frequency_mbsize k)\n", "\n", " X_real, X_fake, Z, G_trainer, D_trainer = \\\n", " build_graph(g_input_dim, d_input_dim, pp_G, pp_D)\n", " \n", " input_map = {X_real: reader_train.streams.features}\n", " for train_step in range(num_minibatches):\n", "\n", " # train the discriminator model for k steps\n", " for gen_train_step in range(k):\n", " Z_data = noise_sample(minibatch_size)\n", " X_data = reader_train.next_minibatch(minibatch_size, input_map)\n", " if X_data[X_real].num_samples == Z_data.shape[0]:\n", " batch_inputs = {X_real: X_data[X_real].data, \n", " Z: Z_data}\n", " D_trainer.train_minibatch(batch_inputs)\n", "\n", " # train the generator model for a single step\n", " Z_data = noise_sample(minibatch_size)\n", " batch_inputs = {Z: Z_data}\n", " G_trainer.train_minibatch(batch_inputs)\n", "\n", " G_trainer_loss = G_trainer.previous_minibatch_loss_average\n", "\n", " return Z, X_fake, G_trainer_loss" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Minibatch[ 1- 12]: loss = 0.435653 * 12288, metric = 0.00% * 12288;\n", " Minibatch[ 1- 6]: loss = 2.737929 * 6144, metric = 0.00% * 6144;\n", " Minibatch[ 13- 24]: loss = 0.518323 * 12288, metric = 0.00% * 12288;\n", " Minibatch[ 7- 12]: loss = 2.355194 * 6144, metric = 0.00% * 6144;\n", " Minibatch[ 25- 36]: loss = 0.465481 * 12288, metric = 0.00% * 12288;\n", " Minibatch[ 13- 18]: loss = 2.486607 * 6144, metric = 0.00% * 6144;\n", " Minibatch[ 37- 48]: loss = 0.504485 * 12288, metric = 0.00% * 12288;\n", " Minibatch[ 19- 24]: loss = 2.644584 * 6144, metric = 0.00% * 6144;\n", " Minibatch[ 49- 60]: loss = 0.892371 * 12288, metric = 0.00% * 12288;\n", " Minibatch[ 25- 30]: loss = 2.253040 * 6144, metric = 0.00% * 6144;\n", " Minibatch[ 61- 72]: loss = 1.121703 * 12288, metric = 0.00% * 12288;\n", " Minibatch[ 31- 36]: loss = 1.814892 * 6144, metric = 0.00% * 6144;\n", " Minibatch[ 73- 84]: loss = 1.036774 * 12288, metric = 0.00% * 12288;\n", " Minibatch[ 37- 42]: loss = 1.942523 * 6144, metric = 0.00% * 6144;\n", " Minibatch[ 85- 96]: loss = 1.019848 * 12288, metric = 0.00% * 12288;\n", " Minibatch[ 43- 48]: loss = 1.879794 * 6144, metric = 0.00% * 6144;\n", " Minibatch[ 97- 108]: loss = 1.080548 * 12288, metric = 0.00% * 12288;\n", " Minibatch[ 49- 54]: loss = 1.765081 * 6144, metric = 0.00% * 6144;\n", " Minibatch[ 109- 120]: loss = 0.934299 * 12288, metric = 0.00% * 12288;\n", " Minibatch[ 55- 60]: loss = 1.822290 * 6144, metric = 0.00% * 6144;\n", " Minibatch[ 121- 132]: loss = 1.006618 * 12288, metric = 0.00% * 12288;\n", " Minibatch[ 61- 66]: loss = 1.897998 * 6144, metric = 0.00% * 6144;\n", " Minibatch[ 133- 144]: loss = 0.956539 * 12288, metric = 0.00% * 12288;\n", " Minibatch[ 67- 72]: loss = 1.741196 * 6144, metric = 0.00% * 6144;\n", " Minibatch[ 145- 156]: loss = 1.048550 * 12288, metric = 0.00% * 12288;\n", " Minibatch[ 73- 78]: loss = 2.027761 * 6144, metric = 0.00% * 6144;\n", " Minibatch[ 157- 168]: loss = 0.960808 * 12288, metric = 0.00% * 12288;\n", " Minibatch[ 79- 84]: loss = 1.706484 * 6144, metric = 0.00% * 6144;\n", " Minibatch[ 169- 180]: loss = 1.020660 * 12288, metric = 0.00% * 12288;\n", " Minibatch[ 85- 90]: loss = 1.785408 * 6144, metric = 0.00% * 6144;\n", " Minibatch[ 181- 192]: loss = 0.934677 * 12288, metric = 0.00% * 12288;\n", " Minibatch[ 91- 96]: loss = 2.024556 * 6144, metric = 0.00% * 6144;\n", " Minibatch[ 193- 204]: loss = 0.815584 * 12288, metric = 0.00% * 12288;\n", " Minibatch[ 97- 102]: loss = 2.117467 * 6144, metric = 0.00% * 6144;\n", " Minibatch[ 205- 216]: loss = 0.822709 * 12288, metric = 0.00% * 12288;\n", " Minibatch[ 103- 108]: loss = 1.818186 * 6144, metric = 0.00% * 6144;\n", " Minibatch[ 217- 228]: loss = 0.831401 * 12288, metric = 0.00% * 12288;\n", " Minibatch[ 109- 114]: loss = 1.985031 * 6144, metric = 0.00% * 6144;\n", " Minibatch[ 229- 240]: loss = 0.679337 * 12288, metric = 0.00% * 12288;\n", " Minibatch[ 115- 120]: loss = 2.341835 * 6144, metric = 0.00% * 6144;\n", " Minibatch[ 241- 252]: loss = 0.600048 * 12288, metric = 0.00% * 12288;\n", " Minibatch[ 121- 126]: loss = 2.026878 * 6144, metric = 0.00% * 6144;\n", " Minibatch[ 253- 264]: loss = 0.677536 * 12288, metric = 0.00% * 12288;\n", " Minibatch[ 127- 132]: loss = 2.154475 * 6144, metric = 0.00% * 6144;\n", " Minibatch[ 265- 276]: loss = 0.590532 * 12288, metric = 0.00% * 12288;\n", " Minibatch[ 133- 138]: loss = 2.299230 * 6144, metric = 0.00% * 6144;\n", " Minibatch[ 277- 288]: loss = 0.643248 * 12288, metric = 0.00% * 12288;\n", " Minibatch[ 139- 144]: loss = 2.050944 * 6144, metric = 0.00% * 6144;\n", " Minibatch[ 289- 300]: loss = 0.671329 * 12288, metric = 0.00% * 12288;\n", " Minibatch[ 145- 150]: loss = 2.090459 * 6144, metric = 0.00% * 6144;\n", " Minibatch[ 301- 312]: loss = 0.685636 * 12288, metric = 0.00% * 12288;\n", " Minibatch[ 151- 156]: loss = 2.059280 * 6144, metric = 0.00% * 6144;\n", " Minibatch[ 313- 324]: loss = 0.724862 * 12288, metric = 0.00% * 12288;\n", " Minibatch[ 157- 162]: loss = 1.968409 * 6144, metric = 0.00% * 6144;\n", " Minibatch[ 325- 336]: loss = 0.763301 * 12288, metric = 0.00% * 12288;\n", " Minibatch[ 163- 168]: loss = 2.121897 * 6144, metric = 0.00% * 6144;\n", " Minibatch[ 337- 348]: loss = 0.654315 * 12288, metric = 0.00% * 12288;\n", " Minibatch[ 169- 174]: loss = 2.104095 * 6144, metric = 0.00% * 6144;\n", " Minibatch[ 349- 360]: loss = 0.686829 * 12288, metric = 0.00% * 12288;\n", " Minibatch[ 175- 180]: loss = 2.183528 * 6144, metric = 0.00% * 6144;\n", " Minibatch[ 361- 372]: loss = 0.789029 * 12288, metric = 0.00% * 12288;\n", " Minibatch[ 181- 186]: loss = 1.974599 * 6144, metric = 0.00% * 6144;\n", " Minibatch[ 373- 384]: loss = 0.839806 * 12288, metric = 0.00% * 12288;\n", " Minibatch[ 187- 192]: loss = 2.055278 * 6144, metric = 0.00% * 6144;\n", " Minibatch[ 385- 396]: loss = 0.744639 * 12288, metric = 0.00% * 12288;\n", " Minibatch[ 193- 198]: loss = 2.087448 * 6144, metric = 0.00% * 6144;\n", " Minibatch[ 397- 408]: loss = 0.839060 * 12288, metric = 0.00% * 12288;\n", " Minibatch[ 199- 204]: loss = 1.878703 * 6144, metric = 0.00% * 6144;\n", " Minibatch[ 409- 420]: loss = 0.824562 * 12288, metric = 0.00% * 12288;\n", " Minibatch[ 205- 210]: loss = 2.082550 * 6144, metric = 0.00% * 6144;\n", " Minibatch[ 421- 432]: loss = 0.837484 * 12288, metric = 0.00% * 12288;\n", " Minibatch[ 211- 216]: loss = 1.993073 * 6144, metric = 0.00% * 6144;\n", " Minibatch[ 433- 444]: loss = 0.883647 * 12288, metric = 0.00% * 12288;\n", " Minibatch[ 217- 222]: loss = 1.815333 * 6144, metric = 0.00% * 6144;\n", " Minibatch[ 445- 456]: loss = 0.885610 * 12288, metric = 0.00% * 12288;\n", " Minibatch[ 223- 228]: loss = 2.002263 * 6144, metric = 0.00% * 6144;\n", " Minibatch[ 457- 468]: loss = 0.797892 * 12288, metric = 0.00% * 12288;\n", " Minibatch[ 229- 234]: loss = 2.010305 * 6144, metric = 0.00% * 6144;\n", " Minibatch[ 469- 480]: loss = 0.857361 * 12288, metric = 0.00% * 12288;\n", " Minibatch[ 235- 240]: loss = 1.923676 * 6144, metric = 0.00% * 6144;\n", " Minibatch[ 481- 492]: loss = 0.835848 * 12288, metric = 0.00% * 12288;\n", " Minibatch[ 241- 246]: loss = 1.892008 * 6144, metric = 0.00% * 6144;\n", " Minibatch[ 493- 504]: loss = 0.866661 * 12288, metric = 0.00% * 12288;\n", " Minibatch[ 247- 252]: loss = 1.977249 * 6144, metric = 0.00% * 6144;\n", " Minibatch[ 505- 516]: loss = 0.784159 * 12288, metric = 0.00% * 12288;\n", " Minibatch[ 253- 258]: loss = 2.022034 * 6144, metric = 0.00% * 6144;\n", " Minibatch[ 517- 528]: loss = 0.758845 * 12288, metric = 0.00% * 12288;\n", " Minibatch[ 259- 264]: loss = 2.077962 * 6144, metric = 0.00% * 6144;\n", " Minibatch[ 529- 540]: loss = 0.751444 * 12288, metric = 0.00% * 12288;\n", " Minibatch[ 265- 270]: loss = 1.996775 * 6144, metric = 0.00% * 6144;\n", " Minibatch[ 541- 552]: loss = 0.832954 * 12288, metric = 0.00% * 12288;\n", " Minibatch[ 271- 276]: loss = 1.938629 * 6144, metric = 0.00% * 6144;\n", " Minibatch[ 553- 564]: loss = 0.795357 * 12288, metric = 0.00% * 12288;\n", " Minibatch[ 277- 282]: loss = 2.064626 * 6144, metric = 0.00% * 6144;\n", " Minibatch[ 565- 576]: loss = 0.777181 * 12288, metric = 0.00% * 12288;\n", " Minibatch[ 283- 288]: loss = 2.031830 * 6144, metric = 0.00% * 6144;\n", " Minibatch[ 577- 588]: loss = 0.849988 * 12288, metric = 0.00% * 12288;\n", " Minibatch[ 289- 294]: loss = 1.954437 * 6144, metric = 0.00% * 6144;\n", " Minibatch[ 589- 600]: loss = 0.867640 * 12288, metric = 0.00% * 12288;\n", " Minibatch[ 295- 300]: loss = 1.958160 * 6144, metric = 0.00% * 6144;\n" ] } ], "source": [ "reader_train = create_reader(train_file, True, d_input_dim, label_dim=10)\n", "\n", "G_input, G_output, G_trainer_loss = train(reader_train)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training loss of the generator is: 1.75\n" ] } ], "source": [ "# Print the generator loss \n", "print(\"Training loss of the generator is: {0:.2f}\".format(G_trainer_loss))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Generating Fake (Synthetic) Images\n", "\n", "Now that we have trained the model, we can create fake images simply by feeding random noise into the generator and displaying the outputs. Below are a few images generated from random samples. To get a new set of samples, you can re-run the last cell." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "image/png": 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/ZrNZ9Ho9NTU1XL16dcUiMBTp2o/FYvMWbslkMkxMTLBlyxb6+/v58MMPGR4e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\nUCgkJznV1tai0+k4duwYAENDQ3dlHIoWyjcj2YtbWlo4cOAAJ0+e5Ac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"text/plain": [ "<matplotlib.figure.Figure at 0x1777df43358>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def plot_images(images, subplot_shape):\n", " plt.style.use('ggplot')\n", " fig, axes = plt.subplots(subplot_shape)\n", " for image, ax in zip(images, axes.flatten()):\n", " ax.imshow(image.reshape(28, 28), vmin = 0, vmax = 1.0, cmap = 'gray')\n", " ax.axis('off')\n", " plt.show()\n", " \n", "noise = noise_sample(36)\n", "images = G_output.eval({G_input: noise})\n", "plot_images(images, subplot_shape =[6, 6])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Larger number of iterations should generate more realistic looking MNIST images. A sampling of such generated images are shown below." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<img src=\"http://www.cntk.ai/jup/GAN_basic_slowmode.jpg\"/>" ], "text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Figure 3\n", "Image(url=\"http://www.cntk.ai/jup/GAN_basic_slowmode.jpg\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note: It takes a large number of iterations to capture a representation of the real world signal. Even simple dense networks can be quite effective in modelling data albeit MNIST is a relatively simple dataset as well." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Suggested Task*\n", "\n", "- Explore the impact of changing the dimension of the input random noise (say from 100 to 10) in terms of computation time, loss and memory footprint for the same number of iterations.\n", "\n", "- Scale the image from 0 to 1. What other changes in the network are needed?\n", "\n", "- Performance is a key aspect to deep neural networks training. Study how the changing the minibatch sizes impact the performance both with regards to quality of the generated images and the time it takes to train a model.\n", "\n", "- Try generating fake images using the CIFAR-10 data set as the training data. How does the network above perform?\n" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "conda env ctnk", "language": "python", "name": "cntk-py35" }, "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
mlund/particletracking	mc/montecarlo.ipynb	1	1457599	null	gpl-2.0
mne-tools/mne-tools.github.io	0.24/_downloads/1abc74aa28d845859c3852be5f0bdd21/30_forward.ipynb	1	14749	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n\n# Head model and forward computation\n\nThe aim of this tutorial is to be a getting started for forward computation.\n\nFor more extensive details and presentation of the general\nconcepts for forward modeling, see `ch_forward`.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import os.path as op\nimport mne\nfrom mne.datasets import sample\ndata_path = sample.data_path()\n\n# the raw file containing the channel location + types\nsample_dir = op.join(data_path, 'MEG', 'sample',)\nraw_fname = op.join(sample_dir, 'sample_audvis_raw.fif')\n# The paths to Freesurfer reconstructions\nsubjects_dir = op.join(data_path, 'subjects')\nsubject = 'sample'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Computing the forward operator\n\nTo compute a forward operator we need:\n\n - a ``-trans.fif`` file that contains the coregistration info.\n - a source space\n - the :term:`BEM` surfaces\n\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Compute and visualize BEM surfaces\n\nThe :term:`BEM` surfaces are the triangulations of the interfaces between\ndifferent tissues needed for forward computation. These surfaces are for\nexample the inner skull surface, the outer skull surface and the outer skin\nsurface, a.k.a. scalp surface.\n\nComputing the BEM surfaces requires FreeSurfer and makes use of\nthe command-line tools `mne watershed_bem` or `mne flash_bem`, or\nthe related functions :func:`mne.bem.make_watershed_bem` or\n:func:`mne.bem.make_flash_bem`.\n\nHere we'll assume it's already computed. It takes a few minutes per subject.\n\nFor EEG we use 3 layers (inner skull, outer skull, and skin) while for\nMEG 1 layer (inner skull) is enough.\n\nLet's look at these surfaces. The function :func:`mne.viz.plot_bem`\nassumes that you have the ``bem`` folder of your subject's FreeSurfer\nreconstruction, containing the necessary surface files. Here we use a smaller\nthan default subset of ``slices`` for speed.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plot_bem_kwargs = dict(\n subject=subject, subjects_dir=subjects_dir,\n brain_surfaces='white', orientation='coronal',\n slices=[50, 100, 150, 200])\n\nmne.viz.plot_bem(plot_bem_kwargs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Visualizing the coregistration\n\nThe coregistration is the operation that allows to position the head and the\nsensors in a common coordinate system. In the MNE software the transformation\nto align the head and the sensors in stored in a so-called trans file.\nIt is a FIF file that ends with ``-trans.fif``. It can be obtained with\n:func:`mne.gui.coregistration` (or its convenient command line\nequivalent `mne coreg`), or mrilab if you're using a Neuromag\nsystem.\n\nHere we assume the coregistration is done, so we just visually check the\nalignment with the following code.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# The transformation file obtained by coregistration\ntrans = op.join(sample_dir, 'sample_audvis_raw-trans.fif')\n\ninfo = mne.io.read_info(raw_fname)\n# Here we look at the dense head, which isn't used for BEM computations but\n# is useful for coregistration.\nmne.viz.plot_alignment(info, trans, subject=subject, dig=True,\n meg=['helmet', 'sensors'], subjects_dir=subjects_dir,\n surfaces='head-dense')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n## Compute Source Space\n\nThe source space defines the position and orientation of the candidate source\nlocations. There are two types of source spaces:\n\n- surface-based source space when the candidates are confined to a\n surface.\n\n- volumetric or discrete source space when the candidates are discrete,\n arbitrarily located source points bounded by the surface.\n\nSurface-based source space is computed using\n:func:`mne.setup_source_space`, while volumetric source space is computed\nusing :func:`mne.setup_volume_source_space`.\n\nWe will now compute a surface-based source space with an ``'oct4'``\nresolution. See `setting_up_source_space` for details on source space\ndefinition and spacing parameter.\n\n<div class=\"alert alert-danger\"><h4>Warning</h4><p>``'oct4'`` is used here just for speed, for real analyses the recommended\n spacing is ``'oct6'``.</p></div>\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "src = mne.setup_source_space(subject, spacing='oct4', add_dist='patch',\n subjects_dir=subjects_dir)\nprint(src)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The surface based source space ``src`` contains two parts, one for the left\nhemisphere (258 locations) and one for the right hemisphere (258\nlocations). Sources can be visualized on top of the BEM surfaces in purple.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "mne.viz.plot_bem(src=src, plot_bem_kwargs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To compute a volume based source space defined with a grid of candidate\ndipoles inside a sphere of radius 90mm centered at (0.0, 0.0, 40.0) mm\nyou can use the following code.\nObviously here, the sphere is not perfect. It is not restricted to the\nbrain and it can miss some parts of the cortex.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sphere = (0.0, 0.0, 0.04, 0.09)\nvol_src = mne.setup_volume_source_space(\n subject, subjects_dir=subjects_dir, sphere=sphere, sphere_units='m',\n add_interpolator=False) # just for speed!\nprint(vol_src)\n\nmne.viz.plot_bem(src=vol_src, plot_bem_kwargs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To compute a volume based source space defined with a grid of candidate\ndipoles inside the brain (requires the :term:`BEM` surfaces) you can use the\nfollowing.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "surface = op.join(subjects_dir, subject, 'bem', 'inner_skull.surf')\nvol_src = mne.setup_volume_source_space(\n subject, subjects_dir=subjects_dir, surface=surface,\n add_interpolator=False) # Just for speed!\nprint(vol_src)\n\nmne.viz.plot_bem(src=vol_src, plot_bem_kwargs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"alert alert-info\"><h4>Note</h4><p>Some sources may appear to be outside the BEM inner skull contour.\n This is because the ``slices`` are decimated for plotting here.\n Each slice in the figure actually represents several MRI slices,\n but only the MRI voxels and BEM boundaries for a single (midpoint\n of the given slice range) slice are shown, whereas the source space\n points plotted on that midpoint slice consist of all points\n for which that slice (out of all slices shown) was the closest.</p></div>\n\nNow let's see how to view all sources in 3D.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fig = mne.viz.plot_alignment(subject=subject, subjects_dir=subjects_dir,\n surfaces='white', coord_frame='mri',\n src=src)\nmne.viz.set_3d_view(fig, azimuth=173.78, elevation=101.75,\n distance=0.30, focalpoint=(-0.03, -0.01, 0.03))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n## Compute forward solution\n\nWe can now compute the forward solution.\nTo reduce computation we'll just compute a single layer BEM (just inner\nskull) that can then be used for MEG (not EEG).\nWe specify if we want a one-layer or a three-layer BEM using the\n``conductivity`` parameter.\nThe BEM solution requires a BEM model which describes the geometry\nof the head the conductivities of the different tissues.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "conductivity = (0.3,) # for single layer\n# conductivity = (0.3, 0.006, 0.3) # for three layers\nmodel = mne.make_bem_model(subject='sample', ico=4,\n conductivity=conductivity,\n subjects_dir=subjects_dir)\nbem = mne.make_bem_solution(model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that the :term:`BEM` does not involve any use of the trans file. The BEM\nonly depends on the head geometry and conductivities.\nIt is therefore independent from the MEG data and the head position.\n\nLet's now compute the forward operator, commonly referred to as the\ngain or leadfield matrix.\nSee :func:`mne.make_forward_solution` for details on the meaning of each\nparameter.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fwd = mne.make_forward_solution(raw_fname, trans=trans, src=src, bem=bem,\n meg=True, eeg=False, mindist=5.0, n_jobs=1,\n verbose=True)\nprint(fwd)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"alert alert-danger\"><h4>Warning</h4><p>Forward computation can remove vertices that are too close to (or outside)\n the inner skull surface. For example, here we have gone from 516 to 474\n vertices in use. For many functions, such as\n :func:`mne.compute_source_morph`, it is important to pass ``fwd['src']``\n or ``inv['src']`` so that this removal is adequately accounted for.</p></div>\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(f'Before: {src}')\nprint(f'After: {fwd[\"src\"]}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can explore the content of ``fwd`` to access the numpy array that contains\nthe gain matrix.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "leadfield = fwd['sol']['data']\nprint(\"Leadfield size : %d sensors x %d dipoles\" % leadfield.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To extract the numpy array containing the forward operator corresponding to\nthe source space ``fwd['src']`` with cortical orientation constraint\nwe can use the following:\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fwd_fixed = mne.convert_forward_solution(fwd, surf_ori=True, force_fixed=True,\n use_cps=True)\nleadfield = fwd_fixed['sol']['data']\nprint(\"Leadfield size : %d sensors x %d dipoles\" % leadfield.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is equivalent to the following code that explicitly applies the\nforward operator to a source estimate composed of the identity operator\n(which we omit here because it uses a lot of memory)::\n\n >>> import numpy as np\n >>> n_dipoles = leadfield.shape[1]\n >>> vertices = [src_hemi['vertno'] for src_hemi in fwd_fixed['src']]\n >>> stc = mne.SourceEstimate(1e-9 * np.eye(n_dipoles), vertices)\n >>> leadfield = mne.apply_forward(fwd_fixed, stc, info).data / 1e-9\n\nTo save to disk a forward solution you can use\n:func:`mne.write_forward_solution` and to read it back from disk\n:func:`mne.read_forward_solution`. Don't forget that FIF files containing\nforward solution should end with :file:`-fwd.fif`.\n\nTo get a fixed-orientation forward solution, use\n:func:`mne.convert_forward_solution` to convert the free-orientation\nsolution to (surface-oriented) fixed orientation.\n\n## Exercise\n\nBy looking at `ex-sensitivity-maps`\nplot the sensitivity maps for EEG and compare it with the MEG, can you\njustify the claims that:\n\n - MEG is not sensitive to radial sources\n - EEG is more sensitive to deep sources\n\nHow will the MEG sensitivity maps and histograms change if you use a free\ninstead if a fixed/surface oriented orientation?\n\nTry this changing the mode parameter in :func:`mne.sensitivity_map`\naccordingly. Why don't we see any dipoles on the gyri?\n\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.0" } }, "nbformat": 4, "nbformat_minor": 0 }	bsd-3-clause
Lyceum519/MusicAnalyzer	jupyter/compressionMp3.ipynb	2	8015	{ "cells": [ { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "21000\n", "5000\n", "('sr2', 13429)\n", "106290__pder__mein-kick-1.wav\n" ] }, { "ename": "ParameterError", "evalue": "Audio buffer is not finite everywhere", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mParameterError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-33-667ac308c8da>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 70\u001b[0m \u001b[0;32mif\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilenames\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0.9\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 71\u001b[0m \u001b[0;32mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"over value : \"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maverageY\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilenames\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 72\u001b[0;31m \u001b[0mlibrosa\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite_wav\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'average.wav'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maverageY\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msr44100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 73\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 74\u001b[0m \u001b[0;31m#if(len(y)>5000):\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/wonyeong91/anaconda/lib/python2.7/site-packages/librosa/output.pyc\u001b[0m in \u001b[0;36mwrite_wav\u001b[0;34m(path, y, sr, norm)\u001b[0m\n\u001b[1;32m 214\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 215\u001b[0m \u001b[0;31m# Validate the buffer. Stereo is okay here.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 216\u001b[0;31m \u001b[0mutil\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalid_audio\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmono\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[0m\u001b[1;32m 217\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 218\u001b[0m \u001b[0;31m# normalize\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/wonyeong91/anaconda/lib/python2.7/site-packages/librosa/util/utils.pyc\u001b[0m in \u001b[0;36mvalid_audio\u001b[0;34m(y, mono)\u001b[0m\n\u001b[1;32m 155\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 156\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misfinite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 157\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mParameterError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Audio buffer is not finite everywhere'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 158\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 159\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mParameterError\u001b[0m: Audio buffer is not finite everywhere" ] } ], "source": [ "import numpy as np\n", "import scipy\n", "import os\n", "\n", "# and IPython.display for audio output\n", "import IPython.display\n", "\n", "# matplotlib for displaying the output\n", "import matplotlib.pyplot as plt\n", "import matplotlib.style as ms\n", "ms.use('seaborn-muted')\n", "%matplotlib inline\n", "\n", "\n", "# Librosa for audio\n", "import librosa\n", "# And the display module for visualization\n", "import librosa.display\n", "\n", "# Search audio files in audio directory\n", "def search(dirname):\n", " try:\n", " global filenames\n", " filenames = os.listdir(dirname)\n", " except PermissionError:\n", " pass\n", "\n", "search(\"/Users/wonyeong91/Documents/GIT/MusicAnalyzer/audio/kick/\")\n", "\n", "\n", "# check continuos zero value of sample at start and end\n", "def filterZero(yData):\n", " res = []\n", " firstZero = 0\n", " lastZero = -1\n", " for i in range( 0, len( yData ) ):\n", " if(yData[i] == 0 or (yData[i]< 7.62939e-04 and yData[i]>-7.62939e-04)):\n", " if(lastZero == -1):\n", " firstZero += 1\n", " else:\n", " lastZero += 1\n", " else:\n", " lastZero = 1\n", " res = yData[firstZero:len(yData)-lastZero]\n", " return res\n", "\n", "\n", "averageY = np.empty(shape=(5000, 1)) \n", " \n", "for filename in filenames:\n", " audio_path = os.path.join(\"/Users/wonyeong91/Documents/GIT/MusicAnalyzer/audio/kick/\", filename)\n", " ext = os.path.splitext(audio_path)[-1]\n", " if ext == '.wav': \n", " sr44100 = 44100\n", " y, sr = librosa.load( audio_path, sr=sr44100 )\n", " newY = filterZero(y)\n", " newSr = sr44100 * 5000 / len( newY )\n", " librosa.output.write_wav('test.wav', newY, sr44100)\n", " y2, sr2 = librosa.load('test.wav', sr=newSr )\n", "\n", " print(len(y))\n", " print(len(y2))\n", " print(\"sr2\", sr2)\n", "\n", " print(filename)\n", " \n", " #generate averageY\n", " for i in range( 0, len(y2) ) :\n", " averageY[i] = averageY[i] + y2[i]/len(filenames)\n", " if(y2[i]/len(filenames) > 0.9 ) :\n", " print(\"over value : \", averageY[i], y2[i], len(filenames))\n", " librosa.output.write_wav('average.wav', averageY, sr44100)\n", "\n", "#if(len(y)>5000):\n", " \n", "averageY2, averageSr = librosa.load('average.wav', sr=44100 )\n", "librosa.display.waveplot( averageY2, 44100 )\n", "\n", "\n", "\n", "\n", "\n", "audio_path = os.path.join(\"/Users/wonyeong91/Documents/GIT/MusicAnalyzer/audio/\", \"Odesza_Above_The_Middle.mp3\")\n", "y, sr = librosa.load( audio_path, sr=sr44100 )\n", "librosa.output.write_wav('test.mp3', y, 10000)\n", "\n", "\n", "\n", "IPython.display.Audio(data=averageY2, rate=44100)" ] }, { "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
CompPhysics/MachineLearning	doc/src/How2ReadData/.ipynb_checkpoints/How2ReadData-checkpoint.ipynb	1	30790	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- dom:TITLE: Data Analysis and Machine Learning: Introduction and Representing data -->\n", "# Data Analysis and Machine Learning: Introduction and Representing data\n", "<!-- dom:AUTHOR: Morten Hjorth-Jensen at Department of Physics, University of Oslo & Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University -->\n", "<!-- Author: --> \n", "Morten Hjorth-Jensen, Department of Physics, University of Oslo and Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University\n", "\n", "Date: Dec 6, 2017\n", "\n", "Copyright 1999-2017, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license\n", "\n", "\n", "\n", "\n", "## What is Machine Learning?\n", "\n", "Machine learning is the science of giving computers the ability to\n", "learn without being explicitly programmed. The idea is that there\n", "exist generic algorithms which can be used to find patterns in a broad\n", "class of data sets without having to write code specifically for each\n", "problem. The algorithm will build its own logic based on the data.\n", "\n", "Machine learning is a subfield of computer science, and is closely\n", "related to computational statistics. It evolved from the study of\n", "pattern recognition in artificial intelligence (AI) research, and has\n", "made contributions to AI tasks like computer vision, natural language\n", "processing and speech recognition. It has also, especially in later\n", "years, found applications in a wide variety of other areas, including\n", "bioinformatics, economy, physics, finance and marketing.\n", "\n", "## Types of Machine Learning\n", "\n", "\n", "The approaches to machine learning are many, but are often split into two main categories. \n", "In supervised learning we know the answer to a problem,\n", "and let the computer deduce the logic behind it. On the other hand, unsupervised learning\n", "is a method for finding patterns and relationship in data sets without any prior knowledge of the system.\n", "Some authours also operate with a third category, namely reinforcement learning. This is a paradigm \n", "of learning inspired by behavioural psychology, where learning is achieved by trial-and-error, \n", "solely from rewards and punishment.\n", "\n", "Another way to categorize machine learning tasks is to consider the desired output of a system.\n", "Some of the most common tasks are:\n", "\n", " * Classification: Outputs are divided into two or more classes. The goal is to produce a model that assigns inputs into one of these classes. An example is to identify digits based on pictures of hand-written ones. Classification is typically supervised learning.\n", "\n", " * Regression: Finding a functional relationship between an input data set and a reference data set. The goal is to construct a function that maps input data to continuous output values.\n", "\n", " * Clustering: Data are divided into groups with certain common traits, without knowing the different groups beforehand. It is thus a form of unsupervised learning.\n", "\n", "## Different algorithms\n", "In this course we will build our machine learning approach on a statistical foundation, with elements \n", "from data analysis, stochastic processes etc before we proceed with the following machine learning algorithms\n", "\n", "1. Linear regression and its variants\n", "\n", "2. Decision tree algorithms, from simpler to more complex ones\n", "\n", "3. Nearest neighbors models\n", "\n", "4. Bayesian statistics \n", "\n", "5. Support vector machines and finally various variants of\n", "\n", "6. Artifical neural networks\n", "\n", "Before we proceed however, there are several practicalities with data analysis and software tools we would \n", "like to present. These tools will help us in our understanding of various machine learning algorithms. \n", "\n", "Our emphasis here is on understanding the mathematical aspects of different algorithms, however, where possible \n", "we will emphasize the importance of using available software. \n", "\n", "\n", "## Software and needed installations\n", "We will make intensive use of python as programming language and the myriad of available libraries. \n", "Furthermore, you will find IPython/Jupyter notebooks invaluable in your work. \n", "You can run R codes in the Jupyter/IPython notebooks, with the immediate benefit of visualizing your data.\n", "\n", "\n", "If you have Python installed (we recommend Python3) and you feel pretty familiar with installing different packages, \n", "we recommend that you install the following Python packages via pip as\n", "1. pip install numpy scipy matplotlib ipython scikit-learn mglearn sympy pandas pillow\n", "\n", "For Python3, replace pip with pip3.\n", "\n", "For OSX users we recommend also, after having installed Xcode, to install brew. Brew allows \n", "for a seamless installation of additional software via for example\n", "1. brew install python3\n", "\n", "For Linux users, with its variety of distributions like for example the widely popular Ubuntu distribution\n", "you can use pip as well and simply install Python as \n", "1. sudo apt-get install python3 (or python for pyhton2.7)\n", "\n", "etc etc. \n", "\n", "## Python installers\n", "If you don't want to perform these operations separately, we recommend two widely used distrubutions which set up \n", "all relevant dependencies for Python, namely\n", "1. [Anaconda](https://docs.anaconda.com/) Anaconda is an open source distribution of the Python and R programming languages for large-scale data processing, predictive analytics, and scientific computing, that aims to simplify package management and deployment. Package versions are managed by the package management system conda\n", "\n", "2. [Enthought canopy](https://www.enthought.com/product/canopy/) is a Python distribution for scientific and analytic computing distribution and analysis environment, available for free and under a commercial license.\n", "\n", "## Installing R, C++, cython or Julia\n", "\n", "You will also find it convenient to utilize R. \n", "Jupyter/Ipython notebook allows you run R code interactively in your browser. The software library R is \n", "tuned to statistically analysis and allows for an easy usage of the tools we will discuss in these texts.\n", "\n", "To install R with Jupyter notebook [following the link here](https://mpacer.org/maths/r-kernel-for-ipython-notebook)\n", "\n", "\n", "\n", "## Installing R, C++, cython or Julia\n", "\n", "\n", "For the C++ affecianodas, Jupyter/IPython notebook allows you also to install C++ and run codes written in this language \n", "interactively in the browser. Since we will emphasize writing many of the algorithms yourself, you can thus opt for\n", "either Python or C++ as programming languages. \n", "\n", "To add more entropy, cython can also be used when running your notebooks. It means that Python with the Jupyter/IPython notebook \n", "setup allows you to integrate widely popular softwares and tools for scientific computing. With its versatility, \n", "including symbolic operations, Python offers a unique computational environment. Your Jupyter/IPython notebook \n", "can easily be converted into a nicely rendered PDF file or a Latex file for further processing.\n", "\n", "This never ends.\n", "If you use the light mark-up language doconce you can convert a standard ascii text file into various HTML \n", "formats, ipython notebooks, latex files, pdf files etc. \n", "\n", "\n", "## Useful packages\n", "\n", "If you already have a Python installation set up, you can use pip or pip3 to install \n", "1. pip3 install numpy scipy ipython \n", "\n", "2. pip3 install pandas matplotlib scikit-learn pillow \n", "\n", "Another useful package is mglearn. \n", "\n", "## Introduction to Jupyter notebook and available tools" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from scipy import sparse\n", "import pandas as pd\n", "from IPython.display import display\n", "eye = np.eye(4)\n", "print(eye)\n", "sparse_mtx = sparse.csr_matrix(eye)\n", "print(sparse_mtx)\n", "x = np.linspace(-10,10,100)\n", "y = np.sin(x)\n", "plt.plot(x,y,marker='x')\n", "plt.show()\n", "data = {'Name': [\"John\", \"Anna\", \"Peter\", \"Linda\"], 'Location': [\"Nairobi\", \"Napoli\", \"London\", \"Buenos Aires\"], 'Age':[51, 21, 34, 45]}\n", "data_pandas = pd.DataFrame(data)\n", "display(data_pandas)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Representing data, more examples" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from scipy import sparse\n", "import pandas as pd\n", "from IPython.display import display\n", "import mglearn\n", "import sklearn\n", "from sklearn.linear_model import LinearRegression\n", "from sklearn.tree import DecisionTreeRegressor\n", "x, y = mglearn.datasets.make_wave(n_samples=100)\n", "line = np.linspace(-3,3,1000,endpoint=False).reshape(-1,1)\n", "reg = DecisionTreeRegressor(min_samples_split=3).fit(x,y)\n", "plt.plot(line, reg.predict(line), label=\"decision tree\")\n", "regline = LinearRegression().fit(x,y)\n", "plt.plot(line, regline.predict(line), label= \"Linear Rgression\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Predator-Prey model from ecology\n", "\n", "The population dynamics of a simple predator-prey system is a\n", "classical example shown in many biology textbooks when ecological\n", "systems are discussed. The system contains all elements of the\n", "scientific method:\n", "\n", " * The set up of a specific hypothesis combined with\n", "\n", " * the experimental methods needed (one can study existing data or perform experiments)\n", "\n", " * analyzing and interpreting the data and performing further experiments if needed\n", "\n", " * trying to extract general behaviors and extract eventual laws or patterns\n", "\n", " * develop mathematical relations for the uncovered regularities/laws and test these by per forming new experiments\n", "\n", "\n", "\n", "\n", "## Case study from Hudson bay\n", "\n", "Lots of data about populations of hares and lynx collected from furs in Hudson Bay, Canada, are available. It is known that the populations oscillate. Why?\n", "Here we start by\n", "\n", "1. plotting the data\n", "\n", "2. derive a simple model for the population dynamics\n", "\n", "3. (fitting parameters in the model to the data)\n", "\n", "4. using the model predict the evolution other predator-pray systems\n", "\n", "\n", "\n", "## Hudson bay data\n", "\n", "\n", "% if FORMAT == 'ipynb':\n", "Most mammalian predators rely on a variety of prey, which complicates mathematical modeling; however, a few predators have become highly specialized and seek almost exclusively a single prey species. An example of this simplified predator-prey interaction is seen in Canadian northern forests, where the populations of the lynx and the snowshoe hare are intertwined in a life and death struggle.\n", "\n", "One reason that this particular system has been so extensively studied is that the Hudson Bay company kept careful records of all furs from the early 1800s into the 1900s. The records for the furs collected by the Hudson Bay company showed distinct oscillations (approximately 12 year periods), suggesting that these species caused almost periodic fluctuations of each other's populations. The table here shows data from 1900 to 1920.\n", "% endif\n", "\n", "<table border=\"1\">\n", "<thead>\n", "<tr><th align=\"center\">Year</th> <th align=\"center\">Hares (x1000)</th> <th align=\"center\">Lynx (x1000)</th> </tr>\n", "</thead>\n", "<tbody>\n", "<tr><td align=\"left\"> 1900 </td> <td align=\"right\"> 30.0 </td> <td align=\"right\"> 4.0 </td> </tr>\n", "<tr><td align=\"left\"> 1901 </td> <td align=\"right\"> 47.2 </td> <td align=\"right\"> 6.1 </td> </tr>\n", "<tr><td align=\"left\"> 1902 </td> <td align=\"right\"> 70.2 </td> <td align=\"right\"> 9.8 </td> </tr>\n", "<tr><td align=\"left\"> 1903 </td> <td align=\"right\"> 77.4 </td> <td align=\"right\"> 35.2 </td> </tr>\n", "<tr><td align=\"left\"> 1904 </td> <td align=\"right\"> 36.3 </td> <td align=\"right\"> 59.4 </td> </tr>\n", "<tr><td align=\"left\"> 1905 </td> <td align=\"right\"> 20.6 </td> <td align=\"right\"> 41.7 </td> </tr>\n", "<tr><td align=\"left\"> 1906 </td> <td align=\"right\"> 18.1 </td> <td align=\"right\"> 19.0 </td> </tr>\n", "<tr><td align=\"left\"> 1907 </td> <td align=\"right\"> 21.4 </td> <td align=\"right\"> 13.0 </td> </tr>\n", "<tr><td align=\"left\"> 1908 </td> <td align=\"right\"> 22.0 </td> <td align=\"right\"> 8.3 </td> </tr>\n", "<tr><td align=\"left\"> 1909 </td> <td align=\"right\"> 25.4 </td> <td align=\"right\"> 9.1 </td> </tr>\n", "<tr><td align=\"left\"> 1910 </td> <td align=\"right\"> 27.1 </td> <td align=\"right\"> 7.4 </td> </tr>\n", "<tr><td align=\"left\"> 1911 </td> <td align=\"right\"> 40.3 </td> <td align=\"right\"> 8.0 </td> </tr>\n", "<tr><td align=\"left\"> 1912 </td> <td align=\"right\"> 57 </td> <td align=\"right\"> 12.3 </td> </tr>\n", "<tr><td align=\"left\"> 1913 </td> <td align=\"right\"> 76.6 </td> <td align=\"right\"> 19.5 </td> </tr>\n", "<tr><td align=\"left\"> 1914 </td> <td align=\"right\"> 52.3 </td> <td align=\"right\"> 45.7 </td> </tr>\n", "<tr><td align=\"left\"> 1915 </td> <td align=\"right\"> 19.5 </td> <td align=\"right\"> 51.1 </td> </tr>\n", "<tr><td align=\"left\"> 1916 </td> <td align=\"right\"> 11.2 </td> <td align=\"right\"> 29.7 </td> </tr>\n", "<tr><td align=\"left\"> 1917 </td> <td align=\"right\"> 7.6 </td> <td align=\"right\"> 15.8 </td> </tr>\n", "<tr><td align=\"left\"> 1918 </td> <td align=\"right\"> 14.6 </td> <td align=\"right\"> 9.7 </td> </tr>\n", "<tr><td align=\"left\"> 1919 </td> <td align=\"right\"> 16.2 </td> <td align=\"right\"> 10.1 </td> </tr>\n", "<tr><td align=\"left\"> 1920 </td> <td align=\"right\"> 24.7 </td> <td align=\"right\"> 8.6 </td> </tr>\n", "</tbody>\n", "</table>\n", "\n", "\n", "\n", "\n", "\n", "## Plotting the data" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "from matplotlib import pyplot as plt\n", "\n", "# Load in data file\n", "data = np.loadtxt('Hudson_Bay.dat', delimiter=',', skiprows=1)\n", "# Make arrays containing x-axis and hares and lynx populations\n", "year = data[:,0]\n", "hares = data[:,1]\n", "lynx = data[:,2]\n", "\n", "plt.plot(year, hares ,'b-+', year, lynx, 'r-o')\n", "plt.axis([1900,1920,0, 100.0])\n", "plt.xlabel(r'Year')\n", "plt.ylabel(r'Numbers of hares and lynx ')\n", "plt.legend(('Hares','Lynx'), loc='upper right')\n", "plt.title(r'Population of hares and lynx from 1900-1920 (x1000)}')\n", "plt.savefig('Hudson_Bay_data.pdf')\n", "plt.savefig('Hudson_Bay_data.png')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "% if FORMAT != 'ipynb':\n", "## Hares and lynx in Hudson bay from 1900 to 1920\n", "\n", "<!-- dom:FIGURE: [fig/Hudson_Bay_data.png, width=700 frac=0.9] -->\n", "<!-- begin figure -->\n", "\n", "<p></p>\n", "<img src=\"fig/Hudson_Bay_data.png\" width=700>\n", "\n", "<!-- end figure -->\n", "\n", "% endif\n", "\n", "\n", "## Why now create a computer model for the hare and lynx populations?\n", "% if FORMAT == 'ipynb':\n", "We see from the plot that there are indeed fluctuations.\n", "We would like to create a mathematical model that explains these\n", "population fluctuations. Ecologists have predicted that in a simple\n", "predator-prey system that a rise in prey population is followed (with\n", "a lag) by a rise in the predator population. When the predator\n", "population is sufficiently high, then the prey population begins\n", "dropping. After the prey population falls, then the predator\n", "population falls, which allows the prey population to recover and\n", "complete one cycle of this interaction. Thus, we see that\n", "qualitatively oscillations occur. Can a mathematical model predict\n", "this? What causes cycles to slow or speed up? What affects the\n", "amplitude of the oscillation or do you expect to see the oscillations\n", "damp to a stable equilibrium? The models tend to ignore factors like\n", "climate and other complicating factors. How significant are these?\n", "% else:\n", " * We see oscillations in the data\n", "\n", " * What causes cycles to slow or speed up?\n", "\n", " * What affects the amplitude of the oscillation or do you expect to see the oscillations damp to a stable equilibrium?\n", "\n", " * With a model we can better understand the data\n", "\n", " * More important: we can understand the ecology dynamics of\n", " predator-pray populations\n", "\n", "% endif\n", "\n", "\n", "\n", "\n", "## The traditional (top-down) approach\n", "\n", "The classical way (in all books) is to present the Lotka-Volterra equations:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\begin{align}\n", "\\frac{dH}{dt} &= H(a - b L)\\\\\n", "\\frac{dL}{dt} &= - L(d - c H)\n", "\\end{align}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here,\n", "\n", " * $H$ is the number of preys\n", "\n", " * $L$ the number of predators\n", "\n", " * $a$, $b$, $d$, $c$ are parameters\n", "\n", "Most books quickly establish the model and then use considerable space on\n", "discussing the qualitative properties of this nonlinear system of\n", "ODEs (which cannot be solved)\n", "\n", "\n", "\n", "\n", "## The \"new\" discrete bottom-up approach\n", "\n", "The bottom-up approach.\n", "\n", " * Start with experimental data and discuss the methods which have been used to collect the data, the assumptions, the electronic devices, the aims etc. That is, expose the students to the theory and assumptions behind the data that have been collected and motivate for the scientific method.\n", "\n", " * Where appropriate the students should do the experiment(s) needed to collect the data.\n", "\n", " * The first programming tasks are to read and visualize the data to see if there are patterns or regularities. This strengthens a research-driven intuition.\n", "\n", " * Now we want to increase the understanding through modeling.\n", "\n", " * Most of the biology lies in the derivation of the model. We shall\n", " focus on an intuitive discrete approach that leads to difference\n", " equations that can be programmed and solved directly.\n", "\n", "\n", "\n", "## Basic (computer-friendly) mathematics notation\n", " * Time points: $t_0,t_1,\\ldots,t_m$\n", "\n", " * Uniform distribution of time points: $t_n=n\\Delta t$\n", "\n", " * $H^n$: population of hares at time $t_n$\n", "\n", " * $L^n$: population of lynx at time $t_n$\n", "\n", " * We want to model the changes in populations, $\\Delta H=H^{n+1}-H^n$\n", " and $\\Delta L=L^{n+1}-L^n$ during a general time interval $[t_{n+1},t_n]$\n", " of length $\\Delta t=t_{n+1}-t_n$\n", "\n", "\n", "\n", "## Basic dynamics of the population of hares\n", "\n", "The population of hares evolves due to births and deaths exactly as a bacteria population:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\Delta H = a \\Delta t H^n\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "However, hares have an additional loss in the population because\n", "they are eaten by lynx.\n", "All the hares and lynx can form\n", "$H\\cdot L$ pairs in total. When such pairs meet during a time\n", "interval $\\Delta t$, there is some\n", "small probablity that the lynx will eat the hare.\n", "So in fraction $b\\Delta t HL$, the lynx eat hares. This\n", "loss of hares and must be accounted for:\n", "subtracted in the equation for hares:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\Delta H = a\\Delta t H^n - b \\Delta t H^nL^n\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Basic dynamics of the population of lynx\n", "\n", "We assume that the primary growth for the lynx population depends on sufficient food for raising lynx kittens, which implies an adequate source of nutrients from predation on hares. Thus, the growth of the lynx population does not only depend of how many lynx there are, but on how many hares they can eat.\n", "In a time interval $\\Delta t HL$ hares and lynx can meet, and in a\n", "fraction $b\\Delta t HL$ the lynx eats the hare. All of this does not\n", "contribute to the growth of lynx, again just a fraction of\n", "$b\\Delta t HL$ that we write as\n", "$d\\Delta t HL$. In addition, lynx die just as in the population\n", "dynamics with one isolated animal population, leading to a loss\n", "$-c\\Delta t L$.\n", "\n", "\n", "\n", "The accounting of lynx then looks like" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\Delta L = d\\Delta t H^nL^n - c\\Delta t L^n\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Evolution equations\n", "\n", "By writing up the definition of $\\Delta H$ and $\\Delta L$, and putting\n", "all assumed known terms $H^n$ and $L^n$ on the right-hand side, we have" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "H^{n+1} = H^n + a\\Delta t H^n - b\\Delta t H^n L^n\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "L^{n+1} = L^n + d\\Delta t H^nL^n - c\\Delta t L^n\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note:\n", "\n", " * These equations are ready to be implemented!\n", "\n", " * But to start, we need $H^0$ and $L^0$ \n", " (which we can get from the data)\n", "\n", " * We also need values for $a$, $b$, $d$, $c$\n", "\n", "\n", "\n", "## Adapt the model to the Hudson Bay case\n", "\n", " * As always, models tend to be general - as here, applicable\n", " to \"all\" predator-pray systems\n", "\n", " * The critical issue is whether the interaction between hares and lynx\n", " is sufficiently well modeled by $\\hbox{const}HL$\n", "\n", " * The parameters $a$, $b$, $d$, and $c$ must be\n", " estimated from data\n", "\n", " * Measure time in years\n", "\n", " * $t_0=1900$, $t_m=1920$\n", "\n", "\n", "\n", "## The program" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "def solver(m, H0, L0, dt, a, b, c, d, t0):\n", " \"\"\"Solve the difference equations for H and L over m years\n", " with time step dt (measured in years.\"\"\"\n", "\n", " num_intervals = int(m/float(dt))\n", " t = np.linspace(t0, t0 + m, num_intervals+1)\n", " H = np.zeros(t.size)\n", " L = np.zeros(t.size)\n", "\n", " print 'Init:', H0, L0, dt\n", " H[0] = H0\n", " L[0] = L0\n", "\n", " for n in range(0, len(t)-1):\n", " H[n+1] = H[n] + adtH[n] - bdtH[n]L[n]\n", " L[n+1] = L[n] + ddtH[n]L[n] - cdtL[n]\n", " return H, L, t\n", "\n", "# Load in data file\n", "data = np.loadtxt('Hudson_Bay.csv', delimiter=',', skiprows=1)\n", "# Make arrays containing x-axis and hares and lynx populations\n", "t_e = data[:,0]\n", "H_e = data[:,1]\n", "L_e = data[:,2]\n", "\n", "# Simulate using the model\n", "H, L, t = solver(m=20, H0=34.91, L0=3.857, dt=0.1,\n", " a=0.4807, b=0.02482, c=0.9272, d=0.02756,\n", " t0=1900)\n", "\n", "# Visualize simulations and data\n", "plt.plot(t_e, H_e, 'b-+', t_e, L_e, 'r-o', t, H, 'm--', t, L, 'k--')\n", "plt.xlabel('Year')\n", "plt.ylabel('Numbers of hares and lynx')\n", "plt.axis([1900, 1920, 0, 140])\n", "plt.title(r'Population of hares and lynx 1900-1920 (x1000)')\n", "plt.legend(('H_e', 'L_e', 'H', 'L'), loc='upper left')\n", "plt.savefig('Hudson_Bay_sim.pdf')\n", "plt.savefig('Hudson_Bay_sim.png')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "% if FORMAT != 'ipynb':\n", "## The plot\n", "\n", "<!-- dom:FIGURE: [fig/Hudson_Bay_sim.png, width=700 frac=0.9] -->\n", "<!-- begin figure -->\n", "\n", "<p></p>\n", "<img src=\"fig/Hudson_Bay_sim.png\" width=700>\n", "\n", "<!-- end figure -->\n", "\n", "\n", "% else:\n", "If we perform a least-square fitting, we can find optimal values for the parameters $a$, $b$, $d$, $c$. The optimal parameters are $a=0.4807$, $b=0.02482$, $d=0.9272$ and $c=0.02756$. These parameters result in a slightly modified initial conditions, namely $H(0) = 34.91$ and $L(0)=3.857$. With these parameters we are now ready to solve the equations and plot these data together with the experimental values.\n", "% endif\n", "\n", "\n", "\n", "## Linear Least squares in R" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " HudsonBay = read.csv(\"src/Hudson_Bay.csv\",header=T)\n", " fix(HudsonBay)\n", " dim(HudsonBay)\n", " names(HudsonBay)\n", " plot(HudsonBay$Year, HudsonBay$Hares..x1000.)\n", " attach(HudsonBay)\n", " plot(Year, Hares..x1000.)\n", " plot(Year, Hares..x1000., col=\"red\", varwidth=T, xlab=\"Years\", ylab=\"Haresx 1000\")\n", " summary(HudsonBay)\n", " summary(Hares..x1000.)\n", " library(MASS)\n", " library(ISLR)\n", " scatter.smooth(x=Year, y = Hares..x1000.)\n", " linearMod = lm(Hares..x1000. ~ Year)\n", " print(linearMod)\n", " summary(linearMod)\n", " plot(linearMod)\n", " confint(linearMod)\n", " predict(linearMod,data.frame(Year=c(1910,1914,1920)),interval=\"confidence\")\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Linear Least squares in R" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " set.seed(1485)\n", " len = 24\n", " x = runif(len)\n", " y = x^3+rnorm(len, 0,0.06)\n", " ds = data.frame(x = x, y = y)\n", " str(ds)\n", " plot( y ~ x, main =\"Known cubic with noise\")\n", " s = seq(0,1,length =100)\n", " lines(s, s^3, lty =2, col =\"green\")\n", " m = nls(y ~ I(x^power), data = ds, start = list(power=1), trace = T)\n", " class(m)\n", " summary(m)\n", " power = round(summary(m)$coefficients[1], 3)\n", " power.se = round(summary(m)$coefficients[2], 3)\n", " plot(y ~ x, main = \"Fitted power model\", sub = \"Blue: fit; green: known\")\n", " s = seq(0, 1, length = 100)\n", " lines(s, s^3, lty = 2, col = \"green\")\n", " lines(s, predict(m, list(x = s)), lty = 1, col = \"blue\")\n", " text(0, 0.5, paste(\"y =x^ (\", power, \" +/- \", power.se, \")\", sep = \"\"), pos = 4)\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.3" } }, "nbformat": 4, "nbformat_minor": 2 }	cc0-1.0
jerrynlp/AutoSum	tools/Data_LTR.ipynb	1	7715	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Construct Data Set for LTR\n", "\n", "## Phrases from Summaries\n", "## Phrases from Stroies" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "class Phrase:\n", " \"\"\"Information of a phrase\"\"\"\n", " def __init__(self, word, word_before, word_after, chapter_id, sentence_id, negation):\n", " self.negation = negation\n", " self.word = word\n", " self.word_before = word_before\n", " self.word_after = word_after\n", " self.chapter_id = chapter_id\n", " self.sentence_id = sentence_id\n", " self.count = 0\n", " self.weight = 0\n", " def add_info(self):\n", " self.count += 1\n", " def output(self):\n", " return str(self.weight) + \"\\t\" + str(self.chapter_id) + \"\\t\" + str(self.sentence_id) + \"\\t\" + self.word \\\n", " + \"\\t\" + self.word_before + \"\\t\" + self.word_after + \"\\t\" + str(self.count)\n", "class PhraseSet:\n", " \"\"\"Set to manage phrases\"\"\"\n", " def __init__(self, story_id, character_id):\n", " self.phrases = {}\n", " self.story_id = story_id\n", " self.character_id = character_id\n", " def add(self, word, chapter_id, sentence_id, negation, word_before, word_after):\n", " if not word in self.phrases:\n", " self.phrases[word] = Phrase(word, word_before, word_after, chapter_id, sentence_id, negation)\n", " self.phrases[word].add_info()\n", " def clear(self):\n", " self.phrases = {}\n", " def sort(self):\n", " return sorted(self.phrases.items(), lambda x, y: cmp(x[1].weight, y[1].weight), reverse=True) " ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1850\t7\t0\t45\t2736\tbusy too\tbe\t,\t6\n", "1850\t7\t0\t22\t1459\tso trustful\tbe\tand\t1\n", "1850\t7\t0\t48\t2882\tmine\tbe\t.\t1\n", "1850\t7\t0\t47\t2778\tonly one\tthe\twho\t2\n", "1850\t7\t0\t47\t2800\tlittle confident too\ta\t,\t2\n", "1850\t7\t0\t37\t2304\tfine\tbe\t.\t1\n", "2300\t2\t0\t13\t1757\tout cold\tbe\t.\t1\n", "2300\t2\t0\t11\t1271\tright\tbe\t;\t1\n", "2300\t2\t0\t10\t922\tday-dreaming\tbe\t,\t5\n", "2300\t2\t0\t11\t1282\tunfazed\tremain\t.\t2\n", "2300\t2\t0\t12\t1380\tREALLY powerful\tbe\tbut\t1\n", "2300\t2\t0\t14\t1908\tmost experienced\tthe\t,\t2\n", "2300\t2\t0\t1\t11\tsmall\tbe\tcompare\t3\n", "2300\t2\t0\t14\t1907\ttrue leader\tthe\t.\t1\n", "2300\t2\t0\t11\t1162\tmore\tlot\tthan\t1\n", "2300\t13\t0\t10\t924\told fart\ta\t.\t3\n", "2540\t0\t0\t9\t683\tex-boyfriend\tmy\t.\t1\n", "2540\t0\t0\t8\t569\tfirst love\tmy\t.\t1\n", "2540\t0\t0\t11\t885\tso muscular\tbe\t.\t2\n" ] } ], "source": [ "import Syntax as sx\n", "BOOK_ID = 0\n", "CHAPTER_ID = 1\n", "SENTENCE_ID = 2\n", "CHARACTER_ID = 15\n", "def sim(phrase1, phrase2):\n", " return 0\n", "def cal_similarity(summarySet, storySet):\n", " for phrase1 in storySet.phrases.values():\n", " max_sim = 0\n", " for phrase2 in summarySet.phrases.values():\n", " similarity = sim(phrase1, phrase2)\n", " if max_sim < similarity:\n", " max_sim = similarity\n", " phrase1.weight = max_sim\n", "def process(summary, story, story_id):\n", " #phrases and characters in summary\n", " characters = {}\n", " pos = 0\n", " for sentence in summary:\n", " for token in sentence:\n", " cid = int(token[CHARACTER_ID])\n", " if cid >= 0:\n", " if not cid in characters:\n", " characters[cid] = [[], [], PhraseSet(story_id, cid), PhraseSet(story_id, cid)]\n", " characters[cid][0].append(pos)\n", " pos += 1\n", " for cid in characters.keys():\n", " for sid in characters[cid][0]:\n", " sentence = summary[sid]\n", " syn = sx.SyntaxTree()\n", " syn.creat(sentence)\n", " labels = syn.extract_label_with_info(cid)\n", " for label in labels:\n", " characters[cid][2].add(label[1], syn.chapterID, syn.sentenceID, label[0], label[2], label[3])\n", " for sentence in story:\n", " for token in sentence:\n", " cid = int(token[CHARACTER_ID])\n", " if cid in characters:\n", " syn = sx.SyntaxTree()\n", " syn.creat(sentence)\n", " labels = syn.extract_label_with_info(cid)\n", " for label in labels:\n", " characters[cid][3].add(label[1], syn.chapterID, syn.sentenceID, label[0], label[2], label[3])\n", " for cid in characters:\n", " cal_similarity(characters[cid][2], characters[cid][3])\n", " sorted_phrases = characters[cid][3].sort()\n", " for phrase in characters[cid][2].phrases:\n", " print str(characters[cid][2].story_id) + \"\\t\" + str(characters[cid][2].character_id) \\\n", " + \"\\t\" + phrase.output()\n", " for phrase in sorted_phrases:\n", " print str(characters[cid][3].story_id) + \"\\t\" + str(characters[cid][3].character_id) \\\n", " + \"\\t\" + phrase[1].output()\n", " return 0\n", "\n", "token_file = open(\"../../2.part.tokens.sample\", 'rb')\n", "story_id = -1\n", "chapter_id = -1\n", "sentence_id = -1\n", "summary = []\n", "story = []\n", "sentence = []\n", "for line in token_file:\n", " terms = line.rstrip().split('\\t')\n", " if not int(terms[BOOK_ID]) == story_id:\n", " #process\n", " process(summary, story, story_id)\n", " #new story\n", " story_id = int(terms[BOOK_ID])\n", " chapter_id = int(terms[CHAPTER_ID])\n", " sentence_id = int(terms[SENTENCE_ID])\n", " summary = []\n", " story = []\n", " sentence.append(terms)\n", " if int(terms[CHAPTER_ID]) == chapter_id and int(terms[SENTENCE_ID]) == sentence_id:\n", " sentence.append(terms)\n", " else:\n", " if len(sentence):\n", " if chapter_id == 0:\n", " summary.append(sentence)\n", " else:\n", " story.append(sentence)\n", " chapter_id = int(terms[CHAPTER_ID])\n", " sentence_id = int(terms[SENTENCE_ID])\n", " sentence = []\n", " sentence.append(terms)\n", "token_file.close()" ] }, { "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 }	mit
sebsch/WT-1_Grundlagen_Datascience	Sentiment_Analyse/Plotting_SPON.ipynb	1	147328	{ "cells": [ { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "#Load libraries\n", "library(ggplot2)\n", "library(dplyr)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "text/html": [ "<table>\n", "<thead><tr><th scope=col>X</th><th scope=col>year</th><th scope=col>month</th><th scope=col>day</th><th scope=col>positiv_abs</th><th scope=col>neutral_abs</th><th scope=col>negativ_abs</th><th scope=col>positiv_rel</th><th scope=col>neutral_rel</th><th scope=col>negativ_rel</th></tr></thead>\n", "<tbody>\n", "\t<tr><td>1 </td><td>2001 </td><td>1 </td><td>1 </td><td> 4 </td><td> 4 </td><td> 12 </td><td>0.02015050</td><td>0.01856187</td><td>0.05886288</td></tr>\n", "\t<tr><td>2 </td><td>2001 </td><td>1 </td><td>2 </td><td>35 </td><td>103 </td><td>135 </td><td>0.01619442</td><td>0.04036150</td><td>0.05168048</td></tr>\n", "\t<tr><td>3 </td><td>2001 </td><td>1 </td><td>3 </td><td>69 </td><td>120 </td><td>120 </td><td>0.03148833</td><td>0.05794016</td><td>0.05850294</td></tr>\n", "\t<tr><td>4 </td><td>2001 </td><td>1 </td><td>4 </td><td>26 </td><td> 49 </td><td> 93 </td><td>0.01867812</td><td>0.03444513</td><td>0.06457513</td></tr>\n", "\t<tr><td>5 </td><td>2001 </td><td>1 </td><td>5 </td><td>45 </td><td>102 </td><td>133 </td><td>0.02115049</td><td>0.04602044</td><td>0.05967728</td></tr>\n", "\t<tr><td>6 </td><td>2001 </td><td>1 </td><td>8 </td><td>42 </td><td>100 </td><td>100 </td><td>0.02182266</td><td>0.05149365</td><td>0.05300146</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "\\begin{tabular}{r\|llllllllll}\n", " X & year & month & day & positiv\\_abs & neutral\\_abs & negativ\\_abs & positiv\\_rel & neutral\\_rel & negativ\\_rel\\\\\n", "\\hline\n", "\t 1 & 2001 & 1 & 1 & 4 & 4 & 12 & 0.02015050 & 0.01856187 & 0.05886288\\\\\n", "\t 2 & 2001 & 1 & 2 & 35 & 103 & 135 & 0.01619442 & 0.04036150 & 0.05168048\\\\\n", "\t 3 & 2001 & 1 & 3 & 69 & 120 & 120 & 0.03148833 & 0.05794016 & 0.05850294\\\\\n", "\t 4 & 2001 & 1 & 4 & 26 & 49 & 93 & 0.01867812 & 0.03444513 & 0.06457513\\\\\n", "\t 5 & 2001 & 1 & 5 & 45 & 102 & 133 & 0.02115049 & 0.04602044 & 0.05967728\\\\\n", "\t 6 & 2001 & 1 & 8 & 42 & 100 & 100 & 0.02182266 & 0.05149365 & 0.05300146\\\\\n", "\\end{tabular}\n" ], "text/plain": [ " X year month day positiv_abs neutral_abs negativ_abs positiv_rel neutral_rel\n", "1 1 2001 1 1 4 4 12 0.02015050 0.01856187 \n", "2 2 2001 1 2 35 103 135 0.01619442 0.04036150 \n", "3 3 2001 1 3 69 120 120 0.03148833 0.05794016 \n", "4 4 2001 1 4 26 49 93 0.01867812 0.03444513 \n", "5 5 2001 1 5 45 102 133 0.02115049 0.04602044 \n", "6 6 2001 1 8 42 100 100 0.02182266 0.05149365 \n", " negativ_rel\n", "1 0.05886288 \n", "2 0.05168048 \n", "3 0.05850294 \n", "4 0.06457513 \n", "5 0.05967728 \n", "6 0.05300146 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "spon_sentiment_df <- na.omit(read.csv(\"/home/hao/workspace/6thSemester/DataScience/data/SPON_All.csv\", fileEncoding=\"UTF-16\"))\n", "head(spon_sentiment_df)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table>\n", "<thead><tr><th scope=col>year</th><th scope=col>sum_positiv_abs</th><th scope=col>sum_neutral_abs</th><th scope=col>sum_negativ_abs</th><th scope=col>sum_positiv_rel</th><th scope=col>sum_neutral_rel</th><th scope=col>sum_negativ_rel</th></tr></thead>\n", "<tbody>\n", "\t<tr><td>2001 </td><td>17061 </td><td>40026 </td><td> 45843 </td><td>7.425997</td><td>16.55480</td><td>21.39019</td></tr>\n", "\t<tr><td>2002 </td><td>19963 </td><td>46281 </td><td> 58506 </td><td>7.344617</td><td>16.39999</td><td>22.20849</td></tr>\n", "\t<tr><td>2003 </td><td>20419 </td><td>47891 </td><td> 58816 </td><td>7.652336</td><td>16.81190</td><td>22.13802</td></tr>\n", "\t<tr><td>2004 </td><td>21964 </td><td>50029 </td><td> 63053 </td><td>7.699428</td><td>16.59420</td><td>22.28493</td></tr>\n", "\t<tr><td>2005 </td><td>28573 </td><td>66727 </td><td> 86556 </td><td>7.424398</td><td>16.82071</td><td>22.16016</td></tr>\n", "\t<tr><td>2006 </td><td>35739 </td><td>82787 </td><td>105757 </td><td>7.410265</td><td>17.30626</td><td>22.47152</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "\\begin{tabular}{r\|lllllll}\n", " year & sum\\_positiv\\_abs & sum\\_neutral\\_abs & sum\\_negativ\\_abs & sum\\_positiv\\_rel & sum\\_neutral\\_rel & sum\\_negativ\\_rel\\\\\n", "\\hline\n", "\t 2001 & 17061 & 40026 & 45843 & 7.425997 & 16.55480 & 21.39019\\\\\n", "\t 2002 & 19963 & 46281 & 58506 & 7.344617 & 16.39999 & 22.20849\\\\\n", "\t 2003 & 20419 & 47891 & 58816 & 7.652336 & 16.81190 & 22.13802\\\\\n", "\t 2004 & 21964 & 50029 & 63053 & 7.699428 & 16.59420 & 22.28493\\\\\n", "\t 2005 & 28573 & 66727 & 86556 & 7.424398 & 16.82071 & 22.16016\\\\\n", "\t 2006 & 35739 & 82787 & 105757 & 7.410265 & 17.30626 & 22.47152\\\\\n", "\\end{tabular}\n" ], "text/plain": [ " year sum_positiv_abs sum_neutral_abs sum_negativ_abs sum_positiv_rel\n", "1 2001 17061 40026 45843 7.425997 \n", "2 2002 19963 46281 58506 7.344617 \n", "3 2003 20419 47891 58816 7.652336 \n", "4 2004 21964 50029 63053 7.699428 \n", "5 2005 28573 66727 86556 7.424398 \n", "6 2006 35739 82787 105757 7.410265 \n", " sum_neutral_rel sum_negativ_rel\n", "1 16.55480 21.39019 \n", "2 16.39999 22.20849 \n", "3 16.81190 22.13802 \n", "4 16.59420 22.28493 \n", "5 16.82071 22.16016 \n", "6 17.30626 22.47152 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "spon_sentiment_byYear <- spon_sentiment_df %>% \n", " group_by(year) %>%\n", " summarise(sum_positiv_abs = sum(positiv_abs), sum_neutral_abs = sum(neutral_abs), sum_negativ_abs = sum(negativ_abs), sum_positiv_rel = sum(positiv_rel), sum_neutral_rel = sum(neutral_rel), sum_negativ_rel = sum(negativ_rel))\n", "head(spon_sentiment_byYear)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "calc_polarity <- function(positiv, neutral, negativ) {\n", " sum <- positiv + neutral + negativ\n", " polarity <- (positiv - negativ) / sum;\n", " polarity\n", "}\n", "\n", "sum_polarity <- mapply(calc_polarity, positiv = spon_sentiment_byYear$sum_positiv_abs, neutral = spon_sentiment_byYear$sum_neutral_abs, negativ = spon_sentiment_byYear$sum_negativ_abs)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table>\n", "<thead><tr><th scope=col>year</th><th scope=col>sum_positiv_abs</th><th scope=col>sum_neutral_abs</th><th scope=col>sum_negativ_abs</th><th scope=col>sum_positiv_rel</th><th scope=col>sum_neutral_rel</th><th scope=col>sum_negativ_rel</th><th scope=col>sum_polarity</th></tr></thead>\n", "<tbody>\n", "\t<tr><td>2001 </td><td>17061 </td><td>40026 </td><td> 45843 </td><td>7.425997 </td><td>16.55480 </td><td>21.39019 </td><td>-0.2796269</td></tr>\n", "\t<tr><td>2002 </td><td>19963 </td><td>46281 </td><td> 58506 </td><td>7.344617 </td><td>16.39999 </td><td>22.20849 </td><td>-0.3089619</td></tr>\n", "\t<tr><td>2003 </td><td>20419 </td><td>47891 </td><td> 58816 </td><td>7.652336 </td><td>16.81190 </td><td>22.13802 </td><td>-0.3020389</td></tr>\n", "\t<tr><td>2004 </td><td>21964 </td><td>50029 </td><td> 63053 </td><td>7.699428 </td><td>16.59420 </td><td>22.28493 </td><td>-0.3042593</td></tr>\n", "\t<tr><td>2005 </td><td>28573 </td><td>66727 </td><td> 86556 </td><td>7.424398 </td><td>16.82071 </td><td>22.16016 </td><td>-0.3188402</td></tr>\n", "\t<tr><td>2006 </td><td>35739 </td><td>82787 </td><td>105757 </td><td>7.410265 </td><td>17.30626 </td><td>22.47152 </td><td>-0.3121859</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "\\begin{tabular}{r\|llllllll}\n", " year & sum\\_positiv\\_abs & sum\\_neutral\\_abs & sum\\_negativ\\_abs & sum\\_positiv\\_rel & sum\\_neutral\\_rel & sum\\_negativ\\_rel & sum\\_polarity\\\\\n", "\\hline\n", "\t 2001 & 17061 & 40026 & 45843 & 7.425997 & 16.55480 & 21.39019 & -0.2796269\\\\\n", "\t 2002 & 19963 & 46281 & 58506 & 7.344617 & 16.39999 & 22.20849 & -0.3089619\\\\\n", "\t 2003 & 20419 & 47891 & 58816 & 7.652336 & 16.81190 & 22.13802 & -0.3020389\\\\\n", "\t 2004 & 21964 & 50029 & 63053 & 7.699428 & 16.59420 & 22.28493 & -0.3042593\\\\\n", "\t 2005 & 28573 & 66727 & 86556 & 7.424398 & 16.82071 & 22.16016 & -0.3188402\\\\\n", "\t 2006 & 35739 & 82787 & 105757 & 7.410265 & 17.30626 & 22.47152 & -0.3121859\\\\\n", "\\end{tabular}\n" ], "text/plain": [ " year sum_positiv_abs sum_neutral_abs sum_negativ_abs sum_positiv_rel\n", "1 2001 17061 40026 45843 7.425997 \n", "2 2002 19963 46281 58506 7.344617 \n", "3 2003 20419 47891 58816 7.652336 \n", "4 2004 21964 50029 63053 7.699428 \n", "5 2005 28573 66727 86556 7.424398 \n", "6 2006 35739 82787 105757 7.410265 \n", " sum_neutral_rel sum_negativ_rel sum_polarity\n", "1 16.55480 21.39019 -0.2796269 \n", "2 16.39999 22.20849 -0.3089619 \n", "3 16.81190 22.13802 -0.3020389 \n", "4 16.59420 22.28493 -0.3042593 \n", "5 16.82071 22.16016 -0.3188402 \n", "6 17.30626 22.47152 -0.3121859 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "spon_sentiment_byYear <- cbind(spon_sentiment_byYear, sum_polarity)\n", "head(spon_sentiment_byYear)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "`geom_smooth()` using method = 'loess'\n" ] }, { "data": {}, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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AAEACIdra+vb+/eve3t7YWFhT/+8Y+lUmkQTpqZmXn58uX29nZGmwZHNeQUnqqN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"text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ggplot(spon_sentiment_byYear, aes(x = year, y = sum_polarity)) + geom_line() + geom_point() + geom_smooth()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": {}, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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wtAKKHYNQOXv0O3fo1itXmu/TMWQAIA6MBoWsjMoYQAtydf7SgAoYRi11TcgX36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\nqatWrcKrWIghzLGDq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"text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ggplot(spon_sentiment_byYear, aes(x = year)) + \n", "geom_line(aes(y=sum_positiv_rel/365, color = \"sum_positiv_rel\")) + \n", "geom_line(aes(y=sum_neutral_rel/365, color = \"sum_neutral_rel\")) +\n", "geom_line(aes(y=sum_negativ_rel/365, color = \"sum_negativ_rel\"))" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": {}, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "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": "3.3.2" } }, "nbformat": 4, "nbformat_minor": 1 }	gpl-3.0
epfl-cosmo/pamm	examples/lj38/.ipynb_checkpoints/pamm-merge-lj-checkpoint.ipynb	1	217227	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import matplotlib\n", "import scipy.sparse.csgraph as csg\n", "from scipy.special import erf\n", "from time import time\n", "matplotlib.rcParams['figure.figsize'] = [7,7]\n", "def gcum(z):\n", " return (1 + erf(z/np.sqrt(2))) * 0.5" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "dim = 15\n", "prefix=\"20000-fp0.05-qs0.9\"\n", "grid = np.loadtxt(prefix+\".grid\")[:]\n", "refgrid = np.asarray(np.loadtxt(\"ref.idxs\"),int)-1\n", "x = np.loadtxt(\"traj9.proj\")[refgrid]\n", "ni = np.asarray(grid[:,dim],int)-1\n", "pi = grid[:,dim+1]\n", "ei = grid[:,dim+2]\n", "ncls = np.max(ni)+1\n", "ngrid = len(ni)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "20000\n" ] } ], "source": [ "print ngrid" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "lcls = []\n", "Qi = np.zeros(ncls)\n", "for i in xrange(ncls):\n", " icls = np.where(ni == i)[0]\n", " Qi[i] = np.exp(pi[icls]).sum()\n", " lcls.append(icls)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "nbs = 64\n", "nibs = np.zeros((nbs,ngrid),int)\n", "nclsbs = np.zeros(nbs,int)\n", "for bs in xrange(nbs):\n", " idx = \"%03d\" % (bs+1)\n", " nibs[bs] = np.asarray(np.loadtxt(prefix+\"-bs-bs\"+idx+\".grid\")[:,dim],int) - 1\n", " nclsbs[bs] = np.max(nibs[bs])+1" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "15\n" ] } ], "source": [ "print nclsbs[1]" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "QA = np.zeros((nbs,np.max(nclsbs)))\n", "for bs in xrange(nbs):\n", " for i in xrange(nclsbs[bs]):\n", " icls = np.where(nibs[bs] == i)[0]\n", " QA[bs,i] = np.exp(pi[icls]).sum()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "QAi = np.zeros((nbs,np.max(nclsbs),ncls))\n", "for bs in xrange(nbs):\n", " for i in xrange(nclsbs[bs]):\n", " icls = np.where(nibs[bs] == i)[0]\n", " for j in xrange(ncls):\n", " inter = np.intersect1d(icls, lcls[j])\n", " QAi[bs,i,j] = np.exp(pi[inter]).sum()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0.99421603 0.99999999 0.97933704 0.77485632 0.64204641 0.75689711\n", " 0.91196604 1. 0.96210583 0.99998791]\n" ] } ], "source": [ "aij = np.zeros((ncls,ncls))\n", "for i in xrange(ncls):\n", " for j in xrange(i+1):\n", " tij = 0\n", " for bs in xrange(nbs):\n", " for k in xrange(nclsbs[bs]):\n", " tij += QAi[bs,k,i] * QAi[bs,k,j]\n", " aij[i,j] = aij[j,i] = tij/(Qi[i]Qi[j]nbs)\n", "print aij.diagonal()\n", "da=aij.diagonal()\n", "gij=aij / np.sqrt(np.multiply.outer(da,da))" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0.96673138 0.99999999 0.99672484 0.75833495 0.72853947 0.87881441\n", " 0.66538676 1. 0.88431119 0.15310106]\n" ] } ], "source": [ "bij = np.zeros((ncls,ncls))\n", "for i in xrange(ncls):\n", " for j in xrange(i+1): \n", " tij = 0\n", " for bs in xrange(nbs):\n", " for k in xrange(nclsbs[bs]):\n", " tij += QAi[bs,k,i] * QAi[bs,k,j] / (QA[bs,k] * QA[bs,k]) \n", " bij[i,j] = bij[j,i] = tij/nbs\n", "print bij.diagonal()" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "collapsed": true }, "outputs": [], "source": [ "nij = np.zeros((ncls,ncls))\n", "for i in xrange(ncls):\n", " for j in xrange(i+1): \n", " tij = 0\n", " for bs in xrange(nbs):\n", " for k in xrange(nclsbs[bs]):\n", " tij += QAi[bs,k,i] * QAi[bs,k,j] / (QA[bs,k]) \n", " nij[i,j] = nij[j,i] = tij/nbs\n", "nij /= np.exp(pi).sum()\n", "py = np.zeros(ncls)\n", "for i in xrange(ncls):\n", " py[i] = np.exp(pi[lcls[i]]).sum()\n", "py/=np.exp(pi).sum()" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0.9619342 0.99999999 0.9864325 0.681594 0.55933118 0.78159033\n", " 0.33218789 1. 0.91978237 0.19441862]\n" ] } ], "source": [ "nnij = nij/ np.sqrt(np.multiply.outer(py,py))\n", "print nnij.diagonal()" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n" ] } ], "source": [ "nnij = nij/ np.sqrt(np.multiply.outer(nij.diagonal(),nij.diagonal()))\n", "print nnij.diagonal()" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7ff99424fc50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.matshow(nnij)\n", "plt.colorbar()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "image/png": 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55b3ZCL6eamht4nz4cSO8NFJlEJ7oG3fZOonJ2dDqWZWm3qkhND9ObcrqtKwH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+WQoQdHDS21CbXidUGFDF9Htp8aSoJO5KUk6HEGMYwr+A5HM+KyoucLZgIagd3LSsrR4rnT6Mxt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t2u\n/34jIu8AOBVAqgWxcD4/3wM4DsAuEUkH0EJVD9RT+xJVrddNVb2HzJ4C5xLD9QPs8+aW1N9pqTSc\nyMXY4VsE4GrXz1cBWFjzABFp5bpeEJEcAAMAbKmvBiaQcD4/r8GuIwBcCkuWSXW1XjfXH1BuI5Ca\nn69gBMG/0xYBGAsAItIPwEH39EAycnxPLBQRGQlgFoAc2GLsDap6voi0B/Ckql6gqlUi4l6MnQag\ngIuxMR3AAldKcxGA3wGAiJwGYIKrDubJAGaLSBXsut2vqim3M0Gwz4+I3AtgjaouBlAAYJ6IbAew\nH/aFndLCvG55InIRgAoAB+D5wyqlicgLAM4GcJSIfAfgHgAZAFRV/1dVl4jIcBHZAeAwgGsarrV1\nj4udiYjIsVJpOJGIiJIMgxgRETkWgxgRETkWgxgRETkWgxgRETkWgxgRETkWgxgRETnW/wMrX3Oj\ntbDCWwAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7ff99701c1d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(x[:,0],x[:,1],c=ni[:], linewidth='0')\n", "plt.axes().set_aspect('equal','datalim')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 91, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3\n" ] } ], "source": [ "cij = nnij > 0.0001\n", "cgraph=csg.csgraph_from_dense(cij, null_value=False)\n", "cc=csg.connected_components(cgraph)\n", "print cc[0]" ] }, { "cell_type": "code", "execution_count": 92, "metadata": { "collapsed": true }, "outputs": [], "source": [ "macro = []\n", "imacro = np.ones(ngrid,int)-1\n", "for i in xrange(cc[0]):\n", " mci = np.zeros(0,int)\n", " for j in xrange(ncls):\n", " if cc[1][j] == i:\n", " mci = np.union1d(mci, lcls[j])\n", " imacro[lcls[j]] = i\n", " macro.append(mci)" ] }, { "cell_type": "code", "execution_count": 93, "metadata": { "collapsed": true }, "outputs": [], "source": [ "si = np.sqrt(np.exp(pi-pi.max()))" ] }, { "cell_type": "code", "execution_count": 94, "metadata": {}, "outputs": [ { "data": { "image/png": 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DB9bD1dWOU6fiWLkyiMWLT6LRKCxceIIJE1ry7bc979rurVuHcfFiMp06eQHQ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\nAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7ff9972832d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(x[:,0],x[:,1],c=imacro, s=(5+si500), linewidth='0')\n", "plt.axes().set_aspect('equal','datalim')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 154, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:1: RuntimeWarning: divide by zero encountered in log10\n", " \"\"\"Entry point for launching an IPython kernel.\n" ] }, { "data": { "image/png": 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"execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python2", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 2 }	gpl-3.0
tommytwoeyes/continuity	Homework_Assignments/HW_11.8_Power_Series.ipynb	2	774	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<hgroup>\n", " <h1>Power Series</h1>\n", " <h5>Section 11.8</h5>\n", "</hgroup>\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }	gpl-3.0
cweinschenk/cweinschenk	Map_Plotting/NIST_Studies/NIST_Study_Map.ipynb	1	25428	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Fire research at the National Institute of Standards and Technology (NIST) dates back to the early 1900's following the need for a standardized fire hydrant coupling after the Great Baltimore Fire. Fast-forward to the 1980s and for the last 30 years, NIST researchers have been performaning experiments and running computer simulations following fire incidents to advance the field of fire safety.\n", "\n", "NIST researchers wrote detailed reports of their work following these fires, but an intuitive, centralized source of this material did not exist. Therefore, we wanted to build an interface to fix this. We felt a map that showed where the fires occured with interactivity to link to the reports would be an interesting project." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", "\n", " <script type=\"text/javascript\">\n", " \n", " (function(global) {\n", " function now() {\n", " return new Date();\n", " }\n", " \n", " if (typeof (window._bokeh_onload_callbacks) === \"undefined\") {\n", " window._bokeh_onload_callbacks = [];\n", " }\n", " \n", " function run_callbacks() {\n", " window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n", " delete window._bokeh_onload_callbacks\n", " console.info(\"Bokeh: all callbacks have finished\");\n", " }\n", " \n", " function load_libs(js_urls, callback) {\n", " window._bokeh_onload_callbacks.push(callback);\n", " if (window._bokeh_is_loading > 0) {\n", " console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", " return null;\n", " }\n", " if (js_urls == null \|\| js_urls.length === 0) {\n", " run_callbacks();\n", " return null;\n", " }\n", " console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", " window._bokeh_is_loading = js_urls.length;\n", " for (var i = 0; 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i < inline_js.length; i++) {\n", " inline_js[i](window.Bokeh);\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>\n", " <div>\n", " <a href=\"http://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n", " <span>BokehJS successfully loaded.</span>\n", " </div>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import pandas as pd\n", "from bokeh.models.glyphs import Circle\n", "from bokeh.plotting import show, output_notebook,figure\n", "from bokeh.models import (\n", " GMapPlot, GMapOptions, Range1d, ColumnDataSource, LinearAxis,\n", " PanTool, WheelZoomTool,HoverTool, TapTool, OpenURL)\n", "output_notebook()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# First, let's load and take a look at the data file of fires." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data = pd.read_csv('study_data.csv')" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Study</th>\n", " <th>Latitude</th>\n", " <th>Longitude</th>\n", " <th>Color</th>\n", " <th>Type</th>\n", " <th>Date</th>\n", " <th>Report</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Simulation of an Attic Fire in a Wood Frame Re...</td>\n", " <td>41.802088</td>\n", " <td>-87.681981</td>\n", " <td>Navy</td>\n", " <td>LODD/LODI</td>\n", " <td>2-Nov-12</td>\n", " <td>http://dx.doi.org/10.6028/NIST.TN.1838</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Simulation of a Fire in a Hillside Residential...</td>\n", " <td>37.739505</td>\n", " <td>-122.439223</td>\n", " <td>Navy</td>\n", " <td>LODD/LODI</td>\n", " <td>2-Jun-11</td>\n", " <td>http://dx.doi.org/10.6028/NIST.TN.1856</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Simulation of a Residential Wind Driven Baseme...</td>\n", " <td>38.965891</td>\n", " <td>-76.917754</td>\n", " <td>Navy</td>\n", " <td>LODD/LODI</td>\n", " <td>24-Feb-12</td>\n", " <td>http://dx.doi.org/10.6028/NIST.TN.1870</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Simulation of the Dynamics of a Wind-Driven Fi...</td>\n", " <td>29.679490</td>\n", " <td>-95.282329</td>\n", " <td>Navy</td>\n", " <td>LODD/LODI</td>\n", " <td>12-Apr-09</td>\n", " <td>http://www.nist.gov/customcf/get_pdf.cfm?pub_i...</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Simulation of the Dynamics of a Fire in a Two-...</td>\n", " <td>40.404689</td>\n", " <td>-91.382416</td>\n", " <td>Red</td>\n", " <td>LODD/LODI & Civilian Loss</td>\n", " <td>22-Dec-99</td>\n", " <td>http://www.nist.gov/customcf/get_pdf.cfm?pub_i...</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Study Latitude Longitude \\\n", "0 Simulation of an Attic Fire in a Wood Frame Re... 41.802088 -87.681981 \n", "1 Simulation of a Fire in a Hillside Residential... 37.739505 -122.439223 \n", "2 Simulation of a Residential Wind Driven Baseme... 38.965891 -76.917754 \n", "3 Simulation of the Dynamics of a Wind-Driven Fi... 29.679490 -95.282329 \n", "4 Simulation of the Dynamics of a Fire in a Two-... 40.404689 -91.382416 \n", "\n", " Color Type Date \\\n", "0 Navy LODD/LODI 2-Nov-12 \n", "1 Navy LODD/LODI 2-Jun-11 \n", "2 Navy LODD/LODI 24-Feb-12 \n", "3 Navy LODD/LODI 12-Apr-09 \n", "4 Red LODD/LODI & Civilian Loss 22-Dec-99 \n", "\n", " Report \n", "0 http://dx.doi.org/10.6028/NIST.TN.1838 \n", "1 http://dx.doi.org/10.6028/NIST.TN.1856 \n", "2 http://dx.doi.org/10.6028/NIST.TN.1870 \n", "3 http://www.nist.gov/customcf/get_pdf.cfm?pub_i... \n", "4 http://www.nist.gov/customcf/get_pdf.cfm?pub_i... " ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The addresses of the fires were geocoded to generate the lattitude and longitude for each fire. The studies were then coded into one of four categories: <span style=\"color:blue\">LODD/LODI</span> (a fire with a firefighter Line of Duty Death or Line of Duty Injury), <span style=\"color:green\">Civilian Loss</span>, <span style=\"color:orange\">WUI</span> (wildland urban interface fire) or <span style=\"color:red\">LODD/LODI and Civilian Loss</span>. The data file also includes the NIST hosting link of the report so that we can link to it through first class bokeh features.\n", "\n", "___\n", "\n", "# The next step is to load in a simple map.\n", "The latitude, longitude, and zoom are set to view all of the United States and Puerto Rico. " ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [], "source": [ "x_range = Range1d()\n", "y_range = Range1d()\n", "\n", "map_options = GMapOptions(lat=39., lng=-98, zoom=3)\n", "\n", "plot = GMapPlot(\n", " x_range=x_range, y_range=y_range,\n", " map_options=map_options,\n", " title = \"NIST Fire Studies\", plot_width=875, plot_height=500\n", ")\n", "plot.map_options.map_type=\"terrain\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Adding Interaction\n", "\n", "We wanted the the study name, type, and date of the fire to be available upon hover. Second, we wanted the glyphs to be clickable to direct users to the full study reports (the main purpose of this exercise)." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "source = ColumnDataSource({'lat':data['Latitude'],'lon':data['Longitude'],'studys': data['Study'],\n", " 'report': data['Report'],'fill':data['Color'],'type':data['Type'],'date':data['Date']})\n", "circle = Circle(x=\"lon\",y=\"lat\",size=15,fill_color=\"fill\")\n", "plot.add_glyph(source, circle)\n", "\n", "pan = PanTool()\n", "wheel_zoom = WheelZoomTool()\n", "hover = HoverTool()\n", "hover.tooltips = [('Study Title','@studys'),('Date','@date'),('Type','@type')]\n", "tap = TapTool()\n", "url = \"@report\"\n", "TapTool.callback = OpenURL(url=url)\n", "\n", "plot.add_tools(pan,wheel_zoom,hover,tap)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "text/html": [ "\n", 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}(this));\n", "</script>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "<bokeh.io._CommsHandle at 0x109fb75f8>" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "show(plot)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "This is clearly a simple example, but that is point. With minimal code, the final product is an interative map that highlights where prior fires have been studied by NIST researchers." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.3" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
pmgbergen/porepy	tutorials/mpsa.ipynb	1	79597	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Multi-point stress approximation (MPSA)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Porepy supports mpsa discretization for linear elasticity problem:\n", "\\begin{equation}\n", "\\nabla\\cdot \\sigma = -\\vec f,\\quad \\vec x \\in \\Omega\n", "\\end{equation}\n", "where $\\vec f$ is a body force, and the stress $\\sigma$ is given as a linear function of the displacement\n", "\\begin{equation}\n", "\\sigma = C:\\vec u.\n", "\\end{equation}\n", "\n", "The convention in porepy is that tension is positive. This means that the Cartesian component of the traction $\\vec T = \\sigma \\cdot \\vec n$, for a direction $\\vec r$ is positive number if the inner product $\\vec T\\cdot \\vec r$ is positive. The displacements will give the difference between the initial state of the rock and the deformed state. If we consider a point in its initial state $\\vec x \\in \\Omega$ and let $\\vec x^* \\in \\Omega$ be the same point in the deformed state, to be consistent with the convention we used for traction, the displacements are given by $\\vec u = \\vec x^* - \\vec x$, that is, $u$ points from the initial state to the finial state.\n", "\n", "To close the system we also need to define a set of boundary conditions. Here we have three posibilities, Neumman conditions, Dirichlet conditions or Robin conditions, and we divide the boundary into three disjont sets $\\Gamma_N$, $\\Gamma_D$ and $\\Gamma_R$ for the three different types of boundary conditions\n", "\\begin{equation}\n", "\\vec u = g_D, \\quad \\vec x \\in \\Gamma_D\\\\\n", "\\sigma\\cdot n = g_N, \\quad \\vec x \\in \\Gamma_N\\\\\n", "\\sigma\\cdot n + W \\vec u= g_R,\\quad \\vec x\\in \\Gamma_R\n", "\\end{equation}" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "To solve this system we first have to create the grid." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import porepy as pp\n", "\n", "# Create grid\n", "n = 5\n", "g = pp.CartGrid([n,n])\n", "g.compute_geometry()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We also need to define the stress tensor $C$. In porepy the constutitive law,\n", "\\begin{equation}\n", "\\sigma = C:u = 2 \\mu \\epsilon +\\lambda \\text{trace}(\\epsilon) I, \\quad \\epsilon = \\frac{1}{2}(\\nabla u + (\\nabla u)^\\top)\n", "\\end{equation}\n", "is implemented, and to get the tensor for this law we call:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# Create stiffness matrix\n", "lam = np.ones(g.num_cells)\n", "mu = np.ones(g.num_cells)\n", "C = pp.FourthOrderTensor(mu, lam)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then we need to define boundary conditions. We set the bottom boundary as a Dirichlet boundary, and the other boundaries are set to Neuman." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# Define boundary type\n", "dirich = np.ravel(np.argwhere(g.face_centers[1] < 1e-10))\n", "bound = pp.BoundaryConditionVectorial(g, dirich, ['dir']dirich.size)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We discretize the stresses by using the multi-point stress approximation (for details, please see: E. Keilegavlen and J. M. Nordbotten. “Finite volume methods for elasticity with weak symmetry”. In: International Journal for Numerical Methods in Engineering (2017))." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now define the values we put on the boundaries. We clamp the bottom boundary, and push down by a constant force on the top boundary. Note that the value of the Neumann condition given on a face $\\pi$ is the integrated traction $\\int_\\pi g_N d\\vec x$." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "top_faces = np.ravel(np.argwhere(g.face_centers[1] > n - 1e-10))\n", "bot_faces = np.ravel(np.argwhere(g.face_centers[1] < 1e-10))\n", "\n", "u_b = np.zeros((g.dim, g.num_faces))\n", "u_b[1, top_faces] = -1 g.face_areas[top_faces]\n", "u_b[:, bot_faces] = 0\n", "\n", "u_b = u_b.ravel('F')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We discretize this system using the Mpsa class. We assume zero body forces $f=0$" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "parameter_keyword = \"mechanics\"\n", "\n", "mpsa_class = pp.Mpsa(parameter_keyword)\n", "f = np.zeros(g.dim * g.num_cells)\n", "\n", "specified_parameters = {\"fourth_order_tensor\": C, \"source\": f, \"bc\": bound, \"bc_values\": u_b}\n", "data = pp.initialize_default_data(g, {}, parameter_keyword, specified_parameters)\n", "mpsa_class.discretize(g, data)\n", "A, b = mpsa_class.assemble_matrix_rhs(g, data)\n", "\n", "u = np.linalg.solve(A.A, b)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And we can plott the y_displacement" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/eke001/python_env/eris/lib/python3.6/site-packages/mpl_toolkits/mplot3d/axes3d.py:753: UserWarning: Attempting to set identical bottom==top results\n", "in singular transformations; automatically expanding.\n", "bottom=0.0, top=0.0\n", " 'bottom=%s, top=%s') % (bottom, top))\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1080x864 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "pp.plot_grid(g, cell_value=u[1::2], figsize=(15, 12))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To understand the inner workings of the discretization, and to recover the traction on the grid faces, some more details are needed. The MPSA discretization creates two sparse matrices \"stress\" and \"bound_stress\". They give define the discretization of the cell-face traction:\n", "\\begin{equation}\n", "T = \\text{stress} \\cdot u + \\text{bound_stress} \\cdot u_b\n", "\\end{equation}\n", "Here $u$ is a vector of cell center displacement and has length g.dim $$ g.num_cells. The vector $u_b$ is the boundary condition values. It is the displacement for Dirichlet boundaries and traction for Neumann boundaries and has length g.dim $$ g.num_faces.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The global linear system is now formed by momentuum balance on all cells. A row in the discretized system reads\n", "\\begin{equation}\n", "-\\int_{\\Omega_k} f dv = \\int_{\\partial\\Omega_k} T(n)dA = [div \\cdot \\text{stress} \\cdot u + div\\cdot\\text{bound_stress}\\cdot u_b]_k,\n", "\\end{equation}\n", "\n", "The call to mpsa_class.assemble_matrix_rhs(), creates the left hand side matrix $ \\text{div} \\cdot \\text{stress} $, the right hand side vector, which consists of $\\text{f}$ and $-\\text{div} \\cdot \\text{bound_stress}$ (note sign change).\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also retrieve the traction on the faces, by first accessing the discretization matrices stress and bound_stress" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<Figure size 1080x864 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Stress discretization\n", "stress = data[pp.DISCRETIZATION_MATRICES][parameter_keyword][mpsa_class.stress_matrix_key]\n", "# Discrete boundary conditions\n", "bound_stress = data[pp.DISCRETIZATION_MATRICES][parameter_keyword][mpsa_class.bound_stress_matrix_key]\n", "\n", "\n", "T = stress * u + bound_stress * u_b\n", "\n", "T2d = np.reshape(T, (g.dim, -1), order='F')\n", "u_b2d = np.reshape(u_b, (g.dim, -1), order='F')\n", "assert np.allclose(np.abs(u_b2d[bound.is_neu]), np.abs(T2d[bound.is_neu]))\n", "\n", "T = np.vstack((T2d, np.zeros(g.num_faces)))\n", "pp.plot_grid(g, vector_value=T, figsize=(15, 12), alpha=0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that the traction on face i: T[2i:2i+g.dim] is the traction on the face as defined by the normal vectors g.face_normals. This means that for the bottom boundary, the traction T[bot] is the force from to box on the outside (since the normal vectors here are [0,1]), while on the top boundary, the traction T[top] is the force applied to to top faces from the outside (since the normals here point out of the domain)." ] } ], "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": "3.6.9" } }, "nbformat": 4, "nbformat_minor": 2 }	gpl-3.0
FCH808/FCH808.github.io	Intro to Machine Learning/notebooks/.ipynb_checkpoints/Lesson 8 - Clustering-checkpoint.ipynb	2	42914	{ "metadata": { "name": "", "signature": "sha256:0f46e1ca0d34faf67da88481e7eac6e94c59671922ef18d01fac6ff04845f5fc" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "%load ../ud120-projects/k_means/k_means_cluster.py" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "#!/usr/bin/python \n", "\n", "\"\"\" \n", " skeleton code for k-means clustering mini-project\n", "\n", "\"\"\"\n", "\n", "\n", "\n", "\n", "import pickle\n", "import numpy\n", "import matplotlib.pyplot as plt\n", "import sys\n", "sys.path.append(\"../ud120-projects/tools/\")\n", "from feature_format import featureFormat, targetFeatureSplit\n", "\n", "\n", "\n", "\n", "def Draw(pred, features, poi, mark_poi=False, name=\"image.png\", f1_name=\"feature 1\", f2_name=\"feature 2\"):\n", " \"\"\" some plotting code designed to help you visualize your clusters \"\"\"\n", "\n", " ### plot each cluster with a different color--add more colors for\n", " ### drawing more than 4 clusters\n", " colors = [\"b\", \"c\", \"k\", \"m\", \"g\"]\n", " for ii, pp in enumerate(pred):\n", " plt.scatter(features[ii][0], features[ii][1], color = colors[pred[ii]])\n", "\n", " ### if you like, place red stars over points that are POIs (just for funsies)\n", " if mark_poi:\n", " for ii, pp in enumerate(pred):\n", " if poi[ii]:\n", " plt.scatter(features[ii][0], features[ii][1], color=\"r\", marker=\"*\")\n", " plt.xlabel(f1_name)\n", " plt.ylabel(f2_name)\n", " plt.savefig(name)\n", " plt.show()\n", "\n", "\n", "\n", "### load in the dict of dicts containing all the data on each person in the dataset\n", "data_dict = pickle.load( open(\"../ud120-projects/final_project/final_project_dataset.pkl\", \"r\") )\n", "### there's an outlier--remove it! \n", "data_dict.pop(\"TOTAL\", 0)\n", "\n", "\n", "### the input features we want to use \n", "### can be any key in the person-level dictionary (salary, director_fees, etc.) \n", "feature_1 = \"salary\"\n", "feature_2 = \"exercised_stock_options\"\n", "feature_3 = \"total_payments\"\n", "poi = \"poi\"\n", "features_list = [poi, feature_1, feature_2]\n", "data = featureFormat(data_dict, features_list )\n", "poi, finance_features = targetFeatureSplit( data )\n", "\n", "\n", "### in the \"clustering with 3 features\" part of the mini-project,\n", "### you'll want to change this line to \n", "### for f1, f2, _ in finance_features:\n", "### (as it's currently written, line below assumes 2 features)\n", "for f1, f2 in finance_features:\n", " plt.scatter( f1, f2 )\n", "plt.show()\n", "\n", "\n", "\n", "from sklearn.cluster import KMeans\n", "features_list = [\"poi\", feature_1, feature_2]\n", "data2 = featureFormat(data_dict, features_list )\n", "poi, finance_features = targetFeatureSplit( data2 )\n", "clf = KMeans(n_clusters=2)\n", "pred = clf.fit_predict( finance_features )\n", "Draw(pred, finance_features, poi, name=\"clusters_before_scaling.pdf\", f1_name=feature_1, f2_name=feature_2)\n", "\n", "\n", "### cluster here; create predictions of the cluster labels\n", "### for the data and store them to a list called pred\n", "\n", "try:\n", " Draw(pred, finance_features, poi, mark_poi=False, name=\"clusters.pdf\", f1_name=feature_1, f2_name=feature_2)\n", "except NameError:\n", " print \"no predictions object named pred found, no clusters to plot\"\n", "\n", "\n", "\n", "\n", "\n" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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CompPhysics/ComputationalPhysics2	doc/LectureNotes/boltzmannmachines.ipynb	1	108695	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Boltzmann Machines\n", "\n", "Why use a generative model rather than the more well known discriminative deep neural networks (DNN)? \n", "\n", "* Discriminitave methods have several limitations: They are mainly supervised learning methods, thus requiring labeled data. And there are tasks they cannot accomplish, like drawing new examples from an unknown probability distribution.\n", "\n", "* A generative model can learn to represent and sample from a probability distribution. The core idea is to learn a parametric model of the probability distribution from which the training data was drawn. As an example\n", "\n", "a. A model for images could learn to draw new examples of cats and dogs, given a training dataset of images of cats and dogs.\n", "\n", "b. Generate a sample of an ordered or disordered Ising model phase, having been given samples of such phases.\n", "\n", "c. Model the trial function for Monte Carlo calculations\n", "\n", "\n", "4. Both use gradient-descent based learning procedures for minimizing cost functions\n", "\n", "5. Energy based models don't use backpropagation and automatic differentiation for computing gradients, instead turning to Markov Chain Monte Carlo methods.\n", "\n", "6. DNNs often have several hidden layers. A restricted Boltzmann machine has only one hidden layer, however several RBMs can be stacked to make up Deep Belief Networks, of which they constitute the building blocks.\n", "\n", "History: The RBM was developed by amongst others Geoffrey Hinton, called by some the \"Godfather of Deep Learning\", working with the University of Toronto and Google.\n", "\n", "\n", "\n", "A BM is what we would call an undirected probabilistic graphical model\n", "with stochastic continuous or discrete units.\n", "\n", "\n", "It is interpreted as a stochastic recurrent neural network where the\n", "state of each unit(neurons/nodes) depends on the units it is connected\n", "to. The weights in the network represent thus the strength of the\n", "interaction between various units/nodes.\n", "\n", "\n", "It turns into a Hopfield network if we choose deterministic rather\n", "than stochastic units. In contrast to a Hopfield network, a BM is a\n", "so-called generative model. It allows us to generate new samples from\n", "the learned distribution.\n", "\n", "\n", "\n", "A standard BM network is divided into a set of observable and visible units $\\hat{x}$ and a set of unknown hidden units/nodes $\\hat{h}$.\n", "\n", "\n", "\n", "Additionally there can be bias nodes for the hidden and visible layers. These biases are normally set to $1$.\n", "\n", "\n", "\n", "BMs are stackable, meaning they cwe can train a BM which serves as input to another BM. We can construct deep networks for learning complex PDFs. The layers can be trained one after another, a feature which makes them popular in deep learning\n", "\n", "\n", "\n", "However, they are often hard to train. This leads to the introduction of so-called restricted BMs, or RBMS.\n", "Here we take away all lateral connections between nodes in the visible layer as well as connections between nodes in the hidden layer. The network is illustrated in the figure below.\n", "\n", "<!-- dom:FIGURE: [figures/RBM.png, width=800 frac=1.0] -->\n", "<!-- begin figure -->\n", "<img src=\"figures/RBM.png\" width=800><p style=\"font-size: 0.9em\"><i>Figure 1: </i></p><!-- end figure -->\n", "\n", "\n", "\n", "\n", "\n", "## The network\n", "\n", "The network layers:\n", "1. A function $\\mathbf{x}$ that represents the visible layer, a vector of $M$ elements (nodes). This layer represents both what the RBM might be given as training input, and what we want it to be able to reconstruct. This might for example be the pixels of an image, the spin values of the Ising model, or coefficients representing speech.\n", "\n", "2. The function $\\mathbf{h}$ represents the hidden, or latent, layer. A vector of $N$ elements (nodes). Also called \"feature detectors\".\n", "\n", "The goal of the hidden layer is to increase the model's expressive power. We encode complex interactions between visible variables by introducing additional, hidden variables that interact with visible degrees of freedom in a simple manner, yet still reproduce the complex correlations between visible degrees in the data once marginalized over (integrated out).\n", "\n", "Examples of this trick being employed in physics: \n", "1. The Hubbard-Stratonovich transformation\n", "\n", "2. The introduction of ghost fields in gauge theory\n", "\n", "3. Shadow wave functions in Quantum Monte Carlo simulations\n", "\n", "The network parameters, to be optimized/learned:\n", "1. $\\mathbf{a}$ represents the visible bias, a vector of same length as $\\mathbf{x}$.\n", "\n", "2. $\\mathbf{b}$ represents the hidden bias, a vector of same lenght as $\\mathbf{h}$.\n", "\n", "3. $W$ represents the interaction weights, a matrix of size $M\\times N$.\n", "\n", "### Joint distribution\n", "\n", "The restricted Boltzmann machine is described by a Bolztmann distribution" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto1\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", "\tP_{rbm}(\\mathbf{x},\\mathbf{h}) = \\frac{1}{Z} e^{-\\frac{1}{T_0}E(\\mathbf{x},\\mathbf{h})},\n", "\\label{_auto1} \\tag{1}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "where $Z$ is the normalization constant or partition function, defined as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto2\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", "\tZ = \\int \\int e^{-\\frac{1}{T_0}E(\\mathbf{x},\\mathbf{h})} d\\mathbf{x} d\\mathbf{h}.\n", "\\label{_auto2} \\tag{2}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is common to ignore $T_0$ by setting it to one. \n", "\n", "\n", "### Network Elements, the energy function\n", "\n", "The function $E(\\mathbf{x},\\mathbf{h})$ gives the energy of a\n", "configuration (pair of vectors) $(\\mathbf{x}, \\mathbf{h})$. The lower\n", "the energy of a configuration, the higher the probability of it. This\n", "function also depends on the parameters $\\mathbf{a}$, $\\mathbf{b}$ and\n", "$W$. Thus, when we adjust them during the learning procedure, we are\n", "adjusting the energy function to best fit our problem.\n", "\n", "\n", "\n", "### Defining different types of RBMs\n", "\n", "There are different variants of RBMs, and the differences lie in the types of visible and hidden units we choose as well as in the implementation of the energy function $E(\\mathbf{x},\\mathbf{h})$. The connection between the nodes in the two layers is given by the weights $w_{ij}$. \n", "\n", "Binary-Binary RBM:\n", "\n", "\n", "RBMs were first developed using binary units in both the visible and hidden layer. The corresponding energy function is defined as follows:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto3\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", "\tE(\\mathbf{x}, \\mathbf{h}) = - \\sum_i^M x_i a_i- \\sum_j^N b_j h_j - \\sum_{i,j}^{M,N} x_i w_{ij} h_j,\n", "\\label{_auto3} \\tag{3}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "where the binary values taken on by the nodes are most commonly 0 and 1.\n", "\n", "\n", "Gaussian-Binary RBM:\n", "\n", "\n", "Another varient is the RBM where the visible units are Gaussian while the hidden units remain binary:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto4\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", "\tE(\\mathbf{x}, \\mathbf{h}) = \\sum_i^M \\frac{(x_i - a_i)^2}{2\\sigma_i^2} - \\sum_j^N b_j h_j - \\sum_{i,j}^{M,N} \\frac{x_i w_{ij} h_j}{\\sigma_i^2}. \n", "\\label{_auto4} \\tag{4}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1. RBMs are Useful when we model continuous data (i.e., we wish $\\mathbf{x}$ to be continuous)\n", "\n", "2. Requires a smaller learning rate, since there's no upper bound to the value a component might take in the reconstruction\n", "\n", "Other types of units include:\n", "1. Softmax and multinomial units\n", "\n", "2. Gaussian visible and hidden units\n", "\n", "3. Binomial units\n", "\n", "4. Rectified linear units\n", "\n", "### Cost function\n", "\n", "When working with a training dataset, the most common training approach is maximizing the log-likelihood of the training data. The log likelihood characterizes the log-probability of generating the observed data using our generative model. Using this method our cost function is chosen as the negative log-likelihood. The learning then consists of trying to find parameters that maximize the probability of the dataset, and is known as Maximum Likelihood Estimation (MLE).\n", "Denoting the parameters as $\\boldsymbol{\\theta} = a_1,...,a_M,b_1,...,b_N,w_{11},...,w_{MN}$, the log-likelihood is given by" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto5\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", "\t\\mathcal{L}(\\{ \\theta_i \\}) = \\langle \\text{log} P_\\theta(\\boldsymbol{x}) \\rangle_{data} \n", "\\label{_auto5} \\tag{5}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto6\"></div>\n", "\n", "$$\n", "\\begin{equation} \n", "\t= - \\langle E(\\boldsymbol{x}; \\{ \\theta_i\\}) \\rangle_{data} - \\text{log} Z(\\{ \\theta_i\\}),\n", "\\label{_auto6} \\tag{6}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "where we used that the normalization constant does not depend on the data, $\\langle \\text{log} Z(\\{ \\theta_i\\}) \\rangle = \\text{log} Z(\\{ \\theta_i\\})$\n", "Our cost function is the negative log-likelihood, $\\mathcal{C}(\\{ \\theta_i \\}) = - \\mathcal{L}(\\{ \\theta_i \\})$\n", "\n", "### Optimization / Training\n", "\n", "The training procedure of choice often is Stochastic Gradient Descent (SGD). It consists of a series of iterations where we update the parameters according to the equation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto7\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", "\t\\boldsymbol{\\theta}_{k+1} = \\boldsymbol{\\theta}_k - \\eta \\nabla \\mathcal{C} (\\boldsymbol{\\theta}_k)\n", "\\label{_auto7} \\tag{7}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "at each $k$-th iteration. There are a range of variants of the algorithm which aim at making the learning rate $\\eta$ more adaptive so the method might be more efficient while remaining stable.\n", "\n", "We now need the gradient of the cost function in order to minimize it. We find that" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto8\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", "\t\\frac{\\partial \\mathcal{C}(\\{ \\theta_i\\})}{\\partial \\theta_i}\n", "\t= \\langle \\frac{\\partial E(\\boldsymbol{x}; \\theta_i)}{\\partial \\theta_i} \\rangle_{data}\n", "\t+ \\frac{\\partial \\text{log} Z(\\{ \\theta_i\\})}{\\partial \\theta_i} \n", "\\label{_auto8} \\tag{8}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto9\"></div>\n", "\n", "$$\n", "\\begin{equation} \n", "\t= \\langle O_i(\\boldsymbol{x}) \\rangle_{data} - \\langle O_i(\\boldsymbol{x}) \\rangle_{model},\n", "\\label{_auto9} \\tag{9}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "where in order to simplify notation we defined the \"operator\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto10\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", "\tO_i(\\boldsymbol{x}) = \\frac{\\partial E(\\boldsymbol{x}; \\theta_i)}{\\partial \\theta_i}, \n", "\\label{_auto10} \\tag{10}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and used the statistical mechanics relationship between expectation values and the log-partition function:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto11\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", "\t\\langle O_i(\\boldsymbol{x}) \\rangle_{model} = \\text{Tr} P_\\theta(\\boldsymbol{x})O_i(\\boldsymbol{x}) = - \\frac{\\partial \\text{log} Z(\\{ \\theta_i\\})}{\\partial \\theta_i}.\n", "\\label{_auto11} \\tag{11}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The data-dependent term in the gradient is known as the positive phase\n", "of the gradient, while the model-dependent term is known as the\n", "negative phase of the gradient. The aim of the training is to lower\n", "the energy of configurations that are near observed data points\n", "(increasing their probability), and raising the energy of\n", "configurations that are far from observed data points (decreasing\n", "their probability).\n", "\n", "The gradient of the negative log-likelihood cost function of a Binary-Binary RBM is then" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto12\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", "\t\\frac{\\partial \\mathcal{C} (w_{ij}, a_i, b_j)}{\\partial w_{ij}} = \\langle x_i h_j \\rangle_{data} - \\langle x_i h_j \\rangle_{model} \n", "\\label{_auto12} \\tag{12}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto13\"></div>\n", "\n", "$$\n", "\\begin{equation} \n", "\t\\frac{\\partial \\mathcal{C} (w_{ij}, a_i, b_j)}{\\partial a_{ij}} = \\langle x_i \\rangle_{data} - \\langle x_i \\rangle_{model} \n", "\\label{_auto13} \\tag{13}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto14\"></div>\n", "\n", "$$\n", "\\begin{equation} \n", "\t\\frac{\\partial \\mathcal{C} (w_{ij}, a_i, b_j)}{\\partial b_{ij}} = \\langle h_i \\rangle_{data} - \\langle h_i \\rangle_{model}. \n", "\\label{_auto14} \\tag{14}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto15\"></div>\n", "\n", "$$\n", "\\begin{equation} \n", "\\label{_auto15} \\tag{15}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To get the expectation values with respect to the data, we set the visible units to each of the observed samples in the training data, then update the hidden units according to the conditional probability found before. We then average over all samples in the training data to calculate expectation values with respect to the data. \n", "\n", "\n", "\n", "\n", "### Kullback-Leibler relative entropy\n", "\n", "When the goal of the training is to approximate a probability\n", "distribution, as it is in generative modeling, another relevant\n", "measure is the Kullback-Leibler divergence, also known as the\n", "relative entropy or Shannon entropy. It is a non-symmetric measure of the\n", "dissimilarity between two probability density functions $p$ and\n", "$q$. If $p$ is the unkown probability which we approximate with $q$,\n", "we can measure the difference by" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto16\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", "\t\\text{KL}(p\|\|q) = \\int_{-\\infty}^{\\infty} p (\\boldsymbol{x}) \\log \\frac{p(\\boldsymbol{x})}{q(\\boldsymbol{x})} d\\boldsymbol{x}.\n", "\\label{_auto16} \\tag{16}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Thus, the Kullback-Leibler divergence between the distribution of the\n", "training data $f(\\boldsymbol{x})$ and the model distribution $p(\\boldsymbol{x}\|\n", "\\boldsymbol{\\theta})$ is" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto17\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", "\t\\text{KL} (f(\\boldsymbol{x})\|\| p(\\boldsymbol{x}\| \\boldsymbol{\\theta})) = \\int_{-\\infty}^{\\infty}\n", "\tf (\\boldsymbol{x}) \\log \\frac{f(\\boldsymbol{x})}{p(\\boldsymbol{x}\| \\boldsymbol{\\theta})} d\\boldsymbol{x} \n", "\\label{_auto17} \\tag{17}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto18\"></div>\n", "\n", "$$\n", "\\begin{equation} \n", "\t= \\int_{-\\infty}^{\\infty} f(\\boldsymbol{x}) \\log f(\\boldsymbol{x}) d\\boldsymbol{x} - \\int_{-\\infty}^{\\infty} f(\\boldsymbol{x}) \\log\n", "\tp(\\boldsymbol{x}\| \\boldsymbol{\\theta}) d\\boldsymbol{x} \n", "\\label{_auto18} \\tag{18}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto19\"></div>\n", "\n", "$$\n", "\\begin{equation} \n", "\t%= \\mathbb{E}_{f(\\boldsymbol{x})} (\\log f(\\boldsymbol{x})) - \\mathbb{E}_{f(\\boldsymbol{x})} (\\log p(\\boldsymbol{x}\| \\boldsymbol{\\theta}))\n", "\t= \\langle \\log f(\\boldsymbol{x}) \\rangle_{f(\\boldsymbol{x})} - \\langle \\log p(\\boldsymbol{x}\| \\boldsymbol{\\theta}) \\rangle_{f(\\boldsymbol{x})} \n", "\\label{_auto19} \\tag{19}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto20\"></div>\n", "\n", "$$\n", "\\begin{equation} \n", "\t= \\langle \\log f(\\boldsymbol{x}) \\rangle_{data} + \\langle E(\\boldsymbol{x}) \\rangle_{data} + \\log Z \n", "\\label{_auto20} \\tag{20}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto21\"></div>\n", "\n", "$$\n", "\\begin{equation} \n", "\t= \\langle \\log f(\\boldsymbol{x}) \\rangle_{data} + \\mathcal{C}_{LL} .\n", "\\label{_auto21} \\tag{21}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The first term is constant with respect to $\\boldsymbol{\\theta}$ since $f(\\boldsymbol{x})$ is independent of $\\boldsymbol{\\theta}$. Thus the Kullback-Leibler Divergence is minimal when the second term is minimal. The second term is the log-likelihood cost function, hence minimizing the Kullback-Leibler divergence is equivalent to maximizing the log-likelihood.\n", "\n", "\n", "To further understand generative models it is useful to study the\n", "gradient of the cost function which is needed in order to minimize it\n", "using methods like stochastic gradient descent. \n", "\n", "The partition function is the generating function of\n", "expectation values, in particular there are mathematical relationships\n", "between expectation values and the log-partition function. In this\n", "case we have" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto22\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", "\t\\langle \\frac{ \\partial E(\\boldsymbol{x}; \\theta_i) } { \\partial \\theta_i} \\rangle_{model}\n", "\t= \\int p(\\boldsymbol{x}\| \\boldsymbol{\\theta}) \\frac{ \\partial E(\\boldsymbol{x}; \\theta_i) } { \\partial \\theta_i} d\\boldsymbol{x} \n", "\t= -\\frac{\\partial \\log Z(\\theta_i)}{ \\partial \\theta_i} .\n", "\\label{_auto22} \\tag{22}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here $\\langle \\cdot \\rangle_{model}$ is the expectation value over the model probability distribution $p(\\boldsymbol{x}\| \\boldsymbol{\\theta})$.\n", "\n", "## Setting up for gradient descent calculations\n", "\n", "Using the previous relationship we can express the gradient of the cost function as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto23\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", "\t\\frac{\\partial \\mathcal{C}_{LL}}{\\partial \\theta_i}\n", "\t= \\langle \\frac{ \\partial E(\\boldsymbol{x}; \\theta_i) } { \\partial \\theta_i} \\rangle_{data} + \\frac{\\partial \\log Z(\\theta_i)}{ \\partial \\theta_i} \n", "\\label{_auto23} \\tag{23}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto24\"></div>\n", "\n", "$$\n", "\\begin{equation} \n", "\t= \\langle \\frac{ \\partial E(\\boldsymbol{x}; \\theta_i) } { \\partial \\theta_i} \\rangle_{data} - \\langle \\frac{ \\partial E(\\boldsymbol{x}; \\theta_i) } { \\partial \\theta_i} \\rangle_{model} \n", "\\label{_auto24} \\tag{24}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto25\"></div>\n", "\n", "$$\n", "\\begin{equation} \n", "\t%= \\langle O_i(\\boldsymbol{x}) \\rangle_{data} - \\langle O_i(\\boldsymbol{x}) \\rangle_{model}\n", "\\label{_auto25} \\tag{25}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This expression shows that the gradient of the log-likelihood cost\n", "function is a difference of moments, with one calculated from\n", "the data and one calculated from the model. The data-dependent term is\n", "called the positive phase and the model-dependent term is\n", "called the negative phase of the gradient. We see now that\n", "minimizing the cost function results in lowering the energy of\n", "configurations $\\boldsymbol{x}$ near points in the training data and\n", "increasing the energy of configurations not observed in the training\n", "data. That means we increase the model's probability of configurations\n", "similar to those in the training data.\n", "\n", "\n", "The gradient of the cost function also demonstrates why gradients of\n", "unsupervised, generative models must be computed differently from for\n", "those of for example FNNs. While the data-dependent expectation value\n", "is easily calculated based on the samples $\\boldsymbol{x}_i$ in the training\n", "data, we must sample from the model in order to generate samples from\n", "which to caclulate the model-dependent term. We sample from the model\n", "by using MCMC-based methods. We can not sample from the model directly\n", "because the partition function $Z$ is generally intractable.\n", "\n", "As in supervised machine learning problems, the goal is also here to\n", "perform well on unseen data, that is to have good\n", "generalization from the training data. The distribution $f(x)$ we\n", "approximate is not the true distribution we wish to estimate,\n", "it is limited to the training data. Hence, in unsupervised training as\n", "well it is important to prevent overfitting to the training data. Thus\n", "it is common to add regularizers to the cost function in the same\n", "manner as we discussed for say linear regression.\n", "\n", "\n", "\n", "## RBMs for the quantum many body problem\n", "\n", "The idea of applying RBMs to quantum many body problems was presented by G. Carleo and M. Troyer, working with ETH Zurich and Microsoft Research.\n", "\n", "Some of their motivation included\n", "\n", "* The wave function $\\Psi$ is a monolithic mathematical quantity that contains all the information on a quantum state, be it a single particle or a complex molecule. In principle, an exponential amount of information is needed to fully encode a generic many-body quantum state.\n", "\n", "* There are still interesting open problems, including fundamental questions ranging from the dynamical properties of high-dimensional systems to the exact ground-state properties of strongly interacting fermions.\n", "\n", "* The difficulty lies in finding a general strategy to reduce the exponential complexity of the full many-body wave function down to its most essential features. That is\n", "\n", "a. Dimensional reduction\n", "\n", "b. Feature extraction\n", "\n", "\n", "* Among the most successful techniques to attack these challenges, artifical neural networks play a prominent role.\n", "\n", "* Want to understand whether an artifical neural network may adapt to describe a quantum system.\n", "\n", "Carleo and Troyer applied the RBM to the quantum mechanical spin lattice systems of the Ising model and Heisenberg model, with encouraging results. Our goal is to test the method on systems of moving particles. For the spin lattice systems it was natural to use a binary-binary RBM, with the nodes taking values of 1 and -1. For moving particles, on the other hand, we want the visible nodes to be continuous, representing position coordinates. Thus, we start by choosing a Gaussian-binary RBM, where the visible nodes are continuous and hidden nodes take on values of 0 or 1. If eventually we would like the hidden nodes to be continuous as well the rectified linear units seem like the most relevant choice.\n", "\n", "\n", "\n", "\n", "## Representing the wave function\n", "\n", "The wavefunction should be a probability amplitude depending on\n", " $\\boldsymbol{x}$. The RBM model is given by the joint distribution of\n", " $\\boldsymbol{x}$ and $\\boldsymbol{h}$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto26\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", " F_{rbm}(\\mathbf{x},\\mathbf{h}) = \\frac{1}{Z} e^{-\\frac{1}{T_0}E(\\mathbf{x},\\mathbf{h})}.\n", "\\label{_auto26} \\tag{26}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To find the marginal distribution of $\\boldsymbol{x}$ we set:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto27\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", " F_{rbm}(\\mathbf{x}) = \\sum_\\mathbf{h} F_{rbm}(\\mathbf{x}, \\mathbf{h}) \n", "\\label{_auto27} \\tag{27}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto28\"></div>\n", "\n", "$$\n", "\\begin{equation} \n", " = \\frac{1}{Z}\\sum_\\mathbf{h} e^{-E(\\mathbf{x}, \\mathbf{h})}.\n", "\\label{_auto28} \\tag{28}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now this is what we use to represent the wave function, calling it a neural-network quantum state (NQS)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto29\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", " \\Psi (\\mathbf{X}) = F_{rbm}(\\mathbf{x}) \n", "\\label{_auto29} \\tag{29}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto30\"></div>\n", "\n", "$$\n", "\\begin{equation} \n", " = \\frac{1}{Z}\\sum_{\\boldsymbol{h}} e^{-E(\\mathbf{x}, \\mathbf{h})} \n", "\\label{_auto30} \\tag{30}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto31\"></div>\n", "\n", "$$\n", "\\begin{equation} \n", " = \\frac{1}{Z} \\sum_{\\{h_j\\}} e^{-\\sum_i^M \\frac{(x_i - a_i)^2}{2\\sigma^2} + \\sum_j^N b_j h_j + \\sum_\\\n", "{i,j}^{M,N} \\frac{x_i w_{ij} h_j}{\\sigma^2}} \n", "\\label{_auto31} \\tag{31}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto32\"></div>\n", "\n", "$$\n", "\\begin{equation} \n", " = \\frac{1}{Z} e^{-\\sum_i^M \\frac{(x_i - a_i)^2}{2\\sigma^2}} \\prod_j^N (1 + e^{b_j + \\sum_i^M \\frac{x\\\n", "_i w_{ij}}{\\sigma^2}}). \n", "\\label{_auto32} \\tag{32}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto33\"></div>\n", "\n", "$$\n", "\\begin{equation} \n", "\\label{_auto33} \\tag{33}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Choose the cost function\n", "\n", "Now we don't necessarily have training data (unless we generate it by using some other method). However, what we do have is the variational principle which allows us to obtain the ground state wave function by minimizing the expectation value of the energy of a trial wavefunction (corresponding to the untrained NQS). Similarly to the traditional variational Monte Carlo method then, it is the local energy we wish to minimize. The gradient to use for the stochastic gradient descent procedure is" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto34\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", "\tC_i = \\frac{\\partial \\langle E_L \\rangle}{\\partial \\theta_i}\n", "\t= 2(\\langle E_L \\frac{1}{\\Psi}\\frac{\\partial \\Psi}{\\partial \\theta_i} \\rangle - \\langle E_L \\rangle \\langle \\frac{1}{\\Psi}\\frac{\\partial \\Psi}{\\partial \\theta_i} \\rangle ),\n", "\\label{_auto34} \\tag{34}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "where the local energy is given by" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto35\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", "\tE_L = \\frac{1}{\\Psi} \\hat{\\mathbf{H}} \\Psi.\n", "\\label{_auto35} \\tag{35}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Mathematical details\n", "\n", "Because we are restricted to potential functions which are positive it\n", "is convenient to express them as exponentials, so that" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto36\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", "\t\\phi_C (\\boldsymbol{x}_C) = e^{-E_C(\\boldsymbol{x}_C)}\n", "\\label{_auto36} \\tag{36}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "where $E(\\boldsymbol{x}_C)$ is called an energy function, and the\n", "exponential representation is the Boltzmann distribution. The\n", "joint distribution is defined as the product of potentials.\n", "\n", "The joint distribution of the random variables is then" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "p(\\boldsymbol{x}) = \\frac{1}{Z} \\prod_C \\phi_C (\\boldsymbol{x}_C) \\nonumber\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "= \\frac{1}{Z} \\prod_C e^{-E_C(\\boldsymbol{x}_C)} \\nonumber\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "= \\frac{1}{Z} e^{-\\sum_C E_C(\\boldsymbol{x}_C)} \\nonumber\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "3\n", "9\n", " \n", "<\n", "<\n", "<\n", "!\n", "!\n", "M\n", "A\n", "T\n", "H\n", "_\n", "B\n", "L\n", "O\n", "C\n", "K" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto38\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", "\tp_{BM}(\\boldsymbol{x}, \\boldsymbol{h}) = \\frac{1}{Z_{BM}} e^{-\\frac{1}{T}E_{BM}(\\boldsymbol{x}, \\boldsymbol{h})} ,\n", "\\label{_auto38} \\tag{38}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "with the partition function" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto39\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", "\tZ_{BM} = \\int \\int e^{-\\frac{1}{T} E_{BM}(\\tilde{\\boldsymbol{x}}, \\tilde{\\boldsymbol{h}})} d\\tilde{\\boldsymbol{x}} d\\tilde{\\boldsymbol{h}} .\n", "\\label{_auto39} \\tag{39}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$T$ is a physics-inspired parameter named temperature and will be assumed to be 1 unless otherwise stated. The energy function of the Boltzmann machine determines the interactions between the nodes and is defined" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "E_{BM}(\\boldsymbol{x}, \\boldsymbol{h}) = - \\sum_{i, k}^{M, K} a_i^k \\alpha_i^k (x_i)\n", "\t- \\sum_{j, l}^{N, L} b_j^l \\beta_j^l (h_j) \n", "\t- \\sum_{i,j,k,l}^{M,N,K,L} \\alpha_i^k (x_i) w_{ij}^{kl} \\beta_j^l (h_j) \\nonumber\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto40\"></div>\n", "\n", "$$\n", "\\begin{equation} \n", "\t- \\sum_{i, m=i+1, k}^{M, M, K} \\alpha_i^k (x_i) v_{im}^k \\alpha_m^k (x_m)\n", "\t- \\sum_{j,n=j+1,l}^{N,N,L} \\beta_j^l (h_j) u_{jn}^l \\beta_n^l (h_n).\n", "\\label{_auto40} \\tag{40}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here $\\alpha_i^k (x_i)$ and $\\beta_j^l (h_j)$ are one-dimensional\n", "transfer functions or mappings from the given input value to the\n", "desired feature value. They can be arbitrary functions of the input\n", "variables and are independent of the parameterization (parameters\n", "referring to weight and biases), meaning they are not affected by\n", "training of the model. The indices $k$ and $l$ indicate that there can\n", "be multiple transfer functions per variable. Furthermore, $a_i^k$ and\n", "$b_j^l$ are the visible and hidden bias. $w_{ij}^{kl}$ are weights of\n", "the \\textbf{inter-layer} connection terms which connect visible and\n", "hidden units. $ v_{im}^k$ and $u_{jn}^l$ are weights of the\n", "\\textbf{intra-layer} connection terms which connect the visible units\n", "to each other and the hidden units to each other, respectively.\n", "\n", "\n", "\n", "We remove the intra-layer connections by setting $v_{im}$ and $u_{jn}$\n", "to zero. The expression for the energy of the RBM is then" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto41\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", "\tE_{RBM}(\\boldsymbol{x}, \\boldsymbol{h}) = - \\sum_{i, k}^{M, K} a_i^k \\alpha_i^k (x_i)\n", "\t- \\sum_{j, l}^{N, L} b_j^l \\beta_j^l (h_j) \n", "\t- \\sum_{i,j,k,l}^{M,N,K,L} \\alpha_i^k (x_i) w_{ij}^{kl} \\beta_j^l (h_j). \n", "\\label{_auto41} \\tag{41}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "resulting in" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "P_{RBM} (\\boldsymbol{x}) = \\int P_{RBM} (\\boldsymbol{x}, \\tilde{\\boldsymbol{h}}) d \\tilde{\\boldsymbol{h}} \\nonumber\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "= \\frac{1}{Z_{RBM}} \\int e^{-E_{RBM} (\\boldsymbol{x}, \\tilde{\\boldsymbol{h}}) } d\\tilde{\\boldsymbol{h}} \\nonumber\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "= \\frac{1}{Z_{RBM}} \\int e^{\\sum_{i, k} a_i^k \\alpha_i^k (x_i)\n", "\t+ \\sum_{j, l} b_j^l \\beta_j^l (\\tilde{h}_j) \n", "\t+ \\sum_{i,j,k,l} \\alpha_i^k (x_i) w_{ij}^{kl} \\beta_j^l (\\tilde{h}_j)} \n", "\td\\tilde{\\boldsymbol{h}} \\nonumber\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "= \\frac{1}{Z_{RBM}} e^{\\sum_{i, k} a_i^k \\alpha_i^k (x_i)}\n", "\t\\int \\prod_j^N e^{\\sum_l b_j^l \\beta_j^l (\\tilde{h}_j) \n", "\t+ \\sum_{i,k,l} \\alpha_i^k (x_i) w_{ij}^{kl} \\beta_j^l (\\tilde{h}_j)} d\\tilde{\\boldsymbol{h}} \\nonumber\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "= \\frac{1}{Z_{RBM}} e^{\\sum_{i, k} a_i^k \\alpha_i^k (x_i)}\n", "\t\\biggl( \\int e^{\\sum_l b_1^l \\beta_1^l (\\tilde{h}_1) + \\sum_{i,k,l} \\alpha_i^k (x_i) w_{i1}^{kl} \\beta_1^l (\\tilde{h}_1)} d \\tilde{h}_1 \\nonumber\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\times \\int e^{\\sum_l b_2^l \\beta_2^l (\\tilde{h}_2) + \\sum_{i,k,l} \\alpha_i^k (x_i) w_{i2}^{kl} \\beta_2^l (\\tilde{h}_2)} d \\tilde{h}_2 \\nonumber\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\times ... \\nonumber\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\times \\int e^{\\sum_l b_N^l \\beta_N^l (\\tilde{h}_N) + \\sum_{i,k,l} \\alpha_i^k (x_i) w_{iN}^{kl} \\beta_N^l (\\tilde{h}_N)} d \\tilde{h}_N \\biggr) \\nonumber\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto42\"></div>\n", "\n", "$$\n", "\\begin{equation} \n", "\t= \\frac{1}{Z_{RBM}} e^{\\sum_{i, k} a_i^k \\alpha_i^k (x_i)}\n", "\t\\prod_j^N \\int e^{\\sum_l b_j^l \\beta_j^l (\\tilde{h}_j) + \\sum_{i,k,l} \\alpha_i^k (x_i) w_{ij}^{kl} \\beta_j^l (\\tilde{h}_j)} d\\tilde{h}_j\n", "\\label{_auto42} \\tag{42}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Similarly" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "P_{RBM} (\\boldsymbol{h}) = \\frac{1}{Z_{RBM}} \\int e^{-E_{RBM} (\\tilde{\\boldsymbol{x}}, \\boldsymbol{h})} d\\tilde{\\boldsymbol{x}} \\nonumber\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto43\"></div>\n", "\n", "$$\n", "\\begin{equation} \n", "\t= \\frac{1}{Z_{RBM}} e^{\\sum_{j, l} b_j^l \\beta_j^l (h_j)}\n", "\t\\prod_i^M \\int e^{\\sum_k a_i^k \\alpha_i^k (\\tilde{x}_i)\n", "\t+ \\sum_{j,k,l} \\alpha_i^k (\\tilde{x}_i) w_{ij}^{kl} \\beta_j^l (h_j)} d\\tilde{x}_i\n", "\\label{_auto43} \\tag{43}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using Bayes theorem" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "P_{RBM} (\\boldsymbol{h}\|\\boldsymbol{x}) = \\frac{P_{RBM} (\\boldsymbol{x}, \\boldsymbol{h})}{P_{RBM} (\\boldsymbol{x})} \\nonumber\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "= \\frac{\\frac{1}{Z_{RBM}} e^{\\sum_{i, k} a_i^k \\alpha_i^k (x_i)\n", "\t+ \\sum_{j, l} b_j^l \\beta_j^l (h_j) \n", "\t+ \\sum_{i,j,k,l} \\alpha_i^k (x_i) w_{ij}^{kl} \\beta_j^l (h_j)}}\n", "\t{\\frac{1}{Z_{RBM}} e^{\\sum_{i, k} a_i^k \\alpha_i^k (x_i)}\n", "\t\\prod_j^N \\int e^{\\sum_l b_j^l \\beta_j^l (\\tilde{h}_j) + \\sum_{i,k,l} \\alpha_i^k (x_i) w_{ij}^{kl} \\beta_j^l (\\tilde{h}_j)} d\\tilde{h}_j} \\nonumber\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto44\"></div>\n", "\n", "$$\n", "\\begin{equation} \n", "\t= \\prod_j^N \\frac{e^{\\sum_l b_j^l \\beta_j^l (h_j) + \\sum_{i,k,l} \\alpha_i^k (x_i) w_{ij}^{kl} \\beta_j^l (h_j)} }\n", "\t{\\int e^{\\sum_l b_j^l \\beta_j^l (\\tilde{h}_j) + \\sum_{i,k,l} \\alpha_i^k (x_i) w_{ij}^{kl} \\beta_j^l (\\tilde{h}_j)} d\\tilde{h}_j}\n", "\\label{_auto44} \\tag{44}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Similarly" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "P_{RBM} (\\boldsymbol{x}\|\\boldsymbol{h}) = \\frac{P_{RBM} (\\boldsymbol{x}, \\boldsymbol{h})}{P_{RBM} (\\boldsymbol{h})} \\nonumber\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto45\"></div>\n", "\n", "$$\n", "\\begin{equation} \n", "\t= \\prod_i^M \\frac{e^{\\sum_k a_i^k \\alpha_i^k (x_i)\n", "\t+ \\sum_{j,k,l} \\alpha_i^k (x_i) w_{ij}^{kl} \\beta_j^l (h_j)}}\n", "\t{\\int e^{\\sum_k a_i^k \\alpha_i^k (\\tilde{x}_i)\n", "\t+ \\sum_{j,k,l} \\alpha_i^k (\\tilde{x}_i) w_{ij}^{kl} \\beta_j^l (h_j)} d\\tilde{x}_i}\n", "\\label{_auto45} \\tag{45}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The original RBM had binary visible and hidden nodes. They were\n", "showned to be universal approximators of discrete distributions.\n", "It was also shown that adding hidden units yields\n", "strictly improved modelling power. The common choice of binary values\n", "are 0 and 1. However, in some physics applications, -1 and 1 might be\n", "a more natural choice. We will here use 0 and 1." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto46\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", "\tE_{BB}(\\boldsymbol{x}, \\mathbf{h}) = - \\sum_i^M x_i a_i- \\sum_j^N b_j h_j - \\sum_{i,j}^{M,N} x_i w_{ij} h_j.\n", "\\label{_auto46} \\tag{46}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto47\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", "\tp_{BB}(\\boldsymbol{x}, \\boldsymbol{h}) = \\frac{1}{Z_{BB}} e^{\\sum_i^M a_i x_i + \\sum_j^N b_j h_j + \\sum_{ij}^{M,N} x_i w_{ij} h_j} \n", "\\label{_auto47} \\tag{47}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto48\"></div>\n", "\n", "$$\n", "\\begin{equation} \n", "\t= \\frac{1}{Z_{BB}} e^{\\boldsymbol{x}^T \\boldsymbol{a} + \\boldsymbol{b}^T \\boldsymbol{h} + \\boldsymbol{x}^T \\boldsymbol{W} \\boldsymbol{h}}\n", "\\label{_auto48} \\tag{48}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "with the partition function" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto49\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", "\tZ_{BB} = \\sum_{\\boldsymbol{x}, \\boldsymbol{h}} e^{\\boldsymbol{x}^T \\boldsymbol{a} + \\boldsymbol{b}^T \\boldsymbol{h} + \\boldsymbol{x}^T \\boldsymbol{W} \\boldsymbol{h}} .\n", "\\label{_auto49} \\tag{49}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Marginal Probability Density Functions\n", "\n", "In order to find the probability of any configuration of the visible units we derive the marginal probability density function." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto50\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", "\tp_{BB} (\\boldsymbol{x}) = \\sum_{\\boldsymbol{h}} p_{BB} (\\boldsymbol{x}, \\boldsymbol{h}) \n", "\\label{_auto50} \\tag{50}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "= \\frac{1}{Z_{BB}} \\sum_{\\boldsymbol{h}} e^{\\boldsymbol{x}^T \\boldsymbol{a} + \\boldsymbol{b}^T \\boldsymbol{h} + \\boldsymbol{x}^T \\boldsymbol{W} \\boldsymbol{h}} \\nonumber\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "= \\frac{1}{Z_{BB}} e^{\\boldsymbol{x}^T \\boldsymbol{a}} \\sum_{\\boldsymbol{h}} e^{\\sum_j^N (b_j + \\boldsymbol{x}^T \\boldsymbol{w}_{\\ast j})h_j} \\nonumber\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "= \\frac{1}{Z_{BB}} e^{\\boldsymbol{x}^T \\boldsymbol{a}} \\sum_{\\boldsymbol{h}} \\prod_j^N e^{ (b_j + \\boldsymbol{x}^T \\boldsymbol{w}_{\\ast j})h_j} \\nonumber\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "= \\frac{1}{Z_{BB}} e^{\\boldsymbol{x}^T \\boldsymbol{a}} \\bigg ( \\sum_{h_1} e^{(b_1 + \\boldsymbol{x}^T \\boldsymbol{w}_{\\ast 1})h_1}\n", "\t\\times \\sum_{h_2} e^{(b_2 + \\boldsymbol{x}^T \\boldsymbol{w}_{\\ast 2})h_2} \\times \\nonumber\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "... \\times \\sum_{h_2} e^{(b_N + \\boldsymbol{x}^T \\boldsymbol{w}_{\\ast N})h_N} \\bigg ) \\nonumber\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "= \\frac{1}{Z_{BB}} e^{\\boldsymbol{x}^T \\boldsymbol{a}} \\prod_j^N \\sum_{h_j} e^{(b_j + \\boldsymbol{x}^T \\boldsymbol{w}_{\\ast j}) h_j} \\nonumber\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto51\"></div>\n", "\n", "$$\n", "\\begin{equation} \n", "\t= \\frac{1}{Z_{BB}} e^{\\boldsymbol{x}^T \\boldsymbol{a}} \\prod_j^N (1 + e^{b_j + \\boldsymbol{x}^T \\boldsymbol{w}_{\\ast j}}) .\n", "\\label{_auto51} \\tag{51}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A similar derivation yields the marginal probability of the hidden units" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto52\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", "\tp_{BB} (\\boldsymbol{h}) = \\frac{1}{Z_{BB}} e^{\\boldsymbol{b}^T \\boldsymbol{h}} \\prod_i^M (1 + e^{a_i + \\boldsymbol{w}_{i\\ast}^T \\boldsymbol{h}}) .\n", "\\label{_auto52} \\tag{52}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Conditional Probability Density Functions\n", "\n", "We derive the probability of the hidden units given the visible units using Bayes' rule" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "p_{BB} (\\boldsymbol{h}\|\\boldsymbol{x}) = \\frac{p_{BB} (\\boldsymbol{x}, \\boldsymbol{h})}{p_{BB} (\\boldsymbol{x})} \\nonumber\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "= \\frac{ \\frac{1}{Z_{BB}} e^{\\boldsymbol{x}^T \\boldsymbol{a} + \\boldsymbol{b}^T \\boldsymbol{h} + \\boldsymbol{x}^T \\boldsymbol{W} \\boldsymbol{h}} }\n", "\t {\\frac{1}{Z_{BB}} e^{\\boldsymbol{x}^T \\boldsymbol{a}} \\prod_j^N (1 + e^{b_j + \\boldsymbol{x}^T \\boldsymbol{w}_{\\ast j}})} \\nonumber\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "= \\frac{ e^{\\boldsymbol{x}^T \\boldsymbol{a}} e^{ \\sum_j^N (b_j + \\boldsymbol{x}^T \\boldsymbol{w}_{\\ast j} ) h_j} }\n", "\t { e^{\\boldsymbol{x}^T \\boldsymbol{a}} \\prod_j^N (1 + e^{b_j + \\boldsymbol{x}^T \\boldsymbol{w}_{\\ast j}})} \\nonumber\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "= \\prod_j^N \\frac{ e^{(b_j + \\boldsymbol{x}^T \\boldsymbol{w}_{\\ast j} ) h_j} }\n", "\t{1 + e^{b_j + \\boldsymbol{x}^T \\boldsymbol{w}_{\\ast j}}} \\nonumber\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto53\"></div>\n", "\n", "$$\n", "\\begin{equation} \n", "\t= \\prod_j^N p_{BB} (h_j\| \\boldsymbol{x}) .\n", "\\label{_auto53} \\tag{53}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From this we find the probability of a hidden unit being \"on\" or \"off\":" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto54\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", "\tp_{BB} (h_j=1 \| \\boldsymbol{x}) = \\frac{ e^{(b_j + \\boldsymbol{x}^T \\boldsymbol{w}_{\\ast j} ) h_j} }\n", "\t{1 + e^{b_j + \\boldsymbol{x}^T \\boldsymbol{w}_{\\ast j}}} \n", "\\label{_auto54} \\tag{54}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto55\"></div>\n", "\n", "$$\n", "\\begin{equation} \n", "\t= \\frac{ e^{(b_j + \\boldsymbol{x}^T \\boldsymbol{w}_{\\ast j} )} }\n", "\t{1 + e^{b_j + \\boldsymbol{x}^T \\boldsymbol{w}_{\\ast j}}} \n", "\\label{_auto55} \\tag{55}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto56\"></div>\n", "\n", "$$\n", "\\begin{equation} \n", "\t= \\frac{ 1 }{1 + e^{-(b_j + \\boldsymbol{x}^T \\boldsymbol{w}_{\\ast j})} } ,\n", "\\label{_auto56} \\tag{56}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto57\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", "\tp_{BB} (h_j=0 \| \\boldsymbol{x}) =\\frac{ 1 }{1 + e^{b_j + \\boldsymbol{x}^T \\boldsymbol{w}_{\\ast j}} } .\n", "\\label{_auto57} \\tag{57}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Similarly we have that the conditional probability of the visible units given the hidden are" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto58\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", "\tp_{BB} (\\boldsymbol{x}\|\\boldsymbol{h}) = \\prod_i^M \\frac{ e^{ (a_i + \\boldsymbol{w}_{i\\ast}^T \\boldsymbol{h}) x_i} }{ 1 + e^{a_i + \\boldsymbol{w}_{i\\ast}^T \\boldsymbol{h}} } \n", "\\label{_auto58} \\tag{58}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto59\"></div>\n", "\n", "$$\n", "\\begin{equation} \n", "\t= \\prod_i^M p_{BB} (x_i \| \\boldsymbol{h}) .\n", "\\label{_auto59} \\tag{59}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto60\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", "\tp_{BB} (x_i=1 \| \\boldsymbol{h}) = \\frac{1}{1 + e^{-(a_i + \\boldsymbol{w}_{i\\ast}^T \\boldsymbol{h} )}} \n", "\\label{_auto60} \\tag{60}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto61\"></div>\n", "\n", "$$\n", "\\begin{equation} \n", "\tp_{BB} (x_i=0 \| \\boldsymbol{h}) = \\frac{1}{1 + e^{a_i + \\boldsymbol{w}_{i\\ast}^T \\boldsymbol{h} }} .\n", "\\label{_auto61} \\tag{61}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Gaussian-Binary Restricted Boltzmann Machines\n", "\n", "Inserting into the expression for $E_{RBM}(\\boldsymbol{x},\\boldsymbol{h})$ in equation results in the energy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "E_{GB}(\\boldsymbol{x}, \\boldsymbol{h}) = \\sum_i^M \\frac{(x_i - a_i)^2}{2\\sigma_i^2}\n", "\t- \\sum_j^N b_j h_j \n", "\t-\\sum_{ij}^{M,N} \\frac{x_i w_{ij} h_j}{\\sigma_i^2} \\nonumber\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto62\"></div>\n", "\n", "$$\n", "\\begin{equation} \n", "\t= \\vert\\vert\\frac{\\boldsymbol{x} -\\boldsymbol{a}}{2\\boldsymbol{\\sigma}}\\vert\\vert^2 - \\boldsymbol{b}^T \\boldsymbol{h} \n", "\t- (\\frac{\\boldsymbol{x}}{\\boldsymbol{\\sigma}^2})^T \\boldsymbol{W}\\boldsymbol{h} . \n", "\\label{_auto62} \\tag{62}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Joint Probability Density Function" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "p_{GB} (\\boldsymbol{x}, \\boldsymbol{h}) = \\frac{1}{Z_{GB}} e^{-\\vert\\vert\\frac{\\boldsymbol{x} -\\boldsymbol{a}}{2\\boldsymbol{\\sigma}}\\vert\\vert^2 + \\boldsymbol{b}^T \\boldsymbol{h} \n", "\t+ (\\frac{\\boldsymbol{x}}{\\boldsymbol{\\sigma}^2})^T \\boldsymbol{W}\\boldsymbol{h}} \\nonumber\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "= \\frac{1}{Z_{GB}} e^{- \\sum_i^M \\frac{(x_i - a_i)^2}{2\\sigma_i^2}\n", "\t+ \\sum_j^N b_j h_j \n", "\t+\\sum_{ij}^{M,N} \\frac{x_i w_{ij} h_j}{\\sigma_i^2}} \\nonumber\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto63\"></div>\n", "\n", "$$\n", "\\begin{equation} \n", "\t= \\frac{1}{Z_{GB}} \\prod_{ij}^{M,N} e^{-\\frac{(x_i - a_i)^2}{2\\sigma_i^2}\n", "\t+ b_j h_j \n", "\t+\\frac{x_i w_{ij} h_j}{\\sigma_i^2}} ,\n", "\\label{_auto63} \\tag{63}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "with the partition function given by" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto64\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", "\tZ_{GB} = \\int \\sum_{\\tilde{\\boldsymbol{h}}}^{\\tilde{\\boldsymbol{H}}} e^{-\\vert\\vert\\frac{\\tilde{\\boldsymbol{x}} -\\boldsymbol{a}}{2\\boldsymbol{\\sigma}}\\vert\\vert^2 + \\boldsymbol{b}^T \\tilde{\\boldsymbol{h}} \n", "\t+ (\\frac{\\tilde{\\boldsymbol{x}}}{\\boldsymbol{\\sigma}^2})^T \\boldsymbol{W}\\tilde{\\boldsymbol{h}}} d\\tilde{\\boldsymbol{x}} .\n", "\\label{_auto64} \\tag{64}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Marginal Probability Density Functions\n", "\n", "We proceed to find the marginal probability densitites of the\n", "Gaussian-binary RBM. We first marginalize over the binary hidden units\n", "to find $p_{GB} (\\boldsymbol{x})$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "p_{GB} (\\boldsymbol{x}) = \\sum_{\\tilde{\\boldsymbol{h}}}^{\\tilde{\\boldsymbol{H}}} p_{GB} (\\boldsymbol{x}, \\tilde{\\boldsymbol{h}}) \\nonumber\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "= \\frac{1}{Z_{GB}} \\sum_{\\tilde{\\boldsymbol{h}}}^{\\tilde{\\boldsymbol{H}}} \n", "\te^{-\\vert\\vert\\frac{\\boldsymbol{x} -\\boldsymbol{a}}{2\\boldsymbol{\\sigma}}\\vert\\vert^2 + \\boldsymbol{b}^T \\tilde{\\boldsymbol{h}} \n", "\t+ (\\frac{\\boldsymbol{x}}{\\boldsymbol{\\sigma}^2})^T \\boldsymbol{W}\\tilde{\\boldsymbol{h}}} \\nonumber\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto65\"></div>\n", "\n", "$$\n", "\\begin{equation} \n", "\t= \\frac{1}{Z_{GB}} e^{-\\vert\\vert\\frac{\\boldsymbol{x} -\\boldsymbol{a}}{2\\boldsymbol{\\sigma}}\\vert\\vert^2}\n", "\t\\prod_j^N (1 + e^{b_j + (\\frac{\\boldsymbol{x}}{\\boldsymbol{\\sigma}^2})^T \\boldsymbol{w}_{\\ast j}} ) .\n", "\\label{_auto65} \\tag{65}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We next marginalize over the visible units. This is the first time we\n", "marginalize over continuous values. We rewrite the exponential factor\n", "dependent on $\\boldsymbol{x}$ as a Gaussian function before we integrate in\n", "the last step." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "p_{GB} (\\boldsymbol{h}) = \\int p_{GB} (\\tilde{\\boldsymbol{x}}, \\boldsymbol{h}) d\\tilde{\\boldsymbol{x}} \\nonumber\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "= \\frac{1}{Z_{GB}} \\int e^{-\\vert\\vert\\frac{\\tilde{\\boldsymbol{x}} -\\boldsymbol{a}}{2\\boldsymbol{\\sigma}}\\vert\\vert^2 + \\boldsymbol{b}^T \\boldsymbol{h} \n", "\t+ (\\frac{\\tilde{\\boldsymbol{x}}}{\\boldsymbol{\\sigma}^2})^T \\boldsymbol{W}\\boldsymbol{h}} d\\tilde{\\boldsymbol{x}} \\nonumber\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "= \\frac{1}{Z_{GB}} e^{\\boldsymbol{b}^T \\boldsymbol{h} } \\int \\prod_i^M\n", "\te^{- \\frac{(\\tilde{x}_i - a_i)^2}{2\\sigma_i^2} + \\frac{\\tilde{x}_i \\boldsymbol{w}_{i\\ast}^T \\boldsymbol{h}}{\\sigma_i^2} } d\\tilde{\\boldsymbol{x}} \\nonumber\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "= \\frac{1}{Z_{GB}} e^{\\boldsymbol{b}^T \\boldsymbol{h} }\n", "\t\\biggl( \\int e^{- \\frac{(\\tilde{x}_1 - a_1)^2}{2\\sigma_1^2} + \\frac{\\tilde{x}_1 \\boldsymbol{w}_{1\\ast}^T \\boldsymbol{h}}{\\sigma_1^2} } d\\tilde{x}_1 \\nonumber\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\times \\int e^{- \\frac{(\\tilde{x}_2 - a_2)^2}{2\\sigma_2^2} + \\frac{\\tilde{x}_2 \\boldsymbol{w}_{2\\ast}^T \\boldsymbol{h}}{\\sigma_2^2} } d\\tilde{x}_2 \\nonumber\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\times ... \\nonumber\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\times \\int e^{- \\frac{(\\tilde{x}_M - a_M)^2}{2\\sigma_M^2} + \\frac{\\tilde{x}_M \\boldsymbol{w}_{M\\ast}^T \\boldsymbol{h}}{\\sigma_M^2} } d\\tilde{x}_M \\biggr) \\nonumber\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "= \\frac{1}{Z_{GB}} e^{\\boldsymbol{b}^T \\boldsymbol{h}} \\prod_i^M\n", "\t\\int e^{- \\frac{(\\tilde{x}_i - a_i)^2 - 2\\tilde{x}_i \\boldsymbol{w}_{i\\ast}^T \\boldsymbol{h}}{2\\sigma_i^2} } d\\tilde{x}_i \\nonumber\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "= \\frac{1}{Z_{GB}} e^{\\boldsymbol{b}^T \\boldsymbol{h}} \\prod_i^M\n", "\t\\int e^{- \\frac{\\tilde{x}_i^2 - 2\\tilde{x}_i(a_i + \\tilde{x}_i \\boldsymbol{w}_{i\\ast}^T \\boldsymbol{h}) + a_i^2}{2\\sigma_i^2} } d\\tilde{x}_i \\nonumber\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "= \\frac{1}{Z_{GB}} e^{\\boldsymbol{b}^T \\boldsymbol{h}} \\prod_i^M\n", "\t\\int e^{- \\frac{\\tilde{x}_i^2 - 2\\tilde{x}_i(a_i + \\boldsymbol{w}_{i\\ast}^T \\boldsymbol{h}) + (a_i + \\boldsymbol{w}_{i\\ast}^T \\boldsymbol{h})^2 - (a_i + \\boldsymbol{w}_{i\\ast}^T \\boldsymbol{h})^2 + a_i^2}{2\\sigma_i^2} } d\\tilde{x}_i \\nonumber\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "= \\frac{1}{Z_{GB}} e^{\\boldsymbol{b}^T \\boldsymbol{h}} \\prod_i^M\n", "\t\\int e^{- \\frac{(\\tilde{x}_i - (a_i + \\boldsymbol{w}_{i\\ast}^T \\boldsymbol{h}))^2 - a_i^2 -2a_i \\boldsymbol{w}_{i\\ast}^T \\boldsymbol{h} - (\\boldsymbol{w}_{i\\ast}^T \\boldsymbol{h})^2 + a_i^2}{2\\sigma_i^2} } d\\tilde{x}_i \\nonumber\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "= \\frac{1}{Z_{GB}} e^{\\boldsymbol{b}^T \\boldsymbol{h}} \\prod_i^M\n", "\te^{\\frac{2a_i \\boldsymbol{w}_{i\\ast}^T \\boldsymbol{h} +(\\boldsymbol{w}_{i\\ast}^T \\boldsymbol{h})^2 }{2\\sigma_i^2}}\n", "\t\\int e^{- \\frac{(\\tilde{x}_i - a_i - \\boldsymbol{w}_{i\\ast}^T \\boldsymbol{h})^2}{2\\sigma_i^2}}\n", "\td\\tilde{x}_i \\nonumber\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto66\"></div>\n", "\n", "$$\n", "\\begin{equation} \n", "\t= \\frac{1}{Z_{GB}} e^{\\boldsymbol{b}^T \\boldsymbol{h}} \\prod_i^M\n", "\t\\sqrt{2\\pi \\sigma_i^2}\n", "\te^{\\frac{2a_i \\boldsymbol{w}_{i\\ast}^T \\boldsymbol{h} +(\\boldsymbol{w}_{i\\ast}^T \\boldsymbol{h})^2 }{2\\sigma_i^2}} .\n", "\\label{_auto66} \\tag{66}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Conditional Probability Density Functions\n", "\n", "We finish by deriving the conditional probabilities." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "p_{GB} (\\boldsymbol{h}\| \\boldsymbol{x}) = \\frac{p_{GB} (\\boldsymbol{x}, \\boldsymbol{h})}{p_{GB} (\\boldsymbol{x})} \\nonumber\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "= \\frac{\\frac{1}{Z_{GB}} e^{-\\vert\\vert\\frac{\\boldsymbol{x} -\\boldsymbol{a}}{2\\boldsymbol{\\sigma}}\\vert\\vert^2 + \\boldsymbol{b}^T \\boldsymbol{h} \n", "\t+ (\\frac{\\boldsymbol{x}}{\\boldsymbol{\\sigma}^2})^T \\boldsymbol{W}\\boldsymbol{h}}}\n", "\t{\\frac{1}{Z_{GB}} e^{-\\vert\\vert\\frac{\\boldsymbol{x} -\\boldsymbol{a}}{2\\boldsymbol{\\sigma}}\\vert\\vert^2}\n", "\t\\prod_j^N (1 + e^{b_j + (\\frac{\\boldsymbol{x}}{\\boldsymbol{\\sigma}^2})^T \\boldsymbol{w}_{\\ast j}} ) }\n", "\t\\nonumber\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "= \\prod_j^N \\frac{e^{(b_j + (\\frac{\\boldsymbol{x}}{\\boldsymbol{\\sigma}^2})^T \\boldsymbol{w}_{\\ast j})h_j } }\n", "\t{1 + e^{b_j + (\\frac{\\boldsymbol{x}}{\\boldsymbol{\\sigma}^2})^T \\boldsymbol{w}_{\\ast j}}} \\nonumber\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto67\"></div>\n", "\n", "$$\n", "\\begin{equation} \n", "\t= \\prod_j^N p_{GB} (h_j\|\\boldsymbol{x}).\n", "\\label{_auto67} \\tag{67}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The conditional probability of a binary hidden unit $h_j$ being on or off again takes the form of a sigmoid function" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "p_{GB} (h_j =1 \| \\boldsymbol{x}) = \\frac{e^{b_j + (\\frac{\\boldsymbol{x}}{\\boldsymbol{\\sigma}^2})^T \\boldsymbol{w}_{\\ast j} } }\n", "\t{1 + e^{b_j + (\\frac{\\boldsymbol{x}}{\\boldsymbol{\\sigma}^2})^T \\boldsymbol{w}_{\\ast j}}} \\nonumber\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto68\"></div>\n", "\n", "$$\n", "\\begin{equation} \n", "\t= \\frac{1}{1 + e^{-b_j - (\\frac{\\boldsymbol{x}}{\\boldsymbol{\\sigma}^2})^T \\boldsymbol{w}_{\\ast j}}} \n", "\\label{_auto68} \\tag{68}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto69\"></div>\n", "\n", "$$\n", "\\begin{equation} \n", "\tp_{GB} (h_j =0 \| \\boldsymbol{x}) =\n", "\t\\frac{1}{1 + e^{b_j +(\\frac{\\boldsymbol{x}}{\\boldsymbol{\\sigma}^2})^T \\boldsymbol{w}_{\\ast j}}} .\n", "\\label{_auto69} \\tag{69}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The conditional probability of the continuous $\\boldsymbol{x}$ now has another form, however." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "p_{GB} (\\boldsymbol{x}\|\\boldsymbol{h})\n", "\t= \\frac{p_{GB} (\\boldsymbol{x}, \\boldsymbol{h})}{p_{GB} (\\boldsymbol{h})} \\nonumber\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "= \\frac{\\frac{1}{Z_{GB}} e^{-\\vert\\vert\\frac{\\boldsymbol{x} -\\boldsymbol{a}}{2\\boldsymbol{\\sigma}}\\vert\\vert^2 + \\boldsymbol{b}^T \\boldsymbol{h} \n", "\t+ (\\frac{\\boldsymbol{x}}{\\boldsymbol{\\sigma}^2})^T \\boldsymbol{W}\\boldsymbol{h}}}\n", "\t{\\frac{1}{Z_{GB}} e^{\\boldsymbol{b}^T \\boldsymbol{h}} \\prod_i^M\n", "\t\\sqrt{2\\pi \\sigma_i^2}\n", "\te^{\\frac{2a_i \\boldsymbol{w}_{i\\ast}^T \\boldsymbol{h} +(\\boldsymbol{w}_{i\\ast}^T \\boldsymbol{h})^2 }{2\\sigma_i^2}}}\n", "\t\\nonumber\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "= \\prod_i^M \\frac{1}{\\sqrt{2\\pi \\sigma_i^2}}\n", "\t\\frac{e^{- \\frac{(x_i - a_i)^2}{2\\sigma_i^2} + \\frac{x_i \\boldsymbol{w}_{i\\ast}^T \\boldsymbol{h}}{2\\sigma_i^2} }}\n", "\t{e^{\\frac{2a_i \\boldsymbol{w}_{i\\ast}^T \\boldsymbol{h} +(\\boldsymbol{w}_{i\\ast}^T \\boldsymbol{h})^2 }{2\\sigma_i^2}}}\n", "\t\\nonumber\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "= \\prod_i^M \\frac{1}{\\sqrt{2\\pi \\sigma_i^2}}\n", "\t\\frac{e^{-\\frac{x_i^2 - 2a_i x_i + a_i^2 - 2x_i \\boldsymbol{w}_{i\\ast}^T\\boldsymbol{h} }{2\\sigma_i^2} } }\n", "\t{e^{\\frac{2a_i \\boldsymbol{w}_{i\\ast}^T \\boldsymbol{h} +(\\boldsymbol{w}_{i\\ast}^T \\boldsymbol{h})^2 }{2\\sigma_i^2}}}\n", "\t\\nonumber\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "= \\prod_i^M \\frac{1}{\\sqrt{2\\pi \\sigma_i^2}}\n", "\te^{- \\frac{x_i^2 - 2a_i x_i + a_i^2 - 2x_i \\boldsymbol{w}_{i\\ast}^T\\boldsymbol{h}\n", "\t+ 2a_i \\boldsymbol{w}_{i\\ast}^T \\boldsymbol{h} +(\\boldsymbol{w}_{i\\ast}^T \\boldsymbol{h})^2}\n", "\t{2\\sigma_i^2} }\n", "\t\\nonumber\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "= \\prod_i^M \\frac{1}{\\sqrt{2\\pi \\sigma_i^2}}\n", "\te^{ - \\frac{(x_i - b_i - \\boldsymbol{w}_{i\\ast}^T \\boldsymbol{h})^2}{2\\sigma_i^2}} \\nonumber\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto70\"></div>\n", "\n", "$$\n", "\\begin{equation} \n", "\t= \\prod_i^M \\mathcal{N}\n", "\t(x_i \| b_i + \\boldsymbol{w}_{i\\ast}^T \\boldsymbol{h}, \\sigma_i^2) \n", "\\label{_auto70} \\tag{70}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto71\"></div>\n", "\n", "$$\n", "\\begin{equation} \n", "\t\\Rightarrow p_{GB} (x_i\|\\boldsymbol{h}) = \\mathcal{N}\n", "\t(x_i \| b_i + \\boldsymbol{w}_{i\\ast}^T \\boldsymbol{h}, \\sigma_i^2) .\n", "\\label{_auto71} \\tag{71}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The form of these conditional probabilities explains the name\n", "\"Gaussian\" and the form of the Gaussian-binary energy function. We see\n", "that the conditional probability of $x_i$ given $\\boldsymbol{h}$ is a normal\n", "distribution with mean $b_i + \\boldsymbol{w}_{i\\ast}^T \\boldsymbol{h}$ and variance\n", "$\\sigma_i^2$.\n", "\n", "\n", "## Neural Quantum States\n", "\n", "\n", "The wavefunction should be a probability amplitude depending on $\\boldsymbol{x}$. The RBM model is given by the joint distribution of $\\boldsymbol{x}$ and $\\boldsymbol{h}$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto72\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", "\tF_{rbm}(\\boldsymbol{x},\\mathbf{h}) = \\frac{1}{Z} e^{-\\frac{1}{T_0}E(\\boldsymbol{x},\\mathbf{h})}\n", "\\label{_auto72} \\tag{72}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To find the marginal distribution of $\\boldsymbol{x}$ we set:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto73\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", "\tF_{rbm}(\\mathbf{x}) = \\sum_\\mathbf{h} F_{rbm}(\\mathbf{x}, \\mathbf{h}) \n", "\\label{_auto73} \\tag{73}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto74\"></div>\n", "\n", "$$\n", "\\begin{equation} \n", "\t\t\t\t= \\frac{1}{Z}\\sum_\\mathbf{h} e^{-E(\\mathbf{x}, \\mathbf{h})}\n", "\\label{_auto74} \\tag{74}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now this is what we use to represent the wave function, calling it a neural-network quantum state (NQS)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto75\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", "\t\\Psi (\\mathbf{X}) = F_{rbm}(\\mathbf{x}) \n", "\\label{_auto75} \\tag{75}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto76\"></div>\n", "\n", "$$\n", "\\begin{equation} \n", "\t= \\frac{1}{Z}\\sum_{\\boldsymbol{h}} e^{-E(\\mathbf{x}, \\mathbf{h})} \n", "\\label{_auto76} \\tag{76}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto77\"></div>\n", "\n", "$$\n", "\\begin{equation} \n", "\t= \\frac{1}{Z} \\sum_{\\{h_j\\}} e^{-\\sum_i^M \\frac{(x_i - a_i)^2}{2\\sigma^2} + \\sum_j^N b_j h_j + \\sum_{i,j}^{M,N} \\frac{x_i w_{ij} h_j}{\\sigma^2}} \n", "\\label{_auto77} \\tag{77}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto78\"></div>\n", "\n", "$$\n", "\\begin{equation} \n", "\t= \\frac{1}{Z} e^{-\\sum_i^M \\frac{(x_i - a_i)^2}{2\\sigma^2}} \\prod_j^N (1 + e^{b_j + \\sum_i^M \\frac{x_i w_{ij}}{\\sigma^2}}) \n", "\\label{_auto78} \\tag{78}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto79\"></div>\n", "\n", "$$\n", "\\begin{equation} \n", "\\label{_auto79} \\tag{79}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The above wavefunction is the most general one because it allows for\n", "complex valued wavefunctions. However it fundamentally changes the\n", "probabilistic foundation of the RBM, because what is usually a\n", "probability in the RBM framework is now a an amplitude. This means\n", "that a lot of the theoretical framework usually used to interpret the\n", "model, i.e. graphical models, conditional probabilities, and Markov\n", "random fields, breaks down. If we assume the wavefunction to be\n", "postive definite, however, we can use the RBM to represent the squared\n", "wavefunction, and thereby a probability. This also makes it possible\n", "to sample from the model using Gibbs sampling, because we can obtain\n", "the conditional probabilities." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto80\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", "\t\|\\Psi (\\mathbf{X})\|^2 = F_{rbm}(\\mathbf{X}) \n", "\\label{_auto80} \\tag{80}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto81\"></div>\n", "\n", "$$\n", "\\begin{equation} \n", "\t\\Rightarrow \\Psi (\\mathbf{X}) = \\sqrt{F_{rbm}(\\mathbf{X})} \n", "\\label{_auto81} \\tag{81}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto82\"></div>\n", "\n", "$$\n", "\\begin{equation} \n", "\t= \\frac{1}{\\sqrt{Z}}\\sqrt{\\sum_{\\{h_j\\}} e^{-E(\\mathbf{X}, \\mathbf{h})}} \n", "\\label{_auto82} \\tag{82}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto83\"></div>\n", "\n", "$$\n", "\\begin{equation} \n", "\t= \\frac{1}{\\sqrt{Z}} \\sqrt{\\sum_{\\{h_j\\}} e^{-\\sum_i^M \\frac{(X_i - a_i)^2}{2\\sigma^2} + \\sum_j^N b_j h_j + \\sum_{i,j}^{M,N} \\frac{X_i w_{ij} h_j}{\\sigma^2}} }\n", "\\label{_auto83} \\tag{83}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto84\"></div>\n", "\n", "$$\n", "\\begin{equation} \n", "\t= \\frac{1}{\\sqrt{Z}} e^{-\\sum_i^M \\frac{(X_i - a_i)^2}{4\\sigma^2}} \\sqrt{\\sum_{\\{h_j\\}} \\prod_j^N e^{b_j h_j + \\sum_i^M \\frac{X_i w_{ij} h_j}{\\sigma^2}}} \n", "\\label{_auto84} \\tag{84}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto85\"></div>\n", "\n", "$$\n", "\\begin{equation} \n", "\t= \\frac{1}{\\sqrt{Z}} e^{-\\sum_i^M \\frac{(X_i - a_i)^2}{4\\sigma^2}} \\sqrt{\\prod_j^N \\sum_{h_j} e^{b_j h_j + \\sum_i^M \\frac{X_i w_{ij} h_j}{\\sigma^2}}} \n", "\\label{_auto85} \\tag{85}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto86\"></div>\n", "\n", "$$\n", "\\begin{equation} \n", "\t= \\frac{1}{\\sqrt{Z}} e^{-\\sum_i^M \\frac{(X_i - a_i)^2}{4\\sigma^2}} \\prod_j^N \\sqrt{e^0 + e^{b_j + \\sum_i^M \\frac{X_i w_{ij}}{\\sigma^2}}} \n", "\\label{_auto86} \\tag{86}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto87\"></div>\n", "\n", "$$\n", "\\begin{equation} \n", "\t= \\frac{1}{\\sqrt{Z}} e^{-\\sum_i^M \\frac{(X_i - a_i)^2}{4\\sigma^2}} \\prod_j^N \\sqrt{1 + e^{b_j + \\sum_i^M \\frac{X_i w_{ij}}{\\sigma^2}}} \n", "\\label{_auto87} \\tag{87}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto88\"></div>\n", "\n", "$$\n", "\\begin{equation} \n", "\\label{_auto88} \\tag{88}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Cost function\n", "\n", "This is where we deviate from what is common in machine\n", "learning. Rather than defining a cost function based on some dataset,\n", "our cost function is the energy of the quantum mechanical system. From\n", "the variational principle we know that minizing this energy should\n", "lead to the ground state wavefunction. As stated previously the local\n", "energy is given by" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto89\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", "\tE_L = \\frac{1}{\\Psi} \\hat{\\mathbf{H}} \\Psi,\n", "\\label{_auto89} \\tag{89}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and the gradient is" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto90\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", "\tG_i = \\frac{\\partial \\langle E_L \\rangle}{\\partial \\alpha_i}\n", "\t= 2(\\langle E_L \\frac{1}{\\Psi}\\frac{\\partial \\Psi}{\\partial \\alpha_i} \\rangle - \\langle E_L \\rangle \\langle \\frac{1}{\\Psi}\\frac{\\partial \\Psi}{\\partial \\alpha_i} \\rangle ),\n", "\\label{_auto90} \\tag{90}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "where $\\alpha_i = a_1,...,a_M,b_1,...,b_N,w_{11},...,w_{MN}$.\n", "\n", "\n", "We use that $\\frac{1}{\\Psi}\\frac{\\partial \\Psi}{\\partial \\alpha_i} \n", "\t= \\frac{\\partial \\ln{\\Psi}}{\\partial \\alpha_i}$,\n", "and find" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto91\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", "\t\\ln{\\Psi({\\mathbf{X}})} = -\\ln{Z} - \\sum_m^M \\frac{(X_m - a_m)^2}{2\\sigma^2}\n", "\t+ \\sum_n^N \\ln({1 + e^{b_n + \\sum_i^M \\frac{X_i w_{in}}{\\sigma^2}})}.\n", "\\label{_auto91} \\tag{91}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This gives" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto92\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", "\t\\frac{\\partial }{\\partial a_m} \\ln\\Psi\n", "\t= \t\\frac{1}{\\sigma^2} (X_m - a_m) \n", "\\label{_auto92} \\tag{92}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto93\"></div>\n", "\n", "$$\n", "\\begin{equation} \n", "\t\\frac{\\partial }{\\partial b_n} \\ln\\Psi\n", "\t=\n", "\t\\frac{1}{e^{-b_n-\\frac{1}{\\sigma^2}\\sum_i^M X_i w_{in}} + 1} \n", "\\label{_auto93} \\tag{93}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto94\"></div>\n", "\n", "$$\n", "\\begin{equation} \n", "\t\\frac{\\partial }{\\partial w_{mn}} \\ln\\Psi\n", "\t= \\frac{X_m}{\\sigma^2(e^{-b_n-\\frac{1}{\\sigma^2}\\sum_i^M X_i w_{in}} + 1)}.\n", "\\label{_auto94} \\tag{94}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If $\\Psi = \\sqrt{F_{rbm}}$ we have" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto95\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", "\t\\ln{\\Psi({\\mathbf{X}})} = -\\frac{1}{2}\\ln{Z} - \\sum_m^M \\frac{(X_m - a_m)^2}{4\\sigma^2}\n", "\t+ \\frac{1}{2}\\sum_n^N \\ln({1 + e^{b_n + \\sum_i^M \\frac{X_i w_{in}}{\\sigma^2}})},\n", "\\label{_auto95} \\tag{95}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "which results in" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto96\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", "\t\\frac{\\partial }{\\partial a_m} \\ln\\Psi\n", "\t= \t\\frac{1}{2\\sigma^2} (X_m - a_m) \n", "\\label{_auto96} \\tag{96}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto97\"></div>\n", "\n", "$$\n", "\\begin{equation} \n", "\t\\frac{\\partial }{\\partial b_n} \\ln\\Psi\n", "\t=\n", "\t\\frac{1}{2(e^{-b_n-\\frac{1}{\\sigma^2}\\sum_i^M X_i w_{in}} + 1)} \n", "\\label{_auto97} \\tag{97}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto98\"></div>\n", "\n", "$$\n", "\\begin{equation} \n", "\t\\frac{\\partial }{\\partial w_{mn}} \\ln\\Psi\n", "\t= \\frac{X_m}{2\\sigma^2(e^{-b_n-\\frac{1}{\\sigma^2}\\sum_i^M X_i w_{in}} + 1)}.\n", "\\label{_auto98} \\tag{98}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us assume again that our Hamiltonian is" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto99\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", "\t\\hat{\\mathbf{H}} = \\sum_p^P (-\\frac{1}{2}\\nabla_p^2 + \\frac{1}{2}\\omega^2 r_p^2 ) + \\sum_{p<q} \\frac{1}{r_{pq}},\n", "\\label{_auto99} \\tag{99}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "where the first summation term represents the standard harmonic\n", "oscillator part and the latter the repulsive interaction between two\n", "electrons. Natural units ($\\hbar=c=e=m_e=1$) are used, and $P$ is the\n", "number of particles. This gives us the following expression for the\n", "local energy ($D$ being the number of dimensions)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto100\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", "\tE_L = \\frac{1}{\\Psi} \\mathbf{H} \\Psi \n", "\\label{_auto100} \\tag{100}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto101\"></div>\n", "\n", "$$\n", "\\begin{equation} \n", "\t= \\frac{1}{\\Psi} (\\sum_p^P (-\\frac{1}{2}\\nabla_p^2 + \\frac{1}{2}\\omega^2 r_p^2 ) + \\sum_{p<q} \\frac{1}{r_{pq}}) \\Psi \n", "\\label{_auto101} \\tag{101}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto102\"></div>\n", "\n", "$$\n", "\\begin{equation} \n", "\t= -\\frac{1}{2}\\frac{1}{\\Psi} \\sum_p^P \\nabla_p^2 \\Psi \n", "\t+ \\frac{1}{2}\\omega^2 \\sum_p^P r_p^2 + \\sum_{p<q} \\frac{1}{r_{pq}} \n", "\\label{_auto102} \\tag{102}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto103\"></div>\n", "\n", "$$\n", "\\begin{equation} \n", "\t= -\\frac{1}{2}\\frac{1}{\\Psi} \\sum_p^P \\sum_d^D \\frac{\\partial^2 \\Psi}{\\partial x_{pd}^2} + \\frac{1}{2}\\omega^2 \\sum_p^P r_p^2 + \\sum_{p<q} \\frac{1}{r_{pq}} \n", "\\label{_auto103} \\tag{103}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto104\"></div>\n", "\n", "$$\n", "\\begin{equation} \n", "\t= \\frac{1}{2} \\sum_p^P \\sum_d^D (-(\\frac{\\partial}{\\partial x_{pd}} \\ln\\Psi)^2 -\\frac{\\partial^2}{\\partial x_{pd}^2} \\ln\\Psi + \\omega^2 x_{pd}^2) + \\sum_{p<q} \\frac{1}{r_{pq}}. \n", "\\label{_auto104} \\tag{104}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto105\"></div>\n", "\n", "$$\n", "\\begin{equation} \n", "\\label{_auto105} \\tag{105}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Letting each visible node in the Boltzmann machine \n", "represent one coordinate of one particle, we obtain" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto106\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", "\tE_L =\n", "\t\\frac{1}{2} \\sum_m^M (-(\\frac{\\partial}{\\partial v_m} \\ln\\Psi)^2 -\\frac{\\partial^2}{\\partial v_m^2} \\ln\\Psi + \\omega^2 v_m^2) + \\sum_{p<q} \\frac{1}{r_{pq}},\n", "\\label{_auto106} \\tag{106}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "where we have that" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto107\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", "\t\\frac{\\partial}{\\partial x_m} \\ln\\Psi\n", "\t= - \\frac{1}{\\sigma^2}(x_m - a_m) + \\frac{1}{\\sigma^2} \\sum_n^N \\frac{w_{mn}}{e^{-b_n - \\frac{1}{\\sigma^2}\\sum_i^M x_i w_{in}} + 1} \n", "\\label{_auto107} \\tag{107}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto108\"></div>\n", "\n", "$$\n", "\\begin{equation} \n", "\t\\frac{\\partial^2}{\\partial x_m^2} \\ln\\Psi\n", "\t= - \\frac{1}{\\sigma^2} + \\frac{1}{\\sigma^4}\\sum_n^N \\omega_{mn}^2 \\frac{e^{b_n + \\frac{1}{\\sigma^2}\\sum_i^M x_i w_{in}}}{(e^{b_n + \\frac{1}{\\sigma^2}\\sum_i^M x_i w_{in}} + 1)^2}.\n", "\\label{_auto108} \\tag{108}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now have all the expressions neeeded to calculate the gradient of\n", "the expected local energy with respect to the RBM parameters\n", "$\\frac{\\partial \\langle E_L \\rangle}{\\partial \\alpha_i}$.\n", "\n", "If we use $\\Psi = \\sqrt{F_{rbm}}$ we obtain" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto109\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", "\t\\frac{\\partial}{\\partial x_m} \\ln\\Psi\n", "\t= - \\frac{1}{2\\sigma^2}(x_m - a_m) + \\frac{1}{2\\sigma^2} \\sum_n^N\n", " \t\\frac{w_{mn}}{e^{-b_n-\\frac{1}{\\sigma^2}\\sum_i^M x_i w_{in}} + 1}\n", "\t\n", "\\label{_auto109} \\tag{109}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto110\"></div>\n", "\n", "$$\n", "\\begin{equation} \n", "\t\\frac{\\partial^2}{\\partial x_m^2} \\ln\\Psi\n", "\t= - \\frac{1}{2\\sigma^2} + \\frac{1}{2\\sigma^4}\\sum_n^N \\omega_{mn}^2 \\frac{e^{b_n + \\frac{1}{\\sigma^2}\\sum_i^M x_i w_{in}}}{(e^{b_n + \\frac{1}{\\sigma^2}\\sum_i^M x_i w_{in}} + 1)^2}.\n", "\\label{_auto110} \\tag{110}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The difference between this equation and the previous one is that we multiply by a factor $1/2$.\n", "\n", "\n", "\n", "\n", "\n", "## Python version for the two non-interacting particles" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Energy: 3.183345614497203\n", "Energy: 3.2529062036963854\n", "Energy: 3.2232673949311432\n", "Energy: 3.3037758094201197\n", "Energy: 3.181455380841746\n", "Energy: 3.2520476925442554\n", "Energy: 3.2271042650905737\n", "Energy: 3.2356563896134065\n", "Energy: 3.2886687225895797\n", "Energy: 3.227899186035562\n", "Energy: 3.2719985629015627\n", "Energy: 3.176378077683709\n", "Energy: 3.2526352494399804\n", "Energy: 3.381301213383767\n", "Energy: 3.3021825088587575\n", "Energy: 3.2237783848528707\n", "Energy: 3.2955256138385893\n", "Energy: 3.216457686169475\n", "Energy: 3.205936920038335\n", "Energy: 3.225496664249184\n", "Energy: 3.232400367865156\n", "Energy: 3.2391218195295735\n", "Energy: 3.202615395083139\n", "Energy: 3.2340999308872496\n", "Energy: 3.201411754556875\n", "Energy: 3.230848786454371\n", "Energy: 3.2561125805987032\n", "Energy: 3.206469174530407\n", "Energy: 3.2423399520468803\n", "Energy: 3.3619856014431933\n", "Energy: 3.305382903715824\n", "Energy: 3.242250245025822\n", "Energy: 3.3279654520758575\n", "Energy: 3.245561322812992\n", "Energy: 3.2875907543852083\n", "Energy: 3.2389614971026846\n", "Energy: 3.2488063026518295\n", "Energy: 3.3197661468132695\n", "Energy: 3.1962539382177355\n", "Energy: 3.272429710751303\n", "Energy: 3.30999563154172\n", "Energy: 3.244777346642188\n", "Energy: 3.255432221161946\n", "Energy: 3.1947049086847357\n", "Energy: 3.233950396635773\n", "Energy: 3.2776922513278697\n", "Energy: 3.2289507285232304\n", "Energy: 3.2456813900163577\n", "Energy: 3.2525014467532523\n", "Energy: 3.2878231649277994\n", " Energy\n", "0 3.183346\n", "1 3.252906\n", "2 3.223267\n", "3 3.303776\n", "4 3.181455\n", "5 3.252048\n", "6 3.227104\n", "7 3.235656\n", "8 3.288669\n", "9 3.227899\n", "10 3.271999\n", "11 3.176378\n", "12 3.252635\n", "13 3.381301\n", "14 3.302183\n", "15 3.223778\n", "16 3.295526\n", "17 3.216458\n", "18 3.205937\n", "19 3.225497\n", "20 3.232400\n", "21 3.239122\n", "22 3.202615\n", "23 3.234100\n", "24 3.201412\n", "25 3.230849\n", "26 3.256113\n", "27 3.206469\n", "28 3.242340\n", "29 3.361986\n", "30 3.305383\n", "31 3.242250\n", "32 3.327965\n", "33 3.245561\n", "34 3.287591\n", "35 3.238961\n", "36 3.248806\n", "37 3.319766\n", "38 3.196254\n", "39 3.272430\n", "40 3.309996\n", "41 3.244777\n", "42 3.255432\n", "43 3.194705\n", "44 3.233950\n", "45 3.277692\n", "46 3.228951\n", "47 3.245681\n", "48 3.252501\n", "49 3.287823\n" ] } ], "source": [ "%matplotlib inline\n", "\n", "# 2-electron VMC code for 2dim quantum dot with importance sampling\n", "# Using gaussian rng for new positions and Metropolis- Hastings \n", "# Added restricted boltzmann machine method for dealing with the wavefunction\n", "# RBM code based heavily off of:\n", "# https://github.com/CompPhysics/ComputationalPhysics2/tree/gh-pages/doc/Programs/BoltzmannMachines/MLcpp/src/CppCode/ob\n", "from math import exp, sqrt\n", "from random import random, seed, normalvariate\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from mpl_toolkits.mplot3d import Axes3D\n", "from matplotlib import cm\n", "from matplotlib.ticker import LinearLocator, FormatStrFormatter\n", "import sys\n", "\n", "\n", "\n", "# Trial wave function for the 2-electron quantum dot in two dims\n", "def WaveFunction(r,a,b,w):\n", " sigma=1.0\n", " sig2 = sigma2\n", " Psi1 = 0.0\n", " Psi2 = 1.0\n", " Q = Qfac(r,b,w)\n", " \n", " for iq in range(NumberParticles):\n", " for ix in range(Dimension):\n", " Psi1 += (r[iq,ix]-a[iq,ix])2\n", " \n", " for ih in range(NumberHidden):\n", " Psi2 = (1.0 + np.exp(Q[ih]))\n", " \n", " Psi1 = np.exp(-Psi1/(2sig2))\n", "\n", " return Psi1Psi2\n", "\n", "# Local energy for the 2-electron quantum dot in two dims, using analytical local energy\n", "def LocalEnergy(r,a,b,w):\n", " sigma=1.0\n", " sig2 = sigma2\n", " locenergy = 0.0\n", " \n", " Q = Qfac(r,b,w)\n", "\n", " for iq in range(NumberParticles):\n", " for ix in range(Dimension):\n", " sum1 = 0.0\n", " sum2 = 0.0\n", " for ih in range(NumberHidden):\n", " sum1 += w[iq,ix,ih]/(1+np.exp(-Q[ih]))\n", " sum2 += w[iq,ix,ih]2 np.exp(Q[ih]) / (1.0 + np.exp(Q[ih]))2\n", " \n", " dlnpsi1 = -(r[iq,ix] - a[iq,ix]) /sig2 + sum1/sig2\n", " dlnpsi2 = -1/sig2 + sum2/sig22\n", " locenergy += 0.5(-dlnpsi1dlnpsi1 - dlnpsi2 + r[iq,ix]2)\n", " \n", " if(interaction==True):\n", " for iq1 in range(NumberParticles):\n", " for iq2 in range(iq1):\n", " distance = 0.0\n", " for ix in range(Dimension):\n", " distance += (r[iq1,ix] - r[iq2,ix])2\n", " \n", " locenergy += 1/sqrt(distance)\n", " \n", " return locenergy\n", "\n", "# Derivate of wave function ansatz as function of variational parameters\n", "def DerivativeWFansatz(r,a,b,w):\n", " \n", " sigma=1.0\n", " sig2 = sigma*2\n", " \n", " Q = Qfac(r,b,w)\n", " \n", " WfDer = np.empty((3,),dtype=object)\n", " WfDer = [np.copy(a),np.copy(b),np.copy(w)]\n", " \n", " WfDer[0] = (r-a)/sig2\n", " WfDer[1] = 1 / (1 + np.exp(-Q))\n", " \n", " for ih in range(NumberHidden):\n", " WfDer[2][:,:,ih] = w[:,:,ih] / (sig2(1+np.exp(-Q[ih])))\n", " \n", " return WfDer\n", "\n", "# Setting up the quantum force for the two-electron quantum dot, recall that it is a vector\n", "def QuantumForce(r,a,b,w):\n", "\n", " sigma=1.0\n", " sig2 = sigma*2\n", " \n", " qforce = np.zeros((NumberParticles,Dimension), np.double)\n", " sum1 = np.zeros((NumberParticles,Dimension), np.double)\n", " \n", " Q = Qfac(r,b,w)\n", " \n", " for ih in range(NumberHidden):\n", " sum1 += w[:,:,ih]/(1+np.exp(-Q[ih]))\n", " \n", " qforce = 2(-(r-a)/sig2 + sum1/sig2)\n", " \n", " return qforce\n", " \n", "def Qfac(r,b,w):\n", " Q = np.zeros((NumberHidden), np.double)\n", " temp = np.zeros((NumberHidden), np.double)\n", " \n", " for ih in range(NumberHidden):\n", " temp[ih] = (rw[:,:,ih]).sum()\n", " \n", " Q = b + temp\n", " \n", " return Q\n", " \n", "# Computing the derivative of the energy and the energy \n", "def EnergyMinimization(a,b,w):\n", "\n", " NumberMCcycles= 10000\n", " # Parameters in the Fokker-Planck simulation of the quantum force\n", " D = 0.5\n", " TimeStep = 0.05\n", " # positions\n", " PositionOld = np.zeros((NumberParticles,Dimension), np.double)\n", " PositionNew = np.zeros((NumberParticles,Dimension), np.double)\n", " # Quantum force\n", " QuantumForceOld = np.zeros((NumberParticles,Dimension), np.double)\n", " QuantumForceNew = np.zeros((NumberParticles,Dimension), np.double)\n", "\n", " # seed for rng generator \n", " seed()\n", " energy = 0.0\n", " DeltaE = 0.0\n", "\n", " EnergyDer = np.empty((3,),dtype=object)\n", " DeltaPsi = np.empty((3,),dtype=object)\n", " DerivativePsiE = np.empty((3,),dtype=object)\n", " EnergyDer = [np.copy(a),np.copy(b),np.copy(w)]\n", " DeltaPsi = [np.copy(a),np.copy(b),np.copy(w)]\n", " DerivativePsiE = [np.copy(a),np.copy(b),np.copy(w)]\n", " for i in range(3): EnergyDer[i].fill(0.0)\n", " for i in range(3): DeltaPsi[i].fill(0.0)\n", " for i in range(3): DerivativePsiE[i].fill(0.0)\n", "\n", " \n", " #Initial position\n", " for i in range(NumberParticles):\n", " for j in range(Dimension):\n", " PositionOld[i,j] = normalvariate(0.0,1.0)sqrt(TimeStep)\n", " wfold = WaveFunction(PositionOld,a,b,w)\n", " QuantumForceOld = QuantumForce(PositionOld,a,b,w)\n", "\n", " #Loop over MC MCcycles\n", " for MCcycle in range(NumberMCcycles):\n", " #Trial position moving one particle at the time\n", " for i in range(NumberParticles):\n", " for j in range(Dimension):\n", " PositionNew[i,j] = PositionOld[i,j]+normalvariate(0.0,1.0)sqrt(TimeStep)+\\\n", " QuantumForceOld[i,j]TimeStepD\n", " wfnew = WaveFunction(PositionNew,a,b,w)\n", " QuantumForceNew = QuantumForce(PositionNew,a,b,w)\n", " \n", " GreensFunction = 0.0\n", " for j in range(Dimension):\n", " GreensFunction += 0.5(QuantumForceOld[i,j]+QuantumForceNew[i,j])\\\n", " (DTimeStep0.5(QuantumForceOld[i,j]-QuantumForceNew[i,j])-\\\n", " PositionNew[i,j]+PositionOld[i,j])\n", " \n", " GreensFunction = exp(GreensFunction)\n", " ProbabilityRatio = GreensFunctionwfnew2/wfold2\n", " #Metropolis-Hastings test to see whether we accept the move\n", " if random() <= ProbabilityRatio:\n", " for j in range(Dimension):\n", " PositionOld[i,j] = PositionNew[i,j]\n", " QuantumForceOld[i,j] = QuantumForceNew[i,j]\n", " wfold = wfnew\n", " #print(\"wf new: \", wfnew)\n", " #print(\"force on 1 new:\", QuantumForceNew[0,:])\n", " #print(\"pos of 1 new: \", PositionNew[0,:])\n", " #print(\"force on 2 new:\", QuantumForceNew[1,:])\n", " #print(\"pos of 2 new: \", PositionNew[1,:])\n", " DeltaE = LocalEnergy(PositionOld,a,b,w)\n", " DerPsi = DerivativeWFansatz(PositionOld,a,b,w)\n", " \n", " DeltaPsi[0] += DerPsi[0]\n", " DeltaPsi[1] += DerPsi[1]\n", " DeltaPsi[2] += DerPsi[2]\n", " \n", " energy += DeltaE\n", "\n", " DerivativePsiE[0] += DerPsi[0]DeltaE\n", " DerivativePsiE[1] += DerPsi[1]DeltaE\n", " DerivativePsiE[2] += DerPsi[2]DeltaE\n", " \n", " # We calculate mean values\n", " energy /= NumberMCcycles\n", " DerivativePsiE[0] /= NumberMCcycles\n", " DerivativePsiE[1] /= NumberMCcycles\n", " DerivativePsiE[2] /= NumberMCcycles\n", " DeltaPsi[0] /= NumberMCcycles\n", " DeltaPsi[1] /= NumberMCcycles\n", " DeltaPsi[2] /= NumberMCcycles\n", " EnergyDer[0] = 2(DerivativePsiE[0]-DeltaPsi[0]energy)\n", " EnergyDer[1] = 2(DerivativePsiE[1]-DeltaPsi[1]energy)\n", " EnergyDer[2] = 2(DerivativePsiE[2]-DeltaPsi[2]energy)\n", " return energy, EnergyDer\n", "\n", "\n", "#Here starts the main program with variable declarations\n", "NumberParticles = 2\n", "Dimension = 2\n", "NumberHidden = 2\n", "\n", "interaction=True\n", "\n", "# guess for parameters\n", "a=np.random.normal(loc=0.0, scale=0.001, size=(NumberParticles,Dimension))\n", "b=np.random.normal(loc=0.0, scale=0.001, size=(NumberHidden))\n", "w=np.random.normal(loc=0.0, scale=0.001, size=(NumberParticles,Dimension,NumberHidden))\n", "# Set up iteration using stochastic gradient method\n", "Energy = 0\n", "EDerivative = np.empty((3,),dtype=object)\n", "EDerivative = [np.copy(a),np.copy(b),np.copy(w)]\n", "# Learning rate eta, max iterations, need to change to adaptive learning rate\n", "eta = 0.001\n", "MaxIterations = 50\n", "iter = 0\n", "np.seterr(invalid='raise')\n", "Energies = np.zeros(MaxIterations)\n", "EnergyDerivatives1 = np.zeros(MaxIterations)\n", "EnergyDerivatives2 = np.zeros(MaxIterations)\n", "\n", "while iter < MaxIterations:\n", " Energy, EDerivative = EnergyMinimization(a,b,w)\n", " agradient = EDerivative[0]\n", " bgradient = EDerivative[1]\n", " wgradient = EDerivative[2]\n", " a -= etaagradient\n", " b -= etabgradient \n", " w -= eta*wgradient \n", " Energies[iter] = Energy\n", " print(\"Energy:\",Energy)\n", " #EnergyDerivatives1[iter] = EDerivative[0] \n", " #EnergyDerivatives2[iter] = EDerivative[1]\n", " #EnergyDerivatives3[iter] = EDerivative[2] \n", "\n", "\n", " iter += 1\n", "\n", "#nice printout with Pandas\n", "import pandas as pd\n", "from pandas import DataFrame\n", "pd.set_option('max_columns', 6)\n", "data ={'Energy':Energies}#,'A Derivative':EnergyDerivatives1,'B Derivative':EnergyDerivatives2,'Weights Derivative':EnergyDerivatives3}\n", "\n", "frame = pd.DataFrame(data)\n", "print(frame)" ] }, { "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": "3.9.12" } }, "nbformat": 4, "nbformat_minor": 4 }	cc0-1.0
pchmieli/h2o-3	h2o-py/demos/deeplearning.ipynb	2	30147	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import h2o\n", "from h2o.estimators.deeplearning import H2ODeepLearningEstimator" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Warning: Version mismatch. H2O is version 3.5.0.99999, but the python package is version UNKNOWN.\n" ] }, { "data": { "text/html": [ "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td>H2O cluster uptime: </td>\n", "<td>58 minutes 48 seconds 43 milliseconds </td></tr>\n", "<tr><td>H2O cluster version: </td>\n", "<td>3.5.0.99999</td></tr>\n", "<tr><td>H2O cluster name: </td>\n", "<td>ludirehak</td></tr>\n", "<tr><td>H2O cluster total nodes: </td>\n", "<td>1</td></tr>\n", "<tr><td>H2O cluster total memory: </td>\n", "<td>4.44 GB</td></tr>\n", "<tr><td>H2O cluster total cores: </td>\n", "<td>8</td></tr>\n", "<tr><td>H2O cluster allowed cores: </td>\n", "<td>8</td></tr>\n", "<tr><td>H2O cluster healthy: </td>\n", "<td>True</td></tr>\n", "<tr><td>H2O Connection ip: </td>\n", "<td>127.0.0.1</td></tr>\n", "<tr><td>H2O Connection port: </td>\n", "<td>54321</td></tr></table></div>" ], "text/plain": [ "-------------------------- -------------------------------------\n", "H2O cluster uptime: 58 minutes 48 seconds 43 milliseconds\n", "H2O cluster version: 3.5.0.99999\n", "H2O cluster name: ludirehak\n", "H2O cluster total nodes: 1\n", "H2O cluster total memory: 4.44 GB\n", "H2O cluster total cores: 8\n", "H2O cluster allowed cores: 8\n", "H2O cluster healthy: True\n", "H2O Connection ip: 127.0.0.1\n", "H2O Connection port: 54321\n", "-------------------------- -------------------------------------" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "h2o.init()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Parse Progress: [##################################################] 100%\n", "Uploaded py2a71800e-2ec6-4f71-b955-854a4f22aeb3 into cluster with 380 rows and 9 cols\n", "Rows: 380 Cols: 9\n", "\n", "Chunk compression summary:\n" ] }, { "data": { "text/html": [ "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b>chunk_type</b></td>\n", 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"<td> 0 B</td>\n", "<td>0.0</td>\n", "<td>0.0</td>\n", "<td>0.0</td></tr>\n", "<tr><td>total</td>\n", "<td> 4.8 KB</td>\n", "<td>380.0</td>\n", "<td>1.0</td>\n", "<td>9.0</td></tr></table></div>" ], "text/plain": [ " size number_of_rows number_of_chunks_per_column number_of_chunks\n", "----------------- ------ ---------------- ----------------------------- ------------------\n", "172.16.2.37:54321 4.8 KB 380 1 9\n", "mean 4.8 KB 380 1 9\n", "min 4.8 KB 380 1 9\n", "max 4.8 KB 380 1 9\n", "stddev 0 B 0 0 0\n", "total 4.8 KB 380 1 9" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Column-by-Column Summary:\n", "\n" ] }, { "data": { "text/html": [ "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b></b></td>\n", "<td><b>ID</b></td>\n", "<td><b>CAPSULE</b></td>\n", "<td><b>AGE</b></td>\n", "<td><b>RACE</b></td>\n", "<td><b>DPROS</b></td>\n", "<td><b>DCAPS</b></td>\n", "<td><b>PSA</b></td>\n", "<td><b>VOL</b></td>\n", "<td><b>GLEASON</b></td></tr>\n", "<tr><td>type</td>\n", "<td>int</td>\n", "<td>int</td>\n", "<td>int</td>\n", "<td>int</td>\n", "<td>int</td>\n", "<td>int</td>\n", "<td>real</td>\n", "<td>real</td>\n", "<td>int</td></tr>\n", "<tr><td>mins</td>\n", "<td>1.0</td>\n", "<td>0.0</td>\n", "<td>43.0</td>\n", "<td>0.0</td>\n", "<td>1.0</td>\n", "<td>1.0</td>\n", "<td>0.3</td>\n", "<td>0.0</td>\n", "<td>0.0</td></tr>\n", "<tr><td>maxs</td>\n", "<td>380.0</td>\n", "<td>1.0</td>\n", "<td>79.0</td>\n", "<td>2.0</td>\n", "<td>4.0</td>\n", "<td>2.0</td>\n", "<td>139.7</td>\n", "<td>97.6</td>\n", "<td>9.0</td></tr>\n", "<tr><td>mean</td>\n", "<td>190.5</td>\n", "<td>0.4</td>\n", "<td>66.0</td>\n", "<td>1.1</td>\n", "<td>2.3</td>\n", "<td>1.1</td>\n", "<td>15.4</td>\n", "<td>15.8</td>\n", "<td>6.4</td></tr>\n", "<tr><td>sigma</td>\n", "<td>109.8</td>\n", "<td>0.5</td>\n", "<td>6.5</td>\n", "<td>0.3</td>\n", "<td>1.0</td>\n", "<td>0.3</td>\n", "<td>20.0</td>\n", "<td>18.3</td>\n", "<td>1.1</td></tr>\n", "<tr><td>zero_count</td>\n", "<td>0</td>\n", "<td>227</td>\n", "<td>0</td>\n", "<td>3</td>\n", "<td>0</td>\n", "<td>0</td>\n", "<td>0</td>\n", "<td>167</td>\n", "<td>2</td></tr>\n", "<tr><td>missing_count</td>\n", "<td>0</td>\n", "<td>0</td>\n", "<td>0</td>\n", "<td>0</td>\n", "<td>0</td>\n", "<td>0</td>\n", "<td>0</td>\n", "<td>0</td>\n", "<td>0</td></tr></table></div>" ], "text/plain": [ " ID CAPSULE AGE RACE DPROS DCAPS PSA VOL GLEASON\n", "------------- ------------- -------------- ------------- -------------- ------------- -------------- ------------- ------------- -------------\n", "type int int int int int int real real int\n", "mins 1.0 0.0 43.0 0.0 1.0 1.0 0.3 0.0 0.0\n", "maxs 380.0 1.0 79.0 2.0 4.0 2.0 139.7 97.6 9.0\n", "mean 190.5 0.402631578947 66.0394736842 1.08684210526 2.27105263158 1.10789473684 15.4086315789 15.8129210526 6.38421052632\n", "sigma 109.840793879 0.491074338963 6.52707126917 0.308773258025 1.00010761815 0.310656449351 19.9975726686 18.3476199673 1.09195337443\n", "zero_count 0 227 0 3 0 0 0 167 2\n", "missing_count 0 0 0 0 0 0 0 0 0" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from h2o.h2o import _locate # private function. used to find files within h2o git project directory.\n", "\n", "prostate = h2o.upload_file(path=_locate(\"smalldata/logreg/prostate.csv\"))\n", "prostate.describe()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "deeplearning Model Build Progress: [##################################################] 100%\n", "Model Details\n", "=============\n", "H2ODeepLearningEstimator : Deep Learning\n", "Model Key: DeepLearning_model_python_1445544453075_137\n", "\n", "Status of Neuron Layers: predicting CAPSULE, 2-class classification, bernoulli distribution, CrossEntropy loss, 322 weights/biases, 8.5 KB, 3,800,000 training samples, mini-batch size 1\n", "\n" ] }, { "data": { "text/html": [ "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b></b></td>\n", "<td><b>layer</b></td>\n", "<td><b>units</b></td>\n", "<td><b>type</b></td>\n", "<td><b>dropout</b></td>\n", "<td><b>l1</b></td>\n", "<td><b>l2</b></td>\n", "<td><b>mean_rate</b></td>\n", "<td><b>rate_RMS</b></td>\n", "<td><b>momentum</b></td>\n", "<td><b>mean_weight</b></td>\n", "<td><b>weight_RMS</b></td>\n", "<td><b>mean_bias</b></td>\n", "<td><b>bias_RMS</b></td></tr>\n", "<tr><td></td>\n", "<td>1</td>\n", "<td>7</td>\n", "<td>Input</td>\n", "<td>0.0</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></tr>\n", "<tr><td></td>\n", "<td>2</td>\n", "<td>10</td>\n", "<td>Tanh</td>\n", "<td>0.0</td>\n", "<td>0.0</td>\n", "<td>0.0</td>\n", "<td>0.1</td>\n", "<td>0.1</td>\n", "<td>0.0</td>\n", "<td>0.1</td>\n", "<td>1.0</td>\n", "<td>0.4</td>\n", "<td>0.7</td></tr>\n", "<tr><td></td>\n", "<td>3</td>\n", "<td>10</td>\n", "<td>Tanh</td>\n", "<td>0.0</td>\n", "<td>0.0</td>\n", "<td>0.0</td>\n", "<td>0.1</td>\n", "<td>0.1</td>\n", "<td>0.0</td>\n", "<td>0.0</td>\n", "<td>1.4</td>\n", "<td>1.1</td>\n", "<td>0.7</td></tr>\n", "<tr><td></td>\n", "<td>4</td>\n", "<td>10</td>\n", "<td>Tanh</td>\n", "<td>0.0</td>\n", "<td>0.0</td>\n", "<td>0.0</td>\n", "<td>0.2</td>\n", "<td>0.2</td>\n", "<td>0.0</td>\n", "<td>-0.2</td>\n", "<td>1.8</td>\n", "<td>-0.2</td>\n", "<td>0.8</td></tr>\n", "<tr><td></td>\n", "<td>5</td>\n", "<td>2</td>\n", "<td>Softmax</td>\n", "<td></td>\n", "<td>0.0</td>\n", "<td>0.0</td>\n", "<td>0.4</td>\n", "<td>0.1</td>\n", "<td>0.0</td>\n", "<td>-0.2</td>\n", "<td>5.7</td>\n", "<td>0.1</td>\n", "<td>0.3</td></tr></table></div>" ], "text/plain": [ " layer units type dropout l1 l2 mean_rate rate_RMS momentum mean_weight weight_RMS mean_bias bias_RMS\n", "-- ------- ------- ------- --------- ---- ---- -------------- --------------- ---------- --------------- ------------- --------------- --------------\n", " 1 7 Input 0.0\n", " 2 10 Tanh 0.0 0.0 0.0 0.113385616509 0.106353402138 0.0 0.0609744639036 1.01239204407 0.39729323576 0.706557273865\n", " 3 10 Tanh 0.0 0.0 0.0 0.127355974298 0.0778207182884 0.0 0.0452940063318 1.41430282593 1.08699401568 0.748293399811\n", " 4 10 Tanh 0.0 0.0 0.0 0.247714677732 0.205178380013 0.0 -0.153704360016 1.77612018585 -0.246065597484 0.803660392761\n", " 5 2 Softmax 0.0 0.0 0.426404607296 0.102490842342 0.0 -0.192942607403 5.6915397644 0.0971629403181 0.319541573524" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "ModelMetricsBinomial: deeplearning\n", " Reported on train data. \n", "\n", "MSE: 0.010708193224\n", "R^2: 0.955478877615\n", "LogLoss: 0.0689458344205\n", "AUC: 0.996818404307\n", "Gini: 0.993636808615\n", "\n", "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.85259659057:\n" ] }, { "data": { "text/html": [ "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b></b></td>\n", "<td><b>0</b></td>\n", "<td><b>1</b></td>\n", "<td><b>Error</b></td>\n", "<td><b>Rate</b></td></tr>\n", "<tr><td>0</td>\n", "<td>224.0</td>\n", "<td>3.0</td>\n", "<td>0.0132</td>\n", "<td> (3.0/227.0)</td></tr>\n", "<tr><td>1</td>\n", "<td>0.0</td>\n", "<td>153.0</td>\n", "<td>0.0</td>\n", "<td> (0.0/153.0)</td></tr>\n", "<tr><td>Total</td>\n", "<td>224.0</td>\n", "<td>156.0</td>\n", "<td>0.0079</td>\n", "<td> (3.0/380.0)</td></tr></table></div>" ], "text/plain": [ " 0 1 Error Rate\n", "----- --- --- ------- -----------\n", "0 224 3 0.0132 (3.0/227.0)\n", "1 0 153 0 (0.0/153.0)\n", "Total 224 156 0.0079 (3.0/380.0)" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Maximum Metrics: Maximum metrics at their respective thresholds\n", "\n" ] }, { "data": { "text/html": [ "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b>metric</b></td>\n", "<td><b>threshold</b></td>\n", "<td><b>value</b></td>\n", "<td><b>idx</b></td></tr>\n", "<tr><td>max f1</td>\n", "<td>0.9</td>\n", "<td>1.0</td>\n", "<td>107.0</td></tr>\n", "<tr><td>max f2</td>\n", "<td>0.9</td>\n", "<td>1.0</td>\n", "<td>107.0</td></tr>\n", "<tr><td>max f0point5</td>\n", "<td>0.9</td>\n", "<td>1.0</td>\n", "<td>107.0</td></tr>\n", "<tr><td>max accuracy</td>\n", "<td>0.9</td>\n", "<td>1.0</td>\n", "<td>107.0</td></tr>\n", "<tr><td>max precision</td>\n", "<td>1.0</td>\n", "<td>1.0</td>\n", "<td>0.0</td></tr>\n", "<tr><td>max absolute_MCC</td>\n", "<td>0.9</td>\n", "<td>1.0</td>\n", "<td>107.0</td></tr>\n", "<tr><td>max min_per_class_accuracy</td>\n", "<td>0.9</td>\n", "<td>1.0</td>\n", "<td>105.0</td></tr></table></div>" ], "text/plain": [ "metric threshold value idx\n", "-------------------------- ----------- -------- -----\n", "max f1 0.852597 0.990291 107\n", "max f2 0.852597 0.996094 107\n", "max f0point5 0.852597 0.984556 107\n", "max accuracy 0.852597 0.992105 107\n", "max precision 1 1 0\n", "max absolute_MCC 0.852597 0.983772 107\n", "max min_per_class_accuracy 0.904469 0.986784 105" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Scoring History:\n" ] }, { "data": { "text/html": [ "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b></b></td>\n", "<td><b>timestamp</b></td>\n", "<td><b>duration</b></td>\n", "<td><b>training_speed</b></td>\n", "<td><b>epochs</b></td>\n", "<td><b>samples</b></td>\n", "<td><b>training_MSE</b></td>\n", "<td><b>training_r2</b></td>\n", "<td><b>training_logloss</b></td>\n", "<td><b>training_AUC</b></td>\n", "<td><b>training_classification_error</b></td></tr>\n", "<tr><td></td>\n", "<td>2015-10-22 14:06:21</td>\n", "<td> 0.000 sec</td>\n", "<td>None</td>\n", "<td>0.0</td>\n", "<td>0.0</td>\n", "<td>nan</td>\n", "<td>nan</td>\n", "<td>nan</td>\n", "<td>nan</td>\n", "<td>nan</td></tr>\n", "<tr><td></td>\n", "<td>2015-10-22 14:06:21</td>\n", "<td> 0.038 sec</td>\n", "<td>115151 rows/sec</td>\n", "<td>10.0</td>\n", "<td>3800.0</td>\n", "<td>0.2</td>\n", "<td>0.2</td>\n", "<td>0.6</td>\n", "<td>0.8</td>\n", "<td>0.3</td></tr>\n", "<tr><td></td>\n", "<td>2015-10-22 14:06:26</td>\n", "<td> 5.047 sec</td>\n", "<td>309126 rows/sec</td>\n", "<td>4100.0</td>\n", "<td>1558000.0</td>\n", "<td>0.0</td>\n", "<td>0.9</td>\n", "<td>0.1</td>\n", "<td>1.0</td>\n", "<td>0.0</td></tr>\n", "<tr><td></td>\n", "<td>2015-10-22 14:06:31</td>\n", "<td>10.051 sec</td>\n", "<td>308783 rows/sec</td>\n", "<td>8160.0</td>\n", "<td>3100800.0</td>\n", "<td>0.0</td>\n", "<td>0.9</td>\n", "<td>0.1</td>\n", "<td>1.0</td>\n", "<td>0.0</td></tr>\n", "<tr><td></td>\n", "<td>2015-10-22 14:06:34</td>\n", "<td>12.360 sec</td>\n", "<td>307717 rows/sec</td>\n", "<td>10000.0</td>\n", "<td>3800000.0</td>\n", "<td>0.0</td>\n", "<td>1.0</td>\n", "<td>0.1</td>\n", "<td>1.0</td>\n", "<td>0.0</td></tr></table></div>" ], "text/plain": [ " timestamp duration training_speed epochs samples training_MSE training_r2 training_logloss training_AUC training_classification_error\n", "-- ------------------- ---------- ---------------- -------- ---------- -------------- ------------- ------------------ -------------- -------------------------------\n", " 2015-10-22 14:06:21 0.000 sec 0 0 nan nan nan nan nan\n", " 2015-10-22 14:06:21 0.038 sec 115151 rows/sec 10 3800 0.183857 0.235583 0.550464 0.803288 0.268421\n", " 2015-10-22 14:06:26 5.047 sec 309126 rows/sec 4100 1.558e+06 0.0152973 0.936399 0.0886012 0.993291 0.0131579\n", " 2015-10-22 14:06:31 10.051 sec 308783 rows/sec 8160 3.1008e+06 0.0182986 0.92392 0.0792696 0.997121 0.0184211\n", " 2015-10-22 14:06:34 12.360 sec 307717 rows/sec 10000 3.8e+06 0.0107082 0.955479 0.0689458 0.996818 0.00789474" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "prostate[\"CAPSULE\"] = prostate[\"CAPSULE\"].asfactor()\n", "model = H2ODeepLearningEstimator(activation = \"Tanh\", hidden = [10, 10, 10], epochs = 10000)\n", "model.train(x = list(set(prostate.columns) - set([\"ID\",\"CAPSULE\"])), y =\"CAPSULE\", training_frame = prostate)\n", "model.show()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "H2OFrame with 380 rows and 3 columns: \n" ] }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>predict</th>\n", " <th>p0</th>\n", " <th>p1</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0</td>\n", " <td>9.993875e-01</td>\n", " <td>6.125394e-04</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>0</td>\n", " <td>9.999998e-01</td>\n", " <td>1.937478e-07</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>0</td>\n", " <td>9.999646e-01</td>\n", " <td>3.535732e-05</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>0</td>\n", " <td>1.000000e+00</td>\n", " <td>2.235483e-12</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>0</td>\n", " <td>9.999950e-01</td>\n", " <td>5.024862e-06</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>1</td>\n", " <td>1.237468e-07</td>\n", " <td>9.999999e-01</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>0</td>\n", " <td>9.992793e-01</td>\n", " <td>7.206910e-04</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>0</td>\n", " <td>1.000000e+00</td>\n", " <td>9.146884e-19</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>0</td>\n", " <td>1.000000e+00</td>\n", " <td>8.434714e-13</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>0</td>\n", " <td>9.999994e-01</td>\n", " <td>6.112821e-07</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " predict p0 p1\n", "0 0 9.993875e-01 6.125394e-04\n", "1 0 9.999998e-01 1.937478e-07\n", "2 0 9.999646e-01 3.535732e-05\n", "3 0 1.000000e+00 2.235483e-12\n", "4 0 9.999950e-01 5.024862e-06\n", "5 1 1.237468e-07 9.999999e-01\n", "6 0 9.992793e-01 7.206910e-04\n", "7 0 1.000000e+00 9.146884e-19\n", "8 0 1.000000e+00 8.434714e-13\n", "9 0 9.999994e-01 6.112821e-07" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "predictions = model.predict(prostate)\n", "predictions.show()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "ModelMetricsBinomial: deeplearning\n", " Reported on test data. \n", "\n", "MSE: 0.010708193224\n", "R^2: 0.955478877615\n", "LogLoss: 0.0689458344205\n", "AUC: 0.996804007947\n", "Gini: 0.993608015894\n", "\n", "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.85259659057:\n" ] }, { "data": { "text/html": [ "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b></b></td>\n", "<td><b>0</b></td>\n", "<td><b>1</b></td>\n", "<td><b>Error</b></td>\n", "<td><b>Rate</b></td></tr>\n", "<tr><td>0</td>\n", "<td>224.0</td>\n", "<td>3.0</td>\n", "<td>0.0132</td>\n", "<td> (3.0/227.0)</td></tr>\n", "<tr><td>1</td>\n", "<td>0.0</td>\n", "<td>153.0</td>\n", "<td>0.0</td>\n", "<td> (0.0/153.0)</td></tr>\n", "<tr><td>Total</td>\n", "<td>224.0</td>\n", "<td>156.0</td>\n", "<td>0.0079</td>\n", "<td> (3.0/380.0)</td></tr></table></div>" ], "text/plain": [ " 0 1 Error Rate\n", "----- --- --- ------- -----------\n", "0 224 3 0.0132 (3.0/227.0)\n", "1 0 153 0 (0.0/153.0)\n", "Total 224 156 0.0079 (3.0/380.0)" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Maximum Metrics: Maximum metrics at their respective thresholds\n", "\n" ] }, { "data": { "text/html": [ "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b>metric</b></td>\n", "<td><b>threshold</b></td>\n", "<td><b>value</b></td>\n", "<td><b>idx</b></td></tr>\n", "<tr><td>max f1</td>\n", "<td>0.9</td>\n", "<td>1.0</td>\n", "<td>155.0</td></tr>\n", "<tr><td>max f2</td>\n", "<td>0.9</td>\n", "<td>1.0</td>\n", "<td>155.0</td></tr>\n", "<tr><td>max f0point5</td>\n", "<td>0.9</td>\n", "<td>1.0</td>\n", "<td>155.0</td></tr>\n", "<tr><td>max accuracy</td>\n", "<td>0.9</td>\n", "<td>1.0</td>\n", "<td>155.0</td></tr>\n", "<tr><td>max precision</td>\n", "<td>1.0</td>\n", "<td>1.0</td>\n", "<td>0.0</td></tr>\n", "<tr><td>max absolute_MCC</td>\n", "<td>0.9</td>\n", "<td>1.0</td>\n", "<td>155.0</td></tr>\n", "<tr><td>max min_per_class_accuracy</td>\n", "<td>0.9</td>\n", "<td>1.0</td>\n", "<td>153.0</td></tr></table></div>" ], "text/plain": [ "metric threshold value idx\n", "-------------------------- ----------- -------- -----\n", "max f1 0.852597 0.990291 155\n", "max f2 0.852597 0.996094 155\n", "max f0point5 0.852597 0.984556 155\n", "max accuracy 0.852597 0.992105 155\n", "max precision 1 1 0\n", "max absolute_MCC 0.852597 0.983772 155\n", "max min_per_class_accuracy 0.904469 0.986784 153" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "performance = model.model_performance(prostate)\n", "performance.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.5" } }, "nbformat": 4, "nbformat_minor": 0 }	apache-2.0
liufuyang/coursera-Applied-Machine-Learning-in-Python	Assigment 4 - Pytorch.ipynb	1	115248	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/fuyang/Workspace/coursera-Applied-Machine-Learning-in-Python/venv/lib/python3.5/site-packages/IPython/core/interactiveshell.py:2698: DtypeWarning: Columns (11,12,31) have mixed types. Specify dtype option on import or set low_memory=False.\n", " interactivity=interactivity, compiler=compiler, result=result)\n" ] } ], "source": [ "import pandas as pd\n", "import numpy as np\n", "\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.neural_network import MLPClassifier\n", "from sklearn.preprocessing import MinMaxScaler\n", "\n", "\n", "\n", "df_train = pd.read_csv('train.csv', encoding = \"ISO-8859-1\")\n", "df_test = pd.read_csv('test.csv', encoding = \"ISO-8859-1\")\n", "\n", "float_cols = [c for c in df_train if df_train[c].dtype == \"float64\"]\n", "float32_cols = {c: np.float32 for c in float_cols}\n", "\n", "df_train = pd.read_csv('train.csv', encoding = \"ISO-8859-1\", dtype=float32_cols)\n", "# df_test is not used in this code example\n", "\n", "\n", "list_to_remove = ['balance_due',\n", " 'collection_status',\n", " 'compliance_detail',\n", " 'payment_amount',\n", " 'payment_date',\n", " 'payment_status']\n", "\n", "list_to_remove_all = ['admin_fee', 'state_fee', # 'clean_up_cost', \n", " 'violator_name', 'zip_code', 'country', 'city', 'state',\n", " 'inspector_name', 'violation_street_number', 'violation_street_name',\n", " 'violation_zip_code', 'violation_description',\n", " 'mailing_address_str_number', 'mailing_address_str_name',\n", " 'non_us_str_code',\n", " 'ticket_issued_date', 'hearing_date']\n", "\n", "df_train.drop(list_to_remove, axis=1, inplace=True)\n", "df_train.drop(list_to_remove_all, axis=1, inplace=True)\n", "df_test.drop(list_to_remove_all, axis=1, inplace=True)\n", "\n", "df_train.drop('grafitti_status', axis=1, inplace=True)\n", "df_test.drop('grafitti_status', axis=1, inplace=True)\n", "\n", "###\n", "df_latlons = pd.read_csv('latlons.csv')\n", "df_address = pd.read_csv('addresses.csv')\n", "df_id_latlons = df_address.set_index('address').join(df_latlons.set_index('address'))\n", "\n", "df_train = df_train.set_index('ticket_id').join(df_id_latlons.set_index('ticket_id'))\n", "df_test = df_test.set_index('ticket_id').join(df_id_latlons.set_index('ticket_id'))\n", "\n", "###\n", "vio_code_freq10 = df_train.violation_code.value_counts().index[0:10]\n", "df_train['violation_code_freq10'] = [list(vio_code_freq10).index(c) if c in vio_code_freq10 else -1 for c in df_train.violation_code ]\n", "df_train.drop('violation_code', axis=1, inplace=True)\n", "df_test['violation_code_freq10'] = [list(vio_code_freq10).index(c) if c in vio_code_freq10 else -1 for c in df_test.violation_code ]\n", "df_test.drop('violation_code', axis=1, inplace=True)\n", "\n", "###\n", "df_train = df_train[df_train.compliance.isnull() == False]\n", "\n", "df_train.lat.fillna(method='pad', inplace=True)\n", "df_train.lon.fillna(method='pad', inplace=True)\n", "#df_train.state.fillna(method='pad', inplace=True)\n", "\n", "df_test.lat.fillna(method='pad', inplace=True)\n", "df_test.lon.fillna(method='pad', inplace=True)\n", "#df_test.state.fillna(method='pad', inplace=True)\n", "\n", "\n", "df_train.lat = df_train.lat.apply(lambda l: l if l < 42.45 else 42.45)\n", "df_train.lat = df_train.lat.apply(lambda l: l if l > 42.25 else 42.25)\n", "\n", "df_train.lon = df_train.lon.apply(lambda l: l if l < -82.9 else -82.9)\n", "df_train.lon = df_train.lon.apply(lambda l: l if l > -83.30 else -83.30)\n", "\n", "\n", "df_train.fine_amount = df_train.fine_amount.apply(lambda l: 50 if l <= 50 else l)\n", "df_train.fine_amount = df_train.fine_amount.apply(lambda l: 100 if l >= 100 and l<=160 else l)\n", "df_train.fine_amount = df_train.fine_amount.apply(lambda l: 200 if l >= 160 and l<250 else l)\n", "df_train.fine_amount = df_train.fine_amount.apply(lambda l: 300 if l >= 250 else l)\n", "df_train.fine_amount = df_train.fine_amount.apply(lambda l: l if l > 0 else 0)\n", "\n", "df_train.late_fee = df_train.late_fee.apply(lambda l: 5 if l <= 5 else l)\n", "df_train.late_fee = df_train.late_fee.apply(lambda l: 10 if l >= 10 and l<=15 else l)\n", "df_train.late_fee = df_train.late_fee.apply(lambda l: 20 if l >= 16 and l<=22 else l)\n", "df_train.late_fee = df_train.late_fee.apply(lambda l: 25 if l >= 22 and l<=30 else l)\n", "df_train.late_fee = df_train.late_fee.apply(lambda l: 40 if l >= 31 else l)\n", "\n", "##\n", "\n", "df_train.drop('agency_name', axis=1, inplace=True)\n", "\n", "one_hot_encode_columns = ['violation_code_freq10', 'disposition', \n", " 'fine_amount', 'late_fee']\n", "\n", "# df_train.drop(one_hot_encode_columns, axis=1, inplace=True)\n", "\n", "df_train_old = df_train\n", "\n", "# df_train = df_train[['fine_amount', 'late_fee', 'compliance','violation_code_freq10','disposition', ]]\n", "\n", "df_train = pd.get_dummies(df_train, columns=one_hot_encode_columns)\n", "df_test = pd.get_dummies(df_test, columns=one_hot_encode_columns)\n", "\n", "###\n", "\n", "train_features = df_train.columns.drop('compliance')\n", "\n", "#######\n", "X_train = df_train[train_features]\n", "y_train = df_train.compliance\n", "\n", "X_train, X_test, y_train, y_test= train_test_split(X_train, \n", " y_train, \n", " random_state=0,\n", " test_size=0.2)\n", "\n", "#######\n", "\n", "scaler = MinMaxScaler()\n", "\n", "X_train_scaled = scaler.fit_transform(X_train)\n", "X_test_scaled = scaler.transform(X_test)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['discount_amount', 'clean_up_cost', 'judgment_amount', 'compliance',\n", " 'lat', 'lon', 'violation_code_freq10_-1', 'violation_code_freq10_0',\n", " 'violation_code_freq10_1', 'violation_code_freq10_2',\n", " 'violation_code_freq10_3', 'violation_code_freq10_4',\n", " 'violation_code_freq10_5', 'violation_code_freq10_6',\n", " 'violation_code_freq10_7', 'violation_code_freq10_8',\n", " 'violation_code_freq10_9',\n", " 'disposition_Responsible (Fine Waived) by Deter',\n", " 'disposition_Responsible by Admission',\n", " 'disposition_Responsible by Default',\n", " 'disposition_Responsible by Determination', 'fine_amount_50.0',\n", " 'fine_amount_95.0', 'fine_amount_100.0', 'fine_amount_200.0',\n", " 'fine_amount_300.0', 'late_fee_5.0', 'late_fee_9.5', 'late_fee_10.0',\n", " 'late_fee_20.0', 'late_fee_25.0', 'late_fee_40.0'],\n", " dtype='object')" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_train.columns" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7fde019b3be0>" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fde036aaac8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df_train[['lat']].plot.hist(stacked=True, bins=20)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "zz =df_train.lat.value_counts().index" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Float64Index([ 42.25, 42.25023, 42.2504467, 42.2504496, 42.2504749,\n", " 42.2505595, 42.2506088, 42.2507538, 42.2507788, 42.2507964,\n", " ...\n", " 42.449841, 42.4498563, 42.4498695, 42.4498717, 42.4498993,\n", " 42.4499021, 42.4499344, 42.4499363, 42.449962, 42.45],\n", " dtype='float64', length=61446)" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "zz.sort_values()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 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F0jDgvnRTSRcSMzPrVo+Hv9KDr34t6YM1yMfMzBpYpd+o3wU8JWkF6QowgIj486pkZWZm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\n/wf871marFbSYAAAAABJRU5ErkJggg==\n", 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F0jDgvnRTSRcSMzPrVo+Hv9KDr34t6YM1yMfMzBpYpd+o3wU8JWkF6QowgIj486pkZWZm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\n/wf871marFbSYAAAAABJRU5ErkJggg==\n", 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fde035ad390>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df_train_old.fine_amount.plot.hist(stacked=True, bins=50)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7fde030a2da0>" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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G/W8CFwE/BrD9D4wylDgiIianVovKy+X+hgEkHde+lCIiolu1WlTWS/ozqpFbVwJ3kwm7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\nUYmIiNqkqERERG1SVCIiojb/HxaG8hwT1XEiAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fde03f309e8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df_train_old.late_fee.plot.hist(stacked=True, bins=50)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<torch._C.Generator at 0x7fde030673c0>" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import torch\n", "from torch.autograd import Variable\n", "import torch.utils.data as Data\n", "import torch.nn.functional as F\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "torch.manual_seed(1) # reproducible" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "dtype('float32')" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y_train.dtype" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "x_tensor = torch.from_numpy(X_train_scaled.astype(np.float32))\n", "y_tensor = torch.from_numpy(y_train.as_matrix().astype(np.int64))\n", "\n", "test_x = torch.from_numpy(X_test_scaled.astype(np.float32))\n", "test_y = torch.from_numpy(y_test.as_matrix().astype(np.int64))" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\n", " 0.0000 0.0000 0.0127 ... 0.0000 0.0000 0.0000\n", " 0.0000 0.0000 0.0277 ... 0.0000 1.0000 0.0000\n", " 0.0000 0.0000 0.0077 ... 0.0000 0.0000 0.0000\n", " ... ⋱ ... \n", " 0.0000 0.0000 0.0227 ... 1.0000 0.0000 0.0000\n", " 0.0000 0.0000 0.0277 ... 0.0000 1.0000 0.0000\n", " 0.0000 0.0000 0.0254 ... 0.0000 0.0000 0.0000\n", "[torch.FloatTensor of size 127904x31]" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x_tensor" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\n", " 0\n", " 0\n", " 0\n", "⋮ \n", " 0\n", " 0\n", " 0\n", "[torch.LongTensor of size 127904]" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y_tensor" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class Net(torch.nn.Module):\n", " def __init__(self, n_feature, n_hidden, n_output):\n", " super(Net, self).__init__()\n", " self.hidden = torch.nn.Linear(n_feature, n_hidden) # hidden layer\n", " self.out = torch.nn.Linear(n_hidden, n_output) # output layer\n", "\n", " def forward(self, x):\n", " x = F.relu(self.hidden(x)) # activation function for hidden layer\n", " x = self.out(x)\n", " return x" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "BATCH_SIZE=800\n", "\n", "torch_dataset = Data.TensorDataset(data_tensor=x_tensor, target_tensor=y_tensor)\n", "loader = Data.DataLoader(\n", " dataset=torch_dataset, # torch TensorDataset format\n", " batch_size=BATCH_SIZE, # mini batch size\n", " shuffle=True, # random shuffle for training\n", " num_workers=2, # subprocesses for loading data\n", ")" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch: 0 \| train loss: 0.7655 \| test accuracy: 0.93\n", "Epoch: 0 \| train loss: 0.2570 \| test accuracy: 0.93\n", "Epoch: 0 \| train loss: 0.2034 \| test accuracy: 0.93\n", "Epoch: 0 \| train loss: 0.2221 \| test accuracy: 0.94\n", "Epoch: 1 \| train loss: 0.1913 \| test accuracy: 0.94\n", "Epoch: 1 \| train loss: 0.2129 \| test accuracy: 0.94\n", "Epoch: 1 \| train loss: 0.1985 \| test accuracy: 0.94\n", "Epoch: 1 \| train loss: 0.2094 \| test accuracy: 0.94\n", "Epoch: 2 \| train loss: 0.1795 \| test accuracy: 0.94\n", "Epoch: 2 \| train loss: 0.1881 \| test accuracy: 0.94\n", "Epoch: 2 \| train loss: 0.2023 \| test accuracy: 0.94\n", "Epoch: 2 \| train loss: 0.2088 \| test accuracy: 0.94\n", "Epoch: 3 \| train loss: 0.1890 \| test accuracy: 0.94\n", "Epoch: 3 \| train loss: 0.1832 \| test accuracy: 0.94\n", "Epoch: 3 \| train loss: 0.1898 \| test accuracy: 0.94\n", "Epoch: 3 \| train loss: 0.1729 \| test accuracy: 0.94\n", "Epoch: 4 \| train loss: 0.1873 \| test accuracy: 0.94\n", "Epoch: 4 \| train loss: 0.1875 \| test accuracy: 0.94\n", "Epoch: 4 \| train loss: 0.2081 \| test accuracy: 0.94\n", "Epoch: 4 \| train loss: 0.2178 \| test accuracy: 0.94\n" ] } ], "source": [ "EPOCH = 5\n", "N_HIDDEN = 100\n", "# net = Net(n_feature=26, n_hidden=2, n_output=2) \n", "\n", "net = torch.nn.Sequential(\n", " torch.nn.Linear(31, N_HIDDEN),\n", " #torch.nn.Dropout(0.5), # drop 50% of the neuron\n", " torch.nn.ReLU(),\n", " torch.nn.Linear(N_HIDDEN, 2),\n", ")\n", "\n", "\n", "optimizer = torch.optim.RMSprop(net.parameters(), lr=0.02)\n", "loss_func = torch.nn.CrossEntropyLoss() # the target label is NOT an one-hotted\n", "\n", "for epoch in range(EPOCH):\n", " for step, (x, y) in enumerate(loader): # gives batch data, normalize x when iterate train_loader\n", " b_x = Variable(x) # batch x\n", " b_y = Variable(y) # batch y\n", "\n", " output = net(b_x) # cnn output\n", " loss = loss_func(output, b_y) # cross entropy loss\n", " optimizer.zero_grad() # clear gradients for this training step\n", " loss.backward() # backpropagation, compute gradients\n", " optimizer.step() # apply gradients\n", "\n", " if step % 50 == 0:\n", " test_output = net(Variable(test_x))\n", " pred_y = torch.max(test_output, 1)[1].data.squeeze()\n", " accuracy = sum(pred_y == test_y) / float(test_y.size(0))\n", " print('Epoch: ', epoch, '\| train loss: %.4f' % loss.data[0], '\| test accuracy: %.2f' % accuracy)\n", "\n" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.909359605911 0.199567567568 0.327304964539\n" ] } ], "source": [ "from sklearn.metrics import recall_score, precision_score, f1_score\n", "\n", "test_output = net(Variable(x_tensor))\n", "train_pred = torch.max(test_output, 1)[1].data.numpy().squeeze()\n", "\n", "print(precision_score(y_train, train_pred),\n", " recall_score(y_train, train_pred),\n", " f1_score(y_train, train_pred))" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.919028340081 0.193438432041 0.319605772615\n" ] } ], "source": [ "test_output = net(Variable(test_x))\n", "test_pred = torch.max(test_output, 1)[1].data.numpy().squeeze()\n", "print(precision_score(y_test, test_pred),\n", " recall_score(y_test, test_pred),\n", " f1_score(y_test, test_pred))" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "image/png": 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kXKQvPDSEHXrdD6/iSgBjY4vr9r8ARr+6AozvNhx0KYDtaiVoAfQy2g+waOLT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\nf+zYMSpVquRy34oVK9pLmJ7SuWPu3Lm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"text/plain": [ "<matplotlib.figure.Figure at 0x7fde3e7fb240>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "test_pro = F.softmax(test_output)\n", "\n", "def draw_roc_curve():\n", " from sklearn.metrics import roc_curve, auc\n", "\n", " fpr_lr, tpr_lr, _ = roc_curve(y_test, test_pro[:,1].data.numpy())\n", " roc_auc_lr = auc(fpr_lr, tpr_lr)\n", "\n", " plt.figure()\n", " plt.xlim([-0.01, 1.00])\n", " plt.ylim([-0.01, 1.01])\n", " plt.plot(fpr_lr, tpr_lr, lw=3, label='LogRegr ROC curve (area = {:0.2f})'.format(roc_auc_lr))\n", " plt.xlabel('False Positive Rate', fontsize=16)\n", " plt.ylabel('True Positive Rate', fontsize=16)\n", " plt.title('ROC curve (1-of-10 digits classifier)', fontsize=16)\n", " plt.legend(loc='lower right', fontsize=13)\n", " plt.plot([0, 1], [0, 1], color='navy', lw=3, linestyle='--')\n", " plt.axes().set_aspect('equal')\n", " plt.show()\n", " \n", "draw_roc_curve()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
metpy/MetPy	v0.12/_downloads/e3a381e26c1f7c055ae74476848708cb/Station_Plot_with_Layout.ipynb	1	10785	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\nStation Plot with Layout\n========================\n\nMake a station plot, complete with sky cover and weather symbols, using a\nstation plot layout built into MetPy.\n\nThe station plot itself is straightforward, but there is a bit of code to perform the\ndata-wrangling (hopefully that situation will improve in the future). Certainly, if you have\nexisting point data in a format you can work with trivially, the station plot will be simple.\n\nThe `StationPlotLayout` class is used to standardize the plotting various parameters\n(i.e. temperature), keeping track of the location, formatting, and even the units for use in\nthe station plot. This makes it easy (if using standardized names) to re-use a given layout\nof a station plot.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import cartopy.crs as ccrs\nimport cartopy.feature as cfeature\nimport matplotlib.pyplot as plt\nimport pandas as pd\n\nfrom metpy.calc import wind_components\nfrom metpy.cbook import get_test_data\nfrom metpy.plots import (add_metpy_logo, simple_layout, StationPlot,\n StationPlotLayout, wx_code_map)\nfrom metpy.units import units" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The setup\n---------\n\nFirst read in the data. We use `numpy.loadtxt` to read in the data and use a structured\n`numpy.dtype` to allow different types for the various columns. This allows us to handle\nthe columns with string data.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "with get_test_data('station_data.txt') as f:\n data_arr = pd.read_csv(f, header=0, usecols=(1, 2, 3, 4, 5, 6, 7, 17, 18, 19),\n names=['stid', 'lat', 'lon', 'slp', 'air_temperature',\n 'cloud_fraction', 'dew_point_temperature', 'weather',\n 'wind_dir', 'wind_speed'],\n na_values=-99999)\n\n data_arr.set_index('stid', inplace=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This sample data has way too many stations to plot all of them. Instead, we just select\na few from around the U.S. and pull those out of the data file.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Pull out these specific stations\nselected = ['OKC', 'ICT', 'GLD', 'MEM', 'BOS', 'MIA', 'MOB', 'ABQ', 'PHX', 'TTF',\n 'ORD', 'BIL', 'BIS', 'CPR', 'LAX', 'ATL', 'MSP', 'SLC', 'DFW', 'NYC', 'PHL',\n 'PIT', 'IND', 'OLY', 'SYR', 'LEX', 'CHS', 'TLH', 'HOU', 'GJT', 'LBB', 'LSV',\n 'GRB', 'CLT', 'LNK', 'DSM', 'BOI', 'FSD', 'RAP', 'RIC', 'JAN', 'HSV', 'CRW',\n 'SAT', 'BUY', '0CO', 'ZPC', 'VIH']\n\n# Loop over all the whitelisted sites, grab the first data, and concatenate them\ndata_arr = data_arr.loc[selected]\n\n# Drop rows with missing winds\ndata_arr = data_arr.dropna(how='any', subset=['wind_dir', 'wind_speed'])\n\n# First, look at the names of variables that the layout is expecting:\nsimple_layout.names()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next grab the simple variables out of the data we have (attaching correct units), and\nput them into a dictionary that we will hand the plotting function later:\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# This is our container for the data\ndata = {}\n\n# Copy out to stage everything together. In an ideal world, this would happen on\n# the data reading side of things, but we're not there yet.\ndata['longitude'] = data_arr['lon'].values\ndata['latitude'] = data_arr['lat'].values\ndata['air_temperature'] = data_arr['air_temperature'].values * units.degC\ndata['dew_point_temperature'] = data_arr['dew_point_temperature'].values * units.degC\ndata['air_pressure_at_sea_level'] = data_arr['slp'].values * units('mbar')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice that the names (the keys) in the dictionary are the same as those that the\nlayout is expecting.\n\nNow perform a few conversions:\n\n- Get wind components from speed and direction\n- Convert cloud fraction values to integer codes [0 - 8]\n- Map METAR weather codes to WMO codes for weather symbols\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Get the wind components, converting from m/s to knots as will be appropriate\n# for the station plot\nu, v = wind_components(data_arr['wind_speed'].values * units('m/s'),\n data_arr['wind_dir'].values * units.degree)\ndata['eastward_wind'], data['northward_wind'] = u, v\n\n# Convert the fraction value into a code of 0-8, which can be used to pull out\n# the appropriate symbol\ndata['cloud_coverage'] = (8 * data_arr['cloud_fraction']).fillna(10).values.astype(int)\n\n# Map weather strings to WMO codes, which we can use to convert to symbols\n# Only use the first symbol if there are multiple\nwx_text = data_arr['weather'].fillna('')\ndata['present_weather'] = [wx_code_map[s.split()[0] if ' ' in s else s] for s in wx_text]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "All the data wrangling is finished, just need to set up plotting and go:\nSet up the map projection and set up a cartopy feature for state borders\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "proj = ccrs.LambertConformal(central_longitude=-95, central_latitude=35,\n standard_parallels=[35])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The payoff\n----------\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Change the DPI of the resulting figure. Higher DPI drastically improves the\n# look of the text rendering\nplt.rcParams['savefig.dpi'] = 255\n\n# Create the figure and an axes set to the projection\nfig = plt.figure(figsize=(20, 10))\nadd_metpy_logo(fig, 1080, 290, size='large')\nax = fig.add_subplot(1, 1, 1, projection=proj)\n\n# Add some various map elements to the plot to make it recognizable\nax.add_feature(cfeature.LAND)\nax.add_feature(cfeature.OCEAN)\nax.add_feature(cfeature.LAKES)\nax.add_feature(cfeature.COASTLINE)\nax.add_feature(cfeature.STATES)\nax.add_feature(cfeature.BORDERS, linewidth=2)\n\n# Set plot bounds\nax.set_extent((-118, -73, 23, 50))\n\n#\n# Here's the actual station plot\n#\n\n# Start the station plot by specifying the axes to draw on, as well as the\n# lon/lat of the stations (with transform). We also the fontsize to 12 pt.\nstationplot = StationPlot(ax, data['longitude'], data['latitude'],\n transform=ccrs.PlateCarree(), fontsize=12)\n\n# The layout knows where everything should go, and things are standardized using\n# the names of variables. So the layout pulls arrays out of `data` and plots them\n# using `stationplot`.\nsimple_layout.plot(stationplot, data)\n\nplt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "or instead, a custom layout can be used:\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Just winds, temps, and dewpoint, with colors. Dewpoint and temp will be plotted\n# out to Farenheit tenths. Extra data will be ignored\ncustom_layout = StationPlotLayout()\ncustom_layout.add_barb('eastward_wind', 'northward_wind', units='knots')\ncustom_layout.add_value('NW', 'air_temperature', fmt='.1f', units='degF', color='darkred')\ncustom_layout.add_value('SW', 'dew_point_temperature', fmt='.1f', units='degF',\n color='darkgreen')\n\n# Also, we'll add a field that we don't have in our dataset. This will be ignored\ncustom_layout.add_value('E', 'precipitation', fmt='0.2f', units='inch', color='blue')\n\n# Create the figure and an axes set to the projection\nfig = plt.figure(figsize=(20, 10))\nadd_metpy_logo(fig, 1080, 290, size='large')\nax = fig.add_subplot(1, 1, 1, projection=proj)\n\n# Add some various map elements to the plot to make it recognizable\nax.add_feature(cfeature.LAND)\nax.add_feature(cfeature.OCEAN)\nax.add_feature(cfeature.LAKES)\nax.add_feature(cfeature.COASTLINE)\nax.add_feature(cfeature.STATES)\nax.add_feature(cfeature.BORDERS, linewidth=2)\n\n# Set plot bounds\nax.set_extent((-118, -73, 23, 50))\n\n#\n# Here's the actual station plot\n#\n\n# Start the station plot by specifying the axes to draw on, as well as the\n# lon/lat of the stations (with transform). We also the fontsize to 12 pt.\nstationplot = StationPlot(ax, data['longitude'], data['latitude'],\n transform=ccrs.PlateCarree(), fontsize=12)\n\n# The layout knows where everything should go, and things are standardized using\n# the names of variables. So the layout pulls arrays out of `data` and plots them\n# using `stationplot`.\ncustom_layout.plot(stationplot, data)\n\nplt.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": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }	bsd-3-clause
kerimlcr/ab2017-dpyo	ornek/osmnx/osmnx-0.3/examples/11-plot-routes-folium-web-map.ipynb	1	3091	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Plot a route as a web map with folium\n", "\n", "Use OSMnx to download a street network, calculate a route between two points, then create a web map.\n", "\n", "- [Overview of OSMnx](http://geoffboeing.com/2016/11/osmnx-python-street-networks/)\n", "- [GitHub repo](https://github.com/gboeing/osmnx)\n", "- [Examples, demos, tutorials](https://github.com/gboeing/osmnx/tree/master/examples)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import osmnx as ox, networkx as nx\n", "from IPython.display import IFrame\n", "ox.config(log_console=True, use_cache=True)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "G = ox.graph_from_place('Piedmont, California, USA', network_type='drive')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# use networkx to calculate the shortest path between two nodes\n", "origin_node = list(G.nodes())[0]\n", "destination_node = list(G.nodes())[-1]\n", "route = nx.shortest_path(G, origin_node, destination_node)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# plot the route with folium\n", "route_map = ox.plot_route_folium(G, route)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "text/html": [ "\n", " <iframe\n", " width=\"600\"\n", " height=\"500\"\n", " src=\"data/route.html\"\n", " frameborder=\"0\"\n", " allowfullscreen\n", " ></iframe>\n", " " ], "text/plain": [ "<IPython.lib.display.IFrame at 0x1b01a25aba8>" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# save as html file then display map as an iframe\n", "filepath = 'data/route.html'\n", "route_map.save(filepath)\n", "IFrame(filepath, width=600, height=500)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.3" } }, "nbformat": 4, "nbformat_minor": 2 }	gpl-3.0
jokedurnez/RequiredEffectSize	Figure1_Power/.ipynb_checkpoints/fig_power-checkpoint.ipynb	1	97504	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Figure: How large should effect sizes be in neuroimaging to have sufficient power?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Specification of alternative\n", "\n", "In a brain map in an MNI template, with smoothness of 3 times the voxelsize, there is one active region with voxelwise effect size D. The (spatial) size of the region is relatively small (<200 voxels). We want to know how large D should be in order to have 80% power to detect the region using voxelwise FWE thresholding using Random Field Theory.\n", "Detect the region means that the maximum in the activated area exceeds the significance threshold.\n", "\n", "### Strategy\n", "\n", "1. Compute the voxelwise threshold for the specified smoothness and volume \n", " * _FweThres = 5.12_\n", "2. Define the alternative hypothesis, so that the omnibus power is 80% \n", "3. How large should the maximum statistic in a (small) region be to exceed the voxelwise threshold with 0.8 power? \n", " * _muMax = 4.00_\n", "5. How does this voxel statistic translate to Cohen's D for a given sample size?\n", " * _See Figure_" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "% matplotlib inline\n", "from __future__ import division\n", "import os\n", "import nibabel as nib\n", "import numpy as np\n", "from neuropower import peakdistribution\n", "import scipy.integrate as integrate\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import palettable.colorbrewer as cb\n", "\n", "if not 'FSLDIR' in os.environ.keys():\n", " raise Exception('This notebook requires that FSL is installed and the FSLDIR environment variable is set')\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. What is the voxelwise threshold?" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ReselSize: 27\n", "Volume: 228483\n", "ReselCount: 8462.33333333\n", "------------\n", "FWE voxelwise GRF threshold: 5.123062\n", "\n" ] } ], "source": [ "# From smoothness + mask to ReselCount\n", "\n", "FWHM = 3\n", "ReselSize = FWHM*3\n", "MNI_mask = nib.load(os.path.join(os.getenv('FSLDIR'),'data/standard/MNI152_T1_2mm_brain_mask.nii.gz')).get_data()\n", "Volume = np.sum(MNI_mask)\n", "ReselCount = Volume/ReselSize\n", "\n", "print(\"ReselSize: \"+str(ReselSize))\n", "print(\"Volume: \"+str(Volume))\n", "print(\"ReselCount: \"+str(ReselCount))\n", "print(\"------------\")\n", "\n", "# From ReselCount to FWE treshold\n", "\n", "FweThres_cmd = 'ptoz 0.05 -g %s' %ReselCount\n", "FweThres = os.popen(FweThres_cmd).read()\n", "\n", "print(\"FWE voxelwise GRF threshold: \"+str(FweThres))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Definition of alternative" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Detect 1 region\n", "We define a 'success' as a situation in which the maximum in the active field exceeds\n", "the threshold." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "Power = 0.8" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. How large statistic in a field be to exceed the threshold with power 0.80?\n", "\n", "We quantify this by computing the expected local maximum in the field (which is a null field elevated by value D).\n", "We use the distribution of local maxima of Cheng&Schwartzman to compute the power/effect size." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The power is sufficient for one region if mu equals: 4.0\n" ] } ], "source": [ "muRange = np.arange(1.8,5,0.01)\n", "muSingle = []\n", "for muMax in muRange:\n", "# what is the power to detect a maximum \n", " power = 1-integrate.quad(lambda x:peakdistribution.peakdens3D(x,1),-20,float(FweThres)-muMax)[0]\n", " if power>Power:\n", " muSingle.append(muMax)\n", " break\n", "print(\"The power is sufficient for one region if mu equals: \"+str(muSingle[0]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 5. From the required voxel statistic to Cohen's D for a given sample size" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Read in data\n", "Data = pd.read_csv(\"../SampleSize/neurosynth_sampsizedata.txt\",sep=\" \",header=None,names=['year','n'])\n", "Data['source']='Tal'\n", "Data=Data[Data.year!=1997] #remove year with 1 entry\n", "David = pd.read_csv(\"../SampleSize/david_sampsizedata.txt\",sep=\" \",header=None,names=['year','n'])\n", "David['source']='David'\n", "Data=Data.append(David)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# add detectable effect\n", "Data['deltaSingle']=muSingle[0]/np.sqrt(Data['n'])" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# add jitter for figure\n", "stdev = 0.01(max(Data.year)-min(Data.year))\n", "Data['year_jitter'] = Data.year+np.random.randn(len(Data))*stdev" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Compute medians per year (for smoother)\n", "Medians = pd.DataFrame({'year':\n", " np.arange(start=np.min(Data.year),stop=np.max(Data.year)+1),\n", " 'TalMdSS':'nan',\n", " 'DavidMdSS':'nan',\n", " 'TalMdDSingle':'nan',\n", " 'DavidMdDSingle':'nan',\n", " 'MdSS':'nan',\n", " 'DSingle':'nan'\n", " })" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/poldrack/anaconda/envs/py3k/lib/python3.5/site-packages/numpy/core/_methods.py:59: RuntimeWarning: Mean of empty slice.\n", " warnings.warn(\"Mean of empty slice.\", RuntimeWarning)\n", "/Users/poldrack/anaconda/envs/py3k/lib/python3.5/site-packages/ipykernel/__main__.py:4: 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", "/Users/poldrack/anaconda/envs/py3k/lib/python3.5/site-packages/ipykernel/__main__.py:5: 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", "/Users/poldrack/anaconda/envs/py3k/lib/python3.5/site-packages/ipykernel/__main__.py:8: 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", "/Users/poldrack/anaconda/envs/py3k/lib/python3.5/site-packages/ipykernel/__main__.py:9: 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", "/Users/poldrack/anaconda/envs/py3k/lib/python3.5/site-packages/ipykernel/__main__.py:12: 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", "/Users/poldrack/anaconda/envs/py3k/lib/python3.5/site-packages/ipykernel/__main__.py:13: 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" ] } ], "source": [ "for yearInd in (range(len(Medians))):\n", " # Compute medians for Tal's data\n", " yearBoolTal = np.array([a and b for a,b in zip(Data.source==\"Tal\",Data.year==Medians.year[yearInd])])\n", " Medians.TalMdSS[yearInd] = np.median(Data.n[yearBoolTal])\n", " Medians.TalMdDSingle[yearInd] = np.median(Data.deltaSingle[yearBoolTal])\n", " # Compute medians for David's data\n", " yearBoolDavid = np.array([a and b for a,b in zip(Data.source==\"David\",Data.year==Medians.year[yearInd])])\n", " Medians.DavidMdSS[yearInd] = np.median(Data.n[yearBoolDavid])\n", " Medians.DavidMdDSingle[yearInd] = np.median(Data.deltaSingle[yearBoolDavid])\n", " # Compute medians for all data\n", " yearBool = np.array(Data.year==Medians.year[yearInd])\n", " Medians.MdSS[yearInd] = np.median(Data.n[yearBool])\n", " Medians.DSingle[yearInd] = np.median(Data.deltaSingle[yearBool]) \n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>DSingle</th>\n", " <th>DavidMdDSingle</th>\n", " <th>DavidMdSS</th>\n", " <th>MdSS</th>\n", " <th>TalMdDSingle</th>\n", " <th>TalMdSS</th>\n", " <th>year</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1.26491</td>\n", " <td>1.26491</td>\n", " <td>10</td>\n", " <td>10</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1996.0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1.20605</td>\n", " <td>1.20605</td>\n", " <td>11</td>\n", " <td>11</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1997.0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1.1547</td>\n", " <td>1.13205</td>\n", " <td>12.5</td>\n", " <td>12</td>\n", " <td>1.1547</td>\n", " <td>12</td>\n", " <td>1998.0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>1.41421</td>\n", " <td>1.41421</td>\n", " <td>8</td>\n", " <td>8</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1999.0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>1.23548</td>\n", " <td>1.23548</td>\n", " <td>10.5</td>\n", " <td>10.5</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>2000.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " DSingle DavidMdDSingle DavidMdSS MdSS TalMdDSingle TalMdSS year\n", "0 1.26491 1.26491 10 10 NaN NaN 1996.0\n", "1 1.20605 1.20605 11 11 NaN NaN 1997.0\n", "2 1.1547 1.13205 12.5 12 1.1547 12 1998.0\n", "3 1.41421 1.41421 8 8 NaN NaN 1999.0\n", "4 1.23548 1.23548 10.5 10.5 NaN NaN 2000.0" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Medians[0:5]" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# add logscale\n", "\n", "Medians['MdSSLog'] = [np.log(x) for x in Medians.MdSS]\n", "Medians['TalMdSSLog'] = [np.log(x) for x in Medians.TalMdSS]\n", "Medians['DavidMdSSLog'] = [np.log(x) for x in Medians.DavidMdSS]\n", "\n", "Data['nLog']= [np.log(x) for x in Data.n]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The figure per List (Tal or David)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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jqpOnSeJeNyG3FlfKX4dcaElHMbMwlmrGxlJ0fhwPRGQqcA5QA/xaVV+tcEjG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\nAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10dd7ae10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "twocol = cb.qualitative.Paired_12.mpl_colors\n", "\n", "fig,axs = plt.subplots(1,2,figsize=(12,5))\n", "fig.subplots_adjust(hspace=.5,wspace=.3)\n", "axs=axs.ravel()\n", "axs[0].plot(Data.year_jitter[Data.source==\"Tal\"],Data['nLog'][Data.source==\"Tal\"],\"r.\",color=twocol[0],alpha=0.5,label=\"\")\n", "axs[0].plot(Data.year_jitter[Data.source==\"David\"],Data['nLog'][Data.source==\"David\"],\"r.\",color=twocol[2],alpha=0.5,label=\"\")\n", "axs[0].plot(Medians.year,Medians.TalMdSSLog,color=twocol[1],lw=3,label=\"Neurosynth\")\n", "axs[0].plot(Medians.year,Medians.DavidMdSSLog,color=twocol[3],lw=3,label=\"David et al.\")\n", "axs[0].set_xlim([1993,2016])\n", "axs[0].set_ylim([0,8])\n", "axs[0].set_xlabel(\"Year\")\n", "axs[0].set_ylabel(\"Median Sample Size\")\n", "axs[0].legend(loc=\"upper left\",frameon=False)\n", "#labels=[1,5,10,20,50,150,500,1000,3000]\n", "labels=[1,4,16,64,256,1024,3000]\n", "axs[0].set_yticks(np.log(labels))\n", "axs[0].set_yticklabels(labels)\n", "\n", "\n", "axs[1].plot(Data.year_jitter[Data.source==\"Tal\"],Data.deltaSingle[Data.source==\"Tal\"],\"r.\",color=twocol[0],alpha=0.5,label=\"\")\n", "axs[1].plot(Data.year_jitter[Data.source==\"David\"],Data.deltaSingle[Data.source==\"David\"],\"r.\",color=twocol[2],alpha=0.5,label=\"\")\n", "axs[1].plot(Medians.year,Medians.TalMdDSingle,color=twocol[1],lw=3,label=\"Neurosynth\")\n", "axs[1].plot(Medians.year,Medians.DavidMdDSingle,color=twocol[3],lw=3,label=\"David et al.\")\n", "axs[1].set_xlim([1993,2016])\n", "axs[1].set_ylim([0,3])\n", "axs[1].set_xlabel(\"Year\")\n", "axs[1].set_ylabel(\"Effect Size with 80% power\")\n", "axs[1].legend(loc=\"upper right\",frameon=False)\n", "plt.savefig('Figure1.svg',dpi=600)\n", "plt.show()\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Print median sample size and power for Neurosynth data" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>year</th>\n", " <th>TalMdSS</th>\n", " <th>TalMdDSingle</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1996.0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1997.0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1998.0</td>\n", " <td>12</td>\n", " <td>1.1547</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>1999.0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>2000.0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>2001.0</td>\n", " <td>8</td>\n", " <td>1.41421</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>2002.0</td>\n", " <td>14</td>\n", " <td>1.06904</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>2003.0</td>\n", " <td>12</td>\n", " <td>1.1547</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>2004.0</td>\n", " <td>13.5</td>\n", " <td>1.09375</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>2005.0</td>\n", " <td>17.5</td>\n", " <td>0.956476</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>2006.0</td>\n", " <td>16</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>2007.0</td>\n", " <td>16</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>2008.0</td>\n", " <td>20</td>\n", " <td>0.894427</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>2009.0</td>\n", " <td>18</td>\n", " <td>0.942809</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>2010.0</td>\n", " <td>28</td>\n", " <td>0.755929</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>2011.0</td>\n", " <td>19.5</td>\n", " <td>0.906045</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>2012.0</td>\n", " <td>44</td>\n", " <td>0.603023</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>2013.0</td>\n", " <td>34</td>\n", " <td>0.685994</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>2014.0</td>\n", " <td>30</td>\n", " <td>0.730297</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>2015.0</td>\n", " <td>43</td>\n", " <td>0.611112</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " year TalMdSS TalMdDSingle\n", "0 1996.0 NaN NaN\n", "1 1997.0 NaN NaN\n", "2 1998.0 12 1.1547\n", "3 1999.0 NaN NaN\n", "4 2000.0 NaN NaN\n", "5 2001.0 8 1.41421\n", "6 2002.0 14 1.06904\n", "7 2003.0 12 1.1547\n", "8 2004.0 13.5 1.09375\n", "9 2005.0 17.5 0.956476\n", "10 2006.0 16 1\n", "11 2007.0 16 1\n", "12 2008.0 20 0.894427\n", "13 2009.0 18 0.942809\n", "14 2010.0 28 0.755929\n", "15 2011.0 19.5 0.906045\n", "16 2012.0 44 0.603023\n", "17 2013.0 34 0.685994\n", "18 2014.0 30 0.730297\n", "19 2015.0 43 0.611112" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Medians.loc[:, lambda df: ['year', 'TalMdSS', 'TalMdDSingle']]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compute median of sample sizes over last 5 years, for use in correlation simulation notebook." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Median sample size (2011-2015): 32.5\n" ] } ], "source": [ "yearBoolTal = np.array([a and b for a,b in zip(Data.source==\"Tal\",Data.year>2010)])\n", "print('Median sample size (2011-2015):',np.median(Data.n[yearBoolTal]))" ] } ], "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
franklx/IHaskell	ihaskell-display/ihaskell-juicypixels/test.ipynb	3	1163	{ "cells": [ { "cell_type": "markdown", "metadata": { "hidden": false }, "source": [ "# Notebook test\n", "\n", "This IHaskell noteook should just test, whether IHaskell and JuicyPixels are properly installed and working.\n", "\n", "Just click in the box below and click on the \"Run\" command in the above menu. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "hidden": false }, "outputs": [], "source": [ "{-# LANGUAGE TypeSynonymInstances, FlexibleInstances #-}\n", "\n", "import IHaskell.Display.Juicypixels\n", "import Codec.Picture\n", " \n", "myImage = generateImage pixelRenderer 250 300\n", " where pixelRenderer x y = PixelRGB8 (fromIntegral x) (fromIntegral y) 128\n", " \n", "myImage " ] } ], "metadata": { "kernelspec": { "display_name": "Haskell", "language": "haskell", "name": "haskell" }, "language_info": { "codemirror_mode": "ihaskell", "file_extension": ".hs", "name": "haskell", "version": "7.10.3" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
TheCamusean/DLRCev3	scripts/siamese_network/siamese_notebook.ipynb	1	66448	{ "cells": [ { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import keras\n", "import numpy as np\n", "from scipy import misc\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([214, 204, 194, ..., 280, 218, 46])" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import os\n", "import random\n", "\n", "directory = '/Users/Jimmy/Desktop/training_data'\n", "def img_paths(directory):\n", " return [os.path.join(root, f) for root, _, files in os.walk(directory)\n", " for f in files if f != \"target.png\" and f != \".DS_Store\"]\n", "\n", "paths = img_paths(directory)\n", "random.shuffle(paths)\n", "paths = paths[:4000]\n", "\n", "def func(x):\n", " x = x.split(\"/\")\n", " return \"_\".join([x[-3], x[-2]])\n", "\n", "classes = [func(x) for x in paths]\n", "classes\n", "\n", "from sklearn import preprocessing\n", "le = preprocessing.LabelEncoder()\n", "classes_encoded = le.fit_transform(list(classes))\n", "classes_encoded" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%\|██████████\| 4000/4000 [00:05<00:00, 770.61it/s]\n" ] } ], "source": [ "import cv2\n", "from tqdm import tqdm\n", "images = []\n", "for img_path in tqdm(paths):\n", " img = misc.imread(img_path, mode='RGB')\n", " images.append(img)\n", "images = np.asarray(images)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Shuffle images\n", "import random\n", "XY = list(zip(images,classes_encoded))\n", "random.shuffle(XY)\n" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(3800, 64, 64, 3) (3800,)\n", "(200, 64, 64, 3) (200,)\n" ] } ], "source": [ "'''Train a Siamese MLP on pairs of digits from the MNIST dataset.\n", "It follows Hadsell-et-al.'06 [1] by computing the Euclidean distance on the\n", "output of the shared network and by optimizing the contrastive loss (see paper\n", "for more details).\n", "[1] \"Dimensionality Reduction by Learning an Invariant Mapping\"\n", " http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf\n", "Gets to 97.2% test accuracy after 20 epochs.\n", "2 seconds per epoch on a Titan X Maxwell GPU\n", "'''\n", "from __future__ import absolute_import\n", "from __future__ import print_function\n", "import numpy as np\n", "\n", "import random\n", "from keras.datasets import mnist\n", "from keras.models import Sequential, Model\n", "from keras.layers import Dense, Dropout, Input, Lambda, Conv2D, Flatten\n", "from keras.optimizers import RMSprop, Adam\n", "from keras import backend as K\n", "import random\n", "from tqdm import tqdm\n", "\n", "num_classes = 10\n", "\n", "\n", "def euclidean_distance(vects):\n", " x, y = vects\n", " return K.sqrt(K.maximum(K.sum(K.square(x - y), axis=1, keepdims=True), K.epsilon()))\n", "\n", "\n", "def eucl_dist_output_shape(shapes):\n", " shape1, shape2 = shapes\n", " return (shape1[0], 1)\n", "\n", "\n", "def contrastive_loss(y_true, y_pred):\n", " '''Contrastive loss from Hadsell-et-al.'06\n", " http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf\n", " '''\n", " margin = 1\n", " return K.mean(y_true * K.square(y_pred) +\n", " (1 - y_true) * K.square(K.maximum(margin - y_pred, 0)))\n", "\n", "\n", "def create_pairs(x, y, digit_indices, classes):\n", " '''Positive and negative pair creation.\n", " Alternates between positive and negative pairs.\n", " '''\n", " pairs = []\n", " labels = []\n", " n = min([len(digit_indices[d]) for d in classes]) - 1\n", " num_classes = len(classes)\n", " eq = False\n", " for i, y1 in enumerate(y):\n", " if len(pairs) > 100:\n", " break\n", " for j,y2 in enumerate(y):\n", " if len(pairs) > 100:\n", " break\n", " if eq and y1 != y2:\n", " pairs += [[x[i], x[j]]]\n", " labels.append(0)\n", " eq = False\n", " elif not eq and y1==y2:\n", " pairs += [[x[i], x[j]]]\n", " labels.append(1)\n", " eq = True\n", " \n", " return np.array(pairs), np.array(labels)\n", "\n", "\n", "def create_base_network(input_dim):\n", " '''Base network to be shared (eq. to feature extraction).\n", " '''\n", " seq = Sequential()\n", " seq.add(Conv2D(32,3, 2, activation='selu', input_shape=(64,64,3),kernel_initializer='truncated_normal'))\n", " seq.add(Conv2D(8,3, 3,activation='selu',kernel_initializer='truncated_normal'))\n", " seq.add(Dropout(0.7))\n", " seq.add(Flatten())\n", " seq.add(Dense(128, activation='selu', kernel_initializer='truncated_normal'))\n", " return seq\n", "\n", "\n", "def compute_accuracy(y_true, y_pred):\n", " '''Compute classification accuracy with a fixed threshold on distances.\n", " '''\n", " pred = y_pred.ravel() < 0.5\n", " return np.mean(pred == y_true)\n", "\n", "\n", "def accuracy(y_true, y_pred):\n", " '''Compute classification accuracy with a fixed threshold on distances.\n", " '''\n", " return K.mean(K.equal(y_true, K.cast(y_pred < 0.5, y_true.dtype)))\n", " \n", "\n", "X = np.asarray([x for x,y in XY])\n", "Y = np.asarray([y for x,y in XY])\n", " \n", "# the data, shuffled and split between train and test sets\n", "(x_train, y_train), (x_test, y_test) = (X[:-200], Y[:-200]), (X[-200:], Y[-200:])\n", "x_train = x_train\n", "x_test = x_test\n", "x_train = x_train.astype('float32')\n", "x_test = x_test.astype('float32')\n", "(x_train-127) /= 127\n", "(x_test-127) /= 127\n", "input_dim = (64,64,3)\n", "\n", "print(x_train.shape, y_train.shape)\n", "print(x_test.shape, y_test.shape)\n", "\n", "classes = np.unique(Y)\n", "classes_dict_train = {}\n", "classes_dict_test = {}\n", "for i,y in enumerate(y_train):\n", " l = classes_dict_train.get(y, [])\n", " l.append(i)\n", " classes_dict_train[y] = l\n", "\n", "# create training+test positive and negative pairs\n", "tr_pairs, tr_y = create_pairs(x_train, y_train, classes_dict_train, classes)\n", "for i,y in enumerate(y_test):\n", " l = classes_dict_test.get(y, [])\n", " l.append(i)\n", " classes_dict_test[y] = l\n", " \n", "classes = np.unique(y_test) \n", "te_pairs, te_y = create_pairs(x_test, y_test, classes_dict_test, classes)\n", "\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "tr_pairs = np.asarray(tr_pairs)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(101, 2, 64, 64, 3)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tr_pairs.shape" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/Jimmy/anaconda3/envs/ai/lib/python3.6/site-packages/ipykernel_launcher.py:76: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 2), activation=\"selu\", input_shape=(64, 64, 3..., kernel_initializer=\"truncated_normal\")`\n", "/Users/Jimmy/anaconda3/envs/ai/lib/python3.6/site-packages/ipykernel_launcher.py:77: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(8, (3, 3), activation=\"selu\", kernel_initializer=\"truncated_normal\")`\n", "/Users/Jimmy/anaconda3/envs/ai/lib/python3.6/site-packages/keras/preprocessing/image.py:524: UserWarning: This ImageDataGenerator specifies `zca_whitening`, but it hasn'tbeen fit on any training data. Fit it first by calling `.fit(numpy_data)`.\n", " warnings.warn('This ImageDataGenerator specifies '\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/2000\n", "50/50 [==============================] - 20s - loss: 249.1564 - accuracy: 0.4950 - val_loss: 0.4947 - val_accuracy: 0.5050\n", "Epoch 2/2000\n", "50/50 [==============================] - 20s - loss: 0.5009 - accuracy: 0.4988 - val_loss: 0.4947 - val_accuracy: 0.5050\n", "Epoch 3/2000\n", "50/50 [==============================] - 19s - loss: 0.5109 - accuracy: 0.4888 - val_loss: 0.4947 - val_accuracy: 0.5050\n", "Epoch 4/2000\n", "20/50 [===========>..................] - ETA: 12s - loss: 0.4903 - accuracy: 0.5094" ] }, { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-24-04fea43bc30e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 80\u001b[0m model.fit_generator(generator(),steps_per_epoch=50,\n\u001b[1;32m 81\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mEPOCHS\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 82\u001b[0;31m validation_data=([te_pairs[:, 0], te_pairs[:, 1]], te_y), callbacks=[checkpoint_callback, tensorboard_callback])\n\u001b[0m\u001b[1;32m 83\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 84\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/anaconda3/envs/ai/lib/python3.6/site-packages/keras/legacy/interfaces.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(args, kwargs)\u001b[0m\n\u001b[1;32m 85\u001b[0m warnings.warn('Update your `' + object_name +\n\u001b[1;32m 86\u001b[0m '` call to the Keras 2 API: ' + signature, stacklevel=2)\n\u001b[0;32m---> 87\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 88\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_original_function\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 89\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/anaconda3/envs/ai/lib/python3.6/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit_generator\u001b[0;34m(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[1;32m 2040\u001b[0m outs = self.train_on_batch(x, y,\n\u001b[1;32m 2041\u001b[0m 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inputs)\u001b[0m\n\u001b[1;32m 2271\u001b[0m updated = session.run(self.outputs + [self.updates_op],\n\u001b[1;32m 2272\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2273\u001b[0;31m self.session_kwargs)\n\u001b[0m\u001b[1;32m 2274\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mupdated\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2275\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/anaconda3/envs/ai/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m 893\u001b[0m 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"#keras_generator.fit(x_train)\n", "\n", "# network definition\n", "base_network = create_base_network(input_dim)\n", "\n", "input_a = Input(input_dim)\n", "input_b = Input(input_dim)\n", "\n", "# because we re-use the same instance `base_network`,\n", "# the weights of the network\n", "# will be shared across the two branches\n", "processed_a = base_network(input_a)\n", "processed_b = base_network(input_b)\n", "\n", "distance = Lambda(euclidean_distance,\n", " output_shape=eucl_dist_output_shape)([processed_a, processed_b])\n", "\n", "model = Model([input_a, input_b], distance)\n", "\n", "# train\n", "opt = Adam(lr=0.01)\n", "model.compile(loss=contrastive_loss, optimizer=opt, metrics=[accuracy])\n", "\n", "# callbacks\n", "import os\n", "filepath = \"/Users/Jimmy/Desktop/siamese5/\"\n", "os.makedirs(filepath, exist_ok=True)\n", "form = \"checkpoint.{epoch:02d}.hdf5\"\n", "\n", "filepath_tensorboard = \"/Users/Jimmy/Desktop/siamese5/logs\"\n", "os.makedirs(filepath_tensorboard, exist_ok=True)\n", "\n", "\n", "checkpoint_callback = keras.callbacks.ModelCheckpoint(filepath + form, verbose=0, \n", " save_best_only=False, save_weights_only=False, \n", " mode='auto', period=1)\n", "tensorboard_callback = keras.callbacks.TensorBoard(log_dir=filepath_tensorboard, \n", " histogram_freq=0, batch_size=BATCH_SIZE, write_graph=True,\n", " write_grads=False, write_images=False, embeddings_freq=0, \n", " embeddings_layer_names=None, embeddings_metadata=None)\n", "\n", "gen1 = keras_generator.flow(tr_pairs[:, 0], tr_y, batch_size=BATCH_SIZE, shuffle=False)\n", "gen2 = keras_generator.flow(tr_pairs[:, 1], tr_y, batch_size=BATCH_SIZE, shuffle=False)\n", "\n", "def generator():\n", " while True:\n", " x1, tr_y = next(gen1)\n", " x2, tr_y = next(gen2)\n", " ind = np.random.choice(len(tr_y), 16)\n", " \n", " yield [x1[ind],x2[ind]], tr_y[ind]\n", "\n", "model.fit_generator(generator(),steps_per_epoch=50,\n", " epochs=EPOCHS,\n", " validation_data=([te_pairs[:, 0], te_pairs[:, 1]], te_y), callbacks=[checkpoint_callback, tensorboard_callback])\n", "\n", "\n", "# compute final accuracy on training and test sets\n", "y_pred = model.predict([tr_pairs[:, 0], tr_pairs[:, 1]])\n", "tr_acc = compute_accuracy(tr_y, y_pred)\n", "y_pred = model.predict([te_pairs[:, 0], te_pairs[:, 1]])\n", "te_acc = compute_accuracy(te_y, y_pred)\n", "\n", "print(' Accuracy on training set: %0.2f%%' % (100 * tr_acc))\n", "print('* Accuracy on test set: %0.2f%%' % (100 * te_acc))" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 15.26025391]]\n" ] }, { "data": { "text/plain": [ "<matplotlib.image.AxesImage at 0x7f5350d19438>" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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3Ariz8/3DAL4H4CtVY5lZ1y2myTucyFLkLqNyXcz1JxFn3u3lBSB2v7VIHF4U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\nfyBQU8TmDwRqitj8gUBNEZs/EKgp/h/s2wn2JVBhrAAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f53502d4c88>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from matplotlib import pyplot as plt\n", "%matplotlib inline\n", "\n", "pred = model.predict([np.expand_dims(images[10], axis=0), np.expand_dims(images[11], axis=0)])\n", "print(pred)\n", "\n", "plt.imshow(images[11])\n" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.image.AxesImage at 0x7f5348b211d0>" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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VY7vlSgLQ0MMC8KayyjuXGITNVe8esKmspuH5CxeGtqFmPzMGLpkJwNyOPLvN0WgqcsEm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\nqdmF3/jG1wfLHxZ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"text/plain": [ "<matplotlib.figure.Figure at 0x7f535072c668>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.imshow(images[10])" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.2" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
psiq/gdsfactory	notebooks/02_components.ipynb	1	3313	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Components\n", "\n", "We store our component functions inside the `pp.components` module. Each\n", "function there returns a Component object\n", "\n", "You can use `dir` or `help` over the `pp.c` module to see the all available\n", "components.\n", "\n", "Some of which are just shapes, but we call them components as they all inherit\n", "from the component class in `pp.Component`" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import pp" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "c = pp.c.mzi()\n", "pp.qp(c)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "c.ports" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "c = pp.c.ring_single_bus()\n", "pp.qp(c)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Phidl components\n", "\n", "Gdsfactory extends phidl. Therefore all phidl components can be easily used in gdsfactory." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import phidl.geometry as pg" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "components = [\n", " pg.tee(size=(4, 2), stub_size=(2, 1), taper_type=None, layer=0),\n", " pg.optimal_hairpin(\n", " width=0.2, pitch=0.6, length=10, turn_ratio=4, num_pts=50, layer=0\n", " ),\n", " pg.optimal_step(\n", " start_width=10,\n", " end_width=22,\n", " num_pts=50,\n", " width_tol=1e-3,\n", " anticrowding_factor=1.2,\n", " symmetric=False,\n", " layer=0,\n", " ),\n", " pg.optimal_90deg(width=100.0, num_pts=15, length_adjust=1, layer=0),\n", " pg.ytron_round(\n", " rho=1,\n", " arm_lengths=(500, 300),\n", " source_length=500,\n", " arm_widths=(200, 200),\n", " theta=2.5,\n", " theta_resolution=10,\n", " layer=0,\n", " ),\n", "]\n", "\n", "for c in components:\n", " pp.qp(c)\n", " c2 = pp.import_phidl_component(component=c)\n", " pp.show(c2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can see all the components available in `gdsfactory`" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "help(pp.c)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.3" } }, "nbformat": 4, "nbformat_minor": 4 }	mit
bflaven/BlogArticlesExamples	extending_streamlit_usage/003_99_ambitieuses_derwenai_spacy_tutorials/Extract_Text_from_PDF_8.ipynb	1	981365	{ "metadata": { "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.3" }, "orig_nbformat": 2, "kernelspec": { "name": "python3", "display_name": "Python 3.8.3 64-bit ('base': conda)" }, "metadata": { "interpreter": { "hash": "15ce07131e6ac9ef2286e9dc443e97a8b7a1385e6fe0cfbd91ad33021bc29612" } }, "interpreter": { "hash": "15ce07131e6ac9ef2286e9dc443e97a8b7a1385e6fe0cfbd91ad33021bc29612" } }, "nbformat": 4, "nbformat_minor": 2, "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import spacy\n", "#importing plotting libraries\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "import plotly.express as px\n", "import plotly.graph_objects as go\n", "import re\n", "from wordcloud import WordCloud\n", "from plotly.offline import plot\n", "\n", "\n", "# pip install pdfx\n", "import pdfx\n", "pdf = pdfx.PDFx(\"/Users/brunoflaven/Documents/01_work/blog_articles/extending_streamlit_usage/003_99_ambitieuses_derwenai_spacy_tutorials/article_bf_2.pdf\")\n", "# print(\"\\n--- step_1 pdf loaded\")\n", "# pdf\n", "\n", "text = pdf.get_text()\n", "# text\n", "# print(\"\\n--- step_2 text loaded\")\n", "# text\n", "\n", "import spacy\n", "from collections import Counter\n", "nlp = spacy.load(\"en_core_web_sm\")\n", "# nlp = spacy.load('fr_core_news_sm')\n", "doc = nlp(text)\n", "# print(\"\\n--- step_3 spacy loaded\")\n", "\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "\n", "# print(\"\\n--- step_7 spacy doc object into pandas dataframe\")\n", "# GREAT load the spacy doc object into into a dataframe of the parsed tokens\n", "import pandas as pd\n", "\n", "cols = (\"text\", \"lemma\", \"POS\", \"explain\", \"stopword\")\n", "rows = []\n", "\n", "for t in doc:\n", " if t.is_stop == False and t.text.isalpha() == True:\n", " # print(t.text)\n", " row = [t.text, t.lemma_, t.pos_, spacy.explain(t.pos_), t.is_stop]\n", " rows.append(row)\n", "\n", "# We can either keep it in dictionary format or put it into a pandas dataframe\n", "pd.set_option('max_colwidth',150)\n", "data_df = pd.DataFrame(rows, columns=cols)\n", "data_df = data_df.sort_index()\n", "\n", "# OUTPUT\n", "# data_df\n", "# data_df.head()\n", "\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " text lemma POS explain stopword token\n", "0 Python Python PROPN proper noun False []\n", "1 Randomization Randomization PROPN proper noun False []\n", "2 Random Random PROPN proper noun False []\n", "3 good good ADJ adjective False []\n", "4 reasons reason NOUN noun False []" ], "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>text</th>\n <th>lemma</th>\n <th>POS</th>\n <th>explain</th>\n <th>stopword</th>\n <th>token</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>Python</td>\n <td>Python</td>\n <td>PROPN</td>\n <td>proper noun</td>\n <td>False</td>\n <td>[]</td>\n </tr>\n <tr>\n <th>1</th>\n <td>Randomization</td>\n <td>Randomization</td>\n <td>PROPN</td>\n <td>proper noun</td>\n <td>False</td>\n <td>[]</td>\n </tr>\n <tr>\n <th>2</th>\n <td>Random</td>\n <td>Random</td>\n <td>PROPN</td>\n <td>proper noun</td>\n <td>False</td>\n <td>[]</td>\n </tr>\n <tr>\n <th>3</th>\n <td>good</td>\n <td>good</td>\n <td>ADJ</td>\n <td>adjective</td>\n <td>False</td>\n <td>[]</td>\n </tr>\n <tr>\n <th>4</th>\n <td>reasons</td>\n <td>reason</td>\n <td>NOUN</td>\n <td>noun</td>\n <td>False</td>\n <td>[]</td>\n </tr>\n </tbody>\n</table>\n</div>" }, "metadata": {}, "execution_count": 5 } ], "source": [ "\n", "from spacy.lang.en.stop_words import STOP_WORDS\n", "# from spacy.lang.fr.stop_words import STOP_WORDS\n", "import string\n", "\n", "from spacy.lang.en import English\n", "# from spacy.lang.fr import French\n", "filtro = list(STOP_WORDS)\n", "pontu = string.punctuation\n", "\n", "parser = English()\n", "# parser = French()\n", "# for EN \n", "blacklist = {}\n", "\n", "# for PT \n", "# blacklist = {'a', 'e', 'o', 'que'}\n", "\n", "# for FR \n", "blacklist = {'j\\'','y', 'a', 'et', 'que'}\n", "\n", "def token_treatment(titulos):\n", " tokens = parser(titulos)\n", " tokens = [word.lemma_.lower().strip() if word.lemma_ != \"-PRON-\" else word.lower_ for word in tokens]\n", " tokens = [word for word in tokens if word not in filtro and word not in pontu]\n", " tokens = [word for word in tokens if word not in blacklist]\n", " return tokens\n", "\n", "data_df['token'] = data_df.text.apply(token_treatment)\n", "data_df.head()\n", "# data_df.sample(5)\n", "# data_df.info()\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "num of rows: 300 \n\n" ] } ], "source": [ "# number of rows\n", "print('num of rows:', len(data_df), '\\n')\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "output_type": "display_data", "data": { "text/plain": "<Figure size 1152x648 with 1 Axes>", "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<svg height=\"460.8pt\" version=\"1.1\" viewBox=\"0 0 907.2 460.8\" width=\"907.2pt\" xmlns=\"http://www.w3.org/2000/svg\" 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\n" }, "metadata": { "needs_background": "light" } } ], "source": [ "\n", "def wordcloud_gen(df, column_name):\n", " regex = r'\\w+'\n", " names_combined = []\n", " for name in df[column_name]:\n", " words = re.findall(regex,name)\n", " words = [word.lower() for word in words]\n", " names_combined.extend(words)\n", " wordcloud = WordCloud(width=800, height=400,max_font_size=50, background_color=\"black\", collocations=False).generate((\" \").join(names_combined))\n", " plt.figure(figsize=(16,9))\n", " plt.imshow(wordcloud, interpolation=\"bilinear\")\n", " plt.axis(\"off\")\n", " # plt.show()\n", " #Saving the plot as an image\n", " plt.savefig('Extract_Text_from_PDF_8.png', bbox_inches='tight', dpi=150)\n", " #Showing the plot\n", " # plt.show() \n", "\n", "# generate most used words in the names of ear buds\n", "wordcloud_gen(data_df, 'text')\n", "# some of the famous words" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ] }	mit
ML4DS/ML4all	C2.kNN_Classification/kNN_Classification_student.ipynb	1	35626	{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "\n", "# The $k$-Nearest Neighbor Classification Algorithm\n", "\n", " Notebook version: 2.2 (Oct 25, 2020)\n", "\n", " Author: Jesús Cid Sueiro ([email protected])\n", " Jerónimo Arenas García ([email protected])\n", " \n", " Changes: v.1.0 - First version\n", " v.1.1 - Function loadDataset updated to work with any number of dimensions\n", " v.2.0 - Compatible with Python 3 (backcompatible with Python 2.7)\n", " Added solution to Exercise 3\n", " v.2.1 - Minor corrections regarding notation\n", " v.2.2 - Adaptation for slides conversion\n", " " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "# To visualize plots in the notebook\n", "%matplotlib inline \n", "\n", "# Import some libraries that will be necessary for working with data and displaying plots\n", "import csv # To read csv files\n", "import random\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from scipy import spatial\n", "from sklearn import neighbors, datasets" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## 1. The binary classification problem.\n", "\n", "In a binary classification problem, we are given an observation vector ${\\bf x}\\in \\mathbb{R}^N$ which is known to belong to one and only one category or class, $y$, in the set ${\\mathcal Y} = \\{0, 1\\}$. The goal of a classifier system is to predict the value of $y$ based on ${\\bf x}$.\n", "\n", "To design the classifier, we are given a collection of labelled observations ${\\mathcal D} = \\{({\\bf x}_k, y_k)\\}_{k=0}^{K-1}$ where, for each observation ${\\bf x}_k$, the value of its true category, $y_k$, is known. All samples are outcomes of an unknown distribution $p_{{\\bf X},Y}({\\bf x}, y)$." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## 2. The Iris dataset\n", "\n", "(Iris dataset presentation is based on this <a href=http://machinelearningmastery.com/tutorial-to-implement-k-nearest-neighbors-in-python-from-scratch/> Tutorial </a> by <a href=http://machinelearningmastery.com/about/> Jason Brownlee</a>) \n", "\n", "To illustrate the algorithms, we will consider the <a href = http://archive.ics.uci.edu/ml/datasets/Iris> Iris dataset </a>, taken from the <a href=http://archive.ics.uci.edu/ml/> UCI Machine Learning repository </a>. Quoted from the dataset description:\n", "\n", "> This is perhaps the best known database to be found in the pattern recognition literature. The data set contains 3 classes of 50 instances each, where each class refers to a type of iris plant. [...] One class is linearly separable from the other 2; the latter are NOT linearly separable from each other. \n", "\n", "The class is the species, which is one of setosa, versicolor or virginica. Each instance contains 4 measurements of given flowers: sepal length, sepal width, petal length and petal width, all in centimeters. \n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "# Taken from Jason Brownlee notebook.\n", "with open('datasets/iris.data', 'r') as csvfile:\n", "\tlines = csv.reader(csvfile)\n", "\tfor row in lines:\n", "\t\tprint(', '.join(row))" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### 2.1. Train/test split\n", "\n", "Next, we will split the data into two sets:\n", "\n", "* Training set, that will be used to learn the classification model\n", "* Test set, that will be used to evaluate the classification performance\n", "\n", "The data partition must be random, in such a way that the statistical distribution of both datasets is the same." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "The code fragment below defines a function `loadDataset` that loads the data in a CSV with the provided filename, converts the flower measures (that were loaded as strings) into numbers and, finally, it splits the data into a training and test sets." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "# Adapted from a notebook by Jason Brownlee\n", "def loadDataset(filename, split):\n", " xTrain = []\n", " cTrain = []\n", " xTest = []\n", " cTest = []\n", "\n", " with open(filename, 'r') as csvfile:\n", " lines = csv.reader(csvfile)\n", " dataset = list(lines)\n", " for i in range(len(dataset)-1):\n", " for y in range(4):\n", " dataset[i][y] = float(dataset[i][y])\n", " item = dataset[i]\n", " if random.random() < split:\n", " xTrain.append(item[0:-1])\n", " cTrain.append(item[-1])\n", " else:\n", " xTest.append(item[0:-1])\n", " cTest.append(item[-1])\n", " return xTrain, cTrain, xTest, cTest" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "We can use this function to get a data split. An expected ratio of 67/33 samples for train/test will be used. However, note that, because of the way samples are assigned to the train or test datasets, the exact number of samples in each partition will differ if you run the code several times." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "xTrain_all, cTrain_all, xTest_all, cTest_all = loadDataset('datasets/iris.data', 0.67)\n", "nTrain_all = len(xTrain_all)\n", "nTest_all = len(xTest_all)\n", "print('Train:', nTrain_all)\n", "print('Test:', nTest_all)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### 2.2. Versicolor vs Virginica\n", "\n", "In the following, we will design a classifier to separate classes \"Versicolor\" and \"Virginica\" using $x_0$ and $x_1$ only. To do so, we build a training set with samples from these categories, and a bynary label $y^{(k)} = 1$ for samples in class \"Virginica\", and $0$ for \"Versicolor\" data." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "# Select two classes\n", "c0 = 'Iris-versicolor' \n", "c1 = 'Iris-virginica'\n", "\n", "# Select two coordinates\n", "ind = [0, 1]\n", "\n", "# Take training test\n", "X_tr = np.array([[xTrain_all[n][i] for i in ind] for n in range(nTrain_all) \n", " if cTrain_all[n]==c0 or cTrain_all[n]==c1])\n", "C_tr = [c for c in cTrain_all if c==c0 or c==c1]\n", "Y_tr = np.array([int(c==c1) for c in C_tr])\n", "n_tr = len(X_tr)\n", "\n", "# Take test set\n", "X_tst = np.array([[xTest_all[n][i] for i in ind] for n in range(nTest_all) \n", " if cTest_all[n]==c0 or cTest_all[n]==c1])\n", "C_tst = [c for c in cTest_all if c==c0 or c==c1]\n", "Y_tst = np.array([int(c==c1) for c in C_tst])\n", "n_tst = len(X_tst)\n", "\n", "# Separate components of x into different arrays (just for the plots)\n", "x0c0 = [X_tr[n][0] for n in range(n_tr) if Y_tr[n]==0]\n", "x1c0 = [X_tr[n][1] for n in range(n_tr) if Y_tr[n]==0]\n", "x0c1 = [X_tr[n][0] for n in range(n_tr) if Y_tr[n]==1]\n", "x1c1 = [X_tr[n][1] for n in range(n_tr) if Y_tr[n]==1]" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "A scatter plot is useful to get some insights on the difficulty of the classification problem" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "# Scatterplot.\n", "labels = {'Iris-setosa': 'Setosa', \n", " 'Iris-versicolor': 'Versicolor',\n", " 'Iris-virginica': 'Virginica'}\n", "plt.plot(x0c0, x1c0,'r.', label=labels[c0])\n", "plt.plot(x0c1, x1c1,'g+', label=labels[c1])\n", "plt.xlabel('$x_' + str(ind[0]) + '$')\n", "plt.ylabel('$x_' + str(ind[1]) + '$')\n", "plt.legend(loc='best')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## 3. Baseline Classifier: Maximum A Priori.\n", "\n", "For the selected data set, we have two clases and a dataset with the following class proportions:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "print(f'Class 0 {c0}: {n_tr - sum(Y_tr)} samples')\n", "print(f'Class 1 ({c1}): {sum(Y_tr)} samples')" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "The maximum a priori classifier assigns any sample ${\\bf x}$ to the most frequent class in the training set. Therefore, the class prediction $y$ for any sample ${\\bf x}$ is" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "y = int(2sum(Y_tr) > n_tr)\n", "print(f'y = {y} ({c1 if y==1 else c0})')" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "The error rate for this baseline classifier is:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "# Training and test error arrays\n", "E_tr = (Y_tr != y)\n", "E_tst = (Y_tst != y)\n", "\n", "# Error rates\n", "pe_tr = float(sum(E_tr)) / n_tr\n", "pe_tst = float(sum(E_tst)) / n_tst\n", "print('Pe(train):', pe_tr)\n", "print('Pe(test):', pe_tst)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "The error rate of the baseline classifier is a simple benchmark for classification. Since the maximum a priori decision is independent on the observation, ${\\bf x}$, any classifier based on ${\\bf x}$ should have a better (or, at least, not worse) performance than the baseline classifier." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## 4. The Nearest-Neighbour Classifier (1-NN).\n", "\n", "\n", "The 1-NN classifier assigns any instance ${\\bf x}$ to the category of the nearest neighbor in the training set.\n", "$$\n", "d = f({\\bf x}) = y_n, {\\rm~where} \\\\\n", "n = \\arg \\min_k \\\|{\\bf x}-{\\bf x}_k\\\|\n", "$$\n", "In case of ties (i.e. if there is more than one instance at minimum distance) the class of one of them, taken arbitrarily, is assigned to ${\\bf x}$." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "def nn_classifier(X1, Y1, X2):\n", " \"\"\" Compute the 1-NN classification for the observations contained in\n", " the rows of X2, for the training set given by the rows in X1 and the\n", " class labels contained in Y1.\n", " \"\"\"\n", " if X1.ndim == 1:\n", " X1 = np.asmatrix(X1).T\n", " if X2.ndim == 1:\n", " X2 = np.asmatrix(X2).T\n", " distances = spatial.distance.cdist(X1,X2,'euclidean')\n", " neighbors = np.argsort(distances, axis=0, kind='quicksort', order=None)\n", " closest = neighbors[0,:]\n", " y_values = np.zeros([X2.shape[0],1])\n", " for idx in range(X2.shape[0]):\n", " y_values[idx] = Y1[closest[idx]]\n", " \n", " return y_values" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Let us apply the 1-NN classifier to the given dataset. First, we will show the decision regions of the classifier. To do so, we compute the classifier output for all points in a rectangular grid from the sample space." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "# Create a regtangular grid.\n", "n_points = 200\n", "x_min, x_max = X_tr[:, 0].min(), X_tr[:, 0].max() \n", "y_min, y_max = X_tr[:, 1].min(), X_tr[:, 1].max()\n", "dx = x_max - x_min\n", "dy = y_max - y_min\n", "h = dy / n_points\n", "xx, yy = np.meshgrid(np.arange(x_min - 0.1 dx, x_max + 0.1 * dx, h),\n", " np.arange(y_min - 0.1 * dx, y_max + 0.1 * dy, h))\n", "X_grid = np.array([xx.ravel(), yy.ravel()]).T\n", "\n", "# Compute the classifier output for all samples in the grid.\n", "Z = nn_classifier(X_tr, Y_tr, X_grid)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we plot the results" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "# Put the result into a color plot\n", "plt.plot(x0c0, x1c0,'r.', label=labels[c0])\n", "plt.plot(x0c1, x1c1,'g+', label=labels[c1])\n", "plt.xlabel('$x_' + str(ind[0]) + '$')\n", "plt.ylabel('$x_' + str(ind[1]) + '$')\n", "plt.legend(loc='best')\n", "\n", "Z = Z.reshape(xx.shape)\n", "plt.contourf(xx, yy, Z)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "We can observe that the decision boudary of the 1-NN classifier is rather intricate, and it may contain small islands covering one or few samples from one class. Actually, the extension of these small regions usually reduces as we have more training samples, though the number of them may increase.\n", "\n", "Now we compute the error rates over the training and test sets." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "# Training errors\n", "Z_tr = nn_classifier(X_tr, Y_tr, X_tr)\n", "E_tr = Z_tr.flatten()!=Y_tr\n", "\n", "# Test errors\n", "Z_tst = nn_classifier(X_tr, Y_tr, X_tst)\n", "E_tst = Z_tst.flatten()!=Y_tst\n", "\n", "# Error rates\n", "pe_tr = float(sum(E_tr)) / n_tr\n", "pe_tst = float(sum(E_tst)) / n_tst\n", "print('Pe(train):', pe_tr)\n", "print('Pe(test):', pe_tst)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "The training and test error rates of the 1-NN may be significantly different. In fact, the training error may go down to zero if samples do not overlap. In the selected problem, this is not the case, because samples from different classes coincide at the same point, causing some classification errors." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### 4.1. Consistency of the 1-NN classifier\n", "\n", "Despite the 1-NN usually reduces the error rate with respect to the baseline classifier, the number of errors may be too large. Errors may be attributed to diferent causes:\n", "\n", " 1. The class distributions are overlapped, because the selected features have no complete information for discriminating between the classes: this would imply that, even the best possible classifier would be prone to errors.\n", " 2. The training sample is small, and it is not enough to obtaing a good estimate of the optimal classifiers.\n", " 3. The classifier has intrinsic limitations: even though we had an infinite number of samples, the classifier performance does not approach the optimal classifiers.\n", "\n", "In general, a classifier is said to be consistent if it makes nearly optimal decisions as the number of training samples increases. Actually, it can be shown that this is the case of the 1-NN classifier if the classification problem is separable, i.e. if there exist a decision boundary with zero error probability. Unfortunately, in a non-separable case, the 1-NN classifier is not consistent. It can be shown that the error rate of the 1-NN classifier converges to an error rate which is not worse than twice the minimum attainable error rate (Bayes error rate) as the number of training samples goes to infinity." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Exercise 1: In this exercise we test the non-consistency of the 1-NN classifier for overlapping distributions. Generate an artificial dataset for classification as follows:\n", "\n", "- Generate $N$ binary labels at random with values '0' and '1'. Store them in vector ${\\bf y}$\n", "- For every label $y_k$ in ${\\bf y}$:\n", " - If the label is 0, take sample $x_k$ at random from a uniform distribution $U(0,2)$.\n", " - If the label is 1, take sample $x_k$ at random from a uniform distribution $U(1,5)$.\n", "\n", "Take $N=1000$ for the test set. This is a large sample to get accurate error rate estimates. Also, take $N=10$, $20$, $40$, $80$,... for the training set. Compute the 1-NN classifier, and observe the test error rate as a function of $N$. \n", "\n", "Now, compute the test error rate of the classifier making decision $1$ if $x_k>1.5$, and $0$ otherwise. \n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# <SOL>\n", "# </SOL>" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## 5. $k$-NN classifier\n", "\n", "A simple extension of the 1-NN classifier is the $k$-NN classifier, which, for any input sample ${\\bf x}$, computes the $k$ closest neighbors in the training set, and takes the majority class in the subset. To avoid ties, in the binary classification case $k$ is usually taken as an odd number.\n", "\n", "The following method implements the $k$-NN classifiers." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "def knn_classifier(X1,Y1,X2,k):\n", " \"\"\" Compute the k-NN classification for the observations contained in\n", " the rows of X2, for the training set given by the rows in X1 and the\n", " components of S1. k is the number of neighbours.\n", " \"\"\"\n", " if X1.ndim == 1:\n", " X1 = np.asmatrix(X1).T\n", " if X2.ndim == 1:\n", " X2 = np.asmatrix(X2).T\n", " distances = spatial.distance.cdist(X1,X2,'euclidean')\n", " neighbors = np.argsort(distances, axis=0, kind='quicksort', order=None)\n", " closest = neighbors[range(k),:]\n", " \n", " y_values = np.zeros([X2.shape[0],1])\n", " for idx in range(X2.shape[0]):\n", " y_values[idx] = np.median(Y1[closest[:,idx]])\n", " \n", " return y_values" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, we can plot the decision boundaries for different values of $k$" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "k = 15\n", "\n", "# Plot the decision boundary. For that, we will assign a color to each\n", "# point in the mesh [x_min, m_max]x[y_min, y_max].\n", "Z = knn_classifier(X_tr, Y_tr, X_grid, k)\n", "\n", "# Put the result into a color plot\n", "plt.plot(x0c0, x1c0,'r.', label=labels[c0])\n", "plt.plot(x0c1, x1c1,'g+', label=labels[c1])\n", "plt.xlabel('$x_' + str(ind[0]) + '$')\n", "plt.ylabel('$x_' + str(ind[1]) + '$')\n", "plt.legend(loc='best')\n", "\n", "Z = Z.reshape(xx.shape)\n", "plt.contourf(xx, yy, Z)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### 5.1. Influence of $k$\n", "\n", "We can analyze the influence of parameter $k$ by observing both traning and test errors." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "# Plot training and test error as a function of parameter k.\n", "pe_tr = []\n", "pe_tst = []\n", "k_list = [2n+1 for n in range(int(np.floor(n_tr/2)))]\n", "\n", "for k in k_list:\n", " # Training errors\n", " Z_tr = knn_classifier(X_tr, Y_tr, X_tr, k)\n", " E_tr = Z_tr.flatten()!=Y_tr\n", "\n", " # Test errors\n", " Z_tst = knn_classifier(X_tr, Y_tr, X_tst, k)\n", " E_tst = Z_tst.flatten()!=Y_tst\n", "\n", " # Error rates\n", " pe_tr.append(float(sum(E_tr)) / n_tr)\n", " pe_tst.append(float(sum(E_tst)) / n_tst)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "# Put the result into a color plot\n", "markerline, stemlines, baseline = plt.stem(k_list, pe_tr, 'r', markerfmt='s', label='Training',\n", " use_line_collection=True)\n", "plt.plot(k_list, pe_tr,'r:')\n", "plt.setp(markerline, 'markerfacecolor', 'r', )\n", "plt.setp(baseline, 'color','r', 'linewidth', 2)\n", "markerline, stemlines, baseline = plt.stem(k_list, pe_tst, label='Test', use_line_collection=True)\n", "plt.plot(k_list, pe_tst,'b:')\n", "plt.xlabel('$k$')\n", "plt.ylabel('Error rate')\n", "plt.legend(loc='best')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Exercise 2: Observe the train and test error for large $k$. Could you relate the error rate of the baseline classifier with that to the $k$-NN for certain value of $k$? " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "The figure above suggests that the optimal value of $k$ is" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "i = np.argmin(pe_tst)\n", "k_opt = k_list[i]\n", "print('k_opt:', k_opt)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "However, using the test set to select the optimal value of the hyperparameter $k$ is not allowed. Instead, we should recur to cross validation." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### 5.2 Hyperparameter selection via cross-validation\n", "\n", "An inconvenient of the application of the $k$-NN method is that the selection of $k$ influences the final error of the algorithm. In the previous experiments, we noticed that the location of the minimum is not necessarily the same from the perspective of the test and training data. Ideally, we would like that the designed classification model works as well as possible on future unlabeled patterns that are not available during the training phase. This property is known as generalization. \n", "\n", "Fitting the training data is only pursued in the hope that we are also indirectly obtaining a model that generalizes well. In order to achieve this goal, there are some strategies that try to guarantee a correct generalization of the model. One of such approaches is known as <b>cross-validation</b>." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Since using the test labels during the training phase is not allowed (they should be kept aside to simultate the future application of the classification model on unseen patterns), we need to figure out some way to improve our estimation of the hyperparameter that requires only training data. Cross-validation allows us to do so by following the following steps:\n", "\n", " 1. Split the training data* into several (generally non-overlapping) subsets. If we use $M$ subsets, the method is referred to as $M$-fold cross-validation. If we consider each pattern a different subset, the method is usually referred to as leave-one-out (LOO) cross-validation.\n", " 2. Train of the system $M$ times. For each run, use a different partition as a validation set, and use the restating partitions as the training set. Evaluate the performance for different choices of the hyperparameter (i.e., for different values of $k$ for the $k$-NN method).\n", " 3. Average the validation error over all partitions, and pick the hyperparameter that provided the minimum validation error.\n", " 4. Rerun the algorithm using all the training data, keeping the value of the parameter that came out of the cross-validation process." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "## k-nn with M-fold cross validation\n", "\n", "# Obtain the indices for the different folds\n", "n_tr = X_tr.shape[0]\n", "M = n_tr\n", "permutation = np.random.permutation(n_tr)\n", "# Initialize sets of indices\n", "set_indices = {n: [] for n in range(M)}\n", "\n", "# Distribute data amont M partitions\n", "n = 0\n", "for pos in range(n_tr):\n", " set_indices[n].append(permutation[pos])\n", " n = (n+1) % M\n", "\n", "# Now, we run the cross-validation process using the k-nn method\n", "k_max = 30\n", "k_list = [2j+1 for j in range(int(k_max))]\n", "\n", "# Obtain the validation errors\n", "pe_val = 0\n", "for n in range(M):\n", " i_val = set_indices[n]\n", " i_tr = []\n", " for kk in range(M):\n", " if not n==kk:\n", " i_tr += set_indices[kk]\n", " \n", " pe_val_iter = []\n", " for k in k_list:\n", " y_tr_iter = knn_classifier(X_tr[i_tr], Y_tr[i_tr], X_tr[i_val], k)\n", " pe_val_iter.append(np.mean(Y_tr[i_val] != y_tr_iter))\n", "\n", " pe_val = pe_val + np.asarray(pe_val_iter).T\n", "\n", "pe_val = pe_val / M" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "# We compute now the train and test errors curves\n", "pe_tr = [np.mean(Y_tr != knn_classifier(X_tr, Y_tr, X_tr, k).T) for k in k_list]\n", "k_opt = k_list[np.argmin(pe_val)]\n", "pe_tst = np.mean(Y_tst != knn_classifier(X_tr, Y_tr, X_tst, k_opt).T)\n", "\n", "plt.plot(k_list, pe_tr,'b--o',label='Training error')\n", "plt.plot(k_list, pe_val.T,'g--o',label='Validation error')\n", "plt.stem([k_opt], [pe_tst],'r-o',label='Test error', use_line_collection=True)\n", "plt.legend(loc='best')\n", "plt.title('The optimal value of $k$ is ' + str(k_opt))\n", "plt.xlabel('$k$')\n", "plt.ylabel('Error rate')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## 6. Scikit-learn implementation\n", "\n", "In practice, most well-known machine learning methods are implemented and available for python. Probably, the most complete library for machine learning is <a href=http://scikit-learn.org/stable/>Scikit-learn</a>. The following piece of code uses the method\n", "\n", " KNeighborsClassifier\n", " \n", "available in Scikit-learn, to compute the $k$-NN classifier using the four components of the observations in the original dataset. This routine allows us to classify a particular point using a weighted average of the targets of the neighbors:\n", "\n", " To classify point ${\\bf x}$:\n", " \n", " - Find $k$ closest points to ${\\bf x}$ in the training set\n", " - Average the corresponding targets, weighting each value according to the distance of each point to ${\\bf x}$, so that closer points have a larger influence in the estimation.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "k = 5\n", "# import some data to play with\n", "iris = datasets.load_iris()\n", "\n", "# Take training test\n", "X_tr = np.array([xTrain_all[n] for n in range(nTrain_all) \n", " if cTrain_all[n]==c0 or cTrain_all[n]==c1])\n", "C_tr = [c for c in cTrain_all if c==c0 or c==c1]\n", "Y_tr = np.array([int(c==c1) for c in C_tr])\n", "n_tr = len(X_tr)\n", "\n", "# Take test set\n", "X_tst = np.array([xTest_all[n] for n in range(nTest_all) \n", " if cTest_all[n]==c0 or cTest_all[n]==c1])\n", "C_tst = [c for c in cTest_all if c==c0 or c==c1]\n", "Y_tst = np.array([int(c==c1) for c in C_tst])\n", "n_tst = len(X_tst)\n", "\n", "for weights in ['uniform', 'distance']:\n", " # we create an instance of Neighbours Classifier and fit the data.\n", " clf = neighbors.KNeighborsClassifier(k, weights=weights)\n", " clf.fit(X_tr, Y_tr)\n", " Z = clf.predict(X_tst)\n", " pe_tst = np.mean(Y_tst != Z)\n", " print(f'Test error rate with {weights} weights = {pe_tst}')" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "<a href = http://scikit-learn.org/stable/auto_examples/neighbors/plot_classification.html> Here</a> you can find an example of the application of `KNeighborsClassifier` to the complete 3-class Iris flower classification problem." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## 7. $k$-NN Classification and Probability Estimation.\n", "\n", "If a sample ${\\bf x}$ has $m$ neighbors from class 1 and $k-m$ neighbors from class $0$, we can estimate the posterior probability* that an observation ${\\bf x}$ belongs to class 1 as\n", "$$\n", "\\hat P\\{y=1\|x\\} = \\frac{m}{k}\n", "$$\n", "Therefore, besides computing a decision about the class of the data, we can modify the $k$-NN algorithm to obtain posterior probability estimates." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Note that the above equation is equivalent to\n", "$$\n", "\\hat P\\{y=1\|x\\} = \\frac{\\sum_{n \\in {\\mathcal N}({\\bf x})} y^{(n)}}{k}\n", "$$\n", "where ${\\mathcal N}({\\bf x})$ is the set of indices for the samples in the neighborhood of $\\bf x$.\n", "\n", "In other words, $\\hat P\\{y=1\|x\\}$ is the average of the neighbor labels. This is actually what the $k$-NN regression algorithm does. Thus, we can estimate the posterior using the `sklearn` regression methods from `KNeighborsRegressor`.\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Exercise 3: Plot a $k$-NN posterior probability map for the Iris flower data, for $k=15$." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "# Select two classes\n", "c0 = 'Iris-versicolor' \n", "c1 = 'Iris-virginica'\n", "# Select two coordinates\n", "coords = [0, 1]\n", "\n", "# Take the selected coordinates only\n", "X_tr = np.array(xTrain_all)[:, coords]\n", "X_tst = np.array(xTest_all)[:, coords]\n", "\n", "# Take training test\n", "ind = [i for i, c in enumerate(cTrain_all) if c==c0 or c==c1]\n", "X_tr = X_tr[ind, :]\n", "C_tr = np.array(cTrain_all)[ind]\n", "Y_tr = (C_tr == c1)\n", "\n", "# Take test set\n", "ind = [i for i, c in enumerate(cTest_all) if c==c0 or c==c1]\n", "X_tst = X_tst[ind, :]\n", "C_tst = np.array(cTest_all)[ind]\n", "Y_tst = (C_tst == c1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "#<SOL>\n", "#</SOL>" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "#<SOL>\n", "#</SOL>" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "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": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 4 }	mit
CalPolyPat/phys202-2015-work	assignments/project/Base Network Tangent Cost Function and L2 Regularization wAdaptive learning constant.ipynb	2	539353	{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ ":0: FutureWarning: IPython widgets are experimental and may change in the future.\n" ] } ], "source": [ "import numpy as np\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "matplotlib.style.use('ggplot')\n", "from mpl_toolkits.mplot3d import Axes3D\n", "import IPython.html.widgets as widg\n", "from IPython.display import clear_output\n", "import sys\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "class Network:\n", " def __init__(self, shape):\n", " \"\"\"The base network class. This defines a simple feed-forward network with appropriate weights and biases.\n", " \n", " Arguments:\n", " shape (list-like): This defines the # of layers and # of neurons per layer in your network.\n", " Each element of the array or list adds a new layer with the number neurons specified by the element.\n", " Variables:\n", " self.shape: see shape.\n", " self.weights: A list of numpy arrays containing the weights corresponding to each channel between neurons.\n", " self.biases: A list of numpy arrays containing the biases corresponding to each neuron.\n", " self.errors: A list of numpy arrays containing the error of each neurons in any iteration of the training process.\n", " self.eta: A float representing the learning rate.\n", " self.lam: A scale factor used in L2 regularization\n", " \"\"\"\n", " \n", " self.shape = np.array(shape) #shape is array-like, i.e. (2,3,4) is a 2 input, 3 hidden node, 4 output network\n", " self.weights = [np.random.ranf((self.shape[i],self.shape[i-1])).1 for i in range(1,len(self.shape))]\n", " self.biases = [np.random.ranf((self.shape[i],)).1 for i in range(1,len(self.shape))]\n", " self.errors = [np.random.ranf((self.shape[i],)) for i in range(1,len(self.shape))]\n", " self.eta = .1\n", " self.lam = .01\n", " self.wrong = 0\n", " self.total = 0\n", " def sigmoid(self, inputs):\n", " \"\"\"Computes the sigmoid function of some input.\n", " \n", " Arguments:\n", " inputs (float or numpy array): The input or inputs to be fed through the sigmoid function.\n", " \"\"\"\n", " \n", " return 1/(1+np.exp(-inputs))\n", " def feedforward(self, inputs):\n", " \"\"\"Feeds inputs through the network and returns the output.\n", " \n", " Arguments:\n", " inputs (numpy array): The inputs to the network, must be the same size as the first(input) layer.\n", " \n", " Variables:\n", " self.activation: A list of numpy arrays corresponding to the output of each neuron in your network.\n", " \"\"\"\n", " \n", " assert inputs.shape==self.shape[0] #inputs must feed directly into the first layer.\n", " self.activation = [np.zeros((self.shape[i],)) for i in range(len(self.shape))]\n", " self.activation[0] = inputs\n", " for i in range(1,len(self.shape)):\n", " self.activation[i]=self.sigmoid(np.dot(self.weights[i-1],self.activation[i-1])+self.biases[i-1])\n", " return self.activation[-1]\n", " def calc_learning_rate(self,grad):\n", " if grad>.85:\n", " self.eta=.1/grad*.11/(.25(2np.pi)*.5)np.exp(-(grad)*2/(2(.25)2))\n", " self.wrong+=1\n", " else:\n", " self.eta=.1/grad.61/(.4(2np.pi).5)np.exp(-(grad)*2/(2(.4)*2))(grad+.08)\n", " self.total+=1\n", " def comp_error(self, answer):\n", " \"\"\"Computes the errors of each neuron.(Typically called Back Propagation)\n", " \n", " Arguments:\n", " answers (numpy array): The expected output from the network.\n", " \"\"\"\n", "# if (self.activation[-1]-answer).any>.15:\n", "# self.eta = .005\n", "# else: \n", "# self.eta = .5\n", " self.calc_learning_rate(np.amax(np.abs((self.activation[-1]-answer))))\n", " #print(np.amax(np.abs((self.activation[-1]-answer))))\n", " assert answer.shape==self.activation[-1].shape\n", " self.errors[-1] = np.pinp.tan(np.pi/2(self.activation[-1]-answer))1/np.cos(np.pi/2(self.activation[-1]-answer))*2np.exp(np.dot(self.weights[-1],self.activation[-2])+self.biases[-1])/(np.exp(np.dot(self.weights[-1],self.activation[-2])+self.biases[-1])+1)*2\n", " for i in range(len(self.shape)-2, 0, -1):\n", " self.errors[i-1] = self.weights[i].transpose().dot(self.errors[i])np.exp(np.dot(self.weights[i-1],self.activation[i-1])+self.biases[i-1])/(np.exp(np.dot(self.weights[i-1],self.activation[i-1])+self.biases[i-1])+1)*2\n", " def grad_descent(self):\n", " \"\"\"Changes each variable based on the gradient descent algorithm.\"\"\"\n", " \n", " #for i in range(len(self.biases)):\n", " # self.biases[i]=self.biases[i]-self.etaself.errors[i]\n", " for i in range(len(self.weights)):\n", " self.biases[i]=self.biases[i]-self.etaself.errors[i]\n", " for j in range(self.weights[i].shape[0]):\n", " for k in range(self.weights[i].shape[1]):\n", " self.weights[i][j,k] = (1-self.etaself.lam/1000)self.weights[i][j,k] - self.etaself.activation[i][k]self.errors[i][j]\n", " def train(self, inputs, answer):\n", " \"\"\"Trains the network.\n", " \n", " Arguments:\n", " inputs (numpy array): The inputs to the network, must be the same size as the first(input) layer.\n", " answers (numpy array): The expected output from the network, must be the same size as the last(output) layer.\n", " \"\"\"\n", " \n", " self.feedforward(inputs)\n", " self.comp_error(answer)\n", " self.grad_descent()\n", " def get_fractional_err(self):\n", " return(self.wrong/(self.total1.0))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# add piecewise def for learning rate\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0.60914045]\n", "[ 0.4997592]\n" ] } ], "source": [ "n1 = Network([2,15,1])\n", "print n1.feedforward(np.array([1,2]))\n", "for i in range(1000):\n", " n1.train(np.array([1,2]), np.array([.5]))\n", "print n1.feedforward(np.array([1,2]))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0. 0. 0.05 0.13 0.09 0.01 0. 0. 0. 0. 0.13 0.15\n", " 0.1 0.15 0.05 0. 0. 0.03 0.15 0.02 0. 0.11 0.08 0. 0.\n", " 0.04 0.12 0. 0. 0.08 0.08 0. 0. 0.05 0.08 0. 0.\n", " 0.09 0.08 0. 0. 0.04 0.11 0. 0.01 0.12 0.07 0. 0.\n", " 0.02 0.14 0.05 0.1 0.12 0. 0. 0. 0. 0.06 0.13 0.1 0.\n", " 0. 0. ]\n" ] } ], "source": [ "from sklearn.datasets import load_digits\n", "digits = load_digits()\n", "print(digits.data[0].01)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "num = []\n", "for i in range(1,21):\n", " num.append(Network([64,i,10]))" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# %timeit num.feedforward(digits.data[89].01)\n", "# %timeit num.comp_error(np.eye(10)[digits.target[89]])\n", "# %timeit num.grad_descent()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def Train_it(num, itera):\n", " iden = np.eye(10)\n", " acc = np.zeros((itera,))\n", " trainer = zip(digits.data,digits.target)\n", " perm = np.random.permutation(trainer)\n", " trains = perm[:1000]\n", " test = perm[1001:]\n", " #num = Network([64, 14, 10])\n", " print num.feedforward(digits.data[89].01)\n", " for i in range(itera):\n", " print(float(100i/(itera1.0)))\n", " for dig, ans in trains:\n", " num.train(dig.01,iden[ans])\n", " cor = 0\n", " tot = 0\n", " for dig, ans in test:\n", " if num.feedforward(dig.01).argmax()==ans:\n", " cor += 1\n", " tot += 1\n", " acc[i] = cor/float(tot)\n", " return acc" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0.58173661 0.57426496 0.58438781 0.58589722 0.57023406 0.56483866\n", " 0.56806882 0.57330475 0.55221257 0.58174784]\n", "0.0\n", "5.0\n", "10.0\n", "15.0\n", "20.0\n", "25.0\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-8-d9bfd5db8b4b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0macc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTrain_it\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnum\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m8\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m20\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[1;32mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0macc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[1;32mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnum\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m8\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_fractional_err\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-7-613328ffb9f0>\u001b[0m in \u001b[0;36mTrain_it\u001b[1;34m(num, itera)\u001b[0m\n\u001b[0;32m 11\u001b[0m \u001b[1;32mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfloat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitera\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;36m1.0\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 12\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mdig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mans\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mtrains\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 13\u001b[1;33m \u001b[0mnum\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdig\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;36m.01\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0miden\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mans\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 14\u001b[0m \u001b[0mcor\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 15\u001b[0m \u001b[0mtot\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m<ipython-input-2-c9718d58e936>\u001b[0m in \u001b[0;36mtrain\u001b[1;34m(self, inputs, answer)\u001b[0m\n\u001b[0;32m 90\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfeedforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 91\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcomp_error\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0manswer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 92\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgrad_descent\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 93\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mget_fractional_err\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 94\u001b[0m \u001b[1;32mreturn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrong\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtotal\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;36m1.0\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-2-c9718d58e936>\u001b[0m in \u001b[0;36mgrad_descent\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 79\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mj\u001b[0m \u001b[1;32min\u001b[0m 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\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mweights\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mj\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0meta\u001b[0m\u001b[1;33m\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlam\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;36m1000\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mweights\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mj\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0meta\u001b[0m\u001b[1;33m\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mactivation\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mj\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 82\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mtrain\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0manswer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 83\u001b[0m \"\"\"Trains the network.\n", "\u001b[1;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ "acc = Train_it(num[8], 20)\n", "print(acc)\n", "print(num[8].get_fractional_err())" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0.50791749 0.52716519 0.5059348 0.52774579 0.51704388 0.50559321\n", " 0.51413791 0.50219507 0.53014809 0.51290328]\n", "0.0\n", "1.0\n", "2.0\n", "3.0\n", "4.0\n", "5.0\n", "6.0\n", "7.0\n", "8.0\n", "9.0\n", "10.0\n", "11.0\n", "12.0\n", "13.0\n", "14.0\n", "15.0\n", "16.0\n", "17.0\n", "18.0\n", "19.0\n", "20.0\n", "21.0\n", "22.0\n", "23.0\n", "24.0\n", "25.0\n", "26.0\n", "27.0\n", "28.0\n", "29.0\n", "30.0\n", "31.0\n", "32.0\n", "33.0\n", "34.0\n", "35.0\n", "36.0\n", "37.0\n", "38.0\n", "39.0\n", "40.0\n", "41.0\n", "42.0\n", "43.0\n", "44.0\n", "45.0\n", "46.0\n", "47.0\n", "48.0\n", "49.0\n", "50.0\n", "51.0\n", "52.0\n", "53.0\n", "54.0\n", "55.0\n", "56.0\n", "57.0\n", "58.0\n", "59.0\n", "60.0\n", "61.0\n", "62.0\n", "63.0\n", "64.0\n", "65.0\n", "66.0\n", 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"42.0\n", "43.0\n", "44.0\n", "45.0\n", "46.0\n", "47.0\n", "48.0\n", "49.0\n", "50.0\n", "51.0\n", "52.0\n", "53.0\n", "54.0\n", "55.0\n", "56.0\n", "57.0\n", "58.0\n", "59.0\n", "60.0\n", "61.0\n", "62.0\n", "63.0\n", "64.0\n", "65.0\n", "66.0\n", "67.0\n", "68.0\n", "69.0\n", "70.0\n", "71.0\n", "72.0\n", "73.0\n", "74.0\n", "75.0\n", "76.0\n", "77.0\n", "78.0\n", "79.0\n", "80.0\n", "81.0\n", "82.0\n", "83.0\n", "84.0\n", "85.0\n", "86.0\n", "87.0\n", "88.0\n", "89.0\n", "90.0\n", "91.0\n", "92.0\n", "93.0\n", "94.0\n", "95.0\n", "96.0\n", "97.0\n", "98.0\n", "99.0\n", "[[ 0.09924623 0.09924623 0.09924623 ..., 0.32537688 0.32537688\n", " 0.32537688]\n", " [ 0.09045226 0.09045226 0.09045226 ..., 0.54773869 0.54773869\n", " 0.54648241]\n", " [ 0.10427136 0.10427136 0.10427136 ..., 0.82160804 0.82160804\n", " 0.82286432]\n", " ..., \n", " [ 0.10678392 0.08542714 0.08542714 ..., 0.95979899 0.95979899\n", " 0.95979899]\n", " [ 0.0879397 0.0879397 0.14070352 ..., 0.95979899 0.95979899\n", " 0.95979899]\n", " [ 0.09924623 0.09924623 0.1620603 ..., 0.97361809 0.97236181\n", " 0.97236181]]\n", "[ 0.00364 0.01066 0.01974 0.01052 0.00667 0.01431 0.00393 0.00991\n", " 0.00474 0.00787 0.00495 0.00946 0.00551 0.00596 0.00822 0.00448\n", " 0.00314 0.01471 0.00853 0.0091 ]\n" ] } ], "source": [ "accu = np.zeros((20,100))\n", "fracerr = np.zeros((20,))\n", "for i in range(20):\n", " accu[i] = Train_it(num[i], 100)\n", " fracerr[i] = num[i].get_fractional_err()\n", "print(accu)\n", "print(fracerr)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "image/png": 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PBtXmzZuTZXQpWOjgFQ6h3fNAms3n9LsMAACAlxDylqid2Z88/GByxvx2gAAA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\nTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f3ab73498d0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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TNA9cpP3PJvnvS9y1shw5cmQpNW54ZfxYZn/0B1L99Tdm6HVf13Y5A294eDjT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\nsZCTJEmSpCljISdJkiRJU+Zv7z6c/4ILimcAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f3ab6d1c810>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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6Rnn0kWTNDSlXX5nyjWtTPf8l6fzMr6VacWrb0QAAoK8ocrSqPLwl5WtfSbn+\nS8nNX0tOOS3V816Yzo/8VKrFR7QdDwAA+pIix5wrD92fcv3VKddfndx1e/KsZ6c678JUb3yLO08C\nAMAUKHLMqrJjR3LPupR1tyXrbku589bk4S2pzj0/nVdcnJx1bqpFNu0GAIADocgxY0opyfq7JpS2\n25L77kqOX55q5RnJM85M57tfk6xYmaqzoO24AAAwsBQ5DkrZuSO55cYnlkouXJjqGc9KTj09nQu+\nM1lxWqpDzbgBAMBMUuQ4YGV8LOXGrybXX93784STU513QTpv/c1UJ57cdjwAAJj3FDmmpHR3pVzz\n+ZQvrU5uvzk5/exeeXvdj6U68ui24wEAwFBR5Niv0u2mfPULKf/wkWTJ0lTf/Z9SXfrLqQ5f3HY0\nAAAYWooce1VKSb72lXQ/+aGksyCd178p+bbnpKqqtqMBAMDQU+R4irLmhnQ/8aHkke3pXPzDyXkX\nKHAAANBHFDkeV26/Od2PfzDZtCHV912S6gUvtk0AAAD0IUWOJEl39adT/vljqb7vDale+LJUCxQ4\nAADoV4oc6f7Hv6Z86m/T+aXfTXX8iW3HAQAAJtFpOwDt6l7z+ZSPfzCdn/8NJQ4AAAaEGbkhVq79\nYsrHLuuVOBt5AwDAwDAjN6TK17+S7of+NJ2f/fVUJ69sOw4AAHAAFLkhVNbckO5fvy+dn/7vqZ5+\nettxAACAA6TIDZly6zfS/Yv/kc5/fXuq057VdhwAAGAaFLkhUu68Nd0P/F46b/qFVGee03YcAABg\nmhS5IVHuuj3dP/nNdP7Lz6T6tue0HQcAADgI7lo5BMraNen+6e+k88NvTnXu+W3HAQAADpIiN8+V\nG69N96/+KJ0f//lU5zyv7TgAAMAMUOTmsfLV/0j3wx9I56fekeqMs9uOAwAAzBBFbp7qXnVFyic/\nks5/e3eqU57RdhwAAGAGKXLzUPeKj6f82z+n84u/neqEk9qOAwAAzDBFbh4ppaR84kMp134xnbf9\nbqqjj2s7EgAAMAsUuXmidLspf/MXKXfc0itxI8vajgQAAMwSRW4eKN1dKX/9vpRND6XzC7+VavER\nbUcCAABmkSI34EopKR+9LGXzxnR+7l2pFh3adiQAAGCWddoOwMEp//KJlFtv7G0xoMQBAMBQUOQG\nWPfL/57y2X9M52d/3XJKAAAYIorcgCq3fiPlb/48nZ/5NXenBACAIaPIDaBy3z3pfuD30nnTW1Ot\nOLXtOADcvlsoAAAgAElEQVQAwBxT5AZMGd2U7v98d6of/NFUZz+n7TgAAEALFLkBUh7Znu6f/Gaq\nF74snRe9rO04AABASxS5AVF27Ur3z38/1ckrU73m9W3HAQAAWqTIDYBSSsrf/Hmya1eqH/mpVFXV\ndiQAAKBFitwAKJ/5u5Q7bknnzb+caqE93AEAYNgpcn2ue/WVKas/3dsr7rDFbccBAAD6gCLXx8ot\nX0/52GXp/Ow7Ux15TNtxAACAPqHI9aly713p/vnvp/OTv5TqpFPajgMAAPQRRa4Plc0b0v2T30hV\n/3iqZz277TgAAECfUeT6TNk+lu7//I1UL3llOheuajsOAADQhxS5PlJ27kz3A+9JdeozU736dW3H\nAQAA+pQi1ydKKSkf/rOksyDVG/6rveIAAIB9UuT6RPnnj6XcdUfv5iYLFrQdBwAA6GOKXB/ofuHf\nUv79s7v3iju87TgAAECfWzjZAXVdX5TkfUkWJLmsaZr37OWYVUnem+SQJA81TbNqZmPOX2X7WMpH\n/zKdX/n9VMuOajsOAAAwAPY7I1fX9YIk709yUZKzk1xS1/VZexxzZJL/leR7m6Y5J8l/nqWs81K5\n9kvJM78t1Ykr2o4CAAAMiMmWVp6fZG3TNOuaptmR5KNJLt7jmDck+bumae5JkqZpHpr5mPNXuXp1\nqgtWtR0DAAAYIJMtrTwpyd0THt+T5II9jjkjySF1XX8uyUiSP26a5oMzF3H+Kps3JutuS/XT/73t\nKAAAwACZbEauTOEchyR5bpJXJ3lVkl+r6/qMgw02DMqXr0p13oWpFh3adhQAAGCATDYjd2+SiRdv\nrUhvVm6iu9O7wcl4kvG6rj+f5Nwkt008aPcNUVY99rhpmoyMjEwv9Tyx9StX5bBLfjKHDPnXod8s\nWrRo6Mcm/cv4pF8Zm/Qz45N+Vtf1uyY8XN00zeqpPG+yIveVJGfUdb0yyfokr09yyR7HfDLJ+3ff\nGOXQ9JZe/tGeJ9odaGKod27dunUqGeelct896W54KOOnnJbtQ/x16EcjIyMZ5rFJfzM+6VfGJv3M\n+KRfjYyMpGmad03nuftdWtk0zc4kb0lyeZKbknysaZo1dV1fWtf1pbuPuTnJZ5J8LcnVSf6yaZqb\nphNmmJSrV6c6/yWpOjb/BgAADkxVylQug5sVZf369W29dqtKKem+4yfTefPbU51yWttx2IPf2tHP\njE/6lbFJPzM+6VfLly9Pkmo6z53sZifMhttvTg5ZlKx4RttJAACAAaTItaC3d9x3pqqmVb4BAIAh\np8jNsbJzR8pX/iPVBd/ZdhQAAGBAKXJz7RvXJSecnOrYp7WdBAAAGFCK3BwrV19pNg4AADgoitwc\nKuNjKTd+NdXzX9R2FAAAYIApcnOoXPfF5JnnpFqytO0oAADAAFPk5lD50upUF6xqOwYAADDgFLk5\nUjZvSL65NtW5L2g7CgAAMOAUuTlSrrkq1XkXplp0aNtRAACAAafIzZFy9ZWpLlzVdgwAAGAeUOTm\nQFl/V7JlU3LmOW1HAQAA5gFFbg6Uq69Mdf5LU3UWtB0FAACYBxS5WVa6XZuAAwAAM0qRm2133Jws\nOjRZ8Yy2kwAAAPOEIjfLyjfvSHXmOamqqu0oAADAPKHIzbYtm5JlR7WdAgAAmEcUudk2ujFZqsgB\nAAAzR5GbZWV0c6plR7cdAwAAmEcUudm2ZVOy7Mi2UwAAAPOIIjfbRjclZuQAAIAZpMjNotLdlTy8\nJRlZ1nYUAABgHlHkZtPWLcniJakWLmw7CQAAMI8ocrNpdFOy1PVxAADAzFLkZtOoPeQAAICZp8jN\norJlUypFDgAAmGGK3GzabDNwAABg5ilys2nL5uRIRQ4AAJhZitwsKqNm5AAAgJmnyM2m0c2pbAYO\nAADMMEVuNm3ZlCyz/QAAADCzFLnZNLopMSMHAADMMEVulpTt40npJocd3nYUAABgnlHkZsvu2biq\nqtpOAgAAzDOK3GwZ3ZQsdX0cAAAw8xS5WVJGNyXLbD0AAADMPEVutmzZlEqRAwAAZoEiN1tsBg4A\nAMwSRW62jG62tBIAAJgVitwsKaMbLa0EAABmhSI3W0Y32wwcAACYFYrcbNmyKVlm+wEAAGDmKXKz\noOzalWzbmowocgAAwMxT5GbD1tFk8ZJUCxa0nQQAAJiHFLnZMLrJ9XEAAMCsUeRmg+vjAACAWaTI\nzYKyeWMqm4EDAACzRJGbDVs2J0cqcgAAwOxQ5GbD6MbEjBwAADBLFLlZUGwGDgAAzCJFbjaMbkzl\nZicAAMAsUeRmwxYzcgAAwOxR5GZYKWX3PnJm5AAAgNmhyM207eNJVaU6bHHbSQAAgHlKkZtpo5uS\npWbjAACA2aPIzbTRTa6PAwAAZpUiN8PKFtfHAQAAs0uRm2mjG1PZDBwAAJhFitxMG92cLFPkAACA\n2aPIzbTRjYocAAAwqxS5GVZGN6dysxMAAGAWLZzsgLquL0ryviQLklzWNM179vj4qiSfTHLH7n/6\nu6ZpfmuGcw6O0Y1udgIAAMyq/Ra5uq4XJHl/kpcnuTfJl+u6/oemadbsceiVTdN83yxlHCxbNtt+\nAAAAmFWTLa08P8napmnWNU2zI8lHk1y8l+OqGU82gMrOncnYw8nI0rajAAAA89hkSytPSnL3hMf3\nJLlgj2NKkhfWdX1DerN2v9g0zU0zF3GAbB1NlixN1VnQdhIAAGAem2xGrkzhHNcmWdE0zblJ/iTJ\nJw461aDasilZ6vo4AABgdk02I3dvkhUTHq9Ib1bucU3TbJ3w90/Xdf2ndV0f3TTNxonH7b4pyqoJ\nx2ZkZGSasfvTjke255Fjjs+SefZ5DZtFixbNu7HJ/GF80q+MTfqZ8Uk/q+v6XRMerm6aZvVUnleV\nsu9Jt7quFya5JcnLkqxPck2SSybe7KSu66cleaBpmlLX9flJmqZpVk7htcv69eunknFgdK+6Irl9\nTTo/+nNtR+EgjIyMZOvWrZMfCC0wPulXxib9zPikXy1fvjyZ5v1G9ru0smmanUnekuTyJDcl+VjT\nNGvqur60rutLdx/2n5N8va7r69PbpuCHphNkXhjdmCy1GTgAADC79jsjN8vm34zchz+QnHByOi97\nTdtROAh+a0c/Mz7pV8Ym/cz4pF/N2owcB6aMbkxlM3AAAGCWKXIzyWbgAADAHFDkZtLmjYkZOQAA\nYJYpcjOklNKbkXOzEwAAYJYpcjNlfCzpLEh12OFtJwEAAOY5RW6mjG5KlpmNAwAAZp8iN1O2bHJ9\nHAAAMCcUuRlSNm9M5Y6VAADAHFDkZsqWzZZWAgAAc0KRmymjG5OlllYCAACzT5GbKaM2AwcAAOaG\nIjdDyujGVG52AgAAzAFFbqZsMSMHAADMDUVupoxutP0AAAAwJxS5GVB27kjGx5IlS9uOAgAADAFF\nbiZsGU2WLEvVWdB2EgAAYAgocjNhdJM95AAAgDmjyM2ELYocAAAwdxS5GdDbekCRAwAA5oYiNxNG\nNydLFTkAAGBuKHIzwdYDAADAHFLkZkAZ3ZzKZuAAAMAcUeRmghk5AABgDilyM2HL5sSMHAAAMEcU\nuYNUSuntI+dmJwAAwBxR5A7W2LbkkENSHXpo20kAAIAhocgdrC1m4wAAgLmlyB2szRsTm4EDAABz\nSJE7SGXL5lSKHAAAMIcUuYM1akYOAACYW4rcwRrd7Bo5AABgTilyB8tm4AAAwBxT5A5S7xo5m4ED\nAABzR5E7WO5aCQAAzDFF7iCUbjd56P7kuBPajgIAAAwRRe5gbN6QLF6S6tDD2k4CAAAMEUXuYDxw\nn9k4AABgzilyB6E8cF+q409sOwYAADBkFLmD8eC3EkUOAACYY4rcQSgP3KfIAQAAc06ROxiWVgIA\nAC1Q5KaplJI86GYnAADA3FPkpmvL5uSQQ1ItXtJ2EgAAYMgoctP14H3JcZZVAgAAc0+RmyZbDwAA\nAG1R5KbLHSsBAICWKHLT9YCllQAAQDsUuWmytBIAAGiLIjddD37L0koAAKAVitw0lG1bk9JNlixt\nOwoAADCEFLnp2H19XFVVbScBAACGkCI3DeWB+1Idd0LbMQAAgCGlyE2HrQcAAIAWKXLT8aAiBwAA\ntEeRmwZbDwAAAG1S5KbD0koAAKBFitwBKuNjySPjybKj244CAAAMKUXuQD1o6wEAAKBdityBevBb\nyXGWVQIAAO1R5A6QG50AAABtU+QOlBudAAAALVs42QF1XV+U5H1JFiS5rGma9+zjuBck+WKSumma\nv5/RlH2kPHBfOi94SdsxAACAIbbfGbm6rhckeX+Si5KcneSSuq7P2sdx70nymSTz+y4gZuQAAICW\nTba08vwka5umWdc0zY4kH01y8V6O+5kk/y/JgzOcr6+URx9JHt6SHH1s21EAAIAhNlmROynJ3RMe\n37P73x5X1/VJ6ZW7P9v9T2XG0vWbB+9Pjj0+VWdB20kAAIAhNlmRm0ope1+StzdNU9JbVjl/l1Y+\nuN7WAwAAQOsmu9nJvUlWTHi8Ir1ZuYmel+SjdV0nybFJvqeu6x1N0/zDxIPqul6VZNVjj5umycjI\nyPRSt2T76KZ0TzoliwcsNwdm0aJFAzc2GR7GJ/3K2KSfGZ/0s7qu3zXh4eqmaVZP5XlVKfuedKvr\nemGSW5K8LMn6JNckuaRpmjX7OP5/J/nHKd61sqxfv34qGftG90N/mpx4Sjove03bUZhFIyMj2bp1\na9sxYK+MT/qVsUk/Mz7pV8uXL0+muaJxv0srm6bZmeQtSS5PclOSjzVNs6au60vrur50Oi84yMqD\n37IZOAAA0Lr9zsjNsoGbkdv1Kz+Rzn97d6qnLW87CrPIb+3oZ8Yn/crYpJ8Zn/SrWZuR4wll545k\n84bkmOPajgIAAAw5RW6qHnogOfKYVAsPaTsJAAAw5BS5qXrwvsT1cQAAQB9Q5KaoPOBGJwAAQH9Q\n5KbqwftsBg4AAPQFRW6KygP3mZEDAAD6giI3VQ+YkQMAAPqDIjcFpbsr2XB/ctzT2o4CAACgyE3J\nxoeSkSNTLTq07SQAAACK3JQ8YOsBAACgfyhyU+BGJwAAQD9R5KbiwfuS405oOwUAAEASRW5KzMgB\nAAD9RJGbige/ZesBAACgbyhykyjdbm9p5fGWVgIAAP1BkZvM6KbksMWpDlvcdhIAAIAkitzkHnCj\nEwAAoL8ocpMoD6x3oxMAAKCvKHKTefBbNgMHAAD6iiI3mQfuc8dKAACgryhykygP3pfq+OVtxwAA\nAHicIrcfpZTejJytBwAAgD6iyO3P1tGksyDVESNtJwEAAHicIrc/D9znRicAAEDfUeT2o9xxc6pT\nTms7BgAAwJMocvtRbro+1dnntR0DAADgSRS5fSg7diRrb06e9ey2owAAADyJIrcvt69Jlq9IdcSS\ntpMAAAA8iSK3D2XNDanOOrftGAAAAE+hyO2D6+MAAIB+pcjtRdm2NbnvnuQZz2o7CgAAwFMocntz\n89eTM85KdcghbScBAAB4CkVuL8pN16c6y7JKAACgPylye1HWXJ/qbDc6AQAA+pMit4fy4LeS7ePJ\nSSvbjgIAALBXitweypressqqqtqOAgAAsFeK3J5uuiGx7QAAANDHFLkJSndXys1fsxE4AADQ1xS5\nie66IxlZluroY9tOAgAAsE+K3ARlzQ2pLKsEAAD6nCI3QW//OMsqAQCA/qbI7VYeeSS589bkzG9v\nOwoAAMB+KXKPWXtTsuLUVIcvbjsJAADAfilyu1lWCQAADApFbrey5no3OgEAAAaCIpekbNmcPHR/\nsvKZbUcBAACYlCKXpNz8teSZ56RauLDtKAAAAJNS5JLkputTnWVZJQAAMBiGvsiVUnZfH+dGJwAA\nwGAY+iKX+9cn3ZKccHLbSQAAAKZk6IvcY3errKqq7SgAAABTosjddENi/zgAAGCADHWRK7t2Jbd+\n3fVxAADAQBnqIpd1tyVHHZtq6VFtJwEAAJiyoS5yj10fBwAAMEiGu8itXZPqmee0HQMAAOCADHWR\ny8Nbk2WWVQIAAINluIvc+Fhy+OK2UwAAAByQIS9y25LDj2g7BQAAwAEZ8iI3lhxmRg4AABgsQ1vk\nyo4dSekmixa1HQUAAOCALJzsgLquL0ryviQLklzWNM179vj4xUl+I0l393+/1DTNv81C1pm1vXd9\nXFVVbScBAAA4IPudkavrekGS9ye5KMnZSS6p6/qsPQ77bNM05zZN85wkP5rkL2Yj6IxzfRwAADCg\nJltaeX6StU3TrGuaZkeSjya5eOIBTdNsm/BwSZKHZjbiLBkfSw47vO0UAAAAB2yypZUnJbl7wuN7\nklyw50F1XX9/kt9NcmKSV85Yutk0ZkYOAAAYTJMVuTKVkzRN84kkn6jr+iVJPpjkzD2Pqet6VZJV\nE56TkZGRKQedaY9WyaMjS7OkxQz0p0WLFrU6NmF/jE/6lbFJPzM+6Wd1Xb9rwsPVTdOsnsrzJity\n9yZZMeHxivRm5faqaZqr6rpeWNf1MU3TbNjjY6uTTAz1zq1bt04l46zobnwoOeTQtJmB/jQyMmJc\n0LeMT/qVsUk/Mz7pVyMjI2ma5l3Tee5kRe4rSc6o63plkvVJXp/kkokH1HV9WpI7mqYpdV0/N0n2\nLHF9abx310oAAIBBs9+bnTRNszPJW5JcnuSmJB9rmmZNXdeX1nV96e7DfjDJ1+u6vi7JHyf5odkM\nPGPGtylyAADAQKpKmdJlcLOhrF+/vq3XTvdv/zoZWZbORT/YWgb6k+UX9DPjk35lbNLPjE/61fLl\ny5NkWhtbT7b9wPw1PuaulQAAwEAa8iJnaSUAADB4hrbIlfFtqczIAQAAA2hoi1xvRu7wtlMAAAAc\nsCEvcmbkAACAwTPkRc41cgAAwOAZ8iJnRg4AABg8Q1nkSndX8ugjyaGHtR0FAADggA1lkcv4eHLY\nYak6w/npAwAAg204m8z4NtfHAQAAA2s4i9x218cBAACDaziL3Jg7VgIAAINrOIucO1YCAAADbCiL\nXBnfluqww9uOAQAAMC1DWeTMyAEAAINsOIvcdtfIAQAAg2s4i5ztBwAAgAE2pEVuLFlsaSUAADCY\nhrPIjY0lh5mRAwAABtNQFrkyvi2VpZUAAMCAGsoi17vZiaWVAADAYBrOIjfurpUAAMDgUuQAAAAG\nzJAWOdsPAAAAg2voilwpJRkfd9dKAABgYA1dkcsj25NDDkm1cGHbSQAAAKZl+IrcuDtWAgAAg20I\ni9y25PDD204BAAAwbUNY5MzIAQAAg21Ii5wbnQAAAINr6IpcUeQAAIABN3RFLuPbUllaCQAADLAh\nLHJj9pADAAAG2hAWuW2WVgIAAANt+Irc9vFksSIHAAAMruErcmPbbD8AAAAMtKErcmV8LJWllQAA\nwAAbuiKX8W1udgIAAAy0ISxyY5ZWAgAAA234itx2G4IDAACDbfiK3PiYu1YCAAADbQiLnLtWAgAA\ng22oilzZ8WhSkiw8pO0oAAAA0zZURa43G7c4VVW1nQQAAGDahqzIjbvRCQAAMPCGrMi5Pg4AABh8\nQ1bkbD0AAAAMviErctsUOQAAYOANVZEr42OpFDkAAGDADVWR6y2tdI0cAAAw2IawyJmRAwAABtuQ\nFTnXyAEAAINvyIqcGTkAAGDwDVWRK/aRAwAA5oGhKnIZH3fXSgAAYOANWZEzIwcAAAy+IStyY8lh\nZuQAAIDBNnxFztJKAABgwA1ZkduWLFbkAACAwTY0Ra7s2pXs2JEcenjbUQAAAA7KwqkcVNf1RUne\nl2RBksuapnnPHh//4SRvS1Il2ZrkzU3TfG2Gsx6c7WPJ4Yenqqq2kwAAAByUSWfk6rpekOT9SS5K\ncnaSS+q6PmuPw+5I8tKmaZ6d5DeT/MVMBz1oY9vc6AQAAJgXpjIjd36StU3TrEuSuq4/muTiJGse\nO6Bpmi9OOP7qJCfPYMaZ4UYnAADAPDGVa+ROSnL3hMf37P63ffnxJJ86mFCzYnzMHnIAAMC8MJUZ\nuTLVk9V1/V1JfizJi6adaLZsNyMHAADMD1MpcvcmWTHh8Yr0ZuWepK7rZyf5yyQXNU2zaS8fX5Vk\n1WOPm6bJyMjIAcadvkdLNzuWLssRc/iaDKZFixbN6diEA2F80q+MTfqZ8Uk/q+v6XRMerm6aZvVU\nnvf/t3d/sZKXZx3Av4eF7e7arX8jdelWiKGxNNqUmBYbTYlyga1Cb3wsCabRaEi0isZobE0arjS9\naETTYGqgBHtB+8SaitEGG+tGE1OhWqORrREjCmygpP6j54+y3fFipnC64SznsLszc975fK7O7ze/\nmXkunt2Z77y/933XJpNzD7hV1aVJ/inJDyY5leShJLd098lt17w2yWeS3Nrdn91lzZNTp07t8tLz\nd+bP/zh58t9yya0/M7f3ZH86evRonn322UWXAS9Kf7Ks9CbLTH+yrI4dO5ZMV/7fs5ecI9fdp5O8\nJ8mDSR5J8vHuPllVt1XVbbPL3p/kG5P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"text/plain": [ "<matplotlib.figure.Figure at 0x7f3ab6ca6a90>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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vaTsKAADQUYpch5TPbUu+uCvVV7+67SgAAECHKXIdUUpJ791/lOq135pq5aq2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\n+iRrgWeBjwDbh8bsA24C9ibZDDxfVUe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"text/plain": [ "<matplotlib.figure.Figure at 0x7f3ab6c501d0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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8LhObTm57JAAAOKJB2wNA28rtX83w//mVZP2GDH7zd7PqzLPbHgkAAI7KjhwrVikl5eoP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\nusz6pKu2bNmSjJ7Ffczmcmr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"text/plain": [ "<matplotlib.figure.Figure at 0x7f3ab6b98b10>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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uLZmRAwAAekCQWyP1iePJzLHkiiubLgUAABhwgtxaOXQwuXq3jpUAAMCqCXJr\nxP44AACgVwS5tWJ/HAAA0COC3BpxhhwAANArgtxaMSMHAAD0iCC3BjodK6eSK65quhQAAGAICHJr\nYXJivmOlXzcAALB6ksUaqA9NpBi3Pw4AAOgNQW4t2B8HAAD0kCC3BpwhBwAA9JIgtxbMyAEAAD0k\nyPVZ/czxZGY6edGVTZcCAAAMCUGu3w7pWAkAAPSWdNFn9WOPprh6d9NlAAAAQ0SQ67djR5Kdlzdd\nBQAAMEQEuX6bOpps39l0FQAAwBAR5PpNkAMAAHpMkOuzevpoCkEOAADoIUGu38zIAQAAPSbI9Zsg\nBwAA9Jgg10f1mTPJ8Zlk2/amSwEAAIaIINdPM1PJlm0pRkebrgQAABgiglw/WVYJAAD0wYalbijL\n8tYkdyUZTfKeqqredYF79ib5d0k2Jnmyqqq9vS1zQAlyAABAHyw6I1eW5WiSu5PcmuTmJLeVZXnT\neffsTPLvk3xbVVVfmuSv9KnWgVNPHU0xJsgBAAC9tdTSyluSPFhV1cNVVZ1Kck+St513z/ckeX9V\nVQeTpKqqJ3tf5oAyIwcAAPTBUksrdyWZWHB9MMlrzrvnxiQby7L8UJKxJD9TVdWv9a7EASbIAQAA\nfbDUjFzdxXNsTPLqJN+S5C1JfrwsyxtXW9hQEOQAAIA+WGpG7tEkexZc70lnVm6hiXQanDyT5Jmy\nLP84ySuTfH7hTfMNUfaeva6qKmNjYyurekDMHJ/O5quuycYh/zmHzaZNm4Z+bDK4jE/aytikzYxP\n2qwsyzsXXO6rqmpfN49bKsh9KsmNZVlel2QyyXclue28e343yd3zjVE2p7P08t+e/0TzBS0s6ien\np6e7qXFgnXn6qcxt3JwTQ/5zDpuxsbEM+9hkcBmftJWxSZsZn7TV2NhYqqq6cyWPXXRpZVVVp5Pc\nkeTeJPuTvK+qqvvLsry9LMvb5+/5yyR/mOR/JflEkndXVbV/JcUMnWlLKwEAgN4r6rqbbXB9UU9O\nTjb12n1Xz81l7v96R0bu/s0UG5Y8ro8W8akdbWZ80lbGJm1mfNJW4+PjSVKs5LFLNTthpWZnkku2\nCHEAAEDPCXL9omMlAADQJ4Jcv0wdEeQAAIC+EOT6pJ46mkKQAwAA+kCQ6xcdKwEAgD4R5Ppl6mgy\ntqPpKgAAgCEkyPWLZicAAECfCHJ9Uk8ds0cOAADoC0GuX8zIAQAAfSLI9YsgBwAA9Ikg1wd1Xeta\nCQAA9I1jUCfUAAAacUlEQVQg1w/HZ5ONm1Js3NR0JQAAwBAS5Pph6mgyZjYOAADoD0GuH+yPAwAA\n+kiQ64NakAMAAPpIkOuHqaPOkAMAAPpGkOsHM3IAAEAfCXL94OgBAACgjwS5PqgtrQQAAPpIkOsH\nSysBAIA+EuT6QZADAAD6SJDrsbquBTkAAKCvBLleO/FMMjKSYvMlTVcCAAAMKUGu18zGAQAAfSbI\n9ZogBwAA9Jkg12tTR5MxQQ4AAOgfQa7HnCEHAAD0myDXa5ZWAgAAfSbI9ZogBwAA9Jkg12OWVgIA\nAP0myPXatBk5AACgvwS5XrO0EgAA6DNBrtemjglyAABAXwlyPVSfPJnMnUkuubTpUgAAgCEmyPXS\n1JFk+84URdF0JQAAwBAT5HrJ/jgAAGANCHK9NH00GdvRdBUAAMCQE+R6yBlyAADAWhDkesnSSgAA\nYA0Icr0kyAEAAGtAkOuhWpADAADWgCDXS/bIAQAAa0CQ66WpY2bkAACAvhPkesnSSgAAYA0Icj1S\nn3o2efZksmVb06UAAABDTpDrlaljydiOFEXRdCUAAMCQE+R6xbJKAABgjQhyvSLIAQAAa0SQ65F6\n6oijBwAAgDUhyPWKGTkAAGCNCHK9Mu0MOQAAYG0Icr1iRg4AAFgjglyP1FNH7ZEDAADWxIalbijL\n8tYkdyUZTfKeqqredd739yb53SQPzX/p/VVV/fMe19l+ZuQAAIA1smiQK8tyNMndSd6c5NEkf1qW\n5e9VVXX/ebf+z6qqvr1PNQ4GQQ4AAFgjSy2tvCXJg1VVPVxV1akk9yR52wXuK3pe2QCpT59OThxP\nto41XQoAALAOLLW0cleSiQXXB5O85rx76iSvK8vys+nM2v1IVVX7e1fiAJg+lmzbnmLElkMAAKD/\nlkoedRfP8ekke6qqemWSn0vyO6uuatBMHU3GLKsEAADWxlIzco8m2bPgek86s3LnVFU1veDv/60s\ny58vy/LyqqqeXnjffFOUvQvuzdjYcCxFPHXqZE5e/qJsG5KfZ73btGnT0IxNho/xSVsZm7SZ8Umb\nlWV554LLfVVV7evmcUVdX3zSrSzLDUkeSPKmJJNJPpnktoXNTsqyvCrJE1VV1WVZ3pKkqqrqui5e\nu56cnOymxtab++j/SO7/Xxn563+v6VLogbGxsUxPTy99IzTA+KStjE3azPikrcbHx5MV9htZdGll\nVVWnk9yR5N4k+5O8r6qq+8uyvL0sy9vnb/srSf68LMvPpHNMwXevpJCBpmMlAACwhhadkeuz4ZmR\nq/5DsuPyjLzlO5ouhR7wqR1tZnzSVsYmbWZ80lZ9m5GjS2bkAACANSTI9UA9dTSFIAcAAKwRQa4X\nzMgBAABrSJDrBUEOAABYQ4LcKtVzZ5LjM8m27U2XAgAArBOC3GrNTCWXbk0xOtp0JQAAwDohyK2W\nZZUAAMAaE+RWS5ADAADWmCC3So4eAAAA1pogt1pm5AAAgDUmyK2WIAcAAKwxQW61BDkAAGCNCXKr\nZI8cAACw1gS51TIjBwAArDFBbrVmp5Nt25uuAgAAWEcEudWamU62jjVdBQAAsI4IcqtQn3o2OXMm\n2XxJ06UAAADriCC3GrPTybaxFEXRdCUAAMA6IsithmWVAABAAwS51ZidSbZua7oKAABgnRHkVmN2\nKtmqYyUAALC2BLlVqGemU2yztBIAAFhbgtxqWFoJAAA0QJBbDUsrAQCABghyqzHTOX4AAABgLQly\nq1DPzqSwtBIAAFhjgtxqWFoJAAA0QJBbDUsrAQCABghyq3Fc10oAAGDtCXIrVNd1Z0Zuqxk5AABg\nbQlyK3XymWR0NMXGTU1XAgAArDOC3ErNztgfBwAANEKQWynLKgEAgIYIcis1OyXIAQAAjRDkVqhz\nGLggBwAArD1BbqWcIQcAADREkFspSysBAICGCHIrNTsjyAEAAI0Q5FbK0koAAKAhgtwK1bPTmp0A\nAACNEORWatY5cgAAQDMEuZWytBIAAGiIILdSx83IAQAAzRDkVqCem0uOzyZbtjVdCgAAsA4Jcivx\nzGyy+dIUo6NNVwIAAKxDgtxKzNofBwAANEeQW4kZ++MAAIDmCHIrMTudbLU/DgAAaIYgtwKdw8C3\nN10GAACwTglyK+EMOQAAoEGC3EpYWgkAADRIkFuJ2enE0koAAKAhgtxKWFoJAAA0aMNSN5RleWuS\nu5KMJnlPVVXvush9X53kY0nKqqp+u6dVtkw9O50RSysBAICGLDojV5blaJK7k9ya5OYkt5VledNF\n7ntXkj9MUvShznaZnbG0EgAAaMxSSytvSfJgVVUPV1V1Ksk9Sd52gft+KMlvJTnc4/raaWbK0koA\nAKAxSwW5XUkmFlwfnP/aOWVZ7kon3P3C/JfqnlXXVrpWAgAADVoqyHUTyu5K8g+qqqrTWVY51Esr\n69Onk1PPJpdubboUAABgnVqq2cmjSfYsuN6TzqzcQl+Z5J6yLJPkiiTfXJblqaqqfm/hTWVZ7k2y\n9+x1VVUZGxu85YlzR5/O9NaxbN9uj9yw2rRp00COTdYH45O2MjZpM+OTNivL8s4Fl/uqqtrXzeOK\nur74pFtZlhuSPJDkTUkmk3wyyW1VVd1/kft/Jcn/12XXynpycrKbGlulPjSRuZ//qYz+s19Y+mYG\n0tjYWKanp5suAy7I+KStjE3azPikrcbHx5MVrmhcdGllVVWnk9yR5N4k+5O8r6qq+8uyvL0sy9tX\n8oIDb2Y62eoTHQAAoDmLzsj12WDOyH3m45n78Acy+kM/3nQp9IlP7Wgz45O2MjZpM+OTturbjBwv\nVM/OpDAjBwAANEiQW66ZaWfIAQAAjRLklmt2yh45AACgUYLccs3OCHIAAECjBLllqmemU1haCQAA\nNEiQW65Zxw8AAADNEuSWS5ADAAAaJsgtl66VAABAwwS55TIjBwAANEyQW4b62ZNJXSebNjddCgAA\nsI4Jcssxv6yyKIqmKwEAANYxQW45LKsEAABaQJBbDkEOAABoAUFuOWZ1rAQAAJonyC1DPTOdwowc\nAADQMEFuOSytBAAAWkCQW47Z6WTrtqarAAAA1jlBbjnMyAEAAC0gyC1DPTOdYtv2pssAAADWOUFu\nOSytBAAAWkCQW47ZmWSrGTkAAKBZgtxyzEw5Rw4AAGicINeluq6T4zOWVgIAAI0T5Lp14plkw6YU\nGzY2XQkAALDOCXLdsqwSAABoCUGuWzpWAgAALSHIdWt2xmHgAABAKwhyXapnphwGDgAAtIIg1y1L\nKwEAgJYQ5LplaSUAANASgly3Zqd1rQQAAFpBkOvWzFSyRZADAACaJ8h1qZ6dSWFGDgAAaAFBrluz\n0/bIAQAArSDIdWtmSpADAABaQZDr1uyMZicAAEArCHJdqOfOJCeOJ1u2Nl0KAACAINeV47PJJVtS\njIw2XQkAAIAg15UZZ8gBAADtIch1Q8dKAACgRQS5bghyAABAiwhyXahnph0GDgAAtIYg1w0zcgAA\nQIsIct0Q5AAAgBYR5Loxq2slAADQHoJcN2amky3bmq4CAAAgiSDXlXp2OsW27U2XAQAAkESQ646l\nlQAAQIsIct2wtBIAAGgRQa4bszOJpZUAAEBLCHJLqE+fSk4/m1xyadOlAAAAJBHklja/rLIoiqYr\nAQAASCLILc2ySgAAoGU2LHVDWZa3JrkryWiS91RV9a7zvv+2JP80ydz8/360qqoP9qHWZsxOJVs1\nOgEAANpj0Rm5sixHk9yd5NYkNye5rSzLm8677Y+qqnplVVWvSvIDSX65H4U2ZmY62eroAQAAoD2W\nWlp5S5IHq6p6uKqqU0nuSfK2hTdUVTW74HJbkid7W2KzOoeBC3IAAEB7LLW0cleSiQXXB5O85vyb\nyrJ8e5J/meSaJN/Us+raYNaMHAAA0C5LzcjV3TxJVVW/U1XVTUm+LcmvrbqqNpmdEeQAAIBWWWpG\n7tEkexZc70lnVu6Cqqr6cFmWG8qyfFFVVU8t/F5ZlnuT7F1wb8bG2h+Qjj97IqO7r83mAaiV3ti0\nadNAjE3WJ+OTtjI2aTPjkzYry/LOBZf7qqra183jirq++KRbWZYbkjyQ5E1JJpN8MsltVVXdv+Ce\nlyZ5qKqquizLVyf5zaqqXtrFa9eTk5Pd1NioMz//Uxl5zRtTfOXrmy6FNTI2Npbp6emmy4ALMj5p\nK2OTNjM+aavx8fEkWdGB1Ysurayq6nSSO5Lcm2R/kvdVVXV/WZa3l2V5+/xt70jy52VZ3pfkZ5J8\n90oKaS1LKwEAgJZZdEauzwZjRu7OH8rI3/j7KXZf33QprBGf2tFmxidtZWzSZsYnbdW3GTnSOUdu\nixk5AACgPQS5RdR13Tl+wDlyAABAiwhyi3n2ZDJSpNi0uelKAAAAzhHkFmNZJQAA0EKC3GIsqwQA\nAFpIkFvMzJSjBwAAgNYR5BZRHzqY4qpdTZcBAADwPILcYg4eSPY4Pw4AAGgXQW4R9cSBFIIcAADQ\nMoLcRdSnTyeHHkl2X9d0KQAAAM8jyF3M448ml784xeZLmq4EAADgeQS5i6gnHkqx54amywAAAHgB\nQe5iJjQ6AQAA2kmQu4h64kCK3YIcAADQPoLcBdR1nUw8ZEYOAABoJUHuQo48lRQjyY7Lmq4EAADg\nBQS5C5k4kOy5IUVRNF0JAADACwhyF1AfdBA4AADQXoLcBdT2xwEAAC0myF3IxIEUL3GGHAAA0E6C\n3HnqE8eTo08nV403XQoAAMAFCXLnO/hwsuvaFCOjTVcCAABwQYLceeoJjU4AAIB2E+TON3FAoxMA\nAKDVBLnz1I88lGKPRicAAEB7CXIL1GfOJIcmkl3XNl0KAADARQlyCz32aLLzRSkuubTpSgAAAC5K\nkFugnnhIoxMAAKD1BLmFDmp0AgAAtJ8gt0A9cSDFSzQ6AQAA2k2Qm1fXtaMHAACAgSDInXXs6c6f\nOy5vtg4AAIAlCHJnzc/GFUXRdCUAAACLEuTmdQ4Ct6wSAABoP0HurIkDyR6NTgAAgPYT5ObVEwfM\nyAEAAANBkEtSnzieHH0quWpX06UAAAAsSZBLkoNfTMZfkmJ0tOlKAAAAliTIJakPWlYJAAAMDkEu\ncRA4AAAwUAS5aHQCAAAMlnUf5OozZ5JHv5jsvq7pUgAAALqy7oNcnphMdl6e4pItTVcCAADQlXUf\n5OpHHrI/DgAAGCjrPshl4kCKPTc0XQUAAEDX1n2Q0+gEAAAYNOs6yNV1nUw8lJiRAwAABsi6DnI5\ndiSp62Tn5U1XAgAA0LX1HeQOdg4CL4qi6UoAAAC6tq6DXK3RCQAAMIA2NF3AWqjrOpmdTqaOJseO\npJ46mkwdTf2pj6T4xrc3XR4AAMCyDG2Qq488lblf+JfJkaeS6WPJ5s3J9suS7TtT7Jj/86u+NsWX\nfVXTpQIAACzL0Aa5fPHBZOPGjPyDf51s35Fi46amKwIAAOiJroJcWZa3JrkryWiS91RV9a7zvv+9\nSX4sSZFkOsnfrqrqf/W41mWpDz+WYtd1KV704ibLAAAA6Lklm52UZTma5O4ktya5OcltZVnedN5t\nDyX5uqqqvjzJP0vyy70udNkOH0quvLrpKgAAAHqumxm5W5I8WFXVw0lSluU9Sd6W5P6zN1RV9bEF\n938iye4e1rgi9eHHMvKKr2y6DAAAgJ7r5viBXUkmFlwfnP/axfz1JP91NUX1xBOPmZEDAACGUjcz\ncnW3T1aW5dcn+cEkr19xRT1QnzmTPH04ueKqJssAAADoi26C3KNJ9iy43pPOrNzzlGX55UneneTW\nqqqOXOD7e5PsPXtdVVXGxsaWWW53zjxxKDPbd2b75S/qy/Mz3DZt2tS3sQmrZXzSVsYmbWZ80mZl\nWd654HJfVVX7unlcUdeLT7iVZbkhyQNJ3pRkMsknk9xWVdX9C+55SZIPJvm+qqo+3mXN9eTkZJe3\nLk+9/zOZ+4Mqoz/6U315fobb2NhYpqenmy4DLsj4pK2MTdrM+KStxsfHk07n/2Vbco9cVVWnk9yR\n5N4k+5O8r6qq+8uyvL0sy9vnb/uJJJcl+YWyLO8ry/KTKymmV+rDj6V4sf1xAADAcFpyRq6P+jYj\nN/dbv5Js2ZaRb/k/+/L8DDef2tFmxidtZWzSZsYnbdXXGblBVB9+LHnxNU2XAQAA0BdDGeTyxGMp\nHD0AAAAMqaELcnVdJ4cPJfbIAQAAQ2roglymjyYbNqbYsq3pSgAAAPpi+ILcE4+ZjQMAAIba0AW5\n+vBjKa7U6AQAABheQxfk7I8DAACG3RAGOUcPAAAAw23oglz9xKEUZuQAAIAhNnRBLocfS5whBwAA\nDLGhCnL1iePJyWeSHZc3XQoAAEDfDFWQ6xw9cE2Komi6EgAAgL4ZriB32BlyAADA8BuqIKfRCQAA\nsB4MVZDrnCHn6AEAAGC4DVWQqw8/ZkYOAAAYekMV5Bw9AAAArAdDE+Tq06eSY08nl1/ZdCkAAAB9\nNTRBLk8+kVx2RYoNG5quBAAAoK+GJ8gdPuToAQAAYF0YmiBXP6HRCQAAsD4MTZBz9AAAALBeDE2Q\nc/QAAACwXgxNkOscPWBGDgAAGH5DEeTqubnkycc1OwEAANaFoQhyOfpUsmVris2XNF0JAABA3w1H\nkDv8mNk4AABg3RiKIFc/cUijEwAAYN0YiiDXmZHT6AQAAFgfhifI6VgJAACsE0MR5JwhBwAArCcD\nH+Tquk6eOGRpJQAAsG4MfJDL7HSSOtk21nQlAAAAa2Lwg9z80QNFUTRdCQAAwJoY+CBXP3HIGXIA\nAMC6MvBBLocfS6FjJQAAsI4MRZDT6AQAAFhPBj7I1U8ccvQAAACwrgx8kDMjBwAArDcDHeTqkyc7\nxw9cdnnTpQAAAKyZgQ5yefKx5IorU4yMNl0JAADAmhnsIHf4kGWVAADAujPQQa5+wtEDAADA+jPQ\nQa4zI6djJQAAsL4MdJCrn3jM0QMAAMC6M9BBzh45AABgPRrYIFefOZMceTK54qqmSwEAAFhTAxvk\n8vThZPtlKTZubLoSAACANTW4Qe4JjU4AAID1aWCDXH34kKMHAACAdWlDky9e/++/WPmDP39/svva\n3hUDAAAwIBoNcnO/8+urevzIm97ao0oAAAAGR6NBbvTH/lWTLw8AADCQugpyZVnemuSuJKNJ3lNV\n1bvO+/6XJPmVJK9K8o+rqvo3vS4UAACAjiWbnZRlOZrk7iS3Jrk5yW1lWd503m1PJfmhJD/d8woB\nAAB4nm66Vt6S5MGqqh6uqupUknuSvG3hDVVVHa6q6lNJTvWhRgAAABboJsjtSjKx4Prg/NcAAABo\nQDdBru57FQAAAHStm2YnjybZs+B6TzqzcstSluXeJHvPXldVlfHx8eU+DayJsbGxpkuAizI+aStj\nkzYzPmmrsizvXHC5r6qqfd08rpsg96kkN5ZleV2SySTfleS2i9xbXOxJ5gs6V1RZlqmq6s5uioS1\nVJblncYmbWV80lbGJm1mfNJWqxmbSwa5qqpOl2V5R5J70zl+4D9UVXV/WZa3z3//l8qyvDrJnybZ\nnmSuLMsfTnJzVVUzKykKAACAi+vqHLmqqv5bkv923td+acHfH8vzl18CAADQJ900O+mXfQ2+Nixm\nX9MFwCL2NV0AXMS+pguARexrugC4iH0rfWBR15pSAgAADJImZ+QAAABYAUEOAABgwHTV7KSXyrK8\nNcld6XTAfE9VVe9a6xrgrLIs9yT5T0muTFIn/3979xJaRxXHcfwbUgPW+kCEqm20QVpoihaDaBWl\nIF34TF39qFApiq4Eq4hiu3DrSnwgXYhtKV1Ef6jUCEItulAQ34piLVgx2ChJxbfiIrVxcYbmeukN\nQnHmXvL7rO6cuXc4i18y858zcw7P2n5a0rnAC8DFwAQg27801tFYsCT1U5aBmbR9a7IZ3ULSOcBz\nwBrK/887ga9IPqNhkrYBm4HjwOeUbJ5Bshk1k7QLuBk4avvSqq3jebzK7l3A38B9tl+f7/i1jshV\nFyTPADcAw8DtklbX2YeINjPAA7bXAOuAe6tMPgIcsL0KeKPajmjCVuAg5UIZks3oHk8Br9leDVwG\nHCL5jIZV6x7fA4xUF879wCaSzWjGbkrd0+qkWZQ0TFmve7j6zQ5J89ZqdT9aeSVw2PaE7RngeWBj\nzX2IOMH2lO1Pq89/AF8Cy4BRYE/1tT3Abc30MBYyScuBmyijHn1Vc7IZjZN0NnCd7V1Q1py1/SvJ\nZzTvN8pN2sWSFgGLge9JNqMBtt8Gfm5r7pTFjcCY7RnbE8BhSu3UUd2PVi4DjrRsTwJX1dyHiJOq\n7uJdDrwHLLU9Xe2aBpY21a9Y0J4AHgLOamlLNqMbDAE/SNoNrAU+Au4n+YyG2f5J0uPAt8BfwH7b\nByQlm9EtOmXxQuDdlu9NUmqnjuoekctaB9GVJC0BXgK22v69dZ/tWZLdqJmkWyjP1H/C3GjcvySb\n0aBFwAiww/YI8Cdtj6oln9EESZdQbiq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"text/plain": [ "<matplotlib.figure.Figure at 0x7f3ab6b36090>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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Am4mQozXl1Knkjk/34u1jH0ouu7K30/b9P57svUawAQDAOQg5hqqcOpXc/qmUj34w5eMf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\nSn4TvgAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f3ab6a9b4d0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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7CgAAYMQIcsvQHPpsqite1HYZAADAiBHklsOKHAAA0AJBbjkOHUyuvLrtKgAA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\nlLGQkyRJkqQpYyEnSZIkSVPGQk6SJEm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"text/plain": [ "<matplotlib.figure.Figure at 0x7f3ab6a27650>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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5AADAMgly68mf/1kyMZbqNW9suxIAAGAFBLl1ZHanyjrVBt04AAAYZILcOlH+/JPJic+l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\n24/X2oGIFpKuB94GPmdm+Hob5Q/HwCV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"text/plain": [ "<matplotlib.figure.Figure at 0x7f3ab69a20d0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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3PtWq1W1PAgAAjBAhdyGmJ5MtW9ueAgAAGDFC7kJMTSRbtrU9BQAAMGKE3AUo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\nBwAAMGCEHAAAwID5/wGvPIq77c3vUgAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f3ab6926c10>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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PAwAAlpwgt5hGnY8DAACWniC3iMqoFTkAAGDpCXKLad/uZMSKHAAAsLQEuUVS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\n36Pbqmoas6uOJbmO5p76lzh2Ne5jzKZ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"text/plain": [ "<matplotlib.figure.Figure at 0x7f3ab68a4410>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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fdhQAAGDAKHItKZ/9ZPLEa1KtuaDtKAAAwIBR5FpSPvmxVM/5jrZjAAAAA0iR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\nJEk6BzZykiRJkrTAzOlhJxdSkpuAe2m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"text/plain": [ "<matplotlib.figure.Figure at 0x7f3ab6831110>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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VmUPJ9GQyfkrbpQAAACNKkDtekxPJ+o2pVq1quxIAAGBECXLHa6+OlQAAQLsE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\nSwEAAFgWQxfksvMbyXkXphp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"text/plain": [ "<matplotlib.figure.Figure at 0x7f3ab67d5fd0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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LsW9PsvXKtlMAAAAjSpFbAnfkAACANilyZ6mUMrgjp8gBAAAtUeTO1szU4J/j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\ncBuqIpe9u0ytBAAAht7QFLl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"text/plain": [ "<matplotlib.figure.Figure at 0x7f3ab6778d50>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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Gnkx1wEYnAABAfwhy/XD8qWRURw4AAOgPQa4PnCEHAAD0kyC3zpqzZ5Pnnkmu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\nQjeUHSsBAIAOW2pq5a1JHi2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"text/plain": [ "<matplotlib.figure.Figure at 0x7f3ab66937d0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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CMBzKoYOZ/tBPpPqatw5kiQMAgEFw1hm5RWZGbsiUA/sy/TMfSPXGt6S67b2pqgX9cqF1\nfmtHlxmfdJWxSZcZn3TV+czIzXkgOMxH+bPPZPpf/8tU7/yOVN/yXQNb4gAAYBAocpyXcuJ4yn/+\n9ZRPfzxjd/xoqtd8RduRAABg6ClyLFjZ80ymf/GnkjXrMvaP7kq1ek3bkQAAYCQocizI9Kc/nvIb\nv5DqW7+7t5zSUkoAAFgyihznpLxwLOXD96Rs+3zG/vb7U119fduRAABg5ChyzFvZ9VSmf+H/SbXp\n6oz9xF2pLryo7UgAADCSFDnmpeyeyPSHfrx3rMDbvslSSgAAaJEix5zK1IFM3/X+VLe9N2Nvf3fb\ncQAAYOSNtR2AbitHn830XR9I9dZ3KXEAANARihxnVI4fz/S/+qepbrgp1bfVbccBAABmKHKcVpk+\nmelf/lCyejzV9/6QZ+IAAKBDFDm+TCkl5d5fSo4cztgP/r1UY8vajgQAAMyiyPFlyu/+Zsqj2zL2\nI/8g1YoVbccBAABOocjxCtN/+Hspf/TR3mHfF13cdhwAAOA0HD/AS8qf/knKb/27jL3vJ1Ot29B2\nHAAA4AzMyJEkKU9tz/Sv3Z2xO3881RUb244DAACchSJHyrHnM/2LP5Wq/sFUW25oOw4AADAHRY6U\nD9+TassNGfvarW1HAQAA5kGRG3Hls3+U8tAXUr33jrajAAAA86TIjbCyb0+m/93/m7Efel+qCy5q\nOw4AADBPityIKidPZvqeD6V693tSXeO5OAAAGCSK3Igqv/PhZMWKVO/+821HAQAAzpEiN4LKIw+k\n/K+PZOwH/k6qMUMAAAAGjZ/iR0x59kimf/mfZ+wv3+nQbwAAGFCK3AgppWT61+5O9aavTfWGN7cd\nBwAAWCBFboSUP/y9ZHJXqr/4V9uOAgAAnAdFbkSUXU+l/Odfz9jf+PupVqxsOw4AAHAeFLkRUKZP\nZvpX7kp123tTXbm57TgAAMB5UuRGQPnY7yQrV6X6c7e2HQUAAOgDRW7IlX2TKb/z4Yz95b+Zqqra\njgMAAPSBIjfESimZ/rc/n+pdt6V61aa24wAAAH2iyA2x8qn/lRzYm+qb/0LbUQAAgD5S5IZUOXwo\npfnljP3Vv5Vq+fK24wAAAH2kyA2p8pu/nOrNb091zWvajgIAAPSZIjeEyoP3pzzyQKr3/KW2owAA\nAItAkRsy5dixTP/bn8/Ye3841QUXth0HAABYBIrckCm//e9TXXNjqq/86rajAAAAi0SRGyLlS4+n\n/PHHUn3PD7YdBQAAWESK3JAoJ09m+td+NtV3/bVUa9a1HQcAAFhEityQKB/9rWT1mlRf941tRwEA\nABaZIjcEytFnU373P2TsL/1IqqpqOw4AALDIFLkhUL7wqeSGm1Nd9qq2owAAAEtAkRsC5bOfSPXV\nb207BgAAsEQUuQFXnjuaPPSFVG94c9tRAACAJaLIDbjyhU8nN7wu1UWr244CAAAsEUVuwJXPWVYJ\nAACjRpEbYOX555Jtn0/1xlvajgIAACwhRW6QffGzybU3prp4vO0kAADAElLkBlj5zB9ZVgkAACNI\nkRtQ5dixlAfvT/XGr207CgAAsMQUuUH1wGeTLTekGl/TdhIAAGCJKXIDyiHgAAAwuhS5AVSOv5Dy\nZ59N9aa3tB0FAABowfK5bqjr+tYkdyVZluSepmk+eMrHL03yb5O8aub9frppmn/d/6i85IHPJa++\nNtWa9W0nAQAAWnDWGbm6rpcluTvJrUluTnJ7Xdc3nXLbnUnub5rmjUm2JvlQXddzFkQWrres8uvb\njgEAALRkrqWVtyR5rGmaHU3THE9yb5LbTrlnV5IXd9xYk2Rf0zQn+huTF5Xjx1O+8OlUb/q6tqMA\nAAAtmWvmbFOSp2Zd70xy6oNZv5TkY3VdTyQZT1L3Lx5fZtufJpuuTrVuQ9tJAACAlsw1I1fm8R7/\nIMmfNk2zMckbk/xcXdfj552M07JbJQAAMNeM3NNJNs+63pzerNxsX5/k/06Spmker+t6e5Ibk3xm\n9k11XW9N7xm6zNyb8XF971yUE8dz6Aufzvh778iY792iWblypbFJZxmfdJWxSZcZn3RZXdcfmHV5\nX9M0983ndXMVuc8kuaGu6y1JJpJ8T5LbT7nnoSTvSvJHdV1fkV6Je+LUN5oJNDvU+w8fPjyfjMwo\nX/xsyhUb8+zKCxLfu0UzPj4eY5OuMj7pKmOTLjM+6arx8fE0TfOBhbz2rEsrZzYtuTPJR5I8mOTD\nTdNsq+v6jrqu75i57Z8m+Zq6rj+f5KNJfrRpmv0LCcPZlc9+ItVX2a0SAABGXVXKfB6DWxRlYmKi\nrc89cMqJE5l+31/N2I/fleqSy9qOM9T81o4uMz7pKmOTLjM+6aqNGzcmSbWQ18612Qld8cgXk8uu\nVOIAAABFblCUz/6RQ8ABAIAkitxAKCdPptz/Sc/HAQAASRS5wfDoA8n6S1Nd9qq2kwAAAB2gyA2A\n8sXPpXrDLW3HAAAAOkKRGwBl+yOprr2x7RgAAEBHKHIdV6ZPJl96PLnmhrajAAAAHaHIdd2uncna\n9akuHm87CQAA0BGKXMeVJx5Ode1r2o4BAAB0iCLXdTseTbYocgAAwMsUuY4rTzxiRg4AAHgFRa7D\nyrHnk8mJ5Kpr2o4CAAB0iCLXZV96PNl0daoVK9pOAgAAdIgi12FlxyOptjh2AAAAeCVFrsueeCTx\nfBwAAHAKRa7Dyo5HU9mxEgAAOIUi11Hl0IHkuaPJFRvbjgIAAHSMItdV2x9NttyQqqraTgIAAHSM\nItdRzo8DAADORJHrqN6OlYocAADw5RS5DirT08mOR5NrHD0AAAB8OUWuiyYnkgsvTrVmXdtJAACA\nDlLkOqj3fNyNbccAAAA6SpHroh2PJFssqwQAAE5Pkeug8sQjqa6x0QkAAHB6ilzHlOMvJLueTF59\nXdtRAACAjlLkuubJJ5IrNqVatartJAAAQEcpch1Tdjya6hobnQAAAGemyHXNE484Pw4AADgrRa5j\nyo5HzMgBAABnpch1SDlyKDl0MLlyU9tRAACADlPkumTHo8nV16caW9Z2EgAAoMMUuQ5xfhwAADAf\nilyH9HasVOQAAICzU+Q6opSSbH8kUeQAAIA5KHJdsXd3snxFqvWXtJ0EAADoOEWuI8p258cBAADz\no8h1xXbnxwEAAPOjyHVE2f5IKjNyAADAPChyHVBOnEie2p5sub7tKAAAwABQ5Lrg6S8ll16R6oKL\n2k4CAAAMAEWuA8r2h50fBwAAzJsi1wXbH3V+HAAAMG+KXAf0NjpR5AAAgPlR5FpWTpxI9uxKrtzc\ndhQAAGBAKHJt278nWbsh1YoVbScBAAAGhCLXtj3PJJe9qu0UAADAAFHkWlb27EqlyAEAAOdAkWvb\n5K7k8ivbTgEAAAwQRa5lZc8zqS5T5AAAgPlT5NrmGTkAAOAcKXItKqX0itzlihwAADB/ilybpg4k\nqy5IdcFFbScBAAAGyPK5bqjr+tYkdyVZluSepmk+eMrH/36S9856v5uSXNo0zcE+Zx0+llUCAAAL\ncNYZubqulyW5O8mtSW5Ocntd1zfNvqdpmp9umuZNTdO8KcmPJblPiZufMrkrlR0rAQCAczTX0spb\nkjzWNM2OpmmOJ7k3yW1nuf/7kvxGv8INvT27zMgBAADnbK4itynJU7Oud8783Zep6/qiJN+c5D/2\nJ9oI2PNM4ugBAADgHM1V5Mo5vNd3JPm4ZZXz1ztDzowcAABwbuba7OTpJJtnXW9Ob1budL43Z1lW\nWdf11iRbX7xumibj4+PzCjmspvY+k9XXXJ+xEf8+dM3KlStHfmzSXcYnXWVs0mXGJ11W1/UHZl3e\n1zTNffN5XVXKmSfd6rpenuThJO9MMpHkU0lub5pm2yn3rU3yRJKrmqZ5bp6Zy8TExDxvHT7l6JFM\n/+gPZuxn701VVW3HYZbx8fEcPny47RhwWsYnXWVs0mXGJ121cePGJFlQGTjr0sqmaU4kuTPJR5I8\nmOTDTdNsq+v6jrqu75h163uSfOQcShwzRw8ocQAAwLk664zcIhvpGbnpT3885TN/mGU//GNtR+EU\nfmtHlxmfdJWxSZcZn3TVos3IsYj27LLRCQAAsCCKXFscPQAAACyQItcSRw8AAAALpci1ZXJXcrkZ\nOQAA4Nwpci0ox19IDk8lGy5tOwoAADCAFLk27N2dbLgs1diytpMAAAADSJFrw+QzyeWejwMAABZG\nkWtBcfQAAABwHhS5Njh6AAAAOA+KXAvK5K5UdqwEAAAWSJFrw55nEksrAQCABVLklliZPpnsm0wu\nvaLtKAAAwIBS5JbagX3J6jWpVq5qOwkAADCgFLmlNrnL0QMAAMB5UeSWWNnzTCo7VgIAAOdBkVtq\nk7tsdAIAAJwXRW6JlT3PJI4eAAAAzoMit9T27EplRg4AADgPitwSKqXMnCFnRg4AAFg4RW4pHTmU\njC1LdfHqtpMAAAADTJFbSpO7PB8HAACcN0VuCRXPxwEAAH2gyC2lSc/HAQAA50+RW0p7nkkuNyMH\nAACcH0VuCVlaCQAA9IMit5QcBg4AAPSBIrdEyvNHk+ePJms3tB0FAAAYcIrcUtmzO7n0Vamqqu0k\nAADAgFPklsqeXYnn4wAAgD5Q5JZI2fNMKkcPAAAAfaDILZVJRw8AAAD9ocgtkd7RA2bkAACA86fI\nLZXJXWbkAACAvlDklkA5cTyZ2p9suLztKAAAwBBQ5JbCvj3JuktSLV/edhIAAGAIKHJLwdEDAABA\nHylyS8DRAwAAQD8pckth8pnkckUOAADoD0VuCfSOHrC0EgAA6A9Fbik4egAAAOgjRW6RlenpZN/u\n5FJFDgAA6A9FbrFNHUguuCjVBRe2nQQAABgSitxi27PLRicAAEBfKXKLrEza6AQAAOgvRW6xTU4k\nl29sOwUAADBEFLlFVnbvSq5Q5AAAgP5R5Bbb5EQqM3IAAEAfKXKLqJTSO0PuCpudAAAA/aPILaaD\n+5MLLkx1wUVtJwEAAIaIIreYbHQCAAAsAkVuEZXdE6ksqwQAAPpMkVtMZuQAAIBFoMgtorJ7VypH\nDwAAAH22fK4b6rq+NcldSZYluadpmg+e5p6tSf5FkhVJ9jZNs7W/MQeUGTkAAGARnHVGrq7rZUnu\nTnJrkpuT3F7X9U2n3LMuyc8l+Y6mab4iyXctUtaBUqank73PJJd7Rg4AAOivuZZW3pLksaZpdjRN\nczzJvUluO+We70vyH5um2ZkkTdPs7X/MAXRgX3LR6lSrLmg7CQAAMGTmWlq5KclTs653JnnLKffc\nkGRFXdd/kGQ8yc80TfPr/Ys4oCyrBAAAFslcM3JlHu+xIslXJfnWJN+c5Cfqur7hfIMNurL7aRud\nAAAAi2KuGbmnk2yedb05vVm52Z5Kb4OT55I8V9f1/0ryhiSPzr5pZkOUrS9eN02T8fHxhaUeAM8d\n2Jtq85ZcMMRf47BauXLlUI9NBpvxSVcZm3SZ8UmX1XX9gVmX9zVNc998XjdXkftMkhvqut6SZCLJ\n9yS5/ZR7fivJ3TMbo6xKb+nlPz/1jWYCzQ71/sOHD88n40A6ufNLGbv6hhwf4q9xWI2Pj2eYxyaD\nzfikq4xNusz4pKvGx8fTNM0HFvLasy6tbJrmRJI7k3wkyYNJPtw0zba6ru+o6/qOmXseSvLfk3wh\nyZ8k+aWmaR5cSJihMjmRWFoJAAAsgqqU+TwGtyjKxMREW597UZWTJzN9Z52xn/n3qVauajsO58hv\n7egy45OuMjbpMuOTrtq4cWOSVAt57VybnbAQ+/cka9YqcQAAwKJQ5BbD7onkik1tpwAAAIaUIrcI\nyuREqsuvbDsGAAAwpBS5xTC5y2HgAADAolHkFkHZPeEwcAAAYNEocothcsKMHAAAsGgUuT4rJ04k\n+/cml13RdhQAAGBIKXL9tm8yWbch1fIVbScBAACGlCLXb5ZVAgAAi0yR67PeRieOHgAAABaPItdv\nZuQAAIBFpsj1Wdm9y9EDAADAolLk+m3302bkAACARaXI9VE5fjyZ2p9ccnnbUQAAgCGmyPXT3meS\nDZelWr687SQAAMAQU+T6abeNTgAAgMWnyPVRmZyw0QkAALDoFLl+2r0rudwZcgAAwOJS5PqoTE6k\nsrQSAACik+leAAAbeElEQVRYZIpcP01OJJZWAgAAi0yR65PywrHk0FSy4bK2owAAAENOkeuXPc8k\nl16RatmytpMAAABDTpHrl92WVQIAAEtDkeuT3kYndqwEAAAWnyLXL5O7HAYOAAAsCUWuT8puh4ED\nAABLQ5Hrl8kJM3IAAMCSUOT6oBx7Pjl6JFl/SdtRAACAEaDI9cPkruTSV6Ua8+0EAAAWn+bRD7uf\ntqwSAABYMopcH/Q2OnH0AAAAsDQUuX5w9AAAALCEFLk+KJOOHgAAAJaOItcPux09AAAALB1F7jyV\n544mx55P1m1oOwoAADAiFLnzNTmRXH5lqqpqOwkAADAiFLnzVCyrBAAAlpgid74mHT0AAAAsLUXu\nfO129AAAALC0FLnzVCYnUilyAADAElLkzkM5cSJ5+snkqi1tRwEAAEaIInc+dm5PLr081UUXt50E\nAAAYIYrceSiPP5TqupvajgEAAIwYRe58PP5Qct2NbacAAABGjCJ3Hsrj28zIAQAAS06RW6Cyf0/y\nwgvJ5c6QAwAAlpYit0Dl8YeT616bqqrajgIAAIwYRW6hLKsEAABaosgtUG/Hyte2HQMAABhBitwC\nlBeOJRNPJluubzsKAAAwghS5hdjxWLLp6lQrV7WdBAAAGEGK3AJYVgkAALRJkVuA8vi25FpFDgAA\naMfyuW6o6/rWJHclWZbknqZpPnjKx7cm+a0kT8z81X9smuaf9DlnZ5RSkscfSvV9/1vbUQAAgBF1\n1iJX1/WyJHcneVeSp5N8uq7r326aZtspt/7Ppmm+c5EydsvkrmTlylQbLm07CQAAMKLmWlp5S5LH\nmqbZ0TTN8ST3JrntNPeNzKnYxflxAABAy+ZaWrkpyVOzrncmecsp95QkX1/X9efTm7X7+03TPNi/\niB3z+EOJjU4AAIAWzTUjV+bxHp9Lsrlpmjck+dkk/+W8U3WYHSsBAIC2zTUj93SSzbOuN6c3K/eS\npmkOz/rv/62u639V1/WGpmn2z75vZlOUrbPuzfj4+AJjt6McPZKpfZMZv+n1qZbPuU8MA2rlypUD\nNzYZHcYnXWVs0mXGJ11W1/UHZl3e1zTNffN5XVXKmSfd6rpenuThJO9MMpHkU0lun73ZSV3XVySZ\nbJqm1HV9S5KmaZot8/jcZWJiYj4ZO6M8cH+mf/c3s+x9/7TtKCyi8fHxHD58eO4boQXGJ11lbNJl\nxiddtXHjxmSB+42cdWll0zQnktyZ5CNJHkzy4aZpttV1fUdd13fM3PZdSf6srus/Te+Ygu9dSJBB\n0Nvo5Ma2YwAAACPurDNyi2zgZuRO/ot/lLF3fHuqN9zSdhQWkd/a0WXGJ11lbNJlxiddtWgzcrys\nTJ9Mtj+SXGujEwAAoF2K3HxNPJmsWZ9qfE3bSQAAgBGnyM1TecyxAwAAQDcocvPlIHAAAKAjFLl5\nKk88lOq6m9qOAQAAoMjNRzl0MDlyKLnyqrajAAAAKHLz8sRDybU3phrz7QIAANqnmcxDeeyhVI4d\nAAAAOkKRm4fyuB0rAQCA7lDk5lBOHE+efDy55jVtRwEAAEiiyM3tySeSyzemuvCitpMAAAAkUeTm\nVB5/KNX1llUCAADdocjNoTy+zUHgAABApyhyZ1FKSR53EDgAANAtitzZ7N+bTE8nl17RdhIAAICX\nKHJn89TjydXXp6qqtpMAAAC8RJE7i7J3MpXZOAAAoGMUubPZN5lcennbKQAAAF5BkTuLsncy1SWK\nHAAA0C2K3Nnsn0wUOQAAoGMUubPZO5lc4hk5AACgWxS5MyjPHU1OHE9Wj7cdBQAA4BUUuTPZ11tW\n6egBAACgaxS5M9m3x/NxAABAJylyZ1D27U7l6AEAAKCDFLkz2TeZbFDkAACA7lHkzqA4DBwAAOgo\nRe5MHAYOAAB0lCJ3JvttdgIAAHSTInca5djzyfPPJWvWtR0FAADgyyhyp7NvMtlwmTPkAACATlLk\nTscZcgAAQIcpcqdR9u1OdcllbccAAAA4LUXudMzIAQAAHabInc6+yeTSK9pOAQAAcFqK3GmUfZOW\nVgIAAJ2lyJ3OvsnkEjNyAABANylypyjHX0iePZysXd92FAAAgNNS5E61b0+y/tJUY741AABAN2kr\np9o3acdKAACg0xS5U/Q2OlHkAACA7lLkTrVvMrlUkQMAALpLkTvV3slkgyIHAAB0lyJ3irJ/MpUZ\nOQAAoMMUuVPttdkJAADQbYrcLOXE8eTIVLLukrajAAAAnJEiN9v+vcnaDamWLWs7CQAAwBkpcrM5\nQw4AABgAitwszpADAAAGgSI3mxk5AABgAChyszkMHAAAGACK3Cxl32SqDZe1HQMAAOCsFLnZ9u1J\nLr2i7RQAAABntXyuG+q6vjXJXUmWJbmnaZoPnuG+Nyf54yR10zT/qa8pl0A5eTI5uD9Z7ww5AACg\n2846I1fX9bIkdye5NcnNSW6v6/qmM9z3wST/PUm1CDkX34G9yZp1qZavaDsJAADAWc21tPKWJI81\nTbOjaZrjSe5Ncttp7vtbSf5Dkj19zrd09u2xYyUAADAQ5ipym5I8Net658zfvaSu603plbufn/mr\n0rd0S6js253qEhudAAAA3TdXkZtPKbsryf/ZNE1Jb1nlYC6t3LcnucRGJwAAQPfNtdnJ00k2z7re\nnN6s3GxfneTeuq6T5NIk31LX9fGmaX579k11XW9NsvXF66ZpMj4+vrDUi+Doof1ZdsPrsqpDmWjH\nypUrOzU2YTbjk64yNuky45Muq+v6A7Mu72ua5r75vK4q5cyTbnVdL0/ycJJ3JplI8qkktzdNs+0M\n9/9qkv9vnrtWlomJiflkXBInP/TjGfuWv5jq5je1HYWWjY+P5/Dhw23HgNMyPukqY5MuMz7pqo0b\nNyYLXNF41qWVTdOcSHJnko8keTDJh5um2VbX9R11Xd+xkE/YWfsmkw02OwEAALrvrDNyi6wzM3Jl\n+mSm/+Z3Z+xf3ptqxcq249Ayv7Wjy4xPusrYpMuMT7pq0WbkRsbBA8nFa5Q4AABgIChySW9ZpaMH\nAACAAaHIJSn7JlM5DBwAABgQilwyMyOnyAEAAINBkUsUOQAAYKAocrG0EgAAGCyKXJLsnUwuVeQA\nAIDBMPJFrkxPJwf2OgwcAAAYGCNf5HLoYHLBhalWrWo7CQAAwLwocjY6AQAABszIF7niMHAAAGDA\njHyRy77JVJdc0XYKAACAeVPkzMgBAAADZuSLXDEjBwAADJiRL3LZt8cZcgAAwEAZ6SJXSkn27ba0\nEgAAGCgjXeRyeCpZsSrVBRe1nQQAAGDeRrvI7dtjNg4AABg4I17kdjsMHAAAGDgjXeTKvj2pFDkA\nAGDAjHSRMyMHAAAMopEucmbkAACAQTTSRS4H9yXrL2k7BQAAwDkZ7SI3dSBZu6HtFAAAAOdkZItc\nOXkyOXIoWbOu7SgAAADnZGSLXA4fTC5anWrZsraTAAAAnJPRLXKWVQIAAANqdIvcwQPJuvVtpwAA\nADhnI1vkytT+VGsVOQAAYPCMbJGztBIAABhUI1zk9idm5AAAgAE0skWuTB1IZUYOAAAYQCNb5HpL\nK83IAQAAg2eEi5yllQAAwGAaySJXSkmmDibrLK0EAAAGz0gWuRw5nKxalWrFyraTAAAAnLPRLHJT\n+x09AAAADKwRLXI2OgEAAAbXSBa5MrU/lSIHAAAMqJEscr0ZOUsrAQCAwTTCRc6MHAAAMJhGs8gd\ndIYcAAAwuEayyJWpA6mcIQcAAAyokSxyveMHzMgBAACDaeSKXCnFZicAAMBAG7kil+ef6/3nBRe2\nmwMAAGCBRq/ITe1P1m1IVVVtJwEAAFiQESxyjh4AAAAG28gVuXJwfyrPxwEAAANs5IqcGTkAAGDQ\njWiRMyMHAAAMrhEscs6QAwAABtvIFbkydSDVOkUOAAAYXMvnuqGu61uT3JVkWZJ7mqb54Ckfvy3J\nP04yPfPnfU3TfGwRsvaHpZUAAMCAO+uMXF3Xy5LcneTWJDcnub2u65tOue2jTdO8oWmaNyX5/iS/\nuBhB+8bSSgAAYMDNtbTyliSPNU2zo2ma40nuTXLb7Buapnl21uXqJHv7G7F/ygvHkheOJRePtx0F\nAABgweZaWrkpyVOzrncmecupN9V1/Z4kP5nkyiTv7lu6fps6kKxZn6qq2k4CAACwYHPNyJX5vEnT\nNP+laZqbknxHkl8/71SLxbJKAABgCMw1I/d0ks2zrjenNyt3Wk3T/GFd18vrur6kaZp9sz9W1/XW\nJFtn3Zvx8aVd4vjCsedz/NLLc/ESf14Gy8qVK5d8bMJ8GZ90lbFJlxmfdFld1x+YdXlf0zT3zed1\nVSlnnnSr63p5koeTvDPJRJJPJbm9aZpts+65LskTTdOUuq6/KslvNk1z3Tw+d5mYmJhPxr6Z/v3/\nmjzzVMbe+8NL+nkZLOPj4zl8+HDbMeC0jE+6ytiky4xPumrjxo1JsqDnvs66tLJpmhNJ7kzykSQP\nJvlw0zTb6rq+o67rO2Zu+4tJ/qyu6/uT/EyS711IkCVhaSUAADAEzjojt8iWfkbuV38muf6mjL29\nu/ux0D6/taPLjE+6ytiky4xPumrRZuSGTZnan8qMHAAAMOBGqshl6kCydkPbKQAAAM7LCBY5M3IA\nAMBgG5kiV06cSI4eSdasbTsKAADAeRmZIpdDB5PVa1KNLWs7CQAAwHkZnSJnWSUAADAkRqjI7bfR\nCQAAMBRGpsiVqQOOHgAAAIbCyBS53oycIgcAAAy+ESpyzpADAACGw8gUOUsrAQCAYTEyRS4HLa0E\nAACGw+gUuakDyTpLKwEAgME3EkWuTE8nh6eSNWbkAACAwTcSRS5HDiUXXJhqxYq2kwAAAJy30Shy\nllUCAABDZESKnI1OAACA4TESRc7RAwAAwDAZiSLXO3rA0koAAGA4jEaRmzpgaSUAADA0RqLIlakD\nZuQAAIChMRJFLlP7PSMHAAAMjREpcgeSdYocAAAwHIa+yJVSZp6Rs7QSAAAYDkNf5PLcs8myZalW\nXdB2EgAAgL4Y/iJnNg4AABgyw1/kDu539AAAADBUhr7IlakDdqwEAACGytAXOUsrAQCAYTP8Re7g\nfkcPAAAAQ2X4i9yUZ+QAAIDhMvRFrveMnKWVAADA8Bj6Itd7Rs6MHAAAMDxGoMjtt9kJAAAwVIa6\nyJVjzycnTiQXXdx2FAAAgL4Z6iL34kYnVVW1nQQAAKBvhrvIHTyQrLOsEgAAGC5DXeSKjU4AAIAh\nNNRFLlP7UylyAADAkBnyInfAjpUAAMDQGfIit9/SSgAAYOgMdZErUwdSmZEDAACGzFAXudjsBAAA\nGEJDXuT2J+sUOQAAYLgMbZErJ44nzx1NVq9tOwoAAEBfDW2Ry9TBZHxtqrHh/RIBAIDRNJQtpzx/\nNNO/8Qupbnhd21EAAAD6buiKXNnzTKb/2f+Rau2GVD/wd9qOAwAA0HfL2w7QT+WRL2b6F38q1bd+\nd6pv/LZUVdV2JAAAgL4bmiI3/Ye/l/Kffz1jf/3vprr5TW3HAQAAWDQDX+TKyZMpv/krKV/8XMZ+\n9J+letWmtiMBAAAsqoEucuXokUz/4k8lpWTsx34q1cWr244EAACw6Aa2yJXJiUz/7D9J9bo3pfru\nH0i1bFnbkQAAAJbEvIpcXde3JrkrybIk9zRN88FTPv7eJD+apEpyOMkPN03zhT5nfYXpe+9J9bVb\nM/Zt9WJ+GgAAgM6Z8/iBuq6XJbk7ya1Jbk5ye13XN51y2xNJvqFpmtcn+b+S/GK/g85Wpk8mj21L\n9fZvWsxPAwAA0EnzmZG7JcljTdPsSJK6ru9NcluSbS/e0DTNH8+6/0+SXNXHjF/u6SeTNetSrVm/\nqJ8GAACgi+ZzIPimJE/Nut4583dn8oNJfvd8Qs2lPPZgqutPnRQEAAAYDfOZkSvzfbO6rr8xyQ8k\neeuCE83How8mr3NWHAAAMJrmU+SeTrJ51vXm9GblXqGu69cn+aUktzZNc+A0H9+aZOuL103TZHx8\n/BzjJqWUHHp8W1a/944sW8DrYS4rV65c0NiEpWB80lXGJl1mfNJldV1/YNblfU3T3Def11WlnH3C\nra7r5UkeTvLOJBNJPpXk9qZpts2659VJPpbkLzVN88l5Zi4TExPzvHXWi/buzvRPvi9jP/1vUlXV\nOb8e5jI+Pp7Dhw+3HQNOy/ikq4xNusz4pKs2btyY9Hb+P2dzPiPXNM2JJHcm+UiSB5N8uGmabXVd\n31HX9R0zt/2jJOuT/Hxd1/fXdf2phYSZj/Log8kNNytxAADAyJpzRm4RLWhGbvrXfy65cnPG3vWd\nixAJ/NaObjM+6Spjky4zPumqRZ2R65ry6IOpbnhd2zEAAABaM1BFrhw5lBzYm1y1pe0oAAAArRmo\nIpfHtiXX3phq2bK2kwAAALRmoIpcb1nlzW3HAAAAaNVgFbnHHkx1vSIHAACMtoEpcuXYsWTnjuSa\nG9uOAgAA0KqBKXLZ8Uiy6epUq1a1nQQAAKBVA1PkPB8HAADQMzhF7jFFDgAAIBmQIlemTyZPPJxc\np8gBAAAMRJHLzh3JuktSja9pOwkAAEDrBqLIlUcfTHX9TW3HAAAA6ISBKHJ59MHkhte1nQIAAKAT\nOl/kSikpj20zIwcAADCj80Uue55JqiSXXtF2EgAAgE7ofJHrHTvwulRV1XYUAACATuh8kcujDyaW\nVQIAALyk80XuxRk5AAAAejpd5MrhqWTqYLLp1W1HAQAA6IxOF7k8+mBy3Y2pxpa1nQQAAKAzOl3k\nymMPprr+5rZjAAAAdEq3i9yjD6a6QZEDAACYrbNFrhx7Ppl4MrnmNW1HAQAA6JTOFrk88XCy+ZpU\nK1a2nQQAAKBTOlvkessqHTsAAABwqu4WORudAAAAnFYni1w5eTLZ/khy/WvbjgIAANA5nSxyefBP\nkys3p7p4vO0kAAAAndPJIjf98f+R6q3vajsGAABAJ3WuyJVDB5Ntn0/15re3HQUAAKCTulfkPvkH\nqd54S6qLLm47CgAAQCd1qsiVUlI+/tFUb3t321EAAAA6q1NFLk88nExPJzc4dgAAAOBMOlXkyswm\nJ1VVtR0FAACgszpT5MrzR1M+94lUX/+OtqMAAAB0WneK3Kc/nrzmK1KtXd92FAAAgE7rTpH7o49m\n7G3f1HYMAACAzutEkSsTTyZ7J5Ov+Oq2owAAAHReN4rcx/9Hqq9/R6ply9qOAgAA0HmtF7ly4njK\nJ+9L9bZ3tR0FAABgILRe5PL5TydXbk51+ca2kwAAAAyE1ovc9Mf/RyqbnAAAAMxbq0Wu7N+TbH8k\n1Vd9fZsxAAAABkq7Re4Tv5/qzW9LtWpVmzEAAAAGSrtF7uMftawSAADgHLX7jNyFFyevvq7VCAAA\nAIOm1SJXvf2bUlVVmxEAAAAGTrtF7i1b2/z0AAAAA6ndInfx6jY/PQAAwEBq/Rw5AAAAzo0iBwAA\nMGAUOQAAgAGjyAEAAAyY5fO5qa7rW5PclWRZknuapvngKR9/bZJfTfKmJP+waZoP9TsoAAAAPXPO\nyNV1vSzJ3UluTXJzktvrur7plNv25f9v7/5D/arrOI4/L7NB5iwi/LF5ayMmODFpiFkUiviH/dr8\n65WCIUbRH/1YIUX6h+w/CZQsQlCcYv4xe1NhC5IlxkVBKqdJkQotGu0qm6JlGv1x525/nOP23cXv\n7evmPed82fPx1/d8zvme+/7jtX3P+3vO9/OBbwC3vuMVSpIkSZKOMcmjlRcDe6tqX1UtAA8AW0cP\nqKqXqmoPsLACNUqSJEmSRkzSyK0D9o9sz7djkiRJkqQeTNLILa54FZIkSZKkiU0y2cnzwOzI9izN\nXbm3JcllwGVvblcVa9eufbunkTqxZs2avkuQxjKfGiqzqSEznxqqJNtHNueqam6S903SyO0BNiZZ\nD7wAfAG4ZsyxM+NO0hZ0pKgkVNX2SYqUupRku9nUUJlPDZXZ1JCZTw3ViWTz/zZyVXUoydeB3TTL\nD+yoqmeTfLXdf2eSs4AngNOBw0m2AZuq6vXjKUqSJEmSNN5E68hV1UPAQ0vG7hx5fYBjH7+UJEmS\nJK2QSSY7WSlzPf5taTlzfRcgLWOu7wKkMeb6LkBaxlzfBUhjzB3vG2cWF52UUpIkSZKmSZ935CRJ\nkiRJx8FGTpIkSZKmzESTnbyTklwJ3E4zA+bdVfX9rmuQ3pRkFvgJcAawCNxVVT9K8n7gp8CHgH1A\nqupfvRWqk1aSVTTLwMxX1efNpoYiyfuAu4Hzaf7/vB74K+ZTPUtyI3AtcBj4M00234PZVMeS3AN8\nFnixqi5ox8Z+jrfZ/RLwBvDNqvrNcufv9I5ce0HyY+BKYBNwTZLzuqxBWmIB+HZVnQ9cAnytzeT3\ngIer6lzgkXZb6sM24BmaC2UwmxqOHwK/rqrzgI8Az2E+1bN23eOvAJvbC+dVwNWYTfXjXpq+Z9Rb\nZjHJJpr1uje177kjybK9WtePVl4M7K2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"text/plain": [ "<matplotlib.figure.Figure at 0x7f3ab667a650>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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n4x45AACgxYTcydi1EgAAaDEhN03d6yVHJpJ165seBQAA4KSE3HQPHklWr0mx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\nV6YYafeYAAAAy6nVheRB4AA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"text/plain": [ "<matplotlib.figure.Figure at 0x7f3ab6589fd0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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TvvyFlI/+fcb+3b93SSUAAAwpITdEyoPHM/2W12Xsph9NtWlL2+MAAADniZAbIuWv/iTV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\ncjtWAgAAo2HgQy57dyVXbLV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"text/plain": [ "<matplotlib.figure.Figure at 0x7f3ab6586250>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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AACQRcq1X13Wy954UV1/X9CgAAEBLCLm2OzKWrF2X4pJnNj0JAADQEkKu5ep796S46tqm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\nWVobcnVde34cAADAObQ25LL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"text/plain": [ "<matplotlib.figure.Figure at 0x7f3ab6488d90>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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27nZ74wAAgCSCXCfUf/OxZNdlybOe33QpAABACwhyHVDv+3DG3vDtZuMAAIAkglzr1Qce\nSo48kjzvlqZLAQAAWkKQa7n643ekeMU3pti4selSAACAlhDkWqw+fSr1Jz+a4tVvaLoUAACgRQS5\nNvv83yTX3ZDimt1NVwIAALSIINdiMx//0xSv/uamywAAAFpGkGup+ujh5IH7Urz4lU2XAgAAtIwg\n11L1J/48xTe8OsWWLU2XAgAAtIwg10L1zEzqT/yZZZUAAMBFCXJt9I+fT7bvSPGUpzddCQAA0EKC\nXAvVf3WHlgMAAMC8BLmWqXvTqe/6XIqXvrbpUgAAgJYS5Fqm/vRHUzz/lhTbdzRdCgAA0FKCXIvU\ndd1fVvkah5wAAADzE+Ta5IF7k9Onkmc+t+lKAACAFhPkWqT++B0pXvX6FEXRdCkAAECLCXItUT/+\nWOrPfiLFK7+x6VIAAICWE+Raor7z48lNz0lx6RVNlwIAALScINcS9cfvyJjecQAAwBIIci1Q738w\nOXIoed4tTZcCAAB0gCDXAvXH70jxytel2LCh6VIAAIAOEOQaVp8+nfpT+1K8yrJKAABgaQS5pt3z\nd8lV16a4ZnfTlQAAAB0hyDWs/tRfpnjZa5suAwAA6BBBrkH144+l/sKdKW55ddOlAAAAHSLINaj+\nu08nT39Wip2XNl0KAADQIYJcg+rPfMyySgAAYNkEuYbUvank/ntSvPBlTZcCAAB0jCDXkPrOj6d4\n3ktSbL2k6VIAAICOEeQaUn/aaZUAAMDKCHINqA8fTA4dSG5+UdOlAAAAHSTINaD+zMdSvORVKTZu\nbLoUAACggxZNEmVZ3prkXUk2JHlfVVXvvOD5K5P8dpJrZ9/vZ6uq+o21L3V9qOs69af/MmP/w9ub\nLgUAAOioBWfkyrLckOTdSW5NcnOS28qyfPYFt709yeeqqnphkr1Jfq4sS1NN83noy8kTJ5OnP6vp\nSgAAgI5abGnlS5PcX1XVV6qqOpXk9iRvveCeA0l2zv5+Z5JHq6o6vbZlrh9nDzkpiqLpUgAAgI5a\nLMhdn+ShOdcPzz4213uTPKcsy/1JPp/kh9euvPWlnjmjCTgAALBqiy2BrJfwHj+e5O+qqtpbluXT\nk9xRluULqqrqzb2pLMu96S+9TJJUVZXx8fFlltttp+76XB6/9PKMf/1zmi6FBWzevHnkxibdYXzS\nVsYmbWZ80mZlWb5jzuW+qqr2LeV1iwW5rybZM+d6T/qzcnO9Msl/SJKqqr5UluUDSb4+yZ1zb5ot\naG5RP9XrPSnrrXszH/1wcstrMmpfd9eMj4/7b0RrGZ+0lbFJmxmftNX4+HiqqnrHSl67WJC7M8lN\nZVk+Ncn+JN+Z5LYL7vnHJK9P8omyLK9JP8R9eSXFrGf1qSdSf+5TGfupX2y6FAAAoOMW3CM3e2jJ\n25N8JMndST5QVdU9ZVm+rSzLt83e9h+T3FKW5eeT/FmSH6mq6uggi+6kL9yZ3PDUFJdd0XQlAABA\nxxV1vZRtcANR79+/v6nPHroz7/npFM99ScZe881Nl8IiLL+gzYxP2srYpM2MT9pq9+7dSbKi4+wX\nO7WSNVCfOJbc8/kUL3ll06UAAADrgCA3BPXffjJ51vNTbNvRdCkAAMA6IMgNQf3pv8zYy/Y2XQYA\nALBOCHIDVk88mjz45eT5tzRdCgAAsE4IcgNW3/25FM99cYpNm5suBQAAWCcEuUF79FBy9XVNVwEA\nAKwjgtygTTyaXHZl01UAAADriCA3YPXRIykEOQAAYA0JcoM2cSS5XJADAADWjiA3aBNHLK0EAADW\nlCA3QPWJ40md5JJtTZcCAACsI4LcIE08mlx+ZYqiaLoSAABgHRHkBmnicHLZFU1XAQAArDOC3ADV\nE486sRIAAFhzgtwgHXXQCQAAsPYEuUGaOKz1AAAAsOYEuQGytBIAABgEQW6QLK0EAAAGQJAbkLqu\n+83ALa0EAADWmCA3KI8dT4oihWbgAADAGhPkBsWySgAAYEAEuUGZeNSySgAAYCAEuQGpJw47sRIA\nABgIQW5QJh61tBIAABgIQW5Qjh5JLrui6SoAAIB1SJAbkHriSIrLr2q6DAAAYB0S5AZlwowcAAAw\nGILcANR13V9a6dRKAABgAAS5QThxPNmwMcVWzcABAIC1J8gNwsRhyyoBAICBEeQGQTNwAABggAS5\nAaiPHtEMHAAAGBhBbhCOHtEMHAAAGBhBbhC0HgAAAAZIkBuAfjNwM3IAAMBgCHKDMPFoctlVTVcB\nAACsU4LcGqvrWvsBAABgoAS5tXa8l2zclGLrJU1XAgAArFOC3FqbeNSJlQAAwEAJcmtN6wEAAGDA\nBLk15sRKAABg0AS5taaHHAAAMGCC3Fo7ekTrAQAAYKAEuTVWTxxJYUYOAAAYIEFurU0cSeyRAwAA\nBkiQW0P9ZuDaDwAAAIMlyK2lY71k0+YUW7Y2XQkAALCOCXJraeKwZZUAAMDACXJrybJKAABgCAS5\nNVQfPZJCkAMAAAZMkFtLmoEDAABDIMitJa0HAACAIRDk1pCllQAAwDAIcmtp4ojDTgAAgIET5NaI\nZuAAAMCwCHJr5dh0smVrii1bmq4EAABY5wS5tXLUskoAAGA4BLm1MnFY6wEAAGAoBLk1Uk88mkLr\nAQAAYAgEubViaSUAADAkgtxa0XoAAAAYEkFujdQTRyytBAAAhkKQWyuWVgIAAEMiyK2BemYmmXzU\nqZUAAMBQCHJr4dhUsvWSFJs1AwcAAAZPkFsLE49aVgkAAAyNILcWjh5JLr+q6SoAAIARIcitgXri\nSAr74wAAgCER5NaCEysBAIAhEuTWgmbgAADAEAlya0AzcAAAYJgEubVw9IgecgAAwNAIcqtUz8wk\nU0ctrQQAAIZGkFut3lRyyfYUmzY3XQkAADAiBLnVsqwSAAAYMkFutZxYCQAADJkgt0r9ZuCCHAAA\nMDyC3GpNHEm0HgAAAIZIkFuto5ZWAgAAwyXIrZKllQAAwLAJcqs1eTS59PKmqwAAAEaIILcKdV0n\n0xPJrsuaLgUAABghgtxqnHwsKcZSbL2k6UoAAIARIsitxtRksvPSpqsAAABGjCC3GlMTghwAADB0\ngtxq9CbtjwMAAIZOkFuFemoixU5BDgAAGC5BbjXskQMAABogyK3G9ESyS5ADAACGS5BbhXp60tJK\nAABg6AS51ZjSDBwAABg+QW41pu2RAwAAhk+QW6G6rgU5AACgEYLcSp04lmzZkmLT5qYrAQAARowg\nt1JTE4mDTgAAgAYIcis1NWFZJQAA0AhBboXq6ckUTqwEAAAaIMitlINOAACAhghyK6WHHAAA0BBB\nbqWm7ZEDAACaIcitUD01mcKplQAAQAMEuZWankx2mZEDAACGb+NiN5RleWuSdyXZkOR9VVW98yL3\n7E3y80k2JTlSVdXetS2zhab1kQMAAJqx4IxcWZYbkrw7ya1Jbk5yW1mWz77gnkuT/FKSN1dV9dwk\n3zGgWlujnjmTHO8l47uaLgUAABhBiy2tfGmS+6uq+kpVVaeS3J7krRfc891Jfq+qqoeTpKqqI2tf\nZsv0ppNtO1Js2NB0JQAAwAhabGnl9UkemnP9cJKXXXDPTUk2lWX50STjSX6hqqrfWrsSW0gPOQAA\noEGLzcjVS3iPTUlenORNSb4lyf9RluVNqy2s1fSQAwAAGrTYjNxXk+yZc70n/Vm5uR5K/4CTx5I8\nVpblx5K8IMl9c2+aPRBl79nrqqoyPj6+sqob9sQTj+XU5Vdle0frZ2GbN2/u7Nhk/TM+aStjkzYz\nPmmzsizfMedyX1VV+5byuqKu5590K8tyY5IvJvmmJPuTfCbJbVVV3TPnnmelfyDKtyTZkuTTSb6z\nqqq7F/nsev/+/UupsXVmPvx7ybHpjP333990KQzA+Ph4er1e02XARRmftJWxSZsZn7TV7t27k6RY\nyWsXXFpZVdXpJG9P8pEkdyf5QFVV95Rl+bayLN82e88/JvmTJH+ffoh77xJCXLfpIQcAADRowRm5\nAevujNx7fzZ53i0Ze/nepkthAPzUjjYzPmkrY5M2Mz5pq4HNyHFx9dRECqdWAgAADRHkVmJ60qmV\nAABAYwS5lZieTHYKcgAAQDMEuWWqT51KHn8s2b6j6VIAAIARJcgtV28yGd+ZYswfHQAA0AxpZLmm\nLKsEAACaJcgtl4NOAACAhglyy1RPaz0AAAA0S5BbrqmJRJADAAAaJMgt1/SEpZUAAECjBLllqvWQ\nAwAAGibILdfUpD1yAABAowS55ZqeSHYJcgAAQHMEueXSRw4AAGiYILcM9cnHk5kzySXbmi4FAAAY\nYYLcckxPJjsvTVEUTVcCAACMMEFuOfSQAwAAWkCQW47pST3kAACAxglyy1BPT6Rw0AkAANAwQW45\npiYtrQQAABonyC2HHnIAAEALCHLLUE9PWloJAAA0TpBbjqkJh50AAACNE+SWY9oeOQAAoHmC3BLV\nda2PHAAA0AqC3FI9diLZuDHFlq1NVwIAAIw4QW6ppicSB50AAAAtIMgtlR5yAABASwhyS1TrIQcA\nALSEILdUesgBAAAtIcgtlR5yAABASwhySzWt9QAAANAOgtwS1VOWVgIAAO0gyC3V9KTDTgAAgFYQ\n5JZKHzkAAKAlBLklqGdmkt5UsnNX06UAAAAIcktyvJds3ZZi46amKwEAABDklmR60omVAABAawhy\nS6GHHAAA0CKC3BLU0xMpzMgBAAAtIcgtxdSkEysBAIDWEOSWQg85AACgRQS5pdBDDgAAaBFBbgnq\nKXvkAACA9hDklmJ60qmVAABAawhyS2GPHAAA0CKC3CLq06eTE8eSHTubLgUAACCJILe4Y1PJ9vEU\nYxuargQAACCJILc4PeQAAICWEeQWY38cAADQMoLcIurpiRRm5AAAgBYR5BYzNZHoIQcAALSIILcY\nPeQAAICWEeQWMz1pRg4AAGgVQW4R9dRECjNyAABAiwhyi5m2Rw4AAGgXQW4xU/bIAQAA7SLILaA+\n9URy6mSybUfTpQAAAJwjyC1k9qCToiiargQAAOAcQW4hUxPJuP1xAABAuwhyC9F6AAAAaCFBbgF1\nbyrFzl1NlwEAAPAkgtxCelOWVgIAAK0jyC2kN5WMm5EDAADaRZBbyPRUYmklAADQMoLcAureZIod\nghwAANAugtxCetNm5AAAgNYR5BbisBMAAKCFBLl51DMzybGpZHxn06UAAAA8iSA3n8eOJ5u3pti4\nqelKAAAAnkSQm4/WAwAAQEsJcvPRegAAAGgpQW4+ZuQAAICWEuTmUfcmUwhyAABACwly8+lNm5ED\nAABaSZCbT29SDzkAAKCVBLn5OOwEAABoKUFuHnVvKsUOzcABAID2EeTm05tKdlpaCQAAtI8gNx/t\nBwAAgJYS5C6iPnMmeex4smO86VIAAAC+hiB3Mcenk0u2pxjb0HQlAAAAX0OQuxg95AAAgBYT5C5m\netJBJwAAQGsJchdR96ZSmJEDAABaSpC7mN5UMq6HHAAA0E6C3MX0ppJxSysBAIB2EuQuRg85AACg\nxQS5i6inp1LsFOQAAIB2EuQupjeZ7BDkAACAdhLkLqY3nZiRAwAAWkqQuxiHnQAAAC0myF2gPnUq\neeJksm1706UAAABclCB3od5UsmNniqJouhIAAICLEuQudEzrAQAAoN0EuQtNTznoBAAAaDVB7gJ1\nbyqFGTkAAKDFBLkL9SYtrQQAAFpNkLtQb1qQAwAAWk2Qu5AZOQAAoOUEuQvU01MpdmoGDgAAtJcg\nd6HZPnIAAABtJchdqDeVmJEDAABabONiN5RleWuSdyXZkOR9VVW9c577viHJJ5OUVVX9/ppWOUw9\nDcEBAIB2W3BGrizLDUneneTWJDcnua0sy2fPc987k/xJkmIAdQ5FffLxJHWyZWvTpQAAAMxrsaWV\nL01yf1VVX6mq6lSS25O89SL3/c9JfjfJ4TWub7imJ5Mdu1IUnc2iAADACFgsyF2f5KE51w/PPnZO\nWZbXpx/u3jP7UL1m1Q3bMT3kAACA9lssyC0llL0ryY9WVVWnv6yyu9NZ0w46AQAA2m+xw06+mmTP\nnOs96c/KzfWSJLeXZZkkVyZ5Y1mWp6qq+tDcm8qy3Jtk79nrqqoyPj6+sqoH5OSpx3Pm8iuzrWV1\nMVybN284efz4AAASvElEQVRu3diEs4xP2srYpM2MT9qsLMt3zLncV1XVvqW8rqjr+SfdyrLcmOSL\nSb4pyf4kn0lyW1VV98xz/68n+cMlnlpZ79+/fyk1Ds3Mh383Od7L2Hd8f9Ol0KDx8fH0er2my4CL\nMj5pK2OTNjM+aavdu3cnK1zRuODSyqqqTid5e5KPJLk7yQeqqrqnLMu3lWX5tpV8YKv1ppJxSysB\nAIB2W3BGbsDaNyP3q/8pefYLM/bKb2y6FBrkp3a0mfFJWxmbtJnxSVsNbEZu1NTTUyl2OrUSAABo\nN0Furt6k9gMAAEDrCXJz9fSRAwAA2k+Qm1XX9exhJ4IcAADQboLcWY8dTzZvTrFpc9OVAAAALEiQ\nO2t6Ktmxs+kqAAAAFiXInXVsKtmphxwAANB+gtxZ0/bHAQAA3SDIzap7UykEOQAAoAMEubP0kAMA\nADpCkDtLDzkAAKAjBLmz9JADAAA6QpCbVU9P2iMHAAB0giB3Vm8q2SnIAQAA7SfIndWbSsb1kQMA\nANpPkEtSz5xJThxLduxsuhQAAIBFCXJJcvxYsnVbig0bmq4EAABgUYJckkw7sRIAAOgOQS5Jjjno\nBAAA6A5BLkltRg4AAOgQQS5JenrIAQAA3SHIJbOtBwQ5AACgGwS5RA85AACgUwS5JHVvKoXDTgAA\ngI4Q5JJ++4EdghwAANANglzSX1ppRg4AAOgIQS5x2AkAANApIx/k6tOnkpOPJdt2NF0KAADAkox8\nkMux6WT7eIoxfxQAAEA3SC/TllUCAADdIsj1ppKdesgBAADdMfJBru5NpTAjBwAAdMjIBzknVgIA\nAF0jyPUmBTkAAKBTBDmHnQAAAB0z8kGuPjadYqcgBwAAdMfIB7lMTyY7BDkAAKA7BLneVGJGDgAA\n6BBBrjeVjOsjBwAAdMdIB7n65MnkzJlk6yVNlwIAALBkIx3kcqx/YmVRFE1XAgAAsGSjHeS0HgAA\nADpotINcb9JBJwAAQOeMdJCre9MpzMgBAAAdM9JBLr1JSysBAIDOGfEgZ48cAADQPaMd5Kb1kAMA\nALpnpINc3ZtMMb6z6TIAAACWZaSDXB49lFxxddNVAAAALMvIBrl65kxy5FBy5bVNlwIAALAsIxvk\nMnE02T6eYsuWpisBAABYltENcocPJFebjQMAALpnZINcffhgiquua7oMAACAZRvZIJfDB5KrzMgB\nAADdM7JBrj4kyAEAAN00skEuhw+muNrSSgAAoHtGMsjVdZ0cPmhGDgAA6KSRDHI51ktSJNvHm64E\nAABg2UYzyM0edFIURdOVAAAALNtIBrl+6wHLKgEAgG4aySCnGTgAANBloxnkDh1MNAMHAAA6aiSD\nXK31AAAA0GEjGeS0HgAAALps5IJcffJkcuJYcukVTZcCAACwIiMX5HLkYHLF1SnGRu9LBwAA1ofR\nSzOzPeQAAAC6auSCXH3ogINOAACAThu5IOegEwAAoOtGLsjVhw6mEOQAAIAOG7kg198jZ2klAADQ\nXSMV5OozZ5KJI8mV1zRdCgAAwIqNVJDL0cPJzktTbNrUdCUAAAArNlpBzrJKAABgHRipIFcfOqj1\nAAAA0HkjFeS0HgAAANaDkQpy9eEDWg8AAACdN1JBrj8jZ2klAADQbSMT5Oq6trQSAABYF0YmyKU3\nmWzalGLb9qYrAQAAWJXRCXKHtB4AAADWh5EJcvWhgw46AQAA1oWRCXL2xwEAAOvFCAU5SysBAID1\nYWSCXH3Y0koAAGB9GJkgl8MHk6vNyAEAAN03EkGufvxEcvLxZNdlTZcCAACwaiMR5HKof9BJURRN\nVwIAALBqoxHknFgJAACsIyMR5OrDBxx0AgAArBsjEeT6M3IOOgEAANaHkQhyWg8AAADryUgEuRw6\nkFwtyAEAAOvDug9y9elTydTR5PKrmy4FAABgTaz7IJcjh5JLr0ixcWPTlQAAAKyJ9R/ktB4AAADW\nmXUf5PqtB5xYCQAArB/rPsjl8MHkakEOAABYP9Z9kNN6AAAAWG/WfZDTegAAAFhv1nWQq2dmkkcf\nSa4U5AAAgPVjXQe5TE0kW7el2HpJ05UAAACsmfUd5A4f0HoAAABYd5bUJbssy1uTvCvJhiTvq6rq\nnRc8/8+S/EiSIkkvyb+squrv17jWZesfdOLESgAAYH1ZdEauLMsNSd6d5NYkNye5rSzLZ19w25eT\n/JOqqp6f5P9K8l/WutAVOaQZOAAAsP4sZUbupUnur6rqK0lSluXtSd6a5J6zN1RV9ck59386yQ1r\nWOPKHT6QPP+WpqsAAABYU0vZI3d9kofmXD88+9h8fjDJH6+mqLVSHzpgaSUAALDuLGVGrl7qm5Vl\n+bokP5DkVRd5bm+SvWevq6rK+Pj4Ut96RaaOPJIdT3tGxgb8OawvmzdvHvjYhJUyPmkrY5M2Mz5p\ns7Is3zHncl9VVfuW8rqlBLmvJtkz53pP+rNyFxbw/CTvTXJrVVUTFz4/W9Dcon6q1+stpcYVqY8f\nS33mdI4VG1IM8HNYf8bHxzPIsQmrYXzSVsYmbWZ80lbj4+OpquodK3ntUoLcnUluKsvyqUn2J/nO\nJLfNvaEsyxuT/H6S76mq6v6VFLLmHrg3uW5PiqJouhIAAIA1tegeuaqqTid5e5KPJLk7yQeqqrqn\nLMu3lWX5ttnbfjLJZUneU5bl58qy/MzAKl6imb/4oxSv+eamywAAAFhzRV0veQvcWqv3798/mDc+\n8FBmfvYnMvYz70uxafNAPoP1y/IL2sz4pK2MTdrM+KStdu/enfR7cS/bUk6t7Jz6jg+meO0bhTgA\nAGBdWndBru5Npf7sJ1K87k1NlwIAADAQ6y/IffSPU7zkVSnGdzVdCgAAwECsqyBXn3oi9V9+OMUb\n3tp0KQAAAAOzvoLcp/YlT3lGiuv2LHovAABAV62bIFfXdeo7Ppgxs3EAAMA6t26CXO7622TDhuRZ\nz2+6EgAAgIFaN0Fu5o4PpnjDt6coVtSGAQAAoDPWRZCrH34g+eqDKV76mqZLAQAAGLj1EeTu+FCK\n170pxcZNTZcCAAAwcJ0PcvXk0dR/96kUe9/YdCkAAABD0f0g99E/TvHS16bYPt50KQAAAEPR6SBX\nnzyZ+mN/kuL1b2m6FAAAgKHpdpD75J8nz3h2imt2N10KAADA0HQ2yNUzM6nv+JAG4AAAwMjpbJDL\nF+5MLtmW3PScpisBAAAYqs4GuZmP35Hidd+qATgAADByOhnk6pmZ5L67U9z8wqZLAQAAGLpOBrkc\neCjZtj3FZVc0XQkAAMDQdTLI1ffeleKZ9sYBAACjqZNBLvfd5ZATAABgZHUuyNV1nfq+u1IIcgAA\nwIjqXJDL4YP9/73q2mbrAAAAaEjngtzZ2ThtBwAAgFHVuSCXe+9KHHQCAACMsM4FOfvjAACAUdep\nIFdPPJqcOJ5ct6fpUgAAABrTrSB3313JTTenGOtU2QAAAGuqW4nIskoAAIBuBbn6vrsFOQAAYOR1\nJsjVx6aTRw8lN35d06UAAAA0qjNBLvffnXzds1Js2NB0JQAAAI3qTJCr77s7hf5xAAAAHQpy996V\n4qabmy4DAACgcZ0IcvXjJ5L9DyZPe2bTpQAAADSuE0EuX/pi8pSnp9i0uelKAAAAGteJIFffd1eK\nm57bdBkAAACt0KEgZ38cAABA0oEgV596IvlvX0qe8aymSwEAAGiF1ge5PHBfcu0NKbZua7oSAACA\nVmh9kKvvu0v/OAAAgDm6EeRuEuQAAADOanWQq8+cSb78xeQZDjoBAAA4q9VBLg8/kFx2ZYrxnU1X\nAgAA0BqtDnL1vfbHAQAAXKj1Qc6ySgAAgCdrbZCrZ2aS+x10AgAAcKHWBrkcfDi5ZHuKy69suhIA\nAIBWaW2Qq+81GwcAAHAxrQ1yue+u5Cb74wAAAC60sckPr3vT8z93710Ze+t3D7EaAACAbmg0yM38\n5L+c/8krrkmuum54xQAAAHREo0Fuw8//TpMfDwAA0Ent3SMHAADARQlyAAAAHSPIAQAAdIwgBwAA\n0DGCHAAAQMcIcgAAAB0jyAEAAHSMIAcAANAxghwAAEDHCHIAAAAdI8gBAAB0jCAHAADQMYIcAABA\nxwhyAAAAHSPIAQAAdIwgBwAA0DGCHAAAQMcIcgAAAB0jyAEAAHSMIAcAANAxghwAAEDHCHIAAAAd\nI8gBAAB0jCAHAADQMYIcAABAxwhyAAAAHSPIAQAAdIwgBwAA0DGCHAAAQMcIcgAAAB0jyAEAAHSM\nIAcAANAxghwAAEDHCHIAAAAdI8gBAAB0jCAHAADQMYIcAABAxwhyAAAAHSPIAQAAdIwgBwAA0DGC\nHAAAQMcIcgAAAB0jyAEAAHSMIAcAANAxghwAAEDHCHIAAAAdI8gBAAB0jCAHAADQMYIcAABAx2xc\n7IayLG9N8q4kG5K8r6qqd17knl9M8sYkJ5J8X1VVn1vrQgEAAOhbcEauLMsNSd6d5NYkNye5rSzL\nZ19wz5uSPKOqqpuS/Isk7xlQrQAAAGTxpZUvTXJ/VVVfqarqVJLbk7z1gnvekuQ3k6Sqqk8nubQs\ny2vWvFIAAACSLB7krk/y0Jzrh2cfW+yeG1ZfGgAAABezWJCrl/g+xQpfBwAAwDItdtjJV5PsmXO9\nJ/0Zt4XuuWH2sScpy3Jvkr1nr6uqyu7du5dRKgzP+Ph40yXAvIxP2srYpM2MT9qqLMt3zLncV1XV\nvqW8brEgd2eSm8qyfGqS/Um+M8ltF9zzoSRvT3J7WZYvTzJZVdUjF77RbEHniirLMlVVvePC+6Bp\nZVm+w9ikrYxP2srYpM2MT9pqNWNzwaWVVVWdTj+kfSTJ3Uk+UFXVPWVZvq0sy7fN3vPHSb5cluX9\nSX4lyQ+tpBAAAACWZtE+clVVfTjJhy947FcuuH77GtcFAADAPBY77GSQ9jX42bCQfU0XAAvY13QB\nMI99TRcAC9jXdAEwj30rfWFR1w6YBAAA6JImZ+QAAABYAUEOAACgYxY97GStlWV5a5J3JdmQ5H1V\nVb1z2DXAWWVZ7knyX5NcnX4j+/9SVdUvlmV5eZIPJHlKkq8kKauqmmysUEZWWZYb0m8F83BVVW82\nNmmLsiwvTfK+JM9J/+/P709yX4xPGlaW5Y8l+Z4kM0m+kP7Y3B5jkyEry/LXknxrkkNVVT1v9rF5\n/x2fHbs/kORMkn9dVdWfLvT+Q52Rm/2G5N1Jbk1yc5LbyrJ89jBrgAucSvJvqqp6TpKXJ/lXs2Py\nR5PcUVXVM5P8+ew1NOGH02//cnZDs7FJW/xCkj+uqurZSZ6f5B9jfNKw2d7H/1OSF89+47whyXfF\n2KQZv55+7pnromOxLMub0+/ZffPsa365LMsFs9qwl1a+NMn9VVV9paqqU0luT/LWIdcA51RVdbCq\nqr+b/f2xJPckuT7JW5L85uxtv5nk25upkFFWluUNSd6U/qxHMfuwsUnjyrLcleQ1VVX9WtLvO1tV\n1VSMT5o3nf4PabeVZbkxybYk+2Ns0oCqqv4qycQFD883Ft+a5P1VVZ2qquorSe5PPzvNa9hLK69P\n8tCc64eTvGzINcBFzf4U70VJPp3kmqqqHpl96pEk1zRVFyPt55P8b0l2znnM2KQNnpbkcFmWv57k\nBUk+m+R/ifFJw6qqOlqW5c8leTDJY0k+UlXVHWVZGpu0xXxjcXeST8257+H0s9O8hj0jp9cBrVSW\n5Y4kv5fkh6uq6s19rqqqOsYuQ1aW5belv6b+czk/G/ckxiYN2pjkxUl+uaqqFyc5nguWqhmfNKEs\ny6en/0OFp6b/jfGOsiy/Z+49xiZtsYSxuOA4HXaQ+2qSPXOu96SfNqExZVluSj/E/VZVVX8w+/Aj\nZVleO/v8dUkONVUfI+uVSd5SluUDSd6f5BvLsvytGJu0w8PpH8DzN7PXv5t+sDtofNKwW5L8dVVV\nj1ZVdTrJ7yd5RYxN2mO+f8cvzEk3zD42r2EHuTuT3FSW5VPLstyc/oa+Dw25BjinLMsiya8mubuq\nqnfNeepDSb539vffm+QPLnwtDFJVVT9eVdWeqqqelv5G/b+oquqfx9ikBaqqOpjkobIsnzn70OuT\n3JXkD2N80qx/TPLysiwvmf03/vXpHxhlbNIW8/07/qEk31WW5eayLJ+W5KYkn1nojYq6Hu7MclmW\nb8z59gO/WlXVTw+1AJijLMtXJ/lYkr/P+enrH0v//zhVkhvjmGIaVpbla5P826qq3jJ7bLGxSePK\nsnxB+gfxbE7ypfSPeN8Q45OGlWX5I+l/gzyT5G+T/I9JxmNsMmRlWb4/yWuTXJn+frifTPLBzDMW\ny7L88fTbD5xOf7vPRxZ6/6EHOQAAAFZn2EsrAQAAWCVBDgAAoGMEOQAAgI4R5AAAADpGkAMAAOgY\nQQ4AAKBjBDkAAICOEeQAAAA65v8HVRqWu6L1rHYAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f3ab6485dd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for i in range(20):\n", " plt.figure(figsize=(15,10))\n", " plt.plot(np.linspace(0,100,100),accu[i])" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#np.savetxt(\"Accuracy_Data_run_10.dat\", accu)\n", "acc0 = np.loadtxt(\"Accuracy_Data_run_10.dat\")" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7f3ab6579550>]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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JktZkwlcRuVb49uyF7oMwO53L+0uSJElamwlfVUyMQv9Afu/fN+C2TkmSJKlgTPgqIM7d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\ncP0WuXmBLOlUx4GK/X3u0HkzKbwTLd0ds/WM6Y7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LCX3l30n7zP8h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4EL6G0J+ETdBUlNlpklzlHSoO4CzgA+BrwMfKfecqTZLyKOBx4Grs/Mf41/r8pc1ITAtxNYOO54\nIb1VPkkDyMyX+393Az+nt11a0mB2RcSpABHxPmCs5nqkRsnMscws+/+k3otzkTStiJhDL+z9JDMf\n6Z8eaC5qQuB7HFgcEYsi4mhgFfBozTVJjRIRx0bE3P7r44DzgSfrrUpqpEeBNf3Xa4BHpmkraYL+\nP6dvuwTnImlKEVEA9wFPZeamcW8NNBcVZTn7d6NExAUcfCzDfZl5a80lSY0SEWfQW9WD3rW7P3Uc\nSdOLiJ8By4GT6F0jcRPwCyCB9+NjGaRpTTKGbgZW0NvOWQLPAVePuxZJ0jgRcR7wO+AvHNy2uQ74\nIwPMRY0IfJIkSZKkwTVhS6ckSZIkaQgGPkmSJElqKQOfJEmSJLWUgU+SJEmSWsrAJ0mSJEktZeCT\nJEmSpJYy8EmSJElSSxn4JEmSJKml/gvmAWXaqvR49AAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f3ab5db1250>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(15,10))\n", "plt.plot(np.linspace(0,20,20), fracerr)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def plot_epochs(az_angle, eleva):\n", " fig = plt.figure(figsize=(15, 10))\n", " ax = fig.add_subplot(111, projection='3d')\n", " X, Y = np.meshgrid(np.linspace(0,100,100), np.linspace(0,20, 20))\n", " ax.plot_surface(X, Y, acc0)\n", " ax.view_init(elev=eleva, azim=az_angle)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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nfx6TJF18cYu5ztSqVYSpsDQ1FXY2syqC9zZKZj1MnTjhaM0auwd/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\nGR4Qp8jdqOefr+m2m9+oUenKyqKLn914TgL9icvl4jtrE+q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"text/plain": [ "<matplotlib.figure.Figure at 0x7f6528552650>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "widg.interact(plot_epochs, az_angle=(0, 360, 1), eleva=(0,20,1))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
FiveEye/FiveEye.github.io	assets/code/rlbook/5_2_SoapBubble.ipynb	1	618515	{ "cells": [ { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "from mpl_toolkits.mplot3d import Axes3D\n", "from matplotlib import cm\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [], "source": [ "N = 20\n", "s = np.random.normal(0,1,(N,N))\n", "dps = np.copy(s)" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "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", " fig.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", " event.shiftKey = false;\n", " // Send a \"J\" for go to next cell\n", " event.which = 74;\n", " event.keyCode = 74;\n", " manager.command_mode();\n", " manager.handle_keydown(event);\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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\" width=\"640\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib notebook\n", "def printS(s):\n", " x = np.linspace(-10,10,N)\n", " y = np.linspace(-10,10,N)\n", " x, y = np.meshgrid(x, y)\n", " fig = plt.figure()\n", " ax = fig.gca(projection='3d')\n", " ax.plot_surface(x,y,s,cmap=cm.coolwarm,linewidth=0, antialiased=False)\n", " plt.show()\n", "printS(s)" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "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", " fig.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", " event.shiftKey = false;\n", " // Send a \"J\" for go to next cell\n", " event.which = 74;\n", " event.keyCode = 74;\n", " manager.command_mode();\n", " manager.handle_keydown(event);\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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\" width=\"640\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def DPupdate(s):\n", " r = np.copy(s)\n", " for i in range(1, N-1):\n", " for j in range(1, N-1):\n", " r[i,j] = (s[i+1,j] + s[i-1,j] + s[i,j+1] + s[i,j-1]) / 4\n", " return r\n", "for _ in range(200):\n", " dps = DPupdate(dps)\n", "printS(dps)\n" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "scrolled": true }, "outputs": [], "source": [ "def update(s,_x,_y,M):\n", " r = 0.0\n", " for i in range(M):\n", " x, y = _x, _y\n", " while True:\n", " c = np.random.randint(4)\n", " if c == 0:\n", " x += 1\n", " elif c == 1:\n", " x -= 1\n", " elif c == 2:\n", " y += 1\n", " else:\n", " y -= 1\n", " if x == 0 or y == 0 or x == N - 1 or y == N - 1:\n", " r += s[x,y]\n", " break\n", " return r / M\n", "def updateAll(s, M):\n", " global T\n", " r = np.copy(s)\n", " for i in range(1,N-1):\n", " for j in range(1,N-1):\n", " r[i,j] = update(s,i,j,M)\n", " s = T / (T + M) * s + M / (T + M) * r\n", " T += M\n", " return s\n", "T = 0" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1800: 6.063749389102973\n", "1900: 5.983036903734684\n", "2000: 5.919951300076669\n", "2100: 5.76030151747774\n", "2200: 5.800827640916654\n", "2300: 5.686088993249514\n", "2400: 5.511176015148434\n", "2500: 5.3442588412985055\n", "2600: 5.215471104180284\n", "2700: 5.099088858690441\n" ] }, { "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", " fig.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", " event.shiftKey = false;\n", " // Send a \"J\" for go to next cell\n", " event.which = 74;\n", " event.keyCode = 74;\n", " manager.command_mode();\n", " manager.handle_keydown(event);\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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\" width=\"640\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for i in range(10):\n", " s = updateAll(s, 100)\n", " print(str(T) + \": \" + str(np.sum(np.abs(s-dps))))\n", "printS(s)\n" ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "15.616353438781161\n" ] } ], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
ThunderShiviah/code_guild	interactive-coding-challenges/stacks_queues/queue_list/queue_list_challenge.ipynb	2	5275	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<small><i>This notebook was prepared by [Donne Martin](http://donnemartin.com). Source and license info is on [GitHub](https://github.com/donnemartin/interactive-coding-challenges).</i></small>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Challenge Notebook" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Problem: Implement a queue with enqueue and dequeue methods using a linked list.\n", "\n", "* [Constraints](#Constraints)\n", "* [Test Cases](#Test-Cases)\n", "* [Algorithm](#Algorithm)\n", "* [Code](#Code)\n", "* [Unit Test](#Unit-Test)\n", "* [Pythonic-Code](#Pythonic-Code)\n", "* [Solution Notebook](#Solution-Notebook)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Constraints\n", "\n", "* If there is one item in the list, do you expect the first and last pointers to both point to it?\n", " * Yes\n", "* If there are no items on the list, do you expect the first and last pointers to be None?\n", " * Yes\n", "* If you dequeue on an empty queue, does that return None?\n", " * Yes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Test Cases\n", "\n", "### Enqueue\n", "\n", "* Enqueue to an empty queue\n", "* Enqueue to a non-empty queue\n", "\n", "### Dequeue\n", "\n", "* Dequeue an empty queue -> None\n", "* Dequeue a queue with one element\n", "* Dequeue a queue with more than one element" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Algorithm\n", "\n", "Refer to the [Solution Notebook](http://nbviewer.ipython.org/github/donnemartin/interactive-coding-challenges/blob/master/stacks_queues/queue_list/queue_list_solution.ipynb). If you are stuck and need a hint, the solution notebook's algorithm discussion might be a good place to start." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Code" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "class Node(object):\n", "\n", " def __init__(self, data):\n", " # TODO: Implement me\n", " pass\n", "\n", "\n", "class Queue(object):\n", "\n", " def __init__(self):\n", " # TODO: Implement me\n", " pass\n", "\n", " def enqueue(self, data):\n", " # TODO: Implement me\n", " pass\n", "\n", " def dequeue(self):\n", " # TODO: Implement me\n", " pass" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Unit Test\n", "\n", "\n", "\n", "The following unit test is expected to fail until you solve the challenge." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# %load test_queue_list.py\n", "from nose.tools import assert_equal\n", "\n", "\n", "class TestQueue(object):\n", "\n", " # TODO: It would be better if we had unit tests for each\n", " # method in addition to the following end-to-end test\n", " def test_end_to_end(self):\n", " print('Test: Dequeue an empty queue')\n", " queue = Queue()\n", " assert_equal(queue.dequeue(), None)\n", "\n", " print('Test: Enqueue to an empty queue')\n", " queue.enqueue(1)\n", "\n", " print('Test: Dequeue a queue with one element')\n", " assert_equal(queue.dequeue(), 1)\n", "\n", " print('Test: Enqueue to a non-empty queue')\n", " queue.enqueue(2)\n", " queue.enqueue(3)\n", " queue.enqueue(4)\n", "\n", " print('Test: Dequeue a queue with more than one element')\n", " assert_equal(queue.dequeue(), 2)\n", " assert_equal(queue.dequeue(), 3)\n", " assert_equal(queue.dequeue(), 4)\n", "\n", " print('Success: test_end_to_end')\n", "\n", "\n", "def main():\n", " test = TestQueue()\n", " test.test_end_to_end()\n", "\n", "\n", "if __name__ == '__main__':\n", " main()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Solution Notebook\n", "\n", "Review the [Solution Notebook](http://nbviewer.ipython.org/github/donnemartin/interactive-coding-challenges/blob/master/stacks_queues/queue_list/queue_list_solution.ipynb) for a discussion on algorithms and code solutions." ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
marinkaz/dm-unipv	Network Analysis.ipynb	1	1057503	null	bsd-3-clause
mne-tools/mne-tools.github.io	0.19/_downloads/bf3ad991f7c7776e245520709f49cb04/plot_cwt_sensor_connectivity.ipynb	2	4059	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n# Compute seed-based time-frequency connectivity in sensor space\n\n\nComputes the connectivity between a seed-gradiometer close to the visual cortex\nand all other gradiometers. The connectivity is computed in the time-frequency\ndomain using Morlet wavelets and the debiased squared weighted phase lag index\n[1]_ is used as connectivity metric.\n\n.. [1] Vinck et al. \"An improved index of phase-synchronization for electro-\n physiological data in the presence of volume-conduction, noise and\n sample-size bias\" NeuroImage, vol. 55, no. 4, pp. 1548-1565, Apr. 2011.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Author: Martin Luessi <[email protected]>\n#\n# License: BSD (3-clause)\n\nimport numpy as np\n\nimport mne\nfrom mne import io\nfrom mne.connectivity import spectral_connectivity, seed_target_indices\nfrom mne.datasets import sample\nfrom mne.time_frequency import AverageTFR\n\nprint(__doc__)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Set parameters\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data_path = sample.data_path()\nraw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'\nevent_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw-eve.fif'\n\n# Setup for reading the raw data\nraw = io.read_raw_fif(raw_fname)\nevents = mne.read_events(event_fname)\n\n# Add a bad channel\nraw.info['bads'] += ['MEG 2443']\n\n# Pick MEG gradiometers\npicks = mne.pick_types(raw.info, meg='grad', eeg=False, stim=False, eog=True,\n exclude='bads')\n\n# Create epochs for left-visual condition\nevent_id, tmin, tmax = 3, -0.2, 0.5\nepochs = mne.Epochs(raw, events, event_id, tmin, tmax, picks=picks,\n baseline=(None, 0), reject=dict(grad=4000e-13, eog=150e-6),\n preload=True)\n\n# Use 'MEG 2343' as seed\nseed_ch = 'MEG 2343'\npicks_ch_names = [raw.ch_names[i] for i in picks]\n\n# Create seed-target indices for connectivity computation\nseed = picks_ch_names.index(seed_ch)\ntargets = np.arange(len(picks))\nindices = seed_target_indices(seed, targets)\n\n# Define wavelet frequencies and number of cycles\ncwt_freqs = np.arange(7, 30, 2)\ncwt_n_cycles = cwt_freqs / 7.\n\n# Run the connectivity analysis using 2 parallel jobs\nsfreq = raw.info['sfreq'] # the sampling frequency\ncon, freqs, times, _, _ = spectral_connectivity(\n epochs, indices=indices,\n method='wpli2_debiased', mode='cwt_morlet', sfreq=sfreq,\n cwt_freqs=cwt_freqs, cwt_n_cycles=cwt_n_cycles, n_jobs=1)\n\n# Mark the seed channel with a value of 1.0, so we can see it in the plot\ncon[np.where(indices[1] == seed)] = 1.0\n\n# Show topography of connectivity from seed\ntitle = 'WPLI2 - Visual - Seed %s' % seed_ch\n\nlayout = mne.find_layout(epochs.info, 'meg') # use full layout\n\ntfr = AverageTFR(epochs.info, con, times, freqs, len(epochs))\ntfr.plot_topo(fig_facecolor='w', font_color='k', border='k')" ] } ], "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
santiago-salas-v/walas	van_den_bussche_froment.ipynb	1	1263574	null	mit
maxentile/msm-learn	notebooks/Gentlest ascent dynamics.ipynb	1	17640	{ "metadata": { "name": "", "signature": "sha256:39f5d729207710797db2c2bd3f29b3806b251cbcb372fef7597c4ab5f4d58087" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Definition: \"The gentlest ascent dynamics [(Weinan and Zhou, 2011)](http://stacks.iop.org/no/24/1831)\n", "\n", "Application: \"Atomistic simulations of rare events using gentlest ascent dynamics\" ([Samanta and Weinan, 2011](http://arxiv.org/pdf/1108.1941v1.pdf))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Background:\n", "- Transition states correspond to saddle points on the potential energy surface\n", "- We can characterize critical points (minima, maxima, saddle points) by computing properties of the matrix of second-order partial derivatives (the \"Hessian\" matrix) at each critical point\n", " - Eigenvalues of the Hessian tell us whether the surface is concave up, concave down, or a mixture of both\n", " - Index of saddlepoint: the fraction of negative eigenvalues of the hessian:\n", " - All negative: concave \"down\"\n", " - All positive: concave \"up\"\n", " - Mixed: inconsistent concavity" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notes on Weinan and Zhou, 2011\n", "- Steepest descent dynamics: Given an energy function $V$ on $\\newcommand{\\reals}{\\mathbb{R}} \\reals^n$, steepest descent dynamics are:\n", "$$ \\dot{ \\newcommand{\\x}{\\mathbf{x}} \\newcommand{\\v}{\\mathbf{v}} \\x} = - \\nabla V (\\x)$$\n", " - If $\\x(\\cdot)$ is a solution, then $V(\\x(t))$ is a decreasing function of $t$\n", " - Stable fixed points: local minima of $V$\n", " - Basins of attraction (aka potential wells of $V$) are separated by \"separatrices\" on which dynamics converges to saddle points\n", "- Goal:\n", " - Opposite: climb out of a basin of attraction\n", "- Strategy:\n", " - Na\u00efve suggestion: flip sign (from $- \\nabla V (\\x)$ to $ \\nabla V (\\x)$)\n", " - Outcome: find local maxima\n", " - We need a dynamics that converges to index-1 saddle points of $V$, of interest to noise-induced transition between metastable states:\n", " $$ \\begin{align}\n", " \\dot{\\x} & = -\\nabla V(\\x) + 2 \\frac{(\\nabla V, \\v)}{(\\v,\\v)} \\v\\\\\n", " \\dot{\\v} & = -\\nabla^2 V (\\x) \\v + \\frac{(\\v,\\nabla^2 V \\v)}{(\\v,\\v)} \\v\\\\\n", " \\end{align}$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notes on Samanta and Weinan 2011\n", "\n", "- Complex system dynamics frequently involve rare transition events between metastable states" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next steps and generalization:\n", "- Can we automatically construct dynamical systems that have the desired properties? (e.g. of hopping out of basins of attraction: create a number of test potentials and see how far a walker gets on each one)\n", "- What is a good set of design criteria for metadynamics systems?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Implementation\n", "\n", "(Based on MATLAB implementation: http://web.math.princeton.edu/string/gad/)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "import numpy.random as npr\n", "import pylab as pl\n", "%matplotlib inline" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 55 }, { "cell_type": "code", "collapsed": false, "input": [ "dt = 0.01\n", "T = 100\n", "ndim=2\n", "\n", "X = np.zeros((T,ndim))\n", "X[0] = [0.45,-0.45]\n", "direction = -np.array([0.87,0.07])\n", "normalize = lambda vec : vec / np.sqrt(sum(vec*2))\n", "direction = normalize(direction)\n", "V = np.zeros((T,ndim))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 56 }, { "cell_type": "code", "collapsed": false, "input": [ "from numpy import pi, sin, cos" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 57 }, { "cell_type": "code", "collapsed": false, "input": [ "def force(x):\n", " return np.array((-picos(pix[0])sin(pix[1]),\n", " -pisin(pix[0])cos(pix[1])))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 58 }, { "cell_type": "code", "collapsed": false, "input": [ "force(X[0])" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 59, "text": [ "array([ 0.48540276, -0.48540276])" ] } ], "prompt_number": 59 }, { "cell_type": "code", "collapsed": false, "input": [ "def hessian(x):\n", " return pi2 np.array([[-sin(pix[0])sin(pix[1]),\n", " cos(pix[0])cos(pix[1])],\n", " [cos(pix[0])cos(pix[1]),\n", " -sin(pix[0])sin(pix[1])]])" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 60 }, { "cell_type": "code", "collapsed": false, "input": [ "hessian(X[0])" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 61, "text": [ "array([[ 9.62807799, 0.24152641],\n", " [ 0.24152641, 9.62807799]])" ] } ], "prompt_number": 61 }, { "cell_type": "code", "collapsed": false, "input": [ "for b in range(10):\n", " #direction = npr.randn(2)\n", " #direction = normalize(direction)\n", " for i in range(T-1):\n", " F = force(X[i])# + npr.randn(ndim)0.01\n", " H = hessian(X[i]).T# + npr.randn(ndim,ndim)0.01\n", " c1 = direction.dot(F)\n", " X[i+1] = X[i] + Fdt - 2dtc1.dot(direction)\n", " direction = normalize(direction - dtH.dot(direction))\n", " pl.plot(X[:,0],X[:,1])" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x1095e8110>" ] } ], "prompt_number": 68 }, { "cell_type": "code", "collapsed": false, "input": [ "c1" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 69, "text": [ "array([ -6.38299509e-14, 6.38299509e-14])" ] } ], "prompt_number": 69 }, { "cell_type": "code", "collapsed": false, "input": [ "direction" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 70, "text": [ "array([[-0.70710678, 0.70710678],\n", " [ 0.70710678, -0.70710678]])" ] } ], "prompt_number": 70 }, { "cell_type": "code", "collapsed": false, "input": [ "normalize(direction)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 41, "text": [ "array([ 0.5741445 , 0.81875399])" ] } ], "prompt_number": 41 }, { "cell_type": "markdown", "metadata": {}, "source": [ "MD-GAD" ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }	mit
bbglab/adventofcode	2018/jordi/Day 9 - Marble Mania.ipynb	1	6917	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "```\n", "\n", "You talk to the Elves while you wait for your navigation system to initialize. To pass the time, they introduce you to their favorite marble game.\n", "\n", "The Elves play this game by taking turns arranging the marbles in a circle according to very particular rules. The marbles are numbered starting with 0 and increasing by 1 until every marble has a number.\n", "\n", "First, the marble numbered 0 is placed in the circle. At this point, while it contains only a single marble, it is still a circle: the marble is both clockwise from itself and counter-clockwise from itself. This marble is designated the current marble.\n", "\n", "Then, each Elf takes a turn placing the lowest-numbered remaining marble into the circle between the marbles that are 1 and 2 marbles clockwise of the current marble. (When the circle is large enough, this means that there is one marble between the marble that was just placed and the current marble.) The marble that was just placed then becomes the current marble.\n", "\n", "However, if the marble that is about to be placed has a number which is a multiple of 23, something entirely different happens. First, the current player keeps the marble they would have placed, adding it to their score. In addition, the marble 7 marbles counter-clockwise from the current marble is removed from the circle and also added to the current player's score. The marble located immediately clockwise of the marble that was removed becomes the new current marble.\n", "\n", "For example, suppose there are 9 players. After the marble with value 0 is placed in the middle, each player (shown in square brackets) takes a turn. The result of each of those turns would produce circles of marbles like this, where clockwise is to the right and the resulting current marble is in parentheses:\n", "\n", "[-] (0)\n", "[1] 0 (1)\n", "[2] 0 (2) 1 \n", "[3] 0 2 1 (3)\n", "[4] 0 (4) 2 1 3 \n", "[5] 0 4 2 (5) 1 3 \n", "[6] 0 4 2 5 1 (6) 3 \n", "[7] 0 4 2 5 1 6 3 (7)\n", "[8] 0 (8) 4 2 5 1 6 3 7 \n", "[9] 0 8 4 (9) 2 5 1 6 3 7 \n", "[1] 0 8 4 9 2(10) 5 1 6 3 7 \n", "[2] 0 8 4 9 2 10 5(11) 1 6 3 7 \n", "[3] 0 8 4 9 2 10 5 11 1(12) 6 3 7 \n", "[4] 0 8 4 9 2 10 5 11 1 12 6(13) 3 7 \n", "[5] 0 8 4 9 2 10 5 11 1 12 6 13 3(14) 7 \n", "[6] 0 8 4 9 2 10 5 11 1 12 6 13 3 14 7(15)\n", "[7] 0(16) 8 4 9 2 10 5 11 1 12 6 13 3 14 7 15 \n", "[8] 0 16 8(17) 4 9 2 10 5 11 1 12 6 13 3 14 7 15 \n", "[9] 0 16 8 17 4(18) 9 2 10 5 11 1 12 6 13 3 14 7 15 \n", "[1] 0 16 8 17 4 18 9(19) 2 10 5 11 1 12 6 13 3 14 7 15 \n", "[2] 0 16 8 17 4 18 9 19 2(20)10 5 11 1 12 6 13 3 14 7 15 \n", "[3] 0 16 8 17 4 18 9 19 2 20 10(21) 5 11 1 12 6 13 3 14 7 15 \n", "[4] 0 16 8 17 4 18 9 19 2 20 10 21 5(22)11 1 12 6 13 3 14 7 15 \n", "[5] 0 16 8 17 4 18(19) 2 20 10 21 5 22 11 1 12 6 13 3 14 7 15 \n", "[6] 0 16 8 17 4 18 19 2(24)20 10 21 5 22 11 1 12 6 13 3 14 7 15 \n", "[7] 0 16 8 17 4 18 19 2 24 20(25)10 21 5 22 11 1 12 6 13 3 14 7 15\n", "The goal is to be the player with the highest score after the last marble is used up. Assuming the example above ends after the marble numbered 25, the winning score is 23+9=32 (because player 5 kept marble 23 and removed marble 9, while no other player got any points in this very short example game).\n", "\n", "Here are a few more examples:\n", "\n", "10 players; last marble is worth 1618 points: high score is 8317\n", "13 players; last marble is worth 7999 points: high score is 146373\n", "17 players; last marble is worth 1104 points: high score is 2764\n", "21 players; last marble is worth 6111 points: high score is 54718\n", "30 players; last marble is worth 5807 points: high score is 37305\n", "What is the winning Elf's score?\n", "```" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from collections import deque\n", "\n", "def high_score(players, points):\n", " \n", " scores = {}\n", " marbles = deque([0])\n", " \n", " # Action remove marble\n", " def play(p if p % 23 == 0):\n", " elf = p % players\n", " marbles.rotate(7)\n", " score = scores.get(elf, 0) + p + marbles.pop() \n", " scores[elf] = score\n", " marbles.rotate(-1) \n", " return score\n", " \n", " # Action add marble\n", " @addpattern(play)\n", " def play(p):\n", " marbles.rotate(-1)\n", " marbles.append(p)\n", " return 0\n", " \n", " # Play\n", " return range(1, points+1) \|> fmap$(play) \|> max" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "37305" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 30 players; last marble is worth 5807 points: high score is 37305\n", "high_score(30, 5807)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "398502" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 428 players; last marble is worth 70825 points\n", "high_score(428, 70825)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```\n", "Amused by the speed of your answer, the Elves are curious:\n", "\n", "What would the new winning Elf's score be if the number of the last marble were 100 times larger?\n", "```" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3352920421" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "high_score(428, 7082500)" ] } ], "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": "3.6.6" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
rbiswas4/simlib	example/Demo_TilingClass.ipynb	1	436269	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import os\n", "import numpy as np\n", "import pandas as pd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note For this to work, you will need the `lsst.sims` stack to be installed. \n", " - opsimsummary uses `healpy` which is installed with the sims stack, but also available from conda" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import opsimsummary as oss\n", "from opsimsummary import Tiling, HealpixTiles\n", "# import snsims\n", "import healpy as hp" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from mpl_toolkits.basemap import Basemap\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## This section pertains to how to write a new Tiling class" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "```\n", "noTile = snsims.Tiling()\n", "---------------------------------------------------------------------------\n", "TypeError Traceback (most recent call last)\n", "<ipython-input-9-5f6f8a94508e> in <module>()\n", "----> 1 noTile = snsims.Tiling()\n", "\n", "TypeError: Can't instantiate abstract class Tiling with abstract methods __init__, area, pointingSequenceForTile, tileIDSequence, tileIDsForSN\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The class `snsims.Tiling` is an abstract Base class. Therefore, this cannot be instantiated. It must be subclassed, and the set of methods outlined have to be implemented for this to work." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "class NoTile(Tiling):\n", " pass" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "ename": "TypeError", "evalue": "Can't instantiate abstract class NoTile with abstract methods __init__, area, pointingSequenceForTile, positions, tileIDSequence, tileIDsForSN", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-6-8ddedac7fb97>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnoTile\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNoTile\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: Can't instantiate abstract class NoTile with abstract methods __init__, area, pointingSequenceForTile, positions, tileIDSequence, tileIDsForSN" ] } ], "source": [ "noTile = NoTile()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "\n", "```\n", "\"\"\"noTile = NoTile()\n", "---------------------------------------------------------------------------\n", "TypeError Traceback (most recent call last)\n", "<ipython-input-4-8ddedac7fb97> in <module>()\n", "----> 1 noTile = NoTile()\n", "\n", "TypeError: Can't instantiate abstract class NoTile with abstract methods __init__, area, pointingSequenceForTile, positions, tileIDSequence, tileIDsForSN\n", "\"\"\"\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The above fails because the methods are not implemented. Below is a stupid (ie. not useful) but minimalist class that would work:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "class MyTile(Tiling):\n", " def __init__(self):\n", " pass\n", " @property\n", " def tileIDSequence(self):\n", " return np.arange(100)\n", " \n", " def tileIDsForSN(self, ra, dec):\n", " x = ra + dec\n", " y = np.remainder(x, 100.)\n", " return np.floor(y)\n", " def area(self, tileID):\n", " return 1.\n", " def pointingSequenceForTile(self, tileID, pointings):\n", " return None\n", " def positions(self):\n", " pass\n", " " ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "myTile = MyTile()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Using the class HealpixTiles" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Currently there is only concrete tiling class that has been implemented. This is the `snsims.HealpixTiles` class.\n", "\n", "This shows how to use the HealpixTiles Class" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "issubclass(HealpixTiles, Tiling)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on class HealpixTiles in module opsimsummary.healpixTiles:\n", "\n", "class HealpixTiles(opsimsummary.tessellations.Tiling)\n", " \| A concrete Tiling class based on Healpix Tiles. The user is\n", " \| allowed to choose the following parameters:\n", " \| \n", " \| Attributes\n", " \| ----------\n", " \| nside : int, power of 2, defaults to 256\n", " \| healpix nside parameter\n", " \| \n", " \| Method resolution order:\n", " \| HealpixTiles\n", " \| opsimsummary.tessellations.Tiling\n", " \| __builtin__.object\n", " \| \n", " \| Methods defined here:\n", " \| \n", " \| __init__(self, nside=256, healpixelizedOpSim=None, preComputedMap=None)\n", " \| nside : int, power of 2, defaults to 256\n", " \| nside parameter of healpix. determines the size of the tiles\n", " \| so that there are 12 * nside 2 equally sized tiles covering\n", " \| the sphere.\n", " \| \n", " \| area(self, tileID)\n", " \| \n", " \| pointingSequenceForTile(self, tileID, allPointings=None, columns=None, kwargs)\n", " \| return a maximal sequence of pointings for a particular tileID.\n", " \| \n", " \| positions(self, tileID, numSamples, rng=None)\n", " \| Return a tuple of (res_phi, res_theta) where res_phi and res_theta are\n", " \| spatially uniform samples of positions of size numSamples within the\n", " \| healpix Tile with ipix=tileID in the nested scheme. The return values\n", " \| should be in degrees, with the convention that theta is 0 on the equator and \n", " \| 90 degrees at the North Pole.\n", " \| \n", " \| Parameters\n", " \| ---------\n", " \| tileID : int, mandatory\n", " \| \n", " \| numSamples : number of positions required\n", " \| \n", " \| rng : instance of `np.random.RandomState`\n", " \| \n", " \| \n", " \| Returns\n", " \| -------\n", " \| \n", " \| .. notes : 1. The inelegant method is sampling a circle with a radius\n", " \| twice that required to have an area equal to the healpix tile. This\n", " \| operation can be done by self.samplePatchOnSphere and returns \n", " \| numSamples, some of which are not on the healpixTiles.\n", " \| 2. `self._angularSamples` returns only those of this sequence which\n", " \| lie on the original tile.\n", " \| 3. by repeating the process till the number obtained matches the number\n", " \| requested, we obtain nsamples on the tile.\n", " \| 4. The method works as long as the radius is large enough so that\n", " \| corners of the tile are not outside the circle sampled.\n", " \| \n", " \| tileIDsForSN(self, ra, dec)\n", " \| Parameters\n", " \| ----------\n", " \| ra : `numpyp.ndarray` or float, degrees, mandatory\n", " \| dec : `numpy.ndarray` or float, degrees, mandatory\n", " \| \n", " \| tilesForPointing(self, pointing, alltiles=None, *kwargs)\n", " \| return a maximal sequence of tile ID s for a particular OpSim pointing\n", " \| \n", " \| ----------------------------------------------------------------------\n", " \| Data descriptors defined here:\n", " \| \n", " \| preComputedEngine\n", " \| \n", " \| preComputedMap\n", " \| \n", " \| tileIDSequence\n", " \| \n", " \| ----------------------------------------------------------------------\n", " \| Data and other attributes defined here:\n", " \| \n", " \| __abstractmethods__ = frozenset([])\n", " \| \n", " \| ----------------------------------------------------------------------\n", " \| Static methods inherited from opsimsummary.tessellations.Tiling:\n", " \| \n", " \| samplePatchOnSphere(phi, theta, delta, size, rng, degrees=True)\n", " \| Uniformly distributes samples on a patch on a sphere between\n", " \| phi \\pm delta, and theta \\pm delta on a sphere. Uniform distribution\n", " \| implies that the number of points in a patch of sphere is proportional\n", " \| to the area of the patch. Here, the coordinate system is the usual\n", " \| spherical coordinate system with the azimuthal angle theta going from\n", " \| 0 degrees at the North Pole, to 90 degrees at the South Pole, through\n", " \| 0. at the equator.\n", " \| \n", " \| This function is not equipped to handle wrap-around the ranges of theta\n", " \| phi and therefore does not work at the poles.\n", " \| \n", " \| Parameters\n", " \| ----------\n", " \| phi: float, mandatory, degrees\n", " \| center of the spherical patch in ra with range \n", " \| theta: float, mandatory, degrees\n", " \| delta: float, mandatory, degrees\n", " \| size: int, mandatory\n", " \| number of samples\n", " \| seed : int, optional, defaults to 1\n", " \| random Seed used for generating values\n", " \| degrees : bool, optional, defaults to True\n", " \| if True, returns angles in degrees, else in\n", " \| radians\n", " \| \n", " \| Returns\n", " \| -------\n", " \| tuple of (phivals, thetavals) where phivals and thetavals are arrays of \n", " \| size size in degrees.\n", " \| \n", " \| ----------------------------------------------------------------------\n", " \| Data descriptors inherited from opsimsummary.tessellations.Tiling:\n", " \| \n", " \| __dict__\n", " \| dictionary for instance variables (if defined)\n", " \| \n", " \| __weakref__\n", " \| list of weak references to the object (if defined)\n", "\n" ] } ], "source": [ "help(HealpixTiles)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "datadir = os.path.join(oss.__path__[0], 'example_data')\n", "opsimdb = os.path.join(datadir, 'enigma_1189_micro.db')\n", "healpixelizedDB = os.path.join(datadir, 'healpixels_micro.db')\n", "#opsimdb = '/Users/rbiswas/data/LSST/OpSimData/minion_1016_sqlite.db'" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "NSIDE = 256" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " reading from database sqlite:////Users/rbiswas/.local/lib/python2.7/site-packages/opsimsummary/example_data/enigma_1189_micro.db\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/miniconda/lib/python2.7/site-packages/pandas/core/indexing.py:476: 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": [ "hpOpSim = oss.HealPixelizedOpSim.fromOpSimDB(opsimdb, NSIDE=NSIDE)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " reading from database sqlite:////Users/rbiswas/.local/lib/python2.7/site-packages/opsimsummary/example_data/enigma_1189_micro.db\n", "SELECT FROM Summary WHERE PROPID in (366, 364)\n" ] } ], "source": [ "opsout = oss.OpSimOutput.fromOpSimDB(opsimdb)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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bz2qSQKMVJS1U77HBVo2wBTi3NjggKKMFnCLRYp6b9vptUdwAXnED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\nfrpz7l3Ou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"text/plain": [ "<matplotlib.figure.Figure at 0x1089dcb90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#x = np.ones(hp.nside2npix(256)) * hp.UNSEEN\n", "#x[1] = -1\n", "x = np.arange(hp.nside2npix(256))\n", "hp.mollview(x, nest=True)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "hptiles = HealpixTiles(healpixelizedOpSim=hpOpSim, nside=NSIDE)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from opsimsummary import HPTileVis" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [], "source": [ "hptvis = HPTileVis(hptiles, opsout)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from opsimsummary import pixelsForAng, plot_south_steradian_view" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([141])" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pixelsForAng(54., -27.5, 4)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "ra, dec = hptvis.pointingCenters(0)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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7zg6tYWumUKtytkw5W+Zs+eyu8azla0kM6X5Iz4mu16VpN7nTu3OoNh4WxOFS\nZaucbGvEv7dHDXPN8iHGUTTLtOl1Mb+4iyjjRXlzsMkwULlgl/JLh5J8jwuxm74buuhCp2AWDjTD\nxdK+QLCQXThS1iE3cJPQiGkLshM4Kh6QvTUKN3BVppzAUZlkpFSartdNNPPl/PKRTNdBFNCwGzQG\nDbzIo2SWWCossZBd2HXupMBzlMXU9my27W3awzZA0oalW9i+zcAfEEQBpm4mnqiThOEEDtfa1xgG\nQ1YLSpu8n/k0KcjkMrkkwcJ+8EOfW51b9LweJauELtT814WOFyrv1EAGh57392PFmBRk4n3iwwil\nQRSw1d+iMWwkwtBKYeWBC7bHgU984hO87W1vg0dMs5yT5UOMw5LlfkQ57ce21wJ8kpBSYvs2fa9P\nKMN9F+DJ78VhH7PsI+113Xjhz2iZJAvPtOtGUgkVTuBQNIuUrXJi7ut7fZUeDz0JUndC59gFj1iw\nib004/Zr2Rpdr7vr/cNed8fZYbO/ycAf8J43vYePfsdH+dYPf+tUDXg/wWbH2eFm5ya60Dm/cP5Y\nnVMiGalx8waEMsTSrd1hG5AQoROodKOO72AZFivFFTShJXuboQzH9ks1NBYLizMJNvH+uB/6Ux3C\nDryXkYl8s7+JG7rkjBxLhaWZtf90G9uD7ddc2D0q5nuWcxw7DkOWsderlHKMKL3AY2OwQctuAbxm\nZpxIRklWH4lUMYSjvK2zfLc1bKn9xEPGiEopk0Dx2AN01uuC0tw6jto7SjwjNVPFAI72lB6ElN/3\n+mz2NrnevU570KZgFXhs4THWSmuHFngadiNZsAuZAkv5JX78R3+c5557jje/+c37fjfO0BSbzDtO\nh57Xo5arcX7h/InuqQ39YZIxx9AMSmYJTWh03W6SuKGQUSQukTTtJqEMWcwvJv1Kk37seBU7YlWz\nVdZKa/v+NtIOYfeTFKHrKGEnjlVcLiyznF8+FOH1vT6b/c37auO1wKNKlvM9yz8EiGS0iyiDKOBO\n7w4tu4WmaayWVscWjQfZtzi7D6gA/kKmMHPmjzjZOyhteVaSk1ImSdOllEk+18NkHJFSpakLooC2\n0yarZclmsrQ85bFYz9Vn3lO8XziBisksGkWylSwaGn7kY3v2nk43cR7ZCJVZp2E32BhsqDysVpmL\nlYsUs0U0NL7qq76K5eXlA/sRe8PmM3le3HqRbXublcIKS4WlE81zCiShCn7o07AbXGlfUUkJslVl\nNk6Z5AVSzkOqAAAgAElEQVSCer5Ow27QtJvJ7yLeH40zPOmBTsksqVSGXo/PbH9mX9IUQiT7tzvO\nDg27QTVXPfTvKt7bdAKHzf4md3p3aAwaLBeWZ9YSi2aRYq2YCMQbvQ22BluPDGk+aphrlg8xZtEs\npZQq4UDks5hfBGCjv0Fj0EDTNOXQcJ/7R0fBJFnFqdIOs6AOPKW9mboKrZjlu7G5tef1iGSUZL85\nLEmmTcVZPYsTOFxpXcEObc6UzvB47fEHQpItu8Xd/t1EC1wprqCh4QQOt3u32RpsEUWRShBhlpBC\nJhU4YsRajB/6FMwCi4XFXfUdv/DCF/JXv+Ov8g0f/AY0TUuSscexhunE9kEUsN5ZBw3Olc+ha/qY\nMHTYcT4MvNBLSrElBTwEicfu5JjEWxOa0FjML+7SAtMJ9zW0hDRjYWituLanedYPfdpOm0hG951E\nIibN5rCJqZmsllYPZZ4FZUW627+bxGyuFlYfSvPso6pZzsnyIcYsZNkejlLYZSvsDHfYGmwBKOmy\nuPzANcnj0uh2nB2lSc0YOzlJcHF+1sPmrrR9O8m4kzOUFrNtb+OGLguWSjQet3/cSehjeIHHRn+D\n6zvX6Xt9TN2kbJUx9PGxNDSDMAzpel36Xp+MnmG1sJpoFV2vS2PQULVKzTJr5bVkP3FS6/y93/s9\nzp47y+raqkqqLYMkdV5Sf5KIttNms7+JpVtcqFyglC0lXqOxJ7GhGRTN4oFerIeBH6q9YTd0dxFj\nmkAzmvK8TGuZscNURstQy9Wmjlm6fR1V73TH3SGKImr5GquF1amkmd7bPo58y07gcLd3l7bTTkgz\nFoJnRZo0j0q8J4lHlSznZthHGF23yzAY4gc+l1uXiaKIxYLSJF+LeKyhP0wSZB+VrMZS82WrSYaQ\n/eAG7lhMWskqHfr+021kjSwZmeFO7w5u6FKxKlxYuJDEnMblzYIomFnj3Qtx/tC+r/YBW0OlSTq+\nQyFT4FTpFIuFxSSNmGmYiYaXXvzSC+R6b12lsdN1qrkqp8unD3S6ad5ucvGUSkO3F27u3MQNXN60\n8iaW8ktJ+as45R3cyyEqpSSXybFSWGGluHLk+RhGYTLPDc2YOidM3aSerydaYXPYHEtjGH+vNWzR\ncTtT93gzeoZ6vp4Qb0jIamEVN3RpO21adovFwiKniqfGnrsmNGq5WlLJJ4iC+0runjWyXKheYMVb\n4W7/Ljc7N9nsbx7Kcco0TM4vnGeluMLd3r02XouwsD9MmGuWDzH20yxt32a9u564s9dzdU6XT78m\nJOmHqgrEQTlZD0IQBbSGLZWaL1c/MEwgjEI6bgcncGbK8bpXG/FibOomOT3Hlr1F22lTyBQ4Wz47\nNTGDH/o0h83k2c9yv3FFjnT5srgyhS50em4PO1B7kHH+1MMgiAKuta/xme3P4IUeF6sXeXb52ZnM\ng9lslh/8wR/kIx/5yNR+3+jcoO20OVU6NTXJQFy3ND76Xp+G3aDn9VR6wVw9KVm1X1L4GLGFouf2\nEEJQtsozxUbCKDn8iLjSlomhr+I3Z9EAxwQwPcfAHyRp7U6VTlHL16Zetz1sk9Ez9y1ExbA9m1vd\nWwz8AdVsldOl04cOR7I9m9u92/S83tQE9g8aj6pmOSfLhxh7kWXX6fLi9ov4oc9KcWXPBf2kEclo\nrGJDxaocKe4RDkc+sYdr3+ujCY2yVZ5JA51sI70YFzNF+n6fjd4GmqZxpnRm6oKYxizkHsds9tye\nqpIxqu2ZLl8WpxiMoohTpVOHJklQHq53eneIoojV0ipZLcvt/m3c0GUxv8ip0ql9BYkwVJVUJk11\nURRxpX2FgTdQSQ4OeCaT6Lt97vTu0HE7Kq9vXPQYbSyBeHrxThNVIaOSsx+WeCbHt2JVyGVy9Nwe\nPa+XOCkdpo2snqU5VJl1SmaJs5WzU0NYWsPWoYSoWTA5vkdx4GnZLe707hBEAcuF5dfElwHmZDnH\nCWCSLKMo4nbvNi81XsIyLF639LpD72ccF+IKEFJKSlYpKYN1FBxGIo+Tn0cyUlUuDpkLFnYvxlJK\nbvcUsSwXlneZ2vZD2mxcy9XIaJmxJNte5CUVOSpWJYnt1DSNvtfnVucWw2B4ZMvAfppHFEVs2VuJ\nAHCqdGrP+fLhD3+Y97///Tz33HPJe17gcaV9BT/yeazy2H2Z8CZrR4ZhSN/vj5V4y2fy9/Ll6iYV\nq3KgdeEgTFoOKlZFZVjy7Zm9q9O5YjNaBolke7C953wJooCm3QRU2bH7vYd0u3d6d2jYDSzdOpJZ\nNc7Z2xgoLflM5cwDj7V+VMlyvmf5iKDv9bm+c52N/gZL+SWeXX722H6Eh8FxmVxj2L6qjZk1slSz\n1T2JL53i7qgm17QzhqVbFK0id/t3E03hKB6umtCoWlWu7lzlWvsaQqjSXfuVbwqigFs7t2gOm+SM\nHE/Wnzx0IH8URaz31pOF81L10q6FU9M0VovKueN27zY3Ozdp2s1dSc0BPvWpT40RpRM4XG5eBuBS\n9dJ9Wy4ma0dmjAwXihdUnKTT5U7vjtp3l8qzdym/hKEZlMX9la3SNbVvmw/ydNwO2/Y2eSOPqZm0\nhi2W8ksHzt84a08hLCQxt2vFNYb+kO3BNjvDnTHTrKEZLOYXaQ1bNIdNqtnqkS0uaRiawbnKORZz\ni9zq3uJy+zLVbJWzlbMz/xY0TeNM+QyL+UVudW5xtX31oTDNPgqYa5YPMWLN8pd/55dZeWIFP/RZ\nzC1yvnqywd97oe/16bk9dE1nIbtw38VeY6eIuIbjXoi1EoE4csHmdFLsilVh6A9Z762joe25B7Uf\n4ryd6RJmcXHi06XTLBWWpn6v63S52blJEAVHNrn2vT43dm4kicpnNcl1nS63urcOTHDe9/pcbV9F\nFzpP1J449kU0LXBljWxSl7JoFhEIem6Pjts5VJWTWTBpVvVDn1xGJSk/jHUinfoub+TZsrfouJ1d\nxBWHdXmhx0J24dBbBQehZbdY760DcK5y7kga4o6zw3pnnYhopq2H48CjqlnOyfIhRkyWP/OrP8MX\nfP4XkMvkqOWOt4zTLLjfMljTEO8d7edskdYEj5LiLm4jNqFljSxFs8jt7m3aTvvQUjkcXMJsr/tK\na4Ils8T5yvlDk9BBZa+O0saFhQuYhsnb3vY2vu3bvo33fu17udy8TC6T4/Ha4ycqlG32N7nbu4uh\nG1xYuLArM9Nk/czjKmUWz+e+18cNXFaLq/sKa9MQRiFtp40XehQyBYIw4Hb/Nhoa5yrnEi0/HQZ1\nEr/dIAq4sXNjKlkfpo1bnVu0nTYVq3LimZjmZDnHsSMmy9/5vd/h0rOXkhylDxKxFH1c2iTMRpSz\nJDM/CG7gsuPsIJFUrApu6HKzcxPgUFJ0HKcXlzCLaw3uVcIs1pjj+0trgkfVJm3P5kbnhlrcDyh7\nNQv6Xp/r7euJhvv93/n9vOer3sPKMyvkMjmeqD1xYs4faZO6qZlEMiKQwb7zYVrpq1hIOWo/e26P\njf4GTuDw2MJjR4qRTFtbCkaBO/079Lwe9Vyds+WzSd/ieqAnJew27AbrnfXEVHuU/eXj0FRnwZws\n5zh2xGT56//Pr/Pmt7z5gTrzHFcZrElMEskkIhnRdbvYvk3WyKqK90fwhEwXdC6bZe7079AcNg+1\nPxNnVYnT7cV1LmdZiHpuj47TUYkSfJX8/UL1wpEWyo2+SmVmGRbnK+ePzfM5iiJuddXeqWd7oEO1\nWD1RopxWNSadJN/UzT3LmcV9jktfxTlR76fCix/6XGtfw/ZtHqs+diSCCKKA9rCNH/mUzBK2Z3O3\nfxdDM3is+lhiPj5pwvQCjxudG/S8Hov5Rc6Uzhx6HNOa6iThHxceVbKcO/g8ApBIqtnqA7teekGr\n5+rH4pwABxNlnAc2Th82a1xdGnEKsjAKqVgVJJKXmy8TRIFyjphB4NhxdtgebNPzepiaeaRsSJZu\nsdHfoDlscrF6kSfqTxz6XrzA49rONQb+4NBeurNA0zTOL5wno2V47LHH+ODf+CDf+9HvPRGiTJsj\nJ03qcRxl1sgmz76SrUwdf01TZchq+RpO4LA12KIxaLA12KKarbJUWDrU3mZGz3CpdolrO9e41bmV\neDUfxmktdujpe316Xo+MluGJ+hOsd9d5pfkKy4VlzpTPUM1VYQitYetECNM0TJ6oP8FWf4s7vTv0\n3N5YMo1Z7+Xx2uOJptpze2OE/7mMuWb5ECPWLD/+Xz/O2z//7Q/kml23e+QyWPvhIKKMzb0ZPUM1\nWz2Sh63t23QcpW1Uc9UkriyXySV7c3shTjQep7bLGSr7zEJ24dDkkS5ZtZhbJCSkbJUPteB0nS7X\nO9fR0E50sbI9m8vty/yHf/cfeP0bX8/SuaVkcT8uxPGoYRQe6OiStgrMmlJwcuziSiqHcVbxQ587\nvTt4oUfJKlHL1Y605eCHPq1hKxFw206bO707lMwSF6oXMDQjSVFZzVVPzP8gri/qBu6R4mPhnrA2\n9IecqZw5NsvWo6pZzsnyIcZRij8fFfdTBusg7EeUUko6rjJXHtXcO7nAljIlbnZv0nbaB5qj4ljE\nrcEWQRQcSTtJ4073DhuDjTFHiXiPdlbC3Ohv7FpgTwIxUVq6xc0XbvL0U0+TqWaO9dpxakBd06nl\najO3lxZ8DqPppa0Clm6xVlybmSgG3iApoo3g0AJOjMl8sUIIrravJoJPIVOg7bRxA/dECTOdeemo\nZtm0Y9pxmWUfVbKcm2HnSMyfEnmoMlizIM4XWzSLu4gy3uuJSeog1/q4mkY6Cbgf+DSdJl7gUbbK\neIHH883n8SOfc6VzLBYWCaJALYCAhoamaYk2sjXYmqnCxEGIU831vB6rhVVOlU8ln8X33XW76ELf\n8z6jKOLazjU6bufYtbtJeIHH1fZVMlqGx2uP86b3vilJd5c38lzvXOel7Zd2abVRFKlE61GgXo/G\nI/03Pm/gD5QAY+Sp5qrYnq0W2ojk+4ZmgDZ9bKMoYtvZZmuwRS1bI2fmxsYviiI0TWUDitsAqOfq\n5PQcdwd3ef7u85i6yVppjXqunpwfL/hBEIy1EYQBXuSRERluDG9gGRalTCnJbhR/d782oihCExp+\n6HOzc1Ptw5oLrHfX+W+3/xunisrJyw1c1rvraqtDt9A0DVMzj80MrmkaF6oXKPQL3OndYegPD7Sw\nTGvjXOUc+Uye9c46tm/zePXxz8mYzDlZfo4jXQbrqObPveCFHjvOTpK8II3Y21UTGrVsDSlkkvEm\niAK80Jta9WKy/Tj+spwtc6entDpd6Jwqn2JruMXWcGvsO/EiHgsHS/kl1kprWIZF3+tjBAamYR5K\n2rc9m6vtq0REU5MDgCLMUIbJPU/uA8clwPzQ52L14olmVQmigCvtKwA8XlXhIS+//DILCwvYnq2S\ngVsLXNu5xo3ODeq5etL/mOT2QhRFDMMhzUGTYTDE0i0yeiYh2FCGCanFiEuCZbQMuqajoxPIgNjq\n5fgOw2iIpVkYurFvG3HidA1FqH2/z46zwyfvfjJJRJHL5PAjf2obAkHf65PP5LF0K6lis2AuYBhG\nUkc0lOHUNuJyYbqm7ieUIU7gKA3ZqtEP+nzi7icoZ8us5Ffo+l2utq+O/fY0tHtl0XQTXbtXLi2u\nx3kYjX+5uEzezHO9fZ2Xmi8dKRvTYn6RvJHnavvqkdt41DE3wz7EOEkzbNrh4ji9XWNMK4sUJxJv\n2A0adgOBwNRN/Mgf+66hGVPrKcYHKIIa+AOyepZ6oc5Wf4vN/iZlq8zZBRVrNqnxbA+22ehtMAyH\nFDPFpGivF3q7iCBesOJFM16k4lR1MVp2i5udm1iGdaDEPVl7NL6XHWeH6+3rZPTMidfJjKKIl5sv\n07bbnKmcAaFCbH74B3+Yt33J23j2855N7l8XOs1hk57Xo5arJVqJqZlERHiBhxM4+KGPF3n4gY8d\nqFqiEknVUtYCXegYuiIaXdfR0JK6mH2vzzAYYns2w2DI0B/iBA6RjJBIEKCjIyOJFJJCpsBqcZWi\npZKxa2gqKX0wxPbtJIm7F3rJ2AsEbuBi+zYSSdkqc7p8mpWCCpMxNTPRgofBkPawzba9TUbLIIRI\n0joWM0VK2RL5TJ6ckSNrZClkVDFz0zCV1k2AjCSBDJKSZX6k9jHj8Bgv8GjaTYpmkQsLF/BCD0u3\nWCouqaot0T0BMREcJ+anoRnJvLQMi7yRT6rS7IW09eNM6cyRQpjS3rKTFpRZMTfDzvHI4ChlsA7b\n/nZ/Gyd0iIyIq+2rDP0hbujS9/o4gcOCtUAtX8MyLLJ6lryZn1p6aho6TgcErBRXKGaKyb7M2crZ\nqT/evtfnduc2w2DIqfIp1oprUz0EYxNjUj0jdJKyT+mFytIt8pk8PbdH3+1zunJ6pr0cIQS1XC2J\n2VzML9K0VUmtarbK+cr5Y/dEnax0crl5mabT5Fz5HI1hI1lw/81P/xsunbvE01/+9C6BoNFv8Er7\nFa63r7NUXMIN3KRaSvw8skaWjJ5B0zVWS6usFdfGtOe4KknfU/lgu8Nu0oYQIkk0YBn3BBNTM5Px\nsAOVxenmzk1udG+Q0TJ4ofq+ZVjkMjkWzAVWKivkzNzUNpzAYcve4krzCq80XuFq6yoFS+U0zhm5\npI0nak9wbuEcutA5WzmLpVls29t03A6WbqFrOrZv44c+buTiui6GbyjiNAvkDVXDNf0MwzBko7/B\njruDmTNZyi9xuX2ZFzZfYLW0ihM43BncYa2oao6WzfKuNqJIEantKaHADZUAEMeeAkke4nR1l1go\nMzQj8dJd763jhA7nKucONZ9ib9l4b96P/BMJL3kYMSfLzzGEUUhz2FSVMvL1Y9uf9AJPaRVuj/Xu\nOrZnU86WyWgZchnlWesGLgvZBVYLqxSswzsQSSlpO6Ni16MKJ3FFjMcqj+1y5AiigNvd2zPnYI33\njEzDpMy4iSm94Nq+zdXWVe727lLL12gPVSaXXCZH0SxSNIt7msnShPnprU8TRAErxZVj258MooCu\n08UO7KQMWFzppOt2iYh4x+l3cKp8amx/7OaNm7vaSJcSi6KIO707tJ02zyw9w6nsqUTA0TQtyfFb\ny9WoZquJyXmsjVTFlYXcQqINTZJzuh+x9jr0h3iBRy1fQyCU13SuiiFGZlkZgYCBP0iIw9CMMbIc\n+AOQcKF2gYXsAq1hC1AmxqpVJRKqjTjpuh3YNAYNzpTPcKZyhpJTYuAPlEZoXQC4NydGlo6N3kZy\n/ZwxqiyTUXPidOU0ZbdMz+uxnFnmqaWneLX5Kn23z6niKXpuj67bxQkdNgYbU9uIn9ck4ucUa+qN\nYSNpw9KtpBh30SxypnwGUzNZ760TRuGRhLRT5VOYhsnNzk280OPx6uN/6AlzboZ9iHHcZti4DJZA\nUM/X78vTMYoi5eXqdceK/w69IYZucKZ05p6nn4Cm3UyKJR8lbnNMG85V0dD2rYix1d9KFovVwuqR\nTE5T+5FywjlTOkPezCeEMPAHiQaaM3JUrArl7G6PyiiKuNy6zPWd65yrnOOpxafuywTe9/p0nW6S\nTxXA1MwxDaPv9Vnvre9Zj/K9X/1evu4DX8fbv+ztU9uITZ5X21cBuFi9mGjnsbevkAKJSjAQVxMx\ntHsaV9Eskjfy+y6q8V52x+ns2YalWey4O4QyTJKUpzXX5rDJZn+THWcH27exDCtJzr5SXEk0P2BX\nBQ5TMxPtt2k3aQ6blK0yC9mFpKh4JCNyRm7PIs+2Zyek1ff6yW8jLgCgo6sC6Waeilnheud64tSl\nCY2iWcTUzWReTbZRskosWAu7NM9pz3JaG3GSf6TK/FMwC0dObdh11J6rZVg8UX9ipjbmZtg5Hmq4\ngUtr2FKFePP1I8VPxjk1O06HntsjIsLUTEpWiTVzLdlvSScyCKKA5kCVK1rMLx6pUsqkNhzJiFea\nrwDwZP3JMUn7OMpe7YUgCrjSusLQH4454aTJ0As8ul6XnttLpHtDMyiZJSpWhbyZ51bnFrZv84bl\nNyjHJrd7qNyksebXcTuJiTi+xkphRS22qb2rmCjruXpClHEbPU8lLfeEhx2qrElLhaVdNSZjPFl/\nkivtK7zSfIXz5fN0/S6b/U2AZF+yZJVUyrU92kgjTkjfdbtjJc1KVokzpTOUs+WpmlRdr9MetmkN\nW5TNstJkU2XRFrILSeWSiAg/Uvt/W4MtSmaJIAooZ8u7KnDEdSqXi8tcqF5IMjiZuqpSEjsqSSmp\n5+oqqcPEnM6b+TEzf/pZx22EUZgk37hQvYDRM5K+AVSzVRbzi0lsY7y/23E6dJ0uDbuxb01QINFC\nJ9tIP2vHd7jbu8tmf5O3rL6FYvZwoTLlbJkn609yuX2Zl7Zf4lL90gPPXf2gMNcsH2Icl2YZV4i3\ndCtxtpkVXuDRclpJqi5AFeQ1SyrLzmhRcAJHLVyp2LTjKIQ7qQ07gXMv5CHlUJNO3ZYzcpytnD3W\nQH4v8Hi19SqhDA9Vsqrv9ZPizx2nw+3ebQxh8IaVN3Bu4VxSW/OgveMgCmjZLdpOe6wGZMlS47DX\nvQZRwEvbL5HRM1xYuJBUSul5PeCetrOQXThQ64N7CQBeuPsCNzo3WMgtcLF6kZXiytR9tr3aiFPW\npYWucrY8k8YUt9FxO1xrX6NhN8ibeRashT3LosH4WKTncjVbpZavKaGis54URz5VPpUQbDyvYy1+\n295mc7CJLnTWimss5hep5WeLI7U9ZbJuDpvc7d9FExqniqeUk1ToYxkWS/kllgpLe7Zne3Zi1UnP\nh2qumiT1PwixFr/Z3+TlxsuA8o5eK6n7OUx4SPr3cbF6cd/f3qOqWc7J8iHGcZBlLEnOmg0F7mmQ\nTbvJwB+goVHJVhKz4uQPOIgCtgfbWIYiY7inyc5S0HkveKHyGoy14a7b5Xr7OgWzMBYwP5kU/LhM\nrjGcwOGV5ivoQj+yt6oXeHx689N0vS5LhaUkFKZiVRAILN1ipbQy9mxjratpNxNSKZklqrnqTFob\nwMvbL7Ntb7OUX2IYDJM2KpYiyLiN1dVVvvu7v5sPfehDU9vpOl2aw6aq5ygDPN+j43bIm3meWXxm\npmc+LRl6TNSzPtO+16dpN5Pi3Tkjp7ynNZ3F/OLMhQa8QIU1xdp5rKVVs1X6Xp/GsJFUdvFCj4Gn\nUg6mBT7bs7neuU7P7aEJDV3TqVgVqtnqzJmfYuGv63QxNIOOpzTHolnk2cVnWSuvHfibTVsa4nJx\n8TyZNdG8Ezi8cPcFVX/TqpI1s0my+v1y9U72I2152Su0ZE6Wcxw77pcsY6KcpVrJtPqMJbNEPVff\n94cvpaRhN5KEBprQEqK0DGvfgs77ISZKU1dlmTpuh+vt65SsEhcWLiSB6dPKTR0nYqKM830exaTr\nBR6vNF9BCJHUh0xrirF5rZKtcKl6CV3XdxFCPVefWXMBNfaf3f4sV9pXOFU6xVJ+aV+t42Mf+xjP\nPfccb37zm5P3bM+m5bQS06GlWyq/qVTOUPVcnc3BJluDrT1DEeI24jJblm4lmtysBOkEDi1b9WOv\nUl1xvPBRKvPEY9EcNpMamoZmMPAGZLQMa8U1pJD37j+FeIvAD33lROV1EwGzmqtSzVYPjEeMpNLW\nvVCZoG93b/NyS+UzfuPSG7lUnx67u9e97Dg7ifUgFnRjAWs/4oyiiFdbrzLwBizmF3FDd0yQqOfr\nM7URO93tRZhzspzj2HE/ZDnr4hEno47rMx7WlLPj7DD0h8l+ZEyUMcndD1Fm9Az1XH0qUdqezbWd\nawcWMr4fpDXKpxafOjainHadO907vLDxAjvODkWzyGJ+kYu1i6wWV2cmlXSO1O2BynxzqXaJpxef\nPtBs/PLLL7O8vEylUhnLsxoXX17MLZLNZBPirOfqyV7dend9jDCjKKLltNgebB+5gHNsqt22txn4\nAwzNoGJVqOfre7ZxP4QZI11DMzZTRkQs5hap5WrKC3TCgzyMQhp2A1D78n7k7yL3xcLiWGztrvsd\nEaaUknq+Tstu8V9v/1d2nB3OV86r32SudqiE/vEWSlxabpYKLTFhDv0hl+qXMDVzbBtm1jb2I8w5\nWc5x7DgqWc6yaMT7LukyR4v5xcNlrhmFC8QVQh4EUcKoZNVg477KXh2EB0mUW4MtWnYrCW8oWyrk\nBlRZsJXCyr5k5wUeG4MNWnZLmTeNAk27yUpphcdrj8/U12w2y7d/z7fzvg+8jyAKEmKKNQkpJc1h\ncxdRxljvrnO7c1vtuwqmtjELgihgq7+VEM0s1o00DmNNOQix6fl29zYbvQ0CAp5ZfIY3rb5p17mT\nhBmba2OzcVzmrZqrslJcmTpnJwmz7/X59NancXwVD2n7dtLGUn7pUNVE4qLl8RzZLwfyJGHG58SC\nRCwwVayKinXeo429CHNOlnMcO45ClvsR5aTEnzNyLBWWjlRA1w99GnZDxcxlF06EKHtuT3kojogy\niIKkZNVqQWmTJxHb5QQOl5uXEULcN1GC8iCdJMpJYWUxt8hycZmBN8D2bSrZCl23S2PQwIu8qZU0\npgo82UWuda4RRiFPLz19YN/7Xp/twTa/9bu/xZlzZ3jd46/bpTUcRJS2Z7M52OSlxkvsDHd4aukp\nXrf0ukOnDNy2t2cilYNwnIQJI2Gkv8ELGy9wq3OLi9WLfOGZL2QhP56ScC/ChOlCwFJhaVdaw0nC\n7Lk9Xth4gZyR4/NWP48dd2dsTqwUVw6VGnEyJ3I8ryaFkZgw3dDd5dA2a4WeNGGmSXdOlnMcOw5L\nlnsRZfxDbQwbiUS4lF86cm5HKSXb9jYCwWJ+MfF6PUmitAN7rHLDSZWsOkmijIWVzf5msshMCisx\nMYVRyHJhGSFE4rE48AeYmqmerwQ3cpVjUHElaWO9u05j0ODJ+pP7ah4tu8XmYDPJ3/pbv/hbPPfF\nz/Hss8+OnRf3xw/9XUksJvu1WFhMPK9nrR3adbpsDjaT2qEHmStnxUEl4Y6CKIr45MYn+f2N36do\nFreo4BMAACAASURBVHm89jjnF86PjV9MmEKIqR7gsXk5/eyX8ktqvz/VRnPYTAiz43T45N1PUs/X\nedPKm9A0jZbdSkzU8XNbzi8fSnicrNs62cZ+hBkjPX5poS8ev2mEOSfLOY4dhyHL2CSaJsogCtjo\nb9AYKGm3lq+xXFi+b7Nl1+0y8AYsFVQuy7QjzlGIMg4PMTRjKlFu2VsPpGRV2mx6VKIMooCXGy8j\npRwjyobdYKO3gRd5+5qv4ja2B9vKgzkVf7m+s85nGp+hMWxQMSs8WX+Sx2v3Mqf0vT6vNF/ZM/EA\nqAXydvc2buiOaTfZbDapOhJjL6LccXa427ub5BWe1G5udm7SsBv7EmbX6XK3f5eBP9hTu7lfnARh\nBlHAzc5NbrZv0vN7lMwSi/lFVkuryb2mCTN2eturf9uDbdpOOykyHpNmmjAX84ts29v8/sbvc7Z8\nltctvy5pI9bq204bQzNYLayOEe8sSGv1mqapYucj0kwT5mRMcxrp7QRN01jMLSaWnzRhPll/kpc+\n/dKcLOc4XuxFlnFgtBeoZNG2Z9N22mSNrErUHHls9bfYHmyrGMd8neXiMqamFrvJUkPpJOUH/ci8\n0KNhN5LK9g27kZDcUYgyXlg0oSUeeK80X1H7kZUL3Ordou20T7xk1V4kd6g2goAXt19kGAy5VL2E\nYRg0Bg02+hs4gUPZKifjEKd+g93joWlakhFopbCCH/hs2kobrVgV6tk6Xb+bLEzLhWUWs4u83HoZ\nQzN4avGpXX1LE1zJLLFWWhsj6zAMkzJUMeIixTFRThLc6fLpPQn/xs4NmsPmriosfa/P7e7tpI21\n4tqJVq+IswvF++rHgR1nB9uzsX1FMqEMk4T7cf3MMArZtrdn+m04gUoMEJNmTLzp30Y9X+fGzg1e\nbb3KU/WnOL9wfqwNL/C4279Lc9jcRbyzIr33rWlaQrwREa82XyWMwgN/G0EUcKd3Z2xuLueVl3RM\nusMbQ97x9nfAnCznOC7EZPmLv/mLPP2Gp5MqBOlSVUGoXMUzeoaSWaI1bNH1ukRRlDjtxFlM0vUG\n94KhGWS0e1U+MnoGU1MlqyzdouN1VFmtUX5TYF/peT/EYSeRjJLF4ZXmK+iazvnK+cTb9bHqYyda\nsiqWfIfB8EDpOT6mVYW41r6G7dmcr5zHjVwadgM/9CmYBZZyyiEjqXsYX3uihmMa6911Fb86SnF2\nqnSKslVOyjYhSZIUDJwBuqbzjrPvGDOXzUpwH/7wh3n/+9/Pc889p77nqtywtVwtybF7WIK70rpC\nz+3xZP1JIiLu9u7S83rkjBxrpbUTHdM04rR36cxS94MwCtkcqAo3fa/Pnd4dDAysjMXAH2DpFqfL\npylkCjTsxlj88X5wAofb3dtJwvb4OcfVexayC3y28Vm2Blu8fun1U0N19iLew2Aa8ZbNMpfbl9E1\nnafqTx0sVE8h3oXsAq+2XuUzn/oMX//ur4dHjCzn6e4eAUgpVTkgs5BU5jANE4GgaTc5Uz6jTGZO\nk+XCMk8vPc2p0qm93dQnCuwGUXCvjmTgEcggqSUZV1eIiOi7Kr/kamGVGzuq8sPp8mm80Du0aTcu\nVxXKkMX8IgKR1Fhcyi2p2EY9w9NLT594+qwbnRvJnoqpmUlVh3Rlh3Q9zbh8V1xCLJfJsdHfoGgV\neaL6BMNgiBZpvH759ZwunT6U5hTHu97auUU1WyWjZThTOcNSfmmMoG3fJpRhUi/x041PUzSLuKHL\n6bK6ZstuEcmIklXas85mjE996lMJUca5RDMiw63OrYTgDltn88LCBT61+Sl++8ZvU8vWKFmlE6/V\nOQ0L2YUkt/BRUy6moWs6+YzKCbxSWEmKZfuhz5nSGTpuh6vtq0l8rBM4SRztfsgaWR6vPY7t2dzt\n3+V65zpW30r8Anpej0u1S7iBy+XWZUzD3PUss0aWC9ULrAVr3O7e5mbnJpv9zUTjnQWmYXJ+4Twr\nxRXu9u6y3lvH1EwWrAUawwZX2lcOTJxuGibnKudYLaxyt6/a2BpssZBTY/EoYq5ZPsTYb88y9prb\ncXZwAzepkbhaWD2RKuZdt8vt7u0kwXPX7ZLL5BKNcr/SQNMQx2fWcjUyWibZ06jlampPzqokMZUn\niZs7N7navko9V8c0zKQ6Bv8/e28WI9mVngd+d4m7xo09IiMj98yqrJVLs0k2TTS7m622GhLQEDxj\nDWTDLWBmJGBgjGxjIMDzNgYswTbgl3maBw9gA4OB1K8SBpAs2bKkbjbJblazyNor18jMyMjYb8Td\n13k4dU/lnhlJtrrYzA8oZDFZefLGjRvnO//2fSBRtszL1EaM2j4dur9No4nN/iZRceG4M1OUJ8EL\nPOyMdtB3+jSC4zmeunmc5DZxt3kXfuSjrJax0d/Ak+4TWIGFSXUSM7kZVNTKAeeK056PpPY0dIfU\nZ3EqMzU2wSWCEY1RAzvDHUwoE3hz5s2fW735LBzOYnxWk/MwComWq6ghLaThBd4BYX+WZWk0nmJJ\n1qeklsZ6JgzPoNG4yBEbsrJSRhiHeNB6AJEXca107dQ1E+LVXZ3q3o57+Nwf8cZxDNd3MZWdOpIK\nPmuNJOJ9cPcBvv9r3we+YJHlJVm+wDiJLOM4xs5oB9v6NliWRV7KX+hDcF7EcYyW2aKO7Ul6TuIl\nEuV4FrWE2u+8sd8aaL88W1JHSvRQk/qWwiuwAgslpTS2z955kThCmL6JreEWtvVtlNQSaunaAYeN\n87rRd4wOPtr9CCzDYlKbRE2rnfsEnyCKIrSsFpqjJliWRU2rHUidJY4tZbV8JN1d1+voWT1cL1/H\n0BmiaTZheRa8wAPHc1A4BRkpQ6Nk4KiLR1pI4/XXX8e/+N/+BV79zqvo231ogkavY9wDS8/qYXu0\njSiKUNWqNIUn8/Iv1MrpcA3wIqWD/dAd4vIyoU5Qc/Pk0DebnUVBKaBn9dAYNTBwBlBSCq4Vr41t\nTzdwBtjWtzF0h5B4CVcKV2B6Jjb1TSiCguvF62cekIfOEFvDLbihS3Rv07Wx34dkjabZhOu7uF25\njdnceJ9TwzPw53/75/iH3/mHwCVZXuLzwnFkGUURHnUeYWe0g5Jcot58P08k3a9KSoHpm8iKWajC\nyR/4k6yBRE6EwAmIEaOqVpGRMtRElokZxEx8aifnRZCkNROnlMTVIoxCtKwWpjJT59psjsP2cBvv\n19+HnJLxyuQrn2kDckMXJaV0bPo8aRaReOnAe510vyZzrnZgH1jjsE1ZIhZ+2GOSZ3n8+//j3+PW\nO7fw6luv4lrhGqaz02NHgZZnYWu4BdM3kZfymNKm6H01PAMr3RXk5fxYEcnnjSAKaA3wot3bCQ5H\nlwB53hIz8uRZTqLsld4K4ijGrYlbqKjj6RdHUYSm0cRqj0SvVwpXAAZom+S5OE/39uFD2bQ2faGD\nXcfq4G7zLrpOF1+d/CquFq+Otcbl6MglPnccJsuBM8CTzhMMnAGWCkt/J2nKZIxB4AS4oXth7U3D\nM9AxO9jUN8GzPPJynsxrPls7LabPPZt3FpzAocLSpkfMgPf7AAqsgCf9J1BT6rkVbg6vv95bx93W\nXeSlPN6ZfWcsNRWApE+3hlvQXR1qSsVMZubUNZIZ2rJSpjW3T3Y/wZ65h6JaPHGNwwbY+91YkoPE\n4/Zj3Nm6A5ZjcaN6AxPqBLV9Os/rSjogO1YHIidiJjNzbH20Z/WwoW987geicZEIaBwezbkIkjLI\nRHriwPcTCcD9WRLbt/HJ3ifQXR3z2XnM5mYvlBK9t3cPPaeHrJBFTs7BCRwoKeXcpHU43X/Ws3cc\ngijA+9vvo67Xca1wDdfKp6eD9+OSLC/xuSMhyw9+8gGKi0W0rTaiKMKV4pWxT6YXRdfqwg1cMAwD\ngRNQVIoXWieKI7TNNjiWg8zJaFttfLT7EZqjJgpyAcvFZcxkZ8YSC98PLyAjLX2nDzd0aQ01cUpJ\nNqUoivC4+xhRHF1olrJltLCtb2NntIOiWsSbU+PX4TpWB9v6NniWHytt2zJbdMTmaecpPmp+hPns\nPK4Ur5x5yNgf9ZWUEqa1aQy9Iep6HUN3iF9Z/hX8m3/3b/Dbv/vbR/xKc3IOBalw4lB6Xa8jiAJU\nteqZg/EJiSwXl39uwhLnQXL4OMsa7Sz4oY+21T52nZbRwvZoG3kpj7nsHJk5jInx995oj3Y4j+uS\nk6zRGDbghR7Kaplma2qZ2rnX2Z/VuIgiVhRFuNO8g53hDi1BnCe7ckmWl/jckZDlH//nP8by7WUo\nKQUlpXRhwhoXiUdlFEdIsalja2bnRdfqkiYUpQwGDB60H2C1t4rJzCSqahV+5EN39bEcDk6yEjvN\nYWFzsIm+3T9T4eYwvMDDpr6JkTeCH/rgGA7Xy9fH2vD3r1GUi5jJzIy1ObmBSwbQ7T4edh6ilq7h\n7dm3xyLrltFCfVhH22xTT9KMlMFf/39/jbfeeAtXr5LoJIoi4v/oDo4V2edZnvqHaoKGuezcuVPZ\njzuP4Yf+ueT4fp5I5khP8408D/aPUB3GwBlQW7mkXuuFZA5ad3UqDjHO/QMISbeMFhpGAz2nB4ER\nIKUk3CrfGrv7+rNoLXuBh0fdRzA9EzIvU9/U0z5bX1SyvBwd+QJA4iRMapNgwByxCPp5IY5j6I4O\nL/Ag8ALycv7CRDlyR3BDl8p/rfZW8bjzGDPZGbw88TL9cO63Slrrrx3rNHGSx+N8dv5MBZiO1UHX\n7mI2OzsWUe6PBEtyCR27g2lteiyiTJpeAFx4fMIObNT1OhrDBqpKFW9NvzX2Ji/xEhATcQk7sCGH\nMtKpNKaqU5Ck55sky7LESFnK0Ci0b/fRHDWx0l2B7uhQBRXXi9dRzYyXUl3ILeBR9xHW++tj17s+\nT+SkHNpWGz27Rw5xF6xfqikVfacPP/SPjKXkJGKOvdZfoyMXAkeidZZlwTM8WmYLD9oPMJ2dPncZ\nIsWlkFfyAAOogoq+1UfH7OBOcAdvz7x9buJlWRa1TA05KYf1wToetR+N5eKTjIis9deQFbNwQgeP\nuo/GjnK/CPjFtKVdYizklTyiOPpMhDUuTJ90trIsSwfhLwI3IJ54mqBB5EU0jSbu7N5BRa3g5erL\nB06xPMujkq7gRvkGbpZvoiSXMHJHeNJ9go93P8bHzY/xyd4nWOuvwQs9VLUqXp54GVeLV1FQTheD\ntzwL2/o2inLx3BtSYmZb1+vIy3nM5+fRs3vIS/lzp86CKMB6fx0b+gY0QcPN8s2xiTKIAmwONrHW\nX0NGyEATNUxlp8aKRKIoQl2vY6W/gqyUxXeXvotJbRK6o6MxauDXf/3X8Sd/8ifH/izLsshJOcxl\n55CTc3AjFxzLkffTbKKu1+EEzrmvReAFLOYXMfJG2B5un/vnPm8wDIOCTNR2Bs7gwuvIKRkcw8H0\nzWP/f0bKYDG/CNMzsdpfRRRFSAtpSLyECKSskpfzqOt1rPZWaTf5WVBSClRBhczLmMpMYam4hJbZ\nwn9d/69wvPO/HwCoiXdJLaExauBx5zG8wDvXz+akHKpqFX23j0mVZIqaZhMP2w/Hei5edFymYV9g\nJGnYP/ubP8PXv/b1UztQP0+EUYjGqEG7K8+jPnLSOm2rjRSbQlEpYugM8Rerf4G0kMY3F755rnSP\n4RmEbAYbMFwDOSWHK/krY43KJFJ2LMOeS30EIOmzul4HAMxmZ5ERMrTWeaN0Y+w1LtJ5CBysCU5n\np9G3++jbfUxpU5jQJs4VWRqegc3BJvzQpzWynt2DFxL3i7pex9rGGq7WrmJ5avnYNY7zD03qxIlA\nvyZomFAnzp0GbBpNNEaNX4hQwX4k4u9ndXmfBsMzMHJHmEhPnHigHTrDA5rHYEjWIjEk0F39wDN3\nnnuSzI92rS7ych5RGOFHWz+CJml4o/bGhZ45wzOw0d+gz9x5D5dPu09hBzauF6/Di7wjz1yCL2oa\n9jKy/AJA5MS/M6IEyPyY7urIiBnkpYunfftOn6aOvcDDX63/FTiOwztz75xKdFEUoWf18LjzGE+6\nT8AyLL429TX8g+v/AK9UXoEd2LTmOXSGZ17H5mATYRyea8YvicDW+mtQUyqNBLeGW3AD94Bw+WnY\nHm7TNa4Xr19o02oaTaz0VyDyIm6Wb4JneYy8Ea4Wr4LneAzds197y2jhSfcJeJbH9fJ1VNIVGJ4B\nJ3CQk3JQBRXXitfwt3/6t/jgkw/wuPP4SGTTsTr0fbhevk5TdAIvoJap4Xb5Nuaz8wiiACv9Fdxv\n3UfLaFGlqJNQTVeRFbOo6/VzRzE/D8gpGWpKxdAdwg/9C62R6M4mnpPHIYkwR+4I64N1IAbyUh5B\nFEB3deSkHG6Wb0JNqVjrr2F7uH3mPWQY8vlSBZXUj2UNX5//OrzQw929u6jr9TPXOIy0kMbN8k0a\n7a7318+1xkJ+ASxYrA/WkRbSuFG6gYJSwPZoG6u91bGv40XDJVl+AfB5OSacB0EUoG21qVTXRes4\nhmfACz1y2o0j/M3m38ANXXxr/lsn1guTZoMH7QfY0DfAMiwW84u4VbmFSroCSZDo5jybnSV2Wv0V\nPGw/pE0Wh9GxOtBdHbPZ2TPTll7g4WnvKToWqUkuFZbAs0RBp2t3MZ2dPjOaDaIAT7tP0TJbqGk1\nLBWWxp7hTNK/jVEDVbWKq0ViHL072oWaUkkDk0iEBrzweJKJogjr/XVsj7ZRUSu4WrhKRSRG7oim\nAQGSZv3B//MDGNtkJvZR+xEMzyAzg4NN1PU6CkoB14rXjn39LMuioBRwo3yDautuj7Zxr30P28Pt\nU9OKc7k5usH+IpERM+BZHn2nj4tk21iGhZySYXrHp2Lp7zlEmBzDIStlYflEYpFneSwVljCtTaNl\ntvC09/TMtCzP8lQysm22MaFO4HblNgCgOWzicff8KVX6elgWc7k5zGfnoTs6Hncfn5lS5Vke8/l5\nmL6JpkFmOWezs/T1Pux8sdOyl2nYFxgXMX/+rNgz9tA0mljML16YpBNj6ERi7Sc7P8G6vo6vz379\nWOeQZFi6ZZJoZBwn+P0GyPtdH4DnnXqJtddpMDzjWL/MIArwoP3gXDOZSfoqQoT57PyF3DSSdGcY\nhwdScQNngLX+2oGRi5bZAsdwR7qjncAhw+uhT5VkgOcpuxjxiQ0tXuBhfbCOgT2AH/lIC+mxUnH7\n12lZLWoPV1KJZdNxaeNEXGFamx57jOLzRDJTrArqhUyjE0eeklI6s8afpGSzUhYL+QWaFq+oFZrG\nTZ4nAOfycO2YHeyMdjCfm4cqqHjUfoQoisjIymd4JpPn6fAzeRIST9X9us77n8n+Wh/f/vq3gS9Y\nGvayG/YSFH7oY8/cQ07KXZgo4zjGwBmAYzlogoYn3SfY0rdws3jzCFEedm0vykVMpifHisSSjs39\n4tO7xi6mMlPEogwsZrIzp66RzMMlhLh/Q68P6gijEJPaJJzAQRiFiOIIMWLEcUy/du0udvQdiCkR\ns5lZ+JGPrtUFwzCIo5iIoMc+wihEEBGh+gQJaY2cEXZGO0hxKdQyNewZe9RmbbW3Cp7l0bW66Nk9\nsAwLL/Bg+Ab80EdaTINneViehbpeP1aE3vCM5+M7h4jyN37jN/A7v/M7+N73vocJZQIbgw3ojo5b\n5VsoSOOnkAVewHRmGhPqBBqjBnZHu9gZ7tAaOMuwB+4jx3C4374PN3Qh8uKB6I5hGDBg6DUnf0++\ncgxHdHnZZ1+f/ffh12h7NukCDslXx3eIgUAYEFH6KITlW7QhLbnGBMnv4BjuyH+zLAuJkzBwBxi5\nI5TVMmROJg4x3EEdWi/0EMcx1JSKB50H2BnuICNmsGfugWd4ZKQMfc680MOWvoX3tt47cO8Sbdv9\nr50Bg53hDvm8lW+CZ3js2ruYyc0gCEiK/CKCEIlCUNJkdlanay1dg+7oWO+v0x4BiZdwo3QDm/om\n7hv3x/r9LwouyfISFI1RA4iBmnbxlu/9G/LWcIuaEV8vX6f/Jooi9JweNUMuykVMpCc+k7atIihY\nKixR8ek7u3fQt/t4o/bGqe4rm/om2lYbBbmAaroKwzMQRsTNo2t2UR/WMZ2ZPtAtyTEc3awTKbM9\nY4+oG6WIpZIXevBDH37sH0ijJQSbrBEjBiKga3cxdIfISBlU1ApxOYkicCxHmke8ERZyC3ACB1Ec\nEaKJY3TMDjpWBzkphz2DzGAmDSSbg00InACRE0m3ZmCiKBWPdd2QJAk8z1P5wcX8IrJilnRGdh+f\nOn8XxzH1WA2iAH7k03sYxRFSXAoT6Qm0zTZW+6vY1DdRUSqYUCfAceQ+TGvTsHwLe8YelkukyYgB\nIbvDB5MYhEijmLjmuLFLyC4MYXgGrMCiaU0ndBCEAb3nPMMTYmEYpLgURE5EiiX3g2M5Mn7EsAAD\nlOQSJWMA1OUl+b3J97zQg+/76IU9DNwBnrSJOpQTOXB8h75nLEu8YyVOgpSSIPMyDM/Alr6FSXUS\nWSlLLL4CEUqK2LmlmTTyUh4dm5gmhHGIslJGzMRHriOIAqiiitXuKizPQlbOYs/Yw8P2Q8zmZjHy\nRrjTuIOCXMBSYQkZMYOslCXi+mdEwkl6OGnKMn3zRCN2lmXpeFDTaFJiZVkWC/kFrKqrp/6uFxWX\nZHkJAETRpGsTu6+LWhj5oU9P5V27i7XeGlRBxXL5uWFsIiztRR7yUh5XtCufqwB8WkhjLjuHttFG\nXiabjB3YmMpMQeZleKFHxd+f9kj3Xk2rQUkpGLkj6uPJMAx0T8dMZoY09Tw7vQdhQBtkTN/E0+5T\nDN0hKukKFEGBF3kQeSKtlwjP8yx/wFpt/wbjhR5We6tQBRUT6gSKahFRHFGySbRMJ9QJVLXqAaPu\nFJvCfH4eHZMoF6mCivncPPJyntp5eaGHoTukNd2+1Ic0Iu4pIi9C4iWkhTT+6I/+CJv6Jppm80Dk\nkJEyWO2t4kn3Ceaz81AEhRwCIp/ahYXx8yiZYzjyOp95bu6P9KYz0wjCgPoc7pq7qKpVmnp9KfUS\ncUzxrTOjH9uzqXer5T8X8U88WzmGg5gSkU8Rm7OEJBN9YpmXIabE556t3PP3JUnHnkcOzws9ch3O\nECNvhJgh88k+fLBgoaQUaKJGD1du4ALMswNGGKAoFyHxEtzIJbq52TnIgnwgHZugZ/VQ1+tgWZbM\na56QgVnKL2HgDHClcAVhFOLj5sdgGAY3yjfQsTpY76/j492PUVSeH5xSXAoKr0ARFGiChqyURVbI\nHomKq+kqtSR71H6EK8XjP7+KoNARkpyUO1BSuUiz24uAy5rlC4y/q5plHMdkLCKKcKN840JNPXEc\no221wYABCxarA9KpupBfwFJhCU7gUG/ErJjFZHpybD3K8+Jp9ykc38FScQkDe4D6sI6RO0JGzGBS\nm0QUR9jWt8ExHJZLpAbIs/wB26bEuHgxv0iJ0fRMeBFplAijEHvGHlJcCteK16gLyzgp5KShKIxD\nLOYXj61J9awe1gZrWMgt0Aad5E9Cph/vfgwAeL32OkpqCSk2deA9HLkj9B3iJOKHPpzQgRu4cAMX\nXuQhjEL86iu/in/yz/8J/un/8k8xpRF7MYZh4Ec+LN/C0+5T6I6OSW0SeTl/gLT3/znvHPB+g+H9\nmrXH1bvCMITu6aRL29GJHus+cf6EkPa7qBwXKe2PgP3IP3AvAZJWTbGEOP3Qp6NT+w2jdVtHz+md\neB2KoMDxHVInLywcex1JBJy431i+hcfdx+iaXdQyNbiBi4JSwFx2Dnkpj6ycpetYnoW1/hoAnEhU\nfujjYfshMlIG87l5DJ0hVvortCbsBA5WuisIwxCTmUlEiDB0nh06nl1PFEdgGZYSZ17OoyAX6HWc\n59lNpCUBHBjZ+qKOjlyS5QuMvyuy7JgdbA23aGrmIkicSSRewuZgE4ZnICNlcL14nTbvnCay/VkR\nxzG80MO2vo2N4QZmtBmkxTQYkHSb7ujo2T34oU9tiq4Wrh4gt0TibXOwiaf9pygpxH+QBel0TDZj\nREB9VAfHcEfWOC+SDQs4edMDgPut+xA44VilG8u38LjzGLqjI51Ko5x+LrKebPosw5L0rpg5tg5t\neRY+bX2K//h//Ud885vfxNLNJTihAz/0wbM8ISJBQ17Mo+N0MPJGmM3OfqZU/eHfv9+pZFKdpAet\nnJwjOrXe6MzN+7MgeXaSP37kU7PokTtCiknBCR1YvkVTwGkhTa5DJNchC891Yc8zc3kYURThUfsR\nunYXaSGNPXMPcRxTgpF5GTk5h4paQTaVxZq+dipRJVJ4VwpXkBbSB6zckkPX0+5TuIGLxfzigc/k\nWYeTnJRDWSkjr+SxpW/B9m3M5+ePbfyxPOuIos8XlSwv07Bfcvihj5bZQlbKQhMu1tSTuIqk2BS2\nhluIEUPiJSi8gkdd0pFX02pnimyPizAK4YYu8Wp8ZoBdH9ZRlsuYykzR1BrDkKHvrJXFT3Z+Ajdw\nUVbKJEoMcMDCy498WlNLOk8VXqHXPXAG2NA3IKfkI81A58XQGWJD30CKTZ2aTks2qeMsrZLuXYET\n8PbM20RXlyFqS8mmnxg5M2AgciJYhoXIi/SaLY9EjCxY/OP//h+jXCojm82SNOuzFK4f+bACUv9T\nBRUCK2BjQAbWPw/PUUVQcK10DY1RA/f37uOD7Q/ghz4G9gBT2Sks5hcxkZ44MS34eYBhGIi8CJEX\nEYYh2nYbe8Yedo1dNIYN8ByPqkpmQsvpMipyBaqonmggLfMyhu6QuoGcByzLYrm8jMcdEom9VnsN\nURxRKb2hOySp69EuWIZFWkhj5I4wdIa4PXH7CFEVlSJ6dg97xh6UvEIkC50htvQtMqfL8rhWvIbV\n/ipW+iuYz87T9CjHcSjIhQNiJEnau+/2oTs69tp7AAAtpcEMTOiejhvFG0e6mZN0bMtsoaAcb2D+\nRcElWX7J0bN7COKAGtheBLqjg4kZ7Bl7YGIGXuhh4AyoMfV+X8PPioQEEnIEAIEToIka9ow90wJQ\ngQAAIABJREFUFJUiHeDfj6Tj9XrpOmROxtPeUzzuPobACeSULOdRUcnA/pX8Fdws3zxyzR2rg7pe\nR1bMXtgeLak77RfWPgltsw01pR6JHBJx7v2EnY7TRASCYYiJNVRYvkWFuqM4wtAdInZJk8zIGWFD\n34DES1gsLGL5yjL+8N/9If7Z//rPjtSsvYC8nwkhW56FD7Y/wOZgEy+VX0JGOl3w/iQYnkE7fnVX\nRxAFpCGJJynNqlbF7YnbP3ex9eQ6ulYXuqsjiiMiKp5bwPXCdaS4FMrpMoIogBu4MAIDRmCQZh2e\n1H/3R7hJbdT27XOTJfC8ieZJ9wkG9gCaqCFGfOBQYns2mmYTbbONGDFW+it41HmE5eIyEQGQC+A4\nDhzLoZKuYGe4g5E7QlbKYjo7jbX+GgbOgOooL+WXsDXcwoa+AS/yTqwVy4KMKWEKU5ii19G222ib\nbZiBiXq/jsftx1jMLeJ29TbKcpkebKrpKnp2DzvDnQtZ4r0ouCTLLzFs34bu6MgImQvbFDmBAzd0\nMXSHZDMJXaz2VrFcXMaV/JXPJeWa1I9s30YYhyRC4kSkhTREXqQbuO7qmNaOmhZvD7fJOAWbgumZ\n6Ed9ZOUsNbMWeIHOdTbNJmpa7QhRJl2i+/0Jx8Vxlk0nwQkcjLwR5rPzB75/EmFLvASWYWF6JrJS\nlpKjJmi04cfyLAzcAXaGO9jWt5EW0pjNzEITNPzZf/4zLF9ZPra5S+AFVNIVVNIVKmS/NdzC49Zj\ntK025nPzKMrFA4L3J8H2bDSMBppGE4ZngGVYFGQiaFCWy5AFmc6aftr6FIiBt6bf+tx9W73Qw85w\nB3vGHj0EZMUsrhSuoKpWaVo1jmO0zBZMz6SvL4ojUvMNXVi+BcMzwDEc5JQMJaVQEh26Q8RxPNYh\nVOIlKkyevOZkTYCQ1oKwgIX8Ao2C77fu41HnEep6HbVMDRW1ghltBpqgQU7J6NpdKCmFjIQJGrb1\nberKk4gPpNgUabwLvXM937IgY1aYxWx2FmEYomf38KDzAE+7T9EwGpjUJjGRnsCkOolyunyAqL+o\nuCTLLykSV5EIFxdoT9YYOkP0nB4QAZv6Jq4Wr+K1ydc+0wYXRiElSD/yiUIKL0NOycfWqbaGW5B5\n+UAaKIgCfNz8GJuDTWTFLCrpCjJSBgW5cEB0YEsnJ+uBPUBRKaKiHEwlJR6Mn8VJIWm5r6iVY4UZ\nDmPP2CNekvvSawnZHkfYDMNATakwPAOaqNERGI7n6CgLAwZhGIJhGLw+9Tpq6RrcyIXt27j78C54\nlYdWJBvsSdFcIqqek3JYyC3gfus+Rs4ILFh07S4EViApvH0ptzAMsWPsoDlqou/0wTIsykoZS4Wl\nAxFIAkVQcKtyCwBwd+8uBE4Y2w7tOCTksjPcodZzJaWE2+XbqKarx6Z4GYZBVsqiZ/fgBA49lMgp\nmR4wvdCD7duUOJN6cRiTMsG4qcecRGqTe8YeWIaF7ujH2vJxHIdqmuj03qrcwuPuY4RhSMQJhjuQ\nefJZEVgBGTGDklLCTHYGD9oP0LJaB6LIWoYcEOt6HXEcH5v6Pwkcx6GcLuOb6W/ipcpLuNe+Bz/0\nadpY5ERMpCfoHOgXVfbukiy/pBh5I1iBBTWlXlh31vRNGK5BBveZEDzDYyG/gK9MfuVCRBnHMSVI\nN3TBgIHES9BEDSInnnhC71gdmL6J5eIyoiii8nRPu08xcAdYyi3hWvnasR6XPEuuOe7FeNJ9AoET\nMPSGlKQawwaVrht3mDtBQpTnJdsgCtC3+2R84Nn1nkaUCZSUAsMz0DJb6FpdiDy5ZyInIi/liXCD\nu4vJ9CTdDEWIyIgZ/Mt//i/xr//tv8bS8hJG3oiOV8gp+cSDVFpI46XKS1jpryDFpqiLScfuoGk2\nYXs2icAil0ZuN8o3UE1Xz9WYc6tyC07goG228aT7BBW1ci5z4cPo2T3SZWt1iICDkMaVwhVa1z4L\nEi9B5ETojg5RPfocCpwAgROQiTM02rR8i6ryTKYnIfHSWBHmdGYatm+ja3WpkflppFtNV8GCpfKG\nIitiZ7SD7RF53ZvDTdwq38JSfgkVtUJM16XCgQxKotKUiLmPQ5gJCkoBL0+8jLX+Gunklcis7q6x\nC8Mz0LE64DtfTNr5Yl71JT4TgiiA6ZlgwBxI8YyDMAqxN9rDnd074DgO14rXYPs2ZrOzY68XRuGB\nlvWk4y45xZ+GKIrQGDWg8iqRhLPXiDC1o0PgBHxr7ltnElQURbB8Cy+VX4KUkrDWXyPem+DQsv9u\niRJ4biacRMkdq3MmUUZxBDuwMXSHaBpNlNUyqmoViqCAZVj0rB42h5soysVjN0HTNMksIMPACUjn\np+7qGLpDagV13PuqCAqu5K9gpb+C3dEuFnOLCMIAD9sP0bbbCKMQE+kJXM1fxWxuPB9RALhauAqG\nYSBzMjpmByN3hLns3JnrhGGIptHEhr4BwzMgciKmtWnUMrULRahZKUvSsb554s8zDENrmIloxMAZ\n0Gg6OZieN4uzkF/Ao/YjNM0mFEE59cAIPH9eEsJ8dfJV3C7fxr32PWwMNnBn9w62hluoqBW4gYud\n0c4RGcjPgzD3e3gCwO3KbRrVf9T4CD9s/XDsNV8EXJLllxBJfVHghAtHlev9dfyw/kOkhTS+MfMN\ntG2iqTmOtqcXenTAn2XIALeaOrnL8Dg86T3B1mALJaUEO7BRUApUXPy8WqNNo4kgCmhbfcfq4E7j\nDgbOAK/VXvvMRFlRK2Olbztmh84yJjXKk4gyOfgkbhdySoaUklDTakiLZFPvWT1s6BsnEiUA/N7v\n/R5+8zd/E++++y5NMUZxRIf9Td88UCfeD0VQMKVO4W+3/hY/3voxqukqJjOTeHvubWREomjUMTtU\np7esls9tyZWRiPON6Zm4kr+CrRFRhTrJoNgLPWz0N7A92oYf+shLebxWfQ3ldPlcv+8k8CxPO1Bl\nXj7zGWUZlqROGeIskjzrhmdQl5OzxD8SYfIH7Qeo63UoKeVMot9PmACJUK8Ur6CklOD4DvpuH22r\njY7VwdPeU3Asd+S52k+YDMNcqEafk3KYz85jQ98A+oT4q+kqvnvlu+iv98de70XAJVl+yeAGLnU3\n4Fhu7HpKFEVY7a/ip42fIi2m8e35bwMA7MDGcvF4L8T9SFKtpmfCj8gsX1YkzTbnTVMlqdbGqIFP\n9z5FVatiIb+AklJCw2hg6A7PTZRBFKBltlBSS/ReeIGHjJiBklJopDYuYbaM1lg1ygQDZwAv8lBW\nyqcSpRu4MDziEpKMEqiCio7VQV7MU+3Z8xAlAHzyySd49913D3yPZVhoooa0kIYTODA8A127C57l\noaZUKCkFfaeP+qCOPXOP1itr6RpuV593sVbTVVSUCgbOAG2rTUZeWAEVtYKSUjozrTqlTeFB+wGG\n3hDXitfQMBpojBoYuSMquabbOjaHm9gzyEjDpDaJ+dzZ4uPjQBM0qh17HrIXOIHUieOQjGaJGlUb\nsnwLAicccH85DmkhjdnsLJ50n2B7uI3l4vKZkelhwqxpNYzcEVRRhSqqKEpF7Bg7eK/+Hv585c9x\ns3wTM9kZTKWnaN32cIR5EcJMRlH2EybP8phMT4691ouAS7L8kmHkEUm3MArHamsHCIms9lexOdiE\nyIl4c/pNFJQC7rfuIytmT92Y4jgmNU7PoKnWolw8EqWchsPC60NniJnsDN6eeRssy9JGnHHcK5pG\nEwAoGSZdr/P5eVTTVWwPt4kRtm+f2cGaIKkvjkuUANC1ulBTKqzAOpYobd/GyBshiAKk2BTyUp7W\nwyzfQhAFROPWN9A22tgabZHu2zPSaT/84cmpMYZhaLTphR5Mz8Rab434UD6TLVwuLmM2Mws3dLHS\nX8Fqb/XAHGpi41VQCjA8A22zje0RubenOZIApBu3pJbQMluopMk9zQgZbOgb+KD+AcI4hB3aEDkR\ni/lFzGRnPhexguPugyZo0F2dqj6d9e9FXoQTOETc4tmhZv/ho2f3wDEcNFE78fNYTVcxckeo63VU\n1Mq5zNgPE2ZaSMPxHYAB7NDGQn4BRbmIDxsfwgkcPGw/xEpvBXNZYsvFcdznTpjsgHTe5uWLe+T+\nInFJll8iuIELL/Qg8zKCKBiLLJNBei/wwDAM5nJzqKVr6FgduKFLnN+PQRzH9DQexRFNJY1T1zzO\nnSQrZLGGNdQ00vDRNJpjE6UXeOiYHVTUCniWR8toUW3UhDynM9NIC2ls9Dfw0H+IpcLSqZFAz+pd\nmCgTE2BN0FDX6yjKRbpBOYFDRRNETkROyR0ghDiOMXJHtCGqaZBZvFqmdqY9GQC8/vrr+P3f/338\n1m/91rH/3/d9+PDRs3u4v3sffa8PNaWimq5iOj2NnELm9hTueQ1zvb9+YJY00W2VeAlT2hTKchkd\np4PmqInWqIWqRnRij3s2qukqOmYHjWEDtUyNWD2ZfTzoPgAHDm9Mv4HbpdtIpc7WNU66MSNEBzoz\nWZYFC/bUA1HSRDV0hsjLeSrunuCwQ4rIiXR2c39EmNQ2/dCH4RkYOAPSyfxs3OMw5rJzJHXafYo3\npt44V92zkq4gAqnph1GIFJeCxEmwfIuI/ksZzGXnEEQBZrIzWOuvYaW3gi19C3O5OcxmZg8QJs/w\nF+oG30+Y45RYXjRckuWXCEmXYxiHVNHlPEiiLU3QqBPGjdINAEBz1CRp1EMNFwlJGp6BMA6pbNq4\nH5aO1aHuJPuF1zcHm+BZHhWlQsXZK2rlXEQZx8SxoT6sw498qIKKLX2LdPDJeQi8QIe+k3+fk3PY\n6G9gZ7iDmewMslL2iFWU6ZlY768jL+ehpBRiVXZO+yiAEK3jOdTTcy43BzcgM6wJSZ7klWgHZAa1\nIBTghi5aVgs8y2MuezCi9H2i9ar7OmyfNAQ5voP5V+ax5q/hB5/+AKZvIgRJ4wZBgCiOaKRqBRYk\nXiIzeykN9/fuE3cNzwHDMERAneeIfZhLxlhqWg0CJ0BJKRBTItJcGlKK6OiGCOF4DtpWGz/e/jEV\nVciKRMCcYzlCYByLntnDnrUHiZNg+zZkXkZNrSFkQvxk5yd41H6EnJgDOCAKIwRRQEXVEwurxFLr\nOD3bxM0lsQ5LvkcdS1geHMPRud+J9AREXqQi+YdJlgGDIArQtbsIwgBySj7yHHAsB1VQIfMyTN9E\n3+lTM4L9pCnwAq4WruKj5kfYHm6fO8qrpqu01KDwCpQ0KXckqeRJbRJPuk8QRAFenngZi/lFrHRX\n8KT7BJuDTSwWFjGVnoKnemiaTRLlj+ltChDCDKIA26Nt9Kze2D//IuCSLL8kSKLKnJjDwB2cq+YS\nRAE2B5vQXZ10VqYUrPXXcKVwBYqgkPm9yMNievHAz1m+hZE7QhiHkHkZmqiN3SHbs3rYNXbhhi4Z\nFs881091Aoc4pGjTNF2Zl/JHIrn9Qtn7/0RxBC/wsNZbQ0kpoWN2sDHYgCZpmM5ME/JjmQNkqAqk\n1lMf1tFzeuBYDlPaFI0s7MBGY9RATspRgvJDH27sHth8EyQbZuJ6wbM8GqMG+m4fV9QrNGr3Qg8C\nJ5yask6iSpmXEccxVrorkBgJMWJ80vyESpTpjg7DM478vJyS8Wu/+2vIKlmoEmnS4hlCDLqjY1Pf\nBGKgrJRR02rISTlEiBAiRBASEfL90oPJTKzpmajrdeyN9sDzPGzPprqrIUIwMSHXjJghurtiGmEQ\nwvd9mKyJolxETswhiAP0nB72zD3cb92HklKwmFuEwAvoOl2wHIsoirClb6En9LCYW0RWIuLjDENs\n1MI4pG4pbuDCjE16aAKeaenyAp2RlHgJMieDZcnBJoxCRIjghz7AAD2nh8awgYyUoVZiAk/s0ERO\npH/XRA1u6IJjOaS4FKI4OvW5CKMQlmehb/ch8RLycp6mcEtqCVW1iqfdp+cevwFIdsQPfewMiVdq\nRa1QQk4LaWiCht3RLnJSDmkhjVcnX4XhGVjpruBh+yE2+htYyi+hKBdR1+tkbvMCYiOVdAVe5OGh\n+XDsn30RcEmWXxIM3SEETkCECAwYyPzpij2HXQUyQgY/3v4x0kIaC7kFRFGE5qiJvJSnUaUbuFS2\n7KIkOXSG2BntwA5sqlJzOGpNBvbTQhor/RVIvISqSrwokw0xjEK6ESVi6omyCsdw2B5uo6SWsFxY\nxtpgDdPZaVwtXD2zJnlLukW7XJtGEwu5BXqoyEk5XCtdO/Y1JxFLYuqbOIYEUQDXczFyR/hg5wOU\nlTL8yMfaYA1KSkFRLlKLp5PQsTpY7a3CCRx8uvcpdFdHRsyQJhJeQEEqICtmUcsQoiuIBWI/pWRp\nKl6SJPzhv/1D/KP/6R/BDogO6Ep3BT27B5EX8frk65jNzxJbL45EhQIrHLAci6IIuqOjZZE5Ty/0\nMJWdwsgdoapVUVJKCPxnh5X42SB/8LzhxfZtRFEEMzBhDk0EUUAjtyR9/+35byPFpXCjcgMKr1B/\nyTAKMfJIXU/3dPLMPHsbZEEmawjKwUjyWQo2eR/2e5ACgBd7EGJCeklNPlnD8i3iIyqSQ6cXERlG\n27fhBA6G3pC+P6ZHOomTsZWMnHl+z/Y9F/ufCdMzobs6EXrgBNoEN5WZwq6xi/ut+/jK5FdOfVb3\nYy47RzIfg3VqIH04uuxZPZoyTUhTt3Ws9Fdwr30PCq9A4slo1XJx+UKuQdOZ6QubNfyicUmWXwI4\ngQM/8lGUi9Bd/cwB6f02QMvFZfoBMT0Tr9deB8uyNKq8ohHPPN3V4QQOGYBX8mN7YnqBh53RDvFl\nTKlUxPy419IyWyjIBdxt3kUQB5jLzaHv9sGAoWbHfOp5mu1w6tcLSJNKSS5hU98EC5Z4Vp5z2D3x\n9Fvrr+Fh+yE9gFwtXD1Z+YZhwTLs8UQaRXiw9wAsWFwtXqV2TxzLwfANGP5zVRiBE6C7OpqjJnaN\nXewZe2iMGmAZFkEYQE2peLP2JqpaFRIrQRVUzORnjr2PyZC46Zn4V//nv8LS7SWikWp30TJbkFIS\n3p18F8vF5WOv2wtIk5XhGzA9E3Zgk9cKlkrslZQSRi7xeZzPzh+JSLzg2UiFb6BjdtA0mjQC3tK3\n8LD9EFZgoapU8XrtdRTVIkYOyVosFYnOaBiFVPR9NjuLT5qfoGW2MMFMQE7JGDkjAIDu6lAFFZrw\nzM5LOr4TNYoiYhwdOLACC27gUqEFgNQh1RTR3k3EGE5bo2N10LW76Nk9tMwWXSPpYE58JPejIBcw\nHU9Tn1k7sBFGIUReREEq4GHnIQRWIMo7z56L0z5zLMvievk6kaVrP8Dtym06M0qjS2P3iNdkVs7i\nq/JX0bN7eNJ5go7VgeVb8AMfr0y+ciHN5y9qN+ylRdcLjM/Loqtttolsl5hF22pT78XjMHSGWOuv\nQeRF6k5geRbe33kfZbmMl6ovkc29/QBKSkFZLVONz4w4vsZsFEVoWS00R02wLIuaVjtSE4niiIqn\nP+0+xdAZEgJkgVulW0iL6QPmvWchsSsSORF+7NMDwbiwPAt/tf5XcAIH7y68e2FT24fth3iv/h4W\n8gt4Y+oN6iUJkKhn4AzwsPUQm/omdo1d2mSVk3IoyAXkpBzyMjmg7Nfj9UMfbatNBBZYDkNnCN3V\naTct8HzTvvPeHdRma3DTLjXLXi4uH0j1BVFw6hqJhdn+e5lkG1a6K4jiCK9UXkHIhNTlJfEHTQ4I\nakpFGIbYHJL0/4Q6gRSTwsPuQ+wau6QhxjEw9IaoZYmTTU2rYS43R30seYbHzmgHpm9iLjdHZgyf\nHQ5Mz6QjNwCZZdQEDZqoISflTn2GnMCB5Vn0cNC3SX2xpJSQk3LISllkhMwRAnEDF127i4paoTZw\nxx0wNFGDJpDr2L9G0kk+ckekM5mXcbd5F3Zg4+WJlw9kUJIUssRLx/YH9Kweftb8GWppkmWQBRk5\nKQfDM6jB92nPcV2v40HrAbYGW5jJz+BX5n7lXE1V+3Fp0XWJFxKJO0dRLsIObCpCfhxOcsRY668R\n78YS8VTsOT1S83imQZqc1sd1LRk6RJDbDV2UlBJqWo1uVn7o05N54i6CGHBCBwJPUrDXS9fHTgUF\nUYCe1aPCBad5SZ6Fjt1BUSlSmbHkusa5lkftR1gdrKKSruAr1a9Qz8ldfRcr/RXUh3UMbCI+nZNz\n+FrtaygqRZTUEjiWQ2PUoJ2US4UlSKnnr8UPfXQsohOabJwyL6MgF5ARMjSlaHs2fuu/+y38zv/+\nO/j+//x9vDLxCrIyabBxAoc4jjg6TN88sEYSlZxGMCIvQos1lJUyPtz5EA/aD3C9eB1FtYicnDuw\nhhd6eNR5hN3RLtJCGm9MvUHHJK6WrmJntIPNwSYGLnFd0VIa7MDGJ81PcL91HxW1grncHOYyc7he\nvo7NwSbqep2O00i8RA9iibC84RnQXZ3Mi+p1qCmVEtbhZyshoQLINYVhiLX+GjWSTkYsZF5GVswi\nI2UOGFF7oQclpaDAF+galDw9AyNvhO3RNrZH27SMkdQR00KaWn+ZvomaVsNqn6Te53Pz8CNiqZYc\nTnRXp2UHkRNp/TYrZTGfnUfbapOGMKZwoHa5Z+6dSpaz2VlU01Xc27uH93feR8/q4e8v/v3PLPrw\nRcAlWf6Sw/RN8CwPkRcxcAaQeflYUkvqcEW5iJnMDCXKJIU0k52BxEsIoxBPu08RRiE1wB23LukF\nHraGWyQtllKxVFiCyIlwQ5cq+iSt9iInUl3MxqgBL/CInFZh8UI1k47VIaleQcVsdvbCQ+s9q4eO\n1cFifhEFpYDV3ipWuisnmuDuRxzHGHkj7Ax30DSbmFBI9NSyWnh/531sj7bh+R5YhsVsdha3yrew\nXFo+MuqTpL3jOMZEeoKky3trGHkjKpye3Mf53DyyYvZI1LPaW8Vafw3/6S//E16bfw3Xpq9h6AxR\n1+sYOkN4kUejnmlt+kjUcxKSGcL9a7xSfQXrg3UqulBQCvTZqet1rPSIGfZycRkL+QV4oUd9IYMo\ngMRLuFG+QWYTwSFEiO/MfwdhFGJdX8fGYAM/2/0ZPmp8BDklY1KbRFbMHuumwbM8MlIGGSmDGmo0\ngh+5I7RMMkKUCGYkEePhND3HcZjMTFKj5yiOaOSdpG2TNdzQhcRJR95DlmXpdQAHo/ckbZtEv1kx\ni5yUgyqo6DN98AyPx93HqCjEXzMROYjiiFrZJR3pyWdJ4iVqqda1u2ibbRpZT6gTWOmvwPCMUz8X\nAifgtdprKMtl/JfN/4K/WP0L3KzcxM3KzZ/LfOuLgkuy/CVGGIVwAgc5KYcgCsjIyDEdlXW9jo7V\nOaJfGkURtofbkHgJFbUCy7fQGDVgeAZemXjlWCeEs5DMIQLAfHYeqqDCDmz07T5ixOBZHkpKOeIR\nGEURtvVtuJGLqlY9t1zaca/V9m0sFZYunDZ1Aod24CajKlcLV7E+WMdafw2z2dkT2+v90Eff6cP2\nbAycAVJI4X7rPlpWCxmBRCJX8lcwqxH7o9NSXH2rj11jF3kxj4yYQctswfZtMGCILB1HmnGSgf39\nJGd4Bu7t3YPu6pjNzuJH/++PoL2jwU/58CKPdjzmRBLZnKee6wQOelaPCIjvWyMjZijZzOfnca91\nDw2jgTAm4vv1IXn+JrVJXCmQGnjLbCGIAnAMB5EnYu+JNupEegJltYy/3vhrfLj7Ia4Xr+PVyVfx\n6uSr8H0fdb2ONX0NjWEDa701uKGLtJDGrfIt/L2Zv3fsa+FZHiWlhJJSotFeYgqeqBblpBxKcunA\nIU1JKRi5I5ieCU3UqPBCco+TqLxjddAYNcjhap/rzXHXcXiNhDz7Th+sTurBeSmPa6Vr+HDnQ3y8\n9zFer71OP9ssw9IoOHnmkixN3+mTmmPoIyfmsGPsoGW26NylyIlom+1zHSJn8jP4Ve5X8cneJ9ga\nbKHn9HCzfPPC8pAvOi7J8pcYpm/SNn7Lt8CAOZKCTYjyuGH+htGA6ZKUjxM4VL1lMj05lgYs8NwK\nq+/0oQkaikoRfuija3fBMRxJM51iDdWyWtjQN3CjdGPsYf8EHatD2+CntYutEUURVnurSHGpAzOM\nLEuahOp6nabj9hNmHMc01WZYBt5rvEdslFIydEfHa5Ov4c3am5jMnq/5wXAMvL/zPvzQh5pSqaHv\nlfwVupmHUQjLt2it0w5syCkZzVETm/ommPi57ud/+L//A9gMi5dfe/nUzfwwkrR20oSSRFIneVum\nhTQWc4vYGm4R0+HBBliGxSsTryAv5zFwBqRbO0VSmSeNyyTWWk2jiabZhO7qWMgvQEpJWCotYalE\nmn929V3c69zDg70H+NPHf4ofbf0IX5/5Om6VbyGrZI9de3+0N52ZhuVZ6Dk9DOwBOlaHuriUlBIE\nnnTqJoP++7M2Sfp0OjONnt3Den8duk2I8zgrs+OQrFFD7cBhpGt3wcQMcmIOHbODld4K7TQ9nDlK\ncSmkuBQ0USPNeI6O7RE5BCMGHrQeIC/lUVAKKCtlbI+2MRWcz7C9lqnB9Ennrh/4uNu8i6ba/KWM\nMi/J8pcUiShAornqBA6tWyRIiPK4SMgJHOwZe6SWFHkIogDpVBrNuDl2N9vAGWBjsAEncFCQClAF\nlSoJneRPeRh3GnegCRr1ObwIfrb7M4i8iJcmXrqw1+amvgk/9HG9fP3YNRLy2U+YQRSgNWzhfuc+\nrbu5gYs3p97EVHYKHMvh5YmXz0xn77cfu7t3Fy2jhXfm3sFMdubYSJtjiYzadGYahmcgjmL8aPNH\n2BpukS5ZbQa2byMv53H/yf1jU40nYeAM0LW60F0dAJAVs5hQJ5CTcmeukZNz+MnOT7DSW8HV0lVU\n1AoMz6A1xfPaWU1qkzADcpjrWl08aj9CTasdOMhNZicxmZ3Et2a/hbt7d/He1nv4y42/xEeNj1DL\n1HCtdA3XCtdOjeAVgXSrTmemMXSGtFu4aTahplRkpSyCMIATOCc2uKWFNCa1SZKh8SxU753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yYnSbagtd0XjWflLG6M3hjq837S8JQsP0WIBMejxh7Ls7DWWIMqqX0D9FF9MnpsXIwTYvBtzKqz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\n2rbagrTttj9blw1nRcRlVphLzrFeW2ejuuGTrncNCT3hm0erlko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/Vd9iIbXgyxr6p3fb\npGbWaFktioboqAWh2aorOiEtBH0xA7jd2OZa+pqo/308ehOQA0zEJuj1e2zWN5mOC6IfRFyPsygv\n8lv/4Lf4xV/9RX7zq7/JVHwKx3UwLMPf+B8WH7Jd20ZVVeYSc9wv3keRFDFaoupEg2KUJqWnCCgB\nZEk+tsZuYxe7Z1M36xy0DzAtE1UVs4+r5VUSoQTr0jqSJPkkndWyRAPigHDeoa/WqZEIJs59bkzH\nZLWyyn5zXxxsQknq3TrlTpkHxQcoksJsZpam1eRG9obfCOVZrFk9IXvoEbrTF84rtmv7fqUeqeqq\nzhcmv8BC5mKqN6fBcR1fSvC88ZfT4Louq9VV2lab2cQsqVDKv37PRaberfPB/gdMx6cJB4UOckyL\nCa1oWcF1XdZr674X50kNZ179Nx0SwgvPOp6T5U8Z0uE0VbPKXnOP+dQ8a9U1ViurF+qQvQw0VeMr\nc1/hP979j3TcDq+NC2Fxzw2i1qlRcAtPv1/W/EaecECQqCZrjIRGqBgV8q38pbQsFVnhxdEXqW/V\nqZpCxWUuMcd2Y5u95h5Fo0gqlCIejLPd3OagfcBiWnTKelqcjuvQ7/XJt/PUzJogMNc6dO3pcJo3\nwm9w0D5AV3QmY5PHUt6/NP9L/Ncn/5Xl8jJfmPqC37G8Vl1DkRWupq/i4rJd2+aH3R+KbttgnIAc\n8CPKcCBMx+n49dp6ty7cMLoFus2ur/G7Wd0k38qzmF701VyiAUEwN9I3qBgVfnTwI96YfuPYexbW\nwmw92aJSqXC/cF9skFqYXr9HQAlgGiIVOhmfJBvKYvdtX6rO7Jm0nTZ1SzTJBOSAn7Ye1DaVJImg\nGqTjdCgaRfKtvB/9duwOtW6Nq9pVLMciHU6TCWWEWP3Hjh3nHfa8NG4mnPHvoeVYWO7TmmnLarHb\n3KXcLiPJEtlQltHoqFBzCkTIt/KMRke5PXabglEgp+aEk43ZEHVSp+PX6BzXEbZ1PdvX0Y2oEUJa\niLAaRpEVNqobBKQA2XCWjt3xBegvosE8CI/ke/3emd3dJ8Fzi2lbbVYqKxi2wXR8Gtu1/UMYIMaw\nlABPSk8IqSF+9fqvHrNQsxyL9dr6uYS9URMWfK+OvXqp3/ezhudk+VOIK8krPCg+4KB1IJo8yits\n1jeZS839WF83G84yn57nhwc/5JWRV/yUoYfB6MzbwAad6j0U26Jb80b3BrqqH7I68kSl4bDAtPc1\nEB/48dg4K+UVMuEMuqKTjWSJB+Pst/bJt/L03T4BJUChWWC9sk4mnCGoBp9akUkShmVQtIrEg3ES\neoJIQOjKBpSA/+HPhDKs1da4X7zPlcQVf7MG0DWd26O3eX/vfaqdKhPRCfbqe0S0CFfiV+hLwvsx\nHoyzXl9no7ZBNpz1B/W9jdWLnKNalFQohSo9dfvo2l06vQ51s86T8hPuF+8T02JEAhH/d5ElmWan\nyUZjw5+H9aI3T/rtX//nf03f7ZNv5VmvrTMaGWU8Mk6+lafltMjqWWZSM0h9iR49nJ4jrLFscRhq\ndBo0bEEqbavt+1vKCIH+gBogqASJqBH6/T6L6UWuZ68TCoTYbezSslrMJmax+zaWY/kdvyA2+j59\nJCS/OUhGRlVU32pup75D3RJOM67rHnomPFeUltVClVRmk7OMx8fRZR1kEQFt1DZYra4S02LcL9xn\nv73PleQVzIo4FHilDS/96PUDeM+kB88qa7exi+3azCRn6PQ6tJ3Dg/iD7j6ek4ssyUg8NcEeNDuo\nGBV2GjsElABzqTlUWcV0zENWe4OWe0ft1fr0qZt1dpu7KJLCQnqBqBYVVmJywD+QSJKEYYsD3Qsj\nLxwjyobZECIbyFzLXDu1J8Ij1PHY+DHhdE/b91nDc7L8KcRgOjalp5hJzLBR3yDQCFy6EWBYvDn5\nJv/u7r/jTv7OsShGldVjBApPB9S9SCAbynK/dJ+6WUcOyRi2gd2zcXEZFo7rkDfy/Onqn3I9e/3Q\nyTapJ8XIgFUXvpeaiATGomNMxadQZHG6dnoOdw/u+jJ3nj5r3z48nZ8JZdhr7vFh/kPSoTTj0XF/\nA43pMQJygLc332YuOYft2swn53ElFwlJDN/HIoxFx/ymJAXhQeg5gni1MW/j67pd+r2Pr0GCoBr0\nlXx26jsUjAJxLS70az9uxIhqUVpOiyeVJ8h9QV4uLv1+H1mS+adf+6f89d/663z5V76MaZl8UP+A\njtUhHowzFhtDQqK8XwZJpIElpEOHlYASYFwb99PQsvQxWcni++mL++/0He4d3CMWjNHr96gZNbYb\n2yL6+tivU5IlQUKujO3auI5Ljx49t0fTbuK6H78XuNAXYws7jR1SoRQdq0NADSD1xf2umTVaTou+\n2ycZEmIPiqxQ6VT8a6936hQ7RRLBBGPRMcpGmRuZG8yl5/xD2rB1f0VWsF2bRrfBfHre/7yd5hPr\n/dt2bEFqH5PeIPaae5SNsnC6CY/7Uo2DOOoipMhCkCGoPG2oanQbzMRnRLPbGfXDO7t3APjC1BcO\nfd3rvI1pMZ+wT8OTyhMkJK6nrx/6eqFVYLu2ff4b+RnEc7L8KUU6nKberbPT3GEps8REbMK3jrqs\np90wSIQTzKfnuVu8y2tjrw3VGSvLMrr81K2esPhaqV065Drv2Rt5f572NW8TTGgJVqorjEfGyUay\nhzZ3EGmt/eY+VbMq5P86RYJKkIXMx68ZgKsZ4as56Cg/eIoHYY02Gh3loHUg9GU7FWaTswTVIJIk\n8fMLP89/uPsf2Kxu8tWlr5IIJfzNbRCZSMafmSt2isyl5s4U2B7cWL2/58I56t06m9VNXFxujtwU\nvo+SxFvTb/Hu9rtoiuYfZHq9Hj16/Nkv/Bm/9Mov8cbsG5TbZR4VHnHQOWAqPsX1zHVhraYoh96/\nwah+2BS/YYmZWa/hpWSUQIabGTFjOngvByPEo685iJpZY626xlJmCV3VsVyLQkt0a2eUDNfC15iK\nTfmEN7hGzayx5q4xHhsnFAjRttsElAA3cjcuVed3XZf16joBJcBE9GkZQZZkZOViZRC7Z7NSEQP+\nL468SC6S8wkRDkefp8FxHdar676W8nk+q4ZtcK9wj6Xckq9yNFifHKbztmE22G/tM5ecIxgI+mt4\nTUmfxMDhJ4nnZPlTjOnENA+KD9hubLOQXsBxnVPlzj5NvDn5Jn9w7w9OjC6HxVh0THTGtgp+7dJP\nrQ255yxkFqiaVXZbu2Sjx8WydVVnLjXHqDXKfmuf/eY+d4t32W3u8urEq77sXKFd4KB14IsC+BvW\nkT1qKjFFMpRko7rBRn2D2cQscV0It4+ER+g4HQzbIBU+vYXem9dbr62zXF72LclOwuDGOXgt2XCW\nqBZltbLK49JjX4xBURRujd7ig90PfEUiRVFQUPid3/4dNE2j2ClSNsss5hb5YvSLbNY3KRpFgoEg\nCe1yghODaFhCKN3LLlQ7VZHW/Nhm7CL310PZKBNSQ8iyLKJzowKIZ8hr0DoJ3iB+IpggE85QbBUx\nHZPx2PilG+LyrTwdp8NSZukT9Qh4HpIuLjdyNy5Un/Tg1cddXP9wch6+t/M94GlU6dVJ7Z49VEOR\nR866qvu2YKZjsl5dp+t0mU3MwrNpZ3nRx/I5fhLwmikuCs+Oqd6tU2iJYf9sOMtOc4dCq3D+ApdE\nIpxgMbPI3eLdS9cnVFklG8kealm/zBoziRlaVouD5gFu/+Q0blgLs5Be4HOTn+P26G3q3Tp//OSP\n+WD3A0zHZCQyQrlTHuo+RLUoS7klQmqIleoK39v+HhWzwptX3mQyPsm94j3fFuo0hLUw17PXiQQi\nrFXXfJPji0BXdW5kb5DQE2zURa3SdV3SoTQziRnWqmtUOhX/+zOZDL/9f/02+XaeidgEC+kFcR2Z\n66RCKV9c/aLXcRR1s04sGEOWZUzHpGk1P9H8neVY7DZ2aXQbPCg+oGbWGImMcDN3k5nEzKlE2TAb\nbFQ3iAVjXElcodap0bbbhAKhS19Py2qRb4sO6k8y35xv5VkuL/sjOpchykKrwHJ5GUVWWMosDUWU\ndaPOo+IjP6osGcLxBjjmeHMS+v0+hXaBarfKdHwaTdWoGBWWy8u4fZel3NKlhVI+C3hOls8Amt0m\nds++1M960dFec4+W1fLbvHeaO8caaz5NfH788ziOw7tb7156DU+BZ7ex+4nWSOtpdpo7vv/fadBV\nnYXMAr989ZdZyiyxXl3nG6vfEA70jsV2fbhaiyqrXM1cFSMR1VW6TpeRyAgvjLxA1+nyo/yP6PV6\n566xkF5gIjZBoV3gcfkxhnWxg4csy8yl5piKTVExKjwsPcSwDK6lrxHTYnyU/wirZ1ExKvzL//tf\n8rkvfo7F1OKhVJ0sy1xJXmEmMUO1U+VB8cG5ZH8aHNehbbd9U+ZCu4Aqq6T1i2+grutSMkq8u/Uu\nO80dwoEwM4kZbuVu+aINp8HTUY0FY8wl52jbbapmFVVRyUVyl4oqHddho7pBJBA5N9V5GizH4nHp\nMXvNPUYiI1xNX73wjLTlWDwpP2GnuUM6nOZ65vrQa7yz/Q6qqvLaxGusVlbZqm+RCqW4kb0xlN9m\nzRRjWlEtymh0lPXqOhv1DWJajOvZ65+K6fxPEs/J8hmAKqtUzapfI7sopmJThAIhNqpC7s0jTE8B\n5MeBRDjh+13u1y+nBanKKmORMcqd8qU3aFmWmU5MIyEJL8Bu89yf0VSNVyde5S8v/mXGwmOs1dYo\nGAU+3P+Q+4X7Q5HWVn0LRVF4c+ZN4sE4j4qP6Pf7XEtfo9KpcL90f6jrH4uOsZRZwu27LJeXybfy\nQ/3cIEaiIyzllpAlmUflRxy0D3gx9yKWa/H15a+zUd9gYnSCWxO3To1AsuEsSzkhRLBcXr5UtOtJ\n7SX1JK7rUjEqZEPZC6UrW1aLzdom94r32KhuYNgGt8du8+Loi2TD5691lCglSWK3sYvjOqRDad/s\n+aJYr67j4p6opjQMSoZQU7J7NoupRabiFzeWrhgVHpUfifnh1CIziZmh19iqb7FR22ApvcRGbYO2\n3WY+Nc+V5HASem2rTdkoY/Us0StQXqFpNZlNzJ7bDPSs4DlZPgMIyAF6bo+qeXZkdBpkWWYuOYeL\naD4A/kII87Wx14hqUd7bee/Sa4xER4TI9pBR3UlIh9OkQinRAWvW6TrdoX4ursd5Y/oNbo/fJhPO\n4PZd7ubv8qD4gIfFh+RbIuI8Cu89nUnMMJuc5Ub2hhBbMAq07Ba5UI7dxq7v/nEewlqYG9kbZCMi\nQ/C49PjCqXld1bmeuc5YZIx8O8+D8gPow35rH6fn8Pd+4+/xx3/0x+evkb3ORGyCUrvkR6rDomkJ\nTVxN1SgZpTPdUwbhCa/fO7jHcnmZZrdJNpRlIjbBeHx86JGoo0QpyzKFtrgnaT0tBA1OEcg/CzuN\nHZ8YLhsJelHczdzNCzfAeEIFXhS3lBsu7TqIt9ffxu7ZRIIRIoEIN3M3hxY8sHqWMF83KrTtthCm\nV4MsZZ7ttOtRPPt0/78AthvbTNlT9NyeP293UWiqxmxilpXqCnuNPSbiE76Um1fP+rSbfgKBAG9N\nv8U3Vr/BvYN7FzaI9jCdmGa5vEzJKA21uZ64Rnyatt0WLvOKSi6cO9Fu6Si8NGRCTxCQA6xV1rB7\nNrqqH7KtyoQyJPWkn94eVDUZtBPzTJLpw+PSYxJagkTo/MYZWZaZigst0Y3qBo+Kj5iITVzonsmy\nzFh0jLpZ517lHpqscTNzk6pZ5b0fvMfV6eEMiceiY8S1OJv1TR6VHzEWGRtKQKLerZMOic2zaBRJ\n6alTycXzhSx3ynScjhjf+bgRx3v+HxYf+jOP5+EkouzYQiAhIAeI6bFjM4XDoGbWKLQLTMQmLkxQ\nFaPiN9xdVo2nZtb8Q9dFbdI8vLPxDg9LD/m5mZ+78Bpu36XaqXLQPmC9ts5kfJKZxMyPtYHwJ4Xn\nZPkMIBvKUuvWfIPbK8krpzrWn4W4Hvcji6gWJa7HfcLcae7guM6lVHPOwkJ2gcniJO/vvs/19PVL\niaxHtSgpPcVec+9Ei7BhENbCZMNZSu0S8WCcSqdCNpwdWlkkqSd5dfxVnJ7Den2dpJ7kWuYahmNQ\nNsqsVdfYb+7T6/f8UYujiOtxXhh9gUApwEZ1g0KrwHd3vstX5r8y9P2MalFu5m6y0xRi8fVunenE\n9NA1pZ26uM9fnP4iDg6FdoG16hp/8Ht/wN//2t/ni28O5xrjNf/kW0JD17uO0w5yhmXguA5xLU7N\nrNHtdf3u4sHvqZk1mlbTNz2OBWOMx8aPacG2rBYdp8NiavHcay0ZJbbqW4dcNRzXodguirRhMEEi\nePFOX088wZvPvMjPbTe2qXfrpPQU04npCz/TlmOx29ylalZJBIUx9EXXcFyHJ+UnfGP1GyykFsRz\neMHIuNguslnbZLWySiaS4XOTn3vma5On4TlZPgPIRXMsZZbYrG+y1diibtZ5aeylSxHmRHyCtt1m\no77Bkrrka5tqisZecw/btZmOT3+q0nhvXXmL/3TvP/Hd3e9e2sJrMjbJg+ID9pp7lzaZnohNUDNr\ndOwOsiRT79YvdJpXZZU3pt7A7QvFl25PNO7MJedYLi8TCURI6Am6vS4Pig8IKkESeoK4FvfFs1VZ\n5UbuBgElwEZlg/X6Ot9c+ya/uPCLQ7/ng5HqdmObB8UHjERGTnRrgcObc0yLsZhY9De0pJ5EQuLr\n/9/XiWVjfP71z/uzccNcx0RcRFTb9W2Wy8tkQhkm45PHNu6aWfNHRlarq4RUIQtXM2vUzTrNbvOQ\nI4c3MnMaARTbYib2vGjuJFeNfr9PpVPBdExUSSWlpwiqw/3OHjx9VUVSjpH+WT+Tb+X9xqbLRIKu\n61IwCuSbef85uEy2xZtD/c72d0gEEvztl/72hYlyo7bBSmWFjtUhF8nxpStf+tRMGz6LkC7bNPIc\nP35IkvQqcOfOnTu8+uqrAOw39nlUfoQkSdzI3GA0NnrhdR3X4VFRrDEosv5pmMiehm9vfJt7hXt8\n7dbXLp1K9RREljJLl27N97z7PLHweDB+4bR2xaiwVl0jrIVpmA0OWgdkwhleGRcSf57r/UkkENEi\nvg5tpVNhr7nH93e/z0Rsgi/Pf/nCqbjBzVOVVd982Pt/g5vz4P87usaT8hO+uf5NMpEMf2nuL32i\nDRhgLDJ2KBV3v3AfTRH2Xx/uf0g6nCYgCxWhkw4VZ8FyLO4V753ruLFZ2zzmqjFIlHWzTkAJMJ+a\nHyolP4jVyirNbvNMybdBDEb12Uj21IPNWWiYDbYb23R7XbJhUbO9aDQ56LRiOzbf2/ken5/6/IXm\noQ3L4HH5MYV2QaTV+8LxaNis1A9+8ANee+01gNf6/f4PLvQL/ATxnCw/wziJLEEIT98riHm9sejY\nmemv02A6JsvlZUJq6BAxerWdoBrkaubqp9bFZts2//buvyUSiPAbL/7GpdZwXZeHpYfIksz1zPVL\nk/nD4kPcvstkbBLDMUjpqQub0j4pPxEdym6fcqdMMpRkNDLKZHzyWBrKsAwaVoNmt0nbavtuLN1e\nF0VWsByL+4X7jMXGWMouDZ1WHcRgWs4z5650KkNvzl/96lf56t/6KvINmZgW42buJlcSVy58KPF0\nUcudMhKSL0F3r3iP0egodbOO1bN4aeQlEnriUn6OW/UtamaNW7lbJ/5OZzlieJmFfr/PQVtoJ1/E\n/9J7/ZJRGqrOaDomu41dP6q/7L31MgORQMQXur/oGoPPx1hojD9a/SOcnsNv3vrNocoj3r3db+1j\nORbz6XnaVhu7Z3Mzd3Ooz6PjOvz3b/93fuXLvwLPGFk+T8M+gwgFQrw0+hI79R2qZpXl8jIpPcVk\nbHLojUdX9RNF1uN6nGuZa6xV13hUfMRiZvFTqUEEAgF+bubn+MbqN/hg9wNen3z9wmt4Xb2Pyo8u\n7UoCMJea41HxES27RSQQoWbWUGTlQmntVDDFnb07ZMNZfnHxF2lZLXbqOzwqPmIkInRavYNGWAsT\n1sJ+dOPZVjW6DXYaOyCJjt0nlSfUOjWWK8tMx6aZSc74YtfnQVOFiXSkFeH7e9+nZtaYjE/y2sRr\nQx2kdF1nPDnO0pUlfpT/ETv1HTpOh0wow3h0/MznynKspwL5PeFQYjs2u61dcTDBJRFIMJ+aZ7u+\nzUJq4dINIJYj5kLHYmMnbs5eHc4bnxhM0za7TQzbIK7FWa+ti2j2gqMihVaBklHyjZtPg+M65Ft5\nSm3hsXmZBp4fZ9r22xvfptap8WtLv3YuUXozrfl2XugMB6KMp8exehZtu821zLVzidJ1XSqmyKR4\nI0TPGp5Hlp9hnBZZejBs0RDhtW67rks6nD5T4usoKkaFjfrGsY5Gy7F4UnlCr9/zZds+DfzJ6p+w\nUl75ROlYrw71SdKxXkr3avqqb7nkGSqfBy+V69kzXctcI67HD21unifmeb6cgx2ZPzz4IflGnpSe\nouMIUfF0KE0uKqyidFUnqAZ9cW9VVtFUzXeg8HRuVVSCgSAdW6yRjWQZi44NnSV4WHzIVn2Liajw\nwrQci7geJxsR98szZTZsg67TPaTJ6zlzBJUgUS2K4Ri8s/UOkUCEeDBOSAnxyvgrl84KbNY2qXfr\nJ0aVR+XdBp8N77MS02LUu3VK7RI3R25e6IDkadCepY/qPQOlTkl0mEdGfHGNYeERXKFdwHXdS6Vt\njxLcYNp2v77PHz76Q14afenMHgKP4PLNPJZrkQwmfaH8qBblcekx2Uj2XK3YklHy10jpKQpPCrzx\nhTfgeWT5HH9RCAfCwonBarKQXBCbQKckhr2H3CDT4TSWawmRdVXzCUxTNa5nr7NZ22SlujL0eMB5\n+PLMl9lr7PE/V//npdOxY9ExIRZe37x0OtYbodiqb3E9c52KWaFslMlFcqfO2g2KQXsbpjcjt6Qt\nibpgfIJsOEu+nSffFMR5FmmGAiGiWhSrZ/Hzsz/Pnf07GI7Bl0a/RMWssFMXTiR2zyYaiFLv1g/J\n/1mORalTomW1CKkhRiOjxEIxVEVYLpWMEvcP7nP/4D65SM6PeH2x8o8lAF+79hr/5J//E772v30N\nVVZpdIUc3GJqEafvsFpdBRDp5uiob1eWCqV8Q+8TMxCmGGnQFZ17pXuMRkbZae5cqt5mOZZfgzz6\nXpaMEjv1HYJqkGupa4cOi12nS82s+fZqxXaRsejYhYjS02pN6akTyeEowV300OqtMUhww0T1J60x\nSHBH17Btm/+x9j9IhpK8MXl6nfIowS1EF4T9WV9IJnpyfINi8UdRMSrst/bp9rokggnmo/OEtTBV\n9XLz4j9pPI8sP8M4L7L0UO1UMR2TTDiDLMkUWuJDC8NHFV4d5iTBZS+Su2yL+kmv9d8e/Tdem3jt\n0kLrhmVcaMbvJJiOyaPiI9LhNJOxSYpGEUVSThwpGRSU9oTJQWzgD4oPSIVSx7oiLcdiv7VPuVP2\n1YhOUplx+y6FdsE/sX93+7sA4r3pQ76dp2JU/Gg1q2dFLay5y4FxQL/fJ6ULQ2vPUNkz+3X7LrZr\nU2gVqHVqIEMymGQkPIKiiKYWWZL5N//vv+Fn3voZbr18y/e4vF+8j+VavDH5BlEtSsWoUOvWAI6l\nmk/DZm2TltVCV3Xa3TbpUJpKt+JHTBeJeE+KKl3XZbuxTblTJhPKHOvktns2JaOEpmik9BTrtXVM\nx+RG7sbQAgRefT+oBI+ZqH9aBFcyShTahRMJblgcJbjx2PixA8y31r/Fo+Ij/tr1v8Z4YvzYGkcJ\nbjwq3FgqnQpWzyIbzlJsF8m381zLXDsxxV8za+w39+k4HX+NwSj/eYPPc3zqGJYsvQ4/27XJhDLC\ni3GgZgLDbW5eh99JLuyDpq/zqflPJBQNTz+0P+l0rJdSXUwtEgqE/I01HUr7hOm5UwSUAAvphWMb\nkDfHd1pdapA0NVkThHeENE3HpNKpkNJT0Ifv7H6HoBLk9cnX0RTNX2O/uU+9W0eRFNLhtBAmOCfV\nO3gdg8SbDWX9Z+Lx48eMjIyQSj0VEbd6Ft/d/i5u3+ULk18gpIUOPVdH1zgK13X56OAjooEoDbvh\n18sc1zl2oBsJj5xJDqZj8qD44FAHrOVYvvbuVGLq2HPkuA5lo4wsyWTDWaqdKlv1La4krwwtlm45\nFsvl5WOd458GwR1d4zSCO2+NilnhoHXgE9xJTWZw+iHVdV1BcEdI0vtMVTtVv37dc3ssl5dPTL9W\njAoH7QM6ToeYJuZjTyLT77z/Hd78wpvwnCyf49PCsGQJIpLwuh89wgSxYew193zbolQoRS6cO5Fc\nvNmxttU+kTDP25wuAtu2+YN7f4CqqPzNG3/zUmIFruvyuPwYt+9yI3vj0nWwx6XH2D2bpdwSPbdH\npVPxCXO3uUuhXSClp7iSOF0nc7WySttuczN389QDyWBdUUY+lqqrdqp0e11y4Rytbos7+Ts+YVo9\ni4PWAaV2iYpZQZVUMqEM2WiWkcjIhTbYQdIE8UxcH7vO7/7u7/KP/tE/OvS9HavD93Y/tm36mDBP\nW+Poc+XVw4OKmGF8YeSFQ2sPEq+LS0pPkYvkTtxgN2ubNLtNv+vyvMObR5SSJJENZ7F7NuvVdaFk\nlZwdKqr06vb9fp9rGZHaNR2TQrtAxaj413xRgrMci4JR8D+vl12jZJQodUo4rnNiBDcI7/Omq7pf\n/vAOLd4aJxGcR5TpUBpN1o593hzXEdfRLmG5FjEtxmhk9Mwehz/8n3/I3/j5vwHPyfI5Pi1chCxB\nEGbZKNPr9w4RJnDiQ53Np27IAAAgAElEQVSL5I5FQq7r8qTyhI7dOZEwz0t7XQT79X3+y+P/wmJm\nkV9Y+IVLreGlUk9Kgw4LL5UaC8ZYSC/Qdbrkm3kO2gcossJkfPJchRbLsXhUfkRIDXE1c7Zs3NHN\nMhFMkAvniAajFNtFFFkhE8pQM2p8a+tbdOwOC+kFcc/CObLhLC7uoY3OW+MijViDz8T7332fawvX\nuLV469gz4RGmLMm8PvG6T5hH17Bci0ggwmh0lKSe5HHpMR27g4t75vC8F2EVjSLdXpdIIEIuLJ5N\nz8rLiyqz4ayvGhTTYieKdA8SZSaUQZIk8s08FbPClcQVYsHzDRWPEqXpmBSNIvVuXTilhNLnRsNH\n0bJaFNtF0YT1KazhHbiGOSx9/fHX2apv8bVbXyOshikaRd+F57Q1PKL0Rqu26ltUjIrf/eodGuDs\nQ/ggdho7vP2dt/k7f/nvwHOyfI5PCxclSzibMOFpyqVoFGnbbTRZIxvJHuoEPY8w4WlDhSqrzKZm\nL6VXC/DB7ge8v/M+Pzf7c5fWjvXSoJdVM4GnnY5e48laZY221WY+Pc9MYmYoWbzG/9/emYfJUZz3\n/1M9187sfWlXK2m1EiAkIQRIoCBx3+aHjTE2MciJRWLsEPCFIdhxSGzHEHzEGCeY2BgH4xibIyQ2\nGIgMGNsQJA7JIHSDrl2tpD1mZndmdu7u+v1R3a3e2bl2JSxM+vs8/exOd/VbNd3v1FvvW++Rjk3K\nGarQhBb0BmmsabRDMbJGlmg6yp7IHjrrOzl7ztkTnFIsGkNjQ6TyKQKegC1Qq13EGIbB9374PXqO\n66FzTqdNoyXUYvNEOYHpfIYDiQHGcmPk9TwjmRE7VeGC9gVVjWUkPaKqw2Tj+DWl3Y9mRjGkquix\nZ3QPqXyq5DPWDZ3h5LAtKD2ah3AyzODYoNK+6qdXfJe2J7iu01bbxmhm1H62HXUdtNS0TOrZjqRH\nbPPkVN9Ppd9sObzS/wpre9eydMZS2mvb7WdbjkahoLSsBFZ5tXg2rmrOljHDF8L6jQ3vGOaiMy4C\nV1i6OFyYirCEygLTQjKbZGBswF6lNtY00hxspsGvtJNKAjObz7JrZBdjubGy6dYqwbnqnaqwc656\np7p/uXtkN68feF1pzI2zaAu1EcvECHgDNNc0VyUwrX3UaivTWxhJjrBjZAd7Y3uJJCP4PD4WtC9Q\nNTBzGdYdWEfIG2JJ15KSXpyJbIKBxACjmVH7fbYGW6saR01NDd/61rdY9fFV4zQXJ09k8pmKAhMU\nX63fv57tQ9sJ+oMsnLaQ2U2zJ+R3LYdkNslQcohd0V2qvmPdNHzCR2uoteSeeTFBGcvEbC2zo7aj\nYvKJbD7La/tfI5wO01rTitfrnZLWHkvHiKajRFNRDIyqzJOFSGQTRFIRRtIjtpm0mDWoHHYM7eDB\nLQ/SFmrjlBmnTNDai2EkPUIylxwnKF/Z9wqGNOio67C9rsvRKIRlean11TK6e9R18HFxeDFVYQnV\nC0wwA73N0ImMnsGreWmqaaIl0EJ/or+swAQVs3ggfgCfx8ecpjmTFlbWfgpQdTaRQlj7l7qhM799\n/qQ9dq3qDbuiu2ivbWfZjGV4NS+ZfIZIKjIpgWntX85vnV/RxFY4qfo1P4Y0GM2Mkswn6Qh10Fbb\nhkd62BLZUlFgQun32RZsK/ludF1HCGFPfhYNS8OwaAQ9QTYNbSorMPNGntf2v0YkHaE50ExdoK5k\n5ZByMAyDl/tfpnekF6/XqybpUAetta3K+9cheIoJynQ+bddYrA/Ul12IJbNJDiQOsGFAFcQ+puUY\nOuo6aAu1VW0mTefTDCeHGUmNkDWytmbcEmqpej8ynU8TSUaU96lJoymoCrhXS8Paz+yP9/PLbb+k\n3l/PBxd8kI76joq/TUtQ1nprSekphhJDbA1vxaN5WNK5hGm10yb9+3b6FhzbdiwbXtvgCksXhxeH\nIixhvMBsCbZUFVeWzCaJpCP2fppP+BhJj+D3+jlu2nElJ7l0Ps2u6C7bRDbZQOzh5DCPbHyE7sZu\nLjn2kqrvc8K5ej2q5aiq7skbefpG++zqDdPrpvNW9K1xe4+WwCz0ki1Hc+uQmmCKxYFaz9iaVAMe\nJYidk6qUkt0ju4mmo3iEB13q5PI5+uJ9tNS0cPrs06t+n8OpYVs7CXgCtIZUOTHn5HvddddxxRVX\ncM455xSl4RyvoatE8k2hJk6deeoEntgX28frA6/TXtvO4mmLbceYQiHQEmyhqaap5OS74cAGNg9t\n5uiWo+0FUCQZIZpWjlCWIGnwN5DKp8YJSt3QGUqqqiJeTZVkK1wwpvNpRtIjRFNRRtOj7Evso85X\nx5KuJVVbOLL5LCPpkXGlxJpqmmgJtlS9NWHRiKajdrWV5mDzhAVBOeSNvBpHMmzTeLHvRfL5PKtO\nXEVjqHxKPyklw8lhBsZUKFLOyKGhEcvEQMDJ00+essVmz8geoqmobfX5Yw0dcZMSvIuhCY3WUCuR\nlNIyrADycrBSs81smEksHSOcCpPTc+yJ7WHv6F7mtc9jdsPsCQmvreLC1ZZsKkRbqI0zZp/Bb3f/\ndsrp8KwKKjujOzmQOFDRKcdZT9CZSmxu81y2h7ezN6YSrge8ARUfmIownBymJdhSNvG2ld5se3i7\nStpeP5NYNkYsEyOWjpE1shUnVSGEnUfUr/nxeDxEU1GyRpYtQ1vYM7qH5bOW01XXVTb5eMgfotvf\nTXdjt/0+rTqcAU+A+kA9TYEmNmzYUFRQWjScPBFNR8npOTYNb2LPyB6Wdi2lp6nHNt/vje9FSsnM\nhpm2VlbjraGroYuuhi4S2QThZJjhlIpP9Gt+6gP1djL1rJFlR3gHmwY3Mbd5LkumL7G/n5NGJKX2\nezenNuPz+JjdoErX1fvqiaQjSCnxCA+1vlp8Hh+GYag0g2aS+4yeQUPDp6lQq3kt82yv11IwDINk\nPmlXS7FolColVgqJbEKVI8vE7WxNjYFGehp7qjZvJrIJYumYvacKUO+vp6exh40HNiKl5JJ5l5QV\nlMlskkgqYpd7a/A30BZqY3pwOvl8HgPjkELFhpPDhFNhuhu7Dznc7EjD1SzfwThUzdKClJJoWiUu\naKppIuSbvBllJD1il8hqCqqgdmuCKyyllMwm6Yv1MZYbm3TOWisd3iXHXjLlUlyV9g2rGZ+VDs8Z\nO5nTc0RSyvuvNdRa1tSbzWfZEdnBtvA2GmsaaaxpHCecqqmuASodnqX11vprMQyDgcQAz/c9TzwT\nZ27TXJqDzeOETaVnbVVFcQpva8KfDI1IKsILvS8wlBhidvNsptVOQ5c6Gwc2sqBtASfPOLnsd7SE\n10jGFBq5FOGk0tBSuRTT66dz5uwzS9JI59OEx8JKC9I04pk4GT3DWGYMTdNoDKhn3hhsJJ6JE8/E\nbVO39V0xYPfoboK+IEe1HFX0nVpaWzwTJ56Nkzfytkm53l9PQ01DRbN/3sjbgs1Jo95fT2OgcVI0\n4tm4ncmpGI1tw9t4ZsczRdPZOSvijGZGyeQzxDNx6vx1zGycybRa5ayTyCbYHt5eNrVfJSSzSZW3\nusBT/Y9Vs3SF5TsYh0tYWhhNjzKWG6PeX1+V+3wx7I3tpTfai8/jo6GmgbHcGABBb1CVn/LVUeev\nw+/1M5wcZl98H4Zh0FnfWVXwfC6X47HtjxHJRPjA/A9M2eGnWAklK+Z0ODlMwBNgVsOssmauXdFd\njKZHx+3X6oZOOBW2035ZptB0Pk0im2AsO0YimyCjZwBsYXRix4lTTh4ey8RIZBO0BA+aabN6lvX7\n1jOUHGJW/SyC/qD9Lqy8rFY5sErWhGQ2yfJTl7Pqr1dx9nvPnhQNXdd5beA1+uP9dNV1sTOyk754\nH6fMO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BKvCVdRcOHChQsXLsrDjbN04cKFCxcuKsAVli5cuHDhwkUFuMLShQsX\nLly4qABXWLpw4cKFCxcV4ApLFy5cuHDhogJcYenChQsXLlxUgCssXbhw4cKFiwpwhaULFy5cuHBR\nAa6wdOHChQsXLirAFZYuXLhw4cJFBbjC0oULFy5cuKgAV1i6cOHChQsXFfD/AT+NR+JTugFOAAAA\nAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10b16b050>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "figoverlaps = hptvis.plotTilePointings(135, projection='cyl', paddingFactors=1,\n", " query='night < 365',\n", " dict(fill=False, color='g', alpha=0.1))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Faster evaluations with preComputedMap" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": true }, "outputs": [], "source": [ "hpchips = HealpixTiles(nside=128, preComputedMap=healpixelizedDB)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([187059, 187073, 194374, 194375, 194419, 194421, 195238, 195240,\n", " 195308, 195309, 196190, 196191, 196234, 196236, 196401, 196402,\n", " 196403, 200849, 201724, 201725, 201731, 201754, 203411, 203413,\n", " 203419, 203421, 203437, 203439, 203454, 203455, 203456, 203478,\n", " 203480, 204359, 204360, 204373, 204375, 204430, 205282, 205298,\n", " 206016, 206062, 206977, 210862, 210909, 210911, 210918, 211229,\n", " 211230, 211231, 211261, 211262, 211279, 211537, 211538, 212803,\n", " 212808, 212812, 214181, 214218, 214247, 215734, 215735, 215777,\n", " 215778, 215912, 215913, 218393, 218394, 218813, 218849, 220581,\n", " 220583, 220620, 220621, 225368, 225424, 230462, 230509, 230524,\n", " 230575, 232648, 232688, 233394, 233395, 233432, 233433, 234056,\n", " 234057, 234073, 234830, 234831, 234874, 234881, 235681, 235724,\n", " 235733, 235760, 235779, 235805, 236380, 236383, 236434, 236435,\n", " 239398, 239435, 243014, 243050, 244790, 246728, 246746, 248059,\n", " 251634, 251666, 251678, 253031, 253033, 253068, 253070, 253742,\n", " 253776, 254454, 254492, 255888, 255892, 255893, 266076, 266477,\n", " 266478, 266489, 266490, 266497])" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hpchips.pointingSequenceForTile(0)" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "132" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.unique(hpchips.pointingSequenceForTile(0)).size" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from opsimsummary import convertToSphericalCoordinates" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": true }, "outputs": [], "source": [ "angs = opsout.summary[['fieldRA', 'fieldDec']]\n", "ra, dec = angs.fieldRA.values, angs.fieldDec.values" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": true }, "outputs": [], "source": [ "convertToSphericalCoordinates??" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": true }, "outputs": [], "source": [ "theta, phi = convertToSphericalCoordinates(ra, dec, unit='radians')" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": true }, "outputs": [], "source": [ "vecs = hp.ang2vec(theta, phi)" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [], "source": [ "vec = hp.pix2vec(128, 0, nest=True)" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1859" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "opsout.summary.iloc[np.abs(np.dot(vecs, vec)).argmin()].fieldID" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": true }, "outputs": [], "source": [ "obsHistIDsStatic = opsout.summary.query('fieldID==1859').index.values" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def staticPoints(tileID):\n", " return obsHistIDsStatic" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([233783, 242822, 242847, 241228, 224988, 231482, 227466, 264801,\n", " 264811, 264825, 264714, 259219, 271100, 270319, 270276, 271594,\n", " 248626, 248582, 244304, 244350, 259175, 250707, 250675, 254369])" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "staticPoints(0)" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": false }, "outputs": [], "source": [ "hpchipsStatic = HealpixTiles(nside=128, preComputedMap=staticPoints)" ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([224988, 227466, 231482, 233783, 241228, 242822, 242847, 244304,\n", " 244350, 248582, 248626, 250675, 250707, 254369, 259175, 259219,\n", " 264714, 264801, 264811, 264825, 270276, 270319, 271100, 271594])" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.unique(hpchipsStatic.pointingSequenceForTile(0))" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": true }, "outputs": [], "source": [ "hpvis = HPTileVis(hpchips, opsout)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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XjAmOoWapmpxOP82hC4c8E10Zlb4Sp91l9xxfNbwqVSOqcjztOAeTD3rmgggN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\nde5Ro0Yxffp0DAYD03tMZ+W9KwF4bvV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rY/I91/UE4IN+H+Dr6qu2eeyrx9S/tYpWTcMs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\n48EvH+SR+o+wc3h1Lg5A8LJgUgtTEfMEy48vZ8a+GbjoXSh8rWbvw9C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HAAAg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\n+V89P8FgNEKY2wAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10d1443d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig2, tc, _ = hpvis.plotTilePointings(140785, projection='cea', paddingFactors=4,\n", " drawPointings=True,\n", " dict(fill=False, color='g', alpha=1.0))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Scratch" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false }, "outputs": [], "source": [ "figtile.savefig('HealpixTile_Pointings.pdf')" ] }, { "cell_type": "code", "execution_count": 276, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from opsimsummary import convertToCelestialCoordinates, healpix_boundaries, pixelsForAng\n", "from matplotlib.patches import Polygon" ] }, { "cell_type": "code", "execution_count": 371, "metadata": { "collapsed": false }, "outputs": [], "source": [ "class HPTileVis(HealpixTiles):\n", " def __init__(self, hpTile, opsout):\n", " \"\"\"\n", " \"\"\"\n", " self.hpTile = hpTile\n", " self.opsout = opsout\n", " self.nside = self.hpTile.nside\n", " \n", " def tileIDfromCelestialCoordinates(self, ra, dec, opsout, units='degrees'):\n", " \"\"\"\n", " Parameters\n", " -----------\n", " ra : \n", " dec : \n", " units: {'degrees', 'radians'}\n", " \"\"\"\n", " return pixelsForAng(lon=ra,lat=dec, unit=units )\n", " def tileCenter(self, tileID):\n", " theta, phi = hp.pix2ang(self.nside, tileID, nest=True)\n", " ra, dec = convertToCelestialCoordinates(theta, phi, input_unit='radians',\n", " output_unit='degrees')\n", " return ra, dec\n", " \n", " def pointingSummary(self, tileID, columns=('ditheredRA', 'ditheredDec'), \n", " allPointings=None):\n", " #if allPointings is None:\n", " # allPointings = opsout.summary\n", " # if query is not None:\n", " # allPointings = allPointings.query(query)\n", " # allPointings = allPointings.index.values\n", " obsHistIDs = self.hpTile.pointingSequenceForTile(tileID=tileID,\n", " allPointings=allPointings)\n", " return self.opsout.summary.ix[obsHistIDs]#[list(columns)]\n", " def pointingCenters(self,\n", " tileID,\n", " raCol='ditheredRA',\n", " decCol='ditheredDec',\n", " query=None):\n", " summary = self.pointingSummary(tileID)#, columns=[raCol, decCol])\n", " \n", " if query is not None:\n", " summary = summary.query(query)\n", " ra = summary[raCol].apply(np.degrees).values\n", " dec = summary[decCol].apply(np.degrees).values\n", " return ra, dec\n", " def plotTilePointings(self, tileID, raCol='ditheredRA', decCol='ditheredDec', radius=1.75,\n", " paddingFactors=1, query=None, ax=None, projection='cyl',*kwargs):\n", " \"\"\"\n", " Parameters\n", " ----------\n", " \n", " \"\"\"\n", " if ax is None:\n", " fig, ax = plt.subplots()\n", " padding = np.degrees(hp.max_pixrad(self.nside)) + radius\n", " ra_tile, dec_tile = self.tileCenter(tileID)\n", " \n", " llcrnrlat = dec_tile - padding paddingFactors\n", " urcrnrlat = dec_tile + padding * paddingFactors\n", " llcrnrlon = ra_tile - padding * paddingFactors\n", " urcrnrlon = ra_tile + padding * paddingFactors\n", " \n", " m = Basemap(llcrnrlat=llcrnrlat, llcrnrlon=llcrnrlon,\n", " urcrnrlat=urcrnrlat, urcrnrlon=urcrnrlon,\n", " projection=projection, lon_0=ra_tile, lat_0=dec_tile,\n", " ax=ax)\n", " \n", " parallels = np.linspace(llcrnrlat, urcrnrlat, 3)\n", " meridians = np.linspace(llcrnrlon, urcrnrlon, 3)\n", " m.drawparallels(parallels, labels=(1, 0, 0, 0)) #np.ones(len(parallels), dtype=bool))\n", " m.drawmeridians(meridians, labels=(0, 1, 1, 1)) #np.ones(len(meridians), dtype=bool))\n", " ra, dec = self.pointingCenters(tileID, raCol=raCol, decCol=decCol, query=query)\n", " lon, lat = healpix_boundaries(tileID, nside=self.nside, units='degrees',\n", " convention='celestial', step=10,\n", " nest=True)\n", " x, y = m(lon, lat)\n", " xy = zip(x, y)\n", " healpixels = Polygon(xy, facecolor='w',fill=False, alpha=1., edgecolor='k', lw=2)\n", " for ra, dec in zip(ra, dec):\n", " m.tissot(ra, dec, radius, 100, kwargs)\n", " ax.add_patch(healpixels)\n", " return fig" ] }, { "cell_type": "code", "execution_count": 365, "metadata": { "collapsed": true }, "outputs": [], "source": [ "tileID = pixelsForAng(54., -27.5, 4, unit='degrees')" ] }, { "cell_type": "code", "execution_count": 366, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([141])" ] }, "execution_count": 366, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tileID" ] }, { "cell_type": "code", "execution_count": 367, "metadata": { "collapsed": false }, "outputs": [], "source": [ "theta, phi = hp.pix2ang(hptiles.nside, 0, nest=True)\n", "ra, dec = oss.convertToCelestialCoordinates(theta, phi)" ] }, { "cell_type": "code", "execution_count": 368, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(array([ 45.]), array([ 9.59406823]))" ] }, "execution_count": 368, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "code", "execution_count": 372, "metadata": { "collapsed": true }, "outputs": [], "source": [ "hptvis = HPTileVis(hptiles, opsout)" ] }, { "cell_type": "code", "execution_count": 388, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from palettable.colorbrewer import sequential" ] }, { "cell_type": "code", "execution_count": 389, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import palettable" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "palettable" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from matplotlib.colorbar import " ] }, { "cell_type": "code", "execution_count": 81, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(<matplotlib.figure.Figure at 0x10ddf5450>,\n", " (202.49999999999997, -30.000000000000014),\n", " (-46.322230670077914,\n", " 186.17776932992206,\n", " -13.677769329922114,\n", " 218.82223067007789))" ] }, "execution_count": 81, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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kYe7cufJ/DFqtFj179sTBgwczPTYzTNbW1oqv9ebkx2TJ0wm8vLwAAPny5cPhw4dRu3Zt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\n+Ph4BAUFwcvLCwDg7u6OjRs3AgB69OgBNzc3PHz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"text/plain": [ "<matplotlib.figure.Figure at 0x10ddf5450>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "hptvis.plotTilePointings(, raCol='ditheredRA', decCol='ditheredDec', projection='gnom',\n", " query=None,#'night <3650',\n", " dict(fill=False, color='g', alpha=1., lw=1., ls='solid'))" ] }, { "cell_type": "code", "execution_count": 376, "metadata": { "collapsed": false }, "outputs": [ { "ename": "AttributeError", "evalue": "'HPTileVis' object has no attribute 'centers'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-376-409616ab5162>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mhptvis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcenters\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mAttributeError\u001b[0m: 'HPTileVis' object has no attribute 'centers'" ] } ], "source": [ "hptvis.centers(0)" ] }, { "cell_type": "code", "execution_count": 115, "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>ditheredRA</th>\n", " <th>ditheredDec</th>\n", " </tr>\n", " <tr>\n", " <th>obsHistID</th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>179157</th>\n", " <td>0.694620</td>\n", " <td>0.013076</td>\n", " </tr>\n", " <tr>\n", " <th>179167</th>\n", " <td>0.694620</td>\n", " <td>0.013076</td>\n", " </tr>\n", " <tr>\n", " <th>179965</th>\n", " <td>0.698441</td>\n", " <td>0.013076</td>\n", 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" <td>0.842995</td>\n", " <td>0.036650</td>\n", " </tr>\n", " <tr>\n", " <th>266500</th>\n", " <td>0.897292</td>\n", " <td>0.032167</td>\n", " </tr>\n", " <tr>\n", " <th>269955</th>\n", " <td>0.865914</td>\n", " <td>0.036650</td>\n", " </tr>\n", " <tr>\n", " <th>269956</th>\n", " <td>0.811530</td>\n", " <td>0.040703</td>\n", " </tr>\n", " <tr>\n", " <th>269957</th>\n", " <td>0.756984</td>\n", " <td>0.043981</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>538 rows × 2 columns</p>\n", "</div>" ], "text/plain": [ " ditheredRA ditheredDec\n", "obsHistID \n", "179157 0.694620 0.013076\n", "179167 0.694620 0.013076\n", "179965 0.698441 0.013076\n", "179998 0.698441 0.013076\n", "180029 0.698441 0.013076\n", "180068 0.698441 0.013076\n", "181880 0.669784 0.016382\n", "182972 0.728329 0.014223\n", "183105 0.673605 0.016382\n", "183107 0.728329 0.014223\n", "183114 0.673605 0.016382\n", "183911 0.677426 0.016382\n", "183913 0.732150 0.014223\n", "183946 0.677426 0.016382\n", "183948 0.732150 0.014223\n", "183962 0.732150 0.014223\n", "183964 0.677426 0.016382\n", "183989 0.677426 0.016382\n", "183990 0.732150 0.014223\n", "184010 0.732150 0.014223\n", "184068 0.732150 0.014223\n", "184871 0.764154 -0.030991\n", "184872 0.735971 0.014223\n", "184873 0.681247 0.016382\n", "184985 0.681247 0.016382\n", "184987 0.735971 0.014223\n", "184988 0.764154 -0.030991\n", "185002 0.764154 -0.030991\n", "185003 0.735971 0.014223\n", "185052 0.764154 -0.030991\n", "... ... ...\n", "258058 0.910659 0.028860\n", "262233 0.656411 0.046140\n", "262249 0.656411 0.046140\n", "262250 0.711137 0.043981\n", "262251 0.739337 -0.001233\n", "262252 0.765688 0.040703\n", "262253 0.739337 -0.001233\n", "262254 0.711137 0.043981\n", "262255 0.656411 0.046140\n", "263659 0.882015 0.032167\n", "263691 0.882015 0.032167\n", "266074 0.893473 0.032167\n", "266075 0.839176 0.036650\n", "266076 0.813114 -0.005157\n", "266077 0.786796 -0.046716\n", "266477 0.788609 0.040703\n", "266478 0.762245 -0.001233\n", "266482 0.707293 0.001730\n", "266483 0.679336 0.046140\n", "266487 0.679336 0.046140\n", "266488 0.734060 0.043981\n", "266489 0.788609 0.040703\n", "266490 0.816933 -0.005157\n", "266491 0.842995 0.036650\n", "266497 0.816933 -0.005157\n", "266498 0.842995 0.036650\n", "266500 0.897292 0.032167\n", "269955 0.865914 0.036650\n", "269956 0.811530 0.040703\n", "269957 0.756984 0.043981\n", "\n", "[538 rows x 2 columns]" ] }, "execution_count": 115, "metadata": {}, "output_type": "execute_result" } ], "source": [ "opsout.summary.ix[hpTileshpOpSim.pointingSequenceForTile(0, allPointings=None)][['ditheredRA', 'ditheredDec']]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [], "source": [ "phi, theta = hpTileshpOpSim.positions(1, 10000)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "mapvals = np.ones(hp.nside2npix(NSIDE)) * hp.UNSEEN" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true }, "outputs": [], "source": [ "mapvals[1] = 100" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([13, 13, 13, ..., 13, 15, 15])" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hp.ang2pix(NSIDE, np.radians(theta), np.radians(phi), nest=True)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true }, "outputs": [], "source": [ "theta_c, phi_c = hp.pix2ang(4, 1, nest=True)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.collections.PathCollection at 0x10d1be190>" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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/f3+efvrp7N69O4cOHcqBAwdy/fXX5/rrrze7BkyUgKNFRFtEGzNOqDFKCwsL+cQnPpHP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\nuwAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x108cfde90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "hp.mollview(mapvals, nest=True)\n", "hp.projscatter(np.radians(theta), np.radians(phi), dict(s=0.0002))\n", "hp.projscatter(theta_c, phi_c, dict(s=8., c='r'))" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "100 loops, best of 3: 2.29 ms per loop\n" ] } ], "source": [ "%timeit hpTileshpOpSim.pointingSequenceForTile(33, allPointings=None)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": true }, "outputs": [], "source": [ "preCompMap = os.path.join(oss.__path__[0], 'example_data', 'healpixels_micro.db')" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import os\n", "os.path.exists(preCompMap)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [], "source": [ "hpTilesMap = HealpixTiles(nside=1, preComputedMap=preCompMap)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "ename": "OperationalError", "evalue": "(sqlite3.OperationalError) no such table: simlib [SQL: 'SELECT obsHistID FROM simlib WHERE ipix == 10']", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mOperationalError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-25-d1c704331bce>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mobsHistIDs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhpTilesMap\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpointingSequenceForTile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallPointings\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m/Users/rbiswas/.local/lib/python2.7/site-packages/opsimsummary/healpixTiles.pyc\u001b[0m in \u001b[0;36mpointingSequenceForTile\u001b[0;34m(self, tileID, allPointings, columns, *kwargs)\u001b[0m\n\u001b[1;32m 124\u001b[0m \u001b[0mobsHistIDs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 125\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpreComputedMap\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 126\u001b[0;31m \u001b[0mobsHistIDs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pointingFromPrecomputedDB\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtileID\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtableName\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'simlib'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 127\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhpOpSim\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 128\u001b[0m \u001b[0mobsHistIDs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pointingFromHpOpSim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtileID\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/rbiswas/.local/lib/python2.7/site-packages/opsimsummary/healpixTiles.pyc\u001b[0m in \u001b[0;36m_pointingFromPrecomputedDB\u001b[0;34m(self, tileID, tableName)\u001b[0m\n\u001b[1;32m 90\u001b[0m \u001b[0msql\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'SELECT obsHistID FROM {0} WHERE ipix == {1}'\u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 91\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtableName\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtileID\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 92\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_sql_query\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msql\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcon\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpreComputedEngine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 93\u001b[0m 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332\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 333\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/miniconda/lib/python2.7/site-packages/pandas/io/sql.pyc\u001b[0m in \u001b[0;36mread_query\u001b[0;34m(self, sql, index_col, coerce_float, parse_dates, params, chunksize)\u001b[0m\n\u001b[1;32m 1082\u001b[0m \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_convert_params\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msql\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1083\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1084\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\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 1085\u001b[0m \u001b[0mcolumns\u001b[0m \u001b[0;34m=\u001b[0m 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"collapsed": true }, "outputs": [], "source": [ "from sqlalchemy import create_engine" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [], "source": [ "engine = create_engine('sqlite:////' + preCompMap)" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "ename": "OperationalError", "evalue": "(sqlite3.OperationalError) no such table: simlib [SQL: 'SELECT FROM simlib Limit 5']", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mOperationalError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-38-9d8a6e1fe67f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m 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parse_dates=parse_dates, chunksize=chunksize)\n\u001b[0m\u001b[1;32m 332\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 333\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/miniconda/lib/python2.7/site-packages/pandas/io/sql.pyc\u001b[0m in \u001b[0;36mread_query\u001b[0;34m(self, sql, index_col, coerce_float, parse_dates, params, chunksize)\u001b[0m\n\u001b[1;32m 1082\u001b[0m \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_convert_params\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msql\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1083\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1084\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\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 1085\u001b[0m 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\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontextual_connect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclose_with_result\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1991\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mconnection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstatement\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\u001b[0m\u001b[0mmultiparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1992\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1993\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mscalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatement\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\u001b[0m\u001b[0mmultiparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/rbiswas/soft/LSST_el_capitan/DarwinX86/sqlalchemy/1.0.8.lsst3+2/lib/python/SQLAlchemy-1.0.8-py2.7-macosx-10.6-x86_64.egg/sqlalchemy/engine/base.pyc\u001b[0m in \u001b[0;36mexecute\u001b[0;34m(self, object, multiparams, *params)\u001b[0m\n\u001b[1;32m 904\u001b[0m \"\"\"\n\u001b[1;32m 905\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobject\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mutil\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstring_types\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 906\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_execute_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobject\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmultiparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 907\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 908\u001b[0m \u001b[0mmeth\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_execute_on_connection\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/rbiswas/soft/LSST_el_capitan/DarwinX86/sqlalchemy/1.0.8.lsst3+2/lib/python/SQLAlchemy-1.0.8-py2.7-macosx-10.6-x86_64.egg/sqlalchemy/engine/base.pyc\u001b[0m in \u001b[0;36m_execute_text\u001b[0;34m(self, statement, multiparams, params)\u001b[0m\n\u001b[1;32m 1052\u001b[0m \u001b[0mstatement\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1053\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1054\u001b[0;31m \u001b[0mstatement\u001b[0m\u001b[0;34m,\u001b[0m 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1147\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1148\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_has_events\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_has_events\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/rbiswas/soft/LSST_el_capitan/DarwinX86/sqlalchemy/1.0.8.lsst3+2/lib/python/SQLAlchemy-1.0.8-py2.7-macosx-10.6-x86_64.egg/sqlalchemy/engine/base.pyc\u001b[0m in \u001b[0;36m_handle_dbapi_exception\u001b[0;34m(self, e, statement, parameters, cursor, context)\u001b[0m\n\u001b[1;32m 1339\u001b[0m util.raise_from_cause(\n\u001b[1;32m 1340\u001b[0m \u001b[0msqlalchemy_exception\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1341\u001b[0;31m \u001b[0mexc_info\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1342\u001b[0m )\n\u001b[1;32m 1343\u001b[0m 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\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[1;32m 1141\u001b[0m self._handle_dbapi_exception(\n", "\u001b[0;32m/Users/rbiswas/soft/LSST_el_capitan/DarwinX86/sqlalchemy/1.0.8.lsst3+2/lib/python/SQLAlchemy-1.0.8-py2.7-macosx-10.6-x86_64.egg/sqlalchemy/engine/default.pyc\u001b[0m in \u001b[0;36mdo_execute\u001b[0;34m(self, cursor, statement, parameters, context)\u001b[0m\n\u001b[1;32m 448\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 449\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mdo_execute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcursor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatement\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 450\u001b[0;31m 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[ { "data": { "text/plain": [ "'/Users/rbiswas/.local/lib/python2.7/site-packages/opsimsummary/example_data/healpixels_micro.db'" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "preCompMap" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "100 loops, best of 3: 2.49 ms per loop\n" ] } ], "source": [ "%timeit hpOpSim.obsHistIdsForTile(34)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": true }, "outputs": [], "source": [ "hpTiles = HealpixTiles(healpixelizedOpSim=hpOpSim)" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([], dtype=int64)" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hpTiles.pointingSequenceForTile(34, allPointings=None)" ] }, { "cell_type": "code", "execution_count": 390, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df = opsout.summary.copy()" ] }, { "cell_type": "code", "execution_count": 393, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2264" ] }, "execution_count": 393, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.fieldID.unique().size" ] }, { "cell_type": "code", "execution_count": 398, "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>fieldRA</th>\n", " <th>fieldDec</th>\n", " <th>ditheredRA</th>\n", " <th>ditheredDec</th>\n", " <th>expMJD</th>\n", " <th>filter</th>\n", " </tr>\n", " <tr>\n", " <th>obsHistID</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>175159</th>\n", " <td>0.0</td>\n", " <td>-0.794553</td>\n", " <td>6.277736</td>\n", " <td>-0.821004</td>\n", " <td>49573.415395</td>\n", " <td>u</td>\n", " </tr>\n", " <tr>\n", " <th>175744</th>\n", " <td>0.0</td>\n", " <td>-0.794553</td>\n", " <td>0.000000</td>\n", " <td>-0.821004</td>\n", " <td>49574.225892</td>\n", " <td>u</td>\n", " </tr>\n", " <tr>\n", " <th>175746</th>\n", " <td>0.0</td>\n", " <td>-0.794553</td>\n", " <td>0.000000</td>\n", " <td>-0.821004</td>\n", " <td>49574.226725</td>\n", " <td>u</td>\n", " </tr>\n", " <tr>\n", " <th>175748</th>\n", " <td>0.0</td>\n", " <td>-0.794553</td>\n", " <td>0.000000</td>\n", " <td>-0.821004</td>\n", " <td>49574.227570</td>\n", " <td>u</td>\n", " </tr>\n", " <tr>\n", " <th>175750</th>\n", " <td>0.0</td>\n", " <td>-0.794553</td>\n", " <td>0.000000</td>\n", " <td>-0.821004</td>\n", " <td>49574.228403</td>\n", " <td>u</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " fieldRA fieldDec ditheredRA ditheredDec expMJD filter\n", "obsHistID \n", "175159 0.0 -0.794553 6.277736 -0.821004 49573.415395 u\n", "175744 0.0 -0.794553 0.000000 -0.821004 49574.225892 u\n", "175746 0.0 -0.794553 0.000000 -0.821004 49574.226725 u\n", "175748 0.0 -0.794553 0.000000 -0.821004 49574.227570 u\n", "175750 0.0 -0.794553 0.000000 -0.821004 49574.228403 u" ] }, "execution_count": 398, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.query('fieldID == 744')[['fieldRA', 'fieldDec', 'ditheredRA', 'ditheredDec', 'expMJD', 'filter']].head()" ] }, { "cell_type": "code", "execution_count": 399, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/miniconda/lib/python2.7/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": [ "df.query('fieldID == 744')['ditheredDec'] = df.query('fieldID == 744')['fieldDec']" ] }, { "cell_type": "code", "execution_count": 405, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def fixdec(mydf, fieldIDs):\n", " return mydf.fieldID.apply(lambda x: x in fieldIDs)" ] }, { "cell_type": "code", "execution_count": 407, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "obsHistID\n", "171077 0\n", "171078 0\n", "171079 0\n", "171080 0\n", "171081 0\n", "171082 0\n", "171083 0\n", "171084 0\n", "171085 0\n", "171086 0\n", "171087 0\n", "171088 0\n", "171089 0\n", "171090 0\n", "171091 0\n", "171092 0\n", "171093 0\n", "171094 0\n", "171095 0\n", "171096 0\n", "171097 0\n", "171098 0\n", "171099 0\n", "171100 0\n", "171101 0\n", "171102 0\n", "171103 0\n", "171104 0\n", "171105 0\n", "171106 0\n", " ..\n", "272045 1\n", "272046 1\n", "272047 1\n", "272048 1\n", "272049 1\n", "272050 1\n", "272051 1\n", "272052 1\n", "272053 1\n", "272054 1\n", "272405 0\n", "272406 0\n", "272407 0\n", "272408 0\n", "272409 0\n", "272410 0\n", "272411 0\n", "272412 0\n", "272413 0\n", "272414 0\n", "272415 0\n", "272416 0\n", "272417 0\n", "272418 0\n", "272419 0\n", "272420 0\n", "272421 0\n", "272422 0\n", "272423 0\n", "272424 0\n", "Name: fieldID, dtype: int64" ] }, "execution_count": 407, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fixdec(df, (744, 1427, 290)).astype(np.int) * df.fieldRA + (1.0 - fixdec(df, (744, 1427, 290)).astype(np.int))" ] }, { "cell_type": "code", "execution_count": 400, "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>fieldRA</th>\n", " <th>fieldDec</th>\n", " <th>ditheredRA</th>\n", " <th>ditheredDec</th>\n", " <th>expMJD</th>\n", " <th>filter</th>\n", " </tr>\n", " <tr>\n", " <th>obsHistID</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>175159</th>\n", " <td>0.0</td>\n", " <td>-0.794553</td>\n", " <td>6.277736</td>\n", " <td>-0.821004</td>\n", " <td>49573.415395</td>\n", " <td>u</td>\n", " </tr>\n", " <tr>\n", " <th>175744</th>\n", " <td>0.0</td>\n", " <td>-0.794553</td>\n", " <td>0.000000</td>\n", " <td>-0.821004</td>\n", " <td>49574.225892</td>\n", " <td>u</td>\n", " </tr>\n", " <tr>\n", " <th>175746</th>\n", " <td>0.0</td>\n", " <td>-0.794553</td>\n", " <td>0.000000</td>\n", " <td>-0.821004</td>\n", " <td>49574.226725</td>\n", " <td>u</td>\n", " </tr>\n", " <tr>\n", " <th>175748</th>\n", " <td>0.0</td>\n", " <td>-0.794553</td>\n", " <td>0.000000</td>\n", " <td>-0.821004</td>\n", " <td>49574.227570</td>\n", " <td>u</td>\n", " </tr>\n", " <tr>\n", " <th>175750</th>\n", " <td>0.0</td>\n", " <td>-0.794553</td>\n", " <td>0.000000</td>\n", " <td>-0.821004</td>\n", " <td>49574.228403</td>\n", " <td>u</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " fieldRA fieldDec ditheredRA ditheredDec expMJD filter\n", "obsHistID \n", "175159 0.0 -0.794553 6.277736 -0.821004 49573.415395 u\n", "175744 0.0 -0.794553 0.000000 -0.821004 49574.225892 u\n", "175746 0.0 -0.794553 0.000000 -0.821004 49574.226725 u\n", "175748 0.0 -0.794553 0.000000 -0.821004 49574.227570 u\n", "175750 0.0 -0.794553 0.000000 -0.821004 49574.228403 u" ] }, "execution_count": 400, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.query('fieldID == 744')[['fieldRA', 'fieldDec', 'ditheredRA', 'ditheredDec', 'expMJD', 'filter']].head()" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df = opsout.summary.copy()" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": false }, "outputs": [], "source": [ "grouped = df.groupby('propID')" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[364, 366]" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ "grouped.groups.keys()" ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "obsHistID\n", "231872 0.925184\n", "231873 0.925184\n", "231874 0.925184\n", "231875 0.925184\n", "231876 0.925184\n", "231877 0.925184\n", "231878 0.925184\n", "231879 0.925184\n", "231880 0.925184\n", "231881 0.925184\n", "231882 0.925184\n", "231883 0.925184\n", "231884 0.925184\n", "231885 0.925184\n", "231886 0.925184\n", "231887 0.925184\n", "231888 0.925184\n", "231889 0.925184\n", "231890 0.925184\n", "231892 0.925184\n", "231893 0.925184\n", "231894 0.925184\n", "231895 0.925184\n", "231896 0.925184\n", "231897 0.925184\n", "231898 0.925184\n", "231899 0.925184\n", "231900 0.925184\n", "231902 0.925184\n", "231903 0.925184\n", " ... \n", "272045 0.925184\n", "272046 0.925184\n", "272047 0.925184\n", "272048 0.925184\n", "272049 0.925184\n", "272050 0.925184\n", "272051 0.925184\n", "272052 0.925184\n", "272053 0.925184\n", "272054 0.925184\n", "272405 2.624318\n", "272406 2.624318\n", "272407 2.624318\n", "272408 2.624318\n", "272409 2.624318\n", "272410 2.624318\n", "272411 2.624318\n", "272412 2.624318\n", "272413 2.624318\n", "272414 2.624318\n", "272415 2.624318\n", "272416 2.624318\n", "272417 2.624318\n", "272418 2.624318\n", "272419 2.624318\n", "272420 2.624318\n", "272421 2.624318\n", "272422 2.624318\n", "272423 2.624318\n", "272424 2.624318\n", "Name: fieldRA, dtype: float64" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "grouped.get_group(366).fieldRA" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
corpusmusic/bb-cluster	obsolete_scripts/kmeans_visualization.ipynb	1	2261180	null	gpl-3.0
josePhoenix/wfirst-tools	notebooks/WFIRST GalSim Demo.ipynb	1	1312613	null	bsd-3-clause
robinzhoucmu/MLab_EXP	Mocap/PythonExp/triangle_pushing.ipynb	1	15208	{ "cells": [ { "cell_type": "code", "execution_count": 355, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import sys\n", "import time\n", "import subprocess\n", "import signal\n", "import os\n", "import math\n", "import rospy\n", "import rosbag\n", "import roslib\n", "roslib.load_manifest(\"robot_comm\")\n", "from robot_comm.srv import \n", "roslib.load_manifest(\"Mocap\")\n", "from Mocap.msg import \n", "from Mocap.srv import \n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 356, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "x: 611.62\n", "y: 435.75\n", "z: 298.72\n", "q0: 0.0004\n", "qx: -0.7058\n", "qy: -0.7084\n", "qz: 0.0004\n", "ret: 1\n", "msg: ROBOT_CONTROLLER: OK." ] }, "execution_count": 356, "metadata": {}, "output_type": "execute_result" } ], "source": [ "get_cart = rospy.ServiceProxy('/robot_GetCartesian', robot_GetCartesian)\n", "set_work_object = rospy.ServiceProxy('/robot_SetWorkObject', robot_SetWorkObject)\n", "set_tool = rospy.ServiceProxy('/robot_SetTool', robot_SetTool)\n", "set_cart = rospy.ServiceProxy('/robot_SetCartesian', robot_SetCartesian)\n", "#Set Speed\n", "set_speed = rospy.ServiceProxy('/robot_SetSpeed', robot_SetSpeed)\n", "set_speed(10, 5)\n", "get_cart()" ] }, { "cell_type": "code", "execution_count": 357, "metadata": { "collapsed": false }, "outputs": [], "source": [ "set_mocap_tf = rospy.ServiceProxy('/mocap_SetMocapTransformation', mocap_SetMocapTransformation)\n", "#set_mocap_tf(666.06, -256.24, 55.61, 0.4922, 0.49687, 0.50185, 0.50893)\n", "#set_mocap_tf(666.16, -256.27, 55.682, 0.49236, 0.49671, 0.50187, 0.50891)\n", "set_mocap_tf(668.54, -260.52, 56.021, 0.49366, 0.49611, 0.50222, 0.50788)\n", "get_mocap_frame = rospy.ServiceProxy('/mocap_GetMocapFrame', mocap_GetMocapFrame)" ] }, { "cell_type": "code", "execution_count": 361, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "mf: \n", " header: \n", " seq: 0\n", " stamp: \n", " secs: 1435534323\n", " nsecs: 723603061\n", " frame_id: ''\n", " number: 0\n", " body_poses: \n", " - \n", " position: \n", " x: 528.757469199\n", " y: -6.80080365991\n", " z: 45.2287401263\n", " orientation: \n", " x: 0.300642320697\n", " y: 0.635914438094\n", " z: 0.637346032424\n", " w: 0.314656298086\n", " uid_markers: \n", " markers: \n", " - \n", " x: 508.782899253\n", " y: 20.262938234\n", " z: 46.3141539341\n", " - \n", " x: 513.558035291\n", " y: -19.1137189706\n", " z: 46.033033762\n", " - \n", " x: 534.19698005\n", " y: -10.6241021869\n", " z: 45.3271757493\n", " - \n", " x: 558.492563078\n", " y: -17.3683924654\n", " z: 46.8091925996\n", " id_marker_sets: \n", " - \n", " markers: \n", " - \n", " x: 508.751215807\n", " y: 20.3087536706\n", " z: 46.3157019833\n", " - \n", " x: 513.580022143\n", " y: -19.1265782548\n", " z: 46.0344851867\n", " - \n", " x: 534.570077159\n", " y: -10.7999228692\n", " z: 45.3303411589\n", " - \n", " x: 558.328886191\n", " y: -17.3387908455\n", " z: 46.8027161403\n", "ret: 1\n", "msg: OK." ] }, "execution_count": 361, "metadata": {}, "output_type": "execute_result" } ], "source": [ "get_mocap_frame()" ] }, { "cell_type": "code", "execution_count": 359, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "x: 500.0\n", "y: 0.0\n", "z: 290.0\n", "q0: 1.0\n", "qx: 0.0\n", "qy: 0.0\n", "qz: 0.0\n", "ret: 1\n", "msg: ROBOT_CONTROLLER: OK." ] }, "execution_count": 359, "metadata": {}, "output_type": "execute_result" } ], "source": [ "set_work_object(0,0,0,1,0,0,0)\n", "set_tool(-125,0,115, 0, 0, 1, 0)\n", "get_cart()" ] }, { "cell_type": "code", "execution_count": 375, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "ret: 1\n", "msg: ROBOT_CONTROLLER: OK." ] }, "execution_count": 375, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Set to pre-push position.\n", "set_speed(10,5)\n", "set_cart(500, 0, 25, 1, 0, 0, 0)\n", "set_speed(5,5)" ] }, { "cell_type": "code", "execution_count": 372, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "x: 500.0\n", "y: 0.0\n", "z: 25.0\n", "q0: 1.0\n", "qx: 0.0\n", "qy: 0.0\n", "qz: 0.0\n", "ret: 1\n", "msg: ROBOT_CONTROLLER: OK." ] }, "execution_count": 372, "metadata": {}, "output_type": "execute_result" } ], "source": [ "get_cart()" ] }, { "cell_type": "code", "execution_count": 321, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 499.99, 0. , 15. ])" ] }, "execution_count": 321, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Set push end position. +x for 50 mm. \n", "# set_cart(550,0,15,1,0,0,0)\n", "# rosbag command\n", "# rosbag record -O bags/robot_push_many.bag /Mocap /netft_data /robot_CartesianLog\n", "#np.random.uniform(0,1,(3,1))\n", "cur_robot_pos" ] }, { "cell_type": "code", "execution_count": 376, "metadata": { "collapsed": false }, "outputs": [], "source": [ "np.random.seed(10)\n", "dir = np.array([[1, 0, 0],[0, -1, 0],[0, 1, 0]]).transpose()\n", "num_pushes = 10\n", "dist_push = 30 # mm\n", "for i in range(0,num_pushes):\n", " #c = np.random.rand(3,1)\n", " c = np.random.uniform(0,1,(3,1))\n", " c = c / c.sum()\n", " rand_disp = dir.dot(np.random.rand(3,1))\n", " #print rand_disp\n", " r_cart = get_cart()\n", " cur_robot_pos = np.array([r_cart.x, r_cart.y, r_cart.z])\n", " nxt_robot_pos = cur_robot_pos + dist_push rand_disp.transpose()\n", " #Sample a random angle between -theta to theta.\n", " angle = np.random.uniform(-np.pi/12,np.pi/12)\n", " if (nxt_robot_pos[0,2] > 24.99 and cur_robot_pos[2] > 24.99): \n", " set_cart(cur_robot_pos[0], cur_robot_pos[1], cur_robot_pos[2], np.cos(angle), 0, 0, np.sin(angle))\n", " time.sleep(0.75)\n", " set_cart(nxt_robot_pos[0,0], nxt_robot_pos[0,1], nxt_robot_pos[0,2], np.cos(angle), 0, 0, np.sin(angle))\n", " #print get_cart()\n", " time.sleep(0.75)\n", " #back up a little bit. \n", " r_cart = get_cart()\n", " cur_robot_pos = np.array([r_cart.x, r_cart.y, r_cart.z])\n", " nxt_robot_pos = cur_robot_pos - 2 * rand_disp.transpose()\n", " #if (nxt_robot_pos[0,2] > 14.99):\n", " # set_cart(nxt_robot_pos[0,0], nxt_robot_pos[0,1], nxt_robot_pos[0,2], np.cos(angle), 0, 0, np.sin(angle))\n", " #set_cart(nxt_robot_pos[0,0], nxt_robot_pos[0,1], nxt_robot_pos[0,2], 1, 0, 0, 0)" ] }, { "cell_type": "code", "execution_count": 304, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "10" ] }, "execution_count": 304, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.random.uniform(-np.pi/4,np.pi/4)\n", "num_pushes" ] }, { "cell_type": "code", "execution_count": 116, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'/home/jiaji/MLab_EXP/Mocap/PythonExp'" ] }, "execution_count": 116, "metadata": {}, "output_type": "execute_result" } ], "source": [ "os.getcwd()" ] }, { "cell_type": "code", "execution_count": 377, "metadata": { "collapsed": false }, "outputs": [], "source": [ "f1 = open(\"exp4/pos3.txt\", \"w\");\n", "f2 = open(\"exp4/force3.txt\", \"w\");\n", "f3 = open(\"exp4/robot3.txt\", \"w\");\n", "bag = rosbag.Bag('bags/robot_multi_push_3out_3.bag')\n", "last_topic = '/netft_data'\n", "ct = 0\n", "last_pos_x = 0\n", "last_pos_y = 0\n", "pose = geometry_msgs.msg.Pose()\n", "force = geometry_msgs.msg.Wrench.force\n", "for topic, msg, t in bag.read_messages(topics=['/Mocap', '/netft_data', '/robot_CartesianLog']):\n", " s = str(t.to_sec()) + '\\n'\n", " if topic == '/Mocap':\n", " pos = msg.body_poses[0].position\n", " ori = msg.body_poses[0].orientation \n", " s = s + str(pos.x) + ' ' + str(pos.y) + ' ' + str(pos.z) + ' '\n", " #Use q0/qw,qx,qy,qz convention.\n", " s = s + str(ori.w) + ' ' + str(ori.x) + ' ' + str(ori.y) + ' ' + str(ori.z)\n", " s = s + '\\n'\n", " f1.write(s); \n", " if topic == '/netft_data':\n", " force = msg.wrench.force\n", " s = s + str(force.x) + ' ' + str(force.y) + ' ' + str(force.z)\n", " s = s + '\\n'\n", " f2.write(s)\n", " if topic == '/robot_CartesianLog':\n", " robot_cart = msg\n", " s = s + str(robot_cart.x) + ' ' + str(robot_cart.y) + ' ' + str(robot_cart.z) + ' '\n", " s = s + str(robot_cart.q0) + ' ' + str(robot_cart.qx) + ' ' + str(robot_cart.qy) + ' ' + str(robot_cart.qz)\n", " s = s + '\\n'\n", " f3.write(s)\n", "bag.close()\n", "f1.close()\n", "f2.close()\n", "f3.close()" ] }, { "cell_type": "code", "execution_count": 278, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "ret: 1\n", "msg: ROBOT_CONTROLLER: OK." ] }, "execution_count": 278, "metadata": {}, "output_type": "execute_result" } ], "source": [ "set_cart(575,0,290,np.sqrt(2)/2,0,0,np.sqrt(2)/2) # Point to the left. +y" ] }, { "cell_type": "code", "execution_count": 281, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "ret: 1\n", "msg: ROBOT_CONTROLLER: OK." ] }, "execution_count": 281, "metadata": {}, "output_type": "execute_result" } ], "source": [ "set_cart(575,0,290,np.sqrt(2)/2,0,0,-np.sqrt(2)/2) # Point to the right. -y\n" ] }, { "cell_type": "code", "execution_count": 354, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "ret: 1\n", "msg: ROBOT_CONTROLLER: OK." ] }, "execution_count": 354, "metadata": {}, "output_type": "execute_result" } ], "source": [ "set_cart(500,0,290,1,0,0,0) # Point to the left. +y" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Just dump the raw mocap body data.\n", "f1 = open(\"mocap_pos_5.txt\", \"w\")\n", "bag = rosbag.Bag('test_human_push_5.bag')\n", "for topic, msg, t in bag.read_messages(topics=['/Mocap']):\n", " if topic == '/Mocap':\n", " pos = msg.body_poses[0].position\n", " ori = msg.body_poses[0].orientation\n", " s = str(t.to_sec()) + '\\n'\n", " s = s + str(pos.x) + ' ' + str(pos.y) + ' ' + str(pos.z) + ' '\n", " s = s + str(ori.w) + ' ' + str(ori.x) + ' ' + str(ori.y) + ' ' + str(ori.z)\n", " s = s + '\\n'\n", " f1.write(s);\n", "f1.close()" ] }, { "cell_type": "code", "execution_count": 86, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1430178331.7931406" ] }, "execution_count": 86, "metadata": {}, "output_type": "execute_result" } ], "source": [ "t.to_sec()" ] }, { "cell_type": "code", "execution_count": 122, "metadata": { "collapsed": true }, "outputs": [], "source": [ "set_tool(-100, 0, 110, 0, 0, 1, 0);\n" ] }, { "cell_type": "code", "execution_count": 121, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "x: 549.99\n", "y: -99.97\n", "z: 375.0\n", "q0: 0.0\n", "qx: -0.7071\n", "qy: 0.7071\n", "qz: 0.0001\n", "ret: 1\n", "msg: ROBOT_CONTROLLER: OK." ] }, "execution_count": 121, "metadata": {}, "output_type": "execute_result" } ], "source": [ "set_cart(550, -100, 375, 1, 0, 0, 0)" ] }, { "cell_type": "code", "execution_count": 123, "metadata": { "collapsed": true }, "outputs": [], "source": [ "get_joints = rospy.ServiceProxy('/robot_GetJoints', robot_GetJoints)" ] }, { "cell_type": "code", "execution_count": 124, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "j1: 0.0\n", "j2: 20.55\n", "j3: 0.92\n", "j4: 0.01\n", "j5: 68.54\n", "j6: 90.0\n", "ret: 1\n", "msg: ROBOT_CONTROLLER: OK." ] }, "execution_count": 124, "metadata": {}, "output_type": "execute_result" } ], "source": [ "get_joints()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.3" } }, "nbformat": 4, "nbformat_minor": 0 }	apache-2.0
eggie5/UCSD-MAS-DSE220	IRI Explore.ipynb	1	56490	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Panel data sets: Category_PANEL_outlet_startweek_endweek.dat\n", "\n", "Panel data is provided for two BehaviorScan markets, Eau Claire, Wisconsin and Pittsfield, Massachusetts.\n", "The naming convention for these is category name then “panel” then outlet then start week and then end week, all separated by underscores, with the extension DAT, so salted snacks drug data for the earliest year would be saltsnck_PANEL_DR_1114_1165\n", "\n", "\n", "PANID\n", "panelist number within a market\n", "\n", "\n", "UNITS\n", "Total number of units purchased by the Buying households.\n", "The sum of total units purchased by the households buying the Product.\n", "\n", "\n", "OUTLET\n", "Channel to which the store/chain belongs MA=Mass\n", "GR=Grocery\n", "DR=drug\n", "DOLLARS\n", "\n", "\n", "Total Paid dollars\n", "This is drawn from the store data, not entered by the panelist, in cases where IRI has store data. In cases where IRI does not receive store data, some panelists do\n", "\n", "\n", "IRI_KEY\n", "Masked store number\n", "\n", "\n", "WEEK\n", "IRI WEEK\n", "\n", "\n", "\n", "COLUPC\n", "(Collapsed UPC). This is the UPC which matches the internal form (e.g. private label collapsed). The information in COLUPC is the same as in the combination of SY, GE, VEND, ITE.\n", "This is the combination of a upc’s system (2 digits), generation (1 digit), vendor (5 digits) and item (5 digits) fields. See product description section for an explanation of these fields. No leading zeroes are shown." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>PANID</th>\n", " <th>WEEK</th>\n", " <th>UNITS</th>\n", " <th>OUTLET</th>\n", " <th>DOLLARS</th>\n", " <th>IRI_KEY</th>\n", " <th>COLUPC</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1212076</td>\n", " <td>1133</td>\n", " <td>1</td>\n", " <td>DR</td>\n", " <td>0.59</td>\n", " <td>8003042</td>\n", " <td>11600012250</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1229641</td>\n", " <td>1157</td>\n", " <td>1</td>\n", " <td>DR</td>\n", " <td>0.59</td>\n", " <td>8003042</td>\n", " <td>11600012250</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1800060</td>\n", " <td>1137</td>\n", " <td>1</td>\n", " <td>DR</td>\n", " <td>0.59</td>\n", " <td>8003042</td>\n", " <td>11600012250</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>1104018</td>\n", " <td>1152</td>\n", " <td>1</td>\n", " <td>DR</td>\n", " <td>0.99</td>\n", " <td>8003043</td>\n", " <td>11600012530</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>1106229</td>\n", " <td>1123</td>\n", " <td>1</td>\n", " <td>DR</td>\n", " <td>0.99</td>\n", " <td>8003043</td>\n", " <td>11600012530</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " PANID WEEK UNITS OUTLET DOLLARS IRI_KEY COLUPC\n", "0 1212076 1133 1 DR 0.59 8003042 11600012250\n", "1 1229641 1157 1 DR 0.59 8003042 11600012250\n", "2 1800060 1137 1 DR 0.59 8003042 11600012250\n", "3 1104018 1152 1 DR 0.99 8003043 11600012530\n", "4 1106229 1123 1 DR 0.99 8003043 11600012530" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "panel = pd.read_csv(\"IRI_Data//Year1/External/saltsnck/saltsnck_PANEL_DR_1114_1165.dat\", delimiter=\"\\t\")\n", "panel.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Panel Demographics\n", "\n", "Panel demographic files have been standardized and are called ads demoN.csv, where\n", "N is the year number: ads demo1.csv, ads demo2.csv ... ads demo5.csv.\n", "\n", "\n", "The panelists included are those who satisfied IRI’s standard 52 week reporting static. This means that (1) the panelists included reported all year, and (2) the panelists are different between years.\n", "For the initial set of data provided, the panelist demos reflect data current at that time. So, for the year 1, 2, and 3 (2001-2003) data, the panelist demos are from early 2007, not 2001. For this reason, there may be panelist records without demographics. For years 4 and 5 (2004-2005) the panelist demos are from later in 2007 and may be slightly different due to the demographic updates. Similarly for years 8-11: the demos reflect information pulled in summer, 2012.\n", "\n", "\n", "The field names and the first two panelist values are shown below. Due to the demographic updates, there are minor differences in the values for the two panelists. For example, the male head in household in 1100180 is now listed as “some college” rather than post-secondary “technical school”, and the male head occupation from laborer to machine operator.\n", "\n", "\n", "In these files, a missing value may appear as an empty field, a blank, a period, or a zero." ] }, { "cell_type": "code", "execution_count": 99, "metadata": { "collapsed": false }, "outputs": [], "source": [ "demos1 = pd.read_csv(\"IRI_Data/demos trips external 1_11 may 13/ads demo1.csv\")\n", "demos11 = pd.read_csv(\"IRI_Data/demos trips external 1_11 may 13/ads demos11.CSV\")\n", "\n", "a=set(demos1.columns.values)\n", "b=set(demos11.columns.values)" ] }, { "cell_type": "code", "execution_count": 112, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['Panelist_ID',\n", " 'Panelist_Type',\n", " 'Combined_Pre-Tax_Income_of_HH',\n", " 'Family_Size',\n", " 'Household_Head_Race',\n", " 'Type_of_Residential_Possession',\n", " 'COUNTY',\n", " 'Age_Group_Applied_to_Household_Head',\n", " 'Education_Level_Reached_by_Household_Head',\n", " 'Occupation_Code_of_Household_Head',\n", " 'Age_Group_Applied_to_Male_HH',\n", " 'Education_Level_Reached_by_Male_HH',\n", " 'Occupation_Code_of_Male_HH',\n", " 'Male_Working_Hour_Code',\n", " 'MALE_SMOKE',\n", " 'Age_Group_Applied_to_Female_HH',\n", " 'Education_Level_Reached_by_Female_HH',\n", " 'Occupation_Code_of_Female_HH',\n", " 'Female_Working_Hour_Code',\n", " 'FEM_SMOKE',\n", " 'Number_of_Dogs',\n", " 'Number_of_Cats',\n", " 'Children_Group_Code',\n", " 'Marital_Status',\n", " 'HH_LANG',\n", " 'ALL_TVS',\n", " 'CABL_TVS',\n", " 'Hispanic_Flag',\n", " 'HISP_CAT',\n", " 'RACE2',\n", " 'RACE3',\n", " 'MICROWAVE',\n", " 'device_type',\n", " 'ZIPCODE',\n", " 'FIPSCODE',\n", " 'market_based_upon_zipcode',\n", " 'IRI_Geography_Number',\n", " 'EXT_FACT']" ] }, "execution_count": 112, "metadata": {}, "output_type": "execute_result" } ], "source": [ "[col.replace(\" \", \"_\") for col in demos11.columns]" ] }, { "cell_type": "code", "execution_count": 101, "metadata": { "collapsed": false }, "outputs": [], "source": [ "intr=a.intersection(b)" ] }, { "cell_type": "code", "execution_count": 106, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['Education_Level_Reached_by_Female_HH',\n", " 'ZIPCODE',\n", " 'Age_Group_Applied_to_Female_HH',\n", " 'Male_Working_Hour_Code',\n", " 'Type_of_Residential_Possession',\n", " 'EXT_FACT',\n", " 'Marital_Status',\n", " 'IRI_Geography_Number',\n", " 'HISP_CAT',\n", " 'Children_Group_Code',\n", " 'Combined_Pre-Tax_Income_of_HH',\n", " 'Panelist_Type',\n", " 'COUNTY',\n", " 'Panelist_ID',\n", " 'Number_of_Cats',\n", " 'FIPSCODE',\n", " 'FEM_SMOKE',\n", " 'Female_Working_Hour_Code',\n", " 'MALE_SMOKE',\n", " 'Family_Size',\n", " 'Occupation_Code_of_Male_HH',\n", " 'Education_Level_Reached_by_Male_HH',\n", " 'Age_Group_Applied_to_Male_HH',\n", " 'market_based_upon_zipcode',\n", " 'Number_of_Dogs',\n", " 'Occupation_Code_of_Female_HH']" ] }, "execution_count": 106, "metadata": {}, "output_type": "execute_result" } ], "source": [ "[col.replace(\" \", \"_\") for col in intr]" ] }, { "cell_type": "code", "execution_count": 97, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "set()" ] }, "execution_count": 97, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#old columns removed...\n", "a-b" ] }, { "cell_type": "code", "execution_count": 98, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "set()" ] }, "execution_count": 98, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#new columns added\n", "b-a" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Store File\n", "\n", "Naming convention: The naming convention for these is category name then outlet then start week and then end week, all separated by underscores, with no extension, so salted snacks drug data for the earliest year would be saltsnck_drug_1114_1165.\n", "\n", "RI_KEY Masked Store number, keyed to delivery_stores file.\n", "\n", "\n", "WEEK IRI Week: see IRI Week Translation.xls file for calendar week translation\n", "\n", "SY\n", "\n", "UPC - System\n", "\n", "GE UPC – Generation\n", "\n", "VEND UPC - Vendor\n", "\n", "ITEM UPC - Item\n", "\n", "UNITS Total Unit sales\n", "\n", "DOLLARS Total Dollar sales\n", "\n", "F Feature: see table below\n", "\n", "D Display: (0=NO, 1=MINOR, 2=MAJOR. MAJOR includes codes lobby and end- aisle)\n", "\n", "PR Price Reduction flag: (1 if TPR is 5% or greater, 0 otherwise)\n" ] }, { "cell_type": "code", "execution_count": 16, "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>IRI_KEY</th>\n", " <th>WEEK</th>\n", " <th>SY</th>\n", " <th>GE</th>\n", " <th>VEND</th>\n", " <th>ITEM</th>\n", " <th>UNITS</th>\n", " <th>DOLLARS</th>\n", " <th>F</th>\n", " <th>D</th>\n", " <th>PR</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>664497</td>\n", " <td>1114</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>41633</td>\n", " <td>2154</td>\n", " <td>3</td>\n", " <td>2.97</td>\n", " <td>NONE</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>664497</td>\n", " <td>1114</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>41633</td>\n", " <td>145</td>\n", " <td>6</td>\n", " <td>13.14</td>\n", " <td>NONE</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>664497</td>\n", " <td>1114</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>41633</td>\n", " <td>2119</td>\n", " <td>3</td>\n", " <td>2.97</td>\n", " <td>NONE</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>664497</td>\n", " <td>1114</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>41633</td>\n", " <td>135</td>\n", " <td>3</td>\n", " <td>6.57</td>\n", " <td>NONE</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>664497</td>\n", " <td>1114</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>41633</td>\n", " <td>2140</td>\n", " <td>1</td>\n", " <td>0.99</td>\n", " <td>NONE</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " IRI_KEY WEEK SY GE VEND ITEM UNITS DOLLARS F D PR\n", "0 664497 1114 0 1 41633 2154 3 2.97 NONE 0 0\n", "1 664497 1114 0 2 41633 145 6 13.14 NONE 0 0\n", "2 664497 1114 0 1 41633 2119 3 2.97 NONE 0 0\n", "3 664497 1114 0 2 41633 135 3 6.57 NONE 0 0\n", "4 664497 1114 0 1 41633 2140 1 0.99 NONE 0 0" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "store = pd.read_fwf(\"../dse-iri-dataset/Year1/External/saltsnck/saltsnck_drug_1114_1165\")\n", "store.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Product Attributes\n", "\n", "The improved file format, which incorporates further information, is prod_category.xls, 16\n", "There are three sets of files.\n", "\n", "The first set of files are applicable to years 1-6 and are provided in a directory called “parsed stub files”.\n", "\n", "The second set of files are applicable to year 7 and are provided in a directory called “parsed stub files 2007”.\n", "\n", "The third set of files are applicable to years 8-11 are are provided in a directory called “parsed stub files 2008-2011”" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "ename": "AttributeError", "evalue": "'module' object has no attribute 'read_'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-20-b8fa7a4c1fc3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mAttributeError\u001b[0m: 'module' object has no attribute 'read_'" ] } ], "source": [ "pd.read_" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Panel Trips\n", "\n", "These files represent the trips made by panelists who purchased at least one item.\n", "These files have been standardized in format from the way they were originally\n", "constructed, and placed in the directory “parsed stub files”. The naming convention is 14\n", "tripsN jul08.csv, where N is the year . Fields are listed below. These files contain the following fields:\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>PANID</th>\n", " <th>WEEK</th>\n", " <th>IRI_Key</th>\n", " <th>MINUTE</th>\n", " <th>CENTS998</th>\n", " <th>CENTS999</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1100016</td>\n", " <td>1114</td>\n", " <td>234140</td>\n", " <td>9701</td>\n", " <td>NaN</td>\n", " <td>4470.835938</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1100016</td>\n", " <td>1115</td>\n", " <td>234140</td>\n", " <td>8088</td>\n", " <td>NaN</td>\n", " <td>7287.500000</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1100016</td>\n", " <td>1116</td>\n", " <td>8046669</td>\n", " <td>5332</td>\n", " <td>NaN</td>\n", " <td>1398.000000</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>1100016</td>\n", " <td>1116</td>\n", " <td>234140</td>\n", " <td>8165</td>\n", " <td>NaN</td>\n", " <td>9771.839844</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>1100016</td>\n", " <td>1116</td>\n", " <td>8003043</td>\n", " <td>9432</td>\n", " <td>NaN</td>\n", " <td>1848.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " PANID WEEK IRI_Key MINUTE CENTS998 CENTS999\n", "0 1100016 1114 234140 9701 NaN 4470.835938\n", "1 1100016 1115 234140 8088 NaN 7287.500000\n", "2 1100016 1116 8046669 5332 NaN 1398.000000\n", "3 1100016 1116 234140 8165 NaN 9771.839844\n", "4 1100016 1116 8003043 9432 NaN 1848.000000" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "trips = pd.read_csv(\"../dse-iri-dataset/demos trips external 1_11 may13/trips1 jul08.csv\")\n", "trips.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Delivery Stores\n", "\n", "This is a flat file with information about the stores. \n", "\n", "This file also contains outlet, estimated acv, the market name so data can be aggregated by market, an open and close week, and finally a “chain” number representing a particular retailer. All the stores belonging to Chain8 are part of the same retailer that year." ] }, { "cell_type": "code", "execution_count": 18, "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>IRI_KEY</th>\n", " <th>OU</th>\n", " <th>EST_ACV</th>\n", " <th>Market_Name</th>\n", " <th>Open</th>\n", " <th>Clsd</th>\n", " <th>MskdName</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>200039</td>\n", " <td>GR</td>\n", " <td>9.602997</td>\n", " <td>BUFFALO/ROCHESTER</td>\n", " <td>539</td>\n", " <td>1219</td>\n", " <td>Chain87</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>200171</td>\n", " <td>GR</td>\n", " <td>28.454990</td>\n", " <td>MILWAUKEE</td>\n", " <td>522</td>\n", " <td>9998</td>\n", " <td>Chain97</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>200197</td>\n", " <td>GR</td>\n", " <td>11.314990</td>\n", " <td>PEORIA/SPRINGFLD.</td>\n", " <td>903</td>\n", " <td>9998</td>\n", " <td>Chain59</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>200233</td>\n", " <td>GR</td>\n", " <td>7.473000</td>\n", " <td>OKLAHOMA CITY</td>\n", " <td>1122</td>\n", " <td>1150</td>\n", " <td>Chain102</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>200272</td>\n", " <td>GR</td>\n", " <td>13.266000</td>\n", " <td>LOS ANGELES</td>\n", " <td>873</td>\n", " <td>9998</td>\n", " <td>Chain124</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " IRI_KEY OU EST_ACV Market_Name Open Clsd MskdName\n", "0 200039 GR 9.602997 BUFFALO/ROCHESTER 539 1219 Chain87\n", "1 200171 GR 28.454990 MILWAUKEE 522 9998 Chain97\n", "2 200197 GR 11.314990 PEORIA/SPRINGFLD. 903 9998 Chain59\n", "3 200233 GR 7.473000 OKLAHOMA CITY 1122 1150 Chain102\n", "4 200272 GR 13.266000 LOS ANGELES 873 9998 Chain124" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stores = pd.read_fwf(\"../dse-iri-dataset/Year1/External/saltsnck/Delivery_Stores\")\n", "stores.head()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The sql extension is already loaded. To reload it, use:\n", " %reload_ext sql\n" ] } ], "source": [ "%load_ext sql" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3 rows affected.\n" ] }, { "data": { "text/html": [ "<table>\n", " <tr>\n", " <th>name</th>\n", " </tr>\n", " <tr>\n", " <td>alex</td>\n", " </tr>\n", " <tr>\n", " <td>john</td>\n", " </tr>\n", " <tr>\n", " <td>david</td>\n", " </tr>\n", "</table>" ], "text/plain": [ "[(u'alex',), (u'john',), (u'david',)]" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%sql postgresql://sharknado:[email protected]/sharknado\n", " select * from test" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true }, "outputs": [], "source": [ "names = _" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table>\n", " <tr>\n", " <th>name</th>\n", " </tr>\n", " <tr>\n", " <td>alex</td>\n", " </tr>\n", " <tr>\n", " <td>john</td>\n", " </tr>\n", " <tr>\n", " <td>david</td>\n", " </tr>\n", "</table>" ], "text/plain": [ "[(u'alex',), (u'john',), (u'david',)]" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "names" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2053 rows affected.\n" ] } ], "source": [ "result = %sql select * from stores\n", "\n", "dataframe = result.DataFrame()" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>iri_key</th>\n", " <th>ou</th>\n", " <th>est_acv</th>\n", " <th>marketname</th>\n", " <th>open</th>\n", " <th>clsd</th>\n", " <th>mskdname</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>200039</td>\n", " <td>GR</td>\n", " <td>9.602997</td>\n", " <td>BUFFALO/ROCHESTER</td>\n", " <td>539</td>\n", " <td>1219</td>\n", " <td>Chain87</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>200171</td>\n", " <td>GR</td>\n", " <td>28.454990</td>\n", " <td>MILWAUKEE</td>\n", " <td>522</td>\n", " <td>9998</td>\n", " <td>Chain97</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>200197</td>\n", " <td>GR</td>\n", " <td>11.314990</td>\n", " <td>PEORIA/SPRINGFLD.</td>\n", " <td>903</td>\n", " <td>9998</td>\n", " <td>Chain59</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>200233</td>\n", " <td>GR</td>\n", " <td>7.473000</td>\n", " <td>OKLAHOMA CITY</td>\n", " <td>1122</td>\n", " <td>1150</td>\n", " <td>Chain102</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>200272</td>\n", " <td>GR</td>\n", " <td>13.266000</td>\n", " <td>LOS ANGELES</td>\n", " <td>873</td>\n", " <td>9998</td>\n", " <td>Chain124</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " iri_key ou est_acv marketname open clsd mskdname\n", "0 200039 GR 9.602997 BUFFALO/ROCHESTER 539 1219 Chain87 \n", "1 200171 GR 28.454990 MILWAUKEE 522 9998 Chain97 \n", "2 200197 GR 11.314990 PEORIA/SPRINGFLD. 903 9998 Chain59 \n", "3 200233 GR 7.473000 OKLAHOMA CITY 1122 1150 Chain102\n", "4 200272 GR 13.266000 LOS ANGELES 873 9998 Chain124" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataframe.head()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10 rows affected.\n" ] }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>iri_key</th>\n", " <th>_year</th>\n", " <th>_type</th>\n", " <th>week</th>\n", " <th>sy</th>\n", " <th>ge</th>\n", " <th>vend</th>\n", " <th>item</th>\n", " <th>units</th>\n", " <th>dollars</th>\n", " <th>f</th>\n", " <th>d</th>\n", " <th>pr</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>664497</td>\n", " <td>None</td>\n", " <td>drug</td>\n", " <td>1114</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>41633</td>\n", " <td>2154</td>\n", " <td>3</td>\n", " <td>2.97</td>\n", " <td>NONE</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>664497</td>\n", " <td>None</td>\n", " <td>drug</td>\n", " <td>1114</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>41633</td>\n", " <td>145</td>\n", " <td>6</td>\n", " <td>13.14</td>\n", " <td>NONE</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>664497</td>\n", " <td>None</td>\n", " <td>drug</td>\n", " <td>1114</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>41633</td>\n", " <td>2119</td>\n", " <td>3</td>\n", " <td>2.97</td>\n", " <td>NONE</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>664497</td>\n", " <td>None</td>\n", " <td>drug</td>\n", " <td>1114</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>41633</td>\n", " <td>135</td>\n", " <td>3</td>\n", " <td>6.57</td>\n", " <td>NONE</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>664497</td>\n", " <td>None</td>\n", " <td>drug</td>\n", " <td>1114</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>41633</td>\n", " <td>2140</td>\n", " <td>1</td>\n", " <td>0.99</td>\n", " <td>NONE</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " iri_key _year _type week sy ge vend item units dollars f d pr\n", "0 664497 None drug 1114 0 1 41633 2154 3 2.97 NONE 0 0\n", "1 664497 None drug 1114 0 2 41633 145 6 13.14 NONE 0 0\n", "2 664497 None drug 1114 0 1 41633 2119 3 2.97 NONE 0 0\n", "3 664497 None drug 1114 0 2 41633 135 3 6.57 NONE 0 0\n", "4 664497 None drug 1114 0 1 41633 2140 1 0.99 NONE 0 0" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "transactions = %sql select * from transactions limit 10\n", "transactions.DataFrame().head()" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10 rows affected.\n" ] }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>l1</th>\n", " <th>l2</th>\n", " <th>l3</th>\n", " <th>l4</th>\n", " <th>l5</th>\n", " <th>l9</th>\n", " <th>level</th>\n", " <th>upc</th>\n", " <th>sy</th>\n", " <th>ge</th>\n", " <th>...</th>\n", " <th>item</th>\n", " <th>stubspec_1431rc</th>\n", " <th>vol_eq</th>\n", " <th>product_type</th>\n", " <th>package</th>\n", " <th>flavor_scent</th>\n", " <th>fat_content</th>\n", " <th>cooking_method</th>\n", " <th>salt_sodium_content</th>\n", " <th>type_of_cut</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>CATEGORY - SALTY SNACKS</td>\n", " <td>POTATO CHIPS</td>\n", " <td>ACTON CO INC</td>\n", " <td>ACTON CO INC</td>\n", " <td>GORDONS</td>\n", " <td>+GORDN BBQ PTCHP THN 6OZ</td>\n", " <td>9</td>\n", " <td>00-02-36300-04650</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>...</td>\n", " <td>4650</td>\n", " <td>+GORDN BBQ PTCHP THN 6OZ 0 2 36300 46...</td>\n", " <td>0.3750</td>\n", " <td>POTATO CHIP</td>\n", " <td>BAG</td>\n", " <td>BARBECUE</td>\n", " <td>MISSING</td>\n", " <td>MISSING</td>\n", " <td>MISSING</td>\n", " <td>THIN</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>CATEGORY - SALTY SNACKS</td>\n", " <td>POTATO CHIPS</td>\n", " <td>ACTON CO INC</td>\n", " <td>ACTON CO INC</td>\n", " <td>GORDONS</td>\n", " <td>+GORDN ORGL PTCHP FLAT 14.5OZ</td>\n", " <td>9</td>\n", " <td>00-01-36300-04632</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>...</td>\n", " <td>4632</td>\n", " <td>+GORDN ORGL PTCHP FLAT 14.5OZ 0 1 36300 46...</td>\n", " <td>0.9063</td>\n", " <td>POTATO CHIP</td>\n", " <td>PLASTIC BAG</td>\n", " <td>ORIGINAL</td>\n", " <td>MISSING</td>\n", " <td>MISSING</td>\n", " <td>MISSING</td>\n", " <td>FLAT</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>CATEGORY - SALTY SNACKS</td>\n", " <td>POTATO CHIPS</td>\n", " <td>ACTON CO INC</td>\n", " <td>ACTON CO INC</td>\n", " <td>GORDONS</td>\n", " <td>+GORDN REDHT PTCHP RPLD 6OZ</td>\n", " <td>9</td>\n", " <td>00-02-36300-04652</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>...</td>\n", " <td>4652</td>\n", " <td>+GORDN REDHT PTCHP RPLD 6OZ 0 2 36300 46...</td>\n", " <td>0.3750</td>\n", " <td>POTATO CHIP</td>\n", " <td>BAG</td>\n", " <td>RED HOT</td>\n", " <td>MISSING</td>\n", " <td>MISSING</td>\n", " <td>MISSING</td>\n", " <td>RIPPLED</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>CATEGORY - SALTY SNACKS</td>\n", " <td>POTATO CHIPS</td>\n", " <td>ACTON CO INC</td>\n", " <td>ACTON CO INC</td>\n", " <td>GORDONS</td>\n", " <td>+GORDN REG PTCHP RPLD 6OZ</td>\n", " <td>9</td>\n", " <td>00-03-36300-04641</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>...</td>\n", " <td>4641</td>\n", " <td>+GORDN REG PTCHP RPLD 6OZ 0 3 36300 46...</td>\n", " <td>0.3750</td>\n", " <td>POTATO CHIP</td>\n", " <td>BAG</td>\n", " <td>REGULAR</td>\n", " <td>MISSING</td>\n", " <td>MISSING</td>\n", " <td>MISSING</td>\n", " <td>RIPPLED</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>CATEGORY - SALTY SNACKS</td>\n", " <td>POTATO CHIPS</td>\n", " <td>ACTON CO INC</td>\n", " <td>ACTON CO INC</td>\n", " <td>GORDONS</td>\n", " <td>+GORDN REG PTCHP THN 6OZ</td>\n", " <td>9</td>\n", " <td>00-02-36300-04640</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>...</td>\n", " <td>4640</td>\n", " <td>+GORDN REG PTCHP THN 6OZ 0 2 36300 46...</td>\n", " <td>0.3750</td>\n", " <td>POTATO CHIP</td>\n", " <td>BAG</td>\n", " <td>REGULAR</td>\n", " <td>MISSING</td>\n", " <td>MISSING</td>\n", " <td>MISSING</td>\n", " <td>THIN</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 21 columns</p>\n", "</div>" ], "text/plain": [ " l1 l2 l3 l4 l5 \\\n", "0 CATEGORY - SALTY SNACKS POTATO CHIPS ACTON CO INC ACTON CO INC GORDONS \n", "1 CATEGORY - SALTY SNACKS POTATO CHIPS ACTON CO INC ACTON CO INC GORDONS \n", "2 CATEGORY - SALTY SNACKS POTATO CHIPS ACTON CO INC ACTON CO INC GORDONS \n", "3 CATEGORY - SALTY SNACKS POTATO CHIPS ACTON CO INC ACTON CO INC GORDONS \n", "4 CATEGORY - SALTY SNACKS POTATO CHIPS ACTON CO INC ACTON CO INC GORDONS \n", "\n", " l9 level upc sy ge \\\n", "0 +GORDN BBQ PTCHP THN 6OZ 9 00-02-36300-04650 0 2 \n", "1 +GORDN ORGL PTCHP FLAT 14.5OZ 9 00-01-36300-04632 0 1 \n", "2 +GORDN REDHT PTCHP RPLD 6OZ 9 00-02-36300-04652 0 2 \n", "3 +GORDN REG PTCHP RPLD 6OZ 9 00-03-36300-04641 0 3 \n", "4 +GORDN REG PTCHP THN 6OZ 9 00-02-36300-04640 0 2 \n", "\n", " ... item stubspec_1431rc \\\n", "0 ... 4650 +GORDN BBQ PTCHP THN 6OZ 0 2 36300 46... \n", "1 ... 4632 +GORDN ORGL PTCHP FLAT 14.5OZ 0 1 36300 46... \n", "2 ... 4652 +GORDN REDHT PTCHP RPLD 6OZ 0 2 36300 46... \n", "3 ... 4641 +GORDN REG PTCHP RPLD 6OZ 0 3 36300 46... \n", "4 ... 4640 +GORDN REG PTCHP THN 6OZ 0 2 36300 46... \n", "\n", " vol_eq product_type package flavor_scent fat_content cooking_method \\\n", "0 0.3750 POTATO CHIP BAG BARBECUE MISSING MISSING \n", "1 0.9063 POTATO CHIP PLASTIC BAG ORIGINAL MISSING MISSING \n", "2 0.3750 POTATO CHIP BAG RED HOT MISSING MISSING \n", "3 0.3750 POTATO CHIP BAG REGULAR MISSING MISSING \n", "4 0.3750 POTATO CHIP BAG REGULAR MISSING MISSING \n", "\n", " salt_sodium_content type_of_cut \n", "0 MISSING THIN \n", "1 MISSING FLAT \n", "2 MISSING RIPPLED \n", "3 MISSING RIPPLED \n", "4 MISSING THIN \n", "\n", "[5 rows x 21 columns]" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "products_query = %sql select * from products limit 10\n", "products_query.DataFrame().head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Join transactions on products\n", "\n", "The transactions table is more interesting if you join products on the `item` column" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5 rows affected.\n" ] }, { "data": { "text/html": [ "<table>\n", " <tr>\n", " <th>iri_key</th>\n", " <th>_year</th>\n", " <th>_type</th>\n", " <th>week</th>\n", " <th>sy</th>\n", " <th>ge</th>\n", " <th>vend</th>\n", " <th>item</th>\n", " <th>units</th>\n", " <th>dollars</th>\n", " <th>f</th>\n", " <th>d</th>\n", " <th>pr</th>\n", " <th>l1</th>\n", " <th>l2</th>\n", " <th>l3</th>\n", " <th>l4</th>\n", " <th>l5</th>\n", " <th>l9</th>\n", " <th>level</th>\n", " <th>upc</th>\n", " <th>sy_1</th>\n", " <th>ge_1</th>\n", " <th>vend_1</th>\n", " <th>item_1</th>\n", " <th>stubspec_1431rc</th>\n", " <th>vol_eq</th>\n", " <th>product_type</th>\n", " <th>package</th>\n", " <th>flavor_scent</th>\n", " <th>fat_content</th>\n", " <th>cooking_method</th>\n", " <th>salt_sodium_content</th>\n", " <th>type_of_cut</th>\n", " </tr>\n", " <tr>\n", " <td>44017</td>\n", " <td>None</td>\n", " <td>drug</td>\n", " <td>1114</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>78907</td>\n", " <td>0</td>\n", " <td>16</td>\n", " <td>15.84</td>\n", " <td>NONE</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>CATEGORY - SALTY SNACKS</td>\n", " <td>TORTILLA/TOSTADA CHIPS</td>\n", " <td>HACIENDA MEXC FOOD PROD</td>\n", " <td>HACIENDA MEXC FOOD PROD</td>\n", " <td>HACIENDA</td>\n", " <td>+HCNDA ORGL TRCHP TRNGL 12OZ</td>\n", " <td>9</td>\n", " <td>01-01-86443-00000</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>86443</td>\n", " <td>0</td>\n", " <td>+HCNDA ORGL TRCHP TRNGL 12OZ 1 1 86443 0 1 1 0.7500RP 08699</td>\n", " <td>0.75</td>\n", " <td>TORTILLA CHIP</td>\n", " <td>BAG</td>\n", " <td>ORIGINAL</td>\n", " <td>MISSING</td>\n", " <td>MISSING</td>\n", " <td>MISSING</td>\n", " <td>MISSING</td>\n", " </tr>\n", " <tr>\n", " <td>44017</td>\n", " <td>None</td>\n", " <td>drug</td>\n", " <td>1114</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>78907</td>\n", " <td>0</td>\n", " <td>16</td>\n", " <td>15.84</td>\n", " <td>NONE</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>CATEGORY - SALTY SNACKS</td>\n", " <td>PRETZELS</td>\n", " <td>AUNTIE ANNES INC</td>\n", " <td>AUNTIE ANNES INC</td>\n", " <td>AUNTIE ANNES</td>\n", " <td>+ATANS SGS#1 PRTZL KNOT 14OZ</td>\n", " <td>9</td>\n", " <td>08-01-95072-00000</td>\n", " <td>8</td>\n", " <td>1</td>\n", " <td>95072</td>\n", " <td>0</td>\n", " <td>+ATANS SGS#1 PRTZL KNOT 14OZ 8 1 95072 0 1 1 0.8750RP 12393</td>\n", " <td>0.875</td>\n", " <td>PRETZEL</td>\n", " <td>BAG</td>\n", " <td>MISSING</td>\n", " <td>MISSING</td>\n", " <td>MISSING</td>\n", " <td>MISSING</td>\n", " <td>MISSING</td>\n", " </tr>\n", " <tr>\n", " <td>44017</td>\n", " <td>None</td>\n", " <td>drug</td>\n", " <td>1114</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>78907</td>\n", " <td>0</td>\n", " <td>16</td>\n", " <td>15.84</td>\n", " <td>NONE</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>CATEGORY - SALTY SNACKS</td>\n", " <td>PRETZELS</td>\n", " <td>BEIGEL & BEIGEL</td>\n", " <td>BEIGEL & BEIGEL</td>\n", " <td>BEIGEL & BEIGEL</td>\n", " <td>+BG&BG SESME PRTZL KNOT 7OZ</td>\n", " <td>9</td>\n", " <td>00-01-08982-00000</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>8982</td>\n", " <td>0</td>\n", " <td>+BG&BG SESME PRTZL KNOT 7OZ 0 1 8982 0 1 1 0.4375RP 12463</td>\n", " <td>0.4375</td>\n", " <td>PRETZEL</td>\n", " <td>BAG</td>\n", " <td>SESAME</td>\n", " <td>MISSING</td>\n", " <td>MISSING</td>\n", " <td>MISSING</td>\n", " <td>MISSING</td>\n", " </tr>\n", " <tr>\n", " <td>44017</td>\n", " <td>None</td>\n", " <td>drug</td>\n", " <td>1114</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>78907</td>\n", " <td>0</td>\n", " <td>16</td>\n", " <td>15.84</td>\n", " <td>NONE</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>CATEGORY - SALTY SNACKS</td>\n", " <td>READY-TO-EAT POPCORN/CARAMEL COR</td>\n", " <td>KETTLE KORN BY THE SEA</td>\n", " <td>KETTLE KORN BY THE SEA</td>\n", " <td>KETTLE KORN</td>\n", " <td>+KLKR1 KTCRN RTEPC 3OZ</td>\n", " <td>9</td>\n", " <td>08-01-50278-00000</td>\n", " <td>8</td>\n", " <td>1</td>\n", " <td>50278</td>\n", " <td>0</td>\n", " <td>+KLKR1 KTCRN RTEPC 3OZ 8 1 50278 0 1 1 0.1875RP 19661</td>\n", " <td>0.1875</td>\n", " <td>RTE POPCORN</td>\n", " <td>BAG</td>\n", " <td>KETTLE CORN</td>\n", " <td>MISSING</td>\n", " <td>MISSING</td>\n", " <td>MISSING</td>\n", " <td>MISSING</td>\n", " </tr>\n", " <tr>\n", " <td>44017</td>\n", " <td>None</td>\n", " <td>drug</td>\n", " <td>1114</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>78907</td>\n", " <td>0</td>\n", " <td>16</td>\n", " <td>15.84</td>\n", " <td>NONE</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>CATEGORY - SALTY SNACKS</td>\n", " <td>READY-TO-EAT POPCORN/CARAMEL COR</td>\n", " <td>MARCIS FUN FOODS</td>\n", " <td>MARCIS FUN FOODS</td>\n", " <td>MARCI'S</td>\n", " <td>+MRC'S KTCRN RTEPC 8OZ</td>\n", " <td>9</td>\n", " <td>08-01-33841-00000</td>\n", " <td>8</td>\n", " <td>1</td>\n", " <td>33841</td>\n", " <td>0</td>\n", " <td>+MRC'S KTCRN RTEPC 8OZ 8 1 33841 0 1 1 0.5000RP 19854</td>\n", " <td>0.5</td>\n", " <td>RTE POPCORN</td>\n", " <td>BAG</td>\n", " <td>KETTLE CORN</td>\n", " <td>MISSING</td>\n", " <td>MISSING</td>\n", " <td>MISSING</td>\n", " <td>MISSING</td>\n", " </tr>\n", "</table>" ], "text/plain": [ "[(44017, None, u'drug', 1114, 0, u'2', 78907, 0, 16, 15.84, u'NONE', 0, 0, u'CATEGORY - SALTY SNACKS', u'TORTILLA/TOSTADA CHIPS', u'HACIENDA MEXC FOOD PROD', u'HACIENDA MEXC FOOD PROD', u'HACIENDA', u'+HCNDA ORGL TRCHP TRNGL 12OZ', 9, u'01-01-86443-00000', 1, 1, 86443, 0, u'+HCNDA ORGL TRCHP TRNGL 12OZ 1 1 86443 0 1 1 0.7500RP 08699', 0.75, u'TORTILLA CHIP', u'BAG', u'ORIGINAL', u'MISSING', u'MISSING', u'MISSING', u'MISSING'),\n", " (44017, None, u'drug', 1114, 0, u'2', 78907, 0, 16, 15.84, u'NONE', 0, 0, u'CATEGORY - SALTY SNACKS', u'PRETZELS', u'AUNTIE ANNES INC', u'AUNTIE ANNES INC', u'AUNTIE ANNES', u'+ATANS SGS#1 PRTZL KNOT 14OZ', 9, u'08-01-95072-00000', 8, 1, 95072, 0, u'+ATANS SGS#1 PRTZL KNOT 14OZ 8 1 95072 0 1 1 0.8750RP 12393', 0.875, u'PRETZEL', u'BAG', u'MISSING', u'MISSING', u'MISSING', u'MISSING', u'MISSING'),\n", " (44017, None, u'drug', 1114, 0, u'2', 78907, 0, 16, 15.84, u'NONE', 0, 0, u'CATEGORY - SALTY SNACKS', u'PRETZELS', u'BEIGEL & BEIGEL', u'BEIGEL & BEIGEL', u'BEIGEL & BEIGEL', u'+BG&BG SESME PRTZL KNOT 7OZ', 9, u'00-01-08982-00000', 0, 1, 8982, 0, u'+BG&BG SESME PRTZL KNOT 7OZ 0 1 8982 0 1 1 0.4375RP 12463', 0.4375, u'PRETZEL', u'BAG', u'SESAME', u'MISSING', u'MISSING', u'MISSING', u'MISSING'),\n", " (44017, None, u'drug', 1114, 0, u'2', 78907, 0, 16, 15.84, u'NONE', 0, 0, u'CATEGORY - SALTY SNACKS', u'READY-TO-EAT POPCORN/CARAMEL COR', u'KETTLE KORN BY THE SEA', u'KETTLE KORN BY THE SEA', u'KETTLE KORN', u'+KLKR1 KTCRN RTEPC 3OZ', 9, u'08-01-50278-00000', 8, 1, 50278, 0, u'+KLKR1 KTCRN RTEPC 3OZ 8 1 50278 0 1 1 0.1875RP 19661', 0.1875, u'RTE POPCORN', u'BAG', u'KETTLE CORN', u'MISSING', u'MISSING', u'MISSING', u'MISSING'),\n", " (44017, None, u'drug', 1114, 0, u'2', 78907, 0, 16, 15.84, u'NONE', 0, 0, u'CATEGORY - SALTY SNACKS', u'READY-TO-EAT POPCORN/CARAMEL COR', u'MARCIS FUN FOODS', u'MARCIS FUN FOODS', u\"MARCI'S\", u\"+MRC'S KTCRN RTEPC 8OZ\", 9, u'08-01-33841-00000', 8, 1, 33841, 0, u\"+MRC'S KTCRN RTEPC 8OZ 8 1 33841 0 1 1 0.5000RP 19854\", 0.5, u'RTE POPCORN', u'BAG', u'KETTLE CORN', u'MISSING', u'MISSING', u'MISSING', u'MISSING')]" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%sql\n", "select * from transactions\n", "join products on products.item = transactions.item\n", "limit 5" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Most popular Items by $ volume\n", "\n", "This query takes a long time:\n", "\n", "* either precompute a join table\n", "* optimize the group by" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10 rows affected.\n" ] }, { "data": { "text/html": [ "<table>\n", " <tr>\n", " <th>product_type</th>\n", " <th>dollar_sum</th>\n", " </tr>\n", " <tr>\n", " <td>POTATO CHIP</td>\n", " <td>18465564852.6</td>\n", " </tr>\n", " <tr>\n", " <td>TORTILLA CHIP</td>\n", " <td>9192142590.74</td>\n", " </tr>\n", " <tr>\n", " <td>RTE POPCORN</td>\n", " <td>8437904236.52</td>\n", " </tr>\n", " <tr>\n", " <td>PRETZEL</td>\n", " <td>5301534431.15</td>\n", " </tr>\n", " <tr>\n", " <td>FRIED PORK RIND</td>\n", " <td>4118536541.46</td>\n", " </tr>\n", " <tr>\n", " <td>CHEESE SNACK</td>\n", " <td>3167251187.26</td>\n", " </tr>\n", " <tr>\n", " <td>CORN CHIP</td>\n", " <td>1505557590.38</td>\n", " </tr>\n", " <tr>\n", " <td>POTATO CRISP</td>\n", " <td>1043139151.73</td>\n", " </tr>\n", " <tr>\n", " <td>ASTSS SALTED SNACKS</td>\n", " <td>905159722.764</td>\n", " </tr>\n", " <tr>\n", " <td>PLANTAIN CHIP</td>\n", " <td>536445576.722</td>\n", " </tr>\n", "</table>" ], "text/plain": [ "[(u'POTATO CHIP', 18465564852.5546),\n", " (u'TORTILLA CHIP', 9192142590.73948),\n", " (u'RTE POPCORN', 8437904236.51907),\n", " (u'PRETZEL', 5301534431.15261),\n", " (u'FRIED PORK RIND', 4118536541.45906),\n", " (u'CHEESE SNACK', 3167251187.2571),\n", " (u'CORN CHIP', 1505557590.38097),\n", " (u'POTATO CRISP', 1043139151.73325),\n", " (u'ASTSS SALTED SNACKS', 905159722.764451),\n", " (u'PLANTAIN CHIP', 536445576.721792)]" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%sql\n", "SELECT products.product_type, sum(transactions.dollars) as dollar_sum\n", "FROM transactions\n", "JOIN products on products.item = transactions.item\n", "GROUP BY products.product_type\n", "ORDER BY dollar_sum desc\n", "LIMIT 10" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
abhijithch/MLDemos.jl	demos/druginteractions.ipynb	1	45985	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO: Recompiling stale cache file /Users/abhijithc/.julia/lib/v0.4/LightXML.ji for module LightXML.\n", "INFO: Recompiling stale cache file /Users/abhijithc/.julia/lib/v0.4/HttpParser.ji for module HttpParser.\n", "INFO: Recompiling stale cache file /Users/abhijithc/.julia/lib/v0.4/Requests.ji for module Requests.\n", "INFO: Recompiling stale cache file /Users/abhijithc/.julia/lib/v0.4/MbedTLS.ji for module MbedTLS.\n", "INFO: Recompiling stale cache file /Users/abhijithc/.julia/lib/v0.4/Codecs.ji for module Codecs.\n" ] } ], "source": [ "using MLDemos, DataFrames" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table class=\"data-frame\"><tr><th></th><th>Disease</th><th>Drug</th><th>Target</th></tr><tr><th>1</th><td>Epilepsy</td><td>Doxaprost</td><td>Gonadotropin-Releasing Hormone Receptor</td></tr><tr><th>2</th><td>Glaucoma</td><td>Doxazosin Mesylate</td><td>Metabotropic Glutamate Receptor 1</td></tr><tr><th>3</th><td>Arrhythmia</td><td>Doxepin Hydrochloride</td><td>Metabotropic Glutamate Receptor 2</td></tr><tr><th>4</th><td>Calculus Urinary</td><td>Doxycycline Hydrochloride</td><td>Metabotropic Glutamate Receptor 3</td></tr><tr><th>5</th><td>Seizures</td><td>Dracotanoside D</td><td>Pancreatic Alpha-Amylase</td></tr><tr><th>6</th><td>Pruritis</td><td>Dragonamide B</td><td>Lysosomal Alpha-Glucosidase</td></tr><tr><th>7</th><td>Cushings Syndrome</td><td>Dragonamide E</td><td>Carbonic Anhydrase 3</td></tr><tr><th>8</th><td>Deep Vein Thrombosis</td><td>Drobuline</td><td>Adenosine A3 Receptor</td></tr><tr><th>9</th><td>Hypertension Ocular</td><td>Drospirenone</td><td>Atp-Binding Cassette Transporter Sub-Family C Member 8</td></tr><tr><th>10</th><td>Arterial Occlusion Peripheral</td><td>Droxinavir Hydrochloride</td><td>Multidrug Resistance Protein 1</td></tr><tr><th>11</th><td>Bipolar Affective Disorder</td><td>Drupanin</td><td>Renin</td></tr><tr><th>12</th><td>Gilberts Syndrome</td><td>Kaad-Cyclopamine</td><td>Gamma-Aminobutyric Acid Receptor Subunit Gamma-1</td></tr><tr><th>13</th><td>Hyperbilirubinemia</td><td>Kabiramide K</td><td>Gamma-Aminobutyric Acid Receptor Subunit Gamma-3</td></tr><tr><th>14</th><td>Rheumatoid Arthritis</td><td>Kadcoccilactone A</td><td>Gamma-Aminobutyric Acid Receptor Subunit Theta</td></tr><tr><th>15</th><td>Rhinitis Seasonal Allergic</td><td>Kadcoccilactone C</td><td>Gamma-Aminobutyric Acid Receptor Subunit Rho-3</td></tr><tr><th>16</th><td>Myeloma Multiple</td><td>Kadcoccilactone D</td><td>Gamma-Aminobutyric-Acid Receptor Subunit Alpha-1</td></tr><tr><th>17</th><td>Opiate Dependence</td><td>Kadcoccilactone F</td><td>Neuronal Acetylcholine Receptor Subunit Alpha-10</td></tr><tr><th>18</th><td>Polycystic Ovarian Syndrome</td><td>Kadsulactone A</td><td>Calcium-Activated Potassium Channel Subunit Alpha 1</td></tr><tr><th>19</th><td>Transplant Bone Marrow</td><td>Kadsuphilol C</td><td>Methylenetetrahydrofolate Reductase</td></tr><tr><th>20</th><td>Central Nervous System Disorder</td><td>Kadsuphilol E</td><td>Glutamate Receptor 1</td></tr><tr><th>21</th><td>Deficiency Vitamin D</td><td>Kadsuracoccinic Acid A</td><td>Estrogen Receptor Beta</td></tr><tr><th>22</th><td>Nephropathy Diabetic</td><td>Kaempferitrin</td><td>Microtubule-Associated Protein 1a</td></tr><tr><th>23</th><td>Infection Rickettsia</td><td>Kaempferol</td><td>Low-Density Lipoprotein Receptor-Related Protein 2</td></tr><tr><th>24</th><td>Shigellosis</td><td>4-Medck Thiolactone</td><td>Atp-Sensitive Inward Rectifier Potassium Channel 1</td></tr><tr><th>25</th><td>Nervousness</td><td>4-Mercaptopyridine</td><td>Glutathione Peroxidase 6</td></tr><tr><th>26</th><td>Anesthesia Local</td><td>N-Heptanoyl-L-Homoserine Lactone</td><td>Glutathione S-Transferase Theta-1</td></tr><tr><th>27</th><td>Hypoglycemia</td><td>N-Heptyl-4-Sulfamoyl-Benzamide</td><td>Maleylacetoacetate Isomerase</td></tr><tr><th>28</th><td>Muscle Relaxation</td><td>N-Heptylcarbamic Acid Quinolin-6-Yl Ester</td><td>Thioredoxin Domain-Containing Protein 12</td></tr><tr><th>29</th><td>Arthritis Psoriatic</td><td>N-Hexanesulfonamide</td><td>G Protein-Activated Inward Rectifier Potassium Channel 2</td></tr><tr><th>30</th><td>Carcinoma Brain</td><td>Ab-Meca</td><td>Tissue-Type Plasminogen Activator</td></tr><tr><th>&vellip;</th><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td></tr></table>" ], "text/plain": [ "254x3 DataFrames.DataFrame\n", "\| Row \| \|-----\|\n", "\| 1 \| \| 2 \| \| 3 \| \| 4 \| \| 5 \| \| 6 \| \| 7 \| \| 8 \| \| 9 \| \| 10 \| \| 11 \| \n", "⋮\n", "\| 243 \| \| 244 \| \| 245 \| \| 246 \| \| 247 \| \| 248 \| \| 249 \| \| 250 \| \| 251 \| \| 252 \| \| 253 \| \| 254 \| \n", "\n", "\| Row \| Disease \|\n", "\|-----\|----------------------------------------------------------------------------\|\n", "\| 1 \| \"Epilepsy\" \|\n", "\| 2 \| \"Glaucoma\" \|\n", "\| 3 \| \"Arrhythmia\" \|\n", "\| 4 \| \"Calculus Urinary\" \|\n", "\| 5 \| \"Seizures\" \|\n", "\| 6 \| \"Pruritis\" \|\n", "\| 7 \| \"Cushings Syndrome\" \|\n", "\| 8 \| \"Deep Vein Thrombosis\" \|\n", "\| 9 \| \"Hypertension Ocular\" \|\n", "\| 10 \| \"Arterial Occlusion Peripheral\" \|\n", "\| 11 \| \"Bipolar Affective Disorder\" \|\n", "⋮\n", "\| 243 \| \"Deficiency Vitamin\" \|\n", "\| 244 \| \"Fibrocystic Breast Disease\" \|\n", "\| 245 \| \"Leprosy Lepromatous\" \|\n", "\| 246 \| \"Onchocerciasis \|River blindness \|Robles disease \|African river blindness\" \|\n", "\| 247 \| \"Onchocerca volvulus \| Onchocerca\" \|\n", "\| 248 \| \"Wolbachia\" \|\n", "\| 249 \| \"filarial infection\" \|\n", "\| 250 \| \"microfilaria\" \|\n", "\| 251 \| \"microfilaricide\" \|\n", "\| 252 \| \"Nematodes\" \|\n", "\| 253 \| NA \|\n", "\| 254 \| NA \|\n", "\n", "\| Row \| Drug \|\n", "\|-----\|-----------------------------\|\n", "\| 1 \| \"Doxaprost\" \|\n", "\| 2 \| \"Doxazosin Mesylate\" \|\n", "\| 3 \| \"Doxepin Hydrochloride\" \|\n", "\| 4 \| \"Doxycycline Hydrochloride\" \|\n", "\| 5 \| \"Dracotanoside D\" \|\n", "\| 6 \| \"Dragonamide B\" \|\n", "\| 7 \| \"Dragonamide E\" \|\n", "\| 8 \| \"Drobuline\" \|\n", "\| 9 \| \"Drospirenone\" \|\n", "\| 10 \| \"Droxinavir Hydrochloride\" \|\n", "\| 11 \| \"Drupanin\" \|\n", "⋮\n", "\| 243 \| \"Droserone\" \|\n", "\| 244 \| \"Droxicam\" \|\n", "\| 245 \| \"Droxinavir\" \|\n", "\| 246 \| \"Rapamycin\" \|\n", "\| 247 \| \"Sirolimus\" \|\n", "\| 248 \| \"Tacrolimus \|fb506\" \|\n", "\| 249 \| \"Clarithromycin\" \|\n", "\| 250 \| \"Imatinib\" \|\n", "\| 251 \| \"Atorvastatin\" \|\n", "\| 252 \| \"Bexarotene\" \|\n", "\| 253 \| NA \|\n", "\| 254 \| NA \|\n", "\n", "\| Row \| Target \|\n", "\|-----\|-------------------------------------------------------------------------------------------------\|\n", "\| 1 \| \"Gonadotropin-Releasing Hormone Receptor\" \|\n", "\| 2 \| \"Metabotropic Glutamate Receptor 1\" \|\n", "\| 3 \| \"Metabotropic Glutamate Receptor 2\" \|\n", "\| 4 \| \"Metabotropic Glutamate Receptor 3\" \|\n", "\| 5 \| \"Pancreatic Alpha-Amylase\" \|\n", "\| 6 \| \"Lysosomal Alpha-Glucosidase\" \|\n", "\| 7 \| \"Carbonic Anhydrase 3\" \|\n", "\| 8 \| \"Adenosine A3 Receptor\" \|\n", "\| 9 \| \"Atp-Binding Cassette Transporter Sub-Family C Member 8\" \|\n", "\| 10 \| \"Multidrug Resistance Protein 1\" \|\n", "\| 11 \| \"Renin\" \|\n", "⋮\n", "\| 243 \| \"Intermediate Conductance Calcium-Activated Potassium Channel Protein 4\" \|\n", "\| 244 \| \"Tubulin Beta-1 Chain\" \|\n", "\| 245 \| \"Signal Transducer And Activator Of Transcription 5b\" \|\n", "\| 246 \| \"Glutathione S-transferase 1 \|EC 2.5.1.18 \|Glutathione S-transferase \|OvGST1\" \|\n", "\| 247 \| \"Ornithine decarboxylase\| ODC\" \|\n", "\| 248 \| \"Ornithine decarboxylase antizyme \|OvAZ\" \|\n", "\| 249 \| \"Fatty-acid and retinol-binding protein \|Antigen maltose-binding protein\| Ov-FAR-1 \| Ov20\" \|\n", "\| 250 \| \"Retinoid-x-receptor \|RXR\" \|\n", "\| 251 \| \"Dihydroorotate dehydrogenase \|DHODH\" \|\n", "\| 252 \| \"Chitinase \|OvCHT1\" \|\n", "\| 253 \| \"Hydroxymethylglutaryl-CoA reductase \|3-hydroxy-3-methylglutaryl-coenzyme A reductase\| HMG-CoA\" \|\n", "\| 254 \| \"Tyrosine-protein kinase CSK \|TK protein kinase \|Tyrosine kinase\" \|" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "terms = readtable(\"$(Pkg.dir())/MLDemos/data/drug/data.csv\", header=true)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "drugs = dropna(terms[:Drug]);\n", "targets = dropna(terms[:Target]);\n", "diseases = dropna(terms[:Disease]);" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(252,)" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "size(drugs)" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "300-element Array{UTF8String,1}:\n", " \"Doxaprost\" \n", " \"Doxazosin Mesylate\" \n", " \"Doxepin Hydrochloride\" \n", " \"Doxycycline Hydrochloride\" \n", " \"Dracotanoside D\" \n", " \"Dragonamide B\" \n", " \"Dragonamide E\" \n", " \"Drobuline\" \n", " \"Drospirenone\" \n", " \"Droxinavir Hydrochloride\" \n", " \"Drupanin\" \n", " \"Kaad-Cyclopamine\" \n", " \"Kabiramide K\" \n", " ⋮ \n", " \"Cardiac Dysrhythmia\" \n", " \"Puberty Precocious\" \n", " \"Aspergillosis\" \n", " \"Meningitis Bacterial\" \n", " \"Ankylosing Spondylitis\" \n", " \"Pain Musculoskeletal\" \n", " \"Hyperlipoproteinemia\" \n", " \"Obstructive Airway Disease\"\n", " \"Iritis\" \n", " \"Carcinoma Endometrial\" \n", " \"Anxiety Disorder\" \n", " \"Somatoform Disorder\" " ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "all = [drugs[1:100];targets[1:100];diseases[1:100]]" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Doxaprost [Title/Abstract] AND\n", " Doxazosin Mesylate [Title/Abstract] AND\n", " Doxepin Hydrochloride [Title/Abstract] AND\n", " Doxycycline Hydrochloride [Title/Abstract] AND\n", " Dracotanoside D [Title/Abstract] AND\n", " Dragonamide B [Title/Abstract] AND\n", " Dragonamide E [Title/Abstract] AND\n", " Drobuline [Title/Abstract] AND\n", " Drospirenone [Title/Abstract] AND\n", " Droxinavir Hydrochloride [Title/Abstract] AND\n", " Drupanin [Title/Abstract] AND\n", " Kaad-Cyclopamine [Title/Abstract] AND\n", " Kabiramide K [Title/Abstract] AND\n", " Kadcoccilactone A [Title/Abstract] AND\n", " Kadcoccilactone C [Title/Abstract] AND\n", " Kadcoccilactone D [Title/Abstract] AND\n", " Kadcoccilactone F [Title/Abstract] AND\n", " Kadsulactone A [Title/Abstract] AND\n", " Kadsuphilol C [Title/Abstract] AND\n", " Kadsuphilol E [Title/Abstract] AND\n", " Kadsuracoccinic Acid A [Title/Abstract] AND\n", " Kaempferitrin [Title/Abstract] AND\n", " Kaempferol [Title/Abstract] AND\n", " 4-Medck Thiolactone [Title/Abstract] AND\n", " 4-Mercaptopyridine [Title/Abstract] AND\n", " N-Heptanoyl-L-Homoserine Lactone [Title/Abstract] AND\n", " N-Heptyl-4-Sulfamoyl-Benzamide [Title/Abstract] AND\n", " N-Heptylcarbamic Acid Quinolin-6-Yl Ester [Title/Abstract] AND\n", " N-Hexanesulfonamide [Title/Abstract] AND\n", " Ab-Meca [Title/Abstract] AND\n", " Abaperidone [Title/Abstract] AND\n", " Abeohyousterone [Title/Abstract] AND\n", " Abiesadine B [Title/Abstract] AND\n", " Doxacurium [Title/Abstract] AND\n", " Doxaminol [Title/Abstract] AND\n", " Doxanthrine [Title/Abstract] AND\n", " Doxazolidine [Title/Abstract] AND\n", " Doxorubicin Analogue [Title/Abstract] AND\n", " Doxorubicin Trifluoroacetate [Title/Abstract] AND\n", " Dpdpe [Title/Abstract] AND\n", " Draconin A [Title/Abstract] AND\n", " Dragmacidin E [Title/Abstract] AND\n", " Dragonamide [Title/Abstract] AND\n", " Drimentine H [Title/Abstract] AND\n", " Droclidinium [Title/Abstract] AND\n", " Droloxifene [Title/Abstract] AND\n", " Drotaverine [Title/Abstract] AND\n", " Droxacin [Title/Abstract] AND\n", " Droxidopa [Title/Abstract] AND\n", " Droxypropine [Title/Abstract] AND\n", " Drummondin C [Title/Abstract] AND\n", " Dsm-121 [Title/Abstract] AND\n", " Dsm-131 [Title/Abstract] AND\n", " K-Strophanthoside [Title/Abstract] AND\n", " Kabiramide G [Title/Abstract] AND\n", " Kadangustin C [Title/Abstract] AND\n", " Kadangustin H [Title/Abstract] AND\n", " Kadangustin J [Title/Abstract] AND\n", " Kadlongilactone A [Title/Abstract] AND\n", " Kadsuphilins B [Title/Abstract] AND\n", " Kadsuphilol A [Title/Abstract] AND\n", " Kadsuphilol D [Title/Abstract] AND\n", " Kadsurenin C [Title/Abstract] AND\n", " Kadsurin [Title/Abstract] AND\n", " 4-Methanesulfonyl-Benzamidine [Title/Abstract] AND\n", " N-Hexadecyl-4-Methoxybenzamide [Title/Abstract] AND\n", " N-Hexadecyl-4-Nitrobenzamide [Title/Abstract] AND\n", " N-Hexadecyl-N'-Methyl-Guanidine Trifluoroacetic Acid [Title/Abstract] AND\n", " N-Hexanoyldihydrosphingosine [Title/Abstract] AND\n", " N-Hexanoylspingosine [Title/Abstract] AND\n", " Dox-Saliform [Title/Abstract] AND\n", " Doxefazepam [Title/Abstract] AND\n", " Doxorubicin 8-(Menthoxycarbonyl)Octanoylhydrazone Hydrochloride [Title/Abstract] AND\n", " Doxorubicin-Pep42 [Title/Abstract] AND\n", " Doxylamine Succinate [Title/Abstract] AND\n", " Dracorhodin [Title/Abstract] AND\n", " Dracotanoside A [Title/Abstract] AND\n", " Draflazine [Title/Abstract] AND\n", " Dramedilol [Title/Abstract] AND\n", " Dronedarone Hydrochloride [Title/Abstract] AND\n", " Drymaritin [Title/Abstract] AND\n", " Dsm-123 [Title/Abstract] AND\n", " Dsm-124 [Title/Abstract] AND\n", " Dsm-73 [Title/Abstract] AND\n", " K76-Cooh [Title/Abstract] AND\n", " Kabiramide C [Title/Abstract] AND\n", " Kabiramide J [Title/Abstract] AND\n", " Kadangustin B [Title/Abstract] AND\n", " Kadangustin G [Title/Abstract] AND\n", " Kadcoccilactone E [Title/Abstract] AND\n", " Kadsulignan L [Title/Abstract] AND\n", " Kadsuphilol B [Title/Abstract] AND\n", " Kadsuralignan J [Title/Abstract] AND\n", " Kaempferol Diacyl Rhamnoside [Title/Abstract] AND\n", " Kaempferol-3-O-(2''-O-Galloyl)-Glucoside [Title/Abstract] AND\n", " N-Glycylaminomethyl-P-Methylphosphinic Acid [Title/Abstract] AND\n", " N-Heptyl-2-Mercapto-Thionicotinamide [Title/Abstract] AND\n", " N-Heptyl-N-Methyl-N-Nitrosoamine [Title/Abstract] AND\n", " N-Heptylpiperazin-1-Amine [Title/Abstract] AND\n", " N-Hexadecanyl Alpha-D-Galactopyranoside [Title/Abstract] AND\n", " Gonadotropin-Releasing Hormone Receptor [Title/Abstract] AND\n", " Metabotropic Glutamate Receptor 1 [Title/Abstract] AND\n", " Metabotropic Glutamate Receptor 2 [Title/Abstract] AND\n", " Metabotropic Glutamate Receptor 3 [Title/Abstract] AND\n", " Pancreatic Alpha-Amylase [Title/Abstract] AND\n", " Lysosomal Alpha-Glucosidase [Title/Abstract] AND\n", " Carbonic Anhydrase 3 [Title/Abstract] AND\n", " Adenosine A3 Receptor [Title/Abstract] AND\n", " Atp-Binding Cassette Transporter Sub-Family C Member 8 [Title/Abstract] AND\n", " Multidrug Resistance Protein 1 [Title/Abstract] AND\n", " Renin [Title/Abstract] AND\n", " Gamma-Aminobutyric Acid Receptor Subunit Gamma-1 [Title/Abstract] AND\n", " Gamma-Aminobutyric Acid Receptor Subunit Gamma-3 [Title/Abstract] AND\n", " Gamma-Aminobutyric Acid Receptor Subunit Theta [Title/Abstract] AND\n", " Gamma-Aminobutyric Acid Receptor Subunit Rho-3 [Title/Abstract] AND\n", " Gamma-Aminobutyric-Acid Receptor Subunit Alpha-1 [Title/Abstract] AND\n", " Neuronal Acetylcholine Receptor Subunit Alpha-10 [Title/Abstract] AND\n", " Calcium-Activated Potassium Channel Subunit Alpha 1 [Title/Abstract] AND\n", " Methylenetetrahydrofolate Reductase [Title/Abstract] AND\n", " Glutamate Receptor 1 [Title/Abstract] AND\n", " Estrogen Receptor Beta [Title/Abstract] AND\n", " Microtubule-Associated Protein 1a [Title/Abstract] AND\n", " Low-Density Lipoprotein Receptor-Related Protein 2 [Title/Abstract] AND\n", " Atp-Sensitive Inward Rectifier Potassium Channel 1 [Title/Abstract] AND\n", " Glutathione Peroxidase 6 [Title/Abstract] AND\n", " Glutathione S-Transferase Theta-1 [Title/Abstract] AND\n", " Maleylacetoacetate Isomerase [Title/Abstract] AND\n", " Thioredoxin Domain-Containing Protein 12 [Title/Abstract] AND\n", " G Protein-Activated Inward Rectifier Potassium Channel 2 [Title/Abstract] AND\n", " Tissue-Type Plasminogen Activator [Title/Abstract] AND\n", " Phosphatidylinositol 3-Kinase Regulatory Subunit Gamma [Title/Abstract] AND\n", " Chromaffin Granule Amine Transporter [Title/Abstract] AND\n", " Dihydrofolate Reductase [Title/Abstract] AND\n", " Small Conductance Calcium-Activated Potassium Channel Protein 2 [Title/Abstract] AND\n", " Prostaglandin E2 Receptor Ep4 Subtype [Title/Abstract] AND\n", " Tachykinin Receptor 1 [Title/Abstract] AND\n", " Vitamin D Receptor Interacting Protein [Title/Abstract] AND\n", " Penicillin Binding Protein 3 [Title/Abstract] AND\n", " Dna Gyrase [Title/Abstract] AND\n", " Phospholipase A2 [Title/Abstract] AND\n", " Beta-Tubulin [Title/Abstract] AND\n", " Calcium Dependent Atpase [Title/Abstract] AND\n", " Opioid Receptor Mu [Title/Abstract] AND\n", " N-Type Calcium Channel [Title/Abstract] AND\n", " Cyclooxygenase-3 [Title/Abstract] AND\n", " Erbb2 [Title/Abstract] AND\n", " Aldosterone Receptor [Title/Abstract] AND\n", " Udp-Glucuronosyltransferase [Title/Abstract] AND\n", " Opioid Receptor Sigma 1 [Title/Abstract] AND\n", " Crth2 Receptor [Title/Abstract] AND\n", " Ent2 [Title/Abstract] AND\n", " Cox-2 [Title/Abstract] AND\n", " Sphingomyelinase [Title/Abstract] AND\n", " Sulfonylurea Receptor 2b [Title/Abstract] AND\n", " Hiv-1 Integrase [Title/Abstract] AND\n", " Mu Opioid Receptor [Title/Abstract] AND\n", " Trace Amine-Associated Receptor 1 [Title/Abstract] AND\n", " 5-Hydroxytryptamine 6 Receptor [Title/Abstract] AND\n", " Cytochrome B [Title/Abstract] AND\n", " Arachidonate 5-Lipoxygenase [Title/Abstract] AND\n", " Sodium Channel Protein Type 5 Subunit Alpha [Title/Abstract] AND\n", " Aldo-Keto Reductase Family 1 Member C3 [Title/Abstract] AND\n", " Gap Junction Alpha-1 Protein [Title/Abstract] AND\n", " Vascular Cell Adhesion Protein 1 [Title/Abstract] AND\n", " Myeloperoxidase [Title/Abstract] AND\n", " Tyrosine-Protein Kinase Fer [Title/Abstract] AND\n", " Peptidyl-Prolyl Cis-Trans Isomerase Fkbp4 [Title/Abstract] AND\n", " 4-Hydroxy-Tetrahydrodipicolinate Synthase [Title/Abstract] AND\n", " Prostaglandin Reductase 1 [Title/Abstract] AND\n", " Xaa-Pro Dipeptidase [Title/Abstract] AND\n", " Hydroxyacid Oxidase 1 [Title/Abstract] AND\n", " Hypoxia-Inducible Factor 1-Alpha [Title/Abstract] AND\n", " Natriuretic Peptides A [Title/Abstract] AND\n", " Dna Polymerase I [Title/Abstract] AND\n", " Cysteinyl Leukotriene Receptor 2 [Title/Abstract] AND\n", " Sphingosine 1-Phosphate Receptor 1 [Title/Abstract] AND\n", " Alpha-Glucosidase [Title/Abstract] AND\n", " Induced Myeloid Leukemia Cell Differentiation Protein Mcl-1 [Title/Abstract] AND\n", " Macrophage Metalloelastase [Title/Abstract] AND\n", " Egl Nine Homolog 1 [Title/Abstract] AND\n", " Nad-Dependent Protein Deacetylase Sirtuin-1 [Title/Abstract] AND\n", " Serine/Threonine-Protein Kinase 17a [Title/Abstract] AND\n", " Hyaluronidase-1 [Title/Abstract] AND\n", " Probable Low Molecular Weight Protein-Tyrosine-Phosphatase [Title/Abstract] AND\n", " Serine/Threonine-Protein Kinase Brsk2 [Title/Abstract] AND\n", " Maternal Embryonic Leucine Zipper Kinase [Title/Abstract] AND\n", " Collagenase 3 [Title/Abstract] AND\n", " Aurora Kinase A [Title/Abstract] AND\n", " Melanin-Concentrating Hormone Receptor 1 [Title/Abstract] AND\n", " Chorismate Synthase [Title/Abstract] AND\n", " Kallikrein-8 [Title/Abstract] AND\n", " Protein Fimh [Title/Abstract] AND\n", " Ns3 [Title/Abstract] AND\n", " Mitogen-Activated Protein Kinase Kinase Kinase 8 [Title/Abstract] AND\n", " Galectin-1 [Title/Abstract] AND\n", " Carbonic Anhydrase 4 [Title/Abstract] AND\n", " Retinoic Acid Receptor Alpha [Title/Abstract] AND\n", " Retinoic Acid Receptor Gamma-1 [Title/Abstract] AND\n", " Retinoic Acid Receptor Rxr-Beta [Title/Abstract] AND\n", " Adenosine A1 Receptor [Title/Abstract] AND\n", " Epilepsy [Title/Abstract] AND\n", " Glaucoma [Title/Abstract] AND\n", " Arrhythmia [Title/Abstract] AND\n", " Calculus Urinary [Title/Abstract] AND\n", " Seizures [Title/Abstract] AND\n", " Pruritis [Title/Abstract] AND\n", " Cushings Syndrome [Title/Abstract] AND\n", " Deep Vein Thrombosis [Title/Abstract] AND\n", " Hypertension Ocular [Title/Abstract] AND\n", " Arterial Occlusion Peripheral [Title/Abstract] AND\n", " Bipolar Affective Disorder [Title/Abstract] AND\n", " Gilberts Syndrome [Title/Abstract] AND\n", " Hyperbilirubinemia [Title/Abstract] AND\n", " Rheumatoid Arthritis [Title/Abstract] AND\n", " Rhinitis Seasonal Allergic [Title/Abstract] AND\n", " Myeloma Multiple [Title/Abstract] AND\n", " Opiate Dependence [Title/Abstract] AND\n", " Polycystic Ovarian Syndrome [Title/Abstract] AND\n", " Transplant Bone Marrow [Title/Abstract] AND\n", " Central Nervous System Disorder [Title/Abstract] AND\n", " Deficiency Vitamin D [Title/Abstract] AND\n", " Nephropathy Diabetic [Title/Abstract] AND\n", " Infection Rickettsia [Title/Abstract] AND\n", " Shigellosis [Title/Abstract] AND\n", " Nervousness [Title/Abstract] AND\n", " Anesthesia Local [Title/Abstract] AND\n", " Hypoglycemia [Title/Abstract] AND\n", " Muscle Relaxation [Title/Abstract] AND\n", " Arthritis Psoriatic [Title/Abstract] AND\n", " Carcinoma Brain [Title/Abstract] AND\n", " Graft Versus Host Disease [Title/Abstract] AND\n", " Metastatic Breast Cancer [Title/Abstract] AND\n", " Amnesia [Title/Abstract] AND\n", " Pinworm [Title/Abstract] AND\n", " Dementia [Title/Abstract] AND\n", " Hyperthyroidism [Title/Abstract] AND\n", " Skin Disease [Title/Abstract] AND\n", " Hypogonadism Male Primary [Title/Abstract] AND\n", " Niemann Pick Disease [Title/Abstract] AND\n", " Inflammatory Bowel Diseases [Title/Abstract] AND\n", " Conjunctival Disease [Title/Abstract] AND\n", " Xerostomia [Title/Abstract] AND\n", " Pneumonia Pneumocystis Carinii [Title/Abstract] AND\n", " Gastritis [Title/Abstract] AND\n", " Hepatitis Chronic Active [Title/Abstract] AND\n", " Drug Hypersensitivity [Title/Abstract] AND\n", " Leukemias [Title/Abstract] AND\n", " Multiple Myeloma [Title/Abstract] AND\n", " Chronic Lymphocytic Leukemia [Title/Abstract] AND\n", " Renal Failure [Title/Abstract] AND\n", " Adult Respiratory Distress Syndrome [Title/Abstract] AND\n", " Anemia [Title/Abstract] AND\n", " Melanoma Malignant [Title/Abstract] AND\n", " Leukemia Chronic Myelogenous [Title/Abstract] AND\n", " Myelodysplastic Syndrome [Title/Abstract] AND\n", " Allergic Rhinitis [Title/Abstract] AND\n", " Urticaria [Title/Abstract] AND\n", " Proteinuria [Title/Abstract] AND\n", " Itching [Title/Abstract] AND\n", " Bone Disorder [Title/Abstract] AND\n", " Gonorrhea [Title/Abstract] AND\n", " Albuminuria [Title/Abstract] AND\n", " Infection Upper Respiratory Tract [Title/Abstract] AND\n", " Edema Pulmonary [Title/Abstract] AND\n", " Seizures Partial [Title/Abstract] AND\n", " Meningitis [Title/Abstract] AND\n", " Lichen Planus [Title/Abstract] AND\n", " Lupus Erythematosus Discoid [Title/Abstract] AND\n", " Necrobiosis Lipoidica [Title/Abstract] AND\n", " Nephrotic Syndrome [Title/Abstract] AND\n", " Conjunctivitis [Title/Abstract] AND\n", " Arthralgia Syndrome [Title/Abstract] AND\n", " Non Insulin Dependent Diabetes Mellitus [Title/Abstract] AND\n", " Chills [Title/Abstract] AND\n", " Malnutrition [Title/Abstract] AND\n", " Soft Tissue Infection [Title/Abstract] AND\n", " Muscle Weakness [Title/Abstract] AND\n", " Headache [Title/Abstract] AND\n", " Pain [Title/Abstract] AND\n", " Hepatitis B [Title/Abstract] AND\n", " Anxiety [Title/Abstract] AND\n", " Aggression [Title/Abstract] AND\n", " Depression [Title/Abstract] AND\n", " Stroke [Title/Abstract] AND\n", " Cerebrovascular Disorder [Title/Abstract] AND\n", " Pneumonia [Title/Abstract] AND\n", " Hypertriglyceridemia [Title/Abstract] AND\n", " Carcinoma Head [Title/Abstract] AND\n", " Cardiac Dysrhythmia [Title/Abstract] AND\n", " Puberty Precocious [Title/Abstract] AND\n", " Aspergillosis [Title/Abstract] AND\n", " Meningitis Bacterial [Title/Abstract] AND\n", " Ankylosing Spondylitis [Title/Abstract] AND\n", " Pain Musculoskeletal [Title/Abstract] AND\n", " Hyperlipoproteinemia [Title/Abstract] AND\n", " Obstructive Airway Disease [Title/Abstract] AND\n", " Iritis [Title/Abstract] AND\n", " Carcinoma Endometrial [Title/Abstract] AND\n", " Anxiety Disorder [Title/Abstract] AND\n", " Somatoform Disorder [Title/Abstract] AND\n", "355.895514 seconds (425.84 k allocations: 668.239 MB, 0.01% gc time)\n" ] }, { "data": { "text/plain": [ "300x27097379 sparse matrix with 2218 Int64 entries:\n", "\t[1 , 7000] = 1\n", "\t[268 , 374316] = 1\n", "\t[8 , 692260] = 1\n", "\t[245 , 842879] = 1\n", "\t[1 , 935524] = 1\n", "\t[212 , 1055005] = 1\n", "\t[298 , 1152496] = 1\n", "\t[245 , 1152537] = 1\n", "\t[290 , 1276700] = 1\n", "\t[198 , 1332684] = 1\n", "\t⋮\n", "\t[266 , 27097347] = 1\n", "\t[5 , 27097348] = 1\n", "\t[16 , 27097348] = 1\n", "\t[111 , 27097354] = 1\n", "\t[15 , 27097360] = 1\n", "\t[15 , 27097363] = 1\n", "\t[247 , 27097363] = 1\n", "\t[96 , 27097368] = 1\n", "\t[15 , 27097373] = 1\n", "\t[281 , 27097379] = 1\n", "\t[284 , 27097379] = 1" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "@time T = tdm(all, \"PUBMED\")" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 0.369356 seconds (42 allocations: 206.779 MB, 5.86% gc time)\n" ] }, { "data": { "text/plain": [ "300x27097379 sparse matrix with 2218 Float64 entries:\n", "\t[1 , 7000] = 16.4218\n", "\t[268 , 374316] = 14.8124\n", "\t[8 , 692260] = 17.1149\n", "\t[245 , 842879] = 15.3232\n", "\t[1 , 935524] = 16.4218\n", "\t[212 , 1055005] = 16.0163\n", "\t[298 , 1152496] = 15.3232\n", "\t[245 , 1152537] = 15.3232\n", "\t[290 , 1276700] = 14.8124\n", "\t[198 , 1332684] = 15.3232\n", "\t⋮\n", "\t[266 , 27097347] = 14.8124\n", "\t[5 , 27097348] = 14.8124\n", "\t[16 , 27097348] = 14.8124\n", "\t[111 , 27097354] = 14.8124\n", "\t[15 , 27097360] = 14.8124\n", "\t[15 , 27097363] = 14.8124\n", "\t[247 , 27097363] = 14.8124\n", "\t[96 , 27097368] = 14.8124\n", "\t[15 , 27097373] = 14.8124\n", "\t[281 , 27097379] = 14.8124\n", "\t[284 , 27097379] = 14.8124" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "@time tdmf=T .* log(size(T,2)./sum(T,2))" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 0.107908 seconds (61 allocations: 164.844 KB)\n" ] }, { "data": { "text/plain": [ "27097379x300 sparse matrix with 2218 Float64 entries:\n", "\t[7000 , 1] = 16.4218\n", "\t[935524 , 1] = 16.4218\n", "\t[22131637, 2] = 14.8124\n", "\t[23385960, 2] = 14.8124\n", "\t[23983168, 2] = 14.8124\n", "\t[25007615, 2] = 14.8124\n", "\t[25668796, 2] = 14.8124\n", "\t[25840026, 2] = 14.8124\n", "\t[26201344, 2] = 14.8124\n", "\t[26328143, 2] = 14.8124\n", "\t⋮\n", "\t[27096571, 299] = 14.8124\n", "\t[26863248, 300] = 14.8124\n", "\t[26919056, 300] = 14.8124\n", "\t[26932754, 300] = 14.8124\n", "\t[26944392, 300] = 14.8124\n", "\t[26951025, 300] = 14.8124\n", "\t[26984121, 300] = 14.8124\n", "\t[26997020, 300] = 14.8124\n", "\t[27037573, 300] = 14.8124\n", "\t[27045631, 300] = 14.8124\n", "\t[27064523, 300] = 14.8124" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "@time dtmf=T' .* log(size(T',1)./sum(T',1))" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "cosine_vectorized (generic function with 1 method)" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "function cosine_vectorized(i::SparseMatrixCSC{Float64, Int64}, j::SparseMatrixCSC{Float64, Int64})\n", " return sum(i .* j)/sqrt(sum(i.i)sum(j.*j))\n", "end" ] }, { "cell_type": "code", "execution_count": 91, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "3-element Array{UTF8String,1}:\n", " \"Epilepsy\" \n", " \"Doxaprost\" \n", " \"Gonadotropin-Releasing Hormone Receptor\"" ] }, "execution_count": 91, "metadata": {}, "output_type": "execute_result" } ], "source": [ "array(terms[1,:])[:]" ] }, { "cell_type": "code", "execution_count": 92, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Epilepsy [Title/Abstract] OR Doxaprost [Title/Abstract] OR Gonadotropin-Releasing Hormone Receptor [Title/Abstract] OR\n" ] }, { "data": { "text/plain": [ "MLDemos.PubIds(Nullable(UTF8String[\"Epilepsy\",\"Doxaprost\",\"Gonadotropin-Releasing Hormone Receptor\"]),Nullable([27097007,27096812,27096250,27095821,27095588,27095555,27095099,27095080,27095079,27094525]),10)" ] }, "execution_count": 92, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dbsearch(\"PUBMED\",array(terms[1,:])[:], \"OR\")" ] }, { "cell_type": "code", "execution_count": 104, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Doxaprost [Title/Abstract] OR\n" ] }, { "data": { "text/plain": [ "MLDemos.PubIds(Nullable(UTF8String[\"Doxaprost\"]),Nullable([935524,7000]),2)" ] }, "execution_count": 104, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d=dbsearch(\"PUBMED\",[terms[1,2]], \"OR\")" ] }, { "cell_type": "code", "execution_count": 115, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2-element Array{Int64,1}:\n", " 935524\n", " 7000" ] }, "execution_count": 115, "metadata": {}, "output_type": "execute_result" } ], "source": [ "drug = get(d.pubids)" ] }, { "cell_type": "code", "execution_count": 114, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "10-element Array{Int64,1}:\n", " 27097007\n", " 27096812\n", " 27096250\n", " 27095821\n", " 27095588\n", " 27095555\n", " 27095099\n", " 27095080\n", " 27095079\n", " 27094525" ] }, "execution_count": 114, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dis = get(di.pubids)" ] }, { "cell_type": "code", "execution_count": 113, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "10-element Array{Int64,1}:\n", " 27063262\n", " 27045358\n", " 26953247\n", " 26920257\n", " 26892063\n", " 26645560\n", " 26580281\n", " 26550267\n", " 26373374\n", " 26345908" ] }, "execution_count": 113, "metadata": {}, "output_type": "execute_result" } ], "source": [ "target=get(t.pubids)" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 0.000692 seconds (9 allocations: 703.422 KB)\n" ] } ], "source": [ "@time D = zeros(r,r)\n", "for i = 1:r\n", " for j = i+1:r\n", " D[i,j] = cosine_vectorized(tdmf[:,i],tdmf[:,j])\n", " end\n", "end" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "300x300 sparse matrix with 44850 Float64 entries:\n", "\t[1 , 2] = NaN\n", "\t[1 , 3] = NaN\n", "\t[2 , 3] = NaN\n", "\t[1 , 4] = NaN\n", "\t[2 , 4] = NaN\n", "\t[3 , 4] = NaN\n", "\t[1 , 5] = NaN\n", "\t[2 , 5] = NaN\n", "\t[3 , 5] = NaN\n", "\t[4 , 5] = NaN\n", "\t⋮\n", "\t[289, 300] = NaN\n", "\t[290, 300] = NaN\n", "\t[291, 300] = NaN\n", "\t[292, 300] = NaN\n", "\t[293, 300] = NaN\n", "\t[294, 300] = NaN\n", "\t[295, 300] = NaN\n", "\t[296, 300] = NaN\n", "\t[297, 300] = NaN\n", "\t[298, 300] = NaN\n", "\t[299, 300] = NaN" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Ds = sparse(D)" ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "9670-element Array{Float64,1}:\n", " 1.0\n", " NaN \n", " NaN \n", " NaN \n", " NaN \n", " NaN \n", " NaN \n", " NaN \n", " NaN \n", " NaN \n", " NaN \n", " NaN \n", " NaN \n", " ⋮ \n", " NaN \n", " NaN \n", " NaN \n", " NaN \n", " NaN \n", " NaN \n", " NaN \n", " NaN \n", " NaN \n", " NaN \n", " NaN \n", " 0.1" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Ds.nzval\n" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "27097348x1 sparse matrix with 10 Float64 entries:\n", "\t[22131637, 1] = 14.8124\n", "\t[23385960, 1] = 14.8124\n", "\t[23983168, 1] = 14.8124\n", "\t[25007615, 1] = 14.8124\n", "\t[25668796, 1] = 14.8124\n", "\t[25840026, 1] = 14.8124\n", "\t[26201344, 1] = 14.8124\n", "\t[26328143, 1] = 14.8124\n", "\t[26716887, 1] = 14.8124\n", "\t[26735908, 1] = 14.8124" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dtmf[:,2]" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "\"Gonadotropin-Releasing Hormone Receptor\"" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "all[11]" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "\"Doxaprost\"" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "all[1]" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "\"Epilepsy\"" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "all[21]" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "27097348x1 sparse matrix with 10 Float64 entries:\n", "\t[26345908, 1] = 14.8124\n", "\t[26373374, 1] = 14.8124\n", "\t[26550267, 1] = 14.8124\n", "\t[26580281, 1] = 14.8124\n", "\t[26645560, 1] = 14.8124\n", "\t[26892063, 1] = 14.8124\n", "\t[26920257, 1] = 14.8124\n", "\t[26953247, 1] = 14.8124\n", "\t[27045358, 1] = 14.8124\n", "\t[27063262, 1] = 14.8124" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dtmf[:,11]" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "27097348x1 sparse matrix with 2 Float64 entries:\n", "\t[7000 , 1] = 16.4218\n", "\t[935524 , 1] = 16.4218" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dtmf[:,1]" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Gonadotropin-Releasing Hormone Receptor [Title/Abstract] AND\n" ] }, { "data": { "text/plain": [ "MLDemos.PubIds(Nullable(UTF8String[\"Gonadotropin-Releasing Hormone Receptor\"]),Nullable([27063262,27045358,26953247,26920257,26892063,26645560,26580281,26550267,26373374,26345908]),10)" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dbsearch(\"PUBMED\", [all[11]], \"AND\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Julia 0.4.5", "language": "julia", "name": "julia-0.4" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", "version": "0.4.5" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
tongwang01/tensorflow	tensorflow/examples/udacity/4_convolutions-TongCopy1.ipynb	1	13048	{ "cells": [ { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "4embtkV0pNxM" }, "source": [ "Deep Learning\n", "=============\n", "\n", "Assignment 4\n", "------------\n", "\n", "Previously in `2_fullyconnected.ipynb` and `3_regularization.ipynb`, we trained fully connected networks to classify [notMNIST](http://yaroslavvb.blogspot.com/2011/09/notmnist-dataset.html) characters.\n", "\n", "The goal of this assignment is make the neural network convolutional." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "cellView": "both", "colab": { "autoexec": { "startup": false, "wait_interval": 0 } }, "colab_type": "code", "collapsed": true, "id": "tm2CQN_Cpwj0" }, "outputs": [], "source": [ "# These are all the modules we'll be using later. Make sure you can import them\n", "# before proceeding further.\n", "from __future__ import print_function\n", "import numpy as np\n", "import tensorflow as tf\n", "from six.moves import cPickle as pickle\n", "from six.moves import range" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "cellView": "both", "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "output_extras": [ { "item_id": 1 } ] }, "colab_type": "code", "collapsed": false, "executionInfo": { "elapsed": 11948, "status": "ok", "timestamp": 1446658914837, "user": { "color": "", "displayName": "", "isAnonymous": false, "isMe": true, "permissionId": "", "photoUrl": "", "sessionId": "0", "userId": "" }, "user_tz": 480 }, "id": "y3-cj1bpmuxc", "outputId": "016b1a51-0290-4b08-efdb-8c95ffc3cd01" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training set (200000, 28, 28) (200000,)\n", "Validation set (10000, 28, 28) (10000,)\n", "Test set (10000, 28, 28) (10000,)\n" ] } ], "source": [ "pickle_file = 'notMNIST.pickle'\n", "\n", "with open(pickle_file, 'rb') as f:\n", " save = pickle.load(f)\n", " train_dataset = save['train_dataset']\n", " train_labels = save['train_labels']\n", " valid_dataset = save['valid_dataset']\n", " valid_labels = save['valid_labels']\n", " test_dataset = save['test_dataset']\n", " test_labels = save['test_labels']\n", " del save # hint to help gc free up memory\n", " print('Training set', train_dataset.shape, train_labels.shape)\n", " print('Validation set', valid_dataset.shape, valid_labels.shape)\n", " print('Test set', test_dataset.shape, test_labels.shape)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "L7aHrm6nGDMB" }, "source": [ "Reformat into a TensorFlow-friendly shape:\n", "- convolutions need the image data formatted as a cube (width by height by #channels)\n", "- labels as float 1-hot encodings." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "cellView": "both", "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "output_extras": [ { "item_id": 1 } ] }, "colab_type": "code", "collapsed": false, "executionInfo": { "elapsed": 11952, "status": "ok", "timestamp": 1446658914857, "user": { "color": "", "displayName": "", "isAnonymous": false, "isMe": true, "permissionId": "", "photoUrl": "", "sessionId": "0", "userId": "" }, "user_tz": 480 }, "id": "IRSyYiIIGIzS", "outputId": "650a208c-8359-4852-f4f5-8bf10e80ef6c" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training set (200000, 28, 28, 1) (200000, 10)\n", "Validation set (10000, 28, 28, 1) (10000, 10)\n", "Test set (10000, 28, 28, 1) (10000, 10)\n" ] } ], "source": [ "image_size = 28\n", "num_labels = 10\n", "num_channels = 1 # grayscale\n", "\n", "import numpy as np\n", "\n", "def reformat(dataset, labels):\n", " dataset = dataset.reshape(\n", " (-1, image_size, image_size, num_channels)).astype(np.float32)\n", " labels = (np.arange(num_labels) == labels[:,None]).astype(np.float32)\n", " return dataset, labels\n", "train_dataset, train_labels = reformat(train_dataset, train_labels)\n", "valid_dataset, valid_labels = reformat(valid_dataset, valid_labels)\n", "test_dataset, test_labels = reformat(test_dataset, test_labels)\n", "print('Training set', train_dataset.shape, train_labels.shape)\n", "print('Validation set', valid_dataset.shape, valid_labels.shape)\n", "print('Test set', test_dataset.shape, test_labels.shape)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "cellView": "both", "colab": { "autoexec": { "startup": false, "wait_interval": 0 } }, "colab_type": "code", "collapsed": true, "id": "AgQDIREv02p1" }, "outputs": [], "source": [ "def accuracy(predictions, labels):\n", " return (100.0 * np.sum(np.argmax(predictions, 1) == np.argmax(labels, 1))\n", " / predictions.shape[0])" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "5rhgjmROXu2O" }, "source": [ "Let's build a small network with two convolutional layers, followed by one fully connected layer. Convolutional networks are more expensive computationally, so we'll limit its depth and number of fully connected nodes." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "cellView": "both", "colab": { "autoexec": { "startup": false, "wait_interval": 0 } }, "colab_type": "code", "collapsed": true, "id": "IZYv70SvvOan" }, "outputs": [], "source": [ "batch_size = 16\n", "patch_size = 5\n", "depth = 16\n", "num_hidden = 64\n", "\n", "graph = tf.Graph()\n", "\n", "with graph.as_default():\n", "\n", " # Input data.\n", " tf_train_dataset = tf.placeholder(\n", " tf.float32, shape=(batch_size, image_size, image_size, num_channels))\n", " tf_train_labels = tf.placeholder(tf.float32, shape=(batch_size, num_labels))\n", " tf_valid_dataset = tf.constant(valid_dataset)\n", " tf_test_dataset = tf.constant(test_dataset)\n", " \n", " # Variables.\n", " layer1_weights = tf.Variable(tf.truncated_normal(\n", " [patch_size, patch_size, num_channels, depth], stddev=0.1))\n", " layer1_biases = tf.Variable(tf.zeros([depth]))\n", " layer2_weights = tf.Variable(tf.truncated_normal(\n", " [patch_size, patch_size, depth, depth], stddev=0.1))\n", " layer2_biases = tf.Variable(tf.constant(1.0, shape=[depth]))\n", " layer3_weights = tf.Variable(tf.truncated_normal(\n", " [image_size // 4 * image_size // 4 * depth, num_hidden], stddev=0.1))\n", " layer3_biases = tf.Variable(tf.constant(1.0, shape=[num_hidden]))\n", " layer4_weights = tf.Variable(tf.truncated_normal(\n", " [num_hidden, num_labels], stddev=0.1))\n", " layer4_biases = tf.Variable(tf.constant(1.0, shape=[num_labels]))\n", " \n", " # Model.\n", " def model(data):\n", " conv = tf.nn.conv2d(data, layer1_weights, [1, 2, 2, 1], padding='SAME')\n", " hidden = tf.nn.relu(conv + layer1_biases)\n", " conv = tf.nn.conv2d(hidden, layer2_weights, [1, 2, 2, 1], padding='SAME')\n", " hidden = tf.nn.relu(conv + layer2_biases)\n", " shape = hidden.get_shape().as_list()\n", " reshape = tf.reshape(hidden, [shape[0], shape[1] * shape[2] * shape[3]])\n", " hidden = tf.nn.relu(tf.matmul(reshape, layer3_weights) + layer3_biases)\n", " return tf.matmul(hidden, layer4_weights) + layer4_biases\n", " \n", " # Training computation.\n", " logits = model(tf_train_dataset)\n", " loss = tf.reduce_mean(\n", " tf.nn.softmax_cross_entropy_with_logits(logits, tf_train_labels))\n", " \n", " # Optimizer.\n", " optimizer = tf.train.GradientDescentOptimizer(0.05).minimize(loss)\n", " \n", " # Predictions for the training, validation, and test data.\n", " train_prediction = tf.nn.softmax(logits)\n", " valid_prediction = tf.nn.softmax(model(tf_valid_dataset))\n", " test_prediction = tf.nn.softmax(model(tf_test_dataset))" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "cellView": "both", "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "output_extras": [ { "item_id": 37 } ] }, "colab_type": "code", "collapsed": false, "executionInfo": { "elapsed": 63292, "status": "ok", "timestamp": 1446658966251, "user": { "color": "", "displayName": "", "isAnonymous": false, "isMe": true, "permissionId": "", "photoUrl": "", "sessionId": "0", "userId": "" }, "user_tz": 480 }, "id": "noKFb2UovVFR", "outputId": "28941338-2ef9-4088-8bd1-44295661e628" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Initialized\n", "Minibatch loss at step 0: 2.660036\n", "Minibatch accuracy: 12.5%\n", "Validation accuracy: 10.0%\n", "Minibatch loss at step 500: 0.752207\n", "Minibatch accuracy: 87.5%\n", "Validation accuracy: 81.1%\n", "Minibatch loss at step 1000: 0.524167\n", "Minibatch accuracy: 87.5%\n", "Validation accuracy: 83.0%\n", "Minibatch loss at step 1500: 0.653026\n", "Minibatch accuracy: 81.2%\n", "Validation accuracy: 84.7%\n", "Minibatch loss at step 2000: 0.092819\n", "Minibatch accuracy: 100.0%\n", "Validation accuracy: 85.4%\n", "Minibatch loss at step 2500: 0.879358\n", "Minibatch accuracy: 68.8%\n", "Validation accuracy: 85.5%\n", "Minibatch loss at step 3000: 0.706744\n", "Minibatch accuracy: 87.5%\n", "Validation accuracy: 86.2%\n", "Test accuracy: 92.8%\n" ] } ], "source": [ "num_steps = 3001\n", "\n", "with tf.Session(graph=graph) as session:\n", " tf.initialize_all_variables().run()\n", " print('Initialized')\n", " for step in range(num_steps):\n", " offset = (step * batch_size) % (train_labels.shape[0] - batch_size)\n", " batch_data = train_dataset[offset:(offset + batch_size), :, :, :]\n", " batch_labels = train_labels[offset:(offset + batch_size), :]\n", " feed_dict = {tf_train_dataset : batch_data, tf_train_labels : batch_labels}\n", " _, l, predictions = session.run(\n", " [optimizer, loss, train_prediction], feed_dict=feed_dict)\n", " if (step % 500 == 0):\n", " print('Minibatch loss at step %d: %f' % (step, l))\n", " print('Minibatch accuracy: %.1f%%' % accuracy(predictions, batch_labels))\n", " print('Validation accuracy: %.1f%%' % accuracy(\n", " valid_prediction.eval(), valid_labels))\n", " print('Test accuracy: %.1f%%' % accuracy(test_prediction.eval(), test_labels))" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "KedKkn4EutIK" }, "source": [ "---\n", "Problem 1\n", "---------\n", "\n", "The convolutional model above uses convolutions with stride 2 to reduce the dimensionality. Replace the strides by a max pooling operation (`nn.max_pool()`) of stride 2 and kernel size 2.\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "klf21gpbAgb-" }, "source": [ "---\n", "Problem 2\n", "---------\n", "\n", "Try to get the best performance you can using a convolutional net. Look for example at the classic [LeNet5](http://yann.lecun.com/exdb/lenet/) architecture, adding Dropout, and/or adding learning rate decay.\n", "\n", "---" ] } ], "metadata": { "colab": { "default_view": {}, "name": "4_convolutions.ipynb", "provenance": [], "version": "0.3.2", "views": {} }, "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.9" } }, "nbformat": 4, "nbformat_minor": 0 }	apache-2.0
denricoNBHS/machine-learning	examples/MNIST-TF.ipynb	1	15056	{ "cells": [ { "cell_type": "code", "execution_count": 33, "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": [ "from tensorflow.examples.tutorials.mnist import input_data\n", "mnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAAAMElEQVR4nGP8z4AbMOGRGxBJFijN\nCBf5jykJF2ZkwJTE6t1B589RSWyS//FJ0sFBAA0FBTtwTifFAAAAAElFTkSuQmCC\n", "text/plain": [ "<PIL.Image.Image image mode=L size=28x28 at 0x10733AEF0>" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from PIL import Image\n", "import numpy as np\n", "\n", "img = Image.open('img/IMG_5638.JPG')\n", "img = img.convert('L').rotate(-90)\n", "img = img.crop((1100, 900, 2000, 1800))\n", "img = img.resize((28, 28), Image.ANTIALIAS)\n", "\n", "img_array = np.asarray(img)\n", "grad = img_array[:,0]\n", "bg = np.transpose(np.array([grad for i in range(28)]))\n", "div = img_array/((bg.astype(np.float32)+1)/256)\n", "rescaled = div(div<255)+255np.ones(np.shape(div))(div > 255)\n", "final = rescaled.astype('uint8')\n", "#final = final.point(lambda x: 0 if x < 160 else 255)\n", "final = Image.fromarray(final)\n", "final = final.point(lambda x: 0 if x < 238 else 255)\n", "final" ] }, { "cell_type": "code", "execution_count": 218, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 255., 255., 255., 255., 255., 255., 255., 255., 255.,\n", " 255., 255., 255., 255., 255., 255., 255., 255., 255.,\n", " 255., 255., 255., 255., 255., 255., 255., 255., 255.,\n", " 255., 255., 255., 255., 255., 255., 255., 255., 255.,\n", " 255., 255., 255., 255., 255., 255., 255., 255., 255.,\n", " 255., 255., 255., 255., 255., 255., 255., 255., 255.,\n", " 255., 255., 255., 255., 255., 255., 255., 255., 255.,\n", " 255., 255., 255., 255., 255., 255., 255., 255., 255.,\n", " 255., 255., 255., 255., 255., 255., 255., 255., 255.,\n", " 255., 255., 255., 255., 255., 255., 255., 255., 255.,\n", " 255., 255., 255., 255., 255., 255., 255., 255., 255.,\n", " 255., 255., 255., 255., 255., 255., 255., 255., 255.,\n", " 255., 255., 255., 255., 255., 255., 255., 255., 255.,\n", " 255., 255., 255., 255., 255., 255., 255., 255., 255.,\n", " 255., 255., 255., 255., 255., 255., 255., 255., 255.,\n", " 255., 255., 255., 255., 255., 255., 255., 255., 255.,\n", " 255., 255., 255., 255., 255., 255., 255., 255., 255.,\n", " 255., 255., 255., 255., 255., 255., 255., 255., 255.,\n", " 255., 255., 255., 255., 255., 255., 255., 255., 255.,\n", " 255., 255., 255., 255., 0., 0., 0., 0., 0.,\n", " 0., 0., 0., 0., 0., 255., 255., 255., 255.,\n", " 255., 255., 255., 255., 255., 255., 255., 255., 255.,\n", " 255., 255., 255., 255., 255., 0., 0., 0., 0.,\n", " 255., 255., 255., 255., 255., 0., 255., 255., 255.,\n", " 255., 255., 255., 255., 255., 255., 255., 255., 255.,\n", " 255., 255., 255., 255., 255., 255., 255., 255., 255.,\n", " 255., 255., 255., 255., 255., 255., 0., 255., 255.,\n", " 255., 255., 255., 255., 255., 255., 255., 255., 255.,\n", " 255., 255., 255., 255., 255., 255., 255., 255., 255.,\n", " 255., 255., 255., 255., 255., 255., 255., 0., 255.,\n", " 255., 255., 255., 255., 255., 255., 255., 255., 255.,\n", " 255., 255., 255., 255., 255., 255., 255., 255., 255.,\n", " 255., 255., 255., 255., 255., 255., 255., 255., 0.,\n", " 255., 255., 255., 255., 255., 255., 255., 255., 255.,\n", " 255., 255., 255., 255., 255., 255., 255., 255., 255.,\n", " 255., 255., 255., 255., 255., 255., 255., 255., 255.,\n", " 0., 255., 255., 255., 255., 255., 255., 255., 255.,\n", " 255., 255., 255., 255., 255., 255., 255., 255., 255.,\n", " 255., 255., 255., 255., 255., 255., 255., 255., 255.,\n", " 255., 0., 255., 255., 255., 255., 255., 255., 255.,\n", " 255., 255., 255., 255., 255., 255., 255., 255., 255.,\n", " 255., 255., 255., 255., 255., 255., 255., 255., 255.,\n", " 255., 255., 0., 255., 255., 255., 255., 255., 255.,\n", " 255., 255., 255., 255., 255., 255., 255., 255., 255.,\n", " 255., 255., 255., 255., 255., 255., 255., 255., 255.,\n", " 255., 255., 255., 0., 255., 255., 255., 255., 255.,\n", " 255., 255., 255., 255., 255., 255., 255., 255., 255.,\n", " 255., 255., 255., 255., 255., 255., 255., 255., 255.,\n", " 255., 255., 255., 255., 0., 255., 255., 255., 255.,\n", " 255., 255., 255., 255., 255., 255., 255., 255., 255.,\n", " 255., 255., 255., 255., 255., 255., 255., 255., 255.,\n", " 255., 255., 255., 255., 255., 0., 255., 255., 255.,\n", " 255., 255., 255., 255., 255., 255., 255., 255., 255.,\n", " 255., 255., 255., 255., 255., 255., 255., 255., 255.,\n", " 255., 255., 255., 255., 255., 255., 0., 255., 255.,\n", " 255., 255., 255., 255., 255., 255., 255., 255., 255.,\n", " 255., 255., 255., 255., 255., 255., 255., 255., 255.,\n", " 255., 255., 255., 255., 255., 255., 255., 0., 255.,\n", " 255., 255., 255., 255., 255., 255., 255., 255., 255.,\n", " 255., 255., 255., 255., 255., 255., 255., 255., 255.,\n", " 255., 255., 255., 255., 255., 255., 255., 255., 0.,\n", " 255., 255., 255., 255., 255., 255., 255., 255., 255.,\n", " 255., 255., 255., 255., 255., 255., 255., 255., 255.,\n", " 255., 255., 255., 255., 255., 255., 255., 255., 255.,\n", " 0., 255., 255., 255., 255., 255., 255., 255., 255.,\n", " 255., 255., 255., 255., 255., 255., 255., 255., 255.,\n", " 255., 255., 255., 255., 255., 255., 255., 255., 255.,\n", " 255., 0., 255., 255., 255., 255., 255., 255., 255.,\n", " 255., 255., 255., 255., 255., 255., 255., 255., 255.,\n", " 255., 255., 255., 255., 255., 255., 255., 255., 255.,\n", " 255., 255., 0., 255., 255., 255., 255., 255., 255.,\n", " 255., 255., 255., 255., 255., 255., 255., 255., 255.,\n", " 255., 255., 255., 255., 255., 255., 255., 255., 255.,\n", " 255., 255., 255., 255., 255., 255., 255., 255., 255.,\n", " 255., 255., 255., 255., 255., 255., 255., 255., 255.,\n", " 255., 255., 255., 255., 255., 255., 255., 255., 255.,\n", " 255., 255., 255., 255., 255., 255., 255., 255., 255.,\n", " 255., 255., 255., 255., 255., 255., 255., 255., 255.,\n", " 255., 255., 255., 255., 255., 255., 255., 255., 255.,\n", " 255., 255., 255., 255., 255., 255., 255., 255., 255.,\n", " 255., 255., 255., 255., 255., 255., 255., 255., 255.,\n", " 255., 255., 255., 255., 255., 255., 255., 255., 255.,\n", " 255., 255., 255., 255., 255., 255., 255., 255., 255.,\n", " 255., 255., 255., 255., 255., 255., 255., 255., 255.,\n", " 255., 255., 255., 255., 255., 255., 255., 255., 255.,\n", " 255., 255., 255., 255., 255., 255., 255., 255., 255.,\n", " 255., 255., 255., 255., 255., 255., 255., 255., 255.,\n", " 255.]], dtype=float32)" ] }, "execution_count": 218, "metadata": {}, "output_type": "execute_result" } ], "source": [ "img = np.asarray(final, dtype=np.float32).flatten()\n", "img = np.reshape(img, (1, 784))\n", "img" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.9064\n" ] } ], "source": [ "# Softmax layer\n", "import tensorflow as tf\n", "\n", "x = tf.placeholder(tf.float32, [None, 784])\n", "y_ = tf.placeholder(tf.float32, [None, 10])\n", "W = tf.Variable(tf.zeros([784,10]))\n", "b = tf.Variable(tf.zeros([10]))\n", "\n", "y = tf.nn.softmax(tf.matmul(x, W) + b)\n", "\n", "cross_entropy = tf.reduce_mean(\n", " tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y))\n", "\n", "train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)\n", "\n", "sess = tf.InteractiveSession()\n", "\n", "tf.global_variables_initializer().run()\n", "\n", "for _ in range(1000):\n", " batch_xs, batch_ys = mnist.train.next_batch(100)\n", " sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})\n", " \n", "correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))\n", "\n", "accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n", "print(sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<function write_graph at 0x10e8cc6a8>\n" ] } ], "source": [ "import tensorflow as tf\n", "\n", "x = tf.placeholder(tf.float32, [None, 784])\n", "y_ = tf.placeholder(tf.float32, [None, 10])\n", "\n", "# Define helper functions\n", "\n", "def weight_variable(shape):\n", " initial = tf.truncated_normal(shape, stddev=0.1)\n", " return tf.Variable(initial)\n", "\n", "def bias_variable(shape):\n", " initial = tf.constant(0.1, shape=shape)\n", " return tf.Variable(initial)\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')\n", "\n", "W_conv1 = weight_variable([5, 5, 1, 32])\n", "b_conv1 = bias_variable([32])\n", "\n", "x_image = tf.reshape(x, [-1, 28, 28, 1])\n", "\n", "h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)\n", "h_pool1 = max_pool_2x2(h_conv1)\n", "\n", "W_conv2 = weight_variable([5, 5, 32, 64])\n", "b_conv2 = bias_variable([64])\n", "\n", "h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)\n", "h_pool2 = max_pool_2x2(h_conv2)\n", "\n", "W_fc1 = weight_variable([7764, 1024])\n", "b_fc1 = bias_variable([1024])\n", "\n", "h_pool2_flat = tf.reshape(h_pool2, [-1, 77*64])\n", "h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)\n", "\n", "keep_prob = tf.placeholder(tf.float32)\n", "h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)\n", "\n", "W_fc2 = weight_variable([1024, 10])\n", "b_fc2 = bias_variable([10])\n", "\n", "y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2\n", "\n", "cross_entropy = tf.reduce_mean(\n", " tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv))\n", "\n", "train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)\n", "correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_, 1))\n", "accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n", "\n", "with tf.Session() as sess:\n", " \n", "# saver = tf.train.Saver()\n", "# saver.restore(sess, 'tmp/checkpoint.ckpt')\n", "#\n", "#with tf.Session() as sess:\n", "# sess.run(tf.global_variables_initializer())\n", "# saver = tf.train.Saver()\n", " \n", "# for i in range(10000):\n", "# batch = mnist.train.next_batch(50)\n", "# if i%100 == 0:\n", "# train_accuracy = accuracy.eval(feed_dict={\n", "# x:batch[0], y_:batch[1], keep_prob: 1.0})\n", "# print(\"step %d, training accuracy %g\" % (i, train_accuracy))\n", "# \n", "# train_step.run(feed_dict={x:batch[0], y_:batch[1], keep_prob: 0.5})\n", "# if i%1000 == 0:\n", "# saver.save(sess, 'tmp/checkpoint.ckpt')\n", "# \n", "# \n", "# print(\"test accuracy %g\" % accuracy.eval(feed_dict={\n", "# x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0\n", "# }))\n", " \n", " \n", "\n", " \n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda root]", "language": "python", "name": "conda-root-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 1 }	gpl-3.0
gilmana/La-noLA_RNA_Data	5GB1_La_Experiment .ipynb	2	153517	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/alexeygilman/anaconda/lib/python3.4/site-packages/matplotlib/__init__.py:872: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n", " warnings.warn(self.msg_depr % (key, alt_key))\n" ] } ], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from scipy.stats import gaussian_kde\n", "import seaborn\n", "seaborn.set()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data = pd.read_csv(\"5G.rpkm.La.csv\")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.fillna(0, inplace = True)\n", "len(data[pd.isnull(data[\"5GB1_vial_wLa_TR3\"])]) #some magic to check if column contains NaN values " ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Create a column of log2 of expression level \n", "data[\"Log2_5GB1_vial_wLa_TR3\"]=data[\"5GB1_vial_wLa_TR3\"].apply(np.log2)\n", "data[\"Log2_5GB1_vial_woLa_TR2\"]=data[\"5GB1_vial_woLa_TR2\"].apply(np.log2)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#Replace the -inf values with zero\n", "data[\"Log2_5GB1_vial_wLa_TR3\"].replace(float('-inf'), 0, inplace = True)\n", "data[\"Log2_5GB1_vial_woLa_TR2\"].replace(float('-inf'), 0, inplace = True)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#Log2(expression) difference. Control - Treatment. Positive values = high expression w/o La. Neg = High expression w La\n", "data[\"M_Log_Diff\"]=(data[\"Log2_5GB1_vial_woLa_TR2\"]-data[\"Log2_5GB1_vial_wLa_TR3\"])\n", "data[\"A_Log_Ave\"]=(data[\"Log2_5GB1_vial_woLa_TR2\"]+data[\"Log2_5GB1_vial_wLa_TR3\"])/2" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#raplce the zero values with a nan (float value)\n", "#data[\"M_Log_Diff\"].replace(0, np.nan, inplace = True)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(array([ 1.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n", " 1.00000000e+00, 4.00000000e+00, 5.00000000e+00,\n", " 3.00000000e+00, 7.00000000e+00, 1.90000000e+01,\n", " 2.80000000e+01, 5.80000000e+01, 1.67000000e+02,\n", " 6.10000000e+02, 1.89400000e+03, 1.14000000e+03,\n", " 3.35000000e+02, 1.16000000e+02, 4.30000000e+01,\n", " 2.80000000e+01, 2.40000000e+01, 1.20000000e+01,\n", " 1.10000000e+01, 3.00000000e+00, 6.00000000e+00,\n", " 6.00000000e+00, 4.00000000e+00, 1.00000000e+00,\n", " 1.00000000e+00, 2.00000000e+00, 1.00000000e+00,\n", " 1.00000000e+00, 1.00000000e+00, 1.00000000e+00,\n", " 2.00000000e+00, 1.00000000e+00, 0.00000000e+00,\n", " 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n", " 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n", " 0.00000000e+00, 1.00000000e+00, 1.00000000e+00,\n", " 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n", " 1.00000000e+00, 1.00000000e+00]),\n", " array([ -5. , -4.64, -4.28, -3.92, -3.56, -3.2 , -2.84, -2.48,\n", " -2.12, -1.76, -1.4 , -1.04, -0.68, -0.32, 0.04, 0.4 ,\n", " 0.76, 1.12, 1.48, 1.84, 2.2 , 2.56, 2.92, 3.28,\n", " 3.64, 4. , 4.36, 4.72, 5.08, 5.44, 5.8 , 6.16,\n", " 6.52, 6.88, 7.24, 7.6 , 7.96, 8.32, 8.68, 9.04,\n", " 9.4 , 9.76, 10.12, 10.48, 10.84, 11.2 , 11.56, 11.92,\n", " 12.28, 12.64, 13. ]),\n", " <a list of 50 Patch objects>)" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { 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5cOFCLViw4B/vn9fQnz9/XjNnzuR6OKPA9YXcde3afz6RGovxYbvROHTokI4ePSq/3y9J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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10ab5c9e8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "plt.style.use('ggplot')\n", "axes = plt.gca()\n", "axes.set_ylim([0,100])\n", "plt.hist(data[\"M_Log_Diff\"],bins = 50, range = [-5,13]) #[np.nanmin(data[\"Log_Diff\"]), np.nanmax(data[\"Log_Diff\"])]) " ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x10a932240>" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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ADdWrV4Bx43y8+qol6mu33eajUyd1BJGe7uWJJ6z85z+RwenUKat2Y/f54K23zNou5sRE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\nQghAAoIQQohyEhCEEEIAEhCEEEKUk4AghBACkIA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"text/plain": [ "<matplotlib.figure.Figure at 0x10ab5c518>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#MA plot\n", "plt.scatter(data[\"A_Log_Ave\"],data[\"M_Log_Diff\"])\n", "axes = plt.gca()\n", "axes.set_xlim([0,20])\n", "axes.set_ylim([-5,15])\n", "plt.ylabel('M')\n", "plt.xlabel('A')\n", "#plt.imshow" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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JaoYB7QEDs1nBIXPLhNjn7JOBoGkaE8YkkeIzU98YY9zBSRTkdK/ZJRqDR5+r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\nvQqNLen+308kLRLJgedHe22eXNjJ7X9v4I23ekgm33+D9rggO6yh7VM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"text/plain": [ "<matplotlib.figure.Figure at 0x10abda6d8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "xy = np.array([data[\"A_Log_Ave\"],data[\"M_Log_Diff\"]]) #create 2d array using the data columns\n", "\n", "#xy = np.vstack([data[\"A_Log_Ave\"],data[\"M_Log_Diff\"]]) #can also do it by stacking arrays, must be same length \n", "\n", "z = gaussian_kde(xy) #create a probability density distribution function for a 2d array\n", "\n", "fig, ax = plt.subplots()\n", "\n", "ax.scatter(data[\"A_Log_Ave\"], data[\"M_Log_Diff\"], c=z(xy), s=20, cmap=plt.cm.coolwarm) # z(xy) is input to get the\n", "# probability function output\n", "\n", "axes = plt.gca()\n", "axes.set_xlim([0,20])\n", "axes.set_ylim([-5,15])\n", "plt.ylabel('M')\n", "plt.xlabel('A')\n", "plt.title(\"log2(-La) - log2(+La)\")\n", "plt.savefig('MA_plot_plut_minus_La.png', bbox_inches='tight')" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 0_1\n", "1 0_1\n", "2 0_1\n", "3 0_1\n", "4 0_1\n", "5 0_1\n", "6 0_1\n", "7 0_1\n", "8 0_1\n", "9 0_1\n", "10 0_1\n", "11 0_1\n", "12 0_1\n", "13 0_1\n", "14 0_1\n", "15 0_1\n", "16 0_1\n", "17 0_1\n", "18 0_1\n", "19 0_1\n", "20 0_1\n", "21 0_1\n", "22 0_1\n", "23 0_1\n", "24 0_1\n", "25 0_1\n", "26 0_1\n", "27 0_1\n", "28 0_1\n", "29 0_1\n", " ... \n", "4510 8_9\n", "4511 6_7\n", "4512 5_6\n", "4513 5_6\n", "4514 7_8\n", "4515 6_7\n", "4516 8_9\n", "4517 6_7\n", "4518 6_7\n", "4519 7_8\n", "4520 5_6\n", "4521 6_7\n", "4522 6_7\n", "4523 6_7\n", "4524 6_7\n", "4525 7_8\n", "4526 8_9\n", "4527 8_9\n", "4528 7_8\n", "4529 9_10\n", "4530 10_18\n", "4531 7_8\n", "4532 6_7\n", "4533 6_7\n", "4534 5_6\n", "4535 6_7\n", "4536 6_7\n", "4537 6_7\n", "4538 6_7\n", "4539 7_8\n", "Name: A_Log_Ave, dtype: category\n", "Categories (11, object): [0_1 < 1_2 < 2_3 < 3_4 ... 7_8 < 8_9 < 9_10 < 10_18]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Slicing the the A values into bins in order to pick out outliers in every bin \n", "A_bins = [-1,1,2,3,4,5,6,7,8,9,10,18]\n", "A_group_names = [\"0_1\",\"1_2\",\"2_3\",\"3_4\",\"4_5\",\"5_6\",\"6_7\",\"7_8\",\"8_9\",\"9_10\",\"10_18\"]\n", "categories = pd.cut(data[\"A_Log_Ave\"], A_bins, labels = A_group_names)\n", "categories" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>locus_tag</th>\n", " <th>product</th>\n", " <th>5GB1_vial_wLa_TR3</th>\n", " <th>5GB1_vial_woLa_TR2</th>\n", " <th>Log2_5GB1_vial_wLa_TR3</th>\n", " <th>Log2_5GB1_vial_woLa_TR2</th>\n", " <th>M_Log_Diff</th>\n", " <th>A_Log_Ave</th>\n", " <th>A_Categories</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>MBURv2_10001</td>\n", " <td>protein of unknown function</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0_1</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>MBURv2_10002</td>\n", " <td>KfrB</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0_1</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>MBURv2_10003</td>\n", " <td>Protein traN</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0_1</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>MBURv2_10004</td>\n", " <td>Protein TraM</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0_1</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>MBURv2_10005</td>\n", " <td>Protein TraL</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0_1</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " locus_tag product 5GB1_vial_wLa_TR3 \\\n", "0 MBURv2_10001 protein of unknown function 0 \n", "1 MBURv2_10002 KfrB 0 \n", "2 MBURv2_10003 Protein traN 0 \n", "3 MBURv2_10004 Protein TraM 0 \n", "4 MBURv2_10005 Protein TraL 0 \n", "\n", " 5GB1_vial_woLa_TR2 Log2_5GB1_vial_wLa_TR3 Log2_5GB1_vial_woLa_TR2 \\\n", "0 0 0 0 \n", "1 0 0 0 \n", "2 0 0 0 \n", "3 0 0 0 \n", "4 0 0 0 \n", "\n", " M_Log_Diff A_Log_Ave A_Categories \n", "0 0 0 0_1 \n", "1 0 0 0_1 \n", "2 0 0 0_1 \n", "3 0 0 0_1 \n", "4 0 0 0_1 " ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data[\"A_Categories\"] = pd.cut(data[\"A_Log_Ave\"], A_bins, labels = A_group_names)\n", "data.head()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>count</th>\n", " <th>mean</th>\n", " <th>std</th>\n", " <th>min</th>\n", " <th>25%</th>\n", " <th>50%</th>\n", " <th>75%</th>\n", " <th>max</th>\n", " </tr>\n", " <tr>\n", " <th>A_Categories</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>0_1</th>\n", " <td>227</td>\n", " <td>0.075638</td>\n", " <td>0.590478</td>\n", " <td>-2.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>2.000000</td>\n", " </tr>\n", " <tr>\n", " <th>1_2</th>\n", " <td>56</td>\n", " <td>0.801561</td>\n", " <td>2.262906</td>\n", " <td>-3.807355</td>\n", " <td>-0.622963</td>\n", " <td>1.000000</td>\n", " <td>2.807355</td>\n", " <td>3.906891</td>\n", " </tr>\n", " <tr>\n", " <th>2_3</th>\n", " <td>82</td>\n", " <td>0.060360</td>\n", " <td>1.667077</td>\n", " <td>-4.700440</td>\n", " <td>-0.807355</td>\n", " <td>0.000000</td>\n", " <td>0.807355</td>\n", " <td>5.169925</td>\n", " </tr>\n", " <tr>\n", " <th>3_4</th>\n", " <td>239</td>\n", " <td>0.053501</td>\n", " <td>1.160904</td>\n", " <td>-3.222392</td>\n", " <td>-0.530515</td>\n", " <td>0.000000</td>\n", " <td>0.477456</td>\n", " <td>7.266787</td>\n", " </tr>\n", " <tr>\n", " <th>4_5</th>\n", " <td>517</td>\n", " <td>0.035413</td>\n", " <td>0.740998</td>\n", " <td>-2.690316</td>\n", " <td>-0.362570</td>\n", " <td>0.000000</td>\n", " <td>0.430634</td>\n", " <td>4.108524</td>\n", " </tr>\n", " <tr>\n", " <th>5_6</th>\n", " <td>850</td>\n", " <td>0.017148</td>\n", " <td>0.589281</td>\n", " <td>-1.912537</td>\n", " <td>-0.285402</td>\n", " <td>0.000000</td>\n", " <td>0.283287</td>\n", " <td>7.081084</td>\n", " </tr>\n", " <tr>\n", " <th>6_7</th>\n", " <td>1122</td>\n", " <td>-0.022970</td>\n", " <td>0.577669</td>\n", " <td>-1.584963</td>\n", " <td>-0.252069</td>\n", " <td>-0.055497</td>\n", " <td>0.167182</td>\n", " <td>12.895954</td>\n", " </tr>\n", " <tr>\n", " <th>7_8</th>\n", " <td>801</td>\n", " <td>-0.011982</td>\n", " <td>0.671898</td>\n", " <td>-1.230780</td>\n", " <td>-0.233409</td>\n", " <td>-0.049469</td>\n", " <td>0.107714</td>\n", " <td>12.396872</td>\n", " </tr>\n", " <tr>\n", " <th>8_9</th>\n", " <td>400</td>\n", " <td>-0.027070</td>\n", " <td>0.597742</td>\n", " <td>-1.651196</td>\n", " <td>-0.201252</td>\n", " <td>-0.042253</td>\n", " <td>0.100014</td>\n", " <td>10.841040</td>\n", " </tr>\n", " <tr>\n", " <th>9_10</th>\n", " <td>152</td>\n", " <td>-0.012441</td>\n", " <td>0.893437</td>\n", " <td>-0.794292</td>\n", " <td>-0.206006</td>\n", " <td>-0.085117</td>\n", " <td>0.059721</td>\n", " <td>10.620449</td>\n", " </tr>\n", " <tr>\n", " <th>10_18</th>\n", " <td>94</td>\n", " <td>-0.145381</td>\n", " <td>0.400636</td>\n", " <td>-3.350989</td>\n", " <td>-0.259141</td>\n", " <td>-0.088727</td>\n", " <td>0.015368</td>\n", " <td>0.878276</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " count mean std min 25% 50% \\\n", "A_Categories \n", "0_1 227 0.075638 0.590478 -2.000000 0.000000 0.000000 \n", "1_2 56 0.801561 2.262906 -3.807355 -0.622963 1.000000 \n", "2_3 82 0.060360 1.667077 -4.700440 -0.807355 0.000000 \n", "3_4 239 0.053501 1.160904 -3.222392 -0.530515 0.000000 \n", "4_5 517 0.035413 0.740998 -2.690316 -0.362570 0.000000 \n", "5_6 850 0.017148 0.589281 -1.912537 -0.285402 0.000000 \n", "6_7 1122 -0.022970 0.577669 -1.584963 -0.252069 -0.055497 \n", "7_8 801 -0.011982 0.671898 -1.230780 -0.233409 -0.049469 \n", "8_9 400 -0.027070 0.597742 -1.651196 -0.201252 -0.042253 \n", "9_10 152 -0.012441 0.893437 -0.794292 -0.206006 -0.085117 \n", "10_18 94 -0.145381 0.400636 -3.350989 -0.259141 -0.088727 \n", "\n", " 75% max \n", "A_Categories \n", "0_1 0.000000 2.000000 \n", "1_2 2.807355 3.906891 \n", "2_3 0.807355 5.169925 \n", "3_4 0.477456 7.266787 \n", "4_5 0.430634 4.108524 \n", "5_6 0.283287 7.081084 \n", "6_7 0.167182 12.895954 \n", "7_8 0.107714 12.396872 \n", "8_9 0.100014 10.841040 \n", "9_10 0.059721 10.620449 \n", "10_18 0.015368 0.878276 " ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "grouped = data.groupby(\"A_Categories\")[\"M_Log_Diff\"].describe()\n", "grouped.unstack()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def get_outliers(x):\n", " mu = x.mean()\n", " std = x.std()\n", " return x[(x < mu - 3std) \| (x > mu + 3std)]" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "result = data.groupby(\"A_Categories\")[\"M_Log_Diff\"].apply(get_outliers)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "A_Categories \n", "0_1 148 2.000000\n", " 204 2.000000\n", " 724 2.000000\n", " 756 -2.000000\n", " 1545 2.000000\n", " 1649 2.000000\n", " 2068 2.000000\n", " 2733 2.000000\n", " 2755 -2.000000\n", " 2894 2.000000\n", " 3082 -2.000000\n", " 3083 2.000000\n", " 3406 2.000000\n", " 4108 2.000000\n", "2_3 994 5.169925\n", "3_4 3933 6.727920\n", " 3934 7.266787\n", "4_5 281 2.747234\n", " 992 -2.690316\n", " 3166 4.108524\n", " 3167 2.938599\n", " 3619 2.382470\n", " 3803 3.402098\n", "5_6 206 -1.912537\n", " 631 -1.798366\n", " 1519 2.195016\n", " 2717 2.506960\n", " 3931 5.351675\n", " 3932 3.718818\n", " 3941 7.081084\n", "6_7 3936 12.895954\n", " 3944 5.972693\n", " 3945 3.798742\n", "7_8 3937 12.396872\n", " 3939 7.108524\n", " 3940 4.805445\n", " 3942 6.219814\n", " 3943 5.565854\n", "8_9 3935 10.841040\n", "9_10 3938 10.620449\n", "10_18 3833 -3.350989\n", "dtype: float64" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "result" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "my_index = result.index.droplevel(0)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [], "source": [ "final = data.iloc[my_index]" ] }, { "cell_type": "code", "execution_count": 19, "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>locus_tag</th>\n", " <th>product</th>\n", " <th>5GB1_vial_wLa_TR3</th>\n", " <th>5GB1_vial_woLa_TR2</th>\n", " <th>Log2_5GB1_vial_wLa_TR3</th>\n", " <th>Log2_5GB1_vial_woLa_TR2</th>\n", " <th>M_Log_Diff</th>\n", " <th>A_Log_Ave</th>\n", " <th>A_Categories</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>148</th>\n", " <td>MBURv2_20062</td>\n", " <td>protein of unknown function</td>\n", " <td>0</td>\n", " <td>4</td>\n", " <td>0.000000</td>\n", " <td>2.000000</td>\n", " <td>2.000000</td>\n", " <td>1.000000</td>\n", " <td>0_1</td>\n", " </tr>\n", " <tr>\n", " <th>204</th>\n", " <td>MBURv2_20118</td>\n", " <td>conserved membrane protein of unknown function</td>\n", " <td>1</td>\n", " <td>4</td>\n", " <td>0.000000</td>\n", " <td>2.000000</td>\n", " <td>2.000000</td>\n", " <td>1.000000</td>\n", " <td>0_1</td>\n", " </tr>\n", " <tr>\n", " <th>724</th>\n", " <td>MBURv2_30137</td>\n", " <td>CRISPR-associated endoribonuclease Cas2</td>\n", " <td>0</td>\n", " <td>4</td>\n", " <td>0.000000</td>\n", " <td>2.000000</td>\n", " <td>2.000000</td>\n", " <td>1.000000</td>\n", " <td>0_1</td>\n", " </tr>\n", " <tr>\n", " <th>756</th>\n", " <td>MBURv2_30169</td>\n", " <td>Linear gramicidin synthase subunit D [Includes...</td>\n", " <td>4</td>\n", " <td>1</td>\n", " <td>2.000000</td>\n", " <td>0.000000</td>\n", " <td>-2.000000</td>\n", " <td>1.000000</td>\n", " <td>0_1</td>\n", " </tr>\n", " <tr>\n", " <th>1545</th>\n", " <td>MBURv2_60318</td>\n", " <td>transposase</td>\n", " <td>0</td>\n", " <td>4</td>\n", " <td>0.000000</td>\n", " <td>2.000000</td>\n", " <td>2.000000</td>\n", " <td>1.000000</td>\n", " <td>0_1</td>\n", " </tr>\n", " <tr>\n", " <th>1649</th>\n", " <td>MBURv2_80010</td>\n", " <td>protein of unknown function</td>\n", " <td>0</td>\n", " <td>4</td>\n", " <td>0.000000</td>\n", " <td>2.000000</td>\n", " <td>2.000000</td>\n", " <td>1.000000</td>\n", " <td>0_1</td>\n", " </tr>\n", " <tr>\n", " <th>2068</th>\n", " <td>MBURv2_130160</td>\n", " <td>Cobalbumin biosynthesis protein</td>\n", " <td>0</td>\n", " <td>4</td>\n", " <td>0.000000</td>\n", " <td>2.000000</td>\n", " <td>2.000000</td>\n", " <td>1.000000</td>\n", " <td>0_1</td>\n", " </tr>\n", " <tr>\n", " <th>2733</th>\n", " <td>MBURv2_130828</td>\n", " <td>conserved protein of unknown function</td>\n", " <td>0</td>\n", " <td>4</td>\n", " <td>0.000000</td>\n", " <td>2.000000</td>\n", " <td>2.000000</td>\n", " <td>1.000000</td>\n", " <td>0_1</td>\n", " </tr>\n", " <tr>\n", " <th>2755</th>\n", " <td>MBURv2_130850</td>\n", " <td>protein of unknown function</td>\n", " <td>4</td>\n", " <td>0</td>\n", " <td>2.000000</td>\n", " <td>0.000000</td>\n", " <td>-2.000000</td>\n", " <td>1.000000</td>\n", " <td>0_1</td>\n", " </tr>\n", " <tr>\n", " <th>2894</th>\n", " <td>MBURv2_130989</td>\n", " <td>DNA polymerase V, subunit C</td>\n", " <td>0</td>\n", " <td>4</td>\n", " <td>0.000000</td>\n", " <td>2.000000</td>\n", " <td>2.000000</td>\n", " <td>1.000000</td>\n", " <td>0_1</td>\n", " </tr>\n", " <tr>\n", " <th>3082</th>\n", " <td>MBURv2_160163</td>\n", " <td>conserved protein of unknown function</td>\n", " <td>4</td>\n", " <td>0</td>\n", " <td>2.000000</td>\n", " <td>0.000000</td>\n", " <td>-2.000000</td>\n", " <td>1.000000</td>\n", " <td>0_1</td>\n", " </tr>\n", " <tr>\n", " <th>3083</th>\n", " <td>MBURv2_160164</td>\n", " <td>transposase</td>\n", " <td>1</td>\n", " <td>4</td>\n", " <td>0.000000</td>\n", " <td>2.000000</td>\n", " <td>2.000000</td>\n", " <td>1.000000</td>\n", " <td>0_1</td>\n", " </tr>\n", " <tr>\n", " <th>3406</th>\n", " <td>MBURv2_160487</td>\n", " <td>protein of unknown function</td>\n", " <td>0</td>\n", " <td>4</td>\n", " <td>0.000000</td>\n", " <td>2.000000</td>\n", " <td>2.000000</td>\n", " <td>1.000000</td>\n", " <td>0_1</td>\n", " </tr>\n", " <tr>\n", " <th>4108</th>\n", " <td>MBURv2_210464</td>\n", " <td>conserved protein of unknown function</td>\n", " <td>0</td>\n", " <td>4</td>\n", " <td>0.000000</td>\n", " <td>2.000000</td>\n", " <td>2.000000</td>\n", " <td>1.000000</td>\n", " <td>0_1</td>\n", " </tr>\n", " <tr>\n", " <th>994</th>\n", " <td>MBURv2_50227</td>\n", " <td>protein of unknown function</td>\n", " <td>0</td>\n", " <td>36</td>\n", " <td>0.000000</td>\n", " <td>5.169925</td>\n", " <td>5.169925</td>\n", " <td>2.584963</td>\n", " <td>2_3</td>\n", " </tr>\n", " <tr>\n", " <th>3933</th>\n", " <td>MBURv2_210289</td>\n", " <td>protein of unknown function</td>\n", " <td>0</td>\n", " <td>106</td>\n", " <td>0.000000</td>\n", " <td>6.727920</td>\n", " <td>6.727920</td>\n", " <td>3.363960</td>\n", " <td>3_4</td>\n", " </tr>\n", " <tr>\n", " <th>3934</th>\n", " <td>MBURv2_210290</td>\n", " <td>DNA binding response regulator MxaB, LuxR fami...</td>\n", " <td>0</td>\n", " <td>154</td>\n", " <td>0.000000</td>\n", " <td>7.266787</td>\n", " <td>7.266787</td>\n", " <td>3.633393</td>\n", " <td>3_4</td>\n", " </tr>\n", " <tr>\n", " <th>281</th>\n", " <td>MBURv2_20195</td>\n", " <td>protein of unknown function</td>\n", " <td>7</td>\n", " <td>47</td>\n", " <td>2.807355</td>\n", " <td>5.554589</td>\n", " <td>2.747234</td>\n", " <td>4.180972</td>\n", " <td>4_5</td>\n", " </tr>\n", " <tr>\n", " <th>992</th>\n", " <td>MBURv2_50225</td>\n", " <td>Integrase catalytic region (fragment)</td>\n", " <td>71</td>\n", " <td>11</td>\n", " <td>6.149747</td>\n", " <td>3.459432</td>\n", " <td>-2.690316</td>\n", " <td>4.804589</td>\n", " <td>4_5</td>\n", " </tr>\n", " <tr>\n", " <th>3166</th>\n", " <td>MBURv2_160247</td>\n", " <td>MotA/TolQ/ExbB proton channel family protein</td>\n", " <td>4</td>\n", " <td>69</td>\n", " <td>2.000000</td>\n", " <td>6.108524</td>\n", " <td>4.108524</td>\n", " <td>4.054262</td>\n", " <td>4_5</td>\n", " </tr>\n", " <tr>\n", " <th>3167</th>\n", " <td>MBURv2_160248</td>\n", " <td>MotA/TolQ/ExbB proton channel family domain pr...</td>\n", " <td>6</td>\n", " <td>46</td>\n", " <td>2.584963</td>\n", " <td>5.523562</td>\n", " <td>2.938599</td>\n", " <td>4.054262</td>\n", " <td>4_5</td>\n", " </tr>\n", " <tr>\n", " <th>3619</th>\n", " <td>MBURv2_190107</td>\n", " <td>protein of unknown function</td>\n", " <td>14</td>\n", " <td>73</td>\n", " <td>3.807355</td>\n", " <td>6.189825</td>\n", " <td>2.382470</td>\n", " <td>4.998590</td>\n", " <td>4_5</td>\n", " </tr>\n", " <tr>\n", " <th>3803</th>\n", " <td>MBURv2_210159</td>\n", " <td>conserved protein of unknown function</td>\n", " <td>7</td>\n", " <td>74</td>\n", " <td>2.807355</td>\n", " <td>6.209453</td>\n", " <td>3.402098</td>\n", " <td>4.508404</td>\n", " <td>4_5</td>\n", " </tr>\n", " <tr>\n", " <th>206</th>\n", " <td>MBURv2_20120</td>\n", " <td>protein of unknown function</td>\n", " <td>64</td>\n", " <td>17</td>\n", " <td>6.000000</td>\n", " <td>4.087463</td>\n", " <td>-1.912537</td>\n", " <td>5.043731</td>\n", " <td>5_6</td>\n", " </tr>\n", " <tr>\n", " <th>631</th>\n", " <td>MBURv2_30044</td>\n", " <td>protein of unknown function</td>\n", " <td>80</td>\n", " <td>23</td>\n", " <td>6.321928</td>\n", " <td>4.523562</td>\n", " <td>-1.798366</td>\n", " <td>5.422745</td>\n", " <td>5_6</td>\n", " </tr>\n", " <tr>\n", " <th>1519</th>\n", " <td>MBURv2_60292</td>\n", " <td>conserved protein of unknown function</td>\n", " <td>19</td>\n", " <td>87</td>\n", " <td>4.247928</td>\n", " <td>6.442943</td>\n", " <td>2.195016</td>\n", " <td>5.345436</td>\n", " <td>5_6</td>\n", " </tr>\n", " <tr>\n", " <th>2717</th>\n", " <td>MBURv2_130812</td>\n", " <td>putative TonB-dependent receptor</td>\n", " <td>19</td>\n", " <td>108</td>\n", " <td>4.247928</td>\n", " <td>6.754888</td>\n", " <td>2.506960</td>\n", " <td>5.501408</td>\n", " <td>5_6</td>\n", " </tr>\n", " <tr>\n", " <th>3931</th>\n", " <td>MBURv2_210287</td>\n", " <td>MxaD-like protein, involved in methanol oxidation</td>\n", " <td>6</td>\n", " <td>245</td>\n", " <td>2.584963</td>\n", " <td>7.936638</td>\n", " <td>5.351675</td>\n", " <td>5.260800</td>\n", " <td>5_6</td>\n", " </tr>\n", " <tr>\n", " <th>3932</th>\n", " <td>MBURv2_210288</td>\n", " <td>protein of unknown function</td>\n", " <td>12</td>\n", " <td>158</td>\n", " <td>3.584963</td>\n", " <td>7.303781</td>\n", " <td>3.718818</td>\n", " <td>5.444372</td>\n", " <td>5_6</td>\n", " </tr>\n", " <tr>\n", " <th>3941</th>\n", " <td>MBURv2_210297</td>\n", " <td>putative MxaS protein involved in methanol oxi...</td>\n", " <td>5</td>\n", " <td>677</td>\n", " <td>2.321928</td>\n", " <td>9.403012</td>\n", " <td>7.081084</td>\n", " <td>5.862470</td>\n", " <td>5_6</td>\n", " </tr>\n", " <tr>\n", " <th>3936</th>\n", " <td>MBURv2_210292</td>\n", " <td>Protein MoxJ</td>\n", " <td>1</td>\n", " <td>7622</td>\n", " <td>0.000000</td>\n", " <td>12.895954</td>\n", " <td>12.895954</td>\n", " <td>6.447977</td>\n", " <td>6_7</td>\n", " </tr>\n", " <tr>\n", " <th>3944</th>\n", " <td>MBURv2_210300</td>\n", " <td>MxaK protein involved in methanol oxidation</td>\n", " <td>10</td>\n", " <td>628</td>\n", " <td>3.321928</td>\n", " <td>9.294621</td>\n", " <td>5.972693</td>\n", " <td>6.308274</td>\n", " <td>6_7</td>\n", " </tr>\n", " <tr>\n", " <th>3945</th>\n", " <td>MBURv2_210301</td>\n", " <td>MxaL protein involved in methanol oxidation</td>\n", " <td>24</td>\n", " <td>334</td>\n", " <td>4.584963</td>\n", " <td>8.383704</td>\n", " <td>3.798742</td>\n", " <td>6.484333</td>\n", " <td>6_7</td>\n", " </tr>\n", " <tr>\n", " <th>3937</th>\n", " <td>MBURv2_210293</td>\n", " <td>Cytochrome c-L</td>\n", " <td>2</td>\n", " <td>10786</td>\n", " <td>1.000000</td>\n", " <td>13.396872</td>\n", " <td>12.396872</td>\n", " <td>7.198436</td>\n", " <td>7_8</td>\n", " </tr>\n", " <tr>\n", " <th>3939</th>\n", " <td>MBURv2_210295</td>\n", " <td>Protein MoxR</td>\n", " <td>11</td>\n", " <td>1518</td>\n", " <td>3.459432</td>\n", " <td>10.567956</td>\n", " <td>7.108524</td>\n", " <td>7.013694</td>\n", " <td>7_8</td>\n", " </tr>\n", " <tr>\n", " <th>3940</th>\n", " <td>MBURv2_210296</td>\n", " <td>MxaP protein</td>\n", " <td>27</td>\n", " <td>755</td>\n", " <td>4.754888</td>\n", " <td>9.560333</td>\n", " <td>4.805445</td>\n", " <td>7.157610</td>\n", " <td>7_8</td>\n", " </tr>\n", " <tr>\n", " <th>3942</th>\n", " <td>MBURv2_210298</td>\n", " <td>putative MxaA protein involved in methanol oxi...</td>\n", " <td>15</td>\n", " <td>1118</td>\n", " <td>3.906891</td>\n", " <td>10.126704</td>\n", " <td>6.219814</td>\n", " <td>7.016798</td>\n", " <td>7_8</td>\n", " </tr>\n", " <tr>\n", " <th>3943</th>\n", " <td>MBURv2_210299</td>\n", " <td>MxaC protein involved in methanol oxidation</td>\n", " <td>19</td>\n", " <td>900</td>\n", " <td>4.247928</td>\n", " <td>9.813781</td>\n", " <td>5.565854</td>\n", " <td>7.030854</td>\n", " <td>7_8</td>\n", " </tr>\n", " <tr>\n", " <th>3935</th>\n", " <td>MBURv2_210291</td>\n", " <td>Methanol dehydrogenase [cytochrome c] subunit 1</td>\n", " <td>9</td>\n", " <td>16509</td>\n", " <td>3.169925</td>\n", " <td>14.010965</td>\n", " <td>10.841040</td>\n", " <td>8.590445</td>\n", " <td>8_9</td>\n", " </tr>\n", " <tr>\n", " <th>3938</th>\n", " <td>MBURv2_210294</td>\n", " <td>Methanol dehydrogenase [cytochrome c] subunit 2</td>\n", " <td>20</td>\n", " <td>31485</td>\n", " <td>4.321928</td>\n", " <td>14.942377</td>\n", " <td>10.620449</td>\n", " <td>9.632153</td>\n", " <td>9_10</td>\n", " </tr>\n", " <tr>\n", " <th>3833</th>\n", " <td>MBURv2_210189</td>\n", " <td>putative dehydrogenase XoxF</td>\n", " <td>7622</td>\n", " <td>747</td>\n", " <td>12.895954</td>\n", " <td>9.544964</td>\n", " <td>-3.350989</td>\n", " <td>11.220459</td>\n", " <td>10_18</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " locus_tag product \\\n", "148 MBURv2_20062 protein of unknown function \n", "204 MBURv2_20118 conserved membrane protein of unknown function \n", "724 MBURv2_30137 CRISPR-associated endoribonuclease Cas2 \n", "756 MBURv2_30169 Linear gramicidin synthase subunit D [Includes... \n", "1545 MBURv2_60318 transposase \n", "1649 MBURv2_80010 protein of unknown function \n", "2068 MBURv2_130160 Cobalbumin biosynthesis protein \n", "2733 MBURv2_130828 conserved protein of unknown function \n", "2755 MBURv2_130850 protein of unknown function \n", "2894 MBURv2_130989 DNA polymerase V, subunit C \n", "3082 MBURv2_160163 conserved protein of unknown function \n", "3083 MBURv2_160164 transposase \n", "3406 MBURv2_160487 protein of unknown function \n", "4108 MBURv2_210464 conserved protein of unknown function \n", "994 MBURv2_50227 protein of unknown function \n", "3933 MBURv2_210289 protein of unknown function \n", "3934 MBURv2_210290 DNA binding response regulator MxaB, LuxR fami... \n", "281 MBURv2_20195 protein of unknown function \n", "992 MBURv2_50225 Integrase catalytic region (fragment) \n", "3166 MBURv2_160247 MotA/TolQ/ExbB proton channel family protein \n", "3167 MBURv2_160248 MotA/TolQ/ExbB proton channel family domain pr... \n", "3619 MBURv2_190107 protein of unknown function \n", "3803 MBURv2_210159 conserved protein of unknown function \n", "206 MBURv2_20120 protein of unknown function \n", "631 MBURv2_30044 protein of unknown function \n", "1519 MBURv2_60292 conserved protein of unknown function \n", "2717 MBURv2_130812 putative TonB-dependent receptor \n", "3931 MBURv2_210287 MxaD-like protein, involved in methanol oxidation \n", "3932 MBURv2_210288 protein of unknown function \n", "3941 MBURv2_210297 putative MxaS protein involved in methanol oxi... \n", "3936 MBURv2_210292 Protein MoxJ \n", "3944 MBURv2_210300 MxaK protein involved in methanol oxidation \n", "3945 MBURv2_210301 MxaL protein involved in methanol oxidation \n", "3937 MBURv2_210293 Cytochrome c-L \n", "3939 MBURv2_210295 Protein MoxR \n", "3940 MBURv2_210296 MxaP protein \n", "3942 MBURv2_210298 putative MxaA protein involved in methanol oxi... \n", "3943 MBURv2_210299 MxaC protein involved in methanol oxidation \n", "3935 MBURv2_210291 Methanol dehydrogenase [cytochrome c] subunit 1 \n", "3938 MBURv2_210294 Methanol dehydrogenase [cytochrome c] subunit 2 \n", "3833 MBURv2_210189 putative dehydrogenase XoxF \n", "\n", " 5GB1_vial_wLa_TR3 5GB1_vial_woLa_TR2 Log2_5GB1_vial_wLa_TR3 \\\n", "148 0 4 0.000000 \n", "204 1 4 0.000000 \n", "724 0 4 0.000000 \n", "756 4 1 2.000000 \n", "1545 0 4 0.000000 \n", "1649 0 4 0.000000 \n", "2068 0 4 0.000000 \n", "2733 0 4 0.000000 \n", "2755 4 0 2.000000 \n", "2894 0 4 0.000000 \n", "3082 4 0 2.000000 \n", "3083 1 4 0.000000 \n", "3406 0 4 0.000000 \n", "4108 0 4 0.000000 \n", "994 0 36 0.000000 \n", "3933 0 106 0.000000 \n", "3934 0 154 0.000000 \n", "281 7 47 2.807355 \n", "992 71 11 6.149747 \n", "3166 4 69 2.000000 \n", "3167 6 46 2.584963 \n", "3619 14 73 3.807355 \n", "3803 7 74 2.807355 \n", "206 64 17 6.000000 \n", "631 80 23 6.321928 \n", "1519 19 87 4.247928 \n", "2717 19 108 4.247928 \n", "3931 6 245 2.584963 \n", "3932 12 158 3.584963 \n", "3941 5 677 2.321928 \n", "3936 1 7622 0.000000 \n", "3944 10 628 3.321928 \n", "3945 24 334 4.584963 \n", "3937 2 10786 1.000000 \n", "3939 11 1518 3.459432 \n", "3940 27 755 4.754888 \n", "3942 15 1118 3.906891 \n", "3943 19 900 4.247928 \n", "3935 9 16509 3.169925 \n", "3938 20 31485 4.321928 \n", "3833 7622 747 12.895954 \n", "\n", " Log2_5GB1_vial_woLa_TR2 M_Log_Diff A_Log_Ave A_Categories \n", "148 2.000000 2.000000 1.000000 0_1 \n", "204 2.000000 2.000000 1.000000 0_1 \n", "724 2.000000 2.000000 1.000000 0_1 \n", "756 0.000000 -2.000000 1.000000 0_1 \n", "1545 2.000000 2.000000 1.000000 0_1 \n", "1649 2.000000 2.000000 1.000000 0_1 \n", "2068 2.000000 2.000000 1.000000 0_1 \n", "2733 2.000000 2.000000 1.000000 0_1 \n", "2755 0.000000 -2.000000 1.000000 0_1 \n", "2894 2.000000 2.000000 1.000000 0_1 \n", "3082 0.000000 -2.000000 1.000000 0_1 \n", "3083 2.000000 2.000000 1.000000 0_1 \n", "3406 2.000000 2.000000 1.000000 0_1 \n", "4108 2.000000 2.000000 1.000000 0_1 \n", "994 5.169925 5.169925 2.584963 2_3 \n", "3933 6.727920 6.727920 3.363960 3_4 \n", "3934 7.266787 7.266787 3.633393 3_4 \n", "281 5.554589 2.747234 4.180972 4_5 \n", "992 3.459432 -2.690316 4.804589 4_5 \n", "3166 6.108524 4.108524 4.054262 4_5 \n", "3167 5.523562 2.938599 4.054262 4_5 \n", "3619 6.189825 2.382470 4.998590 4_5 \n", "3803 6.209453 3.402098 4.508404 4_5 \n", "206 4.087463 -1.912537 5.043731 5_6 \n", "631 4.523562 -1.798366 5.422745 5_6 \n", "1519 6.442943 2.195016 5.345436 5_6 \n", "2717 6.754888 2.506960 5.501408 5_6 \n", "3931 7.936638 5.351675 5.260800 5_6 \n", "3932 7.303781 3.718818 5.444372 5_6 \n", "3941 9.403012 7.081084 5.862470 5_6 \n", "3936 12.895954 12.895954 6.447977 6_7 \n", "3944 9.294621 5.972693 6.308274 6_7 \n", "3945 8.383704 3.798742 6.484333 6_7 \n", "3937 13.396872 12.396872 7.198436 7_8 \n", "3939 10.567956 7.108524 7.013694 7_8 \n", "3940 9.560333 4.805445 7.157610 7_8 \n", "3942 10.126704 6.219814 7.016798 7_8 \n", "3943 9.813781 5.565854 7.030854 7_8 \n", "3935 14.010965 10.841040 8.590445 8_9 \n", "3938 14.942377 10.620449 9.632153 9_10 \n", "3833 9.544964 -3.350989 11.220459 10_18 " ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "final" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "148 0_1\n", "204 0_1\n", "724 0_1\n", "756 0_1\n", "1545 0_1\n", "1649 0_1\n", "2068 0_1\n", "2733 0_1\n", "2755 0_1\n", "2894 0_1\n", "3082 0_1\n", "3083 0_1\n", "3406 0_1\n", "4108 0_1\n", "994 2_3\n", "3933 3_4\n", "3934 3_4\n", "281 4_5\n", "992 4_5\n", "3166 4_5\n", "3167 4_5\n", "3619 4_5\n", "3803 4_5\n", "206 5_6\n", "631 5_6\n", "1519 5_6\n", "2717 5_6\n", "3931 5_6\n", "3932 5_6\n", "3941 5_6\n", "3936 6_7\n", "3944 6_7\n", "3945 6_7\n", "3937 7_8\n", "3939 7_8\n", "3940 7_8\n", "3942 7_8\n", "3943 7_8\n", "3935 8_9\n", "3938 9_10\n", "3833 10_18\n", "Name: A_Categories, dtype: category\n", "Categories (11, object): [0_1 < 1_2 < 2_3 < 3_4 ... 7_8 < 8_9 < 9_10 < 10_18]" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "final[\"A_Categories\"]" ] }, { "cell_type": "code", "execution_count": 22, "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>locus_tag</th>\n", " <th>product</th>\n", " <th>5GB1_vial_wLa_TR3</th>\n", " <th>5GB1_vial_woLa_TR2</th>\n", " <th>Log2_5GB1_vial_wLa_TR3</th>\n", " <th>Log2_5GB1_vial_woLa_TR2</th>\n", " <th>M_Log_Diff</th>\n", " <th>A_Log_Ave</th>\n", " <th>A_Categories</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>994</th>\n", " <td>MBURv2_50227</td>\n", " <td>protein of unknown function</td>\n", " <td>0</td>\n", " <td>36</td>\n", " <td>0.000000</td>\n", " <td>5.169925</td>\n", " <td>5.169925</td>\n", " <td>2.584963</td>\n", " <td>2_3</td>\n", " </tr>\n", " <tr>\n", " <th>3933</th>\n", " <td>MBURv2_210289</td>\n", " <td>protein of unknown function</td>\n", " <td>0</td>\n", " <td>106</td>\n", " <td>0.000000</td>\n", " <td>6.727920</td>\n", " <td>6.727920</td>\n", " <td>3.363960</td>\n", " <td>3_4</td>\n", " </tr>\n", " <tr>\n", " <th>3934</th>\n", " <td>MBURv2_210290</td>\n", " <td>DNA binding response regulator MxaB, LuxR fami...</td>\n", " <td>0</td>\n", " <td>154</td>\n", " <td>0.000000</td>\n", " <td>7.266787</td>\n", " <td>7.266787</td>\n", " <td>3.633393</td>\n", " <td>3_4</td>\n", " </tr>\n", " <tr>\n", " <th>281</th>\n", " <td>MBURv2_20195</td>\n", " <td>protein of unknown function</td>\n", " <td>7</td>\n", " <td>47</td>\n", " <td>2.807355</td>\n", " <td>5.554589</td>\n", " <td>2.747234</td>\n", " <td>4.180972</td>\n", " <td>4_5</td>\n", " </tr>\n", " <tr>\n", " <th>992</th>\n", " <td>MBURv2_50225</td>\n", " <td>Integrase catalytic region (fragment)</td>\n", " <td>71</td>\n", " <td>11</td>\n", " <td>6.149747</td>\n", " <td>3.459432</td>\n", " <td>-2.690316</td>\n", " <td>4.804589</td>\n", " <td>4_5</td>\n", " </tr>\n", " <tr>\n", " <th>3166</th>\n", " <td>MBURv2_160247</td>\n", " <td>MotA/TolQ/ExbB proton channel family protein</td>\n", " <td>4</td>\n", " <td>69</td>\n", " <td>2.000000</td>\n", " <td>6.108524</td>\n", " <td>4.108524</td>\n", " <td>4.054262</td>\n", " <td>4_5</td>\n", " </tr>\n", " <tr>\n", " <th>3167</th>\n", " <td>MBURv2_160248</td>\n", " <td>MotA/TolQ/ExbB proton channel family domain pr...</td>\n", " <td>6</td>\n", " <td>46</td>\n", " <td>2.584963</td>\n", " <td>5.523562</td>\n", " <td>2.938599</td>\n", " <td>4.054262</td>\n", " <td>4_5</td>\n", " </tr>\n", " <tr>\n", " <th>3619</th>\n", " <td>MBURv2_190107</td>\n", " <td>protein of unknown function</td>\n", " <td>14</td>\n", " <td>73</td>\n", " <td>3.807355</td>\n", " <td>6.189825</td>\n", " <td>2.382470</td>\n", " <td>4.998590</td>\n", " <td>4_5</td>\n", " </tr>\n", " <tr>\n", " <th>3803</th>\n", " <td>MBURv2_210159</td>\n", " <td>conserved protein of unknown function</td>\n", " <td>7</td>\n", " <td>74</td>\n", " <td>2.807355</td>\n", " <td>6.209453</td>\n", " <td>3.402098</td>\n", " <td>4.508404</td>\n", " <td>4_5</td>\n", " </tr>\n", " <tr>\n", " <th>206</th>\n", " <td>MBURv2_20120</td>\n", " <td>protein of unknown function</td>\n", " <td>64</td>\n", " <td>17</td>\n", " <td>6.000000</td>\n", " <td>4.087463</td>\n", " <td>-1.912537</td>\n", " <td>5.043731</td>\n", " <td>5_6</td>\n", " </tr>\n", " <tr>\n", " <th>631</th>\n", " <td>MBURv2_30044</td>\n", " <td>protein of unknown function</td>\n", " <td>80</td>\n", " <td>23</td>\n", " <td>6.321928</td>\n", " <td>4.523562</td>\n", " <td>-1.798366</td>\n", " <td>5.422745</td>\n", " <td>5_6</td>\n", " </tr>\n", " <tr>\n", " <th>1519</th>\n", " <td>MBURv2_60292</td>\n", " <td>conserved protein of unknown function</td>\n", " <td>19</td>\n", " <td>87</td>\n", " <td>4.247928</td>\n", " <td>6.442943</td>\n", " <td>2.195016</td>\n", " <td>5.345436</td>\n", " <td>5_6</td>\n", " </tr>\n", " <tr>\n", " <th>2717</th>\n", " <td>MBURv2_130812</td>\n", " <td>putative TonB-dependent receptor</td>\n", " <td>19</td>\n", " <td>108</td>\n", " <td>4.247928</td>\n", " <td>6.754888</td>\n", " <td>2.506960</td>\n", " <td>5.501408</td>\n", " <td>5_6</td>\n", " </tr>\n", " <tr>\n", " <th>3931</th>\n", " <td>MBURv2_210287</td>\n", " <td>MxaD-like protein, involved in methanol oxidation</td>\n", " <td>6</td>\n", " <td>245</td>\n", " <td>2.584963</td>\n", " <td>7.936638</td>\n", " <td>5.351675</td>\n", " <td>5.260800</td>\n", " <td>5_6</td>\n", " </tr>\n", " <tr>\n", " <th>3932</th>\n", " <td>MBURv2_210288</td>\n", " <td>protein of unknown function</td>\n", " <td>12</td>\n", " <td>158</td>\n", " <td>3.584963</td>\n", " <td>7.303781</td>\n", " <td>3.718818</td>\n", " <td>5.444372</td>\n", " <td>5_6</td>\n", " </tr>\n", " <tr>\n", " <th>3941</th>\n", " <td>MBURv2_210297</td>\n", " <td>putative MxaS protein involved in methanol oxi...</td>\n", " <td>5</td>\n", " <td>677</td>\n", " <td>2.321928</td>\n", " <td>9.403012</td>\n", " <td>7.081084</td>\n", " <td>5.862470</td>\n", " <td>5_6</td>\n", " </tr>\n", " <tr>\n", " <th>3936</th>\n", " <td>MBURv2_210292</td>\n", " <td>Protein MoxJ</td>\n", " <td>1</td>\n", " <td>7622</td>\n", " <td>0.000000</td>\n", " <td>12.895954</td>\n", " <td>12.895954</td>\n", " <td>6.447977</td>\n", " <td>6_7</td>\n", " </tr>\n", " <tr>\n", " <th>3944</th>\n", " <td>MBURv2_210300</td>\n", " <td>MxaK protein involved in methanol oxidation</td>\n", " <td>10</td>\n", " <td>628</td>\n", " <td>3.321928</td>\n", " <td>9.294621</td>\n", " <td>5.972693</td>\n", " <td>6.308274</td>\n", " <td>6_7</td>\n", " </tr>\n", " <tr>\n", " <th>3945</th>\n", " <td>MBURv2_210301</td>\n", " <td>MxaL protein involved in methanol oxidation</td>\n", " <td>24</td>\n", " <td>334</td>\n", " <td>4.584963</td>\n", " <td>8.383704</td>\n", " <td>3.798742</td>\n", " <td>6.484333</td>\n", " <td>6_7</td>\n", " </tr>\n", " <tr>\n", " <th>3937</th>\n", " <td>MBURv2_210293</td>\n", " <td>Cytochrome c-L</td>\n", " <td>2</td>\n", " <td>10786</td>\n", " <td>1.000000</td>\n", " <td>13.396872</td>\n", " <td>12.396872</td>\n", " <td>7.198436</td>\n", " <td>7_8</td>\n", " </tr>\n", " <tr>\n", " <th>3939</th>\n", " <td>MBURv2_210295</td>\n", " <td>Protein MoxR</td>\n", " <td>11</td>\n", " <td>1518</td>\n", " <td>3.459432</td>\n", " <td>10.567956</td>\n", " <td>7.108524</td>\n", " <td>7.013694</td>\n", " <td>7_8</td>\n", " </tr>\n", " <tr>\n", " <th>3940</th>\n", " <td>MBURv2_210296</td>\n", " <td>MxaP protein</td>\n", " <td>27</td>\n", " <td>755</td>\n", " <td>4.754888</td>\n", " <td>9.560333</td>\n", " <td>4.805445</td>\n", " <td>7.157610</td>\n", " <td>7_8</td>\n", " </tr>\n", " <tr>\n", " <th>3942</th>\n", " <td>MBURv2_210298</td>\n", " <td>putative MxaA protein involved in methanol oxi...</td>\n", " <td>15</td>\n", " <td>1118</td>\n", " <td>3.906891</td>\n", " <td>10.126704</td>\n", " <td>6.219814</td>\n", " <td>7.016798</td>\n", " <td>7_8</td>\n", " </tr>\n", " <tr>\n", " <th>3943</th>\n", " <td>MBURv2_210299</td>\n", " <td>MxaC protein involved in methanol oxidation</td>\n", " <td>19</td>\n", " <td>900</td>\n", " <td>4.247928</td>\n", " <td>9.813781</td>\n", " <td>5.565854</td>\n", " <td>7.030854</td>\n", " <td>7_8</td>\n", " </tr>\n", " <tr>\n", " <th>3935</th>\n", " <td>MBURv2_210291</td>\n", " <td>Methanol dehydrogenase [cytochrome c] subunit 1</td>\n", " <td>9</td>\n", " <td>16509</td>\n", " <td>3.169925</td>\n", " <td>14.010965</td>\n", " <td>10.841040</td>\n", " <td>8.590445</td>\n", " <td>8_9</td>\n", " </tr>\n", " <tr>\n", " <th>3938</th>\n", " <td>MBURv2_210294</td>\n", " <td>Methanol dehydrogenase [cytochrome c] subunit 2</td>\n", " <td>20</td>\n", " <td>31485</td>\n", " <td>4.321928</td>\n", " <td>14.942377</td>\n", " <td>10.620449</td>\n", " <td>9.632153</td>\n", " <td>9_10</td>\n", " </tr>\n", " <tr>\n", " <th>3833</th>\n", " <td>MBURv2_210189</td>\n", " <td>putative dehydrogenase XoxF</td>\n", " <td>7622</td>\n", " <td>747</td>\n", " <td>12.895954</td>\n", " <td>9.544964</td>\n", " <td>-3.350989</td>\n", " <td>11.220459</td>\n", " <td>10_18</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " locus_tag product \\\n", "994 MBURv2_50227 protein of unknown function \n", "3933 MBURv2_210289 protein of unknown function \n", "3934 MBURv2_210290 DNA binding response regulator MxaB, LuxR fami... \n", "281 MBURv2_20195 protein of unknown function \n", "992 MBURv2_50225 Integrase catalytic region (fragment) \n", "3166 MBURv2_160247 MotA/TolQ/ExbB proton channel family protein \n", "3167 MBURv2_160248 MotA/TolQ/ExbB proton channel family domain pr... \n", "3619 MBURv2_190107 protein of unknown function \n", "3803 MBURv2_210159 conserved protein of unknown function \n", "206 MBURv2_20120 protein of unknown function \n", "631 MBURv2_30044 protein of unknown function \n", "1519 MBURv2_60292 conserved protein of unknown function \n", "2717 MBURv2_130812 putative TonB-dependent receptor \n", "3931 MBURv2_210287 MxaD-like protein, involved in methanol oxidation \n", "3932 MBURv2_210288 protein of unknown function \n", "3941 MBURv2_210297 putative MxaS protein involved in methanol oxi... \n", "3936 MBURv2_210292 Protein MoxJ \n", "3944 MBURv2_210300 MxaK protein involved in methanol oxidation \n", "3945 MBURv2_210301 MxaL protein involved in methanol oxidation \n", "3937 MBURv2_210293 Cytochrome c-L \n", "3939 MBURv2_210295 Protein MoxR \n", "3940 MBURv2_210296 MxaP protein \n", "3942 MBURv2_210298 putative MxaA protein involved in methanol oxi... \n", "3943 MBURv2_210299 MxaC protein involved in methanol oxidation \n", "3935 MBURv2_210291 Methanol dehydrogenase [cytochrome c] subunit 1 \n", "3938 MBURv2_210294 Methanol dehydrogenase [cytochrome c] subunit 2 \n", "3833 MBURv2_210189 putative dehydrogenase XoxF \n", "\n", " 5GB1_vial_wLa_TR3 5GB1_vial_woLa_TR2 Log2_5GB1_vial_wLa_TR3 \\\n", "994 0 36 0.000000 \n", "3933 0 106 0.000000 \n", "3934 0 154 0.000000 \n", "281 7 47 2.807355 \n", "992 71 11 6.149747 \n", "3166 4 69 2.000000 \n", "3167 6 46 2.584963 \n", "3619 14 73 3.807355 \n", "3803 7 74 2.807355 \n", "206 64 17 6.000000 \n", "631 80 23 6.321928 \n", "1519 19 87 4.247928 \n", "2717 19 108 4.247928 \n", "3931 6 245 2.584963 \n", "3932 12 158 3.584963 \n", "3941 5 677 2.321928 \n", "3936 1 7622 0.000000 \n", "3944 10 628 3.321928 \n", "3945 24 334 4.584963 \n", "3937 2 10786 1.000000 \n", "3939 11 1518 3.459432 \n", "3940 27 755 4.754888 \n", "3942 15 1118 3.906891 \n", "3943 19 900 4.247928 \n", "3935 9 16509 3.169925 \n", "3938 20 31485 4.321928 \n", "3833 7622 747 12.895954 \n", "\n", " Log2_5GB1_vial_woLa_TR2 M_Log_Diff A_Log_Ave A_Categories \n", "994 5.169925 5.169925 2.584963 2_3 \n", "3933 6.727920 6.727920 3.363960 3_4 \n", "3934 7.266787 7.266787 3.633393 3_4 \n", "281 5.554589 2.747234 4.180972 4_5 \n", "992 3.459432 -2.690316 4.804589 4_5 \n", "3166 6.108524 4.108524 4.054262 4_5 \n", "3167 5.523562 2.938599 4.054262 4_5 \n", "3619 6.189825 2.382470 4.998590 4_5 \n", "3803 6.209453 3.402098 4.508404 4_5 \n", "206 4.087463 -1.912537 5.043731 5_6 \n", "631 4.523562 -1.798366 5.422745 5_6 \n", "1519 6.442943 2.195016 5.345436 5_6 \n", "2717 6.754888 2.506960 5.501408 5_6 \n", "3931 7.936638 5.351675 5.260800 5_6 \n", "3932 7.303781 3.718818 5.444372 5_6 \n", "3941 9.403012 7.081084 5.862470 5_6 \n", "3936 12.895954 12.895954 6.447977 6_7 \n", "3944 9.294621 5.972693 6.308274 6_7 \n", "3945 8.383704 3.798742 6.484333 6_7 \n", "3937 13.396872 12.396872 7.198436 7_8 \n", "3939 10.567956 7.108524 7.013694 7_8 \n", "3940 9.560333 4.805445 7.157610 7_8 \n", "3942 10.126704 6.219814 7.016798 7_8 \n", "3943 9.813781 5.565854 7.030854 7_8 \n", "3935 14.010965 10.841040 8.590445 8_9 \n", "3938 14.942377 10.620449 9.632153 9_10 \n", "3833 9.544964 -3.350989 11.220459 10_18 " ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "final_list = final[final.A_Categories != \"0_1\"]\n", "final_list" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>count</th>\n", " <th>mean</th>\n", " <th>std</th>\n", " <th>min</th>\n", " <th>25%</th>\n", " <th>50%</th>\n", " <th>75%</th>\n", " <th>max</th>\n", " </tr>\n", " <tr>\n", " <th>A_Categories</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>0_1</th>\n", " <td>0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1_2</th>\n", " <td>0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2_3</th>\n", " <td>1</td>\n", " <td>5.169925</td>\n", " <td>NaN</td>\n", " <td>5.169925</td>\n", " <td>5.169925</td>\n", " <td>5.169925</td>\n", " <td>5.169925</td>\n", " <td>5.169925</td>\n", " </tr>\n", " <tr>\n", " <th>3_4</th>\n", " <td>2</td>\n", " <td>6.997353</td>\n", " <td>0.381036</td>\n", " <td>6.727920</td>\n", " <td>6.862637</td>\n", " <td>6.997353</td>\n", " <td>7.132070</td>\n", " <td>7.266787</td>\n", " </tr>\n", " <tr>\n", " <th>4_5</th>\n", " <td>6</td>\n", " <td>2.148102</td>\n", " <td>2.443965</td>\n", " <td>-2.690316</td>\n", " <td>2.473661</td>\n", " <td>2.842917</td>\n", " <td>3.286224</td>\n", " <td>4.108524</td>\n", " </tr>\n", " <tr>\n", " <th>5_6</th>\n", " <td>7</td>\n", " <td>2.448950</td>\n", " <td>3.381941</td>\n", " <td>-1.912537</td>\n", " <td>0.198325</td>\n", " <td>2.506960</td>\n", " <td>4.535247</td>\n", " <td>7.081084</td>\n", " </tr>\n", " <tr>\n", " <th>6_7</th>\n", " <td>3</td>\n", " <td>7.555796</td>\n", " <td>4.750735</td>\n", " <td>3.798742</td>\n", " <td>4.885717</td>\n", " <td>5.972693</td>\n", " <td>9.434323</td>\n", " <td>12.895954</td>\n", " </tr>\n", " <tr>\n", " <th>7_8</th>\n", " <td>5</td>\n", " <td>7.219302</td>\n", " <td>3.015754</td>\n", " <td>4.805445</td>\n", " <td>5.565854</td>\n", " <td>6.219814</td>\n", " <td>7.108524</td>\n", " <td>12.396872</td>\n", " </tr>\n", " <tr>\n", " <th>8_9</th>\n", " <td>1</td>\n", " <td>10.841040</td>\n", " <td>NaN</td>\n", " <td>10.841040</td>\n", " <td>10.841040</td>\n", " <td>10.841040</td>\n", " <td>10.841040</td>\n", " <td>10.841040</td>\n", " </tr>\n", " <tr>\n", " <th>9_10</th>\n", " <td>1</td>\n", " <td>10.620449</td>\n", " <td>NaN</td>\n", " <td>10.620449</td>\n", " <td>10.620449</td>\n", " <td>10.620449</td>\n", " <td>10.620449</td>\n", " <td>10.620449</td>\n", " </tr>\n", " <tr>\n", " <th>10_18</th>\n", " <td>1</td>\n", " <td>-3.350989</td>\n", " <td>NaN</td>\n", " <td>-3.350989</td>\n", " <td>-3.350989</td>\n", " <td>-3.350989</td>\n", " <td>-3.350989</td>\n", " <td>-3.350989</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " count mean std min 25% 50% \\\n", "A_Categories \n", "0_1 0 NaN NaN NaN NaN NaN \n", "1_2 0 NaN NaN NaN NaN NaN \n", "2_3 1 5.169925 NaN 5.169925 5.169925 5.169925 \n", "3_4 2 6.997353 0.381036 6.727920 6.862637 6.997353 \n", "4_5 6 2.148102 2.443965 -2.690316 2.473661 2.842917 \n", "5_6 7 2.448950 3.381941 -1.912537 0.198325 2.506960 \n", "6_7 3 7.555796 4.750735 3.798742 4.885717 5.972693 \n", "7_8 5 7.219302 3.015754 4.805445 5.565854 6.219814 \n", "8_9 1 10.841040 NaN 10.841040 10.841040 10.841040 \n", "9_10 1 10.620449 NaN 10.620449 10.620449 10.620449 \n", "10_18 1 -3.350989 NaN -3.350989 -3.350989 -3.350989 \n", "\n", " 75% max \n", "A_Categories \n", "0_1 NaN NaN \n", "1_2 NaN NaN \n", "2_3 5.169925 5.169925 \n", "3_4 7.132070 7.266787 \n", "4_5 3.286224 4.108524 \n", "5_6 4.535247 7.081084 \n", "6_7 9.434323 12.895954 \n", "7_8 7.108524 12.396872 \n", "8_9 10.841040 10.841040 \n", "9_10 10.620449 10.620449 \n", "10_18 -3.350989 -3.350989 " ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "final_list.groupby(\"A_Categories\")[\"M_Log_Diff\"].describe().unstack()" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "locus_tag 27\n", "product 27\n", "5GB1_vial_wLa_TR3 27\n", "5GB1_vial_woLa_TR2 27\n", "Log2_5GB1_vial_wLa_TR3 27\n", "Log2_5GB1_vial_woLa_TR2 27\n", "M_Log_Diff 27\n", "A_Log_Ave 27\n", "A_Categories 27\n", "dtype: int64" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "final_list.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": "3.4.4" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
CoderDojoTC/python-minecraft	classroom-code/exercises/Exercise 0 -- Where are we? What are we doing?.ipynb	1	2551	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Introduction to our environment\n", "\n", "This notebook document gives you an introduction to the environment we will use during today's session.\n", "\n", "\n", "## Where are we?\n", "\n", "As you are working in our lab environment, there are a couple of different computers involved. The diagram below shows the programs running on each computer, so you can understand what runs where:\n", "\n", "![The Lab Programming Environment](images/programming-environment.png)\n", "\n", "As you can see in the diagram above, you have your laptop itself, which is running the Minecraft game and a web browser.\n", "\n", "The Minecraft game on your laptop talks across the network to a computer named `python.coderdojotc.org`. On that computer, inside a container shown here as Student Instance 1, there is a program called the Canary Server. The Canary Server is an instance of the Minecraft game that hosts a multi-player server.\n", "\n", "Also on that server is a program called the Jupyter Notebook Server. The Jupyter Notebook Server acts as a web server, so you can connect to it with a web browser. It displays notebooks, which are contained in files on the server.\n", "\n", "\n", "## What are we doing?\n", "\n", "In today's class, we will run Python programs inside the notebooks on the Jupyter Notebook Server. When you open a notebook and tell it to run using the Cell menu at the top of each page, the Jupyter Notebook Server will run the Python code it finds. When this code runs, it can talk across the local network to the Canary Server. This is how Python can make changes inside the Minecraft world. You can see the effects of these changes inside the Minecraft game on your laptop, which is also connected to the canary Server.\n", "\n", "\n", "## Next Steps\n", "\n", "Close this notebook with the File --> Close and Halt command. Open the next exercise, where you will run your first Python program." ] } ], "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
ornlneutronimaging/ResoFit	ResoFit/IPTS_foil_samples-Copy2.ipynb	1	2500455	null	bsd-3-clause
agile-geoscience/xlines	notebooks/05_Read_and_write_SHP.ipynb	1	190219	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### x lines of Python\n", "\n", "# Read and write SHP files\n", "\n", "This notebook goes with [the blog post of the same name](https://agilescientific.com/blog/2017/8/10/x-lines-of-python-read-and-write-a-shapefile), published on 10 August 2017.\n", "\n", "We're going to load a shapefile containing some well data. Then we'll change its CRS, make a new attribute, and save a new shapefile.\n", "\n", "We'll lean on `geopandas` for help, but we'll also inspect the file with `fiona`, a lower-level library that `geopandas` uses under the hood.\n", "\n", "Install `geopandas` and its dependencies (like `gdal`, `proj`, and `fiona`) with \n", "\n", " conda install geopandas\n", " conda install fiona # I had to do this too to get fiona to work." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import fiona\n", "import matplotlib.pyplot as plt\n", "import folium" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'geometry': {'coordinates': (-59.9170497222, 43.9346961111), 'type': 'Point'},\n", " 'id': '0',\n", " 'properties': OrderedDict([('Well_No_', 1.0),\n", " ('D__', 1.0),\n", " ('Well_Name', 'Sable Island'),\n", " ('Well_Nam_1', 'C-67'),\n", " ('Company', 'Mobil et al'),\n", " ('Drilling_U', 'Bawden Rig 18'),\n", " ('Spud_Date', '1967-06-07'),\n", " ('Well_Termi', '1968-01-02'),\n", " ('RT_Elevati', 8.2),\n", " ('Water_Dept', 3.9),\n", " ('Total_Dept', 4604.0),\n", " ('Total_De_1', 15106.0),\n", " ('Well_Type', 'Exploratory'),\n", " ('Well_Symb', 'Plugged dry hole')]),\n", " 'type': 'Feature'}\n" ] } ], "source": [ "import pprint\n", "with fiona.open('../data/offshore_wells_2011_Geographic_NAD27.shp') as src:\n", " pprint.pprint(src[0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Using Geopandas" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import geopandas as gpd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Load our data into a GeoDataFrame (gdf):" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "gdf = gpd.read_file('../data/offshore_wells_2011_Geographic_NAD27.shp')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Company</th>\n", " <th>D__</th>\n", " <th>Drilling_U</th>\n", " <th>RT_Elevati</th>\n", " <th>Spud_Date</th>\n", " <th>Total_De_1</th>\n", " <th>Total_Dept</th>\n", " <th>Water_Dept</th>\n", " <th>Well_Nam_1</th>\n", " <th>Well_Name</th>\n", " <th>Well_No_</th>\n", " <th>Well_Symb</th>\n", " <th>Well_Termi</th>\n", " <th>Well_Type</th>\n", " <th>geometry</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Mobil et al</td>\n", " <td>1.0</td>\n", " <td>Bawden Rig 18</td>\n", " <td>8.2</td>\n", " <td>1967-06-07</td>\n", " <td>15106.0</td>\n", " <td>4604.0</td>\n", " <td>3.9</td>\n", " <td>C-67</td>\n", " <td>Sable Island</td>\n", " <td>1.0</td>\n", " <td>Plugged dry hole</td>\n", " <td>1968-01-02</td>\n", " <td>Exploratory</td>\n", " <td>POINT (-59.9170497222 43.9346961111)</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Shell</td>\n", " <td>2.0</td>\n", " <td>Sedneth 1</td>\n", " <td>25.9</td>\n", " <td>1969-09-01</td>\n", " <td>13085.0</td>\n", " <td>3988.0</td>\n", " <td>57.9</td>\n", " <td>E-84</td>\n", " <td>Onondaga</td>\n", " <td>2.0</td>\n", " <td>Plugged gas well</td>\n", " <td>1969-11-11</td>\n", " <td>Exploratory</td>\n", " <td>POINT (-60.2214388889 43.721147222222)</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Shell</td>\n", " <td>3.0</td>\n", " <td>Sedneth 1</td>\n", " <td>25.9</td>\n", " <td>1969-11-16</td>\n", " <td>13516.0</td>\n", " <td>4120.0</td>\n", " <td>82.3</td>\n", " <td>O-25</td>\n", " <td>Oneida</td>\n", " <td>3.0</td>\n", " <td>Plugged dry hole</td>\n", " <td>1970-02-10</td>\n", " <td>Exploratory</td>\n", " <td>POINT (-61.5601366667 43.2492655556)</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Shell</td>\n", " <td>4.0</td>\n", " <td>Sedneth 1</td>\n", " <td>26.0</td>\n", " <td>1970-02-16</td>\n", " <td>7235.0</td>\n", " <td>2205.0</td>\n", " <td>95.1</td>\n", " <td>N-30</td>\n", " <td>Naskapi</td>\n", " <td>4.0</td>\n", " <td>Plugged dry hole</td>\n", " <td>1970-03-19</td>\n", " <td>Exploratory</td>\n", " <td>POINT (-62.5665408333 43.4963302778)</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Shell</td>\n", " <td>5.0</td>\n", " <td>Sedco H</td>\n", " <td>31.4</td>\n", " <td>1970-05-03</td>\n", " <td>6975.0</td>\n", " <td>2126.0</td>\n", " <td>117.0</td>\n", " <td>B-93</td>\n", " <td>Mohawk</td>\n", " <td>5.0</td>\n", " <td>Plugged dry hole</td>\n", " <td>1970-05-23</td>\n", " <td>Exploratory</td>\n", " <td>POINT (-64.7315288889 42.7029227778)</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Company D__ Drilling_U RT_Elevati Spud_Date Total_De_1 \\\n", "0 Mobil et al 1.0 Bawden Rig 18 8.2 1967-06-07 15106.0 \n", "1 Shell 2.0 Sedneth 1 25.9 1969-09-01 13085.0 \n", "2 Shell 3.0 Sedneth 1 25.9 1969-11-16 13516.0 \n", "3 Shell 4.0 Sedneth 1 26.0 1970-02-16 7235.0 \n", "4 Shell 5.0 Sedco H 31.4 1970-05-03 6975.0 \n", "\n", " Total_Dept Water_Dept Well_Nam_1 Well_Name Well_No_ \\\n", "0 4604.0 3.9 C-67 Sable Island 1.0 \n", "1 3988.0 57.9 E-84 Onondaga 2.0 \n", "2 4120.0 82.3 O-25 Oneida 3.0 \n", "3 2205.0 95.1 N-30 Naskapi 4.0 \n", "4 2126.0 117.0 B-93 Mohawk 5.0 \n", "\n", " Well_Symb Well_Termi Well_Type \\\n", "0 Plugged dry hole 1968-01-02 Exploratory \n", "1 Plugged gas well 1969-11-11 Exploratory \n", "2 Plugged dry hole 1970-02-10 Exploratory \n", "3 Plugged dry hole 1970-03-19 Exploratory \n", "4 Plugged dry hole 1970-05-23 Exploratory \n", "\n", " geometry \n", "0 POINT (-59.9170497222 43.9346961111) \n", "1 POINT (-60.2214388889 43.721147222222) \n", "2 POINT (-61.5601366667 43.2492655556) \n", "3 POINT (-62.5665408333 43.4963302778) \n", "4 POINT (-64.7315288889 42.7029227778) " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gdf.head()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7fe08ce2d0f0>" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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498RZmBnuzsNbGvY6ZsHMO1l1+6OMn3F2l8U6c6RNV335Eqyijq5x9zp3H+zuMeBCYK27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\nFXkRkRBTkRcRCbH/D8PJ28oy0m/nAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fe08ce32ba8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "gdf.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's look at the first 5 rows of the geometry attribute." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 POINT (-59.9170497222 43.9346961111)\n", "1 POINT (-60.2214388889 43.721147222222)\n", "2 POINT (-61.5601366667 43.2492655556)\n", "3 POINT (-62.5665408333 43.4963302778)\n", "4 POINT (-64.7315288889 42.7029227778)\n", "Name: geometry, dtype: object" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gdf.geometry[:5]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice we're in lat, lon:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'init': 'epsg:4267'}\n" ] } ], "source": [ "print(gdf.crs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Visiting [EPSG 4267](http://spatialreference.org/ref/epsg/4267/) tells us the datum is NAD27." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Write a new file\n", "\n", "Let's cast the SHP to a new CRS: [EPSG 26920](http://spatialreference.org/ref/epsg/26920/):" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "gdf = gdf.to_crs({'init': 'epsg:26920'})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we're in a UTM coordinate system: UTM Zone 20N, with a NAD83 datum:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 POINT (747510.4372073817 4869250.818941217)\n", "1 POINT (723875.2971043108 4844664.069700127)\n", "2 POINT (616945.850614059 4789511.343230169)\n", "3 POINT (535101.4182156855 4816032.927423127)\n", "4 POINT (358242.7522037996 4729291.710525603)\n", "Name: geometry, dtype: object" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gdf.geometry[:5]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we'll also add a new attribute with the two-way seismic travel time to the seafloor (in milliseconds)." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "gdf['seafl_twt'] = 2 * 1000 * gdf.Water_Dept / 1485" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Company</th>\n", " <th>D__</th>\n", " <th>Drilling_U</th>\n", " <th>RT_Elevati</th>\n", " <th>Spud_Date</th>\n", " <th>Total_De_1</th>\n", " <th>Total_Dept</th>\n", " <th>Water_Dept</th>\n", " <th>Well_Nam_1</th>\n", " <th>Well_Name</th>\n", " <th>Well_No_</th>\n", " <th>Well_Symb</th>\n", " <th>Well_Termi</th>\n", " <th>Well_Type</th>\n", " <th>geometry</th>\n", " <th>seafl_twt</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Mobil et al</td>\n", " <td>1.0</td>\n", " <td>Bawden Rig 18</td>\n", " <td>8.2</td>\n", " <td>1967-06-07</td>\n", " <td>15106.0</td>\n", " <td>4604.0</td>\n", " <td>3.9</td>\n", " <td>C-67</td>\n", " <td>Sable Island</td>\n", " <td>1.0</td>\n", " <td>Plugged dry hole</td>\n", " <td>1968-01-02</td>\n", " <td>Exploratory</td>\n", " <td>POINT (747510.4372073817 4869250.818941217)</td>\n", " <td>5.252525</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Shell</td>\n", " <td>2.0</td>\n", " <td>Sedneth 1</td>\n", " <td>25.9</td>\n", " <td>1969-09-01</td>\n", " <td>13085.0</td>\n", " <td>3988.0</td>\n", " <td>57.9</td>\n", " <td>E-84</td>\n", " <td>Onondaga</td>\n", " <td>2.0</td>\n", " <td>Plugged gas well</td>\n", " <td>1969-11-11</td>\n", " <td>Exploratory</td>\n", " <td>POINT (723875.2971043108 4844664.069700127)</td>\n", " <td>77.979798</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Shell</td>\n", " <td>3.0</td>\n", " <td>Sedneth 1</td>\n", " <td>25.9</td>\n", " <td>1969-11-16</td>\n", " <td>13516.0</td>\n", " <td>4120.0</td>\n", " <td>82.3</td>\n", " <td>O-25</td>\n", " <td>Oneida</td>\n", " <td>3.0</td>\n", " <td>Plugged dry hole</td>\n", " <td>1970-02-10</td>\n", " <td>Exploratory</td>\n", " <td>POINT (616945.850614059 4789511.343230169)</td>\n", " <td>110.841751</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Shell</td>\n", " <td>4.0</td>\n", " <td>Sedneth 1</td>\n", " <td>26.0</td>\n", " <td>1970-02-16</td>\n", " <td>7235.0</td>\n", " <td>2205.0</td>\n", " <td>95.1</td>\n", " <td>N-30</td>\n", " <td>Naskapi</td>\n", " <td>4.0</td>\n", " <td>Plugged dry hole</td>\n", " <td>1970-03-19</td>\n", " <td>Exploratory</td>\n", " <td>POINT (535101.4182156855 4816032.927423127)</td>\n", " <td>128.080808</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Shell</td>\n", " <td>5.0</td>\n", " <td>Sedco H</td>\n", " <td>31.4</td>\n", " <td>1970-05-03</td>\n", " <td>6975.0</td>\n", " <td>2126.0</td>\n", " <td>117.0</td>\n", " <td>B-93</td>\n", " <td>Mohawk</td>\n", " <td>5.0</td>\n", " <td>Plugged dry hole</td>\n", " <td>1970-05-23</td>\n", " <td>Exploratory</td>\n", " <td>POINT (358242.7522037996 4729291.710525603)</td>\n", " <td>157.575758</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Company D__ Drilling_U RT_Elevati Spud_Date Total_De_1 \\\n", "0 Mobil et al 1.0 Bawden Rig 18 8.2 1967-06-07 15106.0 \n", "1 Shell 2.0 Sedneth 1 25.9 1969-09-01 13085.0 \n", "2 Shell 3.0 Sedneth 1 25.9 1969-11-16 13516.0 \n", "3 Shell 4.0 Sedneth 1 26.0 1970-02-16 7235.0 \n", "4 Shell 5.0 Sedco H 31.4 1970-05-03 6975.0 \n", "\n", " Total_Dept Water_Dept Well_Nam_1 Well_Name Well_No_ \\\n", "0 4604.0 3.9 C-67 Sable Island 1.0 \n", "1 3988.0 57.9 E-84 Onondaga 2.0 \n", "2 4120.0 82.3 O-25 Oneida 3.0 \n", "3 2205.0 95.1 N-30 Naskapi 4.0 \n", "4 2126.0 117.0 B-93 Mohawk 5.0 \n", "\n", " Well_Symb Well_Termi Well_Type \\\n", "0 Plugged dry hole 1968-01-02 Exploratory \n", "1 Plugged gas well 1969-11-11 Exploratory \n", "2 Plugged dry hole 1970-02-10 Exploratory \n", "3 Plugged dry hole 1970-03-19 Exploratory \n", "4 Plugged dry hole 1970-05-23 Exploratory \n", "\n", " geometry seafl_twt \n", "0 POINT (747510.4372073817 4869250.818941217) 5.252525 \n", "1 POINT (723875.2971043108 4844664.069700127) 77.979798 \n", "2 POINT (616945.850614059 4789511.343230169) 110.841751 \n", "3 POINT (535101.4182156855 4816032.927423127) 128.080808 \n", "4 POINT (358242.7522037996 4729291.710525603) 157.575758 " ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gdf.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also get a statistical summary of the data frame:" ] }, { "cell_type": "code", "execution_count": 13, "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>D__</th>\n", " <th>RT_Elevati</th>\n", " <th>Total_De_1</th>\n", " <th>Total_Dept</th>\n", " <th>Water_Dept</th>\n", " <th>Well_No_</th>\n", " <th>seafl_twt</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>207.000000</td>\n", " <td>207.000000</td>\n", " <td>207.000000</td>\n", " <td>207.000000</td>\n", " <td>207.000000</td>\n", " <td>207.000000</td>\n", " <td>207.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>237.246377</td>\n", " <td>34.719952</td>\n", " <td>13026.159662</td>\n", " <td>3969.690821</td>\n", " <td>142.545894</td>\n", " <td>104.043478</td>\n", " <td>191.981002</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>127.765494</td>\n", " <td>11.185937</td>\n", " <td>4081.887038</td>\n", " <td>1262.667988</td>\n", " <td>350.268990</td>\n", " <td>59.936723</td>\n", " <td>471.742748</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>0.000000</td>\n", " <td>6.400000</td>\n", " <td>1503.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>1.000000</td>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>129.500000</td>\n", " <td>25.900000</td>\n", " <td>9984.000000</td>\n", " <td>3043.000000</td>\n", " <td>39.500000</td>\n", " <td>52.500000</td>\n", " <td>53.198653</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>267.000000</td>\n", " <td>31.400000</td>\n", " <td>13202.000000</td>\n", " <td>4024.000000</td>\n", " <td>53.300000</td>\n", " <td>104.000000</td>\n", " <td>71.784512</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>349.500000</td>\n", " <td>41.500000</td>\n", " <td>15683.000000</td>\n", " <td>4800.500000</td>\n", " <td>82.650000</td>\n", " <td>156.500000</td>\n", " <td>111.313131</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>403.000000</td>\n", " <td>82.000000</td>\n", " <td>21404.000000</td>\n", " <td>6676.000000</td>\n", " <td>2091.500000</td>\n", " <td>207.000000</td>\n", " <td>2816.835017</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " D__ RT_Elevati Total_De_1 Total_Dept Water_Dept \\\n", "count 207.000000 207.000000 207.000000 207.000000 207.000000 \n", "mean 237.246377 34.719952 13026.159662 3969.690821 142.545894 \n", "std 127.765494 11.185937 4081.887038 1262.667988 350.268990 \n", "min 0.000000 6.400000 1503.000000 0.000000 0.000000 \n", "25% 129.500000 25.900000 9984.000000 3043.000000 39.500000 \n", "50% 267.000000 31.400000 13202.000000 4024.000000 53.300000 \n", "75% 349.500000 41.500000 15683.000000 4800.500000 82.650000 \n", "max 403.000000 82.000000 21404.000000 6676.000000 2091.500000 \n", "\n", " Well_No_ seafl_twt \n", "count 207.000000 207.000000 \n", "mean 104.043478 191.981002 \n", "std 59.936723 471.742748 \n", "min 1.000000 0.000000 \n", "25% 52.500000 53.198653 \n", "50% 104.000000 71.784512 \n", "75% 156.500000 111.313131 \n", "max 207.000000 2816.835017 " ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gdf.describe()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "gdf.to_file('../data/offshore_wells_2011_UTM20_NAD83.shp')" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[0m\u001b[01;32m../data/offshore_wells_2011_Geographic_NAD27.shp\u001b[0m\r\n", "../data/offshore_wells_2011_UTM20_NAD83.shp\r\n" ] } ], "source": [ "ls ../data/.shp" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Extra: GeoJSON\n", "\n", "GeoJSON is a standard format for encoding geospatial data. Think of it as a more web-friendly shapefile. (It's friendly because it's all contained in a single file, and the file is in JSON format, which JavaScript can process natively and other languages can easily consume)." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Must be geographic coords, so casting to WGS84.\n", "gdf = gdf.to_crs({'init': 'epsg:4326'})\n", "\n", "with open('../data/offshore_wells.geojson', 'w') as f:\n", " f.write(gdf.to_json())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can easily load GeoJSON into [QGIS](http://www.qgis.org/en/site/) for your usual GIS workflow. \n", "\n", "It's pretty cool how GeoJSON files show up [in GitHub](https://github.com/agile-geoscience/xlines/blob/master/data/offshore_wells.geojson)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Extra: Maps with folium\n", "\n", "You can get slippy maps right in Jupyter Notebook with `folium`. 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2539.0, "Water_Dept": 41.6, "Well_Nam_1": "E-70", "Well_Name": "Margaree", "Well_No_": 206.0, "Well_Symb": "Injection well", "Well_Termi": "2010-03-31", "Well_Type": "Development", "highlight": {}, "seafl_twt": 56.02693602693603, "style": {}}, "type": "Feature"}, {"geometry": {"coordinates": [-60.68862630191208, 43.59636123316576], "type": "Point"}, "id": "205", "properties": {"Company": "ExxonMobil", "D__": 401.0, "Drilling_U": "Rowan Gorilla III", "RT_Elevati": 49.73, "Spud_Date": "2009-06-08", "Total_De_1": 13074.1, "Total_Dept": 3985.0, "Water_Dept": 66.6, "Well_Nam_1": "N-76", "Well_Name": "Alma 4", "Well_No_": 205.0, "Well_Symb": "Gas well", "Well_Termi": "2009-08-14", "Well_Type": "Development", "highlight": {}, "seafl_twt": 89.6969696969697, "style": {}}, "type": "Feature"}, {"geometry": {"coordinates": [-60.68862630191208, 43.59636123316576], "type": "Point"}, "id": "206", "properties": {"Company": "ExxonMobil", "D__": 403.0, "Drilling_U": "Rowan Gorilla III", "RT_Elevati": 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</script>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>" ], "text/plain": [ "<folium.folium.Map at 0x7fe08a9e4128>" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import folium\n", "\n", "# Must be geographic coords, so casting to WGS84.\n", "gdf = gdf.to_crs({'init': 'epsg:4326'})\n", "\n", "# Make the map, add the features via GeoJSON.\n", "mymap = folium.Map(location=[45, -62], zoom_start=7)\n", "features = folium.features.GeoJson(gdf.to_json())\n", "mymap.add_child(features)\n", "\n", "mymap" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<hr />\n", "\n", "<div>\n", "<img src=\"https://avatars1.githubusercontent.com/u/1692321?s=50\"><p style=\"text-align:center\">© Agile Geoscience 2016</p>\n", "</div>" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.8" } }, "nbformat": 4, "nbformat_minor": 1 }	apache-2.0
alvason/diffusion-computation	stochasticD/.ipynb_checkpoints/binomial_random_distribution-checkpoint.ipynb	1	232225	{ "metadata": { "name": "", "signature": "sha256:7612c55e33a9c15f0a2e5ab842c10b39fd96a4072447bec36a995583ced9c27f" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Diffusion computation\n", "https://github.com/alvason/diffusion-computation\n", "\n", "### Section003 --- Stochastic solution for the diffusion equation\n", "https://github.com/alvason/diffusion-computation/stochasticD\n", "##### Random distribution --- Binomial distribution (discrete)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "'''\n", "author: Alvason Zhenhua Li\n", "date: 03/19/2015\n", "'''\n", "%matplotlib inline\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import os\n", "dir_path = '/Users/al/Desktop/GitHub/diffusion-computation/stochasticD/figure'\n", "file_name = 'binomial-distribution'\n", "\n", "import alva_machinery_diffusion as alva\n", "\n", "AlvaFontSize = 23\n", "AlvaFigSize = (16, 7)\n", "numberingFig = 0\n", "\n", "'''Binomial process --- Binomial distribution'''\n", "figure_name = '-equation'\n", "file_suffix = '.png'\n", "save_figure = os.path.join(dir_path, file_name + figure_name + file_suffix)\n", "\n", "numberingFig = numberingFig + 1\n", "plt.figure(numberingFig, figsize=(9, 6))\n", "plt.axis('off')\n", "plt.title(r'$ Binomial-distribution---probability-mass-function $',fontsize = AlvaFontSize)\n", "plt.text(0, 5.0/6, r'$ P_{b}(n\|N) = \\frac{N!}{n!(N - n)!} p^n (1 - p)^{N - n} $', fontsize = 1.2AlvaFontSize)\n", "plt.text(0, 4.0/6, r'$ 1-- \\ a \\ set \\ of \\ N \\ events $', fontsize = AlvaFontSize)\n", "plt.text(0, 3.0/6, r'$ 2-- \\ either \\ Success \\ or \\ Failure \\ for \\ each \\ event $', fontsize = AlvaFontSize)\n", "plt.text(0, 2.0/6, r'$ 3-- \\ the \\ probability-p \\ is \\ the \\ same \\ for \\ all \\ events $', fontsize = AlvaFontSize)\n", "plt.text(0, 1.0/6, r'$ 4-- \\ P(n\|N) \\ is \\ the \\ probability \\ with \\ n-success \\ events \\ in \\ a \\ total \\ of \\ N \\ events $',\n", " fontsize = AlvaFontSize)\n", "plt.savefig(save_figure, dpi = 300)\n", "plt.show()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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mIipQrxNvts5r9C/5aWaZSe5+RUHYVutZCBiRaWs/C3efZma/Id5wvg/RHKVZ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\n44H3twm3PXBlbtp5wLrpjlETtSrUqzqEmQ5c7e5/H3xymsH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"text": [ "<matplotlib.figure.Figure at 0x107427b50>" ] } ], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "def AlvaProduct(i):\n", " if type(i) != np.ndarray:\n", " i = np.array([i])\n", " A_product = 0.0i + 1\n", " for j in range(np.size(i)):\n", " for k in range(1, i[j] + 1): \n", " A_product[j] = A_product[j]k\n", " if np.size(i) == 1:\n", " A_product = A_product[0]\n", " return (A_product)\n", "\n", "#testing\n", "i = 100\n", "print ('Alva = ', AlvaProduct(i))\n", "print ('NumP = ', np.prod(np.arange(1, i + 1), dtype=np.float64))\n", "\n", "\n", "def AlvaBinomialD(i, N, p):\n", " B_distribution = 0.0i\n", " B_distribution[:] = AlvaProduct(N)/(AlvaProduct(i[:])AlvaProduct(N - i[:])) p*i[:] (1 - p)*(N - i[:])\n", " return (B_distribution)\n", "\n", "\n", "total_event = int(30)\n", "i_event = np.arange(total_event + 1)\n", "totalPoint_Input = i_event.shape[0]\n", "probability_each = 0.5\n", "plt.figure(figsize = (12, 6))\n", "plt.plot(i_event, AlvaBinomialD(i_event, total_event, 0.3), marker ='o', color = 'red', label = 'p=3/10 Distribution')\n", "plt.plot(i_event, AlvaBinomialD(i_event, total_event, probability_each), marker ='o'\n", " , color = 'green', label = 'p=5/10 Distribution')\n", "plt.plot(i_event, AlvaBinomialD(i_event, total_event, 0.7), marker ='o', color = 'blue', label = 'p=7/10 Distribution')\n", "plt.xlabel(r'$ output-level$', fontsize = AlvaFontSize)\n", "plt.ylabel(r'$ input/level $', fontsize = AlvaFontSize)\n", "plt.title(r'$ Binomial \\ distribution $', fontsize = AlvaFontSize)\n", "if totalPoint_Input < 100:\n", " plt.axes().set_xticks(i_event, minor = True) \n", " plt.axes().set_yticks(AlvaBinomialD(i_event, total_event, probability_each), minor = True) \n", " plt.grid(True, which = 'minor')\n", "else:\n", " plt.grid(True, which = 'major')\n", "plt.legend(loc = (1, 0))\n", "plt.show()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "('Alva = ', 9.3326215443944102e+157)\n", "('NumP = ', 9.3326215443944102e+157)\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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PjypyxpAhkXcnVUop5f+G4VagSaH7TYAtYT7390AfY8xGYBpwmTHm38U9cOzY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i05fzYnzjPH0IjLB5YbY0oBW4AuwF7gayBaRDblOCYS6CcikcaYG4BRItLOGFMLqCUi64wxlYBv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\nI6iz+ftFdcrWnMfQ7s7fdS6/7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"text": [ "<matplotlib.figure.Figure at 0x109805850>" ] } ], "prompt_number": 19 }, { "cell_type": "code", "collapsed": false, "input": [ "'''Binomial randomness --- Binomial distribution (discrete)'''\n", "\n", "totalPoint_Input = int(100)\n", "gInput = np.arange(totalPoint_Input)\n", "output_level = 100\n", "probability_peak = 0.5\n", "randomSeed = np.random.binomial(output_level, probability_peak, totalPoint_Input)\n", "\n", "sumI = 0\n", "for i in range(totalPoint_Input):\n", " sumI = sumI + randomSeed[i]\n", "meanI = sumI/(totalPoint_Input)\n", "\n", "totalLevel = int(totalPoint_Input/1)\n", "category = alva.AlvaLevel(randomSeed, totalLevel, False)\n", "gLevel = category[0]\n", "numberLevel = category[1]\n", "print category[2].shape\n", "\n", "binomial_D = 100AlvaBinomialD(np.arange(totalLevel), totalLevel, probability_peak)\n", "\n", "figure_name = ''\n", "file_suffix = '.png'\n", "save_figure = os.path.join(dir_path, file_name + figure_name + file_suffix)\n", "\n", "numberingFig = numberingFig + 1\n", "figure = plt.figure(numberingFig, figsize = AlvaFigSize)\n", "plot1 = figure.add_subplot(1, 2, 1)\n", "plot1.plot(gInput, randomSeed, color = 'gray', marker = 'o', label = 'data')\n", "plot1.plot(gInput, alva.AlvaMinMax(randomSeed), color = 'red', marker = 'o', label = 'minMaxSorting')\n", "if totalPoint_Input < 100:\n", " plot1.set_xticks(gInput, minor = True) \n", " plot1.set_yticks(randomSeed, minor = True)\n", " plot1.grid(True, which = 'minor')\n", "else:\n", " plot1.grid(True, which = 'major')\n", "plt.title(r'$ Binomial \\ (total-input = %i,\\ mean = %f) $'%(totalPoint_Input, meanI)\n", " , fontsize = AlvaFontSize)\n", "plt.xlabel(r'$ input $', fontsize = AlvaFontSize)\n", "plt.ylabel(r'$ output $', fontsize = AlvaFontSize)\n", "plt.legend(loc = (0, -0.2))\n", "\n", "plot2 = figure.add_subplot(1, 2, 2)\n", "plot2.plot(numberLevel, gLevel, color = 'red', marker = 'o', label = 'category') \n", "plot2.plot(binomial_D, np.arange(totalLevel), color = 'blue', marker = 'o', label = 'Binomial distribution') \n", "if totalPoint_Input < 100:\n", " plot2.set_xticks(numberLevel, minor = True) \n", " plot2.set_yticks(gLevel, minor = True)\n", " plot2.grid(True, which = 'minor')\n", "else:\n", " plot2.grid(True, which = 'major')\n", "plt.title(r'$ Binomial \\ distribution\\ (data = %i,\\ level = %i) $'%(totalPoint_Input, totalLevel)\n", " , fontsize = AlvaFontSize)\n", "plt.xlabel(r'$ input/level $', fontsize = AlvaFontSize)\n", "plt.ylabel(r'$ output-level $', fontsize = AlvaFontSize)\n", "plt.legend(loc = (0, -0.2))\n", "\n", "figure.tight_layout()\n", "plt.savefig(save_figure, dpi = 300)\n", "plt.show()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "(100, 2)\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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JzzZ8rALLnLQC+DrOtDKMMU8DV1hr00dhzCHDZ7q19vfu/ZqAt5AhH1ARZWrH+RX9obTt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pAAAg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\niIiIiIiI1sl/AXfEBxfCNPgVAAAAAElFTkSuQmCC\n", "text": [ "<matplotlib.figure.Figure at 0x10a065590>" ] } ], "prompt_number": 27 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 13 } ], "metadata": {} } ] }	gpl-2.0
nkmk/python-snippets	notebook/str_re_sub.ipynb	1	3670	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import re" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "s = '[email protected] [email protected] [email protected]'" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[email protected] [email protected] [email protected]\n" ] } ], "source": [ "print(re.sub('[a-z]@', 'ABC@', s))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[email protected] [email protected] [email protected]\n" ] } ], "source": [ "print(re.sub('[a-z]@', 'ABC@', s, 2))" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[email protected] [email protected] [email protected]\n" ] } ], "source": [ "print(re.sub('[xyz]', '1', s))" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[email protected] [email protected] [email protected]\n" ] } ], "source": [ "print(re.sub('aaa\|bbb\|ccc', 'ABC', s))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[email protected] [email protected] [email protected]\n" ] } ], "source": [ "print(re.sub('([a-z])@', '\\\\1-123@', s))" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[email protected] [email protected] [email protected]\n" ] } ], "source": [ "print(re.sub('([a-z])@', r'\\1-123@', s))" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "('[email protected] [email protected] [email protected]', 3)\n" ] } ], "source": [ "t = re.subn('[a-z]*@', 'ABC@', s)\n", "print(t)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'tuple'>\n" ] } ], "source": [ "print(type(t))" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[email protected] [email protected] [email protected]\n" ] } ], "source": [ "print(t[0])" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3\n" ] } ], "source": [ "print(t[1])" ] } ], "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 }	mit
cokelaer/colormap	notebooks/colormap package demonstration.ipynb	1	188123	{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Using the colormap package" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "- You can use the class Colormap as shown below\n", "- You can use the cmap_builder function and test_cmap function as well (see end of notebook)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "%pylab inline\n", "from colormap import Colormap" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "c = Colormap()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "cmap = c.cmap('cool')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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WpiB5DxaDdTtIHgXwCWT4wZJsij/FQtS6uD6lLdriW7S1si1Ve60IxYRdKjk3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\nDWIlABfpDRQAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f5a24677be0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f5a07a15080>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# let us see what it looks like\n", "c.test_colormap(cmap)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f59f2c960b8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Would be nice to plot a bunch of colormap to pick up one interesting\n", "c.plot_colormap('diverging')" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f59f2c96080>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# This list is implemented in colormap package itself\n", "c.plot_colormap(c.diverging_black)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Well, I have not found the one I wanted...I wanted from red to white to green" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f5a06ddb400>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f5a078caa90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mycmap = c.cmap_linear('red', 'white', 'green(w3c)')\n", "c.test_colormap(mycmap)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f59f01105c0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f5a079ee908>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mycmap = c.cmap_bicolor('red', 'green(w3c)')\n", "c.test_colormap(mycmap)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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Ho+1U24xV9U5V3auqlwF4B4C/UNV3AXgQwNu61W4FcG/tPgghZBT6FJoDbcbd\nONldAB5X1Q8nVfdh1v8Bmf3goAE8h/cD+AMReRIzG/JdI+yDEEKG0cab4joAvwXgBhF5uPu7GcAH\nAbxRRJ4A8Ibu80KaBH2o6kMAHuqWnwLwuqINCGavLNZrS7pc8rpmmTRqX8FqzCAlAxnpsUZBE319\n2qaEqP0Zo94yWdQmAopeDWvOy9uXdd+sdt6rc/RcRfWWea3k1dxqX/O9KFl36GCnd6w1A3zedWtF\nAzOFqv4V/CfsxpJtTSMCjxBClglzUywgZwDPG6jofzVPBfXe7BaRgjltlFkDVF69pbzOBOtaajmt\nj5Szl+gnGoy0jilS7tZgoFe/YZTVYukRq2yoS2M06NXSncvaVzSwHNV7x2LtK1Lx0b6iwdASN76x\nB/AmFg49nc6YEEKWCZWxw7wyjtxyItuc5frmqYJezXnK2VKzNcodsG2q1rqWmkwpCfqI3PBSIpXc\n47m7Rco3CjqpSaEZ2Yy9dWuUZ4m7WO72PTtrpCYjO23NM+qda3Ssua5r3nc4142vFZyQlBBCJgKV\nsUHvTVFiE45sf9YvsKdsI+VtpY20ji9Szum6XtBFlKLSOtaIyD6cHotlH7fsv5E3RUqJci4hUr7W\netFzEz0Xkc3YeoZK1KC1/8jDIFLDJetGxxodi3etIvt8dHxjeFOwMyaEkBVDM8UCtsH/VY4UhlVv\nqdTIdheFO0c256g+XfbUlPVr3fIX3FK+1nl7D6p1rla9Z/MeQ4149kRLzUX1kR9viQdCrk24pH2J\nf32kfKP6SLmWeCTVnKt3rK2gNwUhhEwAmikIIWTFMOhjAWO6tkWvUFEIcBSoEQV9WK/pnmva2K5t\nPZ5JJnpA++1GWd8i1zaPdXJtyzUjtHRtqzGDpOXRtmoHIyOTztRc2wDajAkhZBJQGRv0rm3R4EKt\nW46lbC0V7LXXufXm68/M/ffap8ueslxUllLyINXmNu4pcW2Lthmp/IgSlRyFS0cDu1GIbqSMawat\nhirjksHCksHEoQN0ud/hZSUKmuDs0NPojAkhZNlQGTtsQ/yr6Cms3HpP2UbtrbSTUaCEZ9O1lKX1\nq+8pgci1zKJEGUdqMkoU1BMpZ2//EdG2PJW7qL4kEGGoMi4J6ojc0XKVt7etoa5nQ930SpT7GK5t\ntBkTQsiKoTeFg+B8ZVxjT/LmbbN+daN530qUseVB4C1bnhtjB3pECqDG/hsp46g+51h6SlRRiTK2\nlGkU7lzFubZmAAAQPklEQVQSYhzZfDeNsho7bI4yXpbyja7FUJtyS6iMCSFkAlAZGwhmR+L58Ube\nCtav7najveVB4bW39l+rjGsS5dQ+KJYd1Bo19uotm7ClnFumyEypGTWfejh02n7TqI/aW23S5RwP\nhFYqO9rX9qDe2/7m3P9Fyy2gNwUhhEwEKmOHUptxutyfRfpLdzqot1RwejUsZeupXesq5vgcWwx9\nQHLtYN5+oglRrWsReWNE3ha1RDZha70Sm3ErZVziJxwp56je+l6ky169pcJLVLy1/ai+JpagJbQZ\nE0LIiqE3BSGETAR2xgZWOHTJ65xlZjgd1KevKFYghmWGSG9eOlBhhTin9RFjPxTWq7nnzmbNkWeZ\nITyTzTaj3jo/b7A0InpdjZLLREEXUYKqoTmCSwboItc3a9ArGkDz6mv2VWNSiQYjc9z0WkEzBSGE\nrBh6UyygVBlHrmnWYFw0gOcNtFmudZFyrh3AyyWa180bzJxfb76+L7feHNLyyI2vJIXm0MGZEte1\ndRzAK1GjJQNoNW50Jcp56LUYWxnTTEEIIROAnbFBrs3Ys/mOYTNOb5SlfFMsm3EJLRPlRG0im7H1\nFjHUZpwSnWuNco6CPrxtDQ2HHmozzrXT1tqMLZXq1Vv7supb2oxrAmBawQlJCSFkIlAZO+QoYyuQ\nIy230lqm5Z7yjeprgj5qiVJYRm0im7FVH81eXWMzhlO/aL30+HJYVtBHlPwmmqqo1k6bq1YjtZsu\n1wZ95O4rUr5pWW060FZwAI8QQlYMgz4cahIFWcrUU6uWHdTyrLB8h+e3a9VblEw7VINnx7T8hEuS\nw/fth75FwKlfVFZK7ltEibeEVW95UKTlJZN4Rmqwxs4aeVCkyyXK1vJJjvblKd9ImS87URBAmzEh\nhEwCKmOD3pvC83bo1VqtN0TkIRDZNGuUXVQ/1EPA8xOOpjy3bMJWe0/t1tjfLcZSxim5ytd7y7Cu\npaVya31nLeUdKeNUrW4zyjw1uX1g/VCbcY1N2bOlt2CC3hTV7vYi8koReVBEHhORR0Xk9q78EhF5\nQESe6P5f3O5wCSGkEZr5FyAinxSR4yLySFJW3A8OiX06BeC9qnoVgGsBvFtErgJwB4CDqnoFgIPd\nZ0IImQ59OHTOX8ynANw0V1bcD1abKVT1GIBj3fJPRORxAJcCuAXA9d1qdwN4CMD7F27MGsCzXt28\neiuc2XqN9swU1uvK2GaK6CZ7P5OWa5r1Gh4N8FmBGml5ZKaI6uHUL1qvlGiwzlovMkNEQSElQR+5\n7lppmxIzRPRqb7Urqa8xQ5SkNKgJcGlJI5uxqv6liFw2V1zcDzY5xe5ArgFwCMDurqMGgGcA7Hba\n7AewHwBetbfFURBCSAH5NuNdInI4+XxAVQ8EbbL6wZTBnbGIvBzAFwG8R1V/LHJWZqiqioj5+9Od\nzAEA2He1aLFrW4m7Vb9cm9Zy6EwVkXK18JTvqQXbTJdLEgFZg6UliYBq3iKWOdNHifKtSaHpDeAN\nTaGZG4iREw6dq2y99juMesv1bewBwJbkK+MTqrqvejcL+sGUQfmyRGQ7Zh3xp1X1S13xsyKyp6vf\nA+D4kH0QQkhzcgfv6k0Zxf1g9e+NzCTwXQAeV9UPJ1X3AbgVwAe7//fGG0N5Cs0ohaW1HNWXBDKM\n5RYTuaZZZVE4tGUT9sKhh6bQtLZZkxzIo8QlMFLOrcKhS+pXHQ69zHrruGptymOEQ4/r2lbcDw4R\n/9cB+C0A3xSRh7uyf9vt/PMichuA7wJ4+4B9EEJIexomlxeRz2A2WLdLRI4C+AAq+sEh3hR/Bd/y\neWPRxgSzX9GWyjaaFqlk+2OTKqeTC8rSci+0+/TceoBtE/bqLeUbJV1CRX1LvKfQCuqI6q3Q5yl5\nU1jr5thZ++UdTv0Oo6zG2yKa1snb/vYFZfPLrWjnTfFOp6qoH5xGBB4hhCwbhkMb1CQKKrH5Luss\nPQUVeVOcwvlMyZvCstXTm2L9vSkslZyWlSjXVvZtJgoihJALCKbQJISQicDk8gbWAF5NpjDvly4K\n4Y1mNB6bEncr69XZGsCLXNss00S6XW8AL7pW6zSAZ11rawBvSlnbrHVLXNuiAbwo9DoKV44G4Eqy\nxq3JAF4rptEZE0LIMplgCs2JdMYCyA5g44WzRemRWbM7Wyp3u1E2Xz4mkZpNl9N10wG8bQvK0vKS\nREGWirbUcFqeXt90/9Fbxvx6Xr23bkRuDmNv3ZpEQdFMHyX1YwR91CYKslRytK1I+W449bn5ir1r\nIS9JPiT9xBCojAkhZAJQGVtsANgJbJ44W6SJdKuxCS9LDQNxCLPlDhUp5xJ3rEj5WirYSyfKOfDO\nrx97Drxa17aaoI/INa02KCMK+shV1udsM93AzmT5WQyG3hSEEDIR6E1h0Snj1FC6/Ydnl2vCmS2i\n9hElo/Il27KUc/qgWPZjT/laas5a16vv2zOFZr63RYm3RKScIztqSX1kE7ZUbIlyrlG+Jcr7HDXc\nWBkDVMaEELJy6E3hsYnzlPE5Kvn52f8a22RJfclIvdUu8ndN1/HskLlpHWv9jPt23rRLudMqcXbo\n6c8OXWITrlHGNYl+Qj/iX0wKPWXcCCpjQgiZAOyMLQybcbos3fLmT8+WjTHJ5VjK2FJmnp9wlBw+\nV/l6NmU1ykqSMkXJ5XPvyzJtxiU24Zrk8pE3RYlybmkzbqmMrXorqVCJt0V6LHJRt+Cp4cbKmGYK\nQgiZCPSmIISQCUAzhUVgpuiXN5KyHY1CIoGzr5knF64VmyG8gSDr1dQLd7bq0/ZWOHQ004eVj9ga\ntEvbRwN4Ja5tY5iUgNgkYa03Rjh0ZMZYVTh0NNNGSb7hmqANazk1bWykIc475/4vWm4Agz4IIWQi\n0GZskamMz2mShE5vN1Ryy1+9XAUVhTjnrBu5W/Vq56RRBtgDfJby9eqt2aNLwqHntzNPjfthNLCa\n41JorRu5vlnKd2iiIGsOO+8tpkYZl8y0YQVteIl+WrmunaOGdyXLS1bGAJUxIYSsnIazQ7diIp3x\nJs79lcwhUcubz3ULjh05UlYW3uzM1jZLEgVFIbSnFpQB+bM/R+HSm059TVBH5PqWsszk8rmuayVv\nKSXh0LmJgmpd26x76W3LUr6RTXhoop9z7M+9IvbU7q65/4uWG0FlTAghE4A2Y4sgHPrFZWsa5XQz\nzyUfAm+LSNmeDOqthO9evaVM01OJgj5KvCmsEFtLOZfMDl2SKMhqEymQki9FlIzJU7lW+1azQ5fU\nl3hT5E51VGIzjgJESmzGkbLetLwlLDtxukxvCkIIubCgMraosRlHm0xUsmT6JHuqylK+NWG1abln\nE45sxlG4s6V8rfqSCUeZQvMsY6TQTLcf2YRLbMbRtEcl4dJRfe8/bPoOA2e/31ZZukybMSGEXDjQ\nm8JjBGWcnlrvk5xG7UVTIaXk+hF7CiuyGUfJ5S3PCS/RzxjKOPK2gFHvqY4xXg2HpjaNvCVaKmNr\nW1G0pqWG0/Icm7GV6CeyKZfYjF9UxJFNOFK+VMaEEHJhQZsxIYSsGHpTeFSEQ5sEp7ORDOptS3Ij\nR2aIKCGMlaPYcn1LyyPXN2+Arv81P2WUpe3TMivA47RTb+UzLpnpwyorUSA14dApnqnIKotCz617\nVRIOHZkpNo2yGte2yJ0tXSdyTStxbXsxBzFgu6ZZJgvP9MBw6Il0xoQQsmRoprDIHcCLDterN0Yn\nJFne0c2xV+LaVqKMLTc1TxlH4cxWCsxoAM8ajIvCmZepjEsUSslsLGMrY0v5rkIZL3MAL5yjbqgy\nXtIAHr0pCCFkItBMYbECZWwtb08DRZKfzaE2Y6t8LGUcpcCMlLE1R15NCs11VcaWTThKJFSbfH5K\nythMJJR+yLXprokyBibXGUeR/lWIyE0i8m0ReVJE7hhjH4QQUk0/IWnO35JoroxFZAPAnwB4I4Cj\nAL4mIvep6mOLD2MCyjgtS8OpN7pgkVqbcYk3xdjKODd5fFRfMyP0ovIh5MzK3VMyKUCkjHOTz4+t\njFt6U4gXzkxlPDZjKOPXAXhSVZ9S1Z8B+CyAW0bYDyGE1HM6829JjGEzvhTA95PPRwH8+vxKIrIf\nwP7u4wsi8sgIx7JqdgE4Ea61XmzFcwK25nlVnFOaVOtZZ3nl/NrQDRwBvir5cnspz8XKBvBU9QCA\nAwAgIodVdd+qjmUstuJ5bcVzArbmeW3FcwJm5zV0G6p6U4tjackYZoqnAbwy+by3KyOEEOIwRmf8\nNQBXiMjlIrIDwDsA3DfCfgghZMvQ3EyhqqdE5HcAfBWzMdxPquqjQbMDrY9jImzF89qK5wRszfPa\niucEbNHzEtWJ+XcQQsgFyChBH4QQQspgZ0wIIRNg5Z3xVgidFpFXisiDIvKYiDwqIrd35ZeIyAMi\n8kT3/+JVH2spIrIhIl8Xkfu7z5eLyKHufn2uG6RdK0Rkp4jcIyLfEpHHReT1W+Re/X73/D0iIp8R\nkZeu4/0SkU+KyPE09sC7PzLjj7vz+4aIvHZ1Rz6MlXbGSej0mwBcBeCdInLVKo+pklMA3quqVwG4\nFsC7u/O4A8BBVb0CwMHu87pxO4DHk88fAvARVX01gB8BuG0lRzWMjwH4iqpeCeA1mJ3fWt8rEbkU\nwO8B2Keq/xizwfN3YD3v16cAzPsBe/fnTQCu6P72A/j4ko6xPaq6sj8Arwfw1eTznQDuXOUxNTqv\nezHLzfFtAHu6sj0Avr3qYys8j72YPfg3ALgfs8wLJwBsWvdvHf4wS8j7HXSD10n5ut+rPvL1Esy8\npO4H8C/W9X4BuAzAI9H9AfCnAN5prbduf6s2U1ih05eu6FiaICKXAbgGwCEAu1X1WFf1DIDdKzqs\nWj4K4H04mwroFQCeU9U+9dE63q/LAfwAwJ915pdPiMhFWPN7papPA/gjAN8DcAzA8wCOYP3vV493\nf7ZMH7LqznhLISIvB/BFAO9R1R+ndTr72V4bP0IReTOA46p6ZNXH0phNAK8F8HFVvQbATzFnkli3\newUAnQ31Fsx+bP4RgItw/qv+lmAd708Oq+6Mt0zotIhsx6wj/rSqfqkrflZE9nT1ewAcX9XxVXAd\ngLeIyN9hlnnvBsxsrTtFXpyzah3v11EAR1X1UPf5Hsw653W+VwDwBgDfUdUfqOpJAF/C7B6u+/3q\n8e7PlulDVt0Zb4nQaRERAHcBeFxVP5xU3Qfg1m75VsxsyWuBqt6pqntV9TLM7stfqOq7ADwI4G3d\namt1TgCgqs8A+L6I9Jm/bgTwGNb4XnV8D8C1IvKy7nnsz2ut71eCd3/uA/CvOq+KawE8n5gz1otV\nG60B3AzgfwP4WwD/btXHU3kOv4HZa9M3ADzc/d2MmY31IIAnAPwPAJes+lgrz+96APd3y78C4K8B\nPAngCwBesurjqzifqwEc7u7XfwNw8Va4VwD+EMC3ADwC4L8AeMk63i8An8HM7n0SszeZ27z7g9mg\n8p90/cc3MfMmWfk51PwxHJoQQibAqs0UhBBCwM6YEEImATtjQgiZAOyMCSFkArAzJoSQCcDOmBBC\nJgA7Y0IImQD/H0eBntFRZh5AAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f59efc635f8>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAD8CAYAAAB0IB+mAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAA5FJREFUeJzt1MENwCAQwLDS/Xc+tgCJ2BPklTUzHwDv+28HAHCG4QNE\nGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QY\nPkCE4QNEGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+\nQIThA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5A\nhOEDRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QYPkCE\n4QNEGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+QITh\nA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5AhOED\nRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QYPkCE4QNE\nGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QY\nPkCE4QNEGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+\nQIThA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5A\nhOEDRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QYPkCE\n4QNEGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+QITh\nA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5AhOED\nRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QYPkCE4QNE\nGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QY\nPkCE4QNEGD5AhOEDRBg+QMQGL4sE9RSocXsAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f5a07a936d8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# there is also 2 extra maps from R\n", "mycmap = c.get_cmap_heat()\n", "c.test_colormap(mycmap)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "# color can be given a a name available in \n", "import colormap.xfree86 as colors" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "['Light Sea Green', 'Pale Turquoise', 'White Smoke', 'Tomato', 'Rosy Brown']" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "list(colors.XFree86_colors.keys())[0:5]" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "['#20B2AA', '#AFEEEE', '#F5F5F5', '#FF6347', '#BC8F8F']" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#or \n", "list(colors.XFree86_colors.values())[0:5]" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "# or as RGB, HLS, HSV, YUX, Hexa format\n", "from colormap import Color" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "co = Color('white')" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "'#FFFFFF'" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "co.hex" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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/aj2Y5w3aAc3DELHFeep2sdCE194LXcTo+6bwHr1jv0RS13eZmjevkz5KFqAq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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f59efe00c18>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f5a07a4bf28>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mycmap = c.cmap_linear('red', '#FFFFFF', 'green(w3c)')\n", "c.test_colormap(mycmap)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "(1.0, 0.0, 0.0)" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Conversion between colors\n", "c = Color('red')\n", "c.rgb" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "(0.0, 0.5, 1.0)" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c.hls" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "'#FF0000'" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c.hex" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Color Red\n", " hexa code: #FF0000\n", " RGB code: (1.0, 0.0, 0.0)\n", " RGB code (un-normalised): [255.0, 0.0, 0.0]\n", "\n", " HSV code: (0.0, 1.0, 1.0)\n", " HSV code: (un-normalised) 0.0 100.0 100.0\n", "\n", " HLS code: (0.0, 0.5, 1.0)\n", " HLS code: (un-normalised) 0.0 50.0 100.0\n", "\n", "\n" ] } ], "source": [ "print(c)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Using cma_builder and test_cmap" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Instead of using the Colormap class, you can also use the cmap_builder alias function andthe test_cmap function" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "from colormap import cmap_builder, test_cmap" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x4a51bd0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEACAYAAABI5zaHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAAYtJREFUeJztwTEBAAAAwqD1T20KP6AAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAB4Gt1LAAHo3iMrAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x4a727d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mycm = cmap_builder('red', 'white', 'green')\n", "test_cmap(mycm)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "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": 0 }	bsd-3-clause
briennakh/BIOF509	Wk02/Wk02-partial-solutions.ipynb	1	343094	{ "cells": [ { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import itertools\n", "import math\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import random\n", "\n", "%matplotlib inline\n", "\n", "\n", "def distance(coords):\n", " distance = 0\n", " for p1, p2 in zip(coords[:-1], coords[1:]):\n", " distance += ((p2[0] - p1[0]) 2 + (p2[1] - p1[1]) 2) ** 0.5\n", " return distance\n", "\n", "\n", "def new_path(existing_path):\n", " path = existing_path[:]\n", " point = random.randint(0, len(path)-2)\n", " path[point+1], path[point] = path[point], path[point+1]\n", " return path\n", "\n", "\n", "def simulated_annealing_optimizer(starting_path, cost_func, new_path_func, start_temp, min_temp, steps):\n", " current_path = starting_path[:]\n", " current_cost = cost_func(current_path)\n", " temp_factor = -np.log(start_temp / min_temp)\n", " history = []\n", " for s in range(0, steps):\n", " temp = start_temp * np.exp(temp_factor * s / steps)\n", " new_path = new_path_func(current_path)\n", " new_cost = cost_func(new_path)\n", " if (new_cost < current_cost) or (random.random() <= np.exp(-(new_cost - current_cost)/temp)):\n", " current_path = new_path\n", " current_cost = new_cost\n", " record = {'step':s, 'temperature':temp, 'current_cost':current_cost, }\n", " history.append(record)\n", " return (current_path, current_cost, history)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def new_path_random(existing_path):\n", " path = existing_path[:]\n", " point1 = random.randint(0, len(path)-1)\n", " point2 = random.randint(0, len(path)-1)\n", " path[point1], path[point2] = path[point2], path[point1]\n", " return path" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ 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history_original])\n", "ax[1,0].plot([i[0] for i in best_path_original], [i[1] for i in best_path_original])\n", "\n", "ax[0,1].plot([i['current_cost'] for i in history_random])\n", "ax[1,1].plot([i[0] for i in best_path_random], [i[1] for i in best_path_random])\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Original: 21908.07881814338\n", "Random: 9568.080729433717\n" ] }, { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAt8AAAK+CAYAAAB6onI7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xm4ZVV5oPH3q2IeBRRQJkFAcWTQckBjoRE1nQimH5HW\n", "BIzYnQi2GjuJYDqh2miiJiqmE0zHEYiGEKOCBieaVBI7CIRBCAVYDgjFUCrIpExV9fUfe526+x7u\n", "cO695+59z9nv73nOs/deZ+99vnUp1v3uOmuvFZmJJEmSpMW3rO0AJEmSpK4w+ZYkSZIaYvItSZIk\n", "NcTkW5IkSWqIybckSZLUEJNvSZIkqSGzJt8RsXVEXBoRV0XEdRHxx6X89IhYFxFXltcratecFhFr\n", 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best_path_original], [i[1] for i in best_path_original])\n", "\n", "ax[0,1].plot([i['current_cost'] for i in history_random])\n", "ax[1,1].plot([i[0] for i in best_path_random], [i[1] for i in best_path_random])\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def select_best(population, cost_func, num_to_keep):\n", " scored_population = [(i, cost_func(i)) for i in population]\n", " scored_population.sort(key=lambda x: x[1])\n", " return [i[0] for i in scored_population[:num_to_keep]]\n", "\n", "def recombine(population):\n", " # Randomly choose two parents\n", " options = list(range(len(population)))\n", " random.shuffle(options)\n", " partner1 = options[0]\n", " partner2 = options[1]\n", " # Choose a split point, take the first parents order to that split point, \n", " # then the second parents order for all remaining points\n", " split_point = random.randint(0, len(population[0])-1)\n", " child = population[partner1][:split_point]\n", " for point in population[partner2]:\n", " if point not in child:\n", " child.append(point)\n", " return child\n", "\n", "\n", "def genetic_algorithm_optimizer(starting_path, cost_func, new_path_func, pop_size, generations):\n", " # Create a starting population by randomly shuffling the points\n", " population = []\n", " for i in range(pop_size):\n", " new_path = starting_path[:]\n", " random.shuffle(new_path)\n", " population.append(new_path)\n", " history = []\n", " # Take the top 25% of routes and recombine to create new routes, repeating for generations\n", " for i in range(generations):\n", " pop_best = select_best(population, cost_func, int(pop_size / 4))\n", " new_population = []\n", " for i in range(pop_size):\n", " new_population.append(new_path_func(pop_best))\n", " population = new_population\n", " record = {'generation':i, 'current_cost':cost_func(population[0]),}\n", " history.append(record)\n", " return (population[0], cost_func(population[0]), history)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def recombine_mutant(population):\n", " # Randomly choose two parents\n", " options = list(range(len(population)))\n", " random.shuffle(options)\n", " partner1 = options[0]\n", " partner2 = options[1]\n", " # Choose a split point, take the first parents order to that split point, \n", " # then the second parents order for all remaining points\n", " split_point = random.randint(0, len(population[0])-1)\n", " child = population[partner1][:split_point]\n", " for point in population[partner2]:\n", " if point not in child:\n", " child.append(point)\n", " return new_path(child)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Original: 2524.817502805437\n", "Mutant: 2691.4617944034358\n" ] }, { "data": { "image/png": [ 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"xoMwpb9eKw74PbwTOKYKufLnWYt0yuftwHdIT5hXIdcCwB+BhQe8VoVcHyL9RXYHcCEwd0Vy/ZzU\n", "t3cbMKHU14v0l8PvgRdJfY37kR4cGlUO4O35z8pU4Ixefd168PvgNXvsGb1mjyyb1+zR5fKaPfss\n", "xdZtHxBjZmZmZtYFRaeCmJmZmZk1hQtrMzMzM7MucGFtZmZmZtYFLqzNzMzMzLrAhbWZmZmZWRe4\n", "sDYzMzMz6wIX1mZmZmZmXfD/AaXEM/1pRsf0AAAAAElFTkSuQmCC\n" ], "text/plain": [ "<matplotlib.figure.Figure at 0x7f5f6edec080>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "random.seed(0)\n", "\n", "coords = [(random.randint(0,1000), random.randint(0,1000)) for i in range(15)]\n", "\n", "best_path_original, best_cost_original, history_original = genetic_algorithm_optimizer(coords, \n", " distance, recombine, 500, 100)\n", "best_path_mutant, best_cost_mutant, history_mutant = genetic_algorithm_optimizer(coords, \n", " distance, recombine_mutant, 500, 100)\n", "print(\"Original:\", best_cost_original)\n", "print(\"Mutant:\", best_cost_mutant)\n", "\n", "fig, ax = plt.subplots(2,2, figsize=(12,12), sharey='row')\n", "ax[0,0].plot([i['current_cost'] for i in history_original])\n", "ax[1,0].plot([i[0] for i in best_path_original], [i[1] for i in best_path_original])\n", "\n", "ax[0,1].plot([i['current_cost'] for i in history_mutant])\n", "ax[1,1].plot([i[0] for i in best_path_mutant], [i[1] for i in best_path_mutant])\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Original: 17145.499698878888\n", "Mutant: 12200.130490826468\n" ] }, { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAtwAAAK+CAYAAACRlck4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xm4ZFV57/HvCy0gQzOIgDSz0IAISoMNCMZGFESNoAkE\n", "I4IGjVcxot5oRGNAr7NXhSSCiSACaggOCaAEvIpEiUwKCMjUogzN0Co9IKBAw3v/2Kvo6tNnqHO6\n", "9qlhfz/Pc56uWrV31VrdsM7vrLOGyEwkSZIk1WO1XldAkiRJGmYGbkmSJKlGBm5JkiSpRgZuSZIk\n", "qUYGbkmSJKlGBm5JkiSpRhMG7ohYMyKujIhrI+IXEfHxUn5CRCyIiGvK18vb7jk+IuZHxM0RcWBb\n", 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plt.subplots(2,2, figsize=(12,12), sharey='row')\n", "ax[0,0].plot([i['current_cost'] for i in history_original])\n", "ax[1,0].plot([i[0] for i in best_path_original], [i[1] for i in best_path_original])\n", "\n", "ax[0,1].plot([i['current_cost'] for i in history_mutant])\n", "ax[1,1].plot([i[0] for i in best_path_mutant], [i[1] for i in best_path_mutant])\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.3" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
mtasende/Machine-Learning-Nanodegree-Capstone	notebooks/prod/n06_hyperparameter_tuning.ipynb	1	335870	{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# In the last notebooks the best parameters for the datasets (base period, training period) were found, for some models, and for the prediction of 1, 7, 14, 28 and 56 days ahead. Two models were finally considered: Linear Regression and Random Forest. In this notebook the hyperparameters will be tuned for the Random Forest, and the best model will be chosen for each \"ahead day\" (1, 7, 14, 28, 56)." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "# Basic imports\n", "import os\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import datetime as dt\n", "import scipy.optimize as spo\n", "import sys\n", "from time import time\n", "from sklearn.metrics import r2_score, median_absolute_error\n", "\n", "%matplotlib inline\n", "\n", "%pylab inline\n", "pylab.rcParams['figure.figsize'] = (20.0, 10.0)\n", "\n", "%load_ext autoreload\n", "%autoreload 2\n", "\n", "sys.path.append('../../')\n", "import utils.misc as misc" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Let's first organize the results from the previous notebooks" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "res_df = pd.read_pickle('../../data/results_ahead1_linear_df.pkl')" ] }, { "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>base_days</th>\n", " <th>ahead_days</th>\n", " <th>train_val_time</th>\n", " <th>step_days</th>\n", " <th>GOOD_DATA_RATIO</th>\n", " <th>SAMPLES_GOOD_DATA_RATIO</th>\n", " <th>x_filename</th>\n", " <th>y_filename</th>\n", " <th>train_days</th>\n", " <th>scores</th>\n", " <th>r2</th>\n", " <th>mre</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>7</td>\n", " <td>1</td>\n", " <td>-1</td>\n", " <td>7</td>\n", " <td>0.99</td>\n", " <td>0.9</td>\n", " <td>x_base7_ahead1.pkl</td>\n", " <td>y_base7_ahead1.pkl</td>\n", " <td>252</td>\n", " <td>(0.834741188062, 0.0153118291225)</td>\n", " <td>0.834741</td>\n", " <td>0.015312</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>14</td>\n", " <td>1</td>\n", " <td>-1</td>\n", " <td>7</td>\n", " <td>0.99</td>\n", " <td>0.9</td>\n", " <td>x_base14_ahead1.pkl</td>\n", " <td>y_base14_ahead1.pkl</td>\n", " <td>252</td>\n", " <td>(0.900241572634, 0.0167286326766)</td>\n", " <td>0.900242</td>\n", " <td>0.016729</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>28</td>\n", " <td>1</td>\n", " <td>-1</td>\n", " <td>7</td>\n", " <td>0.99</td>\n", " <td>0.9</td>\n", " <td>x_base28_ahead1.pkl</td>\n", " <td>y_base28_ahead1.pkl</td>\n", " <td>252</td>\n", " <td>(0.952896891193, 0.015095253567)</td>\n", " <td>0.952897</td>\n", " <td>0.015095</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>56</td>\n", " <td>1</td>\n", " <td>-1</td>\n", " <td>7</td>\n", " <td>0.99</td>\n", " <td>0.9</td>\n", " <td>x_base56_ahead1.pkl</td>\n", " <td>y_base56_ahead1.pkl</td>\n", " <td>252</td>\n", " <td>(0.974173264084, 0.0156013450074)</td>\n", " <td>0.974173</td>\n", " <td>0.015601</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>112</td>\n", " <td>1</td>\n", " <td>-1</td>\n", " <td>7</td>\n", " <td>0.99</td>\n", " <td>0.9</td>\n", " <td>x_base112_ahead1.pkl</td>\n", " <td>y_base112_ahead1.pkl</td>\n", " <td>252</td>\n", " <td>(0.985143962052, 0.0170847165167)</td>\n", " <td>0.985144</td>\n", " <td>0.017085</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " base_days ahead_days train_val_time step_days GOOD_DATA_RATIO \\\n", "0 7 1 -1 7 0.99 \n", "1 14 1 -1 7 0.99 \n", "2 28 1 -1 7 0.99 \n", "3 56 1 -1 7 0.99 \n", "4 112 1 -1 7 0.99 \n", "\n", " SAMPLES_GOOD_DATA_RATIO x_filename y_filename \\\n", "0 0.9 x_base7_ahead1.pkl y_base7_ahead1.pkl \n", "1 0.9 x_base14_ahead1.pkl y_base14_ahead1.pkl \n", "2 0.9 x_base28_ahead1.pkl y_base28_ahead1.pkl \n", "3 0.9 x_base56_ahead1.pkl y_base56_ahead1.pkl \n", "4 0.9 x_base112_ahead1.pkl y_base112_ahead1.pkl \n", "\n", " train_days scores r2 mre \n", "0 252 (0.834741188062, 0.0153118291225) 0.834741 0.015312 \n", "1 252 (0.900241572634, 0.0167286326766) 0.900242 0.016729 \n", "2 252 (0.952896891193, 0.015095253567) 0.952897 0.015095 \n", "3 252 (0.974173264084, 0.0156013450074) 0.974173 0.015601 \n", "4 252 (0.985143962052, 0.0170847165167) 0.985144 0.017085 " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "res_df.head()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "base_days 14\n", "train_days 504\n", "r2 0.924353\n", "mre 0.0145049\n", "ahead_days 1\n", "train_val_time -1\n", "step_days 7\n", "GOOD_DATA_RATIO 0.99\n", "SAMPLES_GOOD_DATA_RATIO 0.9\n", "x_filename x_base14_ahead1.pkl\n", "y_filename y_base14_ahead1.pkl\n", "model linear\n", "Name: 6, dtype: object" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "RELEVANT_COLUMNS = ['base_days', \n", " 'train_days', \n", " 'r2',\n", " 'mre',\n", " 'ahead_days',\n", " 'train_val_time',\n", " 'step_days',\n", " 'GOOD_DATA_RATIO',\n", " 'SAMPLES_GOOD_DATA_RATIO',\n", " 'x_filename',\n", " 'y_filename']\n", "\n", "best_params_df = res_df[RELEVANT_COLUMNS].loc[np.argmin(res_df['mre']),:]\n", "best_params_df['model'] = 'linear'\n", "best_params_df" ] }, { "cell_type": "code", "execution_count": 5, "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>GOOD_DATA_RATIO</th>\n", " <th>SAMPLES_GOOD_DATA_RATIO</th>\n", " <th>ahead_days</th>\n", " <th>base_days</th>\n", " <th>model</th>\n", " <th>mre</th>\n", " <th>r2</th>\n", " <th>step_days</th>\n", " <th>train_days</th>\n", " <th>train_val_time</th>\n", " <th>x_filename</th>\n", " <th>y_filename</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0.99</td>\n", " <td>0.9</td>\n", " <td>1.0</td>\n", " <td>14.0</td>\n", " <td>linear</td>\n", " <td>0.014505</td>\n", " <td>0.924353</td>\n", " <td>7.0</td>\n", " <td>504.0</td>\n", " <td>-1.0</td>\n", " <td>x_base14_ahead1.pkl</td>\n", " <td>y_base14_ahead1.pkl</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " GOOD_DATA_RATIO SAMPLES_GOOD_DATA_RATIO ahead_days base_days model \\\n", "0 0.99 0.9 1.0 14.0 linear \n", "\n", " mre r2 step_days train_days train_val_time \\\n", "0 0.014505 0.924353 7.0 504.0 -1.0 \n", "\n", " x_filename y_filename \n", "0 x_base14_ahead1.pkl y_base14_ahead1.pkl " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_df = pd.DataFrame()\n", "test_df.append(best_params_df, ignore_index=True)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "RELEVANT_COLUMNS = ['base_days', \n", " 'train_days', \n", " 'r2',\n", " 'mre',\n", " 'ahead_days',\n", " 'train_val_time',\n", " 'step_days',\n", " 'GOOD_DATA_RATIO',\n", " 'SAMPLES_GOOD_DATA_RATIO',\n", " 'x_filename',\n", " 'y_filename']\n", "\n", "ahead_days_list = [1, 7, 14, 28, 56]\n", "models_list = ['linear', 'random_forest']\n", "\n", "results_df = pd.DataFrame()\n", "for ahead_days in ahead_days_list:\n", " for model in models_list:\n", " res_df = pd.read_pickle('../../data/results_ahead{}_{}_df.pkl'.format(ahead_days, model))\n", " best_params_df = res_df[RELEVANT_COLUMNS].loc[np.argmax(res_df['r2']),:]\n", " best_params_df['ahead_days'] = ahead_days\n", " best_params_df['model'] = model\n", " results_df = results_df.append(best_params_df, ignore_index=True)" ] }, { "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>GOOD_DATA_RATIO</th>\n", " <th>SAMPLES_GOOD_DATA_RATIO</th>\n", " <th>ahead_days</th>\n", " <th>base_days</th>\n", " <th>model</th>\n", " <th>mre</th>\n", " <th>r2</th>\n", " <th>step_days</th>\n", " <th>train_days</th>\n", " <th>train_val_time</th>\n", " <th>x_filename</th>\n", " <th>y_filename</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0.99</td>\n", " <td>0.9</td>\n", " <td>1.0</td>\n", " <td>112.0</td>\n", " <td>linear</td>\n", " <td>0.015856</td>\n", " <td>0.986599</td>\n", " <td>7.0</td>\n", " <td>504.0</td>\n", " <td>-1.0</td>\n", " <td>x_base112_ahead1.pkl</td>\n", " <td>y_base112_ahead1.pkl</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>0.99</td>\n", " <td>0.9</td>\n", " <td>1.0</td>\n", " <td>112.0</td>\n", " <td>random_forest</td>\n", " <td>0.018002</td>\n", " <td>0.984864</td>\n", " <td>7.0</td>\n", " <td>756.0</td>\n", " <td>-1.0</td>\n", " <td>x_base112_ahead1.pkl</td>\n", " <td>y_base112_ahead1.pkl</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>0.99</td>\n", " <td>0.9</td>\n", " <td>7.0</td>\n", " <td>112.0</td>\n", " <td>linear</td>\n", " <td>0.042367</td>\n", " <td>0.923348</td>\n", " <td>7.0</td>\n", " <td>756.0</td>\n", " <td>-1.0</td>\n", " <td>x_base112_ahead7.pkl</td>\n", " <td>y_base112_ahead7.pkl</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>0.99</td>\n", " <td>0.9</td>\n", " <td>7.0</td>\n", " <td>112.0</td>\n", " <td>random_forest</td>\n", " <td>0.044267</td>\n", " <td>0.915048</td>\n", " <td>7.0</td>\n", " <td>756.0</td>\n", " <td>-1.0</td>\n", " <td>x_base112_ahead7.pkl</td>\n", " <td>y_base112_ahead7.pkl</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>0.99</td>\n", " <td>0.9</td>\n", " <td>14.0</td>\n", " <td>112.0</td>\n", " <td>linear</td>\n", " <td>0.060167</td>\n", " <td>0.865259</td>\n", " <td>7.0</td>\n", " <td>756.0</td>\n", " <td>-1.0</td>\n", " <td>x_base112_ahead14.pkl</td>\n", " <td>y_base112_ahead14.pkl</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>0.99</td>\n", " <td>0.9</td>\n", " <td>14.0</td>\n", " <td>112.0</td>\n", " <td>random_forest</td>\n", " <td>0.063327</td>\n", " <td>0.829452</td>\n", " <td>7.0</td>\n", " <td>756.0</td>\n", " <td>-1.0</td>\n", " <td>x_base112_ahead14.pkl</td>\n", " <td>y_base112_ahead14.pkl</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>0.99</td>\n", " <td>0.9</td>\n", " <td>28.0</td>\n", " <td>112.0</td>\n", " <td>linear</td>\n", " <td>0.091966</td>\n", " <td>0.758046</td>\n", " <td>7.0</td>\n", " <td>756.0</td>\n", " <td>-1.0</td>\n", " <td>x_base112_ahead28.pkl</td>\n", " <td>y_base112_ahead28.pkl</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>0.99</td>\n", " <td>0.9</td>\n", " <td>28.0</td>\n", " <td>112.0</td>\n", " <td>random_forest</td>\n", " <td>0.096087</td>\n", " <td>0.715802</td>\n", " <td>7.0</td>\n", " <td>756.0</td>\n", " <td>-1.0</td>\n", " <td>x_base112_ahead28.pkl</td>\n", " <td>y_base112_ahead28.pkl</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>0.99</td>\n", " <td>0.9</td>\n", " <td>56.0</td>\n", " <td>112.0</td>\n", " <td>linear</td>\n", " <td>0.127913</td>\n", " <td>0.590426</td>\n", " <td>7.0</td>\n", " <td>756.0</td>\n", " <td>-1.0</td>\n", " <td>x_base112_ahead56.pkl</td>\n", " <td>y_base112_ahead56.pkl</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>0.99</td>\n", " <td>0.9</td>\n", " <td>56.0</td>\n", " <td>112.0</td>\n", " <td>random_forest</td>\n", " <td>0.136095</td>\n", " <td>0.512861</td>\n", " <td>7.0</td>\n", " <td>756.0</td>\n", " <td>-1.0</td>\n", " <td>x_base112_ahead56.pkl</td>\n", " <td>y_base112_ahead56.pkl</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " GOOD_DATA_RATIO SAMPLES_GOOD_DATA_RATIO ahead_days base_days \\\n", "0 0.99 0.9 1.0 112.0 \n", "1 0.99 0.9 1.0 112.0 \n", "2 0.99 0.9 7.0 112.0 \n", "3 0.99 0.9 7.0 112.0 \n", "4 0.99 0.9 14.0 112.0 \n", "5 0.99 0.9 14.0 112.0 \n", "6 0.99 0.9 28.0 112.0 \n", "7 0.99 0.9 28.0 112.0 \n", "8 0.99 0.9 56.0 112.0 \n", "9 0.99 0.9 56.0 112.0 \n", "\n", " model mre r2 step_days train_days train_val_time \\\n", "0 linear 0.015856 0.986599 7.0 504.0 -1.0 \n", "1 random_forest 0.018002 0.984864 7.0 756.0 -1.0 \n", "2 linear 0.042367 0.923348 7.0 756.0 -1.0 \n", "3 random_forest 0.044267 0.915048 7.0 756.0 -1.0 \n", "4 linear 0.060167 0.865259 7.0 756.0 -1.0 \n", "5 random_forest 0.063327 0.829452 7.0 756.0 -1.0 \n", "6 linear 0.091966 0.758046 7.0 756.0 -1.0 \n", "7 random_forest 0.096087 0.715802 7.0 756.0 -1.0 \n", "8 linear 0.127913 0.590426 7.0 756.0 -1.0 \n", "9 random_forest 0.136095 0.512861 7.0 756.0 -1.0 \n", "\n", " x_filename y_filename \n", "0 x_base112_ahead1.pkl y_base112_ahead1.pkl \n", "1 x_base112_ahead1.pkl y_base112_ahead1.pkl \n", "2 x_base112_ahead7.pkl y_base112_ahead7.pkl \n", "3 x_base112_ahead7.pkl y_base112_ahead7.pkl \n", "4 x_base112_ahead14.pkl y_base112_ahead14.pkl \n", "5 x_base112_ahead14.pkl y_base112_ahead14.pkl \n", "6 x_base112_ahead28.pkl y_base112_ahead28.pkl \n", "7 x_base112_ahead28.pkl y_base112_ahead28.pkl \n", "8 x_base112_ahead56.pkl y_base112_ahead56.pkl \n", "9 x_base112_ahead56.pkl y_base112_ahead56.pkl " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "results_df" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "results_df.to_pickle('../../data/best_dataset_params_raw_df.pkl')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Which is the best model before hyperparameter tuning?" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>GOOD_DATA_RATIO</th>\n", " <th>SAMPLES_GOOD_DATA_RATIO</th>\n", " <th>ahead_days</th>\n", " <th>base_days</th>\n", " <th>model</th>\n", " <th>mre</th>\n", " <th>r2</th>\n", " <th>step_days</th>\n", " <th>train_days</th>\n", " <th>train_val_time</th>\n", " <th>x_filename</th>\n", " <th>y_filename</th>\n", " </tr>\n", " <tr>\n", " <th>ahead_days</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>1.0</th>\n", " <td>0.99</td>\n", " <td>0.9</td>\n", " <td>1.0</td>\n", " <td>112.0</td>\n", " <td>linear</td>\n", " <td>0.015856</td>\n", " <td>0.986599</td>\n", " <td>7.0</td>\n", " <td>504.0</td>\n", " <td>-1.0</td>\n", " <td>x_base112_ahead1.pkl</td>\n", " <td>y_base112_ahead1.pkl</td>\n", " </tr>\n", " <tr>\n", " <th>7.0</th>\n", " <td>0.99</td>\n", " <td>0.9</td>\n", " <td>7.0</td>\n", " <td>112.0</td>\n", " <td>linear</td>\n", " <td>0.042367</td>\n", " <td>0.923348</td>\n", " <td>7.0</td>\n", " <td>756.0</td>\n", " <td>-1.0</td>\n", " <td>x_base112_ahead7.pkl</td>\n", " <td>y_base112_ahead7.pkl</td>\n", " </tr>\n", " <tr>\n", " <th>14.0</th>\n", " <td>0.99</td>\n", " <td>0.9</td>\n", " <td>14.0</td>\n", " <td>112.0</td>\n", " <td>linear</td>\n", " <td>0.060167</td>\n", " <td>0.865259</td>\n", " <td>7.0</td>\n", " <td>756.0</td>\n", " <td>-1.0</td>\n", " <td>x_base112_ahead14.pkl</td>\n", " <td>y_base112_ahead14.pkl</td>\n", " </tr>\n", " <tr>\n", " <th>28.0</th>\n", " <td>0.99</td>\n", " <td>0.9</td>\n", " <td>28.0</td>\n", " <td>112.0</td>\n", " <td>linear</td>\n", " <td>0.091966</td>\n", " <td>0.758046</td>\n", " <td>7.0</td>\n", " <td>756.0</td>\n", " <td>-1.0</td>\n", " <td>x_base112_ahead28.pkl</td>\n", " <td>y_base112_ahead28.pkl</td>\n", " </tr>\n", " <tr>\n", " <th>56.0</th>\n", " <td>0.99</td>\n", " <td>0.9</td>\n", " <td>56.0</td>\n", " <td>112.0</td>\n", " <td>linear</td>\n", " <td>0.127913</td>\n", " <td>0.590426</td>\n", " <td>7.0</td>\n", " <td>756.0</td>\n", " <td>-1.0</td>\n", " <td>x_base112_ahead56.pkl</td>\n", " <td>y_base112_ahead56.pkl</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " GOOD_DATA_RATIO SAMPLES_GOOD_DATA_RATIO ahead_days base_days \\\n", "ahead_days \n", "1.0 0.99 0.9 1.0 112.0 \n", "7.0 0.99 0.9 7.0 112.0 \n", "14.0 0.99 0.9 14.0 112.0 \n", "28.0 0.99 0.9 28.0 112.0 \n", "56.0 0.99 0.9 56.0 112.0 \n", "\n", " model mre r2 step_days train_days train_val_time \\\n", "ahead_days \n", "1.0 linear 0.015856 0.986599 7.0 504.0 -1.0 \n", "7.0 linear 0.042367 0.923348 7.0 756.0 -1.0 \n", "14.0 linear 0.060167 0.865259 7.0 756.0 -1.0 \n", "28.0 linear 0.091966 0.758046 7.0 756.0 -1.0 \n", "56.0 linear 0.127913 0.590426 7.0 756.0 -1.0 \n", "\n", " x_filename y_filename \n", "ahead_days \n", "1.0 x_base112_ahead1.pkl y_base112_ahead1.pkl \n", "7.0 x_base112_ahead7.pkl y_base112_ahead7.pkl \n", "14.0 x_base112_ahead14.pkl y_base112_ahead14.pkl \n", "28.0 x_base112_ahead28.pkl y_base112_ahead28.pkl \n", "56.0 x_base112_ahead56.pkl y_base112_ahead56.pkl " ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def keep_max_r2(record):\n", " return record.loc[np.argmax(record['r2']),:]\n", "\n", "best_r2_df = results_df.groupby('ahead_days').apply(keep_max_r2)\n", "best_r2_df" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f8ee2607e10>" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Y/djtyQPfTMqRan7h+pZTRDuqU0UDCzr3DgAAANSKQAgmQ09fsvrqalz7r6pnJ16smlaf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\nfTW2/Gz73KmX2m9BO/RAdbJ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Vv11VH6yqX6+qJ7tz3hWSCEJws34syesmzr0p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\nAABMGUEIAAAAYMr8fwiL3kv6l58iAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f8ee26311d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "best_r2_df[['mre', 'r2']].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Before hyperparameter tuning, it seems like the linear regression is doing better in all the predictions. Clearly, as the days ahead are more, the r2 value drops, and the mre goes up." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Let's search for better hyperparameters for the Random Forest models" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>GOOD_DATA_RATIO</th>\n", " <th>SAMPLES_GOOD_DATA_RATIO</th>\n", " <th>base_days</th>\n", " <th>model</th>\n", " <th>mre</th>\n", " <th>r2</th>\n", " <th>step_days</th>\n", " <th>train_days</th>\n", " <th>train_val_time</th>\n", " <th>x_filename</th>\n", " <th>y_filename</th>\n", " </tr>\n", " <tr>\n", " <th>ahead_days</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>1.0</th>\n", " <td>0.99</td>\n", " <td>0.9</td>\n", " <td>112.0</td>\n", " <td>random_forest</td>\n", " <td>0.018002</td>\n", " <td>0.984864</td>\n", " <td>7.0</td>\n", " <td>756.0</td>\n", " <td>-1.0</td>\n", " <td>x_base112_ahead1.pkl</td>\n", " <td>y_base112_ahead1.pkl</td>\n", " </tr>\n", " <tr>\n", " <th>7.0</th>\n", " <td>0.99</td>\n", " <td>0.9</td>\n", " <td>112.0</td>\n", " <td>random_forest</td>\n", " <td>0.044267</td>\n", " <td>0.915048</td>\n", " <td>7.0</td>\n", " <td>756.0</td>\n", " <td>-1.0</td>\n", " <td>x_base112_ahead7.pkl</td>\n", " <td>y_base112_ahead7.pkl</td>\n", " </tr>\n", " <tr>\n", " <th>14.0</th>\n", " <td>0.99</td>\n", " <td>0.9</td>\n", " <td>112.0</td>\n", " <td>random_forest</td>\n", " <td>0.063327</td>\n", " <td>0.829452</td>\n", " <td>7.0</td>\n", " <td>756.0</td>\n", " <td>-1.0</td>\n", " <td>x_base112_ahead14.pkl</td>\n", " <td>y_base112_ahead14.pkl</td>\n", " </tr>\n", " <tr>\n", " <th>28.0</th>\n", " <td>0.99</td>\n", " <td>0.9</td>\n", " <td>112.0</td>\n", " <td>random_forest</td>\n", " <td>0.096087</td>\n", " <td>0.715802</td>\n", " <td>7.0</td>\n", " <td>756.0</td>\n", " <td>-1.0</td>\n", " <td>x_base112_ahead28.pkl</td>\n", " <td>y_base112_ahead28.pkl</td>\n", " </tr>\n", " <tr>\n", " <th>56.0</th>\n", " <td>0.99</td>\n", " <td>0.9</td>\n", " <td>112.0</td>\n", " <td>random_forest</td>\n", " <td>0.136095</td>\n", " <td>0.512861</td>\n", " <td>7.0</td>\n", " <td>756.0</td>\n", " <td>-1.0</td>\n", " <td>x_base112_ahead56.pkl</td>\n", " <td>y_base112_ahead56.pkl</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " GOOD_DATA_RATIO SAMPLES_GOOD_DATA_RATIO base_days \\\n", "ahead_days \n", "1.0 0.99 0.9 112.0 \n", "7.0 0.99 0.9 112.0 \n", "14.0 0.99 0.9 112.0 \n", "28.0 0.99 0.9 112.0 \n", "56.0 0.99 0.9 112.0 \n", "\n", " model mre r2 step_days train_days \\\n", "ahead_days \n", "1.0 random_forest 0.018002 0.984864 7.0 756.0 \n", "7.0 random_forest 0.044267 0.915048 7.0 756.0 \n", "14.0 random_forest 0.063327 0.829452 7.0 756.0 \n", "28.0 random_forest 0.096087 0.715802 7.0 756.0 \n", "56.0 random_forest 0.136095 0.512861 7.0 756.0 \n", "\n", " train_val_time x_filename y_filename \n", "ahead_days \n", "1.0 -1.0 x_base112_ahead1.pkl y_base112_ahead1.pkl \n", "7.0 -1.0 x_base112_ahead7.pkl y_base112_ahead7.pkl \n", "14.0 -1.0 x_base112_ahead14.pkl y_base112_ahead14.pkl \n", "28.0 -1.0 x_base112_ahead28.pkl y_base112_ahead28.pkl \n", "56.0 -1.0 x_base112_ahead56.pkl y_base112_ahead56.pkl " ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "initial_performance_df = results_df[results_df['model']=='random_forest']\n", "initial_performance_df.set_index('ahead_days', inplace=True)\n", "initial_performance_df" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "112.0" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "initial_performance_df.loc[14, 'base_days']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Build the hyperparameters DataFrame" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>n_estimators</th>\n", " <th>max_depth</th>\n", " <th>n_jobs</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>50</td>\n", " <td>5</td>\n", " <td>-1</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>50</td>\n", " <td>10</td>\n", " <td>-1</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>100</td>\n", " <td>5</td>\n", " <td>-1</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>100</td>\n", " <td>10</td>\n", " <td>-1</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " n_estimators max_depth n_jobs\n", "0 50 5 -1\n", "1 50 10 -1\n", "2 100 5 -1\n", "3 100 10 -1" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "n_estimators = [50, 100]\n", "max_depth = [5, 10]\n", "hyper_df = pd.DataFrame([(x, y) for x in n_estimators for y in max_depth], columns=['n_estimators', 'max_depth'])\n", "hyper_df['n_jobs'] = -1\n", "hyper_df" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "GOOD_DATA_RATIO 0.99\n", "SAMPLES_GOOD_DATA_RATIO 0.9\n", "base_days 112\n", "model random_forest\n", "mre 0.0180017\n", "r2 0.984864\n", "step_days 7\n", "train_days 756\n", "train_val_time -1\n", "x_filename x_base112_ahead1.pkl\n", "y_filename y_base112_ahead1.pkl\n", "Name: 1.0, dtype: object" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "params_df = initial_performance_df.loc[1]\n", "params_df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Ahead days = 1" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Evaluating: {'n_estimators': 100, 'max_depth': 10, 'n_jobs': -1}\n", "Evaluating: {'n_estimators': 50, 'max_depth': 10, 'n_jobs': -1}\n", "Evaluating: {'n_estimators': 100, 'max_depth': 5, 'n_jobs': -1}\n", "Generating: base112_ahead1_train756\n", "Evaluating: {'n_estimators': 50, 'max_depth': 5, 'n_jobs': -1}\n", "Generating: base112_ahead1_train756\n", "Generating: base112_ahead1_train756\n", "Generating: base112_ahead1_train756\n", "Evaluating approximately 77 training/evaluation pairs\n", "Evaluating approximately 77 training/evaluation pairs\n", "Evaluating approximately 77 training/evaluation pairs\n", "Evaluating approximately 77 training/evaluation pairs\n", "Approximately 101.3 percent complete. (0.98381408302069029, 0.019143977213481194)\n", "Approximately 90.9 percent complete. (0.98418220377123244, 0.019091244957495992)\n", "Approximately 72.7 percent complete. (0.98612570882134876, 0.016850455554563416)\n", "Approximately 101.3 percent complete. (0.98606697491007356, 0.016820250789550569)\n", "Minimum MRE param set: \n", " n_estimators 100\n", "max_depth 10\n", "n_jobs -1\n", "scores (0.98606697491, 0.0168202507896)\n", "r2 0.986067\n", "mre 0.0168203\n", "Name: 3, dtype: object\n", "Maximum R^2 param set: \n", " n_estimators 50\n", "max_depth 10\n", "n_jobs -1\n", "scores (0.986125708821, 0.0168504555546)\n", "r2 0.986126\n", "mre 0.0168505\n", "Name: 1, dtype: object\n", "Elapsed time: 1398.6756625175476 seconds.\n" ] }, { "data": { "image/png": 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Ll+nHDUW2kwAAADyK4zi69/18fbRit+66oLdGJHW0neSzGDgBQBPatq9Sdyxa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"text/plain": [ "<matplotlib.figure.Figure at 0x7f8edee5a6a0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "AHEAD_DAYS = 1\n", "\n", "# Get the normal parameters set\n", "params_df = initial_performance_df.loc[AHEAD_DAYS].copy()\n", "params_df['ahead_days'] = AHEAD_DAYS\n", "\n", "tic = time()\n", "\n", "from predictor.random_forest_predictor import RandomForestPredictor\n", "PREDICTOR_NAME = 'random_forest'\n", "\n", "# Global variables\n", "eval_predictor_class = RandomForestPredictor\n", "step_eval_days = 60 # The step to move between training/validation pairs\n", "\n", "# Build the params list\n", "params = {'params_df': params_df,\n", " 'step_eval_days': step_eval_days,\n", " 'eval_predictor_class': eval_predictor_class}\n", "\n", "results_df = misc.parallelize_dataframe(hyper_df, misc.search_mean_score_eval, params)\n", "\n", "# Some postprocessing... -----------------------------------------------------------\n", "results_df['r2'] = results_df.apply(lambda x: x['scores'][0], axis=1)\n", "results_df['mre'] = results_df.apply(lambda x: x['scores'][1], axis=1)\n", "# Pickle that!\n", "results_df.to_pickle('../../data/hyper_ahead{}_{}_df.pkl'.format(AHEAD_DAYS, PREDICTOR_NAME))\n", "results_df['r2'].plot()\n", "\n", "print('Minimum MRE param set: \\n {}'.format(results_df.iloc[np.argmin(results_df['mre'])]))\n", "print('Maximum R^2 param set: \\n {}'.format(results_df.iloc[np.argmax(results_df['r2'])]))\n", "# -----------------------------------------------------------------------------------\n", "\n", "toc = time()\n", "print('Elapsed time: {} seconds.'.format((toc-tic)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Ahead days = 7" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Evaluating: {'n_estimators': 100, 'max_depth': 5, 'n_jobs': -1}\n", "Evaluating: {'n_estimators': 50, 'max_depth': 5, 'n_jobs': -1}\n", "Evaluating: {'n_estimators': 50, 'max_depth': 10, 'n_jobs': -1}\n", "Evaluating: {'n_estimators': 100, 'max_depth': 10, 'n_jobs': -1}\n", "Generating: base112_ahead7_train756\n", "Generating: base112_ahead7_train756\n", "Generating: base112_ahead7_train756\n", "Generating: base112_ahead7_train756\n", "Evaluating approximately 77 training/evaluation pairs\n", "Evaluating approximately 77 training/evaluation pairs\n", "Evaluating approximately 77 training/evaluation pairs\n", "Evaluating approximately 77 training/evaluation pairs\n", "Approximately 101.3 percent complete. (0.92867427413659043, 0.042073389045121791)\n", "Approximately 101.3 percent complete. (0.92901657711577335, 0.042002474848861167)\n", "Approximately 101.3 percent complete. (0.92530656870090644, 0.041440316788257971)\n", "Approximately 101.3 percent complete. (0.92659030746215731, 0.041326296086241686)\n", "Minimum MRE param set: \n", " n_estimators 100\n", "max_depth 10\n", "n_jobs -1\n", "scores (0.926590307462, 0.0413262960862)\n", "r2 0.92659\n", "mre 0.0413263\n", "Name: 3, dtype: object\n", "Maximum R^2 param set: \n", " n_estimators 100\n", "max_depth 5\n", "n_jobs -1\n", "scores (0.929016577116, 0.0420024748489)\n", "r2 0.929017\n", "mre 0.0420025\n", "Name: 2, dtype: object\n", "Elapsed time: 1414.774272441864 seconds.\n" ] }, { "data": { "image/png": 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Ite311E0d9M2Wwxowa6UOZ+XajgXATVE4lZF7r2qjXh0a6JmPNumbLYdtxwEA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"text/plain": [ "<matplotlib.figure.Figure at 0x7f8edc52b198>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "AHEAD_DAYS = 7\n", "\n", "# Get the normal parameters set\n", "params_df = initial_performance_df.loc[AHEAD_DAYS].copy()\n", "params_df['ahead_days'] = AHEAD_DAYS\n", "\n", "tic = time()\n", "\n", "from predictor.random_forest_predictor import RandomForestPredictor\n", "PREDICTOR_NAME = 'random_forest'\n", "\n", "# Global variables\n", "eval_predictor_class = RandomForestPredictor\n", "step_eval_days = 60 # The step to move between training/validation pairs\n", "\n", "# Build the params list\n", "params = {'params_df': params_df,\n", " 'step_eval_days': step_eval_days,\n", " 'eval_predictor_class': eval_predictor_class}\n", "\n", "results_df = misc.parallelize_dataframe(hyper_df, misc.search_mean_score_eval, params)\n", "\n", "# Some postprocessing... -----------------------------------------------------------\n", "results_df['r2'] = results_df.apply(lambda x: x['scores'][0], axis=1)\n", "results_df['mre'] = results_df.apply(lambda x: x['scores'][1], axis=1)\n", "# Pickle that!\n", "results_df.to_pickle('../../data/hyper_ahead{}_{}_df.pkl'.format(AHEAD_DAYS, PREDICTOR_NAME))\n", "results_df['r2'].plot()\n", "\n", "print('Minimum MRE param set: \\n {}'.format(results_df.iloc[np.argmin(results_df['mre'])]))\n", "print('Maximum R^2 param set: \\n {}'.format(results_df.iloc[np.argmax(results_df['r2'])]))\n", "# -----------------------------------------------------------------------------------\n", "\n", "toc = time()\n", "print('Elapsed time: {} seconds.'.format((toc-tic)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Ahead days = 14" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Evaluating: {'n_estimators': 100, 'max_depth': 5, 'n_jobs': -1}\n", "Evaluating: {'n_estimators': 50, 'max_depth': 5, 'n_jobs': -1}\n", "Evaluating: {'n_estimators': 100, 'max_depth': 10, 'n_jobs': -1}\n", "Evaluating: {'n_estimators': 50, 'max_depth': 10, 'n_jobs': -1}\n", "Generating: base112_ahead14_train756\n", "Generating: base112_ahead14_train756\n", "Generating: base112_ahead14_train756\n", "Generating: base112_ahead14_train756\n", "Evaluating approximately 77 training/evaluation pairs\n", "Evaluating approximately 77 training/evaluation pairs\n", "Evaluating approximately 77 training/evaluation pairs\n", "Evaluating approximately 77 training/evaluation pairs\n", "Approximately 70.1 percent complete. (0.86854483835222773, 0.059552721522095425)\n", "Approximately 93.5 percent complete. (0.86892702579234082, 0.059514978873294676)\n", "Approximately 101.3 percent complete. (0.85556550478037119, 0.059458013790502723)\n", "Approximately 101.3 percent complete. (0.86161618765643722, 0.05932458230969568)\n", "Minimum MRE param set: \n", " n_estimators 100\n", "max_depth 10\n", "n_jobs -1\n", "scores (0.861616187656, 0.0593245823097)\n", "r2 0.861616\n", "mre 0.0593246\n", "Name: 3, dtype: object\n", "Maximum R^2 param set: \n", " n_estimators 100\n", "max_depth 5\n", "n_jobs -1\n", "scores (0.868927025792, 0.0595149788733)\n", "r2 0.868927\n", "mre 0.059515\n", "Name: 2, dtype: object\n", "Elapsed time: 1405.0895755290985 seconds.\n" ] }, { "data": { "image/png": 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jta9XUS98F68dxy7YHQcAAACS4o6c1/gVO3Vb0yp6OLyu3XGAYhMcWE7LxnVS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"text/plain": [ "<matplotlib.figure.Figure at 0x7f8edee1aa90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "AHEAD_DAYS = 14\n", "\n", "# Get the normal parameters set\n", "params_df = initial_performance_df.loc[AHEAD_DAYS].copy()\n", "params_df['ahead_days'] = AHEAD_DAYS\n", "\n", "tic = time()\n", "\n", "from predictor.random_forest_predictor import RandomForestPredictor\n", "PREDICTOR_NAME = 'random_forest'\n", "\n", "# Global variables\n", "eval_predictor_class = RandomForestPredictor\n", "step_eval_days = 60 # The step to move between training/validation pairs\n", "\n", "# Build the params list\n", "params = {'params_df': params_df,\n", " 'step_eval_days': step_eval_days,\n", " 'eval_predictor_class': eval_predictor_class}\n", "\n", "results_df = misc.parallelize_dataframe(hyper_df, misc.search_mean_score_eval, params)\n", "\n", "# Some postprocessing... -----------------------------------------------------------\n", "results_df['r2'] = results_df.apply(lambda x: x['scores'][0], axis=1)\n", "results_df['mre'] = results_df.apply(lambda x: x['scores'][1], axis=1)\n", "# Pickle that!\n", "results_df.to_pickle('../../data/hyper_ahead{}_{}_df.pkl'.format(AHEAD_DAYS, PREDICTOR_NAME))\n", "results_df['r2'].plot()\n", "\n", "print('Minimum MRE param set: \\n {}'.format(results_df.iloc[np.argmin(results_df['mre'])]))\n", "print('Maximum R^2 param set: \\n {}'.format(results_df.iloc[np.argmax(results_df['r2'])]))\n", "# -----------------------------------------------------------------------------------\n", "\n", "toc = time()\n", "print('Elapsed time: {} seconds.'.format((toc-tic)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Ahead days = 28" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Evaluating: {'n_estimators': 50, 'max_depth': 5, 'n_jobs': -1}\n", "Evaluating: {'n_estimators': 100, 'max_depth': 5, 'n_jobs': -1}\n", "Generating: base112_ahead28_train756\n", "Evaluating: {'n_estimators': 50, 'max_depth': 10, 'n_jobs': -1}\n", "Generating: base112_ahead28_train756\n", "Evaluating: {'n_estimators': 100, 'max_depth': 10, 'n_jobs': -1}\n", "Generating: base112_ahead28_train756\n", "Generating: base112_ahead28_train756\n", "Evaluating approximately 77 training/evaluation pairs\n", "Evaluating approximately 77 training/evaluation pairs\n", "Evaluating approximately 77 training/evaluation pairs\n", "Evaluating approximately 77 training/evaluation pairs\n", "Approximately 100.0 percent complete. (0.76511197091963712, 0.090310449239112411)\n", "Approximately 100.0 percent complete. (0.76483623247521459, 0.090362126076648244)\n", "Approximately 100.0 percent complete. (0.75795817643584429, 0.090145606122285907)\n", "Approximately 100.0 percent complete. (0.75846744620443518, 0.090220889159736098)\n", "Minimum MRE param set: \n", " n_estimators 50\n", "max_depth 10\n", "n_jobs -1\n", "scores (0.757958176436, 0.0901456061223)\n", "r2 0.757958\n", "mre 0.0901456\n", "Name: 1, dtype: object\n", "Maximum R^2 param set: \n", " n_estimators 50\n", "max_depth 5\n", "n_jobs -1\n", "scores (0.76511197092, 0.0903104492391)\n", "r2 0.765112\n", "mre 0.0903104\n", "Name: 0, dtype: object\n", "Elapsed time: 1366.953284740448 seconds.\n" ] }, { "data": { "image/png": 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sJ8ECf18fTRserxZhgbrh7WRl7y62nQQAdYphycM0DwvUi4NitHLbPj38/nLb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"text/plain": [ "<matplotlib.figure.Figure at 0x7f8edc40f5f8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "AHEAD_DAYS = 28\n", "\n", "# Get the normal parameters set\n", "params_df = initial_performance_df.loc[AHEAD_DAYS].copy()\n", "params_df['ahead_days'] = AHEAD_DAYS\n", "\n", "tic = time()\n", "\n", "from predictor.random_forest_predictor import RandomForestPredictor\n", "PREDICTOR_NAME = 'random_forest'\n", "\n", "# Global variables\n", "eval_predictor_class = RandomForestPredictor\n", "step_eval_days = 60 # The step to move between training/validation pairs\n", "\n", "# Build the params list\n", "params = {'params_df': params_df,\n", " 'step_eval_days': step_eval_days,\n", " 'eval_predictor_class': eval_predictor_class}\n", "\n", "results_df = misc.parallelize_dataframe(hyper_df, misc.search_mean_score_eval, params)\n", "\n", "# Some postprocessing... -----------------------------------------------------------\n", "results_df['r2'] = results_df.apply(lambda x: x['scores'][0], axis=1)\n", "results_df['mre'] = results_df.apply(lambda x: x['scores'][1], axis=1)\n", "# Pickle that!\n", "results_df.to_pickle('../../data/hyper_ahead{}_{}_df.pkl'.format(AHEAD_DAYS, PREDICTOR_NAME))\n", "results_df['r2'].plot()\n", "\n", "print('Minimum MRE param set: \\n {}'.format(results_df.iloc[np.argmin(results_df['mre'])]))\n", "print('Maximum R^2 param set: \\n {}'.format(results_df.iloc[np.argmax(results_df['r2'])]))\n", "# -----------------------------------------------------------------------------------\n", "\n", "toc = time()\n", "print('Elapsed time: {} seconds.'.format((toc-tic)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Ahead days = 56" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Evaluating: {'n_estimators': 50, 'max_depth': 5, 'n_jobs': -1}\n", "Evaluating: {'n_estimators': 100, 'max_depth': 5, 'n_jobs': -1}\n", "Evaluating: {'n_estimators': 50, 'max_depth': 10, 'n_jobs': -1}\n", "Evaluating: {'n_estimators': 100, 'max_depth': 10, 'n_jobs': -1}\n", "Generating: base112_ahead56_train756\n", "Generating: base112_ahead56_train756\n", "Generating: base112_ahead56_train756\n", "Generating: base112_ahead56_train756\n", "Evaluating approximately 76 training/evaluation pairs\n", "Evaluating approximately 76 training/evaluation pairs\n", "Evaluating approximately 76 training/evaluation pairs\n", "Evaluating approximately 76 training/evaluation pairs\n", "Approximately 101.3 percent complete. (0.61541202680466478, 0.12640953814082068)\n", "Approximately 101.3 percent complete. (0.61381359078137865, 0.12647375426109192)\n", "Approximately 101.3 percent complete. (0.59592620102113891, 0.12788679297679451)\n", "Approximately 101.3 percent complete. (0.60070055912489662, 0.12775455708581532)\n", "Minimum MRE param set: \n", " n_estimators 50\n", "max_depth 5\n", "n_jobs -1\n", "scores (0.615412026805, 0.126409538141)\n", "r2 0.615412\n", "mre 0.12641\n", "Name: 0, dtype: object\n", "Maximum R^2 param set: \n", " n_estimators 50\n", "max_depth 5\n", "n_jobs -1\n", "scores (0.615412026805, 0.126409538141)\n", "r2 0.615412\n", "mre 0.12641\n", "Name: 0, dtype: object\n", "Elapsed time: 1321.0567960739136 seconds.\n" ] }, { "data": { "image/png": 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qG6f9ovdTt7mOBADlgsLJx/WPj1Sf2IZ69Zt1+nEd85wAAADKm7VWDy5YrrSt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\nSnse9SD999lNOyU1KrnU7i5JM40x4b/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oZduybCfByzFwAjzE6Mua6aLm1fX0Z2u1fh/7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"text/plain": [ "<matplotlib.figure.Figure at 0x7f8edc467d68>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "AHEAD_DAYS = 56\n", "\n", "# Get the normal parameters set\n", "params_df = initial_performance_df.loc[AHEAD_DAYS].copy()\n", "params_df['ahead_days'] = AHEAD_DAYS\n", "\n", "tic = time()\n", "\n", "from predictor.random_forest_predictor import RandomForestPredictor\n", "PREDICTOR_NAME = 'random_forest'\n", "\n", "# Global variables\n", "eval_predictor_class = RandomForestPredictor\n", "step_eval_days = 60 # The step to move between training/validation pairs\n", "\n", "# Build the params list\n", "params = {'params_df': params_df,\n", " 'step_eval_days': step_eval_days,\n", " 'eval_predictor_class': eval_predictor_class}\n", "\n", "results_df = misc.parallelize_dataframe(hyper_df, misc.search_mean_score_eval, params)\n", "\n", "# Some postprocessing... -----------------------------------------------------------\n", "results_df['r2'] = results_df.apply(lambda x: x['scores'][0], axis=1)\n", "results_df['mre'] = results_df.apply(lambda x: x['scores'][1], axis=1)\n", "# Pickle that!\n", "results_df.to_pickle('../../data/hyper_ahead{}_{}_df.pkl'.format(AHEAD_DAYS, PREDICTOR_NAME))\n", "results_df['r2'].plot()\n", "\n", "print('Minimum MRE param set: \\n {}'.format(results_df.iloc[np.argmin(results_df['mre'])]))\n", "print('Maximum R^2 param set: \\n {}'.format(results_df.iloc[np.argmax(results_df['r2'])]))\n", "# -----------------------------------------------------------------------------------\n", "\n", "toc = time()\n", "print('Elapsed time: {} seconds.'.format((toc-tic)))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
david-hoffman/pyOTF	notebooks/Phase Retrieval Development Tests/Phase Retrieval MATLAB Clone.ipynb	1	812809	{ "cells": [ { "cell_type": "markdown", "source": [ "# Following the MATLAB example code\n", "\n", "Following Hanser et. al.," ], "metadata": {} }, { "cell_type": "code", "execution_count": 1, "source": [ "%load_ext autoreload\n", "%autoreload 2\n", "import os\n", "import tqdm\n", "from skimage.external import tifffile as tif\n", "%pylab inline\n", "from psfotf import \n", "from dphtools.utils import scale\n", "from dphtools.display import mip, slice_plot, display_grid\n", "import time" ], "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 2, "source": [ "test_data = tif.imread(\"../fixtures/BIL113_zstack_z300nm_current.tif\")\n", "params = dict(\n", " size = test_data.shape[-1],\n", " zsize = test_data.shape[0],\n", " na = 1.1,\n", " res = 90.5,\n", " zres = 300,\n", " wl = 605,\n", " ni = 1.33,\n", " vec_corr=\"none\",\n", " condition=\"none\"\n", ")" ], "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "C:\\Anaconda3\\lib\\site-packages\\skimage\\external\\tifffile\\tifffile.py:1398: UserWarning: tags are not ordered by code\n", " warnings.warn(\"tags are not ordered by code\")\n" ] } ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 3, "source": [ "mip(test_data)" ], "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "(<matplotlib.figure.Figure at 0x268680c03c8>,\n", " array([<matplotlib.axes._subplots.AxesSubplot object at 0x000002686B478588>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x000002686C9846D8>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x000002686C992E80>], dtype=object))" ] }, "metadata": {}, "execution_count": 3 }, { "output_type": "display_data", "data": { 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", "text/plain": [ "<matplotlib.figure.Figure at 0x268680c03c8>" ] }, "metadata": {} } ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 4, "source": [ "psf = HanserPSF(params)" ], "outputs": [], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 5, "source": [ "mip(psf.PSFi)" ], "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "(<matplotlib.figure.Figure at 0x2686c9fbf28>,\n", " array([<matplotlib.axes._subplots.AxesSubplot object at 0x000002686C9CF3C8>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x000002686D7554A8>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x000002686D7A0710>], dtype=object))" ] }, "metadata": {}, "execution_count": 5 }, { "output_type": "display_data", "data": { "image/png": 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", "text/plain": [ "<matplotlib.figure.Figure at 0x2686c9fbf28>" ] }, "metadata": {} } ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 6, "source": [ "def retrieve_phase(mag, psf, pupil=None):\n", " \"\"\"This function acurately reproduces the matlab code\"\"\"\n", " if pupil is None:\n", " # if first iteration, start with pupil of 1 over pass band\n", " pupil = psf._gen_pupil()\n", " psf._gen_psf(pupil)\n", " phase = angle(psf.PSFa.squeeze())\n", " #replace magnitude with sqrt of PSF\n", " new_psf = mag exp(1j * phase)\n", " new_pupils = fftn(ifftshift(new_psf, axes=(1,2)), axes=(1,2))\n", " # undo focus\n", " new_pupil = (new_pupils / psf._calc_defocus()).mean(0)\n", " # save only the phase information, magnitude will be restricted to 1\n", " new_pupil = exp(1j * angle(new_pupil))\n", " return psf._gen_pupil() * new_pupil, new_pupils" ], "outputs": [], "metadata": { "collapsed": true } }, { "cell_type": "code", "execution_count": 7, "source": [ "start = time.time()\n", "npix = 257\n", "PSF_measured = test_data.copy()\n", "nz, ny, nx = PSF_measured.shape;\n", "zc, yc, xc = unravel_index(PSF_measured.argmax(), PSF_measured.shape);\n", "PSF_offset = 1.5 * bincount(PSF_measured.ravel()).argmax()\n", "PSF = zeros((nz, npix, npix))\n", "PSF[:, npix // 2 - yc:npix // 2 + ny - yc, npix // 2-xc:npix // 2 + nx - xc] = PSF_measured\n", "PSF = sqrt(PSF)\n", "PSF = PSF - sqrt(PSF_offset)\n", "PSF = PSF > 0\n", "\n", "params.update(dict(size = npix))\n", "psf = HanserPSF(params)\n", "pupil = None\n", "psf._gen_kr()\n", "for _ in range(25):\n", " pupil, pupils = retrieve_phase(PSF, psf, pupil, True)\n", "fig, axs = subplots(1, 2, figsize = (12, 6))\n", "img1 = axs[0].matshow(fftshift(angle(conj(pupil)) psf._gen_pupil().real), cmap=\"jet_r\")\n", "colorbar(img1, ax=axs[0])\n", "img2 = axs[1].matshow(fftshift(abs(pupil)))\n", "colorbar(img2, ax=axs[1])\n", "print(time.time()-start)" ], "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "19.699891567230225\n" ] }, { "output_type": "display_data", "data": { "image/png": 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", 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cN5Tv3J/F75u/P8ft/zY9C9bL8ZrAk75AT6dFTR/5uTPJ3clNY9v33uTAXPoCnPSF99zJOeP0q+lT0LZM9m3NrMBPm9uB1duX2XiTi3K0rN13OLlvXxY/snfsc748OfKlYXlb+pvuaX/0GNr7s1YBPdL0DQCFqVHDSTuS2QCz0VhD/k76GvOnhvUtw/FjEXtwWJ5Oudoz+a6l32vwGKyf6XSq+3P0ud+5JcfWprcfZ3lP+vI8Ld/rR1DDQxoDdH8W91dvSzI8EjR7M2vmTmYv2Bing9yVWdBnsm9sOrs2s5A/Xkh7EhmsnbnMyvW+zIJ67/BvDN5pmd+ZWZP2jvRTKMe5W9OxK2tfTgU1PKRpuE7fC70/yY3D8nz6WvRY+P8k/d33nsm585kV4n3D/rFwfzAnJqRh7UynQyazcvpgjn2GwXjcvswCfOnN9FzW81W0+qgBoDA1aliR6V3zODo76e+wN2XxaM+7M6thb83i2vXCcM5CgI0wfeLgWIs+XrfTdErX9AFFc5P19W3xEtRwynZm1se8NX1/1TjF6t5h21iQ71h8ag6mL/jr11wGnMj0Jnvsa56+onKpaVmdzwnfprfGNH0DQGFq1HDKdmfxyNFk1gS2kMXvlD7RnTpw+o3N3XNZedP1vsxaxdafGjWsyvSVdtN50POZPXls/De/+NQNG8k999CHwMPecuXyZM8dZ3Hsz3qXKTVqWJWxn2vpM8GX9j3PpU6Nem3flQtnnoXM4u9Ux4mMb7lbf2rUAFCYoIZV2ZdZf9WOJfumTWrL1WLnc2pNb8Cp25++Vr3aPubDWdz9tdTalG1N37Amxmd3T51MU3eV5nA42+w5Db+xNuVbjRoAChPUAFCYoIZ1pQ8a6jmzyqQ+alhX+qChnjOrXKpRA0BhghoAChPUAFCYoAaAwgQ1ABQmqAGgMEENAIUJagAoTFADQGGCGgAKE9QAUJigBoDCBDUAFCaoAaAwQQ0AhQlqAChMUANAYYIaAAoT1ABQmKAGgMIENQAUJqgBoDBBDQCFCWoAKExQA0BhghoAChPUAFCYoAaAwgQ1ABQmqAGgMEENAIUJagAoTFADQGGCGgAKE9QAUJigBoDCBDUAFCaoAaAwQQ0AhQlqAChMUANAYYIaAAoT1ABQmKAGgMIENQAUJqgBoDBBDQCFCWoAKExQA0BhghoAChPUAFCYoAaAwgQ1ABQmqAGgMEENAIUJagAoTFADQGGCGgAKE9QAUJigBoDCBDUAFCaoAaAwQQ0AhQlqAChMUANAYYIaAAoT1ABQmKAGgMIENQAUJqgBoDBBDQCFCWoAKExQA0BhghoAChPUAFCYoAaAwgQ1ABQmqAGgMEENAIUJagAoTFADQGGCGgAKE9QAUJigBoDCBDUAFCaoAaAwQQ0AhQlqAChMUANAYYIaAAoT1ABQmKAGgMIENQAUJqgBoDBBDQCFCWoAKExQA0BhghoAChPUAFCYoAaAwgQ1ABQmqAGgMEENAIUJagAoTFADQGGCGgAKE9QAUJigBoDCBDUAFCaoAaAwQQ0AhQlqAChMUANAYYIaAAoT1ABQmKAGgMIENQAUJqgBoDBBDQCFCWoAKExQA0BhghoAChPUAFCYoAaAwgQ1ABQmqAGgMEENAIUJagAoTFADQGGCGgAKE9QAUJigBoDCBDUAFCaoAaAwQQ0AhQlqAChMUANAYYIaAAoT1ABQmKAGgMIENQAUJqgBoDBBDQCFCWoAKExQA0BhghoAChPUAFCYoAaAwgQ1ABQmqAGgMEENAIUJagAoTFADQGGCGgAKa13XbfQ1AADLUKMGgMIENQAUJqgBoDBBDQCFCWoAKExQA0BhghoAChPUAFCYoAaAwgQ1ABQmqAGgMEENAIUJagAoTFADQGGCGgAKE9QAUJighlPUWruhtXawtbbzOPte11r7fmvtBa21/zksn+jf1zbivwGor3Vdt9HXAGek1trjktyZ5Itd11072X5JktuTfKjrupe01q5M8oRlvubaJC9L8r6u61663tcMnHkENaxCa+3lSX4jycu6rrth2PaRJFclubTrum+e4NwLk9yW5ECSp3Zd9+3TcMnAGUZQwyq11j6R5ElJnpzk+Ul+K8mruq571wnOaUn+MMnVSZ7Tdd3nTse1AmceQQ2r1Fq7NMktST6Q5NlJ/rzruqse4pw3JXlTktd3XfeW9b9K4EwlqGENtNauT/L6JEeSPKPruttOcOxzk9yY5GNd1z3/NF0icIYy6hvWxn3D5zeS/PFyB7XWzk/fNP6tJD9xGq4LOMMJalil1trFSX4pyZeTXJzktSc4/IYk25L8067r7j0Nlwec4QQ1rN47knRJfiTJ+5O8obW2Y+lBrbXXJLkuyVu7rrvxdF4gcObSRw2r0Fr7sSS/neRfdl33X1prfy3JHUk+1XXdCyfHXZnkj5LclH6U919uyAUDZxxBDaeotbY5fSjfk+SZ3VCYWms/m+RtSX6867rfbq09JsmtSR6T5G93XfdnG3XNwJlHUMMpaq39epJ/nuTKrutumWx/RJLPp++LfkqS/57kx5O8L8mHl/u+rut+c10vGDgjCWo4Ba21pyb5bJJ3dV33c8fZ//Qkn0nff/3iJI9/qO/suu6Ra32dwJlPUANAYUZ9A0BhghoAChPUAFCYoAaAwgQ1ABQmqAGgsE0bfQEnq7Vd5pFRzLYkh4flfUm2JnlwWB+3bx0+Dw6fc8Pnpsm2pH875qYlx8LG6rpdbaOvATVqACjtjKlRw/qbz8pqs/uHc5K+dr0zyd3D+uH0teuxBj2X5ILJ+r7h8+Dk+LEWfjIWht8HHu4ENRw1huYYvkfSh+u02XraZL19yfnz6cN6um8M5IuS3DVZ33ac3942WT6cWbP5kWHbNJiFNJwtNH0DQGFq1LCssfn6gmH9wfS12x3D+sHMat8Ztl/UL/7osLjpkn79riQ3bksO3Dwcuy997fvgZH1s+j6cvmgemXz3fNSi4ewkqOEY037qMaxHF2TWJD2fvq94PGd7cs2w+vQk/yB5zM57kiTfvvHC5ECSG39gOGBsAh+L4N4k42uqjwy/sX9y7Hg8cLbR9A0AhalRwwkdzKyGvZDk3sxq0dtzdADYOecm5yW5ol+de9X+/ND5n8627E2S3PTCp+drB/5Wcs4w6vvWbX1zeO4fvmsuyZZh+f4kd2TW9L2S0eDAw42ghpO2P31ojlOwdiabz+0Xr0kf0tf0q5ee/5W8JO/Lxfl6kmRLHszXnvWE5MA5/QGXJXl/kt3jTcCeJLuH5Wl/NXC20/QNAIWpUcOKjHOck2R3cmAY1f1A+pbqYdD3u/PKXPUfb+tbypNc+4sfy9e3X5zfv+fF/Ybbk9yXZGgaT26JR4cCxyOoYcXunizf1X/cdFFyXrJ1Z7/vqvfclptfnXxwOGrXd5Mnve2r+f1PDhtuTHJkf/om70RIA8vR9A0AhalRw4pNH+35uX7x0J7k9mdl3+3Do0NfsPhRKGMTeM4bT705/aCxcWT31pgrDRyPoIYVG4N6LrOgnU8O5ejA7V/4wTfmVz77y9k1zL66+wVb88G8KLlw/I4fSPKlzPqoFzKL9mnTOnC20/QNAIWpUcNJm8/iWvSOyb6nJuckGQaL/eo9b86vXvvm2e4Ppx939sCwfs5ccmghs9de7kj/kJNMtplLDSSt67qNvoaT0tquM+NCeZiZvvd5If3Twy6a7L98+DyY5Il9WCf95zmZ9UlvGtb3DOv37U3yncyeI74ni5/tPZdZszhsjK7b1Tb6GtD0DQClafqGZR3v1ZILOTp3OgtJPjUsX53kI8mh4Xndh65MHjs3a+o+lGH5K8OG3Zk1cY/GoeFbYl41MBLUcHS09dJwPJjF06YOp2+OHl7EkT2Z9VfvTx+8Y7/yXcl5lwxPHxsP+0pmr7IcX/YxjvDekllxPJLFr9YEzmaavgGgMDVqOGEz8/QhJAfT15qXjs5O+lrwUzJ7A9bnk92fz6yInZ++Nr51cs6+HNu0nqx/bXpa8weqE9SwImMTd9I3V0+bxXdnFvrTkeIZts9nNux7X/r3WY/HTYvipqxvH7WQhjOJpm8AKEyNGlZsLDbTWu++LG4mH2vd40C1vcPxOybn7s5iRnoDxxLUsGLTQF1uxPjh9GH94JL9yzU7C2ng+DR9A0BhatSwKktr19P1wzm2Br30zVjL1cgBemfMs74B4Gyk6RsAChPUAFCYoAaAwgQ1ABQmqAGgMEENAIUJagAoTFADQGGCGgAKE9QAUJigBoDCBDUAFCaoAaAwQQ0AhQlqACjs/wMs8Qr0ULhtxgAAAABJRU5ErkJggg==", "text/plain": [ "<matplotlib.figure.Figure at 0x29238ff5240>" ] }, "metadata": {} } ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 9, "source": [ "display_grid({z:fftshift(abs(pupil) * psf._gen_pupil().real) for z, pupil in zip(psf.zrange, pupils / psf._calc_defocus())});" ], "outputs": [ { "output_type": "display_data", "data": { "image/png": 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", "text/plain": [ "<matplotlib.figure.Figure at 0x292389db8d0>" ] }, "metadata": {} } ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 10, "source": [ "display_grid({z:fftshift(angle(pupil) * psf._gen_pupil().real) for z, pupil in zip(psf.zrange, pupils)});" ], "outputs": [ { "output_type": "display_data", "data": { "image/png": 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", "text/plain": [ "<matplotlib.figure.Figure at 0x292396ec630>" ] }, "metadata": {} } ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 11, "source": [ "display_grid({z:fftshift(angle(pupil) * psf._gen_pupil().real) for z, pupil in zip(psf.zrange, pupils/psf._calc_defocus())});" ], "outputs": [ { "output_type": "display_data", "data": { "image/png": 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", "text/plain": [ "<matplotlib.figure.Figure at 0x292393ca6a0>" ] }, "metadata": {} } ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 12, "source": [ "display_grid({z:p for z, p in zip(psf.zrange, PSF)});" ], "outputs": [ { "output_type": "display_data", "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x292396e3ba8>" ] }, "metadata": {} } ], "metadata": { "collapsed": false } }, { "cell_type": "markdown", "source": [ "The problem is that this data set gets clipped too much when removing background." ], "metadata": { "collapsed": true } }, { "cell_type": "code", "execution_count": null, "source": [], "outputs": [], "metadata": { "collapsed": true } } ], "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": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 1 }	apache-2.0
danielfrg/danielfrg.github.io-source	content/blog/notebooks/2013/08/relevant-content-blog-crawler.ipynb	1	11543	{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "We all know that the most important aspect of data science or machine learning is data; with enough quality data you can do everything. Is also not a mistery that the problem of big data is to get that amount of data into a queryable, reportable or undestandable format; now we have a lot of amazing new tools to store that amount of data (casandra, hbase and more) but I still believe that almost nothing beats the fact of collecting a good amount (not necessarily huge, but the more you have the better) but structured data, and there is nothing more structured than SQL.\n", "\n", "There is a lot of information power in the web and crawl it gives you that power (or is at least the first step), Google does it and I am pretty sure I don't have to say more. I cannot even begin to imagine the amount of work that they do to understand that data. So I created my own mini crawler to crawl what I call relevant content of websites, more specificly blogs, yes I believe blogs and not twitter have a lot of information power, that is why I am writing this in a blog.\n", "\n", "All I needed was python + some libraries, mainly the readability API. The idea is very simple, get the feed of each blog to get the posts and ask readability to give me the text content of each post. For now this code only works with blogspot and wordpress blogs because is easy to get more than 10 posts from their feed. Also most of the blogs are just on those services.\n", "\n", "The readability api is beautiful because I dont have to write beautifulsoup code for each site. I tried some implementations of the arc90 readability (javascript and python) without very good results. But if you are looking to pass the 1000 posts per hours of readability API that is the way to go, they just work. But I don't care to wait 3.6 seconds for each post if the content is better.\n", "\n", "OK, here is the code!" ] }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "SQLite" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import sqlalchemy as sql\n", "from sqlalchemy.ext.declarative import declarative_base" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "engine = sql.create_engine('sqlite:///blogs.db')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "Base = declarative_base()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "class Post(Base):\n", " __tablename__ = 'post'\n", " \n", " url = sql.Column(sql.String(50), primary_key=True)\n", " date = sql.Column(sql.DateTime)\n", " content = sql.Column(sql.String(10000))\n", "\n", " def __init__(self, url, date, content):\n", " self.url = url\n", " self.date = date\n", " self.content = content\n", "\n", " def __repr__(self):\n", " return \"<Post('%s','%s')>\" % (self.url, self.date)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "Base.metadata.create_all(engine)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "Crawler" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from __future__ import division\n", "import math\n", "import time\n", "import logging\n", "import requests\n", "import feedparser\n", "import dateutil\n", "from datetime import datetime\n", "import readability\n", "from bs4 import BeautifulSoup\n", "import sqlalchemy as sql\n", "from sqlalchemy.orm import sessionmaker" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "logger = logging.getLogger('crawler')\n", "logger.setLevel(logging.DEBUG)\n", "handler = logging.FileHandler('crawler.log')\n", "f = logging.Formatter(\"%(asctime)s %(message)s\")\n", "handler.setFormatter(f)\n", "logger.addHandler(handler)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "blogs = [\n", " {'url': 'http://mypreciousconfessions.blogspot.com', 'kind': 'blogspot'},\n", " {'url': 'http://cupcakesandcashmere.com', 'kind':'wordpress' }\n", "]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 8 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Don't ask why those are fashion blogs, I just needed the data." ] }, { "cell_type": "code", "collapsed": false, "input": [ "def parse_info(blog):\n", " feed = ''\n", " kind = ''\n", " if 'feed' in blog:\n", " feed = blog['feed']\n", " if 'blogger.com' in blog['feed']:\n", " kind = 'blogspot'\n", " elif 'wordpress.com' in blog['feed']:\n", " kind = 'wordpress'\n", " else:\n", " kind = blog['kind']\n", " elif 'url' in blog:\n", " if 'blogspot.com' in blog['url'] or blog['kind'] == 'blogspot':\n", " r = requests.get(blog['url'])\n", " html = r.text\n", " soup = BeautifulSoup(html)\n", " feed = soup.find('link', rel='service.post')['href']\n", " kind = 'blogspot'\n", " elif 'wordpress.com' in blog['url'] or blog['kind'] == 'wordpress':\n", " feed = blog['url'] + '/feed/'\n", " kind = 'wordpress'\n", " return feed, kind" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "def get_posts(blog, limit=10000):\n", " feed, kind = parse_info(blog)\n", " \n", " posts = []\n", " if kind == 'blogspot':\n", " feed = feed + '?max-results=%i' % limit\n", " json_feed = feedparser.parse(feed)\n", " for entry in json_feed['entries']:\n", " date = dateutil.parser.parse(entry['published'])\n", " posts.append((entry['link'], date))\n", " elif kind == 'wordpress':\n", " page = 1\n", " while True and page <= math.ceil(limit / 10):\n", " url = feed + '?paged=%i' % page\n", " r = requests.get(url)\n", " if r.status_code == 200:\n", " json_feed = feedparser.parse(r.text)\n", " for entry in json_feed['entries']:\n", " if len(posts) < limit:\n", " date = dateutil.parser.parse(entry['published'])\n", " posts.append((entry['link'], date))\n", " page += 1\n", " else:\n", " break\n", " return posts" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "def insert_post(post_link, date, content):\n", " session = Session()\n", " post = Post(post_link, date, content)\n", " session.add(post)\n", " session.commit()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "def exists(post_link):\n", " session = Session()\n", " response = session.query(Post).filter(Post.url == post_link).all()\n", " return len(response) == 1" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 12 }, { "cell_type": "code", "collapsed": false, "input": [ "def crawl(blogs):\n", " parser = readability.ParserClient('YOUR_READABILITY_API')\n", " for blog in blogs[4:]:\n", " logger.info('---------------------------------------------------------------------------')\n", " posts = get_posts(blog, limit=1000)\n", " n_posts = len(posts)\n", " if 'url' in blog:\n", " logger.info('{0} ({1})'.format(blog['url'], n_posts))\n", " else:\n", " logger.info('{0} ({1})'.format(blog['feed'], n_posts))\n", " logger.info('---------------------------------------------------------------------------')\n", " for i, (post_link, post_date) in enumerate(posts):\n", " if exists(post_link):\n", " logger.info('{0}/{1} Already exists: {2}'.format(i, n_posts, post_link))\n", " else:\n", " parser_response = parser.get_article_content(post_link)\n", " \n", " try:\n", " soup = BeautifulSoup(parser_response.content['content'])\n", " content = soup.get_text(\" \", strip=True)\n", " content = content.replace('\\t', ' ')\n", " content = content.replace('\"', '')\n", " insert_post(post_link, post_date, content)\n", " except Exception as e:\n", " logger.info('{0}/{1} FAIL: {2}'.format(i + 1, n_posts, post_link))\n", " logger.info(str(e))\n", " else:\n", " logger.info('{0}/{1} OK: {2}'.format(i + 1, n_posts, post_link))\n", " time.sleep(3.6)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 13 }, { "cell_type": "markdown", "metadata": {}, "source": [ "That is it! just need to call `crawl(blogs)`\n", "\n", "Q: I need to crawl faster!\n", "\n", "A: One easy way to double the speed of crawling is to create another readbility account and [cycle](http://docs.python.org/2/library/itertools.html#itertools.cycle) though the parsers or even better just contact readability ;)\n", "\n", "Q: Why is this data useful (spoiler of my next post)?\n", "\n", "A: https://code.google.com/p/word2vec/" ] } ], "metadata": {} } ] }	apache-2.0
zhmcclient/python-zhmcclient	docs/notebooks/02_connections.ipynb	1	4899	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Tutorial 2: Connecting to an HMC" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In order to use the zhmcclient package in a Jupyter notebook, it must be installed in the Python environment that was used to start Jupyter. Trying to import it shows whether it is installed:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import zhmcclient" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If it was not installed, close Jupyter, [install zhmcclient](https://python-zhmcclient.readthedocs.io/en/latest/intro.html#installation), and start Jupyter again." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When connecting to an HMC, the user needs to create two objects:\n", "\n", "* A `Session` object that represents a session with the HMC. A `Session` object can be created with or without credentials. It automatically logs on using the provided credentials if a particular HMC operation requires to be logged on. There are a few HMC operations that work without being logged on (e.g. retrieving the API version).\n", "\n", "* A `Client` object that is created on top of a `Session` object, and that provides the main entry point for the resources managed by the HMC. For example, it can list the CPCs managed by the HMC.\n", "\n", "The following code creates these two objects for a particular HMC without providing credentials:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "zhmc = '9.152.150.65'\n", "\n", "session = zhmcclient.Session(zhmc)\n", "client = zhmcclient.Client(session)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following code prints the API version supported by that HMC. If you have no connection to the HMC, a `ConnectionError` will be raised after a while." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "vi = client.version_info()\n", "print(\"HMC API version: {}.{}\".format(vi[0], vi[1]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The previous code section most likely will also show a `InsecureRequestWarning` because certificates are not used in the communication with the HMC.\n", "\n", "The following code section turns off that warning and repeats the version gathering:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import requests\n", "requests.packages.urllib3.disable_warnings()\n", "\n", "vi = client.version_info()\n", "print(\"HMC API version: {}.{}\".format(vi[0], vi[1]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This code section attempts to list the CPCs managed by that HMC:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(\"Listing CPCs managed by HMC %s ...\" % zhmc)\n", "cpcs = client.cpcs.list()\n", "for cpc in cpcs:\n", " print(cpc)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Executing the previous code section reveals that listing the CPCs requires to be logged on, but we did not specify credentials. As a result, an `AuthError` was raised.\n", "\n", "The following code section specifies credentials and performs the list opereration again:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "import getpass\n", "\n", "userid = raw_input('Enter userid for HMC %s: ' % zhmc)\n", "password = getpass.getpass('Enter password for %s: ' % userid)\n", "\n", "session = zhmcclient.Session(zhmc, userid, password)\n", "client = zhmcclient.Client(session)\n", "\n", "print(\"Listing CPCs managed by HMC %s ...\" % zhmc)\n", "cpcs = client.cpcs.list()\n", "for cpc in cpcs:\n", " print(cpc)" ] } ], "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
sanket-patil/speech-vectors	notebooks/classification.ipynb	1	171782	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "from matplotlib import pyplot as plt\n", "from matplotlib import style\n", "import librosa\n", "import IPython.display\n", "import librosa.display\n", "import os\n", "import random\n", "from matplotlib.pyplot import specgram\n", "import glob" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Speech Features\n", "#### Tonnetz\n", "\n", "Networks or lattices of tones. The tonnetz tonal centroids — the “central” tones. These are features that help in Detecting Harmonic Change in Musical Audio or variances due to tones in audio.\n", "\n", "#### STFT\n", "Short-term fourier transform. Segments the signal into short frames and computes the fourier transform on each short segment.\n", "\n", "#### Spectral Contrast\n", "Relative distribution of energies.\n", "\n", "#### Chromagrams\n", "Chromagrams are based on the pitch scales" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def extract_feature(file_name):\n", " X, sample_rate = librosa.load(file_name)\n", " np.nan_to_num(X)\n", " stft = np.abs(librosa.stft(X))\n", " mfccs = np.mean(librosa.feature.mfcc(y=X, sr=sample_rate, n_mfcc=40).T,axis=0)\n", " chroma = np.mean(librosa.feature.chroma_stft(S=stft, sr=sample_rate).T,axis=0)\n", " mel = np.mean(librosa.feature.melspectrogram(X, sr=sample_rate).T,axis=0)\n", " contrast = np.mean(librosa.feature.spectral_contrast(S=stft, sr=sample_rate).T,axis=0)\n", " tonnetz = np.mean(librosa.feature.tonnetz(y=librosa.effects.harmonic(X), sr=sample_rate).T,axis=0)\n", " return mfccs, chroma, mel, contrast, tonnetz\n", "\n", "\n", "def parse_audio_files(parent_dir, sub_dirs, classes, file_ext='.ogg'):\n", " features, labels = np.empty((0, 193)), np.empty(0)\n", " for label, sub_dir in enumerate(sub_dirs):\n", " for fn in glob.glob(os.path.join(parent_dir, sub_dir, file_ext)):\n", " mfccs, chroma, mel, contrast,tonnetz = extract_feature(fn)\n", " ext_features = np.hstack([mfccs,chroma,mel,contrast,tonnetz])\n", " features = np.vstack([features,ext_features])\n", " labels = np.append(labels, classes.get(sub_dir))\n", " return np.array(features), np.array(labels, dtype = np.int)\n", "\n", "\n", "def one_hot_encode(labels):\n", " n_labels = len(labels)\n", " n_unique_labels = len(np.unique(labels))\n", " one_hot_encode = np.zeros((n_labels, n_unique_labels))\n", " one_hot_encode[np.arange(n_labels), labels] = 1\n", " return one_hot_encode" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sound Sample Classes\n", "--------------------\n", "rooster\n", "coughing\n", "insects\n", "laughing\n" ] } ], "source": [ "data_dir = '../data/esc-50'\n", "sample_dir = os.path.join(data_dir, 'sample')\n", "train_dir = os.path.join(data_dir, 'train')\n", "test_dir = os.path.join(data_dir, 'test')\n", "\n", "print 'Sound Sample Classes'\n", "print '--------------------'\n", "for d in os.listdir(sample_dir):\n", " print d" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "samples_dict = dict()\n", "for d in os.listdir(sample_dir):\n", " sample_class_dir = os.path.join(sample_dir, d)\n", " samples_dict[d] = [os.path.join(sample_class_dir, f) for f in os.listdir(sample_class_dir)]" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "mfccs, chroma, mel, contrast,tonnetz = extract_feature(samples_dict.get('insects')[0])\n", "ext_features = np.hstack([mfccs,chroma,mel,contrast,tonnetz])" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "193\n" ] } ], "source": [ "mfccs, chroma, mel, contrast,tonnetz = extract_feature(samples_dict.get('insects')[0])\n", "ext_features = np.hstack([mfccs,chroma,mel,contrast,tonnetz])\n", "print len(ext_features)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(0, 193)\n" ] } ], "source": [ "features = np.empty((0,193))\n", "print features.shape" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ -2.71008667e+02 1.11177134e+02 -2.66969894e+01 1.64818867e+01\n", " -4.28178103e-01 -1.32390633e+01 1.21125811e+00 -5.36048044e+00\n", " -1.12257893e+01 -1.54257043e+01 -1.81542393e+01 -1.80371958e+01\n", " -1.07932755e+01 -8.53481359e+00 -6.64451044e+00 -1.93843792e+00\n", " -1.12454952e+01 -1.68509149e+01 -1.28325805e+01 -1.23412747e+01\n", " -9.02519197e+00 -5.68524379e+00 -5.22570074e+00 -7.41173323e+00\n", " -1.38022800e+01 -1.52868561e+01 -1.31671584e+01 -9.81586918e+00\n", " -6.73705615e+00 -8.49936622e+00 -1.17248014e+01 -1.46298240e+01\n", " -1.01961189e+01 3.74741798e+00 2.14096003e+01 3.56850792e+01\n", " 3.83353708e+01 2.89771481e+01 1.19066973e+01 -2.89713765e+00\n", " 1.59088475e-01 3.19025165e-01 3.33885749e-01 1.52062355e-01\n", " 1.77843989e-01 2.93502119e-01 8.13136173e-01 8.85314120e-01\n", " 3.31957176e-01 1.98507119e-01 3.94651468e-01 3.02763397e-01\n", " 8.68044150e-02 1.83963744e-02 1.35715236e-02 2.39104148e-02\n", " 1.39743146e-01 6.72779917e+00 4.90004694e+02 2.73282780e+02\n", " 8.93248234e-01 5.59734983e-02 1.70156021e-02 2.59211479e-02\n", " 9.61986968e-01 4.17390167e+01 8.62789041e+01 5.58839101e+00\n", " 4.88813554e-02 6.79680806e-03 4.78441937e-03 1.48819929e-01\n", " 3.20957326e+00 1.22941049e+01 4.12018888e+00 9.93672660e-02\n", " 4.37581389e-03 2.98869747e-03 1.00605985e-02 2.59755824e-01\n", " 7.90625275e-01 6.10696306e-01 7.46318680e-02 5.72895169e-03\n", " 3.96717497e-03 9.20663652e-02 1.41673099e+00 6.02122098e+00\n", " 6.44586128e+00 1.63331443e+00 1.09673649e-01 5.98863026e-03\n", " 1.22476459e-02 7.09265921e-02 2.40077103e-01 3.19510478e-01\n", " 8.61640183e-02 6.65731303e-03 1.16871322e-02 1.31723973e-01\n", " 2.78013311e-01 1.51962472e-01 4.00416660e-02 6.17122649e-03\n", " 7.81794417e-02 1.96918157e-01 1.51670048e-01 3.32580226e-02\n", " 7.61031437e-03 3.29178922e-02 6.04684941e-02 1.53753055e-02\n", " 7.11120307e-03 1.68225158e-02 2.11778417e-02 8.03014126e-03\n", " 1.11053473e-02 3.95009299e-02 3.38501458e-02 1.31685055e-02\n", " 2.22509392e-02 2.41385683e-02 1.13896898e-02 1.60631428e-02\n", " 1.59461956e-02 6.22479372e-03 8.93443731e-03 1.07098259e-02\n", " 6.18478379e-03 9.12808856e-03 7.54452137e-03 9.15637982e-03\n", " 7.81352579e-03 6.35637785e-03 8.36853838e-03 6.21336188e-03\n", " 6.53785908e-03 4.66720774e-03 4.59873942e-03 3.29128604e-03\n", " 2.36636812e-03 1.52167571e-03 1.33269869e-03 1.14961995e-03\n", " 1.01464148e-03 5.93145876e-04 4.99635008e-04 4.98640086e-04\n", " 5.03423621e-04 4.61343924e-04 4.50671235e-04 4.80561314e-04\n", " 3.64230392e-04 3.45524261e-04 3.06339818e-04 2.57827978e-04\n", " 1.84518869e-04 1.36144799e-04 9.49975459e-05 9.42402109e-05\n", " 9.45126118e-05 9.04012059e-05 7.84361819e-05 5.63061283e-05\n", " 3.90798580e-05 2.93396714e-05 2.11918386e-05 1.61612281e-05\n", " 1.21721234e-05 9.67922533e-06 6.89963830e-06 5.43285045e-06\n", " 3.83645753e-06 2.90355287e-06 2.12411428e-06 1.89239558e-06\n", " 1.59997136e-06 1.24610975e-06 5.06919169e-07 1.81620320e-07\n", " 3.73000535e+01 2.72948934e+01 2.49868627e+01 2.53811632e+01\n", " 1.81814493e+01 1.97994342e+01 3.85436245e+01 3.70963525e-02\n", " -2.24606709e-02 1.88734775e-01 -2.03544718e-01 5.38986268e-02\n", " 1.56129083e-02]]\n" ] } ], "source": [ "features = np.vstack([features,ext_features])\n", "print features" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "sample_dir = os.path.join(data_dir, 'sample')\n", "sub_dirs = ['laughing', 'coughing', 'insects', 'rooster']\n", "classes = {'laughing': 0, 'coughing': 1, 'insects': 2, 'rooster': 3}\n", "features, labels = parse_audio_files(sample_dir, sub_dirs, classes)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(156, 193)\n" ] } ], "source": [ "print features.shape" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "one_hot = one_hot_encode(labels)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(132, 193)\n", "(132, 4)\n" ] } ], "source": [ "from sklearn.model_selection import train_test_split\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(features, \n", " one_hot, \n", " test_size=0.15, \n", " random_state=42)\n", "print X_train.shape\n", "print y_train.shape" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "collapsed": true }, "outputs": [], "source": [ "n_hidden_units_one = 50 \n", "n_hidden_units_two = 50\n", "\n", "n_classes = 4\n", "n_dim = X_train.shape[1]" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from keras.models import Sequential\n", "from keras.layers import Dense\n", "import numpy" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "collapsed": true }, "outputs": [], "source": [ "model = Sequential()\n", "model.add(Dense(n_hidden_units_one, input_dim=n_dim, kernel_initializer='uniform', activation='relu'))\n", "model.add(Dense(n_hidden_units_two, kernel_initializer='uniform', activation='relu'))\n", "model.add(Dense(n_classes, kernel_initializer='uniform', activation='softmax'))" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "collapsed": true }, "outputs": [], "source": [ "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/10\n", "132/132 [==============================] - 0s - loss: 1.3210 - acc: 0.3636 \n", "Epoch 2/10\n", "132/132 [==============================] - 0s - loss: 1.1869 - acc: 0.5530 \n", "Epoch 3/10\n", "132/132 [==============================] - 0s - loss: 1.0305 - acc: 0.6136 \n", "Epoch 4/10\n", "132/132 [==============================] - 0s - loss: 0.8609 - acc: 0.6970 \n", "Epoch 5/10\n", "132/132 [==============================] - 0s - loss: 0.7235 - acc: 0.7500 \n", "Epoch 6/10\n", "132/132 [==============================] - 0s - loss: 0.6231 - acc: 0.7879 \n", "Epoch 7/10\n", "132/132 [==============================] - 0s - loss: 0.5371 - acc: 0.8258 \n", "Epoch 8/10\n", "132/132 [==============================] - 0s - loss: 0.4258 - acc: 0.8788 \n", "Epoch 9/10\n", "132/132 [==============================] - 0s - loss: 0.3891 - acc: 0.8712 \n", "Epoch 10/10\n", "132/132 [==============================] - 0s - loss: 0.3196 - acc: 0.9015 \n" ] }, { "data": { "text/plain": [ "<keras.callbacks.History at 0x7f03e13c67d0>" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.fit(X_train, y_train, nb_epoch=10, batch_size=20)" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "24/24 [==============================] - 0s\n", "acc: 83.33%\n" ] } ], "source": [ "scores = model.evaluate(X_test, y_test)\n", "print(\"%s: %.2f%%\" % (model.metrics_names[1], scores[1] 100))" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Shape of the transformed feature vector: (156, 5)\n", "Original training sample: [-211.95342873967343, 126.77311337329317, -34.790516564858109, -10.512615152990707, -36.476548766848765, -15.265458386539253, -25.452147024852891, -5.373759791325627, -17.113109621494466, 9.623134344549225, -10.928963826555067, -10.522028894620606, -8.9315416259630567, -4.2521794394544665, -10.441037332644855, -7.5970087415701366, -8.8785380383648729, -1.1939344747472949, -7.0908558885145743, 0.76262883957394723, -6.841079475167918, -6.7085224890121546, -4.9157763803929528, -2.7260106406212623, -6.4026770846192411, -2.3801003698167018, -6.0828079226609626, -1.005822607342773, -4.4659888523414537, -1.1620847727575108, -4.0551530678673311, -0.10143759270967795, -5.5333870628773516, -1.0524975933573175, -3.2479466396041592, -1.5511760810055846, -3.3662660397266997, -2.0985671973177058, -3.8624834878166756, -0.23053952005125813, 0.51834445566621268, 0.50731462792430559, 0.54732341782571448, 0.54456663467689226, 0.55724919803408046, 0.63912478985697185, 0.69991761882011527, 0.71094263997045593, 0.70488867331070892, 0.66897406320086661, 0.58024313023707208, 0.54668903682846348, 0.00030898208816695281, 0.0011037863654963064, 0.016088965129742488, 0.048029955612885047, 0.062542814079642906, 0.13947951636564837, 0.23387052426737462, 0.22624280428373164, 0.19506449493940808, 0.29063113954904291, 0.4549219218919513, 0.73933110146098002, 0.69277632511016096, 0.50986683000985145, 0.60366819441108444, 0.80981155644544678, 1.3543601534067828, 1.320178095013623, 1.5462867580937649, 0.95649853417901898, 1.0331258896554507, 1.8075995803564224, 2.6111118874054506, 2.7215665094646804, 3.3259071652120826, 5.0641070479079797, 6.0979488185737845, 7.1051678508019638, 7.535051336622236, 7.6710790670789937, 2.8148214272133782, 2.7522209223856491, 2.5889673357936207, 1.4791161752630282, 1.1608426587244347, 1.2826584912174799, 1.5683734273342689, 1.0036368136086244, 0.72538112258986454, 0.49079046565747814, 0.39725536292546915, 0.31822184116950575, 0.54006440146321222, 0.48064187985263823, 0.34986304285334202, 0.24974973458894739, 0.19838699669755813, 0.17349815778111813, 0.12901942709396025, 0.11860289924781658, 0.15270080616908899, 0.17868360046591006, 0.19715498444407642, 0.21941677772091334, 0.23211492768070943, 0.25149226279331072, 0.18776629751326335, 0.17411642277448908, 0.17889784925497204, 0.096644062036501277, 0.082507450725597647, 0.066139808612824799, 0.05174036781666911, 0.046590424206075565, 0.038960488665702071, 0.038236938679630399, 0.02557265558608306, 0.021394920531427115, 0.016140895581957311, 0.015475472783039103, 0.011011644560546208, 0.010356935038617588, 0.0099145786292644516, 0.011856377501241988, 0.014797492405052352, 0.013112522923081406, 0.015755074069384874, 0.020782871349228575, 0.022437630686839058, 0.021883271850380963, 0.01657473185440838, 0.018241886561177423, 0.022016965476869915, 0.015009248627246756, 0.013437150754335971, 0.0084712427644161557, 0.0078142587165301747, 0.006294118115443466, 0.0046241263855647703, 0.0046417836148709589, 0.0040455707002778384, 0.0035629338770325389, 0.0035737071288845118, 0.0031781355610891746, 0.0022911490950632291, 0.0019606707835456614, 0.0014439297620097654, 0.0012277669175469535, 0.0010700037495356531, 0.0010706933391297537, 0.0012123657109182897, 0.0011216158135388798, 0.0010196895226450143, 0.0013053526270285333, 0.0017245984804699101, 0.0018016985150939903, 0.0016583987379953005, 0.0014503483148989862, 0.0011736064986719386, 0.0011487815179094805, 0.00098804534315929753, 0.00097006047956999658, 0.00076859565645174838, 0.00074170568164964358, 0.00055791487710688448, 0.00056102393166497023, 0.00038626005762788242, 0.00034570871649306054, 0.00029332994200811788, 0.00018045878544668763, 0.00014152537898716433, 0.00012862934087420268, 0.00011241898990175759, 0.00010035055321958268, 8.6017515487433528e-05, 5.7183426737683098e-05, 1.5404341109415376e-05, 1.0205875575903759e-06, 23.027444126555952, 12.259515859469532, 15.806141550857673, 17.774268658297565, 18.162222180882441, 18.832451488517545, 38.495340230619959, 0.0039265619510271983, 0.0031208547093051074, 0.016112377021180464, -0.0033134670188252908, -0.0038187426081049991, -0.0027186458454636149]\n", "Training sample after PCA: [-2.6068373184520963, 2.4242388793647009, -0.65683308107782468, 4.2573390545248051, 1.079539326884505]\n", "\n", "\n", "Explained variance ratio (first five components)\n", "------------------------------------------------\n", "Principal Component 0 : 0.18766387691\n", "Principal Component 1 : 0.0877785919013\n", "Principal Component 2 : 0.071878311726\n", "Principal Component 3 : 0.0639192643987\n", "Principal Component 4 : 0.0450622371579\n" ] } ], "source": [ "from sklearn.decomposition import PCA\n", "from sklearn import preprocessing\n", "import pandas as pd\n", "\n", "features_df = pd.DataFrame(features)\n", "data_scaled = pd.DataFrame(preprocessing.scale(features_df), columns=features_df.columns)\n", "pca = PCA(n_components=5)\n", "pca_results = pca.fit_transform(data_scaled)\n", "\n", "print 'Shape of the transformed feature vector:', pca_results.shape\n", "print 'Original training sample:', list(features_df.loc[0].values)\n", "print 'Training sample after PCA:', list(pca_results[0])\n", "print '\\n'\n", "# Percentage of variance explained for each components\n", "print 'Explained variance ratio (first five components)'\n", "print '------------------------------------------------'\n", "for idx, r in enumerate(pca.explained_variance_ratio_):\n", " print 'Principal Component', idx, ':', r " ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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SlZaWym5v2+cQu92uYDBocUUdy2azKTo6Wl6vV5395UI/Igv9iCz0I7LQj8gS\n6f1ISUkJdwk4zhnuAjoDj8fT5nXj4uJUU1NjYTUdLyoqSm63W1VVVfL5fOEu54zQj8hCPyIL/Ygs\n9COyRHo/CNeRg2EhAAAAgEUI1wAAAIBFCNcAAACARQjXAAAAgEUI1wAAAIBFCNcAAACARQjXAAAA\ngEUI1wAAAIBFCNcAAACARQjXAAAAgEUI1wAAAIBFCNcAAACARQjXAAAAgEUI1wAAAIBFCNcAAACA\nRQjXAAAAgEUI1wAAAIBFCNcAAACARQjXAAAAgEUI1wAAAIBFCNcAAACARQjXAAAAgEUI1wAAAIBF\nCNcAAACARQjXAAAAgEWc4S6gPWzYsEFbtmzR4cOHNWTIEF133XWSpLKyMj311FOKiooKLTt27Fjl\n5uaGq1QAAAB0IV0yXCclJWncuHHatWuXfD5fg/kPPPCAHA5HGCoDAABAV9Ylw/WgQYMkSUVFRY2G\n6+ZUVFSosrKy3jSv16uEhIQ21eJwOOqdKe+MnE5nvT87M/oRWehHZKEfkYV+RJau0A90jM7/am+D\nJ598UpLUr18/XXnllfWC88aNG7V69ep6y+fm5mrChAkdWmMkSklJCXcJOAn9iCz0I7LQj8hCP3A2\nsRljTLiLaC8rVqxQRUVFaMy1x+NRSUmJevbsqZqaGi1dulRer1e33HJLaB2rz1zHxMTI4/G0/SAi\ngNPpVEpKisrKyuT3+8NdzhmhH5GFfkQW+hFZ6EdkifR+pKenh7sEHHdWnbmOiYlRVlaWJCkxMVF5\neXl6/PHH5fF4FBMTI0lyuVxyuVz11mvL8JITnE5nm9eNNH6/v9MfC/2ILPQjstCPyEI/IktX6gfa\n11l9KT6bzSZJ6sIn7wEAANCBuuSZ60AgoGAwKGOMjDHy+Xyy2+06cOCAYmNjlZqaqtraWi1fvlx9\n+vRRbGxsuEsGAABAF9Alw/WaNWvqfSnxiy++UG5urtLS0rRixQpVVVUpJiZGffv21ZQpU8JYKQAA\nALqSLhmuJ0yY0OTVPYYOHdrB1QAAAOBscVaPuQYAAACsRLgGAAAALEK4BgAAACxCuAYAAAAsQrgG\nAAAALEK4BgAAACxCuAYAAAAsQrgGAAAALEK4BgAAACxCuAYAAAAsQrgGAAAALEK4BgAAACxCuAYA\nAAAsQrgGAAAALEK4BgAAACxCuAYAAAAsQrgGAAAALEK4BgAAACxCuAYAAAAsQrgGAAAALEK4BgAA\nACxCuAZQ+gP4AAAgAElEQVQAAAAsQrgGAAAALGIzxphwFxHpSktLZbe37XOI3W5XMBi0uKKOZbPZ\nFB0dLa/Xq87+cqEfkYV+RBb6EVnoR2SJ9H6kpKSEuwQc5wx3AZ2Bx+Np87pxcXGqqamxsJqOFxUV\nJbfbraqqKvl8vnCXc0boR2ShH5GFfkQW+hFZIr0fhOvIwbAQAAAAwCKEawAAAMAihGsAAADAIoRr\nAAAAwCKEawAAAMAihGsAAADAIoRrAAAAwCKEawAAAMAihGsAAADAIoRrAAAAwCKEawAAAMAihGsA\nAADAIoRrAAAAwCKEawAAAMAihGsAAADAIoRrAAAAwCKEawAAAMAihGsAAADAIoRrAGgHFVW1Wrpu\nu/6/RSv08ML39dqKLTpcVhnusgCgy0pMTGx2/p49ezRkyJDT2ubMmTP1+uuvn9Y6ztNaGgDQouLy\nSj31lw9VfqwmNO1g6TFt2LZXsydfou+ckx7G6gAA7Ykz1wBgsb+s+LxesD7B6wto0TufKRAMhqEq\nADg7VFZW6vLLL9cFF1ygoUOHasmSJaF5fr9f06dP18CBA3XDDTeourpakrRx40bl5uZqxIgRmjhx\nog4cONDm/ROuAcBCZceq9dXew03OLz9Wox17mp4PADgzsbGx+tvf/qZNmzZp5cqV+ulPfypjjCTp\n66+/1l133aUdO3bI5XLpf/7nf+Tz+fSTn/xEr7/+ujZu3Kjbb79dDz74YJv3z7AQnLWqvQF5fEG5\n4pxy2G3hLgddRPmx2tAP8SaXqWx4VhsAYA1jjH7+859rzZo1stvtKiws1KFDhyRJ2dnZGjNmjCTp\n5ptv1rx58/S9731PW7du1ZVXXilJCgQCysjIaPP+Cdc46xSVebT8yxJ9c7BaxkiuOIcuznFrwsAU\n2W2EbJyZVFe8bHabTLDpgJ2WnNCBFQHA2eWll15ScXGxNm7cqKioKPXp00e1tbWSJNsp/87bbDYZ\nYzR48GB9/PHHluyfYSE4qxSV1erplQX6+kBdsJakipqA3v2yVK9/yq/qceaSE2M1pG/PJud3cyfo\nO9l8oREA2svRo0fVvXt3RUVFaeXKldq7d29o3r59+0Ih+uWXX9bYsWPVv39/FRcXh6b7fD5t27at\nzfvvkmeuN2zYoC1btujw4cMaMmSIrrvuutC83bt3a+nSpTp69Kh69eqlyZMny+12h7FadKTlnx+W\nx9f4l8k++7ZCl37HrQx3TAdXha5m2mXDdbD0mIpPufRefGy0bssbKTvDkACg3UyfPl3XXnuthg4d\nqgsvvFADBgwIzevfv7/mz5+v22+/XYMGDdKcOXMUHR2t119/XXPnztXRo0fl9/t1zz33aPDgwW3a\nf5cM10lJSRo3bpx27doln88Xml5VVaXXXntNkyZN0ne+8x2tXLlSixcv1qxZs8JYLTqKLxDU1oKK\nZpf5Yn8l4RpnzJ0Up/unT9D6bXv1+c4DCgSD+k52usaef67ciXHhLg8AuqTKyroTGmlpaU0O8fjq\nq68anT58+HCtWbOmwfSFCxeedh1dMlwPGjRIklRUVFQvXO/YsUPp6emhTyLjx4/XY489puLiYqWn\n1/2atqKiItScE7xerxIS2jZG0uFwKCoqqk3rRgqn01nvz84qoICMaTjeqt4yxhbx/eoq/ZC69vsj\nKipKV44aqCtHDQxHWW3SlfvRGdGPyNIV+oGO0flf7aehuLhYPXv+YyxkdHS0UlNT64XrjRs3avXq\n1fXWy83N1YQJEzq01kiUkpIS7hLOWHbaQRWVVTc5//ycnkpPT+vAitquK/SjK6EfkYV+RBb6gbPJ\nWRWuvV6v4uPj602LiYmRx+MJPR4xYoT69+/fYL3i4uI27fPU7XdGTqdTKSkpKisrk9/vD3c5Z+TS\n/m79+cPGh4Z0S4xSdlKwzb3uKF2pH7w/Igv9iCz0I7JEej9OnCRE+J1V4To6OrrBG6O2tlYxMf8Y\nY+tyueRyueotc+rwktPhdDrbvG6k8fv9nf5YLuqbrKKSFK386ohOvkleWlKUbrs0Q8GAX8FA+Oo7\nHV2hH7w/Igv9iCz0I7J0pX6gfZ1V4To9PV2ff/556LHX61VZWRmf9s4yE4d20+h+yfpi/zHV+oLK\nSo3VgIx4rnENAADOWJe8znUgEJDP55MxRsYY+Xw+BQIBDRw4UIcPH9b27dvl8/m0atUq9ejRg3B9\nFkqOd+rS/im6ckg3DcpMIFgDAABLdMlwvWbNGv32t7/V2rVr9cUXX+i3v/2t1qxZo4SEBE2dOlUr\nVqzQo48+qsLCQt1www3hLhcAAAAWeOedd9S/f3/l5OTokUceaTDfGKO5c+cqJydHw4YN06ZNmyyv\noUsOC5kwYUKTV/fo16+ffvKTn3RwRQAAAGhPgUBAd999t95//3316tVLI0eO1KRJk0KXaJak5cuX\nKz8/X/n5+dqwYYPmzJmjDRs2WFpHlwzXAAAA6Hh9Lvxeu+9jz2fvNDr9k08+UU5Ojvr27StJuvHG\nG7VkyZJ64XrJkiWaMWOGbDabRo8erfLych04cEAZGRmW1dclh4UAAADg7FJYWKjs7OzQ4169eqmw\nsPC0lzlThGsAAADAIoRrAAAAdHpZWVnav39/6HFBQYGysrJOe5kzRbgGAABApzdy5Ejl5+fr22+/\nldfr1auvvqpJkybVW2bSpElatGiRjDFav369kpOTLR1vLfGFRgAAAFikqS8bdgSn06k//OEPmjhx\nogKBgG6//XYNHjxYzzzzjCRp9uzZysvL07Jly5STk6P4+HgtWLDA+jos3yIAAAAQBnl5ecrLy6s3\nbfbs2aG/22w2zZ8/v11rYFgIAAAAYBHCNQAAAGARwjUAAABgEcI1AAAAYBHCNQAAAGCRNl0txBgj\nY0zosd1ORgcAAABanYqLiop0/fXXq1u3bnI6nYqKigr9BwAAAITT7bffru7du2vIkCGNzjfGaO7c\nucrJydGwYcO0adOmdqmj1eH6Rz/6kaKiorRixQolJiZq06ZNmjRpUujC3AAAAEC4zJw5U++80/RN\nbJYvX678/Hzl5+frj3/8o+bMmdMudbR6WMi6deu0b98+JSQkyGaz6fzzz9cLL7ygSy65RLNmzWqX\n4gAAANB5DJ32ULvv48vXGt/HuHHjtGfPnibXW7JkiWbMmCGbzabRo0ervLxcBw4csPz2560+c+1w\nOOR01mVxt9ut4uJiJSQkqLCw0NKCAAAAAKsVFhYqOzs79LhXr17tkmNbHa5HjRqlZcuWSZImTpyo\nadOm6frrr9eFF15oeVEAAABAZ9TqYSEvvviigsGgJOnJJ5/U448/rmPHjumee+5pt+IAAAAAK2Rl\nZWn//v2hxwUFBcrKyrJ8P60O1263O/T3uLg4/eIXv7C8GAAAAKA9TJo0SX/4wx904403asOGDUpO\nTrZ8vLV0GuH6+uuv17333qtLL700NO3DDz/UU089pddff93ywgAAANC5NPVlw45w0003adWqVSop\nKVGvXr3061//Wj6fT5I0e/Zs5eXladmyZcrJyVF8fLwWLFjQLnW0OlyvXr1aixcvrjdt9OjRmjx5\nsuVFAQAAAKfjlVdeaXa+zWbT/Pnz272OVn+hMTY2VlVVVfWmVVVVcRMZAAAA4DibOfk+5s24/fbb\nVVNTo2effVYul0sVFRW666675HQ6tXDhwnYuM7xKS0vbfIt3u90e+iJoZ2Wz2RQdHS2v16tWvlwi\nFv2ILPQjstCPyEI/Ikuk9yMlJSXcJeC4Vg8Lefzxx3XzzTcrJSVF3bp105EjR3T11VfrxRdfbM/6\nIoLH42nzunFxcaqpqbGwmo4XFRUlt9utqqqq0Nilzop+RBb6EVnoR2ShH5El0vtBuI4crQ7XKSkp\nWrp0qQ4ePKj9+/crOztbPXv2bM/aAAAAgE6l1eFaksrLy7VixQoVFRUpMzNTeXl5fFICAAAAjmv1\nQOIPPvhAffr00bx58/Tpp5/qv//7v3XuuedqxYoV7VkfAAAA0Gm0Olz/+Mc/1h//+Edt2LBBf/nL\nX7R+/Xo999xzuvvuu9uzPgAAAKBF+/fv14QJEzRo0CANHjxYTz31VINljDGaO3eucnJyNGzYMG3a\ntMnyOlodrouKijRlypR606677jodPHjQ8qIAAACA0+F0OvX4449r+/btWr9+vebPn6/t27fXW2b5\n8uXKz89Xfn6+/vjHP2rOnDnW19HaBW+55RbNnz9fc+fODU17+umnNWPGDMuLAiJZUZlHpVU+uWId\n6p0WF+5yAACIGJf8/J1238e6332v0ekZGRmh25knJSVp4MCBKiws1KBBg0LLLFmyRDNmzJDNZtPo\n0aNVXl6uAwcOWHob9FaH682bN+uZZ57RY489pqysLBUWFurw4cMaNWqUxo0bF1puzZo1lhUHRJLi\nY169tuGQ9pXWhqb1SI7WD0b20DndYsNYGQAAONmePXu0efNmjRo1qt70wsJCZWdnhx736tVLhYWF\n4QnXs2bN0qxZsyzbMdCZVHsD+uOqQh2t9tebfuioV8+vLtS/XJWtbonRYaoOAACcUFlZqSlTpujJ\nJ5+Uy+Xq8P23Olzfeuut7VkHENE+3V3RIFifUOsLau03R/X9C9I7uCoAAHAyn8+nKVOmaPr06br+\n+usbzM/KytL+/ftDjwsKCpSVlWVpDW26p/fQoUMtLQKIdN8crG52/tcHqzqoEgAA0BhjjO644w4N\nHDhQ//qv/9roMpMmTdKiRYtkjNH69euVnJxs6ZAQ6TRvInPC3r17LS0C7af4mFfr8o+q4EitoqPs\nOj87Ud/tnaQoR5s+VwEAADSpqS8bdoSPPvpIL774ooYOHarhw4dLkn73u99p3759kqTZs2crLy9P\ny5YtU05OjuLj47VgwQLL62hTuDbGWF0H2sG2wkq9tO6g/MF/9Cv/YLU27KrQneOzFBN1egF716Fq\nrd5RosKyWsVGOTS8d5Iu7pd82tvpjPpnxCv/UNNnrwdmJHRgNQAA4FRjx45tMaPabDbNnz+/Xeto\ndSq69957tWXLFkl11whEZKv1BfXqhkP1gvUJ+4/U6t2tpae1vRVfHND/rNirLwsqdaTKr6Jyj5Z9\nXqKnPyhQjTdgVdkRa+S5LrnjG/8sGhdt15jz3B1cUUPllTVa9vEOPf/WBr36983aXXR6PQYAAGeu\n1eE6EAho4sSJGjJkiD766CMVFBS0Z104Q1v2HZPHF2xy/mffVsgfaN1vIMqrfXpl7R419mGwqNyj\nv28/0tYyO424aId+NCFL56bXv651pjtGs3KzlJoYFabK6ny564AeeuE9LVu3Q1u+KdTaz7/VE6+s\n1svvbeI3TQAAdKBWDwuZN2+e/uu//kvLly/XSy+9pIcfflijRo3SjBkzdP311ysxMbE968RpOlLp\nkzFG1d6gvP6gbDYpNsqh2ONDOGp9QdX4AkpyNHwJGGP09cFqfVlQqUDQqMoTlD/YdFDf+G2Frjk/\nTTabrd2OJxJ0S4zWnMt66VCFV6XHvEqOdyorJfzXt66oqtULb38iv7/hbxDWfblHvXumaMywc8NQ\nGQAAZ5/TGizrcDh0zTXX6JVXXtH69etVXFysmTNnqmfPnvrnf/5nFRYWtledOE12m3Sowquyap+q\nvAFVegIqqfSq5JhXxhjFRNlDQftkXn9Qz60u1J/WFOnT3RXatOeYNuw6qsLSGvkCjQfsam9Qvlae\nBe8KeriiNSgrMSKCtSR9vHVvo8H6hDWf7+7AagAAOLudVriuqKjQCy+8oAkTJmjcuHEaNWqUPvzw\nQ+3YsUOJiYm6+uqr26tOnAZjjL4oqFRjWbjWH9TRGr++e07jVwxZ9kWJdh6qqTfN6bApEAyqtNLX\n6P5ccQ5FO7v+lxoj1cHSihbmH+ugSgAAQKuHhdxwww169913NW7cOM2ePVuTJ09WTExMaP4TTzyh\n5OTkdikSp2fn4RqVHPMpJcGpsiqfTj2n7A8YTRiU0mA9jy+ojXsaBrH4aIeO1QbkDxjV+oINzniP\n6kvfwykxPqb5+XHNzwcAANZp9enG0aNHKz8/X0uXLtW0adPqBWtJstvtOnTokOUF4vQdLPdIqgvF\n3V3RSoh2KMphV7TDLnecU90So1TrbXhau6za1+iXIB12m9Jcdf32n3I6/Lwe8Ro/sGFQR8e5aNA5\nLczP7qBKAAAIn9raWl100UU6//zzNXjwYP3qV79qsIwxRnPnzlVOTo6GDRumTZs2WV5Hq89c33ff\nfS0uEx8ff0bFwBpx0Y7Q36McdqUkNPwMFX/SMidPs9nU6FVBEmKc6pEcrZzucXLYbYqNsuu7vZM0\nOCtRDnvX/iJjpMvu7taEETlauXFng3k9u7l0xcjvhKEqAAA6VkxMjD744AMlJibK5/Np7Nixuvrq\nqzV69OjQMsuXL1d+fr7y8/O1YcMGzZkzRxs2bLC0jjbdRAaRbUivRL256bC8/sa/ZNive5ySG7lm\nsyvOqZzuTd8sJTHWqTvGtf7mM8YYfb2vWF/tPSybpMF9eyqnV1qrjwOtN2X8MJ3Tw63VW3arqLhC\niXHRGjnoHF0+IkfxsdHhLg8AcJaY+EJ5u+/j3Tsav7eEzWYLXb3O5/PJ5/M1uJLZkiVLNGPGDNls\nNo0ePVrl5eU6cOCApbdAJ1x3QbFRdl0zPF1vfHa4iXlNB9xrhqfp2ZUFqm5k2Mj3L+jR6mBdVevV\nM39bp2+L/nEN7Pc//Ub9e3fXrEmjFRvNS89qIweeo5EDmx8iAgBAVxYIBDRixAjt3LlTd999t0aN\nGlVvfmFhobKz/zFcslevXiosLLQ0XHOJhy5qdL9k3T4uU/26x8luk6IcNl3QJ0l3X5Hd7CXkMtwx\n+vEV2RrZ16W4aLucDpu+k5Ggn04apNE5rb8L4YvLP6sXrE/4eu9hvbZiS5uOCQAAoDkOh0NbtmxR\nQUGBPvnkE23durXDa+D0YRc2ICNBAzISZIw5rRu8pCVF6wcje+gHI3tIkqKiopSe7lZxcXGr1j9c\nVqlt3zb95dZNXxfounFD5EqIjOtEAwCArsXtdmvChAl65513NGTIkND0rKws7d+/P/S4oKBAWVlZ\nlu6bM9dngY6+c+L+Q+XN3nI7EAiqsPhoB1YEAAC6uuLiYpWX1435rqmp0fvvv68BAwbUW2bSpEla\ntGiRjDFav369kpOTLR0SIp2lZ64XLFiggoIC2e11ny1cLpd+8pOfhLmq9hc0RrsO16jKE1APV7Qy\n3O1z/ePYmJZfVrHRUe2ybwAAED5NfdmwIxw4cEC33nqrAoGAgsGgpk6dqmuuuUbPPPOMJGn27NnK\ny8vTsmXLlJOTo/j4eC1YsMDyOs7KcC1JeXl5GjFiRLjLaKDGG9CGXUf1RUGlfAGjPmmxGnOeWz2T\nzywIby+q0psbD6u82h+a1jc9TjeO7iF3vLVBt/853ZUYH6PKak+j87u5E9Qng2tjAwAA6wwbNkyb\nN29uMH327Nmhv9tsNs2fP79d6zhrw3VTKioqVFlZWW+a1+tVQkJCm7bncDgUFdW68FpR49f8DwpV\nXOENTTtc4dOmvZW6ZUyWhmYntamGvSU1+vO6gwoE64+9/rakVs+vOaD78s5t9FboJzidznp/tiQq\nSvrBZcP1v8s+bTA8xGa3adrl31V0dHguD3c6/YhUp9uPSEY/Igv9iCz0I7J0hX6gY9hMc4Nju6gF\nCxaouLhYxhilpaXpsssu07nnnitJWrlypVavXl1v+dzcXE2YMKFda9p18Jh+v2S7dh8+JrvNpsRY\npxJjnaEwHBft0BMzL1RMVMObv7TkD8u/0oZvSnSs1i9/ICiH3aakOKeinXXb+ucrztMl/dMtPR5J\n2rhjr95ctVnf7Kv7cuPAczM05bILNDSnl+X7AgAAiARnZbguKChQenq6HA6Htm7dqmXLlmn27NlK\nTU21/Mx1TEyMPJ7Gh0ecsC6/TIs/OaDCMo90UjeinTalJUXLfjxg//DiTF3YN/m0a7h74TYVH/M2\nmJ4U51ByXJRGnJus6ZdkNrm+0+lUSkqKysrK5Pf7m1yuKbVen6TIGGfdmn5EujPtRyShH5GFfkQW\n+hFZIr0f6enWnyRD23T+39O0Qa9e/zhzOnz4cH355ZfKz8/XqFGj5HK55HK56i1fVFQkn8/Xpn05\nnc5m1y2v9umvnxyQP2AaDKHw+I2O1fjliqtrU8mxWvl8p3eL+aIyj0oqvTJq+BmqosavaIdNJhho\n1fH5/f42PQ+O4yNR2vocWqmlfnQmbe1HJKEfkYV+RBb6EVm6Uj/QvrgUn+oGt4frBP5n31YoaOoC\nqE0NL5lX5QmE/p6acPpnfj/edVSxzdxVsdIT0MDMtp2VBwAAQH1nXbiuqanRzp075fP5FAgE9MUX\nX2jv3r3KyckJSz0nrt5hs9kUH9OwHb6A0dFqn4LGaFDm6Z21lqTDFV4lxToaDe6S5LTbNDgr8bS3\nCwAAgIbOumEhwWBQH3zwgUpKSmSz2ZSWlqYbb7xRaWlpYakn5aSz0clxTnn9Rr5AUMGgFAgaySZV\neoKKiTL6/Tv7NGNMhrJTW39nw8QYh6IcdqUlRam82i9fIChJskmKjXJoYGaCHPaOvckMAABAV3XW\nheuEhATdeeed4S4j5MI+Lr2/rVTBoGS32dTdVReCj1b7ZbNJcVEOdUuMktNh09Fqv/60pkj/ltdb\ncdGtu2rIiD5J+rKgUjFOu3q4ouuCu6k7Y+2w2zS63+l/QRIAAACNO+uGhUSa5HinplzYXSdOHttk\nkz9g5HTYlBDjUHdXXbCWJI8/qIIjtXryvX1a9VVZvfHYTRmQmaBh2f8Y9hHlsCvGaZfDblPf9Dhd\neK6rmbUBAABwOs66M9eRaOS5ycpyx2rdznIVlXtUWuVTanyU4qLtoetcl1b6VOOrC9O7DteorMqv\nD7Yf0W2XZurc9Lgmt2232fTDi3uqX/ej2rCrQiWVXiXHOTWyb7LGnJfc7M1jAAAAcHoI1xEiMyVG\nN4zsIUl66M3dqj7prHRFjT8UrGWkYNDIHwiqVtKijw7o59f2qReSC8tqtaekVlEOmwZlJigx1qmL\nc9y6OMfdkYcEAABw1iFcR6DzsxP18c6joccnhn8EgkbBoBRUQLUVQUU77HLHO/X5vkpdeK5LVZ6A\nXvr4gHYeqgmt67TblDsgRROHduvw4wAAADjbMCYgggSN0faiKjnsUiAYrAvTxihgjPyBumBtsyl0\nUT1vIKjiYz59c7BaUt1Z7JODtST5g0Yrth/RR/nlrarB6w/KHzjrbtoJAABgCc5cR4jdh6v1hxUF\nKjnmlTGS01F3ZeqYKLuMqbsrut0u2W0KjcOWJCOjvaU12lNSo2+La5rc/pqvy3RxTnLoVuqn2rLv\nmFZ/VabCMo/sNmlgZoKuHNxNmSkxFh8pAABA18WZ6whQXOHVw299q4NHPfIH685Ue/xBefx116Tu\nluiUwyY57LZ6wVqqu7pIeZW/2WAtSWVVfpVVNX7b1tVflenljw+qsMwjSQoaaVthlf7ng/3af6TW\ngiMEAAA4OxCuI8Cij4pU6wvWn2jq7s5YUulTaaVfQVM35tqYuv+Cx/9zxdplOx68W+K0N2x3lSeg\nd7eWNrq812+0dEtJq4+jvLJGX+09rILDrRuCAgAA0NUwLCTMarwBfXPKOGkZyRc8PhZEdWOx68Zh\nS8dPZocCdZU3qN5pTg3JStSyz0sUbGK4dHZqrJLjG7Z7a0Fls2OsdxfXqLzap/RmjqGq1qvX/r5F\nm/MLZY4XkJnu0g8mDNd52eG58yUAAEA4cOY6zGq8QZnj2dYcPzvtC5jQOGuj46E6oFBwdjpsdWOy\nbXXLHzzq1dEav0b2bfxui3abNHFoaqPzGpwxb2wZb9PLBING8//6kTZ9XRAK1pJUVFyh/3njI+3n\nLDYAADiLEK7DLCnOIXe8U8YodEWQxs4jnxj1URe2zfFpNqUkRCnaadcHO47ouhHpunJwquJj/nFr\n9Ex3jGZemqnv9ExodP9ZLXxhMT7arm6JUU3O/3L3Ae07WNboPJ8/oPc/+abZ7QMAAHQlDAsJo0DQ\naMu+Y7Lb6oJ1cxfAO3nkhs0mdUuIUmy0XbbjF+bLP345viuHdNP4gSkqrvApymlTelJ0szXk9IhX\nhjtaB8q9jc4f1S9ZUc6mP4N9uetAs9tvaT4AAEBXwpnrMPEHjBZ8WKRFaw+o4Ehts8G6IZvsdlso\nWJ8qymFXZkpMi8H6hBljMpWW1PDs9NBeibpqSAs3n2mhcGO4ZjYAADh7cOY6TD7eWa6Neyp0tMav\nYKNDmk3D4Gqr+1/w+M1lTnZez/gmr2Hdkm6JUfrp93prW2Glvi2uldNh07DsRGWnxra4bv/e3bV+\n294m5w/o3b1NNQEAAHRGhOsw+Sj/qCpq/E3MbSRYH58sW90ftd6g4qLqxlbb7dKEgY1/YbG1HHab\nhmUnaVh20mmt993vZGn5+h06fKSywTy73aYrL/rOGdUFAADQmTAsJEyKyj0ykoLBf3xBMaS5kRTH\nz1hXeQOq9gaUkuDUjEsy1CslWvn7S5S/v0Q+f+C0ajHG6Ku9h7V68y5tyS88rfWdDrvm3nCpcnrV\nv+RecmKcbr/mIvXL4lJ8AADg7MGZ6zCJPX5b8wbB+iQnzzl5wEdctOSOi1J3V7Tu/6c+ev+Tb7Tw\nzXxV19Z9KTEhLkZXjjxPV4xs+axxweFy/entT3S47B9nnhPiYjT9qu9qWE5mq47FnRSne6aNU2Hx\nURWVHFV8bLQG9O4uRyM3rQEAAOjKSD9hMrx3UoNx0yc7dY4JTTNyxzoVF+3QsdqAln28Q2+t3RYK\n1pJUVePRm2u26v1Pm78MXlWNV3/460f1gvWJ9V94+xPtO9T4JfaakpWerJEDz9Hgc3sSrAEAwFmJ\nBBQmN1zYXdGOxp/+Zq+vEfQpNqbuFw4Ou/TBxp2hWTUejw6XlqnwYLEOFpfq9RUbVevxNbmpdVv3\nqIvsayMAACAASURBVLLa0+i8QCBYb9sAAABoGeE6TKKddk0YmKKo43da/AfT+AX2jJFMUDYZ1XiD\nOlLl1bHKGpXW2BU0RuUVx3SouFTVNTXy+X2q9XhUeKhUv3vm/2fvvsPjus5733/XLlPROwiwF7CA\nFClSpESrkKK6bEuyXKTYjiM7x3ac+CT3JDc5efLcnDzn3Cc3iW9O7k07KTeW49iWqxRJlmJZtiRS\nlaTYxN7AIhJEr9Nn773uHwMCGKIQBEBgCLyfx3pkzN6zZw0WSP1m4d3vegbHHb6G+tSFtlHHePLD\n0Y8LIYQQQohsEq6n0b31pcwp9lMctjCNzEr0ZQr6bl7U2Y8ZNm29SbojSZo7eug1irnkFNPeExv2\nNU6fvcAb7+wZ9pg1wsr5wPHxtfYTQgghhJitJFxPo7klAe6tL6UwaBOwTUxDYQ5axlYqE6gVHoby\nUEqDUripBCodRem+FWnLh1UyH8fJbpitABuHHe/tHfb11ywe/YbFsd7QKIQQQgghMiRcT7P760v5\nT1tq2LiogIBlEPKbWMpB6TQKD6U9gqkOihIfYmoHMx1FuQnAAwUGHmgwAwV4pt1XPaLRGoLEMZSm\no6tn2Ne+ua6GmorCYY/lhfzcvX7JdXznQgghhBAzj9KyP/VVtbe3Y4yz+4VhGHjDb8E49HUiKXYc\nbefNY22cbe4mmUiQjrTj9jZh2QHC1ctIp5JcUaSN44GHQaLlFG60E9DoeBdWuouyokLqlizgT//w\nt4Z9zUg8yfdf3cueo+dxXA+lYPmCKp68dz3VZQUAKKXw+XykUqkbfjvza5mPXCXzkVtkPnKLzEdu\nkfmYOsXFxdM9BNFH+lyPQTI5fEeNsQgGg8Tj8TGdGzA0nZEESccFJ4HSLna4GDu/FCMdJ6lttG0D\nGu254DmZzRz7s7YCpfCi7ehYByntcamljcce2kI8HqerN86JD1tRSrFiQQV5QT8m8Nl71/Lo7Stp\n645SGA5QlB8E6B+3bdsUFRURjUZJp0fuPnIjuJb5yFUyH7lF5iO3yHzkFpmPqSPhOndIuM4h757q\nZt+5XiKRGGnHQSmFtoKgTDw7b9CZGqVMtGGhnWQmYDspvFQMZfkxC6rRgQLczvME/RatbV1855U9\n7DxyHt23aY1SiltWzuUz29bity3CQR/hoG963rgQQgghxAwh4TqHvHuqG4BoPAGANgOgjMw/ZLYp\nz5SEqL7/GWDYaDdFqqsR1fc4GCg7SLB8HqV2nDc++JCy6szydspx6eqNk0g5nGvq5I29p/n47at4\nePMKbMuchncthBBCCDFzyA2NOaS1N1OTlk6nSTsenjKzN5RRgPbItOcbqLtOtJzGS/SC0ddhxFAY\nloVnh3AwiXo2AGnXo6UzQiLl9D+3oyfGz3cd519e3DkVb1EIIYQQYkaTcJ1DApbiUksbqbSDxujb\n8lyh0ZmQ3Ze0Vd+GMtpJod00nptm+J1nIImPgN8PQE8kjudl31DieZpU2uVQQ5NsGiOEEEIIMUES\nrnOI6r1IMpXC8zy0vnxHsh7078vpWvVnaa3dgX7XWTLnmkBhfqZeOzbCVuiXr7X/5MWJvQEhhBBC\niFlOwnWOSKcdGo/uJNXbkXnAczP/XG5d1Ldboxrchk97uLEuACzDxBh0zMAjz/L49AO3kkqlaO/s\nxnEvl5QMMA2Fz87UWqed4bdJF0IIIYQQYyM3NOaI3miM7q4Oejtfw1+xFLtkPh6ggvlo18GwbMjc\nsohGo7SDYShM24cqrIJEF6broLUmHLCoKgmztjbEi6+8QUIHiRHEw8R1DSzLxOi7SbIgHOgP7Itq\nSqft/QshhBBCzAQSrnNEfjiE5wGeQ7LpKMmmoxhFtZh55diF1VjhYgzTh9fX49owDPAcAoEw/uJS\nXG8OeU47C0ttblkxFyMV4ds/egGAMDEUmihhHBSO4xLwmxSGA+SHMvXYxflB1tfVTuN3QAghhBDi\nxifhOkfYtsWyRfN4d0+mzEP5wihfGC8VI9l6mlRrps1eoKAcq2QBeA6G8vA0JGIxKstLCAUX8esP\nzqc0z8d//6t/7r+2UhAmTkjH6SVMnCD5gWB/sK4oyePLj9wmrfiEEEIIISZIwnUO+Y1ffZw9B4+S\nSqVRgYKsY5nt1zVGXhmQ2e7cIHPTo0bT3Rsh4PfzzsluilQv+87H8ChGeQ460YVKRbFti7yQJk/F\nWFhexuZb6plTXsDKBZXZtdxCCCGEEGJcJFznkNo5lXzxMx/nhy/+gl5lZbqCKIVpmHjawzAMlBUY\n9rmJRArXc3nl/QbouYhr2KTTDo6rwCpGpzReZyud3b2UlxazpLqAezcum+J3KIQQQggxs0m4zjGf\n/vi91FSX852X3+PDLgeFIhQKoDXE4nHwHDAs1KCuH9rTOJ7LuYutpGOdqJ5mABx3oPuHCpVAohc3\nHae5tZ3lSxaMe4xHTjTw8mtvc/z0eSzT5ObVdTy87XbmVJWP+5pCCCGEEDOBtOLLQR+5ZS3f+P2n\nqFs0j/m11ZSXFBMM+PFcjRNpBzKt9tDgeYq066E9jYfCi7bjuC7JZOrKrnvgLyCVdkg7Ln/+d//K\n099/gZ5I9JrG9vbu/fzf//AdjpxowHUdkqkk7+75gP/x//wLZz9snKTvgBBCCCHEjUnCdY7KC/r5\nzcc/Qihg09LeSXtnF67nkuhsxIm04mpIY+FggOlDW35SvW0ko91orTNtsRWYRmaKtQYME8s0sSyT\nWDzBm7v28Wd/+y1i8cSYxpRIpvjec68wNLVDMpXkmX9/ZfK+AUIIIYQQNyAJ1zls0ZxSyoxOUp0X\nSPe04vY2k24+TuT8ARIdjXipOHgu2nPw0nGUYWGES0inB5WDKIVlmigFhnYxTQPF5Rskoamlje3v\n7R3TePYfOk48MXIQP3nmPK3tnRN6z0IIIYQQNzKpuc5hp89dYPs7e4hE45kt0QFlWtiBfFLdjaRj\nHYSqlqMsH8qwwA5hhUtw83tJtpxEu5ntzj0vs9Lsxbv6rx0OBfv//+79h3lw6+arjqc3GrvqOdFY\nnPLS4mt8p9kSySTv7TlEU2s7hQV5bF6/hsKCvAldUwghhBBiKki4zmEv/fJtenqjeHqgDEPZIbQy\nUcogWLkMZfkGPSNznuEP4yuZR7K1oa/FnofX2wxuCoBQMEDAP/C8ZDI97Ot/2NjMe3sPkko7rK1f\nQVX56Ds42pY94WB98Ngp/te//phEMtn/2LMvvcYTj97Htts3Djk/7bh09sYJB3yEg74hx4UQQggh\nppKE6xy2a9+h7GBt+fEX12BYPgx/GNMXAjJ9ruk7T2sNXhorVEi4IEhtWT6njx2iO96FaZrk54Uo\nzM9jcFvrxQuyd2bUWvOvP/opO/rKRZQy2PHePsLBAMWFBXR29ww73ts2rM5aEb9WHZ3d/N3TPySV\nzg77rufy3Wd/RnVlOSuXLgQyofqld47yzsGzxBIplKFYuaCSx+6sp6q0YLjLZ4nG4ry1az/HTp/D\ntkxuXr2CDTetwDJlIx0hhBBCjJ+E6xzW3RPJ+tpfOh/IpGLD8vc/rvob8+nMP56L32/xX3/9UdYu\nruRicyv/7Rv/gOt6XLlXjGEYbLv9lqzHXt2xsz9YZ42nN4Lfbw8bsJctms8Tj9w3znea8ca7e4cE\n6wGaV3fs7A/X/98LOzl8pmngqKc53NDE2Usd/N6vbKG8aOQykvMXm/jLf/wuvZGB7+/7B47w8+1z\n+N2vfG5CHxCEEEIIMbtJuM5hSikMw8DzPMxgIcq0AdCug/bcK87NLF4rzyUYDFBbXcHcyhIAairL\n+e0vPck/fufZTK/sPn6fn6c+8zHm11b3P6a15hdv7hpxTIlEkic/fj/KUBw7dRbLMlm/egX1yxdP\neJfHq7XyO3s+c/z4+ZasYD1YNJ7i57tO8Nn7bh72uOd5/P2//igrWA9+/e8//3O+9OQj1zhyIYQQ\nQogMCdc5bO6cSnqjMVwHDDuQWbTWoN00TrQDXVKbuZERAAVemnDQR3VFKQvKgpTm2f3XWr1iCX/5\nx7/Drv2HaW3vorS4gI3r6gkG/FmvGYsnaOsYvePHxeZWnnjkPm7fuHZS328gMHrN9OWx7jtxcdTz\n9p24OGK4PnTsNC1tHSM+d+e+QzzxyH2yen0VbV1Rdh45R080SWVJHptWzpeadyGEEAIJ1zntwa2b\naWptp7OrB2Vkyj9QfTXWbhqn8zx26cL+8wO2SXVFKUGfxSM3D90t0e/3ccemdaO+ps+2MU0L13VG\nPCcUHH4L9onatK6e9w8cGfH4xnWrAEil3RHPyRwfeeyNza2jPtdxHNo6uiRcj+KVncf46dtHM/X9\nfX769hGeengjqxdXj/JMIYQQYuaTPtc57P4tt7FpXT1zqsoJmy6GYWCaAxvBkOjCaT2BTnRj4jJ/\nTikbFxfx9XvnUlM8vgBs2xbr1ywf5QzFrTfXj+8NXcW6+jpWLls07LHKslLuvXMTAItrRu9asmjO\nyMcL86/W0k+Rnxe+yjmz16GGJl5860hWsIbMB55/+ekuOnqu3q7xRtHQ2M6e4xc43yy924UQQoyd\nrFznMNu2+C9f/hXe3XOQd/cc5GRznF7HJj8vhGEYRGNxXM8j3+zmjz53K/OrSibldR97YCtHT5yh\nNzp0a/T7t9xGRdnkvM6VDMPgt7/0JC+++ibb39tLbySC3+fn1pvreezBLf2ryRuWz+Wld4/SExl+\nQ5ttG5aO+Brr16wgFAxm1Z4PtmrZIkqKrt5tZLZ6Y++pEY85jsvbH5zhY7evmsIRTb6zlzr4zit7\naWofuGl3XmURn39gA9Vl8rMhhBBidBKuc5xlWdyxaR13bFqH1pod+xt4Y99pWjsjlBQVsGbJHD66\necWY2s9dTXcS9jRZNMcq2fSp36H56Dsc3fMmrusxr7aarbfdzOYNaybhXY3Mti0+8dBWHn3gLuKJ\nJH6/b0h7PL/P4jc/8RH+6YX3aO8a+ABgWSaP3rFq1NIEn8/mC596mH/8zrN4npd1LC8c4slH75/c\nNzTDXGjtntDxXNfeHeVvf/I2iSt6v59v7uKvf/Qmf/Rr95AX9I/wbCGEEGKWhutYLMYLL7zA6dOn\nCYVCbNu2jTVrrm9onIjW9k5++dZuDp9oQAFrVi5l80e3UFZSiM+anL7MR9sNfnLCxu3Pm2GovZcH\n1t7No8tcamuqaW1tJT1iq7zJZRjGqHXPNeWF/Len7uPwmSYa27oJB/2sW1ZD+Co3RQLcsnYVJcWF\nvPLGuxw/fb6v48ly7t9yG6XFhZP5NmackN8mEkuOfHwM3/9c9sa+00OC9WW9sSRvf3CW+zfVTfGo\nhBBC3EhmZbh++eWXMU2T3/u936OpqYnvfe97VFVVUVFRMd1DG+L0uQv8z3/8LvHEQAnEhUvNvLlz\nP7//tc9TUzXxMUdSXBGsBxxsM1lQYlBbM+GXmXSGoVi9uHpcN9Etnl/L177wqeswqpltw4q5vPzO\n0ZGPL68d8diN4OjZ5qsel3AthBBiNLMuXKdSKY4cOcLXvvY1/H4/8+fPp66ujgMHDnDvvffS09ND\n5IoeyKlUinB4fDe5maaJbdtXP3EE3/z+CySSKZTKvvc0Eo3x7R+9zB//l/807mtf9sElhafVkA1m\nLtt9yeCjN2VKVG50E52PXHB5HqZjPu7duJz9Jxu51DZ0l851dbXctLT2mvqd59p8GIYx6vhN0xgy\n3umcj8mWa/MxHjIfuUXmQ8xGN/5P+zVqb2/HMAzKysr6H6uqquLs2bMA7Nmzh+3bt2c956677mLr\n1q1TOUwADh8/TVtH95C/lFzXJRqLc+DICY6f+ZDbNw7f03msoufjWPbI7es6+qoAiouLJ/Q6YnJN\n13z82X/+FM++vo/te44TiSepKM7n3k0refiONZjGjd2A6LablvLCjgMjHt+8to7y8qFtLkH+fOQa\nmY/cIvMhZpNZF65TqRR+f/YNSX6/n2QykyDXr19PXV3dkOe0to7eH3kkg699rXbuP0p7yk+STB2r\njzRutJ3ervb+Vmh/9Kd/w7r6On7ri58ZQ5u54RmOgZMeebWuwJ851tnZieOMHMJvBBOZj1xhWRbF\nxcXTOh8PblzCgxuX4Lhu/w2nHe3t13ydXJuPDcuqeeWdg0TjqSHHigtCrF5QNuTvglyYj8mSa/Mx\nHjIfuUXmY+qM9MFfTL1ZF659Pt+QPxyJRKI/cBcUFFBQkN15o7Gxcdw38lmWNa7nnr7YzgvvnibB\nwA1iMdfEsUvBl4BkBK01qVSK9w8c4U//+pv8n7//G+Ma4+oSxVvnR74RbW1lJsg7jjNlNzRe5nke\n7Z3dmKY5KS3yxjsfuWg65mM4aW+YYv0xyrX5yA/afP2Tt/Pdn+/lfNNAf+sltWV89v6b8VlqxPHm\nynxMRK7Nx0TIfOQWmQ8xm8y6cF1aWpoJbO3tlJZmNhtpbm7OuU98P3xtP0ZffdflP8xuX4gx8qtI\nxo6jtaazuxeAto4u/uxvv8XvfuVz2Pa1TWtFWPORWpe3LwztPFIR1tw+Vw/zrOvvl2/t4mevv0t7\nZxcAC+fW8ImHtrKqbvG0jEfMDjXlhfz+Z7dysbWbrkic0sIwVSX50z0sIYQQN4gbu0ByHHw+HytW\nrOD1118nlUpx7tw5jh8/zk033TTdQ+vX2NbDxZZMv+DykiJMw0RrBnbFUyZGXgX+soWYZYsxi+ej\ngkXs3HeIb//4pXG95j3zHT61PM38Qo+ABSVBzZZ5Dk/VpwhOw0ew5/7jdb777H/0B2uAMx9e5H/+\n0/c4eHTkjUyEmCw15YWsWlglwVoIIcQ1mXUr1wAPP/wwzz//PN/4xjcIBoM8/PDDOdWGLxofKFvx\n2TZzqsrp7o3Q3pEJ3NowsQsqQbuZkwwLww6QIs3buz/gsQe3jquEYmWpx8rS8f2Kv6OrB631pPSJ\n7olEefm1d4Y9prXHj1/6JatXLJnw6wghhBBCTLZZGa5DoRBPPvnkdA9jRJUl+RiGwvMGyjEMZWBZ\nJp4VwgyXorWLG+/BiXYCGlC4pp+o9jh26ux130nxsr0Hj/H8K9v5sLEJgOqKcj567+3ctn78r7//\n0Alcd+QbXz5sbKKptZ2q8tJxv4YQQgghxPUwK8N1risIB1i7rIa9xy4QTyRpae9AA1bZYoxAARpA\na+z8Crx0nPil42g3jet5dCXUQPnIdfbenoP803efIxPuMy61tPLP332OeDzJ3bffMq7rpsZww0gq\nJTeVCCGEECL3zLqa6xvFZ7atpaa8gJb2TrTWmIW1GIFCQKEAlAKlMH1hgtV1GJYNKDwMunt6J308\niVSaNw808G8/e58f/GI/x84186Of/oLBwXqwZ//j9TGF5OEsXTh31OPhUIjqirJRzxFCCCGEmA6y\ncp2jwgEfN88Lc2hfN3EVRodKuVz+0R+w+5i+MIY/Dy8Vx3Icdh84ykPbbp+0sVxs7eIf/vlntHb0\n9K+Kv7r7GB1dmkIYdmfHWDzO4eMNrKu/9q2i59dWs2LJQo6eOjPs8W2333LNHVGEEEIIIaaCrFzn\nsMbmNvwqhWeHUOpyoB5+pdiwgygUrla8tWs/v/Ff/4xv/fBF2ju7JzQGz9P8/U/eprM3lvW41poU\nPqKERnxuOj3+DQO++qufZOmi+VmPKWVw163r+fh9d477ukIIIYQQ15Ms/+WwgvwwaW3ieBr7aidr\nD+05aGVg+QMkU0l2vLeXA4dP8ke//UXKSorGNYbDZ5po745iXbFS7LdtlFLEdYCwjg1ZvVbKYNH8\nmnG9JkB+Xog//K1f49SZDzl2+iw+22ZdfR3lpZktdD9sbOatXfvp7olQWV7CnbfePCmdSoQQQggh\nJkLCdQ7bvGEN33t1H47jYqbjGPYIq8TaJR1pAy+zUpwXDvcf6u7t5YWf7+CLT3x8XGO42Dr8yrdh\nGOSFQvRGo3gYmGS38NuwZsW4A/1gSxbOZckVNdg/efk1XvrFm1mPvfTLt/jiE49MWZcUIYQQQojh\nSFlIDmvsSGCGStBAqqsRGL4Hdarzw/5gbRmKsD/7M9N7ew8SSXq822jy3Embn52xuNg7TKH0MMKB\nkbdFLykqIBwMoq4oValfvoSnnvjYmK5/rfYdOj4kWENmm/Rvfv95LrW0XZfXFUIIIYQYC1m5zlFN\nHb387U/eIpHWGMrASyVItDRgF1RgBjIbxOh0DB1rw0x0YRoGhmFiGpouClFa4ydJkATpYCV/vddP\n2hv4LLWz0WRdpcvHFjtDSjqSqTSRaIz8vBDr6mp4dsehYceolOLujSv56K0f48Dhk3jao375EhbU\nVl+378sv39o14jHP83jjnT08+ej91+31hRBCCCFGI+E6B3X1xvmrZ7bT3p25idA0DDzXw0snSLaf\nzzq3OD+Iv6iA1s4eNAoPF40JQBqLhJlP+apHs4L1ZfuaTarCmo3VmZ0eeyMxfvzSL9m59yCpdBq/\nz8etN6/mwU11/MeuE0OeHw76eOSOVVSVFlBTNTU7XDY2tU7ouBBCCCHE9SRlITnojX2niQzaAt31\nhi8HMQxFIplm3bK5WApMXK4s9jDLlmL680Z8rd2XMkE8kUzy53/3Ld7cube/P3UylWL7e3t49+0d\n/PYT21g6txxlKPw+i1vr5/N7v7KFqtJr32Z9IvLCI3cnAcjLG/24EEIIIcT1JCvXOejg6UsopQj6\nbWKJFN4w3feUAssw8IC2niiV5SU0t3XgXRHEg0WVuN5Ah+wrtcYUJzoMdu8/zcW27mHPOX+xia7W\nS/zur2wlPc6NYSbL5g038cMXfz7KcbmhUQghhBDTR1auc9DllerCcGDU85QCNDR3RPD7fMyZv5TS\neSspKJ9LQX4ecyrLCfsMPK2H3RI9klK0xA2eOWrzZvc8WPdF9NyPoIeJ2G/u3Dsp722itn5kAwvn\nDt/ib+O6eurrFk/xiIQQQgghBsjKdQ5aNq+ctq4oPtsk6LdIOe7QkzQ4rodlmhQUl9FTsALPX4RF\nZlJVKoLV9gFEGvFKlqCuuGsxklb0pBRhOxO6Pe2BacOcm8Ew4dyOrPOTydSwY22LKXY3mbTEFEEL\n1pS7LCvxMMbWjOSa+X02//vXPs8rb7zH27sP0NXTS2V5KVtvW8+WzeuHvE8hhBBCiKkk4ToHbV23\nhN1HPiTluKTSmTrqwevOl/OjpzWG7SO/biud7XE81wWlMAwDfHkkqzbiv/gWy/N7iTCwwYrWmVVr\n04A8X+bKfp9vYEfFinr0xd0oJ97/nGWLFwwZ5wetBs+ftLPKVo62G9SVeHx6efq6BeyA388j99/F\nI/ffdX1eQAghhBBinKQsJAdVlxXwlUdvI+TPBFdjUEq9Mq/WLF7FxbYe3HQSx/VwHJdUOp0pLTEs\nCueu5DfvKOaRpWmq8jRKgWFA0NKUBT3MvgsW5A1sPINhQuG8/i8ty+SjV2w53pNkSLC+7HiHwXuN\n5kS/DUIIIYQQNxwJ1zlq+fwKPn7HKkylUEphGgrDABQoFD7LpKwwREwHcVwXQ2ksHAw8lNY4Thql\nHVJGmJaWNtZWeHzlphR/vDnJE8ui5Pt0f7AG8NkW5aXFA2UVff8K+P381lOfoba6Mmt8+1rMYYP1\nZXuaJVwLIYQQYvaRspAcdaihiR++dgDDNDC1Rpk2vvLFWIXV2JZF0Okk0XQM7ab6V7OVAqVdXG2C\nYRJPufR0NfPF3/07Nty0koceepDt+xpo6oiSnL+VQDBMYV4An5UJwuFggOCcSmKxGBvW1TKnpI5b\n1q4kP29oK7/OxOifyzoSUvs8U3kakg7YJljy8VwIIYTIIuE6R73w1iG0pynKC9CegPCyrRh2poez\nVpC2SrHza0k3foAqqM2sNCsL7c/HMgZPq8IuX8r7J5o40fUL5lSWowCzq4G4sZxEyqGyJK8/YBtK\nsWVxgIcX3z7q+PJ9oyxbA/kj75o+LolkkoZzFzFNk0XzarBt+dGdap6Gty6YvN9s0ZvMBOv6Mpet\n8xwK/NM9OiGEECI3SELJQS2dERpbewAI+m2Kazfg+kJkd9NTpLFJFS3BSCUx/SGMQBFqUFW21h7a\nTVO88Qk6D71KJNaB47pYpondfQaUgVO4iK5IgoqiMKYB6ytd7l/oXHWMaytc3rpoZt9pOci6imE6\nnIyD53k897M3+MWOXSRTmY118sNhPnrvHdx756YRn5dIJumNxCjIz8PvsydlLLPdj4/bHG0fWKp2\nPNjfYnKm2+TX1yTJm+QPVEIIIcSNSMJ1DkqlB8KtZ4Uw8soxyPS/dj0PrSGddjAMhRkoIHJyO+FF\nm1HK4HIC124KJ9YF2gXTJlRTT8+J7URjcQrzM2UedtdprO6z6FApj9x8G8vKTEJjzKGlQc3d8xxe\nOzf0R2hOnmZzzdUD+lj84IVXeXXHe1mP9UajPPPvP8MwFNtu35h1rKc3wg9f/AW79h/GcRz8Ph+3\nrV/D4w/fTTgUnJQxzUYNXUZWsB6sOwlvX7TG9KFMCCGEmOmkYjIHVZbkEw5mfs+u7UwgdFwXx3HR\nXmZDGK01ruuhbD95S27HDJcACs9Nk460k+5pBTezm6JC4SuqHva1lHYxoi3Mz0uOOVhfdkety+dW\npllW4lHoh6qw5r6FDl+oT+GfhPsZeyJRXnv7/RGPv/jqmzjuwAp5NBbn//qbb/HO+wdwnEzQS6ZS\nvPHu+3zjf/1b/7bu4todahv9r4pDbXIDqxBCCAGycp2TbMtky82LeentIygngdcXpAFQBoZpgdYo\ny4eyAmjt9ReDGJYPI78M7aRRpon2XLx0Ap1OYBgGwcDQXR+L8oMUhse3qru42GNxsXf1E8fhyPEG\nXHfk1dCe3ghnzzeyZOFcALa/u5fmtvZhzz1/8RLv7jnIA1s/cl3GOtOl3NFvUE1OThWQEEIIccOT\nlescdf/GOu5cuwjTjeH0toACwx/GDBVh+PMxAgUY/jCg0Z6DiYuhQBkmyrQwfAFQBsq0MQP59+Wl\nIAAAIABJREFUKNPC9gWxraErjHetXZzVS/tadXT18MY7e/jFm7u4cKllAu86mzfMlu1X0oOKvnft\nPzzqubuvclyMrDpv9A9Qc/KuPldCCCHEbCAr12Pg9/szux6Og2EYBIPjWxX+wkdv46N3rOG//eML\ntBQ8iLL9oPvuIVR941EKZfpQnoMGlGGgtR7YxhFAa6xAHsGyefTGOynKGxjP7WsX89E71owarpVS\nxGIxbNvGsgZ+ZDzP4zs/eZlXd7w3sLIOrF1Vx9e/+BmCwaGr5Ndi3eoVWNaLWdceLD8cYsXSxf2d\nQxzXy9Sdj8D19ITmI1eMNB/X0+b58HZjpgXfcO5cwLi+rzIfuUXmI7fIfOSWmTAfYmrc2D/pUySZ\nTI77ucFgkHg8fvUTRxDyGSydU0hbOgaANv1kdpJRaDLdOpQdBCeKwiOrfYdS4DkoJ45lGgRqlxFo\nPcCWdYsI+GzWLauhuqyAZDIx6hhs26aoqIhoNEo6naYxoth9yeT9021caCyD4qXQdhylMyF436Gj\n/L//8gy//aUnxv2+IdMpZfOGNex4b++wx++9axOOk8ZxMrXUi+bNobGpecTrLaipwvO8Cc1HLrhy\nPqaCAj69TPGDYz4SgwK2UrBlrsOiPJfxfFsn+ucjF0zHfFwvMh+5ReYjt+T6fBQXF0/3EEQfCdc3\ngDl1G+BwGtJRVDqK1grPCmL484FMKYhhWpi2Tbrvhke0h5GKoAaHbdOP1poHb1tB0D++9nR7m01+\netrC86CxFyicm/mnbAX62PMonSm+PXD4OI1NrcypKp/Qe//c4w9hKIM3d+7D9TLX9vt83L/lNh7e\nlt2L+547NvLO+wfwvKEr3bZlc/ftt0xoLLPdgkLN/7YhyaE2k9aYImRr1pS7FEqPayGEEKKfhOsc\nd+rMhxw50YFpLiCZTGGaRqa22omBL4QyTSzTpLIwj54UOKm+JzoJPM9Fa42hFIZhoNJR/D4L/zg3\nYOlOwkunLbSGVDqdHWILamDOeri4q/+h4w3nJhyuLdPkVz/1MI8+cBfHT5/DMAxWLltEMDA00c2r\nqeKrn3+cf3nmhf6e2AChYJCvfP4TlJfKp/qJ8plwc6XcvSiEEEKMRMJ1jtJa860fvMibu/ahA0Ww\n5nMYhoHrevh9Nj6fj2DQI44/s+Oi0lgKUAbaieEkYv3XcgGlXAIdDWxcPW/cNy/ubVJ4fQvhSg1z\njYpVWeHaMievPVtBfh63rF111fM23LSS+uWL2bXvCB1d3VSUlbDhphX4bNlIRgghhBDXn4TrHLX9\n3b28uWsfACrRhW4/gVFWh2GYeJ6mrKQQ0zDI1x7FAc2F3syKtp8UjmFjBPLxEhEyNdia2Nn3ibWd\nZtuvbb7qazdFFe9etDjTY2AqTWVYsbAiyaFWhavBVGDbFrZlkXYGFeD68tAoFBrTtLhp1bLr8825\nioDfz523rpuW1xZCCCHE7CbhOke99vbu7AcafgluCspXog2TSDTGnJI8avI9TnQY5PkyS8qRrm6U\nozGsIChINB0l3XwcX7yJIAkOHT3Jls3rR3zdk50GPzhm43rgauhImBxphx0X4gQMRcxR5NmaoKUJ\n5RfS3d0DXt9NKp4Lc29Da4/Ny4ooyAtfr2+PEEIIIUROkj7XOepiU2vW10q7qLNvwL6n4fiL1Ha9\ny39en6Q5mj2FyVQKvDQq1YNJmnDr+xQnzhBWcQylOXX2wxFf0/XghVOZYK2B9rhBuq+81tOQ8hSe\nB50JRVPMIK4DWPllqHA5BMvAX4BZewvFK7dxvvhOfnjMxrk++8sIIYQQQuQkCdc5Kj8vNOzjyomj\nus4yJ5igN6XovqJL4JBa6IK5WV8G/CO3djjdZRDpuyEy6SqcvpDtepByIJYGp6/P9uV7GQ1loCwf\nhu2nrDBM7ZxKCvMzK9ZH2w1eOWPRnURCthBCCCFmBSkLyVGbN6zhZ6+/M+rx4e5LDIeCRKIDNzOi\ns1PtLWtXApnA/PIZi/cvmUTSiuKAZk7Yw9NgKEi5mRA9OBQP2YNPZeqvPRcMA1JY5PWd5WroSSl+\netpm1yWToAU3VbjcPd/BP3n3OQohhBBC5BQJ1znq4W2388GRkzQ2tw45du+dtzKvpgrQVIY1zdGB\nlF2Yn0csnsi0ydMauhr6j61fvYK6xfOJpODPd/ppiQ384iKSUlyKKEBRFvRQZALySDSZy6f1QOhO\nOAqtMxuSt8UNLm+s6HiQdGHXJZPGiMEX6lNY8jsTIYQQQsxAEnFyVDgU5A+//hQfvedOSooK8fv8\nLJpfy5c/+wmefPT+/vO2znMy2+f1sS2T6ooyQsEAqvUIKtlLfl4eH7/vLr7yq48D8O3Ddlawvszx\nFGkPImmFz8yE59FcPuxp+lv0AUTTisE7lg9eYb/QqzjSLj92QgghhJiZZOU6h4VDQT7x0FY+8dDW\nEc+pK/H4VF2aX56z6IhnUmx+wOTuBQXcet9GEombKMzPwzQzgbYlqjjZOXJdhiJTbx20NIYaFJoV\n2Turk/naUJk+2q6GoKVRCuLOQJq2TT1klfpwm8ma8hu/CNvzNK7nYVtS5yKEEEKIDAnXM8DKUo8V\nJSmaY5mbECtDGtsE8BO6YifDS1GVtco8nOKAx73zHZ47YZNwM72tPZ0J0TBoobzv/5hqIFzDQAZX\nCgp8Q18sfYPn6vbuKC+/e5Q9xy/iOC5VpQVsvXkxH1mzcLqHJoQQQohpJuF6hlAKqsJXSc2A3wTb\ngMQo51SGNHfOdYmmFbsumYAi4hr0xDVaD9RiX16Qtk3INzW1+ZqOhMJnajwF+T6Nb5hF3dq8kcfp\n9JWK2zm6GNzWFeUvn3mD3thAm5am9h6eeXUfTR29PL5lzTSOTgghhBDTTcL1LLOkOLOjYySthq2p\ntgy4a25m18V75ju0xhVnu03y/YqeRGax2m9CoS+z86NS4DNgboHmS2tSxB1oiij+7Yhv2OvbJqyv\ncoY8fq5bseOCRUO3ARpq8zV31DosK8mtZe6X3j2aFawHe2Pvae64aREVxXlTPCohhBBC5Aq5s2yW\nsQx4aJFDccDjypbYKPhIjcPqvnpo24TPr0zzq6s97lzgY1mJptCvKQ95BCxNwMoEbdOAbfMzuzQG\nLVhYpHlkSXrI6nPIhieWpym8otX2yU6Dbx/20dBl9NeUXOhVPHPMZn9z7ixhu67HvhMXRzyutWb3\n0fNTOCIhhBBC5BpZuZ6F1lW6hG3Na+ctTnUaOBoqQpoHF6ZZV5m9UqwULC3RbK4LcE9NL2+e83j3\nkkVv3+Lt3ALN3fPSLCjMXqa+qcKjriTJoTaT3pSiJOCxqswbtgXfz89Yw9eBa/j5OYv6cjcnWvel\nHRfHcUc9J54cuiovhBBCiNlDwvUstazEY1lJirSbCdBjCa9KwW01LpvmuHQnFZahyfeNfH7Agg1V\no4fRSxFFW3yY3XD6xNOZnSPrcqA8JOC3KS/Oo7UzMuI5tRWFUzgiIYQQQuSaHFgPFNdbW1yx40OT\n185ZnOw0slaJbXNswXowQ0FxYPRgPVbJ0bM3AIkcWgy+a93iEY/lh/ysr6udwtEIIYQQItfIyvUM\n5ml46bTF3qy6ZZPykOZXVqQoCkzb0PpVhDSmQdamM1eqHqW7yFS7a+0iLrX18PYHZ7Iezw/5+Y1P\nbJae10IIIcQsJ+F6BnvrgnlFsM5ojSm+f8zHV25KkXLh3UaT/S0WMQfKgppbqlzWVrhDb3i8DkI2\nrC5z2d8yfChdVORREcqdcK2U4sl713Hn2kW8f+xDEkmHeVVFrK+rxWfLHychhBBitpM0cAM735PZ\nbVHrTAhdVDSw/Ot6sKtp5OltjipOdBi88aFFU3QgRV+KKF44ZXGhV/GxJVNTj/HgIoeelMp0Cxmk\nOk/z2NL0lIzhWtWUF1JTLvXVQgghhMgm4foGlHThh8fsrDD69kWT2vxMuUfQhu6kIpoa/TpvXjCz\ngvVge5tNbqpwmVdw/VeNfSZ8flWac92KE50mXt+HhSVFQ9sFtsQUh1pNki5U53nUj9CBRAghhBBi\nOki4vgG93GAPWeWFTG/o50/ZPLEijc+8eii+GBk9lX7QajKvYOruJpxfqJlfOPzraQ3/ccZi96Xs\n+vFfnIPPrkjlVF22EEIIIWYvWfO7wURScKh15Gk73mnQEVfk+TJhdSTGGNrvRdMTK7r2NDR0GXzQ\nanCxd2LX2t1kXhGsM6Ip+N5RH870d+oTQgghhJCV6xtNU9QYfsOVyzRciipKgppt89N8+9DwwXNz\njcvZboMLo4Te8uD4V4MbugxePG3RlRi4/pw8zSeWpSkdx3V3DhOsL4uk4HCbwU0VkrCFEEIIMb1m\nXbh++umnuXDhAoaRWbYtKCjg61//+jSPauz8Yyj38PXl0Ln5ml+rT/H6hxan+7YWLwlqbpvjsqHK\n5WCrwYVee9hrGApurhxfSUhLVPHMUXtIqG+MKP71sI/fXJvEfw0/eUkHOkbZaAbgUtTgJiRcCyGE\nEGJ6zbpwDfDQQw+xfv366R7GuNTka4oCOmtFeLCwDQsLvazzP7cyTcIBx8scv3yT4Opyj4sRl52N\n2avCpgGPLU2Puw/2O43miGUavUnY32qyqXoMu8f0sQyu2gs7MIYPHUIIIYQQ19usDNej6enpIRLJ\n3t46lUoRDofHdT3TNLHt4VeHx+uhJfD9Iwp9ZZ5U8MASj6B/6OuNNISPLYObqz32NRlE01Ae0qyv\n1hT6TSATui3Lyvr31ZztMUftkX22x+L2eWMv97eB+gr4oHnki66rNrDtq1/zeszHVLvW+chlMh+5\nReYjt8h85JaZMB9iaiith0S0Ge3pp5+mtbUVrTVlZWXcfffdLFy4sP/466+/zvbt27Oec9ddd7F1\n69apHuqojrU6/Ox4ilPtmRXgBcUm9y/1sbp6+v8C+z9eidCZGPnHanWVxVc2Ba/pmq1Rj7/cESOS\n1HBFxt6yyOaTq3Ngu0khhBBCzHqzLlxfuHCB8vJyTNPk0KFDvPzyy3z1q1+lpKQEmPyVa7/fTzKZ\nnPC4R5J0QAOB65ipLcuiuLiYzs5OHOfqddgvnDTY3TjyKvMjyzw2VF/bj93h4w089/oejieroGQJ\nwVCIheUhti3xsXHO2K91vedjKlzrfOQymY/cIvORW2Q+ckuuz0d5efl0D0H0mf5lzkn09NNPc+7c\nuWGPzZ07ly996UvU1tb2P7Z27VoOHjzIyZMn2bRpE5C5wbGgoCDruY2NjaTT49sp0LKscT93LC4X\nQlzHl+jnOM6Y3svGSsWBZh/JYf4eLQ1qVhanrmm87+09xD9/9zm09oADaH5OzDA5aSg+9pXPki6f\nP+ZrXe/5mEpjnY9cJvORW2Q+covMR26ZSfMhrq8ZFa6feuqpa36OUopZtnh/3ZUGNb+6KsVPT9tc\nivStYCtYUuTxscVp7JG76g2RTjs889zP+oL15Utp8BzSHjzz76/wJ7/75Ul+B0IIIYQQ4zOjwvXV\nxONxLl68yPz58zEMg8OHD3Pu3DkeeOCB6R7ajDMnT/Plm1K0RBW9aUVJQFMcuPYPMYeOn6Y3Gh3x\n+PmLl7hwqYXa6oqJDFcIIYQQYlLMqnDteR6vvfYabW1tKKUoKyvjiSeeoKysbLqHdkPrScLeZpOm\nmEHQ1Kyp8PrbAVaENRWM/zcD0Vh8Us4RQgghhJgKsypch8NhvvxlKSGYTCc6DH543M7qQb2/xaS+\n3OOxpWmMie16Tu2cylGPm6ZFdUXpxF5ECCGEEGKSjL3ZsBBXiKXhxyfsYTd3OdRqsGuULcvHakFt\nNUsWzB3x+Ma1KynIz5vw6wghhBBCTAYJ12Lc9reYpEfZaHF308TDNcBXP/84VeVDS3cWz5/L5x5/\naFJeQwghhBBiMsyqshAxudrio9d8dMQVnmbCpSElxYX899//Kns+OMqRE2cwDYN19XWsqluEYcjn\nQyGEEELkDgnXYtzCV9kFNmhPPFhfZpkmm9bVs2ld/eRcUAghhBDiOpBlPzFuN1Vk14RoDdE0tMYV\nTVGDhAP7WwykjXhuau+O8tqeU7y66wRnGjumezhCCCHEjCAr12LcyoKaO2pd3rx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"text/plain": [ "<matplotlib.figure.Figure at 0x7f0d3ea5ead0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "<ggplot: (8730928897673)>" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from ggplot import *\n", "\n", "df_pca = features_df.copy()\n", "df_pca['x-pca'] = pca_results[:,0]\n", "df_pca['y-pca'] = pca_results[:,1]\n", "df_pca['label'] = labels\n", "chart = ggplot( df_pca, aes(x='x-pca', y='y-pca', color='label') ) \\\n", " + geom_point(size=75,alpha=0.8) \\\n", " + ggtitle(\"First and Second Principal Components colored by gender\")\n", "chart" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Computing t-SNE embedding\n", "[t-SNE] Computing pairwise distances...\n", "[t-SNE] Computing 151 nearest neighbors...\n", "[t-SNE] Computed conditional probabilities for sample 156 / 156\n", "[t-SNE] Mean sigma: 46.734307\n", "[t-SNE] KL divergence after 100 iterations with early exaggeration: 1.395291\n", "[t-SNE] Error after 400 iterations: 1.395291\n" ] } ], "source": [ "from sklearn.manifold import TSNE\n", "\n", "print(\"Computing t-SNE embedding\")\n", "tsne = TSNE(n_components=2, verbose=1, perplexity=50, n_iter=500)\n", "tsne_results = tsne.fit_transform(features_df)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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aiQgsw4Bjm1BKDfrUaAUwuCYiIiLKmGkYyLlMENgbcdSJiIiIiDLC4JqIiIiI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\nNQAARC6SzUwCJ2xvkwAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f0d3e7d9750>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "<ggplot: (8730928702225)>" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_tsne = features_df.copy()\n", "df_tsne['x-tsne'] = tsne_results[:,0]\n", "df_tsne['y-tsne'] = tsne_results[:,1]\n", "df_tsne['label'] = labels\n", "chart = ggplot( df_tsne, aes(x='x-tsne', y='y-tsne', color='label') ) \\\n", " + geom_point(size=70,alpha=0.1) \\\n", " + ggtitle(\"tSNE dimensions colored by gender\")\n", "chart" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Computing t-SNE embedding\n", "[t-SNE] Computing pairwise distances...\n", "[t-SNE] Computing 151 nearest neighbors...\n", "[t-SNE] Computed conditional probabilities for sample 156 / 156\n", "[t-SNE] Mean sigma: 3.671794\n", "[t-SNE] KL divergence after 100 iterations with early exaggeration: 1.414827\n", "[t-SNE] Error after 325 iterations: 1.414827\n" ] } ], "source": [ "from sklearn.manifold import TSNE\n", "\n", "print(\"Computing t-SNE embedding\")\n", "tsne = TSNE(n_components=2, verbose=1, perplexity=50, n_iter=1000)\n", "tsne_results = tsne.fit_transform(pca_results)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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CRvm7EBERTbxUKoUZM2Zg3rx5ePDBB8ve/6WXXsLy5cuRSqWwxx574K677io4LYmIqJ44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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f0d3e56df50>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "<ggplot: (8730928573909)>" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_tsne = features_df.copy()\n", "df_tsne['x-tsne'] = tsne_results[:,0]\n", "df_tsne['y-tsne'] = tsne_results[:,1]\n", "df_tsne['label'] = labels\n", "chart = ggplot( df_tsne, aes(x='x-tsne', y='y-tsne', color='label') ) \\\n", " + geom_point(size=70,alpha=0.1) \\\n", " + ggtitle(\"tSNE dimensions colored by gender\")\n", "chart" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 2 }	apache-2.0
jamesfolberth/NGC_STEM_camp_AWS	notebooks/machineLearning_notebooks/object_detection/object_detection_tutorial.ipynb	2	13547	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "(This tutorial adapted from https://github.com/tensorflow/models/blob/master/research/object_detection/object_detection_tutorial.ipynb)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Object Detection Demo\n", "Welcome to the object detection inference walkthrough! This notebook will walk you step by step through the process of using a pre-trained model to detect objects in an image. Most of the code below is related to installation so you can run it without reading any of it if you want to - just run each cell and head down to the section \"Let's Try it Out!\" below." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Imports" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "import numpy as np\n", "import os\n", "import six.moves.urllib as urllib\n", "import sys\n", "import tarfile\n", "import tensorflow as tf\n", "import zipfile\n", "\n", "from collections import defaultdict\n", "from io import StringIO\n", "from matplotlib import pyplot as plt\n", "from PIL import Image\n", "\n", "import object_detection\n", "OBJECT_DETECTION_DIR = os.path.dirname(object_detection.__file__)\n", "from object_detection.utils import ops as utils_ops\n", "\n", "if tf.__version__ < '1.4.0':\n", " raise ImportError('Please upgrade your tensorflow installation to v1.4.* or later!')\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Env setup" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# This is needed to display the images.\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Object detection imports\n", "Here are the imports from the object detection module." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from object_detection.utils import label_map_util\n", "from object_detection.utils import visualization_utils as vis_util" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Model preparation " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Variables\n", "\n", "Any model exported using the `export_inference_graph.py` tool can be loaded here simply by changing `PATH_TO_CKPT` to point to a new .pb file. \n", "\n", "By default we use an \"SSD with Mobilenet\" model here. See the [detection model zoo](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md) for a list of other models that can be run out-of-the-box with varying speeds and accuracies." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# What model to download.\n", "MODEL_NAME = 'ssd_mobilenet_v1_coco_2017_11_17'\n", "MODEL_FILE = MODEL_NAME + '.tar.gz'\n", "DOWNLOAD_BASE = 'http://download.tensorflow.org/models/object_detection/'\n", "\n", "# Path to frozen detection graph. This is the actual model that is used for the object detection.\n", "PATH_TO_CKPT = MODEL_NAME + '/frozen_inference_graph.pb'\n", "\n", "# List of the strings that is used to add correct label for each box.\n", "PATH_TO_LABELS = os.path.join(OBJECT_DETECTION_DIR, 'data', 'mscoco_label_map.pbtxt')\n", "\n", "NUM_CLASSES = 90" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Download Model" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#opener = urllib.request.URLopener()\n", "#opener.retrieve(DOWNLOAD_BASE + MODEL_FILE, MODEL_FILE)\n", "tar_file = tarfile.open(MODEL_FILE)\n", "for file in tar_file.getmembers():\n", " file_name = os.path.basename(file.name)\n", " if 'frozen_inference_graph.pb' in file_name:\n", " tar_file.extract(file, os.getcwd())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load a (frozen) Tensorflow model into memory." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "detection_graph = tf.Graph()\n", "with detection_graph.as_default():\n", " od_graph_def = tf.GraphDef()\n", " with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:\n", " serialized_graph = fid.read()\n", " od_graph_def.ParseFromString(serialized_graph)\n", " tf.import_graph_def(od_graph_def, name='')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Loading label map\n", "Label maps map indices to category names, so that when our convolution network predicts `5`, we know that this corresponds to `airplane`. Here we use internal utility functions, but anything that returns a dictionary mapping integers to appropriate string labels would be fine" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "label_map = label_map_util.load_labelmap(PATH_TO_LABELS)\n", "categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True)\n", "category_index = label_map_util.create_category_index(categories)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Helper code" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def load_image_into_numpy_array(image):\n", " (im_width, im_height) = image.size\n", " return np.array(image.getdata()).reshape(\n", " (im_height, im_width, 3)).astype(np.uint8)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Detection" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# For the sake of simplicity we will use only 2 images:\n", "# image1.jpg\n", "# image2.jpg\n", "# If you want to test the code with your images, just add path to the images to the TEST_IMAGE_PATHS.\n", "PATH_TO_TEST_IMAGES_DIR = os.path.join(OBJECT_DETECTION_DIR, 'test_images')\n", "TEST_IMAGE_PATHS = [ os.path.join(PATH_TO_TEST_IMAGES_DIR, 'image{}.jpg'.format(i)) for i in range(1, 3) ]\n", "\n", "# Size, in inches, of the output images.\n", "IMAGE_SIZE = (12, 8)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def run_inference_for_single_image(image, graph):\n", " with graph.as_default():\n", " with tf.Session() as sess:\n", " # Get handles to input and output tensors\n", " ops = tf.get_default_graph().get_operations()\n", " all_tensor_names = {output.name for op in ops for output in op.outputs}\n", " tensor_dict = {}\n", " for key in [\n", " 'num_detections', 'detection_boxes', 'detection_scores',\n", " 'detection_classes', 'detection_masks'\n", " ]:\n", " tensor_name = key + ':0'\n", " if tensor_name in all_tensor_names:\n", " tensor_dict[key] = tf.get_default_graph().get_tensor_by_name(\n", " tensor_name)\n", " if 'detection_masks' in tensor_dict:\n", " # The following processing is only for single image\n", " detection_boxes = tf.squeeze(tensor_dict['detection_boxes'], [0])\n", " detection_masks = tf.squeeze(tensor_dict['detection_masks'], [0])\n", " # Reframe is required to translate mask from box coordinates to image coordinates and fit the image size.\n", " real_num_detection = tf.cast(tensor_dict['num_detections'][0], tf.int32)\n", " detection_boxes = tf.slice(detection_boxes, [0, 0], [real_num_detection, -1])\n", " detection_masks = tf.slice(detection_masks, [0, 0, 0], [real_num_detection, -1, -1])\n", " detection_masks_reframed = utils_ops.reframe_box_masks_to_image_masks(\n", " detection_masks, detection_boxes, image.shape[0], image.shape[1])\n", " detection_masks_reframed = tf.cast(\n", " tf.greater(detection_masks_reframed, 0.5), tf.uint8)\n", " # Follow the convention by adding back the batch dimension\n", " tensor_dict['detection_masks'] = tf.expand_dims(\n", " detection_masks_reframed, 0)\n", " image_tensor = tf.get_default_graph().get_tensor_by_name('image_tensor:0')\n", "\n", " # Run inference\n", " output_dict = sess.run(tensor_dict,\n", " feed_dict={image_tensor: np.expand_dims(image, 0)})\n", "\n", " # all outputs are float32 numpy arrays, so convert types as appropriate\n", " output_dict['num_detections'] = int(output_dict['num_detections'][0])\n", " output_dict['detection_classes'] = output_dict[\n", " 'detection_classes'][0].astype(np.uint8)\n", " output_dict['detection_boxes'] = output_dict['detection_boxes'][0]\n", " output_dict['detection_scores'] = output_dict['detection_scores'][0]\n", " if 'detection_masks' in output_dict:\n", " output_dict['detection_masks'] = output_dict['detection_masks'][0]\n", " return output_dict" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Let's Try it Out!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's identify some objects in a couple of example pictures. Run the code below and see how it does." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "for image_path in TEST_IMAGE_PATHS:\n", " image = Image.open(image_path)\n", " # the array based representation of the image will be used later in order to prepare the\n", " # result image with boxes and labels on it.\n", " image_np = load_image_into_numpy_array(image)\n", " # Expand dimensions since the model expects images to have shape: [1, None, None, 3]\n", " image_np_expanded = np.expand_dims(image_np, axis=0)\n", " # Actual detection.\n", " output_dict = run_inference_for_single_image(image_np, detection_graph)\n", " # Visualization of the results of a detection.\n", " vis_util.visualize_boxes_and_labels_on_image_array(\n", " image_np,\n", " output_dict['detection_boxes'],\n", " output_dict['detection_classes'],\n", " output_dict['detection_scores'],\n", " category_index,\n", " instance_masks=output_dict.get('detection_masks'),\n", " use_normalized_coordinates=True,\n", " line_thickness=8)\n", " plt.figure(figsize=IMAGE_SIZE)\n", " plt.imshow(image_np)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now try it on some images yourself. The code below will let you feed in any picture you same into this directory. Have a look through some pictures in datasets on http://host.robots.ox.ac.uk/pascal/VOC/databases.html and try them out using the code below." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def find_objects(image_path):\n", " image = Image.open(image_path)\n", " # the array based representation of the image will be used later in order to prepare the\n", " # result image with boxes and labels on it.\n", " image_np = load_image_into_numpy_array(image)\n", " # Expand dimensions since the model expects images to have shape: [1, None, None, 3]\n", " image_np_expanded = np.expand_dims(image_np, axis=0)\n", " # Actual detection.\n", " output_dict = run_inference_for_single_image(image_np, detection_graph)\n", " # Visualization of the results of a detection.\n", " vis_util.visualize_boxes_and_labels_on_image_array(\n", " image_np,\n", " output_dict['detection_boxes'],\n", " output_dict['detection_classes'],\n", " output_dict['detection_scores'],\n", " category_index,\n", " instance_masks=output_dict.get('detection_masks'),\n", " use_normalized_coordinates=True,\n", " line_thickness=8)\n", " plt.figure(figsize=IMAGE_SIZE)\n", " plt.imshow(image_np)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# run it like this:\n", "find_objects('Example.jpg')" ] } ], "metadata": { "colab": { "version": "0.3.2" }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 }	bsd-3-clause
jobliz/solid-state-kinetics	notebooks/english/single_kinetic_model_simulation.ipynb	1	79799	{ "metadata": { "name": "", "signature": "sha256:f671dde910b1088c0faa80364fc2b782ab0f690550793830b78415edd7cf03ed" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Solid-state kinetics: Single-model simulation through optimization.\n", "\n", "This is a quick note on how to do single-model simulations with solid-state kinetic models using Python. Isothermal simulation is done rearranging the isothermal rate law, the non-isothermal case is solved using an optimization method with scipy's optimize submodule (Levenberg-Mardquart). The Avrami-Erofeev A2 kinetic model will be used for both simulations, setting $E_{a} = 100 kJ/mol$ and $A = 10^{13} min^{-1}$.\n", "\n", "We first do all required imports and define our kinetic triplet (model, A, E):" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from __future__ import division\n", "import time\n", "import functools\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from scipy.optimize import leastsq\n", "from scipy.constants import R\n", "\n", "E = 100 * 1000 # kJ/mol\n", "A = 1013 # min -1\n", "\n", "def iA2(a):\n", " \"\"\"Integral form of Avrami-Erofeev model (n=2)\"\"\"\n", " return (-np.log(1-a))*(1/2)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 31 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Isothermal simulation.\n", "\n", "The integral isothermal rate law is expressed as:\n", "\n", "$$\n", "\\begin{align}\n", " g(\\alpha) = A e ^ {-\\frac{E_{a}}{RT}} t \\\\\n", "\\end{align}\n", "$$\n", "\n", "which can be rearranged as:\n", "\n", "$$\n", "\\begin{align}\n", " t = \\frac{g(\\alpha)}{A e ^ {-\\frac{E_{a}}{RT}}}\n", "\\end{align}\n", "$$\n", "\n", "Accordingly, we can get the time associated with each $\\alpha$ value by setting a model $g(\\alpha)$ and kinetic parameters $E_{a}$ and $A$ as constants." ] }, { "cell_type": "code", "collapsed": false, "input": [ "alphas = np.arange(0.01, 1, 0.01) # Transformed fraction, numbers between 0 and 1\n", "temps = [340, 345, 350, 355, 360] # Simulated temperatures, in Kelvins\n", "times = []\n", "\n", "for T in temps:\n", " curve = iA2(alphas) / A np.exp(E/(RT))\n", " times.append(curve)\n", "\n", "plots = [plt.plot(t, alpha)[0] for t in times]\n", "plt.title('Simulated Isothermal Reaction')\n", "plt.xlabel('Time (min)')\n", "plt.ylabel('Transformed Fraction')\n", "plt.legend(plots, map(str, temps), loc='lower right')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 32, "text": [ "<matplotlib.legend.Legend at 0x5936e90>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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GmmdXq8SWLUDHjkCHDtqZjyVyinMwMGwg1vdZj+4tuvMtTq1CLAa+/hr45hvg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\nKAC0aNECzZo1w6RJk5Cbm6v4fVJSEnx9fRXHPj4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48cUX8c033xi87D179uDN\nN980eLmMPQw/OTDGGNPATw6MMcY0cHBgjDGmgYMDY4wxDRwcGGOMaeDgwBhjTAMHB8YYYxr+H+Z6\ntwlA3FgXAAAAAElFTkSuQmCC\n", "text": [ "<matplotlib.figure.Figure at 0x583ce10>" ] } ], "prompt_number": 32 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Non-isothermal simulation.\n", "\n", "As presented on [this paper by Kwawam & Flanagan](http://www.sciencedirect.com/science/article/pii/S0040603105002856), non-isothermal data can be simulated with an optimization method by calculating the temperature (T) for transformed fraction values ($\\alpha$) according to:\n", "\n", "$$\n", "\\begin{align}\n", " \\psi = \\mid {g(\\alpha) \\frac{\\beta R}{A E_{a}} - p(x)} \\mid\n", "\\end{align}\n", "$$\n", "\n", "where A, E, $g(\\alpha)$ are fixed constants, the heating rate $\\beta$ changes with every curve, and the exponential integral p(x)\n", "\n", "$$\n", "\\begin{align}\n", " p(x) = \\int_{x}^{\\infty} \\frac{e^{-x}}{x^2} dx\n", "\\end{align}\n", "$$\n", "\n", "where $x = \\frac{E}{RT}$, can be approximated using a 3rd degree Senum-Yang polynomial. In the following code $\\psi$ is expressed as an objetive function where the keyword argument $g$ has to be set up explicitly (avoiding the need to make multiple psi functions). This can be done multiple times, or usign [functools.partial](https://docs.python.org/2/library/functools.html) as depicted here." ] }, { "cell_type": "code", "collapsed": false, "input": [ "def senumyang(x, d=3):\n", " \"\"\"Senum-Yang 3rd degree temperature integral approximation. x = Ea / (RT)\"\"\"\n", " return np.exp(-x) / x * (x*2 + 10x + 18) / (x*3 + 12x*2 + 368x + 24)\n", "\n", "def psi(T, alpha, beta, A, Ea, g=None):\n", " \"\"\"Objetive function for nonisothermal simulation.\"\"\"\n", " x = Ea/(RT)\n", " return np.abs((g(alpha) (betaR)/(AEa)) - senumyang(x))\n", "\n", "# we are using the same alpha values from the isothermal simulation\n", "\n", "rates = [1, 2, 4, 8, 16] # Heating Rate in Kelvin Degrees\n", "guess = 300 # Initial guess for local optimization. Your mileage may vary.\n", "ofunc = functools.partial(psi, g=iA2)\n", "start = time.time()\n", "temperatures = []\n", "\n", "for b in rates:\n", " curve = [leastsq(ofunc, guess, args=(a, b, A, E))[0][0] for a in alphas]\n", " temperatures.append(curve)\n", "\n", "print \"Done all curves in\", time.time()-start, \"seconds\"\n", "\n", "plots = [plt.plot(T, alpha)[0] for T in temperatures]\n", "\n", "plt.title('Simulated Non-isothermal Reaction')\n", "plt.xlabel('Temperature (K)')\n", "plt.ylabel('Transformed Fraction')\n", "plt.legend(plots, map(str, rates), loc='upper left')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Done all curves in 6.1081430912 seconds\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 33, "text": [ "<matplotlib.legend.Legend at 0x5476390>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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zoS1FOdu3Mz9rCMzIwAcmJmhpyH5znKLoIgjvCWE3qX5UXq2KWhuJk5OT0bRp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\ncHvLjR6bekhjkPxp5MyhCcnPV/owHDsG338PffqopyW9IJ2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"text": [ "<matplotlib.figure.Figure at 0x5476d10>" ] } ], "prompt_number": 33 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 33 } ], "metadata": {} } ] }	mit
anhaidgroup/py_entitymatching	notebooks/guides/step_wise_em_guides/Performing Matching Using a ML Matcher.ipynb	2	15531	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Introduction\n", "\n", "This IPython notebook illustrates how to performing matching with a ML matcher. In particular we show examples with a decision tree matcher, but the same principles apply to all of the other ML matchers." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# Import py_entitymatching package\n", "import py_entitymatching as em\n", "import os\n", "import pandas as pd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Read in the orignal tables and a set of labeled data into py_entitymatching." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# Get the datasets directory\n", "datasets_dir = em.get_install_path() + os.sep + 'datasets'\n", "\n", "path_A = datasets_dir + os.sep + 'dblp_demo.csv'\n", "path_B = datasets_dir + os.sep + 'acm_demo.csv'\n", "path_labeled_data = datasets_dir + os.sep + 'labeled_data_demo.csv'" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Metadata file is not present in the given path; proceeding to read the csv file.\n", "Metadata file is not present in the given path; proceeding to read the csv file.\n" ] } ], "source": [ "A = em.read_csv_metadata(path_A, key='id')\n", "B = em.read_csv_metadata(path_B, key='id')\n", "# Load the pre-labeled data\n", "S = em.read_csv_metadata(path_labeled_data, \n", " key='_id',\n", " ltable=A, rtable=B, \n", " fk_ltable='ltable_id', fk_rtable='rtable_id')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Training the ML Matcher\n", "\n", "Now, we can train our ML matcher. In this notebook we will demonstrate this process with a decision tree matcher. First, we need to split our labeled data into a training set and a test set. Then we will exract feature vectors from the training set and train our decision tree with the fit command." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# Split S into I an J\n", "IJ = em.split_train_test(S, train_proportion=0.5, random_state=0)\n", "I = IJ['train']\n", "J = IJ['test']" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# Generate a set of features\n", "F = em.get_features_for_matching(A, B, validate_inferred_attr_types=False)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# Convert I into feature vectors using updated F\n", "H = em.extract_feature_vecs(I, \n", " feature_table=F, \n", " attrs_after='label',\n", " show_progress=False)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "# Instantiate the matcher to evaluate.\n", "dt = em.DTMatcher(name='DecisionTree', random_state=0)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "# Train using feature vectors from I \n", "dt.fit(table=H, \n", " exclude_attrs=['_id', 'ltable_id', 'rtable_id', 'label'], \n", " target_attr='label')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Getting Predictions with the ML Matcher\n", "\n", "Since we now have a trained decision tree, we can use our matcher to get predictions on the test set. Below, we will show four different ways to get the predictions with the predict command that will be useful in various contexts. \n", "\n", "### Getting a List of Predictions\n", "\n", "First up, we will demonstrate how to get just a list of predictions using the predict command. This is the default method of getting predictions. As shown below, the resulting variable, predictions, is just an array containing the predictions for each of the feature vectors in the test set. " ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0, 0, 0, 1, 1, 1, 0, 1, 0, 0])" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Convert J into a set of feature vectors using F\n", "L1 = em.extract_feature_vecs(J, feature_table=F,\n", " attrs_after='label', show_progress=False)\n", "\n", "# Predict on L \n", "predictions = dt.predict(table=L1, exclude_attrs=['_id', 'ltable_id', 'rtable_id', 'label'])\n", "\n", "# Show the predictions\n", "predictions[0:10]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Getting a List of Predictions and a List of Probabilities\n", "\n", "Next we will demonstrate how to get both a list of prediction for the test set, as well as a list of the associated probabilities for the predictions. This is done by setting the 'return_probs' argument to true. Note that the probabilities shown are the probability for a match. " ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Predictions for first ten entries: [0 0 0 1 1 1 0 1 0 0]\n", "Probabilities of a match for first ten entries: [0. 0. 0. 1. 1. 1. 0. 1. 0. 0.]\n" ] } ], "source": [ "# Convert J into a set of feature vectors using F\n", "L2 = em.extract_feature_vecs(J, feature_table=F,\n", " attrs_after='label', show_progress=False)\n", "\n", "# Predict on L \n", "predictions, probs = dt.predict(table=L2, exclude_attrs=['_id', 'ltable_id', 'rtable_id', 'label'], return_probs=True)\n", "\n", "# Show the predictions and probabilities\n", "print('Predictions for first ten entries: {0}'.format(predictions[0:10]))\n", "print('Probabilities of a match for first ten entries: {0}'.format(probs[0:10]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Appending the Predictions to the Feature Vectors Table\n", "\n", "Often, we want to include the predictions with the feature vector table. We can return predictions appended to a copy of the feature vector table if we use the 'append' argument to true. We can choose the name of the new predictions column using the 'target_attr' argument. We can also append the probabilites by setting 'return_probs' to true and setting the new probabilities column name with the 'probs_attr'." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>_id</th>\n", " <th>ltable_id</th>\n", " <th>rtable_id</th>\n", " <th>label</th>\n", " <th>prediction</th>\n", " <th>probability</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>124</th>\n", " <td>124</td>\n", " <td>l1647</td>\n", " <td>r366</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>54</th>\n", " <td>54</td>\n", " <td>l332</td>\n", " <td>r1463</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>268</th>\n", " <td>268</td>\n", " <td>l1499</td>\n", " <td>r1725</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>293</th>\n", " <td>293</td>\n", " <td>l759</td>\n", " <td>r1749</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>230</th>\n", " <td>230</td>\n", " <td>l1580</td>\n", " <td>r1711</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " _id ltable_id rtable_id label prediction probability\n", "124 124 l1647 r366 0 0 0.0\n", "54 54 l332 r1463 0 0 0.0\n", "268 268 l1499 r1725 0 0 0.0\n", "293 293 l759 r1749 1 1 1.0\n", "230 230 l1580 r1711 1 1 1.0" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Convert J into a set of feature vectors using F\n", "L3 = em.extract_feature_vecs(J, feature_table=F,\n", " attrs_after='label', show_progress=False)\n", "\n", "# Predict on L \n", "predictions = dt.predict(table=L3, exclude_attrs=['_id', 'ltable_id', 'rtable_id', 'label'], \n", " target_attr='prediction', append=True,\n", " return_probs=True, probs_attr='probability')\n", "\n", "# Show the predictions and probabilities\n", "predictions[['_id', 'ltable_id', 'rtable_id', 'label', 'prediction', 'probability']].head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Appending the Prediction to the Original Feature Vectors Table In-place\n", "\n", "Lastly, we will show how to append the predictions to the original feature vector dataframe. We can accomplish this by setting the 'append' argument to true, setting the name of the new column with the 'target_attr' argument and then setting the 'inplace' argument to true. Again, we can include the probabilites with the 'return_probs' and 'probs_attr' arguments. This will append the predictions and probabilities to the original feature vector dataframe as opposed to the mthod used above which will create a copy of the feature vectors and append the predictions to that copy." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>_id</th>\n", " <th>ltable_id</th>\n", " <th>rtable_id</th>\n", " <th>label</th>\n", " <th>prediction</th>\n", " <th>probabilities</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>124</th>\n", " <td>124</td>\n", " <td>l1647</td>\n", " <td>r366</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>54</th>\n", " <td>54</td>\n", " <td>l332</td>\n", " <td>r1463</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>268</th>\n", " <td>268</td>\n", " <td>l1499</td>\n", " <td>r1725</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>293</th>\n", " <td>293</td>\n", " <td>l759</td>\n", " <td>r1749</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>230</th>\n", " <td>230</td>\n", " <td>l1580</td>\n", " <td>r1711</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " _id ltable_id rtable_id label prediction probabilities\n", "124 124 l1647 r366 0 0 0.0\n", "54 54 l332 r1463 0 0 0.0\n", "268 268 l1499 r1725 0 0 0.0\n", "293 293 l759 r1749 1 1 1.0\n", "230 230 l1580 r1711 1 1 1.0" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Convert J into a set of feature vectors using F\n", "L4 = em.extract_feature_vecs(J, feature_table=F,\n", " attrs_after='label', show_progress=False)\n", "\n", "# Predict on L \n", "dt.predict(table=L4, exclude_attrs=['_id', 'ltable_id', 'rtable_id', 'label'], \n", " target_attr='prediction', append=True,\n", " return_probs=True, probs_attr='probabilities',\n", " inplace=True)\n", "\n", "# Show the predictions and probabilities\n", "L4[['_id', 'ltable_id', 'rtable_id', 'label', 'prediction', 'probabilities']].head()" ] } ], "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": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 1 }	bsd-3-clause
smcl/ProjectEulerJupyter	Problem 040 - Champernowne's constant.ipynb	1	2109	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "An irrational decimal fraction is created by concatenating the positive integers:\n", "\n", " 0.123456789101112131415161718192021...\n", "\n", "It can be seen that the 12th digit of the fractional part is 1.\n", "\n", "If dn represents the nth digit of the fractional part, find the value of the following expression.\n", "\n", " d1 × d10 × d100 × d1000 × d10000 × d100000 × d1000000\n" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "210" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "open System.Text\n", "\n", "let limit = 1000000\n", "\n", "let appendChampernowne (sb:StringBuilder) n =\n", " sb.Append (string n) \|> ignore\n", " n\n", "\n", "let rec buildChamp' (sb:StringBuilder) n =\n", " if (sb.Length) > limit then (sb.ToString())\n", " else buildChamp' sb (appendChampernowne sb (1 + n))\n", " \n", "let buildChamp n = \n", " buildChamp' (new StringBuilder()) n\n", " \n", "let champernowne = (buildChamp 0).ToCharArray()\n", "\n", "[1; 10; 100; 1000; 10000; 100000; 1000000;]\n", "\|> List.map (fun i -> champernowne.[i-1])\n", "\|> List.map (string >> int)\n", "\|> List.fold (fun acc element -> acc * element) 1" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "F#", "language": "fsharp", "name": "ifsharp" }, "language": "fsharp", "language_info": { "codemirror_mode": "", "file_extension": ".fs", "mimetype": "text/x-fsharp", "name": "fsharp", "nbconvert_exporter": "", "pygments_lexer": "", "version": "4.3.1.0" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
giacomov/3ML	docs/notebooks/extended_models.ipynb	1	2002	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Extended models\n", "\n", "Coming soon! But checkout the existing [documentation](http://astromodels.readthedocs.io/en/latest/Extended_sources_tutorial.html) in astromodels." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "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": "2.7.14" }, "toc": { "colors": { "hover_highlight": "#DAA520", "navigate_num": "#000000", "navigate_text": "#333333", "running_highlight": "#FF0000", "selected_highlight": "#FFD700", "sidebar_border": "#EEEEEE", "wrapper_background": "#FFFFFF" }, "moveMenuLeft": true, "nav_menu": { "height": "21px", "width": "254px" }, "navigate_menu": true, "number_sections": true, "sideBar": true, "threshold": 4, "toc_cell": false, "toc_section_display": "block", "toc_window_display": false, "widenNotebook": false }, "varInspector": { "cols": { "lenName": 16, "lenType": 16, "lenVar": 40 }, "kernels_config": { "python": { "delete_cmd_postfix": "", "delete_cmd_prefix": "del ", "library": "var_list.py", "varRefreshCmd": "print(var_dic_list())" }, "r": { "delete_cmd_postfix": ") ", "delete_cmd_prefix": "rm(", "library": "var_list.r", "varRefreshCmd": "cat(var_dic_list()) " } }, "types_to_exclude": [ "module", "function", "builtin_function_or_method", "instance", "_Feature" ], "window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }	bsd-3-clause
myuuuuun/RepeatedMatrixGame	PrisonersDilemma/experiment3/Experiment-2015-11-30.ipynb	2	1910143	null	mit
y2ee201/Deep-Learning-Nanodegree	intro-to-rnns/Anna_KaRNNa_Exercises.ipynb	1	6511969	null	mit
moagstar/puzzles	Graph/Shortest Paths.ipynb	1	3101	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Shortest Paths\n", "Find all shortest paths in an unweighted undirected graph starting at node a and finishing at node b." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[[u'Frankfurt', u'W\\xfcrzburg', u'M\\xfcnchen'],\n", " [u'Frankfurt', u'Kassel', u'M\\xfcnchen']]" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import sys\n", " \n", " \n", "def find_shortest_paths(graph, start, end, path=None):\n", " \n", " path = path or []\n", " path = path + [start]\n", " \n", " if start == end:\n", " return [path]\n", " \n", " if not graph.has_key(start):\n", " return None\n", " \n", " shortest = []\n", " \n", " for node in graph[start]:\n", " if node not in path:\n", " newpath = find_shortest_paths(graph, node, end, path)\n", " if newpath:\n", " if not shortest or len(newpath[0]) < len(shortest[0]):\n", " shortest = newpath\n", " elif len(newpath[0]) == len(shortest[0]):\n", " shortest += newpath\n", " \n", " return shortest\n", " \n", " \n", "example_graph = {\n", " u'Frankfurt': [u'Mannheim', u'Würzburg', u'Kassel'],\n", " u'Mannheim': [u'Frankfurt', u'Karlsruhe'],\n", " u'Karlsruhe': [u'Augsburg', u'München'],\n", " u'Augsburg': [u'München', u'Karlsruhe'],\n", " u'München': [u'Würzburg', u'Augsburg', u'Nürnberg', u'Kassel'],\n", " u'Nürnberg': [u'München', u'Stuttgart', u'Würzburg'],\n", " u'Stuttgart': [u'Nürnberg'],\n", " u'Kassel': [u'München', u'Frankfurt'],\n", " u'Würzburg': [u'München', u'Nürnberg', u'Frankfurt', u'Erfurt'],\n", " u'Erfurt': [u'Würzburg'],\n", "}\n", "\n", "\n", "find_shortest_paths(example_graph, u'Frankfurt', u'München')" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "100000 loops, best of 3: 9.82 µs per loop\n" ] } ], "source": [ "%%timeit\n", "find_shortest_paths(example_graph, u'Frankfurt', u'München')" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
ngoldbaum/RunNotebook	example/source/custom-display-logic.ipynb	1	12104	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Defining Custom Display Logic for Your Own Objects" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Overview" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In Python, objects can declare their textual representation using the `__repr__` method. IPython expands on this idea and allows objects to declare other, richer representations including:\n", "\n", "* HTML\n", "* JSON\n", "* PNG\n", "* JPEG\n", "* SVG\n", "* LaTeX\n", "\n", "This Notebook shows how you can add custom display logic to your own classes, so that they can be displayed using these rich representations. There are two ways of accomplishing this:\n", "\n", "1. Implementing special display methods such as `_repr_html_`.\n", "2. Registering a display function for a particular type.\n", "\n", "In this Notebook we show how both approaches work." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Parts of this notebook need the inline matplotlib backend:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "import numpy as np\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Implementing special display methods" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The main idea of the first approach is that you have to implement special display methods, one for each representation you want to use. The names of the special methods are self explanatory:\n", "\n", "* `_repr_html_`\n", "* `_repr_json_`\n", "* `_repr_jpeg_`\n", "* `_repr_png_`\n", "* `_repr_svg_`\n", "* `_repr_latex_`\n", "\n", "As an illustration, we build a class that holds data generated by sampling a Gaussian distribution with given mean and variance. Each frontend can then decide which representation it will display be default. Further, we show how to display a particular representation." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The next cell defines the Gaussian class:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from IPython.core.pylabtools import print_figure\n", "from IPython.display import Image, SVG, Math\n", "\n", "class Gaussian(object):\n", " \"\"\"A simple object holding data sampled from a Gaussian distribution.\n", " \"\"\"\n", " def __init__(self, mean=0, std=1, size=1000):\n", " self.data = np.random.normal(mean, std, size)\n", " self.mean = mean\n", " self.std = std\n", " self.size = size\n", " # For caching plots that may be expensive to compute\n", " self._png_data = None\n", " self._svg_data = None\n", " \n", " def _figure_data(self, format):\n", " fig, ax = plt.subplots()\n", " ax.plot(self.data, 'o')\n", " ax.set_title(self._repr_latex_())\n", " data = print_figure(fig, format)\n", " # We MUST close the figure, otherwise IPython's display machinery\n", " # will pick it up and send it as output, resulting in a double display\n", " plt.close(fig)\n", " return data\n", " \n", " # Here we define the special repr methods that provide the IPython display protocol\n", " # Note that for the two figures, we cache the figure data once computed.\n", " \n", " def _repr_png_(self):\n", " if self._png_data is None:\n", " self._png_data = self._figure_data('png')\n", " return self._png_data\n", "\n", "\n", " def _repr_svg_(self):\n", " if self._svg_data is None:\n", " self._svg_data = self._figure_data('svg').encode('utf-8')\n", " return self._svg_data\n", " \n", " def _repr_latex_(self):\n", " return r'$\\mathcal{N}(\\mu=%.2g, \\sigma=%.2g),\\ N=%d$' % (self.mean,\n", " self.std, self.size)\n", " \n", " # We expose as properties some of the above reprs, so that the user can see them\n", " # directly (since otherwise the client dictates which one it shows by default)\n", " @property\n", " def png(self):\n", " return Image(self._repr_png_(), embed=True)\n", " \n", " @property\n", " def svg(self):\n", " return SVG(self._repr_svg_())\n", " \n", " @property\n", " def latex(self):\n", " return Math(self._repr_latex_())\n", " \n", " # An example of using a property to display rich information, in this case\n", " # the histogram of the distribution. We've hardcoded the format to be png\n", " # in this case, but in production code it would be trivial to make it an option\n", " @property\n", " def hist(self):\n", " fig, ax = plt.subplots()\n", " ax.hist(self.data, bins=100)\n", " ax.set_title(self._repr_latex_())\n", " data = print_figure(fig, 'png')\n", " plt.close(fig)\n", " return Image(data, embed=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, we create an instance of the Gaussian distribution, whose default representation will be its LaTeX form:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "x = Gaussian()\n", "x" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can view the data in png or svg formats:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "x.png" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since IPython only displays by default as an ``Out[]`` cell the result of the last computation, we can use the\n", "``display()`` function to show more than one representation in a single cell:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from IPython.display import display" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "display(x.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's create a new Gaussian with different parameters" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "x2 = Gaussian(0.5, 0.2, 2000)\n", "x2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can easily compare them by displaying their histograms" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "display(x.hist)\n", "display(x2.hist)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Adding IPython display support to existing objects" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When you are directly writing your own classes, you can adapt them for display in IPython by following the above example. But in practice, we often need to work with existing code we can't modify. We now illustrate how to add these kinds of extended display capabilities to existing objects. We will use the NumPy polynomials and change their default representation to be a formatted LaTeX expression." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, consider how a numpy polynomial object renders by default:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "p = np.polynomial.Polynomial([1,2,3], [-10, 10])\n", "p" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we define a function that pretty-prints a polynomial as a LaTeX string:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def poly2latex(p):\n", " terms = ['%.2g' % p.coef[0]]\n", " if len(p) > 1:\n", " term = 'x'\n", " c = p.coef[1]\n", " if c!=1:\n", " term = ('%.2g ' % c) + term\n", " terms.append(term)\n", " if len(p) > 2:\n", " for i in range(2, len(p)):\n", " term = 'x^%d' % i\n", " c = p.coef[i]\n", " if c!=1:\n", " term = ('%.2g ' % c) + term\n", " terms.append(term)\n", " px = '$P(x)=%s$' % '+'.join(terms)\n", " dom = r', domain: $[%.2g,\\ %.2g]$' % tuple(p.domain)\n", " return px+dom" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This produces, on our polynomial ``p``, the following:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "poly2latex(p)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from IPython.display import Latex\n", "Latex(poly2latex(p))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "But we can configure IPython to do this automatically for us as follows. We hook into the\n", "IPython display system and instruct it to use ``poly2latex`` for the latex mimetype, when\n", "encountering objects of the ``Polynomial`` type defined in the\n", "``numpy.polynomial.polynomial`` module:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ip = get_ipython()\n", "latex_formatter = ip.display_formatter.formatters['text/latex']\n", "latex_formatter.for_type_by_name('numpy.polynomial.polynomial',\n", " 'Polynomial', poly2latex)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For more examples on how to use the above system, and how to bundle similar print functions\n", "into a convenient IPython extension, see sympy's [`printing` extension](https://github.com/sympy/sympy/blob/master/sympy/interactive/printing.py).\n", "\n", "Once our special printer has been loaded, all polynomials will be represented by their \n", "mathematical form instead:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "p" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "p2 = np.polynomial.Polynomial([-20, 71, -15, 1])\n", "p2" ] } ], "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 }	bsd-2-clause
YihaoLu/statsmodels	examples/notebooks/statespace_sarimax_internet.ipynb	9	8414	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# SARIMAX: Model selection, missing data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The example mirrors Durbin and Koopman (2012), Chapter 8.4 in application of Box-Jenkins methodology to fit ARMA models. The novel feature is the ability of the model to work on datasets with missing values." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "from scipy.stats import norm\n", "import statsmodels.api as sm\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import requests\n", "from io import BytesIO\n", "from zipfile import ZipFile\n", "\n", "# Download the dataset\n", "dk = requests.get('http://www.ssfpack.com/files/DK-data.zip').content\n", "f = BytesIO(dk)\n", "zipped = ZipFile(f)\n", "df = pd.read_table(\n", " BytesIO(zipped.read('internet.dat')),\n", " skiprows=1, header=None, sep='\\s+', engine='python',\n", " names=['internet','dinternet']\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Model Selection\n", "\n", "As in Durbin and Koopman, we force a number of the values to be missing." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Get the basic series\n", "dta_full = df.dinternet[1:].values\n", "dta_miss = dta_full.copy()\n", "\n", "# Remove datapoints\n", "missing = np.r_[6,16,26,36,46,56,66,72,73,74,75,76,86,96]-1\n", "dta_miss[missing] = np.nan" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then we can consider model selection using the Akaike information criteria (AIC), but running the model for each variant and selecting the model with the lowest AIC value.\n", "\n", "There are a couple of things to note here:\n", "\n", "- When running such a large batch of models, particularly when the autoregressive and moving average orders become large, there is the possibility of poor maximum likelihood convergence. Below we ignore the warnings since this example is illustrative.\n", "- We use the option `enforce_invertibility=False`, which allows the moving average polynomial to be non-invertible, so that more of the models are estimable.\n", "- Several of the models do not produce good results, and their AIC value is set to NaN. This is not surprising, as Durbin and Koopman note numerical problems with the high order models." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import warnings\n", "\n", "aic_full = pd.DataFrame(np.zeros((6,6), dtype=float))\n", "aic_miss = pd.DataFrame(np.zeros((6,6), dtype=float))\n", "\n", "warnings.simplefilter('ignore')\n", "\n", "# Iterate over all ARMA(p,q) models with p,q in [0,6]\n", "for p in range(6):\n", " for q in range(6):\n", " if p == 0 and q == 0:\n", " continue\n", " \n", " # Estimate the model with no missing datapoints\n", " mod = sm.tsa.statespace.SARIMAX(dta_full, order=(p,0,q), enforce_invertibility=False)\n", " try:\n", " res = mod.fit()\n", " aic_full.iloc[p,q] = res.aic\n", " except:\n", " aic_full.iloc[p,q] = np.nan\n", " \n", " # Estimate the model with missing datapoints\n", " mod = sm.tsa.statespace.SARIMAX(dta_miss, order=(p,0,q), enforce_invertibility=False)\n", " try:\n", " res = mod.fit()\n", " aic_miss.iloc[p,q] = res.aic\n", " except:\n", " aic_miss.iloc[p,q] = np.nan" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For the models estimated over the full (non-missing) dataset, the AIC chooses ARMA(1,1) or ARMA(3,0). Durbin and Koopman suggest the ARMA(1,1) specification is better due to parsimony.\n", "\n", "$$\n", "\\text{Replication of:}\\\\\n", "\\textbf{Table 8.1} ~~ \\text{AIC for different ARMA models.}\\\\\n", "\\newcommand{\\r}[1]{{\\color{red}{#1}}}\n", "\\begin{array}{lrrrrrr}\n", "\\hline\n", "q & 0 & 1 & 2 & 3 & 4 & 5 \\\\\n", "\\hline\n", "p & {} & {} & {} & {} & {} & {} \\\\\n", "0 & 0.00 & 549.81 & 519.87 & 520.27 & 519.38 & 518.86 \\\\\n", "1 & 529.24 & \\r{514.30} & 516.25 & 514.58 & 515.10 & 516.28 \\\\\n", "2 & 522.18 & 516.29 & 517.16 & 515.77 & 513.24 & 514.73 \\\\\n", "3 & \\r{511.99} & 513.94 & 515.92 & 512.06 & 513.72 & 514.50 \\\\\n", "4 & 513.93 & 512.89 & nan & nan & 514.81 & 516.08 \\\\\n", "5 & 515.86 & 517.64 & nan & nan & nan & nan \\\\\n", "\\hline\n", "\\end{array}\n", "$$\n", "\n", "For the models estimated over missing dataset, the AIC chooses ARMA(1,1)\n", "\n", "$$\n", "\\text{Replication of:}\\\\\n", "\\textbf{Table 8.2} ~~ \\text{AIC for different ARMA models with missing observations.}\\\\\n", "\\begin{array}{lrrrrrr}\n", "\\hline\n", "q & 0 & 1 & 2 & 3 & 4 & 5 \\\\\n", "\\hline\n", "p & {} & {} & {} & {} & {} & {} \\\\\n", "0 & 0.00 & 488.93 & 464.01 & 463.86 & 462.63 & 463.62 \\\\\n", "1 & 468.01 & \\r{457.54} & 459.35 & 458.66 & 459.15 & 461.01 \\\\\n", "2 & 469.68 & nan & 460.48 & 459.43 & 459.23 & 460.47 \\\\\n", "3 & 467.10 & 458.44 & 459.64 & 456.66 & 459.54 & 460.05 \\\\\n", "4 & 469.00 & 459.52 & nan & 463.04 & 459.35 & 460.96 \\\\\n", "5 & 471.32 & 461.26 & nan & nan & 461.00 & 462.97 \\\\\n", "\\hline\n", "\\end{array}\n", "$$\n", "\n", "Note: the AIC values are calculated differently than in Durbin and Koopman, but show overall similar trends." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Postestimation\n", "\n", "Using the ARMA(1,1) specification selected above, we perform in-sample prediction and out-of-sample forecasting." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Statespace\n", "mod = sm.tsa.statespace.SARIMAX(dta_miss, order=(1,0,1))\n", "res = mod.fit()\n", "print(res.summary())" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# In-sample one-step-ahead predictions, and out-of-sample forecasts\n", "nforecast = 20\n", "predict = res.get_prediction(end=mod.nobs + nforecast)\n", "idx = np.arange(len(predict.predicted_mean))\n", "predict_ci = predict.conf_int(alpha=0.5)\n", "\n", "# Graph\n", "fig, ax = plt.subplots(figsize=(12,6))\n", "ax.xaxis.grid()\n", "ax.plot(dta_miss, 'k.')\n", "\n", "# Plot\n", "ax.plot(idx[:-nforecast], predict.predicted_mean[:-nforecast], 'gray')\n", "ax.plot(idx[-nforecast:], predict.predicted_mean[-nforecast:], 'k--', linestyle='--', linewidth=2)\n", "ax.fill_between(idx, predict_ci[:, 0], predict_ci[:, 1], alpha=0.15)\n", "\n", "ax.set(title='Figure 8.9 - Internet series');" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }	bsd-3-clause
jasag/Phytoliths-recognition-system	code/notebooks/Prototypes/Face_Detection/UI.ipynb	1	5334	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from __future__ import print_function\n", "from ipywidgets import interact, interactive, fixed, interact_manual\n", "import ipywidgets as widgets\n", "\n", "%matplotlib inline\n", "#Importamos nuestros módulos y clases necesarias\n", "import Image_Classifier as img_clf\n", "import Labeled_Image as li\n", "import classifiers as clfs\n", "\n", "from skimage import io\n", "from skimage.color import rgb2gray\n", "from skimage.transform import rescale\n", "\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from IPython.display import display\n", "import fileupload\n", "import os\n", "import PIL.Image\n", "import io as io2\n", "import numpy as np\n", "\n", "# Inicializamos la clase que se encarga de clasificar imagenes \n", "clf = img_clf.Image_Classifier(clfs.classifiers.get('svm'))\n", "lbl_img = li.Labeled_Image(clf)\n", "\n", "''' Función que se encarga de aplicar las operaciones \n", "necesarias para convertir los datos obtenidos del FileUpload\n", "en una imagen'''\n", "def imageConverter(change):\n", " ch = change['owner']\n", " image = io2.BytesIO(ch.data)\n", " image = PIL.Image.open(image)\n", " image = np.array(image)\n", " return rgb2gray(image)\n", "\n", "'''Función mediante la que indicamos el clasificador\n", "con el que clasificaremos la imagen'''\n", "def set_classifier_wrapper(classifier_index):\n", " clf.set_classifier(clfs.classifiers[classifier_index][0],\n", " is_probs_classifier = clfs.classifiers[classifier_index][1])\n", " \n", "'''Función que nos permite mostrar la imagen'''\n", "def plotter_wrapper():\n", " lbl_img.boxes_generator_with_nms()\n", " lbl_img.plotter()\n", "\n", "''' Función mediante la que escogemos la imagen'''\n", "def _upload(lbl_img):\n", "\n", " _upload_widget = fileupload.FileUploadWidget()\n", "\n", " def _cb(change):\n", " image = imageConverter(change)\n", " lbl_img.set_image(image)\n", " #lbl_img.predict()\n", " \n", " _upload_widget.observe(_cb, names='data')\n", " display(_upload_widget)\n", " \n", "'''Función que nos permite mostrar la imagen'''\n", "def rescale_image_selector(lbl_img, rescale_coef):\n", " if lbl_img.get_original_image() is not None:\n", " lbl_img.image_rescale(rescale_coef)\n", "\n", "def patch_size_selector(Ni, Nj):\n", " clf.set_patch_size((Ni,Nj))\n", "\n", "clf_button = widgets.Button(description=\"Clasificar\")\n", "\n", "def on_button_clicked(b):\n", " # Etiquetamos imagen\n", " lbl_img.predict()\n", " # Y la mostramos\n", " plotter_wrapper()\n", " \n", "#clf_button.on_click(on_button_clicked)#, clf)\n", "\n", "def step_size_selector(istep, jstep):\n", " clf.set_istep(istep)\n", " clf.set_jstep(jstep)\n", "\n", "def probabilities_selector(probs):\n", " lbl_img.set_probs(probs)\n", " lbl_img.predict()\n", " plotter_wrapper()\n", "\n", "def alfa_selector(alfa):\n", " lbl_img.set_alfa(alfa)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Mostramos el widget que permita elegir el clasificador\n", "interact(set_classifier_wrapper, classifier_index = list(clfs.classifiers.keys()));\n", "\n", "# Mostramos el widget que permita elegir la imagen a clasificar\n", "_upload(lbl_img)\n", "\n", "# Permitimos escoger el rescalado de la imagen, por defecto 1\n", "interact(rescale_image_selector, rescale_coef=(0.3,1,0.001), lbl_img=fixed(lbl_img))\n", "\n", "# Permitimos escoger el tamaño de alto y ancho para\n", "# las subdivisiones de la ventana\n", "#interact(patch_size_selector, Ni=(0,100), Nj=(0,100))\n", "\n", "# Permitimos escoger el tamaño del salto\n", "# en las subdivisiones de la imagen\n", "interact(step_size_selector, istep=(0,100), jstep=(0,100))\n", "\n", "interact(alfa_selector, alfa=(0,1,0.001))\n", "\n", "# Por ultimo, mostramos la imagen y permitimos que muestre las ventanas \n", "# en función de las probabilidades\n", "interact_manual(probabilities_selector, probs=(0.5,1,0.001))\n", "\n", "# LLamar al clasificador\n", "#display(clf_button)" ] } ], "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 }	bsd-3-clause
aemerick/galaxy_analysis	notebooks/Untitled3.ipynb	1	70697	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import yt\n", "\n", "from galaxy_analysis.gizmo import yield_model\n", "from galaxy_analysis.utilities import cy_convert_abundances as ca\n", "#from galaxy_analysis.utilities import convert_abundances as ca\n", "from galaxy_analysis.plot.plot_styles import \n", "\n", "import gizmo_analysis as gizmo\n", "import utilities as gizmo_ut\n", "\n", "\n", "\n", "from scipy.stats import ks_2samp" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "# in utilities.simulation.Snapshot():\n", " reading: home/aemerick/work/gizmo_runs/m12q_res5700_test/snapshot_times.txt\n", "\n", " using snapshot index = 60, redshift = 5.429\n", "\n" ] }, { "ename": "OSError", "evalue": "cannot find snapshot index = 60 in: ['/home/aemerick/work/gizmo_runs/m12q_res5700_test/output/snapshot_000.hdf5', '/home/aemerick/work/gizmo_runs/m12q_res5700_test/output/snapshot_039.hdf5', '/home/aemerick/work/gizmo_runs/m12q_res5700_test/output/snapshot_190.hdf5', '/home/aemerick/work/gizmo_runs/m12q_res5700_test/output/snapshot_600.hdf5']", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mOSError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-7-c48322d54b83>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m part = gizmo.io.Read.read_snapshots(['star', 'dark'], 'index', 60,\n\u001b[0;32m----> 4\u001b[0;31m assign_host_principal_axes=False, simulation_directory = wdir)\n\u001b[0m\u001b[1;32m 5\u001b[0m part190 = gizmo.io.Read.read_snapshots(['star', 'dark'], 'index', 190,\n\u001b[1;32m 6\u001b[0m assign_host_principal_axes=False, simulation_directory = wdir)\n", "\u001b[0;32m~/code/gizmo_analysis/gizmo_io.py\u001b[0m in \u001b[0;36mread_snapshots\u001b[0;34m(self, species, snapshot_value_kind, snapshot_values, simulation_directory, snapshot_directory, simulation_name, properties, element_indices, particle_subsample_factor, separate_dark_lowres, sort_dark_by_id, convert_float32, host_number, assign_host_coordinates, assign_host_principal_axes, assign_host_orbits, assign_formation_coordinates, assign_pointers, check_properties)\u001b[0m\n\u001b[1;32m 639\u001b[0m \u001b[0;31m# read header from snapshot file\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 640\u001b[0m header = self.read_header(\n\u001b[0;32m--> 641\u001b[0;31m 'index', snapshot_index, simulation_directory, snapshot_directory, simulation_name)\n\u001b[0m\u001b[1;32m 642\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 643\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mheader\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'cosmological'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/code/gizmo_analysis/gizmo_io.py\u001b[0m in \u001b[0;36mread_header\u001b[0;34m(self, snapshot_value_kind, snapshot_value, simulation_directory, snapshot_directory, simulation_name, snapshot_block_index, verbose)\u001b[0m\n\u001b[1;32m 901\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 902\u001b[0m path_file_name = self.get_snapshot_file_names_indices(\n\u001b[0;32m--> 903\u001b[0;31m snapshot_directory, snapshot_index, snapshot_block_index)\n\u001b[0m\u001b[1;32m 904\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 905\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_first_print\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~/code/gizmo_analysis/gizmo_io.py\u001b[0m in \u001b[0;36mget_snapshot_file_names_indices\u001b[0;34m(self, directory, snapshot_index, snapshot_block_index)\u001b[0m\n\u001b[1;32m 1417\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0msnapshot_index\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mfile_indices\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1418\u001b[0m raise OSError(\n\u001b[0;32m-> 1419\u001b[0;31m 'cannot find snapshot index = {} in: {}'.format(snapshot_index, path_file_names))\n\u001b[0m\u001b[1;32m 1420\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1421\u001b[0m \u001b[0mpath_file_names\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpath_file_names\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwhere\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile_indices\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0msnapshot_index\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mOSError\u001b[0m: cannot find snapshot index = 60 in: ['/home/aemerick/work/gizmo_runs/m12q_res5700_test/output/snapshot_000.hdf5', '/home/aemerick/work/gizmo_runs/m12q_res5700_test/output/snapshot_039.hdf5', '/home/aemerick/work/gizmo_runs/m12q_res5700_test/output/snapshot_190.hdf5', '/home/aemerick/work/gizmo_runs/m12q_res5700_test/output/snapshot_600.hdf5']" ] } ], "source": [ "wdir = \"/home/aemerick/work/gizmo_runs/m12q_res5700_test/\"\n", "\n", "part = gizmo.io.Read.read_snapshots(['star', 'dark'], 'index', 60,\n", " assign_host_principal_axes=False, simulation_directory = wdir)\n", "part190 = gizmo.io.Read.read_snapshots(['star', 'dark'], 'index', 190,\n", " assign_host_principal_axes=False, simulation_directory = wdir)\n", "\n", "print(part.host_positions)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(-5, 4)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x432 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ex1,ex2 = 'Fe','H'\n", "ey1,ey2 = 'Fe','H'\n", "\n", "\n", "x1 = part['star'].prop('massfraction.' + str.lower(ex1)).astype(np.double)\n", "x2 = part['star'].prop('massfraction.' + str.lower(ex2)).astype(np.double)\n", "y1 = part190['star'].prop('massfraction.' + str.lower(ey1)).astype(np.double)\n", "y2 = part190['star'].prop('massfraction.' + str.lower(ey2)).astype(np.double)\n", "\n", "x = ca.abundance_ratio_array(ex1, x1, ex2, x2, input_type=\"mass\")\n", "y = ca.abundance_ratio_array(ey1, y1, ey2, y2, input_type=\"mass\")\n", "\n", "abund = x1.0\n", "\n", "dbin = 0.1\n", "rmin = -5\n", "rmax = 4\n", "\n", "fig, ax = plt.subplots()\n", "fig.set_size_inches(6,6)\n", "\n", "nbins = int((rmax - rmin)/dbin)\n", "hist, bins = np.histogram(abund, bins = nbins, range = (rmin,rmax))\n", "hist2 = np.ones(np.size(hist)+1)\n", "hist2[:-1] = hist\n", "hist2[-1] = hist2[-2]\n", "\n", "\n", "ax.step(bins, hist2/(1.0np.sum(hist2)), where = 'post', lw = 3, color = 'black', label='39')\n", "\n", "abund = y1.0\n", "\n", "nbins = int((rmax - rmin)/dbin)\n", "hist, bins = np.histogram(abund, bins = nbins, range = (rmin,rmax))\n", "hist2 = np.ones(np.size(hist)+1)\n", "hist2[:-1] = hist\n", "hist2[-1] = hist2[-2]\n", "\n", "ax.step(bins, hist2/(1.0np.sum(hist2)), where = 'post', color = 'C0', lw = 3, label='190')\n", "\n", "#ax.scatter(x,y)\n", "\n", "ax.legend(loc='best')\n", "ax.set_xlabel('[' + ex1 + '/' + ex2 + ']')\n", "ax.set_ylabel('[' + ey1 + '/' + ey2 + ']')\n", "\n", "ax.set_xlim(rmin,rmax)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "# in utilities.simulation.Snapshot():\n", "* reading: home/aemerick/work/gizmo_runs/m12q_res5700_noage/snapshot_times.txt\n", "\n", " using snapshot index = 60, redshift = 5.429\n", "\n", "\n", "# in gizmo_analysis.gizmo_io.Read():\n", "* reading header from: home/aemerick/work/gizmo_runs/m12q_res5700_noage/output/snapshot_060.hdf5\n", " snapshot contains the following number of particles:\n", " dark (id = 1): 16220880 particles\n", " dark2 (id = 2): 4132251 particles\n", " gas (id = 0): 15526126 particles\n", " star (id = 4): 694752 particles\n", " blackhole (id = 5): 0 particles\n", "\n", "* reading species: ['star', 'dark']\n", "* reading particles from:\n", " home/aemerick/work/gizmo_runs/m12q_res5700_noage/output/snapshot_060.hdf5\n", "\n", "! cannot find MUSIC config file: home/aemerick/work/gizmo_runs/m12q_res5700_noage//.conf\n", "! missing cosmological parameters, assuming the following (from AGORA box):\n", " assuming omega_baryon = 0.0455\n", " assuming sigma_8 = 0.807\n", " assuming n_s = 0.961\n", "\n", "* checking sanity of particle properties\n", "\n", "* assigning coordinates for 1 host galaxy/halo[s]:\n", " position = (42344.834, 43284.772, 42141.475) [kpc comoving]\n", " velocity = (-94.0, 177.1, 113.8) [km / s]\n", "\n", "\n", "# in utilities.simulation.Snapshot():\n", "* reading: home/aemerick/work/gizmo_runs/m12q_res5700_noage/snapshot_times.txt\n", "\n", " using snapshot index = 60, redshift = 5.429\n", "\n", "\n", "# in gizmo_analysis.gizmo_io.Read():\n", "* reading header from: home/aemerick/work/gizmo_runs/m12q_res5700_noage/output/snapshot_060.hdf5\n", " snapshot contains the following number of particles:\n", " dark (id = 1): 16220880 particles\n", " dark2 (id = 2): 4132251 particles\n", " gas (id = 0): 15526126 particles\n", " star (id = 4): 694752 particles\n", " blackhole (id = 5): 0 particles\n", "\n", "* reading species: ['star', 'dark']\n", "* reading particles from:\n", " home/aemerick/work/gizmo_runs/m12q_res5700_noage/output/snapshot_060.hdf5\n", "\n", "! cannot find MUSIC config file: home/aemerick/work/gizmo_runs/m12q_res5700_noage//.conf\n", "! missing cosmological parameters, assuming the following (from AGORA box):\n", " assuming omega_baryon = 0.0455\n", " assuming sigma_8 = 0.807\n", " assuming n_s = 0.961\n", "\n", "* checking sanity of particle properties\n", "\n", "* assigning coordinates for 1 host galaxy/halo[s]:\n", " position = (42344.834, 43284.772, 42141.475) [kpc comoving]\n", " velocity = (-94.0, 177.1, 113.8) [km / s]\n", "\n", "[[42344.83396568 43284.7720971 42141.47479642]]\n" ] } ], "source": [ "wdir = \"/home/aemerick/work/gizmo_runs/m12q_res5700_noage/\"\n", "part = gizmo.io.Read.read_snapshots(['star', 'dark'], 'index', 60,\n", " assign_host_principal_axes=False, simulation_directory = wdir)\n", "wdir = \"/home/aemerick/work/gizmo_runs/m12q_res5700_original/\"\n", "part190 = gizmo.io.Read.read_snapshots(['star', 'dark'], 'index', 60,\n", " assign_host_principal_axes=False, simulation_directory = wdir)\n", "\n", "print(part.host_positions)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.7300000365594315e-07 0.009390188381075859\n" ] } ], "source": [ "x1 = part['star'].prop('massfraction.' + str.lower(ex1)).astype(np.double)\n", "\n", "print(np.min(x1),np.max(x1))" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(-5, 4)" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x432 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ex1,ex2 = 'Fe','H'\n", "ey1,ey2 = 'Fe','H'\n", "\n", "\n", "x1 = part['star'].prop('massfraction.' + str.lower(ex1)).astype(np.double)\n", "x2 = part['star'].prop('massfraction.' + str.lower(ex2)).astype(np.double)\n", "y1 = part190['star'].prop('massfraction.' + str.lower(ey1)).astype(np.double)\n", "y2 = part190['star'].prop('massfraction.' + str.lower(ey2)).astype(np.double)\n", "\n", "x = ca.abundance_ratio_array(ex1, x1, ex2, x2, input_type=\"mass\")\n", "y = ca.abundance_ratio_array(ey1, y1, ey2, y2, input_type=\"mass\")\n", "\n", "abund = x1.0\n", "\n", "dbin = 0.1\n", "rmin = -5\n", "rmax = 4\n", "\n", "fig, ax = plt.subplots()\n", "fig.set_size_inches(6,6)\n", "\n", "nbins = int((rmax - rmin)/dbin)\n", "hist, bins = np.histogram(abund, bins = nbins, range = (rmin,rmax))\n", "hist2 = np.ones(np.size(hist)+1)\n", "hist2[:-1] = hist\n", "hist2[-1] = hist2[-2]\n", "\n", "\n", "ax.step(bins, hist2, where = 'post', lw = 3, color = 'black', label = 'tip')\n", "\n", "abund = y1.0\n", "\n", "nbins = int((rmax - rmin)/dbin)\n", "hist, bins = np.histogram(abund, bins = nbins, range = (rmin,rmax))\n", "hist2 = np.ones(np.size(hist)+1)\n", "hist2[:-1] = hist\n", "hist2[-1] = hist2[-2]\n", "\n", "ax.step(bins, hist2, where = 'post', color = 'C0', lw = 3, label = 'correct')\n", "\n", "#ax.scatter(x,y)\n", "\n", "\n", "ax.set_xlabel('[' + ex1 + '/' + ex2 + ']')\n", "ax.set_ylabel('[' + ey1 + '/' + ey2 + ']')\n", "\n", "ax.set_xlim(rmin,rmax)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "# in utilities.simulation.Snapshot():\n", "* reading: home/aemerick/work/gizmo_runs/m12q_res5700_original/snapshot_times.txt\n", "\n", " using snapshot index = 190, redshift = 1.763\n", "\n", "\n", "# in gizmo_analysis.gizmo_io.Read():\n", "* reading header from: home/aemerick/work/gizmo_runs/m12q_res5700_original/output/snapshot_190.hdf5\n", " snapshot contains the following number of particles:\n", " dark (id = 1): 16220880 particles\n", " dark2 (id = 2): 4132251 particles\n", " gas (id = 0): 15501044 particles\n", " star (id = 4): 722209 particles\n", " blackhole (id = 5): 0 particles\n", "\n", "* reading species: ['star', 'dark']\n", "* reading particles from:\n", " home/aemerick/work/gizmo_runs/m12q_res5700_original/output/snapshot_190.hdf5\n", "\n", "! cannot find MUSIC config file: home/aemerick/work/gizmo_runs/m12q_res5700_original//.conf\n", "! missing cosmological parameters, assuming the following (from AGORA box):\n", " assuming omega_baryon = 0.0455\n", " assuming sigma_8 = 0.807\n", " assuming n_s = 0.961\n", "\n", "* checking sanity of particle properties\n", "\n", "* assigning coordinates for 1 host galaxy/halo[s]:\n", " position = (41578.662, 44938.830, 42945.030) [kpc comoving]\n", " velocity = (-85.5, 170.6, 82.2) [km / s]\n", "\n", "\n", "# in utilities.simulation.Snapshot():\n", "* reading: home/aemerick/work/gizmo_runs/m12q_res5700_logage/snapshot_times.txt\n", "\n", " using snapshot index = 151, redshift = 2.337\n", "\n", "\n", "# in gizmo_analysis.gizmo_io.Read():\n", "* reading header from: home/aemerick/work/gizmo_runs/m12q_res5700_logage/output/snapshot_151.hdf5\n", " snapshot contains the following number of particles:\n", " dark (id = 1): 16220880 particles\n", " dark2 (id = 2): 4132251 particles\n", " gas (id = 0): 15706531 particles\n", " star (id = 4): 517258 particles\n", " blackhole (id = 5): 0 particles\n", "\n", "* reading species: ['star', 'dark']\n", "* reading particles from:\n", " home/aemerick/work/gizmo_runs/m12q_res5700_logage/output/snapshot_151.hdf5\n", "\n", "! cannot find MUSIC config file: home/aemerick/work/gizmo_runs/m12q_res5700_logage//.conf\n", "! missing cosmological parameters, assuming the following (from AGORA box):\n", " assuming omega_baryon = 0.0455\n", " assuming sigma_8 = 0.807\n", " assuming n_s = 0.961\n", "\n", "* checking sanity of particle properties\n", "! warning: star massfraction [min, max] = [0.000, 4.849e11]\n", "\n", "* assigning coordinates for 1 host galaxy/halo[s]:\n", " position = (41833.738, 44495.401, 42666.239) [kpc comoving]\n", " velocity = (-70.2, 170.7, 103.1) [km / s]\n", "\n", "[[41578.66198097 44938.82969455 42945.03010933]]\n" ] }, { "data": { "text/plain": [ "(-5, 4)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x432 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "wdir = \"/home/aemerick/work/gizmo_runs/m12q_res5700_original/\"\n", "part = gizmo.io.Read.read_snapshots(['star', 'dark'], 'index', 190,\n", " assign_host_principal_axes=False, simulation_directory = wdir)\n", "wdir = \"/home/aemerick/work/gizmo_runs/m12q_res5700_logage/\"\n", "part190 = gizmo.io.Read.read_snapshots(['star', 'dark'], 'index', 151,\n", " assign_host_principal_axes=False, simulation_directory = wdir)\n", "\n", "print(part.host_positions)\n", "ex1,ex2 = 'Fe','H'\n", "ey1,ey2 = 'Fe','H'\n", "\n", "\n", "x1 = part['star'].prop('massfraction.' + str.lower(ex1)).astype(np.double)\n", "x2 = part['star'].prop('massfraction.' + str.lower(ex2)).astype(np.double)\n", "y1 = part190['star'].prop('massfraction.' + str.lower(ey1)).astype(np.double)\n", "y2 = part190['star'].prop('massfraction.' + str.lower(ey2)).astype(np.double)\n", "\n", "x = ca.abundance_ratio_array(ex1, x1, ex2, x2, input_type=\"mass\")\n", "y = ca.abundance_ratio_array(ey1, y1, ey2, y2, input_type=\"mass\")\n", "\n", "abund = x1.0\n", "\n", "dbin = 0.1\n", "rmin = -5\n", "rmax = 4\n", "\n", "fig, ax = plt.subplots()\n", "fig.set_size_inches(6,6)\n", "\n", "nbins = int((rmax - rmin)/dbin)\n", "hist, bins = np.histogram(abund, bins = nbins, range = (rmin,rmax))\n", "hist2 = np.ones(np.size(hist)+1)\n", "hist2[:-1] = hist\n", "hist2[-1] = hist2[-2]\n", "\n", "\n", "ax.step(bins, hist2, where = 'post', lw = 3, color = 'black', label = 'FIRE - 190')\n", "\n", "abund = y1.0\n", "\n", "nbins = int((rmax - rmin)/dbin)\n", "hist, bins = np.histogram(abund, bins = nbins, range = (rmin,rmax))\n", "hist2 = np.ones(np.size(hist)+1)\n", "hist2[:-1] = hist\n", "hist2[-1] = hist2[-2]\n", "\n", "ax.step(bins, hist2, where = 'post', color = 'C0', lw = 3, label = 'Logage16 - 151')\n", "\n", "#ax.scatter(x,y)\n", "\n", "ax.legend(loc='best')\n", "\n", "ax.set_xlabel('[' + ex1 + '/' + ex2 + ']')\n", "ax.set_ylabel('[' + ey1 + '/' + ey2 + ']')\n", "\n", "ax.set_xlim(rmin,rmax)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "# in utilities.simulation.Snapshot():\n", "* reading: home/aemerick/work/gizmo_runs/m12q_res5700_noage/snapshot_times.txt\n", "\n", " using snapshot index = 39, redshift = 7.229\n", "\n", "\n", "# in gizmo_analysis.gizmo_io.Read():\n", "* reading header from: home/aemerick/work/gizmo_runs/m12q_res5700_noage/output/snapshot_039.hdf5\n", " snapshot contains the following number of particles:\n", " dark (id = 1): 16220880 particles\n", " dark2 (id = 2): 4132251 particles\n", " gas (id = 0): 15887336 particles\n", " star (id = 4): 333543 particles\n", " blackhole (id = 5): 0 particles\n", "\n", "* reading species: ['star', 'dark']\n", "* reading particles from:\n", " home/aemerick/work/gizmo_runs/m12q_res5700_noage/output/snapshot_039.hdf5\n", "\n", "! cannot find MUSIC config file: home/aemerick/work/gizmo_runs/m12q_res5700_noage//.conf\n", "! missing cosmological parameters, assuming the following (from AGORA box):\n", " assuming omega_baryon = 0.0455\n", " assuming sigma_8 = 0.807\n", " assuming n_s = 0.961\n", "\n", "* checking sanity of particle properties\n", "\n", "* assigning coordinates for 1 host galaxy/halo[s]:\n", " position = (42489.739, 43110.835, 42119.164) [kpc comoving]\n", " velocity = (-64.4, 101.9, 43.3) [km / s]\n", "\n", "\n", "# in utilities.simulation.Snapshot():\n", "* reading: home/aemerick/work/gizmo_runs/m12q_res5700_noage/snapshot_times.txt\n", "\n", " using snapshot index = 39, redshift = 7.229\n", "\n", "\n", "# in gizmo_analysis.gizmo_io.Read():\n", "* reading header from: home/aemerick/work/gizmo_runs/m12q_res5700_noage/output/snapshot_039.hdf5\n", " snapshot contains the following number of particles:\n", " dark (id = 1): 16220880 particles\n", " dark2 (id = 2): 4132251 particles\n", " gas (id = 0): 15887336 particles\n", " star (id = 4): 333543 particles\n", " blackhole (id = 5): 0 particles\n", "\n", "* reading species: ['star', 'dark']\n", "* reading particles from:\n", " home/aemerick/work/gizmo_runs/m12q_res5700_noage/output/snapshot_039.hdf5\n", "\n", "! cannot find MUSIC config file: home/aemerick/work/gizmo_runs/m12q_res5700_noage//.conf\n", "! missing cosmological parameters, assuming the following (from AGORA box):\n", " assuming omega_baryon = 0.0455\n", " assuming sigma_8 = 0.807\n", " assuming n_s = 0.961\n", "\n", "* checking sanity of particle properties\n", "\n", "* assigning coordinates for 1 host galaxy/halo[s]:\n", " position = (42489.739, 43110.835, 42119.164) [kpc comoving]\n", " velocity = (-64.4, 101.9, 43.3) [km / s]\n", "\n", "[[42489.73867473 43110.83494349 42119.16401346]]\n" ] }, { "data": { "text/plain": [ "(-5, 4)" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x432 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "wdir = \"/home/aemerick/work/gizmo_runs/m12q_res5700_noage/\"\n", "part = gizmo.io.Read.read_snapshots(['star', 'dark'], 'index', 39,\n", " assign_host_principal_axes=False, simulation_directory = wdir)\n", "part190 = gizmo.io.Read.read_snapshots(['star', 'dark'], 'index', 39,\n", " assign_host_principal_axes=False, simulation_directory = wdir)\n", "\n", "print(part.host_positions)\n", "ex1,ex2 = 'Fe','H'\n", "ey1,ey2 = 'Fe','H'\n", "\n", "\n", "x1 = part['star'].prop('massfraction.' + str.lower(ex1)).astype(np.double)\n", "x2 = part['star'].prop('massfraction.' + str.lower(ex2)).astype(np.double)\n", "y1 = part190['star'].prop('massfraction.' + str.lower(ey1)).astype(np.double)\n", "y2 = part190['star'].prop('massfraction.' + str.lower(ey2)).astype(np.double)\n", "\n", "x = ca.abundance_ratio_array(ex1, x1, ex2, x2, input_type=\"mass\")\n", "y = ca.abundance_ratio_array(ey1, y1, ey2, y2, input_type=\"mass\")\n", "\n", "abund = x1.0\n", "\n", "dbin = 0.1\n", "rmin = -5\n", "rmax = 4\n", "\n", "fig, ax = plt.subplots()\n", "fig.set_size_inches(6,6)\n", "\n", "nbins = int((rmax - rmin)/dbin)\n", "hist, bins = np.histogram(abund, bins = nbins, range = (rmin,rmax))\n", "hist2 = np.ones(np.size(hist)+1)\n", "hist2[:-1] = hist\n", "hist2[-1] = hist2[-2]\n", "\n", "\n", "ax.step(bins, hist2, where = 'post', lw = 3, color = 'black')\n", "\n", "abund = y1.0\n", "\n", "nbins = int((rmax - rmin)/dbin)\n", "hist, bins = np.histogram(abund, bins = nbins, range = (rmin,rmax))\n", "hist2 = np.ones(np.size(hist)+1)\n", "hist2[:-1] = hist\n", "hist2[-1] = hist2[-2]\n", "\n", "ax.step(bins, hist2, where = 'post', color = 'C0', lw = 3)\n", "\n", "#ax.scatter(x,y)\n", "\n", "\n", "ax.set_xlabel('[' + ex1 + '/' + ex2 + ']')\n", "ax.set_ylabel('[' + ey1 + '/' + ey2 + ']')\n", "\n", "ax.set_xlim(rmin,rmax)" ] }, { "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": 2 }	mit
yuhao0531/dmc	notebooks/week-5/05-CNN-CAM-viz-theano.ipynb	3	51596	{ "cells": [ { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "('Training set', (14000, 64, 64), (14000, 1))\n", "('Test set', (6000, 64, 64), (6000, 1))\n" ] } ], "source": [ "import pickle\n", "\n", "pickle_file = '-catsdogs.pickle'\n", "\n", "with open(pickle_file, 'rb') as f:\n", " save = pickle.load(f)\n", " X_train = save['X_train']\n", " y_train = save['y_train']\n", " X_test = save['X_test']\n", " y_test = save['y_test']\n", " del save # hint to help gc free up memory\n", " print('Training set', X_train.shape, y_train.shape)\n", " print('Test set', X_test.shape, y_test.shape)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "np.random.seed(1337) # for reproducibility\n", "\n", "from keras.datasets import mnist\n", "from keras.models import Sequential\n", "from keras.layers import Dense, Dropout, Activation, Flatten\n", "from keras.layers import Convolution2D, MaxPooling2D, AveragePooling2D\n", "from keras.utils import np_utils\n", "from keras.callbacks import ModelCheckpoint\n", "from keras import backend as K\n", "\n", "from keras.datasets import mnist" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(1, 64, 64)\n" ] } ], "source": [ "# number of classes\n", "num_classes = 2\n", "\n", "# image dimensions\n", "img_rows, img_cols = X_train.shape[1], X_train.shape[2]\n", "\n", "if K.image_dim_ordering() == 'th':\n", " X_train = X_train.reshape(X_train.shape[0], 1, img_rows, img_cols)\n", " X_test = X_test.reshape(X_test.shape[0], 1, img_rows, img_cols)\n", " input_shape = (1, img_rows, img_cols)\n", "else:\n", " X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, 1)\n", " X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, 1)\n", " input_shape = (img_rows, img_cols, 1)\n", "\n", "Y_train = np_utils.to_categorical(y_train, num_classes)\n", "Y_test = np_utils.to_categorical(y_test, num_classes)\n", "\n", "print input_shape" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# model hyperparameters\n", "batch_size = 16\n", "nb_epoch = 30\n", "\n", "# network architecture\n", "patch_size_1 = 3\n", "patch_size_2 = 3\n", "patch_size_3 = 3\n", "patch_size_4 = 3\n", "\n", "depth_1 = 128\n", "depth_2 = 256\n", "depth_3 = 512\n", "depth_4 = 1024\n", "\n", "pool_size = 2\n", "\n", "# num_hidden_1 = 512\n", "# num_hidden_2 = 1028\n", "\n", "# dropout = 0.5" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [], "source": [ "model = Sequential()\n", "\n", "model.add(Convolution2D(depth_1, patch_size_1, patch_size_1, border_mode='valid',\n", " input_shape=input_shape))\n", "model.add(Activation('relu'))\n", "model.add(MaxPooling2D(pool_size=(pool_size, pool_size)))\n", "\n", "model.add(Convolution2D(depth_2, patch_size_2, patch_size_2, border_mode='valid'))\n", "model.add(Activation('relu'))\n", "model.add(MaxPooling2D(pool_size=(pool_size, pool_size)))\n", "\n", "model.add(Convolution2D(depth_3, patch_size_3, patch_size_3, border_mode='valid'))\n", "model.add(Activation('relu'))\n", "# model.add(MaxPooling2D(pool_size=(pool_size, pool_size)))\n", "\n", "model.add(Convolution2D(depth_4, patch_size_4, patch_size_4, border_mode='valid'))\n", "model.add(Activation('relu'))\n", "\n", "model.add(AveragePooling2D(pool_size=(10, 10)))\n", "model.add(Flatten())\n", "model.add(Dense(num_classes))\n", "\n", "model.add(Activation('softmax'))" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [], "source": [ "checkpoint_name = \"-model-CAM-theano.hdf5\"\n", "model.load_weights(checkpoint_name)\n", "\n", "model.compile(loss='categorical_crossentropy', optimizer='adadelta', metrics=['accuracy'])" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "category: 0\n", "prediction: 0\n" ] }, { "data": { "image/png": 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MBQs0Fqg96BloxbiYU6UcK52LVsGrrpxIK8bF/qN2pHx7dZW9iL+ERUQ64iQsItIRJ2ER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\nSEechEVEOuIkLCLSESdhEZGO/A+oROgMPZ1mMwAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f41083fcbd0>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "(1, 1, 64, 64)\n" ] } ], "source": [ "%matplotlib inline\n", "\n", "from matplotlib.pyplot import imshow\n", "import matplotlib.pyplot as plt\n", "\n", "img_num = 12\n", "\n", "X_t = X_train[img_num:img_num+1]\n", "\n", "img = X_t[0][0]\n", "\n", "category = np.argmax(Y_train[img_num])\n", "print \"category:\", category\n", "print \"prediction:\", np.argmax(model.predict(X_t)[0])\n", "\n", "mi, ma = np.min(img), np.max(img)\n", "imshow(img, cmap = plt.get_cmap('gray'), vmin = mi, vmax = ma, interpolation='nearest')\n", "plt.axis('off')\n", "plt.show()\n", "\n", "print X_t.shape" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def get_activations(model, layer, X_batch):\n", " get_activations = K.function([model.layers[0].input, K.learning_phase()], [model.layers[layer].output])\n", " activations = get_activations([X_batch,0])\n", " return activations" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(1024, 10, 10)\n" ] } ], "source": [ "my_featuremaps = get_activations(model, 8, X_t)\n", "\n", "maps = my_featuremaps[0][0]\n", "print maps.shape" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVgAAAFdCAYAAABGoXXzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f4108332a90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "lay = maps[5]\n", "\n", "mi, ma = np.min(lay), np.max(lay)\n", "imshow(lay, cmap = plt.get_cmap('gray'), vmin = mi, vmax = ma, interpolation='nearest')\n", "plt.axis('off')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(None, 1024)\n", "(None, 2)\n" ] } ], "source": [ "layer = model.layers[12]\n", "\n", "print layer.input_shape\n", "print layer.output_shape" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(1024, 2)\n" ] }, { "data": { "image/png": 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ahIUQwiMPQpirUzyAZ4JSNkVB4rqyS/BcPrnAexGBaXqCB/w5EWM2RGBLiTBn\narsPpyRNY5qgi6cmqQoHg9cxV9fuDsdo8dgWH17fvIZrigt0m7347AXE3vzWU4jV90RQISkHM0cA\nCogbzC3FZIwxFwVelwUY2xywX3mMwkvvlMPJSTsyIkQVxAm3Ji7IBdHvzIhC4oSk6Aydsk19jeJd\nQto2J061iHyT7+/QUclEw9RJqZkG+MyAuN5CkrJzQYyo1Za4IA2uwSS0nxH2KLiVFa61XU1chRk6\n4bYdEUxHkpZ2bu8he+LirEvi3Ps5rknzCEMuehMWQgiPaBMWQgiPaBMWQgiPaBMWQgiPPAhhLj7F\nZpxenEKMuZ2aiR1bB5hmktVUi3pSa4sIf9/7vW9BbPbRLyF2e+fUVJsRIYqk1wtIzbYwYfWxSLq+\nHsfj4Aial5dYS6+J8HM//ehnEPvw734HYusaxSnXpWeMMV9+8qn199P338J2EAdkM+IYLSYocl4+\nRhFuu8f7dU5KyixHQagfUEgbiACUGHSvjR1J8RiytJX42dgR2Ioc10xAav9t77G+HktbGRIBmQl9\n9cHuf0vezSYJUdxi4j5M8brQ4HUhcdu5XW2IcFu1eP+W1Lo7HHBemoTUVpygslqv7fm7u0eRNiV1\nJlen+KOAY9CbsBBCeESbsBBCeESbsBBCeESbsBBCeORBCHOHJRFoGjyUN0QbuPzAFp7aPX5umpC6\naOQwn2ggJh3w4P477/1tiB32trjx3//Hn+MzMyIgdNj3+QqFqDpC8esQ4meLR05fMQufmZCUj9/9\nAxThvnj9OcTaCAWP57fPIZYUtnBxu76HaxriiDqQ+mlVhekXX73EZ55eoD0pCu0lPhKBbBiJ44rU\n+WtJqsmECFaHFvvVNfjZ6dSeq5KkbmR1FQMiLm7ubiFWTDBN6qZG0bBzBLyEiE4FEdzKDmNdjM/8\naoOOM0MEscrZjsoYvwdZgbFujWPbjbjwmxBjY4R7Q1c5dRpDHI/7G3TSzt47g9gx6E1YCCE8ok1Y\nCCE8ok1YCCE8ok1YCCE88iCEueoUhRcmSIwjKmfxU/u6bo8OtJY4nfIUxanmgKJemqKA0JCaVqmx\nD+//4Hf+AK75rz/4rxAzqMWYuiCpPWsibmBXzTSzhQbmMmyJy6vLcIwmM6LqEe5rTBWaBbZok5Qo\nnrCacDHqRub8BNMSbg4oSsbEkdi4tczIujIkVWZJ5rg3TMzF95g+IGuGqL7V3h6TfsD7p6T+3aRA\n8asjxd1e24AuAAAgAElEQVRqVjORiJzVYK+tZMB7bSvs55CgSDaGS4htDdbh25Pv6ZDY6yGY4LyH\nBTppW1IHsmxRTGOi4f4O+3riCHgDETOjGuequsOxPQa9CQshhEe0CQshhEe0CQshhEe0CQshhEce\nhDC3vUIBgYlwTKwb3rMP6psNplociTOrJ5rTLMCD+6BHAWHc40F9P9rXdQGKa3/vX/w9iFXEJVU3\nGPvkl7+A2IHUJLtu7Jp7YYr/Z7Mpdj4KcQ7+6ktMb7kh6QXncxy3xBGF8hiFkpDM8dCiQLi7Qbfd\n2WoFsed3JMVj5rrSSD28Gd5rIKonizUNqRvYYexQk3Xk1HI7NEQcDVG8bF/jvC8WJPUrecc6EPGv\ncRxhQ4fzMsvQWVYRN2lsUKwLV29DbE/qIy7P7JqGj9/+Bj6TWEBX30AB7z/92V9C7HBAp+GwxS3Q\ndbYO5Pu+e43tf/4JujiN+ZDEbPQmLIQQHtEmLIQQHtEmLIQQHnkQZ8KvozcgttlgmaI8J2fHz+xz\ntNXiEq4JAjzH3Md4PlRM8P4pKVVjFsSs4ZYkIhnZypCcC4541jYO+OEnb2CZorLEsjx9Z/f19etX\ncM2BGB3aBn9ofvrONyGWbPCH96y0ThzbfR1JdrSBZBbLIjw7TkY8x7z+Avs1WeH5ae/MX0OMKjvy\nY39muKhGbFtDzrWDGNsxJNiH2jl77RL8Oq5JprIow9JONy1+NozwHSue4GeHzJ4HlsHwbsT7DzGu\n3SzF8/VDhJ+9J5nm3n3TLiXWpdjWusc5yE8we14/4Nl0dY/rLSVn9futfd5bBLhXzCOSzW2Da/wY\n9CYshBAe0SYshBAe0SYshBAe0SYshBAeeRDC3C/3eMDfGyKy7PFg/fVXduy3T57ANUGIIsCGHPAb\ncrB+foZCQ92gsBU6Ylqe4v0jIuKMA/YpIoJK06IIl05R7NltbWNDfI7ixrxFES4JsW1NjdcFr76C\n2BtXFxArN7Zp5LBBI0VHRL4wwCV5uCNlnEiJn+UJztV9b/dhTUTJZI5jRHwTJgixbTERp6oe568N\ncD1se1s07Mg1QUyMJOS6cUSBKTYkkxgxl7iZAssAvy8BMar0xKyR9jgeW4P3u+9QDP3J56+tv8+I\niSuaoSBW3uHafXrxbYglty8g9vIZxhJHHG5GFCrjmmydX/OVVm/CQgjhEW3CQgjhEW3CQgjhEW3C\nQgjhkQchzH01oMuNiVOh60ozxmwcEWTRnsE10ylz9mDXsxiFrpsDigrTAh1+oWOR60gmtIyMNnOS\n5aRMT0fqIBWkvXunBE+WoPAXhCi49SPGFjMcyzjFLFZdj6JhlNnCWT7B+x+2mIkqDnGOc+ZAq3CM\nXnyGWaz6hT3oeY5r4UBK/gQJZoZzM+UZY0wQo3iU5KT8UI9zFTpOssHgvTYVEXNjMgfEZRkTcXvo\ncT2Ue3uN5wUKleRjJiKu077EfrbERXff3kFs2tvtnSeYHa0w5xDbvMLGLUmZpS83X0IsIu0tK1u8\nzYiTNhsxtnuFYuMx6E1YCCE8ok1YCCE8ok1YCCE8ok1YCCE88iCEOSZukEyQhmgDxjj6TGPQxTPL\nSUo84l471CiSZTmKA7cNCjRpbP8/G0gqxCZEEWAc8Zk05WWE4lFlSLmnif3cQ01caQbvlYbYjqoj\nbj6STrTd3UKsd9rWEcFtXKKjqyLtjWco/AUk3WIzohBVB3Zs3+GYRQWWBuoTdGZlBYo9FRG/Dh1+\nrW5b7H+d2ALb9QbF18XlWxBrydd2d4dlvVhpro4obHsnjenlEseDpZadFShADkS8rInQHE1wfHel\nfV2LU2y2xK2aReT7TYTsp+88hdj6FY5bV9nfhU2FfV8s8Jnx8PW2U70JCyGER7QJCyGER7QJCyGE\nR7QJCyGERx6EMPf40WOIzWZ46F8RZ1NZ2gf1eY5CSUTqllUtClE1SUEYEhfTSAS2qrVFhSjEZ7Yk\ntWAQolDSVKhIBGSqYiLMda3j3GtRKCkSFBVYKstoIK6/BO+XLdFZ15d2Ss0+RcGt2WF6yzHB68Ya\nhRETYdsOPY7RmDgOOZJ6MiCCW0lEoZ7UGnu9RUcliz2/Q3Hx3hmjIUMROGnQhXVyht8Xlo5zvUbB\ntCW13bKZ/dzrEuf4QGrdGVLXLiGOyiHEwewHvN92a183Dvh9DCJSE65G52UU4HdjusT5y5e4X1Sj\nM1ekzGSf4fd2s0WR7xj0JiyEEB7RJiyEEB7RJiyEEB7RJiyEEB55EMJcRlxYuz0KEkmMwlbmCHF3\na+IQS1AsKFs8bU8yPLgfEoztShQ3IucZMUkzWQ0oKjCGGNs2jhjblyhOpaHtmAtIusEDSZ8Z9hhL\nAhSxohYFiUmM7icT2uMRZiw9IqnP1ryGWMFSao44B1WNY9SH9trqQlwLuwPOy4bc60DE3Od3OAd3\nB/xsFWD/24k9N3WPc5XusO9jjW7M2zsU4ZanmEKS1SqsHLE1jnBbaEkqzjsSm88xdjig6Jtnv75t\ndUM8sglZu8RxG5DvWkJq88UF9rXf2/PXdygsLogQmvZEvDwCvQkLIYRHtAkLIYRHtAkLIYRHtAkL\nIYRHHoQw15P0ej1Jo8gEgzCw/4/s9+ieMcSFlZJUehNSW2tdoqjA6o91gd22hgxtR9Jn9izdIBEN\nQ+rAI/XHElsYudu9gGsCklowIbFJSuqiBcRZ16I4lcf2GKXE4RaEKM5sGhSnKkPSVu7vIfb8FmNB\naoslVYftZ4JpMxCXJRGiotUVxNIpvtu0Dd7v1TNHiLrBNV8YXM+rKxTr6mc4vpMJqUW3R2ErzW2h\nrx+wHUmKIuqIjzRlx9JnYt/LA/l+J7bI3hBxNI3J9zHE61qWPpPVL1ygY+72znZyJjkKoW2CwvD5\nW1j/7hj0JiyEEB7RJiyEEB7RJiyEEB55EGfCdYWHS0lCzkA7PIeZTu2zqniCZzxJjudZ2xrPpPKR\nnAMOeLZUkx+fZ6l9BtqXeH5YktRc8Uh+aE4yn7HMauzH55veHqOhv4Br3DMvY4xZnGEGrybD+/cB\nOduN8JxxOrPHLRywLE1IyhFFJ3hW35AsavsKP5s/fQ9irZOJKyHnv/e3mP3KNXkYg2eWxhhzsyYl\nsRI8Jw5vMAPguZOl7u5wB9cEJKPZ85vnEHv/nfchdv2La4hNT/C70JV2H/oAvxvpBM9FB3IWy7Qc\nQzKfhR3er6ztNdISbaQhWeAScv+RxFoy9/kJzmm6sud+7ImWk2I/qxDn+Bj0JiyEEB7RJiyEEB7R\nJiyEEB7RJiyEEB55EMLcdovCy5xkKRqJOWG/s3/MviUizvwMuzmkeP8XNyjQJMUptqNH8ahc20Lc\nQEweGSmPE7T4f7CvUeyJapKVbY8iWedkSBtIbZZ4h+PR7IjgRpJChUQwjSfYr821cz8imIYTnIN8\ngsJLOxKxboqGmc0eM4Tdbe1YTDJusTnerFFkSUjWt55ct16jweJiSgTSnZ35bN5gP+s1zsvY4tra\nBmhIur9Goe/0W9jXobfXyP6A7R+n+EyWgawjWc5OLlD07WK8zi0PtN9hJsXVEktRBSQ5YUcEdZad\nsDjDddm/tEXwOCH9jFEoL4j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"text/plain": [ "<matplotlib.figure.Figure at 0x7f412fee8890>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import scipy.misc\n", "\n", "wts = layer.get_weights()\n", "wts = wts[0]\n", "print wts.shape\n", "\n", "res = np.zeros((10,10))\n", "\n", "for i in range(wts.shape[0]):\n", " m = maps[i]\n", " w = wts[i][category]\n", " r = np.multiply(m, w)\n", " res = res + r\n", "\n", "mi, ma = np.min(img), np.max(img)\n", "imshow(img, vmin = mi, vmax = ma, cmap = plt.get_cmap('gray'), interpolation='nearest')\n", "\n", "plt.hold(True)\n", "\n", "heatmap = scipy.misc.imresize(res, (64,64), interp='bilinear')\n", "# heatmap = scipy.misc.imresize(res, (64,64), interp='nearest')\n", "\n", "mi, ma = np.min(heatmap), np.max(heatmap)\n", "imshow(heatmap, vmin = mi, vmax = ma, alpha=.5, interpolation='nearest')\n", "plt.axis('off')\n", "plt.show()" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 1 }	apache-2.0
lodemo/CATANA	src/data_evaluation/00_Channel Statistics.ipynb	1	923530	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#!/usr/bin/env python\n", "# coding=utf-8\n", "\n", "# Calculates channel statistics, subscriber counts, popularity classes, views, networks, categories\n", "# No collaboration related data\n", "\n", "import pandas as pa \n", "import numpy as np\n", "\n", "import json\n", "import os\n", "import networkx as nx\n", "import pygraphviz as gz\n", "from networkx.drawing.nx_pydot import write_dot\n", "import math\n", "\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "%matplotlib notebook\n", "\n", "import itertools\n", "\n", "import csv\n", "from sqlalchemy import exists, func\n", "\n", "from database import \n", "\n", "from matplotlib import pylab, pyplot\n", "from matplotlib import dates\n", "\n", "import seaborn as sns\n", "sns.set(color_codes=True)\n", "\n", "from scipy import stats, integrate\n", "\n", "from datetime import datetime, timedelta, date\n", "\n", "date_format = '%Y-%m-%dT%H:%M:%S.%fZ'\n", "date_format2 = '%Y-%m-%d %H:%M:%S'\n", "\n", "plt.style.use(['seaborn-paper'])\n", "sns.set_style(\"whitegrid\")\n", "#plt.rc('font', family='serif', serif='Charter')\n", "plt.rc('font', family='serif', serif='DejaVu Serif')\n", "\n", "SMALL_SIZE = 8\n", "MEDIUM_SIZE = 9\n", "BIGGER_SIZE = 13\n", "\n", "plt.rc('font', size=MEDIUM_SIZE) # controls default text sizes\n", "plt.rc('axes', titlesize=MEDIUM_SIZE) # fontsize of the axes title\n", "plt.rc('axes', labelsize=MEDIUM_SIZE) # fontsize of the x and y labels\n", "plt.rc('xtick', labelsize=MEDIUM_SIZE) # fontsize of the tick labels\n", "plt.rc('ytick', labelsize=MEDIUM_SIZE) # fontsize of the tick labels\n", "plt.rc('legend', fontsize=MEDIUM_SIZE) # legend fontsize\n", "plt.rc('figure', titlesize=MEDIUM_SIZE) # fontsize of the figure title\n", "\n", "x_width = 6.8898\n", "x_height = x_width / 1.618\n", "\n", "s_width = 3.4449\n", "s_height = s_width / 1.618\n", "\n", "def save_plot(name, fig, width, height):\n", " fig.tight_layout()\n", " fig.set_size_inches(width, height)\n", " #f.subplots_adjust(top=0.86)\n", "\n", " fig.savefig(CDIR+'/'+name, bbox_inches=\"tight\")\n", " #plt.savefig(CDIR+'/video_view_percentages.pdf', bbox_inches=\"tight\")" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/mlode/intel/intelpython27/lib/python2.7/site-packages/sqlalchemy/engine/default.py:470: Warning: Can't create database 'mlode3m'; database exists\n", " cursor.execute(statement, parameters)\n" ] } ], "source": [ "DIR = '../../data/data_evaluation_3MONTHS'\n", "CDIR = '../../data/data_evaluation_3MONTHS/charts'\n", "\n", "db = YTDatabase()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "with db._session_scope(False) as session:\n", " #channel_stats_first = session.query(ChannelHistory).filter( (func.day(crawlTimestamp) == 28) & (func.month(crawlTimestamp) == 12) ).all()\n", " #channel_stats_last = session.query(ChannelHistory).filter( (func.day(crawlTimestamp) == 28) & (func.month(crawlTimestamp) == 2) ).all()\n", "\n", " df_channel_stats_first = pa.read_sql(session.query(ChannelHistory).filter( (func.day(ChannelHistory.crawlTimestamp) == 28) & (func.month(ChannelHistory.crawlTimestamp) == 12) ).statement, db.engine)\n", " df_channel_stats_last = pa.read_sql(session.query(ChannelHistory).filter( (func.day(ChannelHistory.crawlTimestamp) == 28) & (func.month(ChannelHistory.crawlTimestamp) == 2) ).statement, db.engine)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "7942\n", "7915\n", "947207\n" ] }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>id</th>\n", " <th>channelID</th>\n", " <th>viewCount</th>\n", " <th>subscriberCount</th>\n", " <th>commentCount</th>\n", " <th>videoCount</th>\n", " <th>crawlTimestamp</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>UC--BMyA2X4a9PGAo3lTuopg</td>\n", " <td>123961982</td>\n", " <td>878515</td>\n", " <td>3825</td>\n", " <td>41</td>\n", " <td>2016-12-28 02:57:13</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>UC-27_Szq7BtHDoC0R2U0zxA</td>\n", " <td>1495970510</td>\n", " <td>7783038</td>\n", " <td>9610</td>\n", " <td>401</td>\n", " <td>2016-12-28 02:57:13</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>3</td>\n", " <td>UC--v6kgEGoApDbKVwFR44cA</td>\n", " <td>4531047</td>\n", " <td>46739</td>\n", " <td>3</td>\n", " <td>229</td>\n", " <td>2016-12-28 02:57:13</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>4</td>\n", " <td>UC-63s9JLCZqIDlhXK6VHb7w</td>\n", " <td>8834171</td>\n", " <td>27606</td>\n", " <td>0</td>\n", " <td>89</td>\n", " <td>2016-12-28 02:57:14</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5</td>\n", " <td>UC-4kjzuh4822B9yPSgpZQgA</td>\n", " <td>28671226</td>\n", " <td>1234411</td>\n", " <td>0</td>\n", " <td>49</td>\n", " <td>2016-12-28 02:57:14</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " id channelID viewCount subscriberCount commentCount \\\n", "0 1 UC--BMyA2X4a9PGAo3lTuopg 123961982 878515 3825 \n", "1 2 UC-27_Szq7BtHDoC0R2U0zxA 1495970510 7783038 9610 \n", "2 3 UC--v6kgEGoApDbKVwFR44cA 4531047 46739 3 \n", "3 4 UC-63s9JLCZqIDlhXK6VHb7w 8834171 27606 0 \n", "4 5 UC-4kjzuh4822B9yPSgpZQgA 28671226 1234411 0 \n", "\n", " videoCount crawlTimestamp \n", "0 41 2016-12-28 02:57:13 \n", "1 401 2016-12-28 02:57:13 \n", "2 229 2016-12-28 02:57:13 \n", "3 89 2016-12-28 02:57:14 \n", "4 49 2016-12-28 02:57:14 " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print df_channel_stats_first['channelID'].nunique()\n", "print df_channel_stats_last['channelID'].nunique()\n", "\n", "with db._session_scope(False) as session:\n", " lenc = session.query(ChannelHistory).count()\n", "print lenc\n", "\n", "df_channel_stats_first.head()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "339024.309746\n", "42514.0\n", "4\n" ] } ], "source": [ "print df_channel_stats_first[\"subscriberCount\"].mean()\n", "print df_channel_stats_first[\"subscriberCount\"].median()\n", "print df_channel_stats_first[\"subscriberCount\"][df_channel_stats_first.subscriberCount > 0.0].min()" ] }, { "cell_type": "code", "execution_count": 6, "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 = 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], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# channel subscriber plot\n", "\n", "fig = plt.figure()\n", "ax = sns.distplot(df_channel_stats_first['subscriberCount'], bins=100, kde=False)\n", "ax.set_xlabel('Subscriber')\n", "ax.set_ylabel('Channel')\n", "ax.set_xscale('log')\n", "ax.set_yscale('symlog')\n", "#ax.legend()\n", "\n", "plt.title('Channel Subscriber Counts')\n", "save_plot('channel_subscriber.pdf', fig, s_width, s_height)\n", "#ax.axvline(x=df_channel_stats_first[\"subscriberCount\"].median(), color='red')\n", "#ax.axvline(x=df_channel_stats_first[\"subscriberCount\"].mean(), color='orange')" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "scrolled": 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 = 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], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# plotting popularity classes on subscriber range\n", "\n", "fig = plt.figure()\n", "ax = sns.distplot(df_channel_stats_first['subscriberCount'], kde=False, bins=500)\n", "ax.set_xlabel('Subscriber')\n", "ax.set_ylabel('Channel')\n", "ax.set_xscale('log')\n", "ax.set_yscale('symlog')\n", "#ax.legend()\n", "plt.title('Channel Subscriber Counts')\n", "\n", "classes = [1.0e+3, 1.0e+4, 1.0e+5, 1.0e+6, 1.0e+7, 5.0e+7, 1.0e+8]\n", "for xc in classes:\n", " ax.axvline(x=xc, color='red', linewidth=.5)\n", " \n", "save_plot('channel_subscriber_with_classes.pdf', fig, s_width, s_height)\n" ] }, { "cell_type": "code", "execution_count": 8, "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 = 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\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure()\n", "ax = df_channel_stats_first['subscriberCount'].hist(cumulative=True, bins=1000)\n", "ax.set_xlabel('Subscriber')\n", "ax.set_ylabel('Channel')\n", "ax.set_xscale('log')\n", "ax.set_yscale('log')\n", "#ax.legend()\n", "\n", "plt.title('Channel Subscriber')\n", "\n", "classes = [1.0e+3, 1.0e+4, 1.0e+5, 1.0e+6, 1.0e+7, 5.0e+7, 1.0e+8]\n", "for xc in classes:\n", " ax.axvline(x=xc, color='red', linewidth=.5)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "[(0, 1000.0),\n", " (1000.0, 10000.0),\n", " (10000.0, 100000.0),\n", " (100000.0, 1000000.0),\n", " (1000000.0, 10000000.0),\n", " (10000000.0, 50000000.0),\n", " (50000000.0, 100000000.0)]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bins = [0, 1.0e+3, 1.0e+4, 1.0e+5, 1.0e+6, 1.0e+7, 5.0e+7, 1.0e+8]\n", "def pairwise(iterable):\n", " \"s -> (s0,s1), (s1,s2), (s2, s3), ...\"\n", " a, b = itertools.tee(iterable)\n", " next(b, None)\n", " return zip(a, b)\n", "\n", "popularity_classes = []\n", "\n", "for i, (a, b) in enumerate(pairwise(bins)):\n", " #print a, b\n", " popularity_classes.append((a, b))\n", " \n", " \n", "popularity_classes" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[813, 1575, 2569, 2420, 544, 20, 1]\n", "(0-1000.0) 813\n", "(1000.0-10000.0) 1575\n", "(10000.0-100000.0) 2569\n", "(100000.0-1000000.0) 2420\n", "(1000000.0-10000000.0) 544\n", "(10000000.0-50000000.0) 20\n", "(50000000.0-100000000.0) 1\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" }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>0</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>813</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1575</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2569</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>2420</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>544</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>20</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>1</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " 0\n", "0 813\n", "1 1575\n", "2 2569\n", "3 2420\n", "4 544\n", "5 20\n", "6 1" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "class_counts = [d.count() for d in [df_channel_stats_first[\"subscriberCount\"][(df_channel_stats_first.subscriberCount >= ca) & (df_channel_stats_first.subscriberCount < cb)] for ca, cb in pairwise(bins)]]\n", "\n", "print class_counts\n", "\n", "for ca, cb in pairwise(bins):\n", " print '({}-{}) {}'.format(ca, cb, df_channel_stats_first[\"subscriberCount\"][(df_channel_stats_first.subscriberCount >= ca) & (df_channel_stats_first.subscriberCount < cb)].count())\n", " \n", "# make barplot with counts and popularity classes named (0..) and legend with ranges\n", "fig = plt.figure()\n", "df_counts = pa.DataFrame(class_counts, index=[range(len(class_counts))])\n", "ax = sns.barplot(x=df_counts.index, y=df_counts[0], palette=sns.color_palette(\"Blues_d\"))\n", "ax.set_xlabel('Subscriber Classes')\n", "ax.set_ylabel('Channel')\n", "#ax.set_yscale('symlog')\n", "\n", "plt.title('Channel Subscriber Classes')\n", "save_plot('channel_subscriber_classes_counts.pdf', fig, s_width, s_height)\n", "#[0, 1.0e+3, 1.0e+4, 1.0e+5, 1.0e+6, 1.0e+7, 5.0e+7, 1.0e+8]\n", "#ax.legend(ax.patches, [r'$0: 0-10^3$', r'$1: 10^3-10^4$', r'$0: 0-10^3$', r'$0: 0-10^3$', r'$0: 0-10^3$', r'$0: 0-10^3$', r'$0: 0-10^3$'])\n", "df_counts" ] }, { "cell_type": "code", "execution_count": 11, "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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], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7f8209712a90>" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fig = plt.figure()\n", "ax = sns.distplot(df_channel_stats_first['subscriberCount'], kde=False, bins=bins)\n", "ax.set_xlabel('Subscriber')\n", "ax.set_ylabel('Channel')\n", "ax.set_xscale('symlog')\n", "ax.set_yscale('symlog')\n", "#ax.legend()\n", "\n", "plt.title('Channel Subscriber Classes')" ] }, { "cell_type": "code", "execution_count": 12, "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 = 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' +\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 = 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], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure()\n", "#sns.distplot(df_channel_stats_first['viewCount'], bins=50, kde=False)\n", "\n", "ax = sns.distplot(df_channel_stats_first['viewCount'], kde=False, bins=100)\n", "ax.set_xlabel('Views')\n", "ax.set_ylabel('Channel')\n", "ax.set_xscale('log')\n", "ax.set_yscale('symlog')\n", "plt.title('Channel View Counts')\n", "save_plot('channel_views.pdf', fig, s_width, s_height)\n", "\n", "fig = plt.figure()\n", "ax = sns.distplot(df_channel_stats_first['commentCount'], kde=False, bins=100)\n", "ax.set_xlabel('Comments')\n", "ax.set_ylabel('Channel')\n", "ax.set_xscale('log')\n", "ax.set_yscale('symlog')\n", "plt.title('Channel Comment Counts')\n", "save_plot('channel_comments.pdf', fig, s_width, s_height)" ] }, { "cell_type": "code", "execution_count": 13, "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 = 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], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# this are the numbers of videos from the api, ever uploaded or currently\n", "fig = plt.figure()\n", "ax = sns.distplot(df_channel_stats_first['videoCount'], kde=False, bins=100)\n", "ax.set_xlabel('Number of Videos')\n", "ax.set_ylabel('Channel')\n", "ax.set_xscale('log')\n", "ax.set_yscale('symlog')\n", "#ax.legend()\n", "plt.title('Channel Video Counts')\n", "\n", "save_plot('channel_videos.pdf', fig, s_width, s_height)" ] }, { "cell_type": "code", "execution_count": 14, "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 = 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], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# get numbers of videos actually uploaded in our time-span, in our dataset\n", "\n", "with db._session_scope(False) as session:\n", " uploadsChannelID = pa.read_sql(session.query(Video.channelID).statement, db.engine) \n", "\n", "counts = uploadsChannelID['channelID'].value_counts() \n", "\n", "fig = plt.figure()\n", "ax = sns.distplot(counts, kde=False, bins=100)\n", "ax.set_xlabel('Number of Videos')\n", "ax.set_ylabel('Channel')\n", "ax.set_xscale('log')\n", "ax.set_yscale('symlog')\n", "#ax.legend()\n", "plt.title('Channel Video Uploads')\n", "\n", "save_plot('channel_uploads.pdf', fig, s_width, s_height)" ] }, { "cell_type": "code", "execution_count": 15, "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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\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#fig = plt.figure()\n", "ax = sns.lmplot('subscriberCount', 'viewCount', data=df_channel_stats_first, ci=99)\n", "ax.set_xlabels('Subscriber')\n", "ax.set_ylabels('Views')\n", "plt.legend(['99% CI'])\n", "plt.title('Channel View-Subscriber')\n", "\n", "save_plot('channel_view_subscriber_reg.pdf', ax.fig, s_width, s_height)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "371\n", "2553\n" ] }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>title</th>\n", " <th>keywords</th>\n", " <th>description</th>\n", " <th>dateAdded</th>\n", " <th>uploadsPlaylistID</th>\n", " <th>latestUploadsIDs</th>\n", " <th>unsubscribedTrailer</th>\n", " <th>topicIds</th>\n", " <th>network</th>\n", " <th>crawlTimestamp</th>\n", " <th>thumbnailUrl</th>\n", " </tr>\n", " <tr>\n", " <th>id</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>UC__Pj66OeDibNZNN__L913g</th>\n", " <td>TRACKS - ARTE</td>\n", " <td>[\"ARTE\", \"tracks\", \"culture\", \"musique\", \"unde...</td>\n", " <td>Tracks fait le tour des sons et des cultures q...</td>\n", " <td>2013-11-28T12:52:42.000Z</td>\n", " <td>UU__Pj66OeDibNZNN__L913g</td>\n", " <td>[\"4CLe7teTNJk\",\"1EAxOnyRPYA\",\"uM1SbbVyVTA\",\"Vx...</td>\n", " <td>3znd0FOOqx4</td>\n", " <td>[\"/m/02y26_\",\"/m/04rlf\"]</td>\n", " <td>None</td>\n", " <td>2016-12-28 02:42:58</td>\n", " <td>https://yt3.ggpht.com/-WLT_WJ83cgk/AAAAAAAAAAI...</td>\n", " </tr>\n", " <tr>\n", " <th>UC__PZLSRGtUQiTtvm3hPoEQ</th>\n", " <td>Cripta dos Mistérios</td>\n", " <td>[\"mist\\u00e9rios\", \"lendas\", \"fimes\", \"ovnis\",...</td>\n", " <td>Aqui eu falo das coisas que mais gosto MISTÉRI...</td>\n", " <td>2015-06-21T00:43:12.000Z</td>\n", " <td>UU__PZLSRGtUQiTtvm3hPoEQ</td>\n", " <td>[\"qKNTQsgFghs\",\"cLZ1W22AIyU\",\"VDggyidswBg\",\"7-...</td>\n", " <td>UBv-R3OoK2A</td>\n", " <td>[\"/m/02vxn\"]</td>\n", " <td>BroadbandTV</td>\n", " <td>2016-12-28 02:42:03</td>\n", " <td>https://yt3.ggpht.com/-7V1tMbgQZ1o/AAAAAAAAAAI...</td>\n", " </tr>\n", " <tr>\n", " <th>UC__rmdgxs3ZF0zK_he7Tmig</th>\n", " <td>Contoured Living</td>\n", " <td>[]</td>\n", " <td>My name is Laura. I am always searching for th...</td>\n", " <td>2009-09-28T17:21:58.000Z</td>\n", " <td>UU__rmdgxs3ZF0zK_he7Tmig</td>\n", " <td>[\"K9fFvrTUkeU\",\"LgldWKMjtWs\",\"Hf4rG-Q5r2M\",\"bv...</td>\n", " <td></td>\n", " <td>[\"/m/014trl\",\"/m/025t5bz\",\"/m/025zp2s\",\"/m/027...</td>\n", " <td>None</td>\n", " <td>2016-12-28 02:51:24</td>\n", " <td>https://yt3.ggpht.com/-ceQsyCd1l-4/AAAAAAAAAAI...</td>\n", " </tr>\n", " <tr>\n", " <th>UC_-CxgsxX0tpnm24WO-797Q</th>\n", " <td>Mega Gumelar</td>\n", " <td>[\"\\\"Rachel Goddard\\\"\", \"\\\"Tutorial Makeup\\\"\", ...</td>\n", " <td>Hello Megaliens!\\nLet me introduce myself, i a...</td>\n", " <td>2010-02-19T15:28:50.000Z</td>\n", " <td>UU_-CxgsxX0tpnm24WO-797Q</td>\n", " <td>[\"s7fvV2VKsFk\",\"Jjj4BV4OGvo\",\"J4CIfBp5tgM\",\"y7...</td>\n", " <td>0G4UdSvXdyc</td>\n", " <td>[\"/m/014trl\",\"/m/0157xl\",\"/m/027x0j\",\"/m/0wfzt...</td>\n", " <td>Maker Studios</td>\n", " <td>2016-12-28 02:51:51</td>\n", " <td>https://yt3.ggpht.com/-SVZFEnGePB4/AAAAAAAAAAI...</td>\n", " </tr>\n", " <tr>\n", " <th>UC_1FUFB6TlGeGOyDI4ikkzg</th>\n", " <td>Best Games For Kids TV</td>\n", " <td>[\"\\\"Dora Baby Hazel Peppa Pig Bubble Guppies K...</td>\n", " <td>Best Gameplay! Games for Kids, Children TV.\\nM...</td>\n", " <td>2013-07-19T12:42:48.000Z</td>\n", " <td>UU_1FUFB6TlGeGOyDI4ikkzg</td>\n", " <td>[\"7PKEvGKtrAY\",\"FwU9FoFEZRw\",\"15pC_5Dd-1o\",\"3s...</td>\n", " <td>fPRU0l-ehmM</td>\n", " <td>[\"/m/0215n\",\"/m/02dpv4\",\"/m/0ytgt\",\"/m/03bt1gh...</td>\n", " <td>BroadbandTV</td>\n", " <td>2016-12-28 02:51:00</td>\n", " <td>https://yt3.ggpht.com/-5pUwQKkReE8/AAAAAAAAAAI...</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " title \\\n", "id \n", "UC__Pj66OeDibNZNN__L913g TRACKS - ARTE \n", "UC__PZLSRGtUQiTtvm3hPoEQ Cripta dos Mistérios \n", "UC__rmdgxs3ZF0zK_he7Tmig Contoured Living \n", "UC_-CxgsxX0tpnm24WO-797Q Mega Gumelar \n", "UC_1FUFB6TlGeGOyDI4ikkzg Best Games For Kids TV \n", "\n", " keywords \\\n", "id \n", "UC__Pj66OeDibNZNN__L913g [\"ARTE\", \"tracks\", \"culture\", \"musique\", \"unde... \n", "UC__PZLSRGtUQiTtvm3hPoEQ [\"mist\\u00e9rios\", \"lendas\", \"fimes\", \"ovnis\",... \n", "UC__rmdgxs3ZF0zK_he7Tmig [] \n", "UC_-CxgsxX0tpnm24WO-797Q [\"\\\"Rachel Goddard\\\"\", \"\\\"Tutorial Makeup\\\"\", ... \n", "UC_1FUFB6TlGeGOyDI4ikkzg [\"\\\"Dora Baby Hazel Peppa Pig Bubble Guppies K... \n", "\n", " description \\\n", "id \n", "UC__Pj66OeDibNZNN__L913g Tracks fait le tour des sons et des cultures q... \n", "UC__PZLSRGtUQiTtvm3hPoEQ Aqui eu falo das coisas que mais gosto MISTÉRI... \n", "UC__rmdgxs3ZF0zK_he7Tmig My name is Laura. I am always searching for th... \n", "UC_-CxgsxX0tpnm24WO-797Q Hello Megaliens!\\nLet me introduce myself, i a... \n", "UC_1FUFB6TlGeGOyDI4ikkzg Best Gameplay! Games for Kids, Children TV.\\nM... \n", "\n", " dateAdded uploadsPlaylistID \\\n", "id \n", "UC__Pj66OeDibNZNN__L913g 2013-11-28T12:52:42.000Z UU__Pj66OeDibNZNN__L913g \n", "UC__PZLSRGtUQiTtvm3hPoEQ 2015-06-21T00:43:12.000Z UU__PZLSRGtUQiTtvm3hPoEQ \n", "UC__rmdgxs3ZF0zK_he7Tmig 2009-09-28T17:21:58.000Z UU__rmdgxs3ZF0zK_he7Tmig \n", "UC_-CxgsxX0tpnm24WO-797Q 2010-02-19T15:28:50.000Z UU_-CxgsxX0tpnm24WO-797Q \n", "UC_1FUFB6TlGeGOyDI4ikkzg 2013-07-19T12:42:48.000Z UU_1FUFB6TlGeGOyDI4ikkzg \n", "\n", " latestUploadsIDs \\\n", "id \n", "UC__Pj66OeDibNZNN__L913g [\"4CLe7teTNJk\",\"1EAxOnyRPYA\",\"uM1SbbVyVTA\",\"Vx... \n", "UC__PZLSRGtUQiTtvm3hPoEQ [\"qKNTQsgFghs\",\"cLZ1W22AIyU\",\"VDggyidswBg\",\"7-... \n", "UC__rmdgxs3ZF0zK_he7Tmig [\"K9fFvrTUkeU\",\"LgldWKMjtWs\",\"Hf4rG-Q5r2M\",\"bv... \n", "UC_-CxgsxX0tpnm24WO-797Q [\"s7fvV2VKsFk\",\"Jjj4BV4OGvo\",\"J4CIfBp5tgM\",\"y7... \n", "UC_1FUFB6TlGeGOyDI4ikkzg [\"7PKEvGKtrAY\",\"FwU9FoFEZRw\",\"15pC_5Dd-1o\",\"3s... \n", "\n", " unsubscribedTrailer \\\n", "id \n", "UC__Pj66OeDibNZNN__L913g 3znd0FOOqx4 \n", "UC__PZLSRGtUQiTtvm3hPoEQ UBv-R3OoK2A \n", "UC__rmdgxs3ZF0zK_he7Tmig \n", "UC_-CxgsxX0tpnm24WO-797Q 0G4UdSvXdyc \n", "UC_1FUFB6TlGeGOyDI4ikkzg fPRU0l-ehmM \n", "\n", " topicIds \\\n", "id \n", "UC__Pj66OeDibNZNN__L913g [\"/m/02y26_\",\"/m/04rlf\"] \n", "UC__PZLSRGtUQiTtvm3hPoEQ [\"/m/02vxn\"] \n", "UC__rmdgxs3ZF0zK_he7Tmig [\"/m/014trl\",\"/m/025t5bz\",\"/m/025zp2s\",\"/m/027... \n", "UC_-CxgsxX0tpnm24WO-797Q [\"/m/014trl\",\"/m/0157xl\",\"/m/027x0j\",\"/m/0wfzt... \n", "UC_1FUFB6TlGeGOyDI4ikkzg [\"/m/0215n\",\"/m/02dpv4\",\"/m/0ytgt\",\"/m/03bt1gh... \n", "\n", " network crawlTimestamp \\\n", "id \n", "UC__Pj66OeDibNZNN__L913g None 2016-12-28 02:42:58 \n", "UC__PZLSRGtUQiTtvm3hPoEQ BroadbandTV 2016-12-28 02:42:03 \n", "UC__rmdgxs3ZF0zK_he7Tmig None 2016-12-28 02:51:24 \n", "UC_-CxgsxX0tpnm24WO-797Q Maker Studios 2016-12-28 02:51:51 \n", "UC_1FUFB6TlGeGOyDI4ikkzg BroadbandTV 2016-12-28 02:51:00 \n", "\n", " thumbnailUrl \n", "id \n", "UC__Pj66OeDibNZNN__L913g https://yt3.ggpht.com/-WLT_WJ83cgk/AAAAAAAAAAI... \n", "UC__PZLSRGtUQiTtvm3hPoEQ https://yt3.ggpht.com/-7V1tMbgQZ1o/AAAAAAAAAAI... \n", "UC__rmdgxs3ZF0zK_he7Tmig https://yt3.ggpht.com/-ceQsyCd1l-4/AAAAAAAAAAI... \n", "UC_-CxgsxX0tpnm24WO-797Q https://yt3.ggpht.com/-SVZFEnGePB4/AAAAAAAAAAI... \n", "UC_1FUFB6TlGeGOyDI4ikkzg https://yt3.ggpht.com/-5pUwQKkReE8/AAAAAAAAAAI... " ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "DDIR = '../../data/'\n", "\n", "df_channel = pa.read_sql(session.query(Channel).statement, db.engine) \n", "\n", "df_channel = df_channel.set_index('id')\n", "\n", "#df_channel[df_channel.network == 'Maker_Studios']['network'] = 'Maker Studios'\n", "df_channel.loc[df_channel['network'] == 'Maker_Studios', 'network'] = 'Maker Studios'\n", "\n", "\n", "print df_channel['network'].nunique()\n", "print df_channel.loc[df_channel['network'] == 'None', 'network'].count()\n", "\n", "df_channel.head()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "371 [171 47 171 ..., 164 171 47]\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 = 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], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "1.0\n" ] }, { "data": { "text/plain": [ "count 371.000000\n", "mean 21.407008\n", "std 166.776704\n", "min 1.000000\n", "25% 1.000000\n", "50% 1.000000\n", "75% 3.000000\n", "max 2553.000000\n", "Name: Network, dtype: float64" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Xuniques, X = np.unique(df_channel['network'], return_inverse=True)\n", "\n", "print len(Xuniques), X\n", "\n", "dfnn = pa.DataFrame({ 'Network' : np.sort(X)})\n", "\n", "fig = plt.figure()\n", "ax = sns.distplot(dfnn, kde=False, bins=200)\n", "\n", "ax.set_xlabel('Networks')\n", "ax.set_ylabel('Member')\n", "#ax.set_xscale('log')\n", "ax.set_yscale('symlog')\n", "#ax.legend()\n", "plt.title('Network Member')\n", "ax.set_xticklabels('')\n", "save_plot('network_nof_channel_hist.pdf', fig, s_width, s_height)\n", "\n", "counts = dfnn['Network'].value_counts()\n", "\n", "print counts.median()\n", "counts.describe()" ] }, { "cell_type": "code", "execution_count": 18, "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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\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "371\n", "None 2553\n", "BroadbandTV 1535\n", "Maker Studios 984\n", "Studio71 658\n", "Fullscreen 309\n", "Name: Network, dtype: int64\n", "20\n", "Index([u'None', u'BroadbandTV', u'Maker Studios', u'Studio71', u'Fullscreen',\n", " u'Machinima', u'Curse', u'Freedom!', u'ScaleLab', u'StyleHaul',\n", " u'Social Blade Legacy', u'OmniaMediaCo', u'YouPartnerVSP', u'SonyBMG',\n", " u'TopBeautyBlog', u'ChannelFrederator', u'AIR', u'Mitu', u'Zoomin TV',\n", " u'ScreenwaveMedia'],\n", " dtype='object')\n" ] } ], "source": [ "# Channel networks distribution\n", "dfn = pa.DataFrame({ 'Network' : df_channel['network']})\n", "#ax = sns.countplot(x=\"Network\", data=dfn, palette=\"Greens_d\");\n", "\n", "\n", "fig = plt.figure()\n", "networkcounts = dfn['Network'].value_counts(dropna=False)\n", "print len(networkcounts)\n", "ax = networkcounts[networkcounts > 20.0].sort_values().plot(kind='barh')\n", "networkcounts.to_csv(DIR+'/network_channel_counts.csv')\n", "networks_list = networkcounts[networkcounts > 20.0].index\n", "print networkcounts.head()\n", "print len(networks_list)\n", "print networks_list\n", "ax.set_xlabel('Channel')\n", "ax.set_ylabel('Network')\n", "ax.set_xscale('log')\n", "#ax.set_yscale('log')\n", "#ax.legend()\n", "\n", "plt.title('Network Member')\n", "fig.tight_layout()\n", "save_plot('network_nof_channel.pdf', fig, x_width, x_height)" ] }, { "cell_type": "code", "execution_count": 19, "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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\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Channel topics distribution\n", "topics = [x for x in csv.reader(open(DDIR+'topics.txt','r'), delimiter='\\t')]\n", "\n", "topicIDs = []\n", "topicTitles = {}\n", "for t, tt in topics:\n", " topicIDs.append(t)\n", " topicTitles[t]=str(tt).replace('(parent topic)', '')\n", "\n", "topicIDs.append('/m/None') \n", "topicTitles['/m/None'] = 'None'\n", "\n", "topicIDs.append('/m/NaT') \n", "topicTitles['/m/NaT'] = 'Unknown ID'\n", "\n", "topiclist = []\n", "for ct in df_channel['topicIds']:\n", " if len(json.loads(ct))==0:\n", " topiclist.append('/m/None')\n", " for t in json.loads(ct):\n", " if t in topicIDs: # Filter not supported topics (as of 2017, Freebase deprecated)\n", " topiclist.append(t)\n", " else:\n", " topiclist.append('/m/NaT') \n", " \n", " \n", "dft = pa.DataFrame({ 'Topic' : [topicTitles[t] for t in topiclist]})\n", "fig = plt.figure()\n", "ax = dft['Topic'].value_counts().sort_values(ascending=True).plot(kind='barh')\n", "\n", "ax.set_xlabel('Channel')\n", "ax.set_ylabel('Topic')\n", "ax.set_xscale('symlog', linthreshx=10)\n", "#ax.set_yscale('log')\n", "#ax.legend()\n", "\n", "plt.title('Channel Topics')\n", "fig.tight_layout()\n", "save_plot('channel_topics.pdf', fig, x_width, 2x_height)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "50\n" ] }, { "data": { "text/plain": [ "Unknown ID 32235\n", "Music 3402\n", "Gaming 2733\n", "Lifestyle 2102\n", "Movies 927\n", "Action game 766\n", "Hobby 413\n", "Role-playing video game 406\n", "Sports 400\n", "Fashion 355\n", "Entertainment 307\n", "Food 297\n", "Technology 273\n", "Vehicles 263\n", "Strategy video game 179\n", "Hip hop music 167\n", "Animated cartoon 165\n", "Football 160\n", "Fitness 100\n", "Children's music 91\n", "Action-adventure game 81\n", "Humor 80\n", "Knowledge 78\n", "Society 70\n", "TV shows 68\n", "Sports game 51\n", "None 50\n", "Basketball 43\n", "Pets 39\n", "Electronic music 39\n", "Performing arts 35\n", "Tourism 20\n", "Pop music 17\n", "Rock music 15\n", "Motorsport 15\n", "Professional wrestling 13\n", "Mixed martial arts 9\n", "American football 8\n", "Music of Latin America 4\n", "Country 3\n", "Classical music 2\n", "Simulation video game 2\n", "Reggae 2\n", "Jazz 1\n", "Cricket 1\n", "Music of Asia 1\n", "Name: Topic, dtype: int64" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print len(dft[dft.Topic=='None'])\n", "dft['Topic'].value_counts()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 Film & Animation\n", "2 Cars & Vehicles\n", "10 Music\n", "15 Pets & Animals\n", "17 Sports\n", "19 Travel & Events\n", "20 Gaming\n", "22 People & Blogs\n", "23 Comedy\n", "24 Entertainment\n", "25 News & Politics\n", "26 How-to & Style\n", "27 Education\n", "28 Science & Technology\n", "29 Non-profits & Activism\n", "15\n" ] } ], "source": [ "categories = [x for x in csv.reader(open(DDIR+'categories.txt','r'), delimiter='\\t')]\n", "\n", "\n", "catIDs = []\n", "catTitles = {}\n", "for t, tt in categories:\n", " print t, tt\n", " catIDs.append(int(t))\n", " catTitles[int(t)]=tt\n", " \n", "print len(catTitles)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5891\n", "0\n" ] }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>category</th>\n", " </tr>\n", " <tr>\n", " <th>id</th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>UC__Pj66OeDibNZNN__L913g</th>\n", " <td>Entertainment</td>\n", " </tr>\n", " <tr>\n", " <th>UC__PZLSRGtUQiTtvm3hPoEQ</th>\n", " <td>Entertainment</td>\n", " </tr>\n", " <tr>\n", " <th>UC__rmdgxs3ZF0zK_he7Tmig</th>\n", " <td>How-to & Style</td>\n", " </tr>\n", " <tr>\n", " <th>UC_-CxgsxX0tpnm24WO-797Q</th>\n", " <td>How-to & Style</td>\n", " </tr>\n", " <tr>\n", " <th>UC_1FUFB6TlGeGOyDI4ikkzg</th>\n", " <td>Entertainment</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " category\n", "id \n", "UC__Pj66OeDibNZNN__L913g Entertainment\n", "UC__PZLSRGtUQiTtvm3hPoEQ Entertainment\n", "UC__rmdgxs3ZF0zK_he7Tmig How-to & Style\n", "UC_-CxgsxX0tpnm24WO-797Q How-to & Style\n", "UC_1FUFB6TlGeGOyDI4ikkzg Entertainment" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Gets all videos from every channel and checks for category most occuring\n", "\n", "df_videos = pa.read_sql(session.query(Video).statement, db.engine) \n", "\n", "print len(df_videos['channelID'].unique())\n", "\n", "counts = df_videos.groupby(['channelID'])\n", "\n", "\n", "dfcc = pa.DataFrame(index=df_channel.index, columns = ['category'])\n", "\n", "for ch, s in counts.__iter__():\n", " #print s['category']\n", " dfcc.ix[ch, 'category'] = catTitles[int(s['category'].value_counts().idxmax())] \n", " if len(s) <= 0:\n", " print ch, s\n", " #print s['category'].value_counts().max(), s['category'].value_counts().idxmax()\n", "\n", "dfcc.ix[dfcc.category.isnull(), 'category'] = 'None' \n", " \n", "print len(dfcc[dfcc.category.isnull()] )\n", "dfcc.head()\n" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "7942\n", "2051\n" ] }, { "data": { "text/plain": [ "None 2051\n", "Gaming 1654\n", "Entertainment 1275\n", "People & Blogs 754\n", "Music 452\n", "How-to & Style 410\n", "Comedy 405\n", "Film & Animation 297\n", "Education 191\n", "Science & Technology 171\n", "Sports 119\n", "Cars & Vehicles 54\n", "News & Politics 35\n", "Travel & Events 31\n", "Pets & Animals 27\n", "Non-profits & Activism 16\n", "Name: category, dtype: int64" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print len(dfcc)\n", "print len(dfcc[dfcc.category=='None'])\n", "dfcc.category.value_counts()" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false, "scrolled": 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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\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure()\n", "ax = dfcc['category'].value_counts().sort_values(ascending=True).plot(kind='barh')\n", "\n", "ax.set_xlabel('Channel')\n", "ax.set_ylabel('Category')\n", "ax.set_xscale('log')\n", "#ax.set_yscale('log')\n", "#ax.legend()\n", "\n", "plt.title('Channel Categories')\n", "fig.tight_layout()\n", "save_plot('channel_categories.pdf', fig, 1.5s_width, 1.5s_height)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>topicIds</th>\n", " <th>network</th>\n", " <th>viewCount</th>\n", " <th>subscriberCount</th>\n", " <th>videoCount</th>\n", " <th>commentCount</th>\n", " <th>category</th>\n", " <th>popularity</th>\n", " </tr>\n", " <tr>\n", " <th>id</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>UC__Pj66OeDibNZNN__L913g</th>\n", " <td>[/m/02y26_, /m/04rlf]</td>\n", " <td>None</td>\n", " <td>3253022</td>\n", " <td>23029</td>\n", " <td>967</td>\n", " <td>0</td>\n", " <td>Entertainment</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>UC__PZLSRGtUQiTtvm3hPoEQ</th>\n", " <td>[/m/02vxn]</td>\n", " <td>BroadbandTV</td>\n", " <td>310896</td>\n", " <td>5878</td>\n", " <td>144</td>\n", " <td>0</td>\n", " <td>Entertainment</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>UC__rmdgxs3ZF0zK_he7Tmig</th>\n", " <td>[/m/014trl, /m/025t5bz, /m/025zp2s, /m/027x0j,...</td>\n", " <td>None</td>\n", " <td>1291254</td>\n", " <td>8146</td>\n", " <td>294</td>\n", " <td>121</td>\n", " <td>How-to & Style</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>UC_-CxgsxX0tpnm24WO-797Q</th>\n", " <td>[/m/014trl, /m/0157xl, /m/027x0j, /m/0wfztg3, ...</td>\n", " <td>Maker Studios</td>\n", " <td>625545</td>\n", " <td>18990</td>\n", " <td>67</td>\n", " <td>101</td>\n", " <td>How-to & Style</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>UC_1FUFB6TlGeGOyDI4ikkzg</th>\n", " <td>[/m/0215n, /m/02dpv4, /m/0ytgt, /m/03bt1gh, /m...</td>\n", " <td>BroadbandTV</td>\n", " <td>89020205</td>\n", " <td>106760</td>\n", " <td>288</td>\n", " <td>0</td>\n", " <td>Entertainment</td>\n", " <td>3</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " topicIds \\\n", "id \n", "UC__Pj66OeDibNZNN__L913g [/m/02y26_, /m/04rlf] \n", "UC__PZLSRGtUQiTtvm3hPoEQ [/m/02vxn] \n", "UC__rmdgxs3ZF0zK_he7Tmig [/m/014trl, /m/025t5bz, /m/025zp2s, /m/027x0j,... \n", "UC_-CxgsxX0tpnm24WO-797Q [/m/014trl, /m/0157xl, /m/027x0j, /m/0wfztg3, ... \n", "UC_1FUFB6TlGeGOyDI4ikkzg [/m/0215n, /m/02dpv4, /m/0ytgt, /m/03bt1gh, /m... \n", "\n", " network viewCount subscriberCount \\\n", "id \n", "UC__Pj66OeDibNZNN__L913g None 3253022 23029 \n", "UC__PZLSRGtUQiTtvm3hPoEQ BroadbandTV 310896 5878 \n", "UC__rmdgxs3ZF0zK_he7Tmig None 1291254 8146 \n", "UC_-CxgsxX0tpnm24WO-797Q Maker Studios 625545 18990 \n", "UC_1FUFB6TlGeGOyDI4ikkzg BroadbandTV 89020205 106760 \n", "\n", " videoCount commentCount category popularity \n", "id \n", "UC__Pj66OeDibNZNN__L913g 967 0 Entertainment 2 \n", "UC__PZLSRGtUQiTtvm3hPoEQ 144 0 Entertainment 1 \n", "UC__rmdgxs3ZF0zK_he7Tmig 294 121 How-to & Style 1 \n", "UC_-CxgsxX0tpnm24WO-797Q 67 101 How-to & Style 2 \n", "UC_1FUFB6TlGeGOyDI4ikkzg 288 0 Entertainment 3 " ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_test = pa.DataFrame({ 'topicIds' : df_channel['topicIds'].apply(json.loads)})\n", "\n", "df_test['network'] = df_channel['network']\n", "\n", "df_stats = df_channel_stats_first.set_index('channelID')\n", "\n", "df_test[['viewCount', 'subscriberCount', 'videoCount', 'commentCount']] = df_stats[['viewCount', 'subscriberCount', 'videoCount', 'commentCount']]\n", "\n", "df_test['category'] = dfcc['category']\n", "\n", "def func(x):\n", " for i, (ca, cb) in enumerate(popularity_classes):\n", " if x >= ca and x < cb:\n", " return i\n", "\n", "df_test['popularity'] = df_test['subscriberCount'].apply(func)\n", "\n", "\n", "df_test.head()\n", "\n", "#df_test[np.isnan(df_test.popularity)]\n", "\n", "#df_test['topicIds'].sort_values()\n" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " topicIds category\n", "id \n", "UC__Pj66OeDibNZNN__L913g Music Entertainment\n", "UC__PZLSRGtUQiTtvm3hPoEQ Movies Entertainment\n", "UC__rmdgxs3ZF0zK_he7Tmig Lifestyle How-to & Style\n", "UC_-CxgsxX0tpnm24WO-797Q Lifestyle How-to & Style\n", "UC_1FUFB6TlGeGOyDI4ikkzg Movies Entertainment\n", "UC_1H9v258pXiyLDaW0R5exw American football Sports\n", "UC_1HVMnw-610qx54iEiWk7A Movies Education\n", "UC_23cGTZrzuilpX42sHZcnQ Action game Gaming\n", "UC_2Fn6J_8D3SvpMrKUAFLOw Music None\n", "UC_3gH6zKpk3aCkRkiTik5JA Lifestyle People & Blogs\n", " topicIds \\\n", "id \n", "UC__Pj66OeDibNZNN__L913g [Music] \n", "UC__PZLSRGtUQiTtvm3hPoEQ [Movies] \n", "UC__rmdgxs3ZF0zK_he7Tmig [Lifestyle] \n", "UC_-CxgsxX0tpnm24WO-797Q [Lifestyle, Music] \n", "UC_1FUFB6TlGeGOyDI4ikkzg [Movies] \n", "UC_1H9v258pXiyLDaW0R5exw [American football, American football, Sports] \n", "UC_1HVMnw-610qx54iEiWk7A [Movies] \n", "UC_23cGTZrzuilpX42sHZcnQ [Action game, Gaming] \n", "UC_2Fn6J_8D3SvpMrKUAFLOw [Music] \n", "UC_3gH6zKpk3aCkRkiTik5JA [Lifestyle, Fashion] \n", "\n", " category \n", "id \n", "UC__Pj66OeDibNZNN__L913g Entertainment \n", "UC__PZLSRGtUQiTtvm3hPoEQ Entertainment \n", "UC__rmdgxs3ZF0zK_he7Tmig How-to & Style \n", "UC_-CxgsxX0tpnm24WO-797Q How-to & Style \n", "UC_1FUFB6TlGeGOyDI4ikkzg Entertainment \n", "UC_1H9v258pXiyLDaW0R5exw Sports \n", "UC_1HVMnw-610qx54iEiWk7A Education \n", "UC_23cGTZrzuilpX42sHZcnQ Gaming \n", "UC_2Fn6J_8D3SvpMrKUAFLOw None \n", "UC_3gH6zKpk3aCkRkiTik5JA People & Blogs \n" ] } ], "source": [ "def func(x):\n", " topiclist = []\n", " for t in x:\n", " if t in topicIDs: # Filter not supported topics (as of 2017, Freebase deprecated)\n", " topiclist.append(topicTitles[t])\n", " if len(topiclist)<=0:\n", " topiclist.append(topicTitles['/m/None'])\n", " return topiclist\n", "\n", "def func2(x):\n", " topiclist = []\n", " for t in x:\n", " if t in topicIDs:\n", " return topicTitles[t]\n", " if len(list(x))<=0:\n", " return topicTitles['/m/None']\n", "\n", " \n", "df_test_copy = df_test.copy()\n", "\n", "df_test_copy['topicIds'] = df_test_copy['topicIds'].apply(func)\n", "df_test['topicIds'] = df_test['topicIds'].apply(func2)\n", "\n", "print df_test[['topicIds', 'category']].head(10)\n", "print df_test_copy[['topicIds', 'category']].head(10)\n" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "count 279.000000\n", "mean 27.681004\n", "std 70.105024\n", "min 1.000000\n", "25% 1.500000\n", "50% 5.000000\n", "75% 18.000000\n", "max 573.000000\n", "dtype: float64\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/mlode/intel/intelpython27/lib/python2.7/site-packages/sklearn/utils/validation.py:429: DataConversionWarning: Data with input dtype int64 was converted to float64 by MinMaxScaler.\n", " warnings.warn(msg, _DataConversionWarning)\n", "/home/mlode/intel/intelpython27/lib/python2.7/site-packages/sklearn/preprocessing/data.py:321: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n", "/home/mlode/intel/intelpython27/lib/python2.7/site-packages/sklearn/preprocessing/data.py:356: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n" ] }, { "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" }, { "name": "stderr", "output_type": "stream", "text": [ "/home/mlode/intel/intelpython27/lib/python2.7/site-packages/sklearn/preprocessing/data.py:321: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n", "/home/mlode/intel/intelpython27/lib/python2.7/site-packages/sklearn/preprocessing/data.py:356: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n" ] } ], "source": [ "df_test.ix[df_test.category.isnull(), 'category'] = 'None'\n", "\n", "groups = df_test.groupby(by=['category', 'topicIds'])\n", "#print df_test.head()\n", "test = groups.size()\n", "\n", "print test.describe()\n", "\n", "from sklearn.preprocessing import normalize, MinMaxScaler\n", "\n", "#area = normalize(test.values[:,np.newaxis], axis=0).ravel()\n", "\n", "min_max_scaler = MinMaxScaler(feature_range=(10, 100))\n", "area = min_max_scaler.fit_transform(test.values)\n", "\n", "\n", "Xuniques, X = np.unique(df_test['category'], return_inverse=True)\n", "Yuniques, Y = np.unique(df_test['topicIds'], return_inverse=True)\n", "\n", "fig = plt.figure()\n", "\n", "ax = sns.regplot(x=X, y=Y, fit_reg=False, scatter_kws={\"s\":area})\n", "ax.set(xticks=range(len(Xuniques)), xticklabels=Xuniques,\n", " yticks=range(len(Yuniques)), yticklabels=Yuniques) \n", "ax.set_xticklabels(Xuniques, rotation=90, rotation_mode=\"anchor\")\n", "ax.set_xlabel('Category')\n", "ax.set_ylabel('Topic')\n", "ax.tick_params(axis='x', direction='out', pad=52)\n", "\n", "leg = [1.0, 250.0, 680.0]\n", "area2 = min_max_scaler.fit_transform(leg)\n", "\n", "gll = plt.scatter([],[], s=area2[0], marker='o')\n", "gl = plt.scatter([],[], s=area2[1], marker='o')\n", "ga = plt.scatter([],[], s=area2[2], marker='o')\n", "\n", "plt.legend((gll,gl,ga),\n", " ('1 (min)', '250', '680 (max)'),\n", " scatterpoints=1,\n", " loc='upper center', bbox_to_anchor=(-0.15, -0.05),\n", " ncol=1,\n", " fontsize=9)\n", "\n", "plt.title('Channel Topic-Category')\n", "#fig.tight_layout()\n", "save_plot('channel_topic_category_rel.pdf', fig, x_width, 2.8x_height)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "count 97.000000\n", "mean 71.731959\n", "std 158.997872\n", "min 1.000000\n", "25% 4.000000\n", "50% 13.000000\n", "75% 37.000000\n", "max 739.000000\n", "dtype: float64\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/mlode/intel/intelpython27/lib/python2.7/site-packages/sklearn/preprocessing/data.py:321: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n", "/home/mlode/intel/intelpython27/lib/python2.7/site-packages/sklearn/preprocessing/data.py:356: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n" ] }, { "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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mHx9ZbhzpUOAra04xP+at5Krqr05X9/m9z9tNMt7p4Qsfrdwp2q1kGze9Sl3Tc7mjgmZXLR+v2qdsMzK2sodXe1fXpyzkevqvBVis7K2NoSXO8DiM0r41TzvjsRNgHkcRFxFRLPk4zsQcZoQQti2/QMiWieEEPISW2/5ONUdYM4lRER83lmPiH+Ru78CERe7jrVfCHEFAMRYlvU9Iip2j8AQsSImJuabsgXLVnmMJxBxiBBCyBnoZ8ptZzvHsW37F3JdIQD8tU2bNt9CxGHOfmWfsmxE/BQRpzmjRBlgawAgDRFfsCzrP8KdJ68HWGbpLr4rJYc7J2XxkGkrVOuEJG3GqkYtSupbqiziao9NkltVXdPI8+NPT6nWasTegye5+6DsoP8UZPL2fUdVqzVw+swF7j+xNGyADZzkzRY1zCbAPA8ApAHAACGEQMREAEh2voaInzrbuAIsLdQIzA0R/YOIRgFAMiKWu/ZX4t4u+BKi++tyNCeIaAoivoGISfLYk+S2s51uza7XP0BErwNALyfU3FiW9T1EfBsRR8h9N4zAmsPrDS2TJhU2NDS8e0iOcp9Qy0Mj8kMG2F0p2Vy2YatyP/eyYs3mRp7v5nmj6aZ7mVewLuQ5fStnjXI3Z1m+ZhP3TS8IWB55Nj/g+ap1m5V7hlpMQ0uPExRgdyDidCGE8Pl8P0TENULU318CgMfl9jOCAiwneJ9EdFQIcZV8vMJZHyLACn0+nw8Afhb8dSLaK4/xOAD0l6uvRsQ75Lazg++ftWrV6jtE9BkRTQ7ymeI65v3yft9FB5iXWVdxiHsMyeMuSVk8Zd4m1TohmfBmGbcPUWwfG1PkuftgNbV13DEh0POEB2ek//TYWe6Vmhfg2XNIDh847J3R4rkL1Zz04rKwI7CUKcu5qrpGtWZIKivNCMyzIOJPiagIEfN8Pp9PrhuHiMMQ8SVE/KUQQgCATUQFRPQkEb1DRDPlvaVZiFgJAN3c+yWiCUT0uvxgxR4A+Bsi3oOIu93bImISIk6TI7x/yAaWfwGA7kR0FAD+JoS4GgBeJKLBsiHmTU7jS0QcCwDXBB37HURMCF5HRKMQcSQivu33+2+QAT0PEVe5LnmGxesBxsx86swFXrHWm+HFzHzg8CnulVoQEGJ3peTw8o0fqVYLyfFT5zl99loeOGERf/iJdwIhmCXr9vH9Iwq4c2Im3zMkm3NW7Fat1Ii5Re+HDbAFpR+o1guLCTDDlwIdAozZm79wbg4cPsUT3ijjx8Ys4QETFvOm9z9VrdQsXj+nzPVhu233EV5TtkW1SkguVNXw9Pc2NwqwGZlbPPuhI2Zvfu9V10KDhpgAiz66uOriyex91z0fn+Dc5bt51rxVvOfjE6p1msWL51N1LTRoiAmw6KOLqy6ezPq4Gs/IUV0LDRpiAiz66OKqiyezPq7GM3JU10KDhpgAiz66uOriyayPq/GMHNW10KAhJsCijy6uungy6+NqPCNHdS00aIgJsOiji6sunsz6uBrPyFFdCw0aYgIs+ujiqosnsz6uxjNyVNdCg4aYAIs+urjq4smsj6vxjBzVtdCgISbAoo8url73rK2t4537j/G6ikNcUFLuyXYqwXj9nDp40VN1LfQEiJgjmzwWEdFKOYVUo4l5wxGiIeS7APBYtPwA4Da/339juK8HN9SUs9M/K0TDHIcl4V4bCSbAoo/XXXd9eJwXlO7iqW+v4C0ffOa5+RqZmcu2f8LPzFodMMNF6oxVvGrLQdVqTeL1772DFz2jWde0BRHvlP/2A4BU97qWEjQZ7tWIeDhafu4Z8JvYplFDTafDMxEVR8tFCBNg0aSuro6Pnzrv2Y7MVdW1PH1+42mPxr1exqfPXFCt18DaikMcn1HC/TJKuO+oQn5g5CK+f3ge95Peyzw6xySzHj+nzN70jGZd0x53gMnnAQ0tAcCWTSynOw0k/X7/DUIEBpht2z9AxPeFaNyEEgBi5L5fAYAUAJhBRJacPX4BAKTGxMR8UzandDfOnA8A8XLS311+v/9GRPwdES23LIvcAebz+b4hJw6+RroVCyFE27Ztr5cTEyci4nSfz3etdHlajuCGywabDzZ1nrweYFXVtVxafoD/9d5KPnLirGqdsHz4ySl+9uU1/OS4Yu43bhGXbf9EtVIjMpd+UB8ME4r5gZEL+Z6h2dxvQjHHZ5TwzMytqvWYmbm6ppaHTlvJ/SaU8D3D8rljQhbfOSCTOwzM5LuH5nO/CSWcPHk5n7tQrVq1EWfPV/PGzdtUa7QIE2AeJ2gE1lRDyz5yfQ9EnChEfYAR0QoASEHEQkR8QG4f3ITSaZj5FyGE8Pv9NwLAeGcfzvHl/nq7jtnb5bnhhhtu+H8yUJ3j3IaIlYiYRESZ7iaWToAh4jjbtnvI7bvLGexbI+I217Z7mjtPXg+wKXM3cY/BudwlKYvjM0r49FnvNQisq6vjZ19eE9Cm5KlxRZ5qKV9VXcspU5ZzfEYJd0oMbKcSn1HC/SeW8tGT51Rr8qadn3J8Rgk/nF7IHWR4OUuHgZn8z+cWc3xGCa/e6q1LiUvW7eceg/O4W0o2L91wQLVOs5gA8zhBARa2oSUA3CaEEJZl/RYR84Ro3E9LhtnPwjWhBID+iDiMiEYj4svOPloSYACQTEQPE9HgNm3afEuuC7iEiIhv27b9f3JfToDlIeLvXO65RPRfRFTgel1pc+fJ6wH2z2cXc9fk+o7M9wzN48o93unI63Ds1Dl+clxxQLG9d1g+b9hxWLVaA0dOnG24dBjct+zp8fWjMC90Oy5a/yHHZ5Rwn+ELQza0vC+t/rJn/so9qlUDSHh+acPPafLk5ap1msUEmMcJMQJr1NDSHSZE1NM9AgOAF137ygKA2wDgseAmlIj4SyJaLoQQtm0DIs6Wr+mKiGPl/gY7x0HEobL5pCWEuFr6rHa6KUuvgIaaiPi8M1J0PsQhP6jSUx63BxGNliOwrfJlVzjNM5vC6x2ZEzIWcxfZkbl3Wi6vWu+9Drdbtlbw0+MWBRTaB4bn8vI1m5S7OcvGzdv48VELuW96QaNQeOS5+i7CS1er951bsI77phdw79TckE1Ce6Xmct/0An4z2zudmSsrK3ncK0sbOodPfG2Zcp/mFtOR2cPExcX55YhkiW3bIETYhpZpMgiGE1Gmz+f7obshJBENAoB02Qn5ClEfWi+4m1DKe1z5ADCeiEYRUZnP5/tJbGzsdYi4EADi5chtrmVZ3wOA2+Q9sZlCiCul23wAuFmIhntesxCxkogGEdEziPiKPE4fRNxNRLe0bdv2eiKaKR1nuO6B9ZOfXExCxPLmzpXXR2DHTp7jae9t5iGTl3DFniOqdcKyvvITfnJcEd87LJ8fGJ7L80t2qVZqxCu5FRyfUcJPjSviLolZ3GFgJj8xdgnHZ5Tw+De88XNw5lwVJ76wjJ8eX8zdB+cGhFe3Qbn81PhiHjCplI97sIv0uspDvGDRetUaLaKy0ozAtEcG2K0KFa6SHunR2iER/afzGBELm9ve6wHm4MVfuGBOnD7P5TsO87LV3uweffzUeR4xa02jTyEOnrqCDxz2TmfmrGX1HzZ5anwx3z9iEd8zLJ/vHZbLT46rv9T5buFO1Yph0eHnlNmbntGqgV8J4uLi/PJvxWaK+tHVZQcRH5WX/rpFa59ENFmO3EYBQPvmtjcBFn287HrmXBUXrt3HGW+V89ApRZy17AM+dkr9hzfc1NbW8byi97n/xNLAsJ1Yym8t2sE1NabT8aXiRc9o1UDDVwgTYNFHF1evex45cZYXrt7H7xbu5JffW8WfHvPun1E4eP2cOnjRU3UtNGiICbDoo4urLp7M+rgaz8hRXQsNGmICLPro4qqLJ7M+rsYzclTXQoOGmACLPrq46uLJrI+r8Ywc1bXQoCEmwKKPLq66eDLr42o8I0d1LTRoiAmw6KOLqy6ezPq4Gs/IUV0LDRpiAiz66OKqiyezPq7GM3JU10KDhpgAiz5ed62rq+PDR8/w8jWbPd8ksq6ujs9X1XBFhTdb1Lipqq7lLVu978nszZ9R1bXwKwcAPOcsiJjbpk2bVojYr6nXNNeUEhF/KluuLLQsK1a2cJlNRDtt2/7zRfrd3tyM9CbAoo9XXevq6viNhdv5wZGLGiaevS+tgKfO28QXqmpU6wVw4vR5XlC6iwdPXVE/K8fYRTy36H1PzJgfzOI1+/ifzy3mzknZ3Ckxix9OL+Ql6/ar1moSL/6MXkxtM1wilmX9ByLOc54DwBO2bbdryQS6zTWllLPkD3A9D5id/mJo7lgmwKKPV11H/GuNDK5s7piQxR0SMrlzUjZ3Tc7m+IwSrvbIDBdHTpzltBmr+KnxxdznmQLuOSSX7xmazU+NL+Kh01byoSOfq1ZsYPwbZdx+YONJh9sPzOQX3t2gWi8sXvwZjaS+GSIEAGIQ8UPZ5uRKIYSwbfvviHgcAFIBoBsiLiOiOUIIgYgjEfFf8nGJEPVNKeXEvQmy0eZ35b7DBphsZPkeIiYAwAwhxNdlmG5ExFvlCG6jZVmx7mOFwwRY9PGia9H6/dw1OYc7JWY1KrYdE7K4a3IOv5LrjWaMU+Zu4ifHFgX0V7tzQCZ3HJjJT4wt4glveuNntnz7J9whRHi5+5dt/cCbE1B78Wc0+lXa0CRE9L9yPsVDAPCcEOLr7kt2tm3/AQBeFUIIAEhp3br1t+XrGppSAsC98uv3Oc0y5STDpUSUgYgTEXGuq0PztZZl/Ua+/mlEvEM+nu1MTIyILzsBZkZgl4+d+4/x9Pmb+e3ctapVGjFw0lLukpwdcrRw54D6kdiDIxep1uTDR89wfEYJ9xicE9Kz26D60eL+QydVq3J8RmnY8HKWpBeXqdYMoK6ujucs2cmJEwu5YNVe1ToBRLU4G1qOz+f7IREVA8BDwfecELFctlZp6PflbkoJAC/KzsujiWiQEE2PwFq1avUdAEgHgBQietfVZ8wdYLNNgF1+hk5f2TDx7JET3pq37760Au6UmB220HZKzOK7UnL4vOJ7YeU7DnN8RknYkU37AZncb0Ixr9z8sVJPZua7h+Y1G2D3puar1gxg14HjAZ0IvPRzGp1qbGgRlmXFImKS85yIhiPio4i4SwghAOBnQgghLw9m27b9B2dbV1PKMUTUWQgh5AdAbpWvDRtgABDvhCEA/NPV6flFIvqH3O/ii7mEqLqRXUsWLzbgC17SphVz3/QCfnJ0AW/YtFW5j3u5d2gud0xofPnw35cRM7lrcjZv3Vah1DN7SRn3TS9oMsD6phfwvIJ1ys9pzzCjRPdyz9Ac5Z7uZeXazfzYqIX8yLP5/OSYRVzuoZ/TS6/Khhbj8/mulZf2RsgR0auy6eRkABiPiE8JIQQAtCaizc7r3E0p5chsOgCkIOIky7Ji/X7/j4moCBHzbNtuJ4S42mlwKbtC/wwRS2Q36TmIODcmJuabAPB7RMySIVqIiEMB4HZE3A0AvcK9DzMCix6fn63iNVsP8sq1m1WrNCJ99tqwlxDbD8zkLsnZ3H9iqWpNPnu+mhNfWMa9hzfuHF3fkTmfB05ayic/v6Balce8tr7ZAJv0drlqzUZs33uUX5qzkvceVH8Z1s1lKNuGi+Qq27Z/0NxH61ViAiz6eNH1g4+Oc/dBudw5KTtgdNN+oPwkYkoOL9v4kWpNZmZeULqL+00o5h5DAkc43Qfl8NMTivmtRTtUKzIz82fHz3LnpPCXZbsmZ3vyY//M3vwZVV0LDUEQ0euI+HxsbOx1ql3CYQIs+njVdeHqvdx9cC53Tc5u+JulLsnZfFdKDr+a550/wK2pqeXZOds4PqOEnxhbxH1HFfJDI/M5PqOEp8/fzFXV3vmbtdVbD3LXlBxuH3SZ866UHC6rPKRaLyxe/BlVXQsNGmICLPp42fXgZ6d50tsb+NHRS/iBZ/I4ffZa3nXgmGqtkOz5+AS/vXgHT5+/mSe9vox3fXhctVJIzpyr4tfyK/np8cX8yHMF/ObCSj5zrkq1VpN48WdUdW8ATu0AACAASURBVC00aIgJsOiji6sunsz6uBrPyFFdCw0aYgIs+ujiqosnsz6uxjNyVNdCg4aYAIs+urjq4smsj6vxjBzVtdCgISbAoo8urrp4MuvjajwjR3UtNGiICbDoo4urLp7M+rgaz8hRXQsNGmICLPro4qqLJ7M+rsYzclTXQoOGmACLPrq46uLJrI+r8Ywc1bXQoCEmwC6dz47Vz6DunoXh/hELedvuz1SrNeKtRZUBLVU6DMzkiW9772fg2Ikz/PSE4oBz+viYIk/1AnOo2HOEh89czfcMy+Pug7J5xL9Wc+Veb7ZRcfDi75PqWviVJpLuzEFcSUSzACBVCCEQcah7AuBg5JyKa4ioGBFLiGi7sx9EfIqIPmvJQU2AXRr7Dp5oci68nGUfqFZs4PHRS8J63j0kV7VeAx8cON7kOd2y67BqxQZeL6gMmIXD/R+DdxfvVK0XFi/+Pl1ErTREk0vpzuxGTtab2pJtEfF3bdu2vV4IIXw+nw8RnxVCCNu2f+Hz+X5CRKdash9dAmxbhXemOnLTJTn8XHjOcuGC+qmP3l68o1nPodNXqNZkZm7UyDLUjPReoHzH4ZDh5Z5ncsuuT1VrhsQEmKGBFnRnvh0RhzmtTYhoECLOFqKhl9gC2UJlPACkxsbGXkdEC1ytUv6GiC8AQCoiJgQfHxFHEJHlXvdlCbDFq3c3FIT+GcWqdQLY/P7hZkPhzgGZPOa1dapVQ3ZiDrWoZtWWj1rkmbdC/cg2ZfLyZj1Tp69UrdmIiW+Xc88h2Z6a/5LZBJhSmuvO7Pf745zmkn6/Pw4RXxZCCEScREQ95eMHnBEYEfWRAXYFIn4ghPia3CbP6TUmuZKIXgvh86UIsOCCsHO/d+bte2bm6hYV2x4euDzXEs87B2TymXPVSj0HTGq+y/GdAzL54fRCpZ7MzHelNN8PrPvgHNWaAayrOBTgd8xDs+VHVnkNUSVcd+ZwAUZE+Yj4OyGEsG37T8EBJvuJOfe3BBFNAYC7nOeIeKcTgG4uJsBUN7JragkuCLlF3vEdMGFxi4rtXclZyl1bGmCqG3E+mp7fIs/eqeobRXZObN6za5L67717eTNnTYDf8jUblTs5S6Q113CJtKQ7c9u2ba8nonVCCGHb9n+7RmATXSOw+8OMwHYJIa6S6/PdIzB5KfLqYCciOt0Sd6+PwP6Vvbnhl61Xmrfasy/beKBFxXboNPX3lpq7r+SVS4h5K3e3yPP1vG2qVbl/0CdPQy1JLyxTrdmIlMnLuXNSFo9+da1qlQAiLL+GS6Wl3ZmJ6DV5TyyeiMr8fv+N7dq1a0NEmYg4AhGnE9ESALgJEech4lzLsr4HAH9FxKmIOJKIBrqO+0MAGB/CZQginiOiwTExMd9syt3rAcbMfOrzC7xi7SbVGiFp6ia+s5w+q7578JS5G5v1fHqcN+4xtuSc1tXVqdbkpRua/w/Mmq0HVWuGpLLSfIjD8CVAhwBj9uYvHDNz+fZPmixg0+d7J3jvGZYX1rNTQhZXV9eqVmRm5jVbP27ynBau3adasYGJb5WH9Zw8d6NqvbB48fdJdS00aIgJsEtnx76j3DutIKB4dRuUwwWrdqtWa8TwmasaFdp+E0o8E14OO/Ye4XtTA++H9Ryax5t2eudvwBwK1+7np8YVc6fELO6YkMlPjS/m4rIPVWs1iRd/n1TXQoOGmACLLkdPnOWtW7eq1miWCxeqecMm9feRWsKFqhqu8OjfAQajy8+pFz1V10KDhpgAiz66uOriyayPq/GMHNW10KAhJsCijy6uungy6+NqPCNHdS00aIgJsOiji6sunsz6uBrPyFFdCw0aYgIs+ujiqosnsz6uxjNyVNdCg4aYAIs+urjq4smsj6vxjBzVtdCgISbAoo8urrp4MuvjajwjR3UtNGiIlwPsQlU1v1u4g58eX8IPjcznCW+W8ydHzqjWCsmJ0+e5cO1+frdwJ8+at4oPfua9xovBeLGIhUMXV+MZOapr4VeKoAaWeW3atGkVhX0+RkQXACCViDIQcb4Q4mo5EfACRNzm3t6yrO8h4klEnC7q50O8Qk5TNRwA0hDxzeaaano1wCr2HOHug3O5/cD6vkrtB9T/2ykxy3NtIBav2cf9J5byw+mFfP+Ihdw7LZefHl/Mr+ZVcJXH/kDYjReLWDh0cTWekXOp9dPQQkI1sASAmGjs2z2LPCK+AgAd5Po+iFiOiHe4jhuPiBUA8Cu5fT8AeML19WsQcVpTx/NigJ38/Dx3G5Tz7/ByBVj7gZncISGLl6zbr1qTmZlXbv6YnxxXzN0H53KXpGzukpTNnRIz+a6UHH509BJ+c+F21Yph8WIRC4cursYzcqJRPw0tIFQDS7k+Wc5EPwkR/yKEuIKIChBxHhFNQcTVcXFxfsuy/oeIjgJAR7/ffwMiLgOAvwoROIs8Iubatv0Hub4PIt6NiMvkl68mouGydcuv5DbbAeC7F/NevBhg09/b3BBWwdMeOesfenaxak2ura3jZ2at5nuG5XPnoIaRHQZkcrdBuRyfUeKpnkvMzMdOneceg3O5c2Im7/zwqGqdJplXtJMHTFrKk15f5okJfMNRU1PLmaUf8Iw5K7mm1rueDibAvuIEN7C0bfsXRJQvv3wVEe0UQggAuA0AXpWPBwDAAPn6mbZt/0nUt0sZ6ewXEc8jYhIiTiWiTCHE1+X2vWUfsWUAcDMi3m9ZFiJiiWsEdsLZDwC0loHqOIXEiwF2/4hF4QNMhljHhCw+cfq8Us99B0/yk+OKuWty6MaGXZNz+OH0Qi4pP6DUM5gOQa1VvMqarQe5Y0JW/ah7YCYXrvPOJL7BTHq7nLsm53DnpCxPT+LrYALMIISob2mCiCWImOi+XIeIFW3atGklAyxNiPpRlNPvS4bQm7JVyq3O64IuIfZFxDHOay3LigWA9oiY5YReUIBVWpb1PbcfIh5vyt+LDS17DM6pD6ows3y3H1Bf0Jav2azUc2FpOT80Ip87J2WF9OyUmMm903J59vzVys+pewn2rKioUO4Uapny1vKGn4P2AzJ59Mulyp3CLY+NWsidk7K4c2IWPzlmkXKf5pYNGzYodwheLr0aG1pEqAaWAPAEERUIUX/viYh2yMfBAZbmet16Ipri3rf7EiIRdZYNKxsCTAghEHGrZVm/lY/dAfY0Ij7t2t0VLQkwr/H0+JJmR2BdkrO5qrpGqeexk+c4PqMkbGv5rsk5/PiYIt6081OlnsEMnFTa4Ngr1VtNQt18euws35uazx0SsrhbSjZ/9Okp1UphWbP1IPdKLeCeQ3J4feUnqnWapbLSjMC+soRrYCkv/T1LRJMty/ofIYQAgBlEtMTn8/nkvbAlcXFxPxKi4UMXKc5+iehhpxGlbEr5lt/v/7Ec5c0DgHS3hxy97SaiyUKIK0T9pxAHyMaXzyDiG+4GmKHwYoAt33SAOwzMavjwRnB4tR+YyWkzVqnWZGbmGQu28AMjF3HX5JwA1y5J2XzvsHweNn0l19R475OI+w+d4sVLy1VrNMvJzy9wxZ4jvK58i2qVZqmtreNtZtb8iIl+pTZ8UVwhhLgSEZ/y+Xw/VCnixQCrq6vjxBeWhf0UYs+heXzkxFnVmsxcP0oYOn0lP/jsIu42KJe7Judwl6Qs7jN8IfefWMpbP/hMtWJYvFjEwqGLq/GMHJV10HARWJb1H0T0OgAkq3bxYoAx1/9vdtLb5dxtUG7DaKxTYjYPnFTKh49664+Zj5w4y68XVPLA55fyU+OLuW96AU+Zt4k/OHBctVqTeLGIhUMXV+MZOaproUFDvBpgDtU1tbxhx2Gek7/Ocx9HD+bs+Wo+dORzLtvg/YaWzN4sYuHQxdV4Ro7qWmjQEK8HmIMXf+HCoYurLp7M+rgaz8hRXQsNGmICLPro4qqLJ7M+rsYzclTXQoOGmACLPrq46uLJrI+r8Ywc1bXQoCEmwKKPLq66eDLr42o8I0d1LTRoiAmw6KOLqy6ezPq4Gs/IUV0LDRpiAiz66OKqiyezPq7GM3JU10KDhpgAiw51dXW868PjvHbbIc4tKvN0HzAHr59TNzq4nj5bxeWbzJ9QRIrqWnjRXGpTSES8lYgGhfu6bEFSEuK4wY0j3xNCXA0AtxPRnha634SI5c78hE0hp5maTUQ7AeD2luz/cmEC7NLZvvcoPzd7LcdnlHB8Rgn3TS/gIdNWcKnHZqFnZq6qruX1lZ/w7JxtPOblpbxs40d87kK1aq1m8fL3f/WWg5z04jLulZrPPYfkcMrk5by24pBqrSbx4vlUXQsvii+yKaQbIioOsz5c48iQ24cCEV9uSYAJUT+pLyJmt3TflwsTYJfGzv3HeMCk0obwcgLMeeyVxpvMzGfOVfGY19Zz7+EF3CU5mzsl1E/mO3T6Ss9MzRUOr37/55fs4h6D66cQc9qpdE3O4Z5D8jhn2W7VemHx4vlUXQsvinBNIeXXghtDiri4OD8AzEDEBCKaAwCt5YS1s4UQAhHvQcTn5WS6jzj7aiLAQjaOdEZsbdu2vR4R30PEBACYIWRfLgDoRkRziGgQIhY6ASadn0HEibZt/znE+w0ZYADwN9nschgA9BeivkULES1AxBGI+BIRFRORJV3SiSgDANrL118jX5+IiP9CxL8gYjYiLgaA7wLAQ4iYJ4S4OtR50CHA9nx8gjML13OtBxsFTnizrCGsHhu9mO9KzuZeQzMb1iW9uIzPnvfGCOedxTu455C8gFn+2w/I5G4pOfz8OxtU64Xk/f1H+OlxSzh78RrVKo04cuIs90otaAivTolZ3DEhs+H5fcML+PgptT3rwmECLAoEN4UUQnwtXGNIRJyHiL+Tj2/1+/03yAaPLwshhAyNK+R+l7iOETLAmmgcWSxE/YzzlmX9Rm77NCLeIYS4koj2OdsiYqllWbG2bf8CEQvlrq8moi3BxwsTYFcg4m4AuEbub7Ft2+0QcRIR9ZTrHnB6iDlhLl+3WPo+6cypaNv2DwDgNtu22znvAwAeBAA73PfA6wFWuHYfdxuUy52Tsvi52WtV6wRw6MjnDUH15NjCgFYqvdPyGr62astB1ap87kI1PzWumDsmNO5d1mFgFj/83GI+fMxbc0x+cOBogOfqTR+qVgpgfsmuhrAK+E/BwH+HWM7yD1RrhsQEWBRxmkLKYtstqDHkNgBojYgVfr//Bvfr3AEmG0SOBYBkRPzA2aaFlxAbGkc6I7BWrVp9R7ZKSSGid4mot23bPyCi7c7rAOBV2WSyGyKuRsQkefxsIUPOtW2jAJPv67DzOgB41bbtXxNRvhPWtm3/yQkwIvoHEY2SxyiX66bYtt0jxPvLtyyLgluwBOP1ABsxa3XDpZleqQWqdQLYse9oQ0jd/8zCoFD49yhs4eq9qlX5s+Nn+Z/PLeYOoXqsDczk+59ZyNv3HVWtGcDgqcsDPO8fsVC1UgBT521qCKpQveC6JufwzExvtoExAXaJhGoKiYh95QisUWNIednwZrn+dgCIcQcYIm4iIktuu8LZb6gPcchtQjaOdLYHgHhEHCEf/5OIeov6EdheIcRVcttSGWA/R8Qs1zG7Bh9PBliO89y27T/LAKsUQnxNrvtTbGzsdYg40TUCu98VYEedYzvvUd47TJHr2tq2/Se5/i5EzAaAe5v6PnixI7N7eTVzNXdNru90m/x8oXIf97J8zSbum17AfdML+KERgUXs7sGZDV+bk79WueumLdv44WfzwwZYn7Q8XrlObYfr4GXR0vUBnu9kL1fu5F6mvbuivgtzUlajvnXO+plzVir3DLWYjsyXSLimkEII4W4MSUT/K0T9PTAimiVHH8/K7YYRUZnf778RAPoT0QIAGEBEe23b7iE/hbibiG5xHztc40gAuF1e0usFAD+To8I0IpqDiHNjYmK+6dwDk59gXOmECwD0l5+oTAOA+4Le6zeIaBYiVrqOOdeyrFgi+l8AeFGO9J4RQoh27dq1IaJMua/xiDhMek8goteJaCAR7QGAvwHANYg4FQBSEHGi65OcV8lPPV7T1PfB6yMwZuYPPjrO8xdpcA9sVP09sHtd98ASX/DOPbC3F+/gHoND3wOb5NF7YDv2fMaPjynkBQtXq1ZphLkHFl2iECsGLwAAMT6f71r5+F4i6nORu7hK1AfY8OY21CHAmL35C8fc/KcQC9d661OIo19bZz6FGEWa+hRi9lJv3v9i9ub5jKBUGryIZVm/QcRXEDEBEV8QYT5BGA5EfIGIMizLwua2NQF26VTuafx3YIOnruASD/8d2MvZ23jMy6X1fwfmkRFiU3j5+79y88ec9MK//w4s+cXlvGab+g/uNIUXz2fEBdPw1cUEWHRwZuJYs/Ug55iZOKKODq6nzlzgso1mJo5IUV0LDRpiAiz66OKqiyezPq7GM3JU10KDhpgAiz66uOriyayPq/GMHNW10KAhJsCijy6uungy6+NqPCNHdS00aIgJsOiji6sunsz6uBrPyFFdCw0aYgIs+ujiqosnsz6uxjNyVNdCg4aYAIs+urjq4smsj6vxjBzVtdCgISbAoo8urrp4Mnvftba2jg8fO8Mr12325IwxwXjxfKquhVHnUhteehUA6EhEexExMWj9Q4h4GAC6N7cPObVWESLeKoQQchqtJqeNCoUJsOiji6sunszeda2trePCtfs4bcaqhj9if2bWai5av9/TQebF83mxtcvTXK6Gl6pAxKly1vyvu9a9GaoVSzgAIM0JsEgxARZ9dHHVwbOquoY//uw0b96yTbVKI2pr6/jl7G1hpxF7Na+C6+q8GWJe/N5fSh3zHOEaXsrWI6mydUo8Ed2CiLsQcSQRLSCijLi4OL9s7pgAAC8KOds7AIwnokFENMXv99+IiD9FxI1ElCGnbioUQlwpR0gHAeB2y7IIERcJ0TCZ7uhQzS6JqKdsjXKr3+//MREtJ6L/sm371wAwQ7ZLSXe9vzREfAkAHpTP2yPi3e4AC9UkExFvRcQsOSHwPES8FQBuQsSNTnNN2dAykYhmxcbGXtfUeTYBFj1qa+v4yImzvGVrhWqVFuH1c7q+8hMeNGU5Pz6miJ8cs5BXbv5YtVIAG3ceDgiv4ACLzyjhrR98plozJF783kcrOzxDcMNLv99/o6vZpXD6YCHibKfZoxy5zXV1WB4GAPci4h1ENFO+rp3T9FIGyQNy22wA+JV8XCJnzO+HiFtbt279bQCI9/l83wjT7FIgYqHP5/PJbZ0WJ+uJqK38+r8cLwBItSwLEbFCbjfK7/fHOQEWrkkmIm5y+qIBwKvOCAwRX3YF2F/k1zsQ0ZNNnWMTYNGhpqaWp8zdVD8D/cRCz85Czsw8K3NLw2z0Q6YuV60TkuOnznO/CSUB7VSeGFfkqaabU+ZtagiqR9ILuWNCFndIyORHRhU2rH9pgekH1lIijAnv4zS8RMREd7NLB0Sc7ff741zPK2SblmREnISID8jX5skRXAoRvSNEfYABwG3OflyBkCTbqqTKfXVDxCFChG52Kfd1r9y2oQsyIp50Nbp8iYj+Ibd1uizPJ6LRiHiHO8DCNMm8ChFPOO9Tjs4c39kywL4uW8AMliO81KbOrQmw6LD7oxMB/wsvWu+dWeiDCe4J5kXWVRziboMCe6x1TsriZRs+Uq3WQOpLqxq+53el1Hdlbj8gk7sNymlYP2LWGtWaIfHi71Ok+eBJQjW8BIAnnGaXQgjh9N1yFW9n23cB4Ca5jU1E/0lE/0DE551tELGL/HqaOwRcj3+KiKVE9IhlWb9FxBKnrUmIZpd9hGjo+7WFiEa5jrMmJibm+0IIYdv2r52gBYA0+e/NTmj5fD4fIm6V60M2yUTEjT6f74fy8SvBAWbbdiciek2eh/91jhMOrze0dBYvNuBzL2vLtvAToxdy3/QCfuTZfM4t8u557TEoO6BztGqfUMuipRu417DcgADrMSibc5Z457ymPL+koWlpj0HZ3F72V+sxOKdh/ZDJRco9Qy1e/H1qPhU0IlzDS/x3s8tRtm3/NwD8nIjK5D2xa+Rrfc59J0ScZFnW94QQQu4nVf77VwCIkZcop/j9/h8jYrl7hEdEO+Li4n4khBCIuN/5FGS4Zpdyu2nOiEw+/yUiTpcu40T9COmvRLQEAP7mfs8A8Jy8XNpRPm/UJBMRb5XNLlMRMY+IZgLAr+Q5GOr3+28goiL5yc2XiGiJ8x5CYUZg0eODA8d5XtH7/N6idapVmuXp8cXcOzWXz5+vUq0Skrq6On5xzkbuLkdhXZIyecKbZVzjoU/25a7YE3C/6/4RC7nXsNyAdQtX71WtGRIv/j5FKToMXyVMgEUfXVy97llVXcPLNn7EbxRs5zez1/C5C97qW3b81HkePHVF2A9xDJm2gk+duaBaMyRe/N6rroUGDTEBFn10cdXFk9m7rvsOneTUGasaBVjajFX84SenVOuFxYvnU3UtNGiICbDoo4urLp7M3natrqnl8u2H+d3CnTz5reVcvuMw19R4u6GpF8+n6lpo0BATYNFHF1ddPJn1cTWekaO6Fho0xARY9NHFVRdPZn1cjWfkqK6FBg0xARZ9dHHVxZNZH1fjGTmqa6FBQ0yARR9dXHXxZNbH1XhGjupaaNAQE2DRRxdXXTyZ9XE1npGjuhYaNMQEWPTRxVUXT2Z9XI1n5KiuhQYNMQEWfXRx1cWTWR9X4xk5qmvhV5pIm2/GxMR8HxHnuaefCgYR70bE40Q0uLn92bbdCRE3tNTbBFj00cVVF09mfVyNZ+S0tGYZosylNt8koj5NBZjcZo8zKXAL9lfc0mObAIs+OrgeO3WOV63frFqjxehwTo+cOMtryrzZPiUYL57PltYsQ5RpQfPNcQAQL9e9AgApADCDiCwhAgMMAP5GRFNkH7P+zr6IaG9wgNm2/SfZuHMoIg51bbsCAPrL/Yxpyt0EWPTY8/EJfntRJc/38GS+p89Wcfora7n74FzumpzFg6Ys50+PnVWtFZIDh0/xi3M28sDnl3LatCLe8/EJ1UohOXbqHE96e0PDVFIvvLuRT5z2bj84Zm/+PkWlGBsi4yKab/5FCCH8fv+NADBevtYJsCsQcbczqz4iLrZtu53cplGAAcCvnI7LiPhe69atvy0fVzj7IKICv99/YzhvrwfY6s0fcXvZTiP1pZWqdcKyasvHAa0/ps3z5uhm4tvlAZ53DsjklMnea2p57kI1p0xeHuCZ8PxSPn3We7PnvzhnIz8xtojvGZbPPQZn85PjinjqvE2qtZrEBJghJM013wSA/og4TDaxfFmIfwcYALRGxMNOE0sAeNXpEB0qwHw+30/k6C6ZiNa7OjKXONsQ0RQA6BbO1+sB1ikxK6CIeW1Gcoc+wwsaBYPXqKqu5V5pjT3vSsnhw0e90+mYmbl8x+FGnncOyORVWz5WrRbAidPnOT6jhO9NzeeuyTncOSmLe6Xmc/zEUv7cg2HrYALM0EBLm28i4i+JaLkQQti2DYg4W27vHoFVCiG+Jrf5s8/nu1ZuExBgiHg3EWXatv1n+fwNV4BV+Hy+b8jHC4noP8O5e72hZXCAbd6yTblTqOW+1JxGxVa1U/CydVsF3zO0sWfX5CxevX6zcj/3kr2kLGSAzVu4TrmbeynbuJUfTS/gnkPqw6tzYhbfPSSHHxtVwBs2efNntbLSNLQ0uGhp882YmJhvElE+AIwnolFEVAYAv0fEeYg417Ks78kuyi8CQAoRPSOEEADQnYiOIuKziDgEEYcAQKpt2z0QcaEc7ZUDQLL8FGKlvC82FRHHNuXu9RHYrg+PcceE+hCb9p53L8ts2/NZQKGdU7hTtVJIXlqwuVEojJi1WrVWI6qqazl1xqoAz8FTV/C5894bgc/M3MpPjSvi+9IK+O6hOfzU+GKenbNNtVaTVFaaEZjhS4DXA8zBi79wwRw7eY6Xb/qIS1ZuVK0SlvNVNfzS/M18X1oB9xicw+PeWO/ZpotHTpzl1/Mr+bnZa3n8K0v5E49d5nQ4c66KZ+ds4/4TS/mxUQv51bwKPnPOu5cPmb35+6S6Fho0xARY9NHBtaq6ljdv8fYowY0O5/RCVQ1v2arHOfXi+VRdCw0aYgIs+ujiqosnsz6uxjNyVNdCg4aYAIs+urjq4smsj6vxjBzVtdCgISbAoo8urrp4MuvjajwjR3UtdPM11QKGlmECLPro4qqLJ7M+rsYzcpQVQQB40P2ciCarcjFcHCbAoo8urrp4MuvjajwjR1kRRMTydu3atZEzSbxHRHuVyRguChNg0UcXV108mfVxNZ6Ro6wI2rb9C9lGZAMA/BUAfq5MxnBRmACLPrq46uLJrI+r8Yycy178iKi3swDAg4hYSkR9EHHuZZcxRIQJsOiji6sunsz6uBrPyLnsxU82YuwjQ6yPs7h7Yxm8jQmw6KOLq5c9a2rrOG/Fbn5sdCH3Ss3nB57J4/ml73N1Ta1qtSbx8jl140XPy178nMlliSjDtu1fOOudth4q8Pv9P0bESU4fLkS851L2R0QWEU1p4uv/iYgl7tYpQghhWRYiYg0RDW/BYa4kolkAkCqEEIg41LbtPzT1Atu2/w8Rx8r3ORsA0lvyfoIxARZ9dHH1qufpMxf4oZGLQk7m23t4Ph8/eU61Yli8ek6D8aJnJPUrKhDRAvdzpxfV5QYAvouIa52Z2IUQgohmOjO2f4HHTZOT6Tbc+yOi4UT0cUu7KAPAbU6ANYcMzbdcq64koqKL1BZCmAD7ItDF1aue/SYUhwwvZ+k7qlC1YkiqzFRSl0Qk9SsqIOJIRPyL3++Pk61FmpwB/YsCALoRUYZ7nWVZf0TE9xBxIwCkI+IriPgGET2CiG8BwHNCCEFEDyNiJRE9Q0RLAOBx+d4ecPprheuALAPsbiJ6TT5vDQDxRLTHCbBQnZZ9Pt8PiWgBAKQBwHgASI2Njb2OiBa4OjSPl/3DphDRf0mnqQDQwf0+nbYrPATdEQAAG7dJREFUcXFxfgCYgYiJ8s8ZrmrqnJkAiz5edz1fVcMpk5dz3+cK+MgJb3Vj/ujTU9xhYPjwunNAJrcfmMnv7z+mWjWAp8YubvAbkLFEtU6zePFn9BJK/6WBiLsQcbazEFGZIo9EAEhxr7Msi4hovbzUdr/cbj4A3CYfL3O9viImJuabMTEx30fErc56IioWInwHZDly+hoivm/bdjtETJKtUZweXiE7LSPiJCLqKdc94IzAXP3BGjo4y9HlPLkuDwBuDnMO5iLi7+TjJCeIw2ECLPp43fXpCSUNxbZXaoFqnQBeyd3WZHg5y4vvblCt2sCOfUcb+e39+IRqrSbx4s9oi4r8FwER3eJ+rupj9ADQHREnutdZlvVHOcpJRcRbhRACEWc7zR+dcJLr3Z2M97geFwsRvgOy697VUwAwg4gGydftjYmJ+X6oTsu2bf+aiPKdsLFt+0+hAoyIess+Y8mOh+yyHDACc72HCr/ff4P06tbcH5V7vaGls3ixAZ+uru6mlp2TspT7uJdRs0pbFGDDpi5R7uos7+avbeSXuWitcq+mFi/+jLakxn8hyIaOYxExl4hGO5ezLjeWZX0PEVe778Eh4nQA+Ju8zNcowNyhFRRge4PXh+uADABpQgjRpk2bbyHiR+3atWvj7MM1AnN3Wv5TbGzsdYg40TUCuz84wOR5/VBqfN3l8Z9E9Lrj5zTKFPX3wuY4ozMASEbER5s6Z2YEFn287rpxx2HukpTNHRMyOWfZbtU6AazeepDbNxNe7Qdk8qLVe1WrNnChuraRY3W1+bTkxdLySh9lEHEaAHS3LOu3tm33AIAZqlwsyyLXpxDHAEB3AIghoiJEfAkAfk5EZUQ0yLbtPyDibnn/6h/y8V+cDsgy+O5DxN1EdEuoDsh+v//HRLSEiB5xe7j2kSKEEKE6Lbdr164NEWUi4ghEnC7vvd0U1KH5HXnvLFFehrxZCCFs2/67/BRimnxfvxKi/h4YEc0iosHy05PmHthlRhdXL3rW1dXxPUPzmgyw7oNyuLa2TrVqAGfPV/PUuRs5fVYJn7/gva7RwXjxe/+FBEJLQMRE93PnEprB+5gAiz66uHrVs3z7J9wpMStkeHVMyOQVmz5SrRgWr57TYLzoqawIIuJLcnTyOyLqSUQzlckYLgoTYNFHF1cve27fe5T7TSjhjgn1lww7DMzkx8cs4U3vH1at1iRePqduvOiprAi2bdv2evlHw3mIOLZt27bXK5MxXBQmwKKPLq46eH5+9gJ/+MkpXr9xi2qVFqHDOWX2pqeyIoiIdyg7uOGSMAEWfXRx1cWTWR9X4xk5yoogIhYS0WAAeNzn8/mUiRguGhNg0UcXV108mfVxNZ6Ro6wItmrV6jtC1M9DSETFRLRZmYzhojABFn10cdXFk1kfV+MZOcqKIBENIqLliFiKiImWZZEyGcNFYQIs+ujiqosnsz6uxjNylBVBInqEiCYAQH+fz/cTZSKGi8YEWPTRxVUXT2Z9XI1n5Cgrgs59L0T8qbyEuEOZjOGiMAEWfXRx1cWTWR9X4xk5yoogIq4hopVyktnHiKitMhnDReHlAKupqeH+GUUNf8R6V0o2H/rspGqtkAyfsTLgD257DslRrRSSkyfPN/rj4P0HvXlOHx29OMCz9zP5qpXCUlNTyx99epqXrt7ENR6bJSQUJsBcENEElU0svwwAwG2y5Uuas85p5QIAt39Rx/VqgM1dsiPsVEJ9hntrBvWmpj0q2fiBar0GOic1PcegVzh89PMmPfd8dES1YgN1dXU8Z8lOvi+tgDslZnHHhEzuPbyA3yt+n+vqvBtkJsBc2LbdyXkMAD9zF2FDy0HEMUS0w7bt/3bWtbTJZaR4McD2HTrR7GzkKS+UqNZk5qbDy0vB8Mz0pc16dk7yhqsu55SZefLcjdxhYONprzokZPH09zar1guLCTBR36PKsqxYIsqwLCvWsqxYn8/nQ8Rpl13mSwAApFqW9Rsi2un8aQIApALANYg4FRGTEHG63++PQ8SfIuJGIsqQTToLhRBXCtHQgmUcEY1CxLubOqYXA6xrcrYWRezjw6db5OmFy4kt8fTCOX3+zXUt8hw4sUS1Kh868jl3Tgr/s9olOZs/PeathqHMzBW7j3DatCI+8Mkp1SoBXIYSGwgR9UHEEiLai4glcilExKTLLvMlwBltAUB/RHxbPk4DgCcAIFkIIYjov4hojvM1RHxACCEQMRsAfiWn9drm7BMRN/p8vm+EO6YXA6ylxXb3gaNaeHohGFrqefLkeS08vXBOX87e2qzjmwu3q9YMYM/Hxxvc2g/M5M/PVqlWauCLrK1NAgC/V3bwLxHuy4WImCN7hKUR0WTbtnvIbVo7ASXDzeksPRsRb7Us67dEtMNpnklEc5r6UI0XG1q2tIDNfm+VFp53DsjU5pxmFa7RwtML53TI5CXNOqZNK1Lu6V4yXg28lJxXvF65k7N80fU1LLZttyOimYg4CRHvAICblMlojPveYdu2ba8nou2IOBEAHnf6igHAzUT0jrO9u0knIt4aExPzfURc69pnRyEvLYZC5xHY4aNntPD0wmihpZ7nz5sRWEt5r+j9Zh1zlnnnQzzMzCc/v8AdE+rv2XVJzuaq6hrVSg18YYW1ORBxGhHdIgvw1QAwXpmMphDRLbKhZS9nnWVZf5TNQa9GxKmySeVMv98f5zTpJKIpfr//x4hY7tx7RMR7AGC8nJ8yvqnjejHAHhxRoEUBu3DhQos8h09boVpVm1DY/P7hFnm+V7RNtSqf/PwCd0vJCevYfVAun/bQJTqHC1U1vGz1Rq7yWNfoL7jEhsdpaOnc+yKiwcpkDBeFFwPswoWaZgvYu4t3qNZkZn0+MVdY1vxoYZD5ZOdFs3D1Xu4U4oMcnZOyuXj9ftV6YamsNJ9CbAARpyPiEER8AwD6A8CLymQMF4UXA4yZ+cCnJ8MWr1mZ3vp4sg6Flpl56tyysJ4Pj1yoWi8AXc4pM3PF3iM8aOoK7j44l+9KzuYh01byzv1qP2DUHCbAhBDOH9gCwDUA8AQRTQGAx4QQV112GUNEeDXAHMq3H+QHRizku4dk87T5G1XrhCX4cuKufd7tHJzomt3knmHZqnXCEvwHzQcPf65aqVm8GAyh8KLnZS9+cpaIVLmkyQ8VTJT3bQwa4PUAc/DiL1w4dHHVxZNZH1fjGTmXvfg5f4Pken4/EW23LOuPl13GEBEmwKKPLq66eDLr42o8I0dZEWzVqtV3EPFtRMxp27bt9cpEDBeNCbDoo4urLp7M+rgaz8hRUgBt2/5vItoJAP2VCBguCRNg0UcXV108mfVxNZ6Rc9mLn5wlYodlWb8JWt8r3GsM3sIEWPTRxVUXT2Z9XI1n5Fz24oeIGxFxjOuDHKky1JZcdhlDRJgAiz66uOriyayPq/GMnMte/Jx5+IIholsut4shMkyARR9dXHXxZNbH1XhGjupaaIgSTnNLRJzqXm9Z1v8Q0QUieoSIFgDANUIIgYj9Ij2WCbDoo4urLp7M+rgaz8i51Lpp8BAAkI6Iu92f6iSiUUR0KnhbItob6XFMgEUfr7ueOH2eS8oP8KuZq/nwMbUTIjdHdXUt7z90krdsVT/3YUvw+vfewYuekdYwgweR9xIHIeIIIYSwLOs/iKgnEZ0CgJsQcaNlWbG2bf8dEY/L+4+3I+IwRCwRQgj5+tlNHccEWPSora3jIyfO8patFapVwrLrw+P85Lhi7pKczZ0Ss/jh5xbz+spPVGuFZNGafQ0NIzsmZHJmqbdmdg+mcN1+fj1ztWqNFuHF36fLUVcNlwkASPX5fNcS0d42bdp8CxFHCiG+5ozAEHG2ZVmxQghBRHuc1/n9/jgiKnYeI+LLTR3HBFh0qK2t45Qpy7lrcg73Ts3j46fVtiUJx7Mvr+H2QXML9p9Y6rmZyc+er+aOAwM92w/I5JOfX1CtFpLEF5Zy+4H1joOmLFet0yxe/H36Asup4XLjNLckoglENAER+8rnoQKs4RKiCTA1fHr8DHdNzuGuyTncOSmL81ftUa3UiPNVNfxwemGjyXH/f3tnGh3Ved7x2zY9p8mX9rR1U2O5o9G8z3OaOPFJP7ltWuP0nOa0tgVmsTEmBi8YTJq0IKEVxBYQEFMW2ywGA8ZAAeNlRjtISMKIRSDJwiAZsxkDdmIWsyhCSCPp3w+6dzwazZZhyPu+8P7OuYfRnbkzv7nMff5zl3mfsbPKcf5Cm2y9fjR88tuwA/nuOnRWtlpYhmUVBQJseLa640s6qLg93a5aapCA09zS4/HcT0TnU1NT/8yyLIuZ2yyrf4AR0Ql7me/bjTAP2sv+czyHEGV3Yo1nampqku4Qbfr4SAuenVGCYdk+jMwtQvXej6Q7hU4tLS34xcIdA0Lh+dmlaGo+It0veNp/6OOwAVa7T7312traijEFJUi39xLHziiR7hNrUnF7up311PAHhIgeYuYqj8czKng+M79ERB1E9N/M3EBE0+35rwshFhHRL+2/37bPiU1m5ga32/1gpNcye2DJ40rbTZTWnUZ1nbqj5u89/AWem12B9ExnNPoy+BTrGuywdEtjv/AqXF8vWykiV9tuYs7a/chZVom2djUPcwaj4vZ0e6uq4Y7EBFjyUd215fQlrC06gvlra5W9gMPh1Pkr2Fb5KcprG2WrxIXq//cOKnrKroUGDTEBlnx0cdXFE9DH1XgmjuxaaNAQE2DJRxdXXTwBfVyNZ+LIroUGDTEBlnx0cdXFE9DH1XgmjuxaaNAQE2DJRxdXXTwBfVyNZ+LIroUGDTEBlnx0cdXFE9DH1XgmjuxaaNAQE2DJRxdXXTwBfVyNZ+LIroUGDTEBlnx0cdXFE9DH1XgmjuxaaNAQE2DJRxdXXTwBfVyNZ+LIroUGDdEhwLq7e7Rpp3GzqxtHW9QdjR4Auru7sWPvCax5dw9udHbL1onKF19dR1X9GdTsU/uHzH5/D46duYzy2kb4FRsYOZSenl40f3wUvb29slX6IbsWKkc8jSHDLZeSkvKXRPQuM4+9VYe0tLS/I6L19tBOq4UQhZbVN9CuEGJovM9jt1BpDBr/sOJW3SxL7QD7+ORF5K+ow8jcYgzL8mFCYSXK9p5Gd49aG97vbnRhQ0lLoPVHeoYXv367Hl9e+p1stX50d3dj6NSB4ws+nuHFxctqDea7ruhoWM9lW9X6vHb6e5C3Yg+GTvUFHIdO9WH6yjrlguzilRvYWnkM2a99iImF5chfUYcPak/gqiKdE5JRz+44fp/GkMEw87hkBJg9kvxjQX8vsL0GxxpoNxQiWucEWLJQNcDK93+GJ7J8AwpYeqYXc97cr0yIXW/vxDPTSsMW2yGZXpw4d0W2YoBwjsHT5atqhNiiTYeiehasqpOtCKBvr2tMQVlEz2dnlisTYucvtCF/RR0mL67B5MU1mFhYHrg9c/U+XL7WIVvRBFg4ojWGdB5DRG8JIXKFEKuZOc2yvgkwj8fzAyJqFEK8YD/fImbOY+blbrf7QSL6nr2Xt4yZNxLR9pDXzyWi94UQnuD5RPQGETUJIWbYI87XMPNYl8t1LxHVENHD9vJPMvM79nuotJtY/iS4hQoRvWLfv0II8Q9B7zufiN7weDw/jrR+VAywy9c6MCKnKGJhSM/0omyvGu1KZq6ui1psR+WVoEeBsH1hduRCG9hzyPTK1kRPT09Mz8czvEoEwysbowdt3x6jGoc+F759KBBYoQE2eXENVr1/WLaiCbBwRGkM2eY8hoj+3bIsy+12PyiEWGRZfQEmhHhBCDHP5XLdaz/uMWZeY1l9bU6Yucp+jZnM/JJl9QVkiMK3iGgaEZ1i5gZmftReZnBwry77OcY6znaA/TEzn7Es61v26+8O6gFW7TgR0Urb6W+cVirMfHjQoEF/fd999/2Vx+MRkdaPigH2VklLzMIwcX6VbE1cb++Mq9gePn5Btmpcno9nyA+wOWv3x+U5ZXG1bFUMj/Ily5lG5hbL1sTJc1cCQfU//1uNp/JKMCTTi9HTS78JsSW1uHjlhlTPhAr8nU6UxpBtQY+ZQkQFzLzACRVmHkdEtfZe0j2WZVlElEVEpUSULYTIZeat9vIzhRCDY7l4PJ5/Y+bLgwYN+k6MAJtJRA/bgfRJ0GM2hAmwLCFEjvMYIrpgv9Z/EFElEZU4e5XhUDHA8pbviVkYRuQUw98t91v4p59/HVex3bLjmFRPIP4Au3hVbhEblV8Sl+ewbJ9UTyC+dZquwJeC2sZzgaB6ce6OQOPN9EwvJi2oCtzXLPmLVjz1/K4jjsaQP2LmPfZjhHNeyj6EOE4I8VMies+e9ygRLXOem4iGO6/hHPILhYgK3G73d4P+PpmSkvLttLS0fyGit1JTU//C5XLdy8yZQoif28+3OmgP7DPLsv7UXrY26CKOGvvfx4holWVZVmpq6t8S0QH7vfzEfq7xwc6hqNjQMnPxzpiFYXi2D0dbWqR6Vu1piquIrdxaJ32dxhtgjZIbWz6VF5/nsCyvFus0PUO+57bSekwsLMfEwnI8N/ubxpvpmV68+KvSwH2l1XJrwe9b2+94YjWGZOaxKSkp32bmMvvc1nxmbkhLS/shEb1LRNuFEA8w82fM/LplWX8ihCi0rygsFEL8VAiRwsy7mHlNcFA5CCHSmXmjcxUiEb1sWX1XOjLzLiJalZaWRkIIDzOXM/MvmHmrc6jSOQcmhJjBzHudw4tEdEoI8TP7fb5CRAX2ebUf2fP+j4iyiWidEOKfIq0jFffAKus/DzRcjDQVvLFXtib83T1hLzQJnX6jwNWI8QaYbCr2nY7Lc0PxEdmq+NmM2OcVx82ukK2Jr691YMqS2sCe1vNzdmBkrg/j5+0MzMt5fQ86Ov1SPZNY+g13CyoGWGdXN8bNrojy7bsILacvydYEAGyv+jRqAZu6rFa2IgBg+fbYe4sTCuUXWyC+sFWBsr2xw7by4OeyNQEAG0pbol7EoUJXbtm10KAhKgYYAJz76jqem10xYE9sRE4xKuvVKAoA0NvbiyWbD4YtXpMWVKG9o0u2YoAxBZHPL6lwBaLDybOXo4ZC83F1ukgv2FAf0XPxZnW2rY5OP5a/2xw2wDaUtijxsxTZtdCgIaoGGAB0+bux69BZzFqzH5MX7cDG8lZcua7Gjy5DOfOba3jtnY+QubQW2UsrcPj4BeVGOgCA7VXHBhTa17Y1ydYaQGenHz9fWNXPc/zcHWjvkHuYKxxNn3yFSQuqMDynCMOyfPivX+9C86dfydYaQG9vL46duYyN5a0ofLMGW3Yew+kvrsrWCiC7Fho0ROUAC6a1Vb2x2yKhi6sunoA+rsYzcWTXQoOGmABLPrq46uIJ6ONqPBNHdi00aIgJsOSji6sunoA+rsYzcWTXQoOGmABLPrq46uIJ6ONqPBNHdi00aIgJsOSji6sunoA+rsYzcWTXQoOGmABLPrq46uIJ6ONqPBNHdi00aIgJsOSji6sunoA+rsYzcWTXQoOGmAC7dfz+HrxffRwvzt2JUdNKMXp6MVa+14y29k7ZagOorD+D8fN24oksH4ZM9WLsrHJsq5I/2HAonV3d2FTeiufnVGDUtFI8M70Y64uP4sZN9X4HFozKn9NgVPSUXQvvOBLt6BwOe6zCdaHzhRDPEtEzMZZd6Xa7XfGbx48JsFujo9OPl+bt7Bvh25nsgVKfyi/B2d9el60YYMGGg2HHmEzP8GLKEjWGvAKAq203MXZWRdh1+kxBGS5eaZet2I+bXd3Y3XQOy7c3Y96aGnz40Xl0+btla0VFxe3pdtS3u55EOzqH4na7XeECTDYmwG6N/BV1gQI7IBgy+7ryqkDVwegDJKdneLH6A/lNDQHgl4uq+4WX4+f8PaGwUrZigI5OPxZtbsCkhVUYU1CGUflFmLRwF5ZsaURnl7ohpuL2JLsW3pHE6ugcrpuzy+Vy2yPPT7VHkr/HDrBKIlpCRAeI6CHLsv6IiNbbI9Xfw8x7iGgdEb3BzHvT0tL+3OVyuZ0Ozcz8r0R0QgiRw8xbnU7MzPyB04rF7hC9WQiRQ0RLYr0/1QPM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], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "/home/mlode/intel/intelpython27/lib/python2.7/site-packages/sklearn/preprocessing/data.py:321: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n", "/home/mlode/intel/intelpython27/lib/python2.7/site-packages/sklearn/preprocessing/data.py:356: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n" ] } ], "source": [ "df_test_filtered = df_test[df_test.network.isin(networks_list)]\n", "df_sizes = pa.DataFrame(index=df_test_filtered['network'].unique())\n", "\n", "groups = df_test_filtered.groupby(by=['network', 'popularity'])\n", "#print df_test_filtered.head()\n", "test = groups.size()\n", "\n", "\n", "counts = df_test_filtered['network'].value_counts()\n", "\n", "\n", "Xuniques, X = np.unique(df_test_filtered['network'], return_inverse=True)\n", "#print pa.DataFrame(X)[0].value_counts()\n", "\n", "from sklearn.preprocessing import normalize, MinMaxScaler\n", "\n", "print test.describe()\n", "\n", "min_max_scaler = MinMaxScaler(feature_range=(10, 100))\n", "area = min_max_scaler.fit_transform(test.values)\n", "#print area\n", "\n", "fig = plt.figure()\n", "ax = sns.regplot(y=X, x=df_test_filtered['popularity'], fit_reg=False, scatter_kws={\"s\":area})\n", "ax.set(yticks=range(len(Xuniques)), yticklabels=Xuniques) \n", "#ax.set_xticklabels(Xuniques, rotation=45, rotation_mode=\"anchor\")\n", "#ax.tick_params(axis='x', direction='out', pad=52)\n", "\n", "leg = [1.0, 300.0, 739.0]\n", "area2 = min_max_scaler.fit_transform(leg)\n", "\n", "gll = plt.scatter([],[], s=area2[0], marker='o')\n", "gl = plt.scatter([],[], s=area2[1], marker='o')\n", "ga = plt.scatter([],[], s=area2[2], marker='o')\n", "\n", "plt.legend((gll,gl,ga),\n", " ('1 (min)', '300', '739 (max)'),\n", " scatterpoints=1,\n", " loc='upper center', bbox_to_anchor=(-0.15, -0.05),\n", " ncol=1,\n", " fontsize=9)\n", "\n", "ax.set_xlabel('Popularity Class')\n", "ax.set_ylabel('Network')\n", "\n", "plt.title('Network Popularity Classes')\n", "fig.tight_layout()\n", "save_plot('channel_network_classes_rel.pdf', fig, x_width, 1.5x_height)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "97\n", "20\n", " network popularity count\n", "0 None 1 739\n", "1 BroadbandTV 3 708\n", "2 None 2 706\n", "3 None 0 618\n", "4 BroadbandTV 2 504\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" }, { "name": "stderr", "output_type": "stream", "text": [ "/home/mlode/intel/intelpython27/lib/python2.7/site-packages/matplotlib/scale.py:101: RuntimeWarning: invalid value encountered in less_equal\n", " a[a <= 0.0] = 1e-300\n" ] } ], "source": [ "df_test_filtered = df_test[df_test.network.isin(networks_list)]\n", "df_sizes = pa.DataFrame(index=df_test_filtered['network'].unique())\n", "\n", "groups = df_test_filtered.groupby(by=['network', 'popularity'])\n", "\n", "test = groups.size()\n", "\n", "test = test.sort_values(ascending=False)\n", "print len(test)\n", "print len(test.index.levels[0])\n", "\n", "test2 = test.reset_index()\n", "test2.columns = ['network', 'popularity', 'count']\n", "\n", "print test2.head()\n", "# number of channels per network\n", "#counts = df_test_filtered['network'].value_counts()\n", "\n", "fig = plt.figure()\n", "#test2.plot(kind='bar')\n", "\n", "\n", "ax = sns.barplot(x='network', y='count', data=test2, hue='popularity')\n", "ax.set_xticklabels(ax.get_xticklabels(),rotation=45, ha=\"right\")\n", "\n", "ax.set_xlabel('Network')\n", "ax.set_ylabel('Number of Channels')\n", "#ax.set_yscale('symlog', linthreshx=10)\n", "ax.set_yscale('log')\n", "l = ax.legend(ncol=len(classes))\n", "l.set_title('Popularity Class')\n", "plt.title('Network Popularity Classes')\n", "fig.tight_layout()\n", "\n", "save_plot('channel_network_classes_rel_bar.pdf', fig, 2*x_width, x_height)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "df_uniq = df_test.copy()\n", "\n", "Xuniques, X = np.unique(df_test['category'], return_inverse=True)\n", "Yuniques, Y = np.unique(df_test['topicIds'], return_inverse=True)\n", "Yuniques, Z = np.unique(df_test['network'], return_inverse=True)\n", "\n", "df_uniq['category'] = X\n", "df_uniq['topicIds'] = Y\n", "df_uniq['network'] = Z\n", " \n", "#fig = plt.figure()\n", "#sns.pairplot(df_uniq)" ] }, { "cell_type": "code", "execution_count": 30, "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>topicIds</th>\n", " <th>network</th>\n", " <th>viewCount</th>\n", " <th>subscriberCount</th>\n", " <th>videoCount</th>\n", " <th>commentCount</th>\n", " <th>category</th>\n", " <th>popularity</th>\n", " </tr>\n", " <tr>\n", " <th>id</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>UC__Pj66OeDibNZNN__L913g</th>\n", " <td>Music</td>\n", " <td>None</td>\n", " <td>3253022</td>\n", " <td>23029</td>\n", " <td>967</td>\n", " <td>0</td>\n", " <td>Entertainment</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>UC__PZLSRGtUQiTtvm3hPoEQ</th>\n", " <td>Movies</td>\n", " <td>BroadbandTV</td>\n", " <td>310896</td>\n", " <td>5878</td>\n", " <td>144</td>\n", " <td>0</td>\n", " <td>Entertainment</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>UC__rmdgxs3ZF0zK_he7Tmig</th>\n", " <td>Lifestyle</td>\n", " <td>None</td>\n", " <td>1291254</td>\n", " <td>8146</td>\n", " <td>294</td>\n", " <td>121</td>\n", " <td>How-to & Style</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>UC_-CxgsxX0tpnm24WO-797Q</th>\n", " <td>Lifestyle</td>\n", " <td>Maker Studios</td>\n", " <td>625545</td>\n", " <td>18990</td>\n", " <td>67</td>\n", " <td>101</td>\n", " <td>How-to & Style</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>UC_1FUFB6TlGeGOyDI4ikkzg</th>\n", " <td>Movies</td>\n", " <td>BroadbandTV</td>\n", " <td>89020205</td>\n", " <td>106760</td>\n", " <td>288</td>\n", " <td>0</td>\n", " <td>Entertainment</td>\n", " <td>3</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " topicIds network viewCount \\\n", "id \n", "UC__Pj66OeDibNZNN__L913g Music None 3253022 \n", "UC__PZLSRGtUQiTtvm3hPoEQ Movies BroadbandTV 310896 \n", "UC__rmdgxs3ZF0zK_he7Tmig Lifestyle None 1291254 \n", "UC_-CxgsxX0tpnm24WO-797Q Lifestyle Maker Studios 625545 \n", "UC_1FUFB6TlGeGOyDI4ikkzg Movies BroadbandTV 89020205 \n", "\n", " subscriberCount videoCount commentCount \\\n", "id \n", "UC__Pj66OeDibNZNN__L913g 23029 967 0 \n", "UC__PZLSRGtUQiTtvm3hPoEQ 5878 144 0 \n", "UC__rmdgxs3ZF0zK_he7Tmig 8146 294 121 \n", "UC_-CxgsxX0tpnm24WO-797Q 18990 67 101 \n", "UC_1FUFB6TlGeGOyDI4ikkzg 106760 288 0 \n", "\n", " category popularity \n", "id \n", "UC__Pj66OeDibNZNN__L913g Entertainment 2 \n", "UC__PZLSRGtUQiTtvm3hPoEQ Entertainment 1 \n", "UC__rmdgxs3ZF0zK_he7Tmig How-to & Style 1 \n", "UC_-CxgsxX0tpnm24WO-797Q How-to & Style 2 \n", "UC_1FUFB6TlGeGOyDI4ikkzg Entertainment 3 " ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_test.head()" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df_test.to_csv(DIR+r'/df_channel_statistics_first_day.txt', sep=str('\\t'), encoding='utf-8')\n" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "6" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_test['popularity'].max()" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# number of cluster per channel -> number of persons per channel (content creator)\n", "# average etc.\n", "\n" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": true }, "outputs": [], "source": [ "with db._session_scope(False) as session:\n", " #channel_stats_first = session.query(ChannelHistory).filter( (func.day(crawlTimestamp) == 28) & (func.month(crawlTimestamp) == 12) ).all()\n", " #channel_stats_last = session.query(ChannelHistory).filter( (func.day(crawlTimestamp) == 28) & (func.month(crawlTimestamp) == 2) ).all()\n", "\n", " df_channel_cluster = pa.read_sql(session.query(Video.channelID, VideoFaceCluster.cluster).filter( (VideoFaceCluster.featureID==VideoFeatures.id) & (VideoFeatures.videoID==Video.id)).statement, db.engine)\n" ] }, { "cell_type": "code", "execution_count": 35, "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>channelID</th>\n", " <th>cluster</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>UC--BMyA2X4a9PGAo3lTuopg</td>\n", " <td>9510</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>UC--BMyA2X4a9PGAo3lTuopg</td>\n", " <td>9510</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>UC--BMyA2X4a9PGAo3lTuopg</td>\n", " <td>9510</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>UC--BMyA2X4a9PGAo3lTuopg</td>\n", " <td>12551</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>UC--BMyA2X4a9PGAo3lTuopg</td>\n", " <td>12551</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " channelID cluster\n", "0 UC--BMyA2X4a9PGAo3lTuopg 9510\n", "1 UC--BMyA2X4a9PGAo3lTuopg 9510\n", "2 UC--BMyA2X4a9PGAo3lTuopg 9510\n", "3 UC--BMyA2X4a9PGAo3lTuopg 12551\n", "4 UC--BMyA2X4a9PGAo3lTuopg 12551" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_channel_cluster.head()" ] }, { "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": 2 }	mit
PhilHarnish/forge	src/puzzle/examples/mim/p4_1.ipynb	1	2456	{ "cells": [ { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import forge\n", "from puzzle.puzzlepedia import puzzlepedia\n", "\n", "puzzle = puzzlepedia.parse(\"\"\"\n", "name in {Alistair, Evelyn, Grant, Lyle, Molly, Sandy, Vacant}\n", "number in range(1, 7+1)\n", "\n", "floors = [\n", " [], # None in basement.\n", " [1, 2],\n", " [3, 4, 5, 6, 7],\n", " [], # None on 3rd floor.\n", "]\n", "linked_closets = {\n", " 1: 2,\n", " 3: 4,\n", " 6: 7,\n", "}\n", "under_room = {\n", " 1: 3,\n", " 2: 4,\n", "}\n", "\n", "def floor(n):\n", " # n[1], n[2] return \"1\", else return \"2\".\n", " return 1 + (not (n[1] or n[2]))\n", "\n", "#1:\n", "floor(Alistair) != floor(Evelyn)\n", "\n", "#2: Grant and Lyle have adjacent closets.\n", "for room1, room2 in linked_closets.items():\n", " (Grant == room1) == (Lyle == room2)\n", " (Grant == room2) == (Lyle == room1)\n", "\n", "#3: Sandy is above or below Lyle.\n", "any(Lyle[n] for n in list(under_room.keys()) + list(under_room.values()))\n", "for under, above in under_room.items():\n", " (Lyle == under) == (Sandy == above)\n", " (Sandy == under) == (Lyle == above)\n", "\n", "#4: Molly has an adjacent closet with Vacant.\n", "for room1, room2 in linked_closets.items():\n", " (Molly == room1) == (Vacant == room2)\n", " (Molly == room2) == (Vacant == room1)\n", "\n", "#5:\n", "Alistair.number + Sandy.number == Evelyn.number + Molly.number\n", "\"\"\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0" }, "widgets": { "state": { "88d31afc1a784ce287a4add8a5cd8d46": { "views": [ { "cell_index": 0 } ] } }, "version": "1.2.0" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
edhenry/notebooks	Eigendecomposition+Examples.ipynb	1	3969	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Eigendecomposition of matrices" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this notebook we'll look at the eigendecomposition of matrices. We will be following the lessons taught in the Deep Learning book from Goodfellow et al.\n" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import scipy as sci\n", "from scipy import linalg as la" ] }, { "cell_type": "code", "execution_count": 81, "metadata": { "collapsed": false }, "outputs": [], "source": [ "mat_dim = 5\n", "A = np.mat(np.random.randint(low=1, high=10, size=(mat_dim, mat_dim)))" ] }, { "cell_type": "code", "execution_count": 82, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "matrix([[3, 6, 9, 4, 2],\n", " [7, 8, 5, 1, 1],\n", " [1, 9, 8, 1, 7],\n", " [5, 8, 2, 3, 1],\n", " [8, 8, 6, 6, 4]])" ] }, "execution_count": 82, "metadata": {}, "output_type": "execute_result" } ], "source": [ "A" ] }, { "cell_type": "code", "execution_count": 83, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[-0.09962676, 0.16221648, -0.04536319, -0.16681022, 0.1303474 ],\n", " [ 0.01550388, -0.03100775, 0.03875969, 0.2248062 , -0.12403101],\n", " [ 0.12919897, 0.0749354 , -0.01033592, -0.12661499, -0.03359173],\n", " [ 0.09417169, -0.22538042, -0.06086707, 0.1432673 , 0.0799598 ],\n", " [-0.16681022, -0.03674993, 0.12001148, -0.14097043, 0.1678151 ]])" ] }, "execution_count": 83, "metadata": {}, "output_type": "execute_result" } ], "source": [ "B = la.inv(A)\n", "B" ] }, { "cell_type": "code", "execution_count": 85, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "matrix([[ 1.00000000e+00, -1.11022302e-16, 0.00000000e+00,\n", " -2.22044605e-16, -1.66533454e-16],\n", " [ 1.66533454e-16, 1.00000000e+00, -8.32667268e-17,\n", " -2.22044605e-16, -5.55111512e-17],\n", " [ 0.00000000e+00, 1.11022302e-16, 1.00000000e+00,\n", " 0.00000000e+00, -2.22044605e-16],\n", " [ 1.66533454e-16, 0.00000000e+00, -1.52655666e-16,\n", " 1.00000000e+00, -2.77555756e-16],\n", " [ 2.22044605e-16, -2.22044605e-16, -1.66533454e-16,\n", " -1.11022302e-16, 1.00000000e+00]])" ] }, "execution_count": 85, "metadata": {}, "output_type": "execute_result" } ], "source": [ "I = A * B\n", "I" ] }, { "cell_type": "code", "execution_count": 88, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.9999999999999994" ] }, "execution_count": 88, "metadata": {}, "output_type": "execute_result" } ], "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": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
pjpmarques/Modelling-the-World	Archimedes and Pi.ipynb	1	12401	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Archimedes and Pi\n", "by [Paulo Marques](http://pmarques.eu), 2014/03/09 (Adapted in 2018/10/15 to Python from Julia)\n", "\n", "---\n", "\n", "Since high school I've been fascinated with $\\pi$ -- this infinite non-repeating irrational transcendent number. In fact, not only was I fascinated with $\\pi$ but I was fascinated about everything related to it. In 11th grade I asked my math teacher about how to deduce the area of a circle. Her answer: for that you need to learn how to integrate – wait until university. But I couldn't wait and head to the library where I found a single book that talked about the subject – an obscure calculus book by an author named [Piskunov]( https://archive.org/details/DifferentialAndIntegralCalculus_109). And, to integrate I've learned – just because of $\\pi$. But I digress. ..\n", "\n", "This story is not about calculus or \"symbolic\" integration. It's about how [Archimedes](http://en.wikipedia.org/wiki/Archimedes) calculated $\\pi$ circa 200 B.C. In the \"Measurement of a Circle\" Archimedes states that: \n", "\n", "> \n", "> \"The ratio of the circumference of any circle to its diameter is greater than $3\\tfrac{10}{71}$ but less than $3\\tfrac{1}{7}$\"\n", "> \n", "\n", "This is the first really accurate estimation of $\\pi$. I.e., he calcuated $3.140845070422535 < \\pi < 3.142857142857143$. A good approximation of $\\pi$ is 3.141592653589793. So, this is two decimal places correct. That's pretty impressive.\n", "\n", "According to the story, Archimedes did this by inscribing and circumscribing a circle with regular polygons and measuring their perimeter. As the number of sides increases, the better these polygons approximate a circle. In the end Archimedes was using a 96-sided polygon. The next image illustrates the idea.\n", "\n", "![Polygons](/files/imgs/Archimedes_pi.svg)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "One of the annoying things when books talk about this is that they always show this nice picture but never ever do the actual calculation. So, using Python how can we do this?\n", "\n", "Let's start by assuming that we are going to use a circle with a radius of 1 and we inscribe a square in it. (The square's side is going to be $\\sqrt{2}$.)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![Triangle_Simple](/files/imgs/triang_1.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, assume that the side of this polygon is $s_n$ and you are going to double the number of sides where the length of each new side is $s_{n+1}$. We can draw several triangles in the figure that will help us out:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![Triangle_Simple](/files/imgs/triang_2.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we take the side $\\overline{AB}$, which measures $s_n$, and break it in two, we get the triangle $\\overline{ACD}$. This triangle has a hypotenuse of $s_{n+1}$, an adjacent side of $s_n/2$ and a height of $h$. Note that the new polygon that we are forming is going to have eight sides (i.e., double the number of sides we had), each one measuring $s_{n+1}$. From this we can write:\n", "\n", "$$ h^2 + (\\frac{s_n}{2})^2 = s_{n+1}^2 $$\n", "\n", "\n", "Looking at the triangle $\\overline{BCO}$, which is rectangle, we note that: its hypotenuse is 1, one side measures $1-h$ and the other measures $s_n/2$. Thus, we can write:\n", "\n", "$$ (1-h)^2 + (\\frac{s_n}{2})^2 = 1^2 $$ \n", "\n", "These two relations will always apply as we contantly break the polygons into smaller polygons. As we progress, the perimeter of the polygon $P_n$, obtained after $n$ iterations, will approximate the perimeter of the circle, measuring $2 \\pi$. What this means is that $\\lim_{n \\to \\infty} P_n = 2 \\pi $. \n", "\n", "Also note that every time we create a new polygon the number of sides doubles. Thus, after n steps we have a $2^n$ sided polygon and $P_n$ is:\n", "\n", "$ P_n = 2^n \\times s_n $\n", "\n", "Manipulating the two expressions above we get:\n", "\n", "$$ s_{n+1} = \\sqrt{ 2 - \\sqrt{4 - s_n^2} } $$ \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since we started with a square we have: $s_2 = \\sqrt 2$. We can also consider $s_1 = 2$ representing a diameter line.\n", "\n", "So, with this we have all equations needed to iteratively approximate $\\pi$. Let's start by coding a function that gives us $s_{n+1}$:\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from math import sqrt, pi\n", "\n", "def side_next(side):\n", " return sqrt(2. - sqrt(4. - side2.0))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Our function `aprox_pi()` will compute the aproximation of $\\pi$ for a $2^n$ polygon:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "def aprox_pi(n = 10):\n", " s = 2.0\n", " for i in range(1, n):\n", " s = side_next(s)\n", " return 2.0(n-1) * s" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And the result is:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3.141587725279961" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a_pi = aprox_pi()\n", "a_pi" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So, in 10 iterations we got a very good approximation of $\\pi$ using a 1024-sided polygon. (Note that since $P_\\infty \\rightarrow 2 \\pi$ we need to divide the final result by two. `aprox_pi()` is automatically doing so.)\n", "The error of the result is:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "4.928309832230582e-06" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "abs(pi - a_pi)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That's Interesting. Let's see how good are the approximations generated by `aprox_pi()`." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " i \t Sides \t Pi \t Error\n", "===================================================================\n", " 1 \t 2 \t 2.0000000000 \t 1.14e+00\n", " 2 \t 4 \t 2.8284271247 \t 3.13e-01\n", " 3 \t 8 \t 3.0614674589 \t 8.01e-02\n", " 4 \t 16 \t 3.1214451523 \t 2.01e-02\n", " 5 \t 32 \t 3.1365484905 \t 5.04e-03\n", " 6 \t 64 \t 3.1403311570 \t 1.26e-03\n", " 7 \t 128 \t 3.1412772509 \t 3.15e-04\n", " 8 \t 256 \t 3.1415138011 \t 7.89e-05\n", " 9 \t 512 \t 3.1415729404 \t 1.97e-05\n", " 10 \t 1024 \t 3.1415877253 \t 4.93e-06\n", " 11 \t 2048 \t 3.1415914215 \t 1.23e-06\n", " 12 \t 4096 \t 3.1415923456 \t 3.08e-07\n", " 13 \t 8192 \t 3.1415925765 \t 7.70e-08\n", " 14 \t 16384 \t 3.1415926335 \t 2.01e-08\n", " 15 \t 32768 \t 3.1415926548 \t 1.22e-09\n", " 16 \t 65536 \t 3.1415926453 \t 8.27e-09\n", " 17 \t 131072 \t 3.1415926074 \t 4.62e-08\n", " 18 \t 262144 \t 3.1415929109 \t 2.57e-07\n", " 19 \t 524288 \t 3.1415941252 \t 1.47e-06\n", " 20 \t 1048576 \t 3.1415965537 \t 3.90e-06\n", " 21 \t 2097152 \t 3.1415965537 \t 3.90e-06\n", " 22 \t 4194304 \t 3.1416742650 \t 8.16e-05\n", " 23 \t 8388608 \t 3.1418296819 \t 2.37e-04\n", " 24 \t 16777216 \t 3.1424512725 \t 8.59e-04\n", " 25 \t 33554432 \t 3.1424512725 \t 8.59e-04\n", " 26 \t 67108864 \t 3.1622776602 \t 2.07e-02\n", " 27 \t 134217728 \t 3.1622776602 \t 2.07e-02\n", " 28 \t 268435456 \t 3.4641016151 \t 3.23e-01\n", " 29 \t 536870912 \t 4.0000000000 \t 8.58e-01\n", " 30 \t 1073741824 \t 0.0000000000 \t 3.14e+00\n" ] } ], "source": [ "print(\"%10s \\t %10s \\t %20s \\t %10s\" % (\"i\", \"Sides\", \"Pi\", \"Error\"))\n", "print(\"===================================================================\")\n", "for i in range(1, 31):\n", " sides = 2.*i\n", " a_pi = aprox_pi(i)\n", " err = abs(pi - a_pi)\n", " print(\"%10d \\t %10d \\t %20.10f \\t %10.2e\" % (i, sides, a_pi, err))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So, what's going on? We should expect better approximations as the number of sides increases, right? But, the best result we get is with 14 iterations and a polygon of 16384 sides. After that the approximations of $\\pi$ get worse.\n", "\n", "The problem is that our algorithm is not very good in terms of producing the end result. If you look at the expression $P_n = 2^n \\times s_n$ what we are doing is multiplying a very large number (the number of sides) by a very small number (the length of a side). After a certain point, because we are using floating point, this is a recipe for disaster. In particular, for a 16384-sided polygon, the length of a side is approximatly:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.0003834951969714103" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "2pi/16384" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "----\n", "\n", "On a final note. Archimedes didn't have access to computers nor sofisticated ways of calculating square roots or sofisticated algebra. While in this notebook I've used his ideas of inscribing a polygon inside of a circle, his method was a little different in terms of calculation. He started with an hexagon (not a square) and ended-up with a 96-sided polygon. At the same time he ended up calculating his approximation of $\\pi$ using fractions (which I still think is impressive):\n", "\n", "> \"The ratio of the circumference of any circle to its diameter is greater than $3\\tfrac{10}{71}$ but less than $3\\tfrac{1}{7}$\"\n", "\n", "He did this by using rational approximations of certain square roots. Actually, no one knows where he got his roots! [Check this page](http://itech.fgcu.edu/faculty/clindsey/mhf4404/archimedes/archimedes.html) for a good description of this process of finding out $\\pi$." ] }, { "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": "3.7.1" } }, "nbformat": 4, "nbformat_minor": 1 }	mit
Ttl/scikit-rf	doc/source/tutorials/Introduction.ipynb	2	18614	{ "cells": [ { "cell_type": "raw", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ ".. _introduction:\n", "\|\n", "\|\n", "Download This Notebook: :download:`Introduction.ipynb`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Introduction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is a brief introduction to scikit-rf (aka `skrf`). The intended audience are those who have a working python stack, and are somewhat familiar with python. If you are completely new to python, see scipy's [Getting Started](http://www.scipy.org/Getting_Started). First, import the scikit-rf module `skrf`, as `rf`" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import skrf as rf" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If this produces an error, please see the [installation](installation.rst) tutorial." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Networks" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The central object in `skrf` is a N-port microwave [Network][Network] object. A [Network][Network] can be created in a number of ways, one way is from data stored in a touchstone file.\n", "\n", "[Network]: ../api/network.rst" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ring_slot = rf.Network('data/ring slot.s2p')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you cant find `ring slot.s2p`, then just import it from the `skrf.data` module. \n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from skrf.data import ring_slot" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A short description of the network will be printed out if entered onto the command line" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ring_slot" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The basic attributes of a microwave [Network](../api/network.rst) are provided by the \n", "following properties,\n", "\n", "\n", "* `Network.s` : Scattering Parameter matrix. \n", "* `Network.z0` : Port Impedance matrix.\n", "* `Network.frequency` : Frequency Object. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " The [Network](../api/network.rst) object has numerous other properties and methods which can found in it's docstring. If you are using IPython/Jupyter, then these properties and methods can be 'tabbed' out on the command line. \n", "\n", "\n", "\tIn [1]: ring_slot.s<TAB>\n", "\tring_slot.s ring_slot.s_arcl ring_slot.s_im\n", "\tring_slot.s11 ring_slot.s_arcl_unwrap ring_slot.s_mag\n", "\t..." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Linear Operations " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\t\n", "Element-wise mathematical operations on the s-parameters are accessible through overloaded operators. To illustrate, we load a couple `Networks` stored in the `skrf.data` module. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "short = rf.data.wr2p2_short\n", "delayshort = rf.data.wr2p2_delayshort" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The complex difference between their s-parameters is computed with " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "short - delayshort" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This returns a new [Network](../api/network.rst). Other arrimetic operators are overloaded as well," ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "short/delayshort" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Cascading and De-embedding" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Cascading and de-embeding 2-port Networks can also be done though operators. Cascading is done through the power operator, ````. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "short = rf.data.wr2p2_short\n", "line = rf.data.wr2p2_line\n", "\n", "delayshort = line short" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "De-embedding can be accomplished by cascading the inverse of a network. The inverse of a network is accessed through the property `Network.inv`. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "short = line.inv delayshort" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For more information on the functionality provided by the [Network](../api/network.rst) object, such as interpolation, stitching, n-port connections, and IO support see the [Networks](Networks.ipynb) tutorial." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plotting " ] }, { "cell_type": "raw", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ ".. notice::\n", "\n", " The plotting infrastructure in skrf is under refactoring at the moment to allow for multiple backends, and headless setups. The following assumes you are using `matplotlib` with an interactive session." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "skrf** has a function which updates your [matplotlib rcParams](http://matplotlib.org/users/customizing.html) so that plots appear like the ones shown in these tutorials. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# display plots in notebook\n", "%matplotlib inline \n", "from pylab import \n", "rf.stylely()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The methods of the [Network](../api/network.rst) class provide convenient ways to plot components of the network parameters,\n", "\n", " `Network.plot_s_db()` : plot magnitude of s-parameters in log scale\n", "* `Network.plot_s_deg()` : plot phase of s-parameters in degrees\n", "* `Network.plot_s_smith()` : plot complex s-parameters on Smith Chart" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To plot all four s-parameters of the ``ring_slot`` in Mag, Phase, and on the Smith Chart." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ring_slot.plot_s_db()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Or plot the phase of $S_{12}$" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ring_slot.plot_s_deg(m=0,n=1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ring_slot.plot_s_smith(lw=2)\n", "title('Big ole Smith Chart')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For more detailed information about plotting see the [Plotting](Plotting.ipynb) tutorial" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## NetworkSet " ] }, { "cell_type": "raw", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ ".. currentmodule:: skrf.networkSet" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The [NeworkSet](../api/networkSet.rst) object\n", "represents an unordered set of networks and provides methods for \n", "calculating statistical quantities and displaying uncertainty bounds.\n", "\n", "A [NeworkSet](../api/networkSet.rst) is created from a list or dict of \n", "[Networks](../api/network.rst)'s. This can be done quickly with \n", "`rf.read_all()` , which loads all skrf-readable objects\n", "in a directory. The argument ``contains`` is used to load only files \n", "which match a given substring. \n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [], "source": [ "rf.read_all('data/', contains='ro')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This dictionary can be passed directly to the [NeworkSet](../api/networkSet.rst) constructor, " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from skrf import NetworkSet\n", "\n", "ro_dict = rf.read_all('data/', contains='ro')\n", "ro_ns = NetworkSet(ro_dict, name='ro set') # name is optional\n", "ro_ns" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[NeworkSet](../api/networkSet.rst)'s are list-like. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Statistical Properties" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Statistical quantities can be calculated by accessing \n", "properties of the [NeworkSet](../api/networkSet.rst). For example, to calculate the complex \n", "average of the set, access the ``mean_s`` property" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ro_ns.mean_s" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The returned results are stored in a [Network](../api/network.rst)s s-parameters, regardless of the type of the output. Similarly, to calculate the complex standard deviation of the set, " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ro_ns.std_s" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Because these methods return a [Network](../api/network.rst) object the results can be \n", "saved or plotted in the same way as you would with a [Network](../api/network.rst). To plot the magnitude of the standard deviation of the set, " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [], "source": [ "ro_ns.std_s.plot_s_mag(label='S11')\n", "ylabel('Standard Deviation')\n", "title('Standard Deviation of RO');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plotting Uncertainty Bounds" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Uncertainty bounds on any network parameter can be plotted through the methods " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ro_ns.plot_uncertainty_bounds_s_db(label='S11');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "See the [networkset](networkset.rst) tutorial for more information." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Virtual Instruments" ] }, { "cell_type": "raw", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ " \n", ".. warning::\n", "\n", " To use the virtual instrument classes you must have some other modules\n", " installed, like `PyVISA` or `python-ivi` or both. See the\n", " [Virtual Instruments](virtualinstruments) tutorial for more information.\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " \n", "The [skrf.vi](../api/vi/index.html) module holds classes\n", "for GPIB/VISA instruments that are intricately related to skrf, mostly VNA's.\n", "The VNA classes were created for the sole purpose of retrieving data \n", "so that calibration and analysis could be done offline by skrf, so\n", " most other VNA capabilities is neglected.\n", "\n", "\n", "A list of VNA's that are partially supported.\n", "\n", " " ] }, { "cell_type": "raw", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ ".. currentmodule:: skrf.vi\n", "\n", "\n", " * :class:`~vna.PNA`\n", " * :class:`~vna.ZVA40`\n", " * :class:`~vna.HP8510C`\n", " * :class:`~vna.HP8720`\n", " \n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "An example of using the `PNA` class to retrieve some s-parameter data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " from skrf.vi import vna\n", " my_vna = vna.PNA(address=16) \n", " \n", " #if an error is thrown at this point there is most likely a problem with your visa setup\n", " \n", " dut_1 = my_vna.s11\n", " dut_2 = my_vna.s21\n", " dut_3 = my_vna.two_port" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "See the [Virtual Instruments](VirtualInstruments.ipynb) tutorial for more information." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Calibration" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Calibrations are performed through a [Calibration](../api/calibration/index.rst) class. In most cases, creating\n", "a [Calibration](../api/calibration/index.rst) object requires at least two pieces of information:\n", "\n", "* a list of measured [Network](../api/network.rst)'s\n", "* a list of ideal [Network](../api/network.rst)'s\n", "\n", "The [Network](../api/network.rst) elements in each list must all be similar (same #ports, frequency info, etc) and must be aligned to each other, meaning the first element of ideals list must correspond to the first element of measured list.\n", "\n", "Below is an example script illustrating how to create a [Calibration](../api/calibration/index.rst) .\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### One Port Calibration" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " import skrf as rf\n", " from skf.calibration import OnePort\n", " \n", " my_ideals = rf.read_all('ideals/')\n", " my_measured = rf.read_all('measured/')\n", " duts = rf.read_all('measured/')\n", " \n", " ## create a Calibration instance\n", " cal = rf.OnePort(\n", " ideals = [my_ideals[k] for k in ['short','open','load']],\n", " measured = [my_measured[k] for k in ['short','open','load']],\n", " )\n", " \n", " caled_duts = [cal.apply_cal(dut) for dut in duts.values()]\n", " \n", "See the [Calibration](Calibration.ipynb) tutorial for more details and examples. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Transmission Line Media" ] }, { "cell_type": "raw", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ ".. currentModule:: skrf.media" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Simple transmission-line based networks can be created through methods of the [Media](../api/media/index.rst) class, which represents a transmission line object for a given medium. Once constructed, a [Media](../api/media/index.rst) object contains the neccesary properties such as ``propagation constant`` and ``characteristic impedance``, that are needed to generate microwave circuits.\n", "\n", "The basic usage looks something like this, \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### CPW\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from skrf import Frequency\n", "from skrf.media import CPW, Coaxial \n", "\n", "freq = Frequency(75,110,101,'ghz')\n", "cpw = CPW(freq, w=10e-6, s=5e-6, ep_r=10.6)\n", "cpw" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "cpw.line(d=90,unit='deg', name='line')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Coax" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "freq = Frequency(1,10,101,'ghz')\n", "coax = Coaxial(frequency=freq, Dint=1e-3, Dout=2e-3)\n", "coax" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda env:py35]", "language": "python", "name": "conda-env-py35-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }	bsd-3-clause
kgrodzicki/machine-learning-specialization	course-2-regression/notebooks/Overfitting_Demo_Ridge_Lasso.ipynb	1	316406	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Overfitting demo\n", "\n", "## Create a dataset based on a true sinusoidal relationship\n", "Let's look at a synthetic dataset consisting of 30 points drawn from the sinusoid $y = \\sin(4x)$:" ] }, { "cell_type": "code", "execution_count": 116, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import graphlab\n", "import math\n", "import random\n", "import numpy\n", "from matplotlib import pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create random values for x in interval [0,1)" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [], "source": [ "random.seed(1)\n", "n = 30\n", "x = graphlab.SArray([random.random() for i in range(n)]).sort()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compute y" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": true }, "outputs": [], "source": [ "y = x.apply(lambda x: math.sin(4x))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Add random Gaussian noise to y" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": true }, "outputs": [], "source": [ "e = graphlab.SArray([random.gauss(0,1.0/3.0) for i in range(n)])\n", "y = y + e" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Put data into an SFrame to manipulate later" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n", " <tr>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">X1</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Y</th>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.0656795386913</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.0756188515721</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.0790589698086</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1.00671511342</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.138938870637</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.413297406926</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.14807212852</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.892481205455</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.149821471622</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.562245602232</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.239240046198</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1.26315914115</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.256157891472</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.0912011283189</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.27983601768</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.943369328976</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.295671577899</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.776631047886</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.339005355194</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1.16612986853</td>\n", " </tr>\n", "</table>\n", "[30 rows x 2 columns]<br/>Note: Only the head of the SFrame is printed.<br/>You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.\n", "</div>" ], "text/plain": [ "Columns:\n", "\tX1\tfloat\n", "\tY\tfloat\n", "\n", "Rows: 30\n", "\n", "Data:\n", "+-----------------+-----------------+\n", "\| X1 \| Y \|\n", "+-----------------+-----------------+\n", "\| 0.0656795386913 \| 0.0756188515721 \|\n", "\| 0.0790589698086 \| 1.00671511342 \|\n", "\| 0.138938870637 \| 0.413297406926 \|\n", "\| 0.14807212852 \| 0.892481205455 \|\n", "\| 0.149821471622 \| 0.562245602232 \|\n", "\| 0.239240046198 \| 1.26315914115 \|\n", "\| 0.256157891472 \| 0.0912011283189 \|\n", "\| 0.27983601768 \| 0.943369328976 \|\n", "\| 0.295671577899 \| 0.776631047886 \|\n", "\| 0.339005355194 \| 1.16612986853 \|\n", "+-----------------+-----------------+\n", "[30 rows x 2 columns]\n", "Note: Only the head of the SFrame is printed.\n", "You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns." ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = graphlab.SFrame({'X1':x,'Y':y})\n", "data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Create a function to plot the data, since we'll do it many times" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZAAAAEPCAYAAABsj5JaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nq+pds/z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"text/plain": [ "<matplotlib.figure.Figure at 0x10d663750>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def plot_data(data): \n", " plt.plot(data['X1'],data['Y'],'k.')\n", " plt.xlabel('x')\n", " plt.ylabel('y')\n", "\n", "plot_data(data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Define some useful polynomial regression functions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Define a function to create our features for a polynomial regression model of any degree:" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def polynomial_features(data, deg):\n", " data_copy=data.copy()\n", " for i in range(1,deg):\n", " data_copy['X'+str(i+1)]=data_copy['X'+str(i)]data_copy['X1']\n", " return data_copy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Define a function to fit a polynomial linear regression model of degree \"deg\" to the data in \"data\":" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def polynomial_regression(data, deg):\n", " model = graphlab.linear_regression.create(polynomial_features(data,deg), \n", " target='Y', l2_penalty=0.,l1_penalty=0.,\n", " validation_set=None,verbose=False)\n", " return model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Define function to plot data and predictions made, since we are going to use it many times." ] }, { "cell_type": "code", "execution_count": 111, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def plot_poly_predictions(data, model):\n", " plot_data(data)\n", "\n", " # Get the degree of the polynomial\n", " deg = len(model.coefficients['value'])-1\n", " \n", " # Create 200 points in the x axis and compute the predicted value for each point\n", " xs = graphlab.SFrame({'X1':[i/200.0 for i in range(200)]})\n", " ys = model.predict(polynomial_features(xs,deg))\n", " \n", " # plot predictions\n", " plt.plot(xs['X1'], ys, 'g-', label='degree ' + str(deg) + ' fit')\n", " plt.legend(loc='upper left')\n", " plt.axis([0,1,-1.5,2])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create a function that prints the polynomial coefficients in a pretty way :)" ] }, { "cell_type": "code", "execution_count": 257, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def print_coefficients(model): \n", " # Get the degree of the polynomial\n", " deg = len(model.coefficients['value'])-1\n", "\n", " # Get learned parameters as a list\n", " w = list(model.coefficients['value'])\n", "\n", " # Numpy has a nifty function to print out polynomials in a pretty way\n", " # (We'll use it, but it needs the parameters in the reverse order)\n", " print 'Learned polynomial for degree ' + str(deg) + ':'\n", " w.reverse()\n", " print numpy.poly1d(w)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fit a degree-2 polynomial" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Fit our degree-2 polynomial to the data generated above:" ] }, { "cell_type": "code", "execution_count": 199, "metadata": { "collapsed": true }, "outputs": [], "source": [ "model = polynomial_regression(data, deg=2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Inspect learned parameters" ] }, { "cell_type": "code", "execution_count": 142, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "best degree 2 fit:\n", " 2\n", "-4.808 x + 3.49 x + 0.3082\n" ] } ], "source": [ "print_coefficients(model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Form and plot our predictions along a grid of x values:" ] }, { "cell_type": "code", "execution_count": 143, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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TUmleszkvXfkSsTGxId/niYL199YyXj8FiIiEy4GsA/R7ox8Ar/d9nQpxFTyu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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10d6053d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_poly_predictions(data,model)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Fit a degree-4 polynomial" ] }, { "cell_type": "code", "execution_count": 144, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "best degree 4 fit:\n", " 4 3 2\n", "28.62 x - 50.84 x + 22.88 x - 1.292 x + 0.4735\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10ee0c210>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "model = polynomial_regression(data, deg=4)\n", "print_coefficients(model)\n", "plot_poly_predictions(data,model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fit a degree-16 polynomial" ] }, { "cell_type": "code", "execution_count": 146, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "best degree 16 fit:\n", " 16 15 14 13\n", "-1.096e+06 x + 3.746e+06 x - 2.767e+06 x - 4.327e+06 x \n", " 12 11 10 9\n", " + 8.073e+06 x - 4.228e+06 x + 2.042e+06 x - 4.085e+06 x\n", " 8 7 6 5 4\n", " + 3.563e+06 x - 2671 x - 1.953e+06 x + 1.487e+06 x - 5.633e+05 x\n", " 3 2\n", " + 1.239e+05 x - 1.585e+04 x + 1072 x - 28.21\n" ] } ], "source": [ "model = polynomial_regression(data, deg=16)\n", "print_coefficients(model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###Woah!!!! Those coefficients are crazy! On the order of 10^6." ] }, { "cell_type": "code", "execution_count": 147, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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IP+AboHVBhadOnZrzODg4mODg4BIH4M5V6MVRx68OYzuMZWyHsXaHolxs2rRp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\nVbvDUEqVYSVZD6ItEA+WmJa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"text/plain": [ "<matplotlib.figure.Figure at 0x10ee9f810>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_poly_predictions(data,model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Above: Fit looks pretty wild, too. Here's a clear example of how overfitting is associated with very large magnitude estimated coefficients." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " # " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " # " ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Ridge Regression" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ridge regression aims to avoid overfitting by adding a cost to the RSS term of standard least squares that depends on the 2-norm of the coefficients $\\\|w\\\|$. The result is penalizing fits with large coefficients. The strength of this penalty, and thus the fit vs. model complexity balance, is controled by a parameter lambda (here called \"L2_penalty\")." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Define our function to solve the ridge objective for a polynomial regression model of any degree:" ] }, { "cell_type": "code", "execution_count": 154, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def polynomial_ridge_regression(data, deg, l2_penalty):\n", " model = graphlab.linear_regression.create(polynomial_features(data,deg), \n", " target='Y', l2_penalty=l2_penalty,\n", " validation_set=None,verbose=False)\n", " return model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Perform a ridge fit of a degree-16 polynomial using a very small penalty strength" ] }, { "cell_type": "code", "execution_count": 155, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "best degree 16 fit:\n", " 16 15 14 13\n", "-1.096e+06 x + 3.746e+06 x - 2.767e+06 x - 4.327e+06 x \n", " 12 11 10 9\n", " + 8.073e+06 x - 4.228e+06 x + 2.042e+06 x - 4.085e+06 x\n", " 8 7 6 5 4\n", " + 3.563e+06 x - 2671 x - 1.953e+06 x + 1.487e+06 x - 5.633e+05 x\n", " 3 2\n", " + 1.239e+05 x - 1.585e+04 x + 1072 x - 28.21\n" ] } ], "source": [ "model = polynomial_ridge_regression(data, deg=16, l2_penalty=1e-25)\n", "print_coefficients(model)" ] }, { "cell_type": "code", "execution_count": 156, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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IP+AboHVBhadOnZrzODg4mODg4BIH4M5V6MVRx68OYzuMZWyHsXaHolxs2rRp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\nVbvDUEqVYSVZD6ItEA+WmJa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"text/plain": [ "<matplotlib.figure.Figure at 0x110221b10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_poly_predictions(data,model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Perform a ridge fit of a degree-16 polynomial using a \"good\" penalty strength" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will learn about cross validation later in this course as a way to select a good value of the tuning parameter (penalty strength) lambda. Here, we consider \"leave one out\" (LOO) cross validation, which one can show approximates average mean square error (MSE). As a result, choosing lambda to minimize the LOO error is equivalent to choosing lambda to minimize an approximation to average MSE." ] }, { "cell_type": "code", "execution_count": 239, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# LOO cross validation -- return the average MSE\n", "def loo(data, deg, l2_penalty_values):\n", " # Create polynomial features\n", " polynomial_features(data, deg)\n", " \n", " # Create as many folds for cross validatation as number of data points\n", " num_folds = len(data)\n", " folds = graphlab.cross_validation.KFold(data,num_folds)\n", " \n", " # for each value of l2_penalty, fit a model for each fold and compute average MSE\n", " l2_penalty_mse = []\n", " min_mse = None\n", " best_l2_penalty = None\n", " for l2_penalty in l2_penalty_values:\n", " next_mse = 0.0\n", " for train_set, validation_set in folds:\n", " # train model\n", " model = graphlab.linear_regression.create(train_set,target='Y', \n", " l2_penalty=l2_penalty,\n", " validation_set=None,verbose=False)\n", " \n", " # predict on validation set \n", " y_test_predicted = model.predict(validation_set)\n", " # compute squared error\n", " next_mse += ((y_test_predicted-validation_set['Y'])**2).sum()\n", " \n", " # save squared error in list of MSE for each l2_penalty\n", " next_mse = next_mse/num_folds\n", " l2_penalty_mse.append(next_mse)\n", " if min_mse is None or next_mse < min_mse:\n", " min_mse = next_mse\n", " best_l2_penalty = l2_penalty\n", " \n", " return l2_penalty_mse,best_l2_penalty" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run LOO cross validation for \"num\" values of lambda, on a log scale" ] }, { "cell_type": "code", "execution_count": 240, "metadata": { "collapsed": false }, "outputs": [], "source": [ "l2_penalty_values = numpy.logspace(-4, 10, num=10)\n", "l2_penalty_mse,best_l2_penalty = loo(data, 16, l2_penalty_values)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot results of estimating LOO for each value of lambda" ] }, { "cell_type": "code", "execution_count": 241, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10efc2550>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(l2_penalty_values,l2_penalty_mse,'k-')\n", "plt.xlabel('$\\L2_penalty$')\n", "plt.ylabel('LOO cross validation error')\n", "plt.xscale('log')\n", "plt.yscale('log')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Find the value of lambda, $\\lambda_{\\mathrm{CV}}$, that minimizes the LOO cross validation error, and plot resulting fit" ] }, { "cell_type": "code", "execution_count": 244, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.12915496650148839" ] }, "execution_count": 244, "metadata": {}, "output_type": "execute_result" } ], "source": [ "best_l2_penalty" ] }, { "cell_type": "code", "execution_count": 245, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "best degree 16 fit:\n", " 16 15 14 13 12 11\n", "-0.5706 x - 0.0485 x + 0.3804 x + 0.7024 x + 0.9034 x + 0.9697 x \n", " 10 9 8 7 6 5\n", " + 0.8896 x + 0.6554 x + 0.2686 x - 0.2516 x - 0.8527 x - 1.421 x\n", " 4 3 2\n", " - 1.742 x - 1.463 x - 0.2122 x + 1.549 x + 0.5163\n" ] } ], "source": [ "model = polynomial_ridge_regression(data, deg=16, l2_penalty=best_l2_penalty)\n", "print_coefficients(model)" ] }, { "cell_type": "code", "execution_count": 246, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10de16bd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_poly_predictions(data,model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Perform a ridge fit of a degree-16 polynomial using a very large penalty strength" ] }, { "cell_type": "code", "execution_count": 247, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "best degree 16 fit:\n", " 16 15 14 13 12 11\n", "-0.1183 x - 0.1204 x - 0.1225 x - 0.1245 x - 0.1264 x - 0.1282 x \n", " 10 9 8 7 6 5\n", " - 0.1297 x - 0.1308 x - 0.1312 x - 0.1306 x - 0.1281 x - 0.123 x\n", " 4 3 2\n", " - 0.1135 x - 0.09745 x - 0.07163 x - 0.03448 x + 0.6759\n" ] } ], "source": [ "model = polynomial_ridge_regression(data, deg=16, l2_penalty=100)\n", "print_coefficients(model)" ] }, { "cell_type": "code", "execution_count": 248, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10f54ec10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_poly_predictions(data,model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Let's look at fits for a sequence of increasing lambda values" ] }, { "cell_type": "code", "execution_count": 260, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Learned polynomial for degree 16:\n", " 16 15 14 13\n", "-1.096e+06 x + 3.746e+06 x - 2.767e+06 x - 4.327e+06 x \n", " 12 11 10 9\n", " + 8.073e+06 x - 4.228e+06 x + 2.042e+06 x - 4.085e+06 x\n", " 8 7 6 5 4\n", " + 3.563e+06 x - 2671 x - 1.953e+06 x + 1.487e+06 x - 5.633e+05 x\n", " 3 2\n", " + 1.239e+05 x - 1.585e+04 x + 1072 x - 28.21\n", "\n", "\n", "Learned polynomial for degree 16:\n", " 16 15 14 13\n", "-1.096e+06 x + 3.746e+06 x - 2.767e+06 x - 4.327e+06 x \n", " 12 11 10 9\n", " + 8.073e+06 x - 4.228e+06 x + 2.042e+06 x - 4.085e+06 x\n", " 8 7 6 5 4\n", " + 3.563e+06 x - 2671 x - 1.953e+06 x + 1.487e+06 x - 5.633e+05 x\n", " 3 2\n", " + 1.239e+05 x - 1.585e+04 x + 1072 x - 28.21\n", "\n", "\n", "Learned polynomial for degree 16:\n", " 16 15 14 13 12 11 10\n", "8222 x - 9400 x - 6546 x + 1737 x + 6554 x + 5078 x - 451.4 x \n", " 9 8 7 6 5 4 3\n", " - 5008 x - 4353 x + 1099 x + 5014 x + 884.3 x - 5248 x + 3107 x\n", " 2\n", " - 752.6 x + 80.31 x - 2.41\n", "\n", "\n", "Learned polynomial for degree 16:\n", " 16 15 14 13 12 11\n", "14.17 x - 0.2156 x - 7.579 x - 9.472 x - 7.461 x - 3.131 x \n", " 10 9 8 7 6 5 4\n", " + 1.936 x + 6.192 x + 8.233 x + 7.04 x + 2.466 x - 3.975 x - 8.382 x\n", " 3 2\n", " - 5.657 x + 3.25 x + 2.225 x + 0.2756\n", "\n", "\n", "Learned polynomial for degree 16:\n", " 16 15 14 13 12 11\n", "-0.1183 x - 0.1204 x - 0.1225 x - 0.1245 x - 0.1264 x - 0.1282 x \n", " 10 9 8 7 6 5\n", " - 0.1297 x - 0.1308 x - 0.1312 x - 0.1306 x - 0.1281 x - 0.123 x\n", " 4 3 2\n", " - 0.1135 x - 0.09745 x - 0.07163 x - 0.03448 x + 0.6759\n", "\n", "\n" ] }, { "data": { "image/png": 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V6IFSymYnEk9w+xe380bfN1i6bSlAgTPBwsLCcloenjBbrCjXNbqOEW1H8MCKB/jyzi8t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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10d69d790>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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IP+AboHVBhadOnZrzODg4mODg4BIH4M5V6MVRx68OYzuMZWyHsXaHolxs2rRp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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10f396d50>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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RC1slNWvUJG5oHPG74wnwC+CmdjdZHOHFLFtM0RkqW0zxyn9fycLb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\nCUQppZRd/j8eSFu7zXj0QQAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11040c750>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10deefad0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10f095850>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for l2_penalty in [1e-25, 1e-20, 1e-8, 1e-3, 1e2]:\n", " model = polynomial_ridge_regression(data, deg=16, l2_penalty=l2_penalty)\n", " print_coefficients(model)\n", " print '\\n'\n", " plt.figure()\n", " plot_poly_predictions(data,model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# " ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Lasso Regression" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lasso regression jointly shrinks coefficients to avoid overfitting, and implicitly performs feature selection by setting some coefficients exactly to 0 for sufficiently large penalty strength lambda (here called \"L1_penalty\"). In particular, lasso takes the RSS term of standard least squares and adds a 1-norm cost of the coefficients $\\\|w\\\|$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Define our function to solve the lasso objective for a polynomial regression model of any degree:" ] }, { "cell_type": "code", "execution_count": 281, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def polynomial_lasso_regression(data, deg, l1_penalty):\n", " model = graphlab.linear_regression.create(polynomial_features(data,deg), \n", " target='Y', l2_penalty=0.,\n", " l1_penalty=l1_penalty,\n", " validation_set=None, \n", " solver='fista', verbose=False,\n", " max_iterations=2000, convergence_threshold=1e-10)\n", " return model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Explore the lasso solution as a function of a few different penalty strengths" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We refer to lambda in the lasso case below as \"L1_penalty\"" ] }, { "cell_type": "code", "execution_count": 283, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "l1_penalty = 1.000000e-10\n", "number of nonzeros = 17\n", "Learned polynomial for degree 16:\n", " 16 15 14 13 12 11\n", "17.01 x + 0.08097 x - 8.717 x - 11.13 x - 8.954 x - 3.998 x \n", " 10 9 8 7 6 5\n", " + 1.893 x + 6.906 x + 9.387 x + 8.133 x + 2.955 x - 4.399 x\n", " 4 3 2\n", " - 9.415 x - 6.165 x + 4.185 x + 1.974 x + 0.2908\n", "\n", "\n", "l1_penalty = 1.000000e-02\n", "number of nonzeros = 15\n", "Learned polynomial for degree 16:\n", " 16 15 14 13 12\n", "-1.371 x - 0.3775 x - 0.02277 x - 0.005421 x - 0.0007898 x \n", " 10 9 8 7 6 4\n", " - 0.01347 x + 0.1199 x + 4.082 x + 2.26 x + 0.07193 x - 2.697 x\n", " 3 2\n", " - 6.05 x + 0.05867 x + 3.258 x + 0.2077\n", "\n", "\n", "l1_penalty = 1.000000e-01\n", "number of nonzeros = 5\n", "Learned polynomial for degree 16:\n", " 11 10 3\n", "2.361 x + 0.289 x - 5.297 x + 2.417 x + 0.3663\n", "\n", "\n", "l1_penalty = 1.000000e+00\n", "number of nonzeros = 3\n", "Learned polynomial for degree 16:\n", " 16 4\n", "0.6849 x - 2.07 x + 0.8454\n", "\n", "\n", "l1_penalty = 1.000000e+01\n", "number of nonzeros = 3\n", "Learned polynomial for degree 16:\n", " 5 4\n", "-0.769 x - 0.3617 x + 0.6491\n", "\n", "\n" ] }, { "data": { "image/png": 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XtR6T+k7imOrH0OmVTizbvCzokCTGKYFEyJbdW/j31H9z/LDjWZexjpk3zST5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\ncPJRJ9PyyJZULF8x32Vs2rW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"text/plain": [ "<matplotlib.figure.Figure at 0x10d74c310>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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ZrzZ/FXRIEqPUAomAhRsXcsucW8jMzuTBPg/So1mPfOtpFrdE2ovfvsjNs2/m\nvvPuY9jpw4IORwKgLixftCWQDTs3MPqD0Xy09iPu7XUvV51yVaED5LoQoQRh5daVXPTiRfym+W94\nsM+DGourYGK6C8vM+pjZCjP73szuyKe8p5ntMLPF/u2vQcRZEnsz9jLuw3Gc+p9TaVWnFStGrWDI\nqUN0dpVEpXb12/HZsM/YsGsDvZ/rzU97fwo6JIkRgR7RzKwS8DBwPtAJuMLM2udTdZ5z7nT/dk9E\ngywB5xwvfvsi7R9pz6ptq/jqhq+4+zd3F/vCdikpKfTr149+/fqRkpJSxtFKNElPTyc5OZnk5GTS\n09Mjvv2aVWry5qA3OafZOXSd2pU5S+YEGo/EhkC7sMysGzDOOdfXvz8acM65iSF1egK3OecuKMb6\nAuvC+vanb7l59s1s37+dh/s9XOA4h0h+oqn78uVlL3PlC1eS8WYGfBt8PFK2YrkLqwmwIeT+Rv+x\nvM4ys6/NbJaZdYxMaMWz88BObnnnFn7zzG+4tMOlfDH8CyUPiWmXdbqMriu6wnnAb8ERPeOKEl3i\ngw6gGL4Emjnn9plZX+ANoG1BlcePH5+7nJSURFJSUpkEleNyeG7Jc4z+YDT92/Tnuxu/o0G1BmWy\nLSn/JkyYwKJFi3KXgzZj8gyuufEaFp+8mMxOmezJ2KPfGCkn0tLSSEtLK5V1RUMX1njnXB///hFd\nWPk8Zw3Q2Tm3LZ+yiHRhLd68mFGpo8jKyeLhfg/TtUnXMt+mlG/R1IUVKiM7gxtn3ciXm7/k7Sve\n1u/QlEOx3IW1CGhtZolmlgAMAt4KrWBmDUOWu+IlvSOSRyRs27+NkTNH0nd6X6497VoWDluo5CHl\nWkJcAlMvmMoVJ15Btye68eWmL4MOSaJIoF1YzrlsMxsFvIuXzKY555ab2Q1esUsBLjWzkUAmsB+4\nPNJxZudkM+2radz14V1c1vEylt+0nLpV60Y6DCnHUlJSDptEGk3MjNu7307ruq3pM70PUy+YykXt\nLwo6LIkCmkhYhC83fcmIWSOoEleFh/s9zKmNTi21dWvmucSaLzZ9wYUzLuSWbrdw61m36mKM5YBm\novtKM4HsPLCTuz68ixeXvcj9591fJhMBo7XfW6QwG3ZuoP8L/TmzyZk80u8RKsdVDjokOQqxPAYS\ndQ5NBuz4aEf2Ze7juxu/Y+hpQzWLXMTXtFZT5g+dz8ZdGxkwYwB7MvYEHZIERC2QEKu3ream1JvY\ntHsTjydqOZYkAAASa0lEQVQ/Tvdm3UsxuiOpC0tiWWZ2JiNmjuDrLV8z6w+zaFS9UdAhSRjUheUL\nN4EczDrIxE8m8tBnD3FH9zv4c7c/q1kuUgzOOe7+6G6eWfIMswfPpl39dkGHJCV0NAkkFiYSlqn3\nf3ifG2fdSMcGHVl8w2Ka1WoWdEgiMcPMGJc0jqa1mtLz6Z68dvlrnN307KDDkgipsC2QH/f8yF/m\n/IVPN3zKQ30fYkC7AWUcnUj5NnvVbK5+42pS+qdwcYeLgw5HikmD6CWQnZPNI58/wkmPnUSzWs1Y\nduMyJQ+RUtC3TV/eGfwOo2aP4pHPHwk6HImACtUCOTSn45j4Y3gs+TFOPO7ECEYnUjGs2b6GPtP7\ncFG7i/jHb/8RlWcw6gSWX2kQ3VdQAonEnA4R+dXWfVsZ8MIAWtRpwZMDnqRKfJWgQzqM5mD9Sl1Y\nBdCcDpFg1D+2Ph9c/QH7M/fTd3pfdh7YGXRIUgaKbIGY2c3Af51z2yMTUvhCWyCRntMhIkfKzsnm\nT+/8iXnr5jF78Gya1IyOriJ1Yf2qTLuwzOwevKvkLgaeBOYE9rN/RTAzdyDzgOZ0iEQR5xyTPpnE\no188yuzBs+nYIKp+E67CK/MxEPOumPY7YCjQBXgJ78q5/wtno2XFzFzbKW3pUL8DD/V9SHM6RKLI\nc0ue47b3buOVy17hnMRzgg5HfGU+BuK3OH70b1lAHeAVM5sUzkbL0j97/5M3Br2h5CESZa465Sr+\ne/F/ueSlS3j1u1eDDkdKQZEJxMz+ZGZfApOAT4CTnHMjgc7AJWUcX4lpTodI9Op4TEfaft6WwdMH\n8/d3/x50OHKUinMpk7rA751z60IfdM7lmFn/sglLRMqj4cOH80nqJ1AbJmZPZI/tidq5IlK0Iv9r\nzrlxeZNHSNny0g9JRMq9HXDW8rP4eP3HXP361WRkZwQdkYShQkwkFJHokPf02TrH1eGKV69gX+Y+\nXh34KjWr1Aw4wopHM9F9SiAisScrJ4ubU29mwcYFzB48m8Y1GgcdUoWimehS6tLT00lOTiY5OZn0\n9PSgw5FyLL5SPI8mP8rATgM5+8mzWbF1RdAhSTEpgRSiIh9Ehw8fTmpqKqmpqbldDiJlxcy485w7\nGddzHElPJ/Hphk+DDqnMlKfjihJIIXQQFYmsa069hqcvepqLZlzEGyveCDqcMlGejisV/hcJJX8p\nKSmHDXaKREqf1n2YPXg2F7xwAZt3b2bkGSODDkkKEPggupn1AR7Eaw1Nc85NzKfOQ0BfYC9wjXPu\n6wLWVaqD6Lrgmkhwftj+A33+24fLOl7GPb3uwbuiUuyLtuNKzJ6FZWaVgO+B84BNwCJgkHNuRUid\nvsAo51yymZ0JTHbOdStgfToLS6Qc+Xnvz/R/oT/t67dn6gVTSYhLCDqkcieWz8LqCqxyzq1zzmUC\nM4AL89S5EHgWwDn3GVDLzBpGNkwRCUKDag2Ye/Vcdh3cRe/nevPLvl+CDklCBJ1AmgAbQu5v9B8r\nrE56PnVEpJyqllCNVwe+Srcm3eg2rRsrt64MOiTxlbtB9PHjx+cuJyUlkZSUFFgsIlI6KlklJvae\nSLv67Tj36XN54ZIX6NWiV9BhxaS0tDTS0tJKZV1Bj4F0A8Y75/r490fjXT1+Ykidx4EPnXMv+vdX\nAD2dc1vyWZ/GQETKubS1aVz+yuXc2+tehp0+LOhwYl4sj4EsAlqbWaKZJeD98uFbeeq8BVwNuQln\nR37JQ0QqhqTmScwfOp9/fvpPbnv3NrJzsoMOqcIKNIE457KBUcC7wDJghnNuuZndYGbD/TqpwBoz\nWw38B7gxsIBFpMwVZ6Z2m3ptWHDdAhZvXsyFMy5kx4EdEY5SIArmgZQmdWGJxL7k5GRSU1MB6Nev\nH7NmzSqwbmZ2Jre+eytz/jeH1y9/Xb+3HoZY7sISEQlb5bjKPNT3Icb0GEPPp3vy+vLXgw6pQlEL\nRESiSrgztRelL+KSly5hyClDGJ80nrhKcWUZZrkRszPRS5sSiEjFtmXPFga+MpCq8VV57uLnaFCt\nQdAhRT11YYmIAA2rN+T9q97n1Eanctp/TmPeunlFPqc8XV490tQCEZFyafaq2Qx9cyg3d72ZMeeM\noZLl/325JIP25ZFaICJSYRXUgujbpi9fDv+SOf+bQ5//9mHLnvI1fSwaWk5qgYhITCuqBZGVk8X4\ntPFM+2oajyU/xkXtLzqsPNour15cpdVyOpoWSLm7FpaISKj4SvHc0+se+rbuy5A3hvDGijeY3Gcy\ntY6pBUCTJk0qXLdVaVELRERiWklaEHsy9nD7e7cza9UsnhzwJOe1PC9SYZa60mo56TRenxKIiBTH\nnNVzGPb2MPq36c99591Hnap1gg4pMBpEFxEpgfNbn883I74BoOOjHXluyXPoy2fJqQUiIhXa5+mf\nM3LWSGok1ODR5Ecr3PW01AIREQlT1yZd+XzY51zW8TJ6Pt2T2969jW37twUdVkxQAhGRCi+uUhw3\ndb2JpSOXsidjD+0ebsekTyaxP3N/0KFFNXVhiYjksWLrCu784E4WbVrE7WffzrDTh1G1ctWgwyoT\nOgvLpwQiIqVpUfoi7v34Xj5L/4w/n/lnRnQZkTt/pLxQAvEpgYhIWVi6ZSn3f3I/qatSGdRpEDd1\nvYkTjzsx6LBKhRKITwlERMrS5t2bSfkyhZTFKbSo3YIrT76SyzpeRr1j6wUdWtiUQHxKICISCZnZ\nmbyz+h2mL53O7NWz6ZnYkwHtBtCvTT+Or3F80OGViBKITwlERCJt18FdvLXyLWatmsWc1XNIrJ1I\nr+a9OLvp2XRv1p1G1RsFHWKhlEB8SiAiEqSsnCwWblzIvHXz+HTDp3y64VOqJ1Sn03Gd6Fi/Ix0a\ndCCxViIn1DyBJjWbUCOhBmZhHbtLjRKITwlERKJJjsthzfY1fPfzd3z383cs37qcDbs2kL4rnY27\nNpKRnUGtY2pRs0pNqlWuRlylOCpZpdwbQHZONlk5WWTlZJHtQpZDHjczqidUp2aVmtSsUpPEWom0\nqduG1nVbc3LDk+nQoEOBP6ilBOJTAhGRWHIw6yA7D+5k18Fd7M3YS47LOezmcMRXiie+UjxxFvfr\ncqW4wx53OPZk7GHXwV3sPLCTtTvWsnrbalZtW8WXm79kx4Ed9G3dl0s6XEL/tv2pHFc5N4aYTCBm\nVgd4EUgE1gIDnXM786m3FtgJ5ACZzrmuhaxTCUREJI+Nuzby9sq3eeHbF1i9bTU3nXETfzzzj9So\nUiNmE8hE4Bfn3CQzuwOo45wbnU+9H4DOzrntxVinEoiISCGW/bSM++bfx/s/vM+HQz6k03GdYjKB\nrAB6Oue2mFkjIM051z6femuALs65X4qxTiUQEZFi+O7n72hXrx3xcfExmUC2OefqFnQ/5PEfgB1A\nNpDinJtayDqVQERESiBqfxPdzN4DGoY+BDjgr/lUL+jI3905t9nMGgDvmdly59z8grY5fvz43OWk\npCSSkpJKGraISLmVlpZGWlpaqawryBbIciAppAvrQ+dchyKeMw7Y7Zx7oIBytUBEREogVn9Q6i3g\nGn95CPBm3gpmdqyZVfeXqwG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"text/plain": [ "<matplotlib.figure.Figure at 0x10efbd910>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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cnH4zX3z7BS9d+BInHnpi1CFJCdufBBL5RQdm1sXMvjKzRWb25zymdzSzjWY2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\nhVimEoiIJJUcz2F65nT+t+R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"text/plain": [ "<matplotlib.figure.Figure at 0x10ef9d490>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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xQ7j5+JtpVqdZ1GFJMdSFFUq1BCJS1a3YsILbP7ydF2a/wNXdr+aG3jfQoFaD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\nUOcfZvaNmc00s2MSHWOilLY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"text/plain": [ "<matplotlib.figure.Figure at 0x10ee9f390>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x110444050>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for l1_penalty in [1e-10, 1e-2, 1e-01, 1, 1e1]:\n", " model = polynomial_lasso_regression(data, deg=16, l1_penalty=l1_penalty)\n", " print 'l1_penalty = %e' % l1_penalty\n", " print 'number of nonzeros = %d' % (model.coefficients['value']).nnz()\n", " print_coefficients(model)\n", " print '\\n'\n", " plt.figure()\n", " plot_poly_predictions(data,model)\n", " plt.title('LASSO, lambda = %.2e, # nonzeros = %d' % (l1_penalty, (model.coefficients['value']).nnz()))" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Above: We see that as lambda increases, we get sparser and sparser solutions. However, even for our non-sparse case for lambda=0.01, the fit of our high-order polynomial is not too wild. This is because, like in ridge, coefficients included in the lasso solution are shrunk relative to those of the least squares (unregularized) solution. This leads to better behavior even without sparsity. Of course, as lambda goes to 0, the amount of this shrinkage decreases and the lasso solution approaches the (wild) least squares solution." ] }, { "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": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }	mit

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