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bdearlove/pangea-round2
sequences/regional.ipynb
2
4544
{ "metadata": { "name": "", "signature": "sha256:d8ccc5ad0ee84e171c7fd2be89e045545192b38bf0b1d3a650db34659ed9a91e" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Regional data\n", "\n", "Need to generate:\n", " - Pol only\n", " - Gag/pol/env" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%load_ext rpy2.ipython\n", "%Rdevice svg" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "%%R\n", "library(ape)\n", "library(magrittr)\n", "library(phangorn)\n", "library(adephylo)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "text": [ "Loading required package: ade4\n", "\n", "Attaching package: \u2018adephylo\u2019\n", "\n", "The following object is masked from \u2018package:ade4\u2019:\n", "\n", " orthogram\n", "\n" ] } ], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "%%R\n", "regionaldir <- \"../rawdata/Regional\"\n", "stubs <- c(\"150129_PANGEAsim_Regional_FirstObj_scA_SIMULATED_SEQ\",\"150129_PANGEAsim_Regional_FirstObj_scB_SIMULATED_SEQ\",\"150129_PANGEAsim_Regional_FirstObj_scC_SIMULATED_SEQ\",\"150129_PANGEAsim_Regional_FirstObj_scD_SIMULATED_SEQ\")\n", "numsc <- length(stubs)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "%%R\n", "genes <- c(\"gag\",\"pol\",\"env\")\n", "seqdata <- list()\n", "for(i in 1:numsc){\n", " for(j in 1:length(genes)){\n", " if(j==1){\n", " s <- read.dna(paste(regionaldir,\"/\",stubs[i],\"/\",gsub(\"SEQ\",genes[j],stubs[i],fixed=TRUE),\".fa\",sep=\"\"),format=\"fasta\",as.matrix=TRUE)\n", " snames <- row.names(s)\n", " o <- order(snames)\n", " snames <- snames[o]\n", " s <- s[o,]\n", " }else{\n", " s2 <- read.dna(paste(regionaldir,\"/\",stubs[i],\"/\",gsub(\"SEQ\",genes[j],stubs[i],fixed=TRUE),\".fa\",sep=\"\"),format=\"fasta\",as.matrix=TRUE)\n", " s2names <- row.names(s2)\n", " o <- order(s2names)\n", " s2names <- s2names[o]\n", " s <- cbind(s,s2[o,])\n", " }\n", " }\n", " seqdata[[i]] <- s\n", "}" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "%%R\n", "seqnames.fn <- paste(stubs,\".fas\",sep=\"\")\n", "for(i in 1:numsc){\n", " write.dna(seqdata[[i]],seqnames.fn[i],format=\"fasta\",nbcol=-1,colsep=\"\")\n", "}" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "s=\"\"\"DNA, gag = 1-1440\n", "DNA, pol = 1441-4284\n", "DNA, env = 4285-6807\\n\"\"\"\n", "f=open(\"regional_partition\",'w')\n", "f.write(s)\n", "f.close()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 7 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Also copy over pol sequences" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%%R\n", "for(i in 1:numsc){\n", " s <- read.dna(paste(regionaldir,\"/\",stubs[i],\"/\",gsub(\"SEQ\",\"pol\",stubs[i],fixed=TRUE),\".fa\",sep=\"\"),format=\"fasta\",as.matrix=TRUE)\n", " snames <- row.names(s)\n", " o <- order(snames)\n", " snames <- snames[o]\n", " write.dna(s,paste(gsub(\"SEQ\",\"pol\",stubs[i],fixed=TRUE),\".fas\",sep=\"\"),format=\"fasta\",nbcol=-1,colsep=\"\")\n", "}" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 8 } ], "metadata": {} } ] }
mit
wy36101299/ipynb-file
collaborative-filtering.ipynb
1
15618
{ "metadata": { "name": "", "signature": "sha256:5fafcfb5bc8b6fda388fba2277bafd18f29cd9777c59c190754455eb838b8996" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "# linalg:Linear algebra" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "# Collaborative filtering PPT \u642d\u914d\u670d\u7528\n", "# http://www.slideshare.net/ssuserf88631/collaborative-filtering-45262678" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "# \u65e5\u5f0f \u4e2d\u5f0f \u7f8e\u5f0f \u6cf0\u5f0f \u97d3\u5f0f\n", "# ---------------------------\n", "# sam 2 0 0 4 4\n", "#john 5 5 5 3 3\n", "# tim 2 4 2 1 2\n", "#\u4ee5\u4e0b\u77e9\u9663\u53ef\u770b\u6b64 user \u5c0d item \u9032\u884c\u8a55\u5206\n", "#\u85c9\u7531\u5354\u540c\u904e\u6ffe \u731c\u51fa sam\u5c0d\u4e2d\u5f0f\u548c\u7f8e\u5f0f\u7684\u8a55\u50f9\u5f8c\uff0c\u63a8\u85a6sam\u5403\u4ec0\u9ebc" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "def loadItemScore():\n", " tmp = [[4, 4, 0, 2, 2],\n", " [4, 0, 0, 3, 3],\n", " [4, 0, 0, 1, 1],\n", " [1, 1, 1, 2, 0],\n", " [2, 2, 2, 0, 0],\n", " [5, 5, 5, 0, 0],\n", " [1, 1, 1, 0, 0]]\n", " itemScore = np.array(tmp,dtype=np.float)\n", " return itemScore" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "def loadItemScore2():\n", " tmp = [[0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 5],\n", " [0, 0, 0, 3, 0, 4, 0, 0, 0, 0, 3],\n", " [0, 0, 0, 0, 4, 0, 0, 1, 0, 4, 0],\n", " [3, 3, 4, 0, 0, 0, 0, 2, 2, 0, 0],\n", " [5, 4, 5, 0, 0, 0, 0, 5, 5, 0, 0],\n", " [0, 0, 0, 0, 5, 0, 1, 0, 0, 5, 0],\n", " [4, 3, 4, 0, 0, 0, 0, 5, 5, 0, 1],\n", " [0, 0, 0, 4, 0, 4, 0, 0, 0, 0, 4],\n", " [0, 0, 0, 2, 0, 2, 5, 0, 0, 1, 2],\n", " [0, 0, 0, 0, 5, 0, 0, 0, 0, 4, 0],\n", " [1, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0]]\n", " itemScore = np.array(tmp,dtype=np.float)\n", " return itemScore" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "# SVD \u77e9\u9663\u5947\u7570\u503c\u5206\u89e3\n", "# \u5229\u7528SVD\u5947\u7570\u503c\u5206\u89e3\u53ef\u4ee5\u5927\u5e45\u6e1b\u4f4e\u77e9\u9663\u7684\u5132\u5b58\u91cf\n", "U,sigma,VT = np.linalg.svd(loadItemScore2())\n", "# \u77e9\u9663\u80fd\u91cf\u70ba sum(sigma**2) \u9700\u4fdd\u755990%\u70ba\u5178\u578b\u4f5c\u6cd5\n", "sig2 = sigma**2\n", "print(\"\u77e9\u9663\u7e3d\u80fd\u91cf\u70ba\uff1a\")\n", "print(sum(sig2))\n", "print(\"90%\u77e9\u9663\u80fd\u91cf\uff1a\")\n", "print(sum(sig2)*0.9)\n", "print(\"\u8f49\u63db\u70ba\u4e8c\u7dad\u77e9\u9663\u80fd\u91cf\uff1a\")\n", "print(sum(sig2[:2]))\n", "print(\"\u8f49\u63db\u4e09\u7dad\u77e9\u9663\u80fd\u91cf\uff1a\")\n", "print(sum(sig2[:3]))\n", "# \u8f49\u63db\u70ba\u4e8c\u7dad\u80fd\u91cf\u592a\u4f4e\uff0c\u9700\u70ba\u4e09\u7dad svdMatrix\u5247\u70ba\u9084\u539f\u5f8c\u7684\u77e9\u9663\n", "Sig4 = np.mat(np.eye(3)*sigma[:3])\n", "svdMatrix = U[:,:3] * Sig4 * VT[:3,:]\n", "# \u8cc7\u6599\u4f86\u6e90\uff1ahttp://en.wikipedia.org/wiki/Singular_value_decomposition" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\u77e9\u9663\u7e3d\u80fd\u91cf\u70ba\uff1a\n", "542.0\n", "90%\u77e9\u9663\u80fd\u91cf\uff1a\n", "487.8\n", "\u8f49\u63db\u70ba\u4e8c\u7dad\u77e9\u9663\u80fd\u91cf\uff1a\n", "378.829559511\n", "\u8f49\u63db\u4e09\u7dad\u77e9\u9663\u80fd\u91cf\uff1a\n", "500.500289128\n" ] } ], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "# Euclid Similarity\n", "def eulidSim(a,b):\n", " eulid = np.linalg.norm(a-b)\n", " norEulid = 1.0/(1.0 + eulid)\n", " return norEulid" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "# Adjusted Cosine Similarity \n", "# \u4e0d\u8d85\u904e3 Similarity \uff1d1\n", "def adjCosSim(a,b):\n", " if len(a)<3:\n", " return 1\n", " else:\n", " a = a-a.mean()\n", " b = b-b.mean()\n", " cos = float(np.inner(a,b))/(np.linalg.norm(a)*np.linalg.norm(b))\n", " norCos = 0.5+0.5*cos\n", " return norCos" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "# Cosine Similarity \n", "def cosSim(a,b):\n", " cos = float(np.inner(a,b))/(np.linalg.norm(a)*np.linalg.norm(b))\n", " norCos = 0.5+0.5*cos\n", " return norCos" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "# Pearson Correlation Coefficient Similarity\n", "# \u4e0d\u8d85\u904e3 Similarity \uff1d1\n", "def pearsSim(a,b):\n", " if len(a)<3:\n", " return 1\n", " else:\n", " corcoef = np.corrcoef(a,b,rowvar=0)[0][1]\n", " norCorcoef = 0.5+0.5*corcoef\n", " return norCorcoef\n", "# \u8cc7\u6599\u4f86\u6e90\uff1ahttp://en.wikipedia.org/wiki/Pearson_product-moment_correlation_coefficient" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "# Person Correlation Coefficient Similarity \u548c Adjusted Cosine Similarity \u6bd4\u8f03 \n", "# Person Correlation Coefficient \u662f\u91dd\u5c0d\u7269\u54c1\u7684\u5e73\u5747 Adjusted Cosine Similarity\u662f\u91dd\u5c0d\u4f7f\u7528\u8005\u8a55\u50f9\u7684\u5e73\u5747\n", "# In pearson correlation \u548c cosine \u7684\u6bd4\u8f03 pearson correlation\u6703\u6e1b\u53bb\u5e73\u5747\u9805\uff0c\u964d\u4f4ebias\n", "# In adjusted cosine correlation \u4e0d\u540c\u4f7f\u7528\u8005\u53ef\u80fd\u5c0d\u7269\u54c1\u6703\u6709\u8f03\u9ad8\u6216\u662f\u8f03\u4f4e\u7684\u8a55\u5206\u8868\u73fe \u56e0\u6b64\u6e1b\u53bb\u4f7f\u7528\u8005\u5e73\u5747\u7684\u8a55\u5206 e.x.(4,5)(1,2)\u8996\u70ba\u884c\u70ba\u76f8\u4f3c\n", "# \u8cc7\u6599\u4f86\u6e90\uff1ahttp://www.cs.carleton.edu/cs_comps/0607/recommend/recommender/itembased.html" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "# \u6982\u5ff5\u85c9\u7531\u4f7f\u7528\u8005\u90fd\u8a55\u5206\u7269\u54c1\u7684\u5206\u6578\u505a\u76f8\u4f3c\u5ea6\u904b\u7b97\uff0c\u5176\u7d50\u679c\uff0a\u8a72\u672a\u8a55\u5206\u4e4b\u7269\u54c1 \u7e3d\u548c\u53d6\u5e73\u5747 \u5373\u70ba\u53ef\u80fd\u7684\u5206\u6578\n", "def itemSimRec(itemScore, user, simMethods, item):\n", " # user number : shape(itemScore)[0] item number : shape(itemScore)[1]\n", " n = np.shape(itemScore)[1]\n", " simTotal = 0.0; ratSimTotal = 0.0\n", " for i in range(n):\n", " userItemRating = itemScore[user,i]\n", " # pass norating item\n", " if userItemRating == 0 or i==item:\n", " pass\n", " else:\n", " # find same rating item\n", " sameRatingItem = np.nonzero(np.logical_and(itemScore[:,item] > 0,itemScore[:,i] > 0))[0]\n", " if len(sameRatingItem) == 0: \n", " similarity = 0\n", " else: \n", " similarity = simMethods(itemScore[sameRatingItem,item],itemScore[sameRatingItem,i])\n", "# print(itemScore[sameRatingItem,item])\n", "# print(itemScore[sameRatingItem,i])\n", "# print 'the %d and %d similarity is: %f' % (item, i, similarity)\n", " simTotal += similarity\n", " ratSimTotal += similarity * userItemRating\n", " if simTotal == 0: \n", " return 0\n", " else: \n", " return ratSimTotal/simTotal" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 12 }, { "cell_type": "code", "collapsed": false, "input": [ "def recommend(itemScore, user, N=3, simMethods=cosSim, estMethod=itemSimRec):\n", " #find unrated items \n", " unratedItems = np.nonzero(itemScore[user,:] ==0)[0]\n", " if len(unratedItems) == 0: \n", " return 'you rated everything'\n", " else:\n", " itemScores = []\n", " for item in unratedItems:\n", " estimatedScore = estMethod(itemScore, user, simMethods, item)\n", " itemScores.append((item, estimatedScore))\n", " recItem = sorted(itemScores, key=lambda x: x[1], reverse=True)[:N]\n", " return recItem" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ "# SVD\u5206\u89e3\u518d\u9084\u539f \u53ea\u6709\u6709\u591a\u51faSVD\u7684\u90e8\u5206\uff0c\u5176\u9918\u548citemSimRec\u7b97\u6cd5\u76f8\u540c\n", "def svdItemSimRec(itemScore, user, simMethods, item):\n", " n = np.shape(itemScore)[1]\n", " simTotal = 0.0; ratSimTotal = 0.0\n", " U,Sigma,VT = np.linalg.svd(itemScore)\n", " Sig4 = np.mat(np.eye(3)*Sigma[:3]) #arrange Sig4 into a diagonal matrix\n", " xformedItems = U[:,:3] * Sig4 * VT[:3,:] #create transformed items \n", " for i in range(n):\n", " userItemRating = itemScore[user,i]\n", " # pass norating item\n", " if userItemRating == 0 or i==item:\n", " pass\n", " else:\n", " # find same rating item\n", " sameRatingItem = np.nonzero(np.logical_and(itemScore[:,item] > 0,itemScore[:,i] > 0))[0]\n", " if len(sameRatingItem) == 0: \n", " similarity = 0\n", " else: \n", " similarity = simMethods(itemScore[sameRatingItem,item],itemScore[sameRatingItem,i])\n", "# print(itemScore[sameRatingItem,item])\n", "# print(itemScore[sameRatingItem,i])\n", "# print 'the %d and %d similarity is: %f' % (item, i, similarity)\n", " simTotal += similarity\n", " ratSimTotal += similarity * userItemRating\n", " if simTotal == 0: \n", " return 0\n", " else: \n", " return ratSimTotal/simTotal" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "# [(4, 5.0), (9, 5.0), (10, 4.7297297297297298)]\n", "# \u7269\u54c14 \u5206\u65785 \u7269\u54c19 \u5206\u65785 \u7269\u54c110 \u5206\u65784.7 \u4f9d\u5e8f\u63a8\u85a6\n", "print(\"estMethod=itemSimRec\")\n", "print(\"--------------------\")\n", "print(\"simMethods=eulidSim\")\n", "print(recommend(loadItemScore2(),4,simMethods=eulidSim,estMethod=itemSimRec))\n", "print(\"simMethods=cosSim\")\n", "print(recommend(loadItemScore2(),4,simMethods=cosSim,estMethod=itemSimRec))\n", "print(\"simMethods=adjCosSim\")\n", "print(recommend(loadItemScore2(),4,simMethods=adjCosSim,estMethod=itemSimRec))\n", "print(\"simMethods=pearsSim\")\n", "print(recommend(loadItemScore2(),4,simMethods=pearsSim,estMethod=itemSimRec))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "estMethod=itemSimRec\n", "--------------------\n", "simMethods=eulidSim\n", "[(4, 5.0), (9, 5.0), (10, 4.7297297297297298)]\n", "simMethods=cosSim\n", "[(4, 5.0), (9, 5.0), (10, 4.7999999999999998)]\n", "simMethods=adjCosSim\n", "[(4, 5.0), (9, 5.0), (10, 4.7999999999999998)]\n", "simMethods=pearsSim\n", "[(4, 5.0), (9, 5.0), (10, 4.7999999999999998)]\n" ] } ], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [ "# \u7167\u7406\u8aaa\u7d93\u904eSVD\u5206\u89e3\u5f8c\uff0c\u53ef\u5927\u5e45\u6e1b\u5c11\u5132\u5b58\u91cf\uff0c\u4f46\u5c0d\u65bc\u77e9\u9663\u9084\u539f\u53ef\u80fd\u6703\u5931\u771f\uff0c\u6545\u6703\u6bd4itemSimRec\u4e2d\u6709\u66f4\u591a\u8aa4\u5dee\u5b58\u5728\n", "print(\"estMethod=svdItemSimRec\")\n", "print(\"-----------------------\")\n", "print(\"simMethods=eulidSim\")\n", "print(recommend(loadItemScore2(),4,simMethods=eulidSim,estMethod=svdItemSimRec))\n", "print(\"simMethods=cosSim\")\n", "print(recommend(loadItemScore2(),4,simMethods=cosSim,estMethod=svdItemSimRec))\n", "print(\"simMethods=adjCosSim\")\n", "print(recommend(loadItemScore2(),4,simMethods=adjCosSim,estMethod=svdItemSimRec))\n", "print(\"simMethods=pearsSim\")\n", "print(recommend(loadItemScore2(),4,simMethods=pearsSim,estMethod=svdItemSimRec))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "estMethod=svdItemSimRec\n", "-----------------------\n", "simMethods=eulidSim\n", "[(4, 5.0), (9, 5.0), (10, 4.7297297297297298)]\n", "simMethods=cosSim\n", "[(4, 5.0), (9, 5.0), (10, 4.7999999999999998)]\n", "simMethods=adjCosSim\n", "[(4, 5.0), (9, 5.0), (10, 4.7999999999999998)]\n", "simMethods=pearsSim\n", "[(4, 5.0), (9, 5.0), (10, 4.7999999999999998)]\n" ] } ], "prompt_number": 16 }, { "cell_type": "code", "collapsed": false, "input": [ "# \u53c3\u8003\u4f86\u6e90 Machine Learning in Action" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 17 } ], "metadata": {} } ] }
mit
NeuroDataDesign/seelviz
jon/algorithms/connectivity2.ipynb
2
6301580
null
apache-2.0
ituethoslab/navcom-2017
exercises/Week 7-Situational Mapping/Week 7-Situational Mapping.ipynb
1
3736
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Situational mapping\n", "\n", "Exercise: Mapping actors, relations and discourse (worlds of meaning, including technology)\n", "\n", "Today’s exercise will explore the mapping of actors, their associations, and the social worlds they form together in which technology is present. To conducts this exercise you need to have read the book chapters from Adele Clarke’s Situational Analysis.\n", "\n", "This exercise will aid you in fleshing out prominent and less prominent actors and issues relating to the case you have chosen to investigate. Also, it will help you identify issues that could be interesting and relevant to dive further into. The mapping might then lead to reworking and refining your research question(s).\n", "\n", "### Schedule\n", "1. Introduction to the exercise\n", "2. Make situational maps by going through mapping exercises 1-3\n", "\n", "## Mapping exercise 1 - Messy maps (see Clarke’s example on page 88).\n", "In a messy map, it is important that you start listing what you know about the chosen case. This could be the various actors, such as; human, non-human, key event, organizations, social groups etc. The strength of a messy map is that it is accessible and sharable and allows you to mix all sorts of different elements. Makes notes (memos) of the discussions you engage in when making the maps. Keep a master copy. The master copy give you both something to build later maps of as different themes and concepts become more, or less, important during your research. It also enables you to go back and see what your initial thoughts were and remember elements you might have forgotten in the meantime.\n", "\n", "## Mapping exercise 2 – Relational maps (see Clarke page 104).\n", "Based on the messy maps try relating actors to each other. What is important here is not to come up with a map that accurately visualizes all possible relations between and among actors. The main point in this exercise is to describe the relations you sketch between actors. If actors are related what is significant about their relation? What might the quality of their relation be? Make notes (memos) of the discussions you have when relating actors. Memos are important documents for later analysis.\n", "\n", "## Mapping exercise 3 – Social worlds / arenas maps (see Clarke page 118).\n", "The purpose of identifying social worlds and arenas is to visualize collectives/groups of actors considering them being participants in an arena, where they interact and negotiate what counts as reality. Identify social worlds / arena by discussing how actors form what Clarke calls “universes of discourse”. The arena in which one or more social worlds are placed illustrates the wider ‘battlefield’ of the negotiations. Arenas can be a specific setting (a hospital), an issue (a law), a theme or problematization (health debate), justification (scientific disputes), etc.\n", "\n", "**OBS: Take breaks as you need them.**\n", "\n", "## Presentations\n", "Groups will present select social world/arenas maps, key insights, and new questions that emerged from the mapping (5-8 minutes)" ] } ], "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 }
gpl-3.0
MridulS/BinPy
BinPy/examples/notebook/Gates/NOR.ipynb
1
3926
{ "metadata": { "name": "", "signature": "sha256:12e6d431b372dc30b5061212608ae13fcb2725a5e0d29f7572b6df96d8a68dec" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Examples for NOR class" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# imports\n", "from __future__ import print_function\n", "from BinPy.Gates import *" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "# Initializing the NOR class\n", "\n", "gate = NOR(0, 1)\n", "\n", "# Output of the NOR gate\n", "\n", "print (gate.output())" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "0\n" ] } ], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "# Input changes\n", "\n", "# Input at index 1 is changed to 0\n", "\n", "gate.setInput(1, 0)\n", "\n", "# New Output of the NOR gate\n", "\n", "print (gate.output())" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1\n" ] } ], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "# Changing the number of inputs\n", "\n", "# No need to set the number, just change the inputs\n", "\n", "gate.setInputs(1, 1, 1, 1)\n", "\n", "# To get the input states\n", "\n", "print (gate.getInputStates())" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[1, 1, 1, 1]\n" ] } ], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "# New output of the NOR gate\n", "\n", "print (gate.output())" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "0\n" ] } ], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "# Using Connectors as the input lines\n", "\n", "# Take a Connector\n", "\n", "conn = Connector()\n", "\n", "# Set Output of gate to Connector conn\n", "\n", "gate.setOutput(conn)\n", "\n", "# Put this connector as the input to gate1\n", "\n", "gate1 = NOR(conn, 0)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "# Output of the gate1\n", "\n", "print (gate1.output())" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1\n" ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "# Information about gate instance\n", "\n", "print (gate)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "NOR Gate; Output: 0; Inputs: [1, 1, 1, 1];\n" ] } ], "prompt_number": 8 } ], "metadata": {} } ] }
bsd-3-clause
dereneaton/ipyrad
newdocs/API-analysis/cookbook-locus_builder.ipynb
1
2523
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<h2><span style=\"color:gray\">ipyrad-analysis toolkit:</span> locus_builder</h2>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h5><span style=\"color:red\">(Reference only method)</span></h5>\n", "\n", "When RAD loci are assembled by mapping to a reference genome there may often be many that are in close enough proximity of each other that you wish to concatenate them into a smaller number of loci for gene tree analyses. This can be done based on a fixed window size and distance between windows using the `tree_slider()` tool, or, alternatively you can use the `locus_builder()` tool here to filter and build loci with more options. \n", "\n", "The `locus_builder` tool can be used to filter scaffolds, or find optimal breakpoints in scaffolds to select a suitable number of loci for downstream analyses, and then to prepare those loci for phylogenetic analysis. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Required software" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# conda install ipyrad -c bioconda" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import ipyrad.analysis as ipa" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Short Tutorial:\n", "\n", "The `window_extracter()` tool takes the `.seqs.hdf5` database file from ipyrad as its input file. You can select scaffolds by their index (integer) or by their name (string). If you don't know what these are then first read in the data file without a scaffold argument and check the `.scaffold_table` attribute table. " ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# the path to your HDF5 formatted seqs file\n", "data = \"/home/deren/Downloads/ref_pop2.seqs.hdf5\"" ] } ], "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": 2 }
gpl-3.0
rbiswas4/AnalyzeSN
examples/Demo_usingResChar.ipynb
1
4062
{ "cells": [ { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import analyzeSN" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import sncosmo" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "model = sncosmo.Model(source='salt2')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "data = sncosmo.load_example_data()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "OrderedDict([('x1', 0.5),\n", " ('c', 0.2),\n", " ('z', 0.5),\n", " ('x0', 1.20482820761e-05),\n", " ('t0', 55100.0)])" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.meta" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "model.set(z=0.5)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "res = sncosmo.fit_lc(data, model, vparam_names=['t0', 'x0', 'x1', 'c'], )" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "reschar = analyzeSN.ResChar.fromSNCosmoRes(res)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " mB x1 c\n", "mB 0.001239 0.006771 0.000706\n", "x1 0.006771 0.116810 0.001399\n", "c 0.000706 0.001399 0.000820 [ 1. 0.14 -3.14]\n" ] } ], "source": [ "varmu = reschar.mu_variance_linear(alpha=0.14, beta=3.14)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.08859394617261082\n" ] } ], "source": [ "print(varmu**0.5)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# If we use mcmc to do this:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "res_mcmc = sncosmo.mcmc_lc(data, model, vparam_names=['t0', 'x0', 'x1', 'c'], )" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "reschar_mcmc = reschar.fromSNCosmoRes(res_mcmc)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Note that mu might need additional constants" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.0866330665243906" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "reschar_mcmc.salt_samples(alpha=0.1, beta=-3.14).mu.std()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.2" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
royalosyin/Python-Practical-Application-on-Climate-Variability-Studies
ex00-Introduction Life is short, use Python.ipynb
1
10839
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<style>\n", "\n", ".rendered_html {\n", " font-family: \"proxima-nova\", helvetica;\n", " font-size: 130%;\n", " line-height: 1.5;\n", "}\n", "\n", ".rendered_html h1 {\n", " margin: 0.25em 0em 0.5em;\n", " color: #015C9C;\n", " text-align: center;\n", " line-height: 1.2; \n", " page-break-before: always;\n", "}\n", "\n", ".rendered_html h2 {\n", " margin: 1.1em 0em 0.5em;\n", " color: #26465D;\n", " line-height: 1.2;\n", "}\n", "\n", ".rendered_html h3 {\n", " margin: 1.1em 0em 0.5em;\n", " color: #002845;\n", " line-height: 1.2;\n", "}\n", "\n", ".rendered_html li {\n", " line-height: 1.5; \n", "}\n", "\n", "/*.prompt {\n", " font-size: 120%; \n", "}*/\n", "\n", ".CodeMirror-lines {\n", " font-size: 110%; \n", "}\n", "\n", "/*.output_area {\n", " font-size: 120%; \n", "}*/\n", "\n", "/*#notebook {\n", " background-image: url('files/images/witewall_3.png');\n", "}*/\n", "\n", "h1.bigtitle {\n", " margin: 4cm 1cm 4cm 1cm;\n", " font-size: 300%;\n", "}\n", "\n", "h3.point {\n", " font-size: 200%;\n", " text-align: center;\n", " margin: 2em 0em 2em 0em;\n", " #26465D\n", "}\n", "\n", ".logo {\n", " margin: 20px 0 20px 0;\n", "}\n", "\n", "a.anchor-link {\n", " display: none;\n", "}\n", "\n", "h1.title { \n", " font-size: 250%;\n", "}\n", "\n", "</style>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%load_ext load_style\n", "%load_style talk.css" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Life is short, use Python\n", "\n", "Main objective of this tutorial is the transference of know-how in practical applications and management of statistical tools commonly used to explore meteorological time series, focusing on applications to study issues related with the climate variability and climate change.\n", "\n", "This tutorial starts with some basic statistic for time series analysis as estimation of means, anomalies, standard deviation, correlations, arriving the estimation of particular climate indexes (Niño 3), detrending single time series and decomposition of time series, filtering, interpolation of climate variables on regular or irregular grids, leading modes of climate variability (EOF or HHT), signal processing in the climate system (spectral and wavelet analysis). In addition, this tutorial also deals with different data formats such as CSV, NetCDF, Binary, and matlab'mat, etc.\n", "\n", "It is assumed that you have basic knowledge and understanding of statistics and Python." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Generic libraries for scientific analysis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The default Python library for dealing with large arrays of numeric data (e.g. four dimensional latitude/longitude/altitude/time data arrays) is numpy, while the netCDF data format is commonly used in Atmospheric science and Oceanography as it is convinient to store various variables of many dimensions. The default for reading and writing netCDF files is netCDF4 (the capability to read and write text files, including .csv, is built into numpy). " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## What is NetCDF?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "NetCDF is a set of software libraries and self-describing, machine-independent data formats that support the creation, access, and sharing of array-oriented scientific data.\n", "\n", "NetCDF was developed and is maintained at Unidata. Unidata provides data and software tools for use in geoscience education and research. The NetCDF homepage may be found at http://www.unidata.ucar.edu/software/netcdf/. The NetCDF source-code is hosted at GitHub, and may be found directly at http://github.com/Unidata/netcdf-c." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## How to deal with NetCDF data with Python?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "we mainly use **netCDF4-python**, **NumPy** and **SciPy** to process NetCDF and other data formats.\n", "\n", "* **netCDF4-python**\n", "\n", ">netcdf4-python is a Python interface to the netCDF C library. netCDF version 4 has many features not found in earlier versions of the library and is implemented on top of HDF5. This module can read and write files in both the new netCDF 4 and the old netCDF 3 format, and can create files that are readable by HDF5 clients. The API modelled after Scientific.IO.NetCDF, and should be familiar to users of that module (see more http://unidata.github.io/netcdf4-python/).\n", "\n", "* **NumPy**\n", "\n", ">NumPy is the fundamental package for scientific computing with Python (see more http://www.numpy.org/). <br>It contains among other things:\n", "- a powerful N-dimensional array object\n", "- sophisticated (broadcasting) functions\n", "- tools for integrating C/C++ and Fortran code\n", "- useful linear algebra, Fourier transform, and random number capabilities\n", "\n", ">Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.\n", "\n", "* **SciPy**\n", "\n", ">SciPy is a collection of mathematical algorithms and convenience functions built on the Numpy extension of Python (https://www.scipy.org/index.html). It adds significant power to the interactive Python session by providing the user with high-level commands and classes for manipulating and visualizing data. With SciPy an interactive Python session becomes a data-processing and system-prototyping environment rivaling systems such as MATLAB, IDL, Octave, R-Lab, and SciLab.\n", "\n", ">The additional benefit of basing SciPy on Python is that this also makes a powerful programming language available for use in developing sophisticated programs and specialized applications. Scientific applications using SciPy benefit from the development of additional modules in numerous niches of the software landscape by developers across the world. Everything from parallel programming to web and data-base subroutines and classes have been made available to the Python programmer. All of this power is available in addition to the mathematical libraries in SciPy." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data Sources" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will use the data publicly available as possible.\n", "\n", "The data are mainly downloaded from https://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.derived.surfaceflux.html " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Visualization" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Matplotlib**\n", ">Matplotlib is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. Matplotlib can be used in Python scripts, the Python and IPython shell, the jupyter notebook, web application servers, and four graphical user interface toolkits.\n", "\n", ">Matplotlib tries to make easy things easy and hard things possible. You can generate plots, histograms, power spectra, bar charts, errorcharts, scatterplots, etc., with just a few lines of code. For a sampling, see the screenshots, thumbnail gallery, and examples directory\n", "\n", ">For simple plotting the pyplot module provides a MATLAB-like interface, particularly when combined with IPython. For the power user, you have full control of line styles, font properties, axes properties, etc, via an object oriented interface or via a set of functions familiar to MATLAB users.\n", "\n", ">See more from https://matplotlib.org/\n", "\n", "**Basemap**\n", ">Basemap is a great tool for creating maps using python in a simple way. It’s a matplotlib extension, so it has got all its features to create data visualizations, and adds the geographical projections and some datasets to be able to plot coast lines, countries, and so on directly from the library.\n", "\n", ">Basemap has got some documentation, but some things are a bit more difficult to find. I started this documentation to extend a little the original documentation and examples, but it grew a little, and now covers many of the basemap possibilities.\n", "\n", ">See more from https://basemaptutorial.readthedocs.io/en/latest/" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## More" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will mainly apply these mostly generic libraries to carry out data analysis step by step. The procedures or steps are common to the atmospheric and ocean sciences. Although other advanced libraries will simpilfy the procedures, the underlying ideas should be the same in essense.\n", "\n", "We will also introduce more other libraries such as xarray and iris in the following parts." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda root]", "language": "python", "name": "conda-root-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
wy1iu/sphereface
tools/caffe-sphereface/examples/02-fine-tuning.ipynb
13
480512
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Fine-tuning a Pretrained Network for Style Recognition\n", "\n", "In this example, we'll explore a common approach that is particularly useful in real-world applications: take a pre-trained Caffe network and fine-tune the parameters on your custom data.\n", "\n", "The advantage of this approach is that, since pre-trained networks are learned on a large set of images, the intermediate layers capture the \"semantics\" of the general visual appearance. Think of it as a very powerful generic visual feature that you can treat as a black box. On top of that, only a relatively small amount of data is needed for good performance on the target task." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, we will need to prepare the data. This involves the following parts:\n", "(1) Get the ImageNet ilsvrc pretrained model with the provided shell scripts.\n", "(2) Download a subset of the overall Flickr style dataset for this demo.\n", "(3) Compile the downloaded Flickr dataset into a database that Caffe can then consume." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "caffe_root = '../' # this file should be run from {caffe_root}/examples (otherwise change this line)\n", "\n", "import sys\n", "sys.path.insert(0, caffe_root + 'python')\n", "import caffe\n", "\n", "caffe.set_device(0)\n", "caffe.set_mode_gpu()\n", "\n", "import numpy as np\n", "from pylab import *\n", "%matplotlib inline\n", "import tempfile\n", "\n", "# Helper function for deprocessing preprocessed images, e.g., for display.\n", "def deprocess_net_image(image):\n", " image = image.copy() # don't modify destructively\n", " image = image[::-1] # BGR -> RGB\n", " image = image.transpose(1, 2, 0) # CHW -> HWC\n", " image += [123, 117, 104] # (approximately) undo mean subtraction\n", "\n", " # clamp values in [0, 255]\n", " image[image < 0], image[image > 255] = 0, 255\n", "\n", " # round and cast from float32 to uint8\n", " image = np.round(image)\n", " image = np.require(image, dtype=np.uint8)\n", "\n", " return image" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1. Setup and dataset download\n", "\n", "Download data required for this exercise.\n", "\n", "- `get_ilsvrc_aux.sh` to download the ImageNet data mean, labels, etc.\n", "- `download_model_binary.py` to download the pretrained reference model\n", "- `finetune_flickr_style/assemble_data.py` downloads the style training and testing data\n", "\n", "We'll download just a small subset of the full dataset for this exercise: just 2000 of the 80K images, from 5 of the 20 style categories. (To download the full dataset, set `full_dataset = True` in the cell below.)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Downloading...\n", "--2016-02-24 00:28:36-- http://dl.caffe.berkeleyvision.org/caffe_ilsvrc12.tar.gz\n", "Resolving dl.caffe.berkeleyvision.org (dl.caffe.berkeleyvision.org)... 169.229.222.251\n", "Connecting to dl.caffe.berkeleyvision.org (dl.caffe.berkeleyvision.org)|169.229.222.251|:80... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 17858008 (17M) [application/octet-stream]\n", "Saving to: ‘caffe_ilsvrc12.tar.gz’\n", "\n", "100%[======================================>] 17,858,008 112MB/s in 0.2s \n", "\n", "2016-02-24 00:28:36 (112 MB/s) - ‘caffe_ilsvrc12.tar.gz’ saved [17858008/17858008]\n", "\n", "Unzipping...\n", "Done.\n", "Model already exists.\n", "Downloading 2000 images with 7 workers...\n", "Writing train/val for 1996 successfully downloaded images.\n" ] } ], "source": [ "# Download just a small subset of the data for this exercise.\n", "# (2000 of 80K images, 5 of 20 labels.)\n", "# To download the entire dataset, set `full_dataset = True`.\n", "full_dataset = False\n", "if full_dataset:\n", " NUM_STYLE_IMAGES = NUM_STYLE_LABELS = -1\n", "else:\n", " NUM_STYLE_IMAGES = 2000\n", " NUM_STYLE_LABELS = 5\n", "\n", "# This downloads the ilsvrc auxiliary data (mean file, etc),\n", "# and a subset of 2000 images for the style recognition task.\n", "import os\n", "os.chdir(caffe_root) # run scripts from caffe root\n", "!data/ilsvrc12/get_ilsvrc_aux.sh\n", "!scripts/download_model_binary.py models/bvlc_reference_caffenet\n", "!python examples/finetune_flickr_style/assemble_data.py \\\n", " --workers=-1 --seed=1701 \\\n", " --images=$NUM_STYLE_IMAGES --label=$NUM_STYLE_LABELS\n", "# back to examples\n", "os.chdir('examples')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Define `weights`, the path to the ImageNet pretrained weights we just downloaded, and make sure it exists." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import os\n", "weights = os.path.join(caffe_root, 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel')\n", "assert os.path.exists(weights)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Load the 1000 ImageNet labels from `ilsvrc12/synset_words.txt`, and the 5 style labels from `finetune_flickr_style/style_names.txt`." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loaded ImageNet labels:\n", "n01440764 tench, Tinca tinca\n", "n01443537 goldfish, Carassius auratus\n", "n01484850 great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias\n", "n01491361 tiger shark, Galeocerdo cuvieri\n", "n01494475 hammerhead, hammerhead shark\n", "n01496331 electric ray, crampfish, numbfish, torpedo\n", "n01498041 stingray\n", "n01514668 cock\n", "n01514859 hen\n", "n01518878 ostrich, Struthio camelus\n", "...\n", "\n", "Loaded style labels:\n", "Detailed, Pastel, Melancholy, Noir, HDR\n" ] } ], "source": [ "# Load ImageNet labels to imagenet_labels\n", "imagenet_label_file = caffe_root + 'data/ilsvrc12/synset_words.txt'\n", "imagenet_labels = list(np.loadtxt(imagenet_label_file, str, delimiter='\\t'))\n", "assert len(imagenet_labels) == 1000\n", "print 'Loaded ImageNet labels:\\n', '\\n'.join(imagenet_labels[:10] + ['...'])\n", "\n", "# Load style labels to style_labels\n", "style_label_file = caffe_root + 'examples/finetune_flickr_style/style_names.txt'\n", "style_labels = list(np.loadtxt(style_label_file, str, delimiter='\\n'))\n", "if NUM_STYLE_LABELS > 0:\n", " style_labels = style_labels[:NUM_STYLE_LABELS]\n", "print '\\nLoaded style labels:\\n', ', '.join(style_labels)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2. Defining and running the nets\n", "\n", "We'll start by defining `caffenet`, a function which initializes the *CaffeNet* architecture (a minor variant on *AlexNet*), taking arguments specifying the data and number of output classes." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "from caffe import layers as L\n", "from caffe import params as P\n", "\n", "weight_param = dict(lr_mult=1, decay_mult=1)\n", "bias_param = dict(lr_mult=2, decay_mult=0)\n", "learned_param = [weight_param, bias_param]\n", "\n", "frozen_param = [dict(lr_mult=0)] * 2\n", "\n", "def conv_relu(bottom, ks, nout, stride=1, pad=0, group=1,\n", " param=learned_param,\n", " weight_filler=dict(type='gaussian', std=0.01),\n", " bias_filler=dict(type='constant', value=0.1)):\n", " conv = L.Convolution(bottom, kernel_size=ks, stride=stride,\n", " num_output=nout, pad=pad, group=group,\n", " param=param, weight_filler=weight_filler,\n", " bias_filler=bias_filler)\n", " return conv, L.ReLU(conv, in_place=True)\n", "\n", "def fc_relu(bottom, nout, param=learned_param,\n", " weight_filler=dict(type='gaussian', std=0.005),\n", " bias_filler=dict(type='constant', value=0.1)):\n", " fc = L.InnerProduct(bottom, num_output=nout, param=param,\n", " weight_filler=weight_filler,\n", " bias_filler=bias_filler)\n", " return fc, L.ReLU(fc, in_place=True)\n", "\n", "def max_pool(bottom, ks, stride=1):\n", " return L.Pooling(bottom, pool=P.Pooling.MAX, kernel_size=ks, stride=stride)\n", "\n", "def caffenet(data, label=None, train=True, num_classes=1000,\n", " classifier_name='fc8', learn_all=False):\n", " \"\"\"Returns a NetSpec specifying CaffeNet, following the original proto text\n", " specification (./models/bvlc_reference_caffenet/train_val.prototxt).\"\"\"\n", " n = caffe.NetSpec()\n", " n.data = data\n", " param = learned_param if learn_all else frozen_param\n", " n.conv1, n.relu1 = conv_relu(n.data, 11, 96, stride=4, param=param)\n", " n.pool1 = max_pool(n.relu1, 3, stride=2)\n", " n.norm1 = L.LRN(n.pool1, local_size=5, alpha=1e-4, beta=0.75)\n", " n.conv2, n.relu2 = conv_relu(n.norm1, 5, 256, pad=2, group=2, param=param)\n", " n.pool2 = max_pool(n.relu2, 3, stride=2)\n", " n.norm2 = L.LRN(n.pool2, local_size=5, alpha=1e-4, beta=0.75)\n", " n.conv3, n.relu3 = conv_relu(n.norm2, 3, 384, pad=1, param=param)\n", " n.conv4, n.relu4 = conv_relu(n.relu3, 3, 384, pad=1, group=2, param=param)\n", " n.conv5, n.relu5 = conv_relu(n.relu4, 3, 256, pad=1, group=2, param=param)\n", " n.pool5 = max_pool(n.relu5, 3, stride=2)\n", " n.fc6, n.relu6 = fc_relu(n.pool5, 4096, param=param)\n", " if train:\n", " n.drop6 = fc7input = L.Dropout(n.relu6, in_place=True)\n", " else:\n", " fc7input = n.relu6\n", " n.fc7, n.relu7 = fc_relu(fc7input, 4096, param=param)\n", " if train:\n", " n.drop7 = fc8input = L.Dropout(n.relu7, in_place=True)\n", " else:\n", " fc8input = n.relu7\n", " # always learn fc8 (param=learned_param)\n", " fc8 = L.InnerProduct(fc8input, num_output=num_classes, param=learned_param)\n", " # give fc8 the name specified by argument `classifier_name`\n", " n.__setattr__(classifier_name, fc8)\n", " if not train:\n", " n.probs = L.Softmax(fc8)\n", " if label is not None:\n", " n.label = label\n", " n.loss = L.SoftmaxWithLoss(fc8, n.label)\n", " n.acc = L.Accuracy(fc8, n.label)\n", " # write the net to a temporary file and return its filename\n", " with tempfile.NamedTemporaryFile(delete=False) as f:\n", " f.write(str(n.to_proto()))\n", " return f.name" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, let's create a *CaffeNet* that takes unlabeled \"dummy data\" as input, allowing us to set its input images externally and see what ImageNet classes it predicts." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dummy_data = L.DummyData(shape=dict(dim=[1, 3, 227, 227]))\n", "imagenet_net_filename = caffenet(data=dummy_data, train=False)\n", "imagenet_net = caffe.Net(imagenet_net_filename, weights, caffe.TEST)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Define a function `style_net` which calls `caffenet` on data from the Flickr style dataset.\n", "\n", "The new network will also have the *CaffeNet* architecture, with differences in the input and output:\n", "\n", "- the input is the Flickr style data we downloaded, provided by an `ImageData` layer\n", "- the output is a distribution over 20 classes rather than the original 1000 ImageNet classes\n", "- the classification layer is renamed from `fc8` to `fc8_flickr` to tell Caffe not to load the original classifier (`fc8`) weights from the ImageNet-pretrained model" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def style_net(train=True, learn_all=False, subset=None):\n", " if subset is None:\n", " subset = 'train' if train else 'test'\n", " source = caffe_root + 'data/flickr_style/%s.txt' % subset\n", " transform_param = dict(mirror=train, crop_size=227,\n", " mean_file=caffe_root + 'data/ilsvrc12/imagenet_mean.binaryproto')\n", " style_data, style_label = L.ImageData(\n", " transform_param=transform_param, source=source,\n", " batch_size=50, new_height=256, new_width=256, ntop=2)\n", " return caffenet(data=style_data, label=style_label, train=train,\n", " num_classes=NUM_STYLE_LABELS,\n", " classifier_name='fc8_flickr',\n", " learn_all=learn_all)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Use the `style_net` function defined above to initialize `untrained_style_net`, a *CaffeNet* with input images from the style dataset and weights from the pretrained ImageNet model.\n", "\n", "\n", "Call `forward` on `untrained_style_net` to get a batch of style training data." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "untrained_style_net = caffe.Net(style_net(train=False, subset='train'),\n", " weights, caffe.TEST)\n", "untrained_style_net.forward()\n", "style_data_batch = untrained_style_net.blobs['data'].data.copy()\n", "style_label_batch = np.array(untrained_style_net.blobs['label'].data, dtype=np.int32)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pick one of the style net training images from the batch of 50 (we'll arbitrarily choose #8 here). Display it, then run it through `imagenet_net`, the ImageNet-pretrained network to view its top 5 predicted classes from the 1000 ImageNet classes.\n", "\n", "Below we chose an image where the network's predictions happen to be reasonable, as the image is of a beach, and \"sandbar\" and \"seashore\" both happen to be ImageNet-1000 categories. For other images, the predictions won't be this good, sometimes due to the network actually failing to recognize the object(s) present in the image, but perhaps even more often due to the fact that not all images contain an object from the (somewhat arbitrarily chosen) 1000 ImageNet categories. Modify the `batch_index` variable by changing its default setting of 8 to another value from 0-49 (since the batch size is 50) to see predictions for other images in the batch. (To go beyond this batch of 50 images, first rerun the *above* cell to load a fresh batch of data into `style_net`.)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def disp_preds(net, image, labels, k=5, name='ImageNet'):\n", " input_blob = net.blobs['data']\n", " net.blobs['data'].data[0, ...] = image\n", " probs = net.forward(start='conv1')['probs'][0]\n", " top_k = (-probs).argsort()[:k]\n", " print 'top %d predicted %s labels =' % (k, name)\n", " print '\\n'.join('\\t(%d) %5.2f%% %s' % (i+1, 100*probs[p], labels[p])\n", " for i, p in enumerate(top_k))\n", "\n", "def disp_imagenet_preds(net, image):\n", " disp_preds(net, image, imagenet_labels, name='ImageNet')\n", "\n", "def disp_style_preds(net, image):\n", " disp_preds(net, image, style_labels, name='style')" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "actual label = Melancholy\n" ] }, { "data": { "image/png": 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yvUJS3DfmLUsURECnW6oGgUxGBSVh7180Q52jz0UjoWnYDZxRmdmA1bqxNeau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6EmVpSTd7osozItUDOtN5ksBa2bdacbpJGyRKLhrAxEttNG0O8AWK1TShCtYaElalB19S3dVz6K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\ncDPDu2eav33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"text/plain": [ "<matplotlib.figure.Figure at 0x7f75dc02e4d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "batch_index = 8\n", "image = style_data_batch[batch_index]\n", "plt.imshow(deprocess_net_image(image))\n", "print 'actual label =', style_labels[style_label_batch[batch_index]]" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "top 5 predicted ImageNet labels =\n", "\t(1) 69.89% n09421951 sandbar, sand bar\n", "\t(2) 21.76% n09428293 seashore, coast, seacoast, sea-coast\n", "\t(3) 3.22% n02894605 breakwater, groin, groyne, mole, bulwark, seawall, jetty\n", "\t(4) 1.89% n04592741 wing\n", "\t(5) 1.23% n09332890 lakeside, lakeshore\n" ] } ], "source": [ "disp_imagenet_preds(imagenet_net, image)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also look at `untrained_style_net`'s predictions, but we won't see anything interesting as its classifier hasn't been trained yet.\n", "\n", "In fact, since we zero-initialized the classifier (see `caffenet` definition -- no `weight_filler` is passed to the final `InnerProduct` layer), the softmax inputs should be all zero and we should therefore see a predicted probability of 1/N for each label (for N labels). Since we set N = 5, we get a predicted probability of 20% for each class." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "top 5 predicted style labels =\n", "\t(1) 20.00% Detailed\n", "\t(2) 20.00% Pastel\n", "\t(3) 20.00% Melancholy\n", "\t(4) 20.00% Noir\n", "\t(5) 20.00% HDR\n" ] } ], "source": [ "disp_style_preds(untrained_style_net, image)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also verify that the activations in layer `fc7` immediately before the classification layer are the same as (or very close to) those in the ImageNet-pretrained model, since both models are using the same pretrained weights in the `conv1` through `fc7` layers." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "diff = untrained_style_net.blobs['fc7'].data[0] - imagenet_net.blobs['fc7'].data[0]\n", "error = (diff ** 2).sum()\n", "assert error < 1e-8" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Delete `untrained_style_net` to save memory. (Hang on to `imagenet_net` as we'll use it again later.)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "del untrained_style_net" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3. Training the style classifier\n", "\n", "Now, we'll define a function `solver` to create our Caffe solvers, which are used to train the network (learn its weights). In this function we'll set values for various parameters used for learning, display, and \"snapshotting\" -- see the inline comments for explanations of what they mean. You may want to play with some of the learning parameters to see if you can improve on the results here!" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from caffe.proto import caffe_pb2\n", "\n", "def solver(train_net_path, test_net_path=None, base_lr=0.001):\n", " s = caffe_pb2.SolverParameter()\n", "\n", " # Specify locations of the train and (maybe) test networks.\n", " s.train_net = train_net_path\n", " if test_net_path is not None:\n", " s.test_net.append(test_net_path)\n", " s.test_interval = 1000 # Test after every 1000 training iterations.\n", " s.test_iter.append(100) # Test on 100 batches each time we test.\n", "\n", " # The number of iterations over which to average the gradient.\n", " # Effectively boosts the training batch size by the given factor, without\n", " # affecting memory utilization.\n", " s.iter_size = 1\n", " \n", " s.max_iter = 100000 # # of times to update the net (training iterations)\n", " \n", " # Solve using the stochastic gradient descent (SGD) algorithm.\n", " # Other choices include 'Adam' and 'RMSProp'.\n", " s.type = 'SGD'\n", "\n", " # Set the initial learning rate for SGD.\n", " s.base_lr = base_lr\n", "\n", " # Set `lr_policy` to define how the learning rate changes during training.\n", " # Here, we 'step' the learning rate by multiplying it by a factor `gamma`\n", " # every `stepsize` iterations.\n", " s.lr_policy = 'step'\n", " s.gamma = 0.1\n", " s.stepsize = 20000\n", "\n", " # Set other SGD hyperparameters. Setting a non-zero `momentum` takes a\n", " # weighted average of the current gradient and previous gradients to make\n", " # learning more stable. L2 weight decay regularizes learning, to help prevent\n", " # the model from overfitting.\n", " s.momentum = 0.9\n", " s.weight_decay = 5e-4\n", "\n", " # Display the current training loss and accuracy every 1000 iterations.\n", " s.display = 1000\n", "\n", " # Snapshots are files used to store networks we've trained. Here, we'll\n", " # snapshot every 10K iterations -- ten times during training.\n", " s.snapshot = 10000\n", " s.snapshot_prefix = caffe_root + 'models/finetune_flickr_style/finetune_flickr_style'\n", " \n", " # Train on the GPU. Using the CPU to train large networks is very slow.\n", " s.solver_mode = caffe_pb2.SolverParameter.GPU\n", " \n", " # Write the solver to a temporary file and return its filename.\n", " with tempfile.NamedTemporaryFile(delete=False) as f:\n", " f.write(str(s))\n", " return f.name" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we'll invoke the solver to train the style net's classification layer.\n", "\n", "For the record, if you want to train the network using only the command line tool, this is the command:\n", "\n", "<code>\n", "build/tools/caffe train \\\n", " -solver models/finetune_flickr_style/solver.prototxt \\\n", " -weights models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel \\\n", " -gpu 0\n", "</code>\n", "\n", "However, we will train using Python in this example.\n", "\n", "We'll first define `run_solvers`, a function that takes a list of solvers and steps each one in a round robin manner, recording the accuracy and loss values each iteration. At the end, the learned weights are saved to a file." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def run_solvers(niter, solvers, disp_interval=10):\n", " \"\"\"Run solvers for niter iterations,\n", " returning the loss and accuracy recorded each iteration.\n", " `solvers` is a list of (name, solver) tuples.\"\"\"\n", " blobs = ('loss', 'acc')\n", " loss, acc = ({name: np.zeros(niter) for name, _ in solvers}\n", " for _ in blobs)\n", " for it in range(niter):\n", " for name, s in solvers:\n", " s.step(1) # run a single SGD step in Caffe\n", " loss[name][it], acc[name][it] = (s.net.blobs[b].data.copy()\n", " for b in blobs)\n", " if it % disp_interval == 0 or it + 1 == niter:\n", " loss_disp = '; '.join('%s: loss=%.3f, acc=%2d%%' %\n", " (n, loss[n][it], np.round(100*acc[n][it]))\n", " for n, _ in solvers)\n", " print '%3d) %s' % (it, loss_disp) \n", " # Save the learned weights from both nets.\n", " weight_dir = tempfile.mkdtemp()\n", " weights = {}\n", " for name, s in solvers:\n", " filename = 'weights.%s.caffemodel' % name\n", " weights[name] = os.path.join(weight_dir, filename)\n", " s.net.save(weights[name])\n", " return loss, acc, weights" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's create and run solvers to train nets for the style recognition task. We'll create two solvers -- one (`style_solver`) will have its train net initialized to the ImageNet-pretrained weights (this is done by the call to the `copy_from` method), and the other (`scratch_style_solver`) will start from a *randomly* initialized net.\n", "\n", "During training, we should see that the ImageNet pretrained net is learning faster and attaining better accuracies than the scratch net." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Running solvers for 200 iterations...\n", " 0) pretrained: loss=1.609, acc=28%; scratch: loss=1.609, acc=28%\n", " 10) pretrained: loss=1.293, acc=52%; scratch: loss=1.626, acc=14%\n", " 20) pretrained: loss=1.110, acc=56%; scratch: loss=1.646, acc=10%\n", " 30) pretrained: loss=1.084, acc=60%; scratch: loss=1.616, acc=20%\n", " 40) pretrained: loss=0.898, acc=64%; scratch: loss=1.588, acc=26%\n", " 50) pretrained: loss=1.024, acc=54%; scratch: loss=1.607, acc=32%\n", " 60) pretrained: loss=0.925, acc=66%; scratch: loss=1.616, acc=20%\n", " 70) pretrained: loss=0.861, acc=74%; scratch: loss=1.598, acc=24%\n", " 80) pretrained: loss=0.967, acc=60%; scratch: loss=1.588, acc=30%\n", " 90) pretrained: loss=1.274, acc=52%; scratch: loss=1.608, acc=20%\n", "100) pretrained: loss=1.113, acc=62%; scratch: loss=1.588, acc=30%\n", "110) pretrained: loss=0.922, acc=62%; scratch: loss=1.578, acc=36%\n", "120) pretrained: loss=0.918, acc=62%; scratch: loss=1.599, acc=20%\n", "130) pretrained: loss=0.959, acc=58%; scratch: loss=1.594, acc=22%\n", "140) pretrained: loss=1.228, acc=50%; scratch: loss=1.608, acc=14%\n", "150) pretrained: loss=0.727, acc=76%; scratch: loss=1.623, acc=16%\n", "160) pretrained: loss=1.074, acc=66%; scratch: loss=1.607, acc=20%\n", "170) pretrained: loss=0.887, acc=60%; scratch: loss=1.614, acc=20%\n", "180) pretrained: loss=0.961, acc=62%; scratch: loss=1.614, acc=18%\n", "190) pretrained: loss=0.737, acc=76%; scratch: loss=1.613, acc=18%\n", "199) pretrained: loss=0.836, acc=70%; scratch: loss=1.614, acc=16%\n", "Done.\n" ] } ], "source": [ "niter = 200 # number of iterations to train\n", "\n", "# Reset style_solver as before.\n", "style_solver_filename = solver(style_net(train=True))\n", "style_solver = caffe.get_solver(style_solver_filename)\n", "style_solver.net.copy_from(weights)\n", "\n", "# For reference, we also create a solver that isn't initialized from\n", "# the pretrained ImageNet weights.\n", "scratch_style_solver_filename = solver(style_net(train=True))\n", "scratch_style_solver = caffe.get_solver(scratch_style_solver_filename)\n", "\n", "print 'Running solvers for %d iterations...' % niter\n", "solvers = [('pretrained', style_solver),\n", " ('scratch', scratch_style_solver)]\n", "loss, acc, weights = run_solvers(niter, solvers)\n", "print 'Done.'\n", "\n", "train_loss, scratch_train_loss = loss['pretrained'], loss['scratch']\n", "train_acc, scratch_train_acc = acc['pretrained'], acc['scratch']\n", "style_weights, scratch_style_weights = weights['pretrained'], weights['scratch']\n", "\n", "# Delete solvers to save memory.\n", "del style_solver, scratch_style_solver, solvers" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's look at the training loss and accuracy produced by the two training procedures. Notice how quickly the ImageNet pretrained model's loss value (blue) drops, and that the randomly initialized model's loss value (green) barely (if at all) improves from training only the classifier layer." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7f75d49e1090>" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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ROQeFwjsdShwuuADYvx84etT1dX2eYOtWYP16632UlgI9e/LPRnFISFCzsnYE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\nvXQO+gS2sfonkpC9x6oq6xXnvDkHOcDswgs5dAa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"text/plain": [ "<matplotlib.figure.Figure at 0x7f75d496e890>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot(np.vstack([train_loss, scratch_train_loss]).T)\n", "xlabel('Iteration #')\n", "ylabel('Loss')" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7f75d49e1a90>" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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rQuOqcvDiVuL2qW1z41YqRg4clLQiB7U2kAoeIaZShjGxcyu5UQ1WKEdAmg1O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"text/plain": [ "<matplotlib.figure.Figure at 0x7f75d47cad90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot(np.vstack([train_acc, scratch_train_acc]).T)\n", "xlabel('Iteration #')\n", "ylabel('Accuracy')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's take a look at the testing accuracy after running 200 iterations of training. Note that we're classifying among 5 classes, giving chance accuracy of 20%. We expect both results to be better than chance accuracy (20%), and we further expect the result from training using the ImageNet pretraining initialization to be much better than the one from training from scratch. Let's see." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def eval_style_net(weights, test_iters=10):\n", " test_net = caffe.Net(style_net(train=False), weights, caffe.TEST)\n", " accuracy = 0\n", " for it in xrange(test_iters):\n", " accuracy += test_net.forward()['acc']\n", " accuracy /= test_iters\n", " return test_net, accuracy" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy, trained from ImageNet initialization: 50.0%\n", "Accuracy, trained from random initialization: 23.6%\n" ] } ], "source": [ "test_net, accuracy = eval_style_net(style_weights)\n", "print 'Accuracy, trained from ImageNet initialization: %3.1f%%' % (100*accuracy, )\n", "scratch_test_net, scratch_accuracy = eval_style_net(scratch_style_weights)\n", "print 'Accuracy, trained from random initialization: %3.1f%%' % (100*scratch_accuracy, )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4. End-to-end finetuning for style\n", "\n", "Finally, we'll train both nets again, starting from the weights we just learned. The only difference this time is that we'll be learning the weights \"end-to-end\" by turning on learning in *all* layers of the network, starting from the RGB `conv1` filters directly applied to the input image. We pass the argument `learn_all=True` to the `style_net` function defined earlier in this notebook, which tells the function to apply a positive (non-zero) `lr_mult` value for all parameters. Under the default, `learn_all=False`, all parameters in the pretrained layers (`conv1` through `fc7`) are frozen (`lr_mult = 0`), and we learn only the classifier layer `fc8_flickr`.\n", "\n", "Note that both networks start at roughly the accuracy achieved at the end of the previous training session, and improve significantly with end-to-end training. To be more scientific, we'd also want to follow the same additional training procedure *without* the end-to-end training, to ensure that our results aren't better simply because we trained for twice as long. Feel free to try this yourself!" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Running solvers for 200 iterations...\n", " 0) pretrained, end-to-end: loss=0.781, acc=64%; scratch, end-to-end: loss=1.585, acc=28%\n", " 10) pretrained, end-to-end: loss=1.178, acc=62%; scratch, end-to-end: loss=1.638, acc=14%\n", " 20) pretrained, end-to-end: loss=1.084, acc=60%; scratch, end-to-end: loss=1.637, acc= 8%\n", " 30) pretrained, end-to-end: loss=0.902, acc=76%; scratch, end-to-end: loss=1.600, acc=20%\n", " 40) pretrained, end-to-end: loss=0.865, acc=64%; scratch, end-to-end: loss=1.574, acc=26%\n", " 50) pretrained, end-to-end: loss=0.888, acc=60%; scratch, end-to-end: loss=1.604, acc=26%\n", " 60) pretrained, end-to-end: loss=0.538, acc=78%; scratch, end-to-end: loss=1.555, acc=34%\n", " 70) pretrained, end-to-end: loss=0.717, acc=72%; scratch, end-to-end: loss=1.563, acc=30%\n", " 80) pretrained, end-to-end: loss=0.695, acc=74%; scratch, end-to-end: loss=1.502, acc=42%\n", " 90) pretrained, end-to-end: loss=0.708, acc=68%; scratch, end-to-end: loss=1.523, acc=26%\n", "100) pretrained, end-to-end: loss=0.432, acc=78%; scratch, end-to-end: loss=1.500, acc=38%\n", "110) pretrained, end-to-end: loss=0.611, acc=78%; scratch, end-to-end: loss=1.618, acc=18%\n", "120) pretrained, end-to-end: loss=0.610, acc=76%; scratch, end-to-end: loss=1.473, acc=30%\n", "130) pretrained, end-to-end: loss=0.471, acc=78%; scratch, end-to-end: loss=1.488, acc=26%\n", "140) pretrained, end-to-end: loss=0.500, acc=76%; scratch, end-to-end: loss=1.514, acc=38%\n", "150) pretrained, end-to-end: loss=0.476, acc=80%; scratch, end-to-end: loss=1.452, acc=46%\n", "160) pretrained, end-to-end: loss=0.368, acc=82%; scratch, end-to-end: loss=1.419, acc=34%\n", "170) pretrained, end-to-end: loss=0.556, acc=76%; scratch, end-to-end: loss=1.583, acc=36%\n", "180) pretrained, end-to-end: loss=0.574, acc=72%; scratch, end-to-end: loss=1.556, acc=22%\n", "190) pretrained, end-to-end: loss=0.360, acc=88%; scratch, end-to-end: loss=1.429, acc=44%\n", "199) pretrained, end-to-end: loss=0.458, acc=78%; scratch, end-to-end: loss=1.370, acc=44%\n", "Done.\n" ] } ], "source": [ "end_to_end_net = style_net(train=True, learn_all=True)\n", "\n", "# Set base_lr to 1e-3, the same as last time when learning only the classifier.\n", "# You may want to play around with different values of this or other\n", "# optimization parameters when fine-tuning. For example, if learning diverges\n", "# (e.g., the loss gets very large or goes to infinity/NaN), you should try\n", "# decreasing base_lr (e.g., to 1e-4, then 1e-5, etc., until you find a value\n", "# for which learning does not diverge).\n", "base_lr = 0.001\n", "\n", "style_solver_filename = solver(end_to_end_net, base_lr=base_lr)\n", "style_solver = caffe.get_solver(style_solver_filename)\n", "style_solver.net.copy_from(style_weights)\n", "\n", "scratch_style_solver_filename = solver(end_to_end_net, base_lr=base_lr)\n", "scratch_style_solver = caffe.get_solver(scratch_style_solver_filename)\n", "scratch_style_solver.net.copy_from(scratch_style_weights)\n", "\n", "print 'Running solvers for %d iterations...' % niter\n", "solvers = [('pretrained, end-to-end', style_solver),\n", " ('scratch, end-to-end', scratch_style_solver)]\n", "_, _, finetuned_weights = run_solvers(niter, solvers)\n", "print 'Done.'\n", "\n", "style_weights_ft = finetuned_weights['pretrained, end-to-end']\n", "scratch_style_weights_ft = finetuned_weights['scratch, end-to-end']\n", "\n", "# Delete solvers to save memory.\n", "del style_solver, scratch_style_solver, solvers" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's now test the end-to-end finetuned models. Since all layers have been optimized for the style recognition task at hand, we expect both nets to get better results than the ones above, which were achieved by nets with only their classifier layers trained for the style task (on top of either ImageNet pretrained or randomly initialized weights)." ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy, finetuned from ImageNet initialization: 53.6%\n", "Accuracy, finetuned from random initialization: 39.2%\n" ] } ], "source": [ "test_net, accuracy = eval_style_net(style_weights_ft)\n", "print 'Accuracy, finetuned from ImageNet initialization: %3.1f%%' % (100*accuracy, )\n", "scratch_test_net, scratch_accuracy = eval_style_net(scratch_style_weights_ft)\n", "print 'Accuracy, finetuned from random initialization: %3.1f%%' % (100*scratch_accuracy, )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll first look back at the image we started with and check our end-to-end trained model's predictions." ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "top 5 predicted style labels =\n", "\t(1) 55.67% Melancholy\n", "\t(2) 27.21% HDR\n", "\t(3) 16.46% Pastel\n", "\t(4) 0.63% Detailed\n", "\t(5) 0.03% Noir\n" ] }, { "data": { "image/png": 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yvUJS3DfmLUsURECnW6oGgUxGBSVh7180Q52jz0UjoWnYDZxRmdmA1bqxNeau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6EmVpSTd7osozItUDOtN5ksBa2bdacbpJGyRKLhrAxEttNG0O8AWK1TShCtYaElalB19S3dVz6K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\ncDPDu2eav33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"text/plain": [ "<matplotlib.figure.Figure at 0x7f75d0683e10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.imshow(deprocess_net_image(image))\n", "disp_style_preds(test_net, image)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Whew, that looks a lot better than before! But note that this image was from the training set, so the net got to see its label at training time.\n", "\n", "Finally, we'll pick an image from the test set (an image the model hasn't seen) and look at our end-to-end finetuned style model's predictions for it." ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "actual label = Pastel\n" ] }, { "data": { "image/png": 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Ci+UFEEy1V0UBIE6IGoxuSHmk5BtkaBj2G/btCW13Rr89Zbs7p+9P2O7OaNvOSEkxlFDE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9p9Ybt4Of19W7OXfbsAgKNmoNTwI0vwBGcQKvGY5rN9HPF6L//O5aJjNCQtQWBMX/OJhfm4G1Ge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\nKJVy+/ZPkPUZu9dfpDdYJ8gc51qO7ryOOHqPk7d+SLY8oaEk6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EIa92mO1lg/t8jkyCSB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\nv+3j8qpvhwWF6TsmyabsE1+C94x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FzzNvLUUeNPY7UYZSIpOA1VfGkiSSHlslWq/o8cor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\nQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f75d05ad810>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "batch_index = 1\n", "image = test_net.blobs['data'].data[batch_index]\n", "plt.imshow(deprocess_net_image(image))\n", "print 'actual label =', style_labels[int(test_net.blobs['label'].data[batch_index])]" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "top 5 predicted style labels =\n", "\t(1) 99.76% Pastel\n", "\t(2) 0.13% HDR\n", "\t(3) 0.11% Detailed\n", "\t(4) 0.00% Melancholy\n", "\t(5) 0.00% Noir\n" ] } ], "source": [ "disp_style_preds(test_net, image)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also look at the predictions of the network trained from scratch. We see that in this case, the scratch network also predicts the correct label for the image (*Pastel*), but is much less confident in its prediction than the pretrained net." ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "top 5 predicted style labels =\n", "\t(1) 49.81% Pastel\n", "\t(2) 19.76% Detailed\n", "\t(3) 17.06% Melancholy\n", "\t(4) 11.66% HDR\n", "\t(5) 1.72% Noir\n" ] } ], "source": [ "disp_style_preds(scratch_test_net, image)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Of course, we can again look at the ImageNet model's predictions for the above image:" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "top 5 predicted ImageNet labels =\n", "\t(1) 34.90% n07579787 plate\n", "\t(2) 21.63% n04263257 soup bowl\n", "\t(3) 17.75% n07875152 potpie\n", "\t(4) 5.72% n07711569 mashed potato\n", "\t(5) 5.27% n07584110 consomme\n" ] } ], "source": [ "disp_imagenet_preds(imagenet_net, image)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So we did finetuning and it is awesome. Let's take a look at what kind of results we are able to get with a longer, more complete run of the style recognition dataset. Note: the below URL might be occasionally down because it is run on a research machine.\n", "\n", "http://demo.vislab.berkeleyvision.org/" ] } ], "metadata": { "description": "Fine-tune the ImageNet-trained CaffeNet on new data.", "example_name": "Fine-tuning for Style Recognition", "include_in_docs": true, "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" }, "priority": 3 }, "nbformat": 4, "nbformat_minor": 0 }
mit
dereneaton/ipyrad
testdocs/analysis/cookbook-mb-empirical-calibration.ipynb
1
3710003
null
gpl-3.0
ThierryMondeel/FBA_python_tutorial
FBA_tutorials/minibook-2nd-code-master/chapter4/42-mpl.ipynb
1
5253
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": "## matplotlib and seaborn essentials" }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": "import numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn\n%matplotlib inline" }, { "cell_type": "markdown", "metadata": {}, "source": "### Common plots with matplotlib" }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": "y = np.random.randn(1000)" }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": "plt.plot(y)" }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": "x = np.linspace(-10., 10., 1000)\ny = np.sin(3 * x) * np.exp(-.1 * x**2)" }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": "plt.plot(x, y)" }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": "x = np.linspace(-5., 5., 100)\ny = np.sin(3 * x) * np.exp(-.1 * x ** 2)" }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": "plt.plot(x, y, '--^',\n lw=3, color='#fdbb84',\n mfc='#2b8cbe', ms=8)" }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": "x = np.random.randn(100)\ny = x + np.random.randn(100)" }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": "plt.scatter(x, y)" }, { "cell_type": "markdown", "metadata": {}, "source": "### Customizing matplotlib figures" }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": "# Left panel.\nplt.subplot(1, 2, 1)\nx = np.linspace(-10., 10., 1000)\nplt.plot(x, np.sin(x), '-r', label='sinus')\nplt.plot(x, np.cos(x), ':g', lw=1, label='cosinus')\nplt.xticks([-10, 0, 10])\nplt.yticks([-1, 0, 1])\nplt.ylim(-2, 2)\nplt.xlabel(\"x axis\")\nplt.ylabel(\"y axis\")\nplt.title(\"Two plots\")\nplt.legend()\n\n# Right panel.\nplt.subplot(1, 2, 2, polar=True)\nx = np.linspace(0, 2 * np.pi, 1000)\nplt.plot(x, 1 + 2 * np.cos(6 * x))\nplt.yticks([])\nplt.xlim(-.1, 3.1)\nplt.ylim(-.1, 3.1)\nplt.xticks(np.linspace(0, 5 * np.pi / 3, 6))\nplt.title(\"A polar plot\")\nplt.grid(color='k', linewidth=1, linestyle=':')" }, { "cell_type": "markdown", "metadata": {}, "source": "### Interacting with matplotlib figures in the Notebook" }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": "from ipywidgets import interact" }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": "x = np.linspace(-5., 5., 1000)" }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": "@interact\ndef plot_sin(a=(1, 10)):\n plt.plot(x, np.sin(a*x))\n plt.ylim(-1, 1)" }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": "%matplotlib qt" }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": "lines = plt.plot([0, 1], [0, 1], 'b')" }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": "[<matplotlib.lines.Line2D at 0x7ffa434542e8>]" }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": "lines" }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": "lines[0].set_color('r')\nplt.draw()" }, { "cell_type": "markdown", "metadata": {}, "source": "### High-level plotting with seaborn" }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": " sepal_length sepal_width petal_length petal_width species\n0 5.1 3.5 1.4 0.2 setosa\n1 4.9 3.0 1.4 0.2 setosa\n2 4.7 3.2 1.3 0.2 setosa" }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": "df = seaborn.load_dataset(\"iris\")\ndf.head(3)" }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": "seaborn.pairplot(df, hue=\"species\", size=2.5)" } ], "metadata": {}, "nbformat": 4, "nbformat_minor": 0 }
mit
chbrandt/zyxw
docs/python_notebooks/.ipynb_checkpoints/VO_CatalogServices_search-checkpoint.ipynb
1
106496
{ "metadata": { "name": "", "signature": "sha256:cbf4d23ef1ebe0a367156885dad90271ca5109563a038672023422084ecd25e7" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Searching for X-Ray catalogues on VO for SED studies" ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "1) Search for \"all\" X-Ray catalogues" ] }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "Using the 'PyVO' package, with the use of function 'regsearch', we will search for X-Ray catalogues useful for Spectral Energy Distribution (SED) analysis. We will filter catalogues with flux-related data and spectral/photon index information." ] }, { "cell_type": "code", "collapsed": false, "input": [ "import pyvo\n", "#help(pyvo.regsearch)\n", "import warnings; warnings.simplefilter('ignore') # suppress warnings..." ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "Obs: The registry we will query the catalog services is the default one configured on PyVO:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print(\"%s \\n\" % pyvo.registry.RegistryQuery().baseurl)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "http://vao.stsci.edu/directory/NVORegInt.asmx/ \n", "\n" ] } ], "prompt_number": 2 }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "To do the search we need then to define 'waveband'(=\"xray\"), 'servicetype'(=\"conesearch\"). These two are mandatory for our purpose here. Also, since the spectral/photon index information is a great information to have -- and we want two at this moment -- we will filter the results to accomplish that. So, we will define also the 'keywords'(=\"index\") argument to see if we get a nice answer." ] }, { "cell_type": "code", "collapsed": false, "input": [ "records = pyvo.regsearch(waveband='xray',servicetype='conesearch',keywords='index')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "And we can now inspect the answer to see what are the services returned" ] }, { "cell_type": "code", "collapsed": false, "input": [ "records.nrecs # number of services found" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 4, "text": [ "42" ] } ], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "for r in records: print r.title # Title (name) of the services (catalogues) found" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "HEAO 1 A2 Piccinotti Catalog\n", "Atlas of Radio/X-Ray Associations (ARXA)\n", "CGRO/BATSE 5B Gamma-Ray Burst Spectral Catalog\n", "Chandra Deep Field South AGN Spectral Properties Catalog\n", "CHAMP/SDSS Nearby Low-Luminosity AGN Catalog\n", "M 31 Central Region Chandra X-Ray Point Source Catalog\n", "Chandra Archive Of Galaxies Ultraluminous X-Ray Source Catalog\n", "INTEGRAL IBIS Hard X-Ray Survey of Galactic Center\n", "Second INTEGRAL AGN Catalog\n", "Soft X-ray properties of Seyfert galaxies (Pfefferkorn+, 2001)\n", "Soft X-ray properties of Seyfert galaxies (Pfefferkorn+, 2001)\n", "Spectral analysis of Lockman Hole (Mainieri+, 2002)\n", "AGN in XMM-Newton Hard Bright Survey (Della Ceca+, 2008)\n", "AGN-Host Galaxy Connection (Povic+, 2012)\n", "Spectra of 29 Swift/BAT optical counterparts (Parisi+, 2012)\n", "X-ray and radio emission of type 1 AGNs (Ballo+, 2012)\n", "AGN Torus model comparison of AGN in the CDFS (Buchner+, 2014)\n", "CDFs AGNs X-ray power-law photon index (Saez+, 2008)\n", "X-ray studies of stars in the Pleiades (Micela+, 1990)\n", "X-ray studies of stars in the Pleiades (Micela+, 1990)\n", "M31 Chandra X-ray point sources (Kong+, 2002)\n", "SED of the Fermi blazars (Li+, 2010)\n", "XMM-Newton survey in COSMOS field. IV. (Mainieri+, 2007)\n", "RIXOS source sample X-ray spectra (Mittaz+, 1999)\n", "X-ray+Radio sources in XBootes (El Bouchefry, 2009)\n", "LALA Cetus Field Chandra X-Ray Point Source Catalog\n", "M31 XMM-Newton Spectral Survey X-Ray Point Source Catalog\n", "ROSAT PSPC M31 Source Catalog\n", "ROSAT Radio-Loud Quasars Catalog\n", "ROSAT Radio-Quiet Quasars Catalog\n", "ROSAT Results Archive Sources for the PSPC with Filter\n", "ROSAT Complete Results Archive Sources for the PSPC with Filter\n", "XTE Target Index Catalog\n", "Sloan Digital Sky Survey Quasars Detected by Chandra\n", "Sloan Digital Sky Survey (DR5)/XMM-Newton Quasar Survey Catalog\n", "SSA22 Field Chandra X-Ray Point Source Catalog\n", "Spitzer Wide-Area IR Extra-Galactic Survey Chandra X-Ray Sources\n", "XMM-Newton Bright Serendipitous Survey: AGN X-Ray Spectral Properties\n", "XMM-Newton Large-Scale Structure Optical Identifications Catalog\n", "XMM-Newton 2XMMi-DR3 Selected Source Detections Catalog\n", "XMM-Newton 2XMMi-DR3 Selected Source Classifications Catalog\n", "XTE Archived Public Slew Data\n" ] } ], "prompt_number": 5 }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "You should have notice that the answer returned by the 'pyvo.regsearch' function is a container (i.e, a list) with objects relative to each catalog service found (matching our query). Let's see what are the attributes (except from 'title') of these objects so that we can have a better idea of what we have found so far..." ] }, { "cell_type": "code", "collapsed": false, "input": [ "r = records[0] # let us save a reference (\"r\") for the Piccinotti catalog for what follows" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "from pprint import pprint\n", "from inspect import getmembers,isroutine\n", "attrs = getmembers(r, lambda a:not(isroutine(a)))\n", "print \"Attributes:\"\n", "pprint([ a[0] for a in attrs if not '_' in a[0] ])\n", "print \"\\nRoutines:\"\n", "print([ a[0] for a in getmembers(r, lambda a:isroutine(a)) if not '_' in a[0] ])" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Attributes:\n", "['accessurl',\n", " 'capability',\n", " 'contentlevel',\n", " 'identifier',\n", " 'ivoid',\n", " 'publisher',\n", " 'rec',\n", " 'shortname',\n", " 'standardid',\n", " 'subject',\n", " 'tags',\n", " 'title',\n", " 'type',\n", " 'waveband']\n", "\n", "Routines:\n", "['cachedataset', 'fielddesc', 'get', 'getdataset', 'getdataurl', 'items', 'iteritems', 'iterkeys', 'itervalues', 'keys', 'search', 'values']\n" ] } ], "prompt_number": 7 }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "Some attributes look suggestive, like 'capability', 'contentlevel', 'subject'... But, let's see what they really provide." ] }, { "cell_type": "code", "collapsed": false, "input": [ "for a in attrs:\n", " if not '_' in a[0]: \n", " print(\"%-15s : %s\" % (a[0],a[1]))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "accessurl : http://heasarc.gsfc.nasa.gov/cgi-bin/vo/cone/coneGet.pl?table=a2pic&amp;\n", "capability : ConeSearch\n", "contentlevel : ('Research',)\n", "identifier : ivo://nasa.heasarc/a2pic#1\n", "ivoid : ivo://nasa.heasarc/a2pic#1\n", "publisher : NASA/GSFC HEASARC\n", "rec : ('Catalog', 'A2PIC [1]', 'HEAO 1 A2 Piccinotti Catalog', \"The HEAO 1 A-2 experiment's operations began on day 224 of 1977 (12 August 1977) and ended on day 739 of 1977 (9 January 1979). The A-2 experiment performed two independent, low-background, high-sensitivity surveys of the entire sky 6 months apart, the first scan during days 248 to 437 of 1977 (5 September 1977 - 13 March 1978) and the second scan during days 73 to 254 of 1978 (14 March 1978 - 11 September 1978). The authors analysed the A-2 data in order to obtain a complete flux-limited sample of extragalactic X-ray sources. The region between galactic latitudes of -20 and +20 degrees was excluded to minimize contamination from galactic sources. A circle of 6 degrees radius around the Large Magellanic Cloud sources was also excluded to prevent confusion problems. Therefore, there remained 65.5% of the sky (8.23 steradians) covered by this survey. The lowest statistical significance for the existence of the sources included in this catalog is 5 sigma, as required by the maximum likelihood methods used by the authors to determine the log N - log S parameters. Taking into account this statistical significance requirement, the authors estimated the completeness level of the first and second scans to be 1.25 and 1.8 R15 ct/s, respectively. 1 R15 ct/s is approximately 2.17 x 10^-11 erg/cm^2/s in the 2-10 keV energy band for a power-law spectyrum with a photon index of 1.65. This catalog contains data for 68 nongalactic sources (61 extragalactic and 7 unidentified sources) which were listed in Table 1 of the published catalog. The identified sources fall into several categories, including narrow emission line galaxies, broad emission line galaxies, BL Lacertae objects, and clusters of galaxies.\", 'NASA/GSFC HEASARC', '#X-ray#', 'ivo://nasa.heasarc/a2pic#1', '2013-06-25T00:00:00', '#Extragalactic Source#', '#Catalog#', '#Research#', 1.0, '', 'ivo://nasa.heasarc/a2pic', 'ConeSearch', 'ivo://ivoa.net/std/ConeSearch', '2', 'ParamHTTP', '', 'std?', 'http://heasarc.gsfc.nasa.gov/cgi-bin/vo/cone/coneGet.pl?table=a2pic&amp;', 180.0, 99999, 'ivo://nasa.heasarc/ASD', 'http://heasarc.gsfc.nasa.gov/W3Browse/heao1/a2pic.html')\n", "shortname : A2PIC [1]\n", "standardid : ivo://ivoa.net/std/ConeSearch\n", "subject : ('Extragalactic Source',)\n", "tags : Catalog\n", "title : HEAO 1 A2 Piccinotti Catalog\n", "type : ('Catalog',)\n", "waveband : ('X-ray',)\n" ] } ], "prompt_number": 8 }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "OK, so we have some good informations about the service... In particular, the description found in the 'rec' attribute (the 4th field), which present the main features/purposes of the catalog. We can access the description through record's method 'get':" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print r.get('description')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "The HEAO 1 A-2 experiment's operations began on day 224 of 1977 (12 August 1977) and ended on day 739 of 1977 (9 January 1979). The A-2 experiment performed two independent, low-background, high-sensitivity surveys of the entire sky 6 months apart, the first scan during days 248 to 437 of 1977 (5 September 1977 - 13 March 1978) and the second scan during days 73 to 254 of 1978 (14 March 1978 - 11 September 1978). The authors analysed the A-2 data in order to obtain a complete flux-limited sample of extragalactic X-ray sources. The region between galactic latitudes of -20 and +20 degrees was excluded to minimize contamination from galactic sources. A circle of 6 degrees radius around the Large Magellanic Cloud sources was also excluded to prevent confusion problems. Therefore, there remained 65.5% of the sky (8.23 steradians) covered by this survey. The lowest statistical significance for the existence of the sources included in this catalog is 5 sigma, as required by the maximum likelihood methods used by the authors to determine the log N - log S parameters. Taking into account this statistical significance requirement, the authors estimated the completeness level of the first and second scans to be 1.25 and 1.8 R15 ct/s, respectively. 1 R15 ct/s is approximately 2.17 x 10^-11 erg/cm^2/s in the 2-10 keV energy band for a power-law spectyrum with a photon index of 1.65. This catalog contains data for 68 nongalactic sources (61 extragalactic and 7 unidentified sources) which were listed in Table 1 of the published catalog. The identified sources fall into several categories, including narrow emission line galaxies, broad emission line galaxies, BL Lacertae objects, and clusters of galaxies.\n" ] } ], "prompt_number": 9 }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "Nice. So, until now we could get some high level information that gives us the scope of the catalog. I want now to go to a lower level -- the catalog itself -- to see what information the metadata can give us to see if the catalog can be used on our purpose.\n", "Obs: before going to the next step, I want you to notice the (photon) \"index\" information (which we want) in this description; we should come back to this point later." ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "2) Ivestigating a catalog's metadata" ] }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "To reach the catalog's metadata we will download a null version of the table. As \"null\" I mean a table without data (i.e, sources). The aim here is to further analyse if the catalog/table has the king of information we are looking for: flux-related data and the spectral/photon index." ] }, { "cell_type": "code", "collapsed": false, "input": [ "r_service = r.to_service() # get an object to query the particular (\"r\") service\n", "print r_service.baseurl # check if the URLs match; should be the same printed out by the 'accessurl' attribute above" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "http://heasarc.gsfc.nasa.gov/cgi-bin/vo/cone/coneGet.pl?table=a2pic&amp;\n" ] } ], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "#help(r_service.search)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "cat = r_service.search( pos=(0,0), radius=0.00001 ) # search for a \"null\" point in the sky" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "WARNING:astropy:W48: dal_query:13:3: W48: Unknown attribute 'format' on FIELD\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "WARNING:astropy:W48: dal_query:16:3: W48: Unknown attribute 'format' on FIELD\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "WARNING:astropy:W48: dal_query:19:3: W48: Unknown attribute 'format' on FIELD\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "WARNING:astropy:W48: dal_query:22:3: W48: Unknown attribute 'format' on FIELD\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "WARNING:astropy:W48: dal_query:28:3: W48: Unknown attribute 'format' on FIELD\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\u001b[0;33mWARNING\u001b[0m: W48: dal_query:13:3: W48: Unknown attribute 'format' on FIELD [astropy.io.votable.exceptions]\n", "\u001b[0;33mWARNING\u001b[0m: W48: dal_query:16:3: W48: Unknown attribute 'format' on FIELD [astropy.io.votable.exceptions]\n", "\u001b[0;33mWARNING\u001b[0m: W48: dal_query:19:3: W48: Unknown attribute 'format' on FIELD [astropy.io.votable.exceptions]\n", "\u001b[0;33mWARNING\u001b[0m: W48: dal_query:22:3: W48: Unknown attribute 'format' on FIELD [astropy.io.votable.exceptions]\n", "\u001b[0;33mWARNING\u001b[0m: W48: dal_query:28:3: W48: Unknown attribute 'format' on FIELD [astropy.io.votable.exceptions]\n" ] } ], "prompt_number": 12 }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "Again, let's see what are the attributes of the catalog (\"cat\") object. For convenience we will define a function to give us a higher level to query this kind of information." ] }, { "cell_type": "code", "collapsed": false, "input": [ "def printPublicMembers(obj):\n", " from inspect import getmembers\n", " members = getmembers(obj)\n", " for m in members:\n", " if not '_' in m[0]:\n", " print(\"%-15s : %s\" % (m[0],str(m[1])))\n", "printPublicMembers(cat)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "cursor : <bound method SCSResults.cursor of <pyvo.dal.scs.SCSResults object at 0x3e69210>>\n", "fielddesc : <bound method SCSResults.fielddesc of <pyvo.dal.scs.SCSResults object at 0x3e69210>>\n", "fieldnames : <bound method SCSResults.fieldnames of <pyvo.dal.scs.SCSResults object at 0x3e69210>>\n", "getcolumn : <bound method SCSResults.getcolumn of <pyvo.dal.scs.SCSResults object at 0x3e69210>>\n", "getdesc : <bound method SCSResults.getdesc of <pyvo.dal.scs.SCSResults object at 0x3e69210>>\n", "getrecord : <bound method SCSResults.getrecord of <pyvo.dal.scs.SCSResults object at 0x3e69210>>\n", "getvalue : <bound method SCSResults.getvalue of <pyvo.dal.scs.SCSResults object at 0x3e69210>>\n", "nrecs : 0\n", "protocol : scs\n", "queryurl : http://heasarc.gsfc.nasa.gov/cgi-bin/vo/cone/coneGet.pl?table=a2pic&amp;&RA=0&DEC=0&SR=1e-05&VERB=2\n", "version : 1.0\n", "votable : catalog_designation name ra dec count_rate count_rate_error class Search_Offset\n", "------------------- ---- --- --- ---------- ---------------- ----- -------------" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] } ], "prompt_number": 13 }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "Let's see what the methods 'fielddesc' and 'fieldnames' give to us." ] }, { "cell_type": "code", "collapsed": false, "input": [ "print cat.fieldnames() # print the names of table columns" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[u'catalog_designation', u'name', u'ra', u'dec', u'count_rate', u'count_rate_error', u'class', u'Search_Offset']\n" ] } ], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "for f in cat.fielddesc(): # and what kind of information they store\n", " print(\"%-20s : %-40s %-35s %-10s\" % (f.name,f.description,f.ucd,f.unit))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "catalog_designation : HEAO A2 Catalog Source Name None None \n", "name : Optical Source Identification ID_MAIN None \n", "ra : Right Ascension POS_EQ_RA_MAIN deg \n", "dec : Declination POS_EQ_DEC_MAIN deg \n", "count_rate : R15 (counts/s), 1st 6-month Pass phot.count;em.X-ray ct / s \n", "count_rate_error : Error on First Count Rate stat.error;phot.count;em.X-ray ct / s \n", "class : Browse Object Classification src.class None \n", "Search_Offset : None None None \n" ] } ], "prompt_number": 15 }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "That is the kind of information we are looking for: photon counts, count rates." ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "2.1) Collecting all catalogues UCD and Unit information" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def getUCD(tab):\n", " \"\"\"Returns a list with all (valid) UCDs from a table\"\"\"\n", " from astropy.io.votable import ucd\n", " l1 = []\n", " for f in tab.fielddesc():\n", " if not f.ucd: continue\n", " l2 = []\n", " for u in ucd.parse_ucd(f.ucd):\n", " l2.append(str(u[1]))\n", " l1.extend(l2)\n", " return l1[:]\n", "\n", "def getUnit(tab):\n", " \"\"\"Returns a list of units from a table\"\"\"\n", " l = []\n", " for f in tab.fielddesc():\n", " if not f.unit: continue\n", " l.append(str(f.unit).replace(' ',''))\n", " return l[:]\n", "\n", "# Let's define a function that does the search, exactly what we have already done\n", "def retrieveTable(record):\n", " \"\"\"Retrieve an empty table referenced by the record\"\"\"\n", " serv = record.to_service()\n", " tab = serv.search( pos=(0,0), radius=0.00001 )\n", " return tab\n", "\n", "# Let's loop over all records/catalogues...\n", "Catalogues = {}\n", "for r in records:\n", " print(\"*-> %s\" % r.title)\n", " cat = retrieveTable(r)\n", "# if not cat:\n", "# print(\"NOT %s %s\" % (r.accessurl,r.publisher))\n", "# else:\n", "# print(\"YES %s %s\" % (r.accessurl,r.publisher))\n", " if cat != None:\n", " Catalogues[r] = cat\n", "print \"Done.\"" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "*-> HEAO 1 A2 Piccinotti Catalog\n", "*-> Atlas of Radio/X-Ray Associations (ARXA)" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "*-> CGRO/BATSE 5B Gamma-Ray Burst Spectral Catalog" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "*-> Chandra Deep Field South AGN Spectral Properties Catalog" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "*-> CHAMP/SDSS Nearby Low-Luminosity AGN Catalog" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "*-> M 31 Central Region Chandra X-Ray Point Source Catalog" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "*-> Chandra Archive Of Galaxies Ultraluminous X-Ray Source Catalog" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "*-> INTEGRAL IBIS Hard X-Ray Survey of Galactic Center" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "*-> Second INTEGRAL AGN Catalog" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "*-> Soft X-ray properties of Seyfert galaxies (Pfefferkorn+, 2001)" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "*-> Soft X-ray properties of Seyfert galaxies (Pfefferkorn+, 2001)" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "*-> Spectral analysis of Lockman Hole (Mainieri+, 2002)" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "*-> AGN in XMM-Newton Hard Bright Survey (Della Ceca+, 2008)" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "*-> AGN-Host Galaxy Connection (Povic+, 2012)" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "*-> Spectra of 29 Swift/BAT optical counterparts (Parisi+, 2012)" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "*-> X-ray and radio emission of type 1 AGNs (Ballo+, 2012)" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "*-> AGN Torus model comparison of AGN in the CDFS (Buchner+, 2014)" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "*-> CDFs AGNs X-ray power-law photon index (Saez+, 2008)" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "*-> X-ray studies of stars in the Pleiades (Micela+, 1990)" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "*-> X-ray studies of stars in the Pleiades (Micela+, 1990)" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "*-> M31 Chandra X-ray point sources (Kong+, 2002)" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "*-> SED of the Fermi blazars (Li+, 2010)" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "*-> XMM-Newton survey in COSMOS field. IV. (Mainieri+, 2007)" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "*-> RIXOS source sample X-ray spectra (Mittaz+, 1999)" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "*-> X-ray+Radio sources in XBootes (El Bouchefry, 2009)" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "*-> LALA Cetus Field Chandra X-Ray Point Source Catalog" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "*-> M31 XMM-Newton Spectral Survey X-Ray Point Source Catalog" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "*-> ROSAT PSPC M31 Source Catalog" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "*-> ROSAT Radio-Loud Quasars Catalog" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "*-> ROSAT Radio-Quiet Quasars Catalog" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "*-> ROSAT Results Archive Sources for the PSPC with Filter" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "*-> ROSAT Complete Results Archive Sources for the PSPC with Filter" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "*-> XTE Target Index Catalog" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "*-> Sloan Digital Sky Survey Quasars Detected by Chandra" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "*-> Sloan Digital Sky Survey (DR5)/XMM-Newton Quasar Survey Catalog" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "*-> SSA22 Field Chandra X-Ray Point Source Catalog" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "*-> Spitzer Wide-Area IR Extra-Galactic Survey Chandra X-Ray Sources" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "*-> XMM-Newton Bright Serendipitous Survey: AGN X-Ray Spectral Properties" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "*-> XMM-Newton Large-Scale Structure Optical Identifications Catalog" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "*-> XMM-Newton 2XMMi-DR3 Selected Source Detections Catalog" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "*-> XMM-Newton 2XMMi-DR3 Selected Source Classifications Catalog" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "*-> XTE Archived Public Slew Data" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Done." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] } ], "prompt_number": 16 }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "Let us print the UCDs found on all catalogues:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "UCDs = set()\n", "for cat in Catalogues.itervalues():\n", " if cat is None: continue\n", " l = getUCD(cat)\n", " UCDs.update(set(l))\n", "UCDl = list(UCDs)\n", "UCDl.sort()\n", "for a,b,c in map(None,UCDl[::3],UCDl[1::3],UCDl[2::3]):\n", " print \"{:<30}{:<30}{:<}\".format(a,b,c)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "DATA_LINK ID_MAIN POS_EQ_DEC_MAIN\n", "POS_EQ_RA_MAIN arith.ratio em.IR.J\n", "em.IR.K em.UV em.X-ray\n", "em.X-ray.hard em.X-ray.medium em.X-ray.soft\n", "em.gamma.hard em.opt em.opt.B\n", "em.opt.I em.opt.R em.opt.V\n", "em.radio.750-1500MHz instr.background instr.offset\n", "instr.setup meta.code meta.code.class\n", "meta.code.error meta.code.qual meta.file\n", "meta.fits meta.id meta.id.PI\n", "meta.id.assoc meta.main meta.modelled\n", "meta.note meta.number meta.ref\n", "meta.ref.url obs obs.exposure\n", "obs.field obs.sequence phot\n", "phot.color phot.count phot.flux\n", "phot.flux.density phot.mag phys.angSize\n", "phys.columnDensity phys.luminosity phys.magAbs\n", "phys.mass pos pos.angDistance\n", "pos.eq.dec pos.eq.ra spect\n", "spect.index spect.line.width src\n", "src.class src.class.starGalaxy src.redshift\n", "src.spType stat.error stat.fit.dof\n", "stat.fit.param stat.max stat.min\n", "stat.param stat.snr time.duration\n", "time.end time.epoch time.interval\n", "time.start None None\n" ] } ], "prompt_number": 17 }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "And now let us print the units found:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "UNTs = set()\n", "for cat in Catalogues.itervalues():\n", " if cat is None: continue\n", " l = getUnit(cat)\n", " UNTs.update(set(l))\n", "UNTl = list(UNTs)\n", "UNTl.sort()\n", "for a,b,c in map(None,UNTl[::3],UNTl[1::3],UNTl[2::3]):\n", " print \"{:<30}{:<30}{:<}\".format(a,b,c)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\"Y:M:D\" \"d:m:s\" \"h:m:s\"\n", "% 0.01ct/s 0.1/keV/m2/s\n", "1e+20/cm2 1e+22/cm2 1e+30W\n", "1e-17W/m2 [-] [10+35W]\n", "[10-7W] [Msun] [W/Hz]\n", "[cm-2] aW/m2 arcm\n", "arcs cm^-2 ct\n", "ct/ks ct/s deg\n", "erg/s erg/s/cm^2 fW/m2\n", "keV km/s ks\n", "log mCrab mJy\n", "mW/m2 mag mjd\n", "nJy photon/s/cm^2/keV s\n", "uJy None None\n" ] } ], "prompt_number": 18 }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "Well, it seems to me that the interesting UCDs are:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "UCD_goal=['em.X-ray','phot.flux','phot.flux.density','phot.count','phys.luminosity']" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 19 }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "And the units are:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "UNT_goal=['ct/s','erg/s','erg/s/cm2','erg/s/cm^2','mW/m2','1e-17W/m2','ct/ks','mJy','ct','[10-7W]']" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 20 }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "And then we should be able to filter the catalogues that provides (1st) these UCDs and Units." ] }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "Let's define two little functions to help us on checking the ucds and units:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def checkUCD(tab,UCDs=[]):\n", " \"\"\"Returns a True if tab has one of the given UCDs; False otherwise\"\"\"\n", " if tab is None: return None\n", " l = getUCD(tab)\n", " ok = any( filter(lambda u:u in UCDs, set(l)) )\n", " return ok\n", "\n", "def checkUnit(tab,Units=[]):\n", " \"\"\"Returns a True if tb has one of the given Units; False otherwise\"\"\"\n", " if tab is None: return None\n", " l = getUnit(tab)\n", " ok = any( filter(lambda u:u in Units, set(l)) )\n", " return ok" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 21 }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "And select the catalogues based on UCD_goal and UNT_goal." ] }, { "cell_type": "code", "collapsed": false, "input": [ "SelectCatalogues = {}\n", "for r,t in Catalogues.iteritems():\n", " reason = \"\"\n", " ok1 = checkUCD(t,UCD_goal)\n", " if not ok1:\n", " reason += \"UCD \"\n", " ok2 = checkUnit(t,UNT_goal)\n", " if not ok2:\n", " reason += \"unit \"\n", " if ok1 and ok2:\n", " print(\" + Catalog %s selected\" % r.title)\n", " SelectCatalogues[r] = t\n", " else:\n", " print(\" - Catalog %-80s; reason: %s\" % (r.title+\" NOT selected\",reason))\n", "print(\"%d Catalogues selected:\" % len(SelectCatalogues))\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " + Catalog SSA22 Field Chandra X-Ray Point Source Catalog selected\n", " - Catalog XTE Archived Public Slew Data NOT selected ; reason: UCD unit \n", " + Catalog HEAO 1 A2 Piccinotti Catalog selected\n", " + Catalog X-ray and radio emission of type 1 AGNs (Ballo+, 2012) selected\n", " + Catalog CGRO/BATSE 5B Gamma-Ray Burst Spectral Catalog selected\n", " + Catalog SED of the Fermi blazars (Li+, 2010) selected\n", " + Catalog Spitzer Wide-Area IR Extra-Galactic Survey Chandra X-Ray Sources selected\n", " + Catalog AGN Torus model comparison of AGN in the CDFS (Buchner+, 2014) selected\n", " + Catalog ROSAT Complete Results Archive Sources for the PSPC with Filter selected\n", " - Catalog Atlas of Radio/X-Ray Associations (ARXA) NOT selected ; reason: unit \n", " + Catalog XMM-Newton survey in COSMOS field. IV. (Mainieri+, 2007) selected\n", " + Catalog AGN-Host Galaxy Connection (Povic+, 2012) selected\n", " - Catalog XTE Target Index Catalog NOT selected ; reason: UCD unit \n", " + Catalog XMM-Newton 2XMMi-DR3 Selected Source Detections Catalog selected\n", " - Catalog XMM-Newton Large-Scale Structure Optical Identifications Catalog NOT selected ; reason: unit \n", " + Catalog X-ray+Radio sources in XBootes (El Bouchefry, 2009) selected\n", " + Catalog LALA Cetus Field Chandra X-Ray Point Source Catalog selected\n", " + Catalog Spectral analysis of Lockman Hole (Mainieri+, 2002) selected\n", " + Catalog RIXOS source sample X-ray spectra (Mittaz+, 1999) selected\n", " + Catalog Soft X-ray properties of Seyfert galaxies (Pfefferkorn+, 2001) selected\n", " + Catalog Sloan Digital Sky Survey (DR5)/XMM-Newton Quasar Survey Catalog selected\n", " - Catalog Chandra Archive Of Galaxies Ultraluminous X-Ray Source Catalog NOT selected ; reason: UCD \n", " + Catalog M31 XMM-Newton Spectral Survey X-Ray Point Source Catalog selected\n", " - Catalog XMM-Newton 2XMMi-DR3 Selected Source Classifications Catalog NOT selected ; reason: unit \n", " + Catalog AGN in XMM-Newton Hard Bright Survey (Della Ceca+, 2008) selected\n", " + Catalog Chandra Deep Field South AGN Spectral Properties Catalog selected\n", " + Catalog ROSAT Results Archive Sources for the PSPC with Filter selected\n", " + Catalog Sloan Digital Sky Survey Quasars Detected by Chandra selected\n", " + Catalog XMM-Newton Bright Serendipitous Survey: AGN X-Ray Spectral Properties selected\n", " + Catalog X-ray studies of stars in the Pleiades (Micela+, 1990) selected\n", " + Catalog CDFs AGNs X-ray power-law photon index (Saez+, 2008) selected\n", " - Catalog Spectra of 29 Swift/BAT optical counterparts (Parisi+, 2012) NOT selected ; reason: UCD unit \n", " + Catalog X-ray studies of stars in the Pleiades (Micela+, 1990) selected\n", " + Catalog M 31 Central Region Chandra X-Ray Point Source Catalog selected\n", " - Catalog ROSAT PSPC M31 Source Catalog NOT selected ; reason: UCD \n", " + Catalog Soft X-ray properties of Seyfert galaxies (Pfefferkorn+, 2001) selected\n", " - Catalog ROSAT Radio-Quiet Quasars Catalog NOT selected ; reason: UCD unit \n", " - Catalog INTEGRAL IBIS Hard X-Ray Survey of Galactic Center NOT selected ; reason: unit \n", " + Catalog M31 Chandra X-ray point sources (Kong+, 2002) selected\n", " - Catalog ROSAT Radio-Loud Quasars Catalog NOT selected ; reason: UCD unit \n", " + Catalog Second INTEGRAL AGN Catalog selected\n", " + Catalog CHAMP/SDSS Nearby Low-Luminosity AGN Catalog selected\n", "31 Catalogues selected:\n" ] } ], "prompt_number": 22 }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "And their description follows:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "for r in SelectCatalogues.iterkeys():\n", " print(\"* %s\" % r.title)\n", " print(\" %s\\n\" % r.get('description'))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "* SSA22 Field Chandra X-Ray Point Source Catalog\n", " This table contains the main X-ray point-source catalog for a deep ~400-ks Chandra ACIS-I (Advanced CCD Imaging Spectrometer) exposure of the SSA22 field. The observations were centred on a z = 3.09 protocluster, which is populated by Lyman break galaxies (LBGs), Lyman-alpha emitters (LAEs) and extended Lyman-alpha-emitting blobs (LABs). The survey reached ultimate (3 count) sensitivity limits of ~5.7 x 10&lt;sup&gt;-17&lt;/sup&gt; and ~3.0 x 10&lt;sup&gt;-16&lt;/sup&gt; erg cm&lt;sup&gt;-2&lt;/sup&gt; s&lt;sup&gt;-1&lt;/sup&gt; for the 0.5-2 and 2-8 keV bands, respectively (corresponding to L(2-10 keV) ~ 5.7 x 10&lt;sup&gt;42&lt;/sup&gt; erg s&lt;sup&gt;-1&lt;/sup&gt; and L(10-30 keV) ~ 2.0 x 10&lt;sup&gt;43&lt;/sup&gt; erg s&lt;sup&gt;-1&lt;/sup&gt; at z = 3.09, respectively, for an assumed photon index of Gamma = 1.4). These limits make SSA22 the fourth deepest extragalactic Chandra survey yet conducted, and the only one focused on a known high-redshift structure. In total, the authors detect 297 X-ray point sources and identify one obvious bright extended X-ray source (not included in the current table) over a ~330 arcmin&lt;sup&gt;2&lt;/sup&gt; region. In addition to the X-ray data, the authors provide all available optical spectroscopic redshifts and near-infrared and mid-infrared photometry available for their sources. The basic X-ray and infrared properties of their Chandra sources indicate a variety of source types, although absorbed active galactic nuclei (AGN) appear to dominate. In total, they have identified 12 X-ray sources (either via optical spectroscopic redshifts or LAE selection) at z = 3.06 - 3.12 that are likely to be associated with the SSA22 protocluster. These sources have X-ray and multiwavelength properties that suggest they are powered by AGN with 0.5 - 8 keV luminosities in the range of ~ 10&lt;sup&gt;43&lt;/sup&gt; - 10&lt;sup&gt;45&lt;/sup&gt; erg s&lt;sup&gt;-1&lt;/sup&gt;. The authors have analysed the AGN fraction of sources in the protocluster as a function of local LAE source density and find suggestive evidence for a correlation between AGN fraction and local LAE source density (at the ~96 per cent confidence level), implying that supermassive black hole growth at z ~3 is strongest in the highest density regions.\n", "\n", "* HEAO 1 A2 Piccinotti Catalog\n", " The HEAO 1 A-2 experiment's operations began on day 224 of 1977 (12 August 1977) and ended on day 739 of 1977 (9 January 1979). The A-2 experiment performed two independent, low-background, high-sensitivity surveys of the entire sky 6 months apart, the first scan during days 248 to 437 of 1977 (5 September 1977 - 13 March 1978) and the second scan during days 73 to 254 of 1978 (14 March 1978 - 11 September 1978). The authors analysed the A-2 data in order to obtain a complete flux-limited sample of extragalactic X-ray sources. The region between galactic latitudes of -20 and +20 degrees was excluded to minimize contamination from galactic sources. A circle of 6 degrees radius around the Large Magellanic Cloud sources was also excluded to prevent confusion problems. Therefore, there remained 65.5% of the sky (8.23 steradians) covered by this survey. The lowest statistical significance for the existence of the sources included in this catalog is 5 sigma, as required by the maximum likelihood methods used by the authors to determine the log N - log S parameters. Taking into account this statistical significance requirement, the authors estimated the completeness level of the first and second scans to be 1.25 and 1.8 R15 ct/s, respectively. 1 R15 ct/s is approximately 2.17 x 10^-11 erg/cm^2/s in the 2-10 keV energy band for a power-law spectyrum with a photon index of 1.65. This catalog contains data for 68 nongalactic sources (61 extragalactic and 7 unidentified sources) which were listed in Table 1 of the published catalog. The identified sources fall into several categories, including narrow emission line galaxies, broad emission line galaxies, BL Lacertae objects, and clusters of galaxies.\n", "\n", "* M31 XMM-Newton Spectral Survey X-Ray Point Source Catalog\n", " This table contains the results of a complete spectral survey of the X-ray point sources detected in five XMM-Newton observations along the major axis of M 31 but avoiding the central bulge, aimed at establishing the population characteristics of X-ray sources in this galaxy. One observation of each disc field of M 31 was taken using the EPIC pn and MOS cameras on XMM-Newton in January and June 2002. The authors obtained background-subtracted spectra and lightcurves for each of the 335 X-ray point sources detected across the five observations from 2002. They also correlate their source list with those of earlier X-ray surveys and radio, optical and infra-red catalogs. Sources with more than 50 source counts are individually spectrally fit in order to create the most accurate luminosity functions of M 31 to date. Based on the spectral fitting of these sources with a power law model, the authors observe a broad range of best-fit photon index. From this distribution of best-fit index, they identify 16 strong high mass X-ray binary system candidates in M 31. They show the first cumulative luminosity functions created using the best-fit spectral model to each source with more than 50 source counts in the disc of M 31. The cumulative luminosity functions show a distinct flattening in the X-ray luminosity L_X interval 37.0 &lt;~ log L_X erg s&lt;sup&gt;-1&lt;/sup&gt; &lt;~ 37.5. Such a feature may also be present in the X-ray populations of several other galaxies, but at a much lower statistical significance. The authors investigate the number of AGN present in their source list and find that, above L_X ~1.4 x 10&lt;sup&gt;36&lt;/sup&gt; erg s&lt;sup&gt;-1&lt;/sup&gt;, the observed population is statistically dominated by the point source population of M 31.\n", "\n", "* CGRO/BATSE 5B Gamma-Ray Burst Spectral Catalog\n", " The CGRO/BATSE 5B Gamma-Ray Burst Spectral Catalog contains the results of systematic spectral analyses of gamma-ray bursts (GRBs) detected with the Burst and Transient Source Experiment (BATSE) on board the Compton Gamma-Ray Observatory (CGRO) during its entire nine years of operation. This catalog contains two types of spectra extracted from 2,145 GRBs, and fitted with five different spectral models resulting in a compendium of over 19,000 spectra. The models were selected based on their empirical importance to the spectral shape of many GRBs, and the analysis performed was devised to be as thorough and objective as possible. In their paper, the authors describe in detail their procedures and criteria for the analyses, and present the bulk results in the form of parameter distributions. This catalog should be considered an official product from the BATSE Science Team, and the data files containing the complete results are soon to be available from the HEASARC. This table lists all of the spectroscopy results of gamma-ray bursts observed by a subset of the 8 BATSE Large Area Detectors. BATSE consisted, in part, of an array of 8 sodium iodide Large Area Detectors (LADs) which covered the energy range from ~20 keV - 2 MeV. The LAD detectors were placed at each of the eight corners of the CGRO spacecraft with an outward orientation such that the entire sky not occulted by the Eartt was observed. The spectrum files (&amp;quot;scat&amp;quot; files) available as FITS-format data products associated with this catalog provide parameter values and goodness-of-fit measures for different types of spectral fits and models. These fits are performed using 14-channel data, usually 2-second resolution CONT data. There are currently two spectrum categories: &lt;pre&gt; * Peak flux ('pflx') - a single spectrum over a 2.05-second time range at the peak flux of the burst * Fluence ('flnc') - a single spectrum over the entire burst duration &lt;/pre&gt; The quoted fluxes and fluences are for the 20 keV - 2 MeV energy range, notice. The scat files have two extensions. The first extension gives detector-specific information, including photon fluxes and fluences for each detector, which are provided for each energy channel. The second extension provides derived quantities such as flux, fluence and model parameters for the joint fit of all included detectors. The scat files and their energy-resolved quantities contained in these two extensions will be available soon in the HEASARC data archive. Quantities derived from these spectral fits are available in the present table, as described below and in the Goldstein et al. (2013) reference paper. The spectra are fit with a number of models, with the signal-to-noise ratio of the spectrum often determining whether a more complex model is statistically favored. The current set is: &lt;pre&gt; * Power law ('plaw'), * Comptonized (exponentially attenuated power law; 'comp') * Band ('band') * Smoothly broken power law ('sbpl') * Log_10 Gaussian ('glog') &lt;/pre&gt; The full details of these models are presented in Section 4 of the reference paper. The type of spectrum and spectral model are coded into the parameter names (and the associated file names) using the acronyms given above. Thus for example, the parameters with names beginning with 'flnc_glog' contain the results from fits to the fluence spectra using Log&lt;sub&gt;10&lt;/sub&gt; Gaussian models. The corresponding spectrum file for the burst with trigger number 105 with the results from a fit to the fluence spectrum using a Log&lt;sub&gt;10&lt;/sub&gt; Gaussian model is named scat_0105_flnc_glog_v00.fit. Please note that this table lists the raw results of each spectral fit to each GRB. In cases where the spectral fit failed, the values reported are those that initialized the spectral fit. If the uncertainty on the spectral parameters is reported as zero (no uncertainty), then the fit failed. In a few cases throughout this table, the uncertainties for certain spectral parameters may be reported as '9999.99' which indicates that the uncertainty on that parameter is completely unconstrained. An example of this is when the spectral data from a burst is fitted with a BAND function but is unable to constrain the high-energy index. In this case, the best fit centroid value of the high-energy index parameter is reported, and the '9999.99' value is reported for the uncertainty.\n", "\n", "* Chandra Deep Field South AGN Spectral Properties Catalog\n", " This table contains the results of a detailed X-ray spectral analysis of the sources in the 1 Ms catalog of the Chandra Deep Field South (CDFS, Giacconi et al. 2002, CDS Cat. J/ApJS/139/369, available in Browse as the CHANDFS1MS table), taking advantage of optical spectroscopy and photometric redshifts for 321 extragalactic sources out of the total sample of 347 sources. As a default spectral model, the authors adopt a power law with a slope Gamma with an intrinsic redshifted absorption N_H, a fixed Galactic absorption and an unresolved Fe emission line. For 82 X-ray bright sources, they are able to perform the X-ray spectral analysis leaving both Gamma and N_H free. The weighted mean value for the slope of the power law is 1.75 +/- 0.02, and the distribution of best fit values shows an intrinsic dispersion of 0.30. The authors do not find hints of a correlation between the spectral index Gamma and the intrinsic absorption column density N_H. They then investigate the absorption distribution for the whole sample, deriving the N_H values in faint sources by fixing Gamma to be 1.8. The authors find that the fraction of absorbed sources (with N_H &gt; 10^22 cm^-2) in the sample is constant (at the level of about 75%) or moderately increasing with redshift. Finally, they compare the optical classification to the X-ray spectral properties, confirming that the correspondence of unabsorbed (absorbed) X-ray sources to optical type I (type II) AGN is accurate for at least 80% of the sources with spectral identification (1/3 of the total X-ray sample).\n", "\n", "* AGN Torus model comparison of AGN in the CDFS (Buchner+, 2014)\n", " We present the Bayesian parameter estimation results derived using the torus+pexmon+scattering model. All parameters are shown with their posterior uncertainty, which is summarised using the 1-sigma equivalent quantiles. The prior used on the Photon index was a normal distribution with mean 1.95 and standard deviation 0.15, so if no information was gained this value remains. The KL column measures the information gain measured from the N_H_ posterior in bans. As a reference, the narrowing of a Gaussian from prior to posterior by a factor of 2 corresponds to 0.13ban, and thus values higher than that correspond to significant discriminatory information in the data. Annotations: S when fscat&gt;3%, s when fscat&gt;0.5% with &gt;=90% probability R when R&gt;0.3 with &gt;=90% probability, i.e. strong additional pexmon reflection Compton-thick (CT) if N_H&gt;10^24^cm^-2^, Compton-thin (O) if 10^22^cm^-2^&lt;N_H&lt;10^24^cm^-2^, Unobscured (U, N_H&lt;10^22^cm^-2^, each with &gt;=50% probability.\n", "\n", "* ROSAT Complete Results Archive Sources for the PSPC with Filter\n", " This table is derived from the Second ROSAT Source Catalog of Pointed Observations with the ROSAT PSPC (Roentgen Satellite Position-Sensitive Proportional Counter) Observed Using the Boron Filter, or the 2RXF Catalog. 2XRF contains arcsecond positions and count rates for 2,526 detected sources from 258 ROSAT PSPC Filter observations covering 0.15% of the sky, including 704 high-confidence detections and 20 obvious sources which were not detected by SASS. This table contains the complete version of the list of detections (2,526 entries), whereas the short 'high-confidence' version (the HEASARC's &lt;a href=\"rospspcf.html\"&gt;ROSPSPCF table&lt;/a&gt;) contains 704 detections. The ROSPSPCFTOT table includes many questionable sources that meet the following parameter criteria: false_det = 'f' or deferred = 'D' or not_checked = 'n'. See the documentation below for descriptions of these parameters. The catalog consists of all primary source parameters from the automated detection algorithm employed by the SASS. In addition each observation has been quality checked, both by automatic algorithms and by detailed visual inspection. The results of this quality checking are contained as a set of logical-value flags for a set of principal source parameters. If a source parameter is suspect, the associated flag is set to a corresponding alphabetical value; parameters with no obvious problems maintain the default, '.', value. The Second ROSAT Pointed PSPC Filter Source Catalog includes missing sources, i.e. obvious sources which were not detected by the SASS source detection software but which could be easily detected by visual inspection. Missed sources are marked by negative values of their source identification number, i.e. the parameter 'MPLSX_ID' has a negative value for these sources. The only tabulated quantities for these visually identified missed sources are source positions; other quantities (like count rates, hardness ratios, etc.) are not available. These data have been screened by ROSAT data centers in the US, Germany, and the UK as a step in the production of the ROSAT Results Archive. The RRA contains extracted source and associated products with an indication of reliability for the primary parameters. More information about the ROSAT mission and the SASS can be obtained from the ROSAT User Handbook, available at either &lt;a href=\"http://www.mpe.mpg.de/xray/wave/rosat/doc/ruh/index.php\"&gt;http://www.mpe.mpg.de/xray/wave/rosat/doc/ruh/index.php&lt;/a&gt; or &lt;a href=\"http://heasarc.gsfc.nasa.gov/docs/rosat/rosdocs.html\"&gt;http://heasarc.gsfc.nasa.gov/docs/rosat/rosdocs.html&lt;/a&gt;.\n", "\n", "* XMM-Newton survey in COSMOS field. IV. (Mainieri+, 2007)\n", " We present a detailed spectral analysis of pointlike X-ray sources in the XMM-Newton COSMOS field. Our sample of 135 sources only includes those that have more than 100 net counts in the 0.3-10keV energy band and have been identified through optical spectroscopy. The majority of the sources are well described by a simple power-law model with either no absorption (76%) or a significant intrinsic, absorbing column (20%). The remaining ~4% of the sources require a more complex modeling by incorporating additional components to the power law. For sources with more than 180 net counts (bright sample), we allowed both the photon spectral index Gamma and the equivalent hydrogen column NH to be free parameters. For fainter sources, we fix Gamma to the average value and allow NH to vary. The mean spectral index of the 82 sources in the bright sample is &lt;Gamma&gt;=2.06+/-0.08, with an intrinsic dispersion of ~0.24. Each of these sources has fractional errors on the value of Gamma below 20%. As expected, the distribution of intrinsic absorbing column densities is markedly different between AGNs with or without broad optical emission lines. We find within our sample four type 2 QSO candidates (L_X_&gt;10^44^erg/s, NH&gt;10^22^cm^-2^), with a spectral energy distribution well reproduced by a composite Seyfert 2 spectrum, that demonstrates the strength of the wide-field COSMOS XMM-Newton survey to detect these rare and underrepresented sources. In addition, we have identified a Compton-thick (NH&gt;1.5x10^24^cm^-2^) AGN at z=0.1248. Its X-ray spectrum is well fitted by a pure reflection model and a significant Fe K{alpha} line at rest-frame energy of 6.4keV.\n", "\n", "* Soft X-ray properties of Seyfert galaxies (Pfefferkorn+, 2001)\n", " We have detected 91 out of 99 Seyfert 1 and 47 out of 98 Seyfert 2 galaxies of the Rafanelli sample in the ROSAT X-ray band in pointed and/or All-Sky Survey observations. Spectral information from the survey data could be obtained for 59 Seyfert 1 galaxies and only for one Seyfert 2 galaxy. The tables quote the name of the Seyfert galaxy in column 1 and the ROSAT name in column 2. Columns 3 and 4 contain the ROSAT position. We mostly give the centroid source position from the pointed observation with the higher exposure times. The columns 5 and 6 list the count rates, columns 7 and 8 the corresponding exposure times, columns 9 and 10 the fluxes and columns 11 and 12 the luminosities of the sources detected in ROSAT pointing and survey observations, respectively. The survey count rates were taken from the RASS II catalogue and the pointing count rates were computed from the light curves of the sources. The labels \"(p)\" and \"(h)\" in column 5 indicate that the source data are taken from a PSPC or HRI observation. In the columns 9 and 10 we apply the labels \"(f)\" and \"(c)\" to mark the data produced by spectral fit or by count rates. This specification applies also for columns 11 and 12. The Galactic column density is given in column 13 (Dickey &amp; Lockman, 1990ARA&amp;A..28..215D), while the column density obtained from the spectral fit is given in column 14. The other spectral fit parameters, namely the monochromatic flux at 1keV and the photon index are also given in columns 15 and 16, respectively. The value \"-2.3\" of the photon index was used, if no reliable spectral fit could be obtained. When spectral information was available from the survey as well as from the pointed data, we quote the results from the pointed observations. Columns 17 and 18 show the quality of the X-ray identifications both in the pointing and the survey observations, the identifications labeled either with 1 or 2, 1 and 2 indicating high and lower degree of reliability, respectively. In the last column we have listed the classification of the Seyfert type (Sy1.0, Sy1.2, Sy1.5, Sy1.8, Sy1.9, Sy2.0) taken from the \"Catalogue of Seyfert Galaxies\" (Lipovetski et al., 1987, Cat. &lt;VII/173&gt;). We have modified the Rafanelli et al. conventions (S1 = Sy1.0 + Sy1.2 + Sy1.5 and S2 = Sy1.8 + Sy1.9 + Sy2.0) to S1 = Sy1.0 + Sy1.2 + Sy1.5 + Sy1.8 + Sy1.9 and S2 = S2.0. The classifications marked by a (N) indicate Narrow Line Seyfert 1 galaxies (NLS1) (e.g. Osterbrock &amp; Pogge, 1985ApJ...297..166O; Boller, Brandt &amp; Fink 1996A&amp;A...305...53B; Grupe 1996, Properties of bright soft X-ray selected ROSAT AGN, Dissertation submitted Georg-August University, Gvttingen, Germany ). For each source of the Rafanelli sample we have searched for X-ray detections within the ROSAT All-Sky Survey (RASS II catalogue; see Voges, Aschenbach, Boller et al., 1999, Cat. &lt;IX/10&gt; for references) and public PSPC (Position Sensitive Proportional Counter) and HRI (High Resolution Imager) pointed observations. The sources have been identified on X-ray contour maps overlaid to optical images taken from the Palomar Digitized Sky Survey (DSS). We have generated background subtracted contour maps for each selected ROSAT observation. Overlays with a dark background indicate pointed observations (HRI for HRI-data, without label for PSPC-data), whereas the grey background indicate ROSAT All-Sky survey data (PSPC).\n", "\n", "* AGN-Host Galaxy Connection (Povic+, 2012)\n", " Table presents the X-ray properties of all 1121 objects, including fluxes in six selected energy bands, and public available optical data for X-ray counterparts. X-ray flux in X/O flux ratio is the sum of fluxes in (0.5-2keV) and (2-4.5keV) bands, while the optical flux corresponds to R band. The represented hardness ratio was set using the same X-ray bands. Photometric redshifts and K-corrections were measured using the photometric information in five optical (Furusawa et al., 2008, Cat. J/ApJS/176/1), three NIR (Hewett et al., 2006MNRAS.367..454H; Hodgkin et al., 2009MNRAS.394..675H), and four MIR IRAC (SWIRE survey) bands. Morphological classification was obtained using the galSVM code (Huertas-Company et al., 2008A&amp;A...478..971H, 2009A&amp;A...497..743H), one of the new methods useful especially when dealing with high-redshift sources and low-resolution data. With galSVM we obtained the mean surface brightness, asymmetry, concentration index, Gini, Smoothness, M20 moment of light, and two probabilities p1 and p2 that the galaxy belongs to early- (E, S0, S0/Sa) or late-type, respectively. Stellarity and elongation parameters were obtained with SExtractor. All objects with stellarity parameter greater or equal to 0.9 were considered as compact in this work. We included in our analysis for early-type objects only those sources with p1 probabilities greater or equal to 0.75, and for late-type objects all sources with probabilities p2 greater than 0.5.\n", "\n", "* Spitzer Wide-Area IR Extra-Galactic Survey Chandra X-Ray Sources\n", " This table contains results from deep combined observations with Spitzer and Chandra of the Spitzer Wide-Area Infrared Extragalactic Survey (SWIRE) in the ELAIS N1 region. This survey was used to investigate the nature of the faint X-ray and IR sources in common, to identify active galactic nucleus (AGN)/starburst diagnostics, and to study the sources of the X-ray and cosmic infrared backgrounds (XRB and CIRB). In the 17' x 17' area of the Chandra ACIS-I image there were approximately 3400 SWIRE near-IR sources with 4-sigma detections in at least two Infrared Array Camera (IRAC) bands and 988 sources detected at 24 micron (um) with the Multiband Imaging Photometer (MIPS) brighter than a 24-um flux S_24 ~ 0.1 mJy. Of these, 102 IRAC and 59 MIPS sources have Chandra counterparts, out of a total of 122 X-ray sources present in the area with 0.5 - 8 keV flux &gt; 10-15 erg cm^-2 s^-1. The SWIRE ELAIS N1 field was imaged by the IRAC multiband camera on Spitzer in 2004 January and with MIPS in early 2004 February. The observations were centered at the position (16h 00m, +59d 01'). The X-ray observations were taken as part of the ELAIS Deep X-ray Survey (EDXS) and are described in detail in Manners et al. (2003, MNRAS, 343, 293). For this analysis, the Chandra Advanced CCD Imaging Spectrometer (ACIS) observation of 75 ks centered on (16h 10m 20.11s, +54d 33' 22.3\") (J2000.0) in the ELAIS N1 region. The aim point was focused on the ACIS-I chips, which consist of four CCDs arranged in a 2 x 2 array covering an area of 16.9' x 16.9' (286 square arcmin). Bad pixels and columns were removed, and data were filtered to eliminate high background times (due to strong solar flares), leaving 71.5 ks of good data after filtering. Counts-to-photon calibration assumed a standard power-law model spectrum with photon index Gamma = 1.7. Sources were detected to flux levels of 2.3 x 10^-15 erg s^-1 cm^-2 in the 0.5 - 8 keV band, 9.4 x 10^-16 erg s^-1 cm^-2 in the 0.5 - 2 keV band, and 5.2 x 10^-15 erg s^-1 cm^-2 in the 2 - 8 keV band. Sources are detectable to these flux limits over 90% of the nominal survey area. For this analysis, the authors used sources detected in the full band of ACIS-I only, of which there are 122 in the N1 region. Of the 102 sources in common between Chandra and SWIRE, 83 have significant detections in the separate soft X-ray band (0.5 - 2 keV) and 64 are detected in the hard (2 - 8 keV) band. A simple near-neighbor search was performed to cross-correlate the Spitzer and Chandra source catalogs within the Chandra ACIS-I chip image, using a d = 5\" search radius (roughly the quadratic sum of the astrometric errors). All together, the authors found reliably associated counterparts for 102 of the 122 Chandra sources (84% in total). The vast majority of these were detected with the IRAC channels 1 and 2 (3.6 and 4.5 um): 100 of the 122 Chandra sources in each case. As many as 59 Chandra objects are reliably associated with MIPS 24 um sources (all of them having IRAC counterparts), and just 1 had a MIPS 70 um counterpart. Of the 102 Spitzer-identified Chandra sources, three turned out to correspond to Galactic stars on the basis of their position on color-magnitude plots and optical morphology and were excluded from the subsequent analysis (and this table).\n", "\n", "* XMM-Newton 2XMMi-DR3 Selected Source Detections Catalog\n", " The authors have carried out a classification of 4,330 X-ray sources in the 2XMMi-DR3 catalog. The sources were selected under the requirement of being a point source with multiple XMM-Newton observations and at least one detection with a signal-to-noise ratio larger than 20. For about one-third of the sources, the authors are able to obtain reliable source types from the literature. They mostly correspond to various types of stars (611), active galactic nuclei (AGNs, 753), and compact object systems (138) containing white dwarfs, neutron stars, and stellar-mass black holes. The authors find that about 99% of stars can be separated from other source types based on their low X-ray-to-IR flux ratios and frequent X-ray flares. AGNs have remarkably similar X-ray spectra, with the power-law photon index centered around 1.91 +/- 0.31, and their 0.2-4.5 keV flux long-term variation factors have a median of 1.48, with 98.5% being less than 10. In contrast, 70% of compact object systems can be very soft or hard, highly variable in X-rays, and/or have very large X-ray-to-IR flux ratios, separating them from AGNs. Using these results, the authors derive a source type classification scheme to classify the other sources and find 644 candidate stars, 1,376 candidate AGNs, and 202 candidate compact object systems, whose false identification probabilities are estimated to be about 1%, 3%, and 18%, respectively. There are still 320 sources associated with nearby galaxies and 151 in the Galactic plane, which the authors expect to be mostly compact object systems or background AGNs. There are also 100 candidate ultraluminous X-ray sources. They are found to be much less variable than other accreting compact objects. This table contains the list of 19,637 detections of the 4,330 unique X-ray sources which comprise the authors' sample. The list of 4,330 unique X-ray sources and their classifications is also available as the HEASARC XMMSSCLWBS table.\n", "\n", "* LALA Cetus Field Chandra X-Ray Point Source Catalog\n", " The 174 ks Chandra Advanced CCD Imaging Spectrometer (ACIS) exposure of the Large Area Lyman Alpha Survey (LALA) Cetus field is the second of the two deep Chandra images on LALA fields. In their paper, the authors present the Chandra X-ray sources detected in the Cetus field, along with an analysis of X-ray source counts, the stacked X-ray spectrum, and the optical identifications. A total of 188 X-ray sources were detected: 174 in the 0.5-7.0 keV band, 154 in the 0.5-2.0 keV band, and 113 in the 2.0-7.0 keV band. The X-ray source counts were derived and compared with the 172 ks exposure LALA Bootes field (available as the LALABOOCXO table in Browse). Interestingly, the authors find consistent hard-band X-ray source density, but a (36 +/- 12)% higher soft-band X-ray source density in the Cetus field. The weighted stacked spectrum of the detected X-ray sources can be fitted by a power law with photon index Gamma = 1.55. Based on the weighted stacked spectrum, the authors find that the resolved fraction of the X-ray background drops from (72 +/- 1)% at 0.5-1.0 keV to (63 +/- 4)% at 6.0-8.0 keV. The unresolved spectrum can be fitted by a power law over the range 0.5-7 keV, with a photon index Gamma = 1.22. Optical counterparts are also presented for 154 of the X-ray sources, down to a limiting magnitude of r' = 25.9 (Vega), using a deep r'-band image obtained with the MMT.\n", "\n", "* Spectral analysis of Lockman Hole (Mainieri+, 2002)\n", " We present the results of the X-ray spectral analysis of the first deep X-ray survey with the XMM-Newton observatory during Performance Verification. The X-ray data of the Lockman Hole field and the derived cumulative source counts were reported by Hasinger et al. (2001A&amp;A...365L..45H). We restrict the analysis to the sample of 98 sources with more than 70 net counts (flux limit in the [0.5-7]keV band of 1.6x10^-15^erg/cm^2^/s) of which 61 have redshift identification. We find no correlation between the spectral index and the intrinsic absorption column density NH and, for both the Type-1 and Type-2 AGN populations, we obtain &lt;Gamma&gt;~=2.\n", "\n", "* RIXOS source sample X-ray spectra (Mittaz+, 1999)\n", " We present results of an extensive study of the X-ray spectral properties of sources detected in the RIXOS survey, which is a large, nearly complete sample of objects detected serendipitously in ROSAT PSPC fields down to a flux limit of 3x10^-14^erg/cm2/s (0.5-2keV). We show that for X-ray surveys containing sources with low count rate, such as RIXOS, spectral slopes estimated using simple hardness ratios in the ROSAT band can be biased. Instead, we analyse three-colour X-ray data using statistical techniques appropriate to the Poisson regime which remove the effects of this bias. We also show that the use of three-colour data enables some discrimination between thermal and non-thermal spectra. We have then applied this technique to the RIXOS survey to study the spectral properties of the sample. For the AGN we find an average energy index of 1.05+/-0.05, with no evidence for spectral evolution with redshift. Individual AGN are shown to have a range of properties, including soft X-ray excesses and intrinsic absorption. Narrow-emission-line galaxies (NLLGs) also seem to fit to a power-law spectrum, which may indicate a non-thermal origin for their X-ray emission. We infer that most of the clusters in the sample have a bremsstrahlung temperature &gt;3keV, although some show evidence for a cooling flow. The stars deviate strongly from a power-law model but fit to a thermal model. Finally, we have analysed the whole RIXOS sample (extending the flux cut-off to the sensitivity threshold of each individual observation) containing 1762 sources to study the relationship between spectral slope and flux. We find that the mean spectral slope of the sources hardens at lower fluxes, in agreement with results from other samples. However, a study of the individual sources demonstrates that the majority have relatively soft spectra even at faint flux levels, and the hardening of the mean is caused by the appearance of a population of very hard sources at the lowest fluxes. This has implications for the nature of the soft X-ray background.\n", "\n", "* Soft X-ray properties of Seyfert galaxies (Pfefferkorn+, 2001)\n", " We have detected 91 out of 99 Seyfert 1 and 47 out of 98 Seyfert 2 galaxies of the Rafanelli sample in the ROSAT X-ray band in pointed and/or All-Sky Survey observations. Spectral information from the survey data could be obtained for 59 Seyfert 1 galaxies and only for one Seyfert 2 galaxy. The tables quote the name of the Seyfert galaxy in column 1 and the ROSAT name in column 2. Columns 3 and 4 contain the ROSAT position. We mostly give the centroid source position from the pointed observation with the higher exposure times. The columns 5 and 6 list the count rates, columns 7 and 8 the corresponding exposure times, columns 9 and 10 the fluxes and columns 11 and 12 the luminosities of the sources detected in ROSAT pointing and survey observations, respectively. The survey count rates were taken from the RASS II catalogue and the pointing count rates were computed from the light curves of the sources. The labels \"(p)\" and \"(h)\" in column 5 indicate that the source data are taken from a PSPC or HRI observation. In the columns 9 and 10 we apply the labels \"(f)\" and \"(c)\" to mark the data produced by spectral fit or by count rates. This specification applies also for columns 11 and 12. The Galactic column density is given in column 13 (Dickey &amp; Lockman, 1990ARA&amp;A..28..215D), while the column density obtained from the spectral fit is given in column 14. The other spectral fit parameters, namely the monochromatic flux at 1keV and the photon index are also given in columns 15 and 16, respectively. The value \"-2.3\" of the photon index was used, if no reliable spectral fit could be obtained. When spectral information was available from the survey as well as from the pointed data, we quote the results from the pointed observations. Columns 17 and 18 show the quality of the X-ray identifications both in the pointing and the survey observations, the identifications labeled either with 1 or 2, 1 and 2 indicating high and lower degree of reliability, respectively. In the last column we have listed the classification of the Seyfert type (Sy1.0, Sy1.2, Sy1.5, Sy1.8, Sy1.9, Sy2.0) taken from the \"Catalogue of Seyfert Galaxies\" (Lipovetski et al., 1987, Cat. &lt;VII/173&gt;). We have modified the Rafanelli et al. conventions (S1 = Sy1.0 + Sy1.2 + Sy1.5 and S2 = Sy1.8 + Sy1.9 + Sy2.0) to S1 = Sy1.0 + Sy1.2 + Sy1.5 + Sy1.8 + Sy1.9 and S2 = S2.0. The classifications marked by a (N) indicate Narrow Line Seyfert 1 galaxies (NLS1) (e.g. Osterbrock &amp; Pogge, 1985ApJ...297..166O; Boller, Brandt &amp; Fink 1996A&amp;A...305...53B; Grupe 1996, Properties of bright soft X-ray selected ROSAT AGN, Dissertation submitted Georg-August University, Gvttingen, Germany ). For each source of the Rafanelli sample we have searched for X-ray detections within the ROSAT All-Sky Survey (RASS II catalogue; see Voges, Aschenbach, Boller et al., 1999, Cat. &lt;IX/10&gt; for references) and public PSPC (Position Sensitive Proportional Counter) and HRI (High Resolution Imager) pointed observations. The sources have been identified on X-ray contour maps overlaid to optical images taken from the Palomar Digitized Sky Survey (DSS). We have generated background subtracted contour maps for each selected ROSAT observation. Overlays with a dark background indicate pointed observations (HRI for HRI-data, without label for PSPC-data), whereas the grey background indicate ROSAT All-Sky survey data (PSPC).\n", "\n", "* M31 Chandra X-ray point sources (Kong+, 2002)\n", " We report on Chandra observations of the central region of M31. By combining eight Chandra ACIS-I observations taken between 1999 and 2001, we have identified 204 X-ray sources within the central ~17'x17' region of M31, with a detection limit of ~2x10^35^erg/s. Of these 204 sources, 22 are identified with globular clusters, two with supernova remnants, nine with planetary nebulae, and nine with supersoft sources. By comparing individual images, about 50% of the sources are variable on timescales of months. We also found 13 transients, with light curves showing a variety of shapes. We also extracted the energy spectra of the 20 brightest sources; they can be well fitted by a single power law with a mean photon index of 1.8. The spectral shapes of 12 sources are shown to be variable, suggesting that they went through state changes.\n", "\n", "* AGN in XMM-Newton Hard Bright Survey (Della Ceca+, 2008)\n", " The XMM-Newton Hard Bright Serendipitous Survey (HBSS) is part of a bigger survey project known as XMM Bright Serendipitous Survey (XBS). This latter consists of two flux-limited serendipitous samples of X-ray selected sources at high galactic latitude (|b|&gt;20deg): the XMM BSS sample (389 sources) and the XMM HBSS sample (67 sources, with 56 sources in common with the BSS sample) having an EPIC MOS2 count rate limit, corrected for vignetting, of 10^-2^cts/s (2x10^-3^cts/s) in the 0.5-4.5keV (4.5-7.5keV) energy band; the flux limit is ~7x10^-14^cgs in both energy selection bands. At the time of this writing the spectroscopic identification rate is 87% for the BSS sample and 97% for the HBSS sample. The details on the XMM-Newton fields selection strategy and the source selection criteria of the XMM BSS and HBSS samples are discussed in Della Ceca et al. (2004, Cat. &lt;J/A+A/428/383&gt;), while a description of the optical data and analysis, of the optical classification scheme, and of the optical properties of the extragalactic sources identified so far, is presented in Caccianiga et al. (2008A&amp;A...477..735C). The optical and X-ray properties of the galactic population are discussed in Lopez-Santiago et al. (2007A&amp;A...463..165L).The current classification breakdown of the HBSS sample is as follows: 62 AGN, 1 cluster of galaxies (XBSJ 141830.5+251052), and 2 X-ray emitting stars (XBSJ 014100.6-675328 and XBSJ 123600.7-395217). Two X-ray sources (XBSJ080411.3+650906 and XBSJ 110050.6-344331) are still unidentified at the time of this writing. In table1 we report the basic X-ray spectral properties, (e.g., photon index, intrinsic absorbing column density N_H_, intrinsic 2-10keV luminosity) of the HBSS AGN sample obtained from a complete X-ray spectral analysis of the XMM-Newton data.\n", "\n", "* ROSAT Results Archive Sources for the PSPC with Filter\n", " This table is derived from the Second ROSAT Source Catalog of Pointed Observations with the ROSAT PSPC (Roentgen Satellite Position-Sensitive Proportional Counter) Observed Using the Boron Filter, or the 2RXF Catalog. 2XRF contains arcsecond positions and count rates for 2,526 detected sources from 258 ROSAT PSPC Filter observations covering 0.15% of the sky, including 704 high-confidence detections and 20 obvious sources which were not detected by SASS. The complete version of the list of detections (the HEASARC's &lt;a href=\"rospspcftot.html\"&gt;ROSPSPCFTOT table&lt;/a&gt;) contains 2,526 entries, whereas the short 'high-confidence' version contained in this present table has 704 detection. The ROSPSPCF table excludes sources that meet the following parameter criteria: false_det = 'f' or deferred = 'D' or not_checked = 'n'. See the documentation below for descriptions of these parameters. The catalog consists of all primary source parameters from the automated detection algorithm employed by the SASS. In addition each observation has been quality checked, both by automatic algorithms and by detailed visual inspection. The results of this quality checking are contained as a set of logical-value flags for a set of principal source parameters. If a source parameter is suspect, the associated flag is set to a corresponding alphabetical value; parameters with no obvious problems maintain the default, '.', value. The Second ROSAT Pointed PSPC Filter Source Catalog includes missing sources, i.e. obvious sources which were not detected by the SASS source detection software but which could be easily detected by visual inspection. Missed sources are marked by negative values of their source identification number, i.e. the parameter 'MPLSX_ID' has a negative value for these sources. The only tabulated quantities for these visually identified missed sources are source positions; other quantities (like count rates, hardness ratios, etc.) are not available. These data have been screened by ROSAT data centers in the US, Germany, and the UK as a step in the production of the ROSAT Results Archive. The RRA contains extracted source and associated products with an indication of reliability for the primary parameters. More information about the ROSAT mission and the SASS can be obtained from the ROSAT User Handbook, available at either &lt;a href=\"http://www.mpe.mpg.de/xray/wave/rosat/doc/ruh/index.php\"&gt;http://www.mpe.mpg.de/xray/wave/rosat/doc/ruh/index.php&lt;/a&gt; or &lt;a href=\"http://heasarc.gsfc.nasa.gov/docs/rosat/rosdocs.html\"&gt;http://heasarc.gsfc.nasa.gov/docs/rosat/rosdocs.html&lt;/a&gt;.\n", "\n", "* Sloan Digital Sky Survey Quasars Detected by Chandra\n", " The authors have studied the spectral energy distributions and evolution of a large sample of optically selected quasars from the Sloan Digital Sky Survey (SDSS) that were observed in 323 Chandra images analyzed by the Chandra Multiwavelength Project (ChaMP). Their highest-confidence matched sample (which this HEASARC table comprises) includes 1135 X-ray detected quasars in the redshift range 0.2 &lt; z &lt; 5.4, representing some 36 Msec of effective exposure. In their paper, the authors provide catalogs of QSO properties, and describe their novel method of calculating X-ray flux upper limits and effective sky coverage. Spectroscopic redshifts are available for about 1/3 of the detected sample; elsewhere, redshifts are estimated photometrically. The authors have detected 56 QSOs with redshift z &gt; 3, substantially expanding the known sample. They find no evidence for evolution out to z ~ 5 for either the X-ray photon index Gamma or for the ratio of optical/UV to X-ray flux Alpha_ox. About 10% of detected QSOs show best-fit intrinsic absorbing columns greater than 10&lt;sup&gt;22&lt;/sup&gt; cm&lt;sup&gt;-2&lt;/sup&gt;, but the fraction might reach ~1/3 if most nondetections are absorbed. The authors confirm a significant correlation between Alpha_ox and optical luminosity, but it flattens or disappears for fainter (M_B &gt;~ -23) active galactic nucleus (AGN) alone. They report significant hardening of Gamma both toward higher X-ray luminosity, and for relatively X-ray loud quasars. These trends may represent a relative increase in nonthermal X-ray emission, and their findings thereby strengthen analogies between Galactic black hole binaries and AGN. For uniformly selected subsamples of narrow-line Seyfert 1s and narrow absorption line QSOs, they find no evidence for unusual distributions of either Alpha_ox or Gamma. Much more information on the SDSS is available at the project's web site at &lt;a href=\"http://www.sdss.org/\"&gt;http://www.sdss.org/&lt;/a&gt;.\n", "\n", "* XMM-Newton Bright Serendipitous Survey: AGN X-Ray Spectral Properties\n", " X-ray surveys are a key instrument in the study of active galactic nuclei (AGN). Thanks to their penetrating ability, X-rays are able to map the innermost regions close to the central super massive black hole (SMBH) as well as to detect and characterize its emission up to high redshift. This table contains results from a detailed X-ray spectral analysis of the AGN belonging to the XMM-Newton Bright Serendipitous Survey (XBS, the HEASARC Browse XMMBSS table, CDS Cat. IX/41). The XBS is composed of two flux-limited samples selected in the complementary 0.5 to 4.5 and 4.5 to 7.5 keV energy bands and comprising more than 300 AGN up to redshift ~2.4. The authors have performed an X-ray analysis following two different approaches: by analyzing individually each AGN X-ray spectrum and by constructing average spectra for different AGN types. From the individual analysis, the authors find that there seems to be an anticorrelation between the spectral index and the sources' hard X-ray luminosity, such that the average photon index for the higher luminosity sources (&gt;10&lt;sup&gt;44&lt;/sup&gt; erg s&lt;sup&gt;-1&lt;/sup&gt;) is significantly (&gt;2 sigma) flatter than the average for the lower luminosity sources. They also find that the intrinsic column density distribution agrees with AGN unified schemes, although a number of exceptions are found (3% of the whole sample), which are much more common among optically classified type 2 AGN. The authors also find that the so-called \"soft-excess\", apart from the intrinsic absorption, constitutes the principal deviation from a power-law shape in AGN X-ray spectra and it clearly displays different characteristics, and likely a different origin, for unabsorbed and absorbed AGN. Regarding the shape of the average spectra, they find that it is best reproduced by a combination of an unabsorbed (absorbed) power law, a narrow Fe K-alpha emission line and a small (large) amount of reflection for unabsorbed (absorbed) sources.\n", "\n", "* CHAMP/SDSS Nearby Low-Luminosity AGN Catalog\n", " The combination of the Sloan Digital Sky Survey (SDSS) and the Chandra Multiwavelength Project (ChaMP; Green et al. 2004, ApJS, 150, 43) currently offers the largest and most homogeneously selected sample of nearby galaxies for investigating the relations between X-ray nuclear emission, nebular line emission, black hole masses, and the properties of the associated stellar populations. The authors provide X-ray spectral fits and valid uncertainties for all the galaxies with counts ranging from 2 to 1325 (mean 76, median 19). They present in their paper novel constraints that both X-ray luminosity L&lt;sub&gt;X&lt;/sub&gt; and X-ray spectral energy distribution bring to the galaxy evolutionary sequence HII -&gt; Seyfert/Transition Object -&gt; LINER -&gt; Passive suggested by optical data. In particular, the authors show that both L&lt;sub&gt;X&lt;/sub&gt; and Gamma, the slope of the power law that best fits the 0.5 - 8 keV spectra, are consistent with a clear decline in the accretion power along the sequence, corresponding to a softening of their spectra. This implies that, at z ~ 0, or at low-luminosity active galactic nucleus (AGN) levels, there is an anticorrelation between Gamma and L/L&lt;sub&gt;Edd&lt;/sub&gt;, opposite to the trend which is exhibited by high-z AGN (quasars). The turning point in the Gamma - L/L&lt;sub&gt;Edd&lt;/sub&gt; LLAGN + quasars relation occurs near Gamma ~ 1.5 and L/L&lt;sub&gt;Edd&lt;/sub&gt; ~ 0.01. Interestingly, this is identical to what stellar mass X-ray binaries exhibit, indicating that the authors have probably found the first empirical evidence for an intrinsic switch in the accretion mode, from advection-dominated flows to standard (disk/corona) accretion modes in supermassive black hole accretors, similar to what has been seen and proposed to happen in stellar mass black hole systems. The anticorrelation the authors find between Gamma and L/L&lt;sub&gt;Edd&lt;/sub&gt; may instead indicate that stronger accretion correlates with greater absorption. Therefore, the trend for softer spectra toward more luminous, high-redshift, and strongly accreting (L/L&lt;sub&gt;Edd&lt;/sub&gt; &gt;~ 0.01) AGNs/quasars could simply be the result of strong selection biases reflected in the dearth of type 2 quasar detections. The cross-match of all ChaMP sky regions imaged by Chandra/ACIS with the SDSS DR4 spectroscopic footprint results in a parent sample of 15,955 galaxies on or near a chip and a subset of 199 sources that are X-ray detected. Among those, only 107 sources have an off-axis angle (OAA) Theta &lt;0.2 degrees and avoid ccd=8 due to high serial readout noise; these 107 objects comprise the main sample that the authors employ for this study and that are listed in this table. The authors performed direct spectral fits to the X-ray counts distribution using the full instrument calibration, known redshift, and Galactic 21-cm column nH&lt;sub&gt;Gal&lt;/sub&gt;. Source spectra were extracted from circular regions with radii corresponding to energy encircled fractions of ~90%, while the background region encompasses a 20 arcsec annulus, centered on the source, with separation 4 arcsecs, from the source region. Any nearby sources were excised, from both the source and the background regions. The spectral fitting was done via yaxx ('Yet Another X-ray eXtractor': Aldcroft 2006, BAAS, 38, 376), an automated script that employs the CIAO Sherpa tool. Each spectrum was fitted in the range 0.5 - 8 keV by two different models: (1) a single power law plus absorption fixed at the Galactic 21-cm value (model 'PL'), and (2) a fixed power law of photon index Gamma = 1.9 plus intrinsic absorption of column nH (model 'PLfix'). For the nine objects with more than 200 counts, the authors employed a third model in which both the slope of the power law and the intrinsic absorption were free to vary (model 'PL_abs').\n", "\n", "* X-ray studies of stars in the Pleiades (Micela+, 1990)\n", " Coronal X-ray emission of the Pleiades stars is investigated, and maximum likelihood, integral X-ray luminosity functions are computed for Pleiades members in selected color-index ranges. A detailed search is conducted for long-term variability in the X-ray emission of those stars observed more than once. An overall comparison of the survey results with those of previous surveys confirms the ubiquity of X-ray emission in the Pleiades cluster stars and its higher rate of emission with respect to older stars. It is found that the X-ray emission from dA and early dF stars cannot be proven to be dissimilar to that of Hyades and field stars of the same spectral type. The Pleiades cluster members show a real rise of the X-ray luminosity from dA stars to early dF stars. X-ray emission for the young, solar-like Pleiades stars is about two orders of magnitude more intense than for the nearby solar-like stars.\n", "\n", "* CDFs AGNs X-ray power-law photon index (Saez+, 2008)\n", " Our sources were selected from the CDFs, currently the two deepest X-ray surveys. The on-axis sensitivity limits for the CDF-N are ~2.5x10^-17^erg/cm^2^/s (0.5-2.0keV) and ~1.4x10^-16^erg/cm^2^/s (2-8keV).\n", "\n", "* X-ray+Radio sources in XBootes (El Bouchefry, 2009)\n", " The radio data are from the 2002 version of the FIRST VLA catalogue (Becker et al., 1995, See Cat. VIII/71), and it is derived from 1993 through 2002 observations. The X-ray data (Kenter et al. 2005, Cat. J/ApJS/161/9; Murray et al. 2005ApJS..161....1M) used in this paper are from the Chandra XBootes surveys. The XBootes catalogue contains ~3213 X-ray point sources and is publicly available through the National Optical Astronomy Observatory (NOAO) Deep Wide Field Survey (NDWFS) homepage (http://www.noao.edu/noao/noaodeep/XBootesPublic/index.html) The NDWFS is a deep multiband imaging (Bw, R, I, J, H, K) designed to study the formation and evolution of large-scale structures (Jannuzi et al., 1999, BAAS, 31, 1392; Brown et al., 2003ApJ...597..225B).\n", "\n", "* M 31 Central Region Chandra X-Ray Point Source Catalog\n", " This table countains the M 31 Central Region Catalog of Chandra X-Ray Point Sources. It is based on Chandra observations of the central region of M 31. By combining eight Chandra ACIS-I observations which were taken between 1999 and 2001, the authors have identified 204 X-ray sources within the central ~17'x17' region of M 31, with a detection limit of ~2x10^35 erg/s. Of these 204 X-ray sources, 22 are identified with globular clusters, two with supernova remnants, nine with planetary nebulae, and nine with supersoft sources. By comparing individual images, about 50% of the sources were found to be variable on timescales of months. The authors also found 13 transients, with light curves showing a variety of shapes. They also extracted the energy spectra of the 20 brightest sources; they can be well fitted by a single power law with a mean photon index of 1.8. The spectral shapes of 12 sources were variable, suggesting that they went through state changes. All sources in the catalog have S/N &gt; 2.5 and only five have S/N &lt; 3.0. The detection limit for the sources varies across the image due to the variation of exposure time, background, and instrumental PSF, and is highest near the edges, where the PSF broadens rapidly and the exposure time is lowest. Over the inner 4' of the field, the detection limit is 2.1 x 10^-4 ct/s, which is equivalent to an X-ray luminosity of about 2 x 10^35 erg/s. Additional information about optical identifications and cross-correlated ROSAT X-ray sources not provided in this HEASARC table is available in the published paper (Tables 4 and 5) and/or the CDS at &lt;a href=\"ftp://cdsarc.u-strasbg.fr/pub/cats/J/ApJ/577/738/\"&gt;ftp://cdsarc.u-strasbg.fr/pub/cats/J/ApJ/577/738/&lt;/a&gt; (table4.dat &amp; table5.dat).\n", "\n", "* X-ray studies of stars in the Pleiades (Micela+, 1990)\n", " Coronal X-ray emission of the Pleiades stars is investigated, and maximum likelihood, integral X-ray luminosity functions are computed for Pleiades members in selected color-index ranges. A detailed search is conducted for long-term variability in the X-ray emission of those stars observed more than once. An overall comparison of the survey results with those of previous surveys confirms the ubiquity of X-ray emission in the Pleiades cluster stars and its higher rate of emission with respect to older stars. It is found that the X-ray emission from dA and early dF stars cannot be proven to be dissimilar to that of Hyades and field stars of the same spectral type. The Pleiades cluster members show a real rise of the X-ray luminosity from dA stars to early dF stars. X-ray emission for the young, solar-like Pleiades stars is about two orders of magnitude more intense than for the nearby solar-like stars.\n", "\n", "* X-ray and radio emission of type 1 AGNs (Ballo+, 2012)\n", " Basic information and derived properties are presented for our sample of X-ray selected type 1 AGN (as well as for the X-ray undetected type 1 AGN in the \"control sample\"): coordinates, redshift, X-ray and radio fluxes, optical magnitudes, from the SDSS, 2XMMi, and FIRST catalogues; continuum luminosities at 3000A and in the X-ray band, black hole masses, bolometric luminosities, Eddington ratios; for the sources falling in the FIRST field, optical fluxes at 2500A and 4400 A, X-ray-to-optical index, radio classification, ratio between radio and UV, optical, or X-ray fluxes.\n", "\n", "* Sloan Digital Sky Survey (DR5)/XMM-Newton Quasar Survey Catalog\n", " This table contains the 5th Data Release Sloan Digital Sky Survey (DR5 SDSS)/XMM-Newton Quasar Survey Catalog. This catalog contains 792 SDSS DR5 quasars with optical spectra that have been observed serendipitously in the X-rays with XMM-Newton. These quasars cover a redshift range of z = 0.11 - 5.41 and a magnitude range of i = 15.3 - 20.7. Substantial numbers of radio-loud (70) and broad absorption line (51) quasars exist within this sample. Significant X-ray detections at &gt;=2 sigma account for 87% of the sample (685 quasars), and 473 quasars are detected at &gt;=6 sigma, sufficient to allow X-ray spectral fits. For detected sources, ~60% have X-ray fluxes between F(2-10 keV) = (1-10) x 10&lt;sup&gt;-14&lt;/sup&gt; erg cm&lt;sup&gt;-2&lt;/sup&gt; s&lt;sup&gt;-1&lt;/sup&gt;. The authors fit a single power law, a fixed power law with intrinsic absorption left free to vary, and an absorbed power-law model to all quasars with X-ray signal-to-noise ratio &gt;= 6, resulting in a weighted mean photon index Gamma = 1.91 +/- 0.08, with an intrinsic dispersion sigma(Gamma) = 0.38. For the 55 sources (11.6%) that prefer intrinsic absorption, the authors find a weighted mean N_H = 1.5 +/- 0.3 x 10&lt;sup&gt;21&lt;/sup&gt; cm&lt;sup&gt;-2&lt;/sup&gt;. They find that Gamma correlates significantly with optical color, Delta(g-i), the optical-to-X-ray spectral index (alpha_ox), and the X-ray luminosity. While the first two correlations can be explained as artifacts of undetected intrinsic absorption, the correlation between Gamma and X-ray luminosity appears to be a real physical correlation, indicating a pivot in the X-ray slope.\n", "\n", "* Second INTEGRAL AGN Catalog\n", " The INTEGRAL mission provides a large data set for studying the hard X-ray properties of AGN and allows testing the unified scheme for AGN. This table contains some of the results from the analysis of INTEGRAL IBIS/ISGRI, JEM-X, and OMC data for 199 AGN (and 3 clusters of galaxies) that have been reported to be detected by INTEGRAL at energies above 20 keV. The data analysed therein allowed a significant spectral extraction on 148 objects and optical variability study of 57 AGN. The slopes of the hard X-ray spectra of Seyfert 1 and Seyfert 2 galaxies were found to be consistent within the uncertainties, whereas lower luminosities were measured for the more absorbed/type 2 AGN. The intermediate Seyfert 1.5 objects exhibit hard X-ray spectra consistent with those of Seyfert 1 galaxies. When applying a Compton reflection model, the underlying continua appear still the same in Seyfert 1 and 2 with photon index 2, and the reflection strength is about R = 1, when assuming different inclination angles. A significant correlation is found between the hard X-ray and optical luminosity and the mass of the central black hole, in the sense that the more luminous objects appear to be more massive. There is also a general trend for the absorbed sources and type 2 AGN to have lower Eddington ratios. The black hole mass appears to form a fundamental plane together with the optical and X-ray luminosity of the form L_V being proportional to L_X&lt;sup&gt;0.6&lt;/sup&gt; M_BH&lt;sup&gt;0.2&lt;/sup&gt;, similar to that found between radio luminosity L_R, L_X, and M_BH. The unified model for Seyfert galaxies seems to hold, showing in hard X-rays that the central engine is the same in Seyfert 1 and 2 but seen under different inclination angles and absorption. A catalog of 199 IBIS/ISGRI detected AGN is presented. For those 148 objects significantly detected in the data set analysed here, spectral parameters, fluxes, and luminosities are given. In addition, the photometric table of OMC measurements in the V-band (given for 57 of the AGN) is also included herein. For objects with more complex spectra, notice, the results of a fit to a cut-off power law model were presented in Table 3 of the reference paper, but are not included in this HEASARC table. The JEM-X spectra of the 23 AGN detected by the X-ray monitor were fit with the IBIS/ISGRI data, and the results of this were presented in Table 4 of the reference paper, but are also not included in this HEASARC table.\n", "\n", "* SED of the Fermi blazars (Li+, 2010)\n", " We complied the optical, X-ray, and {gamma}-ray data for 54 Fermi blazars and studied the relationship between the broadband spectral index {alpha}_ox_ and {alpha}_x{gamma}_, as well as the relationship between the intrinsic composite spectral indices {alpha}_xox_ and {alpha}_{gamma}x{gamma}_ for this sample. The relationship between {alpha}_xox_ and {alpha}_{gamma}x{gamma}_ reveals that flat spectrum radio quasars and low-energy peaked BL Lacertae follow a continuous trend, which is consistent with previous results, whereas high-energy peaked BL Lacertae follow a separate distinct trend. Even so, a unified scheme is also revealed from {alpha}_xox_-{alpha}_{gamma}x{gamma}_ diagram when all three subclasses of blazars are considered.\n", "\n" ] } ], "prompt_number": 23 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 23 } ], "metadata": {} } ] }
gpl-2.0
XinyiGong/pymks
notebooks/filter.ipynb
2
42342
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Filter Example\n", "\n", "This example demonstrates the connection between MKS and signal\n", "processing for a 1D filter. It shows that the filter is in fact the\n", "same as the influence coefficients and, thus, applying the `predict`\n", "method provided by the `MKSLocalizationnModel` is in essence just applying a filter." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "%load_ext autoreload\n", "%autoreload 2\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we construct a filter, $F$, such that\n", "\n", "$$F\\left(x\\right) = e^{-|x|} \\cos{\\left(2\\pi x\\right)} $$\n", "\n", "We want to show that if $F$ is used to generate sample calibration\n", "data for the MKS, then the calculated influence coefficients are in\n", "fact just $F$." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ 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This performs the convolution\n", "\n", "$$ p\\left[ s \\right] = \\sum_r F\\left[r\\right] X\\left[r - s\\right] $$\n", "\n", "for each sample." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import scipy.ndimage\n", "\n", "n_space = 101\n", "n_sample = 50\n", "np.random.seed(201)\n", "x = np.linspace(x0, x1, n_space)\n", "X = np.random.random((n_sample, n_space))\n", "y = np.array([scipy.ndimage.convolve(xx, F(x), mode='wrap') for xx in X])\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For this problem, a basis is unnecessary as no discretization is\n", "required in order to reproduce the convolution with the MKS localization. Using\n", "the `ContinuousIndicatorBasis` with `n_states=2` is the equivalent of a\n", "non-discretized convolution in space." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from pymks import MKSLocalizationModel\n", "from pymks import PrimitiveBasis\n", "\n", "prim_basis = PrimitiveBasis(n_states=2, domain=[0, 1])\n", "model = MKSLocalizationModel(basis=prim_basis)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Fit the model using the data generated by $F$." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "model.fit(X, y)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To check for internal consistency, we can compare the predicted\n", "output with the original for a few values" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[-0.41059557 0.20004566 0.61200171 0.5878077 ]\n", "[-0.41059556 0.20004566 0.6120017 0.58780769]\n" ] } ], "source": [ "y_pred = model.predict(X)\n", "print y[0, :4]\n", "print y_pred[0, :4]\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With a slight linear manipulation of the coefficients, they agree perfectly with the shape of the filter, $F$. 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Remember the convolution for the MKS is\n", "\n", "$$ p \\left[s\\right] = \\sum_{l=0}^{L-1} \\sum_{r=0}^{S - 1} \\alpha[l, r] m[l, s - r] $$\n", "\n", "However, when the primitive basis is selected, the `MKSLocalizationModel` solves a modified form of this. There are always redundant coefficients since\n", "\n", "$$ \\sum\\limits_{l=0}^{L-1} m[l, s] = 1 $$\n", "\n", "Thus, the regression in Fourier space must be done with categorical variables, and the regression takes the following form.\n", "\n", "\n", "$$ \\begin{split}\n", "p [s] & = \\sum_{l=0}^{L - 1} \\sum_{r=0}^{S - 1} \\alpha[l, r] m[l, s -r] \\\\\n", "P [k] & = \\sum_{l=0}^{L - 1} \\beta[l, k] M[l, k] \\\\\n", "&= \\beta[0, k] M[0, k] + \\beta[1, k] M[1, k]\n", "\\end{split}\n", "$$\n", "\n", "where\n", "\n", "$$\\beta[0, k] = \\begin{cases}\n", "\\langle F(x) \\rangle ,& \\text{if } k = 0\\\\\n", "0, & \\text{otherwise}\n", "\\end{cases} $$\n", "\n", "This removes the redundancies from the regression, and we can reproduce the filter." ] }, { "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
turbomanage/training-data-analyst
courses/machine_learning/deepdive2/structured/labs/3c_bqml_dnn_babyweight.ipynb
1
13294
{ "cells": [ { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "3o8Qof7Cy165" }, "source": [ "# LAB 3c: BigQuery ML Model Deep Neural Network.\n", "\n", "**Learning Objectives**\n", "\n", "1. Create and evaluate DNN model with BigQuery ML\n", "1. Create and evaluate DNN model with feature engineering with ML.TRANSFORM.\n", "1. Calculate predictions with BigQuery's ML.PREDICT\n", "\n", "\n", "## Introduction \n", "In this notebook, we will create multiple deep neural network models to predict the weight of a baby before it is born, using first no feature engineering and then the feature engineering from the previous lab using BigQuery ML.\n", "\n", "We will create and evaluate a DNN model using BigQuery ML, with and without feature engineering using BigQuery's ML.TRANSFORM and calculate predictions with BigQuery's ML.PREDICT. If you need a refresher, you can go back and look how we made a baseline model in the notebook [BQML Baseline Model](../solutions/3a_bqml_baseline_babyweight.ipynb) or how we combined linear models with feature engineering in the notebook [BQML Linear Models with Feature Engineering](../solutions/3b_bqml_linear_transform_babyweight.ipynb).\n", "\n", "Each learning objective will correspond to a __#TODO__ in this student lab notebook -- try to complete this notebook first and then review the [solution notebook](../solutions/3c_bqml_dnn_babyweight.ipynb)." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "hJ7ByvoXzpVI" }, "source": [ "## Load necessary libraries" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "mC9K9Dpx1ztf" }, "source": [ "Check that the Google BigQuery library is installed and if not, install it. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 609 }, "colab_type": "code", "id": "RZUQtASG10xO", "outputId": "5612d6b0-9730-476a-a28f-8fdc14f4ecde" }, "outputs": [], "source": [ "%%bash\n", "sudo pip freeze | grep google-cloud-bigquery==1.6.1 || \\\n", "sudo pip install google-cloud-bigquery==1.6.1" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "clnaaqQsXkwC" }, "source": [ "## Verify tables exist\n", "\n", "Run the following cells to verify that we have previously created the dataset and data tables. If not, go back to lab [1b_prepare_data_babyweight](../solutions/1b_prepare_data_babyweight.ipynb) to create them." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%bigquery\n", "-- LIMIT 0 is a free query; this allows us to check that the table exists.\n", "SELECT * FROM babyweight.babyweight_data_train\n", "LIMIT 0" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%bigquery\n", "-- LIMIT 0 is a free query; this allows us to check that the table exists.\n", "SELECT * FROM babyweight.babyweight_data_eval\n", "LIMIT 0" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Lab Task #1: Model 4: Increase complexity of model using DNN_REGRESSOR\n", "\n", "DNN_REGRESSOR is a new regression model_type vs. the LINEAR_REG that we have been using in previous labs.\n", "\n", "* MODEL_TYPE=\"DNN_REGRESSOR\"\n", "\n", "* hidden_units: List of hidden units per layer; all layers are fully connected. Number of elements in the array will be the number of hidden layers. The default value for hidden_units is [Min(128, N / (𝜶(Ni+No)))] (1 hidden layer), with N the training data size, Ni, No the input layer and output layer units, respectively, 𝜶 is constant with value 10. The upper bound of the rule will make sure the model won’t be over fitting. Note that, we currently have a model size limitation to 256MB.\n", "\n", "* dropout: Probability to drop a given coordinate during training; dropout is a very common technique to avoid overfitting in DNNs. The default value is zero, which means we will not drop out any coordinate during training.\n", "\n", "* batch_size: Number of samples that will be served to train the network for each sub iteration. The default value is Min(1024, num_examples) to balance the training speed and convergence. Serving all training data in each sub-iteration may lead to convergence issues, and is not advised." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Create DNN_REGRESSOR model\n", "\n", "Change model type to use DNN_REGRESSOR, add a list of integer HIDDEN_UNITS, and add an integer BATCH_SIZE.\n", "* Hint: Create a model_4." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%bigquery\n", "CREATE OR REPLACE MODEL\n", " babyweight.model_4\n", "OPTIONS (\n", " # TODO: Add DNN options\n", " INPUT_LABEL_COLS=[\"weight_pounds\"],\n", " DATA_SPLIT_METHOD=\"NO_SPLIT\") AS\n", "\n", "SELECT\n", " # TODO: Add base features and label\n", "FROM\n", " babyweight.babyweight_data_train" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "AVPXGKZ374v7" }, "source": [ "### Get training information and evaluate" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's first look at our training statistics." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%bigquery\n", "SELECT * FROM ML.TRAINING_INFO(MODEL babyweight.model_4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's evaluate our trained model on our eval dataset." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%bigquery\n", "SELECT\n", " *\n", "FROM\n", " ML.EVALUATE(MODEL babyweight.model_4,\n", " (\n", " SELECT\n", " weight_pounds,\n", " is_male,\n", " mother_age,\n", " plurality,\n", " gestation_weeks\n", " FROM\n", " babyweight.babyweight_data_eval\n", " ))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's use our evaluation's `mean_squared_error` to calculate our model's RMSE." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%bigquery\n", "SELECT\n", " SQRT(mean_squared_error) AS rmse\n", "FROM\n", " ML.EVALUATE(MODEL babyweight.model_4,\n", " (\n", " SELECT\n", " weight_pounds,\n", " is_male,\n", " mother_age,\n", " plurality,\n", " gestation_weeks\n", " FROM\n", " babyweight.babyweight_data_eval\n", " ))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Lab Task #2: Final Model: Apply the TRANSFORM clause\n", "\n", "Before we perform our prediction, we should encapsulate the entire feature set in a TRANSFORM clause as we did in the last notebook. This way we can have the same transformations applied for training and prediction without modifying the queries." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's apply the TRANSFORM clause to the final model and run the query." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%bigquery\n", "CREATE OR REPLACE MODEL\n", " babyweight.final_model\n", "\n", "TRANSFORM(\n", " weight_pounds,\n", " is_male,\n", " mother_age,\n", " plurality,\n", " gestation_weeks,\n", " # TODO: Add FEATURE CROSS of:\n", " # is_male, bucketed_mother_age, plurality, and bucketed_gestation_weeks\n", "\n", "OPTIONS (\n", " # TODO: Add DNN options\n", " INPUT_LABEL_COLS=[\"weight_pounds\"],\n", " DATA_SPLIT_METHOD=\"NO_SPLIT\") AS\n", "\n", "SELECT\n", " *\n", "FROM\n", " babyweight.babyweight_data_train" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's first look at our training statistics." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%bigquery\n", "SELECT * FROM ML.TRAINING_INFO(MODEL babyweight.final_model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's evaluate our trained model on our eval dataset." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%bigquery\n", "SELECT\n", " *\n", "FROM\n", " ML.EVALUATE(MODEL babyweight.final_model,\n", " (\n", " SELECT\n", " *\n", " FROM\n", " babyweight.babyweight_data_eval\n", " ))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's use our evaluation's `mean_squared_error` to calculate our model's RMSE." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%bigquery\n", "SELECT\n", " SQRT(mean_squared_error) AS rmse\n", "FROM\n", " ML.EVALUATE(MODEL babyweight.final_model,\n", " (\n", " SELECT\n", " *\n", " FROM\n", " babyweight.babyweight_data_eval\n", " ))" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "DIuFS56o_F2a" }, "source": [ "## Lab Task #3: Predict with final model.\n", "\n", "Now that you have evaluated your model, the next step is to use it to predict the weight of a baby before it is born, using BigQuery `ML.PREDICT` function." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Predict from final model using an example from original dataset" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", "id": "29cuS1CbADE3" }, "outputs": [], "source": [ "%%bigquery\n", "SELECT\n", " *\n", "FROM\n", " ML.PREDICT(MODEL babyweight.final_model,\n", " (\n", " SELECT\n", " # TODO Add base features example from original dataset\n", " ))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Modify above prediction query using example from simulated dataset\n", "\n", "Use the feature values you made up above, however set is_male to \"Unknown\" and plurality to \"Multiple(2+)\". This is simulating us not knowing the gender or the exact plurality." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%bigquery\n", "SELECT\n", " *\n", "FROM\n", " ML.PREDICT(MODEL babyweight.final_model,\n", " (\n", " SELECT\n", " # TODO Add base features example from simulated dataset\n", " ))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Lab Summary: \n", "In this lab, we created and evaluated a DNN model using BigQuery ML, with and without feature engineering using BigQuery's ML.TRANSFORM and calculated predictions with BigQuery's ML.PREDICT." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Copyright 2019 Google Inc. Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "colab": { "collapsed_sections": [], "name": "10.3.2019-DRAFT-_1 - FeatEnG - LABS.ipynb", "provenance": [], "toc_visible": true }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.3" } }, "nbformat": 4, "nbformat_minor": 4 }
apache-2.0
kubeflow/kfp-tekton-backend
samples/core/ai_platform/ai_platform.ipynb
1
11162
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Chicago Crime Prediction Pipeline\n", "\n", "An example notebook that demonstrates how to:\n", "* Download data from BigQuery\n", "* Create a Kubeflow pipeline\n", "* Include Google Cloud AI Platform components to train and deploy the model in the pipeline\n", "* Submit a job for execution\n", "* Query the final deployed model\n", "\n", "The model forecasts how many crimes are expected to be reported the next day, based on how many were reported over the previous `n` days." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Imports" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%capture\n", "\n", "# Install the SDK (Uncomment the code if the SDK is not installed before)\n", "!python3 -m pip install 'kfp>=0.1.31' --quiet\n", "!python3 -m pip install pandas --upgrade -q\n", "\n", "# Restart the kernel for changes to take effect" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import json\n", "\n", "import kfp\n", "import kfp.components as comp\n", "import kfp.dsl as dsl\n", "\n", "import pandas as pd\n", "\n", "import time" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Pipeline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Constants" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "parameters" ] }, "outputs": [], "source": [ "# Required Parameters\n", "project_id = '<ADD GCP PROJECT HERE>'\n", "output = 'gs://<ADD STORAGE LOCATION HERE>' # No ending slash\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Optional Parameters\n", "REGION = 'us-central1'\n", "RUNTIME_VERSION = '1.13'\n", "PACKAGE_URIS=json.dumps(['gs://chicago-crime/chicago_crime_trainer-0.0.tar.gz'])\n", "TRAINER_OUTPUT_GCS_PATH = output + '/train/output/' + str(int(time.time())) + '/'\n", "DATA_GCS_PATH = output + '/reports.csv'\n", "PYTHON_MODULE = 'trainer.task'\n", "PIPELINE_NAME = 'Chicago Crime Prediction'\n", "PIPELINE_FILENAME_PREFIX = 'chicago'\n", "PIPELINE_DESCRIPTION = ''\n", "MODEL_NAME = 'chicago_pipeline_model' + str(int(time.time()))\n", "MODEL_VERSION = 'chicago_pipeline_model_v1' + str(int(time.time()))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Download data\n", "\n", "Define a download function that uses the BigQuery component" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "bigquery_query_op = comp.load_component_from_url(\n", " 'https://raw.githubusercontent.com/kubeflow/pipelines/01a23ae8672d3b18e88adf3036071496aca3552d/components/gcp/bigquery/query/component.yaml')\n", "\n", "QUERY = \"\"\"\n", " SELECT count(*) as count, TIMESTAMP_TRUNC(date, DAY) as day\n", " FROM `bigquery-public-data.chicago_crime.crime`\n", " GROUP BY day\n", " ORDER BY day\n", "\"\"\"\n", "\n", "def download(project_id, data_gcs_path):\n", "\n", " return bigquery_query_op(\n", " query=QUERY,\n", " project_id=project_id,\n", " output_gcs_path=data_gcs_path\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Train the model\n", "\n", "Run training code that will pre-process the data and then submit a training job to the AI Platform." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "mlengine_train_op = comp.load_component_from_url(\n", " 'https://raw.githubusercontent.com/kubeflow/pipelines/01a23ae8672d3b18e88adf3036071496aca3552d/components/gcp/ml_engine/train/component.yaml')\n", "\n", "def train(project_id,\n", " trainer_args,\n", " package_uris,\n", " trainer_output_gcs_path,\n", " gcs_working_dir,\n", " region,\n", " python_module,\n", " runtime_version):\n", "\n", " return mlengine_train_op(\n", " project_id=project_id, \n", " python_module=python_module,\n", " package_uris=package_uris,\n", " region=region,\n", " args=trainer_args,\n", " job_dir=trainer_output_gcs_path,\n", " runtime_version=runtime_version\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Deploy model\n", "\n", "Deploy the model with the ID given from the training step" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "mlengine_deploy_op = comp.load_component_from_url(\n", " 'https://raw.githubusercontent.com/kubeflow/pipelines/01a23ae8672d3b18e88adf3036071496aca3552d/components/gcp/ml_engine/deploy/component.yaml')\n", "\n", "def deploy(\n", " project_id,\n", " model_uri,\n", " model_id,\n", " model_version,\n", " runtime_version):\n", " \n", " return mlengine_deploy_op(\n", " model_uri=model_uri,\n", " project_id=project_id, \n", " model_id=model_id, \n", " version_id=model_version, \n", " runtime_version=runtime_version, \n", " replace_existing_version=True, \n", " set_default=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Define pipeline" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "@dsl.pipeline(\n", " name=PIPELINE_NAME,\n", " description=PIPELINE_DESCRIPTION\n", ")\n", "\n", "def pipeline(\n", " data_gcs_path=DATA_GCS_PATH,\n", " gcs_working_dir=output,\n", " project_id=project_id,\n", " python_module=PYTHON_MODULE,\n", " region=REGION,\n", " runtime_version=RUNTIME_VERSION,\n", " package_uris=PACKAGE_URIS,\n", " trainer_output_gcs_path=TRAINER_OUTPUT_GCS_PATH,\n", "): \n", " download_task = download(project_id,\n", " data_gcs_path)\n", "\n", " train_task = train(project_id,\n", " json.dumps(\n", " ['--data-file-url',\n", " '%s' % download_task.outputs['output_gcs_path'],\n", " '--job-dir',\n", " output]\n", " ),\n", " package_uris,\n", " trainer_output_gcs_path,\n", " gcs_working_dir,\n", " region,\n", " python_module,\n", " runtime_version)\n", " \n", " deploy_task = deploy(project_id,\n", " train_task.outputs['job_dir'],\n", " MODEL_NAME,\n", " MODEL_VERSION,\n", " runtime_version) \n", " return True\n", "\n", "# Reference for invocation later\n", "pipeline_func = pipeline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Submit the pipeline for execution" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pipeline = kfp.Client().create_run_from_pipeline_func(pipeline, arguments={})\n", "\n", "# Run the pipeline on a separate Kubeflow Cluster instead\n", "# (use if your notebook is not running in Kubeflow - e.x. if using AI Platform Notebooks)\n", "# pipeline = kfp.Client(host='<ADD KFP ENDPOINT HERE>').create_run_from_pipeline_func(pipeline, arguments={})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Wait for the pipeline to finish" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "run_detail = pipeline.wait_for_run_completion(timeout=1800)\n", "print(run_detail.run.status)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Use the deployed model to predict (online prediction)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import os\n", "os.environ['MODEL_NAME'] = MODEL_NAME\n", "os.environ['MODEL_VERSION'] = MODEL_VERSION" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create normalized input representing 14 days prior to prediction day." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%writefile test.json\n", "{\"lstm_input\": [[-1.24344569, -0.71910112, -0.86641698, -0.91635456, -1.04868914, -1.01373283, -0.7690387, -0.71910112, -0.86641698, -0.91635456, -1.04868914, -1.01373283, -0.7690387 , -0.90387016]]}" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "!gcloud ai-platform predict --model=$MODEL_NAME --version=$MODEL_VERSION --json-instances=test.json" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Examine cloud services invoked by the pipeline\n", "- BigQuery query: https://console.cloud.google.com/bigquery?page=queries (click on 'Project History')\n", "- AI Platform training job: https://console.cloud.google.com/ai-platform/jobs\n", "- AI Platform model serving: https://console.cloud.google.com/ai-platform/models\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Clean models" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# !gcloud ai-platform versions delete $MODEL_VERSION --model $MODEL_NAME\n", "# !gcloud ai-platform models delete $MODEL_NAME" ] } ], "metadata": { "celltoolbar": "Tags", "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.7" }, "pycharm": { "stem_cell": { "cell_type": "raw", "metadata": { "collapsed": false }, "source": [] } } }, "nbformat": 4, "nbformat_minor": 2 }
apache-2.0
JasperHG90/reclaimnaija-data
Elections_2015/python/.ipynb_checkpoints/reclaimnaija2015-checkpoint.ipynb
1
181
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mit
vandy-astro-hacks/test-repository
yt_intro.ipynb
1
46589
{ "metadata": { "name": "yt_intro" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "What is yt?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The yt [docs](http://yt-project.org/docs/dev/index.html) give more in depth, but overall yt provides tools for **analyzing numerical simulations produced by a wide variety of codes** including both particle based (e.g. Gadget-2, Gasoline) and mesh based (e.g. ENZO). Using yt you can\n", "\n", "* load simulations snapshots for easy access to fields\n", "* keep track of units\n", "* calculate additional fields and statistics\n", "* plot\n" ] }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Installing yt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For particle codes (which most of us use), we need the development branch. Full instructions for other installation methods can be found [here](http://yt-project.org/docs/dev/installing.html).\n", "\n", "First, clone the repository using mercurial\n", "\n", " $ hg clone https://bitbucket.org/yt_analysis/yt\n", " $ cd yt\n", " $ hg update yt\n", " $ python setup.py install --user --prefix=\n", "\n", "If you are installing on your own machine, leave off the `--user --prefix=`.\n", "\n", "Then open python and make sure you can import yt." ] }, { "cell_type": "code", "collapsed": false, "input": [ "import os\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": "*" }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you got a long messages about conflicting versions of HDF5 that ended with \n", "\n", " Bye...\n", " Aborted\n", "\n", "this is OK. To get around this for now, simple set the environment variable `HDF5_DISABLE_VERSION_CHECK` to 1. The following can be used to accomplish this..." ] }, { "cell_type": "code", "collapsed": false, "input": [ "import os\n", "os.environ[\"HDF5_DISABLE_VERSION_CHECK\"] = \"1\"" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then try to import yt again" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import yt" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Getting the Test Data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "yt provides test data for a wide variety of simulations [here](http://yt-project.org/data/). For this tutorial, we will use three test datasets including two SPH snapshots (one Gadget isolated galaxy and one Gasoline cosmological box) and one ENZO snapshot. \n", "\n", "In your home directory, create a new directory called `yt_datasets`. \n", "\n", " $ cd\n", " $ mkdir yt_datasets\n", "\n", "Then download the `GadgetDiskGalaxy`, `agora_1e11.00400`, and `enzo_tiny_cosmology` data sets from the [yt dataset page](http://yt-project.org/data/). Note: this can take a while...\n", "\n", " $ cd yt_datasets\n", " $ wget http://yt-project.org/data/GadgetDiskGalaxy.tar.gz\n", " $ wget http://yt-project.org/data/agora_1e11.00400.tar.gz\n", " $ wget http://yt-project.org/data/enzo_tiny_cosmology.tar.gz\n", "\n", "and unpack the data sets\n", "\n", " $ tar -xvzf GadgetDiskGalaxy.tar.gz\n", " $ tar -xvzf agora_1e11.00400.tar.gz\n", " $ tar -xvzf enzo_tiny_cosmology.tar.gz\n", "\n", "Alternately, you can use the following kernel after setting `download_datasets = True`." ] }, { "cell_type": "code", "collapsed": false, "input": [ "download_datasets = False\n", "if download_datasets:\n", " !curl -sSO http://yt-project.org/data/GadgetDiskGalaxy.tar.gz\n", " print \"Got GadgetDiskGalaxy\"\n", " !tar xzf GadgetDiskGalaxy.tar.gz\n", " \n", " !curl -sSO http://yt-project.org/data/agora_1e11.00400.tar.gz\n", " print \"agora_1e11.00400\"\n", " !tar xzf agora_1e11.00400.tar.gz\n", " \n", " !curl -sSO http://yt-project.org/data/enzo_tiny_cosmology.tar.gz\n", " print \"Got enzo_tiny_cosmology\"\n", " !tar xzf enzo_tiny_cosmology.tar.gz\n", " \n", " print \"All done!\"" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Loading Data" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import yt\n", "ds = yt.load(\"/home/langmm/yt_datasets/GadgetDiskGalaxy/snapshot_200.hdf5\")" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "yt : [INFO ] 2015-06-25 14:00:28,668 Calculating time from 3.448e-01 to be 1.108e+17 seconds\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "yt : [INFO ] 2015-06-25 14:00:28,669 Assuming length units are in kpc/h (comoving)\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "yt : [INFO ] 2015-06-25 14:00:28,804 Parameters: current_time = 1.10758107325e+17 s\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "yt : [INFO ] 2015-06-25 14:00:28,805 Parameters: domain_dimensions = [2 2 2]\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "yt : [INFO ] 2015-06-25 14:00:28,806 Parameters: domain_left_edge = [ 0. 0. 0.]\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "yt : [INFO ] 2015-06-25 14:00:28,808 Parameters: domain_right_edge = [ 64000. 64000. 64000.]\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "yt : [INFO ] 2015-06-25 14:00:28,809 Parameters: cosmological_simulation = 1\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "yt : [INFO ] 2015-06-25 14:00:28,810 Parameters: current_redshift = 1.89999652869\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "yt : [INFO ] 2015-06-25 14:00:28,811 Parameters: omega_lambda = 0.721\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "yt : [INFO ] 2015-06-25 14:00:28,812 Parameters: omega_matter = 0.279\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "yt : [INFO ] 2015-06-25 14:00:28,812 Parameters: hubble_constant = 0.7\n" ] } ], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can see a list of available particle information called fields, that can be loaded from the snapshot" ] }, { "cell_type": "code", "collapsed": false, "input": [ "ds.field_list" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "yt : [INFO ] 2015-06-25 14:01:31,971 Allocating for 1.191e+07 particles\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "yt : [INFO ] 2015-06-25 14:01:34,771 Identified 8.088e+05 octs\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "yt : [INFO ] 2015-06-25 14:01:35,031 Loading field plugins.\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "yt : [INFO ] 2015-06-25 14:01:35,032 Loaded angular_momentum (8 new fields)\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "yt : [INFO ] 2015-06-25 14:01:35,033 Loaded astro (15 new fields)\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "yt : [INFO ] 2015-06-25 14:01:35,033 Loaded cosmology (22 new fields)\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "yt : [INFO ] 2015-06-25 14:01:35,034 Loaded fluid (63 new fields)\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "yt : [INFO ] 2015-06-25 14:01:35,035 Loaded fluid_vector (95 new fields)\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "yt : [INFO ] 2015-06-25 14:01:35,036 Loaded geometric (111 new fields)\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "yt : [INFO ] 2015-06-25 14:01:35,037 Loaded local (111 new fields)\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "yt : [INFO ] 2015-06-25 14:01:35,037 Loaded magnetic_field (119 new fields)\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "yt : [INFO ] 2015-06-25 14:01:35,038 Loaded my_plugins (119 new fields)\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "yt : [INFO ] 2015-06-25 14:01:35,040 Loaded species (153 new fields)\n" ] }, { "output_type": "pyout", "prompt_number": 6, "text": [ "[('PartType0', 'ArtificialViscosity'),\n", " ('PartType0', 'Coordinates'),\n", " ('PartType0', 'Density'),\n", " ('PartType0', 'ElectronAbundance'),\n", " ('PartType0', 'InternalEnergy'),\n", " ('PartType0', 'Masses'),\n", " ('PartType0', 'Metallicity_00'),\n", " ('PartType0', 'Metallicity_01'),\n", " ('PartType0', 'Metallicity_02'),\n", " ('PartType0', 'Metallicity_03'),\n", " ('PartType0', 'Metallicity_04'),\n", " ('PartType0', 'Metallicity_05'),\n", " ('PartType0', 'Metallicity_06'),\n", " ('PartType0', 'Metallicity_07'),\n", " ('PartType0', 'Metallicity_08'),\n", " ('PartType0', 'Metallicity_09'),\n", " ('PartType0', 'Metallicity_10'),\n", " ('PartType0', 'Metallicity_11'),\n", " ('PartType0', 'NeutralHydrogenAbundance'),\n", " ('PartType0', 'ParticleIDs'),\n", " ('PartType0', 'SmoothingLength'),\n", " ('PartType0', 'StarFormationRate'),\n", " ('PartType0', 'Velocities'),\n", " ('PartType1', 'Masses'),\n", " ('PartType1', 'Coordinates'),\n", " ('PartType1', 'ParticleIDs'),\n", " ('PartType1', 'Velocities'),\n", " ('PartType2', 'Coordinates'),\n", " ('PartType2', 'Masses'),\n", " ('PartType2', 'ParticleIDs'),\n", " ('PartType2', 'Velocities'),\n", " ('PartType4', 'Coordinates'),\n", " ('PartType4', 'Masses'),\n", " ('PartType4', 'Metallicity_00'),\n", " ('PartType4', 'Metallicity_01'),\n", " ('PartType4', 'Metallicity_02'),\n", " ('PartType4', 'Metallicity_03'),\n", " ('PartType4', 'Metallicity_04'),\n", " ('PartType4', 'Metallicity_05'),\n", " ('PartType4', 'Metallicity_06'),\n", " ('PartType4', 'Metallicity_07'),\n", " ('PartType4', 'Metallicity_08'),\n", " ('PartType4', 'Metallicity_09'),\n", " ('PartType4', 'Metallicity_10'),\n", " ('PartType4', 'Metallicity_11'),\n", " ('PartType4', 'ParticleIDs'),\n", " ('PartType4', 'StellarFormationTime'),\n", " ('PartType4', 'Velocities'),\n", " ('PartType5', 'BH_AccreationLength'),\n", " ('PartType5', 'BH_Mass'),\n", " ('PartType5', 'BH_Mass_AlphaDisk'),\n", " ('PartType5', 'BH_Mdot'),\n", " ('PartType5', 'Coordinates'),\n", " ('PartType5', 'Masses'),\n", " ('PartType5', 'ParticleIDs'),\n", " ('PartType5', 'StellarFormationTime'),\n", " ('PartType5', 'Velocities'),\n", " ('all', 'Masses'),\n", " ('all', 'Velocities'),\n", " ('all', 'Coordinates'),\n", " ('all', 'ParticleIDs')]" ] } ], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": {}, "source": [ "As well as fields that can be derived" ] }, { "cell_type": "code", "collapsed": false, "input": [ "ds.derived_field_list" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 7, "text": [ "[('PartType0', 'ArtificialViscosity'),\n", " ('PartType0', 'C_density'),\n", " ('PartType0', 'C_fraction'),\n", " ('PartType0', 'C_mass'),\n", " ('PartType0', 'C_number_density'),\n", " ('PartType0', 'Ca_density'),\n", " ('PartType0', 'Ca_fraction'),\n", " ('PartType0', 'Ca_mass'),\n", " ('PartType0', 'Ca_number_density'),\n", " ('PartType0', 'Coordinates'),\n", " ('PartType0', 'Density'),\n", " ('PartType0', 'ElectronAbundance'),\n", " ('PartType0', 'Fe_density'),\n", " ('PartType0', 'Fe_fraction'),\n", " ('PartType0', 'Fe_mass'),\n", " ('PartType0', 'Fe_number_density'),\n", " ('PartType0', 'He_density'),\n", " ('PartType0', 'He_fraction'),\n", " ('PartType0', 'He_mass'),\n", " ('PartType0', 'He_number_density'),\n", " ('PartType0', 'InternalEnergy'),\n", " ('PartType0', 'Masses'),\n", " ('PartType0', 'Metallicity_00'),\n", " ('PartType0', 'Metallicity_01'),\n", " ('PartType0', 'Metallicity_02'),\n", " ('PartType0', 'Metallicity_03'),\n", " ('PartType0', 'Metallicity_04'),\n", " ('PartType0', 'Metallicity_05'),\n", " ('PartType0', 'Metallicity_06'),\n", " ('PartType0', 'Metallicity_07'),\n", " ('PartType0', 'Metallicity_08'),\n", " ('PartType0', 'Metallicity_09'),\n", " ('PartType0', 'Metallicity_10'),\n", " ('PartType0', 'Metallicity_11'),\n", " ('PartType0', 'Mg_density'),\n", " ('PartType0', 'Mg_fraction'),\n", " ('PartType0', 'Mg_mass'),\n", " ('PartType0', 'Mg_number_density'),\n", " ('PartType0', 'N_density'),\n", " ('PartType0', 'N_fraction'),\n", " ('PartType0', 'N_mass'),\n", " ('PartType0', 'N_number_density'),\n", " ('PartType0', 'Ne_density'),\n", " ('PartType0', 'Ne_fraction'),\n", " ('PartType0', 'Ne_mass'),\n", " ('PartType0', 'Ne_number_density'),\n", " ('PartType0', 'NeutralHydrogenAbundance'),\n", " ('PartType0', 'O_density'),\n", " ('PartType0', 'O_fraction'),\n", " ('PartType0', 'O_mass'),\n", " ('PartType0', 'O_number_density'),\n", " ('PartType0', 'ParticleIDs'),\n", " ('PartType0', 'S_density'),\n", " ('PartType0', 'S_fraction'),\n", " ('PartType0', 'S_mass'),\n", " ('PartType0', 'S_number_density'),\n", " ('PartType0', 'Si_density'),\n", " ('PartType0', 'Si_fraction'),\n", " ('PartType0', 'Si_mass'),\n", " ('PartType0', 'Si_number_density'),\n", " ('PartType0', 'SmoothingLength'),\n", " ('PartType0', 'StarFormationRate'),\n", " ('PartType0', 'Temperature'),\n", " ('PartType0', 'Velocities'),\n", " ('PartType0', 'density'),\n", " ('PartType0', 'mesh_id'),\n", " ('PartType0', 'metallicity'),\n", " ('PartType0', 'particle_angular_momentum_magnitude'),\n", " ('PartType0', 'particle_angular_momentum_x'),\n", " ('PartType0', 'particle_angular_momentum_y'),\n", " ('PartType0', 'particle_angular_momentum_z'),\n", " ('PartType0', 'particle_cylindrical_velocity_theta'),\n", " ('PartType0', 'particle_cylindrical_velocity_z'),\n", " ('PartType0', 'particle_index'),\n", " ('PartType0', 'particle_mass'),\n", " ('PartType0', 'particle_ones'),\n", " ('PartType0', 'particle_position'),\n", " ('PartType0', 'particle_position_cylindrical_radius'),\n", " ('PartType0', 'particle_position_cylindrical_theta'),\n", " ('PartType0', 'particle_position_cylindrical_z'),\n", " ('PartType0', 'particle_position_relative'),\n", " ('PartType0', 'particle_position_relative_x'),\n", " ('PartType0', 'particle_position_relative_y'),\n", " ('PartType0', 'particle_position_relative_z'),\n", " ('PartType0', 'particle_position_spherical_phi'),\n", " ('PartType0', 'particle_position_spherical_radius'),\n", " ('PartType0', 'particle_position_spherical_theta'),\n", " ('PartType0', 'particle_position_x'),\n", " ('PartType0', 'particle_position_y'),\n", " ('PartType0', 'particle_position_z'),\n", " ('PartType0', 'particle_radial_velocity'),\n", " ('PartType0', 'particle_radius'),\n", " ('PartType0', 'particle_specific_angular_momentum_magnitude'),\n", " ('PartType0', 'particle_specific_angular_momentum_x'),\n", " ('PartType0', 'particle_specific_angular_momentum_y'),\n", " ('PartType0', 'particle_specific_angular_momentum_z'),\n", " ('PartType0', 'particle_spherical_position_phi'),\n", " ('PartType0', 'particle_spherical_position_radius'),\n", " ('PartType0', 'particle_spherical_position_theta'),\n", " ('PartType0', 'particle_spherical_velocity_phi'),\n", " ('PartType0', 'particle_spherical_velocity_radius'),\n", " ('PartType0', 'particle_spherical_velocity_theta'),\n", " ('PartType0', 'particle_velocity'),\n", " ('PartType0', 'particle_velocity_cylindrical_radius'),\n", " ('PartType0', 'particle_velocity_cylindrical_theta'),\n", " ('PartType0', 'particle_velocity_cylindrical_z'),\n", " ('PartType0', 'particle_velocity_magnitude'),\n", " ('PartType0', 'particle_velocity_relative'),\n", " ('PartType0', 'particle_velocity_relative_x'),\n", " ('PartType0', 'particle_velocity_relative_y'),\n", " ('PartType0', 'particle_velocity_relative_z'),\n", " ('PartType0', 'particle_velocity_spherical_phi'),\n", " ('PartType0', 'particle_velocity_spherical_radius'),\n", " ('PartType0', 'particle_velocity_spherical_theta'),\n", " ('PartType0', 'particle_velocity_x'),\n", " ('PartType0', 'particle_velocity_y'),\n", " ('PartType0', 'particle_velocity_z'),\n", " ('PartType0', 'smoothing_length'),\n", " ('PartType0', 'thermal_energy'),\n", " ('PartType1', 'Coordinates'),\n", " ('PartType1', 'Masses'),\n", " ('PartType1', 'ParticleIDs'),\n", " ('PartType1', 'Velocities'),\n", " ('PartType1', 'mesh_id'),\n", " ('PartType1', 'particle_angular_momentum_magnitude'),\n", " ('PartType1', 'particle_angular_momentum_x'),\n", " ('PartType1', 'particle_angular_momentum_y'),\n", " ('PartType1', 'particle_angular_momentum_z'),\n", " ('PartType1', 'particle_cylindrical_velocity_theta'),\n", " ('PartType1', 'particle_cylindrical_velocity_z'),\n", " ('PartType1', 'particle_index'),\n", " ('PartType1', 'particle_mass'),\n", " ('PartType1', 'particle_ones'),\n", " ('PartType1', 'particle_position'),\n", " ('PartType1', 'particle_position_cylindrical_radius'),\n", " ('PartType1', 'particle_position_cylindrical_theta'),\n", " ('PartType1', 'particle_position_cylindrical_z'),\n", " ('PartType1', 'particle_position_relative'),\n", " ('PartType1', 'particle_position_relative_x'),\n", " ('PartType1', 'particle_position_relative_y'),\n", " ('PartType1', 'particle_position_relative_z'),\n", " ('PartType1', 'particle_position_spherical_phi'),\n", " ('PartType1', 'particle_position_spherical_radius'),\n", " ('PartType1', 'particle_position_spherical_theta'),\n", " ('PartType1', 'particle_position_x'),\n", " ('PartType1', 'particle_position_y'),\n", " ('PartType1', 'particle_position_z'),\n", " ('PartType1', 'particle_radial_velocity'),\n", " ('PartType1', 'particle_radius'),\n", " ('PartType1', 'particle_specific_angular_momentum_magnitude'),\n", " ('PartType1', 'particle_specific_angular_momentum_x'),\n", " ('PartType1', 'particle_specific_angular_momentum_y'),\n", " ('PartType1', 'particle_specific_angular_momentum_z'),\n", " ('PartType1', 'particle_spherical_position_phi'),\n", " ('PartType1', 'particle_spherical_position_radius'),\n", " ('PartType1', 'particle_spherical_position_theta'),\n", " ('PartType1', 'particle_spherical_velocity_phi'),\n", " ('PartType1', 'particle_spherical_velocity_radius'),\n", " ('PartType1', 'particle_spherical_velocity_theta'),\n", " ('PartType1', 'particle_velocity'),\n", " ('PartType1', 'particle_velocity_cylindrical_radius'),\n", " ('PartType1', 'particle_velocity_cylindrical_theta'),\n", " ('PartType1', 'particle_velocity_cylindrical_z'),\n", " ('PartType1', 'particle_velocity_magnitude'),\n", " ('PartType1', 'particle_velocity_relative'),\n", " ('PartType1', 'particle_velocity_relative_x'),\n", " ('PartType1', 'particle_velocity_relative_y'),\n", " ('PartType1', 'particle_velocity_relative_z'),\n", " ('PartType1', 'particle_velocity_spherical_phi'),\n", " ('PartType1', 'particle_velocity_spherical_radius'),\n", " ('PartType1', 'particle_velocity_spherical_theta'),\n", " ('PartType1', 'particle_velocity_x'),\n", " ('PartType1', 'particle_velocity_y'),\n", " ('PartType1', 'particle_velocity_z'),\n", " ('PartType2', 'Coordinates'),\n", " ('PartType2', 'Masses'),\n", " ('PartType2', 'ParticleIDs'),\n", " ('PartType2', 'Velocities'),\n", " ('PartType2', 'mesh_id'),\n", " ('PartType2', 'particle_angular_momentum_magnitude'),\n", " ('PartType2', 'particle_angular_momentum_x'),\n", " ('PartType2', 'particle_angular_momentum_y'),\n", " ('PartType2', 'particle_angular_momentum_z'),\n", " ('PartType2', 'particle_cylindrical_velocity_theta'),\n", " ('PartType2', 'particle_cylindrical_velocity_z'),\n", " ('PartType2', 'particle_index'),\n", " ('PartType2', 'particle_mass'),\n", " ('PartType2', 'particle_ones'),\n", " ('PartType2', 'particle_position'),\n", " ('PartType2', 'particle_position_cylindrical_radius'),\n", " ('PartType2', 'particle_position_cylindrical_theta'),\n", " ('PartType2', 'particle_position_cylindrical_z'),\n", " ('PartType2', 'particle_position_relative'),\n", " ('PartType2', 'particle_position_relative_x'),\n", " ('PartType2', 'particle_position_relative_y'),\n", " ('PartType2', 'particle_position_relative_z'),\n", " ('PartType2', 'particle_position_spherical_phi'),\n", " ('PartType2', 'particle_position_spherical_radius'),\n", " ('PartType2', 'particle_position_spherical_theta'),\n", " ('PartType2', 'particle_position_x'),\n", " ('PartType2', 'particle_position_y'),\n", " ('PartType2', 'particle_position_z'),\n", " ('PartType2', 'particle_radial_velocity'),\n", " ('PartType2', 'particle_radius'),\n", " ('PartType2', 'particle_specific_angular_momentum_magnitude'),\n", " ('PartType2', 'particle_specific_angular_momentum_x'),\n", " ('PartType2', 'particle_specific_angular_momentum_y'),\n", " ('PartType2', 'particle_specific_angular_momentum_z'),\n", " ('PartType2', 'particle_spherical_position_phi'),\n", " ('PartType2', 'particle_spherical_position_radius'),\n", " ('PartType2', 'particle_spherical_position_theta'),\n", " ('PartType2', 'particle_spherical_velocity_phi'),\n", " ('PartType2', 'particle_spherical_velocity_radius'),\n", " ('PartType2', 'particle_spherical_velocity_theta'),\n", " ('PartType2', 'particle_velocity'),\n", " ('PartType2', 'particle_velocity_cylindrical_radius'),\n", " ('PartType2', 'particle_velocity_cylindrical_theta'),\n", " ('PartType2', 'particle_velocity_cylindrical_z'),\n", " ('PartType2', 'particle_velocity_magnitude'),\n", " ('PartType2', 'particle_velocity_relative'),\n", " ('PartType2', 'particle_velocity_relative_x'),\n", " ('PartType2', 'particle_velocity_relative_y'),\n", " ('PartType2', 'particle_velocity_relative_z'),\n", " ('PartType2', 'particle_velocity_spherical_phi'),\n", " ('PartType2', 'particle_velocity_spherical_radius'),\n", " ('PartType2', 'particle_velocity_spherical_theta'),\n", " ('PartType2', 'particle_velocity_x'),\n", " ('PartType2', 'particle_velocity_y'),\n", " ('PartType2', 'particle_velocity_z'),\n", " ('PartType4', 'C_fraction'),\n", " ('PartType4', 'Ca_fraction'),\n", " ('PartType4', 'Coordinates'),\n", " ('PartType4', 'Fe_fraction'),\n", " ('PartType4', 'He_fraction'),\n", " ('PartType4', 'Masses'),\n", " ('PartType4', 'Metallicity_00'),\n", " ('PartType4', 'Metallicity_01'),\n", " ('PartType4', 'Metallicity_02'),\n", " ('PartType4', 'Metallicity_03'),\n", " ('PartType4', 'Metallicity_04'),\n", " ('PartType4', 'Metallicity_05'),\n", " ('PartType4', 'Metallicity_06'),\n", " ('PartType4', 'Metallicity_07'),\n", " ('PartType4', 'Metallicity_08'),\n", " ('PartType4', 'Metallicity_09'),\n", " ('PartType4', 'Metallicity_10'),\n", " ('PartType4', 'Metallicity_11'),\n", " ('PartType4', 'Mg_fraction'),\n", " ('PartType4', 'N_fraction'),\n", " ('PartType4', 'Ne_fraction'),\n", " ('PartType4', 'O_fraction'),\n", " ('PartType4', 'ParticleIDs'),\n", " ('PartType4', 'S_fraction'),\n", " ('PartType4', 'Si_fraction'),\n", " ('PartType4', 'StellarFormationTime'),\n", " ('PartType4', 'Velocities'),\n", " ('PartType4', 'mesh_id'),\n", " ('PartType4', 'metallicity'),\n", " ('PartType4', 'particle_angular_momentum_magnitude'),\n", " ('PartType4', 'particle_angular_momentum_x'),\n", " ('PartType4', 'particle_angular_momentum_y'),\n", " ('PartType4', 'particle_angular_momentum_z'),\n", " ('PartType4', 'particle_cylindrical_velocity_theta'),\n", " ('PartType4', 'particle_cylindrical_velocity_z'),\n", " ('PartType4', 'particle_index'),\n", " ('PartType4', 'particle_mass'),\n", " ('PartType4', 'particle_ones'),\n", " ('PartType4', 'particle_position'),\n", " ('PartType4', 'particle_position_cylindrical_radius'),\n", " ('PartType4', 'particle_position_cylindrical_theta'),\n", " ('PartType4', 'particle_position_cylindrical_z'),\n", " ('PartType4', 'particle_position_relative'),\n", " ('PartType4', 'particle_position_relative_x'),\n", " ('PartType4', 'particle_position_relative_y'),\n", " ('PartType4', 'particle_position_relative_z'),\n", " ('PartType4', 'particle_position_spherical_phi'),\n", " ('PartType4', 'particle_position_spherical_radius'),\n", " ('PartType4', 'particle_position_spherical_theta'),\n", " ('PartType4', 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'particle_velocity_cylindrical_theta'),\n", " ('PartType5', 'particle_velocity_cylindrical_z'),\n", " ('PartType5', 'particle_velocity_magnitude'),\n", " ('PartType5', 'particle_velocity_relative'),\n", " ('PartType5', 'particle_velocity_relative_x'),\n", " ('PartType5', 'particle_velocity_relative_y'),\n", " ('PartType5', 'particle_velocity_relative_z'),\n", " ('PartType5', 'particle_velocity_spherical_phi'),\n", " ('PartType5', 'particle_velocity_spherical_radius'),\n", " ('PartType5', 'particle_velocity_spherical_theta'),\n", " ('PartType5', 'particle_velocity_x'),\n", " ('PartType5', 'particle_velocity_y'),\n", " ('PartType5', 'particle_velocity_z'),\n", " ('all', 'Coordinates'),\n", " ('all', 'Masses'),\n", " ('all', 'ParticleIDs'),\n", " ('all', 'Velocities'),\n", " ('all', 'mesh_id'),\n", " ('all', 'particle_angular_momentum_magnitude'),\n", " ('all', 'particle_angular_momentum_x'),\n", " ('all', 'particle_angular_momentum_y'),\n", " ('all', 'particle_angular_momentum_z'),\n", " ('all', 'particle_cylindrical_velocity_theta'),\n", " ('all', 'particle_cylindrical_velocity_z'),\n", " ('all', 'particle_index'),\n", " ('all', 'particle_mass'),\n", " ('all', 'particle_ones'),\n", " ('all', 'particle_position'),\n", " ('all', 'particle_position_cylindrical_radius'),\n", " ('all', 'particle_position_cylindrical_theta'),\n", " ('all', 'particle_position_cylindrical_z'),\n", " ('all', 'particle_position_relative'),\n", " ('all', 'particle_position_relative_x'),\n", " ('all', 'particle_position_relative_y'),\n", " ('all', 'particle_position_relative_z'),\n", " ('all', 'particle_position_spherical_phi'),\n", " ('all', 'particle_position_spherical_radius'),\n", " ('all', 'particle_position_spherical_theta'),\n", " ('all', 'particle_position_x'),\n", " ('all', 'particle_position_y'),\n", " ('all', 'particle_position_z'),\n", " ('all', 'particle_radial_velocity'),\n", " ('all', 'particle_radius'),\n", " ('all', 'particle_specific_angular_momentum_magnitude'),\n", " ('all', 'particle_specific_angular_momentum_x'),\n", " ('all', 'particle_specific_angular_momentum_y'),\n", " ('all', 'particle_specific_angular_momentum_z'),\n", " ('all', 'particle_spherical_position_phi'),\n", " ('all', 'particle_spherical_position_radius'),\n", " ('all', 'particle_spherical_position_theta'),\n", " ('all', 'particle_spherical_velocity_phi'),\n", " ('all', 'particle_spherical_velocity_radius'),\n", " ('all', 'particle_spherical_velocity_theta'),\n", " ('all', 'particle_velocity'),\n", " ('all', 'particle_velocity_cylindrical_radius'),\n", " ('all', 'particle_velocity_cylindrical_theta'),\n", " ('all', 'particle_velocity_cylindrical_z'),\n", " ('all', 'particle_velocity_magnitude'),\n", " ('all', 'particle_velocity_relative'),\n", " ('all', 'particle_velocity_relative_x'),\n", " ('all', 'particle_velocity_relative_y'),\n", " ('all', 'particle_velocity_relative_z'),\n", " ('all', 'particle_velocity_spherical_phi'),\n", " ('all', 'particle_velocity_spherical_radius'),\n", " ('all', 'particle_velocity_spherical_theta'),\n", " ('all', 'particle_velocity_x'),\n", " ('all', 'particle_velocity_y'),\n", " ('all', 'particle_velocity_z'),\n", " ('deposit', 'PartType0_cic'),\n", " ('deposit', 'PartType0_cic_velocity_x'),\n", " ('deposit', 'PartType0_cic_velocity_y'),\n", " ('deposit', 'PartType0_cic_velocity_z'),\n", " ('deposit', 'PartType0_count'),\n", " ('deposit', 'PartType0_density'),\n", " ('deposit', 'PartType0_mass'),\n", " ('deposit', 'PartType0_nn_velocity_x'),\n", " ('deposit', 'PartType0_nn_velocity_y'),\n", " ('deposit', 'PartType0_nn_velocity_z'),\n", " ('deposit', 'PartType0_smoothed_C_fraction'),\n", " ('deposit', 'PartType0_smoothed_Fe_fraction'),\n", " ('deposit', 'PartType0_smoothed_He_fraction'),\n", " ('deposit', 'PartType0_smoothed_Mg_fraction'),\n", " ('deposit', 'PartType0_smoothed_N_fraction'),\n", " ('deposit', 'PartType0_smoothed_Ne_fraction'),\n", " ('deposit', 'PartType0_smoothed_O_fraction'),\n", " ('deposit', 'PartType0_smoothed_Si_fraction'),\n", " ('deposit', 'PartType0_smoothed_Temperature'),\n", " ('deposit', 'PartType0_smoothed_density'),\n", " ('deposit', 'PartType0_smoothed_metallicity'),\n", " ('deposit', 'PartType0_smoothed_particle_velocity_x'),\n", " ('deposit', 'PartType0_smoothed_particle_velocity_y'),\n", " ('deposit', 'PartType0_smoothed_particle_velocity_z'),\n", " ('deposit', 'PartType0_smoothed_thermal_energy'),\n", " ('deposit', 'PartType1_cic'),\n", " ('deposit', 'PartType1_cic_velocity_x'),\n", " ('deposit', 'PartType1_cic_velocity_y'),\n", " ('deposit', 'PartType1_cic_velocity_z'),\n", " ('deposit', 'PartType1_count'),\n", " ('deposit', 'PartType1_density'),\n", " ('deposit', 'PartType1_mass'),\n", " ('deposit', 'PartType1_nn_velocity_x'),\n", " ('deposit', 'PartType1_nn_velocity_y'),\n", " ('deposit', 'PartType1_nn_velocity_z'),\n", " ('deposit', 'PartType2_cic'),\n", " ('deposit', 'PartType2_cic_velocity_x'),\n", " ('deposit', 'PartType2_cic_velocity_y'),\n", " ('deposit', 'PartType2_cic_velocity_z'),\n", " ('deposit', 'PartType2_count'),\n", " ('deposit', 'PartType2_density'),\n", " ('deposit', 'PartType2_mass'),\n", " ('deposit', 'PartType2_nn_velocity_x'),\n", " ('deposit', 'PartType2_nn_velocity_y'),\n", " ('deposit', 'PartType2_nn_velocity_z'),\n", " ('deposit', 'PartType4_cic'),\n", " ('deposit', 'PartType4_cic_velocity_x'),\n", " ('deposit', 'PartType4_cic_velocity_y'),\n", " ('deposit', 'PartType4_cic_velocity_z'),\n", " ('deposit', 'PartType4_count'),\n", " ('deposit', 'PartType4_density'),\n", " ('deposit', 'PartType4_mass'),\n", " ('deposit', 'PartType4_nn_velocity_x'),\n", " ('deposit', 'PartType4_nn_velocity_y'),\n", " ('deposit', 'PartType4_nn_velocity_z'),\n", " ('deposit', 'PartType5_cic'),\n", " ('deposit', 'PartType5_cic_velocity_x'),\n", " ('deposit', 'PartType5_cic_velocity_y'),\n", " ('deposit', 'PartType5_cic_velocity_z'),\n", " ('deposit', 'PartType5_count'),\n", " ('deposit', 'PartType5_density'),\n", " ('deposit', 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'Mg_number_density'),\n", " ('gas', 'N_density'),\n", " ('gas', 'N_fraction'),\n", " ('gas', 'N_mass'),\n", " ('gas', 'N_number_density'),\n", " ('gas', 'Ne_density'),\n", " ('gas', 'Ne_fraction'),\n", " ('gas', 'Ne_mass'),\n", " ('gas', 'Ne_number_density'),\n", " ('gas', 'O_density'),\n", " ('gas', 'O_fraction'),\n", " ('gas', 'O_mass'),\n", " ('gas', 'O_number_density'),\n", " ('gas', 'Si_density'),\n", " ('gas', 'Si_fraction'),\n", " ('gas', 'Si_mass'),\n", " ('gas', 'Si_number_density'),\n", " ('gas', 'angular_momentum_magnitude'),\n", " ('gas', 'angular_momentum_x'),\n", " ('gas', 'angular_momentum_y'),\n", " ('gas', 'angular_momentum_z'),\n", " ('gas', 'averaged_density'),\n", " ('gas', 'baryon_overdensity'),\n", " ('gas', 'cell_mass'),\n", " ('gas', 'cutting_plane_velocity_x'),\n", " ('gas', 'cutting_plane_velocity_y'),\n", " ('gas', 'cutting_plane_velocity_z'),\n", " ('gas', 'cylindrical_radial_velocity'),\n", " ('gas', 'cylindrical_radial_velocity_absolute'),\n", " ('gas', 'cylindrical_tangential_velocity'),\n", " ('gas', 'cylindrical_tangential_velocity_absolute'),\n", " ('gas', 'density'),\n", " ('gas', 'density_gradient_magnitude'),\n", " ('gas', 'density_gradient_x'),\n", " ('gas', 'density_gradient_y'),\n", " ('gas', 'density_gradient_z'),\n", " ('gas', 'dynamical_time'),\n", " ('gas', 'entropy'),\n", " ('gas', 'kT'),\n", " ('gas', 'kinetic_energy'),\n", " ('gas', 'mazzotta_weighting'),\n", " ('gas', 'metallicity'),\n", " ('gas', 'radial_velocity'),\n", " ('gas', 'radial_velocity_absolute'),\n", " ('gas', 'shear'),\n", " ('gas', 'specific_angular_momentum_magnitude'),\n", " ('gas', 'specific_angular_momentum_x'),\n", " ('gas', 'specific_angular_momentum_y'),\n", " ('gas', 'specific_angular_momentum_z'),\n", " ('gas', 'sz_kinetic'),\n", " ('gas', 'szy'),\n", " ('gas', 'tangential_over_velocity_magnitude'),\n", " ('gas', 'tangential_velocity'),\n", " ('gas', 'temperature'),\n", " ('gas', 'thermal_energy'),\n", " ('gas', 'velocity_cylindrical_radius'),\n", " ('gas', 'velocity_cylindrical_theta'),\n", " ('gas', 'velocity_cylindrical_z'),\n", " ('gas', 'velocity_divergence'),\n", " ('gas', 'velocity_divergence_absolute'),\n", " ('gas', 'velocity_magnitude'),\n", " ('gas', 'velocity_spherical_phi'),\n", " ('gas', 'velocity_spherical_radius'),\n", " ('gas', 'velocity_spherical_theta'),\n", " ('gas', 'velocity_x'),\n", " ('gas', 'velocity_y'),\n", " ('gas', 'velocity_z'),\n", " ('gas', 'vorticity_magnitude'),\n", " ('gas', 'vorticity_squared'),\n", " ('gas', 'vorticity_stretching_magnitude'),\n", " ('gas', 'vorticity_stretching_x'),\n", " ('gas', 'vorticity_stretching_y'),\n", " ('gas', 'vorticity_stretching_z'),\n", " ('gas', 'vorticity_x'),\n", " ('gas', 'vorticity_y'),\n", " ('gas', 'vorticity_z'),\n", " ('gas', 'xray_emissivity'),\n", " ('index', 'cell_volume'),\n", " ('index', 'cylindrical_r'),\n", " ('index', 'cylindrical_radius'),\n", " ('index', 'cylindrical_theta'),\n", " ('index', 'cylindrical_z'),\n", " ('index', 'disk_angle'),\n", " ('index', 'dx'),\n", " ('index', 'dy'),\n", " ('index', 'dz'),\n", " ('index', 'grid_indices'),\n", " ('index', 'grid_level'),\n", " ('index', 'height'),\n", " ('index', 'ones'),\n", " ('index', 'ones_over_dx'),\n", " ('index', 'path_element_x'),\n", " ('index', 'path_element_y'),\n", " ('index', 'path_element_z'),\n", " ('index', 'radius'),\n", " ('index', 'spherical_phi'),\n", " ('index', 'spherical_r'),\n", " ('index', 'spherical_radius'),\n", " ('index', 'spherical_theta'),\n", " ('index', 'virial_radius_fraction'),\n", " ('index', 'x'),\n", " ('index', 'y'),\n", " ('index', 'z'),\n", " ('index', 'zeros')]" ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
gpl-2.0
phoebe-project/phoebe2-docs
development/tutorials/beaming_boosting.ipynb
2
77857
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Beaming and Boosting\n", "============================\n", "\n", "Due to concerns about accuracy, support for Beaming & Boosting has been disabled as of the 2.2 release of PHOEBE (although we hope to bring it back in a future release)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It may come as surprise that support for Doppler boosting has been dropped in PHOEBE 2.2. This document details the underlying causes for that decision and explains the conditions that need to be met for boosting to be re-incorporated into PHOEBE.\n", "\n", "Let's start by reviewing the theory behind Doppler boosting. The motion of the stars towards or away from the observer changes the amount of received flux due to three effects:\n", "\n", "* the spectrum is Doppler-shifted, so the flux, being the passband-weighted integral of the spectrum, changes;\n", "* the photons' arrival rate changes due to time dilation; and\n", "* radiation is beamed in the direction of motion due to light aberration.\n", "\n", "It turns out that the combined boosting signal can be written as:\n", "\n", "$$ I_\\lambda = I_{\\lambda,0} \\left( 1 - B(\\lambda) \\frac{v_r}c \\right), $$\n", "\n", "where $I_{\\lambda,0}$ is the intrinsic (rest-frame) passband intensity, $I_\\lambda$ is the boosted passband intensity, $v_r$ is radial velocity, $c$ is the speed of light and $B(\\lambda)$ is the boosting index:\n", "\n", "$$ B(\\lambda) = 5 + \\frac{\\mathrm{d}\\,\\mathrm{ln}\\, I_\\lambda}{\\mathrm{d}\\,\\mathrm{ln}\\, \\lambda}. $$\n", "\n", "The term $\\mathrm{d}(\\mathrm{ln}\\, I_\\lambda) / \\mathrm{d}(\\mathrm{ln}\\, \\lambda)$ is called spectral index. As $I_\\lambda$ depends on $\\lambda$, we average it across the passband:\n", "\n", "$$ B_\\mathrm{pb} = \\frac{\\int_\\lambda \\mathcal{P}(\\lambda) \\mathcal S(\\lambda) B(\\lambda) \\mathrm d\\lambda}{\\int_\\lambda \\mathcal{P}(\\lambda) \\mathcal S(\\lambda) \\mathrm d\\lambda}. $$\n", "\n", "In what follows we will code up these steps and demonstrate the inherent difficulty of realizing a robust, reliable treatment of boosting." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's first make sure we have the latest version of PHOEBE 2.4 installed (uncomment this line if running in an online notebook session such as colab)." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "#!pip install -I \"phoebe>=2.4,<2.5\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Import all python modules that we'll need:" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [], "source": [ "import phoebe\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from astropy.io import fits" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pull a set of Sun-like emergent intensities as a function of $\\mu = \\cos \\theta$ from the Castelli and Kurucz database of model atmospheres (the necessary file can be [downloaded from here](http://phoebe-project.org/static/T06000G40P00.fits)):" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "wl = np.arange(900., 39999.501, 0.5)/1e10\n", "with fits.open('T06000G40P00.fits') as hdu:\n", " Imu = 1e7*hdu[0].data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Grab only the normal component for testing purposes:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "Inorm = Imu[-1,:]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's load a Johnson V passband and the transmission function $P(\\lambda)$ contained within:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "pb = phoebe.get_passband('Johnson:V')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Tesselate the wavelength interval to the range covered by the passband:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "keep = (wl >= pb.ptf_table['wl'][0]) & (wl <= pb.ptf_table['wl'][-1])\n", "Inorm = Inorm[keep]\n", "wl = wl[keep]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Calculate $S(\\lambda) P(\\lambda)$ and plot it, to make sure everything so far makes sense:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(wl, Inorm*pb.ptf(wl), 'b-')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's compute the term $\\mathrm{d}(\\mathrm{ln}\\, I_\\lambda) / \\mathrm{d}(\\mathrm{ln}\\, \\lambda)$. First we will compute $\\mathrm{ln}\\,\\lambda$ and $\\mathrm{ln}\\,I_\\lambda$ and plot them:" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "lnwl = np.log(wl)\n", "lnI = np.log(Inorm)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.xlabel(r'$\\mathrm{ln}\\,\\lambda$')\n", "plt.ylabel(r'$\\mathrm{ln}\\,I_\\lambda$')\n", "plt.plot(lnwl, lnI, 'b-')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Per equation above, $B(\\lambda)$ is then the slope of this curve (plus 5). Herein lies the problem: what part of this graph do we fit a line to? In versions 2 and 2.1, PHOEBE used a 5th order Legendre polynomial to fit the spectrum and then sigma-clipping to get to the continuum. Finally, it computed an average derivative of that Legendrian and proclaimed that $B(\\lambda)$. The order of the Legendre polynomial and the values of sigma for sigma-clipping have been set ad-hoc and kept fixed for every single spectrum." ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/lib/python3/dist-packages/ipykernel_launcher.py:1: RankWarning: The fit may be poorly conditioned\n", " \"\"\"Entry point for launching an IPython kernel.\n", "/usr/lib/python3/dist-packages/ipykernel_launcher.py:8: RankWarning: The fit may be poorly conditioned\n", " \n" ] } ], "source": [ "envelope = np.polynomial.legendre.legfit(lnwl, lnI, 5)\n", "continuum = np.polynomial.legendre.legval(lnwl, envelope)\n", "diff = lnI-continuum\n", "sigma = np.std(diff)\n", "clipped = (diff > -sigma)\n", "while True:\n", " Npts = clipped.sum()\n", " envelope = np.polynomial.legendre.legfit(lnwl[clipped], lnI[clipped], 5)\n", " continuum = np.polynomial.legendre.legval(lnwl, envelope)\n", " diff = lnI-continuum\n", " clipped = clipped & (diff > -sigma)\n", " if clipped.sum() == Npts:\n", " break" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.xlabel(r'$\\mathrm{ln}\\,\\lambda$')\n", "plt.ylabel(r'$\\mathrm{ln}\\,I_\\lambda$')\n", "plt.plot(lnwl, lnI, 'b-')\n", "plt.plot(lnwl, continuum, 'r-')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is clear that there is a pretty strong systematics here that we sweep under the rug. Thus, we need to revise the way we compute the spectral index and make it robust before we claim that we support boosting." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For fun, this is what would happen if we tried to estimate $B(\\lambda)$ at each $\\lambda$:" ] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [], "source": [ "dlnwl = lnwl[1:]-lnwl[:-1]\n", "dlnI = lnI[1:]-lnI[:-1]\n", "B = dlnI/dlnwl" ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(0.5*(wl[1:]+wl[:-1]), B, 'b-')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Numerical artifacts dominate and there is little hope to get a sensible (let alone robust) value using this method." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.2" } }, "nbformat": 4, "nbformat_minor": 2 }
gpl-3.0
autumn-lake/Facebook-V-Predicting-Check-Ins
mahalanobis-Copy2.ipynb
2
10028
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import os\n", "import matplotlib.pyplot as plt\n", "from scipy.stats import gaussian_kde\n", "import time\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#ftypes_train = dict(x=np.float32, y=np.float32, time=np.int32, place_id=np.int64)\n", "ftypes_X = dict(x=np.float32, y=np.float32)\n", "ftypes_Y = dict(place_id=np.int64)\n", "X = pd.read_csv(\"maha_train.csv\", usecols=['x','y'], dtype=ftypes_X)\n", "Y = pd.read_csv('maha_train.csv', usecols=['place_id'], dtype=ftypes_Y)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "split = (X.shape[0])*19/20\n", "X_train = X[0:split]\n", "Y_train = Y[0:split]\n", "\n", "X_val = X[split:]\n", "Y_val = Y[split:]\n", "\n", "cova = X_train.cov()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>x</th>\n", " <th>y</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>x</th>\n", " <td>8.165453</td>\n", " <td>-0.003034</td>\n", " </tr>\n", " <tr>\n", " <th>y</th>\n", " <td>-0.003034</td>\n", " <td>8.337269</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " x y\n", "x 8.165453 -0.003034\n", "y -0.003034 8.337269" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.neighbors import KNeighborsClassifier\n", "neigh = KNeighborsClassifier(n_neighbors=30, n_jobs=-1, \n", " algorithm='ball_tree', weights='distance', \n", " metric='mahalanobis',metric_params={'V': cova})" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<bound method KNeighborsClassifier.get_params of KNeighborsClassifier(algorithm='ball_tree', leaf_size=30,\n", " metric='mahalanobis',\n", " metric_params={'V': x y\n", "x 8.165453 -0.003034\n", "y -0.003034 8.337269},\n", " n_jobs=-1, n_neighbors=30, p=2, weights='distance')>" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "neigh.get_params" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "KNeighborsClassifier(algorithm='ball_tree', leaf_size=30,\n", " metric='mahalanobis',\n", " metric_params={'V': x y\n", "x 8.165453 -0.003034\n", "y -0.003034 8.337269},\n", " n_jobs=-1, n_neighbors=30, p=2, weights='distance')" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "neigh.fit(X_train, Y_train.values.ravel())" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "x = neigh.predict_proba(X_val)\n", "\n", "xx = pd.DataFrame(x)\n", "\n", "xx[:1].sort()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>x</th>\n", " <th>y</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>27662119</th>\n", " <td>9.8218</td>\n", " <td>7.9971</td>\n", " </tr>\n", " <tr>\n", " <th>27662120</th>\n", " <td>7.1145</td>\n", " <td>2.6845</td>\n", " </tr>\n", " <tr>\n", " <th>27662121</th>\n", " <td>7.4073</td>\n", " <td>7.0975</td>\n", " </tr>\n", " <tr>\n", " <th>27662122</th>\n", " <td>2.3742</td>\n", " <td>4.3788</td>\n", " </tr>\n", " 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</tr>\n", " <tr>\n", " <th>27662123</th>\n", " <td>4433667163</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " place_id\n", "27662119 5435982367\n", "27662120 2340603043\n", "27662121 6088743117\n", "27662122 8288249811\n", "27662123 4433667163" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Y_val.head()" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [], "source": [ "X1 = np.array(X_val[1:2])" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [], "source": [ "d,ind = neigh.kneighbors(X1,n_neighbors=30)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4537852\n", "7477991\n", "19171625\n", "26558915\n", "2620998\n", "23714790\n", "7119539\n", "26918267\n", "12448902\n", "23386628\n", "17419227\n", "23722127\n", "10580729\n", "11719301\n" ] } ], "source": [ "for loc in ind[0]:\n", " if Y_train.iloc[loc].place_id==3841738458:\n", " print(loc)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ind[0]" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 7.11450005, 2.68449998]], dtype=float32)" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X1" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([3841738458])" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "neigh.predict(X1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "Y_train.iloc[68297].place_id" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
mitliagkas/graphs
Analysis.ipynb
1
43285
{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "%config InlineBackend.figure_format = 'svg' \n", "import numpy as np\n", "import scipy as sp" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Method A: keep edge w.p. $p_s$" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ True, True, True, True, True, True, True, True, True, True], dtype=bool)" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.random.uniform(size=10)>0.1" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def method_a_sim(dout, ps, nsamples=100000):\n", " samples = [0]*nsamples\n", " for i in range(nsamples):\n", " if np.random.uniform() < ps:\n", " nedges = 1 + np.random.binomial(dout-1, ps)\n", " samples[i] = 1/float(nedges)\n", " return np.mean(samples), np.std(samples)\n", " " ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def method_a_analysis(dout,ps):\n", " return ( 1 - (1-ps)**dout )/float(dout)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dout = 3" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "samples = method_a_sim(dout, ps)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(0.33247999999999994, 0.14006682785965657)" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "samples" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.333" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "method_a_analysis(dout,ps)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Method B: $1$ edge uniformly and Binomial from the rest" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def method_b_sim(dout, ps, nsamples=100000):\n", " samples = [0]*nsamples\n", " for i in range(nsamples):\n", " if np.random.uniform() < 1/float(dout):\n", " nedges = 1 + np.random.binomial(dout-1, ps)\n", " samples[i] = 1/float(nedges)\n", " else:\n", " if np.random.uniform() < ps:\n", " nedges = 2 + np.random.binomial(dout-2, ps)\n", " samples[i] = 1/float(nedges)\n", " return np.mean(samples), np.std(samples)\n", "# return sum(samples)/float(len(samples))\n", " " ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def method_b_analysis(dout,ps):\n", " return 1 /float(dout)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ps=0.9" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(0.33333833333333335, 0.11090160793093429)" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "method_b_sim(dout,ps)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.3333333333333333" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "method_b_analysis(dout,ps)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dout=8\n", "allps = np.linspace(0.01, 1)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [], "source": [ "meanstda = [method_a_sim(dout, ps) for ps in allps]\n", "(meana, stda) = zip(*meanstda)\n", "meana = np.array(meana)\n", "stda = np.array(stda)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [], "source": [ "analytica = [method_a_analysis(dout,ps) for ps in allps]" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [], "source": [ "meanstdb = [method_b_sim(dout, ps) for ps in allps]\n", "(meanb, stdb) = zip(*meanstdb)\n", "meanb = np.array(meanb)\n", "stdb = np.array(stdb)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [], "source": [ "analyticb = [method_b_analysis(dout,ps) for ps in allps]" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/svg+xml": [ "<?xml version=\"1.0\" 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"ax.plot(allps, meana, '--', lw=2, label='Method A: analysis', color='black')\n", "ax.plot(allps, analyticb, lw=2, label='Method B: analysis', color='yellow')\n", "ax.plot(allps, meanb, '--', lw=2, label='Method A: analysis', color='black')\n", "ax.fill_between(allps, meana+stda, meana-stda, facecolor='blue', alpha=0.5)\n", "ax.fill_between(allps, meanb+stdb, meanb-stdb, facecolor='yellow', alpha=0.5)\n", "\n", "ax.axis([0, 1, 0, 2/float(dout)]);\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 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", 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mit
srippa/nn_deep
NN playground.ipynb
1
17284
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "## resourcus\n", "* [I am trask blog - simple introduction to NN](http://iamtrask.github.io/)\n", "* [Neural bnetwork tutorial](http://www.existor.com/en/news-neural-networks.html) - walk all the way. [A similar tutorial](http://www.wildml.com/2015/09/implementing-a-neural-network-from-scratch/)\n", "* [Andrew Ng ML course](https://www.coursera.org/learn/machine-learning/)\n", "* [Pedro domingos course](https://www.coursera.org/course/machlearning)\n", "* [NN papers](https://github.com/robertsdionne/neural-network-papers)\n", "* [Brief introduction to deep learning](http://haohanw.blogspot.co.il/2015/01/deep-learning-introduction.html). Based on the [Deep learning lab]()\n", "\n", "\n", "### Courses\n", "* [CSC321 Winter 2015: Introduction to NN - Toronto](http://www.cs.toronto.edu/~rgrosse/csc321/calendar.html)\n", "\n", "### Papers\n", "* [Deep learning in Neural Networks: An Overview (2014)](http://arxiv.org/pdf/1404.7828v4.pdf)\n", "\n", "### Code\n", "* http://upul.github.io/2015/10/12/Training-(deep)-Neural-Networks-Part:-1/ - example of backpropagation in Python" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# General terminology and notations\n", "The notation in here follow the notations in [Chapter 2](http://neuralnetworksanddeeplearning.com/chap2.html) of the deep learning [online book](http://neuralnetworksanddeeplearning.com/index.html). We note that there are different notations in different places, all refer differently to the same mathematical construct.\n", "\n", "* We consider $L$ layers marked by $l=1,\\ldots,L$ where $l=1$ denotes the input layer\n", "* Layer $l$ has $s_l$ units referred to by $a^{l}_j, j = 1,\\ldots,s_l$.\n", "* The matrix $W^l : s_{l} \\times s_{l-1} , l=2,\\ldots, L$ controls the mapping from layer $l-1$ to layer $l$. The vector $\\mathbf{b}^l$ of size $s_{l}$ corresponds to the **bias** term and in layer $l$. The weight $w^l_{ij}$ is the weight associated with the connection of neuron $j$ in layer $l-1$ to neuron $i$ in layer $l$\n", "\n", "* **Forward propagation:** $\\mathbf{a}^l = \\sigma(\\mathbf{z}^l)$ where $\\mathbf{z}^l=W^l \\mathbf{a}^{(l-1)}+ \\mathbf{b}^{(l)} , l =2,\\ldots,L$ where the activation function $\\sigma \\equiv \\sigma_l$ is applied to each component of its argument vector. For simplicity of notations we often write $\\sigma$ instead of $\\sigma_l$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Synonims\n", "* **Neuron** - inspired from biology analogy\n", "* **Unit** - It’s one component of a large network\n", "* **Feature** - It implements a feature detector that’s looking at the input and will turn on iff the sought feature is present in the input\n", "\n", "#### A note about dot product\n", "The activation function works on the outcome of the done way to think about this is in terms of correlation which are [normalized dot products](https://neonwatty.wordpress.com/2012/07/24/correlation-is-a-normalized-inner-product-illustration-using-the-michael-bay-sian-statistic/). Thus what we really measure is the degree of [correlation](https://en.wikipedia.org/wiki/Correlation_and_dependence), or dependence, between the input vector and the coefficient vector. We can view at the dot product as:\n", "* A correlation filter - fires if a correlation between input and weights exceeds a threshold\n", "* A feature detector - Detect if a specific pattern occur in the input\n", "\n", "## Output unit\n", "\n", "The output values are computed by similarly multplying the values oh $h$ by another weight matrix,\n", "\n", "$\\def \\mathbf \\mathbf {}$\n", "\n", "> $\\mathbf{a}^L = \\sigma_L(\\mathbf{W^L} \\cdot \\mathbf{a}^{(L-1)} + \\mathbf{b}^L) = \\sigma_L(\\mathbf{z^L}) $\n", "\n", "#### Linear regression network\n", "Defined when $\\sigma_L=I$. In that case the output is in a form suitable for linear regression.\n", "\n", "#### Softmax function\n", "\n", "For classification problems we want to convert them into probabilities. This is achieved by using the [softmax](https://en.wikipedia.org/wiki/Softmax_function) function.\n", "\n", "\n", "> $\\sigma_L(z) = \\frac{1}{\\alpha}e^z$ where $\\alpha = \\sum_i e^{z^L_i}$ which produces an output vector $\\mathbf{a}^L \\equiv \\mathbf{y} = (y_1,\\ldots,y_{s_L}), y_i = \\frac{e^{z^L_i}}{\\sum_i e^{z^L_i}} , i = 1,\\ldots, s_L$ \n", "\n", "\n", "\n", "The element $y_i$ is the probability that the label of the output is $i$. This is indeed the same expression utilized by [logistic regression](https://en.wikipedia.org/wiki/Logistic_regression) for classification of many labels. The label $i^*$ that corresponds to a given input vector $\\mathbf{a}^1$ ise selected as the index $i^*$ for which $y_i$ is maximal." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Popular types of activation functions\n", "\n", "Example of some popular [activation functions](https://en.wikipedia.org/wiki/Activation_function):\n", " * [Sigmoid](https://en.wikipedia.org/wiki/Sigmoid_function): Transfor inner product into an S shaped curve. There are several popular alternatives for a Sigmoid activation function:\n", " * [The logistic function](https://en.wikipedia.org/wiki/Logistic_function): $\\sigma(z) = \\frac{1}{1+ e^{-z}}$ hase values in [0,1] and thus can be interperable as **probabiliy**.\n", " * [Hyperbolic tangent](https://en.wikipedia.org/wiki/Hyperbolic_function): $\\sigma(z) = \\frac{e^z - e^{-z}}{e^z + e^{-z}}$ with values in $(-1,1)$\n", " * [Rectifier](https://en.wikipedia.org/wiki/Rectifier_(neural_networks): $\\sigma(z) = \\max(0,z)$. A unit that user a rectifier function is called a **rectified linear unit (ReLU)**.\n", " * [softplus](https://en.wikipedia.org/wiki/Rectifier_(neural_networks): $\\sigma(z) = \\ln (1+e^z)$ is a smooth approximation to the rectifier function. \n", " \n", "#### Synonyms for the term \"unit activation\"\n", "* **Unit's value**: View it as a function of the input\n", "* **Activation**: Emphasizes that the unit may be responding or not, or to an extent; it’s most appropriate for logistic units\n", "* **Output**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Python example : some activation functions " ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Scalar product between unit and weights 0.2\n", " Values of Sigmoid activation function 0.549833997312\n", " Values of ta activation function 0.197375320225\n", " Values of sofplus activation function 0.798138869382\n" ] } ], "source": [ "def sigmoid(x):\n", " return 1./(1.+np.exp(-x))\n", "\n", "def rectifier(x):\n", " return np.array([max(xv,0.0) for xv in x])\n", "\n", "def softplus(x):\n", " return np.log(1.0 + np.exp(x))\n", "\n", "x = np.array([1.0,0,0])\n", "w = np.array([0.2,-0.03,0.14])\n", "print ' Scalar product between unit and weights ',x.dot(w)\n", "print ' Values of Sigmoid activation function ',sigmoid(x.dot(w))\n", "print ' Values of ta activation function ',np.tanh(x.dot(w))\n", "print ' Values of sofplus activation function ',softplus(x.dot(w))\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pylab\n", "\n", "z = np.linspace(-2,2,100) # 100 linearly spaced numbers\n", "s = sigmoid(z) # computing the values of \n", "th = np.tanh(z) # computing the values of \n", "re = rectifier(z) # computing the values of rectifier\n", "sp = softplus(z) # computing the values of rectifier\n", "\n", "# compose plot\n", "pylab.plot(z,s) \n", "pylab.plot(z,s,'co',label='Sigmoid') # Sigmoid \n", "pylab.plot(z,th,label='tanh') # tanh\n", "pylab.plot(z,re,label='rectifier') # rectifier\n", "pylab.plot(z,sp,label='softplut') # rectifier\n", "pylab.legend()\n", "pylab.show() # show the plot" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Python example : Simple feed forward classification NN" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " z1 [ 1.2 1.01 1.14]\n", " a1 [ 0.83365461 0.76576202 0.81441409]\n", " z2 [-0.09339804 1.03365429 0.10293268]\n", " y [ 0.18855564 0.58198547 0.22945889]\n", " Input vector [ 1. 0. 0.] is classified to label 1 \n", "\n", "\n", "The probablity for classifying to label 0 is 0.188555644067\n", "The probablity for classifying to label 1 is 0.581985469162\n", "The probablity for classifying to label 2 is 0.229458886771\n" ] } ], "source": [ "def softmax(z):\n", " alpha = np.sum(np.exp(z))\n", " return np.exp(z)/alpha\n", "\n", "# Input\n", "a0 = np.array([1.,0,0])\n", "\n", "# First layer\n", "W1 = np.array([[0.2,0.15,-0.01],[0.01,-0.1,-0.06],[0.14,-0.2,-0.03]])\n", "b1 = np.array([1.,1.,1.])\n", "z1 = W1.dot(a0) + b1\n", "a1 = np.tanh(z1)\n", "\n", "# Output layer\n", "W2 = np.array([[0.08,0.11,-0.3],[0.1,-0.15,0.08],[0.1,0.1,-0.07]])\n", "b2 = np.array([0.,1.,0.])\n", "z2 = W2.dot(a1) + b2\n", "a2 = y = softmax(z2)\n", "imax = np.argmax(y)\n", "\n", "print ' z1 ',z1\n", "print ' a1 ',np.tanh(z1)\n", "print ' z2 ',z2\n", "print ' y ',y\n", "print ' Input vector {0} is classified to label {1} '.format(a0,imax)\n", "\n", "\n", "print '\\n'\n", "for i in [0,1,2]:\n", " print 'The probablity for classifying to label ',i,' is ',y[i]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Cost (or error) functions \n", "\n", "Suppose that the expected output for an input vector ${\\bf x} \\equiv {\\bf a^1}$ is ${\\bf y} = {\\bf y_x}^* = (0,1,0)$, we can now compute the error vector ${\\bf e}= {\\bf e_x}= {\\bf a_x}^L-{\\bf y_x}^*$. With this error, we can now compute a cost $C=C_x$ assotiated with the output $\\bf{y_x}$ of the input vector ${\\bf x}$ (also called loss) function. For convinience of notations we will frequently omitt the subscript $x$.\n", "\n", "Popular loss functions are:\n", "* Absolute cost $C = C({\\bf a}^L)=\\sum_i |e_i|$\n", "* Square cost $C= C({\\bf a}^L) = \\sum_i e_i^2$\n", "* Cross entropy loss $C=C({\\bf a}^L) = -\\sum_i y_i^*\\log{a^L_i} \\equiv -\\sum_i y_i^*\\log{y_i}$. The rationale here is that the output of the softmax function is a probability distribution and we can also view the real label vector $y$ as a probability distribution (1 for the corerct label and 0 for all other labels). The cross entropy function is a common way to measure difference between distributions.\n", "\n", "The total error from all $N$ data vectors is computed as the average of the individual error terms associated with each input vector ${\\bf x}$, that is:$\\frac{1}{N} \\sum_x C_x$\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Error [ 0.18855564 -0.41801453 0.22945889]\n", " Absolute loss 0.836029061676\n", " Square loss 0.262940759619\n", " Cross entropy loss 0.541309798638\n" ] } ], "source": [ "def abs_loss(e):\n", " return np.sum(np.abs(e))\n", "\n", "def sqr_loss(e):\n", " return np.sum(e**2)\n", "\n", "def cross_entropy_loss(y_estimated,y_real):\n", " return -np.sum(y_real*np.log(y_estimated))\n", "\n", "y_real = np.array([0.,1.,0])\n", "err = a2 - ystar\n", "\n", "print ' Error ',err\n", "print ' Absolute loss ',abs_loss(err)\n", "print ' Square loss ',sqr_loss(err)\n", "print ' Cross entropy loss ',cross_entropy_loss(a2,y_real)\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Backpropagation \n", "\n", "Backpropagation is a fast way of computing the derivatives $\\frac{\\partial C}{\\partial w^l_{ij}}$ and $\\frac{\\partial C}{\\partial b_i}$ which are needed for the [Stochastic Gradient Descent](https://en.wikipedia.org/wiki/Stochastic_gradient_descent) procedure used for minimizing the cost function. Backpropagation is a special case of a more general technique called [reverse mode automatic differentiation](https://en.wikipedia.org/wiki/Automatic_differentiation). The backpropagation algorithm is a smart application of the chain rule to allow efficient calculation of needed derivatives. A detailed discussion on the derivation of backpropagation if provided [in this tutorial](http://neuralnetworksanddeeplearning.com/chap2.html). An example for a simple python implementation is provided [in here](https://triangleinequality.wordpress.com/2014/03/31/neural-networks-part-2/).\n", "\n", "http://upul.github.io/2015/10/12/Training-(deep)-Neural-Networks-Part:-1/\n", "\n", "\n", "#### Derivation of backpropagation\n", "Define the vector ${\\mathbf \\delta}^l= \\frac{\\partial C}{\\partial z^l}$, that is $\\delta^l_i = \\frac{\\partial C}{\\partial z^l_i}, i =1,\\ldots,s_l$. \n", "\n", "Recall that $z^{l+1}_i = \\sum_j w^{l+1}_{ij} a^l_j + b^{l+1}_i = \\sum_j w^{l+1}_{ij} \\sigma_l(z^l_j) + b^{l+1}_i$ Then we have\n", "* $\\delta^L_i = \\frac{\\partial C}{\\partial z_i^L} = \\sum_k \\frac{\\partial C}{\\partial a_k^L} \\frac{\\partial a_k^L}{\\partial z_i^L} = \\frac{\\partial C}{\\partial a_i^L} \\frac{\\partial a_i^L}{\\partial z_i^L}= \\frac{\\partial C}{\\partial a^L_i} \\sigma'_L ( z^L_i)$ \n", "\n", "* $\\delta^l_i = \\sum_k \\frac{\\partial C}{\\partial z_k^{l+1}} \\frac{\\partial z_k^{l+1}}{\\partial z_i^L} = \\sum_k \\delta_k^{l+1} w_{ki}^{l+1} \\sigma'_l(z_i^l) = \\sigma'_l(z_i^l) \\cdot ((W^{l+1})^T \\delta^{l+1})_i $\n", "\n", "* $\\frac{\\partial C}{\\partial b^l_{i}} = \\delta^{l}_i$\n", "\n", "* $\\frac{\\partial C}{\\partial w^l_{ij}} = \\frac{\\partial C}{\\partial z^{l}_{i}} \\frac{\\partial z^{l}_{i}}{\\partial w^{l}_{ij}} = \\delta^{l}_i a^{l-1}_j$\n", "\n", "#### In vector form:\n", "\n", "* $\\delta^L = \\frac{\\partial C}{\\partial {\\bf a}^L} \\odot $$\\sigma'_L ({\\bf z}^L)$ where $\\odot$ is the **Hadamard** elementwise product.\n", "\n", "* $\\delta^l = (W^{l+1})^T \\delta^{l+1} \\odot \\sigma'_l({\\bf z}^l)$\n", "\n", "\n", "* $\\frac{\\partial C}{\\partial b^l} = \\delta^{l}$\n", "\n", "* $\\frac{\\partial C}{\\partial w^l_{ij}} = \\delta^{l}_i a^{l-1}_j$\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "http://karpathy.github.io/neuralnets/\n", "\n", "\n", "\n", "Gradient descent example: http://upul.github.io/2015/10/12/Training-(deep)-Neural-Networks-Part:-1/\n", "\n", "http://ufldl.stanford.edu/tutorial/supervised/OptimizationStochasticGradientDescent/\n", "\n", "SGD tricks: http://research.microsoft.com/pubs/192769/tricks-2012.pdf\n", "\n", "http://www.marekrei.com/blog/26-things-i-learned-in-the-deep-learning-summer-school/\n", "\n", "\n", "http://code.activestate.com/recipes/578148-simple-back-propagation-neural-network-in-python-s/\n", "\n", "http://deeplearning.net/tutorial/\n", "\n", "\n", "http://stackoverflow.com/questions/15395835/simple-multi-layer-neural-network-implementation\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": "2.7.8" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
huizhuzhao/jupyter_notebook
tests/nn_manifold_topology.ipynb
1
82991
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### 构造神经网络模型对两条曲线数据点进行二分类,详细信息参考博客\n", "https://huizhuzhao.github.io/2017/01/16/neural-networks-manifolds-topology.html" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "\n", "from sklearn.metrics import roc_curve, auc\n", "import keras\n", "from keras.models import Sequential\n", "from keras.layers import Dense\n", "from keras.callbacks import ModelCheckpoint, CSVLogger\n" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def one_hot(index, dim):\n", " index = np.asarray(index, dtype=np.int32)\n", " I = np.eye(dim)\n", " res = I[index]\n", " return res\n", "\n", "def dataset(n_samples, n_repeat=1):\n", " x1 = np.random.uniform(-1., 1., size=(n_samples, 1))\n", " y1 = x1**2\n", " \n", " x2 = np.random.uniform(-1., 1., size=(n_samples, 1))\n", " y2 = x2**2 + 0.2\n", " \n", " X_1 = np.concatenate([x1, y1], axis=1)\n", " X_2 = np.concatenate([x2, y2], axis=1)\n", " y_1 = one_hot(np.zeros(shape=(n_samples,)), 2)\n", " y_2 = one_hot(np.ones(shape=(n_samples,)), 2)\n", " \n", " X = np.concatenate([X_1, X_2], axis=0)\n", " y = np.concatenate([y_1, y_2], axis=0)\n", " X = np.tile(X, (n_repeat, 1))\n", " y = np.tile(y, (n_repeat, 1))\n", " \n", " return X, y\n", "\n", "def build_model():\n", " model = Sequential()\n", " model.add(Dense(units=13, input_shape=(2, ), activation='relu')) # changing output_dim, you test different model\n", " model.add(Dense(output_dim=3, activation='relu'))\n", " model.add(Dense(units=2, activation='softmax'))\n", " model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n", " return model\n", "\n", "def get_callbacks():\n", " logger = CSVLogger('./logger.csv', append=False)\n", " hdf5_path = './weights.{epoch:02d}-{val_loss:.2f}.hdf5'\n", " checkpoint = ModelCheckpoint(hdf5_path, monitor='val_loss', save_best_only=True, verbose=1)\n", " return [logger, checkpoint]" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/huizhu/anaconda2/lib/python2.7/site-packages/ipykernel/__main__.py:29: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(units=3, activation=\"relu\")`\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Train on 200000 samples, validate on 2000 samples\n", "Epoch 1/20\n", "199168/200000 [============================>.] - ETA: 0s - loss: 0.1373 - acc: 0.9657Epoch 00000: val_loss improved from inf to 0.00353, saving model to ./weights.00-0.00.hdf5\n", "200000/200000 [==============================] - 8s - loss: 0.1367 - acc: 0.9659 - val_loss: 0.0035 - val_acc: 1.0000\n", "Epoch 2/20\n", "198816/200000 [============================>.] - ETA: 0s - loss: 9.1733e-04 - acc: 1.0000Epoch 00001: val_loss improved from 0.00353 to 0.00010, saving model to ./weights.01-0.00.hdf5\n", "200000/200000 [==============================] - 8s - loss: 9.1253e-04 - acc: 1.0000 - val_loss: 1.0425e-04 - val_acc: 1.0000\n", "Epoch 3/20\n", "198848/200000 [============================>.] - ETA: 0s - loss: 2.9801e-05 - acc: 1.0000Epoch 00002: val_loss improved from 0.00010 to 0.00000, saving model to ./weights.02-0.00.hdf5\n", "200000/200000 [==============================] - 8s - loss: 2.9651e-05 - acc: 1.0000 - val_loss: 3.9705e-06 - val_acc: 1.0000\n", "Epoch 4/20\n", "198944/200000 [============================>.] - ETA: 0s - loss: 1.1748e-06 - acc: 1.0000Epoch 00003: val_loss improved from 0.00000 to 0.00000, saving model to ./weights.03-0.00.hdf5\n", "200000/200000 [==============================] - 8s - loss: 1.1698e-06 - acc: 1.0000 - val_loss: 2.3255e-07 - val_acc: 1.0000\n", "Epoch 5/20\n", "199584/200000 [============================>.] - ETA: 0s - loss: 1.4444e-07 - acc: 1.0000Epoch 00004: val_loss improved from 0.00000 to 0.00000, saving model to ./weights.04-0.00.hdf5\n", "200000/200000 [==============================] - 8s - loss: 1.4439e-07 - acc: 1.0000 - val_loss: 1.2085e-07 - val_acc: 1.0000\n", "Epoch 6/20\n", "198976/200000 [============================>.] - ETA: 0s - loss: 1.1926e-07 - acc: 1.0000Epoch 00005: val_loss improved from 0.00000 to 0.00000, saving model to ./weights.05-0.00.hdf5\n", "200000/200000 [==============================] - 8s - loss: 1.1926e-07 - acc: 1.0000 - val_loss: 1.1921e-07 - val_acc: 1.0000\n", "Epoch 7/20\n", "198912/200000 [============================>.] - ETA: 0s - loss: 1.1921e-07 - acc: 1.0000Epoch 00006: val_loss did not improve\n", "200000/200000 [==============================] - 9s - loss: 1.1921e-07 - acc: 1.0000 - val_loss: 1.1921e-07 - val_acc: 1.0000\n", "Epoch 8/20\n", "198752/200000 [============================>.] - ETA: 0s - loss: 1.1921e-07 - acc: 1.0000Epoch 00007: val_loss did not improve\n", "200000/200000 [==============================] - 8s - loss: 1.1921e-07 - acc: 1.0000 - val_loss: 1.1921e-07 - val_acc: 1.0000\n", "Epoch 9/20\n", "198784/200000 [============================>.] - ETA: 0s - loss: 1.1921e-07 - acc: 1.0000Epoch 00008: val_loss did not improve\n", "200000/200000 [==============================] - 8s - loss: 1.1921e-07 - acc: 1.0000 - val_loss: 1.1921e-07 - val_acc: 1.0000\n", "Epoch 10/20\n", "198880/200000 [============================>.] - ETA: 0s - loss: 1.1921e-07 - acc: 1.0000Epoch 00009: val_loss did not improve\n", "200000/200000 [==============================] - 7s - loss: 1.1921e-07 - acc: 1.0000 - val_loss: 1.1921e-07 - val_acc: 1.0000\n", "Epoch 11/20\n", "199712/200000 [============================>.] - ETA: 0s - loss: 1.1921e-07 - acc: 1.0000Epoch 00010: val_loss did not improve\n", "200000/200000 [==============================] - 7s - loss: 1.1921e-07 - acc: 1.0000 - val_loss: 1.1921e-07 - val_acc: 1.0000\n", "Epoch 12/20\n", "199200/200000 [============================>.] - ETA: 0s - loss: 1.1921e-07 - acc: 1.0000Epoch 00011: val_loss did not improve\n", "200000/200000 [==============================] - 9s - loss: 1.1921e-07 - acc: 1.0000 - val_loss: 1.1921e-07 - val_acc: 1.0000\n", "Epoch 13/20\n", "199904/200000 [============================>.] - ETA: 0s - loss: 1.1921e-07 - acc: 1.0000Epoch 00012: val_loss did not improve\n", "200000/200000 [==============================] - 8s - loss: 1.1921e-07 - acc: 1.0000 - val_loss: 1.1921e-07 - val_acc: 1.0000\n", "Epoch 14/20\n", "199584/200000 [============================>.] - ETA: 0s - loss: 1.1921e-07 - acc: 1.0000Epoch 00013: val_loss did not improve\n", "200000/200000 [==============================] - 8s - loss: 1.1921e-07 - acc: 1.0000 - val_loss: 1.1921e-07 - val_acc: 1.0000\n", "Epoch 15/20\n", "199488/200000 [============================>.] - ETA: 0s - loss: 1.1921e-07 - acc: 1.0000Epoch 00014: val_loss did not improve\n", "200000/200000 [==============================] - 8s - loss: 1.1921e-07 - acc: 1.0000 - val_loss: 1.1921e-07 - val_acc: 1.0000\n", "Epoch 16/20\n", "199936/200000 [============================>.] - ETA: 0s - loss: 1.1921e-07 - acc: 1.0000Epoch 00015: val_loss did not improve\n", "200000/200000 [==============================] - 8s - loss: 1.1921e-07 - acc: 1.0000 - val_loss: 1.1921e-07 - val_acc: 1.0000\n", "Epoch 17/20\n", "199808/200000 [============================>.] - ETA: 0s - loss: 1.1921e-07 - acc: 1.0000Epoch 00016: val_loss did not improve\n", "200000/200000 [==============================] - 9s - loss: 1.1921e-07 - acc: 1.0000 - val_loss: 1.1921e-07 - val_acc: 1.0000\n", "Epoch 18/20\n", "199904/200000 [============================>.] - ETA: 0s - loss: 1.1921e-07 - acc: 1.0000Epoch 00017: val_loss did not improve\n", "200000/200000 [==============================] - 9s - loss: 1.1921e-07 - acc: 1.0000 - val_loss: 1.1921e-07 - val_acc: 1.0000\n", "Epoch 19/20\n", "199488/200000 [============================>.] - ETA: 0s - loss: 1.1921e-07 - acc: 1.0000Epoch 00018: val_loss did not improve\n", "200000/200000 [==============================] - 9s - loss: 1.1921e-07 - acc: 1.0000 - val_loss: 1.1921e-07 - val_acc: 1.0000\n", "Epoch 20/20\n", "199840/200000 [============================>.] - ETA: 0s - loss: 1.1921e-07 - acc: 1.0000Epoch 00019: val_loss did not improve\n", "200000/200000 [==============================] - 9s - loss: 1.1921e-07 - acc: 1.0000 - val_loss: 1.1921e-07 - val_acc: 1.0000\n" ] }, { "data": { "text/plain": [ "<keras.callbacks.History at 0x7fd844438c50>" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_X, train_y = dataset(1000, 100)\n", "valid_X, valid_y = dataset(1000)\n", "callbacks = get_callbacks()\n", "model = build_model()\n", "model.fit(train_X, train_y, epochs=20, batch_size=32, validation_data=(valid_X, valid_y), callbacks=callbacks)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "((200000, 2), (200000, 2))" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_X.shape, train_y.shape" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "((100, 100), (100, 100))\n", "(10000, 2)\n", "(100, 100)\n" ] }, { "data": { "image/png": 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cCCdB+TPSiGC80YzvdQHP2ekEGCazrry0UtnEG69nd2QPFq6KqDTjH2DGoyNR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+5n1NipLXNgSoen5gJQjmqZu9bw9jMXAAYmUwC6bmfo2T9jMu0bOKaqPI0NS64HYfa1D5AMPvka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\nQxo68ckXU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"text/plain": [ "<matplotlib.figure.Figure at 0x7fd844443090>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def visualize_model(model, x_min, x_max, y_min, y_max, h):\n", " xx, yy = np.meshgrid(np.linspace(x_min, x_max, h), np.linspace(y_min, y_max, h))\n", " print(xx.shape, yy.shape)\n", " zz = model.predict(np.c_[xx.ravel(), yy.ravel()])\n", " print(zz.shape)\n", " zz = np.reshape(zz.argmax(axis=1), xx.shape)\n", " print(zz.shape)\n", " \n", " \n", " plt.contourf(xx, yy, zz, cmap=plt.cm.coolwarm, alpha=0.7)\n", " plt.scatter(valid_X[:, 0], valid_X[:, 1], c=valid_y.argmax(axis=1), cmap=plt.cm.coolwarm)\n", " plt.show()\n", " \n", "visualize_model(model, -1., 1., 0., 1.5, 100)" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(20, 5)" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "csv_path = './logger.csv'\n", "df = pd.read_csv(csv_path)\n", "df.shape" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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03ZteoHgYeDPwB1X1jO6124A7quqivvrX09tb8fQ5xhsBJicnJxkZGVnwe5Ak6Zfd1NQU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\nN67wOQ0Fcq8AAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fd8448566d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(df.epoch, df.val_acc)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda root]", "language": "python", "name": "conda-root-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 1 }
mit
statkraft/shyft-doc
notebooks/repository/repositories-intro.ipynb
1
581066
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Exposing the API\n", "\n", "## Introduction\n", "At its core, Shyft provides functionality through an API (Application Programming Interface). All the functionality of Shyft is available through this API.\n", "\n", "We begin the tutorials by introducing the API as it provides the building blocks for the framework. Once you have a good understan\n", "\n", "In [Part I](run_nea_nidelva.ipynb) of the simulation tutorials, we covered conducting a very simple simulation of an example catchment using configuration files. This is a typical use case, but assumes that you have a model well configured and ready for simulation. In practice, one is interested in working with the model, testing different configurations, and evaluating different data sources.\n", "\n", "This is in fact a key idea of Shyft -- to make it simple to evaluate the impact of the selection of model routine on the performance of the simulation. In this notebook we walk through a lower level paradigm of working with the toolbox and using the Shyft API directly to conduct the simulations.\n", "\n", "**This notebook is guiding through the simulation process of a catchment. The following steps are described:**\n", "1. **Loading required python modules and setting path to SHyFT installation**\n", "2. **Running of a Shyft simulation**\n", "3. **Running a Shyft simulation with updated parameters**\n", "4. **Activating the simulation only for selected catchments**\n", "5. **Setting up different input datasets**\n", "6. **Changing state collection settings**\n", "7. **Post processing and extracting results**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Loading required python modules and setting path to SHyFT installation\n", "\n", "Shyft requires a number of different modules to be loaded as part of the package. Below, we describe the required steps for loading the modules, and note that some steps are only required for the use of the jupyter notebook." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# Pure python modules and jupyter notebook functionality\n", "# first you should import the third-party python modules which you'll use later on\n", "# the first line enables that figures are shown inline, directly in the notebook\n", "%matplotlib inline\n", "import os\n", "import datetime as dt\n", "import pandas as pd\n", "from os import path\n", "import sys\n", "from matplotlib import pyplot as plt\n", "from netCDF4 import Dataset" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The Shyft Environment\n", "\n", "This next step is highly specific on how and where you have installed Shyft. If you have followed the guidelines at github, and cloned the three shyft repositories: i) shyft, ii) shyft-data, and iii) shyft-doc, then you may need to tell jupyter notebooks where to find shyft. Uncomment the relevant lines below.\n", "\n", "If you have a 'system' shyft, or used `conda install -s sigbjorn shyft` to install shyft, then you probably will want to make sure you have set the SHYFT_DATA directory correctly, as otherwise, Shyft will assume the above structure and fail. __This has to be done _before_ `import shyft`__. In that case, uncomment the relevant lines below.\n", "\n", "**note**: it is most likely that you'll need to do one or the other." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# try to auto-configure the path, -will work in all cases where doc and data\n", "# are checked out at same level\n", "shyft_data_path = path.abspath(\"../../../shyft-data\")\n", "if path.exists(shyft_data_path) and 'SHYFT_DATA' not in os.environ:\n", " os.environ['SHYFT_DATA']=shyft_data_path\n", " \n", "# shyft should be available either by it's install in python\n", "# or by PYTHONPATH set by user prior to starting notebook.\n", "# This is equivalent to the two lines below\n", "# shyft_path=path.abspath('../../../shyft')\n", "# sys.path.insert(0,shyft_path)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "_NamespacePath(['/home/sih/projects/shyft/shyft'])\n" ] } ], "source": [ "from shyft import api\n", "import shyft\n", "\n", "print(shyft.__path__)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. A Shyft simulation\n", "\n", "The purpose of this notebook is to demonstrate setting up a Shyft simulation using existing **repositories**. Eventually, you will want to learn to write your own repositories, but once you understand what is presented herein, you'll be well on your way to working with Shyft.\n", "\n", "If you prefer to take a **high level** approach, you can start by looking at the [Run Nea Nidelva](simulation-yaml.ipynb) notebook. We recommend taking the time to understand the lower level functionality of Shyft, however, as it will be of value later if you want to use your own data and create your own repositories.\n", "\n", "### Orchestration and Repositories\n", "A core philosophy of Shyft is that \"Data should live at the source\". What this means, is that we prefer datasets to either remain in their original format or even come directly from the data provider. To accomplish this, we use \"repositories\". You can read more about repositories at the [Shyft Documentation](https://shyft.readthedocs.io/en/latest/orchestration.html).\n", "\n", "#### Interfaces\n", "Because it is our hope that users will create their own repositories to meet the specifications of their own datasets, we provide 'interfaces'. This is a programming concept that you may not be familiar with. The idea is that it is a basic example, or template, of how the class should work. You can use these and your own class can inherit from them, allowing you to override methods to meet your own specifications. We'll explore this as we move through this tutorial. A nice [explanation of interfaces with python is available here](http://masnun.rocks/2017/04/15/interfaces-in-python-protocols-and-abcs/).\n", "\n", "### Initial Configuration\n", "What is required to set up a simulation? In the following we'll package some basic information into a dictionaries that may be used to configure our simualtion. We'll start by creating a couple of dictionaries that will be used to instantiate an existing **repository** class that was created for demonstration purposes, `CFRegionModelRepository`.\n", "\n", "If it hasn't been said enough, there is *a lot of functionality in the repositories!* You can write a repository to suit your own use case, and it is encouraged to look at this source code. " ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# we need to import the repository to use it in a dictionary:\n", "from shyft.repository.netcdf.cf_region_model_repository import CFRegionModelRepository" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### region specification\n", "\n", "The first dictionary essentially establishes the domain of the simulation. We also specify a repository that is used to read the data that will provide Shyft a `region_model` (discussed below), based on geographic data. The geographic consists of properties of the catchment, e.g. \"forest fraction\", \"lake fraction\", etc." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# next, create the simulation dictionary\n", "RegionDict = {'region_model_id': 'demo', #a unique name identifier of the simulation\n", " 'domain': {'EPSG': 32633,\n", " 'nx': 400,\n", " 'ny': 80,\n", " 'step_x': 1000,\n", " 'step_y': 1000,\n", " 'lower_left_x': 100000,\n", " 'lower_left_y': 6960000},\n", " 'repository': {'class': shyft.repository.netcdf.cf_region_model_repository.CFRegionModelRepository,\n", " 'params': {'data_file': 'netcdf/orchestration-testdata/cell_data.nc'}},\n", " }" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The first keys, are probably quite clear:\n", "\n", "* `start_datetime`: a string in the format: \"2013-09-01T00:00:00\"\n", "* `run_time_step`: an integer representing the time step of the simulation (in seconds), so for a daily step: 86400\n", "* `number_of_steps`: an integer for how long the simulatoin should run: 365 (for a year long simulation)\n", "* `region_model_id`: a string to name the simulation: 'neanidelva-ptgsk'\n", "\n", "We also need to know *where* the simulation is taking place. This information is contained in the `domain`:\n", "\n", "* `EPSG`: an EPSG string to identify the coordinate system\n", "* `nx`: number of 'cells' in the x direction\n", "* `ny`: number of 'cells' in the y direction\n", "* `step_x`: size of cell in x direction (m)\n", "* `step_y`: size of cell in y direction (m)\n", "* `lower_left_x`: where (x) in the EPSG system the cells begin\n", "* `lower_left_y`: where (y) in the EPSG system the cells begin\n", "* `repository`: a repository that can read the file containing data for the cells (in this case it will read a netcdf file)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Model specification\n", "\n", "The next dictionary provides information about the model that we would like to use in Shyft, or the 'Model Stack' as it is generally referred to. In this case, we are going to use the PTGSK model, and the rest of the dictionary provides the parameter values." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "ModelDict = {'model_t': shyft.api.pt_gs_k.PTGSKModel, # model to construct\n", " 'model_parameters': {\n", " 'ae':{\n", " 'ae_scale_factor': 1.5},\n", " 'gs':{\n", " 'calculate_iso_pot_energy': False,\n", " 'fast_albedo_decay_rate': 6.752787747748934,\n", " 'glacier_albedo': 0.4,\n", " 'initial_bare_ground_fraction': 0.04,\n", " 'max_albedo': 0.9,\n", " 'max_water': 0.1,\n", " 'min_albedo': 0.6,\n", " 'slow_albedo_decay_rate': 37.17325702015658,\n", " 'snow_cv': 0.4,\n", " 'tx': -0.5752881492890207,\n", " 'snowfall_reset_depth': 5.0,\n", " 'surface_magnitude': 30.0,\n", " 'wind_const': 1.0,\n", " 'wind_scale': 1.8959672005350063,\n", " 'winter_end_day_of_year': 100},\n", " 'kirchner':{ \n", " 'c1': -3.336197322290274,\n", " 'c2': 0.33433661533385695,\n", " 'c3': -0.12503959620315988},\n", " 'p_corr': {\n", " 'scale_factor': 1.0},\n", " 'pt':{'albedo': 0.2,\n", " 'alpha': 1.26},\n", " }\n", " } " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this dictionary we define two variables:\n", "\n", "* `model_t`: the import path to a shyft 'model stack' class\n", "* `model_parameters`: a dictionary containing specific parameter values for a particular model class\n", "\n", "Specifics of the `model_parameters` dictionary will vary based on which class is used." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Okay, so far we have two dictionaries. One which provides information regarding our simulation domain, and a second which provides information on the model that we wish to run over the domain (e.g. in each of the cells). The next step, then, is to map these together and create a `region_repo` class.\n", "\n", "This is achieved by using a repository, in this case, the `CFRegionModelRepository` we imported above." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "region_repo = CFRegionModelRepository(RegionDict, ModelDict)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The `region_model`\n", "\n", "<div class=\"alert alert-info\">\n", "\n", "**TODO:** a notebook documenting the CFRegionModelRepository\n", "\n", "</div>\n", "\n", "The first step in conducting a hydrologic simulation is to define the **domain of the simulation** and the **model type** which we would like to simulate. To do this we create a `region_model` object. Above we created dictionaries that contain this information, and we instantiated a class called teh `region_repo`. In this next step, we put it together so that we have a single object which we can work with \"at our fingertips\". You'll note above that we have pointed to a 'data_file' earlier when we defined the `RegionDict`. This data file contains all the required elements to fill the cells of our domain. The informaiton is contained in a single [netcdf file](../../../shyft-data/netcdf/orchestration-testdata/cell_data.nc)\n", "\n", "Before we go further, let's look briefly at the contents of this file:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'netCDF4._netCDF4.Dataset'>\n", "root group (NETCDF4 data model, file format HDF5):\n", " dimensions(sizes): cell(4650)\n", " variables(dimensions): float64 \u001b[4mx\u001b[0m(cell), float64 \u001b[4my\u001b[0m(cell), float64 \u001b[4mz\u001b[0m(cell), int32 \u001b[4mcrs\u001b[0m(), float64 \u001b[4marea\u001b[0m(cell), float64 \u001b[4mforest-fraction\u001b[0m(cell), float64 \u001b[4mreservoir-fraction\u001b[0m(cell), float64 \u001b[4mlake-fraction\u001b[0m(cell), float64 \u001b[4mglacier-fraction\u001b[0m(cell), int32 \u001b[4mcatchment_id\u001b[0m(cell)\n", " groups: \n", "\n" ] } ], "source": [ "cell_data_file = os.path.join(os.environ['SHYFT_DATA'], 'netcdf/orchestration-testdata/cell_data.nc')\n", "cell_data = Dataset(cell_data_file)\n", "print(cell_data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You might be surprised to see the dimensions are 'cells', but recall that in Shyft everything is vectorized. Each 'cell' is an element within a domain, and each cell has associated variables:\n", "\n", "* location: x, y, z\n", "* characteristics: forest-fraction, reservoir-fraction, lake-fraction, glacier-fraction, catchment-id\n", "\n", "We'll bring this data into our workspace via the `region_model`. Note that we have instantiated a `region_repo` class using one of the existing Shyft repositories, in this case one that was built for reading in the data as it is contained in the example [shyft-data](https://github.com/statkraft/shyft-data) netcdf files: `CFRegionModelRepository`.\n", "\n", "\n", "Next, we'll use the `region_repo.get_region_model` method to get the `region_model`. Note the name 'demo', in this case is arbitrary. However, depending on how you create your repository, you can specify what region model to return using this string.\n", "<div class=\"alert alert-info\">\n", "\n", "\n", "**note:** *you are strongly encouraged to learn how to create repositories. This particular repository is just for demonstration purposes. In practice, one may use a repository that connects directly to a GIS service, a database, or some other data sets that contain the data required for simulations.*\n", "\n", "<div class=\"alert alert-warning\">\n", "\n", "**warning**: *also, please note that below we call the 'get_region_model' method as we instantiate the class. This behavior may change in the future.*\n", "\n", "</div>\n", "</div>" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "region_model = region_repo.get_region_model('demo')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Exploring the `region_model`\n", "\n", "So we now have created a `region_model`, but what is it actually? This is a very **fundamental class** in Shyft. It is actually one of the \"model stacks\", such as 'PTGSK', or 'PTHSK'. Essentially, the `region_model` contains all the information regarding the simulation type and domain. There are many methods associated with the `region_model` and it will take time to understand all of them. For now, let's just explore a few key methods:\n", "\n", "* `bounding_region`: provides information regarding the domain of interest for the simulation\n", "* `catchment_id_map`: indices of the various catchments within the domain\n", "* `cells`: an instance of `PTGSKCellAllVector` that holds the individual cells for the simulation (*note that this is type-specific to the model type*)\n", "* `ncore`: an integer that sets the numbers of cores to use during simulation (Shyft is very greedy if you let it!)\n", "* `time_axis`: a `shyft.api.TimeAxisFixedDeltaT` class (basically contains information regarding the timing of the simulation)\n", "\n", "Keep in mind that many of these methods are more 'C++'-like than 'Pythonic'. This means, that in some cases, you'll have to 'call' the method. For example: `region_model.bounding_region.epsg()` returns a string. You can use tab-completion to explore the `region_model` further:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'32633'" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "region_model.bounding_region.epsg()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You'll likely note that there are a number of intriguing fucntions, e.g. `initialize_cell_environment` or `interpolate`. But before we can go further, we need some more information. Perhaps you are wondering about forcing data. So far, we haven't said anything about **model input** or the **time** of the simulation, we've only set up a container that holds all the domain and model type information about our simulation. \n", "\n", "Still, we have made *some* progress. Let's look for instance at the cells:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "GeoCellData(mid_point=GeoPoint(204843.73715373065,6994695.209048475,978.344970703125),catchment_id=2305,area=209211.92418108872,ltf=LandTypeFractions(glacier=0.0,lake=0.0,reservoir=0.0,forest=0.0,unspecified=1.0))\n" ] } ], "source": [ "cell_0 = region_model.cells[0]\n", "print(cell_0.geo)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So you can see that so far, each of the cells in the region_model contain information regarding their LandTypeFractions, geolocation, catchment_id, and area. \n", "\n", "A particulary important attribute is `region_model.region_env`. This is a container for each cell that holds the \"environmental timeseries\", or forcing data, for the simulation. By \"tabbing\" from `cell.` you can see that each cell also has and `env_ts` attribute. These are containers customized to provide timeseries as required by the model type we selected, but there is no data yet. In this case we used the `PTGSKModel` (see the `ModelDict`). So for every cell in your simulation, there is a container prepared to accept the forcing data as the next cell shows." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['has_nan_values', 'init', 'precipitation', 'radiation', 'rel_hum', 'temperature', 'wind_speed']\n" ] }, { "data": { "text/plain": [ "4650" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#just so we don't see 'private' attributes\n", "print([d for d in dir(cell_0.env_ts) if '_' not in d[0]]) \n", "region_model.size()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Adding forcing data to the `region_model`\n", "\n", "Clearly the next step is to add forcing data to our `region_model` object. Let's start by thinking about what kind of data we need. From above, where we looked at the `env_ts` attribute, it's clear that this particular model stack, `PTGSKModel`, requires:\n", "\n", "* precipitation\n", "* radiation\n", "* relative humidity (rel_hum)\n", "* temperature\n", "* wind speed\n", "\n", "We have stored this information each in seperate netcdf files which each contain the observational series for a number of different stations. \n", "\n", "<div class=\"alert alert-warning\">\n", "\n", "Again, these files **do not represent the recommended practice**, but are *only for demonstration purposes*. The idea here is just to demonstrate with an example repository, but *you should create your own to match **your** data*.\n", "\n", "</div>\n", "\n", "Our goal now is to populate the `region_env`. \n", "\n", "#### \"Sources\"\n", "\n", "We use the term *sources* to define a location data may be coming from. You may also come across *destinations*. In both cases, it just means a file, database, service of some kind, etc. that is capable of providing data. Repositories are written to connect to *sources*. Following our earlier approach, we'll create another dictionary to define our data sources, but first we need to import another repository:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "from shyft.repository.netcdf.cf_geo_ts_repository import CFDataRepository" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "from shyft.repository.netcdf.cf_geo_ts_repository import CFDataRepository\n", "ForcingData = {'sources': [\n", " \n", " {'repository': shyft.repository.netcdf.cf_geo_ts_repository.CFDataRepository,\n", " 'params': {'epsg': 32633,\n", " 'filename': 'netcdf/orchestration-testdata/precipitation.nc'},\n", " 'types': ['precipitation']},\n", " \n", " {'repository': shyft.repository.netcdf.cf_geo_ts_repository.CFDataRepository,\n", " 'params': {'epsg': 32633,\n", " 'filename': 'netcdf/orchestration-testdata/temperature.nc'},\n", " 'types': ['temperature']},\n", " \n", " {'params': {'epsg': 32633,\n", " 'filename': 'netcdf/orchestration-testdata/wind_speed.nc'},\n", " 'repository': shyft.repository.netcdf.cf_geo_ts_repository.CFDataRepository,\n", " 'types': ['wind_speed']},\n", " \n", " {'repository': shyft.repository.netcdf.cf_geo_ts_repository.CFDataRepository,\n", " 'params': {'epsg': 32633,\n", " 'filename': 'netcdf/orchestration-testdata/relative_humidity.nc'},\n", " 'types': ['relative_humidity']},\n", " \n", " {'repository': shyft.repository.netcdf.cf_geo_ts_repository.CFDataRepository,\n", " 'params': {'epsg': 32633,\n", " 'filename': 'netcdf/orchestration-testdata/radiation.nc'},\n", " 'types': ['radiation']}]\n", " }\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Data Repositories\n", "\n", "In another notebook, further information will be provided regarding the repositories. For the time being, let's look at this configuration dictionary that was created. It essentially just contains a list, keyed by the name `\"sources\"`. This key is known in some of the tools that are built in the Shyft orchestration, so it is recommended to use it.\n", "\n", "Each item in the list is a dictionary for each of the source types, the keys in the dictionaries are: `repository`, `params`, and `types`. The general idea and concept is that in orchestration, the object keyed by `repository` is a class that is instantiated by passing the objects contained in `params`.\n", "\n", "Let's repeat that. From our `Datasets` dictionary, we get a list of `\"sources\"`. Each of these sources contains a class (a repository) that is capable of getting the source data into Shyft. Whatever parameters that are required for the class to work, will be included in the `\"sources\"` dictionary. In our case, the `params` are quite simple, just a path to a netcdf file. But suppose our repository required credentials or other information for a database? This information could also be included in the `params` stanza of the dictionary.\n", "\n", "You should explore the above referenced netcdf files that are available at the [shyft-data](https://github.com/statkraft/shyft-data) git repository. These files contain the forcing data that will be used in the example simulation. Each one contains observational data from some stations in our catchment. Depending on how you write your repository, this data may be provided to Shyft in many different formats.\n", "\n", "Let's explore this concept further by getting the 'temperature' data:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "# get the temperature sources:\n", "tmp_sources = [source for source in ForcingData['sources'] if 'temperature' in source['types']]\n", "\n", "# in this example there is only one\n", "t0 = tmp_sources[0]\n", "\n", "# We will now instantiate the repository with the parameters that are provided\n", "# in the dictionary. \n", "# Note the 'call' structure expects params to contain keyword arguments, and these\n", "# can be anything you want depending on how you create your repository\n", "tmp_repo = t0['repository'](**t0['params'])\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`tmp_repo` is now an instance of the Shyft `CFDataRepository`, and this will provide Shyft with the data when it sets up a simulation by reading the data directly out of the file referenced in the 'source'. But that is just one repository, and we defined many in fact. Furthermore, you may have a heterogenous collection of data sources -- if for example you want to get your temperature from station data, but radiation from model output. You could define different repositories in the `ForcingData` dictionary.\n", "\n", "Ultimately, we bundle all these repositories up into a new class called a `GeoTsRepositoryCollection` that we can use to populate the `region_model.region_env` with data." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "# we'll actually create a collection of repositories, as we have different input types.\n", "from shyft.repository.geo_ts_repository_collection import GeoTsRepositoryCollection\n", "\n", "def construct_geots_repo(datasets_config, epsg=None):\n", " \"\"\" iterates over the different sources that are provided \n", " and prepares the repository to read the data for each type\"\"\"\n", " geo_ts_repos = []\n", " src_types_to_extract = []\n", " for source in datasets_config['sources']:\n", " if epsg is not None:\n", " source['params'].update({'epsg': epsg})\n", " # note that here we are instantiating the different source repositories\n", " # to place in the geo_ts list \n", " geo_ts_repos.append(source['repository'](**source['params']))\n", " src_types_to_extract.append(source['types'])\n", " \n", " return GeoTsRepositoryCollection(geo_ts_repos, src_types_per_repo=src_types_to_extract)\n", "\n", "# instantiate the repository\n", "geots_repo = construct_geots_repo(ForcingData)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`geots_repo` is now a \"geographic timeseries repository\", meaning that the timeseries it holds are spatially aware of their x,y,z coordinates (see `CFDataRepository` for details). It also has several methods. One in particular we are interested in is the `get_timeseries` method. **However, before we can proceed, we need to define the period for the simulation.**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Shyft `TimeAxis`\n", "Time in Shyft is handled with specialized C++ types for computational efficiency. These are custom built objects that are 'calendar' aware. But since in python, most like to use `datetime` objects, we create a function:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1. TimeAxis('2013-09-01T00:00:00Z', 86400s, 360) \n", " [2013-09-01T00:00:00Z,2014-08-27T00:00:00Z>\n", "2. TimeAxis('2013-09-01T00:00:00Z', 86400s, 365) \n", " [2013-09-01T00:00:00Z,2014-09-01T00:00:00Z>\n" ] } ], "source": [ "# next, create the simulation dictionary\n", "TimeDict = {'start_datetime': \"2013-09-01T00:00:00\",\n", " 'run_time_step': 86400, # seconds, daily\n", " 'number_of_steps': 360 # ~ one year\n", " }\n", "\n", "def time_axis_from_dict(t_dict)->api.TimeAxis:\n", " utc = api.Calendar()\n", " \n", " sim_start = dt.datetime.strptime(t_dict['start_datetime'], \"%Y-%m-%dT%H:%M:%S\")\n", " utc_start = utc.time(sim_start.year, sim_start.month, sim_start.day,\\\n", " sim_start.hour, sim_start.minute, sim_start.second)\n", " tstep = t_dict['run_time_step']\n", " nstep = t_dict['number_of_steps']\n", " time_axis = api.TimeAxis(utc_start, tstep, nstep)\n", " \n", " return time_axis\n", "\n", "ta_1 = time_axis_from_dict(TimeDict)\n", "print(f'1. {ta_1} \\n {ta_1.total_period()}')\n", "# or shyft-wise, ready tested, precise and less effort, two lines\n", "utc = api.Calendar() # 'Europe/Oslo' can be passed to calendar for time-zone\n", "ta_2 = api.TimeAxis(utc.time(2013, 9, 1), api.deltahours(24), 365)\n", "print(f'2. {ta_2} \\n {ta_2.total_period()}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now have an object that defines the time dimension for the simulation, and we will use this to initialize the `region_model` with the \"environmental timeseries\" or `env_ts` data. These containers will be given data from the appropriate repositories using the `get_timeseries` function. Following the templates in the `shyft.repository.interfaces` module, you'll see that the repositories should provide the capability to \"screen\" data based on time criteria and *optinally** geo_location criteria. " ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/sih/projects/shyft/shyft/repository/netcdf/utils.py:351: RuntimeWarning: invalid value encountered in greater\n", " pure_arr = nc_var[data_slice][tuple(xy_slice_mask)]\n" ] } ], "source": [ "# we can extract our \"bounding box\" based on the `region_model` we set up\n", "bbox = region_model.bounding_region.bounding_box(region_model.bounding_region.epsg())\n", "\n", "period = ta_1.total_period() #just defined above\n", "\n", "# required forcing data sets we want to retrieve\n", "geo_ts_names = (\"temperature\", \"wind_speed\", \"precipitation\",\n", " \"relative_humidity\", \"radiation\")\n", "\n", "sources = geots_repo.get_timeseries( geo_ts_names, period) #, geo_location_criteria=bbox )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we have a new dictionary, called 'sources' that contains specialized Shyft api types specific to each forcing data type. You can look at one for example:" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10\n" ] } ], "source": [ "prec = sources['precipitation']\n", "print(len(prec))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can explore further and see each element is in itself an `api.PrecipitationSource`, which has a timeseries (ts). Recall from the [first tutorial](run_nea_nidelva.ipynb#Visualizing-the-discharge-for-each-[sub-]catchment) that we can easily convert the `timeseries.time_axis` into datetime values for plotting.\n", "\n", "Let's plot the precip of each of the sources:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/sih/projects/miniconda/envs/shyft_env/lib/python3.6/site-packages/pandas/plotting/_converter.py:129: FutureWarning: Using an implicitly registered datetime converter for a matplotlib plotting method. The converter was registered by pandas on import. Future versions of pandas will require you to explicitly register matplotlib converters.\n", "\n", "To register the converters:\n", "\t>>> from pandas.plotting import register_matplotlib_converters\n", "\t>>> register_matplotlib_converters()\n", " warnings.warn(msg, FutureWarning)\n" ] }, { "data": { "text/plain": [ "Text(0, 0.5, 'precip[mm/hr]')" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1080x720 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(15,10))\n", "\n", "for pr in prec:\n", " t,p = [dt.datetime.utcfromtimestamp(t_.start) for t_ in pr.ts.time_axis], pr.ts.values\n", " ax.plot(t,p, label=pr.mid_point().x) #uid is empty now, but we reserve for later use\n", "fig.autofmt_xdate()\n", "ax.legend(title=\"Precipitation Input Sources\")\n", "ax.set_ylabel(\"precip[mm/hr]\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, the next step will take the data from the sources and connect it to our `region_model.region_env` class:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "def get_region_environment(sources):\n", " region_env = api.ARegionEnvironment()\n", " region_env.temperature = sources[\"temperature\"]\n", " region_env.precipitation = sources[\"precipitation\"]\n", " region_env.radiation = sources[\"radiation\"]\n", " region_env.wind_speed = sources[\"wind_speed\"]\n", " region_env.rel_hum = sources[\"relative_humidity\"]\n", " return region_env\n", "\n", "region_model.region_env = get_region_environment(sources)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And now our forcing data is connected to the `region_model`. We are *almost* ready to run a simulation. There is just one more step. We've connected the sources to the model, but remember that Shyft is a distributed modeling framework, and we've connected point data sources (in this case). So we need to get the data from the observed *points* to each cell. This is done through interpolation." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Shyft Interpolation\n", "In Shyft there are predefined routines for interpolation. In the `interp_config` class below one quickly recognizes the same input source type keywords that are used as keys to the `params` dictionary. `params` is simply a dictionary of dictionaries which contains the parameters used by the interpolation model that is specific for each source type." ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "from shyft.repository.interpolation_parameter_repository import InterpolationParameterRepository\n", "\n", "class interp_config(object):\n", " \"\"\" a simple class to provide the interpolation parameters \"\"\"\n", "\n", " def __init__(self):\n", " \n", " self.interp_params = {'precipitation': {'method': 'idw',\n", " 'params': {'distance_measure_factor': 1.0,\n", " 'max_distance': 600000.0,\n", " 'max_members': 10,\n", " 'scale_factor': 1.02}},\n", " 'radiation': {'method': 'idw',\n", " 'params': {'distance_measure_factor': 1.0,\n", " 'max_distance': 600000.0,\n", " 'max_members': 10}},\n", " 'relative_humidity': {'method': 'idw',\n", " 'params': {'distance_measure_factor': 1.0,\n", " 'max_distance': 600000.0,\n", " 'max_members': 10}},\n", " 'temperature': {'method': 'btk',\n", " 'params': {'nug': 0.5,\n", " 'range': 200000.0,\n", " 'sill': 25.0,\n", " 'temperature_gradient': -0.6,\n", " 'temperature_gradient_sd': 0.25,\n", " 'zscale': 20.0}},\n", " 'wind_speed': {'method': 'idw',\n", " 'params': {'distance_measure_factor': 1.0,\n", " 'max_distance': 600000.0,\n", " 'max_members': 10}}}\n", "\n", " def interpolation_parameters(self):\n", " return self.interp_params\n", "\n", "ip_conf = interp_config()\n", "ip_repo = InterpolationParameterRepository(ip_conf)\n", "\n", "region_model.interpolation_parameter = ip_repo.get_parameters(0) #just a '0' for now" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The next step is to set the intial states of the model using our last repository. This one, the `GeneratedStateRepository` will set empty default values.\n", "\n", "Now we are nearly ready to conduct a simulation. We just need to run a few methods to prepare the model and cells for the simulation. The region_model has a method called `initalize_cell_environment` that takes a `time_axis` type as input. We defined the `time_axis` above, so now we'll use it to initialize the model. At the same time, we'll set the initial_state. Then we can actually run a simulation!" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "from shyft.repository.generated_state_repository import GeneratedStateRepository\n", "\n", "init_values = {'gs': {'acc_melt': 0.0,\n", " 'albedo': 0.65,\n", " 'alpha': 6.25,\n", " 'iso_pot_energy': 0.0,\n", " 'lwc': 0.1,\n", " 'sdc_melt_mean': 0.0,\n", " 'surface_heat': 30000.0,\n", " 'temp_swe': 0.0},\n", " 'kirchner': {'q': 0.01}}\n", "\n", " \n", "state_generator = GeneratedStateRepository(region_model)#, init_values=init_values)\n", "\n", "# we need the state_repository to have the same size as the model\n", "#state_repo.n = region_model.size()\n", "# there is only 1 state (indexed '0')\n", "s0 = state_generator.get_state(0)\n", "not_applied_list=region_model.state.apply_state( # apply state set the current state according to arguments\n", " cell_id_state_vector=s0, # ok, easy to get\n", " cids=[] # empty means apply all, if we wanted to only apply state for specific catchment-ids, this is where to put them\n", ")\n", "assert len(not_applied_list)==0, 'Ensure all states was matched and applied to the model'\n", "region_model.initial_state=region_model.current_state # now we stash the current state to the initial state" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Conduct the simulation\n", "We now have a `region_model` that is ready for simulation. As we discussed before, we still need to get the data from our point observations interpolated to the cells, and we need to get the `env_ts` of each cell populated. But all the machinery is now in place to make this happen. \n", "\n", "To summarize, we've created:\n", "\n", "* `region_repo`, a region repository that contains information related to region of simulation and the model to be used in the simulation. From this we get a `region_model`\n", "* `geots_repo`, a geo-timeseries repository that provides a mechanism to pull the data we require from our 'sources'.\n", "* `time_axis`, created from the TimeAxisFixedDeltaT class of `shyft` to provide the period of simulation.\n", "* `ip_repo`, an interpolation repository which provides all the required parameters for interpolating our data to the distributed cells -- following variable specific protocols/models.\n", "* `state_repo`, a `GeneratedStateRepository` used to provide our simulation an initial state.\n", "\n", "The next step is simply to initialize the cell environment and run the interpolation. As a practive, before simulation we reset to the initial state (we're there already, but it is something you have to do before a new simulation), and then run the cells. First we'll initialize the cell environment:\n" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "region_model.initialize_cell_environment(ta_1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "As a habit, we have a quick \"sanity check\" function to see if the model is runnable. Itis recommended to have this function when you create 'run scripts'." ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "def runnable(reg_mod):\n", " \"\"\" returns True if model is properly configured \n", " **note** this is specific depending on your model's input data requirements \"\"\"\n", " return all((reg_mod.initial_state.size() > 0, reg_mod.time_axis.size() > 0,\n", " all([len(getattr(reg_mod.region_env, attr)) > 0 for attr in\n", " (\"temperature\", \"wind_speed\", \"precipitation\", \"rel_hum\", \"radiation\")])))" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "# run the model, e.g. as you may configure it in a script:\n", "if runnable(region_model):\n", " \n", " region_model.interpolate(region_model.interpolation_parameter, region_model.region_env)\n", " region_model.revert_to_initial_state()\n", " region_model.run_cells()\n", "else:\n", " print('Something wrong with model configuration.')\n", "\n", "\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Okay, so the simulation was run. Now we may be interested in looking at some of the output. We'll take a brief summary glance in the next section, and save a deeper dive into the simulation results for another notebook.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. Simulation results\n", "\n", "The first step will be simply to look at the discharge results for each subcatchment within our simulation domain. For simplicity, we can use a `pandas.DataFrame` to collect the data from each catchment." ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'discharge [m3 s-1]')" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1440x1080 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Here we are going to extact data from the simulation.\n", "# We start by creating a list to hold discharge for each of the subcatchments.\n", "# Then we'll get the data from the region_model object\n", "\n", "# mapping of internal catch ID to catchment\n", "catchment_id_map = region_model.catchment_id_map \n", "\n", "# First get the time-axis which we'll use as the index for the data frame\n", "ta = region_model.time_axis\n", "# and convert it to datetimes\n", "index = [dt.datetime.utcfromtimestamp(p.start) for p in ta]\n", "\n", "# Now we'll add all the discharge series for each catchment \n", "data = {}\n", "for cid in catchment_id_map:\n", " # get the discharge time series for the subcatchment\n", " q_ts = region_model.statistics.discharge([int(cid)])\n", " data[cid] = q_ts.values.to_numpy()\n", "\n", "df = pd.DataFrame(data, index=index)\n", "# we can simply use:\n", "ax = df.plot(figsize=(20,15))\n", "ax.legend(title=\"Catch. ID\")\n", "ax.set_ylabel(\"discharge [m3 s-1]\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Okay, that was simple. Let's look at the timeseries in some individual cells. The following is a bit of a contrived example, but it shows some aspects of the api. We'll plot the temperature series of all the cells in one sub-catchment, and color them by elevation. This doesn't necessarily show anything about the simulation, per se, but rather results from the interpolation step." ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x7f6502f33550>" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1080x720 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from matplotlib.cm import jet as jet\n", "from matplotlib.colors import Normalize\n", "\n", "# get all the cells for one sub-catchment with 'id' == 1228\n", "c1228 = [c for c in region_model.cells if c.geo.catchment_id() == 1228]\n", "\n", "# for plotting, create an mpl normalizer based on min,max elevation\n", "elv = [c.geo.mid_point().z for c in c1228]\n", "norm = Normalize(min(elv), max(elv))\n", "\n", "#plot with line color a function of elevation\n", "fig, ax = plt.subplots(figsize=(15,10))\n", "\n", "# here we are cycling through each of the cells in c1228\n", "for dat,elv in zip([c.env_ts.temperature.values for c in c1228], [c.mid_point().z for c in c1228]):\n", " ax.plot(dat, color=jet(norm(elv)), label=int(elv))\n", " \n", " \n", "# the following is just to plot the legend entries and not related to Shyft\n", "handles, labels = ax.get_legend_handles_labels()\n", "\n", "# sort by labels\n", "import operator\n", "hl = sorted(zip(handles, labels),\n", " key=operator.itemgetter(1))\n", "handles2, labels2 = zip(*hl)\n", "\n", "# show legend, but only every fifth entry\n", "ax.legend(handles2[::5], labels2[::5], title='Elevation [m]')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we would expect from the temperature kriging method, we should find higher elevations have colder temperatures. As an exercise you could explore this relationship using a scatter plot." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we're going to create a function that will read initial states from the `initial_state_repo`. In practice, this is already done by the `ConfgiSimulator`, but to demonstrate lower level functions, we'll reset the states of our `region_model`:" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "state_generator.find_state?" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [], "source": [ "# create a function to reaad the states from the state repository\n", "def get_init_state_from_repo(initial_state_repo_, region_model_id_=None, timestamp=None):\n", " state_id = 0\n", " if hasattr(initial_state_repo_, 'n'): # No stored state, generated on-the-fly\n", " initial_state_repo_.n = region_model.size()\n", " else:\n", " states = initial_state_repo_.find_state(\n", " region_model_id_criteria=region_model_id_,\n", " utc_timestamp_criteria=timestamp)\n", " if len(states) > 0:\n", " state_id = states[0].state_id # most_recent_state i.e. <= start time\n", " else:\n", " raise Exception('No initial state matching criteria.')\n", " return initial_state_repo_.get_state(state_id)\n", " \n", "init_state = get_init_state_from_repo(state_generator, timestamp=region_model.time_axis.start)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Don't worry too much about the function for now, but do take note of the `init_state` object that we created. This is another container, this time it is a class that contains `PTGSKStateWithId` objects, which are specific to the model stack implemented in the simulation (in this case `PTGSK`). If we explore an individual state object, we'll see `init_state` contains, for each cell in our simulation, the state variables for each 'method' of the method stack.\n", "\n", "Let's look more closely:" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4650\n", "CellStateId:\t ['area', 'cid', 'x', 'y']\n", "GammaSnowState:\t ['acc_melt', 'albedo', 'alpha', 'iso_pot_energy', 'lwc', 'sdc_melt_mean', 'surface_heat', 'temp_swe']\n", "KirchnerState:\t ['q']\n" ] } ], "source": [ "def print_pub_attr(obj):\n", " #only public attributes\n", " print(f'{obj.__class__.__name__}:\\t',[attr for attr in dir(obj) if attr[0] is not '_']) \n", " \n", "print(len(init_state))\n", "init_state_cell0 = init_state[0] \n", "# the identifier\n", "print_pub_attr(init_state_cell0.id)\n", "# gam snow states\n", "print_pub_attr(init_state_cell0.state.gs)\n", "\n", "#init_state_cell0.kirchner states\n", "print_pub_attr(init_state_cell0.state.kirchner)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Summary\n", "We have now explored the `region_model` and looked at how to instantiate a `region_model` by using a `api.ARegionEnvironment`, containing a collection of timeseries sources, and passing an `api.InterpolationParameter` class containing the parameters to use for the data interpolation algorithms. The interpolation step \"populated\" our cells with data from the point sources.\n", "\n", "The cells each contain all the information related to the simulation (their own timeseries, `env_ts`; their own model parameters, `parameter`; and other attributes and methods). In future tutorials we'll work with the cells indivdual \"resource collector\" (`.rc`) and \"state collector\" (`.sc`) attributes.\n", "\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.7" } }, "nbformat": 4, "nbformat_minor": 1 }
lgpl-3.0
ahirner/TabulaRazr-OS
.ipynb_checkpoints/TableParser4-checkpoint.ipynb
1
11827
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#TabulaRazr - specific to calculate - TABLE Parser\n", "#Infers a table with arbitrary number of columns from reoccuring patterns in text lines\n", "#(c) Alexander Hirner 2016, no redistribution without permission\n", "#Contributions: ____ (refactoring), UI styling (), ....\n", "\n", "\n", "#Main assumptions Table identificatin:\n", "#1) each row is either in one line or not a row at all\n", "#2) each column features at least one number (=dollar amount)\n", "#2a) each column features at least one date-like string [for time-series only]\n", "#3) a table exists if rows are in narrow consecutive order and share similarities --> scoring algo [DONE] \n", "#4) each column is separated by more than x consecutive whitespace indicators (e.g. ' ' or '..')\n", "\n", "#Feature List Todo:\n", "#1) Acknowledge footnotes / make lower meta-data available\n", "#2) make delimiter length smartly dependent on number of columns (possible iterative approach)\n", "#3) improve captioning: expand non canonical values in tables [DONE] .. but not to the extent how types match up --> use this to further\n", "## delineate between caption and headers\n", "#4) UI: parameterize extraction on the show page on the fly\n", "#5) deeper type inference on token level: type complex [DONE], subtype header (centered, capitalized), \n", "## subtype page nr., type free flow [DONE, need paragraph]\n", "#5a) re\n", "#6) Respect negative values with potential '-' for numerical values\n", "#7)\n", "#8) classify tables with keywords (Muni Bonds) and unsupervised clustering (Hackathon)\n", "#9) Restructure folder and URI around MD5 hash (http://stackoverflow.com/questions/24570066/calculate-md5-from-werkzeug-datastructures-filestorage-without-saving-the-object)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from backend import *\n", "import os" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import json\n", "from flask import Flask, request, redirect, url_for, send_from_directory\n", "from werkzeug import secure_filename\n", "from flask import jsonify, render_template, make_response\n", "\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "TITLE = \"TabulaRazr (XIRR for muni_bonds)\"\n", "\n", "scripts = []\n", "css = [\n", " \"./bower_components/bootstrap/dist/css/bootstrap.min.css\",\n", " \"./css/main.css\",\n", " \"./css/style.css\"\n", "]\n", "\n", "\n", "UPLOAD_FOLDER = './static/ug'\n", "ALLOWED_EXTENSIONS = set(['txt', 'pdf'])\n", "\n", "TITLE = \"TabulaRazr\"\n", "\n", "app = Flask(__name__)\n", "app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER\n", "\n", "def get_extension(filename):\n", " return '.' in filename and \\\n", " filename.rsplit('.', 1)[1] \n", "\n", "def allowed_file(filename):\n", " return get_extension(filename) in ALLOWED_EXTENSIONS\n", "\n", "@app.route('/', methods=['GET', 'POST'])\n", "def upload_file():\n", "\n", " if request.method == 'POST':\n", " \n", " file = request.files['file']\n", " project = request.form['project']\n", " \n", " if file and allowed_file(file.filename):\n", " filename = secure_filename(file.filename)\n", " extension = get_extension(file.filename)\n", " path = os.path.join(app.config['UPLOAD_FOLDER'], project, filename)\n", " \n", " file.save(os.path.join(app.config['UPLOAD_FOLDER'], project, filename))\n", " \n", " if extension == \"pdf\":\n", " txt_path = path+'.txt'\n", " filename += '.txt' \n", " if not os.path.isfile(txt_path):\n", " #Layout preservation crucial to preserve clues about tabular data\n", " cmd = \"pdftotext -enc UTF-8 -layout %s %s \" % (path, txt_path)\n", " os.system(cmd) \n", "\n", " return redirect(url_for('analyze', filename=filename, project=project))\n", "\n", " return render_template('index.html',\n", " title=TITLE ,\n", " css=css)\n", "\n", "@app.route('/analyze/<filename>', methods=['GET', 'POST'])\n", "def analyze(filename): \n", "\n", " project = request.args.get('project')\n", " txt_path = os.path.join(app.config['UPLOAD_FOLDER'], project, filename)\n", " \n", " if not os.path.isfile(txt_path):\n", " return {'error' : txt_path+' not found' }\n", " \n", " tables = return_tables(txt_path)\n", " \n", " #Export tables\n", " with codecs.open(txt_path + '.tables.json', 'w', \"utf-8\") as file:\n", " json.dump(tables, file)\n", "\n", " #Export chart\n", " lines_per_page = 80\n", " nr_data_rows = []\n", " #for t in tables.values():\n", " # print t\n", " for key, t in tables.iteritems():\n", " e = t['end_line']\n", " b = t['begin_line']\n", " for l in range(b, e):\n", " page = l / lines_per_page\n", " if len(nr_data_rows) <= page:\n", " nr_data_rows += ([0]*(page-len(nr_data_rows)+1))\n", " nr_data_rows[page] += 1\n", " dr = pd.DataFrame()\n", " dr['value'] = nr_data_rows\n", " dr['page'] = range(0, len(dr))\n", " \n", " #plot the row density\n", " chart = filename+\".png\"\n", " fig, ax = plt.subplots( nrows=1, ncols=1, figsize=(8,3) ) # create figure & 1 axis\n", " ax.set_xlabel('page nr.')\n", " ax.set_ylabel('number of data rows')\n", " ax.set_title('Distribution of Rows with Data')\n", " ax.plot(dr['page'], dr['value'], )\n", " fig.savefig(txt_path + '.png') # save the figure to file\n", " plt.close(fig) # close the figure\n", "\n", " if request.method == 'POST':\n", " return json.dumps(tables)\n", " \n", " return redirect(url_for('uploaded_file', filename=filename, project=project))\n", " \n", "\n", "@app.route('/show/<filename>')\n", "def uploaded_file(filename):\n", "\n", " project = request.args.get('project') \n", " path = os.path.join(app.config['UPLOAD_FOLDER'], project, filename)\n", " \n", " tables_path = path + '.tables.json'\n", " chart_path = path+\".png\"\n", " \n", " if not os.path.isfile(tables_path):\n", " analyze(path)\n", "\n", " with codecs.open(tables_path) as file:\n", " tables = json.load(file) \n", "\n", " #Create HTML\n", " notices = ['Extraction Results for ' + filename, 'Ordered by lines'] \n", " dfs = (table_to_df(table).to_html() for table in tables.values())\n", " headers = []\n", " for t in tables.values():\n", " if 'header' in t:\n", " headers.append(t['header'])\n", " else:\n", " headers.append('-')\n", " meta_data = [{'begin_line' : t['begin_line'], 'end_line' : t['end_line']} for t in tables.values()]\n", "\n", " return render_template('viewer.html',\n", " title=TITLE + ' - ' + filename,\n", " base_scripts=scripts, filename=filename, project=project,\n", " css=css, notices = notices, tables = dfs, headers=headers, meta_data=meta_data, chart=chart_path)\n", "\n", "@app.route('/inspector/<filename>')\n", "def inspector(filename):\n", " extension = 'txt'\n", " path = os.path.join(app.config['UPLOAD_FOLDER'], extension, filename)\n", " begin_line = int(request.args.get('data_begin'))\n", " end_line = int(request.args.get('data_end'))\n", " margin_top = config[\"meta_info_lines_above\"]\n", " margin_bottom = margin_top\n", " \n", " notices = ['showing data lines from %i to %i with %i meta-lines above and below' % (begin_line, end_line, margin_top)]\n", " with codecs.open(path, \"r\", \"utf-8\") as file:\n", " lines = [l.encode('utf-8') for l in file][begin_line - margin_top:end_line + margin_bottom]\n", " top_lines = lines[:margin_top]\n", " table_lines = lines[margin_top:margin_top+end_line-begin_line]\n", " bottom_lines = lines[margin_top+end_line-begin_line:]\n", " \n", " offset = begin_line-margin_top\n", " table_id = begin_line\n", " \n", " return render_template('inspector.html',\n", " title=TITLE,\n", " base_scripts=scripts, css=css, notices = notices, filename=filename, top_lines=top_lines, \n", " table_lines=table_lines, bottom_lines=bottom_lines, offset=offset, table_id=begin_line)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "ERROR:__main__:Exception on / [POST]\n", "Traceback (most recent call last):\n", " File \"/Library/Python/2.7/site-packages/flask/app.py\", line 1817, in wsgi_app\n", " response = self.full_dispatch_request()\n", " File \"/Library/Python/2.7/site-packages/flask/app.py\", line 1477, in full_dispatch_request\n", " rv = self.handle_user_exception(e)\n", " File \"/Library/Python/2.7/site-packages/flask/app.py\", line 1381, in handle_user_exception\n", " reraise(exc_type, exc_value, tb)\n", " File \"/Library/Python/2.7/site-packages/flask/app.py\", line 1475, in full_dispatch_request\n", " rv = self.dispatch_request()\n", " File \"/Library/Python/2.7/site-packages/flask/app.py\", line 1461, in dispatch_request\n", " return self.view_functions[rule.endpoint](**req.view_args)\n", " File \"<ipython-input-4-004e7bcd31d8>\", line 36, in upload_file\n", " path = os.path.join(app.config['UPLOAD_FOLDER'], project, filename)\n", "NameError: global name 'os' is not defined\n" ] } ], "source": [ "def run_from_ipython():\n", " try:\n", " __IPYTHON__\n", " return True\n", " except NameError:\n", " return False\n", "\n", "if run_from_ipython():\n", " app.run(host='0.0.0.0', port = 8080)\n", "else:\n", " PORT = int(os.getenv('PORT', 8080))\n", " app.run(debug=True, host='0.0.0.0', port = PORT)" ] }, { "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 }
agpl-3.0
theJollySin/data_science_from_scratch
chapters/21_network_analysis/network_analysis.ipynb
1
13131
{ "cells": [ { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'friends': [{'friends': [{...}, {'friends': [{...}, {...}, {'friends': [{...}, {...}, {'friends': [{...}, {'friends': [{...}, {'friends': [{...}, {'friends': [{...}, {'friends': [{...}, {...}], 'id': 7, 'name': 'Devin'}, {'friends': [{...}], 'id': 9, 'name': 'Klein'}], 'id': 8, 'name': 'Kate'}], 'id': 6, 'name': 'Hicks'}, {'friends': [{...}, {'friends': [{'friends': [{...}, {...}], 'id': 6, 'name': 'Hicks'}, {...}, {'friends': [{...}], 'id': 9, 'name': 'Klein'}], 'id': 8, 'name': 'Kate'}], 'id': 7, 'name': 'Devin'}], 'id': 5, 'name': 'Clive'}], 'id': 4, 'name': 'Thor'}], 'id': 3, 'name': 'Chi'}], 'id': 2, 'name': 'Sue'}, {'friends': [{...}, {'friends': [{...}, {...}, {...}], 'id': 2, 'name': 'Sue'}, {'friends': [{...}, {'friends': [{...}, {'friends': [{...}, {'friends': [{...}, {'friends': [{...}, {...}], 'id': 7, 'name': 'Devin'}, {'friends': [{...}], 'id': 9, 'name': 'Klein'}], 'id': 8, 'name': 'Kate'}], 'id': 6, 'name': 'Hicks'}, {'friends': [{...}, {'friends': [{'friends': [{...}, {...}], 'id': 6, 'name': 'Hicks'}, {...}, {'friends': [{...}], 'id': 9, 'name': 'Klein'}], 'id': 8, 'name': 'Kate'}], 'id': 7, 'name': 'Devin'}], 'id': 5, 'name': 'Clive'}], 'id': 4, 'name': 'Thor'}], 'id': 3, 'name': 'Chi'}], 'id': 1, 'name': 'Dunn'}, {'friends': [{...}, {'friends': [{...}, {...}, {'friends': [{...}, {...}, {'friends': [{...}, {'friends': [{...}, {'friends': [{...}, {'friends': [{...}, {'friends': [{...}, {...}], 'id': 7, 'name': 'Devin'}, {'friends': [{...}], 'id': 9, 'name': 'Klein'}], 'id': 8, 'name': 'Kate'}], 'id': 6, 'name': 'Hicks'}, {'friends': [{...}, {'friends': [{'friends': [{...}, {...}], 'id': 6, 'name': 'Hicks'}, {...}, {'friends': [{...}], 'id': 9, 'name': 'Klein'}], 'id': 8, 'name': 'Kate'}], 'id': 7, 'name': 'Devin'}], 'id': 5, 'name': 'Clive'}], 'id': 4, 'name': 'Thor'}], 'id': 3, 'name': 'Chi'}], 'id': 1, 'name': 'Dunn'}, {'friends': [{'friends': [{...}, {...}, {...}], 'id': 1, 'name': 'Dunn'}, {...}, {'friends': [{...}, {'friends': [{...}, {'friends': [{...}, {'friends': [{...}, {'friends': [{...}, {...}], 'id': 7, 'name': 'Devin'}, {'friends': [{...}], 'id': 9, 'name': 'Klein'}], 'id': 8, 'name': 'Kate'}], 'id': 6, 'name': 'Hicks'}, {'friends': [{...}, {'friends': [{'friends': [{...}, {...}], 'id': 6, 'name': 'Hicks'}, {...}, {'friends': [{...}], 'id': 9, 'name': 'Klein'}], 'id': 8, 'name': 'Kate'}], 'id': 7, 'name': 'Devin'}], 'id': 5, 'name': 'Clive'}], 'id': 4, 'name': 'Thor'}], 'id': 3, 'name': 'Chi'}], 'id': 2, 'name': 'Sue'}], 'id': 0, 'name': 'Hero'}\n" ] } ], "source": [ "import math, random, re\n", "from collections import defaultdict, Counter, deque\n", "from functools import partial\n", "\n", "users = [\n", " { \"id\": 0, \"name\": \"Hero\" },\n", " { \"id\": 1, \"name\": \"Dunn\" },\n", " { \"id\": 2, \"name\": \"Sue\" },\n", " { \"id\": 3, \"name\": \"Chi\" },\n", " { \"id\": 4, \"name\": \"Thor\" },\n", " { \"id\": 5, \"name\": \"Clive\" },\n", " { \"id\": 6, \"name\": \"Hicks\" },\n", " { \"id\": 7, \"name\": \"Devin\" },\n", " { \"id\": 8, \"name\": \"Kate\" },\n", " { \"id\": 9, \"name\": \"Klein\" }\n", "]\n", "\n", "friendships = [(0, 1), (0, 2), (1, 2), (1, 3), (2, 3), (3, 4),\n", " (4, 5), (5, 6), (5, 7), (6, 8), (7, 8), (8, 9)]\n", "\n", "for user in users:\n", " user[\"friends\"] = []\n", "\n", "for i, j in friendships:\n", " users[i][\"friends\"].append(users[j])\n", " users[j][\"friends\"].append(users[i])\n", "\n", "\n", "print(users[0]) # What a mess.\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\"\"\" some 2D linear algebra stuff from chapter 4 \"\"\"\n", "from math import sqrt\n", "\n", "\n", "def vector_add(v, w):\n", " '''adds corresponding elements of vectors'''\n", " return [v_i + w_i for v_i,w_i in zip(v, w)]\n", "\n", "\n", "def vector_subtract(v, w):\n", " '''subtract corresponding elements of vectors'''\n", " return [v_i - w_i for v_i,w_i in zip(v, w)]\n", "\n", "\n", "def vector_sum(vectors):\n", " '''sum all corresponding elements'''\n", " result = vectors[0]\n", " for vector in vectors[1:]:\n", " result = vector_add(result, vector)\n", "\n", " return result\n", "\n", "\n", "def vector_sum_fast(vectors):\n", " '''sum all corresponding elements'''\n", " return reduce(vector_add, vectors)\n", "\n", "\n", "def scalar_multiply(c, vector):\n", " '''c is the scalar'''\n", " return [c * v_i for v_i in vector]\n", "\n", "\n", "def vector_mean(vectors):\n", " '''The ith element of the result is the mean of the ith element\n", " of all the input vectors.'''\n", " n = len(vectors)\n", " return scalar_multiply(1/n, vector_sum(vectors))\n", "\n", "\n", "def dot(v, w):\n", " '''the sum of the product of the matching elements\n", " of the input vectors'''\n", " return sum(v_i * w_i for v_i,w_i in zip(v, w))\n", "\n", "\n", "def sum_of_squares(v):\n", " '''add the square of each element'''\n", " return dot(v, v)\n", "\n", "\n", "def magnitude(v):\n", " '''Find the length of a vector in cartesian space'''\n", " return sqrt(sum_of_squares(v))\n", "\n", "\n", "def distance(v, w):\n", " '''Find the distance between two vectors'''\n", " return magnitude(vector_subtract(v, w))\n", "\n", "\n", "def shape(A):\n", " num_rows = len(A)\n", " num_cols = len(A[0] if A else 0)\n", " return num_rows, num_cols\n", "\n", "\n", "def get_row(A, i):\n", " return A[i]\n", "\n", "\n", "def get_col(A, i):\n", " return [A_i[j] for A_i in A]\n", "\n", "\n", "def make_matrix(num_rows, num_cols, entry_fn):\n", " return [[entry_fn(i, j) for j in range(num_cols)] for i in range(num_rows)]\n", "\n", "\n", "def is_diagonal(i, j):\n", " return 1 if i == j else 0\n" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def shortest_paths_from(from_user):\n", " \"\"\" generate the shortest path from one spot to\n", " everywhere else in the graph\n", " \"\"\"\n", " shortest_paths_to = {from_user['id']: [[]]}\n", " frontier = deque((from_user, friend) for friend in from_user['friends'])\n", " \n", " while frontier:\n", " prev_user, user = frontier.popleft()\n", " user_id = user['id']\n", " \n", " paths_to_prev_user = shortest_paths_to[prev_user['id']]\n", " new_paths_to_user = [path + [user_id] for path in paths_to_prev_user]\n", " \n", " old_paths_to_user = shortest_paths_to.get(user_id, [])\n", " if old_paths_to_user:\n", " min_path_length = len(old_paths_to_user[0])\n", " else:\n", " min_path_length = float('inf')\n", " \n", " # only keep short paths that are really new\n", " new_paths_to_user = [path for path in new_paths_to_user\n", " if len(path) <= min_path_length\n", " and path not in old_paths_to_user]\n", " \n", " shortest_paths_to[user_id] = old_paths_to_user + new_paths_to_user\n", " \n", " # and new neighbors to the frontier\n", " frontier.extend((user, friend) for friend in user['friends']\n", " if friend['id'] not in shortest_paths_to)\n", " \n", " return shortest_paths_to" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "for user in users:\n", " user['shortest_paths'] = shortest_paths_from(user)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [], "source": [ "for user in users:\n", " user['betweeness_centrality'] = 0.0" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [], "source": [ "for source in users:\n", " source_id = source['id']\n", " for target_id, paths in source['shortest_paths'].items():\n", " if source_id < target_id:\n", " num_paths = len(paths)\n", " contrib = 1.0 / num_paths\n", " for path in paths:\n", " for id in path:\n", " if id not in [source_id, target_id]:\n", " users[id]['betweeness_centrality'] += contrib" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def farness(user):\n", " \"\"\" the sum of the lengths of the shortest paths to each other user \"\"\"\n", " return sum(len(paths[0])\n", " for paths in user['shortest_paths'].values())\n", "\n", "for user in users:\n", " user['closeness_centrality'] = 1.0 / farness(user)" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\"\"\" Page Rank - Toy Model \"\"\"\n", "\n", "def page_rank(users, damping=0.85, num_iters=100):\n", " \"\"\" A fun little toy model of the Page Rank algorithm \"\"\"\n", " # start with an even distribution\n", " num_users = len(users)\n", " pr = {user['id']: 1.0 / num_users for user in users}\n", " \n", " # the small fraction that each node gets every iteration\n", " base_pr = (1.0 - damping) / num_users\n", " \n", " for _ in range(num_iters):\n", " next_pr = {user['id']: base_pr for user in users}\n", " for user in users:\n", " # distribute page rank to outgoing links\n", " links_pr = pr[user['id']] * damping\n", " for endorsee in user['endorses']:\n", " next_pr[endorsee['id']] += links_pr / len(user['endorses'])\n", " \n", " pr = next_pr\n", " \n", " return pr" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{0: 0.03461538461538463,\n", " 1: 0.03461538461538463,\n", " 2: 0.03461538461538463,\n", " 3: 0.03461538461538463,\n", " 4: 0.07269456570826435,\n", " 5: 0.04344422700587085,\n", " 6: 0.03346379647749512,\n", " 7: 0.03346379647749512,\n", " 8: 0.04344422700587085,\n", " 9: 0.03346379647749512}" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "users = [\n", " {\"id\": 0, \"name\": \"Hero\", \"endorses\": []},\n", " {\"id\": 1, \"name\": \"Dunn\", \"endorses\": []},\n", " {\"id\": 2, \"name\": \"Sue\", \"endorses\": []},\n", " {\"id\": 3, \"name\": \"Chi\", \"endorses\": []},\n", " {\"id\": 4, \"name\": \"Thor\", \"endorses\": []},\n", " {\"id\": 5, \"name\": \"Clive\", \"endorses\": []},\n", " {\"id\": 6, \"name\": \"Hicks\", \"endorses\": []},\n", " {\"id\": 7, \"name\": \"Devin\", \"endorses\": []},\n", " {\"id\": 8, \"name\": \"Kate\", \"endorses\": []},\n", " {\"id\": 9, \"name\": \"Klein\", \"endorses\": []}\n", "]\n", "\n", "endorsements = [(0, 1), (1, 0), (0, 2), (2, 0), (1, 2), (2, 1), (1, 3), (0, 4),\n", " (2, 3), (3, 4), (5, 4), (5, 6), (7, 5), (6, 8), (8, 7), (8, 9)]\n", "\n", "for end in endorsements:\n", " users[end[0]]['endorses'].append(users[end[1]])\n", "\n", "page_rank(users)" ] }, { "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
jforbess/pvlib-python
docs/tutorials/tmy_to_power.ipynb
1
1559771
null
bsd-3-clause
IST256/learn-python
content/lessons/04-Iterations/LAB-Iterations.ipynb
1
15740
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Class Coding Lab: Iterations\n", "\n", "The goals of this lab are to help you to understand:\n", "\n", "- How loops work.\n", "- The difference between definite and indefinite loops, and when to use each.\n", "- How to build an indefinite loop with complex exit conditions.\n", "- How to create a program from a complex idea.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Understanding Iterations\n", "\n", "Iterations permit us to repeat code until a Boolean expression is `False`. Iterations or **loops** allow us to write succinct, compact code. Here's an example, which counts to 3 before [Blitzing the Quarterback in backyard American Football](https://www.quora.com/What-is-the-significance-of-counting-one-Mississippi-two-Mississippi-and-so-on):" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "code_cell_type": "run_code" }, "outputs": [], "source": [ "i = 1\n", "while i <= 3:\n", " print(i,\"Mississippi...\")\n", " i=i+1\n", "print(\"Blitz!\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Breaking it down...\n", "\n", "The `while` statement on line 2 starts the loop. The code indented beneath the `while` (lines 3-4) will repeat, in a linear fashion until the Boolean expression on line 2 `i <= 3` is `False`, at which time the program continues with line 5.\n", "\n", "### Some Terminology\n", "\n", "We call `i <=3` the loop's **exit condition**. The variable `i` inside the exit condition is the only thing that we can change to make the exit condition `False`, therefore it is the **loop control variable**. On line 4 we change the loop control variable by adding one to it, this is called an **increment**.\n", "\n", "Furthermore, we know how many times this loop will execute before it actually runs: 3. Even if we allowed the user to enter a number, and looped that many times, we would still know. We call this a **definite loop**. Whenever we iterate over a fixed number of values, regardless of whether those values are determined at run-time or not, we're using a definite loop.\n", "\n", "If the loop control variable never forces the exit condition to be `False`, we have an **infinite loop**. As the name implies, an Infinite loop never ends and typically causes our computer to crash or lock up. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "code_cell_type": "run_code" }, "outputs": [], "source": [ "## WARNING!!! INFINITE LOOP AHEAD\n", "## IF YOU RUN THIS CODE YOU WILL NEED TO STOP OR RESTART THE KERNEL AFTER RUNNING THIS!!!\n", "\n", "i = 1\n", "while i <= 3:\n", " print(i,\"Mississippi...\")\n", "print(\"Blitz!\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### For loops\n", "\n", "To prevent an infinite loop when the loop is definite, we use the `for` statement. Here's the same program using `for`:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "code_cell_type": "run_code" }, "outputs": [], "source": [ "for i in range(1,4):\n", " print(i,\"Mississippi...\")\n", "print(\"Blitz!\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "One confusing aspect of this loop is `range(1,4)` why does this loop from 1 to 3? Why not 1 to 4? Well it has to do with the fact that computers start counting at zero. The easier way to understand it is if you subtract the two numbers you get the number of times it will loop. So for example, 4-1 == 3.\n", "\n", "### 1.1 You Code\n", "\n", "In the space below, Re-Write the above program to count Mississippi from 10 to 15. You need practice **writing** loops, so make sure you do NOT copy the code.\n", "\n", "Note: How many times will that loop?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "code_cell_type": "write_code", "label": "1.1", "solution": [ "for i in range(10,16):\n", " print(i,\"Mississippi...\")\n", "print(\"Blitz!\")\n" ] }, "outputs": [], "source": [ "# TODO Write code here\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Indefinite loops\n", "\n", "With **indefinite loops** we do not know how many times the program will execute. This is typically based on user action, and therefore our loop is subject to the whims of whoever interacts with it. Most applications like spreadsheets, photo editors, and games use indefinite loops. They'll run on your computer, seemingly forever, until you choose to quit the application. \n", "\n", "The classic indefinite loop pattern involves getting input from the user inside the loop. We then inspect the input and based on that input we might exit the loop. Here's an example:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "code_cell_type": "run_code" }, "outputs": [], "source": [ "name = \"\"\n", "while name != 'mike':\n", " name = input(\"Say my name! : \")\n", " print(f\"Nope, my name is not {name}!\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the above example, the loop will keep on looping until we enter `mike`. The value `mike` is called the **sentinal** value - a value we look out for, and when it exists we stop the loop. For this reason indefinite loops are also known as **sentinal-controlled loops**.\n", "\n", "The classic problem with indefinite/sentinal controlled loops is that its really difficult to get the application's logic to line up with the exit condition. For example we need to set `name = \"\"` in line 1 so that line 2 starts out as `True`. Also we have this wonky logic where when we say `'mike'` it still prints `Nope, my name is not mike!` before exiting.\n", "\n", "### Break statement\n", "\n", "The solution to this problem is to use the break statement. **break** tells Python to exit the loop immediately. We then re-structure all of our indefinite loops to look like this:\n", "\n", "```\n", "while True:\n", " if sentinel-controlled-exit-condition:\n", " break\n", "```\n", "\n", "Here's our program we-written with the break statement. **This is the recommended way to write indefinite loops in this course.**\n", "\n", "NOTE: We always check for the sentinal value immediately AFTER the `input()` function." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "code_cell_type": "run_code" }, "outputs": [], "source": [ "while True:\n", " name = input(\"Say my name!: \")\n", " if name == 'mike':\n", " break\n", " print(\"Nope, my name is not %s!\" %(name))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1.2 You Code: Debug This loop\n", "\n", "This program should count the number of times you input the value `ni`. As soon as you enter a value other than `ni` the program will stop looping and print the count of `ni`'s.\n", "\n", "Example Run:\n", "\n", " What say you? ni\n", " What say you? ni\n", " What say you? ni\n", " What say you? nay\n", " You said 'ni' 3 times.\n", " \n", "The problem of course, is this code wasn't written correctly. Its up to you to get it working!" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "code_cell_type": "debug_code", "label": "1.2", "solution": [ "nicount=0\n", "while True:\n", " say = input(\"What say you? \")\n", " if say != 'ni':\n", " break\n", " nicount = nicount + 1\n", "print(f\"You said 'ni' P {nicount} times.\")\n" ] }, "outputs": [], "source": [ "#TODO Debug this code\n", "nicount=0\n", "while True:\n", " say = input \"What say you? \")\n", " if say == 'ni':\n", " break\n", " nicount = 1\n", "print(f\"You said 'ni' P {nicount} times.\") " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Multiple exit conditions\n", "\n", "This indefinite loop pattern makes it easy to add additional exit conditions. For example, here's the program again, but it now stops when you say my name or type in 3 wrong names. \n", "\n", "Make sure to run this program a couple of times to understand what is happening:\n", "\n", "- First enter mike to exit the program, \n", "- Next enter the wrong name 3 times." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "code_cell_type": "run_code" }, "outputs": [], "source": [ "times = 0\n", "while True:\n", " name = input(\"Say my name!: \")\n", " times = times + 1\n", " if name == 'mike': # sentinal 1\n", " print(\"You got it!\")\n", " break\n", " if times == 3: # sentinal 2\n", " print(\"Game over. Too many tries!\")\n", " break\n", " print(f\"Nope, my name is not {name}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Counting Characters in Text\n", "\n", "Let's conclude the lab with you writing your own program that uses both definite and indefinite loops. This program should input some text and then a character, counting the number of characters in the text. This process will repeat until the text entered is empty. \n", "\n", "The program should work as follows. Example run:\n", "\n", " Enter a text, or press ENTER quit: mississippi\n", " Which character are you searching for? i\n", " There are 4 i's in mississippi\n", " \n", " Enter a text, or press ENTER quit: port-au-prince\n", " Which character are you searching for? -\n", " There are 4 -'s in port-au-prince\n", "\n", " Enter a text, or press ENTER quit:\n", " Goodbye!\n", " \n", " \n", "This seems complicated, so let's break the problem up using the **problem simplification** approach.\n", "\n", "First write code to count the numbers of characters in any text. Here is the algorithm:\n", "\n", " set count to 0\n", " input the text\n", " input the search character\n", " for ch in text\n", " if ch equals the search character\n", " increment the count\n", " print there are {count} {search characters} in {text}\n", " \n", " \n", "### 1.3 You Code\n", "\n", "Implement the algorithm above in code in the cell below." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "code_cell_type": "write_code", "label": "1.3", "solution": [ "count = 0\n", "text = input(\"Enter text: \")\n", "search = input(\"Which character are you searching for? \")\n", "for ch in text:\n", " if ch == search:\n", " count +=1\n", "print(f\"There are {count} {search}'s in {text}.\")\n" ] }, "outputs": [], "source": [ "# TODO Write code here\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we surround the code we wrote in 1.4 with a sentinal-controlled indefinite loop. The sentinal (the part that exits the loop is when the text is empty (`text==\"\"`) The algorithm is:\n", "\n", " loop\n", " set count to 0\n", " input the text\n", " if text is empty quit loop\n", " input the search character\n", " for ch in text\n", " if ch equals the search character\n", " increment the count\n", " print there are {count} {search characters} in {text}\n", "\n", "### 1.4 You Code\n", "\n", "Implement the algorithm above in code." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "code_cell_type": "write_code", "label": "1.4", "solution": [ "while True:\n", " count = 0\n", " text = input(\"Enter text or press ENTER to quit: \")\n", " if text == \"\":\n", " break\n", " search = input(\"Which character are you searching for? \")\n", " for ch in text:\n", " if ch == search:\n", " count +=1\n", " print(f\"There are {count} {search}'s in {text}.\")\n", "print(\"Goodbye!\")\n" ] }, "outputs": [], "source": [ "# TODO Write Code here:\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Metacognition\n" ] }, { "cell_type": "markdown", "metadata": { "label": "comfort_cell" }, "source": [ "\n", "### Rate your comfort level with this week's material so far. \n", "\n", "**1** ==> I don't understand this at all yet and need extra help. If you choose this please try to articulate that which you do not understand to the best of your ability in the questions and comments section below. \n", "**2** ==> I can do this with help or guidance from other people or resources. If you choose this level, please indicate HOW this person helped you in the questions and comments section below. \n", "**3** ==> I can do this on my own without any help. \n", "**4** ==> I can do this on my own and can explain/teach how to do it to others.\n", "\n", "`--== Double-Click Here then Enter a Number 1 through 4 Below This Line ==--` \n", "\n" ] }, { "cell_type": "markdown", "metadata": { "label": "questions_cell" }, "source": [ "### Questions And Comments \n", "\n", "Record any questions or comments you have about this lab that you would like to discuss in your recitation. It is expected you will have questions if you did not complete the code sections correctly. Learning how to articulate what you do not understand is an important skill of critical thinking. Write them down here so that you remember to ask them in your recitation. We expect you will take responsilbity for your learning and ask questions in class.\n", "\n", "`--== Double-click Here then Enter Your Questions Below this Line ==--` \n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# run this code to turn in your work!\n", "from coursetools.submission import Submission\n", "Submission().submit()" ] } ], "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": "3.8.6" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": false, "sideBar": false, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": false, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 4 }
mit
supesolutions/portia-iot
examples/PHP.ipynb
1
19925
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Querying portia - Data fetching with PHP" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Making HTTP requests using Python - Checking credentials" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* ### Unsucessfull request" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\u001b[33;1mPHP warning: file_get_contents(http://io.portia.supe.solutions/api/v1/accesstoken/check): failed to open stream: HTTP request failed! HTTP/1.1 401 Unauthorized\r\n", " in /opt/jupyter-php/pkgs/vendor/litipk/jupyter-php/src/Actions/ExecuteAction.php(115) : eval()'d code on line 6\u001b[39;22m" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "// Portia service URL for token authorization checking\n", "$url = \"http://io.portia.supe.solutions/api/v1/accesstoken/check\";\n", "\n", "// Creates a stream for HTTP request\n", "$options = [\n", " \"http\" => [\n", " \"method\" => \"GET\"\n", " ]\n", "];\n", "$context = stream_context_create($options);\n", "\n", "// Makes the request\n", "$response = file_get_contents($url, false, $context);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* ### Sucessfull request" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Success accessing Portia Service - Status: HTTP/1.1 200 OK\n", "{\"user\":\"teste\",\"isLoggedIn\":true}\n" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "// Portia service URL for token authorization checking\n", "$url = \"http://io.portia.supe.solutions/api/v1/accesstoken/check\";\n", "\n", "// Creates a stream for HTTP request\n", "$options = [\n", " \"http\" => [\n", " \"method\" => \"GET\",\n", " \"header\" => \"Authorization: Bearer bdb6e780b43011e7af0b67cba486057b\\r\\n\" // Setting the header with a token for successful authorization\n", " ]\n", "];\n", "$context = stream_context_create($options);\n", "\n", "// Makes the request\n", "$response = file_get_contents($url, false, $context);\n", "\n", "// Shows response\n", "if($http_response_header[0] == \"HTTP/1.1 200 OK\") {\n", " echo(\"Success accessing Portia Service - Status: \" . $http_response_header[0] . \"\\n\" . $response);\n", "} else {\n", " echo(\"Couldn't access Portia service - Status: \" . $http_response_header[0]);\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Obtaining data from a specific time frame\n", "\n", "Now that we have learned how to authenticate with the service, let's see how to get the data" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Success! For each received dimension: \n" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "Accessing dimension package:\n" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "\tDimension Code: 1\n" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "\tUnity Code: 1\n" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "\tThing Code: 1\n" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "\tDimension Value: 20.8\n" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "\tServer Timestamp: 1508785157578\n" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "Accessing dimension package:\n" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "\tDimension Code: 1\n" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "\tUnity Code: 1\n" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "\tThing Code: 1\n" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "\tDimension Value: 20.9\n" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "\tServer Timestamp: 1508785094581\n" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "Accessing dimension package:\n" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "\tDimension Code: 1\n" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "\tUnity Code: 1\n" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "\tThing Code: 1\n" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "\tDimension Value: 20.9\n" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "\tServer Timestamp: 1508785034294\n" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "Accessing dimension package:\n" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "\tDimension Code: 1\n" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "\tUnity Code: 1\n" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "\tThing Code: 1\n" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "\tDimension Value: 21\n" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "\tServer Timestamp: 1508784973303\n" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "Accessing dimension package:\n" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "\tDimension Code: 1\n" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "\tUnity Code: 1\n" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "\tThing Code: 1\n" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "\tDimension Value: 21.1\n" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "\tServer Timestamp: 1508784913444\n" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "// Example for getting the last 5 minutes of data\n", "$fiveMinutes = 1000 * 60 * 5;\n", "$toTimestamp = time() * 1000; // The time function only gives us the UTC time as seconds since January 1, 1970, so, we multiply by 1000 to get the milliseconds\n", "$fromTimestamp = $toTimestamp - $fiveMinutes;\n", "\n", "// Portia service URL for specific time frame\n", "$url = \"http://io.portia.supe.solutions/api/v1/device/HytTDwUp-j8yrsh8e/port/2/sensor/1\";\n", "\n", "// Adding the calculated timestamps as GET parameters\n", "$url = $url . \"?from_timestamp=$fromTimestamp&?to_timestamp=$toTimestamp\"; // If no parameters, the service default response is for the last 24 hours\n", "\n", "// Creates a stream for HTTP request\n", "$options = [\n", " \"http\" => [\n", " \"method\" => \"GET\",\n", " \"header\" => \"Authorization: Bearer bdb6e780b43011e7af0b67cba486057b\\r\\n\" // Setting the header with a token for successful authorization\n", " ]\n", "];\n", "$context = stream_context_create($options);\n", "\n", "// Makes the request\n", "$response = file_get_contents($url, false, $context);\n", "\n", "// Shows response\n", "if($http_response_header[0] == \"HTTP/1.1 200 OK\") {\n", " // Parses dimensions\n", " $dimensions = json_decode($response);\n", "\n", " echo(\"Success! For each received dimension: \");\n", " foreach($dimensions as $dimension) {\n", " echo(\"Accessing dimension package:\");\n", " echo(\"\\tDimension Code: \" . $dimension -> dimension_code);\n", " echo(\"\\tUnity Code: \" . $dimension -> dimension_unity_code);\n", " echo(\"\\tThing Code: \" . $dimension -> dimension_thing_code);\n", " echo(\"\\tDimension Value: \" . $dimension -> dimension_value);\n", " echo(\"\\tServer Timestamp: \" . $dimension -> server_timestamp);\n", " }\n", "\n", "} else {\n", " echo(\"Couldn't access Portia service - Status: \" . $http_response_header[0]);\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Obtaining the latest data\n", "For the next example, we are requesting only the last data sent by the equipments" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* ### Last dimension" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Success! Accessing dimension package:\n" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "\tDimension Code: 1\n" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "\tUnity Code: 1\n" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "\tThing Code: 1\n" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "\tDimension Value: 20.7\n" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "\tServer Timestamp: 1508785396842\n" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "// Portia service URL for getting the latest data\n", "$url = \"http://io.portia.supe.solutions/api/v1/device/HytTDwUp-j8yrsh8e/port/2/sensor/1/last\";\n", "\n", "// Creates a stream for HTTP request\n", "$options = [\n", " \"http\" => [\n", " \"method\" => \"GET\",\n", " \"header\" => \"Authorization: Bearer bdb6e780b43011e7af0b67cba486057b\\r\\n\" // Setting the header with a token for successful authorization\n", " ]\n", "];\n", "$context = stream_context_create($options);\n", "\n", "// Makes the request\n", "$response = file_get_contents($url, false, $context);\n", "\n", "// Shows response\n", "if($http_response_header[0] == \"HTTP/1.1 200 OK\") {\n", " // Parses dimensions\n", " $dimension = json_decode($response)[0];\n", " echo(\"Success! Accessing dimension package:\");\n", " echo(\"\\tDimension Code: \" . $dimension -> dimension_code);\n", " echo(\"\\tUnity Code: \" . $dimension -> dimension_unity_code);\n", " echo(\"\\tThing Code: \" . $dimension -> dimension_thing_code);\n", " echo(\"\\tDimension Value: \" . $dimension -> dimension_value);\n", " echo(\"\\tServer Timestamp: \" . $dimension -> server_timestamp);\n", "\n", "} else {\n", " echo(\"Couldn't access Portia service - Status: \" . $http_response_header[0]);\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* ### Last three dimensions" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Success! For each received dimension: \n" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "Accessing dimension package:\n" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "\tDimension Code: 1\n" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "\tUnity Code: 1\n" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "\tThing Code: 1\n" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "\tDimension Value: 20.9\n" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "\tServer Timestamp: 1508785518063\n" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "Accessing dimension package:\n" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "\tDimension Code: 1\n" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "\tUnity Code: 1\n" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "\tThing Code: 1\n" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "\tDimension Value: 20.8\n" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "\tServer Timestamp: 1508785457421\n" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "Accessing dimension package:\n" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "\tDimension Code: 1\n" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "\tUnity Code: 1\n" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "\tThing Code: 1\n" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "\tDimension Value: 20.7\n" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "\tServer Timestamp: 1508785396842\n" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "// Portia service URL for getting the latest data\n", "$url = \"http://io.portia.supe.solutions/api/v1/device/HytTDwUp-j8yrsh8e/port/2/sensor/1/last\";\n", "\n", "# Adding GET parameter for specifying that we want the last 3 dimension packages\n", "$url = $url . \"?limit=3\";\n", "\n", "// Creates a stream for HTTP request\n", "$options = [\n", " \"http\" => [\n", " \"method\" => \"GET\",\n", " \"header\" => \"Authorization: Bearer bdb6e780b43011e7af0b67cba486057b\\r\\n\" // Setting the header with a token for successful authorization\n", " ]\n", "];\n", "$context = stream_context_create($options);\n", "\n", "// Makes the request\n", "$response = file_get_contents($url, false, $context);\n", "\n", "// Shows response\n", "if($http_response_header[0] == \"HTTP/1.1 200 OK\") {\n", " // Parses dimensions\n", " $dimensions = json_decode($response);\n", "\n", " echo(\"Success! For each received dimension: \");\n", " foreach($dimensions as $dimension) {\n", " echo(\"Accessing dimension package:\");\n", " echo(\"\\tDimension Code: \" . $dimension -> dimension_code);\n", " echo(\"\\tUnity Code: \" . $dimension -> dimension_unity_code);\n", " echo(\"\\tThing Code: \" . $dimension -> dimension_thing_code);\n", " echo(\"\\tDimension Value: \" . $dimension -> dimension_value);\n", " echo(\"\\tServer Timestamp: \" . $dimension -> server_timestamp);\n", " }\n", "\n", "} else {\n", " echo(\"Couldn't access Portia service - Status: \" . $http_response_header[0]);\n", "}" ] } ], "metadata": { "kernelspec": { "display_name": "PHP", "language": "php", "name": "jupyter-php" }, "language_info": { "file_extension": ".php", "mimetype": "text/x-php", "name": "PHP", "pygments_lexer": "PHP", "version": "7.0.22-0ubuntu0.16.04.1" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
openworm/ChannelWorm
tests/scidash/EGL-19_IV.ipynb
3
48538
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Validating a channel model IV curve with data from an experiment" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import rickpy\n", "rickpy.use_dev_packages(['scidash/sciunit','scidash/neuronunit','neuroml/pyNeuroML'])\n", "\n", "# Imports and preliminaries. \n", "%matplotlib inline\n", "import os,sys\n", "import numpy as np\n", "import quantities as pq\n", "\n", "CW_HOME = os.path.split(os.path.realpath(os.path.pardir))[0] # Location of your ChannelWorm repo\n", "sys.path.insert(1,CW_HOME)\n", "\n", "from channelworm.fitter.initiators import Initiator" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Compare the IV curve predicted from the channel model to that observed from the data" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from neuronunit.tests.channel import IVCurvePeakTest\n", "from neuronunit.models.channel import ChannelModel" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Instantiate the model\n", "channel_model_name = 'EGL-19.channel'\n", "channel_id = 'ca_boyle'\n", "channel_file_path = os.path.join(CW_HOME,'models','%s.nml' % channel_model_name)\n", "\n", "model = ChannelModel(channel_file_path,channel_index=0,name=channel_model_name.split('.')[0])" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Get the experiment data from ChannelWorm and instantiate the test\n", "\n", "import os, sys\n", "import django\n", "\n", "sys.path.append(os.path.join(CW_HOME,'channelworm')) # Change the path if needed\n", "os.environ.setdefault(\n", " \"DJANGO_SETTINGS_MODULE\",\n", " \"web_app.settings\"\n", ")\n", "django.setup()\n", "\n", "from channelworm.ion_channel.models import GraphData\n", "\n", "doi = '10.1083/jcb.200203055'\n", "fig = '2B'\n", "sample_data = GraphData.objects.get(graph__experiment__reference__doi=doi, graph__figure_ref_address=fig)\n", "obs = list(zip(*sample_data.asarray())) \n", "observation = {'i/C':obs[1]*pq.A/pq.F, 'v':obs[0]*pq.mV}\n", "cell_capacitance = 1e-13 * pq.F # Capacitance is arbitrary if IV curves are scaled. \n", "observation['i'] = observation['i/C']*cell_capacitance\n", "\n", "test = IVCurvePeakTest(observation, scale=True)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "pyNeuroML >>> Generating LEMS file to investigate ca_boyle in /Users/rgerkin/Dropbox/dev/openworm/ChannelWorm/models/EGL-19.channel.nml, -100mV->100mV, 6.3degC\n", "pyNeuroML >>> Loading LEMS file: /Users/rgerkin/Dropbox/dev/openworm/ChannelWorm/tests/sciunit/LEMS_Test_ca_boyle.xml and running with jNeuroML\n", "pyNeuroML >>> Executing: (java -Xmx400M -Djava.awt.headless=true -jar \"/Users/rgerkin/Dropbox/dev/neuroml/pyNeuroML/pyneuroml/lib/jNeuroML-0.8.0-jar-with-dependencies.jar\" \"/Users/rgerkin/Dropbox/dev/openworm/ChannelWorm/tests/sciunit/LEMS_Test_ca_boyle.xml\" -nogui) in directory: .\n", "pyNeuroML >>> Command completed. Output: \n", "pyNeuroML >>> jNeuroML >> jNeuroML v0.8.0\n", "pyNeuroML >>> jNeuroML >> Loading: /Users/rgerkin/Dropbox/dev/openworm/ChannelWorm/tests/sciunit/LEMS_Test_ca_boyle.xml with jLEMS, NO GUI mode...\n", "pyNeuroML >>> jNeuroML >> INFO Jul 19,2016 22:30 (INFO) Loading LEMS file from: /Users/rgerkin/Dropbox/dev/openworm/ChannelWorm/tests/sciunit/LEMS_Test_ca_boyle.xml\n", "pyNeuroML >>> jNeuroML >> INFO Jul 19,2016 22:30 (INFO) Reading from: /Users/rgerkin/Dropbox/dev/openworm/ChannelWorm/tests/sciunit/LEMS_Test_ca_boyle.xml\n", "pyNeuroML >>> jNeuroML >> INFO Jul 19,2016 22:30 (INFO) Finished 40000 steps in 3.492 seconds (sim duration: 100.0ms; dt: 0.0025ms)\n", "pyNeuroML >>> jNeuroML >> INFO Jul 19,2016 22:30 (INFO) Written to the file /Users/rgerkin/Dropbox/dev/openworm/ChannelWorm/tests/sciunit/./ca_boyle.e.inf.lems.dat 857630\n", "pyNeuroML >>> jNeuroML >> INFO Jul 19,2016 22:30 (INFO) Written to the file /Users/rgerkin/Dropbox/dev/openworm/ChannelWorm/tests/sciunit/./ca_boyle.e.tau.lems.dat 848483\n", "pyNeuroML >>> jNeuroML >> INFO Jul 19,2016 22:30 (INFO) Written to the file /Users/rgerkin/Dropbox/dev/openworm/ChannelWorm/tests/sciunit/./ca_boyle.f.inf.lems.dat 802077\n", "pyNeuroML >>> jNeuroML >> INFO Jul 19,2016 22:30 (INFO) Written to the file /Users/rgerkin/Dropbox/dev/openworm/ChannelWorm/tests/sciunit/./ca_boyle.f.tau.lems.dat 728480\n", "pyNeuroML >>> jNeuroML >> INFO Jul 19,2016 22:30 (INFO) Written to the file /Users/rgerkin/Dropbox/dev/openworm/ChannelWorm/tests/sciunit/./ca_boyle.i_min100.lems.dat 937004\n", "pyNeuroML >>> jNeuroML >> INFO Jul 19,2016 22:30 (INFO) Written to the file /Users/rgerkin/Dropbox/dev/openworm/ChannelWorm/tests/sciunit/./ca_boyle.i_min80.lems.dat 967943\n", "pyNeuroML >>> jNeuroML >> INFO Jul 19,2016 22:30 (INFO) Written to the file /Users/rgerkin/Dropbox/dev/openworm/ChannelWorm/tests/sciunit/./ca_boyle.i_min60.lems.dat 968239\n", "pyNeuroML >>> jNeuroML >> INFO Jul 19,2016 22:30 (INFO) Written to the file /Users/rgerkin/Dropbox/dev/openworm/ChannelWorm/tests/sciunit/./ca_boyle.i_min40.lems.dat 953985\n", "pyNeuroML >>> jNeuroML >> INFO Jul 19,2016 22:30 (INFO) Written to the file /Users/rgerkin/Dropbox/dev/openworm/ChannelWorm/tests/sciunit/./ca_boyle.i_min20.lems.dat 949250\n", "pyNeuroML >>> jNeuroML >> INFO Jul 19,2016 22:30 (INFO) Written to the file /Users/rgerkin/Dropbox/dev/openworm/ChannelWorm/tests/sciunit/./ca_boyle.i_0.lems.dat 647931\n", "pyNeuroML >>> jNeuroML >> INFO Jul 19,2016 22:30 (INFO) Written to the file /Users/rgerkin/Dropbox/dev/openworm/ChannelWorm/tests/sciunit/./ca_boyle.i_20.lems.dat 995022\n", "pyNeuroML >>> jNeuroML >> INFO Jul 19,2016 22:30 (INFO) Written to the file /Users/rgerkin/Dropbox/dev/openworm/ChannelWorm/tests/sciunit/./ca_boyle.i_40.lems.dat 991260\n", "pyNeuroML >>> jNeuroML >> INFO Jul 19,2016 22:30 (INFO) Written to the file /Users/rgerkin/Dropbox/dev/openworm/ChannelWorm/tests/sciunit/./ca_boyle.i_60.lems.dat 988454\n", "pyNeuroML >>> jNeuroML >> INFO Jul 19,2016 22:30 (INFO) Written to the file /Users/rgerkin/Dropbox/dev/openworm/ChannelWorm/tests/sciunit/./ca_boyle.i_80.lems.dat 984435\n", "pyNeuroML >>> jNeuroML >> INFO Jul 19,2016 22:30 (INFO) Written to the file /Users/rgerkin/Dropbox/dev/openworm/ChannelWorm/tests/sciunit/./ca_boyle.i_100.lems.dat 980531\n", "pyNeuroML >>> jNeuroML >> INFO Jul 19,2016 22:30 (INFO) Written to the file /Users/rgerkin/Dropbox/dev/openworm/ChannelWorm/tests/sciunit/./ca_boyle.rampV.lems.dat 765867\n", "pyNeuroML >>> jNeuroML >> INFO Jul 19,2016 22:30 (INFO) Finished reading, building, running and displaying LEMS model\n", "pyNeuroML >>> jNeuroML >> \n", "=== Model EGL-19 achieved score Fail on test 'IV Curve Test'. ===\n", "The score was computed according to 'The sum-squared difference in the observed and predicted current values over the range of the tested holding potentials.' with raw value 3.087 and pass cutoff 1.0 pA**2\n", "The scaling factor for the model IV curve was 0.924\n" ] } ], "source": [ "# Judge the model output against the experimental data\n", "score = test.judge(model)\n", "score.summarize()\n", "print(\"The score was computed according to '%s' with raw value %s and pass cutoff %s\" \\\n", " % (score.description,score.raw,test.converter.cutoff))\n", "print('The scaling factor for the model IV curve was %.3g' % score.related_data['scale_factor'])" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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NT/4bcGWwA0TkPOBV4O+qerS84tmGYZCWlhZR9derV4+lS5fSt29fUlNTGTVq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\nIvIqJgz7p0ADsNpt5wzJrVPVq1V1D/Cs9zwisgvoo6pNxVN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"text/plain": [ "<matplotlib.figure.Figure at 0x10e06f5c0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib as mpl\n", "import matplotlib.pyplot as plt\n", "mpl.rcParams.update({'font.size':14, 'lines.linewidth':3})\n", "score.plot()\n", "plt.tight_layout()\n", "plt.savefig('/Users/rgerkin/Desktop/iv_curves.eps',format='eps')" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
minesh1291/Practicing-Kaggle
zillow2017/H2Opy_v0.ipynb
1
226257
{ "cells": [ { "cell_type": "markdown", "metadata": { "toc": "true" }, "source": [ "# Table of Contents\n", " <p><div class=\"lev1 toc-item\"><a href=\"#import-Packages\" data-toc-modified-id=\"import-Packages-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>import Packages</a></div><div class=\"lev2 toc-item\"><a href=\"#H2O-init\" data-toc-modified-id=\"H2O-init-11\"><span class=\"toc-item-num\">1.1&nbsp;&nbsp;</span>H2O init</a></div><div class=\"lev2 toc-item\"><a href=\"#import-xy_train,-x_test\" data-toc-modified-id=\"import-xy_train,-x_test-12\"><span class=\"toc-item-num\">1.2&nbsp;&nbsp;</span>import xy_train, x_test</a></div><div class=\"lev2 toc-item\"><a href=\"#27-AUG-2017-dl_model\" data-toc-modified-id=\"27-AUG-2017-dl_model-13\"><span class=\"toc-item-num\">1.3&nbsp;&nbsp;</span>27-AUG-2017 dl_model</a></div><div class=\"lev3 toc-item\"><a href=\"#Model-Details\" data-toc-modified-id=\"Model-Details-131\"><span class=\"toc-item-num\">1.3.1&nbsp;&nbsp;</span>Model Details</a></div><div class=\"lev2 toc-item\"><a href=\"#28-AUG-2017-dl_model_list-1\" data-toc-modified-id=\"28-AUG-2017-dl_model_list-1-14\"><span class=\"toc-item-num\">1.4&nbsp;&nbsp;</span>28-AUG-2017 dl_model_list 1</a></div><div class=\"lev3 toc-item\"><a href=\"#split-the-data-3-ways:\" data-toc-modified-id=\"split-the-data-3-ways:-141\"><span class=\"toc-item-num\">1.4.1&nbsp;&nbsp;</span>split the data 3 ways:</a></div><div class=\"lev3 toc-item\"><a href=\"#desicion\" data-toc-modified-id=\"desicion-142\"><span class=\"toc-item-num\">1.4.2&nbsp;&nbsp;</span>desicion</a></div><div class=\"lev2 toc-item\"><a href=\"#28-AUG-2017-dl_model_list-2\" data-toc-modified-id=\"28-AUG-2017-dl_model_list-2-15\"><span class=\"toc-item-num\">1.5&nbsp;&nbsp;</span>28-AUG-2017 dl_model_list 2</a></div><div class=\"lev2 toc-item\"><a href=\"#28-AUG-2017-dl_model_list-3\" data-toc-modified-id=\"28-AUG-2017-dl_model_list-3-16\"><span class=\"toc-item-num\">1.6&nbsp;&nbsp;</span>28-AUG-2017 dl_model_list 3</a></div><div class=\"lev3 toc-item\"><a href=\"#30,40-nurons,-4,5-layers\" data-toc-modified-id=\"30,40-nurons,-4,5-layers-161\"><span class=\"toc-item-num\">1.6.1&nbsp;&nbsp;</span>30,40 nurons, 4,5 layers</a></div><div class=\"lev3 toc-item\"><a href=\"#tests\" data-toc-modified-id=\"tests-162\"><span class=\"toc-item-num\">1.6.2&nbsp;&nbsp;</span>tests</a></div><div class=\"lev2 toc-item\"><a href=\"#Predict-test_h2o-&amp;-combine\" data-toc-modified-id=\"Predict-test_h2o-&amp;-combine-17\"><span class=\"toc-item-num\">1.7&nbsp;&nbsp;</span>Predict test_h2o &amp; combine</a></div><div class=\"lev2 toc-item\"><a href=\"#Predict-x_test-&amp;-combine\" data-toc-modified-id=\"Predict-x_test-&amp;-combine-18\"><span class=\"toc-item-num\">1.8&nbsp;&nbsp;</span>Predict x_test &amp; combine</a></div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# import Packages" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2017-08-27T13:47:42.434154Z", "start_time": "2017-08-27T13:47:36.719466Z" } }, "outputs": [], "source": [ "import h2o\n", "import time,os" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2017-08-27T17:22:44.951141Z", "start_time": "2017-08-27T17:22:23.849984Z" } }, "outputs": [], "source": [ "%matplotlib inline \n", "#IMPORT ALL THE THINGS\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", "\n", "from h2o.estimators.deeplearning import H2OAutoEncoderEstimator, H2ODeepLearningEstimator\n", "from h2o.estimators.gbm import H2OGradientBoostingEstimator\n", "from h2o.estimators.glm import H2OGeneralizedLinearEstimator\n", "from h2o.estimators.random_forest import H2ORandomForestEstimator" ] }, { "cell_type": "markdown", "metadata": { "ExecuteTime": { "end_time": "2017-08-28T18:13:05.032464Z", "start_time": "2017-08-28T18:13:05.029381Z" } }, "source": [ "## H2O init" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2017-08-27T13:56:31.268045Z", "start_time": "2017-08-27T13:56:30.673475Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Checking whether there is an H2O instance running at http://localhost:54321. connected.\n" ] }, { "data": { "text/html": [ "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td>H2O cluster uptime:</td>\n", "<td>7 mins 36 secs</td></tr>\n", "<tr><td>H2O cluster version:</td>\n", "<td>3.10.4.8</td></tr>\n", "<tr><td>H2O cluster version age:</td>\n", "<td>3 months and 6 days </td></tr>\n", "<tr><td>H2O cluster name:</td>\n", "<td>H2O_from_python_jethva_kxzc79</td></tr>\n", "<tr><td>H2O cluster total nodes:</td>\n", "<td>1</td></tr>\n", "<tr><td>H2O cluster free memory:</td>\n", "<td>16.87 Gb</td></tr>\n", "<tr><td>H2O cluster total cores:</td>\n", "<td>24</td></tr>\n", "<tr><td>H2O cluster allowed cores:</td>\n", "<td>24</td></tr>\n", "<tr><td>H2O cluster status:</td>\n", "<td>accepting new members, healthy</td></tr>\n", "<tr><td>H2O connection url:</td>\n", "<td>http://localhost:54321</td></tr>\n", "<tr><td>H2O connection proxy:</td>\n", "<td>None</td></tr>\n", "<tr><td>H2O internal security:</td>\n", "<td>False</td></tr>\n", "<tr><td>Python version:</td>\n", "<td>3.6.0 final</td></tr></table></div>" ], "text/plain": [ "-------------------------- ------------------------------\n", "H2O cluster uptime: 7 mins 36 secs\n", "H2O cluster version: 3.10.4.8\n", "H2O cluster version age: 3 months and 6 days\n", "H2O cluster name: H2O_from_python_jethva_kxzc79\n", "H2O cluster total nodes: 1\n", "H2O cluster free memory: 16.87 Gb\n", "H2O cluster total cores: 24\n", "H2O cluster allowed cores: 24\n", "H2O cluster status: accepting new members, healthy\n", "H2O connection url: http://localhost:54321\n", "H2O connection proxy:\n", "H2O internal security: False\n", "Python version: 3.6.0 final\n", "-------------------------- ------------------------------" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "h2o.init(max_mem_size = 20) #uses all cores by default\n", "h2o.remove_all()" ] }, { "cell_type": "markdown", "metadata": { "ExecuteTime": { "end_time": "2017-08-28T18:14:43.400150Z", "start_time": "2017-08-28T18:14:43.397239Z" } }, "source": [ "## import xy_train, x_test" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2017-08-27T17:26:56.612758Z", "start_time": "2017-08-27T17:26:36.709062Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Parse progress: |█████████████████████████████████████████████████████████| 100%\n", "Parse progress: |█████████████████████████████████████████████████████████| 100%\n" ] } ], "source": [ "xy_tr = h2o.import_file(path = os.path.realpath(\"../daielee/xy_tr.csv\"))\n", "x_test = h2o.import_file(path = os.path.realpath(\"../daielee/x_test.csv\"))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2017-08-27T17:30:29.958224Z", "start_time": "2017-08-27T17:28:40.322699Z" } }, "outputs": [], "source": [ "xy_tr_df = xy_tr.as_data_frame(use_pandas=True)\n", "x_test_df = x_test.as_data_frame(use_pandas=True)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2017-08-27T17:33:16.499247Z", "start_time": "2017-08-27T17:33:16.492251Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(90275, 58) (2985217, 57)\n" ] } ], "source": [ "print (xy_tr_df.shape,x_test_df.shapepe)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 27-AUG-2017 dl_model\n", "\n", "### Model Details\n", "\n", "\n", "* dl_model = H2ODeepLearningEstimator(epochs=1000)\n", "* dl_model.train(X, y, xy_tr)\n", "\n", "=============\n", "* H2ODeepLearningEstimator : Deep Learning\n", "* Model Key: DeepLearning_model_python_1503841734286_1\n", "\n", "\n", "* ModelMetricsRegression: deeplearning\n", "* ** Reported on train data. **\n", "\n", "* MSE: 0.02257823450695032\n", "* RMSE: 0.15026055539279204\n", "* MAE: 0.06853673758752012\n", "* RMSLE: NaN\n", "* Mean Residual Deviance: 0.02257823450695032" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2017-08-27T17:46:01.328912Z", "start_time": "2017-08-27T17:36:07.648878Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "deeplearning Model Build progress: |██████████████████████████████████████| 100%\n" ] } ], "source": [ "X = xy_tr.col_names[0:57]\n", "y = xy_tr.col_names[57]\n", "dl_model = H2ODeepLearningEstimator(epochs=1000)\n", "dl_model.train(X, y, xy_tr)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2017-08-27T17:49:10.923502Z", "start_time": "2017-08-27T17:49:10.702469Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model Details\n", "=============\n", "H2ODeepLearningEstimator : Deep Learning\n", "Model Key: DeepLearning_model_python_1503841734286_1\n", "\n", "\n", "ModelMetricsRegression: deeplearning\n", "** Reported on train data. **\n", "\n", "MSE: 0.02257823450695032\n", "RMSE: 0.15026055539279204\n", "MAE: 0.06853673758752012\n", "RMSLE: NaN\n", "Mean Residual Deviance: 0.02257823450695032\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>iterations</b></td>\n", "<td><b>samples</b></td>\n", "<td><b>training_rmse</b></td>\n", "<td><b>training_deviance</b></td>\n", "<td><b>training_mae</b></td></tr>\n", "<tr><td></td>\n", "<td>2017-08-27 20:36:08</td>\n", "<td> 0.000 sec</td>\n", "<td>None</td>\n", "<td>0.0</td>\n", "<td>0</td>\n", "<td>0.0</td>\n", "<td>nan</td>\n", "<td>nan</td>\n", "<td>nan</td></tr>\n", "<tr><td></td>\n", "<td>2017-08-27 20:36:14</td>\n", "<td> 6.673 sec</td>\n", "<td>17585 obs/sec</td>\n", "<td>1.1068513</td>\n", "<td>1</td>\n", "<td>99921.0</td>\n", "<td>0.1707692</td>\n", "<td>0.0291621</td>\n", "<td>0.0716218</td></tr>\n", "<tr><td></td>\n", "<td>2017-08-27 20:36:21</td>\n", "<td>13.935 sec</td>\n", "<td>23280 obs/sec</td>\n", "<td>3.3204985</td>\n", "<td>3</td>\n", "<td>299758.0</td>\n", "<td>0.1693846</td>\n", "<td>0.0286912</td>\n", "<td>0.0699298</td></tr>\n", "<tr><td></td>\n", "<td>2017-08-27 20:36:28</td>\n", "<td>20.592 sec</td>\n", "<td>25684 obs/sec</td>\n", "<td>5.5341567</td>\n", "<td>5</td>\n", "<td>499596.0</td>\n", "<td>0.1688644</td>\n", "<td>0.0285152</td>\n", "<td>0.0703825</td></tr>\n", "<tr><td></td>\n", "<td>2017-08-27 20:36:34</td>\n", "<td>26.864 sec</td>\n", "<td>27280 obs/sec</td>\n", "<td>7.7537413</td>\n", "<td>7</td>\n", "<td>699969.0</td>\n", "<td>0.1689997</td>\n", "<td>0.0285609</td>\n", "<td>0.0703055</td></tr>\n", "<tr><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></tr>\n", "<tr><td></td>\n", "<td>2017-08-27 20:45:43</td>\n", "<td> 9 min 35.269 sec</td>\n", "<td>38494 obs/sec</td>\n", "<td>241.5156134</td>\n", "<td>218</td>\n", "<td>21802822.0000000</td>\n", "<td>0.1532783</td>\n", "<td>0.0234942</td>\n", "<td>0.0712851</td></tr>\n", "<tr><td></td>\n", "<td>2017-08-27 20:45:50</td>\n", "<td> 9 min 42.698 sec</td>\n", "<td>38522 obs/sec</td>\n", "<td>244.8331986</td>\n", "<td>221</td>\n", "<td>22102317.0000000</td>\n", "<td>0.1509789</td>\n", "<td>0.0227946</td>\n", "<td>0.0695887</td></tr>\n", "<tr><td></td>\n", "<td>2017-08-27 20:45:55</td>\n", "<td> 9 min 47.743 sec</td>\n", "<td>38536 obs/sec</td>\n", "<td>247.0484520</td>\n", "<td>223</td>\n", "<td>22302299.0000000</td>\n", "<td>0.1519738</td>\n", "<td>0.0230960</td>\n", "<td>0.0705504</td></tr>\n", "<tr><td></td>\n", "<td>2017-08-27 20:46:00</td>\n", "<td> 9 min 52.873 sec</td>\n", "<td>38544 obs/sec</td>\n", "<td>249.2637164</td>\n", "<td>225</td>\n", "<td>22502282.0000000</td>\n", "<td>0.1520505</td>\n", "<td>0.0231193</td>\n", "<td>0.0696316</td></tr>\n", "<tr><td></td>\n", "<td>2017-08-27 20:46:00</td>\n", "<td> 9 min 52.946 sec</td>\n", "<td>38544 obs/sec</td>\n", "<td>249.2637164</td>\n", "<td>225</td>\n", "<td>22502282.0000000</td>\n", "<td>0.1502606</td>\n", "<td>0.0225782</td>\n", "<td>0.0685367</td></tr></table></div>" ], "text/plain": [ " timestamp duration training_speed epochs iterations samples training_rmse training_deviance training_mae\n", "--- ------------------- ---------------- ---------------- ------------------ ------------ ---------- ------------------- -------------------- -------------------\n", " 2017-08-27 20:36:08 0.000 sec 0.0 0 0.0 nan nan nan\n", " 2017-08-27 20:36:14 6.673 sec 17585 obs/sec 1.1068512877319303 1 99921.0 0.17076920858358277 0.029162122600263195 0.07162183855539377\n", " 2017-08-27 20:36:21 13.935 sec 23280 obs/sec 3.3204984768762116 3 299758.0 0.1693846245107908 0.0286911510206616 0.06992976320405175\n", " 2017-08-27 20:36:28 20.592 sec 25684 obs/sec 5.5341567432844085 5 499596.0 0.16886436170309205 0.028515172653392703 0.070382519744771\n", " 2017-08-27 20:36:34 26.864 sec 27280 obs/sec 7.7537413458875655 7 699969.0 0.16899974231011713 0.028560912900885995 0.07030550736729402\n", "--- --- --- --- --- --- --- --- --- ---\n", " 2017-08-27 20:45:43 9 min 35.269 sec 38494 obs/sec 241.51561340348934 218 21802822.0 0.15327830179018073 0.02349423779968172 0.071285078942824\n", " 2017-08-27 20:45:50 9 min 42.698 sec 38522 obs/sec 244.8331985599557 221 22102317.0 0.1509788785278096 0.022794621761515088 0.06958869826467107\n", " 2017-08-27 20:45:55 9 min 47.743 sec 38536 obs/sec 247.04845195236777 223 22302299.0 0.1519738190253394 0.023096041669146614 0.07055043550007233\n", " 2017-08-27 20:46:00 9 min 52.873 sec 38544 obs/sec 249.26371642204376 225 22502282.0 0.1520504536072195 0.023119340442161206 0.06963164259649125\n", " 2017-08-27 20:46:00 9 min 52.946 sec 38544 obs/sec 249.26371642204376 225 22502282.0 0.15026055539279204 0.02257823450695032 0.06853673758752012" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "See the whole table with table.as_data_frame()\n" ] }, { "data": { "text/plain": [ "<bound method ModelBase.summary of >" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dl_model.summary" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "ExecuteTime": { "end_time": "2017-08-27T17:51:45.528823Z", "start_time": "2017-08-27T17:51:45.495320Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Index(['', 'timestamp', 'duration', 'training_speed', 'epochs', 'iterations',\n", " 'samples', 'training_rmse', 'training_deviance', 'training_mae'],\n", " dtype='object')\n" ] } ], "source": [ "sh = dl_model.score_history()\n", "sh = pd.DataFrame(sh)\n", "print(sh.columns)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "ExecuteTime": { "end_time": "2017-08-27T17:54:27.188211Z", "start_time": "2017-08-27T17:54:26.396619Z" } }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f46c82a0128>" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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FZGRkhWX79evHhAkT6N27N6NGjWLgwIHH582cOZPp06fTu3dvunfvzqeffnp8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\nsC2XnB+Lz9ib9bw+Q05hMWv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"text/plain": [ "<matplotlib.figure.Figure at 0x7f46c829edd8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sh.plot(x='epochs',y = ['training_deviance', 'training_mae'])" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "ExecuteTime": { "end_time": "2017-08-27T18:10:02.263576Z", "start_time": "2017-08-27T18:10:02.251270Z" } }, "outputs": [ { "data": { "text/plain": [ "{'activation': 'Rectifier',\n", " 'adaptive_rate': True,\n", " 'autoencoder': False,\n", " 'average_activation': 0.0,\n", " 'balance_classes': False,\n", " 'categorical_encoding': 'AUTO',\n", " 'checkpoint': None,\n", " 'class_sampling_factors': None,\n", " 'classification_stop': 0.0,\n", " 'col_major': False,\n", " 'diagnostics': True,\n", " 'distribution': 'AUTO',\n", " 'elastic_averaging': False,\n", " 'elastic_averaging_moving_rate': 0.9,\n", " 'elastic_averaging_regularization': 0.001,\n", " 'epochs': 10.0,\n", " 'epsilon': 1e-08,\n", " 'export_weights_and_biases': False,\n", " 'fast_mode': True,\n", " 'fold_assignment': 'AUTO',\n", " 'fold_column': None,\n", " 'force_load_balance': True,\n", " 'hidden': [200, 200],\n", " 'hidden_dropout_ratios': None,\n", " 'huber_alpha': 0.9,\n", " 'ignore_const_cols': True,\n", " 'ignored_columns': None,\n", " 'initial_biases': None,\n", " 'initial_weight_distribution': 'UniformAdaptive',\n", " 'initial_weight_scale': 1.0,\n", " 'initial_weights': None,\n", " 'input_dropout_ratio': 0.0,\n", " 'keep_cross_validation_fold_assignment': False,\n", " 'keep_cross_validation_predictions': False,\n", " 'l1': 0.0,\n", " 'l2': 0.0,\n", " 'loss': 'Automatic',\n", " 'max_after_balance_size': 5.0,\n", " 'max_categorical_features': 2147483647,\n", " 'max_confusion_matrix_size': 20,\n", " 'max_hit_ratio_k': 0,\n", " 'max_runtime_secs': 0.0,\n", " 'max_w2': 3.4028235e+38,\n", " 'mini_batch_size': 1,\n", " 'missing_values_handling': 'MeanImputation',\n", " 'model_id': None,\n", " 'momentum_ramp': 1000000.0,\n", " 'momentum_stable': 0.0,\n", " 'momentum_start': 0.0,\n", " 'nesterov_accelerated_gradient': True,\n", " 'nfolds': 0,\n", " 'offset_column': None,\n", " 'overwrite_with_best_model': True,\n", " 'pretrained_autoencoder': None,\n", " 'quantile_alpha': 0.5,\n", " 'quiet_mode': False,\n", " 'rate': 0.005,\n", " 'rate_annealing': 1e-06,\n", " 'rate_decay': 1.0,\n", " 'regression_stop': 1e-06,\n", " 'replicate_training_data': True,\n", " 'reproducible': False,\n", " 'response_column': None,\n", " 'rho': 0.99,\n", " 'score_duty_cycle': 0.1,\n", " 'score_each_iteration': False,\n", " 'score_interval': 5.0,\n", " 'score_training_samples': 10000,\n", " 'score_validation_samples': 0,\n", " 'score_validation_sampling': 'Uniform',\n", " 'seed': -1,\n", " 'shuffle_training_data': False,\n", " 'single_node_mode': False,\n", " 'sparse': False,\n", " 'sparsity_beta': 0.0,\n", " 'standardize': True,\n", " 'stopping_metric': 'AUTO',\n", " 'stopping_rounds': 5,\n", " 'stopping_tolerance': 0.0,\n", " 'target_ratio_comm_to_comp': 0.05,\n", " 'train_samples_per_iteration': -2,\n", " 'training_frame': None,\n", " 'tweedie_power': 1.5,\n", " 'use_all_factor_levels': True,\n", " 'validation_frame': None,\n", " 'variable_importances': False,\n", " 'weights_column': None}" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dl_model.default_params" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "ExecuteTime": { "end_time": "2017-08-27T18:27:02.808623Z", "start_time": "2017-08-27T18:27:02.121443Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "ModelMetricsRegression: deeplearning\n", "** Reported on test data. **\n", "\n", "MSE: 0.025000241400573237\n", "RMSE: 0.1581146463822161\n", "MAE: 0.06741851411740078\n", "RMSLE: NaN\n", "Mean Residual Deviance: 0.025000241400573237\n" ] }, { "data": { "text/plain": [] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ " dl_model.model_performance(test_data=xy_tr)" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "ExecuteTime": { "end_time": "2017-08-27T18:31:14.017671Z", "start_time": "2017-08-27T18:31:14.008576Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Warning: This model doesn't have variable importances\n" ] }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ "Empty DataFrame\n", "Columns: []\n", "Index: []" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.DataFrame(dl_model.varimp())" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "ExecuteTime": { "end_time": "2017-08-27T18:33:33.108344Z", "start_time": "2017-08-27T18:33:15.870591Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "deeplearning prediction progress: |███████████████████████████████████████| 100%\n" ] } ], "source": [ "y_test = dl_model.predict(test_data=x_test)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "ExecuteTime": { "end_time": "2017-08-27T18:33:56.293624Z", "start_time": "2017-08-27T18:33:56.281457Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(2985217, 1)\n" ] } ], "source": [ "print(y_test.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 28-AUG-2017 dl_model_list 1" ] }, { "cell_type": "code", "execution_count": 103, "metadata": { "ExecuteTime": { "end_time": "2017-08-28T09:14:32.549813Z", "start_time": "2017-08-28T08:20:56.715419Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "deeplearning Model Build progress: |██████████████████████████████████████| 100%\n", "deeplearning Model Build progress: |██████████████████████████████████████| 100%\n", 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|██████████████████████████████████████| 100%\n" ] } ], "source": [ "nuron_cnts = [40,80,160]\n", "layer_cnts = [1,2,3,4,5]\n", "acts = [\"Tanh\",\"Maxout\",\"Rectifier\",\"RectifierWithDropout\"]\n", "models_list = []\n", "m_names_list = []\n", "i = 0\n", "# N 3 * L 5 * A 4 = 60n \n", "for act in acts:\n", " for layer_cnt in layer_cnts:\n", " for nuron_cnt in nuron_cnts:\n", " m_names_list.append(\"N:\"+str(nuron_cnt)+\"L:\"+str(layer_cnt)+\"A:\"+act)\n", " print(m_names_list[i])\n", " models_list.append(H2ODeepLearningEstimator(\n", " model_id=m_names_list[i],\n", " hidden=[nuron_cnt]*layer_cnt, # more hidden layers -> more complex interactions\n", " activation = act,\n", " epochs=10, # to keep it short enough\n", " score_validation_samples=10000,\n", " overwrite_with_best_model=True,\n", " adaptive_rate=True,\n", " l1=0.00001, # add some L1/L2 regularization\n", " l2=0.00001,\n", " max_w2=10.0 # helps stability for Rectifier\n", " ))\n", " \n", " models_list[i].train(x=X,y=y,training_frame=xy_tr,\n", " validation_frame=xy_tr)\n", " i+=1" ] }, { "cell_type": "code", "execution_count": 104, "metadata": { "ExecuteTime": { "end_time": "2017-08-28T09:18:16.359964Z", "start_time": "2017-08-28T09:18:11.751532Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "N:40L:1A:Tanh 0.02191104025818047\n", "N:80L:1A:Tanh 0.02866460132997793\n", "N:160L:1A:Tanh 0.02738745782618937\n", "N:40L:2A:Tanh 0.027354476515048527\n", "N:80L:2A:Tanh 0.0213912611589249\n", "N:160L:2A:Tanh 0.03003707631564074\n", "N:40L:3A:Tanh 0.025110226659536748\n", "N:80L:3A:Tanh 0.028280990468011215\n", "N:160L:3A:Tanh 0.024612255460352443\n", "N:40L:4A:Tanh 0.03053287072861051\n", "N:80L:4A:Tanh 0.02361192080682827\n", "N:160L:4A:Tanh 0.026893795364736805\n", "N:40L:5A:Tanh 0.027192312105420283\n", "N:80L:5A:Tanh 0.02680966175827571\n", "N:160L:5A:Tanh 0.025812352247345866\n", "N:40L:1A:Maxout 0.02052945142692426\n", "N:80L:1A:Maxout 0.023988878572638126\n", "N:160L:1A:Maxout 0.023132544480644914\n", "N:40L:2A:Maxout 0.026802401674975062\n", "N:80L:2A:Maxout 0.02556542374159979\n", "N:160L:2A:Maxout 0.027736017276626588\n", "N:40L:3A:Maxout 0.02521875009843506\n", "N:80L:3A:Maxout 0.03075028538701201\n", "N:160L:3A:Maxout 0.023427097603774816\n", "N:40L:4A:Maxout 0.026294584554501673\n", "N:80L:4A:Maxout 0.027574321628114656\n", "N:160L:4A:Maxout 0.02969554448548389\n", "N:40L:5A:Maxout 0.029599484325346585\n", "N:80L:5A:Maxout 0.024038942730959106\n", "N:160L:5A:Maxout 0.028474911836836505\n", "N:40L:1A:Rectifier 0.028346364590309193\n", "N:80L:1A:Rectifier 0.028481820251669787\n", "N:160L:1A:Rectifier 0.025189538051022413\n", "N:40L:2A:Rectifier 0.027264368063950564\n", "N:80L:2A:Rectifier 0.02442926565741489\n", "N:160L:2A:Rectifier 0.024232643998517617\n", "N:40L:3A:Rectifier 0.02451593226524556\n", "N:80L:3A:Rectifier 0.02395960155561946\n", "N:160L:3A:Rectifier 0.02586771050284556\n", "N:40L:4A:Rectifier 0.026499569011630454\n", "N:80L:4A:Rectifier 0.024932048979661853\n", "N:160L:4A:Rectifier 0.020851246602500616\n", "N:40L:5A:Rectifier 0.020170051158055997\n", "N:80L:5A:Rectifier 0.02579208593984897\n", "N:160L:5A:Rectifier 0.027704975682876096\n", "N:40L:1A:RectifierWithDropout 0.027864729600650163\n", "N:80L:1A:RectifierWithDropout 0.025456952506761263\n", "N:160L:1A:RectifierWithDropout 0.025420393720763407\n", "N:40L:2A:RectifierWithDropout 0.02568216346528083\n", "N:80L:2A:RectifierWithDropout 0.028295979836577674\n", "N:160L:2A:RectifierWithDropout 0.02370280221686692\n", "N:40L:3A:RectifierWithDropout 0.02623777253734144\n", "N:80L:3A:RectifierWithDropout 0.02147244398080917\n", "N:160L:3A:RectifierWithDropout 0.024075506716878754\n", "N:40L:4A:RectifierWithDropout 0.025482172219417717\n", "N:80L:4A:RectifierWithDropout 0.02087426269736986\n", "N:160L:4A:RectifierWithDropout 0.028485080565035858\n", "N:40L:5A:RectifierWithDropout 0.029091407690115315\n", "N:80L:5A:RectifierWithDropout 0.027870237362026214\n", "N:160L:5A:RectifierWithDropout 0.02457939327244617\n" ] } ], "source": [ "for i in range(0,639): #range(len(models_list)-1):\n", " try:\n", " sh = models_list[i].score_history()\n", " sh = pd.DataFrame(sh)\n", " perform = sh['validation_deviance'].tolist()[-1]\n", " print(models_list[i].model_id,end=\" \")\n", " print(perform)\n", " except:\n", " print(end=\"\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### split the data 3 ways: \n", "1. 60% for training \n", "2. 20% for validation (hyper parameter tuning) \n", "3. 20% for final testing \n", " \n", "1. We will train a data set on one set and use the others to test the validity of the model by ensuring that it can predict accurately on data the model has not been shown. \n", "2. The second set will be used for validation most of the time. \n", "3. The third set will be withheld until the end, to ensure that our validation accuracy is consistent with data we have never seen during the iterative process. \n", "\n", "### desicion\n", "Use Rect-dropout" ] }, { "cell_type": "code", "execution_count": 181, "metadata": { "ExecuteTime": { "end_time": "2017-08-28T12:27:16.194196Z", "start_time": "2017-08-28T12:27:16.089051Z" } }, "outputs": [], "source": [ "train_h2o, valid_h2o, test_h2o = xy_tr.split_frame([0.6, 0.2], seed=1234)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 28-AUG-2017 dl_model_list 2" ] }, { "cell_type": "code", "execution_count": 112, "metadata": { "ExecuteTime": { "end_time": "2017-08-28T09:56:35.537356Z", "start_time": "2017-08-28T09:53:43.452293Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "N: 40 L: 1 A: RectifierWithDropout\n", "deeplearning Model Build progress: |██████████████████████████████████████| 100%\n", "N: 80 L: 1 A: RectifierWithDropout\n", "deeplearning Model Build progress: |██████████████████████████████████████| 100%\n", "N: 160 L: 1 A: RectifierWithDropout\n", "deeplearning Model Build progress: |██████████████████████████████████████| 100%\n", "N: 40 L: 2 A: RectifierWithDropout\n", "deeplearning Model Build progress: |██████████████████████████████████████| 100%\n", "N: 80 L: 2 A: RectifierWithDropout\n", "deeplearning Model Build progress: |██████████████████████████████████████| 100%\n", "N: 160 L: 2 A: RectifierWithDropout\n", "deeplearning Model Build progress: |██████████████████████████████████████| 100%\n", "N: 40 L: 3 A: RectifierWithDropout\n", "deeplearning Model Build progress: |██████████████████████████████████████| 100%\n", "N: 80 L: 3 A: RectifierWithDropout\n", "deeplearning Model Build progress: |██████████████████████████████████████| 100%\n", "N: 160 L: 3 A: RectifierWithDropout\n", "deeplearning Model Build progress: |██████████████████████████████████████| 100%\n", "N: 40 L: 4 A: RectifierWithDropout\n", "deeplearning Model Build progress: |██████████████████████████████████████| 100%\n", "N: 80 L: 4 A: RectifierWithDropout\n", "deeplearning Model Build progress: |██████████████████████████████████████| 100%\n", "N: 160 L: 4 A: RectifierWithDropout\n", "deeplearning Model Build progress: |██████████████████████████████████████| 100%\n", "N: 40 L: 5 A: RectifierWithDropout\n", "deeplearning Model Build progress: |██████████████████████████████████████| 100%\n", "N: 80 L: 5 A: RectifierWithDropout\n", "deeplearning Model Build progress: |██████████████████████████████████████| 100%\n", "N: 160 L: 5 A: RectifierWithDropout\n", "deeplearning Model Build progress: |██████████████████████████████████████| 100%\n" ] } ], "source": [ "nuron_cnts = [40,80,160]\n", "layer_cnts = [1,2,3,4,5]\n", "acts = [\"RectifierWithDropout\"] #\"Tanh\",\"Maxout\",\"Rectifier\",\n", "models_list = []\n", "m_names_list = []\n", "time_tkn_wall =[]\n", "time_tkn_clk=[]\n", "i = 0\n", "# N 3 * L 5 * A 1 = 15n \n", "for act in acts:\n", " for layer_cnt in layer_cnts:\n", " for nuron_cnt in nuron_cnts:\n", " m_names_list.append(\"N: \"+str(nuron_cnt)+\" L: \"+str(layer_cnt)+\" A: \"+act)\n", " print(m_names_list[i])\n", " models_list.append(H2ODeepLearningEstimator(\n", " model_id=m_names_list[i],\n", " hidden=[nuron_cnt]*layer_cnt, # more hidden layers -> more complex interactions\n", " activation = act,\n", " epochs=10, # to keep it short enough\n", " score_validation_samples=10000,\n", " overwrite_with_best_model=True,\n", " adaptive_rate=True,\n", " l1=0.00001, # add some L1/L2 regularization\n", " l2=0.00001,\n", " max_w2=10.0 # helps stability for Rectifier\n", " ))\n", " str_time_clk = time.clock()\n", " str_time_wall = time.time()\n", " \n", " models_list[i].train(x=X,y=y,training_frame=train,\n", " validation_frame=valid)\n", " time_tkn_clk.append(time.clock()-str_time_clk)\n", " time_tkn_wall.append(time.time()-str_time_wall)\n", " \n", " i+=1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "time.time() shows that the wall-clock time has passed approximately one second while time.clock() shows the CPU time spent on the current process is less than 1 microsecond. time.clock() has a much higher precision than time.time()." ] }, { "cell_type": "code", "execution_count": 115, "metadata": { "ExecuteTime": { "end_time": "2017-08-28T09:59:02.738045Z", "start_time": "2017-08-28T09:59:02.209226Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "N: 40 L: 1 A: RectifierWithDropout clk 0.27000000000003865 wall 3.249885320663452 0.023469934690956554\n", "N: 80 L: 1 A: RectifierWithDropout clk 0.3100000000000023 wall 4.686337471008301 0.025744111295793928\n", "N: 160 L: 1 A: RectifierWithDropout clk 0.339999999999975 wall 7.4652464389801025 0.02426045842498989\n", "N: 40 L: 2 A: RectifierWithDropout clk 0.27000000000003865 wall 3.916215419769287 0.026011432819666936\n", "N: 80 L: 2 A: RectifierWithDropout clk 0.3299999999999841 wall 6.651781797409058 0.02078551153846703\n", "N: 160 L: 2 A: RectifierWithDropout clk 0.35000000000002274 wall 10.531349658966064 0.023994181648258305\n", "N: 40 L: 3 A: RectifierWithDropout clk 0.2899999999999636 wall 4.695176839828491 0.025275593214375437\n", "N: 80 L: 3 A: RectifierWithDropout clk 0.34000000000003183 wall 8.670188903808594 0.02558111504808703\n", "N: 160 L: 3 A: RectifierWithDropout clk 0.4900000000000091 wall 20.546206951141357 0.021774404657216598\n", "N: 40 L: 4 A: RectifierWithDropout clk 0.3199999999999932 wall 5.513561010360718 0.020633118910378\n", "N: 80 L: 4 A: RectifierWithDropout clk 0.38999999999998636 wall 10.835278511047363 0.02253775756289219\n", "N: 160 L: 4 A: RectifierWithDropout clk 0.6999999999999886 wall 33.28201603889465 0.02442749249303816\n", "N: 40 L: 5 A: RectifierWithDropout clk 0.35000000000002274 wall 6.502399444580078 0.02028710252858947\n", "N: 80 L: 5 A: RectifierWithDropout clk 0.39999999999997726 wall 14.082407474517822 0.027109466554412237\n" ] } ], "source": [ "for i in range(len(models_list)-1):\n", " try:\n", " sh = models_list[i].score_history()\n", " sh = pd.DataFrame(sh)\n", " perform = sh['validation_deviance'].tolist()[-1]\n", " print(models_list[i].model_id,end=\" \")\n", " print(\" clk \"+str(time_tkn_clk[i])+\" wall \"+str(time_tkn_wall[i]),end=\" \")\n", " print(perform)\n", " except:\n", " print(end=\"\")" ] }, { "cell_type": "markdown", "metadata": { "ExecuteTime": { "end_time": "2017-08-28T10:12:50.899685Z", "start_time": "2017-08-28T10:12:50.895002Z" } }, "source": [ "## 28-AUG-2017 dl_model_list 3\n", "### 30,40 nurons, 4,5 layers" ] }, { "cell_type": "code", "execution_count": 172, "metadata": { "ExecuteTime": { "end_time": "2017-08-28T12:18:27.341767Z", "start_time": "2017-08-28T12:13:26.620459Z" }, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "N: 30 L: 4 A: RectifierWithDropout\n", "deeplearning Model Build progress: |██████████████████████████████████████| 100%\n", "N: 40 L: 4 A: RectifierWithDropout\n", "deeplearning Model Build progress: |██████████████████████████████████████| 100%\n", "N: 50 L: 4 A: RectifierWithDropout\n", "deeplearning Model Build progress: |██████████████████████████████████████| 100%\n", "N: 30 L: 5 A: RectifierWithDropout\n", "deeplearning Model Build progress: |██████████████████████████████████████| 100%\n", "N: 40 L: 5 A: RectifierWithDropout\n", "deeplearning Model Build progress: |██████████████████████████████████████| 100%\n", "N: 50 L: 5 A: RectifierWithDropout\n", "deeplearning Model Build progress: |██████████████████████████████████████| 100%\n" ] } ], "source": [ "nuron_cnts = [30,40,50]\n", "layer_cnts = [4,5]\n", "acts = [\"RectifierWithDropout\"] #\"Tanh\",\"Maxout\",\"Rectifier\",\n", "dout=0.5\n", "models_list = []\n", "m_names_list = []\n", "time_tkn_wall =[]\n", "time_tkn_clk=[]\n", "\n", "i = 0\n", "# N 1 * L 10 * A 1 = 10n \n", "for act in acts:\n", " for layer_cnt in layer_cnts:\n", " for nuron_cnt in nuron_cnts:\n", " m_names_list.append(\"N: \"+str(nuron_cnt)+\" L: \"+str(layer_cnt)+\" A: \"+act)\n", " print(m_names_list[i])\n", " models_list.append(H2ODeepLearningEstimator(\n", " model_id=m_names_list[i],\n", " hidden=[nuron_cnt]*layer_cnt, # more hidden layers -> more complex interactions\n", " hidden_dropout_ratios=[dout]*layer_cnt,\n", " activation = act,\n", " epochs=500, # to keep it short enough\n", " train_samples_per_iteration=300,\n", " score_validation_samples=10000,\n", " loss=\"absolute\",\n", " overwrite_with_best_model=True,\n", " adaptive_rate=True,\n", " l1=0.00001, # add some L1/L2 regularization\n", " l2=0.0001,\n", " max_w2=10.0, # helps stability for Rectifier\n", " variable_importances=True\n", " ))\n", " str_time_clk = time.clock()\n", " str_time_wall = time.time()\n", " \n", " models_list[i].train(x=X,y=y,training_frame=train,\n", " validation_frame=valid)\n", " time_tkn_clk.append(time.clock()-str_time_clk)\n", " time_tkn_wall.append(time.time()-str_time_wall)\n", " \n", " i+=1" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### tests" ] }, { "cell_type": "code", "execution_count": 142, "metadata": { "ExecuteTime": { "end_time": "2017-08-28T11:18:00.311281Z", "start_time": "2017-08-28T11:18:00.161639Z" } }, "outputs": [], "source": [ "dl_pref=dl_model.model_performance(test_data=test) " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dl_model.mean" ] }, { "cell_type": "code", "execution_count": 155, "metadata": { "ExecuteTime": { "end_time": "2017-08-28T11:50:35.691074Z", "start_time": "2017-08-28T11:50:35.684622Z" } }, "outputs": [ { "data": { "text/plain": [ "0.06796546374064724" ] }, "execution_count": 155, "metadata": {}, "output_type": "execute_result" } ], "source": [ " dl_pref.mae()" ] }, { "cell_type": "code", "execution_count": 163, "metadata": { "ExecuteTime": { "end_time": "2017-08-28T12:06:46.091917Z", "start_time": "2017-08-28T12:06:46.086056Z" } }, "outputs": [ { "data": { "text/plain": [ "'N: 30 L: 4 A: RectifierWithDropout'" ] }, "execution_count": 163, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train.shape\n", "models_list[0].model_id" ] }, { "cell_type": "code", "execution_count": 173, "metadata": { "ExecuteTime": { "end_time": "2017-08-28T12:19:00.360775Z", "start_time": "2017-08-28T12:18:53.938088Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "N: 30 L: 4 A: RectifierWithDropout clk 0.41\twall 0.85\ttr 0.070206\tval 0.067136\tts 0.068964\n", "N: 40 L: 4 A: RectifierWithDropout clk 0.38\twall 0.76\ttr 0.066569\tval 0.066725\tts 0.068954\n", "N: 50 L: 4 A: RectifierWithDropout clk 0.47\twall 1.02\ttr 0.065506\tval 0.066612\tts 0.068913\n", "N: 30 L: 5 A: RectifierWithDropout clk 0.38\twall 0.77\ttr 0.067466\tval 0.066732\tts 0.069265\n", "N: 40 L: 5 A: RectifierWithDropout clk 0.4\twall 0.84\ttr 0.066973\tval 0.067577\tts 0.069282\n", "N: 50 L: 5 A: RectifierWithDropout clk 0.43\twall 0.77\ttr 0.067525\tval 0.06719\tts 0.06935\n" ] }, { "data": { "image/png": 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WFJbyxvpS9h2pJSE2ivlTRnB9rocLzkvv0eu4axu8/GVzOcsLS/jHnkoA5o4d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\nXY11sMltFjq8FQYMhrw7Ie8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"text/plain": [ "<matplotlib.figure.Figure at 0x7f468f3ee908>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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9a1ZoB0dDRSlkpzWuYZoLaEfR3Nn0Lhz8FUa9CR37qaX89S9B4SnYOs/R1mlq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\nrWdbbT01tfVs39ucAFqSwfa99QfP2b2/9WG8OT2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"text/plain": [ "<matplotlib.figure.Figure at 0x7f46900398d0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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kfm7ng3S9Hob/1te58YwOg+1ijp88YLfuzV4EY19pmEO5sxbZtbK8tFLA/Mw8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\nNS6SCQOTuPGiRJ+vBK7NWS40iKj6UnC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"text/plain": [ "<matplotlib.figure.Figure at 0x7f468f7cf908>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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ZaY0xAW/48j10ShpDQq5iUPcRp+MYL74UhNLAHq/laE9bRnUAhqVqe0FE1ohI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\nfWZEpCzQAtjt1ZYtmf2+IAANgW2q+pe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"text/plain": [ "<matplotlib.figure.Figure at 0x7f468fb2bcc0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f46c7870668>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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/qYtJkFoO5+QMa3tN/EqNYYfLG8mtbKKp3Wl1KBNOWO0BahyTICTG6lCGLH6O\nawTvcDt4PbqPXyk1+rYcqeLx556ly9goJ5omv1giI8KZFBZIQkQg8eEBJIQHMqnnFUB8WCD+Dj2f\nG0x9aycpnbnUxacTbXUwwxCQtIAubEjZ8Dp4NfErNQZ1dRs2/fX3vOD/k5OWNzWHc7w1mrLyKIqc\nkZR2R3HYRFNuoqhw/2sLjmFSRBCTwgNcB4gw14EhIcJ1YEiICCQ62B+bzXfHVh4qLOVsKadw8s1W\nhzI8AaFUBUzr6eAd6gheTfxKjUHrth3lq02/pzEylbBrfgaNZdBQRmhjKaENZUxvLOWchv3QXIVw\n8p0dncafusZoKhqjKT0WxbHOCI6aaD4+cYAgmhpbNFFhof1+a0gIDyQ+3HWACA2YmCmiMicbmxii\nZ51tdSjD1hSdyZzSzZTVtTIlKnhI207M36pS41hTu5Oy937JdFsl5ponYdaKfssJQFcnNJa7Dwyl\n0FiGX0MJcQ1lxDWWMa+hFNO4A3GeevdHY2ckx2tjKK+JoqgzkmPOSLYSTYWJotx9kOjyD3d9e+j3\n8lIAk8IDx+Xlpc4i11OsIlKyLI5k+AKmn0V82Zt8cDSHKVlDG3msiV+pMebZDdv5Wtc6GqZeRPgA\nSb+H3Q8ip7peAxBjoLW251sDjaXQUEZYYylhDWXMaChlaeMuaDl+yradEkBtWyxVbdGUVERR0BFB\nYXcU28w/DhBVRBAREkxCRCDTooNdr5jgnvdTIoPws4+tA0No7X7q7NFEhiVYHcqwxc9ZCp9CXe42\n0MSv1PhVXNtC9PafE2LrwH7tz7xTqQgER7tek04zStXZfsrBwa+xjPiGUuIby5jbUIhpLEO6Ok7a\nzCA0SjTVTbEUNsRx+EgMn3fF8zcTz7HueCokhoTIEKZFBzM9Jpip0f84KEyLDiYy2N877fRQS4eT\nae051MSkEzmqe/augER3B+8wRvBq4ldqDPnj6+/wH7KR1gV3Eho3Z3R37giAqBmu1wDEGGipgYaS\nnstL0lhGeEMp4fXFpNQVcmHdp4jtH7efdomD484EiqsmcbQ0liMdMXxuJnHMxHPMxGMPDOv5htD7\noDA9OoTJkYFe/7ZwqKiK+VLCsYQverXeUecfTGXADGIaDgx5BK8mfqXGiJ3Hall+9H/o9A8l9PL/\nsjqc/om47nsPiYHJ/T+qULqcrgNDbQHUFmCvzSe+toD42gIW134Gpvak8s2OSMobEyioj+fwoRh2\nd8fxRrd+pC3xAAAaC0lEQVTrwFBli2Zy5D8OBlPdB4QTnyOC/YbchLIj2SyWbiJnjt/r+yc0x2aS\nVryJsrrWIW2niV+pMcAYw1uv/IH/su+h/cJHXJdlxiu7A6Kmu1584dT1rXU9BwVqCwipzWdmbQEz\nawu4uOtjxHT1FHWKn+vbQuUkcktc3xZ2m3gKzSSKTDyOwFCmxbgOBn0vIU2JDMTRz7eFdnfHbtQE\nSPz+07KILXmdD3IODWk7TfxKjQFv7T7GLbVP0Rg2nbBl37A6nJEVFAlBC2HKqZOjSZcT6ot6DgqO\n2gIm1eYzqbaAs2q2gqk/qXyTI4ryxgTy3d8WdnbH8ddu14HhuC2KKe6+hd4HhYCqvTTawgiLnDZK\nDR45k+acA1uhbogjeDXxK2Wxts4uDr/1a662ldB19fPgGN3OzjHF7oDoZNerP621UJPfc2AIrS1g\nVm0+s2rzuaRrM2K6e4o6xZ9q97eFnJJYcjpi2WHi+bbjAMej0gkb5mMLx5KAxPk4sWMrH1oHryZ+\npSz2/Ad7+FrnC9QnLCUi7SqrwxnbgqIgMQoST51YTbo6Xd8W3AcGR20BCbUFJNTmk1WzBUxjT9n2\njFWjGfXI8QuiIjCFuIYDQ9pME79SFqpqbMex5VEipBnbdY+6Ok/V8Nj9IDrF9errxFiG2nyoLyEg\n+fzRj2+EtMRkkla8YUjbjK1RFW7tzu7BCyk1Afzxrfe5lXdoSr9lwLtklBecGMuQeBZkXOP65jBB\nBExfTJQ0DWmbMZn46xoaBy+k1Dh3uLyRzP1rMfYAwq982Opw1Dg1yT1F81CMycTv31ZNTXPH4AWV\nGsdeffUFLrPvoHv5/RA2yepw1DgVkJhJ5xCv2o/JxB9JI69uHv6DhJUa6zYdLOPa8t/QGDiZoAvu\nszocNZ45AqgI7Kdf4zQ8SvwislJEDotIrois6Wd9mohsFZF2Efn3oWzb7/4wdH32O9qdXYMXVmqc\ncXZ1s+P135BhKyToikfAL8jqkNQ41xybOaTygyZ+EbEDjwNXABnArSKS0adYDXAfsHYY257C6RfG\nDV1vs35ngSdtUGpceXnrIW5v/RO1MYtwzL/R6nDUBNAcmT6k8p6c8S8Bco0xecaYDuBF4NreBYwx\nlcaY7UDnULftjyN8EnHSQMEHzw158iGlxrL61k5a3n+UOKkn8vq1evum8gqnPXBI5T1J/IlAUa/P\nxe5lnhjetgFh1IbP4aqmV9h6tNrDXSk19v35nc3c1v0mdbOuR5LG/1wxanwaM527InKXiGSLSHZV\nVRUhF97PbFsJn773stWhKeUVx463MH3Xo4jNRuQXH7E6HOXDPEn8JUDvx/skuZd5wuNtjTFPGWOy\njDFZcXFx+M9fRZNfLFmlfyGvamiDE5Qai1567RWutn1Cx5J7ISLJ6nCUD/Mk8W8HUkUkWUT8gVuA\nNzysf/jbOvzhnG9wgX0vb23c6OHulBqbtudXs+LYL2nyjyX04getDkf5uEETvzHGCXwTeBc4CLxk\njNkvIneLyN0AIpIgIsXAt4Dvi0ixiIQPtK2nwYWe+3U6JJDEg89Q16IDutT41N1t+OiVJ1lky8Xv\nsocgINTqkJSP82i4lzFmPbC+z7Ine70vx3UZx6NtPRYcTXPGzVy97888/9EO7lw59KHJSlntrR1H\nuaXxGWojM4hafJvV4Sg1djp3BxJ18Woc0o357Hd06ORtapxp7eii9J21JMpxIq57FGxj/r+c8gFj\n/68wZibHky7huq53eGfXUaujUWpIXti4jducr1Iz7XJsyedZHY5SwHhI/EDMigeIliaKPnhGB3Sp\ncaOyoY3IbT/DX7qJvu6nVoejVI9xkfhtM87leMQ8Vja+xrY8HdClxocXXn+T6/iQlkX/3P/DQZSy\nyLhI/IgQdtFqZtrK2L7hBaujUWpQB0rqOSfn57T6RRBx+fesDkepk4yPxA/4Z15Pg/8ksspeIL+6\n2epwlBqQMYZ3XvkdS20HsV38PQiMsDokpU4ybhI/dj9k2T0ssx3gnffesToapQb0933F3FD9v9SG\nziTonH+2OhylTjF+Ej8QtuxrtNmCSDz4DPUtfScCVcp6nV3dHHnz58ywVRB2zc/APrQnIyk1GsZV\n4icwguaML3GFbOX1j7ZbHY1Sp1j30S6+3P5/VE++AMfsS60OR6l+ja/ED8SsWI1dDHz2FJ1dOqBL\njR31LZ3YPvwZIdJGzPWPWh2OUgMad4mfqOlUTb2ca7ve5d1duVZHo1SPF956lxvNezTM+yoSn2Z1\nOEoNaPwlfiDu0m8RIS2UbPqdDuhSY0J+dTMZe39GpyOYqCsfsjocpU5rXCZ+27QlVEYuZGXja2zX\nAV1qDHhz3XNcYNuD8/xvQ3C01eEodVrjMvEDRFx8P9Ntlezc8GerQ1E+7tOccq4ofZy6oGmEnfev\nVoej1KDGbeIPmHcNdQFTOKvsLxQe1wFdyhrd3Yadr/0PqbYSgq/6sesBQkqNceM28WOzY1v2r5xt\nO8KGDW9ZHY3yUW9uO8AtzX+mKvYc/OdebXU4Snlk/CZ+IHzZHbTaQkk69Az1rTqgS42ulg4nTe/9\nhEhpJvbGR0HE6pCU8si4TvwEhNE078tcxqe89dGnVkejfMz/vfMBN3Wtpyb1JmTyAqvDUcpj4zvx\nA3ErVmPEhu2z/9UBXWrUlNe3kbTjp3Tb/Im95hGrw1FqSMZ94icikappV3KVcyPv7cqxOhrlI159\n9QUule20LV0NYZOsDkepIRn/iR+YdNmDhEkrZZue0gFdasTtPVbDF/L/h3r/BCIvvt/qcJQasgmR\n+G1JiyiPyuLypr+yI7/K6nDUBGaMYcsrjzHXVoj/yh+CX5DVISk1ZBMi8QNErbifJKlm97t/tDoU\nNYFt3H2UG+uepSpyAUGL/snqcJQalgmT+AMyrqImcCpnlz1PkQ7oUiOgw9lN2fqfEi91RN2wVm/f\nVOPWhEn82GzYz/0mC2x5bNzwhtXRqAnolU1b+aeOv1Ix/Roc05ZYHY5Sw+ZR4heRlSJyWERyRWRN\nP+tFRB5zr98jIot7rXtARPaLyD4ReUFEAr3ZgN4iln6VZls4SYd+T0ObDuhS3lPT3EHExz/CZhMm\nXf9jq8NR6owMmvhFxA48DlwBZAC3ikhGn2JXAKnu113AE+5tE4H7gCxjzDzADtzitej78g+mef7t\nrCCbtz/8ZMR2o3zPq2+8xpV8TOPieyByqtXhKHVGPDnjXwLkGmPyjDEdwIvAtX3KXAv80bh8CkSK\nyGT3OgcQJCIOIBgo9VLs/Ypf8U26xY79sydx6oAu5QW5FY0sPvgoDY4YYi77jtXhKHXGPEn8iUBR\nr8/F7mWDljHGlABrgWNAGVBvjNkw/HA9EJZAxfQvcqXzfd7fdWREd6V8w/sv/5bFthzkkocgINTq\ncJQ6YyPauSsiUbi+DSQDU4AQEbltgLJ3iUi2iGRXVZ3ZvfgJKx8kWNqp3PTEGdWj1CcHi7m66n+p\nCk0jbMlXrA5HKa/wJPGXAL0vaia5l3lS5hIg3xhTZYzpBF4Fzu1vJ8aYp4wxWcaYrLi4OE/j75d9\ncial0Uu5tOl1duZVnFFdynd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"text/plain": [ "<matplotlib.figure.Figure at 0x7f468f882cf8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for i in range(len(models_list)):\n", " try:\n", " sh = models_list[i].score_history()\n", " sh = pd.DataFrame(sh)\n", " sh.plot(x='epochs',y = ['training_mae', 'validation_mae'])\n", " tr_perform = sh['training_mae'].tolist()[-1]\n", " val_perform = sh['validation_mae'].tolist()[-1]\n", " ts_perform= models_list[i].model_performance(test_data=test).mae() \n", " print(models_list[i].model_id,end=\" \")\n", " print(\"clk \"+str(round(time_tkn_clk[i],2))+\"\\twall \"+str(round(time_tkn_wall[i]/60,2)),end=\"\\t\")\n", " print(\n", " \"tr \" + str(round(tr_perform,6)) +\"\\tval \" + str(round(val_perform,6)) + \"\\tts \" + str(round(ts_perform,6))\n", " )\n", " except:\n", " print(end=\"\")" ] }, { "cell_type": "markdown", "metadata": { "ExecuteTime": { "end_time": "2017-08-28T18:25:37.824489Z", "start_time": "2017-08-28T18:25:37.820196Z" } }, "source": [ "## Predict test_h2o & combine" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Predict x_test & combine" ] }, { "cell_type": "code", "execution_count": 174, "metadata": { "ExecuteTime": { "end_time": "2017-08-28T12:22:10.432113Z", "start_time": "2017-08-28T12:21:43.916601Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/storage/users/jethva/anaconda3/lib/python3.6/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n", " \"This module will be removed in 0.20.\", DeprecationWarning)\n" ] } ], "source": [ "import numpy as np\n", "import pandas as pd\n", "import xgboost as xgb\n", "from sklearn.preprocessing import LabelEncoder\n", "import lightgbm as lgb\n", "import gc\n", "from sklearn.linear_model import LinearRegression\n", "import random\n", "import datetime as dt" ] }, { "cell_type": "code", "execution_count": 175, "metadata": { "ExecuteTime": { "end_time": "2017-08-28T12:22:37.015539Z", "start_time": "2017-08-28T12:22:14.507813Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/storage/users/jethva/anaconda3/lib/python3.6/site-packages/IPython/core/interactiveshell.py:2698: DtypeWarning: Columns (22,32,34,49,55) have mixed types. Specify dtype option on import or set low_memory=False.\n", " interactivity=interactivity, compiler=compiler, result=result)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "90275 2985217 2985217\n" ] } ], "source": [ "np.random.seed(17)\n", "random.seed(17)\n", "\n", "train = pd.read_csv(\"../input/train_2016_v2.csv\", parse_dates=[\"transactiondate\"])\n", "properties = pd.read_csv(\"../input/properties_2016.csv\")\n", "submission = pd.read_csv(\"../input/sample_submission.csv\")\n", "print(len(train),len(properties),len(submission))\n" ] }, { "cell_type": "code", "execution_count": 176, "metadata": { "ExecuteTime": { "end_time": "2017-08-28T12:22:47.245517Z", "start_time": "2017-08-28T12:22:47.215548Z" } }, "outputs": [], "source": [ "def get_features(df):\n", " df[\"transactiondate\"] = pd.to_datetime(df[\"transactiondate\"])\n", " df[\"transactiondate_year\"] = df[\"transactiondate\"].dt.year\n", " df[\"transactiondate_month\"] = df[\"transactiondate\"].dt.month\n", " df['transactiondate'] = df['transactiondate'].dt.quarter\n", " df = df.fillna(-1.0)\n", " return df\n", "\n", "def MAE(y, ypred):\n", " #logerror=log(Zestimate)−log(SalePrice)\n", " return np.sum([abs(y[i]-ypred[i]) for i in range(len(y))]) / len(y)" ] }, { "cell_type": "code", "execution_count": 177, "metadata": { "ExecuteTime": { "end_time": "2017-08-28T12:23:30.002776Z", "start_time": "2017-08-28T12:23:17.720841Z" } }, "outputs": [], "source": [ "\n", "train = pd.merge(train, properties, how='left', on='parcelid')\n", "y = train['logerror'].values\n", "test = pd.merge(submission, properties, how='left', left_on='ParcelId', right_on='parcelid')\n", "properties = [] #memory\n", "\n", "exc = [train.columns[c] for c in range(len(train.columns)) if train.dtypes[c] == 'O'] + ['logerror','parcelid']\n", "col = [c for c in train.columns if c not in exc]\n" ] }, { "cell_type": "code", "execution_count": 178, "metadata": { "ExecuteTime": { "end_time": "2017-08-28T12:23:48.982352Z", "start_time": "2017-08-28T12:23:42.635287Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/storage/users/jethva/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", " \n", "/storage/users/jethva/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:3: 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", " This is separate from the ipykernel package so we can avoid doing imports until\n", "/storage/users/jethva/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:4: 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", " after removing the cwd from sys.path.\n", "/storage/users/jethva/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:5: 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", " \"\"\"\n" ] } ], "source": [ "train = get_features(train[col])\n", "test['transactiondate'] = '2016-01-01' #should use the most common training date\n", "test = get_features(test[col])\n" ] }, { "cell_type": "code", "execution_count": 179, "metadata": { "ExecuteTime": { "end_time": "2017-08-28T12:24:06.552475Z", "start_time": "2017-08-28T12:24:05.547005Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "fit...\n", "0.068370083368\n" ] } ], "source": [ "reg = LinearRegression(n_jobs=-1)\n", "reg.fit(train, y); print('fit...')\n", "print(MAE(y, reg.predict(train)))\n", "train = []; y = [] #memory\n", "\n", "test_dates = ['2016-10-01','2016-11-01','2016-12-01','2017-10-01','2017-11-01','2017-12-01']\n", "test_columns = ['201610','201611','201612','201710','201711','201712']\n" ] }, { "cell_type": "code", "execution_count": 187, "metadata": { "ExecuteTime": { "end_time": "2017-08-28T12:33:58.115127Z", "start_time": "2017-08-28T12:33:47.660142Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "deeplearning prediction progress: |███████████████████████████████████████| 100%\n" ] } ], "source": [ "pred0 = models_list[1].predict(test_data=x_test).as_data_frame(use_pandas=True)" ] }, { "cell_type": "code", "execution_count": 188, "metadata": { "ExecuteTime": { "end_time": "2017-08-28T12:34:05.086180Z", "start_time": "2017-08-28T12:34:05.075080Z" } }, "outputs": [ { "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", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0.001469</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>0.000359</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>0.259057</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>0.030629</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>0.025123</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " predict\n", "0 0.001469\n", "1 0.000359\n", "2 0.259057\n", "3 0.030629\n", "4 0.025123" ] }, "execution_count": 188, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pred0.head(n=5)" ] }, { "cell_type": "code", "execution_count": 189, "metadata": { "ExecuteTime": { "end_time": "2017-08-28T12:36:10.715514Z", "start_time": "2017-08-28T12:34:28.668119Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Predicting with OLS and combining with XGB/LGB/baseline predicitons: ...\n", "predict... 0\n", "predict... 1\n", "predict... 2\n", "predict... 3\n", "predict... 4\n", "predict... 5\n", "\n", "Combined XGB/LGB/baseline/OLS predictions:\n", " ParcelId 201610 201611 201612 201710 201711 201712\n", "0 10754147 -0.0015 -0.0015 -0.0015 -0.0015 -0.0015 -0.0015\n", "1 10759547 -0.0030 -0.0030 -0.0030 -0.0030 -0.0030 -0.0030\n", "2 10843547 0.2924 0.2924 0.2924 0.2924 0.2924 0.2924\n", "3 10859147 0.0313 0.0313 0.0313 0.0313 0.0313 0.0313\n", "4 10879947 0.0256 0.0256 0.0255 0.0256 0.0256 0.0255\n" ] } ], "source": [ "OLS_WEIGHT = 0.0856\n", "\n", "\n", "print( \"\\nPredicting with OLS and combining with XGB/LGB/baseline predicitons: ...\" )\n", "for i in range(len(test_dates)):\n", " test['transactiondate'] = test_dates[i]\n", " pred = OLS_WEIGHT * reg.predict(get_features(test)) + (1-OLS_WEIGHT)*pred0.values[:,0]\n", " submission[test_columns[i]] = [float(format(x, '.4f')) for x in pred]\n", " print('predict...', i)\n", "\n", "print( \"\\nCombined XGB/LGB/baseline/OLS predictions:\" )\n", "print( submission.head() )\n" ] }, { "cell_type": "code", "execution_count": 190, "metadata": { "ExecuteTime": { "end_time": "2017-08-28T12:37:09.685604Z", "start_time": "2017-08-28T12:36:41.445230Z" } }, "outputs": [], "source": [ "from datetime import datetime \n", "submission.to_csv('sub{}.csv'.format(datetime.now().strftime('%Y%m%d_%H%M%S')), index=False)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "h2o.model.regression.h2o_mean_absolute_error(y_actual=,y_predicted=)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0" }, "toc": { "colors": { "hover_highlight": "#DAA520", "running_highlight": "#FF0000", "selected_highlight": "#FFD700" }, "moveMenuLeft": true, "nav_menu": { "height": "12px", "width": "252px" }, "navigate_menu": true, "number_sections": true, "sideBar": true, "threshold": 4, "toc_cell": true, "toc_position": { "height": "836px", "left": "0px", "right": "1499px", "top": "130px", "width": "356px" }, "toc_section_display": "block", "toc_window_display": true, "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()) " } }, "position": { "height": "493px", "left": "1305px", "right": "20px", "top": "133px", "width": "478px" }, "types_to_exclude": [ "module", "function", "builtin_function_or_method", "instance", "_Feature" ], "window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }
gpl-3.0
tensorflow/docs-l10n
site/zh-cn/guide/keras/custom_callback.ipynb
1
22377
{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "b518b04cbfe0" }, "source": [ "##### Copyright 2020 The TensorFlow Authors." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "906e07f6e562" }, "outputs": [], "source": [ "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n", "# you may not use this file except in compliance with the License.\n", "# You may obtain a copy of the License at\n", "#\n", "# https://www.apache.org/licenses/LICENSE-2.0\n", "#\n", "# Unless required by applicable law or agreed to in writing, software\n", "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", "# See the License for the specific language governing permissions and\n", "# limitations under the License." ] }, { "cell_type": "markdown", "metadata": { "id": "d201e826ab29" }, "source": [ "# 编写自己的回调函数" ] }, { "cell_type": "markdown", "metadata": { "id": "71699af85d2d" }, "source": [ "<table class=\"tfo-notebook-buttons\" align=\"left\">\n", " <td> <a target=\"_blank\" href=\"https://tensorflow.google.cn/guide/keras/custom_callback\"><img src=\"https://tensorflow.google.cn/images/tf_logo_32px.png\">在 TensorFlow.org 上查看</a> </td>\n", " <td><a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs-l10n/blob/master/site/zh-cn/guide/keras/custom_callback.ipynb\"><img src=\"https://tensorflow.google.cn/images/colab_logo_32px.png\">在 Google Colab 中运行 </a></td>\n", " <td> <a target=\"_blank\" href=\"https://github.com/tensorflow/docs-l10n/blob/master/site/zh-cn/guide/keras/custom_callback.ipynb\"><img src=\"https://tensorflow.google.cn/images/GitHub-Mark-32px.png\">在 GitHub 上查看源代码</a> </td>\n", " <td> <a href=\"https://storage.googleapis.com/tensorflow_docs/docs-l10n/site/zh-cn/guide/keras/custom_callback.ipynb\"><img src=\"https://tensorflow.google.cn/images/download_logo_32px.png\">下载笔记本</a> </td>\n", "</table>" ] }, { "cell_type": "markdown", "metadata": { "id": "d75eb2e25f36" }, "source": [ "## 简介\n", "\n", "回调是一种可以在训练、评估或推断过程中自定义 Keras 模型行为的强大工具。示例包括使用 TensorBoard 来呈现训练进度和结果的 `tf.keras.callbacks.TensorBoard`,以及用来在训练期间定期保存模型的 `tf.keras.callbacks.ModelCheckpoint`。\n", "\n", "在本指南中,您将了解什么是 Keras 回调函数,它可以做什么,以及如何构建自己的回调函数。我们提供了一些简单回调函数应用的演示,以帮助您入门。" ] }, { "cell_type": "markdown", "metadata": { "id": "b3600ee25c8e" }, "source": [ "## 设置" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "4dadb6688663" }, "outputs": [], "source": [ "import tensorflow as tf\n", "from tensorflow import keras" ] }, { "cell_type": "markdown", "metadata": { "id": "42676f705fc8" }, "source": [ "## Keras 回调函数概述\n", "\n", "所有回调函数都将 `keras.callbacks.Callback` 类作为子类,并重写在训练、测试和预测的各个阶段调用的一组方法。回调函数对于在训练期间了解模型的内部状态和统计信息十分有用。\n", "\n", "您可以将回调函数的列表(作为关键字参数 `callbacks`)传递给以下模型方法:\n", "\n", "- `keras.Model.fit()`\n", "- `keras.Model.evaluate()`\n", "- `keras.Model.predict()`" ] }, { "cell_type": "markdown", "metadata": { "id": "46945bdf5056" }, "source": [ "## 回调函数方法概述\n", "\n", "### 全局方法\n", "\n", "#### `on_(train|test|predict)_begin(self, logs=None)`\n", "\n", "在 `fit`/`evaluate`/`predict` 开始时调用。\n", "\n", "#### `on_(train|test|predict)_end(self, logs=None)`\n", "\n", "在 `fit`/`evaluate`/`predict` 结束时调用。\n", "\n", "### Batch-level methods for training/testing/predicting\n", "\n", "#### `on_(train|test|predict)_batch_begin(self, batch, logs=None)`\n", "\n", "正好在训练/测试/预测期间处理批次之前调用。\n", "\n", "#### `on_(train|test|predict)_batch_end(self, batch, logs=None)`\n", "\n", "在训练/测试/预测批次结束时调用。在此方法中,`logs` 是包含指标结果的字典。\n", "\n", "### 周期级方法(仅训练)\n", "\n", "#### `on_epoch_begin(self, epoch, logs=None)`\n", "\n", "在训练期间周期开始时调用。\n", "\n", "#### `on_epoch_end(self, epoch, logs=None)`\n", "\n", "在训练期间周期开始时调用。" ] }, { "cell_type": "markdown", "metadata": { "id": "82f2370418a1" }, "source": [ "## 基本示例\n", "\n", "让我们来看一个具体的例子。首先,导入 Tensorflow 并定义一个简单的序列式 Keras 模型:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "7350ea602e50" }, "outputs": [], "source": [ "# Define the Keras model to add callbacks to\n", "def get_model():\n", " model = keras.Sequential()\n", " model.add(keras.layers.Dense(1, input_dim=784))\n", " model.compile(\n", " optimizer=keras.optimizers.RMSprop(learning_rate=0.1),\n", " loss=\"mean_squared_error\",\n", " metrics=[\"mean_absolute_error\"],\n", " )\n", " return model\n" ] }, { "cell_type": "markdown", "metadata": { "id": "044db5f2dc6f" }, "source": [ "然后,从 Keras 数据集 API 加载 MNIST 数据进行训练和测试:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "f8826736a184" }, "outputs": [], "source": [ "# Load example MNIST data and pre-process it\n", "(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()\n", "x_train = x_train.reshape(-1, 784).astype(\"float32\") / 255.0\n", "x_test = x_test.reshape(-1, 784).astype(\"float32\") / 255.0\n", "\n", "# Limit the data to 1000 samples\n", "x_train = x_train[:1000]\n", "y_train = y_train[:1000]\n", "x_test = x_test[:1000]\n", "y_test = y_test[:1000]" ] }, { "cell_type": "markdown", "metadata": { "id": "b9acd50b2215" }, "source": [ "接下来,定义一个简单的自定义回调函数来记录以下内容:\n", "\n", "- `fit`/`evaluate`/`predict` 开始和结束的时间\n", "- 每个周期开始和结束的时间\n", "- 每个训练批次开始和结束的时间\n", "- 每个评估(测试)批次开始和结束的时间\n", "- 每次推断(预测)批次开始和结束的时间" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "cc9888d28e79" }, "outputs": [], "source": [ "class CustomCallback(keras.callbacks.Callback):\n", " def on_train_begin(self, logs=None):\n", " keys = list(logs.keys())\n", " print(\"Starting training; got log keys: {}\".format(keys))\n", "\n", " def on_train_end(self, logs=None):\n", " keys = list(logs.keys())\n", " print(\"Stop training; got log keys: {}\".format(keys))\n", "\n", " def on_epoch_begin(self, epoch, logs=None):\n", " keys = list(logs.keys())\n", " print(\"Start epoch {} of training; got log keys: {}\".format(epoch, keys))\n", "\n", " def on_epoch_end(self, epoch, logs=None):\n", " keys = list(logs.keys())\n", " print(\"End epoch {} of training; got log keys: {}\".format(epoch, keys))\n", "\n", " def on_test_begin(self, logs=None):\n", " keys = list(logs.keys())\n", " print(\"Start testing; got log keys: {}\".format(keys))\n", "\n", " def on_test_end(self, logs=None):\n", " keys = list(logs.keys())\n", " print(\"Stop testing; got log keys: {}\".format(keys))\n", "\n", " def on_predict_begin(self, logs=None):\n", " keys = list(logs.keys())\n", " print(\"Start predicting; got log keys: {}\".format(keys))\n", "\n", " def on_predict_end(self, logs=None):\n", " keys = list(logs.keys())\n", " print(\"Stop predicting; got log keys: {}\".format(keys))\n", "\n", " def on_train_batch_begin(self, batch, logs=None):\n", " keys = list(logs.keys())\n", " print(\"...Training: start of batch {}; got log keys: {}\".format(batch, keys))\n", "\n", " def on_train_batch_end(self, batch, logs=None):\n", " keys = list(logs.keys())\n", " print(\"...Training: end of batch {}; got log keys: {}\".format(batch, keys))\n", "\n", " def on_test_batch_begin(self, batch, logs=None):\n", " keys = list(logs.keys())\n", " print(\"...Evaluating: start of batch {}; got log keys: {}\".format(batch, keys))\n", "\n", " def on_test_batch_end(self, batch, logs=None):\n", " keys = list(logs.keys())\n", " print(\"...Evaluating: end of batch {}; got log keys: {}\".format(batch, keys))\n", "\n", " def on_predict_batch_begin(self, batch, logs=None):\n", " keys = list(logs.keys())\n", " print(\"...Predicting: start of batch {}; got log keys: {}\".format(batch, keys))\n", "\n", " def on_predict_batch_end(self, batch, logs=None):\n", " keys = list(logs.keys())\n", " print(\"...Predicting: end of batch {}; got log keys: {}\".format(batch, keys))\n" ] }, { "cell_type": "markdown", "metadata": { "id": "8184bd3a76c2" }, "source": [ "我们来试一下:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "75f7aa1edac6" }, "outputs": [], "source": [ "model = get_model()\n", "model.fit(\n", " x_train,\n", " y_train,\n", " batch_size=128,\n", " epochs=1,\n", " verbose=0,\n", " validation_split=0.5,\n", " callbacks=[CustomCallback()],\n", ")\n", "\n", "res = model.evaluate(\n", " x_test, y_test, batch_size=128, verbose=0, callbacks=[CustomCallback()]\n", ")\n", "\n", "res = model.predict(x_test, batch_size=128, callbacks=[CustomCallback()])" ] }, { "cell_type": "markdown", "metadata": { "id": "02113b8677eb" }, "source": [ "### `logs` 字典的用法\n", "\n", "`logs` 字典包含损失值,以及批次或周期结束时的所有指标。示例包括损失和平均绝对误差。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "629bc145eb84" }, "outputs": [], "source": [ "class LossAndErrorPrintingCallback(keras.callbacks.Callback):\n", " def on_train_batch_end(self, batch, logs=None):\n", " print(\n", " \"Up to batch {}, the average loss is {:7.2f}.\".format(batch, logs[\"loss\"])\n", " )\n", "\n", " def on_test_batch_end(self, batch, logs=None):\n", " print(\n", " \"Up to batch {}, the average loss is {:7.2f}.\".format(batch, logs[\"loss\"])\n", " )\n", "\n", " def on_epoch_end(self, epoch, logs=None):\n", " print(\n", " \"The average loss for epoch {} is {:7.2f} \"\n", " \"and mean absolute error is {:7.2f}.\".format(\n", " epoch, logs[\"loss\"], logs[\"mean_absolute_error\"]\n", " )\n", " )\n", "\n", "\n", "model = get_model()\n", "model.fit(\n", " x_train,\n", " y_train,\n", " batch_size=128,\n", " epochs=2,\n", " verbose=0,\n", " callbacks=[LossAndErrorPrintingCallback()],\n", ")\n", "\n", "res = model.evaluate(\n", " x_test,\n", " y_test,\n", " batch_size=128,\n", " verbose=0,\n", " callbacks=[LossAndErrorPrintingCallback()],\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "742d62e5394a" }, "source": [ "## `self.model` 属性的用法\n", "\n", "除了在调用其中一种方法时接收日志信息外,回调还可以访问与当前一轮训练/评估/推断有关的模型:`self.model`。\n", "\n", "以下是您可以在回调函数中使用 `self.model` 进行的一些操作:\n", "\n", "- 设置 `self.model.stop_training = True` 以立即中断训练。\n", "- 转变优化器(可作为 `self.model.optimizer`)的超参数,例如 `self.model.optimizer.learning_rate`。\n", "- 定期保存模型。\n", "- 在每个周期结束时,在少量测试样本上记录 `model.predict()` 的输出,以用作训练期间的健全性检查。\n", "- 在每个周期结束时提取中间特征的可视化,随时间推移监视模型当前的学习内容。\n", "- 其他\n", "\n", "下面我们通过几个示例来看看它是如何工作的。" ] }, { "cell_type": "markdown", "metadata": { "id": "7eb29d3ed752" }, "source": [ "## Keras 回调函数应用示例" ] }, { "cell_type": "markdown", "metadata": { "id": "2d1d29d99fa5" }, "source": [ "### 在达到最小损失时尽早停止\n", "\n", "第一个示例展示了如何通过设置 `self.model.stop_training`(布尔)属性来创建能够在达到最小损失时停止训练的 `Callback`。您还可以提供参数 `patience` 来指定在达到局部最小值后应该等待多少个周期然后停止。\n", "\n", "`tf.keras.callbacks.EarlyStopping` 提供了一种更完整、更通用的实现。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "5d2acd79cecd" }, "outputs": [], "source": [ "import numpy as np\n", "\n", "\n", "class EarlyStoppingAtMinLoss(keras.callbacks.Callback):\n", " \"\"\"Stop training when the loss is at its min, i.e. the loss stops decreasing.\n", "\n", " Arguments:\n", " patience: Number of epochs to wait after min has been hit. After this\n", " number of no improvement, training stops.\n", " \"\"\"\n", "\n", " def __init__(self, patience=0):\n", " super(EarlyStoppingAtMinLoss, self).__init__()\n", " self.patience = patience\n", " # best_weights to store the weights at which the minimum loss occurs.\n", " self.best_weights = None\n", "\n", " def on_train_begin(self, logs=None):\n", " # The number of epoch it has waited when loss is no longer minimum.\n", " self.wait = 0\n", " # The epoch the training stops at.\n", " self.stopped_epoch = 0\n", " # Initialize the best as infinity.\n", " self.best = np.Inf\n", "\n", " def on_epoch_end(self, epoch, logs=None):\n", " current = logs.get(\"loss\")\n", " if np.less(current, self.best):\n", " self.best = current\n", " self.wait = 0\n", " # Record the best weights if current results is better (less).\n", " self.best_weights = self.model.get_weights()\n", " else:\n", " self.wait += 1\n", " if self.wait >= self.patience:\n", " self.stopped_epoch = epoch\n", " self.model.stop_training = True\n", " print(\"Restoring model weights from the end of the best epoch.\")\n", " self.model.set_weights(self.best_weights)\n", "\n", " def on_train_end(self, logs=None):\n", " if self.stopped_epoch > 0:\n", " print(\"Epoch %05d: early stopping\" % (self.stopped_epoch + 1))\n", "\n", "\n", "model = get_model()\n", "model.fit(\n", " x_train,\n", " y_train,\n", " batch_size=64,\n", " steps_per_epoch=5,\n", " epochs=30,\n", " verbose=0,\n", " callbacks=[LossAndErrorPrintingCallback(), EarlyStoppingAtMinLoss()],\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "939ecfbe0383" }, "source": [ "### 学习率规划\n", "\n", "在此示例中,我们展示了如何在学习过程中使用自定义回调来动态更改优化器的学习率。\n", "\n", "有关更通用的实现,请查看 `callbacks.LearningRateScheduler`。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "71c752b248c0" }, "outputs": [], "source": [ "class CustomLearningRateScheduler(keras.callbacks.Callback):\n", " \"\"\"Learning rate scheduler which sets the learning rate according to schedule.\n", "\n", " Arguments:\n", " schedule: a function that takes an epoch index\n", " (integer, indexed from 0) and current learning rate\n", " as inputs and returns a new learning rate as output (float).\n", " \"\"\"\n", "\n", " def __init__(self, schedule):\n", " super(CustomLearningRateScheduler, self).__init__()\n", " self.schedule = schedule\n", "\n", " def on_epoch_begin(self, epoch, logs=None):\n", " if not hasattr(self.model.optimizer, \"lr\"):\n", " raise ValueError('Optimizer must have a \"lr\" attribute.')\n", " # Get the current learning rate from model's optimizer.\n", " lr = float(tf.keras.backend.get_value(self.model.optimizer.learning_rate))\n", " # Call schedule function to get the scheduled learning rate.\n", " scheduled_lr = self.schedule(epoch, lr)\n", " # Set the value back to the optimizer before this epoch starts\n", " tf.keras.backend.set_value(self.model.optimizer.lr, scheduled_lr)\n", " print(\"\\nEpoch %05d: Learning rate is %6.4f.\" % (epoch, scheduled_lr))\n", "\n", "\n", "LR_SCHEDULE = [\n", " # (epoch to start, learning rate) tuples\n", " (3, 0.05),\n", " (6, 0.01),\n", " (9, 0.005),\n", " (12, 0.001),\n", "]\n", "\n", "\n", "def lr_schedule(epoch, lr):\n", " \"\"\"Helper function to retrieve the scheduled learning rate based on epoch.\"\"\"\n", " if epoch < LR_SCHEDULE[0][0] or epoch > LR_SCHEDULE[-1][0]:\n", " return lr\n", " for i in range(len(LR_SCHEDULE)):\n", " if epoch == LR_SCHEDULE[i][0]:\n", " return LR_SCHEDULE[i][1]\n", " return lr\n", "\n", "\n", "model = get_model()\n", "model.fit(\n", " x_train,\n", " y_train,\n", " batch_size=64,\n", " steps_per_epoch=5,\n", " epochs=15,\n", " verbose=0,\n", " callbacks=[\n", " LossAndErrorPrintingCallback(),\n", " CustomLearningRateScheduler(lr_schedule),\n", " ],\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "c9be225b57f1" }, "source": [ "### 内置 Keras 回调函数\n", "\n", "请务必阅读 [API 文档](https://tensorflow.google.cn/api_docs/python/tf/keras/callbacks/)查看现有的 Keras 回调函数。应用包括记录到 CSV、保存模型、在 TensorBoard 中可视化指标等等!" ] } ], "metadata": { "colab": { "collapsed_sections": [], "name": "custom_callback.ipynb", "toc_visible": true }, "kernelspec": { "display_name": "Python 3", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 0 }
apache-2.0
chichilalescu/bfps
meta/Velocity gradient.ipynb
1
5584
{ "cells": [ { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-Axx**2/2 - Axy*Ayx - Axz*Azx - Ayy**2/2 - Ayz*Azy - Azz**2/2\n", "-Axx*(Axx**2/3 + Axy*Ayx + Axz*Azx) - Axy*(Ayx*Ayy + Ayz*Azx) - Axz*(Ayx*Azy + Azx*Azz) - Ayy*(Ayy**2/3 + Ayz*Azy) - Ayz*Azy*Azz - Azz**3/3\n", "Axx**2 + Axy*(Axy/2 + Ayx) + Axz*(Axz/2 + Azx) + Ayx**2/2 + Ayy**2 + Ayz*(Ayz/2 + Azy) + Azx**2/2 + Azy**2/2 + Azz**2\n" ] } ], "source": [ "import sympy as sp\n", "\n", "A = []\n", "for deriv in ['x', 'y', 'z']:\n", " A.append([])\n", " for field in ['x', 'y', 'z']:\n", " A[-1].append(sp.Symbol('A' + deriv + field))\n", "\n", "A = sp.Matrix(A)\n", "\n", "A2 = A**2\n", "A3 = A**3\n", "Q = -sp.horner(sp.simplify(sum(A2[i, i] for i in range(3))/2))\n", "R = -sp.horner(sp.simplify(sum(A3[i, i] for i in range(3))/3))\n", "print(Q)\n", "print(R)\n", "\n", "S = (A + A.T)/2\n", "S2 = S**2\n", "trS2 = sp.horner(sp.simplify(sum(S2[i, i] for i in range(3))))\n", "print(trS2)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-Axx**2/2 - Axy*Ayx - Axz*Azx - Ayy**2/2 - Ayz*Azy - Azz**2/2\n", "3*DIV + 3*MUL + NEG + 3*POW + 5*SUB\n", "-Axx**3/3 - Axx*Axy*Ayx - Axx*Axz*Azx - Axy*Ayx*Ayy - Axy*Ayz*Azx - Axz*Ayx*Azy - Axz*Azx*Azz - Ayy**3/3 - Ayy*Ayz*Azy - Ayz*Azy*Azz - Azz**3/3\n", "3*DIV + 16*MUL + NEG + 3*POW + 10*SUB\n" ] } ], "source": [ "def alt_measure(expr):\n", " POW = sp.Symbol('POW')\n", " count = sp.count_ops(expr, visual=True).subs(POW, 10)\n", " count = count.replace(sp.Symbol, type(sp.S.One))\n", " return count\n", "\n", "Qalt = sp.simplify(Q, measure = alt_measure)\n", "print(Qalt)\n", "print(sp.count_ops(Qalt, visual=True))\n", "Ralt = sp.simplify(R, measure = alt_measure)\n", "print(Ralt)\n", "print(sp.count_ops(Ralt, visual=True))" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-Axx**2/2 - Axy*Ayx - Axz*Azx - Ayy**2/2 - Ayz*Azy - Azz**2/2\n", "0\n" ] } ], "source": [ "Qalt = - sum(A[i, (i+1)%3] * A[(i+1)%3, i] for i in range(3)) - sum(A[i, i]**2 for i in range(3))/2\n", "print Qalt\n", "print(sp.simplify(Qalt - Q))" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-Axx*(Axx**2/3 + Axy*Ayx + Axz*Azx) - Axy*Ayz*Azx - Axz*Ayx*Azy - Ayy*(Axy*Ayx + Ayy**2/3 + Ayz*Azy) - Azz*(Axz*Azx + Ayz*Azy + Azz**2/3)\n", "6*ADD + 3*DIV + 13*MUL + NEG + 3*POW + 4*SUB\n", "0\n" ] } ], "source": [ "Ralt = - (sum(A[i, i]*(A[i, i]**2/3 + sum(A[i, (i+j)%3]*A[(i+j)%3, i]\n", " for j in range(1, 3)))\n", " for i in range(3)) +\n", " A[0, 1]*A[1, 2]*A[2, 0] + A[0, 2]*A[1, 0]*A[2, 1])\n", "print Ralt\n", "print(sp.count_ops(Ralt, visual=True))\n", "print(sp.simplify(Ralt - R))" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\n", "0\n", "0\n", "-Axx*(Axx**2/3 + Axy*Ayx + Axz*Azx) - Axy*Ayz*Azx - Axz*Ayx*Azy - Ayy*(Axy*Ayx + Ayy**2/3 + Ayz*Azy) - Azz*(Axz*Azx + Ayz*Azy + Azz**2/3)\n", "Axx*Ayy*Azz - Axx*Ayz*Azy - Axy*Ayx*Azz + Axy*Ayz*Azx + Axz*Ayx*Azy - Axz*Ayy*Azx\n" ] } ], "source": [ "AxxAxx = A[0, 0]**2\n", "AyyAyy = A[1, 1]**2\n", "AzzAzz = A[2, 2]**2\n", "AxyAyx = A[0, 1]*A[1, 0]\n", "AyzAzy = A[1, 2]*A[2, 1]\n", "AzxAxz = A[2, 0]*A[0, 2]\n", "\n", "Qalt = - (AxxAxx + AyyAyy + AzzAzz)/2 - AxyAyx -AyzAzy - AzxAxz\n", "print(sp.simplify(Qalt - Q))\n", "Ralt = - (A[0, 0]*(AxxAxx/3 + AxyAyx + AzxAxz) +\n", " A[1, 1]*(AyyAyy/3 + AxyAyx + AyzAzy) +\n", " A[2, 2]*(AzzAzz/3 + AzxAxz + AyzAzy) +\n", " A[0, 1]*A[1, 2]*A[2, 0] +\n", " A[0, 2]*A[1, 0]*A[2, 1])\n", "print sp.simplify(Ralt - R)\n", "#print sp.simplify(Ralt + A.det())\n", "trS2alt = (AxxAxx + AyyAyy + AzzAzz +\n", " ((A[0, 1] + A[1, 0])**2 +\n", " (A[1, 2] + A[2, 1])**2 +\n", " (A[2, 0] + A[0, 2])**2)/2)\n", "print sp.simplify(trS2alt - trS2)\n", "\n", "print Ralt\n", "print A.det()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.9" } }, "nbformat": 4, "nbformat_minor": 0 }
gpl-3.0
TomTranter/OpenPNM
examples/tutorials/Intro to OpenPNM - Advanced.ipynb
1
885497
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Tutorial 3 of 3: Advanced Topics and Usage\n", "\n", "**Learning Outcomes**\n", "\n", "* Use different methods to add boundary pores to a network\n", "* Manipulate network topology by adding and removing pores and throats\n", "* Explore the ModelsDict design, including copying models between objects, and changing model parameters\n", "* Write a custom pore-scale model and a custom Phase\n", "* Access and manipulate objects associated with the network\n", "* Combine multiple algorithms to predict relative permeability" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Build and Manipulate Network Topology\n", "\n", "For the present tutorial, we'll keep the topology simple to help keep the focus on other aspects of OpenPNM." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import warnings\n", "import numpy as np\n", "import scipy as sp\n", "import openpnm as op\n", "%matplotlib inline\n", "np.random.seed(10)\n", "ws = op.Workspace()\n", "ws.settings['loglevel'] = 40\n", "np.set_printoptions(precision=4)\n", "pn = op.network.Cubic(shape=[10, 10, 10], spacing=0.00006, name='net')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Adding Boundary Pores\n", "\n", "When performing transport simulations it is often useful to have 'boundary' pores attached to the surface(s) of the network where boundary conditions can be applied. When using the **Cubic** class, two methods are available for doing this: ``add_boundaries``, which is specific for the **Cubic** class, and ``add_boundary_pores``, which is a generic method that can also be used on other network types and which is inherited from **GenericNetwork**. The first method automatically adds boundaries to ALL six faces of the network and offsets them from the network by 1/2 of the value provided as the network ``spacing``. The second method provides total control over which boundary pores are created and where they are positioned, but requires the user to specify to which pores the boundary pores should be attached to. Let's explore these two options:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "pn.add_boundary_pores(labels=['top', 'bottom'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's quickly visualize this network with the added boundaries:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x720 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#NBVAL_IGNORE_OUTPUT\n", "fig = op.topotools.plot_connections(pn, c='r')\n", "fig = op.topotools.plot_coordinates(pn, c='b', fig=fig)\n", "fig.set_size_inches([10, 10])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Adding and Removing Pores and Throats\n", "\n", "OpenPNM uses a list-based data storage scheme for all properties, including topological connections. One of the benefits of this approach is that adding and removing pores and throats from the network is essentially as simple as adding or removing rows from the data arrays. The one exception to this 'simplicity' is that the ``'throat.conns'`` array must be treated carefully when trimming pores, so OpenPNM provides the ``extend`` and ``trim`` functions for adding and removing, respectively. To demonstrate, let's reduce the coordination number of the network to create a more random structure:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "Ts = np.random.rand(pn.Nt) < 0.1 # Create a mask with ~10% of throats labeled True\n", "op.topotools.trim(network=pn, throats=Ts) # Use mask to indicate which throats to trim" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When the ``trim`` function is called, it automatically checks the health of the network afterwards, so logger messages might appear on the command line if problems were found such as isolated clusters of pores or pores with no throats. This health check is performed by calling the **Network**'s ``check_network_health`` method which returns a **HealthDict** containing the results of the checks:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n", "key value\n", "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n", "disconnected_clusters [array([ 0, 1, 2, ..., 1197, 1198, 1199]), array([1080]), array([1010]), array([1012]), array([1015]), array([1040]), array([1059]), array([1061]), array([1067]), array([1071]), array([1075]), array([1184]), array([1170]), array([1105]), array([1114]), array([1120]), array([1136]), array([1141]), array([1146]), array([1152]), array([1153]), array([1159]), array([1101])]\n", "isolated_pores (22,)\n", "trim_pores [1080, 1010, 1012, 1015, 1040, 1059, 1061, 1067, 1071, 1075, 1184, 1170, 1105, 1114, 1120, 1136, 1141, 1146, 1152, 1153, 1159, 1101]\n", "duplicate_throats []\n", "bidirectional_throats []\n", "headless_throats []\n", "looped_throats []\n", "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n" ] } ], "source": [ "a = pn.check_network_health()\n", "print(a)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The **HealthDict** contains several lists including things like duplicate throats and isolated pores, but also a suggestion of which pores to trim to return the network to a healthy state. Also, the **HealthDict** has a ``health`` attribute that is ``False`` is any checks fail." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "op.topotools.trim(network=pn, pores=a['trim_pores'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's take another look at the network to see the trimmed pores and throats:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x720 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#NBVAL_IGNORE_OUTPUT\n", "fig = op.topotools.plot_connections(pn, c='r')\n", "fig = op.topotools.plot_coordinates(pn, c='b', fig=fig)\n", "fig.set_size_inches([10, 10])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Define Geometry Objects\n", "\n", "The boundary pores we've added to the network should be treated a little bit differently. Specifically, they should have no volume or length (as they are not physically representative of real pores). To do this, we create two separate **Geometry** objects, one for internal pores and one for the boundaries:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "Ps = pn.pores('*boundary', mode='not')\n", "Ts = pn.throats('*boundary', mode='not')\n", "geom = op.geometry.StickAndBall(network=pn, pores=Ps, throats=Ts, name='intern')\n", "Ps = pn.pores('*boundary')\n", "Ts = pn.throats('*boundary')\n", "boun = op.geometry.Boundary(network=pn, pores=Ps, throats=Ts, name='boun')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The **StickAndBall** class is preloaded with the pore-scale models to calculate all the necessary size information (pore diameter, pore.volume, throat lengths, throat.diameter, etc). The **Boundary** class is speciall and is only used for the boundary pores. In this class, geometrical properties are set to small fixed values such that they don't affect the simulation results. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Define Multiple Phase Objects\n", "\n", "In order to simulate relative permeability of air through a partially water-filled network, we need to create each **Phase** object. OpenPNM includes pre-defined classes for each of these common fluids:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "air = op.phases.Air(network=pn)\n", "water = op.phases.Water(network=pn)\n", "water['throat.contact_angle'] = 110\n", "water['throat.surface_tension'] = 0.072" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Aside: Creating a Custom Phase Class\n", "\n", "In many cases you will want to create your own fluid, such as an oil or brine, which may be commonly used in your research. OpenPNM cannot predict all the possible scenarios, but luckily it is easy to create a custom **Phase** class as follows:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "from openpnm.phases import GenericPhase\n", "\n", "class Oil(GenericPhase):\n", " def __init__(self, **kwargs):\n", " super().__init__(**kwargs)\n", " self.add_model(propname='pore.viscosity',\n", " model=op.models.misc.polynomial,\n", " prop='pore.temperature',\n", " a=[1.82082e-2, 6.51E-04, -3.48E-7, 1.11E-10])\n", " self['pore.molecular_weight'] = 116 # g/mol" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Creating a **Phase** class basically involves placing a series of ``self.add_model`` commands within the ``__init__`` section of the class definition. This means that when the class is instantiated, all the models are added to *itself* (i.e. ``self``).\n", "* ``**kwargs`` is a Python trick that captures all arguments in a *dict* called ``kwargs`` and passes them to another function that may need them. In this case they are passed to the ``__init__`` method of **Oil**'s parent by the ``super`` function. Specifically, things like ``name`` and ``network`` are expected.\n", "* The above code block also stores the molecular weight of the oil as a constant value\n", "* Adding models and constant values in this way could just as easily be done in a run script, but the advantage of defining a class is that it can be saved in a file (i.e. 'my_custom_phases') and reused in any project." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n", "main : phase_03\n", "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n", "# Properties Valid Values\n", "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n", "1 pore.molecular_weight 1178 / 1178 \n", "2 pore.pressure 1178 / 1178 \n", "3 pore.temperature 1178 / 1178 \n", "4 pore.viscosity 1178 / 1178 \n", "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n", "# Labels Assigned Locations\n", "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n", "1 pore.all 1178 \n", "2 throat.all 2587 \n", "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n" ] } ], "source": [ "oil = Oil(network=pn)\n", "print(oil)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Define Physics Objects for Each Geometry and Each Phase\n", "\n", "In the tutorial #2 we created two **Physics** object, one for each of the two **Geometry** objects used to handle the stratified layers. In this tutorial, the internal pores and the boundary pores each have their own **Geometry**, but there are two **Phases**, which also each require a unique **Physics**:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "phys_water_internal = op.physics.GenericPhysics(network=pn, phase=water, geometry=geom)\n", "phys_air_internal = op.physics.GenericPhysics(network=pn, phase=air, geometry=geom)\n", "phys_water_boundary = op.physics.GenericPhysics(network=pn, phase=water, geometry=boun)\n", "phys_air_boundary = op.physics.GenericPhysics(network=pn, phase=air, geometry=boun)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> To reiterate, *one* **Physics** object is required for each **Geometry** *AND* each **Phase**, so the number can grow to become annoying very quickly Some useful tips for easing this situation are given below." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Create a Custom Pore-Scale Physics Model\n", "\n", "Perhaps the most distinguishing feature between pore-network modeling papers is the pore-scale physics models employed. Accordingly, OpenPNM was designed to allow for easy customization in this regard, so that you can create your own models to augment or replace the ones included in the OpenPNM *models* libraries. For demonstration, let's implement the capillary pressure model proposed by [Mason and Morrow in 1994](http://dx.doi.org/10.1006/jcis.1994.1402). They studied the entry pressure of non-wetting fluid into a throat formed by spheres, and found that the converging-diverging geometry increased the capillary pressure required to penetrate the throat. As a simple approximation they proposed $P_c = -2 \\sigma \\cdot cos(2/3 \\theta) / R_t$\n", "\n", "Pore-scale models are written as basic function definitions:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "def mason_model(target, diameter='throat.diameter', theta='throat.contact_angle', \n", " sigma='throat.surface_tension', f=0.6667):\n", " proj = target.project\n", " network = proj.network\n", " phase = proj.find_phase(target)\n", " Dt = network[diameter]\n", " theta = phase[theta]\n", " sigma = phase[sigma]\n", " Pc = 4*sigma*np.cos(f*np.deg2rad(theta))/Dt\n", " return Pc[phase.throats(target.name)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's examine the components of above code:\n", "\n", "* The function receives a ``target`` object as an argument. This indicates which object the results will be returned to. \n", "* The ``f`` value is a scale factor that is applied to the contact angle. Mason and Morrow suggested a value of 2/3 as a decent fit to the data, but we'll make this an adjustable parameter with 2/3 as the default.\n", "* Note the ``pore.diameter`` is actually a **Geometry** property, but it is retrieved via the network using the data exchange rules outlined in the second tutorial.\n", "* All of the calculations are done for every throat in the network, but this pore-scale model may be assigned to a ``target`` like a **Physics** object, that is a subset of the full domain. As such, the last line extracts values from the ``Pc`` array for the location of ``target`` and returns just the subset.\n", "* The actual values of the contact angle, surface tension, and throat diameter are NOT sent in as numerical arrays, but rather as dictionary keys to the arrays. There is one very important reason for this: if arrays had been sent, then re-running the model would use the same arrays and hence not use any updated values. By having access to dictionary keys, the model actually looks up the current values in each of the arrays whenever it is run.\n", "* It is good practice to include the dictionary keys as arguments, such as ``sigma = 'throat.contact_angle'``. This way the user can control where the contact angle could be stored on the ``target`` object." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Copy Models Between Physics Objects\n", "\n", "As mentioned above, the need to specify a separate **Physics** object for each **Geometry** and **Phase** can become tedious. It is possible to *copy* the pore-scale models assigned to one object onto another object. First, let's assign the models we need to ``phys_water_internal``:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "mod = op.models.physics.hydraulic_conductance.hagen_poiseuille\n", "phys_water_internal.add_model(propname='throat.hydraulic_conductance',\n", " model=mod)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "phys_water_internal.add_model(propname='throat.entry_pressure',\n", " model=mason_model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now make a copy of the ``models`` on ``phys_water_internal`` and apply it all the other water **Physics** objects:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "phys_water_boundary.models = phys_water_internal.models" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The only 'gotcha' with this approach is that each of the **Physics** objects must be *regenerated* in order to place numerical values for all the properties into the data arrays:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "phys_water_boundary.regenerate_models()\n", "phys_air_internal.regenerate_models()\n", "phys_air_internal.regenerate_models()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Adjust Pore-Scale Model Parameters\n", "\n", "The pore-scale models are stored in a **ModelsDict** object that is itself stored under the ``models`` attribute of each object. This arrangement is somewhat convoluted, but it enables integrated storage of models on the object's wo which they apply. The models on an object can be inspected with ``print(phys_water_internal)``, which shows a list of all the pore-scale properties that are computed by a model, and some information about the model's *regeneration* mode.\n", "\n", "Each model in the **ModelsDict** can be individually inspected by accessing it using the dictionary key corresponding to *pore-property* that it calculates, i.e. ``print(phys_water_internal)['throat.capillary_pressure'])``. This shows a list of all the parameters associated with that model. It is possible to edit these parameters directly:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "phys_water_internal.models['throat.entry_pressure']['f'] = 0.75 # Change value\n", "phys_water_internal.regenerate_models() # Regenerate model with new 'f' value" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "More details about the **ModelsDict** and **ModelWrapper** classes can be found in :ref:`models`.\n", "\n", "## Perform Multiphase Transport Simulations\n", "\n", "### Use the Built-In Drainage Algorithm to Generate an Invading Phase Configuration" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "inv = op.algorithms.Porosimetry(network=pn)\n", "inv.setup(phase=water)\n", "inv.set_inlets(pores=pn.pores(['top', 'bottom']))\n", "inv.run()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* The inlet pores were set to both ``'top'`` and ``'bottom'`` using the ``pn.pores`` method. The algorithm applies to the entire network so the mapping of network pores to the algorithm pores is 1-to-1.\n", "* The ``run`` method automatically generates a list of 25 capillary pressure points to test, but you can also specify more pores, or which specific points to tests. See the methods documentation for the details.\n", "* Once the algorithm has been run, the resulting capillary pressure curve can be viewed with ``plot_drainage_curve``. If you'd prefer a table of data for plotting in your software of choice you can use ``get_drainage_data`` which prints a table in the console." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Set Pores and Throats to Invaded\n", "\n", "After running, the ``mip`` object possesses an array containing the pressure at which each pore and throat was invaded, stored as ``'pore.inv_Pc'`` and ``'throat.inv_Pc'``. These arrays can be used to obtain a list of which pores and throats are invaded by water, using Boolean logic:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "Pi = inv['pore.invasion_pressure'] < 5000\n", "Ti = inv['throat.invasion_pressure'] < 5000" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The resulting Boolean masks can be used to manually adjust the hydraulic conductivity of pores and throats based on their phase occupancy. The following lines set the water filled throats to near-zero conductivity for air flow:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "Ts = phys_water_internal.map_throats(~Ti, origin=water)\n", "phys_water_internal['throat.hydraulic_conductance'][Ts] = 1e-20" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* The logic of these statements implicitly assumes that transport between two pores is only blocked if the throat is filled with the other phase, meaning that both pores could be filled and transport is still permitted. Another option would be to set the transport to near-zero if *either* or *both* of the pores are filled as well.\n", "* The above approach can get complicated if there are several **Geometry** objects, and it is also a bit laborious. There is a pore-scale model for this under **Physics.models.multiphase** called ``conduit_conductance``. The term conduit refers to the path between two pores that includes 1/2 of each pores plus the connecting throat." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Calculate Relative Permeability of Each Phase\n", "\n", "We are now ready to calculate the relative permeability of the domain under partially flooded conditions. Instantiate an **StokesFlow** object:" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "water_flow = op.algorithms.StokesFlow(network=pn, phase=water)\n", "water_flow.set_value_BC(pores=pn.pores('left'), values=200000)\n", "water_flow.set_value_BC(pores=pn.pores('right'), values=100000)\n", "water_flow.run()\n", "Q_partial, = water_flow.rate(pores=pn.pores('right'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The *relative* permeability is the ratio of the water flow through the partially water saturated media versus through fully water saturated media; hence we need to find the absolute permeability of water. This can be accomplished by *regenerating* the ``phys_water_internal`` object, which will recalculate the ``'throat.hydraulic_conductance'`` values and overwrite our manually entered near-zero values from the ``inv`` simulation using ``phys_water_internal.models.regenerate()``. We can then re-use the ``water_flow`` algorithm:" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "phys_water_internal.regenerate_models()\n", "water_flow.run()\n", "Q_full, = water_flow.rate(pores=pn.pores('right'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And finally, the relative permeability can be found from:" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Relative permeability: 0.97898\n" ] } ], "source": [ "K_rel = Q_partial/Q_full\n", "print(f\"Relative permeability: {K_rel:.5f}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* The ratio of the flow rates gives the normalized relative permeability since all the domain size, viscosity and pressure differential terms cancel each other.\n", "* To generate a full relative permeability curve the above logic would be placed inside a for loop, with each loop increasing the pressure threshold used to obtain the list of invaded throats (``Ti``).\n", "* The saturation at each capillary pressure can be found be summing the pore and throat volume of all the invaded pores and throats using ``Vp = geom['pore.volume'][Pi]`` and ``Vt = geom['throat.volume'][Ti]``." ] } ], "metadata": { "@webio": { "lastCommId": null, "lastKernelId": null }, "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": 2 }
mit
RyanAlberts/Springbaord-Capstone-Project
Statistics_Exercises/sliderule_dsi_inferential_statistics_exercise_2.ipynb
1
3820
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Examining Racial Discrimination in the US Job Market\n", "\n", "### Background\n", "Racial discrimination continues to be pervasive in cultures throughout the world. Researchers examined the level of racial discrimination in the United States labor market by randomly assigning identical résumés to black-sounding or white-sounding names and observing the impact on requests for interviews from employers.\n", "\n", "### Data\n", "In the dataset provided, each row represents a resume. The 'race' column has two values, 'b' and 'w', indicating black-sounding and white-sounding. The column 'call' has two values, 1 and 0, indicating whether the resume received a call from employers or not.\n", "\n", "Note that the 'b' and 'w' values in race are assigned randomly to the resumes when presented to the employer." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"span5 alert alert-info\">\n", "### Exercises\n", "You will perform a statistical analysis to establish whether race has a significant impact on the rate of callbacks for resumes.\n", "\n", "Answer the following questions **in this notebook below and submit to your Github account**. \n", "\n", " 1. What test is appropriate for this problem? Does CLT apply?\n", " 2. What are the null and alternate hypotheses?\n", " 3. Compute margin of error, confidence interval, and p-value.\n", " 4. Write a story describing the statistical significance in the context or the original problem.\n", " 5. Does your analysis mean that race/name is the most important factor in callback success? Why or why not? If not, how would you amend your analysis?\n", "\n", "You can include written notes in notebook cells using Markdown: \n", " - In the control panel at the top, choose Cell > Cell Type > Markdown\n", " - Markdown syntax: http://nestacms.com/docs/creating-content/markdown-cheat-sheet\n", "\n", "\n", "#### Resources\n", "+ Experiment information and data source: http://www.povertyactionlab.org/evaluation/discrimination-job-market-united-states\n", "+ Scipy statistical methods: http://docs.scipy.org/doc/scipy/reference/stats.html \n", "+ Markdown syntax: http://nestacms.com/docs/creating-content/markdown-cheat-sheet\n", "</div>\n", "****" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "from scipy import stats" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data = pd.io.stata.read_stata('data/us_job_market_discrimination.dta')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# number of callbacks for black-sounding names\n", "sum(data[data.race=='b'].call)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data.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": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 1 }
mit
mne-tools/mne-tools.github.io
dev/_downloads/64b41c961a5966a9f21a532d9667c8a0/xhemi.ipynb
1
2644
{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n\n# Cross-hemisphere comparison\n\nThis example illustrates how to visualize the difference between activity in\nthe left and the right hemisphere. The data from the right hemisphere is\nmapped to the left hemisphere, and then the difference is plotted. For more\ninformation see :func:`mne.compute_source_morph`.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Author: Christian Brodbeck <[email protected]>\n#\n# License: BSD-3-Clause" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import mne\n\ndata_dir = mne.datasets.sample.data_path()\nsubjects_dir = data_dir / 'subjects'\nstc_path = data_dir / 'MEG' / 'sample' / 'sample_audvis-meg-eeg'\nstc = mne.read_source_estimate(stc_path, 'sample')\n\n# First, morph the data to fsaverage_sym, for which we have left_right\n# registrations:\nstc = mne.compute_source_morph(stc, 'sample', 'fsaverage_sym', smooth=5,\n warn=False,\n subjects_dir=subjects_dir).apply(stc)\n\n# Compute a morph-matrix mapping the right to the left hemisphere,\n# and vice-versa.\nmorph = mne.compute_source_morph(stc, 'fsaverage_sym', 'fsaverage_sym',\n spacing=stc.vertices, warn=False,\n subjects_dir=subjects_dir, xhemi=True,\n verbose='error') # creating morph map\nstc_xhemi = morph.apply(stc)\n\n# Now we can subtract them and plot the result:\ndiff = stc - stc_xhemi\n\ndiff.plot(hemi='lh', subjects_dir=subjects_dir, initial_time=0.07,\n size=(800, 600))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 0 }
bsd-3-clause
quantopian/research_public
notebooks/lectures/Case_Study_Comparing_ETFs/answers/notebook.ipynb
2
78420
{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Exercises: Comparing ETFs - Answer Key\n", "By Christopher van Hoecke, Maxwell Margenot, and Delaney Mackenzie\n", "\n", "\n", "## Lecture Link :\n", "https://www.quantopian.com/lectures/statistical-moments\n", "\n", "https://www.quantopian.com/lectures/hypothesis-testing\n", "\n", "###IMPORTANT NOTE: \n", "This lecture corresponds to the statistical moments and hypothesis testing lecture, which is part of the Quantopian lecture series. This homework expects you to rely heavily on the code presented in the corresponding lecture. Please copy and paste regularly from that lecture when starting to work on the problems, as trying to do them from scratch will likely be too difficult.\n", "\n", "When you feel comfortable with the topics presented here, see if you can create an algorithm that qualifies for the Quantopian Contest. Participants are evaluated on their ability to produce risk-constrained alpha and the top 10 contest participants are awarded cash prizes on a daily basis.\n", "\n", "https://www.quantopian.com/contest\n", "\n", "Part of the Quantopian Lecture Series:\n", "\n", "* [www.quantopian.com/lectures](https://www.quantopian.com/lectures)\n", "* [github.com/quantopian/research_public](https://github.com/quantopian/research_public)\n", "\n", "----" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Key Concepts\n", "t-statistic formula for unequal variances : $ t = \\frac{\\bar{X}_1 - \\bar{X}_2}{(\\frac{s_1^2}{n_1} + \\frac{s_2^2}{n_2})^{1/2}}$\n", "\n", "Where $s_1$ and $s_2$ are the standard deviation of set 1 and set 2; and $n_1$ and $n_2$ are the number of observations we have." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Useful functions\n", "def normal_test(X):\n", " z, pval = stats.normaltest(X)\n", " if pval < 0.05:\n", " print 'Values are not normally distributed.'\n", " else: \n", " print 'Values are normally distributed.'\n", " return" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Useful Libraries\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from scipy import stats\n", "import seaborn as sns" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Data:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Get pricing data for an energy (XLE) and industrial (XLI) ETF\n", "xle = get_pricing('XLE', fields = 'price', start_date = '2016-01-01', end_date = '2017-01-01')\n", "xli = get_pricing('XLI', fields = 'price', start_date = '2016-01-01', end_date = '2017-01-01')\n", "\n", "# Compute returns\n", "xle_returns = xle.pct_change()[1:]\n", "xli_returns = xli.pct_change()[1:]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercise 1 : Hypothesis Testing on Variances. \n", "- Plot the histogram of the returns of XLE and XLI\n", "- Check to see if each return stream is normally distributed\n", "- If the assets are normally distributed, use the F-test to perform a hypothesis test and decide whether they have the two assets have the same variance.\n", "- If the assets are **not** normally distributed, use the Levene test (in the scipy library) to perform a hypothesis test on variance. " ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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GeJnVaAMAQEW4xggAAACA7RGMAAAAANgewQgAAACA7RGMAAAAANiepTdfOH/+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\niBEjtHv3bqtLAAAAAIAKNbC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"text/plain": [ "<matplotlib.figure.Figure at 0x7f200ad63c50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "xle = plt.hist(xle_returns, bins=30)\n", "xli = plt.hist(xli_returns, bins=30, color='r')\n", "\n", "plt.xlabel('returns')\n", "plt.ylabel('Frequency')\n", "plt.title('Histogram of the returns of XLE and XLI')\n", "plt.legend(['XLE returns', 'XLI returns']);" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "XLE\n", "Values are normally distributed.\n", "XLI\n", "Values are not normally distributed.\n" ] } ], "source": [ "# Checking for normality using function above. \n", "\n", "print 'XLE'\n", "normal_test(xle_returns)\n", "print 'XLI'\n", "normal_test(xli_returns)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "LeveneResult(statistic=42.17712538196367, pvalue=2.0149310189859354e-10)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Because the data is not normally distributed, we must use the levene and not the F-test of variance. \n", "\n", "stats.levene(xle_returns, xli_returns)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since we find a pvalue for the Levene test of less than our $\\alpha$ level (0.05), we can reject the null hypothesis that the variability of the two groups are equal thus implying that **the variances are unequal.**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "-----" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercise 2 : Hypothesis Testing on Means.\n", "\n", "Since we know that the variances are not equal, we must use Welch's t-test. \n", "- Calculate the mean returns of XLE and XLI.\n", " - Find the difference between the two means.\n", "- Calculate the standard deviation of the returns of XLE and XLI\n", "- Using the formula given above, calculate the t-test statistic (Using $\\alpha = 0.05$) for Welch's t-test to test whether the mean returns of XLE and XLI are different.\n", "- Consult the [Hypothesis Testing Lecture](https://www.quantopian.com/lectures#Hypothesis-Testing) to calculate the p-value for this test. Are the mean returns of XLE and XLI the same?\n", "\n", "- Now use the t-test function for two independent samples from the scipy library. Compare the results." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "t-test statistic: 0.246212554505\n" ] } ], "source": [ "# Manually calculating the t-statistic\n", "\n", "N1 = len(xle_returns)\n", "N2 = len(xli_returns)\n", "\n", "m1 = xle_returns.mean()\n", "m2 = xli_returns.mean()\n", "\n", "s1 = xle_returns.std()\n", "s2 = xli_returns.std()\n", "\n", "test_statistic = (m1 - m2) / (s1**2 / N1 + s2**2 / N2)**0.5\n", "print 't-test statistic:', test_statistic" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Ttest_indResult(statistic=0.24621255450523835, pvalue=0.80564256071472806)" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Alternative form, using the scipy library on python. \n", "\n", "stats.ttest_ind(xle_returns, xli_returns, equal_var=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "----" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Exercise 3 : Skewness\n", "- Calculate the mean and median of the two assets\n", "- Calculate the skewness using the scipy library" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mean of XLE returns = 0.0011018423017 ; median = 0.000303393455803\n", "Mean of XLI returns = 0.000822235474081 ; median = 0.000546099936288\n" ] } ], "source": [ "# Calculate the mean and median of xle and xli using the numpy library\n", "\n", "xle_mean = np.mean(xle_returns)\n", "xle_median = np.median(xle_returns)\n", "print 'Mean of XLE returns = ', xle_mean, '; median = ', xle_median\n", "\n", "xli_mean = np.mean(xli_returns)\n", "xli_median = np.median(xli_returns)\n", "print 'Mean of XLI returns = ', xli_mean, '; median = ', xli_median" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Skew of XLE returns: 0.090984051661\n", "Skew of XLI returns: -0.332324357079\n" ] } ], "source": [ "# Print values of Skewness for xle and xli returns \n", "\n", "print 'Skew of XLE returns:', stats.skew(xle_returns)\n", "print 'Skew of XLI returns:', stats.skew(xli_returns)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And the skewness of XLE returns of values > 0 means that there is more weight in the right tail of the distribution. The skewness of XLI returns of value > 0 means that there is more weight in the left tail of the distribution.\n", "\n", "----" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercise 4 : Kurtosis\n", "- Check the kurtosis of the two assets, using the scipy library. \n", "- Using the seaborn library, plot the distribution of XLE and XLI returns. \n", "\n", "Recall: \n", "- Kurtosis > 3 is leptokurtic, a highly peaked, narrow deviation from the mean\n", "- Kurtosis = 3 is mesokurtic. The most significant mesokurtic distribution is the normal distribution family. \n", "- Kurtosis < 3 is platykurtic, a lower-peaked, broad deviation from the mean " ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "kurtosis: 0.854981014172\n", "kurtosis: 2.17804780091\n" ] } ], "source": [ "# Print value of Kurtosis for xle and xli returns \n", "\n", "print 'kurtosis:', stats.kurtosis(xle_returns)\n", "print 'kurtosis:', stats.kurtosis(xli_returns)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": 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2cpOyuJLcm1fhlSmpsy9mdSkAAJwU4QYApulgyi6p+EdupEzHNIlwAwDIb4Qb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\nw0CLt77+pzoSwaZpDY7Tnv3bevy641RUVHRa25v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lVvvNb+DqqzNaO0sIIdpDghsh2iuV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"text/plain": [ "<matplotlib.figure.Figure at 0x7f200bc9ab90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Distribution plot of XLE returns in red (for Kurtosis of 1.6). \n", "# Distribution plot of XLI returns in blue (for Kurtosis of 2.0).\n", "\n", "xle = sns.distplot(xle_returns, color = 'r', axlabel = 'xle')\n", "xli = sns.distplot(xli_returns, axlabel = 'xli');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can clearly see from the two graphs that as our kurtosis gets lower, the distribution gets more flat." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*This presentation is for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation for any security; nor does it constitute an offer to provide investment advisory or other services by Quantopian, Inc. (\"Quantopian\"). Nothing contained herein constitutes investment advice or offers any opinion with respect to the suitability of any security, and any views expressed herein should not be taken as advice to buy, sell, or hold any security or as an endorsement of any security or company. In preparing the information contained herein, Quantopian, Inc. has not taken into account the investment needs, objectives, and financial circumstances of any particular investor. Any views expressed and data illustrated herein were prepared based upon information, believed to be reliable, available to Quantopian, Inc. at the time of publication. Quantopian makes no guarantees as to their accuracy or completeness. All information is subject to change and may quickly become unreliable for various reasons, including changes in market conditions or economic circumstances.*" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 2 }
apache-2.0
Danghor/Algorithms
Python/Chapter-09/Dijkstra.ipynb
2
20368
{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from IPython.core.display import HTML\n", "with open('../style.css') as file:\n", " css = file.read()\n", "HTML(css)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Dijkstra's Shortest Path Algorithm" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The notebook `Set.ipynb` implements <em style=\"color:blue\">sets</em> as\n", "<a href=\"https://en.wikipedia.org/wiki/AVL_tree\">AVL trees</a>.\n", "The API provided by `Set` offers the following API:\n", "- `Set()` creates an empty set.\n", "- `S.isEmpty()` checks whether the set `S`is empty.\n", "- `S.member(x)` checks whether `x` is an element of the given set `S`.\n", "- `S.insert(x)` inserts `x` into the set `S`.\n", " This does not return a new set but rather modifies the given set `S`.\n", "- `S.delete(x)` deletes `x` from the set `S`.\n", " This does not return a new set but rather modifies the set `S`.\n", "- `S.pop()` returns the <em style=\"color:blue\">smallest element</em> of the set `S`.\n", " Furthermore, this element is removed from the given set `S`.\n", " \n", "Since sets are implemented as *ordered binary trees*, the elements of a set need to be comparable, i.e. if \n", "`x` and `y` are inserted into a set, then the expression `x < y` has to be defined and has to return a \n", "Boolean value. Furthermore, the relation `<` has to be a \n", "<a href=\"https://en.wikipedia.org/wiki/linear_order\">linear order</a>.\n", " \n", "The class `Set` can be used to implement a priority queue that supports the \n", "<em style=\"color:blue\">removal</em> of elements." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%run Set.ipynb " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The function call `shortest_path` takes a node `source` and a set `Edges`.\n", "\n", "The function `shortest_path` takes two arguments.\n", "- `source` is the start node. \n", "- `Edges` is a dictionary that encodes the set of edges of the graph. For every node `x` the value of `Edges[x]` has the form\n", " $$ \\bigl[ (y_1, l_1), \\cdots, (y_n, l_n) \\bigr]. $$\n", " This list is interpreted as follows: For every $i = 1,\\cdots,n$ there is an edge\n", " $(x, y_i)$ pointing from $x$ to $y_i$ and this edge has the length $l_i$.\n", " \n", "The function returns the dictionary `Distance`. For every node `u` such that there is a path from `source` to \n", "`u`, `Distance[u]` is the length of the shortest path from `source` to `u`. The implementation uses \n", "<a href=\"https://en.wikipedia.org/wiki/Dijkstra%27s_algorithm\">Dijkstra's algorithm</a> and proceeds as follows:\n", "\n", "- `Distance` is a dictionary mapping nodes to their estimated distance from the node\n", " `source`. If `d = Distance[x]`, then we know that there is a path of length `d` leading\n", " from `source` to `x`. However, in general we do not know whether there is a path shorter\n", " than `d` that also connects the source to the node `x`.\n", "- The function `shortest_path` maintains an additional variable called `Visited`.\n", " This variable contains the set of those nodes that have been <em style=\"color:blue\">visited</em> \n", " by the algorithm.\n", " To be more precise, `Visited` contains those nodes `u` that have been removed from the\n", " `Fringe` and for which all neighboring nodes, i.e. those nodes `y` such that\n", " there is an edge `(u,y)`, have been examined. It can be shown that once a node `u` is added to\n", " `Visited`, `Distance[u]` is the length of the shortest path from `source` to `u`.\n", "- `Fringe` is a priority queue that contains pairs of the form `(d, x)`, where `x` is a node and `d`\n", " is the distance that `x` has from the node `source`. This priority queue is implemented as a set,\n", " which in turn is represented by an ordered binary tree. The fact that we store the node `x` and the\n", " distance `d` as a pair `(d,x)` implies that the distances are used as priorities because pairs are\n", " compared lexicographically.\n", " Initially the only node that is known to be\n", " reachable from `source` is the node `source`. Hence `Fringe` is initialized as the\n", " set `{ (0, source) }`.\n", "- As long as the set `Fringe` is not empty, line 7 of the implementation removes that node `u`\n", " from the set `Fringe` that has the smallest distance `d` from the node `source`.\n", "- Next, all edges leading away from `u` are visited. If there is an edge `(u, v)` that has length `l`,\n", " then we check whether the node `v` has already a distance assigned. If the node `v` already has the\n", " distance `dv` assigned but the value `d + l` is less than `dv`, then we have found a\n", " shorter path from `source` to `v`. This path leads from `source` to `u` and then proceeds\n", " to `v` via the edge `(u,v)`.\n", "- If `v` had already been visited before and hence `dv=Distance[v]` is defined, we\n", " have to update the priority of the `v` in the `Fringe`. The easiest way to do this is to remove\n", " the old pair `(dv, v)` from the `Fringe` and replace this pair by the new pair\n", " `(d+l, v)`, because `d+l` is the new estimate of the distance between `source` and `v` and\n", " `d+l` is the new priority of `v`.\n", "- Once we have inspected all neighbours of the node `u`, `u` is added to the set of those nodes that have\n", " been `Visited`.\n", "- When the `Fringe` has been exhausted, the dictionary `Distance` contains the distances of\n", " every node that is reachable from the node `source`" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def shortest_path(source, Edges):\n", " Distance = { source: 0 }\n", " Visited = { source }\n", " Fringe = Set()\n", " Fringe.insert( (0, source) )\n", " while not Fringe.isEmpty():\n", " d, u = Fringe.pop() # get and remove smallest element\n", " for v, l in Edges[u]:\n", " dv = Distance.get(v, None)\n", " if dv == None or d + l < dv:\n", " if dv != None:\n", " Fringe.delete( (dv, v) )\n", " Distance[v] = d + l\n", " Fringe.insert( (d + l, v) )\n", " Visited.add(u)\n", " return Distance" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The version of `shortest_path` given below provides a graphical animation of the algorithm." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def shortest_path(source, Edges):\n", " Distance = { source: 0 }\n", " Visited = { source } # set only needed for visualization\n", " Fringe = Set()\n", " Fringe.insert( (0, source) )\n", " while not Fringe.isEmpty():\n", " d, u = Fringe.pop()\n", " display(toDot(source, u, Edges, Fringe, Distance, Visited))\n", " print('_' * 80)\n", " for v, l in Edges[u]:\n", " dv = Distance.get(v, None)\n", " if dv == None or d + l < dv:\n", " if dv != None:\n", " Fringe.delete( (dv, v) )\n", " Distance[v] = d + l\n", " Fringe.insert( (d + l, v) )\n", " Visited.add(u)\n", " display(toDot(source, None, Edges, Fringe, Distance, Visited))\n", " return Distance" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Code to Display the Directed Graph" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import graphviz as gv" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The function $\\texttt{toDot}(\\texttt{source}, \\texttt{Edges}, \\texttt{Fringe}, \\texttt{Distance}, \\texttt{Visited})$ takes a graph that is represented by \n", "its `Edges`, a set of nodes `Fringe`, and a dictionary `Distance` that has the distance of a node from the node `source`, and set `Visited` of nodes that have already been visited." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def toDot(source, p, Edges, Fringe, Distance, Visited):\n", " V = set()\n", " for x in Edges.keys():\n", " V.add(x)\n", " dot = gv.Digraph(node_attr={'shape': 'record', 'style': 'rounded'})\n", " dot.attr(rankdir='LR', size='8,5')\n", " for x in V:\n", " if x == source:\n", " dot.node(str(x), color='blue', shape='doublecircle')\n", " else:\n", " d = str(Distance.get(x, ''))\n", " if x == p:\n", " dot.node(str(x), label='{' + str(x) + '|' + d + '}', color='magenta')\n", " elif x in Distance and Fringe.member( (Distance[x], x) ):\n", " dot.node(str(x), label='{' + str(x) + '|' + d + '}', color='red')\n", " elif x in Visited:\n", " dot.node(str(x), label='{' + str(x) + '|' + d + '}', color='blue')\n", " else:\n", " dot.node(str(x), label='{' + str(x) + '|' + d + '}')\n", " for u in V:\n", " for v, l in Edges[u]:\n", " dot.edge(str(u), str(v), label=str(l))\n", " return dot" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Code for Testing" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "Edges = { 'a': [ ('c', 2), ('b', 9)], \n", " 'b': [('d', 1)],\n", " 'c': [('e', 5), ('g', 3)], \n", " 'd': [('f', 2), ('e', 4)], \n", " 'e': [('f', 1), ('b', 2)],\n", " 'f': [('h', 5)],\n", " 'g': [('e', 1)],\n", " 'h': []\n", " }" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": false }, "outputs": [], "source": [ "s = 'a'\n", "sp = shortest_path(s, Edges)\n", "sp" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Crossing the Tunnel" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Four persons, Alice, Britney, Charly and Daniel have to cross a tunnel.\n", "The tunnel is so narrow, that at most two persons can cross it together.\n", "In order to cross the tunnel, a torch is needed. Together, they only\n", "have a single torch.\n", " 1. Alice is the fastest and can cross the tunnel in 1 minute.\n", " 2. Britney needs 2 minutes to cross the tunnel.\n", " 3. Charly is slower and needs 4 minutes.\n", " 4. Daniel is slowest and takes 5 minutes to cross the tunnel.\n", " \n", "What is the fastest plan to cross the tunnel?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will model this problem as a graph theoretical problem. The nodes of the graph will be sets \n", "of people. In particular, it will be the set of people at the entrance of the tunnel. In order to model the torch, the torch can also be a member of these sets. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "All = frozenset({ 'Alice', 'Britney', 'Charly', 'Daniel', 'Torch' })" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The timining is modelled by a dictionary." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "Time = { 'Alice': 1, 'Britney': 2, 'Charly': 4, 'Daniel': 5, 'Torch': 0 }" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The function $\\texttt{power}(M)$ defined below computes the power list of the set $M$, i.e. we have:\n", "$$ \\texttt{power}(M) = 2^M = \\bigl\\{A \\mid A \\subseteq M \\bigr\\} $$" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def power(M):\n", " if M == set():\n", " return { frozenset() }\n", " else:\n", " C = set(M) # C is a copy of M as we don't want to change the set M\n", " x = C.pop() # pop removes the element x from the set C\n", " P1 = power(C)\n", " P2 = { A | {x} for A in P1 }\n", " return P1 | P2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If $B$ is a set of persons, then $\\texttt{duration}(B)$ is the time that this group needs to cross the tunnel.\n", "$B$ also contains `'Torch'`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def duration(B):\n", " return max(Time[x] for x in B)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$\\texttt{left_right}(S)$ describes a crossing of the tunnel from the entrance at the left side left to the exit at the right side of the tunnel." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def left_right(S):\n", " return [(S - B, duration(B)) for B in power(S) if 'Torch' in B and 2 <= len(B) <= 3]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$\\texttt{right_left}(S)$ describes a crossing of the tunnel from right to left." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def right_left(S):\n", " return [(S | B, duration(B)) for B in power(All - S) if 'Torch' in B and 2 <= len(B) <= 3]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "Edges = { S: left_right(S) + right_left(S) for S in power(All) }\n", "len(Edges)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The function `shortest_path` is Dijkstra's algorithm. It returns both a dictionary `Parent` containing \n", "the parent nodes and a dictionary `Distance` with the distances. The dictionary `Parent` can be used to\n", "compute the shortest path leading from the node `source` to some other node. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def shortest_path(source, Edges):\n", " Distance = { source: 0 }\n", " Parent = {}\n", " Fringe = Set()\n", " Fringe.insert( (0, source) )\n", " while not Fringe.isEmpty():\n", " d, u = Fringe.pop()\n", " for v, l in Edges[u]:\n", " dv = Distance.get(v, None)\n", " if dv == None or d + l < dv:\n", " if dv != None:\n", " Fringe.delete( (dv, v) )\n", " Distance[v] = d + l\n", " Fringe.insert( (d + l, v) )\n", " Parent[v] = u\n", " return Parent, Distance" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "Parent, Distance = shortest_path(frozenset(All), Edges)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us see whether the goal was reachable and how long it takes to reach the goal." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "goal = frozenset()\n", "Distance[goal]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Given to nodes `source` and `goal` and a dictionary containing the parent of every node, the function\n", "`find_path` returns the path from `source` to `goal`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def find_path(source, goal, Parent):\n", " p = Parent.get(goal)\n", " if p == None:\n", " return [source]\n", " return find_path(source, p, Parent) + [goal]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "Path = find_path(frozenset(All), frozenset(), Parent)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def print_path():\n", " total = 0\n", " print(\"_\" * 81);\n", " for i in range(len(Path)):\n", " Left = set(Path[i])\n", " Right = set(All) - set(Left)\n", " if Left == set() or Right == set():\n", " print(Left, \" \" * 25, Right)\n", " else:\n", " print(Left, \" \" * 30, Right)\n", " print(\"_\" * 81);\n", " if i < len(Path) - 1:\n", " if \"Torch\" in Path[i]:\n", " Diff = set(Path[i]) - set(Path[i+1])\n", " time = duration(Diff)\n", " total += time\n", " print(\" \" * 20, \">>> \", Diff, ':', time, \" >>>\")\n", " else:\n", " Diff = set(Path[i+1]) - set(Path[i])\n", " time = duration(Diff)\n", " total += time\n", " print(\" \" * 20, \"<<< \", Diff, ':', time, \" <<<\")\n", " print(\"_\" * 81)\n", " print('Total time:', total, 'minutes.')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print_path()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "hide_input": false, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.4" }, "latex_envs": { "LaTeX_envs_menu_present": true, "autoclose": false, "autocomplete": true, "bibliofile": "biblio.bib", "cite_by": "apalike", "current_citInitial": 1, "eqLabelWithNumbers": true, "eqNumInitial": 1, "hotkeys": { "equation": "Ctrl-E", "itemize": "Ctrl-I" }, "labels_anchors": false, "latex_user_defs": false, "report_style_numbering": false, "user_envs_cfg": false }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false }, "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 }
gpl-2.0
arcyfelix/Courses
18-03-07-Deep Learning With Python by François Chollet/Chapter 5.3 - Using a pre-trained convnet.ipynb
2
290199
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Chapter 5.3 - Using a pre-trained convnet" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using TensorFlow backend.\n" ] } ], "source": [ "# Importing VGG16\n", "from keras.applications import VGG16" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "network = VGG16(weights = 'imagenet', \n", " include_top = False,\n", " input_shape = (150, 150, 3))" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "input_1 (InputLayer) (None, 150, 150, 3) 0 \n", "_________________________________________________________________\n", "block1_conv1 (Conv2D) (None, 150, 150, 64) 1792 \n", "_________________________________________________________________\n", "block1_conv2 (Conv2D) (None, 150, 150, 64) 36928 \n", "_________________________________________________________________\n", "block1_pool (MaxPooling2D) (None, 75, 75, 64) 0 \n", "_________________________________________________________________\n", "block2_conv1 (Conv2D) (None, 75, 75, 128) 73856 \n", "_________________________________________________________________\n", "block2_conv2 (Conv2D) (None, 75, 75, 128) 147584 \n", "_________________________________________________________________\n", "block2_pool (MaxPooling2D) (None, 37, 37, 128) 0 \n", "_________________________________________________________________\n", "block3_conv1 (Conv2D) (None, 37, 37, 256) 295168 \n", "_________________________________________________________________\n", "block3_conv2 (Conv2D) (None, 37, 37, 256) 590080 \n", "_________________________________________________________________\n", "block3_conv3 (Conv2D) (None, 37, 37, 256) 590080 \n", "_________________________________________________________________\n", "block3_pool (MaxPooling2D) (None, 18, 18, 256) 0 \n", "_________________________________________________________________\n", "block4_conv1 (Conv2D) (None, 18, 18, 512) 1180160 \n", "_________________________________________________________________\n", "block4_conv2 (Conv2D) (None, 18, 18, 512) 2359808 \n", "_________________________________________________________________\n", "block4_conv3 (Conv2D) (None, 18, 18, 512) 2359808 \n", "_________________________________________________________________\n", "block4_pool (MaxPooling2D) (None, 9, 9, 512) 0 \n", "_________________________________________________________________\n", "block5_conv1 (Conv2D) (None, 9, 9, 512) 2359808 \n", "_________________________________________________________________\n", "block5_conv2 (Conv2D) (None, 9, 9, 512) 2359808 \n", "_________________________________________________________________\n", "block5_conv3 (Conv2D) (None, 9, 9, 512) 2359808 \n", "_________________________________________________________________\n", "block5_pool (MaxPooling2D) (None, 4, 4, 512) 0 \n", "=================================================================\n", "Total params: 14,714,688\n", "Trainable params: 14,714,688\n", "Non-trainable params: 0\n", "_________________________________________________________________\n" ] } ], "source": [ "network.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Preparing the data" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import os\n", "import numpy as np\n", "from keras.preprocessing.image import ImageDataGenerator" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "base_dir = 'E:/1_GitHub/arcyfelix/Courses/In Progress-Deep Learning With Python by François Chollet/data/Chapter 5.2 - Using convets with small datasets'" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "train_dir = os.path.join(base_dir, 'train')\n", "validation_dir = os.path.join(base_dir, 'validation')\n", "test_dir = os.path.join(base_dir, 'test')" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "datagen = ImageDataGenerator(rescale = 1. / 255)\n", "batch_size = 20" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def extract_features(directory, sample_count):\n", " # Initializing empty matrixes of a given shape\n", " features = np.zeros(shape = (sample_count, 4, 4, 512))\n", " labels = np.zeros(shape = (sample_count))\n", " \n", " # Generator\n", " generator = datagen.flow_from_directory(\n", " directory,\n", " target_size = (150, 150),\n", " batch_size = batch_size,\n", " class_mode = 'binary')\n", " # Initializing index\n", " i = 0\n", " for inputs_batch, labels_batch in generator:\n", " features_batch = network.predict(inputs_batch)\n", " features[i * batch_size : (i + 1) * batch_size] = features_batch\n", " labels[i * batch_size : (i + 1) * batch_size] = labels_batch\n", " i += 1\n", " if i * batch_size >= sample_count:\n", " # Note that since generators yield data indefinitely in a loop,\n", " # we must `break` after every image has been seen once.\n", " break\n", " return features, labels" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Found 2000 images belonging to 2 classes.\n", "Found 1000 images belonging to 2 classes.\n", "Found 1000 images belonging to 2 classes.\n" ] } ], "source": [ "train_features, train_labels = extract_features(train_dir, 2000)\n", "validation_features, validation_labels = extract_features(validation_dir, 1000)\n", "test_features, test_labels = extract_features(test_dir, 1000)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(2000, 4, 4, 512)" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_features.shape" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(2000,)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_labels.shape" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Flattening the features in order to feed them to a densely-connected classifier\n", "train_features = np.reshape(train_features, (2000, 4 * 4 * 512))\n", "validation_features = np.reshape(validation_features, (1000, 4 * 4 * 512))\n", "test_features = np.reshape(test_features, (1000, 4 * 4 * 512))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Building a densely-connected classifier" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from keras.models import Sequential\n", "from keras.layers import Dense, Dropout\n", "from keras.optimizers import RMSprop" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "model = Sequential()\n", "model.add(Dense(units = 256, \n", " activation = 'relu', \n", " input_dim = 4 * 4 * 512))\n", "model.add(Dropout(rate = 0.5))\n", "model.add(Dense(units = 1, \n", " activation = 'sigmoid'))\n", "\n", "model.compile(optimizer = RMSprop(lr = 2e-5),\n", " loss = 'binary_crossentropy',\n", " metrics = ['acc'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Training" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train on 2000 samples, validate on 1000 samples\n", "Epoch 1/30\n", "2000/2000 [==============================] - 1s - loss: 0.5825 - acc: 0.6875 - val_loss: 0.4301 - val_acc: 0.8450\n", "Epoch 2/30\n", "2000/2000 [==============================] - 1s - loss: 0.4297 - acc: 0.8055 - val_loss: 0.3562 - val_acc: 0.8600\n", "Epoch 3/30\n", "2000/2000 [==============================] - 1s - loss: 0.3550 - acc: 0.8440 - val_loss: 0.3200 - val_acc: 0.8770\n", "Epoch 4/30\n", "2000/2000 [==============================] - 1s - loss: 0.3128 - acc: 0.8730 - val_loss: 0.2966 - val_acc: 0.8850\n", "Epoch 5/30\n", "2000/2000 [==============================] - 1s - loss: 0.2819 - acc: 0.8915 - val_loss: 0.2915 - val_acc: 0.8780\n", "Epoch 6/30\n", "2000/2000 [==============================] - 1s - loss: 0.2630 - acc: 0.8990 - val_loss: 0.2784 - val_acc: 0.8830\n", "Epoch 7/30\n", "2000/2000 [==============================] - 1s - loss: 0.2515 - acc: 0.8980 - val_loss: 0.2629 - val_acc: 0.8900\n", "Epoch 8/30\n", "2000/2000 [==============================] - 1s - loss: 0.2299 - acc: 0.9135 - val_loss: 0.2700 - val_acc: 0.8850\n", "Epoch 9/30\n", "2000/2000 [==============================] - 1s - loss: 0.2129 - acc: 0.9195 - val_loss: 0.2553 - val_acc: 0.8960\n", "Epoch 10/30\n", "2000/2000 [==============================] - 1s - loss: 0.1994 - acc: 0.9240 - val_loss: 0.2530 - val_acc: 0.8930\n", "Epoch 11/30\n", "2000/2000 [==============================] - 1s - loss: 0.1937 - acc: 0.9310 - val_loss: 0.2436 - val_acc: 0.9040\n", "Epoch 12/30\n", "2000/2000 [==============================] - 1s - loss: 0.1898 - acc: 0.9325 - val_loss: 0.2407 - val_acc: 0.9010\n", "Epoch 13/30\n", "2000/2000 [==============================] - 1s - loss: 0.1842 - acc: 0.9350 - val_loss: 0.2392 - val_acc: 0.9030\n", "Epoch 14/30\n", "2000/2000 [==============================] - 1s - loss: 0.1714 - acc: 0.9375 - val_loss: 0.2440 - val_acc: 0.9010\n", "Epoch 15/30\n", "2000/2000 [==============================] - 1s - loss: 0.1588 - acc: 0.9470 - val_loss: 0.2378 - val_acc: 0.9060\n", "Epoch 16/30\n", "2000/2000 [==============================] - 1s - loss: 0.1587 - acc: 0.9455 - val_loss: 0.2423 - val_acc: 0.9010\n", "Epoch 17/30\n", "2000/2000 [==============================] - 1s - loss: 0.1460 - acc: 0.9525 - val_loss: 0.2381 - val_acc: 0.9070\n", "Epoch 18/30\n", "2000/2000 [==============================] - 1s - loss: 0.1500 - acc: 0.9475 - val_loss: 0.2541 - val_acc: 0.8980\n", "Epoch 19/30\n", "2000/2000 [==============================] - 1s - loss: 0.1365 - acc: 0.9520 - val_loss: 0.2496 - val_acc: 0.9000\n", "Epoch 20/30\n", "2000/2000 [==============================] - 1s - loss: 0.1341 - acc: 0.9550 - val_loss: 0.2372 - val_acc: 0.9040\n", "Epoch 21/30\n", "2000/2000 [==============================] - 1s - loss: 0.1253 - acc: 0.9590 - val_loss: 0.2597 - val_acc: 0.8930\n", "Epoch 22/30\n", "2000/2000 [==============================] - 1s - loss: 0.1206 - acc: 0.9615 - val_loss: 0.2482 - val_acc: 0.9030\n", "Epoch 23/30\n", "2000/2000 [==============================] - 1s - loss: 0.1151 - acc: 0.9645 - val_loss: 0.2357 - val_acc: 0.9010\n", "Epoch 24/30\n", "2000/2000 [==============================] - 1s - loss: 0.1100 - acc: 0.9680 - val_loss: 0.2590 - val_acc: 0.8990\n", "Epoch 25/30\n", "2000/2000 [==============================] - 1s - loss: 0.1063 - acc: 0.9650 - val_loss: 0.2374 - val_acc: 0.8980\n", "Epoch 26/30\n", "2000/2000 [==============================] - 1s - loss: 0.1042 - acc: 0.9645 - val_loss: 0.2463 - val_acc: 0.9060\n", "Epoch 27/30\n", "2000/2000 [==============================] - 1s - loss: 0.0994 - acc: 0.9680 - val_loss: 0.2425 - val_acc: 0.9030\n", "Epoch 28/30\n", "2000/2000 [==============================] - 1s - loss: 0.0982 - acc: 0.9715 - val_loss: 0.2379 - val_acc: 0.8980\n", "Epoch 29/30\n", "2000/2000 [==============================] - 1s - loss: 0.0926 - acc: 0.9720 - val_loss: 0.2425 - val_acc: 0.9050\n", "Epoch 30/30\n", "2000/2000 [==============================] - 1s - loss: 0.0889 - acc: 0.9730 - val_loss: 0.2501 - val_acc: 0.9070\n" ] } ], "source": [ "history = model.fit(train_features, \n", " train_labels,\n", " epochs = 30,\n", " batch_size = 20,\n", " validation_data = (validation_features, validation_labels))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Training visualization" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ "acc = history.history['acc']\n", "val_acc = history.history['val_acc']\n", "loss = history.history['loss']\n", "val_loss = history.history['val_loss']" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true }, "outputs": [], "source": [ "epochs = range(len(acc))" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x2ad09f0d780>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize = (10, 6))\n", "\n", "plt.plot(epochs, \n", " acc, \n", " 'bo', \n", " label = 'Training acc')\n", "\n", "plt.plot(epochs, \n", " val_acc, \n", " 'b', \n", " label = 'Validation acc')\n", "\n", "plt.title('Training and validation accuracy')\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "image/png": 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99lq6KwIAAKlCsGoEgwdLb74pde0qnXGG9KtfSe5120ZBgZSbK7VoEW4LClJR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\nlJVJr74aJuh89dXwYfqNb0j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Xx28/kfSsQvcrovNxfIxF+ViLT9JcT1Zx94/jf1TLJP1O\nvH/rLT4+5WlJBe7+THwx798IVHVsee9Gz923SJoraYwieO9ma7BaIOlYM+trZq0lTZT0Qppryhpm\n1iE+mFJm1kHSmZLerflZqKMXJF0W//4ySc+nsZasU/6HM268eP/WS3wA8O8lLXP3uxMe4v3bQNUd\nW9670TCz7mbWOf59O4WT3d5XBO/drDwrUJLip6DOkJQj6UF3n57mkrKGmX1eoZVKklpKepzjW39m\nNkvSKIUrq38s6RZJz0l6UlJvSWslXeDuDMCuh2qO7yiFrhSXtEbStQnjKpAkMztV0uuSlkgqiy++\nSWEsEO/fBqjh2F4k3rsNZmYDFQan5yg0Mj3p7reZWVc18L2btcEKAACgsWVrVyAAAECjI1gBAABE\nhGAFAAAQEYIVAABARAhWAAAAESFYAQAARIRgBQAAEBGCFQAAQET+f70M0o56hZcJAAAAAElFTkSu\nQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x2ad12368208>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize = (10, 6))\n", "\n", "plt.plot(epochs, \n", " loss, \n", " 'bo', \n", " label = 'Training loss')\n", "\n", "plt.plot(epochs, \n", " val_loss, \n", " 'b', \n", " label = 'Validation loss')\n", "\n", "plt.title('Training and validation loss')\n", "plt.legend()\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Running end-to-end model" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from keras.layers import Flatten" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": true }, "outputs": [], "source": [ "end_to_end_model = Sequential()\n", "end_to_end_model.add(network)\n", "end_to_end_model.add(Flatten())\n", "end_to_end_model.add(Dense(units = 256, \n", " activation = 'relu'))\n", "end_to_end_model.add(Dense(units = 1, \n", " activation = 'sigmoid'))" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "vgg16 (Model) (None, 4, 4, 512) 14714688 \n", "_________________________________________________________________\n", "flatten_1 (Flatten) (None, 8192) 0 \n", "_________________________________________________________________\n", "dense_3 (Dense) (None, 256) 2097408 \n", "_________________________________________________________________\n", "dense_4 (Dense) (None, 1) 257 \n", "=================================================================\n", "Total params: 16,812,353\n", "Trainable params: 16,812,353\n", "Non-trainable params: 0\n", "_________________________________________________________________\n" ] } ], "source": [ "end_to_end_model.summary()" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Trainable weights before freezing the VGG base: 30\n" ] } ], "source": [ "print('Trainable weights before freezing the VGG base:', len(end_to_end_model.trainable_weights))" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Freezing VGG part of the network\n", "network.trainable = False" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Trainable weights after freezing the VGG base: 4\n" ] } ], "source": [ "print('Trainable weights after freezing the VGG base:', len(end_to_end_model.trainable_weights))" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Compiling the network\n", "end_to_end_model.compile(loss = 'binary_crossentropy',\n", " optimizer = RMSprop(lr = 2e-5),\n", " metrics = ['acc'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Loading augmented data" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": true }, "outputs": [], "source": [ "train_datagen = ImageDataGenerator(rescale = 1./255,\n", " rotation_range = 40,\n", " width_shift_range = 0.2,\n", " height_shift_range = 0.2,\n", " shear_range = 0.2,\n", " zoom_range = 0.2,\n", " horizontal_flip = True,\n", " fill_mode = 'nearest')\n", "\n", "# The validation data should not be augmented!\n", "test_datagen = ImageDataGenerator(rescale = 1./255)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Found 2000 images belonging to 2 classes.\n" ] } ], "source": [ "train_generator = train_datagen.flow_from_directory(\n", " # This is the target directory\n", " train_dir,\n", " # All images will be resized to 150x150\n", " target_size = (150, 150),\n", " batch_size = 20,\n", " # Since we use binary_crossentropy loss, we need binary labels\n", " class_mode = 'binary')" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Found 1000 images belonging to 2 classes.\n" ] } ], "source": [ "validation_generator = test_datagen.flow_from_directory(validation_dir,\n", " target_size = (150, 150),\n", " batch_size = 20,\n", " class_mode = 'binary')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Training" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/30\n", "44s - loss: 0.5217 - acc: 0.7300 - val_loss: 0.2741 - val_acc: 0.8930\n", "Epoch 2/30\n", "41s - loss: 0.2965 - acc: 0.8675 - val_loss: 0.1732 - val_acc: 0.9270\n", "Epoch 3/30\n", "41s - loss: 0.2317 - acc: 0.8975 - val_loss: 0.2138 - val_acc: 0.9000\n", "Epoch 4/30\n", "41s - loss: 0.1805 - acc: 0.9290 - val_loss: 0.1841 - val_acc: 0.9450\n", "Epoch 5/30\n", "41s - loss: 0.1684 - acc: 0.9325 - val_loss: 0.1057 - val_acc: 0.9530\n", "Epoch 6/30\n", "41s - loss: 0.1327 - acc: 0.9445 - val_loss: 0.1000 - val_acc: 0.9680\n", "Epoch 7/30\n", "41s - loss: 0.1293 - acc: 0.9510 - val_loss: 0.0863 - val_acc: 0.9680\n", "Epoch 8/30\n", "41s - loss: 0.1072 - acc: 0.9615 - val_loss: 0.4918 - val_acc: 0.8740\n", "Epoch 9/30\n", "41s - loss: 0.1066 - acc: 0.9590 - val_loss: 0.1362 - val_acc: 0.9460\n", "Epoch 10/30\n", "41s - loss: 0.0946 - acc: 0.9630 - val_loss: 0.1202 - val_acc: 0.9640\n", "Epoch 11/30\n", "41s - loss: 0.0935 - acc: 0.9650 - val_loss: 0.0830 - val_acc: 0.9680\n", "Epoch 12/30\n", "41s - loss: 0.0946 - acc: 0.9645 - val_loss: 0.0808 - val_acc: 0.9680\n", "Epoch 13/30\n", "41s - loss: 0.0653 - acc: 0.9750 - val_loss: 0.1051 - val_acc: 0.9630\n", "Epoch 14/30\n", "41s - loss: 0.0827 - acc: 0.9685 - val_loss: 0.0798 - val_acc: 0.9650\n", "Epoch 15/30\n", "41s - loss: 0.0667 - acc: 0.9740 - val_loss: 0.0768 - val_acc: 0.9690\n", "Epoch 16/30\n", "41s - loss: 0.0536 - acc: 0.9750 - val_loss: 0.1914 - val_acc: 0.9610\n", "Epoch 17/30\n", "41s - loss: 0.0784 - acc: 0.9740 - val_loss: 0.1278 - val_acc: 0.9660\n", "Epoch 18/30\n", "40s - loss: 0.0513 - acc: 0.9795 - val_loss: 0.1097 - val_acc: 0.9610\n", "Epoch 19/30\n", "41s - loss: 0.0535 - acc: 0.9795 - val_loss: 0.1098 - val_acc: 0.9670\n", "Epoch 20/30\n", "41s - loss: 0.0442 - acc: 0.9815 - val_loss: 0.1255 - val_acc: 0.9690\n", "Epoch 21/30\n", "41s - loss: 0.0584 - acc: 0.9810 - val_loss: 0.1529 - val_acc: 0.9580\n", "Epoch 22/30\n", "41s - loss: 0.0570 - acc: 0.9780 - val_loss: 0.0951 - val_acc: 0.9740\n", "Epoch 23/30\n", "41s - loss: 0.0545 - acc: 0.9820 - val_loss: 0.2190 - val_acc: 0.9650\n", "Epoch 24/30\n", "41s - loss: 0.0515 - acc: 0.9805 - val_loss: 0.1215 - val_acc: 0.9720\n", "Epoch 25/30\n", "41s - loss: 0.0444 - acc: 0.9855 - val_loss: 0.1712 - val_acc: 0.9640\n", "Epoch 26/30\n", "41s - loss: 0.0517 - acc: 0.9790 - val_loss: 0.0748 - val_acc: 0.9750\n", "Epoch 27/30\n", "41s - loss: 0.0358 - acc: 0.9885 - val_loss: 0.1635 - val_acc: 0.9660\n", "Epoch 28/30\n", "41s - loss: 0.0397 - acc: 0.9845 - val_loss: 0.1506 - val_acc: 0.9680\n", "Epoch 29/30\n", "41s - loss: 0.0432 - acc: 0.9845 - val_loss: 0.1244 - val_acc: 0.9600\n", "Epoch 30/30\n", "40s - loss: 0.0441 - acc: 0.9830 - val_loss: 0.1151 - val_acc: 0.9680\n" ] } ], "source": [ "history = end_to_end_model.fit_generator(train_generator,\n", " steps_per_epoch = 100,\n", " epochs = 30,\n", " validation_data = validation_generator,\n", " validation_steps = 50, \n", " verbose = 2)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": true }, "outputs": [], "source": [ "model.save('.\\saved_checkpoints\\Chapter 5.3 - Using a pre-trained convnet\\cats_and_dogs_small_3.h5')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Visualization" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": true }, "outputs": [], "source": [ "acc = history.history['acc']\n", "val_acc = history.history['val_acc']\n", "loss = history.history['loss']\n", "val_loss = history.history['val_loss']" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": true }, "outputs": [], "source": [ "epochs = range(len(acc))" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "image/png": 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533tvuHut7MTM7dq5//jH7i+84L5+ffTnTSopCeFh5Mhwh5rknpvrftdd7suX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\n0jlsxKZNYaR/OtEDAKJG6Ko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"text/plain": [ "<matplotlib.figure.Figure at 0x2ae20da0780>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize = (10, 6))\n", "plt.plot(epochs, \n", " acc, \n", " 'bo', \n", " label = 'Training acc')\n", "plt.plot(epochs, \n", " val_acc, \n", " 'b', \n", " label = 'Validation acc')\n", "plt.title('Training and validation accuracy')\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "image/png": 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mVIUOB6an23aKH6eCVVoakJnJoUAiInIXK6+XITcXmDgRaN7cFvFt3ty+zs31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\nWnXQIDt+hw6qX30V/zZE4/vvVfv3t3Y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"text/plain": [ "<matplotlib.figure.Figure at 0x2ae20da04a8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize = (10, 6))\n", "\n", "plt.plot(epochs, \n", " loss, \n", " 'bo', \n", " label = 'Training loss')\n", "plt.plot(epochs, \n", " val_loss, \n", " 'b', \n", " label = 'Validation loss')\n", "plt.title('Training and validation loss')\n", "plt.legend()\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fine-tuning" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "input_1 (InputLayer) (None, 150, 150, 3) 0 \n", "_________________________________________________________________\n", "block1_conv1 (Conv2D) (None, 150, 150, 64) 1792 \n", "_________________________________________________________________\n", "block1_conv2 (Conv2D) (None, 150, 150, 64) 36928 \n", "_________________________________________________________________\n", "block1_pool (MaxPooling2D) (None, 75, 75, 64) 0 \n", "_________________________________________________________________\n", "block2_conv1 (Conv2D) (None, 75, 75, 128) 73856 \n", "_________________________________________________________________\n", "block2_conv2 (Conv2D) (None, 75, 75, 128) 147584 \n", "_________________________________________________________________\n", "block2_pool (MaxPooling2D) (None, 37, 37, 128) 0 \n", "_________________________________________________________________\n", "block3_conv1 (Conv2D) (None, 37, 37, 256) 295168 \n", "_________________________________________________________________\n", "block3_conv2 (Conv2D) (None, 37, 37, 256) 590080 \n", "_________________________________________________________________\n", "block3_conv3 (Conv2D) (None, 37, 37, 256) 590080 \n", "_________________________________________________________________\n", "block3_pool (MaxPooling2D) (None, 18, 18, 256) 0 \n", "_________________________________________________________________\n", "block4_conv1 (Conv2D) (None, 18, 18, 512) 1180160 \n", "_________________________________________________________________\n", "block4_conv2 (Conv2D) (None, 18, 18, 512) 2359808 \n", "_________________________________________________________________\n", "block4_conv3 (Conv2D) (None, 18, 18, 512) 2359808 \n", "_________________________________________________________________\n", "block4_pool (MaxPooling2D) (None, 9, 9, 512) 0 \n", "_________________________________________________________________\n", "block5_conv1 (Conv2D) (None, 9, 9, 512) 2359808 \n", "_________________________________________________________________\n", "block5_conv2 (Conv2D) (None, 9, 9, 512) 2359808 \n", "_________________________________________________________________\n", "block5_conv3 (Conv2D) (None, 9, 9, 512) 2359808 \n", "_________________________________________________________________\n", "block5_pool (MaxPooling2D) (None, 4, 4, 512) 0 \n", "=================================================================\n", "Total params: 14,714,688\n", "Trainable params: 0\n", "Non-trainable params: 14,714,688\n", "_________________________________________________________________\n" ] } ], "source": [ "network.summary()" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Fine-tuning only block5_conv1, block5_conv2 and block5_conv3\n", "network.trainable = True\n", "\n", "set_trainable = False\n", "for layer in network.layers:\n", " if layer.name == 'block5_conv1':\n", " set_trainable = True\n", " if set_trainable:\n", " layer.trainable = True\n", " else:\n", " layer.trainable = False" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Compiling the network\n", "end_to_end_model.compile(loss = 'binary_crossentropy',\n", " optimizer = RMSprop(lr = 1e-5),\n", " metrics = ['acc'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Training" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/100\n", "100/100 [==============================] - 26s - loss: 0.0158 - acc: 0.9950 - val_loss: 0.1306 - val_acc: 0.9710\n", "Epoch 2/100\n", "100/100 [==============================] - 24s - loss: 0.0155 - acc: 0.9965 - val_loss: 0.1559 - val_acc: 0.9700\n", "Epoch 3/100\n", "100/100 [==============================] - 24s - loss: 0.0146 - acc: 0.9950 - val_loss: 0.1165 - val_acc: 0.9740\n", "Epoch 4/100\n", "100/100 [==============================] - 23s - loss: 0.0177 - acc: 0.9935 - val_loss: 0.1547 - val_acc: 0.9690\n", "Epoch 5/100\n", "100/100 [==============================] - 24s - loss: 0.0211 - acc: 0.9945 - val_loss: 0.1068 - val_acc: 0.9730\n", "Epoch 6/100\n", "100/100 [==============================] - 23s - loss: 0.0138 - acc: 0.9930 - val_loss: 0.1402 - val_acc: 0.9720\n", "Epoch 7/100\n", "100/100 [==============================] - 27s - loss: 0.0111 - acc: 0.9965 - val_loss: 0.1485 - val_acc: 0.9720\n", "Epoch 8/100\n", "100/100 [==============================] - 23s - loss: 0.0244 - acc: 0.9920 - val_loss: 0.1409 - val_acc: 0.9680\n", "Epoch 9/100\n", "100/100 [==============================] - 23s - loss: 0.0130 - acc: 0.9960 - val_loss: 0.1592 - val_acc: 0.9700\n", "Epoch 10/100\n", "100/100 [==============================] - 23s - loss: 0.0063 - acc: 0.9970 - val_loss: 0.1143 - val_acc: 0.9760\n", "Epoch 11/100\n", "100/100 [==============================] - 25s - loss: 0.0073 - acc: 0.9970 - val_loss: 0.1337 - val_acc: 0.9730\n", "Epoch 12/100\n", "100/100 [==============================] - 22s - loss: 0.0106 - acc: 0.9940 - val_loss: 0.1444 - val_acc: 0.9720\n", "Epoch 13/100\n", "100/100 [==============================] - 21s - loss: 0.0136 - acc: 0.9965 - val_loss: 0.0990 - val_acc: 0.9760\n", "Epoch 14/100\n", "100/100 [==============================] - 21s - loss: 0.0101 - acc: 0.9970 - val_loss: 0.1012 - val_acc: 0.9790\n", "Epoch 15/100\n", "100/100 [==============================] - 23s - loss: 0.0136 - acc: 0.9960 - val_loss: 0.1850 - val_acc: 0.9630\n", "Epoch 16/100\n", "100/100 [==============================] - 21s - loss: 0.0074 - acc: 0.9970 - val_loss: 0.1282 - val_acc: 0.9770\n", "Epoch 17/100\n", "100/100 [==============================] - 21s - loss: 0.0092 - acc: 0.9960 - val_loss: 0.1457 - val_acc: 0.9730\n", "Epoch 18/100\n", "100/100 [==============================] - 22s - loss: 0.0078 - acc: 0.9970 - val_loss: 0.1549 - val_acc: 0.9700\n", "Epoch 19/100\n", "100/100 [==============================] - 21s - loss: 0.0055 - acc: 0.9985 - val_loss: 0.1333 - val_acc: 0.9800\n", "Epoch 20/100\n", "100/100 [==============================] - 21s - loss: 0.0146 - acc: 0.9970 - val_loss: 0.1762 - val_acc: 0.9710\n", "Epoch 21/100\n", "100/100 [==============================] - 21s - loss: 0.0114 - acc: 0.9960 - val_loss: 0.1187 - val_acc: 0.9760\n", "Epoch 22/100\n", "100/100 [==============================] - 20s - loss: 0.0106 - acc: 0.9960 - val_loss: 0.1252 - val_acc: 0.9750\n", "Epoch 23/100\n", "100/100 [==============================] - 20s - loss: 0.0054 - acc: 0.9970 - val_loss: 0.1358 - val_acc: 0.9770\n", "Epoch 24/100\n", "100/100 [==============================] - 20s - loss: 0.0083 - acc: 0.9985 - val_loss: 0.1595 - val_acc: 0.9690\n", "Epoch 25/100\n", "100/100 [==============================] - 21s - loss: 0.0103 - acc: 0.9965 - val_loss: 0.2109 - val_acc: 0.9660\n", "Epoch 26/100\n", "100/100 [==============================] - 21s - loss: 0.0117 - acc: 0.9960 - val_loss: 0.1521 - val_acc: 0.9740\n", "Epoch 27/100\n", "100/100 [==============================] - 20s - loss: 0.0122 - acc: 0.9960 - val_loss: 0.1307 - val_acc: 0.9750\n", "Epoch 28/100\n", "100/100 [==============================] - 20s - loss: 0.0029 - acc: 0.9985 - val_loss: 0.1506 - val_acc: 0.9750\n", "Epoch 29/100\n", "100/100 [==============================] - 20s - loss: 0.0079 - acc: 0.9975 - val_loss: 0.1498 - val_acc: 0.9750\n", "Epoch 30/100\n", "100/100 [==============================] - 21s - loss: 0.0073 - acc: 0.9965 - val_loss: 0.2401 - val_acc: 0.9640\n", "Epoch 31/100\n", "100/100 [==============================] - 20s - loss: 0.0080 - acc: 0.9990 - val_loss: 0.2144 - val_acc: 0.9700\n", "Epoch 32/100\n", "100/100 [==============================] - 20s - loss: 0.0093 - acc: 0.9960 - val_loss: 0.1988 - val_acc: 0.9680\n", "Epoch 33/100\n", "100/100 [==============================] - 20s - loss: 0.0048 - acc: 0.9980 - val_loss: 0.1489 - val_acc: 0.9770\n", "Epoch 34/100\n", "100/100 [==============================] - 21s - loss: 0.0134 - acc: 0.9945 - val_loss: 0.1489 - val_acc: 0.9720\n", "Epoch 35/100\n", "100/100 [==============================] - 20s - loss: 0.0158 - acc: 0.9960 - val_loss: 0.1413 - val_acc: 0.9770\n", "Epoch 36/100\n", "100/100 [==============================] - 21s - loss: 0.0198 - acc: 0.9965 - val_loss: 0.1447 - val_acc: 0.9710\n", "Epoch 37/100\n", "100/100 [==============================] - 20s - loss: 0.0044 - acc: 0.9975 - val_loss: 0.1390 - val_acc: 0.9760\n", "Epoch 38/100\n", "100/100 [==============================] - 20s - loss: 0.0102 - acc: 0.9965 - val_loss: 0.1505 - val_acc: 0.9690\n", "Epoch 39/100\n", "100/100 [==============================] - 20s - loss: 0.0077 - acc: 0.9970 - val_loss: 0.1133 - val_acc: 0.9750\n", "Epoch 40/100\n", "100/100 [==============================] - 20s - loss: 0.0066 - acc: 0.9970 - val_loss: 0.1966 - val_acc: 0.9650\n", "Epoch 41/100\n", "100/100 [==============================] - 20s - loss: 0.0063 - acc: 0.9965 - val_loss: 0.1431 - val_acc: 0.9730\n", "Epoch 42/100\n", "100/100 [==============================] - 20s - loss: 0.0053 - acc: 0.9990 - val_loss: 0.1926 - val_acc: 0.9740\n", "Epoch 43/100\n", "100/100 [==============================] - 20s - loss: 0.0068 - acc: 0.9980 - val_loss: 0.1495 - val_acc: 0.9720\n", "Epoch 44/100\n", "100/100 [==============================] - 20s - loss: 0.0108 - acc: 0.9975 - val_loss: 0.1227 - val_acc: 0.9750\n", "Epoch 45/100\n", "100/100 [==============================] - 20s - loss: 0.0087 - acc: 0.9970 - val_loss: 0.1412 - val_acc: 0.9750\n", "Epoch 46/100\n", "100/100 [==============================] - 20s - loss: 0.0131 - acc: 0.9965 - val_loss: 0.1745 - val_acc: 0.9720\n", "Epoch 47/100\n", "100/100 [==============================] - 20s - loss: 0.0070 - acc: 0.9970 - val_loss: 0.1620 - val_acc: 0.9720\n", "Epoch 48/100\n", "100/100 [==============================] - 20s - loss: 0.0081 - acc: 0.9980 - val_loss: 0.1315 - val_acc: 0.9780\n", "Epoch 49/100\n", "100/100 [==============================] - 21s - loss: 0.0042 - acc: 0.9980 - val_loss: 0.1715 - val_acc: 0.9760\n", "Epoch 50/100\n", "100/100 [==============================] - 20s - loss: 0.0093 - acc: 0.9960 - val_loss: 0.1698 - val_acc: 0.9730\n", "Epoch 51/100\n", "100/100 [==============================] - 20s - loss: 0.0029 - acc: 0.9990 - val_loss: 0.1624 - val_acc: 0.9750\n", "Epoch 52/100\n", "100/100 [==============================] - 20s - loss: 0.0150 - acc: 0.9975 - val_loss: 0.1449 - val_acc: 0.9770\n", "Epoch 53/100\n", "100/100 [==============================] - 20s - loss: 0.0039 - acc: 0.9990 - val_loss: 0.1681 - val_acc: 0.9750\n", "Epoch 54/100\n", "100/100 [==============================] - 21s - loss: 0.0080 - acc: 0.9975 - val_loss: 0.1490 - val_acc: 0.9770\n", "Epoch 55/100\n", "100/100 [==============================] - 20s - loss: 0.0033 - acc: 0.9985 - val_loss: 0.2197 - val_acc: 0.9710\n", "Epoch 56/100\n", "100/100 [==============================] - 20s - loss: 0.0067 - acc: 0.9980 - val_loss: 0.2803 - val_acc: 0.9650\n", "Epoch 57/100\n", "100/100 [==============================] - 21s - loss: 0.0087 - acc: 0.9980 - val_loss: 0.1229 - val_acc: 0.9780\n", "Epoch 58/100\n", "100/100 [==============================] - 20s - loss: 0.0036 - acc: 0.9985 - val_loss: 0.1452 - val_acc: 0.9760\n", "Epoch 59/100\n", "100/100 [==============================] - 20s - loss: 0.0076 - acc: 0.9970 - val_loss: 0.2287 - val_acc: 0.9670\n", "Epoch 60/100\n", "100/100 [==============================] - 20s - loss: 0.0052 - acc: 0.9980 - val_loss: 0.1008 - val_acc: 0.9800\n", "Epoch 61/100\n", "100/100 [==============================] - 20s - loss: 0.0017 - acc: 0.9995 - val_loss: 0.1652 - val_acc: 0.9760\n", "Epoch 62/100\n", "100/100 [==============================] - 20s - loss: 0.0053 - acc: 0.9990 - val_loss: 0.1518 - val_acc: 0.9750\n", "Epoch 63/100\n", "100/100 [==============================] - 20s - loss: 0.0154 - acc: 0.9975 - val_loss: 0.2092 - val_acc: 0.9720\n", "Epoch 64/100\n", "100/100 [==============================] - 20s - loss: 0.0057 - acc: 0.9985 - val_loss: 0.1373 - val_acc: 0.9780\n", "Epoch 65/100\n", "100/100 [==============================] - 20s - loss: 0.0139 - acc: 0.9965 - val_loss: 0.1620 - val_acc: 0.9730\n", "Epoch 66/100\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "100/100 [==============================] - 20s - loss: 0.0100 - acc: 0.9975 - val_loss: 0.1641 - val_acc: 0.9760\n", "Epoch 67/100\n", "100/100 [==============================] - 20s - loss: 0.0144 - acc: 0.9975 - val_loss: 0.1325 - val_acc: 0.9790\n", "Epoch 68/100\n", "100/100 [==============================] - 20s - loss: 0.0056 - acc: 0.9975 - val_loss: 0.1862 - val_acc: 0.9730\n", "Epoch 69/100\n", "100/100 [==============================] - 20s - loss: 0.0067 - acc: 0.9985 - val_loss: 0.2030 - val_acc: 0.9720\n", "Epoch 70/100\n", "100/100 [==============================] - 20s - loss: 0.0056 - acc: 0.9985 - val_loss: 0.1574 - val_acc: 0.9740\n", "Epoch 71/100\n", "100/100 [==============================] - 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0.1789 - val_acc: 0.9640\n", "Epoch 79/100\n", "100/100 [==============================] - 20s - loss: 0.0040 - acc: 0.9990 - val_loss: 0.3218 - val_acc: 0.9640\n", "Epoch 80/100\n", "100/100 [==============================] - 20s - loss: 0.0037 - acc: 0.9985 - val_loss: 0.1739 - val_acc: 0.9750\n", "Epoch 81/100\n", "100/100 [==============================] - 20s - loss: 0.0028 - acc: 0.9990 - val_loss: 0.1952 - val_acc: 0.9760\n", "Epoch 82/100\n", "100/100 [==============================] - 21s - loss: 0.0096 - acc: 0.9975 - val_loss: 0.1275 - val_acc: 0.9790\n", "Epoch 83/100\n", "100/100 [==============================] - 22s - loss: 0.0021 - acc: 0.9990 - val_loss: 0.1656 - val_acc: 0.9710\n", "Epoch 84/100\n", "100/100 [==============================] - 22s - loss: 0.0026 - acc: 0.9990 - val_loss: 0.2085 - val_acc: 0.9680\n", "Epoch 85/100\n", "100/100 [==============================] - 22s - loss: 0.0104 - acc: 0.9980 - val_loss: 0.1652 - val_acc: 0.9750\n", "Epoch 86/100\n", "100/100 [==============================] - 22s - loss: 0.0078 - acc: 0.9975 - val_loss: 0.2001 - val_acc: 0.9670\n", "Epoch 87/100\n", "100/100 [==============================] - 22s - loss: 0.0067 - acc: 0.9985 - val_loss: 0.1920 - val_acc: 0.9730\n", "Epoch 88/100\n", "100/100 [==============================] - 22s - loss: 0.0079 - acc: 0.9980 - val_loss: 0.2312 - val_acc: 0.9650\n", "Epoch 89/100\n", "100/100 [==============================] - 25s - loss: 0.0050 - acc: 0.9990 - val_loss: 0.1891 - val_acc: 0.9740\n", "Epoch 90/100\n", "100/100 [==============================] - 21s - loss: 0.0151 - acc: 0.9970 - val_loss: 0.2000 - val_acc: 0.9700\n", "Epoch 91/100\n", "100/100 [==============================] - 24s - loss: 0.0047 - acc: 0.9990 - val_loss: 0.2020 - val_acc: 0.9750\n", "Epoch 92/100\n", "100/100 [==============================] - 24s - loss: 0.0019 - acc: 0.9990 - val_loss: 0.2019 - val_acc: 0.9690\n", "Epoch 93/100\n", "100/100 [==============================] - 24s - loss: 0.0038 - acc: 0.9990 - val_loss: 0.1102 - val_acc: 0.9820\n", "Epoch 94/100\n", "100/100 [==============================] - 23s - loss: 0.0035 - acc: 0.9985 - val_loss: 0.2213 - val_acc: 0.9710\n", "Epoch 95/100\n", "100/100 [==============================] - 23s - loss: 0.0143 - acc: 0.9960 - val_loss: 0.1431 - val_acc: 0.9790\n", "Epoch 96/100\n", "100/100 [==============================] - 24s - loss: 0.0097 - acc: 0.9970 - val_loss: 0.1757 - val_acc: 0.9720\n", "Epoch 97/100\n", "100/100 [==============================] - 24s - loss: 5.5237e-04 - acc: 1.0000 - val_loss: 0.1758 - val_acc: 0.9740\n", "Epoch 98/100\n", "100/100 [==============================] - 23s - loss: 0.0036 - acc: 0.9980 - val_loss: 0.1880 - val_acc: 0.9750\n", "Epoch 99/100\n", "100/100 [==============================] - 24s - loss: 0.0020 - acc: 0.9995 - val_loss: 0.2572 - val_acc: 0.9720\n", "Epoch 100/100\n", "100/100 [==============================] - 24s - loss: 0.0029 - acc: 0.9990 - val_loss: 0.2028 - val_acc: 0.9680.9\n" ] } ], "source": [ "history = end_to_end_model.fit_generator(train_generator,\n", " steps_per_epoch = 100,\n", " epochs = 100,\n", " validation_data = validation_generator,\n", " validation_steps = 50)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": true }, "outputs": [], "source": [ "model.save('.\\saved_checkpoints\\Chapter 5.3 - Using a pre-trained convnet\\cats_and_dogs_small_4.h5')" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": true }, "outputs": [], "source": [ "acc = history.history['acc']\n", "val_acc = history.history['val_acc']\n", "loss = history.history['loss']\n", "val_loss = history.history['val_loss']" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": true }, "outputs": [], "source": [ "epochs = range(len(acc))" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "image/png": 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aAEtSv/2vqk5W1U8AWAfgrdTy91V1p6ruAnA7zB3aAVWdpapTVHVKTU1NwtOL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\n8E/i96sxZvDmyXon8GH1F13EmEuYEsjfZMG4e0UWYy58GUAPEW0GcCe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"text/plain": [ "<matplotlib.figure.Figure at 0x2ae203eaa58>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize = (10, 6))\n", "plt.plot(epochs, \n", " acc, \n", " 'bo', \n", " label = 'Training acc')\n", "plt.plot(epochs, \n", " val_acc, \n", " 'b', \n", " label = 'Validation acc')\n", "plt.title('Training and validation accuracy')\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "image/png": 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CRgalpdHzMWOA+npgy5botouJLEY1uoDQRFdNDR1LTHSxE12xcIOVn091BIH4\nyuuqqaFrsNbpAlh0MUzcouZdVE4XJ9M3T4yq0QOhiS6A3S4vYCW6YiG8KCWJrvPPp+fxFGLU1+hS\nsOhimDhFWfVKdHXuDGRns+hqboTD6QJYdHmBWA8vlpQAx48Dl1xCz5uL6Kqs9Ob5w6KLYUJAiS4V\nXgR8RVKZ5oObTtfp0/QHAFVVobeNCY1Yd7pUPtd55wEdO0ZedNXUAC++GJ5QuX4KIIWXa3Wx6GKY\nENA7XQAl0x84ABw5Ep02MZGlthYoK3PP6VIuF+DNO/XmxqlTse10KdHVty/Qq1fkc7o++wyYNw/4\n+mv3t23mdGVm0iOLLoaJM/Q5XQDndTU3jh6lRxZd8UmsJ9Ln51M5m+xsEl2RdrrUNbKszP1ts9PF\nMM0MI6drxAiq6cOiq3mg7rY7d276WlIS1Q1i0RW7xHp4MS8P6NmTpi3KyQH274/sqNgTJ+jx2DH3\nt11aSudXx47+y1l0MUycYpTTlZwMDBvGoqu5oESX/m4boA4hOZlFVywT64n0+flAnz70f69eFA6P\n5BQ54RRdJSUkuFQ5DEW7drRvWHQxTJxRWUknt75a9ZgxwMaNXGepOWAW4lC0bs2iK5axc7pOnaKy\nDF5ESnK6+val57160WMk87rUYJBwhBf1UwApvFyVnkUXw4SAmgJIz9ixdLH57rvIt4mJLFZOF8Ci\nK9axc7oA32hTr3HsGN0Yap0uILJ5XeF2uszOOxZdDBOHmIkulUy/Zk1k28NEHpVX0qGD8essumIX\nKUl0GU12DfhEl1fzutSci0p0de9OSfXxJLqMnC6ARFckw6hOYdHFMCFQUeGfz6Xo0wc491zg3Xcj\n3yYmspSWkuBq0cL49WBEl+rkWXRFl5oaEl5W4UXAu6JLlYtQoqtlS6BHj/gRXaWl5k5XZiY5XV4L\n/bLoYpgQ0E52rUUIYPZsYPVqqtnFxC9WF34gONGVkUGjzbg4anRRYsouvOjVZPr8fLoWqbAiEPla\nXeoYdlt0NTSY53QB5HSdOuUrWeEVWHQxcc3cucBzz4Vv+2bhRQC47jp6fOed8H0+E33CIbo6dgRS\nU9npijZORZeXna4ePah0iSLStbrC5XRVVtJAJaucLsB7eV0supi4ZtEiYOnS8G3fSnT16kWTzL71\nVvg+n4k+4RBdHToAbdq4K7q2bAHq6tzbXnPATnQFO7dmpMjL84UWFTk5NG1VpAS9El1uj15Uo4at\nnC6ARRexF23nAAAgAElEQVTDRIzTp6lDLCoKz/alNM/pUsyaBWzfDmzbFp42MNEnFkTXwYNAbi7d\nhDDOiZbTtW4d8NBDoW9HW6NLEemyEUp0VVUBZ864t12zKYAULLoYJsKoky1couvUKXIOzJwuALj6\nakqwZrcrPpHSXnQFWkAzHKLr+++prQcPurO95oJTp8tt0fWXvwCPPx5aTt+xY/SnanQpIl024sQJ\nyisD/EfmhopdfbyuXemRRRfDRAg1XLi0lKowu43RFEB6OnUCfvADyutqaHC/DUx0OXmSRriZ3W0D\n3nC61OTryh1gnBGtRHrlQu3fH/w29u6lx2g7XVVVQJcu9L+beV12TlebNhSF8FrZCBZdTNxSWOj7\nX01K7CZORBdAIcaDB7lmVzxiVxgVCEx01dTQe1l0eYNohReVIApFGOnLRSjS02mQRiScroYGEl3Z\n2fTcTdFl53QBvrIRXoJFFxO3aEVXOEKMRvMuGnHVVdSBvvmm+21goouTC38gokuFX8IlusIxFUs8\nE41E+oYGCgcDoYkuVRhVWy4C8JWQiIToUsdvz5706LbTlZJivm8Ab1aldyS6hBCXCSF2CyHyhRDz\nDV6fJYTYJoTYLoT4SggxzOm6DBMutLZyOESXqv9i53S1aQNMn05JzDU17reDiR5Ona7Tp50VadSL\nLrfqdKnjPxpO19GjwO9+570ilU6IhtN1+LAv4TyU8GJ+PlWgN6qmHynRpZLolehyU/RbTQGk8KLo\nMqmh7EMIkQjgZQCXACgEsFEIsVhKqZ1Vbh+A8VLKciHEFACvARjjcF2GCQuFhWSjV1X57vTdxGl4\nEaAQ49//TuUrpk1zvy1MdHAqugASXmbTySjC7XRFQ3T97nfAU08BU6YAAwdG/vNDIRqJ9Fp3K9Tw\noj60qOjVC1ixgoSwSnI3Qkpg61Zy6b/7DkhO9v8bMQKYM8d8fSW6evSgR7edLqtcSoBE15Ej5B4m\neCSu56QZowHkSykLpJRnACwEMFX7BinlV1JKNS5hHYAsp+syTLg4dIguCkB4w4tORNekSUDnzjyK\nMd4IRHQ56Zi1osvN4qjREl1SAv/4B/2vQmaxhJ3oatGCptZxM7yohFa/fuETXTk51ObiYuPXDx8G\nnn4aGDoUGDkSePFFciz37gU2bwZWrqSbyNtvt3YwlVObmQkkJrqf02XndP3qV/QdrYRlpHEiujIB\naAcaFzYuM+MmAMuCXJdhXKOwEOjdmzqwaOZ0AXRxnjkTWLLEtx4T+5SWUmdidQwEkvejd7pqaqjq\ndqgo0XXsmDvbc8qWLT7hEMuiy8qhTElx3+kSAhg/nv63EjW7dlFJiM2b/ZdXVpIosXK6AOMQ4y9/\nSWHJX/0KaNsWeOUVOn42b6Z6g3v20NRmDz9Mx5JVCFw5Xe3a0THttuiyc7o6dKC/WBNdjhFCTACJ\nrvuCWHeOEGKTEGJTicpOZZggqa+nC0VmJg1XDldOV6tWZLM7YfZsytV4/33328JEB1Wjy+qiHoro\nAkJ3u2prfZNyq4K+kWLRIhKlLVqElp8ULU6dovPbKjQVaEkQOwoK6Lp17rnA8ePW+2v1anK0rr/e\nvw1q5KK+RpfCTHR98w3w7LNUX3DPHuCrr2gqtfT0pttQDr/V3IZKdLVtS1NbuR1etHO6vIgT0XUI\nQHfN86zGZX4IIYYCeB3AVCllWSDrAoCU8jUpZa6UMreTnXxlGBuKi0l4ZWWFT3SpKYCc3kXl5tKF\nR39XysQuTi78wYiu9u3dE11Hj5LYGjKEnkcqxKhCi5MmUU5PrDpdVqPjgPA4XTk59Keem7FzJ4na\nnTuBBx/0LTcrF6FQJRz02374YXJtX3nFXLAplLtrJQq1ois93T3Rdfo0OWyxKBWciK6NAPoKIXKE\nEK0AzASwWPsGIUQPAB8AuF5KuSeQdRkmHKhyEZmZVJk4XKLLSWhRIQTZ9l6vCv7448DGjdFuRWwQ\nDtGVmkp5Qm6JLhVajLTo2rqVcoB+8hMavcaiyxmBiq6hQym36rnngH//m5Yr0aUvF6FITqZro9bp\n2rwZ+Ogj4O67yRW1w4nTpUKPqal0w+nW6EUnuZRexVZ0SSnrANwBYAWAnQDek1LuEELcJoS4rfFt\nDwFIB/CKEGKrEGKT1bph+B4M44cqFxEJpysQunf3rx/mNWprac63v/0t2i2JDcIhulSH57boGjyY\nHiMluv7xD3JhfvSj2BZddiNOzcKLGzYEfoNVU0PXLqeia9cuYMAAGh3auzdwww3kLuXl0cg9dQwZ\nkZPjL7oeeoiE0Z13OmtroE6Xm+FFu8muvYyjnC4p5VIpZT8pZW8p5YLGZX+QUv6h8f+bpZQdpJTD\nG/9yrdZlmHCjhI0SXSdP+i4AblFZGbjoysryttOlLqBemzrDq8SS6Iqk06VCixMm0O/TsyeNiIu1\nOnXBOl21tcDkycBjjwX2eQcO0G+Xk0PXlrQ0c9F18iQJ2f796Vj5619p/bvvJqfLLjyordW1bh2V\ns7n3Xkp6d0IgOV3K6XJLdNlNAeRlPFK5gmHcpbCQktwzMnzzfrntdgXjdGVlkcXu9lxtbqFyirxW\nUNCLNDTQvgy36Aq1QKo67lWNrEhUpd+2jTr+n/yEnqscIi/fcBjhRHQZOV0bNpDgCLQ+oBJYyuXK\nyTEfgLB7Nz0OGECP//VfJJpef50+3yyfS9GrF91c1dSQy9WpE3DHHc7b6tTpatOGBiJ07EgDA9yY\nB9fJTBBehUUXE5ccOkQ5C0KEV3QFktMFUHgR8G6IUYkuJ07XXXcBt94a3vZ4mYoKGqwRLtGVmkqP\nbjhdGRl0g5CUFBmnS4UWVSFgVZE81kKMwTpdK1fSY6AD8Y1El5nTtWsXPSrRBQCPPkqOZm2tM9El\nJRU+/de/gPvu8x1zTnDqdKltduxIj26MnmWni2E8RmEhiS7Ae04X4H3Rpao4W7FyJVW1bq44vfB7\nIbzYtSvdgGRkhF90qdDixRf7fhsWXc7Yt48GUXTrRs+V02VUq2vnTnKQtOIqKYnyMTt1IufLCiXs\n7rmHrpFz5wbW1uRkiiZYiaiqKsrnAnxlJ9wIMZaU0Hd3kvDvNVh0MXHJoUM+gdO1Kz26KbpqamjY\ncjCJ9IB3wyxKdNXXU6kBKw4epD83wgWxiNMRVF4RXUBkRNf27VTjacYM37KsLBJ98Si69OHF6mrg\n66/p+wYjunr2JJcQsK4cv3MnJc8nJfkvHz6c3n/RRdafpUY2VlQAv/61/fc0on17e6dLiS7ldLkR\n3i4tJRHnlal9AiEGm8ww1khJTpISXR07UnFGN0WX08mu9Sj3zetOF2Cd11VVRRfrhgbvCshw47bo\nOnOGOmy3RVdRkc/tzcgIf07XokXUGU6f7lvWqhW5N7FWIDUYp2vtWroRufBCOk9On3b+eapchELl\nwhn9brt2URK9EU5qB3bp4isdYTV/ohVpafY5XXrR5ZbTFYv5XACLLiYOOXaMLnRK4CQkAOec467o\nCmQKIC0pKXSH5lWhohVdVnldWtEYyvxwsYzboktbjR5wR3RJScd9pJwuFVocP57mGtWSnd08nK6V\nKylEqPLZAnG7Cgr8a2uZlY2oqyM3UZvPFSgJCTS68vXXnc+qoad9++iIrqNHYzOfC2DRxcQh2hpd\nii5dAh9JZEUgk13rycqKDafLSnRpRSOLLuv3JSSQ0xOo6GrRgtYLRXQdO0YOmhJd6enhFV07dpAD\no0YtaonFWl1Ona4zZ0gIAcDnnwNjx/pcKqei68QJciGNnC79OVZQQG5aKKILoNGOl10W/PppaYEn\n0ocqug4epPBtbq79e70Iiy4m7tDW6FK4XSA12PAi4O0CqeXl5AomJFiHF1l0kXhJTnaWC+Nkfj69\n6ALI7QpFdKkbDa3TVV7uEwhu8/HH9KgNLSp69qTjPpITbodCQwM55k5EF0D7t7ycKrtPnOhzYpyK\nLv3IRYD2f+fOTc+xnTvp0Sy8GCnsnC5tIn1aGoU9QxVdzz9Pjuq8eaFtJ1qw6GL8OH2aKld/+mm0\nWxI82imAFG6LrlCdLi+HFzMySHg5CS9mZjZv0WU32bUiFNEVSp0udcxrc7qk9Hc03WTfPjp2zjmn\n6Ws9e5LYC1cNuNWr6c8t1P5yEl5U7//iC/p9J01yR3Sp5/qcLqNyEdHAidOlRJcabRiK6KqoAF57\nDbjmGt+I2FiDRRfjx5EjFCL48stotyR4Dh2iE1x1NAD9f/Soe3fZweZ0AeR0ebVAqho9l5lp73Sd\ncw5w7rksupwQrOhKTXXf6QLCl0x/6JCv3IEeq6RwN7jrLppyyK2q5yo53qnTVV1N+VwpKcCYMe6J\nruxsY6era9fgrj9uYuV0NTT4O10AhbdDOfb++Efa5r33Br+NaMOii/FDW6cpViksJEHQsqVvWdeu\nJLjc6mxCCS96uVaXEl3dutnndHXvbl28Md6JhOhyO7yoaiWFK6/r8GF/h1lLuGt17d1Lv+EClyab\nC1R0nTpF+VwXXUS5eO3bU15eIKIrNdW3jxQ5OTS9j/aGcefO6IcWARJ9p05RTpseddxqRVcoUwHV\n1AC//z1NrzR8eHDb8AIsuhg/4kF0aWt0KdwukFpRQW5aIBWcFV6uSh+I05WVRR1CcXHT4pDNgVgR\nXampvuNUtTdcosvK6erRgx7DIbrKy+lGqG1b4MUX/SdyDhanokuFF/PzSQxNnEjPVTHaQERXTk7T\ncHVODiXNq5sgKX0TXUcbq6r02nkXFaGIrnfeoeM5ll0ugEUXoyMeRJe2RpciHKKrfXtn+Tx6VNu8\nmNelFV1lZeY1hgoLfU4XEHv1l9wgHKKrTRt/hzZU0aWt0QWEV3TV1lII38zpSkmhkFs4RJdyW3/7\nW/r97r/f+v1GFd71BOp0ffIJPU6a5HutU6fARZce/Tl25AjNYeh10aVyEd1wuqQEnnkGGDoUuOSS\nwNf3Eiy6GD/iRXTpL/yq43HrewUz76LCq+HF2lq6O1XhRcD49zp+nP60oqu5hRhra32DDpzgVHTp\npzVxw+lSoUUgvKJLHStmThcQvrIR6vi74AKa1ubdd4H165u+r74euP12mvzbTngF6nR9/DGJCm3o\ny6noktJedKnv6JWRi4D1pNfK6XJDdC1bRrnG99wT3I2ul2DRxfihRFdxcewM7dZSVUV3XXqnS42m\ncsvpqqwMLp8LoIu0FwukqguncroA47wu1W4VXgSan+hSHUesia6UFGpLOBLpVTjaSnRlZ4fHFdUm\nod97L53v99zjL6xqaoCZM4FXX6XwnN0NmLoWakWDEUqUHToETJjgPzWNU9FVUkL72Uh0de9OQkN9\nR6+MXASchRf1oktNFB8ITz9N15uZM4Nrp5dg0cX4oZ17L9xztIUDJRL0TpfKa3E7vBgsXqzVpc0p\nUh2nlejq3p06t9atm5/ocloYVeEV0QWEr0Cq2bmnpWdPSgp3Et4LhIIC+u3S0ug8f+wxmo7nn/+k\n16uqgCuvpCmK1JyQeXnW28zPp0fthNJGaJ0wlc+lcCq6zEYuAjS3orY0y86dJGSsxG2kCNTpUoME\nAilZsmkTleK4807/0Hus0iLaDWC8hfZkOHLEuN6OG6xZAzz7LF0E1eSubmBUjV7Rtau7oqtv3+DX\n92KtLq3oUh2nUTK9EovqDtxoSHu8Ey7RpZ0CBgitTtfJk9TxaXO6gPBNBeTE6erZk/IEjx5199qi\nD8397Gc00u2++4Dzz6dSEps2AX/5C01RtGgRia7x4823mZdH54J+NKEeFV4E/PO5ABJdFRUUjrYS\nDFaiSy1XDqEaueiFMJuTnC59Ij1ATrHRubNoEY1Cravz/f3rX0C7dsAtt7jb9mjBoovxQy+6wjU0\nd/FiugstKrK+Mw4Uo2r0CjcLpIaS0wWQYPn6a3fa4hZa0dW+PVVbN3O6hPB1rm6VjZAS2LgRGDXK\nGx2KFUq0OJ3/LRSnq6aGnOdAb07Usa53usIlug4dImFhJUS1ZSPcFl2DB/uet2gBPPUU8MMfkkA5\nfRp4/31g6lT6LVu1sne69uwB+vWz/2zldGVmNn2/Oj5KS5vuB337AWvRtWoV/b9rF5VN8ALB5HQB\nxnldpaVNp49KSKB9+fDDJLziAQ4vMn6oaWCA8CbTqyHdbn+GUTV6hZuiK5ScLoBEYVmZt0otaEWX\nEOZlIw4epN9S3bm7Jbq+/JKKSr75ZujbCjeBOl0pKcGJLuUSBBNi1NfoUmRkhC+nq1s3a8Ecjlpd\nDQ3kAukFy+WX00i3hgZKxJ46lZYnJgK9e7snupTTNWlS0+/utEDqvn30XrMSNDk5dG0rLaXf2Qv5\nXAAJISECy+kCjEXXt9/S45IlVPervp7+amrsR6PGEiy6GD/Ky30ndDhFl+qk3ZyaB6C77Y4d/S1/\nhVuiq66OLiihii7AugBppNHXicrMNG6fKhehyMmhi26oU8v85z/0+Oyz7uf8uI0SXXahJ4Vyusy+\nV20tCSsjpwtwV3SFM6fLzrVWosvNZPqiIuqY9aFZIchN37uXEty19O1rLbqqq+k4d5JC0Lo1Je8b\nzQUYiOgyc7kACuFL6ZuezQsjFwFyotq2tXa61DEMOBNdI0fSDV1CnKqTOP1aTLCUl9PdalpaZERX\nOJwuo9AiQKKroiL06XeOH6fHUBPpAW/ldelFV7du5k6XXnQBobtd331Hj1u3UuJsoCxaBLz0UmQG\nKJSWUmeTlOTs/a1bk+NSW2v8ulFhVMAd0WWU0xWOSa+V02VF+/Z0bXHT6VKuuZFoUbXB9PTtS4ny\nDQ3G21RJ9E6cLiEolHneeU1fc0t0qdeWLaNHrzhdAO1TM6crJcU/LG4lunbsoG1ZhWHjARZdjB8q\nxNG1a/hEV0WF787I7c+wuttWnU9xcWifEcq8iwov1uoqLydxkJxMz5XTpXVnpPRVo1e4Jbp27KCO\nKyMDeO65wNefOxf4xS9IEI4dSx2h6jzdJpDCqID/pMhGhEN0FRVRPozejVPtdmuOQoUTpwtwv1aX\nXT6UEX37Up6XmdOsXLBQBssAzkRXfT2N6HQiupYvJxdI7+pFk7Q0Y6dLP+8i4CsobRTe/vZbysvz\nej5nqLDoYs7S0EAnT7hFl7ZzjqTTpe6gQg0xhjLvosKrokvb6XfrRiJBe0GtqCAB4LbTJaVPdN1+\nO+V17NnjfP3KShJCd9xBVcnr62nkWt++wDXXmDsawRILouvIEbrR0IdpwlEg9cQJ+nNSxiBcokuF\nLp2gykCYhRjVsReq6OrYkUSEleg6dIgcUCvRlZlJYqu0lNrupdIJVk6XXnQlJtL79YJfSp/oindY\ndDFnOXGCOidl8YZbdLVo4W5O15kz5GLZOV2hfqYSIaGILi8WSNWLLqOyEdpyEYr27ekvFNFVXEwX\n4kGDSHS1akVD/p2ydy89TpgA/PrXNApy/37gV78C3nuPiisGyunTvnCOnlgRXUahGtVuN5Pp1TES\niNPlVt7evn0k9pRD6wQlpqxEV9eu9oVR7UhMpPPcSnQ5ceoSE31zV3optAiYO11Gogswrkp/5Aht\nY9Cg8LTRS7DoYs6ivfAr0RWOhGaVgzFihLvCTm3LKqcLcE90hRJeBLxXINXI6QL8QzDawqhaQh3B\nqPK5Bg2i0bOzZlFNJachMBVG7N3bt6xnT+CJJ8jpeuABKpYZCK+/TiPgNmxo+lpJSWyILn0+F+AL\nN7rpdDmp0aXIzqa8SKOOOhgKCgIPt2VlkUgzE115eaG7XIpOnax/a6ucNC3Z2fToNdEViNMFGIsu\nlUTPThfTrNBXJK+pce/CqGXfPjpR+/d3V3RZ1egC6OInhDecLsB7BVKdOF3aKYC0hCq6duygx4ED\n6fGuu2gE2WuvOVtfOV1a0QXQ/n7tNWrfzJnOJx8GgH//mx6XLm36WqSdrmAKpBYVWTtdboouJcyd\nhhcB90KMdknoRiQkWJeNcFouwgl2Ven37aPjVDlZZqjv6JWRiworp8uoBIaR6FLnPztdTLNC73QB\n4QkxqoukqhDvlptmNw1JixZ0AfRCThcQu05XQkLTzlxVzA52X+7YQZ+tnJkhQ6gA5IsvUtjYjvx8\ncsiMLvLt2lGIsbQU+OlPneV3SUmzJgDAJ5/4v3bqFDlPkRBddnW69u0Dfv7zptutq6OO3kh0Rdvp\nMhNdVVWUhxeIeD9zhs6hQEUXYF42oqKCfjs3nS470ZWVRSF1K9R39KrTpT/3jRLpAXOnq3Nn58WG\nYxkWXcxZIim6evWizzhzJvT6Tgo7pwugTj3U76Tu6kKtkOy1Aql60ZWcTBdIfU5Xt24kYLXk5FAO\nVLCCdscOusvVjly66y767H/8w379vXut58gbMQJ4/nka/fXUU/bby8+nPLPevWn6GO2IV5UL5bbo\nSklp2vHahReXLgVeeQVYuNB/eXExdYJGoislhf6ciq5vvqHcuHfeMX/PoUPUwTrJgTITXfPm0b65\n/XZn7QJ88zgGK7r27m06+bISYpFyuvLy7Od3BICrriK31mshuLQ0upHRu7Fm4cX09Kb5hOr8bw6w\n6GLOEgnRpa0erVwNtz6jsJA6E6tcKzfmX6yooItJqHNGqrwoLxRIVQVf9U6LvkCqvkaXIpQRjGrk\nogotKi67jEIpToql5uc3DS3qufVWyu968EGfi2WGev2xx+hx+XLfa4FWoweciS79bw/Yi66jR+nx\nlVf8l5vV6FLYVaUvKaGBDCNG0FRgTz8NPP64+fsPH3Y+nVenTvR7aEXXu+8Cb7xBDufy5f6/txXB\nlItQ9O1LN336EH84RFdZmbnDmpfn7LMGDybh67Q2XKQwm3/RKqerosIndtX57zUxGS5YdDFniYTo\nKioiR0SFF938jEOHyD2yqvPiRlX6iorQQ4uAz5HzQl6Xcu/0HX+3bk1Fl5GTGIroKi6mY09/p5uQ\nANx5J7Bli3US/KlT1EY7t0Cb33XjjdZCbvVqEiYzZ9Jxqs3rCkV0mbma5eW+wpFaWrQg98tOdG3a\nRCM2FWbzLiqsqtLPn0/7/c476fNfeolCmHv2UJ6nEYcOOQstArQfevb0VaX//nsSxGPG0HykvXsD\nv/yls+KtTpPQjTAbwbhnD7XRrVpYnTqR4DIaFHLsGP25FcqMBkbzLyrny0x0SekTaQcO0HvZ6WKa\nHeXl5N6kptLJkpLivujS3pm6LbqsanQplOgKJY8s1HkXFcox8kJel1lOkXb+RVUY1cjpUiGjYESX\nVRLtrFnUAX7+ufn6quO1c7oACgnffz+FldS0Q0asWQNceCEJvylTgBUrfNXkI+l0AeR2WYmunj3p\nnNW6XWZTACnMJr2urgZ+9zuas3D7dhJyP/85/Rb19cDOncbbC8TpAnxlI+rqaB83NABvv03f9emn\naTTrn/5kv519+6hmVSCfrVBCR19Ad88eal8gJSissCqQ6lYR1mhi5HSpmwuzRHrAJ0Kb08hFgEWX\nZ6mudpZA7Cbqwi8E/YWjVpeR6HKrVpeTithdutDvGsqoTLecLtVWLzhd6gJo5HQVFVHneOwYuZRG\noislhRLZgxFd2nIRelJTycHats18fTVy0UleDABceSWJqQ8+MH798GESchdeSM+vuII6lK+/pudK\nrASS9Gsnuo4dC050FReTI3P99ZTXpUKG6rxVk9frMRNdmzbRvr79dv9OcMgQety+vek6DQ3OpgDS\nokTXggU00fmrr/qcpR/9CBg/HnjoIeNSBFr27aNtBRPq79aNjlu90+U03OcUK9GlirC6+XmRxsjp\nMprsWmEmutjpYqLKxImBJZS6gf5uO5yiKzubOlS33LSTJ8kxUrVszHCjVldFReg1ugDqiDMyvO90\nNTSQo2JWLkIRbNkINXLRTCAMG0bJ3GYY1eiyIiODOvUPPzR+XeVzKdE1eTKF2VSIsbSUbkrMRJIR\n4XS6OnemKZBOn6baZgCdU+np5iPizHK6lLAcO9Z/ed++tC0j0VVWRi5goE5XaSnlzM2eTW6XQgjK\n4ysrI1FmRTDlIrSf06ePv+iSkoSQm86TndOVkOCtaX0CxcjpCkR07dhBx44bN7KxAIsuD1JQAKxf\nTxP/RpJIiS5VPdpNN239egp//Nd/Wb/PLdHl1gXCK7W6rEQXQC6iWWFURSiiSz9yUcvQoeRmqQu5\nnr17aX8Y5USZMW0aOWy7dzd9bc0auiEYPpyet2tHAkwrujp0aDqC04pQRZdZnS4luoYMoTa++iqJ\nZLNq9Ir0dDqO9RNwf/UVuS760GnLllSqwEh0BVKjS6FujrKzgZdfbvr6yJFU3uP3v/eFj40IRXQB\nTctGlJRQ4dZIOV15eSRA7cpFeJlAnS5VskSJ/m+/bT4uF8Ci6yy1tZEP55mxZAk9qkTTSBEp0aW9\nSLr1GWvXUqd9/vnW73Mjj8ytnC7AO7W6zESX6kgPH3Ymug4edJYArVAjl6wuusOG0aMKQ+hRIxcD\nmSj3Rz+iRyO3a/VqOo60ouqKK0hwHDgQeGFUgERLYqKx6KqtJScrUKerpoaOxc6d6fntt5MA/de/\nzAujKowmvZaSnC6zc2jIEGPRFUiNLsWYMcC559JoPLPSKwsW0D647z7j10+coH0RqugqKPAds27N\nuahF/dZm4cVYzucCrJ0uu5wulSfYXPK5ABZdZ5kzhy6sdkgZnqlxtCxeTI9lZcFVog4WNdm1omtX\nOnkCmYLk5Em6UzSjoKCp6HIjp2vtWnJE7MJ+oTpdDQ3uiq5YcroKC6kTNAsD5uTQRTSQ76NGLurL\nRWhRosssxGhXo8uI7t2BUaOaiq7ychJ3F13kv/zyy+lx2bLApwBStG5tLLrMfntFaqrxOag6cSW6\npk+n/195xd7pMqpKv3cvbdPMLR4yhI4DfV29QOZdVPTuDezaBYwebf6ezEyqD7ZokfHo1VDKRSj6\n9iXRe+AAPQ9HjlXLlnS90IsuKd3PH4sGycnk1GmdLtVvGTld6tp57Bj1B6dPs+hqdkhJdWE2bLAX\nVKmJmfYAACAASURBVP/4B13YwlXQsrycph9R+SluTZXh9LP1ogtw7grV1wOTJlF9JSNqa5tWj3aj\nWGldHd2hX3CB/XvbtaOLRLCiq6qKhJcbOV0Adf7HjkW/QGp5OYkC/YitTp3IoVFOV7du5knLwZSN\ncDL9R48e9Hsbia7aWnKEneZzaZk2jc55rdP45Zd0DVD5XIr+/SkUtnRpcE4XELzoMnO6VLkIJYJb\ntQJuuQX4+GMSR2Y1ugBj0fXVV/RoJbqApo6jCi9afV6w3HMPHYPPPNP0NXWchZIPpcS6CjHm5ZFI\nUqNx3cKoQOrRo3RTG+tOF0BCyml4sUULOp+PHWte0/8oWHSB7nKKisihsavSvGYNvSdcYmjZMhIv\nd9xBzyMVYpQydNH1+uuUW7VunXGS7oEDJFj0Ttfx46GJjm++ITHkRHQJEVqtLremAFKopPRohxjN\ncooSE2kfqZwus9AiED7RJQS5mEYjGA8coPMlUKcLINEFAP/8p2/ZmjXU6eodGCHI7frsMxKgXhJd\nyukCyLEH6Ddx4nRpz9Ovv6abEjPX0WwE4+HD1IZw5CW1aQPcdBOlXOjPEbecLsAnuvbsIREXSL6e\nE4xEVzyUi1CkpTlPpAd8UwEpAW/ldMcbjkSXEOIyIcRuIUS+EGK+wev9hRBfCyFqhBD36F7bL4TY\nLoTYKoTY5FbD3WTdOt//+potetSJouxot1m8mC5gV19NzyMluqqq6EIdrOgqKQF+/WvqlKX0TRas\nxegi6UaOlQo9OBFdQGiiy63JrhVeF12Ar0Cqnejq3p1GYgUqujp2NA9ZKoYNI9Glr+od6MhFLf37\nU3K4NsS4ejWFHVXiu5YrrqCbA685XVrR1aMHlcQA7BPpgaZO19ixtA+NUCPM9KIrkMKowTBnDl1T\nXn/df3lBAYVe1XcJhi5daBtapysc4T4j0RWO/LFoEYjTBfhE144dvpHszQVb0SWESATwMoApAAYC\nuFYIodelxwDMA2BgAgMAJkgph0spc0NpbLhQw6QB56IrHHk4Z86Q03XllXQxSEqKnOgyuvAHIojm\nz6cTbckS6iSMilkahQPcqNW1di2duHaFURW9e1PHYRdKPnOGCmiWlvre67boimSBVKOK2Aor0aWm\nAiostBZdLVvS64Ecs999R3e5dknwQ4fSjYFe0AVao0vPtGl0g6DmwNy0qWloUXHxxb7wq1dFF0CV\n5BMSrCdG1ouu48fJdbAa/SuEcTJ9oIVRAyUnh1IW/vQn/0EaalBOIAMo9GjLRjQ0RFZ05eWRo2ZX\n5iYWMHO61DRWetT8i99+27zyuQBnTtdoAPlSygIp5RkACwFM1b5BSnlUSrkRQK3RBrzOunU0miYh\nwVp0nTnj61DC4XStXk0Xv6uuoraoAoKRwOjCn55OHamd6Pr6a+DPf6YJiocNo07LSHQVFDStHh3q\n/ItSkuhy6nIBwIQJJPLMqmsrfvc7GrreqROJrJEjfSOp3MrpilSB1FWr6HtoXV0tdqJrzx46/u2E\nbSBlI5yMXFSoZHp9iDE/n8SMlatjxfTp5PAuWUKh8bo6c9GVkkLHDhB50XX6dNOJmY8eJRGodwku\nvpgE9tCh1m1p08YnujZsINFhN/p3yBDqKLU3LOF2ugDgtttI3H38sW9ZqOUiFKpsRGEh/c7hcJ46\ndfK/eQPoM8MRyowGeqerqorOF7P8z44daRDN7t3NK58LcCa6MgFou4TCxmVOkQA+E0JsFkLMMXuT\nEGKOEGKTEGJTidWU7C5TU0NuxkUXkcjRVyfWsm+fL7wRrOj6z3/oxDZi8WK6GE6eTM+zs6PrdKn8\nJytBpKpXZ2ZSBWmACrvu3Nl0vX37KPyhPRFDDS8WFJCACkR0TZxIj1ZTywDUEQ8YADz3HFX8Pucc\nunB27x5cOMsIVSD1L38hATB1KvDDH1L+kFH9omB59FE6djdvNn7dLryoHAYrpwug3yUvz9kI36Ii\n4zkXjRg8mI5HfTL93r2Bl4vQMnIkHZMffED5XEIA48aZv1+NcI606AKa5j2qGl1G393JTYG2Kv1X\nX9F2xoyxXmfIEHI01E1CbS21I5xOF0DnQ1YW1SED6PhyU3Tt2+ebGSFcTldtrb8bFA/lIhRGTpdZ\naBEg0bV/P/0m7HS5zwVSyuGg8OTPhRAXGb1JSvmalDJXSpnbKZD5NULkP/+hO/ixY8lmtnK6lCBL\nTg7OmVi2jC7ys2Y17ZSkJNF1ySV0hwD4Twobbswu/HZ1tF59lYq4Pvec745biZpVq/zfa3SRzMig\nO71gRVeg+VwAtSEnB1i50vw95eXkfMyYQeGal16i/bdnDwnuQApx2jF7NjmAe/bQ/j58mI61O+7w\nv7MPljVrfDl2Ko9Ej53TpbATXSNGUBjFyfmhOjk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VusaFp8Gb2ej74zYAMQtV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\nAUIXAABAAoQuAACABAhdAAAACfw/Cemf7XSGp4gAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x2ae213c0ba8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize = (10, 6))\n", "\n", "plt.plot(epochs, \n", " loss, \n", " 'bo', \n", " label = 'Training loss')\n", "plt.plot(epochs, \n", " val_loss, \n", " 'b', \n", " label = 'Validation loss')\n", "plt.title('Training and validation loss')\n", "plt.legend()\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Smothed curves" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def smooth_curve(points, factor = 0.8):\n", " smoothed_points = []\n", " for point in points:\n", " if smoothed_points:\n", " previous = smoothed_points[-1]\n", " smoothed_points.append(previous * factor + point * (1 - factor))\n", " else:\n", " smoothed_points.append(point)\n", " return smoothed_points" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "image/png": 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mArijquN7Prp3765ERET+rF271nba+HhVE4ZVfMTHV//4Bw4c0NTUVO3cubPe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\ntu7ZAmrDBNjZ2RpwSqylS011xKmn1kzvJCInhTL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"text/plain": [ "<matplotlib.figure.Figure at 0x2ae20b0cfd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize = (10, 6))\n", "plt.plot(epochs,\n", " smooth_curve(acc), \n", " 'bo', \n", " label = 'Smoothed training acc')\n", "\n", "plt.plot(epochs,\n", " smooth_curve(val_acc), \n", " 'b', \n", " label = 'Smoothed validation acc')\n", "\n", "plt.title('Training and validation accuracy')\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "image/png": 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93M6dgQ0bbLg6N7z6KjB8OLBtG1CtWu7cs0oVoFkzwDVAAgBYutRy5iZOBD78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\n72LwaJvhRLsML9pmONEuwytV2zBMCQAAkBBhDAAAICHC2GK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"text/plain": [ "<matplotlib.figure.Figure at 0x2ae205da550>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize = (10, 6))\n", "\n", "plt.plot(epochs,\n", " smooth_curve(loss), \n", " 'bo', \n", " label = 'Smoothed training loss')\n", "plt.plot(epochs,\n", " smooth_curve(val_loss), \n", " 'b', \n", " label = 'Smoothed validation loss')\n", "\n", "plt.title('Training and validation loss')\n", "plt.legend()\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Evaluation" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Found 1000 images belonging to 2 classes.\n", "Test acc: 0.973999993801\n" ] } ], "source": [ "test_generator = test_datagen.flow_from_directory(test_dir,\n", " target_size = (150, 150),\n", " batch_size = 20,\n", " class_mode = 'binary')\n", "\n", "test_loss, test_acc = end_to_end_model.evaluate_generator(test_generator, \n", " steps = 50)\n", "print('Test acc:', test_acc)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 }
apache-2.0
Miceuz/rs485-moist-sensor
doc/measurements/Sensor value in free air.ipynb
1
136781
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f59qUCOZtmMeA+wdw9ENHc/RDR9Prr71o9jentE6r8M70YdM5behpAK1GRHXD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\nT2Rp5dIcWpQb7Hj6Pr8vLZ7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"text": [ "<matplotlib.figure.Figure at 0x7f15d7cdb790>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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XoRy0d+y8pBRp77TzcFbZA67ZcpNoiRf9lJ6+iEwBpgH351J4TpH+8MAbfDvZ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\nJK7HaCPwLD5eXN2T2jthhPm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"text": [ "<matplotlib.figure.Figure at 0x7f15d7c6bb10>" ] } ], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "last = 0\n", "r=[]\n", "for v in m:\n", " if last != v:\n", " r.append(v)\n", " last=v\n", "\n", "plot(r)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 6, "text": [ "[<matplotlib.lines.Line2D at 0x7f15d7af5dd0>]" ] }, { "metadata": {}, "output_type": "display_data", "png": 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Uvo2BXC4fer1TlX9ae8ckkAvIcqUq/azsHVXpmza2JsHcKHvn4EF5\nDI5XxD015AErSF8lKB4stHy5mdI3jeTbGMzVlf7Bg7ULQiR5/FMLWhJ7J0zpmwZy1XSrUyKHQSWK\nKNJfulQ2eidP2mPvmJD+0qXy85498hp56l8WL6tX22XvZKH08wzkAjOVfha9d9J02QTMeCTK3jlw\nIH0dzwrWkf7QkPyurS34MTJNIBewM5irk/7AQPoF3VW1kpT02WpJE8idN89Ptynp873bty94NC4g\ng7vLlskGpV6ln5WCNiF9Lrc7dtTeywMH5LiElSvtUvpxpM/LEer1rt5ALpNumL2hkz43okuWyIFg\nzBNpkWZELmDGI1FKf//+9HU8KySYiDUf6ATFShAIDxgdO5Y8qGOrvaN2fZyeTr/il6oukpB+Z6e0\nIFRFzekxDeRyuoMmz9PBFkFLi29nhaG7WzYojaT029pkurdvr72XO3bIp1d1Kci80mkKk0BuV5e0\nGwcGZtZD3WYNQxDpNzfLpyC1DoT9p6tLTqvc3e2Lgddfzy6QmyTfTe2dME//5MnyV/UrXenrK+Ow\negDk+6uvygLCikN9zFy+XA7Uqlbjp0ZtBKUPpF/QXbd3TAK5+hqjSeydoHRHERrg92BhdTg9HX3f\n9HTZEMiNIn22MYKUPm+3tzdOIFctH6+8ItPO16fbO1FBybCxGFHn13vv7NtXm5/79mUXyC3S3uFj\n8PucJH1dlXJBAnyFpLaMakBp3jzZM6ezU6qGKNg4KjeI9NMu6K6qC9NArmrvADPtHdNALqc7zt4Z\nGZFKjX1iIJr0eZ9GCuSy0tfLLW9H5ZFtgVy1fOjXoz6BxwUlw0g/6vx6IFd/Cp6eLn5ELp+7HnuH\njwGUZ+9YR/qqvdPTI1ftURuB/n55s3jxgZ4es5tvYyBX7/oIlBfIVdPAn01H5HK640hfvbdtbfIe\nFqH0i7Z39HKrbkflkW1KXy0fej08eFB+5roXVV7Dps+OU/qqvQPU5qf6fRroXTZN873eLpt8DGAO\n2ztRSr+nR1Yy3u7o8G0eVvZJSL8RlH7aIE9aTz9K6ZuOyOX0mpA+n4v/ExbI5d/VdPE5ky5QXkYg\nVy236rZtSt80kKteT1eXb/Oo9TCsvIZNnx11/rxJP89Abpi9w/e27ECuycpZq4loExHtIKJtRHS7\n9/3NRLSdiKaI6BLtP58jopeJaBcRvTfq+HGBXPWdP+vbJjff1kBumL2TRumn6b3T2Rmu9E0DuZze\nKL8a8G05BlsDYUj6BBKGMgK5QPB7IwZyTethWHmN8vTDzq+TflOT3x2Wz5vV1MpF2Tt8bxshkDsJ\n4E4hxDoAlwP4lLf84TYANwH4obozEa0F8BsA1gL4VQBfJQp33PWgo6oGueeArg71bZObb7vSDwry\nJPX00wZyFy+uXRid02M6IpfTm0bpx9k7RP654tIVhqwCuSbTMLACBoLfo3o42WbvcPlYuFCq1Lh6\nGFS/hJD5n9TeUfv2d3XJThs8R1NPj0xPkkXgo86dJN+XL5cTv0VNvGdq71gbyBVC9AshtnqfhwHs\nArBKCLFbCPEyZi5+fgOAfxJCTAoh9gB4GcClYcfXg46qvcMjabNS+jaTflAgN22XzaSB3KYmWZjz\nDuSq91b9Txi6u2faHWmCuVkGcuOmYQhS+suX+9u22TsmSt+kHoaJlGo1fDWwqPOrSl99GuVz12Pt\n6OdOovTnzZNdnI8cCd8nzt5pqEAuEa0BcDGA5yN2WwVgn7J9wPsuEFH2DhCsKPRtkwLA+6jn0geH\nTE0l94uTYnraJw6d9Lu6/KcW7o4apShU1DM4C5D5GKX01XTr9k5zsxw0kySQy+eM8/R1xaSnK2yA\nj5reMgK5QG0gesmS4KehqSn/Htug9FUS1stHVD0ME1VhQdyw8zN0eydNnY9C2i6bfH6VrCcna+tp\nlL3T0eGnfdkyud6vaR3PCsaXSkTzATwM4A5P8deN9evXo1qVg636+nrR29s7Qw2+611yRjrG298u\nZ/5jvOUtkhzj0++r57O9Zds//GHgP/5H4Prr5fZ998kbc9dd9V9bGP7hH4CtW4G/+qta0l+2DLju\nOn+/1lZ5nUNDvpcZhXp67wDAVVfJeWEY7e21E9T9y78A3/8+8D/+R226Fy0C3vc+qeTiSP/wYXm/\nGJdcIntjhWHVKqC3t/Y7PV233ALcdhvwXi1y9I1vADt3Al/6UvGB3BUrgGuvrSWma6+Vk2zpeXTP\nPXIVpd/93eKVvp6X1arM84MHZT1QJzV717vkjKGMSy+VtiBj2TLg+QApGLWsoX5+FSrpv/nNwDvf\n6f927rnAlVfGX18U1HMnbWyZR9auldt33QWcfz7wiU/I7bCGbtkyyTVsds+bJ+vPkSP+02AQ+vr6\n0NfXZ57AGBiRPhG1QBL+Q0KIx2J2PwBAoQ+s9r6bgfXr10MI4P77ZaGampLEsGyZv8+Xv1z7n9tu\nq92+6ir5MgEHc5n09+6VI/sYe/fmX+n27vVVgkqeS5YAjzwyM72Vihnp19N7BwD++q9nnltVM2pe\nqQ3M/PnAxo3yc1wgd3i4tsH+wAei09fVBfyv/zUzXYcOhd9DxpEj/vdFK/22NuCpp2p/+8535PvY\nWC3pVyo+eRat9DkvGYcOydfhw3K6CLV8PPBA7X9/7/dqtzs6gu89r3Jncn4VKulfeWUtya9cCXz7\n2+HXZQL13EmVflDd6Ojwtw8dCh5pvngx8N3vzjxWpRJN+r29UhAzNmzYYJ7YAJjaOw8C2CmEeCDk\nd9XX3wjgFiJqJaKzAbwJwE/CDsyzQo6Oyoq6aFF+BV/3yXmASdh2HuC5goBa0g9CEs9Pt3dMA7lh\nKkx/XFfzRg3kqjAZnFVPj4u4dKkYH/e/L2pEblgvFRWtrXI/tqRGRvzyUDTp6/aCOiU0kGzO+rB7\nrwfvVcT17Y+y/uqFeu409k4cj0RNLxJ1rCJg0mXzCgC3AriaiLYQ0WYiup6IbiSifQAuA/A4ET0B\nAEKInQD+GcBOAN8D8PtCRM8azYFH3drJGjaSflRhS1Ig1IJrEsidmpKVVFUo+rnVBkfNm7B0m5B+\nvV6smi6e1iGO9IsakWtCVE1Ntd0FVdIv2t5R7QXAzy+eUiFKFOgIu/dRdTqub39cA1oP1HMnbWzj\nhEcSHisjmBvbvgkhngUQpkcfDfnPfQDuM00E2xFJWsg0UB/LRkb8pRgZg4P5VzpeGASIV/pJehwl\ntXe4Qod1ptXPzdNfCBGujOJIv97VjvR0DQ/L8wVVGvX7ou2dOHA+tbXJ+8RPZdVq/fmTFKq9oC7r\nePKkrAtR5VNFWFfUqDrNI+yDkDfp16v0d+3ytwcHay3pJDxWRq/C0kfkAj5JFan09Xf+XKTSD7NJ\nGPXYO3Gkn3Qx+UpFnmNoKLyxKlrpB91DxtiYtC4mJ4sP5MZBzafR0fKUPhCen0nvVdigsyh7p0yl\nv3ixv/ZvPfZOtSqflBpJ6VtF+kUq/UpFFlTe5gDbsWPRfbHrBS8AAWSr9JP23jFZV7hS8b3nwUGZ\nX/390qIIekKIC+RmQfpqnnCawpQ+4M/CmrfS50FIJqStEmSZnj4Qnp9J71WUvRNWp6MGdOVN+jw2\nZWDAX2bUFCqPHDo0sww2vKdfBDjwWKTSHxyUXa7U7e5uqQAOH87n/JOT8th5BHJVe6e9XW5HqdK4\noCoP2hoe9r3ztWtld76wNBel9FVFqt5DFZyOwcFiArl8jqBBSDpUK0T39IsmfV3pc34mteKyDOQm\nyct60NMj13TgZUaT/E/ljXPPlfdwfFw2IHFdMFXMWXuHA49RBSQLBBEG917QVwXKA4cP+/2fATPS\nT6L0mTDUHlFhSLLE5PCwrBhnneVXkiAU0XtHvT+Dg7Lv+KFDMwdocToqlWICuUl6m6j5pCv9Muwd\nlcDWrcte6Se1d/JW+er5Dx5MPp2DziMrVvhdQLkHYpbz82cNK0i/LHtn1SrZb/zoUf/ceba8g4OS\nOEdGJEnlZe8A8T14TCq1Om96T4/cTkv6k5PyVW83PDVP+B52ds6cy318XBLowEByzzYMLS3h1l8S\notJJXw3klmHvqHVi3Trf00/SQKcN5KoWIqMo0ucpopOWjaVLfRtY542kHDZnlX5RgVy1kOnr7DKx\n5emxVSrA6afLx1a2X/Kwd4B4Xz/JusJckPlxOI29E9dbyBTqPYy6Z2NjchFyHl1a73mBaKWfhvSn\np2UvmbIDuWojmlbppwnkdnTI6z1xovb7opV+0oaWpx05fDicR5KkwSn9HJW+WshUwgha9DkP8DlY\nhceR/rJl8inEZD4gfWa/ONJPsq6wmjdRyqilxe/SqSMLPx+QedfSIi2nqHs2Pi5J/8CB7NRzVqTP\nBMnTANgUyL3oonS9d4Ia/IkJeZ+WLAn/X1CDbbvS5/8GLQ4fFbgOgvrUUBSsIf0iArmAX8j0RZ2L\nsHe4gDAhx5F+S0vt4Jko6BZG3Khck8d3XemzvROWZqJwtZ8V6YelK4g4zjhDkn5W6jlrpT8yIu+Z\nDYFcboTYfjx6tH7SP3RICpeogGzQOhd5j8ZlpPX0+b9hPJKEw/ipwWT+sKxgBel3dsrW7tgxs3lm\n6gEX8jKUPpOUKelzek0aobzsHT1vojx9IJr06w3iRqUriPSLVPpJA7ljYzJPurvtCOQyeTU1yfe9\ne5ORPpcJVbGaiLgylX5ae4f/G8UjaY5VFKwg/a4uOTnWkiXJ+sumQZiyLyqQm0Tpq+mNg27vlBHI\nBcJJP4vRuEHp4nsWZO+w0rfN3lGV/pIl0hKrVsuxd9heOHiwdh2APXuSN9J6MNfErg17SivK3okr\nz1H/jeKRNMcqCtaQ/muv5W/tADOVTdGBXFb6o6Pxc+9wek1UQJC9k0cgd3g4uqEqyt4ZGJh5D1WM\njUnSP3jQbnunq8u/V2UEctle+MUv/PrX0yPrY9L7pd97E6sjLB5TlNI/dKh+e6eeQC4fa04q/T17\n8g3iMrq7gVdekYWKl2K0NZAL5GfvpAnk8oCTqEoSNio3S9Lne9jR4S9PGKb0s1TPWQdy+R7wvSpD\n6QMyP3fsqCX9PXuS3y+9B4/t9g7zTZo8zyqQCxQ/Ktca0u/vL07p79hR+yhbViA3bu4dTp+p0td7\n72QxOEtVLy0t0g5I6+lnqfSD7qGK8XE5aKalxSn9OATlZ39/NkrfxN4pK5DLa/+mVfr79sng96JF\n6QO5wBy1d9g7LJL0dVUjhCzkS5ZICyPpOqwmyDOQm8beifNsVTXDlbenJ729k2UgV7+HQaTf3i7T\nbZvSVwO5nZ3lK/2g/ASKs3fKUvq8ml5a0t+5U5YvPo4L5CYAF66i7B3VSuKeCnzzmppkN7Osu1Cp\nfZbzCuRmPSK3vV2+3nijNr9sCOTq9zDI3imS9NNMw8D3gKfMKNPe0fMTSBfI1e0dmwO5fP609o6a\nZwsWyDpuurypfqw5p/TVxZfzhq5mli3zW2p1n6xvgtpnWQ3kmih9ExWQR5dNPv+iRX4lNFH6eXv6\n+j1cvlyOZeAVoITw1ynt6cnW3slyGgab7J2g9zRKX++9Y3Mgl8+fVumr78whceMSwo5lldInotVE\ntImIdhDRNiK63ft+CRE9RUS7iehJIlrkfb+YiL5LRC8S0XNEdGHcOYpW+uo7+9TqufO4CepjXx6B\n3DxG5AJ+nEPdjgvk5u3pB91DdRDb5KSseM3NMv+yUs8tLbM3kBv0XkQglycqU+ffKVrppyH9xYtl\n+dLrRhoOszGQOwngTiHEOgCXA/gUEV0A4G4APxBCnA9gE4DPefvfA2CLEOKtAD4K4CtxJyhS6ave\nNIOj7+o+Wd8EVfUktXfSdtmsN5ALzMybOGVURCA36B6q90wlje5uuwO5qqc/G5R+0kBua+vMCfP4\nKa0IpBUFPIgtikdMYZ29I4ToF0Js9T4PA9gFYDWAGwB809vtm942AFwI2QhACLEbwBoiirz1RQZy\nW1tlKx11s/JQ+moFSEL6S5cCx4/7SyyGIY29Y+LZBuVNmkBukvVW49DWJpV9mCWnEnCWSj+PQK4N\nSj8P0h8bk69Fi8zOr5Iex2OKQFp7h/+bBemrq3gVgUTuExGtAXAxgOcArBBCDACyYQCwwtvtRQDv\n9/a/FMCZkI1EKIq0d/g8UY9lJo9bu3fP3FYfUffu9SfUAmofdbu6/MVJ4vy/piZJ/OrCLq+/PlPF\nx43I1dNrqryT2jtqxX/pJd9nz1LpB6VLbahV0sgzkDs2Ju8zf27kQG5bm5xmHJBBSVbgSaDeey7v\nJrOb6kq3Eewd/m8W9g6v4lWUr298uUQ0H8DDAO4QQgwTkTYLNnj7fgAPENFmANsAbAEQqI/Wr19/\n6vOHPtSLxYt7jRNeDz7xCeCtb/W3b74ZOOccf7u7W45IDMPhw/L/J0/6hfraa4Enn5QLswDApz8N\n3HorcMstcltX+idOhC87qIMboZUr5fYddwDvfz/w27/t7xPVZXPPHuDKK2sL1fHjsp9yHK67rnbq\n20svjV5ZTA3m3XQT8I1vyP9kTfr6PVQbRtUeuPLK7Ii0o0NOWcDYuBH4+78Hvvc9SVRRs0mqYHLk\nhdDLtnfWrAE+8xm/LBIBn/2sX95Mod77I0fMe7HoT9bDw8nPnRbvfGf6/956K/COd/jb73tf/BN5\nGG66Kfwpsq+vD319fekOHAAj0ieiFkjCf0gI8Zj39QARrRBCDBDRSgAVABBCnADwO8p/XwPwatBx\nVdIvEn/0R7Xbv/mbtdtx9s7AgL+e7uLFUrH398vvmfR5mzE4CLz97fJzZ6ck0iSr66hKSD82EG3v\n9PfLYBk3DCdPyv0XLIg/97vfXbu9Zg3wyU+G768G89R0Zk36n/lM7bYaw1CV4tq1/j2pFzwSmKFe\nX5pA7vS0T/onTpSn9NvbgXvvrf1O3zY9jro4jOn91sv34CDwlrckP38aXHCBfKXBxz9eu/0rv5I+\nHV/9avhvvb296O3tPbW9YcOG9CeCub3zIICdQogHlO82AviY9/mjAB4DACJaRETzvM//D4AferGA\nhkFcYEVdrg/wPXe1oeCBGgxd6R87Zj65nB7M1Y8NRI/I5X1ZCXP/6SwWFtHBKnZyUqo9PnfWpK9D\nbeTysgf0cqHehzSBXI5zMOkLkf+Eg3lCXxHM9H7r5TvvdTXmOky6bF4B4FYAVxPRFiLaTETXA/gi\ngGuJaDeAX4G0dQBgLYDtRLQLwHUA7sgn6fkhTumrKw0FvfNnfVv19I8fN6/guhLSjw1E2ztB6cwr\naM4Vnwe38TmzDOQGQY1h5EX6erng+yBE/YHcoaHsVvgqCzrpm97voPJdRKeOuYpYg0EI8SyAMHq6\nJvR70UIAABGcSURBVGD/5wCcX2e6SkVcIFcnUV35j4xIC0VX52lJX1VCY2NSFeqNkm7vqCQYlM48\nSV+9dj53EUr/4EH5Oa/eH0E2xPi49KDrDeQy6Tcy6lH6P/qRv13EYkpzGVaMyLUNCxfKwhs0shSY\nSfKmyl8d3p5W6evnZEQNzgpKZ16Pz6xi9fQWae/k1c9bt3fU/Kx3RO7QUDlB3CyhBnKTTLuhlm9e\n+9jZO/nBkX4AiKIHRVUqsmFQK33Utt5nmT3cNKSvH5uh2ztMIBMTM/+Tp5LiYJ5+ziJIPyiQmyUW\nLJD5zOdRrzFNIFcdnHX0aOMr/SwCuSdOyLLb0ZFPGh0c6YciKpg7OAisW1dLouvW1T4B6Ntq4JQr\nQxp7Rz82Q7d3+Dy89rD6n7yV/vh47TmFmB2BXF0MqNfolH42gVyn8vOHI/0QRAVzK5WZJKo2ApUK\n8OY3S8Jl1asqaw5wpVX6Z58tu/upg690ewfwiTAofXkHctVzVqtyTEKeSraIQC7glwueNfW889LZ\nO2NjtXPvzEZP3zSQq06Y54K4+cORfgiigrmVCnDRRbVK/6KLahXgihX+ZFK6ndLcLB+F0yr9FStm\nNkq6vQP4RMjpLSqQqyv9LOfSD4Ou9PMaxs/l4tAhSVYrV/pKP0kglxfRaWvzR+TONtI3VfotLdIm\n4y6+jvTzhSP9ECSxdyoVOcBjaEhWZFYrUYsld3aak/7ixbVPDUErfIXZO8PDMr0XXlhMIJeXS6xU\n5HKFbW2yV02e1g5QTCAXmLmwDN+HJOdsb5cefleXtIw4b2aDvZMmkAvUrtLm7J184Ug/BGH2zuSk\nJPcLLqi1d1aulMPODx3yCy4X5CD10tVlTvqql8wNiv4kEmbvHDwog2JnnFFMIFe1dzidaRbZTooi\nPH2glpzU+5DU3pmc9J9+OG8aXemnDeQC0XXFIVs40g9BmNI/fFiS+4oV8vP0tB+oZWLmghu03CAj\nCelzetSFl03sHXXBef5/3l3iVHuHz5tmke2kYIsEyJf09fvA20lJH/DzZDYp/bSkH1VXHLKFI/0Q\nhCl9LpTz5skufIcP+/6u/oiqq0IVXV3JZvfTj2Vq7+zZI/fntX+HhmothawRpPSLIP1GUvotLbX3\noLlZ/rfRlX49pB9VVxyyhSP9EIQFctVC2dMjpw/mqWi5oWClr2+rSKr0w47NCLJ3Ojt90ufpW3fu\nzLdStbXJvtYjI/66BXv25B/IZaXPUyLkHcjV70MS0ieS+6qk2Nk5+0g/yT2PqisO2cKRfgjCBmep\niz13dwPbt9duDwzMDPLVG8jlY6vH0pV+mL3z2mvh6c0D7e3A/v2ygeHVhYrw9FktnzxZfCB3cFCe\nN0lD095eS4pdXbPD3kkbyI2qKw7ZwpF+CEyV/o4dtdu//KU/ojDO3kmq9PfskTGE+fNnKv0we+e1\n18LTmwfa2qSFpJ6zCNIHfIunSHunrU3e65Mnk51TV/pdXbNP6btArp1wpB+Crq6ZA6CA2kLZ3S1J\nVFXSKqnqQT/9+EmVPp+LyLz3ztGjM9OXp5JS16bld+6emDfY4ikikKsqUr4nSWI0QaTf6Eq/nt47\n/JQ8OCifEh3ygyP9EDCx6haPWtmDlL6+rapzFWmUvt6gmNg7/N+gY+QBdW1a9X22KH3uW8+xEsBX\n/EmmRXZKvxY9PcDLL8v/FLVU4lyFI/0IBFk8ur0zMDBzW20U+HedENKQvnoutQsmED4il/8bdIw8\nEEb6eQdygVrSz3Nh7aD7npSoZnMgV4jk6ycUUTYdJBzpRyAomKsHcoPeueDOny8rQpCdkiaQq753\ndkqS5/Vrg5ba0xec19/zgLogufo+W5Q+4HfZ5TWGu7uTNzKzOZA7MZF8rqWlS/3Av0O+MFk5azUR\nbSKiHUS0jYhu975fQkRPEdFuInqSiBZ53y8koo1EtNXb/2M5X0NuMFH6Ue9sEQWplzRKX33nz6z2\np6ZmHi/I3tGPkTWYuPgc7M8WSfp59t4B/HvKT29ZKP3ZZO+kmVGVuxQ7pZ8/TJT+JIA7hRDrAFwO\n4FNEdAGAuwH8QAhxPoBNAD7n7f8pADuEEBcDeA+AL3kLqzccgkbl6oFc9X3xYkm8qlrhbn06kpJ+\n0FMDN0pM+EEWEuATbxFKnxUen6O1VeZLUYHcopS+fo+zIP1GV/rqOgFp7ndYXXHIFrGkL4ToF0Js\n9T4PA9gFYDWAGwB809vtmwBu5L8AWOB9XgDgsBBiMstEFwU9kMvT6S5e7P+uvvPjqa7Gs1D6QU8N\n3CgFWTt8jiVL/N+KUPqAJDQ9D4pS+tx7J29PX78+p/Rlo8V1JM39DqsrDtkikadPRGsAXAzgOQAr\nhBADgGwYAPDt+hsAFxLRQQAvogEXRmfo9g53J2v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"text": [ "<matplotlib.figure.Figure at 0x7f15d7b6ea10>" ] } ], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "print (\"Mean: \" + str(mean(r)))\n", "print (\"Median: \" + str(median(r)))\n", "stddev=sqrt(sum((r-median(r))**2)/size(r))\n", "print (\"Standard deviation: \" + str(stddev))\n", "hist(r)\n", "xlabel(\"Value in free air\")\n", "ylabel(\"Count\")\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Mean: 210.634615385\n", "Median: 210.0\n", "Standard deviation: 2.17355905702\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 9, "text": [ "<matplotlib.text.Text at 0x7f15d7f98e50>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x7f15d7d0d950>" ] } ], "prompt_number": 9 }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Initial sensor value in free air: 210+-2**" ] }, { "cell_type": "code", "collapsed": false, "input": [ "d = loadtxt('./initial-value-distribution2.csv', delimiter=',')\n", "plot(d[:,0], label=\"Moisture\")\n", "plot(d[:,1], label=\"Temperature\")\n", "\n", "legend(loc=5)\n", "title(\"Sensor initial value in free air experiment, 102 sensors\")" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 18, "text": [ "<matplotlib.text.Text at 0x7f6d75be5f50>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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55RaYMkVNH9C9uxrJGhnp+Tzpffqo2Rr/+U/bAUnmuIcbblDLr7wCW7ao3zt2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\n3PnU8bHZvmG9htzR9Q4+2/MZOxJ3cH2H64mOjLa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"text": [ "<matplotlib.figure.Figure at 0x7f6d75d45b50>" ] } ], "prompt_number": 18 }, { "cell_type": "code", "collapsed": false, "input": [ "r=d[:,0]\n", "print (\"Mean: \" + str(mean(r)))\n", "print (\"Median: \" + str(median(r)))\n", "stddev=sqrt(sum((r-median(r))**2)/size(r))\n", "print (\"Standard deviation: \" + str(stddev))\n", "figure( figsize=(10, 8))\n", "hist(r)\n", "xlabel(\"Value in free air, batch:1\")\n", "ylabel(\"Count\")\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Mean: 211.450980392\n", "Median: 211.0\n", "Standard deviation: 1.99017193069\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 25, "text": [ "<matplotlib.text.Text at 0x7f6d755f58d0>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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AKAMAGIAoAwAYgCgDABiAKAMAGIAoAwAYgCgDABiAKAMAGIAoAwAYgCgDABiAKAMAGIAo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\nf8ccGFYra+3vAAAAAElFTkSuQmCC\n", "text": [ "<matplotlib.figure.Figure at 0x7f6d7570ed90>" ] } ], "prompt_number": 25 }, { "cell_type": "markdown", "metadata": {}, "source": [ "# RS485 sensors Batch 1 - initial value: 211+-2" ] } ], "metadata": {} } ] }
apache-2.0
drabastomek/learningPySpark
Chapter06/LearningPySpark_Chapter06.ipynb
1
37409
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Introducing ML package of PySpark" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Predict chances of infant survival with ML" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Load the data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, we load the data." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pyspark.sql.types as typ\n", "\n", "labels = [\n", " ('INFANT_ALIVE_AT_REPORT', typ.IntegerType()),\n", " ('BIRTH_PLACE', typ.StringType()),\n", " ('MOTHER_AGE_YEARS', typ.IntegerType()),\n", " ('FATHER_COMBINED_AGE', typ.IntegerType()),\n", " ('CIG_BEFORE', typ.IntegerType()),\n", " ('CIG_1_TRI', typ.IntegerType()),\n", " ('CIG_2_TRI', typ.IntegerType()),\n", " ('CIG_3_TRI', typ.IntegerType()),\n", " ('MOTHER_HEIGHT_IN', typ.IntegerType()),\n", " ('MOTHER_PRE_WEIGHT', typ.IntegerType()),\n", " ('MOTHER_DELIVERY_WEIGHT', typ.IntegerType()),\n", " ('MOTHER_WEIGHT_GAIN', typ.IntegerType()),\n", " ('DIABETES_PRE', typ.IntegerType()),\n", " ('DIABETES_GEST', typ.IntegerType()),\n", " ('HYP_TENS_PRE', typ.IntegerType()),\n", " ('HYP_TENS_GEST', typ.IntegerType()),\n", " ('PREV_BIRTH_PRETERM', typ.IntegerType())\n", "]\n", "\n", "schema = typ.StructType([\n", " typ.StructField(e[0], e[1], False) for e in labels\n", "])\n", "\n", "births = spark.read.csv('births_transformed.csv.gz', \n", " header=True, \n", " schema=schema)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Create transformers" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pyspark.ml.feature as ft\n", "\n", "births = births \\\n", " .withColumn( 'BIRTH_PLACE_INT', \n", " births['BIRTH_PLACE'] \\\n", " .cast(typ.IntegerType()))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Having done this, we can now create our first `Transformer`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "encoder = ft.OneHotEncoder(\n", " inputCol='BIRTH_PLACE_INT', \n", " outputCol='BIRTH_PLACE_VEC')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's now create a single column with all the features collated together. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "featuresCreator = ft.VectorAssembler(\n", " inputCols=[\n", " col[0] \n", " for col \n", " in labels[2:]] + \\\n", " [encoder.getOutputCol()], \n", " outputCol='features'\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Create an estimator" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this example we will (once again) us the Logistic Regression model." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pyspark.ml.classification as cl" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once loaded, let's create the model." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "logistic = cl.LogisticRegression(\n", " maxIter=10, \n", " regParam=0.01, \n", " labelCol='INFANT_ALIVE_AT_REPORT')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Create a pipeline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "All that is left now is to creat a `Pipeline` and fit the model. First, let's load the `Pipeline` from the package." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from pyspark.ml import Pipeline\n", "\n", "pipeline = Pipeline(stages=[\n", " encoder, \n", " featuresCreator, \n", " logistic\n", " ])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Fit the model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Conventiently, `DataFrame` API has the `.randomSplit(...)` method." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "births_train, births_test = births \\\n", " .randomSplit([0.7, 0.3], seed=666)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now run our `pipeline` and estimate our model." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "model = pipeline.fit(births_train)\n", "test_model = model.transform(births_test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here's what the `test_model` looks like." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "test_model.take(1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Model performance" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Obviously, we would like to now test how well our model did." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pyspark.ml.evaluation as ev\n", "\n", "evaluator = ev.BinaryClassificationEvaluator(\n", " rawPredictionCol='probability', \n", " labelCol='INFANT_ALIVE_AT_REPORT')\n", "\n", "print(evaluator.evaluate(test_model, \n", " {evaluator.metricName: 'areaUnderROC'}))\n", "print(evaluator.evaluate(test_model, {evaluator.metricName: 'areaUnderPR'}))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Saving the model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "PySpark allows you to save the `Pipeline` definition for later use." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "pipelinePath = './infant_oneHotEncoder_Logistic_Pipeline'\n", "pipeline.write().overwrite().save(pipelinePath)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So, you can load it up later and use straight away to `.fit(...)` and predict." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "loadedPipeline = Pipeline.load(pipelinePath)\n", "loadedPipeline \\\n", " .fit(births_train)\\\n", " .transform(births_test)\\\n", " .take(1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can also save the whole model" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from pyspark.ml import PipelineModel\n", "\n", "modelPath = './infant_oneHotEncoder_Logistic_PipelineModel'\n", "model.write().overwrite().save(modelPath)\n", "\n", "loadedPipelineModel = PipelineModel.load(modelPath)\n", "test_loadedModel = loadedPipelineModel.transform(births_test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Parameter hyper-tuning" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Grid search" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Load the `.tuning` part of the package." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pyspark.ml.tuning as tune" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next let's specify our model and the list of parameters we want to loop through." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "logistic = cl.LogisticRegression(\n", " labelCol='INFANT_ALIVE_AT_REPORT')\n", "\n", "grid = tune.ParamGridBuilder() \\\n", " .addGrid(logistic.maxIter, \n", " [2, 10, 50]) \\\n", " .addGrid(logistic.regParam, \n", " [0.01, 0.05, 0.3]) \\\n", " .build()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we need some way of comparing the models." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "evaluator = ev.BinaryClassificationEvaluator(\n", " rawPredictionCol='probability', \n", " labelCol='INFANT_ALIVE_AT_REPORT')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create the logic that will do the validation work for us." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "cv = tune.CrossValidator(\n", " estimator=logistic, \n", " estimatorParamMaps=grid, \n", " evaluator=evaluator\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create a purely transforming `Pipeline`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "pipeline = Pipeline(stages=[encoder,featuresCreator])\n", "data_transformer = pipeline.fit(births_train)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Having done this, we are ready to find the optimal combination of parameters for our model." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "cvModel = cv.fit(data_transformer.transform(births_train))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `cvModel` will return the best model estimated. We can now use it to see if it performed better than our previous model." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data_train = data_transformer \\\n", " .transform(births_test)\n", "results = cvModel.transform(data_train)\n", "\n", "print(evaluator.evaluate(results, \n", " {evaluator.metricName: 'areaUnderROC'}))\n", "print(evaluator.evaluate(results, \n", " {evaluator.metricName: 'areaUnderPR'}))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What parameters has the best model? The answer is a little bit convoluted but here's how you can extract it." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "results = [\n", " (\n", " [\n", " {key.name: paramValue} \n", " for key, paramValue \n", " in zip(\n", " params.keys(), \n", " params.values())\n", " ], metric\n", " ) \n", " for params, metric \n", " in zip(\n", " cvModel.getEstimatorParamMaps(), \n", " cvModel.avgMetrics\n", " )\n", "]\n", "\n", "sorted(results, \n", " key=lambda el: el[1], \n", " reverse=True)[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Train-Validation splitting" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Use the `ChiSqSelector` to select only top 5 features, thus limiting the complexity of our model." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "selector = ft.ChiSqSelector(\n", " numTopFeatures=5, \n", " featuresCol=featuresCreator.getOutputCol(), \n", " outputCol='selectedFeatures',\n", " labelCol='INFANT_ALIVE_AT_REPORT'\n", ")\n", "\n", "logistic = cl.LogisticRegression(\n", " labelCol='INFANT_ALIVE_AT_REPORT',\n", " featuresCol='selectedFeatures'\n", ")\n", "\n", "pipeline = Pipeline(stages=[encoder,featuresCreator,selector])\n", "data_transformer = pipeline.fit(births_train)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `TrainValidationSplit` object gets created in the same fashion as the `CrossValidator` model." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "tvs = tune.TrainValidationSplit(\n", " estimator=logistic, \n", " estimatorParamMaps=grid, \n", " evaluator=evaluator\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As before, we fit our data to the model, and calculate the results." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "tvsModel = tvs.fit(\n", " data_transformer \\\n", " .transform(births_train)\n", ")\n", "\n", "data_train = data_transformer \\\n", " .transform(births_test)\n", "results = tvsModel.transform(data_train)\n", "\n", "print(evaluator.evaluate(results, \n", " {evaluator.metricName: 'areaUnderROC'}))\n", "print(evaluator.evaluate(results, \n", " {evaluator.metricName: 'areaUnderPR'}))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Other features of PySpark ML in action" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Feature extraction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### NLP related feature extractors" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Simple dataset." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "text_data = spark.createDataFrame([\n", " ['''Machine learning can be applied to a wide variety \n", " of data types, such as vectors, text, images, and \n", " structured data. This API adopts the DataFrame from \n", " Spark SQL in order to support a variety of data types.'''],\n", " ['''DataFrame supports many basic and structured types; \n", " see the Spark SQL datatype reference for a list of \n", " supported types. In addition to the types listed in \n", " the Spark SQL guide, DataFrame can use ML Vector types.'''],\n", " ['''A DataFrame can be created either implicitly or \n", " explicitly from a regular RDD. See the code examples \n", " below and the Spark SQL programming guide for examples.'''],\n", " ['''Columns in a DataFrame are named. The code examples \n", " below use names such as \"text,\" \"features,\" and \"label.\"''']\n", "], ['input'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, we need to tokenize this text." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "tokenizer = ft.RegexTokenizer(\n", " inputCol='input', \n", " outputCol='input_arr', \n", " pattern='\\s+|[,.\\\"]')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The output of the tokenizer looks similar to this." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "tok = tokenizer \\\n", " .transform(text_data) \\\n", " .select('input_arr') \n", "\n", "tok.take(1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Use the `StopWordsRemover(...)`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "stopwords = ft.StopWordsRemover(\n", " inputCol=tokenizer.getOutputCol(), \n", " outputCol='input_stop')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The output of the method looks as follows" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "stopwords.transform(tok).select('input_stop').take(1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Build `NGram` model and the `Pipeline`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ngram = ft.NGram(n=2, \n", " inputCol=stopwords.getOutputCol(), \n", " outputCol=\"nGrams\")\n", "\n", "pipeline = Pipeline(stages=[tokenizer, stopwords, ngram])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we have the `pipeline` we follow in the very similar fashion as before." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data_ngram = pipeline \\\n", " .fit(text_data) \\\n", " .transform(text_data)\n", " \n", "data_ngram.select('nGrams').take(1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That's it. We got our n-grams and we can then use them in further NLP processing." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Discretize continuous variables" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is sometimes useful to *band* the values into discrete buckets." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "\n", "x = np.arange(0, 100)\n", "x = x / 100.0 * np.pi * 4\n", "y = x * np.sin(x / 1.764) + 20.1234\n", "\n", "schema = typ.StructType([\n", " typ.StructField('continuous_var', \n", " typ.DoubleType(), \n", " False\n", " )\n", "])\n", "\n", "data = spark.createDataFrame([[float(e), ] for e in y], schema=schema)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Use the `QuantileDiscretizer` model to split our continuous variable into 5 buckets (see the `numBuckets` parameter)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "discretizer = ft.QuantileDiscretizer(\n", " numBuckets=5, \n", " inputCol='continuous_var', \n", " outputCol='discretized')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's see what we got." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data_discretized = discretizer.fit(data).transform(data)\n", "\n", "data_discretized \\\n", " .groupby('discretized')\\\n", " .mean('continuous_var')\\\n", " .sort('discretized')\\\n", " .collect()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Standardizing continuous variables\n", "\n", "Create a vector representation of our continuous variable (as it is only a single float)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "vectorizer = ft.VectorAssembler(\n", " inputCols=['continuous_var'], \n", " outputCol= 'continuous_vec')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Build a `normalizer` and a `pipeline`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "normalizer = ft.StandardScaler(\n", " inputCol=vectorizer.getOutputCol(), \n", " outputCol='normalized', \n", " withMean=True,\n", " withStd=True\n", ")\n", "\n", "pipeline = Pipeline(stages=[vectorizer, normalizer])\n", "data_standardized = pipeline.fit(data).transform(data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Classification" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will now use the `RandomForestClassfier` to model the chances of survival for an infant.\n", "\n", "First, we need to cast the label feature to `DoubleType`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pyspark.sql.functions as func\n", "\n", "births = births.withColumn(\n", " 'INFANT_ALIVE_AT_REPORT', \n", " func.col('INFANT_ALIVE_AT_REPORT').cast(typ.DoubleType())\n", ")\n", "\n", "births_train, births_test = births \\\n", " .randomSplit([0.7, 0.3], seed=666)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We are ready to build our model." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "classifier = cl.RandomForestClassifier(\n", " numTrees=5, \n", " maxDepth=5, \n", " labelCol='INFANT_ALIVE_AT_REPORT')\n", "\n", "pipeline = Pipeline(\n", " stages=[\n", " encoder,\n", " featuresCreator, \n", " classifier])\n", "\n", "model = pipeline.fit(births_train)\n", "test = model.transform(births_test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's now see how the `RandomForestClassifier` model performs compared to the `LogisticRegression`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "evaluator = ev.BinaryClassificationEvaluator(\n", " labelCol='INFANT_ALIVE_AT_REPORT')\n", "print(evaluator.evaluate(test, \n", " {evaluator.metricName: \"areaUnderROC\"}))\n", "print(evaluator.evaluate(test, \n", " {evaluator.metricName: \"areaUnderPR\"}))" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Let's test how well would one tree do, then." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "classifier = cl.DecisionTreeClassifier(\n", " maxDepth=5, \n", " labelCol='INFANT_ALIVE_AT_REPORT')\n", "pipeline = Pipeline(stages=[\n", " encoder,\n", " featuresCreator, \n", " classifier]\n", ")\n", "\n", "model = pipeline.fit(births_train)\n", "test = model.transform(births_test)\n", "\n", "evaluator = ev.BinaryClassificationEvaluator(\n", " labelCol='INFANT_ALIVE_AT_REPORT')\n", "print(evaluator.evaluate(test, \n", " {evaluator.metricName: \"areaUnderROC\"}))\n", "print(evaluator.evaluate(test, \n", " {evaluator.metricName: \"areaUnderPR\"}))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Clustering" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this example we will use k-means model to find similarities in the births data." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pyspark.ml.clustering as clus\n", "\n", "kmeans = clus.KMeans(k = 5, \n", " featuresCol='features')\n", "\n", "pipeline = Pipeline(stages=[\n", " encoder,\n", " featuresCreator, \n", " kmeans]\n", ")\n", "\n", "model = pipeline.fit(births_train)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Having estimated the model, let's see if we can find some differences between clusters." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "test = model.transform(births_test)\n", "\n", "test \\\n", " .groupBy('prediction') \\\n", " .agg({\n", " '*': 'count', \n", " 'MOTHER_HEIGHT_IN': 'avg'\n", " }).collect()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "In the field of NLP, problems such as topic extract rely on clustering to detect documents with similar topics. First, let's create our dataset." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "text_data = spark.createDataFrame([\n", " ['''To make a computer do anything, you have to write a \n", " computer program. To write a computer program, you have \n", " to tell the computer, step by step, exactly what you want \n", " it to do. The computer then \"executes\" the program, \n", " following each step mechanically, to accomplish the end \n", " goal. When you are telling the computer what to do, you \n", " also get to choose how it's going to do it. That's where \n", " computer algorithms come in. The algorithm is the basic \n", " technique used to get the job done. Let's follow an \n", " example to help get an understanding of the algorithm \n", " concept.'''],\n", " ['''Laptop computers use batteries to run while not \n", " connected to mains. When we overcharge or overheat \n", " lithium ion batteries, the materials inside start to \n", " break down and produce bubbles of oxygen, carbon dioxide, \n", " and other gases. Pressure builds up, and the hot battery \n", " swells from a rectangle into a pillow shape. Sometimes \n", " the phone involved will operate afterwards. Other times \n", " it will die. And occasionally—kapow! To see what's \n", " happening inside the battery when it swells, the CLS team \n", " used an x-ray technology called computed tomography.'''],\n", " ['''This technology describes a technique where touch \n", " sensors can be placed around any side of a device \n", " allowing for new input sources. The patent also notes \n", " that physical buttons (such as the volume controls) could \n", " be replaced by these embedded touch sensors. In essence \n", " Apple could drop the current buttons and move towards \n", " touch-enabled areas on the device for the existing UI. It \n", " could also open up areas for new UI paradigms, such as \n", " using the back of the smartphone for quick scrolling or \n", " page turning.'''],\n", " ['''The National Park Service is a proud protector of \n", " America’s lands. Preserving our land not only safeguards \n", " the natural environment, but it also protects the \n", " stories, cultures, and histories of our ancestors. As we \n", " face the increasingly dire consequences of climate \n", " change, it is imperative that we continue to expand \n", " America’s protected lands under the oversight of the \n", " National Park Service. Doing so combats climate change \n", " and allows all American’s to visit, explore, and learn \n", " from these treasured places for generations to come. It \n", " is critical that President Obama acts swiftly to preserve \n", " land that is at risk of external threats before the end \n", " of his term as it has become blatantly clear that the \n", " next administration will not hold the same value for our \n", " environment over the next four years.'''],\n", " ['''The National Park Foundation, the official charitable \n", " partner of the National Park Service, enriches America’s \n", " national parks and programs through the support of \n", " private citizens, park lovers, stewards of nature, \n", " history enthusiasts, and wilderness adventurers. \n", " Chartered by Congress in 1967, the Foundation grew out of \n", " a legacy of park protection that began over a century \n", " ago, when ordinary citizens took action to establish and \n", " protect our national parks. Today, the National Park \n", " Foundation carries on the tradition of early park \n", " advocates, big thinkers, doers and dreamers—from John \n", " Muir and Ansel Adams to President Theodore Roosevelt.'''],\n", " ['''Australia has over 500 national parks. Over 28 \n", " million hectares of land is designated as national \n", " parkland, accounting for almost four per cent of \n", " Australia's land areas. In addition, a further six per \n", " cent of Australia is protected and includes state \n", " forests, nature parks and conservation reserves.National \n", " parks are usually large areas of land that are protected \n", " because they have unspoilt landscapes and a diverse \n", " number of native plants and animals. This means that \n", " commercial activities such as farming are prohibited and \n", " human activity is strictly monitored.''']\n", "], ['documents'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, we will once again use the `RegexTokenizer` and the `StopWordsRemover` models." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "tokenizer = ft.RegexTokenizer(\n", " inputCol='documents', \n", " outputCol='input_arr', \n", " pattern='\\s+|[,.\\\"]')\n", "\n", "stopwords = ft.StopWordsRemover(\n", " inputCol=tokenizer.getOutputCol(), \n", " outputCol='input_stop')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next in our pipeline is the `CountVectorizer`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "stringIndexer = ft.CountVectorizer(\n", " inputCol=stopwords.getOutputCol(), \n", " outputCol=\"input_indexed\")\n", "\n", "tokenized = stopwords \\\n", " .transform(\n", " tokenizer\\\n", " .transform(text_data)\n", " )\n", " \n", "stringIndexer \\\n", " .fit(tokenized)\\\n", " .transform(tokenized)\\\n", " .select('input_indexed')\\\n", " .take(2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will use the `LDA` model - the Latent Dirichlet Allocation model - to extract the topics." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "clustering = clus.LDA(k=2, optimizer='online', featuresCol=stringIndexer.getOutputCol())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Put these puzzles together." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "pipeline = Pipeline(stages=[\n", " tokenizer, \n", " stopwords,\n", " stringIndexer, \n", " clustering]\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's see if we have properly uncovered the topics." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "topics = pipeline \\\n", " .fit(text_data) \\\n", " .transform(text_data)\n", "\n", "topics.select('topicDistribution').collect()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Regression" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this section we will try to predict the `MOTHER_WEIGHT_GAIN`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "features = ['MOTHER_AGE_YEARS','MOTHER_HEIGHT_IN',\n", " 'MOTHER_PRE_WEIGHT','DIABETES_PRE',\n", " 'DIABETES_GEST','HYP_TENS_PRE', \n", " 'HYP_TENS_GEST', 'PREV_BIRTH_PRETERM',\n", " 'CIG_BEFORE','CIG_1_TRI', 'CIG_2_TRI', \n", " 'CIG_3_TRI'\n", " ]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, we will collate all the features together and use the `ChiSqSelector` to select only the top 6 most important features." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "featuresCreator = ft.VectorAssembler(\n", " inputCols=[col for col in features[1:]], \n", " outputCol='features'\n", ")\n", "\n", "selector = ft.ChiSqSelector(\n", " numTopFeatures=6, \n", " outputCol=\"selectedFeatures\", \n", " labelCol='MOTHER_WEIGHT_GAIN'\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In order to predict the weight gain we will use the gradient boosted trees regressor." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pyspark.ml.regression as reg\n", "\n", "regressor = reg.GBTRegressor(\n", " maxIter=15, \n", " maxDepth=3,\n", " labelCol='MOTHER_WEIGHT_GAIN')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, again, we put it all together into a `Pipeline`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "pipeline = Pipeline(stages=[\n", " featuresCreator, \n", " selector,\n", " regressor])\n", "\n", "weightGain = pipeline.fit(births_train)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Having created the `weightGain` model, let's see if it performs well on our testing data." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "evaluator = ev.RegressionEvaluator(\n", " predictionCol=\"prediction\", \n", " labelCol='MOTHER_WEIGHT_GAIN')\n", "\n", "print(evaluator.evaluate(\n", " weightGain.transform(births_test), \n", " {evaluator.metricName: 'r2'}))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
gpl-3.0
yashdeeph709/Algorithms
PythonBootCamp/Complete-Python-Bootcamp-master/.ipynb_checkpoints/Chained Comparison Operators-checkpoint.ipynb
2
4411
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Chained Comparison Operators\n", "\n", "An interesting feature of Python is the ability to *chain* multiple comparisons to perform a more complex test. You can use these chained comparisons as a shorthand for larger Boolean Expressions.\n", "\n", "In this lecture we will learn how to chain comparison operators and we will also introduce two other important statements in python: **and** and **or**.\n", "\n", "Let's look at a few examples of using chains:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "1 < 2 < 3" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The above statement check if 1 was less than 2 **and** if 2 was less than 3. We could have written this using an **and** statement in Python:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "1<2 and 2<3" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The **and** is used to make sure two checks have to be true in order for the total check to be true. Let's see another example:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "1 < 3 > 2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The above checks if 3 is larger than both the other numbers, so you could use **and** to rewrite it as:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "1<3 and 3>2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Its important to note that Python is checking both instances of the comparisons. We can also use **or** to write comparisons in Python. For example:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "1==2 or 2<3" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note how it was true, this is beacuse with the **or** operator, we only need one *or* the other two be true. Let's see one more example to drive this home:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "1==1 or 100==1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Great! For an overview of this quick lesson: You should have a comfortable understanding of using **and** and **or** statements as well as reading chained comparison code.\n", "\n", "Go ahead and go to the quiz for this section to check your understanding!" ] } ], "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
jasonding1354/ScalaFAQ
collections/collection_hierarchy.ipynb
1
26137
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## 集合库继承层次\n", "- 集合继承层次从TraversableOnce特质开始。这个特质代表至少能遍历一次的集合。这个特质对Traversable和Iterator进行了抽象。\n", "- Iterator代表了一个数据流,前进到下一个数据项意味着“消费”了当前数据项(也就是只能遍历一次)。\n", "- Traversable代表提供了遍历全部数据的机制的集合,而且能够反复地遍历。\n", "- 最后继承层次分裂为三个分支:sequence、map和set。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![](./images/collections_hierarchy.jpg)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### Gen\\*特质\n", "集合库中有一种generic变种,如GenTraversableOnce、GenIterator和GenSeq。该特质不确保线性执行或并行执行,在并行集合的遍历顺序是不确保的。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1 Traversable\n", "Traversable特质定义了一个foreach方法。foreach方法是一个内部迭代器,其接受一个函数作为参数,对集合的每个元素应用该函数。\n", "\n", "Traversable特质没有提供任何在foreach里停止遍历的方法。为了使某些操作更高效,库使用预初始化的异常来提前跳出迭代。\n", "\n", "> 内部迭代&外部迭代\n", "- 内部迭代器是由集合或迭代器自己负责遍历集合;外部迭代器是由外部迭代器确定何时和如何迭代\n", "- Traversable特质提供了foreach方法,客户代码可以传入函数给它用于遍历处理;Iterable特质提供了iterator方法,客户代码可以获取迭代器,自行遍历" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "\u001b[32mimport \u001b[36mjava.io.FileReader\u001b[0m\n", "\u001b[32mimport \u001b[36mjava.io.BufferedReader\u001b[0m\n", "\u001b[32mimport \u001b[36mjava.io.File\u001b[0m\n", "defined \u001b[32mclass \u001b[36mFileLineTraversable\u001b[0m" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import java.io.FileReader\n", "import java.io.BufferedReader\n", "import java.io.File\n", "\n", "class FileLineTraversable(file: File) extends Traversable[String] {\n", " override def foreach[U](f: String => U): Unit = {\n", " println(\"Opening file\")\n", " val input = new BufferedReader(new FileReader(file))\n", " try {\n", " var line = input.readLine\n", " while (line != null) {\n", " f(line)\n", " line = input.readLine\n", " }\n", " println(\"Done iterating file\")\n", " } finally {\n", " println(\"Closing file\")\n", " input.close()\n", " }\n", " }\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "上面定义了一个Traversable,它打开一个文件,遍历文件的每一行。" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Opening file\n", "Done iterating file\n", "Closing file\n", "Opening file\n", "Done iterating file\n", "Closing file\n" ] }, { "data": { "text/plain": [ "\u001b[36mx\u001b[0m: \u001b[32mFileLineTraversable\u001b[0m = \u001b[33mcmd0\u001b[0m(\n", " \u001b[32m\"Line 1\"\u001b[0m,\n", " \u001b[32m\"Line 2\"\u001b[0m,\n", " \u001b[32m\"Line 3\"\u001b[0m,\n", " \u001b[32m\"Line 4\"\u001b[0m,\n", " \u001b[32m\"Line 5\"\u001b[0m,\n", " \u001b[32m\"Line 6\"\u001b[0m\n", ")" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "val x = new FileLineTraversable(new File(\"test.txt\"))" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Opening file\n", "Done iterating file\n", "Closing file\n" ] }, { "data": { "text/plain": [ "\u001b[36mres2\u001b[0m: \u001b[32mTraversable\u001b[0m[\u001b[32mString\u001b[0m] = \u001b[33mList\u001b[0m(\n", " \u001b[32m\"Line\"\u001b[0m,\n", " \u001b[32m\"1\"\u001b[0m,\n", " \u001b[32m\"Line\"\u001b[0m,\n", " \u001b[32m\"2\"\u001b[0m,\n", " \u001b[32m\"Line\"\u001b[0m,\n", " \u001b[32m\"3\"\u001b[0m,\n", " \u001b[32m\"Line\"\u001b[0m,\n", " \u001b[32m\"4\"\u001b[0m,\n", " \u001b[32m\"Line\"\u001b[0m,\n", " \u001b[32m\"5\"\u001b[0m,\n", " \u001b[32m\"Line\"\u001b[0m,\n", " \u001b[32m\"6\"\u001b[0m\n", ")" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "// 遍历文件,并且遍历每一行,把行拆分成词,最后构造一个词的列表,结果是一个Traversable[String]\n", "for {\n", " line <- x\n", " word <- line.split(\"\\\\s+\")\n", "} yield word" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Opening file\n", "Closing file\n" ] }, { "data": { "text/plain": [ "\u001b[36mres3\u001b[0m: \u001b[32mTraversable\u001b[0m[\u001b[32mString\u001b[0m] = \u001b[33mList\u001b[0m(\u001b[32m\"Line\"\u001b[0m, \u001b[32m\"1\"\u001b[0m, \u001b[32m\"Line\"\u001b[0m, \u001b[32m\"2\"\u001b[0m)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for {\n", " line <- x.take(2)\n", " word <- line.split(\"\\\\s+\")\n", "} yield word" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "FileLineTraversable调用了take方法抽取集合的前n个元素,这里“Done iterating file”没有打印。\n", "\n", "这是因为**Traversable类有一个在必要时高效停止foreach的手段,就是抛出scala.util.control.ControlThrowable**。这是一种预分配的异常,JVM能够高效地抛出和捕获它。这样做法的缺点是take方法实际上会多取一个元素才抛出异常终止迭代。\n", "\n", "由于foreach方法不支持高效的随机访问,所以其对很多算法都是次优的选择,我们可以通过Iterable的外部迭代器来解决。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2 Iterable\n", "Iterable特质提供了iterator方法,iterator方法返回一个能用来遍历集合元素的外部迭代器。这个类允许只是用集合部分元素的方法比Traversable更早提前停止迭代,从而在性能上比Traversable稍有提高。**Iterable特质应该用在明确需要外部迭代器,但不需要随机访问的应用场景。**\n", "\n", "迭代器支持hasNext和next方法。\n", "\n", "Iterable特质的主要优点之一是有能力高效地合作迭代两个集合。" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "\u001b[36mnames\u001b[0m: \u001b[32mIterable\u001b[0m[\u001b[32mString\u001b[0m] = \u001b[33mList\u001b[0m(\u001b[32m\"Josh\"\u001b[0m, \u001b[32m\"Jim\"\u001b[0m)\n", "\u001b[36maddress\u001b[0m: \u001b[32mIterable\u001b[0m[\u001b[32mString\u001b[0m] = \u001b[33mList\u001b[0m(\u001b[32m\"123 Anyroad\"\u001b[0m, \u001b[32m\"125 Anyroad\"\u001b[0m)\n", "\u001b[36mn\u001b[0m: \u001b[32mIterator\u001b[0m[\u001b[32mString\u001b[0m] = non-empty iterator\n", "\u001b[36ma\u001b[0m: \u001b[32mIterator\u001b[0m[\u001b[32mString\u001b[0m] = non-empty iterator" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "val names = Iterable(\"Josh\", \"Jim\")\n", "val address = Iterable(\"123 Anyroad\", \"125 Anyroad\")\n", "\n", "val n = names.iterator\n", "val a = address.iterator" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Josh lives at 123 Anyroad\n", "Jim lives at 125 Anyroad\n" ] }, { "data": { "text/plain": [] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "while(n.hasNext && a.hasNext) {\n", " println(n.next + \" lives at \" + a.next)\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Scala提供了zip方法,用来把两个集合压缩成一个pair的集合" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Josh lives at 123 Anyroad\n", "Jim lives at 125 Anyroad\n" ] }, { "data": { "text/plain": [] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "names.iterator zip address.iterator map {\n", " case (n, a) => n+ \" lives at \" + a\n", "} foreach println" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**当在可变集合上使用外部迭代器时候,可能迭代器不知道集合发生了变化**" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "\u001b[36mx\u001b[0m: \u001b[32mcollection\u001b[0m.\u001b[32mmutable\u001b[0m.\u001b[32mArrayBuffer\u001b[0m[\u001b[32mInt\u001b[0m] = \u001b[33mArrayBuffer\u001b[0m(\u001b[32m1\u001b[0m, \u001b[32m2\u001b[0m, \u001b[32m3\u001b[0m)\n", "\u001b[36mi\u001b[0m: \u001b[32mIterator\u001b[0m[\u001b[32mInt\u001b[0m] = non-empty iterator" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "val x = collection.mutable.ArrayBuffer(1, 2, 3)\n", "val i = x.iterator" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "\u001b[36mres8_1\u001b[0m: \u001b[32mBoolean\u001b[0m = true" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x.remove(0, 3)\n", "i.hasNext" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "ename": "", "evalue": "", "output_type": "error", "traceback": [ "java.lang.IndexOutOfBoundsException: 0", "\tscala.collection.mutable.ResizableArray$class.apply(ResizableArray.scala:43)", "\tscala.collection.mutable.ArrayBuffer.apply(ArrayBuffer.scala:48)", "\tscala.collection.IndexedSeqLike$Elements.next(IndexedSeqLike.scala:65)", "\tcmd9$$user$$anonfun$1.apply$mcI$sp(Main.scala:81)" ] } ], "source": [ "i.next" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "上面remove移除了集合中的全部元素,由于迭代器是外部的,它不知道背后集合已经变化,hasNext方法返回了true,调用next方法时却抛出了异常。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3 Seq\n", "Seq特质是用过length和apply方法定义的。apply方法用来根据序号进行索引操作,length返回集合的大小。Seq特质不对索引或length的性能做任何保证。\n", "\n", "**如果元素插入集合的顺序是很重要的,而且允许重复元素,那么应该使用Seq。**\n", "\n", "Seq经常被用在抽象方法里,因为算法经常以其两个子类之一作为目标数据结果:LinearSeq和IndexedSeq。" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "\u001b[36mx\u001b[0m: \u001b[32mSeq\u001b[0m[\u001b[32mInt\u001b[0m] = \u001b[33mList\u001b[0m(\n", " \u001b[32m2\u001b[0m,\n", " \u001b[32m1\u001b[0m,\n", " \u001b[32m30\u001b[0m,\n", " \u001b[32m-2\u001b[0m,\n", " \u001b[32m20\u001b[0m,\n", " \u001b[32m1\u001b[0m,\n", " \u001b[32m2\u001b[0m,\n", " \u001b[32m0\u001b[0m\n", ")\n", "\u001b[36mres10_1\u001b[0m: \u001b[32mList\u001b[0m[\u001b[32mInt\u001b[0m] = \u001b[33mList\u001b[0m(\u001b[32m3\u001b[0m, \u001b[32m31\u001b[0m, \u001b[32m28\u001b[0m, \u001b[32m18\u001b[0m, \u001b[32m21\u001b[0m, \u001b[32m3\u001b[0m, \u001b[32m2\u001b[0m)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "// 该示例是通过滑动窗口计算元素和\n", "val x = Seq(2, 1, 30, -2, 20, 1, 2, 0)\n", "\n", "x.tails map(_.take(2)) filter(_.length > 1) map (_.sum) toList" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "滑动窗口是通过使用tails方法创建的,tails方法返回一个集合的tail迭代器。也就是说,tails产生的迭代器组的每个迭代器都比前一个迭代器少一个元素。此外,还可以使用Scala提供的sliding方法来代替tails:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "\u001b[36mres12\u001b[0m: \u001b[32mList\u001b[0m[\u001b[32mInt\u001b[0m] = \u001b[33mList\u001b[0m(\u001b[32m3\u001b[0m, \u001b[32m31\u001b[0m, \u001b[32m28\u001b[0m, \u001b[32m18\u001b[0m, \u001b[32m21\u001b[0m, \u001b[32m3\u001b[0m, \u001b[32m2\u001b[0m)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x.sliding(2) map (_.sum) toList" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3.1 LinearSeq\n", "LinearSeq特质代表能够分割为头元素+尾集合的集合。该特质是通过三个“假定高效”的抽象方法来定义的,分别是isEmpty、head和tail。\n", "\n", "Stack是LinearSeq的典型代表,下面的例子是在一个遍历树算法里如何把LinearSeq用作堆栈。" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "defined \u001b[32mtrait \u001b[36mBinaryTree\u001b[0m\n", "defined \u001b[32mobject \u001b[36mNilTree\u001b[0m\n", "defined \u001b[32mclass \u001b[36mBranch\u001b[0m\n", "defined \u001b[32mclass \u001b[36mLeaf\u001b[0m" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "// define a binary tree structure\n", "sealed trait BinaryTree[+A]\n", "case object NilTree extends BinaryTree[Nothing]\n", "case class Branch[+A](value: A,\n", " lhs: BinaryTree[A],\n", " rhs: BinaryTree[A]) extends BinaryTree[A]\n", "case class Leaf[+A] (value: A) extends BinaryTree[A]" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "\u001b[32mimport \u001b[36mscala.collection.LinearSeq\u001b[0m\n", "\u001b[32mimport \u001b[36mscala.annotation.tailrec\u001b[0m\n", "defined \u001b[32mfunction \u001b[36mtraverse\u001b[0m" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "// define algorithm to traverse the binary tree\n", "import scala.collection.LinearSeq\n", "import scala.annotation.tailrec\n", "\n", "def traverse[A, U](t: BinaryTree[A])(f: A => U): Unit = {\n", " @tailrec\n", " def traverseHelper(current: BinaryTree[A],\n", " next: LinearSeq[BinaryTree[A]]): Unit = \n", " current match {\n", " case Branch(value, lhs, rhs) =>\n", " f(value)\n", " traverseHelper(lhs, rhs +: next)\n", " case Leaf(value) if !next.isEmpty =>\n", " f(value)\n", " traverseHelper(next.head, next.tail)\n", " case Leaf(value) => f(value)\n", " case NilTree if !next.isEmpty =>\n", " traverseHelper(next.head, next.tail)\n", " case NilTree => ()\n", " }\n", " traverseHelper(t, LinearSeq())\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "traverseHelper实现了遍历树的核心功能,该方法接受当前要遍历的树和下一个LinearSeq,其中包含之后要迭代的二叉树元素。模式匹配的规则是:\n", "- 当前树是分支时,把分支的值传给f函数,然后递归调用自身,传入左树作为下个要迭代的节点,并用+:方法把右树加到待处理的LinearSeq前面,该方法是O(1)的效率\n", "- 当碰到Leaf,如果next不是空,那么用head和tail分解之,head作为当前树,tail作为next\n", "- 当碰到NilTree时,处理逻辑和Leaf一样" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n", "2\n", "3\n", "4\n" ] }, { "data": { "text/plain": [ "\u001b[36matree\u001b[0m: \u001b[32mBranch\u001b[0m[\u001b[32mInt\u001b[0m] = \u001b[33mBranch\u001b[0m(\u001b[32m1\u001b[0m, Leaf(2), Branch(3,Leaf(4),NilTree))" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "val atree = Branch(1, Leaf(2), Branch(3, Leaf(4), NilTree))\n", "traverse(atree)(println)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "当需要把一个普通递归的算法转化成尾递归或循环算法时,在堆(heap)上手工创建个栈(stack),然后用这个栈来完成实际功能是一种常见的做法。在使用函数式风格的尾递归算法时,LinearSeq是个恰当的选择。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3.2 IndexedSeq \n", "IndexedSeq在随机访问时更为高效,也就是说,其访问元素的开销应该是常量或接近常量。这种集合类型适用于大多数一般的、不涉及头尾分解的算法。" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "\u001b[36mx\u001b[0m: \u001b[32mIndexedSeq\u001b[0m[\u001b[32mInt\u001b[0m] = \u001b[33mVector\u001b[0m(\u001b[32m1\u001b[0m, \u001b[32m2\u001b[0m, \u001b[32m3\u001b[0m)\n", "\u001b[36mres4_1\u001b[0m: \u001b[32mIndexedSeq\u001b[0m[\u001b[32mInt\u001b[0m] = \u001b[33mVector\u001b[0m(\u001b[32m1\u001b[0m, \u001b[32m5\u001b[0m, \u001b[32m3\u001b[0m)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "val x = IndexedSeq(1, 2, 3)\n", "\n", "x.updated(1, 5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "上面使用IndexedSeq对象定义的工厂方法创建IndexedSeq实例,默认情况下,这会创建一个不可变的Vector。IndexedSeq有个update方法,该方法接受一个下标和新值,返回用新值修改了下标位置上的值的新集合。\n", "\n", "IndexedSeq用apply方法来根据下标取值,在Scala里,apply方法调用可以省略。" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "\u001b[36mres5\u001b[0m: \u001b[32mInt\u001b[0m = \u001b[32m3\u001b[0m" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x.apply(2)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "\u001b[36mres6\u001b[0m: \u001b[32mInt\u001b[0m = \u001b[32m3\u001b[0m" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x(2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4 Set\n", "Set集合类型代表一种其每个元素都是唯一的结合,至少对==方法来说是唯一。当需要测试一个集合是否包含某个元素或者要确保集合里没有重复元素的时候,Set是很好用的集合类型。\n", "\n", "Scala支持三种类型的不可变和可变Set:TreeSet、HashSet和BitSet:\n", "> - TreeSet用红黑树实现,具有O(logn)的随机访问复杂度。要创建一个TreeSet,必须提供隐式的Ordering类型类,以便能执行小于和大于比较(因为查找元素时需要比较期望值大小)\n", "- HashSet也是用树结构实现的集合。最大的区别是HashSet用元素的hash值决定把元素放在树的哪个节点上,这意味着hash值相同的元素会被放在相同的节点上。如果hash算法的碰撞几率很小,那么HashSet在查找时的性能一般优于TreeSet\n", "- BitSet是用Long型值的序列实现的。BitSet只能保存整数。BitSet通过把其底层的Long值与欲保存的整数值对应的位置为true来保存整数。BitSet经常用来高效地在内存里跟踪和保存一大批标志位" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 5 Map\n", "Map特质代表键值对的集合,只有有键的值才能存在。\n", "\n", "Scala的Map有两种实现:HashMap和TreeMap,类似HashSet和TreeSet实现。" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "\u001b[36merrorcodes\u001b[0m: \u001b[32mMap\u001b[0m[\u001b[32mInt\u001b[0m, \u001b[32mString\u001b[0m] = \u001b[33mMap\u001b[0m(\n", " \u001b[32m1\u001b[0m -> \u001b[32m\"O NOES\"\u001b[0m,\n", " \u001b[32m2\u001b[0m -> \u001b[32m\"KTHXBAI\"\u001b[0m,\n", " \u001b[32m3\u001b[0m -> \u001b[32m\"ZOMG\"\u001b[0m\n", ")" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "val errorcodes = Map(1 -> \"O NOES\", 2 -> \"KTHXBAI\", 3 -> \"ZOMG\")" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "\u001b[36mres8\u001b[0m: \u001b[32mString\u001b[0m = \u001b[32m\"O NOES\"\u001b[0m" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "errorcodes(1)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "\u001b[36mres9\u001b[0m: \u001b[32mList\u001b[0m[\u001b[32mString\u001b[0m] = \u001b[33mList\u001b[0m(\u001b[32m\"O NOES\"\u001b[0m, \u001b[32m\"ZOMG\"\u001b[0m)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "// Map能用做从键类型到值类型的偏函数\n", "List(1, 3) map errorcodes " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Scala还提供了当键不存在时返回默认值的能力" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "\u001b[36merrorcodes\u001b[0m: \u001b[32mMap\u001b[0m[\u001b[32mInt\u001b[0m, \u001b[32mString\u001b[0m] = \u001b[33mMap\u001b[0m(\n", " \u001b[32m1\u001b[0m -> \u001b[32m\"O NOES\"\u001b[0m,\n", " \u001b[32m2\u001b[0m -> \u001b[32m\"KTHXBAI\"\u001b[0m,\n", " \u001b[32m3\u001b[0m -> \u001b[32m\"ZOMG\"\u001b[0m\n", ")" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "val errorcodes = Map(1 -> \"O NOES\", 2 -> \"KTHXBAI\", 3 -> \"ZOMG\").withDefaultValue(\"Error key\")" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "\u001b[36mres11\u001b[0m: \u001b[32mString\u001b[0m = \u001b[32m\"Error key\"\u001b[0m" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "errorcodes(4)" ] } ], "metadata": { "kernelspec": { "display_name": "Scala 2.11", "language": "scala211", "name": "scala211" }, "language_info": { "codemirror_mode": "text/x-scala", "file_extension": ".scala", "mimetype": "text/x-scala", "name": "scala211", "pygments_lexer": "scala", "version": "2.11.6" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
gogrean/SurfFit
examples/notebooks/SB across core in MACS J0717.ipynb
2
399451
{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "import os\n", "import pickle\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import matplotlib.ticker as mtick\n", "\n", "from pyxel import Image, load_region\n", "from pyxel.models import BrokenPow, IntModel\n", "from pyxel.fitters import CstatFitter" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Region loaded. Its shape and parameters are listed below: \n", "epanda [472.88872, 403.21583, 88.907077, 146.85668, 1.0, 0.0, 0.0, 60.62656, 39.544543, 1.0, 359.99968]\n" ] } ], "source": [ "pkl = 'profile-core-ell.pkl'\n", "\n", "if os.path.exists(pkl):\n", " with open(pkl, 'rb') as f:\n", " p = pickle.load(f)\n", "else:\n", " # Read the images used to create the surface brightness profile.\n", " src_img = Image([\"srcfree_bin4_500-4000_1655_band1_thresh.img\",\n", " \"srcfree_bin4_500-4000_4200_band1_thresh.img\",\n", " \"srcfree_bin4_500-4000_16235_band1_thresh.img\",\n", " \"srcfree_bin4_500-4000_16305_band1_thresh.img\"])\n", " exp_img = Image([\"srcfree_bin4_500-4000_1655_thresh.expmap_nosrcedg\",\n", " \"srcfree_bin4_500-4000_4200_thresh.expmap_nosrcedg\",\n", " \"srcfree_bin4_500-4000_16235_thresh.expmap_nosrcedg\",\n", " \"srcfree_bin4_500-4000_16305_thresh.expmap_nosrcedg\"])\n", " bkg_img = Image([\"1655_bin4_500-4000_bgstow_goodreg.img\",\n", " \"4200_bin4_500-4000_bgstow_goodreg.img\",\n", " \"16235_bin4_500-4000_bgstow_goodreg.img\",\n", " \"16305_bin4_500-4000_bgstow_goodreg.img\"])\n", "\n", " # Read the region file in which the surface brightness profile will be created.\n", " region = load_region(\"core_ell_alt.reg\")\n", " # region = load_region(\"core_sx_ell.reg\")\n", "\n", " # Create the profile and bin it to a minimum of 100 counts/bin\n", " p = region.sb_profile(src_img, bkg_img, exp_img,\n", " min_counts=5, islog=False)\n", " with open(pkl, 'wb') as f:\n", " pickle.dump(p, f)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "rmin = 0.4\n", "rmax = 1.0\n", "\n", "r = np.array([pp[0] for pp in p if rmax >= pp[0] >= rmin])\n", "r_err = np.array([pp[1] for pp in p if rmax >= pp[0] >= rmin])\n", "raw_cts = np.array([pp[2] for pp in p if rmax >= pp[0] >= rmin])\n", "bkg_cts = np.array([pp[4] for pp in p if rmax >= pp[0] >= rmin])\n", "sx = np.array([pp[7] for pp in p if rmax >= pp[0] >= rmin])\n", "sx_err = np.array([pp[8] for pp in p if rmax >= pp[0] >= rmin])\n", "bkg = np.array([pp[9] for pp in p if rmax >= pp[0] >= rmin])\n", "bkg_err = np.array([pp[10] for pp in p if rmax >= pp[0] >= rmin])\n", "t_raw = np.array([pp[11] for pp in p if rmax >= pp[0] >= rmin])\n", "t_bkg = np.array([pp[12] for pp in p if rmax >= pp[0] >= rmin])" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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fPn2oV68eDzzwAMXFxfz73/8OlOeU5dJLL+XRRx/l008/Zffu3dx888307t2b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\nuKHniDRUg7rSlCRJ0uB0msR/DPhGRGwHfIuiTfz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"text/plain": [ "<matplotlib.figure.Figure at 0x10e3c5470>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(nrows=1, ncols=1)\n", "fig.dpi = 300\n", "fig.set_size_inches(12, 5)\n", "\n", "ax.scatter(r, sx, c=\"#1e8f1e\", alpha=0.85, s=35, marker=\"o\",\n", " label=\"0.5-4 keV Source + Sky Bkg\")\n", "ax.errorbar(r, sx, xerr=r_err, yerr=sx_err, linestyle=\"None\", color=\"#1e8f1e\")\n", "ax.step(r, bkg, where=\"mid\", color=\"#1f77b4\", linewidth=2,\n", " label=\"0.5-4 keV instrumental background\")\n", "ax.step(r, bkg - bkg_err, where=\"mid\", color=\"#1f77b4\", linewidth=2, alpha=0.5, linestyle=\"--\")\n", "ax.step(r, bkg + bkg_err, where=\"mid\", color=\"#1f77b4\", linewidth=2, alpha=0.5, linestyle=\"--\")\n", "\n", "ax.semilogy()\n", "ax.get_xaxis().set_major_formatter(mtick.ScalarFormatter())\n", "ax.get_xaxis().set_minor_formatter(mtick.ScalarFormatter())\n", "ax.get_yaxis().set_major_formatter(mtick.LogFormatterMathtext())\n", "#ax.get_yaxis().set_minor_formatter(mtick.FormatStrFormatter(\"%.0e\"))\n", "plt.tick_params(axis=\"both\", which=\"major\", labelsize=14)\n", "plt.tick_params(axis=\"both\", which=\"minor\", labelsize=14)\n", "plt.xlim(rmin, rmax)\n", "plt.ylim(1e-6, 4e-4)\n", "plt.xlabel(\"Radius (arcmin)\", size=15)\n", "plt.ylabel(r\"SB (counts s$^{-1}$ arcmin$^{-2}$)\", size=15)\n", "plt.legend(loc=1)\n", "plt.title(\"Surface Brightness Profile of the Fly-Through Core\", size=15)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "23035\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "//anaconda/lib/python3.5/site-packages/scipy/optimize/_minimize.py:381: RuntimeWarning: Method nelder-mead does not use gradient information (jac).\n", " RuntimeWarning)\n", "/Users/gogrean/code/pyxel/pyxel/optimizers.py:97: OptimizeWarning: Unknown solver options: eps, factr\n", " args=fargs, tol=acc, **kwargs)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Model: MyIntModel\n", "Inputs: ('x',)\n", "Outputs: ('y',)\n", "Model set size: 1\n", "Parameters:\n", " ind1 ind2 ... jump const \n", " -------------- -------------- ... ------------- -----------------\n", " -3.85373204443 0.951988200213 ... 3.44239881766 1.24166921045e-06\n", "{'exit_mode': 0, 'numiter': 310, 'message': 'Optimization terminated successfully.', 'final_func_val': 14.502698230798405, 'num_function_calls': None}\n" ] } ], "source": [ "mod = IntModel(BrokenPow)(widths=r_err, ind1=-3.0, ind2=0.8, norm=4.7e-4, rbreak=0.55, \n", " jump=3.0, const=1.24166921045e-06)\n", "mod.const.fixed = True\n", "\n", "fit = CstatFitter()\n", "m = fit(mod, r, raw_cts, bkg_cts, t_raw, t_bkg, maxiter=500)\n", "print(m)\n", "print(fit.fit_info)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "\n", "FIT SUMMARY:\n", "\n", "| Parameter | Value | Lower Uncertainty | Upper Uncertainty |\n", "|-------------+------------+---------------------+---------------------|\n", "| ind1 | -4.549e+00 | -1.510e+00 | 1.253e+00 |\n", "| ind2 | 1.045e+00 | -1.001e-01 | 1.124e-01 |\n", "| norm | 6.342e-04 | -1.180e-04 | 1.467e-04 |\n", "| rbreak | 5.552e-01 | -9.121e-03 | 1.149e-02 |\n", "| jump | 3.271e+00 | -4.576e-01 | 4.366e-01 |\n", "\n", "\n", "\n" ] }, { "data": { "image/png": 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q6mpGRkasvTYT21vDuXB1+PBhBgYGWLx4MfF4nJycnPPOGTpw4AA7d+4kkUiw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\nvwCLgT8YhnGbaZqj03WPiXt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j/e9/PzabjcrKSkpKSjAMg3nz5jE4OIjb7eb48eOsWbOG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\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VVXz44YfceOONHD58WJiSzpo1SwSKGzZsIC0tjdLSUux2OytWrGDBggVjHn+yVQMSn0Uu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\niYk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XU1pailqtFuaRw5u+T/RYiYyMRCqVotPpSE5ORqvV0tTURG1tLY2NjchkMjQaDQ0N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pg8eTLg+waqTTr/i23kk/NdWvttEqcFK1asYOHChYUykD70rhTJ2UhA4eDAUaNGsW7dum7Z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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x111a37f98>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mc_mod = BrokenPow(*m.parameters)\n", "mc_mod.const.fixed = True\n", "fitted_params = fit.mcmc_err(mc_mod, r, raw_cts, bkg_cts, t_raw, t_bkg,\n", " cl=68.269, save_chain=True, clobber_chain=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }
gpl-3.0
chengsoonong/mclass-sky
mclearn/knfst/python/test.ipynb
3
74253
{ "cells": [ { "cell_type": "code", "execution_count": 347, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import scipy as sp\n", "import pandas as pd\n", "import urllib.request\n", "import os\n", "import shutil\n", "import tarfile\n", "import matplotlib.pyplot as plt\n", "from sklearn import datasets, cross_validation, metrics\n", "from sklearn.preprocessing import KernelCenterer\n", "\n", "%matplotlib notebook" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First we need to download the Caltech256 dataset." ] }, { "cell_type": "code", "execution_count": 348, "metadata": { "collapsed": false }, "outputs": [], "source": [ "DATASET_URL = r\"http://homes.esat.kuleuven.be/~tuytelaa/\"\\\n", "\"unsup/unsup_caltech256_dense_sift_1000_bow.tar.gz\"\n", "DATASET_DIR = \"../../../projects/weiyen/data\"" ] }, { "cell_type": "code", "execution_count": 349, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "../../../projects/weiyen/data/unsup_caltech256_dense_sift_1000_bow.tar.gz exists. Skipping download...\n", "Files extracted.\n" ] } ], "source": [ "filename = os.path.split(DATASET_URL)[1]\n", "dest_path = os.path.join(DATASET_DIR, filename)\n", "\n", "if os.path.exists(dest_path):\n", " print(\"{} exists. Skipping download...\".format(dest_path))\n", "else:\n", " with urllib.request.urlopen(DATASET_URL) as response, open(dest_path, 'wb') as out_file:\n", " shutil.copyfileobj(response, out_file)\n", " print(\"Dataset downloaded. Extracting files...\")\n", "\n", "tar = tarfile.open(dest_path)\n", "tar.extractall(path=DATASET_DIR)\n", "print(\"Files extracted.\")\n", "tar.close()\n", "\n", "path = os.path.join(DATASET_DIR, \"bow_1000_dense/\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Calculate multi-class KNFST model for multi-class novelty detection\n", " \n", " INPUT\n", " K: NxN kernel matrix containing similarities of n training samples\n", " labels: Nx1 column vector containing multi-class labels of N training samples\n", "\n", " OUTPUT\n", " proj: Projection of KNFST\n", " target_points: The projections of training data into the null space\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Load the dataset into memory" ] }, { "cell_type": "code", "execution_count": 351, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ds = datasets.load_files(path)\n", "ds.data = np.vstack([np.fromstring(txt, sep='\\t') for txt in ds.data])\n" ] }, { "cell_type": "code", "execution_count": 352, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data = ds.data\n", "target = ds.target" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Select a few \"known\" classes" ] }, { "cell_type": "code", "execution_count": 354, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(538, 1000)\n", "(538,)\n" ] } ], "source": [ "classes = np.unique(target)\n", "num_class = len(classes)\n", "num_known = 5\n", "\n", "known = np.random.choice(classes, num_known)\n", "mask = np.array([y in known for y in target])\n", "\n", "X_train = data[mask]\n", "y_train = target[mask]\n", "\n", "idx = y_train.argsort()\n", "X_train = X_train[idx]\n", "y_train = y_train[idx]\n", "\n", "print(X_train.shape)\n", "print(y_train.shape)" ] }, { "cell_type": "code", "execution_count": 355, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def _hik(x, y):\n", " '''\n", " Implements the histogram intersection kernel.\n", " '''\n", " return np.minimum(x, y).sum()\n" ] }, { "cell_type": "code", "execution_count": 356, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 4.44089210e-16])" ] }, "execution_count": 356, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from scipy.linalg import svd\n", "\n", "def nullspace(A, eps=1e-12):\n", " u, s, vh = svd(A)\n", " null_mask = (s <= eps)\n", " null_space = sp.compress(null_mask, vh, axis=0)\n", " return sp.transpose(null_space)\n", "\n", "A = np.array([[2,3,5],[-4,2,3],[0,0,0]])\n", "np.array([-4,2,3]).dot(nullspace(A))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Train the model, and obtain the projection and class target points." ] }, { "cell_type": "code", "execution_count": 357, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def learn(K, labels):\n", " classes = np.unique(labels)\n", " if len(classes) < 2:\n", " raise Exception(\"KNFST requires 2 or more classes\")\n", " n, m = K.shape\n", " if n != m:\n", " raise Exception(\"Kernel matrix must be quadratic\")\n", " \n", " centered_k = KernelCenterer().fit_transform(K)\n", " \n", " basis_values, basis_vecs = np.linalg.eigh(centered_k)\n", " \n", " basis_vecs = basis_vecs[:,basis_values > 1e-12]\n", " basis_values = basis_values[basis_values > 1e-12]\n", " \n", " basis_values = np.diag(1.0/np.sqrt(basis_values))\n", "\n", " basis_vecs = basis_vecs.dot(basis_values)\n", "\n", " L = np.zeros([n,n])\n", " for cl in classes:\n", " for idx1, x in enumerate(labels == cl):\n", " for idx2, y in enumerate(labels == cl):\n", " if x and y:\n", " L[idx1, idx2] = 1.0/np.sum(labels==cl)\n", " M = np.ones([m,m])/m\n", " H = (((np.eye(m,m)-M).dot(basis_vecs)).T).dot(K).dot(np.eye(n,m)-L)\n", " \n", " t_sw = H.dot(H.T)\n", " eigenvecs = nullspace(t_sw)\n", " if eigenvecs.shape[1] < 1:\n", " eigenvals, eigenvecs = np.linalg.eigh(t_sw)\n", " \n", " eigenvals = np.diag(eigenvals)\n", " min_idx = eigenvals.argsort()[0]\n", " eigenvecs = eigenvecs[:, min_idx]\n", " proj = ((np.eye(m,m)-M).dot(basis_vecs)).dot(eigenvecs)\n", " target_points = []\n", " for cl in classes:\n", " k_cl = K[labels==cl, :] \n", " pt = np.mean(k_cl.dot(proj), axis=0)\n", " target_points.append(pt)\n", " \n", " return proj, np.array(target_points)" ] }, { "cell_type": "code", "execution_count": 358, "metadata": { "collapsed": false }, "outputs": [], "source": [ "kernel_mat = metrics.pairwise_kernels(X_train, metric=_hik)\n", "proj, target_points = learn(kernel_mat, y_train)" ] }, { "cell_type": "code", "execution_count": 375, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def squared_euclidean_distances(x, y):\n", " n = np.shape(x)[0]\n", " m = np.shape(y)[0]\n", " distmat = np.zeros((n,m))\n", " \n", " for i in range(n):\n", " for j in range(m):\n", " buff = x[i,:] - y[j,:]\n", " distmat[i,j] = buff.dot(buff.T)\n", " return distmat\n", "\n", "def assign_score(proj, target_points, ks):\n", " projection_vectors = ks.T.dot(proj)\n", " sq_dist = squared_euclidean_distances(projection_vectors, target_points)\n", " scores = np.sqrt(np.amin(sq_dist, 1))\n", " return scores\n", "\n" ] }, { "cell_type": "code", "execution_count": 379, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "AUC: 0.269456410256\n", "AUC: 1.0\n", "AUC: 1.0\n", "AUC: 1.0\n", "AUC: 0.272551020408\n", "AUC: 0.990788126919\n", "AUC: 0.501006036217\n", "AUC: 1.0\n", "AUC: 0.809297709105\n", "AUC: 0.4522182861\n", "AUC: 0.502514351831\n", "AUC: 0.228532104602\n", "AUC: 0.32806368427\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-379-74e1cc10e08a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 23\u001b[0m \u001b[1;31m# Train model\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 24\u001b[0m \u001b[0mkernel_mat\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpairwise_kernels\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmetric\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0m_hik\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 25\u001b[1;33m \u001b[0mproj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget_points\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlearn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkernel_mat\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 26\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 27\u001b[0m \u001b[1;31m# Test\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m<ipython-input-357-7d394efbbcaf>\u001b[0m in \u001b[0;36mlearn\u001b[1;34m(K, labels)\u001b[0m\n\u001b[0;32m 23\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0midx2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m \u001b[1;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mcl\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 24\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 25\u001b[1;33m \u001b[0mL\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0midx1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0midx2\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1.0\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m==\u001b[0m\u001b[0mcl\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 26\u001b[0m \u001b[0mM\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mones\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mm\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mm\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mm\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 27\u001b[0m \u001b[0mH\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0meye\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mm\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mm\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mM\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbasis_vecs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mT\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mK\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0meye\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mm\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mL\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ "auc_scores = []\n", "classes = np.unique(target)\n", "num_known = 5\n", "for n in range(20):\n", " num_class = len(classes)\n", " known = np.random.choice(classes, num_known)\n", " mask = np.array([y in known for y in target])\n", "\n", " X_train = data[mask]\n", " y_train = target[mask]\n", " \n", " idx = y_train.argsort()\n", " X_train = X_train[idx]\n", " y_train = y_train[idx]\n", " \n", " sample_idx = np.random.randint(0, len(data), size=1000)\n", " X_test = data[sample_idx,:]\n", " y_labels = target[sample_idx]\n", "\n", " # Test labels are 1 if novel, otherwise 0.\n", " y_test = np.array([1 if cl not in known else 0 for cl in y_labels])\n", " \n", " # Train model\n", " kernel_mat = metrics.pairwise_kernels(X_train, metric=_hik)\n", " proj, target_points = learn(kernel_mat, y_train)\n", " \n", " # Test\n", " ks = metrics.pairwise_kernels(X_train, X_test, metric=_hik)\n", " scores = assign_score(proj, target_points, ks)\n", " auc = metrics.roc_auc_score(y_test, scores)\n", " print(\"AUC:\", auc)\n", " auc_scores.append(auc)\n", "\n", " \n" ] }, { "cell_type": "code", "execution_count": 323, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fpr, tpr, thresholds = metrics.roc_curve(y_test, scores)" ] }, { "cell_type": "code", "execution_count": 324, "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", " fig.waiting = false;\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", " 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", " 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", "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", "\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", " 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", " // 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.send_message('closing', {});\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-danger\" href=\"#\" title=\"Close figure\"><i class=\"fa fa-times icon-remove icon-large\"></i></button>');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Close figure', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\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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\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "plt.plot(fpr, tpr, label='ROC curve')\n", "plt.plot([0, 1], [0, 1], 'k--')\n", "plt.xlim([0.0, 1.0])\n", "plt.ylim([0.0, 1.05])\n", "plt.xlabel('False Positive Rate')\n", "plt.ylabel('True Positive Rate')\n", "plt.title('ROC Curve of the KNFST Novelty Classifier')\n", "plt.legend(loc=\"lower right\")\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 }
bsd-3-clause
pragyasresta29/Restaurant-Recommendation-System
recommendation system/getRecommendations.ipynb
1
9417
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pickle" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def load_obj(name ):\n", " with open( name + '.pkl', 'rb') as f:\n", " return pickle.load(f)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "reviews = load_obj('reviews')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "restaurants = load_obj('restaurants')" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def loadDataset(reviews, restaurants):\n", " restaurant={}\n", " for data in restaurants:\n", " res_id=data['business_id']\n", " name=data['name']\n", " restaurant[res_id]=name\n", " prefs={}\n", " count=0\n", " for data in reviews:\n", " user_id=data['user_id']\n", " res_id=data['restaurant_id']\n", " rating=data['rating']\n", " prefs.setdefault(user_id,{})\n", " prefs[user_id][restaurant[res_id]]=float(rating)\n", " return prefs " ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from math import sqrt\n", "# Returns the Pearson correlation coefficient for p1 and p2\n", "def sim_pearson(prefs,p1,p2):\n", " # Get the list of mutually rated items\n", " si={}\n", " for item in prefs[p1]:\n", " if item in prefs[p2]: si[item]=1\n", " # Find the number of elements\n", " n=len(si)\n", " # if they are no ratings in common, return 0\n", " if n==0: return 0\n", " # Add up all the preferences\n", " sum1=sum([prefs[p1][it] for it in si])\n", " sum2=sum([prefs[p2][it] for it in si])\n", " # Sum up the squares\n", " sum1Sq=sum([pow(prefs[p1][it],2) for it in si])\n", " sum2Sq=sum([pow(prefs[p2][it],2) for it in si])\n", " # Sum up the products\n", " pSum=sum([prefs[p1][it]*prefs[p2][it] for it in si])\n", " # Calculate Pearson score\n", " num=pSum-(sum1*sum2/n)\n", " den=sqrt((sum1Sq-pow(sum1,2)/n)*(sum2Sq-pow(sum2,2)/n))\n", " if den==0: return 0\n", " r=num/den\n", " return r" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Gets recommendations for a person by using a weighted average\n", "# of every other user's rankings\n", "def getRecommendations(prefs,person,similarity=sim_pearson):\n", " totals={}\n", " simSums={}\n", " for other in prefs:\n", " # don't compare me to myself\n", " if other==person: continue\n", " sim=similarity(prefs,person,other)\n", " # ignore scores of zero or lower\n", " if sim<=0: continue\n", " for item in prefs[other]:\n", " # only score movies I haven't seen yet\n", " if item not in prefs[person] or prefs[person][item]==0:\n", " # Similarity * Score\n", " totals.setdefault(item,0)\n", " totals[item]+=prefs[other][item]*sim\n", " # Sum of similarities\n", " simSums.setdefault(item,0)\n", " simSums[item]+=sim\n", " # Create the normalized list\n", " rankings=[(total/simSums[item],item) for item,total in totals.items( )]\n", " # Return the sorted list\n", " rankings.sort( )\n", " rankings.reverse( )\n", " return rankings" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "prefs=loadDataset(reviews,restaurants)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ "rec=getRecommendations(prefs, 'rLtl8ZkDX5vH5nAx9C3q5Q')" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[(5.000000000000001, 'Paprika Mediterranean Experience'),\n", " (5.000000000000001, 'New India Cuisine'),\n", " (5.000000000000001, \"King Solomon's Pizza\"),\n", " (5.000000000000001, \"Chi Chi's Tacos\"),\n", " (5.0, \"Wong's Chinese Dining\"),\n", " (5.0, 'Wild Horse West'),\n", " (5.0, 'Waters Edge Restaurant'),\n", " (5.0, \"Vito & Nick's Ii\"),\n", " (5.0, 'Venice Pizza'),\n", " (5.0, \"Vaquero's\"),\n", " (5.0, 'Uno Chicago Grill'),\n", " (5.0, 'US Pizza'),\n", " (5.0, \"Tom's BBQ\"),\n", " (5.0, \"Togo's Eatery\"),\n", " (5.0, 'Thirsty Camel @ The Phoenician')]" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rec[:15]" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2410" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(rec)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "recommendations={}\n", "data=[]\n", "for i in rec:\n", " list={}\n", " list['sim_score']=i[0]\n", " list['id']=i[1]\n", " data.append(list)\n", "recommendations['recommendations']=data" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'id': 'aC9NkbGoMOHiN4ABxEhPBg', 'sim_score': 5.000000000000001}" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "recommendations['recommendations'][0]" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import json" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true }, "outputs": [], "source": [ "with open('userrec.json', 'w') as userrec:\n", " json.dump(recommendations,userrec)" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": true }, "outputs": [], "source": [ "newrec=''" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": true }, "outputs": [], "source": [ "jsonrec= open('userrec.json')" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [], "source": [ "for line in jsonrec:\n", " newrec= line" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [], "source": [ "testrec=json.loads(newrec)" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[[5.000000000000001, 'Szechuan Cuisine'],\n", " [5.000000000000001, 'Paprika Mediterranean Experience'],\n", " [5.000000000000001, 'New India Cuisine'],\n", " [5.000000000000001, \"King Solomon's Pizza\"],\n", " [5.000000000000001, \"Chi Chi's Tacos\"],\n", " [5.0, \"Wong's Chinese Dining\"],\n", " [5.0, 'Wild Horse West'],\n", " [5.0, 'Waters Edge Restaurant'],\n", " [5.0, \"Vito & Nick's Ii\"],\n", " [5.0, 'Venice Pizza'],\n", " [5.0, \"Vaquero's\"],\n", " [5.0, 'Uno Chicago Grill'],\n", " [5.0, 'US Pizza'],\n", " [5.0, \"Tom's BBQ\"],\n", " [5.0, \"Togo's Eatery\"]]" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "testrec[:15]" ] }, { "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 [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 }
gpl-3.0
martindavid/code-sandbox
data-playground/Inspiration_Exploration_2.ipynb
1
258356
{ "cells": [ { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "from sqlalchemy import create_engine\n", "engine = create_engine('postgresql://localhost:5432/ci_inspirations')" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "action_df = pd.read_csv('data/actions.csv')\n", "custom_insp_df = pd.read_csv('data/custom_inspirations.csv')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>inspiration_uuid</th>\n", " <th>name</th>\n", " <th>excerpt</th>\n", " <th>description</th>\n", " <th>source_inspiration</th>\n", " <th>created_at</th>\n", " <th>updated_at</th>\n", " <th>account</th>\n", " <th>question_id</th>\n", " <th>question_text</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>2aaa820b-b908-41a8-949b-f2001a00a47d</td>\n", " <td>Unconscious bias</td>\n", " <td>Unconscious bias training</td>\n", " <td>Address bias in the workplace by training indi...</td>\n", " <td></td>\n", " <td>2018-01-25T05:32:10.508004</td>\n", " <td>2018-01-25T05:32:10.518295</td>\n", " <td>cimic</td>\n", " <td>71c5d846-f9d1-4488-a759-4a6665ed26c9</td>\n", " <td>My job performance is evaluated fairly</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2aaa820b-b908-41a8-949b-f2001a00a47d</td>\n", " <td>Unconscious bias</td>\n", " <td>Unconscious bias training</td>\n", " <td>Address bias in the workplace by training indi...</td>\n", " <td></td>\n", " <td>2018-01-25T05:32:10.508004</td>\n", " <td>2018-01-25T05:32:10.518295</td>\n", " <td>cimic</td>\n", " <td>0abf1183-464c-46ca-bf3b-7e3cddaa0cb4</td>\n", " <td>I have access to the learning and development ...</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2aaa820b-b908-41a8-949b-f2001a00a47d</td>\n", " <td>Unconscious bias</td>\n", " <td>Unconscious bias training</td>\n", " <td>Address bias in the workplace by training indi...</td>\n", " <td></td>\n", " <td>2018-01-25T05:32:10.508004</td>\n", " <td>2018-01-25T05:32:10.518295</td>\n", " <td>cimic</td>\n", " <td>c430d34c-fd6a-49fb-83e5-a3039188c5a3</td>\n", " <td>My job performance is evaluated fairly</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>2aaa820b-b908-41a8-949b-f2001a00a47d</td>\n", " <td>Unconscious bias</td>\n", " <td>Unconscious bias training</td>\n", " <td>Address bias in the workplace by training indi...</td>\n", " <td></td>\n", " <td>2018-01-25T05:32:10.508004</td>\n", " <td>2018-01-25T05:32:10.518295</td>\n", " <td>cimic</td>\n", " <td>93e616c9-25a0-4b0f-8a29-59793e4c12e1</td>\n", " <td>I have access to the learning and development ...</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>9c63d8d0-27f6-4e40-8838-cf37d3b03f12</td>\n", " <td>Broaden your network</td>\n", " <td>Learning from someone external to the team</td>\n", " <td>Learn from leaders outside the business. 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site/of...</td>\n", " <td></td>\n", " <td>2018-01-25T07:23:14.106600</td>\n", " <td>2018-01-25T07:23:14.112953</td>\n", " <td>cimic</td>\n", " <td>166e361c-532d-4dbd-82f6-0b5c936c337f</td>\n", " <td>I feel I am part of a team</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td>0dbb8ce9-cff4-4d47-8d7f-4284ef378160</td>\n", " <td>Seat shuffle</td>\n", " <td>Get to know others in the business</td>\n", " <td>Allow employees to work from alternate site/of...</td>\n", " <td></td>\n", " <td>2018-01-25T07:23:14.106600</td>\n", " <td>2018-01-25T07:23:14.112953</td>\n", " <td>cimic</td>\n", " <td>0db502e7-06dd-497a-a06e-47dd0638caf7</td>\n", " <td>I feel I am part of a team</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", " </tr>\n", " <tr>\n", " <th>8943</th>\n", " <td>c544ffb6-d586-4bab-8aed-bb2c68bf00c2</td>\n", " <td>D&amp;I as a Company Discussion</td>\n", " <td>D&amp;I Highlights</td>\n", " <td>During department All-hands, talk about D&amp;I co...</td>\n", " <td></td>\n", " <td>2018-11-06T10:11:08.380137</td>\n", " <td>2018-11-06T10:11:08.392112</td>\n", " <td>slack</td>\n", " <td>c5e8570d-280a-4082-b0bd-c207e23b6871</td>\n", " <td>My department lead demonstrates a visible comm...</td>\n", " </tr>\n", " <tr>\n", " <th>8944</th>\n", " <td>183df24e-f316-4063-a766-e7b7c6d12d6e</td>\n", " <td>Respect All Identities</td>\n", " <td>Use language that shows respect for all identi...</td>\n", " <td>Use language that shows respect for all identi...</td>\n", " <td>29f1cb2d-12aa-4584-aa43-efbf8f924092</td>\n", " <td>2018-11-06T10:11:08.543189</td>\n", " <td>2018-11-06T10:11:08.559371</td>\n", " <td>slack</td>\n", " <td>c4e278ba-d261-4222-8361-1811b451f927</td>\n", " <td>I feel comfortable being myself at work.</td>\n", " </tr>\n", " <tr>\n", " <th>8945</th>\n", " <td>8e3ef618-b818-4d7e-9f08-083603e4a9e4</td>\n", " <td>Visit other team meetings</td>\n", " <td>Increase visibility into and empathy between o...</td>\n", " <td>Increase executive visibility and empathy betw...</td>\n", " <td>49c02d4c-512a-4637-a49e-201992d522a4</td>\n", " <td>2018-11-06T10:11:08.728031</td>\n", " <td>2018-11-06T10:11:08.748971</td>\n", " <td>slack</td>\n", " <td>fc5fed4e-0fb0-467b-83f8-04e51cb1cbbe</td>\n", " <td>My department allocates resources toward the h...</td>\n", " </tr>\n", " <tr>\n", " <th>8946</th>\n", " <td>acd5b7ae-3be5-41d7-82f3-0f1afd5d0561</td>\n", " <td>Bridge Courses</td>\n", " <td>Share learning opportunities in your departmen...</td>\n", " <td>Guide employees to enroll in Bridge courses du...</td>\n", " <td></td>\n", " <td>2018-11-06T10:11:09.694531</td>\n", " <td>2018-11-06T10:11:09.710673</td>\n", " <td>slack</td>\n", " <td>2d3bded9-f906-4e8e-822b-66078a13a345</td>\n", " <td>My manager solicits feedback from me about the...</td>\n", " </tr>\n", " <tr>\n", " <th>8947</th>\n", " <td>73f0fad3-ff36-4571-abf7-6b76945f0127</td>\n", " <td>Listening Sessions</td>\n", " <td>Host internal Listening Sessions</td>\n", " <td>Have \"Listening Sessions\" where dept leads mee...</td>\n", " <td></td>\n", " <td>2018-11-06T10:11:08.283155</td>\n", " <td>2018-11-06T10:11:08.299398</td>\n", " <td>slack</td>\n", " <td>11c6d059-559c-4330-bc48-b3af3174660a</td>\n", " <td>I have confidence in my department lead to eff...</td>\n", " </tr>\n", " <tr>\n", " <th>8948</th>\n", " <td>67fec691-dc64-4bc3-aa8e-4020f047d2e2</td>\n", " <td>Respect All Identities</td>\n", " <td>Use language that shows respect for all identi...</td>\n", " <td>Use language that shows respect for all identi...</td>\n", " <td>29f1cb2d-12aa-4584-aa43-efbf8f924092</td>\n", " <td>2018-11-06T10:11:08.406939</td>\n", " <td>2018-11-06T10:11:08.425726</td>\n", " <td>slack</td>\n", " <td>eb2e6fc3-d87d-4785-a926-d2a7b75be13f</td>\n", " <td>I fit in with my teammates.</td>\n", " </tr>\n", " <tr>\n", " <th>8949</th>\n", " <td>4764a325-412b-4481-8db9-0f6ce6c8b569</td>\n", " <td>Alignment workshops</td>\n", " <td>Align work to organization change efforts and ...</td>\n", " <td>Align company work processes to company change...</td>\n", " <td>f6acf8c7-e8ff-4299-97cd-347bbcacf989</td>\n", " <td>2018-11-06T10:11:08.750366</td>\n", " <td>2018-11-06T10:11:08.764085</td>\n", " <td>slack</td>\n", " <td>fc5fed4e-0fb0-467b-83f8-04e51cb1cbbe</td>\n", " <td>My department allocates resources toward the h...</td>\n", " </tr>\n", " <tr>\n", " <th>8950</th>\n", " <td>4b47f43b-8321-4b38-90a4-3aad29b33d6d</td>\n", " <td>Feedback in Slack</td>\n", " <td>Learn more about Feedback @ Slack</td>\n", " <td>Enroll in the SF: Feedback @ Slack course offe...</td>\n", " <td></td>\n", " <td>2018-11-06T10:11:09.715254</td>\n", " <td>2018-11-06T10:11:09.728255</td>\n", " <td>slack</td>\n", " <td>2d3bded9-f906-4e8e-822b-66078a13a345</td>\n", " <td>My manager solicits feedback from me about the...</td>\n", " </tr>\n", " <tr>\n", " <th>8951</th>\n", " <td>87d43634-7b31-40ce-bf9c-7e3c510b2594</td>\n", " <td>Hack the halls</td>\n", " <td>Decorate collaboratively</td>\n", " <td>Get employees involved in improving their desk...</td>\n", " <td>6c2fdcd1-596d-4716-82e2-bc45b0ed6abb</td>\n", " <td>2018-11-06T10:11:08.799994</td>\n", " <td>2018-11-06T10:11:08.815593</td>\n", " <td>slack</td>\n", " <td>96e861cd-b6b9-4b9c-bfc7-725acbe02313</td>\n", " <td>I enjoy my physical workspace.</td>\n", " </tr>\n", " <tr>\n", " <th>8952</th>\n", " <td>8d4a2fdf-8b64-40ed-82d3-adee36674fa5</td>\n", " <td>Rotating team leads</td>\n", " <td>Rotating team leads and assessing impact</td>\n", " <td>Increase ownership for team deliverables. One ...</td>\n", " <td>5260bfce-b7e5-42e1-a794-a54fc6a8c628</td>\n", " <td>2018-11-06T10:11:08.963017</td>\n", " <td>2018-11-06T10:11:08.979410</td>\n", " <td>slack</td>\n", " <td>8e8693f2-6aed-4a9d-8bc8-e833aaafe8ee</td>\n", " <td>I can depend on my teammates to deliver qualit...</td>\n", " </tr>\n", " <tr>\n", " <th>8953</th>\n", " <td>e71cbbd9-5dbb-401e-b124-6e0749198cf3</td>\n", " <td>Donut bot</td>\n", " <td>Set up meetings with employees that don't know...</td>\n", " <td>Encourage employees to join their local #donut...</td>\n", " <td>7c144a98-d41e-491c-b425-64b8ca0851eb</td>\n", " <td>2018-11-06T10:11:09.092417</td>\n", " <td>2018-11-06T10:11:09.144983</td>\n", " <td>slack</td>\n", " <td>c6d4f6ab-cd58-4557-a1ac-811281cda2fc</td>\n", " <td>My team collaborates well with other teams to ...</td>\n", " </tr>\n", " <tr>\n", " <th>8954</th>\n", " <td>5a5c0fc4-3d95-438e-a5a6-19e48131477e</td>\n", " <td>Our vision of the future</td>\n", " <td>Frame leadership's vision for the future</td>\n", " <td>To frame the long-term vision of the company a...</td>\n", " <td>0cc33224-093a-4026-9bb7-29691825b3be</td>\n", " <td>2018-11-06T10:11:09.258558</td>\n", " <td>2018-11-06T10:11:09.274445</td>\n", " <td>slack</td>\n", " <td>d22bb1fa-b61a-4239-8622-30cf021b248c</td>\n", " <td>I clearly understand Slack's vision.</td>\n", " </tr>\n", " <tr>\n", " <th>8955</th>\n", " <td>6420bfbd-5367-402b-a8ce-4e5546b3b48a</td>\n", " <td>Action update emails</td>\n", " <td>Communicate progress addressing feedback</td>\n", " <td>Communicate often and regularly about what is ...</td>\n", " <td>00e41e9a-d260-49ad-98b5-f70f6c92a258</td>\n", " <td>2018-11-06T10:11:09.421226</td>\n", " <td>2018-11-06T10:11:09.436445</td>\n", " <td>slack</td>\n", " <td>f94cd260-cb6e-49bd-9006-71232dd81da3</td>\n", " <td>I believe we will act on the results of this s...</td>\n", " </tr>\n", " <tr>\n", " <th>8956</th>\n", " <td>3d18b58d-d3a4-4fc4-9347-8deed63165eb</td>\n", " <td>Coffeehouse meetings</td>\n", " <td>Enable casual cross-business current work sharing</td>\n", " <td>Create casual forums for cross-team sharing. O...</td>\n", " <td>fcc8b1f2-d92c-48de-a08d-dffde6d83431</td>\n", " <td>2018-11-06T10:11:08.822339</td>\n", " <td>2018-11-06T10:11:08.856082</td>\n", " <td>slack</td>\n", " <td>225c26e6-81b3-44f0-a6bf-babe58c15f80</td>\n", " <td>I'm encouraged and supported in sharing my ide...</td>\n", " </tr>\n", " <tr>\n", " <th>8957</th>\n", " <td>bbe93c4b-d80d-484a-8e42-2034e64fd57e</td>\n", " <td>Process hackathon</td>\n", " <td>Use diverse perspectives to improve processes</td>\n", " <td>Bring together diverse perspectives to improve...</td>\n", " <td>8967feca-5e3f-4239-9c55-91abb8a1e9e5</td>\n", " <td>2018-11-06T10:11:08.989218</td>\n", " <td>2018-11-06T10:11:09.007680</td>\n", " <td>slack</td>\n", " <td>69613580-a5b7-4ad3-ab9a-55664c76d1e7</td>\n", " <td>My team welcomes diverse perspectives.</td>\n", " </tr>\n", " <tr>\n", " <th>8958</th>\n", " <td>3491b471-1799-4dad-8e0a-3bacb82d61f7</td>\n", " <td>Project day</td>\n", " <td>Work together on short term projects</td>\n", " <td>Work together across departments and functions...</td>\n", " <td>62f2e979-0332-438e-9f6c-f4679c419879</td>\n", " <td>2018-11-06T10:11:09.147611</td>\n", " <td>2018-11-06T10:11:09.167998</td>\n", " <td>slack</td>\n", " <td>c6d4f6ab-cd58-4557-a1ac-811281cda2fc</td>\n", " <td>My team collaborates well with other teams to ...</td>\n", " </tr>\n", " <tr>\n", " <th>8959</th>\n", " <td>3a166c46-0695-4156-bef9-e9b44b51637f</td>\n", " <td>The Power of Reiteration</td>\n", " <td>Communicate your company and team vision multi...</td>\n", " <td>Communicate your company and team vision multi...</td>\n", " <td>4c4d757e-1178-497f-9af2-f7188914bbbb</td>\n", " <td>2018-11-06T10:11:09.282259</td>\n", " <td>2018-11-06T10:11:09.299602</td>\n", " <td>slack</td>\n", " <td>d22bb1fa-b61a-4239-8622-30cf021b248c</td>\n", " <td>I clearly understand Slack's vision.</td>\n", " </tr>\n", " <tr>\n", " <th>8960</th>\n", " <td>a000744c-0a94-47de-a703-370ad050bf8f</td>\n", " <td>Synthesis sessions</td>\n", " <td>In-person leader facilitated discussions</td>\n", " <td>Hold open forums to share information about th...</td>\n", " <td>7222a089-95dc-4eed-a181-500d5e1711b2</td>\n", " <td>2018-11-06T10:11:08.857306</td>\n", " <td>2018-11-06T10:11:08.876510</td>\n", " <td>slack</td>\n", " <td>225c26e6-81b3-44f0-a6bf-babe58c15f80</td>\n", " <td>I'm encouraged and supported in sharing my ide...</td>\n", " </tr>\n", " <tr>\n", " <th>8961</th>\n", " <td>6cddce57-d427-4f84-87b9-7bd78825dfd1</td>\n", " <td>Shark tank style competition</td>\n", " <td>Cross-functional teams pitch award ideas</td>\n", " <td>Encourage employees to pitch ideas to leaders ...</td>\n", " <td>58f77db6-d764-4827-9c13-ada9d996f512</td>\n", " <td>2018-11-06T10:11:09.014370</td>\n", " <td>2018-11-06T10:11:09.048700</td>\n", " <td>slack</td>\n", " <td>c6d4f6ab-cd58-4557-a1ac-811281cda2fc</td>\n", " <td>My team collaborates well with other teams to ...</td>\n", " </tr>\n", " <tr>\n", " <th>8962</th>\n", " <td>30b30ab3-d671-407e-b8d2-2e88037210de</td>\n", " <td>Personal Operating Manual</td>\n", " <td>Consult with L&amp;D to foster internal team colla...</td>\n", " <td>Set-up time to speak with an L&amp;D team member a...</td>\n", " <td></td>\n", " <td>2018-11-06T10:11:09.172154</td>\n", " <td>2018-11-06T10:11:09.187534</td>\n", " <td>slack</td>\n", " <td>c6d4f6ab-cd58-4557-a1ac-811281cda2fc</td>\n", " <td>My team collaborates well with other teams to ...</td>\n", " </tr>\n", " <tr>\n", " <th>8963</th>\n", " <td>772b3911-5b6d-46a8-ad13-25acd73690c6</td>\n", " <td>Positive-for-a-week challenge</td>\n", " <td>Broadening the meaning and practice of feedback</td>\n", " <td>Help managers understand and practice providin...</td>\n", " <td>15a34a39-6da8-41b1-a2b1-f112f78f799e</td>\n", " <td>2018-11-06T10:11:09.306710</td>\n", " <td>2018-11-06T10:11:09.346157</td>\n", " <td>slack</td>\n", " <td>bdf77f05-a864-4ffb-8a15-8d87b7e88d19</td>\n", " <td>My manager gives me useful feedback on how I'm...</td>\n", " </tr>\n", " <tr>\n", " <th>8964</th>\n", " <td>1abdae9f-3c8a-49eb-adfc-2430c7ad41e0</td>\n", " <td>Rapid feedback session</td>\n", " <td>Hold sessions focused on sharing admiration of...</td>\n", " <td>Provide employees with an opportunity to give ...</td>\n", " <td>2732a32f-7c0b-4595-a1aa-55c5a81f96e9</td>\n", " <td>2018-11-06T10:11:09.498087</td>\n", " <td>2018-11-06T10:11:09.515823</td>\n", " <td>slack</td>\n", " <td>ed77c4ca-84ae-40e6-917e-5b2526c82a80</td>\n", " <td>I get actionable feedback from my peers to imp...</td>\n", " </tr>\n", " <tr>\n", " <th>8965</th>\n", " <td>bb89188d-449b-45bd-b005-78296fb518e6</td>\n", " <td>Enabling innovative ideas</td>\n", " <td>Surfacing cross-functional innovative ideas</td>\n", " <td>Encourage sharing of ideas across organization...</td>\n", " <td>3bfc9c46-ead5-451a-913d-c72878bac070</td>\n", " <td>2018-11-06T10:11:08.877822</td>\n", " <td>2018-11-06T10:11:08.895797</td>\n", " <td>slack</td>\n", " <td>225c26e6-81b3-44f0-a6bf-babe58c15f80</td>\n", " <td>I'm encouraged and supported in sharing my ide...</td>\n", " </tr>\n", " <tr>\n", " <th>8966</th>\n", " <td>e5a34fd6-bc3f-4a45-8737-90620c7e6561</td>\n", " <td>Information flows exercise</td>\n", " <td>Identify information flow blockers</td>\n", " <td>The information flows exercise focuses on iden...</td>\n", " <td>3dc4a7cb-8908-4581-8d17-b19702df1de8</td>\n", " <td>2018-11-06T10:11:09.049773</td>\n", " <td>2018-11-06T10:11:09.068687</td>\n", " <td>slack</td>\n", " <td>c6d4f6ab-cd58-4557-a1ac-811281cda2fc</td>\n", " <td>My team collaborates well with other teams to ...</td>\n", " </tr>\n", " <tr>\n", " <th>8967</th>\n", " <td>eb8ce9a7-700e-4603-b007-41ee4d00fbf6</td>\n", " <td>Managing Learning Activities</td>\n", " <td>Explore Managing on Bridge</td>\n", " <td>Explore our Managing courses provided through ...</td>\n", " <td></td>\n", " <td>2018-11-06T10:11:09.195655</td>\n", " <td>2018-11-06T10:11:09.207105</td>\n", " <td>slack</td>\n", " <td>b59245cc-d0e4-4064-9946-f02cc46c53e3</td>\n", " <td>I'm confident in my manager’s ability to do th...</td>\n", " </tr>\n", " <tr>\n", " <th>8968</th>\n", " <td>68520dca-bc61-4526-95fe-e5a862847408</td>\n", " <td>Decision-making framework</td>\n", " <td>Include consulting others in your decision-mak...</td>\n", " <td>Making consultation part of the formal decisio...</td>\n", " <td>ef4cdbe2-439e-43a7-a198-895e32847b30</td>\n", " <td>2018-11-06T10:11:09.523174</td>\n", " <td>2018-11-06T10:11:09.549684</td>\n", " <td>slack</td>\n", " <td>30ffab5a-082d-4a54-8f58-2b25dbcd54ee</td>\n", " <td>My manager values my perspectives, even when d...</td>\n", " </tr>\n", " <tr>\n", " <th>8969</th>\n", " <td>0136a984-d306-4741-bf94-297a686d5b76</td>\n", " <td>Team commitments</td>\n", " <td>Weekly team targets and commitments</td>\n", " <td>Encourage greater individual accountability by...</td>\n", " <td></td>\n", " <td>2018-11-06T10:11:08.902533</td>\n", " <td>2018-11-06T10:11:08.958467</td>\n", " <td>slack</td>\n", " <td>8e8693f2-6aed-4a9d-8bc8-e833aaafe8ee</td>\n", " <td>I can depend on my teammates to deliver qualit...</td>\n", " </tr>\n", " <tr>\n", " <th>8970</th>\n", " <td>c3ab7a59-8f66-4032-910f-2e7ee478326c</td>\n", " <td>Department liaison</td>\n", " <td>Facilitate collaboration and prioritization</td>\n", " <td>Create a role that facilitates communication b...</td>\n", " <td>9b4bca01-674d-4864-a501-d94d3e190c27</td>\n", " <td>2018-11-06T10:11:09.071063</td>\n", " <td>2018-11-06T10:11:09.088064</td>\n", " <td>slack</td>\n", " <td>c6d4f6ab-cd58-4557-a1ac-811281cda2fc</td>\n", " <td>My team collaborates well with other teams to ...</td>\n", " </tr>\n", " <tr>\n", " <th>8971</th>\n", " <td>b22e5c61-de4a-4277-bfe3-190b966967a3</td>\n", " <td>Level-up alignment sessions</td>\n", " <td>Connect employees to organization mission, vis...</td>\n", " <td>Connect employees to the organization's missio...</td>\n", " <td>eb0aa1a9-0aaf-4009-a985-167c8780aaa7</td>\n", " <td>2018-11-06T10:11:09.218555</td>\n", " <td>2018-11-06T10:11:09.248060</td>\n", " <td>slack</td>\n", " <td>d22bb1fa-b61a-4239-8622-30cf021b248c</td>\n", " <td>I clearly understand Slack's vision.</td>\n", " </tr>\n", " <tr>\n", " <th>8972</th>\n", " <td>52453839-d2dc-4023-8793-66e1fd908945</td>\n", " <td>Courage to share action stories</td>\n", " <td>Share what your team is doing to address feedback</td>\n", " <td>To maintain momentum, it's essential that the ...</td>\n", " <td>d987ecd5-5e79-4246-aac1-1c3ee4a5f489</td>\n", " <td>2018-11-06T10:11:09.400536</td>\n", " <td>2018-11-06T10:11:09.412575</td>\n", " <td>slack</td>\n", " <td>f94cd260-cb6e-49bd-9006-71232dd81da3</td>\n", " <td>I believe we will act on the results of this s...</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>8973 rows × 10 columns</p>\n", "</div>" ], "text/plain": [ " inspiration_uuid name \\\n", "0 2aaa820b-b908-41a8-949b-f2001a00a47d Unconscious bias \n", "1 2aaa820b-b908-41a8-949b-f2001a00a47d Unconscious bias \n", "2 2aaa820b-b908-41a8-949b-f2001a00a47d Unconscious bias \n", "3 2aaa820b-b908-41a8-949b-f2001a00a47d Unconscious bias \n", "4 9c63d8d0-27f6-4e40-8838-cf37d3b03f12 Broaden your network \n", "5 9c63d8d0-27f6-4e40-8838-cf37d3b03f12 Broaden your network \n", "6 6badccba-9ac5-4592-a23c-80f2aaade083 Embrace failure \n", "7 6badccba-9ac5-4592-a23c-80f2aaade083 Embrace failure \n", "8 2b1d8949-e901-4cbd-972a-fe532462f11c The how sandwich \n", "9 2b1d8949-e901-4cbd-972a-fe532462f11c The how sandwich \n", "10 2b1d8949-e901-4cbd-972a-fe532462f11c The how sandwich \n", "11 2b1d8949-e901-4cbd-972a-fe532462f11c The how sandwich \n", "12 844aed7f-e796-4727-a04b-316f2f1d0b13 One-on-One meetings \n", "13 844aed7f-e796-4727-a04b-316f2f1d0b13 One-on-One meetings \n", "14 844aed7f-e796-4727-a04b-316f2f1d0b13 One-on-One meetings \n", "15 844aed7f-e796-4727-a04b-316f2f1d0b13 One-on-One meetings \n", "16 bee87760-18c4-4872-a157-676060dc3f63 Shared responsibilities \n", "17 bee87760-18c4-4872-a157-676060dc3f63 Shared responsibilities \n", "18 bee87760-18c4-4872-a157-676060dc3f63 Shared responsibilities \n", "19 bee87760-18c4-4872-a157-676060dc3f63 Shared responsibilities \n", "20 ce23b8c3-4e4b-4c30-8d7a-28f3b425a7dc Thank you \n", "21 ce23b8c3-4e4b-4c30-8d7a-28f3b425a7dc Thank you \n", "22 76cc932f-d992-426e-b26d-72ebb0017399 Names on parade \n", "23 76cc932f-d992-426e-b26d-72ebb0017399 Names on parade \n", "24 76cc932f-d992-426e-b26d-72ebb0017399 Names on parade \n", "25 76cc932f-d992-426e-b26d-72ebb0017399 Names on parade \n", "26 0dbb8ce9-cff4-4d47-8d7f-4284ef378160 Seat shuffle \n", "27 0dbb8ce9-cff4-4d47-8d7f-4284ef378160 Seat shuffle \n", "28 0dbb8ce9-cff4-4d47-8d7f-4284ef378160 Seat shuffle \n", "29 0dbb8ce9-cff4-4d47-8d7f-4284ef378160 Seat shuffle \n", "... ... ... \n", "8943 c544ffb6-d586-4bab-8aed-bb2c68bf00c2 D&I as a Company Discussion \n", "8944 183df24e-f316-4063-a766-e7b7c6d12d6e Respect All Identities \n", "8945 8e3ef618-b818-4d7e-9f08-083603e4a9e4 Visit other team meetings \n", "8946 acd5b7ae-3be5-41d7-82f3-0f1afd5d0561 Bridge Courses \n", "8947 73f0fad3-ff36-4571-abf7-6b76945f0127 Listening Sessions \n", "8948 67fec691-dc64-4bc3-aa8e-4020f047d2e2 Respect All Identities \n", "8949 4764a325-412b-4481-8db9-0f6ce6c8b569 Alignment workshops \n", "8950 4b47f43b-8321-4b38-90a4-3aad29b33d6d Feedback in Slack \n", "8951 87d43634-7b31-40ce-bf9c-7e3c510b2594 Hack the halls \n", "8952 8d4a2fdf-8b64-40ed-82d3-adee36674fa5 Rotating team leads \n", "8953 e71cbbd9-5dbb-401e-b124-6e0749198cf3 Donut bot \n", "8954 5a5c0fc4-3d95-438e-a5a6-19e48131477e Our vision of the future \n", "8955 6420bfbd-5367-402b-a8ce-4e5546b3b48a Action update emails \n", "8956 3d18b58d-d3a4-4fc4-9347-8deed63165eb Coffeehouse meetings \n", "8957 bbe93c4b-d80d-484a-8e42-2034e64fd57e Process hackathon \n", "8958 3491b471-1799-4dad-8e0a-3bacb82d61f7 Project day \n", "8959 3a166c46-0695-4156-bef9-e9b44b51637f The Power of Reiteration \n", "8960 a000744c-0a94-47de-a703-370ad050bf8f Synthesis sessions \n", "8961 6cddce57-d427-4f84-87b9-7bd78825dfd1 Shark tank style competition \n", "8962 30b30ab3-d671-407e-b8d2-2e88037210de Personal Operating Manual \n", "8963 772b3911-5b6d-46a8-ad13-25acd73690c6 Positive-for-a-week challenge \n", "8964 1abdae9f-3c8a-49eb-adfc-2430c7ad41e0 Rapid feedback session \n", "8965 bb89188d-449b-45bd-b005-78296fb518e6 Enabling innovative ideas \n", "8966 e5a34fd6-bc3f-4a45-8737-90620c7e6561 Information flows exercise \n", "8967 eb8ce9a7-700e-4603-b007-41ee4d00fbf6 Managing Learning Activities \n", "8968 68520dca-bc61-4526-95fe-e5a862847408 Decision-making framework \n", "8969 0136a984-d306-4741-bf94-297a686d5b76 Team commitments \n", "8970 c3ab7a59-8f66-4032-910f-2e7ee478326c Department liaison \n", "8971 b22e5c61-de4a-4277-bfe3-190b966967a3 Level-up alignment sessions \n", "8972 52453839-d2dc-4023-8793-66e1fd908945 Courage to share action stories \n", "\n", " excerpt \\\n", "0 Unconscious bias training \n", "1 Unconscious bias training \n", "2 Unconscious bias training \n", "3 Unconscious bias training \n", "4 Learning from someone external to the team \n", "5 Learning from someone external to the team \n", "6 Use failure as a learning experience \n", "7 Use failure as a learning experience \n", "8 Directing focus and attention on others vs. self \n", "9 Directing focus and attention on others vs. self \n", "10 Directing focus and attention on others vs. self \n", "11 Directing focus and attention on others vs. self \n", "12 Prioritise One-on-One meetings \n", "13 Prioritise One-on-One meetings \n", "14 Prioritise One-on-One meetings \n", "15 Prioritise One-on-One meetings \n", "16 Setting high standards \n", "17 Setting high standards \n", "18 Setting high standards \n", "19 Setting high standards \n", "20 End each meeting with a thank you \n", "21 End each meeting with a thank you \n", "22 Ensure team member's names are visible \n", "23 Ensure team member's names are visible \n", "24 Ensure team member's names are visible \n", "25 Ensure team member's names are visible \n", "26 Get to know others in the business \n", "27 Get to know others in the business \n", "28 Get to know others in the business \n", "29 Get to know others in the business \n", "... ... \n", "8943 D&I Highlights \n", "8944 Use language that shows respect for all identi... \n", "8945 Increase visibility into and empathy between o... \n", "8946 Share learning opportunities in your departmen... \n", "8947 Host internal Listening Sessions \n", "8948 Use language that shows respect for all identi... \n", "8949 Align work to organization change efforts and ... \n", "8950 Learn more about Feedback @ Slack \n", "8951 Decorate collaboratively \n", "8952 Rotating team leads and assessing impact \n", "8953 Set up meetings with employees that don't know... \n", "8954 Frame leadership's vision for the future \n", "8955 Communicate progress addressing feedback \n", "8956 Enable casual cross-business current work sharing \n", "8957 Use diverse perspectives to improve processes \n", "8958 Work together on short term projects \n", "8959 Communicate your company and team vision multi... \n", "8960 In-person leader facilitated discussions \n", "8961 Cross-functional teams pitch award ideas \n", "8962 Consult with L&D to foster internal team colla... \n", "8963 Broadening the meaning and practice of feedback \n", "8964 Hold sessions focused on sharing admiration of... \n", "8965 Surfacing cross-functional innovative ideas \n", "8966 Identify information flow blockers \n", "8967 Explore Managing on Bridge \n", "8968 Include consulting others in your decision-mak... \n", "8969 Weekly team targets and commitments \n", "8970 Facilitate collaboration and prioritization \n", "8971 Connect employees to organization mission, vis... \n", "8972 Share what your team is doing to address feedback \n", "\n", " description \\\n", "0 Address bias in the workplace by training indi... \n", "1 Address bias in the workplace by training indi... \n", "2 Address bias in the workplace by training indi... \n", "3 Address bias in the workplace by training indi... \n", "4 Learn from leaders outside the business. Invit... \n", "5 Learn from leaders outside the business. Invit... \n", "6 Encourage team members to share learning that ... \n", "7 Encourage team members to share learning that ... \n", "8 The challenge is for managers to apply the 'ho... \n", "9 The challenge is for managers to apply the 'ho... \n", "10 The challenge is for managers to apply the 'ho... \n", "11 The challenge is for managers to apply the 'ho... \n", "12 Embed a consistent practice of one-on-one meet... \n", "13 Embed a consistent practice of one-on-one meet... \n", "14 Embed a consistent practice of one-on-one meet... \n", "15 Embed a consistent practice of one-on-one meet... \n", "16 Use safety interactions, team meetings and dis... \n", "17 Use safety interactions, team meetings and dis... \n", "18 Use safety interactions, team meetings and dis... \n", "19 Use safety interactions, team meetings and dis... \n", "20 Leaders to recognize others in the team or bus... \n", "21 Leaders to recognize others in the team or bus... \n", "22 Ensure team member's names are visible on thei... \n", "23 Ensure team member's names are visible on thei... \n", "24 Ensure team member's names are visible on thei... \n", "25 Ensure team member's names are visible on thei... \n", "26 Allow employees to work from alternate site/of... \n", "27 Allow employees to work from alternate site/of... \n", "28 Allow employees to work from alternate site/of... \n", "29 Allow employees to work from alternate site/of... \n", "... ... \n", "8943 During department All-hands, talk about D&I co... \n", "8944 Use language that shows respect for all identi... \n", "8945 Increase executive visibility and empathy betw... \n", "8946 Guide employees to enroll in Bridge courses du... \n", "8947 Have \"Listening Sessions\" where dept leads mee... \n", "8948 Use language that shows respect for all identi... \n", "8949 Align company work processes to company change... \n", "8950 Enroll in the SF: Feedback @ Slack course offe... \n", "8951 Get employees involved in improving their desk... \n", "8952 Increase ownership for team deliverables. One ... \n", "8953 Encourage employees to join their local #donut... \n", "8954 To frame the long-term vision of the company a... \n", "8955 Communicate often and regularly about what is ... \n", "8956 Create casual forums for cross-team sharing. O... \n", "8957 Bring together diverse perspectives to improve... \n", "8958 Work together across departments and functions... \n", "8959 Communicate your company and team vision multi... \n", "8960 Hold open forums to share information about th... \n", "8961 Encourage employees to pitch ideas to leaders ... \n", "8962 Set-up time to speak with an L&D team member a... \n", "8963 Help managers understand and practice providin... \n", "8964 Provide employees with an opportunity to give ... \n", "8965 Encourage sharing of ideas across organization... \n", "8966 The information flows exercise focuses on iden... \n", "8967 Explore our Managing courses provided through ... \n", "8968 Making consultation part of the formal decisio... \n", "8969 Encourage greater individual accountability by... \n", "8970 Create a role that facilitates communication b... \n", "8971 Connect employees to the organization's missio... \n", "8972 To maintain momentum, it's essential that the ... \n", "\n", " 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2018-11-06T10:11:09.436445 slack \n", "8956 2018-11-06T10:11:08.856082 slack \n", "8957 2018-11-06T10:11:09.007680 slack \n", "8958 2018-11-06T10:11:09.167998 slack \n", "8959 2018-11-06T10:11:09.299602 slack \n", "8960 2018-11-06T10:11:08.876510 slack \n", "8961 2018-11-06T10:11:09.048700 slack \n", "8962 2018-11-06T10:11:09.187534 slack \n", "8963 2018-11-06T10:11:09.346157 slack \n", "8964 2018-11-06T10:11:09.515823 slack \n", "8965 2018-11-06T10:11:08.895797 slack \n", "8966 2018-11-06T10:11:09.068687 slack \n", "8967 2018-11-06T10:11:09.207105 slack \n", "8968 2018-11-06T10:11:09.549684 slack \n", "8969 2018-11-06T10:11:08.958467 slack \n", "8970 2018-11-06T10:11:09.088064 slack \n", "8971 2018-11-06T10:11:09.248060 slack \n", "8972 2018-11-06T10:11:09.412575 slack \n", "\n", " question_id \\\n", "0 71c5d846-f9d1-4488-a759-4a6665ed26c9 \n", "1 0abf1183-464c-46ca-bf3b-7e3cddaa0cb4 \n", "2 c430d34c-fd6a-49fb-83e5-a3039188c5a3 \n", "3 93e616c9-25a0-4b0f-8a29-59793e4c12e1 \n", "4 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\n", "26 6b95bf22-be11-4b82-9e3a-bc8f2fc06090 \n", "27 bb7c8606-fe0f-4f2f-83f0-238a982687fc \n", "28 166e361c-532d-4dbd-82f6-0b5c936c337f \n", "29 0db502e7-06dd-497a-a06e-47dd0638caf7 \n", "... ... \n", "8943 c5e8570d-280a-4082-b0bd-c207e23b6871 \n", "8944 c4e278ba-d261-4222-8361-1811b451f927 \n", "8945 fc5fed4e-0fb0-467b-83f8-04e51cb1cbbe \n", "8946 2d3bded9-f906-4e8e-822b-66078a13a345 \n", "8947 11c6d059-559c-4330-bc48-b3af3174660a \n", "8948 eb2e6fc3-d87d-4785-a926-d2a7b75be13f \n", "8949 fc5fed4e-0fb0-467b-83f8-04e51cb1cbbe \n", "8950 2d3bded9-f906-4e8e-822b-66078a13a345 \n", "8951 96e861cd-b6b9-4b9c-bfc7-725acbe02313 \n", "8952 8e8693f2-6aed-4a9d-8bc8-e833aaafe8ee \n", "8953 c6d4f6ab-cd58-4557-a1ac-811281cda2fc \n", "8954 d22bb1fa-b61a-4239-8622-30cf021b248c \n", "8955 f94cd260-cb6e-49bd-9006-71232dd81da3 \n", "8956 225c26e6-81b3-44f0-a6bf-babe58c15f80 \n", "8957 69613580-a5b7-4ad3-ab9a-55664c76d1e7 \n", "8958 c6d4f6ab-cd58-4557-a1ac-811281cda2fc \n", "8959 d22bb1fa-b61a-4239-8622-30cf021b248c \n", "8960 225c26e6-81b3-44f0-a6bf-babe58c15f80 \n", "8961 c6d4f6ab-cd58-4557-a1ac-811281cda2fc \n", "8962 c6d4f6ab-cd58-4557-a1ac-811281cda2fc \n", "8963 bdf77f05-a864-4ffb-8a15-8d87b7e88d19 \n", "8964 ed77c4ca-84ae-40e6-917e-5b2526c82a80 \n", "8965 225c26e6-81b3-44f0-a6bf-babe58c15f80 \n", "8966 c6d4f6ab-cd58-4557-a1ac-811281cda2fc \n", "8967 b59245cc-d0e4-4064-9946-f02cc46c53e3 \n", "8968 30ffab5a-082d-4a54-8f58-2b25dbcd54ee \n", "8969 8e8693f2-6aed-4a9d-8bc8-e833aaafe8ee \n", "8970 c6d4f6ab-cd58-4557-a1ac-811281cda2fc \n", "8971 d22bb1fa-b61a-4239-8622-30cf021b248c \n", "8972 f94cd260-cb6e-49bd-9006-71232dd81da3 \n", "\n", " question_text \n", "0 My job performance is evaluated fairly \n", "1 I have access to the learning and development ... \n", "2 My job performance is evaluated fairly \n", "3 I have access to the learning and development ... \n", "4 I am given opportunities to develop skills rel... \n", "5 I am given opportunities to develop skills rel... \n", "6 I have access to the learning and development ... \n", "7 I have access to the learning and development ... \n", "8 My Supervisor genuinely cares about my wellbeing \n", "9 My manager genuinely cares about my wellbeing \n", "10 My manager genuinely cares about my wellbeing \n", "11 My manager genuinely cares about my wellbeing \n", "12 My manager gives me useful feedback on how wel... \n", "13 My manager gives me useful feedback on how wel... \n", "14 My manager gives me useful feedback on how wel... \n", "15 My supervisor gives me useful feedback on how ... \n", "16 If I notice a workplace hazard, I would stop a... \n", "17 If I notice a workplace hazard, I would stop a... \n", "18 If I notice a workplace hazard, I would stop a... \n", "19 If I notice a workplace hazard, I would stop a... \n", "20 We acknowledge people who deliver outstanding ... \n", "21 We acknowledge people who deliver outstanding ... \n", "22 I feel I am part of a team \n", "23 I feel I am part of a team \n", "24 I feel I am part of a team \n", "25 I feel I am part of a team \n", "26 I feel I am part of a team \n", "27 I feel I am part of a team \n", "28 I feel I am part of a team \n", "29 I feel I am part of a team \n", "... ... \n", "8943 My department lead demonstrates a visible comm... \n", "8944 I feel comfortable being myself at work. \n", "8945 My department allocates resources toward the h... \n", "8946 My manager solicits feedback from me about the... \n", "8947 I have confidence in my department lead to eff... \n", "8948 I fit in with my teammates. \n", "8949 My department allocates resources toward the h... \n", "8950 My manager solicits feedback from me about the... \n", "8951 I enjoy my physical workspace. \n", "8952 I can depend on my teammates to deliver qualit... \n", "8953 My team collaborates well with other teams to ... \n", "8954 I clearly understand Slack's vision. \n", "8955 I believe we will act on the results of this s... \n", "8956 I'm encouraged and supported in sharing my ide... \n", "8957 My team welcomes diverse perspectives. \n", "8958 My team collaborates well with other teams to ... \n", "8959 I clearly understand Slack's vision. \n", "8960 I'm encouraged and supported in sharing my ide... \n", "8961 My team collaborates well with other teams to ... \n", "8962 My team collaborates well with other teams to ... \n", "8963 My manager gives me useful feedback on how I'm... \n", "8964 I get actionable feedback from my peers to imp... \n", "8965 I'm encouraged and supported in sharing my ide... \n", "8966 My team collaborates well with other teams to ... \n", "8967 I'm confident in my manager’s ability to do th... \n", "8968 My manager values my perspectives, even when d... \n", "8969 I can depend on my teammates to deliver qualit... \n", "8970 My team collaborates well with other teams to ... \n", "8971 I clearly understand Slack's vision. \n", "8972 I believe we will act on the results of this s... \n", "\n", "[8973 rows x 10 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "action_df.fillna('')\n", "custom_insp_df.fillna('')" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "core_insp_df = pd.read_sql_query('select * from core_insp limit 10', con=engine)" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [], "source": [ "matched_df = pd.read_sql_query('select * from matched limit 20', con=engine)" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [], "source": [ "import json" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "'[{\"id\": \"3d227a78-2002-4ef4-98c6-03347faa866c\", \"name\": \"Swap worst tasks\", \"excerpt\": \"Swap your least favorite tasks and see if they can be improved\", \"description\": \"Create empathy and understanding by \\'working in someone else\\'s shoes\\'. Encourage employees to swap their least favorite tasks for a week. The goal is for each person to identify a better way of getting the task done by the end of the week. People get better visibility into the not-so-pleasant parts of each role. Additionally, fresh eyes can often lead to new ideas.\"}, {\"id\": \"c52aefeb-54c5-46fe-b8c6-9285f89e461e\", \"name\": \"Manager lotteries\", \"excerpt\": \"Connect managers across the business to talk about action\", \"description\": \"Pair managers across the organization so that they can meet over lunch or coffee to discuss opportunities and what actions their teams are taking. One company pairs managers who have similar opportunities (identified from recent employee survey results) so that they can learn from each other and break down organizational boundaries. \"}, {\"id\": \"4873cbf7-44c0-44e2-9702-a7216daab121\", \"name\": \"Reverse interview\", \"excerpt\": \"Making dedicated time and formalizing\", \"description\": \"Try reverse-interviewing to help prospective employees understand the job expectations and ensure the job is a match for them. The prospect interviews the people who would be their colleagues about the job, the culture and anything else they\\'re interested in. The employees being interviewed don\\'t ask questions, but focus on responding to the prospect. These sessions usually last 30-45 minutes.\"}, {\"id\": \"b36cb2af-00ac-43bf-9a54-a83a5cc9fa7c\", \"name\": \"Realistic job preview\", \"excerpt\": \"Aligning expectations of new hires\", \"description\": \"Be intentional about setting expectations for newcomers. One company enhances alignment and establishes trust during the recruiting and onboarding processes through Realistic Job Previews (RJPs). RJPs are communicated through video, sound bites, text and graphics and highlight the positive aspects of the role, organization and values. They provide specific examples of what\\'s expected within the first six months of employment. Of equal importance, the RJP covers the challenging aspects of the role, reasons people left in the past and potential challenges new people may face.\"}, {\"id\": \"4ef79dcb-02b0-494e-a5f4-9ea05cab571b\", \"name\": \"Internal communications channels\", \"excerpt\": \"Using internal communication channels to align on decisions\", \"description\": \"Use internal communications channels to align on decisions. One company uses internal collaboration channels to help team members feel more involved in decision making. Individual channels are created for specific projects and teams to support cross-communication. All key decisions are shared and debated in the relevant channels, ensuring that team members are involved in outcomes that affect day-to-day work activities. Team members are not required to be involved in the conversation, but are welcome to participate if they choose to.\"}, {\"id\": \"207205f9-8a8b-4903-937e-288b80d4dca2\", \"name\": \"Team retrospectives and planning\", \"excerpt\": \"Using team retrospectives and planning to learn\", \"description\": \"Achieve role clarity through team retrospectives and planning. Retrospective and planning meetings can be held with managers and team leads to align and improve processes. The meetings provide an opportunity to reflect on previous goals, work and processes; discuss what worked well and what could be improved and plan, redirect and align efforts to company goals. At the meeting decisions can be made and alignment on key priorities and roles improved.\"}, {\"id\": \"d987ecd5-5e79-4246-aac1-1c3ee4a5f489\", \"name\": \"Courage to share action stories\", \"excerpt\": \"Share what your team is doing to address feedback\", \"description\": \"To maintain momentum, it\\'s essential that the actions taken as a result of survey feedback are visible to employees. At one company, individual and team actions resulting from feedback are shared at regular state-of-the-business all hands meetings by leaders from each team.\"}, {\"id\": \"00e41e9a-d260-49ad-98b5-f70f6c92a258\", \"name\": \"Action update emails\", \"excerpt\": \"Communicate progress addressing feedback\", \"description\": \"Communicate often and regularly about what is being done based on employee feedback. One company set up a monthly email update from each leader in the business. The content and format of the email is driven by the leader with no predefined formats or approaches. Leaders get creative in their communications but always make sure to link back to the original feedback and associated action.\"}, {\"id\": \"58f77db6-d764-4827-9c13-ada9d996f512\", \"name\": \"Shark tank style competition\", \"excerpt\": \"Cross-functional teams pitch award ideas\", \"description\": \"Encourage employees to pitch ideas to executives in a \\\\\"Shark Tank-style\\\\\" competition. At one company cross-fuctional teams were formed to build ideas to pitch to the \\\\\"Sharks\\\\\" - a collection of senior leaders. This program encouraged cross-functional collaboration, healthy competition and recognition of great ideas. Once an idea was selected, resources were made available to implement.\"}, {\"id\": \"ef4cdbe2-439e-43a7-a198-895e32847b30\", \"name\": \"Decision-making framework\", \"excerpt\": \"Include consulting others in your decision-making framework\", \"description\": \"Making consultation part of the formal decision-making process facilitates transparency and clarity. Share who has been consulted on a decision whenever a decision is shared. By highlighting the names of people who were consulted, reviewers of the decision can quickly identify the basis for the decision. This practice is particularly helpful in global organizations. It is not to demonstrate consensus, but to show that decision-makers considered other perspectives and implications.\"}]'" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "core_insp_dict = [\n", " {\n", " 'id': str(row['id']),\n", " 'name': row['name'],\n", " 'excerpt': row['excerpt'],\n", " 'description': row['description']\n", " \n", " }\n", " for i, row in core_insp_df.iterrows()\n", "]\n", "json.dumps(core_insp_dict)" ] }, { "cell_type": "code", "execution_count": 51, "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>created_at</th>\n", " 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6d2bb529-caad-4001-8a40-29eaa9cb0cbd a56d35c9-f274-4fd5-a7ac-681ce775529c \n", "1 45ff1309-cce0-4f9f-a220-40615094a5c8 8bf4dd83-7247-416e-9043-94f621d941f6 \n", "2 4b5533e8-0575-48be-b79d-4d7a5705f283 46051fb5-0547-4f2a-8c70-33387a27d0db \n", "3 291ea717-2ff0-4615-883c-5740c10b4016 8f186a80-7ba4-4c59-95e3-cbeb785fba20 \n", "4 0ec59167-327e-4472-b0df-1fba517bb44c 5a29fcb3-c2a3-40fe-9f69-e89ee2cfd999 \n", "\n", " id2 type1 type2 field1 \\\n", "0 4e1e81a2-9f33-4473-83dc-c6b8e4917a93 action core_insp description \n", "1 67e03ed1-02e2-4b2f-89b0-f607c1236212 action core_insp description \n", "2 931d7762-9fa4-4a3f-85e3-f8bf75f8f903 action core_insp description \n", "3 1ea5dcec-a94d-42df-8104-1f85f43c22d9 action core_insp description \n", "4 c43f038b-c1cf-4204-a2f1-c1e6aa86f587 action core_insp description \n", "\n", " field2 similarity_score valuable_score constructive_score \n", "0 description 0.08 0.994987 0.3 \n", "1 description 0.09 0.994987 0.0 \n", "2 description 0.06 0.994987 0.0 \n", "3 description 0.49 0.994987 0.0 \n", "4 description 0.46 0.994987 0.3 " ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "matched_df.head()" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'[{\"_id\": \"6d2bb529-caad-4001-8a40-29eaa9cb0cbd\", \"id1\": \"a56d35c9-f274-4fd5-a7ac-681ce775529c\", \"id2\": \"4e1e81a2-9f33-4473-83dc-c6b8e4917a93\", \"type1\": \"action\", \"type2\": \"core_insp\", \"field1\": \"description\", \"field2\": \"description\", \"similarity_score\": 0.08, \"valuable_score\": 0.994987468671679, \"constructive_score\": 0.3}, {\"_id\": \"45ff1309-cce0-4f9f-a220-40615094a5c8\", \"id1\": \"8bf4dd83-7247-416e-9043-94f621d941f6\", \"id2\": \"67e03ed1-02e2-4b2f-89b0-f607c1236212\", \"type1\": \"action\", \"type2\": \"core_insp\", \"field1\": \"description\", \"field2\": \"description\", \"similarity_score\": 0.09, \"valuable_score\": 0.994987468671679, \"constructive_score\": 0.0}, {\"_id\": \"4b5533e8-0575-48be-b79d-4d7a5705f283\", \"id1\": \"46051fb5-0547-4f2a-8c70-33387a27d0db\", \"id2\": \"931d7762-9fa4-4a3f-85e3-f8bf75f8f903\", \"type1\": \"action\", \"type2\": \"core_insp\", \"field1\": \"description\", \"field2\": \"description\", \"similarity_score\": 0.06, \"valuable_score\": 0.994987468671679, \"constructive_score\": 0.0}, {\"_id\": \"291ea717-2ff0-4615-883c-5740c10b4016\", \"id1\": \"8f186a80-7ba4-4c59-95e3-cbeb785fba20\", \"id2\": \"1ea5dcec-a94d-42df-8104-1f85f43c22d9\", \"type1\": \"action\", \"type2\": \"core_insp\", \"field1\": \"description\", \"field2\": \"description\", \"similarity_score\": 0.49, \"valuable_score\": 0.994987468671679, \"constructive_score\": 0.0}, {\"_id\": \"0ec59167-327e-4472-b0df-1fba517bb44c\", \"id1\": \"5a29fcb3-c2a3-40fe-9f69-e89ee2cfd999\", \"id2\": \"c43f038b-c1cf-4204-a2f1-c1e6aa86f587\", \"type1\": \"action\", \"type2\": \"core_insp\", \"field1\": \"description\", \"field2\": \"description\", \"similarity_score\": 0.46, \"valuable_score\": 0.994987468671679, \"constructive_score\": 0.3}, {\"_id\": \"bcd400ad-0afd-473e-b627-aa73ed34a3a8\", \"id1\": \"4bd4043c-eff1-4d42-b44f-d2e11cc007f7\", \"id2\": \"1f60e471-1c2b-4ba9-8130-b4c3a0fb0956\", \"type1\": \"action\", \"type2\": \"core_insp\", \"field1\": \"description\", \"field2\": \"description\", \"similarity_score\": 0.12, \"valuable_score\": 0.994987468671679, \"constructive_score\": 0.0}, {\"_id\": \"df22dd30-7f48-40c0-ad71-5c5f9b2b1369\", \"id1\": \"938e3a8d-521b-4973-b239-475cf6f2cf25\", \"id2\": \"065c067a-8127-4290-b9e0-695ef5ebb2b3\", \"type1\": \"action\", \"type2\": \"core_insp\", \"field1\": \"description\", \"field2\": \"description\", \"similarity_score\": 0.1, \"valuable_score\": 0.994987468671679, \"constructive_score\": 0.15}, {\"_id\": \"f31dc6d3-a103-44df-978d-0efdd0ba3418\", \"id1\": \"af9ac9e5-5fc0-41a2-b006-76277fd82ba4\", \"id2\": \"fade6361-3514-45f9-8ad7-9be84dafb609\", \"type1\": \"action\", \"type2\": \"core_insp\", \"field1\": \"description\", \"field2\": \"description\", \"similarity_score\": 0.14, \"valuable_score\": 0.994987468671679, \"constructive_score\": 0.3}, {\"_id\": \"00e482fc-e443-4a55-b942-0f5bc9fa5565\", \"id1\": \"dfca426b-58c6-4351-a35e-25bed3696df4\", \"id2\": \"40b6b2a8-0d03-4212-9969-3751d711a118\", \"type1\": \"action\", \"type2\": \"core_insp\", \"field1\": \"description\", \"field2\": \"description\", \"similarity_score\": 0.08, \"valuable_score\": 0.994987468671679, \"constructive_score\": 0.0}, {\"_id\": \"2e206854-09e3-4ce8-9f52-3a00397c145f\", \"id1\": \"c2d30785-e08f-4ea4-9084-373a1a0233f6\", \"id2\": \"622179d7-b06b-4493-9b17-9f8d5ff5b7b0\", \"type1\": \"action\", \"type2\": \"core_insp\", \"field1\": \"description\", \"field2\": \"description\", \"similarity_score\": 0.1, \"valuable_score\": 0.994987468671679, \"constructive_score\": 0.0}, {\"_id\": \"b34ca77c-7808-4026-823b-14acb3e7b1c2\", \"id1\": \"a961148f-df9e-44b7-8a0e-90a40f2a08ad\", \"id2\": \"6b0d0bd5-ce50-480c-b937-7faab0026b19\", \"type1\": \"action\", \"type2\": \"core_insp\", \"field1\": \"description\", \"field2\": \"description\", \"similarity_score\": 0.09, \"valuable_score\": 0.994987468671679, \"constructive_score\": 0.0}, {\"_id\": \"91616d9c-b5ba-470b-98a5-d3863c21d53f\", \"id1\": \"fd8882ac-998b-412e-96dc-cb5e6f9872f7\", \"id2\": \"5bc61e87-87db-4829-bff5-401802755e0a\", \"type1\": \"action\", \"type2\": \"core_insp\", \"field1\": \"description\", \"field2\": \"description\", \"similarity_score\": 0.08, \"valuable_score\": 0.994987468671679, \"constructive_score\": 0.95}, {\"_id\": \"339983af-958d-45f9-9a6d-0c4f9e32a5ad\", \"id1\": \"f5d5f749-dce3-4e52-a151-9d6fda0d11f8\", \"id2\": \"40b6b2a8-0d03-4212-9969-3751d711a118\", \"type1\": \"action\", \"type2\": \"core_insp\", \"field1\": \"description\", \"field2\": \"description\", \"similarity_score\": 0.08, \"valuable_score\": 0.994987468671679, \"constructive_score\": 0.0}, {\"_id\": \"6edcd790-8aea-41eb-944c-ed5189f86bb3\", \"id1\": \"52627568-895f-49ac-9797-f3aad86e1b6f\", \"id2\": \"3a61033b-429c-42ee-aa32-f51d18dfffce\", \"type1\": \"action\", \"type2\": \"core_insp\", \"field1\": \"description\", \"field2\": \"description\", \"similarity_score\": 0.09, \"valuable_score\": 0.994987468671679, \"constructive_score\": 0.15}, {\"_id\": \"a9259ae4-a4cd-4f82-92ad-bdeebb1d392f\", \"id1\": \"34dcfc05-c3c4-4650-bb9c-aed2b1c3d5b9\", \"id2\": \"6f94343e-e9e5-4e69-a69d-7613e969214f\", \"type1\": \"action\", \"type2\": \"core_insp\", \"field1\": \"description\", \"field2\": \"description\", \"similarity_score\": 0.08, \"valuable_score\": 0.994987468671679, \"constructive_score\": 0.0}, {\"_id\": \"d654c09f-42bf-406b-946c-50c80f055bc7\", \"id1\": \"9299eee9-9008-4baf-bf1b-c1afbf9f6b0f\", \"id2\": \"1f60e471-1c2b-4ba9-8130-b4c3a0fb0956\", \"type1\": \"action\", \"type2\": \"core_insp\", \"field1\": \"description\", \"field2\": \"description\", \"similarity_score\": 0.06, \"valuable_score\": 0.994987468671679, \"constructive_score\": 0.0}, {\"_id\": \"21322ca4-0fbc-482e-bd6d-82251c4d3dd0\", \"id1\": \"f0b6ca31-7841-498e-a115-5574f5a6086f\", \"id2\": \"aef097ea-4280-4fc9-a5dc-447d62f31d55\", \"type1\": \"action\", \"type2\": \"core_insp\", \"field1\": \"description\", \"field2\": \"description\", \"similarity_score\": 0.09, \"valuable_score\": 0.994987468671679, \"constructive_score\": 0.913636}, {\"_id\": \"b01d28f9-0695-476b-ad8f-e610e77b24b4\", \"id1\": \"7937820b-a176-4e15-b0d9-1d582ac29243\", \"id2\": \"ba678913-7756-49d6-9351-40680df96bc6\", \"type1\": \"action\", \"type2\": \"core_insp\", \"field1\": \"description\", \"field2\": \"description\", \"similarity_score\": 0.1, \"valuable_score\": 0.994987468671679, \"constructive_score\": 0.15}, {\"_id\": \"32b1f93a-74e8-4cff-a472-cfc236121c6b\", \"id1\": \"9136fa90-140a-4190-ad77-3e6990d4d699\", \"id2\": \"0826f61d-c88d-4842-b6c2-5ed20fd172d1\", \"type1\": \"action\", \"type2\": \"core_insp\", \"field1\": \"description\", \"field2\": \"description\", \"similarity_score\": 0.07, \"valuable_score\": 0.994987468671679, \"constructive_score\": 0.0}, {\"_id\": \"2def62de-3d19-4943-ade3-c4407e583abd\", \"id1\": \"d93ac66c-5f83-4b47-ba1d-ee930a30ee1d\", \"id2\": \"33b35e97-4627-4e79-92bb-64a7842ed118\", \"type1\": \"action\", \"type2\": \"core_insp\", \"field1\": \"description\", \"field2\": \"description\", \"similarity_score\": 0.07, \"valuable_score\": 0.994987468671679, \"constructive_score\": 0.0}]'" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "matched_dict = [\n", " {\n", " \"_id\": str(row['_id']),\n", " \"id1\": str(row['id1']),\n", " \"id2\": str(row[\"id2\"]),\n", " \"type1\": row[\"type1\"],\n", " \"type2\": row[\"type2\"],\n", " \"field1\": row[\"field1\"],\n", " \"field2\": row[\"field2\"],\n", " \"similarity_score\": row[\"similarity_score\"],\n", " \"valuable_score\": row[\"valuable_score\"],\n", " \"constructive_score\": row[\"constructive_score\"]\n", " }\n", " for i, row in matched_df.iterrows()\n", "]\n", "json.dumps(matched_dict)" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(UUID('a56d35c9-f274-4fd5-a7ac-681ce775529c'),\n", " UUID('8bf4dd83-7247-416e-9043-94f621d941f6'),\n", " UUID('46051fb5-0547-4f2a-8c70-33387a27d0db'),\n", " UUID('8f186a80-7ba4-4c59-95e3-cbeb785fba20'),\n", " UUID('5a29fcb3-c2a3-40fe-9f69-e89ee2cfd999'),\n", " UUID('4bd4043c-eff1-4d42-b44f-d2e11cc007f7'),\n", " UUID('938e3a8d-521b-4973-b239-475cf6f2cf25'),\n", " UUID('af9ac9e5-5fc0-41a2-b006-76277fd82ba4'),\n", " UUID('dfca426b-58c6-4351-a35e-25bed3696df4'),\n", " UUID('c2d30785-e08f-4ea4-9084-373a1a0233f6'),\n", " UUID('a961148f-df9e-44b7-8a0e-90a40f2a08ad'),\n", " UUID('fd8882ac-998b-412e-96dc-cb5e6f9872f7'),\n", " UUID('f5d5f749-dce3-4e52-a151-9d6fda0d11f8'),\n", " UUID('52627568-895f-49ac-9797-f3aad86e1b6f'),\n", " UUID('34dcfc05-c3c4-4650-bb9c-aed2b1c3d5b9'),\n", " UUID('9299eee9-9008-4baf-bf1b-c1afbf9f6b0f'),\n", " UUID('f0b6ca31-7841-498e-a115-5574f5a6086f'),\n", " UUID('7937820b-a176-4e15-b0d9-1d582ac29243'),\n", " UUID('9136fa90-140a-4190-ad77-3e6990d4d699'),\n", " UUID('d93ac66c-5f83-4b47-ba1d-ee930a30ee1d'))" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tuple(matched_df['id1'])" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [], "source": [ "id1_tuple = tuple(str(row['id1']) for i, row in matched_df.iterrows())\n", "id2_tuple = tuple(str(row['id2']) for i, row in matched_df.iterrows())" ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "('4e1e81a2-9f33-4473-83dc-c6b8e4917a93',\n", " '67e03ed1-02e2-4b2f-89b0-f607c1236212',\n", " '931d7762-9fa4-4a3f-85e3-f8bf75f8f903',\n", " '1ea5dcec-a94d-42df-8104-1f85f43c22d9',\n", " 'c43f038b-c1cf-4204-a2f1-c1e6aa86f587',\n", " '1f60e471-1c2b-4ba9-8130-b4c3a0fb0956',\n", " '065c067a-8127-4290-b9e0-695ef5ebb2b3',\n", " 'fade6361-3514-45f9-8ad7-9be84dafb609',\n", " '40b6b2a8-0d03-4212-9969-3751d711a118',\n", " '622179d7-b06b-4493-9b17-9f8d5ff5b7b0',\n", " '6b0d0bd5-ce50-480c-b937-7faab0026b19',\n", " '5bc61e87-87db-4829-bff5-401802755e0a',\n", " '40b6b2a8-0d03-4212-9969-3751d711a118',\n", " '3a61033b-429c-42ee-aa32-f51d18dfffce',\n", " '6f94343e-e9e5-4e69-a69d-7613e969214f',\n", " '1f60e471-1c2b-4ba9-8130-b4c3a0fb0956',\n", " 'aef097ea-4280-4fc9-a5dc-447d62f31d55',\n", " 'ba678913-7756-49d6-9351-40680df96bc6',\n", " '0826f61d-c88d-4842-b6c2-5ed20fd172d1',\n", " '33b35e97-4627-4e79-92bb-64a7842ed118')" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" } ], "source": [ "id2_tuple" ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [], "source": [ "core_insp_df = pd.read_sql_query(f\"select * from core_insp where id in {id2_tuple}\", con=engine)" ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'[{\"id\": \"fade6361-3514-45f9-8ad7-9be84dafb609\", \"name\": \"Expanded career conversations\", \"excerpt\": \"Hold mid-year career conversations\", \"description\": \"Hold more regular career-focused conversations with employees. One approach is to hold mid-year career conversations that focus solely on development and career planning. These conversations are led by the employee so that they are encouraged to take ownership of their development. They are provided with a simple template to complete as a guide for the conversation. The employees are encouraged to include a discussion of their personal and job-related goals so the manager can give recommendations for individual skills development opportunities.\"}, {\"id\": \"4e1e81a2-9f33-4473-83dc-c6b8e4917a93\", \"name\": \"Anonymous questions\", \"excerpt\": \"Create anonymous comms channel for employees \", \"description\": \"Make space for feedback and questions from all employees. To facilitate questions at all hands meetings, one company created an anonymous communications channel (using its internal comms software). Employees ask questions anonymously prior to all hands meetings. The questions are then answered by leadership at these meetings. Employees feel that their voices are heard and they can express their opinions without connecting their name to the question. This could also be done at a team level prior to team meetings.\"}, {\"id\": \"622179d7-b06b-4493-9b17-9f8d5ff5b7b0\", \"name\": \"Skip level meetings\", \"excerpt\": \"Implement skip level meetings around decision making\", \"description\": \"Foster open communication and alignment using skip-level meetings. Encourage people leaders who manage other people leaders to implement regular skip-level meetings. A skip level meeting occurs between a people leader and someone (or a group) two levels below them - without the people leader who those people report to attending - thereby skipping a layer of management. The people leader asks open questions to get an insight into how the people are going and can answer questions about the mission and objectives as well as how decisions are made. This fosters an open environment where employees feel included in important decisions. \"}, {\"id\": \"6b0d0bd5-ce50-480c-b937-7faab0026b19\", \"name\": \"Seat shuffle\", \"excerpt\": \"Get to know others in the business\", \"description\": \"Encouraging employees to get to know others outside their team, the seat shuffle moves employees around the office and introduces them to other people in the organization. One company shuffles seats every three months. Not all teams are required to participate and some teams that need to work closely in person each day prefer to keep the shuffle within the team.\"}, {\"id\": \"0826f61d-c88d-4842-b6c2-5ed20fd172d1\", \"name\": \"Who\\'s who in the zoo\", \"excerpt\": \"Keeping employees connected\", \"description\": \"Growing quickly can result in newcomers finding it difficult to feel part of a team. To help people get to know each other, set-up a \\'who\\'s who in the zoo\\' program. A quiz and competitions in each region with questions such as, \\'If you make a change to the pricing, who do you need to involve?\\' and \\'Which team do you go to for advice on contracts?\\' provide fun ways to get to know people and procedures. The quiz also includes more general questions such as, \\'Where is the all staff directory\\', and fun facts like \\'Which team member has nine brothers?\\' and \\'Which customer coach was a finalist in American Idol?\\'.\"}, {\"id\": \"33b35e97-4627-4e79-92bb-64a7842ed118\", \"name\": \"Product advisory group\", \"excerpt\": \"Cross-team oversight to get a fresh perspective\", \"description\": \"Enable cross-functional input into product development through a Product Advisory Board. At one organization a representative from each team has a place on the Product Advisory Board. The board meets once a week with the product manager and designer to discuss the evolution of the product, provide feedback on key decision points and help develop internal and external content. A similar group could be implemented at the team level with collaboration from other teams. Another lightweight alternative would be to invite members from other teams to your team planning meeting to get a fresh perspective.\"}, {\"id\": \"aef097ea-4280-4fc9-a5dc-447d62f31d55\", \"name\": \"Project FAQs\", \"excerpt\": \"Answer and archive cross-department FAQs\", \"description\": \"Create living FAQs for each project and ask employees outside of the team what they want to know. One company uses this approach to ensure all relevant information is easily accessible. The project owner answers questions in the living document as they\\'re asked, keeping the document up-to-date.\"}, {\"id\": \"3a61033b-429c-42ee-aa32-f51d18dfffce\", \"name\": \"Learning passport\", \"excerpt\": \"Fully reimbursed self development programs\", \"description\": \"Give people the opportunity to develop skills relevant to their work. One organization has a learning passport program. Each person receives up to $1000 each year towards courses to help them learn and grow. Their direct manager is responsible for signing off on the course and then the employee receives full reimbursement. They are creating a database of learning opportunities funded by the organization to help generate ideas for other employees and to highlight in employer branding. This organization also plans to connect the training to a Learning Management System. Team Level: Implement skill-sharing sessions within your department. First, develop a skills inventory where individuals can document what topics they are willing to teach. Second, plan brown-bag style lunches so employees can learn new skills from each other.\"}, {\"id\": \"ba678913-7756-49d6-9351-40680df96bc6\", \"name\": \"Adopt a plant\", \"excerpt\": \"Foster team commitment by keeping a plant alive\", \"description\": \"Foster team commitment and engagement. One company encourages teammates across each pod of desks to choose a plant to purchase. They are encouraged to do research based on how much light and water is needed - and also to name their plant creatively. Keeping the plant alive is a team activity and they also decorate the plants during festive holidays.\"}, {\"id\": \"1ea5dcec-a94d-42df-8104-1f85f43c22d9\", \"name\": \"Personalized recognition\", \"excerpt\": \"Ask your employees about how they prefer to be recognized\", \"description\": \"Get to know how each employee prefers to receive recognition, and then document and store these preferences for when an employee is to be recognized. This includes understanding how they like to receive recognition (e.g. in public or one-on-one) and how they like to be rewarded (e.g. vacation days, bonus, dinner, flowers, gifts). This information is stored across the company, so as employees move teams, their recognition preferences go with them.\"}, {\"id\": \"6f94343e-e9e5-4e69-a69d-7613e969214f\", \"name\": \"Termination explanations\", \"excerpt\": \"Giving employees more information about why a termination occurred\", \"description\": \"Provide as much detail as possible to people at your organization on why a termination has occurred to avoid rumors and encourage the right behaviors. When an organization has to let someone go because they are not delivering, the traditional approach is to provide relatively generic communications to the team (for example, \\\\\"Joe found another opportunity and we wish them all the best.\\\\\"). Instead, consider using clearer communications which include more detail about the reason for someone\\'s departure. Communicating a reason for termination, including underperformance, can help employees understand that they are working in an organization that sets high standards.\"}, {\"id\": \"931d7762-9fa4-4a3f-85e3-f8bf75f8f903\", \"name\": \"Celebrate New Learning\", \"excerpt\": \"Acknowledge team members who share lessons learned\", \"description\": \"Acknowledge team members who share the most lessons learned (successes and failures) from trying new ways of working. Strive to do this on a monthly basis in your weekly stand-up meetings.\"}, {\"id\": \"1f60e471-1c2b-4ba9-8130-b4c3a0fb0956\", \"name\": \"Project Retrospectives\", \"excerpt\": \"Conduct team retrospective sessions\", \"description\": \"Conduct post-mortems on projects with your team. Implement a practice wherein after each project is launched and/or completed, a discussion is held to reflect on what could have been done better, and what opportunities have risen to learn more. Where appropriate, involve your team members to own and run the retrospective discussion.\"}, {\"id\": \"67e03ed1-02e2-4b2f-89b0-f607c1236212\", \"name\": \"Lightning presentations\", \"excerpt\": \"Present on personal interests to get to know each other\", \"description\": \"Give your employees a formal opportunity in team meetings to share something important to them from outside of work. Start one team meeting monthly with a lightning presentation. People self-nominate to briefly present on something they care about or are interested in outside work. This can help people identify common interests and helps managers demonstrate an interest in getting to know people.\"}, {\"id\": \"c43f038b-c1cf-4204-a2f1-c1e6aa86f587\", \"name\": \"Support requests in meetings\", \"excerpt\": \"Facilitate giving and receiving support\", \"description\": \"Give employees an easy way to highlight a need for support. One team has a space in their meeting notes for individuals to request support. Support includes sharing workload, sharing expertise, or simply making internal referrals. Not only does this help employees manage their workload, it also increases visibility on what team members are focused on.\"}, {\"id\": \"40b6b2a8-0d03-4212-9969-3751d711a118\", \"name\": \"Weekly weigh-ups\", \"excerpt\": \"Hold team check-ins weekly\", \"description\": \"Hold team check-ins weekly to discuss workloads. Often team members are not aware of each other\\'s workloads, making it hard to know who needs help and how to help them. One company makes managing workloads a shared challenge by having a formalized check-in each week to share what people are working on. When team members are more aware of other\\'s work and challenges, it\\'s easier to offer to jump in and help.\"}, {\"id\": \"065c067a-8127-4290-b9e0-695ef5ebb2b3\", \"name\": \"Cross-training\", \"excerpt\": \"Train employees across positions to ensure adequate coverage\", \"description\": \"Train employees across positions to ensure adequate coverage. One organization uses a cross-training program to ensure every role has adequate coverage if an employee leaves the organization or just needs to take time off. An inventory of high risk roles and associated tasks is created and people are given the opportunity to identify tasks they are most interested in learning. The training typically starts as shadowing and culminates in a small project to make sure the person can successfully perform the task. Team Level: Within your team you can initiate cross-training by encouraging team members to shadow each other, particularly when one individual is starting a new project. You can also incorporate cross-training opportunities into your one-on-one meetings to help achieve team members\\' developmental goals.\"}, {\"id\": \"5bc61e87-87db-4829-bff5-401802755e0a\", \"name\": \"Rewarding V2.0\", \"excerpt\": \"Setup an employee committee to find creative ways to reward top performance\", \"description\": \"Find new and creative ways to reward top performance at your company that fits your culture and employee interests. The more aligned rewards are with employee interests the more successful the reward program will be. One company set up a \\\\\"rewards committee\\\\\" made up of interested employees across functions to evaluate available rewards. Some companies reward employees with adventurous experiences, some by donating to an employee\\'s selected social cause, or providing work time to give back to community, and even (extra) work time for personal development. The most important thing is having employees involved in the selection process to ensure alignment of possible rewards that will fit with employee interests.\"}]'" ] }, "execution_count": 69, "metadata": {}, "output_type": "execute_result" } ], "source": [ "core_insp_dict = [\n", " {\n", " 'id': str(row['id']),\n", " 'name': row['name'],\n", " 'excerpt': row['excerpt'],\n", " 'description': row['description']\n", " \n", " }\n", " for i, row in core_insp_df.iterrows()\n", "]\n", "json.dumps(core_insp_dict)" ] }, { "cell_type": "code", "execution_count": 76, "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>created_at</th>\n", " <th>updated_at</th>\n", " <th>id</th>\n", " <th>name</th>\n", " <th>description</th>\n", " <th>survey_id</th>\n", " <th>src_insp_id</th>\n", " <th>status</th>\n", " <th>account_name</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>2018-01-15 23:19:12.643674</td>\n", " <td>2018-01-15 23:40:01.138296</td>\n", " <td>f0b6ca31-7841-498e-a115-5574f5a6086f</td>\n", " <td>Career Interest</td>\n", " <td>As part of Talent review and Mid-Year discussi...</td>\n", " <td>59dd1b172c86121ff72c1a32</td>\n", " <td>d983d168-6e34-4de8-9359-3e6edece4709</td>\n", " <td>None</td>\n", " <td>nbnco</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2018-01-22 04:43:06.121770</td>\n", " <td>2018-01-23 02:28:36.640499</td>\n", " <td>af9ac9e5-5fc0-41a2-b006-76277fd82ba4</td>\n", " <td>Development Conversation</td>\n", " <td>Hold regular development-focused conversations...</td>\n", " <td>59dd1b172c86121ff72c1a32</td>\n", " <td>1c1f6117-99fb-4d00-adbd-2e78a6abb9bd</td>\n", " <td>None</td>\n", " <td>nbnco</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2018-03-01 20:56:40.563236</td>\n", " <td>2018-03-01 20:56:40.579238</td>\n", " <td>52627568-895f-49ac-9797-f3aad86e1b6f</td>\n", " <td>Health Management Program Development Team</td>\n", " <td>- Continue to include skills development oppor...</td>\n", " <td>59b14bf6e94dd938236ed344</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>healthnowny</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>2018-01-16 21:48:37.866753</td>\n", " <td>2018-01-16 22:00:39.361434</td>\n", " <td>f5d5f749-dce3-4e52-a151-9d6fda0d11f8</td>\n", " <td>2017 - Consult with Women of PSAV Group</td>\n", " <td>Communicate out to the IT department that ther...</td>\n", " <td>59b95a208672356c5f239b0e</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>psav</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>2018-02-22 05:47:46.025119</td>\n", " <td>2018-02-22 05:47:46.031484</td>\n", " <td>8bf4dd83-7247-416e-9043-94f621d941f6</td>\n", " <td>Demonstrate the Importance of People</td>\n", " <td>1. 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This will ensure it is visible and create visibility for who is able to assist. With your employee, have a conversation about strategies that might help move closer to this career goal and support actions that build relevant critical experiences. Connect them to others who can assist them including more senior leaders, networks and relevant teams.\\\\\\\\n\\\\\\\\nAction on team to fill in Talant in workday. Ian to actively review and Talent for team.\", \"survey_id\": \"59dd1b172c86121ff72c1a32\", \"src_insp_id\": \"d983d168-6e34-4de8-9359-3e6edece4709\", \"account_name\": \"nbnco\"}, {\"id\": \"af9ac9e5-5fc0-41a2-b006-76277fd82ba4\", \"name\": \"Development Conversation\", \"description\": \"Hold regular development-focused conversations with all your employees in your team. With care, find time to provide regular feedback to your people \\\\u2013 providing behavioural observations, impact of those behaviours and suggestions for changes. Together the Leader and Employee can discuss their personal and job-related goals and agree on individual skills development opportunities. This may include everything from formal learning opportunities to secondments to filling in for someone on leave. The key is open and regular conversations with all your people. Information on the Leadership portfolio of programs avilable on the Hub. Additional on-line resources available through MIndtools and Lynda.com.\\\\\\\\n\\\\\\\\n\\\\\\\\n1. Get back into 3 questions:\\\\\\\\nWhat are you trying to achieve and why?\\\\\\\\nWhat is the current state?\\\\\\\\nWhat is the next steps?\", \"survey_id\": \"59dd1b172c86121ff72c1a32\", \"src_insp_id\": \"1c1f6117-99fb-4d00-adbd-2e78a6abb9bd\", \"account_name\": \"nbnco\"}, {\"id\": \"52627568-895f-49ac-9797-f3aad86e1b6f\", \"name\": \"Health Management Program Development Team\", \"description\": \"- Continue to include skills development opportunities within 1:1 discussions \\\\\\\\n- Include individual plans related to skills development in Quarterly Check-in discussions & documentation \\\\\\\\n- Review of HealthNow learning opportunities regularly (HN University) \\\\\\\\n- Distribution of continuing education / CEU opportunities as identified \\\\\\\\n- Consideration for staff attendance at national conferences for topics relevant to program development, corporate initiatives, and industry best practice, with reporting back to HN teams and action plan developed \\\\\\\\n- Submit major accomplishments as iConnect article topic suggestions \\\\\\\\n- Team building activity/opportunities, coordinated by team member volunteer(s) \\\\\\\\n- Manager will increase distribution of Tipper points as appropriate\", \"survey_id\": \"59b14bf6e94dd938236ed344\", \"src_insp_id\": \"None\", \"account_name\": \"healthnowny\"}, {\"id\": \"f5d5f749-dce3-4e52-a151-9d6fda0d11f8\", \"name\": \"2017 - Consult with Women of PSAV Group\", \"description\": \"Communicate out to the IT department that there is going to be a survey taking place to get more info on women\\'s perspectives in the workplace \\\\\\\\nContact Women of PSAV Group and gather data on members\\' perspectives around open and honest two-way communication - what does this mean to them, is it working, etc. \\\\\\\\nGather information from the survey and determine next steps\\\\\\\\n\\\\\\\\n\\\\\\\\n\\\\\\\\nWho is taking action? Core IT leadership team \\\\\\\\nBy when? last week February 2018\\\\\\\\nHow will they be held accountable? As a team, hold ourselves accountable - Cathie will check-in end of February, then in 6 months do a progress check\", \"survey_id\": \"59b95a208672356c5f239b0e\", \"src_insp_id\": \"None\", \"account_name\": \"psav\"}, {\"id\": \"8bf4dd83-7247-416e-9043-94f621d941f6\", \"name\": \"Demonstrate the Importance of People\", \"description\": \"1. Action: Identify potential candidate for a rotation and start the discussions and make it happen - SA (2x process and 1X Mech) and possibly Chile or Aus -look to simplify the process.\\\\\\\\n\\\\\\\\n2. Action - regular and informal feedback from leaders - not just paperwork but informal - performance plans - just done procedural purposes - aspirations not used as it should be used - \\\\\\\\n\\\\\\\\n3. Action: Monthly Management Meeting - what is going on - what is in the forecast - what opportunities for the team/members relating to development and interests. (leaders keep me informed)\\\\\\\\n\\\\\\\\nGeneral comments\\\\\\\\n- long site periods and low site uplifts - long hours - missing important personal days that is just as important - look at other rewards and consultations\\\\\\\\n- communicating the benefits of doing site work - other than monetary reasons\\\\\\\\n- flight times - looking at ways of managing flights outside work time - looking at better ways of agreeing these challenges\", \"survey_id\": \"59b5d55950dd771453d8b9d6\", \"src_insp_id\": \"None\", \"account_name\": \"cimic\"}, {\"id\": \"34dcfc05-c3c4-4650-bb9c-aed2b1c3d5b9\", \"name\": \"Monthly NPS Survey Highlights by Location\", \"description\": \"Our purpose here is to communicate to our entire regional team the successes we are having at each location with our Medallia scores. We would enlist the assistance of each location DET to submit their best customer surveys each month. Natalie and I will select the final ones we want to highlight each month and distribute to the entire regional team. We will also include a MTD ranking with each communication to drive some friendly competition. It also gives us an opportunity to recognize our employees mentioned by name in another way in front of the entire region.\\\\\\\\n\\\\\\\\nMonthly newsletter started in Q2\", \"survey_id\": \"59b95a208672356c5f239b0e\", \"src_insp_id\": \"e0d33e54-6b68-4cde-b34c-72b70bcc4aa6\", \"account_name\": \"psav\"}, {\"id\": \"7937820b-a176-4e15-b0d9-1d582ac29243\", \"name\": \"Adopt a plan\", \"description\": \"Foster team commitment and engagement. Encourage team members to do research based on demographics. \\\\\\\\n\\\\\\\\nI am always researching tools for my team and the rest of the Account Managers to assist them in better understand their accounts. In 2017 I forwarded to the team the Guidestar website for them to take a deep dive and look at how their individual accounts are trending based on their strategic goals. In doing so, they can look for opportunities and find solutions to help meet their plans.\", \"survey_id\": \"59b95a208672356c5f239b0e\", \"src_insp_id\": \"ba678913-7756-49d6-9351-40680df96bc6\", \"account_name\": \"psav\"}, {\"id\": \"dfca426b-58c6-4351-a35e-25bed3696df4\", \"name\": \"IAN Action plan\", \"description\": \"Based on a break out session the team concluded that the team spirit should be enhanced by:\\\\\\\\n- Organising a team building day\\\\\\\\n- Starting a IA NL Group app to share information to get to know each other better\\\\\\\\n- A Management drives session facilitated by HR to know about each others prefered drives for performing\\\\\\\\n- Intervision to solve work related barriers to be effective and efficient\\\\\\\\n\\\\\\\\nFurthermore each team member has the responsibility to organise their work better by:\\\\\\\\n- Managing exceeding deadlines in a \\\\u2018week start\\\\u2019 meeting\\\\\\\\n- Each auditor organizes a single point of contact in the business to help them with documentation etc. to conduct the audit\\\\\\\\n\\\\\\\\nSee for more information: the document \\'Employee Engagement Survey IA NL\\'\\\\\\\\n\", \"survey_id\": \"59ad26a71124a24e0cad8ed3\", \"src_insp_id\": \"None\", \"account_name\": \"aegon\"}, {\"id\": \"d93ac66c-5f83-4b47-ba1d-ee930a30ee1d\", \"name\": \"Lessons Learned\", \"description\": \"During the Lead/Manager meeting a designated Lead will gather and discuss results of (i, ii, iii) and summarize for the leadership of IA. When necessary, these results will also be shared with the greater team during the monthly Internal Audit team meeting. We will rotate this monthly responsibility among the Leads.\\\\\\\\n i.\\\\\\\\t Constructive feedback from the customer survey after each audit.\\\\\\\\n ii.\\\\\\\\t Areas of improvement identified in the quality assurance review for each audit .\\\\\\\\n iii. Lessons learned from each audit.\\\\\\\\n\", \"survey_id\": \"59ad26a71124a24e0cad8ed3\", \"src_insp_id\": \"None\", \"account_name\": \"aegon\"}, {\"id\": \"4bd4043c-eff1-4d42-b44f-d2e11cc007f7\", \"name\": \"PSAV - Communication/Meeting Cadence\", \"description\": \"Take the time to ask your team members about what could be done differently/better. Genuinely listen to what they tell you and implement a few of the best ideas.\\\\\\\\nThe PGE Leadership team will establish a regular meeting cadence to share and exchange ideas, improve communication, and direct business discussion and decisions as needed. (The leadership team is defined as VP\\'s and Director level leaders)\\\\\\\\nThe leadership team will meet in person once per quarter, and the Director leadership team will join the established Monday call, once per month. (last Monday of each month)\\\\\\\\nAll PGE Leaders will conduct one-on-one meetings with each team member to set expectations, check on progress and check-in to find out how they are doing. These meeting will be conducted monthly.\", \"survey_id\": \"59b95a208672356c5f239b0e\", \"src_insp_id\": \"f499fbe1-418f-4785-9b36-19e5179afd89\", \"account_name\": \"psav\"}, {\"id\": \"938e3a8d-521b-4973-b239-475cf6f2cf25\", \"name\": \"Restructures\", \"description\": \"Managing a restructure well is a real test of leadership. Restructures must be handled with upmost care and planning. Planning which strongly relates to your strategy and operating model. Strong communication and change management is essential prior, during and post any change to not only those impacted but your whole team and across teams. Reach out to your Leader and HRBPs for guidance and support ahead of your next workforce changes to ensure our nbn frameworks are being leveraged for consistency of approach and employee experience. Also please attend change management training as part of our Learning and Development offers. Organisational needs or roles may change but how we treat our people through restructures will often be what you are remembered for. \", \"survey_id\": \"59dd1b172c86121ff72c1a32\", \"src_insp_id\": \"6167d0c4-63c7-497b-9de6-10c6e771168b\", \"account_name\": \"nbnco\"}, {\"id\": \"5a29fcb3-c2a3-40fe-9f69-e89ee2cfd999\", \"name\": \"2017 -Team Oriented - Skip Level Meetings\", \"description\": \"Give employees an easy way to highlight a need for support. One team has a space in their meeting notes for individuals to request support. Support includes sharing workload, sharing expertise, or simply making internal referrals. Not only does this help employees manage their workload, it also increases visibility on what team members are focused on.\\\\\\\\n\\\\\\\\nShare results of successes and areas of focus from each venue during skip level meetings, help others to understand different challenges throughout Region. Share not only each venue, but other Areas feedback (ex. Cleveland, Michigan, etc.)\\\\\\\\n\\\\\\\\nAction will be completed by RVP / Area Manager / Team Leader\\\\\\\\nThese will be held in August. One in Columbus to include all of Ohio / Kentucky the other in Michigan for all team members without DET\\'s present.\", \"survey_id\": \"59b95a208672356c5f239b0e\", \"src_insp_id\": \"c43f038b-c1cf-4204-a2f1-c1e6aa86f587\", \"account_name\": \"psav\"}, {\"id\": \"46051fb5-0547-4f2a-8c70-33387a27d0db\", \"name\": \"Improving Equipment Dependability\", \"description\": \"IT is in the process of replacing/updating the PC kiosks currently on the floor with new hardware at 4 end cap positions. Player\\'s Club management is appointing a TSA in our department to be responsible for checking all equipment twice a day, this includes kiosks, desk tops, pin pads, scanners, boca machines and radios. A spread sheet will be created in order to track equipment failures, the TSA will be empowered to contact the proper department (IT or Security for radios), completing a work order as soon as a problem is detected. The TSA will then track the progress of the issue and note when resolved, following up with Management for assistance if needed. By designating a team member to this task, we will be creating greater engagement from team members. This will also improve the \\\\\"wait time\\\\\" for repairs, therefore improving team members experience. \\\\\\\\nOur goal is to have all equipment working properly at all times to insure our team members are able to preform to the best of their ability.\", \"survey_id\": \"58211baddb4d0863f100004b\", \"src_insp_id\": \"None\", \"account_name\": \"stofgaming\"}, {\"id\": \"c2d30785-e08f-4ea4-9084-373a1a0233f6\", \"name\": \"2017-Individual Culture Conversations\", \"description\": \"This also applies to \\\\\"proud to work for PSAV\\\\\" and \\\\\"I rarely think about working elsewhere.\\\\\" These items could not be identified as tasks for taking action.\\\\\\\\n\\\\\\\\nRVP to conduct one-on-one meetings with each manager during the annual performance review about driving the PSAV culture, and to encourage each manager to be an ambassador of PSAV. Instill a sense of pride by recognizing the contributions that each of team member is making to the company, and emphasizing their place in the overall business community. Each manager must cascade the conversation down to their direct reports. These conversations will be completed by the end of the annual review process, and will be recorded in the annual reviews.\\\\\\\\n\\\\\\\\n8.1 Update - RVP had one-on-one conversations with each manager during the annual review process to enforce the Culture Conversations. In addition, each monthly team meeting / call includes a segment on company culture.\", \"survey_id\": \"59b95a208672356c5f239b0e\", \"src_insp_id\": \"None\", \"account_name\": \"psav\"}, {\"id\": \"a961148f-df9e-44b7-8a0e-90a40f2a08ad\", \"name\": \"2017 - Transparent team discussions\", \"description\": \"At next town hall, have an open and transparent conversation about whether or not people feel safe to speak up at PSAV - invite comments and conversation around this topic.\\\\\\\\n\\\\\\\\nIn smaller group meetings, model this behavior by being open to others speaking up and sharing his/her thoughts. Reward your team members for good ideas that get shared forward - celebrate the wins!\\\\\\\\n\\\\\\\\n\\\\\\\\nWho is taking action? Cathie will take care of town hall, then all other leaders will be responsible for modeling this moving forward\\\\\\\\nBy when? Town hall in February (tbd)\\\\\\\\nHow will they be held accountable? Survey IT team in 6 months to progress check \\\\\\\\nSanti will also work to find us a Feedback class\", \"survey_id\": \"59b95a208672356c5f239b0e\", \"src_insp_id\": \"None\", \"account_name\": \"psav\"}, {\"id\": \"8f186a80-7ba4-4c59-95e3-cbeb785fba20\", \"name\": \"Personalized recognition\", \"description\": \"Get to know how each employee prefers to receive recognition, and then document and store these preferences for when an employee is to be recognized. This includes understanding how they like to receive recognition (e.g. in public or one-on-one) and how they like to be rewarded (e.g. vacation days, bonus, dinner, flowers, gifts). This information is stored across the company, so as employees move teams, their recognition preferences go with them.\\\\\\\\n\\\\\\\\nTactic:\\\\\\\\n\\\\\\\\n- each manager/supervisor will discuss the employee engagement results with their staff\\\\\\\\n- focus on the items we have selected for our focus for 2018\\\\\\\\n- in the staff meeting, discuss this topic and ask staff to write down how they prefer to be recognized\\\\\\\\n- we will keep an inventory of just the suggestions and determine our next steps towards improving our teams\\' performance in this category -- names are optional\", \"survey_id\": \"59b14bf6e94dd938236ed344\", \"src_insp_id\": \"1ea5dcec-a94d-42df-8104-1f85f43c22d9\", \"account_name\": \"healthnowny\"}, {\"id\": \"9136fa90-140a-4190-ad77-3e6990d4d699\", \"name\": \"PSAV - 2017: Innovation\", \"description\": \"Local management will do a better job on passing down information on new technology that the company is deploying or considering deploying. Our staff was very vocal in the survey that they don\\'t feel we invest in innovating technology to stay ahead of the competition. More transparency on the company\\'s technology path is key here. We will also make initiatives to hear the ideas that are being discussed in this region so we can then pass innovation ideas back up to corporate.\", \"survey_id\": \"59b95a208672356c5f239b0e\", \"src_insp_id\": \"None\", \"account_name\": \"psav\"}, {\"id\": \"a56d35c9-f274-4fd5-a7ac-681ce775529c\", \"name\": \"Open communication\", \"description\": \"Leaders encourage their team members to ask the tough or controversial questions (either privately or in meetings) and share responses transparently at team meetings. Encourage difference of thought openly.\\\\\\\\n\\\\\\\\nleadership - work on how we model/manage behaviors in the - call people on behavours without it being taking badly - plan around this work already on way\\\\\\\\n- what is the action as a team - leaders to call it up and then to action a plan to resolve this behaviour\\\\\\\\n- international offices - happens more frequently - \\\\\\\\nimportantant piece of work for stat as its hurting bottom line\\\\\\\\nhow we mange well - handover to projects - behavioural piece\\\\\\\\n\\\\\\\\nDescribe the action - calling it / coaching /ownership of the action - good coaching - admit we wrong - leadership support - fear of having to own something and run with it and be accountable for it.\\\\\\\\n\\\\\\\\nPeople feel safe to call people on behaviors - build trust that something will be done about it when it happens. Encourage to do the right thin\\\\\\\\n\\\\\\\\ncompany wide \\\\\\\\n\\\\\\\\n\\\\\\\\n\", \"survey_id\": \"59b5d55950dd771453d8b9d6\", \"src_insp_id\": \"77e80d6f-b652-4283-a794-d9eec4da4dae\", \"account_name\": \"cimic\"}, {\"id\": \"fd8882ac-998b-412e-96dc-cb5e6f9872f7\", \"name\": \"Seek a road map that clearly outlines comparitives\", \"description\": \"Provide access to a template to compare and relate too with colleagues. Encourage comparisons of income by staff in similar roles. Develop transparent budgeting in relation to KPI\\'s, salary, and recognised skill and time active in posistion. \\\\\\\\nEncourage staying on, to achieve the best with extra benefits to kiwi saver,holiday allowance \\\\\\\\nDevelop best earning practise by showcasing successful internal company skill and income .Give examples of how high achievers started, how long it took, and what made them a high income source to value. Belief is one thing and understanding your current income and potential income is fair unbiased and not arbituary are positive motivators .\\\\\\\\n Failure is part of successfully improving income potential.\", \"survey_id\": \"59ffde1d1124a245d85fb42e\", \"src_insp_id\": \"None\", \"account_name\": \"mediaworks\"}]'" ] }, "execution_count": 80, "metadata": {}, "output_type": "execute_result" } ], "source": [ "action_dict = [\n", " {\n", " \"id\": str(row[\"id\"]),\n", " \"name\": row[\"name\"],\n", " \"description\": row[\"description\"],\n", " \"survey_id\": row[\"survey_id\"],\n", " \"src_insp_id\": str(row[\"src_insp_id\"]),\n", " \"account_name\": row[\"account_name\"]\n", " }\n", " for i, row in action_df.iterrows()\n", "]\n", "json.dumps(action_dict)" ] }, { "cell_type": "code", "execution_count": 87, "metadata": {}, "outputs": [], "source": [ "question_df = pd.read_sql_query(\"select * from question\", con=engine)\n", "ref_question_df = pd.read_sql_query(\"select * from ref_question\", con=engine)" ] }, { "cell_type": "code", "execution_count": 91, "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>created_at</th>\n", " <th>updated_at</th>\n", " <th>code</th>\n", " <th>description</th>\n", " <th>factor</th>\n", " <th>survey_type</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>All-Goal-3</td>\n", " <td>At least one of my career development goals al...</td>\n", " <td>Goal Alignment</td>\n", " <td>The Alliance Diagnostic</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>Int-Over-2</td>\n", " <td>I would highly recommend an internship at %ACC...</td>\n", " <td>Overall</td>\n", " <td>Intern Survey</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>Ben-Expl-1</td>\n", " <td>The availability of 401k matching is a potenti...</td>\n", " <td>Exploring 401k Matching</td>\n", " <td>Benefits Survey</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>Eng.5</td>\n", " <td>I see myself still working at %ACCOUNT_NAME% i...</td>\n", " <td>Engagement</td>\n", " <td>Onboard Survey (Phased Week 5 )</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>Can-Awar-4</td>\n", " <td>How did you first hear about an opportunity to...</td>\n", " <td>Awareness</td>\n", " <td>Candidate Survey</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " created_at updated_at code \\\n", "0 None None All-Goal-3 \n", "1 None None Int-Over-2 \n", "2 None None Ben-Expl-1 \n", "3 None None Eng.5 \n", "4 None None Can-Awar-4 \n", "\n", " description factor \\\n", "0 At least one of my career development goals al... Goal Alignment \n", "1 I would highly recommend an internship at %ACC... Overall \n", "2 The availability of 401k matching is a potenti... Exploring 401k Matching \n", "3 I see myself still working at %ACCOUNT_NAME% i... Engagement \n", "4 How did you first hear about an opportunity to... Awareness \n", "\n", " survey_type \n", "0 The Alliance Diagnostic \n", "1 Intern Survey \n", "2 Benefits Survey \n", "3 Onboard Survey (Phased Week 5 ) \n", "4 Candidate Survey " ] }, "execution_count": 91, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ref_question_df.head()" ] }, { "cell_type": "code", "execution_count": 92, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'[{\"code\": \"All-Goal-3\", \"description\": \"At least one of my career development goals aligns with the work I do at %ACCOUNT_NAME%\", \"factor\": \"Goal Alignment\", \"survey_type\": \"The Alliance Diagnostic\"}, {\"code\": \"Int-Over-2\", \"description\": \"I would highly recommend an internship at %ACCOUNT_NAME%\", \"factor\": \"Overall\", \"survey_type\": \"Intern Survey\"}, {\"code\": \"Ben-Expl-1\", \"description\": \"The availability of 401k matching is a potential benefit that is top of mind for me as I plan for the future\", \"factor\": \"Exploring 401k Matching\", \"survey_type\": \"Benefits Survey\"}, {\"code\": \"Eng.5\", \"description\": \"I see myself still working at %ACCOUNT_NAME% in two years\\' time\", \"factor\": \"Engagement\", \"survey_type\": \"Onboard Survey (Phased Week 5 )\"}, {\"code\": \"Can-Awar-4\", \"description\": \"How did you first hear about an opportunity to work at %ACCOUNT_NAME%?\", \"factor\": \"Awareness\", \"survey_type\": \"Candidate Survey\"}, {\"code\": \"All-Miss-3\", \"description\": \"I receive clear and constructive feedback about progress toward my performance goals\", \"factor\": \"Mission & Objectives\", \"survey_type\": \"The Alliance Diagnostic\"}, {\"code\": \"Ons-Recr-5\", \"description\": \"Reviewer Comments on Recruitment\", \"factor\": \"Recruitment\", \"survey_type\": \"Onboard Survey (Single Point)\"}, {\"code\": \"On1-Chec-6\", \"description\": \"Reviewer Comments on Organizational Alignment\", \"factor\": \"Checklist\", \"survey_type\": \"Onboard Survey (Phased Week 1)\"}, {\"code\": \"Ind-Mana-7\", \"description\": \"Giving you feedback on how you are progressing\", \"factor\": \"Manager Feedback\", \"survey_type\": \"Individual Effectiveness\"}, {\"code\": \"Mgrcap.1\", \"description\": \"My manager has a good understanding of the challenges our team encounters in our work\", \"factor\": \"Technical Capability\", \"survey_type\": \"Manager Effectiveness\"}, {\"code\": \"Mgremo.1\", \"description\": \"My manager remains calm and productive under pressure\", \"factor\": \"Resilience \", \"survey_type\": \"Manager Effectiveness\"}, {\"code\": \"Mgreff.4\", \"description\": \"I would recommend this person as a manager\", \"factor\": \"Overall Manager Effectiveness\", \"survey_type\": \"Manager Effectiveness\"}, {\"code\": \"Lea.1\", \"description\": \"I have confidence in the leaders at %ACCOUNT_NAME%\", \"factor\": \"Leadership\", \"survey_type\": \"Engagement\"}, {\"code\": \"Mgrcoa.5\", \"description\": \"My manager gives me actionable feedback on a regular basis\", \"factor\": \"Coaching\", \"survey_type\": \"Manager Effectiveness\"}, {\"code\": \"Eng.2\", \"description\": \"%ACCOUNT_NAME% motivates me to go beyond what I would in a similar role elsewhere\", \"factor\": \"Engagement\", \"survey_type\": \"Onboard Survey (Single Point)\"}, {\"code\": \"On1-Chec-5\", \"description\": \"Reviewer Comments on Welcome & Induction\", \"factor\": \"Checklist\", \"survey_type\": \"Onboard Survey (Phased Week 1)\"}, {\"code\": \"Ben-Exis-10\", \"description\": \"I believe investing more of the company\\'s money into improving benefits at %ACCOUNT_NAME% will help attract and retain top talent\", \"factor\": \"Existing Benefits\", \"survey_type\": \"Benefits Survey\"}, {\"code\": \"Ben-Unde-6\", \"description\": \"Select the existing perk you would be most willing to sacrifice to allow for new benefits\", \"factor\": \"Understanding Existing Perks\", \"survey_type\": \"Benefits Survey\"}, {\"code\": \"Tea.2\", \"description\": \"We hold ourselves and our team members accountable for results\", \"factor\": \"Focus and Accountability\", \"survey_type\": \"Team Effectiveness\"}, {\"code\": \"Con.2\", \"description\": \"%ACCOUNT_NAME% effectively directs resources (funding, people and effort) towards company goals\", \"factor\": \"Company Performance\", \"survey_type\": \"Engagement\"}, {\"code\": \"Ind-Mana-2\", \"description\": \"Managing our teams workload (allocating work assignments, setting priorities, scheduling, etc.)\", \"factor\": \"Manager Feedback\", \"survey_type\": \"Individual Effectiveness\"}, {\"code\": \"Fee.2\", \"description\": \"My job performance is evaluated fairly\", \"factor\": \"Fairness\", \"survey_type\": \"Inclusion Survey\"}, {\"code\": \"Ben-Exis-7\", \"description\": \"Should I have or care for a(nother) child, the parental leave policy is sufficient\", \"factor\": \"Existing Benefits\", \"survey_type\": \"Benefits Survey\"}, {\"code\": \"Mgrcom.4\", \"description\": \"My manager is a good listener (allows ample time for others to speak)\", \"factor\": \"Communicating\", \"survey_type\": \"Supplementary ME\"}, {\"code\": \"Tea-Role-1\", \"description\": \"I know how my work contributes to the team\\'s success\", \"factor\": \"Role Clarity\", \"survey_type\": \"Team Effectiveness\"}, {\"code\": \"Ons-Indu-7\", \"description\": \"The onboarding experience gave me the opportunity to establish good relationships with people that are key to my role\", \"factor\": \"Induction\", \"survey_type\": \"Onboard Survey (Single Point)\"}, {\"code\": \"Col.3\", \"description\": \"Other departments at %ACCOUNT_NAME% collaborate well with us to get the job done\", \"factor\": \"Collaboration & Communication\", \"survey_type\": \"Engagement\"}, {\"code\": \"Int-Awar-3\", \"description\": \"My peers on campus are aware of %ACCOUNT_NAME% products or services\", \"factor\": \"Awareness\", \"survey_type\": \"Intern Survey\"}, {\"code\": \"Exi-The -5\", \"description\": \"Which company will you be moving to?\", \"factor\": \"The Future\", \"survey_type\": \"Exit Survey\"}, {\"code\": \"Ben-Expl-2\", \"description\": \"401k matching is something that %ACCOUNT_NAME% should offer, even if the funding has to come from other budgets (such as budgets for salary increases, bonuses, perks, equity, and events)\", \"factor\": \"Exploring 401k Matching\", \"survey_type\": \"Benefits Survey\"}, {\"code\": \"Tea-Comt-1\", \"description\": \"What is one thing you would like our team to do differently?\", \"factor\": \"Comments\", \"survey_type\": \"Team Effectiveness\"}, {\"code\": \"Inn.2\", \"description\": \"We are encouraged to be innovative even though some of our initiatives may not succeed\", \"factor\": \"Innovation\", \"survey_type\": \"Engagement\"}, {\"code\": \"Lea.3\", \"description\": \"The leaders at %ACCOUNT_NAME% have communicated a vision that motivates me\", \"factor\": \"Leadership\", \"survey_type\": \"Quick Engagement Survey\"}, {\"code\": \"All-Mana-2\", \"description\": \"My manager guides problem solving rather than solving problems for me\", \"factor\": \"Manager As Coach\", \"survey_type\": \"The Alliance Diagnostic\"}, {\"code\": \"All-Mana-3\", \"description\": \"My manager has explored my core work values\", \"factor\": \"Manager As Coach\", \"survey_type\": \"The Alliance Diagnostic\"}, {\"code\": \"Ons-Orga-2\", \"description\": \"The organizational values of %ACCOUNT_NAME% align well with my own values\", \"factor\": \"Organizational Alignment\", \"survey_type\": \"Onboard Survey (Single Point)\"}, {\"code\": \"Mgrdev.3\", \"description\": \"My manager helps me understand potential career paths at %ACCOUNT_NAME%\", \"factor\": \"Career Conversations\", \"survey_type\": \"The Alliance Diagnostic\"}, {\"code\": \"Tea-Deve-2\", \"description\": \"Team members give each other constructive feedback\", \"factor\": \"Development\", \"survey_type\": \"Team Effectiveness\"}, {\"code\": \"On1-Expe-1\", \"description\": \"What\\'s one thing we could have done differently to improve the first week of your onboarding experience?\", \"factor\": \"Experience\", \"survey_type\": \"Onboard Survey (Phased Week 1)\"}, {\"code\": \"Eng-Comm-2\", \"description\": \"Are there some things we are not doing so great here?\", \"factor\": \"Comments\", \"survey_type\": \"Engagement\"}, {\"code\": \"All-Inde-2\", \"description\": \"%ACCOUNT_NAME% is a safe place to be open and honest\", \"factor\": \"Alliance Index\", \"survey_type\": \"The Alliance Diagnostic\"}, {\"code\": \"Wel-Over-5\", \"description\": \"I usually feel I am making progress at work\", \"factor\": \"Overall Wellbeing\", \"survey_type\": \"Wellbeing Survey\"}, {\"code\": \"Int-Over-4\", \"description\": \"I felt inspired to go above and beyond the expectations of my role\", \"factor\": \"Overall\", \"survey_type\": \"Intern Survey\"}, {\"code\": \"Eng.4\", \"description\": \"I rarely think about looking for a job at another company\", \"factor\": \"Engagement\", \"survey_type\": \"Onboard Survey (Single Point)\"}, {\"code\": \"Exi-Your-5\", \"description\": \"Please indicate how long you have been considering leaving %ACCOUNT_NAME% for:\", \"factor\": \"Your Decision\", \"survey_type\": \"Exit Survey\"}, {\"code\": \"Eng.1\", \"description\": \"I would recommend %ACCOUNT_NAME% as a great place to work\", \"factor\": \"Engagement\", \"survey_type\": \"Onboard Survey (Single Point)\"}, {\"code\": \"Ons-Indu-3\", \"description\": \"I have had good training on the processes applicable to my role\", \"factor\": \"Induction\", \"survey_type\": \"Onboard Survey (Phased Week 5 )\"}, {\"code\": \"Ons-Expe-3\", \"description\": \"I am feeling welcome here\", \"factor\": \"Experience\", \"survey_type\": \"Onboard Survey (Single Point)\"}, {\"code\": \"Ons-Your-1\", \"description\": \"Please indicate the reasons why you joined %ACCOUNT_NAME%\", \"factor\": \"Your Decision\", \"survey_type\": \"Onboard Survey (Single Point)\"}, {\"code\": \"Di-Bel-3\", \"description\": \"I feel respected at %ACCOUNT_NAME%\", \"factor\": \"Belonging\", \"survey_type\": \"Inclusion Survey\"}, {\"code\": \"Mgrcar.5\", \"description\": \"I would consider speaking with my manager if I was thinking about leaving %ACCOUNT_NAME%\", \"factor\": \"Alliance Index\", \"survey_type\": \"The Alliance Diagnostic\"}, {\"code\": \"Exi-Deve-3\", \"description\": \"I believe there would have been good career opportunities for me at %ACCOUNT_NAME%\", \"factor\": \"Development\", \"survey_type\": \"Exit Survey\"}, {\"code\": \"Ons-Indu-9\", \"description\": \"The information provided has been at the right level for me\", \"factor\": \"Induction\", \"survey_type\": \"Onboard Survey (Phased Week 5 )\"}, {\"code\": \"Di-Dem-1\", \"description\": \"I am included in decisions that affect my work\", \"factor\": \"Decision Making\", \"survey_type\": \"Inclusion Survey\"}, {\"code\": \"Man.3\", \"description\": \"My manager is a great role model for employees\", \"factor\": \"Management\", \"survey_type\": \"Engagement\"}, {\"code\": \"Lea.4\", \"description\": \"The leaders at %ACCOUNT_NAME% keep people informed about what is happening\", \"factor\": \"Leadership\", \"survey_type\": \"Engagement\"}, {\"code\": \"Mgrvis.2\", \"description\": \"My manager helps us set a clear strategy for achieving our goals\", \"factor\": \"Vision and Goal Setting\", \"survey_type\": \"Manager Effectiveness\"}, {\"code\": \"Tea-Comm-2\", \"description\": \"Team members collaborate well with one another to get the job done\", \"factor\": \"Communication and Collaboration\", \"survey_type\": \"Team Effectiveness\"}, {\"code\": \"Ali.1\", \"description\": \"I am appropriately involved in decisions that affect my work\", \"factor\": \"Alignment & Involvement\", \"survey_type\": \"Engagement\"}, {\"code\": \"Ons-Expe-4\", \"description\": \"I am feeling productive\", \"factor\": \"Experience\", \"survey_type\": \"Onboard Survey (Phased Week 5 )\"}, {\"code\": \"Wel-Over-1\", \"description\": \"I usually feel very positive at work\", \"factor\": \"Overall Wellbeing\", \"survey_type\": \"Wellbeing Survey\"}, {\"code\": \"Wel-Over-3\", \"description\": \"I receive support from people around me at work when I need it\", \"factor\": \"Overall Wellbeing\", \"survey_type\": \"Wellbeing Survey\"}, {\"code\": \"All-Trus-1\", \"description\": \"Vision and strategy are well communicated at %ACCOUNT_NAME%\", \"factor\": \"Trust & Communication\", \"survey_type\": \"The Alliance Diagnostic\"}, {\"code\": \"Ind-Supp-1\", \"description\": \"What is one way that I help you or your team to succeed?\", \"factor\": \"Supporting success\", \"survey_type\": \"Individual Effectiveness\"}, {\"code\": \"Ons-Orga-1\", \"description\": \"I know what the organizational values of %ACCOUNT_NAME% are\", \"factor\": \"Organizational Alignment\", \"survey_type\": \"Onboard Survey (Phased Week 5 )\"}, {\"code\": \"Di-Dem-2\", \"description\": \"Perspectives like mine are included in the decision making at %ACCOUNT_NAME%\", \"factor\": \"Decision Making\", \"survey_type\": \"Inclusion Survey\"}, {\"code\": \"Can-Recr-5\", \"description\": \"The recruitment process left me with a clear understanding of the role I was interviewing for\", \"factor\": \"Recruitment\", \"survey_type\": \"Candidate Survey\"}, {\"code\": \"Tea-Supp-1\", \"description\": \"When my teammates say they\\\\u2019ll do something, they follow through with it\", \"factor\": \"Supportive Climate\", \"survey_type\": \"Team Effectiveness\"}, {\"code\": \"Ons-Recr-1\", \"description\": \"Is there something we could have done to improve the recruitment process?\", \"factor\": \"Recruitment\", \"survey_type\": \"Onboard Survey (Single Point)\"}, {\"code\": \"Ind-Mana-8\", \"description\": \"Providing you with responsibility and autonomy\", \"factor\": \"Manager Feedback\", \"survey_type\": \"Individual Effectiveness\"}, {\"code\": \"Ons-Role-1\", \"description\": \"My role so far matches the role description provided to me\", \"factor\": \"Role Perceptions\", \"survey_type\": \"Onboard Survey (Phased Week 5 )\"}, {\"code\": \"Tea-Orga-2\", \"description\": \"%ACCOUNT_NAME% recognizes and celebrates our team\\'s achievements\", \"factor\": \"Organizational Alignment\", \"survey_type\": \"Team Effectiveness\"}, {\"code\": \"Int-Inte-2\", \"description\": \"My actual assignments were similar to my expectations\", \"factor\": \"Intern Experience\", \"survey_type\": \"Intern Survey\"}, {\"code\": \"Ons-Expe-1\", \"description\": \"Is there anything else you would have liked included in your onboarding process?\", \"factor\": \"Experience\", \"survey_type\": \"Onboard Survey (Single Point)\"}, {\"code\": \"Act.3\", \"description\": \"I have seen positive changes taking place based on recent employee survey results\", \"factor\": \"Action\", \"survey_type\": \"Engagement\"}, {\"code\": \"Di-Comm-1\", \"description\": \"What is one thing your company could do to create a more inclusive culture?\", \"factor\": \"Comments\", \"survey_type\": \"Inclusion Survey\"}, {\"code\": \"Eng.2\", \"description\": \"%ACCOUNT_NAME% motivates me to go beyond what I would in a similar role elsewhere\", \"factor\": \"Engagement\", \"survey_type\": \"Engagement\"}, {\"code\": \"Fee.4\", \"description\": \"Generally, the right people are rewarded and recognized at %ACCOUNT_NAME%\", \"factor\": \"Feedback & Recognition\", \"survey_type\": \"Engagement\"}, {\"code\": \"Ons-Expe-3\", \"description\": \"I am feeling welcome here\", \"factor\": \"Experience\", \"survey_type\": \"Onboard Survey (Phased Week 1)\"}, {\"code\": \"Ons-Your-1\", \"description\": \"Please indicate the reasons why you joined %ACCOUNT_NAME%\", \"factor\": \"Your Decision\", \"survey_type\": \"Onboard Survey (Phased Week 1)\"}, {\"code\": \"Ben-Exis-3\", \"description\": \"My benefits package provides quality coverage for myself and, if applicable, my dependents\", \"factor\": \"Existing Benefits\", \"survey_type\": \"Benefits Survey\"}, {\"code\": \"Tea-Team-1\", \"description\": \"We resolve most conflicts or disagreements effectively\", \"factor\": \"Team Processes\", \"survey_type\": \"Team Effectiveness\"}, {\"code\": \"Ben-Eval-7\", \"description\": \"My choice in medical plan was impacted by carrier\", \"factor\": \"Evaluating Medical Plans\", \"survey_type\": \"Benefits Survey\"}, {\"code\": \"Ons-Indu-10\", \"description\": \"My on-the-job training has been effective\", \"factor\": \"Induction\", \"survey_type\": \"Onboard Survey (Phased Week 5 )\"}, {\"code\": \"Act.4\", \"description\": \"I believe action will take place as a result of this survey\", \"factor\": \"Action\", \"survey_type\": \"Engagement\"}, {\"code\": \"Ons-Orga-4\", \"description\": \"I understand how my role contibutes to the organizational goals of %ACCOUNT_NAME%\", \"factor\": \"Organizational Alignment\", \"survey_type\": \"Onboard Survey (Single Point)\"}, {\"code\": \"Mgrvis.1\", \"description\": \"My manager helps me understand how my role contributes to %ACCOUNT_NAME%\\'s success\", \"factor\": \"Vision and Goal Setting\", \"survey_type\": \"Supplementary ME\"}, {\"code\": \"Eng.3\", \"description\": \"I am proud to work for %ACCOUNT_NAME%\", \"factor\": \"Engagement\", \"survey_type\": \"Inclusion Survey\"}, {\"code\": \"Lea.2\", \"description\": \"The leaders at %ACCOUNT_NAME% demonstrate that people are important to the company\\'s success\", \"factor\": \"Leadership\", \"survey_type\": \"Engagement\"}, {\"code\": \"Di-Dvs-1\", \"description\": \"%ACCOUNT_NAME% values diversity\", \"factor\": \"Diversity\", \"survey_type\": \"Inclusion Survey\"}, {\"code\": \"Ben-Unde-1\", \"description\": \"Our perks were chosen to enhance the employee experience at %ACCOUNT_NAME%. Do you think our perks program is effective in enhancing our culture?\", \"factor\": \"Understanding Existing Perks\", \"survey_type\": \"Benefits Survey\"}, {\"code\": \"Ben-Exis-4\", \"description\": \"I believe our benefits package is one of the top reasons why people apply to work at %ACCOUNT_NAME%\", \"factor\": \"Existing Benefits\", \"survey_type\": \"Benefits Survey\"}, {\"code\": \"Can-Inte-1\", \"description\": \"My interview started on time\", \"factor\": \"Interviews\", \"survey_type\": \"Candidate Survey\"}, {\"code\": \"Act.1\", \"description\": \"I have been provided an opportunity to see and discuss recent employee survey results\", \"factor\": \"Action\", \"survey_type\": \"Engagement\"}, {\"code\": \"Di-Fan-3\", \"description\": \"People from all backgrounds have equal opportunities to succeed at %ACCOUNT_NAME%\", \"factor\": \"Fairness\", \"survey_type\": \"Inclusion Survey\"}, {\"code\": \"Eng-Comm-1\", \"description\": \"Are there some things we are doing great here?\", \"factor\": \"Comments\", \"survey_type\": \"Quick Engagement Survey\"}, {\"code\": \"Di-Opr-1\", \"description\": \"When there are career opportunities at %ACCOUNT_NAME%, I am aware of them\", \"factor\": \"Opportunities & Resources\", \"survey_type\": \"Inclusion Survey\"}, {\"code\": \"Ena.1\", \"description\": \"I have access to the things I need to do my job well\", \"factor\": \"Enablement\", \"survey_type\": \"Engagement\"}, {\"code\": \"All-Inde-1\", \"description\": \"I feel like %ACCOUNT_NAME% and I are allies in developing my career\", \"factor\": \"Alliance Index\", \"survey_type\": \"The Alliance Diagnostic\"}, {\"code\": \"Mgrinc.2\", \"description\": \"My manager shows that they value diversity on the team\", \"factor\": \"Inclusion \", \"survey_type\": \"Manager Effectiveness\"}, {\"code\": \"Eng.4\", \"description\": \"I rarely think about looking for a job at another company\", \"factor\": \"Engagement\", \"survey_type\": \"Engagement\"}, {\"code\": \"Eng.1\", \"description\": \"I would recommend %ACCOUNT_NAME% as a great place to work\", \"factor\": \"Engagement\", \"survey_type\": \"Engagement\"}, {\"code\": \"Dev.3\", \"description\": \"I have access to the learning and development I need to do my job well\", \"factor\": \"Learning & Development\", \"survey_type\": \"Engagement\"}, {\"code\": \"Ons-Recr-1\", \"description\": \"Is there something we could have done to improve the recruitment process?\", \"factor\": \"Recruitment\", \"survey_type\": \"Onboard Survey (Phased Week 1)\"}, {\"code\": \"Val-Conn-3\", \"description\": \"I believe these core values are right for a company like ours\", \"factor\": \"Connection\", \"survey_type\": \"Values Survey\"}, {\"code\": \"Tea.3\", \"description\": \"Workloads are divided fairly among people where I work\", \"factor\": \"Teamwork & Ownership\", \"survey_type\": \"Engagement\"}, {\"code\": \"Ind-Gene-1\", \"description\": \"Please describe anything else, not listed above, that you\\'d like to see me continue, improve or do more of.\", \"factor\": \"General\", \"survey_type\": \"Individual Effectiveness\"}, {\"code\": \"Tea-Comm-3\", \"description\": \"There is open and honest two-way communication within our team\", \"factor\": \"Communication and Collaboration\", \"survey_type\": \"Team Effectiveness\"}, {\"code\": \"Exi-The -6\", \"description\": \"What excites you about your new role?\", \"factor\": \"The Future\", \"survey_type\": \"Exit Survey\"}, {\"code\": \"Eng-Comm-3\", \"description\": \"Is there something else you think we should have asked you in this survey?\", \"factor\": \"Comments\", \"survey_type\": \"Engagement\"}, {\"code\": \"Can-Recr-3\", \"description\": \"The recruitment team kept in regular touch with me through the whole process\", \"factor\": \"Recruitment\", \"survey_type\": \"Candidate Survey\"}, {\"code\": \"Can-Awar-2\", \"description\": \"In my experience, %ACCOUNT_NAME% is a company people really want to work for\", \"factor\": \"Awareness\", \"survey_type\": \"Candidate Survey\"}, {\"code\": \"Man.2\", \"description\": \"My manager gives me useful feedback on how well I am performing\", \"factor\": \"Management\", \"survey_type\": \"Engagement\"}, {\"code\": \"Exi-Deve-4\", \"description\": \"Reviewer Comments on Development\", \"factor\": \"Development\", \"survey_type\": \"Exit Survey\"}, {\"code\": \"Fee.2\", \"description\": \"My job performance is evaluated fairly\", \"factor\": \"Feedback & Recognition\", \"survey_type\": \"Engagement\"}, {\"code\": \"On1-Your-4\", \"description\": \"Reviewer Comments on Role Perceptions\", \"factor\": \"Your Decision\", \"survey_type\": \"Onboard Survey (Phased Week 1)\"}, {\"code\": \"Mgrcar.2\", \"description\": \"My manager regularly checks in with how I am doing (not just work related)\", \"factor\": \"Caring\", \"survey_type\": \"Supplementary ME\"}, {\"code\": \"Mgreff.5\", \"description\": \"My manager makes me feel valued\", \"factor\": \"Overall Manager Effectiveness\", \"survey_type\": \"Manager Effectiveness\"}, {\"code\": \"Ons-Indu-8\", \"description\": \"I have had a good quality one on one with my manager\", \"factor\": \"Induction\", \"survey_type\": \"Onboard Survey (Phased Week 5 )\"}, {\"code\": \"Ser.1\", \"description\": \"Day-to-day decisions here demonstrate that quality and improvement are top priorities\", \"factor\": \"Alignment\", \"survey_type\": \"Quick Engagement Survey\"}, {\"code\": \"Di-Opr-3\", \"description\": \"%ACCOUNT_NAME% believes that people can always greatly improve their talents and abilities\", \"factor\": \"Opportunities & Resources\", \"survey_type\": \"Inclusion Survey\"}, {\"code\": \"Eng.3\", \"description\": \"I am proud to work for %ACCOUNT_NAME%\", \"factor\": \"Engagement\", \"survey_type\": \"Onboard Survey (Single Point)\"}, {\"code\": \"All-Comm-1\", \"description\": \"If there was one element that could positively impact careers at %ACCOUNT_NAME% what would that be?\", \"factor\": \"Comments\", \"survey_type\": \"The Alliance Diagnostic\"}, {\"code\": \"Eng.2\", \"description\": \"%ACCOUNT_NAME% motivates me to go beyond what I would in a similar role elsewhere\", \"factor\": \"About the Company\", \"survey_type\": \"Manager Effectiveness\"}, {\"code\": \"Mgrres.3\", \"description\": \"My manager helps me remove barriers in my work\", \"factor\": \"Results-oriented\", \"survey_type\": \"Supplementary ME\"}, {\"code\": \"Ser.1\", \"description\": \"Day-to-day decisions here demonstrate that quality and improvement are top priorities\", \"factor\": \"Service & Quality Focus\", \"survey_type\": \"Engagement\"}, {\"code\": \"Tea-Focu-3\", \"description\": \"Workloads are divided fairly among team members\", \"factor\": \"Focus and Accountability\", \"survey_type\": \"Team Effectiveness\"}, {\"code\": \"All-Inve-1\", \"description\": \"%ACCOUNT_NAME% has committed to providing me opportunities to learn skills relevant to my career development goals\", \"factor\": \"Career Investment\", \"survey_type\": \"The Alliance Diagnostic\"}, {\"code\": \"On1-Recr-5\", \"description\": \"Reviewer Comments on Recruitment\", \"factor\": \"Recruitment\", \"survey_type\": \"Onboard Survey (Phased Week 1)\"}, {\"code\": \"Ali.2\", \"description\": \"I am happy with my current role relative to what was described to me\", \"factor\": \"Alignment & Involvement\", \"survey_type\": \"Engagement\"}, {\"code\": \"Val-Conn-1\", \"description\": \"I know our company values\", \"factor\": \"Connection\", \"survey_type\": \"Values Survey\"}, {\"code\": \"Tea-Open-1\", \"description\": \"If someone makes a mistake, they admit it to the team\", \"factor\": \"Openness\", \"survey_type\": \"Team Effectiveness\"}, {\"code\": \"Ben-Exis-1\", \"description\": \"I understand my benefits package at %ACCOUNT_NAME%\", \"factor\": \"Existing Benefits\", \"survey_type\": \"Benefits Survey\"}, {\"code\": \"Ben-Eval-5\", \"description\": \"My choice in medical plan was impacted by coverage\", \"factor\": \"Evaluating Medical Plans\", \"survey_type\": \"Benefits Survey\"}, {\"code\": \"Exi-Lead-2\", \"description\": \"The leaders at %ACCOUNT_NAME% communicated a vision that motivated me\", \"factor\": \"Leadership\", \"survey_type\": \"Exit Survey\"}, {\"code\": \"Int-My M-4\", \"description\": \"My manager communicated what was important for me to focus on during my internship\", \"factor\": \"My Manager\", \"survey_type\": \"Intern Survey\"}, {\"code\": \"Tea-Role-2\", \"description\": \"Where necessary, we have clearly defined roles and responsibilities for each team member\", \"factor\": \"Role Clarity\", \"survey_type\": \"Team Effectiveness\"}, {\"code\": \"Mgrdev.5\", \"description\": \"My manager helps me find things in my work and career that really inspire me\", \"factor\": \"Development\", \"survey_type\": \"Manager Effectiveness\"}, {\"code\": \"Mgrres.1\", \"description\": \"My manager ensures performance standards are maintained\", \"factor\": \"Results-oriented\", \"survey_type\": \"Manager Effectiveness\"}, {\"code\": \"All-Goal-2\", \"description\": \"My manager has spoken with me about how my career goals align with my work at %ACCOUNT_NAME%.\", \"factor\": \"Goal Alignment\", \"survey_type\": \"The Alliance Diagnostic\"}, {\"code\": \"All-Idea-3\", \"description\": \"%ACCOUNT_NAME% allows us to actively experiment with new ideas\", \"factor\": \"Idea Ecosystem\", \"survey_type\": \"The Alliance Diagnostic\"}, {\"code\": \"Dev.5\", \"description\": \"%ACCOUNT_NAME% is a great company for me to make a contribution to my development\", \"factor\": \"Learning & Development\", \"survey_type\": \"Engagement\"}, {\"code\": \"Dev.4\", \"description\": \"My manager (or someone in management) has shown a genuine interest in my career aspirations\", \"factor\": \"Development\", \"survey_type\": \"Quick Engagement Survey\"}, {\"code\": \"Inn.1\", \"description\": \"At %ACCOUNT_NAME% we act on promising new or innovative ideas\", \"factor\": \"Innovation\", \"survey_type\": \"Engagement\"}, {\"code\": \"Ben-Eval-2\", \"description\": \"The main benefit of my medical coverage is the variety of choice in selecting a doctor\", \"factor\": \"Evaluating Medical Plans\", \"survey_type\": \"Benefits Survey\"}, {\"code\": \"Ons-Indu-6\", \"description\": \"I have a good idea about what I can do to have a successful career here\", \"factor\": \"Induction\", \"survey_type\": \"Onboard Survey (Phased Week 5 )\"}, {\"code\": \"Dev.3\", \"description\": \"I have access to the learning and development I need to do my job well\", \"factor\": \"Enablement\", \"survey_type\": \"Quick Engagement Survey\"}, {\"code\": \"Int-Inte-4\", \"description\": \"I had a clear understanding of my role and responsibilities\", \"factor\": \"Intern Experience\", \"survey_type\": \"Intern Survey\"}, {\"code\": \"Di-Fan-4\", \"description\": \"Administrative tasks that don\\'t have a specific owner (e.g., taking notes in meetings, scheduling events, cleaning up shared space) are fairly divided at %ACCOUNT_NAME%\", \"factor\": \"Fairness\", \"survey_type\": \"Inclusion Survey\"}, {\"code\": \"Int-My M-2\", \"description\": \"My manager made it easy for me to get my work done (e.g., removed roadblocks, supplied the resources I needed, connected me with the right people)\", \"factor\": \"My Manager\", \"survey_type\": \"Intern Survey\"}, {\"code\": \"Ons-Indu-11\", \"description\": \"My induction program was thorough and effective\", \"factor\": \"Induction\", \"survey_type\": \"Onboard Survey (Single Point)\"}, {\"code\": \"Int-Comm-1\", \"description\": \"Is there something we could have done to improve the internship process?\", \"factor\": \"Comments\", \"survey_type\": \"Intern Survey\"}, {\"code\": \"Mgremo.2\", \"description\": \"I believe my manager is aware of how their emotions/moods affects others\", \"factor\": \"Resilience \", \"survey_type\": \"Supplementary ME\"}, {\"code\": \"Ind-Impr-1\", \"description\": \"What are my biggest opportunities to improve that you think could make a real difference?\", \"factor\": \"Improvement Areas\", \"survey_type\": \"Individual Effectiveness\"}, {\"code\": \"Wel-Ener-2\", \"description\": \"In a typical day, I can accomplish all I need to during my normal working hours\", \"factor\": \"Energy\", \"survey_type\": \"Wellbeing Survey\"}, {\"code\": \"Exi-Lead-3\", \"description\": \"My manager was a great role model for me\", \"factor\": \"Leadership\", \"survey_type\": \"Exit Survey\"}, {\"code\": \"Ons-Expe-2\", \"description\": \"What\\'s one thing we could have done differently to improve your onboarding experience?\", \"factor\": \"Experience\", \"survey_type\": \"Onboard Survey (Phased Week 5 )\"}, {\"code\": \"Mgrcoa.3\", \"description\": \"My manager asks questions about how I might solve problems, rather than just giving advice\", \"factor\": \"Coaching\", \"survey_type\": \"Manager Effectiveness\"}, {\"code\": \"Eng.4\", \"description\": \"I rarely think about looking for a job at another company\", \"factor\": \"About the Company\", \"survey_type\": \"Manager Effectiveness\"}, {\"code\": \"Ben-Exis-8\", \"description\": \"The vacation policy allows me sufficient time to recharge\", \"factor\": \"Existing Benefits\", \"survey_type\": \"Benefits Survey\"}, {\"code\": \"Eng.1\", \"description\": \"I would recommend %ACCOUNT_NAME% as a great place to work\", \"factor\": \"About the Company\", \"survey_type\": \"Manager Effectiveness\"}, {\"code\": \"Exi-Your-3\", \"description\": \"Please indicate the 3 main reasons you joined %ACCOUNT_NAME%:\", \"factor\": \"Your Decision\", \"survey_type\": \"Exit Survey\"}, {\"code\": \"Tea-Focu-2\", \"description\": \"We have a shared strategy for how to achieve our team goals\", \"factor\": \"Focus and Accountability\", \"survey_type\": \"Team Effectiveness\"}, {\"code\": \"Col.1\", \"description\": \"At %ACCOUNT_NAME% there is open and honest two-way communication\", \"factor\": \"Voice\", \"survey_type\": \"Inclusion Survey\"}, {\"code\": \"Exi-Alig-4\", \"description\": \"Reviewer Comments on Alignment\", \"factor\": \"Alignment & Involvement\", \"survey_type\": \"Exit Survey\"}, {\"code\": \"Int-Awar-2\", \"description\": \"Before applying for the internship I was already aware of %ACCOUNT_NAME%\", \"factor\": \"Awareness\", \"survey_type\": \"Intern Survey\"}, {\"code\": \"Ben-Eval-4\", \"description\": \"My choice in medical plan was impacted by cost\", \"factor\": \"Evaluating Medical Plans\", \"survey_type\": \"Benefits Survey\"}, {\"code\": \"Can-Inte-2\", \"description\": \"I felt comfortable during the interview process\", \"factor\": \"Interviews\", \"survey_type\": \"Candidate Survey\"}, {\"code\": \"All-Miss-1\", \"description\": \"I have a clear mission objective to accomplish in my current role\", \"factor\": \"Mission & Objectives\", \"survey_type\": \"The Alliance Diagnostic\"}, {\"code\": \"Soc.2\", \"description\": \"%ACCOUNT_NAME%\\'s commitment to social responsibility (e.g. community support, sustainability, etc.) is genuine\", \"factor\": \"Social Connection\", \"survey_type\": \"Engagement\"}, {\"code\": \"Eng.3\", \"description\": \"I am proud to work for %ACCOUNT_NAME%\", \"factor\": \"Engagement\", \"survey_type\": \"Engagement\"}, {\"code\": \"Ben-Eval-6\", \"description\": \"My choice in medical plan was impacted by plan type (HMO, PPO, etc)\", \"factor\": \"Evaluating Medical Plans\", \"survey_type\": \"Benefits Survey\"}, {\"code\": \"Exi-Deve-1\", \"description\": \"%ACCOUNT_NAME% was a great organization for me to make a contribution to my development\", \"factor\": \"Development\", \"survey_type\": \"Exit Survey\"}, {\"code\": \"Ons-Expe-5\", \"description\": \"I\\'ve been supported to take time to complete the onboarding process\", \"factor\": \"Experience\", \"survey_type\": \"Onboard Survey (Single Point)\"}, {\"code\": \"Ali.5\", \"description\": \"I know what I need to do to be successful in my role\", \"factor\": \"Alignment\", \"survey_type\": \"Quick Engagement Survey\"}, {\"code\": \"Mgrcar.4\", \"description\": \"My manager supports me if I need to make use of flexible working arrangements\", \"factor\": \"Caring\", \"survey_type\": \"Supplementary ME\"}, {\"code\": \"Ons-Orga-6\", \"description\": \"I still feel like this is a great organization for me\", \"factor\": \"Organizational Alignment\", \"survey_type\": \"Onboard Survey (Single Point)\"}, {\"code\": \"Di-Dvs-2\", \"description\": \"%ACCOUNT_NAME% builds teams that are diverse\", \"factor\": \"Diversity\", \"survey_type\": \"Inclusion Survey\"}, {\"code\": \"Ons-Indu-5\", \"description\": \"I have a good idea about what is expected of me in my role\", \"factor\": \"Induction\", \"survey_type\": \"Onboard Survey (Phased Week 5 )\"}, {\"code\": \"Mgrdev.1\", \"description\": \"My manager frequently recognises progress I make, not just results\", \"factor\": \"Development\", \"survey_type\": \"Supplementary ME\"}, {\"code\": \"Ben-Unde-2\", \"description\": \"The perks at %ACCOUNT_NAME% are equal to or better than what is offered by similar employers\", \"factor\": \"Understanding Existing Perks\", \"survey_type\": \"Benefits Survey\"}, {\"code\": \"Ons-Enga-6\", \"description\": \"Reviewer Comments on Engagement\", \"factor\": \"Engagement\", \"survey_type\": \"Onboard Survey (Single Point)\"}, {\"code\": \"Tea-Supp-3\", \"description\": \"We genuinely care about each team member\\'s wellbeing\", \"factor\": \"Supportive Climate\", \"survey_type\": \"Team Effectiveness\"}, {\"code\": \"Mgrcar.3\", \"description\": \"My manager takes time to get to know me\", \"factor\": \"Caring\", \"survey_type\": \"Manager Effectiveness\"}, {\"code\": \"Eng.4\", \"description\": \"I rarely think about looking for a job at another company\", \"factor\": \"Engagement\", \"survey_type\": \"Inclusion Survey\"}, {\"code\": \"Int-Inte-8\", \"description\": \"My pay was competitive compared to the pay for other intern programs\", \"factor\": \"Intern Experience\", \"survey_type\": \"Intern Survey\"}, {\"code\": \"Fee.3\", \"description\": \"When it is clear that someone is not delivering in their role we do something about it\", \"factor\": \"Feedback & Recognition\", \"survey_type\": \"Engagement\"}, {\"code\": \"Eng.1\", \"description\": \"I would recommend %ACCOUNT_NAME% as a great place to work\", \"factor\": \"Engagement\", \"survey_type\": \"Inclusion Survey\"}, {\"code\": \"Ali.4\", \"description\": \"I know how my work contributes to the goals of %ACCOUNT_NAME%\", \"factor\": \"Alignment & Involvement\", \"survey_type\": \"Engagement\"}, {\"code\": \"Ali.5\", \"description\": \"I know what I need to do to be successful in my role\", \"factor\": \"Alignment & Involvement\", \"survey_type\": \"Engagement\"}, {\"code\": \"Dev.2\", \"description\": \"I believe there are good career opportunities for me at %ACCOUNT_NAME%\", \"factor\": \"Learning & Development\", \"survey_type\": \"Engagement\"}, {\"code\": \"Can-Over-3\", \"description\": \"From what I saw, %ACCOUNT_NAME% was a place I\\'d love to work\", \"factor\": \"Overall\", \"survey_type\": \"Intern Survey\"}, {\"code\": \"All-Trus-2\", \"description\": \"I trust our leaders to keep us informed about things relevant to us and our work\", \"factor\": \"Trust & Communication\", \"survey_type\": \"The Alliance Diagnostic\"}, {\"code\": \"Ena.5\", \"description\": \"Most of the systems and processes here support us getting our work done effectively\", \"factor\": \"Enablement\", \"survey_type\": \"Engagement\"}, {\"code\": \"Exi-The -3\", \"description\": \"Is the remuneration package for your role:\", \"factor\": \"The Future\", \"survey_type\": \"Exit Survey\"}, {\"code\": \"Ons-Role-2\", \"description\": \"I still feel like this is a great role for me\", \"factor\": \"Role Perceptions\", \"survey_type\": \"Onboard Survey (Single Point)\"}, {\"code\": \"Di-Opr-4\", \"description\": \"%ACCOUNT_NAME% enables me to balance work and personal life\", \"factor\": \"Opportunities & Resources\", \"survey_type\": \"Inclusion Survey\"}, {\"code\": \"Ons-Indu-1\", \"description\": \"What are the three things you have most enjoyed so far working at %ACCOUNT_NAME%\", \"factor\": \"Induction\", \"survey_type\": \"Onboard Survey (Phased Week 5 )\"}, {\"code\": \"Tea.2\", \"description\": \"We hold ourselves and our team members accountable for results\", \"factor\": \"Teamwork & Ownership\", \"survey_type\": \"Engagement\"}, {\"code\": \"Exi-The -1\", \"description\": \"Are you intending to continue working immediately?\", \"factor\": \"The Future\", \"survey_type\": \"Exit Survey\"}, {\"code\": \"Wli.2\", \"description\": \"We are genuinely supported if we choose to make use of flexible working arrangements\", \"factor\": \"Work & Life Blend\", \"survey_type\": \"Engagement\"}, {\"code\": \"Can-Inte-4\", \"description\": \"My interviewers were prepared for the interview (familiar with my background, etc)\", \"factor\": \"Interviews\", \"survey_type\": \"Candidate Survey\"}, {\"code\": \"Ind-Mana-1\", \"description\": \"Setting direction or a strategy for achieving our goals\", \"factor\": \"Manager Feedback\", \"survey_type\": \"Individual Effectiveness\"}, {\"code\": \"Can-Over-5\", \"description\": \"The on-site experience at %ACCOUNT_NAME% was fantastic\", \"factor\": \"Overall\", \"survey_type\": \"Candidate Survey\"}, {\"code\": \"All-Goal-1\", \"description\": \"My manager and I have discussed how my mission here will benefit %ACCOUNT_NAME%\\'s success\", \"factor\": \"Goal Alignment\", \"survey_type\": \"The Alliance Diagnostic\"}, {\"code\": \"Ons-Orga-3\", \"description\": \"I know what the organizational goals of %ACCOUNT_NAME% are\", \"factor\": \"Organizational Alignment\", \"survey_type\": \"Onboard Survey (Single Point)\"}, {\"code\": \"All-Conv-3\", \"description\": \"My manager and I speak openly about the skills and experiences I need to move forward career-wise\", \"factor\": \"Career Conversations\", \"survey_type\": \"The Alliance Diagnostic\"}, {\"code\": \"Man.1\", \"description\": \"My manager genuinely cares about my wellbeing\", \"factor\": \"Management\", \"survey_type\": \"Engagement\"}, {\"code\": \"Int-Comm-4\", \"description\": \"What was the most important lesson you learned during your internship at %ACCOUNT_NAME%?\", \"factor\": \"Comments\", \"survey_type\": \"Intern Survey\"}, {\"code\": \"Ons-Indu-12\", \"description\": \"Reviewer Comments on Onboarding Experience\", \"factor\": \"Induction\", \"survey_type\": \"Onboard Survey (Single Point)\"}, {\"code\": \"Eng.2\", \"description\": \"%ACCOUNT_NAME% motivates me to go beyond what I would in a similar role elsewhere\", \"factor\": \"Engagement\", \"survey_type\": \"Inclusion Survey\"}, {\"code\": \"All-Empl-2\", \"description\": \"I feel my relationship with %ACCOUNT_NAME% would likely continue even if I left\", \"factor\": \"Employee-Employer Relationship\", \"survey_type\": \"The Alliance Diagnostic\"}, {\"code\": \"Ena.4\", \"description\": \"We have enough autonomy to perform our jobs effectively\", \"factor\": \"Enablement\", \"survey_type\": \"Engagement\"}, {\"code\": \"Mgrdev.6\", \"description\": \"My manager ensures I am in the right place in the company to be successful\", \"factor\": \"Development\", \"survey_type\": \"Manager Effectiveness\"}, {\"code\": \"Ind-Impr-2\", \"description\": \"What examples can you give for your selections that will help me know where to improve?\", \"factor\": \"Improvement Areas\", \"survey_type\": \"Individual Effectiveness\"}, {\"code\": \"Ben-Exis-9\", \"description\": \"I believe our benefits package is one of the top reasons why people stay at %ACCOUNT_NAME%\", \"factor\": \"Existing Benefits\", \"survey_type\": \"Benefits Survey\"}, {\"code\": \"Exi-Alig-2\", \"description\": \"I received appropriate recognition for good work\", \"factor\": \"Alignment & Involvement\", \"survey_type\": \"Exit Survey\"}, {\"code\": \"Ons-Indu-2\", \"description\": \"I\\'m confident using the systems I need in my role\", \"factor\": \"Induction\", \"survey_type\": \"Onboard Survey (Phased Week 5 )\"}, {\"code\": \"Int-My M-3\", \"description\": \"My manager gave me actionable feedback to improve and develop\", \"factor\": \"My Manager\", \"survey_type\": \"Intern Survey\"}, {\"code\": \"Ons-Orga-5\", \"description\": \"My experience of the organization has matched my expectations\", \"factor\": \"Organizational Alignment\", \"survey_type\": \"Onboard Survey (Single Point)\"}, {\"code\": \"All-Idea-2\", \"description\": \"We are encouraged to develop ideas here even if they might fail\", \"factor\": \"Idea Ecosystem\", \"survey_type\": \"The Alliance Diagnostic\"}, {\"code\": \"Int-Awar-1\", \"description\": \"What most attracted you to applying at %ACCOUNT_NAME%?\", \"factor\": \"Awareness\", \"survey_type\": \"Intern Survey\"}, {\"code\": \"Exi-Enab-2\", \"description\": \"Most of the systems and processes here supported me getting my work done effectively\", \"factor\": \"Enablement\", \"survey_type\": \"Exit Survey\"}, {\"code\": \"On5-Enga-6\", \"description\": \"Reviewer Comments on Engagement\", \"factor\": \"Engagement\", \"survey_type\": \"Onboard Survey (Phased Week 5 )\"}, {\"code\": \"Int-Inte-5\", \"description\": \"I had several good opportunities to refine my skills\", \"factor\": \"Intern Experience\", \"survey_type\": \"Intern Survey\"}, {\"code\": \"Int-Inte-7\", \"description\": \"Within my team, we worked together effectively to get things done\", \"factor\": \"Intern Experience\", \"survey_type\": \"Intern Survey\"}, {\"code\": \"Ons-Indu-4\", \"description\": \"I have a good idea about what I still need to learn to do my job well\", \"factor\": \"Induction\", \"survey_type\": \"Onboard Survey (Single Point)\"}, {\"code\": \"Ser.1\", \"description\": \"Day-to-day decisions here demonstrate that quality and improvement are top priorities\", \"factor\": \"Alignment & Involvement\", \"survey_type\": \"Exit Survey\"}, {\"code\": \"Ena.2\", \"description\": \"Our physical workspace is enjoyable to work in\", \"factor\": \"Enablement\", \"survey_type\": \"Engagement\"}, {\"code\": \"Mgremo.3\", \"description\": \"My manager copes well in a changing environment\", \"factor\": \"Resilience \", \"survey_type\": \"Manager Effectiveness\"}, {\"code\": \"All-Inde-4\", \"description\": \"I see 2-3 years (or potentially more) at %ACCOUNT_NAME% as a great way to develop my career\", \"factor\": \"Alliance Index\", \"survey_type\": \"The Alliance Diagnostic\"}, {\"code\": \"Di-Bel-1\", \"description\": \"I can be my authentic self at work\", \"factor\": \"Belonging\", \"survey_type\": \"Inclusion Survey\"}, {\"code\": \"All-Conv-2\", \"description\": \"My manager and I have discussed people and/or career paths that inspire me\", \"factor\": \"Career Conversations\", \"survey_type\": \"The Alliance Diagnostic\"}, {\"code\": \"Tea-Team-3\", \"description\": \"Generally, I support how decisions are made in our team\", \"factor\": \"Team Processes\", \"survey_type\": \"Team Effectiveness\"}, {\"code\": \"Ons-Recr-3\", \"description\": \"The recruitment and selection process was professionally conducted\", \"factor\": \"Recruitment\", \"survey_type\": \"Onboard Survey (Phased Week 1)\"}, {\"code\": \"Tea-Focu-4\", \"description\": \"We regularly check in on how we are progressing towards our goals\", \"factor\": \"Focus and Accountability\", \"survey_type\": \"Team Effectiveness\"}, {\"code\": \"Ons-Indu-6\", \"description\": \"I have a good idea about what I can do to have a successful career here\", \"factor\": \"Induction\", \"survey_type\": \"Onboard Survey (Single Point)\"}, {\"code\": \"All-Empl-1\", \"description\": \"Promises concerning my role and development are honored at %ACCOUNT_NAME%\", \"factor\": \"Employee-Employer Relationship\", \"survey_type\": \"The Alliance Diagnostic\"}, {\"code\": \"Lea.3\", \"description\": \"The leaders at %ACCOUNT_NAME% have communicated a vision that motivates me\", \"factor\": \"Leadership\", \"survey_type\": \"Engagement\"}, {\"code\": \"Mgrcap.5\", \"description\": \"My manager shares their knowledge and expertise with the team\", \"factor\": \"Technical Capability\", \"survey_type\": \"Supplementary ME\"}, {\"code\": \"Ons-Indu-11\", \"description\": \"My induction program was thorough and effective\", \"factor\": \"Induction\", \"survey_type\": \"Onboard Survey (Phased Week 5 )\"}, {\"code\": \"Con.3\", \"description\": \"%ACCOUNT_NAME% is in a position to really succeed over the next three years\", \"factor\": \"Company Performance\", \"survey_type\": \"Engagement\"}, {\"code\": \"Ons-Expe-2\", \"description\": \"What\\'s one thing we could have done differently to improve your onboarding experience?\", \"factor\": \"Experience\", \"survey_type\": \"Onboard Survey (Single Point)\"}, {\"code\": \"Tea-Deve-1\", \"description\": \"Working on this team provides me with opportunities that contribute to my development\", \"factor\": \"Development\", \"survey_type\": \"Team Effectiveness\"}, {\"code\": \"All-Chec-2\", \"description\": \"I\\'m confident that I will have the opportunity to speak about my career here\", \"factor\": \"Check Ins\", \"survey_type\": \"The Alliance Diagnostic\"}, {\"code\": \"All-Inde-3\", \"description\": \"%ACCOUNT_NAME% is a place to explore and build great things\", \"factor\": \"Alliance Index\", \"survey_type\": \"The Alliance Diagnostic\"}, {\"code\": \"Exi-Enga-2\", \"description\": \"%ACCOUNT_NAME% motivated me to go beyond what I would in a similar role elsewhere\", \"factor\": \"Engagement\", \"survey_type\": \"Exit Survey\"}, {\"code\": \"On5-Role-3\", \"description\": \"Reviewer Comments on Role Perceptions\", \"factor\": \"Role Perceptions\", \"survey_type\": \"Onboard Survey (Phased Week 5 )\"}, {\"code\": \"Tea.1\", \"description\": \"I feel I am part of a team\", \"factor\": \"Teamwork & Ownership\", \"survey_type\": \"Engagement\"}, {\"code\": \"Eng-Comm-2\", \"description\": \"Are there some things we are not doing so great here?\", \"factor\": \"Comments\", \"survey_type\": \"Quick Engagement Survey\"}, {\"code\": \"On5-Indu-12\", \"description\": \"Reviewer Comments on Onboarding Experience\", \"factor\": \"Induction\", \"survey_type\": \"Onboard Survey (Phased Week 5 )\"}, {\"code\": \"Di-Cbp-2\", \"description\": \"The work that we do at %ACCOUNT_NAME% is important\", \"factor\": \"Contribution to Broader Purpose\", \"survey_type\": \"Inclusion Survey\"}, {\"code\": \"Man.4\", \"description\": \"My manager keeps me informed about what is happening at %ACCOUNT_NAME%\", \"factor\": \"Management\", \"survey_type\": \"Engagement\"}, {\"code\": \"Man-Comt-1\", \"description\": \"Do you have any other feedback you want to share about your manager?\", \"factor\": \"Comments\", \"survey_type\": \"Manager Effectiveness\"}, {\"code\": \"Mgrfai.3\", \"description\": \"My manager ensures that people\\'s ideas and work are attributed to them appropriately\", \"factor\": \"Fair Treatment\", \"survey_type\": \"Supplementary ME\"}, {\"code\": \"Wel-Ener-1\", \"description\": \"I typically wake up feeling fresh and rested for work\", \"factor\": \"Energy\", \"survey_type\": \"Wellbeing Survey\"}, {\"code\": \"Int-Awar-4\", \"description\": \"How did you first hear about an opportunity to intern at %ACCOUNT_NAME%?\", \"factor\": \"Awareness\", \"survey_type\": \"Intern Survey\"}, {\"code\": \"Ali.3\", \"description\": \"I believe my total compensation (base salary+any bonuses+benefits+equity) is fair, relative to similar roles at other companies\", \"factor\": \"Alignment & Involvement\", \"survey_type\": \"Engagement\"}, {\"code\": \"Ons-Recr-3\", \"description\": \"The recruitment and selection process was professionally conducted\", \"factor\": \"Recruitment\", \"survey_type\": \"Onboard Survey (Single Point)\"}, {\"code\": \"Int-My M-1\", \"description\": \"My manager had the relevant expertise required to effectively coach me\", \"factor\": \"My Manager\", \"survey_type\": \"Intern Survey\"}, {\"code\": \"Ons-Indu-8\", \"description\": \"I have had a good quality one on one with my manager\", \"factor\": \"Induction\", \"survey_type\": \"Onboard Survey (Single Point)\"}, {\"code\": \"Col.1\", \"description\": \"At %ACCOUNT_NAME% there is open and honest two-way communication\", \"factor\": \"Collaboration & Communication\", \"survey_type\": \"Engagement\"}, {\"code\": \"Ben-Eval-3\", \"description\": \"The main benefit of my medical coverage is protection in case of an emergency\", \"factor\": \"Evaluating Medical Plans\", \"survey_type\": \"Benefits Survey\"}, {\"code\": \"Tea-Team-4\", \"description\": \"We share or rotate some leadership responsibilities in our team (e.g., running meetings, tracking goals, follow-up)\", \"factor\": \"Team Processes\", \"survey_type\": \"Team Effectiveness\"}, {\"code\": \"Eng.3\", \"description\": \"I am proud to work for %ACCOUNT_NAME%\", \"factor\": \"Engagement\", \"survey_type\": \"Onboard Survey (Phased Week 5 )\"}, {\"code\": \"Tea-Supp-2\", \"description\": \"Team members volunteer to help each other when needed\", \"factor\": \"Supportive Climate\", \"survey_type\": \"Team Effectiveness\"}, {\"code\": \"Tea-Over-3\", \"description\": \"I believe I am on the best team for me right now\", \"factor\": \"Overall Team Effectiveness\", \"survey_type\": \"Team Effectiveness\"}, {\"code\": \"Exi-Enab-4\", \"description\": \"Reviewer Comments on Enablement\", \"factor\": \"Enablement\", \"survey_type\": \"Exit Survey\"}, {\"code\": \"Mgrcap.4\", \"description\": \"My manager is willing to work side by side with the team when necessary\", \"factor\": \"Technical Capability\", \"survey_type\": \"Supplementary ME\"}, {\"code\": \"Tea-Know-3\", \"description\": \"I believe we can successfully accomplish our team goals\", \"factor\": \"Knowledge and Capability\", \"survey_type\": \"Team Effectiveness\"}, {\"code\": \"Ben-Eval-1\", \"description\": \"I understand how to select the best medical coverage\", \"factor\": \"Evaluating Medical Plans\", \"survey_type\": \"Benefits Survey\"}, {\"code\": \"Tea-Orga-1\", \"description\": \"Our team goals are well aligned with the company goals\", \"factor\": \"Organizational Alignment\", \"survey_type\": \"Team Effectiveness\"}, {\"code\": \"Wel-Over-2\", \"description\": \"I often get absorbed in my work and lose track of time\", \"factor\": \"Overall Wellbeing\", \"survey_type\": \"Wellbeing Survey\"}, {\"code\": \"Con.1\", \"description\": \"The products and services %ACCOUNT_NAME% provides are as good as, or better than, our main competitors\", \"factor\": \"Company Performance\", \"survey_type\": \"Engagement\"}, {\"code\": \"On1-Chec-4\", \"description\": \"Reviewer Comments on Onboarding Experience\", \"factor\": \"Checklist\", \"survey_type\": \"Onboard Survey (Phased Week 1)\"}, {\"code\": \"Ons-Role-2\", \"description\": \"I still feel like this is a great role for me\", \"factor\": \"Role Perceptions\", \"survey_type\": \"Onboard Survey (Phased Week 5 )\"}, {\"code\": \"On1-Chec-2\", \"description\": \"Reviewer Comments on Onboarding Checklist\", \"factor\": \"Checklist\", \"survey_type\": \"Onboard Survey (Phased Week 1)\"}, {\"code\": \"Eng.5\", \"description\": \"I see myself still working at %ACCOUNT_NAME% in two years\\' time\", \"factor\": \"Engagement\", \"survey_type\": \"Inclusion Survey\"}, {\"code\": \"Ons-Indu-1\", \"description\": \"What are the three things you have most enjoyed so far working at %ACCOUNT_NAME%\", \"factor\": \"Induction\", \"survey_type\": \"Onboard Survey (Single Point)\"}, {\"code\": \"Ons-Your-3\", \"description\": \"Reviewer Comments on Your Decision\", \"factor\": \"Your Decision\", \"survey_type\": \"Onboard Survey (Single Point)\"}, {\"code\": \"Tea-Orga-3\", \"description\": \"The work our team does makes a positive difference at %ACCOUNT_NAME%\", \"factor\": \"Organizational Alignment\", \"survey_type\": \"Team Effectiveness\"}, {\"code\": \"Ons-Chec-2\", \"description\": \"Reviewer Comments on Onboarding Checklist\", \"factor\": \"Checklist\", \"survey_type\": \"Onboard Survey (Single Point)\"}, {\"code\": \"Ons-Orga-3\", \"description\": \"I know what the organizational goals of %ACCOUNT_NAME% are\", \"factor\": \"Organizational Alignment\", \"survey_type\": \"Onboard Survey (Phased Week 5 )\"}, {\"code\": \"Tea-Focu-1\", \"description\": \"I understand what our team is meant to accomplish\", \"factor\": \"Focus and Accountability\", \"survey_type\": \"Team Effectiveness\"}, {\"code\": \"On5-Orga-7\", \"description\": \"Reviewer Comments on Organizational Alignment\", \"factor\": \"Organizational Alignment\", \"survey_type\": \"Onboard Survey (Phased Week 5 )\"}, {\"code\": \"Eng-Comm-1\", \"description\": \"Are there some things we are doing great here?\", \"factor\": \"Comments\", \"survey_type\": \"Engagement\"}, {\"code\": \"Ind-Mana-3\", \"description\": \"Focusing on performance outcomes\", \"factor\": \"Manager Feedback\", \"survey_type\": \"Individual Effectiveness\"}, {\"code\": \"Ena.1\", \"description\": \"I have access to the things I need to do my job well\", \"factor\": \"Enablement\", \"survey_type\": \"Quick Engagement Survey\"}, {\"code\": \"Ons-Your-3\", \"description\": \"Reviewer Comments on Your Decision\", \"factor\": \"Experience\", \"survey_type\": \"Onboard Survey (Phased Week 5 )\"}, {\"code\": \"Eng.4\", \"description\": \"I rarely think about looking for a job at another company\", \"factor\": \"Engagement\", \"survey_type\": \"Quick Engagement Survey\"}, {\"code\": \"Exi-Your-6\", \"description\": \"Reviewer Comments on Your Decision\", \"factor\": \"Your Decision\", \"survey_type\": \"Exit Survey\"}, {\"code\": \"Ons-Recr-4\", \"description\": \"How long did your recruitment process take from application to offer?\", \"factor\": \"Recruitment\", \"survey_type\": \"Onboard Survey (Single Point)\"}, {\"code\": \"Ons-Chec-1\", \"description\": \"The following list includes things that are important for your successful onboarding here at %ACCOUNT_NAME%. Please tick the things that you believe have NOT been covered or completed so far:\", \"factor\": \"Checklist\", \"survey_type\": \"Onboard Survey (Single Point)\"}, {\"code\": \"Eng.1\", \"description\": \"I would recommend %ACCOUNT_NAME% as a great place to work\", \"factor\": \"Engagement\", \"survey_type\": \"Quick Engagement Survey\"}, {\"code\": \"Ons-Orga-5\", \"description\": \"My experience of the organization has matched my expectations\", \"factor\": \"Organizational Alignment\", \"survey_type\": \"Onboard Survey (Phased Week 5 )\"}, {\"code\": \"Ons-Indu-2\", \"description\": \"I\\'m confident using the systems I need in my role\", \"factor\": \"Induction\", \"survey_type\": \"Onboard Survey (Single Point)\"}, {\"code\": \"Ons-Your-2\", \"description\": \"What were you doing before you began working here?\", \"factor\": \"Your Decision\", \"survey_type\": \"Onboard Survey (Single Point)\"}, {\"code\": \"Tea-Team-2\", \"description\": \"Our team meetings and discussions are effective\", \"factor\": \"Team Processes\", \"survey_type\": \"Team Effectiveness\"}, {\"code\": \"Can-Over-1\", \"description\": \"What is ONE thing you think we could do to attract great talent?\", \"factor\": \"Overall\", \"survey_type\": \"Candidate Survey\"}, {\"code\": \"All-Inve-2\", \"description\": \"I have a good understanding of how contributing at %ACCOUNT_NAME% will also benefit my career\", \"factor\": \"Career Investment\", \"survey_type\": \"The Alliance Diagnostic\"}, {\"code\": \"Mgrcom.1\", \"description\": \"My manager communicates a vision that motivates me\", \"factor\": \"Vision and Goal Setting\", \"survey_type\": \"Manager Effectiveness\"}, {\"code\": \"Ons-Indu-4\", \"description\": \"I have a good idea about what I still need to learn to do my job well\", \"factor\": \"Induction\", \"survey_type\": \"Onboard Survey (Phased Week 5 )\"}, {\"code\": \"Val-Conn-4\", \"description\": \"I believe our company values match our culture\", \"factor\": \"Connection\", \"survey_type\": \"Values Survey\"}, {\"code\": \"Eng-Comm-3\", \"description\": \"Is there something else you think we should have asked you in this survey?\", \"factor\": \"Comments\", \"survey_type\": \"Quick Engagement Survey\"}, {\"code\": \"Exi-Your-1\", \"description\": \"Please indicate in a brief statement the major reason behind your decision to leave %ACCOUNT_NAME%:\", \"factor\": \"Your Decision\", \"survey_type\": \"Exit Survey\"}, {\"code\": \"Mgrfai.2\", \"description\": \"My manager does a good job of managing our team\\'s work (allocating work assignments, setting priorities, scheduling, etc.)\", \"factor\": \"Fair Treatment\", \"survey_type\": \"Supplementary ME\"}, {\"code\": \"Val-Impo-1\", \"description\": \"[INSERT VALUE] is an extremely important value for me in this company\", \"factor\": \"Importance\", \"survey_type\": \"Values Survey\"}, {\"code\": \"Mgrcap.3\", \"description\": \"My manager has the right technical knowledge to help our team\", \"factor\": \"Technical Capability\", \"survey_type\": \"Manager Effectiveness\"}, {\"code\": \"Di-Voc-2\", \"description\": \"I can voice a contrary opinion without fear of negative consequences\", \"factor\": \"Voice\", \"survey_type\": \"Inclusion Survey\"}, {\"code\": \"Can-Awar-3\", \"description\": \"Before applying for the job I was already aware of %ACCOUNT_NAME%\", \"factor\": \"Awareness\", \"survey_type\": \"Candidate Survey\"}, {\"code\": \"Ons-Your-3\", \"description\": \"Reviewer Comments on Your Decision\", \"factor\": \"Your Decision\", \"survey_type\": \"Onboard Survey (Phased Week 1)\"}, {\"code\": \"Ben-Exis-5\", \"description\": \"Should I die, my life insurance plan provides sufficient coverage for my dependents/beneficiaries\", \"factor\": \"Existing Benefits\", \"survey_type\": \"Benefits Survey\"}, {\"code\": \"Lea.4\", \"description\": \"The leaders at %ACCOUNT_NAME% keep people informed about what is happening\", \"factor\": \"Leadership\", \"survey_type\": \"Quick Engagement Survey\"}, {\"code\": \"Tea-Over-1\", \"description\": \"As a team we consistently deliver high quality work\", \"factor\": \"Overall Team Effectiveness\", \"survey_type\": \"Team Effectiveness\"}, {\"code\": \"Ons-Expe-5\", \"description\": \"I\\'ve been supported to take time to complete the onboarding process\", \"factor\": \"Experience\", \"survey_type\": \"Onboard Survey (Phased Week 5 )\"}, {\"code\": \"Ind-Mana-6\", \"description\": \"Helping to remove barriers in your work\", \"factor\": \"Manager Feedback\", \"survey_type\": \"Individual Effectiveness\"}, {\"code\": \"All-Miss-2\", \"description\": \"I know what I need to do to be successful in my current role/mission\", \"factor\": \"Mission & Objectives\", \"survey_type\": \"The Alliance Diagnostic\"}, {\"code\": \"Ons-Orga-6\", \"description\": \"I still feel like this is a great organization for me\", \"factor\": \"Organizational Alignment\", \"survey_type\": \"Onboard Survey (Phased Week 5 )\"}, {\"code\": \"Tea-Comt-2\", \"description\": \"What is one thing that you would like our team to keep doing?\", \"factor\": \"Comments\", \"survey_type\": \"Team Effectiveness\"}, {\"code\": \"Ind-Stre-1\", \"description\": \"What have you observed me excelling at that I should continue?\", \"factor\": \"Strength Areas\", \"survey_type\": \"Individual Effectiveness\"}, {\"code\": \"Col.2\", \"description\": \"Most people here make a good effort to consult other staff where appropriate\", \"factor\": \"Collaboration & Communication\", \"survey_type\": \"Engagement\"}, {\"code\": \"On5-Expe-9\", \"description\": \"Reviewer Comments on Welcome & Induction\", \"factor\": \"Experience\", \"survey_type\": \"Onboard Survey (Phased Week 5 )\"}, {\"code\": \"Dev.1\", \"description\": \"I am given opportunities to develop skills relevant to my interests\", \"factor\": \"Learning & Development\", \"survey_type\": \"Engagement\"}, {\"code\": \"Mgrdec.1\", \"description\": \"My manager makes decisions effectively (i.e. timely, with sufficient information/ perspectives)\", \"factor\": \"Results-oriented\", \"survey_type\": \"Manager Effectiveness\"}, {\"code\": \"Ons-Indu-5\", \"description\": \"I have a good idea about what is expected of me in my role\", \"factor\": \"Induction\", \"survey_type\": \"Onboard Survey (Single Point)\"}, {\"code\": \"Ons-Expe-6\", \"description\": \"Reviewer Comments on Welcome & Induction\", \"factor\": \"Experience\", \"survey_type\": \"Onboard Survey (Single Point)\"}, {\"code\": \"Ons-Orga-7\", \"description\": \"Reviewer Comments on Organizational Alignment\", \"factor\": \"Organizational Alignment\", \"survey_type\": \"Onboard Survey (Single Point)\"}, {\"code\": \"Tea-Know-2\", \"description\": \"Team members readily share their knowledge and lessons learned\", \"factor\": \"Knowledge and Capability\", \"survey_type\": \"Team Effectiveness\"}, {\"code\": \"Ons-Recr-4\", \"description\": \"How long did your recruitment process take from application to offer?\", \"factor\": \"Recruitment\", \"survey_type\": \"Onboard Survey (Phased Week 1)\"}, {\"code\": \"Ons-Chec-1\", \"description\": \"The following list includes things that are important for your successful onboarding here at %ACCOUNT_NAME%. Please tick the things that you believe have NOT been covered or completed so far:\", \"factor\": \"Checklist\", \"survey_type\": \"Onboard Survey (Phased Week 1)\"}, {\"code\": \"Int-Inte-1\", \"description\": \"My intern experience gave me valuable information about my chosen field\", \"factor\": \"Intern Experience\", \"survey_type\": \"Intern Survey\"}, {\"code\": \"Ons-Your-2\", \"description\": \"What were you doing before you began working here?\", \"factor\": \"Your Decision\", \"survey_type\": \"Onboard Survey (Phased Week 1)\"}, {\"code\": \"Eng.2\", \"description\": \"%ACCOUNT_NAME% motivates me to go beyond what I would in a similar role elsewhere\", \"factor\": \"Engagement\", \"survey_type\": \"Quick Engagement Survey\"}, {\"code\": \"All-Exte-2\", \"description\": \"We are encouraged to connect with former employees\", \"factor\": \"External Learning\", \"survey_type\": \"The Alliance Diagnostic\"}, {\"code\": \"Ind-Mana-4\", \"description\": \"Sharing my knowledge and expertise\", \"factor\": \"Manager Feedback\", \"survey_type\": \"Individual Effectiveness\"}, {\"code\": \"Exi-Enab-1\", \"description\": \"I had access to the learning and development I needed to do my job well\", \"factor\": \"Enablement\", \"survey_type\": \"Exit Survey\"}, {\"code\": \"Can-Over-3\", \"description\": \"From what I saw, %ACCOUNT_NAME% was a place I\\'d love to work\", \"factor\": \"Overall\", \"survey_type\": \"Candidate Survey\"}, {\"code\": \"Exi-Deve-2\", \"description\": \"My manager (or someone in management) showed a genuine interest in my career aspirations\", \"factor\": \"Development\", \"survey_type\": \"Exit Survey\"}, {\"code\": \"Int-Inte-6\", \"description\": \"When I did an excellent job my accomplishments were recognized\", \"factor\": \"Intern Experience\", \"survey_type\": \"Intern Survey\"}, {\"code\": \"Ons-Indu-7\", \"description\": \"The onboarding experience gave me the opportunity to establish good relationships with people that are key to my role\", \"factor\": \"Induction\", \"survey_type\": \"Onboard Survey (Phased Week 5 )\"}, {\"code\": \"Di-Voc-1\", \"description\": \"When I speak up, my opinion is valued\", \"factor\": \"Voice\", \"survey_type\": \"Inclusion Survey\"}, {\"code\": \"Soc.1\", \"description\": \"%ACCOUNT_NAME% really allows us to make a positive difference\", \"factor\": \"Social Connection\", \"survey_type\": \"Engagement\"}, {\"code\": \"Fee.1\", \"description\": \"I receive appropriate recognition for good work at %ACCOUNT_NAME%\", \"factor\": \"Alignment\", \"survey_type\": \"Quick Engagement Survey\"}, {\"code\": \"Can-Recr-2\", \"description\": \"Overall, I was provided clear information about %ACCOUNT_NAME% and working there\", \"factor\": \"Recruitment\", \"survey_type\": \"Candidate Survey\"}, {\"code\": \"All-Chec-1\", \"description\": \"My manager encourages me to \\'check-in\\' as a method to stay on track\", \"factor\": \"Check Ins\", \"survey_type\": \"The Alliance Diagnostic\"}, {\"code\": \"Tea-Open-3\", \"description\": \"Team members encourage each other to share their unpolished thoughts and ideas\", \"factor\": \"Openness\", \"survey_type\": \"Team Effectiveness\"}, {\"code\": \"Exi-Lead-1\", \"description\": \"The leaders at %ACCOUNT_NAME% kept people informed about what is happening\", \"factor\": \"Leadership\", \"survey_type\": \"Exit Survey\"}, {\"code\": \"Di-Opr-2\", \"description\": \"I know where to find information to do my job well\", \"factor\": \"Opportunities & Resources\", \"survey_type\": \"Inclusion Survey\"}, {\"code\": \"Ons-Recr-2\", \"description\": \"I was provided accurate information about %ACCOUNT_NAME% during the recruitment process\", \"factor\": \"Recruitment\", \"survey_type\": \"Onboard Survey (Phased Week 1)\"}, {\"code\": \"Di-Cbp-1\", \"description\": \"I understand how my work contributes to %ACCOUNT_NAME%\\\\u2019s mission\", \"factor\": \"Contribution to Broader Purpose\", \"survey_type\": \"Inclusion Survey\"}, {\"code\": \"Wel-Over-4\", \"description\": \"In general, I feel what I do at work is worthwhile\", \"factor\": \"Overall Wellbeing\", \"survey_type\": \"Wellbeing Survey\"}, {\"code\": \"Ons-Orga-2\", \"description\": \"The organizational values of %ACCOUNT_NAME% align well with my own values\", \"factor\": \"Organizational Alignment\", \"survey_type\": \"Onboard Survey (Phased Week 5 )\"}, {\"code\": \"Mgrres.2\", \"description\": \"My manager helps us get workable results from our team meetings\", \"factor\": \"Results-oriented\", \"survey_type\": \"Supplementary ME\"}, {\"code\": \"Ben-Unde-3\", \"description\": \"The perks at %ACCOUNT_NAME% save me a great deal of time and/or money, and add significant value to my employee experience\", \"factor\": \"Understanding Existing Perks\", \"survey_type\": \"Benefits Survey\"}, {\"code\": \"Int-Comm-2\", \"description\": \"What other companies did you consider for your internships?\", \"factor\": \"Comments\", \"survey_type\": \"Intern Survey\"}, {\"code\": \"Ser.2\", \"description\": \"We acknowledge people who deliver outstanding service here\", \"factor\": \"Service & Quality Focus\", \"survey_type\": \"Engagement\"}, {\"code\": \"Ind-Mana-9\", \"description\": \"Helping you with your career and personal development\", \"factor\": \"Manager Feedback\", \"survey_type\": \"Individual Effectiveness\"}, {\"code\": \"Man.4\", \"description\": \"My manager keeps me informed about what is happening at %ACCOUNT_NAME%\", \"factor\": \"Communicating\", \"survey_type\": \"Manager Effectiveness\"}, {\"code\": \"Mgrcom.5\", \"description\": \"My manager collaborates well across organisational boundaries (e.g. teams, departments, etc.)\", \"factor\": \"Communicating\", \"survey_type\": \"Manager Effectiveness\"}, {\"code\": \"Mgrcom.3\", \"description\": \"My manager keeps me informed about important decisions (including those that impact my work)\", \"factor\": \"Trust & Communication\", \"survey_type\": \"The Alliance Diagnostic\"}, {\"code\": \"Int-Inte-3\", \"description\": \"During my internship, I had the time I needed to create and innovate\", \"factor\": \"Intern Experience\", \"survey_type\": \"Intern Survey\"}, {\"code\": \"Eng.1\", \"description\": \"I would recommend %ACCOUNT_NAME% as a great place to work\", \"factor\": \"Engagement\", \"survey_type\": \"Onboard Survey (Phased Week 5 )\"}, {\"code\": \"Ben-Unde-4\", \"description\": \"The wellness offerings at %ACCOUNT_NAME% help me lead a happier, healthier life\", \"factor\": \"Understanding Existing Perks\", \"survey_type\": \"Benefits Survey\"}, {\"code\": \"Ons-Indu-3\", \"description\": \"I have had good training on the processes applicable to my role\", \"factor\": \"Induction\", \"survey_type\": \"Onboard Survey (Single Point)\"}, {\"code\": \"Mgrcom.3\", \"description\": \"My manager keeps me informed about important decisions (including those that impact my work)\", \"factor\": \"Communicating\", \"survey_type\": \"Supplementary ME\"}, {\"code\": \"Eng.4\", \"description\": \"I rarely think about looking for a job at another company\", \"factor\": \"Engagement\", \"survey_type\": \"Onboard Survey (Phased Week 5 )\"}, {\"code\": \"Dev.5\", \"description\": \"%ACCOUNT_NAME% is a great company for me to make a contribution to my development\", \"factor\": \"Development\", \"survey_type\": \"Quick Engagement Survey\"}, {\"code\": \"Dev.4\", \"description\": \"My manager (or someone in management) has shown a genuine interest in my career aspirations\", \"factor\": \"Learning & Development\", \"survey_type\": \"Engagement\"}, {\"code\": \"Act.2\", \"description\": \"My manager, or someone else, has communicated some clear actions based on recent employee survey results\", \"factor\": \"Action\", \"survey_type\": \"Engagement\"}, {\"code\": \"Exi-Enga-4\", \"description\": \"Reviewer Comments on Engagement\", \"factor\": \"Engagement\", \"survey_type\": \"Exit Survey\"}, {\"code\": \"Di-Bel-2\", \"description\": \"Even when something bad happens (e.g., when I get critical feedback from my manager, I have a negative social interaction with a peer, etc.), I don\\\\u2019t question whether or not I belong at %ACCOUNT_NAME%\", \"factor\": \"Belonging\", \"survey_type\": \"Inclusion Survey\"}, {\"code\": \"Can-Over-2\", \"description\": \"Is there something we could have done to improve the candidate/recruitment process?\", \"factor\": \"Overall\", \"survey_type\": \"Candidate Survey\"}, {\"code\": \"Mgrcoa.2\", \"description\": \"My manager regularly gives me feedback I can put to use\", \"factor\": \"Manager As Coach\", \"survey_type\": \"The Alliance Diagnostic\"}, {\"code\": \"Val-Conn-2\", \"description\": \"I think I have a good understanding of what our company values mean\", \"factor\": \"Connection\", \"survey_type\": \"Values Survey\"}, {\"code\": \"Mgrinc.1\", \"description\": \"My manager builds an inclusive team environment\", \"factor\": \"Inclusion \", \"survey_type\": \"Manager Effectiveness\"}, {\"code\": \"Eng.5\", \"description\": \"I see myself still working at %ACCOUNT_NAME% in two years\\' time\", \"factor\": \"Engagement\", \"survey_type\": \"Onboard Survey (Single Point)\"}, {\"code\": \"Wel-Ener-3\", \"description\": \"I usually have enough energy to overcome challenges at work\", \"factor\": \"Energy\", \"survey_type\": \"Wellbeing Survey\"}, {\"code\": \"Exi-Enga-3\", \"description\": \"For most of my time with %ACCOUNT_NAME% I rarely thought about looking for a job at another company\", \"factor\": \"Engagement\", \"survey_type\": \"Exit Survey\"}, {\"code\": \"All-Idea-4\", \"description\": \"%ACCOUNT_NAME% has established effective channels for us to pitch ideas\", \"factor\": \"Idea Ecosystem\", \"survey_type\": \"The Alliance Diagnostic\"}, {\"code\": \"Can-Inte-5\", \"description\": \"The interview sessions increased my excitement for the role\", \"factor\": \"Interviews\", \"survey_type\": \"Candidate Survey\"}, {\"code\": \"Exi-Your-2\", \"description\": \"Is there something we could have done to keep you with %ACCOUNT_NAME%?\", \"factor\": \"Your Decision\", \"survey_type\": \"Exit Survey\"}, {\"code\": \"Tea-Over-2\", \"description\": \"I am proud of what we accomplish as a team\", \"factor\": \"Overall Team Effectiveness\", \"survey_type\": \"Team Effectiveness\"}, {\"code\": \"Exi-The -4\", \"description\": \"Reviewer Comments on The Future\", \"factor\": \"The Future\", \"survey_type\": \"Exit Survey\"}, {\"code\": \"Tea-Over-5\", \"description\": \"I feel I belong on this team\", \"factor\": \"Overall Team Effectiveness\", \"survey_type\": \"Team Effectiveness\"}, {\"code\": \"Ons-Recr-2\", \"description\": \"I was provided accurate information about %ACCOUNT_NAME% during the recruitment process\", \"factor\": \"Recruitment\", \"survey_type\": \"Onboard Survey (Single Point)\"}, {\"code\": \"Exi-Your-4\", \"description\": \"Please indicate the 3 main reasons you are leaving %ACCOUNT_NAME%:\", \"factor\": \"Your Decision\", \"survey_type\": \"Exit Survey\"}, {\"code\": \"Wli.1\", \"description\": \"Generally, I believe my workload is reasonable for my role\", \"factor\": \"Work & Life Blend\", \"survey_type\": \"Engagement\"}, {\"code\": \"Ben-Unde-5\", \"description\": \"Select the perk you think would be the best new addition to the company\\'s offering\", \"factor\": \"Understanding Existing Perks\", \"survey_type\": \"Benefits Survey\"}, {\"code\": \"All-Idea-5\", \"description\": \"%ACCOUNT_NAME% helps us take promising ideas through to implementation/testing\", \"factor\": \"Idea Ecosystem\", \"survey_type\": \"The Alliance Diagnostic\"}, {\"code\": \"On5-Expe-8\", \"description\": \"Reviewer Comments on Onboarding Checklist\", \"factor\": \"Experience\", \"survey_type\": \"Onboard Survey (Phased Week 5 )\"}, {\"code\": \"Di-Bel-4\", \"description\": \"I feel like I belong at %ACCOUNT_NAME%\", \"factor\": \"Belonging\", \"survey_type\": \"Inclusion Survey\"}, {\"code\": \"Mgrcoa.1\", \"description\": \"My manager makes time for one-on-one meetings with me\", \"factor\": \"Coaching\", \"survey_type\": \"Supplementary ME\"}, {\"code\": \"Eng.2\", \"description\": \"%ACCOUNT_NAME% motivates me to go beyond what I would in a similar role elsewhere\", \"factor\": \"Engagement\", \"survey_type\": \"Onboard Survey (Phased Week 5 )\"}, {\"code\": \"Can-Recr-4\", \"description\": \"The recruitment process gave me a good insight into the company\\'s culture and values\", \"factor\": \"Recruitment\", \"survey_type\": \"Candidate Survey\"}, {\"code\": \"Int-Comm-3\", \"description\": \"What would you consider the most desirable companies to work for?\", \"factor\": \"Comments\", \"survey_type\": \"Intern Survey\"}, {\"code\": \"Mgrcoa.4\", \"description\": \"My manager regularly gives me feedback on how well I am performing\", \"factor\": \"Coaching\", \"survey_type\": \"Supplementary ME\"}, {\"code\": \"Man.3\", \"description\": \"My manager is a great role model for employees\", \"factor\": \"Leadership\", \"survey_type\": \"Quick Engagement Survey\"}, {\"code\": \"On1-Your-5\", \"description\": \"Reviewer Comments on Engagement\", \"factor\": \"Your Decision\", \"survey_type\": \"Onboard Survey (Phased Week 1)\"}, {\"code\": \"Ena.3\", \"description\": \"The information I need to do my job effectively is readily available\", \"factor\": \"Enablement\", \"survey_type\": \"Engagement\"}, {\"code\": \"All-Exte-1\", \"description\": \"We are encouraged to seek help or knowledge by connecting with people outside of %ACCOUNT_NAME%\", \"factor\": \"External Learning\", \"survey_type\": \"The Alliance Diagnostic\"}, {\"code\": \"Can-Over-4\", \"description\": \"I would encourage other people to apply at %ACCOUNT_NAME%\", \"factor\": \"Overall\", \"survey_type\": \"Candidate Survey\"}, {\"code\": \"Can-Recr-1\", \"description\": \"The recruitment team was well organized and professional to deal with\", \"factor\": \"Recruitment\", \"survey_type\": \"Candidate Survey\"}, {\"code\": \"Ena.5\", \"description\": \"Most of the systems and processes here support us getting our work done effectively\", \"factor\": \"Enablement\", \"survey_type\": \"Quick Engagement Survey\"}, {\"code\": \"Val-Perf-1\", \"description\": \"%ACCOUNT_NAME% really demonstrates a day-to-day commitment to [INSERT VALUE X]\", \"factor\": \"Performance\", \"survey_type\": \"Values Survey\"}, {\"code\": \"Ben-Exis-2\", \"description\": \"I believe my benefits package at %ACCOUNT_NAME% is equal to or better than what is offered by similar employers\", \"factor\": \"Existing Benefits\", \"survey_type\": \"Benefits Survey\"}, {\"code\": \"Tea-Comm-1\", \"description\": \"Overall, team members are provided equal opportunities to speak during team discussions\", \"factor\": \"Communication and Collaboration\", \"survey_type\": \"Team Effectiveness\"}, {\"code\": \"Mgrcar.5\", \"description\": \"I would speak to my manager if I was thinking about leaving %ACCOUNT_NAME%\", \"factor\": \"Caring\", \"survey_type\": \"Supplementary ME\"}, {\"code\": \"Ons-Orga-4\", \"description\": \"I understand how my role contibutes to the organizational goals of %ACCOUNT_NAME%\", \"factor\": \"Organizational Alignment\", \"survey_type\": \"Onboard Survey (Phased Week 5 )\"}, {\"code\": \"All-Idea-1\", \"description\": \"We are encouraged to take risks if we feel it might positively affect the work\", \"factor\": \"Idea Ecosystem\", \"survey_type\": \"The Alliance Diagnostic\"}, {\"code\": \"Ons-Indu-10\", \"description\": \"My on-the-job training has been effective\", \"factor\": \"Induction\", \"survey_type\": \"Onboard Survey (Single Point)\"}, {\"code\": \"Mgrinn.2\", \"description\": \"My manager encourages innovative ideas and approaches\", \"factor\": \"Fostering Innovation\", \"survey_type\": \"Supplementary ME\"}, {\"code\": \"All-Chec-3\", \"description\": \"Speaking openly about obstacles to accomplishing goals (getting the work done) is encouraged here\", \"factor\": \"Check Ins\", \"survey_type\": \"The Alliance Diagnostic\"}, {\"code\": \"Mgrinn.1\", \"description\": \"My manager gives me enough autonomy to make my own decisions \", \"factor\": \"Fostering Innovation\", \"survey_type\": \"Manager Effectiveness\"}, {\"code\": \"Eng.3\", \"description\": \"I am proud to work for %ACCOUNT_NAME%\", \"factor\": \"Checklist\", \"survey_type\": \"Onboard Survey (Phased Week 1)\"}, {\"code\": \"Mgreff.2\", \"description\": \"My manager helps me stay motivated to do my best work\", \"factor\": \"Overall Manager Effectiveness\", \"survey_type\": \"Manager Effectiveness\"}, {\"code\": \"Exi-Enab-3\", \"description\": \"I had access to the things I needed to do my job well\", \"factor\": \"Enablement\", \"survey_type\": \"Exit Survey\"}, {\"code\": \"Lea.2\", \"description\": \"The leaders at %ACCOUNT_NAME% demonstrate that people are important to the company\\'s success\", \"factor\": \"Trust & Communication\", \"survey_type\": \"The Alliance Diagnostic\"}, {\"code\": \"Dev.2\", \"description\": \"I believe there are good career opportunities for me at %ACCOUNT_NAME%\", \"factor\": \"Development\", \"survey_type\": \"Quick Engagement Survey\"}, {\"code\": \"Fee.1\", \"description\": \"I receive appropriate recognition for good work at %ACCOUNT_NAME%\", \"factor\": \"Feedback & Recognition\", \"survey_type\": \"Engagement\"}, {\"code\": \"Int-Over-3\", \"description\": \"Overall how satisfied were you with your intern experience at %ACCOUNT_NAME%?\", \"factor\": \"Overall\", \"survey_type\": \"Intern Survey\"}, {\"code\": \"Ons-Role-3\", \"description\": \"Reviewer Comments on Role Perceptions\", \"factor\": \"Role Perceptions\", \"survey_type\": \"Onboard Survey (Single Point)\"}, {\"code\": \"Tea-Over-4\", \"description\": \"Being on this team enables me to do my best work\", \"factor\": \"Overall Team Effectiveness\", \"survey_type\": \"Team Effectiveness\"}, {\"code\": \"Can-Awar-5\", \"description\": \"What were you doing before you applied to work at %ACCOUNT_NAME%?\", \"factor\": \"Awareness\", \"survey_type\": \"Candidate Survey\"}, {\"code\": \"Tea-Open-2\", \"description\": \"When I contribute ideas and thoughts, I believe my opinion is valued\", \"factor\": \"Openness\", \"survey_type\": \"Team Effectiveness\"}, {\"code\": \"Ind-Mana-5\", \"description\": \"Working alongside the team\", \"factor\": \"Manager Feedback\", \"survey_type\": \"Individual Effectiveness\"}, {\"code\": \"Can-Awar-1\", \"description\": \"What things most attracted you to applying at %ACCOUNT_NAME%\", \"factor\": \"Awareness\", \"survey_type\": \"Candidate Survey\"}, {\"code\": \"Wli.3\", \"description\": \"I am able to arrange time out from work when I need to\", \"factor\": \"Work & Life Blend\", \"survey_type\": \"Engagement\"}, {\"code\": \"Ons-Expe-3\", \"description\": \"I am feeling welcome here\", \"factor\": \"Experience\", \"survey_type\": \"Onboard Survey (Phased Week 5 )\"}, {\"code\": \"Di-Dem-3\", \"description\": \"I am satisfied with how decisions are made at %ACCOUNT_NAME%\", \"factor\": \"Decision Making\", \"survey_type\": \"Inclusion Survey\"}, {\"code\": \"Eng.1\", \"description\": \"I would recommend %ACCOUNT_NAME% as a great place to work\", \"factor\": \"Engagement\", \"survey_type\": \"Exit Survey\"}, {\"code\": \"On5-Expe-7\", \"description\": \"Reviewer Comments on Recruitment\", \"factor\": \"Experience\", \"survey_type\": \"Onboard Survey (Phased Week 5 )\"}, {\"code\": \"Ben-Exis-6\", \"description\": \"Should I become unable to work, my disability coverage provides sufficient coverage\", \"factor\": \"Existing Benefits\", \"survey_type\": \"Benefits Survey\"}, {\"code\": \"All-Inve-3\", \"description\": \"The effort I invest in my work is a fair exchange for what I have gained career-wise at %ACCOUNT_NAME%\", \"factor\": \"Career Investment\", \"survey_type\": \"The Alliance Diagnostic\"}, {\"code\": \"Tea-Know-1\", \"description\": \"My expertise is well utilized on this team\", \"factor\": \"Knowledge and Capability\", \"survey_type\": \"Team Effectiveness\"}, {\"code\": \"Exi-Lead-4\", \"description\": \"Reviewer Comments on Leadership\", \"factor\": \"Leadership\", \"survey_type\": \"Exit Survey\"}, {\"code\": \"Mgrdev.4\", \"description\": \"My manager has a good understanding of my longer term career aspirations (not necessarily just within %ACCOUNT_NAME%)\", \"factor\": \"Career Conversations\", \"survey_type\": \"The Alliance Diagnostic\"}, {\"code\": \"Ons-Indu-9\", \"description\": \"The information provided has been at the right level for me\", \"factor\": \"Induction\", \"survey_type\": \"Onboard Survey (Single Point)\"}, {\"code\": \"Exi-The -2\", \"description\": \"What will you be moving to when you leave %ACCOUNT_NAME%?\", \"factor\": \"The Future\", \"survey_type\": \"Exit Survey\"}, {\"code\": \"Man.1\", \"description\": \"My manager genuinely cares about my wellbeing\", \"factor\": \"Caring\", \"survey_type\": \"Manager Effectiveness\"}, {\"code\": \"Mgrdev.2\", \"description\": \"My manager shows a genuine interest in my career at %ACCOUNT_NAME% \", \"factor\": \"Development\", \"survey_type\": \"Supplementary ME\"}, {\"code\": \"Mgrinn.3\", \"description\": \"My manager helps me follow through on innovative ideas\", \"factor\": \"Fostering Innovation\", \"survey_type\": \"Manager Effectiveness\"}, {\"code\": \"Ons-Expe-4\", \"description\": \"I am feeling productive\", \"factor\": \"Experience\", \"survey_type\": \"Onboard Survey (Single Point)\"}, {\"code\": \"Eng.5\", \"description\": \"I see myself still working at %ACCOUNT_NAME% in two years\\' time\", \"factor\": \"Engagement\", \"survey_type\": \"Engagement\"}, {\"code\": \"Di-Fan-1\", \"description\": \"I believe that my total compensation (base salary+any bonuses+benefits+equity) is fair, relative to similar roles at %ACCOUNT_NAME%\", \"factor\": \"Fairness\", \"survey_type\": \"Inclusion Survey\"}, {\"code\": \"Ons-Orga-1\", \"description\": \"I know what the organizational values of %ACCOUNT_NAME% are\", \"factor\": \"Organizational Alignment\", \"survey_type\": \"Onboard Survey (Single Point)\"}, {\"code\": \"Exi-Othe-1\", \"description\": \"Reviewer Comments on Other\", \"factor\": \"Other\", \"survey_type\": \"Exit Survey\"}, {\"code\": \"Mgrfai.1\", \"description\": \"My manager frequently recognises when I deliver good work\", \"factor\": \"Fair Treatment\", \"survey_type\": \"Manager Effectiveness\"}, {\"code\": \"Can-Inte-3\", \"description\": \"My interviewers asked me interesting questions that assessed my skills for performing in the role\", \"factor\": \"Interviews\", \"survey_type\": \"Candidate Survey\"}, {\"code\": \"Ons-Role-1\", \"description\": \"My role so far matches the role description provided to me\", \"factor\": \"Role Perceptions\", \"survey_type\": \"Onboard Survey (Single Point)\"}, {\"code\": \"Mgrdev.4\", \"description\": \"My manager has a good understanding of my longer term career aspirations (not necessarily just within %ACCOUNT_NAME%)\", \"factor\": \"Development\", \"survey_type\": \"Supplementary ME\"}, {\"code\": \"Mgrdev.3\", \"description\": \"My manager helps me understand potential career paths at %ACCOUNT_NAME%\", \"factor\": \"Development\", \"survey_type\": \"Manager Effectiveness\"}, {\"code\": \"Ons-Expe-1\", \"description\": \"Is there anything else you would have liked included in your onboarding process?\", \"factor\": \"Experience\", \"survey_type\": \"Onboard Survey (Phased Week 5 )\"}]'" ] }, "execution_count": 92, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ref_question_dict = [\n", " {\n", " 'code': row['code'],\n", " 'description': row['description'],\n", " 'factor': row['factor'],\n", " 'survey_type': row['survey_type']\n", " }\n", " for i, row in ref_question_df.iterrows()\n", "]\n", "json.dumps(ref_question_dict)" ] }, { "cell_type": "code", "execution_count": 93, "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>created_at</th>\n", " <th>updated_at</th>\n", " <th>id</th>\n", " <th>name</th>\n", " <th>excerpt</th>\n", " <th>description</th>\n", " <th>status</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>2017-08-17 11:04:35.065515</td>\n", " <td>2018-01-25 04:20:45.913654</td>\n", " <td>fade6361-3514-45f9-8ad7-9be84dafb609</td>\n", " <td>Expanded career conversations</td>\n", " <td>Hold mid-year career conversations</td>\n", " <td>Hold more regular career-focused conversations...</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2017-08-17 11:01:07.333956</td>\n", " <td>2018-01-25 04:20:45.787361</td>\n", " <td>4e1e81a2-9f33-4473-83dc-c6b8e4917a93</td>\n", " <td>Anonymous questions</td>\n", " <td>Create anonymous comms channel for employees</td>\n", " <td>Make space for feedback and questions from all...</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2017-08-17 11:00:49.831106</td>\n", " <td>2018-01-25 04:20:45.809673</td>\n", " <td>622179d7-b06b-4493-9b17-9f8d5ff5b7b0</td>\n", " <td>Skip level meetings</td>\n", " <td>Implement skip level meetings around decision ...</td>\n", " <td>Foster open communication and alignment using ...</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>2017-08-17 11:00:53.884463</td>\n", " <td>2018-01-25 04:20:45.794741</td>\n", " <td>6b0d0bd5-ce50-480c-b937-7faab0026b19</td>\n", " <td>Seat shuffle</td>\n", " <td>Get to know others in the business</td>\n", " <td>Encouraging employees to get to know others ou...</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>2017-08-17 11:01:02.865682</td>\n", " <td>2018-01-25 04:20:45.773347</td>\n", " <td>0826f61d-c88d-4842-b6c2-5ed20fd172d1</td>\n", " <td>Who's who in the zoo</td>\n", " <td>Keeping employees connected</td>\n", " <td>Growing quickly can result in newcomers findin...</td>\n", " <td>None</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " created_at updated_at \\\n", "0 2017-08-17 11:04:35.065515 2018-01-25 04:20:45.913654 \n", "1 2017-08-17 11:01:07.333956 2018-01-25 04:20:45.787361 \n", "2 2017-08-17 11:00:49.831106 2018-01-25 04:20:45.809673 \n", "3 2017-08-17 11:00:53.884463 2018-01-25 04:20:45.794741 \n", "4 2017-08-17 11:01:02.865682 2018-01-25 04:20:45.773347 \n", "\n", " id name \\\n", "0 fade6361-3514-45f9-8ad7-9be84dafb609 Expanded career conversations \n", "1 4e1e81a2-9f33-4473-83dc-c6b8e4917a93 Anonymous questions \n", "2 622179d7-b06b-4493-9b17-9f8d5ff5b7b0 Skip level meetings \n", "3 6b0d0bd5-ce50-480c-b937-7faab0026b19 Seat shuffle \n", "4 0826f61d-c88d-4842-b6c2-5ed20fd172d1 Who's who in the zoo \n", "\n", " excerpt \\\n", "0 Hold mid-year career conversations \n", "1 Create anonymous comms channel for employees \n", "2 Implement skip level meetings around decision ... \n", "3 Get to know others in the business \n", "4 Keeping employees connected \n", "\n", " description status \n", "0 Hold more regular career-focused conversations... None \n", "1 Make space for feedback and questions from all... None \n", "2 Foster open communication and alignment using ... None \n", "3 Encouraging employees to get to know others ou... None \n", "4 Growing quickly can result in newcomers findin... None " ] }, "execution_count": 93, "metadata": {}, "output_type": "execute_result" } ], "source": [ "core_insp_df.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ins" ] }, { "cell_type": "code", "execution_count": 95, "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>created_at</th>\n", " <th>updated_at</th>\n", " <th>code</th>\n", " <th>insp_id</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>2018-08-17 06:38:10.862574</td>\n", " <td>2018-09-24 07:15:20.196662</td>\n", " <td>ALI.5</td>\n", " <td>fade6361-3514-45f9-8ad7-9be84dafb609</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2018-08-17 06:38:10.878194</td>\n", " <td>2018-09-24 07:15:20.212292</td>\n", " <td>COL.1</td>\n", " <td>622179d7-b06b-4493-9b17-9f8d5ff5b7b0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2018-08-17 06:38:10.893819</td>\n", " <td>2018-09-24 07:15:20.212292</td>\n", " <td>COL.1</td>\n", " <td>4e1e81a2-9f33-4473-83dc-c6b8e4917a93</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>2018-08-17 06:38:10.893819</td>\n", " <td>2018-09-24 07:15:20.227911</td>\n", " <td>COL.1</td>\n", " <td>aef097ea-4280-4fc9-a5dc-447d62f31d55</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>2018-08-17 06:38:10.909445</td>\n", " <td>2018-09-24 07:15:20.227911</td>\n", " <td>COL.2</td>\n", " <td>6b0d0bd5-ce50-480c-b937-7faab0026b19</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " created_at updated_at code \\\n", "0 2018-08-17 06:38:10.862574 2018-09-24 07:15:20.196662 ALI.5 \n", "1 2018-08-17 06:38:10.878194 2018-09-24 07:15:20.212292 COL.1 \n", "2 2018-08-17 06:38:10.893819 2018-09-24 07:15:20.212292 COL.1 \n", "3 2018-08-17 06:38:10.893819 2018-09-24 07:15:20.227911 COL.1 \n", "4 2018-08-17 06:38:10.909445 2018-09-24 07:15:20.227911 COL.2 \n", "\n", " insp_id \n", "0 fade6361-3514-45f9-8ad7-9be84dafb609 \n", "1 622179d7-b06b-4493-9b17-9f8d5ff5b7b0 \n", "2 4e1e81a2-9f33-4473-83dc-c6b8e4917a93 \n", "3 aef097ea-4280-4fc9-a5dc-447d62f31d55 \n", "4 6b0d0bd5-ce50-480c-b937-7faab0026b19 " ] }, "execution_count": 95, "metadata": {}, "output_type": "execute_result" } ], "source": [ "core_insp_to_ref_question_df = pd.read_sql_query(f\"select * from core_insp_to_ref_question where insp_id in {id2_tuple}\", con=engine)\n", "core_insp_to_ref_question_df.head()" ] }, { "cell_type": "code", "execution_count": 99, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'[{\"code\": \"ALI.5\", \"insp_id\": \"fade6361-3514-45f9-8ad7-9be84dafb609\", \"created_at\": \"2018-08-17 06:38:10.862574\", \"updated_at\": \"2018-09-24 07:15:20.196662\"}, {\"code\": \"COL.1\", \"insp_id\": \"622179d7-b06b-4493-9b17-9f8d5ff5b7b0\", \"created_at\": \"2018-08-17 06:38:10.878194\", \"updated_at\": \"2018-09-24 07:15:20.212292\"}, {\"code\": \"COL.1\", \"insp_id\": \"4e1e81a2-9f33-4473-83dc-c6b8e4917a93\", \"created_at\": \"2018-08-17 06:38:10.893819\", \"updated_at\": \"2018-09-24 07:15:20.212292\"}, {\"code\": \"COL.1\", \"insp_id\": \"aef097ea-4280-4fc9-a5dc-447d62f31d55\", \"created_at\": \"2018-08-17 06:38:10.893819\", \"updated_at\": \"2018-09-24 07:15:20.227911\"}, {\"code\": \"COL.2\", \"insp_id\": \"6b0d0bd5-ce50-480c-b937-7faab0026b19\", \"created_at\": \"2018-08-17 06:38:10.909445\", \"updated_at\": \"2018-09-24 07:15:20.227911\"}, {\"code\": \"COL.2\", \"insp_id\": \"0826f61d-c88d-4842-b6c2-5ed20fd172d1\", \"created_at\": \"2018-08-17 06:38:10.909445\", \"updated_at\": \"2018-09-24 07:15:20.243538\"}, {\"code\": \"COL.2\", \"insp_id\": \"aef097ea-4280-4fc9-a5dc-447d62f31d55\", \"created_at\": \"2018-08-17 06:38:10.909445\", \"updated_at\": \"2018-09-24 07:15:20.243538\"}, {\"code\": \"COL.2\", \"insp_id\": \"33b35e97-4627-4e79-92bb-64a7842ed118\", \"created_at\": \"2018-08-17 06:38:10.925069\", \"updated_at\": \"2018-09-24 07:15:20.243538\"}, {\"code\": \"COL.3\", \"insp_id\": \"6b0d0bd5-ce50-480c-b937-7faab0026b19\", \"created_at\": \"2018-08-17 06:38:10.925069\", \"updated_at\": \"2018-09-24 07:15:20.259162\"}, {\"code\": \"DEV.1\", \"insp_id\": \"fade6361-3514-45f9-8ad7-9be84dafb609\", \"created_at\": \"2018-08-17 06:38:10.987569\", \"updated_at\": \"2018-09-24 07:15:20.321637\"}, {\"code\": \"DEV.1\", \"insp_id\": \"3a61033b-429c-42ee-aa32-f51d18dfffce\", \"created_at\": \"2018-08-17 06:38:10.987569\", \"updated_at\": \"2018-09-24 07:15:20.321637\"}, {\"code\": \"DEV.1\", \"insp_id\": \"065c067a-8127-4290-b9e0-695ef5ebb2b3\", \"created_at\": \"2018-08-17 06:38:11.003199\", \"updated_at\": \"2018-09-24 07:15:20.337265\"}, {\"code\": \"DEV.4\", \"insp_id\": \"fade6361-3514-45f9-8ad7-9be84dafb609\", \"created_at\": \"2018-08-17 06:38:11.050070\", \"updated_at\": \"2018-09-24 07:15:20.384139\"}, {\"code\": \"ENA.2\", \"insp_id\": \"ba678913-7756-49d6-9351-40680df96bc6\", \"created_at\": \"2018-08-17 06:38:11.096944\", \"updated_at\": \"2018-09-24 07:15:20.415385\"}, {\"code\": \"ENA.3\", \"insp_id\": \"aef097ea-4280-4fc9-a5dc-447d62f31d55\", \"created_at\": \"2018-08-17 06:38:11.096944\", \"updated_at\": \"2018-09-24 07:15:20.431011\"}, {\"code\": \"FEE.1\", \"insp_id\": \"5bc61e87-87db-4829-bff5-401802755e0a\", \"created_at\": \"2018-08-17 06:38:11.175070\", \"updated_at\": \"2018-09-24 07:15:20.493517\"}, {\"code\": \"FEE.3\", \"insp_id\": \"6f94343e-e9e5-4e69-a69d-7613e969214f\", \"created_at\": \"2018-08-17 06:38:11.190695\", \"updated_at\": \"2018-09-24 07:15:20.509142\"}, {\"code\": \"FEE.4\", \"insp_id\": \"5bc61e87-87db-4829-bff5-401802755e0a\", \"created_at\": \"2018-08-17 06:38:11.221945\", \"updated_at\": \"2018-09-24 07:15:20.540393\"}, {\"code\": \"INN.2\", \"insp_id\": \"931d7762-9fa4-4a3f-85e3-f8bf75f8f903\", \"created_at\": \"2018-08-17 06:38:11.253198\", \"updated_at\": \"2018-09-24 07:15:20.571646\"}, {\"code\": \"LEA.1\", \"insp_id\": \"4e1e81a2-9f33-4473-83dc-c6b8e4917a93\", \"created_at\": \"2018-08-17 06:38:11.268819\", \"updated_at\": \"2018-09-24 07:15:20.587281\"}, {\"code\": \"LEA.2\", \"insp_id\": \"622179d7-b06b-4493-9b17-9f8d5ff5b7b0\", \"created_at\": \"2018-08-17 06:38:11.284444\", \"updated_at\": \"2018-08-27 22:33:12.269460\"}, {\"code\": \"SER.1\", \"insp_id\": \"33b35e97-4627-4e79-92bb-64a7842ed118\", \"created_at\": \"2018-08-17 06:38:11.393823\", \"updated_at\": \"2018-09-24 07:15:20.712272\"}, {\"code\": \"SER.1\", \"insp_id\": \"1f60e471-1c2b-4ba9-8130-b4c3a0fb0956\", \"created_at\": \"2018-08-17 06:38:11.409448\", \"updated_at\": \"2018-09-24 07:15:20.712272\"}, {\"code\": \"SOC.1\", \"insp_id\": \"5bc61e87-87db-4829-bff5-401802755e0a\", \"created_at\": \"2018-08-17 06:38:11.440694\", \"updated_at\": \"2018-09-24 07:15:20.743517\"}, {\"code\": \"TEA.1\", \"insp_id\": \"6b0d0bd5-ce50-480c-b937-7faab0026b19\", \"created_at\": \"2018-08-17 06:38:11.471945\", \"updated_at\": \"2018-09-24 07:15:20.774769\"}, {\"code\": \"TEA.1\", \"insp_id\": \"ba678913-7756-49d6-9351-40680df96bc6\", \"created_at\": \"2018-08-17 06:38:11.471945\", \"updated_at\": \"2018-09-24 07:15:20.774769\"}, {\"code\": \"TEA.1\", \"insp_id\": \"67e03ed1-02e2-4b2f-89b0-f607c1236212\", \"created_at\": \"2018-08-17 06:38:11.503175\", \"updated_at\": \"2018-09-24 07:15:20.806025\"}, {\"code\": \"TEA.1\", \"insp_id\": \"c43f038b-c1cf-4204-a2f1-c1e6aa86f587\", \"created_at\": \"2018-08-17 06:38:11.503175\", \"updated_at\": \"2018-09-24 07:15:20.806025\"}, {\"code\": \"TEA.3\", \"insp_id\": \"40b6b2a8-0d03-4212-9969-3751d711a118\", \"created_at\": \"2018-08-17 06:38:11.518820\", \"updated_at\": \"2018-09-24 07:15:20.821646\"}, {\"code\": \"TEA.3\", \"insp_id\": \"c43f038b-c1cf-4204-a2f1-c1e6aa86f587\", \"created_at\": \"2018-08-17 06:38:11.518820\", \"updated_at\": \"2018-09-24 07:15:20.821646\"}, {\"code\": \"WLI.1\", \"insp_id\": \"c43f038b-c1cf-4204-a2f1-c1e6aa86f587\", \"created_at\": \"2018-08-17 06:38:11.534449\", \"updated_at\": \"2018-09-24 07:15:20.837277\"}, {\"code\": \"WLI.3\", \"insp_id\": \"065c067a-8127-4290-b9e0-695ef5ebb2b3\", \"created_at\": \"2018-08-17 06:38:11.565664\", \"updated_at\": \"2018-09-24 07:15:20.868531\"}, {\"code\": \"MGRCOM.5\", \"insp_id\": \"6b0d0bd5-ce50-480c-b937-7faab0026b19\", \"created_at\": \"2018-08-17 06:38:11.628167\", \"updated_at\": \"2018-09-24 07:15:20.915403\"}, {\"code\": \"MGRVIS.2\", \"insp_id\": \"622179d7-b06b-4493-9b17-9f8d5ff5b7b0\", \"created_at\": \"2018-08-17 06:38:11.768788\", \"updated_at\": \"2018-09-24 07:15:21.040406\"}, {\"code\": \"MGRDEV.1\", \"insp_id\": \"1ea5dcec-a94d-42df-8104-1f85f43c22d9\", \"created_at\": \"2018-08-17 06:38:11.925038\", \"updated_at\": \"2018-09-24 07:15:21.243527\"}, {\"code\": \"MGRDEV.2\", \"insp_id\": \"fade6361-3514-45f9-8ad7-9be84dafb609\", \"created_at\": \"2018-08-17 06:38:11.925038\", \"updated_at\": \"2018-09-24 07:15:21.259157\"}, {\"code\": \"MGRDEV.4\", \"insp_id\": \"fade6361-3514-45f9-8ad7-9be84dafb609\", \"created_at\": \"2018-08-17 06:38:11.940663\", \"updated_at\": \"2018-09-24 07:15:21.259157\"}, {\"code\": \"MGRFAI.3\", \"insp_id\": \"1ea5dcec-a94d-42df-8104-1f85f43c22d9\", \"created_at\": \"2018-08-17 06:38:11.956291\", \"updated_at\": \"2018-09-24 07:15:21.274787\"}, {\"code\": \"MGRVIS.1\", \"insp_id\": \"622179d7-b06b-4493-9b17-9f8d5ff5b7b0\", \"created_at\": \"2018-08-17 06:38:11.971917\", \"updated_at\": \"2018-08-27 22:33:12.925637\"}, {\"code\": \"FEE.1\", \"insp_id\": \"1ea5dcec-a94d-42df-8104-1f85f43c22d9\", \"created_at\": \"2018-08-17 06:38:11.175070\", \"updated_at\": \"2018-09-24 07:15:20.493517\"}, {\"code\": \"DI-DEM-3\", \"insp_id\": \"622179d7-b06b-4493-9b17-9f8d5ff5b7b0\", \"created_at\": \"2018-09-24 05:56:02.215782\", \"updated_at\": \"2018-09-24 07:15:21.134153\"}]'" ] }, "execution_count": 99, "metadata": {}, "output_type": "execute_result" } ], "source": [ "core_insp_to_ref_question_dict = [\n", " {\n", " \"code\": row[\"code\"],\n", " \"insp_id\": str(row[\"insp_id\"]),\n", " \"created_at\": str(row[\"created_at\"]),\n", " \"updated_at\": str(row[\"updated_at\"])\n", " }\n", " for i, row in core_insp_to_ref_question_df.iterrows()\n", "]\n", "json.dumps(core_insp_to_ref_question_dict)" ] }, { "cell_type": "code", "execution_count": 101, "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>created_at</th>\n", " <th>updated_at</th>\n", " <th>action_id</th>\n", " <th>question_id</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>2018-08-17 06:38:24.049668</td>\n", " <td>2018-09-24 07:15:30.602841</td>\n", " <td>f0b6ca31-7841-498e-a115-5574f5a6086f</td>\n", " <td>0f5bda39-8c0e-449a-96d3-6c8f7614fc63</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2018-08-17 06:38:29.565089</td>\n", " <td>2018-09-24 07:15:35.696549</td>\n", " <td>52627568-895f-49ac-9797-f3aad86e1b6f</td>\n", " <td>642f3703-ad5c-45af-8c6d-098802a48065</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2018-08-17 06:38:31.299419</td>\n", " <td>2018-09-24 07:15:37.337148</td>\n", " <td>a56d35c9-f274-4fd5-a7ac-681ce775529c</td>\n", " <td>f0f04a05-3b40-4b47-b222-fc7f034f28e4</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>2018-08-17 06:38:31.330669</td>\n", " <td>2018-09-24 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a56d35c9-f274-4fd5-a7ac-681ce775529c f0f04a05-3b40-4b47-b222-fc7f034f28e4 \n", "3 8bf4dd83-7247-416e-9043-94f621d941f6 890c2624-9068-4335-b7b5-745d8a293a01 \n", "4 34dcfc05-c3c4-4650-bb9c-aed2b1c3d5b9 95c9179a-3323-48f1-9f5e-3ecd0e3c4fc0 " ] }, "execution_count": 101, "metadata": {}, "output_type": "execute_result" } ], "source": [ "action_to_question_df = pd.read_sql_query(f\"select * from action_to_question where action_id in {id1_tuple}\", con=engine)\n", "action_to_question_df.head()" ] }, { "cell_type": "code", "execution_count": 103, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'[{\"action_id\": \"f0b6ca31-7841-498e-a115-5574f5a6086f\", \"question_id\": \"0f5bda39-8c0e-449a-96d3-6c8f7614fc63\", \"created_at\": \"2018-08-17 06:38:24.049668\", \"updated_at\": \"2018-09-24 07:15:30.602841\"}, {\"action_id\": \"52627568-895f-49ac-9797-f3aad86e1b6f\", \"question_id\": \"642f3703-ad5c-45af-8c6d-098802a48065\", \"created_at\": \"2018-08-17 06:38:29.565089\", \"updated_at\": 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{\"action_id\": \"d93ac66c-5f83-4b47-ba1d-ee930a30ee1d\", \"question_id\": \"0e9f6c75-57f8-4a01-9277-b983c28e404e\", \"created_at\": \"2018-08-17 06:38:33.314970\", \"updated_at\": \"2018-09-24 07:15:39.134047\"}, {\"action_id\": \"52627568-895f-49ac-9797-f3aad86e1b6f\", \"question_id\": \"2c2f82d6-5a13-4835-ba04-36122f71b6d6\", \"created_at\": \"2018-08-17 06:38:29.565089\", \"updated_at\": \"2018-09-24 07:15:35.696549\"}, {\"action_id\": \"46051fb5-0547-4f2a-8c70-33387a27d0db\", \"question_id\": \"6b9395ea-5821-1bae-db4d-0863f10000cb\", \"created_at\": \"2018-08-17 06:38:33.439970\", \"updated_at\": \"2018-09-24 07:15:39.243424\"}, {\"action_id\": \"a961148f-df9e-44b7-8a0e-90a40f2a08ad\", \"question_id\": \"1dec0eb5-6269-43bf-8d2c-5bfb86630d02\", \"created_at\": \"2018-08-17 06:38:25.330887\", \"updated_at\": \"2018-09-24 07:15:31.774711\"}, {\"action_id\": \"f5d5f749-dce3-4e52-a151-9d6fda0d11f8\", \"question_id\": \"d1e24d2f-77de-4fd0-9236-0ed782fbd2d3\", \"created_at\": \"2018-08-17 06:38:25.330887\", \"updated_at\": \"2018-09-24 07:15:31.774711\"}, {\"action_id\": \"4bd4043c-eff1-4d42-b44f-d2e11cc007f7\", \"question_id\": \"66396665-f99b-4132-8914-8d3f8d172e2f\", \"created_at\": \"2018-08-17 06:38:25.955856\", \"updated_at\": \"2018-09-24 07:15:32.368445\"}, {\"action_id\": \"8f186a80-7ba4-4c59-95e3-cbeb785fba20\", \"question_id\": \"212492ec-a0ac-4f86-a9a8-257c81a25190\", \"created_at\": \"2018-08-17 06:38:27.112070\", \"updated_at\": \"2018-09-24 07:15:33.415311\"}, {\"action_id\": \"af9ac9e5-5fc0-41a2-b006-76277fd82ba4\", \"question_id\": \"150a180d-8ed4-4241-8355-632d3455693b\", \"created_at\": \"2018-08-17 06:38:28.690126\", \"updated_at\": \"2018-09-24 07:15:34.884059\"}, {\"action_id\": \"52627568-895f-49ac-9797-f3aad86e1b6f\", \"question_id\": \"95d6e52f-b70c-48a1-9aca-1fac5e5e364e\", \"created_at\": \"2018-08-17 06:38:29.565089\", \"updated_at\": \"2018-09-24 07:15:35.696549\"}, {\"action_id\": \"9136fa90-140a-4190-ad77-3e6990d4d699\", \"question_id\": \"a25da2c4-8c9b-4b61-8d43-d5b24301c746\", \"created_at\": \"2018-08-17 06:38:30.080714\", \"updated_at\": \"2018-09-24 07:15:36.180928\"}]'" ] }, "execution_count": 103, "metadata": {}, "output_type": "execute_result" } ], "source": [ "action_to_question_dict = [\n", " {\n", " \"action_id\": str(row[\"action_id\"]),\n", " \"question_id\": str(row[\"question_id\"]),\n", " \"created_at\": str(row[\"created_at\"]),\n", " \"updated_at\": str(row[\"updated_at\"])\n", " }\n", " for i, row in action_to_question_df.iterrows()\n", "]\n", "json.dumps(action_to_question_dict)" ] }, { "cell_type": "code", "execution_count": 107, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "('0f5bda39-8c0e-449a-96d3-6c8f7614fc63',\n", " '642f3703-ad5c-45af-8c6d-098802a48065',\n", " 'f0f04a05-3b40-4b47-b222-fc7f034f28e4',\n", " '890c2624-9068-4335-b7b5-745d8a293a01',\n", " '95c9179a-3323-48f1-9f5e-3ecd0e3c4fc0',\n", " '584f047b-236e-4d0f-90e9-2352085ef0cf',\n", " '11c909d1-e81d-457a-bd06-85b7351ca48c',\n", " '11c909d1-e81d-457a-bd06-85b7351ca48c',\n", " '85dca2cf-2967-464e-a3ac-1778fd7cd02c',\n", " '63981d1e-7f97-4d8b-9738-92af7129aa72',\n", " 'c122e65d-fcfe-492b-a1c6-b2503de3b7a8',\n", " '0e9f6c75-57f8-4a01-9277-b983c28e404e',\n", " '2c2f82d6-5a13-4835-ba04-36122f71b6d6',\n", " '6b9395ea-5821-1bae-db4d-0863f10000cb',\n", " '1dec0eb5-6269-43bf-8d2c-5bfb86630d02',\n", " 'd1e24d2f-77de-4fd0-9236-0ed782fbd2d3',\n", " '66396665-f99b-4132-8914-8d3f8d172e2f',\n", " '212492ec-a0ac-4f86-a9a8-257c81a25190',\n", " '150a180d-8ed4-4241-8355-632d3455693b',\n", " '95d6e52f-b70c-48a1-9aca-1fac5e5e364e',\n", " 'a25da2c4-8c9b-4b61-8d43-d5b24301c746')" ] }, "execution_count": 107, "metadata": {}, "output_type": "execute_result" } ], "source": [ "id1_tuple = tuple(str(row['id1']) for i, row in matched_df.iterrows())\n", "question_id_tuple = tuple(str(row['question_id']) for i, row in action_to_question_df.iterrows())\n", "\n", "question_id_tuple" ] }, { "cell_type": "code", "execution_count": 106, "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>created_at</th>\n", " <th>updated_at</th>\n", " <th>id</th>\n", " <th>description</th>\n", " <th>survey_type</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ "Empty DataFrame\n", "Columns: [created_at, updated_at, id, description, survey_type]\n", "Index: []" ] }, "execution_count": 106, "metadata": {}, "output_type": "execute_result" } ], "source": [ "question_df = pd.read_sql_query(f\"select * from question where id in {question_id_tuple}\", con=engine)\n", "question_df.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.6" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
kimkipyo/dss_git_kkp
통계, 머신러닝 복습/160620월_17일차_나이브 베이즈 Naive Bayes/2.실전 예제.ipynb
1
30021
{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# 베르누이의 경우 실습 예제" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[1 0 0]\n", " [1 1 1]\n", " [0 1 1]\n", " [0 1 0]\n", " [0 0 1]\n", " [1 1 1]]\n", "[ 0. 0. 1. 1. 1. 1.]\n" ] } ], "source": [ "X = np.array([[1,0,0],[1,1,1], [0,1,1],[0,1,0],[0,0,1],[1,1,1]])\n", "y0 = np.zeros(2)\n", "y1 = np.ones(4)\n", "y = np.hstack([y0, y1])\n", "print(X)\n", "print(y)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sklearn.naive_bayes import BernoulliNB\n", "clf_bern = BernoulliNB().fit(X, y)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0., 1.])" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf_bern.classes_" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 2., 4.])" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf_bern.class_count_" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 2., 1., 1.],\n", " [ 1., 3., 3.]])" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fc = clf_bern.feature_count_\n", "fc" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 1. , 0.5 , 0.5 ],\n", " [ 0.25, 0.75, 0.75]])" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fc / np.repeat(clf_bern.class_count_[:, np.newaxis], 3, axis=1)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "x_new = np.array([1,1,0])" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "array([[ 0.55862069, 0.44137931]])" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf_bern.predict_proba([x_new])" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0.75 , 0.5 , 0.5 ],\n", " [ 0.33333333, 0.66666667, 0.66666667]])" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.exp(clf_bern.feature_log_prob_)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0.75 , 0.5 , 0.5 ],\n", " [ 0.33333333, 0.66666667, 0.66666667]])" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "theta = np.exp(clf_bern.feature_log_prob_) #자동적으로 스무딩\n", "theta" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0.55862069, 0.44137931])" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "p = ((theta**x_new)*(1-theta)**(1-x_new)).prod(axis=1)*np.exp(clf_bern.class_log_prior_)\n", "p / p.sum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### X값 살짝 바뀐 경우(스무딩을 써야 하는 경우)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": true }, "outputs": [], "source": [ "X1 = np.array([[1,0,0],[1,0,1], [0,1,1],[0,1,0],[0,0,1],[1,1,1]])\n", "y01 = np.zeros(2)\n", "y11 = np.ones(4)\n", "y1 = np.hstack([y01, y11])" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": true }, "outputs": [], "source": [ "clf_bern1 = BernoulliNB().fit(X1, y1)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 2., 0., 1.],\n", " [ 1., 3., 3.]])" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fc1 = clf_bern1.feature_count_\n", "fc1" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 2., 2., 2.],\n", " [ 4., 4., 4.]])" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.repeat(clf_bern1.class_count_[:, np.newaxis], 3, axis=1)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 1. , 0. , 0.5 ],\n", " [ 0.25, 0.75, 0.75]])" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fc1 / np.repeat(clf_bern1.class_count_[:, np.newaxis], 3, axis=1)" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0.38755981, 0.61244019]])" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf_bern1.predict_proba([x_new])" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0.75 , 0.25 , 0.5 ],\n", " [ 0.33333333, 0.66666667, 0.66666667]])" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.exp(clf_bern1.feature_log_prob_)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0.75 , 0.25 , 0.5 ],\n", " [ 0.33333333, 0.66666667, 0.66666667]])" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "theta = np.exp(clf_bern1.feature_log_prob_) \n", "theta " ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0.38755981, 0.61244019])" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "p = ((theta**x_new)*(1-theta)**(1-x_new)).prod(axis=1)*np.exp(clf_bern1.class_log_prior_)\n", "p / p.sum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 다항의 경우 실습 예제" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[4 4 2]\n", " [4 3 3]\n", " [6 3 1]\n", " [4 6 0]\n", " [0 4 1]\n", " [1 3 1]\n", " [1 1 3]\n", " [0 3 2]]\n", "[ 0. 0. 0. 0. 1. 1. 1. 1.]\n" ] } ], "source": [ "X = np.array([[4,4,2],[4,3,3], [6,3,1],[4,6,0],[0,4,1],[1,3,1],[1,1,3],[0,3,2]])\n", "y0 = np.zeros(4)\n", "y1 = np.ones(4)\n", "y = np.hstack([y0, y1])\n", "print(X)\n", "print(y)" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.naive_bayes import MultinomialNB\n", "clf_mult = MultinomialNB().fit(X, y)" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0., 1.])" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf_mult.classes_" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 4., 4.])" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf_mult.class_count_" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 18., 16., 6.],\n", " [ 2., 11., 7.]])" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fc = clf_mult.feature_count_\n", "fc" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 40., 40., 40.],\n", " [ 20., 20., 20.]])" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.repeat(fc.sum(axis=1)[:, np.newaxis], 3, axis=1)" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0.45, 0.4 , 0.15],\n", " [ 0.1 , 0.55, 0.35]])" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fc / np.repeat(fc.sum(axis=1)[:, np.newaxis], 3, axis=1)" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1.0" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf_mult.alpha" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "array([[ 0.44186047, 0.39534884, 0.1627907 ],\n", " [ 0.13043478, 0.52173913, 0.34782609]])" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(fc + clf_mult.alpha) / (np.repeat(fc.sum(axis=1)[:, np.newaxis], 3, axis=1) + clf_mult.alpha * X.shape[1])" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 43., 43., 43.],\n", " [ 23., 23., 23.]])" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.repeat(fc.sum(axis=1)[:, np.newaxis], 3, axis=1) + clf_mult.alpha * X.shape[1]" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0.54574281, 0.45425719]])" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x_new1 = np.array([1,1,1])\n", "clf_mult.predict_proba([x_new1])" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0.59072628, 0.40927372]])" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x_new2 = np.array([2,2,2])\n", "clf_mult.predict_proba([x_new2])" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0.63424066, 0.36575934]])" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x_new3 = np.array([3,3,3])\n", "clf_mult.predict_proba([x_new3])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# 문제1" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "* feature와 target이 다음과 같을 때, 베르누이 나이브 베이지안 방법을 사용하여 다음 문제를 푸세요." ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": true }, "outputs": [], "source": [ "X = np.array([\n", " [1, 0, 0],\n", " [1, 0, 1],\n", " [0, 0, 1],\n", " [0, 0, 0],\n", " [1, 1, 1],\n", " [0, 1, 1],\n", " [0, 0, 1],\n", " [0, 1, 0],\n", " ])\n", "y = np.array([0,0,0,0,1,1,1,1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "(1) 사전 분포(prior) p(y)를 구하세요." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* p(y=0) = 0.5\n", "* p(y=1) = 0.5" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(0.5, 0.5)" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "py0, py1 = (y==0).sum()/len(y), (y==1).sum()/len(y)\n", "py0, py1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "(2) 스무딩 벡터 알파=0 일 때, 다음 x_new에 대해 우도(likelihood)함수 p(x|y)를 구하고 조건부 확률 분포 p(y|x)를 구하세요.(normalize 된 값이 아님!)\n", "* x_new = [1 1 0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<img src=\"1.png.jpg\" style=\"width:70%; margin: 0 auto 0 auto;\">" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "collapsed": true }, "outputs": [], "source": [ "x_new = np.array([1, 1, 0])" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0.5, 0. , 0.5])" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "theta0 = X[y==0, :].sum(axis=0)/len(X[y==0, :])\n", "theta0" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0.25, 0.75, 0.75])" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ "theta1 = X[y==1, :].sum(axis=0)/len(X[y==1, :])\n", "theta1" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.0" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" } ], "source": [ "likelihood0 = (theta0**x_new).prod()*((1-theta0)**(1-x_new)).prod()\n", "likelihood0" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.046875" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ "likelihood1 = (theta1**x_new).prod()*((1-theta1)**(1-x_new)).prod()\n", "likelihood1" ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.0234375" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "px = likelihood0 * py0 + likelihood1 * py1\n", "px" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(0.0, 1.0)" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ "likelihood0 * py0 / px, likelihood1 * py1 / px" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0., 1.]])" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.naive_bayes import BernoulliNB\n", "model = BernoulliNB(alpha=0).fit(X, y)\n", "model.predict_proba([x_new])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "(3) 스무딩 팩터 알파=0.5일 때, 문제(2)를 다시 풀어보세요." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<img src=\"22.png.jpg\" style=\"width:70%; margin: 0 auto 0 auto;\">" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0.5, 0.1, 0.5])" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ "theta0 = (X[y==0, :].sum(axis=0) + 0.5*np.ones(3))/(len(X[y==0,:])+1)\n", "theta0" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0.3, 0.7, 0.7])" ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "theta1 = (X[y==1, :].sum(axis=0) + 0.5*np.ones(3))/(len(X[y==1,:])+1)\n", "theta1" ] }, { "cell_type": "code", "execution_count": 69, "metadata": { "collapsed": true }, "outputs": [], "source": [ "x_new = np.array([1, 1, 0])" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.025000000000000001" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ "likelihood0 = (theta0**x_new).prod()*((1-theta0)**(1-x_new)).prod()\n", "likelihood0" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.063" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "likelihood1 = (theta1**x_new).prod()*((1-theta1)**(1-x_new)).prod()\n", "likelihood1" ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.043999999999999997" ] }, "execution_count": 72, "metadata": {}, "output_type": "execute_result" } ], "source": [ "px = likelihood0 * py0 + likelihood1 * py1\n", "px" ] }, { "cell_type": "code", "execution_count": 73, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(0.28409090909090912, 0.71590909090909094)" ] }, "execution_count": 73, "metadata": {}, "output_type": "execute_result" } ], "source": [ "likelihood0 * py0 / px, likelihood1 * py1 / px" ] }, { "cell_type": "code", "execution_count": 75, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0.28409091, 0.71590909]])" ] }, "execution_count": 75, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.naive_bayes import BernoulliNB\n", "model = BernoulliNB(alpha=0.5).fit(X, y)\n", "model.predict_proba([x_new])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 문제2\n", "### 문제 1을 다항 나이브 베이지안(Multinomial Naive Bayesian) 방법을 사용하여 (1), (2), (3)을 다시 풀어보세요" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "(1) 사전 분포(prior) p(y)를 구하세요." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* p(y = 0) = 0.5\n", "* p(y = 1) = 0.5" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "(2) 스무딩 팩터 알파=0 일 때, 다음 x_new에 대해 우도(likelihood)함수 p(x|y)를 구하고 조건부 확률 분포 p(y|x)를 구하세요.(normalize 된 값이 아님!)\n", "* x_new = [2 3 1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<img src=\"3.png.jpg\" style=\"width:70%; margin: 0 auto 0 auto;\">" ] }, { "cell_type": "code", "execution_count": 76, "metadata": { "collapsed": true }, "outputs": [], "source": [ "x_new = np.array([2, 3, 1])" ] }, { "cell_type": "code", "execution_count": 77, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0.5, 0. , 0.5])" ] }, "execution_count": 77, "metadata": {}, "output_type": "execute_result" } ], "source": [ "theta0 = X[y==0, :].sum(axis=0)/X[y==0, :].sum()\n", "theta0" ] }, { "cell_type": "code", "execution_count": 78, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0.14285714, 0.42857143, 0.42857143])" ] }, "execution_count": 78, "metadata": {}, "output_type": "execute_result" } ], "source": [ "theta1 = X[y==1, :].sum(axis=0)/X[y==1, :].sum()\n", "theta1" ] }, { "cell_type": "code", "execution_count": 79, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.0" ] }, "execution_count": 79, "metadata": {}, "output_type": "execute_result" } ], "source": [ "likelihood0 = (theta0**x_new).prod()\n", "likelihood0" ] }, { "cell_type": "code", "execution_count": 80, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.00068848863993744083" ] }, "execution_count": 80, "metadata": {}, "output_type": "execute_result" } ], "source": [ "likelihood1 = (theta1**x_new).prod()\n", "likelihood1" ] }, { "cell_type": "code", "execution_count": 81, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.00034424431996872042" ] }, "execution_count": 81, "metadata": {}, "output_type": "execute_result" } ], "source": [ "px = likelihood0 * py0 + likelihood1 * py1\n", "px" ] }, { "cell_type": "code", "execution_count": 82, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(0.0, 1.0)" ] }, "execution_count": 82, "metadata": {}, "output_type": "execute_result" } ], "source": [ "likelihood0 * py0 / px, likelihood1 * py1 / px" ] }, { "cell_type": "code", "execution_count": 83, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0., 1.]])" ] }, "execution_count": 83, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.naive_bayes import MultinomialNB\n", "model = MultinomialNB(alpha=0).fit(X, y)\n", "model.predict_proba([x_new])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "(3) 스무딩 팩터 알파=0.5일 때, 문제(2)를 다시 풀어보세요." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<img src=\"4.png.jpg\" style=\"width:70%; margin: 0 auto 0 auto;\">" ] }, { "cell_type": "code", "execution_count": 84, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0.45454545, 0.09090909, 0.45454545])" ] }, "execution_count": 84, "metadata": {}, "output_type": "execute_result" } ], "source": [ "theta0 = (X[y==0, :].sum(axis=0) + 0.5*np.ones(3))/ (X[y==0, :].sum() + 1.5)\n", "theta0" ] }, { "cell_type": "code", "execution_count": 85, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0.17647059, 0.41176471, 0.41176471])" ] }, "execution_count": 85, "metadata": {}, "output_type": "execute_result" } ], "source": [ "theta1 = (X[y==1, :].sum(axis=0) + 0.5*np.ones(3))/ (X[y==1, :].sum() + 1.5)\n", "theta1" ] }, { "cell_type": "code", "execution_count": 86, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "7.0559241256722174e-05" ] }, "execution_count": 86, "metadata": {}, "output_type": "execute_result" } ], "source": [ "likelihood0 = (theta0**x_new).prod()\n", "likelihood0" ] }, { "cell_type": "code", "execution_count": 87, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.00089524342737249145" ] }, "execution_count": 87, "metadata": {}, "output_type": "execute_result" } ], "source": [ "likelihood1 = (theta1**x_new).prod()\n", "likelihood1" ] }, { "cell_type": "code", "execution_count": 88, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.0004829013343146068" ] }, "execution_count": 88, "metadata": {}, "output_type": "execute_result" } ], "source": [ "px = likelihood0 * py0 + likelihood1 * py1\n", "px" ] }, { "cell_type": "code", "execution_count": 89, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(0.073057616787152349, 0.92694238321284772)" ] }, "execution_count": 89, "metadata": {}, "output_type": "execute_result" } ], "source": [ "likelihood0 * py0 / px, likelihood1 * py1 / px" ] }, { "cell_type": "code", "execution_count": 90, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0.07305762, 0.92694238]])" ] }, "execution_count": 90, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.naive_bayes import MultinomialNB\n", "model = MultinomialNB(alpha=0.5).fit(X, y)\n", "model.predict_proba([x_new])" ] } ], "metadata": { "kernelspec": { "display_name": "Python [Root]", "language": "python", "name": "Python [Root]" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
arvind-iyer/socdata
Explainer notebook.ipynb
2
29253
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Explainer Notebook" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is a notebook produced in the course 02806 - Social data analysis and visualization at DTU in Denmark, Spring 2017 [2]. The intention of this notebook is to present details about the data, reasonings for choices made and several discussions. It should give a better in depth understanding of what we have created for our final project. The requirements for the project can be found here [3].\n", "\n", "The story the group wants to present is a story about Arvind winning the lottery. \n", "He receives a huge amount of money and has in mind that he will move to New York City. \n", "He has questions such as how he can spend his money wisely by investing in a place to live.\n", "The Data Scientist David plays a central role, as he will be the one in charge of analyzing the different data sets in the story. Arvind has another request for David; He wants to live in a neighborhood considered good. David tells Arvind that he will figure things out and then come back to him. He is ready to do some data analysis and visualisation!\n", "\n", "** Participants: **\n", "\n", "* David Coleman\n", "* Arvind Iyer\n", "* Uyen Dan Nguyen" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Motivation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Data sets\n", "\n", "\n", "The group has used a set of data sets to realize the final project.\n", "Firstly we used a data set with NYC GeoJSON for mapping the actual NYC data [10]. Secondly we used a data set called *Median House Value Per Sq Ft* from 2014-2016 from Zillow [11], the leading real estate and home-related information marketplace in the US. \n", "To look at the *quality* in different areas, the group used the 311 Service Requests data sets from 2010 to 2015. The NYC311 is a 24/7 help with more than 3,600 non-emergency government services such as noise complaints and illegal parkings [12]. The last data set we used contained individual income tax statistics with agi from 2014 in all states [19]. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Reasoning\n", "\n", "Our initial idea was to understand the Real Estate market in the city and predicting the *quality of life* in a certain area, as the prices in New York City were known to be high. The intial plan to predict the future housing prices with several Zillow data sets. The problem with this idea was that our main data sets from Zillow had a lot of holes in it, which made them difficult to use. The future housing market is also dependent on a lot of outside parameters, which made it hard for us to do this prediction with a high percentage of certainty. With this is mind, we took some inspiration from the other groups to intercoperate existing 311 data sets. We still wanted to predict the *quality of an area* in terms of non-criminal complaints in a neighborhood or a borough. This way, we could somehow predict the kinds of non-criminal complaints that were most likely to happend in an area or a neighborhood, similar to what was done with crime in San Francisco. \n", "By looking at the frequency of certain types of complaints in an area, it was also possible for us to differ a specific area from another - and still keep the story we presented earlier. To do so, we used the parameters from \"Location\", \"Longitude\", \"Latitude\" and \"Complaint type\" from the 311 data sets. \n", "Also by comparing the zip codes for 311 requests and individual income tax, we could find out what kind of 311 requests were more frequent for people in an area with a certain income. To do so, we took the parameters \"Zip\" and \"Complaint type\" from the 311 and \"Zip\", \"Agi(1-6)\" and an additional row for everybody with an income in the agi bracket. \n", "Lastly the only Zillow data set we ended up using was the *Median House Value Per Sq Ft*, which contained data that helped us to estimate what the mean price per square foot in each borough was. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The end user's experience\n", "\n", "Our story is about Arvind and David's quest to find where Arvind can live in terms of quality of life. The group thus made the visualizations to fit the story. We offer the user some kind of interaction with the website such as zooming in and out of NYC maps. By moving the cursor over an area, one can can see what neighborhood and what borough the cursor is currently on with information dependent on what kind of visualization it is. \n", "\n", "What we had in mind prior creating the website was that it should be easy and intuitive for the user. The visualizations should support the story and the user should understand why the different things are on the page. The user should be able to click on buttons for some of the visualizations to change, such as choose different focus for complaints. Without knowing anything about the K-means, the user should get the message of what it does. The SVM is a diagram that is not that intuitive to the user, but by looking at it and reading the description, we think that it will be somehow understandable.\n", "\n", "The optimal solution which we intially had in mind was for the user to choose to toggle on or off some parameters and all the visualizations would update automatically. This would help an individual to find a good area after own requirements if the individual was planning on moving to NYC. By toggling on a parameter, the user would see on a map what kind of areas that fit that individual the most. As time was a big factor for each of the members in the group, we decided to only follow the story we presented earlier to find Arvind a good area to live with a limited level of interactive visualizations. The website currently offers a list of standard or one chosen complaint type parameter in the focus at one time. More information about this can be read in the section about visualizations." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Basic stats" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Choices in data cleaning and preprocessing\n", "First we had to look through the relevant data sets to see the size, rows and the different parameters. The data sets we used were large and had parameters or data we were not too interested in to tell our story. A concrete example was the income data set. It contained data about all the states in the United States, while we were only interested in New York City. The data set needed to be cleaned two times to fit our purpose. Another concrete example was the Zillow data set, which also contained data about other states which we were not interested in. We cleaned it to only show data from New York City to fit our purpose. The third example was the cleaning we did with the 311 data sets. The data sets contain data from all the departments in NYC, but the only department that served our purpose was the one about housing. The data sets were thus cleaned to only contain information with where the \"Agency Name\" was \"Department of Housing Preservation and Development\". The fourth example was a dataset we created from a 311 file which contained zip codes and the sum for five different complaint categories (Sanitation, Food, Homeless, Neighbourhood, Noise) for each zip code in 2016." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Discussion of the data set stats\n", "\n", "The first data set with NYC GeoJSON consists of 635 kB of data and 527 rows. We did not feel an urge to clean this, as the information was only used in the map of New York City.\n", "\n", "Before we cleaned the second data set from Zillow, it was a data set containing 5820 rows with data from 1996 to 2004. The file was 5.1 MB before cleaning, as it contained data from other cities than New York City as well. \n", "\n", "If we used the 311 data set that contained all the data from 2010-2015, we would have to deal with 7 columns and over 15 million rows [13]. The data set was big and hard to process, so we downloaded several data sets for each year instead. The data sets from 2011 to 2015 can be found in the references: [14], [15], [16], [17] and [18]. Each 311 data set is approximately 1.3 GB. In the following visualizations the 311 open dataset from New York was utilised. This data was reduced down by zip-code, so that it only included the 240 zip-codes contained within New York City itself; discarding the zip-codes from New York State. Although there are 240 zip-codes contained with the New York City limits the 311 data did not contain information on all of them. On researching it was found that some zip-codes are entirely contained within other zip-codes. These larger zip-codes contain the data for both. For this reason the dataset contains 214 entries.\n", "\n", "The data set with information about individual income tax statistics was cleaned to only contain data from New York State and once again cleaned to only contain the New York City data. After cleaning, the 6 MB data set contained 9265 rows. \n", "\n", "The fourth data set was 8 kB big and with 203 rows. To realize the fourth type of visualization we had to combine the first and fourth data set. More information is available in the section about visualizations." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Theory" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Machine learning tools\n", "\n", "##### Radial Basis Function Kernel\n", "The RBF kernel is in the form of a radial basis function [4]. We let X represent the input space and consider a function Φ: X→F such that it takes the input space X and maps it to a point in a new space F. Let us say that we have mapped all the data points from X to F. If you try to solve the normal linear svm in the new space F, we will notice that all the earlier working looks the same, except for that all the points xi are represented as Φ(xi). Instead of using the dot product, which is the natural inner product for Euclidean space, we replace it with ⟨Φ(x),Φ(y)⟩ - a representation of the natural inner product in the new space F. Let us then say that there exists a function k: X x X -> R such that k(xi,xj) = ⟨Φ(x),Φ(y)⟩. We can then replace all the dot products above with k(xi,xj) - also known as a kernel function [5].\n", "\n", "This algorithm is commonly used to support vector machine classification, which consists of supervising learning models to analyze data used for classification and regression analysis. The RBF kernel is well-studied and many SVM packages include it as a default method [7]. Kernel methods are commonly used for pattern analysis to find and study general types of relations as well [6]. \n", "\n", "We used this algorithm to implement a Support Vector based regressor as a prediction model that attempts to estimate future patterns of median house prices based on past fluctuations. Using the median value per square foot of area gave us a good understanding of the average cost of living in terms of real estate space. The general trends of the housing market as a whole are significantly easier to predict than the valuation estimation of a specific plot of land or locality. The SVM reflects the quality of living somehow by looking at the house prices in an area, which is something that is relevant for someone being interested in living in New York City.\n", "\n", "##### K-Nearest Neighbors\n", "The KNN algorithm is used for classification and regression, and will make predictions using the training dataset directly. *Data Sciene From Scratch* explains this algorithm this way: Imagine that you are voting soon, but you have no idea about who you are gonna vote for. By looking at the neighbors, you might get a better idea. You take relevant factors in the account to find the most accurate prediction. This is the idea behind nearest neighbors classification [8]. The KNN needs some notion of distance and an assumption that points that are close to each other are similar. This is one of the simplest algorithms for prediction, and it has some downsides such as focusing too much on the points around and assuming that they are similar. On the other hand, it can give us the general idea or prediction with the plotted parameters or factors. \n", "\n", "In the project, we used the KNN for predicting the areas or districts where some kind of complaint was most common. This gave us a general idea of where some complaints were reported more frequently than other places. This was also relevant for the presented story, as Arvind would have the chance to pick between districts or neighborhoods after what kind of preferences he has for most common complaints in the area.\n", "\n", "##### K-Means Clustering\n", "K-means clustering is a method used for cluster analysis in data mining. The method aims to partition ***n*** observations into ***k*** clusters, where each observation belongs to the cluster with the nearest mean based on the features that are provided. The results of the algorithm are centroids of K clusters, which can be used to label new data and labels for the training data as each data point is assigned to a single cluster. Each centroid is defined by a collection of features, and instead of defining groups before looking at the data, clustering will allow us to find and analyze groups that have formed organically [9]. The algorithm uses the squared Euclidean distance to assign each point to its nearest centroid and it recomputes the centroids by taking the mean of the assigned data points to a centroid's cluster. To find the right number of K, we had to run the K-means clustering algorithm for a range of K values and compare the results.\n", "\n", "In this project, the K-means clustering was done from the 311 data per zip code and for the income per zip code. The reasoning for this was to comment on the relationship between the type of 311 calls and the income of the people living in that zip code, and to see if the clustered groups were similiar to each other. By combining the 311 and income data sets, we had 3.5 million rows to measure the K-means clustering. After observing the k-means partitioning of the 311 data it was contemplated as to whether the distribution of the different 311 partitions and their complaint types had in any way an economic component, wherein people living in similar economic areas experience the same types of complaints. A dataset of the average gross income for each zip-code was found. Unfortunately it was from 2014 the last year that the data had been collected. The dataset also has only 177 zip-codes contained\n", "wihin. The data set had 6 different income brackets ranging from less than 25,000 up to 200,000+. Performing another k-means utilizing the zip-code and the income bracket and the results plotted as before." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Model selection and performance\n", "\n", "For the housing data by Zillow, 24 of 36 months were used as training data for the SVM. The training data was then tested for the last 12 months. \n", "\n", "The KNN was trained using all the locations of complaints available and we used the model to generate a grid of points with a 0.01° difference between neighbouring points, each with a predicted complaint type. \n", "\n", "For the K-means, we only had clustering. We intended to test it with a distance algorithm, plot the curve and find the elbow which is the best k value. \n", "\n", "We used sci-kit learn's inbuilt cross validation support(sklearn.model_selection.cross_val_score) to get the accuracy, mean error and standard deviation of the models used above. We experimented with different scoring functions, but lack of understanding of the scien\n", "\n", "To measure the results of the SVM and regressions for the housing data, we looked at the error values between the three models. \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Visualizations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "One of the visualization we chose was one of a map of New York City divided by the different boroughs. Each borough has a different color and each zip code or area in the borough has a difference in opacity. This opacity is calculated from five main categories in a cleaned 2016 311-csv: Sanitation, Food, Homeless, Noise and Neighbourhood. The different categories are divided in smaller categories, but we only take the main categories in mind. Since there are some differences between the total sum of those five categories for each zip code in 2016, we used scaling, as scales provide a convenient way to map those data values to new values useful for visualization purposes [1]. The scaled value was used to set the opacity in the bind-function. A zip code with a high opacity means that the area had few reported complaints in 2016 compared to the other zip codes. A zip code with a low opacity means that the area had a lot of complaints in 2016. The visualization shows the user how the quality of life was in the different areas with the five main categories in mind in 2016 and was thus relevant for our story. By clicking on one of the areas, a corresponding bar graph will reveal itself and show the frequency of the different main complaints. By hovering over each bar, one can also see the frequency for the specific crime. \n", "\n", "Another visualization type we went for was a heat map which shows the total number of complaints in each month in 2015 for all crimes in the whole city. It shows the patterns of which days that are more prone for non-criminal complaints such as in the weekends. Each main category of complaints has additionally a corresponding heat map so the user can see which days had more registred complaints for that specific complaint type.\n", "\n", "The third type of visualization we went for was one showing the relationship between income and complaint types by zip codes as described in the K-means section. This is an intereactive map visualization, as the user is able to click some buttoms for numbers of clusters. \"Click k2\" means two clusters, \"Click k3\" means three and etc. The user can see the areas where people approximately earn the same, and by putting the one for 311 complaints and the other one for income next to each other, the user can compare the data visually. In the first visualisation a k-means algorithm was applied to the 311, 2016 dataset with respect to the ‘Complaint Types’ which were assigned a numeric value and the ‘Incident Zip’ of the complaint. K-means is an unsupervised learning algorithm which seeks to partition the observations into k clusters based on the nearest mean. This algorithm was run with a k values of 2-10 and the output values stored. All duplicates from the output dataset were removed leaving only one instance of each zip-code with its corresponding cluster number. Unfortunately when it came to plot the visualisation a geojson file for the New York zip codes could not be found. Other New York geojson were available and so it was decided to use a neighbourhood geojson with the zip-codes plotted as points at their appropriate latitude and longitude. This is to see what kind of neighborhoods that are related to each other in terms of types of complaints and income level.\n", "\n", "The fourth type of visualization was similar to the third one; we have a map of New York City and the user is able to click through different types of complaints. By doing so, the user can see which areas that are more prone to noise, neighborhood, food, homeless or sanitation complaints as described in the first visualization. The circles are scaled and the bigger circle it is, the more complaints were registered in the corresponding area in 2016. The user are thus able to see the areas for most homeless reported complaints and areas with a lot of noise. This is relevant for our story because Arvind might have different kinds of preferences such as not liking a lot of noise. Manhattan had a lot of reported noise and homeless complaints in 2016 and should not be in the top list of areas to live with Arvinds preferences in mind. It was decided to further investigate some complaints and to form subsets out of similar complaints and then graph them across the zip-codes so as to make it easier to get an understanding of the type of complaints that could affect your quality of life. The subsets are: \n", "Noise complaints; it is comprised of complaints types 'Noise ', 'Noise - Vehicle', 'Noise - Residential', 'Noise - Street/Sidewalk', 'Noise - Commercial'. \n", "Neighbourhood groups complaints about the upkeep of the area. It contains complaints about - 'Street Condition', 'Street Light Condition', 'Sweeping/Inadequate', 'Graffiti', and ‘Derelict Vehicle'.\n", "Food contains complaints about 'Food Poisoning' and 'Food Establishment'.\n", "Homeless contains complaints about 'Homeless Person Assistance' and 'Homeless Encampment'. \n", "Sanitation contains complaints about 'Rodent', 'Dirty Conditions', 'Sanitation Condition', 'Sewer’, 'Overflowing Recycling Baskets', 'Missed Collection (All Materials)',’Unsanitary Conditions’. This is not a complete list and many other complaints could be included but processing only the higher occurring complaints were taken. Select Subsets using the Buttons and the graph is zoomable for acloser inspection. Each plot is has its own scale for that type so you can visualise the amount of the complaint across the city, but the plots should be be compared against each other size wise. \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Discussion" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Good\n", "\n", "What went well was that the group managed to create something from the different data sets we found. We managed to clean the data sets and combine them in a way that it matched the story we wanted to tell. Additional to this, we managed to use three machine learning tools and visualize the results. The exercises and tools from the class helped us in realizing the project. The design of the visualizations was clean and simple." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Bad\n", "\n", "The project was very open since we could choose whatever topic regarding a city and fulfill the requirements from the project page [3]. There were too many choices to pick between, so we ended up wasting a lot of time on what topic we should go for. We ended up being unsecure about our topic and therefore changing it several times. When we finally went for something, we found out that the data sets were not sufficient enough for what we wanted to project. The reasoning for this could be that we had several topics in mind, but not the relevant data sets. We were also too indecisive and scared of choosing something simple when the project requirements were so open. We learned that sometimes you should go for something and go 100% for it rather than changing the topic several times when there is a deadline. \n", "\n", "What could be improved in our project was for example the user interaction part and the simple design. With more possible ways for the user to interact with the page and a more interesting and intuitive design, the website would be more interesting. Another improvement could be making a better video after seeing all the other videos during the presentation. The website could look more coherent, and the machine learning tool used on the Zillow data set could be more relevant for our project. We would like to add more prediction, as we only had a few of them. \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## References\n", "* [1] S. Murray, Interactive Data Visualization for the Web, 1st ed. Sebastopol: O'Reilly Media, 2013. http://chimera.labs.oreilly.com/books/1230000000345/index.html\n", "\n", "\n", "* [2] \"suneman/socialdataanalysis2017\", GitHub, 2017. [Online]. Available: https://github.com/suneman/socialdataanalysis2017. [Accessed: 07- May- 2017].\n", "\n", "\n", "* [3] \"suneman/socialdataanalysis2017/wiki/Final-Project\", GitHub, 2017. [Online]. Available: https://github.com/suneman/socialdataanalysis2017/wiki/Final-Project. [Accessed: 07- May- 2017].\n", "\n", "\n", "* [4] \"The Radial Basis Function Kernel\", pages.cs.wisc.edu/~matthewb. [Online]. Available: http://pages.cs.wisc.edu/~matthewb/pages/notes/pdf/svms/RBFKernel.pdf.\n", "\n", "\n", "* [5] \"Non-linear SVM classification with RBF kernel\", Stats.stackexchange.com. [Online]. Available: https://stats.stackexchange.com/questions/43779/non-linear-svm-classification-with-rbf-kernel. [Accessed: 07- May- 2017].\n", "\n", "\n", "* [6] \"Kernel Methods for Pattern Analysis\", http://read.pudn.com/. [Online]. Available: http://read.pudn.com/downloads167/ebook/769401/Kernel%20Methods%20for%20Pattern%20Analysis.pdf. \n", "\n", "\n", "* [7] W. SVM?, \"Why is RBF kernel used in SVM?\", Stats.stackexchange.com. [Online]. Available: https://stats.stackexchange.com/questions/172554/why-is-rbf-kernel-used-in-svm. [Accessed: 07- May- 2017].\n", "\n", "\n", "* [8] J. Grus, Data Science from Scratch, 1st ed. Sebastopol, Calif.: O'Reilly, 2015.\n", "\n", "\n", "* [9] A. Trevino, \"Introduction to K-means Clustering\", Datascience.com. [Online]. Available: https://www.datascience.com/blog/introduction-to-k-means-clustering-algorithm-learn-data-science-tutorials.\n", "\n", "\n", "* [10] \"NYC Zip Code Tabulation Areas\", Catalog.civicdashboards.com. [Online]. Available: http://catalog.civicdashboards.com/dataset/11fd957a-8885-42ef-aa49-5c879ec93fac/resource/28377e88-8a50-428f-807c-40ba1f09159b/download/nyc-zip-code-tabulation-areas-polygons.geojson. \n", "\n", "\n", "* [11] \"Zillow Median Home Value\". [Online]. Available: http://files.zillowstatic.com/research/public/Neighborhood/Neighborhood_MedianValuePerSqft_AllHomes.csv. \n", "\n", "\n", "* [12] \"Our Story\", Www1.nyc.gov. [Online]. Available: http://www1.nyc.gov/311/our-story.page. [Accessed: 07- May- 2017].\n", "\n", "\n", "* [13] \"311 Service Requests from 2010 to Present | NYC Open Data\", Data.cityofnewyork.us. [Online]. Available: https://data.cityofnewyork.us/Social-Services/311-Service-Requests-from-2010-to-Present/erm2-nwe9. [Accessed: 07- May- 2017].\n", "\n", "\n", "* [14] \"311 Service Requests from 2011 NYC Open Data\", Data.cityofnewyork.us. [Online]. Available: https://data.cityofnewyork.us/dataset/311-Service-Requests-From-2011/fpz8-jqf4\n", "\n", "\n", "* [15] \"311 Service Requests from 2012 NYC Open Data\", Data.cityofnewyork.us. [Online]. Available: https://data.cityofnewyork.us/dataset/311-Service-Requests-From-2012/as38-8eb5\n", "\n", "\n", "* [16] \"311 Service Requests from 2013 NYC Open Data\", Data.cityofnewyork.us. [Online]. Available: https://data.cityofnewyork.us/dataset/311-Service-Requests-From-2013/hybb-af8n\n", "\n", "\n", "* [17] \"311 Service Requests from 2014 NYC Open Data\", Data.cityofnewyork.us. [Online]. Available: https://data.cityofnewyork.us/dataset/311-Service-Requests-From-2014/vtzg-7562\n", "\n", "\n", "* [18] \"311 Service Requests from 2015 NYC Open Data\", Data.cityofnewyork.us. [Online]. Available: https://data.cityofnewyork.us/dataset/311-Service-Requests-From-2015/57g5-etyj \n", "\n", "\n", "* [19] \"SOI Tax Stats - Individual Income Tax Statistics - 2014 ZIP Code Data (SOI)\", Irs.gov, 2017. [Online]. Available: https://www.irs.gov/uac/soi-tax-stats-individual-income-tax-statistics-2014-zip-code-data-soi." ] }, { "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": 1 }
bsd-3-clause
adamsteer/nci-notebooks
.ipynb_checkpoints/Point cloud to HDF-checkpoint.ipynb
1
1036690
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## What is the proposed task:\n", "- ingest some liDAR points into a HDF file\n", "- ingest the aircraft trajectory into the file\n", "- anything else\n", "\n", "...and then **extract** data from the HDF file at different rates using a spatial 'query'\n", "\n", "#### What do the data look like?\n", "ASCII point clouds with the following attributes, currently used as a full M x N array:\n", "- time (GPS second of week, float)\n", "- X coordinate (UTM metres, float)\n", "- Y coordinate (UTM metres, float)\n", "- Z coordinate (ellipsoidal height, float)\n", "- return intensity (unscaled, float)\n", "- scan angle (degrees, float)\n", "\n", "Everything above is easily stored in the binary .LAS format (or .LAZ). It is kept in ASCII because the following additional data have no slots in .LAS:\n", "\n", "- X uncertainty (aircraft reference frame, m, float)\n", "- Y uncertainty (aircraft reference frame, m, float)\n", "- Z uncertainty (aircraft reference frame, m, float)\n", "- 3D uncertainty (metres, float)\n", "\n", "...and optionally (depending on the use case):\n", "\n", "- aircraft trajectory height (to ITRF08, metres)\n", "- aicraft position uncertainty X (metres, relative to aircraft position)\n", "- aircraft and sensor attributes\n", "\n", "...and derived data:\n", "\n", "- sea ice elevations (m, float)\n", "- estimted snow depths (m, float)\n", "- estimated snow depth uncertainty (m, float)\n", "- estimated ice thickness (m, float)\n", "- estimated ice thickness uncertainty (m, float)\n", "\n", "So, you can see how quickly .LAS loses it's value. ASCII point clouds are conceptually simple, but very big - and not well suited to use in a HPC context. Too much storage overhead, and you have to read the entire file in order to extract a subset. Six million points gets to around 50MB, it's pretty inefficient.\n", "\n", "So lets look at about 6 million points...\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Let's look at a small set of points\n", "\n", "Scenario: A small set of LiDAR and 3D photogrammetry points collected adjacent to a ship (RSV Aurora Australia) parked in sea ice.\n", "\n", "Source: AAD's APPLS system (http://seaice.acecrc.org.au/crrs/)\n", "\n", "https://data.aad.gov.au/aadc/metadata/metadata.cfm?entry_id=SIPEX_II_RAPPLS\n", "\n", "https://data.aad.gov.au/aadc/metadata/metadata.cfm?entry_id=SIPEX_LiDAR_sea_ice\n", "\n", "Pretty cover photo:\n", "<img src=\"http://seaice.acecrc.org.au/wp-content/uploads/2013/09/geometry2.png\">" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from matplotlib.colors import LogNorm\n", "%matplotlib inline\n", "#import plot_lidar\n", "from datetime import datetime" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### import a LiDAR swath" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "swath = np.genfromtxt('../../PhD/python-phd/swaths/is6_f11_pass1_aa_nr2_522816_523019_c.xyz')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pandas as pd\n", "\n", "columns = ['time', 'X', 'Y', 'Z', 'I','A', 'x_u', 'y_u', 'z_u', '3D_u']\n", "swath = pd.DataFrame(swath, columns=columns)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>time</th>\n", " <th>X</th>\n", " <th>Y</th>\n", " <th>Z</th>\n", " <th>I</th>\n", " <th>A</th>\n", " <th>x_u</th>\n", " <th>y_u</th>\n", " <th>z_u</th>\n", " <th>3D_u</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1</th>\n", " <td>522816.00544</td>\n", " <td>-113.710607</td>\n", " <td>-6077.889536</td>\n", " <td>-27.795405</td>\n", " <td>8995.0</td>\n", " <td>56.595811</td>\n", " <td>0.2184</td>\n", " <td>0.3094</td>\n", " <td>0.1440</td>\n", " <td>0.4052</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>522816.00547</td>\n", " <td>-112.621341</td>\n", " <td>-6077.699073</td>\n", " <td>-27.805177</td>\n", " <td>9252.0</td>\n", " <td>56.795901</td>\n", " <td>0.2184</td>\n", " <td>0.3077</td>\n", " <td>0.1431</td>\n", " <td>0.4036</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>522816.00550</td>\n", " <td>-111.563030</td>\n", " <td>-6077.515571</td>\n", " <td>-27.856036</td>\n", " <td>8995.0</td>\n", " <td>56.995991</td>\n", " <td>0.2185</td>\n", " <td>0.3060</td>\n", " <td>0.1422</td>\n", " <td>0.4020</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>522816.00554</td>\n", " <td>-110.491277</td>\n", " <td>-6077.328057</td>\n", " <td>-27.878673</td>\n", " <td>9252.0</td>\n", " <td>57.196111</td>\n", " <td>0.2185</td>\n", " <td>0.3044</td>\n", " <td>0.1413</td>\n", " <td>0.4004</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " time X Y Z I A \\\n", "1 522816.00544 -113.710607 -6077.889536 -27.795405 8995.0 56.595811 \n", "2 522816.00547 -112.621341 -6077.699073 -27.805177 9252.0 56.795901 \n", "3 522816.00550 -111.563030 -6077.515571 -27.856036 8995.0 56.995991 \n", "4 522816.00554 -110.491277 -6077.328057 -27.878673 9252.0 57.196111 \n", "\n", " x_u y_u z_u 3D_u \n", "1 0.2184 0.3094 0.1440 0.4052 \n", "2 0.2184 0.3077 0.1431 0.4036 \n", "3 0.2185 0.3060 0.1422 0.4020 \n", "4 0.2185 0.3044 0.1413 0.4004 " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "swath[1:5]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Now load up the aircraft trajectory" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "air_traj = np.genfromtxt('../../PhD/is6_f11/trajectory/is6_f11_pass1_local_ice_rot.3dp')" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "columns = ['time', 'X', 'Y', 'Z', 'R', 'P', 'H', 'x_u', 'y_u', 'z_u', 'r_u', 'p_u', 'h_u']\n", "air_traj = pd.DataFrame(air_traj, columns=columns)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>time</th>\n", " <th>X</th>\n", " <th>Y</th>\n", " <th>Z</th>\n", " <th>R</th>\n", " <th>P</th>\n", " <th>H</th>\n", " <th>x_u</th>\n", " <th>y_u</th>\n", " <th>z_u</th>\n", " <th>r_u</th>\n", " <th>p_u</th>\n", " <th>h_u</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1</th>\n", " <td>522815.012</td>\n", " <td>30.90322</td>\n", " <td>-6092.56695</td>\n", " <td>193.840</td>\n", " <td>4.073</td>\n", " <td>-3.345</td>\n", " <td>-8.85948</td>\n", " <td>0.054</td>\n", " <td>0.083</td>\n", " <td>0.053</td>\n", " <td>0.034</td>\n", " <td>0.037</td>\n", " <td>0.104</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>522815.016</td>\n", " <td>30.89646</td>\n", " <td>-6092.37386</td>\n", " <td>193.842</td>\n", " <td>4.068</td>\n", " <td>-3.339</td>\n", " <td>-8.85748</td>\n", " <td>0.054</td>\n", " <td>0.083</td>\n", " <td>0.053</td>\n", " <td>0.034</td>\n", " <td>0.037</td>\n", " <td>0.104</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>522815.020</td>\n", " <td>30.89081</td>\n", " <td>-6092.18033</td>\n", " <td>193.846</td>\n", " <td>4.053</td>\n", " <td>-3.327</td>\n", " <td>-8.85448</td>\n", " <td>0.054</td>\n", " <td>0.083</td>\n", " <td>0.053</td>\n", " <td>0.034</td>\n", " <td>0.037</td>\n", " <td>0.104</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>522815.024</td>\n", " <td>30.88405</td>\n", " <td>-6091.98725</td>\n", " <td>193.848</td>\n", " <td>4.027</td>\n", " <td>-3.304</td>\n", " <td>-8.85048</td>\n", " <td>0.054</td>\n", " <td>0.083</td>\n", " <td>0.053</td>\n", " <td>0.034</td>\n", " <td>0.037</td>\n", " <td>0.104</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " time X Y Z R P H x_u \\\n", "1 522815.012 30.90322 -6092.56695 193.840 4.073 -3.345 -8.85948 0.054 \n", "2 522815.016 30.89646 -6092.37386 193.842 4.068 -3.339 -8.85748 0.054 \n", "3 522815.020 30.89081 -6092.18033 193.846 4.053 -3.327 -8.85448 0.054 \n", "4 522815.024 30.88405 -6091.98725 193.848 4.027 -3.304 -8.85048 0.054 \n", "\n", " y_u z_u r_u p_u h_u \n", "1 0.083 0.053 0.034 0.037 0.104 \n", "2 0.083 0.053 0.034 0.037 0.104 \n", "3 0.083 0.053 0.034 0.037 0.104 \n", "4 0.083 0.053 0.034 0.037 0.104 " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "air_traj[1:5]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### take a quick look at the data" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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uByLLWykyJR2Azj5IhgRfVlIrTPpQdhRUiYeE3m3+QKU0qPKLaop6YcWpZ1Ff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\nFfJCOaPs/klKtsPj+kzuFw/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WGaa8IQsHppnBfHoNdiGqVolWowmIYdRY2pIaMDqaI732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\nfBg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"text/plain": [ "<matplotlib.figure.Figure at 0x11c065278>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize = ([30/2.54, 6/2.54]))\n", "ax0 = fig.add_subplot(111) \n", "a0 = ax0.scatter(swath['Y'], swath['X'], c=swath['Z'] - np.min(swath['Z']), cmap = 'gist_earth',\n", " vmin=0, vmax=10, edgecolors=None,lw=0, s=0.6)\n", "a1 = ax0.scatter(air_traj['Y'], air_traj['X'], c=air_traj['Z'], cmap = 'Reds',\n", " lw=0, s=1)\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Making a HDF file out of those points" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import h5py" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#create a file instance, with the intention to write it out\n", "lidar_test = h5py.File('lidar_test.hdf5', 'w')" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "swath_data = lidar_test.create_group('swath_data')" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<HDF5 dataset \"GPS_SOW\": shape (1957932,), type \"<f8\">" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "swath_data.create_dataset('GPS_SOW', data=swath['time'])" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<HDF5 dataset \"3D_UNCERT\": shape (1957932,), type \"<f8\">" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#some data\n", "swath_data.create_dataset('UTM_X', data=swath['X'])\n", "swath_data.create_dataset('UTM_Y', data=swath['Y'])\n", "swath_data.create_dataset('Z', data=swath['Z'])\n", "swath_data.create_dataset('INTENS', data=swath['I'])\n", "swath_data.create_dataset('ANGLE', data=swath['A'])\n", "swath_data.create_dataset('X_UNCERT', data=swath['x_u'])\n", "swath_data.create_dataset('Y_UNCERT', data=swath['y_u'])\n", "swath_data.create_dataset('Z_UNCERT', data=swath['z_u'])\n", "swath_data.create_dataset('3D_UNCERT', data=swath['3D_u'])" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#some attributes\n", "lidar_test.attrs['file_name'] = 'lidar_test.hdf5'\n", "\n", "lidar_test.attrs['codebase'] = 'https://github.com/adamsteer/matlab_LIDAR'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### That's some swath data, now some trajectory data at a different sampling rate" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "traj_data = lidar_test.create_group('traj_data')" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#some attributes\n", "traj_data.attrs['flight'] = 11\n", "traj_data.attrs['pass'] = 1\n", "traj_data.attrs['source'] = 'RAPPLS flight 11, SIPEX-II 2012'" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<HDF5 dataset \"pos_z\": shape (51495,), type \"<f8\">" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#some data\n", "traj_data.create_dataset('pos_x', data = air_traj['X'])\n", "traj_data.create_dataset('pos_y', data = air_traj['Y'])\n", "traj_data.create_dataset('pos_z', data = air_traj['Z'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### close and write the file out" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [], "source": [ "lidar_test.close()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## OK, that's an arbitrary HDF file built\n", "\n", "The generated file is substantially smaller than the combined sources - 158 MB from 193, with no attention paid to optimisation.\n", "\n", "The .LAZ version of the input text file here is 66 MB. More compact, but we can't query it directly - and we have to fake fields! Everything *in* the swath dataset can be stored, but we need to pretend uncertainties are RGB, so if person X comes along and doesn't read the metadata well, they get crazy colours, call us up and complain. Or we need to use .LAZ extra bits, and deal with awkward ways of describing things.\n", "\n", "It's also probably a terrible HDF, with no respect to CF compliance at all. That's to come :)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### And now we add some 3D photogrammetry at about 80 points/m^2:" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [], "source": [ "photo = np.genfromtxt('/Users/adam/Documents/PhD/is6_f11/photoscan/is6_f11_photoscan_Cloud.txt',skip_header=1)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [], "source": [ "columns = ['X', 'Y', 'Z', 'R', 'G', 'B']\n", "photo = pd.DataFrame(photo[:,0:6], columns=columns)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#create a file instance, with the intention to write it out\n", "lidar_test = h5py.File('lidar_test.hdf5', 'r+')" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": true }, "outputs": [], "source": [ "photo_data = lidar_test.create_group('3d_photo')" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<HDF5 dataset \"B\": shape (20151164,), type \"<f8\">" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "photo_data.create_dataset('UTM_X', data=photo['X'])\n", "photo_data.create_dataset('UTM_Y', data=photo['Y'])\n", "photo_data.create_dataset('Z', data=photo['Z'])\n", "photo_data.create_dataset('R', data=photo['R'])\n", "photo_data.create_dataset('G', data=photo['G'])\n", "photo_data.create_dataset('B', data=photo['B'])" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#del lidar_test['3d_photo']" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": true }, "outputs": [], "source": [ "lidar_test.close()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Storage is a bit less efficient here.\n", "\n", "- ASCII cloud: **2.1 GB**\n", "- .LAZ format with same data: **215 MB**\n", "- HDF file containing LiDAR, trajectory, 3D photo cloud: **1.33 GB**\n", "\n", "So, there's probably a case for keeping super dense clouds in different files (along with all their ancillary data). Note that .LAZ is able to store all the data used for the super dense cloud here. But - how do we query it efficiently?\n", "\n", "Also, this is just a demonstration, so we push on!\n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## now, lets look at the HDF file... and get stuff" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from netCDF4 import Dataset" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": true }, "outputs": [], "source": [ "thedata = Dataset('lidar_test.hdf5', 'r')" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<class 'netCDF4._netCDF4.Dataset'>\n", "root group (NETCDF4 data model, file format HDF5):\n", " file_name: lidar_test.hdf5\n", " codebase: https://github.com/adamsteer/matlab_LIDAR\n", " dimensions(sizes): \n", " variables(dimensions): \n", " groups: 3d_photo, swath_data, traj_data" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "thedata" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are the two groups - swath_data and traj_data" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": true }, "outputs": [], "source": [ "swath = thedata['swath_data']" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<class 'netCDF4._netCDF4.Group'>\n", "group /swath_data:\n", " dimensions(sizes): phony_dim_1(1957932)\n", " variables(dimensions): float64 \u001b[4m3D_UNCERT\u001b[0m(phony_dim_1), float64 \u001b[4mANGLE\u001b[0m(phony_dim_1), float64 \u001b[4mGPS_SOW\u001b[0m(phony_dim_1), float64 \u001b[4mINTENS\u001b[0m(phony_dim_1), float64 \u001b[4mUTM_X\u001b[0m(phony_dim_1), float64 \u001b[4mUTM_Y\u001b[0m(phony_dim_1), float64 \u001b[4mX_UNCERT\u001b[0m(phony_dim_1), float64 \u001b[4mY_UNCERT\u001b[0m(phony_dim_1), float64 \u001b[4mZ\u001b[0m(phony_dim_1), float64 \u001b[4mZ_UNCERT\u001b[0m(phony_dim_1)\n", " groups: " ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "swath" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [], "source": [ "utm_xy = np.column_stack((swath['UTM_X'],swath['UTM_Y']))" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [], "source": [ "idx = np.where((utm_xy[:,0] > -100) & (utm_xy[:,0] < 200) & (utm_xy[:,1] > -100) & (utm_xy[:,1] < 200) )" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "43264" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "chunk_z = swath['Z'][idx]\n", "chunk_z.size" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1.1131660000000001" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "max(chunk_z)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "43264" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "chunk_x = swath['UTM_X'][idx]\n", "chunk_x.size" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "43264" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "chunk_y = swath['UTM_Y'][idx]\n", "chunk_y.size" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "43264" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "chunk_uncert = swath['Z_UNCERT'][idx]\n", "chunk_uncert.size" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.collections.PathCollection at 0x394484898>" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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iXKXBTIYw12s4HFCHblitCm08y4T1jRsdu92o/brEdmvwfuSppzq8N2y3hs3G\naMCUntXFizWPP178pnTAfqvbUjE8IHvrW/8Fv/Iru/nGFpYI6oxQHFww7LZ1rNeJpkmUZVBmS6e0\nzEGFwQAKnKt0OKvEGBEMy7z/nCCcTwlbhZoM3udM7LyqkIy2UYqnOMFx9PMchfdenTrab8jNwiyJ\nINBHURS0baaVFgg8kSmqI8djzzR5fY3z62mMBKDMHJKKpeD+aXGBLwLTFO8LbAaQYFvXlTppyDMc\n43jOkApBhqNABsxkOC4RY09KNbDGmA3r9ZbNxrLdJto2YszINBmGIXA4TDr4lvs4JcbApUslx6PR\nAbKEzGtOlKVk2nUtkh8ypFeSUmQYjshQo9OeSElZCo1WrtWI9wf6/kiMIyHI7EuMJdZulXgwME07\nYAAaimLLdlvTtnGGBKfJMwwjfT9yPB51fiSzfnLlV2LMiqKoKUvDaiVyI3VtKEsAYVh13cDxGLSv\n4xEWmJlppAITRT03aDIhEifOlVSVY70OnJ31hGBnKmtKuWchE/8yn5AYR5kI77pRk4lCh0HheJRK\npaoijz5as91KZbleO27fPnJ2NnJ6eqAsDU0T6TppqI/jNCsOfP7nn/Af/sPvfyDP/cNiS8XwkNrr\nXnfCr/7qmXK0hVIaY6HYqzBl+n7SIbJJ6YjyYIo+0cA4jvo74kylYSiMj3EUHNj7QFnWNA1YG2nb\niraF1aqZYYVxDByPieNRpByEYioMDmsPiHaOV7aHNFqtFUriZlOw2VSs1+WMH/d9YLcbODsTcbUY\nB4yJWNtTFOXMrpmmfOxZdyjQNFaH8cQ5CsMlav8jzcNsQqf0yBxC1GZtnKELGQYUmEoaj4bP+7yW\nj33sbMbum6aiaSSjFQZPxHvPMAwcj5Hj0TJNov7pfcnp6YazMw90xDgggViCl+Doxcy2ErZOIsZJ\ns3LHudQICLwiMJkwf+J8zVKSrN7aUmm/51pGKXngcF91BWXpKIoVKUmDWqRJLONYYcwKMHRdx34W\nopVZlKIQaZMLF1rKcqWNciEKHI+WszPHNJVKHJCkIMNw3k+kJP2BzBprW8dmU2CtJAXGFAyD0esy\naW/JzxVJUUSFSyeuXDlocDcztFrXSfsahQ7dTRhjWK8dly6tuXhxjXNGf0eEJY0JnJ0JU+7WrcDh\nMDAMHcMwUVVw4YJlvZbp92lKtK0EwUyYyLTXxT69LYHhAdlHP3qm0JGZewkhSNYl2aefy3MZ9LKM\nYw4WPdMxClWHAAAgAElEQVTUIVBGoCwrtttGs8JWGU2SzQpX3s80veOxV+hjRCasz+cojHE4Jzzy\n7bbRGQgJAoBWC5M6ilGrEWGR3L07cI7d58zQqtNyiuM7ZUjJwNO3fusX8Tf/5ocAT1kKbLBanQcF\nyY6FUSLOJWo2n7RRnSG1MPcSRLqhYL1u2GzE8UtfQhhVFy40qmGU2O06Yuz0/BrOq4WjVldOJ5cn\n6nqgrqUSk6Evp9fHKnzjFc4q9G9Huu6oIn8T0CJsH6kCRdakR4QSS0Cmcquq1P9mjr5TpplUQ+PY\nIRpUNcejKN1KZSVkBGkGj1RVwXZbYq2QEFIq1Hl6jseBYUhaSQ5Y65Uh1uP9qP2ECljPzWXJ7tEZ\niKiNXrkvmyZRFMyQ5DTlOYJJp8WzzHWWEREJijyDYoz0zi5dErkN6UdZ1d+KdF3WCpPXvncvMAyd\nKv1GrcikkX75sgzZGVNTliXOOW7d6hnHgXEM2qsQNt5+P3A4eB2MFAhyv088++zAbjex3ZYP3hF8\nltoCJT0g+9qv/Zf8yq+c3UdjRNVV0aww0jQlJyeW9dpRVZGyzHpCPcdjp5m+x3th/wiLpAbOb3RA\nHSNstxXrdaGidTK9KoJvAgH0fVKuujCIxNnFGbuXZmEe4GKmkwqDx2jz1+sktNWgY5Rt1SDDZ45x\nFLaV0HAFcxYcOSkcJcN4zolEhFRUhQr31QpjeLw3CkWNeh4M3/RNn8973vOMQlny+Yui4Iu+6AIf\n/vAdhZ9kmEmydkPWLhJoRXo3wkA613MqyxZj1giOn7R6yl9WnbNw/WXeZATukVJPUaxZr1eq0SON\n2HEc6LpB1UHPr1WuxuScltocr1ViBNbrERk2rHRYK7DbweFQqiaU7FeQYFcppMh9MxOGlII2oiNF\nkeGzqM1nYZTBBuca2laCq7X93L8RRlLQz+5nKrUkOE7hvkIVYCseecTMjKGUpPI4Hkfu3u01gAjt\nuSzN3AcSnS+hvTon1VjWVKprqYy9l+rae8vZmce5ggsX8gxKQQiGw8FzdtbhXODSJat0WOl/DYPX\nJEiCscCpjosXW97//m/gkUfqB+0GXjJboKSH1N7ylsscDh1FUSgrSETvZF+BU0pg1GnnPGk8EsKo\nDWev/YeSk5OWzaamaUR7aBxR0bfI2VmndEij+kjCWxceeFKqYx5IkynYppGHVCS3BfIQ9VB5wMW5\nykOcne+b3nSZD37wFrvdODfLc/M6hIQxo/Yj3OwErQ2sVlbZJJIty3tahdI8fR8VShs5O4MYj3O1\nIPBSVmCV+Yb3vvdpVqvqPshKjvv69SPbbaPc98ib3nSRZ57pOD0d58A3TVF7O1LJgVQ/ecZCaMFm\nhnqqaqOT2aIfladxpynw2te23L594JlnjGbmHTCqM83BMGiQKpVJ46hrtIEqVNT9viRGoXZa6+m6\nHhl+m7SfMCGQVpYcr+bAIjBboef//N4rClGDFTZOxPuCYTjOg2gpiZ5V1i5KaVLCQ9Ime6E04NyA\nTnrvjjrJnRMcoTZ3XdI+GmT5FGtFCny7rbh8WSi5Uj0JTNj3UvX2fdReS4GI6onK8DRN9L1UDN4L\niULOnWHQXUFCrU089ljLo49Kw3oYJAgdDgPPPpt0GHHkwgUJZH2fuHXrwI0b3cs6MLxQWyqGB2SP\nPPKDeB9nvD3z73PTWRrOTuEcuZlTyoNIk/LWo8pqn88AiOaSwEhybuSrLNHAIRBFVtUUSqFRKQNh\nd1SVUGLF0eUqQL7OB83i7HgB1uuSaRJoKzNRzqmpRiuiicNBFEqF2ZR0OMorjdRxLhiXdZIcm42w\niwTCMEo3zA3wvOWrmNlARdHMWbLQaiPrteMVr9jw5JNn5J0JmWIpNEzJJkWbx/C6122oqopPfOKM\nvp/oOkvXrXSAagRanNuStZ6kVxK1BxOo6yOr1ZlOcK+oKhlczMKDAhWGueGfJ3xDmBjHdB+zqlEq\naqAsA8YM5CE+7xs9VxVVVeu9ItdA+kwlw4DKrju9FzxlKYt98nS3MY66NvcN2LU0TU2MAv3JIJuI\nNp6zkDxZvgSCTpIHRCbEzvITUtVIgDkcwkzpNcbPVUAekDvvZUSlu8r3RXW11OlloU3n6mWzKWjb\nSqs+EYYcRxkWtNYiiq49dd0rPGc5OxNYqa4Nr3hFy8WL1dzLAZlNef/730Zduwf2/L/Utkw+P4Tm\nfeRP/Ikf41d/dacCY0Gz40CWZYZiZunIwNPI+cRxIg+GlWU5ayHJ9wx9LxCO7ABIlKVjvS4VJ5Zy\nf5pgGJjhowwNyGQwCg+gD21U2QQZOmtbUc4E5mb5btfrchU5NmmEy3wAZB7/pO8leLUcd1II6TwQ\n5YdfxN0Sb3jDIxyPE9evD+o4RFJbApsMYwlHPusvufl9MmOqaYSGKZpO57pL4xh0hmNE9JosooUk\n512kyS3WCrtHmuORsiwxpialkq5zHA4nDEOL92ekdBdjbmPtXqfPs1TFucRDSllJtZhnJ6QqCeSJ\n4ZQ2uqfAUlXS7A3hSN8PyqZpkcw+L3fyM6FAlFNrPcdWZz4MZTlpX8hzPGY1U6tOXarVlCQ7997N\ncJm1QZV67RwEpskzjiOiAJx3Mhiqyup9nKuloMFF+jjSqM5VaRaKNDowaWjbkvW60Yl2SXZOTzv2\ne+njrFboc5GTI7lvm6bU45PnQWZrDOu1pe8HjseevKjpcBAK9zhGDS4CPYlYoefGjT/OK17x8pXG\nWALDQ2hnZyOvf/0/1Kwwy04wc/lzM1QcUlJaqDQJpeEsDkWkFDKt0Shuj2ouJaWwJsW9UcdyPsAk\nGkKCA69W6CQvmpEJfNH308z4kb9LerxxznQFY5e/E2kP6RWA1XmGoMNm0sQ2RhxVVUFRCI1U5iSE\nhVJVjmG4X3spaqA7/55QPuWBlsnsgi/90lfwi794m3v3Jna7qANzpb6n0/MZ53Nx3oOBppHGaF2X\nCpvFeWBumgLTVGpjOcz9j1e96pV8/OMTQs98Fc69kraVoFAUp6S0U6dX6jVwOhQHRRF47LGWECL3\n7onUhkwdy/zENNXE2OjUt0BPQm0d9Jhb2vaEtl1RltKDyT0ncfhJHbucb2kOj8jUupkJAsY4ZeYI\nAUBgxYphMITgFA4S6KdpRKFWJFgmvZ4ykJjvyzzNLgrBzOdYFgtVbDaSzYcQ52RIEp2o/Qkz37vT\nVBCCCAhevBjYbOSZyMEpX8vDIXL7tjSjiwK222LO9kXlVvpjjzwiAU7o1lErEOZ7br0u6HvHF37h\nRX7yJxe66qezpcfwAKxpRD/n7GzAuRKR0hZxt7xdLC+5yXLFInEtWZKsc8wMnWlm64iJw1+vCy5c\nKFVOu1T8Nq+UzLIGWVvowDAY7t4VWENw4KgNaDMPb1UVc9Na3jvMuHAe8hKtp4wVi1PN0JNg96L3\nk3X3pVJIcwadUuLtb/9C3vvep4nRceFCwWtes+EjHzlTCCbO7JJxtPS9584dqaI+8YkDzjl++2+/\nxIc+dMZ2KxPFTVMo9TXDWROHw6hBJuH9pBmtpe/lWfE+90Qkg5dlPFmbR5zJk0/eRVaxRoriHnW9\n4uREKKLWRoZhVDZMUieZnaBcv7OzM4YhS5PL60tVVrBer7TpbebG+DAMCsVEpqnCe9jtJj2nonMk\nWlLyeSXQZ9mLRAgFfZ+dtyjKStaetCdQadNXcP4QRrJUu7WGvp+UDJAU3rE6FV8pZVUYdH2f7oM6\n82xJ4t49ocwa45+T0efGc1FYNpsaa6XCHAbL3buiKnz3buLePVitxIE7J/fB6emA91LZnpxI0Knr\ncyqq97KnO0bDnTsFr351y6OPFjp5PXH79hHv4e7dkeNxom0Luu74GfMFn622VAwPwFJKfPu3/z+8\n612/pHMD8hmkXK9x7rz3IHBIT1V5qgqdgnWqTZMlq6VKkAnSoNm9m522QEOSjQtEkfcwCB1VIBiQ\npua5DIfshTYKv5wLy+XyXZyU1cZ4RPSPsvpm0MBSaNAzrNc1TSNURBnmGjXD7e8b0kPPhfQXMrac\nHbkM38l0cVVVs2MJITJNKFtIdJq225azM48xBa973YYPf/gM78cZ087rIQW7l93RkKE8aSa/9a2v\n4pd/+S67nSjRZixbNsOBbLWrgYtY+ygyWb0HjhhzUxu3jrqu52snMJxs4TseJw10uT8hMuH5XMqs\nRwSyNtGELHYS1pAxDdZW2hgPiGSG7OrO11WkRyadWj5X6pUqymu2PhJCptSWNA1sNk73i0sS0PeZ\n/pzZS1L5tG3UmYSgAVDYXpnSul7XuqNZ4MTDYWK3k/6Q3F/yeXMAE8p2UtqssOpWK5FlSamc1XEh\nS7BLZXJygm6Wk+b9xYuWO3c6+j5wOEg/rqosJycCUZ6edvT9SFnWXL7c8MgjAje+6lUr3v3u3/cA\nPcBLbwuU9JDao4/+XYZBFrSsVsW8y1bohCJlIZztAyLTLI5K2EjShMuVQl4Gk2Ep4d6fM4a8h2E4\nV1YVVkp2rpbzhTR+dvqS2eb3FJxeaLGQHahzMpm6XpfKqCmAQrH7qDx2yZRDMJoZ2zkTl+xbmr6S\necqCF2EmiRqrbJkblTEkn1GasQKlSeWUl98kbWwaLl1a8aY3Xebnf36n9FJRmBUaqlB/Zd+DwzlP\njBPWGg6HTvsNWdxPNJVkClrYWwLrlHr+ZHe1c1tSuoj3NX3f0/fXOBz22pyvFD4yOhCXkCHFqFUE\n2qSO2gStCcHptZgQmKanKKIG1kr7GzUhVPq7ozr7PLUsAVoqNhnCMybQNIa2FcnpomioKjRjPhBj\nPSvqCiVVTHpcct8Iay3RtlY36wms9xVfcZGPfOQezzzTM005oZG/kz0gMqcjPQmr1NTzPRvWlnNV\nJ/dPUvE72G5rmkbu53F0jKNMmm826OvDNIlarVR2cl2LInLnTqdEjlYrdembyD3vuX79oFRXw+XL\nkvwMQ+LZZ/8kRWF5udoSGB5S+77v+0W+7dvejziaQFXJQhTROMqa9oYYR0Ty2pC3mWUKqMA1uWGd\nKaQFZVlq5poVSaPi/FGDRZbtlqYz8BzoSOQC7l8Hmtcy5u1kcebFy+s5zdLzsNy54qkwhbL8tGgf\nCTNHMrg8+Zz/RmYYvMIl8mBmmCrLa5xvc0v6WXKfJTucmje/+TE++tEDd+5MOgkcVOP/XMQub6MT\nFdLEW9/6an7yJz+uzk/mPaqqUJZTwTRNDIOfA6cEjoqUSlK6jDGXgIi1E8bcIaVhlh2RDWTicIXO\nOyK7EuQzSEA0WiU4ZHeGSJzLgNxA3rYnEGNDjDJXIUJ0lratZlaZtYWeQ5nzkMrD672F9mwqZO3m\nAalSmhmqaVuRosjXfb9PHI/j3GcqCk/bJs34PW1r2O+TCjkyX7fzzWuj9nzkZ01jODmR4xUqau4F\nCNQk94bM5uS5EZHZKOblR3Ut1W3TlEBkGCYOh47T08TJicidbLeJW7f2hODm2QqRFpFlPXUt8FQ+\nb10XWa9rfumX3v4iP/EPlz0UgcEY8wPAfwHcSCl9iX7vHcD/ADyrv/ZXUkrv0Z99B/DfIfXzn0sp\n/dineN3PysBwejrwyCN/l6LIDsgodASS8buZvQKDZj9GuelZ9VMe2HEM5MlfoXpmh2k1E8uUP6NC\nciKRnDX/u27SbWKjOk70b8/hFnHE4b5+Q6mO3s6BLA8NHQ4D03R+TTLd8BzyYaZpyrrPAucGDX6F\nfrbsgETzqCxhva40Wy4RxlBQRlEevksKnWX+vkP0g9w84Nc06LYugRLAq5LnwH7f63EL3fN+efAQ\nKu27yD5qEGaTOP0KY1rgAsasEXE9aRQXhacsozK9kuL3XiuSiRgn7Tc48s5oaWTXczCpqqgKshER\nx4sqV+IYxxXS2Ia8Q0EqvTxNL8ciG/GEzbPZCD1UpqlFcPFwyFm+VUaZfFYRTEx6XtLs6Os6zhIi\n8vpJv5wKQpbzwh7Zv+2V8jvp5j2vFWOu+mR+QxhaVis6p1CnsKSOx8DpqUCOVZV0ot1otRAYhryQ\nJ699lSZ2vucESrNUlex7bhqrzDah4OYgs98nyrLi3e/+upe1wurD0nx+F/C/Az/4a77/zpTSO+//\nhjHmjcAfA94IvAb4N8aYL/ysjACfwkR2Wxxr24pkQ5YS8N5oppgYxx6I7HYD92vqCAtIKJOyK7hW\nqp44CWGniBrp8Tgg+kFiedfAuay1PKBN47hwodYH2yEqooav+ZpX89M/fYXr1w+EENQJjwyDZKV5\nn0R2zIJ/x5mKmZ1Glv4WiQ2njdZCsfAakTuYNFCVHI+ZVSXrO8/ORKnVGK+OS/B4YxJ1XbFeO3UK\nwmIRWYZCdyLLrTMMqHKn9BAynGMM6szysSWVSbAMg9MsVsTxrPXa48kZvgQPCdBZDDBXUvk9wgxf\nZIkJoY9Ws5SE99K0PRwED79fQ6oogkJQKLxSIrpIcc5421YqTgmaSafL8/USiGYcRU4ihIG+L4hx\nQ11bhkGcuAQ7cebCGjLKFALnAuu1Y7MxVFUgJa+KsXli2pEDYtMMWtnkVbJSJRWFYbstuHRppeqt\n0PcynTwMkaqKOCd9K+mVifz4OGa4ES5eFKVZuR7CxBsGac7LHvGazUZg0fyZu84wjszBar/vuXNH\nqK9N4zQgiPLAel1x+XLNU08dXtaB4YXaiwYlGWN+G/Avfk3FsE8p/Y1f83t/GUgppe/R//9R4LtS\nSv/+eV7zszZe/L2/90H+7J99P3nQ6rzpa3Soy7FaiQplppCK45jY7bIOfV6ZeK6ImZvIWaZCHvbc\n9Mw7eJn5+5IxZThJsvq8OjHvGTgfZpK+QlGI3EKMUimIE5PdD5LdG7bbkrpuEappnB/ecZzmzyqZ\nvVFYJksxmzlTfPWrN7zudRf46EdPKYpCK5zAbtdzPEbNZM0MseTlOjnAigYR6vgzz/58v0Oe6BU5\nZ6MaRdJMlm12icPBKINrQGYGesWxpXLwvuEbvuG38eM/fg9rK71m5bwIx1pRMZWGqiHGwzyLYsz9\narKSsWd9JpELTzrnEfF+0t0VEjxiXONcq3pAVuE0VIguKUMpYcyk8iVRG9Eiax3jhtXqc7h4scL7\nkdVK9JmsjbO0dtdN2ldKOtcivZiUJmTpUtJzJkFeoLdKA6ZIigvjy8+DbdIDQhloZh6ALIq86S7o\n/eBU6luIBEUxcXJi2WwaisISo7Cd7t4dGQaPcyNNI/Ru6cvk9aayIKhpchJR0nVHnSxHoSkJcNYW\njKOnKApOTixPP/2HP1Pu4DNuD0vF8Kns24wx/w3ws8BfSCndAz4H+Kn7fueKfu9lZR/84G1+x++4\nRF4MnxeiZxqnOJOJW7f87DysLXSASGYPRBVUBpgyjTWzkqRZK7BClqQW+CLy9V//efzIj3x8bkrK\nulA7BxeZck5zpmstOthWKhYusJUMLyUOhzTj7pLdw+EQANG1l9eJWuVYdZpOB+7yFHalWXfUv/Fc\nuXKPq1f3M0QkPxOYS2YYzie2z/cx5BWekunLMJU02Ysi6z4xBzyBpGTD3fEYNVj1iIOuKIqak5NC\nYThDWdba4wkcj5G+L/h3/+4ObVvqcTsOh8h+PyJQijSPjbE6J1FT1wJtOWc1M5dNZnneQzbiybIh\n+XxeAwPzORDI8HyORJq7eYuf0b5Oom0zvCJOV9Z7NvT9ihh77tyRa9B1MtHtvddsW2Ci1apitbK0\nrUA7ITi6zrHbdRyPcp/KvuaGtpXGfFEY3a/g1UnLkKU06rNiqgQqYWzJdrqyTAyDsN5kS5/V5EeU\nYIchsN8fGEdJCE5ORE7kwoXVrGTb9yNnZ4l79ya8l6pBqjGpLrbbnnHsGEchDYj8OLq4ylIUDX0v\nsuyHw8R6vQjpPZ89yMDwt4D/JaWUjDH/K/A3gG/9T32R7/qu75r//cQTT/DEE0+8WMf3QO2pp/b8\nwi/cmamqGc5YrQybTcZaLTEKTDEMogdkdHF5xtXPZSvybMKsxjY/dLlhmXfkvv/9T7PdSpA5X1F5\nPqF8PGauv5TkMcrqy8NB6JLCV7dzoBIqomG9LufsO0Z5kHe7cV42BBIkZENdXhCUF8RneY2kfQmn\nmXBWY83zBDlLtTrYJs3ElJLOImTNftkLII1zmbyVJrcESGkSJ23awitfuWazqbhzp2eaIsejUdmN\nieMxcu+esJdkvsRpJp17QQVVhTZFrW67i8RYEUKlwVqy9xi97i2wyDa8OF/LvLtAsHyHbJJDhf3y\nPmxHVTVst2tWq2a+bkIyyJRk0dbKTk9mUyaVNBFacVVFLl4UufTcc4px0l6T1SAtVOTdTtaIyuvK\n9HBZRrZbo0QHEc4TtpwkKDIdn5AteeiEc1Tdqby60+Lckf0edjun0i6WopAFP5tNxcWLFatV1GdA\ngvHt2x3jCGdnnrKUCmiaRrwfaVurkFU135+SJFXIkiJwbsXJiSxRkqAtVfd+P7LbCcGgLB1/4S/8\nAn/7b3/1A/YEnxl73/vex/ve974X7fUeGJT0qX72PFDSe4B3vNygpJ/4iaf5I3/kXyljRGiQskFL\nYBRRFs3Tt7nBmtdlWp0YLsjL1mXyOGlD23DhwoqmKdSRyQSyNJmDMmtyg/fczrWVUAdvlUabM/xM\nJxQMuO/zxrSkDjKQJTVEjym/pkBe1p4vf5fghsJo53LasnZUVp4KewTN6Ce8twp35QM35F3SX/Zl\nj/BzP3drdiptW1LXFU3j+PIvv8zP/Mx19vtBl8rIPmZr5UvECCt1DoNCHbVOlmdxOglkefBKoIiI\n9zXW1lRVMw8B5uHDEBxZBkMcUofMN0gWLRpG59Pc1qKSG4VWN6JJJMt0Ju25tBizwhiRwvDezBCk\nwHBO9zQkVqvzwUHZjZyrjhZrNxjTkCfCMzVWtqhlphzIPvBMeBiRCemoGbZUAiEYpsnivZlhp6yc\nK/fE/RP6VvtKju22ZBwPiIyLOOauk+sKpfZyki5ySjPrbr22c+ValnDr1pHdbo+1Qaeez3t2wu6C\nomhpGhny7DqpDIWIIVCWiFd6Ll4slTZe8sY3XuCf/bPfTdu+/DSTHgpWkh7IaxHn/8X6/4+nlK7r\nv/8n4KtSSn/SGPMm4B8BvxOBkH4ceN7m82dzYPjyL/+/ePLJe2QN+ywpIZxvzzQNKjuQJTAkCzo5\nWenGKkdevH48TurwZSWiOOLzay7Ow8wOqixhtTJK+SuU3ZEZL5PCLEaDUN76lhfkSJkt0gnnryfQ\nSAEE5ZZLlSIUU6MibcXMDZd+QeRwECxaBPiyfIOZh+kyUyZGkZper0suXXL6YGfNHGlE3r49qric\nnWEk5yyPP15x9epBZTekl5GX1IgOkQxZ5alkwehRKRLBrS9dalXi2zAMtQ6lOaSobmcWTV79Kf2S\nQqeQt6rHc8SYA9N0VGcvMNw4TvO1EjlsgEHnHATqE8ZNSduuaJo1VbXSfopMKk/TuS6UWAAGhqFn\nHAWak2M1FMWKqlpTlq02sUtE+kKCgNB1A9YGvO9UpdST0qiyHJ48pS6Cf4bMpGqagu2WmfklvSWp\naCFLsxSIbPZAjJNWn07hTOkLVFVBSnDv3sDZ2ZEsGZ6n+fNE9fE4YcyoxyuVUR6UzIN+xlR6fQU2\nk2VMIBBXVtYVinRRJF29WlNVNf/2376V3/27H32xHvuHxh6KHoMx5h8DTwCXjTGfBN4B/F5jzJsR\nMPMp4E8DpJQ+bIz5YeDDCC3jz3zWev9PY1/yJY9y9epBmRGyNtG5PNgV6LqSw0EWhmT5itPTwNlZ\np/IDMtmcM3yBogodlhP5amtFzrvvPbtdVjSVobWuQ+l8nqyRL8NQ56J04jDznuSkKquQh97y1rL1\nutSBM6HTZg5+Vsv0XpqmKQmUkWmUwgBybLfVPBUszV/0b4KydAIxlsi2ucR2u+JjH7unAUNmE4Ra\nK4Hu5OR8h3YIEuyaxnA45Ocg90Myzg/GBM1284xGrmyEhhkjHI+WwyERo5wz50rads12K83NokjK\nChN8uu8jxyOEUHLvHpRlh7VHoCMHvaqybLdmhvwEhvMMg+N4FOG4PGzX9xXeF3SdJ6WBaRp0aE9w\ncBn8k10ZMKkcR6/XvNIp5YaqKhERQTQBSBo05foXhexYkHkM6QVstzIfUtei3SSid4HjMa9INXNV\ncXZ2PgNhTB4stNoEN1oJS1Uiar+lNtFlNe00wW4nS6VgmhvcQsDw3Lsne7Hr2nHxYs3ly/VMbJAF\nRFFVcAN566AQDXKgzsqrqMCgI0/on54G2tZxPMpiqh//8WdfloHhhdoy4PaA7A//4X/F+99/deZ0\nZ+ckMIZgrc4lisLOE8F5AC5n7n0vlFTv/Zyl5yna873KeakM2kj26oSMQjVJm3RZSVMWwmy3rVIy\nrTo7yRoFksiSEWjjUfoh0txGqwWnDKVSWTqlyh7nYxeIYBji/L0Yua+RbMlT3YBOGmdYS86NNIoD\nr33thrIs+OAH7xDjuVhe1nSaJk/XDXMzEmS4TZyU1SauU8ybuYeRm/sydCYSGNIfEKE5a1tkmU4W\nKAz34dioFPaapjmhritiHBiGIyHc1SU1wvpxbprnPYQ5JDMEkuXnIb4CY9YY0+gMQqYgizaS9Ipy\nJugZxx2Hw14H6QSakVWhNdBibVZmPaFpCjYbr2QAaXQLM0kCT0peg6GwkjIVNMtnCG26UMgtw4vn\nPS8JArLfWXovQdlhHpFucRqgSh1Ek2b3dhu1QpHqc5o8u93Ift8je7Fl611KEyKZLhLgIchujkwI\n8N5xPEoi0DSVHovMNgijzWjj3yhJoMK5Cucc3/iNr+SHfuirHoAHeGntoYGSHoR9tgaGGBOPPfb9\nSkmUxmVV5alYr2VtpnHK36Tk1fmKvr7cyFmyIs1lvDSt866BczqgMTIYtF7XlGVB1tCRcnzkcMhK\nl/J+ed+wNETzpHTe+nUu0HZ/Zi6wgSh8CgJWIAyaNENbuTkustziSF7/+gtstxVPPbVXamvSoJee\nAybG5GIAACAASURBVDGVpShrys4ACXxZEiE34TNmLucZnb2QDFjou6Lhf75CVDLdYfAcDlLlSANX\nYDQJVPL6WXMqpYqmaWiaFW1ba9Yr7zEMsqhIAmRFShuMaTUAOWRX9F1S6kkpUBS5EZ53VwubS9hM\nsn84D9J53zJNFcMgk9ECZclnLct8vGDtRFEcsXZQumtJ27ak5BgGy25XcDi0OOeZphprW6pK5DpC\nkOE776eZDVRVk851CG03Q5LGFDq3Uus9F1WkUWBQEfSL5N0M+ZrnRKauBe6RuZNSt6pFpAIMiP6S\nnPeyFOhTZh2YJ8qnKXB6uqfrek5O8s5vp3TrXKE4Pb95F7jV85eUFo4SFkRq3HunO6lFj+natf9y\nTmpeLrYEhofQjseJixf/Dnlfbh7TjzGodEHegCWYt2Sy5jnUU3k4paLYbktOTmqVbzA6mBU4HDKE\nJFmbsIkCsiMgT0eH+fWryiiVVGSswTGOsvZzvz+y240KJSX9u3LuGQjUlJvC0qhtmoLVqla6ZKEP\nZVIHwxz0BK5wSsvN8JcEh/uH8/IUsTS28/yG1QCbh7wk6OV91N4n/tAfejXvetcvIwqmTlkomeZ5\nHvjKUlhc0ntx9/HlJ87O8hpV2Ycs+xlaikLmN2RKeELWsqJzEfI7zrVIYMlaTZ6ynCjLgaKQkyDH\n65XBNBDjiAzfZdpxAWwoipU22Att1Dt12jIJP00jIfSEsNe+geDl0jOxyhKqKIoNbVtjrbCchIkl\n7B5hLgXqWoKC9HFk3kUkOsQBC43YMU2OvLtCqkTLem3IKruyKzzpfZJnSPIekFLvx1Kp15IsCFQl\njDZpSHuaJqkqatQp/ajrXmXntTHCXup7cfTWllqxyrWUFbZCHsjJmHOJ/X4kBEtZ1jRNzWpVq7SM\nMKs++MGv1+D/8rElMDyk9ra3/Ut+9mevaxPOa0PMaxku8hObTaX4e6kO3yiXe2S/l96DWNQHq9IM\nX5hLWdaiKGCzqWbHL5kvWimIJIRkybnkN8oikUnq3ACVLxliq+tSHxbZrHY+j5HxedniJesfnTbZ\nreoERWXSoBh+hnTyFjtHXtspLBuj8uF52ligLZkjiPPcRoaiMoyU1VglM466PtNoxSQNa2EZne+u\nkP7DedNdGDlZcgSF6xqsrTGmIctLpyQrN62Fuja0raFpKkD2OMhWssC56m0PHAkhb8bLmXKirr3O\nDggkJXMXBeNYEUKBqJc2+hkzqytTUSes7amqSeEag/clwyDKqwDONVTVhrK8QIwVsihqQOBBgRvF\naY5kob8QRiCoWKLV1y7oeztXWf8/e28Wa9l1ngd+a9jTGe65tyZSRZaogbSlFmWjpXQrkdoinURD\nFLe7gXZsBHnoRrofkoekkRdHBgRICvIiv+TNQAN2gnZgIx7Q7sGy25YD0ZEDD7Kl2C1RYkmhxUnF\nKtate+8Z9rj2Xv3w/f/ehzH94HaVVGTfDRAsFu9wpr3+4Zv4+vWCcfQjndfaYbTJYOcdUNdGDnd+\njpQr0XVUKRvD9z5No0yoQNe1aBp6HHFlGnF4yP/Xth3W6xpVFWFtB2tVCe4kuIqfhdksw2pFkVya\nshl75ZUS261GmjIRLsuycaJumh++F0fAd/W6L8Dn8+vPXk888Sa8/PIaSaK5BZ0wdJj73LZcpWw2\nNbxvxoOeXf+A5dJLVq7DbJZJh69OqjykmZccsd1WqKqpw9ZrGPpXgdbskEgBZOeuhagBoyg5qWjO\nrwa/6+HYtkbYSnqI8uvJ8ph0CGqHMZvZPaYS1wJVFbHZDKK65eG+2VAwRoBYfZEUbOVa6+goE4aV\nRd8HfOQjD+EXfuFZnJ52Qpsky6htHR5+OMGNGztRf6sZoIV6E2kWxrRWc6J2zuA9RW99n6Hr3Pi6\nKCg/DExH63sjh80gayMWpDTNMZvlgl141PUOu51aTHcAWDiZMNaJcI3xnQDfG+8TOMdDn9Mgi7Yx\nHZKkHWMtSde0o4ZEC49zLTTLw3uKzpZLZQUZWJsghBaad9H3nNRobc73WXEc7y1WK51cWBhpydIJ\nG0rXfaph4NTENROJBsyKGIRKy8Pc+4g8Z3EEBpQl8QV1fc0yWniUZYfbtznFZJnHAw+kmM9nYBof\ncSHnNCPCiYi0EafVOGImRUF7l7qmiE9Fghcvnuc+v9Z1Xhju0fWNb6zxjW9sxA+m3aOlDpjNPC5d\nSsXOOgUprLw51usW63WH9bqDMUZoko105WoBMQGTSRJRFIUIjuhrxNAf5gZrnsMw6JokyLohCk4x\nJcYRYCVg2DQYDfy49uLvc47ePctlJjYLDI0hFZaxiU0zYLtlcMu+WR87YDcWsH3QXUFwzQQmaKuO\nrhZl6QQUpzfRL//yi2hbjP5BxjiQUw/cubNDVfX4sR97G37+558V/UCPoiCja8p3UGquWnBTZDgM\nGTTzWp9vlqWijHZC02TmwG6nClwv3XtA11XQJDiK3iBEAycAqgcQ0LbcyxuTAPAypaWI0aNt1VML\n0PWU9y2s7VHXNQBON0mSjNnKSWLGQr3bWWEjlei6IKpvdYztBafoR1ozmUQWda0ZHMposlIEG0w5\n472s5nj4q5Gj5i3QVp1da11HobNC/i4gy6KAwzV2O67J0hRYrRIJnerHpoPU7H5co92+rcFAVJsP\nAzGH2axA0xjUNdeeeR4xm3lZ0QVst1PYlboXd12P27ere3H7v+6v81XSPbo+97kX8PGP/x5C4EG2\nXtfYbBrUdSN2FMQE6F9jR+CTAiEL71M59DPp1BzUYI/CMXaKjJIc5Ofogd/LodeLEEnzHCDdMmQH\nrPkD/HvvB8xmDnluZLeutNIoqXA91KJB3xYeHAxoYedNe4o8N5jPjbhnqq4B2G4Dqor0SVIInYjd\nIMC7ri2AD33oAfzbf3tTuj8a8dHHB7KeiqKxoBCLxm+dWFC40WvpH/2jx/AzP/M0uBYDgF5WKJqp\n7fdyEHL0fYquc/I6azpdJ68ZhMHTC+jP4kHabwpjUlAQlmM22yKEDWiGR+owgd+Atu2ETeZk/eTl\nez2YXUAqMZlaZOQMQ4O2bYW6qascJsyRhtsJYJ3CmMuYzQpkWS8gbtjTK9ASg+6wFtY2ohWJAuA6\nKYpqoKi5D4wAzbKIyRp9SoxjTrSSAvj5YGdOyjNBYr4OXBFq0xJlCuN7m2VxFBNSsc31atuSkXVw\nQN1MjA5tS8NBmvFBijCwWFi5NyzKMsB7EgqKIkPXDTg762Bth49+9AH80i994J6fB9/p63yVdJ9e\nX/rSbfzpn66hQfS0cOCON8+9dEdObjCqf3e7VuiDgCaOpSn/Pw8h3YtbGZ2H8YDiOqeRsT2MbBtS\nYDWD2YpwjgD3dtsLPbAbQUeuDxRYjMIM4oNUX6KiSCVOk4wkdqZe/HEKMBCmxjCU0KQ6VQrHaGVN\nQMqgtcnIxOKNrbqFFL//+2tcvDgb2UV9r66iUX4eefZcXVSyJ3dCJXXi/2Tw5S+f4Id/+DH8yZ8c\n4+bNLTYbyPTk8KEPvRm/8RsvycppspxQ+22G6/TjqoU4AqBrmzyHqKY1a6EVWmTAZrMbX9f1mmwg\nspFUZJahKKj1iDGXA9giBO7zQ+iw2fQ4PVUjxABrw0h9nc1oUxGCkWLLFRnBc4MQGqg1NQ/1HrOZ\n2nFwSmSzkYh2Jgr7iHnQTWNBN9lePhskMgyDHYszsYqJfNB1neSDhFHjMgwDqqocV1nea+QrZEJS\nHMdCGVwaRMU8bgj2EsC8j4C6Jl5gLYWHReHEfbhH0xixPmnhnEFRZPDeYr0OOD5mIVqtgAsXMrz1\nrfN7dwi8jq/zwnCPrsceW+HgIAXTqDTlaoCGuYRA0FhdUin/t1D7ia6z6PsEIfQCOKtjJ0VytLOw\no4aArCcqQ6vKYL3mWkq7ZrOXu6uqY2NIN10uM3jPDF2NpeQhGFCWFHOpXQetmg3aNgU77wZMhlNQ\nfAPn1oJTkF9P0JRrE9UQpGkiBSgVwRhfo4997E34lV+5gb5PRqtkFkkIqMvDK8uM5GcrrbFDCB40\n+2OmQ9dxXfDlL5/iS186Rd+TX08nWh6Mv/u7r2C5zECuPTn7DBOK8nr5kXlDpTTZOQBfD+ZFREy0\nYmYZcBKij1AIzCvQYs5O28iKxchr0wHwIhJTfQULZlkOUEX3JCrLpCvnTt85I4ZwDsOQwpge2tEz\nPQ6CLU1Z3YqfTNNCL++Zh9p3zOcpksRKUaYzq2pPyP6yIjbrZFIY5H3mNKymkeoDludsPOiQ66AR\nsrQ+4fusmI73+tgdjo4KwV0a3Lq1E2A7wJhUJoyIENjkMPaVDrjEHSKaxkpeN5uMqurx8ssD/s7f\neeReHQGv6+u8MNyjq2kozhkGglykKZL6SMCYXkTKoVe2EBkTKebzVKwxWDi6jlL+9brG2RmDSwCM\nO/wkUVC1l27OY7Uq4BztC5jDYPcOMyMmeg00ba1tyXVn6hlk/WChqWBcc+XSGVagLkO9kyhWms08\nZjN6GFF7wElguUyxXM7wzDMblGUEufwBIexAMRMT4L7whZu4epX5E3RQ7SXDAZITbKB5DXnusNtx\n504jNSpw1caDBwmZMDxEaY3xQz/0EH7rt16SdZmVgqK+R2ozzrUW/xzgHIWBxIWs+PVYwVWAJ564\nhN/4jRcBEOR0rgazJXqZwqivIOWYxYyrHc3Y1okyA40HddXXI01JeSaW68dVngY1kQkUZWJJ0HUO\nSoUGoii0w0gj5cEdkedGSA1WGhgIvZefk6YBqqqB5k9Q8xDBrO5pxUurFL7eTRNAQzuLuu7G6Uoz\nxmnvoWQAfey6uhxQ1zW22w6npw2sJYhsrUdVdYILBaSpx5UrDDFqWycmjgMAj8PDDFSyRxwfN2ia\nKAwoK19jsFgkyHOSQL7ne84nhte6zjGGe3T9q3/1dfzDf/jboHvpZJd8cECWx2LhRo58CMB6Te95\nrnV4qOpKRDnWDM3poRGamtjGeE5d6agFt5HDhjeeMkx4qBh0nXkVrZSxn9wTxxhANauXTo5d7Gw2\nExroICsDHtZ1rfGUGlGajAWP3TepsezwDdKU0waB+U4cQq2sv1TU5QTM5QRDHYTDFIFqMJ87VFWL\ntqUFNjMEUmGjqB0zBXU/8AMP4N/9u2/LY9T9s2ItHkAimQOJTE3Mq5jPHQ4PB1y/vpaDVT2ldEog\ntrBYODjXyrrOyoShuc+ddMPtaFMxueQ6wXccqF7m86dlNaDZENQsEH9RUZh2+qpIZ1HKkKaFTH+6\n4qJWI8ZBtBj8zVNuBlPtmN7Hg5VFkasd2lMMo4iN1hq9fK700I+yRhtgbSbrHSP4kEffB5ydVSjL\nBmrJospzzW5Q9hWpyz2qqsJmw0anKJxQVK2s3JRqShPJJIlI0wSAETwDaBoC/HmeoiioJqfrbY8s\ni7h8OcVXv/qhu3rf3y/XuY7hPr1+6qeexr/8l08jBJrIlWW7t3dlgIt2hHQUtbJOUvsBJy6sZmTN\nNE0rI303gn8EnDXredrJ0lRvX9VshI0xgcfc95LBwQLDTotiqICm0ZFfxU48/MmKoo0Def2QbAAV\n6jnBMAbhwVsw4nMQvIB2z/QCAjS2kqubFDE6WXeQwqkpZ8ztZUeudtZk0NhxD/7QQzmee66Rgkex\nX5alWK1mMl1B1OBKma3Awy+Bc5lMaDRu00CkLFPwuUMIRrpe0nk56RRwrsIwlKL6ZcEAIMWKk0MI\nKi5j8UoS5jfwkCd9mHqKKKycflwtqs4kTamfIBtskMO4l9eRBY4FPRm7dTKsohTMgBgJwushr4wi\n5khHmVZefd9NmhkjWhHiXwqkEzymjoPF2aHvNcCHk1iW9SgKTm3DYLHbsYnheomFhIp9FpGmod2H\n9xA7DlJd6bnkBBtJx+Ky3QaZHmpZFzJvum2tYFERy2WOy5fZ7Lz//Rfxsz/7xrPDAM7B5/v2euml\nHb761RPxSuJNTY+iZOTkA7qu6dB1E77AuM4UbRuhLpdKr1PPIYCHjjFkmNAoTwsEu1bNNCiKdDx4\nuCcOI2OprnsxjuPvoNhr8u/hWstK50dVcddFsfZW/QD3wnWtj9XI1JPAWo/5nMZmvJmpNlbPoimz\n2cK5HCFYUUQTzFTQlQBmIqsBzU9muNFsliJNE1g7oCgGrFZK1V0gxhnaNuLWLYthcHj88Rx/8icb\nkFZpsVpFpOmAJKG5ne7RhyFIh65MHWUosag652WSc1LsDwC0GIZKnpviNVxhcMUCAOl4MNMAsJXG\ngI0AQ37I5OIabioOvM/NqIUhrTPIROeheQ+KKZFYFkVgFsfnFYKX9RCB5a7zoDU815GLhRFQnK8x\nsZAAzYemVsEiRuYlcJqAAMYZ1NqFjKqIEJy8XgbbbYXtVnGHfcfhFlVlpSGiurooclGoQxqNgKrS\ngh3la4mD0Vyvx2KR4KGHDpEkpK7udmyAHnkkQd93OD7e4YUXBqSpwzvece0engCv7+t8YrhH1+c/\n/xL+3t/7zdGnSJkmaTpgNmO6lMZ0EthV0Zh6FlmZKvTQJm2SaxQnnHwrFhAUqlXVIBbXkIOZh1mW\nFei6HAx0KREjPXLUm0YB56bhjVdVkDWCEcU1ee50Vu2hWc0A9qYAIx2iskscrM0ldtGMwDq7bVpL\nUEcRYUwCKnR1KqE9c56nWCx42JOayC5zt8v2rC4uoCj4uFQVDFSyLprDuRxAEFVtgr5vEeNzMKaR\njpK/mwWAz0unN66trOzlo3T/TrQMNPjjgcmc7L5vhIbci1DRQw3dyNyxwuoZJMZTGU66OjTj54EX\n12NUqe/bTHDS3NcOsNhQVDfRiY18nhR/aZFlFlmWyIqyx3rdIwRqJzhV7rOzpnuPOJUdFfp1HVDX\nAcNQw7kGtL1O5HMTRUhJfIUGjlYaA51e1Y2VdOrdrsN2Sw+pxYIAtnNOhHTED9I0wXw+w+XLGZjv\n7XB2xqI1mzHHgXhNRFlSKFcUOZIkxaVLFlVVI0bqbdbrgM9+9oP48IffdPdv/vvgOp8Y7tMrSYzk\nEeiYTyWpcxkIeGpGA9c07JrZBak1BDslxkFuNkHCbJQtpHYZpJPqKolBPqm4nTphIBXYblucnW0R\nYwNVOJcl6ZHWklXCNZN2w/S4Z9DNlFftfQJVaHMdQgxBgVQ+H04Lw2BlsnCwNghYSjwky1J4z9dD\nQ9/JYGHHGyNDZY6PVYdBEZh2xwcHKbLMYLHYwfsGt293oNNogAbTAMfQZDiK1uZw7kUYs5F1lR7C\nClo7SQgjnsG0uB7qSssDl1TWd7+7wB//8THoQUU/K4L/bALqOoCZ2h3alkVm3zRRCyvxIr6HyvbR\n95FW52bUBdCtVK0pSNdtmgg1kctzK/RR5jLwtQ9SrHRigbjo9vCeU0Ca6mrKCiZiRDdhZZKC0F9V\nKKfeXNTPMKq2G5/TbJZguaSlRdtCci4cKNKzQlulAp4iPuZnHB0VuHBhASbN8Z/l0qLvPZgJTj3F\n889XqOsBAGmoFy4kI9ZWFJPlxnpNS/flkgFSh4cZbt3a4eSkQowWm82Uk3F+vfo6nxjuwbXbdbh6\n9eew2ezAgJKIg4NEfJGmKE7u8nvxl1ffGnauqlhWpSqN1azYCBCLIK3UCAjXy1TipdtKRp0B9/Fk\nvWjil+6MNUWM6xkChc55sbJw0GAbdsZB9AN6QNKRU1lLmsA1m+XIc9p5KMd+t+tQVf14MNI738La\nHAQ+O2GPKCeeP1uN5zgBeVibI0kWInxq0HU16GgaYIxDlhlcupTj+DgAaEAzQI+isMhz5hfoikZz\nglnIAGsLKaQpaH8BKdZGHqsC4lzh8d/ENmJkrCew7w/FoqoTIUFbM04P1HCw098vClrkJ8uOiCyL\nIsLjVMEGoRcKtBtfU3bkmqYGAZQB53pZSzEpUA3wYlRPKM2YVsGjmjICmodBEz3N8Rjk9WfONJlQ\nVp4rHW+N6QSTIH4zm2Xw3qFpOmy3xDtoBU4rlapqBK/gxDWf86CPUXEJivf4enn0PWm0SrN2bhjJ\nF/T14jQL5LhyhXYot2/vUNctrlyZ4xd/8YP4a3/t8t29+e+T63xiuA+vvo84OspAR06LEDqcnQ3Y\nbpuRJaIjL8CDIMuihOIwG5pgc8B2247gKjszixgTaACPcr75GVCwlqAxefwNhoEHP7UKBrNZAu95\n8DRNlDWN/jcPg82mw/d+7wJPP30yUjazzI4rLK4OuPtlZwowH8FjvR5welpBvZDUjTVJPBYLB01U\n6zoHptgxaIXsGDMqjkOgdsM5Bw0m0s6aGcktgBpJ0mM2s3jzmxd48cVKJqMW9FCKowvryUknB5a6\nzZIWTNaMFeM3K99Hiw2K6Cj4UpU4FdfD3sE5yN97OVDjiBEoFjP1N1MsJR9/gjQ1ODhgkBELYoQ6\n5hJH4GRJ23OABAAe3PQA4oFcVYM0FRbWdtCsCaXDxhiERaQMt0SKPAsZd/teMhB6tC1XMq36WUi4\nU5pyAuHnikQH2ss7mSiD0F+NqNA5xW23DNYhtToTcaCRQ50NwW5HK++yDKL3Ic4wDG6kHashooLT\nZ2cdFgs7UlsPD2e4di2F9x3qusfJSYfT0wFd1+DatQRnZwPW6xoPPpjfqyPgdX+dF4Z7cM1mHo89\ntsLJSY3ZzMh6xws4x2CZ7bbDdst1CYFjHiw8oNSzZ5Dpwo87Y2vVf6bFZJ5GsHOxYA4ymRicEPg1\nUfAF0kubppZuUhWw2HN59aDr5IAXXtgIxsGRu67587ZbXYNpJjOfIwFvh76PcoCR6gjwYKP+gRYb\nfN4WPBx7oWX2CIHCtXe9a4mvfOUOkiRDlrHg9X2CpkkFyK0wDMxY5uGd4MUXB2y3xC+cG7BYWDz0\n0BI3b27B1DT1VuIunhx/FXl5DANDklTFy+Kktwgprsyrnnyl6C2l+/gIjf7UfIksc3LYEpCPkQe8\n+hBNITX0X9IIWHb66koLUf9GAFz1sGOns2jbtsKEUvos8zJoYaKurhhJBAAQQoIQUrRtAo1WDQE4\nO6tHcoO1wHzusFoVyDJiGVXVYb2mNQfXegOShKQAdvoMpuLU0CLPMzhHB1qa7FFbkOf07FJlfFX1\nsgLkSpPdP7BeB8xmah+TY7FIkWWJkDdImb11a4vbt3c4Pe1g7RKz2YDdrkVZNthua8xmDs4VeOtb\n58KKIpNvNnvjZT3freu8MNyDy3uLj33sGv7gD16WLp+2wc7RHoErmUGEaSnodaQ+R8Noz00ao0Ge\nR8zn+chWoWeM7p05LXZdgs2GnvRTRxlxcMDDqSgyEWVBmEgED5tGE7h0XcB1iB5QWeawXOrumtOO\nPr5JActYTu7CVQms+APXWvx7L+sUHohFEZBlBNS5suKkU9cD7txpUFVcQ9FKwkmnz+85OrKy6vKo\nKqCqOpycqLU0u/vdzuAb3zhD1wW84x0LXL9ejjoIYwiKquqcz5c/L0Y7muvpxWSyAWVphNHENROn\nJ8jhTfdYNtgtHn/8Er785dsA9GdZ+Z1AlmUCAGPs2lmYDKwld5/U0X0RITt1vt9UKgMQfCEZu/ZJ\nz9LL9MWDercbsNmotoVhTbMZJA6Ua8euo023hu2EQDHmyUkl72sD5zRUhxGfdU01NcF1iKEfleRt\ny1WZ2pIrfXoYemw2XMuxYXJYrbIxK8M5dX61uHFjg6ZpQLpuj/m8BW1gghSUAZcvZ1itZvDeoW0Z\nJ1uWFi+9NGC73cFaRr86R5bYyUmFPD8//v686/yVuUdXljms18QGmKNrZR9tZfyPQmuMyLIEmjRG\n3MGI2Rk70bIcpEPL5NAgu2W1orNmnidyI0LAvgFN04gILUiO7hSlOTGf1N1UD1knO/f9TpN78bIc\nBEztR0qsaiOYKaBrll445A5ZlgNIEIJDXavq1chqZQvNodbpQznqaUrV6mrlRZWcjIXBmBp022yE\nBdRCM5xpsaFJaeyuScEc8O1vr/GmNy0ktIXCOYLaRlZZdmTkxKgWE3yvnPMjlZVeRr1MeZpkNsi+\nfsAwkN+fJB7PPHOGNKU2gDnEyq3nKqfvDXY7RmDyPeFrrWZ9pKKy+JBJFKEaD04d+4lmEWdnARoX\nam2USM9EciM6oZsCIaSgnxLZSGS0dfI7+b0qrnSOBAJjnOBKmu7HhoKrJo1itcJo4rTDaZVYV54D\nh4d0QU0SLxGqEScnJTabDYxJsN06zOcOIVQiTAQAh/nc4uhoLitQfl6bpkddsxE6O6MbcZIQ6wEi\nqqrFyUmNsgyij+FKra55D3jvsVicH39/3nVXwGdjzM8A+CEAN2OM3yd/dwTgFwA8AuBbAH40xngm\n/+8nAPx9sD39n2OMv/nn/NzXJfgMAD/4g5/FCy9skecGmhdMla6uL7i/JpVQv2sQxoYDE7ysuKvy\ncKmqQZLPCDLz8PNjZzcBmHo4DUgSThB9r3vbaRqgPQPXItz3xxFUVosLYNrp8u+MBLJ4WRtx56/s\nEgrrLGLMMAwJ1K1TE+RooMf1VF23qKp+nDAIbtrxdw6DHmCJUDMDGFQ/AGCQvLXUWzB6sxPfJn1N\nNbc6jl0+MYsUFy8uQOM8ixAsmsaAOct2BG4VoNXHpjbctIMYEEKLpqkQwsRuIX4SBRfSfb+8K6Oq\nWdXgU2iSc1GwB/Uk6ka6KIF29Z7ic8qyaT3pXIIQuI7SbHGNg9UCMxUfD+dyOJeCnlJemGgQtpiV\n501lOm241SeKAkdVWwOkTs/nGRaLDIsF8RMmApKRZwwB5GHg5ErPKP0cWjn0+ZnnZ5GNxm7XjgmF\nzANPRwaY972sNa1YobQy8fQIwWG5XGC1ctjtGpRlwGplkWUzWJuIQpqv31NPfQTqNvxGu+4L5bMx\n5r8CsAXws3uF4TMAjmOMP2mM+acAjmKMHzfG/GcAfg7AfwHgYQC/BeCx16oAr+fC8MEP/jr+6I9u\nikiKe3SuEGh8N5tFGWUH0Fl1wG5XjYAjFc00NKN9BcAVgBV2khOlMr3767qVHbQH/ZEGeD+A/3+x\nlAAAIABJREFUfkT7qWoMaqG75b6ZHtclWYbxcFDGCf3vAaVJOqd2GbricGNWgfek2obg0bZuxCC0\nGJEu2wrbahKMUcinLCw+V2MyFEWGNKVb6TB00ukHmRYaedz6s4irUCjm8OCDBaz1uHWrlLVMJweJ\nx4//+HvxL/7F/zMWWbK53EjdVEsPxQ74GBM5MDt0HZ8HextGdKoYkXoTTgdZliHLjGgerOBDEfvD\nOnUIWpCdqJu1yKj+Y5CJROmqRtZNqmvQ7GMWJrXQVsdcjVcl7TNDCNSFqEUKC6xmd+vUwAKTpgaz\nGf24hiHIFNqhqgZZVam5oQZNGbEzd7LGJIV0Ps/ksCYW1LZAkrQSIsWCySaEayfVzNDYkESJNGXT\ndPlygtkskdeb7LDdLoguY5Ai1WG9bhEC1el5zvf1zp0SgMV/+A9/G48/fng3b/v75rovCoM8kEcA\n/F97heHrAJ6IMd40xjwI4KkY4zuMMR8HEGOMn5Gv+3UAn4ox/v5r/MzXbWH4ylfu4Ed+5LdEuMMs\nBk0aY7fe73V0OoYPAlQ7AeW8WBjrAZyAwTGaikaHSu6mB6gGQXML2F1zKiEQCuQ5HThZTDiu61pF\nixczD3iz0aTNC2vECHc+iAhOny3Vqroe0fAUY7hGoC6AYTP7FFKuV6iUblsI3gFMBzUPNrV8pro2\nQZoCTCnTjAH+WYVlEhwm4j/mPtO+O+LOnUpWSU6C6fk1f+NvvBm/93snAjobqBJcP38EaCH/HWBM\nQJ4HZJkRFk+UCWJKoGNR1JUWA3lYOC28Z6KexqbqegqYiqT6I2mR0mmFtFl1m+XrTuU412G0eteu\nPECT9UhrtYIJJfA+R1HQmhygaeB2ywCiGBuoJ5VzFKKRSkpmF9eQBnleyCRmBPDt5euZ0e19ijTl\nZ4UrMBUETp9LqszVLTfBaqU6Hi8YkMN63WKz6XB6Wsm9Q9t6zR3JMk5gtHMniL1YqONsgqYhtpPn\nBmXZ4vi4xAsv/CgWi+Re3P7f9et+pqteiTHeBIAY48vGmCvy9w8B+N29r3tJ/u4NdX3iE1/GSy+V\ncsAaLJeZ7G3NHtgaZFfOnbemYlHF6oR9ww6SE4QV4G+OW7c6LJekcua5JozxYCBHvxdxnBFAu8d2\n66TTBlThmySM4FQGzKRObqUThNx8QfbTAexQ2akmCZPLCLhqJ8usAbKcmHnddS1CaEF7Zs07AFSd\ny7hHs2cjTssIAtIKdvO5TPgCoGA5DxInPP+p4FI5bLFedxgGj67L0HW1rGV4UHqf4gtfOAYAKSwW\nWWagWcXMZOYExZUMd9m0ulCMhN5HSWLGrAoK3ZgPwN/H6ZFU316YRHEskNS6JDg5qeU1skLljKMu\npe+7vZAbVSp3oO25E+wqQk0G0zSVCS7KagZCkx4AtNhuw7jqoo2GxWLhURR8TSl+BIAUfe/QdW4E\ntTnNGJRlLz5gg7yGnJ74vjMngil+FqtVPqqo6cbaiBK5xnpNP6qmoa+Wfhb73ouYMcHDD2fouh7z\n+Qw0GSRIfXpaC1suYDZL4RxwfLzDnTv0j0qSHPO5w9lZj+PjCrOZwTPPnOG97710j06A1/f1nURf\n/j+1/p/61KfGPz/55JN48skn79LDubdXVbUy6iq7qBNAbhCr6CCUUnaTao/RNP3YpRUFaXkEl9Ui\nmoKzg4MZ+r4S+b8avMVXdbRJYkfg0jkHOq6SBWNMhhgzxNggxoiyVN8ddq+qPaAVBvUF3kfpePeD\nWcgmUtM+XRtpytjjjx/ia1+7hTQNmM0MnCMLq2mY06uMG3rnaxGqBQTFWAzzXHMgelkH8fVibgCV\nrVxF9fJYNGfAyL/DWAzUUiTL2DmH4IRNpM817nXtgN4madrLiocCONqOd8IQ6sRGwqIsowjGAGVJ\nsbhzkuIU4EaxY5ZRDJdlTjpyTgr6WDgxqM0GD8P5PJECpKwfYLttUVUT3rHddvL+sYhT+JZgPk+R\npplMgB5lGaC25TH2YDgUNTL8nAKq3A+hGx8/Q3g6NA1po2nKACrNlybDLGK7beVrnBTQHl1H4gEt\nVfj5vnqVmpLl0iOEDtYalGXAyQkDi9p2g9WKNiabTYndrkPTDOg6YLVKcXCgan9SeGcz6jTKkqLQ\ngwMK4fq+QJIo4eGNcT311FN46qmn7trPu5erpK8BeHJvlfT5GOM7X2OV9H8D+OQbbZX06U9/Gf/8\nn/+xjMxK0aM9soqkZjOL+ZwmcLRSiNLpq5BnkIPNQSMTAUAN1/I8lU6f+9euC+OIzh0xVxlqQa0r\nHu6jqQ1Qtau1Ay5c8Hj00QLXr59Cg4WqqkHTdOOqSSeN6W3xmPKbU8E9BpkuDLquFuYRb3Qqb7WD\nJv6QZQne8pYlXnqplMdvR/CXB5sCsoN02QMeeyzDs8+eiW6gh+YO01qE3S7ZSRZMfOvltUxlveXx\nwAMH+Pa3K6gKe1+L8fjjR/id37mNGC2WyxRXrjg899wWNPRrx0OU7wffY+JJ+jqbsWilaSavJ/Mk\n1AyPwKzFapXhlVdKGDPIGsaICZ8VTUjAdtuIvoWvuQLyXN1xfUgmUhzXKqoPYFazGd+rPM/BnG76\nDakTrQr30pQGfcSSiEl5n+HyZQ/ahw8iwsOIN2ggD2nYg4DcTvQbnCQpKMxQFBYhULx5504llFNg\nNiOAbcyUIdE0nYjWPFYrJz+fj327bYRllmGx8Lh6dQa1EKFSv0XbOiRJIhNcFA1GjYODAk8++SB+\n+Zd/8B6fBN+d637CGN4CFoZ3y39/BsCdGONn/hzw+X3gCulzeAOCz9evn+GDH/w12b0DTdOhLIP4\n0athWS84wJTJHCNH9KmjZRg7g1ZYMChO488IwUAdUbk6GcRVsx+FSzyEKAyabBXc6E9EgRY9+TVy\nEYDs7NlB0sKBxYkK3Em8RlzDg7TJAZNHPw92MoZ6Odg1AY3rAQqVNHR+2HvMmiUcXiW4ovW2FfO1\nTvAOtQNRXr/aO+huX2m5BtZmiFG7WTtiGlx1KIWVv4/7bS9MMUgx4MHoXIBzkJUJWURNowlt+1iB\ngVpWOMciyI7dyu+D4FDliNlwt2/Hn6OaFK62jIDgfsya0Pvfe2oIAAgWpEFDk2UGJ0cnwHQyTi7L\nJYOEnGvRdS12uwqbTYcQGgBkLxUFoJkQDL2xoFAswWKRSj4I12ubTS+fO/08OVjLvIS2DdhuaygL\njysvK8AyPZ7W617S2AY4l+DSpQxXryZijwKs1y12u06U8xkODxOo91XTcN1Jn6UURZGOyv6jI48L\nFxLkucETTzyAT37yP/+L3Navm+u+KAzGmJ8H8CSAiwBuAvgkgP8dwC8BuAbgOZCueipf/xMA/kcw\n3eUNSVf9J//kD/FTP3UdNI/j7pOgI+A9LSloa2GENTTlDCu4GIJ23ftup+p/pJ0YGSdpajGf03hP\nvWoYtmIQo5eu0mNS6Bqo8VqW8dDqOmYUVFU3dpEAkCTsVAmQarertFo7FgaG8kCecy+PU9lAYW/K\ncNBkOnXR5GujYicjKyX6/OtKh/5NA7bbdgTGdUqghkGFXaR+cl0wYD5PRS9gpfAksl7hBMWQmH1v\nIogDrhWsg/oNBu/0UBt1qtODsKGMqM89LlzIUJa00ybYquA1Y0eV+cPLyE5eXW75dzohaZF0jgWa\nLrSJfCb09ZwcXRX4BiBYT4L5PMdsxmwCChytTKbAFBgU4X0P50gSUGsPMum8NBSpFHhiCsQ6dErQ\n1R8BfU5fTIvj59bBmBSAw8lJNSYQkj2WSkASm4Ozs0a+pocyxS5doh19WXboul40GqnkPHscHRXo\n+wFFkaKuuWLablu0LVPd6rrFdkuG3WJBbMz7BM8//yN/yTv9/rzui8Jwr67Xc2F473t/DS+8UKGq\nKjkkO3EspSp3UpZOhwyfKi0PyBRi95umDvN5LkIrHrRUBPN3kd7ay6GleQVMeCNOwfAWBaPbtoMx\nGehqacdDi90y/e7VKkPpmWrHYK2VNZYbu0/FGNiJMj+BU4K+d8zkLQpOCLQFYQ4vu1rtaPnVk91z\nQJIMyLJ0zBRQzx9GPUK6dmUR6X6d+oDHHjvEzZs1isLg7Ex33FwrULDlEQJjNDWRTe3O1VNIDd68\nH5AkJA+QAqyrDmYYhBBkDUQ21HbbynOidbnmLND2RNeEUYpjhHPDCPSrIeI0KfVSoHT64CTJw9HJ\noT/lZFRVP+IdurJSGxAtTMSfpgkyRupKdrsG1jIYydoeISSwNoVzkwqbeAEEF7FCi+bni4C1w2Lh\nZKXHHAx+/hyqKuDkZIMYO1F+E6zXcKiuY2b2bEYH3cWCE1ZRWLRti82mxulpgzt3aN0xm2VwLpXP\nADEvur5qipsRQduAoyNOFsYYbDYB63XEjRv/3d287e+b67ww3KfXlSv/G7bbHvSr54fVORYFro60\ny+KemZ0VRF07oK7pmqn7ePXxAdSqwkr2sO7he0l4I9ecY36Etakc/OwmCSgDzmWg4tePI/gwBDjX\njwwlKqUb0OCO3Z+qlPlnTTBjweN+l2ujuqaXkK5zAIxrGPVpslbzj60A4+ySFYNxrpfixAlAQeSH\nHprjlVcqyQNQ7IUTTpJYzGapsLt6wToGOYwdNABI3VonLMCMRVRtP6qqFx+iThg7vM/6vn3Veus9\n77mMZ57ZCgFgEhFyEjLCFjPyHOM4nfCzrf8oScGOB+6UWAfp0nvUNQ/ZvqdmQwFUYlcRmnOgfkLU\nm1C5zsxsYkykuKpavIMGPjlHajCFkWSiOZcLA4qMJI1hnc2o5tcQqa4LIwnA2mH8WoonOS0kScTR\nkcXhoRXBo9qO99hsaty6tcPZWY0YCWCnaYbl0kgzRF8xTThUyxXagpOW2vdsXg4OUjF4HHBy0mK5\nNLhwIcNiwXXn8XHA6emAF1/8b7FcpvfwJPjuXOeF4T693v/+z+P69RMUxSCGZ1FEO0o1VN66jv8T\n08Y5Hm4M4kmhJntkHTk5VIyAnx1CoGW1WmpTrKSHjx1/Lk3PrBQch6pStS/AqQUjjsCLhaIoetFW\nGAGk41i4eOgn0rEBXD8FmSzoxOm9lXVMJyparhtYKJ08LxVqRWg2BddFgxx+tFWm6liLSJDDh8+P\nRU/XcZACR5FYkvCQnIznFIiPwhZSxfYAVUqrNYTmXPP966AZDjyAifVwypvyvVncgCmDwkBT7JLE\nieoccmhr9Gv7qnUWM7DVwdXK+i9DmibIMoLetHmgvcTkneRHPILKdMh7xemFFNYohUjXR1EMAgfk\nOVdXdd2ibYG29WgaCGssCA6WCM4S4ZxHlhGbYW62g3PDaLMSArDbWdy5Q/M9awPyHGIFM4hYjk1D\nUVgURYLFQic7Pn4CzTXmc4vFgiw7hiUZtC0zKEIwI4APGGEy0R7j4MBiPs8xDANOT4PoLVL86q8+\ngY9+9I0X1nM/6xj+f309/3xEWXrsdhWsDeMh45xFnjPrVyMb1RaZgSrKWfeoKvMqxTL9gNTsjgcB\n6X86fUwCp6bpJN4S4KGval6AVggR3pPNkWU8RI0xo5EdV1k70JXVYbMJUPWyMX68OSFBQU2jYjQK\nrdRigRgDg4CUDjs5hpJayyIxjGZuSosksM7Di4WUh+5UuPox2OihhxaIMeL27XrUcPyVv/IgvvCF\nG1IsdILRgpAhSaLswiddBimdgxzMfO05wXVifTE1KsPgsdt14/NWHcZs5sXFNKKu6Ui620X5/gHW\nNvIem1cVkIODXCwulNlFNS+Bfl7eE3SvqjhOehS5OfERYmHkWoUYhTEJnEtQFE5wGupkNMqV0wpX\nOQqwt22NKUc6RZalWCw8ZrMCec7Hx1yFVkgFmu/BQ57WJAZdR3W+tbS/uHixwHxu5P3osd220MRB\n7+mVVBQZDg78yEIjtjOB7H3fYbdrwLxvL/8OWC69FFyLsmRDcflyjgcfXErx4tR9etrixRcrnJ52\n+Pa3y7t2z7+RrvOJ4R5c223AE0/8Dl58cYfNpkJdcx2jAS3sqBJ03ZTCxgMjyrqCmgU9ABkSY2Tk\njmJd3QnIXAndNIAhM8pkGaR7Y+cOKHvGoOs8+t4jxhTGAG99a4Hr1zfgwZiInYGHtR1CWIvFt2Ih\ngPep4B16uEPWSspeiphU11TqksIYoC6qOjmp8pgxmOx02IX28J7cfoKqvey4OzFnc1Abhxh1EiJg\nz0MdIr7TzOJBmDgMq8+yGbynYpnrNzuudxQ41/WOGtxlmRoETsI3Os1OKmlOcgFMooO8XlbsIXQ1\nZMdOn55Z/F59n/mYtBmIYzEmgNvL46Vegyp6uvRqjnjbMrdBKc5q5+F9OjYPqhD3fhjxE50UQqBa\nf7uN6PtEJkxdjxn5jAZoKp3+TP25MQJ1XaKqLPo+B030LC5eTDGbWaRpHA0ez85qbLe14F1OYmBZ\nVKuqkxxxYLmknufCBVqMlGWDLMuhGR20/WZqHACUJY312rbDfE7GHPGwiMPDFJpumKYe16//7bt9\nBHzXr/OJ4T68Qhjwla9sZcUDORiNHNA88Gl1jdFbyHseUBx9wwjYkU/u9lYKHJWZ6OVg7Qx0zvTC\ngOmhZnYc0cmqmaIkLbKMamcWEoPj40a86Y3sai36PoiaNYwrL3a4PCS4DlIuPqmMzk0B9l3XgWKz\nATFmANo9UJmRllTYMhKTthhcD4XQom1p78zHr5NPxPd//xG++tU7Ujyc7NY5TVgLFIVmCxMsZVCO\nH58/7UKMdJSDHNperMVVHKVALG0uAHoy9T1dQSfxnLLMrACpCYxh2hwFY+zeuZqi51XTBPleDf6h\n4SGBdyuv17R+ynPFY5KRKhpjL5OABc38vHx2OpDlBcFa/JjvzFWLFkl12e3AQKd+dHmt6xp9X8vj\nZoqfJgaSSQf5HHp5HLoe08cehVLqZTobRhxAU/y4+gro+xbMAslw4QKV8gcH6n8E1HWH9Trg9LRC\n1zF/5M4dBldxPRZgjAdNCY3Yr/fIc4urV1M8/PBcipcRDYnF8XGN9XoQrUiGd77z4F4dA6/r63xi\nuAfXjRsVrl79rHRKUW5uHnC8MYa9r1Zvn146Rt7URZHJzebQdQFl2YmZnXoHsQPXlDLtzqfQdu7F\n9XDWUHvvMxweLtB1Drdvc42jwDLAXTQPf1omOFcJ/z9Abb27Tl1I5RkYBbrVGmKQlZfiAVZUztyB\nOxdx4UKO01PVKPAAUUEezdvCSHVVPEYPu8uXC9y4UULTylRspp5CCsiqejpNM2QZfYr4eBIwWY8Z\nCOpImiQGly6luHFjK2QBizSlyywdaI2sqab37wMfeBBf/OIJVODGdZVqTTTTWQNhrExEDsxzcLKH\n5+6eOogo75mC/FEOPsVcougqtLhzDaMHPhli2jGqxoTrPjU05N8bmTJ7AekHVFUNgAFKScK1GIN2\nvEyrA/q+k39TzAcoDdeIpgDCvuL3LhZkAqldSIxMMizLDrdubVGWQRodJy6zk4W7hh0lSUTfB+x2\nLZbLVKjEmZAI7Dhp1TWw3fa4cWOHJLF44AGKR42xEqHL95lmegZlGbHbAXfu/Nejq+8b5ToHn+/T\n65/9s6/h05++LqM/wDUMweH5XIVa6dgl641N9scgHSKk6yK3fBr3eaNQ/BXGA5C78kF28urYqSwf\ntXxOkSQpnMul2+NB2nWqkh0weRlFGENsQD2JrE0k3jMZO3MelkE0B9241uLjo30E1bpqM01Quiw1\nyJ4rgCTRwBuqafu+G8FcgJ3qBM4apCn3+Y88MsfpKQVZLLwdqJDW9QYBciaokY3kHEVmtAvh68b7\nqBOlsKapGTDbOMjhCjlYdW2i9uc9FODPc1JJ+XM1YjVKF22gn2laeAxQttekIFcrbhYvmuJ1QnHV\ndRdTyKhnAVQvQFaSHQFYdtdGCj4PRDYO1JsMQzcyfhiVOey9vk5WUDqtqmgvigiTCnzvrTjxDths\nGOUZIw0f9f9z9caJJ8852amnEoCxOFhrpUgG1DXksxaFzWXE28lIA5SOWM3ZWYvTUwreisLhwQdz\neG/FeoXTaJp6HBzw67fbAXfuDMhzjy9+8Qfw6KOLu3fz3wfX+SrpPr3qOuLhh3PxconiixSFnQPc\nvg2hHBKATVPeBKT7pWga7s+VNTKBuTpxcBWi6lLupXk4cW1F5ogeplPhgYCZHdTGe/JrspjNBjFu\no2ZCnVfVZ0jjPMtSCwi7MEZHYjwotXskhbQYd7pcC0XsdsrI6oXRw0wH9QVKkoi/+lffhG996xQP\nPbTE88+vUVXKQjJQN9nttsGzzxIc5p7eyOuYSPfZo2ksaDE9UVQ5QdHXiAVUmUiMp+Tra2USseIw\n62S/Pggbxop5nb6PGMkEdT0xjAC6wioTR8V9XTeMvkhKEghhwGYThV6r088gbBsjqnYA0DhYNxY/\n0jutsMCiiCOJn1AgGQVYprqctGZSTJPEYbmcY7EgBZrUUitrp25vTQkpAkBdAxNgTxykKJJRr6GP\nJ00TobPSvZXhOZpgyPVdCD222yDTLVX+WWYBBNy+3aKquJKigj0VZ1rSf3e7AWdnEcYwZ+Ptb59h\nuXTiK6bCxoj5nDTbO3canJ114tcU8G/+zUv4xCe+967e/6/363xiuEfXhz/8u/jCF07HGz5NB3jf\nyU7aoWmsMEN6eN9LZ6pB99NO3XsFbdUagSB1lg0ymkMA2U7WEJOJGg9xTY8zYr2dIsZU1jssEiqs\n4wHP9dXUqA9gsDxXKxBn1QnEtLC2FTqnJotpToEexMoTjyONtSgobKI6uB2VtCxYYQQygX27awrI\neIiluHp1gf/4H09x4cJMtAIR2y0PaVJANb/B4nu+5yK++c0SyuunEpzPxXsj4rsoh1I/OoiyKHJl\nRyWwJuA5ee5etCTUpGi6GoFlfR+8mA5ywlLAXi/vNSiHehMm3OnrqxMIoBYaSkDQaQFQi++p8GLM\naPAoihR5nqMoOCkQyO8kG6EDs7PV/kN1NikowNRgpAF5PrGuJtNCoKomVb7iG/sUZBVlti1JEbOZ\nuraSeaWPlwUKI423aaJgEcDhYYbLl3PMZqkI+gzKkgX3W9/a4uSkx3YLADkeftgK0YJTLKdcrrxO\nTqimz/McjzyywNFRgkcfXeBf/+v3/CXu9vvvOp8Y7tPruec6ZJkVimWDrtuhrnfwPkVRXETTZNAU\nq7ZVJg+E8ggZ3we5OZ2sItip0i6DDCRjBtmPT3YSdMXsJQy+g+YbeA9st4NMKip0goCKFpr1qwdm\nkqgAiboD7uRfbcWg2Q2Kc6Sphqnw4GQIfBSwENAoze2WVtg8/GjnsFgkogynPTUVwJMKWg/irhtw\nfFzjzp1GikEDY4xYZfAx930UUDgRtkuC1SpDXWNPu8GDnh79nfj7tAA6YfuwA07TRHAXM7qfchKh\ncjkEqmrLkoC9Mok4eXEqU8B+XxiXZW70TOIhqeByhOY4qO6Ck42y2AgGMw8hRZJwxRcCbS7KktYP\nqs1oGi9kgmEsNlxNGqxWDLHh4/GyvurFS8qI0Z6+bzzw6zrIekjfVz++L13H9DUe9tPxkiRUQ+c5\niRZ13SJJPI6OdHVmJJCJIPbZWYOzMyaw1XWPW7canJ4y1IfEAoO25WpsPrc4OMhweOjhfYorV3LU\ndY/VymC1StC2/HymqXpTtbh+fYNvfWuDV15x+LEfe+PpGP6y1/nEcI+ua9f+Pe7cYcpYCCe4eDGF\nMQX6/ghpOhc1aIthaME0sB003pHrH04KtCdQENMI+4QOrCHUoO3BZlTl6oqFgjcD53owGYve+TEm\nsDYXJhTGMZ5rjQggBRPd6NgJtPC+kW7OyCrEQ7MG6rqRrpJrCWs7mX50/eTlkNS8YzcyeliAeHF3\n3Qu42UAtOgBlwagHUC/fR5+nxSLB933fZTz//GZMaqODq5oKUhG7WOTS2bK4XL6cY7OhjUieA6en\nqlPga06Q2OLatTm++c1T+VlKkR2gym3iDL2Al3ZcZ6mBHplI6t/kxuerE5E+N/074gxRmES0GWFX\nTsEZD1wr4LYZleOqw5j8o4AkYSwqmWZRjOT0fWIhp7hNjRSJeVRVJ4K2OD42Pq5EqLEGarzIHJFU\nbCcsFosU8zkjNDl96vTQy5TTCuOLsa6AAs+cuBhfG0ayweXLBS5dyrFcphKIRJyqbSlYOz7mtHBy\n0qCuLQ4OCjhXYD5nc1DXAcZ0mM1ImV2vO+x2JDQcHeXIc75nX/nK37pr9/79cJ2Dz/fp9eijf4Qb\nNypU1QZAgyRZ4PDwCrbbbAQxjWngXI00bWHMDkAjmgH1PPJy8xuhEJL3r52+RjYOQ4NhCHIg086A\nbJRB9qw9GAGZyven4HqAXakxUTAOAtN0dGWoO1dUNTRulH47yoJS9hH37AQyK2GrBExFIRPKohEa\nLvfBOi1M7JsBxnSgNxFfRxZCFsN9628+7sm5VDMUNE6S9t88WCnYSwSE5YF55UqOmzfrvdULMyyc\nUywIUtjUI2rYc2glPsEpQCc5LURTwaNS2QudU43buOvvOieHYCMaET43nRq5qkvGn0eWFNc0GnoD\nqJbCQi3aWVwGeM+CQJzEjgc/cYVOiAEd1Dqc7/cAtbOw1mM+T3B4qCwgNz4uPm+K03Y7Wm7TiVVt\nUowwpyxIOoBgY9S2HBwkuHjRYbn0mM81ZY+fbe8HaObGSy/tcHzMxDcWCT+a7bUtQe26LsFpmCr6\nGB2axqGuOcldumRx+XKKxcJKChzXhJwGSXzYbntcv/6Ru3r/f7ev81XSfXrVNQ+F5XKOJMkBAFW1\nRttmCKEGBUKaBKY3aARQwzkKoegRROYRWSqkklIvMIBxmHroJ+BhzBF7YhFFcf10SNMMfc+de9dp\nGAxprcNgsV730BxlWj54sX1wsh83mLjz076drJsezClQW203At9tq8/NCZuEVNYYGaRCrYSqv9WS\nQgFh9TJysof3QpU049rFWoN3vesIzzxzB30fxKPKCSgapWs2yDIFcyNOTmrJtu6lsJEPdPnoAAAg\nAElEQVR3TxW62mzoxY63qiLK0ozPj0wasnO4huGqjdkYXFsQ+GW3zgNZJx/Vr5jRyI6qZaV+6gQ4\npd0R/FbFsxZvBb3VMwvymnLNGMIghVctsAfMZlZwhxQUCBKkpZ9SLQaNPcoyoq6Zh6B05v11FycO\nh8UiwUMP5UjTAvN5Bk12I14yyOeV1Nj1usZu1+O55yoAPYrCoShyKZwJnItYr1ucntJd1Xtabh8d\nTVkfVdXDuQTDMGCxYK4IHXMTtC3dWOuaRTtJIlarVHQ2Fsulmk6ycM/nFpcv53fnpn8DXecTwz24\nqmrA3/27z+LXfu0Uw1DLTl/XEymMaaQz9qDWoBI1bYNEImipXm32wGR2+lxf0GLAeyqeaYaWyqqh\nBPN+e9BllQcZAWUD9ebnegrQ3AbaCnDloGsAYzRLekoBKwpy+9frFqpb4OEb4BzxBmVesfgA7D/o\n6T8F39OWwrk4Ao7WdvJ4mAzGLteBQiYjRSzssZKsHOqclFSIB3DnrtoFa1PEqH79EWrKx6lNpw/V\nhvQjpVbB9mFo5f3oxY9H85UNJmfXuEet5f9nMlkiBc1As6T5Oy3U2ppg6yBMoihWFMp+mhxjNcFP\nQX21HE9TtbuA8P4TYZ/ZcY1V18xZ0CLMac/BWnWB5WNWhpJSZouCE49qMazlz6qqgPWabqjrdQvm\nkWfIMtXS0A47z0m3bpoWx8e7UcOTpgaHhw6HhxnS1I2va9Mw4ZCq5YiiSLBapbh8mTkcTBpU7KdD\nmlqoQeIwEE8iCxB4+ulTdF2La9cS5LkTB1dOnnlOzEMzTZ555qN349a/b67zieE+vEKI+OxnN6Az\n6gBjWiTJFkXBNUjfA3XNGEdevdhjtOABEKRTn4zGgER8d4IAxBSaeZ+haYAYGwFIOykUTjrPXsRq\n7PiYC8HPS9u2UiyUocOucgpiMf8JSMqOuKogWAEpjzy4CdzuWyMQuPZyKBvs5wuwSAzy+yze9KYC\nf/qnJdQaRIuOgs76M7kiYdfPvIpeOnxd+/DgjBGoqgkUJzDu5bl4mVi0A4aItpjpzNQ3A6DB5Cbb\nI0k48Vy9usBm02Gz0Uxj9XHimk9dQTndQIrklEehivPJ/iJgX6/hPb2W5nMv2dO0ldYDr+vsaGo3\nDD2qqsVuB3DasHtsJRb3JAGKArhwwcmkwAcyDB3q2kn8ZhBjRK6S2GF7VNUAdeRVAV9ZNthueylg\nvay2ciyXA2YzN7KbqorrxBAittsok6yqzVMcHWW4cCGRlZMRWxP114o4PW2x2/U4Pa1RVSwkfG9b\nvPwyV3BcWWXwPkGaFrDWYrPpcXxMlt7b3z7DwYHDwUGKsuxx5UqCCxcsLlxIkKa8Vz/4wSs4v159\nnU8M9+Da7Qb89b/+LP7gD04B3AZwCmtrsPtMhWs/FzZJELA0oG0n0JWRnBoCA9ATP5NOFXLztNDk\ntBh34/6ahyaLyXQwquW3XhHMcCYoHKMbBUhkS7FYvNbLzxu/E7YSRW0qtJuopSyCdIPVG1qN+FJQ\nsRxkPaV5C7V43ajtBKmjtN1IsF43UkxYsFQ1zu7e4a1vXeLZZzfCpjHC3Xci4vMyAWmOhe71DbxX\nTn0voKw+78kpdaLPTvbZIQwCrAb5Ouo/mLCmYDSZad5zHZZltCTh43doGkZ2Ng0V1fw+gBNmL+s4\nfR3VWypFkngx64siDGOxVm5/XbOz18QzEhn6MWWNam4yiobBjSslXbGQmqu2KaqL4CRYlqXQrC3y\n3MGYHEWR4dKlXOzClWVloVkUIQSUZcDNm7vR6BCg9oWRn17A6igqeYL2ITjMZlxXaZYCbdQbXLqU\nY7Wiwpq6CQdN1QOAV16pMAwRX//6bbRtxGqVoyhSABEvvLBDWfa4eDHFJz7xLvyDf/D2v/B9fj9f\n5xPDfXjpTvngIMrOXMVSHhomUlUltlt1JOWBRKM5ArKcILTjVYUo08C4moJ8r4N2tuw6uZ6YzXT8\np0cMAVhl9PDwo0gLAhazGHGVYKExopOITTMYAKAT5giZUzzAhhFT4OGSCK+dmQ0KuKrfDx8rO0q1\nm6AQj8+MALQT627mIk92ERpl2o9TQ4wO168PQkXlGuQDH3gQL7xQo6oCTk97EWjpax6lqKjtdoCm\nkU0FNGLKTKDNOFcunFzyPJPDno+V7rQRVQWUJe1PVHSo0aZMNZu8ryimI5Po4ICrJybqAZwkaXxY\n10boxwkoajOo64C+d6jrTtZTcSQbUMTWjc8jSVgUuG7i+8mJ1sqqCFIIqEanD9cwqrEBSHdusNsl\nODnZ4OwsYLs16DpiAjdv8mtWK4u+d/A+h3MUWVZVECdWOuxmWYqjoxQPP5yJd1eKyemWLDYWAa5m\nN5sOp6cNNpsGqxVNHne7gGef3aGqgBDozHp0lGC1oqCuLAcRTTpcuuTRdRZVNeDGjRqbzYBr1zJc\nu7bAyy8rrnZ+6XU+MdyDa73ucXT0NQzDMYBX4P1GbkaLtvV7zptqZcHQlUkBG7FfFOgk6qHupZq4\nRaDRoWlaTM0BbSx4eLjxEFIfInbMg4Cb+j1Wvt5C3Th5TTnKuibhuisFA32i4AIRGnk57f+Vgqn5\n1mZk3PCwiNJVQvx9FDzXQ5t/vnZthueeOxkfk3aEXGups6cZGUB6aMbosFikWK9bPPzwAb75zY2s\nPqafw3AayOMnTXg+T4TdRAxHsQ3Gk0JEbwZAIq95/6rnq1oL58iUYXIbhV+KI9R1j7Jk9rFGrQI8\nsGkbra6xvRz0VsBrJRmoeyx/Nv2O3N5j68Gej6K0xcJjPvcoiogsC1A9DNlFBnU9YLtV0F3ZYWac\nVqhH0WaiQ1Vt0TQd6O5aIM95sF+5UuDoyGE+t6hrg8ViJsUGMl0O4gRAXOKVV2rcvFmjqvpxZXlw\nUODgIBVKLMYJlow0KyaJRqI7O9y+zfXr0dEcb35zgdUqwWzG52RtitmMyvzdrkNZMtFtseA0t90G\nPP30GU5Ofmgsfm+U65yuep9eP/ETz+Mnf/JrSJJjeF8LQyPdO5x07aPAdBRsgGwS+tAH+fopTEcL\nBDt1I50qD3PNLmCXr15BilGksjoYxvWAFh/v85HqyJ+thmmD7Ny1y9bLQb2B1LyPaxgjqxhVbisw\nrEZ3TgqHxo9qh0rgksB0gBrRkb3F4qOZBQBkclDwePJQ0qyFKdiHcZoHBxm897JSYhFomjAKwcL4\npni8+92HePrpU9CxNY6FkuZ+tPKmk2oKdRzVA5xixChAv5eCnMjahoVO2WTEHjTtrB+LEf2khvF3\n80DWVdxUbPI8RVF46eLVM4nOpmVJvyJGfDbje+VcQJJEWdtxkotRH6fSUT1mM2VLOQGgGe1JZlgv\ntORBxG7Abkeblpdf3qIsG3nuzIiezxMpTKqZGVBV/KcsW3QdV2FFQYdVa7k6UlxsXzDJCVYtRwaJ\nWeUa7PbtFicnAScnFYoix1vfmmI2K/DAAwnW61oiWD2SxGOxoFXHMABlGfC5z/3A3brt75vrvl8l\nGWO+BeAMPFm6GON/aYw5AvALAB4B8C0APxpjPLvXj+U7eR0dEchUR1HuP9l90jdGWTRTzoEeeOSR\nkwdODcPEeCHLSf30jey4+TunA4zrIh7UTPQCIDtcjPx7so3oNspOs5ODmgew5jsz4ETZRlYOYBXA\ncXfd9/s5xcpUsrLighQDrtRU+8DuPxHANMjhNgjDaBi7+EkbwDyCLDOyzlF7aitTkBs1ICx03Eff\nuUMjQNUraBYB4ykTKRpMC7txY4f53Ip2YnpteahAtAikb9LfqYf6PRGAVtWykdddze90MjDIc05o\n1toxC5qcen6/ZkQ0TS8HaIOqIi2YSm/AmFaehx0LI4u5FmVOZ/O5x3JZYLlMcOmSJtrpeeGE9su1\nFzMMWrzwQo8QWmk8yDBKU4vDQ0jRGlDXfI3rmgWuLEk3ttaJKjvH4SHXRUXhpOGZhHxkaQ0jU+j2\n7R7PPbdF00SkqcUDD+QoCpIXWHh433jPArZYKIuKk2xRJDg8pEPuyUmN557r8OCDA7qO4PeNGxWY\nL52JPoMrrqOjN16s59247vnEYIx5FsB7Y4wne3/3GQDHMcafNMb8UwBHMcaPv8b3vi4nhq4b8O53\n/w6uX38FwEa6xUnsQ5CYNxjpg9NKA1DRl9IaHZRSOTme0rdI1z9KbWToC3+SHo5TePyU2cwsAwVU\nPUJI0PecAhQH4dXu/V4FJdm9Tnt33flTm8FphClonGD0VVF9gwc7WP1vrptYgBKkKX2gCMgPEs7D\n8JgQLDR/WSeb6WdyPTKJwKx06/wajbYkUK5iNCPFejpYQ+hETLf/jnKFweKsFhdWpjke9ix0urah\nAy39m4j9kFHWiBZiECxl+iW0W1exnxZI6iBY0FJ4T5+i2cxjscgkb8GKv5Em3ykuQUxlt+vQdQOa\nphGH1ihF1YP5EfyMafhP2w5jQ6JZEPRqcq+i1yq5gQezFXyF1uE0ATQ4OWnQtgTQi8Lj8mWH5TLB\ncullHcoiVdfEAdbrFmU5YL0uUZYGqxWTDvNc1ePAet0JgM8CNJslUoxIdS3LiJdfrtG2LS5dyrBa\nsSh2He24Dw8THBwUeNvb5igKi/e8Z4V//I/f9he4u18f130/MUAJ2K++/hsAT8if/1cATwH4M4Xh\n9XoxMCaRrjWK4IeHu47Ck+pY9+ZWDu5+ZBdxt6sH/jB2xgRyWSCAqatWARRXMmqBYWR9w+LB9dQk\n4KJlhGYl6+OiPxJ3zVMHrEEyzEcgBVTBaVoVJLLiYuGi9bSFsmuY5zAIfXNyddUiQufUgHe+8wBf\n+9odaJqb2mPPZpwO3v/+q/jt334RZdntqaEHsetQB1Qr+oxhj4Wka6ZJPKe7a74WRiipXM+Rdpog\nSTz+5t+8ii9+8RUBgDGyf972tgOs1x2efXY90iybpoUx7Yhh8LWmB5S1DkWRYLm0wvmnVoDUXmVb\nQZqGKF5FxBk46ZH1dXbWYL1uoLYaaqOi2dRkJqmxohIcAtJUxW0z6eRpNcHnP6AsWRg3mxrHx504\n8UbBSA6wWvW4dMkIjVrTCNU2PEFRGCwWxC8uXvS4dWuHs7MO222F7ZaCwIMDi+WS8bVF4VHXFGE2\nDf2cqKkZsF4DBwe9TCBOGGv8vuXSY7UqcOmSgtdWHqemvwVstwHrdY/drsVb3pJhNpshBIOnnz7F\nH/7hGWazAR/72Hvv6r3/Rrm+UxPDKXg3/i8xxp82xpzEGI/2vuZOjPHCa3zv63Ji+Pa3azz00G+D\nG7QNeAg5AQrjeEgqw0ZtCQBiB1MI/MTfV5rmRLdUlpN27X/2cXCHrKsf/YJObuIIaz2GYQYqkAHN\nXph8jCZglIIqpo0lySBg+iCHJIsN8xMoplPKJ1lMmjamWROqaHaCPZjxwKO4bmJIKW7B1ZLqKzD6\nRVHFSpuEt71tgWef3Yq6+9VU2yRhOhh1DIOoniF78kFSxRpUVYPJ08hDA3e45lFFL/EMDZJh98oQ\nIdUecBJRP6GIpmGaXl0bYSvtZxHoigfj54PF3AqFmLkPOploHkKaOrG5VlBfC0svVN1eOvZJP6LZ\nDZzSSPGk4aE2ATqtTRjM2VmNsnQIQWmz1MIwO8JhNvOCW+hn1sm0o6E8DNkBKGxbLlO8+c1zLBY5\nViuProMAzlOkKQBZM7VjSBXAArpe92jbiFdeaXF2RiZWnjtcucJQIAWn61o9s6jhGIaI7XbA7dsB\nZ2cDvPf41V99Lz7ykTeejuH1MDF8IMZ4wxhzGcBvGmOewasJ9XiN/x6vT33qU+Ofn3zySTz55JP3\n4jHe1evq1RzveU+KL31JfX0iVCfACWE68NRWgt0eTd+UnURfG8juWrs6PXQVuJ4u/bnM6A0ypehn\nQztlXWEBzlEYxI5vGA8kdr5T3rEK4LhbblBV08TDgjGIPw87+/2ISvXwoTajF1yA6yR1HCXeQY+d\nt73tAM88c4zJsdWKloHWCrSRoJtmmjK799FHV9hsWux2DX78x9+DZ545w+c/fwMh9LhwIUdR5Gia\nHu973wV87nPP4datDpPOgq+psnDo8Oplr+7HjGddOU3ZERHbbTfu5mMETk4amTQGOah1/08NAKcM\nD7Vi5/sJAZM1E1oTySCvuUEIVhhHGNdlpJkqbpPIawNpKvi9zFGOsoILQjHGOG3udsOonlbWkGaA\ncK1EkLmqggj0DIAMJDR45DkxhAsXnFBInbDm9LOqBV2nlwG3b1e4caPC9etrVNUJ6MzKnf/Fi5Cp\nmMWYxUCJCGodQtv0tlVKq+p8PLzP8eCDuTxvfuaShOZ5zhEP6jrg+7/fo6oirl2bvWGKwlNPPYWn\nnnrqrv287ygryRjzSQBbAP8TgCdjjDeNMQ8C+HyM8Z2v8fWvy4lhs+nw4IO/jrIsAdTQfTq7Mrzq\nxiX7RYVM+k8/AnV0RwVoh6DdOA9f7epVQcwdsAWjGHUH72QlEaCupTxwE9BiI5WfRXM1/l477vA1\nPpGmcrTZUJM7es8kmM8THB9XY8HhAaYaAGXWaMKc4grA/iqJwCDZKWTpcAXy+OMX8PWv38LNmyXq\nOuxlGSRjkb18ucArr5SgIE5ptez285z7+CwzmM3sqHhW0zhluDRNiymHm7t3pcVSU8AJYZ9ZxfdK\n8QqyrjSKkq+fHo5acAHAC80WUAovQV4zFiAe7nx/SQ6g4It+Rr0c1MN4gE6qcCvMNj3oAbUY4e9W\nQgLxlzQlA2i1SpFl7OYZOco1IosmG5jtNgiuQi1A00ScnvbYbDzm8xy3bu1kKulQFEwo5KrKiM0L\nJ9vNpsXZWSsUXOIbQILHHkuR5xbLJUF9rpY6LBZeGEk0IaSCniK4vmcoUwjMbrhzp8Pzz28BAFeu\nZHj4YXog9X3EZhOQpgMWiwzLJckDjzwyx0//9Lv/0vf7/Xjd1xODMWYGwMYYt8aYOYAPA/g0gP8T\nwP8A4DMA/nsA/8e9fBzf6Ws+Jz2Ph5gbD8EsS1HXmmn8aoBYD1KulTTHYbKzSBKPLPMwJsMwOOm0\ndefcSWemegHiCfvpX8b0SFPuX3lwJ0KrVMppkC6XDBwyYzAe9PvaCk4BVg6qCjFW49rIOZrV0bSP\nWb8qVCKA2I0qX2UqqZ14XQecnjbQMHhrB5ycbKFMq9UqG51TeYiSavmmN+V43/suAQC+9KVjsSSf\nvJo2mxqbDQWEGg5PPyZd1QyvWuepVxLXNHREVUEYxYKddJ8dQlAgPshzp2HbNAHoZKhrvSlNr2nC\nWKAAQO9jHt4KzRnBCSzUXkO9q3TKyzKLxSLHcmklyIZdMldxKrCjPQonnIDtNmKzKXHrFoF9LU5Z\nFpHncSxQLDJRbMPp2EqrbVpYd53FbteMWo40NTg4YITm4SGtPBS4HgYWnLqmn9J2CwwDHYSfeeY2\n1usKALBapVguE8SYSlFr0TR8v6gPYQFNUy/TAd+f1YoCt92uF1A7IE3JUnr55RpZ5nDlSkTbppjN\n0v8ke/382r/u9SrpAQC/YtiGegA/F2P8TWPMHwL4RWPM3wfwHIAfvceP4zt60VhtUueSdRQFdLay\nxjFCt4uCHUA6+31xm64vPJqGTpX/L3tvGjNZep2HPe9yl7pV9S399TIcjj0jiqQWUqJIi7JIiOYk\nQ5miFElOItiSAxt2AIEGISC2/1gJBEf+4Tj5E1OIQcCALFimEG2QSEOCQlOiOSSdISGKojykKYoz\n1Ow9Pb18a1Xd7X3vmx/POffWMLSRSNOa7vF3gcYs3V1f1a2qc97znGfxPgctDCCOnAyD18UjoSRd\nRhs5yZrxxMpCrxi6edFz5mk2jn/fGKaH5XmC9xmyLCDPc8Gh6WuzXlPnwKJJ4zu6vvZQrYUa96nN\nREo6IWQjXZMhLVGWpz3Wa6plz87aEVLTk7F6QanJ3XPPnQjLhVGUajc90VLdeLJWvYNmDdBGPAoU\nYaFLfEZl6q6HzClqC6zAggFdx0S8H/zB+/HBDz4uz49sKuLiYevnGqhl+YtFhE4sPSAneI+iyIW7\nbwXqM3ICVNFXkJS4gM1mkKxuCrhirOVeDyN0R/iOVF8WWiNMpX5kPjFF0KEoaJS4s1MAsGI0p3Cf\nHkzYdJjhAQBWDjxGMsOZlPbkk2sMA1CWmeScZ9jfd0KHHsbFf1FkuHSpQFVRVxAC3XGdi1ivCSOF\nALFEaVEUOYrCY7GA7Jd0GvWoqgIpcZ+zs0PYqSxnCIH6mPUa2N3NMJtxz3F+ff3rXOB2m653veuj\n+MxnrstpPsoJ3GAyOHvx69KTs55cJ6/8HoShMikYFYbBoyguoq5Vk9BgGNZSDBTCCFIgqTMgjjzx\n0olje8Gwg8RyElrRBSBDZ6bphUtuNjfnLN75zkt45JEbssS0IxSh+DgjHTFmKXDxOYw6CT43C6Xj\ncvEdRSRmoVGlMbYC8ViBS8jguXy5grUGL7ywHu8rmU/KNmKxLUsvjp9ua0lrRn1F13Wo64Qf+qH7\n8aEPPYnTU81rZsOg3bkHjQJV0zHI/XEjLZXsKYv5fLLiVtEaRX9WGugw3u+6ZnAMw2OmgCPaqzsx\nh6MAjY1QBYQ6MfI9V83HMExBPVVFAz02dzuetJknoewqLpnrmpYTjMPs5b3iTmc2A4qiRFHMkGWV\nsIC4F1J6rUZ5pkQsf7Vi4hqN+RysjZjNPBaLEhcvkq0XI5lsfc/J5vh4I/fFi3VHgd3dTN5Lj82G\nkNBi4bC7W+LiRZ78aX3OBt73hDmbJshnr5fIVuDmzQ5PPFHj1i0KQH/5l9+Cv/JXXpnpbXc0lPSf\n82VtRNvWcI6nIjYFQPnzCruoGI0FehhPsrwIJ/AkGhFCgzxfgHYGPbyntbQxBazN4H2DGPvRRqFp\nOkzRmErR9DINTFkM2lBooeDEaE3DZZJ8iYEQyFLpOtIoH374htBv9WQepClwgalhP9rseIq3yLIo\nU8jEONJ8BcIzLCZkMRFbns0s5nMNjGGx3dkpJb/XiwCK+wK1+zAGYsTWo2kM5nNaON+4QUNDff16\nvz/84T8GQ+OZDTCbqR4hH++H7knUhZaQUJTiHnF6GnB42CGETqA9hyn03kO1Fzq5MK97EuSpYpsN\n3EiTyWV3MUGFCrfp8p5aD30cYDvPgt5USUwbubeahJADioK6Bu8TNhtgtQoClVkxC+T/cy6iLDuU\nZcRmA9llGWgYkHMaRcuJUm3beUjwSKmEMRn29grMZtyd7O1l2GwCiiJHWe5IBjXJDow05dSQUsDe\nnsfFizm6bsDpaYvHH18hxhzGGCwWCRcu0IrEWuD4mPu0quKuZLFwOD4OEvxDKu3zz597JP3HrvOJ\n4TZdb33rr+JLXzoErbAnOT8hhWkZq2It75N45WCkawJBCmUUeqZD3++BhWwJJnwFOFdKoVf+/CDL\nyDguQ/W0rZ5HXGZq2D0ZKRpWPy1OVYEMqBpXC9I0URiQpaICsCCNYBsv70f2U5YVY6OjdsIK3g+E\n0CIE5d7r1GJAS/EB2x8FQjIOIXRS+Cg8U4U0HWzVjoEW1vfeu8QDD+zi85+/DrJzooiyeoFlIuq6\nB/2B3AhFMVksG/+bxXeCZTTngfeHVE+9N2UJlGUpcFkOVevq4YD6kqmw66TDJXoU2FAT0QxU8a5T\nGaDvYYLGs1J8FjC53AITJKaKcyOQoOZfkFLbdQF13csuQ2nODtZWyPMCOzsl9vaYvrZcZjKFuvF+\nKS2WQjoA0IVzj6OjgFu3OoGbuHu7eDGhqoCTkwKXL3us1x1CYFIgl+7cr1QVm/V8TsX98XGHzcbA\nuRL7+xb33FPg4sVs3HE1TQfvLebzhNPTKN8pK9NExPXrDR555EGoad8r7Tr3SrpDr7/6Vz+Cj370\nacnuJSVPmUUUc/WjhQEdOJUeqroGpVSS8VIUHrNZhbLcF9giAwt8Qoweh4cGxtCm2towsmno2KrN\nyIhQaspcpgWGCvEmlsswJDnNDmPh3jayKwojith8axFMdpSG0jdNh67r0PcBITiBJwyMKeU5EpZS\nD37acg/CjDHirFrKMjcKBMbnmFLCfJ7jLW+5jC984Rbe8pZ78Ju/+dQo7iJrh4+r/kUslsTaifsH\nqB3HNK1hPLFnWY48z1GWmUBC+rO1MWHE2fu+FxhE1eKQ3Ac2AnoskURAWEhP/mwM1IEkwf71uVsh\nF+h9CwJt2bE5aCIdxYwTE0zZY2SqqViPMFxZcjeVEhfXk26Gf09P/TF6tG1C2wJ1bUdo5uysR9No\nQ1KqqNmC+qw0RdqsHB018D7D0VEj+go+n/nc4f7757j3Xotr1zpU1RKLRY4QlG1Elh1hSC676zpg\ns5mgVcJiCbdu9bhxo0eMPe6/P8PuLt8bsqkiqmrAfJ7DWqYeHh8HfOUr70JZvrLM8/Q6bwx34NX3\nEXt7PyewEbMO1Al1GKx47qhpnoqaFBpglGdKPahs5kU2Romuc1LogsA7DjEWiHEGTYkrih6aMQCQ\nMqnq5bo+we7uHoYhg7qu6hcZgOwReqEFBnEV1SWjUm4NVHHLfxLyUu8nwlL670EKEwNciFdXUtzU\n9kMjKcnUISzTCE7cC3YdoS6dnD7I8nr1qxe4dq1BVZF+eenSTCY1Pl9VO/OkHQX+UPfQbQaYOs8G\nqPMol8xOlvVhxOkn4Z2VRqGqdvWMYqEHzPiL+hO13NZirO9uGpuvChUnkeEEnTGlzY3BSZwudaej\newO6pDLjocd63covsqiI61uZ2Myom5hosgM06pWv38rS2cv0q55ZRtTozErgTqVAWdqtz3WQz2mQ\nhpBQ13x+h4cNXnhhjaZppcg79P0unKuws1Pi3ntJi9VENu5TgjRlJ/BhJjAb8NhjK4RAQ8E3v9mi\naaK42A7CaBswnzPFkFkPwKOPfo/sf1551/mO4Q68sszh/e9/O973vk8KnTFCDen4xdFcgwgNYFe6\nHxk4Uf7OVIQ58oetAp2kUHEhHSOVrMAA53IMQ5QTf48QAl71qjfhmWc8YnwcLytY1EkAACAASURB\nVLzwRVDH8A0yMUR5PkEK1DYModbgpEBS3ZuNfjWKl1O8RYUqrQ0CmqaXUx+LhPc9Tk+dnGTdOKkA\nfP0xGrz97Zfw6U9fw3odROiXpIBVolhWLySeus/OAsoyEz0C/XCodwDUBZZwh8F8buA9F96MNnVQ\nOxHl+jOvWQ3sosAaU94EHytKU8jGCYL3jRi7LqzVZtx7UiupiuZSWnUL/PkqaDRisGjQ90DTULdA\n9a9CeorbRyjRgCp2J9RUgMZ9OoVsM7qSiPaALCuxWOTY2XGjHQejXnmAiJEqb6bZsTk6N4UTsXl6\nABZ1rRNFJ9NvQJYNKEvGzhbFZJsdAu/ratUiBO7glktGlXICoaEgM6wVHiTcSbIEIU9rHfb2DLqu\nx9WrEfN5hbo2aNuI2YwTcQge+/sWfc/nQ8YX7+1rX7t8xTaFl+I6bwy36To4mCEEfoAJjRTQLxz5\n61Oe73R6M2KnQBqkRh6y6Fq0bSa+Oepiqj42PYyZ6KH0SQrSZCg6Oj5+ClxsrrCzcxFVNWC5XKEo\nXiWYutI3o0ArvWDwFjSwI7xR1wPWay5v2UwslFfPxpVkeTzIa2PxZ4EgI8r7XJ4nRlaUFuaHH74l\nITMQCM3K6djLP7koveeeOc7OetR1BzKm2FyYd2wF6we0cTln8Y533IOPfexZdJ0G4WwL7SBwTBz3\nFUXhJReAz4G5A5Dmzp0Bl7Y8tasWpW25VD47i1iteqxWAX3fjMI/FnVCWjp9qJWE2pOrbuFrrU7Y\n1AyqihCbThG0xUgCManiOAjTLGCz6ccMg81mQAgdrl9vwcYfpXk5sfLWnUEuC2QPawfZr6SRPRVC\ngHOZNJAoTa1HnmtgFOm71D1A8icg7DED9TaiQaHDej1DSksMQ47dXTZJtQ0ZhiRTIokcCrtam3Bw\nMCDGDsMQ8cQTx/j0p9cwJsN8XmJvjySMJ57YICWDqnIwJsPTT68xDN9y3hz+I9d5Y7hN17/5N8/g\nNa9ZgCpi8r9pIMeMY7qGGuFsk8XTNEDbhnGaoJWCAU/3GThFODllkTUzn8/hfQ7vdZegWQpcXGtU\nYtvWUPuEEJaCY5fSPKzAVgHqlkqb6iiFIQosBqhdNdXBgPogqSsrl8UB6onEL16UU960i7DW4oEH\nFnjyyRVUQ5BlHPNZjLdprZnoLxLqmkV7d9fi1q1GJgv6NHHf4jBlQQCqCPce+Oxnr+Oee0o5OXo5\nIfMi/TPK+wI0jRXtiKqGGb3adZMpnrWt/Az9eUmmPiONXyFEVTfzHnJf5FEUxTg9EOKyI2ausI3q\nW/QXH1+bHtA0Peq6xZSTbcVEMAi0Nci+KMhr2bbmZpPSYk3rDEB9qrZhXOcwNm9lVBmTA+B9ZJjR\ngKJgUM/ODgVneZ6EEUVLdDLTBuS5RQhz3LixxnodcPNmQJ4D+/sBR0crPPNMB2MylGXEbKaT6RS5\nqvuztg1CL3bY36dy+uBgibp2mM9LhBBw770eb3rTLuZzh82GPlLf8R0H503hP3Gd7xhu03Xx4geF\nn21kz5AJvBFHHNf7JAvBhNnMjji8ThPD0IIccc0H6KW4OwxDhhByofbp0tjJMlVFda2cuLwU2ohJ\nWOeR50v0fSEFhKfavk9iDzHlFwB8bEJhGpyiIjO14rbQcJ+JSRUEc+9B+2gNiOEJ/v77czz1VINh\nKKDBRGovYUxAngeBrAoslzNZnJYycUQ4V0OLcUrAxYs53vCGA3z0o8/JfoQ7Cy6/gyz+6SGl9t1q\nS8Fi10vxsxgGFj3NxXBuQJ4PYpqnC2kVz+nUpNndEIKBGemszG3QxfAwsou0IWYZbbCpmdDF9LTk\nZjFPQj9W4aT6W6kBHvcZ3jNPmyaNmtinGhWNNwV4YmeAzWJhMZuV2N3Nx9dISCwT2GzSXzBPGqhr\nJ+I6oCwHlGUv7zen1fW6BfNFIpzLkGVJ9m0D6rpGSkHgwQJAjrOzKzg4WGJvz4v7LDUcJFQQyiwK\nZR21YPwtG7kxzLh+5pkaJycWp6eMW33Vq2jMeONGjdWKB5SydPjGb1zgV37l7Xjd65Z/RhXhz/Y6\nXz7fodc//sefx8///FfRdcz/bRp63XDhOxVonQ7oPZQLZTEipQ7DUIPGZnYs0sSJS6GolgAKOeVF\nqLqZRb2DiuSorG7A+MQMZTlDnldwbglmMQQpaDVCICumbTuoQ6ayeHiiTVCTN8IhkOWwgWZTq70G\nRXtRGqDbKjiEg4j/5zDGC34+SAPsUNct1usebWvRthY//uPfiA984FFpHK+WgvW43MMCwCBiLofV\nKo7PVXF42og7TCFBbNjTIprvAz9vmTQyN56wCbHp6TpBvZJIjbWYPK2i3A/6+8RoZApR+2uABVkX\n2AoPKXuNJ2OdGDhxqH3I5DPF18QFPwVebrzPnEJ0N6QeVxjpsG0bR+0D6bqTNQcbtOZXFDLtcu8w\nGftl8D4DvaF4OIixQ9N0KMuE5dJgd9dib0/f88ltlX5UBkURRPgYcXzc4dq1DHm+D+c8zs4CTk4i\n6ppMLMKQhWRr84DT9x36ntNSCHb0Z+I99MK+s7C2xj330AxxGAiXDQNwzz0l/vk//07cc8/sz6Yg\n/Blf58vnO/R64IEdPPtsLR/QyfeGY/ogFhCAZgfwNLYev6xkczRQgzsW00GcIkvMZgWcy8YCFILB\ne95zGR/+8HNyiguYQoA6AC345aZ6VYVJxGY7hNDKzqMHkENtLLyvUBQNqoqsGLKKcsHVNSfaSJZv\nRNN0WK8NmqYeHTVZ0Hppio1AOx6krfZQIZvqJchSos/UMLAg/dzPfQWLhRFs/1hO6wtpQCXUMjpG\noKqiiN0S2rZF0wRo0t2UPY0xmYzCNcITbBITZDNFdgYMQy/Fnv+PcaQDgA5qP8LnTBiHrqiTNuHF\nV5JsBI/ZLENZFijLDFOO87SzYLNOopSOaJpmjMdMKUiiWxD2UICyxXjFEdoiDKR6EM38IMxTVQbz\neSG7C1UU5yO9Vj22CLNxElqvaei3XrdomgFlCRRFRN8HPP98jyef1EajJIoAlpyAGDcIwYyCxbre\nAdBiZyehLD3uu4/Z1sYUiDFhNstlSlXmlZdmPIcxFLQ9/3yN9TpgGEpcvGjx6ldnqGuP55+ndqIs\nHXZ2qIIPoR/3F+fX//s6nxhu0/U3/sYn8Tu/cxXDYEVByjxm7yH+QF4WfAHUH3TIsoDViuM+HTR7\nmR4IH/Dk7uDcEs7twJhMlr6QU9ggpzB1x+xhbYeiIENkuSxQVfuoqosIoQCjHbm4TomeRBTSDaPg\nqu87MG84BwN+yKXnBKM6BCMYtsZyJrDBTCEvtFZQVg4N9miHoTbhaaQRNk2Dn/qpN+Anf/IL0JAZ\nY1pYOyDLAorCi36CkxOdRCPUupzPJYoLaIcpcnS6JpO4iS68fdpnAfM4OCjxbd+2i8985ppQgEkd\nLgqLH/qhb8ATT2zw7LOn46KVOyUz0k3ZVKI0KbKdrKVyN0YvS30nsJHfml5ITFARnE5nhCYJrZCd\nxc+Upqxxf6H5zVQdazMgrJRGnUffd/J+YxSS0VjRQF1kSSdmBeV/e5kYpslJ2VhtW4uDacRi4UZo\nVPMeVABYFEDbNojRYLUKODxMMGYPRbHA8bFCQ2S+7e97gRM5OXUdFdhVxc/myUknKXURiwX1Qt7n\nAAYcHdXo+4DZzGFvb4b53GG16rDZsHH/6q++E9/7veeWGF/379/Jhfdubgz33ffLo22AsmPUflrh\nCFL7OsTYIKUW3ke07WRXoZRUniqtxDoWKIoFrF1A/YSUCdT39NjnkrQF9wMNjGnACMYKIexgGJaI\nsZRi2MGYRk6aqpKl2Mx74sFU3XqBkTy8n6HvaznlM2MghFqeQy9GgAM0fU1hE40UnWiiShVVponF\nu9/9anzoQ08hhAx13WMYIv7JP3kjfv/3X8Cv//pX4FwP7yvs7S2w2VhcuTLH1avH8kUgzXf6yNBZ\ndrrvSYReTvYEEKbTFHWqU0qMU+5z30ecnW3QNC3alrRLXaar9cQUhKQLeN0L6BTEPQwA/IN/8Eb8\n03/6RZRlJkE3PKXP5xoHuj21QIo40HWtWET0YqI3yF4EUE8tDVSiPcmkldDFscJSusTlvsOPTLjZ\njDBRWRYj5DeZ90HEfBAxn0FZcsm+WPBgAqgGRc0SnbDtgsBaQAhRWFId2tag6yqkVMGYmdiQqOqc\nE6lapQAReW4xnwNUVLe4dWsD5ywODryICCl8ixGoa0JbGgiU5wZnZwwO6rqIX/iFt+Ev/IWLL+n3\n/k65zhvDHXq9+c3/GkdHHbIsE/wY8qUn84SnpwBjqDNoW+Y21HUPKmE1FH7y/XcuA1BgGGYj+0aL\nnoa48Hb1svALsHaD2SygKDIUxRIpLWHMAiEUIB++A1DLnkFVr8pooeCL6lbNGsiEumhgbSkfwBwx\nbuR0T8Usm0PA5csFdnctvvKVE4FdGHEJiSTlYhxySk+YzYDVymAY8nEZ7X2Ecz3yvEdVWVy4UOG9\n7/12/NqvPYe3v/0ADz/8PCaRHPF8KqAHoVACfd+NHky8Vz1UswH0ssD20AKu4T1kUiVY20tjdEKd\nnfykisIIHGVx+fICeW5x9eoGGq36l/7SPXj00UM8/vgZ2rZD0/ToOoeuI+tJ2UucCLYT/BTp1VM/\nCQG6uNYlNJlXw9ZzmnYMrA0qsiONltTRSeGsOhSAQjb1dNKdBx/HQ32wpn2Fmvg5xBixWjUIoQZz\nMIDZLIkYMckOi6IzTidRIMYZjJnD+wWWyxIHB04W8Zw2sgw4OwtyWOJUeHCQcHzcoml6XLt2hs2G\n05X3JeZzj2Eg8221qgHQSiPPE9brHiEA8zm1Et/xHXv4yEceup1l4GW7zhvDHXp95CPP4D3v+RgA\njsUUNKmrqQbxULTGL3wni1vlf6sa1QqLyCDPM5AiWMhiVLMSBqizqBaLYWhgTAtjOuF/c+GcZbuI\nsRKefMQwtIixk7Qu6gKUxUM4hOpgteHWJSshhRLqpaQwAS/N3+VStaroh7NYODGls1K4WIx4SgdU\nzc2FM83wmqZA254hxlqKe5LlKP8urTUmLyAVfvGabCK4tA4CZRWSO2BRlglFYTB5/TiB/8ijp1iv\nQ9MENI1qHIbxvVJ1uuYnLJcFnDM4PGyhWgQyhFTNrCwvYvcsWvmo19DMCECZUurIG8W/iO/xj/3Y\nA/jEJ57FtWub8bUry4kFfJDPBScxNkfNw+jB4KbtT6yXRuhloZyPjYHhRg5qRaKCQEJ0HllWIEa6\nmFYVMJsNmM8N8jzJ5yBJTKiR5feA4+MNNhuDvi+QUgmGRnmUZQJQCLMqyr5mcrF1LuLo6AwhdCgK\nYG/Poyw51XivFhl04i0KWn+UpRUNR4fr12s0zYDLlx3e9KYL+JVfeRCaOPdKus6Xz3fodXLSoSx5\niqHHD5fHk69NLxh+j74nPZQXw8+ZqezA0HWevDh5ZOAp28mXE+h7I0WVJ+VhCOj7FjHWiLGVBpNQ\nFCWYKOcEuomgQR1xWyCKH78Tfx8ncYoUp1HFS1ZJnpfQPGnlybOxRLQtsePNhgEvx8cRR0dJfnYS\njByY7Ky5aByGVh6vFJgtwZgZKP7jUnJnx6CqaIVQFH5sCjTUmwM4FLGVGrAN0uiYOcwFao22tTg5\n2abkeim6Xk7qPJFT+MdmQ+GXWoDonmKQ/6fJcTxF7+zkQjogi4dXksfXqcTCuXw8BKhP1TRNqdAv\nQZP3FNP/hV94DCG00NAkbdxKX9VITS5/t80buY+h35VCatzZlKUTfYoRYoEZf6ZClXy8CPX3ohEj\nP39HRzUODxOyrEOWccLh39MmxNc7n/N9XyxyZNkMeT4TthzdWknJpfULaceEsBYLoKos8rzErVtU\nut+6FVBVbvTR0sQ6PSR03YD1mlPvzo4Xjybg6adXePrpFR555Dre+c57Xpov/SvoOm8Mt+l64okN\nLl1aIEbNQdDFIX+fambSCWM0aBojFDyeqpqmB1DL0tUgy3JkWQXaArTCRTdQ4Rl5/SxWeQ7s7fEU\nWlUVZrMSRVEiz0uEQPsM1Q2E4NB1Cm+Y0RuJJ2QWdT2lUvBGjD2lDVLyAstETCprQN1NnfPCZCow\nm1kxb9P4SD5/vg4rcNISDJEBYiRVtetqgaZyxFjh+LjBrVstUgooS83RNrA2h7X7SGkNnaAm/6OI\nGFtM4TW6GIUUoxyamsalq5cGwslJozVZhFmomSIXZfpxomcYpKkMUKEfiQdaHINMLyp6dAL3qdvq\n5F6r2oXJLmXayWgUKrMfvPD9NbdhkCXxpJAnKwvYbDohFkCgQ4hALMCYRqZTLnAn0R6gZoTq56Q0\nWFprcOKZzUrM5zN4H1EUzNGo6wYpDbh5s8Fmw5zsEAJOTwkbql8WQ5sGWNtBBZq6ZxkGKs+NMbh1\nq0cIK1SVxXKZ4+JFOqXGaNC2vUw1A6oqx2xG9+Dj4xZ9D2QZsL+fYzaz8j2Jwog6F7l9vescSrpN\n1w//8MP4xCdujvYQui9QqwrdBaTUCGbPMfnChR1UVQFre8znDotFibJkALsxGfreieCN00Fdh61f\nLbqOWQB0DlU2E7n51lawdn9kuEw5zzz9kRar/56k4BSwNqAsqWPI84A8L2GtlyYFUW/TK4nQS0Lb\nupEVNAxnoBc/YTX6Jk1L6ckew8nrC8JucvIcjTS+CnmeI8ta7O56fMu37OKLXzwG9xqDUH5ZEGnj\nAfCUzsWleibNZl5sLbD1HJR9ox5B9Bpi8teAyaJC/aSSNBozNhU1vZuK+zAWeTZwFkNy+z3KspDC\nynuxu5vj/vtn+OIXj0FbcKaZqTiOi2/I50eXyKqUpo6EduB2bCQaLUuIKo1wE/F7jclU+EkzFAzU\nrZVqc51GWdh1D8bldA6K12bwvpNmWKNtG4RABhKfWyZqZ4MsK+VA4BFjDudmAt1RfOg9MzFmM05B\nylYbBmA2a3HpEuHIqjI4Pe3RthhdBTjZ0Rbk+LjG4WGDGBPKMsNsxs8w/Zo2YJ5Jwn/4D/8t7rmn\nuu014c/yOt8x3KHXz//8E/jAB74inH21bGb8pXOqhu3QNGv58ndIKZPTXTcuQ2PMQSM+frmsZeQi\nl6TEzVlwAOYOB3g/IMsGYYN4YeFU8H6OrqOXkBY9wi4t2rYTNlM/whbWUpHsXABFaD28DwKFZBgG\nL3uOiEkIpgvmHNZGFEWQE3Yx7komxk3aei69TFEzpNSDTcqhLJd473sv41/9qyeQZZUUM3V5VRU5\nYSdCc6o2Vv1CEB0BIRRAl7yataAW3GRH6SKYf24Y9zx57gXuYEMpiqmpaBBTSsAP/MB9+PVffwrr\ntYYaRckfgCzx2Ty0IW7vR2hV7XBy0kEV3Xx+U7youvKqIy5tPQj9kDbai3pdG6TaXzhkme6xctFK\n6OParebntqir0/3ibonuuOpyy+nBo+89vM+w2TRiJ9+MU83urof3XuI4ubfIMu6VKP4zGIZCbNqD\nNL8Bea7vAWnHmw0bHmEqnTAJfabEk3+MwHpNS5fFwuHKFSfNiMwyZp5kODggBHl42ODmzTU+8Ynv\nx2tfu3vbasHLcZ03hjvwapqIvb3fgLUZUqqhgewUK7WY8qB7kBVER1XGODqUJVBVJaqqwHw+B/2R\nqFmI0QubJYxWC3XdSPZBg5QaqJiMjcPC2hJZtg/vq5F1wiLZy8m8lyKgJ3XCQ84VAnsEeF8ipVqa\nRAZ6I2XQPGYamlk5PbPwffM372A2a/GVr7SybDXwvkKMLYAWzECG2Gcop96hbRMm64aZTDS9wBya\nGTxpJXiKH6T5Kl0zveiUrAlo9CVSSjAXtHz+usw34/Qx+RNBCr9mU/Tj39Fph/oFi93dDGdnk+Jc\n/aJYTPWE7saGoIwz0o418rMX7F73ThY6+Wm2gtp/a9PQBbsu8LnLwEgOYMJaQowdlGrLRqIK7Sg/\nJ4NOTtzrWCwWRgwErTDrVJ1v0TRkLLVtxNlZPepQhoFCu9lMleSF7JOUZWflvwthLqknl0PTBKFp\nA3t7JC/o5GCtGx18U4ri6cQ912oVcHJCMWNVOezvD3Lw4cTO/ZgTEWaL5ZKP94533IMPf/i/vH0F\n4WW47trGYIz5PgDvBz+F/yKl9L99nT9zVzYGAPhH/+jL+PCHr2EYIrKMRYIL0ChUyYiua6BOpmwa\nmo3M4Pi+t1J8MtACIgP9kGgnTXqo2mTTW4l7iQFqyEaB0Q6AOYASIUzOmKQQtmCucifNKkDN8qiI\nLZBl+ciWYYYCxV86LbCI6gTihLmjvk4JQA71yFHcWl1PAap2SbFN42meuHsSiILaCWtPUVULvPrV\nJZ59tgUdZRlspApbisiC0FWVxTQ52erJXPMUpufituA+LV4YC7ry8b2nIZ+msE1iNkDVxWz8CvVM\nlGM2ZTsWct7HABWf6aQ2JeDp39PTvXotcUqZvhuqiVEKqBOYhFnKFAZ6+WXHxscdQxT4DbIDUBPC\n9KK9FSeL6TUNg5UGSz+lomDTorVLQte1UJ8pZQsRDjVywHEylWQgw21AXXNiaBo209ksYrmk0JMi\nPIMQMoGRCNUxd4EkjKIwuHCBDYwHJcKiZUn4cLMJuH69QdcNuHLFYnfXy2Lb4pln/puXvAa8nNdd\n2RgMAd2vAHgIwFUAnwXwoymlL3/Nn7trG8O3f/vv4Pr1ATSyIxbctj2GoZHFaif/rvkLSg1l0eGy\nuIL39AJiEcnHpSRPV2R+AAEh1KDpXi+iLYeqqpDnDjs7lxAjqa4MkGGMIxW5Ddq2lWI6WUdoTrO1\nM5DTrvCHQkXYOnFPJ0g9qeZ5Qpa1YpMwQ1GwwWjTIRZPzUTXDajrDqvVBnXdgHnJHWjNsQChkgLO\nzZHnDg88UOCJJ05BZlcHFW2pgIxFjT5NTIHzKAotWmakNNLym+yluu4FpzaYaLeQBbFqA9Q2Q+Mr\nSSJQTv/khTRNIsAUpsOFs91qBHyuk2cSVdiqeSlLirK0qLOhQBq7kSkjiGdQAzrQKilBpwV1z3Vj\nU1LK7NQY7fjcvM9RFA6aP7Gdy60iOR5gILuMbc1EgFpvd12CtZnsHzihEo7U5qNLfu7MACDPDaoq\nYT5ntkTXBTQNPw9Vxf0aUGE2K7BYAE89tUaMEWWZYX/fY38/g5Ikjo83GAbIUpsw1bVrtOEwxmF/\nH8jzAcfHLdo24g1vuILPf/774dwrg7p6t9JVvwvAYymlpwDAGPNLAH4YwJf/k3/rLrr+2l+7H+9/\n/9Ow1qNtmZXARZ6mg1E0ludJvvBGuNceVZWhKAowI5jLPe9zpMTGQIfLVnj2HKsJqfDE2bYObQuc\nnW2QZUtcu0Z17DBoEeWimSdCUlWdA+bziZqqUZBFkQlcoZbeLAhtayQrmQwqqpdZtKw1aBougDuy\nRKHWH695zRKPP74WZgzNAnVJzudCSqJz+3DO4MKFAkCGtmWBGgaHw8MBVVUgBI+/9be+ET/7s3+M\nlHpouMvf+TsP4Gd/9stSzNkMrlypcO1ajbZl4ZhYNgkaWK+L2Yn+ye/VYsG857OzAWqTrvTPSVTo\nZLej982O8JU6sLKBGqHQKnQU5fSuDTYJTZOxlMDEZFNFNZsLJwPnOJECTijFPBToxDdNNXzOw6Bw\nVRwLfAh2XDhT68B7NKX0cdehl05BpPjyxE7oiLst0px5SCC9mqJFawn30OKDOH9RFNjbc5jPHapK\n9y0Uy6UUkecMF+KBhffu9LTF0dGApuFE2jQDrl1rcesWtQ0APbs2mwHeD7hwIcOFCxnuvbcCc1Fo\ntnjhQpJ9RUJVeRwddbh4sbxdJeGuul6uxvBqAM9s/fezYLN4xVx5DrzudV6stj1o82xxeLjAZtOi\nrlcIYS0nTmoSum7AZmNwfNxuMUimExu9k5QN04HumkEoggbLJRd8VOZ6OXHO0fdzaLyosl3ok9MK\nlTKJzxHQNFzgUXQX4NwMdGbllGCtQ0qFTAcazUkztLI0cvIc5P87sdUAVNDXth3y3MlEQpvqCaai\nTYf3K1i7gnM59vcr1LXi9ppyx8J08WKJxx5rUFWl5CSwWP3czz0h91OVuQNu3GhxdNRiyiKYLi7v\niaPPZvmL4jOdc7h8mfYQzz23EZX6IJBHRF1DoCqewkMYUNcJxnTC3IrQEJ7J6whQt1KyaDxms0GK\nuZXFuMatagMCNJNCjf30/VR1Mw8NFOaR6tlv/Xk7Qn+6VNaLorbJXlydXidDQzMSIFSvkpKR5+3E\ny2gm1iLKECP0A1DTE6PFet2NsBubC0ax3eEhRipznvOzvl7XSKlHWTqUZYELFzIAuXh+Odl18N6c\nnPRiK97D+wxZluP0tMOtWy2uX2e8aVFQnOicwbPPtqO2paoc9vYy/MiP/A4efvi/egmrwN173fE6\nhp/+6Z8e//3BBx/Egw8++LI9l/8/l7UWX/jCmWD5LWhpMcD7ChrJCXRYLNzo8KmZDHmeIcsoZOOC\nVvFYKzbJHUIwaJoNNGc4xg5976VAkq3jfYbXvnaOJ56o0ffqwhqEDtqBi+8Je+di0wjPn/x0QimV\nFHeFAByUxaN8fe4UEib7bRWNqWfPAMDj1i0uPzWdDkj4nu+5jI9//Cq4jK+gAixrI/7gD46RUoFh\nyORndfL7EU3Toq4bvPvd9+CTn7yOohiQZUmomDMoRs8mQfvmphnQthAoTRe1PC0zmCfg7GxST8cI\nfPnLyrhS+EwtJvg+ayMmW0g1GpycdLpQdTinRU4+E6NK7UdY1FmQuRyegpd0SlFVtIrv+Hx0IuFj\n6p5DJxonUxDf27JM0vysqJy1cTnZjUD2NVF8kfg4IShlVqmmA9q2edESPcuCTC0Ml5rPHZbLErNZ\nJgvgDM7lYq1CLyT6PiWxy+hQFAkXLhgcH1usVgabTY/1OuHwsIFzPaxtZO9GzY73SWjShCbz3ODC\nhQyLBVlkfU8IyzlOM/N5hitXCrzwwgZHRz3qusd8XmJ/f3Fba8LtvB5+VL7WcAAAIABJREFU+GE8\n/PDDL9njvVw7hu8G8NMppe+T//5JAOlrF9B3847hs5+9hfe97/dlOdvJkqzHZqOun8SIeRKPAOqR\nzklaH4NiaH1hQQtnhTK0qGthpzdSnjv54lmxS57h/vv38Md/3KHrvEAUvXz5yYbSUBwynyBxlsqc\nyaDmf+oC2vdKZ0xQewjNqFZKpfc1rFXKpQrfHJxjfgTzmtWDqENVKeWWhWs7+IaNL0ffZ2O0qbXA\ne95zCR/+8LNjQSMkFbdO0WGcLlhgCTsoq4kQU5TiQo3IhK2zYOkuhxnQCU3TynsAbJ+peM916lEL\n7CDFHrLwVzsLvRjtqWIx5kJYec2TxYm+HmU9GZPGaYb512aEazTIh7sbzfw20IQ2khYANpBt2418\nfGx1wtUIUWZYeOzs8DXStTdAE+dCgMCZCavVpKthgBT3Wnk+oCw7MMiIp37qWaaJSJXqw9AjywZc\nuhSRZbqMhzTuiK7z6PsZYizQ98DeXsJiwf3ceh1wdkaLlOWSec+bDQN9rI0j1Hp42KDvLZzrsVyS\nWLHZcL/0W7/1X+Bd77r7HVfv1uWzA/BH4PL5eQC/C+DHUkp/+DV/7q5tDH/5Lz+M3/u9G8Kc0S+A\nsnSIsYZAFW7X0as+zwdhgDjE6McRPgSeZtkUBpCS2gJooKls3hMKoXWGhfcFjCnA8HgPxjYmgaM4\ncqt9APF5XYJmI47Nf8+gmgmdBoiFN8gyL41kgGY5W9vD+w4qmtM8AdpmOITgcOVKhaLw+PKX16BX\nU/0imAhQcVaQe1eC+cPFuAjPc7K86PpqYUw/QjPAgO/+7ot45JGb4uLZQmNO2cQyOFfC+zjSPoFv\nwGy2Qt/fhDqsaiYDabNJmo16HqkLKqCwkIrYyDQKUDYR/yxgLa1OyGqyUlx56lfrahXnTaKtODaI\nF1+lNKCv9+ljAScsQ1gsyzL5fLD5KiWVmdBA26rafYoNNcYjz9W7a4p31UlPJ4UYc8znVnZNPDAU\nRQE1fswymjXyXnr0fQFrc/ns8Pn2PVXYTdNhNgOWyx7eDyKqI8uJn2Fan2TZHEVBmq3qH/I8is7E\n4OysRwg9yjJgd5eZ1bduNeh7eidduVLhwgUn6nCym7w3+Lt/95vx3vd+85/0a3/HXHdlYwBGuurP\nYKKr/q9f58/clY0hpYTXvOZfY73uEEKPonAIIcoy1MuoHoSBQlw+BArJyHu3Ap1YWVY62UV00hx6\nDEONlFr5iQxlV0YFaYGFNJfJ+0czA0i5DCiKYfRAIr7LP8tmwqbU9wabTUDbkvnStgFtu5aCRLiL\nRS+X0zhtPNS3Z6JdEv/nlKEmgU5Oy600OP4eT77EwrknoKssX6OXP5vGJkoaL9ldmgexs0PYio2w\nh+ZCa0KbprdxUtLAIZ7y3/nOV+FjH7s+QjoAKZiaRlaWVvQmuSzaMyyXBuv1Cpq1zYKuAjOdPrZP\n6Wnrnwofqe0FxWQUhqntBBfafN2EBZ3LxEuoFzEd/Yw43akQUSegSWU+TTvKztJcashrndTR1K1E\nqKKak4wRppbeH1Jjuy7BmFw+RwP6vgX1JmFcbofAbI8Y2Zz4fAFjemSZRVUlhNBgvW6gNtskI0SQ\nwp2hKObwnuwkZo5Qo5DnFkr7PjxscHbWA2iws6PW5QFnZz2sLbBYRIET+Tz29jixHBzM8dWv/tcv\nZTl4Wa67tjH8f7nu1sYAAD/4gx/Dl750LDoFg9WKC7vVygoLRn/18L4U3QGLqGoQWOCnxSUhn4g8\nH+B9j6riqbAouCwE1NrBC+Tj0DQWTZMkV6AH4z7DOCVocSJ9lJbFNKQzIwSjUBGxfbKHaDVdiCUC\nTdE4fTQwppa/x6UnH3tqEJxIDDR4iBdDbujho7bZcYQelCKrjUa9p5yzApWoW+zk2aQOqHQuTSjL\nKIvSctQhaNyksoSIc1vUNfn9X+/jp01PC60xV5BlAcAtqHPuJKqz4yKZjY8NkgZvSXZGKmojBKSF\nnD+LewM2Uo3ZZAYCE+RIVybLTRX2UwQr74NmNivd1SIED+cKFMWA1WoDCgQJDargjGlp7ThpEZay\nUH8rDUDShkFzRTZpNqLJCoaU1ExEnKVMMtxxFAVQFEmW4hHr9Qa3bq2wXjcwJmE20wkuQ4ykMKdU\njBNbllEFT9ozyQwHB1Z8ukhdtZZCPWszXLqUY7Xq8MwzJ2gawprLZS5ZDh5f+tK7/9Tf/5f7ulvp\nqq/4681vPsBv/dZz0PQsYzyKglm4ZL6wWBVFEgfVDJOdcZLFnpWCPtkbMPqzQUodjo4UriFjRL1z\nhsEjJQass/CqzQOXxN6TxkfTNbq3KmU2JSfePDTU48m9hXLuFe9mXgKhhBAsmFGtQrkWIfRgMhlP\nqFOR0pMxYRdOP2pxMPkJec+oSe8L0XVksntx0AjOSS1MquTZWZKcA4w4fUpJmC8DNhuecI3ZAJjg\nmcnbKMlzZMTmcqnEAKWfqmqar4VWJgPa9niLdrptOKd0U045fN0KRQ3yXHQ3wzzi2cyNTUQL/GTK\nZ8bnSeGjLvGtLH51Kc7nxxQ+I4ykJNnOFmqRwedSA6BVBncK3KmQ4tpishQheynPiy2LcAfv1Tdr\nkMJOOKltB3l/ouyMjLxnEet1i7p2UI8p54y8R7RCWS4HzOfAfJ5vNTQnk+8MKdGOhToLj6riBH50\n1KGuSd3lRAOcnkas1/y89T2ZX4eHNW7eXGMYCOvt7RkcHXXwfkDTNLh5s8PFi/lLWQ7uuuu8Mdym\n6/7757hyJcN6XQOIOD0FmsajaTY4OcmgfjNk3+hewUBzgvXLyxOysnSALIvCXU/CKHFjseEJygGY\nIQT60ZARM/nucLnrt6YQbBXFJII7Fmjvo2gHyO1XyMPaDGofwedHIR+hsCisICvQD43WaEegdExV\n7zIf2BgvISqTDcUwDCOVFgBOTyeb8JR6xDglpQEJb3/7Hh555CbURG02I31WPfnVLtxa3mfi2Txh\n0tlWOaxqdOjEFNBLM+DvstjpNKUYv05rCYzaNGJXMZ38CeuR1aTNkcU4Gwsc75kuy9VCRacZNkG+\nD3rPe7kn/TjB6CFRtSdUFvM+VBVpnISoMtHNzJFSDi6sh3Fq4f1XW3eAwVBm1KbQVsXCOdq40w+s\nRds26LoOjB4lu47Rsk5YbqptsFB/p7pu0TRRltpR3jMvmopBNDFkbXEyJOuNpngRZ2dxbCxlSVvt\nEHo8+SQFkGwytCMJIeH0dMDOTiG/cnhvcXQ0oChKWJvhD//wDO94x8FtqQt3y3UOJd2m601v+k08\n8cQhrA3CES9A9gxxXTI6LICNnCCZMxyCntamQHfnSAmlGKcFjey4OFX7BqUz0kuJPkZU8arbJ6cL\nhSIYgrJtAdGDBU6FST00S5g5wpmEnkBgKzdi503To+9bUbx2oE025PVZOUV3473RYv66183x2GPH\neOtbL+NznzsG7biBosigaXEMN8pl78ITJ0OFgKbRPIthC96BTBR0AGXjmywqWFwmOw4K2zzUJoNL\n4Qku0gUrMXIWdsJjmgjHZS6588MIF/HPD+NUo1g6P89B7jObEZ+36j+mz7vaUGjMJU/YlehFIFYh\nQeBKK0vrNC6u2bgTgBmUBUUjvhbGtGNDIPNL3Xj1HkaZlIZxgjJGw3tINabokBYX3kesVhusVg1i\nrOG9FSsVHg7olZQJW0k/Z0aU0hExNvA+oKoGODfIQSEIROVENb/AfD5DlhWYzXJUlZU91GSkSO0N\nYdqm6XHz5gpd14lTKxv2MCRxiFXbdwPvZyjLCstlhm/91gV++7ff9lKVgpflOoeS7tDrbW/bxwsv\nHGMYMuGDN1J05tBQliyLGAYgz6niVVWsFnt+MZStM0BpkPw1CLtI8xL4RePyb0BZesznmdhjWPmi\nGzmt0i9fOeqEQ6zkMXSC8xsMA3URZFbVX7PAzLCzk2OxcHjhhTOoJw9tKDyqqhQoRHF1xfSTZBMk\nnJy02N3N8dhjKyyX5biXIHSmi1qlnfpxPwIoLRYAkhi76U4hjkWdmotpEpkmJ71XgPcF8rxCUTTj\nPoDiPo28jNBMato+W6xWantN2Ian6gFNY6TBKqtLyQUWVaXRqGZkJmkTCAHCvImSsBbllA5MiuMA\nYAZre6zXUaApFnu+FlJtAQYgLRa5NCxODUVRjOSDrktYrwehmqpGwsn7FOUxMng/YBgKmRQgRXRi\nm6mOhQW5R9+TJUffJKrr+Xey8aCjUBybmkaiZvDeg1oYNrqqMqhrh9WKQT1dlwD00owCjOkADKgq\nVYNTu9E0dBfe3fXY3XWYzeZIaQb6RRFS3WxIr61rhveklLBeW7RtwtWrDd73vvtf6nJw113njeE2\nXXUdcHSk3kNqMDaDUgm5CyCTpu/7ccnL0ytQVZBkqhxFQWsA73MpRDwpE+Nu0bb8wjQNl8Z9n20t\nvLUAESIClAHDwkIoiDoCirUs5nMrOxBadpMpBShllRCDRd8nnJ6u4FwzaiQUcuKENEiBiNLUdNkd\noPsK/vkADZdhwdOTrtpy69/hcpLPE/iRH7kPv/Ebzwlzx8pkBSnsdhSwvf71Czz7bIeTkyAML/35\nelrdoG2ViqlFh0Vb7S6cIyPpwQcv4ZFHrgPIR74/pyK+Fr4vw6hSVn8h0iqn+zA9Nq0t2NDU4wly\n4nZyQle9C1ljzOXgwSClHFQAD+Pv6wSk8JBOWoSHGrTtGpO3lU4oCdPERbiLj29G2IiNnjnXmoed\n5x7eRyjjLMYGfR8F/oHsOIwcbOK4a2BiYBgnV+6kejgXsFxSSV9VtIchbdrBWk4+6usUY8TJScDp\nKS1fssxgf582HXXd4fS0Q5bRmn61ihgGJ8wuTsGAw2pF00UaAXrM5x7PPadsv/98r3Mo6TZd73jH\nb6BtIeMriwq/oNQqnJ7yhLheB2w2wHrNk/sknuIiTl0uJ368RlA28vuD/BknbI8Cea5WxlZO1QBP\n4gExRsleUBsHnu5YlHTvoP+P1EJ1J6W3jhU4QPcLFMpxYlBGVQ5gPr5eNgotOHRoVQ8jUngzgafU\nKVUbnBXIiIpvqp5JN4W4tKp2Qe8PIaOpiRjTycnUgyl42bh3yDIjz9GhbUmdJCW3FZgIAApo85ws\nxQfZ3agh3qTiJi2YNFjN4dAMCj1pT7bekEIJIQAE+fNJWFN8ferpxAJbQNXmhLYmJbSKyvi8FV4x\nIKvLwvsWeT5gNotwjrGtnCAtVLehqXVdNwUUDYOD7lE4nWhyoBHKMHUKKXXoug2AFt7nyPMS3tNA\nkcy7Agx3MmKa2KJtW/FDUp0P7cabppE9iVKoPYAS1ORo5KlGfbqRch1jwGrVomk6VFXChQtkAa5W\nEasVD0WLRYnLlwuhjlvZURD+K0uHd73rIv7lv3zLS1YLXo7rHEq6Q69HH61lQcqgHi51VaeQQQNh\n9NSYkpMFnYawuLE48KTFiMmUBjQNR9/pxM3mGSNFSkzFGqS4U3Ws1hm0TgY0+J3LS8XKlTFkpBjY\ncT+RUhS7Dg9d+vK5GmQZXWDVDC/GDH3vRc0aBWrZNtRL43KUJ3cqsUmhVfdWqpF1itnd1cYH4ddz\n2vmu79rFI48cYbXqpbg3YxPRVLaUvJxciTufnUWoZ5AWHGXWqDCsKLzg45ncF4VaAA20oQBRRV7c\n11CXkUnBnKwiWMwi1IlVIRht/nxPCOMQNlGIcBh/LhlZXvywSrEvSUI4UJU1DyDcMWSyf4noug1i\nHLDZUH3PgqvPg2WAxX4Y9ws0wyPziMZ8NL7TZTjvOXUUQIOq6lBVAFlDVkRrCWdnVP8rM45wE1AU\nZMbRvgMj+ykETgAU5vHgMAwFyrIQhhOnZmpY2BCsBcoy4fQ0Qv3EnDOixO5FyBfhXI6+D7hxIwmU\nSkNIwOHgIJe/89LWgrvxOm8Mt+EKIeLee2dyMgzwnqNw13H0pu894QGeVLAF0XBpGgLkAzoIpZOP\nRfM88vCVcaIiOVpYZEL/pJqU2G4YWSv8AhlZiCf5whfQEBgNNaH3vREXToVFlLECAAZdR/orldhq\noJfL9BFGyuV87kaFsSbZOZfw1rdexGc+cySqbojnvuYme6hBnzHqIjtZZbCZFrh+fUDf0w6Dr4He\n+2o3AXwT7r33BGdntSw0jXj/dFAKLoCxmFAspfi82jhPymROTRoqoxoKneJULDaMjY/3XO0+vPhh\nQR6f90kN8IZBtQyEN+hrlQTyCdJcesTI4jVpHcz4/tLbinYTNJozoi7O4FyPEBiqMwxOXFH5vIn9\nB6gyXxXrXWcRYzfuWJSxxj0Bp769PX6Gssygrjcjq0z9oQhHsfE4xz1NVQ1YLnOUpR/zM2hKGKVB\nTLqLrqN1NgVpagbI501bef778TEbX1E4XLxY4dIlWmWw4albrsXxcYfNJiHPE/b3yZTquoSTkx63\nbm3wz/7ZG176onCXXeeN4TZcXFJ6PP98KwWvBQ3IeJJV9a8WP01uI3zEguwcxgKsUAwhES4W+567\nCEZMUgQXo0XXWfmZEzRF51RleKjhnUIlaWvZqjkLfG7Ko8+yKHbctFbQnGBCH2S20LzPouvoYaMs\nnmGwaJoMw9CArKgAZfv8u393jOPjF9tYQ7IEJmaQTjFWph4z7m2AFl13JCpuiu9YdC2cmyGliBA2\nUmgd2taL1qDH5Bs0CexS6sb9yZRloMwhNhoVZuV5NqrFyUSy0KAgGvUNo/4EAN785h18/vOH42dE\noS+dCNlECP0QgjJYLDIp7G4s2H2fgxoSivEmarNOH/zl/QBrS2kWul9q5f1Vsz1VREN2QUqJTVCF\nMxsxGzRFdoO4nfLzyIlCiz+bOFXYCkHx9ZEhBaTUY7OhvUtdJ2iGQ9tqyhotsQ8OMoGguDPxnuQM\nNms2hrOzHjdudLhxg3TugwODS5eAvu9w7doGL7xAVlVV8bN8dkajPT6Ox4ULObpuQJ7zM7a3l+PK\nlRyXLhV/wm/+K+c63zHcpusDH/gj/MRP/B62YT7nImYzi/m8hPcBV64cYLJMHoQNpP44Bn3vcHIS\nEcIKxjC8hqfgArQWUKiIozBP7JARm/RHsj4U1zdQ9TQL4SBNREftQYzMAJ4ZFI5KAoHlUqAhz5ea\nAv58K39nBhqwKb0SUI6/0iL5uFpovCxZVdFsBGojd550TxVr6al50k/QsRaYz4GjI/o/TbuIDsAB\njDmBcwXyfAdF4VAUHbwP+HN/bo4bN6JAQwNi7KSgq5iQtEdCLuoNNNlGT3sGD9UW6KSgWgZOZgZl\nqdTiQSCfDl0XRsU14TozNgmlz6p+gsU1Q0pz2QWFsZHy/dWfP6W+ASouTNLg+V5NzVOt1PXwwKUt\nVedJhIIqrJsM+LhP8iKUhHw2ImJs0TS9wF2FECdK5HmOPM+FXq3ahBZ13SKEKAcONYnUPPQBal3C\nwB9lL7FpxpjGpfNslrBYZNjfZ74J6dMN2taN08fenpPHdzg7G3B01CDLMuzuznBwMENVeaiL7aOP\n/iUoVfxuvc4tMe7Q6+Mfv4Yf/uFPommSQA8tgB5FUcEYegxNOcKTdbXaVNP0jF+uEFZwrkZRJDiX\nkOcz0LYiQsNeWNiVa6+CKy/7gnqkyBrTjIVOCwuwbdKWpLBnUEM6QgdaFM1Y4Bj8U8NaJsw5l8N7\n7gVIH1Tx3DBCUYonEyIjXlxVViiJPMWyQPI1sCA4ODcINAIpulb46srwiVJ0LiGEVyHGDVJ6DEWx\nxmbj0PdRIJ8GGlRkbYYQdLlMLF6jLAn1WKj1Bu9FkqaoIjwLNdTT+6g0WL2n0y+lbg5Q9pMygQBN\ntUsicEuCpesuRncBBawtpZBSOU3l8wRzUTMB2Uep26zeowRrJ1aUKsPVX0pN8rjIpdqcxnuQxT2d\ngAn79GiaAZtNh76PyPMezHXQfYyHxtJyn2YEHlOobRi9uvh69HWSweV9hhCs7I282H2TNbSzQzEg\nkHB01AKglfhsZnDx4pTG1zRcOG82LWitwdfQdcBmk9A0Bt5HZNkcu7tsOHU94Nat9/ypv/8v93W+\nfL5Dr1/+5adFGMbcAi7ziKkak2MYnMAbUfyMgiwmyY5QLjtwDGsbyRJIsLaUL/JEO1XsVP15SH/U\nxStpjEAQjYQRWCRCg+rp5ZOBAS3qvokRI+aJXacZqqOpSJ5onRNMpA1I6adKgXQjO0gtt1ncMxwc\nZNjdLfHVr25GGmzX2S1mTEDfA696Ff8MgLFQ05tImVQR1i4wK6+A/vvP4t57DQ4PezSN0kLL8YSs\nHj5aHElxHQSqogleK8xF3R282Ol0Wkhvn+71z097HSMMMYbgMG6UanBVQfNeR5lWIjQ4SZlByg6y\nlsWY98C/6PVzcUzm1HJpodYiGgDE5zo1EL6fKthTgRgdUI0xoiUhpNa29bjUZmPV1z1IDKk2Un72\nJhGkg7VkmVGbkARu60STYDGbeSyXSrSYvJjaltDSej3Zex8fR6zXPeZzhmEBtENfrai+X61oH17X\nPHisVlSi7+7muO++OTQHpO9pTcPmRxHm0VGLv/f3vvFP+c1/ZVznE8Ntuh566OP49//+GNY6+fK1\nsDZALbDpZZQJMyghhDNY20PjGIuChmEUDbVIaRD2BBelChlwsWzEo98IdY8LNcIunfxS182IELbT\n0JxAIXY8NX7tPVexlj4+6Z5qNcECRRaKOrFC/JZIf5z0ElOoPBlABjs7OWazAmdnTD4jfKA/00Bz\nDnRK4O5iKqDf93334EMfehq0Ec/x0DuW+Id///14z3/3QcT4f0F3Fgp/pZTwxjfu4AtfOBYoxY44\n/KQ01ykAUAqq/jef92SbbS0npGEge+r1r6/w2c/ehAbvTPdUhWpmfFyFAieVNWnCLOh8PIXlaCNB\n2ukwMB+CrqrdeK9f/J4luXd+67UlORhgbCgK9xFu0mbmoC7Aykzrun5sJJotPQwB3hsxqzMSQOXH\n3QKbLBluxvDfKbCj02nTdKA+AcgytejmZ4XNhDBd23KHNp97LJceV654lCWpwGrlQntwGuZdu7bB\nem3RNMD+vpPF9YCzs4C2NUjJoyz5eE0DbDaEAC9ftnjHOy7il37p7g+TPIeS7sCrrgMeeugT+P3f\nP5IvYAZ6zp9huVyChmklgGxcfqlrapYZhMARu649YmTASd8b8aYPQrVUiEKznvnF10lBqaA8CQb5\n4pEjr4Iw9eY3JgNV12nrxGqgGQ6qd2CBVSWyFtpJBMbJRDUVBn/xLx7gD/7gxkizJDYcxQOpl9Nn\nFPxaLbyzURWrYfUK30zKZWoissyIUIu/dne/C//LT30AJnX4+//wJ7Bef072IIDuObwHZjNSIb/3\ne+/Dpz51Q2AcNeVLYtOQRhEZ9y5+6x6T7vli7J0Xn3IUGi9tRPLcSeylEWW1ZgwEmRR1QgjQyM6p\nOfoRemRoEzC580ZsO8lmGSEojQelDbn6T/HPayHWRluWCdZ6zOdWmEMY90Hql8SdSBJIkI1F75Ee\nKKhKV8jLyWTkhFiRiRuvk30Snz/3PV7cgRPqmif91Srg7CxhtQK6jruq+dxL4xpQ15ymjYlicsjP\n0+lpI3u8DGU5w3KZjZRV7y2WywxVVWA284gRqOuEpqHn2MWLGZxL+OAH3/KKWD6fN4Y78Oq6iB/9\n0U/j937vhnzpkyzVWoTA8BvvZ3L6bcFIxSCFZgAdLyfBGQtLLl8sGujpidKYABU/9T3hJlpcBPR9\nK48XoIHuyghi0eWJeTv4Xk+uVM96+X+q1lbc2kiB0vfGjRNOCBE00yMGzuYxCai0wFOsN2w9xgwU\nZ3loFObXXix+pDseHDh827ft4tFHD8dC9v3f/yr823874OrVhK57Rrj7248w7UsA1YDo/8dIr9Wi\nqTGnhGOc3CcWP2L5Bi/OUdbf15hThfRyeZ+iNFdVfU/2ErzHxNHJyOLfZ1FmDoEqx8lei/KcDcoy\nF7zey46ErzaEflyi0947CB1Z70cCGWpRlrpc+Kqxo8JSqqlh2h2Df7xnky7LQnyUeP/Y9AdsNmQA\nbTZR8HzIVJfk80u7FtKeNa+Z1tz8vGcC9TDQJ8+t7B6MNBQ63jZNj5OTGptND+dolqe5FIeHHU5P\no2RZzPHqV2ej6HS1CkIZ91gsOJkeHfW4efMH/mRf+jvsOt8x3IFXnpNlcetWwCQYclguZyM/vih0\nUasiMws1XNtsMnRdA9IHc6zXBZomg7UrECefoAEVRemegTWPp7r5nIwUspQmvr46ZpL9pFMFbQ0A\nzX228ntOTrJRWEUDJnHWdIo+OPB4+ukNrM0RoxE2UxypkGrTwBNekiZHAztOBE6UznbUM1BvoM2K\nDWoYeqzXBnVNkdPVq5sRX//FX3wSzDMYpIHk49Kdew1l0ABqY6HBNiEEpKTPgfkIE5WT90YL8xRr\nOnk0aTznVEwniIgFUQ3neD+GQd+rbEzuy/NttTNJBIRirDShOOoauC8CAKabke7ZyRJ+WjjzUlaS\nxe5uhqrKUFWcEuhEa6QRcUJQEdxmE3F21uLsrAXFmh5Ki+X06QT+meziVdDIi5MiM5Yz7OxUKEsN\nk8KoSQghwDlGxjrnx2m16wgHvfBCDYCuqYuFQ1EM8J734+SkQV3TN8l7GkauVky922wismwQMV2P\nkxMA8LLo5gFqtWpxeupw330Z3vWuyy9ZDbjbr/OJ4TZch4ctHnjgN7BataACN6Isd6H4snPcL9Df\nJskJs5dTI0A6J8VPZVkCmCFGLhpprEfMl/7247cQZAYZga+iQANTOpgun8n8AdS0bhjSCF3wcQBj\nlK1jRihpGHrQJE8N4XI5LTqo136ez3DjxhV4/zioIzACM3hMVE/dO1j52Ub+mydshWe4NAWyzMni\nVnFzLq9/4idej5/5mT/EzZs0T+NCfMBf/+t/Hr/4i1/9moag1NkJ/uBz4f0GDF7zmh08/zxxezYk\nzbcYRDk9vcdq4pdlxNfL0kvTA1772jmefHKFzaYXjr6K96KYFGonhIlAAAAgAElEQVRTneiw2tz0\n5/H3J7dbTjIJZQnMZizmLHi5TDTcA2lGBHUOYczxYIrdlNhGmKkfpzZtOmw21FPw3nGnNJuVqKoM\nZcl9D8DmReW3kWkkykSixZf0Ur6/bLY6MfJg40bYbDI25HNkpC2NHVMaRHXP98lai6LgUj2EgOPj\nFnXdwbkc995LJlVV5agqg9PTVvZBBbLMCUSIUSdkTMIzz6xxdNThd3/3QXznd+7/Sb/2d9R1DiXd\ngVdKCX/zb34Gv/ZrT8H7HlmWUJaVYMQOTePRtkYUuys41yNGMod08UwLZw03GeB9iZQKsQsgM4Rv\nvqqAqV9Q8zlCDSw8EzMobbGDEvLcYwpZwSjIUjdL9Q9iY9BiTh8iqqz9VqEB1JnT2rdgGD63tawG\nrlxZyuMHKQ5+DCIizq4B8vwsTzsRC81ySAZIwwADFrt3vvMSHnnkKr7pmy7gs589A+Mpk1A5dZk7\nhfAUhcPb334Bjzxyc4vBhfHfSdGdkvQ0TU73L8Tqt8V7qlrX188mfXDgcHTUjk1vGOwWO4mJcrNZ\njtmMp1/+bG2aPLVr8t5mY0Xxu71jYSAO77XSknn/udDW6UQzKHSiUXKCEQGcgVp9a7a1/gxNZwOA\nyZ4d8rmzmKjQPDgo04kUWp18uCuicM2g66LAptxbMISKP9daj7IssFh4LJeZqMQp8ONj0CHYGDfS\nZdu2F3GaWmzXWK2AlAh77e1Z+Y4YNA1EYc9dx4UL+Ti1Xr/eYj63+NSnvgevf/3ipSsEL+N1DiXd\ngRcxzA77+8SxrbVomgFN49C2PGVNHjiDQA0QaAnSDLzADhYhtMJMarC3N0eMXpabLDia3KV2FVTE\nGikoRk68Dm0bQPVrAnUCnCzYVNSMT43iHGYzj8VCT8fMZFboikpfJ3CMBvqwICyXEVevPoCqugqa\n/RUoCofj4w6rVZSTJeQE6zAtUj2MN7CFQZpbxL0cYZlj2M2RDjKY3Qzm/76Gb5sZbJqET//uDRhj\n8dWvnuDCBb0nTqYKPi71Hmwkb3vbRTzyyPPiJwU89NAV/PZvv7D1vmnincZMbr+n9GjiaZS5z5pV\noap0tUePccDeXibMIVqIq6KYsaUUYR0e1pjovtgq/GovouIuh9kMwvoZBJoroDbmbFxRGm2PulYd\ngy6pMQody5IWLWy8KoaL48QQI6EuLplVr8L321rVIGgjsWgaVa7rUohT02JRYn/fYXfXjw1JxWYA\nWU3bmovj4x7r9YAbN2pcv16DMaCk9c5maTyAtC2XzzTDG7CzQ0gshISy9NjbK1FVHru7HvM5NQuk\nuuohgySO09OIp5+uEQLFb3/7b//5V0xTeCmu29YYjDH/M4AfB3Bd/tf/lFL6iPze/wjgvwePWP9D\nSumjt+t5vFzXen2EpulxfEyu/zDM4H2GssywWBRgOhoAFJjNyK0nhx9gwDudNMmbH9B1DiFssF5f\nhzEB8/ll4ZlPPHiFRHSRSZdOO4rO6KET5DSRQfMYqBRVWABQiiYnDC7mVN2qhntkAylmThdTRk16\nGLdB8hnabg8xnmBWJjz+eAO1vhiEGZMAGGvh/IByN8c3/My7sYkVfG4BA/QRiC0Q24Qf/NaEj88N\n3v3eDf6Pqwb9BQ/80VPAj38SxgN20IlJNRh267TMovipT92AMcDFizSFu3atxRvfeGFcrNLaWjMj\nuLDdbMibX62Avrc4OwPOzihu0H2AMpG2BXCEy6YFP6cSMnHKcmKEOeek+KpQMaJpzJikti2eyzLN\n+x6EtcVdEptDkCakdtkO22FANJ3j4YSHDEKY3AdFaXz8rOhieD7PURRWqKH8LGgglE5KjAtV3QBk\n2oxo2xpPPaU2LFMWA+0trMBRVg5BBuoddulSgarKMZuV2NmhdQV1CZM4rqqsWM13uHGjxtWrPegB\n5uB9j7YFrl7tsNkEgU6tNHW+v5vNgJMTLuF3dngAOj2dINnz6zZCSdIYzlJK//vX/P9vAfB/Angr\ngPsA/A6A1309zOhuhZIA4IknjvBjP/ZreOih1+Fzn7uBP/qjBk89tYOUaMxmbQ9jVmDebhK+uQrV\nIDGIBZQxRDFUj5RqZP9Pe28eZfdV3fl+zm+8U82lKg0eZMsjYXAMsZkxvABuCC8kK+0Q0ukhnbfo\nJmQeOomTBd0JvR7JSl4nKxMhdAIEg4EAScxobKshxsEmtvFsy4NsSdZYkqrqDr/5vD/2Pr9bAssE\nLFsl5/dlFS7dUl397nT2OXt/h7AiCDoaGCM7dhkMejqcdDYVKNtE+tniXlpqSytQGqYsKq997QLX\nXntIr94QBB3te5f6O1lNTxWI8jgIIIxyut2KdqvDy19+GvMvz/jSjgSbvoyVfBfhBYeoZiKYNgTT\nPvsOnk6RTVGEEadfvp9yoqAqYOkLZ5D9rY8twQZgNwEtiNqWz/zgEDPXJsDjd3bBwwkEfoVZPkLQ\ntWS/dydEAXZLQO+mQyRLljT1GQ5LRqOEJBlbT8hg3LV3xuZqriU3FqeN2zIyv3FtmLJm7UhLxTm1\nykLurD9kQB3U9yuiv7LWCwhrqeJFL5rl5pv3MxoVWpA8HeA6/Ye0GYVmWuJsN5yq3D2usVZE2jmt\nllHDukBPN5KDLHRRcewdjSrStGQ4zBkMCgaDcYaIe63dwh4EMvuQeYapVeHSegvqIBwxZnRxnr7+\n2WhBEa3L8nLG6mrBYCDPt+8but0WLhLWDeql0I1nae22RxzDaJSTJCWrq5Jh3u2GdLsB7bbMv0Yj\nOQENBlYDlISMsLAQk+cicEsSMR3sduGqq57HS14y/TStBs881u2MQQtD31r7+990+68B1lr7Hv3z\n54B3WWu/9gT3ccoWBoAvfOFBzjlnlptv3sc73/mP+H7Ali3bKIqSQ4cGHDzYJ4pSVletHv/HvHhj\n2kgSW0t3vOCsmMsyV6aKs/YWR9Lx+6DUdpBRsZajlrrbY/J8pP+OtIE6HVPTYoNAdrbGRNr+MOR5\nwQUX9PjFX7wAO3ct+6c+ysZeztte+UZ27/Qpy5LSs0y+5w382BVH+Ju7zuecV+4jCwsZsVpZen8g\n3cBH9kf0ds5QeLC4tY+/sY9nfaaymJ3XtVl9OKC31efwAlzynK+wpXsPC9Fu/CNv5Dduu1T24cbg\n+eBFYJyo14C/ZYh9w+coHswxRLrQ5/VO3u22pS3jr9l5C6VW9AVipyAMGzcYl78nc5Oq/n2hXMpw\n3lmcSMyp2F5L3oNrtYz1JuBmE8LycWZ7ggBniyIMImmntNt+zQaSZLZizbBZaKEu/U6KFTpgdv+e\nc6Yd+3K52QJqCyK9fUOrFdPpGFqtUNtlchJ19tjDYcHqasbqaqaOqNJ8EDqpFAWJ9nQUXje3smqy\n5/QoHnEseRy9nrgQR1GgQ2ijp+eK4VBOI+12ydxcWFtoZJlkRouxX0yv58wmqV/H5WXL6qq0qowJ\n6PWCWohXltDvp9x88yu46KKpE/fhP8lY7zOGdxhjfgL4OvBL1tplYAtw05q/s0dve1bhi198hFe9\naiutVsC2bbPs27fKAw8c5vzz25xzzgx/+Zd3s2+fWEEniU+eu91hqPcg7A75kDt3TNf3tQRBSRy3\ndPcmPkIy2AyIIlOreq21avOdE0UeL33pAldfvYswFMaULFoe/X6k7QEpGLK7G+DaU54HyysDLnz1\n99Iqn8PttkunCvjTz36JveVZdGcrbu9t46YDKcNuwlwxorrqHBJTcXjZp596FKXPPfro/sOlFR//\nnj57AG9VervGWOwrRyTDDt2uR24Mj5Sb6PmPM51MU0T7ueL513Dtva/Fq1p4AfgWbAmVX3Deplv4\n533fR3bly7H3H8C+8y6clkAEcR6tVliH9DijOklekxaMtGMchXVcaF1baGwL4jQhOU7V7Nxopb3k\n1wl4zgvIZRzIz53/VFHz+IUmW9QnBqFzViwvj1helnahzB6ExSaDYDl5CPsHZRGFtNsSzen7MWKP\n7cSQTr2ckyRFPdhemy8ur39JkkhbyBUwN5CWBdflg6CPHcLQnRICdQtGZ0/gFOZB4CI2jTKY3K49\nYHKyje9Ly0cKg8XzYpJETsZLSwlHjmTs3j0gijymp0VxXpYlBw4MsXaARHhGmost3kerq/L6tdtt\nNm/u0OuFGncrbaxzztnA6ae3T+wCcIrjKRUGY8y1wOLam5AV7ErgT4H/Ya21xpjfAX4f+Knv9N94\n17veVX9/2WWXcdlllz2FK35mMBrlvPGNf8vLXrZAHHvkecnddy9x6aWb+bM/E4Oubdvm+YM/uJ0P\nfOAePA96vVCjDA3dbkwUtVQl64Z0lQrXSkYj4WhnWal96JClJSkqks2c4YrCWEAm4qJ77tmDMSFh\nK6fVgYlJn86UpTNX0J2HeM7jtPku6ey9lPMbMPMeTBd40znRjOWdfJBfHt7Jyw9+nkGvy1dOv4ie\n9xgALdpMb76XEYbDR57LoUcCbEe281EAoW8xHnjGcM3dHlvmQtJWBZkhmLCMWoYLA8NjL7I88gko\nD8C//6mz+YV/Pgu/8gh8Q9eHdgCBtmNKoCiV8z+aYiE6RPjSIQfsFFksoT/SjhMPHecgu3bXLouL\nYxyNT6jGSIRotyvUR2ESQRyHOPaXhP/IazEa5YxGGaMROv+RDGMnKhwn5Mmu3Sm2hRnkCpUMucMw\noN2WASqIL5C1krhWlr4GzJTaCrIq2HJHpwJnCCgmcZ6K5pxIESRbWWYRbvF2Xk9ygrJIWwzcx1ra\njn5NeBgrnaUg9HqxUlp9pYgKo84lrlWVKI17Pcd885Cc64yDBwsOHsy0uEY4K/owdAp02dlHUcCm\nTRGzs/J5caQJQIuux3BYcORIRr+fq1WMJBtaK8K2nTtXEI8mSSh8+OERBw5kzM1FJ3gleOawfft2\ntm/ffsLu7xmhqxpjzgT+wVr7/CdoJX0eeOezrZX01rf+LY89tnzMINNajygyfOYzb2VxUXbJmzf/\nCe9+9yv48z9/iAcfHJCmYjwmR/YxJVJ27s6vR3a8rZavPeR4TXsjIQg8XvjCjVzw/Fl+b0+bQRzw\nm687xLVbvkFnImOqOxLhG5YAj3mOkBIhAY0tIGITO9nLFkrcIijX8BL2ssidFEQUeFQYrJ4qBnTY\nyZnsZpFP3PWDHPyDjZz3No9DXUseQNkH+xVoG0PLh+5ERd6xmJbFO7MiXSyp/JKVFUPy8TZmyaO8\nTBUOOug1erCygPWhsNrI0dujIGF67ijDG3OSt91AHMd0uzHdrhN0yWvghICyYBkV/Tmbikr70NJy\nkZ2y1dMV2u4zjKNEnaDM5TeEuvMNlL2E2lAb3bmXag0ii6JYQYipnPTSW7jToWRMBLr7D3Rxr6iq\nhDRFsy/c9YDYioDvF0qDdewql5ImpyFn1+77nlp3ePR6ng7GPdptZ8BodI4lz4HTSBSFtIMk1Gms\nYZBh9riDIYNmZ68e6GxHCorc5tHtisag1epogRSRoWOxDQYydO90pLC5TdFolNZOsy4bvKoMvZ5X\nixizzKiiWijCc3OuxSr/LUtLrxdx7bWX0us9e0ia67aVZIzZaK3dp3/8YeAu/f7vgQ8bY/4/pIV0\nDnDz03UdJwuvfvXZvOMdn1ujFJYQ+8sv38bExNiL5bOf/RHOPXeGooAPfvBuhsOYIDBcfvnZbNrU\n5aMffYjDh1OOHl0hSZaZmttMllaUWYvRKGW1v0SSecQtH386oG8NBCF7t8zy2eeey+YLDFs23smu\nc77GeUFBSE5AhaHEErGSBdwzei6XTvXZS18Lg2Wes1ngKwR0CaiIqPAp8ajoM4vBw2Dw3c4b6JFz\nAfcwX+3kb4dvwrzEsONrFvtGwMAFh+FhICktaWFIzioxL8hJNSjFeBWebynahvJsYJelc71h0oPp\nrRA8FyIPfCXqWKPFwUBuIa2gPxHT+9W78W5dwrQjPV2lHDgwDngfm8UZzj67x4MP9nU37rQebn4g\nKWiyMEvfWphLjmZb1loHtwGQwa2Psw2xVuZHaepODqIDcVqBoshJU8Ob33wGn/jEbhyrShZ30bN4\nXlnrBURUJjkGzpfI/X2xavfpdmM6HTRIqFL+P6rDKBgOc1X9SrbzYJCyupqztER9XzJ7sbqAWsbi\nSVMzmKqqot2OauGbeCL5agQpg3EZEGcsL5esrKSas4Cq/wsgZmVlRFn6QKbtO0/Fgi5CVlhJq6sl\nvZ4o+jdsCAjDmDyvtNCIivrIkYKjRzPy3AUqddi6VdLqkqTi8OFEBXIhcSxuunv2DJ5VReFE4Ol8\nNn7XGHMRsoXaCbwNwFp7jzHmY8A9SH/j7afsseBJsLo64kUvWqx7oNYalpZGHDkyotMJ67930UXS\niXvJSzby8z9/DZ5nOOusOW68ccDBQwm7dhW88CVbedkPXMDfXfV1lhZCtr7phex+b4y/ILbe3mJM\ndLqH/xp48bkp93YGGD/n0Xstjx6EOy41/HGQECEUSg+DT8RGm/D4ssfBQ23+r6mPcAcT9JklwDLB\nQWIGZAyAYk3ysDJf6v/K/1s8LAYPy4y3yvM23cX1928i+IbHGT+gcqxtcGYLJnZA6IM9GDJxl8fh\niyoGFZSVIQXO8+H0y0o+fzQgvw32Z7B3BcwWOTF4FkwshYFQbvOtiri8inw5I1/J8P2QqSkX8uIG\nzl69wPm+YcOGWNsaKDMH5ebnSEvGKvtq3JcWFbJQQaUAOIooyM7cmRkKXLaCy9eW7Gd3ChE9wCc/\nuRfPC9TCOlKGjbSynHI7DEEsyNuUZanKauHoy65eCtTKSsLKSqVD90JZUpaxA6toRmSYHjA9HTAx\n0VE1daimdnJiEPX0mLW0upqTpsKC8/2Sfj/X2YGH54mHk/hHyTxDbhe66ORkqFYrLr9a6MVRFCsz\nLCDLPPr9nNVVEVW6Qrdpk1iSuJNCnlfEsViT+L5Era6uFhw9mqugsNLiknLggK+fyQJJ7wuZmRGi\nh3OKbXAsGuXz04Tf/M3rueGGnTz44HJ9zBblZ8lodCXOyG0tfuzH/p6iyPmt33o5193wGL/yjyW2\nHcH3nIOZmcQsrRBOxnQmY0Kg7UMQgrEw3YJXboTZiz7OXDTFNH/B6nLMlmIPl8zdTEmAuO7HlEQU\naLvKQuqF5CZgRI8KW58M2kBATGRjVmlzkOeS7bPYfsWmcy8iYoq4nMB8/He47xVt8oUFCGUn8DgL\n/Madv8Hod+fonm1II8iNDIqND562g7zYkv+gxRrLa1pwfV7R9S3/LoKrtnsEXw6YvAhGd0GrgqAC\nvwPBayBtS8EZGhhGGa2fvpbRvgHpoQSbybBY1K3SQur1ArrdiOGwwLnPOmWzMGZKXBSq2HeIGND3\nS3w/1MGse93G1FCxrHbqaRHXtVqtOv7SKdiNcWrqsk6KSxI3dB5rD5zBnhQnRx2VhXqtBYok2MlO\nvtPxNf/Dp912w1fX3hqfFoT9VuoA3WVoF3oCEeHa2iQ40ak4mxR53PI8yI68qkpcGpzMcSqKQszt\nBoNSxYSePo8+7XbIxERIGDqPpVB9kgxZhraOsppgEUVuGO1OWJDnIzyvIIpc+p9oIyQzuqLTaTEx\nYZiYiOn1fKz1abd9lpdTlpdTVlcLhkOjokGfXi/g7rtfe0wL7FTHuqWrngicyoUB4I479vPVr+5h\naqrFzEzM9HTM9HSLCy6Y/5a/e8sB+Kv7YSmDgwksJ3AggUEJWQ5lpTtkM/4vZjztjwwc+Qm40fsy\nZ/GbRDzIwyyyzHlUTLORWY5Wh8irABNMyaIMQEUOnM6lnM4LmSTG0MPQxuiBMh1OA3386H9zzf9z\nA7tvuol33HcfN/AujvAIZ/79N1i6YJIjZ01jQoCAERP8zs5f4dH/9RwWNnh4Ld3ZGykKRh1AjYHO\nqwuWpqTdMTQlo2Wf4ftiqMAfQbQCrRDiUHfPMXiXQLER8qjCfOYRiqNHGH10B/kw5YorzuLDH96N\n7IrRIWzA5GSLubmIxx8Xg8K1XH1ZBD1+/MdP52Mfe5iigFYrpN22dcrYWJglC2OeF4xGGf1+xWCQ\nMByKx49oVcBlOsvCKnTh8cwI3KvnXFLb7ZB22y3uRtlTQoctS488l4U4SQpNKSv1lOBjbVFTnUUd\n7QR3VucZ1KcaWS/Gu+o4rvQ05KkRXaGGeFZPDOKzJJbepnY7bbfHxohCoy61yMniniTOMVXabhL5\n6ay3jQ6yI8RFtlKtiYfvF3S7PtPTIVNTMe22FKM4DsjzjCNHhgwGKYOBFDrJrQiIIiks4i5gCUNx\nnfX9gCyT040xhqmpkG5XhtfWWjqdgJtvfjVxfKyF+qmMpjA8C/DVvfDDn4PDhezgKxlJAMLR942M\nIzsBdEPoxhD50NKdN0BgYL4Nv3kx3Dl3DQ+wnz6GPpYMn4vZyvkrH2WltZcymqOk0mVJ/gcRL+Qt\nnMMryRnqDOFWKo5Q2d14yftIoxfy9Qe6HL3rCDP/9lwGHMCSYanU3MMCOZV+faH8fj7wif+C2eXR\n2gHBVmh5cu1xCzLVIPhnVGTnVAysIa8gNYbikIFHDXxC2k5+BJ5NaXsJppvBhpyKkjxPSN9/F8Wh\nPmMDOJkFxHGLXi9QJ1GxcXYtIM+zfP/3b+DTn35cd7ZW/XdKnNmfUw2P9QbgigKMNSAiQKy0H9+m\n3Q7rICM3n3DsmbH1RMVwaHSIK5YlLgPBWmEVuaIh7SCPqooYC9lkgD1Oh5O2UBy7mYArRDB2hLU4\nkzp3ehEvqUotPaRN5uy6JUVQsg6qKqhnFWJJHqpQTphAZVmq+rqqSRO+79Fuhzr8l4U4jmWzIcN9\np7Z3Hl1VnZHgBtthKEUriiRXIUlyJCJXhHtBIG03EcnJNYrnlVEjQeceC0liGQyE2up5IRMTAXNz\nEVu3drnuulec6I/1ScW6HT43eGJkGTz0kOyqfN/wvOcFbN8DF05DaYTWlxSQWkgqWE3lttzCyMBg\nBFaSHWU3quIhA5wzBbt5jHv4Z+ZZYp6E+zkfwwz3s5uHJl9KicelxMzzT6QcJtFRdEXBrXyIW/kI\nlojNBJzP1UAApkvV7pJwL8MLz8S7sMcyu3TCYPGpKHUKYSmoyDnAHLuq07HGYruWUc/gD+Wx9C34\nm6CaByx4A49gycNOQmglp8zMgleCt9kyf0lJO6wott9H+7aH8X2ji2Qs/fPTY9KFUFszzhpCwt1H\nI49+X1g8VZXXA2BjSh5+eJWjR7M17K9KWTpCZWy1ItptT8VaLocZ3vSmLXzqU7tZWRkyGGSMRiMG\nAw9rna4kZxybmiFZFo4VZHX3nq1hN41zpqOoqN1rHcXUKaidBYmcBHylv/r1NYNTuksrSnyyXAtG\n6LhOuyK2HSVOeCe0XeqWkjuS+r7QcycmolpZ7Hb9zuKkKEptjwk7SYJ0KqWi5gyHFQcPpvrchGqx\nEtbFSDQNUnhbLY/5+Vj1EEYLV6mD4pwkCVlaGnH0aMbhwyWel5IkJZ2ODNyTxDAcJipQlKzqTkee\nsySp9IRh6HQqwrBUCvizf/P5naI5MTzD+KM/git/a4gXpEQhHNw3w5YPwEB89LDVmr6umqUaK0wc\n30DoqRGzP1b8lpUoi2MPoqDiSz96gEPmBh7gM0BASoghZhcz7GAbp7OB13AdAS12231kJlLKaUkb\nn9O5kf0sssJmLJ6eBEplIQmjSYiMFZvYQ582AxYwhPU4WuYMG/l8/3XcfuPL2JAEmH2Wjftz/Aty\n/CTDH2bYlYTyaE65MuTSi6a488Z93PFPh1g9POKKK7byvvc9QJZVNR3UmEJpm9Pad89xkZ0O0u+P\nmJqKldIb6GI7Nshzyl8nbDvW2M9TampRi8iEtintlM2bQ/bvH5LnTmhmgYCimMSYQIvIuEUEEosp\nuc1GDeoqHcC6YXaF2IwbnVm4f1c4/876IstKvTb5EkfZsVBPZiduAC7XLScWOdnEsbPr9pW1JKcN\niWAVrUCaQpIUDIeFamVsXZA8T+YtUoAc/VYKWJ6XqqIWtlIUSZCPeEIF2p7ySFMJ8JEZkKHbdZRo\no+0xkFNZjot3lcxzsUQfDHIkJU4G9UHgqyOxZKW7GE9rJdN5w4aQhYWg1rOIqNDRb+EXfuE83v72\ns56eD/xJQtNKWucYZrCSwnIKRxLYvQQf+JDlkbsHrOxJ+S9/NsfQh7+5H6oSUv1KSuHol2ucEkIP\nggja2o4JLYRq2e9baXz0Ynj7mx/nG9EBfoIv8Tg3kdKixOMhzuBhziIi5mXcxr/JXs+nw79hmUVy\n0yMnYECPLn0y4AjzeMSE+rXIJBeygz6r5IRktIjIGZIxYorT+EcsCZfwZRIb8qjdyq/8w8/zjSv2\nEFRZbT3hUs2ghbVRPQQVqwcZtIZhRa8nC93amEzxjAKY0MJQKMsnJUkgSSrNnPZ461tP56//WvKg\ng6CD78PMjKcni0yN6ypdtN3UJVAuPUSRS//y1VPJq1swRZHhYkXT1CfLYqydIIqcHYTEqQqrqVDb\n7FDnBJ4GA7nEPmnEAdpCcvoBEdG5wa+E/3gahymagzD0ahaRm1+4k1OaSjtIgpa+OaJVFmJhD5W6\nqK8V98nOWgqrxHM6DySXSJhlhVptW83lMDj7C2udXmKsLxAnVWfD7dc+XlI85XGKFsIjTYWaKz5I\nPhMTfh0IJLf7dDohvu9pwbWkqRhW5rnRkwD0+xV5nil11avT3UBmSK1WxBvesJkPfejip3EVeObR\nFIZ1jrd+HP55n3zU01JYOWdOwx+9CY4O4T03wSWnwccfBlNB4YmraFbCSItEUcncoTJq9WbHzB5P\nTw2mDaOR/KzVS/jsjya8KFzlFt7Gqj2TtJohrGYZBV3uMwtEdDnXTjFIPstDrefjGZ8Ou3iUaSDG\nI6WkpCAgx8PHcgEr3EObSs8FAIYSQ0mXPi/ndkoyXsKjvP2Kl/Hoo12Opj2OHJqhM4JWq62LDcTx\nBL4vi7vshoekaUm/n2loe0JRR4EFunMUodeWLZMcPhzoYpwC4bkAACAASURBVJtRlrnmOYCkhoU1\nG0ksEjw8T4KHZmdDlpdHrK4WZFlGlo378OIOKxTRTiei1ZLvZfcvbZ+yzJG86kx3sDlJ0gYmGAsQ\nKzwvdR9OpUi62YRkGYgnUUCnQx2+I+Ezbj5g1EK70mjMSu+nqFtfYvdh6gXZOa06do0MvmWX73mm\nViSHIThGk2snZVmu4jVniS3FKstKJIPbtagck8sZEpo1s4bxLsYJ/6SAiFjThTGFoU+3K4Plqamo\nniG4k4nYZGf0+xlLSwmjkWR4SAsrBEzNUCpLEQBKATM6TxHrkImJgMnJmCiSPIiiEPGiWJvAkSM5\nBw5k3HLLq7noomePgR40hWHdY/cyXHE19IHH9sL0NPR98EN485nwwP85yi0TPc47M+DuQ3JKUGsZ\nF9eAZ2TQPNGGroFuAJ1IWTqe2EO0p+HQYQgNjArIjWU5s7z45UM+Nz/kyEc24GXwI69LuPjsj3C/\nsbXYDUSLMM0SjzPLgDYxORElvo3oVIbpfAPn5fexIz+dcmWSYtknWTKMDsFgj0f/sZLlh3KOHipY\n2hty9GjBaDTS3eAsYn1s8f0QsQV3eccVkv9cIO6vsjicdVabokjZvXukO/USz2thTMVf/MVLufrq\nA3zhC/vVSFAUxy42VVodEkYkPkEBxojdgQjLcu2Bp1QVXH75Jj72scfXLLBWhWylLtJ+Td901y3i\nL2mHBEGPKGrr6cJoK8n5K7kBLjrMNRrTOQ75cQaHsmse9/5drz8IZBQooTTFGmaP7LjHw2WZXZV6\nzJQBsOzoxXbdJQPKKy4FoVSPKJcH7dfXJMNoDUmyLu/Dq9+cssAbZmZkqCwnPHAhQc5+ROJqS+3x\nV5qiJmFNIszzNFzJ05OFJNGFIUxNyXyj13OMK+dCLKdP0TWU7NkzZDSSa52YCJQp5VqCMs+zVuis\nSSKaCWmrxVx55Xn86q+e83QtAScFTWE4BXDzblnsP34HXPcI5B5s7kKaW7rX7CZ/7gSfuHKan7ke\n9q4KHbXQ4XM/g1EuGoC0hLKASrMMKgueiyhE2TO+MJeSc0Zs7a7w8BYf04Z4xWfCr5hpW/7z0d0M\nqr2U2YDkSERyOGB1HwyXLCv7YXkvrO4r6R/y6a96jEYVZemT5zl5nlKWlQ5LheLo+y1tHxSUZaYD\nVqNCJxcE1NGFOieKppTC6awZMsapZLIYGpMpb1+yo6WdFBOGXV71qq2MRiVf//ohiiLV1o5QHSWC\n0wXMOHpmgLUxjrFUVS4FTwR/ch0hrZZHp+NsRgKiyNY2DjJczRkOxYNnMCjUzjlE2mK+7szdTr3U\n3bXL1XZUSFnoJdA+0BaHVbWw7MLld8raTtsNi+U+ZYA7Tm4bJ69J37ystQiSjFYhamuQcCZwnknO\nWE+KnxTRIAjodlu0WlF9gpH2TlUPaodDmT9IEZIv0SlUWpgrPb14uLhQz/NotWR4PTERq9FfQFEI\ni2k4LOj3C1wcq7SEpIikqcwDZB4ihSHLIElyNTP0mZz0ar2K+90s8xiNhHAwPx/heVIMlpdLINbX\nIeZTn3oB3/u9k0/3MvCMoikMpwgqC3N/CB9+E7xhG3zmNnjTv+tj7l9iZtrwl189g5/bDkczDYIH\nrJ4abCUnA2OgFUAcQM8Xfn7kO+GR/BuJFo63/psR75s4CsAUPhf++Ne47bYj6sUU1sEt4qcfsDaZ\nyx3HyzKgLH1djGzd5pAPv68DTXSgWqq9w9gzx6WgeZ4linzNBZCwoiCIj2HQ5HlCmhYkSUqS5Nra\ncCIvN2Dt4HmTQFdbSBnjogJB0CKOfXo9j3bb0Ol42hcPtUXhbLUz0rRgNCpJEtnZj5XKzmrC4HIa\nul2P6emA3bv7OKdbec4jPM8nCCJ8XzQT0k4RyqlYZJQ6bHb+PRWzswEXXLCF225bRdLY5LWQWYfE\nnDoh3DgX3GUu+Bgj7RQXrTpuGckMJo5RV1dx2m23x75beS46CKGEJiRJhosN9TxPTy++FlN3TU7V\nLc9RHFs6Henx93ohksstu3OhnGb0+6KBiCIfZxAo7yvxMxm3o4wqu8HpOmS+UNU0XHktRGDYaoX1\n/UnLETodn27Xp9czOnNA6a85hw8PGY0KIFD/J8eACpBs6g4/8AObuPrq557Qz/vJRlMYTiF8aSe8\neDP01MTxVa9P2L9rwJ//aY/u+TF/twO+vAdetgVevAl++yY4NIC0gCqAIhvrFgjAlDDfg4kYVjNo\nRfD8OShWBtz6l9czuCBguGfIaOeI/M5VssxH+vW+GphZosilu0lugTGWqamY7/meWb785QO64Pn1\nEFj6wS7HwBUGYfrIggtJIvMOiRh1Slp0gBtijESHVpWni12J7xd4XolEiFaEoQw/49jqvKBFEHSx\ntqVziYKqyimKjKJAFcSeLqoub8BDPJDWmub5lGW2xmsIXDiSDHRFfyDPiyxKLjp1eXlYs3TENiPE\n82SeISrc8Q7dsZ4cZVQWXKcl6BIELcZOq7nu3EvdybvhtzCV2m10yOxCl6QPn2WW0ahkNJIiNxgk\nWkgKxL7dR1xWS8Suexw+FAQBUVTqacyrrbKdZbjvy+nLDeed86ycYowWMCnIZenmIm5Q7gb5a4OP\nbF1wi0LEgdKq8pmcDOl0nC25FLM8dwN7U6vDk8QNpMfOwa7AOHaXMJ9kfjI15TM1JVbrniePsapK\nhkNxpx0MLINByBVXLPD+91+Isxx/NqApDM8y/OyX4C0XwEtPgw/eBUkOs22Y68B0JN9PxfD7X4eH\nluGqN8rvffBeGBZw0WAfP/mT1/H444M1w0S3a5MBYasFvV5MGLY0x0EWYVHDWlzk5IEDwkHPMo88\nz9fw/dEFznHe3ZzAtW5cyI8kz0mSWEC7HSlN0sf3I12sC/K8IE1T+v2ElZWE0SjVBUgem3D1JwiC\nCGgrm6egLFNtB4ExsqhI8SprpbIMWNEFgzooRtxCcyS8Xto5Yr/gPJGAOuZTHEQlNCbXYarTIBg8\nL9LiiTKASuQkIwNnscjw6HYDOp2IiYlWLXyzVrj+SVKokjdlOJQdvSTxufmDr0PttQNnV5w93bVX\ndWxoFAXakqm0tWcZDKQFJME6IAu5tG9cVoTs3gOl9I6NAIW1VCk7ym0knI7BKotJZiwSKCStojA0\nOq/w9P4qVSEXDAYyyJfNSqEtsqp+7WVDEKrzqhMKWlqtiChyRcfW+dZiOQP9fsFwKAZ+rr3lCq5s\nTiSsSlyMQ6anY+677xImJp49sq6mMDRgz54BN964n6WlhEcfXeWGG3brrtpnNKp04FbpEV1iP0Xs\nVOobyKxZ1OXDJv3oUFslnoqCxI653TZ18llVye5cFs6SJBmRJCXDIaSpuw85+k9MRAyHLpdA7B2M\nKaiqAgmfkbaJW6zGbqddjIkRamugC0xOWcrA2hnjubAa9+/Jjt8qdVLsKtLU1tcq7TOXPyA7ZbfA\nO7iBtPD35ZpEExHqjCAgDCOc9UZZFqRpps9Foc6qHkURUJaBzjfc7n0sthNfI1TPYHTugArd5AQh\nLB+fPPfU9C9hNBpQlh4uhlWuQ7O3Vf08Hia7AboUtHF2tLPJkNdfBITu78lz4AwIJfNAnF7lMZbq\nUVTUNFCgfv7L0mkb5LQaBEYt0IOaDisnLnSBt6ysZAyHsLyckWWOOuvrAi+UXndCkRwJw3CY4ftS\nfONY7Drcc5znlgMHMtJU2p+dToeZmZAwDCgKsUi/4YYXsHXr2PX4VEdTGBrw3//77bznPd/QD4zw\nuqWdYqiqALfrlA8Y9HqGTkcGgWIr4OsMwWUTVGSZIU09BoOyHgIKoycjz6XtEQRBzUAR9o3j91sd\nyoYY00IiSn1+6Ie2cu21S/UHVhZtKVZlmelA2OoAWwqVtGjktOB5EcYEOgcpcdkCZVmwuBhTVVIk\nnc05uF2tE5Y5eqYsKkJL9eh2Q3o9MZ6LY+lh57kMl0ejnOGwYDCQ3rks6qJ3cAusMX49aHa7fFmk\npaXRasW02x09jTj2jae2EymjUVY7pQosLmhHjPlALDDEFiPLPET8leA8koTxZXGMHVFGo3Mkq4XT\nEMeeDtTbtNs+nleQZSmjkSjGi6LQ11GKalWNGU7iyWT0VGm1UHjqCOvXLUaJ3ZSZgwysRXDmdCbu\nI+0MBqUdJMaGcSwnV4kTlXaUEwKu1WZ4nmFmRrQcUhgKTaIT+/EgkJPS8nLOaGSVTdWh00koipCq\n8rUNGOL7MT/0Q5v467/e+rR+Tp9JNIWhATfeuJ+f+ZmbMCaj1ZLBoSywjr1RMhqNcNRJ6bOL544s\ncmNRlQiS5MMp1EUZOPq+x9atHSYmDA8+uKJ+/MJGEdVwTppmpKnswmR2ECIpaJ62kmJVqHraAik5\n//wejz22wp49K7o4paRpWT82Y9paEEQQJ3ODsr5mmQ1YJid9JiZC4hjOOKPNkSOZ7pQrVdsKtVF2\n80LPlNOOsGfGxnYCFzkptFNpr8iCGBEEYtAm7Rr3IXSW2LkO7q3qMyxV1aIoJglD8P0Ep1p2p62q\ncmI3q4+5wvcLjDoNCq1VrK5lHlIhyWyWIGjT6UAcC9sqy9CWWUWaZrU9BZrs5jjQxvQQewp5Hl2b\nyOV9t9uh5ijL2iLsIFRXkTMaFWsWeNcGAhEbWn3+hNgg1uHSDrLWLeay23f5z/1+irUhxgR0u9Bq\nxczN+RohKo9nMMgoigpnoy5OrqUOqqnFblkmRabbDZiejonjFrOzMb6P+mFBv28YDj2GQ4+yjHnV\nq6a55przdNh96qPxSmrARz/6II89drTuQYshmww+81yEPaKoLbVFI62hMAyIY1RbIAugMe73ZVYg\nlMcSazP27884dMjToajLPDYqTpLecprGjEYRSeIGrTK8HA5hNCoZRzGWtFqWbdvaPPLIMtaKEK3T\naatlg/TJ2+2O9vBjIKwVrklSMRikGjqTs7SUsn+/zDq63XluvfUw8vY22ksOGNtqSLKZCO0ka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"text/plain": [ "<matplotlib.figure.Figure at 0x11e526828>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(chunk_x, chunk_y, c=chunk_z, lw=0, s=2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### That gave us a small chunk of LIDAR points, without loading the whole point dataset. Neat!\n", "\n", "...but being continually dissatisfied, we want more! Lets get just the corresponding trajectory:" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": true }, "outputs": [], "source": [ "traj = thedata['traj_data']" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<class 'netCDF4._netCDF4.Group'>\n", "group /traj_data:\n", " flight: 11\n", " pass: 1\n", " source: RAPPLS flight 11, SIPEX-II 2012\n", " dimensions(sizes): phony_dim_2(51495)\n", " variables(dimensions): float64 \u001b[4mpos_x\u001b[0m(phony_dim_2), float64 \u001b[4mpos_y\u001b[0m(phony_dim_2), float64 \u001b[4mpos_z\u001b[0m(phony_dim_2)\n", " groups: " ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "traj" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Because there's essentiually no X extent for flight data, only the Y coordinate of the flight data are needed..." ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pos_y = traj['pos_y']" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [], "source": [ "idx = np.where((pos_y[:] > -100.) & (pos_y[:] < 200.))" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": true }, "outputs": [], "source": [ "cpos_x = traj['pos_x'][idx]" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": true }, "outputs": [], "source": [ "cpos_y = traj['pos_y'][idx]" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": true }, "outputs": [], "source": [ "cpos_z = traj['pos_z'][idx]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Now plot the flight line and LiDAR together" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.collections.PathCollection at 0x3944c9e80>" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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T40fC2AGIz4wq1SuuYA+rx7CeQZpBH52+W9HtWH7ZKmuCZhn1hKrBlacX04OE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"text/plain": [ "<matplotlib.figure.Figure at 0x3944931d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(chunk_x, chunk_y, c=chunk_z, lw=0, s=3, cmap='gist_earth')\n", "plt.scatter(cpos_x, cpos_y, c=cpos_z, lw=0, s=5, cmap='Oranges')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "...and prove that we are looking at a trajectory *and* some LiDAR" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from mpl_toolkits.mplot3d import Axes3D" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "image/png": 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OE9sBi66yIfWCgpyQHJjFoG3WUulJ6Ja5FZimmdm4xbkR2fBp9uAnpa4v7bwC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uJ3337Kd242vLM5kHKhCP/UK/f4yqvnNF3gW5s3+ea7v831ePPMPKhWLY0p2vHa1FweLhnd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\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3Xj760Y9y3333Pe/rdvXqVT74wQ+ys7ODlJJf/uVf5kMf+hAf+9jH+Mu//EtOnDgBwMc/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zQOn9M/kbExpRTf/OY3+fSnP80555zDRRddxO23387Xv/51Nm/ezO7du9m9ezcveMELhsdlyJCT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\nGc6rVkz3rp0NpX3OWxL/PcSNR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"text/plain": [ "<matplotlib.figure.Figure at 0x3fc59f898>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#set up a plot\n", "plt_az=310\n", "plt_elev = 40.\n", "plt_s = 3\n", "cb_fmt = '%.1f'\n", "\n", "cmap1 = plt.get_cmap('gist_earth', 10)\n", "\n", "#make a plot\n", "fig = plt.figure()\n", "fig.set_size_inches(35/2.51, 20/2.51)\n", "ax0 = fig.add_subplot(111, projection='3d')\n", "a0 = ax0.scatter(chunk_x, chunk_y, (chunk_z-min(chunk_z))*2,\n", " c=np.ndarray.tolist((chunk_z-min(chunk_z))*2),\\\n", " cmap=cmap1,lw=0, vmin = -0.5, vmax = 5, s=plt_s)\n", "ax0.scatter(cpos_x, cpos_y, cpos_z, c=np.ndarray.tolist(cpos_z),\\\n", " cmap='hot', lw=0, vmin = 250, vmax = 265, s=10)\n", "ax0.view_init(elev=plt_elev, azim=plt_az)\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### plot coloured by point uncertainty" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "image/png": 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OE9sBi66yIfWCgpyQHJjFoG3WUulJ6Ja5FZimmdm4xbkR2fBp9uAnpa4v7bwC\nSTJ1nduqUMYCP87hOVPx9vRfo/Pke2Fi78EvBWAC8Psb/6kgKom1cABGdt4eDAZKmcPphErnOIpx\n97YQ1kld++XydrFF0LZtjEaj4N9Z3k7ygIKckAzJyqCt0+lkKg5l6ryH3PO8IODh+z7a7TYWFxdz\nMwYq2xl+GmaZ36hs+MLCQmbZ8DqiY9BMJ5KIN9k4LPYe9jyYj74H7f7HYGB18898ADDhtk7B6LB3\n531IREIIq3HmcGl624uWWnVCt2OOu7ende0X726Wt5Mi4GqJkBkp06AtDbpmyNOOOxzwsCwLvV6v\nkOi2rnOdlCKy4VHoMq9px8hFXbGME29hA6nguTF6EtbSdbDsLwNwDn4QNjLiAAATw8bZcH/uo0D7\nmIKPqDh0Cjhm0dtel+PNEp2POereFuXtsmt/VHl7+LgnlbdHZc91nTdSPBTkhKQkC4M2y7JmMmhL\ngy5iJsy0e9+j2moVEfCQ0Wmuk461iG0VVYALMX0ZayD19H14xpPvgIUHJfF9EB/w0YXduRJri78P\no9UJhABRC1Xd+VVEZ0EeJqq8Xazj5PL2ZrMJz/MSl7eLzwqXtwtxzvJ2MgkKckKmQI6urq2tod1u\nJyp3kzOJpmmW6jKtk0iUSTLuOCOxMkvJdJrrcWONyoandaCvMyKgIZz8WeaoPqZpwnzyNiw8eRNM\n/OzQDzwEotzD4eg/450wjnwzAMAfDGDW4LxWQawlcedPKtKqSBXOcRyGYcCyrCAwI29dcV0XhmFg\nOBwm8h0Il7cLw9jBYBD8THwXPQxIGApyQiYQZ9AmBN+40jYVM4lC2Or2kh0nyMsqnZ5E2d8/DVFj\nVf0a1onwNWoYxqZsnLxfmSiCa6P5yNthrf81THMQ/HNw5RmAg+Ox9swPwemeCdd1Ya6vo9FoBM9Y\noh/j/AWEyErkL0C0IlzeLrarANhU3p7kWR2uqhDJnMFgEAh0ubydz31CQU5IDJMM2qJcv6Pcu/M0\naEuDeFHoLshVFYsyOglHeaxi76zI4qp2DeuCuEYHgwFc10Wr1cK2bdtgmmZQ1iiXTIoKGvFs0e0e\nrQzDZbT+x6+jOfoqgtmXsuGACcd6MUY7/gzo7IAFwMLmCirx7nBdt9LBlqpfo2GRtrq6Csuygi1R\nac3hdKHuzyGR1QY2+w7Ipm5JDN2iBDrL24kMBTkhEtMYtMkCRrycR6NRIe7ds6KTUBTI4kXMteql\n0zrNs7jOS7OdAAAgAElEQVT2V1ZWlA1wCFSfV9kMbDAYbLlG5bGHSyY9zwsycWtra8yeF8nTX0J7\n6S1o4pGtP/MBD23Y86+Gs/2DQHPr3nC59Fk4UxuGMbVxGFEbWTClNYfTjaocxzSEAxFxvgNTd2ZA\nfHm7MPlleXv9oCAnBOkM2gzDCCKcwqCtKPfuWVFd0IQR0WTXdXHgwAGlxaKMDvMsBzh832c2PCVR\n/gUAsG3btsSfIS/SPM9Dp9OpxWK/bIzlP0Dnyf8bJtYO/aMU+3DxDAyOfDdw1HWJP1MIclGWOsk4\nTNdgS52yp1HP8qqbw9Xp/IYZd+xR5nDhtnpZlrfL2XNdnxVkPBTkpLbI5YVJe4YDm/eCGoaBbreL\nubk5rR6SOvQijyr/B4DDDjtMqwWCioJcLqX2PA/tdhsLCwtYWVmhK/SUiIXTaDTaZNgIIMh2pCVq\nse84DveeZ4G9Duvh62D1/xYG3C0/9gG45vEY7vgosO1FM39dlHFY2N1ZLn3W6RlXN5KItEnmcLqc\nYwryZMcu+w4A0eXtSe/vKIEu9z43TROtVkur64hMhoKc1Io4g7Yk7crk/cqtVgvdbheu66LT6RR4\nBNmgcubW87xgruXyf8MwsG/fPq1ePqqNVc6GN5tNdLvdIBuu6vUQRdnjjXPzF4sx8TtZEs7IxJXK\nJnEDLhuRPS6F1e+j/dNfR9N5INQ3fAMfBpz2L2N03O1A+8jchhG1VUHHzGqp57JgphWn48zhpi1x\nLou6CvJZ987nXd4uG86FzeHqeL6qAAU5qQWTDNrG/U2cQZsoVdeRsgVNGHlvuG3bkeX/Yrw6LRBU\nmGd5b1oSfwOd5rdowr3tO51OaW7+cdnzacsla8MTn0Jn6f9AA09F/thDC/bi6+Ec8wdABvM1bXat\napnVKjLLszFsDhdV4qyiOVzd3wdZHHuS8vak5z4qey6eFeLnItCn0nVEJkNBTirLNAZtMlEZ2qj9\nyjqUfcehglAEtgqcdruNXq8X+RLR0R3eMIxSrpGoYJKcDY9ClzkFir1+k2TDy6ZK2fNMcYawfvom\nWKv/GQYigqc+4OJwDLbfBBz5muLHF4NOmVWdnscqEVfiLIJq9Isolzz7zY8rb5/23LO8vTqos6Ig\nJCPSGLTJLSjiMrRhVBG1aTBNMwhSFE2cwEnystBtzoseb1y5f9IouW4BjzyJChZNkw2XKzuKns/a\nZ8/3fxXtn7wJTe8nkWXpAOA0fxHD424HFp5X9OimYlxmVZSthsvbST7keS8n8YsoYwtDXd8HRW7F\nSGoMyPL2akNBTirBrAZtohfkuAxtGGbIp0M2wxPtyqYt96Ug34pYoA8GA9i2jWazOZPbv07zmzUi\nmDcYDErPhmd1HmqTPfd9NP7H+9D62Udhoh/6GQAD8GHC6V2I0S/cDlhzZYxyZuTsWrvdDt57cma1\nyOx5nQRbUccaVeIcNgAsagtDnc6vTFnHHbd9JSoAx/L2akFBTrQlS4O2WRbdOr6wihK24bmetV2Z\nboI8T6Ky4UmDSXHoch1nvRVg1mx41uT5vZXLnq/9K1o/eT2ao/thIOLZ4AOeuYDBUTfAf9ZvFzas\not4LoiRVfKcO+5LJdMQZABaxhaFOpn0yqqzrogJwabc2sLxdbSjIiXakMWgDNjtMiwxtq9WayaRF\nZMlV74cdJm9hG57rrHpb6ybIsx5vEvO7WdBtfmchai6Tbp2oCjpnz409H0Fn700wsT+yJB0AnOYv\nYfjs/wQsnFzs4Eoi6b5kFc+nDqgg0sYZhE2bQU2CCsdcBqoed1Hl7Y7jBGtkPi+KgYKcaEHeBm1p\n0VXA5FFuL7t5i97WWc41oN98ZzVecR2Ll2UW2XCdmWVepzESrBuTsuelZ1tHT6P10LVorn0Jm576\nHgJR7qOF0TOugrPzVsCs9xKniGoIVYVLHqh4rFlmUKNQ8ZiLQIfKgLjydmEACST3l5DX0sLnp9ls\nBlskWN6eP/V+WxHlSWvQlmcWUUbXfeRZCcU0bt6zUCdBHnUdz83N5WrcpNv8JqWMuZS/W8cFrVLZ\n8yc+j87D16PhPx77K66xHYPj/h/giF/NbxxToNp5T3o+6eqtN9OYwyURVapdx0Wh43EnrZCZdI+L\nNYCcTAmXtzcaDXz729/G8573PGzbti3nI6sHFOREOWYxaBPZ8KIyX7oKGDHutC+dWd2806LbfKcZ\nb9kZXJ3mdxLhyoJOp1PoXOq2oBtH4dlz10HzwTehtfJpGEZ0RwgfBpzeSzF6zu2Addjs31kjkpS+\nJsme6yhc0qJD1lQmiTmcfN9GnccqvQ+moQrXdVrn/qjrPFze7nkePvjBD+JP//RPKcgzgoKcKEHW\nBm1FRfizNpcqirTu20VVHsShmyAHki1oyszgyuiyABl3Hagyl1UmLtuadK/y2MXuyv3o/PAaNNyf\nHvwybLikS+tDDwsY7Hg3/Ge9KfNjqyNRpa9CuImMWJRw0+1ZPCu6i7RJ5nBxe891Pua06H6uw8R5\nDziOs8m5X/w8SXn7E088gaOPPjr/wdcECnJSKqoYtKXFNE1tFyVJDenKztjK6BYAkReuUdemSnML\n6BnwEOThOk+SkUX2vPGT96H9+IdhYLj5BwdblgGA03ouhr94BzD3i/ke0Izoeg8J4oRblHGU+H2i\nF0nM4cS9WjVxOolZqgd1QS5vlz2aZJ+m4XA4Nqjq+752hsYqQ0FOCke++W3bxtraGrZt2za1QVur\n1crcNGxaDMMIHl66MSnLaNs2hsPhpn7MZe8t1E0wxpUAqji3uiFX1Yge7GVUbZDNJMmeC5Hnrz2C\n9g9fDWv0ndjP89HC6Jm/AecXbgY0C65U4RpMYhw1GAxq0SqpyiItyhzOtm24rov19fVaOvTX4RiB\nQxlv0T5xMBgE6yzZAPJzn/scTj31VBx//PHBv9VljoqAgpwURpRBm2mam1zT4/5GlJ/mbRo2LTpn\nyKPErTD4GQ6HQeVBmf2Yw+gmyIHN+/VV6nUdhU7z63keVlZWlM+G6zKfeRG5V/nhj2Lb3v8LDaxt\nZL8jTpvbeBYGx/8ZcPg5hY+ZxCMLN7EX2TTN2LJnlZ5vs1JlQR5G9DZ3XRfdbjcXh35VqdN5jqPZ\nbMKyLACHtoZ++ctfxnve8x5YloVzzjkH7XYbTz/9NA47jP4dWUBBTnIliUGbEAHyA1C1Ut44dCuh\nlpGForwPP492ZVmhk2AEDomx1dXVTR4HwgVVRVSeX7EgFM8F1feGT9qHVyucNbS/9xpYq1/GRh26\nxMG2ZT4MDOYvgn3Cx2C25uo3R5oRzqyN64mt8n1KopG9fOLM4cZ5DOhK3QV5+PjFGvwjH/kIPM/D\n97//fXzmM5/B1772Nfz8z/88TjrpJFx00UW4+OKLcfrppyda3wyHQ5x77rkYjUZwHAdXXHEF3ve+\n92Hfvn246qqr8PDDD2Pnzp248847sbi4CAC4+eabcdttt6HZbOLWW2/FRRddlNsclIG6q0KiLdMa\ntAlRaxiGdqW8OmfIgY3SpLW1NTQaDXQ6HWUqD+LQRZDLreB830ez2cTCwoLScwuoKRLlygKRDZ+f\nn0e/3w8i+ERhfvZFdH/072D6T0b/3Ae8xiLWj7sZ3lGvDhb63tpa+X3PU1KXBX3Uwj1c9iz2pYrt\nCnJWVbc5qst5FcQd77TmcLrNWd3Oc5hxx2+aJk466ST8+Mc/xlFHHYW3vvWtuO+++3DPPffgTW96\nEx5++GGcd955uOiii3Dttdei0+lEfk673caXvvQl9Ho9uK6Ls88+G5dccgk+85nP4IILLsA73/lO\nfOADH8DNN9+MW265Bd///vdx55134oEHHsCePXtwwQUX4MEHH6zUeaIgJ5mR1qDNMAz0+304jqNk\nmfQ4dMuQy+LGdV00m01ls+FRqCzI4/aGr62tlWI4mAaV5lc2bgxvVXEcp+zhkXF4HqwfvhHWU38D\nA/HPR6d7KoYn/jXQPRoGgAYwdu953faw6o5pmomy5zoEXFR5LhZJEmGaxBxOtyqJugtyz/Mm3o/L\ny8s49thj0el0cP755+P888/HBz7wATz66KO49957sXv3bvzGb/zG2M/o9XoAEKyZDMPAXXfdhd27\ndwMArr32WrzkJS/BLbfcgs997nN41atehWaziZ07d+KEE07A17/+dZxxxhnZHLQCUJCTmZAN2uS9\n4JMM2sLC0DRNrYShYJKLtgqIF2RY3AhRo9OcqyQYBZP23as4ZlUR2yeEqUxR/e1VQ9trZv930Pne\nq9FwHzn0b6FT56OF1SPfDO/Z7x1b2hjeey4W+bn1PSdTM817Lyp7rmPARdVxqUIVzrOo2KwjSd87\ny8vLOOuss7b8+/bt2/Ha174Wr33tayd+hud5OO200/DjH/8Yv/Vbv4XTTz8djz32WNBKbfv27Xj8\n8ccBAEtLS5u+b8eOHVhaWko0Vl2gICepiDJoSyLCowzaROsynYShQByzioI8qg2ULG7Ei1In5H3v\nZc53VDZ8YWEhUmDoJK7KqvgYlw2PQqc5rQONh96L9t4/hQE79nfc5nEYnPgJYNvzMez3Mc1mA3mR\nDzB7XgUmtcpTzTSs7HdOGWRxzLqdZwC1buclbzEdx/LyMo455piZvss0TXzrW9/CysoKXv7yl+N7\n3/velu+t0z1HQU4Sk8SgLQrZoA3AFoM2WdTriOjnrcLLJBz0GNcGSjjj6kTZD+c0LvS6iceixqqT\nmWBaJrUWLPt6non+Mtr//RVoDr4f+yu+acI+/ArY/+ajgJndeU2aPS+rRFb7c5uQrI4zrlWeOKci\n4FKmaVhdzqmM7/uZrmvGnWeVzOHqeK4FSasDHnvsMTzrWc/K5Du3bduGl7zkJbj77rtx9NFHB1ny\nRx99FEcddRSAjYz4I48cqrzas2cPduzYkcn3qwIFORnLtAZt8t8lNWjTURjKqCC40rjSqzDuNBRd\nkTCrcNRpnouYU7G3UGTD05gJ6jSnlWPvp9F96N/DxFrsr3jm4ej/m48Az7wk8udZ3r/MnlePuKzq\naDRSNqtaRfJ+z0a2RDwozsV5LsMcrs6CPEkQRqzvhT9EGn72s5/BsiwsLi6i3+/j7/7u7/A7v/M7\nuOyyy3D77bfjXe96Fz7+8Y/j8ssvBwBcdtlluPrqq3HDDTdgaWkJDz30EF70ohel/n4VoSAnkaQ1\naEuTQRQZZl0pa/zTBD2i0FXUFDXuqGtZF3O2tOQ1t3XIhlca30fzBzeg9djtMA0v2BcevlLshXMw\nOulOoDlX+BAFqmfPq0QRwkWVllt1FGlFHrN8nlutVnDvOo4TJGyKMoer47kWJDl2eZtqWvbu3Ytr\nr70WnufB8zxcddVV+NVf/VWceeaZuPLKK3HbbbfhuOOOw5133gkA2LVrF6688krs2rULlmXhwx/+\ncOXOEQU5CZjFoK0uGcQoih5/mqBHFLrOu9jnnIeYy0M46jrPWRC+VnVorUckhk+jff+laPa/jeCM\nGdhQ4gf/wUcX/V94P/xjritnjGNIkj3Pow1XnRf0eRPXciucVRWijec0PWUec9S9G26hl1flSx3P\ntSDJsT/55JN45jOfOdP3nHzyyfjmN7+55d8PP/xw3HvvvZF/c+ONN+LGG2+c6XtVhoKcZGbQlnax\nzQz5ZPIQirrOex4CVy6jzlo46iTIsxhrEdlwneZ0HMou/J76GrrfvQqmvw9bRucDvgk41k4MT/7P\nwNyzyxhhKpg9z5ayr99xWdV+vw8Am8rbeU6no+zzKxPXQi9rc7gqvFdmwfM8WNZ4y83l5eXK7d9W\nAQrympKXQVsaVHHOTkueztSy+3ReGUbd5j0rMVZUGXVZzuVpSTu3zIZvJe5albszqIT5kw+i8z9u\nhhnjlu4DsBcvxujkTwGNaXzS1aOs7DnJD/mcytlz27YxGAxm2pOs23tyVlR7NsnE3btZbGNI6jJe\nVZJc50tLSzM7rJOtUJDXiKwM2izLwtzcXGZZBBEIyKsMOW+yNqWTe7R7nperUCzaIC0LZhUy4RZb\neQtHFYVXHNPOgXg2DAaDUvaG63btKolro3X/VbAO3Ls1G34QDw0Mfv7d8J79jkKHViRx2fPBYACg\nuP2rJBui9p5HndOk2fO6Pmt0OOYszeHqep4FSY6fGfJ8oCCvAUUatKVFJ9ESJqsyXzHfSXsxZ4GO\n855mzCIbPhgM4Ps+Wq0WTcUiSDq3ZRve6b5gEotFsQ+ylONZ+yk63/w1NOxHNoS4GEKwNxzwjMPR\nf/5fA4edUfz4SkTOwLXb7amz53VZ1GfdFitP5HMqytuFYdhgMGBFRAhdr+Gk5nBxgRhdjzsLklaq\nLi8v43nPe15Bo6oPFOQVpSyDtrToup8ZmG3snucF8+37PtrtNhYXFwtb5OhYmWCaZmJBHs6GFxHk\nCKNj0COKcDacQY3pCM9fo9EItgwV2sZp+TPo/eBNMDEIDRCAsfFfTvf5GJ7yOaD9jHzHognMnlcL\nsQ6K2pMcd051Cj5kQVWEaXgbQ1QgRjaHq8pxpyFpuf7y8jJL1nOAgrxipDVoC2dni97/qbMgn1Zw\nhQ3xLMtCr9crZTGno1ictCdbLvkvI8gRRqc5jhpr2dnwOHTZbuF5HtbX17fMn23bW7YRye7BQLaL\n4uZ/vx7txz8WW5buAxgddQ3sXX8MKD6nZZIkey7OmQ7X5yxU5fiSnFMhyKtyzJOo4nGOC8SILYLi\n/hX/u04kPed79+7FscceW8CI6gUFeQWY1aBtNBqVLlx0Ei1hkprSyYZ4hmFkYog3KzrOe9SYyyr5\nT4JOcyxfy+Ee9wsLC4GJDhmPmD/XdbG2toZ2ux07f1H7Hx3HAQCsra3N5vw9WkX76y9Ds/+dDY0t\n/iPhoYP1X/oj4FmvSnewNScqey72rq6vrzN7riFR51QE0MX7Rd6TXEWqKMjDRJnDiUD++vp6EBzN\nu8e9KiQ55yLh0e12CxpVfeDqSlPkzIpt21hbW8PCwkIqg7aysrMypmkGpfW6Mc4cLUrYzM/PK/Nw\n10ksCuQxl13ynwSd5lgE9Pbv3w/TNHP1jZgVFec1HHQzTROdTgftdjvR38v7H23bRrfbDQT6VM7f\nT38T3f/28ui2ZdjIhruNn8Pg1M8DC7+Y+njTUtXFvljge54H3/dhWValndureh5lxDkdjUZot9sw\nDCOyoqVqoq0O5zaMuCeFSC+qx70qeJ6X+JiqduwqQEGuGXEGbSKjEneTyGWnKi60s3YqLxpRci+E\nYJGGeLOg61YBz/Owurpaesl/UlQTjjLhoBEAzM3NTexFSjaI2oIigm6rq6szXZNisT9u77JsTmT+\n+CPo/OQ/wIQTMVDA9wH7sLMxOvUuoNFKPS6SDO49rw5CoMZVtMiirTA/iBxR+Z2VJ/I+6rr1uE/i\nk7B//34sLCwUNKJ6QUGuCeKhH2fQJva8yAZLYYM2lctOJ+0LVh0xfiHCy2gBlQad5l1kwweDATzP\nQ6vVKr3kPwmqvqjD2dxOp4P5+Xns27dPyWeEaoSrMzqdTq7XY9Q+V8dxYA+HsL79BvRWvrCRDRdf\nL112HgwMj30b3F/6vVzGRiYTd/50zp7XMYsqI4s2YHM/bPFcTdsPWwV0G28WxGWJk5jD6Xb/hkki\nyNnyLD+46tIE8ZCPK5ORM7RlG7SlYRrnbNUQe/dXV1e1mW+BimW/MlHZx263i/X1dW32MCX1GCiC\nuC0UsgDXxSytrGtXdu4vszrDHO7DwlcvQHP041ijNhdzWD/5dhjbLy50bHUnyf1jmuZULt+kXJKe\n0yT9sHUoea6bq7wgyXEnMYfT0WcgacszOqznAwW5JogHfRyGYWA4HGJ9fV3ZvbTjEAEFHYQAsNnJ\nWwRCRIZMJ1QV5HL2FsCm7KMuGX2BCtdzlKHguC0UKl4TZRLuYz/N8zXzuXxsN3rfejVMrEZ/nwe4\n7WOx8oK/hdM6ckMI9PubWvsQtdA1e67L+3pW0tzDSUqeVc6e1+XcyqR9VkeZw+noM5DEWX5paYmC\nPCcoyDUh6gYW2a7RaATHcdBoNJTfSxvHOGM0VRjn5C1esLqhksCNyobPzc1tuZ5VDSKMo4xrW8zn\nYDCIzYZHoer9F0Xe14HIWKZ17s9yLhs/vBmdH98CA/6hcnTp430A9hEXYnTa3wCmiRaAFqCdECDM\nnqvKLHMdLnkWok2UPMuCTYWgi8prsbxI2od7ErpWSiTNkJ977rkFjaheUJBriCwKRbZLRLV0NmJS\nVWglcfLW1SVehTlP2w5OpwVDkfM8bTY8jArXRBLyOvdh741SvSBcB61/fgWs/bs3l6X7CMS4BwPD\nZ78X7nPeEfkRcUKgaiZUKpFlua+q2XMdnhFZkfW7Jrz3XA6aCXPbsg3DdHq/ZkUex6yLOVzS+5l7\nyPODglwjZMOwcLZLZBV1RmRrVTBBi8rWjqs+0MkcTaYs8TXOmXrSC0mHaoqimWU+yQZRnRFarVY5\n87e2B0d85TxY7qOHTNpCeMY81k/7G+DIsxN/bHhxGFdaKUrbee2oR5LseZGL+zpcI3m/a1Q0DKvj\n+7WIY1bxXAPJqwOWl5dx7LHHFjKmukFBrgmGYcC27dhFokqlx2lR4RjSZmt1NaUr2nAs7fyG0SWL\nK8hrvFnNp4wuc5vFOKNM7krtjLD8BfT+5RqY2PBOgIlN2XAfgNs+HoMX/z3QPXzmr4tr4RQ2JmJp\ntJrEOu8XsLivo2ArgnGGYUVtWdDh+Z8HRV/TKpnDJdk/DgCrq6tYXFzMbRx1hoJcEwzDwMLCQuyD\nUgUxOytlZZnjnKenyTDoImLCFJFtzmJ+w+g231mON+le+1m/o8rEtXwrejEm5tn6b29Ca+8nt7ql\nHxTjPgD7yH+L0Qs/CeQ0xnAZrcie51EaTTGXD6plz6tCmddrVNAlfF/mVdVSt2uk7OdSmeZwSY69\n7PmpOhTkGjFuUa+bS3kURWeZo0pU0y7KdQ6IFJm9zUr01FGQy33YAeTW91qn58c0cxoOZCQ1uZuF\nsed9bQ+6uy9CY/gIDBOI6l3m+Q0MnvN+eM/5rdzGGEc4e05xlxwV3sN5Z89VOMaiUOlY4+7LLDOq\nKh1vkah23HEVTHmYwyU59n6/j06no9QcVQkK8opQhX21pmnmvg8+L8Omoku/syTr7O2kPtdZUBdB\nXkQ2PIwuc5v0+OVARlZl/TPxg4/A+s7/jhacjf8vDkMqTXeNBTz5/L9E95hzlHielFkaTbKB2fPq\nEZdRnTV7ruM6JgtU7r0eZQToOA5c183EHC5JyToN3fKFglwjJt1gIkur6gNlEnlmmcULajQaodFo\noNPpTNW+aBI6B0SyEGBytYHoyZ5nCbAuojEtUc7+pYpIzRCCYzAYFBbIGIszgvlfL0bj6a9Gl6UD\n8E3AmTsJw7PvAdrz8FZXlX2eUNzpTRYBFlWvzTzQ5VgneUIk7aigy/FmjU7HbRgGLMva0lotbZA0\nSTBiaWmJgjxHKMg1Iqkg15WsRZbv+4FI9Dwv9/ZFuorEtNdNVDZ8YWEh1xJggW5znWS8USJynLN/\nXugyt1HjlO95JQIZT/wz5r5+FRregY3/H9k/3MDw+N+Gc9J7Nv2pTgtDIe5EWx9Z3MlllXUJKOm0\nsAcYYJmEbucTiPeESLIfWcfjzQJdjzsqex5nBBgXjEly7EtLSzjmmGOyPwACgIJcK6ouyLMYv3gQ\niWx4s9lEt9vNNBseh0pt26ZhWgGW5d77tOgiGgXjxquciIR+pm5yBUyz2SwlkLGJf/73sP71z/CM\nMb/iNo/A+ov/C4zDTypsWHkT5xos99wVATtdF79VZ1KARWTeAP2eE3UmKnvuum7kfuS63ptVOe6k\nRoByMCbJse/duxcveMELCjqK+kFBXiF0F+Sz7MOOKvFdXFwsVNToJhIFSbO3YkHmum7p7aF0m+uo\n8SonIg9S9vcnRVyTo9Eo8IMo+p7fxNrP0PjiuWj0f7KRARf/kfB9wNl+MfxzPgvb8wCNn9dJkBeG\nsggAgPX19cQltKQc4gIsojQWAAaDQeWz5yrvLU6DnFGVgy5iP7I4XsdxKn1eZXT1AEpCEnM4cfzj\nWF5exmWXXVbQqOsHBblGJMmQ522Klifi5Z80yxxleFWmqNE1IDJO3EZlw1utVukvLXGd6IIYb1Q2\nvFQRGYHqwQ65AsY0TfR6vUIqYGL50SfQ/Ma/gylM2mQOGrV5aME5/SPA8Vcf+plG128WyCJgNBqh\n1+tFltDm0b6JZIMcYDEMA67rBusOeXtC1cz9qirUBOH9yKLMOQ83b9Wp8rEB0eXttm1vKm+Xg6Ty\n2mR5eRnHHntsKeOuAxTkGlH1knUgWeuzqHZaZZf4AuoLmTjCgZy8nOizRLe59jwPtm3j6aefLnQb\nRVUIV2iIwJDI8BSO58G451I0n/h7BKdQPpX+hhZ3uzux9it/h+7hXMSEiTIlkg2osmjfVBZVF3AC\n7j2vHkJwNxqNTa3V5G0nVTyvnudV5limQZxr0zTR7XY3mcNdf/31ePjhh3H++efjwgsvxFNPPYXD\nDz+87CFXFr3ecmTsA6MqgjzqGIRIPHDgAPbv3w/P8zA/P49t27ah0+kosWDTdf6FuHVdF+vr63j6\n6acxHA7Rbrdx2GGHodfrKSXGAT0EuciG79+/PwgeLS4uYmFhQYkqgzhUmlvP84JrcjAYbLkmCx/n\nk99B81Pb0fpkD9YTfx/VOhw+gNHx/xvs1wywevE3ge4zix2jhohFoQiuivPrOA7W19exvr4eBAhV\nuTbJZkT2XJzDbrcbBHvX1tbQ7/eD7SW6ncO6BFgE8vGK89rpdCLP6/r6urbnVaZu51hGHLt4Drda\nLXS7XXzoQx/C2972Njz22GN4wxvegO9+97t4/etfj0984hN4/PHHp/qOPXv24LzzzsNzn/tcnHzy\nyfjjP/5jAMDv/d7v4ZhjjsGpp56KU089FXfffXfwNzfffDNOOOEEnHjiibjnnnsyPWYVYYZcM8Yt\nlmU4ibcAACAASURBVIUg1PnBEha1KhiIJUUlIZMU2bBnZWVFyWx4FCrPdXhveLfbDQJKKgSOVKdM\n9/5YvvEfYH3vDzey4eG94QcvQ89cgPPSu4Cfe3EJA9SHJPdteM9jnGOwCp4LdWXcOmOcuZ+oxtIp\ny6rzmioNccc7zlNA93uzaj4B0xDXLnl+fh6XXHIJLrnkEoxGI/zar/0afvmXfxmf/exn8Za3vAXH\nH388Lr74Ylx88cU466yzxlarNZtNfOhDH8Ipp5yC1dVVnHbaabjwwgsBAG9/+9vx9re/fdPvP/DA\nA7jzzjvxwAMPYM+ePbjgggvw4IMPanVNTQsFeYUQF6rOLw+x11aIcFVLpqPQKUMui0ax+F1cXNTm\nulFNkE8q87dtW6nxjqOsuY3aijIp+JbrOIf70fjcOWis/SjiiwEYB8vSDz8D3kX3AFa7+DFqTNJn\nTRLH4Gn67RaBzu/gPAib+8W1xlPpHNaZpNdv1L3pOI7S92Ycdb5nkzqs79y5E2984xvxxje+EbZt\n42tf+xq++MUv4rd/+7fx4IMPYvfu3Xj+858f+ffbt2/H9u3bAWwI/RNPPBFLS0vB94e566678KpX\nvQrNZhM7d+7ECSecgK9//es444wzZjxadaEg14yk+8h1jPSJB7njOEGJlE77bFUTiWHiRKNhGNi/\nf7828wyoM9ciMyCy4XHXrCrjTUKRY40yZpyfn0+UNcvtev3xZ2F9+fUwjFFsNtw3TdgvuAk46W0T\nx6jLedeFJNlzXTKvOpM2o6hj9rxuYi3t8cZ5CujgC1G3cyyT5F4O9yC3LAvnnHMOzjnnHLz//e/H\nE088gcXFxUTf99Of/hT3338/zjjjDPzjP/4j/uRP/gR33HEHXvjCF+IP//APsbi4iKWlJZx11lnB\n3+zYsSMQ8FWFglwzqmbsJrtOiwd2s9nEtm3byh7a1Kg69+ES6rBoTNLuQjXKFDppTe90m+M8Cbcp\nFPsTS1ukeR6ML74SzeUvHNLfEY9ar/1zcH7ty8C24wocHIljUs/sOrlD60rS7HlZ57Buz+2sjlc+\nrwBie2Gr0lWh7iXrk7aELS8vY8eOHbE/P/LIIxN91+rqKq644grceuutmJ+fx5vf/Ga8973vhWEY\nePe73413vOMd+PM///Opxl8VKMg1I4kgV/0FIiKn4X22lmUFJj46IkSiCpFWIRoHg0HQXmuSaFRh\n3EkpY67DfgbTVHDoMq9AvsEOOTg0a5vCTMb51ANo3nUeTHffVgEulaU7x/6v8M+/Y7bvCqHTNaED\n4zKvwh1aztDlNf+qv3+zIo9nb5JzWFb2vC73q2zwlSVxvbBFMkY+r2UIY53WP1mTJBixvLyM5zzn\nOTN9j+M4uOKKK/C6170Ol19+OYDNQv66667DpZdeCmAjI/7II48EP9uzZ8/YgEAVoCCvGKpmaYGt\nWbGoHsw6BBTiUGEPf5Sh2CTRKF6+Or2QihpnVi3gdCtdznKsUcGh0nuv//PvwvrOLZuz4QcFuMBD\nB855fwkcd0nx4yMzE868igyd3Fs5TwGgy7NUZVTInuv0XtQF4eYt3qPi3hRBb5E9zzt4JlPX85w0\nsbG8vIzzzz9/pu96wxvegF27duH6668P/u3RRx8N9pZ/9rOfxUknnQQAuOyyy3D11VfjhhtuwNLS\nEh566CG86EUvmun7VYeCXDOSZMhd1y1oNJOJ2iM6Liumu1N8GQEFuew/reDR0XsgzyDCLNnwKHQS\n5FnNZ3h/fem914eraHzml9FY/UFsNhwA3MOeC/fSrwCtXiZfm/a863TNqI4sAOTS9rAAUKV8VgeK\nfkeXlT3XdS2SljKONyp7XmTwDKjfeQ6TxNTt2GOPTf359913Hz75yU/i5JNPxgte8AIYhoGbbroJ\nn/rUp3D//ffDNE3s3LkTH/3oRwEAu3btwpVXXoldu3bBsix8+MMfrvz5oSDXjCSCXBijlEmUY3KS\nPaIqZJlnQbjE5+0IP67sv7Ty34LJesyi3dZgMMjN3V+H63qWec2qoiAJicf50F2w7n0tDBx8LprY\nkg33AdinvBt44bszHyNRD8MwYFmWsuWzZDJxFRCq7D3XlbLfUeOCZ6PRCED2W09U2WpYBkkTMY8/\n/jiOPvro1N9z9tlnRyYLX/ayl8X+zY033ogbb7wx9XfqBgW5hoxbiJZZsh7VPzipY7KMzmXreY89\nSdl/GuosyKN63bdarUxfzjpuC5iGrCsKZsb3Yfz/r0Bz6QtR3mwBnvUMOJf9A3D4CYUNjahFXPls\nWvOpqt7jYVQ6zvA5zDJ7rtJxFoFqxxsVPAtnz7MKvKh03EWR5HyLgIXqrYd1h4JcQ1QT5FGCZlL/\n4HGIY9Dx5hcZ8iyZtuw/DboK8rRzHc6Gt1otLXrdF0HSayEqAFfkHEaOc+1xNP/qdJjDxw5lwEO3\niO8Bzs9fAv+SzwIlLMBE9dBgMAiEBEul1WGS+ZQQdVk+f0m2ZJk9V02g5o3KxxuVPc8i8KLyMedN\nkmN3XZdVJgVAQV4xinKfzrM0VWVjuklkmSGPyobn1RpKxzlPE0QoIhseh05Bj3HjDG9H6XQ6MwXg\nMuFfvwjrC6+EASf2V3y0YJ9/B3DC5QUO7OB3h4JqrVYLvV4PjuME8ykyEGIxWVV0WvwmyZ7Lpe26\nHFcW6HIek2TPi3Df1wVdziuQPPAy6f7U6ZizJsmxz1quTpJBQa4hkxyz88wwyy7eeZWm6iRcwsya\nIS8iGx6FjnM+SyZ3YWFhYt/NrNFljqOuM3FdDgaDTdtRip7DMJ1/fh+sH3wEhohRyUM/ONXuthPg\nXvE1oD1X9PCCxf/Kygp8f3O/dcdx4Ps+LMsCgCATKxaTAGDbdpCZqOuCUSXC2XPhDC3Ol1j463Cf\n15Vps+d1E2tJWmCpSFzgJXx/RlW31O0cyyTRCnv37q18yzEVoCDXkCTGblkKctnF2/O8XI2aAD2z\ntYK0Lvdy1hHApoV7EeRRap83kwSu53mBy7dpmjNvpZgVXQS5QFTahM0ZS8+Gey7MO8/F3BP/Evsr\nPgD7uW8BfuWDxY1LQgTVRBZ1bm5uYlDNNM1NDtLr6+vwfZ9ZPEWRhV273Q4CKo6zUaXR7/crayqm\n03NsHEmy5yLJUWfRpiPj7s9wdYvnebU9t0mu66WlJRxzzDEFjai+UJBXkCwEbdYu3tOgilN8GqYR\nXVHZ8CQL9zzQTSwC0WOOMxYsO5ML6DfHa2trpV+XmziwhOYdp8N0ntratswDYAC+0YR90V8Bv/hv\nCx9eeBtPp9NBt9uF67pBFjwpQsC1Wq3geV50GyAyHSKg0mg0MBgMYFlW5cuiq3Icgqjsubif19bW\nauHcXtXAQzjgKWfPxTHbtl27Z2pSQT5LyzOSjPJXqWRqkmbI05CXi/c06JitFSSZ+7Qt4fJEx6oE\nuRpByX3NmiFnw4GN+S363o/kh3fB+sJrYOBg5UlE2zKvfQScV30D2Paswoc3zmFeLOZnYVwbIPbQ\nVpO4suiqBFSqKtpkxH0n31912Htel3MrZ8/7/T4Mw0jdWUFnklQHLC8v48wzzyxoRPWFglxDkgjy\naRaBZe1bjkPnPXhxWdDwHlzLslK1hMsL3bK3Atd1ceDAAeWy4VGoOseyL0Sz2USv18OBAwfQ6XTK\nFQtffAusB/58bNuy4TNOg/GafwDMYk3QynSYj2oDJM6hMIajC3h5hAXNuIBKXn2VSXaI85kkyCKL\nOF2pgyCPInxu5c4KugfQ4hDrkSSCnCXr+aPmypXMRNKSbxUztYCe2VqBGLt4qUXN8dzcXOlzHEZV\nsRiF3DbK9330ej0tsuEqzXFUebWcDS8tKOa5aPzFmWg89d2xbcvsU2+Ae/b7ceDAARxWoBhXrRIj\nqQt4lUtsdSMqoFKl7HkdSNJ+S9cgSx0FuXzMcc/UcEWSjuc2jBxsGsdjjz1GU7cCoCDXEHEDpelF\nHrfHVqUHS1Gt2/JAjFf1OQ6jkliMIsrlu9vtYjQaodPplD28RKgwx3J5dbPZjO2SUPhYn/oxmnec\nAdNd3fqzg+XpPizYl30aePbFG2M8GPjKG7HYHgwGQQVR0vu56HmMcgGvgkioKmFhF7f4V610Vsd3\nc1qSHGtVsudlv5/KYtw5Dj9TRfa8CgG0JNe2WHuJ/fckPyjINWVaQR7Vf1nVrKIccFBxfHGI7Bmw\n4bCr8hyHUTUIElVhIOZUZAF1oSxBLoJwg8EAruvm3iVhKr79F7Du/c2NJHj4sjs4VV73mXCu+RYw\nd2ShQxNVBKISI+sKoiT32SzXS9X3MVeRuMW/KJ0V54vbEYpj2neiztnzpBnTqpH0HEe58uu8/cTz\nvInP/qRl7WR2KMgriFj4e54XZGqVW4hPQAQVVF8oRlUcyG2OdEGlIEiUp0FURlKFjPO0FDneqCBc\nq9VKvPDIc6zGXdeg+aM7DwlxA1uM2pztZ8F79X8Fxow3jzGKbHjR3SXCZPl9NIYrjqyeoUm3I4iA\nSpHnTIX3hC7olD3X7X2aBbMGPSdtP1HZlT/JffzUU0/h8MMPL2hE9UYfxUA2Me4mEoZu+/fv3+L6\nqwuq7yOXxY5pmpv2kh44cEDpscdRtsCd1tOg7PFOSxH3X1SAaGFhIVVwKPO5tYdo/Kfno3Hgp/Hf\nCcB+4TuBc//PiR+X5XwqXUWQA+OM4fLOxFLMpSNqO4IIHgHYVPHA+c2OLK9XHbLndbt2sqoKSHJu\nVbtHk7Y84/7xYqAg15TwTSS3LBJtDHq9HtrtdkkjnA1hiKYSScWObkJRUMa4Z+nFrts85znecaX9\nach0sbD8bVifOgeGP4r+uQ/4ZhP2Kz4P7PyV7L43AfK8maY5VRVBVaAxnH7IWdd2ux0EVERQKe/M\nXF2CKnm/X1TLntflvMrkdcxx5zZ8j5ZR4SLwfX/idbW0tESH9YKgINcYEYGTWxaJ8srV1QhzJI1Q\nqfXZtPvvVc/ux1HkuEW/e5Hd6XQ6qffn6rKIyFqQJy3tT0MmY/3qf4S1+z0wxCmNGJLXOxrOr98P\n9J6RaozA9Oc/PG95t8tT5TmWFBrDzUYZzyPTNAPTJR0yc7pRxJyN21ZS1L2ny7s0S4o45qi95+Ie\nFR2RyrhHPc+DZVljf4ctz4qDglxTXNfFyspKYDYktywC1BK0aZi2l3rWhEtYp+kzrFvmVpD3uGfJ\nhkeh0r73JGQ1vyKYIfpOzxLMyAPzjgvRXPrK1h9Ie8SdY18K76q/Hbs/PGvkIJCoIpibm8v12tHh\nuhwHjeH0o4isqy7P3Fkp8zin2Z+c1b1Xl/MqU8Yxh+9RsTYSa86i/CGSHPvevXvxvOc9L5fvJ5uh\nINeUZrOJXq8XK2Z0zdIKkvZSz5pZjLAEZY19VvIS5GEBmaVbtW7Bj1nGKoIZo9EIlmWNvf9nZep5\nXX0CzT87Bebwya0/8wCYgG8A9rk3AWe8PbNxJiE8b7MEgeoMjeH0Q4c9yyqjikAtKnuuyvEWSdnH\nLBIL4QqXsD9E1p4eSbvqMENeHBTkmiKip3HoKgoFRQot0d4oKzd63USiIMtxR/VuzkNA6jTXaY47\nqvVWuBqmVH70eVifvQIG4s+Bb3ZgX7Mb2P78zL8+rkJCvqc9z1Nv3ipAmcZwJB1ZVTyULWLqTpLs\neZrKlSR7iquGatdylD9E2NMji8Bn0nZmFOTFQUGuKXK5bhRVyJDnPX65vVGWbvQqGtIlIYttArK5\nYB69m8PoJsiTjrXs1luTxmr8l+vQ/N4dUdvCA9zFZ8O97ltAszhjyXCFi44dJnQkqTFco9HQ5n5N\ni2oL/DhY8TAZHc7luPM4bW9sHY43a1QPQsR1VwgHPvMIvvi+j8FggF6vN+thkARQkGtMHQR51i+I\nrLPhUei6f38WcSuXBU/aTpElVRLkRVybSYkcqzNE489OQWP/v479W/vEq+Bf/vEcR3cIEfwS15/j\nOMq1LNPl+sySSS26hGhgmbQ6TFPxoLqIyQodBWpe2fOqotM5lrPnwKHAZziIlmXwhZ01ioOCvKLk\nJWiLIusxhwVjnpkzXTPk04rbqGx40WXBVRDkymd1l/8F1h3nwfCHsb/iw4B96ceB515Z2LDE8+3A\ngQMwTROdTmemVm95oNJYykJeRLZaLTiOg9FoRIGgMJMqHoBDGba6Zs91YNrsua7rxVkQbYJ1JBz4\nFEG0JM/WJOd6//79WFhYyPMQiAQFucaMu5l0c6COQgQV0ma6yso4pm3HVDZJxW04uFF0ObWMToJc\nIMYb7mmvUlbXMAxY97wD1vdu2zBCjzm1XmsRzm98E1jcUci4wt4Evu9jbm4O7XZxZfEkPYZhBAKu\n1+tVsky6itnj8MJfGInJplNVbKum2zt8EpOy58DG+13cp3WgKvdrXBBNPFtFYFT8jud5E4977969\n2LGjmHc7oSDXmkkvCiFodX3YpBXkRWbDoxDBkFmCCWUwbpuDquZiOglycf31+32MRiMYhqFeVnf1\nCTT/v1OxbfDExv8Xw5JalgGAs/1F8K75MlDQ+Y/zJjhw4IBW9xjZLHJoDKcf4v1mWRaazWZwzopu\n2VQEVRPkMlHZ87W1tU0Bl6o78OuydkhDVPZcDr6IoIv431Hnd2lpiYK8QCjIK0xV9pEnQYXyaRkd\n95FHiVuVsuFR6CDIRRZQZJM8z8P8/HywD0wJ/uk/wvrye7YmwiUh7gOwz/5d4JzfKWxYYXO7orwJ\nVEH1aztLpjGG013o6Y4sVE3TnNiyqYrZ86oiqo3qsPdcXMdVvy6jgi8i6CIHX0Rnpvn5eQAbgpwO\n68Wh0IqQTEvSDLmuJBFbqgpGHfeRi/n2PC8opy7bXGwSKs+z53lBkMgwDLTbbTiOg263q8Zc2n00\n/vw0NJ7+CcZ0LYNnduFc+w/A9pMLGZbv+0G2bdL1p0NAJo5Jz6iyn2FlM8kYjkJPPcJ+ASIYKe5n\nuZ+yDqKuKuXMSQiL03F7z2WBp/P9V+UKiHGIY261WoFOcF0XX/3qV3HNNdfg1FNPxQUXXIADBw7g\npS99acmjrQ8U5BpTdUEeN37VsuFR6JghF3O9f//+Ukr906CaIBMLl+FwGPRfn5ubC7K64potle98\nAtbfXje2dzgADJ91Doxr7wEKOv+e52EwGATmdu12G61WS+nrbxKqXZ+6Eif0VMze1WGRn+QYhbgL\nZ88dx9GmJLoO51Iw7lir6txep/MbRmxnlYMrF154IX74wx9i9+7duOeee/CFL3wBd9xxBy699FJc\ncsklOP/887G4uJjo8/fs2YNrrrkGjz32GEzTxHXXXYe3vvWt2LdvH6666io8/PDD2LlzJ+68887g\nM2+++WbcdtttaDabuPXWW3HRRRflOQXKYUxYLHAloTBiX28cQhSI8hPdkMcfNnMSglHV8tW1tbXA\nMVtlwsZ3vu9jYWEBlmWVPbREiLGX7QQaFSRqt9tbFicrKytBFUeheC4at70YjZ99e+vPfARPeh9N\n2K/4a9jHX4R+v49t27blOiy5nF+Y27Xb7cTl/KXNZ0Jc18WBAwdw2GGHbfmZ53kYjUaxC1iRUVT1\n2NIiMqbdbjeTz5Ozd47jlG4M1+/3g/3VVWVtbQ3dbje1+JJFneM4yoq6OpxLgQhwTdtzOu7+UznQ\nIrBtG67rKr9OyxrhFzA3Nzf2/Lzyla/E7//+7+Of/umfcPfdd+O+++7DKaecgpe97GV42ctehlNO\nOSX2Xn300Ufx6KOP4pRTTsHq6ipOO+003HXXXfjYxz6GI444Au985zvxgQ98APv27cMtt9yC73//\n+7j66qvxjW98A3v27MEFF1yABx98UOnrZwYiD6r6T5kKIzupR6F7htwwjECEh82cVHlhx6H63Me1\n2lpZWdHqAVh2BjJN//VCx/vQ3bA+/UoYcON/xwDcI3bBfcPXgOZGNgsHHczzQojRwWAQlPOnMbcr\n+/yT8qExnH6Ma8elktt+3TKoaY417v7TIXtet/MrSLp3fm1tDS984Qtx+umn4/rrr0e/38fu3btx\n99134zWveQ2uu+46vOMd74j82+3bt2P79u0ANvakn3jiidizZw/uuusu7N69GwBw7bXX4iUveQlu\nueUWfO5zn8OrXvUqNJtN7Ny5EyeccAK+/vWv44wzzsj24BWGglxzqijI5bJfEXXVzcxJxb3NSdrA\n6SZwyhivPI+e5021ZaKQ69f3YX7iIjSXvjL+1wDYF/4RcNpvbvlZXuOUAxjhcn5CZkUFY7g6LPKz\nPsakQZWiRV0dzqUgi2MN339xgRZVsud1Or8ySXqv+76/ZW3V7XaD7DiwkdhJwk9/+lPcf//9OPPM\nM/HYY4/h6KOPBrAh2h9//HEAGwZyZ511VvA3O3bswNLSUuJjqgIU5BVGCHJdHjoiayay4a1WS9uS\ne9M0A8fKsonLhkddExTk8cjzmHaPfa7jfeSfYH3qYhj++OvOmzsGznXfBDrx5ehZjjPPlnm6Xa+k\nWGgMlz15329JgioqZM+rRh7rRNWz53Uy7ZNJctz9fh+dTmfsNZHEnHZ1dRVXXHEFbr311sgqON6/\nh6Ag15xxF7Nc0q7qRR9lgiWy4QCCRbyq44+jbKEwjVO1jG5VFXnPc9p5jCOP8ZqfvhzNf/0ixlWl\n+wDsM38HeMnvZvrd4wgHMFTpgFAkZT8HyCF0MobTgaLu47igigjcy87tWY9Jx7VHWvI+VhWz53U6\nvzJJjnvv3r0z9yB3HAdXXHEFXve61+Hyyy8HABx99NFBlvzRRx/FUUcdBWAjI/7II48Ef7tnz57a\n9UCnINecpE7rqi0wolpCRe0NVz2gEEdZwjacDZ/WqVo3ASHGm/U1Mus8xpHZ/D76bVifOBeGPxz7\na17nCDhv+Bdg2/apPj7tOEUAYzgcwnEcpVvmkfoiO4CPa+vELKxayEEVIN8tCTq9B7Og6GyxCtlz\nHdeWWZDkuLPoQf6GN7wBu3btwvXXXx/822WXXYbbb78d73rXu/Dxj388EOqXXXYZrr76atxwww1Y\nWlrCQw89hBe96EUzfb9uUJBXHJX2Mk9qCRWFqgGFSRQpbMNZ3FarlVoE6SjIsyIsJlutFhYWFjJ3\n2J1lfo27XgPrR5+N+SEA/2A2/Pm/CVzyR6m/Z1rCAbZOp5PKpG1adLtegc0l/OGFZx0WhyougrM0\nhlPx+LJEpeMrYkuCKseaN2We17htCnlnz1W6lovE87yJ3TtmFeT33XcfPvnJT+Lkk0/GC17wAhiG\ngZtuugnvete7cOWVV+K2227DcccdhzvvvBMAsGvXLlx55ZXYtWsXLMvChz/84dqdGwpyzUmaIS+T\npNnwKFQYfxryytzK5JHFVSmAk5RZqyiKFJOpBOTPfoDmJ18M01kHxpwar70A55qvAYcfP9sgkWyc\n4QBbq9XC/Px8LVoEpSHKg8B13cA7IyzOSTmoYAxHpiduS4IIVodL2+seWAmjUmAzHGjJI3ue9/pM\nZZIc9/LyMk455ZTU33H22WfHmr7de++9kf9+44034sYbb0z9nbrDlZPmqCrIo7KN8/PzU0c3dcyA\nAfnt3887i6uSGV1S0lwjUdUaRYjJaQIexudfD+uHfzXx9+znXAn/8r+YdWiRRF2/4Z7rnU6n1FaE\nKj8fxKLvwIEDW0r4heFmWEAI4S7+XixO67hwVAEaw21GBxEjb0kAEJw3x3HQ7/cBYJNze9Tx6HCc\nWaPi8SbNnqfdXqLiMedNUkF+6aWXFjQiAlCQVx7TNBO3JsiCqGz4LNlGXTPkQLbl9p7nYTAYYDQa\nwTTNmec1Dh0DINOMWe5/DaBwMTlxrE8+iOYnz4TprEX88aH/6Zsd2K/9CnD0ydkPEtGLFCFCpum5\nnjeqLqbC15kI+Ewy4ZTLpsXf1lX4qUiSLGxUuyBSLvJ5ExlX4RdAQ78NdAlAxGXPxfaSpOcySeuv\nKpK0MmDv3r049thjCxoVASjItUfOxEZRRMYzLhueRbax6IBClswqbvOc1ziqKsgdx8FgMEjsXZAX\ncWM17v51WD/4y4l/7xz/q/BeEbOHPGNENl8sdoTLfFYty6pIVJ/1AwcOoNPpRP5+3PVnGEaQ7el2\nu8F5iCq/5bkoh7gsrOu66Pf7lW3PpYtwi0POuEZVpojzVid0Ld+eJXuu4/FmgTjuScf+1FNP4Ygj\njihoVASgIK88eWaYRYQ5q2x4FDruaRaknfuyDLKAagnycGn1NN4FeRKMdd+DaP7lGTDd9fEty4w2\n7CvvBXacXswAgaCcemVlZWLv+jJR4XoVJm0iQyMHLbIam2maseW3dARXA5GFBYBer7dpW8y0xnCk\nOOIM/RzHge/76Pf7tcme635dTpM9r3MP8iT+CQBqOT9lQkFeASZlyLMUtEVnbbNc1BbNNGKhjGx4\nFDpuEQgHbeQspWr9rw3DQPcrb0brJ58e/4sm4BxzHrwr/raYgeHQvvrBYADHcQAAc3NzgRAkmxHb\nSISpYlFBi6jyWwo/NRDPe1HhUEVjuCpnFuWMq2macBwHzWYzs/3KqlLFczopey6yxK7rVupcTiLJ\nubZtm++PEqAgrwCT9iVmUY4U5ehdRNZWR4EoSJLdz3rPfVbo9IIW8yzmUZRWK9X/et+DsO48Ey03\nYm84ILUss2C/8gvAsb9c2NDk/c7yNbiysqJFhLzIgF04aDFLi8EsmOQIztL2cgg/O2kMpx8ig5pV\nOzyV0el9n5bwPTgYDIIqujr5CCTZO793715s3769oBERAQV5BZgkyIWonXbRWFRf5nGIsev4wojb\n/x61qFelXVRe7vB5IcSH67pBKylVsuEAgK+8Da3v/b8bzcHH6EZnx7nwXvlFoMBxq7KvfhaKGmuU\ns/w0gbOi7qco4TeNszTJnyTGcCoHUXR5N8xK+DgnBb9Y9aAPYp3TbDZhWVbmzu0qk6RUf9Ye5CQd\n5SsAkjvTCvKysuFVI5whVzUbHkaFfbnjCAeKGo0GLMvCwsJC2UMLML/2XjS//QfxIrxxMBt+8QGW\nCQAAIABJREFU2eeBY84tbFxiv7PIDozbV6/6dVAEszrLJ9mrl9f9P8lZuirZPZ2JM4YLB1GqKAx0\nh1UPeiM/eyftPZcDmSoGyaYhiSDfu3cvBXkJUJBXgEkP+yRl37IxkUolv7Nk+MtGjFuIR7nftcov\naRFIUG2+5YCG3PptNBqp1TvdXkPzu38QdCnzgaAkHQAGzzwL5pVfKnRIcpBtmn31qgvyPIIGIuAz\nGAxyd5Yv6hkQdpYuM7tX5WzcrMemgz9Alc+fzDSmX/J5a7fbY7siJHG4Lpq6nFOZuGOexbldB5L2\nID/hhBMKGhERUJBXgFkEeVQ2vNVqKfWQ0VGQi325rutifX1dGYfvJKhkpJekvF+5TK64d0wE7uk+\nGrAv/yL8Z52Np/ftwzMKWACFKwmmDbKp9AwogqiAT57PwjLnd1J2j6Xt5VPlEmkdmEWkjuuKAKh3\nf1GQx1O17LnneRPHuby8jPPOO6+gEREBBXkNCO9lVjUbHocurc/kNjfCpRIAtm3bptXLTgWBO015\nvwrj3USzB/v8v0Tzvhvgze2A+7/sBsyNa6GIqyCukmDaa1C5ec2B8D2rkp9DUYT3NIdL2+tidqQ6\nqpRI17VdVFombR1RwTOgboI8rdFxkiCZytnzpMe9vLzMkvUSqM+qo8LIRlxRmKYJ27Y37YdUubdw\nGJUytlHILtUA0Ol00Ov1YBgG9u3bV/LopqcsIRYWR0mNxlQUjv6zXw772S+P/Fkepnl1FZZpz32U\nSZsuFSx5Ei5tF9eVXK7JrGz5TFMiXfdrOi15iNSo+0uF7Hldgyyzzq+u2fNJx/3444/j6KOPLmg0\nRFDt1RrZ5OC6srKifDY8ChVbnyUVjzo5lguKrkgIi6Npy/tVFOT/k713C5EuPe97f+9hHerQp++b\nmW9OGo0tj8GDD1tC2BZSgg1KnMixciFiCwlsgmMwvlMEUURurBvLBuciYASGICICgejC4NjIJhis\nZHtn763JjmVhyZY1kTSa73yYrw9VtQ7vaV+sWq36aqq7q7urq9bqrv/NSP1VV7/rfde71vt/nv/z\nf47DIsd7kcSybfM6D85aS38etPEZUEMI8UTbp7Vx1fFY1Tq3TSLdBixjLefJni9DndLW59NZcdHB\nFmhm9nye666z6Jc9mN9ErGf8kmD68Fw/BOpsOMD29nYrH7p1hr8JqLPh85LHNpKao9q1LRqT9+hZ\nHKxrtG2OFzHeRc1d2zHPXJ63ln6NCvNkZdfS9tXjKGO4RbnrXzXytiwcZby4DDOxq7amy7jeJmbP\n56kfr/uUX6X7oSlYE/JLgvpgWkunvfeHB08pZSul0zVWTbjqjMNkz+Z5CVAbDekucr5ntd06r4P1\nqu+Ps+CsMutJ74c0TS/M/RvaOa+TmPYhOG3v8CagyfM/nZWdlravOhu0xtoY7jxYNUmdl9AtIhDb\n5OfMRWDZa9uU7Pk8172Wq68Oa0J+SVAUBQcHB0fKMNtIDGusSrJ+Xik1tJPUXMSYp/s5X4RUeNUH\nqHlx2jFOd0JYlvdDG+9d4NCVf7LNYBvld22a/2lp+yR5CCE8kT2/zGj6M2gRxnBNv8ZFoGn7bhmB\nlcu+ppNY9T28quz5vIZuzz///ML+5hrzo32nlDVmIoqiY7NldV1wGw9Eyybki5QDN7H+/SQsqoZ8\nVj/ni5AKT5oatuFQMa/Merrd21pm/XbUczlLebE2aVsNZpGHSWl7vV/nkU+ucXFYG8OdjKa+Txbp\nuH9Wx/E2o5ZlNwHLzJ7P88y9devW2mF9RVgT8kuCul7sKLSRGNaYPHRfpJRnOhu+CDlwm7JcNc7r\nar+K3vZtmufjxnqadm8XjWWb+50F3nu89+zu7qKUWopJ22nRpnvzIjAtba9LqiYNx9bS9tVjXmO4\nJpGZi0KbCOq8gZWTsudtud5FoFbtNBFHBVsWUaowj5v+rVu3eMc73nGeS1jjjFgT8kuCeWuZ24iL\nzIBOZ8MXfaBflkHaInEWAjFtnLXsjG6bSM+ssc6SWa8dkWdjWj0QQmBra6uxB6w1vg8hxGHGJ4qi\nC62LXePsOM79GzjsUnBZDfzaRMincVRgpTbGnc6et/laz4q2XPPkPoTzZ8/nue47d+7w0z/90wu7\nhjXmx5qQXxG0mZDD98e/iJf/RRiLHYU2EcUap1EkNMU4q23z3AaZddPmtO5wUMuekySh0+kwGAzW\nZLyFmLcutibnbThAt+WgfxpMu3/X+21tDNd8TAdWJtvg1tnzprxvlom27tPzZs/nrSFfS9ZXgzUh\nvySYzCLPQhsztZNYhHz2orPhs9DGQEg9H0c9vKd7sMdxvHLjrKaRx+MwScSXdR+eBU2Z08l9G0UR\nvV7v8MDR5mfaGk9i1mFz3Uu7mYiiiDiOL21v+rYStuNQnxGns+fGGEIIDIfDK7PHLsP6njZ7DvNd\n9927d3nhhRcufPxrvB1rQn5F0EZiOImz1jVPt4padg/ippCa02LWfM/KUDYlo9v0eZ40uLPWopRa\nm7Qdg3lbvDV93dc4G46TTNfmpJdZMt1U1HutPtRfVmO4y0DYTkK9dvWaxnF8ZfbYZVzfk7Ln85w1\n6nNKHbRZY7lYE/JLhJMy5G0n5KcZ/3SbrWW1ippGW+d9UrY+3YN9MkPZFDSVmE1K+qWUJElyWDvb\ndDK+ijn13pPn+dJbvF0kmnpvtgXTkukm99K+jAf902BeY7jLnoFtG6b3WK2CqwndsvpkLwNX4Vk8\nK3tujME5x3A4PPKZOR1wW2O5WBPyS4TjNlFNDNt6YJhHcr/qbPgsLMMh/qJQG7Q1sb55Gk0iPSdJ\n+uv7c40Ks+ZrY2NjrhKIJq37GsvBIls+rXE6nOY91maVQxvf12fFrGsVQhBF0Yl9spsWmJ8H9fW2\nbdznQR1Q8d6TpukT2fMPf/jDvPTSS/yDf/APeO9738v169dXPdwri2Y9Bdc4F457wJxUY950HDd2\n5xyj0Yjd3V2KoiBNU7a3t+l2uyvPQrZt3q21DIfDw8xGp9Nha2uLTqfTuEPTJJowx3VLp/39/cN6\nvK2trUNFQY0mjHUeXPQ4552vNdY4CjXpqwOG9XOqLEuGwyFZlmGMaaVK6TKhzsDGcUy326Xb7aK1\nxlrLaDRiNBpRliXOuUY8G686IZ9EvXb1HqvPVfVZoWlrdxKu0tpOojZFnn5m/vt//+953/vexx/8\nwR/wMz/zM/zlX/4ln/nMZ3jttddO/dz81V/9VW7cuMGP//iPH/7sM5/5DC+++CLvec97eM973sOf\n/umfHv7bZz/7WV555RV+5Ed+hP/6X//rwq61rRAnbKDm7641DlGTqKOwt7fX2sOuMYYsy9jc3ARm\nZ8OTJFk5AZ+F3d1dNjY2Gjk2mD2XdRQ8TdNVD28u5HmOc45er7f0vz1tOpYkybGZg3quNzY2ljzS\n08E5x8HBAdvb2wv/3ulykrNmWkIIPH78mJ2dncYeso567tb77rj7pFanXCbUHRkuuk5xUnZrrV2K\n7PayrlmNOqvW7XYX9p2TKof6/LJqlcOy7tEmoK71j6Lo1L971No1OXteexx0Op1VD2WpmOee/qM/\n+iP+7M/+jGvXrvGlL32Jhw8f8nM/93P843/8j/m5n/u5E7Pnf/EXf0G/3+eXf/mX+drXvgZUhHxj\nY4N/+S//5ROf/Zu/+Rs+9rGP8dprr3Hz5k0++MEP8q1vfauR98wFYOZFNjfltcapcZl7kddjb3I2\n/CgswiH+InDcXJ7VRG9VWHbWOYRAURTs7+9zcHCAlJKtrS36/f6JNc9tyZAvEjX53N/fZ39/HyEE\nm5ubbGxsnKtG/Iq8vNc4A2rZbZqm9Hq9Q5Kc5zmj0eiJPvaLwlXNvp0Hkxm7Xq93qHIwxhyqHGqZ\n+7JwldbxPNd61No1OXt+ldZ2EvNc97179/h7f+/v8bu/+7t84xvf4LXXXuP9738///k//2d+8Ad/\nkI9+9KPH/v4HPvABdnZ2Zv7tafzhH/4hH/3oR9Fa8/LLL/PKK6/wla985XQXdcnQvlTpGkfishLy\nOtPhvWd/f78RteGnQZPIbe2iWdeHHzWXbSONyxpvnSE6j+lYW+Z2EeOc1ac+juMrdyA66zy24T45\nLUIISy9/mTStukxu4MvGMshME4zhrhJpW+S1zlq7pvk7XKW1nUStejwOd+7c4cd+7McO//873/lO\nfv3Xf51f//VfpygK3njjjTP97d/7vd/jP/7H/8h73/te/u2//bdsbW1x69Yt3ve+9x1+5oUXXuDW\nrVtn+v7LgvWb5wqhbYR8OoMLsLW11ehs+Cw0IUPuvSfLMnZ3d8nznDiOj1UWtO1euUiSW2d3Dw4O\n2N/fJ4TA5uYmm5ubZyKXbSHkZ0UdQBsMBuzt7eGco9/vs7W1RZIkF3IYavJ8HnW9NSE8Knt0FQ+N\ny0JNHDqdDr1e77DzQZZljEajw/KdJt9XVwF1BjZNU7rdLmmaIoRYewQsEBdFUI/yd1il8gGuLiGf\nJxB6584dXnzxxZn/liQJP/zDP3zqv/sbv/EbfPvb3+arX/0qzz77LJ/85CdP/R1XBesM+SXCSQZi\n9cOwyZjs1zztlL67u9vKA9KqMuQ1MaqlmZfZvfoixjsru9vv98/9Mm/L3J52nHXgIs/zpTrzt+lw\nNT1Hk8/kJmSPriJmuYGf1VF6Fdn/ZWKVZOao9ncX0ZrrqpK2i0J9Nl218qH+25d5j87CvJ1+bt++\nfSQhPyuefvrpw//9a7/2a/zCL/wCUGXE33zzzcN/u3nzJi+88MJC/3bbsCbkVwhNznpOS4GTJHlb\n9rEef5uy47D8DPk0kUyS5NREsi2kscaixjur73q/31/4IaFNc3vSi3xy72qt6XQ6re8dvmh47w8z\nr0opOp0OWmuMMYfPB2vtEy2h2rYHLwMmSR/wtp7na2l7c3CR7e+uCiFfVUvWVbbEuyprOwvHXXfd\n9eS8xrj1PVXj7t27PPvsswD8wR/8AT/6oz8KwIc//GE+/vGP84lPfIJbt27x+uuv85M/+ZPn+ttt\nx5qQXzKclCFvEiGfrmc+KYPb1gPqPD3Uz4vpXs7nJZJtm+vzjrc2aSuK4sKzu/V6NP1gcNLLex4v\ngmWgqffqpMv3cDh82xxNjrmWUMdxfPh7tRR3NBo9cTht8j1z2TCL9K0iq9cUNHGfwZMEb+0RcHqs\n8t6dVj5MPjcXrXyAKsh2FfbqJOqWZyfhvGeSj33sY3z5y1/m0aNHvPTSS3zmM5/hz//8z/nqV7+K\nlJKXX36Z3//93wfg1Vdf5Rd/8Rd59dVXiaKIz33uc1duXaaxJuSXDMfd0DUhXzUR8N4ftj2SUs6d\nwW1aQGFeXGSG/KKIZNvmuiZlp723p1twLSO726aXTj2v9ZgvSsZ/mTC9J4UQdLvdma2wZil+andw\nqOrMoyh6IvN31UhgU3BSVk9rPffBt81owz13XmO4VZ+RloUmXmf9/KuDYEeVkJw1e35VJesnrfPe\n3t65W7H+p//0n972s3/+z//5kZ//9Kc/zac//elz/c3LhDUhv0KYrDFf9kN4Vja83++fqid620hi\njYuoIZ/sfa21ptvtLrTvZ1OzjkfhNNd9nE/BsrCqfXhWTPdavwgZ/3nQhHt1Wrpf78nBYPDEPNWB\nI+/9IakDDudz8rA4SQIns0fT0s6m9vy9rDiqnrleG2vtWtXQEJxWHt2EZ8my0PR30EklJKfNnl+l\ntZ3EPOt8586dK1/DvWqsCfklw7xZ5mVFCBeZURNCXLj0+yKwyPrmadOsra2tC13Lpr+wJ3ESya3v\nxVrCOMunYFloQ8CjHt/BwcHS7rezYJX357Rx4nHBnZoMTCo5apLtnDsMNjrnZhKDSWOkSXI+eThd\nk8DVoJa210S8flddNlVDm94HszCvPPqqoG3rOV1CMpk9DyE8UZpwnPKhTde8CKzK0G2N02FNyC8Z\nmiD7XkQ2fBbaGr0+b6nAqmTVbcviziK553Gav2g09V6ezPSGEIjj+LDd0BoVall6nufHGifWa+yc\nO/y3ySx4/bNZGSBrLVCpE+qD6OT33t2/ST/dZKOzNdPU6rKQwLZhWtWwTMOqNU6HWfLouuvBaDS6\n9J0P2vR+n8Y82fPpAGWbr/c8mMcM+datW2tCvmKs/lS6xkJx0sPmIuuZF+HufRzaKlk/y/U3QVa9\nbDXFeTFJyL33h2qCi7gXz4umjKPGrMDF5uYmg8Gg8Y7py1QbTAfHer3ezGzMpCxdKXV4H2qtiaJo\n5gG/JtVlWeKcOyR19d91zpGZIT54Hgzu8u23/oYbmy/wju130Yv7xDo5kgSepnXXGmfH9IF/noxs\nm1QNl7n+tl4rqN4faZoeaQx3WbKsTQ0KnwXzuO5fhjU7C+aVrL///e9f0ojWmIU1Ib9iWDSpnXWQ\nX0Q2fBbaSshh/pZt87R/WxbaIKuehBDibbXORxGmVaMpc3tSprcp41wlZgXHjpLuT9aGA09IzCeN\npUIIT5BzYwxlWQIcGjNO3rOPBg/Yzx5zb/8Wj4b3yE2GDZ5h+Tpv7N6kG/XoJj22021S3aETdYhV\nwlO9p4/MHK3KcfqqZqng7RnZRbbqWmOxOK8xXFvQ5rEfhaO8N4wxhBDIsuzSBVeOwzzP3Fu3bq1r\nyFeMNSG/ZJiUGs9CXed2Xlx0NnwW6utq44HuuDWZJfFfVQupSbSFjNW19c45siwjTdNG1jpPYtVz\n25bAxby4iLmc5X8xKzg2qcqYJOLTn6sPiGmaHio4anJeE7U4jiuC7kqstzwY3CGWMV+7/T+xrkQI\nxZ4ZooTEeI+3hizfJwSPQCCVRCCBQKo7/MTz72Er3SbRKZvpFqlMLy2xaBsmScNRrbqaJm1v47v3\ntJh1jac1hmsLrsJ6TnpvAIeKoav0DJyn1dvt27d5xzvesaQRrTELa0J+xXCeOuxF97o+LdpY11xj\nVna/6S2kVk0aT8K0mqCWhaZpuuqhnYhVzO1pMr01mn4PwOIzPLMc5WcpfqZN2uqxHDeeSZd05xxx\nHBNF0WHt6nA4RAjBNx9+lbdGjyhdTqITcjvCekfpLC4EjDcYZ6tnL+CCo7QG5SVSahSKjIz/8cb/\nSUengGAz3UHJDjvdLbY726SqQz/qok2EMhKJXEvbV4jpjOxF9mJe42icdL6Y1xiuDWt1mUsQZqG+\n3ssYXDkKk++m4zAYDNje3l7GkNY4AmtCfglxUob8tLLvWdnwRfS6PgvaVtdcYzK7PxnUuEiJ/3lx\nkX4DZ8W0mmCytn44HDaePNZYJtE9j7t8Gwj5IjAdrDhOZTFLlj4PES/LkrIsD7M1T8jSReDR4C77\n2WPyMmM/32V39BjwPBw+RCiFdRakwgUPIRCrGOc9cRQThZhOAsE7tIzIywKJJgRPYSHgGR48wAV4\nYw98CPTiDiEIenGXZ3pP81MvvBcl1eHzabJmtm3P21VhUcHiWWZj027Sq5C2tzEYftFo6lrNg6u2\nntMBiOOCK/Wzuv73Jq7fPJjHWf4qvOPbgOaxgDXOjeM23ryEfFY2vAmy1iaSxHkghDiUqK46qDEv\nmuRqP4+a4KqQx3kwK/DTFHf5ReM86z4drEjTdKaJ3byy9GnUB7s6297pdJ442N0/uM2DwV2G+QGP\nRvcpXUlpCwBKUWXfTRBgHc4FtAYlJEhB6S1SgPMBKRWRjEBGxDoh0ikuKIx3EAKZyQlBoESVsevG\nXXpJDx+q770zuMdX732NF7de5LmNG6RitrS9DVm/y4ZZbtJHmY01+X3SFpyHpJ5mrZpQu3wVCflJ\n6odZwZU2Z8/nWeM8z0mS5ErdC03E5TudrXEsTqrDnnSnBkjTtFHEsUkk8STURjC1K7OUshFBjXmx\n6uDHUSUSR5HKVY/3NLio4EFt0lZnZ867fy9rkKM2ojwpWHEeWXpRFHjvD7+/XgPnLVk54v7Bbf7u\n3l8zLPex3hGpmMKWCKUw1hEAJRVRFCGlxDlLCBXJj6RGoYmURuuYEByBgHElu3kGCJwPxDomVhHb\nUUoiYwpXEnwgszmDg3soqehFXa53n8IHzzcf/B3fffwG2+kOW+kWL249i0A8kfWrazCbmvW77JjH\nbOyiAidXgcAt8hqbbgx3FdZzEqe53lnBldqEsU2lCfMoSm/fvs3zzz+/pBGtcRTWhPwS4qQI4LTj\nd1Oz4bPQBqf1aVJUZ8OttURRtOrhzY1VkbGj5u+kl0qbyOOixzrdjqvb7TZy/64StWw8z/MT76uz\nyNLrOvD6sFbXh9e/Myj2EQi+dvs19rJdSpthnEEETaQUXkiskATrscGihMZ4g0RCgG7cxbsAsRgf\n7g3WW7JhhpAaj6STdOkrjZQa5w3WOw7KIYIAQZBECf24i1AdntLXKJ1BCcWdg7vIgaAXdxDEjMqC\nO/sP+d7ubf6P536EftJ728F0naF9O5ZNcGaZjU0HTtaeAM1AE43h1oR8fky3VWvLXpvXYX3dg3z1\nWBPyS4iTNl9NamsZ9WmJzypRZYrcqocxE5NmUFprOp3O4YG8LMvWkMUay844n5dUtkk9sYi5Pa6e\nflFoQ5DjpDF678nz/LAuenJfTqL+Dufc3Nnw+vOTwcz6vq2xn+9yd/8md/duEoJnP3uM9Y4gIVIR\nubMU1gDV35FSsqE38CEAAi0khatM3FzwCAdSKJSKkUKRxBEBKEpDXlqMHaJ1hJSKftJjJ90GAbkt\nMc6wm+2x3dmmG3fpS8mozEiihFEx4ubuAxLVI1ZDpAQpNMZZbvSusZH2sd7Rj7tc6+7QmWjdVWf9\n6qxS07NGlxFHZfRmtbs7q1z6KhC4ZRidNcEYrunP9UVjkR165t1rdXBllXtmnmu+ffv2mpA3AGtC\nfgkxj3lDlmWV9HHiANmGF23TCEKddSuK4ljn6jZk9qexDII7y/n7PKSySffGcVhE3fMy3Pmbtt/m\nRX3AzfP8xFaCFyFLz03GqBzgvOOb97/GXvYY7y02VFn3IASFtUgZcMHSTXpVoDGAF57SlfjxeJxU\nJCpFKYULEMmIgckoncM7h5KghKKTdiB4IrmDsYbSFQxGB7wVHrOZbrLT2WJ7a4fcjHgwesSb+7eI\nZYzxltyUHORDJAmD8jHBV87tAri5d6eSyEtFEsdsRhukccJT3R12Oltc7+3wVPca3YnWXW0wtLrs\nmM7oNU0u3VSsIugwjzHcRWVfr9raX8T1ztprdYIBWOlzcJ4A0+3bt/mJn/iJJY1ojaOwJuRXBJMy\n4Foe1fRezbPQFGI73XLrKDOoGm0kNhc55ums5Wmcv49CmzLkZ8Fk3fNJ9fRXCZP36awa+qOCFbNk\n6Sc9D2uPjdoTIkmSw0NyCIFROUCrmP/nu3/OQb5LpBJyM0LJiIAa14THDItB1Z5MCAgKEQQB0FJV\n5Fp3EIBAYoLBOkdW5AiRUIjAzrjmu7AlIUBuR3jvyG1OGgUQgWc2nyEEiIRmd7THw4NH3Nq9w2Zn\ng0jFXEsTDsoDRFA81XuaG/3nKKxlL9vDOEMkNS4EvA8474ikRHrJbrYHOdzdv48Qgk7cpaP6PNO/\nxruuv8jL154/nKtZfbUv6x5t8nUtQi7d5Ou7TFiG0gGuhtphEvP04l4EJvdaMhGkXJWxn/f+xFLJ\n27dv80/+yT+50HGscTLWp7lLiHqTT2ZxJmXA1tpWtg6D1RLy82RzmxJIOA0WTchnZS0X6fzdpqDH\nvGM9Td3zRaAtRnnee0ajEUVRHFvuUGfB64w4zCdLr512Jz02Dg/MwZOVI75572vsZW+hVIyxBanu\nMjJDYhVxYApKb1EoAhkbySaJTjDekqiYg/IALTXeO4wzSK3IXUaiUwSabrxBR0BhS1zw3Nm/Q6Ri\npJD0kz6bepNYxQyKAS4EhBRkJsON1y6JY4QSCDNkvzggKwu8ixAC4ijCuRFKSXyAftwjtwU2OETw\ndFTE0GQ4Ah5HN+5AAC0jrAdvPI/yffZHQ+4PHlM6wzO9a3TihGSGoVWt8HDOXUppe9OvZVoufVq5\nbdOv77xoGlG9KKVD067zorGq6121sd9ast4erAn5JUVRFGRZdniIn8yG18SojVgFQThPH+dptOkl\neJIj/7yYdO4Xomr5dhES68tEyCcVGNN+BGtUqJ9jdZYvTdOFy9KNMYffnyQJaZoePkd98Egh+evb\n/5P9bJf9/DGZGSJF1SvchkCku5TW0Y032IlSRuUQEAzNgMf5WyipEAFinZLoBKESNnXEoDgg1h0K\nVzmnS2FASLo6RStNp/s0Ukoyk7FfHEAIXOvs0E828MHifeCt/HGlRHEFCon1DhE0uXP4oCl9gfeB\nYTlCAFpFSCGIdUwg0Ik6ICW9uMt2dxMbPPvZAZaAQIx7nVe/04tSrLOM8hH/4zt/hVaa7XSDXtJh\nK+3TT7rEKiLREV0R04lTQgiNkHRedcwrt11GNq8JaPI7epHGcE2+zovAMrwBTsI867dIg8x5z2+P\nHj3iqaeeOvffW+N8WBPyS4oQwpGH+DZma2vU13LRL5NFZ3Prw8yku33TMam0OMtcT6ozluHc33ZC\nfpq652WhiXNay9LrAI9S6tALY/pz8Pbe4eeRpdewzvDXd/4XAvjOo29RuBwtFFrFBBSxUsRBUbgC\nFxx7+R4Ue6Q6xQdPJCO20m2cd1hvKGzBbp6hpUKWil68idIJyhg205i9fA+FpPAlOiigpCu7bCYb\nbNAnMzn3hw/woZJlCgRSSKQQWB+AQGEDzpuqnRqSWFc9y513aKnwPqBQeG9JdIJ1jiACDw7eQkoB\nQRDLLkIEhFBIYSm9xTsHBlzwEKB0hlhFvPn4HrGKCAQSHZPoiI2kyw9fewfvvP481/pbR0o629br\n97JgkjBMmo3VhAHAGLNemwbgvMZwV5GQN+l6Z63forPnk8Hnkz6z3s+rx5qQX1J0u90j3cjbTsin\n27YtEtOGWYvM5raxxvm0hGyWxHrZXgVNe/HOwuS8LktBcBY0iZBPuvBPBnjqe63GWWS0xV6+AAAg\nAElEQVTp9WGolqXHcfyELB0qo7Y3H/9vUt3l/uA2jwYPiHRMrGNS3eHADBiWGRaw3hLrBIVCK42Q\nCoLHOgOADZ5slBOrCC01najDtegpnHcYb3iU72KcY6dzDekdN/rPkJmM0pVkJsd6i3GOkR0hhUIC\nkdSY4LDjdmi5LXE+oEkY2IJIR9VnhaKjY7TUCCHxeCKpyW2Bdw4bAkNbEElJcIFIxESiMpwqXGU4\n57zBeU9HxeTB4YPHOIseHx5d8LjgGZkcBBhrKFREZgoOshGvP7pNpLs83d9kp7fJTqdPN4rZ6HQI\nPrSu1y+047lzGtR7Jo5j4jg+DBa2cW1Og7au42mN4dp6nWdF0693keqHGvOoAowxrTF1vuxYE/Ir\niEVJkVeFRWeaJ2vtJw2zFi2fbEs97iTmJWRNkFifN6O/bHjvGQ6HS1MQtBGzfBumAzz1vppl0nYa\nWXoIgTiO6XQ6b/udewe3+Mbtv2RYDlFScpDvIQ/JbEIZPFJGdJIOeTlC64jCFeQhhzKgpAQhkUi0\n0iQqQQhFCA6BpLAFg7Ig0l26cYfnNjYpbMFBfsCD4SOUFGih6cYdXHAoqRmZDBc8xluct0ilKW2B\nQGM9KDokUhKpiH6qiITGh4AJDoVkaEbVPAFGRXSjDjISY3LtsdZTYhBodrMcLcuqvV4Uo6QiVhEW\nh9YKqFzYnfNEWuG9p5900FKBEOSmQAvFbj5i3xruHgyJdczrDyWJjomU4un+JlpqrvU2eMf2U/zA\ntWcPScVa2r561MHwNE2fIHyXaW2aEnw8L+YxhoPvJwnaul6nQRMk6/PivOqHGvOs7d27d3nuuecu\n4jLWOCXWhPyS4rhNeNFZ5ovGojLNsxyZL9Iwq60Z8qOCCDWZucg+2KdFkzK6szCpIIDqnmhyt4NV\nzees1m6zfBvqwKJzDmPMXCS8/v5all77QkwGQ3zwPBzcpRP3ef3+19kdvcWd/ZtIBHGUomR1EHLe\nkTnD0GbEMqZwBVIKtIggBDbiDYwriVWKD7Yi6SZDIOnGMT5IHIJIJxhTMCgG7Ga7Yxm5RilJoiOM\nswzMgLeyx9XPZdWbPNExwXtCUBRlQUd3cUGyk3Yonal6mQewzlF4gxQCJSR5sHSjLlpIhiZDInlr\ntIsak3YpNN4LYp3inCOOI2IVU8gCFwJlmVXZdyGIdESsI7SWqEjhgsPKKlCQ2QKBhADGB7pqA6Gr\n4IINlY/JqMjwAR6PDthMe+xnQ5QQvLD1FImODtenJoDnyRitsRhMEoaTnKTbuDaXjaDO8gkoy/KQ\n5F2FFnhtDjwcp37w3h+u33RAf55rvnXr1trQrSFYE/JLipM2YdsJ+XkyzdO1zcvqw97GDPmsIMIy\n+2CfFk0l5NNt3tI0ZTgckqZpI+atKZjem0e1dpuUpNf36GAwOKwnj6LobUSgPojWDt/Tbuk1Cpvz\ncHCP//e7f44UFekWSDpRhyBgUIxASEpXAgolJSpIpBR0RAeEwNiSSEXkJkMrjcPSibrc6D9P7ksG\n+T4PR/vYcSue0pXEKkariNJbtFAVmXWBEAABQigSJUmjlNzkdFRVllS6QDfq4FzJyBiU0AzCiETF\ndOOk+qxOKazB4bDBAYJRmY1rvMF4S6pTNCkmOHxwKCUZFTlKCax3hACJjpFjEk4ISCEYlTkH+RCC\nAFntwTRKEEGBl1jrCEGAlHjhKmWBtAQCsdK8sHWdbpwwLHIKZ7g/2OVxNuDbD+/yVH+TZzeucb23\nwVanTyeK6Xa7MzNGxzmDr3F+HHe4P85JuibvbZC2t5m0zYtaGm2tPdwz55VGtwGXZW3nUT9Mrus8\nPchfeOGFZQx9jROwJuSXFPMS8jbiLGNvQm1zWzPkdRZyUtYfx3Ej+2A3iZCfZAw4HA5XPMKTsYz5\nrPdmHe0/bm/OkqUrpej3+4fzXas26qyC1vrwwAkQxxWpm3xGGlfyjTt/Sao7vLn7nepgKjTGlZXM\n2xcEoPAWISRaKLpxH5A4LATBqDxgI9ki0QlxZ5vgwQYDCPbzXQpruLV/n2jcYzwg6EY9cpcTqxhP\n4PHoMZHSGF8gpSaSGiUqIiODwAZLbkpCkFgvsT4Qyy7We7pxl1TFOMa16iJUWW+pyExOP+7Rj7r4\n4HHeV1l2a1AoCuswPqBkJd0XCAIOrRSxjlCuuu7SFQQkIXgCIIFOkhBCgnEWicI6S5aXlBaU0uhx\nYAAgUjGxVkRKIxAMTcbAFOzmQ3pxB6U0z23ssF9kPBjscmv3IX8tv4eQkOiIrbTLU/1tXt55lh+4\n/gzdsXx62hl8FRm/y3LgPy9m1cLOk81rAq7SGtbBzEVIo9uAy7q2x3VJqNe4XsdZ13/79m1eeeWV\nFYx8jWk06zS9xtLQxmxtjdOMfdIIatXto9o65zXBWYas/7xoAiGfVQoxS0HQhnr3i5zP6XaCaZrO\n3JtHuaXPms9JWV8dDCmKAqhIWv3vQggO8l2sdxwUuzwa3OeNt75FCFDYrOqxHQyxSjEhEKmkyvyi\ncL5kaEY4b6vMedylF/V4pn+D0hXs5buMyrr9mUWICCkSkIpER7hgKawBwbhPeYx1jkhFbKWbRCqm\ntCVKSvbyA5SweO+qBLSQBJ8QSw1Ksd1JyU2JdY7M5OQ2RwpFomIU8EzvKXwIDMsBxll28wOUUBBA\niggQJCpGIonjiNyUCASegLEGIcbzHjxKVoZwiY4qN3VvEUKQlXkl33ceYyyRTNjubBLpCGsrY9HM\nFCAE1mUYp/CEqg59PCdaSnJbkDuLQFQlMFoTJZpIjI3xgGGZ8/DuTb559wEbSYcXt3d419M3+OFn\nnjvSGfyyZvyWjbM8q07K5rVd2t5mzFrPs0qjm442eyadBpPBsCRJGI1GSCkPS0mklDx8+JD9/X1e\nffVVpJTcuXOHn/3Zn1310NdgTcgvLSYNrmah7RlyY8yR/z6ZcauNoFZd2wwnj7tJqF/CdZS8LYZj\nqyTk08Gfk0ohmhA8WDbqCH6e54dKi6PaCU7K0k/rlj4pS6+ltPU9ffPhd/HCc2v/O2RuyG72Flpq\nYtUZt+dKMcEjfdXWKzcFsfJkJieJYiIZ81TvaSIVMSyHWG95MLqPEooQKvm4lwLjKoO1fVMgqbLR\nWiqkEKQ6IQAOTSxiQsjxPlD4gsKVjP+RRFcydmsr87jClRhgZKp9mZuMWFay80RVxmrWOzKTYZwl\nNjmFK+mM+4RrkRBJjZQK6wIueHazEVppDorRYY15GiVEWpPomMKVeFfV1Y9MSWENNjiUHJvUiZhU\npIhIQKwovSezln1TEI+z/NvdDQSQm5J8TPRLVxJUhPWWjk6QQrKRdCv1QOwZlVUgZWhLlHcoNL2o\nx0ZXYr1nN8v4n9/b5duPHvCdRw94fmuHZze22On2Dp3Ba3I+S87Z9oxfGzErm7fINk/nxVUgbTVO\nutbTSKPbspfaMMZFI4oqH456v33961/nk5/8JCEEPvjBD3JwcMD169dXPcw1AHHCgfBqnRYvGWrn\n4Fmo6zQ3NjaWPKrzw1rLcDhka2vriZ9POn3XZjOzjKBWBWMMWZaxubm56qHMxKxAhhAC5xz9fn/V\nw5sLw+HwMNu6DMxyAU/TdK5sz+7uLhsbGysPFB0H7z17e3vs7Oyc63tmlYwkSTK3LH3yv8d9/6Qs\nfXrvG1fyrfvf4NsP/4ZROSKRncOsj/WGwuUgBaU1KJVWh1Gqw6Zx5tBVPTc5WlZy9k7Uw3pLpCKM\nKyldWbUk8w4bIggeqTQEUdWSO0OgMlorxnXmPnjSKIUAQlRSdsbu6YKKfDqnAEGkJR3VxePwASBQ\nWjOWoTs8gVhFSCEQVCUysYoovUcLTW4c1lety5Soss5KyTGpD0gkuSuw49rDEAKBgJaa4D3dOEVI\ngWScES/BUgUOEFQO7EKSRjFSSgpT4sekP1IKESCNIrRQOO8pygJHwAZPGGfNfQj4EEijGOst3SjF\nWsnIGpzziCAox5n0bhyznXYO91Csq6DEZtphs9PhqW6fl68/jZ44kNakAhZLAOtyiU6nc67vaSrq\n80SSJAv/7sk2T9balWVjjTE455b2/lglBoMBvV7vTHN70Xtp0fDek2UZvV5v1UNZKo5aY+89f/u3\nf8uf/umf8oUvfIEHDx7w/ve/nw996EP8/M//PO9617vm+v5f/dVf5Y//+I+5ceMGX/va1wB4/Pgx\nv/RLv8Qbb7zByy+/zBe/+MXDs/pnP/tZPv/5z6O15t/9u3/HP/yH/3CxF9wezNwga0J+iWGMOTIL\nboxhNBq9jdS2Ac459vf32dnZeZvTdxzHpGnaSJLjnOPg4IDt7e1VD+UJTAcyJqXDNUFvS+BmNBoh\nhLjwQ/GksZ2U8kzBn729vUPlQVNxXkI+3Q4vSZJzydJnfX/dO7wOwk0fCB8O7/PmW68jEDwY3GVY\njgjBUtgCCAghMTYgRNWXW6sIi8HYEhscAU+kErSQSKEq8hvs+L+OwpakukNmC2KVYDxIKSAoAgEf\nHMZbgg+HNX1plCCRVd21FOTjft0+BAQSJWKkqLLP1ge0VBhXVmZy1iClxHqDRJLqZPx9AhcCIzPC\nEyiNwY/l31J08MFXLvE6JpKKIKpacec9hS3GZNijRGVU19EJQkoiWZFnGxx5WWKtINUxvbiLC4E4\nihkUo3HAImC9A1+RY4RAIBACpBAUxuCCwzhHpKrsuXUWRaUWccEjVSVRB0lR2iqQ4QVCVqeYqn+6\nIDOGQZ7hXCWLl+PuIUoIQhXZoJck/NhzL7LV7bGVdni6v0kvSZDj8qFFEsA1IV8cJtemNqZaRjb2\nqhDyEALD4fDMhHz6u6b3UtPKROr3ULfbXfVQloZ51jiEwD/6R/+IP/mTP+HP/uzP+NKXvsSXvvQl\nNjc3+dCHPsSHPvQh/v7f//tH7vm/+Iu/oN/v88u//MuHhPxTn/oU169f51/9q3/F7/zO7/D48WN+\n+7d/m2984xt8/OMf57XXXuPmzZt88IMf5Fvf+lbjgjdLwsyLbu5JcI1z47gbvY0GYzXqsY9GI8qy\nbJzT91FokkR5luHYLFl/k8Y8Dy5yvIs2tmvD3J5ljPPeW/VnzyJLr2uEq5Zc1TpMHvycd9zc/Q6d\nqMvr97/Ozd03xoStIluRihFCooRmZAuIKmLtnUM6ibGOjaRPLCRSS0ydQUdQugIto7G8O6IbJZTe\noVSX0jtyW5ComNzllUQdWWWttUILReEKMpNX9eAh4JxHCklHp0gZ413AWI/1nsyNiHVMacZydwSR\nUmgZkeoEQqCwJYUrKzKLxIfARtxDRx1SlWBDFeSoggOQ2xIEldxcKmIl2Eh741ZrkgCEALktwDq8\n9CgkiYiIdIyRAoPjYTZACBDFiFhF6EiiqSTvFkdhDQGqHuyVKICNpDNe40BhLIUzlL4yvgvjYETk\nPc666rkuE0amxCMoS4MZH/wREhE8AYkQVC7wPmBKh7MepSUhwP2DPe7v7xFpTTeu+p0/v7XDO689\nxXNbOzzd26Db7R4px63JeZPfK8vCMuXcxxlVwcVlY6+SZB0WI+GelLY31Rjuqq0rfP+aj7tuP+7y\nsbW1xUc+8hE+8pGP4L3nr/7qr/jSl77Eb/7mb/KFL3yBH/qhH5r5+x/4wAd44403nvjZH/7hH/Lf\n/tt/A+BXfuVX+Jmf+Rl++7d/m//yX/4LH/3oR9Fa8/LLL/PKK6/wla98hZ/6qZ9a3EW3HGtCfkVR\n15C36UE1SYigepg00en7KEw6lq9qzuv+y3meI4QgSZJjAxltM6K7iPHOklsvwtiuDYS8xjz37Lxm\ndvVnp7PhJ83ntCy9XofJ7y9szqgc8J1Hf8d3Hn4TgUBJxbXu0+R2hAtVLfjAj3ChIotSRggqki4V\njMwIpSV7ZrfKnJYa5wO9uIsXgY14Ex8cmS2wvmBoSyKZAIJY6aq/t9RIodBSMrI51gUKl1XydJWg\npESLaPyZCGNtRTy9r8zihCLVMd14AyUVxlpscFWm2jlKa4hUVRvYjTuE4Md14Q7rJbl1+CAo7Qgl\nNZ0ooRMlpDomEDgoRpTWMCyzKltcBDpxTGkDkdaoUGXGjbXsD0dEKsFSIEVEomMiFXGtu4l1jkGZ\noZViNxugpSISCqU0nVgTfMAGh5SC0loejwYUrvx+LX0UsxP3cSFAkAwKS+EspbGEYAneo4TAjNuu\nCSGItcZ6D1IiPdgASkikkggkTjpAIgVsJClKCCJdrUXwgW8/vM//fnAPrTRbnQ7PbW3zwtY1bvQ3\nudbv00vTxtU2X2VMGlXFcXwYPLkI0742nYfOg4u8ziYaw12VdZ1ETbaPw/3797lx48YTP5NS8u53\nv5t3v/vd/Jt/829O/Xcnv/PZZ5/l/v37QNXv/H3ve9/h51544QVu3bp16u+/zGgHk1njTDjJsKMN\nLs8wm0Raa0nTtDVkHFY753XG0hhz2H95nhdi25QUiyS503LrRTv0t4GQz3Ot0/N0lJldfa3Oubmz\n4fXna1l6vQ7TxGhY7PO9x99mP9/j8egBIQT6yRaFzTHe8HB4B4TEI5EqZjPdIjc5zlsKk+Hq6iwB\nWiqEVAgvkFJhTEGkBPvmAGMtVbMvQSfewOPoRxsEwAaL9Q4bqkx6GEvNYxWjpCaNKsOykRkRvKSk\nxLgAISaSEVokSCVRcRfnLcMyIzM5PgS0rLLsWir6egOhBJkpsM6wXxYoGRF8Je+OVYQW0aHUPAjI\nTMagHGHGRF5rRT/uVK3GhKT0FTkPBA7yEc4ZFJpO3GOr1yMEcWgMVFjDIB9xUIwIPrCZ9nDOca27\nQfCBkS0oTIErHM47unFKCJ5YxwQsidD4AC44drNhJd33gVilEBRSSFKtKZwFAcVYkm6DRyPZHQ5R\nSlaZdyHR4/ugqjvXaJmQRBGltVWme5yVEz7gCGwmKaWzFMayNxpx/2CP//W979KJY57f2uGl7Wu8\nuPMUz29t002StxHAowhFG96l50FdbrFKzJONbZvR2CqwrHu1KcZw85DTy4Z59uudO3cuvAf5VZv3\n86A9bGaNU2MesuW9X/lLdham3ZinSeRxhnVNxjLnfBGZ3SZk9U+D85LcaU+Cpjj0rxKzgkinmaeL\nkqUPin1u7X6Xa91n+Jt7X+Xe/k3kuA46khE+BCIZIaUiiTew3lOYEcHnHOT7CKkgeGIZsxH3KFxB\nCGBcgfMVmXTOEIREyIRO3CWOBMYarHcMTYZ1lkhqgqBqNaYksegRqGTo1jsKV+BtjhCgRUSVvU3w\nwRNLAUiyMkdIMM6iRCXxjnVMCJ40inC+ktQjBLt2v6qjRtCJOnR0CkKBhFFZkpkhcty6rKMSEKFq\nreYdaZpQjtuk3d17VLUTE4JUR1XbtSihVI4gPAPjGVmPLYYV+fUQR7oizFFcZay1YL8Y4bxHMCIA\nkVTEKkLpqMroe0tmDIWtzO6iuv2bE3SUIncV0TbGg/AEH0BWShcpFGJsVJfGlRy/kyQQPN5Dbopq\n/mV1Lykh0UrRjWKe2dhiWBYE7ymcxQSH84GRqVrOxVGEIBCplH6cMDIFb771kO8+vI8MCT/xjpf4\n6R/4AXZ6XeIxATyubdcay8d0NnZa2j6ZPZ/n/dWEoMMysKr3+UmlCKddr3lxVdZ1EvOs8a1bt3jx\nxRcX+ndv3LjBvXv3uHHjBnfv3uWZZ54Bqoz4m2++efi5mzdvXngwoG1Yv0UuMeYl5E3CtOz1KBLZ\nxLHPg2VkRReZ2W0DCZ/EWed30qRtWZ4EbciQT2NarXJRsvQ6exLH8dtk6bnJKG3BV2/+39w7uIVW\nVVuzbtzHOgcyMCpHhBDYcwMQFWmSQpFGPZy39OI+1htKV+CCZy/fHddYByKp6ccbVHXNnoEtKazD\nuKo/uZCCrk6BDgIBAqyzFKaksAVC5AghQYjKuExpBAoXAs6B8wGwZGVVY66kRGuFDKAjRaoTRiYD\nKpOzrBjSj7u8+swrfPut75GqFInGBciKHCWrunYpBVpUknnrLSHAyFREf1Bk1d9SilhHaARpt5q3\n0hiGWUYg42EQaJWO28BprnW6hBDIygKhBQflCKjIshqPvRtFGOuIdFRJ+U3JwORIBEEEFFU7s0gK\nAorSW0rryA1YAbl1IBypVuTWVb/nAh0dYccZdh88xlVuznbsRp/qiOvpJoIqCCCFxDvH0JQMi4L7\ngwM207Sqz49i+klC8IH9PEOM1QOlNXSjhIOiIJaKftQjjSP2s5Jv3rnDzcePud7rcb3fYzPtsN1J\n2en12Ox0SIV4m7QdODQha9uz8yQ0PSg7KW1PxsqG2mgvz/O5pO1Nv8ZFoQnXOV2KMBmEvYhShDUh\nfzsWQcgnA+0AH/7wh/kP/+E/8KlPfYovfOEL/NN/+k8Pf/7xj3+cT3ziE9y6dYvXX3+dn/zJnzzX\n375sWBPyK4wmkdo64zBvD+cmjf00uKhxz2q/tajMbl2X3YYs8WlJ7rSUv9/vL61OtC2EXAhxeLAt\ny/LYkof65VxnxOvfn1eWXn9/vf+hMmi7s/c9nuo/x+3dN7iz9yYH+S6IwEa6zSAfoKTgIN9FoMmC\nJVYJsYxBVe3KhmZICAWFLfDBo1Vx6GLuhUOpmIConNeF4OHoMaWvDMqEECQ6IVJRVRPtDQflsMpk\nCwjBo0WMjhQqVK29CpsjvSJzOc6DlgnGOWKVVO3FhKAfp7gQ8FQty0rv8N4zKDK6cTquD+/CuO1Y\nYR0BRWEtBIEAEt0hGpNs7wO5LRkUQ4xzuOCJpCISmjiqepX74BnkI7z3REqTlyWduEOsOyRRh9xU\n81XYktIaHgz3Kjm9VMRjmXs3iRkWBT4ESmd5MNwnUTE3Nq9VLeOEAiGwziKFwFjLXp7hA1gnkUKg\nhaITxbjg6YqYwhqcDwgg1orSWvaKjFhqMpORak0njknjBOcsaVQZ3Vmqe804jyegpSCNNQTJM90e\nmTEMXcnjbIQSVYu3rbSLFgpPwI+l7QFBbgVFaXgwyCAENpOEvdGQ+/v7VW90qpZt3STmpZ1r/PyP\n/9ghoagDyXVgb1W1smt8H3VAD76vuGmS0dgq0QRCPon6HVG3q1z0ejXtepcB7z1RFB37mdu3b5+L\nFH/sYx/jy1/+Mo8ePeKll17iM5/5DP/6X/9r/tk/+2d8/vOf553vfCdf/OIXAXj11Vf5xV/8RV59\n9VWiKOJzn/vclVuTk7Bue3bJURugzUKWZYQQVtYKYlbf63l7OGdZhve+dX0lF90nexHtt05CG9pz\n1ZintdxpemJfJFa9/05CHeQZDAaH3g1H7c/zyNJrAlMfxia////73v+FVhF3995ECMF+vle1/6py\n0wQCsUpwwaNUjAeGJkMJQWYyRGBM1DSBqs2XVprM5ITgIQSCACU1iAgpIjwBgiAE8L5qWeZweB8I\noarJ7kc9SluAACUUI1tlu0MIlM5VhC8oZBAkssOwHFVSTaUrR3apcMGihKp6gUtFomO6URePZ5AP\nCQQKV6KIEUIRy7gi5s6ihSCzlcQ+swUCEMiKmI+z/FTdyilLg5BVezMALSQiSITSdHWXQVnifNWu\nTCtF4QxdFSMkRDLCejuWnle13KVz9JMU7wNpFJHqGDHOhlvv2c0G+LFJm/EGRYQWMZGOKI0bBwaq\nfuhQtSuLlMI4e2j2JqWiE0UkWlO6yhRuP6vWbDPtEgj04oTSWbwHh6MoDVpqkkiTG4NWCkkgErLq\nMSMkIXiGxmBDVVKAl2TGEqsYPVFOJBAUpsT6qs87gFbqsFe8kpIfeuYZrvV6PLe9xbObm2wlSRUk\nmag7r0nFpLS9rZm60Wh02FawzZg0Gqv9LOq1KYqisW1TF4laCVYHLJqM6fU6S7BrOBzS6XRau/fO\ngnn267/4F/+C3/qt35q77/gaC8O67dlVxHFZOCklxpglj+j4vtfzQkp52H+2TViEC/ii22+dhLZk\ncuH4sU7fd4s2aTstmupgPx3kEULQ7/ffFm0/qnf4SYeeWvZeluVhFmt6HR4O7rGXPeb1B1/HOkvA\nI4QkUpUBmhQC4y0+BPaKQWXKJjIIoJWm8I5u1ENKybAcIoWkdAYIZGaEVhFKakLwCCQjYylcVaNt\nvSWRCRaLkgpJJSNHVSEAiaBwJWHcNzx3Izqqh3EGJSL6UTKuRbcEAkOXE5SojN+8o6tSfHCkukuk\nNIiAcQbjHQ9Hb42z8hKJohf1KUzAeEdhMqy3SARIUZFPFVVjFGC9H18jZMagpBpn92N6cYd3bD3D\n391/ExM8XggKA4P8gFjHSCGIdbUGmypiaHJs6UCUJFphfZVtT6OYTgiUvjoc71qDdft4AhtJimcc\nDAiVPN06iVYxhTEMS1M5uodAJBUdHaFU1bZMS40kBSHY7HQY5DnWOd4aDtnqdElUxHPP7GC943E2\nYphnPCyrVm/b3R7Xkh7JRoT1nr1sRBppRmWJ9Z5UR+M2b55YKVKliWTEqKz8ATbjmL08JwsO52Fc\nhYAIoTKN05pIVrXpWVmRdB8cr9+/T6Q1X79zm+u9Pr0o4oWdbV66fp1rvV7VZm2iVraWttdGV1c1\nO7tqzDIaqxVAtdKhVj5cJQLXVCzCGO4qZsjnueZ79+6t67gbhDUhv+Q4iZAvixAsWlLdZsn6WQMJ\ns9pKLaL91kloIyGvX0an6Yl91TFLvq+1Zn9//4nPnUeWXgeRatn74SEreB4N7jMsDtjNHvHth39L\nYTOk0MQ6RQtFbvPKUA2HsRapNFpGKBURgse4EoEgK0sCgdKWhOCJVEzhSyKpEUKO8+SQW4sQkpEp\nDrPoCkkcVVkjja7ceaXkoBiACFVmWQhSneCDI5IRIHBOYL2k9A4tS4y3aKnRUrOddNlM+jwa7aKl\nZr8YELwjEQn7WUWIUx1XZFvISsbuHBJJPs68C6ATJ0SqTwie0lpyVzIau7CrMfHe7PRRiMOe5Af5\nAOsMIwJ3DvZwjK9NCpQOOFXJ5a2v/p4NDi0VqYpIkg6lc7gQUDIwMgWZMcRaYQNIm7cAACAASURB\nVKxDioq5duIELRWFNZTWo6SktA6BJlaKzJRoodjqpPSimMI7lBAcFDlFWVbfEwyx1vSSlB997h3c\n3H3EvYM9+mlKZksGZc6DwT6xVnR0wrObO2itsM7y1nDA9x6/hfWWjq7M8rpxMi4J8OO1NpQhMCos\nhfV4V9JPYowt0VLSjTSlk1jhxvOjKJ3F+YC3DhFphJe8eG2HSCpGRUlhDdZXJnR3d3dx3vP6/fsk\nccK1XpfrvR4bnQ6dSLPT7fLDN268rQ3UZHa26S3VLiupmZS2DwYDtK72/WTLu8sWPGlzTfVpjeHa\ncnZZJOYx4q3P5EmSLHFkaxyHNSG/5DhuQy6D1HrvyfP8MCu5KEl1Wwn5WbKip62vXzSamsmdhXpO\n6uDFvP3WV4EmHBYmy0a898c68c8yaZtHll7Xntey9I2NjSe+f2/0Fl95479TZ6+dtxS2IFDJzqvW\nex4tE6ROiIUi1jAoB1hXfW9pq2x71farqtMO42y295bSlxhXEtC4IEBIYtlBCslOpz8mkYbClxAY\ny+IhUhXxrnp9VyZrWigOyoOqNlxU/ce7UcSGrr7PEYhllVXObcHIZDwa7dKLu4zyA3pxByEEuS3p\npB2MszzOD6raa6+RUlSmc8HTizVaKg6KKitswxCtFJGqggdaaySV3Dw3ZRVc8B7vqjY/HZ0SxVXG\n/tFwiJ2UptvysDWbkLIyYBMS4yyZKXBZINGazJTESldSbVXVl3fjzrh225GXJbGOq2y4iPBBsJ32\ngOrQb53DBxiWBVlZEKnqGjc7XbpRjPN+TNolu9mIL//d18cGddXBOlEag6OXJBTWclBk3NnfJYk0\nSkqUkDzV76OEYFQaEIL9bISQEDwoNLHWCCfwwZMojdCCvaJABJBYXKik7UpJlBAIATu9bmUEFwLG\nOgprubd/QDeOUULQG5e5ZGVJUVYKB+s8e3nOnd1djHPVGsQRL12/RqI1z29vEx2Rna2Nx9oubW87\narVOU3poXwQuS3BlHmO4eh9dhuudF/X6nvRuhqs1L03HmpBfYdSkdtEP5+mWSPUhfJGS6iaQmbNg\n3r7e0/X1aZqytbW1kkNam3qRW1s5Me/t7Z2q3/oqsMp7uJal1yTgqLKR+tlgrX3iJX/SfE7L0pMk\neWIdHo8eUZiMx9kjCIHH2UOycogUCvCkUW/ct1qgZEJuMzJvGRX7SCGJVIQSklhXn4uk5qAY4IPF\nWENuMzpxDyUUSdShKxSFddhQ1YFntsB4gw8eB8RCoWTVwxsEG0kfT8A6WxmuubKSm0cdCuO50Xtx\nnI03RFozLDLKYKvaaGfJxvXkWmpEEPSjbtVPXFR11cZZBBqHJ1YJm/G1qo+5q8b31mif4EPl0C4E\nvaRDEFSmc6GS1RemJHbRuJVYVSPvnUUKTaQjDIL90iBMIPhQ1UwLMZa3O2IVIZUc9+Su5Pphoq47\n1RopFVudLkqoSoUQPJFQVbsxFdGNO4igGZYFqU7/f/be7Meu/Drbe37j3vsMVUWyZ6nllmxJhoc4\nyRc7NiwEuQlsQEDuDBvIvf81w99FfBUkBgwhsT/HiZzIsr+4bc3qeeBQwzlnD78xF2ufcokmu8km\nm6xqct1oIKu4zx7OOe9a73peppgoJbOtA85YUsgsfIOed76VQkR8TOQycGe7xVvDwjVoq/nGy6+y\nnSbOhp4+BtI0MKWEM2ZuhmiMNrywWkvToFS0rtzabkgls3ItVcFBK1n1uzEwTVWo7iHSWEOu4Izm\nsJXJfq4Fo/S8BlGgiBDb9gPDOOGMofWe68sl11Dc2m7YhcidnTRJ5D1ZkWtFa4U3Bmc1a9Wcuwhu\nnm34n7/3D6zbjteODnjl4JDXjo5Ytw3LeUJ10dre9/2lA499UUTc/eru9+IHtUrv39eu0rn5Il7L\n+4Hh9t8J+r6/VM/T51kPcn2Pj4+5du3aEzqi5/Ug9VyQf8Hrkx7K/RvY43pzvjs66vOcSt5tTb4q\n9WnT5sexX/+467I3P+5eh7jfzvNlqyd9Xu/FHrhfo+yiJd0Yc+40sNbinLuvvXY/nbiXLR3gZLjD\nT2/9gNu7jzgbjzntj3HGY42d98M9oUbGNLINOwqKsWxpTIPVltZ2GK2Jdd4fH+4IkA1oXUtjWlbN\nGq88x+MdcilswkifomR7a4eznpVfkWuhFAGrDWkk10wuIkpzziitMJh5B3uF055N6PEGzsaBIY60\ntmFK0zy1dpRqWLhuFvAFEGic0pqFsWi/IJdCYyuWlj6OxJSYwpZcK411lFo4WqypVWziRmlO+g2p\nioguNaO0Yek6QkkoIKUZVKYtpRpCNlil8dYKvdwIqC6WRCgFhZLfozRGK1rnsMagqsIbAceVWqil\nEHLBKKGyN9phtMPlQkyF3ZgFllc1uzDNGeNW9q1joHWePkzEUnBa461M91euIdXCQdeRS2FKibNx\n4M5uS8oSdWa05J0bNcnv8p6SC7lW7vQbDpoFWhWB86FojONsGogJrDLkqtBV0ziHqpmjxQJjFFOM\nOG2JJXM6jCy9J1BonWXpPIVKLoUWR86FMUbOTs/op4mDtmUzDKhaGbM0G1IpWGfmnHaB4KVcUVaa\np421NMawnSSO7YPTY3KBZeNpnePGasnr165zY7lk1Tas25bDxeIXnterZG2/6vVJyS53W6UvRt7t\nr81VuD5X7XvTZyml1C+4HZqm+UK6He5VDxp59nx//HLVc0H+Ba9Peyj3U/LPOnm9+0v+k4qO2sOj\nrkoc177uNW2+l6PgMu05X1bL+t1T3v06xNnZ2ZX4gH1SgvxuqvwnsQfuZUvfTxxKKcQYz+nwF8X5\nRZvgRRp7qSLqt9MpN7cf8fdv/TW7sEEjE+nWdUx5ohbJ8t6UHRWZ8NaqsbbBVok+G9MokVy50tgG\nbzwKgbjlUkg5M4Rjcs1YbSk4nGkxCl5aHhFypA89IUe2YUeeaed6vr+tsVhlsaYhlSgE8zxSoqKW\nEYWdp/MGqwyvHbxETIkhTWgldHenDGjoXIc3jjjb73OpnE29xJZUifVqrUxjjXE4PV+nHEklCywu\nTixcQyiJxnkWWpNrYYpir96Mu3lfuiOhWbsGhWVMkUYbxhQpuWC1IeYCSibDC9tQVcUbh1IQSkZX\nRcgyuY8l0zmPwpxbzkFxe7fDaEVKI9Y0WK3x1osLwRgShVJgTIFhGvHaQK28uD6QY0hJBHMpDKEH\npTApsnAepw2mUYQYWTYtY4ryuZITnXV4Y/HakJUi5Yq3ns00kGslpogxhpyhtR6DAPGsUZQsdnil\nFLkIBK6xBqVhZVteWq0ZQ2QbJkJKbMaRWmHhZ+eBMhhrcfMX3DFGpiDND4UiqYwyBq8ty9aDUig8\nSmvGKHvmZ+MoOe1aM+WMBpw1nI0jp33PB6envPn+B4Bi4SUv/Vtf/2W+9sIL5Fo4G3sOmo4F5qEy\ntR/3e8gXvR5GpF60Su/fM/fRjY87Q/vzqGdBkO9rvy//qGC4q1SllE99De+///4jZ5A/r8dbzwX5\nF7weVJA/bO0tqU8aMHax9kLxsgjXB6mLk/2LkLbLuucMl0uQP8iU97JP9C/W53mcF90W1tr7UuXv\nR0u/++/tv9C0bXv+/O/F+X4a0XUdVVVOhtsMsecnt/6FlCMnwx1ySYQ04U1DzIGYJkYSWntyrhQU\n2nTEMjHlwJRGTBokL9yIgHfGkVNgjD27sp2PFbxpRCTaFgpCQadyNh2LSBpPZRJsO4F/uVYgZiWi\nlGYiMKVAX3rUbF83yuJ1B2iiytRayDWRYwZVKUMGZG+9lMzCtuRS6OPALg6MacIqS+satlNg6Vuo\nAm078At2YZQ4tSRgM61kmutwUCu+XRFzQqPJZPK8K28wpFw4ao8wStOHCYennxK5RJwxFA2da+is\n5I9PKeKMZcqRUiSWrY+j0M2VonGOdddh0Qw5sJ0Gci7EkinVoaviened1lpSrYQYKUAfJhoLfRjR\nRtNZT2Ms19oFqRbOxoHjO7cFhDe7CFpvOWg6QorswsRp31OQ98PON6ScUBW8s6y7hdjKS2Y7jTTW\nkWuiFLk/j5pOzimFkhRnY0TVSpghcd5oDrslISa0VqgqDYjtMFGZuAUYpei8xyjwvmGXAptxJJVC\nme3rucg0vCpYLFo6Jw0qVQqxFErO7MaRqkFruT6SH2/IVJzRs6vBMsVIKpUhBVpjKEXi4mKO3N4W\nrNF8uL3FuvN01nN9uabUwrXFki8dXeOl1QGvrA6eSqb2Zftsepz1WUXqRWv7vnF5mcXeVflsfFx1\nr+v6sGC4q1YPAu177733ngvyS1bPBfkzXg8ryC8CxpxzTxwwdrGu0m7z3bXdbkkpPTFHwaPUZTjP\nD0OYvyqC/PO43g9Dlf+s2eH7SdD+/nXOkXLi/eO3ybnw/Q/+lpAnUk1426BR8r9LRCsjU1vr0MYw\nlchUCpvpGG8aQp6w2uKNxxsv2dElk0sl5IF+mie7WsuE1niZQhc4njbIy5ijy6xHa0VjWsY0Uoti\niCMhB6b9PV3BGiegMdOgzIKUixC0qfSlRyuhsu/zqSsVqwy7OJBLoVZJ/LZKBJFG4GFWWZxpGEJC\n45kixDzRGM9YJ7y1dG5FrvkcYjeGSX4n8iXSKCM7zMqTa+ZwIbvtQ4yEIvTwpVtArSyaFl0VY5oY\nY0RZy/EgU/TOevo8YbTQ2CvgcQKwK5k49kJ3L5nWNVAVje1ogZg1pVSGmNhOI1TEvu4srxwcsQuB\nzjlCTowpkHJhO41MWaLRnLW0xtJ6Rylw0veUmudJtAA+F04aB1+9/iKbaeSd41vUDFNI2BnWt247\nfv21L7MdRn740QeMOfLxpscg2eveGA7bhmXTshkHKpBL5YOTUzonX3W8lai7VdOwDZLfnnPiTh+J\nSQjreo6zU0BnLdo6umZ13qCo82tQwNFiwYG1WK0ZQ2QIEzVnUpJGT8oFrRWt85L1nhOLpmE3TSys\nI6QEWu4X7xydKaQSmeLElEYWrkFpxQvLAzbjwPffPaPkQtd4ri9W/MZrr/Plw2vnzzxcfTFx1euy\ni71nDeb1aY2W+4HhrjJo8UEE+QcffMBv/MZvPKEjel4PUs8F+Re8Pu1N90Gmn3dbXpumeWqAsYt1\nlUjrF88hyLFfhnP4IPU0Be7+i8zDEOavkiB/XMd5sWEBfKLb4lFo6fvf772n6zrOxmNUrfzjB9/l\n57d/SJ4haGIhj8Q4EWqgNYv5i6rFqsoQJ8nnxghgTPt5Eu7wyhNywChDyD1KKxrdojF0TUtIIxrF\nWAJDSlQMsYg12xiLRr7oDqknpcTZtJnt7YZaE7WCUQ1qBsgNMTKEgFKOlOq5dV0pjdKyZ10qpJyp\nsy27rwONa86FulOGKQdUVtSqKAW08gwh4I1YrquqXGsOCCmJvb0WTsctGhHJqcjeurOGXCteO6YQ\n2IYJbQwaS8gBqxuc7rDG0rhMSplQM8MYKRSo0GlLSBE7n9NQElRFAYYo7oHOCY3eaIUqmkLCGcsQ\nJko1GJUpVeEMeGNQpbJ0ndjmU2I3jdzpdzitqciutFUG58Qif9AuhF6fRcifjSNKyX63Mx6opCL/\n/nG/A6V488P3sNpQFXzl6AVCTnx8dsoYBs6Gns0wIYwmhTMt1ztHLoVSEcv5NHFzu6WZHTML3/DC\nagkVjnuxyY8xcivucMYQc8YoTaWybBpCSuhaMbM9fioZpzS72cXkjWHdtTTGUoHTfuC4H+YUAEOh\n0u6tsUWy6KeQGbUmzP/WZpKmi9UaZzXGwGrhMFphtMapJd7JPn8fAqf9jo9OT3CzPTrXgtWWVXPK\n0WLJKweHNE1D0zTnVPDHbZ1+FizOn8drfBAK+NOwtn/Rr+XFepiIt4tguP3PXmQF7N0Qlx0M9yD3\n8vvvv/98h/yS1XNB/gzUJ33x11qfUyjvrrvF0P0sr0+rroIgv9c5HIYB7/2VEOPwdOBjj5JZ/ywJ\n8gdtWDyoLf3uukhL39vVrbUMYcc/vf99fvzxv9C4liHsUGimPJGKENkb1wgcqxpCGimlUpVQzSua\nqhRGG6xzIiRKRCvNmAeMkQxvozVaG/rQY7VmO51RMZQEBYs1lj70aDT9HJGmUJIrrgxQaU3LlANO\nK5QyeGPZxR5NQ6kKoxwahVEWZedVkhywyjLkeaqFPKvWepQSYTUGEWmlFskC1xaFYQiBkhW7cDxP\nwf280w6nw4ZSBJCW0JiZ0D2kSY63Qh8jlEJfBg67I9bdmlIhZigVxnknWinZiW6twyoRdyglsWU5\nkUtBqcI2iH3bovHa4WfBP4YJpTWlZEKuMtHXjsa2GGUxSjHkiJmBbalkBiV74Qvf4q3lwIg9e8yB\nIQaJfSuF1juGNLH0DQvn8daym0bhPqRELkpWCZQiZ1g2DWUW5yknzJwDPsZIyIXGeoyxGCzWSoRa\niIFBRbQWnkhjLSnDtcWCVCSr/XToyaUyxsCyafjK+jpn48jN7QZrrFx3LU3p22cbGu+laWA1R22D\nt5YxRFBAqcRS+GizRdUq0/bZjh5S4XQcznPJK5xD7IYQpaEx/96DtqFxDmthSBOKyjaMfLzZyXtf\nydRaub5YsfQea4R0X0oFKt6JI6APE//ppz/gRzc/5MuH17m2XPJrL3+J7i7K9EVr+2WyTl+2+ryb\nDvejgD/p1YNnoblysR7l9d6LFXAVwHAPwoV6vkN++eq5IH8G6tME+UVRe3fc1sOKoSdZl2m3+WJ9\nmqDc266vSj2p83w3pb9t28+UWX9Z74v71cN+YbgbAvhJz+ij2NL37wF309J/evNf+af3/57NeCpE\n6T6glRExZ1qcycQcGcNAJVOAqiHVisRDKWKOIrrKgFKgMBJzVsFryeie6ohRklkNlVgVqUBRij7I\nVLJQ0WiZTtaCrhqlQFdINaOAXeyJJXPYrskFStJYtaRU0EWBhlCj7FfPsK48/2yt0FhPrgWF5mw8\nwxlLreC0JeVKZ9eAZKUrpVktDmisYxt6hiANilLr7ADwaKeouVDkZWGNxilLLZWcEmvX0bqOs6ln\nFzLHg+SAr3yHonK0WDHGwJAipsLp0M+NgYzGgIK174glSXyZccSaGVPibJhkhSAFln5BLRmnPUvr\nKbUSUgEkt72WgrUGVcEZQ+Nltz3VyhAmci2kWjFKUWpl1bQAaK3IRWjot3c7KrKnbZRGa8W6aanz\ndLwPE5XKGMUirpSmcRanNKczn6AxDWPIGCp93OGNwWqDMxJnVlEoFCd9T62zdqbOAD6ZQHduyS6M\n/Pz27ZmuXzkLPV6L+8EZwxsvvQAoYs60zvHNl1/m/3nrLcYQybVijSbngnOWfgoycQdykdg0Z+28\ns1/RCg7bBa13vLBeMcXEbprYjAO71HMyBbwxDDFyfbWSv2sdLx8c4rXA9j7cnJJynptS4mB49eA6\nKWes0VDXnI0jb9++xc9v36Sxlu+9/TMOuyVv3HiBF1drrDa8fu3GI1unnzUR9yRqz93YW9v3Yu/z\ntrY/a9fycb3ei6wAuLxguAdJH6q1MgwDq9XqCR7Z8/q0ei7In4H6pAdzvx98d9zWnlh9md+4tdbE\nGJ/2YZzXRUG5z16+1zm8CpP9i7U//s/rg3z/JeQipf9RMuuviiDfC+MHPa/7afU+gqxt24eypX9a\nx/xetvTFYoFSip/f+iGb6YxC4R/f+TtOx1Na34mg1JLdHUsm5JGQ5Zls7IIhT+dT010W4Sgiw1BK\nnneVW0IOlJyINc3iuqK1TLhzLqSaiTUBlpAnoEr8lqh5KJXOtaScSTO0TGSZQmuDqgaNTH5zzdQi\nk8hcC6YaUk10piHN4kzrhpILyihiimilsMrQtoeyK45HaxhTPm90OOMoVNbtgs41/Oarv0wsmXdP\nPmIzDQxhIJWEwrBerChy0hnjRCkVjUK7hlQ0t/uJVbNGEeicnL9tlP3tXRhIpdIYy1QK/rwRo4k5\n0xjL8XAmrx2FmoV/17R4J7ZyyoIxKmqp9KFgdJC4saYhAwvj6JwX8noqhJwZZpeEM0as9dXQGD1f\n08IYA7kIkd0asa2vO9lHz0Xy1VPJnEwTXdNwfbFkihGlOM9bzzUTU2YskZgUtYLGnsfBrduWmgtV\nIbR1Z9kMI9oUnIbWeb587Ro3txuxyo9CuFdaoZQmpCQW/Vq53nZkKiiFMyJc//HddyQVYJxIWe4P\nFISYCFmu19kwyFReSURcYy3LtkErzdI7GutIJbOdJvoQ+fmtOxgNjdO0rSKOha5pGGOQabY2HLiG\ns1q4vd2w7hZ89cZLtN5zvNsypiS76cCLBwe8f3LMOyfHeKM5aDpeOZgbQOPIW7dvYc0x//n9t8lF\nQHBHiyVfOrzGy+tDcVMYzdduvCSRdleECv4k6mkK1Yti7+Lqweexx/ysCfJHSRH6pLrsrIAHWVd9\nlu6Dq1DPBfkzXBffRM7Ozi5d3Nan1WUQtveifn+aoLwqlup9PaxwfJC6F5fgcVH6r9r5/bS6G6S4\nXC7va0u/OBGHB7el7+9fYwy+8Ux54M2Pv8fxcAuF5sc3/5mYZRIeU0RpRUgTSul5Zzygtdj6rPZM\nJXFnOiXkjNIaXaE1rcCrrJ8FVQCt2IQzjLGoLMeptUUh702RRKmaWBQyC81obSg1k0sCLcAxpTQx\njegqk/Zc02xF13zzxq/y8zsfcDJuZnEqQsoZx9o4MgWjFkw5YpXEf9UUcdqhCxy2B1hj0EqTEuym\nTGM1xlheajvGOKG1ISSJDbvdnxJT5O2TD9DK0NkGozXrboVG0ceR3TQypUDMsg+sjcUbh1MNY414\nKy6CVDKtMyg0pWoKkGsR+z3M8DdDzEns68YTc+awWwmwrFbZJTeWKUmjJFYNVYsrwWiMKmQKDsUY\nAuJj0EwxUpXQ2lECYMu5MKRArYhDIQpJ3M/Z4946rNbEIg6D7ThSaqVWzonvh92CG6s1C+fYjANj\njPQxiHCWwDI6Y1l2Fl0VsYr9PNdKmmT/3Six+9daOVh04i6olTEnvDW8cnTI27fvoJRm2UgTIcQE\ntTLlRAV2MZKpvHooQvUHH31IYx1DCMSSON71LBrPydTTOjffm5obyxXLVtYxci5kKpthoHWemCON\ndRilOegWTCkyxokpj3xwZ8MQJfZNz02Aw27B0XJFnxMH3ZIpTHxwfId3bt2kKnUuoHOtHHVLfnbz\nY07HQXbzU+at/pY8T1RqyfL8GoNWjtZZ+hD48ccf8v+9/845kHDhGv7DL73OG9df5KX1ITcWEkv3\nSZO+Z0HEXabXqLX+3PaYL9Pr/LzrSX0PuExguAe5vmdnZ8+n45ewngvyZ6DufjjvtgYDVwYwdrGe\npvC6e1r5MIJSa03O+Qkc5eOrx3WuHzSK61HqKgny+x3r3WsPbdve9xn9rLb0PVxob0t3reVvfvy/\nMaaRIe7YTmdsw4bWSKb4UXcDBcSUKGRiCcQcJHcMzTTHQ+UUCLMVWquKrgq0TGplQizkdKP34K2F\niN0iwLVt2M52aEmULoDSsqOdS6bmQq6yqxtKQSuDUZpmprLHHHHGMUYhef/k9nvEkmitE6u5ceSa\nSTlxloIQuq1kmTe2obUNjfX7K0QfElDwxrP2S9ZryzYMbMeBs2FHmQWiqrDwHVBpW88QZJJ/Om1F\nHJdKaz2qQuc817oDjHZspsCYIkNInOaNCLuQcFqO6XbYyKS2Mlvf5+z1HASAhuS0W6UJObILI9sw\nkEumcw2t9YyhYrSnFkWuFa0UtRROwohWQnPXGpaduB5A7OA5Z8ZppCLQtAK8vD4klyITc62IuTDO\nYr+WQkFo5k7rc/s6FcYcyUXs7nf6LRUR6bkoDv0BGhHyqVSmGMlBVnuslb1/42QtQGtNTIkpJUqF\nWuXf1lqjleJfP/yI144OUUBIke00okqlKoW1Ync3SsSpn5kCx33Pne0Wa/YCR/HK4QGvHR2hUcT9\nvV0l1/xkHFg5T+sdnbO8dvgKIWU+2mzYTROd87x95yZjGunTKLDDWmmd4eXuiIPFgqXzHPdbToee\nkBJKyYpEax2NNqScGcNEAdZtx/XFkjc/eg9vHHr+uwvfsAsBBefOktvDbrbRK1IpUCtOW9Cw9rK/\n/tHmmOP+FGcFJvfy6ohXD65z1K14dX3tFyZ9++byVXlP/aLV495jftYE+ZOeBD9tMNyDOAI++OCD\n50C3S1jPBfkzUPsv/XfHIe0nuScnJ1fyw3Y/IX+SHzAPOq38pLpKgnFfexv4Z3FPPMzO8+Ooq3R+\n7z7WB117gM9uS99D2gCOp5v85M6brNsjPjx9l3dPfjZDyjJetxw0R9SaiTnx1p2fMKUBrR1aabzp\nMMaysIazOBCrxGaV+XnMJdPaFhF3gaKzmKg11Nlqnovke4ccqWimXNGqQWtFygGDotZMSYWxBECI\nud54jDMYbWRPfNqhgKlEDI4pFlq7RGkNFVrjSVWaYCFFjJa9c6Nlel0BpwxDGjHaEnKWKaNesHYy\nad6Ent14m1gTzrhZBGkMRgBqwMfb27OFX3aYjTZ4LVPjMQZCDFjruNn3oHp09Sx8ey6ufDacjQPW\nGE7HHQbFVBKtlXg3Z+Q1g2SHx5RJMaCUWCRTTlxbrEWwodHKkrMIYqphjJPshaPIqnJ9sUYpySlX\nStNPk8Sf1YrTGmM1TokLAaXQCl5dHfLOyR36OAEKM09yjdakLCI5lcwuRWqVe1opEXarxku8mKpo\nDCkVQlLcnCRGDCUiHRStlWZDyJlQk0SEAUorWudmGnulVoNR0IfIywdrttPEre0WgzqHEb44C/Qx\nRphXKLzR7ELkvdMTjNKsuxaF3JcKePvOMW8fH0OtFKA1lsNFRwWua0OmMATJFI95g1aKo65lzDCG\nhLdgncPFSsgWrTQ3VksO2gUn/Y5tKXS+nXf9Ic1W/CEF2YpXYKzDa8Wqacgx0SpLyolUK2NJdFaA\nhEorYkmUIs2lGBMoaGZLfakFpROJQI4TsSiWbYsKI6um44c33+MHH7+LCYOFIQAAIABJREFUNZaF\n83Su4b95/ev80rWXxKkS4/ne6V4cftFE3cPQuJ9Wfdoe84NMYq/C63xcdRmaD08aDPcgr/m99957\nLsgvYT0X5M9A5Zw5PT09n+Qul8tfeEPeC9urYlXf1+dhpb5X3Q26+6Rp5YPUZbDaP2x9lizye0Ha\n7rfz/DjrKgryB92j/6y29H12eIwRVOWHd/4z6/aQ/+tn3yGVCFRqVVjtyCVjlKFPW4gwxO38/iDQ\ntVAypQTujCckZNLqbYNWlta2NLahcwsqlZBGlLY0tiEV2VdNFWKJQl1HxGVGUYpQ0IsSUFZjGlIR\nobhyS6YUcFqiv6iVYRrkPUtBYztqNRy6NU63bMOIxJUVck3kqnHGSJ61W1CBWBMpZcY0zUR3xWF7\nyDQLlpgq23Qi0DmlcdpQlaIzs125ZsYkjQ1V904kTakSlYaCMY7z/nSl8x1TrpATIcoUeZsG+mmc\ngXdivd/Dvjrf4rThJd+yv5unGEglU5F9ad8ZapHncxcGFr4hlcKUQBUhle93tLWCVdvijOH902NS\nKtxR23MKujOGRdPgtETRDTFilGEX5TUWChrN999/i844ai6yupCrWMopaCUTH43CGzdb6qEWEeln\nfY/SmhznZoVxrLyZGzhlpszLfTIhE+39u+yybTAotiFwvN3hnSMmEcTybyLgtGGQKDnvuL5ek3Lh\n5YMD7ux2TOOA05bWWYzSXF9YYpYd+ZAzGphSxlnNuvEcdB2A5L6nzJ3dDoWicZalbzhsW7RSnA4D\np8OAtQqM7N17q+nHiTKD+w66FlVlav2Vaze43e/YhZEhxNmWDss5fcNqmdLnkgkp8+HmlA82p+Qi\neesGibajCtIu58S+k5BzxRpD5yVn3lmNtZkQ6+wuEKfCR2fHWK356OyONGCMJVOJKeG05mTY8o0X\nv8w3X/4yh75DV2mq3G1t3wuJpy18HrUug3h72LrXHvN+Eguci71nNZP+sl3TJwGGe1BB/pywfvnq\nuSB/BsoY84mT3KsoEPe1P/bPo+N7N+iubdvHYq++SoJxXw9zzI/DRfAo9VmaB0+j9uJ6t9sBfOLa\nw9229P0X4Iexpfd5ww9u/RNH3XXeP30LfWaIaWTVHvLC8lWmPLAZT0Ws5oDWipgD1lxnKomvXP86\nXlumNLAZT7gzbphKYJh28wROn4O5tmGD0VZAXrGX3WBtqPMOr9eeUDJjKfOUWwRVpZJzwmhLKhmn\nncC4SsQZK1AuZai60mqxhxus7F5niGj6uKO1Lc7Y2Z6s2cVBotHCwKAkGs0bQ+dbutoy5ok+jZxN\nI6VoWuOpaDov8K1aJT5MsrMTtVSs1lQ0B+1CIr80pJSpSjPEndjAlUEpR8yZNGUoSrLCFeziSGM8\n3joWSnbDF64ll4KemxIxZ07HHqctxmga62iUm6ndA9vKPEVOOCVfyldtx9I5cgWTk7w/zs9vnpsh\n//WXv8qqabndb+nDKJTy2Tp9lkYU0HlPiJFV08q9lBNDSlitOR57mSZnUFRSLix9Q0GI6ilXhhzm\nab4Q1GsxXOsO+J2v/hI/+PAmt7c7+jAxpTzb/oXEvm5anJWM8FILIRdSyvShJ9d6bt3PKaGVZtVY\nSs4CyAsSbxZyZkoJhWLZNNzcbrFac22xFEDeOFGoUCvGGhrj8EpzfbGc72EBu71/esr15YJS4cZ6\nRUyJkDP9FLjT91xfdHy0PQUKq6XEz4VkSEogeCjFjeWSa4slh23H8dBza7vh9m4rzoQQOOyWKA0h\nZdk5Twk1b/K31qG0orFOPutqkWZQKbOLQ9afvNG01rHqOkqpxJJIJRBKYgoJFZGMc+Q+ssqgjTga\nKpUcC4GIUoqVl/N/a3fGZvox//zhW1zvVry0OuSbr3yFV9ZHtLo95888F3+Xo+41id03Yi9OYvf/\n/VmoyybI767PAwz3oJb13/u933vk439ej7eejafyGa/9A/9Jf35VBfnjFrd326s/D9DdVTzfn3ae\nL7oISik0TfPUuASXveFRSmEcx3OaedM0dF13T0jb/u8/ii19L/TDbmA3nfHWnR+xbNa8fvTLvHL4\nZf75/f+XW7uPePXodRZ+xZQmsQ+jGNLAlDNjnri1+5iQI61t0Vi++dKvcdAc0MeBd05+zul4Qspp\nbojIzm2pGWeEOh1KpFRDpiVVg7UNR9oSSqCUilVaosGs2MGnefo85UBrG0otrJpOdsBzQStHzJL5\nHFOhs15+h1sy5pExjBJXpsAah9bQuQ4z71rHnOjDqQjk+Tw7I4Tj07Ahz5A5KlRVoUq8lpmnGCkl\nrIZbu9MZFgcUyFWxbFpiLpRqKUXjTUMpiUzFGwsorNVYY/nNV9/g3ZObfHB6h7O8QykRV1opGmsl\n4qxEwpAF3oXsooOSKTqepmtIJZOL2LKjCjTG4q3DG82UEloZxhTIqfDjWx+i0bTOsguT0Mzn3Wo/\nA+zGGIkpkWohpiR7kcYSS2bZNBht0ApCLlgt02BpqMi0tXMNnWsoVcvPpUIfEm++/yEfngoF3SjN\num3nKbpY5V85OiSkxPsnp0w5sX8qrDb42a1gtCbN0/FcMlVVrLF0jSfmwo3lEqclb30XAptxPP+i\n6ozh2nKBmYGApYqAddpxp5ckgH3+d+cctzZbSoWzYcBZS2sNR4sWaxTfePkl/uG9wGbccTb0DHFC\nK8OUxOJ9tJD88NvbDW/duYXXBu+crAigcMZxOvaUUlm3Eht30EhjI6R0vhYQUqS1nqrEkp5y5Vq3\nJJYsFPiUOJ1G7gw7jFYYk/BWsfYdXXsg6xhxYkiRMUYq0qRbNws637I6bIU1EOXZl7i+QkyRWgtv\nTz3vnt7izY/fpbGWl9YrbizXvLCUa0UFbywvLa7RGHdp85k/qS67eHuYujiJ9d7/wiR2z7/ZuyK/\nyJn0V+maXmyoPApp/0FWEt5//31ef/31x/0SntcjlvqUL66X91vt83qoCiHcV6TswVHL5fIJH9Wj\n13a7xTlH0zSP9HseVwb2g1StlePjY65du3ZlPiz6OXN3sVj8wv9/UVw+ThfBo9T+/F6/fv2pHcPd\ndS8af9u2DMPw7+7fR7Wl750J3vvzzvrfv/U33Ok/Prej39x9SMqR1w5/iaVf8ZNb/8IYB7xtUCj6\nKGCopAwoTWMbAXbVSihBLNoAWnPYHnHQHGK04Z2Tt+nDjlyE7B2zTPqMbShFEUsllQAoibpCY7Ri\n4ZZMaSLXzJQFUuVNi0Zi1aYyoauiqMIUCwu3QisDVZovVUHMiTEGmK3Vbt7lNtpInFbJM1ROUVXF\nKAFnpVrw2rKbAjFXSpFIq6IqtcgueK5FbMHzNfBG4q2mlNCARoM2NKahFkciYRCLfCnlHBJntSYU\nETAxZ7muWrGwjewMa4HbbSeJiwsp0FiPUtDOeejOGMnD1pazMRGSmpsVzbmVPedMyImQxZZf53iv\nxjixnisNpcqxAHYG5o05YZRQ1q2WnGSLTGZb5wkpE3LEGcMuBJzRtMZRFHTOUXJlypEpZVKUaLJS\nQCuNNprWeZbOkkqlnXPNc62cDSOpZLSSZga1shlHzCwWUinUksX+v38t2tB6h7P2fOc6pCzio5aZ\ne1FpnGU9W/VzKZyNIzEL7GzVNnTOs2g8x31PyplhmtiEQGstrXOEJMelNWhdmVICKlOZaJ1khsvq\nQ+RsHOis4/XrN/jqCy8yxMRPbn5EnWFwY0w01pCLXOtufq+s83PfB7nuMSc677HKnMe9jSmScmFK\nEaMEgCfPECwah6Ky8Ga+vpVSKkMMMOfFL1zDsm05bBdYbcilCvE/jkwxUpCm2Lpd4rRm1XRMMQoJ\nP0k0n9KgVcFYOcfbUXLmT8ctjfN86eAFvn7jK/z+G/8Fa7cg5/wL1vbLvLu82+3ouu5SH+PjqO12\n+wuCD7647ob997k9YO2q1sV1hD0Q+H7XrO97mqb5xCHSH/3RH/Fnf/ZnXLt27XM/9ud1z7rnQ/Zc\nkD8jFWO871R2P9lcr9dP+Kgevfp5otHNu34PU/s3uYu7u23bPpEPpePj4ytFtr/YtLkXIHB/3i5D\nXaaGx93xbvtGz/6673a780bGvSBtF//zfr//IiDGe4/3ns10wvfe+T9ZuCULv+ZfPvo+Q+hx2tP6\nFmc8u2nLcX9LaNOmpY9bxjRIjrddUqlMM4XcaE1VipVb4YzHzDvSIU8yRdvD1rTH25aQi2SSF8Uu\nDFRAK8PSyWQy5ii55Wlkv/tqlMYZT62FhVsSiljFRURbGr2iktF4hih551rr82m+VlqOC5l4ln1j\nowptfWE7Uk1iBa9Cey+loKpniBPeNrOAc1glIj5k2de2WpNzoZBRiPVeVUhkOtOChlossVasMiil\nUcoIFRwlFHLpTdBYh9MWZ+2cya7ZhVFo3jVhtcXPcC09Q91yKfTzJBs0pSiM9jK1V6BQQurWMIYo\nw/paaZ0j5oRGk+amgjYCwktZQHTWGL76wovc2m7YjRNKC8Vdo0glk3LGW8tmmqBWrBYBufDNeZRW\nriLmpljw1uGUoxS5MqUUSi3UClOMNM5S52n7SwdrfvWVV/je2+8whYk8H/eUMt4YIbvXSmNlWq2B\n1w6P2KXIcd8Tc5lTAgReZufnSqMIJbEbA8vGz/ZumXznWll6z2acGGJgShmlFa9fu8Zp33NtuTi3\nxav5+Rti5Ha/wVkR3jkXrDXUUtiGCaM1R+2CLx1dZxcnToeB7ThitWaIk0T/obi926KV4tpihbdG\nIHNIn2eMSeL4rMS0aRQL73HW8luvfYX/9NMfMkaJ5tNas2ya2S+RmdJAJYtLxBg637A3bXgt/84Y\nA0Oc5ihCuQ+XvmPVtCycxxnL2TAw5sg2DDTGMoZAKhGjK1obvFX0QVZUtmGY6fqF1jZYbdjNrpT/\n6kvf4OX1C9Q5DWHlO7527VVeWl1n1S4/N8L0o9SzJMiXy+W5k+zu6fkXKZN+P1n+JIfoVauLYLic\n878Dw/V9/6n38R/8wR/wt3/7t5fq+XvG6rkgf5brYmftXn+22+04PDx8wkf16DUMA6WUh5ru11rP\np+H7DOymaZ7oh8/JyQnr9frSiNhPq4s74Ret1k3TXMo39afd8Lg73q1pmns6B3a73Xnm7EUh/iDZ\n4fvViv3PO+f4lw+/z4dn7/Klw1/iXz/+Rz7efIAznmuLG/Rhx3bakEpkCLsZAJXF6lwLRnsRhKYh\nlUShsrQLtLZiua6y0zumkaoKqioO2iOBjFWZPo8pMaRIxuKM7JC3dsGURmIR6nalnoPDrDIkMl47\n2RcuiVolYswqjzedTFeVZTuNlDK/hymFV46ETIzHOM2ir84NBo8zMzBunuznmkg5Q1XkrDDIXu6i\nWWIUGCMxYyFFNpPs9SsU1histiycuBg208AQBkDx4vqa7GArNVPGK7UoKgILYwacdb6RrOwik2sh\n2UsTwyiNVtDaRvK7VSVEyRVPpWCUBQxW+/PGQEUzRXE9ZaCxlinKPnDMGWc0qVRSzijUvDpgMVqf\nZ1ivvGdWnBy0HX0IxJIppZKLnNfONxglNnil1DwZHwkpE3Oej11yyDUaXQUOt50iKGlkTCnTOtnn\nt2ov8meyfuUcgFcqLJxY9b2Va6cUaCr9FIil4J3nV199hSkEToaBw64jl8KYEie9NJOM0hiluLZc\nsGpadjFwNgxzUyOefxMKOdM52dpbtx2//tqr/O2Pf8IQo0zEhavGwjs6b1m2hm+89Bp/9/Mfzm6K\nSMiFtZdplNUaby3XFyvWTcObH7wHCq4vV9zabPjZnZtopVg1LaUWjrol+zT5hf832v/CN3hjubZY\nEHPGasPL6wN2YeTD01N2cWSMkcKEVoV1uxDngW+4s9sw5cgQg6wAUOf7MuOMobFO1ghKpZSZ1h4D\nZt5lXTUdTkl83MnQsw09SidynVBa0U+DuCsqjDngjcNpwyvrGyx8y8FC9uxv7U44GTbS4LKeMU2s\nu5bf/cqv8d+/8d/eU0g87c+Ri0L1i1p7Zsn9Xue+wbsX6I8DMvY0a+9A+yLvzF9sqOy/4+9f872u\nWa2VP/zDP3wuyJ9uPRfkz3J9kiAvpXB6enol7St7C/BqtfrUv7ufhocQPlEkPYk6PT1lsVhcic5t\nzpm+73/BRXAZvkB9Uj2NhsfdzoF9w+Jex7Dvcl8U7fsP0U86r3uhv78W3nuJLuxvswtb/vrH/+ts\nGU9Y4+lcx3bcMKZ+hnplYkmyK6pFNMsUWM1U8jzvMLfnsLX9jnXMidY1dGaJd57ddEYfhxkgpVFK\no7Wns4vZ4iuvO6RILJGYA6kkvBFhW6jkkuaorDJTtz2lZnIx5KxlP1WJhdwbhzVizTXKELIQx0We\nzvTzWvHOMsSJlGWH3Wg7x5p5jHJUhCYfk+wn78IoFvIa52z0ysJ1UIvEs80grSFOOOOxRtMYhzOO\nX3nxdd49+ZgPN7dJRUPVKGS6XYGSC5l5zxlpRmj0PNlXOKuZYpRd7H2uNhVvHSWDt4bdpOYps1jL\nU8kYLVZmAahlcq24c2dPFUv0HN/VeBFNRkv81b6mFIk50zl/PsG+vlieZ3GfDiNTimIXn6ftqWS8\nsbxycMTrRzf4mx/9hCkUpiyfL4um5VdfeZlbmy27MGGUpipQtXI2Teh5ol6pdM5TS5GpdRFbdal1\nhpIVrJLXve46nJXJcevmpklMxFLoQ6B1DqMUC9/QOUcfJoYY6aO8vsZYlo1HIfF1oCQ6rFRCzuym\nkT5EDtqGxjqshkRhSAGrZEVDrors9Meccday9g1aaY66Bbf6DU6bGcwWeGl9yM3thpAiZ9PIGAOv\nHV7j93/5G0wx8e7JbUKWcznlhEbxh7/2W7x6eMT//oM32cVAH0a208TJ0AuDISdWjeNo4Vl5z5gT\nuSR200jIidY2KCqNa7BakXMl1SxT61KgFoyxNPN+/8JLWgCAVUJVjzmzmXpijVQKqMTCGVnvqJUx\nTUBl6TtePXiBb7z4FZau4+bumDu7U27tTjBa07qWV9cv8Ma1VzDG0jnPJuwYU89/+NJvMsSJd07e\n505/wu9+6bfQ6Afekf086tOE6helSikMw/BAA4yLkLG9tf2zQMaeZj2IffuLVHvIonPuF67Zm2++\nyde//nXW6zXDMPAnf/InfOc733nKR/tM13NB/izXxTfVu+syWXwftmKMDMPAwcHBPf98D2nbW64/\nSSQ9ydpsNucZ05ex7j5v+zf4o6Ojp31oD1Snp6fndPfPu/aOi3GUSeknOQfutqUD5/azvWXwbnF+\nP1u6RF1t+T9+9L9QK9zZ3eR0vDNbr6EPG3KJkpGtNLFE0BpwxP00uYpwr1Ss9rS2YZgp2wowyoBS\nOONx2rELW/ZeWLEdN4AjVTVnWcOYBpx2GG3Z59c7Lbutp5OIFKpYYIV2bkXyz9FkQ0hoGlCK1ogF\nd0+Wrgjt22iNt7N1HjXHm8m0X4k/nsa0s11dkYsmJpmSKySHWqa3iVyy5ECXjFUWoxQhZ4yRSWEp\nVfbcm+58FzzmxHYUu/GUBLRlkH1mmdjOmelKwGdOO7SWzO40v449MG2/26xnyJgzjmkqxKLINWGU\nk6mvVuQiwntKcg7Ecm6JOZHmKLa1bylUnDLnmeIKiLN1v5TCqmlprezBVyViPMRIRbEL47x3L5Z2\nZ4RUr5QSeFsQsN9Ru+anN2+S5rgybw1aGw7aliFMnI0jc3w5pVSWjZcmj5LfWavs5/dh+jc7/9zE\nee1wTR8TZ9MkjY2ydxMooc0beW1aaXLJ9EGI7iEnGmtpjKH1Hq0Vm2FiTBJbpuXWENiVNefRblop\nPt6csg0j1iiMyVij0Bis1Sy8xxvHjeWSWjkn0qt5kl6K2PYrVUS90Xx8dsbZ2JNL5YXliv/pd77F\njz7+kI82Z1ijmFLmjes3eHF1wDvHd7i2WDClxI9vfsRuGrnT73DWYhTiNGkcRoX5PMhEXsBzwhYY\nYyBmabiFFKWBoxTWOF45OOLl9TU+PDsRAN00UGqRNYMkgEPZu6+EPNHYCqqysA3OChNBI5T8pW/Z\nxgFV4bBb8Ttf+Q2O2hUn/RnvnN7kw7Ob7KaJQuaF5RHXF4f8j7/+3/Hx7jZ//bPvctyfcnN3m1Ir\n1xdH/A+/8i2+duN1iYSbBwd7KNmTsrbvBfmDNPavcj2MIL/Xz+6vT875Sljbn5U1hH3tG/Z71s/+\nmv3pn/4pf/mXf8lv//Zv8/u///v86Ec/4s///M+f8tE+0/VckD/Ltbe43q+umoV6XyklttvtvxOK\nd8PG9uL3sjQcttst1lramax7WWo/td3vXu3PWymFzWZzZQT52dkZXdd9rg6Eux0X93MO3I+Wfq97\ncf+cxhjPgUh7OvRFR8fpcMxHZ+/yvXf/jg/P3kWh6GzHkHrGOKK1IRWxaMeaMNqRaiXVMotsZuiY\nO981zUWI6K1t5Wdrnonk03x0mloK1ngSCqoWu/psYRVoWIvSEHNkCPKlPxXZYVbG0NmGPsq03miD\nxuC0JxUISSbfS7+gdQ2xJGKK7KaBzrczTMxilWFMQvS++PEle7MapYT0nYshlUguAn8z8x7vmGRS\nH+fs5sb6eY29kkqGIjnOTssuudaKXGcRXeQfrMVjtROoF4aQIyGl2RosbgGnRUxMeZ+//m+xdU6b\ncyFdqliLVYVcIBeNRu4jaxzeSNZ4zEks5bPwa41jKpGawRgN8+8pF6LBBBhW6VwDVJZNQ8wSr5Vy\nmYFzVlYAjCWkiFaaKUeheSeBd8Usfy8lPTdhZNLfOYc1Zm4wZLbThNWaMk/5qdLMEJAehChANIXC\nW8vv/crX+OlHN7m12UijQonI/uUXXuRsHDje9bTOiovAWKacZ3o/7OPUOucAResspVb6KK6LMMei\nrduG3/7qV/mHt94+t/SPQRgG3hqMEZL/fPYoJbNqF0LFL5UyP6/7Z7e1TqbA1oOqnI0DRmmGJNb4\nXZiIKdE4z2Hb8NLBEQvfcGe35bBd8Pr1G/RRwIWn/cCyaVCqcme342TYSS65Fhv/FBOdV1hXWDcd\n1xYrSinspkCqcg9bbWhnl8MUA0bbGfwmorbO7okxRhrr2Uy7OcZObPMgOfJjHkEHGmNINQtMsCpi\nThw0Ha8evEhV8OWjlzhaHPDW7Q/4YHMLauVWf8LCdVhjaG3D9cUBL60OeefkPX739f+SVCIL1/Hd\nd/6Jv/75/42zjuuLI15Zv8jSd7Sm4eX1C7y8foFvvvC1e+7Ifp6T2UcRqlep7hZsn7XuZW1/kg2U\nBz3GZ8H1cLH2VPZ7MZVOT0/5zne+w1/8xV/wV3/1V3zpS1/i29/+Nt/+9rf51re+9ZmGQ2+88cb5\naqBzju9+97scHx/zx3/8x7z11lu88cYb/Mf/+P+z9y4xl51nvefvva619uW71MXlsp04CaG5NCcH\nUKcbgVpigNIKIEEGIKEMmERijlCCmCBmMCASiBmTHqKoBVJLadTdgm7otA6HhnDgJCEJuRA7tqtc\nVd91770u760Hz7u3K5WyXXbZ5Sq7HqlUcrnq22uvtfbleZ7///f/3CNph32b63FD/l6u12vIH0QD\n83bU7XL7u8HGmqZ5KP1D9wOje6vrbgTwO8/bo2ZreLsUCHfG4t2LLH3bhMG909K312L7c7cbCWMN\np+Mt/ssL/5mvXvtngJ18OeQJVSQHfBPHuk1tmIoArUKSjZ5RBqVV9e9K3JQ1DqstIQc08jOyxDSj\ntUXaUYvXlvOpRyuRdhfAGy/EczLn0wqtZHMr1GwBiAlwzYrXWFtKLpwOa3LWONNgldCqQ4oyTEih\nNm2OVCnlfRzw2tKHAWcaqLTwpjamuTbLEjem0UUGcJrCFDPOGYmRKpKVrepxKq14/8GTHK3PBHqm\nNFkJgTrnUq+fqQA2hzMehch4t8RyawyNsUL6TokpiId6PY44a+r1Fw+2VppNGKq3Wza/qsgQwGhN\nyuJdj7lIsx/LDiSHghQTU5bGUWtTP9qlobfGVtl3whpLYx1ea0LJVdqed0RvRZXJUzBKMs5TSjWf\nOuONFZuAtlA0fZjISeLQtDGklOi8I5fCsmnw1nE+iLoi5ESIAqxDKdp6Dma+Eb949bd7a6otQeON\nYdZ4ShHFQUiJs2HcWa1yyWgkXss5y1gz0c+GEV2vY6mN+tw3TClilUZpTWMNp70AxwTSLmA3TOJg\n1nI4k0ZsNQ0i2x56phBRWkk8nHcCKANSkuFKzJkhBGKK1dIwoZAm+NJsgdaa/bbjyv4BH3nq/fz7\nrZd54fSYq3sHKBTXVyec9j3HmzVDmJhSprXyumq8xSnFvLHMmgathcB+Nmwq08Cw386ZUmAzCj9h\nNQ5oLR79zjVcmu/hjauNe2IzDUwxyjBMGVKJPHNwCe8L37r5XVonkveDbo/W+d17y+2+/IXriCnx\nwtkNphxxxlZ7iq6AukxjHc8cXkapxAcvPMMXX/gyX3zhS0xRBmpOO+Z+xmG3h7eehZ+hUDy5d5kh\njnjj+ImrP8pT+1dECaP9921m32pp+1vVqD7s9VoN25utOyFjpZSHQtr+XlE93F7bNKXXSh36sz/7\nMzabDT/zMz/D5z//eT7/+c/zb//2b/zcz/0cv/ALv8DHP/5xrly5ck+P96EPfYh//Md//J7vhZ/5\nzGe4ePEin/70p/mDP/gDjo+P+f3f//37fm7vsnrckL+Xa0t7frV6q+LDHnRt5fZd1+0iLh5m2Ni2\n3gyM7q2uO+F2dxLA7/y7j5Kt4a2+n99ILN6bpaWHEHb5sNtt+PZalFI4Xt3kSy9+ka9c+yJWO4xx\nDGHDejpFa0cqkVQKU05MJTOEAattpXBrAakVcTIrpWvutquNaWZMArzKpRCKoTFd9W+bGqEVoMaF\naWUIKWC1eLlLkYZJFchKPNhGGQqZvWaPTGGKI7lASg6tDDkXVuO6RnlZzmt0UsyJuZ/X4004LZvk\nQsFri1LSuFK3wWMUqTXZ1iZXBPd9GAkpgew4efbwSW6tzxjTAGjag2zZAAAgAElEQVS8Npi6cX96\neZEXV0dMOVRPeyFlGZ6MsVCSYe4lSaBk+Xkz67DW1Q13Fno1EvNlrSWntPPllzr9CEl8za3zMiCp\nsW+lSp6VgpAjqli0csQovueURQ2gVVUCqK1vQO6fVDKt9Uwx0jgnMupS2ISpeukL3hicscy8p3Oy\n3e1sQ8qFo/U5saS6bW3orON4HYipVEm6+K0ba9hrWhZtx6JpOO43pJQ43gg0zRiNVZo+ymDHalUt\nD5ZCYQwRRRHpuNbMfCObfK3oJyH6W6N3ML0LsxmbSfzMKcuxnG16SpXTg8I7GfQIaCzirWXVDzvL\ngZw3ReccISc6Z1BGhhOQidWSoJRmr22rvL4RKnwMnA0b+ilgjNrllvdBPktX44BBk8jstTMuzZdc\nXixpvSVndlFhQwjstx2H8yVffP5bnPUDWhWGGAkx0jlhQXTWEMqEVnlno2icqFi886hSyEW4DEOU\nqDKKqDsa65k1jTTqMdLHkfUoA5KhxvNlxLbQOYsxmUxhNZ2h0Cz9rBLeLX2YuLw44Iee+ADv33+S\nr1z/FifDiuvnt9hMA6f9iqvLi7x/70nOxjXX1jclrk4HFn7OS+fX2EwSlzamgNWWxjg629A1HT/z\nvp/Easd3z65xY32Lg3aPS/NDDmd7nA4rxjixDmc0VhQzy2aPpV8w9x0/cPChHcjqrZK2v1ca8q3y\n6u1U5t1N2v5OsAG2CrZ3u+rh9rqXmLfPfvazfOQjH+ETn/jE7s+uXbvGX/7lX/L5z3+eZ599lj/8\nwz+8p8f74Ac/yD/8wz9w8eLF3Z/98A//MH/zN3/DlStXuHbtGj/7sz/LV7/61Tf/pN6d9bghf6/X\nlo59t3qYNrb3Wtut7jiOjwxsbFtvBEb3VtebhdsdHR1xcHDwSPixbo8Tu5/a3mNbwvyrxeK9EVn6\n7ZVz3mWHb2Xpd97DJ5tb/Ofv/N+cbo45H085Xd8ilCCe1QyowiYGUBJNNqWJuZsTSyLlgOyI5e9p\nZGNplciqQ5LIMqMMUbS5GDMj5kIfJYorl4y3km1tjSPksJO3hzShMWijMFhmrpN/p1T1ORsUhda2\naOXppwkwdLaldR6F5mQ4I+XMwnccdPvcXB+xDgNTnLBGQ1E0VmTSU4oYLSAz0KQk6gBFpYrnXPOo\nI06Lv1y83AKaUoDWCqss63EDKLLOUJRI07P4qZ1uyEm2k5JnLmoBiYvy9OMowKxRtvYhRYoS2FUo\n4mMPOWO1Ehq5lixzozSqyOZxTELznmIkZPFV5yzU9ZR1HS5sN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4Y40RiHMQJenJJI5Eul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V6IA/BK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\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vOagPudZcJcSOTGVMitTRHWOk0AUhpgA8G+wgSR/S7KVhb7RNqXMCkcZ2HDTXhtqziFGB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\nHDx4kKuvvnps6jbmTGcsyMec2zz88MNIKXnXu97Fxz72sQ1B/nQunK9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WL3JtDW8/Hsfl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\n27eja9eut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"text/plain": [ "<matplotlib.figure.Figure at 0x11fb5e9e8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#set up a plot\n", "plt_az=310\n", "plt_elev = 40.\n", "plt_s = 3\n", "cb_fmt = '%.1f'\n", "\n", "cmap1 = plt.get_cmap('gist_earth', 30)\n", "\n", "#make a plot\n", "fig = plt.figure()\n", "fig.set_size_inches(35/2.51, 20/2.51)\n", "ax0 = fig.add_subplot(111, projection='3d')\n", "a0 = ax0.scatter(chunk_x, chunk_y, (chunk_z-min(chunk_z))*2,\n", " c=np.ndarray.tolist(chunk_uncert),\\\n", " cmap=cmap1, lw=0, vmin = 0, vmax = 0.2, s=plt_s)\n", "ax0.scatter(cpos_x, cpos_y, cpos_z, c=np.ndarray.tolist(cpos_z),\\\n", " cmap='hot', lw=0, vmin = 250, vmax = 265, s=10)\n", "ax0.view_init(elev=plt_elev, azim=plt_az)\n", "plt.tight_layout()\n", "plt.savefig('thefig.png')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### now pull in the photogrammetry cloud\n", "This gets a little messy, since it appears we still need to grab X and Y dimensions - so still 20 x 10^6 x 2 points. Better than 20 x 10^6 x 6, but I wonder if I'm missing something about indexing. " ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": true }, "outputs": [], "source": [ "photo = thedata['3d_photo']" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<class 'netCDF4._netCDF4.Group'>\n", "group /3d_photo:\n", " dimensions(sizes): phony_dim_0(20151164)\n", " variables(dimensions): float64 \u001b[4mB\u001b[0m(phony_dim_0), float64 \u001b[4mG\u001b[0m(phony_dim_0), float64 \u001b[4mR\u001b[0m(phony_dim_0), float64 \u001b[4mUTM_X\u001b[0m(phony_dim_0), float64 \u001b[4mUTM_Y\u001b[0m(phony_dim_0), float64 \u001b[4mZ\u001b[0m(phony_dim_0)\n", " groups: " ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "photo" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": true }, "outputs": [], "source": [ "photo_xy = np.column_stack((photo['UTM_X'],photo['UTM_Y']))" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": true }, "outputs": [], "source": [ "idx_p = np.where((photo_xy[:,0] > 0) & (photo_xy[:,0] < 100) & (photo_xy[:,1] > 0) & (photo_xy[:,1] < 100) )" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.collections.PathCollection at 0x11ea8f4a8>" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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pnBlWplUKyvbcd34EZi3w3J3VeXYO9UxUiJN0jJyCm2sLfC17DDlxYERb3+vA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\n2WUcX9iN2d4vIraJ9lMQyNyEJfQ9CLlk6F7uU6ujLh2bMgQdm1opWb4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5AAAg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\npDK2iXa1KLpyUUpbZ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"text/plain": [ "<matplotlib.figure.Figure at 0x3fc59f390>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(photo['UTM_X'][idx_p], photo['UTM_Y'][idx_p], c = photo['Z'][idx_p],\\\n", " cmap='hot',vmin=-1, vmax=1, lw=0, s=plt_s)" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": true }, "outputs": [], "source": [ "p_x = photo['UTM_X'][idx_p]\n", "p_y = photo['UTM_Y'][idx_p]\n", "p_z = photo['Z'][idx_p]" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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DoGIQGSdnIt8MQsCQYoMTA1JxK5fLSKfTSn0LGzdG6EVVQWwNtVoNADA0NNRT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NY0O30gWzeBhXK5HLshjFcGlfdAEgkFzsHBW+IczWSB0nhLXDaSeKLl7O7u5i//8i/ZsWMHO3bs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\nk3gQjBK0SwcLILJbh7x3pY5ZJttxfBNkxsbGqj4iG8zJCn2S6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DxA71hBzS6BqGQSJVsq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"text/plain": [ "<matplotlib.figure.Figure at 0x130428588>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt_az=310\n", "plt_elev = 70.\n", "plt_s = 2\n", "\n", "#make a plot\n", "fig = plt.figure()\n", "fig.set_size_inches(25/2.51, 10/2.51)\n", "ax0 = fig.add_subplot(111, projection='3d')\n", "\n", "#LiDAR points\n", "ax0.scatter(chunk_x, chunk_y, chunk_z-50, \\\n", " c=np.ndarray.tolist(chunk_z),\\\n", " cmap=cmap1, vmin=-30, vmax=2, lw=0, s=plt_s)\n", "\n", "#3D photogrammetry pointd\n", "ax0.scatter(p_x, p_y, p_z, \n", " c=np.ndarray.tolist(p_z),\\\n", " cmap='hot', vmin=-1, vmax=1, lw=0, s=5)\n", "\n", "#aicraft trajectory\n", "ax0.scatter(cpos_x, cpos_y, cpos_z, c=np.ndarray.tolist(cpos_z),\\\n", " cmap='hot', lw=0, vmin = 250, vmax = 265, s=10)\n", "\n", "\n", "ax0.view_init(elev=plt_elev, azim=plt_az)\n", "plt.tight_layout()\n", "plt.savefig('with_photo.png')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is kind of a clunky plot - but you get the idea (I hope). LiDAR is in blues, the 100 x 100 photogrammetry patch in orange, trajectory in orange. Different data sources, different resolutions, extracted using pretty much the same set of queries." ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "LiDAR points: 43264\n", "photogrammetry points: 817370\n", "trajectory points: 1448\n" ] } ], "source": [ "print('LiDAR points: {0}\\nphotogrammetry points: {1}\\ntrajectory points: {2}'.\n", " format(len(chunk_x), len(p_x), len(cpos_x) ))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### So what's happened here:\n", "\n", "Using HDF as the storage medium, this worksheet has shown how to:\n", "\n", "- put point clouds into HDF, storing many attributes that are not possible with .LAS\n", "- start on provenance data, including the trajectory that LiDAR points were generated from (this could be expanded to other properties, eg airborne GPS positions, IMU observations, survey marks, versions of trajectories, ...)\n", "- query for a point subset without loading all the point cloud\n", "- and query for trajectory data in the same bounding box without loading the whole trajectory.\n", "\n", "#### What could be better\n", "\n", "- Compression for bigger point clouds. .LAZ is obviously better, how can HDF/NetCDF storage happen more efficiently?\n", "\n", "#### next steps:\n", "\n", "- recreate this sheet using a single NetCDF file per dataset, with proper metadata\n", "- break up different point cloud sources (makes obvious sense, put them together here just because)\n", "- recreate this example using Davis Station data, which has topography\n", "\n", "#### use cases to test:\n", "\n", "- Say I want a region that is broken up across different flights/surveys/tiles. How can I get consistent data? and how can I find out if different regions of data have different uncertainties attached?\n", "- ???\n", "\n", "#### Dreams:\n", "\n", "- integration with the plasio viewer (http://plas.io)? This is a webGL point cloud viewer, currently reading .LAZ files. I will contact the developers about reading from other formats. Obviously useful for data preview, but not an infrastructure priority.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
apache-2.0
yihaochen/FLASHtools
synchrotron/Synchrotron_Polarization_OffAxis_Difference_102.ipynb
1
7559
{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import yt\n", "import logging\n", "logging.getLogger('yt').setLevel(logging.ERROR)\n", "import numpy as np\n", "from yt import derived_field\n", "\n", "nus = [(1500, 'MHz'), (150, 'MHz')]\n", "proj_axis = [1,0,2]\n", "\n", "prj = {}\n", "frb_I, frb_Q, frb_U = {}, {}, {}\n", "I_bin, Q_bin, U_bin = {}, {}, {}\n", "psi, frac = {}, {}\n", "\n", "nu0 = nus[0]\n", "nu1 = nus[1]\n", "nus_str = ['%.1f %s' % nu0, '%.1f %s' % nu1]\n", "\n", "postfix = ('_synchrotron_gc0')\n", "\n", "dir = '/home/ychen/d9/FLASH4/2015_production_runs/0529_L45_M10_b1_h1'\n", "fname = dir + '/MHD_Jet_hdf5_plt_cnt_0630' + postfix\n", "ds = yt.load(fname)\n", "ptype = 'lobe'\n", "\n", "\n", "for nu in nus:\n", " stokes = StokesFieldName(ptype, nu, proj_axis, field_type='flash')\n", " north_vector = [0,0,1]\n", " if proj_axis == 'x':\n", " prj[nu] = yt.ProjectionPlot(ds, proj_axis, fields, width=(80, 'kpc'))\n", " else:\n", " prj[nu] = yt.OffAxisProjectionPlot(ds, proj_axis, fields,width=(60, 'kpc'),\n", " north_vector=north_vector)\n", " frb_I[nu] = prj[nu].frb.data[fields[0]].v\n", " frb_Q[nu] = prj[nu].frb.data[fields[1]].v\n", " frb_U[nu] = prj[nu].frb.data[fields[2]].v" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def plot_polarization_histogram(frac, psi, I_bin, fig=None, label=None):\n", " \n", " if not fig:\n", " fig = plt.figure(figsize=(16,4))\n", " \n", " ax1 = fig.axes[0]\n", " null = ax1.hist(frac[I_bin.nonzero()].flatten()*100, range=(0,80), bins=40, alpha=0.5, \\\n", " weights=I_bin[I_bin.nonzero()].flatten(), \n", " normed=True)\n", " ax1.set_xlabel('Polarization fraction (%)')\n", "\n", " ax2 = fig.axes[1]\n", " null = ax2.hist(psi[I_bin.nonzero()].flatten(), bins=50, range=(-0.5*np.pi, 0.5*np.pi), alpha=0.5, \\\n", " weights=I_bin[I_bin.nonzero()].flatten(), \n", " normed=True)\n", " x_tick = np.linspace(-0.5, 0.5, 5, endpoint=True)\n", "\n", " x_label = [r\"$-\\pi/2$\", r\"$-\\pi/4$\", r\"$0$\", r\"$+\\pi/4$\", r\"$+\\pi/2$\"]\n", " ax2.set_xlim(-0.5*np.pi, 0.5*np.pi)\n", " ax2.set_xticks(x_tick*np.pi)\n", " ax2.set_xticklabels(x_label)\n", " #ax2.set_title(ds.basename + ' %.1f %s' % nu)\n", "\n", " ax3 = fig.axes[2]\n", " null = ax3.hist(np.abs(psi[I_bin.nonzero()].flatten()), bins=25, range=(0.0, 0.5*np.pi), alpha=0.5,\\\n", " label=label)\n", " ax3.legend()\n", " ax3.set_xlim(0.0, 0.5*np.pi)\n", " ax3.set_xticks([x_tick[2:]*np.pi])\n", " ax3.set_xticks(x_tick[2:]*np.pi)\n", " ax3.set_xticklabels(x_label[2:])\n", "\n", " return fig" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Binning\n", "for nu in nus:\n", " factor = 1\n", " nx = 800//factor\n", " ny = 800//factor\n", "\n", " I_bin[nu] = frb_I[nu].reshape(nx, factor, ny, factor).sum(3).sum(1)\n", " Q_bin[nu] = frb_Q[nu].reshape(nx, factor, ny, factor).sum(3).sum(1)\n", " U_bin[nu] = frb_U[nu].reshape(nx, factor, ny, factor).sum(3).sum(1)\n", "\n", " psi[nu] = 0.5*np.arctan2(U_bin[nu], Q_bin[nu])\n", " frac[nu] = np.sqrt(Q_bin[nu]**2+U_bin[nu]**2)/I_bin[nu]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fig = plt.figure(figsize=(16,4))\n", "ax1 = fig.add_subplot(131)\n", "ax2 = fig.add_subplot(132)\n", "ax3 = fig.add_subplot(133)\n", "\n", "for nu in reversed(nus):\n", " nu_str = '%.0f%s' % nu\n", " fig = plot_polarization_histogram(frac[nu], psi[nu], I_bin[nu], fig=fig, label=nu_str)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ns, bins = {}, {}\n", "for nu in nus:\n", " frac_nu = frac[nu]\n", " ns[nu], bins[nu], patches = plt.hist(frac_nu[frac_nu.nonzero()]*100, range=(0,80), bins=40, \\\n", " weights=I_bin[nu][frac_nu.nonzero()], normed=True)\n", "\n", "plt.cla()\n", "plt.scatter((bins[nu0][1:]+bins[nu0][:-1])/2, (ns[nu0]-ns[nu1])/(ns[nu0]+ns[nu1]))\n", "plt.hlines(0, 0, 80, linestyle=':')\n", "plt.ylim(-0.3,1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fig = plt.figure(figsize=(20,8))\n", "ax1 = fig.add_subplot(121)\n", "ax1.imshow(frac[nu0], vmin=0, vmax=0.7)\n", "plt.title(nus_str[0])\n", "ax2 = fig.add_subplot(122)\n", "img2 = ax2.imshow(frac[nu1], vmin=0, vmax=0.7)\n", "plt.title(nus_str[1])\n", "cb = plt.colorbar(img2, pad=0)\n", "cb.set_label('polarization fraction')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fig = plt.figure(figsize=(10,8))\n", "ax = fig.add_subplot(111)\n", "img = ax.imshow((frac[nu0]-frac[nu1]), cmap='seismic', vmin=-0.5, vmax=0.5)\n", "cb = plt.colorbar(img, pad=0)\n", "\n", "cb.set_label('polarization fraction difference (%s - %s)' % tuple(nus_str))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fig = plt.figure(figsize=(22,8))\n", "ax1 = fig.add_subplot(121)\n", "ax1.imshow(psi[nu0], vmin=0, vmax=np.pi/2)\n", "plt.title(nus_str[0])\n", "ax2 = fig.add_subplot(122)\n", "img2 = ax2.imshow(psi[nu1], vmin=0, vmax=np.pi/2)\n", "plt.title(nus_str[1])\n", "cb = plt.colorbar(img2)\n", "cb.set_label('polarization angle')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fig = plt.figure(figsize=(10,8))\n", "ax = fig.add_subplot(111)\n", "img = plt.imshow((psi[nu0]-psi[nu1]), cmap='seismic', vmin=-1.5, vmax=1.5)\n", "cb = plt.colorbar(img, pad=0)\n", "\n", "cb.set_label('polarization angle difference (%s - %s)' % tuple(nus_str))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#frb_I = proj.frb.data[fields[0]].v\n", "#frb_Q = proj.frb.data[fields[1]].v\n", "#frb_U = proj.frb.data[fields[2]].v\n", "\n", "#proj.annotate_polline(frb_I, frb_Q, frb_U, factor=16)\n", "#proj.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 1 }
gpl-2.0
nerdcommander/scientific_computing_2017
lesson2/Lesson2_team.ipynb
1
12892
{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Unit 1: Programming Basics" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Lesson 2: Writing Useful Functions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Notebook Authors " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "_(fill in your two names here)_" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Team Roles" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Facilitator: _(fill in name)_ \n", "Spokesperson: _(fill in name)_ \n", "Process Analyst: _(fill in name)_ \n", "Quality Control: _(fill in name)_ \n", "\n", "If there are only three people in your team, have one person serve as both spokesperson and process analyst for the rest of this activity.<p>\n", "At the end of this Lesson, you will be asked to record how long each Model required for your team. The _Facilitator_ should keep track of time for your team." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Computational Focus: Writing Useful Functions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Model 1: The Math Module" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In Python’s math module, you will find a number of useful predefined functions. Table 1 at the end of this lesson lists both constants and several useful functions defined in the math module. This same information is also accessible using Python’s built-in help function.\n", "\n", "**Type** (don't copy and paste) the following code, one line per Jupyter cell, to observe the output (if any):\n", "```python\n", "value = math.cos(0) \n", "import math \n", "value = math.cos(0) \n", "value \n", "math.cos(value) \n", "math.pi \n", "math.cos(math.pi) \n", "help(math) # please clear the output of this cell after running \n", "help(math.cos) \n", "math.cos() \n", "```\n", "(_A hint about help in Jupyter Notebooks, you can also use a_ `?` _before a module or function and get some info about it. For example, try_ `?math.cos` _for fun. You get slightly less info sometimes and this is a JN thing not a general Python thing_.)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Critical Thinking Questions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1\\. Examine the two lines (of the ten above lines of Python code) that had errors in them. \n", "1a. Explain how to fix the first error." ] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1b. Explain how to fix the second error. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "2\\. Identify the assignment statement using the `math.cos` function (from the ten Python lines). \n", "2a. What is the _identifier_ in the assignment statement?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "2b. What is the _argument_ in the assignment statement?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "3\\. What are the assumed units (degrees or radians) of the argument passed to the math.cos function? " ] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "4\\. Based on the first two lines of Python code from Model 1, describe two ways using a function defined in the math module differs from using one of Python’s built-in functions like `type()` or `abs()` from the last lesson. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Model 2: User Defined Functions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In addition to using Python’s built-in functions, you can write your own functions. Type the following ***function definition*** in a single Jupyter code cell (_don't_ copy and paste since it's bad practice, and so you can see how Jupyter automatically indents, the spacing is _important!_):\n", "\n", "```Python\n", "def model_two(): \n", " import math \n", " answer = math.cos(0) \n", " print(answer)\n", "``` \n", "To execute the code (known as a ***function call***), type the function name and parentheses in another Jupyter code cell:\n", "\n", "<pre>model_two()</pre>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Critical Thinking Questions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "5\\. Modify the `model_two` function definition so that it uses the `input` function introduced in the last lesson to ask the user to provide the value of the argument of the `math.cos` function. Place a comment in your code that briefly explains what you did." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Model 3: Passing Arguments to Functions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can write a more flexible function by defining a function that takes a ***parameter***. Define the following function in a single Jupyter code cell (again, _don't_ copy and paste):\n", "\n", "```Python\n", "# This function takes a parameter \n", "\n", "def model_three(angle): \n", " import math \n", " print(angle)\n", " answer = math.cos(angle) \n", " print(answer)\n", "``` \n", "\n", "Note: A single hash sign (#) indicates the beginning of a ***comment*** used for program documentation. All text after the # to the end of the line is ignored. It is good practice to communicate to other programmers (or remind your future self!) what is happening in the code.\n", "\n", "When we call the model function, we need to pass the information the function expects (the parameter) as the ***argument in the function call***. The function parameter in the function definition automatically receives the value of the argument in the function call.\n", "\n", "To see three examples of this, type in the following statements in _four_ Jupyter code cells:\n", "<pre>model_three(5)\n", "model_three(float(input(\"enter a number: \")))\n", "dozen = 12\n", "model_three(dozen)\n", "</pre>" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Critical Thinking Questions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "6\\. Consider the first `model_three` _function call_. \n", "6a. What is the first function call from the above model?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "6b. What is the value of the argument from the first function call?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "6c. What is the name of the parameter (from the first line of the function definition)?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "6d. What is the value of the parameter?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "6e. Write an assignment statement that demonstrates how the parameter directly got its value. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "7\\. Consider the last `model_three` _function call_. \n", "7a. What is the value of the argument?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "7b. What is the value of the parameter?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "7c. When a variable is passed to the function as an argument, does the identifier of the argument need to be the same as the identifier taken by the function as a parameter? " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "7d. Write an assignment statement showing how the parameter indirectly got its value. Briefly explain your answer." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Model 4: Return Values for Functions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can write a function that produced an output by using ***return***. Define the following function in a _single_ Jupyter code cell (again, _don't_ copy and paste):\n", "\n", "```python\n", "# This function takes a parameter and returns a value\n", "\n", "def model_four(angle):\n", " import math\n", " print(angle)\n", " answer1 = math.cos(angle)\n", " answer2 = math.cos(angle*angle)\n", " print(answer1, answer2)\n", " return answer2```\n", "</pre> \n", "\n", "\n", "To see three examples of this, type in the following statements in _six_ separate Jupyter code cells:\n", "<pre>model_four(5) \n", "answer1\n", "answer2\n", "result = model_four(5)\n", "result\n", "result/100\n", "</pre>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Critical Thinking Questions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "8a\\. When you run the 4th line of code above `[result = model_four(5)]`, does `result` get its value from `answer1` or `answer2`? " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "8b\\. Explain how `result` gets its value and what happens to `answer1`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "9\\. Would it be possible to perform an operation on the variable `answer2` (e.g., `answer2/100`) after calling the function (in a separate code cell)? Explain." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "10\\. Describe how it is possible to perform an operation involving the _value_ of `answer2` and _not_ possible to perform an operation involving the _value_ of `answer1` after the function call. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "11\\. Copy into a new code cell, edit, and run the `model_four` definition with the last two lines reversed (`return` then `print`). Describe the result. What does this imply about the return statement?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "12\\. Explain why a function that returns the value of a variable is considered more “useful” than a function that simply prints the value of a variable." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Temporal Analysis Report" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "How much time did it require for your team to complete each Model?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# END OF CLASS" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "**Table 1: partial list of math module functions and constants** ([complete list](https://docs.python.org/3/library/math.html))\n", "\n", "functions | functions | | constants\n", ":---: | :---: | :---: | :---:\n", "sin(x) | log(x) | | e\n", "cos(x) | log10(x) | | pi\n", "tan(x) | exp(x) | | \n", "asin(x) | ceil(x) | | \n", "acos(x) | floor(x)| | \n", "atan(x) | | \n" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 1 }
mit
chichilalescu/python-for-scientific-computing
TA Class Notes/Day1.ipynb
1
2463
{ "metadata": { "name": "", "signature": "sha256:3b3e900f8d18695ad9ca648e296ca5137abab0392f31b2efd5bf40f137cf99e6" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "print 'Hello World'\n", "print type('Hello World')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Hello World\n", "<type 'str'>\n" ] } ], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [ "print 1/3\n", "print 1./3\n", "print 2./3" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "0\n", "0.333333333333\n", "0.666666666667\n" ] } ], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "x = 2./3\n", "y = 2/3\n", "print x,y\n", "print type(x), type(y)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "0.666666666667 0\n", "<type 'float'> <type 'int'>\n" ] } ], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ "def gcd(a,b):\n", " while b != 0:\n", " t = b\n", " b = a % b\n", " a = t\n", " print '{0} {2} {1}'.format(b,b**2,b**3)\n", " return a\n", "\n", "def lcm(a,b):\n", " l = abs(a*b)/gcd(a,b)\n", " return l\n", "\n", "def pair(a,b):\n", " print(gcd(a,b),lcm(a,b))\n", " \n", "pair(49,21)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "7 343 49\n", "0 0 0\n", "7 343 49\n", "0 0 0\n", "(7, 147)\n" ] } ], "prompt_number": 22 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
gpl-3.0
trsherborne/learn-python
lesson4.ipynb
1
15933
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## LSESU Applicable Maths Python Lesson 4\n", "###### 15/11/16" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Today we will be learning about\n", "* Data Structures - Official documentation on Data Structures [here](https://docs.python.org/3/tutorial/datastructures.html#)\n", " * Lists\n", " * Tuples\n", " * Dictionaries\n", "* Introduction to the Pandas library" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** Recap from Week 3**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Strings\n", "\n", "```\n", "day = input('Enter the day of the month you were born')\n", "month = input('Enter the month of the year you were born')\n", "year = input('Enter the year you were born')\n", "\n", "birthday = '{} / {} / {}'.format(day,month,year)\n", "print('Your birthday is \\n{}'.format(birthday))\n", "\n", "```\n", "* File I/O\n", "\n", "```\n", "file = open('test_data.txt','r') \n", "\n", "for line in file:\n", " print(line)\n", "\n", "```\n", "\n", "* Lists\n", "\n", "```\n", "list1 = [1,2,3,4,5,6]\n", "\n", "for item in reversed(list1):\n", " print(item)\n", "\n", "```\n", "\n", "* List comprehensions\n", "\n", "```\n", "squares = [x**2 for x in range(1,11)]\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Lists" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We met lists in lesson 3 and are briefly going to go over them again to get in the mindset of looking at data structures in Python. A data structure is a type of object in Python which stores information in some organised format. The format of this organisation dictates what the data structure is. There are buckets of different kinds of data structures, but when writing Python you will primarily be using lists, dictionaries and tuples." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Remember we declare an empty list as so\n", "my_list = []\n", "\n", "# Add new elements to the end of a previous list\n", "my_list.append(1)\n", "my_list.append('Hello')\n", "my_list.append(0.05)\n", "\n", "# Delete specific elements\n", "del my_list[-1] # Remove by index\n", "my_list.remove('Hello') # Remove by value\n", "\n", "# Replace elements\n", "my_list[0] = 2" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Or we can declare a whole, or part of, a list upon declaration\n", "shopping_list = ['bread','toothpaste','blueberries','milk']\n", "\n", "# Printing each element of a list\n", "for item in shopping_list:\n", " print(item)\n", " \n", "# Find the length of a list\n", "print('The shopping list has {} items'.format(len(shopping_list)))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# We can test for membership in a list in the same fashion as a string\n", "if 'milk' in shopping_list:\n", " print('You can\\'t drink milk!')\n", " shopping_list.remove('milk')\n", " \n", "if 'chocolate' not in shopping_list:\n", " print('You forgot chocolate!')\n", " shopping_list.append('chocolate')\n", " \n", "print(shopping_list)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lists always have their order preserved in Python, so you can guarantee that `shopping_list[0]` will have the value `\"bread\"`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Tuples" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A tuple is another of the standard Python data strucure. They behave in a similar way to the list but have one key difference, they are **immutable**. Let's look at what this means.\n", "\n", "A more detailed intro to Tuples can be found [here](http://www.thomas-cokelaer.info/tutorials/python/tuples.html#tuples)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# A tuple is declared with the curved brackets () instead of the [] for a list\n", "my_tuple = (1,2,'cat','dog')\n", "\n", "# But since a tuple is immutable the next line will not run\n", "my_tuple[0] = 4" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So what can we learn from this? **Once you declare a tuple, the object cannot be changed. **\n", "\n", "For this reason, tuples have more optimised methods when you use them so can be more efficient and faster in your code." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### A closer look at using Tuples" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# A tuple might be immutable but can contain mutable objects\n", "my_list_tuple = ([1,2,3],[4,5,6])\n", "\n", "# This won't work\n", "# my_list_tuple[0] = [3,2,1]\n", "\n", "# But this will!\n", "my_list_tuple[0][0:3] = [3,2,1]\n", "\n", "print(my_list_tuple)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# You can add tuples together\n", "t1 = (1,2,3)\n", "t1 += (4,5,6)\n", "print(t1)\n", "\n", "t2 = (10,20,30)\n", "t3 = (40,50,60)\n", "print(t2+t3)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Use index() and count() to look at a tuple\n", "t1 = (1,2,3,1,1,2)\n", "\n", "print(t1.index(2)) # Returns the first index of 2\n", "\n", "print(t1.count(1)) # Returns how many 1's are in the tuple" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# You can use tuples for multiple assignments and for multiple return from functions\n", "\n", "(x,y,z) = (1,2,3)\n", "print(x)\n", "\n", "\n", "# This is a basic function doing multiple return in Python\n", "def norm_and_square(a):\n", " return a,a**2\n", "\n", "(a,b) = norm_and_square(4)\n", "\n", "print(a)\n", "print(b)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Swap items using tuples\n", "\n", "x = 10\n", "y = 20\n", "print('x is {} and y is {}'.format(x,y))\n", "\n", "(x,y) = (y,x)\n", "\n", "print('x is {} and y is {}'.format(x,y))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Question - Write a function which swaps two elements using tuples" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# TO DO\n", "def my_swap_function(a,b):\n", " # write here!\n", " return b,a\n", "\n", "# END TO DO" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "a = 1\n", "b = 2\n", "x = my_swap_function(a,b)\n", "print(x)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Dictionaries" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Dictionaries are perhaps the most useful and hardest to grasp data structure from the basic set in Python. Dictionaries are not iterable in the same sense as lists and tuples and using them required a different approach.\n", "\n", "Dictionaries are sometimes called _hash maps_, _hash tables_ or _maps_ in other programming languages. You can think of a dictionary as the same as a physical dictionary, it is a collection of key (the word) and value (the definition) pairs. \n", "\n", "Each key is unique and has an associated value, the key functions as the index for the value but it can be anything. In contrast to alphabetical dictionaries, the order of a Python dictionary is not guaranteed." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Declare a dictionary using the {} brackets or the dict() method\n", "my_dict = {}\n", "\n", "# Add new items to the dictionary by stating the key as the index and the value\n", "my_dict['bananas'] = 'this is a fruit and a berry'\n", "my_dict['apples'] = 'this is a fruit'\n", "my_dict['avocados'] = 'this is a berry'\n", "\n", "print(my_dict)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# So now we can use the key to get a value in the dictionary\n", "print(my_dict['bananas'])\n", "\n", "# But this won't work if we haven't added an item to the dict\n", "#print(my_dict['cherries'])\n", "\n", "# We can fix this line using the get(key,def) method. This is safer as you wont get KeyError!\n", "print(my_dict.get('cherries','Not found :('))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# If you are given a dictionary data file you know nothing about you can inspect it like so\n", "\n", "# Get all the keys of a dictionary\n", "print(my_dict.keys())\n", "\n", "# Get all the values from a dictionary\n", "print(my_dict.values())\n", "\n", "# Of course you could print the whole dictionary, but it might be huge! These methods break\n", "# the dict down, but the downside is that you can't match up the keys and values!" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Test for membership in the keys using the in operator\n", "\n", "if 'avocados' in my_dict:\n", " print(my_dict['avocados'])\n", " " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Dictionary values can also be lists or other data structures\n", "my_lists = {}\n", "my_lists['shopping list'] = shopping_list\n", "my_lists['holidays'] = ['Munich','Naples','New York','Tokyo','San Francisco','Los Angeles']\n", "\n", "# Now my I store a dictionary with each list named with keys and the lists as values\n", "print(my_lists)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Wrapping everything up, we can create a list of dictionaries with multiple fields and iterate over a dictionary" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Declare a list\n", "europe = []\n", "\n", "# Create dicts and add to lists\n", "germany = {\"name\": \"Germany\", \"population\": 81000000,\"speak_german\":True}\n", "europe.append(germany)\n", "luxembourg = {\"name\": \"Luxembourg\", \"population\": 512000,\"speak_german\":True}\n", "europe.append(luxembourg)\n", "uk = {\"name\":\"United Kingdom\",\"population\":64100000,\"speak_german\":False}\n", "europe.append(uk)\n", "\n", "print(europe)\n", "print()\n", "\n", "for country in europe:\n", " for key, value in country.items():\n", " print('{}\\t{}'.format(key,value))\n", " print()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Question - Add at least 3 more countries to the `europe` list and use a `for` loop to get a new list of every country which speaks German" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# TO DO - You might need more than just a for loop!\n", "\n", "# END TO DO " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## A peek at Pandas" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We've seen some of the standard library of Data structures in Python. We will briefly look at [Pandas](http://pandas.pydata.org/pandas-docs/stable/index.html) now, a powerful data manipulation library which is a sensible next step to organising your data when you need to use something more complex than standard Python data structures.\n", "\n", "The core of Pandas is the DataFrame, which will look familiar if you have worked with R before. This organises data in a table format and gives you spreadsheet like handling of your information. Using Pandas can make your job handling data easier, and many libraries for plotting data (such as Seaborn) can handle a Pandas DataFrame much easier than a list as input.\n", "\n", "*Note*: Pandas uses NumPy under the hood, another package for simplifying numerical operations and working with arrays. We will look at NumPy and Pandas together in 2 lessons time." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# We import the Pandas packages using the import statement we've seen before\n", "\n", "import pandas as pd" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# To create a Pandas DataFrame from a simpler data structure we use the following routine\n", "\n", "europe_df = pd.DataFrame.from_dict(europe)\n", "\n", "print(type(europe_df))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Running this cell as is provides the fancy formatting of Pandas which can prove useful.\n", "\n", "europe_df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run the previous block now. Here we can see how our list of dictionaries was converted to a DataFrame. Each dictionary became a row, each key became a column and the values became the data inside the object.\n", "\n", "That's all on Pandas for now! For a quick tutorial on using Pandas you can check [this link](https://www.datacamp.com/community/tutorials/pandas-tutorial-dataframe-python#gs.SVv7Puk) out. We'll come back to this in the future, we just have to look at Object Oriented Programming and Classes first!" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
wtchg-kwiatkowski/pfx-paper-2015
supplementary/notebooks/hotspots_discovery.ipynb
1
23860
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "docker image cggh/biipy:v1.6.0\n" ] }, { "data": { "text/html": [ "<style type=\"text/css\">\n", ".container {\n", " width: 96%;\n", "}\n", "#maintoolbar {\n", " display: none;\n", "}\n", "#header-container {\n", " display: none;\n", "}\n", "#notebook {\n", " padding-top: 0;\n", "}\n", "</style>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%run ../../shared_setup.ipynb" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def tabulate(f):\n", " class Tabulated(etl.Table):\n", " def __init__(self, *args, **kwargs):\n", " self.args = args\n", " self.kwargs = kwargs\n", " def __iter__(self):\n", " return f(*self.args, **self.kwargs)\n", " return Tabulated\n", " " ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "@tabulate\n", "def tabulate_core_windows(window_size):\n", " yield 'chrom', 'start', 'stop'\n", " for rec in tbl_regions_1b.eq('region_type', 'Core').records():\n", " for start in range(rec.region_start, rec.region_stop, window_size):\n", " yield rec.region_chrom, start, start + window_size - 1\n", " " ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table class='petl'>\n", "<caption>CO events (1194 rows)</caption>\n", "<thead>\n", "<tr>\n", "<th>0|sample</th>\n", "<th>1|chrom</th>\n", "<th>2|co_pos_mid</th>\n", "<th>3|co_pos_min</th>\n", "<th>4|co_pos_max</th>\n", "<th>5|co_pos_range</th>\n", "<th>6|cross</th>\n", "<th>7|co_from_parent</th>\n", "<th>8|co_to_parent</th>\n", "</tr>\n", "</thead>\n", "<tbody>\n", "<tr>\n", "<td>B1SD/PG0015-C/ERR019044</td>\n", "<td>Pf3D7_01_v3</td>\n", "<td style='text-align: right'>145052</td>\n", "<td style='text-align: right'>144877</td>\n", "<td style='text-align: right'>145227</td>\n", "<td style='text-align: right'>350</td>\n", "<td>hb3_dd2</td>\n", "<td>hb3</td>\n", "<td>dd2</td>\n", "</tr>\n", "<tr>\n", "<td>GC03/PG0021-C/ERR015447</td>\n", "<td>Pf3D7_01_v3</td>\n", "<td style='text-align: right'>163584</td>\n", "<td style='text-align: right'>163145</td>\n", "<td style='text-align: right'>164024</td>\n", "<td style='text-align: right'>879</td>\n", "<td>hb3_dd2</td>\n", "<td>dd2</td>\n", "<td>hb3</td>\n", "</tr>\n", "<tr>\n", "<td>XF12/PG0102-C/ERR029143</td>\n", "<td>Pf3D7_01_v3</td>\n", "<td style='text-align: right'>206769</td>\n", "<td style='text-align: right'>205803</td>\n", "<td style='text-align: right'>207736</td>\n", "<td style='text-align: right'>1933</td>\n", "<td>7g8_gb4</td>\n", "<td>gb4</td>\n", "<td>7g8</td>\n", "</tr>\n", "<tr>\n", "<td>7C159/PG0040-Cx/ERR107475</td>\n", "<td>Pf3D7_01_v3</td>\n", "<td style='text-align: right'>206905</td>\n", "<td style='text-align: right'>206074</td>\n", "<td style='text-align: right'>207736</td>\n", "<td style='text-align: right'>1662</td>\n", "<td>hb3_dd2</td>\n", "<td>hb3</td>\n", "<td>dd2</td>\n", "</tr>\n", "<tr>\n", "<td>CH3_61/PG0033-Cx/ERR175544</td>\n", "<td>Pf3D7_01_v3</td>\n", "<td style='text-align: right'>206905</td>\n", "<td style='text-align: right'>206074</td>\n", "<td style='text-align: right'>207736</td>\n", "<td style='text-align: right'>1662</td>\n", "<td>hb3_dd2</td>\n", "<td>dd2</td>\n", "<td>hb3</td>\n", "</tr>\n", "</tbody>\n", "</table>\n", "<p><strong>...</strong></p>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "tbl_co = (\n", " etl\n", " .frompickle(os.path.join(PUBLIC_DIR, 'tbl_co.pickle'))\n", " .convert('chrom', lambda v: str(v, 'ascii'))\n", ")\n", "display_with_nrows(tbl_co, caption='CO events')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table class='petl'>\n", "<thead>\n", "<tr>\n", "<th>0|chrom</th>\n", "<th>1|start</th>\n", "<th>2|stop</th>\n", "</tr>\n", "</thead>\n", "<tbody>\n", "<tr>\n", "<td>Pf3D7_01_v3</td>\n", "<td style='text-align: right'>92901</td>\n", "<td style='text-align: right'>97900</td>\n", "</tr>\n", "<tr>\n", "<td>Pf3D7_01_v3</td>\n", "<td style='text-align: right'>97901</td>\n", "<td style='text-align: right'>102900</td>\n", "</tr>\n", "<tr>\n", "<td>Pf3D7_01_v3</td>\n", "<td style='text-align: right'>102901</td>\n", "<td style='text-align: right'>107900</td>\n", "</tr>\n", "<tr>\n", "<td>Pf3D7_01_v3</td>\n", "<td style='text-align: right'>107901</td>\n", "<td style='text-align: right'>112900</td>\n", "</tr>\n", "<tr>\n", "<td>Pf3D7_01_v3</td>\n", "<td style='text-align: right'>112901</td>\n", "<td style='text-align: right'>117900</td>\n", "</tr>\n", "</tbody>\n", "</table>\n", "<p><strong>...</strong></p>" ], "text/plain": [ "+---------------+--------+--------+\n", "| chrom | start | stop |\n", "+===============+========+========+\n", "| 'Pf3D7_01_v3' | 92901 | 97900 |\n", "+---------------+--------+--------+\n", "| 'Pf3D7_01_v3' | 97901 | 102900 |\n", "+---------------+--------+--------+\n", "| 'Pf3D7_01_v3' | 102901 | 107900 |\n", "+---------------+--------+--------+\n", "| 'Pf3D7_01_v3' | 107901 | 112900 |\n", "+---------------+--------+--------+\n", "| 'Pf3D7_01_v3' | 112901 | 117900 |\n", "+---------------+--------+--------+\n", "..." ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tbl_windows = tabulate_core_windows(5000)\n", "tbl_windows" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table class='petl'>\n", "<thead>\n", "<tr>\n", "<th>0|chrom</th>\n", "<th>1|start</th>\n", "<th>2|stop</th>\n", "<th>3|co_count</th>\n", "<th>4|co_count_3d7_hb3</th>\n", "<th>5|co_count_hb3_dd2</th>\n", "<th>6|co_count_7g8_gb4</th>\n", "</tr>\n", "</thead>\n", "<tbody>\n", "<tr>\n", "<td>Pf3D7_01_v3</td>\n", "<td style='text-align: right'>92901</td>\n", "<td style='text-align: right'>97900</td>\n", "<td>Counter()</td>\n", "<td style='text-align: right'>0</td>\n", "<td style='text-align: right'>0</td>\n", "<td style='text-align: right'>0</td>\n", "</tr>\n", "<tr>\n", "<td>Pf3D7_01_v3</td>\n", "<td style='text-align: right'>97901</td>\n", "<td style='text-align: right'>102900</td>\n", "<td>Counter()</td>\n", "<td style='text-align: right'>0</td>\n", "<td style='text-align: right'>0</td>\n", "<td style='text-align: right'>0</td>\n", "</tr>\n", "<tr>\n", "<td>Pf3D7_01_v3</td>\n", "<td style='text-align: right'>102901</td>\n", "<td style='text-align: right'>107900</td>\n", "<td>Counter()</td>\n", "<td style='text-align: right'>0</td>\n", "<td style='text-align: right'>0</td>\n", "<td style='text-align: right'>0</td>\n", "</tr>\n", "<tr>\n", "<td>Pf3D7_01_v3</td>\n", "<td style='text-align: right'>107901</td>\n", "<td style='text-align: right'>112900</td>\n", "<td>Counter()</td>\n", "<td style='text-align: right'>0</td>\n", "<td style='text-align: right'>0</td>\n", "<td style='text-align: right'>0</td>\n", "</tr>\n", "<tr>\n", "<td>Pf3D7_01_v3</td>\n", "<td style='text-align: right'>112901</td>\n", "<td style='text-align: right'>117900</td>\n", "<td>Counter()</td>\n", "<td style='text-align: right'>0</td>\n", "<td style='text-align: right'>0</td>\n", "<td style='text-align: right'>0</td>\n", "</tr>\n", "</tbody>\n", "</table>\n", "<p><strong>...</strong></p>" ], "text/plain": [ "+---------------+--------+--------+-----------+------------------+------------------+------------------+\n", "| chrom | start | stop | co_count | co_count_3d7_hb3 | co_count_hb3_dd2 | co_count_7g8_gb4 |\n", "+===============+========+========+===========+==================+==================+==================+\n", "| 'Pf3D7_01_v3' | 92901 | 97900 | Counter() | 0 | 0 | 0 |\n", "+---------------+--------+--------+-----------+------------------+------------------+------------------+\n", "| 'Pf3D7_01_v3' | 97901 | 102900 | Counter() | 0 | 0 | 0 |\n", "+---------------+--------+--------+-----------+------------------+------------------+------------------+\n", "| 'Pf3D7_01_v3' | 102901 | 107900 | Counter() | 0 | 0 | 0 |\n", "+---------------+--------+--------+-----------+------------------+------------------+------------------+\n", "| 'Pf3D7_01_v3' | 107901 | 112900 | Counter() | 0 | 0 | 0 |\n", "+---------------+--------+--------+-----------+------------------+------------------+------------------+\n", "| 'Pf3D7_01_v3' | 112901 | 117900 | Counter() | 0 | 0 | 0 |\n", "+---------------+--------+--------+-----------+------------------+------------------+------------------+\n", "..." ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# count COs in windows\n", "tbl_windows_co = (\n", " tbl_windows\n", " .intervalleftjoin(tbl_co, lkey='chrom', lstart='start', lstop='stop',\n", " rkey='chrom', rstart='co_pos_min', rstop='co_pos_max',\n", " include_stop=True)\n", " .cutout(4)\n", " .aggregate(key=('chrom', 'start', 'stop'),\n", " aggregation=lambda vals: collections.Counter([v for v in vals if v is not None]),\n", " value='cross')\n", " .rename('value', 'co_count')\n", " .addfield('co_count_3d7_hb3', lambda row: row.co_count['3d7_hb3'])\n", " .addfield('co_count_hb3_dd2', lambda row: row.co_count['hb3_dd2'])\n", " .addfield('co_count_7g8_gb4', lambda row: row.co_count['7g8_gb4'])\n", ")\n", "tbl_windows_co" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table class='petl'>\n", "<thead>\n", "<tr>\n", "<th>0|co_count_3d7_hb3</th>\n", "<th>1|count</th>\n", "<th>2|frequency</th>\n", "</tr>\n", "</thead>\n", "<tbody>\n", "<tr>\n", "<td style='text-align: right'>0</td>\n", "<td style='text-align: right'>3811</td>\n", "<td style='text-align: right'>0.912814371257485</td>\n", "</tr>\n", "<tr>\n", "<td style='text-align: right'>1</td>\n", "<td style='text-align: right'>314</td>\n", "<td style='text-align: right'>0.07520958083832335</td>\n", "</tr>\n", "<tr>\n", "<td style='text-align: right'>2</td>\n", "<td style='text-align: right'>50</td>\n", "<td style='text-align: right'>0.011976047904191617</td>\n", "</tr>\n", "</tbody>\n", "</table>\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "tbl_windows_co.valuecounts('co_count_3d7_hb3').displayall()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table class='petl'>\n", "<thead>\n", "<tr>\n", "<th>0|co_count_hb3_dd2</th>\n", "<th>1|count</th>\n", "<th>2|frequency</th>\n", "</tr>\n", "</thead>\n", "<tbody>\n", "<tr>\n", "<td style='text-align: right'>0</td>\n", "<td style='text-align: right'>3464</td>\n", "<td style='text-align: right'>0.8297005988023952</td>\n", "</tr>\n", "<tr>\n", "<td style='text-align: right'>1</td>\n", "<td style='text-align: right'>603</td>\n", "<td style='text-align: right'>0.1444311377245509</td>\n", "</tr>\n", "<tr>\n", "<td style='text-align: right'>2</td>\n", "<td style='text-align: right'>101</td>\n", "<td style='text-align: right'>0.024191616766467066</td>\n", "</tr>\n", "<tr>\n", "<td style='text-align: right'>3</td>\n", "<td style='text-align: right'>7</td>\n", "<td style='text-align: right'>0.0016766467065868263</td>\n", "</tr>\n", "</tbody>\n", "</table>\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "tbl_windows_co.valuecounts('co_count_hb3_dd2').displayall()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table class='petl'>\n", "<thead>\n", "<tr>\n", "<th>0|co_count_7g8_gb4</th>\n", "<th>1|count</th>\n", "<th>2|frequency</th>\n", "</tr>\n", "</thead>\n", "<tbody>\n", "<tr>\n", "<td style='text-align: right'>0</td>\n", "<td style='text-align: right'>3688</td>\n", "<td style='text-align: right'>0.8833532934131737</td>\n", "</tr>\n", "<tr>\n", "<td style='text-align: right'>1</td>\n", "<td style='text-align: right'>434</td>\n", "<td style='text-align: right'>0.10395209580838323</td>\n", "</tr>\n", "<tr>\n", "<td style='text-align: right'>2</td>\n", "<td style='text-align: right'>48</td>\n", "<td style='text-align: right'>0.011497005988023952</td>\n", "</tr>\n", "<tr>\n", "<td style='text-align: right'>3</td>\n", "<td style='text-align: right'>4</td>\n", "<td style='text-align: right'>0.0009580838323353293</td>\n", "</tr>\n", "<tr>\n", "<td style='text-align: right'>4</td>\n", "<td style='text-align: right'>1</td>\n", "<td style='text-align: right'>0.00023952095808383233</td>\n", "</tr>\n", "</tbody>\n", "</table>\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "tbl_windows_co.valuecounts('co_count_7g8_gb4').displayall()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table class='petl'>\n", "<thead>\n", "<tr>\n", "<th>0|chrom</th>\n", "<th>1|start</th>\n", "<th>2|stop</th>\n", "<th>3|co_count</th>\n", "<th>4|co_count_3d7_hb3</th>\n", "<th>5|co_count_hb3_dd2</th>\n", "<th>6|co_count_7g8_gb4</th>\n", "<th>7|n_hot</th>\n", "</tr>\n", "</thead>\n", "<tbody>\n", "<tr>\n", "<td>Pf3D7_01_v3</td>\n", "<td style='text-align: right'>202901</td>\n", "<td style='text-align: right'>207900</td>\n", "<td>Counter({'hb3_dd2': 2, '7g8_gb4': 1})</td>\n", "<td style='text-align: right'>0</td>\n", "<td style='text-align: right'>2</td>\n", "<td style='text-align: right'>1</td>\n", "<td style='text-align: right'>1</td>\n", "</tr>\n", "<tr>\n", "<td>Pf3D7_01_v3</td>\n", "<td style='text-align: right'>322901</td>\n", "<td style='text-align: right'>327900</td>\n", "<td>Counter({'3d7_hb3': 2, 'hb3_dd2': 1})</td>\n", "<td style='text-align: right'>2</td>\n", "<td style='text-align: right'>1</td>\n", "<td style='text-align: right'>0</td>\n", "<td style='text-align: right'>1</td>\n", "</tr>\n", "<tr>\n", "<td>Pf3D7_01_v3</td>\n", "<td style='text-align: right'>402901</td>\n", "<td style='text-align: right'>407900</td>\n", "<td>Counter({'7g8_gb4': 2, 'hb3_dd2': 1})</td>\n", "<td style='text-align: right'>0</td>\n", "<td style='text-align: right'>1</td>\n", "<td style='text-align: right'>2</td>\n", "<td style='text-align: right'>1</td>\n", "</tr>\n", "<tr>\n", "<td>Pf3D7_01_v3</td>\n", "<td style='text-align: right'>550312</td>\n", "<td style='text-align: right'>555311</td>\n", "<td>Counter({'7g8_gb4': 2})</td>\n", "<td style='text-align: right'>0</td>\n", "<td style='text-align: right'>0</td>\n", "<td style='text-align: right'>2</td>\n", "<td style='text-align: right'>1</td>\n", "</tr>\n", "<tr>\n", "<td>Pf3D7_02_v3</td>\n", "<td style='text-align: right'>205801</td>\n", "<td style='text-align: right'>210800</td>\n", "<td>Counter({'3d7_hb3': 2})</td>\n", "<td style='text-align: right'>2</td>\n", "<td style='text-align: right'>0</td>\n", "<td style='text-align: right'>0</td>\n", "<td style='text-align: right'>1</td>\n", "</tr>\n", "</tbody>\n", "</table>\n", "<p><strong>...</strong></p>" ], "text/plain": [ "+---------------+--------+--------+---------------------------------------+------------------+------------------+------------------+-------+\n", "| chrom | start | stop | co_count | co_count_3d7_hb3 | co_count_hb3_dd2 | co_count_7g8_gb4 | n_hot |\n", "+===============+========+========+=======================================+==================+==================+==================+=======+\n", "| 'Pf3D7_01_v3' | 202901 | 207900 | Counter({'hb3_dd2': 2, '7g8_gb4': 1}) | 0 | 2 | 1 | 1 |\n", "+---------------+--------+--------+---------------------------------------+------------------+------------------+------------------+-------+\n", "| 'Pf3D7_01_v3' | 322901 | 327900 | Counter({'3d7_hb3': 2, 'hb3_dd2': 1}) | 2 | 1 | 0 | 1 |\n", "+---------------+--------+--------+---------------------------------------+------------------+------------------+------------------+-------+\n", "| 'Pf3D7_01_v3' | 402901 | 407900 | Counter({'7g8_gb4': 2, 'hb3_dd2': 1}) | 0 | 1 | 2 | 1 |\n", "+---------------+--------+--------+---------------------------------------+------------------+------------------+------------------+-------+\n", "| 'Pf3D7_01_v3' | 550312 | 555311 | Counter({'7g8_gb4': 2}) | 0 | 0 | 2 | 1 |\n", "+---------------+--------+--------+---------------------------------------+------------------+------------------+------------------+-------+\n", "| 'Pf3D7_02_v3' | 205801 | 210800 | Counter({'3d7_hb3': 2}) | 2 | 0 | 0 | 1 |\n", "+---------------+--------+--------+---------------------------------------+------------------+------------------+------------------+-------+\n", "..." ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tbl_hotspots = (\n", " tbl_windows_co\n", " .select(lambda row: row.co_count_3d7_hb3 >= 2 or row.co_count_hb3_dd2 >= 2 or row.co_count_7g8_gb4 >= 2)\n", " .addfield('n_hot', lambda row: sum(1 for v in [row.co_count_3d7_hb3, row.co_count_hb3_dd2, row.co_count_7g8_gb4] if v >=2))\n", ")\n", "tbl_hotspots" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table class='petl'>\n", "<thead>\n", "<tr>\n", "<th>0|n_hot</th>\n", "<th>1|count</th>\n", "<th>2|frequency</th>\n", "</tr>\n", "</thead>\n", "<tbody>\n", "<tr>\n", "<td style='text-align: right'>1</td>\n", "<td style='text-align: right'>197</td>\n", "<td style='text-align: right'>0.9656862745098039</td>\n", "</tr>\n", "<tr>\n", "<td style='text-align: right'>2</td>\n", "<td style='text-align: right'>7</td>\n", "<td style='text-align: right'>0.03431372549019608</td>\n", "</tr>\n", "</tbody>\n", "</table>\n" ], "text/plain": [ "+-------+-------+---------------------+\n", "| n_hot | count | frequency |\n", "+=======+=======+=====================+\n", "| 1 | 197 | 0.9656862745098039 |\n", "+-------+-------+---------------------+\n", "| 2 | 7 | 0.03431372549019608 |\n", "+-------+-------+---------------------+" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tbl_hotspots.valuecounts('n_hot')" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "204" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tbl_hotspots.nrows()" ] }, { "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
anshbansal/anshbansal.github.io
udacity_data_science_notes/intro_machine_learning/lesson_15/lesson_15.ipynb
1
609
{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Tying it all together\n", "\n", "![](steps.png)" ] } ], "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
hktxt/MachineLearning
PyTorch Tutorials/HJ.ipynb
1
326722
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from __future__ import print_function, division\n", "\n", "import torch\n", "import torch.nn as nn\n", "import torch.optim as optim\n", "from torch.autograd import Variable\n", "import numpy as np\n", "import torchvision\n", "from torchvision import datasets, models, transforms\n", "import matplotlib.pyplot as plt\n", "import time\n", "import copy\n", "import os\n", "\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "True\n" ] } ], "source": [ "data_transforms = {\n", " 'train': transforms.Compose([\n", " transforms.Scale(260),\n", " transforms.RandomCrop((256,256)),\n", " transforms.RandomHorizontalFlip(),\n", " transforms.ToTensor(),\n", " transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n", " ]),\n", " 'val': transforms.Compose([\n", " transforms.Scale(260),\n", " transforms.CenterCrop((256,256)),\n", " transforms.ToTensor(),\n", " transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n", " ]),\n", " 'test': transforms.Compose([\n", " transforms.Scale(260),\n", " transforms.CenterCrop((256,256)),\n", " transforms.ToTensor(),\n", " transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n", " ]),\n", "}\n", "#'/home/jim/pytorch/hj_pytorch/hj'\n", "data_dir = '/home/jim/Desktop/tyl/data/hj6'\n", "dsets = {x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x])\n", " for x in ['train', 'val','test']}\n", "dset_loaders = {x: torch.utils.data.DataLoader(dsets[x], batch_size=20,\n", " shuffle=True, num_workers=15)\n", " for x in ['train', 'val','test']}\n", "dset_sizes = {x: len(dsets[x]) for x in ['train', 'val','test']}\n", "dset_classes = dsets['train'].classes\n", "\n", "use_gpu = torch.cuda.is_available()\n", "print(use_gpu)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": 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6TaM8Ph6QzWbMJseJl/fQAhEGaSxaO2xZYGcl8f79tKMlDJ768G3o3fupvcOs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\nHu9PSKVo+54YA7evdvT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2LTZndPakaWJ3e2KOlWgyQ93zq7/yFXKeWb96jzZNvPjG2/QP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\nXTXl0s4umhJmc3Q9peu2pBjptlvKsqQRQym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MqhnSlCRR3Lu4m8Nrj1OXDUZr3Kanmrbo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\nF8A51JCnfo+5UPtilmXZpxs5m1mWZVmWZVmWZVmWZdltkqepZ1m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"text/plain": [ "<matplotlib.figure.Figure at 0x7f88fdc85210>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def imshow(inp, title=None):\n", " \"\"\"Imshow for Tensor.\"\"\"\n", " inp = inp.numpy().transpose((1, 2, 0))\n", " mean = np.array([0.485, 0.456, 0.406])\n", " std = np.array([0.229, 0.224, 0.225])\n", " inp = std * inp + mean\n", " plt.imshow(inp)\n", " if title is not None:\n", " plt.title(title)\n", " plt.pause(0.001) # pause a bit so that plots are updated\n", "\n", "\n", "# Get a batch of training data\n", "inputs, classes = next(iter(dset_loaders['test']))\n", "\n", "# Make a grid from batch\n", "out = torchvision.utils.make_grid(inputs)\n", "\n", "imshow(out, title=[dset_classes[x] for x in classes])" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def train_model(model, criterion, optimizer, lr_scheduler, num_epochs=25):\n", " since = time.time()\n", "\n", " best_model = model\n", " best_acc = 0.0\n", " train_loss=[]\n", " train_acc=[]\n", " val_loss=[]\n", " val_acc=[]\n", " for epoch in range(num_epochs):\n", " print('Epoch {}/{}'.format(epoch, num_epochs - 1))\n", " print('-' * 10)\n", "\n", " # Each epoch has a training and validation phase\n", " for phase in ['train', 'val']:\n", " if phase == 'train':\n", " optimizer = lr_scheduler(optimizer, epoch)\n", " model.train(True) # Set model to training mode\n", " else:\n", " model.train(False) # Set model to evaluate mode\n", "\n", " running_loss = 0.0\n", " running_corrects = 0\n", "\n", " # Iterate over data.\n", " for data in dset_loaders[phase]:\n", " # get the inputs\n", " inputs, labels = data\n", "\n", " # wrap them in Variable\n", " if use_gpu:\n", " inputs, labels = Variable(inputs.cuda()), Variable(labels.cuda())\n", " else:\n", " inputs, labels = Variable(inputs), Variable(labels)\n", "\n", " # zero the parameter gradients\n", " optimizer.zero_grad()\n", "\n", " # forward\n", " outputs = model(inputs)\n", " _, preds = torch.max(outputs.data, 1)\n", " loss = criterion(outputs, labels)\n", "\n", " # backward + optimize only if in training phase\n", " if phase == 'train':\n", " loss.backward()\n", " optimizer.step()\n", "\n", " # statistics\n", " running_loss += loss.data[0]\n", " running_corrects += torch.sum(preds == labels.data)\n", "\n", " epoch_loss = running_loss / dset_sizes[phase]\n", " epoch_acc = running_corrects / dset_sizes[phase]\n", "\n", " print('{} Loss: {:.4f} Acc: {:.4f}'.format(\n", " phase, epoch_loss, epoch_acc))\n", " \n", " if phase == 'train':\n", " train_loss.append(epoch_loss)\n", " train_acc.append(epoch_acc)\n", " # deep copy the model\n", " if phase == 'val' and epoch_acc > best_acc:\n", " best_acc = epoch_acc\n", " best_model = copy.deepcopy(model)\n", " \n", " if phase == 'val':\n", " val_loss.append(epoch_loss)\n", " val_acc.append(epoch_acc)\n", "\n", " print()\n", "\n", " time_elapsed = time.time() - since\n", " print('Training complete in {:.0f}m {:.0f}s'.format(\n", " time_elapsed // 60, time_elapsed % 60))\n", " print('Best val Acc: {:4f}'.format(best_acc))\n", " return best_model,train_loss,train_acc,val_loss,val_acc" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def exp_lr_scheduler(optimizer, epoch, init_lr=0.001, lr_decay_epoch=2):\n", " \"\"\"Decay learning rate by a factor of 0.1 every lr_decay_epoch epochs.\"\"\"\n", " #lr = init_lr * (0.1**(epoch // lr_decay_epoch))\n", " lr = init_lr\n", "\n", " if epoch % lr_decay_epoch == 0:\n", " print('LR is set to {}'.format(lr))\n", "\n", " for param_group in optimizer.param_groups:\n", " param_group['lr'] = lr\n", "\n", " return optimizer" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def visualize_model(model, num_images=6):\n", " images_so_far = 0\n", " fig = plt.figure()\n", "\n", " for i, data in enumerate(dset_loaders['val']):\n", " inputs, labels = data\n", " if use_gpu:\n", " inputs, labels = Variable(inputs.cuda()), Variable(labels.cuda())\n", " else:\n", " inputs, labels = Variable(inputs), Variable(labels)\n", "\n", " outputs = model(inputs)\n", " _, preds = torch.max(outputs.data, 1)\n", "\n", " for j in range(inputs.size()[0]):\n", " images_so_far += 1\n", " ax = plt.subplot(num_images//2, 2, images_so_far)\n", " ax.axis('off')\n", " ax.set_title('predicted: {},GT:{}'.format(dset_classes[preds.cpu().numpy()[j]],dset_classes[labels.data[j]]))\n", " imshow(inputs.cpu().data[j])\n", " if images_so_far == num_images:\n", " return " ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class CNN(nn.Module):\n", " def __init__(self):\n", " super(CNN, self).__init__()\n", " self.conv1 = nn.Sequential( \n", " nn.Conv2d(\n", " in_channels=3, # input height\n", " out_channels=64, # n_filters\n", " kernel_size=5, # filter size\n", " stride=1, # filter movement/step\n", " #padding=1, # if want same width and length of this image after con2d, padding=(kernel_size-1)/2 if stride=1\n", " ), # output shape (16, 28, 28)\n", " nn.PReLU(), # activation\n", " nn.MaxPool2d(kernel_size=2), # choose max value in 2x2 area, output shape (16, 14, 14)\n", " )\n", " self.conv2 = nn.Sequential( \n", " nn.Conv2d(64, 64, 3, 1), \n", " nn.PReLU(), \n", " nn.MaxPool2d(2), \n", " )\n", " self.conv3 = nn.Sequential( \n", " nn.Conv2d(64, 128, 3, 1), \n", " nn.PReLU(), \n", " nn.MaxPool2d(2), \n", " )\n", " self.conv4 = nn.Sequential( \n", " nn.Conv2d(128, 256, 3, 1), \n", " nn.PReLU(), \n", " nn.MaxPool2d(2), \n", " )\n", " self.conv5 = nn.Sequential( \n", " nn.Conv2d(256, 512, 3, 1), \n", " nn.PReLU(), \n", " nn.MaxPool2d(2), \n", " )\n", " \n", " self.fc1 = nn.Linear(512 * 6 * 6, 2048) # fully connected layer, output n classes\n", " self.dp1 = nn.Dropout(0.5)\n", " self.fc2 = nn.Linear(2048, 2)\n", "\n", " def forward(self, x):\n", " x = self.conv1(x)\n", " x = self.conv2(x)\n", " x = self.conv3(x)\n", " x = self.conv4(x)\n", " x = self.conv5(x)\n", " x = x.view(x.size(0), -1) # flatten the output of conv2 to (batch_size, 32 * 7 * 7)\n", " x = self.fc1(x)\n", " x = self.dp1(x)\n", " x = self.fc2(x)\n", " return x # return x for visualization\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class Lenet(nn.Module):\n", " def __init__(self):\n", " super(Lenet, self).__init__()\n", " self.conv = nn.Sequential(\n", " nn.Conv2d(3, 6, 3, stride=1, padding=1),\n", " nn.MaxPool2d(2, 2),\n", " nn.Conv2d(6, 16, 5, stride=1, padding=0),\n", " nn.MaxPool2d(2, 2)\n", " )\n", "\n", " self.fc = nn.Sequential(\n", " nn.Linear(62*62*16, 120),\n", " nn.Linear(120, 84),\n", " nn.Linear(84, 2)\n", " )\n", "\n", "\n", " def forward(self, x):\n", " out = self.conv(x)\n", " out = out.view(out.size(0), -1)\n", " out = self.fc(out)\n", " return out\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class AlexNet(nn.Module):\n", "\n", " def __init__(self, num_classes=2):\n", " super(AlexNet, self).__init__()\n", " self.features = nn.Sequential(\n", " nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),\n", " nn.ReLU(inplace=True),\n", " nn.MaxPool2d(kernel_size=3, stride=2),\n", " nn.Conv2d(64, 192, kernel_size=5, padding=2),\n", " nn.ReLU(inplace=True),\n", " nn.MaxPool2d(kernel_size=3, stride=2),\n", " nn.Conv2d(192, 384, kernel_size=3, padding=1),\n", " nn.ReLU(inplace=True),\n", " nn.Conv2d(384, 256, kernel_size=3, padding=1),\n", " nn.ReLU(inplace=True),\n", " nn.Conv2d(256, 256, kernel_size=3, padding=1),\n", " nn.ReLU(inplace=True),\n", " nn.MaxPool2d(kernel_size=3, stride=2),\n", " )\n", " self.classifier = nn.Sequential(\n", " nn.Dropout(),\n", " nn.Linear(256 * 6 * 6, 4096),\n", " nn.ReLU(inplace=True),\n", " nn.Dropout(),\n", " nn.Linear(4096, 4096),\n", " nn.ReLU(inplace=True),\n", " nn.Linear(4096, num_classes),\n", " )\n", "\n", " def forward(self, x):\n", " x = self.features(x)\n", " x = x.view(x.size(0), 256 * 6 * 6)\n", " x = self.classifier(x)\n", " return x" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CNN (\n", " (conv1): Sequential (\n", " (0): Conv2d(3, 64, kernel_size=(5, 5), stride=(1, 1))\n", " (1): PReLU (1)\n", " (2): MaxPool2d (size=(2, 2), stride=(2, 2), dilation=(1, 1))\n", " )\n", " (conv2): Sequential (\n", " (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1))\n", " (1): PReLU (1)\n", " (2): MaxPool2d (size=(2, 2), stride=(2, 2), dilation=(1, 1))\n", " )\n", " (conv3): Sequential (\n", " (0): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1))\n", " (1): PReLU (1)\n", " (2): MaxPool2d (size=(2, 2), stride=(2, 2), dilation=(1, 1))\n", " )\n", " (conv4): Sequential (\n", " (0): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1))\n", " (1): PReLU (1)\n", " (2): MaxPool2d (size=(2, 2), stride=(2, 2), dilation=(1, 1))\n", " )\n", " (conv5): Sequential (\n", " (0): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1))\n", " (1): PReLU (1)\n", " (2): MaxPool2d (size=(2, 2), stride=(2, 2), dilation=(1, 1))\n", " )\n", " (fc1): Linear (18432 -> 2048)\n", " (dp1): Dropout (p = 0.5)\n", " (fc2): Linear (2048 -> 2)\n", ")\n", "Epoch 0/39\n", "----------\n", "LR is set to 0.001\n", "train Loss: 0.0303 Acc: 0.6898\n", "val Loss: 0.0285 Acc: 0.7150\n", "\n", "Epoch 1/39\n", "----------\n", "train Loss: 0.0279 Acc: 0.7328\n", "val Loss: 0.0301 Acc: 0.7043\n", "\n", "Epoch 2/39\n", "----------\n", "LR is set to 0.001\n", "train Loss: 0.0270 Acc: 0.7426\n", "val Loss: 0.0279 Acc: 0.7295\n", "\n", "Epoch 3/39\n", "----------\n", "train Loss: 0.0264 Acc: 0.7448\n", "val Loss: 0.0284 Acc: 0.7208\n", "\n", "Epoch 4/39\n", "----------\n", "LR is set to 0.001\n", "train Loss: 0.0260 Acc: 0.7519\n", "val Loss: 0.0277 Acc: 0.7255\n", "\n", "Epoch 5/39\n", "----------\n", "train Loss: 0.0258 Acc: 0.7517\n", "val Loss: 0.0267 Acc: 0.7452\n", "\n", "Epoch 6/39\n", "----------\n", "LR is set to 0.001\n", "train Loss: 0.0253 Acc: 0.7591\n", "val Loss: 0.0253 Acc: 0.7590\n", "\n", "Epoch 7/39\n", "----------\n", "train Loss: 0.0248 Acc: 0.7678\n", "val Loss: 0.0244 Acc: 0.7685\n", "\n", "Epoch 8/39\n", "----------\n", "LR is set to 0.001\n", "train Loss: 0.0241 Acc: 0.7743\n", "val Loss: 0.0252 Acc: 0.7585\n", "\n", "Epoch 9/39\n", "----------\n", "train Loss: 0.0233 Acc: 0.7834\n", "val Loss: 0.0232 Acc: 0.7830\n", "\n", "Epoch 10/39\n", "----------\n", "LR is set to 0.001\n", "train Loss: 0.0221 Acc: 0.7962\n", "val Loss: 0.0215 Acc: 0.8000\n", "\n", "Epoch 11/39\n", "----------\n", "train Loss: 0.0209 Acc: 0.8154\n", "val Loss: 0.0204 Acc: 0.8247\n", "\n", "Epoch 12/39\n", "----------\n", "LR is set to 0.001\n", "train Loss: 0.0201 Acc: 0.8229\n", "val Loss: 0.0223 Acc: 0.7953\n", "\n", "Epoch 13/39\n", "----------\n", "train Loss: 0.0189 Acc: 0.8337\n", "val Loss: 0.0196 Acc: 0.8263\n", "\n", "Epoch 14/39\n", "----------\n", "LR is set to 0.001\n", "train Loss: 0.0184 Acc: 0.8419\n", "val Loss: 0.0188 Acc: 0.8377\n", "\n", "Epoch 15/39\n", "----------\n", "train Loss: 0.0173 Acc: 0.8516\n", "val Loss: 0.0171 Acc: 0.8528\n", "\n", "Epoch 16/39\n", "----------\n", "LR is set to 0.001\n", "train Loss: 0.0163 Acc: 0.8646\n", "val Loss: 0.0175 Acc: 0.8568\n", "\n", "Epoch 17/39\n", "----------\n", "train Loss: 0.0158 Acc: 0.8680\n", "val Loss: 0.0167 Acc: 0.8600\n", "\n", "Epoch 18/39\n", "----------\n", "LR is set to 0.001\n", "train Loss: 0.0147 Acc: 0.8801\n", "val Loss: 0.0158 Acc: 0.8755\n", "\n", "Epoch 19/39\n", "----------\n", "train Loss: 0.0139 Acc: 0.8870\n", "val Loss: 0.0143 Acc: 0.8862\n", "\n", "Epoch 20/39\n", "----------\n", "LR is set to 0.001\n", "train Loss: 0.0130 Acc: 0.8952\n", "val Loss: 0.0155 Acc: 0.8750\n", "\n", "Epoch 21/39\n", "----------\n", "train Loss: 0.0119 Acc: 0.9058\n", "val Loss: 0.0128 Acc: 0.9062\n", "\n", "Epoch 22/39\n", "----------\n", "LR is set to 0.001\n", "train Loss: 0.0114 Acc: 0.9080\n", "val Loss: 0.0156 Acc: 0.8805\n", "\n", "Epoch 23/39\n", "----------\n", "train Loss: 0.0107 Acc: 0.9167\n", "val Loss: 0.0123 Acc: 0.9050\n", "\n", "Epoch 24/39\n", "----------\n", "LR is set to 0.001\n", "train Loss: 0.0100 Acc: 0.9237\n", "val Loss: 0.0117 Acc: 0.9125\n", "\n", "Epoch 25/39\n", "----------\n", "train Loss: 0.0091 Acc: 0.9302\n", "val Loss: 0.0125 Acc: 0.9070\n", "\n", "Epoch 26/39\n", "----------\n", "LR is set to 0.001\n", "train Loss: 0.0087 Acc: 0.9350\n", "val Loss: 0.0131 Acc: 0.9012\n", "\n", "Epoch 27/39\n", "----------\n", "train Loss: 0.0074 Acc: 0.9438\n", "val Loss: 0.0111 Acc: 0.9243\n", "\n", "Epoch 28/39\n", "----------\n", "LR is set to 0.001\n", "train Loss: 0.0074 Acc: 0.9459\n", "val Loss: 0.0119 Acc: 0.9113\n", "\n", "Epoch 29/39\n", "----------\n", "train Loss: 0.0067 Acc: 0.9519\n", "val Loss: 0.0122 Acc: 0.9180\n", "\n", "Epoch 30/39\n", "----------\n", "LR is set to 0.001\n", "train Loss: 0.0067 Acc: 0.9493\n", "val Loss: 0.0105 Acc: 0.9243\n", "\n", "Epoch 31/39\n", "----------\n", "train Loss: 0.0056 Acc: 0.9578\n", "val Loss: 0.0106 Acc: 0.9277\n", "\n", "Epoch 32/39\n", "----------\n", "LR is set to 0.001\n", "train Loss: 0.0055 Acc: 0.9596\n", "val Loss: 0.0116 Acc: 0.9217\n", "\n", "Epoch 33/39\n", "----------\n", "train Loss: 0.0052 Acc: 0.9637\n", "val Loss: 0.0102 Acc: 0.9285\n", "\n", "Epoch 34/39\n", "----------\n", "LR is set to 0.001\n", "train Loss: 0.0045 Acc: 0.9673\n", "val Loss: 0.0113 Acc: 0.9273\n", "\n", "Epoch 35/39\n", "----------\n", "train Loss: 0.0047 Acc: 0.9656\n", "val Loss: 0.0123 Acc: 0.9247\n", "\n", "Epoch 36/39\n", "----------\n", "LR is set to 0.001\n", "train Loss: 0.0043 Acc: 0.9688\n", "val Loss: 0.0103 Acc: 0.9333\n", "\n", "Epoch 37/39\n", "----------\n", "train Loss: 0.0036 Acc: 0.9745\n", "val Loss: 0.0112 Acc: 0.9327\n", "\n", "Epoch 38/39\n", "----------\n", "LR is set to 0.001\n", "train Loss: 0.0040 Acc: 0.9712\n", "val Loss: 0.0120 Acc: 0.9223\n", "\n", "Epoch 39/39\n", "----------\n", "train Loss: 0.0034 Acc: 0.9768\n", "val Loss: 0.0132 Acc: 0.9237\n", "\n", "Training complete in 44m 34s\n", "Best val Acc: 0.933250\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/jim/anaconda2/lib/python2.7/site-packages/ipykernel/__main__.py:19: VisibleDeprecationWarning: converting an array with ndim > 0 to an index will result in an error in the future\n" ] }, { "data": { "image/png": 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ZwHqHsULaJap1WGsJnWe52aFhwIkhlxlMYbO5gZQYViMZRSl4214Eqkosme20YTWsiPNC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\nI6eGUhaLA20I5cw5RAbrqQ1K1uQlcjoGdCs8uDrw9jtfYXf5A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"text/plain": [ "<matplotlib.figure.Figure at 0x7f88f2b9a990>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "model_ft = CNN()\n", "print(model_ft)\n", "#num_ftrs = model_ft.fc.in_features\n", "#model_ft.fc = nn.Linear(num_ftrs, 2)\n", "\n", "if use_gpu:\n", " model_ft = model_ft.cuda()\n", "\n", "criterion = nn.CrossEntropyLoss()\n", "optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)\n", "\n", "#model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler, num_epochs=60)\n", "model_ft,train_loss,train_acc,val_loss,val_acc = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler, num_epochs=40)\n", "visualize_model(model_ft)\n", "plt.ioff()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "torch.save(model_ft.state_dict(), '1011_2.pkl')" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x7fbba5cd4950>" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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oQGxeJmNaIEscpumtexc+vBuiOsPtcyHxlEZfIj2/lK7R4da+YUwLZInDNB1V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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fbbedf15d90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.rcParams['font.sans-serif']=['SimHei']\n", "\n", "plt.plot(val_acc,label=\"val\")\n", "plt.plot(train_acc,label=\"train\")\n", "plt.xlim((0, 60))\n", "plt.ylim((0.6, 1))\n", "plt.title('Accuracy')\n", "plt.xlabel('Epoch')\n", "plt.ylabel('Accuracy(%)')\n", "plt.legend(loc='lower right')\n", "plt.savefig('acc.png',dpi=600)\n", "plt.show() " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "test_y = np.loadtxt('alex_tests_y.txt');\n", "y_pred = np.loadtxt('alex_preds_y.txt');" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x7fbba5d24750>" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Ro3h6elbL/ezZ/JUINCn1PtJ2rFJlRMQNa0KZaYz5uqSAMebUGF8RmQosKO/DjTFTgClg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\nS7EBXOzuY3LmpmQpvt8Br7r7CGA08XdsfHzunsk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"text/plain": [ "<matplotlib.figure.Figure at 0x7fbbee670810>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.rcParams['font.sans-serif']=['SimHei']\n", "plt.plot(val_loss,label=\"val\")\n", "plt.plot(train_loss,label=\"train\")\n", "plt.xlim((0, 60))\n", "plt.ylim((0, 0.03))\n", "plt.title('Loss')\n", "plt.xlabel('Epoch')\n", "plt.ylabel('Loss(%)')\n", "plt.legend(loc='upper right')\n", "plt.savefig('loss.png',dpi=600)\n", "plt.show() " ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "precision: [ 0.52 0.45]\n", "recall: [ 0.72 0.25]\n", "fscore: [ 0.61 0.32]\n", "support: [5000 4452]\n" ] } ], "source": [ "from sklearn.metrics import precision_recall_fscore_support as score\n", "plt.rcParams['font.sans-serif']=['SimHei']\n", "model = CNN()\n", "model = model.cuda()\n", "#model.load_state_dict(torch.load('1011_2.pkl'))\n", "tests_y = []\n", "preds_y = []\n", "for data in dset_loaders['test']:\n", " inputs, labels = data\n", " if use_gpu:\n", " inputs, labels = Variable(inputs.cuda()), Variable(labels.cuda())\n", " else:\n", " inputs, labels = Variable(inputs), Variable(labels)\n", " test_output = model(inputs)\n", " test_output = test_output.cpu()\n", " pred_y = torch.max(test_output, 1)[1].data.numpy().squeeze()\n", " pred_y = list(pred_y)\n", " labels = labels.cpu()\n", " labels = list(labels.data.numpy())\n", " tests_y += labels\n", " preds_y += pred_y\n", "\n", "#print(preds_y, 'prediction number')\n", "#print(tests_y, 'real number')\n", "\n", "precision, recall, fscore, support = score(tests_y, preds_y)\n", "\n", "print('precision: {}'.format(precision))\n", "print('recall: {}'.format(recall))\n", "print('fscore: {}'.format(fscore))\n", "print('support: {}'.format(support))" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Automatically created module for IPython interactive environment\n", "Confusion matrix, without normalization\n", "[[1529 471]\n", " [ 420 1580]]\n", "Normalized confusion matrix\n", "[[ 0.76 0.24]\n", " [ 0.21 0.79]]\n" ] }, { "data": { "image/png": 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AKLLZzXcEbomIOxe3jwEDNovxj04s2zFYaayw+TGVroIV4bNJlzzRyJyuTbLe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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fbbbdb87810>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fbbedf2c8d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#coding:utf-8\n", "print(__doc__)\n", "\n", "import itertools\n", "import numpy as np\n", "from matplotlib.font_manager import *\n", "import matplotlib.pyplot as plt\n", "\n", "from sklearn import svm, datasets\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.metrics import confusion_matrix\n", "matplotlib.rcParams['axes.unicode_minus'] = False\n", "plt.rcParams['font.sans-serif']=['SimHei']\n", "class_names = dset_classes\n", "\n", "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", " 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", " 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", " fmt = '.2f' if normalize else 'd'\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, format(cm[i, j], fmt),\n", " horizontalalignment=\"center\",\n", " color=\"white\" if cm[i, j] > thresh else \"black\")\n", "\n", " plt.tight_layout()\n", " #plt.ylabel('True label')\n", " plt.ylabel(U'发发')\n", " plt.xlabel('Predicted label')\n", "\n", "# Compute confusion matrix\n", "cnf_matrix = confusion_matrix(tests_y, preds_y)\n", "np.set_printoptions(precision=2)\n", "\n", "# Plot non-normalized confusion matrix\n", "plt.figure()\n", "plot_confusion_matrix(cnf_matrix, classes=class_names,\n", " title='Confusion matrix, without normalization')\n", "plt.savefig('cof_matrix.png')\n", "# Plot normalized confusion matrix\n", "plt.figure()\n", "plot_confusion_matrix(cnf_matrix, classes=class_names, normalize=True,\n", " title='Normalized confusion matrix')\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": true }, "outputs": [], "source": [ "np.savetxt('CNN_train_acc.txt',train_acc);\n", "np.savetxt('CNN_val_acc.txt',val_acc);" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": true }, "outputs": [], "source": [ "np.savetxt('CNN_train_loss.txt',train_loss);\n", "np.savetxt('CNN_val_loss.txt',val_loss);" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": true }, "outputs": [], "source": [ "np.savetxt('CNN_tests_y.txt',tests_y);\n", "np.savetxt('CNN_preds_y.txt',preds_y);" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 }
gpl-3.0
r-shekhar/NYC-transport
15_dataframe_analysis/spatialjoin_geopandas_dask.ipynb
1
12538675
null
bsd-3-clause
ramhiser/Keras-Tutorials
notebooks/06_autoencoder.ipynb
1
246652
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Autoencoders\n", "\n", "I've been exploring how useful [autoencoders](https://en.wikipedia.org/wiki/Autoencoder) are and how painfully simple they are to implement in [Keras](https://keras.io/). In this post, my goal is to better understand them myself, so I borrow heavily from [the Keras blog](https://blog.keras.io/building-autoencoders-in-keras.html) on the same topic. So rather than sprinkling references to the Keras blog throughout the post, just assume I borrowed it from [Francois Chollet](https://twitter.com/fchollet). Thanks to Francois for making his code available!\n", "\n", "For instance, I thought about drawing a diagram overviewing autoencoders, but it's hard to beat the effective simplicity of this diagram.\n", "\n", "![stole from Keras blog](https://blog.keras.io/img/ae/autoencoder_schema.jpg)\n", "\n", "So, autoencoders are legit. They perform data compression but not in the JPEG or MPEG way, which make some broad assumptions about images, sound, and video and apply compression based on the assumptions. Instead, autoencoders **learn** (automatically) a lossy compression based on the data examples fed in. So the compression is specific to those examples.\n", "\n", "## What's Required\n", "\n", "Autoencoders require 3 things:\n", "\n", "1. Encoding function\n", "2. Decoding function\n", "3. Loss function describing the amount of information loss between the compressed and decompressed representations of the data examples and the decompressed representation (i.e. a \"loss\" function).\n", "\n", "The encoding/decoding functions are typically (parametric) neural nets and are differentiable with respect to the distance function. The differentiable part enables optimizing the parameters of the encoding/decoding functions to minimize the reconstruction loss.\n", "\n", "## What Are They Good For\n", "\n", "1. Data Denoising\n", "2. Dimension Reduction\n", "3. Data Visualization (basically the same as 2, but plots)\n", "\n", "For data denoising, think PCA, but nonlinear. In fact, if the encoder/decoder functions are linear, the result spans the space of the PCA solution. The nonlinear part is useful because they can capture, for example, multimodality in the feature space, which PCA can't.\n", "\n", "Dimension reduction is a direct result of the lossy compression of the algorithm. It can help with denoising and **pre-training** before building another ML algorithm. But is the compression good enough to replace JPEG or MPEG? Possibly. Check out [this post](https://hackernoon.com/using-ai-to-super-compress-images-5a948cf09489) based on [a recent paper](https://arxiv.org/abs/1708.00838).\n", "\n", "But this post is not about the cutting edge stuff. Instead, we're going to focus on more of the basics and do the following:\n", "\n", "* Simple Autoencoder\n", "* Deep Autoencoder\n", "* Convolution Autoencoder\n", "* Build a Second Convolution Autoencoder to Denoise Images" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Data Loading and Preprocessing\n", "\n", "For this post, I'm going to use the [MNIST data set](http://yann.lecun.com/exdb/mnist/). To get started, let's start with the boilerplate imports." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/ramey/miniconda3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", " from ._conv import register_converters as _register_converters\n", "Using TensorFlow backend.\n" ] } ], "source": [ "from IPython.display import Image, SVG\n", "import matplotlib.pyplot as plt\n", "\n", "%matplotlib inline\n", "\n", "import numpy as np\n", "import keras\n", "from keras.datasets import mnist\n", "from keras.models import Model, Sequential\n", "from keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D, Flatten, Reshape\n", "from keras import regularizers" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "With that out of the way, let's load the MNIST data set and scale the images to a range between 0 and 1. If you haven't already downloaded the data set, the Keras `load_data` function will download the data directly from S3 on AWS." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# Loads the training and test data sets (ignoring class labels)\n", "(x_train, _), (x_test, _) = mnist.load_data()\n", "\n", "# Scales the training and test data to range between 0 and 1.\n", "max_value = float(x_train.max())\n", "x_train = x_train.astype('float32') / max_value\n", "x_test = x_test.astype('float32') / max_value" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The data set consists 3D arrays with 60K training and 10K test images. The images have a resolution of 28 x 28 (pixels)." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "((60000, 28, 28), (10000, 28, 28))" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x_train.shape, x_test.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To work with the images as vectors, let's reshape the 3D arrays as matrices. In doing so, we'll reshape the 28 x 28 images into vectors of length 784" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "((60000, 784), (10000, 784))" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x_train = x_train.reshape((len(x_train), np.prod(x_train.shape[1:])))\n", "x_test = x_test.reshape((len(x_test), np.prod(x_test.shape[1:])))\n", "\n", "(x_train.shape, x_test.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***\n", "\n", "# Simple Autoencoder\n", "\n", "Let's start with a simple autoencoder for illustration. The encoder and decoder functions are each fully-connected neural layers. The encoder function uses a [ReLU activation function](https://en.wikipedia.org/wiki/Rectifier_(neural_networks)), while the decoder function uses a [sigmoid activation function](https://en.wikipedia.org/wiki/Activation_function#Comparison_of_activation_functions).\n", "\n", "So what are the encoder and the decoder layers doing?\n", "\n", "* The encoder layer \"encodes\" the input image as a compressed representation in a reduced dimension. The compressed image typically looks garbled, nothing like the original image.\n", "* The decoder layer \"decodes\" the encoded image back to the original dimension. The decoded image is a [lossy reconstruction](https://en.wikipedia.org/wiki/Lossy_compression) of the original image.\n", "\n", "In our example, the compressed image has a dimension of 32. The encoder model reduces the dimension from the original 784-dimensional vector to the encoded 32-dimensional vector. The decoder model restores the dimension from the encoded 32-dimensional representation back to the original 784-dimensional vector.\n", "\n", "The compression factor is the ratio of the input dimension to the encoded dimension. In our case, the factor is `24.5 = 784 / 32`.\n", "\n", "The `autoencoder` model maps an input image to its reconstructed image." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Compression factor: 24.5\n", "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "dense_1 (Dense) (None, 32) 25120 \n", "_________________________________________________________________\n", "dense_2 (Dense) (None, 784) 25872 \n", "=================================================================\n", "Total params: 50,992\n", "Trainable params: 50,992\n", "Non-trainable params: 0\n", "_________________________________________________________________\n" ] } ], "source": [ "# input dimension = 784\n", "input_dim = x_train.shape[1]\n", "encoding_dim = 32\n", "\n", "compression_factor = float(input_dim) / encoding_dim\n", "print(\"Compression factor: %s\" % compression_factor)\n", "\n", "autoencoder = Sequential()\n", "autoencoder.add(\n", " Dense(encoding_dim, input_shape=(input_dim,), activation='relu')\n", ")\n", "autoencoder.add(\n", " Dense(input_dim, activation='sigmoid')\n", ")\n", "\n", "autoencoder.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Encoder Model\n", "\n", "We can extract the encoder model from the first layer of the autoencoder model. The reason we want to extract the encoder model is to examine what an encoded image looks like." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "input_1 (InputLayer) (None, 784) 0 \n", "_________________________________________________________________\n", "dense_1 (Dense) (None, 32) 25120 \n", "=================================================================\n", "Total params: 25,120\n", "Trainable params: 25,120\n", "Non-trainable params: 0\n", "_________________________________________________________________\n" ] } ], "source": [ "input_img = Input(shape=(input_dim,))\n", "encoder_layer = autoencoder.layers[0]\n", "encoder = Model(input_img, encoder_layer(input_img))\n", "\n", "encoder.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Okay, now we're ready to train our first autoencoder. We'll iterate on the training data in batches of 256 in 50 epochs. Let's also use [the Adam optimizer](https://arxiv.org/abs/1412.6980) and per-pixel binary [crossentropy](https://en.wikipedia.org/wiki/Cross_entropy) loss. The purpose of the loss function is to reconstruct an image similar to the input image.\n", "\n", "I want to call out something that may look like a typo or may not be obvious at first glance. Notice the repeat of `x_train` in `autoencoder.fit(x_train, x_train, ...)`. This implies that `x_train` is both the input and output, which is exactly what we want for image reconstruction.\n", "\n", "I'm running this code on a laptop, so you'll notice the training times are a bit slow (no GPU)." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train on 60000 samples, validate on 10000 samples\n", "Epoch 1/50\n", "60000/60000 [==============================] - 2s 35us/step - loss: 0.2786 - val_loss: 0.1903\n", "Epoch 2/50\n", "60000/60000 [==============================] - 2s 32us/step - loss: 0.1713 - val_loss: 0.1543\n", "Epoch 3/50\n", "60000/60000 [==============================] - 2s 32us/step - loss: 0.1449 - val_loss: 0.1342\n", "Epoch 4/50\n", "60000/60000 [==============================] - 2s 32us/step - loss: 0.1291 - val_loss: 0.1220\n", "Epoch 5/50\n", "60000/60000 [==============================] - 2s 32us/step - loss: 0.1190 - val_loss: 0.1133\n", "Epoch 6/50\n", "60000/60000 [==============================] - 2s 32us/step - loss: 0.1115 - val_loss: 0.1071\n", "Epoch 7/50\n", "60000/60000 [==============================] - 2s 32us/step - loss: 0.1060 - val_loss: 0.1025\n", "Epoch 8/50\n", "60000/60000 [==============================] - 2s 32us/step - loss: 0.1020 - val_loss: 0.0992\n", "Epoch 9/50\n", "60000/60000 [==============================] - 2s 33us/step - loss: 0.0993 - val_loss: 0.0971\n", "Epoch 10/50\n", "60000/60000 [==============================] - 2s 32us/step - loss: 0.0975 - val_loss: 0.0955\n", "Epoch 11/50\n", "60000/60000 [==============================] - 2s 32us/step - loss: 0.0962 - val_loss: 0.0945\n", "Epoch 12/50\n", "60000/60000 [==============================] - 2s 32us/step - loss: 0.0954 - val_loss: 0.0939\n", "Epoch 13/50\n", "60000/60000 [==============================] - 2s 32us/step - loss: 0.0949 - val_loss: 0.0934\n", "Epoch 14/50\n", "60000/60000 [==============================] - 2s 32us/step - loss: 0.0945 - val_loss: 0.0931\n", "Epoch 15/50\n", "60000/60000 [==============================] - 2s 32us/step - loss: 0.0942 - val_loss: 0.0928\n", "Epoch 16/50\n", "60000/60000 [==============================] - 2s 32us/step - loss: 0.0940 - val_loss: 0.0926\n", "Epoch 17/50\n", "60000/60000 [==============================] - 2s 32us/step - loss: 0.0939 - val_loss: 0.0925\n", "Epoch 18/50\n", "60000/60000 [==============================] - 2s 33us/step - loss: 0.0937 - val_loss: 0.0923\n", "Epoch 19/50\n", "60000/60000 [==============================] - 2s 32us/step - loss: 0.0936 - val_loss: 0.0923\n", "Epoch 20/50\n", "60000/60000 [==============================] - 2s 32us/step - loss: 0.0935 - val_loss: 0.0922\n", "Epoch 21/50\n", "60000/60000 [==============================] - 2s 32us/step - loss: 0.0934 - val_loss: 0.0921\n", "Epoch 22/50\n", "60000/60000 [==============================] - 2s 32us/step - loss: 0.0933 - val_loss: 0.0921\n", "Epoch 23/50\n", "60000/60000 [==============================] - 2s 34us/step - loss: 0.0933 - val_loss: 0.0920\n", "Epoch 24/50\n", "60000/60000 [==============================] - 2s 32us/step - loss: 0.0932 - val_loss: 0.0920\n", "Epoch 25/50\n", "60000/60000 [==============================] - 2s 35us/step - loss: 0.0932 - val_loss: 0.0919\n", "Epoch 26/50\n", "60000/60000 [==============================] - 2s 36us/step - loss: 0.0931 - val_loss: 0.0919\n", "Epoch 27/50\n", "60000/60000 [==============================] - 2s 36us/step - loss: 0.0931 - val_loss: 0.0919\n", "Epoch 28/50\n", "60000/60000 [==============================] - 2s 36us/step - loss: 0.0930 - val_loss: 0.0918\n", "Epoch 29/50\n", "60000/60000 [==============================] - 2s 36us/step - loss: 0.0930 - val_loss: 0.0918\n", "Epoch 30/50\n", "60000/60000 [==============================] - 2s 37us/step - loss: 0.0930 - val_loss: 0.0918\n", "Epoch 31/50\n", "60000/60000 [==============================] - 2s 37us/step - loss: 0.0929 - val_loss: 0.0917\n", "Epoch 32/50\n", "60000/60000 [==============================] - 2s 37us/step - loss: 0.0929 - val_loss: 0.0917\n", "Epoch 33/50\n", "60000/60000 [==============================] - 2s 36us/step - loss: 0.0929 - val_loss: 0.0917\n", "Epoch 34/50\n", "60000/60000 [==============================] - 2s 36us/step - loss: 0.0929 - val_loss: 0.0917\n", "Epoch 35/50\n", "60000/60000 [==============================] - 2s 36us/step - loss: 0.0928 - val_loss: 0.0917\n", "Epoch 36/50\n", "60000/60000 [==============================] - 2s 36us/step - loss: 0.0928 - val_loss: 0.0916\n", "Epoch 37/50\n", "60000/60000 [==============================] - 2s 36us/step - loss: 0.0928 - val_loss: 0.0916\n", "Epoch 38/50\n", "60000/60000 [==============================] - 2s 36us/step - loss: 0.0928 - val_loss: 0.0917\n", "Epoch 39/50\n", "60000/60000 [==============================] - 2s 37us/step - loss: 0.0928 - val_loss: 0.0916\n", "Epoch 40/50\n", "60000/60000 [==============================] - 2s 38us/step - loss: 0.0928 - val_loss: 0.0918\n", "Epoch 41/50\n", "60000/60000 [==============================] - 2s 38us/step - loss: 0.0928 - val_loss: 0.0916\n", "Epoch 42/50\n", "60000/60000 [==============================] - 2s 37us/step - loss: 0.0927 - val_loss: 0.0916\n", "Epoch 43/50\n", "60000/60000 [==============================] - 2s 37us/step - loss: 0.0927 - val_loss: 0.0916\n", "Epoch 44/50\n", "60000/60000 [==============================] - 2s 37us/step - loss: 0.0927 - val_loss: 0.0916\n", "Epoch 45/50\n", "60000/60000 [==============================] - 2s 36us/step - loss: 0.0927 - val_loss: 0.0916\n", "Epoch 46/50\n", "60000/60000 [==============================] - 2s 37us/step - loss: 0.0927 - val_loss: 0.0915\n", "Epoch 47/50\n", "60000/60000 [==============================] - 2s 37us/step - loss: 0.0927 - val_loss: 0.0915\n", "Epoch 48/50\n", "60000/60000 [==============================] - 2s 37us/step - loss: 0.0927 - val_loss: 0.0915\n", "Epoch 49/50\n", "60000/60000 [==============================] - 2s 37us/step - loss: 0.0927 - val_loss: 0.0915\n", "Epoch 50/50\n", "60000/60000 [==============================] - 2s 37us/step - loss: 0.0926 - val_loss: 0.0916\n" ] }, { "data": { "text/plain": [ "<keras.callbacks.History at 0x181b5c4cf8>" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "autoencoder.compile(optimizer='adam', loss='binary_crossentropy')\n", "autoencoder.fit(x_train, x_train,\n", " epochs=50,\n", " batch_size=256,\n", " shuffle=True,\n", " validation_data=(x_test, x_test))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We've successfully trained our first autoencoder. With a mere 50,992 parameters, our autoencoder model can compress an MNIST digit down to 32 floating-point digits. Not that impressive, but it works.\n", "\n", "To check out the encoded images and the reconstructed image quality, we randomly sample 10 test images. I really like how the encoded images look. Do they make sense? No. Are they eye candy though? Most definitely.\n", "\n", "However, the reconstructed images are quite lossy. You can see the digits clearly, but notice the loss in image quality." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1296x288 with 30 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "num_images = 10\n", "np.random.seed(42)\n", "random_test_images = np.random.randint(x_test.shape[0], size=num_images)\n", "\n", "encoded_imgs = encoder.predict(x_test)\n", "decoded_imgs = autoencoder.predict(x_test)\n", "\n", "plt.figure(figsize=(18, 4))\n", "\n", "for i, image_idx in enumerate(random_test_images):\n", " # plot original image\n", " ax = plt.subplot(3, num_images, i + 1)\n", " plt.imshow(x_test[image_idx].reshape(28, 28))\n", " plt.gray()\n", " ax.get_xaxis().set_visible(False)\n", " ax.get_yaxis().set_visible(False)\n", " \n", " # plot encoded image\n", " ax = plt.subplot(3, num_images, num_images + i + 1)\n", " plt.imshow(encoded_imgs[image_idx].reshape(8, 4))\n", " plt.gray()\n", " ax.get_xaxis().set_visible(False)\n", " ax.get_yaxis().set_visible(False)\n", "\n", " # plot reconstructed image\n", " ax = plt.subplot(3, num_images, 2*num_images + i + 1)\n", " plt.imshow(decoded_imgs[image_idx].reshape(28, 28))\n", " plt.gray()\n", " ax.get_xaxis().set_visible(False)\n", " ax.get_yaxis().set_visible(False)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***\n", "\n", "# Deep Autoencoder\n", "\n", "\n", "Above, we used single fully-connected layers for both the encoding and decoding models. Instead, we can stack multiple fully-connected layers to make each of the encoder and decoder functions **deep**. You know because deep learning.\n", "\n", "In this next model, we'll use 3 fully-connected layers for the encoding model with decreasing dimensions from 128 to 64 32 again. Likewise, we'll add 3 fully-connected decoder layers that reconstruct the image back to 784 dimensions. Except for the last layer, we'll use ReLU activation functions again.\n", "\n", "In Keras, this model is painfully simple to do, so let's get started. We'll use the same training configuration: Adam + 50 epochs + batch size of 256." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "dense_3 (Dense) (None, 128) 100480 \n", "_________________________________________________________________\n", "dense_4 (Dense) (None, 64) 8256 \n", "_________________________________________________________________\n", "dense_5 (Dense) (None, 32) 2080 \n", "_________________________________________________________________\n", "dense_6 (Dense) (None, 64) 2112 \n", "_________________________________________________________________\n", "dense_7 (Dense) (None, 128) 8320 \n", "_________________________________________________________________\n", "dense_8 (Dense) (None, 784) 101136 \n", "=================================================================\n", "Total params: 222,384\n", "Trainable params: 222,384\n", "Non-trainable params: 0\n", "_________________________________________________________________\n" ] } ], "source": [ "autoencoder = Sequential()\n", "\n", "# Encoder Layers\n", "autoencoder.add(Dense(4 * encoding_dim, input_shape=(input_dim,), activation='relu'))\n", "autoencoder.add(Dense(2 * encoding_dim, activation='relu'))\n", "autoencoder.add(Dense(encoding_dim, activation='relu'))\n", "\n", "# Decoder Layers\n", "autoencoder.add(Dense(2 * encoding_dim, activation='relu'))\n", "autoencoder.add(Dense(4 * encoding_dim, activation='relu'))\n", "autoencoder.add(Dense(input_dim, activation='sigmoid'))\n", "\n", "autoencoder.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Encoder Model\n", "\n", "Like we did above, we can extract the encoder model from the autoencoder. The encoder model consists of the first 3 layers in the autoencoder, so let's extract them to visualize the encoded images." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "input_2 (InputLayer) (None, 784) 0 \n", "_________________________________________________________________\n", "dense_3 (Dense) (None, 128) 100480 \n", "_________________________________________________________________\n", "dense_4 (Dense) (None, 64) 8256 \n", "_________________________________________________________________\n", "dense_5 (Dense) (None, 32) 2080 \n", "=================================================================\n", "Total params: 110,816\n", "Trainable params: 110,816\n", "Non-trainable params: 0\n", "_________________________________________________________________\n" ] } ], "source": [ "input_img = Input(shape=(input_dim,))\n", "encoder_layer1 = autoencoder.layers[0]\n", "encoder_layer2 = autoencoder.layers[1]\n", "encoder_layer3 = autoencoder.layers[2]\n", "encoder = Model(input_img, encoder_layer3(encoder_layer2(encoder_layer1(input_img))))\n", "\n", "encoder.summary()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train on 60000 samples, validate on 10000 samples\n", "Epoch 1/50\n", "60000/60000 [==============================] - 4s 59us/step - loss: 0.2502 - val_loss: 0.1741\n", "Epoch 2/50\n", "60000/60000 [==============================] - 3s 53us/step - loss: 0.1580 - val_loss: 0.1422\n", "Epoch 3/50\n", "60000/60000 [==============================] - 3s 53us/step - loss: 0.1357 - val_loss: 0.1275\n", "Epoch 4/50\n", "60000/60000 [==============================] - 3s 53us/step - loss: 0.1245 - val_loss: 0.1190\n", "Epoch 5/50\n", "60000/60000 [==============================] - 3s 53us/step - loss: 0.1178 - val_loss: 0.1137\n", "Epoch 6/50\n", "60000/60000 [==============================] - 3s 53us/step - loss: 0.1133 - val_loss: 0.1104\n", "Epoch 7/50\n", "60000/60000 [==============================] - 3s 53us/step - loss: 0.1098 - val_loss: 0.1070\n", "Epoch 8/50\n", "60000/60000 [==============================] - 3s 53us/step - loss: 0.1068 - val_loss: 0.1042\n", "Epoch 9/50\n", "60000/60000 [==============================] - 3s 53us/step - loss: 0.1042 - val_loss: 0.1018\n", "Epoch 10/50\n", "60000/60000 [==============================] - 3s 53us/step - loss: 0.1018 - val_loss: 0.0997\n", "Epoch 11/50\n", "60000/60000 [==============================] - 3s 53us/step - loss: 0.0999 - val_loss: 0.0980\n", "Epoch 12/50\n", "60000/60000 [==============================] - 3s 53us/step - loss: 0.0982 - val_loss: 0.0966\n", "Epoch 13/50\n", "60000/60000 [==============================] - 3s 53us/step - loss: 0.0967 - val_loss: 0.0949\n", "Epoch 14/50\n", "60000/60000 [==============================] - 3s 53us/step - loss: 0.0956 - val_loss: 0.0941\n", "Epoch 15/50\n", "60000/60000 [==============================] - 3s 53us/step - loss: 0.0947 - val_loss: 0.0935\n", "Epoch 16/50\n", "60000/60000 [==============================] - 3s 54us/step - loss: 0.0939 - val_loss: 0.0929\n", "Epoch 17/50\n", "60000/60000 [==============================] - 3s 53us/step - loss: 0.0933 - val_loss: 0.0921\n", "Epoch 18/50\n", "60000/60000 [==============================] - 3s 53us/step - loss: 0.0927 - val_loss: 0.0915\n", "Epoch 19/50\n", "60000/60000 [==============================] - 3s 53us/step - loss: 0.0921 - val_loss: 0.0911\n", "Epoch 20/50\n", "60000/60000 [==============================] - 3s 54us/step - loss: 0.0917 - val_loss: 0.0905\n", "Epoch 21/50\n", "60000/60000 [==============================] - 3s 54us/step - loss: 0.0912 - val_loss: 0.0904\n", "Epoch 22/50\n", "60000/60000 [==============================] - 3s 54us/step - loss: 0.0907 - val_loss: 0.0902\n", "Epoch 23/50\n", "60000/60000 [==============================] - 3s 54us/step - loss: 0.0903 - val_loss: 0.0892\n", "Epoch 24/50\n", "60000/60000 [==============================] - 3s 54us/step - loss: 0.0898 - val_loss: 0.0889\n", "Epoch 25/50\n", "60000/60000 [==============================] - 3s 54us/step - loss: 0.0895 - val_loss: 0.0887\n", "Epoch 26/50\n", "60000/60000 [==============================] - 3s 53us/step - loss: 0.0892 - val_loss: 0.0887\n", "Epoch 27/50\n", "60000/60000 [==============================] - 3s 54us/step - loss: 0.0890 - val_loss: 0.0880\n", "Epoch 28/50\n", "60000/60000 [==============================] - 3s 54us/step - loss: 0.0886 - val_loss: 0.0879\n", "Epoch 29/50\n", "60000/60000 [==============================] - 3s 55us/step - loss: 0.0883 - val_loss: 0.0876\n", "Epoch 30/50\n", "60000/60000 [==============================] - 3s 56us/step - loss: 0.0882 - val_loss: 0.0875\n", "Epoch 31/50\n", "60000/60000 [==============================] - 3s 57us/step - loss: 0.0879 - val_loss: 0.0871\n", "Epoch 32/50\n", "60000/60000 [==============================] - 3s 54us/step - loss: 0.0876 - val_loss: 0.0869\n", "Epoch 33/50\n", "60000/60000 [==============================] - 3s 55us/step - loss: 0.0875 - val_loss: 0.0867\n", "Epoch 34/50\n", "60000/60000 [==============================] - 3s 55us/step - loss: 0.0872 - val_loss: 0.0866\n", "Epoch 35/50\n", "60000/60000 [==============================] - 3s 54us/step - loss: 0.0870 - val_loss: 0.0866\n", "Epoch 36/50\n", "60000/60000 [==============================] - 3s 54us/step - loss: 0.0868 - val_loss: 0.0862\n", "Epoch 37/50\n", "60000/60000 [==============================] - 3s 54us/step - loss: 0.0867 - val_loss: 0.0860\n", "Epoch 38/50\n", "60000/60000 [==============================] - 3s 55us/step - loss: 0.0865 - val_loss: 0.0859\n", "Epoch 39/50\n", "60000/60000 [==============================] - 3s 56us/step - loss: 0.0864 - val_loss: 0.0858\n", "Epoch 40/50\n", "60000/60000 [==============================] - 3s 55us/step - loss: 0.0862 - val_loss: 0.0856\n", "Epoch 41/50\n", "60000/60000 [==============================] - 3s 55us/step - loss: 0.0861 - val_loss: 0.0856\n", "Epoch 42/50\n", "60000/60000 [==============================] - 3s 56us/step - loss: 0.0859 - val_loss: 0.0856\n", "Epoch 43/50\n", "60000/60000 [==============================] - 3s 55us/step - loss: 0.0859 - val_loss: 0.0853\n", "Epoch 44/50\n", "60000/60000 [==============================] - 3s 55us/step - loss: 0.0857 - val_loss: 0.0854\n", "Epoch 45/50\n", "60000/60000 [==============================] - 3s 55us/step - loss: 0.0855 - val_loss: 0.0851\n", "Epoch 46/50\n", "60000/60000 [==============================] - 3s 55us/step - loss: 0.0854 - val_loss: 0.0850\n", "Epoch 47/50\n", "60000/60000 [==============================] - 3s 56us/step - loss: 0.0853 - val_loss: 0.0849\n", "Epoch 48/50\n", "60000/60000 [==============================] - 3s 54us/step - loss: 0.0852 - val_loss: 0.0848\n", "Epoch 49/50\n", "60000/60000 [==============================] - 4s 59us/step - loss: 0.0851 - val_loss: 0.0848\n", "Epoch 50/50\n", "60000/60000 [==============================] - 3s 55us/step - loss: 0.0850 - val_loss: 0.0845\n" ] }, { "data": { "text/plain": [ "<keras.callbacks.History at 0x181e2eb940>" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "autoencoder.compile(optimizer='adam', loss='binary_crossentropy')\n", "autoencoder.fit(x_train, x_train,\n", " epochs=50,\n", " batch_size=256,\n", " validation_data=(x_test, x_test))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As with the simple autoencoder, we randomly sample 10 test images (the same ones as before). The reconstructed digits look much better than those from the single-layer autoencoder. This observation aligns with the reduction in validation loss after adding multiple layers to the autoencoder." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1296x288 with 30 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "num_images = 10\n", "np.random.seed(42)\n", "random_test_images = np.random.randint(x_test.shape[0], size=num_images)\n", "\n", "encoded_imgs = encoder.predict(x_test)\n", "decoded_imgs = autoencoder.predict(x_test)\n", "\n", "plt.figure(figsize=(18, 4))\n", "\n", "for i, image_idx in enumerate(random_test_images):\n", " # plot original image\n", " ax = plt.subplot(3, num_images, i + 1)\n", " plt.imshow(x_test[image_idx].reshape(28, 28))\n", " plt.gray()\n", " ax.get_xaxis().set_visible(False)\n", " ax.get_yaxis().set_visible(False)\n", " \n", " # plot encoded image\n", " ax = plt.subplot(3, num_images, num_images + i + 1)\n", " plt.imshow(encoded_imgs[image_idx].reshape(8, 4))\n", " plt.gray()\n", " ax.get_xaxis().set_visible(False)\n", " ax.get_yaxis().set_visible(False)\n", "\n", " # plot reconstructed image\n", " ax = plt.subplot(3, num_images, 2*num_images + i + 1)\n", " plt.imshow(decoded_imgs[image_idx].reshape(28, 28))\n", " plt.gray()\n", " ax.get_xaxis().set_visible(False)\n", " ax.get_yaxis().set_visible(False)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***\n", "\n", "# Convolutional Autoencoder\n", "\n", "Now that we've explored deep autoencoders, let's use a convolutional autoencoder instead, given that the input objects are images. What this means is our encoding and decoding models will be [convolutional neural networks](http://cs231n.github.io/convolutional-networks/) instead of fully-connected networks.\n", "\n", "Again, Keras makes this very easy for us. Before we get started though, we need to reshapes the images back to `28 x 28 x 1` for the convnets. The 1 is for 1 channel because black and white. If we had RGB color, there would be 3 channels." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "x_train = x_train.reshape((len(x_train), 28, 28, 1))\n", "x_test = x_test.reshape((len(x_test), 28, 28, 1))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To build the convolutional autoencoder, we'll make use of `Conv2D` and `MaxPooling2D` layers for the encoder and `Conv2D` and `UpSampling2D` layers for the decoder. The encoded images are transformed to a 3D array of dimensions `4 x 4 x 8`, but to visualize the encoding, we'll flatten it to a vector of length 128. I tried to use an encoding dimension of 32 like above, but I kept getting subpar results.\n", "\n", "After the flattening layer, we reshape the image back to a `4 x 4 x 8` array before upsampling back to a `28 x 28 x 1` image." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "conv2d_1 (Conv2D) (None, 28, 28, 16) 160 \n", "_________________________________________________________________\n", "max_pooling2d_1 (MaxPooling2 (None, 14, 14, 16) 0 \n", "_________________________________________________________________\n", "conv2d_2 (Conv2D) (None, 14, 14, 8) 1160 \n", "_________________________________________________________________\n", "max_pooling2d_2 (MaxPooling2 (None, 7, 7, 8) 0 \n", "_________________________________________________________________\n", "conv2d_3 (Conv2D) (None, 4, 4, 8) 584 \n", "_________________________________________________________________\n", "flatten_1 (Flatten) (None, 128) 0 \n", "_________________________________________________________________\n", "reshape_1 (Reshape) (None, 4, 4, 8) 0 \n", "_________________________________________________________________\n", "conv2d_4 (Conv2D) (None, 4, 4, 8) 584 \n", "_________________________________________________________________\n", "up_sampling2d_1 (UpSampling2 (None, 8, 8, 8) 0 \n", "_________________________________________________________________\n", "conv2d_5 (Conv2D) (None, 8, 8, 8) 584 \n", "_________________________________________________________________\n", "up_sampling2d_2 (UpSampling2 (None, 16, 16, 8) 0 \n", "_________________________________________________________________\n", "conv2d_6 (Conv2D) (None, 14, 14, 16) 1168 \n", "_________________________________________________________________\n", "up_sampling2d_3 (UpSampling2 (None, 28, 28, 16) 0 \n", "_________________________________________________________________\n", "conv2d_7 (Conv2D) (None, 28, 28, 1) 145 \n", "=================================================================\n", "Total params: 4,385\n", "Trainable params: 4,385\n", "Non-trainable params: 0\n", "_________________________________________________________________\n" ] } ], "source": [ "autoencoder = Sequential()\n", "\n", "# Encoder Layers\n", "autoencoder.add(Conv2D(16, (3, 3), activation='relu', padding='same', input_shape=x_train.shape[1:]))\n", "autoencoder.add(MaxPooling2D((2, 2), padding='same'))\n", "autoencoder.add(Conv2D(8, (3, 3), activation='relu', padding='same'))\n", "autoencoder.add(MaxPooling2D((2, 2), padding='same'))\n", "autoencoder.add(Conv2D(8, (3, 3), strides=(2,2), activation='relu', padding='same'))\n", "\n", "# Flatten encoding for visualization\n", "autoencoder.add(Flatten())\n", "autoencoder.add(Reshape((4, 4, 8)))\n", "\n", "# Decoder Layers\n", "autoencoder.add(Conv2D(8, (3, 3), activation='relu', padding='same'))\n", "autoencoder.add(UpSampling2D((2, 2)))\n", "autoencoder.add(Conv2D(8, (3, 3), activation='relu', padding='same'))\n", "autoencoder.add(UpSampling2D((2, 2)))\n", "autoencoder.add(Conv2D(16, (3, 3), activation='relu'))\n", "autoencoder.add(UpSampling2D((2, 2)))\n", "autoencoder.add(Conv2D(1, (3, 3), activation='sigmoid', padding='same'))\n", "\n", "autoencoder.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Encoder Model\n", "\n", "To extract the encoder model for the autoencoder, we're going to use a slightly different approach than before. Rather than extracting the first 6 layers, we're going to create a new `Model` with the same input as the autoencoder, but the output will be that of the flattening layer. As a side note, this is a very useful technique for grabbing submodels for things like [transfer learning](http://ruder.io/transfer-learning/).\n", "\n", "As I mentioned before, the encoded image is a vector of length 128." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "conv2d_1_input (InputLayer) (None, 28, 28, 1) 0 \n", "_________________________________________________________________\n", "conv2d_1 (Conv2D) (None, 28, 28, 16) 160 \n", "_________________________________________________________________\n", "max_pooling2d_1 (MaxPooling2 (None, 14, 14, 16) 0 \n", "_________________________________________________________________\n", "conv2d_2 (Conv2D) (None, 14, 14, 8) 1160 \n", "_________________________________________________________________\n", "max_pooling2d_2 (MaxPooling2 (None, 7, 7, 8) 0 \n", "_________________________________________________________________\n", "conv2d_3 (Conv2D) (None, 4, 4, 8) 584 \n", "_________________________________________________________________\n", "flatten_1 (Flatten) (None, 128) 0 \n", "=================================================================\n", "Total params: 1,904\n", "Trainable params: 1,904\n", "Non-trainable params: 0\n", "_________________________________________________________________\n" ] } ], "source": [ "encoder = Model(inputs=autoencoder.input, outputs=autoencoder.get_layer('flatten_1').output)\n", "encoder.summary()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train on 60000 samples, validate on 10000 samples\n", "Epoch 1/100\n", "60000/60000 [==============================] - 69s 1ms/step - loss: 0.1986 - val_loss: 0.1361\n", "Epoch 2/100\n", "60000/60000 [==============================] - 69s 1ms/step - loss: 0.1252 - val_loss: 0.1153\n", "Epoch 3/100\n", "60000/60000 [==============================] - 68s 1ms/step - loss: 0.1128 - val_loss: 0.1081\n", "Epoch 4/100\n", "60000/60000 [==============================] - 68s 1ms/step - loss: 0.1072 - val_loss: 0.1039\n", "Epoch 5/100\n", "60000/60000 [==============================] - 69s 1ms/step - loss: 0.1033 - val_loss: 0.1004\n", "Epoch 6/100\n", "60000/60000 [==============================] - 69s 1ms/step - loss: 0.1004 - val_loss: 0.0979\n", "Epoch 7/100\n", "60000/60000 [==============================] - 69s 1ms/step - loss: 0.0983 - val_loss: 0.0963\n", "Epoch 8/100\n", "60000/60000 [==============================] - 69s 1ms/step - loss: 0.0966 - val_loss: 0.0944\n", "Epoch 9/100\n", "60000/60000 [==============================] - 69s 1ms/step - loss: 0.0952 - val_loss: 0.0933\n", "Epoch 10/100\n", "60000/60000 [==============================] - 69s 1ms/step - loss: 0.0940 - val_loss: 0.0921\n", "Epoch 11/100\n", "60000/60000 [==============================] - 68s 1ms/step - loss: 0.0929 - val_loss: 0.0912\n", "Epoch 12/100\n", "60000/60000 [==============================] - 69s 1ms/step - loss: 0.0921 - val_loss: 0.0906\n", "Epoch 13/100\n", "60000/60000 [==============================] - 69s 1ms/step - loss: 0.0912 - val_loss: 0.0896\n", "Epoch 14/100\n", "60000/60000 [==============================] - 68s 1ms/step - loss: 0.0905 - val_loss: 0.0891\n", "Epoch 15/100\n", "60000/60000 [==============================] - 65s 1ms/step - loss: 0.0899 - val_loss: 0.0885\n", "Epoch 16/100\n", "60000/60000 [==============================] - 65s 1ms/step - loss: 0.0894 - val_loss: 0.0884\n", "Epoch 17/100\n", "60000/60000 [==============================] - 65s 1ms/step - loss: 0.0888 - val_loss: 0.0875\n", "Epoch 18/100\n", "60000/60000 [==============================] - 65s 1ms/step - loss: 0.0884 - val_loss: 0.0871\n", "Epoch 19/100\n", "60000/60000 [==============================] - 65s 1ms/step - loss: 0.0880 - val_loss: 0.0868\n", "Epoch 20/100\n", "60000/60000 [==============================] - 65s 1ms/step - loss: 0.0876 - val_loss: 0.0863\n", "Epoch 21/100\n", "60000/60000 [==============================] - 65s 1ms/step - loss: 0.0872 - val_loss: 0.0861\n", "Epoch 22/100\n", "60000/60000 [==============================] - 65s 1ms/step - loss: 0.0869 - val_loss: 0.0859\n", "Epoch 23/100\n", "60000/60000 [==============================] - 65s 1ms/step - loss: 0.0867 - val_loss: 0.0855\n", "Epoch 24/100\n", "60000/60000 [==============================] - 65s 1ms/step - loss: 0.0864 - val_loss: 0.0855\n", "Epoch 25/100\n", "60000/60000 [==============================] - 65s 1ms/step - loss: 0.0862 - val_loss: 0.0853\n", "Epoch 26/100\n", "60000/60000 [==============================] - 66s 1ms/step - loss: 0.0860 - val_loss: 0.0854\n", "Epoch 27/100\n", "60000/60000 [==============================] - 65s 1ms/step - loss: 0.0858 - val_loss: 0.0848\n", "Epoch 28/100\n", "60000/60000 [==============================] - 65s 1ms/step - loss: 0.0856 - val_loss: 0.0846\n", "Epoch 29/100\n", "60000/60000 [==============================] - 65s 1ms/step - loss: 0.0855 - val_loss: 0.0845\n", "Epoch 30/100\n", "60000/60000 [==============================] - 65s 1ms/step - loss: 0.0853 - val_loss: 0.0842\n", "Epoch 31/100\n", "60000/60000 [==============================] - 65s 1ms/step - loss: 0.0852 - val_loss: 0.0843\n", "Epoch 32/100\n", "60000/60000 [==============================] - 65s 1ms/step - loss: 0.0851 - val_loss: 0.0843\n", "Epoch 33/100\n", "60000/60000 [==============================] - 65s 1ms/step - loss: 0.0849 - val_loss: 0.0841\n", "Epoch 34/100\n", "60000/60000 [==============================] - 65s 1ms/step - loss: 0.0848 - val_loss: 0.0837\n", "Epoch 35/100\n", "60000/60000 [==============================] - 72s 1ms/step - loss: 0.0847 - val_loss: 0.0837\n", "Epoch 36/100\n", "60000/60000 [==============================] - 70s 1ms/step - loss: 0.0846 - val_loss: 0.0838\n", "Epoch 37/100\n", "60000/60000 [==============================] - 71s 1ms/step - loss: 0.0845 - val_loss: 0.0834\n", "Epoch 38/100\n", "60000/60000 [==============================] - 70s 1ms/step - loss: 0.0843 - val_loss: 0.0833\n", "Epoch 39/100\n", "60000/60000 [==============================] - 2570s 43ms/step - loss: 0.0842 - val_loss: 0.0832\n", "Epoch 40/100\n", "60000/60000 [==============================] - 80s 1ms/step - loss: 0.0841 - val_loss: 0.0830\n", "Epoch 41/100\n", "60000/60000 [==============================] - 67s 1ms/step - loss: 0.0840 - val_loss: 0.0831\n", "Epoch 42/100\n", "60000/60000 [==============================] - 66s 1ms/step - loss: 0.0840 - val_loss: 0.0832\n", "Epoch 43/100\n", "60000/60000 [==============================] - 66s 1ms/step - loss: 0.0838 - val_loss: 0.0828\n", "Epoch 44/100\n", "60000/60000 [==============================] - 67s 1ms/step - loss: 0.0837 - val_loss: 0.0826\n", "Epoch 45/100\n", "60000/60000 [==============================] - 66s 1ms/step - loss: 0.0836 - val_loss: 0.0826\n", "Epoch 46/100\n", "60000/60000 [==============================] - 66s 1ms/step - loss: 0.0836 - val_loss: 0.0825\n", "Epoch 47/100\n", "60000/60000 [==============================] - 66s 1ms/step - loss: 0.0835 - val_loss: 0.0825\n", "Epoch 48/100\n", "60000/60000 [==============================] - 66s 1ms/step - loss: 0.0834 - val_loss: 0.0824\n", "Epoch 49/100\n", "60000/60000 [==============================] - 66s 1ms/step - loss: 0.0833 - val_loss: 0.0831\n", "Epoch 50/100\n", "60000/60000 [==============================] - 67s 1ms/step - loss: 0.0832 - val_loss: 0.0825\n", "Epoch 51/100\n", "60000/60000 [==============================] - 66s 1ms/step - loss: 0.0832 - val_loss: 0.0828\n", "Epoch 52/100\n", "60000/60000 [==============================] - 66s 1ms/step - loss: 0.0831 - val_loss: 0.0824\n", "Epoch 53/100\n", "60000/60000 [==============================] - 67s 1ms/step - loss: 0.0830 - val_loss: 0.0821\n", "Epoch 54/100\n", "60000/60000 [==============================] - 66s 1ms/step - loss: 0.0829 - val_loss: 0.0819\n", "Epoch 55/100\n", "60000/60000 [==============================] - 66s 1ms/step - loss: 0.0829 - val_loss: 0.0821\n", "Epoch 56/100\n", "60000/60000 [==============================] - 66s 1ms/step - loss: 0.0828 - val_loss: 0.0819\n", "Epoch 57/100\n", "60000/60000 [==============================] - 66s 1ms/step - loss: 0.0828 - val_loss: 0.0821\n", "Epoch 58/100\n", "60000/60000 [==============================] - 66s 1ms/step - loss: 0.0826 - val_loss: 0.0818\n", "Epoch 59/100\n", "60000/60000 [==============================] - 66s 1ms/step - loss: 0.0826 - val_loss: 0.0817\n", "Epoch 60/100\n", "60000/60000 [==============================] - 65s 1ms/step - loss: 0.0825 - val_loss: 0.0816\n", "Epoch 61/100\n", "60000/60000 [==============================] - 65s 1ms/step - loss: 0.0825 - val_loss: 0.0816\n", "Epoch 62/100\n", "60000/60000 [==============================] - 65s 1ms/step - loss: 0.0824 - val_loss: 0.0817\n", "Epoch 63/100\n", "60000/60000 [==============================] - 66s 1ms/step - loss: 0.0824 - val_loss: 0.0817\n", "Epoch 64/100\n", "60000/60000 [==============================] - 65s 1ms/step - loss: 0.0823 - val_loss: 0.0814\n", "Epoch 65/100\n", "60000/60000 [==============================] - 65s 1ms/step - loss: 0.0822 - val_loss: 0.0813\n", "Epoch 66/100\n", "60000/60000 [==============================] - 65s 1ms/step - loss: 0.0822 - val_loss: 0.0812\n", "Epoch 67/100\n", "60000/60000 [==============================] - 64s 1ms/step - loss: 0.0821 - val_loss: 0.0812\n", "Epoch 68/100\n", "60000/60000 [==============================] - 64s 1ms/step - loss: 0.0821 - val_loss: 0.0811\n", "Epoch 69/100\n", "60000/60000 [==============================] - 67s 1ms/step - loss: 0.0820 - val_loss: 0.0811\n", "Epoch 70/100\n", "60000/60000 [==============================] - 69s 1ms/step - loss: 0.0820 - val_loss: 0.0811\n", "Epoch 71/100\n", "60000/60000 [==============================] - 71s 1ms/step - loss: 0.0819 - val_loss: 0.0809\n", "Epoch 72/100\n", "60000/60000 [==============================] - 68s 1ms/step - loss: 0.0819 - val_loss: 0.0813\n", "Epoch 73/100\n", "60000/60000 [==============================] - 68s 1ms/step - loss: 0.0819 - val_loss: 0.0809\n", "Epoch 74/100\n", "60000/60000 [==============================] - 68s 1ms/step - loss: 0.0818 - val_loss: 0.0808\n", "Epoch 75/100\n", "60000/60000 [==============================] - 70s 1ms/step - loss: 0.0818 - val_loss: 0.0809\n", "Epoch 76/100\n", "60000/60000 [==============================] - 68s 1ms/step - loss: 0.0817 - val_loss: 0.0807\n", "Epoch 77/100\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "60000/60000 [==============================] - 68s 1ms/step - loss: 0.0817 - val_loss: 0.0807\n", "Epoch 78/100\n", "60000/60000 [==============================] - 68s 1ms/step - loss: 0.0816 - val_loss: 0.0807\n", "Epoch 79/100\n", "60000/60000 [==============================] - 68s 1ms/step - loss: 0.0816 - val_loss: 0.0809\n", "Epoch 80/100\n", "60000/60000 [==============================] - 69s 1ms/step - loss: 0.0815 - val_loss: 0.0809\n", "Epoch 81/100\n", "60000/60000 [==============================] - 70s 1ms/step - loss: 0.0815 - val_loss: 0.0805\n", "Epoch 82/100\n", "60000/60000 [==============================] - 68s 1ms/step - loss: 0.0814 - val_loss: 0.0806\n", "Epoch 83/100\n", "60000/60000 [==============================] - 67s 1ms/step - loss: 0.0814 - val_loss: 0.0805\n", "Epoch 84/100\n", "60000/60000 [==============================] - 68s 1ms/step - loss: 0.0813 - val_loss: 0.0805\n", "Epoch 85/100\n", "60000/60000 [==============================] - 68s 1ms/step - loss: 0.0813 - val_loss: 0.0803\n", "Epoch 86/100\n", "60000/60000 [==============================] - 67s 1ms/step - loss: 0.0812 - val_loss: 0.0802\n", "Epoch 87/100\n", "60000/60000 [==============================] - 67s 1ms/step - loss: 0.0812 - val_loss: 0.0803\n", "Epoch 88/100\n", "60000/60000 [==============================] - 67s 1ms/step - loss: 0.0811 - val_loss: 0.0802\n", "Epoch 89/100\n", "60000/60000 [==============================] - 67s 1ms/step - loss: 0.0811 - val_loss: 0.0801\n", "Epoch 90/100\n", "60000/60000 [==============================] - 69s 1ms/step - loss: 0.0811 - val_loss: 0.0801\n", "Epoch 91/100\n", "60000/60000 [==============================] - 67s 1ms/step - loss: 0.0810 - val_loss: 0.0802\n", "Epoch 92/100\n", "60000/60000 [==============================] - 66s 1ms/step - loss: 0.0810 - val_loss: 0.0805\n", "Epoch 93/100\n", "60000/60000 [==============================] - 68s 1ms/step - loss: 0.0809 - val_loss: 0.0800\n", "Epoch 94/100\n", "60000/60000 [==============================] - 68s 1ms/step - loss: 0.0809 - val_loss: 0.0799\n", "Epoch 95/100\n", "60000/60000 [==============================] - 68s 1ms/step - loss: 0.0809 - val_loss: 0.0803\n", "Epoch 96/100\n", "60000/60000 [==============================] - 67s 1ms/step - loss: 0.0808 - val_loss: 0.0799\n", "Epoch 97/100\n", "60000/60000 [==============================] - 67s 1ms/step - loss: 0.0808 - val_loss: 0.0801\n", "Epoch 98/100\n", "60000/60000 [==============================] - 67s 1ms/step - loss: 0.0808 - val_loss: 0.0799\n", "Epoch 99/100\n", "60000/60000 [==============================] - 69s 1ms/step - loss: 0.0807 - val_loss: 0.0798\n", "Epoch 100/100\n", "60000/60000 [==============================] - 68s 1ms/step - loss: 0.0807 - val_loss: 0.0798\n" ] }, { "data": { "text/plain": [ "<keras.callbacks.History at 0x182d375320>" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "autoencoder.compile(optimizer='adam', loss='binary_crossentropy')\n", "autoencoder.fit(x_train, x_train,\n", " epochs=100,\n", " batch_size=128,\n", " validation_data=(x_test, x_test))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The reconstructed digits look even better than before. This is no surprise given an even lower validation loss. Other than slight improved reconstruction, check out how the encoded image has changed. What's even cooler is that the encoded images of the 9 look similar as do those of the 8's. This similarity was far less pronounced for the simple and deep autoencoders." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1296x288 with 30 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "num_images = 10\n", "np.random.seed(42)\n", "random_test_images = np.random.randint(x_test.shape[0], size=num_images)\n", "\n", "encoded_imgs = encoder.predict(x_test)\n", "decoded_imgs = autoencoder.predict(x_test)\n", "\n", "plt.figure(figsize=(18, 4))\n", "\n", "for i, image_idx in enumerate(random_test_images):\n", " # plot original image\n", " ax = plt.subplot(3, num_images, i + 1)\n", " plt.imshow(x_test[image_idx].reshape(28, 28))\n", " plt.gray()\n", " ax.get_xaxis().set_visible(False)\n", " ax.get_yaxis().set_visible(False)\n", " \n", " # plot encoded image\n", " ax = plt.subplot(3, num_images, num_images + i + 1)\n", " plt.imshow(encoded_imgs[image_idx].reshape(16, 8))\n", " plt.gray()\n", " ax.get_xaxis().set_visible(False)\n", " ax.get_yaxis().set_visible(False)\n", "\n", " # plot reconstructed image\n", " ax = plt.subplot(3, num_images, 2*num_images + i + 1)\n", " plt.imshow(decoded_imgs[image_idx].reshape(28, 28))\n", " plt.gray()\n", " ax.get_xaxis().set_visible(False)\n", " ax.get_yaxis().set_visible(False)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Denoising Images with the Convolutional Autoencoder\n", "\n", "Earlier, I mentioned that autoencoders are useful for denoising data including images. When I learned about this concept in grad school, my mind was blown. This simple task helped me realize data can be manipulated in very useful ways and that the dirty data we often inherit can be cleansed using more advanced techniques.\n", "\n", "With that in mind, let's add bit of noise to the test images and see how good the convolutional autoencoder is at removing the noise." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1296x288 with 20 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x_train_noisy = x_train + np.random.normal(loc=0.0, scale=0.5, size=x_train.shape)\n", "x_train_noisy = np.clip(x_train_noisy, 0., 1.)\n", "\n", "x_test_noisy = x_test + np.random.normal(loc=0.0, scale=0.5, size=x_test.shape)\n", "x_test_noisy = np.clip(x_test_noisy, 0., 1.)\n", "\n", "num_images = 10\n", "np.random.seed(42)\n", "random_test_images = np.random.randint(x_test.shape[0], size=num_images)\n", "\n", "# Denoise test images\n", "x_test_denoised = autoencoder.predict(x_test_noisy)\n", "\n", "plt.figure(figsize=(18, 4))\n", "\n", "for i, image_idx in enumerate(random_test_images):\n", " # plot original image\n", " ax = plt.subplot(2, num_images, i + 1)\n", " plt.imshow(x_test_noisy[image_idx].reshape(28, 28))\n", " plt.gray()\n", " ax.get_xaxis().set_visible(False)\n", " ax.get_yaxis().set_visible(False)\n", " \n", " # plot reconstructed image\n", " ax = plt.subplot(2, num_images, num_images + i + 1)\n", " plt.imshow(x_test_denoised[image_idx].reshape(28, 28))\n", " plt.gray()\n", " ax.get_xaxis().set_visible(False)\n", " ax.get_yaxis().set_visible(False)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Convolutional Autoencoder - Take 2\n", "\n", "Well, those images are terrible. They remind me of the mask from the movie Scream.\n", "\n", "![Scream mask](https://ae01.alicdn.com/kf/HTB1ZxqFj6ihSKJjy0Feq6zJtpXaS/New-Scary-Ghost-Face-Scream-Mask-Creepy-for-Halloween-Masquerade-Party-Fancy-Dress-Costume.jpg)\n", "\n", "Okay, so let's try that again. This time we're going to build a ConvNet with a lot more parameters and forego visualizing the encoding layer. The network will be a bit larger and slower to train, but the results are definitely worth the effort.\n", "\n", "One more thing: this time, let's use `(x_train_noisy, x_train)` as training data and `(x_test_noisy, x_test)` as validation data." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "conv2d_13 (Conv2D) (None, 28, 28, 32) 320 \n", "_________________________________________________________________\n", "max_pooling2d_5 (MaxPooling2 (None, 14, 14, 32) 0 \n", "_________________________________________________________________\n", "conv2d_14 (Conv2D) (None, 14, 14, 32) 9248 \n", "_________________________________________________________________\n", "max_pooling2d_6 (MaxPooling2 (None, 7, 7, 32) 0 \n", "_________________________________________________________________\n", "conv2d_15 (Conv2D) (None, 7, 7, 32) 9248 \n", "_________________________________________________________________\n", "up_sampling2d_6 (UpSampling2 (None, 14, 14, 32) 0 \n", "_________________________________________________________________\n", "conv2d_16 (Conv2D) (None, 14, 14, 32) 9248 \n", "_________________________________________________________________\n", "up_sampling2d_7 (UpSampling2 (None, 28, 28, 32) 0 \n", "_________________________________________________________________\n", "conv2d_17 (Conv2D) (None, 28, 28, 1) 289 \n", "=================================================================\n", "Total params: 28,353\n", "Trainable params: 28,353\n", "Non-trainable params: 0\n", "_________________________________________________________________\n" ] } ], "source": [ "autoencoder = Sequential()\n", "\n", "# Encoder Layers\n", "autoencoder.add(Conv2D(32, (3, 3), activation='relu', padding='same', input_shape=x_train.shape[1:]))\n", "autoencoder.add(MaxPooling2D((2, 2), padding='same'))\n", "autoencoder.add(Conv2D(32, (3, 3), activation='relu', padding='same'))\n", "autoencoder.add(MaxPooling2D((2, 2), padding='same'))\n", "\n", "# Decoder Layers\n", "autoencoder.add(Conv2D(32, (3, 3), activation='relu', padding='same'))\n", "autoencoder.add(UpSampling2D((2, 2)))\n", "autoencoder.add(Conv2D(32, (3, 3), activation='relu', padding='same'))\n", "autoencoder.add(UpSampling2D((2, 2)))\n", "autoencoder.add(Conv2D(1, (3, 3), activation='sigmoid', padding='same'))\n", "\n", "autoencoder.summary()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train on 60000 samples, validate on 10000 samples\n", "Epoch 1/100\n", "60000/60000 [==============================] - 140s 2ms/step - loss: 0.1661 - val_loss: 0.1143\n", "Epoch 2/100\n", "60000/60000 [==============================] - 132s 2ms/step - loss: 0.1113 - val_loss: 0.1068\n", "Epoch 3/100\n", "60000/60000 [==============================] - 133s 2ms/step - loss: 0.1062 - val_loss: 0.1038\n", "Epoch 4/100\n", "60000/60000 [==============================] - 131s 2ms/step - loss: 0.1036 - val_loss: 0.1017\n", "Epoch 5/100\n", "60000/60000 [==============================] - 119s 2ms/step - loss: 0.1018 - val_loss: 0.1001\n", "Epoch 6/100\n", "60000/60000 [==============================] - 121s 2ms/step - loss: 0.1005 - val_loss: 0.0992\n", "Epoch 7/100\n", "60000/60000 [==============================] - 121s 2ms/step - loss: 0.0996 - val_loss: 0.0984\n", "Epoch 8/100\n", "60000/60000 [==============================] - 123s 2ms/step - loss: 0.0989 - val_loss: 0.0980\n", "Epoch 9/100\n", "60000/60000 [==============================] - 128s 2ms/step - loss: 0.0983 - val_loss: 0.0972\n", "Epoch 10/100\n", "60000/60000 [==============================] - 123s 2ms/step - loss: 0.0978 - val_loss: 0.0971\n", "Epoch 11/100\n", "60000/60000 [==============================] - 123s 2ms/step - loss: 0.0974 - val_loss: 0.0965\n", "Epoch 12/100\n", "60000/60000 [==============================] - 120s 2ms/step - loss: 0.0970 - val_loss: 0.0962\n", "Epoch 13/100\n", "60000/60000 [==============================] - 123s 2ms/step - loss: 0.0967 - val_loss: 0.0959\n", "Epoch 14/100\n", "60000/60000 [==============================] - 121s 2ms/step - loss: 0.0964 - val_loss: 0.0958\n", "Epoch 15/100\n", "60000/60000 [==============================] - 123s 2ms/step - loss: 0.0962 - val_loss: 0.0964\n", "Epoch 16/100\n", "60000/60000 [==============================] - 122s 2ms/step - loss: 0.0960 - val_loss: 0.0952\n", "Epoch 17/100\n", "60000/60000 [==============================] - 123s 2ms/step - loss: 0.0957 - val_loss: 0.0952\n", "Epoch 18/100\n", "60000/60000 [==============================] - 123s 2ms/step - loss: 0.0956 - val_loss: 0.0953\n", "Epoch 19/100\n", "60000/60000 [==============================] - 123s 2ms/step - loss: 0.0954 - val_loss: 0.0949\n", "Epoch 20/100\n", "60000/60000 [==============================] - 118s 2ms/step - loss: 0.0953 - val_loss: 0.0946\n", "Epoch 21/100\n", "60000/60000 [==============================] - 118s 2ms/step - loss: 0.0952 - val_loss: 0.0946\n", "Epoch 22/100\n", "60000/60000 [==============================] - 118s 2ms/step - loss: 0.0951 - val_loss: 0.0944\n", "Epoch 23/100\n", "60000/60000 [==============================] - 118s 2ms/step - loss: 0.0949 - val_loss: 0.0944\n", "Epoch 24/100\n", "60000/60000 [==============================] - 118s 2ms/step - loss: 0.0948 - val_loss: 0.0945\n", "Epoch 25/100\n", "60000/60000 [==============================] - 118s 2ms/step - loss: 0.0947 - val_loss: 0.0942\n", "Epoch 26/100\n", "60000/60000 [==============================] - 117s 2ms/step - loss: 0.0946 - val_loss: 0.0941\n", "Epoch 27/100\n", "60000/60000 [==============================] - 115s 2ms/step - loss: 0.0946 - val_loss: 0.0941\n", "Epoch 28/100\n", "60000/60000 [==============================] - 118s 2ms/step - loss: 0.0945 - val_loss: 0.0940\n", "Epoch 29/100\n", "60000/60000 [==============================] - 118s 2ms/step - loss: 0.0944 - val_loss: 0.0942\n", "Epoch 30/100\n", "60000/60000 [==============================] - 116s 2ms/step - loss: 0.0943 - val_loss: 0.0939\n", "Epoch 31/100\n", "60000/60000 [==============================] - 118s 2ms/step - loss: 0.0943 - val_loss: 0.0938\n", "Epoch 32/100\n", "60000/60000 [==============================] - 119s 2ms/step - loss: 0.0942 - val_loss: 0.0938\n", "Epoch 33/100\n", "60000/60000 [==============================] - 118s 2ms/step - loss: 0.0942 - val_loss: 0.0937\n", "Epoch 34/100\n", "60000/60000 [==============================] - 120s 2ms/step - loss: 0.0941 - val_loss: 0.0936\n", "Epoch 35/100\n", "60000/60000 [==============================] - 118s 2ms/step - loss: 0.0941 - val_loss: 0.0936\n", "Epoch 36/100\n", "60000/60000 [==============================] - 124s 2ms/step - loss: 0.0940 - val_loss: 0.0937\n", "Epoch 37/100\n", "60000/60000 [==============================] - 132s 2ms/step - loss: 0.0940 - val_loss: 0.0937\n", "Epoch 38/100\n", "60000/60000 [==============================] - 126s 2ms/step - loss: 0.0940 - val_loss: 0.0936\n", "Epoch 39/100\n", "60000/60000 [==============================] - 131s 2ms/step - loss: 0.0939 - val_loss: 0.0936\n", "Epoch 40/100\n", "60000/60000 [==============================] - 129s 2ms/step - loss: 0.0939 - val_loss: 0.0934\n", "Epoch 41/100\n", "60000/60000 [==============================] - 137s 2ms/step - loss: 0.0938 - val_loss: 0.0934\n", "Epoch 42/100\n", "60000/60000 [==============================] - 124s 2ms/step - loss: 0.0938 - val_loss: 0.0936\n", "Epoch 43/100\n", "60000/60000 [==============================] - 124s 2ms/step - loss: 0.0938 - val_loss: 0.0934\n", "Epoch 44/100\n", "60000/60000 [==============================] - 123s 2ms/step - loss: 0.0937 - val_loss: 0.0936\n", "Epoch 45/100\n", "60000/60000 [==============================] - 123s 2ms/step - loss: 0.0937 - val_loss: 0.0934\n", "Epoch 46/100\n", "60000/60000 [==============================] - 123s 2ms/step - loss: 0.0937 - val_loss: 0.0935\n", "Epoch 47/100\n", "60000/60000 [==============================] - 124s 2ms/step - loss: 0.0937 - val_loss: 0.0932\n", "Epoch 48/100\n", "60000/60000 [==============================] - 124s 2ms/step - loss: 0.0937 - val_loss: 0.0936\n", "Epoch 49/100\n", "60000/60000 [==============================] - 125s 2ms/step - loss: 0.0936 - val_loss: 0.0934\n", "Epoch 50/100\n", "60000/60000 [==============================] - 124s 2ms/step - loss: 0.0936 - val_loss: 0.0932\n", "Epoch 51/100\n", "60000/60000 [==============================] - 124s 2ms/step - loss: 0.0936 - val_loss: 0.0932\n", "Epoch 52/100\n", "60000/60000 [==============================] - 118s 2ms/step - loss: 0.0935 - val_loss: 0.0932\n", "Epoch 53/100\n", "60000/60000 [==============================] - 118s 2ms/step - loss: 0.0935 - val_loss: 0.0932\n", "Epoch 54/100\n", "60000/60000 [==============================] - 119s 2ms/step - loss: 0.0935 - val_loss: 0.0932\n", "Epoch 55/100\n", "60000/60000 [==============================] - 118s 2ms/step - loss: 0.0935 - val_loss: 0.0940\n", "Epoch 56/100\n", "60000/60000 [==============================] - 119s 2ms/step - loss: 0.0935 - val_loss: 0.0932\n", "Epoch 57/100\n", "60000/60000 [==============================] - 119s 2ms/step - loss: 0.0934 - val_loss: 0.0931\n", "Epoch 58/100\n", "60000/60000 [==============================] - 118s 2ms/step - loss: 0.0934 - val_loss: 0.0932\n", "Epoch 59/100\n", "60000/60000 [==============================] - 118s 2ms/step - loss: 0.0934 - val_loss: 0.0932\n", "Epoch 60/100\n", "60000/60000 [==============================] - 117s 2ms/step - loss: 0.0934 - val_loss: 0.0938\n", "Epoch 61/100\n", "60000/60000 [==============================] - 117s 2ms/step - loss: 0.0934 - val_loss: 0.0930\n", "Epoch 62/100\n", "60000/60000 [==============================] - 118s 2ms/step - loss: 0.0933 - val_loss: 0.0930\n", "Epoch 63/100\n", "60000/60000 [==============================] - 118s 2ms/step - loss: 0.0934 - val_loss: 0.0932\n", "Epoch 64/100\n", "60000/60000 [==============================] - 118s 2ms/step - loss: 0.0933 - val_loss: 0.0933\n", "Epoch 65/100\n", "60000/60000 [==============================] - 119s 2ms/step - loss: 0.0933 - val_loss: 0.0930\n", "Epoch 66/100\n", "60000/60000 [==============================] - 118s 2ms/step - loss: 0.0933 - val_loss: 0.0931\n", "Epoch 67/100\n", "60000/60000 [==============================] - 118s 2ms/step - loss: 0.0933 - val_loss: 0.0933\n", "Epoch 68/100\n", "60000/60000 [==============================] - 118s 2ms/step - loss: 0.0933 - val_loss: 0.0933\n", "Epoch 69/100\n", "60000/60000 [==============================] - 119s 2ms/step - loss: 0.0932 - val_loss: 0.0932\n", "Epoch 70/100\n", "60000/60000 [==============================] - 114s 2ms/step - loss: 0.0932 - val_loss: 0.0930\n", "Epoch 71/100\n", "60000/60000 [==============================] - 118s 2ms/step - loss: 0.0932 - val_loss: 0.0931\n", "Epoch 72/100\n", "60000/60000 [==============================] - 118s 2ms/step - loss: 0.0932 - val_loss: 0.0938\n", "Epoch 73/100\n", "60000/60000 [==============================] - 118s 2ms/step - loss: 0.0932 - val_loss: 0.0937\n", "Epoch 74/100\n", "60000/60000 [==============================] - 119s 2ms/step - loss: 0.0932 - val_loss: 0.0932\n", "Epoch 75/100\n", "60000/60000 [==============================] - 118s 2ms/step - loss: 0.0932 - val_loss: 0.0929\n", "Epoch 76/100\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "60000/60000 [==============================] - 118s 2ms/step - loss: 0.0932 - val_loss: 0.0929\n", "Epoch 77/100\n", "60000/60000 [==============================] - 118s 2ms/step - loss: 0.0931 - val_loss: 0.0929\n", "Epoch 78/100\n", "60000/60000 [==============================] - 118s 2ms/step - loss: 0.0932 - val_loss: 0.0934\n", "Epoch 79/100\n", "60000/60000 [==============================] - 118s 2ms/step - loss: 0.0932 - val_loss: 0.0929\n", "Epoch 80/100\n", "60000/60000 [==============================] - 117s 2ms/step - loss: 0.0932 - val_loss: 0.0930\n", "Epoch 81/100\n", "60000/60000 [==============================] - 118s 2ms/step - loss: 0.0931 - val_loss: 0.0929\n", "Epoch 82/100\n", "60000/60000 [==============================] - 119s 2ms/step - loss: 0.0931 - val_loss: 0.0929\n", "Epoch 83/100\n", "60000/60000 [==============================] - 119s 2ms/step - loss: 0.0931 - val_loss: 0.0931\n", "Epoch 84/100\n", "60000/60000 [==============================] - 119s 2ms/step - loss: 0.0931 - val_loss: 0.0930\n", "Epoch 85/100\n", "60000/60000 [==============================] - 118s 2ms/step - loss: 0.0931 - val_loss: 0.0929\n", "Epoch 86/100\n", "60000/60000 [==============================] - 118s 2ms/step - loss: 0.0931 - val_loss: 0.0929\n", "Epoch 87/100\n", "60000/60000 [==============================] - 120s 2ms/step - loss: 0.0930 - val_loss: 0.0929\n", "Epoch 88/100\n", "60000/60000 [==============================] - 128s 2ms/step - loss: 0.0931 - val_loss: 0.0929\n", "Epoch 89/100\n", "60000/60000 [==============================] - 122s 2ms/step - loss: 0.0930 - val_loss: 0.0930\n", "Epoch 90/100\n", "60000/60000 [==============================] - 125s 2ms/step - loss: 0.0930 - val_loss: 0.0931\n", "Epoch 91/100\n", "60000/60000 [==============================] - 128s 2ms/step - loss: 0.0930 - val_loss: 0.0929\n", "Epoch 92/100\n", "60000/60000 [==============================] - 122s 2ms/step - loss: 0.0930 - val_loss: 0.0930\n", "Epoch 93/100\n", "60000/60000 [==============================] - 130s 2ms/step - loss: 0.0930 - val_loss: 0.0928\n", "Epoch 94/100\n", "60000/60000 [==============================] - 125s 2ms/step - loss: 0.0930 - val_loss: 0.0929\n", "Epoch 95/100\n", "60000/60000 [==============================] - 132s 2ms/step - loss: 0.0930 - val_loss: 0.0928\n", "Epoch 96/100\n", "60000/60000 [==============================] - 127s 2ms/step - loss: 0.0930 - val_loss: 0.0928\n", "Epoch 97/100\n", "60000/60000 [==============================] - 126s 2ms/step - loss: 0.0930 - val_loss: 0.0927\n", "Epoch 98/100\n", "60000/60000 [==============================] - 126s 2ms/step - loss: 0.0930 - val_loss: 0.0927\n", "Epoch 99/100\n", "60000/60000 [==============================] - 126s 2ms/step - loss: 0.0930 - val_loss: 0.0928\n", "Epoch 100/100\n", "60000/60000 [==============================] - 124s 2ms/step - loss: 0.0929 - val_loss: 0.0932\n" ] }, { "data": { "text/plain": [ "<keras.callbacks.History at 0x182fb17160>" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "autoencoder.compile(optimizer='adam', loss='binary_crossentropy')\n", "autoencoder.fit(x_train_noisy, x_train,\n", " epochs=100,\n", " batch_size=128,\n", " validation_data=(x_test_noisy, x_test))" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1296x288 with 20 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Denoise test images\n", "x_test_denoised = autoencoder.predict(x_test_noisy)\n", "\n", "plt.figure(figsize=(18, 4))\n", "\n", "for i, image_idx in enumerate(random_test_images):\n", " # plot original image\n", " ax = plt.subplot(2, num_images, i + 1)\n", " plt.imshow(x_test_noisy[image_idx].reshape(28, 28))\n", " plt.gray()\n", " ax.get_xaxis().set_visible(False)\n", " ax.get_yaxis().set_visible(False)\n", " \n", " # plot reconstructed image\n", " ax = plt.subplot(2, num_images, num_images + i + 1)\n", " plt.imshow(x_test_denoised[image_idx].reshape(28, 28))\n", " plt.gray()\n", " ax.get_xaxis().set_visible(False)\n", " ax.get_yaxis().set_visible(False)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Fantastic, those images almost look like the originals." ] }, { "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": 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
james-prior/cohpy
20170907-dojo-decimal.ipynb
1
4411
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "from decimal import Decimal, getcontext, ROUND_HALF_EVEN" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "Decimal('12.435')" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d = Decimal('12.435')\n", "d" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "Decimal('43.500')" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(d % 1) * 100" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "\"Decimal('0.435')\"" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "repr(d % 1)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "'0.435'" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "str(d % 1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now for some rounding." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "getcontext().rounding = ROUND_HALF_EVEN\n", "getcontext().prec = 2" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "Decimal('12.435')" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Decimal('12')" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d + 0" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "Decimal('0.44')" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d % 1 + 0" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "Decimal('12.435')" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d = Decimal('12.435')\n", "d" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Decimal('44')" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(d % 1) * 100" ] } ], "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
Jackporter415/phys202-project
Base_Question.ipynb
2
20499
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ ":0: FutureWarning: IPython widgets are experimental and may change in the future.\n" ] } ], "source": [ "import numpy as np\n", "import random\n", "from ipythonblocks import BlockGrid as BG\n", "from IPython.html import widgets" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def make_num_grid(width,height,Red_perc,Blue_perc):\n", " grid = np.random.choice((0,1,2),size = (height,width), p = [1.00-((Red_perc+Blue_perc)/100),Red_perc/100,Blue_perc/100])\n", " return grid" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def near_me_num(grid,percent):\n", "\n", " i = 0\n", " j = 0\n", " moving_array = []\n", " moving_ZERO_array = []\n", " moving_ONE_array = []\n", " moving_TWO_array = []\n", " height = grid.shape[0]\n", " width = grid.shape[1]\n", " for row in range(0,height):\n", " for col in range(0,width):\n", " if grid[row,col] == 1: \n", " \n", " \n", " #Every row but first and last\n", " if (row != 0 and row != height-1) and (col != 0 and col != width-1) and (height !=2 and width != 2):\n", " if grid[row-1,col] == 2:\n", " i+=1\n", " if grid[row+1,col] == 2:\n", " i+=1\n", " if grid[row,col+1] == 2:\n", " i+=1\n", " if grid[row,col-1] == 2:\n", " i+=1\n", " if grid[row+1,col+1] == 2:\n", " i+=1\n", " if grid[row+1,col-1] == 2:\n", " i+=1\n", " if grid[row-1,col+1] == 2:\n", " i+=1\n", " if grid[row-1,col+1] == 2:\n", " i+=1\n", " \n", " if i/8 >= percent/100:\n", " moving_ONE_array.append((row,col))\n", "\n", " i = 0\n", " \n", " #Upper Left Corner \n", " if row == 0 and col == 0:\n", " if grid[row+1,col] == 2:\n", " i+=1\n", " if grid[row+1,col+1] == 2:\n", " i+=1\n", " if grid[row,col+1] == 2:\n", " i+=1\n", " \n", " if i/3 >= percent/100:\n", " moving_ONE_array.append((row,col))\n", "\n", " i = 0\n", " \n", " #Lower Left Corner \n", " if row == height-1 and col == 0:\n", " if grid[row-1,col] == 2:\n", " i+=1\n", " if grid[row-1,col+1] == 2:\n", " i+=1\n", " if grid[row,col+1] == 2:\n", " i+=1\n", " \n", " if i/3 >= percent/100:\n", " moving_ONE_array.append((row,col))\n", " i = 0\n", " \n", " #Upper Right Corner \n", " if col == width-1 and row == 0:\n", " if grid[row+1,col] == 2:\n", " i+=1\n", " if grid[row+1,col-1] == 2:\n", " i+=1\n", " if grid[row,col-1] == 2:\n", " i+=1\n", " \n", " if i/3 >= percent/100:\n", " moving_ONE_array.append((row,col))\n", " i = 0 \n", " \n", " #Lower Right Corner\n", " if col == width-1 and row == height-1:\n", " if grid[row-1,col] == 2:\n", " i+=1\n", " if grid[row-1,col-1] == 2:\n", " i+=1\n", " if grid[row,col-1] == 2:\n", " i+=1\n", " \n", " if i/3 >= percent/100:\n", " moving_ONE_array.append((row,col))\n", " i = 0 \n", " \n", " #First col, no corners \n", " if col == 0 and (row!= 0 and row!= height-1):\n", " if grid[row+1,col] == 2:\n", " i+=1\n", " if grid[row+1,col+1] == 2:\n", " i+=1\n", " if grid[row,col+1] == 2:\n", " i+=1\n", " if grid[row-1,col+1] == 2:\n", " i+=1\n", " if grid[row-1,col] == 2:\n", " i+=1\n", " \n", " if i/5 >= percent/100:\n", " moving_ONE_array.append((row,col))\n", " i = 0 \n", " \n", " #Bottom row, no corners\n", " if row == height-1 and (col != 0 and col != width-1):\n", " if grid[row,col-1] == 2:\n", " i+=1\n", " if grid[row-1,col-1] == 2:\n", " i+=1\n", " if grid[row-1,col] == 2:\n", " i+=1\n", " if grid[row-1,col+1] == 2:\n", " i+=1\n", " if grid[row,col+1] == 2:\n", " i+=1\n", " \n", " if i/5 >= percent/100:\n", " moving_ONE_array.append((row,col))\n", " i = 0 \n", " \n", " #Last col, no corners\n", " if col == width-1 and (row != 0 and row != height-1):\n", " if grid[row+1,col] == 2:\n", " i+=1\n", " if grid[row+1,col-1] == 2:\n", " i+=1\n", " if grid[row,col-1] == 2:\n", " i+=1\n", " if grid[row-1,col-1] == 2:\n", " i+=1\n", " if grid[row-1,col] == 2:\n", " i+=1\n", " \n", " if i/5 >= percent/100:\n", " moving_ONE_array.append((row,col))\n", " i = 0 \n", " \n", " #First row, no corners\n", " if row == 0 and (col != 0 and col != width-1):\n", " if grid[row,col-1] == 2:\n", " i+=1\n", " if grid[row+1,col-1] == 2:\n", " i+=1\n", " if grid[row+1,col] == 2:\n", " i+=1\n", " if grid[row+1,col+1] == 2:\n", " i+=1\n", " if grid[row,col+1] == 2:\n", " i+=1\n", " \n", " if i/5 >= percent/100:\n", " moving_ONE_array.append((row,col))\n", " i = 0\n", "\n", " \n", " \n", " if grid[row,col] == 2:\n", "\n", " \n", " #Every row but first and last\n", " if (row != 0 and row != height-1) and (col != 0 and col != width-1) and (height !=2 and width != 2):\n", " if grid[row-1,col] == 1:\n", " j+=1\n", " if grid[row+1,col] == 1:\n", " j+=1\n", " if grid[row,col+1] == 1:\n", " j+=1\n", " if grid[row,col-1] == 1:\n", " j+=1\n", " if grid[row+1,col+1] == 1:\n", " j+=1\n", " if grid[row+1,col-1] == 1:\n", " j+=1\n", " if grid[row-1,col+1] == 1:\n", " j+=1\n", " if grid[row-1,col+1] == 1:\n", " j+=1\n", " \n", " if j/8 >= percent/100:\n", " moving_TWO_array.append((row,col))\n", " j = 0 \n", " \n", " #Upper Left Corner \n", " if row == 0 and col == 0:\n", " if grid[row-1,col] == 1:\n", " j+=1\n", " if grid[row-1,col+1] == 1:\n", " j+=1\n", " if grid[row,col+1] == 1:\n", " j+=1\n", " \n", " if j/3 >= percent/100:\n", " moving_TWO_array.append((row,col))\n", " j = 0 \n", " \n", " #Lower Left Corner \n", " if row == height-1 and col == 0:\n", " if grid[row-1,col] ==1:\n", " j+=1\n", " if grid[row-1,col+1] == 1:\n", " j+=1\n", " if grid[row,col+1] == 1:\n", " j+=1\n", " \n", " if j/3 >= percent/100:\n", " moving_TWO_array.append((row,col))\n", " j = 0 \n", " \n", " #Upper Right Corner \n", " if col == width-1 and row == 0:\n", " if grid[row-1,col] == 1:\n", " j+=1\n", " if grid[row-1,col-1] == 1:\n", " j+=1\n", " if grid[row,col-1] == 1:\n", " j+=1\n", " \n", " if j/3 >= percent/100:\n", " moving_TWO_array.append((row,col))\n", " j = 0 \n", " \n", " #Lower Right Corner\n", " if col == width-1 and row == height-1:\n", " if grid[row-1,col] == 1:\n", " j+=1\n", " if grid[row-1,col-1] == 1:\n", " j+=1\n", " if grid[row,col-1] == 1:\n", " j+=1\n", " \n", " if j/3 >= percent/100:\n", " moving_TWO_array.append((row,col))\n", " j = 0 \n", " \n", " #First col, no corners \n", " if col == 0 and (row!= 0 and row!= height-1):\n", " if grid[row+1,col] == 1:\n", " j+=1\n", " if grid[row+1,col+1] ==1:\n", " j+=1\n", " if grid[row,col+1] == 1:\n", " j+=1\n", " if grid[row-1,col+1] == 1:\n", " j+=1\n", " if grid[row-1,col] == 1:\n", " j+=1\n", " \n", " if j/5 >= percent/100:\n", " moving_TWO_array.append((row,col))\n", " j = 0 \n", " \n", " #Bottom row, no corners\n", " if row == height-1 and (col != 0 and col != width-1):\n", " if grid[row,col-1] == 1:\n", " j+=1\n", " if grid[row-1,col-1] == 1:\n", " j+=1\n", " if grid[row-1,col] == 1:\n", " j+=1\n", " if grid[row-1,col+1] == 1:\n", " j+=1\n", " if grid[row,col+1] == 1:\n", " j+=1\n", " \n", " if j/5 >= percent/100:\n", " moving_TWO_array.append((row,col))\n", " j = 0 \n", " \n", " #Last col, no corners\n", " if col == width-1 and (row != 0 and row != height-1):\n", " if grid[row+1,col] == 1:\n", " j+=1\n", " if grid[row+1,col-1] == 1:\n", " j+=1\n", " if grid[row,col-1] == 1:\n", " j+=1\n", " if grid[row-1,col-1] == 1:\n", " j+=1\n", " if grid[row-1,col] == 1:\n", " j+=1\n", " \n", " if j/5 >= percent/100:\n", " moving_TWO_array.append((row,col))\n", " j = 0 \n", " \n", " #First row, no corners\n", " if row == 0 and (col != 0 and col != width-1):\n", " if grid[row,col-1] == 1:\n", " j+=1\n", " if grid[row+1,col-1] == 1:\n", " j+=1\n", " if grid[row+1,col] == 1:\n", " j+=1\n", " if grid[row+1,col+1] == 1:\n", " j+=1\n", " if grid[row,col+1] == 1:\n", " j+=1\n", " \n", " if j/5 >= percent/100:\n", " moving_TWO_array.append((row,col))\n", " j = 0 \n", " if grid[row,col] == 0:\n", " moving_ZERO_array.append((row,col))\n", "\n", " \n", " return grid, moving_ZERO_array, moving_ONE_array, moving_TWO_array" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[1 1 1]\n", " [2 2 2]\n", " [1 1 1]]\n" ] } ], "source": [ "test_grid = np.array([[1,1,1],[2,2,2],[1,1,1]])\n", "print (test_grid)\n", "near_test = near_me_num(test_grid,50)\n", "assert near_test[3][0] == (1,0)\n", "assert near_test[3][1] == (1,1)\n", "assert near_test[2][0] == (0,0)\n", "assert near_test[2][1] == (0,1)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def Meathead_Movers(newgrid, array0, array1, array2):\n", " i = 0\n", " j = 0\n", " k = 0\n", " np.random.shuffle(array0)\n", " np.random.shuffle(array1)\n", " np.random.shuffle(array2)\n", " LEN_A0 = len(array0)\n", " LEN_A1 = len(array1)\n", " LEN__A1 = len(array1)-1\n", " LEN_A2 = len(array2)\n", " if LEN_A1 < LEN_A2 and LEN_A0 != 0:\n", "\n", " while i <= LEN_A1-1:\n", " \n", " newgrid[array1[i]] = 2\n", " newgrid[array2[k]] = 1\n", "\n", " #print (\"MOVED\",array1[i],1,array2[k],2)\n", " i+=1\n", " k+=1\n", " while i >= LEN_A1 and i < LEN_A2:\n", " \n", " newgrid[array0[j]] = 2\n", " newgrid[array2[i]] = 0\n", " #print (\"MOVED\",array2[i],0)\n", "\n", " i+=1\n", " j+=1\n", " if LEN_A1 > LEN_A2 and LEN_A0 != 0:\n", "\n", " while i <= LEN_A2-1:\n", "\n", " newgrid[array1[i]] = 2\n", " newgrid[array2[k]] = 1\n", " #print (\"MOVED\",array1[i],1,array2[i],2)\n", " i+=1\n", " k+=1\n", " \n", " while LEN__A1 > LEN_A1:\n", " \n", " if LEN_A0 == 0:\n", " break\n", " newgrid[array0[j]] = 1\n", " newgrid[array1[LEN__A1-1]] = 0\n", " #print (\"MOVED\",array1[LEN__A1-1],1,array0[j],0)\n", " yy -=1\n", " j+=1\n", "\n", " if LEN_A2 == LEN_A1:\n", " np.random.shuffle(array1)\n", "\n", " while i < LEN_A2:\n", "\n", " newgrid[array1[i]] = 2\n", " newgrid[array2[i]] = 1\n", " #print (\"MOVED\",array1[i],1,array2[i],2)\n", " i+=1\n", " if LEN_A0 == 0:\n", " if LEN_A1 < LEN_A2:\n", "\n", " while i <= LEN_A1-1:\n", "\n", " newgrid[array1[i]] = 2\n", " newgrid[array2[i]] = 1\n", "\n", " #print (\"MOVED\",array1[i],1,array2[k],2)\n", " i+=1\n", " k+=1\n", " if LEN_A1>=LEN_A2:\n", "\n", " while i <= LEN_A2-1:\n", " \n", " newgrid[array1[i]] = 2\n", " newgrid[array2[i]] = 1\n", " #print (\"MOVED\",array1[i],1,array2[i],2)\n", " i+=1\n", " k+=1\n", " \n", " return newgrid" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[1 1 2]\n", " [1 1 1]\n", " [2 1 2]]\n" ] } ], "source": [ "meat_time = Meathead_Movers(*near_test)\n", "print (meat_time)\n", "assert meat_time[1,0] != 2\n", "assert meat_time[1,1] != 2\n", "assert meat_time[1,2] != 2" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def colorful(grid):\n", " f = BG(grid.shape[1],grid.shape[0],fill=(0, 0, 0),block_size=4)\n", " for row in range(f.height):\n", " for col in range(f.width):\n", " sq = f[row,col]\n", " \n", " if grid[row,col] == 1:\n", " sq.blue = 900000000\n", " if grid[row,col] == 2:\n", " sq.red = 900000000\n", " return f" ] }, { "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": "3.4.0" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
JKeun/project-02-watcha
03_model/05_Standard_Score(random_pick).ipynb
1
2979
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### 랜덤으로 pred할 때의 기준 스코어" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [], "source": [ "x1 = np.random.randint(1,6,1000)\n", "x2 = np.random.randint(1,6,1000)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "mse : 4.065 \n", "mae : 1.613 \n", " r2 : -1.01339788754\n" ] } ], "source": [ "from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error\n", "mse = mean_squared_error(x1, x2)\n", "mae = mean_absolute_error(x1, x2)\n", "r2 = r2_score(x1, x2)\n", "print(\"mse : \", mse ,\n", " \"\\nmae : \", mae,\n", " \"\\n r2 : \", r2)" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "mse_mean : 3.868\n", "mse_list : [4.235, 3.846, 3.839, 3.809, 3.611]\n", "---------------------\n", "mae_mean : 1.558\n", "mae_list : [1.641, 1.562, 1.551, 1.537, 1.499]\n", "---------------------\n", "r2_mean : -0.96284\n", "r2_list : [-1.1596, -0.9293, -0.9179, -0.98, -0.8274]\n" ] } ], "source": [ "mse_list = []\n", "mae_list = []\n", "r2_list = []\n", "\n", "for _ in range(5):\n", " x1 = np.random.randint(1,6,1000)\n", " x2 = np.random.randint(1,6,1000)\n", " \n", " mse = mean_squared_error(x1, x2)\n", " mae = mean_absolute_error(x1, x2)\n", " r2 = r2_score(x1, x2)\n", " \n", " mse_list.append(round(mse, 4))\n", " mae_list.append(round(mae, 4))\n", " r2_list.append(round(r2, 4))\n", "\n", "print(\"mse_mean : \", np.mean(mse_list))\n", "print(\"mse_list : \", mse_list)\n", "print('---------------------')\n", "print(\"mae_mean : \", np.mean(mae_list))\n", "print(\"mae_list : \", mae_list)\n", "print('---------------------')\n", "print(\"r2_mean : \", np.mean(r2_list))\n", "print(\"r2_list : \", r2_list)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
the-deep-learners/study-group
neural-networks-and-deep-learning/src/run_network.ipynb
3
7242
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Network from Nielsen's Chapter 1\n", "http://neuralnetworksanddeeplearning.com/chap1.html#implementing_our_network_to_classify_digits" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Load MNIST Data" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import mnist_loader" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "training_data, validation_data, test_data = mnist_loader.load_data_wrapper()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Set up Network" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import network" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# 784 (28 x 28 pixel images) input neurons; 30 hidden neurons; 10 output neurons\n", "net = network.Network([784, 30, 10])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Train Network" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 0: 9041 / 10000\n", "Epoch 1: 9220 / 10000\n", "Epoch 2: 9290 / 10000\n", "Epoch 3: 9366 / 10000\n", "Epoch 4: 9365 / 10000\n", "Epoch 5: 9386 / 10000\n", "Epoch 6: 9367 / 10000\n", "Epoch 7: 9443 / 10000\n", "Epoch 8: 9415 / 10000\n", "Epoch 9: 9454 / 10000\n", "Epoch 10: 9426 / 10000\n", "Epoch 11: 9426 / 10000\n", "Epoch 12: 9461 / 10000\n", "Epoch 13: 9475 / 10000\n", "Epoch 14: 9472 / 10000\n", "Epoch 15: 9474 / 10000\n", "Epoch 16: 9453 / 10000\n", "Epoch 17: 9469 / 10000\n", "Epoch 18: 9485 / 10000\n", "Epoch 19: 9465 / 10000\n", "Epoch 20: 9458 / 10000\n", "Epoch 21: 9465 / 10000\n", "Epoch 22: 9455 / 10000\n", "Epoch 23: 9490 / 10000\n", "Epoch 24: 9464 / 10000\n", "Epoch 25: 9481 / 10000\n", "Epoch 26: 9488 / 10000\n", "Epoch 27: 9481 / 10000\n", "Epoch 28: 9500 / 10000\n", "Epoch 29: 9510 / 10000\n" ] } ], "source": [ "# Use stochastic gradient descent over 30 epochs, with mini-batch size of 10, learning rate of 3.0\n", "net.SGD(training_data, 30, 10, 3.0, test_data=test_data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercise: Create network with just two layers" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "two_layer_net = network.Network([784, 10])" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 0: 3284 / 10000\n", "Epoch 1: 3776 / 10000\n", "Epoch 2: 4564 / 10000\n", "Epoch 3: 4613 / 10000\n", "Epoch 4: 4637 / 10000\n", "Epoch 5: 4668 / 10000\n", "Epoch 6: 5743 / 10000\n", "Epoch 7: 5764 / 10000\n", "Epoch 8: 6508 / 10000\n", "Epoch 9: 6632 / 10000\n" ] } ], "source": [ "two_layer_net.SGD(training_data, 10, 10, 1.0, test_data=test_data)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 0: 6602 / 10000\n", "Epoch 1: 6623 / 10000\n", "Epoch 2: 6647 / 10000\n", "Epoch 3: 6681 / 10000\n", "Epoch 4: 7274 / 10000\n", "Epoch 5: 7380 / 10000\n", "Epoch 6: 7422 / 10000\n", "Epoch 7: 7440 / 10000\n", "Epoch 8: 7420 / 10000\n", "Epoch 9: 7441 / 10000\n" ] } ], "source": [ "two_layer_net.SGD(training_data, 10, 10, 2.0, test_data=test_data)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 0: 7408 / 10000\n", "Epoch 1: 7400 / 10000\n", "Epoch 2: 7422 / 10000\n", "Epoch 3: 7416 / 10000\n", "Epoch 4: 7459 / 10000\n", "Epoch 5: 8294 / 10000\n", "Epoch 6: 8305 / 10000\n", "Epoch 7: 8264 / 10000\n", "Epoch 8: 8284 / 10000\n", "Epoch 9: 8314 / 10000\n" ] } ], "source": [ "two_layer_net.SGD(training_data, 10, 10, 3.0, test_data=test_data)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 0: 8255 / 10000\n", "Epoch 1: 8278 / 10000\n", "Epoch 2: 8255 / 10000\n", "Epoch 3: 8246 / 10000\n", "Epoch 4: 8260 / 10000\n", "Epoch 5: 8272 / 10000\n", "Epoch 6: 8289 / 10000\n", "Epoch 7: 8309 / 10000\n", "Epoch 8: 8284 / 10000\n", "Epoch 9: 8287 / 10000\n" ] } ], "source": [ "two_layer_net.SGD(training_data, 10, 10, 4.0, test_data=test_data)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 0: 8311 / 10000\n", "Epoch 1: 8277 / 10000\n", "Epoch 2: 8303 / 10000\n", "Epoch 3: 8300 / 10000\n", "Epoch 4: 8293 / 10000\n", "Epoch 5: 8301 / 10000\n", "Epoch 6: 8295 / 10000\n", "Epoch 7: 8292 / 10000\n", "Epoch 8: 8305 / 10000\n", "Epoch 9: 8289 / 10000\n", "Epoch 10: 8307 / 10000\n", "Epoch 11: 8304 / 10000\n", "Epoch 12: 8294 / 10000\n", "Epoch 13: 8291 / 10000\n", "Epoch 14: 8299 / 10000\n", "Epoch 15: 8309 / 10000\n", "Epoch 16: 8314 / 10000\n", "Epoch 17: 8323 / 10000\n", "Epoch 18: 8301 / 10000\n", "Epoch 19: 8311 / 10000\n" ] } ], "source": [ "two_layer_net.SGD(training_data, 20, 10, 3.0, test_data=test_data)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
anne-urai/pupilUncertainty
newstuff/confidence_updating_4Armin.ipynb
2
471928
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "These analyses and simulations use data from Urai et al. 2017, and aim to further pinpoint the relationship between several proxies for confidence on the previous trial.\n", "\n", "If you use this work in any way, or see something interesting, please contact me first ([email protected])! I'm very happy to chat with people about collaborating to better understand these patterns. Thanks." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib\n", "#matplotlib.use('TkAgg')\n", "import matplotlib.pylab as plt\n", "import seaborn as sns\n", "sns.set(style=\"darkgrid\", context=\"talk\")\n", "import scipy as sp\n", "from IPython import embed as shell\n", "import psychofit as psy\n", "# import tqdm # progress bar" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "#### Urai et al. (2018): pupil dilation and reaction times scale with decision uncertainty ####\n", "# try:\n", "# data_urai2017 = pd.read_csv('https://ndownloader.figshare.com/files/12753785')\n", "# data_urai2017['choice'] = data_urai2017.resp\n", "# data_urai2017['evidence'] = data_urai2017.coherence * data_urai2017.stimulus # signed evidence\n", "# data_urai2017['subj_idx'] = data_urai2017.subjnr\n", "\n", "# except:\n", "data_urai2017 = pd.read_csv('/Users/urai/Data/HDDM/NatComm/2ifc_data_hddm.csv')\n", "data_urai2017['choice'] = np.sign(data_urai2017.response - 0.1)\n", "data_urai2017['evidence'] = data_urai2017.stimulus\n", "data_urai2017['sessionnr'] = data_urai2017.session\n", "data_urai2017['blocknr'] = data_urai2017.block\n", "data_urai2017['correct'] = 1 * (np.sign(data_urai2017.stimulus) == data_urai2017.choice)\n", "data_urai2017['decision_pupil'] = data_urai2017.prevpupil.shift(-1)\n", "\n", "data_urai2017['coherence'].values[data_urai2017['coherence'] <= 0.025] = 0.025\n", "data_urai2017['coherence'] = data_urai2017['coherence'] * 100\n", "data_urai2017['evidence'] = data_urai2017['coherence'] * np.sign(data_urai2017.stimulus)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>subj_idx</th>\n", " <th>session</th>\n", " <th>block</th>\n", " <th>trial</th>\n", " <th>stimulus</th>\n", " <th>coherence</th>\n", " <th>response</th>\n", " <th>rt</th>\n", " <th>prevstim</th>\n", " <th>prevresp</th>\n", " <th>...</th>\n", " <th>prev3pupil</th>\n", " <th>choice</th>\n", " <th>evidence</th>\n", " <th>sessionnr</th>\n", " <th>blocknr</th>\n", " <th>correct</th>\n", " <th>decision_pupil</th>\n", " <th>rt_norm</th>\n", " <th>evidence_norm</th>\n", " <th>coherence_norm</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>41999</th>\n", " <td>18</td>\n", " <td>4</td>\n", " <td>3</td>\n", " <td>45</td>\n", " <td>0.1000</td>\n", " <td>10.0</td>\n", " <td>1</td>\n", " <td>1.02</td>\n", " <td>-1</td>\n", " <td>-1</td>\n", " <td>...</td>\n", " <td>2.039595</td>\n", " <td>1.0</td>\n", " <td>10.0</td>\n", " <td>4</td>\n", " <td>3</td>\n", " <td>1</td>\n", " <td>-0.990630</td>\n", " <td>-0.115254</td>\n", " <td>0.744908</td>\n", " <td>-0.347168</td>\n", " </tr>\n", " <tr>\n", " <th>50495</th>\n", " <td>22</td>\n", " <td>2</td>\n", " <td>1</td>\n", " <td>27</td>\n", " <td>-0.3000</td>\n", " <td>30.0</td>\n", " <td>0</td>\n", " <td>0.74</td>\n", " <td>-1</td>\n", " <td>-1</td>\n", " <td>...</td>\n", " <td>0.426876</td>\n", " <td>-1.0</td>\n", " <td>-30.0</td>\n", " <td>2</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>-0.334638</td>\n", " <td>0.099458</td>\n", " <td>-1.730324</td>\n", " <td>1.639987</td>\n", " </tr>\n", " <tr>\n", " <th>60853</th>\n", " <td>26</td>\n", " <td>3</td>\n", " <td>7</td>\n", " <td>41</td>\n", " <td>-0.3000</td>\n", " <td>30.0</td>\n", " <td>0</td>\n", " <td>0.32</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>...</td>\n", " <td>-0.267755</td>\n", " <td>-1.0</td>\n", " <td>-30.0</td>\n", " <td>3</td>\n", " <td>7</td>\n", " <td>1</td>\n", " <td>0.714768</td>\n", " <td>-1.917801</td>\n", " <td>-1.742329</td>\n", " <td>1.560530</td>\n", " </tr>\n", " <tr>\n", " <th>17814</th>\n", " <td>8</td>\n", " <td>3</td>\n", " <td>3</td>\n", " <td>43</td>\n", " <td>-0.0125</td>\n", " <td>2.5</td>\n", " <td>0</td>\n", " <td>0.68</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>...</td>\n", " <td>-1.962683</td>\n", " <td>-1.0</td>\n", " <td>-2.5</td>\n", " <td>3</td>\n", " <td>3</td>\n", " <td>1</td>\n", " <td>-0.690422</td>\n", " <td>0.042006</td>\n", " <td>-0.436038</td>\n", " <td>-0.596708</td>\n", " </tr>\n", " <tr>\n", " <th>10021</th>\n", " <td>5</td>\n", " <td>1</td>\n", " <td>10</td>\n", " <td>28</td>\n", " <td>-0.2000</td>\n", " <td>20.0</td>\n", " <td>0</td>\n", " <td>0.43</td>\n", " <td>-1</td>\n", " <td>-1</td>\n", " <td>...</td>\n", " <td>1.342577</td>\n", " <td>-1.0</td>\n", " <td>-20.0</td>\n", " <td>1</td>\n", " <td>10</td>\n", " <td>1</td>\n", " <td>-1.685843</td>\n", " <td>-0.567258</td>\n", " <td>-1.183189</td>\n", " <td>0.546158</td>\n", " </tr>\n", " <tr>\n", " <th>47465</th>\n", " <td>20</td>\n", " <td>5</td>\n", " <td>8</td>\n", " <td>4</td>\n", " <td>0.2000</td>\n", " <td>20.0</td>\n", " <td>1</td>\n", " <td>0.42</td>\n", " <td>1</td>\n", " <td>-1</td>\n", " <td>...</td>\n", " <td>0.990406</td>\n", " <td>1.0</td>\n", " <td>20.0</td>\n", " <td>5</td>\n", " <td>8</td>\n", " <td>1</td>\n", " <td>-1.911564</td>\n", " <td>-0.926856</td>\n", " <td>1.182197</td>\n", " <td>0.662312</td>\n", " </tr>\n", " <tr>\n", " <th>53111</th>\n", " <td>23</td>\n", " <td>2</td>\n", " <td>6</td>\n", " <td>10</td>\n", " <td>-0.0500</td>\n", " <td>5.0</td>\n", " <td>0</td>\n", " <td>0.50</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>...</td>\n", " <td>1.126950</td>\n", " <td>-1.0</td>\n", " <td>-5.0</td>\n", " <td>2</td>\n", " <td>6</td>\n", " <td>1</td>\n", " <td>0.362542</td>\n", " <td>-0.945887</td>\n", " <td>-0.209642</td>\n", " <td>-0.830185</td>\n", " </tr>\n", " <tr>\n", " <th>10727</th>\n", " <td>5</td>\n", " <td>3</td>\n", " <td>5</td>\n", " <td>23</td>\n", " <td>0.2000</td>\n", " <td>20.0</td>\n", " <td>1</td>\n", " <td>0.55</td>\n", " <td>-1</td>\n", " <td>-1</td>\n", " <td>...</td>\n", " <td>0.395690</td>\n", " <td>1.0</td>\n", " <td>20.0</td>\n", " <td>3</td>\n", " <td>5</td>\n", " <td>1</td>\n", " <td>0.184489</td>\n", " <td>-0.073610</td>\n", " <td>1.258366</td>\n", " <td>0.654115</td>\n", " </tr>\n", " <tr>\n", " <th>13891</th>\n", " <td>6</td>\n", " <td>5</td>\n", " <td>1</td>\n", " <td>26</td>\n", " <td>-0.0125</td>\n", " <td>2.5</td>\n", " <td>1</td>\n", " <td>0.51</td>\n", " <td>-1</td>\n", " <td>-1</td>\n", " <td>...</td>\n", " <td>2.611660</td>\n", " <td>1.0</td>\n", " <td>-2.5</td>\n", " <td>5</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0.778141</td>\n", " <td>-0.770857</td>\n", " <td>-0.340018</td>\n", " <td>-0.587400</td>\n", " </tr>\n", " <tr>\n", " <th>33763</th>\n", " <td>15</td>\n", " <td>1</td>\n", " <td>7</td>\n", " <td>35</td>\n", " <td>0.0500</td>\n", " <td>5.0</td>\n", " <td>1</td>\n", " <td>0.65</td>\n", " <td>-1</td>\n", " <td>-1</td>\n", " <td>...</td>\n", " <td>1.876514</td>\n", " <td>1.0</td>\n", " <td>5.0</td>\n", " <td>1</td>\n", " <td>7</td>\n", " <td>1</td>\n", " <td>0.921922</td>\n", " <td>-0.279468</td>\n", " <td>0.266300</td>\n", " <td>-0.801688</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>10 rows × 30 columns</p>\n", "</div>" ], "text/plain": [ " subj_idx session block trial stimulus coherence response rt \\\n", "41999 18 4 3 45 0.1000 10.0 1 1.02 \n", "50495 22 2 1 27 -0.3000 30.0 0 0.74 \n", "60853 26 3 7 41 -0.3000 30.0 0 0.32 \n", "17814 8 3 3 43 -0.0125 2.5 0 0.68 \n", "10021 5 1 10 28 -0.2000 20.0 0 0.43 \n", "47465 20 5 8 4 0.2000 20.0 1 0.42 \n", "53111 23 2 6 10 -0.0500 5.0 0 0.50 \n", "10727 5 3 5 23 0.2000 20.0 1 0.55 \n", "13891 6 5 1 26 -0.0125 2.5 1 0.51 \n", "33763 15 1 7 35 0.0500 5.0 1 0.65 \n", "\n", " prevstim prevresp ... prev3pupil choice evidence \\\n", "41999 -1 -1 ... 2.039595 1.0 10.0 \n", "50495 -1 -1 ... 0.426876 -1.0 -30.0 \n", "60853 1 1 ... -0.267755 -1.0 -30.0 \n", "17814 1 1 ... -1.962683 -1.0 -2.5 \n", "10021 -1 -1 ... 1.342577 -1.0 -20.0 \n", "47465 1 -1 ... 0.990406 1.0 20.0 \n", "53111 1 1 ... 1.126950 -1.0 -5.0 \n", "10727 -1 -1 ... 0.395690 1.0 20.0 \n", "13891 -1 -1 ... 2.611660 1.0 -2.5 \n", "33763 -1 -1 ... 1.876514 1.0 5.0 \n", "\n", " sessionnr blocknr correct decision_pupil rt_norm evidence_norm \\\n", "41999 4 3 1 -0.990630 -0.115254 0.744908 \n", "50495 2 1 1 -0.334638 0.099458 -1.730324 \n", "60853 3 7 1 0.714768 -1.917801 -1.742329 \n", "17814 3 3 1 -0.690422 0.042006 -0.436038 \n", "10021 1 10 1 -1.685843 -0.567258 -1.183189 \n", "47465 5 8 1 -1.911564 -0.926856 1.182197 \n", "53111 2 6 1 0.362542 -0.945887 -0.209642 \n", "10727 3 5 1 0.184489 -0.073610 1.258366 \n", "13891 5 1 0 0.778141 -0.770857 -0.340018 \n", "33763 1 7 1 0.921922 -0.279468 0.266300 \n", "\n", " coherence_norm \n", "41999 -0.347168 \n", "50495 1.639987 \n", "60853 1.560530 \n", "17814 -0.596708 \n", "10021 0.546158 \n", "47465 0.662312 \n", "53111 -0.830185 \n", "10727 0.654115 \n", "13891 -0.587400 \n", "33763 -0.801688 \n", "\n", "[10 rows x 30 columns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# normalize RT for each participant\n", "zscore = lambda x: (x - x.mean()) / x.std()\n", "data_urai2017['rt_norm'] = data_urai2017.groupby(['subj_idx', 'sessionnr', 'blocknr'])['rt'].transform(zscore)\n", "data_urai2017['evidence_norm'] = data_urai2017.groupby(['subj_idx', 'sessionnr', 'blocknr'])['evidence'].transform(zscore)\n", "data_urai2017['coherence_norm'] = data_urai2017.groupby(['subj_idx', 'sessionnr', 'blocknr'])['coherence'].transform(zscore)\n", "data_urai2017['decision_pupil'] = data_urai2017.groupby(['subj_idx', 'sessionnr', 'blocknr'])['decision_pupil'].transform(zscore)\n", "\n", "data_urai2017.sample(n=10)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/anaconda3/envs/python3.7/lib/python2.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n", " return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 936x288 with 3 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "## FIRST, MAKE A QUICK OVERVIEW OF BASIC BEHAVIORAL DATA\n", "fig, ax = plt.subplots(1, 3, figsize=(13,4))\n", "sns.lineplot(data=data_urai2017, x='evidence', y='choice', color='k', marker='o', ci=None, ax=ax[0])\n", "ax[0].set(title='Psychometric function')\n", "sns.regplot(data=data_urai2017, x='coherence', y='correct', x_bins=5, color='k', marker='o', ci=None, ax=ax[1])\n", "ax[1].set(ylim=[0.5, 1])\n", "sns.lineplot(data=data_urai2017, x='evidence', y='rt', color='k', marker='o', ci=None,ax=ax[2])\n", "ax[2].set(title='Chronometric function')\n", "fig.suptitle('Figure 1. Basic behavior in the 2IFC task', verticalalignment='baseline')\n", "plt.tight_layout()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 936x288 with 3 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "## RT reflects uncertainty\n", "data_urai2017.loc[:,'rt_bin'] = pd.qcut(data_urai2017['rt_norm'], 2, labels=False) # median split\n", "\n", "fig, ax = plt.subplots(1, 3, figsize=(13,4))\n", "sns.lineplot(data=data_urai2017, x='evidence', y='rt_norm', hue='correct', marker='o', ax=ax[0], ci=None, palette={0:\"firebrick\", 1:\"forestgreen\"}, legend=False)\n", "ax[0].set(title='Vevaiometric curve')\n", "sns.regplot(data=data_urai2017, x='rt_norm', y='correct', x_bins=5, color='k', marker='o', ax=ax[1], ci=None)\n", "ax[1].set(title='Confidence calibration')\n", "sns.lineplot(data=data_urai2017, x='coherence', y='correct', hue='rt_bin', marker='o',ci=None, ax=ax[2], palette={0:\"dimgrey\", 1:\"black\"}, legend=False)\n", "ax[2].set(title='Conditional psychometric', ylim=[0.5, 1])\n", "fig.suptitle('Figure 2. Reaction time reflects uncertainty', verticalalignment='baseline')\n", "plt.tight_layout()\n", "# fig.savefig('rt.pdf')" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 936x288 with 3 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "## Pupil reflects uncertainty\n", "data_urai2017.loc[:,'pupil_bin'] = pd.qcut(data_urai2017['decision_pupil'], 2, labels=False) # median split\n", "\n", "fig, ax = plt.subplots(1, 3, figsize=(13,4))\n", "sns.lineplot(data=data_urai2017, x='evidence', y='decision_pupil', hue='correct', marker='o', ci=None, ax=ax[0], palette={0:\"firebrick\", 1:\"forestgreen\"}, legend=False)\n", "ax[0].set(title='Vevaiometric curve')\n", "sns.regplot(data=data_urai2017, x='decision_pupil', y='correct', x_bins=5, color='k', marker='o', ax=ax[1], ci=None)\n", "ax[1].set(title='Confidence calibration')\n", "sns.lineplot(data=data_urai2017, x='coherence', y='correct', hue='pupil_bin', marker='o', ci=None, ax=ax[2], palette={0:\"dimgrey\", 1:\"black\"}, legend=False)\n", "ax[2].set(title='Conditional psychometric', ylim=[0.5, 1])\n", "fig.suptitle('Figure 3. Post-decision pupil dilation reflects uncertainty', verticalalignment='baseline')\n", "plt.tight_layout()\n", "#fig.savefig('pupil.pdf')" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "# RATHER THAN FITTING PSYCHOMETRIC FUNCTION SHIFTS, DO A QUICK HEURISTIC: REPETITION PROBABILITY\n", "data_urai2017['repeat'] = 1 * (data_urai2017['choice'] == data_urai2017.choice.shift(1))\n", "data_urai2017['previous_coherence'] = data_urai2017.coherence.shift(1)\n", "data_urai2017['previous_choice'] = data_urai2017.choice.shift(1)\n", "data_urai2017['previous_correct'] = data_urai2017.correct.shift(1)\n", "data_urai2017['previous_rt_bins'] = pd.qcut(data_urai2017.rt_norm.shift(1), 3, labels=False) \n", "data_urai2017['previous_rt'] = data_urai2017.rt_norm.shift(1) \n", "data_urai2017['previous_pupil_bins'] = pd.qcut(data_urai2017.decision_pupil.shift(1), 3, labels=False) \n", "data_urai2017['previous_pupil'] = data_urai2017.decision_pupil.shift(1) \n", "\n", "# data_urai2017.loc[:,'current_evidence'] = pd.qcut(data_urai2017['coherence'], 2, labels=False) # median split" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [], "source": [ "## DEFINE UPDATING BASED ON THE PSYCHOMETRIC FUNCTION SHIFT\n", "# from ibl_pipeline.analyses import behavior as behavioral_analyses\n", "\n", "def fit_psychfunc(df):\n", " \n", " choicedat = df.dropna(subset=['choice']) # ignore missed trials\n", " choicedat = choicedat.groupby('evidence').agg({'rt':'count', 'response':'mean'}).reset_index() \n", " assert(all(choicedat['response'] >= 0)) # make sure we're not using signed choices\n", " \n", " if sum(choicedat.rt) > 20: # only fit if each datapoint has at least 10 trials\n", " pars, L = psy.mle_fit_psycho(choicedat.values.transpose(), P_model='erf_psycho', \n", " parstart=np.array([choicedat['evidence'].mean(), 20., 0.05]), \n", " parmin=np.array([choicedat['evidence'].min(), 0., 0.]), \n", " parmax=np.array([choicedat['evidence'].max(), 100., 1.]))\n", " df2 = {'bias':pars[0],'threshold':pars[1], 'lapse':pars[2]}\n", " else:\n", " df2 = {'bias':np.nan, 'threshold':np.nan, 'lapse':np.nan}\n", " return pd.DataFrame(df2, index=[0])\n", "\n", "def updating(df, colname):\n", " \n", " # fit psychometric functions\n", " psychfuncfits = df.groupby(['subj_idx', 'previous_choice', 'previous_correct', colname]).apply(fit_psychfunc).reset_index() \n", " \n", " # make a new dataframe to compute the bias shift per group\n", " df2 = pd.DataFrame()\n", " for name, grouped in psychfuncfits.groupby(['previous_correct', 'subj_idx', colname]):\n", " if grouped.shape[0] == 2: # skip if there aren't 2 previous responses in this bin\n", " biasshift = grouped.loc[grouped.previous_choice == -1, 'bias'].item() - grouped.loc[grouped.previous_choice == 1, 'bias'].item()\n", " df3 = grouped.groupby(['previous_correct', 'subj_idx', colname]).mean().reset_index() # average the rest\n", " df3['update'] = biasshift # now insert the computed biasshift\n", " df2 = df2.append(df3)\n", " \n", " print(colname)\n", " df2 = df2.dropna(subset=['update'])\n", " # print(df2.columns)\n", " return df2\n" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "previous_coherence\n", "previous_pupil_bins\n", "previous_rt_bins\n" ] } ], "source": [ "## NOW FIT THE PSYCHOMETRIC FUNCTION SHIFT, DEPENDING ON PREVIOUS COHERENCE\n", "coherence_update = updating(data_urai2017, 'previous_coherence')\n", "pupil_update = updating(data_urai2017, 'previous_pupil_bins')\n", "rt_update = updating(data_urai2017, 'previous_rt_bins')" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 936x288 with 3 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "## HOW DO PUPIL, RT AND COHERENCE AFFECT NEXT-TRIAL UPDATING?\n", "fig, ax = plt.subplots(1, 3, figsize=(13,4), sharey=True)\n", "kwargs = {'y':'update', 'ci':68, 'marker':'o', 'estimator':np.nanmean, 'err_style':\"bars\"}\n", "sns.lineplot(data=coherence_update[coherence_update.previous_correct == 1], x='previous_coherence', ax=ax[0], **kwargs)\n", "sns.lineplot(data=rt_update[rt_update.previous_correct == 1], x='previous_rt_bins', ax=ax[1], **kwargs)\n", "sns.lineplot(data=pupil_update[pupil_update.previous_correct == 1], x='previous_pupil_bins', ax=ax[2], **kwargs)\n", "fig.suptitle('Figure 4. Decision uncertainty reduces repetition', verticalalignment='baseline')\n", "plt.tight_layout()\n", "fig.savefig('updating.pdf')" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [], "source": [ "## DO THE RT-BINNING PER LEVEL OF PREVIOUS COHERENCE\n", "def updating_2split(df, colname1, colname2):\n", " \n", " # fit psychometric functions\n", " psychfuncfits = df.groupby(['subj_idx', 'previous_choice', 'previous_correct', colname1, colname2]).apply(fit_psychfunc).reset_index() \n", " \n", " # make a new dataframe to compute the bias shift per group\n", " df2 = pd.DataFrame()\n", " for name, grouped in psychfuncfits.groupby(['previous_correct', 'subj_idx', colname1, colname2]):\n", " if grouped.shape[0] == 2: # skip if there aren't 2 previous responses in this bin\n", " biasshift = grouped.loc[grouped.previous_choice == -1, 'bias'].item() - grouped.loc[grouped.previous_choice == 1, 'bias'].item()\n", " df3 = grouped.groupby(['previous_correct', 'subj_idx', colname1, colname2]).mean().reset_index() # average the rest\n", " df3['update'] = biasshift # now insert the computed biasshift\n", " df2 = df2.append(df3)\n", " \n", " df2 = df2.dropna(subset=['update'])\n", " print(colname1)\n", " print(colname2)\n", " # print(df2.columns)\n", " return df2" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "previous_rt_bins_percoh\n", "previous_coherence\n", "previous_pupil_bins_percoh\n", "previous_coherence\n" ] }, { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>previous_correct</th>\n", " <th>subj_idx</th>\n", " <th>previous_rt_bins_percoh</th>\n", " <th>previous_coherence</th>\n", " <th>previous_choice</th>\n", " <th>level_5</th>\n", " <th>bias</th>\n", " <th>lapse</th>\n", " <th>threshold</th>\n", " <th>update</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0.0</td>\n", " <td>1</td>\n", " <td>0.0</td>\n", " <td>2.5</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " <td>-1.540719</td>\n", " <td>9.112686e-11</td>\n", " <td>30.034013</td>\n", " <td>-2.513598</td>\n", " </tr>\n", " <tr>\n", " <th>0</th>\n", " <td>0.0</td>\n", " <td>1</td>\n", " <td>1.0</td>\n", " <td>2.5</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " <td>2.321820</td>\n", " <td>5.169082e-02</td>\n", " <td>21.555405</td>\n", " <td>-3.891155</td>\n", " </tr>\n", " <tr>\n", " <th>0</th>\n", " <td>0.0</td>\n", " <td>1</td>\n", " <td>2.0</td>\n", " <td>2.5</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " <td>0.264076</td>\n", " <td>2.606105e-11</td>\n", " <td>18.263647</td>\n", " <td>-6.227803</td>\n", " </tr>\n", " <tr>\n", " <th>0</th>\n", " <td>0.0</td>\n", " <td>1</td>\n", " <td>2.0</td>\n", " <td>5.0</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " <td>1.810546</td>\n", " <td>5.477974e-12</td>\n", " <td>36.786290</td>\n", " <td>4.943931</td>\n", " </tr>\n", " <tr>\n", " <th>0</th>\n", " <td>0.0</td>\n", " <td>1</td>\n", " <td>3.0</td>\n", " <td>2.5</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " <td>1.709106</td>\n", " <td>1.811594e-01</td>\n", " <td>6.523152</td>\n", " <td>-12.502599</td>\n", " </tr>\n", " <tr>\n", " <th>0</th>\n", " <td>0.0</td>\n", " <td>1</td>\n", " <td>3.0</td>\n", " <td>5.0</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " <td>-1.004352</td>\n", " <td>1.529344e-11</td>\n", " <td>27.731667</td>\n", " <td>-5.282243</td>\n", " </tr>\n", " <tr>\n", " <th>0</th>\n", " <td>0.0</td>\n", " <td>2</td>\n", " <td>0.0</td>\n", " <td>2.5</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " <td>0.347577</td>\n", " <td>1.387654e-11</td>\n", " <td>11.537617</td>\n", " <td>2.469326</td>\n", " </tr>\n", " <tr>\n", " <th>0</th>\n", " <td>0.0</td>\n", " <td>2</td>\n", " <td>1.0</td>\n", " <td>2.5</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " <td>-0.774735</td>\n", " <td>7.086719e-11</td>\n", " <td>11.706885</td>\n", " <td>-3.226901</td>\n", " </tr>\n", " <tr>\n", " <th>0</th>\n", " <td>0.0</td>\n", " <td>2</td>\n", " <td>2.0</td>\n", " <td>2.5</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " <td>-2.600332</td>\n", " <td>4.361485e-11</td>\n", " <td>27.633340</td>\n", " <td>13.060322</td>\n", " </tr>\n", " <tr>\n", " <th>0</th>\n", " <td>0.0</td>\n", " <td>2</td>\n", " <td>3.0</td>\n", " <td>2.5</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " <td>0.902776</td>\n", " <td>3.178982e-11</td>\n", " <td>10.616158</td>\n", " <td>0.598255</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " previous_correct subj_idx previous_rt_bins_percoh previous_coherence \\\n", "0 0.0 1 0.0 2.5 \n", "0 0.0 1 1.0 2.5 \n", "0 0.0 1 2.0 2.5 \n", "0 0.0 1 2.0 5.0 \n", "0 0.0 1 3.0 2.5 \n", "0 0.0 1 3.0 5.0 \n", "0 0.0 2 0.0 2.5 \n", "0 0.0 2 1.0 2.5 \n", "0 0.0 2 2.0 2.5 \n", "0 0.0 2 3.0 2.5 \n", "\n", " previous_choice level_5 bias lapse threshold update \n", "0 0.0 0 -1.540719 9.112686e-11 30.034013 -2.513598 \n", "0 0.0 0 2.321820 5.169082e-02 21.555405 -3.891155 \n", "0 0.0 0 0.264076 2.606105e-11 18.263647 -6.227803 \n", "0 0.0 0 1.810546 5.477974e-12 36.786290 4.943931 \n", "0 0.0 0 1.709106 1.811594e-01 6.523152 -12.502599 \n", "0 0.0 0 -1.004352 1.529344e-11 27.731667 -5.282243 \n", "0 0.0 0 0.347577 1.387654e-11 11.537617 2.469326 \n", "0 0.0 0 -0.774735 7.086719e-11 11.706885 -3.226901 \n", "0 0.0 0 -2.600332 4.361485e-11 27.633340 13.060322 \n", "0 0.0 0 0.902776 3.178982e-11 10.616158 0.598255 " ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# NOW DO THE SPLIT INTO PUPIL OR RT BINS, BUT PER LEVEL OF PREVIOUS COHERENCE\n", "def shiftrt_percoh(x):\n", " return pd.qcut(x, 4, labels=False)\n", " \n", "data_urai2017['previous_rt_bins_percoh'] = data_urai2017.groupby(['previous_coherence'])['previous_rt'].apply(shiftrt_percoh)\n", "rt_update_percoh = updating_2split(data_urai2017, 'previous_rt_bins_percoh', 'previous_coherence')\n", "\n", "data_urai2017['previous_pupil_bins_percoh'] = data_urai2017.groupby(['previous_coherence'])['previous_pupil'].apply(shiftrt_percoh)\n", "pupil_update_percoh = updating_2split(data_urai2017, 'previous_pupil_bins_percoh', 'previous_coherence')\n", "rt_update_percoh.head(n=10)" ] }, { "cell_type": "code", "execution_count": 86, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 936x288 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(1, 2, figsize=(13,4), sharey=True)\n", "sns.lineplot(data=rt_update_percoh[rt_update_percoh.previous_correct == 1], x='previous_rt_bins_percoh', \n", " hue='previous_coherence', legend=False, ax=ax[0], palette='viridis', **kwargs)\n", "sns.lineplot(data=pupil_update_percoh[pupil_update_percoh.previous_correct == 1], x='previous_pupil_bins_percoh', \n", " hue='previous_coherence', legend='brief', palette='viridis', ax=ax[1], **kwargs)\n", "lgd = ax[1].legend(loc='center right', bbox_to_anchor=(1.75, 0.7), ncol=1) # move box outside\n", "fig.suptitle('Figure 5. More updating with lower previous contrast, and also with lower previous decision uncertainty')\n", "fig.savefig('updating_percoh.pdf')" ] }, { "cell_type": "code", "execution_count": 87, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<seaborn.axisgrid.FacetGrid at 0x1a18a5aad0>" ] }, "execution_count": 87, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 451.4x360 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.lmplot(data=data_urai2017, x='rt', y='correct', hue='coherence', \n", " x_bins=10, palette='viridis', truncate=True, ci=None)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Stronger coherence -> more correct / more confidence\n", "* Within each coherence level, fast RT -> more correct / more confidence\n", "\n", "*BUT*\n", "\n", "* Stronger coherence -> less updating on next trial\n", "* Within each coherence level, fast RT -> more updating on next trial" ] }, { "cell_type": "code", "execution_count": 75, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: update R-squared: 0.102\n", "Model: OLS Adj. R-squared: 0.096\n", "Method: Least Squares F-statistic: 19.46\n", "Date: Thu, 06 Jun 2019 Prob (F-statistic): 5.74e-12\n", "Time: 15:01:14 Log-Likelihood: -1643.3\n", "No. Observations: 520 AIC: 3295.\n", "Df Residuals: 516 BIC: 3312.\n", "Df Model: 3 \n", "Covariance Type: nonrobust \n", "==============================================================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "--------------------------------------------------------------------------------------------------------------\n", "Intercept 4.5599 0.661 6.899 0.000 3.261 5.858\n", "previous_coherence -0.1378 0.044 -3.128 0.002 -0.224 -0.051\n", "previous_rt_bins_percoh -1.5276 0.334 -4.576 0.000 -2.183 -0.872\n", "previous_coherence:previous_rt_bins_percoh 0.0096 0.023 0.410 0.682 -0.036 0.056\n", "==============================================================================\n", "Omnibus: 79.926 Durbin-Watson: 1.552\n", "Prob(Omnibus): 0.000 Jarque-Bera (JB): 263.923\n", "Skew: 0.693 Prob(JB): 4.90e-58\n", "Kurtosis: 6.203 Cond. No. 84.1\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" ] } ], "source": [ "## test anova\n", "from statsmodels.formula.api import ols\n", "formula = 'update ~ previous_coherence*previous_rt_bins_percoh'\n", "lm = ols(formula, rt_update_percoh).fit()\n", "print(lm.summary())" ] }, { "cell_type": "code", "execution_count": 76, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: update R-squared: 0.042\n", "Model: OLS Adj. R-squared: 0.037\n", "Method: Least Squares F-statistic: 8.103\n", "Date: Thu, 06 Jun 2019 Prob (F-statistic): 2.73e-05\n", "Time: 15:01:24 Log-Likelihood: -1731.7\n", "No. Observations: 561 AIC: 3471.\n", "Df Residuals: 557 BIC: 3489.\n", "Df Model: 3 \n", "Covariance Type: nonrobust \n", "=================================================================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "-----------------------------------------------------------------------------------------------------------------\n", "Intercept 1.9318 0.572 3.377 0.001 0.808 3.056\n", "previous_coherence -0.0690 0.037 -1.842 0.066 -0.143 0.005\n", "previous_pupil_bins_percoh -0.1100 0.301 -0.366 0.714 -0.700 0.480\n", "previous_coherence:previous_pupil_bins_percoh -0.0213 0.020 -1.065 0.287 -0.061 0.018\n", "==============================================================================\n", "Omnibus: 41.918 Durbin-Watson: 1.772\n", "Prob(Omnibus): 0.000 Jarque-Bera (JB): 107.307\n", "Skew: 0.369 Prob(JB): 5.00e-24\n", "Kurtosis: 5.011 Cond. No. 85.8\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" ] } ], "source": [ "\n", "## test anova\n", "formula = 'update ~ previous_coherence*previous_pupil_bins_percoh'\n", "lm = ols(formula, pupil_update_percoh).fit()\n", "print(lm.summary())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Conclusion**: the weaker the previous coherence, the larger the choice update (i.e. more information about the bound placement). However, _within_ each level of coherence, faster RTs (so a more confident choice) also lead to stronger updating. \n", "Even when ignoring previous coherence, and just grouping by RT, this captures the \n" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Logit Regression Results \n", "==============================================================================\n", "Dep. Variable: response No. Observations: 64343\n", "Model: Logit Df Residuals: 64338\n", "Method: MLE Df Model: 4\n", "Date: Sun, 02 Jun 2019 Pseudo R-squ.: 0.2936\n", "Time: 16:32:03 Log-Likelihood: -31501.\n", "converged: True LL-Null: -44595.\n", " LLR p-value: 0.000\n", "======================================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "--------------------------------------------------------------------------------------\n", "const -0.0030 0.014 -0.209 0.834 -0.031 0.025\n", "evidence 0.1281 0.001 109.410 0.000 0.126 0.130\n", "previous_coherence 0.0018 0.001 1.892 0.058 -6.51e-05 0.004\n", "previous_choice 0.1737 0.014 12.034 0.000 0.145 0.202\n", "prev_coh_resp -0.0095 0.001 -9.872 0.000 -0.011 -0.008\n", "======================================================================================\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/anaconda3/envs/python3.7/lib/python2.7/site-packages/ipykernel_launcher.py:7: 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", " import sys\n" ] } ], "source": [ "### CAN ALSO USE STATSMODELS!\n", "import statsmodels.api as sm\n", "import statsmodels.formula.api as smf\n", "\n", "data_urai2017 = data_urai2017.dropna(subset=['previous_coherence', 'previous_rt', 'previous_pupil'])\n", "designM = data_urai2017[['evidence', 'previous_coherence', 'previous_choice']]\n", "designM['prev_coh_resp'] = designM.previous_choice * designM.previous_coherence\n", "designM = sm.add_constant(designM, prepend=True)\n", "\n", "logit_mod = sm.Logit(data_urai2017.response, designM)\n", "logit_res = logit_mod.fit(disp=0)\n", "print(logit_res.summary())\n", "\n", "# md = smf.mixedlm(\"response ~ evidence\", data_urai2017, groups=data_urai2017[\"subj_idx\"], family=sm.families.Binomial())\n", "# mdf = data_urai2017.fit()\n", "# print(mdf.summary())" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Logit Regression Results \n", "==============================================================================\n", "Dep. Variable: response No. Observations: 64343\n", "Model: Logit Df Residuals: 64338\n", "Method: MLE Df Model: 4\n", "Date: Sun, 02 Jun 2019 Pseudo R-squ.: 0.2931\n", "Time: 16:32:04 Log-Likelihood: -31525.\n", "converged: True LL-Null: -44595.\n", " LLR p-value: 0.000\n", "===================================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "-----------------------------------------------------------------------------------\n", "const 0.0152 0.010 1.566 0.117 -0.004 0.034\n", "evidence 0.1281 0.001 109.454 0.000 0.126 0.130\n", "previous_rt 0.0148 0.010 1.504 0.133 -0.004 0.034\n", "previous_choice 0.0684 0.010 7.045 0.000 0.049 0.087\n", "prev_coh_resp -0.0686 0.010 -6.986 0.000 -0.088 -0.049\n", "===================================================================================\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/anaconda3/envs/python3.7/lib/python2.7/site-packages/ipykernel_launcher.py:6: 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", " \n" ] } ], "source": [ "### CAN ALSO USE STATSMODELS!\n", "import statsmodels.api as sm\n", "import statsmodels.formula.api as smf\n", "\n", "designM = data_urai2017[['evidence', 'previous_rt', 'previous_choice']]\n", "designM['prev_rt_resp'] = designM.previous_choice * designM.previous_rt\n", "designM = sm.add_constant(designM, prepend=True)\n", "\n", "logit_mod = sm.Logit(data_urai2017.response, designM)\n", "logit_res = logit_mod.fit(disp=0)\n", "print(logit_res.summary())" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Logit Regression Results \n", "==============================================================================\n", "Dep. Variable: response No. Observations: 64343\n", "Model: Logit Df Residuals: 64338\n", "Method: MLE Df Model: 4\n", "Date: Sun, 02 Jun 2019 Pseudo R-squ.: 0.2927\n", "Time: 16:32:31 Log-Likelihood: -31542.\n", "converged: True LL-Null: -44595.\n", " LLR p-value: 0.000\n", "===================================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "-----------------------------------------------------------------------------------\n", "const 0.0159 0.010 1.642 0.101 -0.003 0.035\n", "evidence 0.1280 0.001 109.424 0.000 0.126 0.130\n", "previous_pupil 0.0079 0.010 0.804 0.421 -0.011 0.027\n", "previous_choice 0.0684 0.010 7.057 0.000 0.049 0.087\n", "prev_pupil_resp -0.0410 0.010 -4.159 0.000 -0.060 -0.022\n", "===================================================================================\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/anaconda3/envs/python3.7/lib/python2.7/site-packages/ipykernel_launcher.py:6: 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", " \n" ] } ], "source": [ "### CAN ALSO USE STATSMODELS!\n", "import statsmodels.api as sm\n", "import statsmodels.formula.api as smf\n", "\n", "designM = data_urai2017[['evidence', 'previous_pupil', 'previous_choice']]\n", "designM['prev_pupil_resp'] = designM.previous_choice * designM.previous_pupil\n", "designM = sm.add_constant(designM, prepend=True)\n", "\n", "logit_mod = sm.Logit(data_urai2017.response, designM)\n", "logit_res = logit_mod.fit(disp=0)\n", "print(logit_res.summary())" ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Logit Regression Results \n", "==============================================================================\n", "Dep. Variable: response No. Observations: 64343\n", "Model: Logit Df Residuals: 64334\n", "Method: MLE Df Model: 8\n", "Date: Sun, 02 Jun 2019 Pseudo R-squ.: 0.2950\n", "Time: 16:34:59 Log-Likelihood: -31440.\n", "converged: True LL-Null: -44595.\n", " LLR p-value: 0.000\n", "======================================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "--------------------------------------------------------------------------------------\n", "const -0.0144 0.015 -0.980 0.327 -0.043 0.014\n", "evidence 0.1283 0.001 109.443 0.000 0.126 0.131\n", "previous_coherence 0.0028 0.001 2.773 0.006 0.001 0.005\n", "previous_rt 0.0238 0.010 2.328 0.020 0.004 0.044\n", "previous_pupil 0.0085 0.010 0.855 0.392 -0.011 0.028\n", "previous_choice 0.2044 0.015 13.857 0.000 0.175 0.233\n", "prev_coh_resp -0.0123 0.001 -12.292 0.000 -0.014 -0.010\n", "prev_rt_resp -0.0981 0.010 -9.608 0.000 -0.118 -0.078\n", "prev_pupil_resp -0.0395 0.010 -3.980 0.000 -0.059 -0.020\n", "======================================================================================\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/anaconda3/envs/python3.7/lib/python2.7/site-packages/ipykernel_launcher.py:4: 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", " after removing the cwd from sys.path.\n", "/anaconda3/envs/python3.7/lib/python2.7/site-packages/ipykernel_launcher.py:5: 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", " \"\"\"\n", "/anaconda3/envs/python3.7/lib/python2.7/site-packages/ipykernel_launcher.py:6: 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", " \n" ] } ], "source": [ "## NOW COMBINE ALL 3 IN ONE MODEL..\n", "\n", "designM = data_urai2017[['evidence', 'previous_coherence', 'previous_rt', 'previous_pupil', 'previous_choice']]\n", "designM['prev_coh_resp'] = designM.previous_choice * designM.previous_coherence\n", "designM['prev_rt_resp'] = designM.previous_choice * designM.previous_rt\n", "designM['prev_pupil_resp'] = designM.previous_choice * designM.previous_pupil\n", "designM = sm.add_constant(designM, prepend=True)\n", "\n", "logit_mod = sm.Logit(data_urai2017.response, designM)\n", "logit_res = logit_mod.fit(disp=0)\n", "print(logit_res.summary())" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:python3.7] *", "language": "python", "name": "conda-env-python3.7-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.15" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
statsmodels/statsmodels.github.io
v0.13.1/examples/notebooks/generated/pca_fertility_factors.ipynb
2
315749
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# statsmodels Principal Component Analysis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*Key ideas:* Principal component analysis, world bank data, fertility\n", "\n", "In this notebook, we use principal components analysis (PCA) to analyze the time series of fertility rates in 192 countries, using data obtained from the World Bank. The main goal is to understand how the trends in fertility over time differ from country to country. This is a slightly atypical illustration of PCA because the data are time series. Methods such as functional PCA have been developed for this setting, but since the fertility data are very smooth, there is no real disadvantage to using standard PCA in this case." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "execution": { "iopub.execute_input": "2021-11-12T23:40:25.651626Z", "iopub.status.busy": "2021-11-12T23:40:25.651161Z", "iopub.status.idle": "2021-11-12T23:40:26.870223Z", "shell.execute_reply": "2021-11-12T23:40:26.871115Z" }, "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import matplotlib.pyplot as plt\n", "import statsmodels.api as sm\n", "from statsmodels.multivariate.pca import PCA\n", "\n", "plt.rc(\"figure\", figsize=(16, 8))\n", "plt.rc(\"font\", size=14)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The data can be obtained from the [World Bank web site](http://data.worldbank.org/indicator/SP.DYN.TFRT.IN), but here we work with a slightly cleaned-up version of the data:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "execution": { "iopub.execute_input": "2021-11-12T23:40:26.875448Z", "iopub.status.busy": "2021-11-12T23:40:26.874381Z", "iopub.status.idle": "2021-11-12T23:40:26.913812Z", "shell.execute_reply": "2021-11-12T23:40:26.914587Z" }, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Country Name</th>\n", " <th>Country Code</th>\n", " <th>Indicator Name</th>\n", " <th>Indicator Code</th>\n", " <th>1960</th>\n", " <th>1961</th>\n", " <th>1962</th>\n", " <th>1963</th>\n", " <th>1964</th>\n", " <th>1965</th>\n", " <th>...</th>\n", " <th>2004</th>\n", " <th>2005</th>\n", " <th>2006</th>\n", " <th>2007</th>\n", " <th>2008</th>\n", " <th>2009</th>\n", " <th>2010</th>\n", " <th>2011</th>\n", " <th>2012</th>\n", " <th>2013</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Aruba</td>\n", " <td>ABW</td>\n", " <td>Fertility rate, total (births per woman)</td>\n", " <td>SP.DYN.TFRT.IN</td>\n", " <td>4.820</td>\n", " <td>4.655</td>\n", " <td>4.471</td>\n", " <td>4.271</td>\n", " <td>4.059</td>\n", " <td>3.842</td>\n", " <td>...</td>\n", " <td>1.786</td>\n", " <td>1.769</td>\n", " <td>1.754</td>\n", " <td>1.739</td>\n", " <td>1.726</td>\n", " <td>1.713</td>\n", " <td>1.701</td>\n", " <td>1.690</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Andorra</td>\n", " <td>AND</td>\n", " <td>Fertility rate, total (births per woman)</td>\n", " <td>SP.DYN.TFRT.IN</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>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1.240</td>\n", " <td>1.180</td>\n", " <td>1.250</td>\n", " <td>1.190</td>\n", " <td>1.220</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Afghanistan</td>\n", " <td>AFG</td>\n", " <td>Fertility rate, total (births per woman)</td>\n", " <td>SP.DYN.TFRT.IN</td>\n", " <td>7.671</td>\n", " <td>7.671</td>\n", " <td>7.671</td>\n", " <td>7.671</td>\n", " <td>7.671</td>\n", " <td>7.671</td>\n", " <td>...</td>\n", " <td>7.136</td>\n", " <td>6.930</td>\n", " <td>6.702</td>\n", " <td>6.456</td>\n", " <td>6.196</td>\n", " <td>5.928</td>\n", " <td>5.659</td>\n", " <td>5.395</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Angola</td>\n", " <td>AGO</td>\n", " <td>Fertility rate, total (births per woman)</td>\n", " <td>SP.DYN.TFRT.IN</td>\n", " <td>7.316</td>\n", " <td>7.354</td>\n", " <td>7.385</td>\n", " <td>7.410</td>\n", " <td>7.425</td>\n", " <td>7.430</td>\n", " <td>...</td>\n", " <td>6.704</td>\n", " <td>6.657</td>\n", " <td>6.598</td>\n", " <td>6.523</td>\n", " <td>6.434</td>\n", " <td>6.331</td>\n", " <td>6.218</td>\n", " <td>6.099</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Albania</td>\n", " <td>ALB</td>\n", " <td>Fertility rate, total (births per woman)</td>\n", " <td>SP.DYN.TFRT.IN</td>\n", " <td>6.186</td>\n", " <td>6.076</td>\n", " <td>5.956</td>\n", " <td>5.833</td>\n", " <td>5.711</td>\n", " <td>5.594</td>\n", " <td>...</td>\n", " <td>2.004</td>\n", " <td>1.919</td>\n", " <td>1.849</td>\n", " <td>1.796</td>\n", " <td>1.761</td>\n", " <td>1.744</td>\n", " <td>1.741</td>\n", " <td>1.748</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 58 columns</p>\n", "</div>" ], "text/plain": [ " Country Name Country Code Indicator Name \\\n", "0 Aruba ABW Fertility rate, total (births per woman) \n", "1 Andorra AND Fertility rate, total (births per woman) \n", "2 Afghanistan AFG Fertility rate, total (births per woman) \n", "3 Angola AGO Fertility rate, total (births per woman) \n", "4 Albania ALB Fertility rate, total (births per woman) \n", "\n", " Indicator Code 1960 1961 1962 1963 1964 1965 ... 2004 \\\n", "0 SP.DYN.TFRT.IN 4.820 4.655 4.471 4.271 4.059 3.842 ... 1.786 \n", "1 SP.DYN.TFRT.IN NaN NaN NaN NaN NaN NaN ... NaN \n", "2 SP.DYN.TFRT.IN 7.671 7.671 7.671 7.671 7.671 7.671 ... 7.136 \n", "3 SP.DYN.TFRT.IN 7.316 7.354 7.385 7.410 7.425 7.430 ... 6.704 \n", "4 SP.DYN.TFRT.IN 6.186 6.076 5.956 5.833 5.711 5.594 ... 2.004 \n", "\n", " 2005 2006 2007 2008 2009 2010 2011 2012 2013 \n", "0 1.769 1.754 1.739 1.726 1.713 1.701 1.690 NaN NaN \n", "1 NaN 1.240 1.180 1.250 1.190 1.220 NaN NaN NaN \n", "2 6.930 6.702 6.456 6.196 5.928 5.659 5.395 NaN NaN \n", "3 6.657 6.598 6.523 6.434 6.331 6.218 6.099 NaN NaN \n", "4 1.919 1.849 1.796 1.761 1.744 1.741 1.748 NaN NaN \n", "\n", "[5 rows x 58 columns]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = sm.datasets.fertility.load_pandas().data\n", "data.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we construct a DataFrame that contains only the numerical fertility rate data and set the index to the country names. We also drop all the countries with any missing data." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "execution": { "iopub.execute_input": "2021-11-12T23:40:26.918219Z", "iopub.status.busy": "2021-11-12T23:40:26.917155Z", "iopub.status.idle": "2021-11-12T23:40:27.042500Z", "shell.execute_reply": "2021-11-12T23:40:27.043252Z" }, "jupyter": { "outputs_hidden": false } }, "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>1960</th>\n", " <th>1961</th>\n", " <th>1962</th>\n", " <th>1963</th>\n", " <th>1964</th>\n", " <th>1965</th>\n", " <th>1966</th>\n", " <th>1967</th>\n", " <th>1968</th>\n", " <th>1969</th>\n", " <th>...</th>\n", " <th>2002</th>\n", " <th>2003</th>\n", " <th>2004</th>\n", " <th>2005</th>\n", " <th>2006</th>\n", " <th>2007</th>\n", " <th>2008</th>\n", " <th>2009</th>\n", " <th>2010</th>\n", " <th>2011</th>\n", " </tr>\n", " <tr>\n", " <th>Country Name</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Aruba</th>\n", " <td>4.820</td>\n", " <td>4.655</td>\n", " <td>4.471</td>\n", " <td>4.271</td>\n", " <td>4.059</td>\n", " <td>3.842</td>\n", " <td>3.625</td>\n", " <td>3.417</td>\n", " <td>3.226</td>\n", " <td>3.054</td>\n", " <td>...</td>\n", " <td>1.825</td>\n", " <td>1.805</td>\n", " <td>1.786</td>\n", " <td>1.769</td>\n", " <td>1.754</td>\n", " <td>1.739</td>\n", " <td>1.726</td>\n", " <td>1.713</td>\n", " <td>1.701</td>\n", " <td>1.690</td>\n", " </tr>\n", " <tr>\n", " <th>Afghanistan</th>\n", " <td>7.671</td>\n", " <td>7.671</td>\n", " <td>7.671</td>\n", " <td>7.671</td>\n", " <td>7.671</td>\n", " <td>7.671</td>\n", " <td>7.671</td>\n", " <td>7.671</td>\n", " <td>7.671</td>\n", " <td>7.671</td>\n", " <td>...</td>\n", " <td>7.484</td>\n", " <td>7.321</td>\n", " <td>7.136</td>\n", " <td>6.930</td>\n", " <td>6.702</td>\n", " <td>6.456</td>\n", " <td>6.196</td>\n", " <td>5.928</td>\n", " <td>5.659</td>\n", " <td>5.395</td>\n", " </tr>\n", " <tr>\n", " <th>Angola</th>\n", " <td>7.316</td>\n", " <td>7.354</td>\n", " <td>7.385</td>\n", " <td>7.410</td>\n", " <td>7.425</td>\n", " <td>7.430</td>\n", " <td>7.422</td>\n", " <td>7.403</td>\n", " <td>7.375</td>\n", " <td>7.339</td>\n", " <td>...</td>\n", " <td>6.778</td>\n", " <td>6.743</td>\n", " <td>6.704</td>\n", " <td>6.657</td>\n", " <td>6.598</td>\n", " <td>6.523</td>\n", " <td>6.434</td>\n", " <td>6.331</td>\n", " <td>6.218</td>\n", " <td>6.099</td>\n", " </tr>\n", " <tr>\n", " <th>Albania</th>\n", " <td>6.186</td>\n", " <td>6.076</td>\n", " <td>5.956</td>\n", " <td>5.833</td>\n", " <td>5.711</td>\n", " <td>5.594</td>\n", " <td>5.483</td>\n", " <td>5.376</td>\n", " <td>5.268</td>\n", " <td>5.160</td>\n", " <td>...</td>\n", " <td>2.195</td>\n", " <td>2.097</td>\n", " <td>2.004</td>\n", " <td>1.919</td>\n", " <td>1.849</td>\n", " <td>1.796</td>\n", " <td>1.761</td>\n", " <td>1.744</td>\n", " <td>1.741</td>\n", " <td>1.748</td>\n", " </tr>\n", " <tr>\n", " <th>United Arab Emirates</th>\n", " <td>6.928</td>\n", " <td>6.910</td>\n", " <td>6.893</td>\n", " <td>6.877</td>\n", " <td>6.861</td>\n", " <td>6.841</td>\n", " <td>6.816</td>\n", " <td>6.783</td>\n", " <td>6.738</td>\n", " <td>6.679</td>\n", " <td>...</td>\n", " <td>2.428</td>\n", " <td>2.329</td>\n", " <td>2.236</td>\n", " <td>2.149</td>\n", " <td>2.071</td>\n", " <td>2.004</td>\n", " <td>1.948</td>\n", " <td>1.903</td>\n", " <td>1.868</td>\n", " <td>1.841</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 52 columns</p>\n", "</div>" ], "text/plain": [ " 1960 1961 1962 1963 1964 1965 1966 1967 \\\n", "Country Name \n", "Aruba 4.820 4.655 4.471 4.271 4.059 3.842 3.625 3.417 \n", "Afghanistan 7.671 7.671 7.671 7.671 7.671 7.671 7.671 7.671 \n", "Angola 7.316 7.354 7.385 7.410 7.425 7.430 7.422 7.403 \n", "Albania 6.186 6.076 5.956 5.833 5.711 5.594 5.483 5.376 \n", "United Arab Emirates 6.928 6.910 6.893 6.877 6.861 6.841 6.816 6.783 \n", "\n", " 1968 1969 ... 2002 2003 2004 2005 2006 \\\n", "Country Name ... \n", "Aruba 3.226 3.054 ... 1.825 1.805 1.786 1.769 1.754 \n", "Afghanistan 7.671 7.671 ... 7.484 7.321 7.136 6.930 6.702 \n", "Angola 7.375 7.339 ... 6.778 6.743 6.704 6.657 6.598 \n", "Albania 5.268 5.160 ... 2.195 2.097 2.004 1.919 1.849 \n", "United Arab Emirates 6.738 6.679 ... 2.428 2.329 2.236 2.149 2.071 \n", "\n", " 2007 2008 2009 2010 2011 \n", "Country Name \n", "Aruba 1.739 1.726 1.713 1.701 1.690 \n", "Afghanistan 6.456 6.196 5.928 5.659 5.395 \n", "Angola 6.523 6.434 6.331 6.218 6.099 \n", "Albania 1.796 1.761 1.744 1.741 1.748 \n", "United Arab Emirates 2.004 1.948 1.903 1.868 1.841 \n", "\n", "[5 rows x 52 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "columns = list(map(str, range(1960, 2012)))\n", "data.set_index(\"Country Name\", inplace=True)\n", "dta = data[columns]\n", "dta = dta.dropna()\n", "dta.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are two ways to use PCA to analyze a rectangular matrix: we can treat the rows as the \"objects\" and the columns as the \"variables\", or vice-versa. Here we will treat the fertility measures as \"variables\" used to measure the countries as \"objects\". Thus the goal will be to reduce the yearly fertility rate values to a small number of fertility rate \"profiles\" or \"basis functions\" that capture most of the variation over time in the different countries." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The mean trend is removed in PCA, but its worthwhile taking a look at it. It shows that fertility has dropped steadily over the time period covered in this dataset. Note that the mean is calculated using a country as the unit of analysis, ignoring population size. This is also true for the PC analysis conducted below. A more sophisticated analysis might weight the countries, say by population in 1980." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "execution": { "iopub.execute_input": "2021-11-12T23:40:27.046795Z", "iopub.status.busy": "2021-11-12T23:40:27.045817Z", "iopub.status.idle": "2021-11-12T23:40:27.295059Z", "shell.execute_reply": "2021-11-12T23:40:27.295843Z" }, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "text/plain": [ "(0.0, 51.0)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1152x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax = dta.mean().plot(grid=False)\n", "ax.set_xlabel(\"Year\", size=17)\n", "ax.set_ylabel(\"Fertility rate\", size=17)\n", "ax.set_xlim(0, 51)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next we perform the PCA:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "execution": { "iopub.execute_input": "2021-11-12T23:40:27.299507Z", "iopub.status.busy": "2021-11-12T23:40:27.298508Z", "iopub.status.idle": "2021-11-12T23:40:27.396970Z", "shell.execute_reply": "2021-11-12T23:40:27.397824Z" }, "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "pca_model = PCA(dta.T, standardize=False, demean=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Based on the eigenvalues, we see that the first PC dominates, with perhaps a small amount of meaningful variation captured in the second and third PC's." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "execution": { "iopub.execute_input": "2021-11-12T23:40:27.401597Z", "iopub.status.busy": "2021-11-12T23:40:27.400474Z", "iopub.status.idle": "2021-11-12T23:40:27.669585Z", "shell.execute_reply": "2021-11-12T23:40:27.670383Z" }, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1152x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = pca_model.plot_scree(log_scale=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next we will plot the PC factors. The dominant factor is monotonically increasing. Countries with a positive score on the first factor will increase faster (or decrease slower) compared to the mean shown above. Countries with a negative score on the first factor will decrease faster than the mean. The second factor is U-shaped with a positive peak at around 1985. Countries with a large positive score on the second factor will have lower than average fertilities at the beginning and end of the data range, but higher than average fertility in the middle of the range." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "execution": { "iopub.execute_input": "2021-11-12T23:40:27.674062Z", "iopub.status.busy": "2021-11-12T23:40:27.673015Z", "iopub.status.idle": "2021-11-12T23:40:27.942651Z", "shell.execute_reply": "2021-11-12T23:40:27.943432Z" }, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_8050/427128218.py:3: UserWarning: FixedFormatter should only be used together with FixedLocator\n", " ax.set_xticklabels(dta.columns.values[::10])\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 576x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(8, 4))\n", "lines = ax.plot(pca_model.factors.iloc[:, :3], lw=4, alpha=0.6)\n", "ax.set_xticklabels(dta.columns.values[::10])\n", "ax.set_xlim(0, 51)\n", "ax.set_xlabel(\"Year\", size=17)\n", "fig.subplots_adjust(0.1, 0.1, 0.85, 0.9)\n", "legend = fig.legend(lines, [\"PC 1\", \"PC 2\", \"PC 3\"], loc=\"center right\")\n", "legend.draw_frame(False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To better understand what is going on, we will plot the fertility trajectories for sets of countries with similar PC scores. The following convenience function produces such a plot." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "execution": { "iopub.execute_input": "2021-11-12T23:40:27.947142Z", "iopub.status.busy": "2021-11-12T23:40:27.946129Z", "iopub.status.idle": "2021-11-12T23:40:27.951133Z", "shell.execute_reply": "2021-11-12T23:40:27.951852Z" }, "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "idx = pca_model.loadings.iloc[:, 0].argsort()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First we plot the five countries with the greatest scores on PC 1. These countries have a higher rate of fertility increase than the global mean (which is decreasing)." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "execution": { "iopub.execute_input": "2021-11-12T23:40:27.955391Z", "iopub.status.busy": "2021-11-12T23:40:27.954384Z", "iopub.status.idle": "2021-11-12T23:40:27.960720Z", "shell.execute_reply": "2021-11-12T23:40:27.961472Z" }, "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "def make_plot(labels):\n", " fig, ax = plt.subplots(figsize=(9, 5))\n", " ax = dta.loc[labels].T.plot(legend=False, grid=False, ax=ax)\n", " dta.mean().plot(ax=ax, grid=False, label=\"Mean\")\n", " ax.set_xlim(0, 51)\n", " fig.subplots_adjust(0.1, 0.1, 0.75, 0.9)\n", " ax.set_xlabel(\"Year\", size=17)\n", " ax.set_ylabel(\"Fertility\", size=17)\n", " legend = ax.legend(\n", " *ax.get_legend_handles_labels(), loc=\"center left\", bbox_to_anchor=(1, 0.5)\n", " )\n", " legend.draw_frame(False)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "execution": { "iopub.execute_input": "2021-11-12T23:40:27.964828Z", "iopub.status.busy": "2021-11-12T23:40:27.963845Z", "iopub.status.idle": "2021-11-12T23:40:28.181583Z", "shell.execute_reply": "2021-11-12T23:40:28.182453Z" }, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 648x360 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "labels = dta.index[idx[-5:]]\n", "make_plot(labels)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here are the five countries with the greatest scores on factor 2. These are countries that reached peak fertility around 1980, later than much of the rest of the world, followed by a rapid decrease in fertility." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "execution": { "iopub.execute_input": "2021-11-12T23:40:28.186246Z", "iopub.status.busy": "2021-11-12T23:40:28.185175Z", "iopub.status.idle": "2021-11-12T23:40:28.430964Z", "shell.execute_reply": "2021-11-12T23:40:28.431796Z" }, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 648x360 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "idx = pca_model.loadings.iloc[:, 1].argsort()\n", "make_plot(dta.index[idx[-5:]])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally we have the countries with the most negative scores on PC 2. These are the countries where the fertility rate declined much faster than the global mean during the 1960's and 1970's, then flattened out." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "execution": { "iopub.execute_input": "2021-11-12T23:40:28.435745Z", "iopub.status.busy": "2021-11-12T23:40:28.434656Z", "iopub.status.idle": "2021-11-12T23:40:28.646850Z", "shell.execute_reply": "2021-11-12T23:40:28.647667Z" }, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 648x360 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "make_plot(dta.index[idx[:5]])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also look at a scatterplot of the first two principal component scores. We see that the variation among countries is fairly continuous, except perhaps that the two countries with highest scores for PC 2 are somewhat separated from the other points. These countries, Oman and Yemen, are unique in having a sharp spike in fertility around 1980. No other country has such a spike. In contrast, the countries with high scores on PC 1 (that have continuously increasing fertility), are part of a continuum of variation." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "execution": { "iopub.execute_input": "2021-11-12T23:40:28.651326Z", "iopub.status.busy": "2021-11-12T23:40:28.650307Z", "iopub.status.idle": "2021-11-12T23:40:28.871249Z", "shell.execute_reply": "2021-11-12T23:40:28.872017Z" }, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "text/plain": [ "array(['Oman', 'Yemen, Rep.'], dtype=object)" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<Figure size 1152x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "pca_model.loadings.plot.scatter(x=\"comp_00\", y=\"comp_01\", ax=ax)\n", "ax.set_xlabel(\"PC 1\", size=17)\n", "ax.set_ylabel(\"PC 2\", size=17)\n", "dta.index[pca_model.loadings.iloc[:, 1] > 0.2].values" ] } ], "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": "3.8.12" } }, "nbformat": 4, "nbformat_minor": 4 }
bsd-3-clause
mdeff/ntds_2017
projects/reports/face_manifold/NTDS_Project.ipynb
1
542102
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Manifold Learning on Face Data\n", "\n", "**Atul Kumar Sinha, Karttikeya Mangalam and Prakhar Srivastava**\n", "\n", "In this project, we explore manifold learning on face data to embed high dimensional face images into a lower dimensional embedding. We hypothesize that euclidean distance in this lower dimensional embedding reflects image similarity in a better way. This hypothesis is tested by choosing path(s) that contain a number of points (images) from this lower dimensional space which represent an ordererd set of images. These images are then combined to generate a video which shows a smooth morphing." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " <div class=\"bk-root\">\n", " <a href=\"https://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n", " <span id=\"9b18e597-ea23-43d5-a176-e412d58abdea\">Loading BokehJS ...</span>\n", " </div>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "\n", "(function(root) {\n", " function now() {\n", " return new Date();\n", " }\n", "\n", " var force = true;\n", "\n", " if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n", " root._bokeh_onload_callbacks = [];\n", " root._bokeh_is_loading = undefined;\n", " }\n", "\n", "\n", " \n", " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", " root._bokeh_timeout = Date.now() + 5000;\n", " root._bokeh_failed_load = false;\n", " }\n", "\n", " var NB_LOAD_WARNING = {'data': {'text/html':\n", " \"<div style='background-color: #fdd'>\\n\"+\n", " \"<p>\\n\"+\n", " \"BokehJS does not appear to have successfully loaded. 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i < js_urls.length; i++) {\n", " var url = js_urls[i];\n", " var s = document.createElement('script');\n", " s.src = url;\n", " s.async = false;\n", " s.onreadystatechange = s.onload = function() {\n", " root._bokeh_is_loading--;\n", " if (root._bokeh_is_loading === 0) {\n", " console.log(\"Bokeh: all BokehJS libraries loaded\");\n", " run_callbacks()\n", " }\n", " };\n", " s.onerror = function() {\n", " console.warn(\"failed to load library \" + url);\n", " };\n", " console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", " document.getElementsByTagName(\"head\")[0].appendChild(s);\n", " }\n", " };var element = document.getElementById(\"9b18e597-ea23-43d5-a176-e412d58abdea\");\n", " if (element == null) {\n", " console.log(\"Bokeh: ERROR: autoload.js configured with elementid '9b18e597-ea23-43d5-a176-e412d58abdea' but no matching script tag was found. \")\n", " return false;\n", " }\n", "\n", " var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.7.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.7.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.7.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.12.7.min.js\"];\n", "\n", " var inline_js = [\n", " function(Bokeh) {\n", " Bokeh.set_log_level(\"info\");\n", " },\n", " \n", " function(Bokeh) {\n", " \n", " },\n", " \n", " function(Bokeh) {\n", " \n", " document.getElementById(\"9b18e597-ea23-43d5-a176-e412d58abdea\").textContent = \"BokehJS is loading...\";\n", " },\n", " function(Bokeh) {\n", " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.7.min.css\");\n", " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.7.min.css\");\n", " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.7.min.css\");\n", " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.7.min.css\");\n", " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.7.min.css\");\n", " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.7.min.css\");\n", " }\n", " ];\n", "\n", " function run_inline_js() {\n", " \n", " if ((root.Bokeh !== undefined) || (force === true)) {\n", " for (var i = 0; i < inline_js.length; i++) {\n", " inline_js[i].call(root, root.Bokeh);\n", " }if (force === true) {\n", " display_loaded();\n", " }} else if (Date.now() < root._bokeh_timeout) {\n", " setTimeout(run_inline_js, 100);\n", " } else if (!root._bokeh_failed_load) {\n", " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", " root._bokeh_failed_load = true;\n", " } else if (force !== true) {\n", " var cell = $(document.getElementById(\"9b18e597-ea23-43d5-a176-e412d58abdea\")).parents('.cell').data().cell;\n", " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", " }\n", "\n", " }\n", "\n", " if (root._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", "}(window));" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import os\n", "import numpy as np\n", "from sklearn.tree import ExtraTreeRegressor\n", "from sklearn import manifold\n", "import matplotlib.pyplot as plt\n", "from matplotlib.pyplot import imshow\n", "from matplotlib import animation\n", "from PIL import Image\n", "import pickle\n", "from scipy.linalg import norm\n", "\n", "import networkx as nx\n", "from scipy import spatial\n", "\n", "\n", "from bokeh.plotting import figure, output_file, show, ColumnDataSource\n", "from bokeh.models import HoverTool\n", "from bokeh.io import output_notebook\n", "output_notebook()\n", "\n", "%matplotlib inline\n", "\n", "plt.rcParams[\"figure.figsize\"] = (8,6)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import warnings\n", "warnings.filterwarnings('ignore')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "PATH = './img_align_celeba'" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "def load_image(filepath):\n", " ''' Loads an image at the path specified by the parameter filepath '''\n", " im = Image.open(filepath)\n", " return im" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "def show_image(im):\n", " ''' Displays an image'''\n", " fig1, ax1 = plt.subplots(1, 1)\n", " ax1.imshow(im, cmap='gray');\n", " return" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "#Loads image files from all sub-directories\n", "\n", "imgfiles = [os.path.join(root, name)\n", " for root, dirs, files in os.walk(PATH)\n", " for name in files\n", " if name.endswith((\".jpg\"))]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Dataset\n", "\n", "We are using [`CelebA Dataset`](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html) which is a large-scale face attributes dataset with more than 200K celebrity images, each with 40 attribute annotations. The images in this dataset cover large pose variations and background clutter.\n", "\n", "We randomly downsample it by a factor of 30 for computational reasons." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of images = 6753\n" ] } ], "source": [ "#N=int(len(imgfiles)/30)\n", "N=len(imgfiles)\n", "print(\"Number of images = {}\".format(N))\n", "test = imgfiles[0:N]" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'./img_align_celeba/110369.jpg'" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test[1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Loading the data" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "sample_path = imgfiles[0]\n", "sample_im = load_image(sample_path)\n", "sample_im = np.array(sample_im)\n", "img_shape = (sample_im.shape[0],sample_im.shape[1])" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "ims = np.zeros((N, sample_im.shape[1]*sample_im.shape[0]))\n", "for i, filepath in enumerate(test):\n", " im = load_image(filepath)\n", " im = np.array(im)\n", " im = im.mean(axis=2)\n", " im = np.asarray(im).ravel().astype(float)\n", " ims[i] = im" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Learning the Manifold\n", "\n", "We are using [Isomap](https://en.wikipedia.org/wiki/Isomap) for dimensionality reduction as we believe that the face image data lies on a structured manifold in a higher dimension and thus is embeddable in a much lower dimension without much loss of information.\n", "\n", "Further, Isomap is a graph based technique which aligns with our scope." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "#iso = manifold.Isomap(n_neighbors=2, n_components=3, max_iter=500, n_jobs=-1)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "#Z = iso.fit_transform(ims) #don't run, can load from pickle as in below cells" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "#saving the learnt embedding\n", "\n", "#with open('var6753_n2_d3.pkl', 'wb') as f: #model learnt with n_neighbors=2 and n_components=3\n", "# pickle.dump(Z,f)\n", "\n", "#with open('var6753_n2_d2.pkl', 'wb') as f: #model learnt with n_neighbors=2 and n_components=2\n", "# pickle.dump(Z,f)\n", "\n", "#with open('var6753_n4_d3.pkl', 'wb') as f: #model learnt with n_neighbors=4 and n_components=3\n", "# pickle.dump(Z,f)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "with open('var6753_n2_d2.pkl', 'rb') as f:\n", " Z = pickle.load(f)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", " <div class=\"bk-root\">\n", " <div class=\"bk-plotdiv\" id=\"9279ffcd-6497-4acd-9900-cf5addd62bf6\"></div>\n", " </div>\n", "<script type=\"text/javascript\">\n", " \n", " (function(root) {\n", " function now() {\n", " return new Date();\n", " }\n", " \n", " var force = false;\n", " \n", " if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n", " root._bokeh_onload_callbacks = [];\n", " root._bokeh_is_loading = undefined;\n", " }\n", " \n", " \n", " \n", " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", " root._bokeh_timeout = Date.now() + 0;\n", " root._bokeh_failed_load = false;\n", " }\n", " \n", " var NB_LOAD_WARNING = {'data': {'text/html':\n", " \"<div style='background-color: #fdd'>\\n\"+\n", " \"<p>\\n\"+\n", " \"BokehJS does not appear to have successfully loaded. 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0Cd0S7d8a22wBRf5J4q78pA1IoZ8fwV1UDJ0o6KX/u9QK8zIygFOblAQ36AqpqNxUCa+INmyDy4QFu8zYN+QsBAHSZBwk+hvMBbbkOq7YrFQH6yfopjytNAXOld6mbs4cCsr80elyrpwPiDiZf6WsNA10WqSlQdtUCIs5mZWLa5wJEpgv/TB7nARjkECHtEg0AZdqPJw9KlQCE45rqUMtJAPaTD1Si+ocDQ++cPmcLBQCC7es/8gqlAnZQclwzav0AnETgHbQPpwF9Nnb9yPtRApoIS7+521EDxNxRJkka3wMKK/FJv3sFAjz1pzeKnz0AI2tOBODPawBzm/OTc5MbA7YjBEBq6t0CaQydNnfrEQKAQHljAF8NAAlhZFDXd0kCvgDwwxQCUQDU8s30DtcjAuxuTVdPixcBr2gWL8lXOQB8PU6NlXN1A7ZpteHEtwMBejR1Y7Ua9QBo5ah9sVtvAFVui7WpaykCb9mW29TCwQKpVokOenLRAV/2v67GpnECrj/zhDP3YQMhqQiYnIc/ADhDwq6PBwEAGzJ24E5CHQAWLx662dNFAVFT1q/U6q0Cco8JDkSDIQOlj/je1D9VAKUgyOqucwcDZ94TSRwPKwDpn+0YAOLFAwhNNGf5/r0AucbtYqGu/wCCQakamNafADY9XP0PWxsAPFavU5lW2wJiw6gUNJbLAjmkd2XwWqcCfH4fFGF6zQGGmSmB/B8NAba6EyULHtsB4vz5rl5LQQBppBV7JWLPAES7AbCr3qsCbTL/3Ay3iwJboE04bE7PAX7133NmNv8DTUwZZu1jKQDIupb/HJbtAvd5W9fg6wkC0efvvZXTGwBCwDUzgfslA7bSVxfpcsMDUUEl0xLOrwL2pnSrrh8nAUNfCAqSfz8Acum5SUjuxQIZjOV2z9sHAk+p5khoxqcDEcBWh2zDOQGYJB+4UXcBALAo3G5m/yMCnAXuL7LrBQKdFh7Jl0djAh3koWO2uzMCUGP5V9gLBQHQ2/4hw0qfApXfphcIUtkBivPgWhCjfwFpo2lEZlrfAPkp0VSlW0ECzCG2iaEyiQLfTAq2OfqHAFCn7rjYJs8C9+cUt0mvRQEE8pi7FPs5A39ISfhMGskBQVvSLvp/MQFj0nzHsBb/AhNKvdfqLv0A+lBxeP0vOQIvt4GSS6dfAeRjSTDuGwsAvJcTRWEvTwAEkIio+YMrASeerj52n5MCEROiAK7rEQBEFcztP8dFAdOP2cTrNvUB8xvNFeqa5QDPQZ20DObJA2CXHzJPtwMCbDIzFnDDEQMPSSiHeSq9AkShizyg/kEBz98dNTOedQAQRyuazarfAi+w5WCbEu0Cmp7WoCoyWQEnLcAo/zLjAGTSE2V+oz8Bxkf9iTxGgQEScYb5p3cRADoDKVfOtwMDGwvtQD9LHQGdhYtay+sZA1JEWXZM94MAuuJGDIRSqQN3rKAZy/ZTAEpVDElFNr0C0PiP/+X/DQAtU/dzg7mTAIAPDBIcXw0CtIx6SccbCQBJcrL0GisBAU5n0/JTbusDYwKRh3Bu0wPvBa9noMLpAXXsNPEaPsUCcDtqbXpjiwK4a6MUY+MDAKCTGEjN63MDR3gWqRYbTwK6QsMlmacRA3awPIQcEyEBqWE1UidTbwFotK+3WDefA8z05k36Lu0BcI+1yGP3AwDVXSQWPw7DA+n8xYanU1sCmzdL2hgHBwOYw3btR1MdAEPCueDywwsC8fIH3YTnDQDfTyxI0VbzA6L9xyyI+xUAQJ6wmL+zTQAqZZQb/H8zAAuKfJ+B3ysCmFIlGVBzgwBwXxU+BDt9AivOXzsppvkB3HtAs/3+uQCuv5+nNotVA4iVqZVzezEA6KbDWXlO2QF+AOwWdccnA8HgDeZbpx0DPrRNVyMvWQFnF3hZnLdTAu+5XBgIagcBdfQ89ydWawDKZN0A0orDAgQYgR+Kzq0A0xE9EEjvLQG/EyUsqs+zADYQ467i34MBn/PhMQA/TQCzQDaq4Ys3Ao+BrGMFMvkAL4aCGfhuIwBOF+DgIe+XAAlJHCCPhoEBqgcoXJ/67wO/lDyFrbtRA4WjSMn8jtkD6uUyRCkbFwDBe1t9Vo+bASaJRorxkgMDR7Xs1k9WsQD/BFCarNdLAfjIrokrVwcAQ1Nep8x3HwF6rmEd91K1ArE2T9wfJYkBCCa0YftLaQNsSxXm6c8xAhWX07FNgvUBJjXnoHH6pQIDAF78KY+3AqaZu65ky1UBIi6la12zHQFbzPd/ziK5Aryn20Scp00Drr3bHTubKQBTin8MR38hA/DUgI/l42sCX9b7MNey9QFY7vGG6gtPAp0ymzzpgtsAu2Qw4TEzQQLp+KdcjitDAZFj4SLIPw0BlhBeTHpqkwJjjfWYDT5LAHwJ7bY5mxkANR3jQ4nnRQO99kFkSJ7lAL/NEENW9rcBvQa3HKY+pQC1bI3nSSbtAuO4EQpXYwEBcDnF+TcjFQLc2CxhivsfAeblOINLI1ED4CL7ped+wQPSrvNgCkrxAQ5EM/XJQ0UBeLzylPO2nQGfmy+kazdbAClop0ucdpEBJVMFQc7zXQBa3XPyctc3A1Qeihcro1UBIGg6813mQwLmegkJU7NTA+HPsJBocwsC8KMclMJXMQPPW1WQRbs/AQj2JFvZjxUAvelsj3m6pQHgxQlyqxNLAU9Rk5v1MxUA9G2iYvPjCQB6VQns4ScZAy5bK15RmwMBX4RoPFBG8wJp/9pUp97FAvRNuddNFtUAgT3v5TeG8QAGEIpINI7TAiXEnFKpryUBcEyPNTwq7QFMaoVtf78dAdYEkUTpY0MB9F2N3sIfRQGuXJ85Rq4RANsQjzhVEpkCArqb20ejTQDgSx2OBg7DAbi2LImduwcBtYXHRL8K4QFaHjRaDpNtARyQXrkskucDrwd8ltFLBwIXkUKfj68jA5o3+rCMrp8A4OnYhwijewKqK75PEFa1AXb0rkPin0kBU3kG3OkaowEU2r45L7btA7d+p+yilwUB7SIjWngDDwHjPqzbwtrJAIUA0sSMI2MCbY8A77xp2wCh66Ohb0dDADBlUewT/x0CKZ4/Kaz+uQGHfsaVhf9DAXRWNX1rvqMAXrSFheFaTwCZXVxCRvsvAbJniJqJP5MCvy+L/Dpu1QJ0sLKiyr8ZAbG9Cl42LwsDn8sk80hHWQCIJ5fIFUanAuORmlwL61ED4v1zMkueRwDyViArsu9FALa07a2Gbw0Dookc0ibe1QIk/Za+mkcdA85lec8j2wsAaviugGCy5QMtfEpXb6c7ASdsP672x2kDjC7d1oKdxQEHg63+fL47ASeQ2IAxol0Dvh4SHVBDawPBJRhCLAsNALn+lKjmF0MChIUrWYty5QDRJKbmzdbrAM5rn4BkPpEBJH39pWHfXwJZIwerWYs9ACDcVt982wsAgnoLeSBXAQOXVRaQ3V7TASYA/Q+5t1ED8pRdYBofFQGnfL0zIx7bAmD0nx+Uf1ECvW4iLLZXEwHyLjliDycRAOwkz/gCVqkAZ+CtK1hbXwLkmFgGomcHAEfcPkZFYmcAaij00kou4QHieaBNFM7PAM+YKqKYPzECuMMcW/QeyQBSIZ5at6MjAMJMBeyn6xkAjzSrVv1ewQAxI+HV8YeJAoDAUUv4u0MCS2wLZCQfIQAtLvVWY8MHABtqfbXTQ0cAFowVJSDzIwAAggKtDwrRAehnkPUrqyUASYVE05Jy/wBLwP2QFUaZAKkMXZ/6h1UBF0A29lSqoQJ/CmOh0VJ/AM14cf9JZs0BOAX6VSSDAQPZsK1/gXqVAHTkCOy4YxkDcsjmRUJ/SwORyMChFqKfAaIMqRHsG0EABmBMpgr3cwFBZFmgpxcRA6Oue4mkry0CqRCPxDNnbwCviFFEzKq/AdkOUsQ+7xsCoDymhncXMQAwQA6tGWMRAcYo+JfSPo8CTGBW8vq25wGKtqte93sjASyLJ7mivxEB/uxiuh8HRQA8PIOiYxclABWU17V8n6MBs8UQu9pW4QMFDINSNaq5AyVfxqx4DwECzsoDhWUKXwCfqMYinApdACPjM5+iPuUA8pY1nd0bEQKhQHe4XjaPA9OZ4c9+YxMCLwErjjM+RwCe179EdWcBAWrIcDl8p1MDXSUn9Y1POQNKfdUX8vrZAs/AIQgP+zkAIM21tJxm0wIOsi6g7q+DAef62dwnJ10AN+JJPhtLTQAROxOC/+LxAlnkSyJcGn8BF0oIl3RiCQMH7jqFp6crA33C+VDUGu0BDsyAp4bWwwGDOjKuMMMPAHcLjsX/2lsCHv3j2GCDTQEicmv9ESLvAWouEvLbwx0CcuYu/J5iAwBU8VoCi/sdAJGD27GlrrUAtN9p9FZTFQIvlSXM0jcpAMkJK/Kq/4MCJzwAKMf7bwBf6hkI5NMRApO8a7aWe1EDNjDCj9rDVQCNe9PAmwaxAolsUbzsgYEBcVN8aJjXdQNryvj3Q4b9A7UV/T/E00sDtwWpKzmnNQAxo4tQxrLnANtGjoKh5vUAII159o1fRwJ2Kcs86SJ9AKiSjBNwPxUDBra2c2pidwJ1+CXMTu7dAjPgvsmGktUBmh90w9NDEQNtDKPHqc3jAb9TCtwqAwcAyUvzZoP7EwDAOeduJKdBAkgDHGwS2wECb/oXm+FWcwF9R2kc44MxAJ8rjbt9uvECPAPmJ/CvYQOXH39jC46XASKoC7CbDwcC/sHVOJji6QOyKQ4+M0sFA8918gHSN0MCbEyu3y3CsQDEakYIeRt3A8iPDuFhTzEBskU6fY6G2wPLygWl5mdxAqrkFuvIgrsBXCk6GNvPBwE5z9Bfuh7XAMoWveFicy8As6A2TdBfMQB0sGCoyM9zAMz6VC0oG0sBSlV/oY8+2QHqqDYrLRaBAJ/F/T5A7mMAU1+G9Yvq8QGinX1MXO9HAl1t6SG4RwkD1pW1enJ3KQFtH9g4dd8XAsc2W9k/Fq0AWe4DkEJzMQDPhsCw5+sJAShw3sSLen8D4VMQc+jPMQIljHdC859HAjmKbwECbwEB/EPye2nLBQBOnPULUGN7AOV8o7a9+w0D0MuqDJh/QQMMqz+OizcFAbjZn0VGgz0A/Cjx501+SwCxlH1xBb9JAA3WDiFFWzkCXoDLYXFCZQA3G4ub1FNDAAa4dxTmg1ECklKOw32rAQI1CWZnHls9A7spT7/G8j0CxR2QS6ijhwD2TBOKXQJxAJ4dm2cQ5zUDtirC4U9ebwOdK0TIlgMnAU95QDpXf3MBTdz1dmQ68QKSSuHwyKcBAjrg6o96w0MDmDvi4jEi/QAGjAi2AwMHAKHAft4sRw0DuNoARA3uJwGVHWTQPyG5Aza9Mnb1EiMDsdgO7esezwHVhtLl9d8FAtrnHko7stUBLwc1Ve861wCYaw7MgOtDA9p80WbWlzEBGH/yvZeHEQCOODsgUPLbA61AXSOOHoECU/N/i182xQGMbKqZ/jLxAZhRtVsUTsEBYDQ6Me7emQB9G+jdH8r/AhLUP0tAmv8Abp6S3qhzIQFp7l5nYJdJAFYD/V+OC5cBN09+d77eswNE18FXTx3FAFvl9JMk10ED8a7FVktHRQLs3rnvr+dRAWfTVRT+u0MBsc0mY18LEwKVGV/bsEcZA+pER4fQrxEB8t/FpCCrNQPChWUIlS5DApGlu9v+fs0DDelhsdIS9wCdYaXw3O8BA5JR0+fdStcBdZWsEL4KRwC8l6f4cGc5AGrjMxso+scDQWjE6A16iwMCncnw6CLdATmOPTrP/xUD47x2r+yiqQEeLqdxRYb5ACHkqe0ysmECWjLpjrsbTQJ/USJbrh7rAPUkq4ZOZ1UBDlCzuKMHGwC6B2hDYxtHA7tyiOhyZxkBaU5OJsP7KQMubZ5Rs7rtACDoN9s/7oUCxPozZ14rCwHupVS0imLVA090cNjRpqMAs18xtQ+HNQCYA2DiPWbvAjMQcWoButkA9eCSjslG2wKIll0czYbvAu8ABB88a00DlDxJvL6rXwAVeheqcwrXAB+PFXowxqcCtYNa3eGjZQKBJYGQYNbVAg03GMkmn1MAy9RalJwe8QDQKyFdTMMPAbtytIAsehcBWTvFicEa8QJ39Dn33SNBA7SamHOx/w0CoGcymW72pQC4alY7rrLPAuJZZnCOP3MB1LWQMwUfbwI6acs7cuK9AaJR8haJXtMAXDjnWgkmnwKnmMQNuC9zABPrCUzZ5x0B8YeNJZ1S3QNWbFSkgitBAvVYOMfnc1MA9OlAZtdjLwEtjM8W9y5zAGsHueANzw8BjN2VZ6rmxQOuX2NMT16DAQkJTdxZyzkD47LN1by3CwGo63LBxnrxAntuiq0GwdECKv02dJezEQKWgcpCqnrlAen/Evffyw8AMh6n0++bJwISz9SuuSpdArOCOEfVJwsDoL57UqFOzQHlePbshPsNA5ucpPz15wsDYBjryWqfVQINyEfJLJtTA++JA3Fdfs0DcMcV1eqSqQAv2KcEn89FA/T2Dmm/SzkBSJx4b+PuuQFkvh+ych4VAXW+Hsm3XocAFtlDKrN3JQPf7I7Tl4cnA7l45+F6CcEBJGEN+7S1vwP2rhUZPQ5pAw6ak4U+pwkB5+ZO7LjrawGHB4F5XEbxAuUromgHBuMA0vhD+lTLAQDSun1ATordAU/+X0ySKsEAt+dCxbhXdwF9vgpZbgMZAfy9ETGampcAES4Ym8ojCwCajlaLvYrNARdC4kZkCzcAMJ89wMx3PwEWgGdqhZcTAZOcIm5bQysCXnW8CiNDDQEiQhGEXsbfACZpgDdbrr8BtHBVdJ1/JQHferUjUpbRAAs6AEL3I38DeYKx3OxG3wDi8bc2r5cLAfRcGEn+pwcCu3gstEFHNwL3jMKT1srhANsWozulNscCd7KVi2EPewH8x+HdCh9DA5RO5+DFgwcAinHjLHJzUQP8avPHuhZNAbTiE+1hu0cDUZg7DLNHWQLFC6fn6XsdAESUM0GLBx0BNSoiM7HnFQFhgIYp67NBAqcFnXf+J1sDhJ+JngMGowBJiPyKspKPAueX1Os5TtMD7NQSYN3amQHic3UBfd73AMZvPKAe0vEDZIQty5cumQFlCrvcuzcbA4HIiKLNnycCFRSVbUzfGwHZec01Ci+rAOJiEvMV2wkB9AAspze/KwAXB9qMPAMHAbJYVKK5NgMBcU5P/4M/FwEnBXM5+lcxAvSynSl635cCn8CTsKK61QKIwM3/gU6tAryu38o+T2MDuL99v2tLVwAHPYrdKSL7As6zU+U8DvsDa5nEbyALAwJVCJjqoV7/A7HiKFhLvusBiI4goeou6wJj3fCkizNXAromxz8bKyUDVAmuzPbjIwAi5A66sRs1AzJZqkN9B0kCW5JPUJVO6wMt666cY4K7Ag7jR8d/KskBkUSbPCxiqQPuN7fJR981AXGZQ1DDnyUDEkG6IL1bHQOwRj8sRTMPAS94F+shNxkA1pxZhqvHawDmMTMBMx97AqZLUAjkYuUCFuzAxE3rRwEMa2YwDyrdACZc8tOEhiUAr6sL1VbnAQFGpUtoSXbdAniad2FHmxkDujVgyjynGQA3wAoCEjWPAgZ+LwooZ1cCblEgWMD/DwLO7aJ/xpc1ArGaSRrz8YcDz131kLXrHQFOgpHffTtFA0Ayhe9bgtMAN90qS89unwBgpAFRqKK1AyL92IzyywsD/6+cIK4agQExmSZp5MsVA9vlqeY8PwMA3E4SBwsnGwEVP2HDLd7ZA+aN/ZutDr0B9sJZ82EzAwE2g/RLMKM9APH9C6KL+1ECLMwuEfl2iQGL2vZxf3qFAZyDPFrVDxEAx8pGgNJ3DwE3Bh9q6K79ABZ5uMWXLrMBTLmwNUZSYwCeQjD9qtLNA56gpkukzukAQ9eHrKtXKQG8w1PpBONpA5oBcfBVlz0C6FswmGlrcwPEHvdK0yKxAyPVwSRL/t0AVJHbZYgPCQMiVcWmCHKtAl0qEu4JPo0C0nGPakEawQDgLppDxINLA7MnBDx5QwkBPokyPwTKlQDmthUtLnsFAz6Sw5DOAw0C9VCp6sV6qwPrHyqKJr8HAboC3QhcLz0CowZBF7FPEwClon3lM1tNA+hoUMlOC0kC3XdnugqzPQFB2+dEa+LXAdSKkROJrtkCH5vvSUBWPQOqj/SRTdctA7Dge9mxKw0CH3cwuV9jAQOs0VhCXgMZA6RKvdjxPxUAyqu1wcXO6QDgDAChJ1sRAD4aa+usFc8DvKLxbiSbLQATQxEwTzrhAbLHPiTFp2sBTw49QmiDAwJoSak5rOMlASCAoo2UJ18AM3OmrplW6wKx3ibJgqaXA9mJrLUMCyECDtW3CaZvPwONjR2t5PLPAasYMbsoLw0CCgmazcPDIQMe7cYhsHNBAYeBoWNq9w0BUJFVxbg3gwFqdDh3nnsFA1zNzh435wcCjf/OySRujQFmWwat6e65Ah9W5cfEeyMBYd5WofZq3QHuPHYlxzMDA13SeouZIzEBXD0dUvmepQMqUE1RMesVAq+PViQEYrcBVallLpoeuQIYa3NUQjKfAFFS/EKNh4cCe0HGJG1K5QPj+IMT/EZzA0q09Z1F9s0AcIeag61+6wJ9agAEvZMnAMuKCbvNosUBax75D7/O0wNzNQ/2kzsdA70ugQBH9u0BX2TKh4VyEQL8QCtaQqabAuiybu9HPucB87m9umgG1QN8KPmChiMZA24lg2II9z0DFRkghL1ytwPPHZk9XMKrAH9Hfn1LznkD51KFurCKzQE4Z3FSvE7NAdKoRQWovyMBCc9aCa4OgQKNQNhJdVrjAInfsDkm54MBkmcvOfg7gwHjC6JnQyeLAXCJFsk8stECtVThDYSnGQEva1rKcS7jAoaJhKS4/wkBVcYDI4B7TQPeZGN65asLANxKr7scoysAa+ah3VzurQAMPvATb09NAR+BcP1xqs0BFE6AgR5iyQD8RXutj3knA3nHHf0v25cBoU7eUL3DGQLGIAyZx285As4u10m58ucBRtULX/q5kwDxm/FopmpPA20Y5OS8oxkAFRYCIwffDwACqrTiEj45Ahlxqog0Z0sCl93hdW9CbQMyWJTnflL/Aop/nVdrew0CX3zyRG5+IwJBUb8UZFdVAnj0x9x/PysDLbsdx5kOzQP3V0vtMuNFAy0lnWFrUskBvnSUjhE6iQDMIY2EItLzAIc2KAV+M1cCf73PXUSOpwDixpTi/S8fANNX4lK9o1EBoAIFLvKDCwP4wD0ke7KRAZZDM7rmzwMA0Iw/eVWPVQEOhFMirsOHAd56DJmt0vkBQ4Zf/ApPGQLksnbln8rBAOJtuxLskh0B/o426fp23QKt82ukea+TAS4qeZ3VmtUAkDTUBmCbZwDlUpM47FcRA/LxbI4mfkcD/0aQ06MzPwMiRDr4ExNzADTSRuQZV3sDg67vZhF22QFuQT4r2o8ZA8d2Vrr95rUAH6dZqBR2wwO5xuVcAcLFA7H4CK+Flw0Bl/xV3kcjLQHpqBDj8g8VA54inkSpLv0CykMPXutfPQBc/GWwos8pAbRuCkBTNvkDyCK710mLJQJ9vKH3RAKpAiLxtXhWt0UA+Q842cgzjwLaQLCNvctDALt6pYdl9zkDPNZEpCgirQFn+8VwVxNVAejWRQECY00Adavlp/DtvwJjh+sOc9qXAm6HQq1xj2UCi6m05k9jDQMNBZQdTwNfAfauiu9yItsD2zImwfjrQwFyuqcJKPLLAbuYG1o0cmUBfYTrrYAK4wKLOiQkU3c3A9e/HoYRTzUAl4DaVve+5QLsFWcL0AqBAK+Ub2oVqwkDdo/BuBEvCwIXW9pY7nrFAfVitiDaEwsBM6akKkKy2wIAcs28rlcHAenRrcyV8t8CvgNRGWU28QBiU4kMA7bxAhcZkarhws0DzxhPsZVHBwEUiT0hFfbRAkDmorBnxvsA7avvAzcbOwAppC9+LmsBANCARqZlAn8Cd1xnQfdXWwFi/xlpiuYNAi2DIeb5ZyUD70ymAT323wP0++O4v1cfA64gPgiuf0kC//817hcixQFCYyzUUi8HAHbZtK/bIukBNSyXYmxTNQHoiPAltoarAhX0T2rRz1UAT9levBR7ZwA7uDVfQtq/AvuvYKFHkuEDpqFZiroHRQPCiQdC8NJlAlg68cnaBwEC5cbbV06HIQF6PS4Cnt8LAy2CiJe/DvUB5Ip0yORzBQOKUgQXfJ8DAQ8o37BHMv0D/w/SvVQ26wBCiMWY4g7rAbdbujCt6x0BWZ+//t2G2wC0njSW/a85AO/T1LEUTwMC/T/4aJR3dQINVUBC4J9vAWUKsQpDhcEDO3aXyDzXNQFBT5SdjGbbALtzJsN3VvMC/DDBEu+mzQN8JimTj3ZnAHgAEwhs6wUBme+IQAFTFwJAhVxK8LtJAR6E+mvb3tkCCj/L53dmrQBjVB8ziCsdAwhSlEAAO0MCu98XTOv67wDuEdTyrnsBAL+TySbdG0UC5ZuVUYDimwOJT5EYIlcdAx6+o+3uA00CFeyO7+9vGQOa62OSiLsHAzBB2un9z20Cc0YCkE56sQIXGvoJ41NzAjBmGatpl1UA04y29IKSgQEWbAJO2Es5A6SIb3weMwcB6IS2kJfnlwI59wZ0VHZFAL+JP78jTuEBaflbScKqwwL+1HYrwfaxAd0ViKY1VscD/0kvNLnSvwEyK7xx6Pd3AFe+EYECc0EBXwwF+qXzSwMfLPFN04aNA17tlim/w2sBqfzsXnrbGQDkiWZEIAOzAMZdOKPaL4UBqtECxT1qyQMCk5XQEp8LAuNCgQ8YvdkBIzhBHEeCtQPR9UxLgypZAhu2QHPhyyMDA6Epr6GjHQLelkleRob/A7XUxKtwzvEANE/X8Q2jJQI2yWBG1ndRAuvYsXIMBqEAfy9Bd0qnUQN05zDH0wbbAJ3UtS8WrtcD6vNsB1j3PQIrMdVR6GdBAi7UA+l6bvkBjjxmnNhi4wM097X1JoclAaQ1Uur6Zq0Dhp1vi0V+6wGIHk+XH8apAX10lQpsF1MCoRiOwf6vZQFcrovbAeLhAJoyCkRbH0sBWbuqBqQfpwLkDs6rjx6LA9bEMTLRut0D+9fjZfxrVwNDXWsACBtFAJOPkm6Wl1cAYz75q+8vbwK2bwC9JL7XABBnEpDEOzECJGBsSsJjbQBEQ72ifSqVA1LtE1rmmo0B8I3+ke77CwBILYgSAjsVAzrP3kmpFwsD9aIOh8P7VwKH8bTxf9sRA4Tvi2r3zvMCZO06bN3K5QAfzbpFlCqxAzwsq+FOIoUDxf9pxYkC7wMfkWQkWMtHAuRvy9SEAnEBVAI+gLinAQOxjh444UsFAmZPIJ2IurcAMZTWg7inLQDv5HM+j+bdAsFk7ZWoxw8BkKHliW9LFwBVCpbObIblAt4r9ru2G4MBgkgePFCy2QIivzmRRTt7A6dUrMejgzUBoPAkMssvUwN2PoW/J1atAUnQrnMU8wcC8084JsEzAwDnhdIeexMRAz6S4jBJ3wkCz+jOXkKuTwMYFaAN8n7BAI+OhMoGcxUBzzAtcYdHLQJIJZY7nM6hAXOJ3g8zB3UARLru8XizIQCtShcZhMa/ACi0rxK3B0cD+adYkVSm6QMYSJEfwqsxAjDe3pG/Qs0C37SZ0plTqwGLG+0uDWMLANgunAVmRr0AHoTSU2gGmQIa/LDfQMbNALl8gYN4W1ECvraGU8NTbwBPfhUrnH6RA0RoUYkcmr0BRxm13HIGewMN3/P+c6N9AH+yIqb3aycDvQg+0Sa6WwK7VBIdVUNXAgkd6N26HoUA6IFcJhALHwDdE2byE9b/AVscmr46GsUCI1EnaYGnVwIpaA8FYMtRAj3y6l6G00kCykHet8ASuwADFlHyR8d1A9Jhww+xU6MB7Jru1uf2ZQM98pTq9oL/AKEk6XBBjxUCYzMYvYUOFQBdUUwxZFtHAemBYTqFJx0C6aQk9yA/EQCYYPkA8JLjAQKO8oRf/1MA2P3XnRFHAwEko+IpSnpZA9ocMHBvUvsCj/TCSQ8qGQBinjFQZLMTAHUaOaW8s0MClpMtcg9TZQP7tbEsELnvA1vsFJdx6x0BFTeuSapzSwFIbaOsrgr7ACuuXho3Us0AMRIbjNqLBwPAGxGY0erVABYjJAcPsxMCOHG/x9BShQOcB1VXyJ7VAd+Fz1ioOxUDmOXpCdamkQOhxs7+HXsbAI+If1QidzEB2d/uU6TnBwPlQ6Bi9KrjAY5bxVVASxcCuVjMw8MDawHCSpUCDbKxAFoFfAzuY1MDrNwdjiNqxwMvSt9rdPdNARhCsX5YfsMCz78IDKAnQQFHfZYq68MHAlae0mv0rtMB/id41CSLBQFwuCyO6+bNAurcMWSN33cAH4V83BTeiwOTbyqOrFcLAQhkpcjl2wcCgXrBbdnSewC0TiUgvgLBARHdRKTYw1EA8Dgsm6ny6QJkgm0DcV5/Aasuy5GOO2sA12WTdlLLZwEo8PFWwO8bAfI4WZnhgwMAGkli4pBrZwBDr7TicE8LAzcogjTyOxkA1aVuqVO/CQFh6Vwm46LJADW+CMVFrvMDAUX4naHuzQFbaobkfjLpA+do7zX0XpkBwEfKS5nWwQJF+GK1C76NAQq2DllNdn8A7AZH0cf3QQBz7puQXSsjAaOubrKqf20CBd9pzBYPRQNeKSnQjEqHAIXgjJald3sCTTXt/bvPPwN/L8vFQqcRAW04pGET8p0DRcWivj4W4QFoP3tUSr6XAB3hQ3BKLk8A56VwS2pTAwCgaUsM5p7BAUWJOYCecxsBBpGFK7SzHQBKa8wa828LABgED6/d+yUAg7i03zKnAQHDf+o6xCM1AXfiwMY51ucAx8T3vXWqzQEm3IeVFcMDAGQw5HjftqMDnA5GZqpasQACaeHD5ctNAVOLEXuJUuMCDxBa3rmHKQDjSyRKpG71AmSWao9q01cCRIczdO0i0wAJp0UaecLlApLWIt1WO4sC5O7gkiNyrwPLiiTQe4MdAT9pGnSo3xcBI/EPo7J7BwFLlzCD7GOjAjhswjmQOwkD9Q6kWx3/AQJlfjouxhctAHm2yk1EIxUBwFmS9k+vDQMXVIYhz/LzAWn0Vl28/wkDo04S3QF/ZwDUe5VrWfbpAFcdoiMpGxUC8dHEW0/zJQMRwL5DqsMZA/eVKzo6M1kDRitE8iwnbQKrImYgIgaZAexCFiBoc3sBpHdJySmijwN32Belz7LpAJX35S+6FxkD7c5wbzJTCwAhXLWKS5shAQYxB1zh7vsDt/30NLurAQGEi9SD44MlAA78tezqecMB/Aw7SfIKuwN94sNfrWqnA/oAMtdeEwUAHc0GZaKqqQLnOVGGUSYbA5EQPrGNPvcDScupYgyKiwFlKRGWAQsJAY9Wrahu6ssAjaSCXYKfCwB3cn/WS6JRAswVcCJ4+hMA6FqK3hMGwwNfJaNeiW7hAUCOVK4B4wcBcPJg64O/QwMS5nyjrJItAPSoYp9xI0EB+s+YgYmXIQCspXpLX8LHA2vV1J9k23sBdjga2i0/ZwCeJIbrXsqrAJ2g0migWdkArXIDkLDrOQBlmV/jkOdNA6118hpY5q0C4cXrsA/mxQLpnMdOd/ebAFHI/k/4buECbZgdwAXGuQGN5iAmOp8BAchSGKfX/ncAz/mhe8PjXwLbzoEj1jc7AZR4AT9OhzEB7F6fXAv/gwESAJGlU56rAHyC654EzxcB8ce0xXDS2wOloYrGCZHbAWMayv3U4xEApa64NrJXGwIBBFLfY4M1A5vWpmEybxcAnQ1eiVjm1QMZ9ug8GcVbAsZWFl1pbQEB4dYKZsdTPQKmKzg0qJNNA9Y9zWE85hkAQIbSs4wjEwAsj4xtPY6lApxXEeQ0Kx0BXcv72A4q5QO//OTTE8NFAFLWP+Hgn2MCKiCtabzWvQIvsK6mgtsRAO/kKEwL7w0BHbGxFtnDFwO/59IU68s/AyzWGK6uL1sBCwaXgR8/XQPSaXob2z6XAi73RsvVWwkABcuf156qwQMDhac4tz7vAM4kUkE4HyUCtjHE8QEisQMosNhV4mMtAV3t4o17Jm0AqmMARsl7SwHmKV6ogh4/Ag9bVnGV+l0BBK+kcDfvZQIenimiCc6ZAvdciwr1BpsAQ4MLhQpHJQF6mjxAIJ7lAqRHxdMe5ykCGtI6C6jC4QOXrYjicT33AmY8ufMsk00BizZFwo7q/wPr/dCODmcPAGXBEsYzy00CE9eezo4DVQLwLCPnwlqrAVziJHBTG0MCapIpbr2jCQMHOThNsjtFAEdyj2z+ZuMDBkmnpZdmmQIof1i9UhaNAtnMeP9Xbu8C4e2vxx3bDQHxctyBRsuTAjp3w5GtT20C1RqlMjhWzQKWM/EQMRMXAKa3570cVzUC+4EGchfJgwM/VUEE3MMvACsi/b1yhtMAUZsUQEUi9wHyeO/h5RMRArc5G3Y23tED5gtpVIAXQQAA8avh2LdbA/eK+oZwn5cC+zymp/8fOwNudKh5e28JAF3nD+KAVtkCwe5/45mO7QAMnVdFpFb1AIXOj2uuhk0BvK0BbuhKUwGSRKcqavItABC8Jl0gyqMCp2ZsysOa0QP4TKIHXi7/Afx4sKZvH2EBjkDH0jxW/QJjuLgzi5bpAIbCsdhDC2kB7BlqlLlLHQDKd5sAGXdXA8l1UC49es0C4m+d2IpnEQFBEzahdTrZASQma8XQGxEB9stkENjPRQACMjSvR8pZAi2KgStfOpEDeh+ct4YyzQBOjvyNdoeHA1tYHWNrO48DOBHRQVFLAwH1Y+2+EqZ7AWkmh6fHnz0DZOvU17J3GQN+5xKzK3pjAH0NJbkStj0A47nQUEx65QN/mvatQjKJAnBi7hmXDxsATESHQpqDGQAo/iG83ldhALQpnJJK7xkBvPdXtXAq/QNidbQ+kOrRAPOhPKRS+1cB5Cf0aD8OnQGreno9qBsnAJF3aDRkNn0DL8TKeQb6bwAZ4ck3AzbzABKmUlVX4ykCCBgtsG8nHQGspf3UkYZxA1Sf0wePk4MC05Js606uvQGbIhKF2rbnACYldTafaxkAZhG6pABjaQJPccIxEx9xArBnU86LAysCTZ7BnvsvBQIa9qnHqRsHA43cDOod5yMC4mwtlJuGvwFRNut1bwuDA0XtETeCC3kDxVsz7NufFQA2re/hXt8HA/MF3yEcruUDHJJw/CC/KwH3dyASqNc5AI8fNxX94s0AM7p9lfvzIwPcLuigcDurAt+tbBRS/zkCUXnKSVUS1QNlXnUk2EdNA0M1m4IND4MBOh4Gl7oilwDh8D1hHl9BArm3rNB842sCamh0luyu6wOeNbmzgh8HARRlv9h3bucA1y6lKCR/bwO2munZgoKJAHaHLJHj6okD+ygFhHPm8wDuZWRx/H7pAmJyHkctV1MBYzXdVMB7RwEgvPAjpqcfA/ZSWnW5oukButh7a1kXAwOJsEwdEtcbALUm1+51oqkAAc6W7v6bSQFTCf4Bq0dpAPJbeB1VpskAeaszhmXqjQIOuS9l2VNrA7thWC70GwEAwJC7CiizGQCMdchLFnbfAeRK4mYLPskASznXvkUXkwFe15fWHvc3ALfTsdTZImsDZBOYzykPiwF+bc0OMzaDAuiuvfVvq0MAoxNiGXxTDQOWIzGxv0NhAnM4uIDN/5sBRlV+9N6u7wGoxQTqsBb5A/+xuImq4tsBeW5DXzQfOwNOwFHNlmZzA9uhg4xW61cDLMDwOLJZSQJvbk1Ix5NvA8ABRb0DqwEAhOdO3AhnDwOpGbl7trtZA2xPFtRvCrsA/rmD9MuW+QKqDoyu92uTA0bjpY/ndx0BX1FLnATLGQGRQEl6JfMJA7FBglJRFykBi/qTqFx+9QB9EK1mwYqbAqM/LOefb4cCAIFhl6V+8QJDmG9NATr/Av5fcFNafz0BXmINHnoHHQA0HUJz6b7JAjVCCIQzQucDlwHwbMwnFQHmayV5VlshA+MK+G9PmwsBnDGH6Wn7jwBxqUqUELdXAV3X0S7QdyEANRZ+AIUarQMngmrTzU6/ARwWbeW023sDXbHpzr/zHwNtlNw4XzNhANwmYCNKlxECbHD+W5VaZQELU3vF4k97AdIDJtS+Az0BUpnp2QMjEQER4APQEDubA7yDpG593hcDgb0/mvGe1QM40ZuAlp9ZAPCmee7dqpkD4GqEIBpvAQEVJyzt/TrDAapN+xeTetsAUuJUhzsrTQCj89LbHnWHAfhifqmuBskBAsGN5fUbLwAtL94miMKBAc4bqb6JJycAcVoky+5nQwK62tyTV+MdAte+MCaWWvEBvud1JPcGlwAZJm1mGUZXAC71lvqeV0MDQ1BhnW9q2QL1VLQCwD8lAo0tlJ4OopUAY1VWtaHaPQJf/1Mpe9shATHyEQoVPzkBkbAqbl6TXQD7SAzGijdNAH4u1f9WxrcCs0UFc1Oq6wIlAzvHic89A4mf49mjv2EAmMcm5s4OgQH3++ycxUrdAa6qeaF56w8CorEeFOZnEQNDpvDKZ+MRAkteRPvjcs0ARmvgSv5mrwM2f4P+Az8RAhgu3gm6i6MASGTOhsbSvwAzTcAPoystAgm06s5Shv0D/+yYpNkrMwHSA9WBjnozAofiF4AYrqkCRWzMaIdq2QE9HZ53Cd6JAuKo7pHWMtsDVqfn0R8HLQBzHb7L/StbAEygSk3LXxkB8oYoaZkqzQMPLPZx6x6vAUSUK8ICmr8A2ADPLO8rHQM+/I/6xGL9AMar972e+0cCrIyY6ujSwQFh9TIwxh9FAXy5OapeLmMDV4e6KHNLGQDIQiosMoczADQbYPgw6osBoA10Ta7yxQJPqofMKMqLAmnjVJ71pw0BSYw1PhlKrwKsQV2DTgdVAkOWUzvoLyMAUi/nbN+etwCylb4aRs4LA+b1++aNrq0DvjM7QDIvIQBWyDFGSw8XAwfpMCPF1wkC9e6WtssvMQHF1X4R7jcVAlJNcdRx/w0Cr4apqgN+4QIH7DYO/9NVA6BR5tA/11MCpcbY3MyKYwIMJcaSjVclAULLR3nKKx0AsXNNd5pC6QKHWEWfmJb9AWanmvBb8yUD/OyuIOeipQKy2PTfiWZlACtUgddENm8A8Mt7sOqrDQF44C9yKVtTA7sFUSbcB3sBBCnhqMwWtwF4nc7E/lbpAvSv40Q+6kMAGrr3rrIrRwB6+i4evgL7AUHykq0N24sAHVPj1LXLVwCcuN6CLGrBA5FWcYOXqwsAuOV5aXK2UwIfHVkdLMNpAgbTPa82KtUD6qdi6gXS/wL+Zd19VKcJArowaRlX3wkBiFfJkF26OQP23SJ4NvcrAC68q2LYlyMD1cdOQuUDFQClLEhLfF+PA3C8q8MhCpUDAtOiG77F0wJHh+bN4duHA20v7nrL7kcAhtx5vVvqZwKzvQQlUn7zAgxXGi9lKdcCoE1MrILK3QL3WcsUqF9JAund1f4U/0UDdjRzAZZvHQF1j8EzaS8dAZJOonq68rUCtN78i4gHOQGopP+DSzdTABiGqkOkfxUDLnhxILjHEQBGWQBfDi8DApLxjVbIlx0CiHfzwIre+wDpf2vd6Z7DAEAqgYENMucC6VylcSpbrwP5YRCpL8K1ARXnFqyOrvsBdolObjMGJQKB0ZYDGFM5ArGmLM48Y6MDqHjtYEBnLQI0ecPGQ87RAlordXY6rxUAEgaRNFWS+wGNlYqr3mcPAjbM/2UN0u8AnJ66a5pGzQJ12yG5QJrBA5ytBaF4ar8DKKC1x4XmiQN/0ZRGTVNbAnEQHbaB4jMDtDHv+R/6mwAJxURMbU8fAXpnLGxVKskDUMrNqO8zQQDDj793pbL/AZh/yEIIfkEDaf00ptk/IwH8nT49YTchA0Qrj4jxMxMDORPvYSwfSwKiZylaGALFAAwBCwYsAzECdthvNWM7HQCrHE3Z5odNAawnIz+L03MD+dedtBeSYwNyU0YG+VI/AY7iY9iHmo0DPEyFlctnJwKHOr8BI4YjAmk4pRdn0yMBFZJIr8hK1QF4z2wmEO8JA9pLUZiF/v8DTZdgx0CK1QC8mfdiM8cBAS//+aAbBy0AIPlk0x5XKQLoboSjZe7pAcHMpU8pvoEASpoooEJq8wCY4e15qgLVAX9T3ox8Fx8DeiEyvA77DwL/C4MOM3MLAhAMq10VvycAEERqyQJjlwE+OjBIreatAaNMJ77K3s0CmZfo1wX3hwH4gZFpoCq9AK/z2eYIX1EDQ9+qYPYu8QKQVEQKp485ADVwAV4x04cCBRwkhciLUQDDEsxkSibfAL9HTXUEAzEDFnL8hboHPwCjh11bLU7lAJRQkJ3bNzsBut4znnODCQIpcp0QritTApd3LWKNts0D9LE3OncmcwPKHXy89YLbAm04tIry3t0BA82/fX8KgQE33hdHTeazAIQiD6ZEhoECrJ9q3/W3NQI/64VpOc6tAG9XflN0tvUCLuzn2Gr+uQK1cLHbMoLPAK09csXTKskAn6HveFjGwQB8afq1iO7dAr8DTqdjd0kAR+0g/Cpe1QBiVFb0q/rlAtB3Vw04Bw8Al3DGWHLzVQOZsQxIETLVAXixNxu6O1MACIOAYeQfVQLcaxmA2ssZA2tVXQQSaukA/oXjYT9zCQMKWR2QpaNRAzA3yL+eftkD/baQEvjO2wI7W0XZ5BtPASmgmLA2ex8B876pHyazWQLJhIlAL8LhAUgd58F1sz8BbFuXb2D3awDErAZfnMpZACQpYzow3xUBWkJOZPr6xQFLCPPe6WeBAPH8ugZXhp8BZGIFgsz3DwLhU+KHHqMJAL9Go6FVEwEDZ/82vRDjdQHbV8jk9KdNAFhNJ1cZY0EAesMQfqSvJQP56KcpiQ8PAW5T4JkaAysDHI+oUiWjAQAZ4xfucAMBAwtq29jG+ukCkp0tNmmPUQPNpPwtPzcVAFN5k3BEZwsCKiVUbmPm8QHHrVsDD47ZApl8N8UR8vsC/nJrgGZqiwJGAl3Q9MtBA4iMdHe5gwcB1uf0EVBi1QFk5DEZcoMdAsfzOBcKtvEBU+bXMDry3QLiy7GQc3ZLA/nITb6KFq0BNtsY3ADDGwMKQFEL4kMZAqhUVu1b33cA1BRTWaGjHQGUqyRASYN5Ah+z8gmPdzUAJ6txXIlzRQPuhEOChu8tAJKuXijeSyUBpAu1Mtq/BQEtDXHz1McTAM1dneei908DJHHkY9kTDQE2WR5KtPbjAfTjS4gEEwsBagHRPl0S+QAIZKwKSCcXAlQ4KLG/Yw0A4UWKAqDWywFOnpmfL88/AjSa2d15QyEBb2G4FLejIwIe+4AptvspAEwLeTSm50kCaWKhFZFjTQB5s3WUjSqtAL8eYa+L8sUBUbuFiOrPVQJsKH9A7EcJAC8pMi19EwECxncECNkrSQOx6uoAW4MtAboPDqyxLzcC/pnN2gbScwMQSi4oI3eHA6I4/T3iVu8DSCKsSGULLQLIDTGqjGNZABmWNEIPEyEC+Kh1qZhy7QLoYM950l8tAdGn5t7/2xkDuJ9vwsF+SwOKT1koy6tHA345SEj3Gj0C1uEr78DOxwA/w1hznwMLAGfkZgkcoyEDfd5M+lpHTQDQJXcBGx6xAB9grv8cMxcBWhFRlGpGfwMjnbB4J27dACGU5rjRuu0CEs4ZGBRPOQJh3824RmrBARv2CG0IAxUCEh8iX7mfSQK2o6Or7XMnAy1U8lDP0sUDyBaF4AsTDQKD0GRIVZsJA+GePSjqq48CO2+IxttiQQNeAl9GY/qtA3Cd5YlzI0EB84zTXB8HHQFgqZzYYD7XAHazxRoSYnsDk+R7sfkvTwEVfU/OxlLvAnCnKy3xKlMBLRwcwh3DVQIL6uW6ck8tAt9U5VJyRtkBvB9UVB9ipQC4Eq0Ef641A+hTF3UNf0kAWE/hRdveVwI6D4rhdXrFARcNVKu8+ycBU1A+m2DJ/wA4uFGUBEcLALCs284q5xMD98F6HbDHdwNwwuAqT1LdAcePxDSIYysD6PlPugBfKQDHO7gYE7rlAEi+9v958v0ALj+dlgELUQOzrQqxLmcdAHRI+cW63ykBP/JHi+6bIQILP92hni9HAfyD+wh2xxUABNc9Co72/QJiiIfDTmM7AMTA36O7pxEBsxNTQMizbwAIGYFeJI9dAExxKlqbZrkDoa5osCzHAQLhCCg+niODA5rbWdASvm8CnEZ6ygwq/QMDOcW6kKZPAnc62sriE00Bs1Gp3hX7EQLEBHxnT6rDAaUiAmEu0n8BgWc7DlyzBwP7ww7pqbbdAYsH4vFsGasCACvBZmgmqwMGDuesVF6tApqKnWPRbsMBUccYCiCbNQJrGUhYSFuPA4w47WdPGpkBFecOgdHjPQDr2olOD4qPAjy+8+/4B0ECx4xLHqmaTwLip/l1t7MpAOO5sdGg5w8BTMwzdbSzMQJqqCR9ba6VA8MS1cLsbxEAycZuCXM/iwHqkp8trlcBAnzhZ7Tpmw8C2AQ48K8bMQL9YDuqvhLBAKrzcM8l4w8BIuqHPVorAQLBGl78b6MvAjAF5ZpdFxcDozMdZwn7DQApxnQuCCajAyq7xl4tqrkDnQEB4scDTQEQ/pn/rGctAvuGmVE1Ns8BTdEDdZWzDQIDyjFSwDc/AU2D1c56xxUAruyjowTbFwFW9XhT2csZAQAe9utUDxUA75y+/T5beQOfYl+lj5c1AYVHeY8GGmsCjuAmwBoPJQIfx2mePushAn0QiBrgYpMBgfzS0HPTRQI+jusqAVlzAL1x+nh1yu0CjwudKJq2gQAqR9uHHOaZA4Oux08qpzcDs2EUF5LaDQCVF31NMXslAfNvEb6y6psDD62GHd+/HQG7oCgjsjLBAGhdlVsp7z8CVvlA3MlfCwG5A8I2bZonAjzKzCFoz4sD2ZsrlcGXjwIOj8zPdO7LAJyz/2dPy4sBPRQJglwGqQBtl9oTdv8VAhOW6tewa0cDXSceMYoCywHCPD90N1+LA28/BE/Stz0DuneMKZ824wGjrb88vEs1AplATgon0xkBy5GdIKm26QFLzb3NhBcpAFi/Xq4FO1UB6SkHv6QLVwBC+gsV0qbPAy/JfZjpvwkAvmXF2LiSfQB19I8v8bHLAm+Try97Oz0AikPzHuqTRwCxdlxgur6XAn8yMQR9pwkAEBVx6QXa0wNfiS44OzK5A5J6VApOS48Cp/7cvUhywwDcUBlOZv9NAmg0OjPrzlcDzrQrsWzjEQCX9k4R3cL7A12dZOa5ywEAMJy0A4k3UwMD+SW4BxcLAyZILctfJukA7Vyl6grmMQPILGWkUK9ZAIWc7OFUT3sAv85VNIYbCwHWqyq5/xuPAZlCCWUWImkAxf4elcuW7QMX3Tp7ekLDAqAypk30K2sBoU56M5PjCQKybZFSywcFA6Wk7kVnE7sBOdh5CDlCjwFBUQNBintJAJQnQvu3AwUApR6kmhH2dwP8D459zYXJASb1QZfyx1kCTJxEFqpXIwNJZmY9LAsPA8yi4ZioOqMCiZ94cxobCQF+UDB1NrsNAniwA+sfqiUAiT5lHl3TCwB7Vs9Q4mK9AWRqhP6PQy8Am8OAIvsB6QHFlINtzb7/AR0QCxsn4wcDSPfmOSfiwwCuhVbYcUMZAfk5SfQHN0UCL74asTiOXQKgC9nD7NrvAytLLO/Fpr0BNqR6PoAKmwKS5vKrLn9fA9qViNHhV0UCwQbsQf97HwI37DQCfosFA/zyoATg/uEBOeSnwh5/FwEGg2Z5zwcVAkFTdaK8assAuEpC/YyS5QKmihmer5a5AdUDy9zm1nUDaXVWy55aTQOEzgVbnepVAi0eYduQ0z8A8QjUhEpW+QLaIiifWnqpAJ6AHIzpywsDkOQApB0vMwMdicvjXBJ1AE1mv5Gh8w0BsVsc9NSC5QO080lKxjsvAltMNgBna38A1xrt4qMbSQMkOE3qAm8tAEOUg5R4KzsCPznId4gmyQFkJue2IEblAMexl1EUCzUB1xX0ueFjjwARm8p6bncPAL0eKALPgkkDSY7ZSdU/YwCbi8GaU57NA//i1DUDttkArmA1/KWzcQH2UvedIsKBAOU+b/A3FxUDlM6AS0DfEwHqLI4PbCs9A8JQRY3ka3kCAGl7NTnnHQK+lcv9VrbJAtFT0NJSItUDe+9exIDfewPsD/Mm3G8VAKx6nneTonkDOFr9lVYXRQHsKjkB3UKZAFKmpRe1KrkA72K8+Hg+yQJ+BSwsZpMdAzGxkOj3CuUDivNzRi6KvQOkiLLy2rbxAZCeZX+ugwcBrqT9/N4/BQIM7lju+3KBA9BlaqHTRxMC/JXkrJqrVQBu0NNoQMMJAWizf/k8d3cDvP4+iYNOowOK+d9eApWjAoVaWueZB0MBFV4atq3vQQAunOKgNS8PAbrTl1Sh6yEDsxjYoaQCDQGUG2rFpt8hABhs2QZQ7qkD6NzRlNcjPwLVk83rzQ8zARKmYtGOG3sCSTExuSUnFQGfVBhZhCJ9AAAEThQh9dUDoKsH5ZADDQIhbiyw4EaDAH2Y7STqUo0DP4+pOtArfQGMohEXYBLbAOnFmXZGsv0DkGMElSt+wQCHodrixHc9A3ydqxdlk3sBJY1JyDPzXQPX8dBePsJ3A7bBIRGMK2EAed8mJ60DgwGsPpsdfmM/AVpaDm42JnsBzQzupVA3BwPeEe/NGgsJANJa3Ru/ezMAbar94kbjXwLaLvP65J8vA3MwEESjvx0Bryi/PlMKkwNzIQo4sBaXADCdCrPUavUBVwWLcMRCTwN5FfuJLFrvAaaP0QGXT0kBWFCQPfdSuQFEnnnbqZJfA++mjaU1px0A+U9+DQ3y7QFew1pqV4KrATB/REuDPp0CzEkhrzuzLQDC1tHpUrKPAzYHz0+Lwz0BM7uxoAuVoQKfOLSU0HspABylHp1fAuEAh4EeqdhHLQMofgv4g7IlAjesKjuM22EAdXGqTYK7JQFnYfJBMzqxAuT7MOkn4wkBE52MeonSYQGYZm+/2v3ZARKnaDj4y10B4ssoWzDy2wGM4BawjlrJANBK6+5NuxEB5b7IxZV3CwCL23s84FuLAmOk0m2rbxcCj+zzBIijSQMrgyjkn6X3ACo29uM2PrkBUtMX00XTEQORYzhtKA8BA6lPYLGakuUCWNpRQhI7gwCzofq6HetRAQZkl7UwPuMDkHiiignjDQMS4vhH+wpvAvUGO40zWzkC+HL1OLGexQLUbazBaL+nAbooSJNkrx8AmiyLcxaekwKg1gcLv67lAcX3trQ6luEAnsEMTc3C4QHpeXW5yOb9Amcrah0e+ucC4To+RAn6+wD0jBGMoPr7Ac1PjUoMrz0Bu5Wuyrqe7wMSiB93EIcZASnCGm44su0BIBjCTFu3ewEZLBME2x4DAHoxQJE8b6sB8Gw/y1/3BwGEuLSCtYrpA7z/LgPcz4MB411KCKIG9wDPk57V8c7JAPS1/ROIQykC4T1bHPp/JQJbeHWLYV+DArqJXKkLS00Af96JChL7WwB7CZ9rWw8BAjYfGFa+7tEDIoZL27ajEwAZcst2c0Z5Ad+5fZpHExMDVLFtVlr6+wBjFepnh957A2lZY1Gag10A4bgW1yzfCQJMl17VDYMRABeNgmGEIw0C23PsovOnVwFGdjz0rlrhABaeOCH8nxEA32+oAd5rUQGxzRutqecvAotJWol8prcAtAdzNSgzQQFeoQeiGDc7Azdr6N5rUvMD5jVYXuhbPQI1pPVP3PZ5Apl8a6zu8qsAC3upxjg7VwMLktT5NtsBAHxgKXqu1wED3knAxsCfLQF9semO7qdhAAFONsjUKisAaNcMTLRGTwOCfWljPn9rACccJ04pFwkD9WAmHhybAwMJ+Pz6t6LdAx4q1q6DQ0MA7tS3Ia9F6wNsgmeO4L9bA4DrUBzV9x0DKM/GKbhLcwJJ1DbrxIrzArspc1uawtUDeyEI9QIbSwLa51Wd1I7fA5LaTA8tK6cCeNKua1yG7wPoto3Ou+8VADp1fT8ppoEDU9ffiDsiUQNPNNbQAAbBAO5CdSoHD2EAsUkQtObvEwNfVoUB5KMnAjUzbjxp1y0BnYM+S9JTAQCC2bJda6drAwZapMKMpwMCFP/eJwcW9wDuh2UQXxbHA06zoDhtjrcAHPxT+NNaTwFsglp0Va9/Af/W8iYY8ukDiJP8tYtDJQCmCJ4ITR4RAmZjmOPNH0ECestngWMO9wGy2FSyaANNAshw63Yx8mUA0DO2EAkuTQCUqL9NOx7TAeaSeX2vuw0D8MpafNFG3wLgL4/Ma+MBAmcBR6wk/qEDZBNmjTGnQQJx0FUps0NFAkSh24O0h5sBuNRUOBmnNQK6izR6c7MPAQvPwsTaWiMA6OQfIp/y5wCH1XY9sDrTAYgtujRnKr0BmtRv885rFQBvQgWlYm8FA5LqYa22lrUDn7LsTPIexQAWQsbHeO8fAnXW3c6sSyMAFgkAHJ6mhwNlOwzC8v7FAc/0OEWc7ksCm8mjQXc/JQHkBZhqWVcZAqD69Xx4vvsBAMsfA9wy5QK9yuUVVp9BAItgBaVCgwsCzEtbWzRy3QIr4F/FjY7RA2M3jTEKqxMD4YEpG9mfUQOTU1h9owMbADhIvFTiOvUDmEt73XvLKwKXZNHmzIaTA16qYCnezoUC7x9Eg8o+5wGGz6JQxhLbAMKu/UvPzyUB70Igt3kPAwN0oHvQ3ULtAA46ifrMiwcD3OQtEeaLBwCaL0yket+DAXKDJ/1Wq3sCVFJ00ck6zQEQn49bF9r9AS4pgZalkwsDJHIIu25jnwPBhmjHMMcnAnqv7LrQKxcCuOPnL8nmaQIAlDf1iOdNA94Q3oyXSwcAB8ZU7cKi/QCkL/QiL6MVA1BZKZKztgMC+3jchZJLMQD7pC9pe27lAlK6FRs/1mcC67PQGsgXQQBpxr0YNs7bAPKJqRKh25MCNC34QDHmwQCm1UnXSmsDAH0XHCvtzvkBvkc2KtHvHQFzbqyaFP8hABfYeXilO38D3hBr2mRhiwCu+Nhn3a7JAIHQoX2St08Do3fHzTOChQHKoTzj79NZAJUsMGkrgu0DPn3gMItnQQMfaP6zp3rPALw6GtMIpzMBNHdcmyybnwEdN1ovdLsTAgJWXaxcz4sAYGJ76utrSQBkSmhf0NtBAn11wPwg90UDVYNkjTPXIwIc3B5WW4pXA/6NI4rc7wsBYWJc1dRjSQI2OuapJJ85A6VQ0fggHx0DdUzoavaa5wEtf5oxSVM9A7xkO6mK100DAIChiFYO4QO2FJ8CAFL/AA8sZYo0guEDhhplQSRLEQNZGsfxMWsVALRvbOQKBxED30iqFXxzFwPYDRFqVtbpAywLkW890x0AX6vpeU1rTQGcKSpQNy9NA/W9bNb80z0BUGwbZeFyiwMOvnc991JzALXwhGi/ptcBRZzvnlBybwJWBIFJvHLBAlLTjdgKPskB385rNIcitQP7gxOwh7cJAO7OCVmIJw8A1yV71QFjPwHBqm7Oe9ZzAE0JVUKrUwECfyCZDS1C2QNU/edVLm7NA4KGM6SNnkEC70SmPdQjawLb2EtvmIbhA+DmrSh7pysA9/8UpTdupQEDwJJW6bsVAvYc+gKv9v8BXImChOGTGQBgN6BPhHMFAYcDuR9hvsUCWmVG92+S/wFoXzC/PlczA9mciwYNnvMDJPSHIh6+1wNfrylTYTcNAanvZit81oUAaQWqu4xTswFxFRBYjor9AwLk7GRFCz8AmEcIbeAfEQLmobYLWD8NAgze5okBDqsBtIYLCJdJ9wHAp7ykQyM1AAdMuy8MIyUBVX457BByuQHsNn/7FDrtADLF/W+v1wkDt3RuVnnDXQNSRXkmxu7FAhIcVPHwMhkCBsExp9onSwNDXb/TQBsRAvOdtPbcKu8BPnXzV/9eqwKAz7v/pncLAKa7VeN2k5MB6y4rdRYTSwII0TqVbUsfAL3uKVBiRsUAzzrFKw+zBQLrXcSaea81AMWzae+1fysAQAuWYB/LTwOoNEKj8mYPA8vQ9rQ124MD/dnA9hrnTwFUg1iq3bt/AlGbaSiA93cA8aXbqMBnSQIctQ7rzQK7AgHxUF8dzu8ADVHF2SQjPwMRB0hONjsVAQDRj+CoqvUC+qx95Y+C0QIbkZulBz5JAf/WdhjVm0EAGjxHiXjuOwP10B4wS/slA/EePYpwi00DINz94HjriwKGmn5j59cbAlT40pZXQs0CAtpQG04zNwO51dGSwKsDADy+Iqrw4vsDHhPE48hPQwCS0Zg6H+MRAY68wOiPB0kDeSwIaZynHwN0yvkVkg9rAOdLQLwKbukDPBvYMi8zUwID8bSiPLdRAEnuQyq2fw0DknIUgeKO+QNaAdqhlN5pAP7bsE5ysfkBGWbQqqhB3wJ20zTSrGr7AMrWN/QYnv0AUg/JlPU++wBbED/V1uLrARLc0XuxVxMCDcSkexfXRwIm9YoVY0bPASyeLcvNot0D+Cui+DY+0QEwiUsOY66HAZ4LR8lNIssAYy0Z3Bty8wMNjn7M/zbpAfd1AvlZP4sDVQ8th6bPJQAmdRtKUydRAg5ZRUZshz0A4XEvz8xW0QC/yFZn7rMTAM2KLfOVaxcDwmyVIXmzDwAwkF/4gnqrA29FJaSnw2cDrbaZqA63HQPt8UkxEdKfA8NHvnpzwvsABwXpqBeevwMIiW74fssDAs+R1+e2Jp0AiL0LN8428QJLuno59LcZA+thXaZvF00ATBJaGCFrDwE3wdmHTINPAEM7NdIaYycBHQl142ga2wLD4i7EqAsnA91Io1e0EzsBr/FFxebC+wOytyiLcCsFATZglgjqJqED0PXpQQ5bAwAI3rFY3I9pA8j8nRmi9xEBc9B0FEdzAwMhSdnje1LTAjm0qZ4Z/4cAREtwfEwO5QAUz5hf1xMHA3QXvDoSiuMCEdslSFjDAQLdj3mcUuN/A05hY6OaVx8D74LMpLACwQO0rJtC9/sRA+qqNkpLodEACOA2Yb0vewAeR0Qm0XKHABir5eBShl0Ai4DzHtHK4wPyq2kY0OcTA2eNIrf9DxsBNAVUEdZPBwExqiRyQLchAEqXpT9f8sUBO+aD/skDFwDqLYnnHspjA9kVvZMye5sCkZgC2Aka8QEaUFqql7NBAJrFzh+4ewsBEBYEVt63YQIvb93UcZsHA2y/RhJInrkC3PDu6Z0XPQHfx7b7UbM/AkF/VTdHrskCWZ9VAATC3QGBlZy+j88DAOumcS0tFz0DcxG2hcNvAQOXCs6z/9pxApbkLwq8zw8C0Q2uT1Di+QEVzr0AZv7tAeQqsjvLHy8DSUwPOg5XOQJh/GX0qqNbAOdY5FXGmrUDXiobUyxipwHQNaoCg583ASYhpGm/QssCewgW1zLvNQHC99D1IxNTA40q3xkBCe8CYrsktaE+wwGzesGXig8PAwDR/eznXx8B9oSBY9FnHwMn/5DBZ4tBAHiAhZ46Cx0AvAibfQMjTQAY8G7uJAtFA8rOyGgHPwsCYYIfynu/CwIbXZjlogbhAr3LziY46pMD82EAaX0a8QMIZ3idqC7LADcs1fteL20A6e4/7NxvfwE2h7QjMK8ZA3bzs7XmlncAXRA/Y9uHCQKZWMjiq09fA6OuP15/uycCjpVReYbywQKYm7BE198JAX1dbv5eTvcAlrM62j8rOQPxIFeW8n79AF3Ev3mFG4cDQKmR+J7/OQFELLWRe2cpA4uRTABE/xEC2vDf0nfLBwLKIgOE548dAxkFDEZD/xUBm8kQ7os+2QMMqfPdgk5TAzuGixFYS18DBDMHIx5m1wA2OwfwhJqfAcfF0x/8vxsATfxYhHb67wIhMBHELtZjAODLazEPmxEBxsTuqx6qPwM3c9PA1lcPAIlrcuedhtEB3nSjZzrHIwEtqZlVEBNFACCrgh0oiicDRWTrcF4LRwNdQd4OA2sfA8gVeT+Rn0cBZ5CMtTmDRQJtBs8V2xbZA3fqsRVOGycBOpop/nKLNQFX27379FtPAomZTLcRAz8ALTWY4lenVQE/LNZWJhOHANNCjs9aQqsCq+RLiCM++QB7vz0PrncBAoHpLIBRdxMAxz7Rs/OvBQJjvVvQFiapAuphiYHsr0ECWT9FemtzDwEOEgWKbYdFAtaxte2QZvEDbruy7vwTTQMyW8mi1QtJA7dFStb920sDc5EGc8JbMwEJCl/S4dYrAHVbqvO672UDHg1K+u7+1QFx1/pyps8hAROa1HFm5xUDC0MN5FgHLQBwnOr1uUsNAe7TsQC/Rx0CoOWO49uPMwP6p5T5Lf6jArzBEwOo0xkAUs3Zu3HW4QD+F/2qkor9AJkAximzavEDRO/ooJGC8wBTCWkxLZsBAV89f87fqxUDZfgCf/YfYQNPAmYHc0MZArL+Dky9x4MDNJMEcbZ+pQLaKm6pFDdBAd+eABg57wcBZp4To1uW6QCt/Aig4UcnAry1TeuwM0MBvEKCQ3VTQQKjwEPlBcstA73lf38P44sAAwvCpUeG5QFIaLpLW1tZAoVYJuech2EBBG7QxFYnOwIX7SKtMltDAcvYBOPKxwUAiTBToViHCQM8uhfGWac/Abk28akp7tUBqBEmt7hvTQLtNZ9eft8bAYFdyiMI/sECH3m7xylrCQFD/qt/VUrbAuNDTP1hUvkCLteSxnozBwFLHAWee5djAjDUirrZetUATsC6prpCwQDhopx8UVdJAgIUHxAf7wcCS/FgtiDW+wNk1NK1yZMdAjfGQnYehwUAsYQ9doxTDQNPEp+qZccVAmmNmFmEyvsCP7AnJnkC9QD/6vfpy4cVA2bXsVSfwpEAb7K9hGn66QEomE2Lzg+DAGYtMryy5zMAZaqCREPrDQF8qygFhlLFAMiAiN9H0lsAzznp3e3LUQCMuneWNAblACaQpnsSH2EA4wWtr3kWAwHk++/9dE8xAuHp0z/ygw8C5rZzUYJvZwDMCP9HvsctA1q4tn/lWwED3kbykQhTMwD7GHb0EccdAqO74/+emtUDp5a9YQ8+xQG8+mcfFaZBACfqL97WiwMDZST2jSdWxQIWHf40kfsZAKHEZfH77w0Bqa5cFyObgwGKL+rpI1tFAlC6cNTFJxkDClb0J58DNwEZ65oJUYqJA+mVq7OMPv8As5NfYRVyhQCQ5K5acKcVAW74FmNonzUBQBF2egeKkQB0eTzp31J1A/7D7CaZ7zUAxAaCNyuTDwEMcKGzolsPAfjv6riSLwsA9uyui1ZzPwKM5f6Vq08vARe6gquQB00DMP59aayK/QH+SmUcG28DAle9/C3ndrcB9ryWRfqbIwKH83APuxNFAcQS0P4DqqMB2cDyTA0/DwBIa1v5E28DAgMV7gdTC0cAUrH85r1G1QEyWzlh/9uPAt2dl/ujfukAECVGfWxmSwP4EOhJ5etTAeopAL6HNwEAF2oURUPe6QCObDnBcPdjAkGoXNkzDvED6n3Sms2jHQKjhHxl3UuDAvth0j6ASxEB79Sl52K/BQC8wUKOf+cZAAts6OODyqMBM9sn0xCexwCg6tnNFkNBAJC2Su5cEwMBCYTgtXeu4wLnRzufdX8lA09ryBqJh0EDCzm5qB83FQGMiqL7yvsRAtFI9aJHHtMB9m6QH0ZS3wJslK6OxKLBAdlFqAaZStEDQ0TNaEDXMQNzNR0vft81AQZjcBWarzMDIMLwiXHrDQCK6+6UMg8RA14tTUJ1dt8Dgdkeoe0HAQGygdangKbRANdUyhLJro0CLmpRGEqnIQK/pOUY/8cLAKEnZ66hzwEBYAuJk0cxmQAnRxeuN/otA54hEVzyLwcBW17PnvceoQHYWqfypdcHA/G8CtEc5xkAyur+oC4XVQGebI6bpObxAQ76kq3i9wUBxRsgKxzmkQIGWlcxRoLnABIPmwt/wtEAWnmN5EiCqQANs04pfVa3AdD5fIr1UucC+CGjbNenjwNt3+vnug5XAOLgBS4wAx8AuauvoeMPMQEuU4f37WKFA7Wd+alkVwkBoTve/DqbBwPQZRqcZrsZA4VFF+qBg7cBcbFKxLKulwH8grTu9bMPA7AB7bwRrxcCBYipn+1LSwHDE9UiIYaxAwcZCYyTwxkD6J/OmsoPcQO9tU1lGM87AUVN6Gr6PpMAkaWrrqtrIQGTYwRHgHMZAIeM/VWWdg8C2VIdOwE+xQKRVIaHB+cLAnA7Dr+uwyEAdHw8mpDmjQO8vZbYsZcPA8jmv0MpzokAJYOnGXXezQC9OGd9O+pTABk9+vs1hnsDpT0Kf5H20wNm3OTInN8dAohobk2JPmcC5vHoao7LTwFzAHrzQyNlAVPuTKUj3wMDgiQ3ngD/TQDZoKiOJyLbAnu4d27CkocC5F89CtOTKQLuHAW5mQ93ANZ71iHDgt0Cr6JhWbt23QIYEfyw5NcTABh9Yc0fuv0CnfIn/FDPCQCJaZQzaCdNAStXM+BLluUA3hc0aRUe6QGVHIETiSLxA27eef1kYwsBD38CNXf3hwN3DV7PyWcHA/UYBzvkoy0D3DtU/SEPTwAzzUBuHAc1AwWfofMXYxMBfzaKWg0bFQAwK6y1TZsjAJeQjQVJTv8CL5j8qM2LDwELaTdu5jb1AkVzkXSpDxsDVNosnj7jBwOkYuIhAV7PAsY/5i8eKwkD6mlmeJmnOwN7++1Wxc+BAWj/fj7NcoUBogkcR8v/EQLWPtSAQ8chA1SSyxVY/xMD4T8hVzfO6wJvtd401PZBArjSjDnyRv0B6Ll18n6ybwJNVhWnc+9FAmb9Gp37JyMDKAUavoPLCwEw3EqjgJKXAT8vQBBl4y0AdzodIEoqaQG8Hrl4RPNXAHqZJjGYd1kBdiihk8qTbwKebj8AIbMRAhiWFh4utu8BzOXO240ajQO2qMnJ/EqdA/MHYuztgwEC2jxReLTKhQG88ZAYFprpAz/MQz7yzr0CgBWskq/3AwACTnL8ILshAVhvobWjQmkBawX9rL63PwBqpQnzsPrpAUyAfPxZ9wsBMypowsfTIQAcrxo+01ZjAdYChNCBO1UAsqce3mADSQBSxeVr4yqjA1GjM9E+Qx8CJcZTO2ViowHrbiVmvDrdAMSbcNouDjMAGejigx8flwOt6JC7b66NAA603zQ1kvsC3d+SuqvDNwMmO/tHlDLdAf8krpv+eoUAxVcM9pX3PwPZ6YBLMCNdATxl2AptS4cDhjzjvrtHGwHpx+Fv96brAV00240n1xcA98XZpylmtwJzGCLVw7qZALFJS32yJysAKoBuBge58wHx3ONktQb7ADe7acdG/y0BD3JvgYOqcQDw5rFlNiITAGFB249IQ68B/ylavf5vBQJbF1dNz+61AdFrYL1PdtkDVcQzo/sKywEoy6/8NttbANXu5pBJ/sMDtRQoBDMPJQFrcnhJ6RMJA0kI7HkS2jEAclKXmMVbSwO5jyYDFEMFAfBhRaTIejcD5nHKeIRDHwFnEKxHkoIXA/GWG0EaAxsB/T6JfeUXRQNx0yewD08nARCGQpC6IusDh2wzEjezEwO8CmXzTssnAPkmHfVkBusAGyy8cvrfFQN2oH+CIa9dAPxVemILAysD4uilHjtTewKfg2PepDMTA0dwcf8h4ikBWbYWATefNQLZiE//ljdNAFDFJEd6lvUBCtOyuL+OwQHEsh0sgVNbA9k7COczoyEDVEG5Z5kPVwLFF43XKfL1AS6C+dDevw0AMlCo94lDBQMiA9alidcDAZNak01WSncCREWeC3mNvQCPe/rgf4YHAJN61V9/gtEA0X5VWdwijwNMSdYMH9ODAjZTeFztRnUC3AYhHDPrBQDmqAa5cTrrAkXYwSums2UDB2QZap3fCwHcfhAaev5hAuvxIAuWQw0CiJx6UInnBwCC+TBnNYctA+mL36C71ysDatUI3e0blwOtBsFNAvczAKEgHtK3PyEA8FKtK+8epwCdZhawjl6tAmDs787m0ysAkeKdsFuegwJWN/Y8PecXAQPSXfDoOyEA6c+7/hcO3QH3/B7VfqMbA3gA8OlvIkcARkGuoCpXWQEO14sw/981ArQCBBa4Xo0AD3zezWGLIQOTpNZcdRqnAI00QON2K0cBn956NpvfbwNyO17O+E7JAV7yBjI4iwkAiM2PRDCbSwOmLUHtfhMFAh2dLGJMRxECHLn9phgHYQKkoknreT8PACRNSmlrTu0CLoWK0vLvPwC7p5fw1l5dAGpCsY0a9w8A3I6rxEeXKwGIS0VEAFtBAVJP9SfzBu8Di4rd1NiG+wJ3v9HABLMJAakEdJMxuwMCC6ZciLXuzQHbeVHQqdn5Ao7fhuPcY08C5waOgYAi9wDwSCyxfCrNAGiip0WRcx0BlI/qL7kHFwGcFozmIJsDAQ+KUR0TIzUDOMJ8TerbRwMzn+1mtH+DANvffoZiE1EDVGHe1zHS8QE4xf81FtddAXgVA+wHVssAh7rUHHKCBQANTysQyqrpAPMu8yszywkCXFBxIraTOwHYgDCkOb6dA5I97FjeLg8Bz/INBz67bQJrooaE6aM5ASU83IycwwUAbqjrdeA/TQF7w7AMvfr1ADrb4Y6yPxED85Rp05LOpwAvB4zuuvdNAxBBbVZc5w0Aqfa/gLX21QPJPS3f6dqlAvKS7GpUUwsCbA1f3xqO2QNJGJ+wk0cTABO+QbhECw0CzGWZozIuOwKUoTzEV0sJAcAEvwNstuECT1/n5pUHUwFYIB6BM5rhAbENzuhC2x8AJzRlq9ZzIQHhNigEBJ7vAwU4f33z/zEB3XBPWh8rdQPC7GKJZ5rRA+DiEqrNHz0BOt9e6HKfRQMtEiR5yVarAhClWO9HEtMA/SoL8cdfLQMd4gS5LVMjAcrlOP07KqcD+Xqz6jiTBwEvGLwI3D6JAJBq18pwq4sD2djXCVnq9QITgYQ4J0MHA8hujOKaV2kDPzpSWlDPpwPPbVdXtZctAW2WQZBxZ2UAEknTPRCCswB1NLhJnLLHAJ4igr51W2sCwL4cpEJzHQN6JKdOQmtfANTi/vXqoksAXKwm3z7zXwHj2+aggNcLAHyyZ8vBqV8D/7m8cFoSdwBuQvEfZuM5A2AeuNzJtwsBRTjiLWNvMQAa5UtpePL1APCrCfdJYycCASko6UMLSwP0y3RAM1q5AWaK2P84RwkDCm/JQRcTGQCKpAmSWQaXAiAQjPVk718D/06x1hy7eQHCgyf0WgMlARd20GcBgyEC7yUWXRjmkQMClVvgoNN3Ar1hEjc7+uUC1hnkvpNO1QO/M0GbB2qbAKPXXKJkcoECyQ2kfTNjAwCZvrm8mO4hAstRu0bxHysDLp6p0+p7bwN+Q0rqNicrAyyir1/nYy8BjbvvEwAXTQH0kS6198ZNAEjsGGk8C0EAz9GES2A3BQP54BO2h6sbAkUQxhOKV18CHGY97tpHVQOwwTrIgyMHAMHik6Q1mzUB4n37iVDrQQIqSGY3aqrrAPDNVGYOlkUCITc4j98u3QMs8zTOvjKFAfRyzArcYxEBqHhpI82fRQJhcXQXtWqvA7cmSdDzu30DGLNORyzK5QKEGCQVWT8nAFYWjj4Q8v8AZp6tOmavIQLhgjY8sa+TA61b/usVzvMCGQ7396vi+wARU5uczGMVAJg8uUegs0EC9SWgA3OjLwGA6elNIltLA/1tUGMj+1kC+F+03UDXBwIeCzbsjGdFAmoGMCS8Gs8Bex7SlmSTVwOm3JoueY6pA0s8wqIzJzkDWwkauSf6wQPGQ3GIObcpAZY+VWt094cATGRgMzqbJwH4IhwC5nuXAUPpv2tPgxUB7pgbS95ikQLn7X+roj8HAZqS3BLfB0kCTxy9+lNLIwOhAaY5yeujAaZRazsdl1cDMO8E8ijvFQLKlOkc+eeLADrOitkEwrkD1GMJPAEurQBe7sIgiL9TAye981jai0kD4l3oif+y3QFYg+zQOur1Aygn0BXXQtUDPtBTLw+2oQMXAyr5Kn8nAVTld5KHQwkDH2sKhT4iWwKSyE9OGq6fAJA4bn/ZpxcCBLsHAWgbHwDi4LYn6O+LAiqTcYc11ssCU2gUbruq7wN4tLoB8b7pAYQLbEb291EAfwUL3aLrAwB8hHBXE8bBABcSpspSqcsCWl3752XXHQMVYsNFvXbJAQSi9UeO+psBey1HE4fbTQKEkDuqOIJ9AiI/gF7jKvcDSL60RgK/CQFHGeul7gcDA4dm26WrIxMA3pKpx+J+bwAMk9LRTpaBAAa8HLYpwzkAOu66jT+PEQHby/GhJaMxA5E0/nznx2ECcHwWcZbCVwLxOzzAL2tbA7cIHjU9RlkCjM6um8zfEQPMAXjQEDrfA3Fo9JHmswUCRPFXOwV+2QKf1IttS4bNA0ZAahgqXtECnEYmO6ZDEwA0fBlBeEopAYsJzgafjxEBXV9ro6N7GQEgmLHCBAMRAa/59O7YFuUAhOqkfIHHEwCi/SdkRpcpAS2c7Rqloy8DsvTHUi5zIQHSwyZ8GMNLAfDnuaXuSyUAmKuCvgcOrwN0C+0DontfACyaoHU9T3sCnnv/vMN/TQMz4mRtc4NzAvy+IInJTpUBVERBygffHQFxpEek+ZMDA+fqNqK050MA3owNDdjqzQF8ys0wujMVA7yrACpcWyUB3Pqb33Jy1QGvLOOst+bDA+GozHhA90kAV/qG+5iuFwFeJIQrDDtXA/+CHhcqpdEDq19wb2Iy1wExlGZMO3t/AFp5eQgzY48DZU/ziss+bQCiTO92y9sLAjF7+XlyhtMBNE8J70FSxQOQDfnkIDbhA5shks9xjvUBoHgILlky7wEG3Te52wunAUFHkxQ8shMDoP2ehd0KVwIRAuNbFAqFAC4et3XXpl8DX4XQ+ycLBQI/mE1M4u7HA93WciAb6sMCmYzAsUQG3QNkL38EMkcHArOdD3gnJsMA2ea8WsIzCQOau8mCXb7xAQYsbIzfbz0BC3A5fOe3EwP939k+K/7DA/bSVPqakwcD1d+094o/FQD82Ejsd/uDAYWS5/WEDzMB3Z9HwF8vDQEE8titeNm5AZEn2Jb8Cz0Ct7gp/R/XEQOKTM4OYALNAzKSz/r1B1UCbNs/OY/ajQLJqvjVUecRAjtqMiCQcukCvXNsWdMmuQO2bCyRLFs3AS4TpglSTxEAyEs9iFNfDQLEbXBHW0NFApktHDOJJqECI68hIsHviwDA5NWwP0qpABihlOS0zt8CPDEmEylqjwGn9f1JigcJA1zeZ9lFCwcBUUWGDVe6gQIsUd2Wax+HAiSq19q6ZwcCa63r2Ati9wKrwhEKTB7FA7ZoXOusTrUDnB4wDRWLFQNVB1+RA4sVAUJx2O2MOw0DBFEfH5imkQPDo+JRuk8TA6n16F3CTjMCzp6pZyyDMQJN7f6UBYcVAy2H1bmpRx0DKMPyknBG4wEy6+kBBpcTAPVhGz3xLvkBOm0Qzs1K8wO0tS/5Ent7AFjKwmzAgukD3j/vuzai4QEiMRVV6wsJA3sckgLiwmEB/3ptk4+rQQH23kbePb8LAIfDtOq8Hu0BqkB5a/KWgwH+ucq7aarfAywknAUXDrkCVhupZpv7NQK+4NmvWEr5AAbQ0S+8CpsCMD7VMebXLQIRpt/ah/czAjyYPZY5ClMD7rntc1bXKwJiya7v+hdXAXT9CBCLefkB/cgSv8X3KQALGFpESxs1Auh7K1w62uUCEUggs6jnIQErC9+8xA8XA9kbphIF34sAUZ3gyVxDSwLyHrMhpIpbAiBqTerO31sAknSjxsqS5wHPixznTHKlAKEPPuvxT4cA/PcSMlk++QLZ9wiajSsPAMu+3jJW2yUAjg2fe+e+sQKeEat9hgODAPD9NbhSLyECfUjl6PFfFQPOvgCWR8dJAHggy8SGHsUCtKFC5GmjBQNQsM5qQ87xAHM5+LioSp8AzuXD9SibQQHQEulvljLjAdg84FT/kq0AxZsLZ5iLCQMp7U6oY0uDAMaP9s8NIxEA6+ErqvmCwwBMBMbGg4rhA0q5Q0TSRwsC+xr2baXLEQIGv+0hzYsLAYm8oXyVu6sBBWTWpv/m7QGZ3HzyOTT3AfWt7RDdV08Cs3Hv7ArrKQAtzbEwAarfASECwvdlozEAX/RXKtsXBQOI2M3v0i71AB4mbrhVdwkCxZ5Qnr2rBwGhO8UTid8pAfL6xOCE3tkDvOhpQggGTwM2S4UQ8qcxAjW4wDkb7ykDt5sDQgCDCwDdTvSziMs1AfWLocVnzv8B3RJCKE2+JQEj7bF5Sd77AFqhZcV6XycCSnOt8BKygQBkx+SjOc63AmiaksENuk8BL1oC2XSnEwHgopJiABc1Aa4kjMUGtp8CKvzznHEXAQGtpkw5iwLpAOOHmXrqbvMBhsKS/nbviwD5hCHYLMcHAuu9dWaf6vUC87UQqmiG2QK2hHvKz5bhAF73sXDtdx0DYeBw8/nSwwFEsMD1JqtjAPyMPVsaWpUCOyPk6JVazQM7SqQCgNdtAmmfmv1prj0BW20yO/i7FQDRGzVUUx7PAwRmOk6N9qMBvB9vCnW+8QKhbf+9M77hANFOUYhPvsMBUURn6YHDGwL4K50+aLNXAWHAMWeFOt8DJKuFYJxm3QAAZBZD96LZAZ+hA+6mss8B7SxH3XPW4wEWMjfPsPsZApSAsWzPg4MDRnoZ+l4qyQCa88WA0RspAL+pUYZs7cEDLdD5+IjDTQPFvojVq6cDAlM3grB26x8DYSg2h2b22QFzmclK1COHAVxPYIEezucDhPqdXiZHWQNXROibE2sLAJRESRtlstEASy+0Y+Y3ewEMxtcSLiMnAmooLBhPqxMAAma+VMn+LQKRw21+COK9Avnh8QUS+yEBpfTmpcYLDQLy9mYGi1LPAv1onGZJ1wUBMrVht/ojAQPqNkt2RirbAeABg1tLonUCMjjfvkECzQHad7tPYscJAhy4VbqrjzMDgGwo3YviTwIdraNIepq/AIY1VikGXqkBTBkbS+qPAwN/ZykYNzbvAVm5NtdXNzEDJlFr7H1/CwEBTkR+KBLDAEFwlRII5kECDsUIlg6fNwMEIrYiyUp5AR9xnoNTQv0CXsdi+qtfAwC+burlM27DAi4s4znO2qEABI/CV7ceoQJ8wlyQl6JhArw8UnT4fyUB9hEP+mmfDQEXNveORecNAlETloACzlMCKvBFIbHy+QOaotnkyPLVAMvipQagq0EB3eQwjGn7SwNR7PWYrHrXABGrm+T8MzkC4I3tN61XGQDe6GYSz4tPANEMBeKfrxMAMh2UO0g2WwNrYCViau9/AzbCrQi4QvECpOkjigtLJwKlhNgD5uJDA+wfQjmYEuUBcNh2Iae24wHs7sNZJ6sxA567uXuKp2kBiXNKJSHLPQNsO4uzzGshAt4eGBxCUvMCYAC96TS+3QE25XKKa06XAhyCVarkm2cBTJYUf+4nFQN+IjUxgndNAijbhLBcwxsAhPJAaZQG6QDsuH55UJ8HA3xMcJ9G5h8B3kWoKSBqNwKz6xAD5idHAgHotT9Xzr0B/PshSMszBQFUPUfNQ+6DAl0lQawqw10BtzghetRuwwFdv6vOd0dVAjI/p2g33rUCEILmPWM6wwLI/Jnh2fs1AOfMFqqGks0BAfy3MWZfEQDHzcBVQddDA/7/loDqiqUBbB0mej+m8QEDyP3OoJ8RAQfTdA0o9xMCtgGB6JFe9wKqFkNrHOKTAeXEs9G6uzcCpCWFpDYDJwOFWJomGUcpAx36O2AIOwsCXIobD5QWzQMVS+fQ4yslAvivLn4ZDtEDIfvkycqvAQGXEYgV3fMBAECRhiqIl0ECV7iT1Q1WwQIShuwxMwsjAeVm1TfkByEA0W9GoPJK1QPpE+dmK6MLAaZnR3uJCukCC3fyVR265QOI15lUkh6bALc5wMI8wkcAz4Q+G/9GwQCO4xVXCWMNA/X3szk2ApcANGHYm56q9QKAV/dXD4K7AZBD553zcvUCj2/g2MT60QP9ax+ZsHt5AFHTW/zJJ0UBsqBQkJO7QQA3O7gzUcrhAU5VoGJnYk8B0zjyzpAHjwF7oM6pusdNAuZoYatXUzkDwCVnkvE2iwOmA7UROLZhArpyjos7pwsBSY9eXNsLRwN06lO4Oz7RAHCZx5jbkxsDZ+mKMyPTBQM921tl9FMfAVZZe2P+FwUBpzGtvKTi/wMlMZ/Zn2dJA03dm0C35u8CtiNA94PLQwPppREa8l53AR8R6RMvWosCC3JqusoGEQGL9lMgmbKpAbiA14nHZv8AMSXZ7AXS3wNK6ROoAfM9AcdH+kGFOq0CBsirk3fm0QGY7W2m+WblA3K9Z09mnwkCRUTAJvJ69wIG1i9d0sJjAbpK4AaU11kA7ikKKczPKQCcvDgL7G8XARTkNRLCXqMCUEgq0vl6wwGXMPxVzXdBAHAgT3OA/pkAF8LM7vDnBQPNhaKgcTq5AxRpb2NSevMB3ITDlm57awFLVb+b0EMDACwLWwB7VpcA+roTIuljDwF6AERsQR5zA2dZe7pjk10AYP7emfJKhwNQFTUtkFbRAA5AUax3Ls0Anz3nbnPCfwN0GSVfF+8jAzFO20xzTyECw4OY4TNPCwHKStCx4HZpAj95PulZo3cAU4rlI6XecwF/sUGEJVMRADBv1KXjcy8BXo/r82oO3wNCudNnCRMdAFDYbG5dWx0CEVAC6gq6AQCbFSuNUI7LA9vPUFNLxw0CXTsN9+s7QQKFnxdbMbbpA6cOeQ4tYuUBoKPXQiNW2wPIYyLi3wMXAROnHsOexhUCpH3iBlxS/wOHzl1QugrdAdtt8+A8qw8CCs5CsqvzTQGrcTFtKfM/A+FtpXqK/tsDnZXKLHfi2QHPXe9qlOMxAnlbqswGnm8BcSYEN0bSXQL4tm/yNqrZANIkREtWtsMBuTZQePZ61wG8mflBIiblAN2pey98utsBGTOf4IvTUQBbGRZZDg8BAb1d6La4WwkBjs68pJ6m2QNIn5fhG6rPArGgPg5nw08A0AKIR58ymQAgB/2r/XJJATSEyqrri4MAFagLIs+WswNxmjWBJU7/AVAqWB5P2yEAbTL7dH0erQD+xvZSnGL9A2il6l4Omv0DHWmxHAOObQLecf+CGnMHAuG9Ry0nemEB2jy9HRUDYwC6tIUI218pAJYoBQDTMnUBY+vveFk3JQFGauMxEtKJAWm2jahTAuUDqIbykGIKowGRj7QJTE7NArA3zJwJosUDS6plynDeowN3dzuW+gqtAUlUqWKeL1cAVhJ3RLRyxQJFXijHgwdLAmSqwBobNtUCNfzfPbpKlQN++C5jNsOLAOHVhRa/hlkDQLkq4z2LhwHX9wGUS8LzAOvUDB+XzxkCEYO0EUPvIQNttTJLkGqJA7kPnwhwtxUAvNaiawnrJQJO1x42SVrtATfpwHlb+tECQDZ5XkGrGQMFsbFuJz9zAGQvQmWT918D6sFZZoDnZQJB7aiiBMcNAbB/EtG6AykA7hxwwfeLGQM/g+AftULjAs37QvZEc3MBKRvQ0WcyWwEEbYkipG7LAQO90G7Aip8AFTAQBmji6QNSfRAxaxsbAe6TVuwK4ukA5ow5oeBPIQAQdJDzvCcdAq4uUGFi6u8CpFK/EUHTVQI3Y/+6ndK/AYCCygGALtUBhOVJeuBRtwCam2be6SsxAPze0tns/qMAtNYn3OWTKQJ8uO/GNwuTA3jZqZ4qTzUDWd0g1xRTBwICbp4P2565Aq9cBswBersAP2YAxchCvQLPNN89dOq5AJnJyV4bZukAHDqZCRhPgwPW65784Hq5A2AAA6Szjf8AyQCGtxSrJQMzXV1DG3LlAhWPaC3LRnEAxfCG5J52xwK+MLen9X8JA/Dy+tmtY1MCc9wxrdnG8QN1yPCEFsKXAJdhf7M1Ox8BEGm+8UKm4wDuWSz/SM8zAL3W1nOYA2MAJ8IgDny/JwINOpxFTEJRA2zmuhD/0ykC4Pf3gCZO2QC0uOnJm8MLAtZdjHr5kx0BqM68sGGDFwJjQsltk8MZA9Wd3EvU4wkBWT/ODN5G8QAwkEGTz8slAvFTIS42N1kBun0gRXl20QJhKvHRR/7TA3m4lCAFY0EA0usGh8qO6wApq+14N08XAOV5G5/oYt0Dw/UGKxTC4QGdeba1CxNNA8LsNoJjLsUD+1Uo6FhfAQIteB+8zadvAbNlZw36ousCzvqO2002zQOX3WVywULbAgWPVIScmqUBDoa40zlq6wBe6Qc9gTdnAarYcuSeIzUBwd67OKwamQIpsY4i0lLVAvCwd7jh2tECuzpSbtZOsQE+u9lKQGrfA8lUxXr+AoMCAXPaXU5CwQEmHRhqfl6TAuDGv4xGbwUBwx43TpF69QL4GhOU9osbArtsIfYGin8AHM5qNcPjCQMXeoAXMrrrA25E2f5Mu1EDq+SDBxdKFwFbeMrMq68tAVsOeiE97nEAZtb/xeKvWwCfQYYXgr8lAPf2EESsrtUBT/Wtcv9GkwOYRvY6kmdvAWw0XIvZirUAhEdMPtSrCQMwp9GuMlsfArUVtICK4nsBoL/pjt9S/QMuAANR36dFAJ67NwNFIwkCOKbTDRzibwFMfOQaatdtAmEzo67qZw8DlbUhyAW/KQA0xXrvKesxA6So6ZkCXpMD0GYPydTPUwPK2AMBXJbzA0XPLvoRGvkAiOS3N8STIwEx2VF6b2s/AhIwrNM9uw0CMQ+Wq2FzDQGoU6jiJ/MLApc/IJLMktMD99tK5vFLDQOct/lfNzNFA07OF1gOGsEDQ+3bh/LjCwMmsklf6X6bALXwZ+UzBx8Co41Yhn3/FwPk/L6HQbsTAYja9Ix3YjkCTPut+TvShwGD2zq9UreLAXG6oiAJuy8D3buMgxFrBQG5kL2S77c5AKTvPSl926cBnkdvUfCLMwIu92RKsh6hApz3RjBV92cBTvd3RzqjIQFHS6h+pzKtArEe6NaqkuUBTFQxbKN/EQIp3buMyqLvA+TB44MQexUCFGT+sNMXJwAfXUPcc6cfAaDSWk63qW8BRIzJeZMe2QOyqufeGRNJAyI7h0rgd1ECE13CyDPm6wIcW3AO+DtNAf4SnLTJ4xkCvX4RYybifwLa0PgfQ47lAL4d6WCHWhUCZ/CxgkIrIwH/f6fU6jK3An123Qhf0wED74kiublVzQKSv0RZzJ5BAfPLuGnNZyUByYxiVBxXgwBvlsNGD/LdASCMBKZDZv0AWc4YEPnzewGBLx4ysUrRA3795KkUpr8DehrS2z5iEwIvqG6ce3cXAROjO0MFZvECpXq5t2oXRwNzVe+iTjNpAYMAWtpF8ysBxZ4wdpx/OQJmRN1DKUNRAQj3kG/9izcAz6o6Bp+u6QNEBGWFFs7xAxcEmnSR2g8D/Hjj+dHOywLP5+dGdDsZAbNVZexuyvkA/ICB/hqaqwAkAxLoV9dTAIweZ4W2Z0EBt9DwFgRC3wPYyWtn6a5vABiR6mF92x8B7SiE8/oLQQHScgeb/3r9A3GgC6yySt0DaHLIwI23XQOrFTgAof9NAYSzmhnalusCv0gIonCi/wAQQNA7etdrA6bqlgdunqsDO/d4Th/zAQEsGPKykdJJAscbRPBcByED5WdznMaO2wKA1IjUSftdABWwJpCn9sEDWq7NHdDPUwI6KxKnzfOHA8omOlADkwcB4IamMk03FQJXvcCy5LcjA7ul3bhk+xUCfZtwdZP7EwPPJKrbI9rnAFzJGkn/UlMDTayFv6JiwwKt519RFxrXASvFDS7+tx0B+hpBVQM/GQP5A4xmDAL3AjCM4pn9jxkAgLIRNucTOQC2dzpFfP5nArJgFKhRau8COy2jm0+jVQI127WrddLNAj1gPZcj/tUCKQ/7mXBHIQNBktYbBf8bAiWL3aC81wsBvQ8wS5N7BQPY1PY9qrbhAlnW8GutHncDfbUoQ0ZvSwNfzSYeA4MhATYph/+dmrkAoXf7NGG6tQPGj+VPsHb7AuS1LxhptyUAoxasefP+7QHJCbcUkJ9FA/bB9+Uioz8Dq+ViLM/+5wBw9Wr6iIKbABrGNoKngy0BrwpGHdBvNQDitFdAWrOPAs0YSt90t0EDrpw9oWt+2QBmswJmiLLDAHBXf0odMwUDvmcoSpgLQQAlfBbE2acrANvO8nA5N5MBc46KyxE7DQIjjCqnNIL3AVHGa40LD1EAJZb3nNTaiwB9tPzt5/7HARcEzrZXUvcBV7GO8JaLIwDZTda+HSrZAS1BreoNc00CNlo1LKHenQFhOYoyHqnZAnTTni+aS1UAzqCy1wITSwCyczJTM1sBAUVc0zLHX1kAtxQgztSW2wMGyegRud+FAtmwonB9W2EDdm8TEitnBQETNFN+8GWrAqqj0uZNtrMCKCD/GkJzDQLNYz7dThdFA5c/r/tKSuMB/QOev0KKcwPRNsKUVf7FA9DWUgxI7rUB2ASGqi4TSQM1sjPs6BMtAGZ3203mQvUAzFRnbKdOFwNPjtpGCI8rAMzY6z0hl1sCktUFoJUGyQINZsUEauL7AHZHu83euokB3GAWLhHzSwJ8CHnhNj8FA/EJzXKtUzUBndIq+wQKmQDwZspyEAYdAEAKsHNnAykCRCBYXyaypQJw1mddZ58lAKds3t07nzEBlo2DLpLOAQJUAXV8T49TAAeURjTZCyEDywGm3S3W3QP31B/2ly8lAlONMq4ZAzcBs51OvDm+zwMC3cuHPCtHAzW8a70LyvkAWCbYZSJ7TwDfoVz03T8TA3gctjLfewkA5cV1+bAe2wDcmpPB9qsZAb0/NPVoIs8Aa/9HcONjmwK7bJUqg85tAmDLKsd9MyMBPi5Acf8nUQEb1j4nYz6vAvEMB2Z9D0MCvE99U9eyrwMvT3ubDkKlAaltL3+qluUAZ2lq9qpjFwGO38LEu3Z/AIVdWwxP3tkDZGFG5tLu1wFRNFCMska7AWLmcB5ew0kAz66NdJRe7QKsfARSohL7AcZbuXCrtx8AnNAukY/i8QDEIotlwcMTAZLUJ0ZgGt8BgGRpcocOSQByZe0RARbZA+fdmANWei0Bjd3rVD2TAQGai2bdiN7bAqJ7t+d3Ow0Cs5pfMXX21QArMBgj225HA0oVvCm4iukCqoVTh+obHwD9+/bWXTdJAZ4o3S8gevECpd9In2Dy1QBhLwgT4N8jAgEhuIAaE0UC9+gVNX/2/QAA2iuyUZtrAwqNjT2kTtUCpPXyp9dDCwJlLG4ZeIs1ASuCqhWG7pUD7TxvuLaHQwJwpBTDB/INA7Ygif1JfwsD1mWHztr7ewMQJlEcL5cVAiAGXIyjK0ECDFrz3OTbGQGabMimg/adAhIAwUzp9tECmftMQlGa2QIiUpfB5c9hAHSmPtrehscAF0WI0Wy/RQAAd48rk/c/ABdY7HtwLn0DUsLBKgbexQJzxLNfC+4PAdgevqwIPssBYYm/ckdO/QLVS/7UaO67AJRYX9fXix0ANKrd/ztvDQOnZf3IGuYxAZh6o28lWskAA1/ARkMfLQArF4sEQOarAQuwuMQFMy0D8dJPlIhCBQLS2aLa3hrTA3eKQoGKCzkBh9wCfbGZcQLEwChc7j6/A+2FVmy7jsMDKvyx26CK1QEcHuicsh7pAIuCTM08IwkCAsgBG65fKQCEzJ175d6tAYC+ln68uuMB/Km5um0y/QNQ4KNx1lb7Ak5oEolE2w8AjQ6boDYJ0QMkmkWKjP5DAJvj2n4APvMDP4baQVJi5wN/IhPhl1K3AzHLXDzlmtEAIwOLVNdi+wAsjDyYliphARJsmkxXjw0C8v3GvvFmYQEMVzACvktjASY6MlcmYvkCUyIsXnwGywBL1zXWDM6FAPYm1h5dW2ECzKpzQQ0fCQIGT6SjeXsxAifj5Fht7tEDQPGXmXdSgwIsQ0/yUrcjA6arIi/tKyMD+0bJd11jNQH1URPD7D5HA9vyXxHhXwkCOWgyiuyyxwHB6mwuQAdRA2Nd7LCEmwsD3fjUQErKzQL2OuQmad6NAk29mK8LF1cDqbVBQbizAQA4WNBL+Tr3A8ptQKiXg4sAnjSHz1IDGQK//+rR1SqNAfVqYo95cycDR0ICBVN/CwApSFThYb6JAAK3EJhfPx0Dq5PppVKK+QObvBuRZy6xAYkBroqU+qUDIUk4st7XTwDkMFBUTG83AnW+9wweawEA+1Kiurku2wBuUFTLvraBAwn+/GCway0CBqVqYmLODwCylRFhKtLBA8SR5zbFrvkBUdLd3zlazwKyKRVSzB5fAhAhvO8DJyMClGv4uvIyyQEPFiLBndLNAoOFzf6qdqEDnMRjWOSLeQL+SeLNqNuDAM5tmGWTLuEDV766Hao+jwP4w88jKwJBABrbSNsVM1UD8FYYCGQu1QGVXCq5x2KtASU/p/DLww0CejwXDJtSdwB26hOQ2dK7AOAPakirJxkBtjnzNkoXTQGAWSf/gIMfABUp72z6VxEB5CwcpDry7QL+ZbFjTqM5AjjSVG0PqxkCFv6F5Z1LAwBzLvkr2V+LAR5QQlQOlmEBhws8UXFXQQP9sEE0wy6rAgck60u6m0kDJGNh12JPCQDMV/I4PlKVAfdG2xhAss0B9TUHnXXPEQE43Y77Y6MXAhywPxWfQucChQsdS6SfHQAuB3VIvAuLA7lqb5zzHvsDXz0kq1m3RwJOsBmBlHslAmEa0++fjpMDNVDwXSTjKwI9VgmIBvbJAAv5kXOcGxEDXA7ZzLRyswPwnyhLBH2rAL2VcBMdp0kAAmcHcm02dQPYgCejGAaHAQGxVu6DupcB+54T84M3CQBOJ8yd6HKHA3wYUvaQH0cD4rx/4YdONQKhJqulVgZzA6ES5lGHjtECN2waU4TGgQNqk/c8qZM7AQ8udvf6UtUAdJAl38VPLwElcWHo1eMhAmrDy1enW0sAaK9jzFlnNQJ/zddGt0ojA/hpd2clvt0AIB9Dxr0W6QG2x21qGStVA68Og5mEmosCoZ8r9gzu3wI8FTrFMuZPAPqy07KOihUAaPvsfrzOnQHrpQg1x9sBAMWlX0sz9uUAgb521ffzSQB1OJfIvEdnAP7DlbyzsxUAZ7LCPgSnGwJMRg8Vwae7AC6fjO7DjxUAJflwR9uzGQAYtUBqXiMTAo1KIHToduUCZhB4watZ+wI6zJBwUK9RAPDUxwfy4p0BQ1KVyKdbHQEW9HDSAm8FAiBAV0l6Hu8DZnMFn3lSUwN2ZwceIc7rAGr3daRxPjcDFoHk+R17UQAqa5CnTVq5AJXY2A2F1wMDCGaltTOPJwPdGLZ+RzMFAQ0qFEox5vcCALkWHqxPKwJQGm9oVEL9AZvntSOCBwcBVl5jL0JzJQLRwWiHdV8XAKmYiK5uzx8CAXWwa+g+mQABe5daRG6LA5i6BbTqZvcBCSxXcHpLDQDMDlSUIc8lAS+dwvLR1xUC0cTX+A0KWQGtsBx8mLcJArAccw9E+v8DMD9By7b3AwEy9dVQBzq1AWKAEs3aa5cBIEaThLy/OQIzOPWITPNhASWw+VoVDsECxjydVYUqyQKWORwn0kUxA5Tpi4svOl0AlhTG0fPrSQAFsLWnlZc1ADYcMx2jC28CRsv4W6a6VQAJbYsPQlLRACfXgUsLM0EAr+o0/xG3SQKYeiQ8G1alA64CQgpEgwkCFooZ66znRwJ2m6jDRoKxA6VbtQUp/30B+/r1nhlW5QFeSpdeQ48hAXbUsJX3ikUDuWhNwGPK5wLtLkqDmFqlAVZdl3RwC2MBQ1J8yZ1HZQNcMTGSrN7pAsrAN5wX91kDfZSpagp64QLOZyJcYt4LA8/iZh705nECJ5pGjt6jRQF7j+K1zBtNAFxJ4cwqDwkDbPzswcT7BQL4KewUbYtVA/MpyL01stMBUASuVl33AQCxT4TRxT+LAh+K7EGG/x0Bx1XWj5qfCwMH5xTDo0qZALCGUtW3nr0C2a8PtBi+bwAYHaGBOno7ACx5uEUH9lsBxSq4xuEyOQHbIA6wZfLBArD62A5sJrMD+wW6sRbPGwDANlhhhn81AiCSAS3tAykDNwzAWaEmhwC2KpE52M9lAOfVHE7Hmp0DP15T0eWu7wJCraGyBB7lAZl2Q/d3ivsCHeLhu7dOoQLYt92w1NcHAiyDkpKql1ECEDeFUpF/GQN4sVKa61rnANYV9ZJ16vMC5lP+Tu7aRQE6vQzfo8LFAB2VWYzmOrECJavHNBYGDwDFhz1ewjLHAbj1fHahuukDYn4a3ZJ+PwJNp8Kk+pM1AsupexzeE3cDEbo/EB9yaQFtS3jIQG81AvlFltoWjuUAKECFcjdqkwD/1q3hTgpLAAb7jUZJGuEA0bstinW67wM8CPFPRCbxAa5D+s/UpsMADCTAWpXLKwGMYyuM+wb9AaRCqOqzywUDI1vNjlBnEwBN5C/qXLeLAoFYd+LylpMCUchdY2g6qwDm2xBqrY83Az8hvtO3jcMBZSR+kxii6QDjTaS32/sJAPwdj8s5Qz0BPKOGVu+ndwNT2RjVmrLLAwAr1Cpod28CyesrQCi/CwDojrW0QocjAIiISaEl62cDBq2Io5IXCQJsEmAb8OtVAI+6tClnwzcBc7TjOlxfDwMzE/P86c9BAp/QfU4VclMCy/HMIfo7kwPt4KL5LyajAGBUXrzchwUAgbQDNDXWwQNq1ZpKpxX7Au418OowMo8CqDIVgwiLBQLMrUUfCD7DAdHcI2zHDwUDMAKeIsQmtQLbmjMgKi6tAcSXZRVnlxsDHDBG+Dde/wIC2stJKdrJAvt2Pq+0ZvECsCXH/PjnUwG/sUA/rQc5AgURiWt7otcDi8hBg0yXNQNySps70LcvAdbohbK5A5sBXLUPGYVy/QC+FG4diBohAw+y45yBk0MAfvL4NO63OwGc02J7cSrjAbyFvsM04xcCl08MKwHu6QG9roWbsiM/Al+IpspfJx0CxNB7Cm5bCQFut1sI9V7ZA33+wo1hzxUBseNgt7Z7UQKlpki6/zYnAeBbRf4qblEBU/R0UETy8QPmhAlJ4qLVAFpiKPa5s0cDhdqrREDbIwM0XAV6s/ZHAFQ7EIszf1UCl0UMKt7S7QPdsT5TzPOBAiCFE+hBp0UCs1PruMZG+QNDiHCRw2sRAV239Gs/DyEAMDM7B9lOlQGwiillxbKVA8ojq4D5G0EDkl61caKu2wINEL+P5McNAIDfHm7nIzUA0s/B/H1DVQJfr0ehrdL7AjwXDpTGd6cBmnNvtIzi3QEBjJT1WUbhA4PIaOWz+skA+UMdk6oG3QJ35vLxD4tLArL319nyKvcBUwecS35K+QDz26Ud/mKnA+OIz4Mq2icAQ3u7s+nrOQJCuIGIwX6xApiZfPzBrxECgvI67ugqzQBjcOnXlCY3AWNH3wkNBt0BinNQMFsDFwGGnJdaaO9FA/tNyLlsTzUAz39Bkf4DGQE0j2AqjGLPAkaAHzKp/qMCb+82ygAe3wDiduoVXocFANgf63iWqt0CXghE0KkXGQGJVxBB2gLlACxtJwNR308B+OyEEf67cQHMc2VmPerZAp6h4o9P52sAwiKYjQd2SQNQcVBGRp5nAXgcS2L8T1sCMWET8bsvNQFig3E/bMuDAB77FwlyIpkAZphDGN5LQQAR1ZB4mD8HAz/gDybvewMBumRv1bCC9wBhJjJFvD8dAkzNxc6eHwkDMT15gJPu1wAlpvlKoF8xAeyB2jsTAw0ApWS8DyhjEQD3jpjYD5sRAuhILkiv3ykCoBvUMOKO/wLW8ATabMsZAjJc1RRrtxMCxEkANI2fQQAr9gJ8BB9LAzov64N7u0kBWiOwXzdrJQOiu5OYCCMXAW+1epX5AwcCulvsm7mGzQPwHrojzENbAOWTWw9JZcsDR2AcynbHCQHUypzwLQaTAdVji+ryooUD6avOO1XiQwF7RAt9UZs5A5t/ahYZ7y0B71hFHkU3YwPxC0LRZhMZA0BY+xB8Uw8Ax2TIo0FyTQGbsRLuGZMdArzUby64y1EC9CerrDs3bQDoMEel4nsDAN9qyE87uxMAIxYntWeK0wG7OG96MgLzAONWyMfEQ2cAVfdiDGZi7QKqzb/9XE8vA4jG/p7zk2sDPdb+0QOC1wMZqq5maAZDAEdxVgk9LtUBOcmlmcrG4wP0ZGAK7b8xA4DWN6rLJwEAECVdk+k24QHuJdCBcyZXAtU4XPltp08AnBykNNz3TQBa8XF16CMVApWp1+imtwUCpmowJuJbgwH9SNK53PcLAGzSyI0lI4EB/osyiEpfeQNMaNuAy57VA1M+COfLxw0Bm/tYNdyydQN5KLiHkrMnAQOV97trjyMBaIGHRD2qkwNcEz7iNMs5AZrRfgGS0rEAt22asBz2xwCiKkoWvKb1AqW/YQmY8wEAkfwL3qVeUwMG26BZcmLhAxA69ltTI48B84H7MmhOxwPs1VrqASrRAjOsjssBbxUAYBkjBg1GtQB0i9Ziitb9AMOSCREFd08Cp4DLss4eSQEV0rjG7pKfA3EMqZquE2UDBQhrEvUHPwFpi8MSBYcPAr8Mm+dTWzsBaQ4/uaubCQJZu2mwtTsPAC7GebB4BmMB+qcGIwY6xQGtnh3aGxb5A53tLdE2jxcCg5tz99xfCQPt3YocRN7XAXKkkPtX138DmUY/U5R6uQEC79n+Va6nAYwrNWba1oUCUHryfX5yyQLXwWVxPjcBAVOms+YTcx0DOKg+F0kbiQEqqqXwKTsBATUk+/K5Kx0DHj8I/cpChQCZ++VEXz79AtvFz4Nvzm0BjEfUytOC1QOXdYNkxsqFA+dMAhHh6xkBwcX/AamfMwNISqIyQR9hAf/J5fhdPxEB1qSMP1EWvwPN2J462FspA9y7MsLytw8CT4JUW3ZrOQBMo1VELE7xAe0IgJ/Alt0DquEjTpIzQQGOljYlX+8PAvpmN+ESUt0AuTEnFAmnpwOf0gKQurcXA9o9g7bNGx0ATBuTj9JvLwAe5fLJ+XsJAmQpsRJFijkACmu+Zz/CswGUHeZtjUdzA8pxnAts3vkAIil1EDVa4QHXIOw6se8PAwbeuuvZ/gMC4I+nUXD2PwJ6SMGeKLc5ALbbzAiI4ykB1nvHq0D/iwCd/Z/aePq/AYh06c7Hh1ECgswj6GGGpQDrAQZ6dHcjAdCN1aU5OsUAAo5XvXQTGwHEu7AH8nr9AipOeUr4ub0B0//oLV2yaQEyF9yGEB9JAMBhZ2FUoxECrcG4OZx+ZQN8Omu77Xr/A/3P2oh0PzEA2amqwMum3wKa/5HFymrfAGMAjCUyMxMAUBv+93aTHQH380YuF2KVAIIl5s5hMx0DqWmla41PIQPqGK9mhpLlAkWakN7IJrcCKL5jT2LLVQNElsPJjgrvA/S+zS5d+rkDs1nkBczvkwJ2rurf3HNdAZvUMJCLNyMDoFDx+xbixQAwc1k86otpA2WlC+0MlqUDRTdOnCRrgwGZntItfQ8xA8pzYIKvd38A7DumF0ajCQBmGG5CgSaVA7cLDYMpSqMC1Ve8voQfhwDH2pw/GyMvA4toZ8t745sAhvhQk9UunwMU/jB9unLtAvS08Ao4Sd8D8g4SGDs3OQBfSMJqb97pAtB7FKe0uu0B863oqjBq4QOiK/5vDld3ATNRv349LtEAvwMHdTEzCQC/ZPybJ4MJAvqpG20+NzUCj8/qvIoivQAavvBCMQcLAwZYODn8l4sBWc4e4yvy/QBYpydKRs4FAWKVjqvuptMAcKoqmnaLTwMAsPkoRSuvA231rrPXRsMDudwlC30WrQMzC9dJPO+vAwe933gZJ1UDZPKY6uTPGQF8bNHd75M9Ai33dDUMu0kCGKd3VxujGwDEe8BYBnL1ALRG4tmuvpcD+mnmAnsjgwG3wgEVxsLtADQmeRsK0wsCOSfHUXtLBwKWlgYj+sbVAj6CadV2f0ED+1zsGTnXPwJ70FduPCrLANELQKA2Sm0BuqfWt8+3SwMkgP2oTDspAYgPutD4LxUCVnVPRF9K2wIQDZbemENDAx3kXEpwyqMDVuwxlBWS+wFDjKWMhCMHAQVWkL4iaxcCrWZytTTTUQM6GDh76qM1AGltcQZrWk8CL+AeTo7G/wF/QmNVUqKjAMWuODrlmq0CwfagZJNnHwDwwysB+28TALC3mOlZb38Bn6BUGhpKtwHpHf837l77AXICSSD1OvkB4q1C8HjXNwAGdUoi5hqVA4+wcALmCxkB8fFAsJkHBwIqmkgcen9VAmZpMy6pgzkA15KMnGQ7EQEu+7jx7PsxAdCo2tUpRn8BvoQ28Wp7CQNYFZ/Fb5cTABqohJrRNtsCq+oVoZS3SQH369Y/Ild9A4Kv+CNJtw0B64B2fAoe+QN2MzY2AlLpAYkMhC3kvzUC8yDt8qde4QDTIxV5w8unA+xlK/c4a4sCrDSc/O9mrwLvwAiMGYshAxfnFiEauyUBzbGZ77B2uQMFYAiEyQb7AifRMoMyvvUCEWYafdVClQMNh9rgLRbFAh/3clM591UAwV8fFrwWnQOLIK8PoV8JAQ1b5i1YutECiIiv2JUfAQIta9VfckpbAr7z8awJRi0Baw71lcL3EwCJr3qPxhM5ASsnt2Y4ts0B2wPuREoDGwPv5ew9mweHAm7wzwCCzl8Azy86jgXuUwCrKwwLPJMjAJxOsAc2Ov8A4gx55V53TQIh0K5LAfMvAT2PEaKK8qkBSt9ioTmfUwFgAFznA1c7AiTeOCfOdx0CM/0LHtZvRwLuNuIl/nrVAsZZ+bY5stkBMcC/dAiGswIzUq9N33LPAB75HpeDzokDj5bNXlwSRwFkk0ttPrsnAmVwumpUEtUBTDLX8u9vlwKU+Vg60gaBAkZswfhClwUALl00YXcHCwLYsaz/MkshAcyfIC8M7z0Boo0nk/57PQKlZ35i1r71AiZ/QZWVVz0AtoHLMNxHQQNMFkz2RfrNAIEKjyrtm0MD6wEqKgyLhwLnFCknBsszAdiPqD7ihukBOuqThN0LSwFvzO0fXjs5AqIcOEO7Yy0BZoK8lg/uxwM7OU+sb8MtAqBHOOmHSyEAs01Y3CBazwNDu0PRLzsrAdn3id8WkwcDdzjhZkbLXwFAH09N5PshAP4At+xiIvsBGmkTMqrLPQK3cTgGyeLfAfrztCFrrjUAioS8WOq2bQOGHaBHdt8dAHpeEV8dHvMD+8X1G+AXEwOHGzU3mz4BAq6Zcmy0nw8B1TtB+oKK2wGNSlTJC08dAcFiOan8pssDrMce7jKvUwGxdN/cZn+jAUBgxu5EVx8CaXdsxSRi+wGNgO6vdgMFA2BlQmodXukDj+WyKL2ymQP8JszaQDbLAHFfKaBh6scAOr5gOsrOewGvzQYUEqqTAb5lb9mfq1cAgVwg19O6pwM+dQ/Xj/a/Ajc9WQMVAwcAbIWREN9S+QNrjzzbp0M1AEOxFb6im2UCahMVeOV3BQPqGxoDsU75AokKZaY+JqEDH2RKgc3TRQOLl+PIWQM9AdocV4RILzsBcyYvLDwGrwCklaP1RjLZA8XckSruMo8CTCpMMLI6RwDXTuCy2qKbAC6sg+AnzxUAAmgKHD1PXQG9NSpsuIrdADRC3dYkNw0BC90QYRI6UwMX2OPNhRslAufqojfM0wkDIIQ0akUnEwLn+IBc6SM5AoJYi5fUozUCyuLxzwovAQIuY3dif3NVADSPDBGJq1EDwC5egRoHEQCWUf29KD7jAuAXQam35vsC5GM9XwdO1QOEoUwXQIrbAR7dSgqWn00D+HZ7HJLXFQKifFu7GLbjArjMh05lBw8BffsXO0N3TQNu7dspzbtFAh2PiKPvru0DPfs0TItXNQG9EvfWSscfAP3Xn5p5Dv0DWyIyoEdaLwBbiYsi5VbVAA7JY1ReZycDCcRMGyueHQFNj3TwxWsbArr1SpVpzt8AmIFNDNFKmQGIplBN/XrBAoo1RVb2XwUAJdeKyw4PEwFGEzIV72r9AR8KVFcRWqEDQqiWWsaPFQCmF345RA5dAKYujkzEAosDeTSr4kR3FQBFVk8zG97lA/ueBm3Lvy8CA67SattvYQNJz3KY+ONDAwczxFMz600DF3/7LgMWywEnNFLR0CsPAaqJfc6snzUBcQyX1+li1QPcxjuxw5rTAqJPxwlEj0UBC1NdtEky/QC3p8VfosLDApDmGsWcU0MCkMBoniQPgwLWDx7Faq6JAKC7isxecUUDqsmZdHwigQBrthw/hTb9A5UQLAZYIxMAraNi9F3XUQJyv6mB2ItNAQjWhd/auisBDnt9EHGfRQG4egaj8IdHAtcOfN0nHrsCwuXGIll67wMcVkNzi8rBAvsRC+tQ+v0BiM2q/hlnVQFz8PgXuTbZAyaNEaMEAt0BDi088NE6qQCs10OI+6ePAOsVhxiTQkEDHitl0vfK1QBwAPlf2l79A+8l3CXmDuECrWgx6ogi2wMS//9jc9MhAqOMlVIbBqEDUmSPKY0nMwBbJW8aaDclAMtgNoneEpUDgdGfqF/jEQPn0nZD3e5JAZrV2yevXxUALk/KJa/63wMIRVe8pG97AOjxgKL8Uz0ChnCiI/bbUwP3KLuayi6jA+BmVCqzxy8AqyJoSDCHIQEnm7uQimNFAULyI7slhsUBG3eRMMU/XQDvP1/5l/bvAlrEpgvoLyECA2G6bYivcwEiym8PGEIRAHUp95+h218B5kxCC6OLWwNDdy977DtnAjifcsjhkycBfYqPhTbHjwO54AKMlp85AVYWGOnvzp0BMl8vx1CrCwJxRQQFovHtADlMU1k/HvEB2O59SA9eYwAPs5nX427xACbjZohKGo0CltEa2c6DBQPB1eH1rPtPAhXFnDMTYu0Dy3yEESLrQQDzmQVZHtrbAmi5434Y/rkDNQoGR/TLcQN6KXHQnkdFAUBBwunkkz0DtNi7omATNwJ829J7H+8LAYuiJJPlY1cA075aon5uzQAK9dDPGPrZACrmUa4kgsUADcfWCCWvZwNiM6ZR8HMRAaEv5PM8Ns0AN0CIRY+jVQE6jcb2vvaxAP+w7a2GHvUATkHQLAfCXQK739YnlWblAQTlI578DuECLQn4zjXWgwBlYqVFMG+HAAN0JmtGsvUBQUclF/STAQPwEnHd3BtvAWCwPI6JCfMCZ4OZevdi1wP7aehKZGuXA9wEXstmOsUAMDjUrpiShQHXh/ogLy6/Av3Fjc8zgzMBCqgmUrCrEQILtuVKi+rBAzWyBc+4OysAU+QonpzuuwNit4ZOI7bJAm88opQFOtUCYEGwiRdbPwHwGQMP7n9NAqdLXnefx2EBPoQdI89XLwKHIdjj2HshAj6P5Au5L2MBd9OttswPCwA4I4Do7v67A6m2PkScGvEBpkuCROwWuwMTwaEF4NMDA1jT55Tmt2sAq9l5hDgDBwGHrV8DV/q1A//dmFNrzssAV5FdsNp/dQDr+WL53HNLAc0VL1mYb0UDC2HUdD525wLU5e0jVObpA84wPYoI00kACTFDaKs2rQJFJzEnxgqtAFRQ3o1KDxkAxoYnbcrOgQIug0iobyNlAfCM4JmolxUBsDl/PZqm7QJjwlatQceTAgbKkBDpKykBELopEiTvnwC7OJ7DuIMJAxxacBazu1ECSd5w71XHRQJzcf0+rWsXAIGXz1vuho0Dch7Ahqe65QPITRli7kMdAKRxfrzm+t0BaeIFYTiHbQKZPTwyPeMPAMN4vPIZkqsBmUiYX3H7CwHhgryQqEMHAASki8sPducDub8QXi7bHwMzMQ7zEP4ZAiNuDmjOaoEA2+Tt0h+XcwFMABvrjobjAPWJv/WosvcD+PtXRYPirQPMTM+aNdMFAFntnuUp36sDFcwGFmJWuQDjb5O+aboPAR9s1Ic3gfsBK+8qS8Om/wMpXh7x2KbTAJggdTzTelUDFi9fAQaq7wEUOmXLXTa1AIUePneykzUBY6loGM6TEwCgFvgTtIM1AhLTrfRf1w8Csv0pLGay7QAEhxayq2LvAbEyF0nAevUDxpmwQ8H24QBcsXbvVNsBA/TOqBCAW0EAF9TUcMCWmwDKVXTlCF7xA7KUmbf8EtkAXciXr2MzMQOojCBQzgr5AK3e9lTadxMAuwqDDGmnNwBKQI1NHs7bAVStrY3GN1UALkTQOZoHCQE8qLmn927rAQRYQOZpi0MCgnqpSYCjOwFWc89Z8mdjAlWaqLHa5lUBRZhucjlTkwMjKwzdGuarAD8wckFW3p0DOIKFLHhenwCeuMFmVG6TAL/tlZAwwp0Cn06O9P1WnwAyuG+quYMZAi09OUYx/vEBnnVbJ+1C0wBZxcnJZXslAxwup4yB4lsCSTaxLOeG6QMS78gUaj8hAK1t87yxAn0C+gW9Uou+4QLRUbd54wq5AX/9KE4N71MD+NFSu7DDWwPBtLWnfFNBAWFDn/Rww1EA2BLqgv1zYQHtqIZGZRMJA2uqCpp71nUBXIDLEDsfJQLkNJQQjh8xArS68I7F1uECaZNg6xcvUQC+RMfgDSOLACAwj8F4xy0AJVosyr8LVQKJPxpVZMLlAQ9nHRpcvw0Dz3XLYmT3eQJ7g6c/Ooc5ABy+bbRWiv8CNt9iszarSwODmHczKNdLAjG7HAG7ItsBf/e8xhLXQQLFOHGDx0sHAuJlIISIbskBn9iraWSLBQLj71+f4nsZAN4sdpBiLsUChZT94Jf/cwKP04V/Hx8BAdGwAnj9qssDJlI+T647TQLVFhGMRccRAaDqMzU11yEArB0nXgvXGQNc8811JL53Ai0Zm7SsIrsD20irRsNWYwMbmdtrrGLBABhupsqw21EAil2ybA4KkwE9fwhahStRAYmzsMEL/00BXrJjnf6/awGNBVdn/1qTA9r6aRWQxz0D4AyAvoULCwGA93Qb2uL9AEdMLJxzHx0BxEAhhtCrCwImszBjGUNBAUyHbuNV+x0CCCF2xyOzGwAh23JPmQcZAHCLQIu9usMC+eaXw8Z28wAZflTW9d7lA/MKIVWkD0EBRyxE7s4+xQHcjIUEqq6LAgJMhPoXnwUALp+wVmuTUQBElA/gSrZ1A2T1kEa0vu8DKC4nnoCSvQAyH0Q4Dn8nA5n96d4lczUDMvYrNaKTEQBPK/IgczKlAjotj5kA0rkAMZH6f08rgwGv2ttuux3/AplpX5BJ6xcABn6u+EXDBQMPTSdbRB7bAYtUVfHMKucBxe4xMqO/FQMxnQctH0JHAH5q4kRHrw0B9MrHEcsPMQG8DzwSq37xA3sIuenF3dcABLTp0exLBwH6/lMPitM9AIrATBCzN3cDtXptzX4m9QNVcV3B+RJ3A/g7AN6Gm48Ay03q9VK7IQFT+Bne/84RAfHrMf3h/00DmrkPyNpPDQHeXuEiK/NPALMXo/oU74MBMlPh2m7zEwJdiFXzGWeDAKHNGSsczykBy8RMWU0G+QDcS8wuFN7LA5c3BMgulnUBSf0JRw+DSwIlc65FCLKlApHcmECQmvUBRJMpS2XDGwLLvgboDicjAAamv3M/kxUA2Mluavn3QQBrIM/141r/Aa/9IjuP3wEAZb7AZ69PIQA3K3cDiAp7A7JYEjv8pssAd9m6gGqzgwPdisRoP3VTArRSiPdzfxcDaqV7ie4ODwAtQmrb4zsZASrtDdFRexkCId72IXNnDwOuj6WqbG77A1o1zVdNZosDM+hKtOxnAwNNNhN9AdbDARXME0MNjtMCx9I6gujfbwD3Xq25+ZapALZ1aNCfur8BMFxfsub6iwDl9L938J8dAtm7AXE3UwcAfPN0XiObZQMVmmFwnZ6jAUihqRIgt0MDSAN/m42jQwA5gUnvwIujAGiX3hSlyoUBP66DKR7ayQHN2BogQzcrALsTx8Zrv0UAHgrM3l4LEQF9GkvfTtaJAsHxXf8+UwcAvl8gc1cK6QClJuOcPFoDAdfxC0B0No0DYCQWEfU3CQEbQZwuS0ZzA5lm8fhGfw8Dbu+oEhdzZwIWWFV6NmsHAYQfQsqqs2cDGQ3KegOeywN07iuYQocBA5zpI3TClxUBv+/rtk1C5QAzHeRzyH9LAHL232OsGu8AMpRUJhG7AQLEiklDfQ8lAkPCkNGXVxMBrcZ5m2DK5QM8RRe9YmpVAD/uhto9twMB5UaPFB7XCwCX//ftxHNZAZz/7h+Rms8CFn+lhITawQIIdQMNVHK/ALBwzvBMy4cCTomzj34+MQMwhAql9CbRAYKSR0vl1xUCCz3HIIGK2wDb+vT0deePAW6eAO2+KwkAZ6pLC1CLQwMuMP1WnqrLAeZ3nEZWA2kBuBuSnuBnBwJ/ouA5IC61AMuqPbyfIo0DeAfmSasG3QGzqn1JYleDAbWo/eCiiu8DRtNZIhwGtQIoXP8J8Aj5A6NnGRKgyxkDlgptyB++nwDgBMkyGAK1AAjj9BP8TssBmMYCmbyfHQO3cwmpHVbtAkryHkkROxEAaDkmxudu9QBmf/kuOLMBAcvV+Cdayu0CDwu0WeiHJQByJJW99tMbAhJDYNcrc5MAfTUiyZ4HgwCOpVSWl5bLAhN2luxQv5sCK/uouFfrGQKFK8o4QfejAxzjUtSjiyEDtE43FCL7VwAkmVdZz0r/AVwEQbRbcpUAl0VaPoDiJQFvSYahI/r/AzXhA/1Ahs8DUB5NP6QzgQEkQVoPeSchAb0sAdntn0EDB7OrFUUnNQLXAwSAre6jA/XGUK3IVxUASu4CPfQR/wG0xinq828JAqW5GD4umwkAdxqwObmjawI18IHIyGOHA7OgRYSkHu8AalDx2NHiowOcl3fhFWrZA2oCCpf0W38CF0ZO9w0HRwMJGHfjNxsdAf9r7pG4VocAxDo+gPXOlwFzy0brCXbhAVJoanSCXy0BDCAddWUagwCQXb8+4G+LAAtVFQRjGrMCQlnPMeALJQAeFINCDKshAPcpNRrX6tUAgpK//cqffwCu1c0zyYcVAEq8nfXxnxECVMTYylvHewMgSERA+4s3AKtZtJd4X10DFnRj/TLawQI4uPLDYHJ7Ann6YBdP+qEA29bFgmrLQQJ4by/4Alc5ARoBL4tOruEA/G9ZuggDQQE5XzwWnCqbAhwPDyi/4zED5fjMl6Qi2QDngoOmisLbAaY+wmhk2uEBwPEccegTcwIXGcXd9SInAaN6+maekzkD0XGmMkezKQCRQDkMq9dFAA+RmK06gtsD20dWQF1nEwDz4WCLsruXAzgZcc7uck0Bamne1yyDAwJLpaA6mbMhAeYQMGBjJzUC0ZJiPAyzDwCiJzaGrasVAT/dmzMVEyEDiIdIwKLG5wHpCx5RECKtATWvbtVpQ00B9FG2CrVTLQCIWQrzOfrnAasN0XdqVyUBoYy7VRmXBwJWPPras7bdA/b1njsMHxcCgQY2ONtTSQKXnMfmdONFAtK1ddtLwyMCXdfCTGELKwD8GVqzQubNAaKIvBEX6ukBMgT4qUb61QCKzSlew+rTA7U9gcmfjy0BLEwHXoTScwCJj2mdGL4nA4y6STJ/9u8C0Rj78VI+1QH+boTbq+cLA7iyYxKnUq0ASUCOoV4nZwNQ118oNptjA2lZD+O+/wUAsWOJeB2y7wMN6wUwTIL7AyxoieGXWw0DwkpqaR0/KQAQjr1rBuXvAre6D53q9wED5LtskWF3BwHrw1MZJYsdADynFCkqbvEC2VtgNUDHYwLd59Ej4vdRAxzAb5VHSwUDkOtK65RPAwMRj73Wkr7jAfgtJTLxww0AhNf/TXliyQKRjR4tfhbRA5l3MLcuAxUAN/uqrfYexQBPzC+cmka1AP9bNwOafrkDOnlMLdH/VQEAZTYIQQtBAJHoFt7qIyMDV6qz494XQQKvPvEisacLA/x3EDxIar0AIjVBHAcSSQALhBND/MMNAg+A4OYBO18DMUw0CetGwQCZejDG6dLbALoXXFh+X4sDy3ALLi1GawH/cMJy9csBABZgFNANnw8A4L4ZAkfipQKPiGB4YRMFAmrbDkx8+vkBk/yT1PW+wwG8nQE91wsdAKdWcrXTk00ANoMn+qsrMQOAgs54uhsJAL8Z4MydI0ECeX/YqLpXQwNEyC/Ky57VAPV2OV9g9y0CLhH8zFpPLQET+/60aVKrAgp8zbw96xEAztZOaeUjCQD9guK7/t7dAqNgJKQklx0DHsAl9KUrBQI4UR0Meg9FA7wfyrPaM18AHLOfSxHWkQIjw/UbyoJxAw5zyYHewzMBhzr0QR1nIQB/+YUgLn7hAj2zdA2hw1cC/xGJQR31gQHsbxRtNhnHAgrnZbOVWpECEu2j96HhfQKVKDRkv3drAkrkbB3oydUB8rG/0hdPTQCVVSplM7bRAZdsa9uRPrkCyMUbFulq6wCzE2UV0vc5A3F2LtCUZlECK8Wc/wVarwM9Tx6Oh1rdAAKa9Gz1trEDoSHp3bLChQLquYpXFFclAxXEOfSjLikCec/xBuF+UwFfBhvwfQcXAAxop2AEXhsDcf0pZIX22QA0tJPjvf3RAzyhc6GOpwsBQxgwy/MG9QA6kgAyDmrlAYdfph+2au0Cz/PP36EG9QGCNYmRrBLtATFslt0QAwsBFFJX33q+9wJStw4CWGXjAazMz3brS2sCanKCJvULBwH35qa4vq4PAkRAYcdYp2kBaG5SFjUF6QGclYivpKNzArXuvSoJgwsCk17PeU+2/QMRauGTVlK/Avp9iLy4NzUAgDvdci2u8wLzqNvHvQdXAcBcd2XXEyEAYk35xXT/cwORFcEg3NcBAaBDUtdXEykCRQ32htqzGQIlpiZ0z6rdAoO+mR8SP0UCq53vblWamQAbZObC3Vb/AJYbgUX6/wEAfhIlotZCdQAoIGz4CA65AOvR1OOqQ0kDEVwWuJw/PQChwodzH3sFALjZOcTI3vMAgNinXY/3AwCy3czn/IsNA7zoPzarLp8BnpVweCmu6wN/ouZJMxtdANUsfQxPhtUAoM3En4ojRwMXszeQHjKTAGkeCWhp9ikBKvmuBYtm7QDT1OGJ3EuHAJa9ThdKAv0BKFn9v0BXAQNsepBhlIsJAjJ3upG8mtEAfRPnhA6zLQM5ccbRLa7lAuVpNBqAhssDJgQX49SGoQFmeFMa7Z71AtZt9AWyBp8Bu5mlAHUm8wACyGeVy8cfAgPMrK/S2c8A2ThTyh3KmwIVEiqV0x7fA4xi0fwmaxkDG8NkchGrNwAVfbOTGRNBABVpiFDS1isCHAYIKX/a4QJoes4GO/MBAOk0tvedtxkAK9DKGl8rNQPQhPCuLyLZAVBwBhk9Gt8AFD0NrYhvOQDelQQpHQ9XA+tRZ486sx8A2DwriQz3GQBTlcF2bjb5AHID4Ua7z38CJMdDO0D/CQJqwKlsvLrzAyGjXa8IGsEA2wwpUbSDDQLu7gR1q55ZAoR6+2wlexsDo6jtck9S7wEqE1CEketjANID1ro1rwEBinS5Idp+ewPfHb68zhrNAN/1T4Gw838AN5/7EvS/ewFF2Abp6bdRAwM5fuF/DwUD26lMge1nCQGyZPZXHCrrAwKQgJet7wsCN9EfxbDDHQF9l+SaNCdVA0qTx2K87wUAB7kvanAO0wEHIu77H/OLALxjwMZb3t0C64JSLtfqaQHp5EWAkJbBAX8h0Xy4x0sBLP1uquG/DwI+GtTQjK81A1Q34dMGWvkDIlhEJFDPBwF9PsKugA97AaYmWWz8+3sDscT1Q5hXTQKk1v1H1Qa1AWwVVfgzp1EAS3fcZi5+xQLp5Icye28xAugJzZQj5zUA6bDfgDJbQQHW5D+qGoLpAw+srpn1npMD5+EpkhnTKQDUCH2DZUrrAHfYrYuZJ0MB65OP19M/SwHDmdyM8HpvAqT5DnXDj0cA4ScrmPH2uQFU14XkUQODAQfs0kAtQlkAd3a7607rAQPR8a5ZkWK9Az6kNrFuez0CDKOpJiR3CwKdktyR8NuPAMC+a/bExasBJZnnAbSjAQOz1mRoUaMJAMd3E6MkjqUAAHEkBrsu4QLVbT7SJCshAUQoPRoDdj0DM/eVneg7dwDVrYDi+ucFAmGkiJSKLrsBZCYb5/k/FwHK0bb1yqbvALVCmCKGM2EBn7q8ISEXhwNot9cpjsLdAQjyRKBmDyEBVpU78BkS6wB6XfUi2rrxAMglG4bNMtEBOyXdgpuWpwNRbgbAAyb1AKZBz/QFqzUB37twLYX/hwLRuEv8TUc9At0l739oLu8ANT4FiZfnEwK5Js5I1L9pAxyfMUp0wzUA7egCQ5T24wEw23+fmDJhAHIDBt6sRw0D3r6GMU+6yQMAnS6SvrMJAAR8VsCQCo8AKhBje7sThwB7U4ON8I8nAvnZnHhi1z0C1JPu4ig+9QGFSV4tpYbbAicbnNEwZyUA1cJpblDLgwLgNWzYS9NzAnVhsJswPzkBvXnydkEXBQDlrF3N1RcpAVE2zpD5Hr8DJAOPdNpzJwJtmrm4+9bdAmjFCdqG4vkBzZoCY/DLAQPSgB8mo3JnAu5CGzN9p0UDRfGb0gcC2QMBhX/vZqsfA4EJezoSDzEDm2ZMEBmvCQPe1MUt+rrBA8LPZRm3sv0ACqdY8ABW7wDgfyqdcycJAzuUXzHJf0MDQCZhmxFfBwBHB6jCjEcBABg6cM4Ku0kDY522F8u3gwJVQf1booYrAXgSzV+HAmsDUtXBkEyfVQHSpK1J067PA3a1CIJPJxkC6wEVGsNnBQIknu56OvtFASTO7UTJ+3UBZO0Aq8RzSQFON69bgy9JAoO3Z5uRFxEBM1vliIXu4wA8JIXNPq45AoOcY0rwJw0BC3ctHOGGbwFsX0v61cMHAzLO0jKHHxkAIv0epjhSVQHzt0ktFVpXAk9doCHyboUBiN5vOL3TMQBirJWUFvc5AR5ox4bKA00BQECzKTy6iwF34rqmdUcPAIoBOUMRXwcDcbqv2mw23QHXTDqeZd5xAwsyJ0w/3gkD8IF0zwEevQEEBgr0yNMfAR22KuEoYyECskZ7eDCbOQBcEbYp2Xs3AMGir/W2k0kAheFrF4eGnQNiznrM8OLTAhKo+vG34tMBJ0tcSVISkQL+thKW2y8HAxcVr6dVD0sCOI1FwQXWzQFC0alKGbrJAxF46ppqIw0BaNALaYxvBQGm/yqA4q8JAekQR3WLuu0BHfqojnoTTwDXcJFWIYLBAOwfzzMVG0UCa2bkr2JFNQEEyUZznwZDA2I8b7rS42sDBEucHSXTVQKBNhKL0wKZAZ1Dabb+9yUAMNZfEEJvBQFfxkKSjcq9AEKm6RP5NxEBEKpugcbCkwLaHUT3vzcJAC351FkRBr8CHAasKdaDJwNCgdPvdheDAO5Hflcov3cCGjurwEkmtQH7R65Mu1cJA4azAHuOws0AEvWlMUX3CQA0wsYkLEtRA1YPp1hOawkBCw/dHo+rdwEpcXKXkidFAC0HpMG7xpkCvJc7ILOTDQNQdQxgsn9hAJBGeazwbrMDeUPGRZo+5QPxLd/WhmZDAumbUt2eHy0AocUQXNCLMQFXRCefyfKHAz8XNgnzSxEAaVQQxXNfiwJqyrP/SV71AV4s1+3lTxEBLRIlxlzt1wP4SLGC4fc5A4CYniuO8yEDUBHhzoI11wFLPVwjzGNHAfSEsePlbwkB9DbfbqYPrwILnsE46iaZAtkzJ5jgsuUASJCuPph3gwNR1TmecvNJAlhvEUJTjsUBgQS6yMfbXwJZO38MSmahAthD+u3Rg3ED5a/2VVPHDQAqmojMIQMBAdlDJzK1cwUB0mx2bZMGkQDwcDO+MZNVAJbPnlBZX5L9WC+ekQp3DwGk9Rh1pAdzAC4Wu1a2stkC04UrQD/22QK98z9t99qFAuGj7nmBZvcBKuDU89LvCQBcZIAGWZODABuTkCSKMdMClGIutZaXRQI7ZcT6LJZ1AzdAmuz2MtsDRby+i/O23QNDMgRq8TMRA8CiAqGWEd8CvW3jW3sjEQCi3BI4ECsdA9A/qQA9ZxkAd/0GZ28/kwCIJHv7HiMhALrD6+iy3wUBN52CKUNGwQCxFIzH7lrxAWQTpz3Pcr0DKuxSPVIOwwIk3qtUcMoFAnqvxX5oA08Dm5bZ+pvvDwK7Ro+2rmNJAmVgonVONpEBphAvluVqiQGGsw8+U65XAT7T/EhVq5cCzmJldBezBQBndh1pWFczAMx2jfuNpwkD7/Xap5nXOQHEnmNMae7ZAY6hil2aS1EC5liflRYS3QEmX2eQSarVAUtFOq9aCm8AIAqIWh9ibwJP68M1owqTASBDlTIvTxED/pNmwnMeQQF5wOxelQuLALFtbKCsetcBweG7GtJ6yQJTvXlxX+69APjm1isusxcD6grJ4CsnWQG6W7PVRxN/AJGyOa7o01ECy4rpm+uulwPZg4pNNarPAzzUGF5Dby0B+EPEb6MjXwPCOdyRnWdJA8BV0d0QFxECY+KNAY9uhwLzebTTdw9PASoBn+iGSv0AiCgy2e7biwHm2oA3djYhApDCvQObr3kD+1JbZHwGjQFn+/I4OzqVA1ldL7Doe4cDxgc7Tr+PFwCo2n/U8oMRAJgYW2fM3xECHj46eK0exQIVp/KRV5VDA0X7x8DIRxUCUqBaYqAvTwASVjbn4pdJAb6ey8BRe00Ak1oPmPM21QFB4MlOXtipA0NlpqYe04cBQ2SAs573FwMr87Vz3uqrAqF2+Rl5Ky8DG+ODIG/S1QDJt8JvjIsFAr+JRdUj1v8DFBu8zF6S+QKRrPaJmIaFA0UCVhPaqpUAW+Rhb6+nFQHfSGwSQi8DA1iQHnylIxEDBdRCs8HSqQPqMYx3b6OTAWPz4eyqqzUDCnNOEqxzAwHlqQEBKrLTAwNfoqDE0u8DR5hpM2/WxwEpW7a7RKp3AqSDLwTXOqECsQ9qzqZViQLyI8gXCjbtADEYk82FkrMBvgKZjGAjYwBHx7HDInMfAvvVZqWu31MDiaqiU+GifwMBWxQoKacJA9lYhOBlFt0Ay5qTu0nmsQC0gih0s1cVA4detoaLEuUBry0m1TEjCwFclMEilaMFAj8L4nQkV1UDbxOh0QC/CwF9dcdmiLYBAF16XXRHn4sAx6kcx9GHQQE0zAj4H0rRAaTiRtE16x0DLgsSWOavSQGgHT9KSja7A6RQtEYOSzUC8+waoBhe3wEFP27WQeMhAlx1t6qORt8CAIlozmu3CQHMmish7qL/Aj4DozXQEtUAKiQBdQA7YQGyJ3Aok3sVAkcLB6ZtCwsDpB3iHEu7RQCfsKoocc7LA1SevtpMtwsApFyRWHgPEQJ/5kMCESatArb4ZMuULy8AIiBEhiZy3QOlERlroPZ1A0F0OEfHklMD86900Q7W2QP4QM8w5prNAUTMrDEZL3MBGEifuf+7YwGx/sh/hab/AyOn5y0CBnUDxBSP99rLDwHtCScLBl8RAooH4TAKBvMCdL7N9Y8PAQMTHh8PrVMLABEYTsTMA4cBvakwzsx7gwNFo7eF0qMNAho9lu1zJ1cChcXbL+9FQwBQJ4ztzlNHAyrqcXXNElEDrLXFBhR7PQAZpxbFikbhAH9sRqGBj00AxXPGhmSa5wBaPwMSQa6FAWkYPHrckx0Drd6Fpc7u9wAUYrnxipsTALUW4rO1c4MAva9DFpFi7wPBVzwS53YZAusD82gzLk0BR/oWamDbFwANGYLrxNMVAdFDXRr440kB5mtrv4KDFQBGBZ+L+q7LAlyMk/KDDxUAzbTbnmGLMQLWHLs9msr5A0hMjXUEvv0CCDHYbamfHQJwNsYb2d9BAzDCtCz/X20Bw2pYqskWyQGC32NVL7cHAlJDvR76mu0B9mKYPLD27QCr+x0aG5b1AGEJO0HmbuUAtBOhGZx6qwK8WfARldshApL4/Vhi1pkArIIeo1MvAQMl7s8XXnaTAhlnbtAjA0EA8coNlf4fIwIz82WbZl6dAtG2HLzFIuUD6F36AlDbMwOAPzQz9v6jAj36kNxkVxEBVko+aLOLIQKLeE+DzisBAdk9L12jiyMApmtE8eqiqQC9dOrjUw8JA71YZXfB0w0Cb/gbPL5mbQIQpvdgKFsnAbmLavud9x0ByT3ZsVoy2QPkYzLnHlcHA9HyBZtKlu0D5yuevLFDaQFwlTz7DeMZAwfvcjJVB0EDljRCcss/PQPoIdDlDgMtACIBLshAqosCvqMEHGfexwN3nfp9o98BAHl2UV8RDxkDpTVQjDNm/QFTcXkKe7b5AN+Xjmb3Ps8CTIvouhwrAQO2wntXylsDAPHRHJzecyEBCUkBpcVbEQFW+HHuIjNFAsLrNSMP11cBeKWDthWxHwBOPuGkkApzARK0ShnI3v0ABojGbB/zewLmL4GMJXs/Amd1DrnbezUAzyJdHmo7HQFO8LOgMfMlAKCpQ76Tz4sD4K9SIUBvCwDnvMWzqAMrAhXp7sZuemkA+POfcr9DIwFu4tIJ6GLtAbkifYuZC1UBp5CZRz328QKNJ3NC6EuHAR6jKgR6e0UDxKylJQLV9QAJQKjrBILjAHwPuCz/9tcDvA2EdsTGRwOSDf3zGbL3ACDjxBt8TvMAA92VQ5AuyQPpQ1b2xgsfAvUXaVzEKtUDWJaL8X0ymQMRC16eV0uTA62uX9U/2tcByTgTodVuwwEG9Xru6Y8lAT5Y+lKuZwUCGS/DviVG9QJt2G9YsHLHADYnr614g0EBRB8AhvA6yQEn7ngGB0N3Af1ebcN+erkA1Nu5uZ/PJQA3DO/5DBLbApmDuurxZucDyKiFawG+AwLxSO+OYRaNAgsqz90UFq0DsL9ynvijDwOYKt1uuJ7tAAH6MGnKW3kDjO9w2U3LHQFgNSWXoDMrAqQM/uj18r0CDCSJH1lTMwGQfJOovnMFAODTsw7rwyUDnd1nbkDLhwF9MKamWPc9AjPtdXSYApkCDYW8OwnOtQJFlpsewnc1AFTs3WdvwuUCGrBaB8uy0QJ2jDpyzesDAn0SOs6EVqMB2mihGiDrBwMJ3zBmd8tTA2hZEyG6huUB1y1L43VDLQOkTvmocsslADUMGP+9a1kARdVm7dnXEQDK7Zez7B89ARDZFNZz+1cC9PZenTRjZwN9OpSRb4NdAHPgAAkioukD0qINxLPnhwDhQcMeFdZ7AF4lIx3HR0MC5dEYp6nzAQAzEdoVtFXRAz4c9Gyg50EDXS4OIgwe+QMmb4ZWUC8NA3W/Hrcxz2sAX2C3JTCHCQH/c9Pk97sVAwHAFU9Mp3sD75BNVna2hwP0vHuqq0cxAqg1Pjqv8v0DN5xYaM0SGQEEfFOPhm9pAD4pM+Ks/ncAAvWCds4rEwFH8KOow77RAHzpg0xOA1UB2GKrw3yDHQGhpq7wJXL9A2OJeUcgr3cAb15nn57jLwKRGop6Q1btAJXPNYjXauUC9MpUavBDAQNi4rTuSVLvAiqhS/lf7zMBFx3+ET4/CwNeJrvBIUb9ABuUl/hMcw8DjY65bcvC2wGRuLX/lC7XAYmNieiyIqsA4cSg9zF3CwNMRv6djc9BAcQfcKNuJu0BMqBOv3XzFQDsIBiEspL1Akn74ASuuwsCMXe8zi4bfQNZSy90kmNFAsnq2yDyHdkCLLy2bm6KUQFK+ZVQx5GRAaNCFBMrl18ApWBpPexvEwDxzTP0SPLPAcdTOer/fs0BFQA1unoysQLV9PtkpIdfAsk/dzcN2wsAjWG832d/TwLuzB4VXS59AlhJ4QhB6ssAUGUDF3/C3QH5EQIgmfcNAMvg5VTbKsMAvyGc9SjzAQLWB7o7kRLJA5pFcvMRHv0AiBFngbv2owM7IRPfUh9vA8EkR+nXzykB37egX7wLcQH46jwj6C9VAM2WaQ8uOt0DZAMi++5PKQGn37m1MMmxAX3EeRdfYtcA0qm4zwmu+wFTusWwVpdNAmfKTzL69y0C3kB2MvsqlQIMCIiXrncpAEVcAiejQhMDgqVkwccDFQLmqA7Z3mdPA7G+lmyl62UCXwDHGbE+rQHd3JsIQyeDAI+Vs/YCg1kD+FviWd+6FQBk70xpsU5rAhf0v/nOKzcCeZIWQmnDKQA0tfq8fKsPAm7ZKL3vspcBxl7NjLAmXwIB9tvYvP4lAaw7ib01FxUD4tMMXWkjCwJOMe1cAP5pA0SNiKvZ4tkDlD0R0tGe+QOubh4LRXOHA2H9PzCsM0MAzPcNDUmDQQJih16JaAeDAnTjGXYpp4sDtkz3kuBjGwMkv6Jln/cHAzvYYpZT60kDk6vep9gLCQJMrL7NxF8FASA6H40otuECKxShFnjDcwLG87SHJdqpAh1TtKkNfvsCwZHx6XsPJQCmPD+BOos/ADx+CjSABtMDsqq5MMxeDwJszlgd4meBAsyOEjZSwoEAB0r1V8MSxQHg4pTJQrctA3sHyd0Wa1EAmndGzcFPCQAYdF0iCrbBAfN5dpfkM0UBvSZkKgjnhwBIAnKjNj6zAUgbOwodQYkDsXAucv6iyQPp7cCpYJ8dA5nIRWVkiw8DvRSQA/1W7QAd4/7xGgMfAzNjow9tgskBKz7fOVwDCwI1cgFyqBcBAsHiclefpx8AoN/BYTPB3wKAD8ffl49fAVPJATV1Pt8AXEc06donOQLLLagEmSc1Ax1pqHUWstUC+2BkSyxl7QLw2gopPKMjAmg3qKUKBw0Dppmh0alfMwObPCil20d/A1akCuhxGm8C2esE+SGilwPr2Xg1YhLTAK7CHHJn6yEBMcY9cKWWhwDx6ew6VSNzA1tw/7YBYu8AxZFOLRKS7QHOr8fyPhNPAoicO2OYFmUARoTFpqVq6wOmASqSaNJHA/mYHeK/5zsC8Axansei+wBj8Dib1Wb7AGLaEsGsKxEAGMujFUTS2QG3xGmIioa1A47m9NNL3sMAjLFrUDwTQQIGDtkEoqNbAfxgP7rEIqMAKRQRAG4OxwKWPETodSsRAWN5X+0GQmUBKwlEQLurHQBfdyv2+B9vApbkEno88vkDHokuM2AOlQNwq3OG0tMdAsp+t4h/ly0Cmp60fm9m+QBwVNcNMqchAz2mByHA/0MCPBD+yoQTFwDESN5bYPrhAacumLouHyUChPQN4PpjDwPuYWFd3ooPAmpxrBeLmsEDlv3nuPxu1QM3+8OxNb8TA7TRhPmxPcsDy1pvGxqy7QFK1OagUYMJA3g9qBKyxwUBsmjpscV20QMWD/kekSpNACzrNkpXCwEBdSB9qI7/RwIpShEWCC+DAs+d2dyebx0Ag7r5lqUzBQJNuLRlMcqxALGOpZad1v0DWlgj7vVrcwGFcqE+cpKrAJ0jYefjnocD7mbokaOmgwNNJKe4TwsLA0FH8AuxUr8BJ8oq/K1DNQBkgtg/kV9HA3pCogRhCuUD7P3HnaMjCQHxRZvKoba5AdLqviZqzucAIlUd7N+3FwOjfc9OmALDATijpUK2xzUARvEloribDQI6B6/YvfLJALcW89TMTmcA2DF5mq6nCwKr0uKtoB8ZAoQQVr01mzcADfK46G0TEQDELcZiH68dAePY6Kiq+zkC9lPFXo6XDwFOciRp2q7lAzgWboiDF1cAkYtb6IZXWwBXFcoSkBOLAS4Vt75Jaq8CMzm5vR4u9QC1JpD3FtaTA1nbdNs9fwMBwVO36oI7BwBh87FukLpzAYRBXWOpCxUAmAMJZXmrfwH42agTIs71A\",\"dtype\":\"float64\",\"shape\":[6753]}}},\"id\":\"f1d5af84-6bf5-4644-92d1-a7e667b69c1f\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"callback\":null},\"id\":\"95dd5d80-cd11-41c3-bde4-0bab36532e59\",\"type\":\"DataRange1d\"},{\"attributes\":{\"formatter\":{\"id\":\"07cbe68b-5ab5-4309-80d0-ad6f901d9147\",\"type\":\"BasicTickFormatter\"},\"plot\":{\"id\":\"68bcde05-1b43-4e4b-8678-e1bb055784a4\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"f784a7e8-b04a-41b9-8812-74927ecd0112\",\"type\":\"BasicTicker\"}},\"id\":\"cb457bd1-8802-45cf-8d2c-abad74e055f2\",\"type\":\"LinearAxis\"}],\"root_ids\":[\"68bcde05-1b43-4e4b-8678-e1bb055784a4\"]},\"title\":\"Bokeh Application\",\"version\":\"0.12.7\"}};\n", " var render_items = [{\"docid\":\"277f1c4e-2546-4624-84ba-38a9f448eeee\",\"elementid\":\"9279ffcd-6497-4acd-9900-cf5addd62bf6\",\"modelid\":\"68bcde05-1b43-4e4b-8678-e1bb055784a4\"}];\n", " \n", " Bokeh.embed.embed_items(docs_json, render_items);\n", " };\n", " if (document.readyState != \"loading\") fn();\n", " else document.addEventListener(\"DOMContentLoaded\", fn);\n", " })();\n", " },\n", " function(Bokeh) {\n", " }\n", " ];\n", " \n", " function run_inline_js() {\n", " \n", " if ((root.Bokeh !== undefined) || (force === true)) {\n", " for (var i = 0; i < inline_js.length; i++) {\n", " inline_js[i].call(root, root.Bokeh);\n", " }if (force === true) {\n", " display_loaded();\n", " }} else if (Date.now() < root._bokeh_timeout) {\n", " setTimeout(run_inline_js, 100);\n", " } else if (!root._bokeh_failed_load) {\n", " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", " root._bokeh_failed_load = true;\n", " } else if (force !== true) {\n", " var cell = $(document.getElementById(\"9279ffcd-6497-4acd-9900-cf5addd62bf6\")).parents('.cell').data().cell;\n", " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", " }\n", " \n", " }\n", " \n", " if (root._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", " }(window));\n", "</script>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Visualizing the learnt 3D-manifold in two dimensions\n", "\n", "source = ColumnDataSource(\n", " data=dict(\n", " x=Z[:, 0],\n", " y=Z[:, 1],\n", " desc=list(range(Z.shape[0])),\n", " )\n", " )\n", "\n", "hover = HoverTool(\n", " tooltips=[\n", " (\"index\", \"$index\"),\n", " (\"(x,y)\", \"($x, $y)\"),\n", " (\"desc\", \"@desc\"),\n", " ]\n", " )\n", "\n", "p = figure(plot_width=700, plot_height=700, tools=[hover],title=\"Mouse over the dots\")\n", "\n", "p.circle('x', 'y', size=10, source=source)\n", "show(p)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Regeneration from Lower Dimensional Space\n", "\n", "While traversing the chosen path, we are also sub sampling in the lower dimensional space in order to create smooth transitions in the video. We naturally expect smoothness as points closer in the lower dimensional space should correspond to similar images. Since we do not have an exact representation for these sub-sampled points in the original image space, we need a method to map these back to the higher dimension.\n", "\n", "We will be using [Extremely randomized trees](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.65.7485&rep=rep1&type=pdf) for regression.\n", "\n", "As an alternative, we would also be testing convex combination approach to generate representations for the sub-sampled points." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Path Selection Heuristic\n", "\n", "### Method 1\n", "\n", "Generating k-nearest graph using the Gaussian kernel. We further generate all pair shortest paths from this graph and randomly choose any path from that list for visualization. For regeneration of sub-sampled points, we use Extremely randomized trees as mentioned above." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "ExtraTreeRegressor(criterion='mse', max_depth=19, max_features='auto',\n", " max_leaf_nodes=None, min_impurity_decrease=0.0,\n", " min_impurity_split=None, min_samples_leaf=1, min_samples_split=2,\n", " min_weight_fraction_leaf=0.0, random_state=None,\n", " splitter='random')" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Mapping the regressor from low dimension space to high dimension space\n", "\n", "lin = ExtraTreeRegressor(max_depth=19)\n", "lin.fit(Z, ims)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.90199726393173751" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lin.score(Z, ims)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5,1,'Reconstructed')" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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f//rXAThz5gzRaFQl8SqVClu2bOHQoUOAK4D/yU9+kgcffJCDBw8yPT1NJBLR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\nUDtx4sSJEydzLP8fZZqmdoe0bdwAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x198b726d8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pred = lin.predict(Z[502].reshape(1, -1));\n", "fig_new, [ax1,ax2] = plt.subplots(1,2)\n", "ax1.imshow(ims[502].reshape(*img_shape), cmap = 'gray')\n", "ax1.set_title('Original')\n", "ax2.imshow(pred.reshape(*img_shape), cmap = 'gray')\n", "ax2.set_title('Reconstructed')" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": 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XpVJJrksiP3k8HtkvXBteg4rD7XZjd3cXb5HelKxLJpPORz/6UXmPvMJxcTyxWEzkVbVa\nHdk7/Fw4HJbnTCQS8Pl8ACBKz74m5yUWi12lGKnYuAaUH5yrbreLYrEofEEZzd/dbhftdhsul0sU\nYafTgc/ngzEGzWZT1oLPQfnT6XQwNTUFl8uFcrkMl8uFQCAAl8uFUCgk8trj8cDn88Hj8QivB4NB\neL1e4Y1kMgmv14tqtSrjCofD6Ha78Hg8WFtbQyKRQCKRkLnib5tXyuUygIFMDIfDIwZks9kUJ6bV\namFqagrBYBDGGAQCAeRyOZRKJfT7fczMzCAUCok+CofDqFarKBaLeP/732+3Yb7tdKuMgk2Mtp9c\nxH77zm8pGiewx3kstkdg/20rjhu97zgv6F1K7xqeI9l8RCWskRwK9Gt91/5fe6AAxDC3vdNroVE3\nei99nXFkIwD8vo1O2GjgIdKb5juNPlIpUWFoxeP1euH3+0fQEv19v98vRrc2+OlY0fjid4CBt0pD\nHhis7UGkURxtyNoOGg0IGhRcg2KxiFAoJI4Qn6HdboujQMNUO1N+v18MHI1Ueb1eMQqMMchkMvI5\nYwwikYjMYb1eF4csHA4jGAyi0WiIocT70VjgazSmjDHiePZ6Pfj9frmvpmAwiHa7jXq9Dr9/cOo1\njaFoNCpzxXXSa5HPH83T7m+VUfBNAN9mjDmNQSelvwfg79+ie912uh5Ezs/YdC2D4EYgZv0ZG9p7\nF9K7iucO4p1xIQUbltahA4av7O/ahoH2JsdB0AfRuDAfr6d/6zHbY9TPaD/r9dCca9HNCOPgTfId\nDTcNmwODefD7/SNj6na7CAaDCIVCI0q93+/D4/EgFAqJt06FEwgERHHq0Ar/5jWAfe/4oBCkJmMM\nvF7vSPgCwIgxRh4hIlSr1USZj4PsAYjC1EgOr6UNWyIihO19Ph8WFhbEAycK22g0EAgE0Ol0BJ2i\n8aLRDSp/IhCaz4ggBYNBQSA00sk5a7VaiMVi6Ha7qFQqsi6cD6IY/N/n8wnq1e/3EYncyPlHh0+3\nxChwHKdrjPk5AP8fBmU6n3Yc59Vbca/bTbaw5W9tZdvxOdvq528dy72ewLoeQvFuo3cTz5HGIQUA\nBGovFosCv1MoEzangiCPUujp10gU7ACu8hrfzDi18TEuhs2/7WcaZ1gctJ/09d6M8fJW6c3yHRWd\n9kwBIBQKiXIPBAISFrUNNKIAoVAIiURCFBRDOVSeOh4PDEJDXHNgPyauDQPyB0nPIcfVbrfFA+fY\n+B2GOxjKmZubG/Hs+VsjBSQqc4ZKObZut4tAIIBer4dWqyUQv36f6ACh+36/j2g0ikgkInOdy+UQ\niUQQj8cRDAbH5mn1ej3U63U0Gg1EIhGEw2HMzMyIcaHzwDweD9rtNorFIjqdDiKRCPx+P4LBIABI\nfgPRkHa7LcYdDbqZmSOVSiB0y5oXOY7zpzicgyfeEXSzlfg4gfduNgqAdxfPHRSO6vV6AmeWy2XU\n63XJt9GxXK2UxiFVByWc6tfeynjfCo9eL7R2PbTgEAyDN8V3fB6d3AYMoHOdS0WvkwabDUUTTrfz\nhEh2CIgJbtqbHff5cagl762TBPkePW2iBMwr8Pv9gkRp46PZbMpneA1eLxgMikLVCpsOE2PyRA/I\nm8xNCIVC8Hq9Ej7g89A75zxpo0nPU6fTQbvdRr/fR7lclrwNfo7Pz/DNxsYGXC4X5ubmxEDq9Xoj\nzl29XgcwmjTL+TiKdCTwZg1J6RgVsG/B6cx8e5GZ4MLP8/u0JO0sWE28p77fuM1xkGAZ9zqhomaz\nKcl/GqLS99DwbbPZRK/XQzgcFmgLGECCOt5lowu8js62ZawM2E861F7ejeQwcF54DzK87cFp+JLr\nxGx3ro/2EPR6anTk7UDBb4f4PHy+GyXbI71Rz1TPm16Pcd7zQV65jovyWlzPdruNQqGAWq2GXq8H\nn88nsVfyDAVvIBAQD6/f7yMQCGBmZgbJZFK8PCaOUcjrcdJjBPaTDDl+3m+cx0/PlnyjjQZ6ZJq3\ntRLlGJgsSfh4nCEzbj20YL/VBsNBpGUYeZ9efrfblcoRetO2EUiFSOXKa9g8wb3FJFO/3z9yXy3r\nNFrU7XZH0ABjBjF87utSqSR5BJxLIgiJREISUYks6BABPX6/3494PC68yWRFQv/VahXAgJ8rlYoo\nYvKwz+dDt9tFqVSCMQahUAjNZlMgf86Tz+fD9PQ0AoEAQqGQGBEARgwT8hEAQdmYCM19wLHSQAkG\ng4hGowgEAjLvrVZL5qzVaiEcDose4H1oeBxFOpqmyruIrgV9Tuhbg2xInL91bPagENRBhpuO0VKJ\nNhoNFAoF7O7uYmdnB9VqVZS5Vtwsd+X3jTGIxWIjgs5xHITDYUn8CgQCCAaDI9npujyR3qM2qmgM\nX8/YsV+z50snTFJJ2oaVfpZxoYgbWZvDIGbW81m0gqbCJRoADMIF5BXt6eoY97hKApJWjvRkaRSS\n/3h/jVBwLvX1ZmdnxXjY29tDoVAQviESpfMY+LwAUK1WEQgEEI/HEY/HBVHodDqSTNlsNsWQDQQC\nkrgXDofRaDSQTqdhjMHs7CwKhYJUhLVarRFPnhA+ky4BiGGiQyraiKKxGQqFMD09DY/Hg0qlgkql\nAgCIRqMyH8wLYDJhrVZDOp3GzMyMrCnHwyqJcDgs4RfyMMd51GhiFBwy2bFQ+3VgYhi800krPNsb\n0Ip+XLycr9+oQtMKgwKJ4QN6PpqfdKmpNkyoiHw+nyRqEWrWHhIFI5UTFZSN9FDI8ru6IoJoIK/H\ncfGZ+b72ZnlfKrFx86cV2bhESY2s2fN4WMSEwnEICclG/2gMcKwaXeI19dzxfa6L/TrnnvC6rRxp\n4NFQoayihx8KheD3+5HNZtHpdDA/Py/Ggu6Hsbq6img0ikQigUgkIt5/IpFAvV6XPAqfz4dcLidx\ne/08Xq8Xy8vLmJ+fx+zsrCQxHj9+HGtra3C73ZKw12q1kE6nsbCwIM+l+27oEIs2xrShwPnudruI\nxWKoVqtioJGHeV3HcRCJREZK2mkUa9SW5aVcEwCyn44iTYyC20DjBIBmynd5FcE7kg7y6LUndi0j\nkK/ZSuIghTXOqGSiVKVSQbVaHUlU0/A7vUIAI304dBxYK3AN8+tMbg3nc6zjQm/8vv6OzgKnd6ob\njtGgYDyX16eQ1wKf741bg6O2nwiBc84INZNsRT8u5GLPv14DHafWBpROZLbDs3boRlcAaJ6Jx+Mo\nl8soFotIpVJwu93I5/OoVquS7KdDtvScc7kcdnZ2JBGP3jXzAxzHwdbWliQUkvr9PqamppBKpdDv\n91GtVgWKf/nll+H1ehEMBgV5YZJguVxGKBSC4zhoNpuSd6HRN8dxJOzM9/WcdzodCa15vV7p5+Jy\nuUZKG4PB4IghrY1m/q0NYN6H+RlHkSZGwSHTOKSAf2tPR3tOEzraZCsjG/7WhgFfs40CncVN4XMt\nw0ArC2Z8VyoVXL58Gel0GpubmxJb5WcInzJ3hfAtsF8aRgFGzwbYT1LT2e0UbvSYdLMsXsPn841k\nqmtjg3Azr6cbcbHBEj02NvDhdbXhw0x3zrG9j8atye0mGj+6253t/QOQOD3XxuVySZMnzhe9Xr0m\nNumSOhpuGmGiYcbwj64m0N57qVRCIBBAOBxGpVKB1+tFKpXCysoKGo2GKOVSqYRMJoMLFy5IGIDl\nkx6PB2fOnMF3fMd3YGFhAS+//DJWVlbw8MMP45VXXkEmk8Ha2hpqtRrq9Tqmp6cF+YrFYjh+/DhO\nnDiBu+66S/iBRlE8Hsc999wjED37CzBnplarIRwOy9zSkNLJgT6fTwyvWq0mn9va2oLX68X09DSm\npqakhwGVvjFGmnhpg4BGBK/NnAht0B41mhgFt4EO8hiBN9/tbkJHm66VF3CjYYKDrstrUHEyvsrk\nOyYzUchTcRPCJESvr6nDCjrBVMesbeVDeFsLRI4vEAiMdE7USkYjBQddd1wYgRUWrVZLjBJ7TvQz\nHSWDADi4D8BB71NJMZRD5aoNIfs645wPDW3rpGE7lKOvwTbZ5AnHGdTxs6yvWCxic3MThUIBuVwO\njUYDrVYLtVpNOicCQCaTgdfrxdTUFLa2tvD6668jEAhIB87t7W28/PLLSKfT2NvbG+lv4PP5UK/X\nkc/n0W63kc/n4XINMv6j0ajkyHCu/H4/KpWKVChwPLrzK/nQ7XYjGo2i0WhcxSc6iZM5BEwo5DzR\nIOD19J7RqA95VqMSE6TgOmTHyjTcqROQaGVrZrc3PpmJwkxDbrq9Mb+rx8ANwnvYHodN2tq2x0xG\n0bAcG1gwtsjxMhmI3bBarRaazeZIcoweC61gKgA2K+E4mO0KQLL/7biZ9kztJCUdz+Sa8LeOqdnr\nxznnZzlObipuRB1/0+t9O0g/M4CrNutBSv2tkuZd7TXouL3uq65RIw0Du1wuaWncbDZRKpWQTqdx\n8eJF9Pt9pNNpAEAikYAx+xnXzKjmNegdsYeBbivL7xDa1X0OyCPa4yXETZ6iEKYw5PMyCZGVETa6\nYFeCxGIxaRBDAU9e1PPFueLY7G5/JL3/D9NoGDcWHarR+ROcS50XoMfNZ+Z+13KCMDhbRtsGqA7b\nsCohm83C4/FgaWkJhUIBbrcbwWAQvV5PPOVyuYxqtYrt7W3s7e1hd3dXeMTtduOuu+7CxsYG2u22\ntJcPBAI4ceKEVFNUq1Wsra1hdnZW0I+nn34a6XRaKrZcLpcYlHytWCwin8+PlB3ec889MGZQ5siu\nhZwHwv+RSASpVEqScO2+BkQieF2+z7FyPVqtFvL5vFQyEInTPFav14Uvua/0WnPutfw7anRkjIJ3\nC42LBU/oW5O00aN/a0U0LhmOf5MI99O4o9dIRV6tVpHNZrG2toZsNgsA0tyGMLuOkTIGTFi33x8c\nnkTEgR3dKPS0AcL3tOeoPVEaC/rcEl1eyMNq2DaWhoEudeXfhL357PRa+aNLfW1PW6MdumufTirj\nPB+mYWCMkVAAvX1gNLmQa2uPjUqefMIQAtETYB8N0samdlKYCMh7M4zTbrel2U+pVEIul0M6nUa9\nXpcEvueff15gcxqMnU5HuvqFw2FcunQJsVgMzWYTMzMzonjpYdMInZ6exgsvvAAAyGazKBQKKBaL\nI2iINgjoXDiOg0ajgfPnz+ONN97A5z73OTz44IO45557BNpPpVJIpVIS/igWi4IoLC0tSTkuEwS9\nXi+SyaRUP5Afe70eGo0GGo0GgsEgIpEIIpEISqXSVQqfxgErdAKBgMw7jSGuib1mR40mRsERoIlh\n8K1JtsAn6YQnXS9P5UcBQ2VGxabbvOq6aHYwbLfbWFtbk4Ni6AW2223UajW5ty5vCwaDI1CmyzXo\nlgdArkmjwI6FasNA/w/s9+oYl41t59MwnMFrM/6tu8Hx3tpg0AidTiC7XshG0+0IL9iIKBUUX7eN\nAm0ccL71YUT6szoRkxUkXANt8AH7h0YxcS6RSCCfzyOdTktM//z586K0eSgQYfBIJCIoJ1saA4PD\ns4rFImq1GiKRiOQh8Jr07Dc2NuD1elEoFNBoNORQJI7R6/VK9QINVCp67olQKIQXX3wRzz//PILB\nIH7sx34MjuOIEUDFz7LFYDAohqbX65UwVCqVEmOBc8UzGHSfBRroRD71mhIloBFjG878n/M/7kyS\no0ATo+AI0FFNOJnQ2yMN7WplpGP0OkzATHTtaVD49Xo91Go1yRnI5XIS3y0UCnIi5b333ot6vS7X\nzGazWF9fR7vdliZYoVAIoVAIsVgM8Xgc/X5fTkYMh8OizOmZamVEAUkhp5P8qND57OPi/YTFdZjJ\nLqlkeIO167rUUh/yo8lG37RA1vvLjsG/nbyOt0raEKGC0AaADnfquLU2Jhgi0eEF8pA2mrSRxh+7\nnNTv96PRaGBzc1OS6y5fvoytrS2sra1JKCmXy8EYI8qSSYVzc3OYmppCPB7H+vq6HPTD1r/tdlva\nbQODxkCVSgV7e3tyXkGv15O8AybjNRoNVCoVLC0twePxYGZmBo7joF6vY3NzU1oH1+t1HrmOp556\nCvPz82g2mzhz5gySyaQYMgBGjAIaHuT3er2OXq8nJx7qlsZEKdiAjfyq23/r5ELOvQ4d20jBUaWJ\nUXDIpOP6VAwTo+Bbl2xPFoBA7MC+kmTuSKlUwu7uLi5duoS9vT2Uy2Xs7u6iUqkgl8sBgGRaE55k\nK1bmq7B2enp6GouLizh9+jSSnIGCAAAgAElEQVQKhYL01OexrzQQdOdLejz0pHjELL1Mfey45l16\nbxSW2kDQHj7nhD3q+X6pVBJEgOEOGgI0RmgQ8T2NXNAD0zH5cR4ZY+z8vG78cxhEJU+FoRUM8y70\niXr0dm20QCNJ9FxpbGljQJ+DQGNOXyuXy4nH3u/38eKLL2J9fV3QJqJQOsGOhiuVJT3x9fV1nDhx\nAgDw/ve/H8888wzC4TBcLhcKhcIIapTP5+HxeNBsNhGLxWROGBrShyhtbm7KHBEdmJ2dRbVaRTAY\nHDnOOZ1OI5PJoFAo4MSJE1hcXMS5c+cQjUYBQDx+hlnYHCoQCGBpaQn9fl+OhI/H46jVakilUtJr\nw+PxoFqtijHFcbLxEY1djWbo0BCwfy7CUZX7E6NgQhO6RTQOVtevU1gzjrq6uorV1VWsr6/j1Vdf\nRblcFsgVgGQ/MzeAMDtb4moFV6vVpC87MOhGRyEWjUZFQNXrdfHOqHBYk93v9yXmrNECGjla0Wsj\ngWPodDojUCpJJ6XyfsxadxwH1Wp15DQ5els6AVG/ruFZfQ+OyzbItNIEDrfiR3vqOsHQhpLHGTg6\n4VqjLOOeX19DIzPaS+VaMbHw85//PPb29pBIJCQjPxaLycFLNBibzSYymYzA+dlsVgzSQqGASqWC\nkydPotfrSRJiuVwWI4IhC8L3HDcNm8XFRTEat7e3EYvF0Gq1UCgUJJnamEFnQxoQiURiJA+GFRGZ\nTAanT59GNBpFOp0eOQyJPTri8TiazaYYUAy9cY8RDQMgCd02+mTM4IwDGmzAftdR3TtCG25MxDxq\ndCSMAp1gxEYUugpAM7Jmfk46mdOGZmgVa4hQVxUc5B1oi5zMwEx+m3h93kvHW91ut2wKVgjwPTsW\nq0u6dOwsEomMfFdnfdvJVfYz6Kxjbjg9n7ZgZfUDE6G0gNeQLb87Lh47bkw6/qs/ZyfeHVVI7Xrj\n0kJDP4deZ60YtfdgjMGVK1ewvr6OL3zhCyKc/H4/EokE/H4/5ufnR3hSe+GaT+ndcx/Ru3McB3t7\neyOCKB6PS+tiANLauNfrYWZmBoFAQMIQVMipVArdbhdTU1OYn58HAAlfEMUg33A/lEolJJNJ6aVA\nr4zCneVilUpFjtvd2toaCROQX4kQUPCTp+zER81bel3G7ZHb0Q/EVvY0vnQugA4R6BwQPgefGYB8\nh8+rQw0knRTH8wGazSauXLmCvb09vPTSS+j3+7jzzjuxs7ODRx55BPl8Hi+88AJ8Ph8KhYJcy+v1\n4oMf/CDC4TByuRyuXLkCr9eLWCyGYrEIx3HwpS99CXNzcyiXy6J4OT7HcaRcUZ/JQQO0Wq2KAcAe\nFWw/zDLUy5cvo1ar4dixY/jO7/xOAEA+n8fKygpmZ2eRSqVQrVaxurqKz372swgGg/iu7/ouBINB\nhMNhBINB9Pt94Xs930yGbLVamJqaGjGoWq0WotHoSO4B5SuRj16vh0gkMhI+JHFtHceRnglHjY6E\nUfBuIzvRylaO4+ioKExbsI7zUCY0IO3FaSNQG2f1eh1XrlzBc889h0KhgEgkIp48IV0mXvEa2gjR\nXjsVOJWNrv9nTBSAeOUejwdXrlyBMQbRaBT9/qDcr9FoYGFhAb1eD4uLi4jFYggEApienpas7t3d\nXWSzWSmH1DkQwD6/BgIBFIvFEVSEcelKpYJmsykn0zUaDeTzeTmOltfRTXXGzaWOz4/zrvk+Y+Pa\noDhsY5ThAW20UOE3Go2RzwGjbYe5tnbIhp/X39EGE4nwPLv+lUolqViZm5vD7u4ugAH0n06ncenS\nJTEweZzwvffei3PnzuFrX/saXnvtNSmrpvcbCoVknYkC0FjVa8dYPNeUYS32DWBIgcbh1NTUSLLh\nlStXRAm/9NJLoujf9773IZ1Oy15g6aAxBi+++CIqlQpmZ2dx9uxZxONxFItFTE9Py/xo506jOEQk\ntPPJeeda6oZfNN60w8jP28jZUaOJUXBE6CCP+6gYA+PoKDP2YZJuG6tRAb12GlHS81atVpFOp7G6\nuopkMolYLAafz4dqtSowPj1mLWh0QyCt2LTQ0r0I6MlEIpERhO3kyZMynlKpJHH7lZUVyTmgIGu3\n25idnUW9Xsfy8jIymYworGw2K8ZFOByW2nCiE8YYKeNiKaFOHNT9FphcyOciZDuO9JzYr9t7iMaY\n/ZnDDh/YSAYNH4aJ+Dmt3DnP4wwCKlUbDdUoKf9utVool8vI5XLY3d2VezabTRw7dgydTgdXrlwR\nJRgMBtFut7GwsICHHnoI5XIZr776Knw+HxYWFrC3t4e9vT1ZT55cSEXKZ2R4odFoiAHKhkE6rEAU\njIqZ46Ahq0s2yRfFYhGtVgvxeBx33HGHJA3ympqfvv3bvx3hcFhOQWQIQqN3NFI5twxxAft5AToU\no/NDdFiI39WhMm2sTXIKJgRgtAzJVhK2kNIekPbADlOI2TRu3O9244AZ+lwbnVCks8m1gKEXf+nS\nJWxubqLb7SKVSsFxHJTLZTEGdNMgLWhIGi4nxA7s17vr+Lk+wY1KhgrJ5/NJ5vnJkycRDAZRr9eR\nzWZRLBbFU3322WdhjBHFHwqFUCgUkEql0Gq1sLu7K0KYHt7x48elgRITIguFgoyl3x+cJFcul1Gp\nVEaUiW5idBBKpfeDnVyoFSL/1+iDzhI/DNJGjl5T3WJax6A1tE0F4ziOoAqa35ioqb1SEuHwer2O\nlZUVbG1tIZfLodVqIRQK4dixY0in02g0Gjh58iT29vakJt9xBgf/8Dv5fF56CLDEUDetIr+xLLBU\nKo0oc/aY8Pv9UtZYqVTkWWKxGGKxGKLRKCKRCKrVKqrVKhqNBjqdDsrlMmZmZqRqwXEGFQmlUgnA\noEdHsVjE/Py8VCi4XC5sb2/jL/7iL+D3+/HAAw/ggx/8oIQp5ubmZM50y26uC/sk6DMLbFmo8x0c\nx5F9wAREhiF0Y62jSBOj4AjTQdDm7UYP3u1GwI2SNuCYIW7HjOmxOI4jipiZ6FQcTGyiErXvoRWj\nrUA1rKwb3LjdbgQCATmznt4bvXaOlUKSiYhu96BFbKlUkvwHtldm7Jjx436/j3w+j4sXLyIej2Nh\nYQGLi4uiBKn02YuAWey6OsPuc2CTznvRuS5UTvpHz8u4vJ7DIB3acBxnRJFTGWmE4Hp7nc/FZ+Ha\nag+WSrNWq6Hb7SKbzSKbzaJcLssZAuvr69LMil0KHcfB6dOnpeKDSYS9Xg+JREIU+/r6uuSC6THZ\nISy+R34misQW3W63W6oZ2EyrWq2OxPhp9BJRMsZgenoawWAQmUwGKysrePDBB3Hs2DE5I0NXcxw7\ndgw+nw/ZbBYXLlzAnXfeiUajMdIYjCEQKnFWWtj5J3YOCw0JzavkX91xkut7VJsXTRrtHzJp4fNW\nBNHtNghIE8Ngn7TQodBj5j1fI5xKRc94K6sLdAkeBRQ/x+9QQOn4LH/oMbFtMJUu769Lp1yuQYMY\neueEX3l9GiLGGEn+arVa2NvbQy6Xk54GXq8XgUAAiURCKiMYPmCpVzweh9vtxu7uLl544QVsbGxI\n+IAIGJ8P2O/hYMfIgasT9Chk7Qx8e27sTHFeU3/2MIlrodEjnQuiEUI+t012rgBLNnWCJpUUkzMZ\njiKPAQMEYXt7Www69g8oFApwuVxYXFwcUdqJRALz8/PodrtiJESjUcTjcQl90bvWB3Kx3LLVakkS\nLPmb3jwheFYIcN+wy6BuW+xyuTA1NQW/3y+GBTAweJ999llJICT/9no9HD9+HE8++ST6/T5OnTqF\nzc1NaQVOFIXlsNyfmvc41zq/R3+G66iTQrUhznDGuKTXo0RHBinghmAMiZOtoTR7QZg8xe8y5qRh\nRVso8D27VIoLqT/HWCzHp4UNP8NzwtvtthwDSkvY7/dLzI6Zrty4+mAOnU1eq9Vw/PhxiXNxQzMe\na9cs69iWZlw7mcXuwc0mMbSAdQxSf043mdEerp1trwUbhZFeN+3NahiU3fm4cQ+T7Pju9bxSnTCl\nY6Zs5kJlSoGSz+cFYs1msyKUw+Gw8Oru7q70a69Wq8I3fM1WWNoztGFijksry0AgIHwE7J98xyYs\nS0tLkohIKJheok726/V6UuvdaDRw5coVyTSPRqOYmZmR7nM0Fqgg5ubmhI93d3exubmJlZUVPPTQ\nQ4hGo/B4PPjGN74h4RYqDAp1Ywad5GwBrXmV71E5Mrtc7y1beGt+O0whrQ0trqfOGyEiw9/acNHI\niCbuL+5zyoxerzfS5vry5cvCc4lEArlcTsbz4osvYm5uDoFAAGfOnIHL5cJjjz2G+fl5pFIpbG5u\nSjJdvV4XRe92uxEOh1Gv1xGNRsWoZeip3x90SyQaxtCHzpsA9s+AKRaLUiYbDAZx5swZZLNZbG5u\nSg8NvhYMBsXgYSlhv9/Hk08+ibNnzyIQCGB9fR3T09MoFouYnZ3FxYsXsbu7i+npafh8PiQSCWSz\nWXziE5/A5uYmjh8/juXlZSSTyREji3MfCoWkKZPOPbCTCT0ejyQNc2/qdb6dYeBr0ZEwCrQlrKEz\nKix+xk7WOii2qiFXrczHJYEYY0bij7yWPS5gtNxHw3JM4KJCIDMz6QbAVbASm5JQEVSrVYlb0aqM\nxWLS3YvX9ng8ckiJrTBoROm54bzo8dtzz8/oxCwKF+116MxZ+zv2OhwVRONm0rgkMRqejHkSCgcg\nB8w4jiNejc/nkxg9BeA4r5+8yftoqJKvjRsf36NSIWJAvnS5XNjd3UW/35fSKhoB3HO1Wg1+v1/G\noZPUWNPNY4t1XLzb7SIWiwHAVTzo8/nkPAYmkhWLRVSrVQQCAUQiEall5zNoXqIy0rynn1l7bNrA\n1PX/2ijQcuSwPTed7zGukY0d8tFjOyg5Uq+9ToCjB8xr5fN5MTyZs8JmRaFQSOLrOzs7aLVa0ryq\nUqlcxad0bhiC0iXldHjoGdMIJRKlnQjKG94LgITQwuGwNAuiwVksFnHx4kXMzc2NQPs0InmK4/b2\nNlKpFGZnZ5HNZhGLxTA7OysGb7VaRaVSgcfjwenTp/HUU0/hPe95Dy5duoS5ubkRw1iXTtpy1OZJ\nJjVy3WyZyM8flDx7u+lIGAUkeut6MqmcdIxMJ07xc1wIXoeeMAUiX9MC1/Zotcc4Dh7SSoE/jAPz\nrG56Yhr+5QahZc17MIbaarUQDocllsqz4+mdsYuX7SHp2KkevxaY4yCwcXExXse23jWz62vq+bCv\nfZDgeqfQQXAyW7tynnRdNcvzKJC8Xi9mZ2cFRmdmts/nQz6fF2+4VCqJcBgHd9tJpiTtIevXGcfV\nnewcxxkpp6JSYA8MJkTxdDiGPvhdbWBzbRl/Zp5DoVCAMQbHjh0bafqikT4dn/3whz+MZ555Bjs7\nO0ilUlhYWBgxTjQ/ApDSNz4zSXtxGj2xhbL2tO1YsD23h0E0CnR5KslG/cahAvyc/g1AEgCDwaCU\nduo8gUwmA5fLhUgkgnq9josXL8JxHIRCIQSDQZw7d07KVCORCE6fPj0SYmLOC8/EYLMhOkU8p4AJ\nhr3eoFU1GxxpvuX3GU6p1WrybAxB1Ot1LCwsABjkEUSjUTnUi9UyTFakgdJqtTA9PY2VlRUAgxLL\nfD6ParWKqakpQc6Yu3Dffffh7Nmz2NzcxPLyMh566KGRk2WZiMu9Q7RWV8hQl+gSy+s5R3Qejhod\nCaPAhqM5oewtzUVgrJaKjpPKhWi1WiKEgf0OWcBo8yAd8+I9KYj0UaO2BU/Sm7RcLktHLSp/xsy2\nt7fxzDPP4Omnn0YulxNPjUfA8vOxWGwERiRaAACLi4uYmZnBI488gng8DmOMtPckIkLhey0kALj6\nOGgKJNuSHeeF6GuT+TViYkPvegNxzvQ4jrLBYHtmJBptNO4ymYycB+92u6XXO5sGsea+0WhIq2Im\nPy0uLqLdbqNSqQCAxFQ1DKkFjj1fmkf1mlLwcn9opCyXy8kRsZFIBMYMurDRaO50OqjVasjlcvD5\nfNIDniGveDwup8bRACJKRuSgWCzKOHq9npww1+l00Gg0JDZ8/vx53HfffVhcXEQ2m8X09LT0vrc9\nUXtttKKk4cDP0+ggsqD3Np+R1yT/93o9OTDqVpNGl9g6V5fg2d6lriDR+0xXj2gEkwqWirler2Nr\nawutVkuqXMhLDHuyN4DP50O5XMbe3h7OnDmDTqeDr3/961I6qCtb2AK50+mIQexyucQoIA/xdVa2\nkFc471xDHodtjMHU1BQikYj0x1hbW0O1WhXjxePxIJvNSpiNOS40Vnmq49zcHJrNJr75zW/i7Nmz\nyGQy2NjYkJyEcDiMvb09fPGLX8S9996L06dPY2FhAbu7uzh9+rTMMeU0kxvZ8EsfMKUTiTlHdl6B\nNu6NMRKOO2p0JIwCYNTq5cZh6Y6Go+iZ8DeNCCpZnYilFZdWalw8Wznq+nIbJaD1B4xCm9PT08hm\ns9jb28PMzIycFf7Zz34WjjMoS4nH4+L5s692NBqV0hmNKFDh0jO6cuUKVlZW8PLLL2NpaQnvfe97\n8b3f+724fPmyMCeTluyYvBagOj9CE59fhzd01rD2sHhNG6bVSstOgNLXsL1aPc53AtHj7vf70u6V\nfMEYKOuxGe7JZDIolUrY3t7G7u4uCoUC7r33XgADg5KxSV4fOLjqRBuo2qDj/GulYJc/sSd8s9nE\n0tKSlHmx/pqhBEKqwWBQYqdUToyTUrHrcfG5y+WylICRF3ReDveO3+9HqVSSODAPZ2KGvEb7NB9r\nHtLGLF9jySPHZ1cx6GvqpMrDKg/jmpB04plu68wf7eRoFMlGQkhEdvhDFJOHCxFBYLMpHhlM+ZhO\npxGLxXDhwgVkMhkkk0kxmGZnZ+X+gUBg5ERPji8SiYhs1W2J2aOARiTzAJhkyu8QzaKcDIfDmJ6e\nhtvtxiuvvCJzwvM9KDtZ5stwA41ZGiKZTAbBYBC5XE4cFRqLMzMzuO+++7C6uopGo4GPfOQj2NjY\nwPHjx0d4Te8tLW85J+Rtkn5dlyRqvXMU6cgbBWyNasPa3DwUesCgVavuC2/HbDSkD0CSmLgh9GbU\nVjsXUsNFpO3tbczMzODYsWN49NFH8eyzzwo8zJ7uhKuYiEgomRtFC28tFGioGGPQaDSwurqKra0t\nfPOb38THP/5xKeHhPNgla7aA0XE8nVyo51wbWdr4YgKXDu9oeFcbTJzncWjAQQaANjaOKpFH6O3T\n2teJfCyx63Q6kt1cLpfFo2k0GnAcRw440rXd5IFxIRyb6GXyc1w/KnFgP+OZteE0VjTqxiYv+hnp\n2bNMkGMi/9OzNMZIfgGVRKVSESOYeTP69Md+f1CGxrMezpw5Iy2Xgf0Da7QRrj1k7TUDV/eI4H10\nEhyNbNsr53gOM9Rlh9d0zodG3vjc9m8t97RCJlHm8EwCrg35MBgMSg4A+WR6ehoLCwtYXV2F2+0W\nXo1EIuj1epidnZV8AxqRHs/gJE/yG5EgvT40PAjTU+7Nzc1JTonmcSK35FeXyyU5KolEAqdOnUKh\nUJD9p5P9eE/mIWSzWXi9XkFXy+UyIpHIiPFFfgmFQvjDP/xD3H333Th+/Di2t7cl1KxlIvlZly/q\nxHZtQOj1slEgrv9RLUk8MkYBMJpMyIVjJjcXQG98DZnSGqNVyu/1+/2R9pOszwb2rXTeh0KVjTTI\nDPrcbfbOZk/vV155Rcq0eDwoN6zH40EymRSLlRm7OoGPR3vqzW4zkxbK9Mx/53d+B6lUCsViEadO\nncKHP/xhafZBqIveazabFU+Qc0HFpee03W5LKY+eWwDS3YxeHj0tMjib0eh4o46R8zs01Li+VKh8\n5sOM7ZLPbLKFrTaYeJQs+YA1++z0Vy6XRQBvbW1hb29P6qd5pCwh0mQyiY2NDcRiMTFWabgxdsuY\nrNvtHlGuPLOe8HwoFMLMzIzkCTAfhUrn/vvvl77s5XIZ1WoVbrdbxmzMflY1+www+YsCW4eqUqmU\nfIbGUSKRkCoEjrfVao20wmXnwtdeew2BQADlcllKGln7TiiZ5Wf0Apn7wKRcKhTdNIbKirXmFPqc\nV+brkI8ZUtje3r6lvEZyHEcqI7SxEw6HUalURN7oHCg7NMC51CFD7jO+5na7UalURBYw54koAdcO\nGIS5tra2JGla73WuPdEEwuI0LDjfkUgEkUgEly5dEjlAJEgbr+TXWCyGcrkMYFDJ4Ha7kUql0G63\nsbS0JAd4MW/A7/fj2LFjSCQSyGQyyGaz0lhJK+VsNitrS6eSc7y1tYVUKiVyb2NjA41GQ46Ebjab\nuHDhAhYWFuD3+1EsFuUwKMplXdWh5Tj5UMswrgmdUPIn1/2oOkFHwijQXjow2qnMRgY0XGs36tDx\nHW4qXl8nPPH7ugpBQ5E6HqQrIGZnZ2VzeL1efOUrX8H6+jqSySSi0ai0+9S9zW+U7HiT7Snq67Xb\nbbH+U6kUtra28OlPfxo/+ZM/KSVBhNYKhQJCodAIRMjr8/n4mxnibrdb5pLEv4ncUFlpqNcWTDSE\n+AzcMFwvYDSpc1xS1e0gWvEUxnw+etperxfhcHgEAcrlcoIINBoN1Go1bG9vY3t7G3t7e9ja2kKt\nVkMgEJCz59vtNlZXV8UjYiKiFjacdyZ5URlQ4VJx81hkGhbMa+GzaOSGiVlErYwxooj1UbL6/n6/\nX5QKMEDlpqam4PV6r2oqw/vRIA+FQhI/ZTkcWxoTXXjve98rnqWGzHWnP2A/N4jvs9kRP0tDgImV\nxhi5HpMpgf3eDdobPAzSMoaKheMBRr1L3XqX49P5JjpEw+8CkL3JuanVaiMwvUaGmEDIfUvFZ+cC\n8YfyjQaCx+PB8ePHEQwGxXFhDkooFJJEVmA/zs4yWS3Te70ecrmc5B8wZ4DnLvA6PLeBISA9Rv7N\nhFXygzZuuI8p/5rNppRT7uzsYH5+HrFYDPl8/ioURqPMvL+dqE4Zd1RRgBuhI2EUAKOGgYauuYF0\n7E0LHX4XGPUYNLNREDB+qI0JG7an4KAXQfSg3x+cs/3cc8/h0UcfRTKZxNzcHM6cOYNarYZyuSxM\nxJpvG1YCMALx256xHZqgYcS/+VwUpIwHhsNhnDhxAo899hgAYG5uDj/4gz8o1i6FPOEvbk4mJDFn\ng4KA866VNJWkbhJCwaQTwowxImT4/HZMmffQcTZbwN1OsmN+RIKazSZOnjwpxk42mxXhzuQpAHjp\npZewurqKy5cvI5PJoFqtolgsIhQK4fjx4zh79iwuXbqEr33taygWi4JOMdlLx+2J9tAjI7+y0kWf\nhuj1esUIo2HH/RSNRkUY08BoNptIJBKYmpqSFrb1eh2RSES8JcLD5BEiTpFIBFNTU6jX61hbWxNv\nNJ1OS7Ifz1lgdY3L5cLe3h6AAULWbrfFS0smkyiXy2g2m5ibmxuJi1MYU5lpo4HohcvlkqY32qOl\nUuBe10dEc5yHjVBpZUPFpTPidViPCkYjCxyvNij4oxNFqWAzmcyIUUHFyD1ow+mEyCkjtfLTcXEa\nVKVSSZAnetasguA8M7GVTa9Y7aC7djIfYGdnB91uF8FgEIVCAfPz89Lvw0YVbZ0BQM5roAKnEcPr\nT01NAQAKhQLi8TjS6TTOnDkj+ROPPfYYHnjgAXFuaIiTVzgGPj9lIeeGcoD8p51OO+/gKNLbMgqM\nMWsAKgB6ALqO4/wHxpgkgC8AOAVgDcAPOo5TOOgawOjRulo56LO2tYLUVqYaywjCoOE2HafkNcis\n/C4wmjFP5CEej0uLzS9/+ct45plnkEwmhVl2d3fF6GAmuQ4B2PCWHSfk/W3lbxsH2qsAIH3J5+fn\n0ev1sLq6ioceegg7Ozt444038Oqrr+K+++4TaJldwTT6AkBK47S3pP/XBgyfjZuC75HR7fwCPj/X\nQ99bP7OOHd8I3Sy+uxbxmbRAnp2dFeiaQlJnZOfzeWxsbOCNN95AoVCQboXMb6F3urOzg9dffx0b\nGxvw+XyIRqOSzKURF0KPNAZCoZB4wEy0YgiAc0e0gJB7MBgUo4IGDNEOfpbIBysp6vU6CoWCfD8S\niYiRwjPjKSRDoRAWFhZEubM/PQ10ZqDXajW5HwAxRgn1kq+08cPPdTodycGhkNVVBFwvHdPt9fYb\nTXEsumRMI3E3QjeL57Qy4bj5myiRTqLWhrJOXBt3XcdxJB+AbbL1PDOEwrnjfPCH86whbh1G5Q/3\nMeebISCWZ5PXyYdEd/R+D4fDiMfjI7xL7549FLrdLqanp+UzDFmRT21PnddgmFafxEjjg3xLJJVo\nVT6fR6fTwcbGBtrtNubn50eMAo5bv8ZraqdnyCvy25a3Wm6+Gf47TLoZSMHfchwnq/7/ZQB/6TjO\nbxpjfnn4/z++3kW0ktYZ7RSOwNUw+rhJ1aU6/M3P2vEeGh0aQrdRBwDY29vDysoKnn/+eWHUmZkZ\nAAMGZUIOvSzbIAAO7pKnN53N5GRcrZj4OVreuVwOsVgMyWQSy8vLCIfDOHfuHJ544gmsrKzge77n\ne6QUTDfVoHBlmSQFhha0ev74LKFQSAQ/cHVnLiIrJG5oChqtvHjda63nNeim8N040gpEt/ulAqaQ\npYDsdrtYWVnB+vq6nE9PQcOERACizF9++WW89tprknuhhQmNWK30mRvA1+id0PAF9g1ahgE6nc7I\nme6lUmmktJeGApWAbh9bLpdRKBREYTvOoF0xPS6dLOh2uzE1NYVutyvrXq1W5XAjNrOhN08Eg//T\nw+/1eiOH/GgFor0rLXD1b43s6Hit/lsbefz9JtGpt81zHIM2/vm8TNbUiJxteGvkQxvt/F+3Ly6X\ny3K4FtE7JpBSFtIQIx/pfCSSDnfodaEC18/ApkA0HnW/Cp0XFg6HEYvF0O12RUa43W4kk0lJagSA\npaUlOfgomUzC4/EIopROp2VctsNlhxQASFiElQgsXyTy99xzzwEATp06hddff12SabWRpHO8mIel\n9RUNp3FhBa4jddFRRRmC5LgAACAASURBVAxuRfjg4wA+NPz7swC+iutsFGOMePOEXxgX1RAomZlN\nVjSMxIWi4NIQsF4g7W0zsYYLRCEJ7DPQN77xDTz++OPY3NzE9PS03Jc93N/73vcil8tJlm+xWBzZ\ndBqCG4cU6LEDo8fw0rPUsUVgwFg8o54ncPEY0GQyCccZlBptbW3hM5/5DD72sY9hYWFBLOVoNIqd\nnR1MTU2J0NDwJBlcGzd2+Q0FK5+B8WC9GY0x0taU3ikwiANrWFx70m8Dxn3TfEdBpD0BDZPmcjl0\nu11JgGICIte00WhgZWUFr7zyCsrlMur1Our1OjKZDPb29iQhsdPpSGe1TqeDF154AaVSCV6vF1NT\nU3C5XEgmk+KV01AjP9Aj5ymKjUYDhcLAISUUqxPwmIFtjBH0KhqNSr5DIBCQ8wl4KJLL5cL09LTE\nbmn8ZLNZNJtN5PN5zM7OSkij3++jUChI3fbU1BR8Ph8WFxcl9ru1tYV2uy1lt7qum39XKhV84AMf\nwHPPPYdGo4ETJ06IYZ1IJLC3tydjJB8ynMd1IIKgDU295/Sxt9oz5vy+DXrTPMdkO42eccwcj5aB\nVOQk8obOE3G5XJLLQaSFhiPDOjrPiiiSDqdwbHYyt5bL2kHgfAeDQUxPT8sasftgq9VCqVRCJBKR\n0JkxBnNzc5iZmUEikUAqlUI+nxfjlsZsvz9oBR4Oh7G9vY1Wq4VisYjp6WnEYjFsb2+PyC1tXLGd\nM6/HsJd2OiuVCqrVKlKpFCKRiPDxhQsXsLS0JActpdNprKys4K677kK1WpUcCRpEjrN/tgIRKY1W\nO87+Eec6THrU6e0aBQ6Af2eMcQD8ruM4vwdgznGcNAA4jpM2xsxe9yLDCaU1yYkjPMPJ1ApTe1f8\nobDi69pqo7LTCSper1dKaUKhEICBwqrVapienkY0GsXjjz8uliUFNKFexqaYZMOEGm5azawUUDq+\npA0EzSz6Pf2aPWcUktpqzWazEg8mbPfiiy+i3W7jzjvvRCaTke5zmUxGyiTte1Jo2h4a/7a9W220\naHSHEDLnn4JIX/NasOgBdFP4zib9jPRk7SY49MAB4JVXXsGFCxfQ7XZRqVRQqVSkIoAxUR0v7Xa7\n2NnZkZIxltAydk8e51zQICRCUKvVJMGQAokeIT0e9q7Qnjfj+iTeg8gCy2Y5Du4FQvAApCpAG4gs\ni+x0Otjd3UUoFBLjoN/vY25uTvYlEQzC2cxKP3HiBE6ePInz58+LUcEuj0w0o6LTPKRj7lrBMryi\nHQbyt70nx+3FW81zWtZpI1h7kBrN0PJDe5z8vjYcqARZGsiEPD470QLOHf+2DXnOI+dFoy7kp1gs\nJvJQO2304lnBxYotLTOSyaQgq+QjY4yUH1Lp0ug7efIk7rnnHsldiUaj2Nragt/vlxCJ1gv03nWn\nUBrLWgYzAZPPTeRAH/1N5IR5LS6XS8qKe72eVLawXJPykPxIvUA+I2kj66jR2zUKHnYcZ3u4Gf7c\nGHPxRr9ojPlpAD8N7CtXOzxgb1Qdn9FogP2+/T3GI8kcmrl5T7ambbfbSCaTAIBnn30WW1tbImhp\ntFSrVUSjUczOzkqCoTOMZdHSHT7jiPdiw1v8jJoTe47Gvq4ZTHvn2mOiV1epVJBOp9HtduXYWm4A\n7enrMdv30POsBZlOWuSG0OumBRgFk0Zv9H25fjdIN4XvlpaW7PcE3iZSpIUnY5WO4yCdTmNtbU1e\no9Lk0bKMaVIYu91u8fCNMVI+Sq+dgpPEZFdWJBAdYvJWOBwGgBFhrysXWK/ObG/+TWFFoUdhxu6h\nbrcb8XhcwkhUygCkrwFLV3ltdjdsNBqYmpoSI5FIHw3DcDgs75fLZeRyOZw5cwYLCwv4+te/jmAw\niMXFRXlOokk6jAEMED4Kf86ZTkYG9pNl+Tc/Y/MmefIGjIKbwnMsu9QeruM4Iw3XKKfsMAH3kA4n\n6h+NnlCJcZ9pNI5zaecp0RDQMotzR4SKc6dzhDY2NqT/AcsMObeNRgOxWEz4iugYm3rpRl/r6+vy\nvj64a2dnB0tLS3KaIyt/7JAkAElypLzXITMdLiJiQ9QkFArB5/NJabnuTsiSYPI0S8+1XOSaqDUf\ncQT13rbzDI4avS2jwHGc7eHvPWPMYwAeBLBrjFkYWs4LAPYO+O7vAfg9ADh16pRjJ+cROh9+duR1\nACMeHJlCx66A/QQTMpi2kgk7ulyD1pylUgnAAJ5bXl7G448/Lq0yW62W9AQgNP++971P+th3u12B\njwknUalohUgGHqfkuRG1dT7OYOBmtsuV+v2+CMudnR2cO3cOiUQC29vbqFarKBQK+NKXvoQHH3wQ\nJ0+eFGNGeyr6nuPWA9jP/LahO9am67BDv99HJpMRa5zxZULI2iuywyqHwXf333+/3IzzSU+Bikh7\n2cYYbG1tYWVlBVtbW5ifn0coFMJrr70mJYmET3nwEZtTsTSU+Si6fI5Kl2gB8zL4dyaTkZg9BaJ9\nZgEAyebnelLI87mYOMheATr/plKpCLLE+ziOg1gsNlKCxoqVTCYjlQ88crdYLEp4ivdyuQYZ8Ftb\nW9ja2sJrr70Gj8cj8eHt7W38+Z//OXK5HCKRCL761a/KSX1UajS6mBCpldX6+rrsdw3Fc26Aq8t9\ntYd9o3SzeO748eOOHSrT+4v/Uw7o/aHDfCz54xrScaH3W6lUkM/nxXM3xozIIs6X9mh5Dy0rafjT\nCOOYdnZ24PP5EIvFcO7cOXg8g9bDTALs9/tSfTIzM4MTJ07gAx/4APL5PJ588kns7u4im80Kb9IA\n5vHb2uFIJpOo1+s4deoU/H4/Njc3kUwmkcvlRsICwEAHUM44jiOGMp+BBrBGYCjfmSz7/PPPSzOn\ns2fPIhqNIpVKyb2i0ahcnzokHA6PoNK6Gov7kORyueQclaNIb9koMMaEAbgcx6kM//4eAL8O4P8F\n8BMAfnP4+0+udy3bc9YKyVbkNqSlFShzAnTmMgWEtqiB0f772gBxu9348pe/jGw2i7m5OSmFoofn\ndrtFGVSrVXi9XonBautZ1+qPQz+0lWl75fyt4U072YqbUwu6breLTCYjh51EIhHMzs4ik8kgGo2i\nUChIgtu5c+ckCc62ZsfNlYbn9H05NhpDvIZGCezOko5zdX9//ezXopvJd7ynjk3Ty9CxQvJhOBzG\nSy+9JMfONptNLC8vi4dERUSBwMxmets8pU03iNJwPwUXhQivRYNSl9Ky33sgEJAjmimAuE6cT/Yf\nII8QHaBnCQwgXRppNG6JPHEuuBf6/b6c39Hv9yVkwEoDvRd7vR6OHTuGWCyGhYUFbG9vCxLjdrux\nubkpnma/38f58+fxyCOPjCgnXTPP1/X66fwLGi96Ha63/jfwmZvKc8DVLcA1aqflBfeONg7Is9yL\n9KhZz18sFqVnBtdYrzXRETotwD4qQF4jz+qcA/KIdtpCoZAYiJVKRcbLUC4TnGdnZxGNRiXHgc+v\nYXqiSAyDBAIBzM7OSn5MNpuV5MnV1VVBmMcZV1oOkT8YbrBlFB0dVl8UCgXpO0MEjcgdeYvITqlU\nkj1iG5o0RvR6aoTnbeaz3DJ6O6OaA/DYcII9AP5Px3H+zBjzTQB/bIz5BwDWAfwn17uQrRi0t6o9\nWWB0kW1jQgsSACNMrg0KbjAdL2VZWDqdRrFYlAYvFDKsMvB6vaJgCSOxEQvhYnryGsqzjREtzDh+\n/bdtKGjlfdD8cVMQFkskEuL1sZHJxsYGms0m7r777pFuY3Y1gM5+tuEubfgw7q3zOfQm0O1ldUyY\nz6839A3STeM7zh1/U1lx7nTZG8uVKpUKUqkUQqEQzp8/j3Q6Lc1PGO9nKIXKERgI1FQqhXg8LgYA\nBbX2wvk9Km6WDPJ1ol8UNiwhJCysDxRiJjrvQ8+GiWDkPyZm0Yjp9Xojve2JauiyLn6eyZR+vx+J\nRAL5fF4qHQDIEeKsoGDpF/voU9Az/4JjI19S8ZG3CDdzbhlG0byn95teY5veRLjqpvKcbWxz32rZ\np50e7h0dHiBR3jAXiigQS0B1FQEND8o1bfTa7zE8RASNoQCiHFzfWq2G+fl56ZWRyWSkRwDzhzwe\nj/QaYK5IKBSSLqjAqPHDZFKuL2P3zC1hy+OLFy8KT2qDQO8vnRulw0jaMOj3+5LISr7q9/tyfDRl\neSgUkr1m6xNd4qllnE22cXcU6S0bBY7jrAC4b8zrOQDf/SavNbJBdSKT7WUC+wYAF0YvgEYF+D7L\nyehhUdhVq1VMT0+j0+mgWCzi0qVLeOyxx8T7SSQS2NnZAQCBtGZnZ+WkrWAwiJWVlZHTFu1QAC1L\n/Z42RnSZlw4J0IomaaGnY6f8LIVnOBxGoVDA3t4evF4vYrEYlpaWpFRuamoKGxsbePrpp/Hwww+j\nVCqh2+1ibm5O4F9mR+t1oQLnJuEaEDXg4T7hcFjmt9FoSOmk3nS6GoTGgW3hX4NXbhrf8RnoYbJf\n+vT0tKA/9H6Xl5eRTqdx6tQpZLNZLC8vY29vD36/H9vb22g0GigWi9J1UEOLPIKW3gTRLD430QFd\nW14sFoVX+D0AI/xL4cv+BXweQqQ6h4DfpULViYzBYFCOx6URwLHT82dcuF6vS5MlCsjd3V3ZD8wJ\nYDvdRqMBv9+PYDCIeDyOO++8Uzp/5nI5nD59WhrfLCwsjCTHke+4p4lyRKNR9HqDGnOeqFer1eSE\nRm3I6n3E8ksqAvLuQQL8VvAcnwnYV4D6Na6NzmTXlRtEbXRHS6JcLtegOVQ2m5WDkIgKaONXhw2I\nJlABU05xPLpRD71ljo2GXrFYRK1Wk0oUGgOnTp0CABw/flySnNfW1rCxsSEykY4WlX+v15MKHLZD\nJr+z1wr5lN/RIUmG02ydQiNWK2M9D0TamMhImU4jl8mV5Gtt9GtjgEY514zGk9ZP3HfjdNtRoCOD\nX4zblBoJsF/XIQUNd2sIh9ekV0QPjMKC0PqxY8dw4cIF/Omf/incbrcwFXuRU9CyR304HEY0GhVG\n5GfspEdboWpLdByEpL9Pz84WGnb8UaMqAMSLrNfrqFarIqxTqRQqlQrK5TJcLhfeeOMNHDt2DHfc\ncQe8Xi+2trYwPT0tKInO6NW/dQwS2IdstfAgjE6lqhM7KYC0YaC95MMkjpdzR8Wsy72o9Nh2tVKp\nYHd3F7VaTU65JGxrI1d2jFTnCwBXV3gQpaBRxkQ7ChnOLb0wjpm8Qt4mWsZsa+YBMEmRXh35j+Og\nsNMNZwgfU+DZPQY0oqS9JIYrKIypSMLhMObm5kbCbURPuE+1J6rDUNxrNBBs709Dtdr4sXs7aIPh\ndpIOCQKjhj9/U3lq6F8racaxGS5hjwDW9tN50hUbOqeCc0pHhbzG++p4uA418LMul0tOxfR4PCiV\nShJOrNfrOHPmDO6++24sLy/j4sWLyGazwj+8Xq/Xk1wcja5RTjJMAAyQx3w+j3w+j93dXUHDOJc6\nn4BEmcnP2CgSZSafkzztcrmwtraGs2fPIhAIYH19XQwGjpM8RtmuUQD+rw1V5gppZPWo0ZExCoBR\nxQeMxrJJesJto0B/FxjtVGhDii6XS6CvQqGAJ554AplMRs6Q7/f7KBaLI+fJGzPoEZ9IJOD1epHJ\nZGSj6ficvr+OAdqxL1qMHJdWtjacT8ayFYn2AJh5S0+PJ4PNzs5Kf/x8Pg9jBglzzz//PBYXFyVh\nqd8fNKWhN6KFFO9HKE2PV8cGdaINrWUtfOyESl5Hx4UPmyg4uVmpPOmR7e3toVqtymEsTHByuVwS\nv9X9/6mc6Okw5s5no3GkPRZjjAhzJl7RSGFFA1EEhgrIV1QMfM9OJGOyJNeCyALHQiJEyxADDQ4A\nooRoRBACZvkgjSKGEhjaYEiNgpdVF8YYSRrUiZL0oKjAaZzxNQpxxtBpyNAA0glg2gjQc8G9ZHfm\nPCzS3irlglbUmvjc5BkqQQBiwNKQZD6BRqs0MmBD5lSiunKD+5byjLkFzCnRuQSBQACxWEzQ2EAg\nIPkyTKpNJBKCaJHnuA90907HceQgLvIAc3WImnGdAUhjLBq5zIXxeDwiq7VzqB1HrgFJ51voOeh2\nuzh//jzi8Tg+/vGPy6FSOv+D9+Na6tbKvLZGgTTK+q2YU3BTSceYyby6rEh72/rz2kPRTOBy7Z+e\nSAHA61GYsnrgxRdfRKlUwuLioiwwa7KZ5c3T2hjT2tzcxM7OjggZAFLmp0MIvV5PhDuFG40M7bHZ\nno6GGLlp+Rw83Ijf5evMjOU4O53B8b0zMzPisc7Pzwus+NJLL+HVV1/FJz/5SZw6dQq5XA6zs7MS\nV+Zc2qVeHB8NBzI+PWMtyFqtlsCOFMJ8Fl1eRTpM61mjNvSOdS1zvV7HlStXkE6npSnUzs4O+v1B\nXfLy8jIqlYpApxQm5L+pqSnMz88jHo+PCAbdzwKAxNDpKdPzYlJWrVYbyRy354jClQYNsG+86Uxo\nvs5103FqGhlUskQoGCKwT4aLxWLilW1ubgr/ud2DFrZU/BpCZZiO/RWOHTsmVQ+sOCiXy9jd3YXj\nONLxrlKpiILSmeScIxrXVBjA1XxkG9FUMoxJHybpNdRID5WjbYxzfYm2UA4SWeLZK47jSDhAowtE\neDgHVPRaoek9bIyRszIYIvD7/ZiZmREeYHJjOBzGHXfcIc/Gfc2wFmVcvV5HKpWSsfR6PVl7LReA\nUceAxi7DFbVaTfanNgx1YuO4udTX00YZDVk9t3yv3W7jlVdewauvvor3vOc9+OAHPygond7nnD+i\nccw/0CEvGr9cOxvNOEp0ZIwCLqBmZpJGBfT/tpVvQ+1kAM0kmiGAQQvjxx9/XBSXFtq63zqVB2tZ\n2QXO9vLJMJpZtCWuQw52uIHeAP/X8W4+s/Z49DMDGLF2eQ1a0MFgECdPnsTy8jLa7TYKhQJmZmaQ\nzWaRTCbF8uaz24iMHivnURtqOqat550bnAaRzrK3BaBeu8MiWwAbYySuzbyCaDSKubk5bG5uShMe\nNivSAkyHh+gJ63mh8cdnpWBl8hw9cfIgBSbnmYYteUorDQ0RA/vekQ496BAa1w/AyImIWnCxLwEN\nPyINfM8YI53qeH1CqwAkTMJscwByCic9KvI8vTwiDZwX7gMabRpRYsY7+YywtW7dTdSCcLUNfeu1\nPyzSydBaTuhx8H/uYY24aRifsoBroJ2ScbIBgCSKanlDo5II1fz8PILBoOSPBAIBJJNJCTUwjBQI\nBHDq1ClRikSH9LPVajUEg0GptOFx8VNTU6JIdUtmjfB4PB5MT0+LkehyuWRcly9fFrRAh1A0dK/n\ndNzZL3qcwGg1i+7S+vjjjyMWi+H973+/NK+j08i10c4UUUM734HXttfnKNGRMAoohLT1ZRsA2ijQ\nylhvZh2r5/XsHAOtiNLpNJ544gl4PIPjP3d3d5FIJEagUwpKHXfa3d0VOJXCURsEwD6ETINAC2sy\nPKFVKgJ+XytcrVQ5bh2K4H14PWBwWBKZMhgMCgLg9/sRj8fhOI604fX7/VhdXZWzHKgwbANDe1o6\nJGKHeHQCl43yaCvd9txIhymcbY+ByVzdblcyuSlkGo0G9vb2RFESbdHf59pSKemDfbSw13zJe9Io\n4Pss+QMgZV22UaWvNw5p00KRBjfhfypMKgLdZ4Klh16vVxpyEY7n9ZkkG41GBSKmwUweYiyYSaZU\nHGzsxM6O4XAYpVIJrVYLMzMzctgXQ3U0NuwjeLmP6HVxj9IL5jPrueE4OH92yPJWk5ZLmn+0ocBn\n0XuLfKQTVPk/jS/yh5YxfEbtXGiEj/NDI4tdNnltIjCBQAChUAjhcFiQBm1EUFkz/Oj1erG8vIzT\np0/j/2fvzWIkTa+6z/8bkUtk7EtGRq5VlVXd1VXttrtNg9oytpmWDcyMkMAXwy6N8UjfBeMrhGCA\ni+EGMUgIJG4aPiEEgwSj+UBojDyAzScG43aX2m67u6u62rVXZlZVLhGRseYey1xE/06ceDvLbpA7\nO0eaRyplVmbkuzzPec75n/9ZnvPnz+vKlSs6OjqyVtrdbtfyDzDg2Wx2JAwHGLp27ZokWZ+YTCaj\nIBicrIns0Bdmd3f3XU7lcWFQDwy8M4kNYW/C9L722msqFAqanp620kkPPPhb79T6OfYhGOb8/wcF\n32NASfk6VyYznLyBIfXeHYrKl6L4ZDFJVlJCTOz3f//3tbW1ZdnL/X5f5XLZYvIoD76SS0DjH+g3\nPi/JTptjwaH0+SxZ4bTUpPSm0WjY3549e9Y893a7LWkguPQK9zFk8gHYvAgftCqgpdVqKZPJKB6P\n6+DgQHNzc2q320qn0/rzP/9zff7zn1cymVSxWFStVnuXMfPG2yc1esPKqYCenvaszfj4uNGd/lx7\nFAnlZSc1kCWUIPFJgOD+/r6da3H79m2Vy2W1Wi1Vq1XrDSC9GxBwQFYmk1E0Oijlo2Mh1KpXUAA7\nYsPh8AJUPs/nj0BG/nziErkBGBroYNovo7RR9MlkUouLi8bm4JnT7317e9tKLgHLKLepqSktLy9r\nY2PDlH2v1zP5JZs8CAY5E3S8i0SGZy3g3W9ubqparer555+3ZjIc78z9mQtJIydM0iTKx75Z03Dz\nJa8XmJ+TBKPkfjAAoh4EeObRvw96LexA9Ho9m1ufB0CyKPuRPCOqCKDbyX9hH5ZKJWUyGeXzeZOT\n6elp60fBXg07Yjxrp9PRxYsXTY9/4hOfsD4gmUzGkmmptAGIe0aV8BlhCwAJiYk+jOHzGWDPyLPw\nTci8bUBeAJXYEXJuYKEkaW9vT//yL/+i69ev67d/+7d18eJFbW9vKxaL2fkJPjkcpxG2AYBDi2QY\nyNM4Tg0okDSCtqThIRIe3QVBYJ5TOG6E8fZMAQtPuU02m7VjYWu1mtLptDX78WCDe3nlQXmWp4nD\ng79FeNnYvkuYPzCJz/DexGXZ2LwbPREeN1eg1nC5C58DdED30oyJz7311lt64YUXTPGG554BQuZ+\nngb1wMGjbg8sfOgDRsJ7OmHm4P0e4UTPw8PBsalvvPGGHZTC2tGohQ6HyBgKOpFIjJwf4LO6fWhF\n0ojsotx9whKUOGvZ6XQszotHFPZ2iKl6L9GH2Vg7b3DS6bRSqZSddwCF75Mhea52uz0ic/68BSpW\nYDgwAOxHZHtsbMy8On8eA8rYg8Z4PK5WqzUCpsP0L2dIeFYq/P7+Hl6XwNCEk5Dfz+HXnGciROA9\nR+bMy4d/n3AoiKQ/aVhGKGnkHIB4PK6FhQU7tI3zOHBw4vG4rSVfU6mUAV0Aoc+693oQnYEeQubC\noGxpack6aiLXPmlyY2NjJDaPA8E+6HQG3T0rlYo9D9fLZrN2ABM6yIMwnpt16PUGpY3+2gAO8qAi\nkYgB9larpVu3blklWqfTMfnz+SF870FdEAxKMNEnXp+fpnFqQIGntkjYCm9uT9MT18Hj8Qvh6Z9+\nv29eUrvd1t7engqFgv70T//UBBqKmJg6ghZ+tkajYUqZbH4Ok8FL9M18vAdPIhCeDd4pSYOFQsHK\nHiWNHOtJm2Y2PwJFJqx/dm9MuDcIe3t721rPZrNZpVIp8wK//OUva2JiQs8995wZBendhxX5WLZH\n1/yOefdgyCdWAXoARJ5J+CASvnyCIf3bHzx4YBUGmUxG1WrVEv7W19fV7/fNU6JRDElZPoZNMivs\ngK/QwBh5cOBDUf6ku2azabIEcJFkJWkYO08xS6PVO0EQjMTax8bGrDIFYMyc4LX5d8hkMrp///7I\nvqPR0Pj4uJ2OWKlUbJ81m02lUikzehj6er1u2eWwKpJUKpXU7Xb1ne98R7Ozs9Ybod/vj/Qs8Y2O\nqGfvdgenWjLPPuzm6WRklTAQuRsnBQpgYdg/rLm/fzgEhJz4vYP8MCfNZtPaBgN2/J5bWFjQ+Pi4\nnduC3sPTzeVyOn/+vNLptOLxuAFFDoJj7cLhUGnIJmIEeQcfQ+f3gGYo+P39fdVqNWsO1+l0NDc3\nZ0B7e3vbjL1PnF1bW1OhUFC73dbDhw+NhSIUSs8VaSgD5LswtwAm2FgfRvFsGNc4OjpSs9nUK6+8\nolgspuXlZcvvIRE93DwsCIaneGLPeI9wmOO0jFMDCvzAM/Gd3Pxi+thZ2OPy8Ww+T+c1vIpr167p\n3r17dn485TIYNbwtX55DjTrPx+aThv2tw7kQfvjYHTS/T5DCeJD97b19SSMH7KAgpFFPlw1LGSVK\nB1oOBoL3pDKg3W5rcnJSb7/9tj72sY+ZwHrDH07M8iyBz4Hw7++91OM8HQZr5ruvncQAhPIOgIRK\npWJVHNCbu7u7FtMEiDYaDZvHQqFg7wVYlGQxUrxi5NSDAn7OV5RHv983WhwwGC5JZP5QOh5YhIGb\n9+6gNj2w4F8ikRgxLqzN1NSUJVp6JqvVapky9B0GoWupSJFkYMSzBLxDvz84V4Quh8grZZm+oRNV\nOL5xE3sm/M4o7rDhR4a9fJ7EQNbC4zhD6xlPvwfxahnValUrKysmCz7cBMOEEYQVOjo6siOMp6en\nLWQAQCbpk/X3Os173t5xCDOGYWDKfZFB9BLUP/vMg9GtrS21Wi1LBueo7l6vZ6fZbm5u2nPXajUL\nYzJX3hizD3gnTjpkTTwY4DO+pBg2gZwswiW8bzg8ARhi//Izz3idpnFqQIEXHJ9HwPAGB7pZGnpF\nnhLy1+n3+2b0UqmUEomE/u3f/s28bhQIxyeHY+n+/t77DoLADC+/98PTZng6XtBArh55RyKDRi6+\nxzx/wwb1JWd4Sgg/ngAnPnJfEoIon8OoRSIRS5iLx+NaXV1VLBaz9wrncnjD5QXaGxueIzwnx4Ek\nTy2H2ZmTGt4TI9mN5La1tTW1222bN8AjoSFCCz7ZiuQ7jD7eNIOfh6lDvFvmAk/Pg1LPCGAsfE6H\nNGpw/Nx6Wh0lzCmNxFylIahhbjgoDFBAMqQkS1rc2dmx0BqhJxIDfRMcnsl3nPMhCk7UGxsbMyDL\nnu33+8aY+JwW/4g+ZAAAIABJREFUX41B1YI0qi+4L88dbvJ0kgwVYaIwWJZG+3WE/4bPYeD8/sOY\ntlotW0uuk0wmlU6nbZ9nMpmR8z0ADTADPkSAPMNYhZ/JP3c4rAETKI2yjRhhHy7jOdBDhIe73UFr\n5WKxaIwOc5TL5Qwwx+NxlUolTU5OamNjQ0EQjPTs2NraUr8/OOCMvBrPaHA/H3qWRpkP5tM7ZIBW\nr8t8EimsMfLf6/XsmcmvOo3j1IACNjZoj01LIh4C1e8PDqHgBC4vfCSmeGXY7/c1Ozurhw8fqtvt\n6vd///e1u7trFPCjR49MUZIo4xkCKC3qqxuNhp5//nk9ePBAlUpFhUJhpLYbry6dTlvWtiRTbCRe\nYZAzmYwl/USjUWWzWavTZpO0Wi1NTU1Zpm0ul1O327XugyTlJBIJlctlYwu814mnQE09sbZkMqlk\nMqlqtapIJKJGo2HAxDMBx4GlsALwORQYxLBCAWjweQ718e17T3pQKgUoK5VKunHjhtbX1yXJzroI\ngkH8vNPpaHt7W4eHg2O25+bmtL6+bizM0dGgFS89Lkg0PDw8PLaxDLI+NjZmclMul82bR1F7BgAv\nnufDsHoPhX9QthjgRCKhbDarXC5nXmTYiwIYSLJERv41Gg07QlmSCoWCAVzKMJlPKjUoZ+MkOuLE\neE5HR0eWjzA1NWXHJ6fTaVPEiURC29vbqtfr1lUUsIZXyfwAnhmAE98y2Xt1J5lT4PMY2A8+5h4O\nJ/j8IL7CAuzt7Rlwm56eNuM/Pj6ufD5voIA9Cq1Oj4lz585paWlJ09PTmpmZMTkAIPjeD55Z846A\nN/rImGc2wiwi7Zk9M0hvFcIZXJP704mV5F+8ewBzMplUNpu1RGnfT2VhYUGTk5NaWVnRw4cPLdnP\n59aEc9Gk0bNzCKNEIoOGZRh/AIZn1mCmqehhDVmzsIN02sapAQUIEb31fawZL5syKLxs4reUOXmv\n3S/0ysqKZmZmdHBwoEajoXa7rWKxqEajYZ42SjWMiGkG0u0O6v3z+bxu3bql8fFxa8YhyY7+3N7e\ntg1Fhm0+n9fq6qpKpZLGx8dVrVYVi8WUy+WM5gJAeOE8ODgYSUxhoxNm2N3dtSQZFPL6+rq63a7y\n+byhZYxzq9VSsVjUjRs3lE6nNTExoWq1apsgmUxaGMNvGuZUGtLbPgnSez38HX9D3gCKjq/e48MY\noBBPamCcotFBW2iyryORiB49eqRKpaKlpSVtb28bfbm3t6dKpaKjo8GhPiQhLSwsWFjA1yijJHd2\ndkbyK7zChIHyVQY8n4+Je0CFJ02Fib8me8mHqur1ulWfUH2TTqctx8Qza+wdQEAymTTDS1IuHhBK\nkf1Xq9XMQHc6HQOqlB76HA6AKgllhOx4Zw4b47loh0w9PLqBvhHJZFLxeNxYCl/JxHpglH1/iDAr\n+X4PX37o180bJL8HPWhhLrzXy/f9/qCTIM4JuQOHh4dWCdNutxWLxVSv160XAcDTG3Cfm+XpduQS\n2p+8IR8elYaJxtJoKIS/590ZHpRyXXQX7wWYyOVyikQG53IQvmKO2Cuzs7Pa2Niwfb2/v69Lly5Z\nbtX9+/dHzltgvwRBoOnpabVarRGmi+TXqakp3bt3T1euXFG5XNbnP/95AwJUt1HVBVj3dsYnE5/W\ncWpAgTckXsDw3lFAnmr2KNXnGXgE3u8P6qVpoAF9vru7a1QbiuK4TRimwMNxKpSKR9Q+oxejCMUM\n+ob+l0br5fm/b+KC4UeJIcw+qQ0KjNIcABaGLgiGh9rQYY++BdSg93o93bp1Sx/+8Icf+94+pOE/\n4z0cP3d8xntnzLd/d0+1ndRAlngm4uTSAAzmcjkzHiRfNptNy9qWhrkeXIO/994gSW3MEx6VD12g\nFL3i8EoWA8j3niJmMPcoOB++8mCXEjMfhvMsBOuHvErDBFGy1JFFn8HP83owQR6OT+bDcHsPk8Gx\nzRh2Dr+BUSOvxitVQjpUOySTSSvP8+/H3IXzCE4SEPAsPpfFhz69weRnYdBCHpQ03HcYNxwq2AES\nBD0L5JPpPAhgfb1DEAYJx4Vlwkyg/8rzer1+3GfCOtD/nHfn2flcNpuVNPTCmVv0YqfTMTnAaYvF\nBscxd7uDE2PZ/5lMRplMxo6cTqVSI2CNfAzCXPfv31ej0dCP/uiPWqk6gA7Gg2fzDhNzzn4/jeNU\ngAKf9OGTafACmDwMNvQWIIEEo3AJl4/dlEolvfTSS1YbOjU1ZfEdafRIY98kxQszwkFG+PT0tMVA\nofd7vZ59hmx2KFJqytlsZFSjQDOZjNLptB49emSUdaFQsLjY2NiYlpaWrO0up5JBt46Pj1v/cK7N\ncxHrfvjwobLZrNbW1ix+h5FrNpv60pe+pPn5eWsx62nu72awMRpsxnB8lHnxnjIIm78/6dABynZ3\nd1edTse8EZLa5ufn9corr6jRaFh/ina7rUQiYVUiAEyuxztTOUIvARSCB4rIhacUCV9RFua96bD8\nS+8+Q4I8GZ+Qd3R0ZN3gotGoSqWSpqenreKAfRIGBig6wiCUj3FdyiJJNIRlonIB9kMaskHch3iq\nD3VMTEyoUqlYRQTZ3r3eoOPh9PS0GT2SGAH2nMOAnHulDPhCNmFs/DhpMOpBigeG0rvzDnxCoQeD\n5FhwLf4ehoh5yWQy2tjYMOZyYmJC+XzeaHcArzfegEH/cx+W5fc+XODl6DiW0TOyHuiEcyvC/2fA\nEiOTsFPewOIE9vuDSq+1tTXdunXL2C6uu7y8rKOjIwud7u/vWyg5EomoVqtZGTvAwOeiSDLdjl0A\neGA3Go2GGo2GCoWC6TxAl1/T0zZOBSjwKNEjaBbRC5kPJ3iPy3v5fgHwXL71rW/pxo0bJliABoSa\nxQqjci+4PkSBZ+mTUQAjnkL2SSc8P+9E0hZggl7hvsbXJyMSIoAC5m+SyaTq9bp1lgP5Eg6Bujo4\nOLCmGRhhusp5WrhcLiubzY4or++lNDE4Xskzz35d/foAwFDSoOqTGj7cwRqicJj77e1tY3VYKwZt\ndmFzPAMSiUTscCNJZoB5P+SCZjBeWRJ75HPMKZUMsER+8Jl4PD6ivPwhNpIsyRBZ9kmuPnzD/vBJ\nWNLwfA8MzuTkpKrV6ki4zldA+DJhHyaLRqMjpXm+r4NnVXgWaXgCaNiD9kldhBeY6zBNz/Ce8PcC\nvN/PETZ4HhB42Xgcm8E7+V4Z/nM4F5HIIBkPOWL9kTUYRBwD3/yK63qWLzzCYDQ8n8cxWQx0JrrQ\n78Pw+/Dz8Bx45iMMZtGLhBh84uvR0ZHpSrq4UsHQ7Q7KWjc2NlQsFi0/jYRCnp3704wIxy78vNgV\nHwpCt38QuVPvdZwKUMAIU9Q+QxSj6NvBeq/M0/pe2LrdQVOZf/zHfzSviw5UKEYfizouo9srahYU\nGh5FhZADRo6OjuyZIpHIiCLmndiIk5OTVitLJ0JYBU5uxDOUBvXceGNch3AAFQqdTscUOu8MSOj3\n+5bMU6lU7O8Z9+7d06VLl2wDeEE/Lnzg58tT3tLo4SasKTSnD6OwVic5eGZkC++eDVutVk3eqE7p\n9/tWbkh2tJdVn2fBOmCwvJfKV0Agv/MsS7/ft2eCWWANAcQe1OCd+5i1j9NKskoU3pm/9YDO7z+f\n5Mhz+cYsdH4D0AFaHicDJJSSiMm9qOyYnJw0UOArPPg81+L5wtR5eI4BIN6IsU5+L57UCIdovts4\nDhBwjb29PVtHX97qDwfC4JN8iO5qt9vK5XLWZIvrhMN9YR3owxz8nn0svbuBmQc64fCHv25Yx/h7\nIB8+1MbnmUt/Pc8qw5IGQWDgADYL56jRaOiJJ57Q/v6+5ubmtLu7q7//+783JtOHlRmEIABdXo7Z\nT4TZ0B2eZfHg/zSOUwEKfEzLCyXeFooZpdzv963xCcotm81a/BJKETT3a7/2a5ZtD83olRxCEqa8\npSEgwDsiRkXSFVnnIEnf/pNWubwLiTIoMQ7cwfPb3d3V9va2JNlxvOl0Wv3+oFKCBK7NzU3t7OzY\niYYYLN5vbGxMt2/fNpqblrN4DxyfvLe3p5mZGcusz2QyajQaunXrlj7zmc+MeO4I8OMSZLwHKA0P\nAvHr6ePs3jtPJpP2+8d5F+/HiEQiRn1jxCcmJnRwcKCHDx+qUqmMKGBCLCTnbW9vG22O8cJ4kwjI\nunlDQOZ+t9u1a5FgG41GrXyWPgnIHUAP7wMFiRIiGx2jCpOF8o5EBp09c7mcEomEla56A8s/9gJV\nExhkOl6iDEl+5XyDRqNhLIM0PLhof3/fwBRhCQALzZIwZgBtZCgajRrwIEGW/dVqtcxZ4O/Z9wAQ\nn7gMaACEfhDK2YcvkAnmg595lvS4nAL0Wb1eN4OH9z82NmZr3O8PEjR9g56trS1NTU0pn88rHo8b\nMOBvww6Z/+oNtt/DAAJ/pLcHcN6j94DVG38f4vFhXdbK562QaM29uQYgnLmbm5vTk08+aeGTcrms\naDSqRqOhYrGoaHRQEXPu3DkLCX/84x/Xt7/9bZPrcPiEPgmLi4uWQJxKpYx5I/mX+SFvixAyJbyn\ntc3xyR5J95jhPScMNJ3iMExUDkDJRiIR60jmO/v5chLABIaVzeRRsaet+d7HkYjxSoO4KF7N/v6+\nlQfOzc3ZPTE0QRBYJ0HQeqlUso5vXBdjjrJF+RFvBoUCHqCr9/f3de7cORNGaGGAEwlbKMFWq2XV\nCpyaCItCgxCy0tvttg4ODiwXIZFImEfsKUNpdNP6PAGyw71C8ygfA8jcMCcnPVgjaWDAarWams2m\nnTHB3LL5M5mMlSVKo+cSIC/e64UlQIYpoePeGH4UCF44uQiSRsABcgk4JcwBOPE9OTjMyIMNwKEH\nAgAADC3lXihvaGaMBuEr3jGdTo+E5XheSSMMh4+j1+t1AwS8597e3kgyF4mzvV7PrkcSL38L4CLH\ngPny4ROoYwD6cbT8SQ3kyb9DuArCP7uPv3uaHOBZr9dVq9W0sLCg2dlZTUxMqFAoWCgJQEWFRi6X\n0/LysiRZmZ00ZAJ87ko4yRHZw3jzlfAD8XQ+5wcyFg75ch2fT+GdRP75axL29HraO279ft8cw7Gx\nMc3MzJh8krQOoDx79qzp7UKhoNXVVU1PT1uyNhUwdOZEr0uyyo4gCGz9aIXMMwEGaIiHrQmXe56m\ncSqYgnDsEGXjS/T4XBC8O0mJOC5f+VwsFtPm5qZ1nsNIYvxR1n6zfa/B5iEc4A+xwdtFWRFn4j1Q\n9Agwz8KBLrAiPBPPyH0xDBhdn2jE/aAKqYlHGP0xtt1u14wH10Khkzz26NEjXbhwwTYxIQbWCPDl\nFZmPK3qkf1y+SDgM8V5zF76fwz+HlwFKnXy+SDjZtdcbVpyg2MIJbB6kYigBAChgH27wddtcL+zZ\nYkjwjvH+fUjNhw88+0YNuE9ECw8flgvHtQEO0tDb9zHXTqdjgAnWhWf3c+4NH1+pRvByw7MQFmDf\n8t7s6XDpm18jwi2sh3/P8M8+iBFmKcLxZtbchw4I4dRqNUmyevh2u63FxUX1eoNOf/V63ZKdH3d9\nPzCqeOOeNfJ7xF8LR87vea9TpXdXH3Bt7/3zbu9lPfyeCX+eueIz7A9fZQVwODo6UqPRsByrWq2m\nCxcuqN/v6+mnn9a1a9cs+RjdjP6VBqBqd3fXnoGmZYAB3x8EeWQ+w6Xvp2mciifDgIdjUp46O45C\nw1DBGMAMsHg7Ozv6q7/6K01OTqpWq4189QaO64Xvcdxzcm2ysNvttprN5khL4iAIVK/XJUm1Wk39\nfn+EYsUTItZUrVatnpt+3qBQPC3eE9TPu3gKGa9IGnhd9+7ds9IkGn5wut+DBw/0xBNPaGtrS81m\n07xMGht98Ytf1Be+8AXFYjGjdvEKpFHPgv9L71Y6YVDHZ73SZ874+5MaYeVHi+OrV68a2PReCUYQ\n4ATLw5r60AjvA2Pl+xT41rysNZTocXPglYmPvxIump6eVqfTsRp0jKc0YDJo9LO4uGid34h34umg\n4Lm/Zz7wpAhNUZY5NjZmYTcSEEulkhKJhLa2tqzHBwyI71THu3jvEZaNefM13V4++RnzDuCFRseT\n3t3d1d7ensXZfWUScwT4OskQQhj4MEe+RNl746w9n4MpyuVyZpikoecfiUQ0MzOja9eu2f8x+Mw9\nAJHjkAHAGE1kwetgv06AU97D73EfhvX72+8zz5Z4p8LnCjCYCz9nzJMHkv4+XreTUBgEgSqVijmV\nHHg2Pz+v69ev67nnnlM8HtfNmzf11FNP6c0331ShUFA0GrVQIWxhIpHQzs6Otre3R1gA9gXDAyev\nq49jU07LOBWgwE+Oj2dJGkFYeNGeasIb8B4Jm2d1dVVbW1tG/aBQETpPIYZpxccNngVlDyr0Agog\n8Vn/INPx8XEDMF45drtdqw7AULBR+OdpRq7JJoDO9W1eOZJYGoAEFA+5GhwjjefplTDgaWJiws4o\nZ+69Rxb2vnh/PnOcwg1v+A8CEDB8Yh1UYb1et/kmXOUPiQpToOHvGcik99p9VjYyQAWCNPTW/ODa\nrB2eM0CCkE24zIm9Mz4+rnQ6bYyC9O5GMscp4fB+8MoeQ89ewvgEQWD5ENvb2yajGGY/N7wDShTm\nxHtS/oAj5sC/J8DZJxk+bp3D7+uBwUnKngdd3iDyu/DcH8ekYYi8cSV3o1gs6uDgwEKB9IvgbylF\npLNq2Ps/DiAx973esKzOx9rDwwNNALUHN49zFvwcSHrX2ngdfhyzgCwiB559zufzliPDs+3t7Wln\nZ0fZbFarq6v69Kc/rfv37+vBgwcqFAqW0C3JgAAgqt1u25xwzDjv40FRWCec9nEqQAFGlsUGbRGH\n9BvZI0Mfx5aGSgvBvXPnjo6OBqexQZf6ZCaaXHwvhoDB4kLlc143m5PkMq9wfIc1klB4X5LL8FYk\nmaCFs8x5f2/AAQkg+6mpKfOEUKp8HhYB7zgajWpvb0+JRMJQLoKMl4Di8Z3MPCDgmfzc+HXwiD1M\nKfqN743gSYcPfDjHt4zt9XrWEhoFCFvi59bLLoN39uyRDz/wf++NwVzxTFyHdeZvuDfeOQlX5J6E\nmbVodFA6mM1mrRwN0OafGUV9nGIOK2AfJgKI+jMPKHH1Bpf8AL73cooXj3H31C/P5HMS6GaK3LAH\nw7KD58u1AR28i6d1T3L4SoAwQJCGYDJseHlmdCQtozm6l/kjKRZAH9YjNDhDV/m8E/YDCYPeePue\nD16eAateP3jmycsN+8cbTvS1z/nwa3McKOC6YVBynH7CScxms8pms5bvhN6kAmtiYkLlclmSLP+q\nUqnYdcjDIqmaMG2r1bLTd/2BZX6fHcdEn7TcvddxKkABAs9Ccsww1C4DwfPlTyBijBuKaWxsTP/8\nz/9sR7UuLy+bgcR7Ok4xfDej5BPhdnZ2LE7c7w8SW6CYpqamLNlkbm5OnU5HGxsblqQGCCBHgIRK\ncgLi8bhlarNJ2fAkejUaDfX7fWum0263deHCBXU6g3MaKHMkeWtsbEzr6+tWRhmLxew8cpLjstns\nCCWIQQJEEbf2SoZ/Xpnz9yh1GBG/UX0ZT7jq5KQGz7S/v69Wq6X79+/rrbfe0sLCglZWVrS1tWXJ\nWoA4BvR0KpWy8A7vTZIc2cWeyQqCYCTBiMZFAE2UDkmC3W7XklWZfyhMrl8ul0fOnSdPZH5+XktL\nS5qfn9f09PSIJw8z5ftnHBeq80wWP8cjDYJBvTsVM8lkUo1GwzKuaRFdr9dHOvBJw7I6DLcPRVUq\nFVPCxGQ5wKfT6Vi4yyeIejaF+2AgfVY990LmTzp04JlFn0QYZpyOMx7ojSAIrCMruR0Av1QqpWq1\naq19l5aWdOfOHWu7jjH0+5J7Uq6HrPraewxdGLjyfFzPz7GXcw8WCPcw/4AK3t+HTnyOjs8V8cwP\nn+Nv0JWeXY1EBueaLC4uWjhAkiVVcyw6LEo8Htf6+rolgKfTaT355JPqdDq6d++eNjc3tbCwoIcP\nH6rT6Wh5eVmNRsPKIEmY39vb0/z8vM25Zxv9u52mcSpAAYiPmCML4T0CJpVNheFHwPBaiKFWq1XN\nz8+rUqkok8lY1zoEUDr+tD9ptDlG2EvyyWfkCxD/94q90+nYqXuSrJyL4z+9Z8c7AFoAGiQKMtiQ\nxBCr1aoxKpHI4DCjo6Mjo2/xzPhbWAmfhNjtdm2uSbBDWWEwYDx8Zz2MBe/gveLjPFBptJ87oRWf\nsOjX4CQGwBJgsr29bc+BfBHKkWSUIR4TihbjhOyyHoAA3h3AiBxisEjwJJ7OvHovGjkglJHJZCwE\nRI4CeQXSgCKem5tTqVSy6gCuB4PggZo09K68x+jzGRi9Xs/6XrTbbUWjUfOsKIvc29tTJpPR/v6+\ngXze24eqPEvC+RJebv1JpD484L3OTqdjtC6fk2TdPj24wpPz8naSMueZGg+QfK8V9Af7A0eCufIJ\nq71ez84yoLSXng581jfC2t3d1dmzZ0f6nnBPnAf2vN/XniHDcPN/ZBNw2+l0RuQM0OYTSzGQnoUI\n6xTuw/A66bjwqQcU/j7oWvR6Pp9Xq9XS3Nyc1tbWLCeMhm3dblfb29smM+h8gEA2m1WlUrEeCPV6\nXZVKRY1GQ91uV9lsVk8++aSmpqaUTqdHgJgPY5+kA/TvGacCFEjDBkRjY2NGh0nD/AC/GfCWmWDC\nAuPj49rc3FQ2m9Xf/u3fqlqtmoD6TFeviLwAfi8FEc6ypUc2Hr8ki+0Sg/aKzNPO0NUYek/JenDC\npuR3KAqenTnh8wArygvr9brlBPCeIPQgCKx7IcqADQTFR5XC3t6ehRPCKD4s7N7T9JvVtznGCwlv\n+pMePvENlH///n1jRnxPc7wKz3Awp7TjjUQimp6etiTUnZ0d9Xo96zIXVoowRgAy5hLlB8UryQDl\n+Pi4NjY2zNB7QJFOp1UoFHTmzBmdO3du5JwDn3ntqWhp9AQ+n6zlw3G9Xs9YsE6nY2WKgEbkHqOS\ny+UkDTx/X3URBIGBTNYAmYhEIgaUPE1M90hAdK83PDyN98KwQJtTY86ZH+H47gcxPK0uDfNFfGkr\nDhJrEj491LNQkcggOXNpackYIEKKHlzBjESjUSWTSXO20ANQ6ay7D1PB5LF26FtfmeN/z1dvoPk5\nISzmAhnn/+EwpC+r9uwAz8R+8nPqq3rC8+Z7u9CCOB6Pq1KpKJ1Oa2NjQ/V6XcViUfF4XOVy2eS6\n3W5ra2tLpVLJGN75+XlL9lxYWFAqlbKj1wFVnMIaZqVOa37BqQAFGEVP/wAQKAXxHrvvwIciRUBS\nqZRWVlZ08+ZNAxSe/nyc4ffX/14Iznt/GDt+RqwUY+jvRSw1zEIwB3w97ntpuEH9nHla11Osfk7C\n8T2fB8A/b9R9whfKFOHmnuHYHe/KczH8Zz115hPr/DuepNfGs3iwBPWOYcb4eE+HuWGDE6NEwaFY\nw/X5fPX9CwB1Xu69gebv/HrwOxQtXtPU1JQuXLhgLVwBAiQkhufWrxP38axAeD3pCeBPFuW+/f6w\n+ZNPfKRiwdPLHvx4WWc+kTkfcsDIeToYQwYDxvewbewDnksabSH8QY3wHvchTEKjgEHmAtnCANZq\nNZVKJdMphG4AnzgI9MzAkRgfHzfv97jnkTSiYzzw97kNYaftuPn0shzOL+D3vBNz4MMXDA9a+H/4\n2sfp9nAoBhCJDBNGlYYGm2oaHD3kGQN+cHBgVV/s2263q3K5bMDMt5cOs6F+/k5rWeKpeSpKEtnI\ndC3zPb5Bt97TZ6AQstms/vEf/9EOR5FGa1f9ZgyHB/j3uFiPV5ph4+o/g4H2YRGUIQr8OGDA8Nd9\nHIDxVB+fpTsf88GG4Jm8hx+mg3kHqH2a7BAfk4b903lGfw0fyw2/E/f2MVSfx8HPwuGckxg8L4bf\ne2rS6HkW0lAh+WclNu8NfTiRzit2wl7dbtcSS6PR6EjynDeQx4Em9glGZHJyUvl8XmfOnLFEMmLp\nHkx4hcr3HuCF187/H2Xplbk0yCUg5BWPx03WCVHBJgCspNEkT2nIovneHGEwz7U5/Mu/v39GPGDm\n2q9VWA98EOM4/eKbF3lmkff0rAHMgW9hPDk5qXK5bAwDBtCHMz2rGtYrYeDqGTLvBEnHdzU9Tk+x\nfjCe3smABUCWCImGZd07KwwPnj2bxO+4jl9rZI+zQWiKRYUGo9PpKJfL2YFIyWTSSmH9gV+UtxI+\nOzw8NGfCAzl0hQe9YSfptI1TAwq8AacUDrQG7ewb6KCger2etfPsdrv62te+ppdffnmEJvUshPdo\npX9/YltYWYdj6cSMAQZQT7wfz+9Pf/RKKuy9+Wt7Qfc5CXjwCCinhPX7fTtg5riNBbAg8cnT4ZJ0\n5coVvfjiizo6OlKhUDDP+bhmMOF5CM+vZ4P89xjHMJ19EoN35mRElCvygcfrm2P5RCZpGF/3JXHU\n5yeTSTPMfk7wMsg/wHih0D0b470Kfw0MRywW08WLF1UsFlUsFpXJZKwroacxpaHBJIfFr6U3VLy7\n/z33hEHhTAYAr6+0oaseinh+fl6NRkPValX1et3kLJxo5+lgAC8ePwlgXv6ZC9aEREzkmnXy4Bkd\n4OX8pGXOyznvz/P6ufAsIkaHPKszZ86o3W5rfHzczkLJ5XK2DlyD0wSlIUNHiMGHiFh/7k8SqGcN\n/DP7PeDBpAf1vvzVr7XfK8iPdwqYC+7F+vlrww5JsvwFaZgPA5CHWfJOVLfbtfyTfD6v3d1dnT9/\nXtvb28aCUX7ocyIODw/VaDQUj8cNdK+urmppaclOzIUZC7MUHvyeBrbqu43v2eY4CII/C4JgKwiC\na+5n+SAIvhIEwa13vube+XkQBMEfBUFwOwiCN4Mg+IH3+iB4VlBFIGHOFQDdegGSRhMMDw4O9O1v\nf9uEDQX4OIreIzhvdB83jvMyPLXsk3LwQNmAKCMfK3xnziQNY9v++l4xhBUX18fAwqwQT+T/bB6/\n6cKeuVdncrKnAAAgAElEQVRCXlmvrKyM9I/3n0Nh+DppSSNzwab0Xgrej783htB7xScldyjAVqs1\n0mCI50d5ksjnjQnv4ulYZJj55x7MLV4b5Y6skTSaje3/cS0vCyQpxuNxzczMqFgsmlIChPruf8ex\nAZ4xOI7ZCRsnaQjeWTcARiaTUaFQMECPcuz3+yMlcP75/cFK/pn4jDdKxIF9vNvvN2hsv7d84yXW\n5nuN91vujtMzzLOvxGDOve7odAZlnZyG2mq1lEgkND8/r52dHQuvYISpzvKhKXTrcTrBs3jMvdfB\nfo346r1ir2dhRQ8ODmwdwvLoS/tgfTx48MydW4uReZM08m7hZwjPN3vbg4hkMql8Pm/7ud1uK51O\nj+hCntl3kp2amrIqJNg+whxh23Lcs58kGP33jPdy9sGfS/pvQz/7XyT9136//6Sk//rO/yXpv5P0\n5Dv//pOkl97TQ0SGBx8x+cR3ydhOp9NW0gfKI+ZIqU0sFtPW1pYkGZVLaZ83Pvyfr2Hlzfd+o/T7\nfUv04vlAofQrAD3GYjE1m03bVGw0H2eGvmq1WgZsGBh7FISnSL03y+Zl42FgOp2OLl++rI2NDUtC\n5G98CRzC7t8Fg00L52w2awYR4+fnkYx71pGNRZKeB3EYOR875Oc+VnxScicNMtTr9brVJzebTfOu\nMGbSMMnQe/8+yQ0vzudzMMIeFWvrwQTr3G63rWoFheiPfYWmHxsbU6lU0lNPPaW5uTlls9mR44dp\nserXAKXlm2f5BFzAh4/lUwrJe/qkXtbYl5hRRkiZF8c9wwoBbnimcBiJUAwAEbnnmhj/cIdEPM5I\nJGLMmS+T9XOCZ0xjKs9ynZTc+Ri6NNzzrLE35Ds7O9Z9jzXu9QYJlwAEstzxmjHChKQ8S4Du8yEd\nDxAJb+FQYNw5th155xl9wmEY2LDG6F+Sl/2JpJzj4EGb10fsDdg0/g5dzLV86BSnkB4e2AxkhD3A\n/K6srCiZTGp6enok6dcn/FKN1Ov1dOfOHe3u7mpmZsYqFdBd7DHCDN6x9TlljwtTf9Dje4YP+v3+\nV4MgOBf68U9K+m/e+f4vJP0/kn79nZ//7/2B1F0JgiAbBMFcv99f/273gAL3MVmQl0en3ngnEglT\nFBwWRBIIgsIhRD5xxaPycIyH7z2t+DgUzPCbt1qtDibVxQKl4eZjw1BDLA3oKxrmoMzCDAdUFolC\nKGc+y7sTbqnX63r99ddH4oOABsCWPwaaZ/PxXkl64403rHYXxQCC9t6LF24UwXHeUPh71uE45uQk\n5C4IAmvhnEwmlU6nlUql1Gw2LSTAGBsbs1PlpKFSPzo6stbTzB9yg3L2YNDHd5Er/sHyICdeoVMr\nzemcc3NzymQyBgZQWnRbQ068V4cs+hACpWvQozwb/2A+kBUMKPJ4dHSkSqWier2uXq+nzc1NM2rk\nuAAueE48wmQyaaeBQv37MKKn/okD4w1jlABvVFjwTAAGPwBZGBdfGnxScofh9zLIevt8KsqGJVny\nWzweV7fbVbPZVBAEmp+ft1BOLpez3/E+nvlD/lgPfode4NnQKRg0mM0gCKwDKvLI73z1ATknGEDe\nkfn3bBvv60EMsou8+gRJf45At9s1gAxIIWyXTqdHQLW/N9f0MrG0tKQPfehDevXVV9XtDkoKNzY2\n7FCxUqmkRqOh/f19TU1NGXC4evWqnnvuOS0tLSmfzyuTyRggIFehXC4rn8+P6ATa8lOdc9rGfzSn\noITg9/v99SAIZt75+YKkNfe5B+/87LsqZy+MnprFw2DhERCfZ0BPg/Hxcd24ccPij/zcx8q89+aF\nTho1Zt5IeWPGZxAuWASvyFGenrLFe5qYmNDi4qLVeHNkMUcmswnn5+c1OzurSqWivb09RSIRlctl\nYz04x4DNzjPTyEiSNjc3bSMACrxy5/vwe/p52NnZUavVsr4HMCQeTAEOwt4x1wonJ/rvUQ4+KfJ7\njO+r3PV6Pauvj8fj1v0No4KRIdkSpYVi8/SsNGQCvDfMfXzliTfU/nN44yhAX/YVBINEO0qeMpmM\n0um0NVp5XBjAGwjvlYf3A++Dt+epW+8JAio522B/f1/b29tqNBqKRCJmwGgrC9CXBvvg0aNHFjbh\nlEjm1TMGvDP7ipiwZ136/f5IfwbkkOG/DwNeD8b8zx8zvq9yF5YBT9vzbJ5J86FAgCVOEqwOcw/Y\nw+nw5dJcm7UmtMO60r1PknWpzOVyJoswQwAbwAVr4J0v327dM0PeofKhHQCKD0l4He3DZ/wO4AHT\neXBwoHa7rXv37qlYLFpo0JdkM6dcC3CRzWYNdE9MTGhtbW0ETLMnj46O7FybXC5nXXOLxaLJOgwE\n88Kce7DtGZvTNr7fiYbHBUmOffMgCP6TBpSbCoXCiAJj42PUUVbQ9AgDwh6LxXRwcKBXXnllpJ//\n9PS0IdfwAvj4ddhY+YQfT2GCfBEwaDI2BErc0/0TExPK5/OmzD/84Q+bQqTjIPHWaHTQkhZQsLm5\naRTgwcGB6vW6Hj16pC9/+cuSBkc5t1qtkdh2EAQqFotaXl5WEAwOAFlfX7ekTJ7fbzC/UdkIUHKb\nm5vWlZFqEN7NK13m7jjDdNw8e2/p+1Ca8x+Su8XFRTtOdnx83BiUsAeP1+nzJHzM9HGsiPdUURIY\nOc+AMV/es8L7cs9tIIS15HlgFMINY6Tjm0Id97wYIU9twiCxN3d2dlStVtVqtfTw4UMLWwE4x8bG\nVCwWjRWAqud+nNrnSz5RpP45w7RqJBKxEi//bsgp80E2u39mvx7SMNHwODbrPzDek9x5mSNsyLsy\nPNPEnuRznILowSfvBYiTBgCAY+KpvmLeGL66hVALuqVer5uxymaz9rexWMyYKkI5PnzIXkAWvfcf\nlnEPOv0aoU99YqNPDPdAib24u7ur3d1dA5p7e3vWV2Bzc1O5XM7YlWq1anPv9yX7qN1ua2ZmxsIZ\nvMPk5KQ5C9gZwk6JREJHR0fa3NzU1atX9eKLL1oDJMIb/vwbD/pO67HJ0n8cFGxCkwVBMCdp652f\nP5C05D63KOnRcRfo9/v/WdJ/lqRz5871fRKR97CYYCgr6CoykY+OjvTWW2/pK1/5imq1mgqFghno\nvb29kWxPlEU4qY2fIaBeIfsQRj6fVywWMyMSBIG1Mya2TvzX9y4AZdKFjg5l2WxW8/PzyuVy1gSD\nTXp0dKTz58+bsK+srNjGXVxc1OHhoZLJpDUempyc1OzsrD72sY9pfn7ewNT+/r42Njb09a9/Xa+/\n/roBCF/6dVy4BO/zy1/+sp566in7GaEQ70GGvS+3xu9iXfhsKpWyDcravwf0/H2Vu+eff75fq9W0\nurqq1dVVNZtNPXjwQL1eT1tbWxbXJDxFOZIHND4UwAAUeuXtvdxwQpSvQsEDLBaLSiaT1gTLJ5h1\nu11tbGyYnCIDAEpOQaSED5nmeUiMIkRFXgzr6oEPOSeNRkPXr19Xs9m0hkzcY35+XqlUaqSdMMwY\n4cB+f3BS6MrKiqampgzgp9NpRSIR1Wq1EXAMNU1eEV6xb1hE+Ic9hk4gfCFpRJ8wxxgVz3CFWa7v\np9x5mUun030fQgj368AocYIq3rQHBWNjYyMHGnlvmTbT9BXBA/fNo4jVT0xM6JVXXrGyxkwmY8xe\npVIZOXUVEDc9Pa1SqaSzZ8/amhOyIhzj85hgAsNhMr9n+u+Ee3H+YIPZXzAZt2/fto6i5XJZ29vb\nZrwjkUHFwJkzZ7S9va0HDx7ojTfe0Pb2ts6cOaOlpSUVi0W7PqwuYazDw0Pl83nt7+/r7t272t/f\nVyaTMftDwy72DIclweSurKzoq1/9ql544QUtLCyYrksmkwZuAPGEwf4/m1PwmPFFSf+jpP/tna//\nl/v5F4Ig+D8kvSCp8d3iawwWxy+U98r95PmYFOzB3t6eNjY27O9J3tvZ2dHCwoKazea77hn2cv2z\noCzC/yYmJjQ1NWWbUZIlnk1OTqrRaKhSqahWq2lra8sMM8cVd7uDFps0xCBOyObY29uzzcq74h3u\n7OyoXC7r4cOHarVaRtfSxrZQKOjZZ58178Dfc39/X6VS6V3v6eeWd/bIvNPpaGVlxRQvysQjXoYH\nT8cZvLBXRpa0B2nvQTl/X+Wu1+upXC5rdXXV+sUT8yMZis95AOmVM+DSv7MHWV7R8xkoXq7tvSYA\n5OzsrKanp62VajqdVrFY1N7entrttra3t41pWl9fHwFtiURC6XRas7OzZkABgRhNnpEQCXQv68sz\nASybzabW19dHDA5njhAbDYLArsP74KnBZAAkJFnyqiQ7zyC856ShwaQjH54eNDPgDSDuaWk+xzOF\n2Zf3IHPS+yB3PIunsmEd2fPMGc/qG1Dh5Hi2hLlIJpOmQ6hS8Gwc8zg+Pq65uTlJg7ylYrFoIcnt\n7W3t7u5aox5pELaEMarX65b8PT4+bt/zfMlk0u7n9znrI2nEKfEJjXTApFQYh2ttbc1CCa1Wy5LO\n0WPJZFLnz5/XmTNn1Gg0rGvh/fv3dfPmTU1PT48wMNwbB8eHB0m0REbpjhuPx7W5uWmhWoBXs9m0\nbojMKWuJs+UZ6/cgcx/Y+J6gIAiCv9YgyWY6CIIHkv5XDTbH/xkEwf8kaVXS//DOx/9vSf+9pNuS\ndiX90nt5CLwjJs4rXZ/4hJcE/UKC0quvvqpCoWAJexwolEqlTAgxgiwKySygUDYpn/NeHs+Hlw06\npCcBKPrs2bNaXFxUvV7XG2+8oVgsZsJJQtb9+/fVbDatX70knTt3zjYqcTXAj2/JWa1WLbESgJLP\n51UoFKz97Pj4uGq12kh5ks8W9x4s84fQejqSPvTdblf37t3TRz/60ZFYqA8V8LwYAjYa1wwbTA8Q\nQNDMsTOo77vcdTod29SECKg88KDUZ2lDk3tD6kNZXJd54H2RNa+UotGoeejIE+VRpVLJeg4kEgnL\ne4jH40qn08pms2q322o2myqXy9rd3VWz2bSzGFDQqVTKknh98hXgO5zUxbsCIvb29lStVrW5ualI\nJGKyT2JmIpGwhDhoZM4foT8GIGh8fFyZTMYAba1Ws/3vw27kdwCmyEHg9z78QqiCWDB/D5AgqdFT\n1u/IlzFmnk5+v+UurF+QM1gO1oUWvBhBDFU0GlWj0dD8/LyBqFKppBs3bliG+9ramqanp7W7u2vs\nEg16PAt6dHSk6elp81xnZ2dtHVKplPb29vTVr35Vb7/9ti5cuKCdnR3t7u5qY2NDlUpFBwcHpn+Q\n0fPnz9teyuVylm/Fe0lDMABQxqgfHh5qc3NTd+/etQTe1dVVCwfH43EDJfl83kIaFy9e1OzsrFXz\n7O3taXZ21lgLbANtrwlVwEBNTEzo/v37mpub097ens6cOaPV1VXbp+hanIRMJmNVQtFoVM1mU6lU\nSuvr63r55Zd14cIFxeNxk3WeH4eN/Y6zddrGe6k++LnH/OrTx3y2L+l//vc+BIrSd+uSZIrXx7CD\nIDA6d2JiQul0Wjs7OzbBzWbTDJSvFfdGDyPGNT1Y8IYMwSEW3Ol07PS6IAjUbretJ3s0GrWSxGw2\nqx/7sR/TvXv3VCgUtLW1Zdna9+7d06NHj5RIJLS8vGyCMj09bRuIe8OCtFotlctlNRoNHR4eKpfL\nKZlMKpPJ6Nw7/e07nY5u3bplhz9JsoQ53jeRSIzMM+WH76zdCABKp9PWNvVP/uRP9Jd/+ZcWQ/a1\n+97Qe/obI+qrM3zc2idXAYRCoYf3Xe6i0ejIEb+bm5sKgsDKSb2hl96d/MrzcgANYIC/xcCGQyLM\nCe/PvGezWZVKJS0sLGhhYcFi5Hh9KHbYDEJauVzOkv7q9bolsb799tvK5/Oanp5WPp+XNBqiYB3x\n1Hy5KJ438j4xMaHp6WlLtqpUKtrY2LC1Q2FCi0YiET399NPa2tqy/gUwIBzAFQSBMR4oTklWEdLr\n9SzO7StufM8S5sc3jfFJbgCIsIfIXPA55wS8r3LnnRRpaCA5YRUm6OzZs6abfIXAxMSEUeGApTfe\neEMXLlxQu91Wq9VSPp+3RmbNZlPVatX2IAbY51Qg0/7wL2T5mWee0Y/8yI/Y+QpjY2O6ceOGyuWy\n0um0Dg8PdefOHdNb1WpVuVxOuVxupDyZ6wZBMHKqKKGDWq2m+/fva2dnx2QCgz45OWm9OBKJhJaW\nlnTu3DndvXvX1l0a5ljFYjG1222zE6lUypq49XqDpmLIEACG/Tg+Pq5CoaB0Oq3XX39d4+Pjajab\nJr/kNOAkIu84T91uV7/zO7+jl156SdVqVdlsVisrKwbu0ZndbneEMTxN41R0NPT0CkrLZ137DY2n\ngGDfvXvXBM0vEAlJPn7LYFE8OxFWGGEK0wMX7tHr9eyY1/HxcYvlc0Ld8vKy0dB4KlC+MB8wAZ62\n94k1GAAaZuAtxmIxpdNpNZtNtdtt6yYnyQw+uQ14TlQs+IQyT4uHPX+ULF4ASNuDKe8JS8Padn8f\nP+9h8CXJNijrfVKDOCRyUqvVzJuGQfEGCYAaDp94IOtp63ASoqeN/Rxi6Dj6FkqU4ecdahmA5g04\nz82zAyqQMZ7ZNyDi75Bpz5z52DfPu729rVarpWazaeACqhcWi+9TqZR58cwBBpy5B1DzGeZckoU7\nwiCK3/mKheP2MR62j0+zPn4NPkgql/cJl6vClrCPeFcPZDGG/ihrQqa93qA0b2try8o9MWS8s08c\n9rkf/X5f1WpVTz/9tHZ3d3XlyhWj0J955hmVSiWVy2U98cQTkqStrS1zYgg3IVfhcJr/6tlFdFY0\nGlU2m7UOqqVSSfl8Xt1uV4VCQfv7+yqXywqCQA8fPrTQLbrfh7yYP7/GJEPyfF7WmQtyZmZnZ7W2\ntqZEImGsViaTGekSiZ3hVNrV1VWVSiUDJJJMx/B+POf3IcH6fRmn5qm89+iTozwVjWLo9XpaXFzU\n22+/rZdeekmpVMp6VIeTwPzRxdIwVOFjwT62x2aBVvIHqfiOc3jCCCEAASG5evWqtWq+ePGiFhYW\nVC6X7UQtOmMRx6YECAH2zES9XreEq4mJCTUaDa2srOhf//VfNT8/r3K5rHv37unSpUuKRAalSalU\nSv3+oO3yk08+qWw2qw996EM6PDzU2tqa7ty5I0kj4IP398aamv0/+qM/0he+8AVtb2+PHMXK39FI\nhGt49oXhAZpPKEKZhz//fg8Shs6cOWNHoRJDJA/FJ0p5JsArNkkjHgAeCUaJ66B8vKdGUmAymVSh\nUDCvxitVjmZdX183IICSisViKhQKkoatX4kXE45Ip9MW4hgbG7Nsdrwnn/sA6KD3AsfKwtpFo1HN\nzs4qm82qXC4bkCIMgdGKRqNaW1uzHAmAI15ku91WEATKZDLa3NzUvXv3jL1g+BCOb8rjs/QnJyeV\nSqUscZX9y15ib/pES8AGeiASiVjo8f0eXs9IepeTQSIljgDNw5hbAB3nSTQaDTvKt1QqKZFImEHk\nVD/yNfx5Hv7cgb29PassAaC++eab+trXvqaHDx9qc3PTmvsQtp2cnNSVK1d08eJFpVIpnTlzRmfO\nnBnpY9JoNDQ5OWknZ/qQD/oUls53BXzqqaesdT3hhP39fT18+NDW/OjoSNevX1cymdTs7Kzdt9ls\nanp62ioJer2epqenlUqlNDk5aU4coBUHjTWAZYBlIYegWCwqCAJr003iK4mKhDuQpT/8wz/U5z//\neVWrVWUymXflDuF8nMZxKkABCjfsqfK7cDLQxMSENjY29Oabb9pn0+m0NQ8CQHha0Cfz8Du/KOEE\nEDYMTX68B8VGZqGJ9aHIO52OXnjhBb366quq1Wp68803NTc3p6WlJYu11mo1Yw3YHBgKnxhFLCqb\nzVoDjc3NTW1sbGhnZ0fPP/+8ZmZmdO7cOX3uc5/Tyy+/rLt372plZcVCKJIs3nh4eKjt7W1Jw2Sl\nMGvA+4N+9/f3dfXqVd29e1fZbNY2Nd6nTxSUNAIywkl6x/08vAYnOcbGxqwqZH5+3sAMzXh4XtbC\nh5bCYQRPCTOnx5XH8XMyttPptJVP0dOC+e10Onr48KHK5fKIfNOPAiMBG8B5BygtQg7IKEraJ5Uy\nwqwRyV/8PZUC1IJzmBMHIu3v71syYjKZVLFYtD0DsPBdHDm2dmJiQtvb20okEqpWqyPzzTMSUycM\nxT4mdhuPxy2D3ecXMO/hd2UNvPd8EoN5De83nsOHTgFv0pBRAJAnk0nrbUKjIfrycz4CoVRfgRV+\nf1g9jCCedblcVrvdtiY+P/3TP616va719UEeZSqV0pUrV3T79m1ls1nTj+l0WrFYzMJZvJs0elgV\nawMQ9WV/xO+RL0LEDx48MD159+5dA0DkUpFsSBIkwAemMwgCNRoN092xWMz6apBgidePjisUCqpU\nKhbS8geCSUPAOj4+bvNfqVR08+ZNvfzyy/rYxz42okMODw8tN8MnY56mcTI74T0Mr2DZvB7Js5nI\nfL5586auXbumUqlkDALxTTZYOHuXr55q9LE1T/X6eKWvCPDeCt5gOp1WqVSyc8ojkYgePnyofr+v\nz372syoUCrpz547K5bJ5Tj6my6b1wuO9dsogvXcwNTWlQqGgu3fv6vbt24rFYnruuef04z/+4/rI\nRz6iIAgMbKA0MDp4sB6IhdeB2Ca01+7urr7+9a+bJ+FjhGx0vH/oZLw0n19wHM3un+EkgQH3BDAV\ni0Uzzv4zHkSinMNhKX4HEKOU8Lh3I8ZKNn4qlTIlRmgIloiEJtq14gXW63U1Gg1jkQhhkQPg74Nn\ngmFmHTxj4efDMxoAXYCpNIh/k1x19uxZAzJ4Y6w3YS0AlK9wQB6np6dVLBaNMiZ266lVgABGwzd2\nogKCZEqoZJ+fE15vZM/ripMKIXg58rKELpGGuVSULrN+MEv+mZPJpDE1m5ubmpmZMUNZq9XMUHKf\n8LsT656YmFC5XNbNmzf1+uuva3NzU48ePdIP/MAP6Jd+6Zf0W7/1W5IGYOTJJ5/UJz7xCX30ox/V\nU089ZdUpW1tbFsYYHx+cIUBs37Os6E6/r+iLQhigWq2qUqkonU4rnU6rXC5bw6xGo6GlpSWTi7t3\n7+qNN97Q1atXValUFIlELHmSxEMMvs9tonMk+92H+TxDmEwmLWm2VCoZKCVfi/LbUqlkMnhwcKB/\n+qd/MvYgHP5rtVojrNhpGqeCKUDp+DhqEAQWa8coo2wmJib02muvSRokFpLlf1z5ImV+0rBxBvdB\nWYVj+RhqkgbxKOhm1ev1jHbNZrNaXFzU2bNn9eDBA21ublrJzC//8i/r2Wef1U/91E/p7/7u7/TP\n//zP+shHPqJMJqP19XVdu3bNjCjGJ5vNWtb1+Pi4ZfB2u13V63UtLS3p53/+5/WNb3xDr732miYm\nJnTp0iV95jOfUb1e15UrV7S2tqaxsTEtLi5qdnZWH/3oRw0pN5tNLS4u6urVq8Z4SKNeIs1lfCfD\nWCymf/mXf9HR0ZF+8id/0uaDcAieq58rH6cOGyHvtYRzRk5qsI40L0kmk1bOVywWJQ1q+lutlvb2\n9syAo1QwmL6CgHr5sbEx63zmk1r7/b4BgFgspieffNIMWywWs78lWRR6d3t7Wzs7O4rH4yoUCnrq\nqafUarXsfAqYqq2tLfPElpaWjB2C+oe2JeSDgWFtuA6hNxIVI5GIrl69avHRXC5nB8KwJ1OplC5d\nuqT19XU9evRI4+PjNp+U7kajUattp0IikUhodXVV7XZbi4uL2tzcVK1WM6+V0Eer1bJ+/1Dqnopm\nEJ/2846XiiEIJ5Ge1AjvN54ThwcmgMom9F8ul7MKAJKAkd98Pq9araZ4PK7V1VU7KKlWq2lsbMyM\nczKZtLmKRCLKZDKamZmxRGYSQDkX4KMf/ag+9alPKZlM6v79+4pGoyoWi3rw4IHu3r1rlS6zs7P6\n9Kc/beFb7gXI8Z0KCV9hqGEJSIKenJzU7du3lc/nVSwWrbfKZz/7WfV6Pa2tranf7+vtt9/WE088\nYZU2ly9fVrPZNMfltddes4Rd8hOi0ag1N4pEIrY/j46OVCqVDFydPXtW9+/fNxtEFVY0GlU+n7cu\niYQM6IXwjW98Q5cvX7ZjrFOplP74j/9Yv/mbv2ml54RkSSY9jeNUgAJpNG7oDQS0n294Ua/X7ZhL\nKgBQhD55C+XtPSDPEiC0PnfBMwooEbzrSGRQNuZL0Vqtlh48eKB6vW496bPZrD7xiU/omWee0Ztv\nvqnNzU0tLy9rbm5OlUpFy8vL2tnZUbFYNO/Qo2hp6HkS64ZSq1Qqeumll3R4eKhYLKZnn31WKysr\n+uu//mu9/vrrOnv2rBqNhorFoi5cuKClpSXrGEn8jHfEkHlD7L1Gz54AyK5cuaJf+IVfsNpnfw0f\nM+PvPCPgvVL/c//5kx48PxRit9tVu91Wtzs4Bc0nINH3gbnwiVXIHEaLag9pKEvMKTFwDmDxHQDx\nPjy1ioGOxWKamZnRhQsXlMvlrIc6lS1Q+RjNer1uxyn7ODz0Os8bzn3Ae6Q0jb4X5XLZ8h9IhsTQ\n9/t9O65ZkjEBnM/A52DeeBZJIxnqqVRK+/v71rbYfw7mwuf98PNwPpKXM9Y4TJ/z+5MKHTBgYXwG\nPs4KOgsgtLe3p3w+b+8wMTFhIDWTyWhsbMyYyHw+b1Q688j1yfMAyJKoSPgmCALrSUCYp9FoWFgn\nEono537u5/R7v/d7VnZ49+5dzc/Pa3NzU0888YTR4hMTE9rd3R0JvYYTRNHPJMmiv3n+yclJbW1t\n6fLly7p06ZLOnTun2dlZrays6Omnn7beGFRbzM3NKZfL6f79+9Y9cGxszCoNkGsfjkFGmEv2ubcR\ntEimymx2dlaRyKAkFybDn3lDKWav17Pj2CnpRldQKg/wP23jVICCcGIhg4xYvIVkMqkgCHTz5s0R\nOohmLD5OGI7lSjKK1VNontrhWdigXAslirIkzl+tVk0QNjY2rAHI0dGR/st/+S/6m7/5G+3s7Ojc\nuaQ9WGYAACAASURBVHN69tlndfnyZV29elXPP/+8xsbGND8/ryAILCkQQ+Cf5cyZM5qamrKMWOgs\nNgXzE4vF9MILL2hjY8OSCpeXl63DIx4d8+qNPnPDV0I1/N9/rtlsKpFIqFKpWFyTE9q8cpOGtKjP\nXfAUnb9uOEP5JEYQBJaMh1dA/BqQ6nNH+J2vGvGMSBAMmwz5Y5FJHuM9C4WCcrmcEomENcIi1s/8\nMS+UZWEIM5mMMQq0FObaKODp6WmNj4/r/v37mpycNHk5ODiwGDPvh7EG4HW7XYuRcq9ms6nNzU0r\nJ0wmk8bAAcgx1rx3sVjU7OysXYfwm4+pIyMwLIBu36mPAZuB3PhnJgznqWhOt5SG4Qf/f7/vwyG0\n93P4e3nQx/vxfIC8RqOhc+fOqdPpaHV1VTMzM3ZmyuHhoTFX6XRakiyBDr3F3l9YWJA0PEeAe3e7\ng9LuTCaj5eVlo8fT6bQ2Nzd17do11Wo11Wo1feYzn9EP//AP69q1a8rn81pYWNCtW7f09NNPSxqw\nG4Q2/SmL0mi/F2902Ts4czT/2dzcVLPZ1DPPPGMA+Gd+5me0sbGhb3zjG1peXjZG89lnn1Uul9O9\ne/dMRi9fvqzl5WULM7Tb7ZEQRpglxSbwc8KA29vbxnLRD4RD06RhD4O9vT2dPXvWcr7i8bgePXqk\nhYUFPXr0SE8//bTdm4Ti09rq+NSAAu+F4VWRDUod+M7Ojr71rW/pm9/8piWzZLNZ6+jG8J4t9BsC\nCEoDMftkRM8y+Ngfz8QpeU899ZR2dna0tbWlr3/96/acnJTFYkMB0nozHo/rxRdf1NzcnBlyyndI\nBEMp0wCGcsaPfexjJmi93qANL2GG5eVltVotnT17Vi+88IIymYzOnDljm79er1vSGgbIMyPew/IA\nzSdp8vmJiQn91m/9ln7lV37FQgx+s/O3XDdclsf1WRPWKQzgTmL0+32jsG/duqVqtapisahGo6Gx\nsTFbPxI8Jycntb29bZnDXtGRv9Fut5XNZpXP57W1tWVzT95Fp9PR4uKi0e++nzwGHuPIvJ09e1at\nVmukN/36+rpyuZyy2az29/fNc4dOj8VieuaZZ2wdY7GYdnZ27D2i0ehjlTeMCUY/nU4bo1GtVkfa\n2gZBoPv37xuggdWCNcMIcZqhT9DiXaCy9/b2NDk5qZ2dHQNdMGTQ6l5m2Ze+wggGh/vASjCQudOS\n+Q1AJzxCaIU5mJ+ft34AMzMzun37th3Zy0E+d+7c0blz5/Sd73zHEvW2trY0NzentbU1NZtNfe5z\nn9PNmzetWyA6qNFoWE7B/Py89bjAg5+ZmdHi4qJWVlb0B3/wB/rQhz6kn/qpn9LCwoLu37+vRCKh\nfD6vcrmss2fPWvOtyclJ3blzx1hVgIBfDwBBNBq1UEY6ndZrr72mixcvmhxev37dTh38oR/6IX3y\nk59UpVIx3f3222/rypUreuKJJ/TJT35SyWRSi4uLtof8yZHIPrlW0WhUZ86cUa/Xs2OhO52OlXGv\nr69bsmMkEtGdO3f0kY98RGNjY8pms/rmN7+ppaUlc9zoKRGLxbS8vKwHDx7o29/+tmZmZnTp0iW7\nN+c0nMZxKkCBNxgYIJCk9/ibzaZu3LghadiNjjgNyXRhTxRh8ImMvr7ZP0OYTvcUuE9KIqZKA6FH\njx6pXC5bnTnZ1yD2S5cuWSwTw0wSEYoV44iC85QmHtP8/LxKpZIp393dXa2vr1u8ihwHssvZ2HiG\nxBMBYMd5ST6nwitfX1v7ne98R1//+tf18Y9/3Donomh9uMEDrsfdR5IZMg9OTmogP4Su/Lvz/jwj\nbAueqqekfQgCGp9zEpAZaGGyoX1PAZLnAAWEt2DL6IDJ7/l7ZA0gG4/HLYbrS9C8h02MlP0VptCp\nNODgF8BgvV43hYbik6RisWjAg+oUGAifu+Ape7x7/k8CISwT8+zLMwHt4QRC1oXSR9/FMCxTYebr\npIffE/7/eOxeDiRZv35kotPpWHgqn8/bYWerq6v2fhzag+dNeSqsIY4G84TM+/BrMpnUc889p42N\nDa2vr2tmZkbtdluTk5Pa3NxUqVTSzMyMVldXLcQEszQ5OalqtTqS2CgNQQAyzz+frJdMJnXu3DkL\nQ9XrdWNX0+m0vva1r2lyclILCwu6fv26otGoLl++rGw2a+2FFxcXLQ/GMwL0xPA6iUE4FrkFhKRS\nKWsaR2UD+QLdblelUsn2tQ+n7u7uKpPJaHZ2VtVqVQ8fPtSHPvQhC+HgdJzGcapAgffQpSHyJ3t1\nZ2dHGxsbdp41XtLExIS19vXNVvxXxuMykjFiYfqer4AF6Dhit5lMxppVeCpwbm5OvV5P58+ftzjZ\n+fPnrWUs5TJkmHtPm/sRE6NEBqaCjNZCoaDZ2Vnt7u6OtLKNRqMWF8cI7O3t2aZEYQJUPFvgQYH3\n3r1nnEgk9A//8A968cUXjXL39D/UrjRqkDyFGwZe4fU4icGaMt8AJs4YACwwfJtdDA7eDMqUuDeK\nzsfS+R1JhZ6+BgD4vhgwRsytz8qngx3nUbAO0J5BMGju4kNfyKZPLGS9/PBlqsT2fYIesslz4mFN\nTU3Z8xMv9u21oathVkj+IiSRSCSshNHrAJ9A7EEB4RwOEPIhHundssXP+PpB5LIga97IA6gBWt75\nCILATvjb2NiQNACoOzs7ymQyunPnjuLxuO7evWunmRImOjg4sK6ksC75fN46dwI28eBZZ+YznU4r\nHo9rY2PD1vj8+fOq1Wra3d1VpVIxpyAej1sCXSKR0NbW1rFgDBk8LmQrDY58f/rpp/Xmm29akuTT\nTz+to6MjbW1t6ZOf/KTK5bJ6vZ4+/OEP69atW0bnHxwc6FOf+pS14kaHse88qGew7/f29mxfYuDJ\nrfBVHPV63c7Z2d3d1YULF3Tz5k0dHR1pdnbWutcGweCE2WeffVZ37twx8IY+oBncaRynAhRIw4Qv\nT18Tiw2CQanKN7/5TctsJSsfehelJQ0byvg4KZtxenpa9Xrdsqj5LD3wu92u1d9Ho1Ht7OwYesco\nUno4Pj44CGRmZmbEi0GRgfxppIGwUHdLkxvKtmKxmHlZ/gwEPChoWzwzDAeNPghjkFhEZi8bgcYc\nxL2bzabR3t5DxVjiqcHEsDb9/qAl6ssvv6yf+Imf0O3bt20D+kOEmH/+79vP+jXxIZuTBAWeHQqC\nwGL80PFeefiERMpOW62WPTNsB8cKX7p0SfF43NaFro3tdtvWWJKtuX8O5Ie5yOVy6nQ6Rv9T0oRR\nYV6RPeYU+hSlyL36/b4ZcACED5WNjY1ZDfjMzIyxA8g3e8afoAfIwSslR8Ind0nDBC+fgBYEgTVZ\n2t/f1+zsrPL5vLa3t81r4/nYV8gWh0D1+8PujL72nnv6kllfTux1zkkNnhEwQIjk8PDQmlJNTU1Z\nwmgQBLbe0NozMzO6cuWKnawKK9lqtbSzs2OhsWKxqH6/r5s3b1pFDGfDkGgoDTv99Xo966rZ7Q4O\nU0OOnn32WR0cHFj+wrlz5wy8YIRJjkwkEpa579kISSNJpJ7JocnS2tqaMpmM5ZHBbI6Pj1upN8CW\nCoRisWjdXmkfTK4Z7+WTU0nyo8R3cnLSTh6FZfPAl1yZbDarra0tra+va2FhQXt7e5ZDRGiQ/hH7\n+/t6++23FY/Hde/ePb388svGgjQaDWM2Tts4FaDAe6jhDdrr9VQsFvXyyy+rXq9bHD6bzVqcHPT5\nuOx27w2ijML3Cocw+Bmf94k5CLRPCPOUKcoZzw+EHPYMHlf14O/rvXAUGNcPezv+vbgXHgPJcUEw\nSGzkxDAUfjgbGqXq469c9+DgQMlkUl/96lf1gz/4g+YlMCcAKeaLa3oWx4eIPEtxkl5b2DhBYe7u\n7lpyk/dYMXqwB2HFxrv6bnTU62P4pNF1RIb8WvIZD5iI7yPHyA/M0NjY8IAXrk3/BQAGyXwYdT8H\nPpyGIeJZAMB44Z4Jwhhwb8Ia4eRHBjIQrhjgaHHmms8QAqMCyLdoDs8TP/fg1iezhd/Tr/9Jyh30\nMfvNn86H8SJrXhqWtAIU8O7pJLi+vq4zZ87YaYnEwPF6d3d3dfnyZX35y1/W0tKSrR/r4ve412fo\nCtaKRj9+vn0IAH3Bex13yij7hHuj93hHgJ/XoXTkJKlVGpaTezmHmfP5YtJQ1gCGni3qdDqq1+vW\nStkDVd4REOVbS9MThPMM6PSazWbtREUSmJFhHLBer2dlkqdxnApQIL2b6sP7GB8f16NHj3T9+nU9\nePBAs7OzRsOAOEGRj6Ok8br53jcN4l4+58CHFBAkvC0WVRqtVPDGW9JIy2J/Pa+kuLbPheC6XqB9\nnsNxwMn/n43pgQzPzP1ojes73oWTr7inj+dC+2IY7t27py9+8Yv62Z/9WSvLQdBJAPNGx9eS+3cJ\nx1lPeviEVOK7lPHxO2nIfniF6JkovtJUiBPoSKAjcVEa9syQ3i2v3ngFQWC0PfsBBgoZ4rkAAOGw\nhAdh0mhIJwxIvIL3Hh7g1ue8eO8QOfZAhHfBAABiGH5fsN/ZZwAogKykERDFM/hnCVPD3jAwfB4I\nz/ZByJ7XBayPDxORL0Wv/1KppFgsprNnz1pm/szMjJLJpGW537hxw9jPXC5nVS5TU1O6fv26fv3X\nf11/9md/pmQyaQ2A6OPPWiLHgF/fvhuZg+kAJPo9wT73uoS5Zm24hgcLkkyGfVkuOpZGX8iqD4My\nV2FGivsABDwoCMuF70WAM8NempiYsHbfsG2Tk5N2Ei9MFnLIPvfglvwdn38BI33axqkABdAzTKo3\n4K+++qquXr0qSSqVSuaNNJtNa96CgErD5iAMYkZQvHzGG23/c4yCP1TDH9Thu8JBY/lcAOhNPue9\nALpZ+eYwABSuyfAxU0n2jNzLI2ZpWOvMZgMQ+BIcNmYqldLly5ct9udZB5RUp9MxShIwQNw6m81a\naeLf/M3f6MaNG/qN3/gNK9khxuhj9swzRscrBgxb2JN7vwfvSZiKrG+8E94nEolYy1VivoR1fOdG\n1npra0vlclmLi4tWekTCHmEqgGrYgKOIvKH2TYaQBdbfhxk8sOX9+J719wrKr3cYbCLHeP0kyPr7\neEXPupI4y3v43As+y7oTcqAjIyW9pVLJwmvf+c53VCwWre4dYwBz4sFOGNgyvNEaGxs21uKZPPt3\nEgMQRaMg5ozkZHoEUFqZzWYNTL311lvqdrvWbfArX/mKnnjiCV25ckXz8/MWJuIMAE7N/MY3vqFf\n/dVfVaFQMACMB4sO8sbVM5wwVbAG6Mkw2Oz3+0bLe6MsaWSNMLzsG88y8H90LtViMJTSaN8PgCLr\nyTuRY9Ltdq0boQdfPuQnyfQk15dk4ITn9mE6QjSNRkOpVEqFQsHuvbi4qEqlYozQ+PjgYCdKPw8P\nD+1I5dM4TgUokEZjjSwKh15EIhFtbGzozJkzJkDr6+u2sTj1yiPFMB3omQH+L42GALi/F3KP6A8P\nD42ig97CM4I+ZTMgtGEFynuGN03YW/af9bFvH3KQhhS2N66eBsOw+CQ5TlekHDLMlHjPC2qXDHfQ\nLWg4kUjo+vXr1pVRGpZZobT9GktD+vy4uThJ5cy8sF54RTy/N7wgf69IvFFk/r3Cw/ulpz/y0mq1\nzCB5IBz+5z0Vb7SJs0uj4aaw9+VDNx508X7hRMvjQgm8G3933PDelwe5HuAfx0Z4MOOTMonNLi8v\n66233nrXtXzSIfPkWT+/j3y2uQeorPtJhg0YrL1P+iSvCENEvgbVKhhAMuF7vZ5u3LhhJdskP6+u\nrlq/gEKhYE154vG4fvd3f1f5fN5YJw+K8cw9yERn4viEQSEhKS8vPmzJenvw6/Ua7ASywZ7hK3uS\nqhLypby+Pk6X+LAkzyNpxD4AJvgZut2/i8+z8k2+sBl0iDw6OlI6ndbdu3dHKgpgEKiU29nZsbkO\ny+lpGqfiqR7neZCZvL6+PuI9pdNpM1gYO75yveOu74XaD091ssjEgHwffwDB3t6etboMx0ah3H0J\nVhh0eIENhwW8sQ+zHuG/5735RycvWAdvZPkbEsGOjo4s9hW+P8br8PDQTnSk2iIIBslylIqBhB88\neGDhgjDD4WOjfvPxrMwrivAkh2d8YGN8W2xpKB+PU47+s/7nlB2l02mjHPF8pNEqE4waPwNkEtLw\neQjeoPp5RKnzvMytPwmOe3hGwhvL4xS39654X54NwEhyYTh84RkCvDP/vtyfhFmUb7FY1OLion3O\nv5cPH/jwE/8Pz5UHDrAGjwM47/dgTgBZHsywZ9ir0Na1Ws3mOJVKqVaraXp6WrVaTT/0Qz+kWCym\nc+fOWYttTuWDoSIR+8GDB2q1WpIGSX2E+PCwvTH3JaHsDZ7XyyZJfTy3d6LC4Uj/OQ+KvBFHp4Wf\nJQgGJ2pmMhkLLVH+iI6BXfB7xjsd/hk84KGMlnM3pqamDKB6sOTZDMIu2ArCD7RrJrQN00yHUBjH\nDwKMvtdxKpgCFmtsbHgm9+HhoV5++WVVq9V3dePa3Ny01q5kiYPQfBwKJeYReK1WM8WDlxz21lGG\n/X7fMmgRHuJabCQywok1+bg0G5tkMEoDOeQGutl7816RUlFBLNYrtDCrwvfQ1CS7cTgK3vAP/uAP\nKhqN6ktf+pLW1tYkyVo34w1jPPACSESkN8P6+roajcZI5vtf/MVf6Bd/8Ret/SebI0wt9vujLV3x\nCrwncZKDHAv6ydNfgKOtkZOZmRlT2iTLAYzCIS86oXmjls/nNTY27G4I+AzT/Z6a9Uba05gecHrK\nHznySaUoS2QNA+mpV2lUaSO/3jsCaKJMPZVL+WYYtPj38MoZMI9ypblMr9czhZ9IJKwbHSfKea+Q\nwfzT98ODdA9GGLwvjEQ4bPd+D6h7D64IceZyOWuA0+0OjsdeWlqSNNg/tVpNlUpFCwsLqtVq+uxn\nP6s7d+7o7t27unjxou7evauZmRm98cYb1sGSd0UvffOb39Ts7KwuXrw4ste8nFExgi7gcz6pFb3l\n85UODw8tpIsMElLybGrYAeQeMAG+ORiMRr/ft5MKOWeDz4Xzvqg6wNngGQEBPDcVBX6f+6oMKoXI\nL2JvRqNR5XI57e/vK5fLWcVRsVj8f9l70+BIz+u+99/d2IEGuoEG0NgxM5iNnBluw5EyYkQ6Ek3F\npUjlJS5fO1GqlMSWSortlKNK3TgVX0dx5UNiXmWzKvfa8q1KfK9sx3GKtq5o0hRNU+KIHJEznBnO\nAmCwDNBAd6NXLI2tgb4fwN/B6eZoiW8I40OeKtTMYHp53+c9z1n+53/OMSSAvg5URvAdS0tLikaj\nplMO4zpUTgENUurq6jQ9Pa27d+9aDjAQ2GPC8jB91FMLdxJ9Ydh9FEi6obae2jsTHrZH0IhwcVhQ\njij2+yEG/B9KFkeiVCpZqSBeqI/uayF1bzD5XB9Z1hpT3svinhDE6elpXblypepzfFqiVomzb5Rg\ncri51nK5rMnJSd25c0cPPPCA7b0vKfXGyDPk+b303XPC7+fCscQBw6tnihmoCXvIfnqD4yF3lCUK\nEWPp5yR4I+UjXem9Y79r00pSNRGxNg3FM+R7vcPikSD/fd4BqX0tcon889le3qg08TLKXrAHtakD\noixJ1k6a7wWBAn6tdYJqo3yuFTmqTbHxfm+IfTTMnh7U4vv9WYf9Lu07ZVQ0cT+ZTMacS3qbzM/P\nq7e3V4VCQYFAwNJUlcpeJ7+Wlhbl83ktLCzoyJEj1l2VlKtvfMY+4Lh4dNbrqtoKEN7rK53Qdx7p\nqf0Of95ro3qfhvPPn89aW1uTtN8qGj2Obvb7yw+t4rk2nBXKt5Hx1dXVKoeFsld4bwRbHkXgutl7\nPpvflcvV02O538O4DoVTQH6Ghhwvv/yy3n77be3u7lo/aaDpTCZT1bKX92N8vRJEWOjVjlLyxozI\nigcEVOUFijw6LYwbGxur8se8HsIUh0rahwUR/nw+b0xWhAXli2DiJPB+FKt3DlCo/nfeUKytrVnE\nu7a2pnw+rw9+8INVUx4hDHo2PXtS64zk83mtrq5aDT/R2dramlZWVhSNRvX7v//7+umf/mmNjY3Z\noeO5eCfAOyI+TwyUeVCLKIaW0uvr6+a90/2MGudKZW+6IXJIaoXn6CHV2g583DO/Y+a9z3Gy/x7y\n9oaMZ+IVsLSPDqB0vZGnI6FHI1DqOA2ed8B7fcklaBhNWwKBPY4JuWnOkTcU/rvY29rcLkob9ri0\n71zQsIsOijj2fF8trOvlHl3BlD5fkeH32Du8vsLh/V6hUKhq4BN99UHSMB7xeFzd3d3KZDKSpPHx\ncbW1tZm+ePLJJ/XKK6/YIKKdnR319fXp6tWr6u3t1fb2tu7du2dcBVofA40vLCwoGo1WOZV8tk8b\neTKcT0HxJ7oEx9G/1qcG0GvovNrUKUbSo4+h0P7YY5qzra+vm86qRXhAYyVV8RLQzzR0o8lSNBo1\nI+4dFlJi0v6UVCJ9SrmlfaQQvUAre+TQ96Ph3rBfpG4O4zoUToEk81q3t7d1/fp1a5yCQWRTaz18\nlhdmjx4QsSIkKAvvQdYqXh9FcRB89UC5XK5i53qCIc4KB4zrweNfWlqy6/TEQH8ftYZTqh4YVAsZ\nc+98HnuEp10qlWxMaDgc1ssvv2zoB0qWPfHX4p0u8nw0RJmYmJAkxWIxS/vs7u7q7bff1tDQkCl2\nT0LzSsc7Mr7V80F6zx5u98bV5595PpRE+ejHG2OiAo+K+NwqbHlKmWpljs/yqQJew7PHEfSRfi2S\nALS/srJijg1DW3y+1zsfPiryjgEyhVwzCAvmNdUDPtfvZV7ah5wxDv6ceoPOArpeXl7W3NycyYaX\n+1p0jD32fBr2DV3hr02qRkgOchE8IEc4BT4YaGhoUHt7u1VZLS4uqqGhQd3d3TZeuqGhQY8//riW\nl5d1+/ZtnT17Vrdv3zaUgX3nhxp/uDuVSkWnT59+T7TvESL/zNgzPp9zzb57pIcfHxj55RFQ/7v7\n/d0HO/7fGGR/Bnm+6GjknSifIBFnGfnz55ogEEeLNAINjuBkEPSgGzj73C/3V1dXVzXXZnl52dKx\n/7N50fdYXvncuXPH2m+CHnCAyRXVCom0n4/1JDGUu4+qfS7XQ2MoDe8xorz4e6lUUiAQsB7fQFYe\nSkegETKEo6GhQcViUZlMxjqGecJd7UHjnjykhuDhhbO8svepDL+3sVhM/f39Wl5eVrFY1KlTp5TL\n5awlqPewvfH2ny/tEZTi8bgikYhB7CAx4XDY5q57Bn4tisMhxgj5XDGd/g5icX8oaRxQab/Uk6iF\n2QM+1QGU7RWKlyPkzacdfJdBH+17Q+edJmk/5+oNnI9wkL3d3V1DiIrFoqQ9JxBmvy+7YuGIeKVG\nNM2e0OIWR3Z5eVlLS0sWdRLBStUTCD0/hUjf8208GhIMBq3cs1QqKZ/Pa2pqyiKv2mtj3/z1cz++\nWoTnXGv8vANai9i8n8vD6rX1+CwqmnC+Njc3rRFWV1eXIpGI8vm8jWKn/S5zUAgIaAMfDAaN91RX\nV2cGMpvNKhKJ3Fen8uONfa1D5dFUHBHSZMi9dzxBE2qdNOTSk8V5jv410r6D7B15/0xrdakkC+bQ\nSRjlUGhvDkEqlapCf1tbWxWLxZROpxUOh5XL5SyV47+fIWAgGKBvOP441sgwDgdOSTgcfj9F7S+8\nDoVTsLu7N8nv0qVLNqN7Y2PDOhiiiOrq6pROp99TZuQ9TowLB82TY6T9bmIIrK+/9wgDn43SghAl\nyfJ5QPrcA/kjVigUsrkEvvQNZ4KDEArtzSoIBoNmpCuVihETyclyzUxN4zt8+kGSCT3X7Hucj4+P\n65VXXlGxWLQ59/F4XNPT0+ru7tb8/Lza2tqs3wD7ubOzY54yecxyuaxsNquGhgYNDw/r4x//uL78\n5S/rjTfe0MWLF9XW1qbl5eUqtARY2xPT7lcydBArEAhUTTeDtb29va1kMmm9MGKxmBFaV1dXtby8\nbHPWeU4QnDB6kmwP0+m0zboPh8PW6Yw2yRh70i3Im5cjjL6Heb0iLZf3pgNms1mL0o4ePVqV8uJc\neLKYN0beAd3c3LSUSLlcNke2o6ND09PTSiQSKpVKSiaT1qKYiNQ/RxQjnBRa+q6urtrwJpyYWCym\njo4O3b59W1//+td17do1lct7A4AgghF5cf8Yz+3tbYseuV8cJ76b81qLThwkYgBayX6TmiQAam5u\nVktLi9WxZzIZ9fX1qbm5Wfl8XmfOnNHdu3c1MDCgF198USdOnNDp06f18ssvV91zMpnUqVOntLm5\naY1ymGMQiUSUy+W0sLCgrq4ue26kw5DPWo4DiKGP5Le3t80xQedghCuViqUa4RcRFHhnQ5JxBDo7\nOy1Kh7Qn7QUjkE0pC8ZhoDcCcxuQFd+PAv2ZTqeVy+Wskgqkobe3tyqAWV1d1ZkzZ4wnMTs7a7KG\n3QAt851sSctAiCRdxJnMZDJqbm5WR0eHXfdhW4fCKUA53b59W7u7u9Y0xXcsxNhS+vHdPqfW80aZ\nekgLZQeMxvKQLqsWOYCM4hsQefgYRe17mwMZYkxQvHjVm5ubVouMI9TY2GglesDNVDigqH3KgENE\nZEHjJT5jaWlJdXV1+rVf+zXbXwwMB1aSKWo8Z0nviRDIs+E57+zs2EELhUJ66623NDY2pt7eXjM0\nXjkHAoGqqoP7pXIOYrF/3Ge5XLbWxuwlaSIfrdQuGmFhSOloePnyZXV3d1uZmLQftfiSK6Jqz5PB\nmGL8pOpOcPweZ4Tn7XkaOF3eIPqo26fLPEqB8+oRD16D/CGPvB+OhH+9pConifMBwrKzs2OytLa2\npvX1dc3OzurKlSsaHx+35j18jv9clkfV/J/cm3dQalN1tc7BQaxAYI8wjS4Bncrn81XNcjA+7BnO\nUalU0sDAgJaXlzU8PKzW1lZNTk5qfX3dGhNxftljgiNPhMMZQ5fRBA79Ie076cjB6uqqGT/kQHTT\npQAAIABJREFU2A9Ew1DyHQzsQrY9goDsexnzZEb0Kw2Z0Gnt7e2WrmRsu7Tf+4Hz4J2Yzc1NQ2W5\nJ5AD0jXoI9KZvrNi7WwaGpwFAgGbeEqFl7SfdvZOP7JLNUN3d/d7CLOHZR2KqwoEAvrP//k/WxTR\n1dWl2dnZKliJOevfy6OvjXxYPhVQm4P18NP9/s31Sft19mtrazaDwQ9P8jPffZdFL5zJZNLyy/X1\n9drY2LBoW5INAGlqalKhUFAikVC5XLa6bQwvglfL+vY5YIxIS0uLnnrqKX32s59Ve3u7ZmZmbIoj\ncBrX3tbWZn28USQYF5wf4N14PG6QdSQS0erqqp5++mk9//zzeuedd4wEhRFA2eDw3I9Qd5ALWeDA\nBwIBe75EK5457w+9d2g8kdSnfCgzo50sinN3d1eLi4taWVkxZ8Gz/H2qyCNiPk3k+TU4m8gNnwdc\nSqrLp8ww5rXwq09feSgXxY8TWS7vdXcEaUHh+mfqES/eyx6xVldXlU6nVanskVkLhYJmZ2eVSqUs\n4vfpp9p1v5SbRwNwcPi3vzf/+4NCCtANXmZYVEVxXiRZqWVDQ4MGBwdVLBatPHZoaEjZbFY3b960\nM8UUVWZFgB5CGJVU5ZRiRFdWVtTa2ipJVoWDocTRxOktl8taWVkxHhFl2pxvAjd0nK8EQ/Z4pqRY\nGxoa1NLSYnuDXqJEEKfEp19AU7zjhDPiHQSatPn0G8FcKBSyQIiW5KAky8vL5lDzXhxhHA8IkL5d\nNNezvr5uMyx8ypAUw/8cnfw9Vj6f10c/+lHduXNHJ06c0OzsrNra2rS6umoQNkayubnZyB61C7jI\nR1/82xsdT0DBqErVHeDu50CguHO5nKQ940BnP7xG4HCMHq1wA4GAuru79YEPfEB37tzRnTt3DPqF\nVHX06FGbWR4Oh7W7u1eOFQ6Hdfz4ccViMWOuc8C84wSpEKIM0W59fb3OnTunpaUlra2taWhoyA68\ntKeYOzs7VS6XLU+G0JJvr02tFAoFa/qxu7tryp1W1FSPPPHEE1ZVIe1PJwsGg/a82OeDTh94Q899\nLC8vK5vNKpfLGeLj26YiR76sjBRVLBZTsVjU448/rg9+8IO6dOmSpD25ovlMpVLRCy+8oGvXrikU\n2psF39jY+B6Eorm5ucoh8GmcnZ0dU0pbW1uWevL5/XK5rIWFBasy8c6k74/gc7u1PAfqwbe2tlQo\nFJROp5XP5y0nzUIh1hpmZAbHcGdnryqG3h2VSkVf+9rX9Oqrr1qL6d7eXn3+85/XwMCAXn31VYs6\n/TmszYH7c0pqpFameG4eGfluOf33c3m5ITUKyRpCGmx7aY/IW19fr5GREUsLUut+584dzc/PWytk\nyrk3NzetvwGIA7NiiPL5nkqlYk5wpVKx1uYgTOggSVYRQgRM2sjvH4Yd3cczAGZnPPH6+rpSqVSV\nPkCfMYWRFs1bW1t2XTiODB+Cm0Eqhs/L5XLK5XJGEMQpoW05TYUikYghVpyd+vp6rays6O2339Yj\njzyio0ePamhoyHrcoPPhBnD9TFDEiWKPlpaW7PnjQIDsHsZ1KJyCjY0NgyVXVlYM2vdelc+nfrfl\noepashcwHYxpD+3XpghqoUj+jz/xYonOyGF5oou0TzbE866vr9eDDz6o0dFRnT9/Xm+++abNKuf1\npVLJ2MeDg4Pmbbe3txsMWBtB1kbZKExfAvTaa6/ph37oh/TNb35TMzMzOnHihI2mBdZjf/GM8fhx\nQPgTxba6umrKi2va3t5WLBZTLpfTxMSEPvKRj9hhxNBhgPzzvB8T/aAWzh6GC4PnSW0+x8q/eQag\nPCjkU6dOKRwOa25uTtlsVtlsVqdOnbIqkLfffttKora2ttTa2mrT1XCeSLN4mUS2uF7gW6J0Osx5\nngkpOPKeRI/cC2kFfsc+oIxptpXNZk258TmcHeRFqh5CxP7hNHnkAUWcTCatZKu+fm80bjQa1UMP\nPaS33367Ko1Ra/xrOTwo4lrn0iMjkMn8a/n7QSz21jvDPHMCGfZqfX1dzc3NhgzMz88rEomovb1d\npVJJMzMz1nhrc3NTY2NjmpqaMpga2ZD2nRGcrM3NTWUymSrdm8/ntb29bd0pfdvpSqViHVB3d3et\nkVvtucAJ8H0o4FHQ3RPOkkd+kdWOjg4Fg3tjyVdXVxWLxapQAAi/qVTKfofDQPBRqVTMKZBk5EtQ\nWE9uBIH2+wMJHB0fiUTU0dGhXC5ncgj6hmy3t7fbULiWlhZzTqgA4lpZu7u7hswctnUonAJJunLl\nikZHR5XJZBQKhapyNM3NzSbgPkKpXRgW7xRI+8rJl5l5KLU2mvhuUDYeoE8j4BRAJvSoA/+H0imX\nyxoYGNDm5qb6+voUj8eVTCaVTqctD9jV1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lXqhwNKdMh9d3d3W84SlElSlSL3HANgS3p2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\nN5TT06RKxy5SE3/xF3+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"text/plain": [ "<matplotlib.figure.Figure at 0x11ba7c0b8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "person1 = 34\n", "person2 = 35\n", "test = ((Z[person1] + Z[person2]) / 2) #+ 0.5*np.random.randn(*Z[person1].shape)\n", "pred = lin.predict(test.reshape(1, -1))\n", "fig_newer, [ax1, ax2, ax3] = plt.subplots(1, 3)\n", "ax1.imshow(ims[person1].reshape(*img_shape), cmap = 'gray')\n", "ax1.set_title('Face 1')\n", "ax2.imshow(ims[person2].reshape(*img_shape), cmap = 'gray')\n", "ax2.set_title('Face 2')\n", "ax3.imshow(pred.reshape(*img_shape), cmap = 'gray')\n", "ax3.set_title('Face between lying on manifold');" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "distances = spatial.distance.squareform(spatial.distance.pdist(Z, 'braycurtis'))" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "kernel_width = distances.mean()\n", "weights = np.exp(-np.square(distances) / (kernel_width ** 0.1))\n", "for i in range(weights.shape[0]):\n", " weights[i][i] = 0" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "NEIGHBORS = 2\n", "#NEIGHBORS = 100\n", "# Your code here.\n", "\n", "#Find sorted indices of weights for each row\n", "indices = np.argsort(weights, axis = 1)\n", "\n", "#Create a zero matrix which would later be filled with sparse weights\n", "n_weights = np.zeros((weights.shape[0], weights.shape[1]))\n", "\n", "#Loop that iterates over the 'K' strongest weights in each row, and assigns them to sparse matrix, leaving others zero\n", "for i in range(indices.shape[0]):\n", " for j in range(indices.shape[1] - NEIGHBORS, indices.shape[1]):\n", " col = indices[i][j]\n", " n_weights[i][col] = weights[i][col] \n", "\n", "#Imposing symmetricity\n", "big = n_weights.T > n_weights\n", "n_weights_s = n_weights - n_weights * big + n_weights.T * big" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "G = nx.from_numpy_matrix(n_weights_s)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "image/png": 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tpC2KgufvuSeWl5fj/PnzU1HU6e5YNc3w8xNh4mMx3G+//fCioUPxs7o6oWCO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4+VXFEbKWWvhQ/ZJCAUxZo3gl5jZfZ2s6MtLgaKinC0cWMD6yJVQpwuvjd36GDtHmJ36cxBU/T2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\n6r2u5yoAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x198b72438>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pos = {}\n", "for i in range(Z.shape[0]):\n", " pos[i] = Z[i,0:2]\n", "\n", "fig2,ax2 = plt.subplots()\n", "nx.draw(G, pos, ax=ax2, node_size=10)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[0, 2264, 874, 1330, 6412, 1325, 781, 3607, 483, 1106, 6316, 1405, 102]" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "imlist=nx.all_pairs_dijkstra_path(G)[0][102] #choosing the path starting at node 0 and ending at node 102\n", "imlist" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "N=25 #number of sub-samples between each consecutive pair in the path\n", "lbd = np.linspace(0, 1, N)\n", "counter = 0\n", "for count, i in enumerate(imlist):\n", " if count != len(imlist) - 1:\n", " person1 = i\n", " person2 = imlist[count + 1]\n", " for j in range(N):\n", " test = (lbd[j] * Z[person2]) + ((1 - lbd[j]) * Z[person1])\n", " pred = lin.predict(test.reshape(1, -1))\n", " im = Image.fromarray(pred.reshape(*img_shape))\n", " im = im.convert('RGB')\n", " im.save('{}.png'.format(counter))\n", " counter += 1" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "os.system(\"ffmpeg -f image2 -r 10 -i ./%d.png -vcodec mpeg4 -y ./method1.mp4\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Please check the generated video in the same enclosing folder." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Observing the output of the tree regressor we notice sudden jumps in the reconstructed video. We suspect that these discontinuities are either an artefact of the isomap embedding in a much lower dimension or because of the reconstruction method. \n", "\n", "To investigate further we plot the frobenius norm of the sampled image in the isomap domain and that of the reconstructed image in the original domain. Since, we are sampling on a linear line between two images, the plot of the norm of the image of expected to be either an increasing or a decreasing linear graph. This indeed turnout the case for the sampled images in the isomap domain.\n", "\n", "However, as we suspected, after reconstruction we observed sudden jumps in the plot. Clearly, this is because of the tree regressor which is overfitting the data, in which case there are sudden jumps in the plot." ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "norm_vary = list()\n", "norm_im = list()\n", "lbd = np.linspace(0, 1, 101)\n", "person1=12\n", "person2=14\n", "for i in range(101):\n", " test = (lbd[i] * Z[person2]) + ((1-lbd[i]) * Z[person1])\n", " norm_vary.append(norm(test))\n", " pred = lin.predict(test.reshape(1, -1))\n", " im = Image.fromarray(pred.reshape(*img_shape))\n", " norm_im.append(norm(im))" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "Text(0.5,1,'Norm for the mean image in projected space')" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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MULJgbj56tDl31C/HP77ZyLOfrOZswgW/yxIRkRwgNZeUlXTIkyuSwV0bUL1kft7+djO7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\nBD4G1gHfAP2ccxe8c+j9gZn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pZGblgSeBGOdcXSAS6Iq2w5SMB9onarvcdtcBqOk9egPD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\niIJdREQkjCjYRUREwoiCXUREJIz8P0dAvq7SnvzxAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11ba2ebe0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "f, ax = plt.subplots(1,1)\n", "ax.plot(norm_vary)\n", "ax.set_title('Norm for the mean image in projected space')" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5,1,'Norm for mean image in original space')" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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8APgN4NeMMe8TkaeH/38y8DTgyvDv8cDvAI8XkcPArwLXAiZ8n1uMMafC5zwf\n+AiBYb8BeF92u6kUiZuTKt6pSIOaoJ1s8fl1GHiCXdcrfGSsMp6doVC8ba4ySyj+9X/1Zd784a+k\nft3HfvkpXHCgk/p1RdFNO92tpuI51/PpuX61PXZjzAngRPj4jIjcBVxMYJwPhk87DzgePn4G8Aeh\nx/0RETkkIhcSGP0PGGMeBAhvDm4QkQ8CB40xfxcu/wPgmahhry15qeJt3r5fsiq+rAEwEDcYPgdW\nStkEZQTDofhGQ1hpNWYy7A/s9HjEkTXe9XP/ONHz/+zTJ/jVW+6sfNi/H/WKX2zx3G74m5epik91\nSyEilwGPA24HXgS8X0R+kyBXb4/Ci4F7Yi+7N1w2afm9I5aP+vznE3j2PPzhD0+z6UqB9D1DKwdV\nvPXYvbJV8bt9Lj5UjlXtxDx2pTpY8dxGrHZ5teXMpIrfDG8cj24k877PXw9uMqtuAG2uvJUwhdSq\nqXhu145sLbGOPfEni8gG8C7gRcaY0yLycuD/Nca8S0R+DHgj8BRg1K9mZlh+7kJj3gC8AeDaa6+t\n169dMU7t9Pj65l6q1zziyBoHVqaHoF0/r5ayYblbyTn2rd0eV190cPoTcyDusSvVISp3i4VfV1rO\nTF706b1+ZKyTMGi9Wu1L4qDcbbF7xe/Yka1V99hFpEVg1N9qjHl3uPi5wAvDx/8H+L3w8b3ApbGX\nX0IQpr+XIBwfX/7BcPklI56v5MgPv/7D3H1yJ9VrnnjFEd76M0+Y+rxAFZ99KF5EaDYEr/RQfL+U\nGnY4O8euVIftfZf1tkMjFqlabTnsz5Af3tzrc9nR9cTPtyLKfsUNYM/1aQiJrw11Fc9FHnuVc+yh\nyv2NwF3GmFfFVh0HvofAOF8PfClcfgvwAhG5mUA8t2WMOSEi7wf+i4icHz7vqcBLjTEPisgZEXkC\nQYj/OcD/nH/XlHG4ns9X7t/hB7/jIn7wsRcmes3v/+1X+NqDu4me2/f8XELxEITjyxTP7fc99vpe\nKTXsoB57VdnuDia7WWb12Lf2+qkGDEUGsOKGve+l629RV/FcXTz2JwLPBj4jIp8Kl/0S8K+B14hI\nE9gnzH0TqNqfDhwjKHf7KYDQgP9n4GPh837dCumAn2NQ7vY+VDiXK98408U38N3fcoSnXp2sROYj\ndz/Ip+/dTPRc18/HY4dAQFdm57nNqE+8euzKgDPdc3uDr7ad1OI5PxwJfF4Kwx4NS6m4Aey6fqpK\njsEQmGrv1zC71rBXOcdujPkQo/PgAN814vkGuGnMe70JeNOI5XcA3zZtW5RsOBHm1i9MIQA7stFm\np+ex3/ci4zKOvBrUQOCxlxntwSxGAAAgAElEQVSKjya7leyx60z2arETm+xmmUU8d2bfxRg4L8Xx\n1bLHRMUNYM/zo+M3CbX12KNQvHaeUwrk+NY+ABedt5r4NYdDMc+DO72pz3V9n1YODWoguIsvUzw3\naCerHrsyYHt/VCi+kToUv7UXRITSeOxWjFZ1j73v+omFc1DfznNV8NjVsC8hs3jsSQ277xt8Q24e\ne7PRKLXczYbiy/bYNcdeLba757YQXWk57Ke8AdsMJwemyrGHx0TZw5Gm0fP8KLqQBHsT0K34Dcsw\n6rErpXBia5+NTpODCUrXLEdCw/7AFMNum8fkMQQGglB8mQ1qyg7Fq8deTUaJ51ZbDvszeuxpIkL2\nXKt8HXtKj11EaDuN2oXiI49dp7spRXJ8c48Lz0vXYGXgsXcnPs8q1vMY2wpBKL5MVXzZ4jn12KvJ\n9qgcezt9jt0eX6lC8dZjr0Ede9o2yC1Haiee2+l5tBwpteWzGvYl5MTWPhceSp5fBziyHnTBemB7\nssceGfYcPfYyx7Zu7vZYbTlTBYR5oR579TDGjMmxpzfsUY49lcce3ETXQTyXNpLXbtbQY++6pXrr\noIZ9KTmxtcdFKT32g6tNmg2ZmmMfhOLz8tgbpd7BnypxAAyox15Fuq6P65tzyt1WWg77fT/ViN1Z\nxHO293rVxXOzeOx1NOw7Pa/U/DqoYV86uq7H/ds9LkyhiIcg33X+enuqYR+E4vM5tJpO+R57Wc1p\nIIiENBuiHnuF2B6a7GZZjaIryQ2TjQh1mskNQ6sunedSlrtBaNgrvl/D7PbcUhXxoIZ96bgvLHVL\no4i3HFlvTxfPhSdhfnXsjZLL3cprJ2vpNBvqsVeI4clultVWcHlNU/KWtjkNxMRzFfds+1468RwE\n+1Y3w77TVY9dKZjjm+lr2C2Hk3jsodHNLRTfENxSQ/HljWy1rLQc9dgrxKgBMBCI54BUefbNGVI9\nzYYgUgOP3Z0hx15TVbzm2JVCObGVvobdksiwW489x1C8W2oovtwcO6jHXjWika0jxHOQzrDP4rGL\nCC2nUX3x3Aw59k4dc+xdj/WOeuyl87Jb7uTl7/1c2ZtRCCdm6DpnObLe5oHtyeVu/ZzL3ZqNRmke\nu++baFZ2mQQee70udouMDcUf6JxtkK1hT9MvfhbDDtBxGgtZ7lZH8VwVPPZyP70i3HXidNmbUBjH\nN/c4tNaKwoRpOLze4fS+O3FKk+vbHPviiefOdF18U14Nu6XdbKQeLqLkxziPfXVGwz7L8dVqllst\nkoSeZ2Yqd6tbdGq3px57JWjX4KTIihNb+6kV8ZbDG4GnaruvjSLy2HNrKSultc7cjPrEq8euDIhy\n7EMX8yjH3kujip/NY285UnnPtud6qVXxZZe3zsJuzyvdY1fDTrnGomiOb6avYbccXpveL96GyfMa\nAtNsNKKoQNGcivrEVyHHrh57VRiUu519XKymzLF3XY+9vjfTjWMdnJOeN0MovmbiOWMMOz1XVfFV\noI53hbMSdJ2b0bDbtrITus9ZYVtuHnuJ4rlT6rErI9jed3Eawkrr7Mup/X/SmzDbnObgTB57TcRz\nC955LmhIVO5kN9AcO1CP/FQW7PZctvb6M4fij2xMHwRjv8e8yt2ajfJ6xW9GA2DK99i7FfXYX/WB\nL/JHH/1a2ZuRirbT4DU3XsO1lx2e6fXBZDcHkbOP+bSq+C07h2AGw952GpXuPOeFUx9nybHX6SZ2\np2dLH8v12NWwE54USxCKj2rY5/XYJ4bi8+481yhNPHdqp9yRrZYqe+wfufsBBPi+xzy07E1JRNf1\nePcnvs6dx0/PZdgPjJiUmFY8N0s7WUvVQ/HW654lFF/l/RpmNxzZWnaOXQ079ZwgNAtRDfuMHvv5\na21EJnvsA1V8nuK5cn6rzd0eIrOFSrOkyjn2nuvz6Icd4L/+i28ve1MScWa/z7s/8fW5jqntfZeN\nEaHXgXgu2W81z+TAqndom9mw16yl7E40slVz7KWzLDn2E3N0nYNgstqh1dbE0a028pHXPPZyc+yB\nYtnJqUY/KVX22Htu+n7gZTKYZT6HYe+6I8ubVpopQ/F2FvvqDOK5itex2+930cVz0Sx27RVfPq2a\nHTyzcnxrDxF46Hmdmd9jWve5yGNfwAY1m3v9mfKfWVNpj30G5XOZtDPos77dddkYEYpvNCRVHfbm\nHKH4VrPa4rnIsKeM5NVNPLcThuI1x14BgvxUde92s+LE5j5HNzqpJkcNc2S9M3Eme+4eeyM/j/2v\nv3iSzx7fGrv+zq9vla6Ih2p77JOaF1WRRkNSp3de+eef5xNfPRX9/wv3neH6b33IyOeutpzkOfYw\n1XNgJf1lueriuXlC8a5v8H1Do+RIWRIij11z7OVTZt62SI7PMId9mMPrbb58cnvsejfvBjVOIzfD\n/gvv/Hu+cXpyy9yfePzDc/nsNHSagYCwikZ0lpKmsmmlFM++9SNfZa3d5BFH1gB47CXn8QOPvXDk\nc1dbTuIc+9Zen4MrrZkMWLtZ7WtYZNiddE5FPFWy0ijXC05C5LGX3HlODTvBweP6BmPMOSUri8SJ\nrX2uuGBjrvc4vNHmY19JEorP0WPP6QK22/N4znc/gl96+mPGPqcK+eOV2JzvShr2CnxHaUjbta3n\n+dx4zUUTjxPLattJnGPfnLGdLFRfPDdrGaw933qeHx33VaYqHnu9zsCcsBeiRQ7HG2M4sbk3c3Ma\ny5H1Nqd2e/hjvOZBKD6/BjW+Yeznz0PX9VlrN1lpOWP/qnDj10nZ+KRI6pZjh/SlYn3PJI5KpNFD\nzDoABqofiu/OEYqH6s+at+z0quGx1+sMzAlrhKocypqX0/suOz1vZkW85fB6G98MhD7DRGNbc/Ik\nrYeadTjeGFMbRbdVW1cxz17HUHwa5bXnGzw/+TCTVB77jH3iIRDPVdljn6eOPf76qrPbdREZnKNl\nUa8zMCfsSbrIhn2eOexxBk1qRueio5ayOQldbKlZ1v3irZHstKp/SlTVYzfG1NJjT9N5MgopN5Md\n36nEc3N67FU2fvZ7m2UITPz1VWen57HWckoX+tXrDMyJLGpZq46tYZ+1OY3lyHpQKjdOGT/IpeWX\nY4fsPfbIsJd8p50Eu43dio2zDHQq1M5jTyOeG5RtJfTYW8k99llHtkL1K3vsTccsLWXjr686uz23\n9Bp2UMMODE7SKp8Y83I89NhnbSdrmdZW1vUMIuTWxCUy7Bn/Vl03uPjWIRQfeexutTz2gTdb/e8w\nThrhWdqQ8ko7mSreGBMY9hma00AoAKywYzJzg5rw+VVMO41ip+uVXsMOatiBQVityuKTeTmxuY/T\nEB5yYE7x3JRBMH3fz21kKwxy91kr4633WwfDvlJRj31Q0lT97zBOmiYo/ZQe+0rTSdSgZrvr4vlm\njlC8E+X/q8isx0a7Wa9o6m7PLV0RD2rYgUFpVl3yOLNwfGuPhx7ozO1Jnz9lJrvrmdxq2GEgdMwt\nFF+DkpqqeuyzCqTKpp1iVkTakPJqO5kq3vaJP2/WcrdmtQXAvRlTdFE0tSZO127PK10RD2rYgeXI\nsR/f3OPCQ/Pl1yG4aB9YaU4w7H5uwjkAp2E99uUNxVfWY58x3Fo2aWZF9FPuY9Ic+zyT3SCmHq/o\nNaznzhYRq5vHvtPz1GOvCu1mPnnbKnFia58L5+w6Zzmy3p4Qik9eCjQLA489J1V8DYyS9di7VfXY\naxaKD3LsCcVzbrqWydawGzP5/QcDYGYXz0F1PdtlKnergsde/q1FBUhbUvHVB3Z476dPzPWZ56+1\nedZ1lxbW8OTkmS4PPZiNYQ8GwYwpd/P8XEPxTl6q+CjHXv5JOY2o85x67JmQJsfeS1m2tdJ2MCZ4\n3aRja+5QfMU99rSRDkv9VPHV8NjL34IKkPak+L2/+Qf+8CNfnftzv/uRR7j86Prc7zMNzzfs9ryR\nM6Nn4fB6h3tP7Y5c5/omt3aykJ8eIgrF16GOvVnxHHvNPPb2DKH4pB67TZvs9yYb9nlGtkI8F13N\nqOPc5W4VvWEZZqfnVkIVr4aduMee7KTY63tceN4Kf/UL3zvT5/3F5+7jBW/7ZOLhEPNi+xdnZdiP\nrLf59L2bI9e5nsmtnSwMQvFZq3/rFIqvqsde33K3WcRzCRvUtAcz2c9jvDe+uRektubpPAfVNYA9\nz0ckfeOq+oXivUrUsZe/BRUgrfLSth6dNeS4Fp7sRZ2Eg4lDGXnsG0G/+FFDc1zfz62dLAxC8Vn3\nHKhXg5pqdp7r1tRjb6Xos5423bDaGhj2SWzt9Wk3G6zMGDGqugG0rYbTph7r5LH3XJ+e51fCY596\nFInIpSJym4jcJSJ3isgLw+VvF5FPhX9fEZFPxV7zUhE5JiJfEJHvjy2/IVx2TEReElt+uYjcLiJf\nCt+30KHXaUtF5p1gZUcXFnUSbncDjz0rUceR9TZ9z3B63z1nXd8zuariW7nVsddHFd9yGjgNqVzT\njtqWu6Xos542pGyjK9Oic1u7fQ6ttmbW3LRrUO42yw1f1W9Y4tjfuAo59iTftAu82BjzGOAJwE0i\ncpUx5seNMdcYY64B3gW8G0BErgJuBK4GbgBeJyKOiDjAa4GnAVcBzwqfC/BK4NXGmCuBU8DzstvF\n6di8bdKTe9452INuSsV4XDvdbEPxk7rPuTnPCLcee26h+Brk2CHd1LCimLWkqWxaKfqsp+15vpKw\n58A8feKh+uK5WZ2hOonndqKRrTXw2I0xJ4wxnwgfnwHuAi626yW4xfwx4I/CRc8AbjbGdI0x/wAc\nA64L/44ZY+42xvSAm4FnhK+/Hnhn+Pq3AM/MYueS0o68wOT9oufxSjoFH6zWsGd1JzlpEIzrF9Og\npr/EveIh8ASr5rEPRvbWy7Cn6bOeukFNy4rnJhv2zd3Z+8RD9Ru5zGrY6zQEJprFXoEce6pvWkQu\nAx4H3B5b/CTgG8aYL4X/vxi4J7b+3nDZuOVHgE1jjDu0vDDShuK7c46mLPoudDtjj33SIJi+l3NL\n2fC9vczr2OsTioeKeuxesD11C8WnEc+lblDTTpZj35zXY694Lro/ozNkb+Rr4bFbLVMdPHaLiGwQ\nhNxfZIw5HVv1LAbeOsAod83MsHzUNjxfRO4QkTtOnjyZbMMTkPaucO4ce8En4W7PiueyOeAOb0wK\nxefrsecmnqtRr3gItrNqHnttc+yOg+sb/ARRoF7KqERS8dzpvT7nzVjqBtXPRfdmTNGJCO1mg25F\nb1jiDELxNfHYRaRFYNTfaox5d2x5E/gXwNtjT78XuDT2/0uA4xOW3w8cCt8rvvwcjDFvMMZca4y5\n9oILLkiy6YkY5KeSGYv+jEIQi31tUeVK2XvsoWHfHeGx+yZXVXwrZdokKd3wZq2ohkHzspJizndR\nDIxePb5Di43YJbnRTlurb8Vz0wbBbO725vLYo85zFe2e2Zsjytmp+Kx5y243WwdqHpKo4gV4I3CX\nMeZVQ6ufAnzeGHNvbNktwI0i0hGRy4ErgY8CHwOuDBXwbQKB3S0m6LV4G/Aj4eufC7xnnp1KS7tg\njz1qCVpYuZtVxWdj2FdaDmtthwdHhOJdz6eVoyq+mVtLWa823jpU22PvOOVf2NKQ5vxPG4pfSeCx\n9z2fnZ43V4594JxU62bP0p3jmpmmM2CZ1M1jfyLwbOD6WHnb08N1N3J2GB5jzJ3AO4DPAX8O3GSM\n8cIc+guA9xMI8N4RPhfgF4GfF5FjBDn3N865X6mIBFkpalnnMuwFl7vtdF1EBmHBLDh/rV1KKD6/\neeyTO4NVjU4VPfaahuLTNKiatUHNJPFc1HVuHvFc1Cu+mh77PFHONEN6yiTrlOc8TL21MMZ8iNF5\ncIwxPzlm+SuAV4xYfitw64jldxOo5kthkLdNKKBxsyl3K04857HWcmhk6Ekf2Rg9CKbv+/m2lHWs\neC77HHvdPPYzI/oIlEndDXuS87Hv+TSExOmmlfC7mOSxzzvZDWIis4oawJ7rz+zJ1sZjz7j6aB7K\n34IKICK000x4mtNjL6OOPaswvOXwenukKr4oj72fRyi+JjXsEIR4T54ZPYinLKzRc3JMxeTBID+d\nLMee5qa+6TRoO42Jhj0aADNPjn3CzcnWbp9Xvv/zhbWwHsXd9+/wnQ8/f6bXpmkgVCa7UYOaGnjs\ny0LLkcTdzOYtd3MaQrMhxYXie25mwjnLodUWXz65fc7yYB57nuVuGoqHwGOvmhcz7w1vWaTxdmfZ\nx5XW5NLErTn7xMPkm5OPf+1B3nb713jYwZXSfp+DKy3+yRVHZ3ptuybiuZ2eS7vZqEQfBzXsIa1m\n8jyO7RU/D0WGl/Lw2Nc6zZEeQDCPPU/xXKiKz6FBTZ2MUiVV8XPe8JZFGvHcLPs47bca5NhnL3eb\nlE7Y6wXL3vLT1/Hohx2Y+TPKotVMHk0tk72eV4kadkjZoGaRaaUIxc/bUhaKDS/tdL3MBR3rbScK\nPcXJex77wGPPvld83XLslVPFez7tGkU9LJF4LoHwbJZGK6ttZ2IYPItQfLMhiIy+ObFpgCzFs0US\nlLtV6yZ2FDvdasxiBzXsEUlnMruej2/mFwgVGV7a7mYfil9rN9nteec09XC9nOexR+VueYTi63M6\nVNdjr1d+HeINo6Z/n33PpL6pX205icRzB1dmP0dFZKxzYo+TWSfHlU1dxHO7PbcSinjQUHxE0raS\nacc2jqNdoMe103Mzv5Nci7XKjIf5+76fbyi+kV+DmqM18jYr6bHXLJ1hGYSxk5W7pT2+V1oOxzf3\nue0L3xy5/nPHT3Og05y7sdM4ZyEy7BUJE6el3WywuVetY30UO73qeOzV2IoKkLRW0obr5s0lFil+\nCkLx2efYIVCCxt87UMXnO91NJKcGNTXyaFZaQRvUIPVRje2uq2FPM/J0lnTD0Y0Of3nXN/ip3//Y\n2Oc8+qHz577bY3RC+zUPxddFPLfbVY+9cjSdRqI79m4YrmvN7bEXN51rp+uykUOOHexEo2AojDEG\n1ze5dp4DaDUa2Yfia1jHDkGkoSqGPQvtSRmkmRUxS7rhVT/+HXz5m+dWkMS59PBaqvccRcsZXWmz\n1/dwGlLL3wassLn64rmdnsf567MLILNEDXtIO2koPmqbWQ/xnOebc8LlWWBD8XaiEQzy3nkbGqeR\nvDQxKT2vXuVugx7k2f+2s1LXcrc0DaNmEc8dXGnxuBlruNMwzmPf6/m19dahRh57z1VVfNVoOY1E\n4d2sumt1nAbdAsRPtn9xHuI5gL3+oPuZzXvnqYq375+9x14/VTxQqTz7vP0dymLQZz37BjVFEojn\nRoTiXa+2wjkoVo80DztdrxKz2EE99oiW00hY7pJubOM4Oq1GNHUtT7IeAGOxuaS4x267weU5jx2C\n0p5cGtTU6OIX99irQt/zM7+BLIJBHXuycreNOdTreTJWPNfzouOljgR6pPKP8689sMupERMtLdvd\nfmU89moeoSXQajbYC8tOJpGVx15UeGnQvzjbA261ZcVzZXjsyaIrSTHG1LLzHFTLY++5Pu21+twc\nWVLl2GcodyuKseI5t96GPahYKjfHfvJMlyf/5m1MCxRqjr1iJM6xh+K5LMrdijHswfZm7UmN8tht\n3jvvHHsrY4/dhi/rFIqvosdeX1V88hx7z/Uqu4/jQvF7Pa/eOfYK9Ir/6gM7+Ab+/VMfxVUXHRz5\nnIYIj7/8SMFbNho17CFJy916GZW7FZU3yisUb3Psu/14KD5MU+SsincyzrHb36FOhr2SHntNxXPR\n2OZE89hNZXUE7THpxP1+3cVzDp5v8HxT2oChE1v7ADz16ofxqAxKE/OmmkdoCQSGPUGDiqhBzXwH\nWFF17DaPn5fHvtuNh+KL8tizLXfr9kPDXqOLX6eCHvu844zLIr14rprd9VpjPNu9fr16NAxT9Jjr\nUZzY2gPgwvNWStuGNNT3186Y5pga0GGiHLsznxEoKrxkVfFZe+wrYT56J9YDeyAszNljz7jczY7P\nVY99PurqsUfiuZx6xRdFe8w1bL9f/1A8lGvYj2/us9FpcmBl9n7+RVLNI7QEkvaKz0485xTksQdG\nK2u1ZqMhrLUd9uLiuVDQlmeveAgiAlmKaeoYiq9ijr2u5W4NO0Y5Qa/4Kpe7Teo8V2fxXDvFWN28\nOLG1VxtvHdSwRwR17Mnu2IPnz+eVBjn2AurYc8qxQ5Bnj3vsRaniW47gZaiKj0LxqoqfiyzGGZdF\nmlRcVT32seK5RfHYSzTs923tc+Gh1dI+Py3VPEJLIKhjL7BBTdgmcXg6Wtbsdl1Esi93g+A94zn2\nrG56puE0shbPhaH4GuUhrQdWRJOjpNS1pSyMb8caxxgTGPaK7mN7zDVsv+/XvkENlByK39rnwoPq\nsdeOVlMS3RF2M5zuBvnfhW53PdbbTUSyN7ZrQzPZo5ayOYfiW41GpuVudQzF25uQqnjsWY0zLotx\nYew4nm8wZv6KmLwIxHPnnhd7fa+2k91goGcqy7D3XJ/7t7tceEgNe+1ImmO3d8SdOcVznYIM+06O\nE4fWO82zDHs/UsUX4bFnKZ6rXyjeiherkmPPapxxWSRpGBWJQyu6j8E+nH08+L6h5/rR8VJH0pQj\n5sE3Tu9jDFx0nobia0fLaeCb4K58EvYC1pqz3M1eAG1+Ny+2e25uQ0LW2k6kuodBjj3vcGwz405U\nNpxdJ4+95QTja6visVtFeW1D8Qk8dmv4q7qP7RFT0PZDQ79aZ4+9ZD2JrWF/mIrn6kfStpKDcrf5\nc+xQkMfezs+w78Y6z3lRKD5v8Vxj6g1YGuwFo055SBFhpelUxmPvZtSRsSySiOeqHpVoOeemE/dD\nx2EhxHOlGfaghv0iDcXXj1bCkoqe69OQ+ZuwFHWw7na9/ELx7Sa7/VHiufzHtmYZlqtjKB6CPHtV\nPPasxhmXxThFeZzIsFe0QU28Q5tlL7zxq9NN6zBFOUHjOL4ZeOwXaii+flhjNE2UlZXytyhByHbX\nzW3i1lrnbI99MI+9iHK3HFTxFfXExlEljz2rapGyGNfcJU6/4vto04Pxm969njXs9bppjVO2eO6+\nrT0OrjRzS2nmQTWP0BJIGorvZjTooiiPfSfXHHvzrBx7JJ7LWRXv5NVSVj32mclqnHFZJFHF9wqK\nSM2KTQ/GPdv9fv0N+6gbliI5vrVfK28d1LBHRKH4KRfKnpdNE45Bg5F8Pa5AFZ9fjn2/70fes1tQ\nS9lWXqH4moUr1WPPjiRDoLLS1+SF/e7jtez2+Kh1jt0pP8dep1I3UMMeEZ0UU07ufkZtM4vy2PMM\nxVtRns3jRS1lC1DF5xGKr+oFexxV8tizGmdcFkGOPWlFTDX3cdQwGyueq7PHXrZ47j712OvLIBQ/\n/eTO4sSOSjhyDC+5ns9+38+l6xwMSmhs97koHJv32NZG9r3i206DRkkjIWelWh57NuOMyyJJ58l+\nxQWC0TXMPVc8V2uPvYBr5Ti6rsf9271a9YkHNewRacrdMvHYCwgv2VnpuXnsdnRrKNApbGxrDr3i\n6yacg6p57NmMMy6LToJpi5VvUDNCPR6F4tvV3OYkDKbvFX+s37dlFfFq2GtJmnK3LMKNKwW0BM1z\nAAwE4jkYjIYtShXfzLylbD3nVXeaThRqLZusxhmXRcuZrtuw6YbqiufO1QntRc2X6vm7QLlDYGyp\n20U1GgADatgjWgnvCns1KnfL37Cf7bEPQvEFdJ7LuKVsHS98gcdelVD8Aojnpp37FU83jNIJDTz2\n+h3fljLFc/edDprTqMdeU6I69mktZWtU7mZnsW/k1KAm8tjDG4hBKD5vjz1r8Vw9Q/ErTSf3lsRJ\nKWqyX16MG6ASp+rphlHpxEUod2s6DRpSjmGvY3MaUMMekTgUn1G528Cw5+dxRR57Ti1lbY7dNsHo\nF9RSttkIesUbk41x7/a9Wnqa6rFnx6gBKsP0K55uaI3wbPd6oSq+pr+LpZ1AA5EHJ7b2OLTWql3E\no96/doYkDcX3M5rHPKhjz9NjzzcUvx7l2AfiOachuYyIjWPFeVk57V3Xp1NDj2alQjn2rMYZl8Wo\nASrDZDUAKi9Giudcj5YjuQta86aVYPpeHtSx1A3UsEcM8lPTQ/GZ5NgLCMXv9vI17FG5W0w8l7e3\nDoNQf1ZNarquV8tQfJU89qzGGZdFEvGcXV/ZHPtIj92rdRjekqRqIQ+Ob+7XLr8OatgjUpW7ZWAE\nmo1g7GaeB6vNsec5BAbi4rlsbnqmYW8esmorW+cce98zmeoNZqXq3uw0Wk7Qptif8F1GY1sreqyM\nck667mIY9nZJHvuJrb3FNOwicqmI3CYid4nInSLywti6fyciXwiX/0Zs+UtF5Fi47vtjy28Ilx0T\nkZfEll8uIreLyJdE5O0i0s5yJ5NQdLmbiOR+sNoce1517CutBiKDBjWuZ3IXzsGgF72XUclbnevY\nIf+2xEmoervVaSQpqepV3GMf5Zzs9bxaN6extJvFG/a9nsep3X7tSt0gmcfuAi82xjwGeAJwk4hc\nJSLfCzwDeKwx5mrgNwFE5CrgRuBq4AbgdSLiiIgDvBZ4GnAV8KzwuQCvBF5tjLkSOAU8L7M9TEg7\nqcfumcy80k4z3wYjO12XhuTXdUpEWGs5gxy77+c+AAZiofiMSt6CUHz9Ln5WEFWFPHtW44zLIsn5\nbzu6VbWOfdS8i/2+vxCGPUkv/6y573Q9m9NAAsNujDlhjPlE+PgMcBdwMfBzwH8zxnTDdd8MX/IM\n4GZjTNcY8w/AMeC68O+YMeZuY0wPuBl4hgRKq+uBd4avfwvwzKx2MCnNpHXsGeZj200nd/HceruZ\nq5htrdM8q469iHKnyGNf8lC8FfxVwWMvKg2TF0laSvc8D6chOBVtPTwq6rDX92o9i91Shsd+YjOo\nYX/YIhr2OCJyGfA44HbgUcCTwhD6X4nIPwqfdjFwT+xl94bLxi0/AmwaY9yh5YViDdLUOnYvm1A8\nhIKQPMVzXY+1nPLrlqM6VH4AACAASURBVPW2MxDPeX4xofjMxXN+LTvP2Qt2FTz2rMYZl0USjU3f\nM5UNw8MY8Vx/QXLsJYjnjoftZC9aZFW8iGwA7wJeZIw5DTSB8wnC878AvCP0vkdd2c0My0dtw/NF\n5A4RuePkyZNJNz0RoyYjjSLLkzvvg3U7x1nsltV2k53uoI49765zEBPPZZZjr2co3m5zFTz2rPo7\nlEWSKpWgIqaa3jqM7jzXXRTD7hQ/F+G+rfp67Imu+iLSIjDqbzXGvDtcfC/wbhN0CfmoiPjA0XD5\npbGXXwIcDx+PWn4/cEhEmqHXHn/+WRhj3gC8AeDaa6/NVAo8ajLSMJ4fKJCzzLHn3aAmL+GcpRyP\nPVmXwKTU1ShZj/2Or5zi1E5/pvdwGsI1lx6a29vOajhSWSQRzwbRuuoayZHiub7HhYtg2JsNzuy7\n05+YIce39jm83q7ljdHUq37ohb8RuMsY86rYqj8hyI1/UEQeBbQJjPQtwNtE5FXARcCVwEcJPPMr\nReRy4OsEArt/aYwxInIb8CMEeffnAu/JaP8SY3Nnk0JxWXfXahcgnsur65xlrdPk9F5gVFzPFCOe\ni8rd5v/ujDG1zbGfvxYUj/zKn3x2rvf5Tz9wFT/9Ty6f6z36GY0zLosk4rng5qW6Hrs9L4bFcwuR\nYy9BPHdis56lbpDMY38i8GzgMyLyqXDZLwFvAt4kIp8FesBzQ+/9ThF5B/A5AkX9TcYYD0BEXgC8\nH3CANxlj7gzf7xeBm0Xk5cAnCW4kCmdak4rMDXvO5W7bXY+LD+VbObjedqKQVd8vSjyXXSg+aE1L\nLTvPXXPpIW55wROjVMgsPPuNt3P/dnfubam/xz49YtfPUF+TByISpvfOnsdet3aooyhFPLe1zyXn\nrxX6mVkx1bAbYz7E6Dw4wL8a85pXAK8YsfxW4NYRy+8mUM2XSsuZnPMe1LFmY7zyPliDUHy+J/Vq\n24kMSxCKz//Cl3RgTxJsfrqOHruI8NhLDs31HuudZtTvYB6y6u9QFgNF+fibpDoo/4edhf2a6keG\nKUM8d2Jrn+suP1zoZ2ZFvnHamjEt3DOY7pRdjn07g4vqOHZ7Lmu559ib0cznIBSfv8fuRB77/Ce6\nTYXU0bBnwUanGfUhmIcsq0XKYDBAZXLnucob9ubZ17D9RfHYwxuWLM75JOz2Pbb2+rUUzoEa9rNo\nOjIxvJtLjj3HUqXtAsRzax0n8vj6vp97Th8G5W7ZeOzWsNf/4jcLa20nM4+96kZvEnYU6+Qbe1P5\nm5d4OtH1fPqeWYgGNatthxNb+1zxy+8r9HMvrmHXOVDDfhbTQvGDIRDZnCjtppNbeMn1fPb7+Rva\ntVaTruvj+abwlrJZ5Ni7YbShjnXsWbDeaWYSNep5fu43kXmSpI6953qV1xHEp6Dth/8ugnjup594\nOQ850CGjSc2J6LQaPOUxDy3uAzOkvmdiDgSh+ARDILLKseconrPh1bwGwFjs++/2XPpesS1ls1DF\nayh+0DlwHnquT3utvt/hqFnmw9TB+43novfC37Xq25yEy46u84Lrryx7M2pDfc/EHGg5jYktZbs1\nKnfLewCMZS024c0tSBXfytJj11B8JqH4OgjLJpFoCEzFG9TA2c7CfhSNWs5je5mp75mYA61mseVu\nwRCYfBrU2It13uK5tVCYs9N1C1PFOxnWsUeh+CX22DMJxdddFZ+gV3zVy93gbPGcNeyL4LEr6aj2\nUVowyXPs1e8Vb0PxeZe7WcO+2/OCITAFqOKT9vVPQuSxL0AechbWswzFV9zoTSJZjr36UYlWLJ1o\nZwjUsXOaMh/VPkoLZtpowDxU8T3Px+SgCLEee97iOduLPgjFF9NS1smwQc3Sh+I7TkbiueorxifR\nSjBYqA4lfS1HouvUnnrsS0u1j9KCmSqey7iOve00MCa7nudx7MU67yEwUSi+54aqeG1QUyc22k16\nrj93u846KMYnkWQITN+rfne9eKVNFIpvV3ublezRXzxGUMdeYCg+DP/mIaArXDzX9QIBVQGh+EgV\nn0WDmv5ye+xRxGWOtrRQD292EkmmO9YhFN8e4bEv67G9zFT7KC2YIMc+3gvsRuVu2XnsMNlLmJWd\ngj323Z6L5xfjsdtQfF9z7HNjyxW3e/OF45ejV3z10w0jxXML0HlOSUe1j9KCmdpSNuOaZzsCMhfD\nXlgd+yDH3veLaVBjy928TFrKLnco3v5+85S8eb7BN9nd8JZB0umOVd/HuE7IGnYVzy0f1T5KC6bw\n6W4J8nqzstN1aUj+wpmzc+x+ZHTzRFvKZoc17PMI6LI+L8qiPaEqxhhTi3RDvPPcIjWoUdJR7aO0\nYKY1qLFGP6u7dusl5lHLvh3OYhfJ14PuNBs0JLiR8A3FtpTNwrD3F8MozcpGBjn2RTHscUX5MFZU\nW+V57GArbcJytwVqKaukQ3/xGK3m5Bx7Xh57XuK5vPPrEIwOXW832drrA8WEYzMVz7keLUeivP2y\nYSMuc3nsGVeLlMXwZLQ4/ZrsY9B5LrhJsx77ypJGo5aZah+lBZNkbKsImY0mTdLGclZ2ul7u+XXL\natthay8wDEWMbbWfMak0MSld11/aMDwMPPZ5cuyRYa+4NzuNSX0sso7W5UVwc2I9do92s0FjSW9a\nl5lqH6UFMzXHHtaxZhXe7uSoii9iZKtlvdPkdOixF6GKFwk8bC+jOvZlFc5BXPyoOfb2hE6QddnH\n+DVsv+dpfn1JqfZRWjBNpzF1HnuWJT22xCoPw77bc6Ma87xZazuxUHwx3kGzIfQz6RXvL7dhb1vx\nXAY59ozGGZdFvB3rML2aeOwtp4HrG3zfsN/31bAvKdU+SgvG9oof1+I1637Y9kKYR459u+sVkmOH\nwDhEHnsBqvjgcwQvq1D8El/8VloD8eOsDMLU9Q75TpoVMbh5qfYlM57e2+t7KpxbUvRXj9GeUkaV\nuWHPudwt7wEwltW2w+l9G4ovyGMPPZN5WfZQvIiw3mmyM0coPutxxmXRnpCKi1TxFd/HqOlVZNiX\n96Z1man2UVow0yY8ZT1zenB3nX25W1GqeAia4JQSis9EFb/coXgIIi5ziecWxbAnyLHXIRQP0Hd9\n9tWwLy3FXPlrwlltJdvnrs+6QUVnDo/9zz97H/dvd8euP7NfnHhurd2MPJrCQvFORuK5/nKr4iG4\nMduZJ8fuZduRsSwmqeLrUtJnt6/vGfb7Kp5bVtSwx2hNKT/LWjw3ax371zf3+Nn//fGpz3v4kbWZ\ntista7Fe1EWUuwWfM3kSX1K6bnFahKqyMWcovl8Tb3YaLacRtWIeZuCxV1tH0IpV2uz3fY5uLPex\nvazorx6jPWUmc9Yzp2fNsZ/a6QHw33/ksXzPoy8Y+RxHhCMbnfk2MCFx9X0R5W7B5wheFqp41+f8\ntXobpHlZmzcUXxNvdhqTOk/2axKVGBbP6QCY5UQNe4xpOfasZ07b90rrsdt89qWH13jIgZXMtmdW\n1uMee6Hlblmp4qt9sc6b9U6Tr2/uzfz6uijGp9FpjlfF16ZBTXj+9TTHvtRU+ygtmKYzyE+NImtV\n/Kw5dltadt5qK7NtmYe4V1DEEBgIQvFZtZRd9hz7RseZr0HNwnjs4wWZdREIxp0TNezLS7WP0oKZ\nHorP1rCLCG2nMbPHfrAihj2eoy6u3C1L8dxynwZrnYxU8RX3ZqcxKRRflwY1A/Gcz552nltaqn2U\nFszUcjfXZC6emVRiMw5bM14Vjz0unius3G1Cl7A0aLlbIJ7Tsa2Th0DV5eblLPGc62uDmiVFf/UY\nU3Psnk8747BtMGYxXanR6b1g1vp6RYQx63HxXJGd57JqULPkXs16u8l+35/5+1yUUHx8MtowtWlQ\nE27fTs/D84167EtKtY/Sghnc7U7IsWd8x96ZwWPf2utzcLWV+6z1pKyVJZ6bM8dujFGPHaIpgLOW\nvC1KuVt8Mtow1uBXfR/t9elMGNXTHPtyUu2jtGDazWJz7MFnps+xn97vVyYMD0GO1lLUha/pyNwt\nZV3fYEz1S5jyZn3O0a1ZjzMui0niubp47Pb8swJbNezLSbWP0oKZXu7mZz5zOgj/pVfFH1ypjmFf\nL6lBzbyG3d5QLbsqfmDYZ+s+ZyNZVYkgzUp8MtowA/FctffR3nic3g9u0tSwLydq2GO0Ci53g9nE\nc1t71fLYzyp3K8hjbzkyd7lbtx8YsqWvYw9/v3k89qp7skmIzv8RjY/qI54Lbjysx6459uWk2kdp\nwbQKLneDyU0xxnF63+XganV6C50lnivIo3EyEM8NPPblPg3mDsXnoD0pg0l9JYIBUFL5qMTAYw8N\ne7v+v4uSHv3VY0wKxXu+wfNN5h5pu9mg259BPFehUPxaJx6KLyrHPn5gR1I0FB9ghwWN65M+jTwi\nWWUwKWLXc7Od7JgX7SjHHobil/zYXlaqf6QWyCTD3s+ppKfddOim9dgrFopvOw2cMLde5NjW+XPs\nYSh+AYzSPKxpKB6Yfv7XYR8j8ZxVxVekJFYpluofqQUSlbuNuGPv5pRjSyue2+97dF2/Ml3nIOig\nZ41DYUNgGg3cORvU2EjJsufYrcc+a5OaIExd/++wFeuzPkyvJvs4HIpXj305mXqkisilInKbiNwl\nIneKyAvD5S8Tka+LyKfCv6fHXvNSETkmIl8Qke+PLb8hXHZMRF4SW365iNwuIl8SkbeLyIhp6Plj\njfaotpJ5TXfqtMY3xRiFPWGrZNhhkGcvShXfcgR3zuluGooP0Bx7QHwy2jA919RiH+35Z0PxOt1t\nOUlypLrAi40xjwGeANwkIleF615tjLkm/LsVIFx3I3A1cAPwOhFxRMQBXgs8DbgKeFbsfV4ZvteV\nwCngeRntXypaE+rYezk14eg46cRz9oQ9uFId8RwM8uxFeTXZiOc0FA+Bclpk9hx7d0Fy7O0Jofi6\npBtEhHazMfDYlzwataxM/dWNMSeMMZ8IH58B7gIunvCSZwA3G2O6xph/AI4B14V/x4wxdxtjesDN\nwDMkkJleD7wzfP1bgGfOukPzMCnHllc/7LTiuaoNgLGstQPj4BTmsc/fKz4KxS+5x95oCGstZ2aP\nvS7552lE5/+IzpP9GkUl2k5Dy92WnFRHqohcBjwOuD1c9AIR+bSIvElEzg+XXQzcE3vZveGyccuP\nAJvGGHdoeeHYMNZIVWxu4rmUHnvFBsBY1trNwka2QnADMXcdu6s5dsv6HBPeFiUU35oUivf8KKJX\ndVqOYINZ2qBmOUl8NorIBvAu4EXGmNPA7wCPBK4BTgC/ZZ864uVmhuWjtuH5InKHiNxx8uTJpJue\nGBEZ21Yyt1B8ygY19k68SuVuEDQ5KaqGHbJpKauh+AEbnebs5W4L4rHbm5Nxdex1uXmJ/xZ6bC8n\niX51EWkRGPW3GmPeDWCM+YYxxjPG+MDvEoTaIfC4L429/BLg+ITl9wOHRKQ5tPwcjDFvMMZca4y5\n9oILLkiy6alpjamPztVjn8GwV9FjL7JXeEtbymbKWmf2UPyieOyTZkXUpY4dBs5HoJ2oR5RByZYk\nqngB3gjcZYx5VWz5hbGn/XPgs+HjW4AbRaQjIpcDVwIfBT4GXBkq4NsEArtbjDEGuA34kfD1zwXe\nM99uzc643K01vp3My90c3LD5TRJsD+gDVRPPtZ1CL3xWPBccPrMRtZRVr4b19uwz2fueicLYdWai\nxqZGUQm7nSqcW16SWIcnAs8GPiMinwqX/RKBqv0agrD5V4B/A2CMuVNE3gF8jkBRf5MxxgMQkRcA\n7wcc4E3GmDvD9/tF4GYReTnwSYIbiVJojVGp5ymes++fpDTl9F6fTrNRudzZjdc9nO98xPnTn5gR\ntubY9c3MTXE0xz5go9PkG2f2Z3rtonjsUxvU1GQf2zGPXVlOphp2Y8yHGJ0Hv3XCa14BvGLE8ltH\nvc4YczeDUH6ptB2ZWMeeR44dkhv2qg2AsXzXI87nuwo07LYRjusZZr1+5dV0qI6sdZrs3L/k5W7h\nPowao1zHUHzVbv6V4qjHkVogreaYHHvOHnvXS3ZRPb3fr1ypWxnYfP48TWq6rkezIYV1y6syGx1n\n5lB8z/UWIp0xqGMfUe7mmdrcvAxC8WrYl5V6HKkFMjbHnqN4Dkhcy15Vj71oIsM+Ry17t+8vhEHK\ngvV2k915cuwVn1OehGl9LOrjsQe/hebYlxf95YcYq4rPKWzbmVA7O4rTe27lus6VgTNhdnZSuq5P\nR70aIAzF9zz8GSoN6iQsm8Sksc112sd2WOWh7WSXl3ocqQUyto49J4990gzoUWgoPqAVeuzztJXt\nLkgIOQs2wpbAu/10eXY7zrjt1N+ItCeci4F4rh5RCbudKp5bXvSqNsS0crfMp7ulNOwaig+Ii+dm\npetqKN5iB8GkDcfnNc64DAbTHUdH7Oqyj3Y/NBq1vNTjSC2QliPFlruFns4oJe4wxhhO7/Ur13Wu\nDAbiuXlz7Hrxg8F0vrQCum7UkbEe3uwkJvaKr8nYVhhco9RjX17qcaQWyLgce17lbmk89u2ui2+q\n13WuDGz72nn6xXddT2vYQwajW9OF4qPGTTXxZifhNASncW4qzvdNKBCsxz4Oyt3qsb1K9ugvP0R7\ningua89kMAN6+gXVdp07uKriuUkDe5KiofgB62GOfaeXzmPPS3tSFu0RDaqsQLMu+9jSBjVLTz2O\n1AJpOY2RobhuqIrNuvdyGvFcVQfAlEEznCQ3n3hOQ/EWG4pP2y++7+YTySqLliPnnIt56WvyoqN1\n7EtPPY7UAmk1GyNLqPquyeXEntTtapitig6AKQMbip+v3E1V8RYbik+bY184j31EgyobFarLPg7q\n2NWwLyv1OFILZHy5m5fLiW1vFpIY9shjV8Oejcfe9zXHHrJhVfEpR7fWzZudxiiNTV4jm/NCO88p\n9ThSC6TVGB2Kz2vQRapQfJhjV4895rHPJZ6rz2CPvFmzOfYl99hHlbvWraRPc+xKPY7UAmk1x3js\nOdWx2hxvEsO+pTn2iExayrqe5thDZi13WzSPvd1snJtj9/IRzuZFZNjbi/GbKOlRefUQ48a25tUP\ne6CKTx6K39CWslGDmhNbe3z1gZ2Z3mOvp+VuFqchrLac2UPxNfFmpzHq/K/bzUskntOb1qVFLcQQ\n48rdgtGU2Z8oaYbAbO31ObDSxGnUw3PIk/WwD/Yvvuszc72PzS0rQcnbzB77ghj29giNTV1D8Sva\nK35p0avaEJOmu+VxYtumGMnq2LXrnOWKh2zwe8+5ltP7/ZnfoyHC9zzqggy3qt6sd5rpy91yatxU\nFgslnlOPfWlRwz5Ey2ng+QbfNzRinnHfzW8IRGdEXm8Up/dcFc6FiAhPueqhZW/GQrHeTm/YF008\nNynHXpd9HOTY1bAvK/U4Uguk1RxdH53n2MZRF5NRnN7ra9c5JTc2Os3ULWW7Ncs/TyPIsZ8dsaub\nx25bya6rYV9a6nGkFoi9QA2H4/Mqd7OfmaiOXUPxSo6sdZzULWVt2HpRGv0EnSdHN6ipyz5+37c+\nlFf+8LdzxUM2yt4UpSTqcaQWSNSDfERbybI9dh3ZquTJeqc5s3iuLt7sNNojyl3rto+rbYcf/0cP\nz7z9tVIf6nGkFkiraT32c5WxeZ3YnWaDbsJyN+06p+TFRrvJ7ozT3eqSf57GyCEwNcuxK4oeqUNY\n4z18cndz9didqR676/ns9Dz12JXcWOs46cVzC2bYR4Xi69agRlEW42zMkLE5di+/EZ/t5vQcezSy\nVZvTKDmx0Wmy03MxJnk3P+vNNhekt0KrOV48tygCQWXx0SN1iJZTQijeadBzJ4dAdQCMkjfrnSa+\ngf0EzZIseY0zLotRDao0FK/UDT1Sh7DhtlEzmfO6Y++0povnbCMWDcUreWHLo9II6HquT2eBPNmR\ndew1E88pih6pQ1jxnOuPKHfLKxQ/pj99nC312JWcsTPZ0+TZ8zwvymDU2Gb12JW6oUfqEO0RoXjf\nN7i+ybXcbVqv+NN7OrJVyZfIsKeoZc8zRVUGLaeBG3aetFiPfVF0BMriszhnZEaMqmPv5dwPu91M\n4bFrgxolJ+zo1jTd5xbPYw9v7P34+W8WSkegLD6Lc0ZmhA3Fxw1tL+fuWkl6xdscu7aUVfJivRPk\n2FOF4nNstVwG9hyPn499Lz99jaLkgR6tQ4wqd8u7VjdJ57nTe31aTjAzW1HyYGOGUHzPNQtl9Fpj\nzv9FunlRFh89WocYVe4WiWdy6xXvTK1j39oL+sRrOFDJi7VZxHOeH0W5FoFx5782p1HqxOKckRlh\nT+D4iZ13uUsij31fR7Yq+bIR5ti3U+XYvYUqdxtV7tpzF0sgqCw+mrAdImop655r2PMKx3VC8Zwx\nZqxHvrXX54AadiVH1sIc+2fu3eQv7rwv0WtOnuly4XmreW5WobTHaGw0FK/UCTXsQ7RH1LF3C8ix\ng21bOzqHfnqvr+1klVxpOQ2ObrT5k08d508+dTzx666+6Lwct6pYRpW75tmcSlHyQC3FEGXk2ONK\n3LGGfb/PJecvjmekVJP3vfCf8o3T+6le88gLFmfud3T+u4Mb+7567ErNUMM+RHNMjg3y99i7rs+B\nMc/5/9u7/1ir6zqO48/XPfcCghIoWHKBAKOS3BJihsnKUTlQJrrVqlUyZmNrbGHTmdmaq9YfbU2L\nVWxMSd2c1ZAla5ZzgktbMn+QooKTmSKKQhpqwoAL7/74fq4ejt9zf3Dvud/7/d7XY7u75/u5n3PP\n5372Pvd9vp8f369v2WpDYfJpo5l82uiim1GYZttdPcduZeJobZC73a3Fl5TM2ztbLyJ4+5AXz5m1\nWt7iuaMV29Jn1ddrtEqaJmmLpB2SnpG0uuHn10oKSZPSsSStkbRL0lOS5tXVXS7p+fS1vK78M5K2\np+esUYF7ugrZ7tZLYj/cdZwjx477qnNmLdb9Ifto4xm7h+KtRPoSrV3ANRFxDrAAWCVpDmRJH/gy\nsLuu/hJgdvpaCaxNdU8HbgQ+C5wP3ChpYnrO2lS3+3mLB/Znnbxam2jTEG93q6Urfh3p4uix4x/4\neuPdI4CvOmfWankf7LPFc97HbuXRa6aIiL3A3vT4HUk7gE7gWeBm4DrgnrqnLAPuiIgAHpE0QdJZ\nwEXA/RHxJoCk+4HFkh4ExkfEP1P5HcDlwF8H5S88CR0Nd1tr9ar4sel2mZeuebjHehPHjmrJ65tZ\nptmInRfPWZn06xRQ0gxgLrBV0mXAKxHxZMPIeSfwct3xnlTWU/menPK8119JdmbP9OnT+9P0fhlV\na6Mr55KyrbpW/AVnn8GPl87hUA+X8hzTUeMLH5/cktc3s8x717FoWGPjxXNWJn1O7JJOBe4GriYb\nnv8RcHFe1ZyyOInyDxZGrAPWAcyfPz+3zmDoaG9r+MSevVSr3txjOmpctXBmS363mfVd7k1gvI/d\nSqZP0Sqpgyyp3xkRG4GzgZnAk5JeBKYCT0j6CNkZ97S6p08FXu2lfGpOeWE6amqYY8susenhOLNq\ny51j9+I5K5m+rIoXcCuwIyJuAoiI7RFxZkTMiIgZZMl5XkS8BmwCrkyr4xcAb6V5+vuAiyVNTIvm\nLgbuSz97R9KC9FpXcuKc/ZBrb2vjSNfQbXczs+Gh2b0ifMZuZdKXofgLgW8D2yX9K5XdEBH3Nql/\nL3AJsAs4CKwAiIg3Jf0MeDTV+2n3Qjrgu8BtwClki+YKWzgHWQLPG4r3m9us2rrPzB98bj//S3e5\nO3T0mD/UW6n0ZVX8w+TPg9fXmVH3OIBVTeqtB9bnlD8GnNtbW4ZK41D84fe2u3nLi1mVje2oMeVD\nY9i8cx+bd+57r3zWpHEFtsqsf7wxOkdHrS13KM73QjertvZaGw/9YNEJ73+JpvdwMBuOnNhzZPvY\nT9zu5qE4s5Gh1iZqbU7kVl7OVjmyfewnXqDCw/BmZlYGTuw5Otobt7v5jN3MzMrBQ/E5OmptbNt9\ngCt+9w8AXnrjIONGe2jOzMyGPyf2HFfM7eTY8ffn2D81ZTwLPzapwBaZmZn1jRN7jmXndbLsvNzL\n1ZuZmQ1rnjg2MzOrECd2MzOzCnFiNzMzqxAndjMzswpxYjczM6sQJ3YzM7MKcWI3MzOrECd2MzOz\nCnFiNzMzqxAndjMzswpxYjczM6sQJ3YzM7MKcWI3MzOrEEVE77WGIUn7gZcG8VdOAv4ziL9vJHIf\nDpz7cODch4PD/Thwg92HH42Iyb1VKm1iH2ySHouI+UW3o8zchwPnPhw49+HgcD8OXFF96KF4MzOz\nCnFiNzMzqxAn9vetK7oBFeA+HDj34cC5DweH+3HgCulDz7GbmZlViM/YzczMKmTEJ3ZJiyU9J2mX\npOuLbk9ZSJomaYukHZKekbQ6lZ8u6X5Jz6fvE4tu63AnqSZpm6S/pOOZkramPvyjpFFFt3E4kzRB\n0gZJO1M8XuA47B9J30/v46cl3SVpjOOwZ5LWS9on6em6sty4U2ZNyjNPSZrXyraN6MQuqQb8FlgC\nzAG+IWlOsa0qjS7gmog4B1gArEp9dz3wQETMBh5Ix9az1cCOuuNfADenPvwvcFUhrSqPXwN/i4hP\nAp8m60vHYR9J6gS+B8yPiHOBGvB1HIe9uQ1Y3FDWLO6WALPT10pgbSsbNqITO3A+sCsiXoiII8Af\ngGUFt6kUImJvRDyRHr9D9s+0k6z/bk/VbgcuL6aF5SBpKnApcEs6FrAI2JCquA97IGk88HngVoCI\nOBIRB3Ac9lc7cIqkdmAssBfHYY8i4u/Amw3FzeJuGXBHZB4BJkg6q1VtG+mJvRN4ue54TyqzfpA0\nA5gLbAU+HBF7IUv+wJnFtawUfgVcBxxPx2cAByKiKx07Jns2C9gP/D5NZ9wiaRyOwz6LiFeAXwK7\nyRL6W8DjOA5PRrO4G9JcM9ITu3LKvE2gHySdCtwNXB0RbxfdnjKRtBTYFxGP1xfnVHVMNtcOzAPW\nRsRc4F087N4vjiOhqwAAAa5JREFUaR54GTATmAKMIxs6buQ4PHlD+r4e6Yl9DzCt7ngq8GpBbSkd\nSR1kSf3OiNiYil/vHmJK3/cV1b4SuBC4TNKLZNNAi8jO4CekIVFwTPZmD7AnIram4w1kid5x2Hdf\nAv4dEfsj4iiwEfgcjsOT0SzuhjTXjPTE/igwO63+HEW2YGRTwW0qhTQXfCuwIyJuqvvRJmB5erwc\nuGeo21YWEfHDiJgaETPIYm9zRHwT2AJ8JVVzH/YgIl4DXpb0iVT0ReBZHIf9sRtYIGlsel9396Hj\nsP+axd0m4Mq0On4B8Fb3kH0rjPgL1Ei6hOwsqQasj4ifF9ykUpC0EHgI2M7788M3kM2z/wmYTvYP\n46sR0bjAxBpIugi4NiKWSppFdgZ/OrAN+FZEHC6yfcOZpPPIFh+OAl4AVpCdtDgO+0jST4Cvke12\n2QZ8h2wO2HHYhKS7gIvI7uD2OnAj8Gdy4i59YPoN2Sr6g8CKiHisZW0b6YndzMysSkb6ULyZmVml\nOLGbmZlViBO7mZlZhTixm5mZVYgTu5mZWYU4sZuZmVWIE7uZmVmFOLGbmZlVyP8BVVj7h6Hq/EAA\nAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x19eafc128>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "norm_vary = list()\n", "norm_im = list()\n", "lbd = np.linspace(0, 1, 101)\n", "for i in range(101):\n", " test = (lbd[i] * Z[person1]) + ((1-lbd[i]) * Z[person2])\n", " norm_vary.append(norm(test))\n", " pred = lin.predict(test.reshape(1, -1))\n", " im = Image.fromarray(pred.reshape(*img_shape))\n", " norm_im.append(norm(im))\n", "f, ax = plt.subplots(1,1)\n", "ax.plot(norm_im)\n", "ax.set_title('Norm for mean image in original space')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Even after extensive hyperparamter tuning, we are unable to learn a reasonable regressor hence we use the convex combination approach in him dim." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Method 2\n", "\n", "Instead of choosing a path from the graph, manually choosing a set of points which visibbly lie on a 2D manifold. For regeneration of sub-sampled points, we use convex combinations of the consecutive pairs in high dimensional space itself." ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "\n", "#Interesting paths with N4D3 model\n", "#imlist = [1912,3961,2861,4870,146,6648]\n", "#imlist = [3182,5012,5084,1113,2333,1375]\n", "#imlist = [5105,5874,4255,2069,1178]\n", "#imlist = [3583,2134,1034, 3917,3704, 5920,6493]\n", "#imlist = [1678,6535,6699,344,6677,5115,6433]\n", "\n", "#Interesting paths with N2D3 model\n", "imlist = [1959,3432,6709,4103, 4850,6231,4418,4324]\n", "#imlist = [369,2749,1542,366,1436,2836]\n", "\n", "#Interesting paths with N2D2 model\n", "#imlist = [2617,4574,4939,5682,1917,3599,6324,1927]" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [], "source": [ "N=25\n", "lbd = np.linspace(0, 1, N)\n", "counter = 0\n", "for count, i in enumerate(imlist):\n", " if count != len(imlist) - 1:\n", " person1 = i\n", " person2 = imlist[count + 1]\n", " for j in range(N):\n", " im = (lbd[j] * ims[person2]) + ((1 - lbd[j]) * ims[person1])\n", " im = Image.fromarray(im.reshape((218, 178)))\n", " im = im.convert('RGB')\n", " im.save('{}.png'.format(counter))\n", " counter += 1" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "os.system(\"ffmpeg -f image2 -r 10 -i ./%d.png -vcodec mpeg4 -y ./method2.mp4\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Please check the generated video in the same enclosing folder.\n", "\n", "Now we can obbserve that the video has quite smooth transitions in terms of either similar head poses, hair styles or face shapes, etc." ] } ], "metadata": { "kernelspec": { "display_name": "Python [conda root]", "language": "python", "name": "conda-root-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.2" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
ccwang002/2014-ggplot2-intro
Rcode/play_earthquake_map/raw_earthquake.ipynb
4
3058
{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import pandas as pd\n", "import re" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 18 }, { "cell_type": "code", "collapsed": false, "input": [ "df = pd.read_excel(\"earthquake_month.xlsx\", \"2\").dropna()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [ "import datetime as dt" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 29 }, { "cell_type": "code", "collapsed": false, "input": [ "r = re.match(\"(\\d+)\u6708(\\d+)\u65e5(\\d+)\u6642(\\d+)\u5206\", \"2\u670802\u65e502\u664203\u5206\", re.UNICODE).groups()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 31 }, { "cell_type": "code", "collapsed": false, "input": [ "dt.datetime(2013, *(int(d) for d in r))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 35, "text": [ "'2013-02-02'" ] } ], "prompt_number": 35 }, { "cell_type": "code", "collapsed": false, "input": [ "def parse_sheet(df, month):\n", " df.columns = [\"no\", \"date\", \"lon\", \"lat\", \"depth\", \"scale\"]\n", " df[\"date\"] = df.date.apply(\n", " lambda t: dt.datetime(2013, \n", " *(int(d) for d in re.match(\"(\\d+)\u6708(\\d+)\u65e5(\\d+)\u6642(\\d+)\u5206\", t, re.UNICODE).groups())\n", " ))\n", " df[\"month\"] = month\n", " return df" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 39 }, { "cell_type": "code", "collapsed": false, "input": [ "df_month_list = [parse_sheet(pd.read_excel(\"earthquake_month.xlsx\", str(m)).dropna(), str(m)) for m in range(1, 12)]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 41 }, { "cell_type": "code", "collapsed": false, "input": [ "df_combined = pd.concat(df_month_list)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 42 }, { "cell_type": "code", "collapsed": false, "input": [ "df_combined.to_csv(\"earthquake_month_parsed.csv\")" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 47 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
mit
dietmarw/EK5312_ElectricalMachines
Chapman/Ch5-Problem_5-10.ipynb
1
4480
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Excercises Electric Machinery Fundamentals\n", "## Chapter 5" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Problem 5-10" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "slideshow": { "slide_type": "skip" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "%pylab notebook" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Description" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A synchronous machine has a synchronous reactance of $1.0\\,\\Omega$ per phase and an armature resistance of $0.1\\,\\Omega$ per phase. \n", "\n", "* If $\\vec{E}_A = 460\\,V\\angle-10°$ and $\\vec{V}_\\phi = 480\\,V\\angle0°$, is this machine a motor or a generator? \n", "* How much power P is this machine consuming from or supplying to the electrical system?\n", "* How much reactive power Q is this machine consuming from or supplying to the electrical system?" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "Ea = 460 # [V]\n", "EA_angle = -10/180*pi # [rad]\n", "EA = Ea * (cos(EA_angle) + 1j*sin(EA_angle))\n", "Vphi = 480 # [V]\n", "VPhi_angle = 0/180*pi # [rad]\n", "VPhi = Vphi*exp(1j*VPhi_angle)\n", "Ra = 0.1 # [Ohm]\n", "Xs = 1.0 # [Ohm]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### SOLUTION" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**This machine is a motor, consuming power from the power system, because $\\vec{E}_A$ is lagging $\\vec{V}_\\phi$**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is also consuming reactive power, because $E_A \\cos{\\delta} < V_\\phi$ . The current flowing in this machine is:\n", "\n", "$$\\vec{I}_A = \\frac{\\vec{V}_\\phi - \\vec{E}_A}{R_A + jX_s}$$" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "IA = 83.9 A ∠ -12.96°\n" ] } ], "source": [ "IA = (VPhi - EA) / (Ra + Xs*1j)\n", "IA_angle = arctan(IA.imag/IA.real)\n", "print('IA = {:.1f} A ∠ {:.2f}°'.format(abs(IA), IA_angle/pi*180))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Therefore the real power consumed by this motor is:\n", "\n", "$$P =3V_\\phi I_A \\cos{\\theta}$$" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "P = 117.7 kW\n", "============\n" ] } ], "source": [ "theta = abs(IA_angle)\n", "P = 3* abs(VPhi)* abs(IA)* cos(theta)\n", "print('''\n", "P = {:.1f} kW\n", "============'''.format(P/1e3))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and the reactive power consumed by this motor is:\n", "\n", "$$Q = 3V_\\phi I_A \\sin{\\theta}$$" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Q = 27.1 kvar\n", "=============\n" ] } ], "source": [ "Q = 3* abs(VPhi)* abs(IA)* sin(theta)\n", "print('''\n", "Q = {:.1f} kvar\n", "============='''.format(Q/1e3))" ] } ], "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 }
unlicense
mne-tools/mne-tools.github.io
0.21/_downloads/9cb26d39ca23b6aac4c0d201a4775849/plot_brainstorm_data.ipynb
1
3233
{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n\n# Brainstorm raw (median nerve) dataset\n\nHere we compute the evoked from raw for the Brainstorm\ntutorial dataset. For comparison, see [1]_ and:\n\n https://neuroimage.usc.edu/brainstorm/Tutorials/MedianNerveCtf\n\n## References\n.. [1] Tadel F, Baillet S, Mosher JC, Pantazis D, Leahy RM.\n Brainstorm: A User-Friendly Application for MEG/EEG Analysis.\n Computational Intelligence and Neuroscience, vol. 2011, Article ID\n 879716, 13 pages, 2011. doi:10.1155/2011/879716\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Authors: Mainak Jas <[email protected]>\n#\n# License: BSD (3-clause)\n\nimport numpy as np\n\nimport mne\nfrom mne.datasets.brainstorm import bst_raw\nfrom mne.io import read_raw_ctf\n\nprint(__doc__)\n\ntmin, tmax, event_id = -0.1, 0.3, 2 # take right-hand somato\nreject = dict(mag=4e-12, eog=250e-6)\n\ndata_path = bst_raw.data_path()\n\nraw_path = (data_path + '/MEG/bst_raw/' +\n 'subj001_somatosensory_20111109_01_AUX-f.ds')\n# Here we crop to half the length to save memory\nraw = read_raw_ctf(raw_path).crop(0, 180).load_data()\nraw.plot()\n\n# set EOG channel\nraw.set_channel_types({'EEG058': 'eog'})\nraw.set_eeg_reference('average', projection=True)\n\n# show power line interference and remove it\nraw.plot_psd(tmax=60., average=False)\nraw.notch_filter(np.arange(60, 181, 60), fir_design='firwin')\n\nevents = mne.find_events(raw, stim_channel='UPPT001')\n\n# pick MEG channels\npicks = mne.pick_types(raw.info, meg=True, eeg=False, stim=False, eog=True,\n exclude='bads')\n\n# Compute epochs\nepochs = mne.Epochs(raw, events, event_id, tmin, tmax, picks=picks,\n baseline=(None, 0), reject=reject, preload=False)\n\n# compute evoked\nevoked = epochs.average()\n\n# remove physiological artifacts (eyeblinks, heartbeats) using SSP on baseline\nevoked.add_proj(mne.compute_proj_evoked(evoked.copy().crop(tmax=0)))\nevoked.apply_proj()\n\n# fix stim artifact\nmne.preprocessing.fix_stim_artifact(evoked)\n\n# correct delays due to hardware (stim artifact is at 4 ms)\nevoked.shift_time(-0.004)\n\n# plot the result\nevoked.plot(time_unit='s')\n\n# show topomaps\nevoked.plot_topomap(times=np.array([0.016, 0.030, 0.060, 0.070]),\n time_unit='s')" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 0 }
bsd-3-clause
ramabrahma/data-sci-int-capstone
.ipynb_checkpoints/data-exploration-life-insurance-checkpoint.ipynb
1
311728
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Exploration of Prudential Life Insurance Data\n", "\n", "### Data retrieved from: \n", "https://www.kaggle.com/c/prudential-life-insurance-assessment\n", "\n", "\n", "###### File descriptions:\n", "\n", "* train.csv - the training set, contains the Response values\n", "* test.csv - the test set, you must predict the Response variable for all rows in this file\n", "* sample_submission.csv - a sample submission file in the correct format\n", "\n", "###### Data fields:\n", "\n", "Variable |\tDescription\n", "-------- | ------------\n", "Id |\tA unique identifier associated with an application.\n", "Product_Info_1-7 |\tA set of normalized variables relating to the product applied for\n", "Ins_Age |\tNormalized age of applicant\n", "Ht |\tNormalized height of applicant\n", "Wt |\tNormalized weight of applicant\n", "BMI |\tNormalized BMI of applicant\n", "Employment_Info_1-6 |\tA set of normalized variables relating to the employment history of the applicant.\n", "InsuredInfo_1-6 |\tA set of normalized variables providing information about the applicant.\n", "Insurance_History_1-9 |\tA set of normalized variables relating to the insurance history of the applicant.\n", "Family_Hist_1-5 |\tA set of normalized variables relating to the family history of the applicant.\n", "Medical_History_1-41 |\tA set of normalized variables relating to the medical history of the applicant.\n", "Medical_Keyword_1-48 |\tA set of dummy variables relating to the presence of/absence of a medical keyword being associated with the application.\n", "Response |\tThis is the target variable, an ordinal variable relating to the final decision associated with an application\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following variables are all categorical (nominal):\n", "\n", "Product_Info_1, Product_Info_2, Product_Info_3, Product_Info_5, Product_Info_6, Product_Info_7, Employment_Info_2, Employment_Info_3, Employment_Info_5, InsuredInfo_1, InsuredInfo_2, InsuredInfo_3, InsuredInfo_4, InsuredInfo_5, InsuredInfo_6, InsuredInfo_7, Insurance_History_1, Insurance_History_2, Insurance_History_3, Insurance_History_4, Insurance_History_7, Insurance_History_8, Insurance_History_9, Family_Hist_1, Medical_History_2, Medical_History_3, Medical_History_4, Medical_History_5, Medical_History_6, Medical_History_7, Medical_History_8, Medical_History_9, Medical_History_11, Medical_History_12, Medical_History_13, Medical_History_14, Medical_History_16, Medical_History_17, Medical_History_18, Medical_History_19, Medical_History_20, Medical_History_21, Medical_History_22, Medical_History_23, Medical_History_25, Medical_History_26, Medical_History_27, Medical_History_28, Medical_History_29, Medical_History_30, Medical_History_31, Medical_History_33, Medical_History_34, Medical_History_35, Medical_History_36, Medical_History_37, Medical_History_38, Medical_History_39, Medical_History_40, Medical_History_41\n", "\n", "The following variables are continuous:\n", "\n", "Product_Info_4, Ins_Age, Ht, Wt, BMI, Employment_Info_1, Employment_Info_4, Employment_Info_6, Insurance_History_5, Family_Hist_2, Family_Hist_3, Family_Hist_4, Family_Hist_5\n", "\n", "The following variables are discrete:\n", "\n", "Medical_History_1, Medical_History_10, Medical_History_15, Medical_History_24, Medical_History_32\n", "\n", "Medical_Keyword_1-48 are dummy variables." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "My thoughts are as follows:\n", "\n", "The main dependent variable is the Risk Response (1-8)\n", "What are variables are correlated to the risk response?\n", "How do I perform correlation analysis between variables?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Import libraries" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "# Importing libraries\n", "\n", "%pylab inline\n", "%matplotlib inline\n", "import pandas as pd \n", "import matplotlib.pyplot as plt\n", "from matplotlib.colors import LogNorm\n", "from sklearn import preprocessing\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Convert variable data into categorical, continuous, discrete, \n", "# and dummy variable lists the following into a dictionary" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Define categorical data types" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['Id', 'Medical_Keyword_1', 'Medical_Keyword_2', 'Medical_Keyword_3', 'Medical_Keyword_4', 'Medical_Keyword_5', 'Medical_Keyword_6', 'Medical_Keyword_7', 'Medical_Keyword_8', 'Medical_Keyword_9', 'Medical_Keyword_10', 'Medical_Keyword_11', 'Medical_Keyword_12', 'Medical_Keyword_13', 'Medical_Keyword_14', 'Medical_Keyword_15', 'Medical_Keyword_16', 'Medical_Keyword_17', 'Medical_Keyword_18', 'Medical_Keyword_19', 'Medical_Keyword_20', 'Medical_Keyword_21', 'Medical_Keyword_22', 'Medical_Keyword_23', 'Medical_Keyword_24', 'Medical_Keyword_25', 'Medical_Keyword_26', 'Medical_Keyword_27', 'Medical_Keyword_28', 'Medical_Keyword_29', 'Medical_Keyword_30', 'Medical_Keyword_31', 'Medical_Keyword_32', 'Medical_Keyword_33', 'Medical_Keyword_34', 'Medical_Keyword_35', 'Medical_Keyword_36', 'Medical_Keyword_37', 'Medical_Keyword_38', 'Medical_Keyword_39', 'Medical_Keyword_40', 'Medical_Keyword_41', 'Medical_Keyword_42', 'Medical_Keyword_43', 'Medical_Keyword_44', 'Medical_Keyword_45', 'Medical_Keyword_46', 'Medical_Keyword_47', 'Medical_Keyword_48', 'Response']\n" ] } ], "source": [ "s = [\"Product_Info_1, Product_Info_2, Product_Info_3, Product_Info_5, Product_Info_6, Product_Info_7, Employment_Info_2, Employment_Info_3, Employment_Info_5, InsuredInfo_1, InsuredInfo_2, InsuredInfo_3, InsuredInfo_4, InsuredInfo_5, InsuredInfo_6, InsuredInfo_7, Insurance_History_1, Insurance_History_2, Insurance_History_3, Insurance_History_4, Insurance_History_7, Insurance_History_8, Insurance_History_9, Family_Hist_1, Medical_History_2, Medical_History_3, Medical_History_4, Medical_History_5, Medical_History_6, Medical_History_7, Medical_History_8, Medical_History_9, Medical_History_11, Medical_History_12, Medical_History_13, Medical_History_14, Medical_History_16, Medical_History_17, Medical_History_18, Medical_History_19, Medical_History_20, Medical_History_21, Medical_History_22, Medical_History_23, Medical_History_25, Medical_History_26, Medical_History_27, Medical_History_28, Medical_History_29, Medical_History_30, Medical_History_31, Medical_History_33, Medical_History_34, Medical_History_35, Medical_History_36, Medical_History_37, Medical_History_38, Medical_History_39, Medical_History_40, Medical_History_41\",\n", " \"Product_Info_4, Ins_Age, Ht, Wt, BMI, Employment_Info_1, Employment_Info_4, Employment_Info_6, Insurance_History_5, Family_Hist_2, Family_Hist_3, Family_Hist_4, Family_Hist_5\",\n", " \"Medical_History_1, Medical_History_10, Medical_History_15, Medical_History_24, Medical_History_32\"]\n", " \n", "\n", "varTypes = dict()\n", "\n", "\n", "#Very hacky way of inserting and appending ID and Response columns to the required dataframes\n", "#Make this better\n", "\n", "varTypes['categorical'] = s[0].split(', ')\n", "#varTypes['categorical'].insert(0, 'Id')\n", "#varTypes['categorical'].append('Response')\n", "\n", "varTypes['continuous'] = s[1].split(', ')\n", "#varTypes['continuous'].insert(0, 'Id')\n", "#varTypes['continuous'].append('Response')\n", "\n", "varTypes['discrete'] = s[2].split(', ')\n", "#varTypes['discrete'].insert(0, 'Id') \n", "#varTypes['discrete'].append('Response')\n", "\n", "\n", "\n", "varTypes['dummy'] = [\"Medical_Keyword_\"+str(i) for i in range(1,49)]\n", "varTypes['dummy'].insert(0, 'Id')\n", "varTypes['dummy'].append('Response')\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#Prints out each of the the variable types as a check\n", "#for i in iter(varTypes['dummy']):\n", " #print i" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Importing life insurance data set" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following variables are all categorical (nominal):\n", "\n", "Product_Info_1, Product_Info_2, Product_Info_3, Product_Info_5, Product_Info_6, Product_Info_7, Employment_Info_2, Employment_Info_3, Employment_Info_5, InsuredInfo_1, InsuredInfo_2, InsuredInfo_3, InsuredInfo_4, InsuredInfo_5, InsuredInfo_6, InsuredInfo_7, Insurance_History_1, Insurance_History_2, Insurance_History_3, Insurance_History_4, Insurance_History_7, Insurance_History_8, Insurance_History_9, Family_Hist_1, Medical_History_2, Medical_History_3, Medical_History_4, Medical_History_5, Medical_History_6, Medical_History_7, Medical_History_8, Medical_History_9, Medical_History_11, Medical_History_12, Medical_History_13, Medical_History_14, Medical_History_16, Medical_History_17, Medical_History_18, Medical_History_19, Medical_History_20, Medical_History_21, Medical_History_22, Medical_History_23, Medical_History_25, Medical_History_26, Medical_History_27, Medical_History_28, Medical_History_29, Medical_History_30, Medical_History_31, Medical_History_33, Medical_History_34, Medical_History_35, Medical_History_36, Medical_History_37, Medical_History_38, Medical_History_39, Medical_History_40, Medical_History_41\n", "\n", "The following variables are continuous:\n", "Product_Info_4, Ins_Age, Ht, Wt, BMI, Employment_Info_1, Employment_Info_4, Employment_Info_6, Insurance_History_5, Family_Hist_2, Family_Hist_3, Family_Hist_4, Family_Hist_5\n", "\n", "The following variables are discrete:\n", "Medical_History_1, Medical_History_10, Medical_History_15, Medical_History_24, Medical_History_32\n", "\n", "Medical_Keyword_1-48 are dummy variables." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Import training data \n", "d = pd.read_csv('prud_files/train.csv')" ] }, { "cell_type": "code", "execution_count": 93, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "def normalize_df(d):\n", " min_max_scaler = preprocessing.MinMaxScaler()\n", " x = d.values.astype(np.float)\n", " return pd.DataFrame(min_max_scaler.fit_transform(x))" ] }, { "cell_type": "code", "execution_count": 130, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Import training data \n", "d = pd.read_csv('prud_files/train.csv')\n", "\n", "#Separation into groups\n", "\n", "df_cat = pd.DataFrame(d, columns=[\"Id\",\"Response\"]+varTypes[\"categorical\"])\n", "df_disc = pd.DataFrame(d, columns=[\"Id\",\"Response\"]+varTypes[\"categorical\"])\n", "df_cont = pd.DataFrame(d, columns=[\"Id\",\"Response\"]+varTypes[\"categorical\"])" ] }, { "cell_type": "code", "execution_count": 159, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "('could not convert string to float: A8',)\n", "Error on Product_Info_2 w error: could not convert string to float: A8\n" ] } ], "source": [ "d_cat = df_cat.copy()\n", "\n", "#normalizes the columns for binary classification\n", "norm_product_info_2 = [pd.get_dummies(d_cat[\"Product_Info_2\"])]\n", "\n", "a = pd.DataFrame(normalize_df(d_cat[\"Response\"]))\n", "a.columns=[\"nResponse\"]\n", "d_cat = pd.concat([d_cat, a], axis=1, join='outer')\n", "\n", "for x in varTypes[\"categorical\"]:\n", " try:\n", "\n", " a = pd.DataFrame(normalize_df(d_cat[x]))\n", " a.columns=[str(\"n\"+x)]\n", " d_cat = pd.concat([d_cat, a], axis=1, join='outer')\n", " except Exception as e:\n", " print e.args\n", " print \"Error on \"+str(x)+\" w error: \"+str(e)" ] }, { "cell_type": "code", "execution_count": 145, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 163, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>nResponse</th>\n", " <th>nProduct_Info_1</th>\n", " <th>nProduct_Info_3</th>\n", " <th>nProduct_Info_5</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> 1.000000</td>\n", " <td> 0</td>\n", " <td> 0.243243</td>\n", " <td> 0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> 0.428571</td>\n", " <td> 0</td>\n", " <td> 0.675676</td>\n", " <td> 0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> 1.000000</td>\n", " <td> 0</td>\n", " <td> 0.675676</td>\n", " <td> 0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> 1.000000</td>\n", " <td> 0</td>\n", " <td> 0.243243</td>\n", " <td> 0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> 1.000000</td>\n", " <td> 0</td>\n", " <td> 0.675676</td>\n", " <td> 0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " nResponse nProduct_Info_1 nProduct_Info_3 nProduct_Info_5\n", "0 1.000000 0 0.243243 0\n", "1 0.428571 0 0.675676 0\n", "2 1.000000 0 0.675676 0\n", "3 1.000000 0 0.243243 0\n", "4 1.000000 0 0.675676 0" ] }, "execution_count": 163, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\n", "d_cat.iloc[:,62:66].head(5)\n", "\n", "# Normalization of columns\n", "# Create a minimum and maximum processor object" ] }, { "cell_type": "code", "execution_count": 107, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Define various group by data streams\n", "\n", "df = d\n", " \n", "gb_PI2 = df.groupby('Product_Info_1')\n", "gb_PI2 = df.groupby('Product_Info_2')\n", "\n", "gb_Ins_Age = df.groupby('Ins_Age')\n", "gb_Ht = df.groupby('Ht')\n", "gb_Wt = df.groupby('Wt')\n", "\n", "gb_response = df.groupby('Response')\n", "\n" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "collapsed": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Product_Info_1 : ['1, ', '2, ']\n", "Product_Info_2 : ['C2, ', 'B2, ', 'E1, ', 'A4, ', 'B1, ', 'A1, ', 'A3, ', 'A2, ', 'A5, ', 'C1, ', 'A7, ', 'A6, ', 'C3, ', 'A8, ', 'D4, ', 'C4, ', 'D2, ', 'D3, ', 'D1, ']\n", "Product_Info_3 : ['1, ', '2, ', '3, ', '4, ', '5, ', '6, ', '8, ', '9, ', '10, ', '11, ', '12, ', '13, ', '15, ', '16, ', '17, ', '18, ', '19, ', '20, ', '21, ', '22, ', '23, ', '24, ', '26, ', '27, ', '28, ', '29, ', '30, ', '31, ', '32, ', '33, ', '34, ', '36, ', '37, ', '38, ']\n", "Product_Info_5 : ['2, ', '3, ']\n", "Product_Info_6 : ['1, ', '3, ']\n", "Product_Info_7 : ['1, ', '2, ', '3, ']\n", "Employment_Info_2 : ['1, ', '2, ', '3, ', '4, ', '5, ', '6, ', '7, ', '9, ', '10, ', '11, ', '12, ', '13, ', '14, ', '15, ', '16, ', '17, ', '18, ', '19, ', '20, ', '21, ', '22, ', '23, ', '25, ', '26, ', '27, ', '28, ', '29, ', '30, ', '31, ', '32, ', '33, ', '34, ', '35, ', '36, ', '37, ', '38, ']\n", "Employment_Info_3 : ['1, ', '3, ']\n", "Employment_Info_5 : ['2, ', '3, ']\n", "InsuredInfo_1 : ['1, ', '2, ', '3, ']\n", "InsuredInfo_2 : ['2, ', '3, ']\n", "InsuredInfo_3 : ['1, ', '2, ', '3, ', '4, ', '5, ', '6, ', '7, ', '8, ', '9, ', '10, ', '11, ']\n", "InsuredInfo_4 : ['2, ', '3, ']\n", "InsuredInfo_5 : ['1, ', '3, ']\n", "InsuredInfo_6 : ['1, ', '2, ']\n", "InsuredInfo_7 : ['1, ', '3, ']\n", "Insurance_History_1 : ['1, ', '2, ']\n", "Insurance_History_2 : ['1, ', '2, ', '3, ']\n", "Insurance_History_3 : ['1, ', '2, ', '3, ']\n", "Insurance_History_4 : ['1, ', '2, ', '3, ']\n", "Insurance_History_7 : ['1, ', '2, ', '3, ']\n", "Insurance_History_8 : ['1, ', '2, ', '3, ']\n", "Insurance_History_9 : ['1, ', '2, ', '3, ']\n", "Family_Hist_1 : ['1, ', '2, ', '3, ']\n", "Medical_History_2 : ['1, ', '2, ', '3, ', '5, ', '6, ', '7, ', '8, ', '9, ', '10, ', '12, ', '13, ', '14, ', '15, ', '16, ', '17, ', '18, ', '19, ', '20, ', '21, ', '22, ', '23, ', '24, ', '25, ', '26, ', '27, ', '28, ', '29, ', '30, ', '32, ', '33, ', '34, ', '35, ', '36, ', '37, ', '38, ', '39, ', '40, ', '41, ', '42, ', '43, ', '44, ', '45, ', '46, ', '47, ', '48, ', '50, ', '51, ', '52, ', '53, ', '54, ', '55, ', '56, ', '57, ', '58, ', '59, ', '60, ', '61, ', '62, ', '63, ', '64, ', '66, ', '67, ', '68, ', '69, ', '70, ', '71, ', '72, ', '73, ', '74, ', '75, ', '76, ', '77, ', '78, ', '79, ', '81, ', '82, ', '84, ', '85, ', '86, ', '87, ', '88, ', '89, ', '90, ', '91, ', '93, ', '94, ', '95, ', '96, ', '97, ', '98, ', '99, ', '100, ', '101, ', '102, ', '104, ', '105, ', '106, ', '107, ', '108, ', '109, ', '110, ', '111, ', '112, ', '113, ', '114, ', '115, ', '116, ', '117, ', '120, 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', '622, ', '623, ', '624, ', '626, ', '627, ', '628, ', '629, ', '630, ', '631, ', '632, ', '633, ', '634, ', '635, ', '636, ', '637, ', '638, ', '639, ', '640, ', '641, ', '642, ', '643, ', '644, ', '645, ', '646, ', '647, ', '648, ']\n", "Medical_History_3 : ['1, ', '2, ', '3, ']\n", "Medical_History_4 : ['1, ', '2, ']\n", "Medical_History_5 : ['1, ', '2, ', '3, ']\n", "Medical_History_6 : ['1, ', '2, ', '3, ']\n", "Medical_History_7 : ['1, ', '2, ', '3, ']\n", "Medical_History_8 : ['1, ', '2, ', '3, ']\n", "Medical_History_9 : ['1, ', '2, ', '3, ']\n", "Medical_History_11 : ['1, ', '2, ', '3, ']\n", "Medical_History_12 : ['1, ', '2, ', '3, ']\n", "Medical_History_13 : ['1, ', '2, ', '3, ']\n", "Medical_History_14 : ['1, ', '2, ', '3, ']\n", "Medical_History_16 : ['1, ', '2, ', '3, ']\n", "Medical_History_17 : ['1, ', '2, ', '3, ']\n", "Medical_History_18 : ['1, ', '2, ', '3, ']\n", "Medical_History_19 : ['1, ', '2, ', '3, ']\n", "Medical_History_20 : ['1, ', '2, ', '3, ']\n", "Medical_History_21 : ['1, ', '2, ', '3, ']\n", "Medical_History_22 : ['1, ', '2, ']\n", "Medical_History_23 : ['1, ', '2, ', '3, ']\n", "Medical_History_25 : ['1, ', '2, ', '3, ']\n", "Medical_History_26 : ['1, ', '2, ', '3, ']\n", "Medical_History_27 : ['1, ', '2, ', '3, ']\n", "Medical_History_28 : ['1, ', '2, ', '3, ']\n", "Medical_History_29 : ['1, ', '2, ', '3, ']\n", "Medical_History_30 : ['1, ', '2, ', '3, ']\n", "Medical_History_31 : ['1, ', '2, ', '3, ']\n", "Medical_History_33 : ['1, ', '3, ']\n", "Medical_History_34 : ['1, ', '2, ', '3, ']\n", "Medical_History_35 : ['1, ', '2, ', '3, ']\n", "Medical_History_36 : ['1, ', '2, ', '3, ']\n", "Medical_History_37 : ['1, ', '2, ', '3, ']\n", "Medical_History_38 : ['1, ', '2, ']\n", "Medical_History_39 : ['1, ', '2, ', '3, ']\n", "Medical_History_40 : ['1, ', '2, ', '3, ']\n", "Medical_History_41 : ['1, ', '2, ', '3, ']\n", "Response : ['1, ', '2, ', '3, ', '4, ', '5, ', '6, ', '7, ', '8, ']\n" ] } ], "source": [ "#Outputs rows the differnet categorical groups\n", "\n", "for c in df.columns:\n", " if (c in varTypes['categorical']):\n", " if(c != 'Id'):\n", " a = [ str(x)+\", \" for x in df.groupby(c).groups ]\n", " print c + \" : \" + str(a)\n", " \n", " " ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "ename": "TypeError", "evalue": "f() takes at least 2 arguments (2 given)", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-28-49219c99689d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[0mdf_emp_info\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0md\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"Response\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m+\u001b[0m \u001b[1;33m[\u001b[0m \u001b[1;34m\"Employment_Info_\"\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m6\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[0mdf_bio\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0md\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"Response\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"Ins_Age\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"Ht\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"Wt\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"BMI\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mdf_med_kw\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0md\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"Response\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m+\u001b[0m \u001b[1;33m[\u001b[0m \u001b[1;34m\"Medical_Keyword_\"\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m48\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m \u001b[1;34m\"Medical_Keyword_\"\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m48\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 5\u001b[0m \u001b[0mdf_med_kw\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdescribe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mTypeError\u001b[0m: f() takes at least 2 arguments (2 given)" ] } ], "source": [ "df_prod_info = pd.DataFrame(d, columns=([\"Response\"]+ [ \"Product_Info_\"+str(x) for x in range(1,8)])) \n", "df_emp_info = pd.DataFrame(d, columns=([\"Response\"]+ [ \"Employment_Info_\"+str(x) for x in range(1,6)])) \n", "df_bio = pd.DataFrame(d, columns=[\"Response\", \"Ins_Age\", \"Ht\", \"Wt\",\"BMI\"])\n", "df_med_kw = pd.DataFrame(d, columns=([\"Response\"]+ [ \"Medical_Keyword_\"+str(x) for x in range(1,48)])).add(axis=[ \"Medical_Keyword_\"+str(x) for x in range(1,48)])\n", "df_med_kw.describe()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Id</th>\n", " <th>Product_Info_1</th>\n", " <th>Product_Info_2</th>\n", " <th>Product_Info_3</th>\n", " <th>Product_Info_4</th>\n", " <th>Product_Info_5</th>\n", " <th>Product_Info_6</th>\n", " <th>Product_Info_7</th>\n", " <th>Ins_Age</th>\n", " <th>Ht</th>\n", " <th>...</th>\n", " <th>Medical_Keyword_40</th>\n", " <th>Medical_Keyword_41</th>\n", " <th>Medical_Keyword_42</th>\n", " <th>Medical_Keyword_43</th>\n", " <th>Medical_Keyword_44</th>\n", " <th>Medical_Keyword_45</th>\n", " <th>Medical_Keyword_46</th>\n", " <th>Medical_Keyword_47</th>\n", " <th>Medical_Keyword_48</th>\n", " <th>Response</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> 2</td>\n", " <td> 1</td>\n", " <td> D3</td>\n", " <td> 10</td>\n", " <td> 0.076923</td>\n", " <td> 2</td>\n", " <td> 1</td>\n", " <td> 1</td>\n", " <td> 0.641791</td>\n", " <td> 0.581818</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> 8</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> 5</td>\n", " <td> 1</td>\n", " <td> A1</td>\n", " <td> 26</td>\n", " <td> 0.076923</td>\n", " <td> 2</td>\n", " <td> 3</td>\n", " <td> 1</td>\n", " <td> 0.059701</td>\n", " <td> 0.600000</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> 4</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> 6</td>\n", " <td> 1</td>\n", " <td> E1</td>\n", " <td> 26</td>\n", " <td> 0.076923</td>\n", " <td> 2</td>\n", " <td> 3</td>\n", " <td> 1</td>\n", " <td> 0.029851</td>\n", " <td> 0.745455</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> 8</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> 7</td>\n", " <td> 1</td>\n", " <td> D4</td>\n", " <td> 10</td>\n", " <td> 0.487179</td>\n", " <td> 2</td>\n", " <td> 3</td>\n", " <td> 1</td>\n", " <td> 0.164179</td>\n", " <td> 0.672727</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> 8</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> 8</td>\n", " <td> 1</td>\n", " <td> D2</td>\n", " <td> 26</td>\n", " <td> 0.230769</td>\n", " <td> 2</td>\n", " <td> 3</td>\n", " <td> 1</td>\n", " <td> 0.417910</td>\n", " <td> 0.654545</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> 8</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 128 columns</p>\n", "</div>" ], "text/plain": [ " Id Product_Info_1 Product_Info_2 Product_Info_3 Product_Info_4 \\\n", "0 2 1 D3 10 0.076923 \n", "1 5 1 A1 26 0.076923 \n", "2 6 1 E1 26 0.076923 \n", "3 7 1 D4 10 0.487179 \n", "4 8 1 D2 26 0.230769 \n", "\n", " Product_Info_5 Product_Info_6 Product_Info_7 Ins_Age Ht \\\n", "0 2 1 1 0.641791 0.581818 \n", "1 2 3 1 0.059701 0.600000 \n", "2 2 3 1 0.029851 0.745455 \n", "3 2 3 1 0.164179 0.672727 \n", "4 2 3 1 0.417910 0.654545 \n", "\n", " ... Medical_Keyword_40 Medical_Keyword_41 Medical_Keyword_42 \\\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", " Medical_Keyword_43 Medical_Keyword_44 Medical_Keyword_45 \\\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", " Medical_Keyword_46 Medical_Keyword_47 Medical_Keyword_48 Response \n", "0 0 0 0 8 \n", "1 0 0 0 4 \n", "2 0 0 0 8 \n", "3 0 0 0 8 \n", "4 0 0 0 8 \n", "\n", "[5 rows x 128 columns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head(5)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Id</th>\n", " <th>Product_Info_1</th>\n", " <th>Product_Info_3</th>\n", " <th>Product_Info_4</th>\n", " <th>Product_Info_5</th>\n", " <th>Product_Info_6</th>\n", " <th>Product_Info_7</th>\n", " <th>Ins_Age</th>\n", " <th>Ht</th>\n", " <th>Wt</th>\n", " <th>...</th>\n", " <th>Medical_Keyword_40</th>\n", " <th>Medical_Keyword_41</th>\n", " <th>Medical_Keyword_42</th>\n", " <th>Medical_Keyword_43</th>\n", " <th>Medical_Keyword_44</th>\n", " <th>Medical_Keyword_45</th>\n", " <th>Medical_Keyword_46</th>\n", " <th>Medical_Keyword_47</th>\n", " <th>Medical_Keyword_48</th>\n", " <th>Response</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td> 59381.000000</td>\n", " <td> 59381.000000</td>\n", " <td> 59381.000000</td>\n", " <td> 59381.000000</td>\n", " <td> 59381.000000</td>\n", " <td> 59381.000000</td>\n", " <td> 59381.000000</td>\n", " <td> 59381.000000</td>\n", " <td> 59381.000000</td>\n", " <td> 59381.000000</td>\n", " <td>...</td>\n", " <td> 59381.000000</td>\n", " <td> 59381.000000</td>\n", " <td> 59381.000000</td>\n", " <td> 59381.000000</td>\n", " <td> 59381.000000</td>\n", " <td> 59381.000000</td>\n", " <td> 59381.000000</td>\n", " <td> 59381.000000</td>\n", " <td> 59381.000000</td>\n", " <td> 59381.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td> 39507.211515</td>\n", " <td> 1.026355</td>\n", " <td> 24.415655</td>\n", " <td> 0.328952</td>\n", " <td> 2.006955</td>\n", " <td> 2.673599</td>\n", " <td> 1.043583</td>\n", " <td> 0.405567</td>\n", " <td> 0.707283</td>\n", " <td> 0.292587</td>\n", " <td>...</td>\n", " <td> 0.056954</td>\n", " <td> 0.010054</td>\n", " <td> 0.045536</td>\n", " <td> 0.010710</td>\n", " <td> 0.007528</td>\n", " <td> 0.013691</td>\n", " <td> 0.008488</td>\n", " <td> 0.019905</td>\n", " <td> 0.054496</td>\n", " <td> 5.636837</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td> 22815.883089</td>\n", " <td> 0.160191</td>\n", " <td> 5.072885</td>\n", " <td> 0.282562</td>\n", " <td> 0.083107</td>\n", " <td> 0.739103</td>\n", " <td> 0.291949</td>\n", " <td> 0.197190</td>\n", " <td> 0.074239</td>\n", " <td> 0.089037</td>\n", " <td>...</td>\n", " <td> 0.231757</td>\n", " <td> 0.099764</td>\n", " <td> 0.208479</td>\n", " <td> 0.102937</td>\n", " <td> 0.086436</td>\n", " <td> 0.116207</td>\n", " <td> 0.091737</td>\n", " <td> 0.139676</td>\n", " <td> 0.226995</td>\n", " <td> 2.456833</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td> 2.000000</td>\n", " <td> 1.000000</td>\n", " <td> 1.000000</td>\n", " <td> 0.000000</td>\n", " <td> 2.000000</td>\n", " <td> 1.000000</td>\n", " <td> 1.000000</td>\n", " <td> 0.000000</td>\n", " <td> 0.000000</td>\n", " <td> 0.000000</td>\n", " <td>...</td>\n", " <td> 0.000000</td>\n", " <td> 0.000000</td>\n", " <td> 0.000000</td>\n", " <td> 0.000000</td>\n", " <td> 0.000000</td>\n", " <td> 0.000000</td>\n", " <td> 0.000000</td>\n", " <td> 0.000000</td>\n", " <td> 0.000000</td>\n", " <td> 1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td> 19780.000000</td>\n", " <td> 1.000000</td>\n", " <td> 26.000000</td>\n", " <td> 0.076923</td>\n", " <td> 2.000000</td>\n", " <td> 3.000000</td>\n", " <td> 1.000000</td>\n", " <td> 0.238806</td>\n", " <td> 0.654545</td>\n", " <td> 0.225941</td>\n", " <td>...</td>\n", " <td> 0.000000</td>\n", " <td> 0.000000</td>\n", " <td> 0.000000</td>\n", " <td> 0.000000</td>\n", " <td> 0.000000</td>\n", " <td> 0.000000</td>\n", " <td> 0.000000</td>\n", " <td> 0.000000</td>\n", " <td> 0.000000</td>\n", " <td> 4.000000</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td> 39487.000000</td>\n", " <td> 1.000000</td>\n", " <td> 26.000000</td>\n", " <td> 0.230769</td>\n", " <td> 2.000000</td>\n", " <td> 3.000000</td>\n", " <td> 1.000000</td>\n", " <td> 0.402985</td>\n", " <td> 0.709091</td>\n", " <td> 0.288703</td>\n", " <td>...</td>\n", " <td> 0.000000</td>\n", " <td> 0.000000</td>\n", " <td> 0.000000</td>\n", " <td> 0.000000</td>\n", " <td> 0.000000</td>\n", " <td> 0.000000</td>\n", " <td> 0.000000</td>\n", " <td> 0.000000</td>\n", " <td> 0.000000</td>\n", " <td> 6.000000</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td> 59211.000000</td>\n", " <td> 1.000000</td>\n", " <td> 26.000000</td>\n", " <td> 0.487179</td>\n", " <td> 2.000000</td>\n", " <td> 3.000000</td>\n", " <td> 1.000000</td>\n", " <td> 0.567164</td>\n", " <td> 0.763636</td>\n", " <td> 0.345188</td>\n", " <td>...</td>\n", " <td> 0.000000</td>\n", " <td> 0.000000</td>\n", " <td> 0.000000</td>\n", " <td> 0.000000</td>\n", " <td> 0.000000</td>\n", " <td> 0.000000</td>\n", " <td> 0.000000</td>\n", " <td> 0.000000</td>\n", " <td> 0.000000</td>\n", " <td> 8.000000</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td> 79146.000000</td>\n", " <td> 2.000000</td>\n", " <td> 38.000000</td>\n", " <td> 1.000000</td>\n", " <td> 3.000000</td>\n", " <td> 3.000000</td>\n", " <td> 3.000000</td>\n", " <td> 1.000000</td>\n", " <td> 1.000000</td>\n", " <td> 1.000000</td>\n", " <td>...</td>\n", " <td> 1.000000</td>\n", " <td> 1.000000</td>\n", " <td> 1.000000</td>\n", " <td> 1.000000</td>\n", " <td> 1.000000</td>\n", " <td> 1.000000</td>\n", " <td> 1.000000</td>\n", " <td> 1.000000</td>\n", " <td> 1.000000</td>\n", " <td> 8.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>8 rows × 127 columns</p>\n", "</div>" ], "text/plain": [ " Id Product_Info_1 Product_Info_3 Product_Info_4 \\\n", "count 59381.000000 59381.000000 59381.000000 59381.000000 \n", "mean 39507.211515 1.026355 24.415655 0.328952 \n", "std 22815.883089 0.160191 5.072885 0.282562 \n", "min 2.000000 1.000000 1.000000 0.000000 \n", "25% 19780.000000 1.000000 26.000000 0.076923 \n", "50% 39487.000000 1.000000 26.000000 0.230769 \n", "75% 59211.000000 1.000000 26.000000 0.487179 \n", "max 79146.000000 2.000000 38.000000 1.000000 \n", "\n", " Product_Info_5 Product_Info_6 Product_Info_7 Ins_Age \\\n", "count 59381.000000 59381.000000 59381.000000 59381.000000 \n", "mean 2.006955 2.673599 1.043583 0.405567 \n", "std 0.083107 0.739103 0.291949 0.197190 \n", "min 2.000000 1.000000 1.000000 0.000000 \n", "25% 2.000000 3.000000 1.000000 0.238806 \n", "50% 2.000000 3.000000 1.000000 0.402985 \n", "75% 2.000000 3.000000 1.000000 0.567164 \n", "max 3.000000 3.000000 3.000000 1.000000 \n", "\n", " Ht Wt ... Medical_Keyword_40 \\\n", "count 59381.000000 59381.000000 ... 59381.000000 \n", "mean 0.707283 0.292587 ... 0.056954 \n", "std 0.074239 0.089037 ... 0.231757 \n", "min 0.000000 0.000000 ... 0.000000 \n", "25% 0.654545 0.225941 ... 0.000000 \n", "50% 0.709091 0.288703 ... 0.000000 \n", "75% 0.763636 0.345188 ... 0.000000 \n", "max 1.000000 1.000000 ... 1.000000 \n", "\n", " Medical_Keyword_41 Medical_Keyword_42 Medical_Keyword_43 \\\n", "count 59381.000000 59381.000000 59381.000000 \n", "mean 0.010054 0.045536 0.010710 \n", "std 0.099764 0.208479 0.102937 \n", "min 0.000000 0.000000 0.000000 \n", "25% 0.000000 0.000000 0.000000 \n", "50% 0.000000 0.000000 0.000000 \n", "75% 0.000000 0.000000 0.000000 \n", "max 1.000000 1.000000 1.000000 \n", "\n", " Medical_Keyword_44 Medical_Keyword_45 Medical_Keyword_46 \\\n", "count 59381.000000 59381.000000 59381.000000 \n", "mean 0.007528 0.013691 0.008488 \n", "std 0.086436 0.116207 0.091737 \n", "min 0.000000 0.000000 0.000000 \n", "25% 0.000000 0.000000 0.000000 \n", "50% 0.000000 0.000000 0.000000 \n", "75% 0.000000 0.000000 0.000000 \n", "max 1.000000 1.000000 1.000000 \n", "\n", " Medical_Keyword_47 Medical_Keyword_48 Response \n", "count 59381.000000 59381.000000 59381.000000 \n", "mean 0.019905 0.054496 5.636837 \n", "std 0.139676 0.226995 2.456833 \n", "min 0.000000 0.000000 1.000000 \n", "25% 0.000000 0.000000 4.000000 \n", "50% 0.000000 0.000000 6.000000 \n", "75% 0.000000 0.000000 8.000000 \n", "max 1.000000 1.000000 8.000000 \n", "\n", "[8 rows x 127 columns]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Grouping of various categorical data sets" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Histograms and descriptive statistics for Risk Response, Ins_Age, BMI, Wt" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "count 59381.000000\n", "mean 5.636837\n", "std 2.456833\n", "min 1.000000\n", "25% 4.000000\n", "50% 6.000000\n", "75% 8.000000\n", "max 8.000000\n", "Name: Response, dtype: float64\n", "\n", "count 59381.000000\n", "mean 0.405567\n", "std 0.197190\n", "min 0.000000\n", "25% 0.238806\n", "50% 0.402985\n", "75% 0.567164\n", "max 1.000000\n", "Name: Ins_Age, dtype: float64\n", "\n", "count 59381.000000\n", "mean 0.469462\n", "std 0.122213\n", "min 0.000000\n", "25% 0.385517\n", "50% 0.451349\n", "75% 0.532858\n", "max 1.000000\n", "Name: BMI, dtype: float64\n", "\n", "count 59381.000000\n", "mean 0.292587\n", "std 0.089037\n", "min 0.000000\n", "25% 0.225941\n", "50% 0.288703\n", "75% 0.345188\n", "max 1.000000\n", "Name: Wt, dtype: float64\n", "\n" ] }, { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAZEAAAEZCAYAAABWwhjiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAALEgAACxIB0t1+/AAAIABJREFUeJzt3XmcHFW5//HPN4QEAoGIbCFACBiWKEqILIpiXC4XFFnu\n", 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"plt.xlabel(\"Normalized BMI [0,1]\")\n", "plt.ylabel(\"Frequency\")\n", "plt.hist(df.BMI)\n", "plt.savefig('images/hist_BMI.png')\n", "print df.BMI.describe()\n", "print \"\"\n", "\n", "plt.figure(3)\n", "plt.title(\"Continuous - Histogram for Wt\")\n", "plt.xlabel(\"Normalized Wt [0,1]\")\n", "plt.ylabel(\"Frequency\")\n", "plt.hist(df.Wt)\n", "plt.savefig('images/hist_Wt.png')\n", "print df.Wt.describe()\n", "print \"\"\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Histograms and descriptive statistics for Product_Info_1-7" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The iteration is: 1\n", "count 59381.000000\n", "mean 1.026355\n", "std 0.160191\n", "min 1.000000\n", "25% 1.000000\n", "50% 1.000000\n", "75% 1.000000\n", "max 2.000000\n", "Name: Product_Info_1, dtype: float64\n", "\n", "The iteration is: 2\n", "count 59381\n", "unique 19\n", "top D3\n", "freq 14321\n", "Name: Product_Info_2, dtype: object\n", "\n", "The iteration is: 3\n", "count 59381.000000\n", "mean 24.415655\n", "std 5.072885\n", "min 1.000000\n", "25% 26.000000\n", "50% 26.000000\n", "75% 26.000000\n", "max 38.000000\n", "Name: Product_Info_3, dtype: float64\n", "\n", "The iteration is: 4\n", "count 59381.000000\n", "mean 0.328952\n", "std 0.282562\n", "min 0.000000\n", "25% 0.076923\n", "50% 0.230769\n", "75% 0.487179\n", "max 1.000000\n", "Name: Product_Info_4, dtype: float64\n", "\n", "The iteration is: 5\n", "count 59381.000000\n", "mean 2.006955\n", "std 0.083107\n", "min 2.000000\n", "25% 2.000000\n", "50% 2.000000\n", "75% 2.000000\n", "max 3.000000\n", "Name: Product_Info_5, dtype: float64\n", "\n", "The iteration is: 6\n", "count 59381.000000\n", "mean 2.673599\n", "std 0.739103\n", "min 1.000000\n", "25% 3.000000\n", "50% 3.000000\n", "75% 3.000000\n", "max 3.000000\n", "Name: Product_Info_6, dtype: float64\n", "\n", "The iteration is: 7\n", "count 59381.000000\n", "mean 1.043583\n", "std 0.291949\n", "min 1.000000\n", "25% 1.000000\n", "50% 1.000000\n", "75% 1.000000\n", "max 3.000000\n", "Name: Product_Info_7, dtype: float64\n", "\n" ] }, { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAZYAAAEZCAYAAAC0HgObAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xu4HFWZ7/HvjwTCLSTGaMiViwYkKggI0VFxI8qJ6OGi\n", "KAgyUXO8EBV0HEdQHKLjBWbOgHhGOIooATUSRQE1BwlINIPGAAaIhEiCRHIhGwgYIiLm8p4/1mpS\n", "6exL71DVnd75fZ6nn1216vau6t71dtVaXaWIwMzMrCw7tToAMzPrX5xYzMysVE4sZmZWKicWMzMr\n", "lROLmZmVyonFzMxK5cRipZO0TtK+z3EdV0r6t3Ii6nL943Kcqmob7UDSFyQ9KmlVk7e7r6RNkrbL\n", "Y5CkEZJ+JelJSf/R6njazXb5phpIOk3SHfngt0rSLEmvaXDZTZL2rzrG7kTE4IhY9lxXk1991t1B\n", "q5isIuKhHGeP25D0HklztyWO7Z2kccA/AS+JiFFdTO/I+3FdPsAulvSepgfag758AZG0TNIxDa76\n", "A8AjEbFXRHxyG2PbWdIPJT2Y9+Prt2U97ciJZTsk6Z+Ai4EvAC8ExgJfA47vy2oqCK3nDUoDy15l\n", "yevb5mRVBWUtDGEcsCYi1vQwz8qcgPcCPgVcLumg+pkkDagqyBL15f3fB7ivhG3+Cng3sLoP225/\n", 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df['Product_Info_'+str(i)].describe()\n", " print \"\"\n", " \n", " plt.figure(i)\n", "\n", " if(i == 4):\n", " plt.title(\"Continuous - Histogram for Product_Info_\"+str(i))\n", " plt.xlabel(\"Normalized value: [0,1]\")\n", " plt.ylabel(\"Frequency\")\n", " else:\n", " plt.title(\"Categorical - Histogram of Product_Info_\"+str(i))\n", " plt.xlabel(\"Categories\")\n", " plt.ylabel(\"Frequency\")\n", " \n", " if(i == 2):\n", " df.Product_Info_2.value_counts().plot(kind='bar')\n", " else:\n", " plt.hist(df['Product_Info_'+str(i)])\n", " \n", " plt.savefig('images/hist_Product_Info_'+str(i)+'.png')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Split dataframes into categorical, continuous, discrete, dummy, and response" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "catD = df.loc[:,varTypes['categorical']]\n", "contD = df.loc[:,varTypes['continuous']]\n", "disD = df.loc[:,varTypes['discrete']]\n", "dummyD = df.loc[:,varTypes['dummy']]\n", "respD = df.loc[:,['id','Response']]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Descriptive statistics and scatter plot relating Product_Info_2 and Response" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "prod_info = [ \"Product_Info_\"+str(i) for i in range(1,8)]\n", "\n", "a = catD.loc[:, prod_info[1]]\n", "\n", "stats = catD.groupby(prod_info[1]).describe()" ] }, { "cell_type": "code", "execution_count": 192, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.collections.PathCollection at 0x419ea828>" ] }, "execution_count": 192, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAYoAAAETCAYAAAAoF0GbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAALEgAACxIB0t1+/AAAFaFJREFUeJzt3X/wZXV93/HnywUaoklWUAFhlYksijrqRkPpVMMXUnDd\n", 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"BoUkqcugkCR1GRSSpC6DQpLUZVBIkrr+H57znrZs+nACAAAAAElFTkSuQmCC\n" ], "text/plain": [ "<matplotlib.figure.Figure at 0x34ee4898>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "c = gb_PI2.Response.count()\n", "plt.figure(0)\n", "\n", "plt.scatter(c[0],c[1])" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'Product_Info_3'" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "plt.figure(0)\n", "plt.title(\"Histogram of \"+\"Product_Info_\"+str(i))\n", "plt.xlabel(\"Categories \" + str((a.describe())['count']))\n", "plt.ylabel(\"Frequency\")" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "count 59381.000000\n", "mean 1.026355\n", "std 0.160191\n", "min 1.000000\n", "25% 1.000000\n", "50% 1.000000\n", "75% 1.000000\n", "max 2.000000\n", "Name: Product_Info_1, dtype: float64\n", "\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-61-64afcafe78d9>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 9\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 10\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Histogram of \"\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;34m\"Product_Info_\"\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 11\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Categories \"\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcatD\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdescribe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'count'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 12\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Frequency\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 13\u001b[0m \u001b[1;31m#fig, axes = plt.subplots(nrows = 1, ncols = 2)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", 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series.mean(), series.std(), series.min()] +\n\u001b[1;32m-> 3795\u001b[1;33m [series.quantile(x) for x in percentiles] + [series.max()])\n\u001b[0m\u001b[0;32m 3796\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0md\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mstat_index\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mseries\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3797\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32mC:\\Users\\Robbie\\Anaconda\\lib\\site-packages\\pandas\\core\\series.pyc\u001b[0m in \u001b[0;36mquantile\u001b[1;34m(self, q)\u001b[0m\n\u001b[0;32m 1264\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0m_quantile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m 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\u001b[0mqs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1260\u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[0mcom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_list_like\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mqs\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 1261\u001b[0m return Series([_quantile(values, x*100)\n\u001b[0;32m 1262\u001b[0m for x in qs], index=qs)\n", "\u001b[1;32mC:\\Users\\Robbie\\Anaconda\\lib\\site-packages\\pandas\\core\\common.pyc\u001b[0m in \u001b[0;36mis_list_like\u001b[1;34m(arg)\u001b[0m\n\u001b[0;32m 2501\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2502\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mis_list_like\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2503\u001b[1;33m return (hasattr(arg, '__iter__') and\n\u001b[0m\u001b[0;32m 2504\u001b[0m not isinstance(arg, compat.string_and_binary_types))\n\u001b[0;32m 2505\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mKeyboardInterrupt\u001b[0m: " ] }, { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEKCAYAAADpfBXhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAALEgAACxIB0t1+/AAAE5BJREFUeJzt3X+QXWddx/H3x6SgtZQfxkFNU4oQ+SXtoENoQWSxHQlV\n", "6IgKhIL8GqxoGXVGLaC2QQYUB8dfaK21VGTGZkZgJGBtB4EMFfqDjrSlNOkkQmqSQqE/KIVWTOjX\n", "P84Jub3d3Xt3c/fu5un7NbPTe/Y89znf++y9n332OeekqSokSW35nuUuQJI0eYa7JDXIcJekBhnu\n", "ktQgw12SGmS4S1KDDPeGJbkxyU8vdx3LKckvJNmT5J4kJ0352LuTnDrNYy5EkouT3JnkquWuRZNn\n", "uB+hZguOJK9JcsXB7ar68ar61Ih+Tkhyf5JW3wvvBn69qh5RVdcP7+xf+zf78N+b5M8mOBbVfy3a\n", "8M90RNt/TPL2Mds+FzgN+JGqOvkw6nt7ks8n2Z/kvMX2o8lr9QP9UHDYwTEkE+zrUKfJqqXod8xj\n", "BzgeuGlE0xOr6hHAqcArgDfM0tfqyVc4cQt5TzwO2F1V/3uYx9wJ/C7wbws4tqbAcG/LAz5c/ez+\n", "Z/rHG5Jcm+TuJF9J8u6+2cGZ/df72euz0vmD/vm3JXlfkmMH+v2VJLckuX2g3cHjbE7ygSTvT3I3\n", 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plt.figure(i)\n", " plt.title(\"Histogram of \"+\"Product_Info_\"+str(i))\n", " plt.xlabel(\"Categories \" + str((catD.groupby(key).describe())['count']))\n", " plt.ylabel(\"Frequency\")\n", " \n", " #fig, axes = plt.subplots(nrows = 1, ncols = 2)\n", " #catD[key].value_counts(normalize=True).hist(ax=axes[0]); axes[0].set_title(\"Histogram: \"+str(key))\n", " #catD[key].value_counts(normalize=True).hist(cumulative=True,ax=axes[1]); axes[1].set_title(\"Cumulative HG: \"+str(key))\n", " \n", " if a.dtype in (np.int64, np.float, float, int):\n", " a.hist()\n", " \n", "# Random functions\n", "#catD.Product_Info_1.describe()\n", "#catD.loc[:, prod_info].groupby('Product_Info_2').describe()\n", "#df[varTypes['categorical']].hist()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Id</th>\n", " <th>Product_Info_1</th>\n", " <th>Product_Info_2</th>\n", " <th>Product_Info_3</th>\n", " <th>Product_Info_5</th>\n", " <th>Product_Info_6</th>\n", " <th>Product_Info_7</th>\n", " <th>Employment_Info_2</th>\n", " <th>Employment_Info_3</th>\n", " <th>Employment_Info_5</th>\n", " <th>...</th>\n", " <th>Medical_History_33</th>\n", " <th>Medical_History_34</th>\n", " <th>Medical_History_35</th>\n", " <th>Medical_History_36</th>\n", " <th>Medical_History_37</th>\n", " <th>Medical_History_38</th>\n", " <th>Medical_History_39</th>\n", " <th>Medical_History_40</th>\n", " <th>Medical_History_41</th>\n", " <th>Response</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> 2</td>\n", " <td> 1</td>\n", " <td> D3</td>\n", " <td> 10</td>\n", " <td> 2</td>\n", " <td> 1</td>\n", " <td> 1</td>\n", " <td> 12</td>\n", " <td> 1</td>\n", " <td> 3</td>\n", " <td>...</td>\n", " <td> 1</td>\n", " <td> 3</td>\n", " <td> 1</td>\n", " <td> 2</td>\n", " <td> 2</td>\n", " <td> 1</td>\n", " <td> 3</td>\n", " <td> 3</td>\n", " <td> 3</td>\n", " <td> 8</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> 5</td>\n", " <td> 1</td>\n", " <td> A1</td>\n", " <td> 26</td>\n", " <td> 2</td>\n", " <td> 3</td>\n", " <td> 1</td>\n", " <td> 1</td>\n", " <td> 3</td>\n", " <td> 2</td>\n", " <td>...</td>\n", " <td> 3</td>\n", " <td> 1</td>\n", " <td> 1</td>\n", " <td> 2</td>\n", " <td> 2</td>\n", " <td> 1</td>\n", " <td> 3</td>\n", " <td> 3</td>\n", " <td> 1</td>\n", " <td> 4</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> 6</td>\n", " <td> 1</td>\n", " <td> E1</td>\n", " <td> 26</td>\n", " <td> 2</td>\n", " <td> 3</td>\n", " <td> 1</td>\n", " <td> 9</td>\n", " <td> 1</td>\n", " <td> 2</td>\n", " <td>...</td>\n", " <td> 3</td>\n", " <td> 3</td>\n", " <td> 1</td>\n", " <td> 3</td>\n", " <td> 2</td>\n", " <td> 1</td>\n", " <td> 3</td>\n", " <td> 3</td>\n", " <td> 1</td>\n", " <td> 8</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> 7</td>\n", " <td> 1</td>\n", " <td> D4</td>\n", " <td> 10</td>\n", " <td> 2</td>\n", " <td> 3</td>\n", " <td> 1</td>\n", " <td> 9</td>\n", " <td> 1</td>\n", " <td> 3</td>\n", " <td>...</td>\n", " <td> 3</td>\n", " <td> 3</td>\n", " <td> 1</td>\n", " <td> 2</td>\n", " <td> 2</td>\n", " <td> 1</td>\n", " <td> 3</td>\n", " <td> 3</td>\n", " <td> 1</td>\n", " <td> 8</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> 8</td>\n", " <td> 1</td>\n", " <td> D2</td>\n", " <td> 26</td>\n", " <td> 2</td>\n", " <td> 3</td>\n", " <td> 1</td>\n", " <td> 9</td>\n", " <td> 1</td>\n", " <td> 2</td>\n", " <td>...</td>\n", " <td> 3</td>\n", " <td> 3</td>\n", " <td> 1</td>\n", " <td> 3</td>\n", " <td> 2</td>\n", " <td> 1</td>\n", " <td> 3</td>\n", " <td> 3</td>\n", " <td> 1</td>\n", " <td> 8</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 62 columns</p>\n", "</div>" ], "text/plain": [ " Id Product_Info_1 Product_Info_2 Product_Info_3 Product_Info_5 \\\n", "0 2 1 D3 10 2 \n", "1 5 1 A1 26 2 \n", "2 6 1 E1 26 2 \n", "3 7 1 D4 10 2 \n", "4 8 1 D2 26 2 \n", "\n", " Product_Info_6 Product_Info_7 Employment_Info_2 Employment_Info_3 \\\n", "0 1 1 12 1 \n", "1 3 1 1 3 \n", "2 3 1 9 1 \n", "3 3 1 9 1 \n", "4 3 1 9 1 \n", "\n", " Employment_Info_5 ... Medical_History_33 Medical_History_34 \\\n", "0 3 ... 1 3 \n", "1 2 ... 3 1 \n", "2 2 ... 3 3 \n", "3 3 ... 3 3 \n", "4 2 ... 3 3 \n", "\n", " Medical_History_35 Medical_History_36 Medical_History_37 \\\n", "0 1 2 2 \n", "1 1 2 2 \n", "2 1 3 2 \n", "3 1 2 2 \n", "4 1 3 2 \n", "\n", " Medical_History_38 Medical_History_39 Medical_History_40 \\\n", "0 1 3 3 \n", "1 1 3 3 \n", "2 1 3 3 \n", "3 1 3 3 \n", "4 1 3 3 \n", "\n", " Medical_History_41 Response \n", "0 3 8 \n", "1 1 4 \n", "2 1 8 \n", "3 1 8 \n", "4 1 8 \n", "\n", "[5 rows x 62 columns]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "catD.head(5)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Id</th>\n", " <th>Medical_History_1</th>\n", " <th>Medical_History_10</th>\n", " <th>Medical_History_15</th>\n", " <th>Medical_History_24</th>\n", " <th>Medical_History_32</th>\n", " <th>Response</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td> 59381.000000</td>\n", " <td> 50492.000000</td>\n", " <td> 557.000000</td>\n", " <td> 14785.000000</td>\n", " <td> 3801.000000</td>\n", " <td> 1107.000000</td>\n", " <td> 59381.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td> 39507.211515</td>\n", " <td> 7.962172</td>\n", " <td> 141.118492</td>\n", " <td> 123.760974</td>\n", " <td> 50.635622</td>\n", " <td> 11.965673</td>\n", " <td> 5.636837</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td> 22815.883089</td>\n", " <td> 13.027697</td>\n", " <td> 107.759559</td>\n", " <td> 98.516206</td>\n", " <td> 78.149069</td>\n", " <td> 38.718774</td>\n", " <td> 2.456833</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td> 2.000000</td>\n", " <td> 0.000000</td>\n", " <td> 0.000000</td>\n", " <td> 0.000000</td>\n", " <td> 0.000000</td>\n", " <td> 0.000000</td>\n", " <td> 1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td> 19780.000000</td>\n", " <td> 2.000000</td>\n", " <td> 8.000000</td>\n", " <td> 17.000000</td>\n", " <td> 1.000000</td>\n", " <td> 0.000000</td>\n", " <td> 4.000000</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td> 39487.000000</td>\n", " <td> 4.000000</td>\n", " <td> 229.000000</td>\n", " <td> 117.000000</td>\n", " <td> 8.000000</td>\n", " <td> 0.000000</td>\n", " <td> 6.000000</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td> 59211.000000</td>\n", " <td> 9.000000</td>\n", " <td> 240.000000</td>\n", " <td> 240.000000</td>\n", " <td> 64.000000</td>\n", " <td> 2.000000</td>\n", " <td> 8.000000</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td> 79146.000000</td>\n", " <td> 240.000000</td>\n", " <td> 240.000000</td>\n", " <td> 240.000000</td>\n", " <td> 240.000000</td>\n", " <td> 240.000000</td>\n", " <td> 8.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Id Medical_History_1 Medical_History_10 \\\n", "count 59381.000000 50492.000000 557.000000 \n", "mean 39507.211515 7.962172 141.118492 \n", "std 22815.883089 13.027697 107.759559 \n", "min 2.000000 0.000000 0.000000 \n", "25% 19780.000000 2.000000 8.000000 \n", "50% 39487.000000 4.000000 229.000000 \n", "75% 59211.000000 9.000000 240.000000 \n", "max 79146.000000 240.000000 240.000000 \n", "\n", " Medical_History_15 Medical_History_24 Medical_History_32 \\\n", "count 14785.000000 3801.000000 1107.000000 \n", "mean 123.760974 50.635622 11.965673 \n", "std 98.516206 78.149069 38.718774 \n", "min 0.000000 0.000000 0.000000 \n", "25% 17.000000 1.000000 0.000000 \n", "50% 117.000000 8.000000 0.000000 \n", "75% 240.000000 64.000000 2.000000 \n", "max 240.000000 240.000000 240.000000 \n", "\n", " Response \n", "count 59381.000000 \n", "mean 5.636837 \n", "std 2.456833 \n", "min 1.000000 \n", "25% 4.000000 \n", "50% 6.000000 \n", "75% 8.000000 \n", "max 8.000000 " ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Exploration of the discrete data\n", "disD.describe()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Id</th>\n", " <th>Medical_History_1</th>\n", " <th>Medical_History_10</th>\n", " <th>Medical_History_15</th>\n", " <th>Medical_History_24</th>\n", " <th>Medical_History_32</th>\n", " <th>Response</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> 2</td>\n", " <td> 4</td>\n", " <td>NaN</td>\n", " <td> 240</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td> 8</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> 5</td>\n", " <td> 5</td>\n", " <td>NaN</td>\n", " <td> 0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td> 4</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> 6</td>\n", " <td> 10</td>\n", " <td>NaN</td>\n", " <td> NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td> 8</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> 7</td>\n", " <td> 0</td>\n", " <td>NaN</td>\n", " <td> NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td> 8</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> 8</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td> NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td> 8</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Id Medical_History_1 Medical_History_10 Medical_History_15 \\\n", "0 2 4 NaN 240 \n", "1 5 5 NaN 0 \n", "2 6 10 NaN NaN \n", "3 7 0 NaN NaN \n", "4 8 NaN NaN NaN \n", "\n", " Medical_History_24 Medical_History_32 Response \n", "0 NaN NaN 8 \n", "1 NaN NaN 4 \n", "2 NaN NaN 8 \n", "3 NaN NaN 8 \n", "4 NaN NaN 8 " ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "disD.head(5)" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "collapsed": false }, "outputs": [ { "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-60-515469ef3abc>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 8\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 9\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Histogram of \"\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 10\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Categories \"\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdescribe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'count'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 11\u001b[0m \u001b[1;31m#fig, axes = plt.subplots(nrows = 1, ncols = 2)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 12\u001b[0m \u001b[1;31m#catD[key].value_counts(normalize=True).hist(ax=axes[0]); axes[0].set_title(\"Histogram: \"+str(key))\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32mC:\\Users\\Robbie\\Anaconda\\lib\\site-packages\\pandas\\core\\groupby.pyc\u001b[0m in \u001b[0;36mdescribe\u001b[1;34m(self, percentile_width, percentiles, include, exclude)\u001b[0m\n", "\u001b[1;32mC:\\Users\\Robbie\\Anaconda\\lib\\site-packages\\pandas\\core\\groupby.pyc\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 559\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 560\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 561\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcurried\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 562\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 563\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32mC:\\Users\\Robbie\\Anaconda\\lib\\site-packages\\pandas\\core\\groupby.pyc\u001b[0m in \u001b[0;36mapply\u001b[1;34m(self, func, *args, **kwargs)\u001b[0m\n\u001b[0;32m 660\u001b[0m \u001b[1;31m# ignore SettingWithCopy here in case the user mutates\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 661\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0moption_context\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'mode.chained_assignment'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 662\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_python_apply_general\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 663\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 664\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_python_apply_general\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32mC:\\Users\\Robbie\\Anaconda\\lib\\site-packages\\pandas\\core\\groupby.pyc\u001b[0m in \u001b[0;36m_python_apply_general\u001b[1;34m(self, f)\u001b[0m\n\u001b[0;32m 664\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_python_apply_general\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 665\u001b[0m keys, values, mutated = self.grouper.apply(f, self._selected_obj,\n\u001b[1;32m--> 666\u001b[1;33m self.axis)\n\u001b[0m\u001b[0;32m 667\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 668\u001b[0m return self._wrap_applied_output(keys, values,\n", "\u001b[1;32mC:\\Users\\Robbie\\Anaconda\\lib\\site-packages\\pandas\\core\\groupby.pyc\u001b[0m in \u001b[0;36mapply\u001b[1;34m(self, f, data, axis)\u001b[0m\n\u001b[0;32m 1272\u001b[0m hasattr(splitter, 'fast_apply') and axis == 0):\n\u001b[0;32m 1273\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1274\u001b[1;33m \u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmutated\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msplitter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfast_apply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgroup_keys\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1275\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mgroup_keys\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmutated\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1276\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mInvalidApply\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32mC:\\Users\\Robbie\\Anaconda\\lib\\site-packages\\pandas\\core\\groupby.pyc\u001b[0m in \u001b[0;36mfast_apply\u001b[1;34m(self, f, names)\u001b[0m\n\u001b[0;32m 3444\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3445\u001b[0m \u001b[0msdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_sorted_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3446\u001b[1;33m \u001b[0mresults\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmutated\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_frame_axis0\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnames\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstarts\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mends\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3447\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3448\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mresults\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmutated\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32mpandas\\src\\reduce.pyx\u001b[0m in \u001b[0;36mpandas.lib.apply_frame_axis0 (pandas\\lib.c:38246)\u001b[1;34m()\u001b[0m\n", "\u001b[1;32mC:\\Users\\Robbie\\Anaconda\\lib\\site-packages\\pandas\\core\\groupby.pyc\u001b[0m in \u001b[0;36mf\u001b[1;34m(g)\u001b[0m\n\u001b[0;32m 656\u001b[0m \u001b[1;33m@\u001b[0m\u001b[0mwraps\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 657\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 658\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mg\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 659\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 660\u001b[0m \u001b[1;31m# ignore SettingWithCopy here in case the user mutates\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32mC:\\Users\\Robbie\\Anaconda\\lib\\site-packages\\pandas\\core\\groupby.pyc\u001b[0m in \u001b[0;36mcurried\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m 544\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 545\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mcurried\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 546\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 547\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 548\u001b[0m \u001b[1;31m# preserve the name so we can detect it when calling plot methods,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32mC:\\Users\\Robbie\\Anaconda\\lib\\site-packages\\pandas\\core\\generic.pyc\u001b[0m in \u001b[0;36mdescribe\u001b[1;34m(self, percentile_width, percentiles, include, exclude)\u001b[0m\n\u001b[0;32m 3841\u001b[0m \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mselect_dtypes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minclude\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0minclude\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexclude\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mexclude\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3842\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3843\u001b[1;33m \u001b[0mldesc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mdescribe_1d\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpercentiles\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0m_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ms\u001b[0m \u001b[1;32min\u001b[0m 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"\u001b[1;32mC:\\Users\\Robbie\\Anaconda\\lib\\site-packages\\pandas\\core\\generic.pyc\u001b[0m in \u001b[0;36mdescribe_numeric_1d\u001b[1;34m(series, percentiles)\u001b[0m\n\u001b[0;32m 3794\u001b[0m d = ([series.count(), series.mean(), series.std(), series.min()] +\n\u001b[0;32m 3795\u001b[0m [series.quantile(x) for x in percentiles] + [series.max()])\n\u001b[1;32m-> 3796\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0md\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mstat_index\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mseries\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3797\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3798\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32mC:\\Users\\Robbie\\Anaconda\\lib\\site-packages\\pandas\\core\\series.pyc\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, data, index, dtype, name, copy, fastpath)\u001b[0m\n\u001b[0;32m 129\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 130\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mindex\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 131\u001b[1;33m \u001b[0mindex\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_ensure_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 132\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 133\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mdata\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32mC:\\Users\\Robbie\\Anaconda\\lib\\site-packages\\pandas\\core\\index.pyc\u001b[0m in 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in \u001b[0;36m_reset_identity\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 249\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_reset_identity\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 250\u001b[0m \u001b[1;34m\"\"\"Initializes or resets ``_id`` attribute with new object\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 251\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_id\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_Identity\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 252\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 253\u001b[0m \u001b[1;31m# ndarray compat\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mKeyboardInterrupt\u001b[0m: " ] }, { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEKCAYAAADpfBXhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", 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str((df.groupby(key).describe())['count']))\n", " #fig, axes = plt.subplots(nrows = 1, ncols = 2)\n", " #catD[key].value_counts(normalize=True).hist(ax=axes[0]); axes[0].set_title(\"Histogram: \"+str(key))\n", " #catD[key].value_counts(normalize=True).hist(cumulative=True,ax=axes[1]); axes[1].set_title(\"Cumulative HG: \"+str(key))\n", " if df[key].dtype in (np.int64, np.float, float, int):\n", " df[key].hist()\n", " \n", " i+=1" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "\n", "#Iterate through each 'discrete' column of data\n", "#Perform a 2D histogram later\n", "\n", "i=0 \n", "for key in varTypes['discrete']:\n", " \n", " #print \"The category is: {0} with value_counts: {1} and detailed tuple: {2} \".format(key, l.count(), l)\n", " plt.figure(i)\n", " fig, axes = plt.subplots(nrows = 1, ncols = 2)\n", " \n", " #Histogram based on normalized value counts of the data set\n", " disD[key].value_counts().hist(ax=axes[0]); axes[0].set_title(\"Histogram: \"+str(key))\n", " \n", " #Cumulative histogram based on normalized value counts of the data set\n", " disD[key].value_counts().hist(cumulative=True,ax=axes[1]); axes[1].set_title(\"Cumulative HG: \"+str(key))\n", " i+=1" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#2D Histogram\n", "\n", "i=0 \n", "for key in varTypes['categorical']:\n", " \n", " #print \"The category is: {0} with value_counts: {1} and detailed tuple: {2} \".format(key, l.count(), l)\n", " plt.figure(i)\n", " #fig, axes = plt.subplots(nrows = 1, ncols = 2)\n", " \n", " x = catD[key].value_counts(normalize=True)\n", " y = df['Response']\n", " \n", " plt.hist2d(x[1], y, bins=40, norm=LogNorm())\n", " plt.colorbar()\n", " \n", " #catD[key].value_counts(normalize=True).hist(ax=axes[0]); axes[0].set_title(\"Histogram: \"+str(key))\n", " #catD[key].value_counts(normalize=True).hist(cumulative=True,ax=axes[1]); axes[1].set_title(\"Cumulative HG: \"+str(key))\n", " i+=1" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#Iterate through each categorical column of data\n", "#Perform a 2D histogram later\n", "\n", "i=0 \n", "for key in varTypes['categorical']:\n", " \n", " #print \"The category is: {0} with value_counts: {1} and detailed tuple: {2} \".format(key, l.count(), l)\n", " plt.figure(i)\n", " #fig, axes = plt.subplots(nrows = 1, ncols = 2)\n", " #catD[key].value_counts(normalize=True).hist(ax=axes[0]); axes[0].set_title(\"Histogram: \"+str(key))\n", " #catD[key].value_counts(normalize=True).hist(cumulative=True,ax=axes[1]); axes[1].set_title(\"Cumulative HG: \"+str(key))\n", " if df[key].dtype in (np.int64, np.float, float, int):\n", " #(1.*df[key].value_counts()/len(df[key])).hist()\n", " df[key].value_counts(normalize=True).plot(kind='bar')\n", " \n", " i+=1" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "ename": "AttributeError", "evalue": "'_LocIndexer' object has no attribute 'head'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-12-78d3c441c23a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Product_Info_1'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mAttributeError\u001b[0m: '_LocIndexer' object has no attribute 'head'" ] } ], "source": [ "df.loc('Product_Info_1')" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.9" } }, "nbformat": 4, "nbformat_minor": 0 }
gpl-3.0
g-weatherill/notebooks
gmpe-smtk/Ground Motion IMs Short.ipynb
1
18234
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Calculating Ground Motion Intensity Measures" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The SMTK contains two modules for the characterisation of ground motion:\n", "\n", "1) smtk.response_spectrum\n", "\n", "This module contains methods for calculation of the response of a set of single degree-of-freedom (SDOF) oscillators using an input time series. Two methods are currently supported:\n", "\n", " i) Newmark-Beta\n", " \n", " ii) Nigam & Jennings (1969) {Preferred}\n", "\n", "The module also includes functions for plotting the response spectra and time series\n", "\n", "2) smtk.intensity_measures \n", "\n", "This module contains a set of functions for deriving different intensity measures from a strong motion record\n", "\n", "i) get_peak_measures(...) - returns PGA, PGV and PGD\n", "\n", "ii) get_response_spectrum(...) - returns the response spectrum\n", "\n", "iii) get_response_spectrum_pair(...) - returns a response spectrum pair\n", "\n", "iv) geometric_mean_spectrum(...) - returns the geometric mean of a pair of records\n", "\n", "v) arithmetic_mean_spectrum(...) - returns the arithmetic mean of a pair of records\n", "\n", "vi) geometric_mean_spectrum(...) - returns the envelope spectrum of a pair of records\n", "\n", "vii) larger_pga(...) - Returns the spectrum with the larger PGA\n", "\n", "viii) rotate_horizontal(...) - rotates a record pair through angle theta\n", "\n", "ix) gmrotdpp(...) - Returns the rotationally-dependent geometric fractile (pp) of a pair of records\n", "\n", "x) gmrotipp(...) - Returns the rotationally-independent geometric fractile (pp) of a pair of records\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Example Usage of the Response Spectrum" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Import modules\n", "%matplotlib inline\n", "import numpy as np # Numerical Python package\n", "import matplotlib.pyplot as plt # Python plotting package\n", "# Import\n", "import smtk.response_spectrum as rsp # Response Spectra tools\n", "import smtk.intensity_measures as ims # Intensity Measure Tools\n", "\n", "\n", "periods = np.array([0.01, 0.02, 0.03, 0.04, 0.05, 0.075, 0.1, 0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19,\n", " 0.20, 0.22, 0.24, 0.26, 0.28, 0.30, 0.32, 0.34, 0.36, 0.38, 0.40, 0.42, 0.44, 0.46, 0.48, 0.5, \n", " 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 1.0, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, \n", " 1.9, 2.0, 2.2, 2.4, 2.6, 2.8, 3.0, 3.2, 3.4, 3.6, 3.8, 4.0, 4.2, 4.4, 4.6, 4.8, 5.0, 5.5, 6.0, \n", " 6.5, 7.0,7.5, 8.0, 8.5, 9.0, 9.5, 10.0], dtype=float)\n", "number_periods = len(periods)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Load record pair from files\n", "x_record = np.genfromtxt(\"data/sm_record_x.txt\")\n", "y_record = np.genfromtxt(\"data/sm_record_y.txt\")\n", "\n", "x_time_step = 0.002 # Record sampled at 0.002 s \n", "y_time_step = 0.002" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Get Response Spectrum - Nigam & Jennings" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Create an instance of the Newmark-Beta class\n", "nigam_jennings = rsp.NigamJennings(x_record, x_time_step, periods, damping=0.05, units=\"cm/s/s\")\n", "sax, time_series, acc, vel, dis = nigam_jennings.evaluate()\n", "\n", "# Plot Response Spectrum\n", "rsp.plot_response_spectra(sax, axis_type=\"semilogx\", filename=\"images/response_nigam_jennings.pdf\",filetype=\"pdf\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plot Time Series" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "rsp.plot_time_series(time_series[\"Acceleration\"],\n", " x_time_step,\n", " time_series[\"Velocity\"],\n", " time_series[\"Displacement\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Intensity Measures" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Get PGA, PGV and PGD" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "pga_x, pgv_x, pgd_x, _, _ = ims.get_peak_measures(0.002, x_record, True, True)\n", "print \"PGA = %10.4f cm/s/s, PGV = %10.4f cm/s, PGD = %10.4f cm\" % (pga_x, pgv_x, pgd_x)\n", "pga_y, pgv_y, pgd_y, _, _ = ims.get_peak_measures(0.002, y_record, True, True)\n", "print \"PGA = %10.4f cm/s/s, PGV = %10.4f cm/s, PGD = %10.4f cm\" % (pga_y, pgv_y, pgd_y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Get Durations: Bracketed, Uniform, Significant" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print \"Bracketed Duration (> 5 cm/s/s) = %9.3f s\" % ims.get_bracketed_duration(x_record, x_time_step, 5.0)\n", "print \"Uniform Duration (> 5 cm/s/s) = %9.3f s\" % ims.get_uniform_duration(x_record, x_time_step, 5.0)\n", "print \"Significant Duration (5 - 95 Arias ) = %9.3f s\" % ims.get_significant_duration(x_record, x_time_step, 0.05, 0.95)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Get Arias Intensity, CAV, CAV5 and rms acceleration" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print \"Arias Intensity = %12.4f cm-s\" % ims.get_arias_intensity(x_record, x_time_step)\n", "print \"Arias Intensity (5 - 95) = %12.4f cm-s\" % ims.get_arias_intensity(x_record, x_time_step, 0.05, 0.95)\n", "print \"CAV = %12.4f cm-s\" % ims.get_cav(x_record, x_time_step)\n", "print \"CAV5 = %12.4f cm-s\" % ims.get_cav(x_record, x_time_step, threshold=5.0)\n", "print \"Arms = %12.4f cm-s\" % ims.get_arms(x_record, x_time_step)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Spectrum Intensities: Housner Intensity, Acceleration Spectrum Intensity" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Get response spectrum\n", "sax = ims.get_response_spectrum(x_record, x_time_step, periods)[0]\n", "print \"Velocity Spectrum Intensity (cm/s/s) = %12.5f\" % ims.get_response_spectrum_intensity(sax)\n", "print \"Acceleration Spectrum Intensity (cm-s) = %12.5f\" % ims.get_acceleration_spectrum_intensity(sax)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Get the response spectrum pair from two records" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sax, say = ims.get_response_spectrum_pair(x_record, x_time_step,\n", " y_record, y_time_step,\n", " periods,\n", " damping=0.05,\n", " units=\"cm/s/s\",\n", " method=\"Nigam-Jennings\")\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Get Geometric Mean Spectrum" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sa_gm = ims.geometric_mean_spectrum(sax, say)\n", "rsp.plot_response_spectra(sa_gm, \"semilogx\", filename=\"images/geometric_mean_spectrum.pdf\", filetype=\"pdf\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Get Envelope Spectrum" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sa_env = ims.envelope_spectrum(sax, say)\n", "rsp.plot_response_spectra(sa_env, \"semilogx\", filename=\"images/envelope_spectrum.pdf\", filetype=\"pdf\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Rotationally Dependent and Independent IMs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### GMRotD50 and GMRotI50" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "gmrotd50 = ims.gmrotdpp(x_record, x_time_step, y_record, y_time_step, periods, percentile=50.0,\n", " damping=0.05, units=\"cm/s/s\")\n", "gmroti50 = ims.gmrotipp(x_record, x_time_step, y_record, y_time_step, periods, percentile=50.0,\n", " damping=0.05, units=\"cm/s/s\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Plot all of the rotational angles!\n", "plt.figure(figsize=(8, 6))\n", "for row in gmrotd50[\"GeoMeanPerAngle\"]:\n", " plt.semilogx(periods, row, \"-\", color=\"LightGray\")\n", "plt.semilogx(periods, gmrotd50[\"GMRotDpp\"], 'b-', linewidth=2, label=\"GMRotD50\")\n", "plt.semilogx(periods, gmroti50[\"Pseudo-Acceleration\"], 'r-', linewidth=2, label=\"GMRotI50\")\n", "plt.xlabel(\"Period (s)\", fontsize=18)\n", "plt.ylabel(\"Acceleration (cm/s/s)\", fontsize=18)\n", "plt.legend(loc=0)\n", "plt.savefig(\"images/rotational_spectra.pdf\", dpi=300, format=\"pdf\")\n" ] }, { "cell_type": "raw", "metadata": { "collapsed": false }, "source": [ "rotd50 = ims.rotdpp(x_record, x_time_step, y_record, y_time_step, periods, percentile=50.0,\n", " damping=0.05, units=\"cm/s/s\")[0]\n", "plt.semilogx(periods, rotd50[\"Pseudo-Acceleration\"], 'b-', linewidth=2, label=\"RotD50\")\n", "plt.xlabel(\"Period (s)\", fontsize=18)\n", "plt.ylabel(\"Acceleration (cm/s/s)\", fontsize=18)\n", "plt.legend(loc=0)\n", "plt.savefig(\"images/rotd50_spectrum.pdf\", dpi=300, format=\"pdf\")\n", "roti50 = ims.rotipp(x_record, x_time_step, y_record, y_time_step, periods, percentile=50.0,\n", " damping=0.05, units=\"cm/s/s\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fourier Spectra, Smoothing and HVSR" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Show the Fourier Spectrum" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ims.plot_fourier_spectrum(x_record, x_time_step,\n", " filename=\"images/fourier_spectrum.pdf\", filetype=\"pdf\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Smooth the Fourier Spectrum Using the Konno & Omachi (1998) Method " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from smtk.smoothing.konno_ohmachi import KonnoOhmachi\n", "# Get the original Fourier spectrum\n", "freq, amplitude = ims.get_fourier_spectrum(x_record, x_time_step)\n", "\n", "# Configure Smoothing Parameters\n", "smoothing_config = {\"bandwidth\": 40, # Size of smoothing window (lower = more smoothing)\n", " \"count\": 1, # Number of times to apply smoothing (may be more for noisy records) \n", " \"normalize\": True} \n", "\n", "# Apply the Smoothing\n", "smoother = KonnoOhmachi(smoothing_config)\n", "smoothed_spectra = smoother.apply_smoothing(amplitude, freq)\n", "\n", "# Compare the Two Spectra\n", "plt.figure(figsize=(7,5))\n", "plt.loglog(freq, amplitude, \"k-\", lw=1.0,label=\"Original\")\n", "plt.loglog(freq, smoothed_spectra, \"r\", lw=2.0, label=\"Smoothed\")\n", "plt.xlabel(\"Frequency (Hz)\", fontsize=14)\n", "plt.xlim(0.05, 200)\n", "plt.ylabel(\"Fourier Amplitude\", fontsize=14)\n", "plt.tick_params(labelsize=12)\n", "plt.legend(loc=0, fontsize=14)\n", "plt.grid(True)\n", "plt.savefig(\"images/SmoothedFourierSpectra.pdf\", format=\"pdf\", dpi=300)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Get the HVSR" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Load in the Time Series" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Load in a three component data set\n", "record_file = \"data/record_3component.csv\"\n", "record_3comp = np.genfromtxt(record_file, delimiter=\",\")\n", "\n", "time_vector = record_3comp[:, 0]\n", "x_record = record_3comp[:, 1]\n", "y_record = record_3comp[:, 2]\n", "v_record = record_3comp[:, 3]\n", "time_step = 0.002\n", "\n", "# Plot the records\n", "fig = plt.figure(figsize=(8,12))\n", "fig.set_tight_layout(True)\n", "ax = plt.subplot(311)\n", "ax.plot(time_vector, x_record)\n", "ax.set_ylim(-80., 80.)\n", "ax.set_xlim(0., 10.5)\n", "ax.grid(True)\n", "ax.set_xlabel(\"Time (s)\", fontsize=14)\n", "ax.set_ylabel(\"Acceleration (cm/s/s)\", fontsize=14)\n", "ax.tick_params(labelsize=12)\n", "ax.set_title(\"EW\", fontsize=16)\n", "ax = plt.subplot(312)\n", "ax.plot(time_vector, y_record)\n", "ax.set_xlim(0., 10.5)\n", "ax.set_ylim(-80., 80.)\n", "ax.grid(True)\n", "ax.set_xlabel(\"Time (s)\", fontsize=14)\n", "ax.set_ylabel(\"Acceleration (cm/s/s)\", fontsize=14)\n", "ax.set_title(\"NS\", fontsize=16)\n", "ax.tick_params(labelsize=12)\n", "ax = plt.subplot(313)\n", "ax.plot(time_vector, v_record)\n", "ax.set_xlim(0., 10.5)\n", "ax.set_ylim(-40., 40.)\n", "ax.grid(True)\n", "ax.set_xlabel(\"Time (s)\", fontsize=14)\n", "ax.set_ylabel(\"Acceleration (cm/s/s)\", fontsize=14)\n", "ax.set_title(\"Vertical\", fontsize=16)\n", "ax.tick_params(labelsize=12)\n", "plt.savefig(\"images/3component_timeseries.pdf\", format=\"pdf\", dpi=300)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Look at the Fourier Spectra" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "x_freq, x_four = ims.get_fourier_spectrum(x_record, time_step)\n", "y_freq, y_four = ims.get_fourier_spectrum(y_record, time_step)\n", "v_freq, v_four = ims.get_fourier_spectrum(v_record, time_step)\n", "plt.figure(figsize=(7, 5))\n", "plt.loglog(x_freq, x_four, \"k-\", lw=1.0, label=\"EW\")\n", "plt.loglog(y_freq, y_four, \"b-\", lw=1.0, label=\"NS\")\n", "plt.loglog(v_freq, v_four, \"r-\", lw=1.0, label=\"V\")\n", "plt.xlim(0.05, 200.)\n", "plt.tick_params(labelsize=12)\n", "plt.grid(True)\n", "plt.xlabel(\"Frequency (Hz)\", fontsize=16)\n", "plt.ylabel(\"Fourier Amplitude\", fontsize=16)\n", "plt.legend(loc=3, fontsize=16)\n", "plt.savefig(\"images/3component_fas.pdf\", format=\"pdf\", dpi=300)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Calculate the Horizontal To Vertical Spectral Ratio" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Setup parameters\n", "params = {\"Function\": \"KonnoOhmachi\",\n", " \"bandwidth\": 40.0,\n", " \"count\": 1.0,\n", " \"normalize\": True\n", " }\n", "# Returns\n", "# 1. Horizontal to Vertical Spectral Ratio\n", "# 2. Frequency\n", "# 3. Maximum H/V\n", "# 4. Period of Maximum H/V\n", "hvsr, freq, max_hv, t_0 = ims.get_hvsr(x_record, time_step, y_record, time_step, v_record, time_step, params)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plt.figure(figsize=(7,5))\n", "plt.semilogx(freq, hvsr, 'k-', lw=2.0)\n", "# Show T0\n", "t_0_line = np.array([[t_0, 0.0],\n", " [t_0, 1.1 * max_hv]])\n", "plt.semilogx(1.0 / t_0_line[:, 0], t_0_line[:, 1], \"r--\", lw=1.5)\n", "plt.xlabel(\"Frequency (Hz)\", fontsize=14)\n", "plt.ylabel(\"H / V\", fontsize=14)\n", "plt.tick_params(labelsize=14)\n", "plt.xlim(0.1, 10.0)\n", "plt.grid(True)\n", "plt.title(r\"$T_0 = %.4f s$\" % t_0, fontsize=16)\n", "plt.savefig(\"images/hvsr_example1.pdf\", format=\"pdf\", dpi=300)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.2" } }, "nbformat": 4, "nbformat_minor": 0 }
agpl-3.0
jasti/CrossfitAnalyser
CrossfitAnalyser.ipynb
1
89774
{ "metadata": { "name": "", "signature": "sha256:031d4f09d4428b6bbea15479ea61ffc68f20a563c263a7ec4aebd375631d052e" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline\n", "import csv\n", "import pandas as pd\n", "from datetime import datetime\n", "import calendar\n", "import numpy as np\n", "import matplotlib.pyplot as plt \n", "\n", "#Set pandas column width\n", "pd.options.display.max_colwidth =40" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 72 }, { "cell_type": "code", "collapsed": false, "input": [ "##Import data from a tab seperated file\n", "#Define your columns\n", "cols =['Date','Workout']\n", "df = pd.read_csv('input/workouts.tsv', sep='\\t', converters={'Date': str}) \n", "#Drop invalid data. e.g. blank inputs\n", "df = df.dropna()\n", "print df.head(5)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " Date Workout\n", "0 20020131 Row 2000 meters Rest for 2000 meter ...\n", "1 20020131 For time: 50 Push-ups 50 Pull-ups 75...\n", "2 20020130 Row 2000 meters Rest for 2000 meter ...\n", "3 20020129 Complete as many rounds in 20 minute...\n", "4 20020128 Bike ten minutes, hard. Rest five mi...\n", "\n", "[5 rows x 2 columns]\n" ] } ], "prompt_number": 73 }, { "cell_type": "code", "collapsed": false, "input": [ "df.to_csv('output/workouts_refine.csv')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 74 }, { "cell_type": "code", "collapsed": false, "input": [ "# Reimport the file from Refine\n", "cols =['Date','Workout','HeroWod']\n", "df = pd.read_csv('input/workouts_refine.tsv', sep='\\t', converters={'Date': str}) \n", "df = df.dropna()\n", "print df.head(5)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " Date Workout HeroWod\n", "0 20020131 row 2000 meters rest for 2000 meter ... False\n", "1 20020131 for time: 50 push-ups 50 pull-ups 75... False\n", "2 20020130 row 2000 meters rest for 2000 meter ... False\n", "3 20020129 complete as many rounds in 20 minute... False\n", "4 20020128 bike ten minutes, hard. rest five mi... False\n", "\n", "[5 rows x 3 columns]\n" ] } ], "prompt_number": 75 }, { "cell_type": "code", "collapsed": false, "input": [ "# Define a function that allows us the date column into Year, Month and Day\n", "def splitDate(date):\n", " # print type(date)\n", " strDate = str(date);\n", " # print strDate\n", " try:\n", " mydate = datetime.strptime(strDate, '%Y%m%d')\n", " # mydate = datetime.strptime(strDate,'%Y-%m-%d %H:%M:%S')\n", " \n", " return [str(mydate.year), calendar.month_name[mydate.month], str(mydate.day)]\n", " except ValueError:\n", " print \"Bad Date encountered : \" + strDate\n", " return [np.NaN,np.NaN,np.NaN]\n", "\n", "#Invoke the previously defined function\n", "df['Year'], df['Month'], df['Day'] = zip(*df[\"Date\"].map(splitDate))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 99 }, { "cell_type": "code", "collapsed": false, "input": [ "print df.tail(5)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " Date Workout HeroWod Year \\\n", "4182 20131205 rest day False 2013 \n", "4183 20131204 five rounds for time of: 10 kettlebe... False 2013 \n", "4184 20131203 back squat 2-2-2-2-2-2-2-2-2-2 reps False 2013 \n", "4185 20131202 five rounds for time of: 20 ghd sit-... False 2013 \n", "4186 20131201 rest day False 2013 \n", "\n", " Month Day \n", "4182 December 5 \n", "4183 December 4 \n", "4184 December 3 \n", "4185 December 2 \n", "4186 December 1 \n", "\n", "[5 rows x 6 columns]\n" ] } ], "prompt_number": 101 }, { "cell_type": "code", "collapsed": false, "input": [ "#Workouts per year\n", "df_year_total=df.groupby(['Year']).size()\n", "print \"Total workouts per year : \"\n", "print df_year_total" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Total workouts per year : \n", "Year\n", "2002 352\n", "2005 364\n", "2007 362\n", "2008 366\n", "2009 364\n", "2010 365\n", "2011 365\n", "2012 366\n", "2013 364\n", "dtype: int64\n" ] } ], "prompt_number": 103 }, { "cell_type": "code", "collapsed": false, "input": [ "# Plot a histogram for sanity checking the data\n", "plt.figure();\n", "df.groupby(['Year']).size().plot(kind='bar');" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x108bcdd10>" ] } ], "prompt_number": 79 }, { "cell_type": "code", "collapsed": false, "input": [ "# Apply filter to get just the years that has good data\n", "yearRange = [\"2002\",\"2005\",\"2007\",\"2008\",\"2009\",\"2010\",\"2011\",\"2012\",\"2013\"]\n", "df = df[df['Year'].isin( yearRange)]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 104 }, { "cell_type": "code", "collapsed": false, "input": [ "# Get the rows that is a Hero WOD or a Benchmark WOD\n", "dfHero = df[df['HeroWod'].astype(str) == \"True\"]\n", "# Reset the internal data frame index\n", "dfHero.reset_index(inplace=True)\n", "# Group By year\n", "df_hero_year=dfHero.groupby(['Year']).size()\n", "# Hero/ Benchmark workout distribution\n", "plt.figure();\n", "df_hero_year.plot(kind='bar');\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Hero WODS per year : \n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x109e01bd0>" ] } ], "prompt_number": 95 }, { "cell_type": "code", "collapsed": false, "input": [ "# Takes a search string and data frame. \n", "# Returns a series of the term occurences over year\n", "def searchForTerm(searchString, df):\n", " # Filter that ignores case\n", " df = df[df['Workout'].str.contains(searchString, case = 'false')]\n", " df.reset_index(inplace=True)\n", " #Group by the year\n", " df_grouped=df.groupby(['Year']).size()\n", " return df_grouped" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 105 }, { "cell_type": "code", "collapsed": false, "input": [ "df_restDays = searchForTerm(\"rest day\", df)\n", "# \"Rest Days\" distribution\n", "plt.figure();\n", "ax= df_restDays.plot(kind='bar');\n", "#Setting the Y axis label\n", "ax.set_ylabel(\"No of rest days\")" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 114, "text": [ 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"text": [ "<matplotlib.figure.Figure at 0x105779e90>" ] } ], "prompt_number": 114 }, { "cell_type": "code", "collapsed": false, "input": [ "df_deadlift = searchForTerm(\"deadlift\", df)\n", "print df_deadlift\n", "# \"For time\" workout day distribution\n", "plt.figure();\n", "df_deadlift.plot(kind='bar');" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Year\n", "2002 33\n", "2005 37\n", "2007 42\n", "2008 44\n", "2009 41\n", "2010 48\n", "2011 44\n", "2012 37\n", "2013 43\n", "dtype: int64\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x108c6b190>" ] } ], "prompt_number": 109 }, { "cell_type": "code", "collapsed": false, "input": [ "# Find series that have running over the years\n", "df_run = searchForTerm(\"run\", df)\n", "# Find series that have rowing over the years\n", "df_row = searchForTerm(\"row\", df)\n", "# Find series that have rowing over the years\n", "df_bike = searchForTerm(\"bike\", df)\n", "# Find series that have rowing over the years\n", "df_swim = searchForTerm(\"swim\", df)\n", "\n" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 115, "text": [ "<matplotlib.text.Text at 0x109222150>" ] }, { "metadata": {}, "output_type": "display_data", "text": [ "<matplotlib.figure.Figure at 0x108d04150>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x108ceec50>" ] } ], "prompt_number": 115 }, { "cell_type": "code", "collapsed": false, "input": [ "# Concatenate all these terms into one dataframe\n", "df_cardio = pd.concat([df_run, df_row,df_bike,df_swim], axis=1)\n", "#Name the columns\n", "df_cardio.columns = ['Running', 'Rowing','Biking','Swimming']\n", "\n", "plt.figure();\n", "#Create a stacked bar chart\n", "ax =df_cardio.plot(kind='bar',stacked = 'true');\n", "#Label the Y axis\n", "ax.set_ylabel(\"Workout Occurence\")" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 116, "text": [ "<matplotlib.text.Text at 0x1092e5fd0>" ] }, { "metadata": {}, "output_type": "display_data", "text": [ "<matplotlib.figure.Figure at 0x1091b4ed0>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x109e01bd0>" ] } ], "prompt_number": 116 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 94 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 94 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
mit
bps90/bps90.github.io
assets/files/papers/7/WCNC-2016/.ipynb_checkpoints/ParseEdgeList-checkpoint.ipynb
1
3165
{ "metadata": { "name": "", "signature": "sha256:ff125a7e55001c106f8fbc3a423fce14d3050550a263c5c1554c142a03638144" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "from random import randrange\n", "from os import listdir\n", "from os.path import isfile, join\n", "\n", "def readFile(fileName, numNodes):\n", " f = open(fileName, 'r')\n", " outFileName = \"./topology/newTopologys/edge\"+numNodes+\".edgelist\"\n", " outputFile = open(outFileName, 'w')\n", " \n", " for l in f.readlines():\n", " split = l.split()\n", " gain = int(randrange(20)+50)\n", " gain = -gain\n", " outputFile.write(split[0] +\" \"+ split[1] +\" \"+ str(gain) + \"\\n\")\n", "\n", "#readFile(\"./topology/newTopologys/10-edge-100.txt\", \"100\")\n", "#readFile(\"./topology/newTopologys/10-edge-200.txt\", \"200\")\n", "#readFile(\"./topology/newTopologys/10-edge-300.txt\", \"300\")\n", "#readFile(\"./topology/newTopologys/10-edge-400.txt\", \"400\")\n", "#readFile(\"./topology/newTopologys/10-edge-500.txt\", \"500\")\n", " " ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "\n", "\n", "def parseFile(directory, numNodes):\n", " onlyfiles = [ f for f in listdir(directory) if isfile(join(directory,f)) ]\n", " \n", " for f in onlyfiles:\n", " idTopology = f.split(\"-\")[0]\n", " inputFile = open(directory+f, 'r')\n", " outFileName = directory+\"out/\"+idTopology+\"-edge-\"+str(numNodes)+\".edgelist\"\n", " outputFile = open(outFileName, 'w')\n", "\n", " for l in inputFile.readlines():\n", " split = l.split()\n", " gain = int(randrange(20)+50)\n", " gain = -gain\n", " outputFile.write(split[0] +\" \"+ split[1] +\" \"+ str(gain) + \"\\n\")\n", " \n", " \n", "\n", "directory = \"./topology/100-nodes-EdgeList/\"\n", "onlyfiles = [ f for f in listdir(directory) if isfile(join(directory,f)) ]\n", "print onlyfiles\n", "#parseFile(directory, 100)\n", "\n", "for i in range(100, 600, 100):\n", " directory = \"./topology/\"\n", " directory += str(i)\n", " directory += \"-nodes-EdgeList/\"\n", " parseFile(directory, i)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "['1-edge-100.edgelist', '1-edge-100.txt', '10-edge-100.txt', '2-edge-100.txt', '3-edge-100.txt', '4-edge-100.txt', '5-edge-100.txt', '6-edge-100.txt', '7-edge-100.txt', '8-edge-100.txt', '9-edge-100.txt']\n" ] } ], "prompt_number": 23 } ], "metadata": {} } ] }
mit
scraperwiki/databaker
databaker/tutorial/Finding_your_way.ipynb
2
53859
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Opening and previewing\n", "\n", "This uses the tiny excel spreadsheet example1.xls. It is small enough to preview inline in this notebook. But for bigger spreadsheet tables you will want to open them up in a separate window." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading example1.xls which has size 7168 bytes\n", "Table names: ['beatles', 'stones']\n", "The unordered bag of cells for this table looks like:\n", "{<B4 2.0>, <C6 1.0>, <D1 ''>, <B5 4.0>, <C5 3.0>, <C1 ''>, <B3 'Cars'>, <D3 'Trains'>, <C2 ''>, <D4 1.0>, <D6 3.0>, <B6 4.0>, <D5 2.0>, <B2 ''>, <A1 'Date'>, <B1 2014.0>, <C7 5.0>, <A7 'George'>, <A6 'Ringo'>, <A3 ''>, <A2 ''>, <C4 2.0>, <A4 'John'>, <C3 'Planes'>, <D7 5.0>, <D2 ''>, <A5 'Paul'>, <B7 2.0>}\n" ] } ], "source": [ "\n", "# Load in the functions\n", "from databaker.framework import *\n", "\n", "# Load the spreadsheet\n", "tabs = loadxlstabs(\"example1.xls\")\n", "\n", "# Select the first table\n", "tab = tabs[0]\n", "\n", "print(\"The unordered bag of cells for this table looks like:\")\n", "print(tab)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Selecting cell bags\n", "\n", "A table is also \"bag of cells\", which just so happens to be a set of all the cells in the table. \n", "\n", "A \"bag of cells\" is like a Python set (and looks like one when you print it), but it has extra selection functions that help you navigate around the table.\n", "\n", "We will learn these as we go along, but you can see the full list on the tutorial_reference notebook.\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div id=\"injblock1001\">\n", "<style>\n", "table.ex, table.exkey { border: thin black solid }\n", "table.ex td, table.ex tr { border: none }\n", "table.ex td:hover { border: thin blue solid }\n", "table.ex td.excOBS:hover { border: thin red solid }\n", "table.ex td.selected { border: thick red solid }\n", "</style>\n", "\n", "\n", "\n", "<table class=\"ex\">\n", "<caption style=\"text-align:center; padding:0px; caption-side:bottom\">beatles</caption>\n", "<tr><td title=\"0 0\">Date</td><td title=\"1 0\">2014.0</td><td title=\"2 0\"></td><td title=\"3 0\"></td></tr>\n", "<tr><td title=\"0 1\"></td><td title=\"1 1\"></td><td title=\"2 1\"></td><td title=\"3 1\"></td></tr>\n", "<tr><td title=\"0 2\"></td><td title=\"1 2\">Cars</td><td style=\"font-weight:bold\" title=\"2 2\">Planes</td><td style=\"font-weight:bold\" title=\"3 2\">Trains</td></tr>\n", "<tr><td title=\"0 3\">John</td><td title=\"1 3\">2.0</td><td title=\"2 3\">2.0</td><td title=\"3 3\">1.0</td></tr>\n", "<tr><td title=\"0 4\">Paul</td><td title=\"1 4\">4.0</td><td title=\"2 4\">3.0</td><td title=\"3 4\">2.0</td></tr>\n", "<tr><td title=\"0 5\">Ringo</td><td title=\"1 5\">4.0</td><td title=\"2 5\">1.0</td><td title=\"3 5\">3.0</td></tr>\n", "<tr><td title=\"0 6\">George</td><td title=\"1 6\">2.0</td><td title=\"2 6\">5.0</td><td title=\"3 6\">5.0</td></tr>\n", "</table>\n", "\n", "</div>\n" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Preview the table as a table inline\n", "savepreviewhtml(tab)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The cells with bold font are {<C3 'Planes'>, <D3 'Trains'>}\n" ] } ], "source": [ "bb = tab.is_bold()\n", "print(\"The cells with bold font are\", bb)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The 2 cells immediately below these bold font cells are {<C4 2.0>, <D4 1.0>}\n" ] } ], "source": [ "print(\"The\", len(bb), \"cells immediately below these bold font cells are\", bb.shift(DOWN))" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The single cell with the text 'Cars' is {<B3 'Cars'>}\n" ] }, { "data": { "text/plain": [ "{<B3 'Cars'>}" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cc = tab.filter(\"Cars\")\n", "print(\"The single cell with the text 'Cars' is\", cc)\n", "\n", "cc.assert_one() # proves there is only one cell in this bag" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Everything in the column below the 'Cars' cell is {<B7 2.0>, <B6 4.0>, <B5 4.0>, <B4 2.0>}\n" ] } ], "source": [ "print(\"Everything in the column below the 'Cars' cell is\", cc.fill(DOWN))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "If you wanted to include the 'Cars' heading, then use expand {<B3 'Cars'>, <B7 2.0>, <B6 4.0>, <B5 4.0>, <B4 2.0>}\n" ] } ], "source": [ "hcc = tab.filter(\"Cars\").expand(DOWN)\n", "print(\"If you wanted to include the 'Cars' heading, then use expand\", hcc)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "You can print the cells in row-column order if you don't mind unfriendly code\n", "[<B3 'Cars'>, <B4 2.0>, <B5 4.0>, <B6 4.0>, <B7 2.0>]\n" ] } ], "source": [ "print(\"You can print the cells in row-column order if you don't mind unfriendly code\")\n", "shcc = sorted(hcc.unordered_cells, key=lambda Cell:(Cell.y, Cell.x))\n", "print(shcc)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "It can be easier to see the set of cells coloured within the table\n" ] }, { "data": { "text/html": [ "<div id=\"injblock1002\">\n", "<style>\n", "table.ex, table.exkey { border: thin black solid }\n", "table.ex td, table.ex tr { border: none }\n", "table.ex td:hover { border: thin blue solid }\n", "table.ex td.excOBS:hover { border: thin red solid }\n", "table.ex td.selected { border: thick red solid }\n", "</style>\n", "\n", "\n", "<table class=\"exkey\">\n", "<tr><td class=\"xc1\" style=\"background-color:LightGreen\">item 0</td></tr></table>\n", "\n", "<table class=\"ex\">\n", "<caption style=\"text-align:center; padding:0px; caption-side:bottom\">beatles</caption>\n", "<tr><td title=\"0 0\">Date</td><td title=\"1 0\">2014.0</td><td title=\"2 0\"></td><td title=\"3 0\"></td></tr>\n", "<tr><td title=\"0 1\"></td><td title=\"1 1\"></td><td title=\"2 1\"></td><td title=\"3 1\"></td></tr>\n", "<tr><td title=\"0 2\"></td><td style=\"background-color:LightGreen\" title=\"1 2\">Cars</td><td style=\"font-weight:bold\" title=\"2 2\">Planes</td><td style=\"font-weight:bold\" title=\"3 2\">Trains</td></tr>\n", "<tr><td title=\"0 3\">John</td><td style=\"background-color:LightGreen\" title=\"1 3\">2.0</td><td title=\"2 3\">2.0</td><td title=\"3 3\">1.0</td></tr>\n", "<tr><td title=\"0 4\">Paul</td><td style=\"background-color:LightGreen\" title=\"1 4\">4.0</td><td title=\"2 4\">3.0</td><td title=\"3 4\">2.0</td></tr>\n", "<tr><td title=\"0 5\">Ringo</td><td style=\"background-color:LightGreen\" title=\"1 5\">4.0</td><td title=\"2 5\">1.0</td><td title=\"3 5\">3.0</td></tr>\n", "<tr><td title=\"0 6\">George</td><td style=\"background-color:LightGreen\" title=\"1 6\">2.0</td><td title=\"2 6\">5.0</td><td title=\"3 6\">5.0</td></tr>\n", "</table>\n", "\n", "</div>\n" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print(\"It can be easier to see the set of cells coloured within the table\")\n", "savepreviewhtml(hcc)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Note**: As you work through this tutorial, do please feel free to temporarily insert new Jupyter-Cells in order to give yourself a place to experiment with any of the functions that are available. (Remember, the value of the last line in a Jupyter-Cell is always printed out -- in addition to any earlier print-statements.)\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(\"All the cells that have an 'o' in them:\",\n", " {<A6 'Ringo'>, <A4 'John'>, <A7 'George'>})" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"All the cells that have an 'o' in them:\", tab.regex(\".*?o\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Observations and dimensions\n", "Let's get on with some actual work. In our terminology, an \"Observation\" is a numerical measure (eg anything in the 3x4 array of numbers in the example table), and a \"Dimension\" is one of the headings.\n", "\n", "Both are made up of a bag of cells, however a Dimension also needs to know how to \"look up\" from the Observation to its dimensional value." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div id=\"injblock1003\">\n", "<style>\n", "table.ex, table.exkey { border: thin black solid }\n", "table.ex td, table.ex tr { border: none }\n", "table.ex td:hover { border: thin blue solid }\n", "table.ex td.excOBS:hover { border: thin red solid }\n", "table.ex td.selected { border: thick red solid }\n", "</style>\n", "\n", "\n", "<table class=\"exkey\">\n", "<tr><td class=\"xc1\" style=\"background-color:LightGreen\">item 0</td></tr></table>\n", "\n", "<table class=\"ex\">\n", "<caption style=\"text-align:center; padding:0px; caption-side:bottom\">beatles</caption>\n", "<tr><td title=\"0 0\">Date</td><td title=\"1 0\">2014.0</td><td title=\"2 0\"></td><td title=\"3 0\"></td></tr>\n", "<tr><td title=\"0 1\"></td><td title=\"1 1\"></td><td title=\"2 1\"></td><td title=\"3 1\"></td></tr>\n", "<tr><td title=\"0 2\"></td><td title=\"1 2\">Cars</td><td style=\"font-weight:bold\" title=\"2 2\">Planes</td><td style=\"font-weight:bold\" title=\"3 2\">Trains</td></tr>\n", "<tr><td title=\"0 3\">John</td><td style=\"background-color:LightGreen\" title=\"1 3\">2.0</td><td style=\"background-color:LightGreen\" title=\"2 3\">2.0</td><td style=\"background-color:LightGreen\" title=\"3 3\">1.0</td></tr>\n", "<tr><td title=\"0 4\">Paul</td><td style=\"background-color:LightGreen\" title=\"1 4\">4.0</td><td style=\"background-color:LightGreen\" title=\"2 4\">3.0</td><td style=\"background-color:LightGreen\" title=\"3 4\">2.0</td></tr>\n", "<tr><td title=\"0 5\">Ringo</td><td style=\"background-color:LightGreen\" title=\"1 5\">4.0</td><td style=\"background-color:LightGreen\" title=\"2 5\">1.0</td><td style=\"background-color:LightGreen\" title=\"3 5\">3.0</td></tr>\n", "<tr><td title=\"0 6\">George</td><td style=\"background-color:LightGreen\" title=\"1 6\">2.0</td><td style=\"background-color:LightGreen\" title=\"2 6\">5.0</td><td style=\"background-color:LightGreen\" title=\"3 6\">5.0</td></tr>\n", "</table>\n", "\n", "</div>\n" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# We get the array of observations by selecting its corner and expanding down and to the right\n", "obs = tab.excel_ref('B4').expand(DOWN).expand(RIGHT)\n", "savepreviewhtml(obs)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div id=\"injblock1004\">\n", "<style>\n", "table.ex, table.exkey { border: thin black solid }\n", "table.ex td, table.ex tr { border: none }\n", "table.ex td:hover { border: thin blue solid }\n", "table.ex td.excOBS:hover { border: thin red solid }\n", "table.ex td.selected { border: thick red solid }\n", "</style>\n", "\n", "\n", "<table class=\"exkey\">\n", "<tr><td class=\"xc1\" style=\"background-color:LightGreen\">item 0</td><td class=\"xc2\" style=\"background-color:MistyRose\">item 1</td></tr></table>\n", "\n", "<table class=\"ex\">\n", "<caption style=\"text-align:center; padding:0px; caption-side:bottom\">beatles</caption>\n", "<tr><td title=\"0 0\">Date</td><td title=\"1 0\">2014.0</td><td title=\"2 0\"></td><td title=\"3 0\"></td></tr>\n", "<tr><td title=\"0 1\"></td><td title=\"1 1\"></td><td title=\"2 1\"></td><td title=\"3 1\"></td></tr>\n", "<tr><td title=\"0 2\"></td><td style=\"background-color:LightGreen\" title=\"1 2\">Cars</td><td style=\"background-color:LightGreen;font-weight:bold\" title=\"2 2\">Planes</td><td style=\"background-color:LightGreen;font-weight:bold\" title=\"3 2\">Trains</td></tr>\n", "<tr><td style=\"background-color:MistyRose\" title=\"0 3\">John</td><td title=\"1 3\">2.0</td><td title=\"2 3\">2.0</td><td title=\"3 3\">1.0</td></tr>\n", "<tr><td style=\"background-color:MistyRose\" title=\"0 4\">Paul</td><td title=\"1 4\">4.0</td><td title=\"2 4\">3.0</td><td title=\"3 4\">2.0</td></tr>\n", "<tr><td style=\"background-color:MistyRose\" title=\"0 5\">Ringo</td><td title=\"1 5\">4.0</td><td title=\"2 5\">1.0</td><td title=\"3 5\">3.0</td></tr>\n", "<tr><td style=\"background-color:MistyRose\" title=\"0 6\">George</td><td title=\"1 6\">2.0</td><td title=\"2 6\">5.0</td><td title=\"3 6\">5.0</td></tr>\n", "</table>\n", "\n", "</div>\n" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# the two main headings are in a row and a column\n", "r1 = tab.excel_ref('B3').expand(RIGHT)\n", "r2 = tab.excel_ref('A3').fill(DOWN)\n", "\n", "# here we pass in a list containing two cell bags and get two colours\n", "savepreviewhtml([r1, r2])\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div id=\"injblock1005\">\n", "<style>\n", "table.ex, table.exkey { border: thin black solid }\n", "table.ex td, table.ex tr { border: none }\n", "table.ex td:hover { border: thin blue solid }\n", "table.ex td.excOBS:hover { border: thin red solid }\n", "table.ex td.selected { border: thick red solid }\n", "</style>\n", "\n", "\n", "<table class=\"exkey\">\n", "<tr><td class=\"xc1\" style=\"background-color:LightGreen\">Vehicles</td><td class=\"xc2\" style=\"background-color:MistyRose\">item 1</td></tr></table>\n", "\n", "<table class=\"ex\">\n", "<caption style=\"text-align:center; padding:0px; caption-side:bottom\">beatles</caption>\n", "<tr><td title=\"0 0\">Date</td><td title=\"1 0\">2014.0</td><td title=\"2 0\"></td><td title=\"3 0\"></td></tr>\n", "<tr><td title=\"0 1\"></td><td title=\"1 1\"></td><td title=\"2 1\"></td><td title=\"3 1\"></td></tr>\n", "<tr><td title=\"0 2\"></td><td style=\"background-color:LightGreen\" title=\"1 2\">Cars</td><td style=\"background-color:LightGreen;font-weight:bold\" title=\"2 2\">Planes</td><td style=\"background-color:LightGreen;font-weight:bold\" title=\"3 2\">Trains</td></tr>\n", "<tr><td title=\"0 3\">John</td><td title=\"1 3\">2.0</td><td title=\"2 3\">2.0</td><td title=\"3 3\">1.0</td></tr>\n", "<tr><td title=\"0 4\">Paul</td><td title=\"1 4\">4.0</td><td style=\"background-color:MistyRose\" title=\"2 4\">3.0</td><td title=\"3 4\">2.0</td></tr>\n", "<tr><td title=\"0 5\">Ringo</td><td title=\"1 5\">4.0</td><td title=\"2 5\">1.0</td><td title=\"3 5\">3.0</td></tr>\n", "<tr><td title=\"0 6\">George</td><td title=\"1 6\">2.0</td><td title=\"2 6\">5.0</td><td title=\"3 6\">5.0</td></tr>\n", "</table>\n", "\n", "</div>\n" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# HDim is made from a bag of cells, a name, and an instruction on how to look it up \n", "# from an observation cell. \n", "h1 = HDim(r1, \"Vehicles\", DIRECTLY, ABOVE)\n", "\n", "# Here is an example cell\n", "cc = tab.excel_ref('C5')\n", "\n", "# You can preview a dimension as well as just a cell bag\n", "savepreviewhtml([h1, cc])\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Cell {<C5 3.0>} matches (<C3 'Planes'>, 'Planes') in dimension Vehicles\n" ] } ], "source": [ "# !!! This is the important look-up stage from a cell into a dimension\n", "print(\"Cell\", cc, \"matches\", h1.cellvalobs(cc), \"in dimension\", h1.label)\n" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Obs {<B4 2.0>} maps to (<B3 'Cars'>, 'Cars')\n", "Obs {<C4 2.0>} maps to (<C3 'Planes'>, 'Planes')\n", "Obs {<D4 1.0>} maps to (<D3 'Trains'>, 'Trains')\n", "Obs {<B5 4.0>} maps to (<B3 'Cars'>, 'Cars')\n", "Obs {<C5 3.0>} maps to (<C3 'Planes'>, 'Planes')\n", "Obs {<D5 2.0>} maps to (<D3 'Trains'>, 'Trains')\n", "Obs {<B6 4.0>} maps to (<B3 'Cars'>, 'Cars')\n", "Obs {<C6 1.0>} maps to (<C3 'Planes'>, 'Planes')\n", "Obs {<D6 3.0>} maps to (<D3 'Trains'>, 'Trains')\n", "Obs {<B7 2.0>} maps to (<B3 'Cars'>, 'Cars')\n", "Obs {<C7 5.0>} maps to (<C3 'Planes'>, 'Planes')\n", "Obs {<D7 5.0>} maps to (<D3 'Trains'>, 'Trains')\n" ] } ], "source": [ "# You can start to see through to the final result of all this work when you \n", "# print out the lookup values for every observation in the table at once. \n", "for ob in obs:\n", " print(\"Obs\", ob, \"maps to\", h1.cellvalobs(ob))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Note** the value of `h1.cellvalobs(ob)` is actually a pair composed of the heading cell and its value. This is is because we can over-ride its output value without actually rewriting the original table, as we shall see. \n" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Obs {<B4 2.0>} maps to (<B3 'Cars'>, 'Horses')\n", "Obs {<C4 2.0>} maps to (<C3 'Planes'>, 'Planes')\n", "Obs {<D4 1.0>} maps to (<D3 'Trains'>, 'Trains')\n", "Obs {<B5 4.0>} maps to (<B3 'Cars'>, 'Horses')\n", "Obs {<C5 3.0>} maps to (<C3 'Planes'>, 'Planes')\n", "Obs {<D5 2.0>} maps to (<D3 'Trains'>, 'Trains')\n", "Obs {<B6 4.0>} maps to (<B3 'Cars'>, 'Horses')\n", "Obs {<C6 1.0>} maps to (<C3 'Planes'>, 'Planes')\n", "Obs {<D6 3.0>} maps to (<D3 'Trains'>, 'Trains')\n", "Obs {<B7 2.0>} maps to (<B3 'Cars'>, 'Horses')\n", "Obs {<C7 5.0>} maps to (<C3 'Planes'>, 'Planes')\n", "Obs {<D7 5.0>} maps to (<D3 'Trains'>, 'Trains')\n" ] } ], "source": [ "# You can change an output value like this:\n", "h1.AddCellValueOverride(\"Cars\", \"Horses\")\n", "\n", "for ob in obs:\n", " print(\"Obs\", ob, \"maps to\", h1.cellvalobs(ob))" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Obs {<B4 2.0>} maps to (<B3 'Cars'>, 'Horses')\n", "Obs {<C4 2.0>} maps to (<C3 'Planes'>, 'Submarines')\n", "Obs {<D4 1.0>} maps to (<D3 'Trains'>, 'Trains')\n", "Obs {<B5 4.0>} maps to (<B3 'Cars'>, 'Horses')\n", "Obs {<C5 3.0>} maps to (<C3 'Planes'>, 'Submarines')\n", "Obs {<D5 2.0>} maps to (<D3 'Trains'>, 'Trains')\n", "Obs {<B6 4.0>} maps to (<B3 'Cars'>, 'Horses')\n", "Obs {<C6 1.0>} maps to (<C3 'Planes'>, 'Submarines')\n", "Obs {<D6 3.0>} maps to (<D3 'Trains'>, 'Trains')\n", "Obs {<B7 2.0>} maps to (<B3 'Cars'>, 'Horses')\n", "Obs {<C7 5.0>} maps to (<C3 'Planes'>, 'Submarines')\n", "Obs {<D7 5.0>} maps to (<D3 'Trains'>, 'Trains')\n" ] } ], "source": [ "# Alternatively, you can override by the reference to a single cell to a value \n", "# (This will work even if the cell C3 is empty, which helps with filling in blank headings)\n", "h1.AddCellValueOverride(tab.excel_ref('C3'), \"Submarines\")\n", "for ob in obs:\n", " print(\"Obs\", ob, \"maps to\", h1.cellvalobs(ob))" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Obs {<B4 2.0>} maps to (None, 'Clouds')\n", "Obs {<C4 2.0>} maps to (<C3 'Planes'>, 'Submarines')\n", "Obs {<D4 1.0>} maps to (<D3 'Trains'>, 'Trains')\n", "Obs {<B5 4.0>} maps to (<B4 2.0>, 'Clouds')\n", "Obs {<C5 3.0>} maps to (<C3 'Planes'>, 'Submarines')\n", "Obs {<D5 2.0>} maps to (<D3 'Trains'>, 'Trains')\n", "Obs {<B6 4.0>} maps to (<B4 2.0>, 'Clouds')\n", "Obs {<C6 1.0>} maps to (<C3 'Planes'>, 'Submarines')\n", "Obs {<D6 3.0>} maps to (<D3 'Trains'>, 'Trains')\n", "Obs {<B7 2.0>} maps to (<B4 2.0>, 'Clouds')\n", "Obs {<C7 5.0>} maps to (<C3 'Planes'>, 'Submarines')\n", "Obs {<D7 5.0>} maps to (<D3 'Trains'>, 'Trains')\n" ] } ], "source": [ "# You can override the header value for an individual observation element. \n", "b4cell = tab.excel_ref('B4')\n", "h1.AddCellValueOverride(b4cell, \"Clouds\")\n", "for ob in obs:\n", " print(\"Obs\", ob, \"maps to\", h1.cellvalobs(ob))" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div id=\"injblock1006\">\n", "<style>\n", "table.ex, table.exkey { border: thin black solid }\n", "table.ex td, table.ex tr { border: none }\n", "table.ex td:hover { border: thin blue solid }\n", "table.ex td.excOBS:hover { border: thin red solid }\n", "table.ex td.selected { border: thick red solid }\n", "</style>\n", "\n", "\n", "<table class=\"exkey\">\n", "<tr><td class=\"xc1\" style=\"background-color:LightGreen\">Vehicles</td><td class=\"xc2\" style=\"background-color:MistyRose\">item 1</td></tr></table>\n", "\n", "<table class=\"ex\">\n", "<caption style=\"text-align:center; padding:0px; caption-side:bottom\">beatles</caption>\n", "<tr><td title=\"0 0\">Date</td><td title=\"1 0\">2014.0</td><td title=\"2 0\"></td><td title=\"3 0\"></td></tr>\n", "<tr><td title=\"0 1\"></td><td title=\"1 1\"></td><td title=\"2 1\"></td><td title=\"3 1\"></td></tr>\n", "<tr><td title=\"0 2\"></td><td style=\"background-color:LightGreen\" title=\"1 2\"><strike>Cars</strike>Horses</td><td style=\"background-color:LightGreen;font-weight:bold\" title=\"2 2\"><strike>Planes</strike>Submarines</td><td style=\"background-color:LightGreen;font-weight:bold\" title=\"3 2\">Trains</td></tr>\n", "<tr><td title=\"0 3\">John</td><td style=\"background-color:MistyRose\" title=\"1 3\"><strike>2.0</strike>Clouds</td><td style=\"background-color:MistyRose\" title=\"2 3\">2.0</td><td style=\"background-color:MistyRose\" title=\"3 3\">1.0</td></tr>\n", "<tr><td title=\"0 4\">Paul</td><td style=\"background-color:MistyRose\" title=\"1 4\">4.0</td><td style=\"background-color:MistyRose\" title=\"2 4\">3.0</td><td style=\"background-color:MistyRose\" title=\"3 4\">2.0</td></tr>\n", "<tr><td title=\"0 5\">Ringo</td><td style=\"background-color:MistyRose\" title=\"1 5\">4.0</td><td style=\"background-color:MistyRose\" title=\"2 5\">1.0</td><td style=\"background-color:MistyRose\" title=\"3 5\">3.0</td></tr>\n", "<tr><td title=\"0 6\">George</td><td style=\"background-color:MistyRose\" title=\"1 6\">2.0</td><td style=\"background-color:MistyRose\" title=\"2 6\">5.0</td><td style=\"background-color:MistyRose\" title=\"3 6\">5.0</td></tr>\n", "</table>\n", "\n", "</div>\n" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# The preview table shows how things have changed\n", "savepreviewhtml([h1, obs])" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Wrong-Obs {<A1 'Date'>} maps to (None, None) <--- ie Nothing\n" ] } ], "source": [ "wob = tab.excel_ref('A1')\n", "print(\"Wrong-Obs\", wob, \"maps to\", h1.cellvalobs(wob), \" <--- ie Nothing\")\n" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "After giving a default value Wrong-Obs {<A1 'Date'>} now maps to (None, 'Who knows?')\n" ] } ], "source": [ "h1.AddCellValueOverride(None, \"Who knows?\")\n", "print(\"After giving a default value Wrong-Obs\", wob, \"now maps to\", h1.cellvalobs(wob))" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Obs {<B4 2.0>} maps to (None, 'Beatles')\n", "Obs {<C4 2.0>} maps to (None, 'Beatles')\n", "Obs {<D4 1.0>} maps to (None, 'Beatles')\n", "Obs {<B5 4.0>} maps to (None, 'Beatles')\n", "Obs {<C5 3.0>} maps to (None, 'Beatles')\n", "Obs {<D5 2.0>} maps to (None, 'Beatles')\n", "Obs {<B6 4.0>} maps to (None, 'Beatles')\n", "Obs {<C6 1.0>} maps to (None, 'Beatles')\n", "Obs {<D6 3.0>} maps to (None, 'Beatles')\n", "Obs {<B7 2.0>} maps to (None, 'Beatles')\n", "Obs {<C7 5.0>} maps to (None, 'Beatles')\n", "Obs {<D7 5.0>} maps to (None, 'Beatles')\n" ] } ], "source": [ "# The default even works if the cell bag set is empty. In which case we have a special \n", "# constant case that maps every observation to the same value\n", "h3 = HDimConst(\"Category\", \"Beatles\")\n", "for ob in obs:\n", " print(\"Obs\", ob, \"maps to\", h3.cellvalobs(ob))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Conversion segments and output\n", "A ConversionSegment is a collection of Dimensions with an Observation set that is going to be processed and output as a table all at once.\n", "\n", "You can preview them in HTML (just like the cell bags and dimensions), only this time the observation cells can be clicked on interactively to show how they look up. " ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div id=\"injblock1011\">\n", "<style>\n", "table.ex, table.exkey { border: thin black solid }\n", "table.ex td, table.ex tr { border: none }\n", "table.ex td:hover { border: thin blue solid }\n", "table.ex td.excOBS:hover { border: thin red solid }\n", "table.ex td.selected { border: thick red solid }\n", "</style>\n", "\n", "\n", "<table class=\"exkey\">\n", "<tr><td class=\"xc0\" style=\"background-color:Aqua\">OBS</td><td class=\"xc1\" style=\"background-color:LightGreen\">TIME</td><td class=\"xc2\" style=\"background-color:MistyRose\">Vehicles</td><td class=\"xc3\" style=\"background-color:LightGray\">Name</td></tr></table>\n", "\n", "<table class=\"ex\">\n", "<caption style=\"text-align:center; padding:0px; caption-side:bottom\">beatles</caption>\n", "<tr><td title=\"0 0\">Date</td><td style=\"background-color:LightGreen\" title=\"1 0\">2014.0</td><td title=\"2 0\"></td><td title=\"3 0\"></td></tr>\n", "<tr><td title=\"0 1\"></td><td title=\"1 1\"></td><td title=\"2 1\"></td><td title=\"3 1\"></td></tr>\n", "<tr><td title=\"0 2\"></td><td style=\"background-color:MistyRose\" title=\"1 2\">Cars</td><td style=\"background-color:MistyRose;font-weight:bold\" title=\"2 2\">Planes</td><td style=\"background-color:MistyRose;font-weight:bold\" title=\"3 2\">Trains</td></tr>\n", "<tr><td style=\"background-color:LightGray\" title=\"0 3\">John</td><td style=\"background-color:Aqua\" title=\"1 3\">2.0</td><td style=\"background-color:Aqua\" title=\"2 3\">2.0</td><td style=\"background-color:Aqua\" title=\"3 3\">1.0</td></tr>\n", "<tr><td style=\"background-color:LightGray\" title=\"0 4\">Paul</td><td style=\"background-color:Aqua\" title=\"1 4\">4.0</td><td style=\"background-color:Aqua\" title=\"2 4\">3.0</td><td style=\"background-color:Aqua\" title=\"3 4\">2.0</td></tr>\n", "<tr><td style=\"background-color:LightGray\" title=\"0 5\">Ringo</td><td style=\"background-color:Aqua\" title=\"1 5\">4.0</td><td style=\"background-color:Aqua\" title=\"2 5\">1.0</td><td style=\"background-color:Aqua\" title=\"3 5\">3.0</td></tr>\n", "<tr><td style=\"background-color:LightGray\" title=\"0 6\">George</td><td style=\"background-color:Aqua\" title=\"1 6\">2.0</td><td style=\"background-color:Aqua\" title=\"2 6\">5.0</td><td style=\"background-color:Aqua\" title=\"3 6\">5.0</td></tr>\n", "</table>\n", "\n", "</div>\n", "\n", "<script>\n", "var jslookup = {\"2 3\":[1,0,2,2,0,3],\"2 6\":[1,0,2,2,0,6],\"2 5\":[1,0,2,2,0,5],\"1 3\":[1,0,1,2,0,3],\"3 6\":[1,0,3,2,0,6],\"1 6\":[1,0,1,2,0,6],\"3 3\":[1,0,3,2,0,3],\"1 4\":[1,0,1,2,0,4],\"3 5\":[1,0,3,2,0,5],\"2 4\":[1,0,2,2,0,4],\"1 5\":[1,0,1,2,0,5],\"3 4\":[1,0,3,2,0,4]}; \n", "var jdividNUM = \"injblock1011\"; \n", "var Dclickedcell = null; \n", "function clickedcell() \n", "{ \n", " Dclickedcell = this; \n", " console.log(\"jjjj\", this); \n", " var rgc = new RegExp('(^|\\b)' + \"selected\".split(' ').join('|') + '(\\b|$)', 'gi'); \n", " Array.prototype.forEach.call(document.querySelectorAll(\"div#\"+jdividNUM+\" table.ex td.selected\"), function(el, i) { \n", " if (el.classList) el.classList.remove(\"selected\");\n", " else el.className = el.className.replace(rgc, ' ');\n", " }); \n", " if (this.classList) this.classList.add(\"selected\");\n", " else this.className += ' ' + \"selected\";\n", "\n", " var dimpairs = jslookup[this.title]; \n", " if (dimpairs !== undefined) {\n", " for (var i = 1; i < dimpairs.length; i += 2) {\n", " var row = document.querySelectorAll(\"div#\"+jdividNUM+\" table.ex tr\")[dimpairs[i]]; \n", " var el = row.querySelectorAll(\"td\")[dimpairs[i-1]]; \n", " if (el.classList) el.classList.add(\"selected\");\n", " else el.className += ' ' + \"selected\";\n", " }\n", " }\n", "}\n", "Array.prototype.forEach.call(document.querySelectorAll(\"div#\"+jdividNUM+\" table.ex td\"), function(item, i) { item.onclick=clickedcell; }); \n", "</script>\n" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "dimensions = [ \n", " HDim(tab.excel_ref('B1'), TIME, CLOSEST, ABOVE), \n", " HDim(r1, \"Vehicles\", DIRECTLY, ABOVE), \n", " HDim(r2, \"Name\", DIRECTLY, LEFT), \n", " HDimConst(\"Category\", \"Beatles\")\n", "]\n", "\n", "c1 = ConversionSegment(obs, dimensions, processTIMEUNIT=False)\n", "savepreviewhtml(c1)\n" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "Written to file: <a href='preview.html' target='_blank'>preview.html</a><br>" ], "text/plain": [ "/home/goatchurch/sensiblecode/src/databaker/databaker/tutorial/preview.html" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# If the table is too big, we can preview it in another file is openable in another browser window.\n", "# (It's very useful if you are using two computer screens.)\n", "savepreviewhtml(c1, \"preview.html\", verbose=False)" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Looking up all the observations against all the dimensions and print them out\n", "{'Vehicles': 'Cars', 'Category': 'Beatles', 'OBS': 2.0, 'TIME': '2014.0', 'Name': 'John'}\n", "{'Vehicles': 'Planes', 'Category': 'Beatles', 'OBS': 2.0, 'TIME': '2014.0', 'Name': 'John'}\n", "{'Vehicles': 'Trains', 'Category': 'Beatles', 'OBS': 1.0, 'TIME': '2014.0', 'Name': 'John'}\n", "{'Vehicles': 'Cars', 'Category': 'Beatles', 'OBS': 4.0, 'TIME': '2014.0', 'Name': 'Paul'}\n", "{'Vehicles': 'Planes', 'Category': 'Beatles', 'OBS': 3.0, 'TIME': '2014.0', 'Name': 'Paul'}\n", "{'Vehicles': 'Trains', 'Category': 'Beatles', 'OBS': 2.0, 'TIME': '2014.0', 'Name': 'Paul'}\n", "{'Vehicles': 'Cars', 'Category': 'Beatles', 'OBS': 4.0, 'TIME': '2014.0', 'Name': 'Ringo'}\n", "{'Vehicles': 'Planes', 'Category': 'Beatles', 'OBS': 1.0, 'TIME': '2014.0', 'Name': 'Ringo'}\n", "{'Vehicles': 'Trains', 'Category': 'Beatles', 'OBS': 3.0, 'TIME': '2014.0', 'Name': 'Ringo'}\n", "{'Vehicles': 'Cars', 'Category': 'Beatles', 'OBS': 2.0, 'TIME': '2014.0', 'Name': 'George'}\n", "{'Vehicles': 'Planes', 'Category': 'Beatles', 'OBS': 5.0, 'TIME': '2014.0', 'Name': 'George'}\n", "{'Vehicles': 'Trains', 'Category': 'Beatles', 'OBS': 5.0, 'TIME': '2014.0', 'Name': 'George'}\n" ] } ], "source": [ "print(\"Looking up all the observations against all the dimensions and print them out\")\n", "for ob in c1.segment:\n", " print(c1.lookupobs(ob))" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>OBS</th>\n", " <th>TIME</th>\n", " <th>Vehicles</th>\n", " <th>Name</th>\n", " <th>Category</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>2.0</td>\n", " <td>2014.0</td>\n", " <td>Cars</td>\n", " <td>John</td>\n", " <td>Beatles</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2.0</td>\n", " <td>2014.0</td>\n", " <td>Planes</td>\n", " <td>John</td>\n", " <td>Beatles</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1.0</td>\n", " <td>2014.0</td>\n", " <td>Trains</td>\n", " <td>John</td>\n", " <td>Beatles</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>4.0</td>\n", " <td>2014.0</td>\n", " <td>Cars</td>\n", " <td>Paul</td>\n", " <td>Beatles</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>3.0</td>\n", " <td>2014.0</td>\n", " <td>Planes</td>\n", " <td>Paul</td>\n", " <td>Beatles</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>2.0</td>\n", " <td>2014.0</td>\n", " <td>Trains</td>\n", " <td>Paul</td>\n", " <td>Beatles</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>4.0</td>\n", " <td>2014.0</td>\n", " <td>Cars</td>\n", " <td>Ringo</td>\n", " <td>Beatles</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>1.0</td>\n", " <td>2014.0</td>\n", " <td>Planes</td>\n", " <td>Ringo</td>\n", " <td>Beatles</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>3.0</td>\n", " <td>2014.0</td>\n", " <td>Trains</td>\n", " <td>Ringo</td>\n", " <td>Beatles</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>2.0</td>\n", " <td>2014.0</td>\n", " <td>Cars</td>\n", " <td>George</td>\n", " <td>Beatles</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>5.0</td>\n", " <td>2014.0</td>\n", " <td>Planes</td>\n", " <td>George</td>\n", " <td>Beatles</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>5.0</td>\n", " <td>2014.0</td>\n", " <td>Trains</td>\n", " <td>George</td>\n", " <td>Beatles</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " OBS TIME Vehicles Name Category\n", "0 2.0 2014.0 Cars John Beatles\n", "1 2.0 2014.0 Planes John Beatles\n", "2 1.0 2014.0 Trains John Beatles\n", "3 4.0 2014.0 Cars Paul Beatles\n", "4 3.0 2014.0 Planes Paul Beatles\n", "5 2.0 2014.0 Trains Paul Beatles\n", "6 4.0 2014.0 Cars Ringo Beatles\n", "7 1.0 2014.0 Planes Ringo Beatles\n", "8 3.0 2014.0 Trains Ringo Beatles\n", "9 2.0 2014.0 Cars George Beatles\n", "10 5.0 2014.0 Planes George Beatles\n", "11 5.0 2014.0 Trains George Beatles" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = c1.topandas()\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# WDA Technical CSV\n", "The ONS uses their own data system for publishing their time-series data known as WDA. \n", "\n", "If you need to output to it, then this next section is for you. \n", "\n", "The function which outputs to the WDA format is `writetechnicalCSV(filename, [conversionsegments])` The format is very verbose because it repeats each dimension name and its value twice in each row, and every row begins with the following list of column entries, whether or not they exist.\n", "\n", "> `observation, data_marking, statistical_unit_eng, statistical_unit_cym, measure_type_eng, measure_type_cym, observation_type, obs_type_value, unit_multiplier, unit_of_measure_eng, unit_of_measure_cym, confidentuality, geographic_area`\n", "\n", "The `writetechnicalCSV()` function accepts a single conversion segment, a list of conversion segments, or equivalently a pandas dataframe. \n" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "observation,data_marking,statistical_unit_eng,statistical_unit_cym,measure_type_eng,measure_type_cym,observation_type,empty,obs_type_value,unit_multiplier,unit_of_measure_eng,unit_of_measure_cym,confidentuality,empty1,geographic_area,empty2,empty3,time_dim_item_id,time_dim_item_label_eng,time_dim_item_label_cym,time_type,empty4,statistical_population_id,statistical_population_label_eng,statistical_population_label_cym,cdid,cdiddescrip,empty5,empty6,empty7,empty8,empty9,empty10,empty11,empty12,dim_id_1,dimension_label_eng_1,dimension_label_cym_1,dim_item_id_1,dimension_item_label_eng_1,dimension_item_label_cym_1,is_total_1,is_sub_total_1,dim_id_2,dimension_label_eng_2,dimension_label_cym_2,dim_item_id_2,dimension_item_label_eng_2,dimension_item_label_cym_2,is_total_2,is_sub_total_2,dim_id_3,dimension_label_eng_3,dimension_label_cym_3,dim_item_id_3,dimension_item_label_eng_3,dimension_item_label_cym_3,is_total_3,is_sub_total_3\r\n", "2.0,,,,,,,,,,,,,,,,,2014.0,2014.0,,,,,,,,,,,,,,,0,,Vehicles,Vehicles,,Cars,Cars,,,,Name,Name,,John,John,,,,Category,Category,,Beatles,Beatles,,,\r\n", "2.0,,,,,,,,,,,,,,,,,2014.0,2014.0,,,,,,,,,,,,,,,0,,Vehicles,Vehicles,,Planes,Planes,,,,Name,Name,,John,John,,,,Category,Category,,Beatles,Beatles,,,\r\n", "1.0,,,,,,,,,,,,,,,,,2014.0,2014.0,,,,,,,,,,,,,,,0,,Vehicles,Vehicles,,Trains,Trains,,,,Name,Name,,John,John,,,,Category,Category,,Beatles,Beatles,,,\r\n", "4.0,,,,,,,,,,,,,,,,,2014.0,2014.0,,,,,,,,,,,,,,,0,,Vehicles,Vehicles,,Cars,Cars,,,,Name,Name,,Paul,Paul,,,,Category,Category,,Beatles,Beatles,,,\r\n", "3.0,,,,,,,,,,,,,,,,,2014.0,2014.0,,,,,,,,,,,,,,,0,,Vehicles,Vehicles,,Planes,Planes,,,,Name,Name,,Paul,Paul,,,,Category,Category,,Beatles,Beatles,,,\r\n", "2.0,,,,,,,,,,,,,,,,,2014.0,2014.0,,,,,,,,,,,,,,,0,,Vehicles,Vehicles,,Trains,Trains,,,,Name,Name,,Paul,Paul,,,,Category,Category,,Beatles,Beatles,,,\r\n", "4.0,,,,,,,,,,,,,,,,,2014.0,2014.0,,,,,,,,,,,,,,,0,,Vehicles,Vehicles,,Cars,Cars,,,,Name,Name,,Ringo,Ringo,,,,Category,Category,,Beatles,Beatles,,,\r\n", "1.0,,,,,,,,,,,,,,,,,2014.0,2014.0,,,,,,,,,,,,,,,0,,Vehicles,Vehicles,,Planes,Planes,,,,Name,Name,,Ringo,Ringo,,,,Category,Category,,Beatles,Beatles,,,\r\n", "3.0,,,,,,,,,,,,,,,,,2014.0,2014.0,,,,,,,,,,,,,,,0,,Vehicles,Vehicles,,Trains,Trains,,,,Name,Name,,Ringo,Ringo,,,,Category,Category,,Beatles,Beatles,,,\r\n", "2.0,,,,,,,,,,,,,,,,,2014.0,2014.0,,,,,,,,,,,,,,,0,,Vehicles,Vehicles,,Cars,Cars,,,,Name,Name,,George,George,,,,Category,Category,,Beatles,Beatles,,,\r\n", "5.0,,,,,,,,,,,,,,,,,2014.0,2014.0,,,,,,,,,,,,,,,0,,Vehicles,Vehicles,,Planes,Planes,,,,Name,Name,,George,George,,,,Category,Category,,Beatles,Beatles,,,\r\n", "5.0,,,,,,,,,,,,,,,,,2014.0,2014.0,,,,,,,,,,,,,,,0,,Vehicles,Vehicles,,Trains,Trains,,,,Name,Name,,George,George,,,,Category,Category,,Beatles,Beatles,,,\r\n", "*********,12\r\n", "\n" ] } ], "source": [ "print(writetechnicalCSV(None, c1))" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "writing 1 conversion segments into /home/goatchurch/sensiblecode/src/databaker/databaker/tutorial/exampleWDA.csv\n", "conversionwrite segment size 12 table 'beatles'; \n", " OBS TIME Vehicles Name Category\n", "0 2.0 2014.0 Cars John Beatles\n", "1 2.0 2014.0 Planes John Beatles\n", "2 1.0 2014.0 Trains John Beatles\n", "3 4.0 2014.0 Cars Paul Beatles\n", "4 3.0 2014.0 Planes Paul Beatles\n", "5 2.0 2014.0 Trains Paul Beatles\n", "6 4.0 2014.0 Cars Ringo Beatles\n", "7 1.0 2014.0 Planes Ringo Beatles\n", "8 3.0 2014.0 Trains Ringo Beatles\n", "9 2.0 2014.0 Cars George Beatles\n", "10 5.0 2014.0 Planes George Beatles\n", "11 5.0 2014.0 Trains George Beatles\n" ] } ], "source": [ "# This is how to write to a file\n", "writetechnicalCSV(\"exampleWDA.csv\", c1)\n", "\n", "# We can read this file back in to a list of pandas dataframes\n", "dfs = readtechnicalCSV(\"exampleWDA.csv\")\n", "print(dfs[0])\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Note** If you were wondering what the `processTIMEUNIT=False` was all about in the ConversionSegment constructor, it's a feature to help the WDA output automatically set the TIMEUNIT column according to whether it should be Year, Month, or Quarter.\n", "\n", "You will note that the TIME column above is `2014.0` when it really should be `2014` with the TIMEUNIT set to `Year`.\n", "\n", "By setting it to `True` the ConversionSegment object will identify the timeunit from the value of the TIME column and then force its format to conform. " ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "TIMEUNIT='Year'\n" ] }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>OBS</th>\n", " <th>TIME</th>\n", " <th>TIMEUNIT</th>\n", " <th>Vehicles</th>\n", " <th>Name</th>\n", " <th>Category</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>2.0</td>\n", " <td>2014</td>\n", " <td>Year</td>\n", " <td>Cars</td>\n", " <td>John</td>\n", " <td>Beatles</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2.0</td>\n", " <td>2014</td>\n", " <td>Year</td>\n", " <td>Planes</td>\n", " <td>John</td>\n", " <td>Beatles</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1.0</td>\n", " <td>2014</td>\n", " <td>Year</td>\n", " <td>Trains</td>\n", " <td>John</td>\n", " <td>Beatles</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>4.0</td>\n", " <td>2014</td>\n", " <td>Year</td>\n", " <td>Cars</td>\n", " <td>Paul</td>\n", " <td>Beatles</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>3.0</td>\n", " <td>2014</td>\n", " <td>Year</td>\n", " <td>Planes</td>\n", " <td>Paul</td>\n", " <td>Beatles</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>2.0</td>\n", " <td>2014</td>\n", " <td>Year</td>\n", " <td>Trains</td>\n", " <td>Paul</td>\n", " <td>Beatles</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>4.0</td>\n", " <td>2014</td>\n", " <td>Year</td>\n", " <td>Cars</td>\n", " <td>Ringo</td>\n", " <td>Beatles</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>1.0</td>\n", " <td>2014</td>\n", " <td>Year</td>\n", " <td>Planes</td>\n", " <td>Ringo</td>\n", " <td>Beatles</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>3.0</td>\n", " <td>2014</td>\n", " <td>Year</td>\n", " <td>Trains</td>\n", " <td>Ringo</td>\n", " <td>Beatles</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>2.0</td>\n", " <td>2014</td>\n", " <td>Year</td>\n", " <td>Cars</td>\n", " <td>George</td>\n", " <td>Beatles</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>5.0</td>\n", " <td>2014</td>\n", " <td>Year</td>\n", " <td>Planes</td>\n", " <td>George</td>\n", " <td>Beatles</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>5.0</td>\n", " <td>2014</td>\n", " <td>Year</td>\n", " <td>Trains</td>\n", " <td>George</td>\n", " <td>Beatles</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " OBS TIME TIMEUNIT Vehicles Name Category\n", "0 2.0 2014 Year Cars John Beatles\n", "1 2.0 2014 Year Planes John Beatles\n", "2 1.0 2014 Year Trains John Beatles\n", "3 4.0 2014 Year Cars Paul Beatles\n", "4 3.0 2014 Year Planes Paul Beatles\n", "5 2.0 2014 Year Trains Paul Beatles\n", "6 4.0 2014 Year Cars Ringo Beatles\n", "7 1.0 2014 Year Planes Ringo Beatles\n", "8 3.0 2014 Year Trains Ringo Beatles\n", "9 2.0 2014 Year Cars George Beatles\n", "10 5.0 2014 Year Planes George Beatles\n", "11 5.0 2014 Year Trains George Beatles" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# See that the `2014` no longer ends with `.0`\n", "c1 = ConversionSegment(obs, dimensions, processTIMEUNIT=True)\n", "c1.topandas()\n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "**Note** Sometimes the TIME value needs to be created by joining two or more other cells (eg one is a month, and the other is the year). \n", "\n", "Such an operation can much more easily be done using the pandas column operations than by using the concept of subdimensions which used to exist in Databaker before we took it out. \n", "\n", "This will be explained in a later worked example." ] }, { "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": 1 }
agpl-3.0
NazBen/impact-of-dependence
notebooks/grid-search.ipynb
1
696987
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Conservative Estimation using a Grid Seach Minimization\n", "\n", "This notebook illustrates the different steps for a conservative estimation using a grid search minimization.\n", "\n", "###### Classic Libraries" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import openturns as ot\n", "import numpy as np\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "\n", "%matplotlib inline\n", "%load_ext autoreload\n", "%autoreload 2\n", "\n", "random_state = 123\n", "np.random.seed(random_state)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Additive model\n", "\n", "The first example of conservative estimation consider an additive model $\\eta : \\mathbb R^d \\rightarrow \\mathbb R$ with Gaussian margins. The objectives are to estimate a quantity of interest $\\mathcal C(Y)$ of the model output distribution. Unfortunately, the dependence structure is unknown. In order to be conservative we aim to give bounds to $\\mathcal C(Y)$.\n", "\n", "### The model\n", "This example consider the simple additive example." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on function func_sum in module depimpact.tests.test_functions:\n", "\n", "func_sum(x, a=None)\n", " Additive weighted model function.\n", " \n", " Parameters\n", " ----------\n", " x : np.ndarray\n", " The input values.\n", " a : np.ndarray\n", " The input coefficients.\n", " \n", " Returns\n", " -------\n", " y : a.x^t\n", "\n" ] } ], "source": [ "from depimpact.tests import func_sum\n", "help(func_sum)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Dimension 2\n", "\n", "We consider the problem in dimension $d=2$ and a number of pairs $p=1$ for gaussian margins." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "dim = 2\n", "margins = [ot.Normal()]*dim" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Copula families\n", "\n", "We consider a gaussian copula for this first example" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "families = np.zeros((dim, dim), dtype=int)\n", "families[1, 0] = 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Estimations\n", "We create an instance of the main class for a conservative estimate." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "from depimpact import ConservativeEstimate\n", "\n", "quant_estimate = ConservativeEstimate(model_func=func_sum, margins=margins, families=families)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, we compute the quantile at independence" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "n = 1000\n", "indep_result = quant_estimate.independence(n_input_sample=n, random_state=random_state)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We aim to minimize the output quantile. To do that, we create a `q_func` object from the function `quantile_func` to associate a probability $\\alpha$ to a function that computes the empirical quantile from a given sample." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "from depimpact import quantile_func\n", "alpha = 0.05\n", "q_func = quantile_func(alpha)\n", "indep_result.q_func = q_func" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The computation returns a `DependenceResult` instance. This object gather the informations of the computation. It also computes the output quantity of interest (which can also be changed)." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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zbsHlJq9KwlbE2zUipwIJJU1hnPL0h5UHTH/cJgMU5300bZACwIpFc0L/vptK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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x23cb13315c0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.jointplot(indep_result.input_sample[:, 0], indep_result.input_sample[:, 1]);" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Output quantile : -2.3035580191761107\n" ] }, { "data": { "image/png": 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BwZSWlmry93HGGEpLSwkODu7WcXSOXylg4fpcckqruTYzgeg+py461p6fXDyUD7cW8Mii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\n89lRUMG1mYlEhgRYHZ7X0cT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"text/plain": [ "<matplotlib.figure.Figure at 0x23ccf6e2630>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "h = sns.distplot(indep_result.output_sample_id, axlabel='Model output', label=\"Output Distribution\")\n", "plt.plot([indep_result.quantity]*2, h.get_ylim(), label='Quantile at %d%%' % (alpha*100))\n", "plt.legend(loc=0)\n", "print('Output quantile :', indep_result.quantity)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A boostrap can be done on the output quantity" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "indep_result.compute_bootstrap(n_bootstrap=5000)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And we can plot it" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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z0as28PINZew+1sFbv/04Hf0jiS4tJairRKLmnOPufSdp6hzkHRfXsLwgtfaT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\nnNAm1G1evL6IV9TiFhFJMmp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"text/plain": [ "<matplotlib.figure.Figure at 0x23cb1331f98>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.distplot(indep_result.bootstrap_sample, axlabel='Output quantile');" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Quantile at independence: -2.29 with a C.O.V at 0.0 %\n" ] }, { "data": { "image/png": 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Vevm2bkp8KAWlFXy3M93eqagORgu/smzFwRWsOLjCZnG2VlFp2Hw4iz5d/fDz\ncG3112+sYZGdCAv0ZMHmFHunojoYLfyq3dhzLJf8knKGOcBoH8DJSZgyOJRfkjI5llNs73RUB6KF\nX7UbG5Oz8PNwIbpL27lStyHXxodRaeCLhLO6oCjVYrTwq3Yhu7CUxPQ8hvQItNvNVpoiKtibIT0C\nWbA5BWN0Tb9qHVr4lWVBXkEEeTXc3thqnC1tOpyFAYb0aLtr9+szJT6UxPR8dh7NtXcqqoPQdfzK\nsgU3LLBpnK1UGsOmQ1n0CvGhk7dbq762LUwe2J0/L9nFgs0pDAj1t3c6qgPQEb9yeIdOFJJdWEZ8\njwB7p9Ik/l6uXBLTmSUJRymr0NsyqpanhV9Z9qflf+JPy/9kszhbSTiSjZuzEzHdHHe0PGVwGCcK\nSlmVmGHvVFQHoFM9yrK1KWttGmcL5ZWV7EjNIaa7H24ujjuOGdsnhCBvNxZuTmV8vy72Tke1c477\nk6IUkHgsn6KyCmLDHHOa5xRXZyeuiuvO97vSySkss3c6qp3Twq8cWsKRLLzdnOnV2cfeqTTbdfFh\nlFZUsnT7UXunoto5LfzKYeUWl7HnWB4DwwIcau1+ffp396N3Fx8WbtaLuVTL0sKvLAvzCyPML8xm\ncc31zY5jlFca4sIde5rnFBFhSnwYmw5lkZxZYO90VDumH+4qy/4z5T82jWuuLxJS6eTtRnig/W76\nYmvXxIXyj2/2sHBLKg9d2tve6ah2Skf8yiGl5xazZv8J4sIDkDbcd7+xuvp7MKpXMAs3p+htGVWL\n0cKvLHvwmwd58JsHbRbXHF9uPYoxEOfgq3nqcl18GClZRWw8lGXvVFQ7pVM9yrKEYwk2jWuOxQmp\nDArzJ9jXvcVfq7Vd1r8L3m7OLNiUwvlRjtd7SLV9OuJXDifpeB47UnO5Oi7U3qm0CC83FyYO7MZX\n29MoLquwdzqqHbJU+EVkgojsFZEkEZldx/O3iMg2EdkuImtEJNbqvko11uItR3ESuDK2m71TaTFT\n4kPJLynnu116W0Zlew0WfhFxBl4BJgIxwE0iEnNG2EFgrDFmIPA0MLcR+yplmTGGpduOMvK8YDr7\netg7nRZzQVQQoQGeLNTbMqoWYGXEfz6QZIw5YIwpBT4Brq4dYIxZY4w59UnUOiDM6r7KcfQO6k3v\noIaXGFqNa4rdaXkknyjkikHtd7QPVbdlvHZwKKsSMziep7dlVLZl5cPdUOBIrccpwPBzxN8FfN3Y\nfUVkOjAun2X5AAAgAElEQVQdICIiwkJaqrXNvXKuTeOa4usdaTgJXBbT/huZXRsfyr9/TGJJwlHu\nvrCnvdNR7YhNP9wVkYuoKvx/bOy+xpi5xpihxpihISEhtkxLtRPGGL7ansYFPYMI8ml/q3nOdF6I\nD3HhAczfpNM9yrasjPhTgfBaj8Oqt51GRAYBbwETjTEnGrOvcgzTv5wONDyitxrXWPuO53Mgo4A7\nRkba9LhtWUQnL5ZsPcrz3+2lm3/9VyjfPFzfJSvrrIz4NwDRIhIlIm7AjcCS2gEiEgEsBH5jjEls\nzL7KcSSeSCTxRKLN4hrr6+3HEIHL+3e1+bHbqkGh/jiLsOVwtr1TUe1IgyN+Y0y5iNwPfAs4A+8Y\nY3aKyIzq518H/hcIAl6tvny+vHraps59W+hcVDv39Y40hvXoRGe/9rGa56NfDzcY4+XuQp+uviQc\nyeby/l3bRRdSZX+Wrtw1xiwDlp2x7fVaX98N3G11X6Uaa39GPnuO5fHklR1vNXB8RCC70nJJOp5P\nn66+9k5HtQN65a5yCN/sOAbAhAEdZ5rnlN5dffByc2bLEe3do2xDe/Uoy+K6xtk0rjGWbU8jPiLg\nnB9wtlcuTk4MCgtgY/JJissq8HB1tndKysFp4VeWvTjhRZvGWXXoRAE7j+byxBX9bHpcRxIfEcC6\nAyfYnprDsEht3KaaR6d6VJv3dQee5jklNMCTEB93thzW6R7VfFr4lWW3LryVWxfearM4q77enkZs\nmD9hgV42O6ajEREGRwSQfKKQE/kl9k5HOTgt/MqylNwUUnIbvorUapyl18wqZGtKDhMHtu/ePFbE\nRwTiJLAhWUf9qnm08Ks27dRqnokdeJrnFD9PV/p29WPT4SzKKyvtnY5yYFr4VZv23a50+nb1pUeQ\nt71TaROGRXaioKSc3Wl59k5FOTAt/KrNOllQysbkkx2iE6dV0V18CPB0ZUPySXunohyYLudUlo0I\nG2HTuIb8uOc4lQYu0cJfw0mEoZGBLN99nJMFpXTydrN3SsoBaeFXlv3tkr/ZNK4hy3en08XPnQHd\n/W1yvPZiSI9OrNh9nA3JJztUwzplOzrVo9qk4rIKfkrM4JJ+XXDSxmSn8fd0pU9XXzYdyqKi0tg7\nHeWAdMSvLLvus+sAWHDDApvEncvflu2hsLQCFycnS10sO5rzIzux59gh9hzLpb++I1KNpIVfWXai\n8ETDQY2IO5fdabm4uThxXoiu5qlLdBdf/Ks/5NXCrxpLp3pUm1NZadhzLJfozj64OOu3aF2cnYQh\nPQLZl55PVkGpvdNRDkZ/qlSbsz01h9zicmK6+dk7lTZtaI9AADYe0qWdqnG08Ks2Z/nudATo00Vv\nOnIuAV5u9O7iy8bkLErL9UpeZZ3O8SvLxkeNt2lcfb7flU6PIG+83PXbsyEX9AzivbXJfL0jjavj\nQu2djnIQ+pOlLPufsf9j07i6HDlZyJ5jeUzS3jyWRHfxIcjbjXlrkrXwK8t0qke1Kct3pwPQT+f3\nLXESYcR5QWw5nE3CkWx7p6MchBZ+ZdnEDycy8cOJNoury/e70onu7EOQj3uT9u+I4iMC8XF34b01\nyfZORTkInepRlhWVFdkkrr4LsopKK1h34AQXRoc0OreOzMPVmalDwvjw10P8aWJfOvt52Dsl1cZZ\nGvGLyAQR2SsiSSIyu47n+4rIWhEpEZE/nPFcsohsF5EEEdloq8RV+7M3PY9Ko9M8TXH7yEjKKw3v\nrU22dyrKATRY+EXEGXgFmAjEADeJSMwZYSeBB4A59RzmImNMnDFmaHOSVe3b7rRcvN1dCAv0tHcq\nDicy2JvLY7rywdpDFJSU2zsd1cZZGfGfDyQZYw4YY0qBT4CrawcYY44bYzYAZS2Qo+oAyisrSUzP\no19XX5xEm7I1xe/G9iS3uJxPNhyxdyqqjbMyxx8K1P5OSgGGN+I1DLBcRCqAN4wxcxuxr2pDJvee\nbNO42g5mFlBSXqnTPM0wOCKQ86M68fbPB7htRA9ctd2FqkdrfLg72hiTKiKdge9FZI8xZtWZQSIy\nHZgOEBER0Qppqcb6w8g/NBzUiLjadqfl4uosnBfi0+h91X/NGNuTO+dtZOm2o1w7OMze6ag2ysqQ\nIBUIr/U4rHqbJcaY1Oq/jwOLqJo6qiturjFmqDFmaEiIruroSIwx7E7Lo1dnX9xcdJTaHON6d6Z3\nFx9e/XG/9upX9bLyU7YBiBaRKBFxA24Ellg5uIh4i4jvqa+By4AdTU1W2de4eeMYN2+czeJOScsp\nJqeojH5dtTdPczk5CbMujmbf8XyWbU+zdzqqjWqw8BtjyoH7gW+B3cBnxpidIjJDRGYAiEhXEUkB\nHgKeEJEUEfEDugCrRWQrsB74yhjzTUudjHJMu9NyEaCvzu/bxBUDuxHd2YeXVuzTUb+qk6U5fmPM\nMmDZGdter/X1MaqmgM6UC8Q2J0HV/u0+lkt4Jy98tCmbTTg5Cb+/JJr7P9rCV9vTuCq2u71TUm2M\nTqgqu8ouLOVodrGu5rGxSQO60buLDy8tT9RRvzqLFn5lV3uO5QHQr5vO79uSk5Pw+/G92Z9RwNJt\nR+2djmpj9L21suyG/jfYNA6q5veDvN0I0aZsNjdxQFf6dPHl5RX7mDyoO85OemGcqqKFX1l277B7\nbRpXXFbBgYwCRp4XhOjVujZ3aq7/3g83s3TbUe3Xr2roVI+yrLCskMKyQpvFJabnUWGMruZpQRP6\nd6VvV19d4aNOo4VfWTbpw0lM+nCSzeL2HMvDy82ZHkFetkhP1aFqrj+aAxkFLNlq+bpL1c5p4Vd2\nUVFp2Hssj77alK3FXd6/KzHd/Hhx+T7KKvSm7EoLv7KTg5kFFJVV0LerTvO0NCcn4eHLenPoRCEL\nNqXYOx3VBuiHu8oudqXl4Oos9O6iyzhbw8V9OxMe6Mnfvt5DaXklLufo3HnzcG2S2N7piF+1ukpj\n2HU0l2htytZqRIRLY7qSU1TG+uST9k5H2ZmO+JVlt8fdbpO41KwicovLiemu0zytqVdnH3oGe7Ny\nbwZDe3TSX7odmBZ+ZZmtCv+utFycBPpqN06bqe8G9me6NKYLb6w6wNoDJxjbW9ufd1T6K19ZllmY\nSWZhZrPjdh7NJSrYGy83HXe0th5B3vTp4suqxAyKyyrsnY6yEy38yrKpn01l6mdTmxWXdDyPzPwS\nYrr72zo9ZdElMV0oKqtgdVLDv8RV+6SFX7Wqb3YcAyBGr9a1m9AAT/p39+OXpEwKSsrtnY6yAy38\nqlV9uzOd8EBP/D1d7Z1Kh3ZJvy6Ullfyy34d9XdEWvhVq0nNLmJ7ao5O87QBXfw86N/dj7X7T1BU\nqnP9HY0WftVqvttZNc3TX6d52oRxfTpTUl7J2gMn7J2KamW6rEJZNnPozGbFfbvzGNGdfQj21d77\nbUH3AE/6dvXll6RMRvUKwt3F2d4pqVaihV9ZNm3AtCbHnSwoZf3Bk9w7rpet01LNcFGfzrz2035+\nPXCSMbquv8PQqR5l2ZGcIxzJOdKkuOW706k0VZ0iVdsR3smLXp19+DkpUzt3diBa+JVlv1n0G36z\n6DdNivt2xzFCAzwZEKrz+23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"text/plain": [ "<matplotlib.figure.Figure at 0x23ccf98bc50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ci = [0.025, 0.975]\n", "quantity_ci = indep_result.compute_quantity_bootstrap_ci(ci)\n", "\n", "h = sns.distplot(indep_result.output_sample_id, axlabel='Model output', label=\"Output Distribution\")\n", "plt.plot([indep_result.quantity]*2, h.get_ylim(), 'g-', label='Quantile at %d%%' % (alpha*100))\n", "plt.plot([quantity_ci[0]]*2, h.get_ylim(), 'g--', label='%d%% confidence intervals' % ((1. - (ci[0] + 1. - ci[1]))*100))\n", "plt.plot([quantity_ci[1]]*2, h.get_ylim(), 'g--')\n", "plt.legend(loc=0)\n", "print('Quantile at independence: %.2f with a C.O.V at %.1f %%' % (indep_result.boot_mean, indep_result.boot_cov))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Grid Search Approach\n", "Firstly, we consider a grid search approach in order to compare the perfomance with the iterative algorithm. The discretization can be made on the parameter space or on other concordance measure such as the kendall's Tau. This below example shows a grid-search on the parameter space." ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "K = 20\n", "n = 10000\n", "grid_type = 'lhs'\n", "dep_measure = 'parameter'\n", "grid_result = quant_estimate.gridsearch(n_dep_param=K, n_input_sample=n, grid_type=grid_type, dep_measure=dep_measure, \n", " random_state=random_state)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The computation returns a `ListDependenceResult` which is a list of `DependenceResult` instances and some bonuses." ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The computation did 200000 model evaluations.\n" ] } ], "source": [ "print('The computation did %d model evaluations.' % (grid_result.n_evals))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lets set the quantity function and search for the minimum among the grid results." ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Minimum quantile: -3.270045262294147 at param: [0.9498828008575777]\n" ] } ], "source": [ "grid_result.q_func = q_func\n", "min_result = grid_result.min_result\n", "print('Minimum quantile: {} at param: {}'.format(min_result.quantity, min_result.dep_param))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can plot the result in grid results. The below figure shows the output quantiles in function of the dependence parameters." ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x23ccf767550>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(grid_result.dep_params, grid_result.quantities, '.', label='Quantiles')\n", "plt.plot(min_result.dep_param[0], min_result.quantity, 'ro', label='minimum')\n", "plt.xlabel('Dependence parameter')\n", "plt.ylabel('Quantile value')\n", "plt.legend(loc=0);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As for the individual problem, we can do a boostrap also, for each parameters. Because we have $K$ parameters, we can do a bootstrap for the $K$ samples, compute the $K$ quantiles for all the bootstrap and get the minimum quantile for each bootstrap." ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\naz-probook\\Anaconda3\\lib\\site-packages\\statsmodels\\nonparametric\\kde.py:494: RuntimeWarning: invalid value encountered in true_divide\n", " binned = fast_linbin(X,a,b,gridsize)/(delta*nobs)\n", "C:\\Users\\naz-probook\\Anaconda3\\lib\\site-packages\\statsmodels\\nonparametric\\kdetools.py:34: RuntimeWarning: invalid value encountered in double_scalars\n", " FAC1 = 2*(np.pi*bw/RANGE)**2\n" ] }, { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x23c800d4a20>" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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2tHTp+c0t+tylszh2TAa7dlGd/uaRtVq1u12b9499BbObzp4cx1QAACCV0SOD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\n77hsYDCi7v4h9QwM6nDfkLr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"text/plain": [ "<matplotlib.figure.Figure at 0x23c80006588>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "grid_result.compute_bootstraps(n_bootstrap=500)\n", "boot_min_quantiles = grid_result.bootstrap_samples.min(axis=0)\n", "boot_argmin_quantiles = grid_result.bootstrap_samples.argmin(axis=0).ravel().tolist()\n", "boot_min_params = [grid_result.dep_params[idx][0] for idx in boot_argmin_quantiles]\n", "\n", "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n", "sns.distplot(boot_min_quantiles, axlabel=\"Minimum quantiles\", ax=axes[0])\n", "sns.distplot(boot_min_params, axlabel=\"Parameters of the minimum\", ax=axes[1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For the parameter that have the most occurence for the minimum, we compute its bootstrap mean." ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Worst Quantile: -3.2667239514489337 at [0.9498828008575777] with a C.O.V of 1.1714362901728483 %\n" ] } ], "source": [ " # The parameter with most occurence\n", "boot_id_min = max(set(boot_argmin_quantiles), key=boot_argmin_quantiles.count)\n", "boot_min_result = grid_result[boot_id_min]\n", "\n", "boot_mean = boot_min_result.bootstrap_sample.mean()\n", "boot_std = boot_min_result.bootstrap_sample.std()\n", "print('Worst Quantile: {} at {} with a C.O.V of {} %'.format(boot_min_result.boot_mean, min_result.dep_param, boot_min_result.boot_cov*100.))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Kendall's Tau\n", "\n", "An interesting feature is to convert the dependence parameters to Kendall's Tau values." ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x23c80023a58>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(grid_result.kendalls, grid_result.quantities, '.', label='Quantiles')\n", "plt.plot(min_result.kendall_tau, min_result.quantity, 'ro', label='Minimum quantile')\n", "plt.xlabel(\"Kendall's tau\")\n", "plt.ylabel('Quantile')\n", "plt.legend(loc=0);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we can see, the bounds" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### With bounds on the dependencies\n", "\n", "An interesting option in the `ConservativeEstimate` class is to bound the dependencies, due to some prior informations. " ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "scrolled": true }, "outputs": [], "source": [ "bounds_tau = np.asarray([[0., 0.7], [0.1, 0.]])\n", "quant_estimate.bounds_tau = bounds_tau\n", "K = 20\n", "n = 10000\n", "grid_type = 'lhs'\n", "grid_result = quant_estimate.gridsearch(n_dep_param=K, n_input_sample=n, grid_type=grid_type, random_state=random_state)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Minimum quantile: -3.3076218755118094 at param: [ 0.98005602]\n" ] } ], "source": [ "grid_result.q_func = q_func\n", "min_result = grid_result.min_result\n", "print('Minimum quantile: {} at param: {}'.format(min_result.quantity, min_result.dep_param))" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x23cd0affd30>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(grid_result.dep_params, grid_result.quantities, '.', label='Quantiles')\n", "plt.plot(min_result.dep_param[0], min_result.quantity, 'ro', label='minimum')\n", "plt.xlabel('Dependence parameter')\n", "plt.ylabel('Quantile value')\n", "plt.legend(loc=0);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Saving the results\n", "\n", "It is usefull to save the result in a file to load it later and compute other quantities or anything you need!" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "No conversion path for dtype: dtype('<U3')", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-38-473176a44a41>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mfilename\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'./result.hdf'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mgrid_result\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_hdf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32mc:\\users\\naz-probook\\onedrive\\git-repo\\impact-of-dependence\\depimpact\\conservative.py\u001b[0m in \u001b[0;36mto_hdf\u001b[1;34m(self, path_or_buf, input_names, output_names, verbose, with_input_sample)\u001b[0m\n\u001b[0;32m 1230\u001b[0m \u001b[0mhdf_store\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mattrs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Fixed Parameters'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m 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"\u001b[1;32mh5py\\h5t.pyx\u001b[0m in \u001b[0;36mh5py.h5t.py_create\u001b[1;34m()\u001b[0m\n", "\u001b[1;32mh5py\\h5t.pyx\u001b[0m in \u001b[0;36mh5py.h5t.py_create\u001b[1;34m()\u001b[0m\n", "\u001b[1;31mTypeError\u001b[0m: No conversion path for dtype: dtype('<U3')" ] } ], "source": [ "filename = './result.hdf'\n", "grid_result.to_hdf(filename)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "ename": "AttributeError", "evalue": "'KeysView' object has no attribute 'remove'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-26-f4ce0ceb49d1>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mdependence\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mListDependenceResult\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mload_grid_result\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mListDependenceResult\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfrom_hdf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mq_func\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mq_func\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwith_input_sample\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32mc:\\users\\naz-probook\\onedrive\\git-repo\\dep-impact\\dependence\\dependence.py\u001b[0m in \u001b[0;36mfrom_hdf\u001b[1;34m(cls, filepath_or_buffer, id_of_experiment, output_id, with_input_sample, q_func)\u001b[0m\n\u001b[0;32m 1253\u001b[0m \u001b[1;31m# All groups of experiments are loaded and concatenated\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1254\u001b[0m \u001b[0mlist_index\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mhdf_store\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1255\u001b[1;33m \u001b[0mlist_index\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'dependence_params'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1256\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1257\u001b[0m \u001b[1;31m# Only the specified experiment is loaded\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mAttributeError\u001b[0m: 'KeysView' object has no attribute 'remove'" ] } ], "source": [ "from dependence import ListDependenceResult\n", "load_grid_result = ListDependenceResult.from_hdf(filename, q_func=q_func, with_input_sample=False)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'load_grid_result' is not defined", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-27-1644477fd2bb>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtesting\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0massert_array_equal\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgrid_result\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moutput_samples\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mload_grid_result\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moutput_samples\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mNameError\u001b[0m: name 'load_grid_result' is not defined" ] } ], "source": [ "np.testing.assert_array_equal(grid_result.output_samples, load_grid_result.output_samples)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "import os\n", "os.remove(filename)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Taking the extreme values of the dependence parameter\n", "\n", "If the output quantity of interest seems to have a monotonicity with the dependence parameter, it is better to directly take the bounds of the dependence problem. Obviously, the minimum should be at the edges of the design space" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [], "source": [ "K = None\n", "n = 1000\n", "grid_type = 'vertices'\n", "grid_result = quant_estimate.gridsearch(n_dep_param=K, n_input_sample=n, grid_type=grid_type, random_state=random_state)" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Kendall's Tau : [ 0.15643447 0.89100652], Quantile: [-2.68011394 -3.2314678 ]\n" ] } ], "source": [] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Kendall's Tau : [ 0.15643447 0.89100652], Quantile: [-2.68011394 -3.2314678 ]\n" ] } ], "source": [ "grid_result.q_func = q_func\n", "print(\"Kendall's Tau : {}, Quantile: {}\".format(grid_result.kendalls.ravel(), grid_result.quantities))" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "image/png": 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LSwssFgsUCgVmz56NXbt24fvvvxeSdTv32TY2NgYssJboDCjRXb2db7bYdzrQ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\ngg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x23380039470>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from depimpact.plots import matrix_plot_input\n", "matrix_plot_input(grid_result.min_result);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Higher Dimension \n", "\n", "We consider the problem in dimension $d=5$." ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Don't forget to change the family matrix.\n", "Don't forget to change the bounds matrix\n" ] } ], "source": [ "dim = 5\n", "quant_estimate.margins = [ot.Normal()]*dim" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Copula families with one dependent pair\n", "\n", "We consider a gaussian copula for this first example, but for the moment only one pair is dependent." ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Dont't foget to update the bounds matrix\n" ] }, { "data": { "text/plain": [ "array([[0, 0, 0, 0, 0],\n", " [0, 0, 0, 0, 0],\n", " [1, 0, 0, 0, 0],\n", " [0, 0, 0, 0, 0],\n", " [0, 0, 0, 0, 0]])" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "families = np.zeros((dim, dim), dtype=int)\n", "families[2, 0] = 1\n", "quant_estimate.families = families\n", "families" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 0. , 0. , 0.99, 0. , 0. ],\n", " [ 0. , 0. , 0. , 0. , 0. ],\n", " [-0.99, 0. , 0. , 0. , 0. ],\n", " [ 0. , 0. , 0. , 0. , 0. ],\n", " [ 0. , 0. , 0. , 0. , 0. ]])" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "quant_estimate.bounds_tau = None\n", "quant_estimate.bounds_tau" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We reset the families and bounds for the current instance. (I don't want to create a new instance, just to check if the setters are good)." ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[3, 0, 0, 0, 0],\n", " [5, 2, 0, 0, 0],\n", " [4, 5, 4, 0, 0],\n", " [1, 4, 5, 1, 0],\n", " [2, 1, 1, 5, 5]])" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "quant_estimate.vine_structure" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's do the grid search to see" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [], "source": [ "K = 20\n", "n = 10000\n", "grid_type = 'vertices'\n", "grid_result = quant_estimate.gridsearch(n_dep_param=K, n_input_sample=n, grid_type=grid_type, random_state=random_state)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The quantile is lower compare to the problem of dimension 1. Indeed, there is more variables, more uncertainty, so a larger deviation of the output." ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Worst Quantile: -4.273422229577796 at [ 0.99999998]\n" ] }, { "data": { "text/plain": [ "<seaborn.axisgrid.PairGrid at 0x23380a91f28>" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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P6fV6Sskkko5E7FUhT2aD3sIbrqnT5KBcq8Lr7x0dtnXEA+YGIhSRC36Gmw8P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\nRyIIiPi3ySKTSRAKRVBm0mHahJHYW21Bi72T1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NE277y4gpxUFOenCyjSM5gzScPupjC7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\ngnSk3nsPPv/sU7S3t+PJJ5/kPz99ftR0I8kkQcQHg3pHri+ZNGkS3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MSkFJi6ZJWhssab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Z0UCDwWOGb2x0B4B9XzahtDAL9c3tKMzT4s6vTcC/PqnFCgmjOdXS3CJFINs6XPjJUx/C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\niIiIVIaDHBERERERkcr8A0D1unRKaJMtAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x23380a914a8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "grid_result.q_func = q_func\n", "min_result = grid_result.min_result\n", "print('Worst Quantile: {} at {}'.format(min_result.quantity, min_result.dep_param))\n", "matrix_plot_input(min_result)" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": 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nQXsktSUuLg5OTk4YNWoUbG1tYWFhodf/z3/+U/c4JiYGrVu3NjpEiIjIvKoNkmftcQgh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\nTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x23382816eb8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(grid_result.dep_params, grid_result.quantities, '.', label='Quantiles')\n", "plt.plot(min_result.dep_param[0], min_result.quantity, 'ro', label='Minimum')\n", "plt.xlabel('Dependence parameter')\n", "plt.ylabel('Quantile value')\n", "plt.legend(loc=0);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Copula families with all dependent pairs\n", "\n", "We consider a gaussian copula for this first example, but for the moment only one pair is dependent." ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [], "source": [ "families = np.zeros((dim, dim), dtype=int)\n", "for i in range(1, dim):\n", " for j in range(i):\n", " families[i, j] = 1" ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Don't forget to change the family matrix.\n" ] }, { "ename": "IndexError", "evalue": "index 0 is out of bounds for axis 0 with size 0", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mIndexError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-69-dc57b31daec4>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mquant_estimate\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmargins\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmargins\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0mquant_estimate\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfamilies\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfamilies\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[0mquant_estimate\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvine_structure\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0mquant_estimate\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbounds_tau\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mquant_estimate\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbounds_tau\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32mc:\\users\\naz-probook\\onedrive\\git-repo\\dep-impact\\dependence\\dependence.py\u001b[0m in \u001b[0;36mmargins\u001b[1;34m(self, margins)\u001b[0m\n\u001b[0;32m 457\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Don't forget to change the R-vine array\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 458\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 459\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvine_structure\u001b[0m \u001b[1;33m=\u001b[0m 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\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlisted_pairs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 605\u001b[0m \u001b[1;31m#if False:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 606\u001b[1;33m \u001b[0mpairs_iter_id\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mget_pair_id\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdim\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpair\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwith_plus\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mpair\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mlisted_pairs\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 607\u001b[0m \u001b[0mpairs_by_levels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_pairs_by_levels\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdim\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpairs_iter_id\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 608\u001b[0m \u001b[0mstructure\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_possible_structures\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdim\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpairs_by_levels\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32mc:\\users\\naz-probook\\onedrive\\git-repo\\dep-impact\\dependence\\dependence.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m 604\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlisted_pairs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 605\u001b[0m \u001b[1;31m#if False:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 606\u001b[1;33m \u001b[0mpairs_iter_id\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mget_pair_id\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdim\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpair\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwith_plus\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mpair\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mlisted_pairs\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 607\u001b[0m \u001b[0mpairs_by_levels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_pairs_by_levels\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdim\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpairs_iter_id\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 608\u001b[0m \u001b[0mstructure\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_possible_structures\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdim\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpairs_by_levels\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32mc:\\users\\naz-probook\\onedrive\\git-repo\\dep-impact\\dependence\\utils.py\u001b[0m in \u001b[0;36mget_pair_id\u001b[1;34m(dim, pair, with_plus)\u001b[0m\n\u001b[0;32m 972\u001b[0m \u001b[0mpair\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 973\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 974\u001b[1;33m \u001b[0mpair_id\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwhere\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpairs\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mpair\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mall\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 975\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mpair_id\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 976\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mIndexError\u001b[0m: index 0 is out of bounds for axis 0 with size 0" ] } ], "source": [ "quant_estimate.margins = margins\n", "quant_estimate.families = families\n", "quant_estimate.vine_structure = None\n", "quant_estimate.bounds_tau = None\n", "quant_estimate.bounds_tau" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "ename": "IndexError", "evalue": "index 1 is out of bounds for axis 0 with size 1", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mIndexError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-71-90f292a31187>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[0mn\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1000\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[0mgrid_type\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'lhs'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mgrid_result\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mquant_estimate\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgridsearch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn_dep_param\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mK\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_input_sample\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m,\u001b[0m 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216\u001b[0m output_samples = self.run_stochastic_models(\n", "\u001b[1;32mc:\\users\\naz-probook\\onedrive\\git-repo\\dep-impact\\dependence\\dependence.py\u001b[0m in \u001b[0;36mrun_stochastic_models\u001b[1;34m(self, params, n_input_sample, return_input_samples, random_state)\u001b[0m\n\u001b[0;32m 253\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mparam\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mparams\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 254\u001b[0m \u001b[0mfull_param\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_corr_dim\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--> 255\u001b[1;33m \u001b[0mfull_param\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_pair_ids\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mparam\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 256\u001b[0m \u001b[0mfull_param\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_fixed_pairs_ids\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_fixed_params_list\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 257\u001b[0m \u001b[0mintput_sample\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_sample\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfull_param\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_input_sample\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mIndexError\u001b[0m: index 1 is out of bounds for axis 0 with size 1" ] } ], "source": [ "K = 100\n", "n = 1000\n", "grid_type = 'lhs'\n", "grid_result = quant_estimate.gridsearch(n_dep_param=K, n_input_sample=n, grid_type=grid_type, random_state=random_state)" ] }, { "cell_type": "code", "execution_count": 153, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Worst Quantile: -10.8510465381 at [-0.97636018 0.59525763 -0.71252831 0.99805621 0.99805621 0.43584383\n", " -0.94199303 0.99225191 0.93110039 0.59525763]\n" ] } ], "source": [ "min_result = grid_result.min_result\n", "print('Worst Quantile: {0} at {1}'.format(min_result.quantity, min_result.dep_param))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### With one fixed pair" ] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Don't forget to change the margins.\n" ] }, { "ename": "IndexError", "evalue": "index 2 is out of bounds for axis 0 with size 2", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mIndexError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-72-a6d6fc4e3e3a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mfamilies\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mquant_estimate\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mConservativeEstimate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel_func\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfunc_sum\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmargins\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmargins\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfamilies\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfamilies\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32mc:\\users\\naz-probook\\onedrive\\git-repo\\dep-impact\\dependence\\dependence.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, model_func, margins, families, fixed_params, bounds_tau, vine_structure, copula_type)\u001b[0m\n\u001b[0;32m 92\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmargins\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmargins\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 93\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfamilies\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfamilies\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 94\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbounds_tau\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbounds_tau\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 95\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfixed_params\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfixed_params\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 96\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvine_structure\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvine_structure\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32mc:\\users\\naz-probook\\onedrive\\git-repo\\dep-impact\\dependence\\dependence.py\u001b[0m in \u001b[0;36mbounds_tau\u001b[1;34m(self, value)\u001b[0m\n\u001b[0;32m 653\u001b[0m \u001b[0mbounds_tau\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdim\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdim\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 654\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mj\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_pairs\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 655\u001b[1;33m \u001b[0mbounds_tau\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mj\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbounds_tau\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_tau_interval\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_families\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mj\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 656\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_custom_bounds_tau\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 657\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mIndexError\u001b[0m: index 2 is out of bounds for axis 0 with size 2" ] } ], "source": [ "families[3, 2] = 0\n", "quant_estimate = ConservativeEstimate(model_func=func_sum, margins=margins, families=families)" ] }, { "cell_type": "code", "execution_count": 73, "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "gridsearch() got an unexpected keyword argument 'q_func'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-73-7ac333112d45>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[0mgrid_type\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'lhs'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m grid_result = quant_estimate.gridsearch(n_dep_param=K, n_input_sample=n, grid_type=grid_type, \n\u001b[1;32m----> 5\u001b[1;33m q_func=q_func, random_state=random_state)\n\u001b[0m", "\u001b[1;31mTypeError\u001b[0m: gridsearch() got an unexpected keyword argument 'q_func'" ] } ], "source": [ "K = 100\n", "n = 10000\n", "grid_type = 'lhs'\n", "grid_result = quant_estimate.gridsearch(n_dep_param=K, n_input_sample=n, grid_type=grid_type, \n", " q_func=q_func, random_state=random_state)" ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Worst Quantile: -4.273422229577796 at [ 0.99999998]\n" ] } ], "source": [ "min_result = grid_result.min_result\n", "print('Worst Quantile: {0} at {1}'.format(min_result.quantity, min_result.dep_param))" ] }, { "cell_type": "code", "execution_count": 75, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[3, 0, 0, 0, 0],\n", " [5, 2, 0, 0, 0],\n", " [4, 5, 4, 0, 0],\n", " [1, 4, 5, 1, 0],\n", " [2, 1, 1, 5, 5]])" ] }, "execution_count": 75, "metadata": {}, "output_type": "execute_result" } ], "source": [ "grid_result.vine_structure" ] }, { "cell_type": "code", "execution_count": 77, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<seaborn.axisgrid.PairGrid at 0x23384e28470>" ] }, "execution_count": 77, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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P6fV6Sskkko5E7FUhT2aD3sIbrqnT5KBcq8Lr7x0dtnXEA+YGIhSRC36Gmw8P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\nRyIIiPi3ySKTSRAKRVBm0mHahJHYW21Bi72T1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NE277y4gpxUFOenCyjSM5gzScPupjC7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\ngnSk3nsPPv/sU7S3t+PJJ5/kPz99ftR0I8kkQcQHg3pHri+ZNGkS3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MSkFJi6ZJWhssab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Z0UCDwWOGb2x0B4B9XzahtDAL9c3tKMzT4s6vTcC/PqnFCgmjOdXS3CJFINs6XPjJUx/C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\nREpmw8lONNmT0t8khwSRHFCN3ADDFRyLa4ZiYaBrPSIxHHn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"text/plain": [ "<matplotlib.figure.Figure at 0x23384e28eb8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from depimpact.plots import matrix_plot_input\n", "\n", "matrix_plot_input(min_result)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "#### Save the used grid and load it again" ] }, { "cell_type": "code", "execution_count": 78, "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "gridsearch() got an unexpected keyword argument 'q_func'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-78-7e2f2a79afd0>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[0mgrid_type\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'lhs'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m grid_result_1 = quant_estimate.gridsearch(n_dep_param=K, n_input_sample=n, grid_type=grid_type, \n\u001b[1;32m----> 5\u001b[1;33m q_func=q_func, save_grid=True, grid_path='./output')\n\u001b[0m", "\u001b[1;31mTypeError\u001b[0m: gridsearch() got an unexpected keyword argument 'q_func'" ] } ], "source": [ "K = 100\n", "n = 1000\n", "grid_type = 'lhs'\n", "grid_result_1 = quant_estimate.gridsearch(n_dep_param=K, n_input_sample=n, grid_type=grid_type, save_grid=True, grid_path='./output')" ] }, { "cell_type": "code", "execution_count": 79, "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "gridsearch() got an unexpected keyword argument 'q_func'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-79-14131258b620>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m grid_result_2 = quant_estimate.gridsearch(n_dep_param=K, n_input_sample=n, grid_type=grid_type, \n\u001b[1;32m----> 2\u001b[1;33m q_func=q_func, use_grid=0, grid_path='./output')\n\u001b[0m", "\u001b[1;31mTypeError\u001b[0m: gridsearch() got an unexpected keyword argument 'q_func'" ] } ], "source": [ "grid_result_2 = quant_estimate.gridsearch(n_dep_param=K, n_input_sample=n, grid_type=grid_type, \n", " q_func=q_func, use_grid=0, grid_path='./output')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Then gather the results from the same grid with the same configurations" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(1000, 1000)" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "grid_result_1.n_input_sample, grid_result_2.n_input_sample" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": true }, "outputs": [], "source": [ "grid_result = grid_result_1 + grid_result_2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Because the configurations are the same, we can gather the results from two different runs" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2000" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "grid_result.n_input_sample" ] } ], "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": 2 }
mit
Disiok/poetry-seq2seq
notebooks/Vera's Experiments.ipynb
1
3061
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using TensorFlow backend.\n", "Building prefix dict from the default dictionary ...\n", "Loading model from cache /tmp/jieba.cache\n", "Loading model cost 0.240 seconds.\n", "Prefix dict has been built succesfully.\n" ] } ], "source": [ "#! /usr/bin/env python\n", "#-*- coding:utf-8 -*-\n", "\n", "from utils import *\n", "from segment import Segmenter\n", "from vocab import get_vocab, VOCAB_SIZE\n", "from quatrains import get_quatrains\n", "from gensim import models\n", "from numpy.random import uniform\n", "\n", "ndim = 128" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## New Word2Vec" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# print \"Generating %d-dim word embedding ...\" %ndim\n", "# int2ch, ch2int = get_vocab()\n", "# ch_lists = []\n", "# quatrains = get_quatrains()\n", "# for idx, poem in enumerate(quatrains):\n", "# for sentence in poem['sentences']:\n", "# ch_lists.append(filter(lambda ch: ch in ch2int, sentence))\n", "# # the i-th characters in the poem, used to boost Dui Zhang\n", "# i_characters = [[sentence[j] for sentence in poem['sentences']] for j in range(len(poem['sentences'][0]))]\n", "# for characters in i_characters:\n", "# ch_lists.append(filter(lambda ch: ch in ch2int, characters))\n", "# if 0 == (idx+1)%10000:\n", "# print \"[Word2Vec] %d/%d poems have been processed.\" %(idx+1, len(quatrains))" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# print \"Hold on. This may take some time ...\"\n", "# model = models.Word2Vec(ch_lists, size = ndim, min_count = 5)\n", "# embedding = uniform(-1.0, 1.0, [VOCAB_SIZE, ndim])\n", "# for idx, ch in enumerate(int2ch):\n", "# if ch in model.wv:\n", "# embedding[idx,:] = model.wv[ch]\n", "# np.save(_w2v_path, embedding)\n", "# print \"Word embedding is saved.\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Seq to Seq" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
pioneers/topgear
robot.ipynb
1
9751
{ "metadata": { "name": "", "signature": "sha256:9960c86d79cec218abef84e916a5e727efaacadf81c4943fb7249106b3e9ad05" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<img src=\"http://mirageforum.com/forum/attachment.php?attachmentid=159&d=1370617142\">" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "back to [Index](index.ipynb)" ] }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Robot" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This class defines a python class for driving the zumocrawler." ] }, { "cell_type": "code", "collapsed": false, "input": [ "from python.mbedrpc import *\n", "import threading\n", "\n", "class Motor:\n", " def __init__(self, a1, a2):\n", " self.a1=a1\n", " self.a2=a2\n", " def cmd(self, speed):\n", " if speed >=0:\n", " self.a1.write(speed)\n", " self.a2.write(0)\n", " else:\n", " self.a1.write(0)\n", " self.a2.write(-speed)\n", "class Robot:\n", " def __init__(self, dev='/dev/ttyACM0'):\n", " self.mbed=SerialRPC(dev, 115200)\n", " a1=PwmOut(self.mbed, p21)\n", " a2=PwmOut(self.mbed, p22)\n", " b1=PwmOut(self.mbed, p23)\n", " b2=PwmOut(self.mbed, p24)\n", " self.m_right = Motor(a1, a2)\n", " self.m_left = Motor(b1, b2)\n", " self.enabled=True\n", " self.last_left=0\n", " self.last_right=0\n", " self.sensors=[]\n", " for i in (p20,p19,p18,p17,p16,p15):\n", " self.sensors.append(AnalogIn(self.mbed, i))\n", " self.rlock=threading.Lock()\n", " def enable(self):\n", "# self.rlock.acquire()\n", " self.enabled=True\n", " self._cmd(self.last_left, self.last_right)\n", "# self.rlock.release()\n", " def disable(self):\n", "# self.rlock.acquire()\n", " self.enabled=False\n", " self._cmd(self.last_left, self.last_right)\n", "# self.rlock.release()\n", " def drive(self, left, right):\n", "# self.rlock.acquire()\n", " self._cmd(left, right)\n", "# self.rlock.release()\n", " def cmd(self, left, right):\n", "# self.rlock.acquire()\n", " self._cmd(left, right)\n", "# self.rlock.release()\n", " def _cmd(self, left, right):\n", " self.last_left=left\n", " self.last_right=right\n", " if self.enabled:\n", " self.m_left.cmd(-left)\n", " self.m_right.cmd(right)\n", " else:\n", " self.m_left.cmd(0)\n", " self.m_right.cmd(0)\n", " def read_sensors(self):\n", " \"\"\" returns an array of the line sensor reflectance values\n", " \"\"\"\n", " def read(sensor): return sensor.read()\n", " return map(read, self.sensors)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "#NBINCLUDE_STOP" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 31 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Example Usage" ] }, { "cell_type": "code", "collapsed": false, "input": [ "r = Robot()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "r.cmd(1,0)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "r.cmd(0,1)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "r.cmd(0,0)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "help(r.read_sensors)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Help on method read_sensors in module __main__:\n", "\n", "read_sensors(self) method of __main__.Robot instance\n", " returns an array of the line sensor reflectance values\n", "\n" ] } ], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "r.read_sensors()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 21, "text": [ "[0.8327229022979736,\n", " 0.6478632688522339,\n", " 0.5623931884765625,\n", " 0.642246663570404,\n", " 0.6019536256790161,\n", " 0.684737503528595]" ] } ], "prompt_number": 21 }, { "cell_type": "code", "collapsed": false, "input": [ "for sensor in r.sensors:\n", " print sensor.read()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "0.612942636013\n", "0.25567767024\n", "0.13821734488\n", "0.287667900324\n", "0.127960935235\n", "0.050549454987\n" ] } ], "prompt_number": 41 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 41 }, { "cell_type": "markdown", "metadata": {}, "source": [ "r.enable()" ] }, { "cell_type": "code", "collapsed": false, "input": [ "r.disable()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "%%file zrobot.py\n", "from python.mbedrpc import *\n", "import threading\n", "\n", "class Motor:\n", " def __init__(self, a1, a2):\n", " self.a1=a1\n", " self.a2=a2\n", " def cmd(self, speed):\n", " if speed >=0:\n", " self.a1.write(speed)\n", " self.a2.write(0)\n", " else:\n", " self.a1.write(0)\n", " self.a2.write(-speed)\n", "class Robot:\n", " def __init__(self, dev='/dev/ttyACM0'):\n", " self.mbed=SerialRPC(dev, 115200)\n", " a1=PwmOut(self.mbed, p21)\n", " a2=PwmOut(self.mbed, p22)\n", " b1=PwmOut(self.mbed, p23)\n", " b2=PwmOut(self.mbed, p24)\n", " self.m_right = Motor(a1, a2)\n", " self.m_left = Motor(b1, b2)\n", " self.enabled=True\n", " self.last_left=0\n", " self.last_right=0\n", " self.sensors=[]\n", " for i in (p20,p19,p18,p17,p16,p15):\n", " self.sensors.append(AnalogIn(self.mbed, i))\n", " self.rlock=threading.Lock()\n", " def enable(self):\n", "# self.rlock.acquire()\n", " self.enabled=True\n", " self._cmd(self.last_left, self.last_right)\n", "# self.rlock.release()\n", " def disable(self):\n", "# self.rlock.acquire()\n", " self.enabled=False\n", " self._cmd(self.last_left, self.last_right)\n", "# self.rlock.release()\n", " def drive(self, left, right):\n", "# self.rlock.acquire()\n", " self._cmd(left, right)\n", "# self.rlock.release()\n", " def cmd(self, left, right):\n", "# self.rlock.acquire()\n", " self._cmd(left, right)\n", "# self.rlock.release()\n", " def _cmd(self, left, right):\n", " self.last_left=left\n", " self.last_right=right\n", " if self.enabled:\n", " self.m_left.cmd(-left)\n", " self.m_right.cmd(right)\n", " else:\n", " self.m_left.cmd(0)\n", " self.m_right.cmd(0)\n", " def read_sensors(self):\n", " def read(sensor): return sensor.read()\n", " return map(read, self.sensors)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Overwriting zrobot.py\n" ] } ], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
apache-2.0
mne-tools/mne-tools.github.io
stable/_downloads/bcaf3ed1f43ea7377c6c0b00137d728f/custom_inverse_solver.ipynb
1
8476
{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n\n# Source localization with a custom inverse solver\n\nThe objective of this example is to show how to plug a custom inverse solver\nin MNE in order to facilate empirical comparison with the methods MNE already\nimplements (wMNE, dSPM, sLORETA, eLORETA, LCMV, DICS, (TF-)MxNE etc.).\n\nThis script is educational and shall be used for methods\nevaluations and new developments. It is not meant to be an example\nof good practice to analyse your data.\n\nThe example makes use of 2 functions ``apply_solver`` and ``solver``\nso changes can be limited to the ``solver`` function (which only takes three\nparameters: the whitened data, the gain matrix and the number of orientations)\nin order to try out another inverse algorithm.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\nfrom scipy import linalg\nimport mne\nfrom mne.datasets import sample\nfrom mne.viz import plot_sparse_source_estimates\n\n\ndata_path = sample.data_path()\nmeg_path = data_path / 'MEG' / 'sample'\nfwd_fname = meg_path / 'sample_audvis-meg-eeg-oct-6-fwd.fif'\nave_fname = meg_path / 'sample_audvis-ave.fif'\ncov_fname = meg_path / 'sample_audvis-shrunk-cov.fif'\nsubjects_dir = data_path / 'subjects'\ncondition = 'Left Auditory'\n\n# Read noise covariance matrix\nnoise_cov = mne.read_cov(cov_fname)\n# Handling average file\nevoked = mne.read_evokeds(ave_fname, condition=condition, baseline=(None, 0))\nevoked.crop(tmin=0.04, tmax=0.18)\n\nevoked = evoked.pick_types(eeg=False, meg=True)\n# Handling forward solution\nforward = mne.read_forward_solution(fwd_fname)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Auxiliary function to run the solver\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def apply_solver(solver, evoked, forward, noise_cov, loose=0.2, depth=0.8):\n \"\"\"Call a custom solver on evoked data.\n\n This function does all the necessary computation:\n\n - to select the channels in the forward given the available ones in\n the data\n - to take into account the noise covariance and do the spatial whitening\n - to apply loose orientation constraint as MNE solvers\n - to apply a weigthing of the columns of the forward operator as in the\n weighted Minimum Norm formulation in order to limit the problem\n of depth bias.\n\n Parameters\n ----------\n solver : callable\n The solver takes 3 parameters: data M, gain matrix G, number of\n dipoles orientations per location (1 or 3). A solver shall return\n 2 variables: X which contains the time series of the active dipoles\n and an active set which is a boolean mask to specify what dipoles are\n present in X.\n evoked : instance of mne.Evoked\n The evoked data\n forward : instance of Forward\n The forward solution.\n noise_cov : instance of Covariance\n The noise covariance.\n loose : float in [0, 1] | 'auto'\n Value that weights the source variances of the dipole components\n that are parallel (tangential) to the cortical surface. If loose\n is 0 then the solution is computed with fixed orientation.\n If loose is 1, it corresponds to free orientations.\n The default value ('auto') is set to 0.2 for surface-oriented source\n space and set to 1.0 for volumic or discrete source space.\n depth : None | float in [0, 1]\n Depth weighting coefficients. If None, no depth weighting is performed.\n\n Returns\n -------\n stc : instance of SourceEstimate\n The source estimates.\n \"\"\"\n # Import the necessary private functions\n from mne.inverse_sparse.mxne_inverse import \\\n (_prepare_gain, is_fixed_orient,\n _reapply_source_weighting, _make_sparse_stc)\n\n all_ch_names = evoked.ch_names\n\n # Handle depth weighting and whitening (here is no weights)\n forward, gain, gain_info, whitener, source_weighting, mask = _prepare_gain(\n forward, evoked.info, noise_cov, pca=False, depth=depth,\n loose=loose, weights=None, weights_min=None, rank=None)\n\n # Select channels of interest\n sel = [all_ch_names.index(name) for name in gain_info['ch_names']]\n M = evoked.data[sel]\n\n # Whiten data\n M = np.dot(whitener, M)\n\n n_orient = 1 if is_fixed_orient(forward) else 3\n X, active_set = solver(M, gain, n_orient)\n X = _reapply_source_weighting(X, source_weighting, active_set)\n\n stc = _make_sparse_stc(X, active_set, forward, tmin=evoked.times[0],\n tstep=1. / evoked.info['sfreq'])\n\n return stc" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Define your solver\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def solver(M, G, n_orient):\n \"\"\"Run L2 penalized regression and keep 10 strongest locations.\n\n Parameters\n ----------\n M : array, shape (n_channels, n_times)\n The whitened data.\n G : array, shape (n_channels, n_dipoles)\n The gain matrix a.k.a. the forward operator. The number of locations\n is n_dipoles / n_orient. n_orient will be 1 for a fixed orientation\n constraint or 3 when using a free orientation model.\n n_orient : int\n Can be 1 or 3 depending if one works with fixed or free orientations.\n If n_orient is 3, then ``G[:, 2::3]`` corresponds to the dipoles that\n are normal to the cortex.\n\n Returns\n -------\n X : array, (n_active_dipoles, n_times)\n The time series of the dipoles in the active set.\n active_set : array (n_dipoles)\n Array of bool. Entry j is True if dipole j is in the active set.\n We have ``X_full[active_set] == X`` where X_full is the full X matrix\n such that ``M = G X_full``.\n \"\"\"\n inner = np.dot(G, G.T)\n trace = np.trace(inner)\n K = linalg.solve(inner + 4e-6 * trace * np.eye(G.shape[0]), G).T\n K /= np.linalg.norm(K, axis=1)[:, None]\n X = np.dot(K, M)\n\n indices = np.argsort(np.sum(X ** 2, axis=1))[-10:]\n active_set = np.zeros(G.shape[1], dtype=bool)\n for idx in indices:\n idx -= idx % n_orient\n active_set[idx:idx + n_orient] = True\n X = X[active_set]\n return X, active_set" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Apply your custom solver\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# loose, depth = 0.2, 0.8 # corresponds to loose orientation\nloose, depth = 1., 0. # corresponds to free orientation\nstc = apply_solver(solver, evoked, forward, noise_cov, loose, depth)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "View in 2D and 3D (\"glass\" brain like 3D plot)\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plot_sparse_source_estimates(forward['src'], stc, bgcolor=(1, 1, 1),\n opacity=0.1)" ] } ], "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": "3.8.0" } }, "nbformat": 4, "nbformat_minor": 0 }
bsd-3-clause
beardeer/playground
notebooks/clustering.ipynb
1
994939
{ "cells": [ { "cell_type": "code", "execution_count": 674, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import csv\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import sklearn.cluster as cluster\n", "import time\n", "import hdbscan\n", "\n", "from sklearn.metrics import silhouette_score\n", "\n", "%matplotlib inline\n", "sns.set_context('poster')\n", "sns.set_color_codes()\n", "plot_kwds = {'alpha' : 0.25, 's' : 80, 'linewidths':0}" ] }, { "cell_type": "code", "execution_count": 675, "metadata": { "collapsed": true }, "outputs": [], "source": [ "plot_x, plot_y = 1, 3\n", "n_clusters = 3\n", "markers = []" ] }, { "cell_type": "code", "execution_count": 676, "metadata": { "collapsed": true }, "outputs": [], "source": [ "s_id = 7104\n", "input_path = r'../local_data/cluster_%d.csv' % s_id\n", "input_file = open(input_path,'rb')" ] }, { "cell_type": "code", "execution_count": 677, "metadata": { "collapsed": false }, "outputs": [], "source": [ "reader=csv.reader(input_file)" ] }, { "cell_type": "code", "execution_count": 678, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data_array=list(reader)" ] }, { "cell_type": "code", "execution_count": 679, "metadata": { "collapsed": true }, "outputs": [], "source": [ "input_file.close()" ] }, { "cell_type": "code", "execution_count": 680, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data=np.array(data_array).astype('float')" ] }, { "cell_type": "code", "execution_count": 681, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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iPKauQHB+Q+W6mvAqGzNLh7SXAHhYCcM++o6f3/jotiwMECGMUhjW7XOyLQPd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"text/plain": [ "<matplotlib.figure.Figure at 0x1f507c18>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(data.T[plot_x], data.T[plot_y], c='b', **plot_kwds)\n", "frame = plt.gca()\n", "frame.axes.get_xaxis().set_visible(False)\n", "frame.axes.get_yaxis().set_visible(False)" ] }, { "cell_type": "code", "execution_count": 682, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def plot_clusters(data, all_data, algorithm, args, kwds):\n", " start_time = time.time()\n", " labels = algorithm(*args, **kwds).fit_predict(data)\n", " end_time = time.time()\n", " palette = sns.color_palette('deep', np.unique(labels).max() + 1)\n", " colors = [palette[x] if x >= 0 else (0.0, 0.0, 0.0) for x in labels]\n", " plt.scatter(all_data.T[plot_x], all_data.T[plot_y], c=colors, **plot_kwds)\n", " frame = plt.gca()\n", "# frame.axes.get_xaxis().set_visible(False)\n", "# frame.axes.get_yaxis().set_visible(False)\n", "# plt.title('Clusters found by {}'.format(str(algorithm.__name__)), fontsize=24)\n", " plt.xlabel('Assignment performance')\n", " plt.ylabel('Past assignment performance')\n", " silhouette_avg = silhouette_score(data, labels)\n", "# plt.text(3, 0.95, 'Silhouette Score: %4f'% silhouette_avg, fontsize=25)\n", " return labels" ] }, { "cell_type": "code", "execution_count": 683, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "image/png": 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YJSXGWjyt6TQDwkDXHni1ghZ76YjR2JDlId5BXvm0UKh2wWqnSaOmWROvo+23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ohlmxwejc5MYsZxRpLD7kYFaZmv7fCklKJadQgTNbXIkFJSrdpL/R5HScZhL2AUFIVMvWKx0XRL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\n1gnyP7LvAP45Ir5WbHjqYC2XvfwP8AeAD0fEVasbnjrUSr7zd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"text/plain": [ "<matplotlib.figure.Figure at 0x1f5092b0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "labels_k2 = plot_clusters(data[:,1:], data, cluster.KMeans, (), {'n_clusters': 2})" ] }, { "cell_type": "code", "execution_count": 684, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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WtALPcyiNJZc5GLWQAEMIIYRYsL1Jeiy4OCrLDfvjlI2VZm2/bxLlzJKcvXFybHGd5oZp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EekqT5jtJemmoOeT5Jcr+S04CxXvqu12+3vXuE4ZnEswP5uMeaQvH+kxFsTwGN2gd9FHoT8LqAM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\nTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1fbacd30>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "labels_k2 = plot_clusters(data[:,:], data, cluster.KMeans, (), {'n_clusters': 2})" ] }, { "cell_type": "code", "execution_count": 685, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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OSgxTKJpeo5JziesvNwVxljOOcpLMkBcFWNAKPM+hMJZM+mBUQgIMIYQQYsH2k/6x4OKo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\nzfbOnLshhPA3JP0wbkhX7Znn+j2Uo/v5SNK87IMhScsUQrgLSQftr8YYP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"text/plain": [ "<matplotlib.figure.Figure at 0x24fde860>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "labels_k2 = plot_clusters(data[:,1:], data, cluster.KMeans, (), {'n_clusters': 3})" ] }, { "cell_type": "code", "execution_count": 686, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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YLfJpNqppkuBfIB3uIo8RN8Oju+fX9bSkLqcS8iwKcY0opQ6KYTMlrULPoxZX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xVBAMbTCdNagmU+oLh13U18x6RZc9gfBIu3z2vJL0PFNdmqNDGnJtNsghI5QkhMw8c3Kjxo3t2m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\nrkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1fc272b0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "labels_k2 = plot_clusters(data[:,:], data, cluster.KMeans, (), {'n_clusters': 3})" ] }, { "cell_type": "code", "execution_count": 687, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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sRRE363Hv0dzH9FzpqFIHCTCEuEWUUgSzlIXkgta1oqJmV4DL0mKKkizJKY1F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1EU1X0lfzXE/VXOm8W5k6/fE0XRIUC/3/8s8PepROL/62Uf3HEkvd7yV6Et9TM4TBgcJORZeexw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\ngFeStThZzu/mGcAleXIx63PADrKaXHVgsyYYd8uXV85ZfxVwW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"text/plain": [ "<matplotlib.figure.Figure at 0x2509a828>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "labels_k2 = plot_clusters(data[:,1:], data, cluster.KMeans, (), {'n_clusters': 4})" ] }, { "cell_type": "code", "execution_count": 688, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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V7TZudxczHFbpR/OCbjMZo7QiWFtHN5c358TrdPGaTcrpyaFmutnAa9Wzg+Ev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oWvn/33y6+9/lXgg263K3q93uvX/BbgR7vd7vcD3wu0KJSvvrfX65X+7wtECsmz2hM+m+wQ5dGp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\nIrKureoeDOfcZuA6oAo8GfgEcLlz7hWLnOMBXwZ+D3gT8ETgd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"text/plain": [ "<matplotlib.figure.Figure at 0x1fbac2e8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "labels_k2 = plot_clusters(data[:,:], data, cluster.KMeans, (), {'n_clusters': 4})" ] }, { "cell_type": "code", "execution_count": 689, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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WoedRSu3Xr+y18w3BoVSEjurlBf6cJqHrqAlq69SgQCmDNhe7gNtb96mCx9vx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\n1fnyrHnrzyIb2WJBKaWfA/4fWeezB0bEd1YnPHWxdsveQ8mS2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"text/plain": [ "<matplotlib.figure.Figure at 0x1f495a20>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "labels_k2 = plot_clusters(data[:,1:], data, cluster.KMeans, (), {'n_clusters': 5})" ] }, { "cell_type": "code", "execution_count": 690, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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qrydpwaffWaNZ8wmG9luYYowpZ9higrU5CoX2W+BcFWCs8AbGLDN85t11nh/M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faP1HhlAnjAJvDNlEHINwN14HuBn+v1el89cyGvWBDCcQje+ggmTTFJXBrt1ep3RjnKWjv3Cp7W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\nRQmGiIiIiIhkRgmGiIiIiIhkRgmGiIiIiIhkRgmGiIiIiIhkR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"text/plain": [ "<matplotlib.figure.Figure at 0x1fa495f8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "labels_k2 = plot_clusters(data[:,:], data, cluster.KMeans, (), {'n_clusters': 5})" ] }, { "cell_type": "code", "execution_count": 691, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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dc0sNWi/73EqppQfNxlj2dxPy7N3G1RlHnlaUhcEPdOP1DlcRtQJ8X7HzJsVa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Gxqhplzcc48s2i32995ieOYx/7drf/W433S/pESb0wA91kHvp40CPl6oAx8N/AzrVbrn5+6kOdk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\nBY7/beDNwD2Ag8ClwAUxxttWIFz1kHbb3qxzXwu8NsboDQK1Z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"text/plain": [ "<matplotlib.figure.Figure at 0x282deda0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "labels_k2 = plot_clusters(data[:,1:], data, cluster.KMeans, (), {'n_clusters': 6})" ] }, { "cell_type": "code", "execution_count": 692, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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EsNhc84uuiit18WPekPOBrawgc0d+HyEwtY7ceRKjMUso9G5GhlgrnkxLQgiH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\nVaI2Me2kabex9ALdiHoFahaAExOIqd5Z8eONy7bfD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pFVWUPbO5Xo9+088ETgEuBVwDs3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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x24995c88>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "labels_k2 = plot_clusters(data[:,:], data, cluster.KMeans, (), {'n_clusters': 6})" ] }, { "cell_type": "code", "execution_count": 693, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# plot_clusters(data, cluster.AffinityPropagation, (), {'preference':-5.0, 'damping':0.95})" ] }, { "cell_type": "code", "execution_count": 694, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# labels_s1 = plot_clusters(data[:,1:], data, cluster.SpectralClustering, (), {'n_clusters': 3})" ] }, { "cell_type": "code", "execution_count": 695, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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6vrw5J626T6PmHw7ZO6oW+TQrSgu90Od0CSlzYjkedR7OfUzHl44qVZAAQ4hr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\nvG+J+9+No8vpbH+zu6O2bNQEY7aTz+ic9aNk12TwWCfI/8i+C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n", "text/plain": [ "<matplotlib.figure.Figure at 0x1f6e07f0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "labels_s2 = plot_clusters(data[:,:], data, cluster.SpectralClustering, (), {'n_clusters': 3})" ] }, { "cell_type": "code", "execution_count": 696, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# plot_clusters(data[:,1:], data, cluster.AgglomerativeClustering, (), {'n_clusters':n_clusters, 'linkage':'ward'})" ] }, { "cell_type": "code", "execution_count": 697, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# plot_clusters(data[:,1:], data, cluster.DBSCAN, (), {'eps':0.025})" ] }, { "cell_type": "code", "execution_count": 698, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [], "source": [ "# plot_clusters(data[:,1:], data, hdbscan.HDBSCAN, (), {'min_cluster_size':15})" ] }, { "cell_type": "code", "execution_count": 699, "metadata": { "collapsed": true }, "outputs": [], "source": [ "output_path = r'local_data/label_%d.csv' % s_id\n", "output_file = open(output_path, 'wb')" ] }, { "cell_type": "code", "execution_count": 700, "metadata": { "collapsed": true }, "outputs": [], "source": [ "writer = csv.writer(output_file)\n", "writer.writerow(['l1', 'l2'])" ] }, { "cell_type": "code", "execution_count": 701, "metadata": { "collapsed": false }, "outputs": [ { "ename": "NameError", "evalue": "name 'labels_k1' is not defined", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-701-36cba4681224>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels_k1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0mwriter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwriterow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mlabels_k1\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabels_k2\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mNameError\u001b[0m: name 'labels_k1' is not defined" ] } ], "source": [ "for i in range(len(labels_k1)):\n", " writer.writerow([labels_k1[i], labels_k2[i]])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "output_file.close()" ] } ], "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 }
unlicense
google/or-tools
examples/notebook/contrib/car.ipynb
1
6839
{ "cells": [ { "cell_type": "markdown", "id": "google", "metadata": {}, "source": [ "##### Copyright 2021 Google LLC." ] }, { "cell_type": "markdown", "id": "apache", "metadata": {}, "source": [ "Licensed under the Apache License, Version 2.0 (the \"License\");\n", "you may not use this file except in compliance with the License.\n", "You may obtain a copy of the License at\n", "\n", " http://www.apache.org/licenses/LICENSE-2.0\n", "\n", "Unless required by applicable law or agreed to in writing, software\n", "distributed under the License is distributed on an \"AS IS\" BASIS,\n", "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", "See the License for the specific language governing permissions and\n", "limitations under the License.\n" ] }, { "cell_type": "markdown", "id": "basename", "metadata": {}, "source": [ "# car" ] }, { "cell_type": "markdown", "id": "link", "metadata": {}, "source": [ "<table align=\"left\">\n", "<td>\n", "<a href=\"https://colab.research.google.com/github/google/or-tools/blob/master/examples/notebook/contrib/car.ipynb\"><img src=\"https://raw.githubusercontent.com/google/or-tools/master/tools/colab_32px.png\"/>Run in Google Colab</a>\n", "</td>\n", "<td>\n", "<a href=\"https://github.com/google/or-tools/blob/master/examples/contrib/car.py\"><img src=\"https://raw.githubusercontent.com/google/or-tools/master/tools/github_32px.png\"/>View source on GitHub</a>\n", "</td>\n", "</table>" ] }, { "cell_type": "markdown", "id": "doc", "metadata": {}, "source": [ "First, you must install [ortools](https://pypi.org/project/ortools/) package in this colab." ] }, { "cell_type": "code", "execution_count": null, "id": "install", "metadata": {}, "outputs": [], "source": [ "!pip install ortools" ] }, { "cell_type": "code", "execution_count": null, "id": "code", "metadata": {}, "outputs": [], "source": [ "# Copyright 2010 Hakan Kjellerstrand [email protected]\n", "#\n", "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", "# you may not use this file except in compliance with the License.\n", "# You may obtain a copy of the License at\n", "#\n", "# http://www.apache.org/licenses/LICENSE-2.0\n", "#\n", "# Unless required by applicable law or agreed to in writing, software\n", "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", "# See the License for the specific language governing permissions and\n", "# limitations under the License.\n", "\"\"\"\n", "\n", " Car sequencing in Google CP Solver.\n", "\n", " This model is based on the car sequencing model in\n", " Pascal Van Hentenryck\n", " 'The OPL Optimization Programming Language', page 184ff.\n", "\n", "\n", " Compare with the following models:\n", " * MiniZinc: http://hakank.org/minizinc/car.mzn\n", " * Comet: http://hakank.org/comet/car.co\n", "\n", " This model was created by Hakan Kjellerstrand ([email protected])\n", " Also see my other Google CP Solver models:\n", " http://www.hakank.org/google_or_tools/\n", "\"\"\"\n", "import sys\n", "\n", "from ortools.constraint_solver import pywrapcp\n", "\n", "\n", "\n", "# Create the solver.\n", "solver = pywrapcp.Solver(\"Car sequence\")\n", "\n", "#\n", "# data\n", "#\n", "nbCars = 6\n", "nbOptions = 5\n", "nbSlots = 10\n", "\n", "Cars = list(range(nbCars))\n", "Options = list(range(nbOptions))\n", "Slots = list(range(nbSlots))\n", "\n", "# car 0 1 2 3 4 5\n", "demand = [1, 1, 2, 2, 2, 2]\n", "\n", "option = [\n", " # car 0 1 2 3 4 5\n", " [1, 0, 0, 0, 1, 1], # option 1\n", " [0, 0, 1, 1, 0, 1], # option 2\n", " [1, 0, 0, 0, 1, 0], # option 3\n", " [1, 1, 0, 1, 0, 0], # option 4\n", " [0, 0, 1, 0, 0, 0] # option 5\n", "]\n", "\n", "capacity = [(1, 2), (2, 3), (1, 3), (2, 5), (1, 5)]\n", "\n", "optionDemand = [\n", " sum([demand[j] * option[i][j] for j in Cars]) for i in Options\n", "]\n", "\n", "#\n", "# declare variables\n", "#\n", "slot = [solver.IntVar(0, nbCars - 1, \"slot[%i]\" % i) for i in Slots]\n", "setup = {}\n", "for i in Options:\n", " for j in Slots:\n", " setup[(i, j)] = solver.IntVar(0, 1, \"setup[%i,%i]\" % (i, j))\n", "setup_flat = [setup[i, j] for i in Options for j in Slots]\n", "\n", "#\n", "# constraints\n", "#\n", "for c in Cars:\n", " b = [solver.IsEqualCstVar(slot[s], c) for s in Slots]\n", " solver.Add(solver.Sum(b) == demand[c])\n", "\n", "for o in Options:\n", " for s in range(0, nbSlots - capacity[o][1] + 1):\n", " b = [setup[o, j] for j in range(s, s + capacity[o][1] - 1)]\n", " solver.Add(solver.Sum(b) <= capacity[o][0])\n", "\n", "for o in Options:\n", " for s in Slots:\n", " solver.Add(setup[(o, s)] == solver.Element(option[o], slot[s]))\n", "\n", "for o in Options:\n", " for i in range(optionDemand[o]):\n", " s_range = list(range(0, nbSlots - (i + 1) * capacity[o][1]))\n", " ss = [setup[o, s] for s in s_range]\n", " cc = optionDemand[o] - (i + 1) * capacity[o][0]\n", " if len(ss) > 0 and cc >= 0:\n", " solver.Add(solver.Sum(ss) >= cc)\n", "\n", "#\n", "# search and result\n", "#\n", "db = solver.Phase(slot + setup_flat, solver.CHOOSE_FIRST_UNBOUND,\n", " solver.ASSIGN_MIN_VALUE)\n", "\n", "solver.NewSearch(db)\n", "num_solutions = 0\n", "while solver.NextSolution():\n", " print(\"slot:%s\" % \",\".join([str(slot[i].Value()) for i in Slots]))\n", " print(\"setup:\")\n", " for o in Options:\n", " print(\"%i/%i:\" % (capacity[o][0], capacity[o][1]), end=\" \")\n", " for s in Slots:\n", " print(setup[o, s].Value(), end=\" \")\n", " print()\n", " print()\n", " num_solutions += 1\n", "\n", " if num_solutions >= num_sol:\n", " break\n", "\n", "solver.EndSearch()\n", "\n", "print()\n", "print(\"num_solutions:\", num_solutions)\n", "print(\"failures:\", solver.Failures())\n", "print(\"branches:\", solver.Branches())\n", "print(\"WallTime:\", solver.WallTime())\n", "\n", "num_sol = 3\n" ] } ], "metadata": {}, "nbformat": 4, "nbformat_minor": 5 }
apache-2.0
nens/python-subgrid
notebooks/slicing.ipynb
1
152633
{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "# We are going to fill in an array\n", "\n", "# with N squares\n", "N = 50000\n", "\n", "# at different slices, starting at:\n", "m = np.random.random_integers(0, 2000, N)\n", "n = np.random.random_integers(0, 2000, N)\n", "\n", "# with some random sizes:\n", "size = 2**(np.random.binomial(7, 0.2, N) + 1)\n", "\n", "# create an empty array, with masked values\n", "img = np.ma.empty((2128, 2128), dtype='int32')\n", "img.mask = True\n", "\n", "# this is a slow version (500ms on my machine)\n", "for i, (m_i, n_i, size) in enumerate(zip(m, n, size)):\n", " img[m_i:(m_i+size),n_i:(n_i+size)] = i \n", "\n", "# I also tried:\n", "# img[m:(m+size), n:(n+size)] = np.ndindex(m.shape)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1 loops, best of 3: 605 ms per loop\n" ] } ], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "plt.imshow(img, cmap='Accent')\n" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 9, "text": [ "<matplotlib.image.AxesImage at 0x2a8ad10>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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ElwrLIPLeXaxjhKSd6TeOdKX57Ax6iruFLq9DqiFX+Ip3bYNod3oWu67ByGFC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"text": [ "<matplotlib.figure.Figure at 0x32d65d0>" ] } ], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
gpl-3.0
ijmbarr/causalgraphicalmodels
notebooks/cgm-examples.ipynb
1
71992
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# An Introduction to `CausalGraphicalModels`\n", "\n", "`CausalGraphicalModel` is a python module for describing and manipulating [Causal Graphical Models](https://en.wikipedia.org/wiki/Causal_graph) and [Structural Causal Models](https://en.wikipedia.org/wiki/Structural_equation_modeling). Behind the curtain, it is a light wrapper around the python graph library [networkx](https://networkx.github.io/).\n", "\n", "This notebook is designed to give a quick overview of the functionality of this package." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# CausalGraphicalModels" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from causalgraphicalmodels import CausalGraphicalModel" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "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", "<!-- Generated by graphviz version 2.38.0 (20140413.2041)\n", " -->\n", "<!-- Title: %3 Pages: 1 -->\n", "<svg width=\"164pt\" height=\"260pt\"\n", " viewBox=\"0.00 0.00 164.25 260.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n", "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 256)\">\n", "<title>%3</title>\n", "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-256 160.246,-256 160.246,4 -4,4\"/>\n", "<!-- sprinkler -->\n", "<g id=\"node1\" class=\"node\"><title>sprinkler</title>\n", "<ellipse fill=\"none\" stroke=\"black\" cx=\"42.2463\" cy=\"-162\" rx=\"42.4939\" ry=\"18\"/>\n", "<text text-anchor=\"middle\" x=\"42.2463\" y=\"-158.3\" font-family=\"Times,serif\" font-size=\"14.00\">sprinkler</text>\n", "</g>\n", "<!-- wet -->\n", "<g id=\"node3\" class=\"node\"><title>wet</title>\n", "<ellipse fill=\"none\" stroke=\"black\" cx=\"85.2463\" cy=\"-90\" rx=\"27\" ry=\"18\"/>\n", "<text text-anchor=\"middle\" x=\"85.2463\" y=\"-86.3\" font-family=\"Times,serif\" font-size=\"14.00\">wet</text>\n", "</g>\n", "<!-- sprinkler&#45;&gt;wet -->\n", "<g id=\"edge4\" class=\"edge\"><title>sprinkler&#45;&gt;wet</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M52.4367,-144.411C57.7064,-135.832 64.2434,-125.191 70.0921,-115.67\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"73.173,-117.341 75.425,-106.988 67.2085,-113.677 73.173,-117.341\"/>\n", "</g>\n", "<!-- rain -->\n", "<g id=\"node2\" class=\"node\"><title>rain</title>\n", "<ellipse fill=\"none\" stroke=\"black\" cx=\"129.246\" cy=\"-162\" rx=\"27\" ry=\"18\"/>\n", "<text text-anchor=\"middle\" x=\"129.246\" y=\"-158.3\" font-family=\"Times,serif\" font-size=\"14.00\">rain</text>\n", "</g>\n", "<!-- rain&#45;&gt;wet -->\n", "<g id=\"edge3\" class=\"edge\"><title>rain&#45;&gt;wet</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M119.262,-145.116C113.749,-136.345 106.783,-125.264 100.594,-115.416\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"103.476,-113.425 95.191,-106.821 97.5495,-117.15 103.476,-113.425\"/>\n", "</g>\n", "<!-- slippery -->\n", "<g id=\"node5\" class=\"node\"><title>slippery</title>\n", "<ellipse fill=\"none\" stroke=\"black\" cx=\"85.2463\" cy=\"-18\" rx=\"38.9931\" ry=\"18\"/>\n", "<text text-anchor=\"middle\" x=\"85.2463\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">slippery</text>\n", "</g>\n", "<!-- wet&#45;&gt;slippery -->\n", "<g id=\"edge5\" class=\"edge\"><title>wet&#45;&gt;slippery</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M85.2463,-71.6966C85.2463,-63.9827 85.2463,-54.7125 85.2463,-46.1124\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"88.7464,-46.1043 85.2463,-36.1043 81.7464,-46.1044 88.7464,-46.1043\"/>\n", "</g>\n", "<!-- season -->\n", "<g id=\"node4\" class=\"node\"><title>season</title>\n", "<ellipse fill=\"none\" stroke=\"black\" cx=\"85.2463\" cy=\"-234\" rx=\"34.394\" ry=\"18\"/>\n", "<text text-anchor=\"middle\" x=\"85.2463\" y=\"-230.3\" font-family=\"Times,serif\" font-size=\"14.00\">season</text>\n", "</g>\n", "<!-- season&#45;&gt;sprinkler -->\n", "<g id=\"edge2\" class=\"edge\"><title>season&#45;&gt;sprinkler</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M75.2731,-216.765C70.084,-208.317 63.623,-197.799 57.7952,-188.312\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"60.6804,-186.322 52.4639,-179.633 54.7159,-189.986 60.6804,-186.322\"/>\n", "</g>\n", "<!-- season&#45;&gt;rain -->\n", "<g id=\"edge1\" class=\"edge\"><title>season&#45;&gt;rain</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M95.4513,-216.765C100.838,-208.195 107.564,-197.494 113.595,-187.9\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"116.738,-189.476 119.097,-179.147 110.812,-185.751 116.738,-189.476\"/>\n", "</g>\n", "</g>\n", "</svg>\n" ], "text/plain": [ "<graphviz.dot.Digraph at 0x7f10a219fb38>" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sprinkler = CausalGraphicalModel(\n", " nodes=[\"season\", \"rain\", \"sprinkler\", \"wet\", \"slippery\"],\n", " edges=[\n", " (\"season\", \"rain\"), \n", " (\"season\", \"sprinkler\"), \n", " (\"rain\", \"wet\"),\n", " (\"sprinkler\", \"wet\"), \n", " (\"wet\", \"slippery\")\n", " ]\n", ")\n", "\n", "# draw return a graphviz `dot` object, which jupyter can render\n", "sprinkler.draw()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "P(season)P(sprinkler|season)P(rain|season)P(wet|rain,sprinkler)P(slippery|wet)\n" ] } ], "source": [ "# get the distribution implied by the graph\n", "print(sprinkler.get_distribution())" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# check for d-seperation of two nodes\n", "sprinkler.is_d_separated(\"slippery\", \"season\", {\"wet\"})" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[('sprinkler', 'rain', {'season'}),\n", " ('sprinkler', 'slippery', {'wet'}),\n", " ('sprinkler', 'slippery', {'rain', 'wet'}),\n", " ('sprinkler', 'slippery', {'season', 'wet'}),\n", " ('sprinkler', 'slippery', {'rain', 'season', 'wet'}),\n", " ('rain', 'slippery', {'wet'}),\n", " ('rain', 'slippery', {'sprinkler', 'wet'}),\n", " ('rain', 'slippery', {'season', 'wet'}),\n", " ('rain', 'slippery', {'season', 'sprinkler', 'wet'}),\n", " ('wet', 'season', {'rain', 'sprinkler'}),\n", " ('wet', 'season', {'rain', 'slippery', 'sprinkler'}),\n", " ('season', 'slippery', {'wet'}),\n", " ('season', 'slippery', {'rain', 'wet'}),\n", " ('season', 'slippery', {'sprinkler', 'wet'}),\n", " ('season', 'slippery', {'rain', 'sprinkler'}),\n", " ('season', 'slippery', {'rain', 'sprinkler', 'wet'})]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# get all the conditional independence relationships implied by a CGM\n", "sprinkler.get_all_independence_relationships()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# check backdoor adjustment set\n", "sprinkler.is_valid_backdoor_adjustment_set(\"rain\", \"slippery\", {\"wet\"})" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "frozenset({frozenset({'sprinkler'}),\n", " frozenset({'season'}),\n", " frozenset({'season', 'sprinkler'})})" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# get all backdoor adjustment sets\n", "sprinkler.get_all_backdoor_adjustment_sets(\"rain\", \"slippery\")" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "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", "<!-- Generated by graphviz version 2.38.0 (20140413.2041)\n", " -->\n", "<!-- Title: %3 Pages: 1 -->\n", "<svg width=\"172pt\" height=\"268pt\"\n", " viewBox=\"0.00 0.00 172.25 268.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n", "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 264)\">\n", "<title>%3</title>\n", "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-264 168.246,-264 168.246,4 -4,4\"/>\n", "<!-- sprinkler -->\n", "<g id=\"node1\" class=\"node\"><title>sprinkler</title>\n", "<ellipse fill=\"none\" stroke=\"black\" cx=\"122\" cy=\"-166\" rx=\"42.4939\" ry=\"18\"/>\n", "<text text-anchor=\"middle\" x=\"122\" y=\"-162.3\" font-family=\"Times,serif\" font-size=\"14.00\">sprinkler</text>\n", "</g>\n", "<!-- wet -->\n", "<g id=\"node3\" class=\"node\"><title>wet</title>\n", "<ellipse fill=\"none\" stroke=\"black\" cx=\"76\" cy=\"-90\" rx=\"27\" ry=\"18\"/>\n", "<text text-anchor=\"middle\" x=\"76\" y=\"-86.3\" font-family=\"Times,serif\" font-size=\"14.00\">wet</text>\n", "</g>\n", "<!-- sprinkler&#45;&gt;wet -->\n", "<g id=\"edge1\" class=\"edge\"><title>sprinkler&#45;&gt;wet</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M111.562,-148.208C105.598,-138.614 98.0078,-126.404 91.3728,-115.73\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"94.3326,-113.862 86.0807,-107.217 88.3876,-117.557 94.3326,-113.862\"/>\n", "</g>\n", "<!-- rain -->\n", "<g id=\"node2\" class=\"node\"><title>rain</title>\n", "<ellipse fill=\"none\" stroke=\"black\" cx=\"31\" cy=\"-166\" rx=\"27\" ry=\"18\"/>\n", "<ellipse fill=\"none\" stroke=\"black\" cx=\"31\" cy=\"-166\" rx=\"31\" ry=\"22\"/>\n", "<text text-anchor=\"middle\" x=\"31\" y=\"-162.3\" font-family=\"Times,serif\" font-size=\"14.00\">rain</text>\n", "</g>\n", "<!-- rain&#45;&gt;wet -->\n", "<g id=\"edge2\" class=\"edge\"><title>rain&#45;&gt;wet</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M42.8232,-145.557C48.3487,-136.471 54.9925,-125.546 60.8787,-115.866\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"64.0234,-117.431 66.2287,-107.068 58.0424,-113.794 64.0234,-117.431\"/>\n", "</g>\n", "<!-- slippery -->\n", "<g id=\"node5\" class=\"node\"><title>slippery</title>\n", "<ellipse fill=\"none\" stroke=\"black\" cx=\"76\" cy=\"-18\" rx=\"38.9931\" ry=\"18\"/>\n", "<text text-anchor=\"middle\" x=\"76\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">slippery</text>\n", "</g>\n", "<!-- wet&#45;&gt;slippery -->\n", "<g id=\"edge3\" class=\"edge\"><title>wet&#45;&gt;slippery</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M76,-71.6966C76,-63.9827 76,-54.7125 76,-46.1124\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"79.5001,-46.1043 76,-36.1043 72.5001,-46.1044 79.5001,-46.1043\"/>\n", "</g>\n", "<!-- season -->\n", "<g id=\"node4\" class=\"node\"><title>season</title>\n", "<ellipse fill=\"none\" stroke=\"black\" cx=\"122\" cy=\"-242\" rx=\"34.394\" ry=\"18\"/>\n", "<text text-anchor=\"middle\" x=\"122\" y=\"-238.3\" font-family=\"Times,serif\" font-size=\"14.00\">season</text>\n", "</g>\n", "<!-- season&#45;&gt;sprinkler -->\n", "<g id=\"edge4\" class=\"edge\"><title>season&#45;&gt;sprinkler</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M122,-223.837C122,-215.058 122,-204.163 122,-194.267\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"125.5,-194.07 122,-184.07 118.5,-194.07 125.5,-194.07\"/>\n", "</g>\n", "</g>\n", "</svg>\n" ], "text/plain": [ "<graphviz.dot.Digraph at 0x7f10a219f550>" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# get the graph created by intervening on node \"rain\"\n", "do_sprinkler = sprinkler.do(\"rain\")\n", "\n", "do_sprinkler.draw()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Latent Variables " ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "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", "<!-- Generated by graphviz version 2.38.0 (20140413.2041)\n", " -->\n", "<!-- Title: %3 Pages: 1 -->\n", "<svg width=\"90pt\" height=\"228pt\"\n", " viewBox=\"0.00 0.00 90.00 227.60\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n", "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 223.6)\">\n", "<title>%3</title>\n", "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-223.6 86,-223.6 86,4 -4,4\"/>\n", "<!-- y -->\n", "<g id=\"node1\" class=\"node\"><title>y</title>\n", "<ellipse fill=\"none\" stroke=\"black\" cx=\"27\" cy=\"-18\" rx=\"27\" ry=\"18\"/>\n", "<text text-anchor=\"middle\" x=\"27\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">y</text>\n", "</g>\n", "<!-- z -->\n", "<g id=\"node2\" class=\"node\"><title>z</title>\n", "<ellipse fill=\"none\" stroke=\"black\" cx=\"55\" cy=\"-90\" rx=\"27\" ry=\"18\"/>\n", "<text text-anchor=\"middle\" x=\"55\" y=\"-86.3\" font-family=\"Times,serif\" font-size=\"14.00\">z</text>\n", "</g>\n", "<!-- z&#45;&gt;y -->\n", "<g id=\"edge2\" class=\"edge\"><title>z&#45;&gt;y</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M48.3644,-72.411C45.0866,-64.2164 41.0558,-54.1395 37.3819,-44.9548\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"40.5516,-43.4548 33.588,-35.4699 34.0523,-46.0546 40.5516,-43.4548\"/>\n", "</g>\n", "<!-- x -->\n", "<g id=\"node3\" class=\"node\"><title>x</title>\n", "<ellipse fill=\"none\" stroke=\"black\" cx=\"55\" cy=\"-162\" rx=\"27\" ry=\"18\"/>\n", "<text text-anchor=\"middle\" x=\"55\" y=\"-158.3\" font-family=\"Times,serif\" font-size=\"14.00\">x</text>\n", "</g>\n", "<!-- x&#45;&gt;z -->\n", "<g id=\"edge1\" class=\"edge\"><title>x&#45;&gt;z</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M55,-143.697C55,-135.983 55,-126.712 55,-118.112\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"58.5001,-118.104 55,-108.104 51.5001,-118.104 58.5001,-118.104\"/>\n", "</g>\n", "<!-- Unobserved_0 -->\n", "<g id=\"node4\" class=\"node\"><title>Unobserved_0</title>\n", "<ellipse fill=\"black\" stroke=\"black\" cx=\"27\" cy=\"-217.8\" rx=\"1.8\" ry=\"1.8\"/>\n", "</g>\n", "<!-- Unobserved_0&#45;&gt;y -->\n", "<g id=\"edge4\" class=\"edge\"><title>Unobserved_0&#45;&gt;y</title>\n", "<path fill=\"none\" stroke=\"black\" stroke-dasharray=\"5,2\" d=\"M26.7296,-215.901C25.5161,-211.815 20.5645,-194.562 19,-180 13.8723,-132.275 15.4079,-119.865 19,-72 19.6366,-63.5179 20.8384,-54.3361 22.1208,-46.0356\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"25.5836,-46.5472 23.7517,-36.1119 18.6763,-45.412 25.5836,-46.5472\"/>\n", "</g>\n", "<!-- Unobserved_0&#45;&gt;x -->\n", "<g id=\"edge3\" class=\"edge\"><title>Unobserved_0&#45;&gt;x</title>\n", "<path fill=\"none\" stroke=\"black\" stroke-dasharray=\"5,2\" d=\"M27.486,-215.866C29.2799,-212.419 35.7477,-199.992 41.9617,-188.052\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"45.0705,-189.66 46.5825,-179.174 38.8611,-186.428 45.0705,-189.66\"/>\n", "</g>\n", "</g>\n", "</svg>\n" ], "text/plain": [ "<graphviz.dot.Digraph at 0x7f10a219fa58>" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dag_with_latent_variables = CausalGraphicalModel(\n", " nodes=[\"x\", \"y\", \"z\"],\n", " edges=[\n", " (\"x\", \"z\"),\n", " (\"z\", \"y\"), \n", " ],\n", " latent_edges=[\n", " (\"x\", \"y\")\n", " ]\n", ")\n", "\n", "dag_with_latent_variables.draw()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "frozenset()" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# here there are no observed backdoor adjustment sets\n", "dag_with_latent_variables.get_all_backdoor_adjustment_sets(\"x\", \"y\")" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "frozenset({frozenset({'z'})})" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# but there is a frontdoor adjustment set\n", "dag_with_latent_variables.get_all_frontdoor_adjustment_sets(\"x\", \"y\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# StructuralCausalModels\n", "\n", "For Structural Causal Models (SCM) we need to specify the functional form of each node:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "from causalgraphicalmodels import StructuralCausalModel\n", "import numpy as np\n", "\n", "scm = StructuralCausalModel({\n", " \"x1\": lambda n_samples: np.random.binomial(n=1,p=0.7,size=n_samples),\n", " \"x2\": lambda x1, n_samples: np.random.normal(loc=x1, scale=0.1),\n", " \"x3\": lambda x2, n_samples: x2 ** 2,\n", "})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The only requirement on the functions are:\n", " - that variable names are consistent \n", " - each function accepts keyword variables in the form of `numpy` arrays and output numpy arrays of shape [n_samples] \n", " - that in addition to it's parents, each function takes a `n_samples` variables indicating how many samples to generate \n", " - that any function acts on each row independently. This ensure that the output samples are independent\n", " \n", "Wrapping these functions in the `StructuralCausalModel` object allows us to easily generate samples: " ] }, { "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>x1</th>\n", " <th>x2</th>\n", " <th>x3</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>1.004020</td>\n", " <td>1.008056</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>0.902045</td>\n", " <td>0.813686</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1</td>\n", " <td>1.150323</td>\n", " <td>1.323242</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>1</td>\n", " <td>0.984634</td>\n", " <td>0.969504</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>1</td>\n", " <td>1.023118</td>\n", " <td>1.046770</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " x1 x2 x3\n", "0 1 1.004020 1.008056\n", "1 1 0.902045 0.813686\n", "2 1 1.150323 1.323242\n", "3 1 0.984634 0.969504\n", "4 1 1.023118 1.046770" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds = scm.sample(n_samples=100)\n", "\n", "ds.head()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f109cf51518>" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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NwzNavY6nVrxBbmkB/5rznLk8eGWtmkd/foecymLev/VJc8DYlZXAiwe+xNPe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\nMNjNn8igHgwI6kFEUA80FqZbD1JKdpw4wEerF1FUXsozdz/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"text/plain": [ "<matplotlib.figure.Figure at 0x7f10a2183d30>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# and visualise the samples\n", "import seaborn as sns\n", "\n", "%matplotlib inline\n", "\n", "sns.kdeplot(\n", " data=ds.x2,\n", " data2=ds.x3,\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And to access the implied CGM\"" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "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", "<!-- Generated by graphviz version 2.38.0 (20140413.2041)\n", " -->\n", "<!-- Title: %3 Pages: 1 -->\n", "<svg width=\"62pt\" height=\"188pt\"\n", " viewBox=\"0.00 0.00 62.00 188.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n", "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) 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30.5001,-118.104\"/>\n", "</g>\n", "<!-- x3 -->\n", "<g id=\"node2\" class=\"node\"><title>x3</title>\n", "<ellipse fill=\"none\" stroke=\"black\" cx=\"27\" cy=\"-18\" rx=\"27\" ry=\"18\"/>\n", "<text text-anchor=\"middle\" x=\"27\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">x3</text>\n", "</g>\n", "<!-- x2&#45;&gt;x3 -->\n", "<g id=\"edge2\" class=\"edge\"><title>x2&#45;&gt;x3</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M27,-71.6966C27,-63.9827 27,-54.7125 27,-46.1124\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"30.5001,-46.1043 27,-36.1043 23.5001,-46.1044 30.5001,-46.1043\"/>\n", "</g>\n", "</g>\n", "</svg>\n" ], "text/plain": [ "<graphviz.dot.Digraph at 0x7f10a219f320>" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "scm.cgm.draw()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And to apply an intervention:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "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", "<!-- Generated by graphviz version 2.38.0 (20140413.2041)\n", " -->\n", "<!-- Title: %3 Pages: 1 -->\n", "<svg width=\"70pt\" height=\"196pt\"\n", " viewBox=\"0.00 0.00 70.00 196.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n", "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 192)\">\n", "<title>%3</title>\n", "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-192 66,-192 66,4 -4,4\"/>\n", "<!-- x1 -->\n", "<g id=\"node1\" class=\"node\"><title>x1</title>\n", "<ellipse fill=\"none\" stroke=\"black\" cx=\"31\" cy=\"-166\" rx=\"27\" ry=\"18\"/>\n", "<ellipse fill=\"none\" stroke=\"black\" cx=\"31\" cy=\"-166\" rx=\"31\" ry=\"22\"/>\n", "<text text-anchor=\"middle\" x=\"31\" y=\"-162.3\" font-family=\"Times,serif\" font-size=\"14.00\">x1</text>\n", "</g>\n", "<!-- x2 -->\n", "<g id=\"node3\" class=\"node\"><title>x2</title>\n", "<ellipse fill=\"none\" stroke=\"black\" cx=\"31\" cy=\"-90\" rx=\"27\" ry=\"18\"/>\n", "<text text-anchor=\"middle\" x=\"31\" y=\"-86.3\" font-family=\"Times,serif\" font-size=\"14.00\">x2</text>\n", "</g>\n", "<!-- x1&#45;&gt;x2 -->\n", "<g id=\"edge1\" class=\"edge\"><title>x1&#45;&gt;x2</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M31,-143.993C31,-136.061 31,-126.915 31,-118.478\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"34.5001,-118.315 31,-108.315 27.5001,-118.315 34.5001,-118.315\"/>\n", "</g>\n", "<!-- x3 -->\n", "<g id=\"node2\" class=\"node\"><title>x3</title>\n", "<ellipse fill=\"none\" stroke=\"black\" cx=\"31\" cy=\"-18\" rx=\"27\" ry=\"18\"/>\n", "<text text-anchor=\"middle\" x=\"31\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">x3</text>\n", "</g>\n", "<!-- x2&#45;&gt;x3 -->\n", "<g id=\"edge2\" class=\"edge\"><title>x2&#45;&gt;x3</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M31,-71.6966C31,-63.9827 31,-54.7125 31,-46.1124\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"34.5001,-46.1043 31,-36.1043 27.5001,-46.1044 34.5001,-46.1043\"/>\n", "</g>\n", "</g>\n", "</svg>\n" ], "text/plain": [ "<graphviz.dot.Digraph at 0x7f109cf82518>" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "scm_do = scm.do(\"x1\")\n", "\n", "scm_do.cgm.draw()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And sample from the distribution implied by this intervention:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>x1</th>\n", " <th>x2</th>\n", " <th>x3</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0</td>\n", " <td>0.258736</td>\n", " <td>0.066944</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>1.142529</td>\n", " <td>1.305371</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2</td>\n", " <td>1.927665</td>\n", " <td>3.715894</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>3</td>\n", " <td>2.878977</td>\n", " <td>8.288507</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>4</td>\n", " <td>4.098250</td>\n", " <td>16.795653</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " x1 x2 x3\n", "0 0 0.258736 0.066944\n", "1 1 1.142529 1.305371\n", "2 2 1.927665 3.715894\n", "3 3 2.878977 8.288507\n", "4 4 4.098250 16.795653" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "scm_do.sample(n_samples=5, set_values={\"x1\": np.arange(5)})" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.3" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
kimkipyo/dss_git_kkp
통계, 머신러닝 복습/160628화_22일차_서포트 벡터 머신_SVM_Support Vector Machine/1.서포트 벡터 머신.ipynb
1
1627158
null
mit
vgm64/highway-radio
ExploreData.ipynb
1
138524
{ "metadata": { "name": "", "signature": "sha256:f9f0a366b19f8b11ea650f7e59ab88e2576446fa890c09b4a01f77cc5010334d" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import MySQLdb\n", "import numpy as np\n", "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "plt.rcParams['figure.figsize'] = (10.0, 10.0)\n", "plt.rcParams['image.aspect'] = 'equal'\n", "plt.rcParams['figure.dpi'] = 300" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 16 }, { "cell_type": "code", "collapsed": false, "input": [ "def get_database_conn():\n", " connection = MySQLdb.connect('localhost', 'root', '', 'insight')\n", " cursor = connection.cursor()\n", " return connection, cursor\n", "connection, cursor = get_database_conn()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 17 }, { "cell_type": "code", "collapsed": false, "input": [ "def get_contour(i):\n", " #cursor.execute(\"SELECT * FROM contours WHERE id = %s\", (str(i)))\n", " query = \"SELECT * FROM contours WHERE id = %s WHERE reg = 'CA'\" % i\n", " cursor.execute(query)\n", " result = cursor.fetchall()\n", " lats = np.fromstring(result[0][-2], sep=',')\n", " lons = np.fromstring(result[0][-1], sep=',')\n", " return np.array([lats, lons])\n", "def show_contour(i):\n", " contour = get_contour(i)\n", " plt.scatter(contour[1], contour[0])\n", " \n", "def get_all_antennas():\n", " query = \"SELECT antlat, antlon FROM contours WHERE antlon < -50 AND antlon > -125 AND antlat < 50 AND antlat > 20\"\n", " cursor.execute(query)\n", " result = cursor.fetchall()\n", " return np.array(result)\n", "def show_all_antennas():\n", " locs = get_all_antennas()\n", " plt.scatter(locs[:,1], locs[:,0])" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 42 }, { "cell_type": "code", "collapsed": false, "input": [ "ax =plt.gca()\n", "ax.set_aspect('equal')\n", "print show_all_antennas()\n", "plt.draw()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "None\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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TXvsXal2LonEgTrBwDHWdPYMKpuNRQee0xYKkMp5GrXQ/mPOqiUiUf/3rXxW6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\nVEQwl+vkrcCnqunDptjGvUP267o4muBcIffffz/r16/n0EO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ViFnvBwT4nIpoQF7G/Oxk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tDZQh9sILL6wSQwSQTqeRkqwyQTgzIXh9h1xMCMNsQgDeakKxnMhknEKc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\nirM7D0oYW2k/B0IC/Glo5ehcqAwSoHqTSwAAkyZNqpW+OE5N45Y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"text": [ "<matplotlib.figure.Figure at 0x108eab350>" ] } ], "prompt_number": 43 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "plt.rcParams" ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 20 } ], "metadata": {} } ] }
mit
bearing/dosenet-analysis
Weather Station Data Query.ipynb
1
205107
{ "cells": [ { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " <div class=\"bk-root\">\n", " <a href=\"https://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n", " <span id=\"0b7fc20c-3a02-49a4-b190-86f4f0b8f95f\">Loading BokehJS ...</span>\n", " </div>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "\n", "(function(root) {\n", " function now() {\n", " return new Date();\n", " }\n", "\n", " var force = true;\n", "\n", " if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n", " root._bokeh_onload_callbacks = [];\n", " root._bokeh_is_loading = undefined;\n", " }\n", "\n", " var JS_MIME_TYPE = 'application/javascript';\n", " var HTML_MIME_TYPE = 'text/html';\n", " var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", " var CLASS_NAME = 'output_bokeh rendered_html';\n", "\n", " /**\n", " * Render data to the DOM node\n", " */\n", " function render(props, node) {\n", " var script = document.createElement(\"script\");\n", " node.appendChild(script);\n", " }\n", "\n", " /**\n", " * Handle when an output is cleared or removed\n", " */\n", " function handleClearOutput(event, handle) {\n", " var cell = handle.cell;\n", "\n", " var id = cell.output_area._bokeh_element_id;\n", " var server_id = cell.output_area._bokeh_server_id;\n", " // Clean up Bokeh references\n", " if (id != null && id in Bokeh.index) {\n", " Bokeh.index[id].model.document.clear();\n", " delete Bokeh.index[id];\n", " }\n", "\n", " if (server_id !== undefined) {\n", " // Clean up Bokeh references\n", " var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", " cell.notebook.kernel.execute(cmd, {\n", " iopub: {\n", " output: function(msg) {\n", " var id = msg.content.text.trim();\n", " if (id in Bokeh.index) {\n", " Bokeh.index[id].model.document.clear();\n", " delete Bokeh.index[id];\n", " }\n", " }\n", " }\n", " });\n", " // Destroy server and session\n", " var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", " cell.notebook.kernel.execute(cmd);\n", " }\n", " }\n", "\n", " /**\n", " * Handle when a new output is added\n", " */\n", " function handleAddOutput(event, handle) {\n", " var output_area = handle.output_area;\n", " var output = handle.output;\n", "\n", " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", " if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", " return\n", " }\n", "\n", " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", "\n", " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", " // store reference to embed id on output_area\n", " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", " }\n", " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", " var bk_div = document.createElement(\"div\");\n", " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", " var script_attrs = bk_div.children[0].attributes;\n", " for (var i = 0; i < script_attrs.length; i++) {\n", " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", " }\n", " // store reference to server id on output_area\n", " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", " }\n", " }\n", "\n", " function register_renderer(events, OutputArea) {\n", "\n", " function append_mime(data, metadata, element) {\n", " // create a DOM node to render to\n", " var toinsert = this.create_output_subarea(\n", " metadata,\n", " CLASS_NAME,\n", " EXEC_MIME_TYPE\n", " );\n", " this.keyboard_manager.register_events(toinsert);\n", " // Render to node\n", " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", " render(props, toinsert[toinsert.length - 1]);\n", " element.append(toinsert);\n", " return toinsert\n", " }\n", "\n", " /* Handle when an output is cleared or removed */\n", " events.on('clear_output.CodeCell', handleClearOutput);\n", " events.on('delete.Cell', handleClearOutput);\n", "\n", " /* Handle when a new output is added */\n", " events.on('output_added.OutputArea', handleAddOutput);\n", "\n", " /**\n", " * Register the mime type and append_mime function with output_area\n", " */\n", " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", " /* Is output safe? */\n", " safe: true,\n", " /* Index of renderer in `output_area.display_order` */\n", " index: 0\n", " });\n", " }\n", "\n", " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", " if (root.Jupyter !== undefined) {\n", " var events = require('base/js/events');\n", " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", "\n", " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", " register_renderer(events, OutputArea);\n", " }\n", " }\n", "\n", " \n", " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", " root._bokeh_timeout = Date.now() + 5000;\n", " root._bokeh_failed_load = false;\n", " }\n", "\n", " var NB_LOAD_WARNING = {'data': {'text/html':\n", " \"<div style='background-color: #fdd'>\\n\"+\n", " \"<p>\\n\"+\n", " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", " \"</p>\\n\"+\n", " \"<ul>\\n\"+\n", " \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n", " \"<li>use INLINE resources instead, as so:</li>\\n\"+\n", " \"</ul>\\n\"+\n", " \"<code>\\n\"+\n", " \"from bokeh.resources import INLINE\\n\"+\n", " \"output_notebook(resources=INLINE)\\n\"+\n", " \"</code>\\n\"+\n", " \"</div>\"}};\n", "\n", " function display_loaded() {\n", " var el = document.getElementById(\"0b7fc20c-3a02-49a4-b190-86f4f0b8f95f\");\n", " if (el != null) {\n", " el.textContent = \"BokehJS is loading...\";\n", " }\n", " if (root.Bokeh !== undefined) {\n", " if (el != null) {\n", " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", " }\n", " } else if (Date.now() < root._bokeh_timeout) {\n", " setTimeout(display_loaded, 100)\n", " }\n", " }\n", "\n", "\n", " function run_callbacks() {\n", " try {\n", " root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n", " }\n", " finally {\n", " delete root._bokeh_onload_callbacks\n", " }\n", " console.info(\"Bokeh: all callbacks have finished\");\n", " }\n", "\n", " function load_libs(js_urls, callback) {\n", " root._bokeh_onload_callbacks.push(callback);\n", " if (root._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", " root._bokeh_is_loading = js_urls.length;\n", " for (var i = 0; i < js_urls.length; i++) {\n", " var url = js_urls[i];\n", " var s = document.createElement('script');\n", " s.src = url;\n", " s.async = false;\n", " s.onreadystatechange = s.onload = function() {\n", " root._bokeh_is_loading--;\n", " if (root._bokeh_is_loading === 0) {\n", " console.log(\"Bokeh: all BokehJS libraries loaded\");\n", " run_callbacks()\n", " }\n", " };\n", " s.onerror = function() {\n", " console.warn(\"failed to load library \" + url);\n", " };\n", " console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", " document.getElementsByTagName(\"head\")[0].appendChild(s);\n", " }\n", " };var element = document.getElementById(\"0b7fc20c-3a02-49a4-b190-86f4f0b8f95f\");\n", " if (element == null) {\n", " console.log(\"Bokeh: ERROR: autoload.js configured with elementid '0b7fc20c-3a02-49a4-b190-86f4f0b8f95f' but no matching script tag was found. \")\n", " return false;\n", " }\n", "\n", " var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.13.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.13.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.13.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.13.0.min.js\"];\n", "\n", " var inline_js = [\n", " function(Bokeh) {\n", " Bokeh.set_log_level(\"info\");\n", " },\n", " \n", " function(Bokeh) {\n", " \n", " },\n", " function(Bokeh) {\n", " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.13.0.min.css\");\n", " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.13.0.min.css\");\n", " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.13.0.min.css\");\n", " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.13.0.min.css\");\n", " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.13.0.min.css\");\n", " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.13.0.min.css\");\n", " }\n", " ];\n", "\n", " function run_inline_js() {\n", " \n", " if ((root.Bokeh !== undefined) || (force === true)) {\n", " for (var i = 0; i < inline_js.length; i++) {\n", " inline_js[i].call(root, root.Bokeh);\n", " }if (force === true) {\n", " display_loaded();\n", " }} else if (Date.now() < root._bokeh_timeout) {\n", " setTimeout(run_inline_js, 100);\n", " } else if (!root._bokeh_failed_load) {\n", " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", " root._bokeh_failed_load = true;\n", " } else if (force !== true) {\n", " var cell = $(document.getElementById(\"0b7fc20c-3a02-49a4-b190-86f4f0b8f95f\")).parents('.cell').data().cell;\n", " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", " }\n", "\n", " }\n", "\n", " if (root._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", "}(window));" ], "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n\n if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n \n\n \n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n var NB_LOAD_WARNING = {'data': {'text/html':\n \"<div style='background-color: #fdd'>\\n\"+\n \"<p>\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"</p>\\n\"+\n \"<ul>\\n\"+\n \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n \"<li>use INLINE resources instead, as so:</li>\\n\"+\n \"</ul>\\n\"+\n \"<code>\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"</code>\\n\"+\n \"</div>\"}};\n\n function display_loaded() {\n var el = document.getElementById(\"0b7fc20c-3a02-49a4-b190-86f4f0b8f95f\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n }\n finally {\n delete root._bokeh_onload_callbacks\n }\n console.info(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(js_urls, callback) {\n root._bokeh_onload_callbacks.push(callback);\n if (root._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 root._bokeh_is_loading = js_urls.length;\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n var s = document.createElement('script');\n s.src = url;\n s.async = false;\n s.onreadystatechange = s.onload = function() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.log(\"Bokeh: all BokehJS libraries loaded\");\n run_callbacks()\n }\n };\n s.onerror = function() {\n console.warn(\"failed to load library \" + url);\n };\n console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.getElementsByTagName(\"head\")[0].appendChild(s);\n }\n };var element = document.getElementById(\"0b7fc20c-3a02-49a4-b190-86f4f0b8f95f\");\n if (element == null) {\n console.log(\"Bokeh: ERROR: autoload.js configured with elementid '0b7fc20c-3a02-49a4-b190-86f4f0b8f95f' but no matching script tag was found. \")\n return false;\n }\n\n var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.13.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.13.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.13.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.13.0.min.js\"];\n\n var inline_js = [\n function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n \n function(Bokeh) {\n \n },\n function(Bokeh) {\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.13.0.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.13.0.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.13.0.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.13.0.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.13.0.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.13.0.min.css\");\n }\n ];\n\n function run_inline_js() {\n \n if ((root.Bokeh !== undefined) || (force === true)) {\n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }if (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n var cell = $(document.getElementById(\"0b7fc20c-3a02-49a4-b190-86f4f0b8f95f\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n\n }\n\n if (root._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}(window));" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "import numpy as np # math\n", "import pandas as pd # manipulating data\n", "import matplotlib.pyplot as plt # graphing\n", "import os # useful for handling filenames etc.\n", "\n", "# calculates the Pearson correlation coefficient, p-value, and does linear regression\n", "from scipy.stats import pearsonr, linregress\n", "\n", "#import seaborn as sns # makes matplotlib beautiful\n", "#sns.set_style('darkgrid')\n", "\n", "import matplotlib as mpl # control formatting\n", "mpl.rcParams['axes.titlesize'] = 16\n", "mpl.rcParams['axes.titleweight'] = 'semibold'\n", "\n", "# interactive graphs\n", "from bokeh.io import output_notebook, show, push_notebook\n", "from bokeh.plotting import figure\n", "from bokeh.layouts import row, column\n", "from bokeh.models import DatetimeTickFormatter, HoverTool\n", "output_notebook()\n", "\n", "# manage date and time\n", "from datetime import datetime, timedelta, date" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Time</th>\n", " <th>TemperatureF</th>\n", " <th>DewpointF</th>\n", " <th>PressureIn</th>\n", " <th>WindDirection</th>\n", " <th>WindDirectionDegrees</th>\n", " <th>WindSpeedMPH</th>\n", " <th>WindSpeedGustMPH</th>\n", " <th>Humidity</th>\n", " <th>HourlyPrecipIn</th>\n", " <th>Conditions</th>\n", " <th>Clouds</th>\n", " <th>dailyrainin</th>\n", " <th>SolarRadiationWatts/m^2</th>\n", " <th>SoftwareType</th>\n", " <th>DateUTC&lt;br&gt;</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>2018-11-01 12:49:32</td>\n", " <td>72.3</td>\n", " <td>48.0</td>\n", " <td>-100.0</td>\n", " <td>SSW</td>\n", " <td>202.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>42.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.00</td>\n", " <td>44.49</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-01 19:49:32</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>&lt;br&gt;</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2018-11-01 12:54:36</td>\n", " <td>72.5</td>\n", " <td>48.2</td>\n", " <td>-100.0</td>\n", " <td>SE</td>\n", " <td>133.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>42.0</td>\n", " <td>0.21</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.04</td>\n", " <td>277.27</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-01 19:54:36</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>&lt;br&gt;</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>2018-11-01 12:59:56</td>\n", " <td>73.4</td>\n", " <td>45.7</td>\n", " <td>-100.0</td>\n", " <td>South</td>\n", " <td>187.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>37.0</td>\n", " <td>0.21</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.04</td>\n", " <td>292.76</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-01 19:59:56</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>&lt;br&gt;</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>2018-11-01 13:04:44</td>\n", " <td>74.3</td>\n", " <td>44.2</td>\n", " <td>-100.0</td>\n", " <td>ESE</td>\n", " <td>114.0</td>\n", " <td>1.8</td>\n", " <td>4.9</td>\n", " <td>34.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.04</td>\n", " <td>288.55</td>\n", " <td>Weather logger 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<td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>2018-11-01 13:14:52</td>\n", " <td>76.3</td>\n", " <td>44.2</td>\n", " <td>-100.0</td>\n", " <td>SE</td>\n", " <td>139.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>32.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.04</td>\n", " <td>274.66</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-01 20:14:52</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>&lt;br&gt;</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>2018-11-01 13:19:56</td>\n", " <td>77.5</td>\n", " <td>43.7</td>\n", " <td>-100.0</td>\n", " <td>SSE</td>\n", " <td>150.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>30.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.04</td>\n", " <td>274.23</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-01 20:19:56</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>&lt;br&gt;</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>2018-11-01 13:24:44</td>\n", " <td>78.8</td>\n", " <td>45.7</td>\n", " <td>-100.0</td>\n", " <td>South</td>\n", " <td>186.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>31.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.04</td>\n", " <td>287.99</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-01 20:24:44</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>&lt;br&gt;</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>2018-11-01 13:29:32</td>\n", " <td>79.7</td>\n", " <td>45.5</td>\n", " <td>-100.0</td>\n", " <td>East</td>\n", " <td>97.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>30.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.04</td>\n", " <td>279.70</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-01 20:29:32</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>&lt;br&gt;</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>2018-11-01 13:34:36</td>\n", " <td>80.6</td>\n", " <td>45.5</td>\n", " <td>-100.0</td>\n", " <td>NE</td>\n", " <td>39.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>29.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.04</td>\n", " <td>290.42</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-01 20:34:36</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>&lt;br&gt;</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>2018-11-01 13:39:56</td>\n", " <td>81.7</td>\n", " <td>46.4</td>\n", " <td>-100.0</td>\n", " <td>NE</td>\n", " <td>46.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>29.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.04</td>\n", " <td>280.35</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-01 20:39:56</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>&lt;br&gt;</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>2018-11-01 13:44:28</td>\n", " <td>82.4</td>\n", " <td>47.8</td>\n", " <td>-100.0</td>\n", " <td>NE</td>\n", " <td>49.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>30.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.04</td>\n", " <td>266.63</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-01 20:44:28</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>&lt;br&gt;</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>2018-11-01 13:49:16</td>\n", " <td>82.9</td>\n", " <td>49.3</td>\n", " <td>-100.0</td>\n", " <td>SSW</td>\n", " <td>212.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>31.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.04</td>\n", " <td>260.77</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-01 20:49:16</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td>&lt;br&gt;</td>\n", " <td>NaN</td>\n", " 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<td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td>2018-11-01 13:59:40</td>\n", " <td>83.3</td>\n", " <td>49.6</td>\n", " <td>-100.0</td>\n", " <td>SW</td>\n", " <td>226.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>31.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.04</td>\n", " <td>245.79</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-01 20:59:40</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td>&lt;br&gt;</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>...</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", " 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<td>0.00</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-02 05:59:57</td>\n", " </tr>\n", " <tr>\n", " <th>245</th>\n", " <td>&lt;br&gt;</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>246</th>\n", " <td>2018-11-01 23:04:45</td>\n", " <td>64.2</td>\n", " <td>51.4</td>\n", " <td>-100.0</td>\n", " <td>SW</td>\n", " <td>214.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>63.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-02 06:04:45</td>\n", " </tr>\n", " <tr>\n", " <th>247</th>\n", " <td>&lt;br&gt;</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " 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<th>250</th>\n", " <td>2018-11-01 23:14:37</td>\n", " <td>64.0</td>\n", " <td>51.3</td>\n", " <td>-100.0</td>\n", " <td>SSW</td>\n", " <td>209.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>63.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-02 06:14:37</td>\n", " </tr>\n", " <tr>\n", " <th>251</th>\n", " <td>&lt;br&gt;</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>252</th>\n", " <td>2018-11-01 23:19:57</td>\n", " <td>63.9</td>\n", " <td>51.4</td>\n", " <td>-100.0</td>\n", " <td>SSW</td>\n", " <td>205.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>64.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-02 06:19:57</td>\n", " </tr>\n", " <tr>\n", " <th>253</th>\n", " <td>&lt;br&gt;</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>254</th>\n", " <td>2018-11-01 23:24:45</td>\n", " <td>63.7</td>\n", " <td>51.3</td>\n", " <td>-100.0</td>\n", " <td>SSW</td>\n", " <td>208.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>64.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-02 06:24:45</td>\n", " </tr>\n", " <tr>\n", " <th>255</th>\n", " <td>&lt;br&gt;</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>256</th>\n", " <td>2018-11-01 23:29:49</td>\n", " <td>63.3</td>\n", " <td>51.4</td>\n", " <td>-100.0</td>\n", " <td>SSW</td>\n", " <td>207.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>65.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-02 06:29:49</td>\n", " </tr>\n", " <tr>\n", " <th>257</th>\n", " <td>&lt;br&gt;</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>258</th>\n", " <td>2018-11-01 23:34:21</td>\n", " <td>63.1</td>\n", " <td>51.3</td>\n", " <td>-100.0</td>\n", " <td>SSW</td>\n", " <td>206.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>65.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-02 06:34:21</td>\n", " </tr>\n", " <tr>\n", " <th>259</th>\n", " <td>&lt;br&gt;</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>260</th>\n", " <td>2018-11-01 23:39:57</td>\n", " <td>62.8</td>\n", " <td>51.3</td>\n", " <td>-100.0</td>\n", " <td>SSW</td>\n", " <td>206.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>66.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-02 06:39:57</td>\n", " </tr>\n", " <tr>\n", " <th>261</th>\n", " <td>&lt;br&gt;</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>262</th>\n", " <td>2018-11-01 23:44:29</td>\n", " <td>62.6</td>\n", " <td>51.1</td>\n", " <td>-100.0</td>\n", " <td>SW</td>\n", " <td>229.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>66.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-02 06:44:29</td>\n", " </tr>\n", " <tr>\n", " <th>263</th>\n", " <td>&lt;br&gt;</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>264</th>\n", " <td>2018-11-01 23:49:17</td>\n", " <td>62.6</td>\n", " <td>50.4</td>\n", " <td>-100.0</td>\n", " <td>SSW</td>\n", " <td>213.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>64.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-02 06:49:17</td>\n", " </tr>\n", " <tr>\n", " <th>265</th>\n", " <td>&lt;br&gt;</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>266</th>\n", " <td>2018-11-01 23:54:53</td>\n", " <td>62.6</td>\n", " <td>49.5</td>\n", " <td>-100.0</td>\n", " <td>SSW</td>\n", " <td>200.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>62.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-02 06:54:53</td>\n", " </tr>\n", " <tr>\n", " <th>267</th>\n", " <td>&lt;br&gt;</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>268</th>\n", " <td>2018-11-01 23:59:57</td>\n", " <td>62.8</td>\n", " <td>49.6</td>\n", " <td>-100.0</td>\n", " <td>SSW</td>\n", " <td>208.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>62.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-02 06:59:57</td>\n", " </tr>\n", " <tr>\n", " <th>269</th>\n", " <td>&lt;br&gt;</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>270 rows × 16 columns</p>\n", "</div>" ], "text/plain": [ " Time TemperatureF DewpointF PressureIn WindDirection \\\n", "0 2018-11-01 12:49:32 72.3 48.0 -100.0 SSW \n", "1 <br> NaN NaN NaN NaN \n", "2 2018-11-01 12:54:36 72.5 48.2 -100.0 SE \n", "3 <br> NaN NaN NaN NaN \n", "4 2018-11-01 12:59:56 73.4 45.7 -100.0 South \n", "5 <br> NaN NaN NaN NaN \n", "6 2018-11-01 13:04:44 74.3 44.2 -100.0 ESE \n", "7 <br> NaN NaN NaN NaN \n", "8 2018-11-01 13:09:48 75.4 44.4 -100.0 SE \n", "9 <br> NaN NaN NaN NaN \n", "10 2018-11-01 13:14:52 76.3 44.2 -100.0 SE \n", "11 <br> NaN NaN NaN NaN \n", "12 2018-11-01 13:19:56 77.5 43.7 -100.0 SSE \n", "13 <br> NaN NaN NaN NaN \n", "14 2018-11-01 13:24:44 78.8 45.7 -100.0 South \n", "15 <br> NaN NaN NaN NaN \n", "16 2018-11-01 13:29:32 79.7 45.5 -100.0 East \n", "17 <br> NaN NaN NaN NaN \n", "18 2018-11-01 13:34:36 80.6 45.5 -100.0 NE \n", "19 <br> NaN NaN NaN NaN \n", "20 2018-11-01 13:39:56 81.7 46.4 -100.0 NE \n", "21 <br> NaN NaN NaN NaN \n", "22 2018-11-01 13:44:28 82.4 47.8 -100.0 NE \n", "23 <br> NaN NaN NaN NaN \n", "24 2018-11-01 13:49:16 82.9 49.3 -100.0 SSW \n", "25 <br> NaN NaN NaN NaN \n", "26 2018-11-01 13:54:52 83.3 50.4 -100.0 South \n", "27 <br> NaN NaN NaN NaN \n", "28 2018-11-01 13:59:40 83.3 49.6 -100.0 SW \n", "29 <br> NaN NaN NaN NaN \n", ".. ... ... ... ... ... \n", "240 2018-11-01 22:49:49 64.6 53.8 -100.0 SSW \n", "241 <br> NaN NaN NaN NaN \n", "242 2018-11-01 22:54:53 64.4 52.0 -100.0 SW \n", "243 <br> NaN NaN NaN NaN \n", "244 2018-11-01 22:59:57 64.2 51.4 -100.0 SSW \n", "245 <br> NaN NaN NaN NaN \n", "246 2018-11-01 23:04:45 64.2 51.4 -100.0 SW \n", "247 <br> NaN NaN NaN NaN \n", "248 2018-11-01 23:09:49 64.0 51.3 -100.0 SW \n", "249 <br> NaN NaN NaN NaN \n", "250 2018-11-01 23:14:37 64.0 51.3 -100.0 SSW \n", "251 <br> NaN NaN NaN NaN \n", "252 2018-11-01 23:19:57 63.9 51.4 -100.0 SSW \n", "253 <br> NaN NaN NaN NaN \n", "254 2018-11-01 23:24:45 63.7 51.3 -100.0 SSW \n", "255 <br> NaN NaN NaN NaN \n", "256 2018-11-01 23:29:49 63.3 51.4 -100.0 SSW \n", "257 <br> NaN NaN NaN NaN \n", "258 2018-11-01 23:34:21 63.1 51.3 -100.0 SSW \n", "259 <br> NaN NaN NaN NaN \n", "260 2018-11-01 23:39:57 62.8 51.3 -100.0 SSW \n", "261 <br> NaN NaN NaN NaN \n", "262 2018-11-01 23:44:29 62.6 51.1 -100.0 SW \n", "263 <br> NaN NaN NaN NaN \n", "264 2018-11-01 23:49:17 62.6 50.4 -100.0 SSW \n", "265 <br> NaN NaN NaN NaN \n", "266 2018-11-01 23:54:53 62.6 49.5 -100.0 SSW \n", "267 <br> NaN NaN NaN NaN \n", "268 2018-11-01 23:59:57 62.8 49.6 -100.0 SSW \n", "269 <br> NaN NaN NaN NaN \n", "\n", " WindDirectionDegrees WindSpeedMPH WindSpeedGustMPH Humidity \\\n", "0 202.0 0.0 0.0 42.0 \n", "1 NaN NaN NaN NaN \n", "2 133.0 0.0 0.0 42.0 \n", "3 NaN NaN NaN NaN \n", "4 187.0 0.0 0.0 37.0 \n", "5 NaN NaN NaN NaN \n", "6 114.0 1.8 4.9 34.0 \n", "7 NaN NaN NaN NaN \n", "8 133.0 0.4 2.5 33.0 \n", "9 NaN NaN NaN NaN \n", "10 139.0 0.0 0.0 32.0 \n", "11 NaN NaN NaN NaN \n", "12 150.0 0.0 0.0 30.0 \n", "13 NaN NaN NaN NaN \n", "14 186.0 0.0 0.0 31.0 \n", "15 NaN NaN NaN NaN \n", "16 97.0 0.0 0.0 30.0 \n", "17 NaN NaN NaN NaN \n", "18 39.0 0.0 0.0 29.0 \n", "19 NaN NaN NaN NaN \n", "20 46.0 0.0 0.0 29.0 \n", "21 NaN NaN NaN NaN \n", "22 49.0 0.0 0.0 30.0 \n", "23 NaN NaN NaN NaN \n", "24 212.0 0.0 0.0 31.0 \n", "25 NaN NaN NaN NaN \n", "26 190.0 0.0 0.0 32.0 \n", "27 NaN NaN NaN NaN \n", "28 226.0 0.0 0.0 31.0 \n", "29 NaN NaN NaN NaN \n", ".. ... ... ... ... \n", "240 210.0 0.0 0.0 68.0 \n", "241 NaN NaN NaN NaN \n", "242 221.0 0.0 0.0 64.0 \n", "243 NaN NaN NaN NaN \n", "244 200.0 0.0 0.0 63.0 \n", "245 NaN NaN NaN NaN \n", "246 214.0 0.0 0.0 63.0 \n", "247 NaN NaN NaN NaN \n", "248 219.0 0.0 0.0 63.0 \n", "249 NaN NaN NaN NaN \n", "250 209.0 0.0 0.0 63.0 \n", "251 NaN NaN NaN NaN \n", "252 205.0 0.0 0.0 64.0 \n", "253 NaN NaN NaN NaN \n", "254 208.0 0.0 0.0 64.0 \n", "255 NaN NaN NaN NaN \n", "256 207.0 0.0 0.0 65.0 \n", "257 NaN NaN NaN NaN \n", "258 206.0 0.0 0.0 65.0 \n", "259 NaN NaN NaN NaN \n", "260 206.0 0.0 0.0 66.0 \n", "261 NaN NaN NaN NaN \n", "262 229.0 0.0 0.0 66.0 \n", "263 NaN NaN NaN NaN \n", "264 213.0 0.0 0.0 64.0 \n", "265 NaN NaN NaN NaN \n", "266 200.0 0.0 0.0 62.0 \n", "267 NaN NaN NaN NaN \n", "268 208.0 0.0 0.0 62.0 \n", "269 NaN NaN NaN NaN \n", "\n", " HourlyPrecipIn Conditions Clouds dailyrainin SolarRadiationWatts/m^2 \\\n", "0 0.00 NaN NaN 0.00 44.49 \n", "1 NaN NaN NaN NaN NaN \n", "2 0.21 NaN NaN 0.04 277.27 \n", "3 NaN NaN NaN NaN NaN \n", "4 0.21 NaN NaN 0.04 292.76 \n", "5 NaN NaN NaN NaN NaN \n", "6 0.00 NaN NaN 0.04 288.55 \n", "7 NaN NaN NaN NaN NaN \n", "8 0.00 NaN NaN 0.04 295.80 \n", "9 NaN NaN NaN NaN NaN \n", "10 0.00 NaN NaN 0.04 274.66 \n", "11 NaN NaN NaN NaN NaN \n", "12 0.00 NaN NaN 0.04 274.23 \n", "13 NaN NaN NaN NaN NaN \n", "14 0.00 NaN NaN 0.04 287.99 \n", "15 NaN NaN NaN NaN NaN \n", "16 0.00 NaN NaN 0.04 279.70 \n", "17 NaN NaN NaN NaN NaN \n", "18 0.00 NaN NaN 0.04 290.42 \n", "19 NaN NaN NaN NaN NaN \n", "20 0.00 NaN NaN 0.04 280.35 \n", "21 NaN NaN NaN NaN NaN \n", "22 0.00 NaN NaN 0.04 266.63 \n", "23 NaN NaN NaN NaN NaN \n", "24 0.00 NaN NaN 0.04 260.77 \n", "25 NaN NaN NaN NaN NaN \n", "26 0.00 NaN NaN 0.04 252.30 \n", "27 NaN NaN NaN NaN NaN \n", "28 0.00 NaN NaN 0.04 245.79 \n", "29 NaN NaN NaN NaN NaN \n", ".. ... ... ... ... ... \n", "240 0.00 NaN NaN 0.00 0.00 \n", "241 NaN NaN NaN NaN NaN \n", "242 0.00 NaN NaN 0.00 0.00 \n", "243 NaN NaN NaN NaN NaN \n", "244 0.00 NaN NaN 0.00 0.00 \n", "245 NaN NaN NaN NaN NaN \n", "246 0.00 NaN NaN 0.00 0.00 \n", "247 NaN NaN NaN NaN NaN \n", "248 0.00 NaN NaN 0.00 0.00 \n", "249 NaN NaN NaN NaN NaN \n", "250 0.00 NaN NaN 0.00 0.00 \n", "251 NaN NaN NaN NaN NaN \n", "252 0.00 NaN NaN 0.00 0.00 \n", "253 NaN NaN NaN NaN NaN \n", "254 0.00 NaN NaN 0.00 0.00 \n", "255 NaN NaN NaN NaN NaN \n", "256 0.00 NaN NaN 0.00 0.00 \n", "257 NaN NaN NaN NaN NaN \n", "258 0.00 NaN NaN 0.00 0.00 \n", "259 NaN NaN NaN NaN NaN \n", "260 0.00 NaN NaN 0.00 0.00 \n", "261 NaN NaN NaN NaN NaN \n", "262 0.00 NaN NaN 0.00 0.00 \n", "263 NaN NaN NaN NaN NaN \n", "264 0.00 NaN NaN 0.00 0.00 \n", "265 NaN NaN NaN NaN NaN \n", "266 0.00 NaN NaN 0.00 0.00 \n", "267 NaN NaN NaN NaN NaN \n", "268 0.00 NaN NaN 0.00 0.00 \n", "269 NaN NaN NaN NaN NaN \n", "\n", " SoftwareType DateUTC<br> \n", "0 Weather logger V3.0. 2018-11-01 19:49:32 \n", "1 NaN NaN \n", "2 Weather logger V3.0. 2018-11-01 19:54:36 \n", "3 NaN NaN \n", "4 Weather logger V3.0. 2018-11-01 19:59:56 \n", "5 NaN NaN \n", "6 Weather logger V3.0. 2018-11-01 20:04:44 \n", "7 NaN NaN \n", "8 Weather logger V3.0. 2018-11-01 20:09:48 \n", "9 NaN NaN \n", "10 Weather logger V3.0. 2018-11-01 20:14:52 \n", "11 NaN NaN \n", "12 Weather logger V3.0. 2018-11-01 20:19:56 \n", "13 NaN NaN \n", "14 Weather logger V3.0. 2018-11-01 20:24:44 \n", "15 NaN NaN \n", "16 Weather logger V3.0. 2018-11-01 20:29:32 \n", "17 NaN NaN \n", "18 Weather logger V3.0. 2018-11-01 20:34:36 \n", "19 NaN NaN \n", "20 Weather logger V3.0. 2018-11-01 20:39:56 \n", "21 NaN NaN \n", "22 Weather logger V3.0. 2018-11-01 20:44:28 \n", "23 NaN NaN \n", "24 Weather logger V3.0. 2018-11-01 20:49:16 \n", "25 NaN NaN \n", "26 Weather logger V3.0. 2018-11-01 20:54:52 \n", "27 NaN NaN \n", "28 Weather logger V3.0. 2018-11-01 20:59:40 \n", "29 NaN NaN \n", ".. ... ... \n", "240 Weather logger V3.0. 2018-11-02 05:49:49 \n", "241 NaN NaN \n", "242 Weather logger V3.0. 2018-11-02 05:54:53 \n", "243 NaN NaN \n", "244 Weather logger V3.0. 2018-11-02 05:59:57 \n", "245 NaN NaN \n", "246 Weather logger V3.0. 2018-11-02 06:04:45 \n", "247 NaN NaN \n", "248 Weather logger V3.0. 2018-11-02 06:09:49 \n", "249 NaN NaN \n", "250 Weather logger V3.0. 2018-11-02 06:14:37 \n", "251 NaN NaN \n", "252 Weather logger V3.0. 2018-11-02 06:19:57 \n", "253 NaN NaN \n", "254 Weather logger V3.0. 2018-11-02 06:24:45 \n", "255 NaN NaN \n", "256 Weather logger V3.0. 2018-11-02 06:29:49 \n", "257 NaN NaN \n", "258 Weather logger V3.0. 2018-11-02 06:34:21 \n", "259 NaN NaN \n", "260 Weather logger V3.0. 2018-11-02 06:39:57 \n", "261 NaN NaN \n", "262 Weather logger V3.0. 2018-11-02 06:44:29 \n", "263 NaN NaN \n", "264 Weather logger V3.0. 2018-11-02 06:49:17 \n", "265 NaN NaN \n", "266 Weather logger V3.0. 2018-11-02 06:54:53 \n", "267 NaN NaN \n", "268 Weather logger V3.0. 2018-11-02 06:59:57 \n", "269 NaN NaN \n", "\n", "[270 rows x 16 columns]" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# An example of what the website query looks like without any processing\n", "CSV_URL = 'https://www.wunderground.com/weatherstation/WXDailyHistory.asp?\\\n", "ID=KCABERKE169&day=01&month=11&year=2018&graphspan=day&format=1'\n", "df = pd.read_csv(CSV_URL, index_col=False)\n", "df" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Time</th>\n", " <th>TemperatureF</th>\n", " <th>DewpointF</th>\n", " <th>PressureIn</th>\n", " <th>WindDirection</th>\n", " <th>WindDirectionDegrees</th>\n", " <th>WindSpeedMPH</th>\n", " <th>WindSpeedGustMPH</th>\n", " <th>Humidity</th>\n", " <th>HourlyPrecipIn</th>\n", " <th>Conditions</th>\n", " <th>Clouds</th>\n", " <th>dailyrainin</th>\n", " <th>SolarRadiationWatts/m^2</th>\n", " <th>SoftwareType</th>\n", " <th>DateUTC&lt;br&gt;</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>2018-11-01 12:49:32</td>\n", " <td>72.3</td>\n", " <td>48.0</td>\n", " <td>-100.0</td>\n", " <td>SSW</td>\n", " <td>202.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>42.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.00</td>\n", " <td>44.49</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-01 19:49:32</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2018-11-01 12:54:36</td>\n", " <td>72.5</td>\n", " <td>48.2</td>\n", " <td>-100.0</td>\n", " <td>SE</td>\n", " <td>133.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>42.0</td>\n", " <td>0.21</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.04</td>\n", " <td>277.27</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-01 19:54:36</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>2018-11-01 12:59:56</td>\n", " <td>73.4</td>\n", " <td>45.7</td>\n", " <td>-100.0</td>\n", " <td>South</td>\n", " <td>187.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>37.0</td>\n", " <td>0.21</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.04</td>\n", " <td>292.76</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-01 19:59:56</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>2018-11-01 13:04:44</td>\n", " <td>74.3</td>\n", " <td>44.2</td>\n", " <td>-100.0</td>\n", " <td>ESE</td>\n", " <td>114.0</td>\n", " <td>1.8</td>\n", " <td>4.9</td>\n", " <td>34.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.04</td>\n", " <td>288.55</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-01 20:04:44</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>2018-11-01 13:09:48</td>\n", " <td>75.4</td>\n", " <td>44.4</td>\n", " <td>-100.0</td>\n", " <td>SE</td>\n", " <td>133.0</td>\n", " <td>0.4</td>\n", " <td>2.5</td>\n", " <td>33.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.04</td>\n", " <td>295.80</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-01 20:09:48</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>2018-11-01 13:14:52</td>\n", " <td>76.3</td>\n", " <td>44.2</td>\n", " <td>-100.0</td>\n", " <td>SE</td>\n", " <td>139.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>32.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.04</td>\n", " <td>274.66</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-01 20:14:52</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>2018-11-01 13:19:56</td>\n", " <td>77.5</td>\n", " <td>43.7</td>\n", " <td>-100.0</td>\n", " <td>SSE</td>\n", " <td>150.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>30.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.04</td>\n", " <td>274.23</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-01 20:19:56</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>2018-11-01 13:24:44</td>\n", " <td>78.8</td>\n", " <td>45.7</td>\n", " <td>-100.0</td>\n", " <td>South</td>\n", " <td>186.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>31.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.04</td>\n", " <td>287.99</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-01 20:24:44</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>2018-11-01 13:29:32</td>\n", " <td>79.7</td>\n", " <td>45.5</td>\n", " <td>-100.0</td>\n", " <td>East</td>\n", " <td>97.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>30.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.04</td>\n", " <td>279.70</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-01 20:29:32</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>2018-11-01 13:34:36</td>\n", " <td>80.6</td>\n", " <td>45.5</td>\n", " <td>-100.0</td>\n", " <td>NE</td>\n", " <td>39.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>29.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.04</td>\n", " <td>290.42</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-01 20:34:36</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>2018-11-01 13:39:56</td>\n", " <td>81.7</td>\n", " <td>46.4</td>\n", " <td>-100.0</td>\n", " <td>NE</td>\n", " <td>46.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>29.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.04</td>\n", " <td>280.35</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-01 20:39:56</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>2018-11-01 13:44:28</td>\n", " <td>82.4</td>\n", " <td>47.8</td>\n", " <td>-100.0</td>\n", " <td>NE</td>\n", " <td>49.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>30.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.04</td>\n", " <td>266.63</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-01 20:44:28</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>2018-11-01 13:49:16</td>\n", " <td>82.9</td>\n", " <td>49.3</td>\n", " <td>-100.0</td>\n", " <td>SSW</td>\n", " <td>212.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>31.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.04</td>\n", " <td>260.77</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-01 20:49:16</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td>2018-11-01 13:54:52</td>\n", " <td>83.3</td>\n", " <td>50.4</td>\n", " <td>-100.0</td>\n", " <td>South</td>\n", " <td>190.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>32.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.04</td>\n", " <td>252.30</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-01 20:54:52</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td>2018-11-01 13:59:40</td>\n", " <td>83.3</td>\n", " <td>49.6</td>\n", " <td>-100.0</td>\n", " <td>SW</td>\n", " <td>226.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>31.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.04</td>\n", " <td>245.79</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-01 20:59:40</td>\n", " </tr>\n", " <tr>\n", " <th>30</th>\n", " <td>2018-11-01 14:04:44</td>\n", " <td>83.3</td>\n", " <td>48.7</td>\n", " <td>-100.0</td>\n", " <td>South</td>\n", " <td>183.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>30.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.04</td>\n", " <td>239.89</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-01 21:04:44</td>\n", " </tr>\n", " <tr>\n", " <th>32</th>\n", " <td>2018-11-01 14:09:48</td>\n", " <td>83.5</td>\n", " <td>48.9</td>\n", " <td>-100.0</td>\n", " <td>SSW</td>\n", " <td>203.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>30.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.04</td>\n", " <td>243.23</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-01 21:09:48</td>\n", " </tr>\n", " <tr>\n", " <th>34</th>\n", " <td>2018-11-01 14:14:52</td>\n", " <td>83.5</td>\n", " <td>48.9</td>\n", " <td>-100.0</td>\n", " <td>NW</td>\n", " <td>325.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>30.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.04</td>\n", " <td>235.37</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-01 21:14:52</td>\n", " </tr>\n", " <tr>\n", " <th>36</th>\n", " <td>2018-11-01 14:19:56</td>\n", " <td>83.8</td>\n", " <td>49.1</td>\n", " <td>-100.0</td>\n", " <td>SSE</td>\n", " <td>162.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>30.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.04</td>\n", " <td>226.00</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-01 21:19:56</td>\n", " </tr>\n", " <tr>\n", " <th>38</th>\n", " <td>2018-11-01 14:24:12</td>\n", " <td>84.0</td>\n", " <td>50.2</td>\n", " <td>-100.0</td>\n", " <td>SSW</td>\n", " <td>195.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>31.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.04</td>\n", " <td>227.39</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-01 21:24:12</td>\n", " </tr>\n", " <tr>\n", " <th>40</th>\n", " <td>2018-11-01 14:29:48</td>\n", " <td>84.2</td>\n", " <td>48.6</td>\n", " <td>-100.0</td>\n", " <td>NE</td>\n", " <td>49.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>29.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.04</td>\n", " <td>219.75</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-01 21:29:48</td>\n", " </tr>\n", " <tr>\n", " <th>42</th>\n", " <td>2018-11-01 14:34:52</td>\n", " <td>84.4</td>\n", " <td>45.9</td>\n", " <td>-100.0</td>\n", " <td>WNW</td>\n", " <td>287.0</td>\n", " <td>0.9</td>\n", " <td>2.5</td>\n", " <td>26.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.04</td>\n", " <td>131.58</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-01 21:34:52</td>\n", " </tr>\n", " <tr>\n", " <th>44</th>\n", " <td>2018-11-01 14:39:56</td>\n", " <td>84.4</td>\n", " <td>44.8</td>\n", " <td>-100.0</td>\n", " <td>NNW</td>\n", " <td>342.0</td>\n", " <td>0.9</td>\n", " <td>4.9</td>\n", " <td>25.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.04</td>\n", " <td>216.19</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-01 21:39:56</td>\n", " </tr>\n", " <tr>\n", " <th>46</th>\n", " <td>2018-11-01 14:44:28</td>\n", " <td>84.6</td>\n", " <td>46.9</td>\n", " <td>-100.0</td>\n", " <td>SSE</td>\n", " <td>152.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>27.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.04</td>\n", " <td>132.06</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-01 21:44:28</td>\n", " </tr>\n", " <tr>\n", " <th>48</th>\n", " <td>2018-11-01 14:49:48</td>\n", " <td>84.6</td>\n", " <td>48.0</td>\n", " <td>-100.0</td>\n", " <td>SSW</td>\n", " <td>196.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>28.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.04</td>\n", " <td>132.49</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-01 21:49:48</td>\n", " </tr>\n", " <tr>\n", " <th>50</th>\n", " <td>2018-11-01 14:54:52</td>\n", " <td>84.7</td>\n", " <td>47.1</td>\n", " <td>-100.0</td>\n", " <td>SE</td>\n", " <td>137.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>27.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.04</td>\n", " <td>193.70</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-01 21:54:52</td>\n", " </tr>\n", " <tr>\n", " <th>52</th>\n", " <td>2018-11-01 14:59:56</td>\n", " <td>84.9</td>\n", " <td>47.3</td>\n", " <td>-100.0</td>\n", " <td>NW</td>\n", " <td>307.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>27.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.04</td>\n", " <td>115.30</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-01 21:59:56</td>\n", " </tr>\n", " <tr>\n", " <th>54</th>\n", " <td>2018-11-01 15:04:44</td>\n", " <td>84.7</td>\n", " <td>50.0</td>\n", " <td>-100.0</td>\n", " <td>SE</td>\n", " <td>130.0</td>\n", " <td>1.3</td>\n", " <td>2.5</td>\n", " <td>30.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.04</td>\n", " <td>125.16</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-01 22:04:44</td>\n", " </tr>\n", " <tr>\n", " <th>56</th>\n", " <td>2018-11-01 15:09:32</td>\n", " <td>84.6</td>\n", " <td>49.8</td>\n", " <td>-100.0</td>\n", " <td>NNW</td>\n", " <td>342.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>30.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.04</td>\n", " <td>187.58</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-01 22:09:32</td>\n", " </tr>\n", " <tr>\n", " <th>58</th>\n", " <td>2018-11-01 15:14:36</td>\n", " <td>84.2</td>\n", " <td>49.5</td>\n", " <td>-100.0</td>\n", " <td>ESE</td>\n", " <td>121.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>30.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.04</td>\n", " <td>130.71</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-01 22:14:36</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>210</th>\n", " <td>2018-11-01 21:34:37</td>\n", " <td>64.0</td>\n", " <td>52.2</td>\n", " <td>-100.0</td>\n", " <td>SSW</td>\n", " <td>212.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>65.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-02 04:34:37</td>\n", " </tr>\n", " <tr>\n", " <th>212</th>\n", " <td>2018-11-01 21:39:57</td>\n", " <td>64.2</td>\n", " <td>51.8</td>\n", " <td>-100.0</td>\n", " <td>SW</td>\n", " <td>223.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>64.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-02 04:39:57</td>\n", " </tr>\n", " <tr>\n", " <th>214</th>\n", " <td>2018-11-01 21:44:45</td>\n", " <td>64.2</td>\n", " <td>51.4</td>\n", " <td>-100.0</td>\n", " <td>South</td>\n", " <td>190.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>63.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-02 04:44:45</td>\n", " </tr>\n", " <tr>\n", " <th>216</th>\n", " <td>2018-11-01 21:49:49</td>\n", " <td>64.4</td>\n", " <td>52.0</td>\n", " <td>-100.0</td>\n", " <td>South</td>\n", " <td>190.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>64.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-02 04:49:49</td>\n", " </tr>\n", " <tr>\n", " <th>218</th>\n", " <td>2018-11-01 21:54:53</td>\n", " <td>64.4</td>\n", " <td>53.2</td>\n", " <td>-100.0</td>\n", " <td>SSW</td>\n", " <td>204.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>67.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-02 04:54:53</td>\n", " </tr>\n", " <tr>\n", " <th>220</th>\n", " <td>2018-11-01 21:59:41</td>\n", " <td>64.2</td>\n", " <td>52.7</td>\n", " <td>-100.0</td>\n", " <td>West</td>\n", " <td>274.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>66.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-02 04:59:41</td>\n", " </tr>\n", " <tr>\n", " <th>222</th>\n", " <td>2018-11-01 22:04:45</td>\n", " <td>64.2</td>\n", " <td>50.5</td>\n", " <td>-100.0</td>\n", " <td>SSW</td>\n", " <td>202.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>61.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-02 05:04:45</td>\n", " </tr>\n", " <tr>\n", " <th>224</th>\n", " <td>2018-11-01 22:09:49</td>\n", " <td>64.4</td>\n", " <td>52.0</td>\n", " <td>-100.0</td>\n", " <td>SW</td>\n", " <td>221.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>64.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-02 05:09:49</td>\n", " </tr>\n", " <tr>\n", " <th>226</th>\n", " <td>2018-11-01 22:14:05</td>\n", " <td>64.4</td>\n", " <td>51.1</td>\n", " <td>-100.0</td>\n", " <td>SSW</td>\n", " <td>202.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>62.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-02 05:14:05</td>\n", " </tr>\n", " <tr>\n", " <th>228</th>\n", " <td>2018-11-01 22:19:57</td>\n", " <td>64.6</td>\n", " <td>50.0</td>\n", " <td>-100.0</td>\n", " <td>SSW</td>\n", " <td>192.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>59.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-02 05:19:57</td>\n", " </tr>\n", " <tr>\n", " <th>230</th>\n", " <td>2018-11-01 22:24:45</td>\n", " <td>64.8</td>\n", " <td>52.7</td>\n", " <td>-100.0</td>\n", " <td>SSW</td>\n", " <td>193.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>65.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-02 05:24:45</td>\n", " </tr>\n", " <tr>\n", " <th>232</th>\n", " <td>2018-11-01 22:29:49</td>\n", " <td>65.1</td>\n", " <td>53.6</td>\n", " <td>-100.0</td>\n", " <td>SSW</td>\n", " <td>192.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>66.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-02 05:29:49</td>\n", " </tr>\n", " <tr>\n", " <th>234</th>\n", " <td>2018-11-01 22:34:37</td>\n", " <td>65.1</td>\n", " <td>54.0</td>\n", " <td>-100.0</td>\n", " <td>SSW</td>\n", " <td>200.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>67.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-02 05:34:37</td>\n", " </tr>\n", " <tr>\n", " <th>236</th>\n", " <td>2018-11-01 22:39:41</td>\n", " <td>64.9</td>\n", " <td>54.1</td>\n", " <td>-100.0</td>\n", " <td>SSW</td>\n", " <td>207.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>68.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-02 05:39:41</td>\n", " </tr>\n", " <tr>\n", " <th>238</th>\n", " <td>2018-11-01 22:44:45</td>\n", " <td>64.8</td>\n", " <td>54.0</td>\n", " <td>-100.0</td>\n", " <td>South</td>\n", " <td>189.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>68.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-02 05:44:45</td>\n", " </tr>\n", " <tr>\n", " <th>240</th>\n", " <td>2018-11-01 22:49:49</td>\n", " <td>64.6</td>\n", " <td>53.8</td>\n", " <td>-100.0</td>\n", " <td>SSW</td>\n", " <td>210.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>68.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-02 05:49:49</td>\n", " </tr>\n", " <tr>\n", " <th>242</th>\n", " <td>2018-11-01 22:54:53</td>\n", " <td>64.4</td>\n", " <td>52.0</td>\n", " <td>-100.0</td>\n", " <td>SW</td>\n", " <td>221.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>64.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-02 05:54:53</td>\n", " </tr>\n", " <tr>\n", " <th>244</th>\n", " <td>2018-11-01 22:59:57</td>\n", " <td>64.2</td>\n", " <td>51.4</td>\n", " <td>-100.0</td>\n", " <td>SSW</td>\n", " <td>200.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>63.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-02 05:59:57</td>\n", " </tr>\n", " <tr>\n", " <th>246</th>\n", " <td>2018-11-01 23:04:45</td>\n", " <td>64.2</td>\n", " <td>51.4</td>\n", " <td>-100.0</td>\n", " <td>SW</td>\n", " <td>214.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>63.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-02 06:04:45</td>\n", " </tr>\n", " <tr>\n", " <th>248</th>\n", " <td>2018-11-01 23:09:49</td>\n", " <td>64.0</td>\n", " <td>51.3</td>\n", " <td>-100.0</td>\n", " <td>SW</td>\n", " <td>219.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>63.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-02 06:09:49</td>\n", " </tr>\n", " <tr>\n", " <th>250</th>\n", " <td>2018-11-01 23:14:37</td>\n", " <td>64.0</td>\n", " <td>51.3</td>\n", " <td>-100.0</td>\n", " <td>SSW</td>\n", " <td>209.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>63.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-02 06:14:37</td>\n", " </tr>\n", " <tr>\n", " <th>252</th>\n", " <td>2018-11-01 23:19:57</td>\n", " <td>63.9</td>\n", " <td>51.4</td>\n", " <td>-100.0</td>\n", " <td>SSW</td>\n", " <td>205.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>64.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-02 06:19:57</td>\n", " </tr>\n", " <tr>\n", " <th>254</th>\n", " <td>2018-11-01 23:24:45</td>\n", " <td>63.7</td>\n", " <td>51.3</td>\n", " <td>-100.0</td>\n", " <td>SSW</td>\n", " <td>208.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>64.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-02 06:24:45</td>\n", " </tr>\n", " <tr>\n", " <th>256</th>\n", " <td>2018-11-01 23:29:49</td>\n", " <td>63.3</td>\n", " <td>51.4</td>\n", " <td>-100.0</td>\n", " <td>SSW</td>\n", " <td>207.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>65.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-02 06:29:49</td>\n", " </tr>\n", " <tr>\n", " <th>258</th>\n", " <td>2018-11-01 23:34:21</td>\n", " <td>63.1</td>\n", " <td>51.3</td>\n", " <td>-100.0</td>\n", " <td>SSW</td>\n", " <td>206.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>65.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-02 06:34:21</td>\n", " </tr>\n", " <tr>\n", " <th>260</th>\n", " <td>2018-11-01 23:39:57</td>\n", " <td>62.8</td>\n", " <td>51.3</td>\n", " <td>-100.0</td>\n", " <td>SSW</td>\n", " <td>206.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>66.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-02 06:39:57</td>\n", " </tr>\n", " <tr>\n", " <th>262</th>\n", " <td>2018-11-01 23:44:29</td>\n", " <td>62.6</td>\n", " <td>51.1</td>\n", " <td>-100.0</td>\n", " <td>SW</td>\n", " <td>229.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>66.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-02 06:44:29</td>\n", " </tr>\n", " <tr>\n", " <th>264</th>\n", " <td>2018-11-01 23:49:17</td>\n", " <td>62.6</td>\n", " <td>50.4</td>\n", " <td>-100.0</td>\n", " <td>SSW</td>\n", " <td>213.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>64.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-02 06:49:17</td>\n", " </tr>\n", " <tr>\n", " <th>266</th>\n", " <td>2018-11-01 23:54:53</td>\n", " <td>62.6</td>\n", " <td>49.5</td>\n", " <td>-100.0</td>\n", " <td>SSW</td>\n", " <td>200.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>62.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-02 06:54:53</td>\n", " </tr>\n", " <tr>\n", " <th>268</th>\n", " <td>2018-11-01 23:59:57</td>\n", " <td>62.8</td>\n", " <td>49.6</td>\n", " <td>-100.0</td>\n", " <td>SSW</td>\n", " <td>208.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>62.0</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>Weather logger V3.0.</td>\n", " <td>2018-11-02 06:59:57</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>135 rows × 16 columns</p>\n", "</div>" ], "text/plain": [ " Time TemperatureF DewpointF PressureIn WindDirection \\\n", "0 2018-11-01 12:49:32 72.3 48.0 -100.0 SSW \n", "2 2018-11-01 12:54:36 72.5 48.2 -100.0 SE \n", "4 2018-11-01 12:59:56 73.4 45.7 -100.0 South \n", "6 2018-11-01 13:04:44 74.3 44.2 -100.0 ESE \n", "8 2018-11-01 13:09:48 75.4 44.4 -100.0 SE \n", "10 2018-11-01 13:14:52 76.3 44.2 -100.0 SE \n", "12 2018-11-01 13:19:56 77.5 43.7 -100.0 SSE \n", "14 2018-11-01 13:24:44 78.8 45.7 -100.0 South \n", "16 2018-11-01 13:29:32 79.7 45.5 -100.0 East \n", "18 2018-11-01 13:34:36 80.6 45.5 -100.0 NE \n", "20 2018-11-01 13:39:56 81.7 46.4 -100.0 NE \n", "22 2018-11-01 13:44:28 82.4 47.8 -100.0 NE \n", "24 2018-11-01 13:49:16 82.9 49.3 -100.0 SSW \n", "26 2018-11-01 13:54:52 83.3 50.4 -100.0 South \n", "28 2018-11-01 13:59:40 83.3 49.6 -100.0 SW \n", "30 2018-11-01 14:04:44 83.3 48.7 -100.0 South \n", "32 2018-11-01 14:09:48 83.5 48.9 -100.0 SSW \n", "34 2018-11-01 14:14:52 83.5 48.9 -100.0 NW \n", "36 2018-11-01 14:19:56 83.8 49.1 -100.0 SSE \n", "38 2018-11-01 14:24:12 84.0 50.2 -100.0 SSW \n", "40 2018-11-01 14:29:48 84.2 48.6 -100.0 NE \n", "42 2018-11-01 14:34:52 84.4 45.9 -100.0 WNW \n", "44 2018-11-01 14:39:56 84.4 44.8 -100.0 NNW \n", "46 2018-11-01 14:44:28 84.6 46.9 -100.0 SSE \n", "48 2018-11-01 14:49:48 84.6 48.0 -100.0 SSW \n", "50 2018-11-01 14:54:52 84.7 47.1 -100.0 SE \n", "52 2018-11-01 14:59:56 84.9 47.3 -100.0 NW \n", "54 2018-11-01 15:04:44 84.7 50.0 -100.0 SE \n", "56 2018-11-01 15:09:32 84.6 49.8 -100.0 NNW \n", "58 2018-11-01 15:14:36 84.2 49.5 -100.0 ESE \n", ".. ... ... ... ... ... \n", "210 2018-11-01 21:34:37 64.0 52.2 -100.0 SSW \n", "212 2018-11-01 21:39:57 64.2 51.8 -100.0 SW \n", "214 2018-11-01 21:44:45 64.2 51.4 -100.0 South \n", "216 2018-11-01 21:49:49 64.4 52.0 -100.0 South \n", "218 2018-11-01 21:54:53 64.4 53.2 -100.0 SSW \n", "220 2018-11-01 21:59:41 64.2 52.7 -100.0 West \n", "222 2018-11-01 22:04:45 64.2 50.5 -100.0 SSW \n", "224 2018-11-01 22:09:49 64.4 52.0 -100.0 SW \n", "226 2018-11-01 22:14:05 64.4 51.1 -100.0 SSW \n", "228 2018-11-01 22:19:57 64.6 50.0 -100.0 SSW \n", "230 2018-11-01 22:24:45 64.8 52.7 -100.0 SSW \n", "232 2018-11-01 22:29:49 65.1 53.6 -100.0 SSW \n", "234 2018-11-01 22:34:37 65.1 54.0 -100.0 SSW \n", "236 2018-11-01 22:39:41 64.9 54.1 -100.0 SSW \n", "238 2018-11-01 22:44:45 64.8 54.0 -100.0 South \n", "240 2018-11-01 22:49:49 64.6 53.8 -100.0 SSW \n", "242 2018-11-01 22:54:53 64.4 52.0 -100.0 SW \n", "244 2018-11-01 22:59:57 64.2 51.4 -100.0 SSW \n", "246 2018-11-01 23:04:45 64.2 51.4 -100.0 SW \n", "248 2018-11-01 23:09:49 64.0 51.3 -100.0 SW \n", "250 2018-11-01 23:14:37 64.0 51.3 -100.0 SSW \n", "252 2018-11-01 23:19:57 63.9 51.4 -100.0 SSW \n", "254 2018-11-01 23:24:45 63.7 51.3 -100.0 SSW \n", "256 2018-11-01 23:29:49 63.3 51.4 -100.0 SSW \n", "258 2018-11-01 23:34:21 63.1 51.3 -100.0 SSW \n", "260 2018-11-01 23:39:57 62.8 51.3 -100.0 SSW \n", "262 2018-11-01 23:44:29 62.6 51.1 -100.0 SW \n", "264 2018-11-01 23:49:17 62.6 50.4 -100.0 SSW \n", "266 2018-11-01 23:54:53 62.6 49.5 -100.0 SSW \n", "268 2018-11-01 23:59:57 62.8 49.6 -100.0 SSW \n", "\n", " WindDirectionDegrees WindSpeedMPH WindSpeedGustMPH Humidity \\\n", "0 202.0 0.0 0.0 42.0 \n", "2 133.0 0.0 0.0 42.0 \n", "4 187.0 0.0 0.0 37.0 \n", "6 114.0 1.8 4.9 34.0 \n", "8 133.0 0.4 2.5 33.0 \n", "10 139.0 0.0 0.0 32.0 \n", "12 150.0 0.0 0.0 30.0 \n", "14 186.0 0.0 0.0 31.0 \n", "16 97.0 0.0 0.0 30.0 \n", "18 39.0 0.0 0.0 29.0 \n", "20 46.0 0.0 0.0 29.0 \n", "22 49.0 0.0 0.0 30.0 \n", "24 212.0 0.0 0.0 31.0 \n", "26 190.0 0.0 0.0 32.0 \n", "28 226.0 0.0 0.0 31.0 \n", "30 183.0 0.0 0.0 30.0 \n", "32 203.0 0.0 0.0 30.0 \n", "34 325.0 0.0 0.0 30.0 \n", "36 162.0 0.0 0.0 30.0 \n", "38 195.0 0.0 0.0 31.0 \n", "40 49.0 0.0 0.0 29.0 \n", "42 287.0 0.9 2.5 26.0 \n", "44 342.0 0.9 4.9 25.0 \n", "46 152.0 0.0 0.0 27.0 \n", "48 196.0 0.0 0.0 28.0 \n", "50 137.0 0.0 0.0 27.0 \n", "52 307.0 0.0 0.0 27.0 \n", "54 130.0 1.3 2.5 30.0 \n", "56 342.0 0.0 0.0 30.0 \n", "58 121.0 0.0 0.0 30.0 \n", ".. ... ... ... ... \n", "210 212.0 0.0 0.0 65.0 \n", "212 223.0 0.0 0.0 64.0 \n", "214 190.0 0.0 0.0 63.0 \n", "216 190.0 0.0 0.0 64.0 \n", "218 204.0 0.0 0.0 67.0 \n", "220 274.0 0.0 0.0 66.0 \n", "222 202.0 0.0 0.0 61.0 \n", "224 221.0 0.0 0.0 64.0 \n", "226 202.0 0.0 0.0 62.0 \n", "228 192.0 0.0 0.0 59.0 \n", "230 193.0 0.0 0.0 65.0 \n", "232 192.0 0.0 0.0 66.0 \n", "234 200.0 0.0 0.0 67.0 \n", "236 207.0 0.0 0.0 68.0 \n", "238 189.0 0.0 0.0 68.0 \n", "240 210.0 0.0 0.0 68.0 \n", "242 221.0 0.0 0.0 64.0 \n", "244 200.0 0.0 0.0 63.0 \n", "246 214.0 0.0 0.0 63.0 \n", "248 219.0 0.0 0.0 63.0 \n", "250 209.0 0.0 0.0 63.0 \n", "252 205.0 0.0 0.0 64.0 \n", "254 208.0 0.0 0.0 64.0 \n", "256 207.0 0.0 0.0 65.0 \n", "258 206.0 0.0 0.0 65.0 \n", "260 206.0 0.0 0.0 66.0 \n", "262 229.0 0.0 0.0 66.0 \n", "264 213.0 0.0 0.0 64.0 \n", "266 200.0 0.0 0.0 62.0 \n", "268 208.0 0.0 0.0 62.0 \n", "\n", " HourlyPrecipIn Conditions Clouds dailyrainin SolarRadiationWatts/m^2 \\\n", "0 0.00 NaN NaN 0.00 44.49 \n", "2 0.21 NaN NaN 0.04 277.27 \n", "4 0.21 NaN NaN 0.04 292.76 \n", "6 0.00 NaN NaN 0.04 288.55 \n", "8 0.00 NaN NaN 0.04 295.80 \n", "10 0.00 NaN NaN 0.04 274.66 \n", "12 0.00 NaN NaN 0.04 274.23 \n", "14 0.00 NaN NaN 0.04 287.99 \n", "16 0.00 NaN NaN 0.04 279.70 \n", "18 0.00 NaN NaN 0.04 290.42 \n", "20 0.00 NaN NaN 0.04 280.35 \n", "22 0.00 NaN NaN 0.04 266.63 \n", "24 0.00 NaN NaN 0.04 260.77 \n", "26 0.00 NaN NaN 0.04 252.30 \n", "28 0.00 NaN NaN 0.04 245.79 \n", "30 0.00 NaN NaN 0.04 239.89 \n", "32 0.00 NaN NaN 0.04 243.23 \n", "34 0.00 NaN NaN 0.04 235.37 \n", "36 0.00 NaN NaN 0.04 226.00 \n", "38 0.00 NaN NaN 0.04 227.39 \n", "40 0.00 NaN NaN 0.04 219.75 \n", "42 0.00 NaN NaN 0.04 131.58 \n", "44 0.00 NaN NaN 0.04 216.19 \n", "46 0.00 NaN NaN 0.04 132.06 \n", "48 0.00 NaN NaN 0.04 132.49 \n", "50 0.00 NaN NaN 0.04 193.70 \n", "52 0.00 NaN NaN 0.04 115.30 \n", "54 0.00 NaN NaN 0.04 125.16 \n", "56 0.00 NaN NaN 0.04 187.58 \n", "58 0.00 NaN NaN 0.04 130.71 \n", ".. ... ... ... ... ... \n", "210 0.00 NaN NaN 0.00 0.00 \n", "212 0.00 NaN NaN 0.00 0.00 \n", "214 0.00 NaN NaN 0.00 0.00 \n", "216 0.00 NaN NaN 0.00 0.00 \n", "218 0.00 NaN NaN 0.00 0.00 \n", "220 0.00 NaN NaN 0.00 0.00 \n", "222 0.00 NaN NaN 0.00 0.00 \n", "224 0.00 NaN NaN 0.00 0.00 \n", "226 0.00 NaN NaN 0.00 0.00 \n", "228 0.00 NaN NaN 0.00 0.00 \n", "230 0.00 NaN NaN 0.00 0.00 \n", "232 0.00 NaN NaN 0.00 0.00 \n", "234 0.00 NaN NaN 0.00 0.00 \n", "236 0.00 NaN NaN 0.00 0.00 \n", "238 0.00 NaN NaN 0.00 0.00 \n", "240 0.00 NaN NaN 0.00 0.00 \n", "242 0.00 NaN NaN 0.00 0.00 \n", "244 0.00 NaN NaN 0.00 0.00 \n", "246 0.00 NaN NaN 0.00 0.00 \n", "248 0.00 NaN NaN 0.00 0.00 \n", "250 0.00 NaN NaN 0.00 0.00 \n", "252 0.00 NaN NaN 0.00 0.00 \n", "254 0.00 NaN NaN 0.00 0.00 \n", "256 0.00 NaN NaN 0.00 0.00 \n", "258 0.00 NaN NaN 0.00 0.00 \n", "260 0.00 NaN NaN 0.00 0.00 \n", "262 0.00 NaN NaN 0.00 0.00 \n", "264 0.00 NaN NaN 0.00 0.00 \n", "266 0.00 NaN NaN 0.00 0.00 \n", "268 0.00 NaN NaN 0.00 0.00 \n", "\n", " SoftwareType DateUTC<br> \n", "0 Weather logger V3.0. 2018-11-01 19:49:32 \n", "2 Weather logger V3.0. 2018-11-01 19:54:36 \n", "4 Weather logger V3.0. 2018-11-01 19:59:56 \n", "6 Weather logger V3.0. 2018-11-01 20:04:44 \n", "8 Weather logger V3.0. 2018-11-01 20:09:48 \n", "10 Weather logger V3.0. 2018-11-01 20:14:52 \n", "12 Weather logger V3.0. 2018-11-01 20:19:56 \n", "14 Weather logger V3.0. 2018-11-01 20:24:44 \n", "16 Weather logger V3.0. 2018-11-01 20:29:32 \n", "18 Weather logger V3.0. 2018-11-01 20:34:36 \n", "20 Weather logger V3.0. 2018-11-01 20:39:56 \n", "22 Weather logger V3.0. 2018-11-01 20:44:28 \n", "24 Weather logger V3.0. 2018-11-01 20:49:16 \n", "26 Weather logger V3.0. 2018-11-01 20:54:52 \n", "28 Weather logger V3.0. 2018-11-01 20:59:40 \n", "30 Weather logger V3.0. 2018-11-01 21:04:44 \n", "32 Weather logger V3.0. 2018-11-01 21:09:48 \n", "34 Weather logger V3.0. 2018-11-01 21:14:52 \n", "36 Weather logger V3.0. 2018-11-01 21:19:56 \n", "38 Weather logger V3.0. 2018-11-01 21:24:12 \n", "40 Weather logger V3.0. 2018-11-01 21:29:48 \n", "42 Weather logger V3.0. 2018-11-01 21:34:52 \n", "44 Weather logger V3.0. 2018-11-01 21:39:56 \n", "46 Weather logger V3.0. 2018-11-01 21:44:28 \n", "48 Weather logger V3.0. 2018-11-01 21:49:48 \n", "50 Weather logger V3.0. 2018-11-01 21:54:52 \n", "52 Weather logger V3.0. 2018-11-01 21:59:56 \n", "54 Weather logger V3.0. 2018-11-01 22:04:44 \n", "56 Weather logger V3.0. 2018-11-01 22:09:32 \n", "58 Weather logger V3.0. 2018-11-01 22:14:36 \n", ".. ... ... \n", "210 Weather logger V3.0. 2018-11-02 04:34:37 \n", "212 Weather logger V3.0. 2018-11-02 04:39:57 \n", "214 Weather logger V3.0. 2018-11-02 04:44:45 \n", "216 Weather logger V3.0. 2018-11-02 04:49:49 \n", "218 Weather logger V3.0. 2018-11-02 04:54:53 \n", "220 Weather logger V3.0. 2018-11-02 04:59:41 \n", "222 Weather logger V3.0. 2018-11-02 05:04:45 \n", "224 Weather logger V3.0. 2018-11-02 05:09:49 \n", "226 Weather logger V3.0. 2018-11-02 05:14:05 \n", "228 Weather logger V3.0. 2018-11-02 05:19:57 \n", "230 Weather logger V3.0. 2018-11-02 05:24:45 \n", "232 Weather logger V3.0. 2018-11-02 05:29:49 \n", "234 Weather logger V3.0. 2018-11-02 05:34:37 \n", "236 Weather logger V3.0. 2018-11-02 05:39:41 \n", "238 Weather logger V3.0. 2018-11-02 05:44:45 \n", "240 Weather logger V3.0. 2018-11-02 05:49:49 \n", "242 Weather logger V3.0. 2018-11-02 05:54:53 \n", "244 Weather logger V3.0. 2018-11-02 05:59:57 \n", "246 Weather logger V3.0. 2018-11-02 06:04:45 \n", "248 Weather logger V3.0. 2018-11-02 06:09:49 \n", "250 Weather logger V3.0. 2018-11-02 06:14:37 \n", "252 Weather logger V3.0. 2018-11-02 06:19:57 \n", "254 Weather logger V3.0. 2018-11-02 06:24:45 \n", "256 Weather logger V3.0. 2018-11-02 06:29:49 \n", "258 Weather logger V3.0. 2018-11-02 06:34:21 \n", "260 Weather logger V3.0. 2018-11-02 06:39:57 \n", "262 Weather logger V3.0. 2018-11-02 06:44:29 \n", "264 Weather logger V3.0. 2018-11-02 06:49:17 \n", "266 Weather logger V3.0. 2018-11-02 06:54:53 \n", "268 Weather logger V3.0. 2018-11-02 06:59:57 \n", "\n", "[135 rows x 16 columns]" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# We can clean-up the odd row structure trivially:\n", "dg = df.drop([2*i + 1 for i in range(df.shape[0] // 2)])\n", "dg" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "code_folding": [] }, "outputs": [], "source": [ "# Method for grabbing data for a specific location and data and cleaning up the website query output\n", "def get_clean_df(location_id, date):\n", " \"\"\"Get weather data from `location_id` on `date`, then\n", " remove all the `<br>` tags in the file.\n", " \n", " `date` should be a list/tuple of 3 strings in the format\n", " [MM, DD, YYYY].\n", " \"\"\"\n", " url = \"https://www.wunderground.com/weatherstation/WXDailyHistory.asp?\" + \\\n", " \"ID={}&\".format(location_id) + \\\n", " \"day={}&\".format(date[1]) + \\\n", " \"month={}&\".format(date[0]) + \\\n", " \"year={}&\".format(date[2]) + \\\n", " \"graphspan=day&format=1\"\n", " print(url)\n", " data = pd.read_csv(url, index_col=False)\n", " # drop every other row because it contains `<br>`\n", " return data.drop([2*i + 1 for i in range(data.shape[0] // 2)])" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "https://www.wunderground.com/weatherstation/WXDailyHistory.asp?ID=KCABERKE169&day=01&month=11&year=2018&graphspan=day&format=1\n" ] } ], "source": [ "# Method to process weather data DataFrame \n", "# - converts temperature/pressure to sensible units and drop unneeded columns\n", "def process_data(data_df):\n", " def deg_f_to_c(deg_f):\n", " return (5. / 9.) * (deg_f - 32)\n", "\n", " def inhg_to_mbar(inhg):\n", " return 33.863753 * inhg\n", "\n", " data_df.reset_index()\n", " for row in data_df.itertuples():\n", " idx = row.Index\n", " itime, tempf, dewf, pressure = row.Time, row.TemperatureF, row.DewpointF, row.PressureIn\n", " data_df.loc[idx, 'Time'] = datetime.strptime(itime, '%Y-%m-%d %H:%M:%S')\n", " data_df.loc[idx, 'Temperature'] = deg_f_to_c(tempf)\n", " data_df.loc[idx, 'Dewpoint'] = deg_f_to_c(dewf)\n", " data_df.loc[idx, 'Pressure'] = inhg_to_mbar(pressure)\n", "\n", " return data_df.drop(['TemperatureF', 'DewpointF', 'PressureIn', 'Conditions', 'Clouds',\n", " 'SoftwareType', 'DateUTC<br>'], axis=1)\n", "\n", "ws_data = process_data(get_clean_df('KCABERKE169', ['11', '01', '2018']))" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Time</th>\n", " <th>WindDirection</th>\n", " <th>WindDirectionDegrees</th>\n", " <th>WindSpeedMPH</th>\n", " <th>WindSpeedGustMPH</th>\n", " <th>Humidity</th>\n", " <th>HourlyPrecipIn</th>\n", " <th>dailyrainin</th>\n", " <th>SolarRadiationWatts/m^2</th>\n", " <th>Temperature</th>\n", " <th>Dewpoint</th>\n", " <th>Pressure</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>2018-11-01 12:49:32</td>\n", " <td>SSW</td>\n", " <td>202.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>42.0</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>44.49</td>\n", " <td>22.388889</td>\n", " <td>8.888889</td>\n", " <td>-3386.3753</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2018-11-01 12:54:36</td>\n", " <td>SE</td>\n", " <td>133.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>42.0</td>\n", " <td>0.21</td>\n", " <td>0.04</td>\n", " <td>277.27</td>\n", " <td>22.500000</td>\n", " <td>9.000000</td>\n", " <td>-3386.3753</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>2018-11-01 12:59:56</td>\n", " <td>South</td>\n", " <td>187.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>37.0</td>\n", " <td>0.21</td>\n", " <td>0.04</td>\n", " <td>292.76</td>\n", " <td>23.000000</td>\n", " <td>7.611111</td>\n", " <td>-3386.3753</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>2018-11-01 13:04:44</td>\n", " <td>ESE</td>\n", " <td>114.0</td>\n", " <td>1.8</td>\n", " <td>4.9</td>\n", " <td>34.0</td>\n", " <td>0.00</td>\n", " <td>0.04</td>\n", " <td>288.55</td>\n", " <td>23.500000</td>\n", " <td>6.777778</td>\n", " <td>-3386.3753</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>2018-11-01 13:09:48</td>\n", " <td>SE</td>\n", " <td>133.0</td>\n", " <td>0.4</td>\n", " <td>2.5</td>\n", " <td>33.0</td>\n", " <td>0.00</td>\n", " <td>0.04</td>\n", " <td>295.80</td>\n", " <td>24.111111</td>\n", " <td>6.888889</td>\n", " <td>-3386.3753</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>2018-11-01 13:14:52</td>\n", " <td>SE</td>\n", " <td>139.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>32.0</td>\n", " <td>0.00</td>\n", " <td>0.04</td>\n", " <td>274.66</td>\n", " <td>24.611111</td>\n", " <td>6.777778</td>\n", " <td>-3386.3753</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>2018-11-01 13:19:56</td>\n", " <td>SSE</td>\n", " <td>150.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>30.0</td>\n", " <td>0.00</td>\n", " <td>0.04</td>\n", " <td>274.23</td>\n", " <td>25.277778</td>\n", " <td>6.500000</td>\n", " <td>-3386.3753</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>2018-11-01 13:24:44</td>\n", " <td>South</td>\n", " <td>186.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>31.0</td>\n", " <td>0.00</td>\n", " <td>0.04</td>\n", " <td>287.99</td>\n", " <td>26.000000</td>\n", " <td>7.611111</td>\n", " <td>-3386.3753</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>2018-11-01 13:29:32</td>\n", " <td>East</td>\n", " <td>97.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>30.0</td>\n", " <td>0.00</td>\n", " <td>0.04</td>\n", " <td>279.70</td>\n", " <td>26.500000</td>\n", " <td>7.500000</td>\n", " <td>-3386.3753</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>2018-11-01 13:34:36</td>\n", " <td>NE</td>\n", " <td>39.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>29.0</td>\n", " <td>0.00</td>\n", " <td>0.04</td>\n", " <td>290.42</td>\n", " <td>27.000000</td>\n", " <td>7.500000</td>\n", " <td>-3386.3753</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>2018-11-01 13:39:56</td>\n", " <td>NE</td>\n", " <td>46.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>29.0</td>\n", " <td>0.00</td>\n", " <td>0.04</td>\n", " <td>280.35</td>\n", " <td>27.611111</td>\n", " <td>8.000000</td>\n", " <td>-3386.3753</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>2018-11-01 13:44:28</td>\n", " <td>NE</td>\n", " <td>49.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>30.0</td>\n", " <td>0.00</td>\n", " <td>0.04</td>\n", " <td>266.63</td>\n", " <td>28.000000</td>\n", " <td>8.777778</td>\n", " <td>-3386.3753</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>2018-11-01 13:49:16</td>\n", " <td>SSW</td>\n", " <td>212.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>31.0</td>\n", " <td>0.00</td>\n", " <td>0.04</td>\n", " <td>260.77</td>\n", " <td>28.277778</td>\n", " <td>9.611111</td>\n", " <td>-3386.3753</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td>2018-11-01 13:54:52</td>\n", " <td>South</td>\n", " <td>190.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>32.0</td>\n", " <td>0.00</td>\n", " <td>0.04</td>\n", " <td>252.30</td>\n", " <td>28.500000</td>\n", " <td>10.222222</td>\n", " <td>-3386.3753</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td>2018-11-01 13:59:40</td>\n", " <td>SW</td>\n", " <td>226.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>31.0</td>\n", " <td>0.00</td>\n", " <td>0.04</td>\n", " <td>245.79</td>\n", " <td>28.500000</td>\n", " <td>9.777778</td>\n", " <td>-3386.3753</td>\n", " </tr>\n", " <tr>\n", " <th>30</th>\n", " <td>2018-11-01 14:04:44</td>\n", " <td>South</td>\n", " <td>183.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>30.0</td>\n", " <td>0.00</td>\n", " <td>0.04</td>\n", " <td>239.89</td>\n", " <td>28.500000</td>\n", " <td>9.277778</td>\n", " <td>-3386.3753</td>\n", " </tr>\n", " <tr>\n", " <th>32</th>\n", " <td>2018-11-01 14:09:48</td>\n", " <td>SSW</td>\n", " <td>203.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>30.0</td>\n", " <td>0.00</td>\n", " <td>0.04</td>\n", " <td>243.23</td>\n", " <td>28.611111</td>\n", " <td>9.388889</td>\n", " <td>-3386.3753</td>\n", " </tr>\n", " <tr>\n", " <th>34</th>\n", " <td>2018-11-01 14:14:52</td>\n", " <td>NW</td>\n", " <td>325.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>30.0</td>\n", " <td>0.00</td>\n", " <td>0.04</td>\n", " <td>235.37</td>\n", " <td>28.611111</td>\n", " <td>9.388889</td>\n", " <td>-3386.3753</td>\n", " </tr>\n", " <tr>\n", " <th>36</th>\n", " <td>2018-11-01 14:19:56</td>\n", " <td>SSE</td>\n", " <td>162.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>30.0</td>\n", " <td>0.00</td>\n", " <td>0.04</td>\n", " <td>226.00</td>\n", " <td>28.777778</td>\n", " <td>9.500000</td>\n", " <td>-3386.3753</td>\n", " </tr>\n", " <tr>\n", " <th>38</th>\n", " <td>2018-11-01 14:24:12</td>\n", " <td>SSW</td>\n", " <td>195.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>31.0</td>\n", " <td>0.00</td>\n", " <td>0.04</td>\n", " <td>227.39</td>\n", " <td>28.888889</td>\n", " <td>10.111111</td>\n", " <td>-3386.3753</td>\n", " </tr>\n", " <tr>\n", " <th>40</th>\n", " <td>2018-11-01 14:29:48</td>\n", " <td>NE</td>\n", " <td>49.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>29.0</td>\n", " <td>0.00</td>\n", " <td>0.04</td>\n", " <td>219.75</td>\n", " <td>29.000000</td>\n", " <td>9.222222</td>\n", " <td>-3386.3753</td>\n", " </tr>\n", " <tr>\n", " <th>42</th>\n", " <td>2018-11-01 14:34:52</td>\n", " <td>WNW</td>\n", " <td>287.0</td>\n", " <td>0.9</td>\n", " <td>2.5</td>\n", " <td>26.0</td>\n", " <td>0.00</td>\n", " <td>0.04</td>\n", " <td>131.58</td>\n", " <td>29.111111</td>\n", " <td>7.722222</td>\n", " <td>-3386.3753</td>\n", " </tr>\n", " <tr>\n", " <th>44</th>\n", " <td>2018-11-01 14:39:56</td>\n", " <td>NNW</td>\n", " <td>342.0</td>\n", " <td>0.9</td>\n", " <td>4.9</td>\n", " <td>25.0</td>\n", " <td>0.00</td>\n", " <td>0.04</td>\n", " <td>216.19</td>\n", " <td>29.111111</td>\n", " <td>7.111111</td>\n", " <td>-3386.3753</td>\n", " </tr>\n", " <tr>\n", " <th>46</th>\n", " <td>2018-11-01 14:44:28</td>\n", " <td>SSE</td>\n", " <td>152.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>27.0</td>\n", " <td>0.00</td>\n", " <td>0.04</td>\n", " <td>132.06</td>\n", " <td>29.222222</td>\n", " <td>8.277778</td>\n", " <td>-3386.3753</td>\n", " </tr>\n", " <tr>\n", " <th>48</th>\n", " <td>2018-11-01 14:49:48</td>\n", " <td>SSW</td>\n", " <td>196.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>28.0</td>\n", " <td>0.00</td>\n", " <td>0.04</td>\n", " <td>132.49</td>\n", " <td>29.222222</td>\n", " <td>8.888889</td>\n", " <td>-3386.3753</td>\n", " </tr>\n", " <tr>\n", " <th>50</th>\n", " <td>2018-11-01 14:54:52</td>\n", " <td>SE</td>\n", " <td>137.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>27.0</td>\n", " <td>0.00</td>\n", " <td>0.04</td>\n", " <td>193.70</td>\n", " <td>29.277778</td>\n", " <td>8.388889</td>\n", " <td>-3386.3753</td>\n", " </tr>\n", " <tr>\n", " <th>52</th>\n", " <td>2018-11-01 14:59:56</td>\n", " <td>NW</td>\n", " <td>307.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>27.0</td>\n", " <td>0.00</td>\n", " <td>0.04</td>\n", " <td>115.30</td>\n", " <td>29.388889</td>\n", " <td>8.500000</td>\n", " <td>-3386.3753</td>\n", " </tr>\n", " <tr>\n", " <th>54</th>\n", " <td>2018-11-01 15:04:44</td>\n", " <td>SE</td>\n", " <td>130.0</td>\n", " <td>1.3</td>\n", " <td>2.5</td>\n", " <td>30.0</td>\n", " <td>0.00</td>\n", " <td>0.04</td>\n", " <td>125.16</td>\n", " <td>29.277778</td>\n", " <td>10.000000</td>\n", " <td>-3386.3753</td>\n", " </tr>\n", " <tr>\n", " <th>56</th>\n", " <td>2018-11-01 15:09:32</td>\n", " <td>NNW</td>\n", " <td>342.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>30.0</td>\n", " <td>0.00</td>\n", " <td>0.04</td>\n", " <td>187.58</td>\n", " <td>29.222222</td>\n", " <td>9.888889</td>\n", " <td>-3386.3753</td>\n", " </tr>\n", " <tr>\n", " <th>58</th>\n", " <td>2018-11-01 15:14:36</td>\n", " <td>ESE</td>\n", " <td>121.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>30.0</td>\n", " <td>0.00</td>\n", " <td>0.04</td>\n", " <td>130.71</td>\n", " <td>29.000000</td>\n", " <td>9.722222</td>\n", " <td>-3386.3753</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", " </tr>\n", " <tr>\n", " <th>210</th>\n", " <td>2018-11-01 21:34:37</td>\n", " <td>SSW</td>\n", " <td>212.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>65.0</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>17.777778</td>\n", " <td>11.222222</td>\n", " <td>-3386.3753</td>\n", " </tr>\n", " <tr>\n", " <th>212</th>\n", " <td>2018-11-01 21:39:57</td>\n", " <td>SW</td>\n", " <td>223.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>64.0</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>17.888889</td>\n", " <td>11.000000</td>\n", " <td>-3386.3753</td>\n", " </tr>\n", " <tr>\n", " <th>214</th>\n", " <td>2018-11-01 21:44:45</td>\n", " <td>South</td>\n", " <td>190.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>63.0</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>17.888889</td>\n", " <td>10.777778</td>\n", " <td>-3386.3753</td>\n", " </tr>\n", " <tr>\n", " <th>216</th>\n", " <td>2018-11-01 21:49:49</td>\n", " <td>South</td>\n", " <td>190.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>64.0</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>18.000000</td>\n", " <td>11.111111</td>\n", " <td>-3386.3753</td>\n", " </tr>\n", " <tr>\n", " <th>218</th>\n", " <td>2018-11-01 21:54:53</td>\n", " <td>SSW</td>\n", " <td>204.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>67.0</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>18.000000</td>\n", " <td>11.777778</td>\n", " <td>-3386.3753</td>\n", " </tr>\n", " <tr>\n", " <th>220</th>\n", " <td>2018-11-01 21:59:41</td>\n", " <td>West</td>\n", " <td>274.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>66.0</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>17.888889</td>\n", " <td>11.500000</td>\n", " <td>-3386.3753</td>\n", " </tr>\n", " <tr>\n", " <th>222</th>\n", " <td>2018-11-01 22:04:45</td>\n", " <td>SSW</td>\n", " <td>202.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>61.0</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>17.888889</td>\n", " <td>10.277778</td>\n", " <td>-3386.3753</td>\n", " </tr>\n", " <tr>\n", " <th>224</th>\n", " <td>2018-11-01 22:09:49</td>\n", " <td>SW</td>\n", " <td>221.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>64.0</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>18.000000</td>\n", " <td>11.111111</td>\n", " <td>-3386.3753</td>\n", " </tr>\n", " <tr>\n", " <th>226</th>\n", " <td>2018-11-01 22:14:05</td>\n", " <td>SSW</td>\n", " <td>202.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>62.0</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>18.000000</td>\n", " <td>10.611111</td>\n", " <td>-3386.3753</td>\n", " </tr>\n", " <tr>\n", " <th>228</th>\n", " <td>2018-11-01 22:19:57</td>\n", " <td>SSW</td>\n", " <td>192.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>59.0</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>18.111111</td>\n", " <td>10.000000</td>\n", " <td>-3386.3753</td>\n", " </tr>\n", " <tr>\n", " <th>230</th>\n", " <td>2018-11-01 22:24:45</td>\n", " <td>SSW</td>\n", " <td>193.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>65.0</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>18.222222</td>\n", " <td>11.500000</td>\n", " <td>-3386.3753</td>\n", " </tr>\n", " <tr>\n", " <th>232</th>\n", " <td>2018-11-01 22:29:49</td>\n", " <td>SSW</td>\n", " <td>192.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>66.0</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>18.388889</td>\n", " <td>12.000000</td>\n", " <td>-3386.3753</td>\n", " </tr>\n", " <tr>\n", " <th>234</th>\n", " <td>2018-11-01 22:34:37</td>\n", " <td>SSW</td>\n", " <td>200.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>67.0</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>18.388889</td>\n", " <td>12.222222</td>\n", " <td>-3386.3753</td>\n", " </tr>\n", " <tr>\n", " <th>236</th>\n", " <td>2018-11-01 22:39:41</td>\n", " <td>SSW</td>\n", " <td>207.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>68.0</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>18.277778</td>\n", " <td>12.277778</td>\n", " <td>-3386.3753</td>\n", " </tr>\n", " <tr>\n", " <th>238</th>\n", " <td>2018-11-01 22:44:45</td>\n", " <td>South</td>\n", " <td>189.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>68.0</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>18.222222</td>\n", " <td>12.222222</td>\n", " <td>-3386.3753</td>\n", " </tr>\n", " <tr>\n", " <th>240</th>\n", " <td>2018-11-01 22:49:49</td>\n", " <td>SSW</td>\n", " <td>210.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>68.0</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>18.111111</td>\n", " <td>12.111111</td>\n", " <td>-3386.3753</td>\n", " </tr>\n", " <tr>\n", " <th>242</th>\n", " <td>2018-11-01 22:54:53</td>\n", " <td>SW</td>\n", " <td>221.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>64.0</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>18.000000</td>\n", " <td>11.111111</td>\n", " <td>-3386.3753</td>\n", " </tr>\n", " <tr>\n", " <th>244</th>\n", " <td>2018-11-01 22:59:57</td>\n", " <td>SSW</td>\n", " <td>200.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>63.0</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>17.888889</td>\n", " <td>10.777778</td>\n", " <td>-3386.3753</td>\n", " </tr>\n", " <tr>\n", " <th>246</th>\n", " <td>2018-11-01 23:04:45</td>\n", " <td>SW</td>\n", " <td>214.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>63.0</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>17.888889</td>\n", " <td>10.777778</td>\n", " <td>-3386.3753</td>\n", " </tr>\n", " <tr>\n", " <th>248</th>\n", " <td>2018-11-01 23:09:49</td>\n", " <td>SW</td>\n", " <td>219.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>63.0</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>17.777778</td>\n", " <td>10.722222</td>\n", " <td>-3386.3753</td>\n", " </tr>\n", " <tr>\n", " <th>250</th>\n", " <td>2018-11-01 23:14:37</td>\n", " <td>SSW</td>\n", " <td>209.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>63.0</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>17.777778</td>\n", " <td>10.722222</td>\n", " <td>-3386.3753</td>\n", " </tr>\n", " <tr>\n", " <th>252</th>\n", " <td>2018-11-01 23:19:57</td>\n", " <td>SSW</td>\n", " <td>205.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>64.0</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>17.722222</td>\n", " <td>10.777778</td>\n", " <td>-3386.3753</td>\n", " </tr>\n", " <tr>\n", " <th>254</th>\n", " <td>2018-11-01 23:24:45</td>\n", " <td>SSW</td>\n", " <td>208.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>64.0</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>17.611111</td>\n", " <td>10.722222</td>\n", " <td>-3386.3753</td>\n", " </tr>\n", " <tr>\n", " <th>256</th>\n", " <td>2018-11-01 23:29:49</td>\n", " <td>SSW</td>\n", " <td>207.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>65.0</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>17.388889</td>\n", " <td>10.777778</td>\n", " <td>-3386.3753</td>\n", " </tr>\n", " <tr>\n", " <th>258</th>\n", " <td>2018-11-01 23:34:21</td>\n", " <td>SSW</td>\n", " <td>206.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>65.0</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>17.277778</td>\n", " <td>10.722222</td>\n", " <td>-3386.3753</td>\n", " </tr>\n", " <tr>\n", " <th>260</th>\n", " <td>2018-11-01 23:39:57</td>\n", " <td>SSW</td>\n", " <td>206.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>66.0</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>17.111111</td>\n", " <td>10.722222</td>\n", " <td>-3386.3753</td>\n", " </tr>\n", " <tr>\n", " <th>262</th>\n", " <td>2018-11-01 23:44:29</td>\n", " <td>SW</td>\n", " <td>229.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>66.0</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>17.000000</td>\n", " <td>10.611111</td>\n", " <td>-3386.3753</td>\n", " </tr>\n", " <tr>\n", " <th>264</th>\n", " <td>2018-11-01 23:49:17</td>\n", " <td>SSW</td>\n", " <td>213.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>64.0</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>17.000000</td>\n", " <td>10.222222</td>\n", " <td>-3386.3753</td>\n", " </tr>\n", " <tr>\n", " <th>266</th>\n", " <td>2018-11-01 23:54:53</td>\n", " <td>SSW</td>\n", " <td>200.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>62.0</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>17.000000</td>\n", " <td>9.722222</td>\n", " <td>-3386.3753</td>\n", " </tr>\n", " <tr>\n", " <th>268</th>\n", " <td>2018-11-01 23:59:57</td>\n", " <td>SSW</td>\n", " <td>208.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>62.0</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>17.111111</td>\n", " <td>9.777778</td>\n", " <td>-3386.3753</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>135 rows × 12 columns</p>\n", "</div>" ], "text/plain": [ " Time WindDirection WindDirectionDegrees WindSpeedMPH \\\n", "0 2018-11-01 12:49:32 SSW 202.0 0.0 \n", "2 2018-11-01 12:54:36 SE 133.0 0.0 \n", "4 2018-11-01 12:59:56 South 187.0 0.0 \n", "6 2018-11-01 13:04:44 ESE 114.0 1.8 \n", "8 2018-11-01 13:09:48 SE 133.0 0.4 \n", "10 2018-11-01 13:14:52 SE 139.0 0.0 \n", "12 2018-11-01 13:19:56 SSE 150.0 0.0 \n", "14 2018-11-01 13:24:44 South 186.0 0.0 \n", "16 2018-11-01 13:29:32 East 97.0 0.0 \n", "18 2018-11-01 13:34:36 NE 39.0 0.0 \n", "20 2018-11-01 13:39:56 NE 46.0 0.0 \n", "22 2018-11-01 13:44:28 NE 49.0 0.0 \n", "24 2018-11-01 13:49:16 SSW 212.0 0.0 \n", "26 2018-11-01 13:54:52 South 190.0 0.0 \n", "28 2018-11-01 13:59:40 SW 226.0 0.0 \n", "30 2018-11-01 14:04:44 South 183.0 0.0 \n", "32 2018-11-01 14:09:48 SSW 203.0 0.0 \n", "34 2018-11-01 14:14:52 NW 325.0 0.0 \n", "36 2018-11-01 14:19:56 SSE 162.0 0.0 \n", "38 2018-11-01 14:24:12 SSW 195.0 0.0 \n", "40 2018-11-01 14:29:48 NE 49.0 0.0 \n", "42 2018-11-01 14:34:52 WNW 287.0 0.9 \n", "44 2018-11-01 14:39:56 NNW 342.0 0.9 \n", "46 2018-11-01 14:44:28 SSE 152.0 0.0 \n", "48 2018-11-01 14:49:48 SSW 196.0 0.0 \n", "50 2018-11-01 14:54:52 SE 137.0 0.0 \n", "52 2018-11-01 14:59:56 NW 307.0 0.0 \n", "54 2018-11-01 15:04:44 SE 130.0 1.3 \n", "56 2018-11-01 15:09:32 NNW 342.0 0.0 \n", "58 2018-11-01 15:14:36 ESE 121.0 0.0 \n", ".. ... ... ... ... \n", "210 2018-11-01 21:34:37 SSW 212.0 0.0 \n", "212 2018-11-01 21:39:57 SW 223.0 0.0 \n", "214 2018-11-01 21:44:45 South 190.0 0.0 \n", "216 2018-11-01 21:49:49 South 190.0 0.0 \n", "218 2018-11-01 21:54:53 SSW 204.0 0.0 \n", "220 2018-11-01 21:59:41 West 274.0 0.0 \n", "222 2018-11-01 22:04:45 SSW 202.0 0.0 \n", "224 2018-11-01 22:09:49 SW 221.0 0.0 \n", "226 2018-11-01 22:14:05 SSW 202.0 0.0 \n", "228 2018-11-01 22:19:57 SSW 192.0 0.0 \n", "230 2018-11-01 22:24:45 SSW 193.0 0.0 \n", "232 2018-11-01 22:29:49 SSW 192.0 0.0 \n", "234 2018-11-01 22:34:37 SSW 200.0 0.0 \n", "236 2018-11-01 22:39:41 SSW 207.0 0.0 \n", "238 2018-11-01 22:44:45 South 189.0 0.0 \n", "240 2018-11-01 22:49:49 SSW 210.0 0.0 \n", "242 2018-11-01 22:54:53 SW 221.0 0.0 \n", "244 2018-11-01 22:59:57 SSW 200.0 0.0 \n", "246 2018-11-01 23:04:45 SW 214.0 0.0 \n", "248 2018-11-01 23:09:49 SW 219.0 0.0 \n", "250 2018-11-01 23:14:37 SSW 209.0 0.0 \n", "252 2018-11-01 23:19:57 SSW 205.0 0.0 \n", "254 2018-11-01 23:24:45 SSW 208.0 0.0 \n", "256 2018-11-01 23:29:49 SSW 207.0 0.0 \n", "258 2018-11-01 23:34:21 SSW 206.0 0.0 \n", "260 2018-11-01 23:39:57 SSW 206.0 0.0 \n", "262 2018-11-01 23:44:29 SW 229.0 0.0 \n", "264 2018-11-01 23:49:17 SSW 213.0 0.0 \n", "266 2018-11-01 23:54:53 SSW 200.0 0.0 \n", "268 2018-11-01 23:59:57 SSW 208.0 0.0 \n", "\n", " WindSpeedGustMPH Humidity HourlyPrecipIn dailyrainin \\\n", "0 0.0 42.0 0.00 0.00 \n", "2 0.0 42.0 0.21 0.04 \n", "4 0.0 37.0 0.21 0.04 \n", "6 4.9 34.0 0.00 0.04 \n", "8 2.5 33.0 0.00 0.04 \n", "10 0.0 32.0 0.00 0.04 \n", "12 0.0 30.0 0.00 0.04 \n", "14 0.0 31.0 0.00 0.04 \n", "16 0.0 30.0 0.00 0.04 \n", "18 0.0 29.0 0.00 0.04 \n", "20 0.0 29.0 0.00 0.04 \n", "22 0.0 30.0 0.00 0.04 \n", "24 0.0 31.0 0.00 0.04 \n", "26 0.0 32.0 0.00 0.04 \n", "28 0.0 31.0 0.00 0.04 \n", "30 0.0 30.0 0.00 0.04 \n", "32 0.0 30.0 0.00 0.04 \n", "34 0.0 30.0 0.00 0.04 \n", "36 0.0 30.0 0.00 0.04 \n", "38 0.0 31.0 0.00 0.04 \n", "40 0.0 29.0 0.00 0.04 \n", "42 2.5 26.0 0.00 0.04 \n", "44 4.9 25.0 0.00 0.04 \n", "46 0.0 27.0 0.00 0.04 \n", "48 0.0 28.0 0.00 0.04 \n", "50 0.0 27.0 0.00 0.04 \n", "52 0.0 27.0 0.00 0.04 \n", "54 2.5 30.0 0.00 0.04 \n", "56 0.0 30.0 0.00 0.04 \n", "58 0.0 30.0 0.00 0.04 \n", ".. ... ... ... ... \n", "210 0.0 65.0 0.00 0.00 \n", "212 0.0 64.0 0.00 0.00 \n", "214 0.0 63.0 0.00 0.00 \n", "216 0.0 64.0 0.00 0.00 \n", "218 0.0 67.0 0.00 0.00 \n", "220 0.0 66.0 0.00 0.00 \n", "222 0.0 61.0 0.00 0.00 \n", "224 0.0 64.0 0.00 0.00 \n", "226 0.0 62.0 0.00 0.00 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287.99 26.000000 7.611111 -3386.3753 \n", "16 279.70 26.500000 7.500000 -3386.3753 \n", "18 290.42 27.000000 7.500000 -3386.3753 \n", "20 280.35 27.611111 8.000000 -3386.3753 \n", "22 266.63 28.000000 8.777778 -3386.3753 \n", "24 260.77 28.277778 9.611111 -3386.3753 \n", "26 252.30 28.500000 10.222222 -3386.3753 \n", "28 245.79 28.500000 9.777778 -3386.3753 \n", "30 239.89 28.500000 9.277778 -3386.3753 \n", "32 243.23 28.611111 9.388889 -3386.3753 \n", "34 235.37 28.611111 9.388889 -3386.3753 \n", "36 226.00 28.777778 9.500000 -3386.3753 \n", "38 227.39 28.888889 10.111111 -3386.3753 \n", "40 219.75 29.000000 9.222222 -3386.3753 \n", "42 131.58 29.111111 7.722222 -3386.3753 \n", "44 216.19 29.111111 7.111111 -3386.3753 \n", "46 132.06 29.222222 8.277778 -3386.3753 \n", "48 132.49 29.222222 8.888889 -3386.3753 \n", "50 193.70 29.277778 8.388889 -3386.3753 \n", "52 115.30 29.388889 8.500000 -3386.3753 \n", "54 125.16 29.277778 10.000000 -3386.3753 \n", "56 187.58 29.222222 9.888889 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figure(plot_width=960, plot_height=480, title='Etcheverry Rooftop Temperature', x_axis_type='datetime')\n", "\n", "p.line(ws_data['Time'], ws_data['Temperature'], line_width=2)\n", "p.add_tools(HoverTool(tooltips=[('Time', '$x'), ('Temp', '$y')]))\n", "\n", "p.xaxis.axis_label = 'Time'\n", "p.yaxis.axis_label = 'Temperature'\n", "\n", "show(p)" ] }, { "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": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.15" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
socallaghan/linearRegressionTutorial
linearRegressionAnswers.ipynb
1
470629
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Some are ideal for tackling large volumes of data while others provide more informative probabilistic outputs.\n", "\n", "In this lab, we'll focus on Linear Regression; a good point of reference for many other regression techniques and still used widely around the world today due to their simplicity and favourable scaling characteristics." ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Part I: Maximum Likelihood Linear Regression" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this section, we're going to explore what linear regression models are and investigate how they can be trained using a \"Maximum Likelihood\" approach." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Import related packages\n", "import numpy as np\n", "import scipy.linalg as linalg\n", "import scipy.stats as stats\n", "import matplotlib.pyplot as pl\n", "\n", "#Set generated images to appear underneath the related cell\n", "%matplotlib inline " ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, lets generate some synthetic data. Here's we create a random 1-D vector $\\mathbf{x}$ of observation locations and evaluate an \"unknown\" ground truth function at these locations before adding some noise to create our observations, $\\mathbf{y}$." ] }, { "cell_type": "code", "collapsed": false, "input": [ "nObs = 20 # number of observations\n", "betaInv = 0.2 # variance of Gaussian noise applied to samples\n", "x = np.sort(np.random.random(nObs)*10)[:,np.newaxis] # observation locations\n", "y = 0.5*x + 1 + 1*np.sin(x)+ (np.random.randn(nObs)*betaInv)[:,np.newaxis] # ground truth (we don't know this!)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lets plot the data first to see what it looks like:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "pl.figure(figsize=(15,5))\n", "pl.plot(x,y,'b.')\n", "pl.axis([0,10,0,7])\n", "pl.xlabel('x')\n", "pl.ylabel('y')\n", "pl.show()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x7fb6e5d08710>" ] } ], "prompt_number": 3 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Linear Model with Polynomial Features" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Okay, now lets try to fit a model to it.\n", "\n", "One of the most straightforward types of regression is maximum likelihood regression using a linear model, (the output is a weighted sum of the features).\n", "For now, lets use a very simple model with just two parameters (or weights), $\\mathbf{w}$.\n", "\\begin{equation}\n", "f(x) = w_0 + w_1 x\n", "\\end{equation}\n", "This is just a line with $w_0$ and $w_1$ representing the bias and slope, respectively. \n", "\n", "In matrix form this is $\\mathbf{f}= \\mathbf{\\theta w}$ where $\\mathbf{\\theta}$ is $[x^0,x^1]$ (the feature matrix) and $\\mathbf{w}$ is $[w_0, w_1]^T$. \n", "\n", "The feature matrix has a dimensionality of $N\\times p$ where $N$ is the number of inputs and $p$ is the number of features. The complexity of the model can be increased by increasing $p$. Polynomial features (increasing powers of $x$) are commonly used in practice. Evaluating a polynomial model at locations $[x_1,...,x_N]$ and weights $[w_0,...,w_p]$ is given as:\n", "\n", "\\begin{equation}\n", "\\begin{bmatrix}\n", "f_1 \\\\\n", "f_2 \\\\\n", "\\vdots \\\\\n", "f_N\n", "\\end{bmatrix} =\n", " \\begin{bmatrix}\n", " x_1^0 & x_1^1 & \\cdots & x_1^{p-1} \\\\\n", " x_2^0 & x_2^1 & \\cdots & x_2^{p-1} \\\\\n", " \\vdots & \\vdots & \\ddots & \\vdots \\\\\n", " x_N^0 & x_N^1 & \\cdots & x_N^{p-1}\n", " \\end{bmatrix} \n", " \\begin{bmatrix}\n", " w_0 \\\\\n", " w_1 \\\\\n", " \\vdots \\\\ \n", " w_{p-1} \n", " \\end{bmatrix}\n", "\\end{equation}\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lets query our model and compare it against the data. To generate a polynomial feature matrix $\\theta$, we'll use\n", "custom function called polyFeatureGen() that takes the observation or query locations ($x$ or $x_{\\text{query}}$) and the number of features ($p$) as inputs. It returns an $n \\times p$ matrix such that:\n", "\\begin{equation}\n", "\\theta_{i,j} = x_i^{j-1}\n", "\\end{equation}" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Define Polynomial basis functions -------------------\n", "# x: input vector (n x d) where n is the number of inputs and d is their dimensionality (you can assume d=1 for this lab)\n", "# p: number of features (scalar)\n", "# theta: the feature matrix (n x p)\n", "\n", "def polyFeatureGen(x,p):\n", " #Solution:\n", " theta = x**np.arange(p)\n", " return theta" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "# Test polyFeatureGen\n", "# Run this cell. The output should be: \n", "# [[1 1 1]\n", "# [1 4 16]\n", "# [1 3 9]\n", "# [1 6 36]]\n", "\n", "xTest = np.array([1,4,3,6])[:,np.newaxis] # 4 1-D inputs\n", "pTest = 3 #This test model will have 3 features\n", "thetaTest = polyFeatureGen(xTest,pTest) #the test feature matrix (should be 4 x 3)\n", "print(thetaTest)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[[ 1 1 1]\n", " [ 1 4 16]\n", " [ 1 3 9]\n", " [ 1 6 36]]\n" ] } ], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "For now, lets stick with two features. Lets pick two arbitriary values for the weights. Say," ] }, { "cell_type": "code", "collapsed": false, "input": [ "p = 2 #number of features\n", "w_guess = np.array([-0.2, 5])[:,np.newaxis]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Generate the query points and the associated feature matrix" ] }, { "cell_type": "code", "collapsed": false, "input": [ "nQuery = 1000 # number of query inputs\n", "xQuery = np.linspace(0,10,nQuery)[:,np.newaxis] #linearly spaced from 0 to 10" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 7 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Write a line of code to generate the associated feature matrix for the query points:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#Uncomment and complete the line of code below to generate thetaQuery, (the feature matrix for the query points):\n", "#\n", "#Solution:\n", "thetaQuery = polyFeatureGen(xQuery,p)\n", "\n", "print(thetaQuery) #Should be a nQuery x p matrix" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[[ 1. 0. ]\n", " [ 1. 0.01001001]\n", " [ 1. 0.02002002]\n", " ..., \n", " [ 1. 9.97997998]\n", " [ 1. 9.98998999]\n", " [ 1. 10. ]]\n" ] } ], "prompt_number": 8 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now evaluate the model at the query locations" ] }, { "cell_type": "code", "collapsed": false, "input": [ "fQuery = thetaQuery.dot(w_guess)\n", "print(fQuery.shape)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "(1000, 1)\n" ] } ], "prompt_number": 9 }, { "cell_type": "markdown", "metadata": {}, "source": [ "And plot the results:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "fig = pl.figure(figsize=(15,5))\n", "pl.plot(x,y,'b.')\n", "pl.plot(xQuery,fQuery,'r')\n", "pl.xlabel('x')\n", "pl.ylabel('f(x)')\n", "pl.show()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x7fb70a23c250>" ] } ], "prompt_number": 10 }, { "cell_type": "markdown", "metadata": {}, "source": [ "It's not a great fit! The Mean Squared Error is:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "fModel = polyFeatureGen(x,p).dot(w_guess)\n", "MSE = np.mean((fModel - y)**2)\n", "print('Mean Squared Error: ' + str(MSE))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Mean Squared Error: 746.150265796\n" ] } ], "prompt_number": 11 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Maximum Likeliood Estimation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now lets learn the parameters of our model to see if we can improve the fit.\n", "The goal here is to estimate the parameter values (i.e. $\\mathbf{w}$) from the data. A popular way of doing this is by optimising the conditional likelihood of the data given a model, $p(y|x,w,\\beta^{-1})$, with respect to the parameters.\n", "\n", "A common assumption to make is that the measurements we gathered are subject to Gaussian noise\n", "i.e. we assume that our observations, y are drawn from a Gaussian distribution with mean $\\mathbf{w\\theta^T}$ and\n", "variance $\\beta^{-1}$ (indicating the level of noise). \n", "\n", "The probability of acquiring a particular set of observation, given a specific model is\n", "\\begin{equation}\n", "p(y|x,w,\\beta^{-1}) = \\prod_{n=1}^{N} \\mathcal{N}(\\mathbf{w\\theta^T}, \\beta^{-1})\n", "\\end{equation}\n", "\n", "It is often referred to as the likelihood of the data. In log form:\n", "\\begin{equation}\n", "\\text{ln}p(\\mathbf{y}|\\mathbf{x},\\mathbf{w},\\beta^{-1}) = \\sum_{n=1}^{N} \\text{ln}\\big( \\mathcal{N}(\\mathbf{w\\theta^T}, \\beta^{-1})\\big) \\\\\n", " = \\frac{N}{2} \\text{ln}(\\beta) - \\frac{N}{2}\\text{ln}(2\\pi) - \\frac{\\beta}{2} \\sum_{n=1}^{N} (\\mathbf{y}-\\mathbf{w\\theta^T})^2 \n", "\\end{equation}\n", "\n", "\n", "The final term is the sum of squares error between the observations and the model.\n", "If we vary the parameters of the model to maximise the likelihood of the data, we train a model\n", "that best fits the data\n", "The gradient of the log likelihood with respect to the weights is:\n", "\\begin{equation}\n", "\\triangledown \\text{ln}p(\\mathbf{y}|\\mathbf{x},\\mathbf{w},\\beta^{-1})= \\sum_{n=1}^{N} (\\mathbf{y}_n - \\mathbf{w\\theta^T})\\theta\n", "\\end{equation}\n", "Find the max by setting the gradient to zero and then solve for $\\mathbf{w}$ to find the \"maximum-likelihood parameters\" (w_ml):\n", "\\begin{equation}\n", "\\mathbf{w}_{ml}^T = (\\mathbf{\\theta}^T\\mathbf{\\theta})^{-1}\\mathbf{\\theta^T y}\n", "\\end{equation}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Generate feature matrix using the observations locations." ] }, { "cell_type": "code", "collapsed": false, "input": [ "#Solution:\n", "theta = polyFeatureGen(x,p)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 12 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then find the parameters that maximise the likelihood as described above." ] }, { "cell_type": "code", "collapsed": false, "input": [ "#Implement a line of code to determine w_ml. Hint: use np.linalg.solve(A,B) \n", "#rather than np.linalg.inv(A)B for better numerical stability\n", "\n", "#Solution:\n", "w_ml = np.linalg.solve((np.transpose(theta).dot(theta)),(np.transpose(theta))).dot(y)\n", "\n", "print(w_ml)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[[ 0.41310195]\n", " [ 0.58601903]]\n" ] } ], "prompt_number": 13 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now evaluate the model at the query locations using the new parameters and plot the results as before:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Solution:\n", "fQuery = thetaQuery.dot(w_ml) # the model's predictions at the query locations. This will be an nQuery x 1 matrix\n", "\n", "fig = pl.figure(figsize=(15,5))\n", "pl.plot(x,y,'b.')\n", "pl.plot(xQuery,fQuery,'r')\n", "pl.axis([0, 10, 0, 8])\n", "pl.xlabel('x')\n", "pl.ylabel('f(x)')\n", "pl.title('Max Likelihood weights with ' + str(p) + ' features')\n", "pl.show()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x7fb6e5af3f90>" ] } ], "prompt_number": 14 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Much better. Calculate and print the mean squared error between the model prediction and the observations using these new parameters to quantively compare with our initial set of parameters " ] }, { "cell_type": "code", "collapsed": false, "input": [ "#Solution\n", "fModel = theta.dot(w_ml) # the model's predictions at the observation locations. This will be an N x 1 matrix\n", "MSE = np.mean((fModel - y)**2) # scalar\n", "print('Mean Squared Error: ' + str(MSE))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Mean Squared Error: 0.392305925411\n" ] } ], "prompt_number": 15 }, { "cell_type": "markdown", "metadata": {}, "source": [ "This MSE is the best we can do with this model." ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Increasing the Model Complexity" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To reduce the MSE further, we will need to increase the model's flexibility by adding higher order polynomial features. \n", "In the cell below, increase the number of feature in the model, retrain it, plot the resulting query outputs as before and evaluate the MSE. \n", "What is the number of features that gives the lowest MSE?" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Set the number of features\n", "p = 8 #number of features\n", "\n", "# Build the design feature matrix\n", "theta = polyFeatureGen(x,p)\n", "\n", "# Determine the maximum likelihood weights\n", "w_ml = np.linalg.solve((np.transpose(theta).dot(theta)),(np.transpose(theta))).dot(y)\n", "\n", "# Build the feature matrix for the query points\n", "thetaQuery = polyFeatureGen(xQuery,p)\n", "\n", "# Evaluate the model at the query points\n", "fQuery = thetaQuery.dot(w_ml)\n", "\n", "fig = pl.figure(figsize=(15,5))\n", "pl.plot(x,y,'b.')\n", "pl.plot(xQuery,fQuery,'r')\n", "pl.axis([0, 10, 0, 8])\n", "pl.xlabel('x')\n", "pl.ylabel('f(x)')\n", "pl.title('Number of Features: '+str(p))\n", "pl.show()\n", "\n", "fModel = theta.dot(w_ml)\n", "MSE = np.mean((fModel - y)**2)\n", "print('Mean Squared Error: ' + str(MSE))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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BkI96s6ZHL9rOGscmBjhJSkxVthg4L+1V1FTv3jBkSNJVSJKUv8aPj8VwjRrR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\nklRlNQlxDdJTyv+sBn4F9AT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"text": [ "<matplotlib.figure.Figure at 0x7fb6e5c06cd0>" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Mean Squared Error: 0.019448867593\n" ] } ], "prompt_number": 16 }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you may have noticed, increasing the flexibility and model complexity decreases the mean squared error because the model's ability to fit all of the observation exactly increases as well. However, it also results in \"overfitting\". This is when the model fits the observations very well but generalises poorly to out locations. If a number of observations are withheld from training, the model would not do well at predicting their value as it is more concerned with overfitting the observations it can see.\n", "\n", "\n" ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Other Types of Features (also known as Basis Functions)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Up until now, the feature matrix has been generated using polynomial basis functions:\n", "\n", "\\begin{equation}\n", "\\theta_{i,j} = x_i^{j-1}\n", "\\end{equation}\n", "\n", "Run the cell below to visualise the basis functions that were used in the last model you trained:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "pl.figure(figsize=(15,5))\n", "for i in range(p):\n", " pl.plot(xQuery,thetaQuery[:,(i-1)])\n", " pl.axis([0, 10, 0, 80])\n", " pl.title('Individual basis functions')\n", "pl.show()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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iK/ooQ2yjqwsiIiApybfzNcXR1NOzj+joGcTG5lgdyqRhGAaV11cyd/NcIuM0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\nlLhcRwWxb+x29jU00N9kYpjFwtDYWIaYzQxVEQ+R9qcAJSIiQUYBTZr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"text": [ "<matplotlib.figure.Figure at 0x7fb6e5c5d790>" ] } ], "prompt_number": 17 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The above graph is simply plotting each column or feature of the feature matrix. \n", "\n", "Multiply each basis functions by the learnt it's associated weight, w_ml[i], and plot the results." ] }, { "cell_type": "code", "collapsed": false, "input": [ "pl.figure(figsize=(15,5))\n", "for i in range(p):\n", " pl.plot(xQuery,thetaQuery[:,(i-1)]*w_ml[i])\n", " pl.axis([0, 10, -80, 80])\n", " pl.title('Individual weighted basis functions')\n", "pl.show()\n", "print('Learnt weights: ' + str(w_ml.transpose()))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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gyxaYMwcGDaoTfUkty2Lf4/s48eUJei/pTXAzzTYptYPT6eSBBx7g66+/ZsWK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\nJCJuJCAAliyBa6+1zsN9+aXpRCLizrSFUqQNVDY0cHVODnH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"text": [ "<matplotlib.figure.Figure at 0x7fb6ea3a0b90>" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Learnt weights: [[ 6.94876283e+00 -2.00026847e+01 2.04606702e+01 -9.13155568e+00\n", " 2.10026572e+00 -2.59423463e-01 1.63943566e-02 -4.17155772e-04]]\n" ] } ], "prompt_number": 18 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Interestingly, the model's complicated looking prediction is just a linear combination of these basis functions. Hence the name \"linear\" regression." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "However, there are many possible variants of basis functions each giving the resulting model different characteristics. Radial basis functions are a commonly used alternative to the polynomial function:\n", "\\begin{equation}\n", "\\theta_{i,j} = \\text{exp}\\Bigg(-\\frac{(x_i-\\mu_j)^2}{2s^2}\\Bigg)\n", "\\end{equation}\n", "\n", "Here, $\\mu_j$, is the mean of the $j^{\\text{th}}$ radial basis function and $s$ corresponds to the basis functions width or its region of influence. \n", "\n", "\n", "Implement the basis function radialFeatureGen() that takes a vector of inputs ($\\textbf{x}$), a vector of $p$ mean locations ($\\mathbf{\\mu}$) with shape $p \\times 1$ and a basis function spatial width ($s$). It then calculates the feature matrix, $\\theta$, using the radial basis function given above. " ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Define Radial basis functions -------------------\n", "# Dimensions:\n", "# x: n x 1\n", "# mu: p x 1\n", "# s: 1 x 1\n", "# theta: n x p\n", "\n", "def radialFeatureGen(x,mu, s):\n", " # Code goes here\n", " #Solution:\n", " theta = np.exp(-(((x-mu.transpose())**2)/(2*s**2))) \n", " return theta" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 19 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using the values for $\\mu$ and $s$ provided below, write some code that finds the weights with the maximum likelihood ($w_{ml}$) for a design matrix generated using the radial basis function. Then evaluate the model at the query locations and plot the results as before." ] }, { "cell_type": "code", "collapsed": false, "input": [ "mu = np.array([1,2,3,4,5])[:,np.newaxis] #The mean of the radial basis functions used in this model\n", "s = 1 # the spatial width of the basis function\n", "\n", "#Solution:\n", "theta = radialFeatureGen(x,mu,s)\n", "w_ml = np.linalg.solve((np.transpose(theta).dot(theta)),(np.transpose(theta))).dot(y)\n", "thetaQuery = radialFeatureGen(xQuery,mu,s)\n", "\n", "# Evaluate the model at the query points\n", "fQuery = thetaQuery.dot(w_ml)\n", "\n", "# Plot the results:\n", "fig = pl.figure(figsize=(15,5))\n", "pl.plot(x,y,'b.')\n", "pl.plot(xQuery,fQuery,'r')\n", "pl.axis([0, 10, 0, 8])\n", "pl.xlabel('x')\n", "pl.ylabel('f(x)')\n", "pl.title('Radial Basis Function')\n", "\n", "pl.figure(figsize=(15,5))\n", "for i in range(mu.shape[0]):\n", " pl.plot(xQuery,thetaQuery[:,(i-1)][:,np.newaxis]*w_ml[i])\n", " pl.title('Individual weighted basis functions')\n", "pl.show()\n", "print('Learnt weights: ' + str(w_ml.transpose()))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x7fb6e5ad0410>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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yD5Vk75RN+aXl5O/3JYaZb2y0QYQmTIA774Qs1dikimBHkJq/11B1fRW5++RS\n8bsKcvfMjXe2REREvrREDOC+APYEmoc5lhoB3Pz58J3vWHOts8+Od25Ekt6gwG3nbCourSBv382s\n0fb74Xvfg48+gkcegfLykc2sxFWoJ0Ttv2upvLqS3L1yqbi0gtw9FMiJiEjySNQAbi+gaZhjyR3A\nOY4NVPKnP1mTyQMPjHeORJJa2B+m9rZaKq+qJHuXSOC2zwg0RXYcuOEG+OMfYe5cDW6SgkK9IWpu\nqWX1VZV4d8wl9K0KWsbl0tgI7e3Q2ws9PQOptxdCIWtZ63INXmZmQk6OpezsgfWSEigttZSXZ+eL\niIh8VYkYwK0E2rAmlP8E/hVzLHkDuN5e+P73B+7qa7ASkc02aoHbUM89B2eeaaNU/vjHKoEnmc5O\nWL4cVqyANWugstKW0bR2LRTnhPi6t5bj2iupL8plyV4VBCtyycxkUMrIAI/HYvtweGAZDluA19kJ\nXV227OyEjg5rkdvQYMnvt0Bu/Hir1C0vh4qKgbTVVpCrikAREdkEiRjATQBqgRLgWeAnwKLIseQM\n4Kqr4ZvftF/s229XvxqRzRT2h6m9NRK4zcim4nejFLjFWrnS+sXttRfcfLOV5CWhNDXBBx/Ahx9a\n9+Jly+Czz6C5GbbdFrbZBqZMWTeVlQ10Pw71hKi9pZbKayrJ2zeP8kvLyd1t5CKqnh4LGOvqLJBc\ntWogrV5tl1lBAUyfDjvuaMvp02HnnWHcuBHLhoiIpIBEDOBiXQp0An+KbDuXXnpp/8FZs2YxK9Gb\nNr34IvzP/9jd+0su0R18kc2wTuB2aQV5e2/BUVu7umx6gcpKeOghmDRpy7229HMcC3bee8/S++9b\namuDGTNg111hhx1g++1tXvYpU778YKKhnhA1/6xhzR/XkLdfHhWXVpAzY/RHrQyHrVbw008tffKJ\nLT/+2Jpf7rGHTRG65562XlY26lkSEZEEsXDhQhYuXNi/ffnll0MCBXBZgAfoALKBBcDlkSUkUw2c\n41j/mRtugLvvhiOOiHeORJJOf1PJP8QpcIvlODbw0I03wj33qF/cFtDbC+++C6+9Bq+/bkuwytDd\ndhtIW2018rM+hLpjArkDIoHcrlt++oFwGL74At55xz6Ld96xlJkJ++4LBx1k3al33x3S0rZ49kRE\nJA4SrQZuK+DhyLoXuAe4KuZ4cgRwbW1w1llQW2uDlUyZEu8ciSSVcCBM3R11rL5yNdk7ZVNx2RZo\nKrmpFiwgjusUAAAgAElEQVSwUWR/8hO4+GLNFzeCOjvhlVfghRdg0SJrEjl9Ouy/PxxwgC3Ly7ds\nQ4ZQd4iaf9Sw5to15B+UT/nvyuM+j5zjWNPLN96wz+uVV6wJ5l57WTB30EGWcjTdnYhISkq0AG5j\nEj+A+/BD6+929NE24qRuiYpssnAwTP3d9az+v9VkbpNJxeUV5O//JeZx21Kqq+G006xt2913Q3Fx\nvHOUlPx+C0JeeMHS++9bE8HDDoODD4a997ZRHRNBqCtE9d+rWXPdGgoOLqDi0gqyd0qQzAGtrQMB\n3csvW23dnnta448jjrDPUtONioikBgVwI8Vx4NZbrZ/bDTdYvzcR2SThYJiGextYdcUqMsozqLi8\ngoKDCuKdrQ0LBODXv4b774f77oP99ot3jhKe49iokE8+adNhvv661bAdfrgFbQcemPhjPIW6QlTf\nVM2aP62h4NACKn5XQfaOiRPIRXV1WS3ms8/aYKqrV8Mhh1gwd/TR1kdQRESSkwK4kdDWBueeC0uX\nwrx5ViIRkY1yQg4N9zWw6vJVpJWlUXF5BYWzCuOdrS/n0Udt4u9LLoELL9RARUP09sJLL1nQ9tRT\ntn3ssZZmzbKRF5NRsDNIzU01rPnzGgoPL6T8t+VkT0+8QC6qvt5qOZ97Dp5+2mo2jz/e0kEHqbGI\niEgyUQD3Vb3xBpxxhpVGrrtOQ4yLbAIn7LD2gbWsunwV3kIvW12xFQWHFUT/ISWflSvt/0BhIdxx\nh032NYZVV8MTT1jQtnChjQ553HH2b3LXXVMrxg12BKm+sZqq66soPLKQit9VkLV9YlcjOo6N4vnE\nE5aWL4cjj4SvfQ2OOUbTFoiIJDoFcJsrHIZrr7V+bv/4B3zjG/HOkUjCc8IOjQ83suqyVbiz3Wx1\nxVYUHlmYvIFbrEAALr/cmlLfeqtFK2PI8uXw8MPw3//aHGzHHGO1O0cdNTa6CAbbg1T/rZqqG6oo\nml1E+W/LydousQO5qLo6qx194gl4/nmbe+4b37Du3FttFe/ciYjIUArgNkdVlc0J1dtrw4lPnRrv\nHIkkNMdxaHy0kVWXrsKd5qbi8gqKjilKjcBtqJdfhm99C0480aYSSdFaecexMZv++19LjY023/lJ\nJ1nTSJ8v3jmMj2B7kKq/VlH9l2qKji2i/P+Vk7VtcgRyYAPLvPii/U0feQQmT7ZA7qST1DtARCRR\nKID7cq9uAdvPfmZDiF9yiYb1EtkAx3FoeqyJVVesgjBUXFFB8fHFqRm4xWppgR/8wGZjvvde2GWX\neOdoRDiOtRp/6CEr4IMV7E86ycZw0YwKA4JtQar+UkXVX6soPr6Yit9WkLl1Zryz9aUEgzaqZTRI\nz8uzYO6b37SJ01P9aywikqgUwG2qtWutQLZsGdx1F+yxR3zyIZIEnLDD2ofWsvrK1bg8Lsp/W864\nr49L/cAtluPAnXfCL35hg5v86ldJecMn2l9q3jwbbDM7G04+2QrxqdafbTQEWgNU3VBF9Y3VjDth\nHOX/r5zMackVyIH1Gli82AK5hx6yfaeeCqefrutARGRLUwC3KR591IK3b30LrrgiZZtEiXxV4WCY\ntfetZfXvV+PJ9VD+23KKjxsDNW4bUlkJ3/2u1crdcQfstFO8c7RJliyxoG3ePCu8n366pZ13VmF9\ncwRaAlRdX0X1TdWM+3okkNsq+QI5sKD+gw8soJ83D9LTB66PHXaId+5ERFKfArgNaWqy5pKvvGIF\nr5kzt9xriySRcCBM/X/qqfxDJWllaZT/tjx1BicZCY4D//oX/OY39j/lF79IyNq45csHCuVtbTZX\n+emn24TQ+lOOjEBzJJC72QK5qb+amjSDnQzHceCttwZqaEtK7Jo57TQNgCIiMloUwA3/KjB3Llx0\nkbUR+f3vISdn9F9XJMmE/WHq7qyj8qpKMqZlUP7bcgoOSeLpAEbb6tVWG9fWBrfdZtVZCZCl+++3\nAnh1NZxyihXA999ffdpGU6A5QPXfqqm+sZqCQwuYeslUcnfPjXe2vpJw2O53zpsHDz5oAdzpp9vP\n6KRJ8c6diEjqUAA31KpVcN55VpL5179g331H9/VEklCoK0Ttv2tZ86c1ZO+UTflvy8k/ID/e2UoO\nsbVx55wDv/sdZG3ZGpjaWnjgAStof/aZDUJy+ulwyCHg8WzRrIx5wc4gtbfYdyln1xym/noqBTOT\ndHbzGMGgTRw+b56NZrnLLnaNnXyy1dKJiMjmUwAXFQzC3/5mtW0XXQQ///nYHQdbZD36Gvuo/ls1\nNX+vIf/gfKb+aip5e+fFO1vJqa7OmlO+/jrceKPNdD2Kmptt8Im5c21QkhNOsAL1EUfoX10iCPvD\n1N1VR+U1laRPSGfqJVNTZqoNvx8WLLBr76mn7L7onDk27URB8seqIiJbnAI4gFdftWkBCgrgn/+E\nbbcd+dcQSWI9q3qo+lMV9ffUU3JyCVN+PiWp++0klAUL4Ic/hN12gxtusIm3RkhXFzz2mBWcX3oJ\njjwSzjjD5hjXWEyJKRwMs/bBtVReVQkuKL+knJKTS3B5kj+QA7smn3jCauZeeAEOO8xuJHzta1u8\nIlpEJGmN7QCupsaG9l640CbcPf109dQXidH5QSeVf6yk+elmJnxvApMvmEz6hPR4Zyv19PTAVVfB\nzTfDxRfbDaX0zfuc+/rgmWds+rn5860vW7S2I0+VpUnDcRyan2pm9R9W01fXx+QLJ1P2v2V4cxJv\n8JvN1dpqzSvnzoU337QbC3PmwFFHbfblLyIyJozNAM7vtzvd114L554Lv/61BikRiXDCDs3PNFN1\nQxVdH3Ux+cLJTPz+RLz5qVNwTFiffWZNuD/9FK67Dk48cZNuKoVCVsM2d67N07XjjlbTpv5GqaHt\n9Taq/lxFy4stTDhnApN+MomMyalVhdrQMNDEd8kSu+EwZw7MmpWQA7aKiMTV2ArgHAcef9z6t22/\nPVx/PWyzzcjlTiSJhbpC1N1VR9VfqnBnuJl84WTGzxmPO11DEW5xCxZY/7jSUvs/NWPGOqdEh2+f\nO9eGby8rswLvaafB1KlxyLOMup4veqj+azV1d9ZRdEwRU342hdw9k3vkyuGsWTMwMuqaNRoZVURk\nqLETwL36qjWXbG21mrdjjhnZnIkkqd7KXqpvrKb2tloKZhYw+cLJ5B+cnxKDJyS1YNBGq7zsMquJ\nu/xymDCBTz6x5pHz5llh9owzNIHyWBNsC1L771qq/lJFxlYZTP7pZMZ9bVzK9JOLFZ2bcO5c6Oy0\nGxRz5liXUf2LEpGxKvUDuE8+sSaS774L//d/cOaZGidbxjzHcWh/rZ2qG6poeaGFsu+UMenHk8ic\nlhnvrMlQLS00//z3ZMy9nftyvsufPL9k9v8UM2cO7LGHCrFjWTgQpvG/jVTdUIW/xs/EH0xkwjkT\nSCtNi3fWRpzjwMcfWyA3b56NnDpnjm5eiMjYlLoBXGUlXHGFDcH2q1/Bj36kYddkzAu2B6n/Tz01\n/6gh3Btm0k8mUXZWGd5cdTJJNJ99ZnO13X+/9Q865+gqfth8JRNeexDXBRfAhRdCbuo1n5PN0/Fu\nB9U3V9P4UCNFxxUx6YeTyNs/LyVr0h0HFi+2QO6++2D8eAvkTj8dysvjnTsRkdGXegHcF1/YaG4P\nPgjf/74Fb5poRsa4jnc6qPlHDWsfXEvhkYVM/P5ECg4twOVOvcJdMlu2zIK2Bx6AtWvhm9+0/j8H\nHhjTcGDFCmtW+dxz8Mtfwg9+oPHXpV+gJUDdHXXU3FyDJ8fDxB9OpHROaUqNXhkrFIJFiyyYe+gh\nmwXotNNscvopU+KdOxGR0ZE6Adzy5fCHP1iN23nn2d3pceO2bO5EEkiwM0jDvAZq/1lLoDHAhHMn\nUPa/ZaSXaXzuRBIN2u6/HxobLWg79VQ44ICNtPb++GO49FJ45RU4/3xrZaCbVRLhhB1anmuh+qZq\n2l5uo+TkEsrOLiNvv9SslQMIBOy+xn332Vxz06ZZIHfSSbDddvHOnYjIyEn+AO6jj2wOt/nzbe6k\n88+HwsL45E4kzpywQ9uiNuruqKPxkUbyD85n4g8mUnRUUUoOcJCMHMe65D76qM2B1dhow/1Ha9q+\n9Ch7n34K11xjI+x+73vw059amzKRCH+Nn/q766m9rRaXx0XZ2WWUfauMtPGp11cuKhCAl1+2aTUe\nfhiKiweCuV13Vd9REUluyRnAhcM2U+2f/2wTxvz4x3b3OT8/jlkTiZ+ez3uou6uO+rvq8eR6KDur\njNIzSlXbliACAZun7ZFHrJFARoYNLHniiVbTNiJDo69ebXPH3XOPdQa64AKbLkUkwnEc2l5to+62\nOhofbqRgVgFlZ5dRNLsIty91x+cPh+GNNyyY++9/LXg76SSr7d5nH01NICLJJ/kCuFtusXmR0tJs\nnqTTToN0FVJl7Am0Bmh8qJG6O+voXtpN6ZxSys4qI2e3nJRtIpVM2tvh6aetpm3+fGvCdeKJNkHx\nDjuMYg1AfT3ceCPccosNU3n++XD00SqlyiDBjiBr719L7e219CzroeTkEkrPKCX/wPyU7hvrOPDB\nBwPBXHMzHHccHH88HHEEZGfHO4ciIhuXfAHcccdZ4HbooWoDIWNOsCNI02NNNNzXQOtLrRQeXsj4\nb4+n+Nhi3GkqoMfbihUWrD31lE09eeCBFrB97WswceIWzkxvr3UG+stfbAKtH/8YzjoL8vK2cEYk\n0fWs6qFhXgMN9zYQbA1SenoppWeUkjMj9W8GrVgBTz5pfebefNO+s8cfb0FdRUW8cyciMrzkC+A2\ndyJvkSQV6grR9EQTDfc30PJcC/kz8yk9rZRxJ47Dm5eaI8sli54eaxoZDdq6umD2bDjmGKv0SohY\nyXHgtdfgr3+FBQus7dg558D+++smmKyj86NOGuY2UH9vPZ4sj/2vOWkc2Ttnp3ww195uX5EnnrDv\n8/jxA8Hcvvva3HMiIolAAZxIAgo0B2h6qommR5toXtBM3n55VpD6xjh8hSpFxNPnnw8EbK+8AjNm\nWMB27LG2ntBl3Lo6uOsuuPVWa1J59tnw7W9r0BNZh+M4tL/WztoH17L24bW4vC5KvlHCuJPGkbdv\nXko3swSbnuCttyyYe/JJm6Fo1iw46ig48kjYZpsE/66LSEpTACeSIHo+76HxsUaaHmui450OCg4r\nYNzXxlF8QjFpJak7Wlyiq6+HF16A55+3ZU+P1bIde6z1mUnKQW+jtXK33mpD9M2cCXPmwAknqBOQ\nrMNxHDrf76Txv42sfXgtweYg404cx7hvjKPgkALc6anffLuhwaYoePZZq6VLS7NA7qij4LDDoKgo\n3jkUkbFEAZxInIQDYdrfaKd5fjONjzUSaAz0B2yFhxfiydrQJGAyWtrarFnk889bqq6GQw6Bww+3\ngtqOO6bYnfeODgvi5s2zoG72bBvFcvZsGy5TZIju5d00PtxI48ONdH3SRcEhBRQdU0TR7CIyt8qM\nd/ZGnePY7B0LFlhAt2iRDUx06KH2v+LAAzUotoiMLgVwIluI4zj0LO+heUEzLQtaaH2plcxtMyk6\nqojiE4rJ2yf1myUlovp6G3Bk0SJrErl0Key330DAtsce4B0rXQ0bG21ovnnz4P33rfPPCSdYMJeb\nG+/cSQIKNAVoXtBM89OWvIVeio8ppmh2EfkH5+PJTP0bUX4/vP663fh56SVYvNhm8DjkEEszZ6qG\nTkRGlgI4kVHkr/XT9nIbLc+10PxsM4Sg8KhCCo8spPDwQjWN3MIcx0adiwZrr7wCa9faXGwHHWRp\n771V8QRATY3NgfDYYxbhHnCABXMnnACTJ8c7d5KAnLBD53udNM1vonl+M50fdJK7Vy6FhxZSMKuA\nvP3yxkRzS7/f+s9FA7rXX4dp0+z/y/772w2irbdOsZp8EdmiFMCJjBDHcej9opfWl1tpW9RG28tt\nBJoC5B+UT+HhhRQeVUjWDlkpP5JbImlqsoLU4sW2fPNNmzZy5kwrTM2cCTvtpCnSNqqjA555xoK5\np56yORGOPNLSwQdDVla8cygJKNgepO3VNlpfbKV1YStdn3SRt08eBYcWUDCrgNy9c/FkpH4NXSAA\n77xjLZRff90mFe/ttUAumvbZR5XcIrLpFMCJbKZQT4jO9zvpeKuD9tfbaX25FYCCgwvIn5lP/sx8\nsnfKVrPILaSjwybojQZsixdbi8A997TC0d5723LKlHjnNMkFg/D229b559ln4b337MM98kgbpm/P\nPW2EB5Ehgu1B2ha10bpwIKDL3imbvP3yyNs/j7z98sioyBgTN7mqquyGUjSge+89q6XbY4+BtNtu\nCupEZHgK4EQ2gRNy6Pq0i47FHbQvbqfjrQ66l3aTtUMWefvkkbtvLgUHF5Cx1dgofMST40BlpQVr\n0fT++9bib+edBwdr22+v2rVR19Fh7cSiozl89pkFcdFqzv3314gOMqxQd4iOd+0GWPsb7bS/3o4T\ndiyg2yePnN1zyNk9h/Sy9HhnddT19cFHH1kg9+67lj76CCZNGgjodt8ddtnFZv3Qz4zI2KYATmSI\nQGuArg+76Pyw05YfdNK1pIv0SekWrO2dS+4+ueTsljMmmv/Ei+NAba0NKvLpp5Y++gg+/BAyM23O\ntdi03XZjaLCRRNbeblUKr7xiAd1bb1mHn732srTnnrDrrupoKOtwHAf/Gr8Fc4vb6Xyvk873OnGn\nu/uDuZzdc8jdPddulqV464ZgEJYtGwjo3nvP/geCNf0emkpL45tfEdlyFMDJmBVoDtC9rJvupd22\n/KSbzg86CTYHyd4lm+xds8nZNceWu+TgzVd0MBr8fli1ygoq0UBt6VJLPh9Mn25DdO+wg919njFD\nBZWk0tdnVaTvvGPp7betlm677Syg2333gRJoSUm8cysJxnEc/JV+Ot7r6A/oOt/rJNASIGv7LLJ3\nzCZrehZZO9p6xrQM3N7UrXZ3HBs5d8mSdZPXa1+jHXaAbbe1tM021jRT90tEUosCOElpwfYgvat6\n6f2il+7PLFDrWdZD99Juwn1hsrbPsrRDFlnTs8iZkTMm7uxuSY5jg4msXGnp888H1leuhLo6G9Qw\nGqTFBmzjxsU79zIqenutKvXtt60NbLQE6vOtW62w3XZqMybrCLYF6V7aTdenXXR/0k3XJ110f9pN\nX00fmdtkkrl9JplbW8qYlkHm1pmkT0lP2eAu2mJhyRK7P7J8uY24u3w5rF5tX6FoQLfNNlYpXl4O\nU6dCcbG+XiLJRgGcJC3HcQi2BfFX+fGv9tPzRY8Fa5GArXdVL+HeMBkVGWRUZJC1XRaZ22dasLZ9\nFmllaeqv9hU5jk18XVW1blqzZmDp9dpd4GjaeuuB9SlT1PRRsIuppsZKoJ98MhDULV8OPT120URT\ntAQ6bZp1EkpP/T5SsmlC3SG6P7MbdT2fW+pd2UvP5z30NfSRMSVjUECXPiWd9MkDKRWbxQeD1m94\nxYqBoG7lStu3erW1gpg6dSCgi65PmQITJljKzVWQJ5JIEi2Amw3cAHiAfwPXDDmuAG4MCAfDBJuD\nBBoD9DX00VfTh7/GP+zS5XWRNjGNjIoMMrfK7A/WMraypa/EpyDtS3Ic6Oy0ZjoNDZai67HL+noL\n0Fwu+6GfMsVq0oZLhYXxfleS1NrarOo2mlasGKjKra21WZInTx64CGOX48dbystTCXSMC/WG7Abf\n5730rOyxm39r/Las8uOv9uPN81owFwns0srS8JX6SBufRlppGr7xPtJK0/DkelLmt6Wjw4K5aEAX\nXa+stK9Xba39LkSDubKygfXo9rhxloqLISdHXzWR0ZZIAZwHWAYcAVQDbwFzgE9jzlEAl0TCfWGC\nbUGCbUFCbaH+9WBrkEBTgMDaAIHGIWltgGB7EF+hD984H74SH2kT00ifmD54OSmdtAlpeHNUdTMc\nx7G7qp2d0NoKzc3Q0rLx1NxsAZrHY/3Mxo8fvBy6b/JkKxeLxE0oNHA3Yc2awVW/VVUDdx38/nUv\n4mgqLra7DAUFtowmlUTHFCfsEFgbwF/lp3dNL/4qP4H6AH31ffQ19Nl6Qx+BhgBO0LHALhLU+Yp8\neAu9eAu9+AoH1oduezKTs4avo8Oau0cDuthUV2fN5BsbbRkI2FcqGtANXc/Pt5SXN7Ae3c7K0ldO\nZFMkUgC3P3ApVgsHcHFkeXXMOQrgRojjODghB6fPIewP9y9D3SHC3THLrnW31znWFSLYHiTUHhoU\nsIUDYbz53v7kyff0r/tKIgFaJEjrXx/nw1fow+VJ3f/gjmPjOvT2Wsuw3t6BNHQ7dl9PjwVkHR2D\nl+vb53Zb+XNomTQ2FRWtu6+0FLKz4/0piYywnh5Yu3Zw1XK0Ojl6h6O1deBuRmurffHy8weCu/x8\n+3Lk5AyfYo9lZ9twqRkZ1sQzI2PwutoNJ7VQd6g/mOur77NWIy0Bgi3B/jTcNoA314sn14MnZ5g0\n3P5sD+5MN+6MmDR0O8ONJ9ODK80V95rB3t7BAd3Q9bY2S+3tA+vRFAwOBHZ5eQNBXXb2l1ump68/\npaXZTUqRZLY5Adxo/epMAtbEbFcB+w496c1b26wE7IATjuyMbEdXcQb2DdqGdfcNdw5AOLKPIa/F\neh7nxDw85nFEHxdyIGxBE2Eg7PTvI7pv6DnR47HnxDzOCTm4ovuCDk4gDEEHVyAMkXUCYQg4EIzd\njqy7AJ8bvC7wusHnxkl3Q7oHMtw4kSXpHltPdw8sM3y2LI1sZ3kjyQPZkfU09zq30obG3/3bzZH0\n2fDnOA6Ew+tPodCGj2/KOUOPh0J2JzEYtGVsGrpvY9vR1NtrFQE+30B5LiNjoJy3oX2ZmdYPYfx4\n6/6TmztQVoyux+7TXMqSzBzHIeSECDthu+GEs85yQ8dil2EnbOsFHpyCCTjblW3a8wX6cLe2425v\nx9PajruzC3dXd39ydXXj7mrC3bQmsq8n5ngXLn8frr4+3L1+W/fb0t3rB5cLJy2NcEYaTno6TpqP\ncEY6Tnoajtfbn/B6cLyeyLYHx+vD8XrA48HxRfZ5vP3nEHuuxwNuN47bheOO/D92ufr34XbjuCLH\n3HbMcUXW+x/nApd74DGRx4ddrshj7Dn6ixHR//mx//sj607/sdi/9Jc8f+jzxzxu3fPXfc51nnud\n/Gxc9Ke9P7ldhMdhCRfOkHOjyQOE/S6CfhfBbjf0unF6XNDrwulxQ48Lp9cFDW5Ybfvpcdsy4IK+\n2ITt87twxR4LAT7A5+CkOeB1cDyA1wEPOJElkX0DxxwcL+BxwAuOJ3JeZJ/jjnxObgfc4Lgiz+GK\nlCU8kX2R43aupSw3THXB1OixAqDIznVckXNd9vhw2H4/+4IQ6Iv8loYcgiH7TQ62QbA5sj8MjQEI\nhiAQglAQAkEi5zqEwgxOoYElLgvi3G5we8HtAY8bPF4Hl6f/0rZ5RCPnEv1qxByLfB0GljHnuD32\nWLe7/y2Ca+BriHvg8ovuiz0nel26I+sDx1yD9kePudwD5wy69IcsB60PuzNme7iv7NDTNvZVGvQa\nzkbzNtzjNvgVHcH7FYP+L6zv5ZK4fmG0ArhNqlr7ww9/ETnbxfaevdjBu1f/A2M/eAfsH0LMX9aJ\nuWIHjke2XUMz4Yo5Hnke1+BMDnq9IYGwE/PcuCDsstcMu1w4LlfM0p578L4hy8hrOW6X/ThEni8c\n+cZH14NuN2G3i1Cmm1C2i5DLTcjtIuy2ZcjtJhQ9J/LD3f9uh1yQw12grj7sR6NjA+dsyvNsxjlu\n98ZT/z/jzTjH6x3+uM9nyesdWB9ue1PPid6A190/2VICoQDdgW66A930BHtsGegZdtsf8tMX6iMQ\nCtAX6lsnBcIb3h8MBwmFQ4ScEKFwyLYj61926eDgdrlxu9y4sFqF2KXb5V5n33DL9T3+yz7fOjIt\nucat/9fcHpceSYN5Q5AWdEgLhEkPOqQHHHzBMBmBPnzhPjwhB0/YwRcCT9jBE3Lwhhy8YSyFbJ8n\n6OD1235PZL835vz+MnU4WnZy+tfdjmPHnIFt15B1d3jgvHXPGdgG1llC/69t/z4H6PP66POl0edN\no8+Xhj+S+nxp9Pl8tu5Nw582cMzvSyfg9RLw+gi5PQQ9XoIeTyR5CXk8BKL7vD6Cbg9Bb2Tb7SXo\n9fSfH3ZFfgtdbsKR38TouuNyDT7mckWWA8cctxtXOIwnHMblOHjCIVyOE/mcwuCAy3Fi3rv9HVyO\n0x8QubIi28ScG90maPucgc/QFXOjOrod+9m6IneUXWFI63OR1ge+AHhC4A2CJ+jCG3TwhFx4wi48\nQWw95MITYmAZtH3ucGRf0IUner2EXbjC4Arbutvv4HJcA9dT2DVwbUTW3WFw9a+7cDkx+2OfM3q9\nRApn67umIvFlzGcxcC7R2CDmMa7Y69Gx8/sDpxAQipRtWPexsfkYSaPxnJtWgt50oxKjJMFnmcix\n2ae9H7K098Ov9ByjFcBVA1NitqdgtXCDvPPiWXSGQnSGQjztcpHj6SLH4+lP2THrOR4PeR4PBV4v\nhT4fhV6vrUdSdH+6OzWHFRaR5BUIBWjqaaKxu5HmnmZae1tp97cPm9r8bYO2O/wd/UEbQJYviyxf\nFpm+TFt6MwdtR/ele9JJ86QNSjlpOfg8vnX2p3nS8LkH9vs8PrxuLx6XB4/bM2h9c5bRgEoSRzAc\npiUYpDkYpDkQ6F+2BoO0h0J0BIN0hEIDaZjtzlAIt8tFpttNpttNhttNpsczsL6e/dluN+luN2ku\nFz63G5/LNZCGbKfFbg9zrtflwg14YpcuFx7AvZ590XOjx3VtisiWNXvQlst1z5d+htH6r+XFBjE5\nHKgBFrOBQUwcx8EfDtMVDvcHdJ2hEF0x652hEO3BIC2R1BpdDwQGbXtdLgq8Xsb5fIxPS6PU56M0\nLY3xkWVpdH9kPVPVJyLyJYWdMGu71lLbWUtNRw21HbXUdtaytmstTT1N/cFaU7etd/V1UZRZxLis\ncbBps3wAACAASURBVBRnFVOQUUBeeh55aXm2jKT8jPxB23npeeSm5fYHZj6PL95vXRJUIBymIRCg\nvq+P+r4+6iLL+kCAur4+1vb10Rz5nWwOBOgKhSj0+SjyeimKLKM3Q/O8XnI9Hkux60O2czwefLpp\nKiLylSTSICYAxzAwjcCtwFVDjo/4ICaO49AdDtMSCNAUDFLf10dDX1//j1pDIDB4u6+PTI+HKenp\nTE5PZ0o0ZWQM2qcgT2TscByH+q56VrWu6k+rW1dT02mBWk1HDQ1dDeRn5DMhZwITcicwMXciZdll\nlGSXWJCWWdwfrBVnFpOfkY/bpYKubJ6+cJgqv581fj9renupjKxX9vayxu+nxu+nLRSyG5c+H2Vp\naYyPpOh6ic9HcUzAluvx4FbNk4hI3CVaALcxcR+F0nEcWoLB/h/FNdEfyJjtar+fPK+XbTIzh01F\nPt0RF0k2faE+VrasZFnjMpY1LePz5s9Z1WbBWmVbJXnpeVQUVFjKr2Bq/lQm5k5kYu5EJuROoCyn\njDSPRpaRkdMeDPJ5Tw8renpYHllGU2MgwIS0NKZGbi5OSU/vX5+ans7E9HSKfT48CshERJKOArhR\nEHYc6vr6+Lynp//HdUXMj6zb5WKbzEx2zMpi5+xsdsrOZufsbKakp6tdvUicdfV18XHDx3zU8BFL\nG5eyrGkZyxqXUdlWyZT8KWxfvD3bF2/P1kVbs1XBVlQUVFBeUE6WLyveWZcU5ER+T5Z0dfFxJC3t\n7mZ5Tw+doRBbZ2ay7ZCbhFtnZjIpPV3BmYhIilIAt4U5jkNTIMDynh4+6e7m466u/h/mzlCoP6jb\nNSeHPXNz2S0nh2w1xxQZcY7jsKp1FR/Wf8gH9R/0L6vbq5leMp1dx+/KDsU7sP24gYBNNWgymnpD\nIT7q6uKdjg4+ignYXMDOkRt9O2Vns2N2NttmZjIhLU03/URExiAFcAmkORBgSSSge7+zk3c6O1nS\n1cVWGRnsmZvLnrm57KWgTmSzNHQ1sLh6MW9WvcnimsW8Vf0WWb4sZpTNYNfSXW05fle2K94Or1uT\nLMvo8ofDfNzVxdsdHbzd0cE7HR0s7e5m+6ws9szJYdecnP6ArdTnU6AmIiL9FMAluL5wmCWRO7Lv\ndHbyTkcHS7q62C4ri4Py8zkwL4+D8vOZnJER76yKJIywE+bjho95adVLvLLmFRZXL6a1t5W9J+7N\nvpP2ZZ9J+7D3pL0pyymLd1ZljFjb18erbW28EkkfdnWxTWYme0VuzO2Zm8uu2dkaAEtERDZKAVwS\n6guHebejg1fa2ni1vZ1X2trIdrs5MD+fmfn5HF5YyDaZmbpjK2NGKBzi/br3eWn1S7y0+iVeqXyF\n4sxiDik/hJnlM9l30r5sW7ytRnWULaayt5cXWlr6A7a6vj72z8/noEjaOzeXLAVrIiKyGRTApQDH\ncVje08MrbW3/v707j46qPNw4/s2+h+wbSyBhiSA7UVELiBuIWhdUVCyoiBZ3FESqgtZWqWhpRRGL\niFrXqlBRiwUl4lKWhKCAEkJCQhay7yHJTGbu748J/qhSZUlyZzLP55w5TJJJ7nM4k8k8933v+7Kp\npoYN1dV4eXhwXng454WHMz48nFhfXbsjXcuB2gN8su8T1uWs47P9nxEfHM/YxLGM7T2WMYljSAhJ\nMDuiuJEaq5WNba+/G6qrqW5tZXxYGGPCwji7WzcGBQVpUREREWkXKnBdkGEY7G1q+uGNRFpNDT39\n/LgwIoJLIiM5MzQUb22kKi6mubWZL/K/YN2+dazLWUdZYxnnJ53PhL4TuCD5Ak2HlE5lNwwy6uv5\nsLKST6qq2H3oEGeGhnJ+24mzIcHB2jNNREQ6hAqcG2i120mvr2ddVRVrKyvJb25mQkQEl0RFcWF4\nOGHal06cVF1LHR/t/YjVe1bzSc4nnBpzKhOSJzCh7wRGxI/Ay1NT0KTzHLLZ+LS6mrWVlXxYWUk3\nb28uiYxkQkQEZ4aG4q8pkSIi0glU4NxQYXMzH1ZWsrayki9qaxkVEsKV0dFMjo7WVEsxXVljGf/c\n809W71nNlwe+ZEziGC5PuZxLB1xKdFC02fHEzVRbraypqGB1RQVpNTWMDAnhkshILomMpF+g9v4T\nEZHOpwLn5hptNtZXVfFueTkfVVUxIjiYq2NiuCIqimiVOekktc21vPf9e7y+83UyijO4sO+FXJFy\nBRP7TSTUL9TseOJm6lpb+aCigrfLy/m8poZzw8OZHB3NxIgIIjRjQURETKYCJz9ostlYV1XF22Vl\n/KuqitNDQ5kSE8Pk6GhCvbUvlrSvltYWPs7+mNd3vs763PWM7zOe6wdfz6R+kwjwCTA7nriZJpuN\ntZWVvFVWxqfV1fyqWzeuiYnh11FRev0TERGnogInR3XIZuOjykreKCtjY3U1l0RFMT0ujnPCwnRh\nvpyUHSU7WLF9BW/uepPBMYO5fvD1TB44mfCAcLOjiZsxDIOt9fW8fPAg75SXMyokhCkxMVweFUW4\nRtpERMRJqcDJLyq3WHizrIyXS0qoslqZFhfHtLg4kgM0SiLHpq6ljjd3vsnftv+NssYybh5+M9OH\nTScxLNHsaOKGDra08FppKatKSrAaBtPj4vhNbCw9/f3NjiYiIvKLVODkuOyor2dVSQlvlJUxKCiI\nWQkJXBYVhY+2JZCjyCjOYOm2paz+fjXnJZ3HjBEzOD/pfK0eKZ3Obhh8UlXFsuJivqit5YqoKG6M\ni+Osbt0O/yEUERFxCSpwckIsdjtrKip4rqiIfU1N3BIfz8yEBBL8/MyOJiaz2qy8//37/HXrXyms\nK2TWqFlMHzad2OBYs6OJG6q2Wnm5pITni4ro5u3NrO7duSY6mmBd1yYiIi5KBU5O2s6GBpYVF/NW\nWRnnhYczKyGBsWFhOqvtZsoby3kx40WWpS+jX2Q/7jrtLi4ZcAnennqjLJ0vs76e54qKeK+igkkR\nEdzRvTunh4bqdUlERFyeCpy0m7rWVl4tKeG54mL8PDyY06sXV0dHa3plF5dTlcPirxfz9u63ufKU\nK7nz9DsZEjvE7FjihuyGwUeVlTxVUEBeczO3JSQwIz6eGG2JIiIiXYgKnLQ7wzD4V1UVTxUUkNvU\nxD09ejAjPp4QTVnqUjIPZrLoq0VsyN3Ab0f9ljtPv5OYoBizY4kbarHbeb20lKcKCgj09GRur15c\nGRWFt04eiYhIF6QCJx0qva6OpwoK+LS6mlsSErire3fidZ2cS0vLS+OJL59gV9kuZp8xm5kjZxLi\nF2J2LHFDNVYryw8e5C+FhQwJCmJur16co+nbIiLSxanASafIbWpiSWEhfy8tZUpMDPN69aKXlux2\nKZ/nfc6CtAUU1hXy4NkPMnXIVPy8Vcal81VYLDxdWMiLxcVcFBnJ/T17MjQ42OxYIiIinUIFTjpV\nucXC0wUF/O3gQa6KjubBxEQSVeSc2pcHvmRB2gLyavJ4eMzDTB0yVQuTiCmOfP24uu1EkF4/RETE\n3ajAiSkqLBaeKSxkeXExk6OjebBXL3prY3CnsrlwM49sfITsqmwe+tVD/Gbob/Dx8jE7lrihcouF\nxW3FTSP4IiLi7lTgxFSVVivPFBTwQnExV0ZH80hiIj30xsxUeyv3Mv/T+Wwp2sLDYx5m+rDp+Hpp\nFT/pfJVWK386cIAVRxS3nnp9EBERN6cCJ06hymrlqYICXiwu5ub4eOb16kWEj0Z7OlNpQymPff4Y\nb+9+m/vPvJ+7T7+bAB+NikrnO2Sz8ZfCQp4pLOTKqCh+l5io4iYiItLmRAqc1mWWdhfh48MTSUns\nTE2ltrWVAVu38mR+PodsNrOjdXkNlgYe+/wxBj0/CF8vX/bcsYd5Z89TeZNO12q382JxMf22bCGz\noYGvhg/nhQEDVN5EREROkkbgpMNlHTrEQ/v383VtLY/07s1NcXHaELyd2Q07q3as4qHPHmJc73E8\nPv5xksKTzI4lbsgwDN6vqGB+bi49/Px4MimJ1NBQs2OJiIg4JU2hFKe2ra6Oebm5FFssPJOczMTI\nSLMjdQmbCzdz57/uxMfTh79M+Aup3VPNjiRuaktdHffs20ez3c6ipCTODw/XPm4iIiI/QwVOnJ5h\nGHxUWcnsnBz6BgTwdHIypwQFmR3LJR2sP8iDnz7I+tz1LDpvEdcPvl5vlsUURS0tzMvN5bPqav6Y\nlMQNsbF46rkoIiLyi3QNnDg9Dw8PLo6KYldqKueHhzNmxw7uzs6mymo1O5rLsNgsLP56MYOXDSY2\nKJY9t+9h6pCpKm/S6ZpsNn6fl8eQbdvo5edH1mmnMS0uTuVNRESkA2kETkxVYbGwIC+Pf5SX83Bi\nIr9NSMBb18f9T5/t/4xZH80iKTyJJROW0D+yv9mRxA0ZhsE/ysuZm5PDqJAQnkpOpo/2fhQRETlu\nmkIpLmtXQwP37NtHmdXK8/36cXZYmNmRnEp5Yzn3r7+ftLw0np34LJcOuNTsSOKmvmlo4M7sbOpa\nW/lLv36M1e+qiIjICdMUSnFZpwYHs37oUH6XmMiU775j+vffU2axmB3LdIZh8HLmy5y67FSiAqLY\nPWu3ypuYor61ldn79nH+N99wfWwsGaNGqbyJiIiYQCNw4nTqW1tZmJfHq6WlPNq7N7cmJODlhtfU\n7KnYw20f3kajtZHlFy9nRPwIsyOJGzo8XXL2vn1cEBHBoqQkon19zY4lIiLSJWgKpXQpOxsauD07\nm0M2G8/3789pbrKXlMVm4Y9f/JGlW5eyYOwCZqXOwsvTy+xY4oayDx3ijuxsii0Wlmlqs4iISLtT\ngZMuxzAMXist5YHcXH4dGcmTSUmE+fiYHavDZBRncOM/byQxLJFlk5bRI7SH2ZHEDTXbbDx54ABL\ni4p4MDGRu7p3x0eLC4mIiLQ7FTjpsqqtVubl5vJhZSXP9uvHFdHRZkdqVy2tLTz6+aO8lPkSz1zw\nDNcNvk7bAogp0qqruWXvXoYEBbGkb196+vubHUlERKTLUoGTLm9TTQ23ZGUxKCiIpf36keDnZ3ak\nk7a1aCs3/vNG+kf2Z9mkZcQFx5kdSdxQjdXK3Nxc/lVVxXP9+nFpVJTZkURERLo8Z1qFciFQCGS2\n3SZ00HHEzYwJC+ObUaMYFBTE0PR0lhcXY3fREwHNrc08sP4BLn3zUh4e8zDvX/2+ypuYYnV5Oadu\n24aXhwe7UlNV3kRERJxYR43ALQDqgWd+5jEagZOTsrOhgVuysvD19OTF/v1JCQoyO9Ix21K4hWlr\npjEkdghLL1pKTFCM2ZHEDZW0tHBHdjY7Gxv524ABjNEiJSIiIp3KmUbgwNzpmeIGBgcH89WIEUyO\njubszEx+n5eHxW43O9bPstqsLExbyKVvXcpj5zzGO1e9o/Imnc4wDFYePMiQ9HQGBAbyzahRKm8i\nIiIuoiNH4G4EaoF04D6g5keP0QictJsDzc3ctncvxS0trEpJYVhIiNmRfmJv5V6mvj+ViIAIVv56\nJQkhCWZHEjeU29TEzKwsalpbeSklhaHBwWZHEhERcVudvYjJeuBoF+z8DtgMlLd9/HsgHrj5R49T\ngZN2ZRgGr5SUMDc3l9u7d+fBXr3wdYKlzw3D4IX0F3h448M8Ou5RZqXO0gqT0unshsFzRUU8mpfH\nvF69uKdHD7yd4PdDRETEnZ1IgfM+ieOdf4yPWwGsPdoXFi5c+MP9cePGMW7cuJOII+7Ow8OD6fHx\nnB8RwcysLE7LyDB9NO5g/UFu/uBmyg+V8+VNX5ISlWJaFnFf+5uauCkrixa7na9HjKB/YKDZkURE\nRNxSWloaaWlpJ/UzOmoYIB442Hb/XiAVuO5Hj9EInHSYw6Nxc3JzuT0hgfmJiZ0+Gvf+9+8z66NZ\nzBw5k4fHPIyPV9fdgFyck2EYLC8u5uG8PB7o2ZN7e/bES6O/IiIiTsOZ9oF7FRgGGMB+4Fag9EeP\nUYGTDlfU0sLMrCyKOvHauLqWOu5edzdfHviSVy97ldE9R3f4MUV+rKC5mZvbrnVblZLCQBdapVVE\nRMRdOFOBOxYqcNIpOnM0bmvRVq5971rO7XMuz1z4DMG+WiBCOpdhGLxcUsIDubnc26MHc3v21LVu\nIiIiTkoFTuRndORonN2ws/jrxSz+ejHLJi3jyoFXttvPFjlWxS0t3JKVRbHFwispKQzRCpMiIiJO\nTQVO5BccuVLlnd27M69XL3xOcnSipKGEaWum0Whp5PUrXicxLLGd0oocG8MweL20lNk5OcxKSOB3\niYkn/bwWERGRjqcCJ3KMCpubmZGVRYXVyqqUFE49wZGKf+f8m+lrpjNjxAweGfsI3p4ns7CryPEr\ntVi4be9e9jU18UpKCiOccA9EEREROToVOJHjYBgGKw4eZP7+/dzXowf3H8e1QhabhYc+e4g3dr7B\na5e/xjl9zungtCI/9U5ZGXdlZ3NTfDwLevfGT6NuIiIiLkUFTuQE5Dc3c9OePTTYbKxKSeGUX1it\nL6cqh2vfu5bY4Fhe/vXLRAVGdVJSEYcKi4Xbs7P5trGRV1JSOC001OxIIiIicgJOpMDpdK24vUR/\nf9YPHcq0uDh+lZnJ0wUF2P7HyYU3dr7BGS+dwdQhU/lgygcqb9Lp1pSXMyQ9nZ5+fmwfOVLlTURE\nxM1oBE7kCLlNTdy4Zw82w+DllBT6BQYC0GBp4M5/3cnXBV/z1pVvMTx+uMlJxd3UWK3ctW8fX9fW\nsiolhbPDwsyOJCIiIidJI3AiJykpIICNw4ZxdUwMo7dv56+FhWw/mMmoF0fhgQcZMzNU3qTTfVJV\nxeD0dEK9vPgmNVXlTURExI1pBE7kf8hqbGRiRhqFNfn8qVcU9wy72uxI4mbqW1u5PyeHdVVVrExJ\n4dzwcLMjiYiISDvSCJxIO6k4VMH9H0whMusx7hswmscbElhWVIRdJx2kk3xeU8PQ9HRshsHO1FSV\nNxEREQFMHoFrtbXi5ellYgSRn0rLS+OG1Tdw7anX8vj4x/H18uX7xkam79lDiJcXL6WkkOjvb3ZM\n6aKabDbm79/PO2VlvDhgAJMiI82OJCIiIh3E5Ubgzn/tfIrri82MIPKDVnsrj2x8hOveu44Vl6zg\nT+f/CV8vXwBOCQriq+HDOS88nFEZGawoLkZTgKW9ba6tZVh6OqUWC9+mpqq8iYiIyE+YWuDG9R7H\niOUj+GjvR2bGEKGgtoBzXjmHzYWb2X7rdi7se+FPHuPt6cm8xEQ2Dh3KsuJiJn77LYXNzSakla6m\nxW5nfm4ul+3axR/69OGNgQOJ9PExO5aIiIg4IdMXMfki/wu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"text": [ "<matplotlib.figure.Figure at 0x7fb6e5abb110>" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Learnt weights: [[ 5.20689104 -10.95883545 14.04892152 -10.13378618 7.5608242 ]]\n" ] } ], "prompt_number": 20 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Rerun the above cell but change the basis functions by altering $\\mu$. e.g. **mu = np.array([1,2,3,4,5,6,7,8,9,10])[:,np.newaxis]**\n", "\n", "* What effect does changing $s$ have on the model? \n", "* How might we find the optimal values for $\\mu$ and $s$?\n", "* How does the behaviour of the radial basis function model differ from the poynomial case in regions where data is sparse?\n", " " ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Downsides to this Approach" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "While this approach to regressing over data is appealling, it does have a few shortcomings.\n", "For example, we have no estimate of how confident the model is in its predictions (which is crucial in decision making applications). Also the model learns a single hypothesis given the data. In reality there may be numerous explanations for the same set of observations. In Part II of today's lab, we will investigate a probabilitic approach to regression." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "% Bayesian Linear Regression Tutorial, Alistair Reid, NICTA 2015\n", "\\newcommand{\\W} [0] {\\mathbf{w}}\n", "\\newcommand{\\X} [0] {\\mathbf{x}}\n", "\\newcommand{\\y} [0] {\\mathbf{y}}\n", "\\newcommand{\\Xf} [0] {\\mathbf{\\theta}}\n", "\\newcommand{\\norm} [0] {\\mathcal{N}}\n", "\\newcommand{\\weightprec} [0] {Q}\n", "\\newcommand{\\weightprecsimp} [0] {\\alpha I}\n", "\\newcommand{\\alphs} [0] {\\mathbf{\\alpha}}\n", "\\newcommand{\\invnoise} [0] {\\beta}\n", "\\newcommand{\\noise} [0] {\\beta^{-1}}\n", "\\newcommand{\\Ainv} [0] {A^{-1}}\n", "\\newcommand{\\fq} [0] {\\mathbf{f_*}}\n", "\\newcommand{\\xq} [0] {\\mathbf{x_*}}\n", "$$ " ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Part 2: Bayesian Linear Regression" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "In the previous sections, we have selected the linear regression weights by maximising the likelihood of the data. The learnt model certainly makes useful predictions, but it provides no notion of uncertainty: we cannot distinguish between a prediction that is tightly constrained by the data, and a prediction that is essentially an unconstrained \\`guess'. Also, if there are multiple likely explanations of the data, we have neglected all but one. Recall that for a given feature mapping $\\X \\to \\Xf$, the predictive behaviour of a linear regression is encoded in the weights:\n", "\\begin{equation}\n", "f(x) = \\Xf \\W \n", "\\end{equation}\n", "\n", "Where $\\W$ is $p \\times 1$ and $\\Xf$ is $n \\times p$. Instead of using a single *maximum likelihood* weight vector, we can take a Bayesian approach and consider a probability distribution over all possible weight vectors. The distribution can be inferred from the observations using Bayes rule:\n", "\n", "\\begin{equation}\n", "p(\\W|\\y) \\propto p(\\y|\\W) p(\\W)\n", "\\end{equation}\n", "\n", "The likelihood $p(\\y|\\W)$ is exactly the criterion we were using to select the maximum likelihood weights:\n", "\n", "\\begin{equation}\n", "p(\\y | \\W) = \\norm (y | \\Xf \\W, \\noise I )\n", "\\end{equation}\n", "\n", "In addition, we must now specify a prior distribution over $\\W$ encoding our belief about reasonable weight values. Any probability distribution we consider to be sensible for the problem could be used here, but some are easier to work with than others. In this tutorial, we will use a simple Gaussian prior with precision (inverse covariance) $\\weightprecsimp$ that is $p \\times p$ with a single free parameter:\n", "\n", "\\begin{equation}\n", "\\W \\sim \\norm( 0, \\alphs^{-1}I )\n", "\\end{equation}\n", "\n", "Hence we are assuming the weights are independently distributed & penalised for large magnitudes. While the $\\W$'s are model parameters that directly affect the output, we consider the $\\alphs$'s to be *hyperparameters* because they tune the prior distribution over the parameters $\\W$. \n", "\n", "Because we have assumed a Gaussian form for both the likelihood and the prior, Bayes rule can be computed analytically in this case to obtain a posterior distribution over the weights. This involves expanding the exponents of the two Gaussians and completing the square (an exercise left to the reader).\n", "\n", "The posterior over the weights is given by: \n", "\\begin{equation}\n", "p(\\W|\\Xf,y) \\sim \\norm \\left( \\mu_w, \\Ainv \\right)\n", "\\end{equation}\n", "Where $A$ is the $p \\times p$ posterior precision given by: \n", "\\begin{equation}\n", "A = \\invnoise \\Xf^\\top \\Xf + \\weightprecsimp\n", "\\end{equation}\n", "And the weight means are given by:\n", "\\begin{equation}\n", "\\mu_w = \\invnoise \\Ainv \\Xf^\\top \\y\n", "\\end{equation}\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise: Compute Posterior Weight Distribution\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "import scipy.linalg as linalg\n", "import matplotlib.pyplot as pl\n", "import scipy.optimize # In addition, nlopt would be a great optimisation library to consider\n", "from math import log, pi\n", "%matplotlib inline" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 21 }, { "cell_type": "code", "collapsed": false, "input": [ " # Generate Noisy Data ---------------------------------\n", "nSamp = 5\n", "noiseSTD = 0.5\n", "xdomain = 10\n", "x = np.sort(np.random.random(nSamp)*xdomain)[:,np.newaxis] # x is n by 1\n", "y = x + np.random.randn(nSamp)[:,np.newaxis]*noiseSTD - 5 # y is also n by 1\n", "\n", "pl.figure(figsize=(5,5) )\n", "pl.plot(x,y,'k.')\n", "pl.grid()\n", "pl.title('Noisy observations of the linear target function')\n", "pl.xlabel('x')\n", "pl.ylabel('y')\n", "pl.show()\n" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x7fb6e5bf8f90>" ] } ], "prompt_number": 22 }, { "cell_type": "code", "collapsed": false, "input": [ "# Use 2 features - (1, x) to learn intercept and slope -------------------\n", "p = 2 # n features...\n", "n = len(x)\n", "\n", "# build a feature vector n*[1, x]\n", "theta = polyFeatureGen(x,p) \n", "\n", "# Define a prior\n", "alpha = 2**-2 # Weight precision -> Larger alpha 'relaxes' the complexity penalty - try it\n", "beta = noiseSTD**-2 # noise inverse standard deviation - cheat and use the real value\n", "\n", "# Compute the posterior\n", "A = beta * theta.T.dot(theta) + alpha * np.eye(p)\n", "mu_w = beta * linalg.solve(A, theta.T.dot(y))\n", "\n", "# Plot a countour ellipse of the weight distribution\n", "th = np.linspace(0,2*pi,100)\n", "xy = mu_w - np.log(0.1/2)*linalg.cholesky(linalg.inv(A),lower=False).dot(np.vstack((np.sin(th), np.cos(th))))\n", "\n", "postpl = pl.plot(xy[0], xy[1],'b')\n", "xy = np.zeros(mu_w.shape) - np.log(0.1/2)*np.vstack((np.sin(th), np.cos(th)))/np.sqrt(alpha)\n", "pripl = pl.plot(xy[0], xy[1],'g')\n", "\n", "\n", "pl.xlabel('Intercept')\n", "pl.ylabel('Gradient')\n", "pl.title('Weights (prior=green, posterior=blue) - contour at 0.1 max probability')\n", "pl.axis('equal')\n", "pl.show()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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ueYbpO6bL9eYchBQ5kaEf9v1AxyodKV+kvO4oQjiNoMpBeHl4sS58ne4oAuct\nchWB9cBB4ADwvN44rsdisfDl9i95tsWzuqMI4VQ8PDx4tsWzTN8xXXcUgfMWuUTgJaAecC/wDFBH\nayIXsyVyCwDtK7fXnEQI5zOk4RDWR6zn7NWzuqO4PWctcueAPcb9OOAQUE5fHNfz4/4fGdxgsBw2\nIMRd8PP14/6a97M4dLHuKG7PWYucWSDQBNimOYfLSEpJYvGhxQysP1B3FCGc1iP1H+GnAz/pjuH2\nvHUHyCE/YDHwAqpFd8ukSZNu3Q8ODiY4ODgvczm1deHrCCwWSLXi1XRHEcJpdanahcd+fYyI6AgC\niwXqjmNTSEgIISEhumPYlTP3RfkAy4GVwLR0r1lk99279/iyx2lYuiEvtXpJdxQhnNrTy5+mSrEq\nvN72dd1RssTYPOHMdeE2ztpd6QHMBEK5vcCJHLiRdINlh5fxUL2HdEcRwukNrD+QBQcX6I7h1py1\nyLUBhgAdgN3GrZvWRC5i5bGVNCzTUI6NEyIXtKvUjgvXLnD40mHdUdyWs26T24zzFmiHtuzIMh6s\nI+epFCI3eHl60a92P5YdXkbttrV1x3FLUijELRaLhdXHV9OtujSKhcgtXat3ZdXxVbpjuC0pcuKW\ngxcPks8rH9WLV9cdRQiXERwYzI4zO4i7GXfnN4tcJ0VO3LLq2Cq6VusqB4ALkYv8fP1oXq45IREh\nuqO4JSly4pZVx1fRtXpX3TGEcDldq3Vl9fHVumO4JSlyAoD4xHi2ntpKxyoddUcRwuV0rSbb5XSR\nIicA2HhiI00CmlAkXxHdUYRwOY0CGhGdEE1EdITuKG5HipwA4O/IvwmqHKQ7hhAuydPDk6DKQfx9\n8m/dUdyOFDkBwI4zO2hRvoXuGEK4rHvK3cOOMzt0x3A7UuQEFouF7ae3c0+5e3RHEcJltSjfgu2n\nt+uO4Xac9YwnIhf9d+U/CvoUpGzhsrees1ggOVndUlLA2xs8PdVNjjAQIvualW3G3vN7SUxOxMfL\nR3cctyFFzoUlJ8Px43DsGJw8CWfOwIULcPkyREdDTAxcvQoXy+zgaqUWFCkCN29CYqIqbJ6e6i+A\nl5e6b7GoguftDb6+kD+/uhUsqG5+flC4MBQtCsWKgb8/lCypbmXKQNmyULGiel0Id1I4X2ECiwVy\n4MIBmpRtojuO25Ai54K2boW334ZNm6BUKahRAypXhnLloGFDKFFCFZ+iRVVR+jR0O2WLtODVL1Th\n8vFRRc12OBfLAAAe20lEQVRWiy0lBZKS1O3mTUhIULf4eLh2DeLiVOGMiVGFNCoKwsNh+3Y4fx5O\nn4ZTp9R3VK8OdepA06bQoYPKJq1E4cpalG/BjjM7pMjlIXsWuQHAoiw8J3LZlCmwYwd88w1066aK\nWmaObN/BoOaTs9S68vRUhdDXV7Xc7obFolqTYWEQGqqyfvaZKsIbNqhWohCu6J5y97D99HZGNhup\nO4rbsOfiZBy3FzRbz4lcNmcOzJgBM2fCqFGq+7BmTahSRXUVli8PAQFQurTqRjx4IZQ6JernSbab\nN+HSJTh7Fs6dgytXVEvQ2kIUwpXVL12f+fvn647hVuzROdQd6AE8DCwwfUdhoC6QF/upy5XBDSkp\nEBmpWk3h4er+6dOq6/DiRTgfG8WJB6rg8UE0hQp6ULiwKoqFCqmWWoECkC+fuvn4qFaWl1fa7kzz\nTiqJiaqQ3bihitf166r7MjZWdV8mJKiWZUCAKrjVqkG9etCqlfor3ZXClZ29epZG3zTiwmsXdEex\nyRWvDG6PltwZYCfQx/hrHWGxwEt2+D6RCU9PtT2ucmXbr287FcboFdXZkehBXJwqRnFx6hYfr243\nbqhbYqJqbVn3uLTulGLd69LLSxVCa3dmgQKpO6MUKaJ2RClcWL1XCHcU4BdAfFI8MQkxFM0ve1/l\nBXsUub3GbT6QaIfPF7koLCqMGsVr4OmpClEROauXEHbj4eFB9eLVCYsKo3m55rrjuAV7rlO3BNYA\nYUC4cfvPjt8n7sKxqGPUKF5Ddwwh3Eb14tU5FnVMdwy3Yc8dT2YCLwK7gGQ7fo/IgbCoMLpWk8vr\nCJFXahSvQdjlMN0x3IY9W3LRwErgPHDJdBMOJOxymFwJXIg8VKN4DcKipMjlFXu25NYDU4GlwA3T\n87vs+J0im87GnaV84fK6YwjhNsoXKc/ZuLO6Y7gNexa5ewELkH7ragc7fqfIBovFwqXrlyhVqJTu\nKEK4jVIFS3HpunRq5RV7FrlgO362yAXXEq/hgQcFfe7y1CVCiGwrWbAkF69d1B3Dbdhzm1wAaueT\nP43HdYERdvw+kU3SihMi75UsWJJL1y8hJ6zIG/YscrOB1UA543EYcjC4Q7l0/RIlC5bUHUMIt1LA\npwDent5cS7ymO4pbsGeRKwksJPXwgURAzk7oQC5euyhFTggNpMsy79izyMUB5vPf3wvE2PH7RDZJ\nS04IPUoVKsXF61Lk8oI9dzx5BfgdqApsAUoB/e34fSKbridex8/HT3cMIdxOIZ9CxCfG647hFuxZ\n5HYC7YFaxuMj5N65LLsB0wAvYAbwQS59rltJSknC21Mu3iZEXvP29CYpRbbe5AV7LOE6AWuBB1HH\nyVmvQlDT+Ls0h5/vBXwJdAZOAzuA34BDOfxctyNFTgg9pMjlHXss4YJQRa4Xqsill9Mi1wI4BkQY\njxegLusjRS6bpMgJoYcUubxjjyXcROPvMDt8NkB5INL0+BTqigcim6TICaGHFLm8Y48l3CvG34yO\ndPwkh5+fpSMoJ02adOt+cHAwwcHBOfxa15OYkihFTggNvD29SUzRf7nNkJAQQkJCdMewK3ss4Qqj\nClEt4B7U9jIP4H5gey58/mmgoulxRVRrLg1zkRO2SUtOCD28Pb1JTNZf5NI3ACZPnqwvjJ3YYwk3\nyfi7CWgKXDUeTwRW5MLn/wvUAAKBM8DDwCO58Llux8fTh+uJ13XHEMLtJKYk4uvlqzuGW7Dnanxp\n0h4ykGg8l1NJwLPAKtSeljORnU7uip+vHxeuXdAdQwi3E3czDj9fOUY1L9izyM1FdU8uRXVX9gXm\n5NJnrzRuIgf8fP24evPqnd8ohMhVV29clSKXR+xZ5N5FXYGgHWob3TBgtx2/T2RT4XyFibsZpzuG\nEG5HWnJ5x957HexE7RSSH1XoKgEn7fydIov8fP2kyAmhgRS5vGPPEzT3Rl1e5z8gBHXwtnQxOhAp\nckLoIUUu79izyL0DtAKOAlVQp/vaZsfvE9kk2+SE0OPqzasUzldYdwy3YM8ilwhcMr7DC1gPNLfj\n94lssl6hWAiRd6y9JwW8C2hO4h7suU3uCurA8E3AfOAC6hpzwkGU9SvL+bjzclC4EHnoVOwpKhSp\ngIeHx53fLHLMni25PsB14CXUXpbHUCdtFg7Cx8uHkgVLci7unO4oQriNU7GnqFik4p3fKHKFvYqc\nN7AcSEZ1W84GPgcu2+n7xF2qWLQikTGRd36jECJXWFtyIm/Yq8glASlAMTt9vsglFYpUIDJWipwQ\neSUyJlJacnnInhtirgH7gTXGfVDHyj1vx+8U2VSxSEVOxd52fmshhJ2cij1F44DGumO4DXsWuaXG\nLUuXxhF6VChSQborhchDkbGR9KoluyfkFXsUub5ABeBL4/F2oJRxf4wdvk/kQDX/aoREhOiOIYTb\nCIsKo5p/Nd0x3IY9tsmNQV1DzsoXdXxce2CUHb5P5ECDMg3Yf2G/7hhCuIVrN69xOvY0NUrU0B3F\nbdijyPmS9vyUf6P2qjwJFLLD94kcqOpflcvXLxOTEKM7ihAuL/RiKLVK1pLjUvOQPYqcf7rHz5ju\nl0I4FE8PT+qWqsuBCwd0RxHC5e2/sJ8GpRvojuFW7FHktgEjbTz/NHLuSofUoHQD9p3fpzuGEC5v\n3/l9UuTymD3azC8BvwKDgF3Gc01Rl9vpa4fvEznUsExD2S4nRB7Yf2E/3at31x3DrdijyJ0HWgMd\ngXqoQwiWA+vs8F0iFzQo04DFhxbrjiGES7NYLOw/v58GZaQll5fstfXTAqw1bsLBNQlowp5ze0hM\nTsTHy0d3HCFc0omYE3h5elHWr6zuKG7FnidoFk7Cv4A/1fyrsfPsTt1RhHBZGyI20L5ye7n6QB6T\nIicACKocxMYTG3XHEMJlbTyxkaDKQbpjuB0pcgKQIieEvW08KUVOBylyAoB2ldqx+eRmklOSdUcR\nwuWcuXqGqPgo6paqqzuK25EiJwAo41eGAL8AOZRACDvYdGIT7Sq1w9NDFrl5Tca4uCWochAbIjbo\njiGEy9lwYoN0VWoiRU7c0rVaV1YcW6E7hhAuxWKxsCJsBV2qdtEdxS1JkRO3dK3ela2RW4lOiNYd\nRQiXsff8Xrw8vahfur7uKG5Jipy4xc/Xj6DKQawMW6k7ihAu49fDv9K3Vl85Pk4TZyxyU4FDwF7U\nlceL6o3jWvrW7suyI8t0xxDCZSw7soy+teW0vbo4Y5FbjTonZiPgKDBWbxzX0qtmL/489ic3km7o\njiKE04uIjuBU7ClaV2ytO4rbcsYitwZIMe5vAypozOJyyviVoV7peoREhOiOIoTT++3Ib/Sq2Qsv\nTy/dUdyWMxY5s+GA7A6Yy/rW6suSQ0t0xxDC6S09tJQ+tfrojuHWHPUa7GuAABvPjwN+N+6PB24C\nP9r6gEmTJt26HxwcTHBwcK4GdGWPNHiEhl83ZFq3aRT0Kag7jhBO6b8r/3Hw4kG6Ve+mO0qGQkJC\nCAkJ0R3Drpx1d59hwJNAJyDBxusWi8WSp4FcTbd53RjacCiDGw7WHUUIp/S/9f8jJiGGz7p/pjtK\nlhl7gDprXbDJGbsruwGvAX2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"text": [ "<matplotlib.figure.Figure at 0x7fb6e5d2b550>" ] } ], "prompt_number": 23 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Marginalising over weights\n", "\n", "While it is interesting to see the distribution of weights used by the Bayesian linear regression, the ultimate goal is to predict the outputs $\\fq$ corresponding to inputs $\\xq$. To do this, we *marginalise* out the weights $\\W$:\n", "\n", "\n", "\\begin{equation}\n", "p(\\fq | \\xq, \\Xf, \\y) = \\int p(\\fq|\\xq,\\W) p(\\W|\\Xf,\\y) d\\W\n", "\\end{equation}\n", "\n", "We know from above that:\n", "\\begin{equation}\n", "p(\\W|\\Xf,y) \\sim \\norm ( \\mu_w, \\Ainv )\n", "\\end{equation}\n", "\n", "We have also defined a deterministic mapping to the predictive function:\n", "\\begin{equation}\n", "\\fq = \\xq \\W \n", "\\end{equation}\n", "\n", "This marginalisation has a closed form solution - we can produce predictive distributions, considering all possible weights, without explicity computing them:\n", "\\begin{equation}\n", "\\fq \\sim \\norm \\left( \\invnoise \\xq \\Ainv \\Xf^\\top \\y, \\hspace{0.5cm} \\xq \\Ainv \\xq^\\top \\right)\n", "\\end{equation}\n", "\n", "Note that this equation gives a multivariate Gaussian prediction: for $m$ queries, the covariance will be $m \\times m$. This is useful for joint draws. However, if you are only interested in the envelope of uncertainty, then the diagonal can be extracted to give the marginal variance of each prediction. Don't forget to add the noise variance back on if you want to predict the range of possible *observations* rather than *latent functions*!\n", "\n", "### Exercise:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Make predictions directly from the data-------------------\n", "nQuery = 1000\n", "xQuery = np.linspace(-1, 11, nQuery)[:,np.newaxis]\n", "thetaQuery = polyFeatureGen(xQuery,p)\n", "\n", "# lets make this a reusable function\n", "def linreg(theta, y, thetaQuery, alpha, beta):\n", " I = np.eye(theta.shape[1])\n", " A = beta * theta.T.dot(theta) + alpha*I\n", " mu_w = beta * linalg.solve(A, theta.T.dot(y))\n", " f_mean = thetaQuery.dot(mu_w)\n", "\n", " \n", " #f_covar = thetaQuery.dot(linalg.solve(A, thetaQuery.T))+1/beta\n", " #f_std = np.sqrt(np.diag(f_covar))[:,np.newaxis]\n", " \n", " # Advanced challenge: use cholesky factorisation to compute the marginal variance directly\n", " L = linalg.cholesky(A, lower=True) # Factorise A for faster computation\n", " f_std = np.sqrt( np.sum( linalg.solve(L, thetaQuery.T )**2, axis=0) + 1./beta)[:,np.newaxis] # try with and without noise variance\n", " \n", " #f_std = np.sqrt(np.diag(f_covar))\n", " return f_mean, f_std\n", "\n", "f_mean, f_std = linreg(theta, y, thetaQuery, alpha, beta)\n", "\n", "# plot the mean, and +- 2 standard deviations\n", "pl.figure(figsize=(5,5) )\n", "pl.plot(x,y,'k.')\n", "pl.plot(xQuery, f_mean, 'b')\n", "pl.plot(xQuery, f_mean+2.5*f_std, 'r')\n", "pl.plot(xQuery, f_mean-2.5*f_std, 'r')\n", "pl.xlabel('Input x')\n", "pl.ylabel('Output y')\n", "pl.title('Predictive Envelope with Data and Mean')\n", "pl.show()\n" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x7fb6e5cf2dd0>" ] } ], "prompt_number": 24 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise: Non-linear Basis Functions\n", "\n", "We have already been using simple linear basis functions, but lets try a demo with a non-linear problem. Don't worry about visualising the distribution over weights - with many polynomial features they will be too high dimensional to show, but we will get very interesting predictive marginals:" ] }, { "cell_type": "code", "collapsed": false, "input": [ " # Generate Noisy Sinusoid Data ---------------------------------\n", "nSamp = 30\n", "noiseSTD = 0.1\n", "xdomain = 10\n", "x = np.sort(np.random.random(nSamp)) \n", "x = x-np.min(x)\n", "x = x/np.max(x) * xdomain\n", "y = np.sin(x) + np.random.randn(nSamp)*noiseSTD\n", "x = x[:,np.newaxis]\n", "y = y[:,np.newaxis]\n", "\n", "\n", "pl.figure(figsize=(15,5) )\n", "pl.plot(x,y,'k.')\n", "pl.xlabel('x')\n", "pl.ylabel('y')\n", "pl.title('Noisy observations of the target function')\n", "pl.grid()\n", "pl.show()\n" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x7fb6e5c651d0>" ] } ], "prompt_number": 25 }, { "cell_type": "markdown", "metadata": {}, "source": [ "This data clearly has a non-linear pattern. As we saw in part 1 of this tutorial, linear regression techniques can perform well on non-linear data by introducing basis functions. Lets apply the polynomial basis functions you implemented in polyFeatureGen to increase the dimensionality of the inputs to $p=7$ and run the regression again." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Bayesian linear regression - prediction with polynomial basis functions\n", "# Define prediction locations -------------------\n", "nQuery = 1000\n", "xQuery = np.linspace(0, 10, nQuery)[:,np.newaxis]\n", "\n", "# transform into feature space (now n by p)\n", "p = 7 # n features...\n", "theta = polyFeatureGen(x,p)\n", "thetaQuery = polyFeatureGen(xQuery,p)\n", "\n", "# Define a prior\n", "alpha = 0.7**-2 # per-weight inverse standard deviation - chosen automatically using strategy in final section\n", "beta = 0.1**-2 # noise inverse standard deviation - using the real value for now\n", "\n", "f_mean, f_std = linreg(theta, y, thetaQuery, alpha, beta)\n", "\n", "# plot the mean, and +- 2 standard deviations\n", "pl.figure(figsize=(15,5) )\n", "pl.plot(x,y,'k.')\n", "pl.plot(xQuery, f_mean, 'b')\n", "pl.plot(xQuery, f_mean+2.5*f_std, 'r')\n", "pl.plot(xQuery, f_mean-2.5*f_std, 'r')\n", "pl.xlabel('Input x')\n", "pl.ylabel('Output y')\n", "pl.title('Predictive Envelope with Data and Mean')\n", "pl.show()\n" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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6NNChg9unP3eONW6zZrHmrX17L3hf9GEOHmQG0r//cs9Rt66bJk5MpHNe6dI8\n+VbJideRVSGov6Avk5TEcNuGDcCqVR4VgatXAw8/zNTPjh2ZfdKrV+4SgQB7GfboQafR99+nA1rd\nusCMGTzJ8xh58rAW5M03gYce4mtGCCFEzmfjRtYDfvONR0TgvHnszxsfz/fGDh0kArPLLbcAv/7K\n7N4nnuDbu1v2GAUKUM3/+y/3E7k1CJODyCn/irkvInjxIl1BU1L4T+khxbVjB9CnD1s/DBpEEZg3\nr0eW4pVYFgu8Bw3in2zoUBq6evRNcMECVpuHhACtW3twIUIIIWxl7Vpe5ydOZEGeGzl+HHjtNe4P\nvvuO7aGEef79l14uAIN0Vaq4YdKzZ4FHH2XK06efumFCkVkUEcwNxMbS8atECR61eUAERkdTSzz+\nOG+7d7OOWCLwcvz8gObNgXXrKAY/+IC1g7/95sFFtWjB9NBevVhXKoQQIucREcE0vp9+crsInDuX\n2TC33UYzNYlA+6hcmX2PW7SgKd/s2W6YtEQJliNNn85cX+GzKCLoaxw7RhEYGMhcfzfnZyclsf5t\n2DDWmvfpw+vBlXhzbx5PkpIC/PgjeybVr8/rp8dcRvfvp3FAp07MYVWujhBC5AzCw4Hnn+dG/dFH\n3TbtuXM0L1+1iu91Dz7otqkF2DUqKAho1YrporYbyRw6xLqgTz6hKYTwOIoI5mQOHQIeeYTdzL/8\n0u0icPlyoE4dfvztN4rB9EQgAERFRSEyMhJhYWEIDg526zq9mbx5GUndvZvP5T33AAMHsnbC7VSt\nSlvXOXOY6+9weGARQgghjBIWRhE4Z45bReCqVXxfy58f+PNPiUBPEBDANhP79zP76OBBmyesVAlY\nupTOeHPn2jyZsAMJQV/hn3/4X92tm9ujNydPsvn7K6+wxm3RIqBGjav/ThFnumpAQABCQkLcsErf\nonBhHqD98QfrJ+64g9dQtwe2y5dn+tCffzK3NzHRzQsQQghhjCVL2C5o/nygYUO3TJmSwvez9u2B\nr79mPWDRom6ZWqRDyZLcT7Rpw1TRZctsnrBGDb7eXn2VdTDCp8gpuWA5OzV0zx4W4vXuzcprN2FZ\nzCp56y0eLg4alPlyRF/rzeNpli1j78FbbwXGj+chm1uJj+e7eGIizYduuMHNCxBCCJEtli2jY9vc\nuW4Lx0VHc8r8+YEpU67eGF64n4gIvrV/9BFtAWyNIYSFMeVp1aprRwuEbaiPYE5j507WcQ0Y4NYG\nsNHRbINp7RTKAAAgAElEQVSwdy/www/A/fe7bepcS2Iic/rHjKEJV7dubs7+TU7mid7u3cDChTxW\nFEII4f2sXEkn8dmzWbPlBpYuZQerHj2Avn1lFuet7N/PmsEGDRixtbVu8IcfgMGDWT9UvryNE4mM\nUI1gTmLrVqBx41RnFjcxZw5Qrx5z/f/4QyLQXRQowDT7yEgW2QcGAlFRblxAvnypqv/xx5kTLIQQ\nwrtZtQp47jk2rHWDCExO5ntVly5AaCirVSQCvZeqVanLjh/nljImxsbJunRhanKLFkBcnI0TCVNI\nCHormzYxEjhmDAv03MD580DXrmxxMG8eo1IFC7plapGGO+6gh0vbtszuGTnSjT4ufn60Mn3qKSrR\nY8fcNLEQQogss3Yt8OyzwM8/u6VHw6lTfHtYvx7YvFltIXyFYsWAX35h2eiDDwL79tk42SefMJrw\n3HO0mhdejYSgN7JhA5vPhYTQB9gNrF/P/1sA2LIFeOCBrI8RHByMwMBANGvWDLGxsWYXmMvIm5c1\ngxs3UpQ3bkzTWLfg5wd89hkLCxo1Ag4fdtPEQgghMs369WwWP2UKszhsZutWtj2qW5ct5FQP6Fvk\nyQMMGQK89x4F4e+/2zSRnx97FPv5Ad27e8AFT2QF1Qh6Gxs3UgROmsSPNuNw0Al07Fj+37Zpc/1j\nBQYGIjIyEgAQFBSEGTNmGFpl7iYlhbWDo0fz79S+vRsnHzkSGDeOPUNuvdWNEwshhMiQzZsZmvvf\n/4BmzWyfbvp0mo2MHQt06GD7dPZy6hT9Fw4coCGC63byJHDhAm8JCRQyefOybKJoUcDfn7XzN97I\nBsCVKwNVqgC1avmcwdqvvzIDzNatZlwcQ8Zt2wIffmjTJOJKZBbjy2zezAv6hAnA00/bPt2pU+wY\ncP48s0puuil74zVr1gxhYWEICAhAeHi43EINs3kz3VsDAljw7ban95tvgOHD6UgnJzAhhPAsO3cC\njz1Gi+lnnrF1qpQUGsHMmMHUwrp1bZ3OPEeOMH32t9/Yq+mvv+iSfccdLJ6rWJGbn4oVgTJlKOhu\nuAEoVIiRrJQUFkXGxQFnzvB2/Dgb9P37L51YoqL4+7VrM2T60EP8WLiwpx/9VdmwgS+fQYNstKGI\njmaK2ejRTGEWtiMh6Kts2QI0bcp00FatbJ/u99+Zvh0UxFSB/PmzP6ZaRtjPxYtM61i8mG/MAQFu\nmnjiROb9L1kC3HWXmyYVQghxGfv3A488QhM5m/0D4uIY/btwge83ZcrYOp0ZTp3iG+TixSy2P3+e\nRXEPPsj6lzvuAG6+2WwfheRktvnavp3qas0aYMcOzvfUUzzgr1vXrf2fM8uePVxi585sMWHLErds\nAZ58kk2o3bZpyb1ICPoiW7fyn2TcuOzlZmYCy+I0Awey6Wvr1rZOJ2xi5kygZ092FenZ003vL1On\nUoWGh0sMCiGEuzlyhCLw3Xd54beRw4eZmHTvvSwbMXFYbBt79/JNccECirFHH6X4atQIqFnTMwLs\n4kVg9Wr21gsLoyBt25YtPho0cHNvqKtz7BjwxBN8yoYNs+npmjuXucXr11OIC9uQEPQ1tm2jO+jX\nX/MiYSPx8UBwMA+qZs4Eqle3dTphM3v2MKpbowbw/fdA8eJumDQ0NFUM3nmnGyYUQgiBkycpAjt1\nsr3eassWoGVL4PXXgfff98pAFtXL9Ol8TzpwgOlNTz9N8VeokKdX91+iohhWnTaNorBTJ/btrVzZ\n0ysDwEBq06bsHjV2rE06dcQI/r1Wr2bNpbAFCUFfYscOHsN8+SVPiWzkyBHmgt92G7P8vDx1/eok\nJvJNIDoaOHqUt/PnqXRdN4C9L1y3YsVocVa+PD9WrOjGIjv7SEgA3noLWLGC4r5OHTdMOnUqdwfL\nljHNRgghhH2cPUtX0CeeoLubjfz6K1vBjR/vhSVdDge72I8bRzHRsiXQsSOfm3z5PL26zGFZjFpO\nnEi31wYNgB49qMI83Izx3Dkax1SvzsNl40+pZVH8xsSwYbWaT9qChKCvsHMnewKMGmW7BdeGDcDj\nj5/BjTfOQM2a8zBtWqhv1PA5HMDu3Sxo3LkT+Ptvfjx0CChblmKuQgWKuxIlqG5dNwC4dIm3hARe\n4Y4fT70dOcL71azJ2+23M3f93nspGn2MqVMpCL/+2vYzBfLTTzyVXr6cz50QQgjzXLxIkXD33bzA\n2xSesyy2LR4xgqYw991nyzTXx6lTwA8/UJ36+zMttn17n3Pq/A8XLzKq+e23fIy9e7MZuwcbOF+4\nwJIhf3/q1AIFDE+QmMhSqIAAvtiEcSQEfYFdu3iCNXy47cXeP/7ITL5y5fpgx45hALy4tUNKCkXf\n8uXAunW8lSzJXIU772T0qVYtoFq17BcsWBYjibt387ZzJ7BpE+s1q1blnI8+SrHuI82S/vyTF/D2\n7YHBg91w2DZ5Mu3kVqygmBZCCGGOxESax5UtyzYRNtWVORxM8li8mH4eXpKtyEPfUaO4kXn6aeC1\n1+jG6ZW5qtlk9WpGe7duBd55B+jWzWPpkwkJPFD282OmkfH60NOn6ST6wQc22pXmXiQEvZ29e9lX\n5bPPePJjEw4H0KcPMHs2MH8+8N57Xtra4fRpvvMsWsSUj4oVWTP50ENMmShf3r3rSUxk2sa6dRQ4\nK1fyXbFJE74RPfigV6czxMSwbrBwYabi2/5nnjQJ+PhjPldqLSGEEGZISWG2UFISd+M2pT4mJjIV\n9MABpoWWLGnLNFlj1y4elM+fz8W99Vb2+1v5Clu20LElIgLo14+C0HhY7tokJrLsMn9+ljUaF4NR\nUcDDD3OT2rCh4cFzNxKC3syBAyxk7tePedI2kZBAjRkdTaOm0qW9rLXDhQu8wIeGAqtWMfLWvDk9\njL3NTSo5mVHKpUuZLxMTQ2fXtm15EfNCUZiUxChwWBgwbx6DqLYycSLtS1esYBGqEEKI68ey6Ox2\n4ACdMG1KFYyLYx1goULsJexx74ADB/hesmgR8MYbjAB6hTL1ANu2MVV0zx72+AoKcnsk9NIlbneK\nFmX5ifGziCVLgJdfZv1SpUqGB8+9SAh6K4cPUwS+9RatuGzi1CmawlSsyMw9rzHPsizaBo8fT3XS\noAGLvJ95xrdq8qKigFmzeEIbE8PmO126MJ3Uy5g0ie8jkyZRZ9vK99+zK+3KlUzdFUIIcX189BHN\nuJYvty09MCaG7wu1a/Nt2aNeK8eOsZ5h2jS2GHjnHdb9C74GPviAf6BvvnF7H76EBG7TSpWiNYDx\ns++RI/l3X70aKFLE8OC5k6wKQe9pZJKTOXaMNYHdu9sqAvfvZ+Zigwb8v/IKEXjhAkXCPffQLrl2\nbYqpsDDgxRd9SwQCTH/s25fpG4sX8/G56glDQxmO8xJefpmB11dfpceArbz6Kp+Xxo1Z1yGEECLr\njB3LdLmFC20TgQcOMBuvSROb3CEzS3w8BeBddzHquWsXmxxLBKby+OPAxo00yGnRgkI5NtZt0xcq\nlJoM9fLLzFg2yrvvMm3p1VcZMBBuRxFBuzl5kjWB7dqxlsomfv+dpzb9+tneZzZzxMTQgmz8eL7j\n9OxJkeBFTVSNcekSFde4cUzj6NWLaT2lSnl6ZQCAf/7hyW+TJqy7tzWbddQoICSEKb8+YrIjhBBe\nwc8/07VlzZprOrYEBwcjKioKRYoUQWhoxk7gV94vOtofTZowW8TGc+mrY1kUu++/T6fuESOAW2/1\n0GJ8iNOneeA6fz6fs44d3ZYuevEidWjVqjw8MDptfDz3ie3b8zUhskVWI4I5BcsrOX3asurWtaw+\nfSzL4bBtml9/tawyZSxr/nzbpsg8//5rWa+/blklS1pW9+6WtW+fp1fkXrZssaxOnSzL39+yeva0\nrAMHPL0iy7L4UnzsMctq2dKy4uJsnmzgQMu6+27LOnXK5omEECKHsHSpZd14o2Vt356puzdq1MgC\nYAGwgoKCMnW/xo3ft8qXt6yffjK16Ovgr78sKzDQsmrXtqyVKz24EB9m3TrLqlPHslq0sKzoaLdN\ne/68Zd1/v2W9+64NW9qDBy2rQgXLCgszPHDuw/n/nmlyYHjGSzh3jr1/XA6hNp3aTJlC992FC2lq\n6TGOHWNhd716zCX46y/2xvHC2jlbqVuXxZk7dwLFizMl1lX070FKlmQ2bqlSwCOP0EjINj7+mK/9\npk35fyCEECJjNm4Enn+eUbK77srUrxRx1lMFBAQgJCTkmve7/faXsXXrMHzzje1dq9InIQHo359e\nCW3bAn/8wf2RyDoPPMA0sHr1uOeYPt0t0xYtSh+fJUvY6cIolSoBM2bQ6TAqyvDgIjfgaQF+OXFx\nltWwoWX16GFrJHDMGMuqVIkHbB4jNtay+va1rFKlLOuddywrJsaDi/FCTp5MfX66dmXE1IM4HJb1\n2WeWdcstmT54vv6JevTg/4HtIUghhPBRdu+2rPLlLWvevCz92pkzZ6ygoCDrzJkz17xfo0b9rdKl\nU6yFC7Oz0GwQGWlZNWtaVuvWlnX4sIcWkUP5/XfLuv12y3ruOe433EB0tGVVq2ZZ48bZMPh331lW\nrVqWdfasDYPnDpDFiGBOySF1PnYvID6eidSVKwMTJthSE2dZdFieNg0ID/dQ89fkZEb8Bg1iKLJ/\nf+CWWzywEB/h9GnWz40fz6LoDz90Q5O/jAkNBd5+O/MtfDJbj3IZDgcdVaOj2aDKJgt0IYTwSaKj\n2TO3Xz+ga1dbpli8mD5t06fT08ytnD2b2svoq6+A1q3dvIBcQnw8nWZnz+ab+0MP2T7l/v3MLhox\ngu0ujdKzJ532587Nmb4SNiPXUE9y6RKb8pQrx2paG17ADgfb68yfT7ddj4jAiAimJMydy95xEydK\nBF6LUqWYIrxtGw2EatQAvvySXVs9QMeOtIJu04avpWsRFRWFyMhIhIWFITg4OHOT5MnDwxB/f5ol\neZGjqhBCeJTYWKbPBwfbJgJ/+YWZdvPmeUAERkQAderwfeCvvyQC7aRwYeCLL9he4tln2ZDe4bB1\nyqpVecjw9ttMFzXKl1/yEGHgQMMDi/RQRNAUSUls+Jk3L4/ebPBjTkqife/BgwywuN1h+fBhWv1u\n2MDoVps2bm9wmmPYsYO9gfbvZ2+Hxo09soyNG4GWLengfbW9SLNmzRAWFoaAgACEh4dnLiLoIjGR\nr5XixW1qRCSEED5EfDzw5JM8UP3yS1veR0ND+Xa9cCFL1d1GQgKjUz//zAPxZs3cOLnAoUMM0RUt\nyvfbsmVtnW79eu4hFi4E6tc3OPDx4+yZ+O23zLITmUYRQU+QnMzq6+Rk5mvaIALj47mXjo3lKYxb\nRaDDwZTGevWAmjVphPLssxKB2eGuu3iMNmIEU0XbtaPQdjP16wORkRSCQ4dm3MYnNDQUQUFBWReB\nAFCgADBzJi/s3burV5AQIveSksKUjJtvBkaPtk0Evvcee9K7VQRu2cJ2EAcPAlu3SgR6gkqVgJUr\nuV8LCKApj4088AATf1q1AvbtMzhwuXI0j+nSBdi71+DA4kpyyk7ecxFBhwPo3JmumfPn29LF/cIF\n/pOVLQv8+COQP7/xKTJmzx4KlYQEpoDeeacbJ88lXLzIVI5x4xglfOstiic3Eh0NPPUUTdxGj7Yp\nLT8ujpHPwEA+XiGEyE1YFpv3/f036+ZsuM5Pm8ZIYHi4G9+uLYupicOH8w3Ejf3txFWYPZuHr2PH\n2lDIdznjxzNR7LffDAchv/2We6P164EbbjA4cM4lqxHBnPKf6hkhaFlAt27A7t28qDttmk1y/jyj\n4lWqAD/84MasuuRkXtCHD2eaxxtvKKXPbvbu5SYhOhqYNMnNR7mMNrdsmfpasyGwDZw6xQrzzp3V\nOFYIkbsYMYLpeqtX25LWM306zxHDwzPdhSL7nDrF63lMDNNBq1Rx08QiU2zfDjzzDLO4hg61dR/3\n0UfA8uW0jjC2HbYs1kQlJgJTp+qAIRMoNdRdWBbw5pus9VqwwBYR6GpFWKMGdYHbdNj+/cDDDzMH\n9fffWQ0sEWg/1aszXfTdd/mH/+gjGhC5CX9//smPHwfat7fJx6Z0aWDpUp7wTZxowwRCCOGFTJtG\n58xFi2wRgTNmUAQuXepGEfjbbzywrFkTWLVKItAbuftu7uM2b2ZU4fx526YaPJgvhQ4dGEswgp8f\no4K7djGyKYzjaSHYFMAuAHsA9E7n54EAzgLY4rz1c9vKroZlAb178yK4aBFQrJjxKc6cAZ54gr1C\nv/vOTQ66lsXc0/vvZ81aeHjuawjvafz86PW9dStrMevVozmPmyhShBnOKSk8RIyPt2GSm25iR9qP\nPwbmzLFhAiGE8CIiInhwvHAhawMNM3Mmk3aWLOG+33YcDmYLtW5Ns7ORI91eziCyQOnSfHFUrsxD\n/iNHbJnGz4/+QBcvMrnJWKJe4cJMcx06lAcOwiiejLHmBbAbQGMARwBsBNABwN9p7hMI4B0ALa8x\nlvtSQy2LPeCWLmUldunSxqc4dQpo0oQZdF984aZI+JkzQI8ejHCGhgK1a7thUnFVLCs116dLFzaP\ndNObbVISs32ioykMbTjroLHAk08yneixx2yYQAghPMxff7F3g03Xudmzgdde4z6/Th3jw/+Xc+d4\nWHn8ON+f1DrKd7As4PPP2WZiwQLb9nnnzgGNGjGzqHd6IZ7rZelSpolu3AhUrGhw4JyFL6WG3gdg\nL4ADAJIA/AygVTr3856EYMsC+vSxVQTGxPC9onFjN4rA1asZerzxRv6DSQR6B35+vJJu3crbQw8B\nUVFumTp/fgaHb7uNkekzZ2yYpF49HmW3b8/XnRBC5CSio+mc+cUXtojAOXMoAhcvdpMI3LWLGUMV\nKtBuWiLQt/DzozL7/HNuMpcutWWa4sWpM7/6ir0sjdGkCZvNt23rsR7MORFPCsGbABxK8/Vh5/fS\nYgF4EMBWAIsA3OGepaWDSwQuXmybCDx2jIaKLVvSVNF2EWhZTOkICmLN1tixDMEL76JcOV5VO3em\nGJw40S0tGPLmZVpygwY80D5xwoZJGjWi9/TTT3OTIYQQOYFz5ygCu3dneynDzJ/PJJ6wMJ7j2s78\n+UxTevdd1mwpFdR3ad+eoeQXX2Sk2gZuugmYNw8IDjbcwaJPH9qSvvOOwUFzN54UgpnZyf4BoBKA\nOgC+AjDX1hVlhGUBffvaKgKPH+dmu107YNAgN4jAs2fZmHDmTBYSN29u84QiW/j58eg3IoKCvW1b\n4PRpt0z7xRfUaY0aAUeP2jBJy5Y8oXzySfafEkIIXyYpidfoBx5gKYlhli0DXnmFJYf16hkf/nIc\nDpYlvPYa8OuvnFj4Pg8/zBfSe+/xxNcG7r2XQ7dqZbAsMU8epistXcqPItvYYRCfWY6AIs9FJTAq\nmJa09kZhAMYBKAXgPzvgAQMG/P/ngYGBCAwMNLNKy6J746JF9MUtU8bMuGlwpYN26AB88onx4f/L\n1q18k3LVZxUs6IZJhRHuvJPmMR9+SLe2GTOA++6zdUo/Px5OFCjAw4qVK5kZZJROnVKLY1evNtyI\nSAgh3IRlsfduwYI0UjF8qrtmDfcKc+awX7itXLzIqNHx40zfL1/e5gmFW7n7bqb4uuo/DB9aBAcH\nIyoqCkWKdEbz5i9g7dp8ZloBlijBnNPAQD4G209DvJuIiAhERER4ehnXRT4A+wBUAVAAwJ8Aal1x\nn3JIrRG8D6wnTA/LFhwOy+rb17Jq17asmBhbpjh5ksP362fL8P9l8mTLKlPGsqZOddOEwjZmz+bf\ncuxYvlbdwKBBllWzpmVFR9s0wUcfWda991rW2bM2TSCEEDbyySeWFRBgWXFxxofetMmyypa1rCVL\njA/9X6Kj+ThefNGyEhLcMKHwGIcPW9Ydd1hW795G9xKNGjWywOw/q3LlCKtNG8tKSTE2vGVNn25Z\nt95qWadOGRzU90HmMi7/H0+mhiYD6AVgCYCdAKaDjqHdnDcAaAtgOygSvwTQ3m2rc7mD/vqrbZFA\nV4uIp54CPv3U+PCXk5LCFIBPP2V6YceONk8obKdNG2DdOnZ/b9eONSk2068fy10efdSmNNFBg4D6\n9dm7wo09FIUQIttMmABMmcKabiOhj1T++osVHCEhTJywla1baQrzzDPA5MnKGsrp3HQTI4PLl7Pw\n1OEwMmwRZ3/tgIAAbNhQBydOcA9hjOeeYwuT55/nHldcF97jyJk9nCLYEA4Hm/KsX09PZhtqAmNj\nKQIfeYR+LbbWBJ49y+LgpCSmEpYqZeNkwu3Ex7NHVWQkC8Dd0E34s8+Ynr9ypQ0uzikpFLZ58rAJ\nc968hicQQgjDhIXR2n7VKqBGDaND793LGu3PP+ee11YWLGC7oq+/5kZb5B7On+dpQ40aPHHIZgPr\n2NhYBAcHIyQkBP7+/jh5kucLn3wCvPSSoTUnJ9MB9ZFH3BBR8Q2y2j5CQvBKkpOZ3793Ly+IJUqY\nGTcN587xRO/++4Evv7RZBO7ZQ6ePJk2AUaPYF0DkTCZPZtQ3JISnZDYzZAintEUMJiQATZtS1H71\nlZv6qAghxHWweTOvV/PmAQ8+aHTogwe5x+3blw6MtmFZNCIbPpz1V/ffb+NkwmuJi6PbrSExeCV/\n/81DjV9+oQm6EY4fZ8HsuHHc7+ZyJASzQ2Ii897OnAHmzs1yakdqYWwRhIaGwt/f/z/3iYvj+0Xt\n2uzpaev+dtkyHh8OHkxxK3I+GzcyZfSVV4CPPzZ+Eb8SW8Xg2bN8xwgKomGTEEJ4GwcOcEf71Ve8\n9hrk2DGKwB49gLffNjr05TgcPERcupRWpJUr2ziZ8HpsFoOLFnFLumEDcPPNhgZdv54O5L/9BlSv\nbmhQ30RC8HpJSOCG08+P6ZOFCmV5iMDAQERGRgIAgoKCMGPGjMt+fuFC6v/Wd9/ZvEefNIn9VqZP\n52Za5B6OHgWefZbWnpMnA0WL2jrd0KF8ua1cyVIDoxw9yk3WRx8BXbsaHlwIIbLB6dOMAPbsyXIS\nw0MHBtLg21Y38UuXmNJ6+DAjmiVL2jiZ8BlsFoPDh7OSZdWq69pup8+33zIquH698RpdXyKrQtCT\nZjHeQ2wsWynccANfmdf5qkxbGBsSEnLZzy5eZMS6alWbRaBlsefPoEGsGZMIzH1UqEBV5u/PbvD7\n99s6XZ8+3Ec8/jgzNIxSoQL7d/brx4bGQgjhDSQksEFaixbGRaBrD/7kk0zssI1z51gTlpBAPwSJ\nQOGiaFGG7qKimJNsyEDGxQcfANWqcWjLYkZdYGAgmjVrhtjY2OsbtHt3Ni989VUOKjKFIoKHD9O2\n8/HH2Tk7GwrtysJYFwkJjFiXL8/IiW3eF4mJ/K/auZNup+XK2TSR8Aksi/nHgwe7JTLcvz/z/leu\ntMFfaeNG7ozmzjVYWCCEENeBw0EDNj8/GloZPNlNTOSh8c0304TUtvKRY8d4Tb3/fhrDyJRLpEdc\nHE8kAgKMm1pcvMi3806dgM8/r4Bjx44BAFq1aoW5c+de36Dx8Rz0pZdoopcLUUQwK/z1V+qrcPTo\nbF/M/f39MWPGjMtE4KVL9O0oXdpmEXj2LC/qp09zJy4RKPz8gF69aGceFMSPNjJgAOtfn3ySQXaj\n1K/P9bdpw/9bIYTwFO+/TyE1ebJREehwcP9auDAzh2wTgXv2cO/TujVT6SQCRUYULcq60VWreNpr\nkCJFeLb7+edAXNwD//99v+y88AsXZmbfkCHA6tUGVpnzyb0RwdWrmXw/ahQNYmwgMZGlWoUK8dAw\nXz5bpgEOHaIIbNQIGDNGF3XxX1xNqLp0Ya6RTTsMy+Ih3KZN9B0wXp44dSpzUdeuBSpVMjy4EEJc\ngzFjgPHjeQ0y2IrJsphhum0bs+ELFzY29OVs2cL9wqefykROZJ4TJ+hc9OqrwLvvGh06MhJo0uQ0\nEhPro169ElixYkW6ZotZYskS7nc2bWKJSS5CZjGZYeJEejFPmcJmfjaQlMRWaJZF7xnbujbs2sUQ\nTK9edP2Szb7IiGPHmHN0550s/i5QwJZpHA6gWzd2YFm4kKd+RvniC+ZMrVmjnphCCPcxezbV2tq1\nQJUqRocePBiYNYubYhu6VpF161jXOG4cD8KFyAqHDwMPP8z9s+FDhBEjLmLw4JP466/iuPnmbIpA\nF4MH81RlxQrb9jveiITg1UhOZkrHwoWsoatZ05bFJCcDHTsyVXn2bBtff5s2cWM/ZAjdOoS4Fhcu\nsKXIuXPAnDk0lLGBlBSmOJ08SSO6ggUNT/D++7SJDg+3QWkKIcQV/PYbRdSSJcA99xgdevx4YMQI\n6svy5Y0OncqKFaxrnDyZvghCXA979zL7bNQovp4MYVnUlmfOADNnGsq4djiAZ57hoc3YsQYG9A1U\nI5gRsbF099qxg81LbBKBKSksOTx/nqd7tonAlSuZ3vHttxKBIvO4nHHr1KHt+b//2jJN3rzA//4H\nFCvGyHhSkuEJhg+nBW+7djx5EUIIu4iKYn3yTz8ZF4GzZtHke+lSG0XgggXctM+cKREoskf16jwM\nefNNfjSEnx+97Y4eBT77zNCgefIAP/4IhIXZ7pHgy+QOIbh1K3DffeyHEhZmm0VySgo1WUwMgy2v\nv27ADjc95s7lBnj6dJ52CJEV8ualOVK3bkDDhraZr+TLx5I+h4NluEb1Wp48wA8/cNBu3WQVLYSw\nhxMnKJ4++4xuWAZZvpwtCBcupJW+LUyfzh6sCxaonZQww1138UD5hReAzZuNDVuwIIf97jtu1Y3g\n788N+dtvUwuIHIuVLg6HZX33nWWVKWNZU6akfx9DpKRY1ssvW9ajj1rWhQv8XqNGjSwAFgArKCjI\nzEQ//GBZ5ctb1qZNZsYTuZupUy3rxhsta+1a26aIj7esJ56wrE6d+H9ilPPnLat+fcvq29fwwEKI\nXE9cHK8vH39sfOhNmyyrbFnLiogwPnQqEyZYVsWKlrVtm42TiFzLnDmWVaGCZe3bZ3TYNWu4LTE6\nbPaF3u0AACAASURBVGioZVWrZlmnTxsc1Dtx6o5Mk3MjgrGxrIX6+muaSjz/vG1TORxAjx5Mnf71\n19SSpas1mL8uRo2iR39EBJtmCpFdOnZkzcgzz/BY2gYKFWIQe/9+4K23DAfvXNbWs2YBX31lcGAh\nRK4mORno0AG44w5g4ECjQ0dFsVIlJMTGIN1XX9EZdOVK4O67bZpE5Gpatwb69WOkPCbG2LAPPcRh\n27Rhr0EjdOjAf7oXXuCmXfw/OVMILl7MC5+/v631gAA3ta+/ztLDhQtZguUiNDQUQUFBCA8Pz54V\nrmVRAH7/PUWtjY9H5EKaNuUJRteuzKe3gSJFOMXq1cb3VEDZsqxVGD6cFr1CCJEdXL0c4uOp1gy6\ncR85QqPvwYNtrOwYM4bp/6tWsSRGCLvo2ZN9ilu0oBmdIXr1osF59+4GD49HjKCBx6BBhgbMGeQs\n19CzZ9nfZNkytoh4/HGbJ2WEY8MGFnoXL27TJH37UmUuWwbceKMNkwgB4O+/KQrfeMN4nyAXJ07Q\nfbpnT9aaG2XbNqBxY+Dnn4HHHjM8uBAi1zB8OBAaypMrg2/sZ8/y+texI/Dhh8aGvZwvv2Q0cOVK\n4JZbbJpEiDRYFg0yTp0CfvnFWNPsCxeABg1oA/Daa0aGZButgAAe8DRrZmhQ7yL3uoZOnAjcfjtf\ngNu2uUUEuhzsFy+2UQS+8w5V5sqVEoHCXmrVYsR54kSgd29bDFhuvJEdH774ghmpRqldm6547duz\nabIQQmSVadNoX7hwodE39kuXmEnXqBEvr7YwerREoHA/fn7MWEtKYp2Uob3DDTfQ52XgQO61jVC+\nPDOHXn4Z2LfP0KC+Tc6JCD70ENMh3FA75wrSLV5M1y9belo7HIyNb97MtDeb+r0J8R9OnaJLXkAA\na2yNNPS5nF27gEcfZfcT4+lRtOxlWpRtVnxCiBxHZCTT3JYvN1pX53JOTkjgWVXevMaGTmX0aF6v\nJQKFp4iLY8i7Qwfggw+MDbtgAVNEN20y2GLl66+BCROoMHNYL+Lc21De4TCax381PvmE5hcrVwKl\nS9swQUoKEBwM7N4NLFpkU7hRiKtw7hzQvDl7Bk2YYMvO5Y8/mIk6bZoNAfzx44GRI9mhuVw5w4ML\nIXIcO3fydCo01PgF6YMPuN8MDwcKFzY6NPniC2DcOG5KKlWyYQIhMsnhw8ADD7CBe5s2xobt358v\n7+XLgfz5DQxoWWz6DdAbwU36wR3k3tRQN/0RP/2UfU6WLbNJBCYnAy+9BPzzj405p0Jcg+LF+fo7\nfJiOu8Y7wrMv86xZPDzcsMHw4N27Ay++yMjmuXOGBxdC5Ciio1kvNHKkcRH41VfA/PnAvHk2icBR\noyQChfdw8818sXfrxhCeIfr3p0m4sUCjnx8bFm7fzv+fXExOkcDO1hn2MnQo65oiIgyGp9OSlMQq\n8vPnWXBry7uGEFkgIYGpUnnysDFxoULGp1i4kIaly5axT60xLIvp1bt2MbJesKDBwYUQOYLz54FH\nHgHatgU++sjo0LNn03tr7VqgShWjQ5NRo5hfLxEovI25c/n+u26dsdfm6dNA/fp03O3QwciQ7GvV\noAFPpR9+2NCgniX3pobaLARHjqTJUEQEULGiDRMkJgLPPcdighkzbNlwC3FdJCUxKhgby4u7Dfn0\n06bRfGnVKqBqVYMDp6TQPAagm6gtxTlCCJ8kKQl4+mnW1H33ndHMojVrmBm3ZAlQr56xYVMZMybV\nGEYiUHgjI0cCU6bQfbdYMSND/vkn8MQTwIoVBst4ly5lJt66dTad2LgXO1JD7UiA9CmGDeN7xIoV\nNonApCRuVi2LpxISgcKbyJ+fdTMVKrCoz4ZUyw4deBj/xBPA0aMGB86bl29Ep0/TQMYNmQNCCB/A\nsuhwmDcvU8MMisC//waefZaXHltEYEgIzWGWL5cIFN7Lu+8C993HN/iUFCND1q3Lktg2bXg2bYQm\nTdjPpVUrGt7kMjJz5dsD4E8AkwCEAfDGnZRtEcHBg3kxX74cuOkmGyZISuI/yaVLzCMpUMCGSYQw\ngMPBZj5//sljbhvqV4cMYXRw1SqgZEmDA587RyOIli1ZbCCEyN0MGsRapogIFh8Z4uhR4MEHgQED\nGGQwzpQp3LRGRNDMSwhvJimJtfp3383DC0P06gUcPMgkJSPG5pYFvPoqXdNnz7bFLd1d2BERrAng\newCdAOwFMBRAjetZnC9hWbyQh4byemuLCExOZspdfDwjgRKBwpvJk4cn5/XqAU8+aUtksE8fRgVb\ntuS/hTGKF2ed4JQpdBQVQuReJk8GfviBvvQGReC5c/SceeUVm0TgrFnMoV+6VCJQ+Ab587NnysKF\nwKRJxob94gvg5El6dxjBz4/7m5Mnc91hcVZzIR4DMAXADWCUsA8AU20es4PRiKBlAf360elr+XKb\n+rgnJ9PVMDaWxjBKBxW+gmUxMrhliy2RQYeDm6izZ9kSMF8+g4Pv38+C8DFjaA4hhMhdhIezqV9k\nJHD77caGTUwEWrRgjfO339pgZL5wIdClC6+5desaHlwIm9m1i6ZM8+bRnMUAR47QPGbSJJ5NG+HE\nCaazDh8OtGtnaFD3YodZTBkAz4MRweMAJgD4FUAdALMAVMnqIm3AmBC0LGZdLF5MF8OyZY0Mezkp\nKexfEhNDtSkRKHwNm8VgUhKjghUqABMnGt5Ubd3KsOPPPwOPPWZwYCGEV+P6358926hDoGUBnTsD\nZ87YcHgFcDPSsSPw66/A/fcbHlwIN7FoEdMvN2xgmwkDREbSZ3HDBoM+L1u3Ao0bUwjce6+hQd2H\nHamhvwEoAaAVgGYA5gBIArAJQI7KsbIs1raGh9MYxjYR+PLLwPHjPBmRCBS+iJ8f8M03tqWJ5s/P\nLKidO5kuapQ6dZiq0r49hawQIudz4ADQvDnw9dfGbeL79QN27+bZknERuGYNfQRmzZIIFL5Ns2bs\np/LMM8ZqPxo1YvDm2WcNlpPUqUNDptatDbvXeSeZUYx5ADjsXkg2yXZE0OGgqeDvvzP93qhRRdpJ\nunYF/v2XtQk22PAL4VZsjgyeOgU0bAgEBwNvv210aB7d9+pFZxrV2wiRczl5kheS117jG71Bxo9n\nvdLatTYcHm/cSPE6dSojmUL4OpbF1Gw/P+Cnn4yk+1gWz0qKFDGcQTRoEFOyV670qb7e6iN4HSQl\nMa3j0CFmXpQoYW5h/4/DwZD4vn18Yd1wgw2TCOEBbBaDBw9yDzdkCN8/jBISwlqAtWuB8uUNDy6E\n8DgXLgCPP07XYGPOEmTePHagWL0aqFbN6NDAtm20tf/+e/Y6FCKnEB/PqHy7djQ/MkBcHAPmb77J\ng2MjWBbLuC5eZH9vH+lDbEdqqE/QrFkzxF5HU5H4eEZ/z57lHtY2Edi9O7BnDyOBEoEiJ2Fzmugt\ntzBV/913gbAwo0PzHaNzZ/ZHPHvW8OBCCI+SnMzN5u238yTJIOvX0x103jwbRODevbTcHzNGIlDk\nPAoXZt+HL7809qZetCiTfPr1Y2afEfz8gAkT2IfYkGD1RjIjBBum872HTC8ku4SFhSE4i8cAZ89y\n/1eiBI07bYn8uqIlO3cyEmjQqloIr8ElBuvUoXXehQtGh7/jDr5vdOrEDZhR+vVjyLFVKyAhwfDg\nQgiPYFlAt248iP3+e6OOU1FRLHOaPJmuhUaJjmYksH9/n3UtFOKa3Hwza/U7d2aBrQFq1gS++w4I\nCqIXoxEKFqTCDAsDvvrK0KDeRWaujFsA1MvE9zyJFRAQgPDwcPj7+2fqF2JiKAIfeIB/W1t6R1oW\na5C2bGFIw4YG3EJ4FQ4HLc6PHGGetWEzpIULWWa7ciVQq5bBgVNS6MqXnOxTKSBCiAz4+GOm+axc\naTQL5/hxNozv25fXIqOcPk33i44dbXDJEsILmTgRGDmSYbxixYwM2acPh1uyxKB50z//AA89xKLg\nli0NDWoPJmsEGwB4EMDbAL5Ic99iAFqD7SO8BevMmTOZFoEHD/LArW1b1oIa7/cDUAS++Wbqq9GW\nnFMhvJCUFOD555m0P2cOUKCA0eF//JFBvLVrgUqVDA586RKNGapV48XelguDEMJ2xo1j2plhB5e4\nOCAwkEkPAwYYG5ZcuEBDmAcfBEaM0PVH5B66daOh06xZRl73KSkM9Nx7LzBsmIH1udi4kc6nixbZ\nkApgDpM1ggVA0ZfX+bGo83YOgNd1Ys6sCNyyhdfZbt2AwYNtFIFvv80cNolAkdvIm5duYPnypUbZ\nDNKpEx2on3ySrqLGKFiQOeKbNzMtSwjhe8yaBXz2Gd97DYrApCT2K6tb14bLQ2Ii0KYNc9skAkVu\nY+xY4PBh4PPPjQyXNy8QGgpMm8azaGPUr8+awVat6PmRQ8jM1aYygH/tXkg2yZRraFgYN5Hffsto\noE0rAd57j10uly0DMilQhchxXLrEQppSpRjGM5xu+f77bLG1bNl/M7+Cg4MRFRWFIkWKIDQ0NNMH\nRQCAEydYM/j668at5oUQNhIZyQKhJUtoXmUIy6IxzNGjNIfJn9/Y0KkZFJcusWbKeCNCIXyAw4eB\n++5j4a2hVimuAN7q1fSLMsaECTxsWrMGuOkmgwObwY72ESvT+Z4F4LHMTuIGrikEQ0KATz7h6cCD\nD9q2CqB3b+5Mly3jBliI3Ex8PNMtq1blP6HBYlyHA3j5Zdb7Xrk5CwwMRGRkJAAgKCgIM2bMyNrg\nBw4AjzzCtIFOnYytWQhhE9u3s03EtGn8aJABA2j4HRFh2O/NsoCePYFdu3hSbbimWgifIiICaN+e\n2XRVqhgZ8vvvgdGjgQ0bjJUgkuHDecC9ahVQurTBgbOPHUIwIM3nhQA8CyAZgDd5qWYoBB0O1hPN\nnMm03ttus20FrB4PCwOWL/e6F4YQHiMujnmc9erRmclg2lNSEtu/lCzJg0SXzmzWrBnCwsKQVROp\ny9i1i73Hxo3jJEII7+TgQRo5jBjBjaRBJk7k4f+6dUC5ckaH5uZk8WJgxQqZyQkBULVNmcJomyEr\n/1deYVer6dMNZ11/8AGF4LJlXtURwF0N5TcC8KZKyXSFYFwcIwZHjgDz5wNlytg2Ox3K5s/nBd22\niYTwUc6eBRo3ptPC558bvRpfvMihGzSg+ZifHxAbG4vg4GCEhIRcnwh08ccfrDqfOtVYuooQwiAn\nTrA5dc+eNGgzSFgY9xCrVgE1ahgdmhve8eO54TVYyyiET2NZ9BYoWBCYNMnIXiEhgdUeHTqwH7Ex\nXDnjhw5x/+8lEX07GsqXSnMrA6ApAFNHV00B7AKwB0DvDO4z1vnzrchCy4r9+5kCWqyYG7TZgAFs\ncrZ8uUSgEOlRogTrdpYuBQYONDp0kSJM21q8mAEBgOZRM2bMyJ4IBIB77mE++fPPMyQghPAezp1j\n4/V27YyLwM2bmRU+Z44NIvDHHykEw8MlAoVIi6uJ++bNPCgxQKFCwOzZ3B9ERBgZkvj5sXGhvz/w\n7LOs8/VBMqMYD4A1gQBTQg8AGAhgTTbnzgtgN4DGAI6AUcYOAP5Oc59mAHo5P94PYAyAB9IZ67KI\n4LJlwAsvMEjXs6fNBlwDB7Lv2MqVwI032jiREDmA48dTT+/fesvo0IcPMztswACe4htl8WLgpZco\nZOt4U+ccIXIp8fEUgXfdZTzl3NUy7JtvbMgKd4UZjTdDFSIHsXcvozlz5xoz9ggP59v477+zn70x\nkpIYxbx4kSdHBQsaHDzr2BERrALgVuftNgBPIPsiEADuA7AXFJZJAH4G0OqK+7QEMNn5+QYA/gAy\nzNJ3OKj4W7Y8i/Ll38TChc1w9mysgaVmwKBBTDpesUIiUIjMUK4cT2pGj2bah0FuvplBxz592Mve\nKE2bAl9/zY1nVJThwYUQWSIpiVHAihVpPW9QBJ46xX/3vn1tEIEbN6aGGSUChciY6tW5R3juOeDY\nMSNDPvEE0KsXjYUTE40MSfLnZ7+KwoV9MjKYGSFYGMC7AH4BMAdsMG8iEfYmAIfSfH3Y+b1r3Sdd\nHR8Twyavc+YAtWsHY+vWsQgLC0NwcLCBpabDkCH8w69YYUMFuRA5mFtuoWLr29dwkx9aRM+fD3Tp\nwtIbowQF0UX0iSdoTiGEcD8OB9C1K/uTpnWIMkB8PNCyJbve9OplbFiydy/7j02YYKN1uRA5iObN\ngVdfNarcPvyQW/a33zYyXCr589OxuFAh9qfzITGYmYY1P4JN5MeCocaOAH4CEJTNua/d+I9cedSX\n7u9VqzYAtWvTNGLp0gMAgICAAISEhFz/CjNi2DDm+K9cCZQvb358IXI6t98OLFzIo/fixfmPa4j7\n7qO3S5s2DD7Wrm1saCrMc+coBlet0iGQEO7EsoB33qEJwNKlRhv6udr5Va4MDB1qbFhy4gSvdf37\nUwwKITLHxx8DmzaxP/fYsdkeLk8enh/Vr8+PL71kYI3/196dx9lY938cf9mKkVJKElK6S1oIFW3m\nlrZJq6Zde9Ned7kr2rT9irpLKnWnjcpYWsgaQ2ZsJWUppCEpWixpsg6znN8f75kbGYxxXeecmXk/\nH4/zmMVxXd9h5sz1ub6fpVBhMHjFFQpiBw8OeGZF0dLT00nfheLH4uRTzAWaFONzO6sV8BhqGAPQ\nBcgHum/2nP8C6ShtFNRYpg2w9G/HioweHeGMM/RBYB0Di/Lss7qjl56utBQzK7mJExWxDRsGrYoq\n/y25/v01dH7iRDj44EAPvalBVHq6CsXNLHxPPqmuDwH/3EUicNddMGeOSvgCLfFZs0ZjaM4+G554\nIsADm5UTWVnQsqV6clx5ZSCHnDNHP5bDh+vmcaDy8uDWW2HmTM2ti3ITyTBqBKcDrTf7uBXw9c4t\nq0hfoZrDhsBuwKXA0L89ZyhQOM25FZDF1kEgwP+CQAiwY+DfPf+8plOOH+8g0CwIp5yi23Lnn6+B\n0MWUkpJCYmIiSUlJZGUVXQd8+eXwwAMaYbhsWVALLtC1q0ZhnHMOrF0b8MHNbCuvvKJMnE8/Dfzm\ny/PPK7YMvM9DTo5qnI4+OvBuyWblRs2a+uH817/gm28COeSRR+pyvkMH+O23QA65SaVK6iZ6+um6\nxonzUpLiBIItgcnAT6ixy5SCz30L7Mr/SC7qCDoa7TAORB1Dby54AIwEFqKmMq8Dt+3C+XZNjx7w\n2msKAg/8eymjmZVYUpJSPs46S3U0xZCZmUlGRsYO64DvvFM9JZKSYPXqoBaMmlO88AIcfriKitav\nD/DgZraF1FTo3l3poAGXYwwYAD176sZ9oPFlJAI3F1zKvP56yO3Lzcq4Y46BF19UBtE2bv7urPPP\n14/ohRdq1mCgKlSA//s/SElRTfCXXwZ8guAU55XpoCKeF9nsc4uCXFAJFTlQPjA9e+pCNT0d6tcP\n7zxm5Vnv3irOmTRphzdbkpKSGDVqFC1btiQtLW27u/+RCNxyC/zwg8oSA73jn5enLoB//KFU0TgZ\nKGtWZgwerDSrsWM1KiJA6enasBs3Tpt2gXr0Ue1ejh8P1asHfHCzcuquuzTf5ZNPAmkUFYnoZnG1\natCnT0j3a4YO1eD5l16Cyy4L4QRb2tnU0OI88T2gYzE+F0vhBYKvvKI7/+PHq4rczMLTvbtSRSdM\n2G5e/c7WAefl6YKvYkXtAFSqFOCac3OVh5qdrfql3XYL8OBm5dioUerm8Omn0Lx5oIeePRvattXr\nQdu2gR5aO4DPPQdTpni0lFmQNm7cVHP78MOBHHLtWjj5ZOjYUb2oQvHNN8oe6thRPQYCvQjZUhiB\n4Azg2M0+roxSQne1WUyQwgkEe/XSi3l6OjRsGPzxzWxrXbro7v+4ceooWgwpKSlkZmaSkJBAampq\nkcFhdrZ+dxxxhH60A73zl5OjFteVKunKMsBuhmbl0mef6Vb90KHQuvWOn78TfvlFh+zWTQ3+AvXJ\nJ9rBnDgRGjUK+OBmxq+/qu3nW2+ppCQAP/8MJ5ygXcEzzwzkkFtbulSvaRUrwvvvh9ZrJMhmMQ8C\nq4GjC94WPpaxdVOXsqdXL3UIHT/eQaBZND39tDqEnX9+sRP3i1MzWLWqrtG++CKE5n1VqsDAgVrv\n1Vdrl9DMSmbyZKVQffhh4EHgX3/phtDtt4cQBH7+uVLAhg51EGgWlrp11Rb8mmuUJhqABg1g0CD9\n+p4/P5BDbm3//XWDu00baNFCGQ9xoDgRYzegc9gL2UXB7gj26AEvv6w7kg4CzaIvL0/plnl5enXe\nQRrFztQMLl0KJ50EnTrpxn2gsrOV/lGnDrzzTqjpH2Zl0ldfqbvTe+8Ffmt+40YFgY0bq+oj0KyA\nefPUSfidd3QSMwtXjx56nZg8WUV+AejdW4f94gvYa69ADlm0CRPgqqv0Wte9e6AnCyM1tA1FD3Gf\nUNyTREFwgWD37poT+NlnbgxjFksbNmg8Q6NG8N//bveqbWdrBhcuVFfnF19URmeg1q2D9u3hkEP0\nWyWAgnazcuGbb9Ry/Y03dEMlQJGIynPWrFEpb6D3aH77TZ0Bu3aFa68N8MBmtk2RiG4YV6sGb78d\n2J2dO++E779Xc7lQqzyysjTjasQIXYx06BDI1xBGIDicTYFgVeB4NEcw6PLqXRFMIPjkk9Cvn7Zu\nPSLCLPZWr95UGP7kk4EeetYsXXOmpkK7doEeWlebZ5+tVoSBFySalUHz5qlrS48eqqMJ2AMP6Cb8\nuHGQkBDggVetUqrXxRfDQw8FeGAz26E1a6BVK3UT3c4oqZ2Rm6vKlAMPjNLklwkT4I47oEYN9SU5\n8cRdOlwYgeDf1Qd6AheV4O+GZdcCwUhErZ4//li/JQKeU2Rmu2D5crX0uv12vdgHaMIE3YQbNUpl\niYFatQrOOEMV6C++6GDQbFsWLNANn6eeUt1PwHr00Ob8pElQq1aAB964Uald//gHvPqqf8bNYiEz\nU9cIw4fD8ccHcsjVq5U1dOWVcN99gRxy+/Ly1EDmkUc0n/iee9QIpwQZRdEIBCugAfBHlODvhqXk\ngWAkoi6FI0eqU6FbPZvFn0WL9KrcvXvgHR6GDFGtYEYGHHbYtp9XnM6kW8nKUp3T8cdrhpAvFM22\ntGCBdgIffjiwO/qb69cPOndWGVGDBgEeOD9fnSVCyTU1s50yZIhuFH/9Ney3XyCHXLJEvaoKszaj\nYsMGNZ7r0UOvLZdeqvqVY44p3vXDokVUOPhgCDgQfHmz9ysCzYAfgauKe5IoKFkgGImoY0R6OqSl\nBXyr0MwCNXs2nHaa5gwG1DK60FtvaTNi8uRtd3ROTEwkIyMDgOTkZAYNGlS8gxe2KWzaVGmirhk0\nk/nz9TMdUhA4ZozqAseNC3wWPdx/v14wxo4NrFGFme2CBx+EqVNh9GioXDmQQ06frnu5w4cruSdq\nIhGYNg0++EDdk9eu1QKOPRYOOECbVpUrqyfB4sXw3XeaW/rnn1RYvhwCDgSvZVONYB4KAifv7NcU\nsp0PBPPzdffgyy/1TbP33uGszMyCM3kyXHABDBumuoAAPfOM6gUnTCj65WBnOpNuZfVqpZA1bqyi\nAweDVt7Nn6+dwEcfhZtuCvzw06bpR27wYGWNBapnT3jtNb0e+QayWXzIy9NN4hYtNCQ0IMOH6z7V\nlCkxGiQQiWh7cupUNdRauhSWLdPXW60a1KundKZWreCoo6igIDjQQLAacCgKBhcAxRvsFV07Fwjm\n5cEtt8CcOSoOCrVHrJkFasQIuOEGdfZt0iSww0YicO+9uoAcM2brhhI725l0K2vWqJvowQerM7FT\nyay8CjkInD8fTj1VzYbPPz/gg3/wgep3Jk3yeCmzeLNihQLBHj3gouBambz8sl5PJk+Gkvz6j6Yg\nawSrAP8HXA/8XPC5BsA7aNh8TsmWGIpImzZtile7s3GjckVWrFBOcY0a0VulmQXjvffUoW/SpEAL\nfwrLfv76SzsJAWWXbLJ2rdri162reWOBn8AszhUGgV27avh6wH77TXNCH3wwhMNnZKheZ8wYaNYs\n4IObWSAK0wEmTlQWTkDuuUdjTseMie9s8CADwReBPYB7gNUFn9sTeB5YB9xdsiWG4n/bgdut3Vm3\nTi2ed9sNBgyAqlWjtT4zC1phK8CJE2HffQM7bE6OdhH23Rf69Akhi3PdOqW31qqlgNbBoJUXIQeB\nf/21aZLDww8HfPDZs7X2/v1V12hm8evNN+GFF5ROGdCGT36+9pFWr9aQgXj91R1kILgAOAzI/9vn\nKwHfo3TReBEBtl+789dfcO65cNBBGjwZ6pRIM4uKLl2UIjpuHOyxR2CHXbdOBeLNmoXU7DM7Gy68\nUL+g+vXz65GVffPmaXDnY48ptTtgGzaoPOjII5XGFejP7OLF2mbs1i3wrsVmFpIbb9QYp4EDA3tB\n2LhRN4oPOEBN5uKxEfjOBoLbu9edz9ZBIKhhTFGfj6nk5ORtB4HLl2tG0THHqOOgL7rMyoann1Y7\nwA4d9AodkIQEFYhPnqyxPoGrWlWp6dnZWnt2PJZemwVk5sxNcwJDCALz8uCqq7SL37NnwBdnf/6p\nrr933eUg0Kw0eeUVWLhQO4MB2W03NfGcO1f3ocuC7b1cfgJ8DPT92+c7AsnAeWEtqgS23SxmyRLd\nhbz4YnjiifgM382s5HJz9fNdrZp21wLM5Vy+XE0nrr8+pKGyOTkqSly6FD75xDXLVvZMnaq62F69\n9HMasEgE7rxzU++3QCs+srOVGnDssUpF9/WDWeny008auzBgACQmBnbYP/7QaOMbb1STuXgSZGpo\nPRQIrge+LvhcCyABuBBYUrIlhqLoQHD+fDjjDLjjDs0LNLOyaf165YUdc0zguZxLlugFv3NnuPnm\nwA67SV4e3H67BhaNGuV29FZ2FDZXeecdOOecUE7x+ONq7JSREXAD8Px8DXOuWFF1gR75YlY6paXB\nNddoXFy9eoEddvFijaZ59NFQEh1KLMhAsPDP2wJHojq8ucC4ki4uRFsHgjNmqFX744+HUpRuPwh4\nIwAAIABJREFUZnGmsFNEhw6B53MuWKBD/+c/cPnlgR5aIhFFmiNGqCXZtqbam5UWn36q3e4BA9Rk\nJQQvvaR6wEmTYP/9AzxwJAL/+hfMmqWvw43lzEq3p5/W/OGMDOV3BmT+fGW9P/20Xu7iQdCBYGmx\nZSCYlgZXXqmBrx06xG5VZhZdv/+upg733adZoQGaPRvatVMzsvbtAz30Jt26wRtvwNixmjdoVhoN\nHqyfvyFDoHXrUE7x7rvqDDpxonrABeq553SCiRPjf2iYme1Yfr7mCh54oNLUAzRvnu51/ec/8VFG\nHGSzmNKpXz9VjX/0kYNAs/KmTh3tqD35pCq6A3TUUTB0qOoFx48P9NCbdO6sNPZTT1U1ullp8/77\ncNtt2kkLKQj85BO4/34YPTqEILBfPzWZGDXKQaBZWVGxoppFpqXpJk+AGjfWZUenTvDBB4EeOirK\nzo5gfr7u4vXqBSNHqoe0mZVPM2eqPjiEmV/p6XDJJeoqevzxgR56k/ffh3//WzsqrVqFdBKzgPXo\nocenn0KTJqGcYvx4le6NHAktWwZ88LFjlU302We+hjAri2bPVi5nWprmQwVo1iz1lurVK7b7UOU3\nNfSuu/TiPWpUoMWgZlZKTZigLoUjRsBxxwV66OHDVXo8dqx2CkMxciRce62GFZ17bkgnMQtAYY3r\n0KHapmvQIJTTTJumnjODBgXaAFAKbx59+KF25M2sbBowAB56CL76CvbeO9BDz5ypaTPduqk/TSyU\n39TQWbOUz+8g0MxAF3Nvvqkgat68QA/dvj28+KLu/i1YEOihN0lKUhCbkgKvvx7SScx2UU4OXHed\nbrxMmhRaEDh3rn6U33orhCDwxx8VYb76qoNAs7Lusss00ubyy9W1O0DNmilr4ZFHlGFeGpSdHcH1\n693Zy8y21qcPdO2qi9T69QM99BtvaEZ2RgY0bBjooTdZsECjMa64Ql2QPcvM4sW6dcqTjkS0TVe9\neiinWbRII1yeeUYtAAK1YoUaTN1xhwYSmlnZl5ur36tNm8Lzzwd++EWL1FzuhhuiP3i+/O4IOgg0\ns6Jce60u8M48U1NgA3TTTWpQ2ratZgqF4tBDYcoU1V3dcIN2YMxi7Y8/VH9bq5ZqWUMKAn//HU4/\nXZmngQeBa9dqe/+iixwEmpUnlSvr5tXQobpZHLCGDZUkUVjun58f+CkCU1ZuLRc9UN7MrFDnzsrZ\nGDcO9tgj0EO/8IKm1WRkhDgCcO3aLXdfAv4azIrthx+USnneedC9e2i71CtWqK/DpZdqVESgcnPh\nwgthn310IeiddrPy57vvNCR4yBA48cTAD//HH3DBBWpo/u67UK1a4KfYSvndETQz255nnoGjj1Y7\nr40bAz30vfdqrMRpp8HSpYEeepPq1dU3/8AD9Yvrl19COpHZdkyZAiefrIHrzz4bWgCVlaXeLe3b\nq69DoCIRzTnMyVEdsYNAs/LpiCN0I+jii0NJ66lVSw1Kq1RR5tDy5YGfYpc5EDSz8qFCBfjvfyEh\nAa6+OvAi8S5dVIPerp12MkJRuTL07g3JyRor8fXXIZ3IrAgDB+r29jvvKJAKyerVKt9p0waefjqE\nOO2xx9Rg7sMPdYVmZuVXUhLccw+cf74ybwJWtarGk7ZrByecoJeeeFJWboM5NdTMiic7W/2djzhC\nA38CvMqMRLR7MWqUMlD32SewQ29t8OBNHUUvuijEE1m5F4loR/3112HYMDjmmNBOtXatfjyPPFJN\nPAMPAl9/XTOHp0yB2rUDPriZlUqRiOY9ZGfrhldIWQL9+8Ndd2ncauA1zwXK7xxBB4JmVlyrVqn4\nqH17deIMUCSiBjIZGZozuNdegR5+S9On6y7mrbdqS9Ipbha0jRu1+zdrloLA0IpgYf16jYioX19j\nIioGnbM0ZAjcdptGTTVqFPDBzaxUy87WdcHZZ8Ojj4Z2mm+/1b3bs85Sw9Lddgv2+K4RNDPbkT33\n1LZd//7w8suBHrpCBW04tG6tF/rVqwM9/JaaN4epU7U7eM01sGFDiCezcmf5chXq/fGHWuCFGARu\n3Kgyndq1VbYXeBA4ebJ20IcOdRBoZlurWhU+/lgvQB98ENppjj4apk1TSeIJJ8CcOaGdqlgcCJpZ\n+VS7NowZo4YX/fsHeugKFaBnT40oSkoKORisW1fbj9nZmrTtJjIWhOnT4bjj1Bhm8ODQxkOAerZc\ndpmuw/r2hUqVAj7Bd9/pFvx770HLlgEf3MzKjAMO0M2i225T+nhIatbUy+rtt+vXduvWA2nT5p8k\nJSWRlZUV2nmLUlbyiJwaamYlM3u22n327astvADl5ytrc/ZsbUDuuWegh9/6ZN26qe5xwABN4DYr\nicJCltde0zZdiPLyVCuzerVuxgedJsWvv6ot/BNPqEmUmdmOjBypub2TJoWeQbBgATRvPofVq1cA\nt5GcfCSDBg0q8fGcGmpmtjOOOkq35q6+Gj7/PNBDV6yoa+mmTZVh99dfgR5+65M9+CC8/bYu3l96\nSQWLZsWVlwf336+OR+PGhR4E5ubqx+6PP9TAM/AgMCtLN3duucVBoJkVX1ISdO2qt3/8EeqpDj0U\nTjrpAWAQlStPonbtd1m1KtRTbsGBoJnZiSdqR/CCCwJP2K9YUZt0xx8Pp58Of/4Z6OG3duaZCmjf\nflsXv+vWhXxCKxNWrtSQ+K+/hi+/DLUzKGwKAles0HjMqlUDPsGGDRoYn5gIDzwQ8MHNrMy75RY1\nY7vgApVehKh///dJTl7O3LkVWbeuKo0bq31ByKcFnBpqZrZJaqouGidOhIYNAz10JKLB8xMmaMBs\nqKMlQAFgSoryUj/+GA45JOQTWqn15Zdw6aUKnJ59VvMqQ5Sbq3TQv/7SZnzgQWB+Plx+ud4OGBBC\n0aGZlQv5+XptrFxZwwAD72JVtOnTtSE5cyZ06gTXXVf8DuRODTUzK6krrlBq3BlnwLJlgR66QgV4\n4QVo21YliaENnS+UkKDmGDfcoOHzIXZBs1IqEtFt5/bt1cf8hReiEgReeaUmuIQSBEYiqm9culTf\n/w4CzaykKlaEd9+Fn36CRx6J2mmbN9e0no8+UmPwhg21QTlxomLTIMVqR3AfYCBwELAIuAQoqk3O\nImAVkAfkAMdv43jeETSz4Dz6KIwYAePHB97hJRJRKd+IESrD2m+/QA9ftK++UlvGdu00ybZatSic\n1OLaqlVw443qVPDBB1EZqZCToyBw7Vpd4AQeBILmgg4ZAunpIQ/xNLNyY/lyzYTq1Ekd4KLst99U\n7TFokJZyzjnqB3fSSXDwwVtuVJaWgfLPAisK3j4A7A10LuJ5PwItgJU7OJ4DQTMLTiSivs7z5ql7\nWMBXrJGIYs3BgxUM7r9/oIcv2qpVcPPNqoEcOBCOOCIKJ7W4NGMGXHKJtqZffDGkiGxLOTnK1szO\nVhC4++4hnKRXL309kyZF6YfKzMqNH36AU0/VzdRLLonZMr7/XpOvJk5UO4Dly7VjWLcu1KgBQ4eW\njkBwHtAGWArUAdKBxkU870egJbCjlj0OBM0sWIV97Qu3L6pUCfwUTzyhsoO0NGjQIPDDby0S0W3F\nzp2he3cVHlQoK6XitkP5+UoBffZZDbq84oqonHbDBgWBubnafAwlCOzfH+67T1dHBx8cwgnMrNyb\nNUtd3/r109s4sG4dLFwIv/8O3bu/ytixt0MpCAT/RLuAhWtYudnHm1sI/IVSQ18H3tjG8RwImlnw\ncnKgQwcN037//VDqjXr00DX5mDFw2GGBH75oc+eqAL5JE3j1VahVK0ontphZsgSuuUZR2fvvB94M\naVvWrdOPUEKCejGFEgSOHq0WpOPGaRyMmVlYJk6Eiy5Sfcfx26pYi43ExEQyMjIgTprFpAHfFvE4\n72/PixQ8inIScCxwNnA74AnJZhY9VaooKX/ZMlVqh3DD6Z57VIOemAjffBP44YvWpIk6RdarpzEB\nI0ZE6cQWEx9+qO4D//ynaueiFASuWgVnn6062IEDQwoCv/gCOnZUnrWDQDML2ymnKLPmvPPgu+9i\nvZotJCQk7PTfiWVqaCLwO3AAMJ6iU0M31xVYAzxfxJ9Funbt+r8PEhMTSUxMDGKdZmawZo3SQFq3\nVmpdAOmUKSkpZGZmkpCQQGpqKmPG1OTOOzVTrVWrANZcXBkZcO21aiTzwgsqMrCyYeVKuPtuFZL0\n6wcnnBDVU591FrRoodK9ULquz5mjNrzvvKPBz2Zm0dK3r4r9J0yAgw6K2TLS09NJT08HIDs7m+7d\nu0MpSA19FtX9dUdNYmqydbOYBKASsBqoDowBHi94+3dODTWzcP35p3ZUzj9fnQl30WYpHCQnJzNo\n0CBGjlRMNmCArm+jZvVqDTkcOxbeeivKJ7dQDB6shkfJyfD000pvjpLff9d9k7PPVilqKGWoP/0E\nJ58M3bqpFamZWbT17AkvvaRMi/r1Y70aoPTMEewGnA5kAm0LPgaoCxTmKNUBJgIzganAcIoOAs3M\nwrf33irkGzgQ/vOfXT5cYQpHy5Yt6d27N6BNjQ8+0KSHoUN3+RTFV6MGvPEGvPKKItHrr4c/dtSj\ny+LS8uX6BnrgAaU19+wZ1SDw55/VWO/SS0MMApct06zP++5zEGhmsXP33XDbbbp5+ssvsV5NicQq\nEFwJtAMOA85g0wzBX4FzCt5fCDQreBwFPBPlNZqZbal2be2a9eoFr7++S4dKTU0lOTmZtLQ0atas\n+b/Pt2mjiRUpKdCnzy6ud2edc47S7fbYQ/VW/fuHUhdpIYhElP559NGq/Zw5UztmUTR/voLA226D\nhx8OKQgsLDy85BINjjczi6VOnTSTtW1bDfwrZcpK33CnhppZ9PzwgyK27t1D25GYN0/XuzfeqAH0\nUZ/y8MUXcNNNSnfp1cst+ePZd98p+srKgtdei3KRqXz1lXonPPGEvmdDsW6dfigKu9169ImZxYun\nnlJr5PHjYzrHtLSkhpqZlV6NGqllfadOMGRIKKdo3BgmT1aq6B13aKxhVLVqBdOnq0Nay5Zqbbp2\nbZQXYdu1dq1mQp56qtqZT5sWkyAwLU3x2auvhhgEbtigORQNGujGhINAM4snDz+sTIXTTlP6einh\nQNDMrCSOPFJjF1JSdCUcgrp11dRz3jz9flm/PpTTbFuVKtCli4bo/vADHHGEOtk4AyO2IhHdIWjS\nRPMBv/kG7rwTKleO+lJSU+Gqq9Sb5oILQjpJbi5ccQVUq6YOoaG0IDUz20Vdu8LFF+vm3JIlsV5N\nsZSVW2pODTWz2Jg0SbsxH36oF/8QbNigHi5LlqiJzN57h3KaHZs4UXVZNWpAjx6aDWDRNWWKdqKz\nszXu45//jNlSevTQEkaNCnGEX36+vvmXLdNslVCGEZqZBeg//1HmwtixyiCKIqeGmplF08knq6nK\nxRfrIj0Eu++uPiDHH6/TLV4cyml27JRTVAx21VUqCLvkEvj++xgtppxZsEDfY5deCrfeCl9/HbMg\nMD8f7r8fevdW+nJoQWAkorzoRYvg448dBJpZ6fDvfyubpk0bNWCLYw4Ezcx21WmnwXvvKTdu6tRQ\nTlGxombZ33ADnHiiyvdiolIlpcNmZkLz5opMb7qp1KTBlDqLF2se4Akn6N/7++/h6qtjlh6ZnQ0d\nO2ojfNIkleyFIhLRCIxp02D4cCgYt2JmViqkpMCzz+r6YNq0WK9mmxwImpkF4cwzNe/h3HO1axaS\ne+/VaLgzzwytT03xVK+uRiWZmbDvvtC0KfzrXzHcrixjCgPApk31b/3dd2ofG8OAaMUKaNcOcnJg\n3DioVSvEkz31lHJOP/0U9twzxBOZmYXkiis0o/ecc6I8HLj4HAiamQUlKQnefFMv+jNmhHaaiy7S\nNfIdd8Bzz8W4d8vee8Mzz8Ds2WpW0rSpti0zM2O4qFLsp58UADZrpnmO8+bprnLt2oGdIiUlhcTE\nRJKSksjKytrxX0Abka1aqQx2wAD1bQnNiy9qhz0tLeRo08wsZOeeq8Zyt9yiusE440DQzCxI552n\nPvpnn61umyFp2VKj/lJT1bJ/48bQTlU8BxygAvn585UvePLJkJwMX34Z44WVEp9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"text": [ "<matplotlib.figure.Figure at 0x7fb6e5d08b90>" ] } ], "prompt_number": 26 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now the predictions are able to capture the non-linear structure of this problem! There are two ways of interpreting how this is working.\n", "\n", "The first interpretation is that we are inferring a probability distribution over the weights, and each weight corresponds to a basis function. With the polynomial basis functions, we are therefore considering distributions over the polynomial coefficients in a polynomial equation. You can see in the predictive distribution:\n", "\\begin{equation}\n", "\\fq \\sim \\norm \\left( \\invnoise \\xq \\Ainv \\Xf^\\top \\y, \\hspace{0.5cm} \\xq \\Ainv \\xq^\\top \\right)\n", "\\end{equation}\n", "that our mean function is in fact a linear combination of the basis functions composing $\\Xf$, and the space of models is restricted to the span of the basis functions. \n", "\n", "A second interpretation is that we have used polyFeatureGen to project the inputs and the queries into 7 dimensions, effectively warping the space on the x-axis. The line in the high dimensional space can then project to a nonlinear function in the original space. To understand this principle, think about how a direct path an airline takes around a [3D sphere](https://www.metabunk.org/data/MetaMirrorCache/contrailscience.com_skitch_Google_Earth_20111211_141946.jpg) can appear as a curved arc when projected onto a [2d map](http://www.gcmap.com/map?P=syd-jnb&MS=wls&MR=900&MX=720x360&PM=*) ). You will see this principle applied in the support vector machine tutorial. \n", "\n", "You can investigate the choice of basis functions by modifying the above code:\n", "* What happens to the prediction as you move away from the input data? (try changing the domain above)\n", "* We have been using polynomial basis functions. Can you think of a different class of basis functions that might work for periodic data?\n", "\n", "## Plotting draws from the posterior\n", "\n", "We have been looking at the mean and envelope of the predictive distribution, but it is also useful to look at draws from the posterior distribution. To do this we must look at the *joint* multivariate distribution of the posterior. Note that given our particular choice of Gaussian prior and likelihood, the posterior predictions are also joint-Gaussian. For example, we can compute a posterior multivariate Gaussian over the weights and draw random weights out of this distribution. This will sample a set of deterministic models each drawn with probability consistent with our Bayesian analysis.\n", "\n", "How do we draw values out of a multivariate Gaussian distribution? We can do this by linear operations on a vector of independent univariate Gaussian draws. This can be achieved using the cholesky factorisation: if $L$ is the upper Cholesky factor of the covariance matrix $A^{-1}$, and $\\mathbf{\\Sigma}$ is a vector of iid Gaussians, we may draw from the distribution according to:\n", "\\begin{equation}\n", "\\hat{\\W} = \\mu_w + L \\Sigma\n", "\\end{equation}\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Plotting draws from the function\n", "I = np.eye(theta.shape[1])\n", "A = beta * theta.T.dot(theta) + alpha*I\n", "mu_w = beta * linalg.solve(A, theta.T.dot(y))\n", "L = linalg.cholesky(linalg.inv(A), lower=True)\n", "\n", "nPlots = 10\n", "fig = pl.figure(figsize=(15,5))\n", "pl.plot(x,y,'k.')\n", "for i in range(nPlots):\n", " weights = mu_w + L.dot(np.random.randn(p)[:,np.newaxis])\n", " f_star = thetaQuery.dot(weights)\n", " pl.plot(xQuery, f_star)\n", "pl.xlabel('x')\n", "pl.ylabel('y')\n", "pl.title('Draws from the posterior')\n", "pl.show()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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/Tfs1xXdsAK+P1rH+u0JWWg2lvW4rt0xGdratQoux96hSZRZeXgPlbw0BPH8t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7cDHzx4dyvw9et3B9NrNGBkhCIrC6QICiGEdhQm3CbmYHWuG2p44hXOjzl2dFZf5cKRmYTNVrJN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\nuH2bB8uW4RAezpMSJ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"text": [ "<matplotlib.figure.Figure at 0x7fb6e5ae0990>" ] } ], "prompt_number": 27 }, { "cell_type": "markdown", "metadata": {}, "source": [ "* If we were to average over a very large number of these draws, the density at each input value would be Gaussian distributed with the marginals from the previous section. \n", "\n", "\n", "### Advanced: Hyperparameter selection\n", "\n", "Until now we have been manually specifying values for $\\alphs^2$ and $\\invnoise$. Such manual selection is not ideal, as it requires a deep understanding of every dataset and large amounts of manual tuning to select appropriate noise and prior hyperparameters. Instead, there are two main approaches for hyperparameter selection based on the data. Both rely on the concept of *marginal likelihood* - the probability of drawing the observations out of the prior. We can obtain this criterion by marginalising out the actual weight vectors and noise draws. The log marginal likelihood given all our Gausianity assumptions so far is given by:\n", "\n", "\\begin{equation}\n", "\\log p(y | \\Xf) = \\frac{1}{2} \\left( p \\log \\alphs^2 + n \\log \\invnoise - \\log |A| - n \\log 2\\pi - \\invnoise (y-y^*)^\\top (y-y^*) - \\alphs^2 \\mu^{*\\top} \\mu^* \\right)\n", "\\end{equation}\n", "\n", "\\begin{equation}\n", "\\mu^* = \\invnoise \\Ainv \\Xf^T y\n", "\\end{equation}\n", "\n", "\\begin{equation}\n", "\\y^* = \\Xf \\mu^*\n", "\\end{equation}\n", "\n", "See \"Evaluation of the evidence function\" in Bishop - Pattern recognition and machine learning for how this is derived. Here $|A|$ is the determinant of A, and $\\mu^*$ and $\\y^*$ are the mean weights and mean latent function predictions at $\\X$ respectively using the current hyperparameters. This criterion naturally weights model fit against overfitting. If the prior is too tight to fit the data, we cannot draw the observations, but if it is too general then the probability of drawing any particular dataset is also low. It can become very small or large and is best computed in log form.\n", "\n", "The first hyperparameter strategy is to define a *hyper-prior* over $\\alphs$ and $\\mathbf{\\beta}$. If we carefully choose a conjugate prior (for our scenario this will be an inverse gamma distribution) over these parameters, then it is possible to marginalise over the hyperparameters (in the same way we marginalised the weights out of the predictive distribution) and obtain a closed form solution for the weight distribution. An interested student is directed to Bishop, Pattern Recognition and Machine Learning. Unfortunately, this distribution will be of Student-T form rather than Gaussian, so marginalising out the weights to obtain predictions will then be a (straight forward) exercise in numerical integration.\n", "\n", "A simpler approach that we will take here is to search for the maximum likelihood hyperparameters - this is also called the evidence approximation or type 2 maximum likelihood. . The main drawback of this approach is that we loose a level of Bayesian inference compared to the above alternative. Also we must select these parameters via optimisation strategies and the problem is non-convex, meaning we may end up in a local optimum that is better than its neighbours but not the best set of parameters. However, it is a more principled method of model selection than manually tweaking the parameters.\n", "\n", "Below we will use a simple gradient-free numerical optimisation procedure to find some hyperparameters:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Compute the log marginal likelihood\n", "def nLML(params, theta, y, verbose=True):\n", "\n", " alpha_ = params[0]**2\n", " beta_ = params[1]**2\n", " p_ = theta.shape[1]\n", " I = np.eye(p_)\n", " \n", " if verbose:\n", " print('Querying:', alpha_, beta_)\n", " \n", " A = alpha_*I + beta_*np.dot(theta.T, theta) \n", " \n", " # The log determinant of a positive semi-definite matrix is given by twice the log-trace of its cholesky factorisation.\n", " L = linalg.cholesky(A, lower=True)\n", " mn = beta_*linalg.solve(A, np.dot(theta.T,y))\n", " predY = np.dot(theta, mn)\n", " LML = 0.5* (p_*log(alpha_) + nSamp*log(beta_) - 2*np.sum(np.log(np.diag(L))) - nSamp*log(2*pi)\n", " - beta_*np.sum((y - predY)**2) - alpha_*np.sum(mn**2))\n", " return -LML\n", " \n", "params0 = [1., 1.] # # signal, noise inverse standard deviations\n", "# Note we havent defined numerical gradients (which might be a good idea later), so we will use\n", "# the COBYLA algorithm that can estimate them automatically\n", "nLMLcriterion = lambda params: nLML(params, theta, y)\n", "newparams = scipy.optimize.minimize(nLMLcriterion, params0, method='COBYLA') # gradient free\n", "alpha = newparams.x[0]**2\n", "beta = newparams.x[1]**2\n", "print('Local Optimum:\\n alpha = ',alpha, ', beta=', beta)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Querying: 1.0 1.0\n", "Querying: 4.0 1.0\n", "Querying: 4.0 4.0\n", "Querying: 4.76828283359 8.89825082184\n", "Querying: 9.3754577175 11.9792744431\n", "Querying: 8.17739691358 19.7173997388\n", "Querying: 8.76943353114 29.5418573547\n", "Querying: 4.51310030042 35.7911663426\n", "Querying: 4.34387958267 48.7450174316\n", "Querying: 2.33679570135 61.0469284401\n", "Querying: 3.75265562622 65.6350340256\n", "Querying: 5.46823905781 70.5578503581\n", "Querying: 5.23109951122 79.1608227278\n", "Querying: 7.54717235986 82.6839907651\n", "Querying: 4.15093387108 79.3493677075\n", "Querying: 5.66617807396 80.649749649\n", "Querying: 5.34915052014 76.9988629341\n", "Querying: 4.95181236795 79.006260824\n", "Querying: 4.67758241391 78.9920400065\n", "Querying: 4.93249324811 79.5573563944\n", "Querying: 4.72357053903 78.3881352714\n", "Querying: 4.83701833308 78.6968946056\n", "Querying: 4.85639456883 78.148931342\n", "Querying: 4.83958883157 77.6015171158\n", "Querying: 4.97443783932 77.4768912017\n", "Querying: 4.76869842271 77.1311127118\n", "Querying: 4.80407824714 77.3661361532\n", "Querying: 4.87288733784 77.566486446\n", "Querying: 4.90586755063 77.6074989974\n", "Querying: 4.91916930582 77.4804608515\n", "Querying: 4.93559167424 77.5025583587\n", "Querying: 4.90329080482 77.4530510314\n", "Querying: 4.88758922683 77.4243006346\n", "Querying: 4.90673937187 77.4215257063\n", "Querying: 4.88638391764 77.4387567465\n", "Querying: 4.89483371143 77.4459037241\n", "Querying: 4.89371708667 77.4625089145\n", "Querying: 4.89547424909 77.4289063766\n", "Querying: 4.89526940764 77.4117403783\n", "Querying: 4.89333763665 77.4276326351\n", "Querying: 4.89979206585 77.4296960681\n", "Querying: 4.90404866102 77.4266673638\n", "Querying: 4.90192013242 77.4281817086\n", "Querying: 4.89960159349 77.4254667818\n", "Querying: 4.89784728787 77.4334461373\n", "Querying: 4.8988196286 77.4315710913\n", "Querying: 4.89850635761 77.4356833082\n", "Querying: 4.89743108325 77.4361106622\n", "Querying: 4.89891014643 77.4364044954\n", "Querying: 4.89925228731 77.4375212641\n", "Querying: 4.89954892942 77.4362148735\n", "Querying: 4.89904431399 77.4390749229\n", "Local Optimum:\n", " alpha = 4.89904431399 , beta= 77.4390749229\n" ] } ], "prompt_number": 28 }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "Now try using these values in the previous sections!" ] }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Part III: Stopping Distance Assignment" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that you know the basics of linear regression, lets apply it to a real data problem. The cell below loads a dataset of stopping distances (metres) of a car travelling at different speeds (km/h). If you were in the same car travelling at 21 km/h and you hit the brakes at a distance of 135 metres from a wall, what is the probability of you coming to a stop before hitting the wall?" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#Load data\n", "data = np.load('cars_stopping_dist.npz')\n", "speed= data['speed']\n", "stoppingDist= data['dist']" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 29 }, { "cell_type": "code", "collapsed": false, "input": [ "#Solution\n", "\n", "# plot raw data\n", "pl.figure(figsize=(15,5) )\n", "pl.plot(speed,stoppingDist,'k.')\n", "pl.xlabel('Input x')\n", "pl.ylabel('Output y')\n", "pl.title('Raw Data')\n", "pl.show()\n", "\n", "#whiten data \n", "speed_std = np.var(speed)**0.5\n", "dist_std = np.var(stoppingDist)**0.5\n", "speed_whitened = speed/speed_std\n", "dist_whitened = stoppingDist/dist_std\n" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x7fb6e5c0b350>" ] } ], "prompt_number": 30 }, { "cell_type": "code", "collapsed": false, "input": [ "# Train the model\n", "\n", "nQuery = 1000\n", "speedQuery = np.linspace(0, np.max(speed_whitened)*1.2, nQuery)[:,np.newaxis] # Generates query locations\n", "\n", "# transform into feature space (now n by p)\n", "p = 8 # n features...\n", "\n", "mu = np.linspace(0,np.max(speedQuery),10)[:,np.newaxis] #The mean of the radial basis functions used in this model\n", "s = 5.5 # the spatial width of the basis function\n", "\n", "#Solution:\n", "theta = radialFeatureGen(speed_whitened,mu,s)\n", "thetaQuery = radialFeatureGen(speedQuery,mu,s)\n", "\n", "params0 = np.array([0.7**-2, 0.3**-2 ]) # signal, noise inverse standard deviations\n", "# Note we havent defined numerical gradients (which might be a good idea later), so we will use\n", "# the COBYLA algorithm that can estimate them automatically\n", "nLMLcriterion = lambda params: nLML(params, theta, dist_whitened, verbose=False)\n", "newparams = scipy.optimize.minimize(nLMLcriterion, params0, method='COBYLA') # gradient free\n", "\n", "# Define a prior\n", "alpha = newparams.x[0]**2\n", "beta = newparams.x[1]**2\n", "\n", "# Query the model \n", "f_mean, f_std = linreg(theta, dist_whitened, thetaQuery, alpha, beta)\n", "\n", "#Unwhiten\n", "f_mean_unwhite = f_mean*dist_std\n", "f_std_unwhite = f_std*dist_std\n", "speedQuery_unwhite = speedQuery*speed_std\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 31 }, { "cell_type": "code", "collapsed": false, "input": [ "# plot the mean, and +- 2 standard deviations\n", "pl.figure(figsize=(15,5))\n", "pl.plot(speed,stoppingDist,'k.')\n", "pl.plot(speedQuery_unwhite, f_mean_unwhite, 'b')\n", "pl.plot(speedQuery_unwhite, f_mean_unwhite+2.5*f_std_unwhite, 'r')\n", "pl.plot(speedQuery_unwhite, f_mean_unwhite-2.5*f_std_unwhite, 'r')\n", "pl.grid()\n", "pl.xlabel('Input x')\n", "pl.ylabel('Output y')\n", "pl.title('Predictive Envelope with Data and Mean')\n", "pl.show()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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cVk1lWfapyp+IiIiIVE2FhdYkfPx4uzRtaoFv+XJbQ3kROTk53/TdS0lJCUvf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\n9eoFjRpFe5QiIiJRofAnUgKtna/CCgps7WJgyFu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16/w6MHGEz0vmCQ6S+Tf0\ndmBykX/OkvTXb+LmQVGSpDFl+JMk6cZeyDpuSZ93AQ+nzz8NTEqfXwBmjPB1JgLfAj4HfAD84Vjf\nqCRJNzLSbzYlSap3qRHO880GdhBDNZNtHP4K+BExRPRnZLaN2EH07HUS8wL/c9bXeRX4JREgdwJv\nA/+HWPhFkiRJklRBh4E5lb4JSZLGgsM+JUkaWTn2ApQkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIk\nSZIkSZIkSZIkSZIkSdXt/wOP/UQ34tQ2vQAAAABJRU5ErkJggg==\n", "text": [ "<matplotlib.figure.Figure at 0x7fb6e5abd090>" ] } ], "prompt_number": 32 }, { "cell_type": "code", "collapsed": false, "input": [ "#Query the model at the speed of interest (21km/h)\n", "\n", "speedQuery = 21/speed_std #(whitened)\n", "\n", "thetaQuery = radialFeatureGen(speedQuery,mu,s)\n", "\n", "# Query the model \n", "f_mean, f_std = linreg(theta, dist_whitened, thetaQuery, alpha, beta)\n", "\n", "#Unwhiten\n", "f_mean_unwhite = f_mean*dist_std\n", "f_std_unwhite = f_std*dist_std\n", "speedQuery_unwhite = speedQuery*speed_std\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 33 }, { "cell_type": "code", "collapsed": false, "input": [ "# Determine the probability mass to the left of 135 metres in a \n", "# normal distribution with mean: predStoppingDistMean and standard deviation: predStoppingDistStd \n", "predStoppingDistMean = f_mean_unwhite\n", "predStoppingDistStd = f_std_unwhite\n", "distToWall = 135\n", "prob = stats.norm.cdf(distToWall,predStoppingDistMean,predStoppingDistStd)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 34 }, { "cell_type": "code", "collapsed": false, "input": [ "print('Probability of stopping before hitting the wall: '+ str(prob[0,0]))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Probability of stopping before hitting the wall: 0.159299284463\n" ] } ], "prompt_number": 35 } ], "metadata": {} } ] }
gpl-2.0
Unidata/unidata-python-workshop
notebooks/MetPy_Case_Study/MetPy_Case_Study.ipynb
1
52647
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<a name=\"top\"></a>\n", "<div style=\"width:1000 px\">\n", "\n", "<div style=\"float:right; width:98 px; height:98px;\">\n", "<img src=\"https://raw.githubusercontent.com/Unidata/MetPy/master/src/metpy/plots/_static/unidata_150x150.png\" alt=\"Unidata Logo\" style=\"height: 98px;\">\n", "</div>\n", "\n", "<h1>MetPy Case Study</h1>\n", "\n", "<div style=\"clear:both\"></div>\n", "</div>\n", "\n", "<hr style=\"height:2px;\">\n", "\n", "This is a tutorial on building a case study map for Dynamic Meteorology courses with use of Unidata tools, specifically [MetPy](https://unidata.github.io/MetPy/latest/) and [Siphon](https://unidata.github.io/siphon/latest/). In this tutorial we will cover accessing, calculating, and plotting model output.\n", "\n", "Let's investigate The Storm of the Century, although it would easy to change which case you wanted (please feel free to do so).\n", "\n", "Reanalysis Output: NARR 00 UTC 13 March 1993\n", "\n", "### Data from Reanalysis on pressure surfaces:\n", "* Geopotential Heights\n", "* Temperature\n", "* u-wind component\n", "* v-wind component\n", "\n", "### Calculations:\n", "* Vertical Vorticity\n", "* Advection of Temperature and Vorticity\n", "* Horizontal Divergence\n", "* Wind Speed" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from datetime import datetime\n", "\n", "import cartopy.crs as ccrs\n", "import cartopy.feature as cfeature\n", "from netCDF4 import Dataset, num2date\n", "import numpy as np\n", "from scipy.ndimage import gaussian_filter\n", "from siphon.catalog import TDSCatalog\n", "from siphon.ncss import NCSS\n", "import matplotlib.pyplot as plt\n", "import metpy.calc as mpcalc\n", "from metpy.plots import StationPlot\n", "from metpy.units import units" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Case Study Data\n", "\n", "There are a number of different sites that you can utilize to access past model output analyses and even forecasts. The most robust collection is housed at the National Center for Environmental Information (NCEI, formerly NCDC) on a THREDDS server. The general website to begin your search is\n", "\n", "https://www.ncdc.noaa.gov/data-access\n", "\n", "this link contains links to many different data sources (some of which we will come back to later in this tutorial). But for now, lets investigate what model output is avaiable\n", "\n", "https://www.ncdc.noaa.gov/data-access/model-data/model-datasets\n", "\n", "The gridded model output that are available\n", "\n", "**Reanalysis**\n", " * Climate Forecast System Reanalysis (CFSR)\n", " * CFSR provides a global reanalysis (a best estimate of the observed state of the atmosphere) of past weather from January 1979 through March 2011 at a horizontal resolution of 0.5°.\n", " * North American Regional Reanalysis (NARR)\n", " * NARR is a regional reanalysis of North America containing temperatures, winds, moisture, soil data, and dozens of other parameters at 32km horizontal resolution.\n", " * Reanalysis-1 / Reanalysis-2 (R1/R2)\n", " * Reanalysis-1 / Reanalysis-2 are two global reanalyses of atmospheric data spanning 1948/1979 to present at a 2.5° horizontal resolution.\n", "\n", "**Numerical Weather Prediction**\n", " * Climate Forecast System (CFS) \n", " * CFS provides a global reanalysis, a global reforecast of past weather, and an operational, seasonal forecast of weather out to nine months.\n", " * Global Data Assimilation System (GDAS)\n", " * GDAS is the set of assimilation data, both input and output, in various formats for the Global Forecast System model.\n", " * Global Ensemble Forecast System (GEFS)\n", " * GEFS is a global-coverage weather forecast model made up of 21 separate forecasts, or ensemble members, used to quantify the amount of uncertainty in a forecast. GEFS produces output four times a day with weather forecasts going out to 16 days.\n", " * Global Forecast System (GFS)\n", " * The GFS model is a coupled weather forecast model, composed of four separate models which work together to provide an accurate picture of weather conditions. GFS covers the entire globe down to a horizontal resolution of 28km.\n", " * North American Mesoscale (NAM)\n", " * NAM is a regional weather forecast model covering North America down to a horizontal resolution of 12km. Dozens of weather parameters are available from the NAM grids, from temperature and precipitation to lightning and turbulent kinetic energy.\n", " * Rapid Refresh (RAP)\n", " * RAP is a regional weather forecast model of North America, with separate sub-grids (with different horizontal resolutions) within the overall North America domain. RAP produces forecasts every hour with forecast lengths going out 18 hours. RAP replaced the Rapid Update Cycle (RUC) model on May 1, 2012.\n", " * Navy Operational Global Atmospheric Prediction System (NOGAPS)\n", " * NOGAPS analysis data are available in six-hourly increments on regularly spaced latitude-longitude grids at 1-degree and one-half-degree resolutions. Vertical resolution varies from 18 to 28 pressure levels, 34 sea level depths, the surface, and other various levels.\n", "\n", "**Ocean Models**\n", " * Hybrid Coordinate Ocean Model (HYCOM), Global\n", " * The Navy implementation of HYCOM is the successor to Global NCOM. This site hosts regions covering U.S. coastal waters as well as a global surface model.\n", " * Navy Coastal Ocean Model (NCOM), Global\n", " * Global NCOM was run by the Naval Oceanographic Office (NAVOCEANO) as the Navy’s operational global ocean-prediction system prior to its replacement by the Global HYCOM system in 2013. This site hosts regions covering U.S., European, West Pacific, and Australian coastal waters as well as a global surface model.\n", " * Navy Coastal Ocean Model (NCOM), Regional\n", " * The Regional NCOM is a high-resolution version of NCOM for specific areas. NCEI serves the Americas Seas, U.S. East, and Alaska regions of NCOM.\n", " * Naval Research Laboratory Adaptive Ecosystem Climatology (AEC)\n", " * The Naval Research Laboratory AEC combines an ocean model with Earth observations to provide a synoptic view of the typical (climatic) state of the ocean for every day of the year. This dataset covers the Gulf of Mexico and nearby areas.\n", " * National Centers for Environmental Prediction (NCEP) Real Time Ocean Forecast System (RTOFS)–Atlantic\n", " * RTOFS–Atlantic is a data-assimilating nowcast-forecast system operated by NCEP. This dataset covers the Gulf of Mexico and most of the northern and central Atlantic.\n", "\n", "**Climate Prediction**\n", " * CM2 Global Coupled Climate Models (CM2.X)\n", " * CM2.X consists of two climate models to model the changes in climate over the past century and into the 21st century.\n", " * Coupled Model Intercomparison Project Phase 5 (CMIP5) (link is external)\n", " * The U.N. Intergovernmental Panel on Climate Change (IPCC) coordinates global analysis of climate models under the Climate Model Intercomparison Project (CMIP). CMIP5 is in its fifth iteration. Data are available through the Program for Climate Model Diagnosis and Intercomparison (PCMDI) website.\n", " \n", "**Derived / Other Model Data**\n", " * Service Records Retention System (SRRS)\n", " * SRRS is a store of weather observations, summaries, forecasts, warnings, and advisories generated by the National Weather Service for public use.\n", " * NOMADS Ensemble Probability Tool\n", " * The NOMADS Ensemble Probability Tool allows a user to query the Global Ensemble Forecast System (GEFS) to determine the probability that a set of forecast conditions will occur at a given location using all of the 21 separate GEFS ensemble members.\n", " * National Digital Forecast Database (NDFD)\n", " * NDFD are gridded forecasts created from weather data collected by National Weather Service field offices and processed through the National Centers for Environmental Prediction. NDFD data are available by WMO header or by date range.\n", " * National Digital Guidance Database (NDGD)\n", " * NDGD consists of forecasts, observations, model probabilities, climatological normals, and other digital data that complement the National Digital Forecast Database." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## NARR Output\n", "Lets investigate what specific NARR output is available to work with from NCEI.\n", "\n", "https://www.ncdc.noaa.gov/data-access/model-data/model-datasets/north-american-regional-reanalysis-narr\n", "\n", "We specifically want to look for data that has \"TDS\" data access, since that is short for a THREDDS server data access point. There are a total of four different GFS datasets that we could potentially use.\n", "\n", "\n", "**Choosing our data source**\n", "Let's go ahead and use the NARR Analysis data to investigate the past case we identified (The Storm of the Century).\n", "\n", "https://www.ncei.noaa.gov/thredds/catalog/narr-a-files/199303/19930313/catalog.html?dataset=narr-a-files/199303/19930313/narr-a_221_19930313_0000_000.grb\n", "\n", "And we will use a python package called Siphon to read this data through the NetCDFSubset (NetCDFServer) link.\n", "\n", "https://www.ncei.noaa.gov/thredds/ncss/grid/narr-a-files/199303/19930313/narr-a_221_19930313_0000_000.grb/dataset.html" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Case Study Date\n", "year = 1993\n", "month = 3\n", "day = 13\n", "hour = 0\n", "\n", "dt = datetime(year, month, day, hour)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Read NARR Data from THREDDS server\n", "base_url = 'https://www.ncei.noaa.gov/thredds/catalog/narr-a-files/'\n", "\n", "# Programmatically generate the URL to the day of data we want\n", "cat = TDSCatalog(f'{base_url}{dt:%Y%m}/{dt:%Y%m%d}/catalog.xml')\n", "\n", "# Have Siphon find the appropriate dataset\n", "ds = cat.datasets.filter_time_nearest(dt)\n", "\n", "# Download data using the NetCDF Subset Service\n", "ncss = ds.subset()\n", "query = ncss.query().lonlat_box(north=60, south=18, east=300, west=225)\n", "query.all_times().variables('Geopotential_height_isobaric', 'Temperature_isobaric',\n", " 'u-component_of_wind_isobaric',\n", " 'v-component_of_wind_isobaric').add_lonlat().accept('netcdf')\n", "data = ncss.get_data(query)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Back up in case of bad internet connection.\n", "# Uncomment the following line to read local netCDF file of NARR data\n", "# data = Dataset('../../data/NARR_19930313_0000.nc','r')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's see what dimensions are in the file:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data.dimensions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Pulling Data for Calculation/Plotting\n", "\n", "The object that we get from Siphon is netCDF-like, so we can pull data using familiar calls for all of the variables that are desired for calculations and plotting purposes.\n", "\n", "**NOTE:**\n", "Due to the curvilinear nature of the NARR grid, there is a need to smooth the data that we import for calculation and plotting purposes. For more information about why, please see the following link: http://www.atmos.albany.edu/facstaff/rmctc/narr/\n", "\n", "Additionally, we want to attach units to our values for use in MetPy calculations later and it will also allow for easy conversion to other units.\n", "\n", "<div class=\"alert alert-success\">\n", " <b>EXERCISE</b>:\n", " Replace the `0`'s in the template below with your code:\n", " <ul>\n", " <li>Use the `gaussian_filter` function to smooth the `Temperature_isobaric`, `Geopotential_height_isobaric`, `u-component_of_wind_isobaric`, and `v-component_of_wind_isobaric` variables from the netCDF object with a `sigma` value of 1.</li>\n", " <li>Assign the units of `kelvin`, `meter`, `m/s`, and `m/s` resectively.</li>\n", " <li>Extract the `lat`, `lon`, and `isobaric1` variables.</li>\n", " </ul>\n", "</div>" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Extract data and assign units\n", "tmpk = gaussian_filter(data.variables['Temperature_isobaric'][0], sigma=1.0) * units.K\n", "hght = 0\n", "uwnd = 0\n", "vwnd = 0\n", "\n", "# Extract coordinate data for plotting\n", "lat = data.variables['lat'][:]\n", "lon = data.variables['lon'][:]\n", "lev = 0" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<button data-toggle=\"collapse\" data-target=\"#sol1\" class='btn btn-primary'>View Solution</button>\n", "<div id=\"sol1\" class=\"collapse\">\n", "<code><pre>\n", "# Extract data and assign units\n", "tmpk = gaussian_filter(data.variables['Temperature_isobaric'][0],\n", " sigma=1.0) * units.K\n", "hght = gaussian_filter(data.variables['Geopotential_height_isobaric'][0],\n", " sigma=1.0) * units.meter\n", "uwnd = gaussian_filter(data.variables['u-component_of_wind_isobaric'][0], sigma=1.0) * units('m/s')\n", "vwnd = gaussian_filter(data.variables['v-component_of_wind_isobaric'][0], sigma=1.0) * units('m/s')\n", "\n", "\\# Extract coordinate data for plotting\n", "lat = data.variables['lat'][:]\n", "lon = data.variables['lon'][:]\n", "lev = data.variables['isobaric1'][:]\n", "</pre></code>\n", "</div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next we need to extract the time variable. It's not in very useful units, but the `num2date` function can be used to easily create regular datetime objects." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "time = data.variables['time1']\n", "print(time.units)\n", "vtime = num2date(time[0], units=time.units)\n", "print(vtime)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we need to calculate the spacing of the grid in distance units instead of degrees using the MetPy helper function `lat_lon_grid_spacing`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Calcualte dx and dy for calculations\n", "dx, dy = mpcalc.lat_lon_grid_deltas(lon, lat)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Finding Pressure Level Data\n", "A robust way to parse the data for a certain pressure level is to find the index value using the `np.where` function. Since the NARR pressure data ('levels') is in hPa, then we'll want to search that array for our pressure levels 850, 500, and 300 hPa.\n", "\n", "<div class=\"alert alert-success\">\n", " <b>EXERCISE</b>:\n", " Replace the `0`'s in the template below with your code:\n", " <ul>\n", " <li>Find the index of the 850 hPa, 500 hPa, and 300 hPa levels.</li>\n", " <li>Extract the heights, temperature, u, and v winds at those levels.</li>\n", " </ul>\n", "</div>\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Specify 850 hPa data\n", "ilev850 = np.where(lev==850)[0][0]\n", "hght_850 = hght[ilev850]\n", "tmpk_850 = 0\n", "uwnd_850 = 0\n", "vwnd_850 = 0" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Specify 500 hPa data\n", "ilev500 = 0\n", "hght_500 = 0\n", "uwnd_500 = 0\n", "vwnd_500 = 0" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Specify 300 hPa data\n", "ilev300 = 0\n", "hght_300 = 0\n", "uwnd_300 = 0\n", "vwnd_300 = 0" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<button data-toggle=\"collapse\" data-target=\"#sol2\" class='btn btn-primary'>View Solution</button>\n", "<div id=\"sol2\" class=\"collapse\">\n", "<code><pre>\n", "# Specify 850 hPa data\n", "ilev850 = np.where(lev == 850)[0][0]\n", "hght_850 = hght[ilev850]\n", "tmpk_850 = tmpk[ilev850]\n", "uwnd_850 = uwnd[ilev850]\n", "vwnd_850 = vwnd[ilev850]\n", "\n", "\\# Specify 500 hPa data\n", "ilev500 = np.where(lev == 500)[0][0]\n", "hght_500 = hght[ilev500]\n", "uwnd_500 = uwnd[ilev500]\n", "vwnd_500 = vwnd[ilev500]\n", "\n", "\\# Specify 300 hPa data\n", "ilev300 = np.where(lev == 300)[0][0]\n", "hght_300 = hght[ilev300]\n", "uwnd_300 = uwnd[ilev300]\n", "vwnd_300 = vwnd[ilev300]\n", "</pre></code>\n", "</div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Using MetPy to Calculate Atmospheric Dynamic Quantities\n", "\n", "MetPy has a large and growing list of functions to calculate many different atmospheric quantities. Here we want to use some classic functions to calculate wind speed, advection, planetary vorticity, relative vorticity, and divergence.\n", "\n", "* Wind Speed: `mpcalc.wind_speed()`\n", "* Advection: `mpcalc.advection()`\n", "* Planetary Vorticity: `mpcalc.coriolis_parameter()`\n", "* Relative Vorticity: `mpcalc.vorticity()`\n", "* Divergence: `mpcalc.divergence()`\n", "\n", "Note: For the above, MetPy Calculation module is imported in the following manner `import metpy.calc as mpcalc`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Temperature Advection\n", "\n", "A classic QG forcing term is 850-hPa temperature advection. MetPy has a function for advection\n", "\n", "[`advection(scalar quantity, [advecting vector components], (grid spacing components))`](https://unidata.github.io/MetPy/latest/api/generated/metpy.calc.advection.html#metpy.calc.advection\n", ")\n", "\n", "So for temperature advection our scalar quantity would be the tempertaure, the advecting vector components would be our u and v components of the wind, and the grid spacing would be our dx and dy we computed in an earier cell.\n", "\n", "<div class=\"alert alert-success\">\n", " <b>EXERCISE</b>:\n", " <ul>\n", " <li>Uncomment and fill out the advection calculation below.</li>\n", " </ul>\n", "</div>\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Temperature Advection\n", "# tmpc_adv_850 = mpcalc.advection(--Fill in this call--).to('degC/s')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<button data-toggle=\"collapse\" data-target=\"#sol3\" class='btn btn-primary'>View Solution</button>\n", "<div id=\"sol3\" class=\"collapse\">\n", "<code><pre>\n", "# Temperature Advection\n", "tmpc_adv_850 = mpcalc.advection(tmpk_850, [uwnd_850, vwnd_850],\n", " (dx, dy), dim_order='yx').to('degC/s')\n", "\n", "</pre></code>\n", "</div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Vorticity Calculations\n", "\n", "There are a couple of different vorticities that we are interested in for various calculations, planetary vorticity, relative vorticity, and absolute vorticity. Currently MetPy has two of the three as functions within the calc module.\n", "\n", "**Planetary Vorticity (Coriolis Parameter)**\n", "\n", "[`coriolis_parameter(latitude in radians)`](\n", "https://unidata.github.io/MetPy/latest/api/generated/metpy.calc.coriolis_parameter.html#metpy.calc.coriolis_parameter)\n", "\n", "Note: You must can convert your array of latitudes to radians...NumPy give a great function `np.deg2rad()` or have units attached to your latitudes in order for MetPy to convert them for you! Always check your output to make sure that your code is producing what you think it is producing.\n", "\n", "**Relative Vorticity**\n", "\n", "When atmospheric scientists talk about relative vorticity, we are really refering to the relative vorticity that is occuring about the vertical axis (the k-hat component). So in MetPy the function is\n", "\n", "[`vorticity(uwind, vwind, dx, dy)`](\n", "https://unidata.github.io/MetPy/latest/api/generated/metpy.calc.vorticity.html#metpy.calc.vorticity)\n", "\n", "** Absolute Vorticity**\n", "\n", "Currently there is no specific function for Absolute Vorticity, but this is easy for us to calculate from the previous two calculations because we just need to add them together!\n", "\n", "`ABS Vort = Rel. Vort + Coriolis Parameter`\n", "\n", "Here having units are great, becase we won't be able to add things together that don't have the same units! Its a nice safety check just in case you entered something wrong in another part of the calculation, you'll get a units error.\n", "\n", "<div class=\"alert alert-success\">\n", " <b>EXERCISE</b>:\n", " <ul>\n", " <li>Fill in the function calls below to complete the vorticity calculations.</li>\n", " </ul>\n", "</div>\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Vorticity and Absolute Vorticity Calculations\n", "\n", "# Planetary Vorticity\n", "# f = mpcalc.coriolis_parameter(-- Fill in here --).to('1/s')\n", "\n", "# Relative Vorticity\n", "# vor_500 = mpcalc.vorticity(-- Fill in here --)\n", "\n", "# Abosolute Vorticity\n", "# avor_500 = vor_500 + f" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<button data-toggle=\"collapse\" data-target=\"#sol4\" class='btn btn-primary'>View Solution</button>\n", "<div id=\"sol4\" class=\"collapse\">\n", "<code><pre>\n", "# Vorticity and Absolute Vorticity Calculations\n", "\n", "\\# Planetary Vorticity\n", "f = mpcalc.coriolis_parameter(np.deg2rad(lat)).to('1/s')\n", "\n", "\\# Relative Vorticity\n", "vor_500 = mpcalc.vorticity(uwnd_500, vwnd_500, dx, dy,\n", " dim_order='yx')\n", "\n", "\\# Abosolute Vorticity\n", "avor_500 = vor_500 + f\n", "</pre></code>\n", "</div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Vorticity Advection\n", "\n", "We use the same MetPy function for temperature advection for our vorticity advection, we just have to change the scalar quantity (what is being advected) and have appropriate vector quantities for the level our scalar is from. So for vorticity advections well want our wind components from 500 hPa." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Vorticity Advection\n", "f_adv = mpcalc.advection(f, [uwnd_500, vwnd_500], (dx, dy), dim_order='yx')\n", "\n", "relvort_adv = mpcalc.advection(vor_500, [uwnd_500, vwnd_500], (dx, dy), dim_order='yx')\n", "\n", "absvort_adv = mpcalc.advection(avor_500, [uwnd_500, vwnd_500], (dx, dy), dim_order='yx')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Divergence and Stretching Vorticity\n", "\n", "If we want to analyze another component of the vorticity tendency equation other than advection, we might want to assess the stretching forticity term.\n", "\n", "`-(Abs. Vort.)*(Divergence)`\n", "\n", "We already have absolute vorticity calculated, so now we need to calculate the divergence of the level, which MetPy has a function\n", "\n", "`divergence(uwnd, vwnd, dx, dy)`\n", "\n", "This function computes the horizontal divergence." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Stretching Vorticity\n", "div_500 = mpcalc.divergence(uwnd_500, vwnd_500, dx, dy, dim_order='yx')\n", "\n", "stretch_vort = -1 * avor_500 * div_500" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Wind Speed, Geostrophic and Ageostrophic Wind\n", "\n", "**Wind Speed**\n", "\n", "Calculating wind speed is not a difficult calculation, but MetPy offers a function to calculate it easily keeping units so that it is easy to convert units for plotting purposes.\n", "\n", "[`wind_speed(uwnd, vwnd)`](https://unidata.github.io/MetPy/latest/api/generated/metpy.calc.wind_speed.html#metpy.calc.wind_speed)\n", "\n", "**Geostrophic Wind**\n", "\n", "The geostrophic wind can be computed from a given height gradient and coriolis parameter\n", "\n", "[`geostrophic_wind(heights, coriolis parameter, dx, dy)`](\n", "https://unidata.github.io/MetPy/latest/api/generated/metpy.calc.geostrophic_wind.html#metpy.calc.geostrophic_wind)\n", "\n", "This function will return the two geostrophic wind components in a tuple. On the left hand side you'll be able to put two variables to save them off separately, if desired.\n", "\n", "**Ageostrophic Wind**\n", "\n", "Currently, there is not a function in MetPy for calculating the ageostrophic wind, however, it is again a simple arithmatic operation to get it from the total wind (which comes from our data input) and out calculated geostrophic wind from above.\n", "\n", "`Ageo Wind = Total Wind - Geo Wind`\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Divergence 300 hPa, Ageostrophic Wind\n", "wspd_300 = mpcalc.wind_speed(uwnd_300, vwnd_300).to('kts')\n", "\n", "div_300 = mpcalc.divergence(uwnd_300, vwnd_300, dx, dy, dim_order='yx')\n", "ugeo_300, vgeo_300 = mpcalc.geostrophic_wind(hght_300, f, dx, dy, dim_order='yx')\n", "\n", "uageo_300 = uwnd_300 - ugeo_300\n", "vageo_300 = vwnd_300 - vgeo_300" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Maps and Projections" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Data projection; NARR Data is Earth Relative\n", "dataproj = ccrs.PlateCarree()\n", "\n", "# Plot projection\n", "# The look you want for the view, LambertConformal for mid-latitude view\n", "plotproj = ccrs.LambertConformal(central_longitude=-100., central_latitude=40.,\n", " standard_parallels=[30, 60])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def create_map_background():\n", " fig=plt.figure(figsize=(14, 12))\n", " ax=plt.subplot(111, projection=plotproj)\n", " ax.set_extent([-125, -73, 25, 50],ccrs.PlateCarree())\n", " ax.coastlines('50m', linewidth=0.75)\n", " ax.add_feature(cfeature.STATES, linewidth=0.5)\n", " return fig, ax" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 850-hPa Temperature Advection\n", "\n", "* Add one contour (Temperature in Celsius with a dotted linestyle\n", "* Add one colorfill (Temperature Advection in C/hr)\n", "\n", "<div class=\"alert alert-success\">\n", " <b>EXERCISE</b>:\n", " <ul>\n", " <li>Add one contour (Temperature in Celsius with a dotted linestyle</li>\n", " <li>Add one filled contour (Temperature Advection in C/hr)</li>\n", " </ul>\n", "</div>\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fig, ax = create_map_background()\n", "\n", "# Contour 1 - Temperature, dotted\n", "# Your code here!\n", "\n", "# Contour 2\n", "clev850 = np.arange(0, 4000, 30)\n", "cs = ax.contour(lon, lat, hght_850, clev850, colors='k',\n", " linewidths=1.0, linestyles='solid', transform=dataproj)\n", "\n", "plt.clabel(cs, fontsize=10, inline=1, inline_spacing=10, fmt='%i',\n", " rightside_up=True, use_clabeltext=True)\n", "\n", "# Filled contours - Temperature advection\n", "contours = [-3, -2.2, -2, -1.5, -1, -0.5, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0]\n", "# Your code here!\n", "\n", "# Vector\n", "ax.barbs(lon, lat, uwnd_850.to('kts').m, vwnd_850.to('kts').m,\n", " regrid_shape=15, transform=dataproj)\n", "\n", "# Titles\n", "plt.title('850-hPa Geopotential Heights, Temperature (C), \\\n", " Temp Adv (C/h), and Wind Barbs (kts)', loc='left')\n", "plt.title(f'VALID: {vtime}', loc='right')\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<button data-toggle=\"collapse\" data-target=\"#sol5\" class='btn btn-primary'>View Solution</button>\n", "<div id=\"sol5\" class=\"collapse\">\n", "<code><pre>\n", "fig, ax = create_map_background()\n", "\n", "\\# Contour 1 - Temperature, dotted\n", "cs2 = ax.contour(lon, lat, tmpk_850.to('degC'), range(-50, 50, 2),\n", " colors='grey', linestyles='dotted', transform=dataproj)\n", "\n", "plt.clabel(cs2, fontsize=10, inline=1, inline_spacing=10, fmt='%i',\n", " rightside_up=True, use_clabeltext=True)\n", "\n", "\\# Contour 2\n", "clev850 = np.arange(0, 4000, 30)\n", "cs = ax.contour(lon, lat, hght_850, clev850, colors='k',\n", " linewidths=1.0, linestyles='solid', transform=dataproj)\n", "\n", "plt.clabel(cs, fontsize=10, inline=1, inline_spacing=10, fmt='%i',\n", " rightside_up=True, use_clabeltext=True)\n", "\n", "\\# Filled contours - Temperature advection\n", "contours = [-3, -2.2, -2, -1.5, -1, -0.5, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0]\n", "cf = ax.contourf(lon, lat, tmpc_adv_850*3600, contours,\n", " cmap='bwr', extend='both', transform=dataproj)\n", "plt.colorbar(cf, orientation='horizontal', pad=0, aspect=50,\n", " extendrect=True, ticks=contours)\n", "\n", "\\# Vector\n", "ax.barbs(lon, lat, uwnd_850.to('kts').m, vwnd_850.to('kts').m,\n", " regrid_shape=15, transform=dataproj)\n", "\n", "\\# Titles\n", "plt.title('850-hPa Geopotential Heights, Temperature (C), \\\n", " Temp Adv (C/h), and Wind Barbs (kts)', loc='left')\n", "plt.title(f'VALID: {vtime}', loc='right')\n", "\n", "plt.tight_layout()\n", "plt.show()\n", "</pre></code>\n", "</div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 500-hPa Absolute Vorticity\n", "\n", "<div class=\"alert alert-success\">\n", " <b>EXERCISE</b>:\n", " <ul>\n", " <li>Add code for plotting vorticity as filled contours with given levels and colors.</li>\n", " </ul>\n", "</div>" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fig, ax = create_map_background()\n", "\n", "# Contour 1\n", "clev500 = np.arange(0, 7000, 60)\n", "cs = ax.contour(lon, lat, hght_500, clev500, colors='k',\n", " linewidths=1.0, linestyles='solid', transform=dataproj)\n", "plt.clabel(cs, fontsize=10, inline=1, inline_spacing=4,\n", " fmt='%i', rightside_up=True, use_clabeltext=True)\n", "\n", "# Filled contours\n", "# Set contour intervals for Absolute Vorticity\n", "clevavor500 = [-4, -3, -2, -1, 0, 7, 10, 13, 16, 19,\n", " 22, 25, 28, 31, 34, 37, 40, 43, 46]\n", "\n", "# Set colorfill colors for absolute vorticity\n", "# purple negative\n", "# yellow to orange positive\n", "colorsavor500 = ('#660066', '#660099', '#6600CC', '#6600FF',\n", " '#FFFFFF', '#ffE800', '#ffD800', '#ffC800',\n", " '#ffB800', '#ffA800', '#ff9800', '#ff8800',\n", " '#ff7800', '#ff6800', '#ff5800', '#ff5000',\n", " '#ff4000', '#ff3000')\n", "\n", "# YOUR CODE HERE!\n", "\n", "plt.colorbar(cf, orientation='horizontal', pad=0, aspect=50)\n", "\n", "# Vector\n", "ax.barbs(lon, lat, uwnd_500.to('kts').m, vwnd_500.to('kts').m,\n", " regrid_shape=15, transform=dataproj)\n", "\n", "# Titles\n", "plt.title('500-hPa Geopotential Heights, Absolute Vorticity \\\n", " (1/s), and Wind Barbs (kts)', loc='left')\n", "plt.title(f'VALID: {vtime}', loc='right')\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<button data-toggle=\"collapse\" data-target=\"#sol6\" class='btn btn-primary'>View Solution</button>\n", "<div id=\"sol6\" class=\"collapse\">\n", "<code><pre>\n", "fig, ax = create_map_background()\n", "\n", "\\# Contour 1\n", "clev500 = np.arange(0, 7000, 60)\n", "cs = ax.contour(lon, lat, hght_500, clev500, colors='k',\n", " linewidths=1.0, linestyles='solid', transform=dataproj)\n", "plt.clabel(cs, fontsize=10, inline=1, inline_spacing=4,\n", " fmt='%i', rightside_up=True, use_clabeltext=True)\n", "\n", "\\# Filled contours\n", "\\# Set contour intervals for Absolute Vorticity\n", "clevavor500 = [-4, -3, -2, -1, 0, 7, 10, 13, 16, 19,\n", " 22, 25, 28, 31, 34, 37, 40, 43, 46]\n", "\n", "\\# Set colorfill colors for absolute vorticity\n", "\\# purple negative\n", "\\# yellow to orange positive\n", "colorsavor500 = ('#660066', '#660099', '#6600CC', '#6600FF',\n", " '#FFFFFF', '#ffE800', '#ffD800', '#ffC800',\n", " '#ffB800', '#ffA800', '#ff9800', '#ff8800',\n", " '#ff7800', '#ff6800', '#ff5800', '#ff5000',\n", " '#ff4000', '#ff3000')\n", "\n", "cf = ax.contourf(lon, lat, avor_500 * 10**5, clevavor500,\n", " colors=colorsavor500, transform=dataproj)\n", "plt.colorbar(cf, orientation='horizontal', pad=0, aspect=50)\n", "\n", "\\# Vector\n", "ax.barbs(lon, lat, uwnd_500.to('kts').m, vwnd_500.to('kts').m,\n", " regrid_shape=15, transform=dataproj)\n", "\n", "\\# Titles\n", "plt.title('500-hPa Geopotential Heights, Absolute Vorticity \\\n", " (1/s), and Wind Barbs (kts)', loc='left')\n", "plt.title(f'VALID: {vtime}', loc='right')\n", "\n", "plt.tight_layout()\n", "plt.show()\n", "</pre></code>\n", "</div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 300-hPa Wind Speed, Divergence, and Ageostrophic Wind\n", "\n", "<div class=\"alert alert-success\">\n", " <b>EXERCISE</b>:\n", " <ul>\n", " <li>Add code to plot 300-hPa Ageostrophic Wind vectors using matplotlib's quiver function.</li>\n", " </ul>\n", "</div>" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fig, ax = create_map_background()\n", "\n", "# Contour 1\n", "clev300 = np.arange(0, 11000, 120)\n", "cs2 = ax.contour(lon, lat, div_300 * 10**5, range(-10, 11, 2),\n", " colors='grey', transform=dataproj)\n", "plt.clabel(cs2, fontsize=10, inline=1, inline_spacing=4,\n", " fmt='%i', rightside_up=True, use_clabeltext=True)\n", "\n", "# Contour 2\n", "cs = ax.contour(lon, lat, hght_300, clev300, colors='k',\n", " linewidths=1.0, linestyles='solid', transform=dataproj)\n", "plt.clabel(cs, fontsize=10, inline=1, inline_spacing=4,\n", " fmt='%i', rightside_up=True, use_clabeltext=True)\n", "\n", "# Filled Contours\n", "spd300 = np.arange(50, 250, 20)\n", "cf = ax.contourf(lon, lat, wspd_300, spd300, cmap='BuPu',\n", " transform=dataproj, zorder=0)\n", "plt.colorbar(cf, orientation='horizontal', pad=0.0, aspect=50)\n", "\n", "# Vector of 300-hPa Ageostrophic Wind Vectors\n", "# Your code goes here!\n", "\n", "# Titles\n", "plt.title('300-hPa Geopotential Heights, Divergence (1/s),\\\n", " Wind Speed (kts), Ageostrophic Wind Vector (m/s)',\n", " loc='left')\n", "plt.title(f'VALID: {vtime}', loc='right')\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<button data-toggle=\"collapse\" data-target=\"#sol7\" class='btn btn-primary'>View Solution</button>\n", "<div id=\"sol7\" class=\"collapse\">\n", "<code><pre>\n", "fig, ax = create_map_background()\n", "\n", "\\# Contour 1\n", "clev300 = np.arange(0, 11000, 120)\n", "cs2 = ax.contour(lon, lat, div_300 * 10**5, range(-10, 11, 2),\n", " colors='grey', transform=dataproj)\n", "plt.clabel(cs2, fontsize=10, inline=1, inline_spacing=4,\n", " fmt='%i', rightside_up=True, use_clabeltext=True)\n", "\n", "\\# Contour 2\n", "cs = ax.contour(lon, lat, hght_300, clev300, colors='k',\n", " linewidths=1.0, linestyles='solid', transform=dataproj)\n", "plt.clabel(cs, fontsize=10, inline=1, inline_spacing=4,\n", " fmt='%i', rightside_up=True, use_clabeltext=True)\n", "\n", "\\# Filled Contours\n", "spd300 = np.arange(50, 250, 20)\n", "cf = ax.contourf(lon, lat, wspd_300, spd300, cmap='BuPu',\n", " transform=dataproj, zorder=0)\n", "plt.colorbar(cf, orientation='horizontal', pad=0.0, aspect=50)\n", "\n", "\\# Vector of 300-hPa Ageostrophic Wind Vectors\n", "ax.quiver(lon, lat, uageo_300.m, vageo_300.m, regrid_shape=15,\n", " pivot='mid', transform=dataproj, zorder=10)\n", "\n", "\\# Titles\n", "plt.title('300-hPa Geopotential Heights, Divergence (1/s),\\\n", " Wind Speed (kts), Ageostrophic Wind Vector (m/s)',\n", " loc='left')\n", "plt.title(f'VALID: {vtime}', loc='right')\n", "\n", "plt.tight_layout()\n", "plt.show()\n", "</pre></code>\n", "</div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Vorticity Tendency Terms\n", "\n", "Here is an example of a four-panel plot for a couple of terms in the Vorticity Tendency equation\n", "\n", "Upper-left Panel: Planetary Vorticity Advection\n", "\n", "Upper-right Panel: Relative Vorticity Advection\n", "\n", "Lower-left Panel: Absolute Vorticity Advection\n", "\n", "Lower-right Panel: Stretching Vorticity" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fig=plt.figure(1,figsize=(21.,16.))\n", "\n", "# Upper-Left Panel\n", "ax=plt.subplot(221,projection=plotproj)\n", "ax.set_extent([-125.,-73,25.,50.],ccrs.PlateCarree())\n", "ax.coastlines('50m', linewidth=0.75)\n", "ax.add_feature(cfeature.STATES,linewidth=0.5)\n", "\n", "# Contour #1\n", "clev500 = np.arange(0,7000,60)\n", "cs = ax.contour(lon,lat,hght_500,clev500,colors='k',\n", " linewidths=1.0,linestyles='solid',transform=dataproj)\n", "plt.clabel(cs, fontsize=10, inline=1, inline_spacing=3, fmt='%i', rightside_up=True, use_clabeltext=True)\n", "\n", "# Contour #2\n", "cs2 = ax.contour(lon,lat,f*10**4,np.arange(0,3,.05),colors='grey',\n", " linewidths=1.0,linestyles='dashed',transform=dataproj)\n", "plt.clabel(cs2, fontsize=10, inline=1, inline_spacing=3, fmt='%.2f', rightside_up=True, use_clabeltext=True)\n", "\n", "# Colorfill\n", "cf = ax.contourf(lon,lat,f_adv*10**10,np.arange(-10,11,0.5),\n", " cmap='PuOr_r',extend='both',transform=dataproj)\n", "plt.colorbar(cf, orientation='horizontal',pad=0.0,aspect=50,extendrect=True)\n", "\n", "# Vector\n", "ax.barbs(lon,lat,uwnd_500.to('kts').m,vwnd_500.to('kts').m,regrid_shape=15,transform=dataproj)\n", "\n", "# Titles\n", "plt.title(r'500-hPa Geopotential Heights, Planetary Vorticity Advection ($*10^{10}$ 1/s^2)',loc='left')\n", "plt.title('VALID: %s' %(vtime),loc='right')\n", "\n", "\n", "\n", "# Upper-Right Panel\n", "ax=plt.subplot(222,projection=plotproj)\n", "ax.set_extent([-125.,-73,25.,50.],ccrs.PlateCarree())\n", "ax.coastlines('50m', linewidth=0.75)\n", "ax.add_feature(cfeature.STATES, linewidth=0.5)\n", "\n", "# Contour #1\n", "clev500 = np.arange(0,7000,60)\n", "cs = ax.contour(lon,lat,hght_500,clev500,colors='k',\n", " linewidths=1.0,linestyles='solid',transform=dataproj)\n", "plt.clabel(cs, fontsize=10, inline=1, inline_spacing=3, fmt='%i', rightside_up=True, use_clabeltext=True)\n", "\n", "# Contour #2\n", "cs2 = ax.contour(lon,lat,vor_500*10**5,np.arange(-40,41,4),colors='grey',\n", " linewidths=1.0,transform=dataproj)\n", "plt.clabel(cs2, fontsize=10, inline=1, inline_spacing=3, fmt='%d', rightside_up=True, use_clabeltext=True)\n", "\n", "# Colorfill\n", "cf = ax.contourf(lon,lat,relvort_adv*10**8,np.arange(-5,5.5,0.5),\n", " cmap='BrBG',extend='both',transform=dataproj)\n", "plt.colorbar(cf, orientation='horizontal',pad=0.0,aspect=50,extendrect=True)\n", "\n", "# Vector\n", "ax.barbs(lon,lat,uwnd_500.to('kts').m,vwnd_500.to('kts').m,regrid_shape=15,transform=dataproj)\n", "\n", "# Titles\n", "plt.title(r'500-hPa Geopotential Heights, Relative Vorticity Advection ($*10^{8}$ 1/s^2)',loc='left')\n", "plt.title('VALID: %s' %(vtime),loc='right')\n", "\n", "\n", "\n", "# Lower-Left Panel\n", "ax=plt.subplot(223,projection=plotproj)\n", "ax.set_extent([-125.,-73,25.,50.],ccrs.PlateCarree())\n", "ax.coastlines('50m', linewidth=0.75)\n", "ax.add_feature(cfeature.STATES, linewidth=0.5)\n", "\n", "# Contour #1\n", "clev500 = np.arange(0,7000,60)\n", "cs = ax.contour(lon,lat,hght_500,clev500,colors='k',\n", " linewidths=1.0,linestyles='solid',transform=dataproj)\n", "plt.clabel(cs, fontsize=10, inline=1, inline_spacing=3, fmt='%i', rightside_up=True, use_clabeltext=True)\n", "\n", "# Contour #2\n", "cs2 = ax.contour(lon,lat,avor_500*10**5,np.arange(-5,41,4),colors='grey',\n", " linewidths=1.0,transform=dataproj)\n", "plt.clabel(cs2, fontsize=10, inline=1, inline_spacing=3, fmt='%d', rightside_up=True, use_clabeltext=True)\n", "\n", "# Colorfill\n", "cf = ax.contourf(lon,lat,absvort_adv*10**8,np.arange(-5,5.5,0.5),\n", " cmap='RdBu',extend='both',transform=dataproj)\n", "plt.colorbar(cf, orientation='horizontal',pad=0.0,aspect=50,extendrect=True)\n", "\n", "# Vector\n", "ax.barbs(lon,lat,uwnd_500.to('kts').m,vwnd_500.to('kts').m,regrid_shape=15,transform=dataproj)\n", "\n", "# Titles\n", "plt.title(r'500-hPa Geopotential Heights, Absolute Vorticity Advection ($*10^{8}$ 1/s^2)',loc='left')\n", "plt.title('VALID: %s' %(vtime),loc='right')\n", "\n", "\n", "\n", "# Lower-Right Panel\n", "ax=plt.subplot(224,projection=plotproj)\n", "ax.set_extent([-125.,-73,25.,50.],ccrs.PlateCarree())\n", "ax.coastlines('50m', linewidth=0.75)\n", "ax.add_feature(cfeature.STATES, linewidth=0.5)\n", "\n", "# Contour #1\n", "clev500 = np.arange(0,7000,60)\n", "cs = ax.contour(lon,lat,hght_500,clev500,colors='k',\n", " linewidths=1.0,linestyles='solid',transform=dataproj)\n", "plt.clabel(cs, fontsize=10, inline=1, inline_spacing=3, fmt='%i', rightside_up=True, use_clabeltext=True)\n", "\n", "# Contour #2\n", "cs2 = ax.contour(lon,lat,gaussian_filter(avor_500*10**5,sigma=1.0),np.arange(-5,41,4),colors='grey',\n", " linewidths=1.0,transform=dataproj)\n", "plt.clabel(cs2, fontsize=10, inline=1, inline_spacing=3, fmt='%d', rightside_up=True, use_clabeltext=True)\n", "\n", "# Colorfill\n", "cf = ax.contourf(lon,lat,gaussian_filter(stretch_vort*10**9,sigma=1.0),np.arange(-15,16,1),\n", " cmap='PRGn',extend='both',transform=dataproj)\n", "plt.colorbar(cf, orientation='horizontal',pad=0.0,aspect=50,extendrect=True)\n", "\n", "# Vector\n", "ax.barbs(lon,lat,uwnd_500.to('kts').m,vwnd_500.to('kts').m,regrid_shape=15,transform=dataproj)\n", "\n", "# Titles\n", "plt.title(r'500-hPa Geopotential Heights, Stretching Vorticity ($*10^{9}$ 1/s^2)',loc='left')\n", "plt.title('VALID: %s' %(vtime),loc='right')\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plotting Data for Hand Calculation\n", "\n", "Calculating dynamic quantities with a computer is great and can allow for many different educational opportunities, but there are times when we want students to calculate those quantities by hand. So can we plot values of geopotential height, u-component of the wind, and v-component of the wind on a map? Yes! And its not too hard to do.\n", "\n", "Since we are using NARR data, we'll plot every third point to get a roughly 1 degree by 1 degree separation of grid points and thus an average grid spacing of 111 km (not exact, but close enough for back of the envelope calculations).\n", "\n", "To do our plotting we'll be using the functionality of MetPy to plot station plot data, but we'll use our gridded data to plot around our points. To do this we'll have to make or 2D data into 1D (which is made easy by the `ravel()` method associated with our data objects).\n", "\n", "First we'll want to set some bounds (so that we only plot what we want) and create a mask to make plotting easier.\n", "\n", "Second we'll set up our figure with a projection and then set up our \"stations\" at the grid points we desire using the MetPy class `StationPlot`\n", "\n", "https://unidata.github.io/MetPy/latest/api/generated/metpy.plots.StationPlot.html#metpy.plots.StationPlot\n", "\n", "Third we'll plot our points using matplotlibs `scatter()` function and use our stationplot object to plot data around our \"stations\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Set lat/lon bounds for region to plot data\n", "LLlon = -104\n", "LLlat = 33\n", "URlon = -94\n", "URlat = 38.1\n", "\n", "# Set up mask so that you only plot what you want\n", "skip_points = (slice(None, None, 3), slice(None, None, 3))\n", "mask_lon = ((lon[skip_points].ravel() > LLlon + 0.05) & (lon[skip_points].ravel() < URlon + 0.01))\n", "mask_lat = ((lat[skip_points].ravel() < URlat - 0.01) & (lat[skip_points].ravel() > LLlat - 0.01))\n", "mask = mask_lon & mask_lat" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"alert alert-success\">\n", " <b>EXERCISE</b>:\n", " <ul>\n", " <li>Plot markers and data around the markers.</li>\n", " </ul>\n", "</div>" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Set up plot basics and use StationPlot class from MetPy to help with plotting\n", "fig = plt.figure(figsize=(14, 8))\n", "ax = plt.subplot(111,projection=ccrs.LambertConformal(central_latitude=50,central_longitude=-107))\n", "ax.set_extent([LLlon,URlon,LLlat,URlat],ccrs.PlateCarree())\n", "ax.coastlines('50m', edgecolor='grey', linewidth=0.75)\n", "ax.add_feature(cfeature.STATES, edgecolor='grey', linewidth=0.5)\n", "\n", "# Set up station plotting using only every third element from arrays for plotting\n", "stationplot = StationPlot(ax, lon[skip_points].ravel()[mask],\n", " lat[skip_points].ravel()[mask],\n", " transform=ccrs.PlateCarree(), fontsize=12)\n", "\n", "# Plot markers then data around marker for calculation purposes\n", "# Your code goes here!\n", "\n", "# Title\n", "plt.title('Geopotential (m; top), U-wind (m/s; Lower Left), V-wind (m/s; Lower Right)')\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<button data-toggle=\"collapse\" data-target=\"#sol8\" class='btn btn-primary'>View Solution</button>\n", "<div id=\"sol8\" class=\"collapse\">\n", "<code><pre>\n", "# Set up plot basics and use StationPlot class from MetPy to help with plotting\n", "fig = plt.figure(figsize=(14, 8))\n", "proj = ccrs.LambertConformal(central_latitude=50, central_longitude=-107)\n", "ax = plt.subplot(111, projection=proj)\n", "ax.coastlines('50m', edgecolor='grey', linewidth=0.75)\n", "ax.add_feature(cfeature.STATES, edgecolor='grey', linewidth=0.5)\n", "\n", "\\# Set up station plotting using only every third\n", "\\# element from arrays for plotting\n", "stationplot = StationPlot(ax, lon[::3, ::3].ravel()[mask],\n", " lat[::3, ::3].ravel()[mask],\n", " transform=ccrs.PlateCarree(), fontsize=12)\n", "\n", "\\# Plot markers then data around marker for calculation purposes\n", "ax.scatter(lon[::3, ::3].ravel()[mask], lat[::3, ::3].ravel()[mask],\n", " marker='o', transform=dataproj)\n", "stationplot.plot_parameter((0, 1), hght_500[::3, ::3].ravel()[mask])\n", "stationplot.plot_parameter((-1.5, -1), uwnd_500[::3, ::3].ravel()[mask],\n", " formatter='.1f')\n", "stationplot.plot_parameter((1.5, -1), vwnd_500[::3, ::3].ravel()[mask],\n", " formatter='.1f')\n", "\n", "\\# Title\n", "plt.title('Geopotential (m; top), U-wind (m/s; Lower Left), \\\n", " V-wind (m/s; Lower Right)')\n", "\n", "plt.tight_layout()\n", "plt.show()\n", "</pre></code>\n", "</div>" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
letsgoexploring/economicData
business-cycle-data/python/.ipynb_checkpoints/business_cycle_data-checkpoint.ipynb
1
29859
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# U.S. Business Cycle Data\n", "\n", "This notebook downloads, manages, and exports several data series for studying business cycles in the US. Four files are created in the `csv` directory:\n", "\n", "File name | Description |\n", "---------------------------------------------|------------------------------------------------------|\n", "`rbc_data_actual_trend.csv` | RBC data with actual and trend values |\n", "`rbc_data_actual_trend_cycle.csv` | RBC data with actual, trend, and cycle values |\n", "`business_cycle_data_actual_trend.csv` | Larger data set with actual and trend values |\n", "`business_cycle_data_actual_trend_cycle.csv` | Larger data set with actual, trend, and cycle values |\n", "\n", "The first two files are useful for studying basic RBC models. The second two contain all of the RBC data plus money, inflation, and inflation data." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import fredpy as fp\n", "import matplotlib.pyplot as plt\n", "\n", "plt.style.use('classic')\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# Export path: Set to empty string '' if you want to export data to current directory\n", "export_path = '../Csv/'" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# Load FRED API key\n", "fp.api_key = fp.load_api_key('fred_api_key.txt')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Download and manage data\n", "\n", "Download the following series from FRED:\n", "\n", "FRED series ID | Name | Frequency |\n", "---------------|------|-----------|\n", "GDP | Gross Domestic Product | Q |\n", "PCEC | Personal Consumption Expenditures | Q |\n", "GPDI | Gross Private Domestic Investment | Q |\n", "GCE | Government Consumption Expenditures and Gross Investment | Q |\n", "EXPGS | Exports of Goods and Services | Q |\n", "IMPGS | Imports of Goods and Services | Q |\n", "NETEXP | Net Exports of Goods and Services | Q |\n", "HOANBS | Nonfarm Business Sector: Hours Worked for All Employed Persons | Q |\n", "GDPDEF | Gross Domestic Product: Implicit Price Deflator | Q |\n", "PCECTPI | Personal Consumption Expenditures: Chain-type Price Index | Q |\n", "CPIAUCSL | Consumer Price Index for All Urban Consumers: All Items in U.S. City Average | M |\n", "M2SL | M2 | M |\n", "TB3MS | 3-Month Treasury Bill Secondary Market Rate | M |\n", "UNRATE | Unemployment Rate | M |\n", "\n", "Monthly series (M2, T-Bill, unemployment rate) are converted to quarterly frequencies. CPI and PCE inflation rates are computed as the percent change in the indices over the previous year. GDP, consumption, investment, government expenditures, net exports and M2 are deflated by the GDP deflator. The data ranges for nataional accounts series (GDP, consumption, investment, government expenditures, net exports) and hours are equalized to the largest common date range. " ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# Download data\n", "gdp = fp.series('GDP')\n", "consumption = fp.series('PCEC')\n", "investment = fp.series('GPDI')\n", "government = fp.series('GCE')\n", "exports = fp.series('EXPGS')\n", "imports = fp.series('IMPGS')\n", "net_exports = fp.series('NETEXP')\n", "hours = fp.series('HOANBS')\n", "deflator = fp.series('GDPDEF')\n", "pce_deflator = fp.series('PCECTPI')\n", "cpi = fp.series('CPIAUCSL')\n", "m2 = fp.series('M2SL')\n", "tbill_3mo = fp.series('TB3MS')\n", "unemployment = fp.series('UNRATE')\n", "\n", "# Base year for NIPA deflators\n", "cpi_base_year = cpi.units.split(' ')[1].split('=')[0]\n", "\n", "# Base year for CPI\n", "nipa_base_year = deflator.units.split(' ')[1].split('=')[0]\n", "\n", "# Convert monthly M2, 3-mo T-Bill, and unemployment to quarterly\n", "m2 = m2.as_frequency('Q')\n", "tbill_3mo = tbill_3mo.as_frequency('Q')\n", "unemployment = unemployment.as_frequency('Q')\n", "cpi = cpi.as_frequency('Q')\n", "\n", "# Deflate GDP, consumption, investment, government expenditures, net exports, and m2 with the GDP deflator\n", "def deflate(series,deflator):\n", " \n", " deflator, series = fp.window_equalize([deflator, series])\n", " series = series.divide(deflator).times(100)\n", " \n", " return series\n", "\n", "gdp = deflate(gdp,deflator)\n", "consumption = deflate(consumption,deflator)\n", "investment = deflate(investment,deflator)\n", "government = deflate(government,deflator)\n", "net_exports = deflate(net_exports,deflator)\n", "exports = deflate(exports,deflator)\n", "imports = deflate(imports,deflator)\n", "m2 = deflate(m2,deflator)\n", "\n", "# pce inflation as percent change over past year\n", "pce_deflator = pce_deflator.apc()\n", "\n", "# cpi inflation as percent change over past year\n", "cpi = cpi.apc()\n", "\n", "# GDP deflator inflation as percent change over past year\n", "deflator = deflator.apc()\n", "\n", "# Convert unemployment, 3-mo T-Bill, pce inflation, cpi inflation, GDP deflator inflation data to rates \n", "unemployment = unemployment.divide(100)\n", "tbill_3mo = tbill_3mo.divide(100)\n", "pce_deflator = pce_deflator.divide(100)\n", "cpi = cpi.divide(100)\n", "deflator = deflator.divide(100)\n", "\n", "# Make sure that the RBC data has the same data range\n", "gdp,consumption,investment,government,exports,imports,net_exports,hours = fp.window_equalize([gdp,consumption,investment,government,exports,imports,net_exports,hours])\n", "\n", "# T-Bill data doesn't neet to go all the way back to 1930s\n", "tbill_3mo = tbill_3mo.window([gdp.data.index[0],'2222'])" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'csv_export_path' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/var/folders/2v/szccskxn0w3cwd_5482ld6m00000gp/T/ipykernel_9001/857631556.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mmetadata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'cpi_base_year'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcpi_base_year\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mmetadata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcsv_export_path\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m'/business_cycle_metadata.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mNameError\u001b[0m: name 'csv_export_path' is not defined" ] } ], "source": [ "metadata = pd.Series(dtype=str,name='Values')\n", "metadata['nipa_base_year'] = nipa_base_year\n", "metadata['cpi_base_year'] = cpi_base_year\n", "\n", "metadata.to_csv(export_path+'/business_cycle_metadata.csv')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Compute capital stock for US using the perpetual inventory method\n", "\n", "Next, compute the quarterly capital stock series for the US using the perpetual inventory method. The discrete-time Solow growth model is given by:\n", "\n", "\\begin{align}\n", "Y_t & = A_tK_t^{\\alpha}L_t^{1-\\alpha} \\tag{1}\\\\\n", "C_t & = (1-s)Y_t \\tag{2}\\\\\n", "Y_t & = C_t + I_t \\tag{3}\\\\\n", "K_{t+1} & = I_t + (1-\\delta)K_t \\tag{4}\\\\\n", "A_{t+1} & = (1+g)A_t \\tag{5}\\\\\n", "L_{t+1} & = (1+n)L_t \\tag{6}.\n", "\\end{align}\n", "\n", "Here the model is assumed to be quarterly so $n$ is the *quarterly* growth rate of labor hours, $g$ is the *quarterly* growth rate of TFP, and $\\delta$ is the *quarterly* rate of depreciation of the capital stock. Given a value of the quarterly depreciation rate $\\delta$, an investment series $I_t$, and an initial capital stock $K_0$, the law of motion for the capital stock, Equation (4), can be used to compute an implied capital series. But we don't know $K_0$ or $\\delta$ so we'll have to *calibrate* these values using statistics computed from the data that we've already obtained.\n", "\n", "Let lowercase letters denote a variable that's been divided by $A_t^{1/(1-\\alpha)}L_t$. E.g.,\n", "\n", "\\begin{align}\n", "y_t = \\frac{Y_t}{A_t^{1/(1-\\alpha)}L_t}\\tag{7}\n", "\\end{align}\n", "\n", "Then (after substituting consumption from the model), the scaled version of the model can be written as: \n", "\n", "\\begin{align}\n", "y_t & = k_t^{\\alpha} \\tag{8}\\\\\n", "i_t & = sy_t \\tag{9}\\\\\n", "k_{t+1} & = i_t + (1-\\delta-n-g')k_t,\\tag{10}\n", "\\end{align}\n", "\n", "where $g' = g/(1-\\alpha)$ is the growth rate of $A_t^{1/(1-\\alpha)}$. In the steady state:\n", "\n", "\\begin{align}\n", "k & = \\left(\\frac{s}{\\delta+n+g'}\\right)^{\\frac{1}{1-\\alpha}} \\tag{11}\n", "\\end{align}\n", "\n", "which means that the ratio of capital to output is constant:\n", "\n", "\\begin{align}\n", "\\frac{k}{y} & = \\frac{s}{\\delta+n+g'} \\tag{12}\n", "\\end{align}\n", "\n", "and therefore the steady state ratio of depreciation to output is:\n", "\n", "\\begin{align}\n", "\\overline{\\delta K/ Y} & = \\frac{\\delta s}{\\delta + n + g'} \\tag{13}\n", "\\end{align}\n", "\n", "where $\\overline{\\delta K/ Y}$ is the long-run average ratio of depreciation to output. We can use Equation (13) to calibrate $\\delta$ given $\\overline{\\delta K/ Y}$, $s$, $n$, and $g'$.\n", "\n", "Furthermore, in the steady state, the growth rate of output is constant:\n", "\n", "\\begin{align}\n", "\\frac{\\Delta Y}{Y} & = n + g' \\tag{14}\n", "\\end{align} \n", "\n", "\n", "1. Assume $\\alpha = 0.35$.\n", "2. Calibrate $s$ as the average of ratio of investment to GDP.\n", "3. Calibrate $n$ as the average quarterly growth rate of labor hours.\n", "4. Calibrate $g'$ as the average quarterly growth rate of real GDP minus n.\n", "5. Calculate the average ratio of depreciation to GDP $\\overline{\\delta K/ Y}$ and use the result to calibrate $\\delta$. That is, find the average ratio of Current-Cost Depreciation of Fixed Assets (FRED series ID: M1TTOTL1ES000) to GDP (FRED series ID: GDPA). Then calibrate $\\delta$ from the following steady state relationship:\n", "\\begin{align}\n", "\\delta & = \\frac{\\left( \\overline{\\delta K/ Y} \\right)\\left(n + g' \\right)}{s - \\left( \\overline{\\delta K/ Y} \\right)} \\tag{15}\n", "\\end{align}\n", "6. Calibrate $K_0$ by asusming that the capital stock is initially equal to its steady state value:\n", "\\begin{align}\n", "K_0 & = \\left(\\frac{s}{\\delta + n + g'}\\right) Y_0 \\tag{16}\n", "\\end{align}\n", "\n", "Then, armed with calibrated values for $K_0$ and $\\delta$, compute $K_1, K_2, \\ldots$ recursively. See Timothy Kehoe's notes for more information on the perpetual inventory method:\n", "\n", "http://users.econ.umn.edu/~tkehoe/classes/GrowthAccountingNotes.pdf\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "# Set the capital share of income\n", "alpha = 0.35\n", "\n", "# Average saving rate\n", "s = np.mean(investment.data/gdp.data)\n", "\n", "# Average quarterly labor hours growth rate\n", "n = (hours.data[-1]/hours.data[0])**(1/(len(hours.data)-1)) - 1\n", "\n", "# Average quarterly real GDP growth rate\n", "g = ((gdp.data[-1]/gdp.data[0])**(1/(len(gdp.data)-1)) - 1) - n\n", "\n", "# Compute annual depreciation rate\n", "depA = fp.series('M1TTOTL1ES000')\n", "gdpA = fp.series('gdpa')\n", "\n", "gdpA = gdpA.window([gdp.data.index[0],gdp.data.index[-1]])\n", "gdpA,depA = fp.window_equalize([gdpA,depA])\n", "\n", "deltaKY = np.mean(depA.data/gdpA.data)\n", "delta = (n+g)*deltaKY/(s-deltaKY)\n", "\n", "# print calibrated values:\n", "print('Avg saving rate: ',round(s,5))\n", "print('Avg annual labor growth:',round(4*n,5))\n", "print('Avg annual gdp growth: ',round(4*g,5))\n", "print('Avg annual dep rate: ',round(4*delta,5))\n", "\n", "# Construct the capital series. Note that the GPD and investment data are reported on an annualized basis\n", "# so divide by 4 to get quarterly data.\n", "capital = np.zeros(len(gdp.data))\n", "capital[0] = gdp.data[0]/4*s/(n+g+delta)\n", "\n", "for t in range(len(gdp.data)-1):\n", " capital[t+1] = investment.data[t]/4 + (1-delta)*capital[t]\n", "\n", "# Save in a fredpy series\n", "capital = fp.to_fred_series(data = capital,dates =gdp.data.index,units = gdp.units,title='Capital stock of the US',frequency='Quarterly')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Compute total factor productivity\n", "\n", "Use the Cobb-Douglas production function:\n", "\n", "\\begin{align}\n", "Y_t & = A_tK_t^{\\alpha}L_t^{1-\\alpha} \\tag{17}\n", "\\end{align}\n", "\n", "and data on GDP, capital, and hours with $\\alpha=0.35$ to compute an implied series for $A_t$." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Compute TFP\n", "tfp = gdp.data/capital.data**alpha/hours.data**(1-alpha)\n", "tfp = fp.to_fred_series(data = tfp,dates =gdp.data.index,units = gdp.units,title='TFP of the US',frequency='Quarterly')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Additional data management\n", "\n", "Now that we have used the aggregate production data to compute an implied capital stock and TFP, we can scale the production data and M2 by the population." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "# Convert real GDP, consumption, investment, government expenditures, net exports and M2\n", "# into thousands of dollars per civilian 16 and over\n", "gdp = gdp.per_capita(civ_pop=True).times(1000)\n", "consumption = consumption.per_capita(civ_pop=True).times(1000)\n", "investment = investment.per_capita(civ_pop=True).times(1000)\n", "government = government.per_capita(civ_pop=True).times(1000)\n", "exports = exports.per_capita(civ_pop=True).times(1000)\n", "imports = imports.per_capita(civ_pop=True).times(1000)\n", "net_exports = net_exports.per_capita(civ_pop=True).times(1000)\n", "hours = hours.per_capita(civ_pop=True).times(1000)\n", "capital = capital.per_capita(civ_pop=True).times(1000)\n", "m2 = m2.per_capita(civ_pop=True).times(1000)\n", "\n", "\n", "# Scale hours per person to equal 100 on October (Quarter III) of GDP deflator base year.\n", "hours.data = hours.data/hours.data.loc[base_year+'-10-01']*100" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot aggregate data" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fig, axes = plt.subplots(3,4,figsize=(6*4,4*3))\n", "\n", "axes[0][0].plot(gdp.data)\n", "axes[0][0].set_title('GDP')\n", "axes[0][0].set_ylabel('Thousands of '+base_year+' $')\n", "\n", "axes[0][1].plot(consumption.data)\n", "axes[0][1].set_title('Consumption')\n", "axes[0][1].set_ylabel('Thousands of '+base_year+' $')\n", "\n", "axes[0][2].plot(investment.data)\n", "axes[0][2].set_title('Investment')\n", "axes[0][2].set_ylabel('Thousands of '+base_year+' $')\n", "\n", "axes[0][3].plot(government.data)\n", "axes[0][3].set_title('Gov expenditure')\n", "axes[0][3].set_ylabel('Thousands of '+base_year+' $')\n", "\n", "axes[1][0].plot(capital.data)\n", "axes[1][0].set_title('Capital')\n", "axes[1][0].set_ylabel('Thousands of '+base_year+' $')\n", "\n", "axes[1][1].plot(hours.data)\n", "axes[1][1].set_title('Hours')\n", "axes[1][1].set_ylabel('Index ()'+base_year+'=100)')\n", "\n", "axes[1][2].plot(tfp.data)\n", "axes[1][2].set_title('TFP')\n", "\n", "axes[1][3].plot(m2.data)\n", "axes[1][3].set_title('M2')\n", "axes[1][3].set_ylabel('Thousands of '+base_year+' $')\n", "\n", "axes[2][0].plot(tbill_3mo.data*100)\n", "axes[2][0].set_title('3mo T-Bill')\n", "axes[2][0].set_ylabel('Percent')\n", "\n", "axes[2][1].plot(pce_deflator.data*100)\n", "axes[2][1].set_title('PCE Inflation')\n", "axes[2][1].set_ylabel('Percent')\n", "\n", "axes[2][2].plot(cpi.data*100)\n", "axes[2][2].set_title('CPI Inflation')\n", "axes[2][2].set_ylabel('Percent')\n", "\n", "axes[2][3].plot(unemployment.data*100)\n", "axes[2][3].set_title('Unemployment rate')\n", "axes[2][3].set_ylabel('Percent');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Compute HP filter of data" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# HP filter to isolate trend and cyclical components\n", "gdp_log_cycle,gdp_log_trend= gdp.log().hp_filter()\n", "consumption_log_cycle,consumption_log_trend= consumption.log().hp_filter()\n", "investment_log_cycle,investment_log_trend= investment.log().hp_filter()\n", "government_log_cycle,government_log_trend= government.log().hp_filter()\n", "exports_log_cycle,exports_log_trend= exports.log().hp_filter()\n", "imports_log_cycle,imports_log_trend= imports.log().hp_filter()\n", "# net_exports_log_cycle,net_exports_log_trend= net_exports.log().hp_filter()\n", "capital_log_cycle,capital_log_trend= capital.log().hp_filter()\n", "hours_log_cycle,hours_log_trend= hours.log().hp_filter()\n", "tfp_log_cycle,tfp_log_trend= tfp.log().hp_filter()\n", "deflator_cycle,deflator_trend= deflator.hp_filter()\n", "pce_deflator_cycle,pce_deflator_trend= pce_deflator.hp_filter()\n", "cpi_cycle,cpi_trend= cpi.hp_filter()\n", "m2_log_cycle,m2_log_trend= m2.log().hp_filter()\n", "tbill_3mo_cycle,tbill_3mo_trend= tbill_3mo.hp_filter()\n", "unemployment_cycle,unemployment_trend= unemployment.hp_filter()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot aggregate data with trends" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fig, axes = plt.subplots(3,4,figsize=(6*4,4*3))\n", "\n", "axes[0][0].plot(gdp.data)\n", "axes[0][0].plot(np.exp(gdp_log_trend.data),c='r')\n", "axes[0][0].set_title('GDP')\n", "axes[0][0].set_ylabel('Thousands of '+base_year+' $')\n", "\n", "axes[0][1].plot(consumption.data)\n", "axes[0][1].plot(np.exp(consumption_log_trend.data),c='r')\n", "axes[0][1].set_title('Consumption')\n", "axes[0][1].set_ylabel('Thousands of '+base_year+' $')\n", "\n", "axes[0][2].plot(investment.data)\n", "axes[0][2].plot(np.exp(investment_log_trend.data),c='r')\n", "axes[0][2].set_title('Investment')\n", "axes[0][2].set_ylabel('Thousands of '+base_year+' $')\n", "\n", "axes[0][3].plot(government.data)\n", "axes[0][3].plot(np.exp(government_log_trend.data),c='r')\n", "axes[0][3].set_title('Gov expenditure')\n", "axes[0][3].set_ylabel('Thousands of '+base_year+' $')\n", "\n", "axes[1][0].plot(capital.data)\n", "axes[1][0].plot(np.exp(capital_log_trend.data),c='r')\n", "axes[1][0].set_title('Capital')\n", "axes[1][0].set_ylabel('Thousands of '+base_year+' $')\n", "\n", "axes[1][1].plot(hours.data)\n", "axes[1][1].plot(np.exp(hours_log_trend.data),c='r')\n", "axes[1][1].set_title('Hours')\n", "axes[1][1].set_ylabel('Index ()'+base_year+'=100)')\n", "\n", "axes[1][2].plot(tfp.data)\n", "axes[1][2].plot(np.exp(tfp_log_trend.data),c='r')\n", "axes[1][2].set_title('TFP')\n", "\n", "axes[1][3].plot(m2.data)\n", "axes[1][3].plot(np.exp(m2_log_trend.data),c='r')\n", "axes[1][3].set_title('M2')\n", "axes[1][3].set_ylabel('Thousands of '+base_year+' $')\n", "\n", "axes[2][0].plot(tbill_3mo.data*100)\n", "axes[2][0].plot(tbill_3mo_trend.data*100,c='r')\n", "axes[2][0].set_title('3mo T-Bill')\n", "axes[2][0].set_ylabel('Percent')\n", "\n", "axes[2][1].plot(pce_deflator.data*100)\n", "axes[2][1].plot(pce_deflator_trend.data*100,c='r')\n", "axes[2][1].set_title('PCE Inflation')\n", "axes[2][1].set_ylabel('Percent')\n", "\n", "axes[2][2].plot(cpi.data*100)\n", "axes[2][2].plot(cpi_trend.data*100,c='r')\n", "axes[2][2].set_title('CPI Inflation')\n", "axes[2][2].set_ylabel('Percent')\n", "\n", "axes[2][3].plot(unemployment.data*100)\n", "axes[2][3].plot(unemployment_trend.data*100,c='r')\n", "axes[2][3].set_title('Unemployment rate')\n", "axes[2][3].set_ylabel('Percent')\n", "\n", "\n", "ax = fig.add_subplot(1,1,1)\n", "ax.axis('off')\n", "ax.plot(0,0,label='Actual')\n", "ax.plot(0,0,c='r',label='Trend')\n", "\n", "ax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.05),ncol=2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot cyclical components of the data" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fig, axes = plt.subplots(3,4,figsize=(6*4,4*3))\n", "\n", "axes[0][0].plot(gdp_log_cycle.data)\n", "axes[0][0].set_title('GDP')\n", "axes[0][0].set_ylabel('Thousands of '+base_year+' $')\n", "\n", "axes[0][1].plot(consumption_log_cycle.data)\n", "axes[0][1].set_title('Consumption')\n", "axes[0][1].set_ylabel('Thousands of '+base_year+' $')\n", "\n", "axes[0][2].plot(investment_log_cycle.data)\n", "axes[0][2].set_title('Investment')\n", "axes[0][2].set_ylabel('Thousands of '+base_year+' $')\n", "\n", "axes[0][3].plot(government_log_cycle.data)\n", "axes[0][3].set_title('Gov expenditure')\n", "axes[0][3].set_ylabel('Thousands of '+base_year+' $')\n", "\n", "axes[1][0].plot(capital_log_cycle.data)\n", "axes[1][0].set_title('Capital')\n", "axes[1][0].set_ylabel('Thousands of '+base_year+' $')\n", "\n", "axes[1][1].plot(hours_log_cycle.data)\n", "axes[1][1].set_title('Hours')\n", "axes[1][1].set_ylabel('Index ()'+base_year+'=100)')\n", "\n", "axes[1][2].plot(tfp_log_cycle.data)\n", "axes[1][2].set_title('TFP')\n", "\n", "axes[1][3].plot(m2_log_cycle.data)\n", "axes[1][3].set_title('M2')\n", "axes[1][3].set_ylabel('Thousands of '+base_year+' $')\n", "\n", "axes[2][0].plot(tbill_3mo_cycle.data)\n", "axes[2][0].set_title('3mo T-Bill')\n", "axes[2][0].set_ylabel('Percent')\n", "\n", "axes[2][1].plot(pce_deflator_cycle.data)\n", "axes[2][1].set_title('PCE Inflation')\n", "axes[2][1].set_ylabel('Percent')\n", "\n", "axes[2][2].plot(cpi_cycle.data)\n", "axes[2][2].set_title('CPI Inflation')\n", "axes[2][2].set_ylabel('Percent')\n", "\n", "axes[2][3].plot(unemployment_cycle.data)\n", "axes[2][3].set_title('Unemployment rate')\n", "axes[2][3].set_ylabel('Percent');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Create data files" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Create a DataFrame with actual and trend data\n", "data = pd.DataFrame({\n", " 'gdp':gdp.data,\n", " 'gdp_trend':np.exp(gdp_log_trend.data),\n", " 'gdp_cycle':gdp_log_cycle.data,\n", " 'consumption':consumption.data,\n", " 'consumption_trend':np.exp(consumption_log_trend.data),\n", " 'consumption_cycle':consumption_log_cycle.data,\n", " 'investment':investment.data,\n", " 'investment_trend':np.exp(investment_log_trend.data),\n", " 'investment_cycle':investment_log_cycle.data,\n", " 'government':government.data,\n", " 'government_trend':np.exp(government_log_trend.data),\n", " 'government_cycle':government_log_cycle.data,\n", " 'exports':exports.data,\n", " 'exports_trend':np.exp(exports_log_trend.data),\n", " 'exports_cycle':exports_log_cycle.data,\n", " 'imports':imports.data,\n", " 'imports_trend':np.exp(imports_log_trend.data),\n", " 'imports_cycle':imports_log_cycle.data,\n", " 'hours':hours.data,\n", " 'hours_trend':np.exp(hours_log_trend.data),\n", " 'hours_cycle':hours_log_cycle.data,\n", " 'capital':capital.data,\n", " 'capital_trend':np.exp(capital_log_trend.data),\n", " 'capital_cycle':capital_log_cycle.data,\n", " 'tfp':tfp.data,\n", " 'tfp_trend':np.exp(tfp_log_trend.data),\n", " 'tfp_cycle':tfp_log_cycle.data,\n", " 'real_m2':m2.data,\n", " 'real_m2_trend':np.exp(m2_log_trend.data),\n", " 'real_m2_cycle':m2_log_cycle.data,\n", " 't_bill_3mo':tbill_3mo.data,\n", " 't_bill_3mo_trend':tbill_3mo_trend.data,\n", " 't_bill_3mo_cycle':tbill_3mo_cycle.data,\n", " 'cpi_inflation':cpi.data,\n", " 'cpi_inflation_trend':cpi_trend.data,\n", " 'cpi_inflation_cycle':cpi_cycle.data,\n", " 'pce_inflation':pce_deflator.data,\n", " 'pce_inflation_trend':pce_deflator_trend.data,\n", " 'pce_inflation_cycle':pce_deflator_cycle.data,\n", " 'unemployment':unemployment.data,\n", " 'unemployment_trend':unemployment_trend.data,\n", " 'unemployment_cycle':unemployment_cycle.data,\n", " })" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# RBC data\n", "columns_ordered =[]\n", "names = ['gdp','consumption','investment','hours','capital','tfp']\n", "for name in names:\n", " columns_ordered.append(name)\n", " columns_ordered.append(name+'_trend')\n", " \n", "data[columns_ordered].dropna().to_csv(export_path+'rbc_data_actual_trend.csv',index=True)\n", "\n", "# Create a DataFrame with actual, trend, and cycle data\n", "columns_ordered =[]\n", "names = ['gdp','consumption','investment','hours','capital','tfp']\n", "for name in names:\n", " columns_ordered.append(name)\n", " columns_ordered.append(name+'_trend')\n", " columns_ordered.append(name+'_cycle')\n", " \n", "data[columns_ordered].dropna().to_csv(export_path+'rbc_data_actual_trend_cycle.csv',index=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# More comprehensive Business Cycle Data\n", "columns_ordered =[]\n", "names = ['gdp','consumption','investment','hours','capital','tfp','real_m2','t_bill_3mo','pce_inflation','unemployment']\n", "for name in names:\n", " columns_ordered.append(name)\n", " columns_ordered.append(name+'_trend')\n", "\n", "data[columns_ordered].dropna().to_csv(export_path+'business_cycle_data_actual_trend.csv',index=True)\n", "\n", "# Create a DataFrame with actual, trend, and cycle data\n", "columns_ordered =[]\n", "names = ['gdp','consumption','investment','hours','capital','tfp','real_m2','t_bill_3mo','pce_inflation','unemployment']\n", "for name in names:\n", " columns_ordered.append(name)\n", " columns_ordered.append(name+'_trend')\n", " columns_ordered.append(name+'_cycle')\n", " \n", "data[columns_ordered].dropna().to_csv(export_path+'business_cycle_data_actual_trend_cycle.csv')" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.7" } }, "nbformat": 4, "nbformat_minor": 1 }
mit
dmittov/misc
BikeSharing-Linear.ipynb
1
36202
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Linear methods" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "https://www.kaggle.com/c/bike-sharing-demand" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# !pip install -U kaggle\n", "# register the token in you kaggle profile & save it to ~/.kaggle/kaggle.json\n", "# !kaggle competitions download -c bike-sharing-demand" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "from sklearn import linear_model\n", "from scipy import stats\n", "import seaborn as sns\n", "from matplotlib import pyplot as plt\n", "from sklearn.metrics import mean_squared_error, mean_absolute_error, make_scorer\n", "import numpy as np\n", "from dateutil.parser import parse\n", "from sklearn.model_selection import GridSearchCV, RandomizedSearchCV\n", "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n", "from sklearn.pipeline import Pipeline\n", "from sklearn.base import BaseEstimator, TransformerMixin\n", "from sklearn.model_selection import cross_val_score\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# to prevent warining inside sklearn code\n", "pd.options.mode.chained_assignment = None" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(10886, 12)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv(\"train.csv\")\n", "df.shape" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>datetime</th>\n", " <th>season</th>\n", " <th>holiday</th>\n", " <th>workingday</th>\n", " <th>weather</th>\n", " <th>temp</th>\n", " <th>atemp</th>\n", " <th>humidity</th>\n", " <th>windspeed</th>\n", " <th>casual</th>\n", " <th>registered</th>\n", " <th>count</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>2011-01-01 00:00:00</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>9.84</td>\n", " <td>14.395</td>\n", " <td>81</td>\n", " <td>0.0</td>\n", " <td>3</td>\n", " <td>13</td>\n", " <td>16</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2011-01-01 01:00:00</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>9.02</td>\n", " <td>13.635</td>\n", " <td>80</td>\n", " <td>0.0</td>\n", " <td>8</td>\n", " <td>32</td>\n", " <td>40</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2011-01-01 02:00:00</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>9.02</td>\n", " <td>13.635</td>\n", " <td>80</td>\n", " <td>0.0</td>\n", " <td>5</td>\n", " <td>27</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>2011-01-01 03:00:00</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>9.84</td>\n", " <td>14.395</td>\n", " <td>75</td>\n", " <td>0.0</td>\n", " <td>3</td>\n", " <td>10</td>\n", " <td>13</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>2011-01-01 04:00:00</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>9.84</td>\n", " <td>14.395</td>\n", " <td>75</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " datetime season holiday workingday weather temp atemp \\\n", "0 2011-01-01 00:00:00 1 0 0 1 9.84 14.395 \n", "1 2011-01-01 01:00:00 1 0 0 1 9.02 13.635 \n", "2 2011-01-01 02:00:00 1 0 0 1 9.02 13.635 \n", "3 2011-01-01 03:00:00 1 0 0 1 9.84 14.395 \n", "4 2011-01-01 04:00:00 1 0 0 1 9.84 14.395 \n", "\n", " humidity windspeed casual registered count \n", "0 81 0.0 3 13 16 \n", "1 80 0.0 8 32 40 \n", "2 80 0.0 5 27 32 \n", "3 75 0.0 3 10 13 \n", "4 75 0.0 0 1 1 " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "At first, we need custom score function, described in task.\n", "https://www.kaggle.com/c/bike-sharing-demand/overview/evaluation\n", "\n", "* Why do we need +1 in score function?" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "def rmsle(y_true, y_pred):\n", " y_pred_clipped = np.clip(y_pred, 0., None)\n", " return mean_squared_error(np.log1p(y_true), np.log1p(y_pred_clipped)) ** .5" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* What happens without np.clip?\n", "\n", "Let's start with the exisiting features and simple linear regression.\n", "\n", "All that feature extractors and grid search would be more clear further." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "class SimpleFeatureExtractor(BaseEstimator, TransformerMixin):\n", " \n", " def fit(self, X, y=None):\n", " return self \n", " \n", " def transform(self, X, y=None):\n", " return X[[\"holiday\", \"workingday\", \"season\", \"weather\", \"temp\", \"atemp\", \"humidity\", \"windspeed\"]].values" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fitting 5 folds for each of 1 candidates, totalling 5 fits\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n", "[Parallel(n_jobs=4)]: Done 5 out of 5 | elapsed: 2.0s finished\n" ] }, { "data": { "text/plain": [ "GridSearchCV(cv=5, error_score='raise-deprecating',\n", " estimator=Pipeline(memory=None,\n", " steps=[('extractor', SimpleFeatureExtractor()), ('regression', LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n", " normalize=False))]),\n", " fit_params=None, iid='warn', n_jobs=4, param_grid={},\n", " pre_dispatch='2*n_jobs', refit=False, return_train_score='warn',\n", " scoring=make_scorer(rmsle, greater_is_better=False), verbose=1)" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "exctractor = SimpleFeatureExtractor()\n", "clf = Pipeline([\n", " (\"extractor\", exctractor),\n", " (\"regression\", linear_model.LinearRegression()),\n", "])\n", "param_grid = {}\n", "scorerer = make_scorer(rmsle, greater_is_better=False)\n", "researcher = GridSearchCV(clf, param_grid, scoring=scorerer, cv=5, n_jobs=4, verbose=1, refit=False)\n", "researcher.fit(df, df[\"count\"].values)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Hyperparameters Searcher always maximizes the score function, so if we need to decrease it, it just adds the minus." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "-1.4687746950336822" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "researcher.best_score_" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Add regularization and grid search the hyperparameters\n", "\n", "Now it's more clear why we have Grid Searcher ;-)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "exctractor = SimpleFeatureExtractor()\n", "clf = Pipeline([\n", " (\"extractor\", exctractor),\n", " (\"regression\", linear_model.ElasticNet()),\n", "])" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fitting 5 folds for each of 100 candidates, totalling 500 fits\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n", "[Parallel(n_jobs=4)]: Done 76 tasks | elapsed: 1.8s\n", "[Parallel(n_jobs=4)]: Done 500 out of 500 | elapsed: 6.8s finished\n" ] }, { "data": { "text/plain": [ "GridSearchCV(cv=5, error_score='raise-deprecating',\n", " estimator=Pipeline(memory=None,\n", " steps=[('extractor', SimpleFeatureExtractor()), ('regression', ElasticNet(alpha=1.0, copy_X=True, fit_intercept=True, l1_ratio=0.5,\n", " max_iter=1000, normalize=False, positive=False, precompute=False,\n", " random_state=None, selection='cyclic', tol=0.0001, warm_start=False))]),\n", " fit_params=None, iid='warn', n_jobs=4,\n", " param_grid={'regression__alpha': array([1.00000e-03, 3.59381e-03, 1.29155e-02, 4.64159e-02, 1.66810e-01,\n", " 5.99484e-01, 2.15443e+00, 7.74264e+00, 2.78256e+01, 1.00000e+02]), 'regression__l1_ratio': array([0. , 0.11111, 0.22222, 0.33333, 0.44444, 0.55556, 0.66667,\n", " 0.77778, 0.88889, 1. ])},\n", " pre_dispatch='2*n_jobs', refit=False, return_train_score='warn',\n", " scoring=make_scorer(rmsle, greater_is_better=False), verbose=1)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "param_grid = {\n", " \"regression__alpha\": np.logspace(-3, 2, 10),\n", " \"regression__l1_ratio\": np.linspace(0, 1, 10)\n", "}\n", "scorerer = make_scorer(rmsle, greater_is_better=False)\n", "researcher = GridSearchCV(clf, param_grid, scoring=scorerer, cv=5, n_jobs=4, verbose=1, refit=False)\n", "researcher.fit(df, df[\"count\"].values)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "-1.4196641068278841" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "researcher.best_score_" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'regression__alpha': 27.825594022071257,\n", " 'regression__l1_ratio': 0.3333333333333333}" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "researcher.best_params_" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Try to add some custom features" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "class FeatureExtractor(BaseEstimator, TransformerMixin):\n", " \n", " ohe = OneHotEncoder(categories='auto', sparse=False)\n", " scaler = StandardScaler()\n", " \n", " categorical_columns = [\"week_day\", \"hour\", \"season\", \"weather\"]\n", " numerical_columns = [\"temp\", \"atemp\", \"humidity\", \"windspeed\"]\n", " \n", " def _add_features(self, X):\n", " X[\"week_day\"] = X.datetime.apply(lambda dttm: parse(dttm).weekday())\n", " X[\"hour\"] = X.datetime.apply(lambda dttm: parse(dttm).hour)\n", " \n", " def _combine(self, *feature_groups):\n", " return np.hstack(feature_groups)\n", " \n", " def collect_stats(self, X):\n", " self._add_features(X)\n", " self.ohe.fit(X[self.categorical_columns])\n", " self.scaler.fit(X[self.numerical_columns])\n", " \n", " def fit(self, X, y=None):\n", " return self \n", " \n", " def transform(self, X, y=None):\n", " self._add_features(X)\n", " custom_binary_features = self.ohe.transform(X[self.categorical_columns])\n", " scaled_features = self.scaler.transform(X[self.numerical_columns])\n", " return self._combine(\n", " custom_binary_features, \n", " scaled_features,\n", " X[[\"holiday\", \"workingday\"]].values\n", " ) " ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/anaconda3/lib/python3.7/site-packages/sklearn/preprocessing/data.py:645: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n", " return self.partial_fit(X, y)\n" ] } ], "source": [ "exctractor = FeatureExtractor()\n", "exctractor.collect_stats(df)\n", "clf = Pipeline([\n", " (\"extractor\", exctractor),\n", " (\"regression\", linear_model.ElasticNet()),\n", "])" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Fitting 5 folds for each of 100 candidates, totalling 500 fits\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=4)]: Done 42 tasks | elapsed: 1.2min\n", "[Parallel(n_jobs=4)]: Done 192 tasks | elapsed: 5.2min\n", "[Parallel(n_jobs=4)]: Done 442 tasks | elapsed: 11.6min\n", "[Parallel(n_jobs=4)]: Done 500 out of 500 | elapsed: 12.9min finished\n" ] }, { "data": { "text/plain": [ "GridSearchCV(cv=5, error_score='raise-deprecating',\n", " estimator=Pipeline(memory=None,\n", " steps=[('extractor', FeatureExtractor()), ('regression', ElasticNet(alpha=1.0, copy_X=True, fit_intercept=True, l1_ratio=0.5,\n", " max_iter=1000, normalize=False, positive=False, precompute=False,\n", " random_state=None, selection='cyclic', tol=0.0001, warm_start=False))]),\n", " fit_params=None, iid='warn', n_jobs=4,\n", " param_grid={'regression__alpha': array([1.00000e-03, 3.59381e-03, 1.29155e-02, 4.64159e-02, 1.66810e-01,\n", " 5.99484e-01, 2.15443e+00, 7.74264e+00, 2.78256e+01, 1.00000e+02]), 'regression__l1_ratio': array([0. , 0.11111, 0.22222, 0.33333, 0.44444, 0.55556, 0.66667,\n", " 0.77778, 0.88889, 1. ])},\n", " pre_dispatch='2*n_jobs', refit=False, return_train_score='warn',\n", " scoring=make_scorer(rmsle, greater_is_better=False), verbose=1)" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "param_grid = {\n", " \"regression__alpha\": np.logspace(-3, 2, 10),\n", " \"regression__l1_ratio\": np.linspace(0, 1, 10)\n", "}\n", "pd.options.mode.chained_assignment = None\n", "scorerer = make_scorer(rmsle, greater_is_better=False)\n", "researcher = GridSearchCV(clf, param_grid, scoring=scorerer, cv=5, n_jobs=4, verbose=1, refit=False)\n", "researcher.fit(df, df[\"count\"].values)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "-1.1171565953466736" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "researcher.best_score_" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'regression__alpha': 0.046415888336127795,\n", " 'regression__l1_ratio': 0.7777777777777777}" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "researcher.best_params_" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "156.66559636027856" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "scorerer = make_scorer(mean_squared_error, greater_is_better=False)\n", "scores = cross_val_score(clf, df, df[\"count\"].values, cv=5, n_jobs=4, scoring=scorerer)\n", "np.mean((-np.array(scores)) ** .5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What we can theoretically get if we optimize RMSE " ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fitting 5 folds for each of 100 candidates, totalling 500 fits\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n", "[Parallel(n_jobs=4)]: Done 42 tasks | elapsed: 1.2min\n", "[Parallel(n_jobs=4)]: Done 192 tasks | elapsed: 5.5min\n", "[Parallel(n_jobs=4)]: Done 442 tasks | elapsed: 11.8min\n", "[Parallel(n_jobs=4)]: Done 500 out of 500 | elapsed: 13.5min finished\n" ] }, { "data": { "text/plain": [ "GridSearchCV(cv=5, error_score='raise-deprecating',\n", " estimator=Pipeline(memory=None,\n", " steps=[('extractor', FeatureExtractor()), ('regression', ElasticNet(alpha=1.0, copy_X=True, fit_intercept=True, l1_ratio=0.5,\n", " max_iter=1000, normalize=False, positive=False, precompute=False,\n", " random_state=None, selection='cyclic', tol=0.0001, warm_start=False))]),\n", " fit_params=None, iid='warn', n_jobs=4,\n", " param_grid={'regression__alpha': array([1.00000e-03, 3.59381e-03, 1.29155e-02, 4.64159e-02, 1.66810e-01,\n", " 5.99484e-01, 2.15443e+00, 7.74264e+00, 2.78256e+01, 1.00000e+02]), 'regression__l1_ratio': array([0. , 0.11111, 0.22222, 0.33333, 0.44444, 0.55556, 0.66667,\n", " 0.77778, 0.88889, 1. ])},\n", " pre_dispatch='2*n_jobs', refit=False, return_train_score='warn',\n", " scoring=make_scorer(rmse, greater_is_better=False), verbose=1)" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "param_grid = {\n", " \"regression__alpha\": np.logspace(-3, 2, 10),\n", " \"regression__l1_ratio\": np.linspace(0, 1, 10)\n", "}\n", "pd.options.mode.chained_assignment = None\n", "\n", "def rmse(y_true, y_pred):\n", " return mean_squared_error(y_true, y_pred) ** .5\n", "\n", "scorerer = make_scorer(rmse, greater_is_better=False)\n", "researcher = GridSearchCV(clf, param_grid, scoring=scorerer, cv=5, n_jobs=4, verbose=1, refit=False)\n", "researcher.fit(df, df[\"count\"].values)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "-129.4953879296039" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "researcher.best_score_" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'regression__alpha': 0.01291549665014884,\n", " 'regression__l1_ratio': 0.4444444444444444}" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "researcher.best_params_" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* 11 min!!! Now we also learn FeaureExtractor every time and the pipeline becomes heavier. Why? Can you speed it up?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# What was the point about Maximum Likelihood" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The process is described by possion distribution better" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "https://en.wikipedia.org/wiki/Poisson_distribution\n", "\n", "In probability theory and statistics, the Poisson distribution (French pronunciation: ​[pwasɔ̃]; in English often rendered /ˈpwɑːsɒn/), named after French mathematician Siméon Denis Poisson, is a discrete probability distribution that expresses the probability of a given number of events occurring in a fixed interval of time or space if these events occur with a known constant rate and independently of the time since the last event.[1] The Poisson distribution can also be used for the number of events in other specified intervals such as distance, area or volume.\n", "\n", "\n", "The other point of view: we have 200 people with 3% probability to pick up the bike.\n", "\n", "What about CLT??? It works when $n \\rightarrow \\inf$. For poisson distribution there is a special case called De Moivre–Laplace theorem." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The list of different kinds of Generalized Linear Regression methods in sklearn: https://scikit-learn.org/stable/modules/linear_model.html\n", "\n", "And there is no Poisson regression there.\n", "\n", "So, let's write a probabilistic model for poisson distribution and optimize maximum likelihood.\n", "\n", "Hausaufgaben: try to do it.\n", "\n", "Hint: \n", "\n", "start from the assumption $\\hat{y} = \\exp{\\langle x, \\theta \\rangle}$ and find the derivative of log-likelihood by $\\theta$. It's zero + check the sign of the second derivative.\n", "\n", "The conclusion: we can simulate poisson regression with simple wrapper." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Poisson hierarchical regression" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Check if we have issues with np.log(y == 0)" ] }, { "cell_type": "code", "execution_count": 37, "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>datetime</th>\n", " <th>season</th>\n", " <th>holiday</th>\n", " <th>workingday</th>\n", " <th>weather</th>\n", " <th>temp</th>\n", " <th>atemp</th>\n", " <th>humidity</th>\n", " <th>windspeed</th>\n", " <th>casual</th>\n", " <th>registered</th>\n", " <th>count</th>\n", " <th>week_day</th>\n", " <th>hour</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ "Empty DataFrame\n", "Columns: [datetime, season, holiday, workingday, weather, temp, atemp, humidity, windspeed, casual, registered, count, week_day, hour]\n", "Index: []" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[df[\"count\"] == 0]" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: RuntimeWarning: divide by zero encountered in log\n", " \"\"\"Entry point for launching an IPython kernel.\n" ] }, { "data": { "text/plain": [ "-inf" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.log(0)" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [], "source": [ "class PoissonRegression(linear_model.ElasticNet):\n", " \n", " def __init__(self, alpha=1.0, l1_ratio=0.5, fit_intercept=True,\n", " normalize=False, precompute=False, max_iter=1000,\n", " copy_X=True, tol=1e-4, warm_start=False, positive=False,\n", " random_state=None, selection='cyclic'):\n", " super().__init__(alpha, l1_ratio, fit_intercept, normalize, precompute, max_iter,\n", " copy_X, tol, warm_start, positive, random_state, selection)\n", " \n", " def fit(self, X, y, *args):\n", " return super().fit(X, np.log(y), *args)\n", " \n", " def predict(self, X):\n", " return np.exp(super().predict(X))" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fitting 5 folds for each of 200 candidates, totalling 1000 fits\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/anaconda3/lib/python3.7/site-packages/sklearn/preprocessing/data.py:645: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n", " return self.partial_fit(X, y)\n", "[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n", "[Parallel(n_jobs=4)]: Done 42 tasks | elapsed: 1.4min\n", "[Parallel(n_jobs=4)]: Done 192 tasks | elapsed: 5.7min\n", "[Parallel(n_jobs=4)]: Done 442 tasks | elapsed: 12.8min\n", "[Parallel(n_jobs=4)]: Done 792 tasks | elapsed: 23.4min\n", "[Parallel(n_jobs=4)]: Done 1000 out of 1000 | elapsed: 30.3min finished\n" ] }, { "data": { "text/plain": [ "GridSearchCV(cv=5, error_score='raise-deprecating',\n", " estimator=Pipeline(memory=None,\n", " steps=[('extractor', FeatureExtractor()), ('regression', PoissonRegression(alpha=1.0, copy_X=True, fit_intercept=True, l1_ratio=0.5,\n", " max_iter=1000, normalize=False, positive=False, precompute=False,\n", " random_state=None, selection='cyclic', tol=0.0001,\n", " warm_start=False))]),\n", " fit_params=None, iid='warn', n_jobs=4,\n", " param_grid={'regression__alpha': array([1.00000e-05, 2.06914e-05, 4.28133e-05, 8.85867e-05, 1.83298e-04,\n", " 3.79269e-04, 7.84760e-04, 1.62378e-03, 3.35982e-03, 6.95193e-03,\n", " 1.43845e-02, 2.97635e-02, 6.15848e-02, 1.27427e-01, 2.63665e-01,\n", " 5.45559e-01, 1.12884e+00, 2.33572e+00, 4.83293e+00, 1.00000e+01]), 'regression__l1_ratio': array([0. , 0.11111, 0.22222, 0.33333, 0.44444, 0.55556, 0.66667,\n", " 0.77778, 0.88889, 1. ])},\n", " pre_dispatch='2*n_jobs', refit=False, return_train_score='warn',\n", " scoring=make_scorer(rmsle, greater_is_better=False), verbose=1)" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "exctractor = FeatureExtractor()\n", "exctractor.collect_stats(df)\n", "clf = Pipeline([\n", " (\"extractor\", exctractor),\n", " (\"regression\", PoissonRegression()),\n", "])\n", "param_grid = {\n", " \"regression__alpha\": np.logspace(-5, 1, 20),\n", " \"regression__l1_ratio\": np.linspace(0, 1, 10)\n", "}\n", "pd.options.mode.chained_assignment = None\n", "scorerer = make_scorer(rmsle, greater_is_better=False)\n", "researcher = GridSearchCV(clf, param_grid, scoring=scorerer, cv=5, n_jobs=4, verbose=1, refit=False)\n", "researcher.fit(df, df[\"count\"].values)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'regression__alpha': 0.001623776739188721,\n", " 'regression__l1_ratio': 0.1111111111111111}" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "researcher.best_params_" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "-0.7326059519929896" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "researcher.best_score_" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In terms of MSE the score is worse. But it doesn't mean MSE is the most relevant metric. At least poisson regression never predicts negative values.\n", "\n", "* When you expect poisson regression to have better MSE score?" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "193.30622303311725" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "scorerer = make_scorer(mean_squared_error, greater_is_better=False)\n", "scores = cross_val_score(clf, df, df[\"count\"].values, cv=5, n_jobs=4, scoring=scorerer)\n", "np.mean((-np.array(scores)) ** .5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Skill vs Education\n", "\n", "When you need to predict counts, try to use Poisson Regression.\n", "You can get good enough results with experience, but you can't handle on just your skills when face a new type of tasks. More complicated tasks you have less your previous experience can help you.\n", "\n", "The key to success is to have good enough education. With education you can do research." ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [], "source": [ "df_test = pd.read_csv(\"test.csv\")\n", "cols = df_test.columns\n", "\n", "all_data = pd.concat([df[cols], df_test[cols]])" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/anaconda3/lib/python3.7/site-packages/sklearn/preprocessing/data.py:645: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n", " return self.partial_fit(X, y)\n", "/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:27: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n", "/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:27: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n" ] } ], "source": [ "exctractor = FeatureExtractor()\n", "exctractor.collect_stats(all_data)\n", "clf = Pipeline([\n", " (\"extractor\", exctractor),\n", " (\"regression\", PoissonRegression(alpha=0.001623776739188721, l1_ratio=0.1111111111111111)),\n", "])\n", "clf.fit(df, df[\"count\"].values)\n", "df_test[\"count\"] = clf.predict(df_test)" ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [], "source": [ "df_test[[\"datetime\",\"count\"]].set_index(\"datetime\").to_csv(\"linear.csv\")" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [], "source": [ "# !kaggle competitions submit -f linear.csv -m \"linear regression\" bike-sharing-demand\n", "# score 0.64265" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Further steps: use Random Forest Regressor & Catboost to get into top 10%." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![](https://i.kym-cdn.com/photos/images/original/000/517/111/fbd.jpg)" ] } ], "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
m-labs/artiq
artiq/examples/artiq_ipython_notebook.ipynb
1
24533
{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "%pylab inline\n", "\n", "import os\n", "import logging\n", "import time\n", "import asyncio\n", "import datetime\n", "import glob\n", "from pprint import pprint\n", "\n", "import numpy as np\n", "np.set_printoptions(precision=3)\n", "import matplotlib.pyplot as plt\n", "import seaborn\n", "seaborn.set_style(\"whitegrid\")\n", "import pandas as pd\n", "import h5py\n", "\n", "from sipyco.pc_rpc import (Client, AsyncioClient,\n", " BestEffortClient, AutoTarget)\n", "from artiq.master.databases import DeviceDB\n", "from artiq.master.worker_db import DeviceManager" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# let's assume artiq_master and artiq_ctlmgr are already running\n", "# then move to a location where we have our artiq setup\n", "os.chdir(os.path.expanduser(\"~/work/nist/artiq/run\"))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# we can directly use the artiq controller infrastructure\n", "# and access any artiq device\n", "\n", "# we can have artiq prepare that connection for us:\n", "ddb = DeviceDB(\"device_db.py\")\n", "devmgr = DeviceManager(ddb)\n", "lda = devmgr.get(\"lda\")\n", "lda.set_attenuation(42)\n", "assert lda.get_attenuation() == 42\n", "\n", "# ... or we can wire it up ourselves if you know where it is\n", "assert ddb.get(\"lda\", resolve_alias=True)[\"host\"] == \"::1\"\n", "assert ddb.get(\"lda\", resolve_alias=True)[\"port\"] == 3253\n", "\n", "# there are different Client types tailored to different use cases:\n", "\n", "# synchronous\n", "lda = Client(\"::1\", 3253)\n", "assert lda.get_attenuation() == 42\n", "\n", "# asyncio\n", "lda = AsyncioClient()\n", "async def test_lda():\n", " await lda.connect_rpc(\"::1\", 3253, AutoTarget)\n", " return await lda.get_attenuation()\n", "assert asyncio.get_event_loop().run_until_complete(test_lda()) == 42\n", "\n", "# best effort\n", "lda = BestEffortClient(\"::1\", 3253, AutoTarget)\n", "assert lda.get_attenuation() == 42" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "current schedule\n", "{}\n", "experiments:\n", "['ex/',\n", " 'test_analyzer.py',\n", " 'notebook_test.py',\n", " 'speed_benchmark.py',\n", " 'histograms.py',\n", " 'arguments_demo.py',\n", " '.git/',\n", " '__pycache__/',\n", " 'flopping_f_simulation.py',\n", " 'test_crash.py',\n", " 'run_forever.py',\n", " 'transport.py',\n", " 'pdq2_simple.py']\n" ] } ], "source": [ "# let's connect to the master\n", "\n", "schedule, exps, datasets = [\n", " Client(\"::1\", 3251, \"master_\" + i) for i in\n", " \"schedule experiment_db dataset_db\".split()]\n", "\n", "print(\"current schedule\")\n", "pprint(schedule.get_status())\n", "print(\"experiments:\")\n", "pprint(exps.list_directory(\"repository\"))" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "current schedule\n", "{4722: {'due_date': None,\n", " 'expid': {'arguments': {'F0': 1500, 'noise_amplitude': 0.3},\n", " 'class_name': 'FloppingF',\n", " 'file': 'repository/flopping_f_simulation.py',\n", " 'log_level': 30},\n", " 'flush': False,\n", " 'pipeline': 'main',\n", " 'priority': 0,\n", " 'repo_msg': None,\n", " 'status': 'preparing'}}\n" ] } ], "source": [ "# we can submit experiments to be run\n", "\n", "expid = dict(\n", " file=\"repository/flopping_f_simulation.py\",\n", " class_name=\"FloppingF\",\n", " log_level=logging.WARNING,\n", " arguments=dict(\n", " F0=1500,\n", " noise_amplitude=.3,\n", " ),\n", ")\n", "if not schedule.get_status():\n", " rid = schedule.submit(pipeline_name=\"main\", expid=expid,\n", " priority=0, due_date=None, flush=False)\n", "print(\"current schedule\")\n", "pprint(schedule.get_status())" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# wait for experiment to finish\n", "# this can be written nicer by subscribing and reacting to scheduler changes\n", "while rid in schedule.get_status():\n", " time.sleep(.1)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "flopping_f: 1499.944285221012\n" ] }, { "data": { "image/png": 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FTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fc5187c8668>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# now that the experiment has completed, we can get the\n", "# current value of the (live) dataset and plot it\n", "# had we done this earlier, the dataset would have been incomplete\n", "fig, ax = plt.subplots()\n", "d = datasets.get(\"flopping_f_brightness\")\n", "ax.plot(d)\n", "print(\"flopping_f:\", datasets.get(\"flopping_freq\"))" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# this is how you would clear all pipelines\n", "for i in schedule.get_status():\n", " schedule.delete(i)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "available datasets ['artiq_version', 'flopping_f_brightness']\n" ] } ], "source": [ "# we can easily find and use the data that was saved as part\n", "# of the experiment\n", "\n", "t = datetime.datetime.now()\n", "f = os.path.join(\n", " \"results\", t.strftime(\"%Y-%m-%d\"), #t.strftime(\"%H-%M\"),\n", " \"*\", \"{:09d}-FloppingF.h5\".format(rid))\n", "\n", "# we would usually like to use pandas but our data does not have\n", "# the metadata pandas want\n", "#d = pd.HDFStore(glob.glob(f)[0])\n", "\n", "with h5py.File(glob.glob(f)[0]) as f:\n", " print(\"available datasets\", list(f))\n", " assert np.allclose(f[\"datasets/flopping_f_brightness\"], d)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Overwriting repository/notebook_test.py\n" ] } ], "source": [ "%%writefile repository/notebook_test.py\n", "\n", "# we can also write experiments in the notebook and submit them\n", "# we don't have submit-by-content yet (and there would be questions\n", "# about other modules that would need to be imported) so we just export\n", "# this cell and submit it by filename\n", "\n", "from artiq.experiment import *\n", "\n", "class Hello(EnvExperiment):\n", " def build(self):\n", " pass\n", " \n", " def run(self):\n", " print(\"Hello world!\")" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4724\n" ] } ], "source": [ "expid = dict(\n", " file=\"repository/notebook_test.py\",\n", " class_name=\"Hello\",\n", " log_level=logging.WARNING,\n", " arguments=dict(),\n", ")\n", "rid = schedule.submit(pipeline_name=\"misc\", expid=expid,\n", " priority=1, due_date=None, flush=False)\n", "print(rid)\n", "# on the master you should see the message." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
lgpl-3.0
erccarls/vectorsearch
notebooks/04 - doc2vec- training.ipynb
1
28620
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " business_id date review_id stars \\\n", "10 UsFtqoBl7naz8AVUBZMjQQ 2013-11-08 Di3exaUCFNw1V4kSNW5pgA 5 \n", "11 UsFtqoBl7naz8AVUBZMjQQ 2014-03-29 0Lua2-PbqEQMjD9r89-asw 3 \n", "\n", " text type \\\n", "10 All the food is great here. But the best thing... review \n", "11 We checked this place out this past Monday for... review \n", "\n", " user_id votes_cool votes_funny votes_useful \\\n", "10 uK8tzraOp4M5u3uYrqIBXg 0.0 0.0 0.0 \n", "11 I_47G-R2_egp7ME5u_ltew 0.0 0.0 0.0 \n", "\n", " cleaned_tokenized \n", "10 [[food, great], [best, thing, wing], [wing, si... \n", "11 [[checked, place, past, monday, wing, night], ... \n", " business_id date \\\n", "84215 8781c06a4e2407f5e027cd503f4aab675e76615b NaN \n", "84216 8781c06a4e2407f5e027cd503f4aab675e76615b NaN \n", "\n", " review_id stars \\\n", "84215 0e446098-6893-4315-9ed8-243c1926dae6 4.0 \n", "84216 a5eb8ce2-2f30-4f4b-885b-5d163e606629 5.0 \n", "\n", " text type \\\n", "84215 Buffalo wings w/ hotter sauce - just the right... NaN \n", "84216 It's thinly sliced steak covered with cheese o... NaN \n", "\n", " user_id votes_cool votes_funny \\\n", "84215 d1d2fa20-3413-41ee-adc2-b58bc9b160e8 NaN NaN \n", "84216 4585e5c9-f4b7-4bdb-94d4-39dc8e124db6 NaN NaN \n", "\n", " votes_useful cleaned_tokenized \n", "84215 NaN [[buffalo, wing, w, hotter, sauce, -, right, a... \n", "84216 NaN [[thinly, sliced, steak, covered, cheese, warm... \n" ] } ], "source": [ "import pandas as pd\n", "import pickle\n", "import numpy as np\n", "\n", "# Load the bar review dataset \n", "review = pd.read_pickle('../output/bar_reviews_cleaned_and_tokenized_SF.pickle')\n", "print review.head(2)\n", "print review.tail(2)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Load the training users\n", "user_set_training = pickle.load(open('../output/training_users.pickle', 'rb'))\n", "# Make the active review set training only \n", "review = review[review.user_id.isin(user_set_training)]\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Fraction Processed 0.99928762244\n", "5615\n" ] } ], "source": [ "from collections import OrderedDict\n", "from itertools import chain\n", "\n", "\n", "# n_reviews = 100 # all of them... \n", "# Flatten the reviews, so each review is just a single list of words.\n", "reviews_merged_bus = OrderedDict()\n", "business_set = list(set(review.business_id.values[:]))\n", "for i_bus, bus_id in enumerate(business_set):\n", " if ((i_bus%5)==0):\n", " print '\\r Fraction Processed',float(i_bus+1)/len(business_set),\n", " # This horrible line first collapses each review of a corresponding business into a list\n", " # of lists, and then collapses the list of sentences to a long list of words\n", " reviews_merged_bus[bus_id] = list(chain.from_iterable(chain.from_iterable( \n", " review.cleaned_tokenized[review.business_id==bus_id] )) )\n", "docs_bus = reviews_merged_bus.values()\n", "print \n", "print len(docs_bus)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "path /data/insight_yelp/input/\n", "\n", "First Doc: \n", "-----------------\n", "TaggedDocument(['crooning', 'gay', 'men', u'friend', 'tear', 'joint', 'regularly', 'recognized', u'medium', '-', 'straight', 'gay', 'best', 'karaoke', 'bar', 'valley', 'coming', 'ever', 'since', 'used', 'hop', 'fence', 'old', 'apartment', 'complex', u'block', 'away', 'tell', 'place', 'cant', 'beat', 'fun', 'value', 'dept', 'mean', 'else', 'get', '4', 'absolut', u'tonic', '-', 'plus', 'free', 'oftentimes', 'campy', 'klassy', 'entertainment', 'crowd', 'singing', u'skill', 'singing', u'taste', 'run', 'gamut', 'thats', 'part', 'appeal', 'crowd', 'mostly', 'gay', 'men', u'30', u'40', u'go', 'older', 'younger', u'lesbian', 'also', 'hold', 'court', 'well', 'mean', 'else', u'honor', 'sing', 'pat', 'benatar', 'melissa', 'many', 'gay', 'men', 'bring', 'straight', u'girlfriend', 'most', 'likely', 'someone', 'sing', 'grease', 'duet', 'believe', 'not', 'handful', 'straight', 'men', 'dragged', 'not', 'fear', 'crowd', 'very', 'friendly', 'welcoming', u'bartender', u'owner', 'take', 'care', 'people', 'kind', 'music', 'expect', 'people', 'completely', 'hammered', 'singing', 'public', 'very', 'first', 'time', 'others', 'serious', 'seasoned', '-', 'classical', 'training', 'via', 'phoenix', u'men', 'choir', u'guy', 'would', 'make', 'american', 'idol', 'drool', 'desire', 'song', 'list', 'large', 'respectable', 'think', 'george', 'dragon', 'better', 'list', '-', 'see', 'review', u'song', 'run', 'spectrum', 'liza', 'cabaret', 'tim', 'mcgraw', 'hip-hop', 'rap', u'80', 'alt', 'rock', 'u2', 'acdc', 'elton', 'john', 'stevie', 'wonder', 'frank', 'sinatra', 'place', 'totally', 'small', 'divey', '-and', 'bit', 'weathered', 'looking', 'speed', 'past', '7th', 'st', 'useless', 'trivia', 'alert', 'phoenix', 'architectural', 'history', u'buff', 'bar', 'identical', 'chez', 'nous', 'without', u'booth', 'not', 'dark', u'building', 'built', 'man', 'karaoke', u'run', 'thursday', 'night', 'saturday', 'night', 'other', 'themed', u'evening', 'throughout', 'week', 'incl', 'not', 'making', 'greek', 'god', 'revue', 'stripper', 'show', 'hosted', 'catty', 'drag', u'queen', 'sunday', 'night', u'gay-men', u'their-friends', u'the-media', u'the-valley', u'the-fence', u'this-place', u'the-fun', u'the-crowd', u'the-gamut', u'the-appeal', u'the-crowd', u'their-30s', u'the-honors', u'straight-men', u'the-crowd', u'the-bartenders', u'the-people', u'what-kind', u'some-people', u'the-very-first-time', u'the-phoenix', u'these-guys', u'the-song-list', u'my-review', u'the-songs', u'the-spectrum', u'hip-hop-rap', u'this-place', u'this-bar', u'the-booths', u'the-same-man', u'thursday-night', u'saturday-night', u'the-week', u'sunday-night', 'went', 'last', 'night', 'karaoke', 'flamin', 'steve', 'jenni', 'work', 'blast', 'gay', 'bar', 'bunch', u'u', 'great', 'selection', u'song', 'guy', u'run', 'running', 'karaoke', '15', u'year', 'nicely', 'decorated', 'guess', 'used', 'real', 'dive', u'goddess', 'room', 'clean', 'no', 'water', 'floor', 'gotta', 'say', 'though', 'gay', 'men', 'sing', u'song', 'werent', 'danzig', 'acdc', 'right', 'said', 'fred', u'rendition', 'would', 'fallen', 'asleep', 'staff', 'awesome', 'everyone', 'crowd', 'really', 'friendly', 'definitely', 'go', 'back', u'a-bunch', u'15-years', u'a-real-dive', u'the-floor', u'gay-men', u'the-staff', u'the-crowd', u'here', 'place', 'go', 'everybody', u'know', 'name', 'most', u'patron', 'happen', 'gay', 'love', 'coming', u'gay', 'hanging', u'drink', 'neighborhood', 'bar', 'crowd', 'supa', 'mixed', 'mean', 'else', 'go', 'hardened', 'biker', 'boy', 'compliment', 'nail', 'polish', 'choice', 'awesome', 'ive', 'also', 'threatened', 'stealing', u'shoe', 'well', 'asked', 'one', 'buy', 'lipstick', 'drag', 'queen', u'bartender', 'courteous', 'super', 'funny', 'fave', 'bartender', 'michael', u'he', 'cutie', 'great', 'pour', 'whether', 'youre', u'short', 't-shirt', 'dressed', u'nine', 'stop', 'youll', 'find', 'corner', 'fit', u'a-place', u'your-name', u'the-patrons', u'my-gays', u'some-drinks', u'a-neighborhood-bar', u'the-crowd', u'my-shoes', u'the-bartenders', u'my-fave-bartender', u'a-cutie', u'a-great-pour', u'a-t-shirt', u'the-nines', u'a-corner', 'went', 'drag', 'king', 'contest', u'woman', 'very', 'over', 'weight', 'not', 'very', 'entertaining', 'bar', 'size', 'shack', 'definetly', 'older', 'male', 'crowd', 'lesbian', 'between', u'age', '21-35', 'not', 'spot', u'all-the-women', u'the-bar', u'the-size', u'a-shack', u'the-ages', u'your-spot', 'im', 'giving', u'apollo', 'four', u'star', 'lot', u'thing', 'look', 'bar', 'clean', 'well-maintained', 'including', u'goddess', 'restroom', 'also', 'friendly', 'staff', 'diverse', 'group', u'patron', 'great', 'outdoor', 'patio', 'featuring', 'roman', u'column', 'perhaps', 'most', 'importantly', u'feature', 'karaoke', 'thursday', '-', 'saturday', u'night', 'husband', 'went', 'friday', 'night', 'karaoke', 'stevey', 'p', 'kristin', u'so', 'great', 'time', u'singer', 'talent', 'definitely', 'better', 'average', 'karaoke', 'dive', 'bar', 'song', 'choice', 'good', 'avid', 'karaoke', 'singer', 'didnt', 'like', 'fact', 'no', 'separate', 'stage', 'area', 'carved', 'current', 'singer', 'stand', 'stand', u'chair', 'between', u'table', 'strain', u'neck', 'look', u'lyric', 'karaoke', 'also', 'wasnt', 'central', 'focus', 'place', 'much', 'very', 'loud', 'talking', 'socializing', 'going', 'spite', 'singing', 'im', 'not', 'going', 'penalize', u'apollo', 'much', 'though', 'know', 'serious', 'karaoke', u'freak', 'like', 'want', 'focus', 'one', 'other', 'complaint', '-', u'u', 'group', 'also', 'little', 'disappointed', 'weak', u'drink', '-', 'dont', 'know', 'crowd', 'control', 'final', 'tab', '70', 'expected', 'feeling', 'little', 'loopy', 'most', 'night', 'speaking', 'feeling', 'loopy', 'appreciative', 'helpful', u'bartender', 'wanted', 'call', 'cab', 'one', 'came', 'fairly', 'quickly', 'after', 'called', 'one', u'u', 'apparently', 'company', 'karaoke', 'one', 'kobalt', 'another', 'gay', 'bar', 'park', 'central', 'complex', 'social', 'dynamic', 'bar', 'scene', 'pretty', 'diverse', 'crowd', 'karaoke', 'part', 'experience', 'really', 'like', 'apollo', 'however', 'ever', 'want', 'go', 'somewhere', 'little', 'low-key', 'really', 'focus', 'karaoke', 'part', 'might', 'want', 'check', 'kobalt', u'four-stars', u'a-lot', u'a-bar', u'a-diverse-group', u'my-husband', u'a-friday-night', u'kristin-s', u'a-great-time', u'the-fact', u'our-chairs', u'our-necks', u'the-lyrics', u'the-place', u'the-focus', u'the-group', u'the-night', u'the-bartenders', u'a-cab', u'the-company', u'the-one', u'another-gay-bar', u'a-pretty-diverse-crowd', u'the-experience', u'the-karaoke-part', 'spilled', 'drink', 'over', 'significant', 'other', 'table', 'others', 'party', 'christine', 'kristin', 'etc', 'spilled', 'kamikaze', u'shot', 'over', u'table', 'karaoke', u'book', 'still', 'not', 'get', 'kicked', 'dumped', 'said', 'score', 'reason', 'updating', 'bar', u'owner', 'ron', 'lee', 'asked', 'update', 'since', 'not', 'divey', 'anymore', 'arent', 'classroom', 'carpet', 'gave', 'way', 'faux-finish', 'wood', u'floor', 'new', 'plasma', u'screen', 'replaced', 'old', u'tv', 'old', u'furnishing', 'gave', 'way', 'new', u'table', 'barstools', 'wrought', 'iron', u'back', 'og', 'swivel', 'naugahyde', u'chair', 'still', 'lurk', 'bar', 'thanks', u'guy', 'original', 'review', 'almost', 'year', 'half', 'ago', 'interior', 'remodel', 'new', 'outdoor', 'patio', 'response', '2006', 'smoking', u'bar', 'ban', u'my-drink', u'the-tables', u'the-reason', u'the-bar-owners', u'new-tables', u'the-bar', u'my-original-review', u'a-response', 'went', u'friend', 'birthday', 'gathering', 'co-worker', 'hadnt', u'apollo', u'year', 'wasnt', 'really', 'looking', 'forward', 'since', 'last', 'time', 'bunch', 'old', u'guy', 'say', 'wasnt', 'bad', 'experience', 'place', 'definitely', 'changed', 'way', 'bright', 'crowd', 'mixed', 'old', u'v', 'younger', 'although', 'not', 'young', u'bartender', 'pleasant', 'one', 'bartender', 'little', 'overboard', 'talking', 'bad', 'other', u'bar', 'kind', u'turn', 'still', 'friendly', 'overall', 'wont', 'hangout', 'fun', 'night', 'long', 'youre', 'group', 'know', u'a-few-friends', u'a-birthday-gathering', u'a-co-worker', u'the-last-time', u'a-bunch', u'old-guys', u'a-bad-experience', u'the-place', u'its-way', u'the-crowd', u'the-bartenders', u'one-bartender', u'other-bars', u'which-kind', u'my-hangout', u'the-night', u'a-group', 'went', u'apollo', 'saturday', 'night', 'group', 'included', 'kristin', 'christine', 'significant', 'others', 'apparently', u'weekend', 'karaoke', 'let', 'tell', 'not', 'average', 'karaoke', 'experience', 'mean', 'karaoke', '34', 'people', 'sing', 'actually', 'decent', 'really', 'good', u'voice', 'geez', 'horrible', 'screeching', u'rendition', 'total', 'eclipse', 'heart', 'oh', 'right', 'husband', 'sang', 'awful', 'funny', 'way', 'go', 'participate', 'karaoke', 'plenty', 'tv', u'screen', u'word', 'really', 'liked', u'tv', 'reserved', 'show', 'football', 'sportscenter', u'apollo', 'moderately', 'sized', 'establishment', 'outdoor', 'patio', 'saw', u'lady', 'bathroom', 'cleverly', 'marked', u'goddess', 'sign', 'tiny', 'however', 'never', 'line', 'most', 'crowd', 'male', 'persuasion', 'husband', 'beer', 'cant', 'comment', u'price', 'mixed', u'drink', u'shot', 'beer', 'cheaper', 'most', u'place', 'valley', 'bud', 'light', u'draft', '275', 'fat', 'tire', u'draft', '3', 'since', 'designated', 'driver', 'evening', 'switched', 'soda', 'halfway', 'night', '2', 'included', 'free', u'refill', u'bartender', 'extremely', 'friendly', 'very', 'prompt', 'service', 'also', 'waiter', u'float', 'throughout', 'space', 'dont', 'want', 'go', 'bar', 'crowd', 'flowed', 'throughout', 'evening', 'never', 'got', 'full', 'people', 'getting', 'knocked', 'impossible', 'move', 'very', 'laid', 'back', 'place', 'hang', 'chat', 'enjoy', 'singing', 'along', 'sweet', 'caroline', u'a-group', u'the-weekends', u'the-people', u'total-eclipse', u'the-heart', u'my-husband', u'a-funny-way', u'tv-screens', u'the-words', u'a-few-tvs', u'an-outdoor-patio', u'the-ladies-bathroom', u'a-line', u'the-crowd', u'the-male-persuasion', u'my-husband', u'the-prices', u'mixed-drinks', u'most-places', u'the-valley', u'light-drafts', u'the-designated-driver', u'the-night', u'free-refills', u'the-bartenders', u'a-waiter', u'the-space', u'the-bar', u'the-crowd', u'the-evening', u'sweet-caroline', 'crowd', u'apollo', 'always', 'seems', 'pretty', 'diverse', 'stopped', 'second', 'third', 'time', 'friend', 'saturday', 'night', 'virtually', 'every', 'group', 'seemed', 'represented', u'apollo', 'charming', 'sense', 'completely', u'lack', 'pretentiousness', 'hip', u'bar', 'scottsdale', 'phoenix', 'karaoke', 'night', 'always', 'great', 'idea', 'dude', 'singing', 'creed', 'weird', 'right', u'drink', 'nice', 'stiff', 'even', u'apollo', 'isnt', 'weekly', 'hangout', 'list', 'definitely', 'like', 'overall', 'vibe', 'think', 'also', 'one', 'oldest', 'gay', u'bar', 'phoenix', u'the-crowd', u'a-friend', u'a-saturday-night', u'the-sense', u'the-pretentiousness', u'karaoke-night', u'a-great-idea', u'some-dude', u'the-overall-vibe', 'ahhh', u'apollo', 'little', 'gay', 'bay', 'street', u'apollo', 'evolved', 'over', u'year', 'ive', 'good', u'time', 'makeover', 'almost', 'complete', u'bathroom', 'renovated', 'soon', 'hope', 'might', 'going', 'back', 'often', 'surly', u'bartender', 'long', 'replaced', 'huge', 'patio', 'constructed', 'although', 'no', 'longer', 'smoke', 'enjoyable', 'area', 'although', 'not', 'first', 'choice', u'bar', 'stop', 'time', 'time', 'enjoy', 'well', 'made', 'read', 'strong', 'cocktail', 'catch', u'bud', 'best', u'night', 'opinion', 'karaoke', 'nites', 'place', 'normally', u'pack', 'very', 'diverse', 'crowd', 'aside', u'issue', u'men', 'bathroom', u'padlock', 'worse', 'overly', 'bright', 'lighting', 'bar', 'getting', 'better', 'after', 'chatting', 'owner', 'great', 'know', 'touch', u'patron', 'quickly', 'address', u'issue', 'im', 'really', 'looking', 'forward', 'completion', u'renovation', u'the-years', u'some-good-times', u'the-best-nights', u'my-opinion', u'the-place', u'a-very-diverse-crowd', u'my-issues', u'the-mens-bathroom', u'this-bar', u'the-owner', u'their-patrons', u'any-issues', u'the-completion', u'the-renovations', 'went', 'last', 'night', 'cheap', u'drink', 'friendly', 'staff', 'return', 'headed', 'over', u'apollo', 'last', 'friday', 'night', 'possibly', 'drink', 'enough', 'sing', 'karaoke', 'started', 'night', 'saying', 'absolutely', 'would', 'not', 'singing', 'going', 'make', 'fun', 'forward', u'hour', 'meredith', u'brook', 'bitch', 'coming', 'mouth', 'blame', 'strong', u'drink', 'great', 'service', u'apollo', 'crowd', 'great', 'would', 'definitely', 'go', 'back', 'patio', 'also', 'great', 'addition', 'since', 'many', u'companion', u'smoker', 'could', 'take', u'drink', 'outside', u'some-karaoke', u'my-mouth', u'the-strong-drinks', u'great-service', u'the-crowd', u'the-patio', u'a-great-addition', u'my-companions', u'their-drinks', 'im', 'not', 'really', 'fan', 'place', 'seems', 'awfully', 'crowded', 'hard', 'navigate', 'worse', 'though', 'charge', '6', 'red', 'bull', 'no', 'liquor', 'bunkhouse', 'usually', u'charge', '2', 'back', 'patio', 'small', 'skinny', 'whereas', 'usual', 'clientele', 'not', u'a-fan', u'the-place', u'a-red-bull', u'no-liquor', u'the-back-patio', u'the-usual-clientele', 'one', 'time', u'apollo', 'gay', u'dude', 'asked', 'friend', 'real', u'woman', 'knew', 'waxed', 'mustache', 'going', 'night', 'never', 'mind', 'always', 'feel', 'like', 'diva', u'girl', u'apollo', 'handful', u'time', 'karaoke', 'im', 'always', 'drunken', 'mess', 'every', 'time', 'leave', 'well', 'certainly', 'cant', 'handle', 'liquor', u'drink', 'poured', 'well', 'cheap', 'cheap', u'drink', 'first', 'time', 'think', 'pink', 'panther', u'martini', '4', 'great', 'place', 'gather', 'bunch', u'friend', 'karaoke', 'yes', 'gay', 'bar', 'coolest', 'one', 'ive', 'always', 'met', 'great', u'guy', 'never', 'interested', 'always', 'nice', 'willing', 'sing', 'duet', 'karaoke', 'host', 'always', 'fun', 'not', 'mic', 'hog', 'like', u'place', 'complaint', 'gay', u'friend', 'like', 'sing', u'lot', 'show', u'tune', 'boring', 'definitely', 'great', u'performer', 'most', 'good', 'time', 'dont', 'feel', 'like', 'need', 'belt', 'like', 'whitney', 'houston', 'come', 'favorite', u'thing', u'apollo', '1', 'free', 'fresh', 'popcorn', 'best', 'thing', 'munch', 'after', 'youve', 'lot', 'drink', '2', u'lady', 'room', 'never', 'line', 'mainly', 'gay', u'dude', 'frequent', 'place', 'love', u'apollo', u'my-friend', u'real-women', u'my-mustache', u'a-diva', u'the-girls', u'a-handful', u'a-drunken-mess', u'my-liquor', u'the-drinks', u'a-bunch', u'great-guys', u'a-duet', u'the-karaoke-host', u'some-places', u'my-only-complaint', u'my-gay-friends', u'show-tunes', u'a-good-time', u'whitney-houston', u'my-favorite-things', u'best-thing', u'a-lot', u'the-ladies-room', u'a-line', 'much', 'better', u'experience', 'experienced', 'initial', 'review', u'price', 'line', 'other', u'bar', 'like', 'phoenix', u'owner', 'very', 'supportive', 'community', 'count', 'something', 'may', 'cranky', 'mood', 'posted', 'initial', 'review', 'nice', 'patio', 'good', 'seating', 'shade', u'my-initial-review', u'the-prices', u'other-bars', u'the-owners', u'the-community', u'my-initial-review', u'good-seating', 'youre', 'going', 'go', u'apollo', 'karaoke', 'probably', 'start', 'practicing', 'fake', 'im', u'writ', 'routine', 'youre', 'going', 'need', 'think', u'le', 'hip-hop', 'along', u'line', 'show', u'tune', 'old', u'ballad', 'shit', 'around', 'born', 'dont', 'get', 'wrong', 'though', 'dig', 'free', 'popcorn', 'free', u'condom', 'pours', 'strong', 'enough', 'get', 'date', 'put', 'im', 'guessing', 'lot', 'putting', 'gone', u'men', 'bathroom', 'door', 'permanently', 'propped', 'open', u'the-lines', u'show-tunes', u'your-date', u'a-lot', u'the-mens-bathroom-door', 'breeder', 'favorite', 'gay', 'bar', u'bartender', 'hilarious', u'drink', 'cheap', u'wall', 'salmon', 'scene', 'usually', 'mix', 'gay', 'men', 'pretty', u'girl', 'win', 'like', 'sing', 'karaoke', 'place', 'sing', 'prince', 'mostly', 'awful', 'falsetto', 'still', 'get', 'cheered', u'my-favorite-gay-bar', u'the-bartenders', u'the-drinks', u'the-walls', u'the-scene', u'a-mix', u'gay-men', u'pretty-girls', u'a-place', 'never', u'year', 'back', 'treated', 'horrible', 'service', 'approached', 'owner', 'treated', u'u', 'like', 'crap', 'boycotted', 'bar', '2', u'year', 'tried', 'prove', 'a-holes', 'plenty', 'friendly', u'bar', 'appreciate', 'business', u'horrible-service', u'the-owner', u'this-bar', u'2-years', u'the-business', 'im', 'feeling', 'low', 'looking', 'great', 'atmosphere', 'cheer', 'go', 'great', u'drink', 'staff', 'very', 'nice', 'throw', 'little', 'karaoke', 'fun', 'most', 'definetly', 'place', u'a-great-atmosphere', u'the-staff', u'the-place', 'very', 'upset', 'place', 'im', 'outside', 'beer', 'garden', 'cigarette', 'owner', 'came', 'said', 'smell', 'pot', 'person', 'told', 'medical', 'patient', 'medicated', 'got', 'bar', 'told', 'not', 'smoke', 'parking', 'lot', 'told', 'havent', 'know', u'law', 'accused', 'smoking', 'parking', 'lot', 'really', 'feel', 'discriminated', 'not', 'come', 'back', 'even', 'u', 'respect', u'rule', 'feel', 'discriminate', 'u', 'call', 'u', 'front', 'everyone', 'definitely', 'not', 'coming', 'back', u'this-place', u'the-beer-garden', u'a-cigarette', u'the-owner', u'my-person', u'the-bar', u'the-parking-lot', u'the-laws', u'the-parking-lot', u'their-rules', u'apollo', 'fun', 'experience', 'checked', 'bank', 'account', 'find', 'erroneously', 'overcharged', 'over', '3500', 'called', 'tried', 'tell', 'debit', 'hold', 'tip', 'not', 'week', 'later', 'charge', 'went', 'said', 'would', 'someone', 'call', 'back', 'not', 'got', 'run', 'around', 'gave', 'very', 'generous', 'tip', 'expense', 'fun', 'check', 'bank', 'account', 'dont', 'play', u'rule', u'a-fun-experience', u'my-bank-account', u'the-charge', u'the-run', u'a-very-generous-tip', u'my-expense', u'your-bank-account', u'the-rules', 'fun'], [u'lLI8ObL8aCVbkrrtAW0EHw'])\n" ] } ], "source": [ "import gensim\n", "from itertools import chain\n", "import sys\n", "sys.path.append('../vectorsearch/')\n", "import nltk_helper\n", "import doc2vec\n", "from gensim.models.doc2vec import TaggedDocument\n", "import pandas as pd\n", "n_epochs = 10\n", "n_docs = 10 # -1 for almost all of them...\n", "\n", "# Generate the tagged document list. \n", "\n", "docs = [TaggedDocument(words, [business_set[index],])\n", " for index, words in enumerate(docs_bus[:])]\n", "\n", "print '\\nFirst Doc: \\n-----------------\\n', docs[0]\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from gensim.models import doc2vec\n", "\n", "model = doc2vec.Doc2Vec(min_count=4, window=5, size=200, sample=1e-4, negative=10, workers=12)\n", "# Build the vocab from list of sentences.\n", "model.build_vocab(docs) \n", "\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training Epoch 10, alpha 0.0160\n" ] } ], "source": [ "from random import shuffle\n", "\n", "for epoch in range(10):\n", " print '\\rTraining Epoch %i, alpha %1.4f'%(epoch+1, model.alpha),\n", " #model.train(np.random.permutation(docs))\n", " shuffle(docs)\n", " model.train(docs)\n", " model.alpha -= 0.001 # decrease the learning rate\n", " model.min_alpha = model.alpha # fix the learning rate, no decay\n", "\n", "#model.init_sims(replace=True) \n", "# # Normalize the word vectors.\n", "# vec_norms = np.sqrt(np.sum(model.syn0**2, axis=1))\n", "# model.syn0 = (model.syn0/vec_norms[:, numpy.newaxis])\n", "# # Normalize the doc vectors.\n", "# vec_norms = np.sqrt(np.sum(model.docvecs.doctag_syn0**2, axis=1))\n", "# model.docvecs.doctag_syn0 = (model.docvecs.doctag_syn0/vec_norms[:, numpy.newaxis])\n", "\n", "model.save('../output/doc2vec_bars_200_neg_10_win_5.model')" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[(u'yyUJKvG-C4VipITrAS0nIQ', 0.6710823774337769), (u'g3fipTPN2LBe_U42niTDcw', 0.6688743829727173), (u'ANaGwB8tVAc1qM1QAJecsQ', 0.6611914038658142), (u'LVjRN5pMJ8hhDmX0lbclpQ', 0.6471917629241943), (u'YP-sxa8i95v_scvXN2o4_w', 0.6342615485191345), (u'XkyZAQAaGO9i3on-b3fswg', 0.6326159834861755), (u'qdTtkZVgcdu3SEA6tzBPdw', 0.6320397853851318), (u'L7eGNKkuy_XdQ_35Y1Kacg', 0.629540741443634), (u'cd46siFt_-08j9-kSbVEgA', 0.6246991157531738), (u'3mp5jXdxC2yqSK6sgRQfEg', 0.6225901246070862)] \n", "\n", "[('draft', 0.7733069062232971), (u'wine', 0.7592223882675171), ('tap', 0.7554447054862976), ('micro-brews', 0.7395221590995789), ('brew', 0.7344582080841064), (u'cocktail', 0.7317838072776794), ('draught', 0.7313833236694336), ('selection', 0.723392128944397), ('bottled', 0.71184903383255), ('whisky', 0.6925665140151978)] \n", "\n", "[(u'W3SROyBvrFKT5C2ySdx1qw', 0.44849711656570435), (u'6w6gMZ3iBLGcUM4RBIuifQ', 0.43213915824890137), (u'KCP4tSmVRD6Gk3xbPEAf3w', 0.3971535861492157), (u'3WsATGkAIXV-56eWjdzecw', 0.3879733681678772), (u'3jzEz2q9HZYF2XU1Gm41nA', 0.3710145354270935), (u'JS0gYaJR5HZDhZG0TJRRGg', 0.3570679724216461), (u'QSmI5Y9bhCLIw9YYKOiQkg', 0.34995037317276), (u'Bxn0LTYR9BxEeXReFbXDJA', 0.34519267082214355), (u'Ejw0lND0g8WBQj4pCllUnQ', 0.3383505046367645), (u'EaAo1G89msEiSQLi1jX_Hw', 0.33163875341415405)] \n", "\n", "[ u'My brother and I make the trek from N Scottsdale to The Drummer almost every weekend. Jesse makes the HOTTEST suicide grilled wings on the planet - we love \\'em! Service is great and the \"regulars\" are pretty friendly too. Drink prices are good and there are plenty of TV\\'s. One of the better \"dive bars\" in the area.']\n", "[('rory', 0.5823439359664917), ('carin', 0.5686616897583008), ('biotch', 0.5440913438796997), ('seit', 0.5418999195098877), ('uwe', 0.5400148034095764), ('reaffirms', 0.5130881071090698), ('accoustic', 0.512511134147644), ('gaurd', 0.5113624930381775), ('wrist', 0.5096732974052429), ('self-guided', 0.4946335256099701)] \n", "\n" ] } ], "source": [ "# Can find similar documents..\n", "print model.docvecs.most_similar(positive=['KUinHkKyGhznElgIzx0yIw']), '\\n'\n", "\n", "# Can find similar words...Re: Dream Companies and contact from recruiters\n", "print model.most_similar(positive=['beer']), '\\n'\n", "\n", "# Can find documents that are most similar to keywords.... \n", "print model.docvecs.most_similar(positive=[model['beer'], model['music']]), '\\n'\n", "\n", "# Can find words that are most common in documents\n", "print review.text[review.review_id=='KUinHkKyGhznElgIzx0yIw'].values\n", "print model.most_similar(positive=[model.docvecs['KUinHkKyGhznElgIzx0yIw']]), '\\n'\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }
apache-2.0
CullenGao/LSTM_PittsRoutine
script/.ipynb_checkpoints/smooth_theta-checkpoint.ipynb
1
13832
{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3644\n" ] } ], "source": [ "# HEADS-UP: using inline to display instead of plt.show() everytime on notebook.\n", "# %matplotlib inline \n", "\n", "import sys\n", "import numpy as np\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "from sklearn.gaussian_process import GaussianProcess\n", "from statsmodels.nonparametric.smoothers_lowess import lowess\n", "from scipy.interpolate import interp1d\n", "\n", "# Read proc data\n", "raw_itsc = np.loadtxt('../data/torque_participants/csv_S01_intersection/proc_intersection.csv', delimiter=',')\n", "raw_y0 = np.loadtxt('../data/torque_participants/csv_S01_intersection/proc_y0.csv', delimiter=',')\n", "raw_acc = np.loadtxt('../data/torque_participants/csv_S01_intersection/proc_acc.csv', delimiter=',')\n", "# p intersection.shape[0]\n", "\n", "# Group up intersections\n", "intersections = []\n", "cnt = 0\n", "for i in range (1, raw_itsc.shape[0]):\n", " if (raw_itsc[i, 1] == raw_itsc[i - 1, 1]):\n", " continue\n", " else:\n", " intersections.append(raw_itsc[cnt: i])\n", " cnt = i + 1\n", "print len(intersections)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "##############################################################\n", "# Sample disturbution for different itsc_type \n", "\n", "tmp_itsc_type = np.unique(raw_itsc[:, 3]) # [-1. 1. 4. 5. 6. 8.]\n", "distributions = {i: list() for i in tmp_itsc_type}\n", "\n", "# for cnt in range(len(intersections)):\n", "cnt = 3\n", "# Select a specific sample right now\n", "itsc = intersections[cnt]\n", "\n", "# Params\n", "start_step = itsc[0, 0]\n", "end_step = itsc[-1, 0]\n", "# p start_step, end_step\n", "\n", "# Select according window in ACC & Y0 datas,\n", "# [start_step, end_step]\n", "for i in range(raw_y0.shape[0]):\n", " ts = raw_y0[i, 0]\n", " if ts >= start_step:\n", " start_index = i - 1\n", " for j in range(start_index, raw_y0.shape[0]):\n", " tss = raw_y0[j, 0]\n", " if tss >= end_step:\n", " end_index = j\n", "# p start_step - raw_y0[start_index, 0], raw_y0[end_index, 0] - end_step\n", " break\n", " break\n", "y0 = raw_y0[start_index: end_index + 1]\n", "\n", "for i in range(raw_acc.shape[0]):\n", " ts = raw_acc[i, 0]\n", " if ts >= start_step:\n", " start_index = i - 1\n", " for j in range(start_index, raw_acc.shape[0]):\n", " tss = raw_acc[j, 0]\n", " if tss >= end_step:\n", " end_index = j\n", "# p start_step - raw_acc[start_index, 0], raw_acc[end_index, 0] - end_step\n", " break\n", " break\n", "acc = raw_acc[start_index: end_index + 1]\n", "\n", "# Gaussian Process for interpolation\n", "# for theta in np.linspace(0.0001, 1e-2, 10):\n", "gp_y0 = GaussianProcess(theta0=1e-3, thetaL=1e-4, thetaU=1e-1)\n", "gp_acc = GaussianProcess(theta0=1e-3, thetaL=1e-4, thetaU=1e-1)\n", "gp_y0.fit(y0[:, 0].reshape(-1, 1), y0[:, 1].reshape(-1, 1))\n", "gp_acc.fit(acc[:, 0].reshape(-1, 1), acc[:, 1].reshape(-1, 1))\n", "\n", "yy = gp_y0.predict(itsc[:, 0].reshape(-1, 1))\n", "aa = gp_acc.predict(itsc[:, 0].reshape(-1, 1))\n", "\n", "# lowess_y0 = lowess(y0[:, 1], y0[:, 0], frac=.1)\n", "# lowess_acc = lowess(acc[:, 1], acc[:, 0], frac=.3)\n", "\n", "# y0_x = list(zip(*lowess_y0))[0]; y0_y = list(zip(*lowess_y0))[1]\n", "# acc_x = list(zip(*lowess_acc))[0]; acc_y = list(zip(*lowess_acc))[1]\n", "\n", "# f_y0 = interp1d(y0_x, y0_y, bounds_error=False)\n", "# f_acc = interp1d(acc_x, acc_y, bounds_error=False)\n", "\n", "# yy = f_y0(itsc[:, 0].reshape(-1, 1))\n", "# aa = f_acc(itsc[:, 0].reshape(-1, 1))\n", "\n", "theta = (np.arcsin(yy / aa) * 180 / 3.1415926).reshape(-1, 1)\n", "# Do the differencing\n", "delta_theta = np.diff(theta, axis=0)\n", "\n", "# Form statistical data\n", "for i in range (itsc.shape[0] - 1):\n", " distributions[itsc[i + 1, -1]].append(delta_theta[i, 0])\n", "\n", " # if cnt >= 600:\n", " # break\n", " # p cnt * 1. / len(intersections)\n", " # sys.stdout.write(\"Progress: %d%% \\r\" % (cnt * 1. / len(intersections)) )\n", " # sys.stdout.flush()\n", "\n", " ##############################################################\n", " # Visual validation\n", "\n", "fig_1 = plt.figure()\n", "# plt.plot(itsc[:, 0], itsc[:, 2])\n", "plt.plot(itsc[:, 0], itsc[:, 3], 'k', label=\"itsc_type\")\n", "# plt.plot(y0[:, 0], y0[:, 1], label=\"y0_discrete\")\n", "# plt.plot(acc[:, 0], acc[:, 1], label=\"acc_discrete\")\n", "plt.plot(itsc[:, 0], yy, 'b--', linewidth=0.8, label=\"y0_lowessed\")\n", "plt.plot(itsc[:, 0], aa, 'c--', linewidth=0.8, label=\"acc_lowessed\")\n", "plt.plot(itsc[:, 0], theta, 'g', linewidth=1, label=\"raw_theta\")\n", "plt.plot(itsc[1:, 0], delta_theta, 'r', label=\"delta_theta\")\n", "plt.plot(itsc[:, 0], np.zeros(itsc.shape), 'grey', linewidth=0.2)\n", "\n", "plt.xlim(itsc[0, 0] - 1000, itsc[-1, 0] + 1000)\n", "ymin, ymax = plt.ylim()\n", "plt.ylim(ymin - 1, ymax + 1)\n", "plt.legend(loc='lower right')\n", "plt.title(\"Data Demo on Intersection NO.13 (LEFT TURN)\")\n", "plt.xlabel(\"Time Stamp\")\n", "plt.ylabel(\"Degree\")\n", "\n", "plt.show()\n", "\n", "# print gp_y0.score(y0[:, 0].reshape(-1, 1), y0[:, 1].reshape(-1, 1)), \\\n", "# gp_acc.score(acc[:, 0].reshape(-1, 1), acc[:, 1].reshape(-1, 1))\n", "# plt.legend(['wheel_pos', 'itsc_type', 'theta'],\n", "# loc='center left',\n", "# bbox_to_anchor=(1, 0.5))\n", "\n", "# # plt.figure()\n", "# for itsc_type in distributions:\n", "# if itsc_type != 1 and itsc_type != -1 and itsc_type != 5:\n", "# sns.distplot(distributions[itsc_type], hist=True, rug=False)\n", "# plt.legend([i for i in distributions if i != 1 and i != -1 and i != 5],\n", "# loc = 'center left', bbox_to_anchor=(1, 0.5))\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.0\n", "0.0137211855104\n", "0.0274423710209\n", "0.0411635565313\n", "0.0548847420417\n", "0.0686059275521\n", "0.0823271130626\n", "0.096048298573\n", "0.109769484083\n", "0.123490669594\n", "0.137211855104\n" ] } ], "source": [ "##############################################################\n", "# Sample disturbution for different itsc_type \n", "\n", "# tmp_itsc_type = np.unique(raw_itsc[:, 3]) # [-1. 1. 4. 5. 6. 8.]\n", "# distributions = {i: list() for i in tmp_itsc_type}\n", "\n", "# data = np.empty((0, raw_itsc.shape[1] + 4))\n", "data = np.empty((0, raw_itsc.shape[1] + 2))\n", "for cnt in range(len(intersections)):\n", "# cnt = 3\n", "# Select a specific sample right now\n", " if cnt % 50 == 0:\n", " print cnt * 1. / 3644\n", " try:\n", " itsc = intersections[cnt]\n", "\n", " # Params\n", " start_step = itsc[0, 0]\n", " end_step = itsc[-1, 0]\n", " # p start_step, end_step\n", "\n", " # Select according window in ACC & Y0 datas,\n", " # [start_step, end_step]\n", " for i in range(raw_y0.shape[0]):\n", " ts = raw_y0[i, 0]\n", " if ts >= start_step:\n", " start_index = i - 1\n", " for j in range(start_index, raw_y0.shape[0]):\n", " tss = raw_y0[j, 0]\n", " if tss >= end_step:\n", " end_index = j\n", " # p start_step - raw_y0[start_index, 0], raw_y0[end_index, 0] - end_step\n", " break\n", " break\n", " y0 = raw_y0[start_index: end_index + 1]\n", "\n", " for i in range(raw_acc.shape[0]):\n", " ts = raw_acc[i, 0]\n", " if ts >= start_step:\n", " start_index = i - 1\n", " for j in range(start_index, raw_acc.shape[0]):\n", " tss = raw_acc[j, 0]\n", " if tss >= end_step:\n", " end_index = j\n", " # p start_step - raw_acc[start_index, 0], raw_acc[end_index, 0] - end_step\n", " break\n", " break\n", " acc = raw_acc[start_index: end_index + 1]\n", "\n", " # Gaussian Process for interpolation\n", " gp_y0 = GaussianProcess(theta0=1e-2, thetaL=1e-4, thetaU=1e-1)\n", " gp_acc = GaussianProcess(theta0=1e-2, thetaL=1e-4, thetaU=1e-1)\n", " gp_y0.fit(y0[:, 0].reshape(-1, 1), y0[:, 1].reshape(-1, 1))\n", " gp_acc.fit(acc[:, 0].reshape(-1, 1), acc[:, 1].reshape(-1, 1))\n", "\n", " yy = gp_y0.predict(itsc[:, 0].reshape(-1, 1))\n", " aa = gp_acc.predict(itsc[:, 0].reshape(-1, 1))\n", " \n", "# if cnt >= 100:\n", "# break\n", "\n", " except Exception or IndexError as err:\n", " print \"Bad Example: \", cnt\n", " print \"*****\", err\n", "# dele.append(cnt)\n", " continue\n", "\n", " theta = (np.arcsin(yy / aa) * 180 / 3.1415926).reshape(-1, 1)\n", " delta_theta = np.diff(theta, axis=0)\n", " tmp_data = np.hstack((itsc[1:], theta[1:], delta_theta))\n", "# tmp_data = np.hstack((itsc[1:], theta[1:], delta_theta, yy[1:], aa[1:]))\n", " \n", " data = np.vstack((data, tmp_data))\n", " \n", " dele = []\n", " for i in range(data.shape[0]):\n", " if np.isnan(data[i, -1]) or np.isnan(data[i, -2]):\n", " dele.append(i)\n", " data = np.delete(data, dele, axis=0)\n", "\n", "print data.shape\n", "# np.savetxt('../data/torque_participants/csv_S01_intersection/S01_itsc_.csv', data, delimiter=',')\n", "np.savetxt('../data/torque_participants/csv_S01_intersection/S01_itsc.csv', data, delimiter=',')\n", " \n", "\n", " # Form statistical data\n", "# for i in range (itsc.shape[0]):\n", "# distributions[itsc[i, -1]].append(theta[i, 0])\n", "\n", "# if cnt >= 100:\n", "# break\n", "# p cnt * 1. / len(intersections)\n", "# sys.stdout.write(\"Progress: %d%% \\r\" % (cnt * 1. / len(intersections)) )\n", "# sys.stdout.flush()\n", "\n", " ##############################################################\n", " # Visual validation\n", "\n", "# fig_1 = plt.figure()\n", "# # plt.plot(itsc[:, 0], itsc[:, 2])\n", "# plt.plot(itsc[:, 0], itsc[:, 3])\n", "# plt.plot(y0[:, 0], y0[:, 1])\n", "# plt.plot(acc[:, 0], acc[:, 1])\n", "# plt.plot(itsc[:, 0], yy)\n", "# plt.plot(itsc[:, 0], aa)\n", "# plt.plot(itsc[:, 0], theta)\n", "\n", "# plt.xlim(itsc[0, 0] - 1000, itsc[-1, 0] + 1000)\n", "# ymin, ymax = plt.ylim()\n", "# plt.ylim(ymin - 1, ymax + 1)\n", "# plt.legend(['itsc_type', 'y0', 'acc', 'yy', 'aa', 'theta'],\n", "# loc='center left',\n", "# bbox_to_anchor=(1, 0.5))\n", "# p gp_y0.score(y0[:, 0].reshape(-1, 1), y0[:, 1].reshape(-1, 1)), \\\n", "# gp_acc.score(acc[:, 0].reshape(-1, 1), acc[:, 1].reshape(-1, 1))\n", " \n", "# plt.legend(['wheel_pos', 'itsc_type', 'theta'],\n", "# loc='center left',\n", "# bbox_to_anchor=(1, 0.5))\n", "\n", "plt.figure()\n", "for itsc_type in distributions:\n", " if itsc_type != 1 and itsc_type != -1 and itsc_type != 5:\n", "# sns.distplot(distributions[itsc_type], hist=True, rug=False)\n", " plt.hist(distributions[itsc_type])\n", "plt.legend([i for i in distributions if i != 1 and i != -1 and i != 5],\n", " loc = 'center left', bbox_to_anchor=(1, 0.5))\n", "plt.title(\"Degree Distribution for Different types (LEFT, RIGHT, STRAIGHT)\")\n", "plt.xlabel(\"Degree\")\n", "plt.ylabel(\"Number of samples * 1e3\")" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [Root]", "language": "python", "name": "Python [Root]" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
shanot/imp
modules/pmi/examples/analysis/analyse_crosslink_from_a_cluster.ipynb
2
1576
{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This script when it is run in a clustering directory" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import IMP\n", "import IMP.pmi\n", "import IMP.pmi.analysis\n", "\n", "model=IMP.Model()\n", "rmf_file='0.rmf3'\n", "frame_number=0\n", "prot=IMP.pmi.analysis.get_hier_from_rmf(model,frame_number,rmf_file)\n", "\n", "\n", "\n", "cm=IMP.pmi.analysis.CrossLinkTable()\n", "\n", "# Since the protein names and the residue numbers are retrieved from the stat file,\n", "# you have to provide the positions within the key of the corresponding fields\n", "# after splitting at every \"_\" , \"-\" and \":\"\n", "# example key ISDCrossLinkMS_Distance_intrarb_937-State:0-108:PROT1_55:PROT2-1-1-0.1_None\n", "# resid1 = 6 , chain1 = 7 , resid2 = 8 , chain2 = 9\n", "\n", "namemap={}\n", "namemap[\"Protein1\"]=7\n", "namemap[\"Protein2\"]=9\n", "namemap[\"Residue1\"]=6\n", "namemap[\"Residue2\"]=8\n", "\n", "\n", "cm.set_crosslinks(\"stat.out\",search_label='ISDCrossLinkMS_Distance_',mapping=namemap)\n", "cm.set_hierarchy(prot)\n", "cm.plot(filename=\"xl_all\")" ], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
gpl-3.0
shantnu/WebScraping
Ipython/Selenium_4.ipynb
1
3713
{ "metadata": { "name": "", "signature": "sha256:adfae323dc758a930b3d9fc30a92f490f930dafbdd26b6688020a3c01e3b13be" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "from selenium import webdriver\n", "from selenium.webdriver.common.keys import Keys" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 62 }, { "cell_type": "code", "collapsed": false, "input": [ "def get_code(driver):\n", " '''\n", " Function to get the code from pre blocks, and write to a file\n", " '''\n", " \n", " code = \"\"\n", " \n", " for code_block in driver.find_elements_by_tag_name(\"pre\"):\n", " code += code_block.text\n", " \n", " with open(\"code.txt\", \"a\") as f:\n", " f.write(code)\n", " " ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 63 }, { "cell_type": "code", "collapsed": false, "input": [ "driver = webdriver.Firefox()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 64 }, { "cell_type": "code", "collapsed": false, "input": [ "driver.get(\"http://pythonforengineers.com/articles/\")" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 65 }, { "cell_type": "code", "collapsed": false, "input": [ "elem = driver.find_element_by_name(\"s\")" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 66 }, { "cell_type": "code", "collapsed": false, "input": [ "elem.send_keys(\"reddit\")" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 67 }, { "cell_type": "code", "collapsed": false, "input": [ "elem.send_keys(Keys.RETURN)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 68 }, { "cell_type": "code", "collapsed": false, "input": [ "link = driver.find_element_by_link_text(\"Build a Reddit Bot Part 1\")" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 69 }, { "cell_type": "code", "collapsed": false, "input": [ "link.click()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 70 }, { "cell_type": "code", "collapsed": false, "input": [ "get_code(driver)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 71 }, { "cell_type": "code", "collapsed": false, "input": [ "while True:\n", " try:\n", " link = driver.find_element_by_link_text(\"Next Part\")\n", " link.click()\n", " get_code(driver)\n", " except:\n", " break" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 72 }, { "cell_type": "code", "collapsed": false, "input": [ "driver.close()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 73 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 73 } ], "metadata": {} } ] }
mit
Caoimhinmg/PmagPy
data_files/Essentials_Examples/Notebooks/.ipynb_checkpoints/essentials_ps_1_template-checkpoint.ipynb
1
5495
{ "metadata": { "name": "", "signature": "sha256:a6165acf97d4696c9378c9b99ebc3b28b834325b1eb95f1053b9b2995e733a8b" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "IPython Notebook for turning in solutions to the problems in the Essentials of Paleomagnetism Textbook by L. Tauxe" ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Problems in Chapter 1" ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Problem 1: " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Given that:\n", "\n", "$$\n", "\\nabla V_m = - \\bigl(\n", "{ {\\partial}\\over {\\partial r} }\n", "{ {m \\cos \\theta} \\over {4 \\pi r^2}} + \n", "{ {1\\over r} }\n", "{ {\\partial}\\over {\\partial \\theta} }\n", " { { m\\cos \\theta}\\over { 4 \\pi r^2} }\n", " \\bigr)\n", "$$\n", "\n", "it follows that:\n", "\n", "Complete this text using LaTeX formatting. see the above example. Notice how stand alone equations look like this: \n", "$$\n", "Type your equation here\n", "$$\n", "and inline math looks like this: $\\alpha,\\beta,\\gamma$\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# code to calculate H_r and H_theta\n", "import numpy as np\n", "deg2rad=np.pi/180. # converts degrees to radians\n", "# write code here to calculate H_r and H_theta and convert to B_r, B_theta\n", "\n", "\n", "# This is how you print out nice formatted numbers\n", "# floating point variables have the syntax: \n", "# '%X.Yf'%(FP_variable) where X is the number of digits and Y is the \n", "# number of didgets after the decimal. \n", "# uncomment this line to print\n", "#print 'H_r= ','%7.1f'%(H_r), 'H_theta= ', '%7.1f'%(H_theta)\n", "# to format integers: use the syntax:\n", "# '%i'%(INT_variable)\n", "#print 'B_r = ','%i'%(B_r*1e6), 'uT' # B_r in microtesla\n", "#print 'B_theta =','%i'%(B_theta*1e6),'uT' # B_theta in microtesla" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Problem 2a:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Some text to describe what you are doing. (Edit this!)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# to get information from the user, you can use the command: \n", "# raw_input(\"TYPE A MESSAGE HERE\"). For example: \n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Problem 2b: " ] }, { "cell_type": "code", "collapsed": false, "input": [ "# take your program from 2a and modify it here to go back and forth. " ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Problem 3a:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "a) This problem boils down to finding the value for ${\\bf m}$ in Equation 1.8 in Chapter 1 that would give rise to a radial field of 10$\\mu$T at a depth of 2890 km (radius of the Earth minus radius of the dipole source). \n", "\n", "Write text here about how you solve the problem.... " ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Write code here to calculate the moment, m and print it in ZAm^2\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 6 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Problem 3b" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "b) To compare 10 $\\mu$T with the field produced by an axial dipole of 80 ZAm$^2$, we need the second part of Equation 1.8 in the text:\n", "\n", "Type your answer here with nice LaTeX formatting. " ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Write some code here that calculates H_r, H_theta, the total field\n", "# in H and converted to microtesla. Use nicely formated print statements \n", "# display your results. " ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 7 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Problem 4:" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "\n", "Write your answer in this markdown cell. \n", "\n" ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
bsd-3-clause
proto-n/Alpenglow
examples/external_models/libfm/evaluate.ipynb
2
143165
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import pandas as pd\n", "import sys\n", "import shutil\n", "from alpenglow.experiments import BatchFactorExperiment, ExternalModelExperiment\n", "from alpenglow.evaluation import DcgScore\n", "import numpy as np\n", "import matplotlib\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "data = pd.read_csv('http://info.ilab.sztaki.hu/~fbobee/alpenglow/tutorial_dataset.csv', header=None, names=['time', 'user', 'item'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# libfm model" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "exp = ExternalModelExperiment(\n", " period_length=60 * 60 * 24 * 7 * 4,\n", " in_name_base=\"batches/batch\",\n", " mode=\"read\",\n", ")" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "running experiment...\n" ] } ], "source": [ "res = exp.run(data)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "res['dcg'] = DcgScore(res)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.01446016560516256" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "res['dcg'].mean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# alpenglow batch factor model" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "running experiment...\n" ] } ], "source": [ "exp = BatchFactorExperiment(\n", " period_length=60 * 60 * 24 * 7 * 4,\n", " negative_rate=5\n", ")\n", "res2 = exp.run(data)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "res2['dcg'] = DcgScore(res2)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.0173458838786909" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "res2['dcg'].mean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# plotting" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x7f0f9ba0f1d0>" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1080x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(15,8))\n", "res.groupby(res.time//(60*60*24)).dcg.mean().plot()\n", "res2.groupby(res2.time//(60*60*24)).dcg.mean().plot()\n", "plt.ylabel('daily average ndcg')\n", "plt.legend(['libfm', 'alpenglow'])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "py3-alpenglow", "language": "python", "name": "py3-alpenglow" }, "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" }, "notebook_module": { "alias": "notebook_module", "code": "", "compatibility": false } }, "nbformat": 4, "nbformat_minor": 2 }
apache-2.0
linsalrob/EdwardsLab
jupyter/liz_spreadsheets.ipynb
1
71960
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import os\n", "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 34, "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>superkingdom</th>\n", " <th>phylum</th>\n", " <th>class</th>\n", " <th>order</th>\n", " <th>family</th>\n", " <th>genus</th>\n", " <th>species</th>\n", " <th>taxid</th>\n", " <th>Philippines OPH_001</th>\n", " <th>Philippines OPH_002</th>\n", " <th>...</th>\n", " <th>Class</th>\n", " <th>Order</th>\n", " <th>Family</th>\n", " <th>Genus</th>\n", " <th>Species</th>\n", " <th>Taxid</th>\n", " <th>July182018_Philipinnes_WS_11_S85</th>\n", " <th>July182018_Philipinnes_WS_174_S83</th>\n", " <th>July182018_Philipinnes_WS_180_S86</th>\n", " <th>July182018_Philipinnes_WS_76_S84</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>s:Bacteria</td>\n", " <td>p:Proteobacteria</td>\n", " <td>c:Gammaproteobacteria</td>\n", " <td>o:Pseudomonadales</td>\n", " <td>f:Pseudomonadaceae</td>\n", " <td>g:Pseudomonas</td>\n", " <td>s:Pseudomonas aeruginosa</td>\n", " <td>taxid: 1000561</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>...</td>\n", " <td>c:Gammaproteobacteria</td>\n", " <td>o:Pseudomonadales</td>\n", " <td>f:Pseudomonadaceae</td>\n", " <td>g:Pseudomonas</td>\n", " <td>s:Pseudomonas aeruginosa</td>\n", " <td>taxid: 1000561</td>\n", " <td>26.0</td>\n", " <td>34.0</td>\n", " <td>58.0</td>\n", " <td>37.0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>s:Bacteria</td>\n", " <td>p:Proteobacteria</td>\n", " <td>c:Betaproteobacteria</td>\n", " <td>o:Nitrosomonadales</td>\n", " <td>f:Sterolibacteriaceae</td>\n", " <td>g:Methyloversatilis</td>\n", " <td>s:Methyloversatilis universalis</td>\n", " <td>taxid: 1000565</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>...</td>\n", " <td>c:Betaproteobacteria</td>\n", " <td>o:Nitrosomonadales</td>\n", " <td>f:Sterolibacteriaceae</td>\n", " <td>g:Methyloversatilis</td>\n", " <td>s:Methyloversatilis universalis</td>\n", " <td>taxid: 1000565</td>\n", " <td>171.0</td>\n", " <td>82.0</td>\n", " <td>50.0</td>\n", " <td>166.0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>s:Bacteria</td>\n", " <td>p:Firmicutes</td>\n", " <td>c:Negativicutes</td>\n", " <td>o:Veillonellales</td>\n", " <td>f:Veillonellaceae</td>\n", " <td>g:Megasphaera</td>\n", " <td>s:Megasphaera sp. 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UPII 199-6 taxid: 1000568 0.0 \n", "3 s:Megasphaera sp. UPII 135-E taxid: 1000569 0.0 \n", "4 s:Streptococcus anginosus taxid: 1000570 0.0 \n", "... ... ... ... \n", "9300 NaN NaN NaN \n", "9301 NaN NaN NaN \n", "9302 NaN NaN NaN \n", "9303 NaN NaN NaN \n", "9304 NaN NaN NaN \n", "\n", " Philippines OPH_002 ... Class Order \\\n", "0 0.0 ... c:Gammaproteobacteria o:Pseudomonadales \n", "1 0.0 ... c:Betaproteobacteria o:Nitrosomonadales \n", "2 0.0 ... c:Negativicutes o:Veillonellales \n", "3 0.0 ... c:Negativicutes o:Veillonellales \n", "4 0.0 ... NaN NaN \n", "... ... ... ... ... \n", "9300 NaN ... c:Spirochaetia o:Leptospirales \n", "9301 NaN ... c:Spirochaetia o:Leptospirales \n", "9302 NaN ... c:Mollicutes o:Mycoplasmatales \n", "9303 NaN ... c:Mollicutes o:Mycoplasmatales \n", "9304 NaN ... c:Mollicutes o:Mycoplasmatales \n", "\n", " Family Genus \\\n", "0 f:Pseudomonadaceae g:Pseudomonas \n", "1 f:Sterolibacteriaceae g:Methyloversatilis \n", "2 f:Veillonellaceae g:Megasphaera \n", "3 f:Veillonellaceae g:Megasphaera \n", "4 NaN NaN \n", "... ... ... \n", "9300 f:Leptospiraceae g:Leptospira \n", "9301 f:Leptospiraceae g:Leptospira \n", "9302 f:Mycoplasmataceae g:Mycoplasma \n", "9303 f:Mycoplasmataceae g:Mycoplasma \n", "9304 f:Mycoplasmataceae g:Ureaplasma \n", "\n", " Species Taxid \\\n", "0 s:Pseudomonas aeruginosa taxid: 1000561 \n", "1 s:Methyloversatilis universalis taxid: 1000565 \n", "2 s:Megasphaera sp. UPII 199-6 taxid: 1000568 \n", "3 s:Megasphaera sp. UPII 135-E taxid: 1000569 \n", "4 NaN NaN \n", "... ... ... \n", "9300 s:Leptospira interrogans taxid: 1291337 \n", "9301 s:Leptospira interrogans taxid: 996849 \n", "9302 s:Mycoplasma canis taxid: 1131452 \n", "9303 s:Mycoplasma pneumoniae taxid: 1238993 \n", "9304 s:Ureaplasma urealyticum taxid: 626095 \n", "\n", " July182018_Philipinnes_WS_11_S85 July182018_Philipinnes_WS_174_S83 \\\n", "0 26.0 34.0 \n", "1 171.0 82.0 \n", "2 0.0 0.0 \n", "3 0.0 3.0 \n", "4 NaN NaN \n", "... ... ... \n", "9300 0.0 0.0 \n", "9301 0.0 0.0 \n", "9302 1.0 0.0 \n", "9303 0.0 0.0 \n", "9304 0.0 1.0 \n", "\n", " July182018_Philipinnes_WS_180_S86 July182018_Philipinnes_WS_76_S84 \n", "0 58.0 37.0 \n", "1 50.0 166.0 \n", "2 0.0 1.0 \n", "3 1.0 0.0 \n", "4 NaN NaN \n", "... ... ... \n", "9300 0.0 0.0 \n", "9301 1.0 0.0 \n", "9302 0.0 0.0 \n", "9303 0.0 0.0 \n", "9304 0.0 0.0 \n", "\n", "[9305 rows x 65 columns]" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ws1 = pd.read_excel(\"C:\\\\Users\\\\edwa0468\\\\Downloads\\\\whaleshark taxa annotated 2020.xlsx\", sheet_name=\"Sheet1\", header=2)\n", "ws2 = pd.read_excel(\"C:\\\\Users\\\\edwa0468\\\\Downloads\\\\philippine whale shark reseq 2020.xlsx\", sheet_name=\"all_taxonomy\", header=0)\n", "ws3 = pd.merge(ws1, ws2, left_on='taxid', right_on='Taxid', how='outer')\n", "ws3" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 8, "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>superkingdom</th>\n", " <th>phylum</th>\n", " <th>class</th>\n", " <th>order</th>\n", " <th>family</th>\n", " <th>genus</th>\n", " <th>species</th>\n", " <th>taxid</th>\n", " <th>Philippines OPH_001</th>\n", " <th>Philippines OPH_002</th>\n", " <th>...</th>\n", " <th>Philippines OPH_037</th>\n", " <th>Philippines OPH_038</th>\n", " <th>Philippines OPH_039</th>\n", " <th>Philippines OPH_040</th>\n", " <th>Philippines OPH_041</th>\n", " <th>Philippines OPH_042</th>\n", " <th>Philippines OPH_044</th>\n", " <th>Philippines OPH_045</th>\n", " <th>Philippines OPH_046</th>\n", " <th>Philippines OPH_054</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>s:Bacteria</td>\n", " <td>p:Proteobacteria</td>\n", " <td>c:Gammaproteobacteria</td>\n", " <td>o:Pseudomonadales</td>\n", " <td>f:Pseudomonadaceae</td>\n", " <td>g:Pseudomonas</td>\n", " <td>s:Pseudomonas aeruginosa</td>\n", " <td>taxid: 1000561</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>5</td>\n", " <td>25</td>\n", " <td>19</td>\n", " <td>6</td>\n", " <td>11</td>\n", " <td>4</td>\n", " <td>2</td>\n", " <td>4</td>\n", " <td>4</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>s:Bacteria</td>\n", " <td>p:Proteobacteria</td>\n", " <td>c:Betaproteobacteria</td>\n", " <td>o:Nitrosomonadales</td>\n", " <td>f:Sterolibacteriaceae</td>\n", " <td>g:Methyloversatilis</td>\n", " <td>s:Methyloversatilis universalis</td>\n", " <td>taxid: 1000565</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>5</td>\n", " <td>14</td>\n", " <td>7</td>\n", " <td>5</td>\n", " <td>0</td>\n", " <td>4</td>\n", " <td>5</td>\n", " <td>11</td>\n", " <td>8</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>s:Bacteria</td>\n", " <td>p:Firmicutes</td>\n", " <td>c:Negativicutes</td>\n", " <td>o:Veillonellales</td>\n", " <td>f:Veillonellaceae</td>\n", " <td>g:Megasphaera</td>\n", " <td>s:Megasphaera sp. 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UPII 135-E</td>\n", " <td>taxid: 1000569</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>2</td>\n", " <td>0</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>s:Bacteria</td>\n", " <td>p:Firmicutes</td>\n", " <td>c:Bacilli</td>\n", " <td>o:Lactobacillales</td>\n", " <td>f:Streptococcaceae</td>\n", " <td>g:Streptococcus</td>\n", " <td>s:Streptococcus anginosus</td>\n", " <td>taxid: 1000570</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>...</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>9226</th>\n", " <td>s:Bacteria</td>\n", " <td>p:Actinobacteria</td>\n", " <td>c:Actinobacteria</td>\n", " <td>o:Micromonosporales</td>\n", " <td>f:Micromonosporaceae</td>\n", " <td>g:Salinispora</td>\n", " <td>s:Salinispora pacifica</td>\n", " <td>taxid: 999545</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>9227</th>\n", " <td>s:Bacteria</td>\n", " <td>p:Actinobacteria</td>\n", " <td>c:Actinobacteria</td>\n", " <td>o:Micromonosporales</td>\n", " <td>f:Micromonosporaceae</td>\n", " <td>g:Salinispora</td>\n", " <td>s:Salinispora arenicola</td>\n", " <td>taxid: 999546</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>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>9228</th>\n", " <td>s:Bacteria</td>\n", " <td>p:Proteobacteria</td>\n", " <td>c:Betaproteobacteria</td>\n", " <td>o:Nitrosomonadales</td>\n", " <td>f:Sterolibacteriaceae</td>\n", " <td>g:Methyloversatilis</td>\n", " <td>s:Methyloversatilis universalis</td>\n", " <td>taxid: 999628</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>31</td>\n", " <td>53</td>\n", " <td>43</td>\n", " <td>51</td>\n", " <td>32</td>\n", " <td>23</td>\n", " <td>43</td>\n", " <td>56</td>\n", " <td>35</td>\n", " <td>23</td>\n", " </tr>\n", " <tr>\n", " <th>9229</th>\n", " <td>s:Bacteria</td>\n", " <td>p:Firmicutes</td>\n", " <td>c:Bacilli</td>\n", " <td>o:Bacillales</td>\n", " <td>f:Bacillaceae</td>\n", " <td>g:Bacillus</td>\n", " <td>s:Bacillus amyloliquefaciens</td>\n", " <td>taxid: 999891</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>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>9230</th>\n", " <td>s:Bacteria</td>\n", " <td>p:Actinobacteria</td>\n", " <td>c:Actinobacteria</td>\n", " <td>o:Propionibacteriales</td>\n", " <td>f:Propionibacteriaceae</td>\n", " <td>g:Cutibacterium</td>\n", " <td>s:[Propionibacterium] humerusii</td>\n", " <td>taxid: 999892</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>9231 rows × 53 columns</p>\n", "</div>" ], "text/plain": [ " superkingdom phylum class \\\n", "0 s:Bacteria p:Proteobacteria c:Gammaproteobacteria \n", "1 s:Bacteria p:Proteobacteria c:Betaproteobacteria \n", "2 s:Bacteria p:Firmicutes c:Negativicutes \n", "3 s:Bacteria p:Firmicutes c:Negativicutes \n", "4 s:Bacteria p:Firmicutes c:Bacilli \n", "... ... ... ... \n", "9226 s:Bacteria p:Actinobacteria c:Actinobacteria \n", "9227 s:Bacteria p:Actinobacteria c:Actinobacteria \n", "9228 s:Bacteria p:Proteobacteria c:Betaproteobacteria \n", "9229 s:Bacteria p:Firmicutes c:Bacilli \n", "9230 s:Bacteria p:Actinobacteria c:Actinobacteria \n", "\n", " order family genus \\\n", "0 o:Pseudomonadales f:Pseudomonadaceae g:Pseudomonas \n", "1 o:Nitrosomonadales f:Sterolibacteriaceae g:Methyloversatilis \n", "2 o:Veillonellales f:Veillonellaceae g:Megasphaera \n", "3 o:Veillonellales f:Veillonellaceae g:Megasphaera \n", "4 o:Lactobacillales f:Streptococcaceae g:Streptococcus \n", "... ... ... ... \n", "9226 o:Micromonosporales f:Micromonosporaceae g:Salinispora \n", "9227 o:Micromonosporales f:Micromonosporaceae g:Salinispora \n", "9228 o:Nitrosomonadales f:Sterolibacteriaceae g:Methyloversatilis \n", "9229 o:Bacillales f:Bacillaceae g:Bacillus \n", "9230 o:Propionibacteriales f:Propionibacteriaceae g:Cutibacterium \n", "\n", " species taxid Philippines OPH_001 \\\n", "0 s:Pseudomonas aeruginosa taxid: 1000561 0 \n", "1 s:Methyloversatilis universalis taxid: 1000565 0 \n", "2 s:Megasphaera sp. UPII 199-6 taxid: 1000568 0 \n", "3 s:Megasphaera sp. UPII 135-E taxid: 1000569 0 \n", "4 s:Streptococcus anginosus taxid: 1000570 0 \n", "... ... ... ... \n", "9226 s:Salinispora pacifica taxid: 999545 0 \n", "9227 s:Salinispora arenicola taxid: 999546 0 \n", "9228 s:Methyloversatilis universalis taxid: 999628 1 \n", "9229 s:Bacillus amyloliquefaciens taxid: 999891 0 \n", "9230 s:[Propionibacterium] humerusii taxid: 999892 0 \n", "\n", " Philippines OPH_002 ... Philippines OPH_037 Philippines OPH_038 \\\n", "0 0 ... 5 25 \n", "1 0 ... 5 14 \n", "2 0 ... 0 0 \n", "3 0 ... 0 0 \n", "4 0 ... 0 0 \n", "... ... ... ... ... \n", "9226 0 ... 0 0 \n", "9227 0 ... 0 0 \n", "9228 0 ... 31 53 \n", "9229 0 ... 0 0 \n", "9230 0 ... 0 1 \n", "\n", " Philippines OPH_039 Philippines OPH_040 Philippines OPH_041 \\\n", "0 19 6 11 \n", "1 7 5 0 \n", "2 0 0 0 \n", "3 0 0 0 \n", "4 0 0 0 \n", "... ... ... ... \n", "9226 1 0 0 \n", "9227 0 0 0 \n", "9228 43 51 32 \n", "9229 0 0 0 \n", "9230 0 0 0 \n", "\n", " Philippines OPH_042 Philippines OPH_044 Philippines OPH_045 \\\n", "0 4 2 4 \n", "1 4 5 11 \n", "2 0 1 0 \n", "3 0 0 2 \n", "4 0 0 0 \n", "... ... ... ... \n", "9226 0 0 1 \n", "9227 0 0 1 \n", "9228 23 43 56 \n", "9229 0 0 1 \n", "9230 0 0 0 \n", "\n", " Philippines OPH_046 Philippines OPH_054 \n", "0 4 2 \n", "1 8 3 \n", "2 0 3 \n", "3 0 1 \n", "4 0 0 \n", "... ... ... \n", "9226 0 1 \n", "9227 0 0 \n", "9228 35 23 \n", "9229 0 0 \n", "9230 0 0 \n", "\n", "[9231 rows x 53 columns]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ws1 = pd.read_excel(\"C:\\\\Users\\\\edwa0468\\\\Downloads\\\\whaleshark taxa annotated 2020.xlsx\", sheet_name=\"Sheet1\", header=2)\n", "ws1" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Superkingdom</th>\n", " <th>Phylum</th>\n", " <th>Class</th>\n", " <th>Order</th>\n", " <th>Family</th>\n", " <th>Genus</th>\n", " <th>Species</th>\n", " <th>Taxid</th>\n", " <th>July182018_Philipinnes_WS_11_S85</th>\n", " <th>July182018_Philipinnes_WS_174_S83</th>\n", " <th>July182018_Philipinnes_WS_180_S86</th>\n", " <th>July182018_Philipinnes_WS_76_S84</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>s:Archaea</td>\n", " <td>p:Candidatus Korarchaeota</td>\n", " <td>c:</td>\n", " <td>o:</td>\n", " <td>f:</td>\n", " <td>g:Candidatus Korarchaeum</td>\n", " <td>s:Candidatus Korarchaeum cryptofilum</td>\n", " <td>taxid: 374847</td>\n", " <td>10</td>\n", " <td>4</td>\n", " <td>2</td>\n", " <td>4</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>s:Archaea</td>\n", " <td>p:Crenarchaeota</td>\n", " <td>c:Thermoprotei</td>\n", " <td>o:Desulfurococcales</td>\n", " <td>f:Desulfurococcaceae</td>\n", " <td>g:Aeropyrum</td>\n", " <td>s:Aeropyrum pernix</td>\n", " <td>taxid: 272557</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>4</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>s:Archaea</td>\n", " <td>p:Crenarchaeota</td>\n", " <td>c:Thermoprotei</td>\n", " <td>o:Desulfurococcales</td>\n", " <td>f:Desulfurococcaceae</td>\n", " <td>g:Desulfurococcus</td>\n", " <td>s:Desulfurococcus amylolyticus</td>\n", " <td>taxid: 490899</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>s:Archaea</td>\n", " <td>p:Crenarchaeota</td>\n", " <td>c:Thermoprotei</td>\n", " <td>o:Desulfurococcales</td>\n", " <td>f:Desulfurococcaceae</td>\n", " <td>g:Ignicoccus</td>\n", " <td>s:Ignicoccus hospitalis</td>\n", " <td>taxid: 453591</td>\n", " <td>16</td>\n", " <td>8</td>\n", " <td>6</td>\n", " <td>7</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>s:Archaea</td>\n", " <td>p:Crenarchaeota</td>\n", " <td>c:Thermoprotei</td>\n", " <td>o:Desulfurococcales</td>\n", " <td>f:Desulfurococcaceae</td>\n", " <td>g:Staphylothermus</td>\n", " <td>s:Staphylothermus marinus</td>\n", " <td>taxid: 399550</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>4</td>\n", " <td>0</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", " </tr>\n", " <tr>\n", " <th>7367</th>\n", " <td>s:</td>\n", " <td>p:</td>\n", " <td>c:</td>\n", " <td>o:</td>\n", " <td>f:Inoviridae</td>\n", " <td>g:</td>\n", " <td>s:Vibrio virus Vf33</td>\n", " <td>taxid: 127511</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>7368</th>\n", " <td>s:</td>\n", " <td>p:</td>\n", " <td>c:</td>\n", " <td>o:</td>\n", " <td>f:Inoviridae</td>\n", " <td>g:</td>\n", " <td>s:[no name] taxid: 10871</td>\n", " <td>taxid: 10871</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>3</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>7369</th>\n", " <td>s:</td>\n", " <td>p:</td>\n", " <td>c:</td>\n", " <td>o:</td>\n", " <td>f:</td>\n", " <td>g:</td>\n", " <td>s:Phage Gifsy-1</td>\n", " <td>taxid: 129861</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>7370</th>\n", " <td>s:</td>\n", " <td>p:</td>\n", " <td>c:</td>\n", " <td>o:</td>\n", " <td>f:</td>\n", " <td>g:</td>\n", " <td>s:Phage Gifsy-2</td>\n", " <td>taxid: 129862</td>\n", " <td>4</td>\n", " <td>4</td>\n", " <td>2</td>\n", " <td>4</td>\n", " </tr>\n", " <tr>\n", " <th>7371</th>\n", " <td>s:</td>\n", " <td>p:</td>\n", " <td>c:</td>\n", " <td>o:</td>\n", " <td>f:</td>\n", " <td>g:</td>\n", " <td>s:Salmonella phage Fels-1</td>\n", " <td>taxid: 128975</td>\n", " <td>4</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>7</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>7372 rows × 12 columns</p>\n", "</div>" ], "text/plain": [ " Superkingdom Phylum Class \\\n", "0 s:Archaea p:Candidatus Korarchaeota c: \n", "1 s:Archaea p:Crenarchaeota c:Thermoprotei \n", "2 s:Archaea p:Crenarchaeota c:Thermoprotei \n", "3 s:Archaea p:Crenarchaeota c:Thermoprotei \n", "4 s:Archaea p:Crenarchaeota c:Thermoprotei \n", "... ... ... ... \n", "7367 s: p: c: \n", "7368 s: p: c: \n", "7369 s: p: c: \n", "7370 s: p: c: \n", "7371 s: p: c: \n", "\n", " Order Family Genus \\\n", "0 o: f: g:Candidatus Korarchaeum \n", "1 o:Desulfurococcales f:Desulfurococcaceae g:Aeropyrum \n", "2 o:Desulfurococcales f:Desulfurococcaceae g:Desulfurococcus \n", "3 o:Desulfurococcales f:Desulfurococcaceae g:Ignicoccus \n", "4 o:Desulfurococcales f:Desulfurococcaceae g:Staphylothermus \n", "... ... ... ... \n", "7367 o: f:Inoviridae g: \n", "7368 o: f:Inoviridae g: \n", "7369 o: f: g: \n", "7370 o: f: g: \n", "7371 o: f: g: \n", "\n", " Species Taxid \\\n", "0 s:Candidatus Korarchaeum cryptofilum taxid: 374847 \n", "1 s:Aeropyrum pernix taxid: 272557 \n", "2 s:Desulfurococcus amylolyticus taxid: 490899 \n", "3 s:Ignicoccus hospitalis taxid: 453591 \n", "4 s:Staphylothermus marinus taxid: 399550 \n", "... ... ... \n", "7367 s:Vibrio virus Vf33 taxid: 127511 \n", "7368 s:[no name] taxid: 10871 taxid: 10871 \n", "7369 s:Phage Gifsy-1 taxid: 129861 \n", "7370 s:Phage Gifsy-2 taxid: 129862 \n", "7371 s:Salmonella phage Fels-1 taxid: 128975 \n", "\n", " July182018_Philipinnes_WS_11_S85 July182018_Philipinnes_WS_174_S83 \\\n", "0 10 4 \n", "1 0 0 \n", "2 0 2 \n", "3 16 8 \n", "4 1 0 \n", "... ... ... \n", "7367 0 0 \n", "7368 0 2 \n", "7369 0 0 \n", "7370 4 4 \n", "7371 4 0 \n", "\n", " July182018_Philipinnes_WS_180_S86 July182018_Philipinnes_WS_76_S84 \n", "0 2 4 \n", "1 4 1 \n", "2 0 0 \n", "3 6 7 \n", "4 4 0 \n", "... ... ... \n", "7367 0 0 \n", "7368 3 0 \n", "7369 2 0 \n", "7370 2 4 \n", "7371 0 7 \n", "\n", "[7372 rows x 12 columns]" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ws2 = pd.read_excel(\"C:\\\\Users\\\\edwa0468\\\\Downloads\\\\philippine whale shark reseq 2020.xlsx\", sheet_name=\"all_taxonomy\", header=0)\n", "ws2" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>superkingdom</th>\n", " <th>phylum</th>\n", " <th>class</th>\n", " <th>order</th>\n", " <th>family</th>\n", " <th>genus</th>\n", " <th>species</th>\n", " <th>taxid</th>\n", " <th>Philippines OPH_001</th>\n", " <th>Philippines OPH_002</th>\n", " <th>...</th>\n", " <th>Class</th>\n", " <th>Order</th>\n", " <th>Family</th>\n", " <th>Genus</th>\n", " <th>Species</th>\n", " <th>Taxid</th>\n", " <th>July182018_Philipinnes_WS_11_S85</th>\n", " <th>July182018_Philipinnes_WS_174_S83</th>\n", " <th>July182018_Philipinnes_WS_180_S86</th>\n", " <th>July182018_Philipinnes_WS_76_S84</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>s:Bacteria</td>\n", " <td>p:Proteobacteria</td>\n", " <td>c:Gammaproteobacteria</td>\n", " <td>o:Pseudomonadales</td>\n", " <td>f:Pseudomonadaceae</td>\n", " <td>g:Pseudomonas</td>\n", " <td>s:Pseudomonas aeruginosa</td>\n", " <td>taxid: 1000561</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>...</td>\n", " <td>c:Gammaproteobacteria</td>\n", " <td>o:Pseudomonadales</td>\n", " <td>f:Pseudomonadaceae</td>\n", " <td>g:Pseudomonas</td>\n", " <td>s:Pseudomonas aeruginosa</td>\n", " <td>taxid: 1000561</td>\n", " <td>26.0</td>\n", " <td>34.0</td>\n", " <td>58.0</td>\n", " <td>37.0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>s:Bacteria</td>\n", " <td>p:Proteobacteria</td>\n", " <td>c:Betaproteobacteria</td>\n", " <td>o:Nitrosomonadales</td>\n", " <td>f:Sterolibacteriaceae</td>\n", " <td>g:Methyloversatilis</td>\n", " <td>s:Methyloversatilis universalis</td>\n", " <td>taxid: 1000565</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>...</td>\n", " <td>c:Betaproteobacteria</td>\n", " <td>o:Nitrosomonadales</td>\n", " <td>f:Sterolibacteriaceae</td>\n", " <td>g:Methyloversatilis</td>\n", " <td>s:Methyloversatilis universalis</td>\n", " <td>taxid: 1000565</td>\n", " <td>171.0</td>\n", " <td>82.0</td>\n", " <td>50.0</td>\n", " <td>166.0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>s:Bacteria</td>\n", " <td>p:Firmicutes</td>\n", " <td>c:Negativicutes</td>\n", " <td>o:Veillonellales</td>\n", " <td>f:Veillonellaceae</td>\n", " <td>g:Megasphaera</td>\n", " <td>s:Megasphaera sp. UPII 199-6</td>\n", " <td>taxid: 1000568</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>...</td>\n", " <td>c:Negativicutes</td>\n", " <td>o:Veillonellales</td>\n", " <td>f:Veillonellaceae</td>\n", " <td>g:Megasphaera</td>\n", " <td>s:Megasphaera sp. UPII 199-6</td>\n", " <td>taxid: 1000568</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>s:Bacteria</td>\n", " <td>p:Firmicutes</td>\n", " <td>c:Negativicutes</td>\n", " <td>o:Veillonellales</td>\n", " <td>f:Veillonellaceae</td>\n", " <td>g:Megasphaera</td>\n", " <td>s:Megasphaera sp. UPII 135-E</td>\n", " <td>taxid: 1000569</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>...</td>\n", " <td>c:Negativicutes</td>\n", " <td>o:Veillonellales</td>\n", " <td>f:Veillonellaceae</td>\n", " <td>g:Megasphaera</td>\n", " <td>s:Megasphaera sp. UPII 135-E</td>\n", " <td>taxid: 1000569</td>\n", " <td>0.0</td>\n", " <td>3.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>s:Bacteria</td>\n", " <td>p:Firmicutes</td>\n", " <td>c:Bacilli</td>\n", " <td>o:Lactobacillales</td>\n", " <td>f:Streptococcaceae</td>\n", " <td>g:Streptococcus</td>\n", " <td>s:Streptococcus anginosus</td>\n", " <td>taxid: 1000570</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>...</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>9300</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>c:Spirochaetia</td>\n", " <td>o:Leptospirales</td>\n", " <td>f:Leptospiraceae</td>\n", " <td>g:Leptospira</td>\n", " <td>s:Leptospira interrogans</td>\n", " <td>taxid: 1291337</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>9301</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>c:Spirochaetia</td>\n", " <td>o:Leptospirales</td>\n", " <td>f:Leptospiraceae</td>\n", " <td>g:Leptospira</td>\n", " <td>s:Leptospira interrogans</td>\n", " <td>taxid: 996849</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>9302</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>c:Mollicutes</td>\n", " <td>o:Mycoplasmatales</td>\n", " <td>f:Mycoplasmataceae</td>\n", " <td>g:Mycoplasma</td>\n", " <td>s:Mycoplasma canis</td>\n", " <td>taxid: 1131452</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>9303</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>c:Mollicutes</td>\n", " <td>o:Mycoplasmatales</td>\n", " <td>f:Mycoplasmataceae</td>\n", " <td>g:Mycoplasma</td>\n", " <td>s:Mycoplasma pneumoniae</td>\n", " <td>taxid: 1238993</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>9304</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>c:Mollicutes</td>\n", " <td>o:Mycoplasmatales</td>\n", " <td>f:Mycoplasmataceae</td>\n", " <td>g:Ureaplasma</td>\n", " <td>s:Ureaplasma urealyticum</td>\n", " <td>taxid: 626095</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>9305 rows × 65 columns</p>\n", "</div>" ], "text/plain": [ " superkingdom phylum class \\\n", "0 s:Bacteria p:Proteobacteria c:Gammaproteobacteria \n", "1 s:Bacteria p:Proteobacteria c:Betaproteobacteria \n", "2 s:Bacteria p:Firmicutes c:Negativicutes \n", "3 s:Bacteria p:Firmicutes c:Negativicutes \n", "4 s:Bacteria p:Firmicutes c:Bacilli \n", "... ... ... ... \n", "9300 NaN NaN NaN \n", "9301 NaN NaN NaN \n", "9302 NaN NaN NaN \n", "9303 NaN NaN NaN \n", "9304 NaN NaN NaN \n", "\n", " order family genus \\\n", "0 o:Pseudomonadales f:Pseudomonadaceae g:Pseudomonas \n", "1 o:Nitrosomonadales f:Sterolibacteriaceae g:Methyloversatilis \n", "2 o:Veillonellales f:Veillonellaceae g:Megasphaera \n", "3 o:Veillonellales f:Veillonellaceae g:Megasphaera \n", "4 o:Lactobacillales f:Streptococcaceae g:Streptococcus \n", "... ... ... ... \n", "9300 NaN NaN NaN \n", "9301 NaN NaN NaN \n", "9302 NaN NaN NaN \n", "9303 NaN NaN NaN \n", "9304 NaN NaN NaN \n", "\n", " species taxid Philippines OPH_001 \\\n", "0 s:Pseudomonas aeruginosa taxid: 1000561 0.0 \n", "1 s:Methyloversatilis universalis taxid: 1000565 0.0 \n", "2 s:Megasphaera sp. UPII 199-6 taxid: 1000568 0.0 \n", "3 s:Megasphaera sp. UPII 135-E taxid: 1000569 0.0 \n", "4 s:Streptococcus anginosus taxid: 1000570 0.0 \n", "... ... ... ... \n", "9300 NaN NaN NaN \n", "9301 NaN NaN NaN \n", "9302 NaN NaN NaN \n", "9303 NaN NaN NaN \n", "9304 NaN NaN NaN \n", "\n", " Philippines OPH_002 ... Class Order \\\n", "0 0.0 ... c:Gammaproteobacteria o:Pseudomonadales \n", "1 0.0 ... c:Betaproteobacteria o:Nitrosomonadales \n", "2 0.0 ... c:Negativicutes o:Veillonellales \n", "3 0.0 ... c:Negativicutes o:Veillonellales \n", "4 0.0 ... NaN NaN \n", "... ... ... ... ... \n", "9300 NaN ... c:Spirochaetia o:Leptospirales \n", "9301 NaN ... c:Spirochaetia o:Leptospirales \n", "9302 NaN ... c:Mollicutes o:Mycoplasmatales \n", "9303 NaN ... c:Mollicutes o:Mycoplasmatales \n", "9304 NaN ... c:Mollicutes o:Mycoplasmatales \n", "\n", " Family Genus \\\n", "0 f:Pseudomonadaceae g:Pseudomonas \n", "1 f:Sterolibacteriaceae g:Methyloversatilis \n", "2 f:Veillonellaceae g:Megasphaera \n", "3 f:Veillonellaceae g:Megasphaera \n", "4 NaN NaN \n", "... ... ... \n", "9300 f:Leptospiraceae g:Leptospira \n", "9301 f:Leptospiraceae g:Leptospira \n", "9302 f:Mycoplasmataceae g:Mycoplasma \n", "9303 f:Mycoplasmataceae g:Mycoplasma \n", "9304 f:Mycoplasmataceae g:Ureaplasma \n", "\n", " Species Taxid \\\n", "0 s:Pseudomonas aeruginosa taxid: 1000561 \n", "1 s:Methyloversatilis universalis taxid: 1000565 \n", "2 s:Megasphaera sp. UPII 199-6 taxid: 1000568 \n", "3 s:Megasphaera sp. UPII 135-E taxid: 1000569 \n", "4 NaN NaN \n", "... ... ... \n", "9300 s:Leptospira interrogans taxid: 1291337 \n", "9301 s:Leptospira interrogans taxid: 996849 \n", "9302 s:Mycoplasma canis taxid: 1131452 \n", "9303 s:Mycoplasma pneumoniae taxid: 1238993 \n", "9304 s:Ureaplasma urealyticum taxid: 626095 \n", "\n", " July182018_Philipinnes_WS_11_S85 July182018_Philipinnes_WS_174_S83 \\\n", "0 26.0 34.0 \n", "1 171.0 82.0 \n", "2 0.0 0.0 \n", "3 0.0 3.0 \n", "4 NaN NaN \n", "... ... ... \n", "9300 0.0 0.0 \n", "9301 0.0 0.0 \n", "9302 1.0 0.0 \n", "9303 0.0 0.0 \n", "9304 0.0 1.0 \n", "\n", " July182018_Philipinnes_WS_180_S86 July182018_Philipinnes_WS_76_S84 \n", "0 58.0 37.0 \n", "1 50.0 166.0 \n", "2 0.0 1.0 \n", "3 1.0 0.0 \n", "4 NaN NaN \n", "... ... ... \n", "9300 0.0 0.0 \n", "9301 1.0 0.0 \n", "9302 0.0 0.0 \n", "9303 0.0 0.0 \n", "9304 0.0 0.0 \n", "\n", "[9305 rows x 65 columns]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ws3 = pd.merge(ws1, ws2, left_on='taxid', right_on='Taxid', how='outer')\n", "ws3" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "ws3.to_excel(\"attempt1.xlsx\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## How Rob would do it" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "taxa = {}\n", "data = {}\n", "headerlist = []\n", "alldatalen = None\n", "with open(\"C:\\\\Users\\\\edwa0468\\\\Downloads\\\\whaleshark taxa annotated 2020.txt\", \"r\") as w1:\n", " c = 0\n", " for l in w1:\n", " c += 1\n", " if c == 3:\n", " headerlist = l.rstrip().split(\"\\t\")\n", " elif c > 3:\n", " p = l.rstrip().split(\"\\t\")\n", " taxid = p[7]\n", " taxstr = \"\\t\".join(p[0:7])\n", " taxa[taxid] = taxstr\n", " data[taxid] = p[8:]\n", " datalen = len(data[taxid])\n", " if not alldatalen:\n", " alldatalen = datalen\n", " if datalen != alldatalen:\n", " sys.stderr.write(f\"ERROR: {datalen} and {alldatalen} are not the same length!\\n\")\n", "\n", "header2=False\n", "with open(\"C:\\\\Users\\\\edwa0468\\\\Downloads\\\\philippine whale shark reseq 2020.txt\", \"r\") as w2:\n", " for l in w2:\n", " if not header2:\n", " header2 = True \n", " header = l.rstrip().split(\"\\t\")\n", " headerlist += header[8:]\n", " else:\n", " p = l.rstrip().split(\"\\t\")\n", " taxid = p[7]\n", " taxstr = \"\\t\".join(p[0:7])\n", " if taxid in taxa and taxa[taxid] != taxstr:\n", " sys.stderr.write(f\"ERROR: Had {taxa[taxid]} now have {taxstr} for {taxid}\\n\")\n", " else:\n", " taxa[taxid] = taxstr\n", " if taxid not in data:\n", " data[taxid] = [\"\" for v in range(alldatalen)]\n", " data[taxid] += p[8:]\n", "\n", "with open(\"example2.tsv\", 'w') as out: \n", " out.write(\"\\t\".join(headerlist) + \"\\n\")\n", " for tid in data:\n", " out.write(\"\\t\".join(map(str, [taxa[tid], tid] + data[tid])) + \"\\n\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.8" } }, "nbformat": 4, "nbformat_minor": 1 }
mit
qxcv/joint-regressor
keras/flow-rgb-graph-net-surgery-poselet.ipynb
1
138269
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Net surgery to produce a `Graph` model for regressing poselets" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "from keras.optimizers import SGD\n", "\n", "import numpy as np\n", "\n", "import models\n", "from vggnet.upgrade_weights import upgrade_weights\n", "from vggnet.vgg16_keras import VGG_16" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "models = reload(models)\n", "solver = SGD()\n", "rgb_shape = (6, 224, 224)\n", "flow_shape = (2, 224, 224)\n", "regressor_outputs = 6\n", "init = 'glorot_normal'" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "huge = models.vggnet16_poselet_class_flow_norm_bn({\n", " 'images': (6, 224, 224),\n", " 'flow': (2, 224, 224),\n", " 'poselet': (901,)\n", "}, solver, init)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "flow_seq = huge.nodes['flow_conv']\n", "rgb_seq = huge.nodes['rgb_conv']\n", "ilsvrc_weights_path = './vggnet/vgg16_weights.h5'\n", "ilsvrc_model = VGG_16(ilsvrc_weights_path)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Concatenating old L1 dimensions to change shape, (64, 3, 3, 3)->(64, 1, 3, 3)\n", "First layer done\n", "WARNING: layer count differs (24 new v. 37 old)\n", "Skipping BatchNormalization layer in new model\n", "Changing ZeroPadding2D\n", "Changing Convolution2D\n", "Skipping BatchNormalization layer in new model\n", "Changing MaxPooling2D\n", "Changing ZeroPadding2D\n", "Changing Convolution2D\n", "Skipping BatchNormalization layer in new model\n", "Changing ZeroPadding2D\n", "Changing Convolution2D\n", "Skipping BatchNormalization layer in new model\n", "Changing MaxPooling2D\n", "Changing ZeroPadding2D\n", "Changing Convolution2D\n", "Skipping BatchNormalization layer in new model\n", "Changing ZeroPadding2D\n", "Changing Convolution2D\n", "Skipping BatchNormalization layer in new model\n", "Changing ZeroPadding2D\n", "Changing Convolution2D\n", "Skipping BatchNormalization layer in new model\n", "Changing MaxPooling2D\n", "0 new layers ignored and 20 old layers ignored\n", "Concatenating old L1 dimensions to change shape, (64, 3, 3, 3)->(64, 6, 3, 3)\n", "First layer done\n", "WARNING: layer count differs (24 new v. 37 old)\n", "Skipping BatchNormalization layer in new model\n", "Changing ZeroPadding2D\n", "Changing Convolution2D\n", "Skipping BatchNormalization layer in new model\n", "Changing MaxPooling2D\n", "Changing ZeroPadding2D\n", "Changing Convolution2D\n", "Skipping BatchNormalization layer in new model\n", "Changing ZeroPadding2D\n", "Changing Convolution2D\n", "Skipping BatchNormalization layer in new model\n", "Changing MaxPooling2D\n", "Changing ZeroPadding2D\n", "Changing Convolution2D\n", "Skipping BatchNormalization layer in new model\n", "Changing ZeroPadding2D\n", "Changing Convolution2D\n", "Skipping BatchNormalization layer in new model\n", "Changing ZeroPadding2D\n", "Changing Convolution2D\n", "Skipping BatchNormalization layer in new model\n", "Changing MaxPooling2D\n", "0 new layers ignored and 20 old layers ignored\n" ] } ], "source": [ "ol1 = upgrade_weights(flow_seq.layers, ilsvrc_model.layers)\n", "ol2 = upgrade_weights(rgb_seq.layers, ilsvrc_model.layers)\n", "assert ol1 == ol2\n", "old_left = ol1 # Number of layers remaining in the old model" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Concatenating old L1 dimensions to change shape, (512, 256, 3, 3)->(512, 512, 3, 3)\n", "First layer done\n", "WARNING: layer count differs (25 new v. 20 old)\n", "Skipping BatchNormalization layer in new model\n", "Changing ZeroPadding2D\n", "Changing Convolution2D\n", "Skipping BatchNormalization layer in new model\n", "Changing ZeroPadding2D\n", "Changing Convolution2D\n", "Skipping BatchNormalization layer in new model\n", "Changing MaxPooling2D\n", "Changing ZeroPadding2D\n", "Changing Convolution2D\n", "Skipping BatchNormalization layer in new model\n", "Changing ZeroPadding2D\n", "Changing Convolution2D\n", "Skipping BatchNormalization layer in new model\n", "Changing ZeroPadding2D\n", "Changing Convolution2D\n", "Skipping BatchNormalization layer in new model\n", "Changing MaxPooling2D\n", "Changing Flatten\n", "Changing Dense\n", "Changing Dropout\n", "Changing Dense\n", "Changing Dropout\n", "0 new layers ignored and 1 old layers ignored\n" ] } ], "source": [ "front_layers = len(flow_seq.layers)\n", "assert front_layers == len(rgb_seq.layers), \"Flow and RGB pipelines should be same length\"\n", "back_ilsvrc_layers = ilsvrc_model.layers[-old_left:]\n", "back_seq = huge.nodes['shared_layers']\n", "upgrade_weights(back_seq.layers, back_ilsvrc_layers)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [], "source": [ "huge.save_weights('vggnet/vgg16-2stream-pslt-clas-norm-bn.h5')" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from keras.utils.visualize_util import to_graph\n", "\n", "from IPython.display import SVG" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "image/svg+xml": [ "<svg height=\"4424pt\" viewBox=\"0.00 0.00 979.00 4424.00\" width=\"979pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n", "<g class=\"graph\" id=\"graph0\" transform=\"scale(1 1) rotate(0) translate(4 4420)\">\n", "<title>G</title>\n", "<polygon fill=\"white\" points=\"-4,4 -4,-4420 975,-4420 975,4 -4,4\" stroke=\"none\"/>\n", "<!-- layer0 -->\n", "<g class=\"node\" id=\"node1\"><title>layer0</title>\n", "<polygon fill=\"none\" points=\"321.5,-1 321.5,-47 662.5,-47 662.5,-1 321.5,-1\" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"420\" y=\"-20.3\">poselet (keras.layers.core.Dense)</text>\n", "<polyline fill=\"none\" points=\"518.5,-1 518.5,-47 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"546\" y=\"-31.8\">input:</text>\n", "<polyline fill=\"none\" points=\"518.5,-24 573.5,-24 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"546\" y=\"-8.8\">output:</text>\n", "<polyline fill=\"none\" points=\"573.5,-1 573.5,-47 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"618\" y=\"-31.8\">(None, 4096)</text>\n", "<polyline fill=\"none\" points=\"573.5,-24 662.5,-24 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"618\" y=\"-8.8\">(None, 901)</text>\n", "</g>\n", "<!-- layer1 -->\n", "<g class=\"node\" id=\"node2\"><title>layer1</title>\n", "<polygon fill=\"none\" points=\"337,-85 337,-131 647,-131 647,-85 337,-85\" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"420\" y=\"-104.3\">(keras.layers.core.Dropout)</text>\n", "<polyline fill=\"none\" points=\"503,-85 503,-131 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"530.5\" y=\"-115.8\">input:</text>\n", "<polyline fill=\"none\" points=\"503,-108 558,-108 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"530.5\" y=\"-92.8\">output:</text>\n", "<polyline fill=\"none\" points=\"558,-85 558,-131 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"602.5\" y=\"-115.8\">(None, 4096)</text>\n", "<polyline fill=\"none\" points=\"558,-108 647,-108 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"602.5\" y=\"-92.8\">(None, 4096)</text>\n", "</g>\n", "<!-- layer1&#45;&gt;layer0 -->\n", "<g class=\"edge\" id=\"edge1\"><title>layer1-&gt;layer0</title>\n", "<path d=\"M492,-84.5931C492,-76.1177 492,-66.2974 492,-57.104\" fill=\"none\" stroke=\"black\"/>\n", "<polygon fill=\"black\" points=\"495.5,-57.0958 492,-47.0959 488.5,-57.0959 495.5,-57.0958\" stroke=\"black\"/>\n", "</g>\n", "<!-- layer2 -->\n", "<g class=\"node\" id=\"node3\"><title>layer2</title>\n", "<polygon fill=\"none\" points=\"342.5,-169 342.5,-215 641.5,-215 641.5,-169 342.5,-169\" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"420\" y=\"-188.3\">(keras.layers.core.Dense)</text>\n", "<polyline fill=\"none\" points=\"497.5,-169 497.5,-215 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"525\" y=\"-199.8\">input:</text>\n", "<polyline fill=\"none\" points=\"497.5,-192 552.5,-192 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"525\" y=\"-176.8\">output:</text>\n", "<polyline fill=\"none\" points=\"552.5,-169 552.5,-215 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"597\" y=\"-199.8\">(None, 4096)</text>\n", "<polyline fill=\"none\" points=\"552.5,-192 641.5,-192 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"597\" y=\"-176.8\">(None, 4096)</text>\n", "</g>\n", "<!-- layer2&#45;&gt;layer1 -->\n", "<g class=\"edge\" id=\"edge2\"><title>layer2-&gt;layer1</title>\n", "<path d=\"M492,-168.593C492,-160.118 492,-150.297 492,-141.104\" fill=\"none\" stroke=\"black\"/>\n", "<polygon fill=\"black\" points=\"495.5,-141.096 492,-131.096 488.5,-141.096 495.5,-141.096\" stroke=\"black\"/>\n", "</g>\n", "<!-- layer3 -->\n", "<g class=\"node\" id=\"node4\"><title>layer3</title>\n", "<polygon fill=\"none\" points=\"337,-253 337,-299 647,-299 647,-253 337,-253\" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"420\" y=\"-272.3\">(keras.layers.core.Dropout)</text>\n", "<polyline fill=\"none\" points=\"503,-253 503,-299 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"530.5\" y=\"-283.8\">input:</text>\n", "<polyline fill=\"none\" points=\"503,-276 558,-276 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"530.5\" y=\"-260.8\">output:</text>\n", "<polyline fill=\"none\" points=\"558,-253 558,-299 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"602.5\" y=\"-283.8\">(None, 4096)</text>\n", "<polyline fill=\"none\" points=\"558,-276 647,-276 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"602.5\" y=\"-260.8\">(None, 4096)</text>\n", "</g>\n", "<!-- layer3&#45;&gt;layer2 -->\n", "<g class=\"edge\" id=\"edge3\"><title>layer3-&gt;layer2</title>\n", "<path d=\"M492,-252.593C492,-244.118 492,-234.297 492,-225.104\" fill=\"none\" stroke=\"black\"/>\n", "<polygon fill=\"black\" points=\"495.5,-225.096 492,-215.096 488.5,-225.096 495.5,-225.096\" stroke=\"black\"/>\n", "</g>\n", "<!-- layer4 -->\n", "<g class=\"node\" id=\"node5\"><title>layer4</title>\n", "<polygon fill=\"none\" points=\"339,-337 339,-383 645,-383 645,-337 339,-337\" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"416.5\" y=\"-356.3\">(keras.layers.core.Dense)</text>\n", "<polyline fill=\"none\" points=\"494,-337 494,-383 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"521.5\" y=\"-367.8\">input:</text>\n", "<polyline fill=\"none\" points=\"494,-360 549,-360 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"521.5\" y=\"-344.8\">output:</text>\n", "<polyline fill=\"none\" points=\"549,-337 549,-383 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"597\" y=\"-367.8\">(None, 25088)</text>\n", "<polyline fill=\"none\" points=\"549,-360 645,-360 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"597\" y=\"-344.8\">(None, 4096)</text>\n", "</g>\n", "<!-- layer4&#45;&gt;layer3 -->\n", "<g class=\"edge\" id=\"edge4\"><title>layer4-&gt;layer3</title>\n", "<path d=\"M492,-336.593C492,-328.118 492,-318.297 492,-309.104\" fill=\"none\" stroke=\"black\"/>\n", "<polygon fill=\"black\" points=\"495.5,-309.096 492,-299.096 488.5,-309.096 495.5,-309.096\" stroke=\"black\"/>\n", "</g>\n", "<!-- layer5 -->\n", "<g class=\"node\" id=\"node6\"><title>layer5</title>\n", "<polygon fill=\"none\" points=\"330,-421 330,-467 654,-467 654,-421 330,-421\" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"409\" y=\"-440.3\">(keras.layers.core.Flatten)</text>\n", "<polyline fill=\"none\" points=\"488,-421 488,-467 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"515.5\" y=\"-451.8\">input:</text>\n", "<polyline fill=\"none\" points=\"488,-444 543,-444 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"515.5\" y=\"-428.8\">output:</text>\n", "<polyline fill=\"none\" points=\"543,-421 543,-467 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"598.5\" y=\"-451.8\">(None, 512, 7, 7)</text>\n", "<polyline fill=\"none\" points=\"543,-444 654,-444 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"598.5\" y=\"-428.8\">(None, 25088)</text>\n", "</g>\n", "<!-- layer5&#45;&gt;layer4 -->\n", "<g class=\"edge\" id=\"edge5\"><title>layer5-&gt;layer4</title>\n", "<path d=\"M492,-420.593C492,-412.118 492,-402.297 492,-393.104\" fill=\"none\" stroke=\"black\"/>\n", "<polygon fill=\"black\" points=\"495.5,-393.096 492,-383.096 488.5,-393.096 495.5,-393.096\" stroke=\"black\"/>\n", "</g>\n", "<!-- layer6 -->\n", "<g class=\"node\" id=\"node7\"><title>layer6</title>\n", "<polygon fill=\"none\" points=\"274,-505 274,-551 710,-551 710,-505 274,-505\" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"402\" y=\"-524.3\">(keras.layers.convolutional.MaxPooling2D)</text>\n", "<polyline fill=\"none\" points=\"530,-505 530,-551 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"557.5\" y=\"-535.8\">input:</text>\n", "<polyline fill=\"none\" points=\"530,-528 585,-528 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"557.5\" y=\"-512.8\">output:</text>\n", "<polyline fill=\"none\" points=\"585,-505 585,-551 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"647.5\" y=\"-535.8\">(None, 512, 14, 14)</text>\n", "<polyline fill=\"none\" points=\"585,-528 710,-528 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"647.5\" y=\"-512.8\">(None, 512, 7, 7)</text>\n", "</g>\n", "<!-- layer6&#45;&gt;layer5 -->\n", "<g class=\"edge\" id=\"edge6\"><title>layer6-&gt;layer5</title>\n", "<path d=\"M492,-504.593C492,-496.118 492,-486.297 492,-477.104\" fill=\"none\" stroke=\"black\"/>\n", "<polygon fill=\"black\" points=\"495.5,-477.096 492,-467.096 488.5,-477.096 495.5,-477.096\" stroke=\"black\"/>\n", "</g>\n", "<!-- layer7 -->\n", "<g class=\"node\" id=\"node8\"><title>layer7</title>\n", "<polygon fill=\"none\" points=\"260,-589 260,-635 724,-635 724,-589 260,-589\" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"402\" y=\"-608.3\">(keras.layers.normalization.BatchNormalization)</text>\n", "<polyline fill=\"none\" points=\"544,-589 544,-635 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"571.5\" y=\"-619.8\">input:</text>\n", "<polyline fill=\"none\" points=\"544,-612 599,-612 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"571.5\" y=\"-596.8\">output:</text>\n", "<polyline fill=\"none\" points=\"599,-589 599,-635 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"661.5\" y=\"-619.8\">(None, 512, 14, 14)</text>\n", "<polyline fill=\"none\" points=\"599,-612 724,-612 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"661.5\" y=\"-596.8\">(None, 512, 14, 14)</text>\n", "</g>\n", "<!-- layer7&#45;&gt;layer6 -->\n", "<g class=\"edge\" id=\"edge7\"><title>layer7-&gt;layer6</title>\n", "<path d=\"M492,-588.593C492,-580.118 492,-570.297 492,-561.104\" fill=\"none\" stroke=\"black\"/>\n", "<polygon fill=\"black\" points=\"495.5,-561.096 492,-551.096 488.5,-561.096 495.5,-561.096\" stroke=\"black\"/>\n", "</g>\n", "<!-- layer8 -->\n", "<g class=\"node\" id=\"node9\"><title>layer8</title>\n", "<polygon fill=\"none\" points=\"274,-673 274,-719 710,-719 710,-673 274,-673\" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"402\" y=\"-692.3\">(keras.layers.convolutional.Convolution2D)</text>\n", "<polyline fill=\"none\" points=\"530,-673 530,-719 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"557.5\" y=\"-703.8\">input:</text>\n", "<polyline fill=\"none\" points=\"530,-696 585,-696 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"557.5\" y=\"-680.8\">output:</text>\n", "<polyline fill=\"none\" points=\"585,-673 585,-719 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"647.5\" y=\"-703.8\">(None, 512, 16, 16)</text>\n", "<polyline fill=\"none\" points=\"585,-696 710,-696 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"647.5\" y=\"-680.8\">(None, 512, 14, 14)</text>\n", "</g>\n", "<!-- 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text-anchor=\"middle\" x=\"558.5\" y=\"-764.8\">output:</text>\n", "<polyline fill=\"none\" points=\"586,-757 586,-803 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"648.5\" y=\"-787.8\">(None, 512, 14, 14)</text>\n", "<polyline fill=\"none\" points=\"586,-780 711,-780 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"648.5\" y=\"-764.8\">(None, 512, 16, 16)</text>\n", "</g>\n", "<!-- layer9&#45;&gt;layer8 -->\n", "<g class=\"edge\" id=\"edge9\"><title>layer9-&gt;layer8</title>\n", "<path d=\"M492,-756.593C492,-748.118 492,-738.297 492,-729.104\" fill=\"none\" stroke=\"black\"/>\n", "<polygon fill=\"black\" points=\"495.5,-729.096 492,-719.096 488.5,-729.096 495.5,-729.096\" stroke=\"black\"/>\n", "</g>\n", "<!-- layer10 -->\n", "<g class=\"node\" id=\"node11\"><title>layer10</title>\n", "<polygon fill=\"none\" points=\"260,-841 260,-887 724,-887 724,-841 260,-841\" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"402\" y=\"-860.3\">(keras.layers.normalization.BatchNormalization)</text>\n", "<polyline fill=\"none\" points=\"544,-841 544,-887 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"571.5\" y=\"-871.8\">input:</text>\n", "<polyline fill=\"none\" points=\"544,-864 599,-864 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"571.5\" y=\"-848.8\">output:</text>\n", "<polyline fill=\"none\" points=\"599,-841 599,-887 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"661.5\" y=\"-871.8\">(None, 512, 14, 14)</text>\n", "<polyline fill=\"none\" points=\"599,-864 724,-864 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"661.5\" y=\"-848.8\">(None, 512, 14, 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points=\"273,-1009 273,-1055 711,-1055 711,-1009 273,-1009\" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"402\" y=\"-1028.3\">(keras.layers.convolutional.ZeroPadding2D)</text>\n", "<polyline fill=\"none\" points=\"531,-1009 531,-1055 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"558.5\" y=\"-1039.8\">input:</text>\n", "<polyline fill=\"none\" points=\"531,-1032 586,-1032 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"558.5\" y=\"-1016.8\">output:</text>\n", "<polyline fill=\"none\" points=\"586,-1009 586,-1055 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"648.5\" y=\"-1039.8\">(None, 512, 14, 14)</text>\n", "<polyline fill=\"none\" points=\"586,-1032 711,-1032 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"648.5\" y=\"-1016.8\">(None, 512, 16, 16)</text>\n", "</g>\n", "<!-- layer12&#45;&gt;layer11 -->\n", "<g class=\"edge\" id=\"edge12\"><title>layer12-&gt;layer11</title>\n", "<path d=\"M492,-1008.59C492,-1000.12 492,-990.297 492,-981.104\" fill=\"none\" stroke=\"black\"/>\n", "<polygon fill=\"black\" points=\"495.5,-981.096 492,-971.096 488.5,-981.096 495.5,-981.096\" stroke=\"black\"/>\n", "</g>\n", "<!-- layer13 -->\n", "<g class=\"node\" id=\"node14\"><title>layer13</title>\n", "<polygon fill=\"none\" points=\"260,-1093 260,-1139 724,-1139 724,-1093 260,-1093\" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"402\" y=\"-1112.3\">(keras.layers.normalization.BatchNormalization)</text>\n", "<polyline fill=\"none\" points=\"544,-1093 544,-1139 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"571.5\" y=\"-1123.8\">input:</text>\n", "<polyline fill=\"none\" points=\"544,-1116 599,-1116 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"571.5\" y=\"-1100.8\">output:</text>\n", "<polyline fill=\"none\" points=\"599,-1093 599,-1139 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"661.5\" y=\"-1123.8\">(None, 512, 14, 14)</text>\n", "<polyline fill=\"none\" points=\"599,-1116 724,-1116 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"661.5\" y=\"-1100.8\">(None, 512, 14, 14)</text>\n", "</g>\n", "<!-- layer13&#45;&gt;layer12 -->\n", "<g class=\"edge\" id=\"edge13\"><title>layer13-&gt;layer12</title>\n", "<path d=\"M492,-1092.59C492,-1084.12 492,-1074.3 492,-1065.1\" fill=\"none\" stroke=\"black\"/>\n", "<polygon fill=\"black\" points=\"495.5,-1065.1 492,-1055.1 488.5,-1065.1 495.5,-1065.1\" stroke=\"black\"/>\n", "</g>\n", "<!-- layer14 -->\n", "<g class=\"node\" id=\"node15\"><title>layer14</title>\n", "<polygon fill=\"none\" points=\"274,-1177 274,-1223 710,-1223 710,-1177 274,-1177\" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"402\" y=\"-1196.3\">(keras.layers.convolutional.Convolution2D)</text>\n", "<polyline fill=\"none\" points=\"530,-1177 530,-1223 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"557.5\" y=\"-1207.8\">input:</text>\n", "<polyline fill=\"none\" points=\"530,-1200 585,-1200 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"557.5\" y=\"-1184.8\">output:</text>\n", "<polyline fill=\"none\" points=\"585,-1177 585,-1223 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"647.5\" y=\"-1207.8\">(None, 512, 16, 16)</text>\n", "<polyline fill=\"none\" points=\"585,-1200 710,-1200 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"647.5\" y=\"-1184.8\">(None, 512, 14, 14)</text>\n", "</g>\n", "<!-- layer14&#45;&gt;layer13 -->\n", "<g class=\"edge\" id=\"edge14\"><title>layer14-&gt;layer13</title>\n", "<path d=\"M492,-1176.59C492,-1168.12 492,-1158.3 492,-1149.1\" fill=\"none\" stroke=\"black\"/>\n", "<polygon fill=\"black\" points=\"495.5,-1149.1 492,-1139.1 488.5,-1149.1 495.5,-1149.1\" stroke=\"black\"/>\n", "</g>\n", "<!-- layer15 -->\n", "<g class=\"node\" id=\"node16\"><title>layer15</title>\n", "<polygon fill=\"none\" points=\"273,-1261 273,-1307 711,-1307 711,-1261 273,-1261\" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"402\" y=\"-1280.3\">(keras.layers.convolutional.ZeroPadding2D)</text>\n", "<polyline fill=\"none\" points=\"531,-1261 531,-1307 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"558.5\" y=\"-1291.8\">input:</text>\n", "<polyline fill=\"none\" points=\"531,-1284 586,-1284 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"558.5\" y=\"-1268.8\">output:</text>\n", "<polyline fill=\"none\" points=\"586,-1261 586,-1307 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"648.5\" y=\"-1291.8\">(None, 512, 14, 14)</text>\n", "<polyline fill=\"none\" points=\"586,-1284 711,-1284 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"648.5\" y=\"-1268.8\">(None, 512, 16, 16)</text>\n", "</g>\n", "<!-- layer15&#45;&gt;layer14 -->\n", "<g class=\"edge\" id=\"edge15\"><title>layer15-&gt;layer14</title>\n", "<path d=\"M492,-1260.59C492,-1252.12 492,-1242.3 492,-1233.1\" fill=\"none\" stroke=\"black\"/>\n", "<polygon fill=\"black\" points=\"495.5,-1233.1 492,-1223.1 488.5,-1233.1 495.5,-1233.1\" stroke=\"black\"/>\n", "</g>\n", "<!-- layer16 -->\n", "<g class=\"node\" id=\"node17\"><title>layer16</title>\n", "<polygon fill=\"none\" points=\"274,-1345 274,-1391 710,-1391 710,-1345 274,-1345\" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"402\" y=\"-1364.3\">(keras.layers.convolutional.MaxPooling2D)</text>\n", "<polyline fill=\"none\" points=\"530,-1345 530,-1391 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"557.5\" y=\"-1375.8\">input:</text>\n", "<polyline fill=\"none\" points=\"530,-1368 585,-1368 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"557.5\" y=\"-1352.8\">output:</text>\n", "<polyline fill=\"none\" points=\"585,-1345 585,-1391 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"647.5\" y=\"-1375.8\">(None, 512, 28, 28)</text>\n", "<polyline fill=\"none\" points=\"585,-1368 710,-1368 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"647.5\" y=\"-1352.8\">(None, 512, 14, 14)</text>\n", "</g>\n", "<!-- layer16&#45;&gt;layer15 -->\n", "<g class=\"edge\" id=\"edge16\"><title>layer16-&gt;layer15</title>\n", "<path d=\"M492,-1344.59C492,-1336.12 492,-1326.3 492,-1317.1\" fill=\"none\" stroke=\"black\"/>\n", "<polygon fill=\"black\" points=\"495.5,-1317.1 492,-1307.1 488.5,-1317.1 495.5,-1317.1\" stroke=\"black\"/>\n", "</g>\n", "<!-- layer17 -->\n", "<g class=\"node\" id=\"node18\"><title>layer17</title>\n", "<polygon fill=\"none\" points=\"260,-1429 260,-1475 724,-1475 724,-1429 260,-1429\" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"402\" y=\"-1448.3\">(keras.layers.normalization.BatchNormalization)</text>\n", "<polyline fill=\"none\" points=\"544,-1429 544,-1475 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"571.5\" y=\"-1459.8\">input:</text>\n", "<polyline fill=\"none\" points=\"544,-1452 599,-1452 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"571.5\" y=\"-1436.8\">output:</text>\n", "<polyline fill=\"none\" points=\"599,-1429 599,-1475 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"661.5\" y=\"-1459.8\">(None, 512, 28, 28)</text>\n", "<polyline fill=\"none\" points=\"599,-1452 724,-1452 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"661.5\" y=\"-1436.8\">(None, 512, 28, 28)</text>\n", "</g>\n", "<!-- layer17&#45;&gt;layer16 -->\n", "<g class=\"edge\" id=\"edge17\"><title>layer17-&gt;layer16</title>\n", "<path d=\"M492,-1428.59C492,-1420.12 492,-1410.3 492,-1401.1\" fill=\"none\" stroke=\"black\"/>\n", "<polygon fill=\"black\" points=\"495.5,-1401.1 492,-1391.1 488.5,-1401.1 495.5,-1401.1\" stroke=\"black\"/>\n", "</g>\n", "<!-- layer18 -->\n", "<g class=\"node\" id=\"node19\"><title>layer18</title>\n", "<polygon fill=\"none\" points=\"274,-1513 274,-1559 710,-1559 710,-1513 274,-1513\" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"402\" y=\"-1532.3\">(keras.layers.convolutional.Convolution2D)</text>\n", "<polyline fill=\"none\" points=\"530,-1513 530,-1559 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"557.5\" y=\"-1543.8\">input:</text>\n", "<polyline fill=\"none\" points=\"530,-1536 585,-1536 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"557.5\" y=\"-1520.8\">output:</text>\n", "<polyline fill=\"none\" points=\"585,-1513 585,-1559 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"647.5\" y=\"-1543.8\">(None, 512, 30, 30)</text>\n", "<polyline fill=\"none\" points=\"585,-1536 710,-1536 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"647.5\" y=\"-1520.8\">(None, 512, 28, 28)</text>\n", "</g>\n", "<!-- layer18&#45;&gt;layer17 -->\n", "<g class=\"edge\" id=\"edge18\"><title>layer18-&gt;layer17</title>\n", "<path d=\"M492,-1512.59C492,-1504.12 492,-1494.3 492,-1485.1\" fill=\"none\" stroke=\"black\"/>\n", "<polygon fill=\"black\" points=\"495.5,-1485.1 492,-1475.1 488.5,-1485.1 495.5,-1485.1\" stroke=\"black\"/>\n", "</g>\n", "<!-- layer19 -->\n", "<g class=\"node\" id=\"node20\"><title>layer19</title>\n", "<polygon fill=\"none\" points=\"273,-1597 273,-1643 711,-1643 711,-1597 273,-1597\" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"402\" y=\"-1616.3\">(keras.layers.convolutional.ZeroPadding2D)</text>\n", "<polyline fill=\"none\" points=\"531,-1597 531,-1643 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"558.5\" y=\"-1627.8\">input:</text>\n", "<polyline fill=\"none\" points=\"531,-1620 586,-1620 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"558.5\" y=\"-1604.8\">output:</text>\n", "<polyline fill=\"none\" points=\"586,-1597 586,-1643 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"648.5\" y=\"-1627.8\">(None, 512, 28, 28)</text>\n", "<polyline fill=\"none\" points=\"586,-1620 711,-1620 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"648.5\" y=\"-1604.8\">(None, 512, 30, 30)</text>\n", "</g>\n", "<!-- layer19&#45;&gt;layer18 -->\n", "<g class=\"edge\" id=\"edge19\"><title>layer19-&gt;layer18</title>\n", "<path d=\"M492,-1596.59C492,-1588.12 492,-1578.3 492,-1569.1\" fill=\"none\" stroke=\"black\"/>\n", "<polygon fill=\"black\" points=\"495.5,-1569.1 492,-1559.1 488.5,-1569.1 495.5,-1569.1\" stroke=\"black\"/>\n", "</g>\n", "<!-- layer20 -->\n", "<g class=\"node\" id=\"node21\"><title>layer20</title>\n", "<polygon fill=\"none\" points=\"260,-1681 260,-1727 724,-1727 724,-1681 260,-1681\" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"402\" y=\"-1700.3\">(keras.layers.normalization.BatchNormalization)</text>\n", "<polyline fill=\"none\" points=\"544,-1681 544,-1727 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"571.5\" y=\"-1711.8\">input:</text>\n", "<polyline fill=\"none\" points=\"544,-1704 599,-1704 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"571.5\" y=\"-1688.8\">output:</text>\n", "<polyline fill=\"none\" points=\"599,-1681 599,-1727 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"661.5\" y=\"-1711.8\">(None, 512, 28, 28)</text>\n", "<polyline fill=\"none\" points=\"599,-1704 724,-1704 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"661.5\" y=\"-1688.8\">(None, 512, 28, 28)</text>\n", "</g>\n", "<!-- layer20&#45;&gt;layer19 -->\n", "<g class=\"edge\" id=\"edge20\"><title>layer20-&gt;layer19</title>\n", "<path d=\"M492,-1680.59C492,-1672.12 492,-1662.3 492,-1653.1\" fill=\"none\" stroke=\"black\"/>\n", "<polygon fill=\"black\" points=\"495.5,-1653.1 492,-1643.1 488.5,-1653.1 495.5,-1653.1\" stroke=\"black\"/>\n", "</g>\n", "<!-- layer21 -->\n", "<g class=\"node\" id=\"node22\"><title>layer21</title>\n", "<polygon fill=\"none\" points=\"274,-1765 274,-1811 710,-1811 710,-1765 274,-1765\" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"402\" y=\"-1784.3\">(keras.layers.convolutional.Convolution2D)</text>\n", "<polyline fill=\"none\" points=\"530,-1765 530,-1811 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"557.5\" y=\"-1795.8\">input:</text>\n", "<polyline fill=\"none\" points=\"530,-1788 585,-1788 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"557.5\" y=\"-1772.8\">output:</text>\n", "<polyline fill=\"none\" points=\"585,-1765 585,-1811 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"647.5\" y=\"-1795.8\">(None, 512, 30, 30)</text>\n", "<polyline fill=\"none\" points=\"585,-1788 710,-1788 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"647.5\" y=\"-1772.8\">(None, 512, 28, 28)</text>\n", "</g>\n", "<!-- layer21&#45;&gt;layer20 -->\n", "<g class=\"edge\" id=\"edge21\"><title>layer21-&gt;layer20</title>\n", "<path d=\"M492,-1764.59C492,-1756.12 492,-1746.3 492,-1737.1\" fill=\"none\" stroke=\"black\"/>\n", "<polygon fill=\"black\" points=\"495.5,-1737.1 492,-1727.1 488.5,-1737.1 495.5,-1737.1\" stroke=\"black\"/>\n", "</g>\n", "<!-- layer22 -->\n", "<g class=\"node\" id=\"node23\"><title>layer22</title>\n", "<polygon fill=\"none\" points=\"273,-1849 273,-1895 711,-1895 711,-1849 273,-1849\" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"402\" y=\"-1868.3\">(keras.layers.convolutional.ZeroPadding2D)</text>\n", "<polyline fill=\"none\" points=\"531,-1849 531,-1895 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"558.5\" y=\"-1879.8\">input:</text>\n", "<polyline fill=\"none\" points=\"531,-1872 586,-1872 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"558.5\" y=\"-1856.8\">output:</text>\n", "<polyline fill=\"none\" points=\"586,-1849 586,-1895 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"648.5\" y=\"-1879.8\">(None, 512, 28, 28)</text>\n", "<polyline fill=\"none\" points=\"586,-1872 711,-1872 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"648.5\" y=\"-1856.8\">(None, 512, 30, 30)</text>\n", "</g>\n", "<!-- layer22&#45;&gt;layer21 -->\n", "<g class=\"edge\" id=\"edge22\"><title>layer22-&gt;layer21</title>\n", "<path d=\"M492,-1848.59C492,-1840.12 492,-1830.3 492,-1821.1\" fill=\"none\" stroke=\"black\"/>\n", "<polygon fill=\"black\" points=\"495.5,-1821.1 492,-1811.1 488.5,-1821.1 495.5,-1821.1\" stroke=\"black\"/>\n", "</g>\n", "<!-- layer23 -->\n", "<g class=\"node\" id=\"node24\"><title>layer23</title>\n", "<polygon fill=\"none\" points=\"260,-1933 260,-1979 724,-1979 724,-1933 260,-1933\" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"402\" y=\"-1952.3\">(keras.layers.normalization.BatchNormalization)</text>\n", "<polyline fill=\"none\" points=\"544,-1933 544,-1979 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"571.5\" y=\"-1963.8\">input:</text>\n", "<polyline fill=\"none\" points=\"544,-1956 599,-1956 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"571.5\" y=\"-1940.8\">output:</text>\n", "<polyline fill=\"none\" points=\"599,-1933 599,-1979 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"661.5\" y=\"-1963.8\">(None, 512, 28, 28)</text>\n", "<polyline fill=\"none\" points=\"599,-1956 724,-1956 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"661.5\" y=\"-1940.8\">(None, 512, 28, 28)</text>\n", "</g>\n", "<!-- layer23&#45;&gt;layer22 -->\n", "<g class=\"edge\" id=\"edge23\"><title>layer23-&gt;layer22</title>\n", "<path d=\"M492,-1932.59C492,-1924.12 492,-1914.3 492,-1905.1\" fill=\"none\" stroke=\"black\"/>\n", "<polygon fill=\"black\" points=\"495.5,-1905.1 492,-1895.1 488.5,-1905.1 495.5,-1905.1\" stroke=\"black\"/>\n", "</g>\n", "<!-- layer24 -->\n", "<g class=\"node\" id=\"node25\"><title>layer24</title>\n", "<polygon fill=\"none\" points=\"274,-2017 274,-2063 710,-2063 710,-2017 274,-2017\" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"402\" y=\"-2036.3\">(keras.layers.convolutional.Convolution2D)</text>\n", "<polyline fill=\"none\" points=\"530,-2017 530,-2063 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"557.5\" y=\"-2047.8\">input:</text>\n", "<polyline fill=\"none\" points=\"530,-2040 585,-2040 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"557.5\" y=\"-2024.8\">output:</text>\n", "<polyline fill=\"none\" points=\"585,-2017 585,-2063 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"647.5\" y=\"-2047.8\">(None, 512, 30, 30)</text>\n", "<polyline fill=\"none\" points=\"585,-2040 710,-2040 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"647.5\" y=\"-2024.8\">(None, 512, 28, 28)</text>\n", "</g>\n", "<!-- layer24&#45;&gt;layer23 -->\n", "<g class=\"edge\" id=\"edge24\"><title>layer24-&gt;layer23</title>\n", "<path d=\"M492,-2016.59C492,-2008.12 492,-1998.3 492,-1989.1\" fill=\"none\" stroke=\"black\"/>\n", "<polygon fill=\"black\" points=\"495.5,-1989.1 492,-1979.1 488.5,-1989.1 495.5,-1989.1\" stroke=\"black\"/>\n", "</g>\n", "<!-- layer25 -->\n", "<g class=\"node\" id=\"node26\"><title>layer25</title>\n", "<polygon fill=\"none\" points=\"273,-2101 273,-2147 711,-2147 711,-2101 273,-2101\" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"402\" y=\"-2120.3\">(keras.layers.convolutional.ZeroPadding2D)</text>\n", "<polyline fill=\"none\" points=\"531,-2101 531,-2147 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"558.5\" y=\"-2131.8\">input:</text>\n", "<polyline fill=\"none\" points=\"531,-2124 586,-2124 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"558.5\" y=\"-2108.8\">output:</text>\n", "<polyline fill=\"none\" points=\"586,-2101 586,-2147 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"648.5\" y=\"-2131.8\">(None, 512, 28, 28)</text>\n", "<polyline fill=\"none\" points=\"586,-2124 711,-2124 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"648.5\" y=\"-2108.8\">(None, 512, 30, 30)</text>\n", "</g>\n", "<!-- layer25&#45;&gt;layer24 -->\n", "<g class=\"edge\" id=\"edge25\"><title>layer25-&gt;layer24</title>\n", "<path d=\"M492,-2100.59C492,-2092.12 492,-2082.3 492,-2073.1\" fill=\"none\" stroke=\"black\"/>\n", "<polygon fill=\"black\" points=\"495.5,-2073.1 492,-2063.1 488.5,-2073.1 495.5,-2073.1\" stroke=\"black\"/>\n", "</g>\n", "<!-- layer26 -->\n", "<g class=\"node\" id=\"node27\"><title>layer26</title>\n", "<polygon fill=\"none\" points=\"323.5,-2185 323.5,-2231 660.5,-2231 660.5,-2185 323.5,-2185\" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"402\" y=\"-2204.3\">(keras.layers.core.Merge)</text>\n", "<polyline fill=\"none\" points=\"480.5,-2185 480.5,-2231 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"508\" y=\"-2215.8\">input:</text>\n", "<polyline fill=\"none\" points=\"480.5,-2208 535.5,-2208 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"508\" y=\"-2192.8\">output:</text>\n", "<polyline fill=\"none\" points=\"535.5,-2185 535.5,-2231 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"598\" y=\"-2215.8\"> </text>\n", "<polyline fill=\"none\" points=\"535.5,-2208 660.5,-2208 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"598\" y=\"-2192.8\">(None, 512, 28, 28)</text>\n", "</g>\n", "<!-- layer26&#45;&gt;layer25 -->\n", "<g class=\"edge\" id=\"edge26\"><title>layer26-&gt;layer25</title>\n", "<path d=\"M492,-2184.59C492,-2176.12 492,-2166.3 492,-2157.1\" fill=\"none\" stroke=\"black\"/>\n", "<polygon fill=\"black\" points=\"495.5,-2157.1 492,-2147.1 488.5,-2157.1 495.5,-2157.1\" stroke=\"black\"/>\n", "</g>\n", "<!-- layer27 -->\n", "<g class=\"node\" id=\"node28\"><title>layer27</title>\n", "<polygon fill=\"none\" points=\"44,-2269 44,-2315 480,-2315 480,-2269 44,-2269\" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"172\" y=\"-2288.3\">(keras.layers.convolutional.MaxPooling2D)</text>\n", "<polyline fill=\"none\" points=\"300,-2269 300,-2315 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"327.5\" y=\"-2299.8\">input:</text>\n", "<polyline fill=\"none\" points=\"300,-2292 355,-2292 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"327.5\" y=\"-2276.8\">output:</text>\n", "<polyline fill=\"none\" points=\"355,-2269 355,-2315 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"417.5\" y=\"-2299.8\">(None, 256, 56, 56)</text>\n", 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stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"327.5\" y=\"-2383.8\">input:</text>\n", "<polyline fill=\"none\" points=\"300,-2376 355,-2376 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"327.5\" y=\"-2360.8\">output:</text>\n", "<polyline fill=\"none\" points=\"355,-2353 355,-2399 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"417.5\" y=\"-2383.8\">(None, 256, 56, 56)</text>\n", "<polyline fill=\"none\" points=\"355,-2376 480,-2376 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"417.5\" y=\"-2360.8\">(None, 256, 56, 56)</text>\n", "</g>\n", "<!-- layer28&#45;&gt;layer27 -->\n", "<g class=\"edge\" id=\"edge28\"><title>layer28-&gt;layer27</title>\n", "<path d=\"M251.826,-2352.59C253.273,-2344.12 254.949,-2334.3 256.519,-2325.1\" fill=\"none\" stroke=\"black\"/>\n", "<polygon fill=\"black\" points=\"259.995,-2325.54 258.228,-2315.1 253.094,-2324.36 259.995,-2325.54\" stroke=\"black\"/>\n", "</g>\n", "<!-- layer29 -->\n", "<g class=\"node\" id=\"node30\"><title>layer29</title>\n", "<polygon fill=\"none\" points=\"30,-2437 30,-2483 466,-2483 466,-2437 30,-2437\" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"158\" y=\"-2456.3\">(keras.layers.convolutional.Convolution2D)</text>\n", "<polyline fill=\"none\" points=\"286,-2437 286,-2483 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"313.5\" y=\"-2467.8\">input:</text>\n", "<polyline fill=\"none\" points=\"286,-2460 341,-2460 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"313.5\" y=\"-2444.8\">output:</text>\n", "<polyline fill=\"none\" points=\"341,-2437 341,-2483 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"403.5\" y=\"-2467.8\">(None, 256, 58, 58)</text>\n", "<polyline fill=\"none\" points=\"341,-2460 466,-2460 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"403.5\" y=\"-2444.8\">(None, 256, 56, 56)</text>\n", "</g>\n", "<!-- layer29&#45;&gt;layer28 -->\n", "<g class=\"edge\" id=\"edge29\"><title>layer29-&gt;layer28</title>\n", "<path d=\"M248,-2436.59C248,-2428.12 248,-2418.3 248,-2409.1\" fill=\"none\" stroke=\"black\"/>\n", "<polygon fill=\"black\" points=\"251.5,-2409.1 248,-2399.1 244.5,-2409.1 251.5,-2409.1\" stroke=\"black\"/>\n", "</g>\n", "<!-- layer30 -->\n", "<g class=\"node\" id=\"node31\"><title>layer30</title>\n", "<polygon fill=\"none\" points=\"29,-2521 29,-2567 467,-2567 467,-2521 29,-2521\" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"158\" 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id=\"edge30\"><title>layer30-&gt;layer29</title>\n", "<path d=\"M248,-2520.59C248,-2512.12 248,-2502.3 248,-2493.1\" fill=\"none\" stroke=\"black\"/>\n", "<polygon fill=\"black\" points=\"251.5,-2493.1 248,-2483.1 244.5,-2493.1 251.5,-2493.1\" stroke=\"black\"/>\n", "</g>\n", "<!-- layer31 -->\n", "<g class=\"node\" id=\"node32\"><title>layer31</title>\n", "<polygon fill=\"none\" points=\"16,-2605 16,-2651 480,-2651 480,-2605 16,-2605\" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"158\" y=\"-2624.3\">(keras.layers.normalization.BatchNormalization)</text>\n", "<polyline fill=\"none\" points=\"300,-2605 300,-2651 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"327.5\" y=\"-2635.8\">input:</text>\n", "<polyline fill=\"none\" points=\"300,-2628 355,-2628 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"327.5\" 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id=\"node55\"><title>layer54</title>\n", "<polygon fill=\"none\" points=\"512,-2437 512,-2483 948,-2483 948,-2437 512,-2437\" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"640\" y=\"-2456.3\">(keras.layers.convolutional.Convolution2D)</text>\n", "<polyline fill=\"none\" points=\"768,-2437 768,-2483 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"795.5\" y=\"-2467.8\">input:</text>\n", "<polyline fill=\"none\" points=\"768,-2460 823,-2460 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"795.5\" y=\"-2444.8\">output:</text>\n", "<polyline fill=\"none\" points=\"823,-2437 823,-2483 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"885.5\" y=\"-2467.8\">(None, 256, 58, 58)</text>\n", "<polyline fill=\"none\" points=\"823,-2460 948,-2460 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"885.5\" y=\"-2444.8\">(None, 256, 56, 56)</text>\n", "</g>\n", "<!-- layer54&#45;&gt;layer53 -->\n", "<g class=\"edge\" id=\"edge54\"><title>layer54-&gt;layer53</title>\n", "<path d=\"M730,-2436.59C730,-2428.12 730,-2418.3 730,-2409.1\" fill=\"none\" stroke=\"black\"/>\n", "<polygon fill=\"black\" points=\"733.5,-2409.1 730,-2399.1 726.5,-2409.1 733.5,-2409.1\" stroke=\"black\"/>\n", "</g>\n", "<!-- layer55 -->\n", "<g class=\"node\" id=\"node56\"><title>layer55</title>\n", "<polygon fill=\"none\" points=\"511,-2521 511,-2567 949,-2567 949,-2521 511,-2521\" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"640\" y=\"-2540.3\">(keras.layers.convolutional.ZeroPadding2D)</text>\n", "<polyline fill=\"none\" points=\"769,-2521 769,-2567 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"796.5\" y=\"-2551.8\">input:</text>\n", "<polyline fill=\"none\" points=\"769,-2544 824,-2544 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"796.5\" y=\"-2528.8\">output:</text>\n", "<polyline fill=\"none\" points=\"824,-2521 824,-2567 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"886.5\" y=\"-2551.8\">(None, 256, 56, 56)</text>\n", "<polyline fill=\"none\" points=\"824,-2544 949,-2544 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"886.5\" y=\"-2528.8\">(None, 256, 58, 58)</text>\n", "</g>\n", "<!-- layer55&#45;&gt;layer54 -->\n", "<g class=\"edge\" id=\"edge55\"><title>layer55-&gt;layer54</title>\n", "<path d=\"M730,-2520.59C730,-2512.12 730,-2502.3 730,-2493.1\" fill=\"none\" stroke=\"black\"/>\n", "<polygon fill=\"black\" points=\"733.5,-2493.1 730,-2483.1 726.5,-2493.1 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56, 56)</text>\n", "<polyline fill=\"none\" points=\"837,-2628 962,-2628 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"899.5\" y=\"-2612.8\">(None, 256, 56, 56)</text>\n", "</g>\n", "<!-- layer56&#45;&gt;layer55 -->\n", "<g class=\"edge\" id=\"edge56\"><title>layer56-&gt;layer55</title>\n", "<path d=\"M730,-2604.59C730,-2596.12 730,-2586.3 730,-2577.1\" fill=\"none\" stroke=\"black\"/>\n", "<polygon fill=\"black\" points=\"733.5,-2577.1 730,-2567.1 726.5,-2577.1 733.5,-2577.1\" stroke=\"black\"/>\n", "</g>\n", "<!-- layer57 -->\n", "<g class=\"node\" id=\"node58\"><title>layer57</title>\n", "<polygon fill=\"none\" points=\"512,-2689 512,-2735 948,-2735 948,-2689 512,-2689\" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"640\" y=\"-2708.3\">(keras.layers.convolutional.Convolution2D)</text>\n", "<polyline fill=\"none\" points=\"768,-2689 768,-2735 \" 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font-size=\"14.00\" text-anchor=\"middle\" x=\"886.5\" y=\"-2803.8\">(None, 256, 56, 56)</text>\n", "<polyline fill=\"none\" points=\"824,-2796 949,-2796 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"886.5\" y=\"-2780.8\">(None, 256, 58, 58)</text>\n", "</g>\n", "<!-- layer58&#45;&gt;layer57 -->\n", "<g class=\"edge\" id=\"edge58\"><title>layer58-&gt;layer57</title>\n", "<path d=\"M730,-2772.59C730,-2764.12 730,-2754.3 730,-2745.1\" fill=\"none\" stroke=\"black\"/>\n", "<polygon fill=\"black\" points=\"733.5,-2745.1 730,-2735.1 726.5,-2745.1 733.5,-2745.1\" stroke=\"black\"/>\n", "</g>\n", "<!-- layer59 -->\n", "<g class=\"node\" id=\"node60\"><title>layer59</title>\n", "<polygon fill=\"none\" points=\"498,-2857 498,-2903 962,-2903 962,-2857 498,-2857\" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"640\" 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id=\"edge59\"><title>layer59-&gt;layer58</title>\n", "<path d=\"M730,-2856.59C730,-2848.12 730,-2838.3 730,-2829.1\" fill=\"none\" stroke=\"black\"/>\n", "<polygon fill=\"black\" points=\"733.5,-2829.1 730,-2819.1 726.5,-2829.1 733.5,-2829.1\" stroke=\"black\"/>\n", "</g>\n", "<!-- layer60 -->\n", "<g class=\"node\" id=\"node61\"><title>layer60</title>\n", "<polygon fill=\"none\" points=\"512,-2941 512,-2987 948,-2987 948,-2941 512,-2941\" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"640\" y=\"-2960.3\">(keras.layers.convolutional.Convolution2D)</text>\n", "<polyline fill=\"none\" points=\"768,-2941 768,-2987 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"795.5\" y=\"-2971.8\">input:</text>\n", "<polyline fill=\"none\" points=\"768,-2964 823,-2964 \" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"795.5\" 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fill=\"none\" stroke=\"black\"/>\n", "<polygon fill=\"black\" points=\"736.5,-4341.1 733,-4331.1 729.5,-4341.1 736.5,-4341.1\" stroke=\"black\"/>\n", "</g>\n", "</g>\n", "</svg>" ], "text/plain": [ "<IPython.core.display.SVG object>" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "SVG(to_graph(huge, show_shape=True).create_svg())" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "ename": "AttributeError", "evalue": "'OrderedDict' object has no attribute 'layers'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-25-1c9b0b758727>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mhuge\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnodes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlayers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mAttributeError\u001b[0m: 'OrderedDict' object has no attribute 'layers'" ] }, { "name": "stdout", "output_type": "stream", "text": [ "> \u001b[1;32m<ipython-input-25-1c9b0b758727>\u001b[0m(1)\u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n", "\u001b[1;32m----> 1 \u001b[1;33m\u001b[0mhuge\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnodes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlayers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[0m\n", "ipdb> huge.nodes.keys()\n", "['rgb_conv', 'flow_norm', 'flow_conv', 'shared_layers', 'fc_pslt']\n", "ipdb> huge.nodes['shared_layers']\n", "<keras.models.Sequential object at 0x7f8b2b03be50>\n", "ipdb> q\n" ] } ], "source": [ "huge.nodes.layers.keys()" ] }, { "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 }
apache-2.0
magenta/magenta-demos
colab-notebooks/MusicXML_Document_Structure_Documentation.ipynb
1
6164017
null
apache-2.0
psygrammer/coco
part3/bayes/ch14/Baye_Chap14.ipynb
1
1034287
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 14. Multinominal processing trees\n", "\n", " ## 14.1 Multinomial processing model of pair-clustering\n", " \n", " The finding that semantically related items are often recalled consecutively can be taken as evidence for the idea that they were stored and retrieved as a cluster. An MPT model assumes that observed behavior arises from a sequence of cognitive events, able to be represented by a rooted tree architecture such as the one shown in Figure 14.1.\n", " \n", " ### The pair-clustering MPT model describes a simple sequence of cognitive processes that can produce these four behavioral outcomes, controlled by three parameters.\n", " - The cluster-storage parameter c is the probability that a word pair is clustered and stored in memory. \n", " - The cluster-retrieval parameter r is the conditional probability that a word pair is retrieved from memory, given that is was clustered. \n", " - The unique storage-retrieval parameter u is the conditional probability that a member of a word pair is stored and retrieved from memory, given that the word pair was not stored as a cluster.\n", " \n", " " ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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y0NBQOErB0AHVERMTg7Dy5cCJxRUoQ6LxZ57BeOwjPV11FZWdPSN6veF6fqkj\nKzU9LUo038IbzbK5PKmcJ53sgMcX8viTH6Z+M9k8oYR3K8hl6ijD63F5PD4U/hAJOAi4ZyHmAblx\nkTOXI5KrNAqlX8TgyFzrcSaLzRdJ8GeqRRabzWffr/KYuJrJ5gokCsFUU2woj6lm59lhArMICXdn\nOw3t8iUK/lS7CGNns3kzx8jkcAUyxL3QRgSIwGwESH7MRoWOEQEiQASIwGIRmMhMBD9ff+kKf5UG\nv7EBbQc+LlYnC2wnIDkwArxgR1Vy5MyFQxR8q+BhBUMHXK2QhwpiY+vWrQHVAQUy91J5gX0+nNMQ\nHNLTVHnx8k0zSxOjcVZcveKxc6ITwoVI5rSUIxKpo5Kys66dafjs41OeXevioiPEfJbdMNrTUnm6\nsLpfGLMnXnu3k9hSjojaJgJEYNkRIOerZXdLaEBEgAgQgUeIgM9pNY6OjJqdLKlCgWpuTotRpzM4\nvRypQqVWwWtoSRfDfpATdg6/6MHLeMSRw8oB4YFQ8rq6uilDB2LEUYIDwgNbbGwsgsUfoVsQ5Kl4\ndP0tF0+fOl9Qx5OGpGanqyQcY19rW0uzQZy0Zd+TT+3LUaFwe5AHRd0RASKwnAiQ/FhOd4PGQgSI\nABF4tAigZkJTdXH+9bIhC1uqjcxICrcPdtW1dhkN9vCsLfsPPpYeJluit/FThg7Ei08ZOpAtFzlz\nYeiAexXMGgjkyMzMDKgOJMNdcB6qR+smLcFsfC67fnSwo7Oze2DYaPOIZaqY2LiEuGhk3SXhsQS8\nqUkisMIIkPPVCrthNFwiQASIwMoh4DMNNV2+fObGgC+Mbf3ik09FmujokAiZ0N1TdPZq85AoJjNZ\nK+Ut3qvwKUNHIGEuDB1wrwoYOvr6+qA6zGYzSm0gddWJEyegPZAGV6FQrByeK2akTASohMfhz/oV\nM2QaKBEgAsEjQPIjeKypJyJABIjAKiPg6m2uHxr3bTv2srbtXP7nRZ642ANvfG8Ls+EDR1sVL1wk\nRBTyIiCZbugISI7h4WEYOlpbW7ED1YETkpKSDhw4EIjogKFjWpKiRRgANUEEiAARIAILJ0DyY+Gs\n6EwiQASIABG4HwIeu8nO0SqTNmq9JQXtBlnc0e2PH98Yw9Axdj3z3fWiyNzM8AcuBj3d0AGbRkB1\noLg4Ijp6e3vhW4V8VrBsZGVlHT16FIaOhIQEfFyhceT3A53OJQJEgAgsdwIkP5b7HaLxEQEiQARW\nKgEmNzJtoyDKJ7K39be2hyWkrN2aJoHdISLpsYikB5tUwNCBn4joCEgOGDdQ0Q/b0NAQVAfKd8Cl\nau/evYGIDlQJRNWOB+uLriICRIAIEIGlIEDyYymoUptEgAgQASKActjC6MSUaK+17HR+W+uYNlub\nFKVA8t0HQIO8VdggLaYMHTBxwNDR09MDyYEAD6lUirKAhw4dguqAoUOlUpGh4wE40yVEgAgQgSAQ\nIPkRBMjUBREgAkRg9RLw2A19/QOtRu06bXqE/D5KPkwZOlBzGwIDtg4oDeStampqQrEO7CORLqoB\n7ty5E5ID7lVRUVECARV6W71PGs2cCBCBlUKA5MdKuVM0TiJABIjAiiRgMwz3D3ZZ1aqwpDiFYP4y\nH1OGDqvVGkiYC0MHipEjgVXA0CEWi9PT0/fv3w/VgYBytVpNho4V+WTQoIkAEVitBEh+rNY7T/Mm\nAkSACASDgE8/ODTU1hGiSUpJjOKxWLP2Od3QYTAYYOhAAfLphg5EeiCKA8XIAxEdMHSIRKJZm6KD\nRIAIEAEisMwJkPxY5jeIhkcEiAARWNEE3CbdqG7AEJ4clhSruiPwA4YOCA9EdEwZOgYGBurr6zs7\nOxFQDhECZ6q0tLTdu3cH3KtQm5wMHSv6aaDBEwEiQARAgOQHPQZEgAgQASKwdARYQllY0pqD7Mwd\n8RoBws6hN7BBeDidTuTGDRg6Ojo64F4F7QH3KkiRiIiI3NzcgKED0R3wtlq68VHLRIAIEAEiEGQC\nTPwzEOQuqTsiQASIABFYPQQ8TofN4WRwBAIeC7mrbDYbJAc2iI3GxkYIDxg6EOOBfLyoCZiZmRkw\ndISGhpKhY/U8JDRTIkAEVhUBkh+r6nbTZIkAESACwSMwYefwGzqQoipg6IDMgGMVDB39/f0wdCCL\nbkhIyJo1awLFyGNjY5E/N3jjo56IABEgAkTgYRAg+fEwqFOfRIAIEIFHl0DAtwqqY8rQgYKAyJaL\nUHIYOqA6WCwWTByoRx5wrwoLC8ORR5cHzYwIEAEiQARmEKDYjxk46AMRIAJEgAg8AIHphg6TyQTf\nKlTqQKpcVAbs6+uD6rBYLMiQO2XoQGFymUz2AB3RJUSACBABIrDSCZD1Y6XfQRo/ESACROChEZgy\ndCAx7kRAx/jw8HDA0IEdGDowMpTmmDJ0hIeHczj02uuh3S/qmAgQASKwHAiQ/FgOd4HGQASIABFY\nMQSmDB1ut3vK0NHd3Q1DB+oDol4HwjyUSiUkB3Lmwr0qPj5eLpdTHPmKucE0UCJABIjAEhMg+bHE\ngKl5IkAEiMAjQSBg6MDPgKEDvlWwb7RMbAjtgKED5TsSEhKys7MRR56cnIwqgUhm9UhMnSZBBIgA\nESACi0mA5Mdi0qS2iAARIAKPGAFEkGODoQPBG4GIjp6eHhg68BOSAxXKEcIBQ0d6enrA0AG7Bxk6\nHrFngKZDBIgAEVhcAiQ/FpcntUYEiAARWPEEphs6IDCgOhA73tra2tzcPDg4CPcqqBEkyYWhA5ID\nho6oqCg+n7/ip00TIAJEgAgQgaAQIPkRFMzUCREgAkRg2RMIGDrgQzVl6EAsR319PeI6ID+gQyQS\nCawc2OBeBT8rZLIiQ8eyv6s0QCJABIjAsiNA8mPZ3RIaEBEgAkQgaASmDB0OhyNg6IBLFQp0wNCB\nquTYx/Ho6GgkzA3U6IChQygUBm141BERIAJEgAg8egRIfjx695RmRASIwGohAPEQmOr9WiFg6MC1\n0w0dKEMOQwdKkkNywNtKJBIhb1XA0IHMuRqN5n67WC33gOZJBIgAESAC90mA5Md9AqPTiQARIAIP\nm0DAZOF0OmGaQBgGj8dD6AXqabDZ7HsMbcrQgQsDhg5EcbS3tzc2NgYMHUhphXRVU6mrYPQQi8X3\naJC+IgJEgAgQASLwAARIfjwANLqECBABIvDQCEBFIDYDBcXhIoWQDJvNhpCMkJAQBGMgHFwgENxh\nppgydFit1kDqKhg6GhoaAoYOnU4H9QJDR0ZGRiCOHE3d0cJDmyp1TASIABEgAo8iAZIfj+JdpTkR\nASLwiBKA9oBgKC0t/fDDDy9cuAAdgiNQCyqVav/+/SdPntywYQPUCA5ig/CAoQNFAKE6sHV0dEB1\nQHvAvQoXhoWFIaIDQeRQHTExMbjqEWVG0yICRIAIEIHlRYDkx/K6HzQaIkAEiMA9CMCCcfHixX/5\nl38pLi7G/tSZUCCwe+zdu/enP/3pli1bXC4XVAoqAyJPLnyr4GEFOwlcreCgBbGRmZkZiCOHAiFD\nxxRD2iECRIAIEIHgEOAEpxvqhQgQASJABL4+AQiJjz/+GNaP6doDzcLWAS+sa9euwQyCgBAID1QG\nnDJ0aLXaQEQHVAcctFAo8OuPhFogAkSACBABIvBgBEh+PBg3uooIEAEiEGwCsGlUVlbC+gHXqVn7\nxvG8vDw4WSE3LsLQURDw8OHDAUNHeHj4vQPTZ22QDhIBIkAEiAARWHQCJD8WHSk1SASIABFYEgJm\nsxnOVLBywNYxawcI9oDdA+mqXnvtNYSSx8XFKRSKWc+kg0SACBABIkAEHhYBkh8Pizz1SwSIABG4\nPwLwqoICQbGOe1yG6A5Ekx86dEgqlbJYLAgViu64By76iggQASJABIJPgORH8JlTj0SACBCBByGA\n4h4QFRAYUBRzGUAgThAfcuXKFSSzUiqVCAWBMQRuV7gEauRBeqVriAARIAJEgAgsKgGSH4uKkxoj\nAkSACCwZAWiPgKhAVqtZ5QeUCfQGcuy+9dZbarUa8R4o6IEIEOgQbHK5HCU+AjqETCJLdpeoYSJA\nBIgAEZiHAMmPeQDR10SACBCBZUIgkDY3Nze3r6/vjsxXGCFMHMhwdezYMYSbI8cuSnzU1taWlZVB\nh6CSIOJA4JQVERERkCIwicAYQiaRZXJnaRhEgAgQgVVFYE4L/qqiQJMlAkSACKwIAna7/aOPPvrH\nf/zH3t5eBJrD1SoQ3cHlcmH32L179xtvvLFz504IFWTBQmX05uZm1P1AEt6BgQGRSAQpAgUCk0hi\nYiJ0CC5BEl5cG5AiZBJZEc8ADZIIEAEisNIJkPxY6XeQxk8EiMBqIQClMTw8jLof2BCGDoGBYoLI\nxouYkMjIyE2bNp04cWLHjh2QE9OJuN1unNbS0gIpAnsIfuIIhEdoaGhCQgKkCHaQIAtqBPoEOiQg\nRaa3QPtEgAgQASJABBaRAMmPRYRJTREBIkAElooAtIfJZLpw4cJ7770HZ6qjR48ajUZoCeTCgnhA\nVUE4ZUFI3NuCgZNRFWTKJDI0NIR4Eo1GA5NIenp6fHx8wCQSCHAP6JB7N7hUs6V2iQARIAJE4NEl\nQPLj0b23NDMiQAQeIQIo94Fi53/84x9h9/jOd74DK8fXKSPodDqhPWASwQaTCH4CFVyzED2CUHVE\niWAHUgTCBhUMJywi/kCRRwgnTYUIEAEiQAQeGgGSHw8NPXVMBIgAEVggAbhLNTU1we7R1tb21FNP\nPfHEEzBQLPDaeU+DFQXNQoHU19cjSgRh6wgIgXdWdHQ0ahfCJIJ9WEgkEgnZQ+aFSScQASJABIjA\nvARIfsyLiE4gAkSACDxMAnC7gqXik08+gefVli1bXnrppaioqKWwRSCuHTm1oHOQNauqqgr1QwQC\nARy6EFgCHYIoEegQ2EOgfBBtggEsxRgeJmjqmwgQASJABIJCgORHUDBTJ0SACBCBByWAkI+8vLx3\n330XqgOJraAEYIV40MYWep1er0fKLGw1NTUwiWAMcMdCiAiMIRgAyo9Ah2BDtHpAh5AUWShZOo8I\nEAEisOoJkPxY9Y8AASACRGAZE0DIR0lJCcoIwv/qhz/84datW79OyMcDTBQD6OnpgQ6BSaS6uhrJ\nfBENEhYWFnDNQpQIQkRQ0BAmkUBNQ9IhDwCZLiECRIAIrCoCJD9W1e2myRIBIrCSCCCpLhb9sHtg\n0f/CCy+gniCW/g9rAigzgmrrExYRv0kEgSIofQjXLJhEUEUEJhH4aMEeAilCJpGHdY+oXyJABIjA\niiBA8mNF3CYaJBEgAquOAEI+EInx5z//uaCgAPUEn3/++Xnz6gaNEYRHZ2dnwCQCKYIaiAhMnzKJ\nIHcWokSgQ3AQRUgC3llBGxt1RASIABEgAsucAMmPZX6DaHhEgAisUgIGg+H06dMffvghHJxef/11\nrOmDEPJxv6xRdh01DQPR6tAhECRI6RuIVseAUUskPDw8YBJBFDtFidwvXjqfCBABIvBIEiD58Uje\nVpoUESACK5sAzAswerz55puwHvzkJz/ZsGFDkEM+HgAfahoiWRakCELVUUtkcHAQCXxhEkGcOlyz\nkpKSAlEiMIlwOBwyiTwAYbqECBABIvBoECD58WjcR5oFESACjw4BGBBgSXjnnXeQb/eb3/zmgQMH\nsF5fQdNDyMrw8HAgSgTR6hAkcCSDDkFwSEpKChL4wjwSMIkggW/ApEMB6yvo/tJQiQARIAJfkwDJ\nj68JkC4nAkSACCwmAUR4I6zigw8+KC8vP3jw4NNPP404ipW7OkfG3tbWVkiRgElkdHQUwgNSJC4u\nLjMzE2l88RGbWCyGeYdMIov5JFFbRIAIEIHlSoDkx3K9MzQuIkAEViUBFB3/4osvPv/887Vr1772\n2mtYoK9c7TH9BsKkMzAwMGUSQZF1mHQCUSIIbkGUCOqKQIcEahoGyqtPv5z2iQARIAJE4JEhQPLj\nkbmVNBEiQARWPAGLxXLlypW3334bC/Ef/ehHUCBLGG4OjygAexjixmg0Njc3I6cwTCLYxsfHYeGB\nSQRaCyaR2NhYRImAABL4BnTIwxjjin+WaAJEgAgQgWVLgOTHsr01NDAiQARWFwGHwwGHK2gPrM6/\n+93v7tmzZ4nCzaE7fF632aAz2zxSTaiEz2Y+JNJ2ux3JhQOJs6qqqhC5jgRZMImgvjvsIYgSgSyZ\nMolAhJAOeUg3irolAkSACCwmAZIfi0mT2iICRIAIPBgBZLBFjMR7772Hcn7Hjx8/duwY6mY8WFP3\nuMrrdjsdWPNbxvo7bxYW9NmFu55+OSdK/tD0x7Sx6vX6gGsWwu5hEkHQCNyxUNMQJhEkzkL6LOgQ\nbFTTcBoz2iUCRIAIrEgCJD9W5G2jQRMBIvAoEYA1AqmiPvnkkwsXLuTm5p48eTI6OnoJ3vT7dL1d\n9ZU3O4f6bhZcu3K5JHr30Z/89/+xO0nFYT0s+8cst9Fms/X09ECKwDsLibNQ8R213uGaBSbQIQgU\nCSTwRZQIj8ebsIgso8HPMh86RASIABEgAjMJrKRkjjNHTp+IABEgAo8IAYR8FBYW5uXlITXtkSNH\n4Hq0BNoDrDyG/rYbeWcaHWyby8cRIvBjOQKE2EB+XmxHjx7V6XRTJpEbN25cvXoVrlkwiSQmJkKK\nABfsITATkUlkOd5IGhMRIAJEYA4CZP2YAwwdJgJEgAgEhQBe9peWlr711lsol/GDH/xg27ZtSxTy\nMTEbhJvjj7cl/8N//3//sSdk31/9t2BYP/zRJj5/lDvTL3j8+4h6Z95P2DvqMCIfccAkAu+s3t5e\nlC+cMolAq0zVNEShxomOlqW0CsoTRZ0QASJABJY5AbJ+LPMbRMMjAkTgUSYAyYEUtKdPn0ZAxnPP\nPbf01c2x/scfj9ftdTuDBNZlt+jHdRYnUyyTy6V8t82sN5icXrZYKlfIJdyF+X3BvgFzBzYEyYyM\njASi1aFDYDVCrjDoEJhEkpOTEbAeHh4eMIkgij2gQwKiJ0izpW6IABEgAkRgPgIkP+YjRN8TASJA\nBJaGAKwACPm4ePEigs537dq1fft2+B0tTVd3tgrTAPvOY0vy2eeydDXeuFpQPGDiKCPjspPDrf3t\n1U0dBpM7Knvzvv27k7SShQmQW8ODaQhiA9vu3bvNZjOSZUGKIFS9trb25s2bMpkMXyFOHUIlKSkp\nkMAXNQ1RY4RMIktyg6lRIkAEiMD9EyD5cf/M6AoiQASIwGIQQHKngoKC/Px8LJRR4BxRDY/ce3qf\nebj5woUvr3fZw3hOxNZL1JHhCo1I6B24kXeptpsdmhK3O4nHeUBHKfhfrZnYTpw4ASEXiBJBtDrs\nIdgCNQ3hl4UEvtgPmET4fH6glMojh3oxnkhqgwgQASIQFAIkP4KCmTohAkSACMwkgJCPkpISrMhh\n8Xj22WfhOLSEFQZndn1fn/xRG4jVWMA2m3nB1dtU2z/m2vb0NyM6LhR/WeyIjDzwgx9uZzX8ydVa\nwdXy+ZxFCX9HvAfC0LHt27cPog7WJEiRgEkEcTUQHjCJxMXFoaYh0vjiIzaYRGBImW3MC5gqnUIE\niAARIAJfgwDJj68Bjy4lAkSACDwQAYR8oL7HqVOncPUrr7yyfv36pQw3f6AhBi7yOPRjI8OjJh+b\n7Y8Wn3ODQOFIFarQEMWMWA6PbdzsU0kTNmsZZYXtY+LYI9sPPbU5jjnq2/n0d9eJYrZkh3MXu+YI\nEvKum9icTufAwEDAJIKahufPn7906VLAJAJ7CDbUFYEOwfkBk8gKtYfMEIdTc5g4OvVpzvtGXxAB\nIkAEHgYBkh8Pgzr1SQSIwCom4PV6kbgJaXZR8Pvxxx/ftGkTIhOWJw+PXVdXfOGLi9VuHo/huYeL\nlNvLkWRv2vvU0W1KwTRXKiY3Mj13Z6RP6uzub+0IS05Yuz1dBlNFVOrBqNSlnjKqgsRObHBsQyH5\n5uZmFBKBSQRbcXEx6qkjSD1Q0xBnBaJEEOAOG9QKMYn48CC5HHaL2WK1OzxeH5vHl0ikEpGAzfJa\njGaniyFVyWeowaUmPtm+317m882P0ed1Q4i7kU3AzWBxeAIhb7G16OSI7vnb58MAXG7/OJgsDrQo\n+76ike7ZNn1JBIjArASW6b95s46VDhIBIkAEHgECKO+N+hXwvMrOzoazENbBy/ctNZMrVWjj4+K9\nXNY9vaS8LoZYpZJz4M00/Q6xRLHJ6bE+W/mZwraWoZDM3OQoJZb3008Jzj5C0jdObMgwBtUXSJwF\nkwgMUNCBcM1CrRVkzYJJBLdjyiQy/wI6OKO/qxefD/nDDP3d3e2tbV09/WMmB5vH5vBFSk1MdlZm\ntNJeU1Ix5gl77OgOtXCaGryrnSU54HUax4YHR81ChSY0RM2frigm0i1Pdeo0Dne01DX36lH3RqSN\nydq4OU4lZE19Hawdr8vU19VW19JjMlmlmqjs9evDFaLpow7WQKgfIrCKCJD8WEU3m6ZKBIjAQyeA\nlRZevWPVi2UuAqYRkLA8Qz4CoNgizdpdR9bu+lrYvHZDX99gi16zVpMRqRQ8DPVxe/zIxouShdie\neOIJ6MBAIRFkzYIUQRoAuGMhgW/AJIL0Wf4YEYViudU09LpsY4Md5UX5p05frWob0YTFxMUlpyTJ\nxoc6vziTfzUkITvJWl7cGLb9jX1MX/ClntM4UHr+Lx+cq07YcujkyadjFfwJReFz2h0ut1cgFk5J\nVOt4V8XVjz86X9vW1hG97dhPYrJiFILgq1Ovw9BSU/jeB2dampo12Xt+qI3XSIXsoKu228/o8t7z\neZwOp8vDEohgqwr+47W84dDoFk6A5MfCWdGZRIAIEIGvRcDhcKBUxRdffAFvq5deeglJmx5OyMdE\n6cFAKMfCosq/1qxthpH+oS6LSh2aFKcQLKPXypAWWyY2pAHo6ekJSBEkzvr8888RKALXLJhEkMA3\nNTU1UNMQUSJw6Hq4JhGPw9zVUvXlB+9+daHEHbnhyLdfe+GJ7bFaqX8h6La0lJ556xe/fPM3rfy0\nnZvXJEk4wcmuPOPxsIwO1FcU5hdUmgSafUcOx8j5E3YzZ09rY++wbe32XAXyDUxs8pgNT30/KT7x\nw1//8z/r2YzgC4/AMDiSyN1HvhkfHffum7+odgXf+jKD3vL/4DQMNHX2WCQp6xO00x0tl//IaYTL\nigDJj2V1O2gwRIAIPLIE4FmOut1nz57V6XTHjh2DK1DwQz58HrfT/+7SbjJb7S6fxWwbGR4xhHKF\nfB6Xz1+aOAGffmhosL1Do41LToxG1ttleIORfAz5ebEdPXoUdycQrQ6heOPGDbjJIVodJhEYTCBF\nkFwLokUul9+vSQSxEAjVCMwdAubBTF4+l7WrqfS3v/3dZ5cbkrcc/Mbrrx3dnCJkTyLliJNz9x99\nerCx+VfuEFVqnJb9MGiLtZGpuXu2DilSMtZoJVBrE5N2Wxoqi6/XGCPWr5PxOYER+wMtxHK5TC7h\nsfQP8bEIjEOhVsikrPFJmA9xPMu7a/NIa2HRNX2MLDNaJViuQWvLGyGNzk+A5Ac9B0SACBCBJSeA\n1efY2BiKUeDlem5uLkrmYQm75L3e1YHDMt7SUN3Q1t1RXTFs543195z/7M+OjsSI2PiMnHWRcsES\nOFO4jSODI91jYUmbkmJVD+sN910kZj8AVaDRaHZMbFarFXIxYBKBFKmsrDx37hyiRKKjo6FDoFUC\nJhHUHkEs/T1MIrj1aAqV2vEAIPIE9hO1Wg0vL2T+vU8R4h7rqvvkD3/86GxV4ubDr3/njSO5cXfm\nDePKYxLiszPjelWx0SGy+1UfGOokF0yIwcBn/yH//sTnyS8DvyGoJs+fPn2eLGrfU2/sOOJmcQXC\nQOSHz2cz6sZGRm0+CQs9TDQ52RY+T6iywJPnF2k4MnuPtzuc3p9/lLca8182NeRbumeyn1l/T1yK\nH/4L59xud+sf192n+hvx/xf4DnuBKeDmztGq/+Rb20Rr+IzNv3t341M3YeKCiS6mhoqLpjUz0cit\nQUw2P8fve85oop1AwzO78zfmc9vHRnRjwxZJrH9yE7OeoxM6TATuSYDkxz3x0JdEgAgQgcUggJCP\nwsLCgEvPkSNH4NUz20pjMXq6Zxteh7GzsfLc5Womm5O8/WAay2UyNl/Oa4pdu1WdkBkuWwrXKCZf\nHBKbticxa0eCVvhwAz/uyebOL2HfgMzA5na7R0dHA9Hq0CG4j5CR0CEwiaBaCwLW4aYVMIkgsCSw\nYpu6uTB5DQ4OovYIrChlZWWojQjRsmHDhj179kCFQsks3ALmMo/cuHgu74t8kXbt7sefemxDzJ3a\nwz8DplDEk2qjlao0rfiWkeHOic3x2et22qxms9Xh8jAEQqlczLaajAaL1cvgCMVSmUwquJ2XCimr\nnCbD+OjouM3hZvGECpVGrZLxOCym122zIheXzen0sPkitj/1ltdmHKsuLLief90Rsc2AgBuGgMvj\ni0QIArk9FOzi0vERu9liZ3K4QjGSBdzuEetlp82sQ39Gq8fHwui0Wo1UxGMxEVJiM5vMDqebyRVI\npGKP1TBusLIFUqVKIeaj1P3tLmbuYQoYqtloNNmdHkTtu20Oz+RqfupMnIRTdGNjRrPdx+JJ5Aok\nJhADxK3n2J+6CycYkGfMw+ALJWIRx6Yf7h8Y9PAU0fGJIXLRXRLEiwEDkMPp9LH4Ermc47UbDQZ0\nzuIJMGeJWAiKtwaADANOm0GvG9MZHG4fTyhWqtRymZiHDA+MqXZcDA4mLvLYTHq9hSmQKJUKiQB6\neGoSM3YwYrvFOHprRlyJVKFUKaVCXuC9gH++JsPY6JjRYmew+TKVSq1SCHkTZlEMxmUb7WrIv3C9\n4ObYjmS9waD3Ovl8oVDAvQfnGb3TByIwRYDkxxQK2iECRIAILAkBvPMuLy//7LPP8Ob7hRdeQPG7\n+3ztvWijEmkTj732n469tmgNLqAhTsyG3d/P2sLiisSCFfkvDhQCxAY22KzMZnN7ezukCLL3Qorc\nvHkTS0Z8hTh1CBVUrw8k8IVlI6ArkGH5DxMbdvAYBHDBkIJ0W08//fTrr7++0HKTPvdAc8316/mt\nbu6GtdkHdmeK54jrYLNZ4ckR8ugEKff+Aj/shqGqG9dLazr1FlZ0ckZWgqirpX3YZLHZnCxB6IbN\nWzesTYSXFJa1Lpupu62usOBGc2ufl4OEzDxpWMru3TtzMmIEHkNrTenVwpoxvUUdn/X4scOhvtHK\ny6fe//hU3s26SAvny8+1UVJhaHxS7qbcELhmTT5ADouxvbZ40DAwZLB7GT62IOx2jz63cbSnpqyo\nrKJ5wOCCwsFCPG3z7h2b10UpOf2tNdfzS7pH9Ux5WGJSnLOnrqqxm6NO27Vv/84N8WL+rBB8Tqux\nt6O1qqqub3DMw2QjjprrNXQMGNw89eSIGB6ndbinveJGWV1Tu9HphgyTKFUpmety1mTFhCm5bJbb\nbu7taCy7Ud09MAbjBZcnkiv4pr7qwuKSYXbKy9//6fN7MyGBphr073gdgx31RddKO4dGGLLwtOws\niW2oo2vAbLO43KzQpDWbN29MjFD6FYjPYzGOtNVXlZbXtveNM5heLl+iCk9EjaCstFi5wD3ZzphP\nqo1PjPcMNNU0djEUibv27t+1KWnWiSNpwWhve0VpUWV9y6jDC49LqUSRmL0ld2NOlFrK8jnx7Y3C\nkpqmboPF4vAw5SER67ds3rA2K1QhYrotva1Vpz748KtPzzR7Nbwr51SWRoksJClrw/q0CAEy49FG\nBO6HAPtnP/vZ/ZxP5xIBIkAEiMB9EMC7c1ScQHVzvPl+5plnsIRFYYH7uH7ln8pCSlg+H6/GV/5U\nGBCQCAWBgNy+ffuuXbuQOhl6A45V0CEIFIGuQGkRmDsQyw6xgRf9iGL/xS9+0d3djcdgavowieAr\neHbBawvpBxB5MmUtmTrnjh2fc/zGxbMfvpdnkifvPPzCsV2pojl4sgXKiNjM9MQomXDOV+B3NB74\naNP1VZVcPXfhctHVwoamttbupn4rJz0jzTva8Jf3/lLX50rOyYlQCLAMba+7/utf/vajvNbE9btP\nHN/Bt3V98t6XN1qssRlp4WJHQ3Xxp59/VZifN+TmpW3YJhpvKrycX99rshv1Ti/XZdP3dXU4mbyY\n1CyNGGrGO9pRe6Mgv65vrKun18KSr1m7ZqLHP9/qUc6zjXfmf/abN996v4EbLjQAAEAASURBVJsV\ns/fY8e0pwvbCz7+8cMMliUyJDxlpqbl86kx+SeH5/MKSirru7p7erqYrVxtsbvXGraly0W2FMzVr\nyKeWivw333wb6bnEIQkbNqZ69F1nvjh9o6JFGhm/aceeeK2U43MMtld99Lvf/+njUklcztGnnsiO\nZHfe+Or0hesDDmlMTKRcyBpoKXv7P37zhy+r5BFpaVGCpqufv/feFyMMUUbuJrtdoNAmrEmLQCmT\nqX79O157T2Pt1TPn8ouLCqtqm9tampt7uOr4lAh2c+Gpv5yvtIqi1qRFi3gch2mk8urHv3/n3bIh\nXs6eJ47szmGZe7/4y1fXS/vkyIkQJhxprb96+mx+8bW8a0XFFXWdnd293U3XrjVYPaqNW9NmmbjX\nOdJV/9lbb/7+/dMOZcLeQ8dyIpy1V/70lystbE1qRkIow9Kf95ff/6/fXPJEbPnGC3vDOMb8Ux+f\nL6zla5MTY0M5rvGa8qKCylqTx6o32dlel36kv2vExpVHpieGCu5T685gQh9WJQGSH6vyttOkiQAR\nCAoB+FEPDQ0hzS6sHwgoQLJXrFbnXWsGZWjUydcigJRlsHskJCSgaiQ8qfAzLi4OugKGESRWxu2G\nbeT69etwt4PMwPE7OsODAYmCYHREtOPCea1hdl1H/tWzX5U1R2XmPPncM2uiFJP+P3c0zGDzhBJ4\nTiEn0ZRlAT76XtTUw/v7OcMR0Apfps3I3ZYVxu6vuVozaE7ZdPDv/ubHO9dlI7+Rpb+8ctAZmbI+\nO0ZpHa7/9P3fvX9p4LGnv/WTv3omNTY2MTqUa2k/X1DrkERv35Sdmb0uK5TVX1NsVSRs3rV/TUbG\n5v0H14QxBmsbQ3Kf/8d/+h/fOvn8nq25oQFLCuOW/Gge52za++Tf/O2Pd67PStDwp3rMiuDVF37+\n5m/eHVfv+vZf/fj41oyY+ASNhN9ceL5uyBK7bm/u+vU7d2b6rP2FpS0xa3b+6L/97c409biJlb4m\nZ/36BCmfOw3DBCufa6Ch5P23fnu+1rL/he/99fefXZ+ZtjY7K0nO7GuocSojN273yw+3vjPvkz++\ne6ox/fGXfvCjF9ckRkXGpyUlROrbSi8W1XvlsSnh7PIrX73zUWXqjhM/+dtXd23JVvO9HRUNrKjc\nEy++8vS+TTlrEjUyeBvO7J/FD4lN3b5zDceru3at2iGJP/Ttv/rB8/vXZaXwfI6KS+Uernbd1jUq\nga/95qV3fvtumzf51Td++OK+nMjwyLTUlHChpfjS5aYBT0L2mrXr1m3fnsV2jZSUtYalbXrjpz/e\nkx1ptnJSMlG5JPHuiTuM/fmfv//2+5cUGx//wV//+LGN6Sqeo6e1Nr/GJtEkb90Q7x5uvn7h86Ie\nVnru3qcObcnKTBLz7NculhtdinVbstQKdULq+gM7cthOXYtJ+fQb//Uf/u6NF47vz02PJO1x5/+E\n9HkBBGaaBRdwAZ1CBIgAESACCyQAXx1Uk0CoAFaZhw4dwotz0h4LRLeCTkNC3nUTm9PpHBgYCCTO\nqqioKCoqgqfWdLvH9Emh3ndAosAgNm8EiGVcrx/s9/m4IeqQhOg5I/iR2czjgZcQ93b8gL9Lj3Gw\ne2B4XBKLuivCmcvh6SPCPhbLLLyuD0nduOnxp2NVCF1giBQqZXiUp9vtdCFG3NnTWF1zvUgdsylj\nQ6rQYzebHAyBLDIiKdpYYWxvH7fvlvsru/gbuXtDpLI/sHy2LTJ9446DT8cpRbCRTe/Rqe+rqq4r\n7JDuSE9JiZAgWgMebMqI6OQ1STXNtubusZ0JSpjXODweSxSWnJS9KSNFmha7fpuTJ5bLJIGSIzP6\n8zrGGyrLrha0xuUefOJQbqhU4LfK8eUxqUnZa7TFVv8nJsPd21xXVlDAi0rftDc36la6aHZobMba\nzTvOXPmg/MqV7RmHdYYxk1guiYpRIsyGy5ar5CFaZ4N1aNwt3JkQJ5zbH8lPGXHmfO2a9E1HN0Aq\nYCUmVMsV0QqGk+HCDXSZBmqrygqafTkHN+RmRfEmbAsowpOVs27X+gsfN5VfLdudFa0SstmYOFMU\nGp+wJjcjVZuTsn7rkzwRIkgmJjVj3r7RrqbK8sIRRUxu7v70GBUCeRx8WUh0xvpsZ1KERgCnPTFs\nP8nZw45IGceOuB+2Vy6Wp6gcNteoyeYK3Df8nOsOzuiNPhCB+QiQ/JiPEH1PBIgAEXggAni9jdLm\ncLuCd82zzz67UC//B+qLLloOBOCaFTuxHTx4EDrk17/+9c9//nN/rPUcGxy0IFBhCZnj+zsP+5hy\noRBuP3N5VXkhM/qGdNL4rCjl7SRmTsvYzfMfXa4c3v2D/x4mF8xUJnd2EfgcohTGh8sD4chcHo8v\nEDKYZv9XbsvQgK69y20LH+luLC3x9TD8GZtcnf1jXDWsQf4MYLO3OHEUS3v/6n4imxN+Tj9ZLhOG\nhsiZWJUzGNN7tIyPjA90IfJ8dLi3pKRIi+QILKbL0Ku3s5H/mI+kw8xb9EQR0dq0LCmHJ+IKRWJ/\nL7NuLot+cKx/gKWO1aZHKG9nLWZzcZ2KaZ/QTR7zQN9Ac7NRuAZ6D1E2k5PiiUNU6jiFbczWP2px\ncTgCvtvuNOmtDrdH5IP4dLoYXC7bn+/rnhwCA2NK1OKwaOmt2hlwUOQhF5qX5VcmFsPwcH+7TSAQ\natXCaRkGxEqlOjLSVNrX3zti97iFEw0JwyK1qRlSnlAk4opEs04aB52G4aHBjiGlNCwhSuvnxmBI\nw9MPv/LTnc94BBK5P1qdn7zn+Hciszr0FnNJ/kW7xdLdVNUyPCoN6Rs12jweBefuUoyBZ3chs51r\nXHR8tRIg+bFa7zzNmwgQgaUkgHfbCAOA2xX+gT558iQCRh9OhcGlnCO1fQ8CMImEhIQEcvLOJTCw\nbFvgyg2rWf/S3Of0em14Oz7r5nOZ6wouXizr3vX9pHDFrVWtz2PvrS8+dymvjZu+L7D2n/XimQeR\nUdeDt9wTeVX9Jotp3yIuHJ5kdv1IT31Fo1ePhLF+9SRU73jh2cS0bBnfP8ppp8+y63Mjm5PJ4RWE\naKUTcsN/zlw9+tv3MJkcx5ilt7KuPkp0SwpoEjc9E5q4NlqBlX7As00qEYWp5BzmPJN0OV0Om50p\nEt+xsp+uF5A6F1Ykh8/L8fpf9k9734+bwELMP07mijWRobHxvgt91cWFpSk5kb6a6uYxtyQ+ISU+\nVH5bscwCYPIQECPL2Gy0PPjrw2FneHAbJk8O/Pb3z2LCsQ7JqCY1kUwiDFPJAopi5tnTPiFVmdVp\nM/rEKuR9FrEDFJkcoUQplPjTBjOZPrvdOtjTWnjui6KKRpc4JjUlRchkO3Fr4LvnwyPgfxqmtejf\nt5mNyHImUKglcHKb/uW082iXCMxKgOTHrFjoIBEgAkTgwQnAp7+vrw/ZjVBL+/HHH9+8eTOWoQ/e\nHF25AgnA5IX6Hvg519ghPFAzRKVSLUSB8IQChHT4Q0SwEpwsXzijZZ9H39NU0dpt0KQmhkwu0hle\n02DHzdKKui6nPGHG6fN8mGspyWShQiX8lURrtpz8m/97b4p6MgDeZ7daHU6PyJ+cdzLIfmYjWGZD\n0KBfj3nw5rWbPfao55/JFfMmBzLz5Mmj8CPjonwIUxK3effL/9df74eqCnyFAhQWf7Zaf3DFrSW6\nXyf5V8l+56m5Nx5SxYolDFTfRDJdt19Z3D57Ys9/O5gcHp8nErCdWP9PV3se+/i4YWDY4wtjMjhi\npVyVGiO+YRq6/NmnLTLnQHtn2JZjTz77Ykqo7G47wdwjmuUbNtzn/EmcHT4GInb80AKbP0fv8JDX\nA0Hm8w92YsnvL78CJeNXgXNvSOsrF0nVrDaLbWjI6EGO4UmQDpsNuZaFIk5PfeHvf/mvV5pc2w6f\nfP2145lxmq6Sz2yNV9o4TKTMMhl0XJFcMNEJfkz89gw1VZdXt0XtOZITq7mdlnnuUdA3RGCKwDzv\nCabOox0iQASIABFYIAGDwZCfn48QZBSF2LdvH5ahC1liLrBxOm1FEICxK5CNd65EZ1hXI4MWpOlC\nzGJiTVRcamaozDUy2tvSNerConTa5vO6TGOdhecvtHQ71+3aESK6FW/tsupqb1boXZJjxw5G8qdW\n+tOuvGsXi1isLLEADyxtJ76fMIP4V5xMBlsUEh4eH68YG9U3tvQ5JkPqYXhpqSm/er1MhxU9zpxs\nZNobcVztdHus+A+1JXp7BurbjC6smaedPGuPIpU2PCZWYhnTdTb16223SnP4vMaBtpLC/NK2Mb+V\nxr8x/Yvwedbg/vNQjUQmU8icY/q+hj6dxR1oEbH5bpfD6UITXo+PyRGFhoUmxcstNmvvkM55a5o+\nWCTMFouVJRLJQuQCD4qGMGRpz3zntRef3Zqzc88r/+Uffvq3P9qeETXvQvzWtPGXwpT15xYzQPYj\nESlCwiITRCg6MtyntzhvjRFmI6vJbLaIxUJUWWHfiq/xW5/mEx/+eUsVMk2E0mo09nT2odZJgJrP\nbe5orLheWDI0PtjYUFdU0Rebkf3sKycy4kK5LJfdghAQL9PHcul6mmqK6wcMTrf/OhdKpqCwi89r\n7u/vrW3UW25Fhvj50kYEFkaA5MfCONFZRIAIEIGFEUCFQcQcf/nllyhF99RTT8XHx8+b12hhDdNZ\nK4wAkurC7w710RETMl1/Yh+x5hAnhw8fhvyYN+4c02YJ1ety1j+5OQrVHf788bnazkGz3YWEWshn\n5bAae9rq8z754Hxxc+S6xx5fG3krWABuVw3V1X02+aZDUWESRGwHVpxzQUTYugMlA20OJ0KfnS6L\nyWyzI5gB+XLHTUaj0+EwGA1mm0cTn75x12aWru/6hWtVSLzqxLLdOthaffbMV5er27CCd1pvNeJ2\nufx78C/C4pcHI4YdGqK9T49Vu4PpFEfKYcpBpcN79+hgSZKScnLjOD2d168WV48YLNAJFuNwRUHe\nqbOXOk3o0IGyg3iDD/PL6Mio3oiShxNr5DnmyRar0pLX7khU6vvrSkurh/VmF0oHGgaaG6ub2gd1\n4+a+gWGDxaWKSV63NZdpGS2+XtTUM2zDvGym9vrKgpvVZm169qZ9ySFSFs87ahlrb+9z+ARymchl\nGWmuraqqbegZ1FlvSYa7BwHM/knbHXBrmiAEq5HD6bCaxvUmg9Vug2Oa0eThypOS1q4PZw23FRXd\naBzFAt/lNI50VJYW1LTp0tZk7tuaLGB7/PfIZkdrY2Nj40YzSkD6hc3sG0sdnbZ2yy6FW1ddcLmk\noUdvxZTso50NF89+fr6symQwWq0mK3KjIYieCfuKy6zraW1p7BzCM+DQDXT0tDfDWgTXMy6Hb8WN\nbOkYNxhNbptHIpAj8n6a0Jy9fzpKBGYSoMS7M3nQJyJABIjA1yDgcDiqqqo++ugj7Lz22mtbtmxZ\nyOLya3RIly5fAlAd8K3CA4Ci6Qgx96/sJjZ4ZCET2ksvvfTyyy9rNJrpymTuybCk6hBkIkJxvvqW\n+pZBK4rEGcbHhvo662rK8r786kqlOePAyVde3B0iDpg+fOaRtmv5NXpmwon9sX0N1U29xvQtu2KV\nwkBA+d0dIUK9pa7scv71mqbOcRfP5xVJBHyGfbj47Ff510s6hx1el1shF4bFp8eGStjGxrKm9uZB\nK99r6+9oKLh4tWaYt/noiRyVq60ahUNQuq7T6BYwOXK5VBmmVbDcpqHepsp2o83HsXTUtfaOp+3Y\nm650tzfcnKdHpTwxM0uj5TdUVLS3tNgYHJtuqO5m8bWSenli7jMH1rqGOgvz8lCSsK1zxGWzuVkc\nJl8aqpIgJuTuOU4c4ShD1DKVsK22ur2l1c7mIc67vvzKhfNf3WzqHzEiltwjFvG1sSkxoTK2rqmu\nqa1zzO5zWXqbqk6fPVfQYN657/CLz+yNkCMfcXNNwanTeZcuXryUd+7cmVNfnD57pqiifmDUJRBr\ntFoZFvJ3DgLlRHpai/KvlZZXdfaOclhekVSIuInRjpunz10prm6zw9uKo4B1JS09Vq7k1lc1tjV3\nOBk+02hvRf7p8xev2iN3Hjzxwt7sMMtgZ/HFi4WlN1s7h50WqwfGEJ4kRAUpMPtrZY5QoVZIha7+\nqsaOls5xJL4a7W8rvXS9upuRfeD41tRIl8nQ2diEeucoY+8yjdTevFZWWT5iZzrtbsgcl0eQnLMx\nRiMyj4803qxFaXku29LY2mGQJO7cukY5W32VOfjTYSLgJ0Dyg54DIkAEiMDiEMC76I6ODlSaQ6mH\nI0eOPPbYY6K5M9EsTpfUyvImIJfL4WEVFRUFKRIZGRkdHQ1jSG5uLoTHK6+8gnLpC9MeE5Nk8UNj\n4lIzYtg2XVdzTVnZzYqq6sobhaXljaLIlJPf/+6JfWsVyLk0ca7PZai+llfb2pO5e5vYY2iprmzs\n0mlTsyJQEUQwUbr8Lm5uy3BFKer/9cuiELwgHB80snkamXes9EqJRRSSHKnhWgadbF58Rk5cbGJy\n+lqZz9peU1Zxo6S8stYhizl+8uVjGxNcurYb+WeK2oyyqNQQqUA3YuZJojLTw+RY+kqkrpGutqb6\nxl5v0trDJ/ZnCz1jeJ0/b49JOdv8pTkS5ObhtvIbFaWlZXXtVlQCfPnkkRgFq7e24vLZfD1blpIc\nJmKMdw67hKEJWbEISrlz5T81Y/hWhUfHpScoYbq4WVlTWlHd2OdCkcCcRNQy53kto3YvKyIhMzkp\nJSVjjdhna6wsKy8pqahptPFDDr/wysvPHIjTCE29Lde+vHSz0RyTs27bju2563IykmJD1WIs3CuK\nKrvG2Bkb0jSIkrljFF5ra83Ns2cKUF89KSFMzDZaXCy1WjbaXlnRqVfHJWlkolGjg6+OyUpNSEpM\nyYqTmsaaykrLSkvLW3osEdkHXn3tlcc3Joq4nr766vzz10Z9wsSJdrpHHXxNbHqslj8ZjjM138kd\npkgZHpecqeV7BttrK8tLb5bXjHO1+55+4fiOVJlIogkJjVKLjUPDzQ3+p6tJJ8p47OSJ7dnukWGd\nV5aw8eCeDQk4TaVQyMTe3p7WxroGnyx6/+EjmTHaadm5Jnuj30TgngTgNTinre6eF9KXRIAIEAEi\ncJsA/i5F9evPPvvs7NmzGzZswOIS3jX3sbi83RLtPYIEkJQVNhCTyYRwc8QCCQSCB54k4oD1Y8MD\nQzqL3c0VSkLCwrVq+R3xBm5Da97pjy5XjaVn53BcptobhdfqdWsOPvvSgR25OUmiibyrDzyAWxf6\n3E67zWJxeJGzViwV8mZ/6T6tFx+8feCa6GPxJVLp3FUxpl0xc9cNByj05/Iis65UIpy3v5lXz/IJ\nflBWi8Xu8vEEIomY60E8usPNhlLkoXDKZPM+j3+aVqcXeaJEQr+pAorCayr99IN/++dT8j0v/OSv\nn0wIgbHF377Xbeutu/bOm7+utIR96yd/vz8z8lYCslk6X+ghv7OWxYKiKxwBEgoLZ5STXGgbM89D\nGmM7wjpsXibb3+KMpFWYLZhYAZktEEql6A7B9y6vl8nl3o4dQqSMH4l94kYI+XeZeGZ2R5+IwGwE\nKPPVbFToGBEgAkTgPgkEQj7OnTuHV9rHjh3De27SHveJ8FE+/f+w9x5QcV5Zom7lnANFkXMOEgKU\nUALlaEXLlu12aLvtid13pmd6bt+3nte9b9213nuTuns62m5ntywrJxSQEIgcRBA551RAQeVcdxcI\nhBCFABEK2GUs/vrD+ff5zk/V2WcnmNJ6eHjMSw9JUJ/C3Rd+pmmNzPbctPut4AQt5DiyGQfBUadl\ngJoYHeYj40Pa2GkunMUhSA/FhGoT3BlfQqTSmAKa01RgL2yHQqVxBbSZ3++FDULRPi7/aYNkOpNG\nf+4ioqOO/GSpbVaD3WKVcsQ+IibFDsnI7KNmDvA2E4ll7m6MDqj/4czN7blbTLsDhOTwn079pz13\nZgeJ4F3Fgp+pzobe0rnw8/QYkUymk5+tIgkRIEw2n+m8uMrTq3ELCTghgOqHEzC4GwkggdVHYMQa\n7PgHNIdZKQ9QPw6qXIPpA2aZr776KvjbYLj56nt8XKjHsFLPF3vxxQSzfriruR8CtKEch85gMRNo\nTn1zXEh8lxeFwvLy8Q9jp9WlX74lJKyLCpGJ2GSbWTnQVZp9u7i0SRqRIuWCWWCeND2X54ECIoHZ\nEkD1Y7bE8HwkgARWIgG7HTL4aCEpj0ZnsRGhMgCPx2fSZ5S+32KxNDY2pqamggEEqpvHx8fPJJXq\nSoSIfXI5AkZV36P87JpOrYBPL89+AKUqvN354FLjcoIuM4FoPtHrj72nvXLpesaNczU1EZF+7lSr\ntq62tqqxyz9q55Hjp6M8hXNwMFtmGFBcJDBXAhj7MVdyeB0SQAIrhYAdyhYoexurq8vrm/vVRqrN\nTKExQtZsSIiL5Y7F8jrrK9hKuru7z58//+DBg6SkpNOnT8vlclz1dIYL9yOBFUTAZtANdba2NDe3\n9CqUFiJD7O4TEBzk4yFjO6q/4wsJIAGnBND64RQNHkACSGBVELCZh3oaslLPX7yV1ccO27Zzh4+2\n5vpXv39Yso/v9f+s9eI9ySXkhAUkVM3Ozk5PT4f6HlDGYXa5jJy0ibuRABJYDgRIDJYoMBx+4paD\ntCgjEnAhAqh+uNBgoChIAAksOgG7drAl48Jnn357n7n24M8+/GB7JK/wQo9GSbJZ7OrRMl7OlzH1\nen1BQcHFixehksPJkychpyqGfCz6COINkQASQAJIYJkRGEstt8zERnGRABJAAvNAwGZSV+XfuXDl\n5pA0bs/R00mRHhSCncTnSWPjIkJDJGwoVu30Lmazuaam5saNGzabDUrIQbJdDPlwCgsPIAEkgASQ\nABIYI4DWjzES+BsJIIHVR0Df31Jc9CinjbP96LqUxMCRil282C0H/++YZCqbJ+KxnKkfEPLR1dV1\n9+7dtra2lJSU9evXQz3r1ccPe4wEkAASQAJIYNYEUP2YNTK8AAkggZVCwD7U29XbVE8VussCwsSs\n0XxARAab587mTd/HoaGhzMzM3NxcyLG7c+dOiUSC4ebTE8OjSAAJIAEkgARGCaDzFT4JSAAJrFoC\nFrWyX9HdJ5IKfQM8GeN1jgkEu9VsMhpMFtuUaCDBbl5e3tWrV/l8/iuvvBIQEIAhH1OCwp1IAAkg\nASSABJ4ngOrH80xwDxJAAquEAJFCZ9BYTC6bJhWwiE8+Du02s76nua6woKShT2OzT0ZhNBorKiqu\nXbsGkR6vv/76mjVrMORjMiN8jwSQABJAAkjAOQFUP5yzwSNIAAmscAIUkdzbOzjYZLYMDqmMJovV\natFrhpqqi7//7ts/nkurU2itUAJ9wstqtUKwx507dxQKxd69exMSEigUdGGdAAg3kQASQAJIAAm8\niAB+cb6IEB5HAkhg5RLguvmFx29Jq8vOvH1LRuoTMUmDne0FBcU1Wvaeo6/sjpRRJ1QPg3DzwcFB\nKC9YUlISFxe3fft2gUCwctlgz5AAEkACSAAJLAgBrHq+IFixUSSABJYLAYOyM/P21Uup2ToSgUyj\nGrSc0DXbj5/cFuEjJj2b9woqDN67d++rr76SSqUfffRRdHQ0hnwsl1FGOZEAEkACSMB1CKD64Tpj\ngZIgASSwRATsNpNBp9XqbCQqi8VmMmjPy2EwGIqKij7//HOdTvfhhx8mJSVhyMfzlHAPEkACSAAJ\nIIEXEkDnqxciwhOQABJY6QSIJBqTAz/O+mmxWJqamm7evKlSqU6cOIEVBp2Bwv1IAAkgASSABF5I\nAEPPX4gIT0ACSGBVE4CQDwg0B7crqHG+YcMGsHuw2exVTQQ7jwSQABJAAkjgJQig+vES8PBSJIAE\nVgEBCPnIzs6+f/++r6/vvn375HI5VhhcBcOOXUQCSAAJIIGFIoDqx0KRxXaRABJYAQT0en1hYeHF\nixeZTObJkydDQ0Mx3HwFDCt2AQkgASSABJaQAKofSwgfb40EkIBLEzCbzbW1tTdu3IByH6dPn8aQ\nD5ceLRQOCSABJIAElgkBVD+WyUChmEgACSwuAQj56O7uTktLa21t3bZtG0R90GhTZMRaXKHwbkgA\nCSABJIAElj0BVD+W/RBiB5AAElgIAkNDQ5mZmRD1ERISsnPnTolEgiEfC8EZ20QCSAAJIIHVRgDV\nj9U24thfJIAEXkwAinvk5+dfvXqVx+MdO3YsMDAQQz5eTA3PQAJIAAkgASQwAwKofswAEp6CBJDA\naiJgMpkqKiquX78OKsfrr7++Zs0arDC4msYf+4oEkAASQAILSwDVj4Xli60jASSwvAhAlHlbW9ud\nO3d6enp2796dkJBAoWB51uU1higtEkACSAAJuDQBVD9cenhQOCSABBaZgFKpzMjIKC4ujomJ2bFj\nh1AoXGQB8HZIAAkgASSABFY2AVQ/Vvb4Yu+QABKYBQGtVpubm5uamurm5nb48GGoM4jh5rPAh6ci\nASSABJAAEpgBAVQ/ZgAJT0ECSGAVEDAajaWlpZcvXwZvq9deey06OhrDzVfBsGMXkQASQAJIYLEJ\noPqx2MTxfkgACbggAYvF0tTUBHYPlUp15MiR+Ph4DDd3wWFCkZAAEkACSGAFEED1YwUMInYBCSCB\nlyIAFQYVCsW9e/eqqqoSExO3bNnCZrNfqkW8GAkgASSABJAAEnBCANUPJ2BwNxJAAquGgEajycnJ\nAfXDx8dn//79Hh4eGPKxagYfO4oEkAASQAKLTQDVj8UmjvdDAkjApQjo9fqioqJLly4xmcyTJ0+G\nhYVhyIdLDRAKgwSQABJAAiuMAKofK2xAsTtIAAnMgoDZbK6rq7tx4wZsnDp1at26dRjyMQt8eCoS\nQAJIAAkggdkTQPVj9szwCiSABFYEAQj5gNqCaWlpLS0tEO+xceNGOp2+InqGnUACSAAJIAEk4LoE\nUP1w3bFByZAAElhQAsPDww8fPszKygoKCtq1a5dUKsWQjwUFjo0jASSABJAAEgACqH7gY4AEkMBq\nJKDT6fLz86HKB4fDOX78OGggGPKxGp8D7DMSQAJIAAksOgFUPxYdOd4QCSCBpSZgMpkqKyuvX78O\n5o4zZ86sXbsWQz6Wekzw/kgACSABJLBaCKD6sVpGGvuJBJDAKAGbzdbe3n7nzp3u7u7du3dDoQ8o\nc45wkAASQAJIAAkggcUhgOrH4nDGuyABJOAqBJRKZUZGBiTbjY6OTk5OFgqFriIZyoEEkAASQAJI\nYBUQQPVjFQwydhEJIIExAlqtNjc39+bNmxBofuTIEV9fXww3H2ODv5EAEkACSAAJLAYBVD8WgzLe\nAwkgAVcgYDQaS0tLIdwcvK1Onz4N1g8MN3eFcUEZkAASQAJIYFURQPVjVQ03dhYJrF4CVqu1qanp\n1q1bkG/38OHDCQkJGG6+ep8G7DkSQAJIAAksHQFUP5aOPd4ZCSCBxSIAFQYVCkV6ejokvALFA4oM\nQr7dxbo53gcJIAEkgASQABJ4SgDVj6cscAsJIIGVSkCj0eTk5ECBcy8vr/3793t6emLIx0oda+wX\nEkACSAAJuDgBVD9cfIBQPCSABF6WgF6vhzxXly5dYjAYp06dCg8Px5CPl2WK1yMBJIAEkAASmCsB\nVD/mSg6vQwJIYDkQMJvNdXV1qampUGrwxIkT69atw5CP5TBuKCMSQAJIAAmsWAKofqzYocWOIQEk\nACEfPT099+7dg6DzpKSkTZs2gQEEsSABJIAEkAASQAJLSADVjyWEj7dGAkhgYQmoVKqsrKzMzMzA\nwEAocO7m5oYhHwtLHFtHAkgACSABJPAiAqh+vIgQHkcCSGB5EtDpdPn5+RDywWazjx8/HhQUhCEf\ny3MkUWokgASQABJYUQRQ/VhRw4mdQQJIYJQARHpUVVVdu3YNzB1vvvlmXFwchnzgs4EEkAASQAJI\nwBUIoPrhCqOAMiABJDCfBGw2W0dHx507d7q7u3fu3AmFPqDM+XzeANtCAkgACSABJIAE5koA1Y+5\nksPrkAAScFUCSqUyIyOjsLAwKioqJSVFJBK5qqQoFxJAAkgACSCBVUcA1Y9VN+TYYSSwsglotdrc\n3NwbN26IxeIjR474+flhuPnKHnHsHRJAAkgACSwvAqh+LK/xQmmRABKYjoDRaCwrK7t69Sp4W732\n2msxMTEYbj4dLzyGBJAAEkACSGDRCaD6sejI8YZIAAksDAGr1drc3Hz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qrVptsxFJZCKZ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00V5fdbW6g59acs+eNf6JoYqzZ2FLwv4hvd/tDpAoRDpXJuKATchM1uafUIRSMU/E7G9rGejt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"text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import Image\n", "Image(filename='Bayesian_Cognitive_Modeling_pdf__page_203_of_280_.png')" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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dXpcvvocwJVhyzLB7dZvqlXwmk8z2H0ipSmedX7lzd8EprRTgsJ7KFkQOcmW9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"text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Image(filename='Bayesian_Cognitive_Modeling_pdf__page_204_of_280__.png') " ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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pWQdeO/9cCt2vMzgjs62bKRDL8+1DYKuF7IoIkfmilNSMVLzT5pyZtPtDoXAw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\nqiuFvkkBmSWqPnimOhIE4cyNFCtWGFhhqxZne3r6XDHt0hA8y4Fn1dZ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"text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Image(filename='Bayesian_Cognitive_Modeling_pdf__page_204_of_280_.png') " ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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S1Xjn7oO61n6b1tjfWnb7jkbEl27KKQ6NCOEJrBODrbcvfH6xvNssDIsQWwcG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4qbGiMCRGAOAiyLyBGD3wLBDP81BYPB2S7NUSddIgJLJcAifvmCSS+WDfg8Hn+QL5JKRfylDnyV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\n8Oqpc7/66bWS0uKgSTczZ1e3HS3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"text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Image(filename='Bayesian_Cognitive_Modeling_pdf__page_205_of_280_.png') " ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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ouxrDZdkkDlMIVhJmD7CGxEnNFCIblloniQoCKuYlFWc7JyzYnHLr1kf+vk7v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3V602U4IK6A+WR9k2zSYnEtF5IrYkbYugxzOqUhsU2+LqAHOoXmtFIcAGRtdrTfzlAy84/BE75Y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\nSqS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JPSRYygcDfoiL4IXpLEoVixSSaLdtdsLorpfly8HOC3DNm+xuP8JjLQ/4kK6T2QLw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\nkXAFCCNMBxEgAkSACBABIkAE0pQ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"text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Image(filename='Bayesian_Cognitive_Modeling_pdf__page_206_of_280_.png') " ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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1gtUCg5ykwcR2ezKvx+e4OJ3YWscb4nGySlN89Pj7OXkPBiZsTofNaNSf/2ZE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zaMugad+Pzzz9EI2ZulpAU0P2zDO3bsEMqc302/+waEoZdJh2SixKPfE1BEvBDBHuABGgm0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\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es4xGI1yFuLg4SEgdSycgR4eJgO8QcBmbG5tKShmW2GZ21Rbd7hZy2Bx3Z21e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\nGvE8Boogk5gbvXrm5IlpVvn+L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"text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Image(filename='Bayesian_Cognitive_Modeling_pdf__page_207_of_280_.png') " ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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HQBht+DGXEqMCAcwqLIll9SsrANBQ4ZSJzg/rrKcSUcdMaWEJcLt0zFMquZ8g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\nNmPXeE7WgcbyjHQ5mdA3ylJ1kwBznX4msj0ShHBqyLI/Nkzm24FA/CuJJVI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"text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Image(filename='Bayesian_Cognitive_Modeling_pdf__page_210_of_280_.png') " ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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+Q6Y9Mm6E6VbJpi0H0xM0UYnJeYTsSUjzONmuRtejV9/x907LbWKT8w6HJrHv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\nUltdeaUNx5bnFJWVZMpg3gitCOfremzI3B2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7gh0Sjw0MU5Ny0RO4CeXLNuWswKOWz+Eh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\nJQU8FrCQa25oaetqaWvvH4T/+nv6XKkpkynQLiDXyMJygcRQUrZ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"text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Image(filename='Bayesian_Cognitive_Modeling_pdf__page_210_of_280__.png') " ] }, { "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
billzhao1990/CS231n-Spring-2017
assignment2/BatchNormalization.ipynb
1
222781
{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Batch Normalization\n", "One way to make deep networks easier to train is to use more sophisticated optimization procedures such as SGD+momentum, RMSProp, or Adam. Another strategy is to change the architecture of the network to make it easier to train. One idea along these lines is batch normalization which was recently proposed by [3].\n", "\n", "The idea is relatively straightforward. Machine learning methods tend to work better when their input data consists of uncorrelated features with zero mean and unit variance. When training a neural network, we can preprocess the data before feeding it to the network to explicitly decorrelate its features; this will ensure that the first layer of the network sees data that follows a nice distribution. However even if we preprocess the input data, the activations at deeper layers of the network will likely no longer be decorrelated and will no longer have zero mean or unit variance since they are output from earlier layers in the network. Even worse, during the training process the distribution of features at each layer of the network will shift as the weights of each layer are updated.\n", "\n", "The authors of [3] hypothesize that the shifting distribution of features inside deep neural networks may make training deep networks more difficult. To overcome this problem, [3] proposes to insert batch normalization layers into the network. At training time, a batch normalization layer uses a minibatch of data to estimate the mean and standard deviation of each feature. These estimated means and standard deviations are then used to center and normalize the features of the minibatch. A running average of these means and standard deviations is kept during training, and at test time these running averages are used to center and normalize features.\n", "\n", "It is possible that this normalization strategy could reduce the representational power of the network, since it may sometimes be optimal for certain layers to have features that are not zero-mean or unit variance. To this end, the batch normalization layer includes learnable shift and scale parameters for each feature dimension.\n", "\n", "[3] Sergey Ioffe and Christian Szegedy, \"Batch Normalization: Accelerating Deep Network Training by Reducing\n", "Internal Covariate Shift\", ICML 2015." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "# As usual, a bit of setup\n", "from __future__ import print_function\n", "import time\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from cs231n.classifiers.fc_net import *\n", "from cs231n.data_utils import get_CIFAR10_data\n", "from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array\n", "from cs231n.solver import Solver\n", "\n", "%matplotlib inline\n", "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n", "plt.rcParams['image.interpolation'] = 'nearest'\n", "plt.rcParams['image.cmap'] = 'gray'\n", "\n", "# for auto-reloading external modules\n", "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n", "%load_ext autoreload\n", "%autoreload 2\n", "\n", "def rel_error(x, y):\n", " \"\"\" returns relative error \"\"\"\n", " return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "X_val: (1000, 3, 32, 32)\n", "X_test: (1000, 3, 32, 32)\n", "X_train: (49000, 3, 32, 32)\n", "y_train: (49000,)\n", "y_val: (1000,)\n", "y_test: (1000,)\n" ] } ], "source": [ "# Load the (preprocessed) CIFAR10 data.\n", "\n", "data = get_CIFAR10_data()\n", "for k, v in data.items():\n", " print('%s: ' % k, v.shape)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Batch normalization: Forward\n", "In the file `cs231n/layers.py`, implement the batch normalization forward pass in the function `batchnorm_forward`. Once you have done so, run the following to test your implementation." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Before batch normalization:\n", " means: [ -2.3814598 -13.18038246 1.91780462]\n", " stds: [ 27.18502186 34.21455511 37.68611762]\n", "After batch normalization (gamma=1, beta=0)\n", " mean: [ 7.32747196e-17 7.82707232e-17 1.03389519e-17]\n", " std: [ 0.99999999 1. 1. ]\n", "After batch normalization (nontrivial gamma, beta)\n", " means: [ 11. 12. 13.]\n", " stds: [ 0.99999999 1.99999999 2.99999999]\n" ] } ], "source": [ "# Check the training-time forward pass by checking means and variances\n", "# of features both before and after batch normalization\n", "\n", "# Simulate the forward pass for a two-layer network\n", "np.random.seed(231)\n", "N, D1, D2, D3 = 200, 50, 60, 3\n", "X = np.random.randn(N, D1)\n", "W1 = np.random.randn(D1, D2)\n", "W2 = np.random.randn(D2, D3)\n", "a = np.maximum(0, X.dot(W1)).dot(W2)\n", "\n", "print('Before batch normalization:')\n", "print(' means: ', a.mean(axis=0))\n", "print(' stds: ', a.std(axis=0))\n", "\n", "# Means should be close to zero and stds close to one\n", "print('After batch normalization (gamma=1, beta=0)')\n", "a_norm, _ = batchnorm_forward(a, np.ones(D3), np.zeros(D3), {'mode': 'train'})\n", "print(' mean: ', a_norm.mean(axis=0))\n", "print(' std: ', a_norm.std(axis=0))\n", "\n", "# Now means should be close to beta and stds close to gamma\n", "gamma = np.asarray([1.0, 2.0, 3.0])\n", "beta = np.asarray([11.0, 12.0, 13.0])\n", "a_norm, _ = batchnorm_forward(a, gamma, beta, {'mode': 'train'})\n", "print('After batch normalization (nontrivial gamma, beta)')\n", "print(' means: ', a_norm.mean(axis=0))\n", "print(' stds: ', a_norm.std(axis=0))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "After batch normalization (test-time):\n", " means: [-0.03927354 -0.04349152 -0.10452688]\n", " stds: [ 1.01531428 1.01238373 0.97819988]\n" ] } ], "source": [ "# Check the test-time forward pass by running the training-time\n", "# forward pass many times to warm up the running averages, and then\n", "# checking the means and variances of activations after a test-time\n", "# forward pass.\n", "np.random.seed(231)\n", "N, D1, D2, D3 = 200, 50, 60, 3\n", "W1 = np.random.randn(D1, D2)\n", "W2 = np.random.randn(D2, D3)\n", "\n", "bn_param = {'mode': 'train'}\n", "gamma = np.ones(D3)\n", "beta = np.zeros(D3)\n", "for t in range(50):\n", " X = np.random.randn(N, D1)\n", " a = np.maximum(0, X.dot(W1)).dot(W2)\n", " batchnorm_forward(a, gamma, beta, bn_param)\n", "bn_param['mode'] = 'test'\n", "X = np.random.randn(N, D1)\n", "a = np.maximum(0, X.dot(W1)).dot(W2)\n", "a_norm, _ = batchnorm_forward(a, gamma, beta, bn_param)\n", "\n", "# Means should be close to zero and stds close to one, but will be\n", "# noisier than training-time forward passes.\n", "print('After batch normalization (test-time):')\n", "print(' means: ', a_norm.mean(axis=0))\n", "print(' stds: ', a_norm.std(axis=0))" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Batch Normalization: backward\n", "Now implement the backward pass for batch normalization in the function `batchnorm_backward`.\n", "\n", "To derive the backward pass you should write out the computation graph for batch normalization and backprop through each of the intermediate nodes. Some intermediates may have multiple outgoing branches; make sure to sum gradients across these branches in the backward pass.\n", "\n", "Once you have finished, run the following to numerically check your backward pass." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "dx error: 1.70292583282e-09\n", "dgamma error: 7.42041421625e-13\n", "dbeta error: 2.87950576558e-12\n" ] } ], "source": [ "# Gradient check batchnorm backward pass\n", "np.random.seed(231)\n", "N, D = 4, 5\n", "x = 5 * np.random.randn(N, D) + 12\n", "gamma = np.random.randn(D)\n", "beta = np.random.randn(D)\n", "dout = np.random.randn(N, D)\n", "\n", "bn_param = {'mode': 'train'}\n", "fx = lambda x: batchnorm_forward(x, gamma, beta, bn_param)[0]\n", "fg = lambda a: batchnorm_forward(x, a, beta, bn_param)[0]\n", "fb = lambda b: batchnorm_forward(x, gamma, b, bn_param)[0]\n", "\n", "dx_num = eval_numerical_gradient_array(fx, x, dout)\n", "da_num = eval_numerical_gradient_array(fg, gamma.copy(), dout)\n", "db_num = eval_numerical_gradient_array(fb, beta.copy(), dout)\n", "\n", "_, cache = batchnorm_forward(x, gamma, beta, bn_param)\n", "dx, dgamma, dbeta = batchnorm_backward(dout, cache)\n", "\n", "print('dx error: ', rel_error(dx_num, dx))\n", "print('dgamma error: ', rel_error(da_num, dgamma))\n", "print('dbeta error: ', rel_error(db_num, dbeta))" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Batch Normalization: alternative backward (OPTIONAL, +3 points extra credit)\n", "In class we talked about two different implementations for the sigmoid backward pass. One strategy is to write out a computation graph composed of simple operations and backprop through all intermediate values. Another strategy is to work out the derivatives on paper. For the sigmoid function, it turns out that you can derive a very simple formula for the backward pass by simplifying gradients on paper.\n", "\n", "Surprisingly, it turns out that you can also derive a simple expression for the batch normalization backward pass if you work out derivatives on paper and simplify. After doing so, implement the simplified batch normalization backward pass in the function `batchnorm_backward_alt` and compare the two implementations by running the following. Your two implementations should compute nearly identical results, but the alternative implementation should be a bit faster.\n", "\n", "NOTE: This part of the assignment is entirely optional, but we will reward 3 points of extra credit if you can complete it." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "dx difference: 5.96415594171e-13\n", "dgamma difference: 0.0\n", "dbeta difference: 0.0\n", "speedup: 2.74x\n" ] } ], "source": [ "np.random.seed(231)\n", "N, D = 100, 500\n", "x = 5 * np.random.randn(N, D) + 12\n", "gamma = np.random.randn(D)\n", "beta = np.random.randn(D)\n", "dout = np.random.randn(N, D)\n", "\n", "bn_param = {'mode': 'train'}\n", "out, cache = batchnorm_forward(x, gamma, beta, bn_param)\n", "\n", "t1 = time.time()\n", "dx1, dgamma1, dbeta1 = batchnorm_backward(dout, cache)\n", "t2 = time.time()\n", "dx2, dgamma2, dbeta2 = batchnorm_backward_alt(dout, cache)\n", "t3 = time.time()\n", "\n", "print('dx difference: ', rel_error(dx1, dx2))\n", "print('dgamma difference: ', rel_error(dgamma1, dgamma2))\n", "print('dbeta difference: ', rel_error(dbeta1, dbeta2))\n", "print('speedup: %.2fx' % ((t2 - t1) / (t3 - t2)))" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Fully Connected Nets with Batch Normalization\n", "Now that you have a working implementation for batch normalization, go back to your `FullyConnectedNet` in the file `cs2312n/classifiers/fc_net.py`. Modify your implementation to add batch normalization.\n", "\n", "Concretely, when the flag `use_batchnorm` is `True` in the constructor, you should insert a batch normalization layer before each ReLU nonlinearity. The outputs from the last layer of the network should not be normalized. Once you are done, run the following to gradient-check your implementation.\n", "\n", "HINT: You might find it useful to define an additional helper layer similar to those in the file `cs231n/layer_utils.py`. If you decide to do so, do it in the file `cs231n/classifiers/fc_net.py`." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Running check with reg = 0\n", "Initial loss: 2.26119551013\n", "W1 relative error: 1.10e-04\n", "W2 relative error: 2.85e-06\n", "W3 relative error: 4.05e-10\n", "b1 relative error: 0.00e+00\n", "b2 relative error: 0.00e+00\n", "b3 relative error: 1.01e-10\n", "beta1 relative error: 7.33e-09\n", "beta2 relative error: 1.89e-09\n", "gamma1 relative error: 6.96e-09\n", "gamma2 relative error: 1.96e-09\n", "\n", "Running check with reg = 3.14\n", "Initial loss: 6.99653322011\n", "W1 relative error: 1.98e-06\n", "W2 relative error: 2.28e-06\n", "W3 relative error: 1.11e-08\n", "b1 relative error: 0.00e+00\n", "b2 relative error: 0.00e+00\n", "b3 relative error: 2.10e-10\n", "beta1 relative error: 6.65e-09\n", "beta2 relative error: 4.23e-09\n", "gamma1 relative error: 6.27e-09\n", "gamma2 relative error: 5.28e-09\n" ] } ], "source": [ "np.random.seed(231)\n", "N, D, H1, H2, C = 2, 15, 20, 30, 10\n", "X = np.random.randn(N, D)\n", "y = np.random.randint(C, size=(N,))\n", "\n", "for reg in [0, 3.14]:\n", " print('Running check with reg = ', reg)\n", " model = FullyConnectedNet([H1, H2], input_dim=D, num_classes=C,\n", " reg=reg, weight_scale=5e-2, dtype=np.float64,\n", " use_batchnorm=True)\n", "\n", " loss, grads = model.loss(X, y)\n", " print('Initial loss: ', loss)\n", "\n", " for name in sorted(grads):\n", " f = lambda _: model.loss(X, y)[0]\n", " grad_num = eval_numerical_gradient(f, model.params[name], verbose=False, h=1e-5)\n", " print('%s relative error: %.2e' % (name, rel_error(grad_num, grads[name])))\n", " if reg == 0: print()" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Batchnorm for deep networks\n", "Run the following to train a six-layer network on a subset of 1000 training examples both with and without batch normalization." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(Iteration 1 / 200) loss: 2.340975\n", "(Epoch 0 / 10) train acc: 0.137000; val_acc: 0.133000\n", "(Epoch 1 / 10) train acc: 0.319000; val_acc: 0.274000\n", "(Epoch 2 / 10) train acc: 0.407000; val_acc: 0.308000\n", "(Epoch 3 / 10) train acc: 0.464000; val_acc: 0.318000\n", "(Epoch 4 / 10) train acc: 0.513000; val_acc: 0.300000\n", "(Epoch 5 / 10) train acc: 0.563000; val_acc: 0.318000\n", "(Epoch 6 / 10) train acc: 0.632000; val_acc: 0.339000\n", "(Epoch 7 / 10) train acc: 0.700000; val_acc: 0.347000\n", "(Epoch 8 / 10) train acc: 0.736000; val_acc: 0.360000\n", "(Epoch 9 / 10) train acc: 0.775000; val_acc: 0.325000\n", "(Epoch 10 / 10) train acc: 0.805000; val_acc: 0.323000\n", "(Iteration 1 / 200) loss: 2.302332\n", "(Epoch 0 / 10) train acc: 0.129000; val_acc: 0.131000\n", "(Epoch 1 / 10) train acc: 0.245000; val_acc: 0.212000\n", "(Epoch 2 / 10) train acc: 0.316000; val_acc: 0.270000\n", "(Epoch 3 / 10) train acc: 0.340000; val_acc: 0.260000\n", "(Epoch 4 / 10) train acc: 0.382000; val_acc: 0.291000\n", "(Epoch 5 / 10) train acc: 0.411000; val_acc: 0.294000\n", "(Epoch 6 / 10) train acc: 0.457000; val_acc: 0.310000\n", "(Epoch 7 / 10) train acc: 0.485000; val_acc: 0.316000\n", "(Epoch 8 / 10) train acc: 0.554000; val_acc: 0.316000\n", "(Epoch 9 / 10) train acc: 0.594000; val_acc: 0.308000\n", "(Epoch 10 / 10) train acc: 0.595000; val_acc: 0.309000\n" ] } ], "source": [ "np.random.seed(231)\n", "# Try training a very deep net with batchnorm\n", "hidden_dims = [100, 100, 100, 100, 100]\n", "\n", "num_train = 1000\n", "small_data = {\n", " 'X_train': data['X_train'][:num_train],\n", " 'y_train': data['y_train'][:num_train],\n", " 'X_val': data['X_val'],\n", " 'y_val': data['y_val'],\n", "}\n", "\n", "weight_scale = 2e-2\n", "bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, use_batchnorm=True)\n", "model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, use_batchnorm=False)\n", "\n", "bn_solver = Solver(bn_model, small_data,\n", " num_epochs=10, batch_size=50,\n", " update_rule='adam',\n", " optim_config={\n", " 'learning_rate': 1e-3,\n", " },\n", " verbose=True, print_every=200)\n", "bn_solver.train()\n", "\n", "solver = Solver(model, small_data,\n", " num_epochs=10, batch_size=50,\n", " update_rule='adam',\n", " optim_config={\n", " 'learning_rate': 1e-3,\n", " },\n", " verbose=True, print_every=200)\n", "solver.train()" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Run the following to visualize the results from two networks trained above. You should find that using batch normalization helps the network to converge much faster." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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WgScdLjTtLqAFxl66+ogvSpO6ANYq67PpQIqeLuN2zYCO+8O86WZb04LBOhER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fUfngGsRcLLji+9JQkKt8Hpd8O0qyJugfDK6gALNu71QtzqXWzd63WlnJiZwk0nj+GU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"text/plain": [ "<matplotlib.figure.Figure at 0x7f540dd3cc88>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.subplot(3, 1, 1)\n", "plt.title('Training loss')\n", "plt.xlabel('Iteration')\n", "\n", "plt.subplot(3, 1, 2)\n", "plt.title('Training accuracy')\n", "plt.xlabel('Epoch')\n", "\n", "plt.subplot(3, 1, 3)\n", "plt.title('Validation accuracy')\n", "plt.xlabel('Epoch')\n", "\n", "plt.subplot(3, 1, 1)\n", "plt.plot(solver.loss_history, 'o', label='baseline')\n", "plt.plot(bn_solver.loss_history, 'o', label='batchnorm')\n", "\n", "plt.subplot(3, 1, 2)\n", "plt.plot(solver.train_acc_history, '-o', label='baseline')\n", "plt.plot(bn_solver.train_acc_history, '-o', label='batchnorm')\n", "\n", "plt.subplot(3, 1, 3)\n", "plt.plot(solver.val_acc_history, '-o', label='baseline')\n", "plt.plot(bn_solver.val_acc_history, '-o', label='batchnorm')\n", " \n", "for i in [1, 2, 3]:\n", " plt.subplot(3, 1, i)\n", " plt.legend(loc='upper center', ncol=4)\n", "plt.gcf().set_size_inches(15, 15)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Batch normalization and initialization\n", "We will now run a small experiment to study the interaction of batch normalization and weight initialization.\n", "\n", "The first cell will train 8-layer networks both with and without batch normalization using different scales for weight initialization. The second layer will plot training accuracy, validation set accuracy, and training loss as a function of the weight initialization scale." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Running weight scale 1 / 20\n", "Running weight scale 2 / 20\n", "Running weight scale 3 / 20\n", "Running weight scale 4 / 20\n", "Running weight scale 5 / 20\n", "Running weight scale 6 / 20\n", "Running weight scale 7 / 20\n", "Running weight scale 8 / 20\n", "Running weight scale 9 / 20\n", "Running weight scale 10 / 20\n", "Running weight scale 11 / 20\n", "Running weight scale 12 / 20\n", "Running weight scale 13 / 20\n", "Running weight scale 14 / 20\n", "Running weight scale 15 / 20\n", "Running weight scale 16 / 20\n", "Running weight scale 17 / 20\n", "Running weight scale 18 / 20\n", "Running weight scale 19 / 20\n", "Running weight scale 20 / 20\n" ] } ], "source": [ "np.random.seed(231)\n", "# Try training a very deep net with batchnorm\n", "hidden_dims = [50, 50, 50, 50, 50, 50, 50]\n", "\n", "num_train = 1000\n", "small_data = {\n", " 'X_train': data['X_train'][:num_train],\n", " 'y_train': data['y_train'][:num_train],\n", " 'X_val': data['X_val'],\n", " 'y_val': data['y_val'],\n", "}\n", "\n", "bn_solvers = {}\n", "solvers = {}\n", "weight_scales = np.logspace(-4, 0, num=20)\n", "for i, weight_scale in enumerate(weight_scales):\n", " print('Running weight scale %d / %d' % (i + 1, len(weight_scales)))\n", " bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, use_batchnorm=True)\n", " model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, use_batchnorm=False)\n", "\n", " bn_solver = Solver(bn_model, small_data,\n", " num_epochs=10, batch_size=50,\n", " update_rule='adam',\n", " optim_config={\n", " 'learning_rate': 1e-3,\n", " },\n", " verbose=False, print_every=200)\n", " bn_solver.train()\n", " bn_solvers[weight_scale] = bn_solver\n", "\n", " solver = Solver(model, small_data,\n", " num_epochs=10, batch_size=50,\n", " update_rule='adam',\n", " optim_config={\n", " 'learning_rate': 1e-3,\n", " },\n", " verbose=False, print_every=200)\n", " solver.train()\n", " solvers[weight_scale] = solver" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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12niflegtsTnhfFg91Y5DW/O9TUAadoUBL9jWLLfG2oSF2+7WZmfCuf+zXbB/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\neGrA9ZqoKYfJPe2UKobgRjD4LTj6Cyyf4u5oPE5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Anr2tFZO+3kHbKd9xLjWdan4+pFzK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"text/plain": [ "<matplotlib.figure.Figure at 0x7f540dcaa7b8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot results of weight scale experiment\n", "best_train_accs, bn_best_train_accs = [], []\n", "best_val_accs, bn_best_val_accs = [], []\n", "final_train_loss, bn_final_train_loss = [], []\n", "\n", "for ws in weight_scales:\n", " best_train_accs.append(max(solvers[ws].train_acc_history))\n", " bn_best_train_accs.append(max(bn_solvers[ws].train_acc_history))\n", " \n", " best_val_accs.append(max(solvers[ws].val_acc_history))\n", " bn_best_val_accs.append(max(bn_solvers[ws].val_acc_history))\n", " \n", " final_train_loss.append(np.mean(solvers[ws].loss_history[-100:]))\n", " bn_final_train_loss.append(np.mean(bn_solvers[ws].loss_history[-100:]))\n", " \n", "plt.subplot(3, 1, 1)\n", "plt.title('Best val accuracy vs weight initialization scale')\n", "plt.xlabel('Weight initialization scale')\n", "plt.ylabel('Best val accuracy')\n", "plt.semilogx(weight_scales, best_val_accs, '-o', label='baseline')\n", "plt.semilogx(weight_scales, bn_best_val_accs, '-o', label='batchnorm')\n", "plt.legend(ncol=2, loc='lower right')\n", "\n", "plt.subplot(3, 1, 2)\n", "plt.title('Best train accuracy vs weight initialization scale')\n", "plt.xlabel('Weight initialization scale')\n", "plt.ylabel('Best training accuracy')\n", "plt.semilogx(weight_scales, best_train_accs, '-o', label='baseline')\n", "plt.semilogx(weight_scales, bn_best_train_accs, '-o', label='batchnorm')\n", "plt.legend()\n", "\n", "plt.subplot(3, 1, 3)\n", "plt.title('Final training loss vs weight initialization scale')\n", "plt.xlabel('Weight initialization scale')\n", "plt.ylabel('Final training loss')\n", "plt.semilogx(weight_scales, final_train_loss, '-o', label='baseline')\n", "plt.semilogx(weight_scales, bn_final_train_loss, '-o', label='batchnorm')\n", "plt.legend()\n", "plt.gca().set_ylim(1.0, 3.5)\n", "\n", "plt.gcf().set_size_inches(10, 15)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Question:\n", "Describe the results of this experiment, and try to give a reason why the experiment gave the results that it did." ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Answer:\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" }, "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": "174px", "width": "252px" 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mit