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train · ~165k rows (showing the first 47.2k)

repo_name stringlengths 6 92	path stringlengths 7 220	copies stringclasses 78 values	size stringlengths 2 9	content stringlengths 15 1.05M ⌀	license stringclasses 15 values
sauloal/ipython	probes/probes_SL2.40sc04878.ipynb	1	587888	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Parameters" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Column Names\n", "\tK-mer Coverage\n", "\tSequencing Coverage\n", "\tNs\n", "\tAGP Contig\n", "\tAGP Gap\n", "\tAGP Unknown\n", "\tAGP Other\n", "\tK-mer Coverage averaged: 500 bp\n", "\tK-mer Coverage averaged: 2.5 Kbp\n", "\tK-mer Coverage averaged: 5 Kbp\n", "\tK-mer Coverage averaged: 50 Kbp\n", "\tK-mer Coverage averaged: 1 Mbp\n", "\tK-mer Coverage averaged: 5 Kbp before\n", "\tK-mer Coverage averaged: 5 Kbp after\n", "Config\n", "\tRUN : run_BWA_JKL_23_JU_1_orig\n", "\tchromosome : SL2.40ch12\n", "\tdata_folder : probes\n", "\tout_folder : reports\n", "\tscaffold : SL2.40sc04878\n" ] } ], "source": [ "MAX_ROWS = 100000\n", "\n", "HISTOGRAM_COLOR = (0.5,0.5,0.5)\n", "ALL_GRAPH_COLOR = (0.2,0.2,0.2)\n", "OUTPUT_FORMAT = 'eps'\n", "\n", "REPORT_SIZE = True\n", "\n", "FULL_FIG_W , FULL_FIG_H = (19 if REPORT_SIZE else 16) , 8\n", "CHROM_FIG_W, CHROM_FIG_H = FULL_FIG_W, 20\n", "\n", "\n", "config = {\n", " 'data_folder': 'probes',\n", " 'RUN' : 'run_BWA_JKL_23_JU_1_orig',\n", " 'chromosome' : 'SL2.40ch12',\n", " 'scaffold' : 'SL2.40sc04878',\n", " 'out_folder' : 'reports'\n", "}\n", "\n", "if False:\n", " config['BAC' ] = 'JBPP0904'\n", " config['BAC_coord_start'] = 967164\n", " config['BAC_coord_end' ] = 970727\n", " config['BAC_coord' ] = '%012d-%012d' % ( config['BAC_coord_start'], config['BAC_coord_end' ] )\n", " config['RUN' ] = 'run_BWA_JKL_23_JU_1_orig_PROBES'\n", "\n", " \n", "BAC_MODE = False\n", "cols_to_plot = [\n", " # Col name limit # ticks\n", " [ \"K-mer Coverage\" , [0,1] , 5 ], \n", " [ \"Sequencing Coverage\" , None , 5 ],\n", " [ \"Ns\" , [0.1,1] , 0 ],\n", " [ \"AGP Contig\" , [0.1,1] , 0 ],\n", " [ \"AGP Gap\" , [0.1,1] , 0 ],\n", " [ \"AGP Unknown\" , [0.1,1] , 0 ],\n", " [ \"AGP Other\" , [0.1,1] , 0 ],\n", " [ \"K-mer Coverage averaged: 500 bp\" , [0,None], 2 ],\n", " [ \"K-mer Coverage averaged: 2.5 Kbp\" , [0,None], 2 ],\n", " [ \"K-mer Coverage averaged: 5 Kbp\" , [0,None], 2 ],\n", " [ \"K-mer Coverage averaged: 50 Kbp\" , [0,None], 2 ],\n", " [ \"K-mer Coverage averaged: 1 Mbp\" , [0,None], 2 ],\n", " [ \"K-mer Coverage averaged: 5 Kbp before\", [0,None], 2 ],\n", " [ \"K-mer Coverage averaged: 5 Kbp after\" , [0,None], 2 ]\n", " ]\n", "\n", "if 'BAC' in config:\n", " BAC_MODE = True\n", " cols_to_plot.insert(2, ['BLAST Coverage', [0, None], 5 ])\n", "\n", "print \"Column Names\"\n", "for col_to_plot in cols_to_plot:\n", " print \"\\t\", col_to_plot[0]\n", " \n", "print \"Config\"\n", "for cfg in sorted(config.keys()):\n", " print \"\\t%-15s: %s\" % (cfg, config[cfg])\n", " \n", " \n", "## For debugging only\n", "NROWS = 1000\n", "NROWS = 10000\n", "NROWS = None\n", "\n", "PARSE_VERBOSE = False\n", "#PARSE_VERBOSE = True\n" ] }, { "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": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from matplotlib import gridspec\n", "import os\n", "from IPython.display import HTML\n", "import operator\n", "\n", "#import pylab\n", "#pylab.show()\n", "\n", "%pylab inline\n", "\n", "pd.set_option('display.notebook_repr_html', True)\n", "#pd.set_option('display.max_columns', 20)\n", "#pd.set_option('display.max_rows', 25)\n", "\n", "def addHeader(level, text):\n", " display( HTML('''<h%(level)d>%(text)s</h%(level)d>''' % {'level':level, 'text':text}) )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Constants" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Input Files" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "KmerCoverageFile : True probes/run_BWA_JKL_23_JU_1_orig/S_lycopersicum_chromosomes.fa_S_lycopersicum_scaffolds.sam.SL2.40ch12.sam.SL2.40sc04878.sam.cov.prop.cov\n", "SequencingCoverageFile: True probes/mapping/out/S_lycopersicum_chromosomes.fa_S_lycopersicum_chromosomes.pos.SL2.40ch12.pos.cov.SL2.40sc04878.cov\n", "AgpContigFile : True probes/agp/S_lycopersicum_scaffolds_from_contigs.2.40.agp.SL2.40sc04878.agp.contig.agp.cov\n", "AgpGapFile : True probes/agp/S_lycopersicum_scaffolds_from_contigs.2.40.agp.SL2.40sc04878.agp.gap.agp.cov\n", "AgpOtherFile : True probes/agp/S_lycopersicum_scaffolds_from_contigs.2.40.agp.SL2.40sc04878.agp.other.agp.cov\n", "AgpUnknownFile : True probes/agp/S_lycopersicum_scaffolds_from_contigs.2.40.agp.SL2.40sc04878.agp.unknown.agp.cov\n", "NsFile : True probes/Ns/S_lycopersicum_scaffolds.fa_NONE.tab.SL2.40ch12.tab.SL2.40sc04878.tab.cov\n", "\n", "all files present\n" ] } ], "source": [ "\"\"\"\n", "S_lycopersicum_chromosomes.fa_S_lycopersicum_scaffolds.sam.SL2.40ch12.sam.SL2.40sc06147.sam.cov\n", "S_lycopersicum_chromosomes.fa_S_lycopersicum_scaffolds.sam.SL2.40ch12.sam.SL2.40sc06147.sam.cov.prop.cov\n", "S_lycopersicum_chromosomes.fa_S_lycopersicum_scaffolds.sam.SL2.40ch12.sam.SL2.40sc06147.sam.pos\n", "\"\"\"\n", " \n", "KmerCoverageFile = \"%(data_folder)s/%(RUN)s/S_lycopersicum_chromosomes.fa_S_lycopersicum_scaffolds.sam.%(chromosome)s.sam.%(scaffold)s.sam.cov.prop.cov\" % config\n", "SequencingCoverageFile = \"%(data_folder)s/mapping/out/S_lycopersicum_chromosomes.fa_S_lycopersicum_chromosomes.pos.%(chromosome)s.pos.cov.%(scaffold)s.cov\" % config\n", "AgpContigFile = \"%(data_folder)s/agp/S_lycopersicum_scaffolds_from_contigs.2.40.agp.%(scaffold)s.agp.contig.agp.cov\" % config\n", "AgpGapFile = \"%(data_folder)s/agp/S_lycopersicum_scaffolds_from_contigs.2.40.agp.%(scaffold)s.agp.gap.agp.cov\" % config\n", "AgpOtherFile = \"%(data_folder)s/agp/S_lycopersicum_scaffolds_from_contigs.2.40.agp.%(scaffold)s.agp.other.agp.cov\" % config\n", "AgpUnknownFile = \"%(data_folder)s/agp/S_lycopersicum_scaffolds_from_contigs.2.40.agp.%(scaffold)s.agp.unknown.agp.cov\" % config\n", "NsFile = \"%(data_folder)s/Ns/S_lycopersicum_scaffolds.fa_NONE.tab.%(chromosome)s.tab.%(scaffold)s.tab.cov\" % config\n", "infiles = [KmerCoverageFile, SequencingCoverageFile, \n", " AgpContigFile, AgpGapFile, \n", " AgpUnknownFile, AgpOtherFile, NsFile]\n", "\n", "if BAC_MODE:\n", " \"\"\"\n", "\n", " S_lycopersicum_chromosomes.fa_S_lycopersicum_scaffolds.sam.SL2.40ch12.SL2.40sc05611.JBPP0904_primer.000000967164-000000970727.S_lycopersicum_chromosomes.fa_S_lycopersicum_chromosomes.pos.SL2.40ch12.pos.cov.SL2.40sc05611.cov\n", " S_lycopersicum_chromosomes.fa_S_lycopersicum_scaffolds.sam.SL2.40ch12.SL2.40sc05611.JBPP0904_primer.000000967164-000000970727.S_lycopersicum_chromosomes.fa_S_lycopersicum_scaffolds.sam.SL2.40ch12.sam.SL2.40sc05611.sam.cov.prop.cov\n", " S_lycopersicum_chromosomes.fa_S_lycopersicum_scaffolds.sam.SL2.40ch12.SL2.40sc05611.JBPP0904_primer.000000967164-000000970727_Product.fasta.blast.cov\n", " S_lycopersicum_chromosomes.fa_S_lycopersicum_scaffolds.sam.SL2.40ch12.SL2.40sc05611.JBPP0904_primer.000000967164-000000970727.S_lycopersicum_scaffolds_from_contigs.2.40.agp.SL2.40sc05611.agp.contig.agp.cov\n", " S_lycopersicum_chromosomes.fa_S_lycopersicum_scaffolds.sam.SL2.40ch12.SL2.40sc05611.JBPP0904_primer.000000967164-000000970727.S_lycopersicum_scaffolds_from_contigs.2.40.agp.SL2.40sc05611.agp.gap.agp.cov\n", " S_lycopersicum_chromosomes.fa_S_lycopersicum_scaffolds.sam.SL2.40ch12.SL2.40sc05611.JBPP0904_primer.000000967164-000000970727.S_lycopersicum_scaffolds_from_contigs.2.40.agp.SL2.40sc05611.agp.other.agp.cov\n", " S_lycopersicum_chromosomes.fa_S_lycopersicum_scaffolds.sam.SL2.40ch12.SL2.40sc05611.JBPP0904_primer.000000967164-000000970727.S_lycopersicum_scaffolds_from_contigs.2.40.agp.SL2.40sc05611.agp.unknown.agp.cov\n", " S_lycopersicum_chromosomes.fa_S_lycopersicum_scaffolds.sam.SL2.40ch12.SL2.40sc05611.JBPP0904_primer.000000967164-000000970727.S_lycopersicum_scaffolds.fa_NONE.tab.SL2.40ch12.tab.SL2.40sc05611.tab.cov\n", " \"\"\"\n", " \n", " config['in_base_name'] = \"%(data_folder)s/%(RUN)s/S_lycopersicum_chromosomes.fa_S_lycopersicum_scaffolds.sam.%(chromosome)s.%(scaffold)s.%(BAC)s_primer.%(BAC_coord)s\" % config\n", " KmerCoverageFile = \"%(in_base_name)s.S_lycopersicum_chromosomes.fa_S_lycopersicum_scaffolds.sam.%(chromosome)s.sam.%(scaffold)s.sam.cov.prop.cov\" % config\n", " SequencingCoverageFile = \"%(in_base_name)s.S_lycopersicum_chromosomes.fa_S_lycopersicum_chromosomes.pos.%(chromosome)s.pos.cov.%(scaffold)s.cov\" % config\n", " BlastCoverageFile = \"%(in_base_name)s_Product.fasta.blast.cov\" % config\n", " AgpContigFile = \"%(in_base_name)s.S_lycopersicum_scaffolds_from_contigs.2.40.agp.%(scaffold)s.agp.contig.agp.cov\" % config\n", " AgpGapFile = \"%(in_base_name)s.S_lycopersicum_scaffolds_from_contigs.2.40.agp.%(scaffold)s.agp.gap.agp.cov\" % config\n", " AgpOtherFile = \"%(in_base_name)s.S_lycopersicum_scaffolds_from_contigs.2.40.agp.%(scaffold)s.agp.other.agp.cov\" % config\n", " AgpUnknownFile = \"%(in_base_name)s.S_lycopersicum_scaffolds_from_contigs.2.40.agp.%(scaffold)s.agp.unknown.agp.cov\" % config\n", " NsFile = \"%(in_base_name)s.S_lycopersicum_scaffolds.fa_NONE.tab.%(chromosome)s.tab.%(scaffold)s.tab.cov\" % config\n", " \n", " infiles = [KmerCoverageFile, SequencingCoverageFile, \n", " BlastCoverageFile, AgpContigFile, \n", " AgpGapFile, AgpUnknownFile, \n", " AgpOtherFile, NsFile]\n", "\n", "\n", "print \"%-22s: %-5s %s\" % ( \"KmerCoverageFile\" , os.path.exists(KmerCoverageFile) , KmerCoverageFile )\n", "print \"%-22s: %-5s %s\" % ( \"SequencingCoverageFile\", os.path.exists(SequencingCoverageFile), SequencingCoverageFile )\n", "print \"%-22s: %-5s %s\" % ( \"AgpContigFile\" , os.path.exists(AgpContigFile) , AgpContigFile )\n", "print \"%-22s: %-5s %s\" % ( \"AgpGapFile\" , os.path.exists(AgpGapFile) , AgpGapFile )\n", "print \"%-22s: %-5s %s\" % ( \"AgpOtherFile\" , os.path.exists(AgpOtherFile) , AgpOtherFile )\n", "print \"%-22s: %-5s %s\" % ( \"AgpUnknownFile\" , os.path.exists(AgpUnknownFile) , AgpUnknownFile )\n", "print \"%-22s: %-5s %s\" % ( \"NsFile\" , os.path.exists(NsFile) , NsFile )\n", "\n", "\n", "\n", "if BAC_MODE:\n", " print \"%-22s: %-5s %s\" % ( \"BlastCoverageFile\" , os.path.exists(BlastCoverageFile) , BlastCoverageFile )\n", "\n", "print\n", "\n", "\n", "\n", "if not all([os.path.exists(x) for x in infiles]):\n", " print \"missing file\"\n", " #quit()\n", "else:\n", " print \"all files present\"\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Output Files" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Combined graph : reports/eps/run_BWA_JKL_23_JU_1_orig_SL2.40ch12_SL2.40sc04878_prop.Combined_graph.eps\n", "Gaps Distribution : reports/eps/run_BWA_JKL_23_JU_1_orig_SL2.40ch12_SL2.40sc04878_prop.Gaps_Distribution.eps\n", "K-mer Coverage Distribution : reports/eps/run_BWA_JKL_23_JU_1_orig_SL2.40ch12_SL2.40sc04878_prop.K-mer_Coverage_Distribution.eps\n", "K-mer Coverage Stats : reports/eps/run_BWA_JKL_23_JU_1_orig_SL2.40ch12_SL2.40sc04878_prop.K-mer_Coverage_Stats.eps\n", "Ns Distribution : reports/eps/run_BWA_JKL_23_JU_1_orig_SL2.40ch12_SL2.40sc04878_prop.Ns_Distribution.eps\n", "Sequencing Coverage Distribution: reports/eps/run_BWA_JKL_23_JU_1_orig_SL2.40ch12_SL2.40sc04878_prop.Sequencing_Coverage_Distribution.eps\n", "Sequencing Coverage Stats : reports/eps/run_BWA_JKL_23_JU_1_orig_SL2.40ch12_SL2.40sc04878_prop.Sequencing_Coverage_Stats.eps\n", "all_data : reports/eps/run_BWA_JKL_23_JU_1_orig_SL2.40ch12_SL2.40sc04878_prop.csv\n", "all_data_full : reports/eps/run_BWA_JKL_23_JU_1_orig_SL2.40ch12_SL2.40sc04878_prop_full.csv\n" ] } ], "source": [ "config['out_extension'] = OUTPUT_FORMAT\n", "config['out_bn' ] = \"%(out_folder)s/%(out_extension)s/%(RUN)s_%(chromosome)s_%(scaffold)s_prop\" % config\n", "config['out_bn_img' ] = \"%(out_bn)s.%%s.%(out_extension)s\" % config\n", "\n", "if BAC_MODE:\n", " config['out_bn' ] = \"%(out_folder)s/%(out_extension)s/%(RUN)s_%(chromosome)s_%(scaffold)s_%(BAC)s_%(BAC_coord)s\" % config\n", " config['out_bn_img' ] = \"%(out_bn)s.%%s.%(out_extension)s\" % config\n", "\n", "\n", "\n", "output_files = {\n", " 'all_data' : '%(out_bn)s.csv' % config,\n", " 'all_data_full' : '%(out_bn)s_full.csv' % config,\n", " 'K-mer Coverage Stats' : config['out_bn_img'] % 'K-mer_Coverage_Stats',\n", " 'Sequencing Coverage Stats' : config['out_bn_img'] % 'Sequencing_Coverage_Stats',\n", " 'K-mer Coverage Distribution' : config['out_bn_img'] % 'K-mer_Coverage_Distribution',\n", " 'Sequencing Coverage Distribution': config['out_bn_img'] % 'Sequencing_Coverage_Distribution',\n", " 'Gaps Distribution' : config['out_bn_img'] % 'Gaps_Distribution',\n", " 'Ns Distribution' : config['out_bn_img'] % 'Ns_Distribution',\n", " 'Combined graph' : config['out_bn_img'] % 'Combined_graph'\n", "}\n", "\n", "\n", "if BAC_MODE:\n", " output_files['BLAST Coverage Stats' ] = config['out_bn_img'] % 'BLAST_Coverage_Stats'\n", " output_files['BLAST Coverage Distribution'] = config['out_bn_img'] % 'BLAST_Coverage_Distribution'\n", "\n", " \n", "for of in sorted(output_files.keys()):\n", " print \"%-32s: %s\" % ( of, output_files[of] )" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "class size_controller(object):\n", " def __init__(self, w, h):\n", " self.w = w\n", " self.h = h\n", " \n", " def __enter__(self):\n", " self.o = rcParams['figure.figsize']\n", " rcParams['figure.figsize'] = self.w, self.h\n", " return None\n", " \n", " def __exit__(self, type, value, traceback):\n", " rcParams['figure.figsize'] = self.o\n", " \n", "col_type_int = np.int64\n", "col_type_flo = np.float64\n", "col_type_str = np.object\n", "col_type_bol = np.int8" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Read Files" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## K-mer Coverage File" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "col_info = [\n", " [ \"Position\" , col_type_int ], \n", " [ \"K-mer Coverage\" , col_type_flo ], \n", " [ \"K-mer Coverage averaged: 500 bp\" , col_type_flo ], \n", " [ \"K-mer Coverage averaged: 2.5 Kbp\" , col_type_flo ], \n", " [ \"K-mer Coverage averaged: 5 Kbp\" , col_type_flo ], \n", " [ \"K-mer Coverage averaged: 50 Kbp\" , col_type_flo ], \n", " [ \"K-mer Coverage averaged: 1 Mbp\" , col_type_flo ], \n", " [ \"K-mer Coverage averaged: 5 Kbp before\", col_type_flo ], \n", " [ \"K-mer Coverage averaged: 5 Kbp after\" , col_type_flo ]\n", "]\n", "\n", "col_names=[cf[0] for cf in col_info]\n", "col_types=dict(zip([c[0] for c in col_info], [c[1] for c in col_info]))\n", "\n", "if PARSE_VERBOSE:\n", " print \"\\n\".join( col_names )" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "probes/run_BWA_JKL_23_JU_1_orig/S_lycopersicum_chromosomes.fa_S_lycopersicum_scaffolds.sam.SL2.40ch12.sam.SL2.40sc04878.sam.cov.prop.cov\n", "Loaded 5717763 rows and 9 columns\n" ] } ], "source": [ "SKIP_ROWS = 1\n", "\n", "print KmerCoverageFile\n", "\n", "KmerData = pd.read_csv(KmerCoverageFile, header=None, names=col_names, dtype=col_types, nrows=NROWS, \\\n", " skiprows=SKIP_ROWS, verbose=PARSE_VERBOSE, delimiter=\"\\t\", comment=\"#\")\n", " \n", "print \"Loaded %d rows and %d columns\" % ( KmerData.shape[0], KmerData.shape[1] )\n", "\n", "if PARSE_VERBOSE:\n", " print KmerData.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Sequencing Coverage File" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "col_info = [\n", " [ \"Position\" , col_type_int ], \n", " [ \"Sequencing Coverage\" , col_type_int ]\n", "]\n", "\n", "col_names=[cf[0] for cf in col_info]\n", "col_types=dict(zip([c[0] for c in col_info], [c[1] for c in col_info]))\n", "\n", "if PARSE_VERBOSE:\n", " print col_names\n", " col_types" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "probes/mapping/out/S_lycopersicum_chromosomes.fa_S_lycopersicum_chromosomes.pos.SL2.40ch12.pos.cov.SL2.40sc04878.cov\n", "Loaded 5717763 rows and 2 columns\n" ] } ], "source": [ "SKIP_ROWS = 0\n", "\n", "print SequencingCoverageFile\n", "\n", "SequencingCoverageData = pd.read_csv(SequencingCoverageFile, header=None, names=col_names, dtype=col_types, nrows=NROWS, \\\n", " skiprows=SKIP_ROWS, verbose=PARSE_VERBOSE, delimiter=\"\\t\", comment=\"#\")\n", "\n", "print \"Loaded %d rows and %d columns\" % ( SequencingCoverageData.shape[0], SequencingCoverageData.shape[1] )\n", "\n", "if PARSE_VERBOSE:\n", " print SequencingCoverageData.head()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "if BAC_MODE:\n", " addHeader(1,'BLAST')\n", "\n", " col_info = [\n", " [ \"Position\" , col_type_int ], \n", " [ \"BLAST Coverage\" , col_type_flo ]\n", " ]\n", "\n", " col_names=[cf[0] for cf in col_info]\n", " col_types=dict(zip([c[0] for c in col_info], [c[1] for c in col_info]))\n", "\n", " if PARSE_VERBOSE:\n", " print col_names\n", " col_types" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "if BAC_MODE:\n", " SKIP_ROWS = 0\n", " \n", " print BlastCoverageFile\n", " \n", " BlastCoverageData = pd.read_csv(BlastCoverageFile, header=None, names=col_names, dtype=col_types, nrows=NROWS, \\\n", " skiprows=SKIP_ROWS, verbose=PARSE_VERBOSE, delimiter=\"\\t\", comment=\"#\")\n", " \n", " print \"Loaded %d rows and %d columns\" % ( BlastCoverageData.shape[0], BlastCoverageData.shape[1] )\n", " \n", " if PARSE_VERBOSE:\n", " print BlastCoverageData.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##AGP" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "SKIP_ROWS = 0" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Contig" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "col_info = [\n", " [ \"Position\" , col_type_int ], \n", " [ \"AGP Contig\" , col_type_bol ]\n", "]\n", "\n", "col_names=[cf[0] for cf in col_info]\n", "col_types=dict(zip([c[0] for c in col_info], [c[1] for c in col_info]))\n", "\n", "if PARSE_VERBOSE:\n", " print col_names\n", " col_types" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "probes/agp/S_lycopersicum_scaffolds_from_contigs.2.40.agp.SL2.40sc04878.agp.contig.agp.cov\n", "Loaded 5717763 rows and 2 columns\n" ] } ], "source": [ "print AgpContigFile\n", "\n", "AgpContigData = pd.read_csv(AgpContigFile, header=None, names=col_names, dtype=col_types, nrows=NROWS, \\\n", " skiprows=SKIP_ROWS, verbose=PARSE_VERBOSE, delimiter=\"\\t\", comment=\"#\")\n", "\n", "print \"Loaded %d rows and %d columns\" % ( AgpContigData.shape[0], AgpContigData.shape[1] )\n", "\n", "if PARSE_VERBOSE:\n", " print AgpContigData.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Gap" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "col_info = [\n", " [ \"Position\" , col_type_int ], \n", " [ \"AGP Gap\" , col_type_bol ]\n", "]\n", "\n", "col_names=[cf[0] for cf in col_info]\n", "col_types=dict(zip([c[0] for c in col_info], [c[1] for c in col_info]))\n", "\n", "if PARSE_VERBOSE:\n", " print col_names\n", " col_types" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "probes/agp/S_lycopersicum_scaffolds_from_contigs.2.40.agp.SL2.40sc04878.agp.gap.agp.cov\n", "Loaded 5717763 rows and 2 columns\n" ] } ], "source": [ "print AgpGapFile\n", "\n", "AgpGapData = pd.read_csv(AgpGapFile, header=None, names=col_names, dtype=col_types, nrows=NROWS, \\\n", " skiprows=SKIP_ROWS, verbose=PARSE_VERBOSE, delimiter=\"\\t\", comment=\"#\")\n", " \n", "print \"Loaded %d rows and %d columns\" % ( AgpGapData.shape[0], AgpGapData.shape[1] )\n", "\n", "if PARSE_VERBOSE:\n", " print AgpGapData.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Unknown" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "col_info = [\n", " [ \"Position\" , col_type_int ], \n", " [ \"AGP Unknown\" , col_type_bol ]\n", "]\n", "\n", "col_names=[cf[0] for cf in col_info]\n", "col_types=dict(zip([c[0] for c in col_info], [c[1] for c in col_info]))\n", "\n", "if PARSE_VERBOSE:\n", " print col_names\n", " col_types" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "probes/agp/S_lycopersicum_scaffolds_from_contigs.2.40.agp.SL2.40sc04878.agp.unknown.agp.cov\n", "Loaded 5717763 rows and 2 columns\n" ] } ], "source": [ "print AgpUnknownFile\n", "\n", "AgpUnknownData = pd.read_csv(AgpUnknownFile, header=None, names=col_names, dtype=col_types, nrows=NROWS, \\\n", " skiprows=SKIP_ROWS, verbose=PARSE_VERBOSE, delimiter=\"\\t\", comment=\"#\")\n", " \n", "print \"Loaded %d rows and %d columns\" % ( AgpUnknownData.shape[0], AgpUnknownData.shape[1] )\n", "\n", "if PARSE_VERBOSE:\n", " print AgpUnknownData.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Other" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [], "source": [ "col_info = [\n", " [ \"Position\" , col_type_int ], \n", " [ \"AGP Other\" , col_type_bol ]\n", "]\n", "\n", "col_names=[cf[0] for cf in col_info]\n", "col_types=dict(zip([c[0] for c in col_info], [c[1] for c in col_info]))\n", "\n", "if PARSE_VERBOSE:\n", " print col_names\n", " col_types" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "probes/agp/S_lycopersicum_scaffolds_from_contigs.2.40.agp.SL2.40sc04878.agp.other.agp.cov\n", "Loaded 5717763 rows and 2 columns\n" ] } ], "source": [ "print AgpOtherFile\n", "\n", "AgpOtherData = pd.read_csv(AgpOtherFile, header=None, names=col_names, dtype=col_types, nrows=NROWS, \\\n", " skiprows=SKIP_ROWS, verbose=PARSE_VERBOSE, delimiter=\"\\t\", comment=\"#\")\n", "\n", "print \"Loaded %d rows and %d columns\" % ( AgpOtherData.shape[0], AgpOtherData.shape[1] )\n", "\n", "if PARSE_VERBOSE:\n", " print AgpOtherData.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Ns" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [], "source": [ "col_info = [\n", " [ \"Position\" , col_type_int ], \n", " [ \"Ns\" , col_type_bol ]\n", "]\n", "\n", "col_names=[cf[0] for cf in col_info]\n", "col_types=dict(zip([c[0] for c in col_info], [c[1] for c in col_info]))\n", "\n", "if PARSE_VERBOSE:\n", " print col_names\n", " col_types" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "probes/Ns/S_lycopersicum_scaffolds.fa_NONE.tab.SL2.40ch12.tab.SL2.40sc04878.tab.cov\n", "Loaded 5717763 rows and 2 columns\n" ] } ], "source": [ "print NsFile\n", "\n", "NsData = pd.read_csv(NsFile, header=None, names=col_names, dtype=col_types, nrows=NROWS, \\\n", " skiprows=SKIP_ROWS, verbose=PARSE_VERBOSE, delimiter=\"\\t\", comment=\"#\")\n", " \n", "print \"Loaded %d rows and %d columns\" % ( NsData.shape[0], NsData.shape[1] )\n", "\n", "if PARSE_VERBOSE:\n", " print NsData.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Merge" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Saved 5717763 rows and 15 columns\n" ] } ], "source": [ "Data = pd.DataFrame(KmerData, copy=True)\n", "\n", "Data.combine_first( KmerData )\n", "Data[\"AGP Contig\" ] = AgpContigData[ \"AGP Contig\" ]\n", "Data[\"AGP Gap\" ] = AgpGapData[ \"AGP Gap\" ]\n", "Data[\"AGP Unknown\" ] = AgpUnknownData[ \"AGP Unknown\" ]\n", "Data[\"AGP Other\" ] = AgpOtherData[ \"AGP Other\" ]\n", "Data[\"Ns\" ] = NsData[ \"Ns\" ]\n", "Data[\"Sequencing Coverage\"] = SequencingCoverageData[\"Sequencing Coverage\"]\n", "if BAC_MODE:\n", " Data[\"BLAST Coverage\" ] = BlastCoverageData[ \"BLAST Coverage\" ]\n", "\n", "print \"Saved %d rows and %d columns\" % ( Data.shape[0], Data.shape[1] )\n", "\n", "if PARSE_VERBOSE:\n", " print Data.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Pot" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## K-mer Coverage Stats" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ 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500 bp\",\n", " \"K-mer Coverage averaged: 2.5 Kbp\",\n", " \"K-mer Coverage averaged: 5 Kbp\",\n", " \"K-mer Coverage averaged: 50 Kbp\",\n", " \"K-mer Coverage averaged: 1 Mbp\",\n", " \"K-mer Coverage averaged: 5 Kbp before\",\n", " \"K-mer Coverage averaged: 5 Kbp after\"], return_type='dict', rot=45)\n", " plt.setp(bqc['boxes' ], color='black')\n", " plt.setp(bqc['medians' ], color='black')\n", " plt.setp(bqc['whiskers'], color='black')\n", " plt.setp(bqc['fliers' ], color='black')\n", " \n", " savefig(output_files['K-mer Coverage Stats'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Sequencing Coverage Stats" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAABFQAAAHfCAYAAAB+sozsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAALEgAACxIB0t1+/AAAIABJREFUeJzt3X+wbedd3/f3lfXDPwALRa0lC0fXgyGNMwUZN5bbQnSY\n", 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"outputs": [], "source": [ "if BAC_MODE:\n", " addHeader(2,'BLAST Coverage Stats')\n", " \n", " with size_controller(FULL_FIG_W, FULL_FIG_H):\n", " bqc = Data.boxplot(column=['BLAST Coverage'], return_type='dict')\n", " plt.setp(bqc['boxes' ], color='black')\n", " plt.setp(bqc['medians' ], color='black')\n", " plt.setp(bqc['whiskers'], color='black')\n", " plt.setp(bqc['fliers' ], color='black')\n", " savefig(output_files['BLAST Coverage Stats'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## K-mer Coverage Distribution" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3743704\n" ] }, { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAABHEAAAHfCAYAAADXxkR4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAALEgAACxIB0t1+/AAAIABJREFUeJzt3X+wpXddH/D3LksIaCQGbQIhkkyBkXRoKcyAv6qPghA7\n", "LThTB8JMhWlTZzSdqu1UATvFi3QU/IfgdACnIgmMRZhSFCtGwo/TXwMEKKloTJPQxElWNjjBBWqr\n", 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"AAAAAAAAABr7//FA5eHSdEiLAAAAAElFTkSuQmCC\n" ], "text/plain": [ "<matplotlib.figure.Figure at 0x7f143facaf50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "with size_controller(FULL_FIG_W, FULL_FIG_H):\n", " hf = Data[ Data[\"K-mer Coverage\"] > 0 ]['Position']\n", " print hf.size\n", " if hf.size > 0:\n", " hs = hf.hist(color=HISTOGRAM_COLOR)\n", " savefig(output_files['K-mer Coverage Distribution'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Sequencing Coverage Distribution" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5452930\n" ] }, { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAABHEAAAHfCAYAAADXxkR4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAALEgAACxIB0t1+/AAAIABJREFUeJzt3X+MZWd5H/DvLottSBwWk8hrjIutBhSoaElQ7aRJyTSA\n", "caoKkIrASAWUWpHAbRNaKcWOIhiERE3/oUQVP7Sh2EQtAYWYH4UA5oebtAqYUNw4cVzbZBzhNetE\n", 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savefig(output_files['Sequencing Coverage Distribution'])" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [], "source": [ "if BAC_MODE:\n", " addHeader(2,'BLAST Coverage Distribution')\n", " \n", " with size_controller(FULL_FIG_W, FULL_FIG_H):\n", " hf = Data[ Data[\"BLAST Coverage\"] > 0 ]['Position']\n", " print hf.size\n", " if hf.size > 0:\n", " hs = hf.hist(color=HISTOGRAM_COLOR)\n", " savefig(output_files['BLAST Coverage Distribution'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Gaps Distribution" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "328216\n" ] }, { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAABGoAAAHfCAYAAAAIiD5nAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAALEgAACxIB0t1+/AAAIABJREFUeJzs3X+wZnd9H/b3LmtYcGRvwa6WX0bbGGpIyWAzMXZjk68N\n", 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f146023d8d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "with size_controller(FULL_FIG_W, FULL_FIG_H):\n", " hf = Data[ Data[\"AGP Gap\"] > 0 ]['Position']\n", " print hf.size\n", " if hf.size > 0:\n", " hs = hf.hist(color=HISTOGRAM_COLOR)\n", " savefig(output_files['Gaps Distribution'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Ns Distribution" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "271199\n" ] }, { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAABGoAAAHfCAYAAAAIiD5nAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAALEgAACxIB0t1+/AAAIABJREFUeJzs3X/wZWd9H/b3LmsQdhRvMa6WX0bbGGpIyWAzMXbjkMcG\n", "UzmTAEMZfsyEalLFM0VpYtKZxJBp6+8/JeCZDiGTAU9iHAQTY4jJCtxgCUz1NGnHoISwCbasIDkr\n", "imQke0QtOYmMQbv945z1flmv2O93H+35nPPd12vmzr3Pc8/96n3ZZ88u773nuQkAAAAAAAAAAAAA\n", 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"metadata": {}, "output_type": "display_data" } ], "source": [ "with size_controller(FULL_FIG_W, FULL_FIG_H):\n", " hf = Data[ Data[\"Ns\"] > 0 ]['Position']\n", " print hf.size\n", " if hf.size > 0:\n", " hs = hf.hist(color=HISTOGRAM_COLOR)\n", " savefig(output_files['Ns Distribution'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# CSV output" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Original Size 5717763 rows and 15 columns\n", "SAMPLING EVERY 58 ROWS\n", "Saving full data to: reports/eps/run_BWA_JKL_23_JU_1_orig_SL2.40ch12_SL2.40sc04878_prop_full.csv\n", "New Size 5717763 rows and 15 columns\n", "Saving data to : reports/eps/run_BWA_JKL_23_JU_1_orig_SL2.40ch12_SL2.40sc04878_prop.csv\n" ] } ], "source": [ "SAMPLE_EVERY = 1\n", "\n", "while ( Data.shape[0] / SAMPLE_EVERY ) > MAX_ROWS:\n", " SAMPLE_EVERY += 1\n", " \n", "print \"Original Size %d rows and %d columns\" % ( Data.shape[0], Data.shape[1] )\n", "\n", "if SAMPLE_EVERY != 1:\n", " print \"SAMPLING EVERY %d ROWS\" % SAMPLE_EVERY\n", " \n", " print \"Saving full data to: \", output_files['all_data_full']\n", " \n", " Data.to_csv(output_files['all_data_full'], sep='\\t', index=False)\n", " \n", " DataSampled = Data[::SAMPLE_EVERY]\n", "\n", " print \"New Size %d rows and %d columns\" % ( Data.shape[0], Data.shape[1] )\n", " \n", " if PARSE_VERBOSE:\n", " print Data.head()\n", "\n", "else:\n", " print \"no need to sample\"\n", " DataSampled = Data\n", "\n", "print \"Saving data to :\", output_files['all_data']\n", "DataSampled.to_csv(output_files['all_data'], sep='\\t', index=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Combined graph" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAABFQAAAQVCAYAAACc+Z6ZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", 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CHROM_FIG_H):\n", " if True:\n", " #fig, axes = plt.subplots(nrows=len(cols_to_plot)+2, ncols=1)\n", " plt.subplots_adjust(wspace=0.5, hspace=0.5)\n", "\n", " num_extra_rows = 2\n", " num_row_span = 3\n", " \n", " if BAC_MODE:\n", " num_extra_rows = 3\n", " num_row_span = 3\n", " \n", " num_cols_e = num_cols + (num_extra_rowsnum_row_span)\n", " axes = []\n", "\n", " axes.append( plt.subplot2grid((num_cols_e,1), (0 , 0), rowspan=num_row_span) )\n", " axes.append( plt.subplot2grid((num_cols_e,1), (num_row_span, 0), rowspan=num_row_span) )\n", " \n", " \n", " if BAC_MODE:\n", " axes.append( plt.subplot2grid((num_cols_e,1), (num_row_span2, 0), rowspan=num_row_span) )\n", "\n", " \n", " for i in xrange ( num_cols - num_extra_rows ):\n", " axes.append( plt.subplot2grid((num_cols_e,1), ((num_extra_rowsnum_row_span)+i, 0) ) )\n", "\n", " \n", " for col_to_plot_i, col_to_plot_info in enumerate(cols_to_plot):\n", " col_to_plot, col_ylim, col_yticks = col_to_plot_info\n", " \n", " axis = axes[col_to_plot_i]\n", " DataSampled[col_to_plot].plot(ax=axis, kind='area', stacked=False, color=ALL_GRAPH_COLOR)\n", " axis.set_title(col_to_plot)\n", " \n", " \n", " if col_ylim is not None:\n", " col_ylim_min, col_ylim_max = col_ylim\n", " \n", " if col_ylim_min is not None:\n", " axis.set_ylim( bottom = col_ylim_min )\n", " \n", " if col_ylim_max is not None:\n", " axis.set_ylim( top = col_ylim_max )\n", " \n", " \n", " if col_yticks is not None:\n", " if col_yticks == 0:\n", " axis.set_yticks([])\n", " \n", " else:\n", " ylim_min, ylim_max = axis.get_ylim()\n", " ylim_diff = ylim_max - ylim_min\n", " ylim_step = ylim_diff / (col_yticks1.0)\n", " #print col_to_plot, ylim_min, ylim_max, ylim_diff, ylim_step\n", " axis.set_yticks(np.arange(ylim_min,ylim_max+ylim_step,ylim_step))\n", " savefig(output_files['Combined graph'])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#%install_ext https://raw.githubusercontent.com/rasbt/python_reference/master/ipython_magic/watermark.py\n", "%reload_ext watermark\n", "%watermark --author \"Saulo Aflitos\" --date --updated --python --hostname --machine --githash --packages numpy,scipy,matplotlib,pandas,IPython" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<script>\n", "code_show=true; \n", "function code_toggle() {\n", " var classes_to_hide = ['div.input', 'div.output_stderr', 'div.output_prompt', 'div.input_prompt', 'div.prompt'];\n", " if (code_show){\n", " for ( var c in classes_to_hide ) {\n", " $(classes_to_hide[c]).hide();\n", " $(classes_to_hide[c]).css('visibility', 'hidden');\n", " }\n", " } else {\n", " for ( var c in classes_to_hide ) {\n", " $(classes_to_hide[c]).show();\n", " $(classes_to_hide[c]).css('visibility', 'visible');\n", " }\n", " }\n", " code_show = !code_show\n", "} \n", "$( document ).ready(code_toggle);\n", "</script>\n", "<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off the raw code.\"></form>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#http://stackoverflow.com/questions/27934885/how-to-hide-code-from-cells-in-ipython-notebook-visualized-with-nbviewer\n", "#from IPython.display import HTML\n", "\n", "HTML('''<script>\n", "code_show=true; \n", "function code_toggle() {\n", " var classes_to_hide = ['div.input', 'div.output_stderr', 'div.output_prompt', 'div.input_prompt', 'div.prompt'];\n", " if (code_show){\n", " for ( var c in classes_to_hide ) {\n", " $(classes_to_hide[c]).hide();\n", " $(classes_to_hide[c]).css('visibility', 'hidden');\n", " }\n", " } else {\n", " for ( var c in classes_to_hide ) {\n", " $(classes_to_hide[c]).show();\n", " $(classes_to_hide[c]).css('visibility', 'visible');\n", " }\n", " }\n", " code_show = !code_show\n", "} \n", "$( document ).ready(code_toggle);\n", "</script>\n", "<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off the raw code.\"></form>''')" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
dsacademybr/PythonFundamentos	Cap05/Notebooks/DSA-Python-Cap05-02-Objetos.ipynb	1	8486	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# <font color='blue'>Data Science Academy - Python Fundamentos - Capítulo 5</font>\n", "\n", "## Download: http://github.com/dsacademybr" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Versão da Linguagem Python Usada Neste Jupyter Notebook: 3.8.8\n" ] } ], "source": [ "# Versão da Linguagem Python\n", "from platform import python_version\n", "print('Versão da Linguagem Python Usada Neste Jupyter Notebook:', python_version())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Objetos" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Em Python, tudo é objeto!" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# Criando uma lista\n", "lst_num = [\"Data\", \"Science\", \"Academy\", \"Nota\", 10, 10]" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "list" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# A lista lst_num é um objeto, uma instância da classe lista em Python\n", "type(lst_num)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lst_num.count(10)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'int'>\n", "<class 'list'>\n", "<class 'tuple'>\n", "<class 'dict'>\n", "<class 'str'>\n" ] } ], "source": [ "# Usamos a função type, para verificar o tipo de um objeto\n", "print(type(10))\n", "print(type([]))\n", "print(type(()))\n", "print(type({}))\n", "print(type('a'))" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class '__main__.Carro'>\n" ] } ], "source": [ "# Criando um novo tipo de objeto chamado Carro\n", "class Carro(object):\n", " pass\n", "\n", "# Instância do Carro\n", "palio = Carro()\n", "\n", "print(type(palio))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# Criando uma classe\n", "class Estudantes:\n", " def __init__(self, nome, idade, nota):\n", " self.nome = nome\n", " self.idade = idade\n", " self.nota = nota" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "# Criando um objeto chamado Estudante1 a partir da classe Estudantes\n", "Estudante1 = Estudantes(\"Pele\", 12, 9.5)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'Pele'" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Atributo da classe Estudante, utilizado por cada objeto criado a partir desta classe\n", "Estudante1.nome" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "12" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Atributo da classe Estudante, utilizado por cada objeto criado a partir desta classe\n", "Estudante1.idade" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "9.5" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Atributo da classe Estudante, utilizado por cada objeto criado a partir desta classe\n", "Estudante1.nota" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "# Criando uma classe\n", "class Funcionarios:\n", " def __init__(self, nome, salario):\n", " self.nome = nome\n", " self.salario = salario\n", "\n", " def listFunc(self):\n", " print(\"O nome do funcionário é \" + self.nome + \" e o salário é R$\" + str(self.salario))" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "# Criando um objeto chamado Func1 a partir da classe Funcionarios\n", "Func1 = Funcionarios(\"Obama\", 20000)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "O nome do funcionário é Obama e o salário é R$20000\n" ] } ], "source": [ "# Usando o método da classe\n", "Func1.listFunc()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "** Usando atributos *\n" ] } ], "source": [ "print(\" Usando atributos ***\")" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hasattr(Func1, \"nome\")" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hasattr(Func1, \"salario\")" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "setattr(Func1, \"salario\", 4500)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hasattr(Func1, \"salario\")" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "4500" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "getattr(Func1, \"salario\")" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "delattr(Func1, \"salario\")" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hasattr(Func1, \"salario\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Fim" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Obrigado\n", "\n", "### Visite o Blog da Data Science Academy - <a href=\"http://blog.dsacademy.com.br\">Blog DSA</a>" ] } ], "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 }	gpl-3.0
dinkelk/PyDyGraphs	examples/PyDyGraphTester.ipynb	1	388517	{ "cells": [ { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Import pydygraphs and numpy\n", "import sys\n", "sys.path.append(\"../\")\n", "\n", "import dygraphs.graph as dy\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " <script src=\"http://dygraphs.com/dygraph-combined.js\"></script>\n", " <table style=\"width: 800px; border-style: hidden;\">\n", " <tr><td style=\"border-style: hidden;\"><div id='Figure48' style=\"width: 800px; height: 400px;\"></div></td></tr>\n", " <tr><td style=\"border-style: hidden;\"><div style=\"text-align:right; width: 800px; height: auto;\"; id='Figure48_legend'></div></td></tr>\n", " </table>\n", " " ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", " <script type=\"text/javascript\">\n", "\n", " function handle_output_Figure48(out) {\n", "\n", " g = new Dygraph(document.getElementById('Figure48'), [[0.0, 0.030852882195163394, -0.7867089370940125, 0.6024768697965741], [1.0, 0.4161446636299534, -0.49282025529426343, 0.40595604118590445], [2.0, 0.8106333657130256, -0.7343133765451211, 0.47315899712285503], [3.0, 0.126484376992434, -0.8326959257134761, 0.732366541609166], [4.0, 0.6575869098640671, -0.8054117453909584, 0.8291323949574178], [5.0, 0.5412417797220964, -0.05871433321687293, 0.8864529975414083], [6.0, 0.6881379543496275, -0.5520870871085185, 0.6548339243705505], [7.0, 0.7961589150940227, -0.8377455704397647, 0.20891251976528102], [8.0, 0.5619626330857797, -0.7930174017054452, 0.7676788829444697], [9.0, 0.6034732073245458, -0.785651210528219, 0.8311451421814076], [10.0, 0.777631767838908, -0.5133885560495564, 0.3954809212298426], [11.0, 0.646538871968208, -0.7240280778459531, 0.729577341294859], [12.0, 0.5917784752429297, 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-0.04764732371756238, 0.7819237952594407], [98.0, 0.7148621070464718, -0.5158717760040907, 0.1108157129566496], [99.0, 0.05921376324163618, -0.612259482086364, 0.5963087674207308]], \n", " \n", " {\"labels\": [\"these\", \"are\", \"the\", \"labels\"], \"fillGraph\": true, \"fillAlpha\": 0.6, \"colors\": [\"red\", \"orange\", \"teal\"], \"title\": \"Plot Title\", \"xlabel\": \"X Axis\", \"ylabel\": \"Y Axis\"}\n", "\n", " );\n", " }\n", " var kernel = IPython.notebook.kernel;\n", " var callbacks_Figure48 = { 'iopub' : {'output' : handle_output_Figure48}};\n", " kernel.execute(\"sys.modules['dygraphs.graph'].__PYDYGRAPH__FIGURE__JSON__[48]\", callbacks_Figure48, {silent:false});\n", " </script>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Single Figure Example\n", "fig = dy.figure(width=800)\n", "\n", "# Form data for plot\n", "size = 100\n", "x = np.array(range(size))\n", "y = [np.sin(np.random.rand(size)),-np.sin(np.random.rand(size)), np.random.rand(size)]\n", "y = np.array(y)\n", "# Plot figure\n", "options = {'labels': 'these are the labels'.split(' '), 'fillGraph': True, 'fillAlpha': 0.6,\n", " 'colors': 'red orange teal'.split(' ')}\n", "fig.plot(x,y, *options)\n", "fig.title('Plot Title')\n", "fig.xlabel('X Axis')\n", "fig.ylabel('Y Axis')\n", "\n", "# Show figure:\n", "fig.show()" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<script src=\"http://dygraphs.com/dygraph-combined.js\"></script>\n", " <table style=\"width: 1050px; border-style: hidden;\">\n", " <tr>\n", " <th COLSPAN='1'>\n", " <h1 align=\"center\">PyDyGraphs Example Subplot</h1>\n", " </th>\n", " <tr><tr><td style=\"border-style: hidden;\">\n", " <script src=\"http://dygraphs.com/dygraph-combined.js\"></script>\n", " <table style=\"width: 1050.0px; border-style: hidden;\">\n", " <tr><td style=\"border-style: hidden;\"><div id='Figure42' style=\"width: 1050.0px; height: 200.0px;\"></div></td></tr>\n", " <tr><td style=\"border-style: hidden;\"><div style=\"text-align:right; width: 1050.0px; height: auto;\"; id='Figure42_legend'></div></td></tr>\n", " </table>\n", " </td></tr><tr><td style=\"border-style: hidden;\">\n", " <script src=\"http://dygraphs.com/dygraph-combined.js\"></script>\n", " <table style=\"width: 1050.0px; border-style: hidden;\">\n", " <tr><td style=\"border-style: hidden;\"><div id='Figure43' style=\"width: 1050.0px; height: 200.0px;\"></div></td></tr>\n", " <tr><td style=\"border-style: hidden;\"><div style=\"text-align:right; width: 1050.0px; height: auto;\"; id='Figure43_legend'></div></td></tr>\n", " </table>\n", " </td></tr></table>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", " <script type=\"text/javascript\">\n", "\n", " function handle_output_Figure42(out) {\n", "\n", " g = new 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"<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Subplot example\n", "[fig1],[fig2] = dy.subplot(2,1, title=\"PyDyGraphs Example Subplot\")\n", "\n", "# Form data for plot 1\n", "size = 4000\n", "x = np.array(range(size))\n", "y1 = [np.sin(np.array(range(size)) np.pi / 180.), np.sin(np.array(range(size)) * np.pi / 360)]\n", "\n", "# Plot figure 1\n", "fig1.plot(x,y1, labels='label1 label2 label3'.split(' '), fillGraph=True, fillAlpha=0.4)\n", "fig1.title(\"Figure 1\")\n", "fig1.xlabel('Series')\n", "fig1.ylabel('Value')\n", "\n", "# Form data for plot 2\n", "y2 = [np.sin(np.random.rand(size))]\n", "\n", "# Plot figure 2\n", "fig2.plot(x, y2, labels=[\"x_var\", \"Random Variable\"], color=['orange'], showRangeSelector=True, showRoller=False)\n", "fig2.title(\"Figure 2\")\n", "fig2.xlabel('Series')\n", "fig2.ylabel('Value')\n", "\n", "# Show plots:\n", "fig1.show()\n", "fig2.show()" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " <script src=\"http://dygraphs.com/dygraph-combined.js\"></script>\n", " <table style=\"width: 1050px; border-style: hidden;\">\n", " <tr><td style=\"border-style: hidden;\"><div id='Figure47' style=\"width: 1050px; height: 400px;\"></div></td></tr>\n", " <tr><td style=\"border-style: hidden;\"><div style=\"text-align:right; width: 1050px; height: auto;\"; id='Figure47_legend'></div></td></tr>\n", " </table>\n", " " ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", " <script type=\"text/javascript\">\n", "\n", " function handle_output_Figure47(out) {\n", "\n", " g = new Dygraph(document.getElementById('Figure47'), [[0.0, 0.1018693850278454, 1.0], [1.0, 0.07440315561460131, 0.8090169943749475], [2.0, 0.09248695608593135, 0.30901699437494745], [3.0, 0.07815409114904007, -0.30901699437494734], [4.0, 0.10698197639551711, 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[995.0, 0.0041599021358432325, -1.0], [996.0, 0.023300704773725398, -0.8090169943749534], [997.0, -0.012801806846225054, -0.30901699437498076], [998.0, 0.06359777820476777, 0.3090169943748904], [999.0, 0.028242472987354147, 0.8090169943748975]], \n", " \n", " {\"stepPlot\": true, \"colors\": [\"green\", \"blue\"], \"labels\": [\"a\", \"label1\", \"label2\"], \"title\": \"Plot Title 3\"}\n", "\n", " );\n", " }\n", " var kernel = IPython.notebook.kernel;\n", " var callbacks_Figure47 = { 'iopub' : {'output' : handle_output_Figure47}};\n", " kernel.execute(\"sys.modules['dygraphs.graph'].__PYDYGRAPH__FIGURE__JSON__[47]\", callbacks_Figure47, {silent:false});\n", " </script>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "## Dataframe example\n", "import pandas as pd\n", "import numpy as np\n", "\n", "# Form a dataframe\n", "df = pd.DataFrame(columns=['a', 'label1', 'label2'])\n", "df.a=np.arange(0, 1000)\n", "df.label1=np.sin(df.a0.02np.pi/10) + (np.random.ranf(size=len(df.a))-0.5)0.25\n", "df.label2=np.cos(df.anp.pi/5)\n", "\n", "# Create a plot directly from a Pandas dataframe\n", "fig3 = dy.figure()\n", "\n", "options = {'stepPlot': True, 'colors': 'green blue'.split(' ')}\n", "fig3.plotDataFrame(df, **options)\n", "fig3.title('Plot Title 3')\n", "\n", "# fig3.xlabel('a');fig3.ylabel('Units of Awesome');fig3.title('Dataframe Example')\n", "fig3.show()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "http://dygraphs.com/dygraph-combined.js\n" ] } ], "source": [ "print (dy.__PYDYGRAPH__DYGRAPHS_LIB_STRING__)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 1 }	mit
tschinz/iPython_Workspace	01_Mine/MachineLearning/tensorflow-examples_nb/2_BasicModels/gradient_boosted_decision_tree.ipynb	3	32353	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Gradient Boosted Decision Tree\n", "\n", "Implement a Gradient Boosted Decision tree (GBDT) with TensorFlow to classify\n", "handwritten digit images. This example is using the MNIST database of\n", "handwritten digits as training samples (http://yann.lecun.com/exdb/mnist/).\n", "\n", "- Author: Aymeric Damien\n", "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from __future__ import print_function\n", "\n", "import tensorflow as tf\n", "from tensorflow.contrib.boosted_trees.estimator_batch.estimator import GradientBoostedDecisionTreeClassifier\n", "from tensorflow.contrib.boosted_trees.proto import learner_pb2 as gbdt_learner\n", "\n", "# Ignore all GPUs (current TF GBDT does not support GPU).\n", "import os\n", "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"\"" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" ] } ], "source": [ "# Import MNIST data\n", "# Set verbosity to display errors only (Remove this line for showing warnings)\n", "tf.logging.set_verbosity(tf.logging.ERROR)\n", "from tensorflow.examples.tutorials.mnist import input_data\n", "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=False,\n", " source_url='http://yann.lecun.com/exdb/mnist/')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Parameters\n", "batch_size = 4096 # The number of samples per batch\n", "num_classes = 10 # The 10 digits\n", "num_features = 784 # Each image is 28x28 pixels\n", "max_steps = 10000\n", "\n", "# GBDT Parameters\n", "learning_rate = 0.1\n", "l1_regul = 0.\n", "l2_regul = 1.\n", "examples_per_layer = 1000\n", "num_trees = 10\n", "max_depth = 16" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Fill GBDT parameters into the config proto\n", "learner_config = gbdt_learner.LearnerConfig()\n", "learner_config.learning_rate_tuner.fixed.learning_rate = learning_rate\n", "learner_config.regularization.l1 = l1_regul\n", "learner_config.regularization.l2 = l2_regul / examples_per_layer\n", "learner_config.constraints.max_tree_depth = max_depth\n", "growing_mode = gbdt_learner.LearnerConfig.LAYER_BY_LAYER\n", "learner_config.growing_mode = growing_mode\n", "run_config = tf.contrib.learn.RunConfig(save_checkpoints_secs=300)\n", "learner_config.multi_class_strategy = (\n", " gbdt_learner.LearnerConfig.DIAGONAL_HESSIAN)\\\n", "\n", "# Create a TensorFlor GBDT Estimator\n", "gbdt_model = GradientBoostedDecisionTreeClassifier(\n", " model_dir=None, # No save directory specified\n", " learner_config=learner_config,\n", " n_classes=num_classes,\n", " examples_per_layer=examples_per_layer,\n", " num_trees=num_trees,\n", " center_bias=False,\n", " config=run_config)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Active Feature Columns: ['images_0', 'images_1', 'images_2', 'images_3', 'images_4', 'images_5', 'images_6', 'images_7', 'images_8', 'images_9', 'images_10', 'images_11', 'images_12', 'images_13', 'images_14', 'images_15', 'images_16', 'images_17', 'images_18', 'images_19', 'images_20', 'images_21', 'images_22', 'images_23', 'images_24', 'images_25', 'images_26', 'images_27', 'images_28', 'images_29', 'images_30', 'images_31', 'images_32', 'images_33', 'images_34', 'images_35', 'images_36', 'images_37', 'images_38', 'images_39', 'images_40', 'images_41', 'images_42', 'images_43', 'images_44', 'images_45', 'images_46', 'images_47', 'images_48', 'images_49', 'images_50', 'images_51', 'images_52', 'images_53', 'images_54', 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'images_768', 'images_769', 'images_770', 'images_771', 'images_772', 'images_773', 'images_774', 'images_775', 'images_776', 'images_777', 'images_778', 'images_779', 'images_780', 'images_781', 'images_782', 'images_783']\n", "WARNING:tensorflow:From /Users/aymeric.damien/anaconda2/lib/python2.7/site-packages/tensorflow/contrib/learn/python/learn/estimators/head.py:678: __new__ (from tensorflow.contrib.learn.python.learn.estimators.model_fn) is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "When switching to tf.estimator.Estimator, use tf.estimator.EstimatorSpec. You can use the `estimator_spec` method to create an equivalent one.\n", "INFO:tensorflow:Create CheckpointSaverHook.\n", "WARNING:tensorflow:Issue encountered when serializing resources.\n", "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n", "'_Resource' object has no attribute 'name'\n", "INFO:tensorflow:Graph was finalized.\n", "INFO:tensorflow:Running local_init_op.\n", "INFO:tensorflow:Done running local_init_op.\n", "WARNING:tensorflow:Issue encountered when serializing resources.\n", "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n", "'_Resource' object has no attribute 'name'\n", "INFO:tensorflow:Saving checkpoints for 0 into /var/folders/p7/9nxn5g_s5kv636j86zdg2hrr0000gr/T/tmpE_8oiQ/model.ckpt.\n", "WARNING:tensorflow:Issue encountered when serializing resources.\n", "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n", "'_Resource' object has no attribute 'name'\n", "INFO:tensorflow:loss = 2.3025992, step = 1\n", "INFO:tensorflow:Saving checkpoints for 2 into /var/folders/p7/9nxn5g_s5kv636j86zdg2hrr0000gr/T/tmpE_8oiQ/model.ckpt.\n", "WARNING:tensorflow:Issue encountered when serializing resources.\n", "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n", "'_Resource' object has no attribute 'name'\n", "INFO:tensorflow:Saving checkpoints for 94 into /var/folders/p7/9nxn5g_s5kv636j86zdg2hrr0000gr/T/tmpE_8oiQ/model.ckpt.\n", "WARNING:tensorflow:Issue encountered when serializing resources.\n", "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n", "'_Resource' object has no attribute 'name'\n", "INFO:tensorflow:global_step/sec: 0.199624\n", "INFO:tensorflow:loss = 0.32783023, step = 101 (500.943 sec)\n", "INFO:tensorflow:Requesting stop since we have reached 10 trees.\n", "INFO:tensorflow:Saving checkpoints for 161 into /var/folders/p7/9nxn5g_s5kv636j86zdg2hrr0000gr/T/tmpE_8oiQ/model.ckpt.\n", "WARNING:tensorflow:Issue encountered when serializing resources.\n", "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n", "'_Resource' object has no attribute 'name'\n", "INFO:tensorflow:Loss for final step: 0.21336032.\n" ] }, { "data": { "text/plain": [ "GradientBoostedDecisionTreeClassifier(params={'head': <tensorflow.contrib.learn.python.learn.estimators.head._MultiClassHead object at 0x127568650>, 'weight_column_name': None, 'feature_columns': None, 'center_bias': False, 'num_trees': 10, 'logits_modifier_function': None, 'use_core_libs': False, 'learner_config': num_classes: 10\n", "regularization {\n", " l2: 0.0010000000475\n", "}\n", "constraints {\n", " max_tree_depth: 16\n", "}\n", "learning_rate_tuner {\n", " fixed {\n", " learning_rate: 0.10000000149\n", " }\n", "}\n", "pruning_mode: POST_PRUNE\n", "growing_mode: LAYER_BY_LAYER\n", "multi_class_strategy: DIAGONAL_HESSIAN\n", ", 'examples_per_layer': 1000})" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Display TF info logs\n", "tf.logging.set_verbosity(tf.logging.INFO)\n", "\n", "# Define the input function for training\n", "input_fn = tf.estimator.inputs.numpy_input_fn(\n", " x={'images': mnist.train.images}, y=mnist.train.labels,\n", " batch_size=batch_size, num_epochs=None, shuffle=True)\n", "\n", "# Train the Model\n", "gbdt_model.fit(input_fn=input_fn, max_steps=max_steps)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Active Feature Columns: ['images_0', 'images_1', 'images_2', 'images_3', 'images_4', 'images_5', 'images_6', 'images_7', 'images_8', 'images_9', 'images_10', 'images_11', 'images_12', 'images_13', 'images_14', 'images_15', 'images_16', 'images_17', 'images_18', 'images_19', 'images_20', 'images_21', 'images_22', 'images_23', 'images_24', 'images_25', 'images_26', 'images_27', 'images_28', 'images_29', 'images_30', 'images_31', 'images_32', 'images_33', 'images_34', 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'images_679', 'images_680', 'images_681', 'images_682', 'images_683', 'images_684', 'images_685', 'images_686', 'images_687', 'images_688', 'images_689', 'images_690', 'images_691', 'images_692', 'images_693', 'images_694', 'images_695', 'images_696', 'images_697', 'images_698', 'images_699', 'images_700', 'images_701', 'images_702', 'images_703', 'images_704', 'images_705', 'images_706', 'images_707', 'images_708', 'images_709', 'images_710', 'images_711', 'images_712', 'images_713', 'images_714', 'images_715', 'images_716', 'images_717', 'images_718', 'images_719', 'images_720', 'images_721', 'images_722', 'images_723', 'images_724', 'images_725', 'images_726', 'images_727', 'images_728', 'images_729', 'images_730', 'images_731', 'images_732', 'images_733', 'images_734', 'images_735', 'images_736', 'images_737', 'images_738', 'images_739', 'images_740', 'images_741', 'images_742', 'images_743', 'images_744', 'images_745', 'images_746', 'images_747', 'images_748', 'images_749', 'images_750', 'images_751', 'images_752', 'images_753', 'images_754', 'images_755', 'images_756', 'images_757', 'images_758', 'images_759', 'images_760', 'images_761', 'images_762', 'images_763', 'images_764', 'images_765', 'images_766', 'images_767', 'images_768', 'images_769', 'images_770', 'images_771', 'images_772', 'images_773', 'images_774', 'images_775', 'images_776', 'images_777', 'images_778', 'images_779', 'images_780', 'images_781', 'images_782', 'images_783']\n", "INFO:tensorflow:Starting evaluation at 2018-07-26-01:00:06\n", "INFO:tensorflow:Graph was finalized.\n", "INFO:tensorflow:Restoring parameters from /var/folders/p7/9nxn5g_s5kv636j86zdg2hrr0000gr/T/tmpE_8oiQ/model.ckpt-161\n", "INFO:tensorflow:Running local_init_op.\n", "INFO:tensorflow:Done running local_init_op.\n", "INFO:tensorflow:Finished evaluation at 2018-07-26-01:00:07\n", "INFO:tensorflow:Saving dict for global step 161: accuracy = 0.9273, global_step = 161, loss = 0.23841818\n", "WARNING:tensorflow:Issue encountered when serializing resources.\n", "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n", "'_Resource' object has no attribute 'name'\n", "Testing Accuracy: 0.9273\n" ] } ], "source": [ "# Evaluate the Model\n", "# Define the input function for evaluating\n", "input_fn = tf.estimator.inputs.numpy_input_fn(\n", " x={'images': mnist.test.images}, y=mnist.test.labels,\n", " batch_size=batch_size, shuffle=False)\n", "\n", "# Use the Estimator 'evaluate' method\n", "e = gbdt_model.evaluate(input_fn=input_fn)\n", "print(\"Testing Accuracy:\", e['accuracy'])" ] } ], "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 }	gpl-2.0
marcinofulus/ProgramowanieRownolegle	CUDA/iCSE_PR_map2d.ipynb	2	137134	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Zastosowanie indeksowania wielowymiarowego\n", "\n", "\n", "Zadanie: Oblicz wartości funkcji $\\sin(x^2+y^2)$ na siatce w zadanym obszarze.\n", "\n", "### Krok pierwszy\n", "\n", "Napiszemy jądro obliczające wartości funkcji $\\sin(x^2)$ na zadanym wektorze danych. Jest to zadanie, które można by wykonać używając `gpuarray`, ale dla celów dydaktycznych wykonamy je własnym kernelem." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Ok\n" ] } ], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "print(\"Ok\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7fe64c9652e8>]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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HH6ozcqSuWn/+eduVUFU2bgRefVUXv3LxZOXy8vSxYq+c6vndRMqRavPFyAQI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\nrE2kghAgnNdvBgjyXXY2sHSp7Sr2PxE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"text/plain": [ "<matplotlib.figure.Figure at 0x7fe64c1a9c50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "import pycuda.driver as cuda\n", "import pycuda.autoinit\n", "from pycuda.compiler import SourceModule\n", "\n", "mod = SourceModule(\"\"\"\n", " __global__ void sin1d(float x,float y)\n", " {\n", " int idx = threadIdx.x + blockDim.xblockIdx.x;\n", " y[idx] = sinf(powf(x[idx],2.0f));\n", " }\n", " \"\"\")\n", "\n", "Nx = 128\n", "x = np.linspace(-3,3,Nx).astype(np.float32)\n", "y = np.empty_like(x)\n", "func = mod.get_function(\"sin1d\")\n", "func(cuda.In(x),cuda.Out(y),block=(Nx,1,1),grid=(1,1,1))\n", "plt.plot(x,y)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Krok drugi\n", "\n", "Nie będziemy teraz przesyłać wartości $x$ do jądra, ale obliczymy je w locie, ze wzoru:\n", "\n", "$$ x = x_0 + i \\frac{\\Delta x}{N}$$" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7fe64c1b5278>]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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FdiQUBa+/Dqxera+roBsxQpP9jz+2HUl8OEs40+W8L6Y5jcEEIhG9emnxyp496R+LavfJ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\nGhMIIiIiShoTCCIiIkoaEwgiIiJK2v8H+9IScht26mIAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fe65c23f240>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import pycuda.driver as cuda\n", "import pycuda.autoinit\n", "from pycuda.compiler import SourceModule\n", "\n", "mod = SourceModule(\"\"\"\n", " __global__ void sin1da(float y)\n", " {\n", " int idx = threadIdx.x + blockDim.xblockIdx.x;\n", " float x = -3.0f+6.0ffloat(idx)/blockDim.x;\n", " y[idx] = sinf(powf(x,2.0f));\n", " }\n", " \"\"\")\n", "\n", "Nx = 128\n", "x = np.linspace(-3,3,Nx).astype(np.float32)\n", "y = np.empty_like(x)\n", "func = mod.get_function(\"sin1da\")\n", "func(cuda.Out(y),block=(Nx,1,1),grid=(1,1,1))\n", "plt.plot(x,y,'r')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Krok trzeci\n", "\n", "Wykonamy probkowanie funkcji dwóch zmiennych, korzystając z wywołania jądra, które zawiera $N_x$ wątków w bloku i $N_y$ bloków. \n", "\n", "Proszę zwrócić szczególną uwagę na linie:\n", "\n", " int idx = threadIdx.x;\n", " int idy = blockIdx.x;\n", " \n", "zawierające wykorzystanie odpowiednich indeksów na CUDA, oraz sposób obliczania globalnego indeksu tablicy wartości, która jest w formacie \"row-major\"!\n", "\n", " int gid = idx + blockDim.xidy;" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pycuda.driver as cuda\n", "import pycuda.autoinit\n", "from pycuda.compiler import SourceModule\n", "\n", "mod = SourceModule(\"\"\"\n", " __global__ void sin2d(float z)\n", " {\n", " int idx = threadIdx.x;\n", " int idy = blockIdx.x;\n", "\n", " int gid = idx + blockDim.xidy;\n", "\n", " float x = -4.0f+6.0ffloat(idx)/blockDim.x;\n", " float y = -3.0f+6.0ffloat(idy)/gridDim.x;\n", " \n", " z[gid] = sinf(powf(x,2.0f)+powf(y,2.0f));\n", " }\n", " \"\"\")\n", "\n", "Nx = 128\n", "Ny = 64\n", "x = np.linspace(-4,2,Nx).astype(np.float32)\n", "y = np.linspace(-3,3,Ny).astype(np.float32)\n", "XX,YY = np.meshgrid(x,y)\n", "z = np.zeros(NxNy).astype(np.float32)\n", "\n", "func = mod.get_function(\"sin2d\")\n", "func(cuda.Out(z),block=(Nx,1,1),grid=(Ny,1,1))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Porównajmy wyniki:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.contour.QuadContourSet at 0x7fe64c941e48>" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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U3AW8bzDvc8C9u5dlyoBS6meB7wZeYIz5LzPLHY8MpEwP1CICsGr0RWhXBFqOBnhLQGsN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\n6giEDWCVFihNBKyjAndy2E8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"text/plain": [ "<matplotlib.figure.Figure at 0x7fe64ca8ac88>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.contourf(XX,YY,z.reshape(Ny,Nx) )" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.contour.QuadContourSet at 0x7fe64c8b9b38>" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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LlCYBMPks8j4WS4CdOJY2KgFKyTTtd0oK7MDPh7T7U3dMavph39jU9N9eKWj6fM941YwX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\nYdv6onPu9/MdmVyMMe8GzgFngT87LMAHeNQ59xe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"text/plain": [ "<matplotlib.figure.Figure at 0x7fe64c99b5f8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.contourf(XX,YY,np.sin(XX2+YY2))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Krok czwarty\n", "\n", "Algorytm ten nie jest korzystny, gdyż rozmiar bloku determinuje rozmiar siatki na, której próbkujemy funkcje. \n", "\n", "Optymalnie było by wykonywać operacje w blokach o zadanym rozmiarze, niezależnie od ilości próbek danego obszaru. \n", "\n", "Poniższy przykład wykorzystuje dwuwymiarową strukturę zarówno bloku jak i gridu. Dzielimy wątki tak by w obrębie jednego bloku były wewnatrz kwadratu o bokach 4x4.\n", "\n" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pycuda.driver as cuda\n", "import pycuda.autoinit\n", "from pycuda.compiler import SourceModule\n", "\n", "mod = SourceModule(\"\"\"\n", " __global__ void sin2da(float z)\n", " {\n", " int ix = threadIdx.x + blockIdx.x blockDim.x;\n", " int iy = threadIdx.y + blockIdx.y * blockDim.y;\n", " \n", " int gid = ix + iy * blockDim.x * gridDim.x;\n", " float x = -4.0f+6.0ffloat(ix)/(blockDim.xgridDim.x);\n", " float y = -3.0f+6.0ffloat(iy)/(blockDim.ygridDim.y);\n", " \n", " z[gid] = sinf(powf(x,2.0f)+powf(y,2.0f));\n", " }\n", " \"\"\")\n", "\n", "block_size = 4\n", "Nx = 32block_size\n", "Ny = 32block_size\n", "x = np.linspace(-4,2,Nx).astype(np.float32)\n", "y = np.linspace(-3,3,Ny).astype(np.float32)\n", "XX,YY = np.meshgrid(x,y)\n", "z = np.zeros(Nx*Ny).astype(np.float32)\n", "\n", "func = mod.get_function(\"sin2da\")\n", "func(cuda.Out(z),\\\n", " block=(block_size,block_size,1),\\\n", " grid=(Nx//block_size,Ny//block_size,1) )\n" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.contour.QuadContourSet at 0x7fe64c834a58>" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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QbFmqYt4/pQtB+fMM9EhRL1DTDbDmOQQ5qbJWIAFSJ+BOcfdrCPEtKIr37DPb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\n0CGisEyMpy5q97eE6vwhnABAXfe9yEA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"text/plain": [ "<matplotlib.figure.Figure at 0x7fe64c909cf8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.contourf(XX,YY,z.reshape(Ny,Nx) )" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }	gpl-3.0
ual/hedonic-models	sales-hedonic.ipynb	1	28218	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Tutorial on Hedonic Regression\n", "\n", "This material uses Python to demonstrate some aspects of hedonic regression, but the objective here is not to learn to program, but to understand the hedonic regression methodology. We begin with an example in which we generate some synthetic data using a set of coefficients and a mathematical model, and learn those coefficients using a statistical method called multiple regression." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Startup steps\n", "import pandas as pd, numpy as np, statsmodels.api as sm\n", "import matplotlib.pyplot as plt, matplotlib.cm as cm, matplotlib.font_manager as fm\n", "import matplotlib.mlab as mlab\n", "from scipy.stats import pearsonr, ttest_rel\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Generate data using a model we define:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "nsample = 100\n", "X = np.linspace(0, 10, 100)\n", "beta = np.array([0, 2])\n", "e = np.random.normal(size=nsample)\n", "X = sm.add_constant(X)\n", "y = np.dot(X, beta) + e" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot the data and the model. Note that the intercept" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plt.figure(1, figsize=(10,8), )\n", "plt.plot([0, 10], [0, 20])\n", "plt.scatter(x, y, marker=0, s=10, c='g')\n", "plt.axis([0, 10, 0, 20])\n", "plt.show();" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "model = sm.OLS(y, X)\n", "results = model.fit()\n", "print(results.summary())" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print('Parameters: ', results.params)\n", "print('R2: ', results.rsquared)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "nsample = 100\n", "x = np.linspace(0, 10, 100)\n", "X = np.column_stack((x, x*2))\n", "beta = np.array([1, 2, .5])\n", "e = np.random.normal(size=nsample)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "X = sm.add_constant(X)\n", "y = np.dot(X, beta) + e" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "X[:10]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plt.scatter(x,y, s=5)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "model = sm.OLS(y, X)\n", "results = model.fit()\n", "print(results.summary())" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sf = pd.read_csv('data/redfin_2017-03-05-17-45-34-san-francisco-county-1-month.csv')\n", "sf.columns" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sf1 = sf.rename(index=str, columns={'SALE TYPE': 'saletype',\n", " 'SOLD DATE': 'solddate', 'PROPERTY TYPE': 'proptype', 'ADDRESS': 'address',\n", " 'CITY': 'city', 'STATE': 'state', 'ZIP': 'zip', 'PRICE': 'price', 'BEDS': 'beds',\n", " 'BATHS': 'baths', 'LOCATION': 'location', 'SQUARE FEET': 'sqft', 'LOT SIZE': 'lotsize',\n", " 'YEAR BUILT': 'yrbuilt', 'DAYS ON MARKET': 'daysonmkt', '$/SQUARE FEET': 'pricesqft',\n", " 'LATITUDE': 'latitude', 'LONGITUDE': 'longitude', 'HOA/MONTH': 'hoamonth',\n", " 'URL (SEE http://www.redfin.com/buy-a-home/comparative-market-analysis FOR INFO ON PRICING)': 'url',\n", " 'STATUS': 'status', 'NEXT OPEN HOUSE START TIME': 'nextopenstart', 'NEXT OPEN HOUSE END TIME': 'nextopenend',\n", " 'SOURCE': 'source', 'MLS#': 'mls', 'FAVORITE': 'favorite', 'INTERESTED': 'interested'\n", " })\n", "\n", "sf1.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sf1.describe()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plt.figure(1, figsize=(10,8), )\n", "plt.scatter(sf1['sqft'], sf1['price'], marker=0, s=10, c='g')\n", "#plt.axis([12, 16, 12, 16])\n", "plt.show();" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import statsmodels.api as sm\n", "import numpy as np\n", "from patsy import dmatrices\n", "y, X = dmatrices('np.log(price) ~ np.log(sqft) + C(baths)', \n", " data=sf1, return_type='dataframe')\n", "mod = sm.OLS(y, X)\n", "res = mod.fit()\n", "residuals = res.resid\n", "predicted = res.fittedvalues\n", "observed = y\n", "print(res.summary())" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plt.hist(residuals, bins=25, normed=True, alpha=.5)\n", "mu = residuals.mean()\n", "variance = residuals.var()\n", "sigma = residuals.std()\n", "x = np.linspace(-3, 3, 100)\n", "plt.plot(x,mlab.normpdf(x, mu, sigma));" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plt.figure(1, figsize=(10,8), )\n", "plt.plot([12, 16], [0, 0], c='b')\n", "plt.scatter(predicted, residuals, marker=0, s=10, c='g');\n", "plt.axis([12, 16, -0.8, 0.8])\n", "plt.show();" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plt.figure(1, figsize=(10,8), )\n", "plt.plot([12, 16], [12, 16])\n", "plt.scatter(observed, predicted, marker=0, s=10, c='g')\n", "plt.axis([12, 16, 12, 16])\n", "plt.show();" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "vars = pd.read_csv('data/ba_block_variables.csv', dtype={'block_id':object})" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "vars.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "smallvars = vars[['block_id','block_groups_sum_persons','block_groups_sum_acres','block_groups_total_jobs',\n", " 'block_groups_median_children', 'block_groups_median_persons' , 'block_groups_median_income',\n", " 'block_groups_prop_tenure_2', 'nodes_low_income_hh_1500m', 'nodes_high_income_hh_1500m',\n", " 'nodes_jobs_5000m', 'nodes_jobs_30km', 'nodes_population_400m', 'nodes_population_800m',\n", " 'nodes_jobs_800m', 'prop_race_of_head_1', 'prop_race_of_head_2', 'pumas_density_households',\n", " 'nodes_jobs_3000m_agg1', 'nodes_jobs_3000m_agg2', 'pumas_density_jobs', 'nodes_jobs_40km',\n", " 'nodes_jobs_3000m_agg3', 'nodes_jobs_3000m_agg4', 'nodes_jobs_3000m_agg5',\n", " 'block_groups_prop_persons_1', 'block_groups_prop_persons_2', 'block_groups_median_age_of_head',\n", " 'puma10_id_is_0607501', 'puma10_id_is_0607502', 'puma10_id_is_0607503', 'puma10_id_is_0607504']]\n", "sv = smallvars.rename(index=str, columns={'block_groups_sum_persons': 'bgpop',\n", " 'block_groups_sum_acres': 'bgacres',\n", " 'block_groups_total_jobs': 'bgjobs',\n", " 'block_groups_median_children': 'bgmedkids',\n", " 'block_groups_median_persons': 'bgmedhhs',\n", " 'block_groups_median_income': 'bgmedinc',\n", " 'block_groups_prop_tenure_2': 'proprent',\n", " 'nodes_low_income_hh_1500m': 'lowinc1500m',\n", " 'nodes_high_income_hh_1500m': 'highinc1500m',\n", " 'nodes_jobs_5000m': 'lnjobs5000m',\n", " 'nodes_jobs_30km': 'lnjobs30km',\n", " 'nodes_jobs_40km': 'lnjobs40km',\n", " 'nodes_population_400m': 'lnpop400m',\n", " 'nodes_population_800m': 'lnpop800m',\n", " 'nodes_jobs_800m': 'lnjobs800m',\n", " 'prop_race_of_head_1': 'propwhite',\n", " 'prop_race_of_head_2': 'propblack',\n", " 'pumas_density_households': 'pumahhden',\n", " 'pumas_density_jobs': 'pumajobden',\n", " 'nodes_jobs_3000m_agg1': 'lnbasic3000m',\n", " 'nodes_jobs_3000m_agg2': 'lntcpuw3000m',\n", " 'nodes_jobs_3000m_agg3': 'lnret3000m',\n", " 'nodes_jobs_3000m_agg4': 'lnfire3000m',\n", " 'nodes_jobs_3000m_agg5': 'lnserv3000m',\n", " 'block_groups_prop_persons_1': 'prop1per',\n", " 'block_groups_prop_persons_2': 'prop2per',\n", " 'block_groups_median_age_of_head': 'bgmedagehd',\n", " 'puma10_id_is_0607501': 'puma1',\n", " 'puma10_id_is_0607502': 'puma2',\n", " 'puma10_id_is_0607503': 'puma3',\n", " 'puma10_id_is_0607504': 'puma4'\n", " })\n", "sv.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Recoded detailed race code\n", " 1 .White alone\n", " 2 .Black or African American alone\n", " 3 .American Indian alone\n", " 4 .Alaska Native alone\n", " 5 .American Indian and Alaska Native tribes specified; or American\n", " .Indian or Alaska native, not specified and no other races\n", " 6 .Asian alone\n", " 7 .Native Hawaiian and Other Pacific Islander alone\n", " 8 .Some other race alone\n", " 9 .Two or more major race groups " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "with open('pg_engine_redfin.txt') as f:\n", " pg_engine = f.readlines()\n", "from sqlalchemy import create_engine\n", "engine = create_engine(pg_engine[0])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%time\n", "import pandas as pd\n", "df = pd.read_sql_query('select from \"rental_listings\"',con=engine)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(df.dtypes)\n", "df.describe()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# convert the date column to yyyy-mm-dd date format\n", "df['date'] = pd.to_datetime(df['date'], format='%Y-%m-%d')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# load the 2014 census data set of MSAs\n", "census = pd.read_csv('data/census_pop_income.csv', encoding='ISO-8859-1')\n", "census['median_income'] = census['2014_median_income'].str.replace(',','').astype(int)\n", "census['population'] = census['2014_pop_est'].str.replace(',','').astype(int)\n", "census = census.drop(labels='notes', axis=1, inplace=False)\n", "census = census.set_index('region')\n", "census.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# these are the 15 most populous metros by population, defined by census bureau 2014 estimates\n", "most_populous_regions = census['2014_pop_est'].sort_values(ascending=False, inplace=False)\n", "print(most_populous_regions.head(15))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df = df.merge(census, left_on='region', right_index=True)\n", "df.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Create an HDF5 file if desired, in the data directory\n", "#df.to_hdf('data/rents.h5','rents',append=False)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Load from HDF5 if desired\n", "#df = pd.HDFStore('data/rents.h5')\n", "#df" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "upper_percentile = 0.998\n", "lower_percentile = 0.002\n", "\n", "# how many rows would be within the upper and lower percentiles?\n", "upper = int(len(df) * upper_percentile)\n", "lower = int(len(df) * lower_percentile)\n", "\n", "# get the rent/sqft values at the upper and lower percentiles\n", "rent_sqft_sorted = df['rent_sqft'].sort_values(ascending=True, inplace=False)\n", "upper_rent_sqft = rent_sqft_sorted.iloc[upper]\n", "lower_rent_sqft = rent_sqft_sorted.iloc[lower]\n", "\n", "# get the rent values at the upper and lower percentiles\n", "rent_sorted = df['rent'].sort_values(ascending=True, inplace=False)\n", "upper_rent = rent_sorted.iloc[upper]\n", "lower_rent = rent_sorted.iloc[lower]\n", "\n", "# get the sqft values at the upper and lower percentiles\n", "sqft_sorted = df['sqft'].sort_values(ascending=True, inplace=False)\n", "upper_sqft = sqft_sorted.iloc[upper]\n", "lower_sqft = sqft_sorted.iloc[lower]\n", "\n", "print('valid rent_sqft range:', [lower_rent_sqft, upper_rent_sqft])\n", "print('valid rent range:', [lower_rent, upper_rent])\n", "print('valid sqft range:', [lower_sqft, upper_sqft])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# create a boolean vector mask to filter out any rows with rent_sqft outside of the reasonable values\n", "rent_sqft_mask = (df['rent_sqft'] > lower_rent_sqft) & (df['rent_sqft'] < upper_rent_sqft)\n", "\n", "# create boolean vector masks to filter out any rows with rent or sqft outside of the reasonable values\n", "rent_mask = (df['rent'] > lower_rent) & (df['rent'] < upper_rent)\n", "sqft_mask = (df['sqft'] > lower_sqft) & (df['sqft'] < upper_sqft)\n", "\n", "# filter the thorough listings according to these masks\n", "filtered_listings = pd.DataFrame(df[rent_sqft_mask & rent_mask & sqft_mask])\n", "len(filtered_listings)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "filtered_listings.describe()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#sfbay = filtered_listings[filtered_listings['region']=='sfbay']\n", "sfbay = df[df['region']=='sfbay']\n", "sfbay.describe()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#sfbay['rent_sqft'].quantile(.01)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#sfbay['sqft'].quantile(.01)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# create a boolean vector mask to filter out any rows with rent_sqft and sqft in Bay Area under 1 percentile\n", "#sfbay_rent_sqft_mask = (sfbay['rent_sqft'] > sfbay['rent_sqft'].quantile(.01) )\n", "\n", "# create boolean vector masks to filter out any rows with rent or sqft outside of the reasonable values\n", "#sfbay_sqft_mask = (sfbay['sqft'] > sfbay['sqft'].quantile(.01) )\n", "\n", "# filter the thorough listings according to these masks\n", "#sfbay_filtered = pd.DataFrame(sfbay[sfbay_rent_sqft_mask & sfbay_sqft_mask])\n", "#len(sfbay_filtered)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "upper_percentile = 0.998\n", "lower_percentile = 0.002\n", "\n", "# how many rows would be within the upper and lower percentiles?\n", "upper = int(len(sfbay) * upper_percentile)\n", "lower = int(len(sfbay) * lower_percentile)\n", "\n", "# get the rent/sqft values at the upper and lower percentiles\n", "rent_sqft_sorted = sfbay['rent_sqft'].sort_values(ascending=True, inplace=False)\n", "upper_rent_sqft = rent_sqft_sorted.iloc[upper]\n", "lower_rent_sqft = rent_sqft_sorted.iloc[lower]\n", "\n", "# get the rent values at the upper and lower percentiles\n", "rent_sorted = sfbay['rent'].sort_values(ascending=True, inplace=False)\n", "upper_rent = rent_sorted.iloc[upper]\n", "lower_rent = rent_sorted.iloc[lower]\n", "\n", "# get the sqft values at the upper and lower percentiles\n", "sqft_sorted = sfbay['sqft'].sort_values(ascending=True, inplace=False)\n", "upper_sqft = sqft_sorted.iloc[upper]\n", "lower_sqft = sqft_sorted.iloc[lower]\n", "\n", "print('valid rent_sqft range:', [lower_rent_sqft, upper_rent_sqft])\n", "print('valid rent range:', [lower_rent, upper_rent])\n", "print('valid sqft range:', [lower_sqft, upper_sqft])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# create a boolean vector mask to filter out any rows with rent_sqft outside of the reasonable values\n", "rent_sqft_mask = (sfbay['rent_sqft'] > lower_rent_sqft) & (sfbay['rent_sqft'] < upper_rent_sqft)\n", "\n", "# create boolean vector masks to filter out any rows with rent or sqft outside of the reasonable values\n", "rent_mask = (sfbay['rent'] > lower_rent) & (sfbay['rent'] < upper_rent)\n", "sqft_mask = (sfbay['sqft'] > lower_sqft) & (sfbay['sqft'] < upper_sqft)\n", "\n", "# filter the thorough listings according to these masks\n", "sfbay_filtered = pd.DataFrame(sfbay[rent_sqft_mask & rent_mask & sqft_mask])\n", "len(sfbay_filtered)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sfbay_filtered.describe()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sf = sfbay_filtered.merge(sv, left_on='fips_block', right_on='block_id')\n", "sf['northsf'] = sf['puma1']+sf['puma2']+sf['puma3']+sf['puma4']\n", "sf['sqft1'] = sf['sqft']sf['sqft']<1500\n", "sf['sqft2'] = sf['sqft']sf['sqft']>1499\n", "sf['pct1per'] = sf['prop1per']100\n", "sf['pct2per'] = sf['prop2per']100\n", "sf['pctrent'] = sf['proprent']100\n", "sf['pctblack'] = sf['propblack']100\n", "sf.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sf.describe()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "sf['bgpopden'] = sf['bgpop']/sf['bgacres']\n", "sf['bgjobden'] = sf['bgjobs']/sf['bgacres']\n", "sf['highlowinc1500m'] = sf['highinc1500m']/sf['lowinc1500m']" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sf.dtypes" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Use either filtered_listings for the national dataset or sfbay_filtered for the Bay Area subset\n", "\n", "import statsmodels.api as sm\n", "import numpy as np\n", "from patsy import dmatrices\n", "y, X = dmatrices('np.log(rent) ~ np.log(sqft) + bedrooms + bathrooms \\\n", " ', \n", " data=sf, return_type='dataframe')\n", "mod = sm.OLS(y, X)\n", "res = mod.fit()\n", "residuals = res.resid\n", "predicted = res.fittedvalues\n", "observed = y\n", "print(res.summary())" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Use either filtered_listings for the national dataset or sfbay_filtered for the Bay Area subset\n", "\n", "import statsmodels.api as sm\n", "import numpy as np\n", "from patsy import dmatrices\n", "y, X = dmatrices('np.log(rent) ~ np.log(sqft) + bedrooms + bathrooms +\\\n", " np.log(bgmedinc) + lnjobs5000m + lnjobs40km + pctblack \\\n", " + pumahhden + pctrent + pct1per + pct2per + bgmedagehd \\\n", " ', \n", " data=sf, return_type='dataframe')\n", "mod = sm.OLS(y, X)\n", "res = mod.fit()\n", "residuals = res.resid\n", "predicted = res.fittedvalues\n", "observed = y\n", "print(res.summary())" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plt.hist(residuals, bins=25, normed=True, alpha=.5)\n", "mu = residuals.mean()\n", "variance = residuals.var()\n", "sigma = residuals.std()\n", "x = np.linspace(-3, 3, 100)\n", "plt.plot(x,mlab.normpdf(x, mu, sigma));" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plt.figure(1, figsize=(10,8), )\n", "plt.plot([7, 9], [0, 0], c='b')\n", "plt.scatter(predicted, residuals, marker=0, s=2, c='g');\n", "plt.axis([7.25, 9, -1.5, 1.5])\n", "plt.show();" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plt.figure(1, figsize=(10,8), )\n", "plt.plot([6, 9.5], [6, 9.5])\n", "plt.scatter(observed, predicted, marker=0, s=2, c='g')\n", "plt.axis([6.5, 9.5, 6.5, 9.5])\n", "plt.show();" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(residuals.mean())\n", "print(residuals.std())" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# If we want to use WLS we need a useful set of weights. The default produces the same results as OLS \n", "mod = sm.WLS(y, X, weights=1.)\n", "res = mod.fit()\n", "print(res.summary())" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Warning, this is a very intensive process and will take a while\n", "%%time\n", "from pymc3 import Model, NUTS, sample\n", "from pymc3.glm import glm\n", "\n", "with Model() as model_glm:\n", " glm('np.log(rent) ~ np.log(sqft) + bedrooms + bathrooms', sfbay_filtered)\n", " trace = sample(5000)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from pymc3 import traceplot\n", "%matplotlib inline\n", "traceplot(trace);" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from scipy import optimize\n", "from pymc3 import find_MAP\n", "with model_glm:\n", "\n", " # obtain starting values via MAP\n", " start = find_MAP(fmin=optimize.fmin_powell)\n", "\n", " # draw 2000 posterior samples\n", " trace = sample(5000, start=start)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "traceplot(trace);" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import theano\n", "import pymc3 as pm" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fig = plt.figure(figsize=(7, 7))\n", "ax = fig.add_subplot(111, xlabel='x', ylabel='y', title='Generated data and underlying model')\n", "ax.plot(np.log(sfbay['sqft']), np.log(sfbay['rent']), 'o', markersize=.5, color='blue', label='sampled data')\n", "#ax.plot(x, true_regression_line, label='true regression line', lw=2.)\n", "#pm.glm.plot_posterior_predictive(trace, samples=100,\n", "# label='posterior predictive regression lines')\n", "plt.legend(loc=0);" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "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": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 0 }	bsd-3-clause
btw2111/intro-numerical-methods	0_intro_numerical_methods.ipynb	8	82547	{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Introduction and Motivation: Modeling and methods for scientific computing" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Why are we here?\n", "\n", "Cannot solve everything\n", "\n", "$$x^5 + 3x^2+ 2x + 3 = 0$$\n", "\n", "$$f(x,y,z,t) = 0$$" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Problems can be too big...\n", "\n", "![Linear Regression](./images/linear_regression.png)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Actually want an answer..." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Numerics compliment analytical methods" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Why should I care?" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "#### The Retirement Problem\n", "\n", "$$A = \\frac{P}{r} \\left((1+r)^n - 1 \\right)$$\n", "\n", "$P$ is the incremental payment\n", "\n", "$r$ is the interest rate per payment period\n", "\n", "$n$ is the number of payments\n", "\n", "$A$ is the total amount after n payments\n", "\n", "Note that these can all be functions of $r$, $n$, and time" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "image/png": 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TBEGoQzNo6NboyN2Ti893PvQf3d9sc1cZDKzW6diZk0PE9OlMHzzYbHM3S3g4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\n1mGq8fEqAunDD9Xd8amn4Ic/bFMNB03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"text/plain": [ "<matplotlib.figure.Figure at 0x105a20e90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "import numpy\n", "import matplotlib.pyplot as plt\n", "\n", "def A(P, r, n):\n", " return P / r * ((1 + r)*n - 1)\n", "\n", "n = numpy.linspace(0, 20, 100)\n", "target = 5000\n", "plt.hold(True)\n", "for r in [0.02, 0.05, 0.08, 0.1, 0.12]:\n", " plt.plot(n, A(100, r, n))\n", "plt.plot(n, numpy.ones(n.shape) target, 'k--')\n", "plt.legend([\"r = 0.02\", \"r = 0.05\", \"r = 0.08\", \"r = 0.1\", \"r = 0.12\", \"Target\"], loc=2)\n", "plt.xlabel(\"Years\")\n", "plt.ylabel(\"Annuity Value (Dollars)\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "#### Boat race\n", "Given a river (say a sinusoid) find the total length actually rowed over a given interval\n", "\n", "$$f(x) = A \\sin x$$" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1091a4310>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = numpy.linspace(0, 4 * numpy.pi)\n", "plt.plot(x, 2.0 * numpy.sin(x))\n", "plt.title(\"River Sine\")\n", "plt.xlabel(\"x\")\n", "plt.ylabel(\"y\")\n", "plt.axis([0, 4numpy.pi, -2, 2])\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "-" } }, "source": [ "We need to calculate the function $f(x)$'s arc-length from $[0, 4 \\pi]$\n", "\n", "$$L = \\int_0^{4 \\pi} \\sqrt{1 + \|f'(x)\|^2} dx$$\n", "\n", "In general need numerical quadrature." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "#### Non-Linear population growth\n", "\n", "Lotka-Volterra Predator-Prey model\n", "\n", "$$\\frac{d R}{dt} = R \\cdot (a - b \\cdot F)$$\n", "$$\\frac{d F}{dt} = F \\cdot (c \\cdot R + d)$$\n", "\n", " - Where are the steady states?\n", " - How do we solve the initial value problem?\n", " - How do we understand the non-linear dynamics?\n", " - How do we evaluate whether this is a good model?" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "#### Interpolation and Data Fitting\n", "\n", "Finding trends in real data represented without a closed form (analytical form).\n", "\n", "Sunspot counts" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "slideshow": { "slide_type": "-" } }, "outputs": [ { "data": { "image/png": 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gDjGl7cWki08aByEC0QbErsHciINggQhtjZ86RTeWLcSjDvADwh2EK8Sk3/hA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\nPvAOoVQC/u//TcYPhJzf1VcneYhHHgF+/ufDPmv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"text/plain": [ "<matplotlib.figure.Figure at 0x109515fd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data = numpy.loadtxt(\"./data/sunspot.dat\")\n", "data.shape\n", "plt.plot(data[:, 0], data[:, 1])\n", "plt.xlabel(\"Year\")\n", "plt.ylabel(\"Number\")\n", "plt.title(\"Number of Sunspots\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Why Python?\n", "(Based on Jake Vanderplas and extended)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### C, C++, Fortran\n", "\n", "#### Pros:\n", " - Performance and legacy computing codes available\n", "\n", "#### Cons:\n", " - Syntax not optimized for casual programming\n", " - No interactive facilities\n", " - Difficult visualization, text processing, etc.\n", " " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### IDL, Matlab, Mathematica, etc.\n", "\n", "#### Pros:\n", " - Interactive with easy visualization tools\n", " - Extensive scientific and engineering libraries available\n", "\n", "#### Cons:\n", " - Costly and proprietary\n", " - Unpleasant for large-scale computing and non-mathematical tasks" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### Python\n", "#### Pros:\n", " - Python is free (BSD license) and highly portable (Windows, Mac OS X, Linux, etc.)\n", " - Interactive interpreter\n", " - Readability\n", " - Simple\n", " - Extensive documentation\n", " - Memory management is (mostly) transparent\n", " - Clean and object-oriented\n", " - Built-in types" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "#### Pros:\n", "- Comprehensive standard library\n", " - Well-established 3rd-party packages (NumPy, SciPy, matplotlib, etc.)\n", " - Easily wraps existing legacy code in C, C++ and Fortran\n", " - Python mastery is marketable\n", " - Scalability\n", " - Interactive experimentation\n", " - Code can be one-line scripts or million-line projects\n", " - Used by novices and full-time professionals alike" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "#### Cons:\n", " - Can be slow\n", " - Packaging system is a bit crufty\n", " - Too many Monty Python jokes (not really a con)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## IPython Notebooks\n", "\n", "The notebook environment gives us a convenient means for working with code while annotating it. We will only cover the key concepts here and hope that you will explore on your own the environments." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### Toolbar\n", "\n", " - Notebooks are modal, they have an edit mode (editing cells) and command mode.\n", " - Highly recommend taking the tour and checking out the help menu " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### Content types\n", " - Markdown\n", " - LaTeX $x^2$\n", " - Python\n", " - NumPy, SciPy, and other packages" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Obtaining the Notebooks\n", "\n", "All notebooks are found on [github](http://github.com/mandli/intro-numerical-methods).\n", "\n", "Highly recommend obtaining a github account if you do not already have one. Will allow you start to become comfortable with `git`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Clone* the repository\n", "\n", "`$> git clone git://github.com/mandli/intro-numerical-methods`\n", "\n", "Pull in new changes\n", "\n", "`$> git pull`\n", "\n", "Push new changes (you do not have permission to do this\n", "\n", "`$> git push`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Also note that you can watch what changes were made and submit issues to the github project page if you find mistakes (PLEASE DO THIS!)." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Installation\n", "\n", "A few options\n", " 1. Install on your own machine\n", " 2. Use a cloud service" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### Your own machine\n", "\n", "The easiest way to install all the components you will need for the class is to use Continuum Analytics' [Anaconda](http://continuum.io/downloads) distribution. We will be using python 2.7.x for all in class demos and homework so I strongly suggest you do not get the Python 3.4 version.\n", "\n", "Alternatives to using Anaconda also exist in the form of Enthought's [Canopy](https://www.enthought.com/products/canopy/) distribution which provides all the tools you will need as well along with an IDE (development environment)." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### The \"cloud\"\n", "\n", "Instead of running things locally on your machine there are a number of cloud services that you are welcome to use in order to get everything running easily.\n", " 1. Sage-Math-Cloud - Create an account on [Sage-Math-Cloud](https://cloud.sagemath.com) and interact with python via the provided terminal or Ipython notebook inteface.\n", " 1. Wakari - Continuum also has a free cloud service called [Wakari](https://wakari.io) that you can sign up for which provides an Anaconda installation along with similar tools to Sage-Math-Cloud." ] } ], "metadata": { "celltoolbar": "Slideshow", "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.3" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
computational-class/computational-communication-2016	qinqiang/homework3/homework_3.ipynb	1	13080	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "吸大麻真的不会上瘾吗？High一下的代价比你想象得大\n", "2016-04-20\n", "\n", "微信ID:ibookreview『每个早晨，与534000人一起阅读』据说，今天是国际大麻日。书评君也是第一次知道还有这么个节日，说是在1971年，美国一群高中生约好在4月20日的下午4点20分在学校一起吸食大麻。此后，4月20日就成为国际大麻日，旨在开启大麻合法化运动。但是，吸食大麻真的像听起来那么轻松随意，甚至很酷吗？其实，要High那么一下的代价比你想象中更大，它的危害一点都不比A类毒品差。一名在摩洛哥从事了一辈子大麻走私的毒贩把大麻的特性概括地很好：它一点一点地勾引你，让你慢慢地成瘾，实际上它比A类毒品更邪恶。现在，全世界的人口中有4.4％的人（约1.9亿人）吸食大麻，有0.6％的人（约2500万人）每天吸食。我国现在则有两百万至三百万吸毒者，其中至少有一百万人抽大麻。而在1949年新中国成立时，吸毒者的数字更曾经达到过两千万。在一些贫困地区，已经出现了秘密的大麻农场。大麻是一种利润丰厚的经济作物，全球的总产值为358亿美元，超出玉米（233亿美元）和小麦（75亿美元）产值的总和。如此巨额的经济利益，加之在世界范围内对大麻走私网络打击不力，造成大麻泛滥，打击大麻的道路仍然路漫漫其修远。要说大麻，不能不提的是哈希。在世界范围内，大麻加工成哈希的形式来运输和走私是非常常见的形式。什么是哈希？今天书评君想跟大家聊聊大麻（或者说哈希）在世界范围内种植、加工、走私、分销的利益链条。读完这篇文章，你会发现，大麻的破坏力可一点都不比海洛因等A类毒品小啊。大麻烟叶与哈希制品1哈希是什么？它是大麻的浓缩制品，在各种大麻制品中纯度最高哈希（hash，hashish）是大麻的浓缩制品，由雌性大麻植物的树脂制成。它所含的四氢大麻酚，简称THC，可以导致吸食者产生欣快亢奋的感觉。哈希中的THC含量最高可达35%，而其他种类的大麻制品的THC含量通常仅有5%—15%。哈希的THC强度取决于用于提炼它的大麻植物的THC强度。不过，就连哈希这种毒品制造业，也在世界范围内存在着山寨和造假。市面上许多哈希当中并不是只有大麻这一种成分，毒贩们为了降低成本，常常将橡胶、塑料、泥土等压制成粉末状添加在毒品中贩卖。2011 年 6 月 3 日巴西，一些当地民众在参加大麻合法化游行。2哈希不会上瘾吗？比A类毒品更邪恶，一点点勾引你，让你逐渐上瘾哈希往往被视为最易于为社会所接受的娱乐性药物（recreationaldrug）。哈希吸食者坚称，它可以改变人的感官体验以及对周围世界的看法，它对人体是无害的。甚至，在很多时候，吸食哈希或者大麻成为“酷”的象征。但批评者则认为，经常吸食哈希可以导致心理依赖性，并且摧毁人的动力感官。美国国家药物滥用研究所（NIDA）的研究报告显示：长期吸食大麻会导致成瘾，并且接触大麻年龄越小和吸食频率高者更容易成瘾。关于哈希本身的药物效力问题。摩洛哥一位富有的哈希大亨总结了这一毒品给他个人带来的危害。他亲口说道：“我希望我这辈子从未见过大麻，更别提卷入这门“生意” 了。我们很多人从事这一行，是因为觉得它比做可卡因和海洛因的风险低，然而，单单由于哈希交易的数量之大，就意味着它是一条永不停顿的传送带，一旦你站上去了，就很难下得来。我自己的儿子抽哈希上了瘾，几乎完全丧失了行为能力。最后，我不得不把他送到一家诊所里去戒毒。其实从很多方面来说，哈希比A类毒品更邪恶。它一点点地勾引着你，渐渐地将你变成冷漠麻木、丧失自主意识的行尸走肉。我为我的孩子感到十分悲哀，尤其是因为由于我介入这一行，使他最初沾染上了哈希。人们需要了解哈希的真实故事，明白它是怎么到达他们家里的。他们应当懂得，哈希的害处绝对不比任何其他毒品要小。”西方某国军事基地正在焚烧大麻制品3除了上瘾，哈希还有哪些危害？致癌物比烟草多50%-70%实际上，大麻除了能让人短时间感觉很“飞”这一点所谓的“好处”之外，副作用极多。而人类在吸食大麻后感觉到的放松和亢奋实际是思维和记忆等身体功能的受阻和扭曲。吸食大麻会导致心跳过快，提高心脏病发作的机率。大麻烟含有的致癌性碳氢化合物比普通烟草多50%-70%，对呼吸器官的危害也极大。此外，这个年利润高达数十亿美元的行业里充满了邪恶行径和犯罪活动，其触角伸至世界的各个角落，从贫困的农民到职业犯罪分子，不计其数的人以此谋生。在这个犯罪网络里，聚集着无数毒枭、黑帮、商人和腐败的警察，甚至包括恐怖主义分子，他们为了达到攫取巨额利润的目的，不惜采取性利诱、恐吓、贿赂和凶杀等各种毒辣的手段。专家们认为，在过去的二十年中，由于黑帮和恐怖分子向大麻种植农持续施压，不断要求更多供货，促使世界范围内的大麻产量几乎翻了一番。如同商业消费的许多陷阱一样，由于巨大利益的诱惑，这些黑帮和毒枭会利用各种机会宣扬“人类需要大麻”的论调。但其实很少人真的想过，自己是否真的天生有这样的需求？大麻的植物形态4哈希从哪里来？42%产于摩洛哥世界上的主要哈希产地均在一些最贫穷的国家，那里的农民只能通过种植大麻来维持生计。摩洛哥的哈希产量比地球上任何其他国家都要多。西方的影响刺激了摩洛哥的大麻种植业，最初是通过殖民主义扩张；自1960年代以来，西方的嬉皮士文化也稳步推动了摩洛哥的哈希生产。据欧盟估计，哈希生产是摩洛哥的主要外汇来源，在其国民生产总值中占相当大的比例。全球约42%的哈希产于摩洛哥。其余的哈希是由世界上其他近九十个国家生产的，包括巴基斯坦（18%）、阿富汗（17%）、黎巴嫩（9%）和印度（9%）。它们主要被运往西欧和中欧市场销售，如英国、西班牙、法国、意大利、葡萄牙、瑞典、比利时和捷克共和国。 5哈希是如何制作的？分为“筛分法”和“手搓法”制作哈希可以采用两种方法，这取决于世界各地使用的不同技术。在哈希的最大产地摩洛哥，他们采用的是“筛分法”，即在植物收获和晒干之后，采集并筛分大麻花序的树脂腺—其中含有主要的活性物质—浓缩的四氢大麻酚。在黎巴嫩的贝卡谷地，人们也喜欢用筛分法。贝卡谷地出产的黎巴嫩红大麻一直以其品质高档而著称于世，不过自1990年代初以来，中东地区的暴力冲突导致了其产量下降。另一种哈希制作技术是“手搓法”，即用手掌和手指来回搓磨开花大麻的枝条，直到树脂积淀到手上。这种操作程序比“筛分法”的技术性低，见于亚洲的一些地区，主要是印度，也包括克什米尔和尼泊尔。筛分比手搓容易，生产效率也比较高。一个手搓采集者用一个完整的工作日才能获得10—25克的哈希，而用“筛分法”仅几个小时就可制作1公斤的哈希。用“筛分法”制作的哈希成品药效更强，因为植物上的树脂几乎没有任何残留。一名大麻的贩卖者正在检测大麻的花苞6哈希如何运输到世界各地？最新技术是利用无线电浮标运输由于仅需跨越7.7海里的水路便可从摩洛哥抵达西班牙，因此西班牙成为大麻流向世界各地的“交通枢纽”。西班牙对大麻的缉获量比欧洲所有其他国家的缉获量加在一起还要多。然而，似乎没有任何手段可以有效地遏制哈希从摩洛哥穿越直布罗陀海峡，涌入欧洲。几乎每一天都有大量货物来自北非，贩运者总能发明各种新奇的走私方式。最新的一种技巧是，黑帮们将带有无线电发射浮标的哈希货袋扔进大西洋，之后再驾船来收集它们。来自英格兰东南部肯特郡的托尼是世界上最早的一批职业大麻走私犯。近四十年前，他在一个黑帮的支持下成立了一家“运输公司”，开始从阿富汗和土耳其走私大麻。当时搞这种贩运是个危险的差事，现在依然如此。托尼的手下多年来一直如此驱车上万公里往返运输，因为哈希的利润始终非常丰厚。几乎在货运抵达英国的一个月内，托尼及其强大的秘密专业组织就能保证从他们的“现金投资”中得到五倍的回报。在世界各地，由黑社会资助贩运大宗哈希的案例十分多见。托尼公司的卡车总是以运输水果和蔬菜等合法货物作掩护，这些货物本身往往也在英国出售，获得额外的合法利润。位于荷兰的大麻博物馆7哈希为何屡禁不止？走私大麻比其他毒品风险小得多据世界各国的执法机构声称，为铲除哈希走私犯罪活动，他们每年的开支总计超过10亿美元，然而，收效甚微。大麻一直是世界范围内最难以禁绝的毒品。一名在摩洛哥从事多年缉毒工作的侦探老莫总结道：人人都知道哈希可以赚钱。在摩洛哥，没有人，甚至连国王本人，都不会做出任何危及哈希业的事。它在经济上至关重要，几乎是摩洛哥的经济支柱型“产业”。如果减缓哈希业的发展，将意味着成千上万的人失业，给摩洛哥带来灾难。摩洛哥警察与与毒品走私集团关系紧密，两者勾结的记录历历在案。在大麻生产基地的源头摩洛哥，这样的状况决定了大麻总是在源源不断地被种植、制造，总是有充足的货源等待着销往世界各地。黑帮们从事大麻走私，为了把从中获得的暴利隐藏起来，在世界各地他们大力投资购买物业，将非法所得进行洗白。在世界范围内，毒贩运输、贩卖大麻的成本，包括经济、时间以及被抓之后的刑罚，都比贩卖A类毒品代价小得多。警察和司法当局往往将更多精力和金钱投入在缉拿A类毒品走私犯上，面对大麻犯罪活动的规模及其复杂性往往无力招架，不堪重负。作者：温斯利·克拉克森著译者：珍栎译版本：生活·读书·新知三联书店 2015年9月版本文根据《哈希的故事》一书整合，图片来源于本书及网页。本文撰文、整合与编辑：走走。未经授权不得转载。新京报书评周刊ibookreview投稿&合作邮箱：[email protected]长按识别二维码关注点击以下关键词查看精彩内容封闭小区 \| 民科 \| 2016期待之书 \| 2015遗珠之书 \| 知更鸟 \| 引力波 \|《美人鱼》\| 孔飞力 \| 2015年度好书 \| 奇葩翻译 \| 剩女 \| 丰子恺 \| 偷书 \| 在岛屿写作 \| 同性恋群像 \| 弟子规 \| 康夏 \| 权力的游戏 \| 小王子 \| 孤独图书馆 \| 黄家驹 \| 腋毛禁忌 \| 二十四节气 \| 伍迪·艾伦 \| 夏日翻书 \| 禁烟令 \| 玛丽莲·梦露幸福也在左下角的“阅读原文”里哟。\n", "\n" ] } ], "source": [ "import urllib2\n", "from bs4 import BeautifulSoup\n", "\n", "url = \"http://mp.weixin.qq.com/s?src=3&timestamp=1461137776&ver=1&signature=EhzGzNpWxh-i1UzSM02QbraXYpRadtlqhM9lAbAHLtIQIrXz9Xa7Kb9z1B2kpF9DMQAessIVgOrtVv1zeOzyXt4BmSqGlY8TGUYmA8ey9GPDiWtBtGLcxyVln7h-bbla7Ip-tcnUAnQ0iBJJ0VMLA3T59x4oyFZRNrDWribpU=\"\n", "content = urllib2.urlopen(url).read() #获取网页的html文本\n", "soup = BeautifulSoup(content, 'html.parser') \n", "print soup.title.text\n", "print soup.find('div', {'class', 'rich_media_meta_list'}).find(id = 'post-date').text\n", "print soup.find('div', {'class', 'rich_media_content'}).get_text()" ] } ], "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
matthewzimmer/traffic-sign-classification	Traffic_Signs_Recognition_Cifar10.ipynb	1	2515092	null	mit
hande-qmc/hande	tools/pyhande/tutorials/3_custom_get_results_ccmc.ipynb	1	8243	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This demonstration shows how CCMC [1] data (analysis) results can be analysed in a more customised way. \n", "This applies to FCIQMC [2] as well." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from pyhande.data_preparing.hande_ccmc_fciqmc import PrepHandeCcmcFciqmc\n", "from pyhande.extracting.extractor import Extractor\n", "from pyhande.error_analysing.blocker import Blocker\n", "from pyhande.error_analysing.hybrid_ana import HybridAna\n", "from pyhande.results_viewer.get_results import get_results, analyse_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Part A\n", "\n", "For now, still using the default, quick `get_results` function but this time specify `merge_type` to not merging\n", "(no effect here as calculations are independent, the default is merge using UUIDs btw),\n", "the `analyser` to hybrid [3] (not blocking [4] as by default) and while we don't specify analysis start MC iterations,\n", "we specify that the MSER find starting iteration function should be used to automatically find them\n", "(the default is 'blocking' for the blocking find starting iteration function)." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "results = get_results([\"data/0.01_ccsd.out.gz\", \"data/0.002_ccsd.out.gz\"], merge_type='no', analyser='hybrid', start_its='mser')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `summary` table shows the analysed data by the `analyser`.\n", "The hybrid `analyser` analyses the instantaneous projected energy (as prepared by the `preparator` object)." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "results.summary" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The hybrid `analyser`'s output can be viewed." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "results.analyser.opt_block" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(results.analyser.start_its) # Used starting iterations, found using MSER find starting iteration function.\n", "print(results.analyser.end_its) # Used end iterations, the last iteration by default." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Part B\n", "\n", "Now, we don't use `get_results` to get the `results` object but define the `extractor`, `preparator` and `analyser` objects ourselves.\n", "Even though it doesn't have an effect here as there is no calculation to merge, we state that we want to merge using\n", "the 'legacy' way, i.e. don't use UUID for merging but simply determine whether iterations from one output file to the next\n", "(order matters here) are consecutive. If shift is already varying across that continuation, don't merge if 'shift_damping'\n", "differs from one output file to the next ('md_shift' specifies that this restriction only applies when shift is already varying,\n", "otherwise use 'md_always' for this restriction to always hold).\n", "Since no merge is possibly, these options are ignored and just shown here for demonstration purposes." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "extra = Extractor(merge={'type': 'legacy', 'md_shift': ['qmc:shift_damping'], 'shift_key': 'Shift'})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Define `preparator` object. It contains the hard coded mapping of column name meaning to column name, i.e. 'ref_key' : 'N_0,\n", "for the case of HANDE CCMC/FCIQMC. If you use a different package, you'll need to create your own `preparator` class." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "prep = PrepHandeCcmcFciqmc()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Define `analyser`. Use class method `inst_hande_ccmc_fciqmc` to pre-set what should be analysed (inst. projected energy), name\n", "of iteration key ('iterations'), etc.\n", "Use 'blocking' start iteration finder and specify that a graph should be shown by the start iteration finder." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ana = HybridAna.inst_hande_ccmc_fciqmc(start_its = 'blocking', find_start_kw_args={'show_graph': True})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, we can execute those three objects. 'analyse_data' is a handy helper to call their `.exe()` methods.\n", "For each calculation, a graph is shown by the find starting iteration method." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "results2 = analyse_data([\"data/0.01_ccsd.out.gz\", \"data/0.002_ccsd.out.gz\"], extra, prep, ana)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Have used different starting iteration finder, so these will be different." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "results2.analyser.start_its" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "But results are comparable. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "results2.summary_pretty" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "But what if we want to analyse the shift instead of the instantaneous projected energy with hybrid analysis?\n", "-> BEWARE this is untested. Only used for illustration here!\n", "Don't use class method for `analyser` instantiation anymore.\n", "Keep default settings (find start iterations using 'mser' etc).\n", "Note that when doing blocking [4], not hybrid [3], the order is a bit different, the columns to be analysed are 'cols'\n", "for blocking [4] and 'hybrid_col' for hybrid analysis [3]. You might need to define both for a given `analyser` if you\n", "are using the starting iteration function of the other type ('blocking' with start_its='mser' or 'hybrid' with\n", "start_its='blocking'). Consult the docstring." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ana2 = HybridAna('iterations', 'Shift', 'replica id')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "results3 = analyse_data([\"data/0.01_ccsd.out.gz\", \"data/0.002_ccsd.out.gz\"], extra, prep, ana2)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "results3.summary_pretty" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[1] - A. J. W. Thom (2010), Phys. Rev. Lett. 105, 236004.\n", "\n", "[2] - G. H. Booth et al. (2009), J. Chem. Phys. 131, 054106; Cleland, et al. (2010), J. Chem. Phys. 132, 041103.\n", "\n", "[3] - T. Ichibha et al., [arXiv:1904.09934 [physics.comp-ph]].\n", "\n", "[4] - H. Flyvbjerg, H. G. Petersen (1989), J. Chem. Phys. 91, 461 (1989)." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3.6.10", "language": "python", "name": "python3610" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.10-final" } }, "nbformat": 4, "nbformat_minor": 4 }	lgpl-2.1
PYPIT/PYPIT	doc/comparisons/PYPIT_vs_LowRedux_Kastb.ipynb	1	1350742	null	gpl-3.0
nmiculinic/psiml2017-facs	TestSpredSheet.ipynb	1	2720	{ "cells": [ { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from openpyxl import load_workbook\n", "import os" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import cv2\n", "import matplotlib.py" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "True\n" ] }, { "ename": "TypeError", "evalue": "Required argument 'mat' (pos 2) not found", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-36-32b7112f786f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mCV_LOAD_IMAGE_COLOR\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;31m# set flag to G0 to give a grayscale one\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCV_8UC1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mTypeError\u001b[0m: Required argument 'mat' (pos 2) not found" ] } ], "source": [ "image_path= \"./data/10k FACES/Face Annotations/Images and Annotations/11.jpg\"\n", "print(os.path.exists(image_path))\n", "\n", "CV_LOAD_IMAGE_COLOR = 1 # set flag to 1 to give colour image\n", "CV_LOAD_IMAGE_COLOR = 0 # set flag to G0 to give a grayscale one\n", "img = cv2.imread(image_path, cv2.CV_8UC1)\n", "cv2.imshow(img)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
ComputationalModeling/spring-2017-danielak	past-semesters/fall_2016/day-by-day/day15-Schelling-1-dimensional-segregation-day2/Day_15_Pre_Class_Notebook.ipynb	1	5010	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Getting ready to implement the Schelling model\n", "\n", "## Goal for this assignment\n", "\n", "The goal of this assignment is to finish up the two functions that you started in class on the first day of this project, to ensure that you're ready to hit the ground running when you get back to together with your group. \n", "\n", "You are welcome to work with your group on this pre-class assignment - just make sure to list who you worked with below. Also, everybody needs to turn in their own solutions!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Your name" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "// put your name here!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Function 1: Creating a game board\n", "\n", "Function 1: Write a function that creates a one-dimensional game board composed of agents of two different types (0 and 1, X and O, stars and pluses... whatever you want), where the agents are assigned to spots randomly with a 50% chance of being either type. As arguments to the function, take in (1) the number of spots in the game board (setting the default to 32) and (2) a random seed that you will use to initialize the board (again with some default number), and return your game board. (Hint: which makes more sense to describe the game board, a list or a Numpy array? What are the tradeoffs?) Show that your function is behaving correctly by printing out the returned game board.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Put your code here, using additional cells if necessary.\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Function 2: deciding if an agent is happy\n", "\n", "Write a function that takes the game board generated by the function you wrote above and determines whether an agent at position i in the game board of a specified type is happy for a game board of any size and a neighborhood of size N (i.e., from position i-N to i+N), and returns that information. Make sure to check that position i is actually inside the game board (i.e., make sure the request makes sense), and ensure that it behaves correctly for agents near the edges of the game board. Show that your function is behaving correctly by giving having it check every position in the game board you generated previously, and decide whether the agent in each spot is happy or not. Verify by eye that it's behaving correctly. (Hint: You're going to use this later, when you're trying to decide where to put an agent. Should you write the function assuming that the agent is already in the board, or that you're testing to see whether or not you've trying to decide whether to put it there?)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Put your code here, using additional cells if necessary.\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Assignment wrapup\n", "\n", "Please fill out the form that appears when you run the code below. You must completely fill this out in order to receive credit for the assignment!" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from IPython.display import HTML\n", "HTML(\n", "\"\"\"\n", "<iframe \n", "\tsrc=\"https://goo.gl/forms/M7YCyE1OLzyOK7gH3?embedded=true\" \n", "\twidth=\"80%\" \n", "\theight=\"1200px\" \n", "\tframeborder=\"0\" \n", "\tmarginheight=\"0\" \n", "\tmarginwidth=\"0\">\n", "\tLoading...\n", "</iframe>\n", "\"\"\"\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Congratulations, you're done!\n", "\n", "Submit this assignment by uploading it to the course Desire2Learn web page. Go to the \"Pre-class assignments\" folder, find the dropbox link for Day 15, and upload it there.\n", "\n", "See you in class!" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }	agpl-3.0
andypetrella/spark-notebook	notebooks/core/Spark-101.snb.ipynb	5	13224	{ "metadata" : { "name" : "Spark 101", "user_save_timestamp" : "1970-01-01T01:00:00.000Z", "auto_save_timestamp" : "2015-01-10T00:02:12.659Z", "language_info" : { "name" : "scala", "file_extension" : "scala", "codemirror_mode" : "text/x-scala" }, "trusted" : true, "customLocalRepo" : null, "customRepos" : null, "customDeps" : null, "customImports" : null, "customArgs" : null, "customSparkConf" : null }, "cells" : [ { "metadata" : { "id" : "46CEC7C56C6A46358F2BEB3E10B556D0" }, "cell_type" : "markdown", "source" : "<style>\n h1, h2, h3, h4, h5, p, ul, li {\n color: #2C475C;\n }\n .output_html {\n color: skyblue;\n }\n hr { height: 2px; color: lightblue; }\n</style>" }, { "metadata" : { "id" : "44C028B12E234BD88A96264AEEC2E801" }, "cell_type" : "markdown", "source" : "# Spark 101" }, { "metadata" : { "trusted" : true, "input_collapsed" : false, "collapsed" : false, "id" : "B9C94C7E25F044A58CDDFC7704C2BDF9" }, "cell_type" : "code", "source" : "import org.apache.spark._\nimport org.apache.spark.SparkContext._\nimport org.apache.spark.rdd._", "outputs" : [ ] }, { "metadata" : { "id" : "3E7214F9051D4FE5B7D270D403724E6F" }, "cell_type" : "markdown", "source" : "### First create a dataset using the local `syslog` file" }, { "metadata" : { "id" : "5092C5E55D3E4D1884C459C7217BD0DC" }, "cell_type" : "markdown", "source" : "We will \n\n* load the file\n* convert each line keeping its size\n* remove the duplicates" }, { "metadata" : { "id" : "3D29F319694B49668F0063E62F122F29" }, "cell_type" : "markdown", "source" : "For that, we'll use the `sparkContext`, which\n\n* is the driver\n* can define job (read inputs, transform, group, etc)\n* constructs DAG\n* schedules tasks on the cluster" }, { "metadata" : { "trusted" : true, "input_collapsed" : false, "collapsed" : false, "id" : "12D7D03CCF3A4CBE8F2EC92AB2E79505" }, "cell_type" : "code", "source" : "val dta:RDD[Int] = sparkContext.textFile(\"/var/log/syslog\")\n .map(_.size)\n .distinct", "outputs" : [ ] }, { "metadata" : { "id" : "AE43FAFD60784CBD98C40212EA016E68" }, "cell_type" : "markdown", "source" : "MappedRDD is actually an instance of `RDD[Int]` that will contain the distinct sizes of the lines." }, { "metadata" : { "id" : "E4C91D776DC24CADB4F46D8CB09D8348" }, "cell_type" : "markdown", "source" : "_Note_: there is NO computations happening! → [see UI](http://localhost:4040/stages/)" }, { "metadata" : { "id" : "19DB25C9982E4CA4859C44FF20B893B0" }, "cell_type" : "markdown", "source" : "-----" }, { "metadata" : { "id" : "0D7C5C9A9731442FA3F74601876C1F92" }, "cell_type" : "markdown", "source" : "### Now we can use the size for fancy operations like grouping per last digit" }, { "metadata" : { "trusted" : true, "input_collapsed" : false, "collapsed" : false, "id" : "4F238AE4CC96481A8AE60F84C5D06137" }, "cell_type" : "code", "source" : "val rdd1:RDD[(Int, Iterable[Int])] = dta.groupBy(_ % 10)", "outputs" : [ ] }, { "metadata" : { "id" : "960FCC7D876048AFA8CFEDE073622214" }, "cell_type" : "markdown", "source" : "### But we can also get rid of even sizes (... non trivially...), then _tupling_ with some other computations" }, { "metadata" : { "trusted" : true, "input_collapsed" : false, "collapsed" : false, "id" : "828B2401BAB84FEA8E8258C49FF6FB68" }, "cell_type" : "code", "source" : "val rdd2 = dta.map(_ + 1)\n .filter(_ % 2 == 0)\n .map(x => (x%10, xx))", "outputs" : [ ] }, { "metadata" : { "id" : "B124A3E5C36B4AF495F4E17C1555602D" }, "cell_type" : "markdown", "source" : "-----" }, { "metadata" : { "id" : "3C998EA9FBB34ADBB67A6E37AED29291" }, "cell_type" : "markdown", "source" : "### We can combine distributed datasets into single ones, by _joining_ them for instance." }, { "metadata" : { "trusted" : true, "input_collapsed" : false, "collapsed" : false, "id" : "0ECFCF9DC9DE4893BE258C0E28126A7F" }, "cell_type" : "code", "source" : "val joined = rdd1.join(rdd2)", "outputs" : [ ] }, { "metadata" : { "id" : "99D1D1AF025445B781FD195852AEA041" }, "cell_type" : "markdown", "source" : "_Note (again)_: still nothing done on the cluster up to here → [see ui](http://localhost:4040/stages/)" }, { "metadata" : { "id" : "A0AD7A11A3724A3480C504A883437277" }, "cell_type" : "markdown", "source" : "-----" }, { "metadata" : { "id" : "0B6EC3D5FD224D7980EB3CD1D7E2C09E" }, "cell_type" : "markdown", "source" : "#### Now we ask the cluster to do the whole thing: Action" }, { "metadata" : { "trusted" : true, "input_collapsed" : false, "collapsed" : false, "id" : "16463E75AFD04BE88E1C589B088341BA" }, "cell_type" : "code", "source" : "joined.take(10).toList.mkString(\"\\n\")", "outputs" : [ ] }, { "metadata" : { "id" : "CBFB95C4F51442C886C8C81E2E3BE5B3" }, "cell_type" : "markdown", "source" : "_Note (yeah)_: NOW there were some computations in the cluster → [see stages](http://localhost:4040/stages/) and [see tasks](http://localhost:4040/stages/stage/?id=3&attempt=0)" }, { "metadata" : { "id" : "F9856142C7D04653BB80CB33944AD7E7" }, "cell_type" : "markdown", "source" : "-----" }, { "metadata" : { "id" : "220F90FFC9464168A97C2A8D6F5BA3F7" }, "cell_type" : "markdown", "source" : "## But what just happened?" }, { "metadata" : { "id" : "5E9CEC67863944C19882273526A0DEBD" }, "cell_type" : "markdown", "source" : "### First Spark created a DAG based on the job definition" }, { "metadata" : { "trusted" : true, "input_collapsed" : false, "collapsed" : false, "id" : "A91299C34A6A4B758B46D8280DA27CB7" }, "cell_type" : "code", "source" : "joined.toDebugString", "outputs" : [ ] }, { "metadata" : { "id" : "0248A08A23894C3A8FB617ADC1B4CC12" }, "cell_type" : "markdown", "source" : "### Then it scheduled it to the executors in the cluster <small>only one when running in local mode<small>" }, { "metadata" : { "id" : "D6C71E9A63894B4E8B20A2662B5361DF" }, "cell_type" : "markdown", "source" : "We can check the <strong>Total tasks</strong> activity in the [UI](http://localhost:4040/executors/)" }, { "metadata" : { "id" : "DAB44BF35B4141EF8C61E10E322E2199" }, "cell_type" : "markdown", "source" : "-------" }, { "metadata" : { "id" : "DE361E8132064C35B4C4EFFCF8B2A40E" }, "cell_type" : "markdown", "source" : "## Now we will prepare the dataset and then using it several times" }, { "metadata" : { "id" : "93108F8366B54B04B4989B6FC56A5DF1" }, "cell_type" : "markdown", "source" : "So we'll read a file about stock price per day, so let's create a type holding relevant data." }, { "metadata" : { "trusted" : true, "input_collapsed" : false, "collapsed" : false, "id" : "A94CC65A4C6742F38A7DE218C3E9429D" }, "cell_type" : "code", "source" : "case class Quote(stock:String, date:String, price:Double) extends java.io.Serializable", "outputs" : [ ] }, { "metadata" : { "id" : "E55DDA65D10C4F128F2B4B677B4E98DB" }, "cell_type" : "markdown", "source" : "The file will contain lines like:\n``` \nASTE,2011-12-06,33.93\nASTE,2012-03-14,36.84\n```" }, { "metadata" : { "id" : "8D7E41D23D544E36A1B986DB370AAB0A" }, "cell_type" : "markdown", "source" : "Let's download the data first" }, { "metadata" : { "trusted" : true, "input_collapsed" : false, "collapsed" : false, "id" : "F3168A0EFCEA4813A8F5F44D57423710" }, "cell_type" : "code", "source" : "import sys.process._\n\"mkdir -p /tmp/data\"!!\n\nif (!new java.io.File(\"/tmp/data/closes.csv\").exists)\n \"wget https://s3-eu-west-1.amazonaws.com/spark-notebook-data/closes.csv -O /tmp/data/closes.csv\"!!", "outputs" : [ ] }, { "metadata" : { "trusted" : true, "input_collapsed" : false, "collapsed" : false, "id" : "F41892F4F09947488E5688C59A140C2C" }, "cell_type" : "code", "source" : ":sh du -h /tmp/data/closes.csv", "outputs" : [ ] }, { "metadata" : { "trusted" : true, "input_collapsed" : false, "collapsed" : false, "id" : "64A4F31B05B34470826FEDD14D3220C6" }, "cell_type" : "code", "source" : "val closes:RDD[Quote] = sparkContext.textFile(\"/tmp/data/closes.csv\")\n .map(_.split(\",\").toList)\n .map{ case s::d::p::Nil => Quote(s, d, p.toDouble)}", "outputs" : [ ] }, { "metadata" : { "id" : "B42BE2395000448788544554D90EF13F" }, "cell_type" : "markdown", "source" : "We have date, so we can group stock prices per day" }, { "metadata" : { "trusted" : true, "input_collapsed" : false, "collapsed" : false, "id" : "7E461A6F79064E82BEF457C44A6FD290" }, "cell_type" : "code", "source" : "val byDate = closes.keyBy(_.date)", "outputs" : [ ] }, { "metadata" : { "id" : "5B898CB11B214AFB8A8300A1E67CC853" }, "cell_type" : "markdown", "source" : "Now we can compute the minimum stocks per date" }, { "metadata" : { "trusted" : true, "input_collapsed" : false, "collapsed" : false, "id" : "F8E9582D50F4449E874EAD7BFD09BED2" }, "cell_type" : "code", "source" : "def minByDate = byDate.combineByKey[(String, Double)]( // `def` to force spark recomputing... otherwise it's smart enough to reuse previous RDDs...\n (x:Quote) => (x.stock, x.price), \n (d:(String, Double), l:Quote) => if (d._2 < l.price) d else (l.stock, l.price),\n (d1:(String, Double), d2:(String, Double)) => if (d1._2 < d2._2) d1 else d2\n)", "outputs" : [ ] }, { "metadata" : { "trusted" : true, "input_collapsed" : false, "collapsed" : false, "id" : "35B0B9FEC268438196978CB4856AA4D6" }, "cell_type" : "code", "source" : "<pre>{minByDate.take(2).toList.mkString(\"\\n\")}</pre>", "outputs" : [ ] }, { "metadata" : { "id" : "D6A99D77B1C1404E821A8A68011A4733" }, "cell_type" : "markdown", "source" : "It took ~2 seconds (in local[8] and 24G of RAM)" }, { "metadata" : { "trusted" : true, "input_collapsed" : false, "collapsed" : false, "id" : "9C8B7A146E404C5087F4734C48C5E251" }, "cell_type" : "code", "source" : "<pre>{minByDate.take(2).toList.mkString(\"\\n\")}</pre>", "outputs" : [ ] }, { "metadata" : { "id" : "994354908DA34A638C2C99BFA5573B2A" }, "cell_type" : "markdown", "source" : "Once again.... 2 seconds!!!" }, { "metadata" : { "id" : "CCAE12ADF4BC4195B4D5C2428CEFA221" }, "cell_type" : "markdown", "source" : "#### Solution: caching!" }, { "metadata" : { "trusted" : true, "input_collapsed" : false, "collapsed" : false, "id" : "0E3E7BA6B0654C1082700F1CB46545E5" }, "cell_type" : "code", "source" : "val maxByDate2 = byDate.combineByKey[(String, Double)](\n (x:Quote) => (x.stock, x.price), \n (d:(String, Double), l:Quote) => if (d._2 > l.price) d else (l.stock, l.price),\n (d1:(String, Double), d2:(String, Double)) => if (d1._2 > d2._2) d1 else d2\n)\n\nmaxByDate2.cache()", "outputs" : [ ] }, { "metadata" : { "id" : "A87AE9D86357497F8F56B65231041D12" }, "cell_type" : "markdown", "source" : "Ask some data" }, { "metadata" : { "trusted" : true, "input_collapsed" : false, "collapsed" : false, "id" : "E6A6AF0C873B41A4834CCE29B222BB5C" }, "cell_type" : "code", "source" : "<pre>{maxByDate2.take(2).toList.mkString(\"\\n\")}</pre>", "outputs" : [ ] }, { "metadata" : { "id" : "72317C1AFA09488E9B5E61BC76FC4C38" }, "cell_type" : "markdown", "source" : "Go to [UI](http://localhost:4040/storage/)" }, { "metadata" : { "trusted" : true, "input_collapsed" : false, "collapsed" : false, "id" : "051E00657F2041DD8A874D89566D9D2C" }, "cell_type" : "code", "source" : "<pre>{maxByDate2.take(2).toList.mkString(\"\\n\")}</pre>", "outputs" : [ ] }, { "metadata" : { "id" : "6C954E9F8CD24401878288E3B86FD287" }, "cell_type" : "markdown", "source" : "BLAZING FAST* => Reuses the cache!" } ], "nbformat" : 4 }	apache-2.0
minesh1291/Practicing-Kaggle	MNIST_2017/dump_/talking_keras_notebook.ipynb	1	16708	{ "cells": [ { "metadata": { "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19", "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5", "trusted": false, "collapsed": true }, "cell_type": "code", "source": "# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load in \n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n\nimport os\nprint(os.listdir(\"../input\"))\n\n# Any results you write to the current directory are saved as output.", "execution_count": null, "outputs": [] }, { "metadata": { "_cell_guid": "24d70ace-2774-4e74-81c8-f8ae991a6111", "_uuid": "f4e672bf05a8350d1a1a8143940cf537d5c6c798", "trusted": false, "collapsed": true }, "cell_type": "code", "source": "%%bash\nls -lh ../input", "execution_count": null, "outputs": [] }, { "metadata": { "_cell_guid": "f6afde22-b309-4592-be16-57203c02e943", "_uuid": "3f98618108095a33c169cda1c6a9a36cfa3d79ef", "trusted": false, "collapsed": true }, "cell_type": "code", "source": "%%bash\nwc -l ../input/", "execution_count": null, "outputs": [] }, { "metadata": { "_cell_guid": "5507e31e-3742-480e-bd76-38b53011531c", "_uuid": "19b3e6fa625311d9faec7d4cdf02391ab6f9a631", "trusted": false, "collapsed": true }, "cell_type": "code", "source": "40000000100/184903891", "execution_count": null, "outputs": [] }, { "metadata": { "_cell_guid": "19d7f9d4-f441-4539-9650-042a4f278a3a", "_uuid": "113268912ee6cf7e5f5388f689c86dead72ad331", "trusted": false, "collapsed": true }, "cell_type": "code", "source": "%%bash\nhead ../input/sample_submission.csv", "execution_count": null, "outputs": [] }, { "metadata": { "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0", "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a", "trusted": false, "collapsed": true }, "cell_type": "code", "source": "#train = pd.read_csv(\"../input/train.csv\")\n#test = pd.read_csv(\"../input/test.csv\")\n\npath = \"../input/\"\n\ndtypes = {\n 'ip' : 'uint32',\n 'app' : 'uint16',\n 'device' : 'uint16',\n 'os' : 'uint16',\n 'channel' : 'uint16',\n 'is_attributed' : 'uint8',\n 'click_id' : 'uint32'\n }\n\nprint('loading train data...')\ntrain_df = pd.read_csv(path+\"train.csv\", nrows=40000000, dtype=dtypes, usecols=['ip','app','device','os', 'channel', 'click_time', 'is_attributed'])\n\nprint('loading test data...')\ntest_df = pd.read_csv(path+\"test.csv\", dtype=dtypes, usecols=['ip','app','device','os', 'channel', 'click_time', 'click_id'])\n\nsub = pd.read_csv(\"../input/sample_submission.csv\")\n", "execution_count": null, "outputs": [] }, { "metadata": { "_cell_guid": "016da536-95f0-46ec-a1a3-d70bf00831a0", "_uuid": "65205b7fe693b7732d8e26962d1985f02a8bbc0a", "trusted": false, "collapsed": true }, "cell_type": "code", "source": "import gc\ngc.collect()", "execution_count": null, "outputs": [] }, { "metadata": { "_cell_guid": "3bbac495-74f5-46da-9f07-cd3a7d3085d0", "_uuid": "01fb2f91d2d207e5372dc05d26ba88e079961d35", "trusted": false, "collapsed": true }, "cell_type": "code", "source": "print(test_df.shape)\ntest_df.head()", "execution_count": null, "outputs": [] }, { "metadata": { "collapsed": true, "_cell_guid": "24832bd1-ac3e-4fea-a692-ca9797ea94c7", "_uuid": "7c15281dd83a8b507d1fd244839e9e3576df4a0e", "trusted": false }, "cell_type": "code", "source": "len_train = len(train_df)\ntrain_df=train_df.append(test_df)", "execution_count": null, "outputs": [] }, { "metadata": { "_cell_guid": "f6d65848-4b85-4873-bc4c-5dafdefa2213", "_uuid": "4bad0e7f86a5e46fe86ae1e2c5a6a48411b5d2e9", "trusted": false, "collapsed": true }, "cell_type": "code", "source": "del test_df\ngc.collect()\n\nprint('Extracting new features...')\ntrain_df['hour'] = pd.to_datetime(train_df.click_time).dt.hour.astype('uint8')\ntrain_df['day'] = pd.to_datetime(train_df.click_time).dt.day.astype('uint8')\ntrain_df['wday'] = pd.to_datetime(train_df.click_time).dt.dayofweek.astype('uint8')\n\ngc.collect()", "execution_count": null, "outputs": [] }, { "metadata": { "_cell_guid": "77c2a323-d2a7-4063-aaa5-1772f83a88ef", "_uuid": "38c43880da75ffdde32cc44f71dc67e68ec96c5e", "trusted": false, "collapsed": true }, "cell_type": "code", "source": "print('grouping by ip-day-hour combination...')\ngp = train_df[['ip','day','hour','channel']].groupby(by=['ip','day','hour'])[['channel']].count().reset_index().rename(index=str, columns={'channel': 'qty'})\ntrain_df = train_df.merge(gp, on=['ip','day','hour'], how='left')\ndel gp\ngc.collect()", "execution_count": null, "outputs": [] }, { "metadata": { "_cell_guid": "fcd3213a-ae7e-4d79-9288-31658eaf165a", "_uuid": "e8bf74e7bb60e7bb12b742608978835797cab014", "trusted": false, "collapsed": true }, "cell_type": "code", "source": "print('group by ip-app combination...')\ngp = train_df[['ip', 'app', 'channel']].groupby(by=['ip', 'app'])[['channel']].count().reset_index().rename(index=str, columns={'channel': 'ip_app_count'})\ntrain_df = train_df.merge(gp, on=['ip','app'], how='left')\ndel gp\ngc.collect()", "execution_count": null, "outputs": [] }, { "metadata": { "_cell_guid": "07f41fb6-67f6-4099-b08f-6d48cf845c9d", "_uuid": "7e3473fea4c6a8b87e5b967312277f21af7da6c3", "trusted": false, "collapsed": true }, "cell_type": "code", "source": "print('group by ip-app-os combination...')\ngp = train_df[['ip','app', 'os', 'channel']].groupby(by=['ip', 'app', 'os'])[['channel']].count().reset_index().rename(index=str, columns={'channel': 'ip_app_os_count'})\nprint(\"merging...\")\ntrain_df = train_df.merge(gp, on=['ip','app', 'os'], how='left')\ndel gp\ngc.collect()", "execution_count": null, "outputs": [] }, { "metadata": { "_cell_guid": "f429ef85-155b-4f5b-9444-d786f8aabb13", "_uuid": "5fa3659283942f76af13bed53d19a00e1bd5f7e2", "trusted": false, "collapsed": true }, "cell_type": "code", "source": "print(\"vars and data type: \")\ntrain_df.info()\ntrain_df['qty'] = train_df['qty'].astype('uint16')\ntrain_df['ip_app_count'] = train_df['ip_app_count'].astype('uint16')\ntrain_df['ip_app_os_count'] = train_df['ip_app_os_count'].astype('uint16')\n", "execution_count": null, "outputs": [] }, { "metadata": { "collapsed": true, "_cell_guid": "f72eecc7-a36d-4412-8611-8a3843442005", "_uuid": "6b521ed0f7e31446d715ab163577f6678d2edd44", "trusted": false }, "cell_type": "code", "source": "test_df = train_df[len_train:]\ntrain_df = train_df[:len_train]\n", "execution_count": null, "outputs": [] }, { "metadata": { "_cell_guid": "8c5270bd-a1ac-45aa-94ea-ceb50a64a2b5", "_uuid": "6d417bf111dfe18a3bad52af60572ea14e9c7605", "trusted": false, "collapsed": true }, "cell_type": "code", "source": "train_df.head()", "execution_count": null, "outputs": [] }, { "metadata": { "collapsed": true, "_cell_guid": "02808831-d2f5-4594-9b18-32a39a58c5a9", "_uuid": "8001869a044f4864e2bf346d427e9d3dacdc9733", "trusted": false }, "cell_type": "code", "source": "train_df = train_df.sample(frac=1).reset_index(drop=True)", "execution_count": null, "outputs": [] }, { "metadata": { "_cell_guid": "83ca6243-b508-444e-bd15-70dcb508cc55", "_uuid": "7cc51110b2573827dcf4ee3638f1eee617cea8aa", "trusted": false, "collapsed": true }, "cell_type": "code", "source": "train_df.head()", "execution_count": null, "outputs": [] }, { "metadata": { "collapsed": true, "_cell_guid": "1cec11bb-aa0c-4412-9970-eaf34d9cdd49", "_uuid": "8102de6620618b9a86acca8c753b0f1c6af168ec", "trusted": false }, "cell_type": "code", "source": "val_df = train_df[(len_train-3000000):len_train]\ntrain_df = train_df[:(len_train-3000000)]", "execution_count": null, "outputs": [] }, { "metadata": { "_cell_guid": "fabf1cb8-294e-486e-b91e-14e948deb285", "_uuid": "6f79dc377a72544c61a4e722bd5e69bb97554a83", "trusted": false, "collapsed": true }, "cell_type": "code", "source": "print(\"train size: \", len(train_df))\nprint(\"valid size: \", len(val_df))\nprint(\"test size : \", len(test_df))\n\ntarget = 'is_attributed'\npredictors = ['app','device','os', 'channel', 'hour', 'day', 'wday', 'qty', 'ip_app_count', 'ip_app_os_count']\ncategorical = ['app', 'device', 'os', 'channel', 'hour', 'day', 'wday']\n\nsub = pd.DataFrame()\nsub['click_id'] = test_df['click_id'].astype('int')", "execution_count": null, "outputs": [] }, { "metadata": { "_cell_guid": "59f80c05-7618-4894-acc8-483c5259a7fc", "_uuid": "2e2f83566fb53159e91d40b826b6194864959372", "trusted": false, "collapsed": true }, "cell_type": "code", "source": "from sklearn.linear_model import LogisticRegressionCV\nmodel = LogisticRegressionCV(cv=5,scoring=\"neg_log_loss\",random_state=1\n #,penalty=\"l1\"\n #,Cs= Cs_#list(np.arange(1e-7,1e-9,-0.5e-9)) # [0.5,0.1,0.01,0.001] #list(np.power(1, np.arange(-10, 10)))\n #,max_iter=1000, tol=1e-11\n #,solver=\"liblinear\"\n ,n_jobs=4\n )\nmodel.fit(train_df[predictors], train_df[target])\n\n#---\nCs = model.Cs_\nlist(np.power(10.0, np.arange(-10, 10)))\ndir(model)\nsco = model.scores_[1].mean(axis=0)\n#---\nimport matplotlib.pyplot as plt\nplt.plot(Cs\n #np.log10(Cs)\n ,sco)\n# plt.ylabel('some numbers')\nplt.show()\nsco.min()", "execution_count": null, "outputs": [] }, { "metadata": { "collapsed": true, "_cell_guid": "b909244c-9cfa-41f4-9260-da4acfface26", "_uuid": "0ddac85a951aae16a0d33ce4c1d063892904f84f", "trusted": false }, "cell_type": "code", "source": "", "execution_count": null, "outputs": [] }, { "metadata": { "_cell_guid": "10f54298-ffec-48ae-8543-13a924153aed", "_uuid": "2e21cd33416340eda73d0e122f7eba12992844b9", "trusted": false, "collapsed": true }, "cell_type": "code", "source": "from sklearn.metrics import roc_auc_score\ny_pred=model.predict(val_df[predictors],verbose=1,batch_size=10000)\ny_test=val_df[target]\nroc_auc_score(y_test, y_pred)", "execution_count": null, "outputs": [] }, { "metadata": { "_cell_guid": "fc3896a2-9cff-417f-917b-9711df0b31b7", "_uuid": "b0129083bc6b534115a59d1f3b5be1e4c77691cd", "trusted": false, "collapsed": true }, "cell_type": "code", "source": "y_test.hist()", "execution_count": null, "outputs": [] }, { "metadata": { "_cell_guid": "a752c700-4c54-433e-9ada-b291c7bcef58", "_uuid": "567000c607a161e6e39f7a8354d887fea558616b", "trusted": false, "collapsed": true }, "cell_type": "code", "source": "y_pred[:6]\nnumpy.histogram(y_pred)", "execution_count": null, "outputs": [] }, { "metadata": { "collapsed": true, "_cell_guid": "8bc33895-4ffb-4937-80b0-82c1a35b5452", "_uuid": "51859da9355aeed4c1e8581c42dad158d192ba3d", "trusted": false }, "cell_type": "code", "source": "almost1 = y_pred >= 0.96\ny_pred[almost1] = 1", "execution_count": null, "outputs": [] }, { "metadata": { "_cell_guid": "38eeaef7-ed5a-44ea-8d1e-a42b5ac56e31", "_uuid": "0c8348ce4bb8a9e2372defe6bfa8856d3ec583ea", "trusted": false, "collapsed": true }, "cell_type": "code", "source": "y_pred[almost1]", "execution_count": null, "outputs": [] }, { "metadata": { "_cell_guid": "ecb594e9-a1e7-49de-b226-0fe0750f12cc", "_uuid": "49babe8d611bd29210b37783042b192e28b4f7ff", "trusted": false, "collapsed": true }, "cell_type": "code", "source": "roc_auc_score(y_test, y_pred) - 0.9235889250504977\n", "execution_count": null, "outputs": [] }, { "metadata": { "collapsed": true, "_cell_guid": "5b898502-7640-4094-812a-afaab1d92130", "_uuid": "9ebf5fd8000e5ead831faa4e2020685f8a417603", "trusted": false }, "cell_type": "code", "source": "gc.collect()\n\nprint(\"Training...\")\nstart_time = time.time()\n\n\nparams = {\n 'learning_rate': 0.19,\n #'is_unbalance': 'true', # replaced with scale_pos_weight argument\n 'num_leaves': 15, # 2^max_depth - 1\n 'max_depth': 4, # -1 means no limit\n 'min_child_samples': 100, # Minimum number of data need in a child(min_data_in_leaf)\n 'max_bin': 100, # Number of bucketed bin for feature values\n 'subsample': .7, # Subsample ratio of the training instance.\n 'subsample_freq': 1, # frequence of subsample, <=0 means no enable\n 'colsample_bytree': 0.7, # Subsample ratio of columns when constructing each tree.\n 'min_child_weight': 0, # Minimum sum of instance weight(hessian) needed in a child(leaf)\n 'scale_pos_weight':99 # because training data is extremely unbalanced \n}\nbst = lgb_modelfit_nocv(params, \n train_df, \n val_df, \n predictors, \n target, \n objective='binary', \n metrics='auc',\n early_stopping_rounds=50, \n verbose_eval=True, \n num_boost_round=350, \n categorical_features=categorical)\n\nprint('[{}]: model training time'.format(time.time() - start_time))\n", "execution_count": null, "outputs": [] }, { "metadata": { "collapsed": true, "_cell_guid": "08cc486d-d7f0-4758-81bb-fa4c8e5a97e0", "_uuid": "9804bc8e0aeca5855f013b4597a0b045012e21f7", "trusted": false }, "cell_type": "code", "source": "print(\"Predicting...\")\nsub['is_attributed'] = bst.predict(test_df[predictors])\nprint(\"writing...\")\nsub.to_csv('sub_lgb_balanced99.csv',index=False)\nprint(\"done...\")", "execution_count": null, "outputs": [] }, { "metadata": { "collapsed": true, "_cell_guid": "f876763a-2d7a-4c09-aaea-9e0e1d084902", "_uuid": "49789db1bc9c10ec0be9107bffe536d115f5fbcf", "trusted": false }, "cell_type": "code", "source": "", "execution_count": null, "outputs": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "version": "3.6.4", "file_extension": ".py", "nbconvert_exporter": "python", "name": "python", "pygments_lexer": "ipython3", "codemirror_mode": { "version": 3, "name": "ipython" }, "mimetype": "text/x-python" } }, "nbformat": 4, "nbformat_minor": 1 }	gpl-3.0
UWSEDS/LectureNotes	Fall2018/09_UnitTests/unit-tests.ipynb	1	17995	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Unit Tests" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Overview and Principles\n", "Testing is the process by which you exercise your code to determine if it performs as expected. The code you are testing is referred to as the code under test. \n", "\n", "There are two parts to writing tests.\n", "1. invoking the code under test so that it is exercised in a particular way;\n", "1. evaluating the results of executing code under test to determine if it behaved as expected.\n", "\n", "The collection of tests performed are referred to as the test cases. The fraction of the code under test that is executed as a result of running the test cases is referred to as test coverage.\n", "\n", "For dynamical languages such as Python, it's extremely important to have a high test coverage. In fact, you should try to get 100% coverage. This is because little checking is done when the source code is read by the Python interpreter. For example, the code under test might contain a line that has a function that is undefined. This would not be detected until that line of code is executed.\n", "\n", "Test cases can be of several types. Below are listed some common classifications of test cases.\n", "- Smoke test. This is an invocation of the code under test to see if there is an unexpected exception. It's useful as a starting point, but this doesn't tell you anything about the correctness of the results of a computation.\n", "- One-shot test. In this case, you call the code under test with arguments for which you know the expected result.\n", "- Edge test. The code under test is invoked with arguments that should cause an exception, and you evaluate if the expected exception occurrs.\n", "- Pattern test - Based on your knowledge of the calculation (not implementation) of the code under test, you construct a suite of test cases for which the results are known or there are known patterns in these results that are used to evaluate the results returned.\n", "\n", "Another principle of testing is to limit what is done in a single test case. Generally, a test case should focus on one use of one function. Sometimes, this is a challenge since the function being tested may call other functions that you are testing. This means that bugs in the called functions may cause failures in the tests of the calling functions. Often, you sort this out by knowing the structure of the code and focusing first on failures in lower level tests. In other situations, you may use more advanced techniques called mocking. A discussion of mocking is beyond the scope of this course.\n", "\n", "A best practice is to develop your tests while you are developing your code. Indeed, one school of thought in software engineering, called test-driven development, advocates that you write the tests before you implement the code under test so that the test cases become a kind of specification for what the code under test should do." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Examples of Test Cases\n", "This section presents examples of test cases. The code under test is the calculation of entropy." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Entropy of a set of probabilities\n", "$$\n", "H = -\\sum_i p_i \\log(p_i)\n", "$$\n", "where $\\sum_i p_i = 1$." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "# Code Under Test\n", "def entropy(ps):\n", " items = ps * np.log(ps)\n", " return np.abs(-np.sum(items))" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.5004024235381879" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Smoke test\n", "entropy([0.2, 0.8])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Suppose that all of the probability of a distribution is at one point. An example of this is a coin with two heads. Whenever you flip it, you always get heads. That is, the probability of a head is 1.\n", "\n", "What is the entropy of such a distribution? From the calculation above, we see that the entropy should be $log(1)$, which is 0. This means that we have a test case where we know the result!" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Test completed!\n" ] } ], "source": [ "# One-shot test. Need to know the correct answer.\n", "entries = [\n", " [0, [1]],\n", "]\n", "\n", "for entry in entries:\n", " ans = entry[0]\n", " prob = entry[1]\n", " if not np.isclose(entropy(prob), ans):\n", " print(\"Test failed!\")\n", "print (\"Test completed!\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Question: What is an example of another one-shot test? (Hint: You need to know the expected result.)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "One edge test of interest is to provide an input that is not a distribution in that probabilities don't sum to 1." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/ubuntu/miniconda3/lib/python3.6/site-packages/ipykernel_launcher.py:4: RuntimeWarning: invalid value encountered in log\n", " after removing the cwd from sys.path.\n" ] }, { "data": { "text/plain": [ "nan" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Edge test. This is something that should cause an exception.\n", "entropy([-0.5])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's consider a pattern test. Examining the structure of the calculation of $H$, we consider a situation in which there are $n$ equal probabilities. That is, $p_i = \\frac{1}{n}$.\n", "$$\n", "H = -\\sum_{i=1}^{n} p_i \\log(p_i) \n", "= -\\sum_{i=1}^{n} \\frac{1}{n} \\log(\\frac{1}{n}) \n", "= n (-\\frac{1}{n} \\log(\\frac{1}{n}) )\n", "= -\\log(\\frac{1}{n})\n", "$$\n", "For example, entropy([0.5, 0.5]) should be $-log(0.5)$." ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Worked!\n" ] } ], "source": [ "# Pattern test\n", "def test_equal_probabilities(n):\n", " prob = 1.0/n\n", " ps = np.repeat(prob , n)\n", " if np.isclose(entropy(ps), -np.log(prob)):\n", " print(\"Worked!\")\n", " else:\n", " import pdb; pdb.set_trace()\n", " print (\"Bad result.\")\n", " \n", " \n", "# Run a test\n", "test_equal_probabilities(100000)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You see that there are many, many cases to test. So far, we've been writing special codes for each test case. We can do better." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Unittest Infrastructure" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are several reasons to use a test infrastructure:\n", "- If you have many test cases (which you should!), the test infrastructure will save you from writing a lot of code.\n", "- The infrastructure provides a uniform way to report test results, and to handle test failures.\n", "- A test infrastructure can tell you about coverage so you know what tests to add.\n", "\n", "We'll be using the `unittest` framework. This is a separate Python package. Using this infrastructure, requires the following:\n", "1. import the unittest module\n", "1. define a class that inherits from unittest.TestCase\n", "1. write methods that run the code to be tested and check the outcomes.\n", "\n", "The last item has two subparts. First, we must identify which methods in the class inheriting from unittest.TestCase are tests. You indicate that a method is to be run as a test by having the method name begin with \"test\".\n", "\n", "Second, the \"test methods\" should communicate with the infrastructure the results of evaluating output from the code under test. This is done by using `assert` statements. For example, `self.assertEqual` takes two arguments. If these are objects for which `==` returns `True`, then the test passes. Otherwise, the test fails." ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "...\n", "----------------------------------------------------------------------\n", "Ran 3 tests in 0.008s\n", "\n", "OK\n" ] } ], "source": [ "import unittest\n", "\n", "# Define a class in which the tests will run\n", "class UnitTests(unittest.TestCase):\n", "\n", " # Each method in the class to execute a test\n", " def test_success(self):\n", " self.assertEqual(1, 1)\n", " \n", " def test_success1(self):\n", " self.assertTrue(1 == 1)\n", "\n", " def test_failure(self):\n", " self.assertLess(1, 2)\n", " \n", "suite = unittest.TestLoader().loadTestsFromTestCase(UnitTests)\n", "_ = unittest.TextTestRunner().run(suite)\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "# Function the handles test loading\n", "#def test_setup(argument ?):\n", " \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Code for homework or your work should use test files. In this lesson, we'll show how to write test codes in a Jupyter notebook. This is done for pedidogical reasons. It is NOT not something you should do in practice, except as an intermediate exploratory approach. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As expected, the first test passes, but the second test fails." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercise\n", "- Rewrite the above one-shot test for entropy using the unittest infrastructure." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "# Implementating a pattern test. Use functions in the test.\n", "import unittest\n", "\n", "# Define a class in which the tests will run\n", "class TestEntropy(unittest.TestCase):\n", " \n", " def test_equal_probability(self):\n", " def test(count):\n", " \"\"\"\n", " Invokes the entropy function for a number of values equal to count\n", " that have the same probability.\n", " :param int count:\n", " \"\"\"\n", " raise RuntimeError (\"Not implemented.\")\n", " #\n", " test(2)\n", " test(20)\n", " test(200)\n", "\n", "#test_setup(TestEntropy)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "import unittest\n", "\n", "# Define a class in which the tests will run\n", "class TestEntropy(unittest.TestCase):\n", " \"\"\"Write the full set of tests.\"\"\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Testing For Exceptions\n", "\n", "Edge test cases often involves handling exceptions. One approach is to code this directly." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "import unittest\n", "\n", "# Define a class in which the tests will run\n", "class TestEntropy(unittest.TestCase):\n", " \n", " def test_invalid_probability(self):\n", " try:\n", " entropy([0.1, 0.5])\n", " self.assertTrue(False)\n", " except ValueError:\n", " self.assertTrue(True)\n", " \n", "#test_setup(TestEntropy)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`unittest` provides help with testing exceptions." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ ".\n", "----------------------------------------------------------------------\n", "Ran 1 test in 0.004s\n", "\n", "OK\n" ] } ], "source": [ "import unittest\n", "\n", "# Define a class in which the tests will run\n", "class TestEntropy(unittest.TestCase):\n", " \n", " def test_invalid_probability(self):\n", " with self.assertRaises(ValueError):\n", " entropy([0.1, 0.5])\n", " \n", "suite = unittest.TestLoader().loadTestsFromTestCase(TestEntropy)\n", "_ = unittest.TextTestRunner().run(suite)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Test Files\n", "Although I presented the elements of `unittest` in a notebook. your tests should be in a file. If the name of module with the code under test is `foo.py`, then the name of the test file should be `test_foo.py`.\n", "\n", "The structure of the test file will be very similar to cells above. You will import `unittest`. You must also import the module with the code under test. Take a look at `test_prime.py` in this directory to see an example." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Discussion\n", "Question: What tests would you write for a plotting function?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Test Driven Development\n", "Start by writing the tests. Then write the code.\n", "\n", "We illustrate this by considering a function geomean that takes a list of numbers as input and produces the geometric mean on output." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "import unittest\n", "\n", "# Define a class in which the tests will run\n", "class TestEntryopy(unittest.TestCase):\n", " \n", " def test_oneshot(self):\n", " self.assertEqual(geomean([1,1]), 1)\n", " \n", " def test_oneshot2(self):\n", " self.assertEqual(geomean([3, 3, 3]), 3)\n", " \n", "#test_setup(TestGeomean)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "#def geomean(argument?):\n", "# return ?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercise\n", "- Create a python module entropy.py with the entropy function\n", "- Create a python module test_entropy.py with the test codes\n", "- Try running `python test_entropy.py`.\n", "- Try using nosetests to get coverage information (`nosetests --with-coverage test_entropy.py`). \n", " - You can install nosetests with `conda install nose`\n", " - You may have to install the coverage module (look at https://stackoverflow.com/questions/14488601/how-to-fix-python-nose-coverage-not-available-unable-to-import-coverage-module)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Other infrastructures\n", "- pytest\n", "- nose\n", "- Use binary functions that being with \"test\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## References\n", "\n", "https://www.youtube.com/watch?v=GEqM9uJi64Q (Pydata 2015)\n", "https://www.youtube.com/watch?v=yACtdj1_IxE (Pycon 2017)\n", "\n", "The first talk mentions some packages:\n", "engarde - https://github.com/TomAugspurger/engarde\n", "Hypothesis - https://hypothesis.readthedocs.io/en/latest/\n", "Feature Forge - https://github.com/machinalis/featureforge\n", "\n", "\n", "Detlef Nauck talk: \n", "http://ukkdd.org.uk/2017/info/talks/nauck.pdf\n", "He also had a list of R tools but I could not find the slides form the talk I saw.\n", "\n", "Test Driven Data Analysis:\n", "https://www.youtube.com/watch?v=TGwZnZYg0jw\n", "\n", "Profiling for Pandas:\n", "https://github.com/pandas-profiling/pandas-profiling" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 }	bsd-2-clause
xmnlab/notebooks	jupyter/Introducción.ipynb	1	66814	{ "cells": [ { "cell_type": "markdown", "metadata": { "toc": "true" }, "source": [ "# Table of Contents\n", " <p><div class=\"lev1 toc-item\"><a href=\"#Introducción-a-Jupyter-Notebook\" data-toc-modified-id=\"Introducción-a-Jupyter-Notebook-1\"><span class=\"toc-item-num\">1  </span>Introducción a Jupyter Notebook</a></div><div class=\"lev2 toc-item\"><a href=\"#¿Qué-es-Jupyter-Notebook?\" data-toc-modified-id=\"¿Qué-es-Jupyter-Notebook?-11\"><span class=\"toc-item-num\">1.1  </span>¿Qué es Jupyter Notebook?</a></div><div class=\"lev2 toc-item\"><a href=\"#¿Qué-es-un-notebook-science?\" data-toc-modified-id=\"¿Qué-es-un-notebook-science?-12\"><span class=\"toc-item-num\">1.2  </span>¿Qué es un notebook science?</a></div><div class=\"lev2 toc-item\"><a href=\"#Estructura-del-notebook-science\" data-toc-modified-id=\"Estructura-del-notebook-science-13\"><span class=\"toc-item-num\">1.3  </span>Estructura del notebook science</a></div><div class=\"lev2 toc-item\"><a href=\"#Comando-mágicos\" data-toc-modified-id=\"Comando-mágicos-14\"><span class=\"toc-item-num\">1.4  </span>Comando mágicos</a></div><div class=\"lev3 toc-item\"><a href=\"#Ejecutar-códigos-con-otros-kernels\" data-toc-modified-id=\"Ejecutar-códigos-con-otros-kernels-141\"><span class=\"toc-item-num\">1.4.1  </span>Ejecutar códigos con otros kernels</a></div><div class=\"lev2 toc-item\"><a href=\"#Cargar-datos\" data-toc-modified-id=\"Cargar-datos-15\"><span class=\"toc-item-num\">1.5  </span>Cargar datos</a></div><div class=\"lev2 toc-item\"><a href=\"#Gráficos\" data-toc-modified-id=\"Gráficos-16\"><span class=\"toc-item-num\">1.6  </span>Gráficos</a></div><div class=\"lev2 toc-item\"><a href=\"#Widgets\" data-toc-modified-id=\"Widgets-17\"><span class=\"toc-item-num\">1.7  </span>Widgets</a></div><div class=\"lev2 toc-item\"><a href=\"#Help\" data-toc-modified-id=\"Help-18\"><span class=\"toc-item-num\">1.8  </span>Help</a></div><div class=\"lev2 toc-item\"><a href=\"#LaTeX\" data-toc-modified-id=\"LaTeX-19\"><span class=\"toc-item-num\">1.9  </span>LaTeX</a></div><div class=\"lev2 toc-item\"><a href=\"#Instalación\" data-toc-modified-id=\"Instalación-110\"><span class=\"toc-item-num\">1.10  </span>Instalación</a></div><div class=\"lev1 toc-item\"><a href=\"#Referencias\" data-toc-modified-id=\"Referencias-2\"><span class=\"toc-item-num\">2  </span>Referencias</a></div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Introducción a Jupyter Notebook" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## ¿Qué es Jupyter Notebook?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> El IPython Notebook es ahora conocido como el Jupyter Notebook. Es un entorno computacional interactivo, en el que se pueden combinar ejecución de código, texto enriquecido, matemáticas, gráficos y contenidos multimedia [1]." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## ¿Qué es un notebook science?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> El Open Notebook Science es la práctica de hacer que el registro primario completo de un proyecto de investigación este disponible públicamente, en línea y tal como está registrado. Esto consiste en colocar el notebook personal, de laboratorio o del investigador en línea junto con todos los datos crudos y procesados así como cualquier material asociado, tal como se genera este material. El enfoque puede resumirse por el lema \"ninguna información privilegiada\". Este es el extremo lógico de enfoques transparentes a la investigación e incluye explícitamente la puesta a disposición de experimentos inéditos fallidos, menos importantes y de otra índole; llamados \"datos oscuros\". La práctica de la ciencia del cuaderno abierto, aunque no es la norma de la comunidad académica, ha ganado considerable atención en la investigación reciente general, and peer-reviewed y de medios de comunicación revisados como parte de una tendencia general hacia enfoques más abiertos en la práctica de la investigación y publicación. La Ciencia del cuaderno abierto, por tanto, puede ser descrito como parte de un más amplio movimiento de ciencia abierta que incluye la promoción y adopción de la publicación de acceso abierto, datos abiertos, datos de crowdsourcing y ciencia ciudadana. Está inspirado en parte por el éxito del software de código abierto y se basa en muchas de sus ideas [2]." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Estructura del notebook science" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Una manera sencilla de probar Jupyter sin tenerlo instalado en la computadora \n", "es por medio de un servicio web gratuito:\n", "\n", "https://try.jupyter.org/\n", "\n", "\n", "Basicamente el notebook está compuesto por celdas [3]. \n", "Hay algunos tipos de celdas:\n", "\n", "* texto;\n", "* código;\n", "* resultado;\n", "\n", "En la celda de texto podemos utilizar formatación con Markdown. Ej:\n", "\n", "```md\n", "\n", "negrito;\n", "itálico;\n", "*negrito y itálico;\n", "```\n", "\n", "A parte de formatación de texto, con Markdown podemos inserir imágenes:\n", "\n", "![Jupyter Logo](http://jupyter.org/assets/nav_logo.svg)\n", "\n", "```md\n", "\n", "![Jupyter Logo](http://jupyter.org/assets/nav_logo.svg)\n", "```\n", "\n", "```python\n", "print(a+b)\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Comando mágicos" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Los comando mágicos de Jupyter son comando própios del entorno Jupyter,\n", "y empiezan por % o %%. \n", "\n", "Ejemplo de algunos comandos mágicos [4]:\n", "\n", " Ejecutar códigos python (%run)\n", "* Inserir código desde archivo (%load)\n", "* Listar todas variables del escopo global (%who)\n", "* Visualizar tiempo de ejecución (%%time)\n", "* Visualizar tiempo de ejecución (%%timeit)\n", "* Profile (%prun)\n", "* Depuración (%pdb)\n", "* Comandos shell (!)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "a = 1\n", "b = 2.2\n", "c = 3\n", "d = 'a'" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "a\t b\t c\t d\t \n" ] } ], "source": [ "%who" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def f1(n):\n", " for x in range(n):\n", " pass" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 0 ns, sys: 0 ns, total: 0 ns\n", "Wall time: 26 µs\n" ] } ], "source": [ "%%time \n", "f1(100)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "100000 loops, best of 3: 3.65 µs per loop\n" ] } ], "source": [ "%%timeit\n", "f1(100)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Ejecutar códigos con otros kernels\n", "\n", "En la celda de código también es posible ejecutar códigos de otras lenguajes.\n", "A continuación, algunas comandos mágicos para ejecutar comandos de otros \n", "lenguajes:\n", "\n", " %%bash\n", "* %%HTML\n", "* %%python2\n", "* %%python3\n", "* %%ruby\n", "* %%perl" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "total 80K\n", "drwxr-xr-x 4 xmn xmn 4,0K ago 12 10:36 .\n", "drwxr-xr-x 13 xmn xmn 4,0K ago 10 15:49 ..\n", "drwxr-xr-x 2 xmn xmn 4,0K ago 9 18:38 data\n", "-rw-r--r-- 1 xmn xmn 57K ago 12 10:36 Introducción.ipynb\n", "drwxr-xr-x 2 xmn xmn 4,0K ago 12 10:36 .ipynb_checkpoints\n", "-rw-r--r-- 1 xmn xmn 72 ago 12 10:36 Untitled.ipynb\n" ] } ], "source": [ "%%bash\n", "\n", "ls -lah" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Cargar datos" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "\n", "df = pd.read_csv('data/kaggle-titanic.csv')" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>PassengerId</th>\n", " <th>Survived</th>\n", " <th>Pclass</th>\n", " <th>Name</th>\n", " <th>Sex</th>\n", " <th>Age</th>\n", " <th>SibSp</th>\n", " <th>Parch</th>\n", " <th>Ticket</th>\n", " <th>Fare</th>\n", " <th>Cabin</th>\n", " <th>Embarked</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>Braund, Mr. Owen Harris</td>\n", " <td>male</td>\n", " <td>22.0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>A/5 21171</td>\n", " <td>7.2500</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n", " <td>female</td>\n", " <td>38.0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>PC 17599</td>\n", " <td>71.2833</td>\n", " <td>C85</td>\n", " <td>C</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>3</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>Heikkinen, Miss. Laina</td>\n", " <td>female</td>\n", " <td>26.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>STON/O2. 3101282</td>\n", " <td>7.9250</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>4</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n", " <td>female</td>\n", " <td>35.0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>113803</td>\n", " <td>53.1000</td>\n", " <td>C123</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>Allen, Mr. William Henry</td>\n", " <td>male</td>\n", " <td>35.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>373450</td>\n", " <td>8.0500</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " PassengerId Survived Pclass \\\n", "0 1 0 3 \n", "1 2 1 1 \n", "2 3 1 3 \n", "3 4 1 1 \n", "4 5 0 3 \n", "\n", " Name Sex Age SibSp \\\n", "0 Braund, Mr. Owen Harris male 22.0 1 \n", "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n", "2 Heikkinen, Miss. Laina female 26.0 0 \n", "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n", "4 Allen, Mr. William Henry male 35.0 0 \n", "\n", " Parch Ticket Fare Cabin Embarked \n", "0 0 A/5 21171 7.2500 NaN S \n", "1 0 PC 17599 71.2833 C85 C \n", "2 0 STON/O2. 3101282 7.9250 NaN S \n", "3 0 113803 53.1000 C123 S \n", "4 0 373450 8.0500 NaN S " ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'pandas.core.frame.DataFrame'>\n", "RangeIndex: 891 entries, 0 to 890\n", "Data columns (total 12 columns):\n", "PassengerId 891 non-null int64\n", "Survived 891 non-null int64\n", "Pclass 891 non-null int64\n", "Name 891 non-null object\n", "Sex 891 non-null object\n", "Age 714 non-null float64\n", "SibSp 891 non-null int64\n", "Parch 891 non-null int64\n", "Ticket 891 non-null object\n", "Fare 891 non-null float64\n", "Cabin 204 non-null object\n", "Embarked 889 non-null object\n", "dtypes: float64(2), int64(5), object(5)\n", "memory usage: 83.6+ KB\n" ] } ], "source": [ "df.info()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>PassengerId</th>\n", " <th>Survived</th>\n", " <th>Pclass</th>\n", " <th>Age</th>\n", " <th>SibSp</th>\n", " <th>Parch</th>\n", " <th>Fare</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>891.000000</td>\n", " <td>891.000000</td>\n", " <td>891.000000</td>\n", " <td>714.000000</td>\n", " <td>891.000000</td>\n", " <td>891.000000</td>\n", " <td>891.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>446.000000</td>\n", " <td>0.383838</td>\n", " <td>2.308642</td>\n", " <td>29.699118</td>\n", " <td>0.523008</td>\n", " <td>0.381594</td>\n", " <td>32.204208</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>257.353842</td>\n", " <td>0.486592</td>\n", " <td>0.836071</td>\n", " <td>14.526497</td>\n", " <td>1.102743</td>\n", " <td>0.806057</td>\n", " <td>49.693429</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>1.000000</td>\n", " <td>0.000000</td>\n", " <td>1.000000</td>\n", " <td>0.420000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>223.500000</td>\n", " <td>0.000000</td>\n", " <td>2.000000</td>\n", " <td>20.125000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>7.910400</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>446.000000</td>\n", " <td>0.000000</td>\n", " <td>3.000000</td>\n", " <td>28.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>14.454200</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>668.500000</td>\n", " <td>1.000000</td>\n", " <td>3.000000</td>\n", " <td>38.000000</td>\n", " <td>1.000000</td>\n", " <td>0.000000</td>\n", " <td>31.000000</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>891.000000</td>\n", " <td>1.000000</td>\n", " <td>3.000000</td>\n", " <td>80.000000</td>\n", " <td>8.000000</td>\n", " <td>6.000000</td>\n", " <td>512.329200</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " PassengerId Survived Pclass Age SibSp \\\n", "count 891.000000 891.000000 891.000000 714.000000 891.000000 \n", "mean 446.000000 0.383838 2.308642 29.699118 0.523008 \n", "std 257.353842 0.486592 0.836071 14.526497 1.102743 \n", "min 1.000000 0.000000 1.000000 0.420000 0.000000 \n", "25% 223.500000 0.000000 2.000000 20.125000 0.000000 \n", "50% 446.000000 0.000000 3.000000 28.000000 0.000000 \n", "75% 668.500000 1.000000 3.000000 38.000000 1.000000 \n", "max 891.000000 1.000000 3.000000 80.000000 8.000000 \n", "\n", " Parch Fare \n", "count 891.000000 891.000000 \n", "mean 0.381594 32.204208 \n", "std 0.806057 49.693429 \n", "min 0.000000 0.000000 \n", "25% 0.000000 7.910400 \n", "50% 0.000000 14.454200 \n", "75% 0.000000 31.000000 \n", "max 6.000000 512.329200 " ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Gráficos" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from matplotlib import pyplot as plt" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f9a23439198>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df.Survived.value_counts().plot(kind='bar')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Pixiedust database opened successfully\n" ] }, { "data": { "text/html": [ "\n", " <div style=\"margin:10px\">\n", " <a href=\"https://github.com/ibm-watson-data-lab/pixiedust\" target=\"_new\">\n", " <img src=\"https://github.com/ibm-watson-data-lab/pixiedust/raw/master/docs/_static/pd_icon32.png\" style=\"float:left;margin-right:10px\"/>\n", " </a>\n", " <span>Pixiedust version 1.0.9</span>\n", " </div>\n", " " ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Unable to check latest version <urlopen error [Errno -2] Name or service not known>\n" ] } ], "source": [ "import pixiedust" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "pixiedust": { "displayParams": { "aggregation": "SUM", "handlerId": "downloadFile", "keyFields": "Sex", "rendererId": "matplotlib", "rowCount": "500", "valueFields": "Survived" } } }, "outputs": [ { "data": { "text/html": [ "<style type=\"text/css\">.pd_warning{display:none;}</style><div class=\"pd_warning\"><em>Hey, there's something awesome here! To see it, open this notebook outside GitHub, in a viewer like Jupyter</em></div>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display(df)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Widgets" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true }, "outputs": [], "source": [ "π = np.pi" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def show_wave(A, f, φ):\n", " ω = 2πf\n", " t = np.linspace(0, 1, 10000)\n", " f = Anp.sin(ωt+φ)\n", " \n", " plt.grid(True)\n", " plt.plot(t, f)\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/xmn/anaconda3/lib/python3.6/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['serif'] not found. Falling back to DejaVu Sans\n", " (prop.get_family(), self.defaultFamily[fontext]))\n" ] }, { "data": { "image/png": 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zOHXqFAAglUrh6NGjWF1dnXhhTtLzRDgZBFCqa0jGwohG3H2pZZBcmlOP2Msc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\nEIuIiBj0A/br+IfLa/SXAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f9a0c9f1160>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_wave(A=5, f=5, φ=2)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import ipywidgets as widgets\n", "from IPython.display import display" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "6fc11a09bede42d892ed26d7c767f90d" } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "params = dict(value=1, min=1, max=100, step=1, continuous_update=False)\n", "\n", "wA = widgets.IntSlider(params)\n", "wf = widgets.IntSlider(params)\n", "wφ = widgets.IntSlider(value=0, min=0, max=10, step=1, continuous_update=False)\n", "\n", "widgets.interact(show_wave, A=wA, f=wf, φ=wφ);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Para más informaciones sobre ipywidgets, consulte el manual de usuario [6]." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Help" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Para ver la documentación de una determinada función o clase puedes ejecutar el comando:\n", "\n", "```\n", "?str.replace()\n", "```\n", "\n", "Este comando abrirá una sección en la página con la documentación deseada.\n", "\n", "Otro modo de ver la documentación es usando la función help, ej.:\n", "\n", "```\n", "help(str.replace)\n", "```" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": true }, "outputs": [], "source": [ "?str.replace()" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on method_descriptor:\n", "\n", "replace(...)\n", " S.replace(old, new[, count]) -> str\n", " \n", " Return a copy of S with all occurrences of substring\n", " old replaced by new. If the optional argument count is\n", " given, only the first count occurrences are replaced.\n", "\n" ] } ], "source": [ "help(str.replace)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## LaTeX" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Con Jupyter Notebook, también se puede escribir en las celdas de texto en formato LaTeX.\n", "\n", "Por ejemplo:\n", "\n", "```latex\n", "\n", "$$\n", "\\begin{equation}\n", "\\omega = \\alpha + \\beta + \\sum_{n=1}^{\\infty} 2^{-n}\n", "\\end{equation}\n", "$$\n", "\n", "```\n", "\n", "Y su resutado es:\n", "\n", "$$\n", "\\begin{equation}\n", "\\omega = \\alpha + \\beta + \\sum_{n=1}^{\\infty} 2^{-n}\n", "\\end{equation}\n", "$$\n", "\n", "Para más informaciones sobre el uso de LaTeX en Jupyter, pueden ver en [6]." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Instalación" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Para utilizar Jupyter notebook, necesitamos instalarlo. \n", "La mejor manera para instalar librerías es hacerlo en un entorno separado \n", "(del entorno python del sistema operativo). Una distribución Python que está\n", "enfocada en temas científicos es Anaconda y se puede bajar su instalador en https://www.continuum.io/downloads [7].\n", "\n", "En el entorno principal (root) de Anaconda, Jupyter ya viene instalado. Caso empiecemos un entorno nuevo, podemos instalar Jupyter con el comando:\n", "\n", "```sh\n", "conda install jupyter\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Referencias" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* [1] http://ipython.org/notebook.html\n", "* [2] https://es.wikipedia.org/wiki/Open_notebook_science\n", "* [3] https://es.slideshare.net/jileon/introduccion-a-jupyter-antes-i-python-notebook\n", "* [4] https://www.dataquest.io/blog/jupyter-notebook-tips-tricks-shortcuts/\n", "* [5] http://jupyter.readthedocs.io/en/latest/install.html\n", "* [6] http://ipywidgets.readthedocs.io/en/latest/user_guide.html\n", "* [7] http://jupyter-contrib-nbextensions.readthedocs.io/en/latest/nbextensions/latex_envs/README.html\n", "\n", "Tutoriales\n", "\n", "* https://www.youtube.com/watch?v=nktmFUFWpO0\n", "* https://www.youtube.com/watch?v=fTRkm3d6ebw\n", "* https://pybonacci.es/2013/05/16/entrevista-a-fernando-perez-creador-de-ipython/\n", "* https://jupyter-notebook-beginner-guide.readthedocs.io" ] } ], "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" }, "latex_envs": { "LaTeX_envs_menu_present": true, "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": { "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": "495px", "width": "252px" }, "navigate_menu": true, "number_sections": true, "sideBar": true, "threshold": 4, "toc_cell": true, "toc_section_display": "block", "toc_window_display": true, "widenNotebook": false } }, "nbformat": 4, "nbformat_minor": 2 }	mit
kgourgou/set-phasers-to-stan	birth-death-paths-stan/.ipynb_checkpoints/paths-checkpoint.ipynb	1	2375	{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from matplotlib.pyplot import plot\n", "from ssa import SSA\n", "from pystan import stan\n", "\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "N = 100\n", "alpha = 1\n", "mu = 10\n", "\n", "x, t = SSA(3,N,a=alpha,mu=mu)\n", "\n", "x = x.astype(int) # path data supposed to be integers.\n", "\n", "path_data = {\n", " 'N' : N,\n", " 't' : t,\n", " 'x' : x\n", "}\n", "\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Setup STAN :\n", "model_description = \"\"\"\n", "data{\n", " int<lower=0> N; ## number of time steps\n", " vector[N] t; ## time value at each time step\n", " int<lower=0> x[N]; ## population value at each time step\n", "}\n", "\n", "transformed data{\n", " int<lower=0> x0; ## starting population value\n", " x0 <- x[1];\n", "}\n", "\n", "parameters{\n", " real<lower=0> alpha; ## birth rate parameter \n", " real<lower=0> mu; ## death rate parameter\n", "}\n", "\n", "model {\n", " x ~ poisson(alpha/mu+(x0-alpha/mu)exp(-mut)); \n", "}\n", "\"\"\"\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fit = stan(model_code=model_description,\n", " data=path_data, chains=1,iter=1000)\n", "\n", "print fit" ] }, { "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
yl565/statsmodels	examples/notebooks/ols.ipynb	3	12634	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Ordinary Least Squares" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "from __future__ import print_function\n", "import numpy as np\n", "import statsmodels.api as sm\n", "import matplotlib.pyplot as plt\n", "from statsmodels.sandbox.regression.predstd import wls_prediction_std\n", "\n", "np.random.seed(9876789)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## OLS estimation\n", "\n", "Artificial data:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "nsample = 100\n", "x = np.linspace(0, 10, 100)\n", "X = np.column_stack((x, x2))\n", "beta = np.array([1, 0.1, 10])\n", "e = np.random.normal(size=nsample)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Our model needs an intercept so we add a column of 1s:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "X = sm.add_constant(X)\n", "y = np.dot(X, beta) + e" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Fit and summary:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "model = sm.OLS(y, X)\n", "results = model.fit()\n", "print(results.summary())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Quantities of interest can be extracted directly from the fitted model. Type ``dir(results)`` for a full list. Here are some examples: " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print('Parameters: ', results.params)\n", "print('R2: ', results.rsquared)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## OLS non-linear curve but linear in parameters\n", "\n", "We simulate artificial data with a non-linear relationship between x and y:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "nsample = 50\n", "sig = 0.5\n", "x = np.linspace(0, 20, nsample)\n", "X = np.column_stack((x, np.sin(x), (x-5)2, np.ones(nsample)))\n", "beta = [0.5, 0.5, -0.02, 5.]\n", "\n", "y_true = np.dot(X, beta)\n", "y = y_true + sig * np.random.normal(size=nsample)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Fit and summary:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "res = sm.OLS(y, X).fit()\n", "print(res.summary())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Extract other quantities of interest:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print('Parameters: ', res.params)\n", "print('Standard errors: ', res.bse)\n", "print('Predicted values: ', res.predict())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Draw a plot to compare the true relationship to OLS predictions. Confidence intervals around the predictions are built using the ``wls_prediction_std`` command." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "prstd, iv_l, iv_u = wls_prediction_std(res)\n", "\n", "fig, ax = plt.subplots(figsize=(8,6))\n", "\n", "ax.plot(x, y, 'o', label=\"data\")\n", "ax.plot(x, y_true, 'b-', label=\"True\")\n", "ax.plot(x, res.fittedvalues, 'r--.', label=\"OLS\")\n", "ax.plot(x, iv_u, 'r--')\n", "ax.plot(x, iv_l, 'r--')\n", "ax.legend(loc='best');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## OLS with dummy variables\n", "\n", "We generate some artificial data. There are 3 groups which will be modelled using dummy variables. Group 0 is the omitted/benchmark category." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "nsample = 50\n", "groups = np.zeros(nsample, int)\n", "groups[20:40] = 1\n", "groups[40:] = 2\n", "#dummy = (groups[:,None] == np.unique(groups)).astype(float)\n", "\n", "dummy = sm.categorical(groups, drop=True)\n", "x = np.linspace(0, 20, nsample)\n", "# drop reference category\n", "X = np.column_stack((x, dummy[:,1:]))\n", "X = sm.add_constant(X, prepend=False)\n", "\n", "beta = [1., 3, -3, 10]\n", "y_true = np.dot(X, beta)\n", "e = np.random.normal(size=nsample)\n", "y = y_true + e" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Inspect the data:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(X[:5,:])\n", "print(y[:5])\n", "print(groups)\n", "print(dummy[:5,:])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Fit and summary:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "res2 = sm.OLS(y, X).fit()\n", "print(res2.summary())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Draw a plot to compare the true relationship to OLS predictions:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "prstd, iv_l, iv_u = wls_prediction_std(res2)\n", "\n", "fig, ax = plt.subplots(figsize=(8,6))\n", "\n", "ax.plot(x, y, 'o', label=\"Data\")\n", "ax.plot(x, y_true, 'b-', label=\"True\")\n", "ax.plot(x, res2.fittedvalues, 'r--.', label=\"Predicted\")\n", "ax.plot(x, iv_u, 'r--')\n", "ax.plot(x, iv_l, 'r--')\n", "legend = ax.legend(loc=\"best\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Joint hypothesis test\n", "\n", "### F test\n", "\n", "We want to test the hypothesis that both coefficients on the dummy variables are equal to zero, that is, $R \\times \\beta = 0$. An F test leads us to strongly reject the null hypothesis of identical constant in the 3 groups:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "R = [[0, 1, 0, 0], [0, 0, 1, 0]]\n", "print(np.array(R))\n", "print(res2.f_test(R))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can also use formula-like syntax to test hypotheses" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(res2.f_test(\"x2 = x3 = 0\"))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Small group effects\n", "\n", "If we generate artificial data with smaller group effects, the T test can no longer reject the Null hypothesis: " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "beta = [1., 0.3, -0.0, 10]\n", "y_true = np.dot(X, beta)\n", "y = y_true + np.random.normal(size=nsample)\n", "\n", "res3 = sm.OLS(y, X).fit()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(res3.f_test(R))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(res3.f_test(\"x2 = x3 = 0\"))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Multicollinearity\n", "\n", "The Longley dataset is well known to have high multicollinearity. That is, the exogenous predictors are highly correlated. This is problematic because it can affect the stability of our coefficient estimates as we make minor changes to model specification. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from statsmodels.datasets.longley import load_pandas\n", "y = load_pandas().endog\n", "X = load_pandas().exog\n", "X = sm.add_constant(X)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Fit and summary:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ols_model = sm.OLS(y, X)\n", "ols_results = ols_model.fit()\n", "print(ols_results.summary())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Condition number\n", "\n", "One way to assess multicollinearity is to compute the condition number. Values over 20 are worrisome (see Greene 4.9). The first step is to normalize the independent variables to have unit length: " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "norm_x = X.values\n", "for i, name in enumerate(X):\n", " if name == \"const\":\n", " continue\n", " norm_x[:,i] = X[name]/np.linalg.norm(X[name])\n", "norm_xtx = np.dot(norm_x.T,norm_x)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then, we take the square root of the ratio of the biggest to the smallest eigen values. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "eigs = np.linalg.eigvals(norm_xtx)\n", "condition_number = np.sqrt(eigs.max() / eigs.min())\n", "print(condition_number)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Dropping an observation\n", "\n", "Greene also points out that dropping a single observation can have a dramatic effect on the coefficient estimates: " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ols_results2 = sm.OLS(y.ix[:14], X.ix[:14]).fit()\n", "print(\"Percentage change %4.2f%%\\n\"7 % tuple([i for i in (ols_results2.params - ols_results.params)/ols_results.params100]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also look at formal statistics for this such as the DFBETAS -- a standardized measure of how much each coefficient changes when that observation is left out." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "infl = ols_results.get_influence()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In general we may consider DBETAS in absolute value greater than $2/\\sqrt{N}$ to be influential observations" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "2./len(X)**.5" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(infl.summary_frame().filter(regex=\"dfb\"))" ] } ], "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
kiaderouiche/hilbmetrics	notebooks/convexGeometry.ipynb	2	1108	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## This IPython notebook contains the example code presented symbolic calculs in Convex Hull " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "%config InlineBackend.figure_formats = {'svg', 'png'}" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from sympy import init_printing, symbols, Symbol" ] }, { "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": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 }	apache-2.0
margulies/gradient_analysis	06_supplementary_analysis_and_figures.ipynb	2	82762	{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Components variance" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Applications/miniconda3/envs/topography/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n", " warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n" ] } ], "source": [ "% matplotlib inline\n", "\n", "import numpy as np\n", "import matplotlib as mpl\n", "import matplotlib.pyplot as plt\n", "mpl.rcParams['svg.fonttype'] = 'none'\n", "\n", "emb = np.load('gradient_data/embedded/embedding_dense_res.npy')" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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6NX799Vfs27cPEolEtDiIiEyB2HW+Pho3bhz8/Pzw4YcfltvH8iKqHZpM7MJl\nHEgX9LrlLzU1FS1btoSbmxukUilCQ0OxZcuWSo+Pi4vD6NGjdRlStY0ZMwZZWVnYu3ev2KEQEZGJ\nefr0KbZu3Yo333xT7FCITJa6E7tYWFhwGQfSWxa6vHhmZqZK9xQXF5dKJ04pLCxEQkICvvvuO12G\nVG0WFhaIjIxEREQEevfuzdY/IiKqNbt27YKfnx8aNWpU6TGRkZHKr4ODgxEcHKz7wIhMBMf3kViS\nk5ORnJystevpNPnTxLZt29CjRw80bNiw0mPEfrCNGjUK//73v5GQkIABAwbU6r2JiIyZth9uxkad\nnjHPPyOJSHuY+JGYXsx5oqKianQ9nY75O3z4MCIjI5WzZM6bNw8SiQTTp08vd+zrr7+OkSNHIjQ0\ntOJA9WQ8w//+9z/MnTsXR48eZesfEZGO6Eudrw8eP34MNzc3pKenw8bGpsJjWF5EuqFu4mdlZYVd\nu3Yx8SOd0+sxf507d8a1a9eQkZGB4uJixMfHY8iQIeWOe/jwIfbv34+hQ4fqMhytGD58OEpLS186\ndpGIiEhbrKyscO/evUoTPyLSDblcjtdee63KxI/r95Eh0WnyZ25ujuXLl6Nfv35o06YNQkND4e3t\njZUrV+KHH35QHrd582a8+uqrsLS01GU4WmFmZobZs2cjIiICCoVC7HCIiIiISAemTp2KgoKCSvdz\nYhcyRFzkvRoEQUBAQACmTZuGUaNGiR0OEZHR0ac63xCwvIi0KyUlBb169ar0RT/H95FYalrfM/mr\npj179uD//b//h3PnzsHCQm/mzSEiMgr6VufrO5YXkfZUNc6PiR+JSa/H/BmzkJAQNGrUCOvWrRM7\nFCIiIiKqIblcjmHDhqFXr16VJn7m5uZM/MigseWvBvbv34/33nsPly5dglQqFTscIiKjoY91vj5j\neRHVTEpKCl577bWXjvEDgKFDh2Lz5s21FBVReWz5E1FQUBDc3d3xyy+/iB0KEREREVVD2ayeVSV+\n1tbWWLRoUS1FRaQbbPmroUOHDiE0NBRXrlxB3bp1xQ6HiMgo6Gudr69YXkTVN3ToUGzduvWlx3Cc\nH+kLtvyJLDAwEG3btsWPP/4odihEREREpKayMX7btm176XHW1tZM/MhosOVPC44fP44hQ4bg2rVr\nBrFWIRGRvtPnOl8fsbyINKPOGD8zMzMMGjQIixcv5jp+pDe41IOeeP3119GjRw9MmzZN7FCIiAye\nvtf5+ob92LFAAAAgAElEQVTlRaQ+uVyO9u3bvzTxMzc3x969e9naR3qHyZ+eOHv2LPr27Yu0tDRY\nW1uLHQ4RkUHT9zpf37C8iNSnzhg/zupJ+opj/vREu3bt0Lt3byxbtkzsUIiIiIjoBeqO8fP09OSs\nnmS02PKnRZcvX0aPHj1w7do1NGjQQOxwiIgMliHU+fqE5UX0cnK5HCEhIUhLS6v0GI7xI0PAlj89\n0rp1awwcOJBvi4iIiIj0yNSpU1+a+Jmbm2Pfvn3YsmULEz8yamz507K0tDR07doVly9fhoODg9jh\nEBEZJEOp8/UFy4uoYnK5HB9//DG2bt360n8jHONHhoITvuihCRMmwMHBAXPnzhU7FCIig2RIdb4+\nYHkRladOV0/g2Ri/xMREtviRQWDyp4du3LiBjh074uLFi2jcuLHY4RARGRxDqvP1AcuLqLyqZvWU\nSCQYPHgwx/iRQWHyp6cmT56MOnXq4NtvvxU7FCIig2Nodb7YWF5EquRyOXx8fFBUVFTpMezqSYaI\nyZ+eun37Ntq0aYNz585BJpOJHQ4RkUExtDpfbCwvor/J5XL07t0b169fr/QYdvUkQ8XZPvWUk5MT\n3nvvPXz11Vdih0JERERkEsrG+VWW+EkkEgwZMoSJH5kstvzp0L179+Dl5YUTJ07Azc1N7HCIiAyG\nIdb5YmJ5ET1T1Tg/dvUkQ8dun3ruX//6F+7du4dVq1aJHQoRkcEw1DpfLCwvoqrH+bGrJxkDJn96\nLicnBx4eHujZsyfy8/Ph7OyM6OhoVjxERC9hqHW+WFheZOqqGufXvHlz7N27l39/kcFj8qfn5HI5\n/Pz88ODBA+U2vnkiIno5Q63zxcLyIlNW1Xp+lpaWOH/+PP/uIqPACV/0XEREhEriBwBpaWmIiIgQ\nKSIiIiIi41DW4veyhdz79evHxI/o/zD507HMzMwKt2dlZdVyJERERETGo6qZPYFnva0WLVpUe0ER\n6TkLsQMwds7OzhVud3JyquVIiIiIiIyDOmv5NW/enMNsiF7Alj8di46Ohqenp8q2OnXq4P79+8jJ\nyREpKiIiIiLDpG6LHyd4ISqPyZ+Oubu7IzExEWFhYejVqxfCwsJw5swZeHt7w9fXF8nJyWKHSERE\nRGQwIiIiXjrGjy1+RJXjbJ8i2rVrF8aNG4exY8ciMjISUqlU7JCIiPSCMdb5usTyIlMhl8sREBCA\nu3fvVrifM6qTseNsnwZswIABOHnyJE6cOIFXXnkF6enpYodEREREpJfKuntWlvixxY+oakz+RNak\nSRPs2LEDoaGh6Nq1K2JiYsQOiYiIiEivVLWkA8f4EamH3T71yKlTpzB69Gj4+/vju+++g62trdgh\nERGJwhTqfG1ieZExq2oR9yZNmuDQoUNM/MgksNunEfH19cWxY8dgZWWFjh074siRI2KHRERERCSq\nqiZ46du3LxM/IjWx5U9Pbdy4ER988AGmTJmC6dOnw9zcXOyQiIhqjanV+TXF8iJjxQleiFTVtL5n\n8qfHbt68iTFjxkAikWDt2rVwcXEROyQiolphinV+TbC8yBhV1d2zefPmHOdHJofdPo2Yq6srkpKS\nEBISAj8/P2zatEnskIiIiIhqxcu6e3KCF6LqYcufgTh8+DDCwsIQEhKChQsXwsrKSuyQiIh0xtTr\nfE2xvMjYvKy7Jyd4IVPGlj8TERAQgJMnT6KgoAD+/v44ffo05HI5wsPD0atXL4SHh0Mul4sdJhER\nEVGNVLWeHyd4Iao+nbf8JSQkYOrUqVAoFBg3bhymT59e7pjk5GR8/PHHePr0KRo1aoR9+/aVD5Rv\nNZViYmIwefJkmJub4/79+8rtHPRMRMaCdf7fHj58iPfffx/nzp2DmZkZfv75Z3Tt2lXlGJYXGZPw\n8HDExsZWuI9/65Cp0+sJXxQKBVq1aoWkpCTIZDJ07twZ8fHx8PLyUh7z8OFDdOvWDXv27IGzszOy\ns7Ph6OhYPlA+2FQMHToUW7duLbc9LCyMC8UTkcFjnf+3d999F0FBQRg7dixKSkrw+PHjcuvAsrzI\nWLC7J9HL6XW3z9TUVLRs2RJubm6QSqUIDQ3Fli1bVI5Zt24d3njjDTg7OwNAhYkflZeXl1fh9qys\nrFqOhIiIdCUvLw9//PEHxo4dCwCwsLAol/gRGQt29yTSPZ0mf5mZmXB1dVV+7+LigszMTJVjrly5\ngpycHPTq1QudO3fG2rVrdRmS0ShLll8kk8lqORIiItIVuVwOR0dHjB07Fp06dcKECRNQWFgodlhE\nOlHV7J7R0dG1HBGR8RF9wpeSkhKcOHECu3btQkJCAqKjo3Ht2jWxw9J70dHR8PT0VNnGipGIyLiU\nPSM/+ugjnDhxAlZWVpg3b57YYRFpnVwuR2JiYoX7mjRpwnF+RFpiocuLOzs748aNG8rvb926Va7F\nysXFBY6OjqhXrx7q1auHnj174vTp02jRokW560VGRiq/Dg4ORnBwsK5C13vu7u5ITEzEJ598gq1b\nt2LUqFGYM2cOK0YiMkjJyclITk4WOwy94+LiAldXV/j7+wMARowYgfnz51d4LJ+RZKjY3ZOoctp+\nPup0wpfS0lK0bt0aSUlJcHJyQpcuXRAXFwdvb2/lMZcuXcLkyZORkJCAJ0+eoGvXrli/fj18fHxU\nA+Vg9kq5uLggJSUFHh4eYodCRKQVrPP/FhQUhFWrVqFVq1aIiorC48ePyyWALC8yZJzdk0h9Na3v\nddryZ25ujuXLl6Nfv37KpR68vb2xcuVKSCQSTJgwAV5eXnj11VfRvn17mJubY8KECeUSP3o5f39/\nHDt2jMkfEZERWrp0KcLCwvD06VN4eHhg9erVYodEpFUvzgdRht09ibRP5+v8aQvfalZuzpw5yMvL\nw4IFC8QOhYhIK1jna4blRYZKLpejd+/euH79erl9XL6KqDy9XuqBakdZyx8RERGRoSgb61dR4sdJ\n7Ih0g8mfEfDz88Px48ehUCjEDoWIiIhILZUt7dC8eXN29yTSESZ/RqBRo0aws7PjEhlERERkMCob\n6+fu7s7Ej0hHmPwZCXb9JCIiIkMgl8sRHh6OCxcuVLhfJpPVckREpoPJn5Fg8kdERET6rmycX2xs\nbIXr+nGsH5FuMfkzEkz+iIiISN9VNs6vSZMmCAsL41g/Ih3T6Tp/VHv8/Pxw8uRJlJaWwtzcXOxw\niIiIiMqpbJyfj48Pl3UgqgVs+TMSdnZ2aNq0KS5fvix2KEREREQqOM6PSD+w5c+IlHX99PHxETsU\nIiIiIgB/j/OrqLsnwHF+RLWJLX9GhOP+iIiISN9wnB+R/mDLnxHx9/fH//73P7HDICIiIlLiOD8i\n/cGWPyPSsWNHnD59GiUlJWKHQkRERAS5XI7r169XuI/j/IhqH5M/I2Jra4tmzZpVOpiaiIiIqLaU\njfWrKPnjOD8icTD5MzIc90dERET6oLKxfs2bN+c4PyKRMPkzMkz+iIiISB9UNtbP3d2diR+RSJj8\nGRkmf0RERKQPnJ2dK9zOsX5E4mHyZ2R8fX1x7tw5FBcXix0KERERmaCyBd3Pnj1bbh/H+hGJi0s9\nGJn69evD09MT586dQ6dOncQOh4iIiExIRQu6W1tbo23btsrEj10+icTDlj8jxK6fREREJIaKJnkp\nKCiAp6cnYmJimPgRiYzJnxFi8kdERERiqGySl6ysrFqOhIgqwuTPCDH5IyIiIjFwkhci/cbkzwi1\nb98ely5dQlFRkdihEBERkQkom+Tl1KlT5fZxkhci/cEJX4yQpaUlWrdujTNnzqBLly5ih0NERERG\njJO8EBkOtvwZKXb9JCIiotrASV6IDAeTPyPF5I+IiIhqAyd5ITIcTP6MFJM/IiIiqg2c5IXIcDD5\nM1Jt27bFtWvX8PjxY7FDISIiIiP2+uuvw9zcXGUbJ3kh0k+c8MVI1a1bF23atMGpU6fQrVs3scMh\nIiIiIyOXyzFjxgxs27YN7du3h6urK/Lz8yGTyTjJC5GeYvJnxMq6fjL5IyIiIm16cYbPkydPIi8v\nD4mJiUz6iPQYu30aMY77IyIiIl2oaIbPtLQ0REREiBQREamDyZ8RY/JHREREusAZPokME5M/I+bj\n44OMjAzk5+eLHQoREREZEXt7+wq3c4ZPIv3G5M+ISaVStG/fHidPnhQ7FCIiIjIi1tbWaNCggco2\nzvBJpP844YuRK+v62bNnT7FDISIiIgMml8uVY/1OnDiB9evXY8OGDcjKyuIMn0QGgsmfkfP398fu\n3bvFDoOIiIgM2IuzewLAp59+ytk9iQwMu30aOU76QkRERDXF2T2JjAOTPyPn5eWF27dvIzc3V+xQ\niIiIyEBxdk8i46Dz5C8hIQFeXl5o1aoV5s+fX27//v370bBhQ3Tq1AmdOnXCnDlzdB2SSTE3N4ev\nry9OnDghdihERERkoJydnSvcztk9iQyLTpM/hUKBSZMmYffu3Th//jzi4uJw6dKlcsf17NkTJ06c\nwIkTJzBz5kxdhmSS2PWTiEg8S5YsQV5eHgRBwLhx49CpUyfs2bNH7LCINBIdHQ1LS0uVbZzdk8jw\n6DT5S01NRcuWLeHm5gapVIrQ0FBs2bKl3HGCIOgyDJPH5I+ISDw///wzbG1tsWfPHjx48ABr167F\n559/rvb5zZs3R4cOHdCxY0d06dJFh5ESVS49PR2NGjXC6NGj0atXL4SFhXGyFyIDpNPZPjMzM+Hq\n6qr83sXFBampqeWOO3ToEHx9feHs7Iyvv/4aPj4+ugzL5Pj7+7NFlYhIJGUvOHfu3IkxY8agTZs2\nGr30NDMzQ3JyMuzs7HQVIlGFypZ2yMzMxNmzZxEREYEpU6aIHRYR1YDoSz34+fnhxo0bsLKywq5d\nuzBs2DBcuXJF7LCMSsuWLZGTk4Ps7Gw4OjqKHQ4RkUnx8/NDv379IJfLMXfuXOTn58PMTP2ON4Ig\nQKFQ6DBCovIqWtph6dKlGDJkCFv7iAyYTpM/Z2dn3LhxQ/n9rVu3yg0Ytra2Vn49YMAAfPjhh8jJ\nyYG9vX2560VGRiq/Dg4ORnBwsNZjNkZmZmbo1KkTjh8/jldffVXscIiIyklOTkZycrLYYejETz/9\nhFOnTsHDwwNWVla4f/8+Vq9erfb5EokEISEhMDc3x4QJEzB+/HgdRkv0TEVLO6SnpyMiIgIxMTEi\nRUVENaXT5K9z5864du0aMjIy4OTkhPj4eMTFxakc89dff6FJkyYAno0RFAShwsQPUE3+SDNl4/6Y\n/BGRPnrxhV5UVJR4wWiZmZkZ3N3dceXKFRQVFWl8/p9//gknJyfcu3cPISEh8Pb2Ro8ePXQQKdHf\nuLQDkXHSafJnbm6O5cuXo1+/flAoFBg3bhy8vb2xcuVKSCQSTJgwARs2bMD3338PqVQKS0tLrF+/\nXpchmSx/f3/Ex8eLHQYRkcn58ccfsWTJEty6dQu+vr44fPgwAgMDsXfvXrXOd3JyAgA0atQIw4cP\nR2pqaoXJH3vHkDZxaQci/aDtnjESwUCm2pRIJJwVtAbS0tIQHByMmzdvih0KEVGVjKnOb9euHY4e\nPYqAgACcOnUKly5dwr/+9S9s3LixynMfP34MhUIBa2trPHr0CP369cOsWbPQr18/leOMqbxIP8jl\ncnTu3Bn3799XbvP09OQMn0Qiq2l9L/qEL1Q7PDw8UFBQgDt37qBp06Zih0NEZDLq1auHevXqAQCe\nPHkCLy8vXL58Wa1z//rrLwwfPhwSiQQlJSUICwsrl/gR6UKjRo0gkUgwYMAAFBUVQSaTITo6mokf\nkYFj8mciJBIJ/P39cfz4cbz22mtih0NEZDJcXFyQm5uLYcOGISQkBHZ2dnBzc1PrXHd3d5w6dUrH\nERI98/zSDjk5OQgICMC2bdvEDouItIjdPk3IF198gXr16mHWrFlih0JE9FLGWufv378fDx8+RP/+\n/VGnTh2tXddYy4tqT0VLOzRr1gzJycls7SPSIzWt79VfaIgMXtmMn0REpHs5OTnl/mvXrh169OiB\ngoICscMjUlHR0g43btxARESESBERkS6w26cJ6dy5MyZNmgRBECCRSMQOh4jIqPn5+Snf0N64cQN2\ndnYQBAG5ublo1qwZ5HK52CESKXFpByLTwJY/E+Lq6orS0lJW5EREtUAulyM9PR19+/bFtm3bkJ2d\njfv372P79u2ctIX0Dpd2IDINTP5MSNmkL+z6SURUew4fPoyBAwcqvx8wYAAOHjwoYkRE5UVHR6NZ\ns2Yq2zw9PREdHS1SRESkC+z2aWLKkr+hQ4eKHQoRkUmQyWSYM2cOwsPDAQCxsbFsTSG94+7ujg4d\nOqBhw4ZwcHDg0g5ERorJn4nx9/fH999/L3YYREQmIy4uDlFRURg+fDgAoGfPnoiLixM5KiJVp0+f\nRmpqKq5evQobGxuxwyEiHeFSDyYmKysLHTp0wN27dznpCxHpLWOs8x8+fAgzMzOd/GFtjOVFtWvQ\noEEICQnBlClTxA6FiF6CSz2QRmQyGaRSKW7cuCF2KEREJuHo0aNo164dOnTooPz/8ePHxQ6LCHK5\nHOHh4ejUqRP27duH/v37ix0SEekYu32aoLJxf25ubmKHQkRk9MaNG4cVK1bglVdeAQAcOHAAY8eO\nxZkzZ0SOjExZRYu6v/baa0hMTOQ4PyIjxpY/E8QZP4mIao+5ubky8QOAHj16wMKC715JXBUt6p6W\nlsZF3YmMHJ8+Jsjf3x+LFi0SOwwiIqN24sQJAEBQUBAmTpyI0aNHQyKRYP369QgODhY3ODJ5XNSd\nyDQx+TNBfn5+OHbsGARB4KQvREQ68sknn6h8HxUVpfyadS+JjYu6E5kmzvZpopo1a4Z9+/bB09NT\n7FCIiMphna8Zlhdp6sqVK2jTpg1KSkqU2zw9PTnmj0jP1bS+Z8ufiSob98fkj4hIt3Jzc7FmzRpc\nv35d5Q/tpUuXihgVmbr9+/fD398fHh4euH37Nhd1JzIRTP5MVFnyN2rUKLFDISIyagMHDkRAQADa\ntWsHMzPOs0biKywsxOzZs/Hbb78hICBA7HCIqBYx+TNR/v7+mDt3rthhEBEZvaKiIixcuFDsMIgg\nl8sRERGBw4cPAwCaNGkickREVNs45s9E3b9/Hx4eHnjw4AHfRBOR3jGmOn/RokWwtrbGoEGDULdu\nXeV2e3t7rd3DmMqLdKOidf04xo/I8NS0vudf/SYqLy8PpaWlCAwMRHh4OORyudghEREZpTp16uCf\n//wnAgMD4efnBz8/P/j7+4sdFpkYrutHRAC7fZqksrd/jx49QmpqKlJTU3H48GG+/SMi0oFvv/0W\n165dg6Ojo9ihkAnjun5EBLDlzyTx7R8RUe1p0aIFrKysxA6DTBzX9SMigC1/Jqmyt3+VbSciouqr\nX78+fH190atXL5Uxf1zqgWrTu+++i7i4OCgUCuU2T09PREdHixgVEdU2Jn8mqLK3f2fOnMHZs2fR\nrl27Wo6IiMh4DRs2DMOGDRM7DDJxy5Ytw2effYabN28iKyuL6/oRmSiNZvu8e/cuioqKlN83a9ZM\nJ0FVhDOZaU9lM3699957WLRoEf7xj39g5syZKm+oiYhqE+t8zbC86GVSUlLwzjvv4OLFi6hXr57Y\n4RBRDdS0vlcr+du6dSs++eQTZGVloXHjxsjIyIC3tzfOnz9f7Rtrig827Spb6+fFt39ZWVn46KOP\ncPnyZfz444/o1q2b2KESkQkypjr/6tWr+OKLL3DhwgWVF6jp6elau4cxlRdpl0KhQEBAAKZOnYq3\n3npL7HCIqIZqZamHsgVBW7VqBblcjqSkJAQEBFT7piQ+d3d3xMTEYO/evYiJiVF2+5DJZNi4cSNm\nz56NESNGYPLkycjPz4dcLkd4eDh69erFpSGIiDQwduxYfPDBB7CwsMC+ffvw9ttvIzw8XOywyESs\nX78eCoUCoaGhYodCRHpAreRPKpXCwcEBCoUCCoUCvXr1wrFjx3QdG4lEIpFgxIgROHfuHAoKCtC6\ndWt0794dsbGxSE5ORmxsLEJCQpgAEhGpobCwEH369IEgCHBzc0NkZCR27Nghdlhk5ORyOUaPHo2x\nY8eiYcOGyMjIEDskItIDaiV/DRs2REFBAXr27ImwsDBMmTIF9evX13VsJDJ7e3usXr0a3t7euH37\ntso+Lg1BRKSeunXrQqFQoGXLlli+fDk2bdqEgoICscMiI1Y2tj8+Ph5PnjxBUlISX9oSEQA1k78t\nW7bA0tISixYtQv/+/eHp6Ylt27bpOjbSE89PC/08LgxLRFS1JUuW4PHjx1i6dCmOHz+OmJgYrFmz\nRuywyIhxPV8iqoxaSz0838r3zjvv6CwY0k9cGJaIqPo6d+4MALC2tsbq1asBAJ9++im6du0qZlhk\nxCpbt5cvbYnopS1/NjY2sLW1rfQ/Mg3R0dHw9PRU2caFYYmIqu+///2v2CGQEavsbzS+tCWil7b8\n5efnA3jWfcDJyQljxoyBIAiIjY0tNwaMjJe7uzsSExMRERGBzZs3o0ePHvj++++5MCwRUTVxWQbS\npdLSUtjZ2eHBgwfKbXxpS0SAmuv8dejQAadPn65ymy5xDSP98OGHH6JVq1aYOnWq2KEQkREzhjo/\nJyenwu2CIKBDhw64deuW1u5lDOVF2pGamorhw4djz549mDt3brn1fInIsNW0vld7zF9sbCxCQ0Mh\nkUgQFxfH2T5NVGBgILZt28bkj4ioCn5+fpU+pOvUqSNCRGTsBEHAp59+iqioKLRp0wYxMTFih0RE\nekatlr/r169jypQp+PPPPwEAPXr0wOLFi9G8eXNdx6fEt5r6IS0tDcHBwbh586bYoRCREWOdrxmW\nFwHPZmefMWMGTp8+DXNzc7HDISIdqGl9r9ZSD82bN8eWLVuQnZ2N7OxsbN68We3ELyEhAV5eXmjV\nqhXmz59f6XFHjx6FVCrFxo0b1bouicPDwwNPnjxh8kdERKRHnj59iunTp2PBggVM/IioUmolf+np\n6Rg8eDAaNWqExo0bY+jQoUhPT6/yPIVCgUmTJmH37t04f/484uLicOnSpQqP+/zzz/Hqq69q/hNQ\nrZJIJOjWrRsOHjwodihEREQmTy6XIzw8HG3btkVubi68vLzEDomI9Jhayd9bb72FkSNH4vbt28jK\nysKbb76J0aNHV3leamoqWrZsCTc3N0ilUoSGhmLLli3ljlu2bBlGjBiBxo0ba/4TUK3r1q0bDh06\nJHYYREREJk0ulyMkJASxsbG4cuUK/vrrL/Tr1w9yuVzs0IhIT6mV/D1+/BhjxoyBhYUFLCwsEB4e\njqKioirPy8zMhKurq/J7FxeXcguPZmVlYfPmzfjggw84XsFABAYGsuWPiEgDBw4cUC7wfu/ePf5x\nTloRERGBtLQ0lW1paWmIiIgQKSIi0ncvTf5ycnKQk5ODAQMGYN68ebh+/ToyMjKwYMECDBw4UCsB\nTJ06VWUsIBNA/efv74/z58+jsLBQ7FCIiPReVFQU5s+fj7lz5wJ4NjYrPDxco2soFAp06tQJQ4YM\n0UWIZKBefKFeJisrq5YjISJD8dKlHl6cpnrlypXKfRKJRPkgq4yzszNu3Lih/P7WrVtwdnZWOebY\nsWMIDQ2FIAjIzs7Grl27IJVKK3zARUZGKr8ODg5GcHDwS+9PumFpaYk2bdrg2LFjeOWVV8QOh4iM\nQHJyMpKTk8UOQyc2bdqEkydPolOnTgAAmUyG/Px8ja6xZMkS+Pj4IC8vTxchkoF68W+qMjKZrJYj\nISJDodZSD9VVWlqK1q1bIykpCU5OTujSpQvi4uLg7e1d4fFjx47F4MGD8frrr5cPlNNY65WpU6fC\nyckJ06dPFzsUIjJCxlTnd+nSBampqejUqRNOnDiBR48eITAwEGfOnFHr/Fu3bmHs2LGYMWMGFi5c\niK1bt5Y7xpjKi9R35swZdOzYEQqFQrnN09MTiYmJXNCdyEjVyiLvpaWl2LFjB65fv46SkhLl9mnT\npr30PHNzcyxfvhz9+vWDQqHAuHHj4O3tjZUrV0IikWDChAkqx0skkmr8CCSGbt26Yd26dWKHQUSk\n90aOHImJEyciNzcXq1atws8//4zx48erff7HH3+Mr7/+Gg8fPtRhlGSIduzYgUGDBsHGxgZZWVmQ\nyWSIjo5m4kdElVKr5W/gwIGoV68e2rVrBzOzv4cJzpo1S6fBPY9vNfXLzZs34efnh7/++otJOxFp\nnbHV+YmJidizZw8EQcCrr76KkJAQtc7bsWMHdu3aheXLlyM5ORnffvsttm3bVu44YysvqlpBQQE8\nPDyQnJwMHx8fscMholpSKy1/t27dUrt7CpkGV1dX1K1bF+np6fD09BQ7HCIivbVw4UKMGjVK7YTv\neX/++Se2bt2KnTt3orCwEPn5+Xj77bexZs2acsdyXLxp+c9//oOgoCAmfkRGTttj4tVq+Zs+fTr6\n9OmDfv36ae3GmuJbTf0zatQoDBo0CGPGjBE7FCIyMsZU50dFReG///0v7O3tMWrUKLz55pto0qSJ\nxtfZv38/vv32W475IxQWFsLDwwMJCQno0KGD2OEQUS2qaX2v1jp/AQEBGD58OCwtLWFrawsbGxvY\n2tpW+6ZkHLjeHxFR1WbNmoXz58/ju+++w+3btxEUFIS+ffuKHRYZsFWrVqFLly5M/IhIY2p1+5w2\nbRoOHTqEdu3acXwXKXXr1g2//PKL2GEQERmExo0bo2nTpnBwcMDdu3c1Pj8oKAhBQUE6iIwMyZMn\nT7BgwQJs3rxZ7FCIyACp1fLn6uqKtm3bMvEjFb6+vrh27RrXnSIieokVK1YgODgYffr0wf3797Fq\n1SqOo6dqW716Ndq3bw9/f3+xQyEiA6RWy5+HhweCg4MxYMAA1K1bV7m9qqUeyLjVqVMHHTt2RGpq\nKrswERFV4ubNm1i8eDF8fX3FDoUMmFwux4wZM7Bx40YEBQVBLpdzSQci0phayZ+7uzvc3d1RXFyM\n4tJeQwIAACAASURBVOJiXcdEBqRbt244dOgQkz8iohfk5eXB1tYW//znPwEAOTk5Kvvt7e3FCIsM\nkFwuR0hICNLS0gAAe/bsQUhICBdzJyKNqTXbpz7gTGb6afPmzVi5ciV27doldihEZESMoc4fNGgQ\ntm/fDnd393I/j0QiQXp6utbuZQzlRZULDw9HbGxsue1hYWGIiYkRISIiEkutrPN37949LFiwAOfP\nn0dRUZFy+969e6t9YzIOgYGBGDt2LBQKBczM1BpCSkRkErZv3w7gWasNUU1kZmZWuD0rK6uWIyEi\nQ6fWX+thYWHw8vKCXC7HrFmz0Lx5c3Tu3FnXsZEBaNKkCRwcHHDp0iWxQyEi0kt9+vRRaxtRZZo2\nbVrhdplMVsuREJGhUyv5u3//PsaNGwepVIqgoCD8/PPPbPUjJa73R0RUXlFREXJycpCdnY0HDx4g\nJycHOTk5uH79eqUtOUQV8fb2hqWlpco2T09PREdHixQRERkqtbp9SqVSAICTkxN27NgBmUxWbuA6\nma6ySV/ef/99sUMhItIbK1euxOLFi5GVlQU/Pz/lGA1bW1tMmjRJ5OjIUBQWFmLlypWIi4vDb7/9\nhqysLMhkMkRHR3OyFyLSmFoTvmzfvh2vvPIKbt68icmTJyMvLw+RkZEYPHhwbcQIgIPZ9dmpU6cw\nevRoXLx4UexQiMhIGFOdv2zZMkyePFmn9zCm8iJVixYtQkpKCjZt2iR2KESkB2pa31d7ts/Fixdj\n6tSp1b6xpvhg018lJSWwt7fH9evXOXU5EWmFsdX5586dw4ULF1QmTXv77be1dn1jKy96pqCgAC1a\ntMCePXvQvn17scMhIj1Q0/q+2tMzLly4sNo3JeNiYWGBLl264PDhw2KHQkSkd6KiojB58mRMnjwZ\n+/btw2effYatW7eKHRYZgGXLliE4OJiJHxFpTbWTP75hpOdx0hcioopt2LABSUlJaNq0KVavXo3T\np0/j4cOHYodFeu7hw4dYuHAhIiMjxQ6FiIxItZM/iUSizTjIwJVN+kJERKosLS1hZmYGCwsL5OXl\noXHjxrh586bYYZGeW7RoEQYOHAgvLy+xQyEiI/LS2T5tbGwqTPIEQUBhYaHOgiLDExAQgKNHj6Kk\npAQWFmpNIktEZBL8/f2Rm5uL8ePHw8/PD9bW1ggMDBQ7LNJj9+/fx7Jly5Camip2KERkZKo94Utt\n42B2/efj44N169bB19dX7FCIyMAZa51//fp15OXlaX0Ml7GWl6n64osvkJOTg5UrV4odChHpmZrW\n92yiIa3p1q0bDh48yOSPiAjAiRMnXrqvU6dOtRgN6Tu5XI6IiAjI5XIcO3YMe/fuFTskIjJCbPkj\nrfnpp5+wb98+xMTEiB0KERk4Y6jze/XqVek+iUSi1T/ujaG8TJlcLkdISAjS0tKU2zw9PZGYmMiF\n3IlIhWjr/NU2Ptj038WLFzFo0CCVhxcRUXWwztcMy8uwhYeHIzY2ttz2sLAwvlAlIhXs9kl6o3Xr\n1njw4AHu3LmDpk2bih0OEZFeWLNmTYXbtbnIOxm2zMzMCrdnZWXVciREZOyY/JHWmJmZISAgAIcO\nHcLw4cPFDoeISC8cPXpU+XVRURGSkpLQqVMnJn+k5OzsXOF2mUxWy5EQkbFjt0/SqmnTpiEhIQFN\nmjSBs7MzoqOjOV6BiDRmzHV+bm4uQkNDkZCQoLVrGnN5mYK0tDS0adMGT548UW7jmD8iqgi7fZLe\nkMvliI+Px+3bt3Hx4kUAwOHDh/nwIiJ6Tv369SGXy8UOg/TIn3/+CS8vL7Rp0wa3b9+GTCbjy1Mi\n0gkmf6Q1ERERuH37tsq2tLQ0REREcMA6EZmswYMHQyKRAAAUCgUuXLiAkSNHihwV6YuCggL861//\nwoYNGxAQECB2OERk5Jj8kdZwwDoRUXmffvqp8msLCwu4ubnBxcVFxIhIn8yfPx/BwcFM/IioVjD5\nI63hgHUiovKCgoIAAHl5eSgpKQEA5OTkwN7eXsywSA9kZGRgxYoVOHXqlNihEJGJ4IQvpDVcpJaI\ntMWY6vwffvj/7d17XFT1vv/xNyCmlVpZXkAiJFE0lDQkLylQ2DYyqlOGW7bboswyTfeu7LFrDA/t\nLns/up2sc3J3clegZrYNS8OQJNMk8l5q+RDHGyRmHipTlMv6/dGP2ZKDMjAza2bW6/l48Hgway3W\nfPx+5fvlM+t7matZs2apXbt2Cg4OlmEYCgoK0u7du932HoFUXlaSkZGhPn36KDs72+xQAPgJNnmH\nT7Hb7bLZbPrmm2+0bds2lZaWKi4uzuywAPiZQGrze/XqpXXr1uniiy/22HsEUnlZxdq1a5WRkaFv\nv/1W5557rtnhAPATrW3vg90YC6CoqCjl5uZq/fr1uu2227Ro0SKzQwIAU0VHR/PHPRqpr6/X9OnT\n9cwzz/B/A4BX8eQPHrN//37Fx8dry5YtLG4AwCWB1OZv2rRJd955pxITE3XOOec4jv/Xf/2X294j\nkMrLCt566y29+uqr+vzzzxUczOfwAJqPff7gsyIiIjR58mQ99thjevPNN80OBwBMce+99yolJUVx\ncXH8oW9hDdMi9u3bp/Xr1ys3N5f/DwC8jid/8Kiff/5ZMTExWrZsmQYOHGh2OAD8RCC1+VdeeaU2\nbdrk0fcIpPIKRCyIBsBdmPMHn9ahQwdlZ2frz3/+M3+YALCk0aNHa+7cufruu+905MgRxxesw2az\nNUr8JKmsrEw2m82kiABYFU/+4HG1tbUaMGCAnn76ad10001mhwPADwRSm+/syQ5bPVhLcnKyiouL\nnR7/5JNPvB8QAL/FnD/4vDZt2ujvf/+7ZsyYodGjRys0NNTskADAa+x2u9khwGTh4eFOj4eFhXk5\nEgBW5/EnfwUFBZo+fbrq6+uVlZWlmTNnNjq/dOlS2Ww2BQcHKzQ0VC+88IKGDRt2eqB8qunXDMPQ\nqFGjlJ6ergceeMDscAD4uEBq89966y2nxydMmOC29wik8gpE27dv14ABA1RbW+s4xpw/AC3h05u8\n19fXKyYmRkVFRQoLC1NCQoIWLlyoPn36OK45duyYY4+br776SmPHjtWOHTtOD5SOze9t2bJFKSkp\nuvbaa/X9998rPDxcOTk5dHwAThNIbf7UqVMd31dXV6uoqEgDBw7U4sWL3fYegVRegeixxx7T1q1b\n1alTJ1VUVCgsLIz+D0CL+PSwz9LSUvXq1UuRkZGSpIyMDOXn5zdK/k7d3PTo0aMsexzAOnbsqJqa\nGr377ruOYyUlJXzyCSCgvfzyy41eV1VVKSMjw6Ro4G07duzQ3LlztWXLFoZ5AjCdRzOt8vJyRURE\nOF736NFD5eXlp133/vvvKzY2VmPGjNEbb7zhyZBgIpvNpp9//rnRMVY7A2A15513HvMALcIwDE2Z\nMkU2m43ED4BP8IkFX26++WbdfPPNWrNmjR5//HEVFhY6vS47O9vxfVJSkpKSkrwTINzCWeIvSRUV\nFV6OBICvKS4udroaYiAYM2aMgoKCJP06HWL79u0aO3asyVHBG+bPn6//+7//0/333292KAAgycPJ\nX3h4uPbt2+d4feDAgSZXvJKk4cOHa/fu3Tpy5Iguuuii086fmvzB/zRV9926dfNyJAB8zW8/0Js9\ne7Z5wbjZQw895Pi+TZs2ioyMVI8ePZr1sydOnNCIESN08uRJ1dbW6rbbbtMTTzzhqVDhRlVVVXr4\n4Ye1ZMkStWnjE5+1A4Bnk7+EhATt2rVLe/fuVffu3bVw4UItWLCg0TVlZWWKjo6WJG3cuFEnT550\nmvjB/+Xk5KikpKTRRrft27fXjh07HEOgbDabysvLWQwGgN/btWuXKisrNXLkyEbH165dqxMnTjj6\nvjM555xztGrVKp177rmqq6vTsGHDNHr0aA0ePNhTYcNNbDabxowZo8TERLNDAQAHjyZ/ISEhmjNn\njkaNGuXY6iE2NlavvfaagoKCNGnSJL333nt666231LZtW7Vv316LFi3yZEgwUVRUlAoLC2Wz2Ryr\nnc2ePVv5+fkaNGiQ2rZtq8rKSsf1LAYDwJ9Nnz5dTz/99GnHO3bsqOnTp+uDDz5o1n0aFkY7ceKE\namtrHUNI4bs2bNigd999V9u3bzc7FABoxOP7/LkLy1gHttGjR6ugoOC04+PHj1dubq4JEQEwUyC0\n+QkJCfryyy+dnouLi9NXX33VrPvU19dr0KBBKisr05QpU5wmlIFQXoGirq5OQ4YM0f3336+JEyea\nHQ6AAOPTWz0AzVVdXe30OIvBAPBXVVVVTZ47fvx4s+8THBysTZs26aefftLNN9+s7du3q2/fvqdd\nx6JovmHu3Lk655xzNGHCBLNDARAA3L0gGskffEJTi8GwNDYAf3XVVVfpH//4h+65555Gx19//XUN\nGjTI5ft17NhRycnJKigoOGvyB3NUVlZq1qxZWrVqFfsWA3ALdy+IxrBP+AS73a7U1NRGi8FERkZq\n1apVzPkDLCgQ2vzKykrdcsstatu2rSPZW79+vU6ePKklS5Y0a6Xjw4cPKzQ0VJ06ddLx48d1/fXX\n69FHH9UNN9zQ6LpAKC9/ZrfbZbPZtHLlSl100UVatmwZfRcAj2hte0/yB5/R0HlWVFTo0KFDCgsL\n04oVK1jcALCgQGrzV61apa+//lqS1K9fP6WkpDT7Z7/66iv98Y9/VH19verr63XHHXfoscceO+26\nQCovf+Psw8vo6GgWLAPgESR/CEjV1dUaOHCgsrOz2QwZsCDafNdQXubJzMxUXl7eacdZsAyAJ7S2\nvWdAOnxSu3btNG/ePD344IP6/vvvzQ4HAACnysvLnR5nwTIAvojkDz4rMTFRmZmZmjZtmtmhAADg\n1EUXXeT0OAuWAfBFDPuETzt+/LhiY2MVGRmp4OBghYeHKycnh3kUQICjzXcN5WWeO+64QytWrNCP\nP/7oOMacPwCewj5/CGgHDx5UbW2tVq9e7ThWUlJCpwoAMN2GDRu0evVqrV69Wn/7299UUVGhsLAw\nPqQE4LN48gefxkR6wJpo811DeXmfYRgaPny47rrrLmVlZZkdDgCLYMEXBDQm0gMAfNH8+fN14sQJ\n3XnnnWaHAgDNxrBP+LTw8HCnx5lIDwAwy9GjRzVz5kwtWrRIwcF8jg7AfzDsEz6NzXMBa6LNdw3l\n5V1/+ctftH//fr399ttmhwLAYtjkHQHPbrfLZrNp/fr1OnnypIqKikj8gABHm+8ayst7ysrKlJiY\nqK1btzIKBYDXkfzBMrZt26b09HTt2rXL7FAAeBhtvmsoL+9JT0/XkCFD9Oijj5odCgALYqsHWEZs\nbKwOHz6sQ4cOqUuXLmaHAwCwmBUrVmjbtm1atGiR2aEAQIswSxl+Izg4WImJiSopKTE7FACAxdTU\n1Gj69Ol6/vnndc4555gdDgC0CMkf/MrQoUO1bt06s8MAAFjMnDlzFBkZqTFjxpgdCgC0GMkf/MqQ\nIUNI/gAAXnXo0CE99dRTevHFFxUUFGR2OADQYiz4Ar/y448/qkePHjpy5IhCQ0PNDgeAh9Dmu4by\n8qy7775bHTt21PPPP292KAAsjgVfYCmdOnVSZGSktm7dqkGDBpkdDgAgwG3YsEHLli3TN998Y3Yo\nANBqDPuE32HoJwDAGwzD0NSpU/Xkk0+qU6dOZocDAK1G8ge/Q/IHAPAku92uzMxM9evXT99++62S\nkpLMDgkA3II5f/A7O3bsUFpamnbv3m12KAA8hDbfNZSX+9jtdqWmpqqsrMxxLDo6WoWFhYqKijIx\nMgBofXvPkz/4nd69e6uqqkqVlZVmhwIACDA2m61R4idJZWVlstlsJkUEAO5D8ge/ExwcrKuvvpqh\nnwAAtysvL3d6vKKiwsuRAID7kfzBLzHvDwDgCV26dHF6PCwszMuRAID7kfzBLw0ZMkSff/652WEA\nAAJMTU2NOnTo0OhYdHS0cnJyTIoIANyHff7glwYPHqxNmzbp5MmTatu2rdnhAAACQF5enrZv3651\n69bp6aefVkVFhcLCwpSTk8NiLwACAqt9wm/1799f//u//6uEhASzQwHgZrT5rqG8Wm/37t1KTExU\nYWGh4uPjzQ4HAJxitU9YFvP+AADuUFNTo3Hjxunxxx8n8QMQ0Ej+4LeGDh1K8gcAaLUnnnhCF198\nsaZNm2Z2KADgUSR/8Fs9evTQBx98oOTkZGVmZsput5sdEgDAz3zyySd68803NW/ePAUFBZkdDgB4\nFHP+4JfsdrtSU1MbbcQbHR2twsJCJuUDAYA23zWUV8scPnxY8fHxmjdvnlJTU80OBwDOijl/sCSb\nzdYo8ZOksrIy2Ww2kyICAPgTwzB011136fe//z2JHwDLYKsH+KXy8nKnxysqKrwcCQDAH73yyiv6\n7rvvtHjxYrNDAQCvIfmDXwoPD3d6PCwszMuRAAD8zdatWzV79mx9/vnn7BULwFI8PuyzoKBAffr0\nUUxMjJ599tnTzs+fP18DBgzQgAEDNHz4cH311VeeDgkBICcnR9HR0Y2ORUdHKycnx6SIAAD+4Nix\nYxo3bpyee+459erVy+xwAMCrPLrgS319vWJiYlRUVKSwsDAlJCRo4cKF6tOnj+OakpISxcbGqlOn\nTiooKFB2drZKSkpOD5TJ7PgNu90um82m/fv364svvtDSpUs1atQos8MC4Aa0+a6hvJpv8uTJ+vnn\nn5Wbm8vqngD8Tmvbe48O+ywtLVWvXr0UGRkpScrIyFB+fn6j5O/qq69u9H1Tc7mA34qKilJubq4k\n6emnn9abb75J8gcAaNK//vUvFRYWatOmTSR+ACzJo8M+y8vLFRER4Xjdo0ePMyZ3r7/+ukaPHu3J\nkBCgpkyZoo8//lg7d+40OxQAgA/av3+/7rvvPs2fP18dO3Y0OxwAMIXPbPWwatUqzZs3z+m8QOBs\nOnbsqKlTp+qpp54yOxQAgI+pq6tTZmampk+frsTERLPDAQDTeHTYZ3h4uPbt2+d4feDAAaerNG7d\nulWTJk1SQUGBLrzwwibvl52d7fg+KSlJSUlJ7gwXfm7atGmKjo7W7t271bNnT7PDAeCC4uJiFRcX\nmx0GAtRTTz2lNm3a6JFHHjE7FAAwlUcXfKmrq1Pv3r1VVFSk7t27a/DgwVqwYIFiY2Md1+zbt0/X\nXnut3n777Ubz/04LlMnsaIapU6fqo48+UkREhMLDw5WTk6OoqCizwwLgItp811BejTUsCFZeXq7Q\n0FBt3LhRW7duZTsgAH7Ppxd8CQkJ0Zw5czRq1CjV19crKytLsbGxeu211xQUFKRJkyYpJydHR44c\n0f333y/DMBQaGqrS0lJPhoUAZbfb9eGHH2rPnj0qKyuT9OtqsoWFhSSAAPzSgQMHNGHCBFVWVio4\nOFj33HOPpk2bZnZYPs1utys1NdXRD0hSt27ddOLECROjAgDf4NEnf+7Ep5o4m8zMTOXl5Z12fPz4\n8Y5VQQH4B9r8Xx08eFAHDx5UfHy8jh49qkGDBp22arZEeZ2KvgBAIGtte+8zC74ArdXUSrJlZWXK\nzMxUcnKyMjMzZbfbvRwZALRMt27dFB8fL0k6//zzFRsby5ZIZ9FU+VRUVHg5EgDwPR4d9gl4k7PF\nhCRp/fr1KikpcbxmKCgAf7Rnzx5t3ryZ1SrPoqm+gPl+AMCwTwQQZ/M8zjvvPP3yyy+nXcvwH8C3\n0eY3dvToUSUlJclmsyk9Pf2080FBQXriiSccr628Iva2bdt05ZVXqqamxnEsOjqaD/0A+KXfroY9\ne/bsVvWPJH8IKA0rvFVUVCgsLExlZWWNnvo1SE5O1ieffGJChACagzb/32pra3XjjTdq9OjRevDB\nB51eQ3n928SJE3X06FG1a9fO0Rew8jOAQOHTq30C3hYVFdXoiV5mZqbT5I/hPwD8xV133aW+ffs2\nmfjh3958802Vlpbqyy+/1HnnnWd2OADgc3jyh4DmbChop06dNHLkSP3000/sBQj4KNr8X61du1Yj\nRoxQXFycgoKCFBQUpKeeekq/+93vGl1HeUnbt2/XyJEjtWrVKl1xxRVmhwMAHtHa9p7kDwHv1KGg\nwcHBWrVqlerr6x3nmQsC+B7afNdYvbyOHTumwYMHa8aMGcrKyjI7HADwGJI/wAXs/wT4B9p811i9\nvO655x4dP35cb7/9toKCgswOBwA8hjl/gAvY/wkAAsv8+fO1evVqrV+/nsQPAM6C5A+Wwv5PABA4\ndu7cqQcffFArV65Uhw4dzA4HAHwewz5hKc4WgOnZs6dWrlzJnD/Ah9Dmu8aK5VVdXa2rr75akydP\n1uTJk80OBwC8orXtfbAbYwF8XlRUlAoLCzV+/HglJSXpkksu0V133UXiBwB+5k9/+pN69+6te++9\n1+xQAMBv8OQPlrZp0yalpqYqJSVF33//PVs/AD6CNt81ViuvRYsW6S9/+Ys2bNigTp06mR0OAHgN\nq30CrWC32zVgwAD9/PPPjmNs/QCYjzbfNVYqr7KyMg0ZMkQfffSRBg0aZHY4AOBVDPsEWsFmszVK\n/KRf/7Cw2WwmRQQAaMqJEyc0duxY2Ww2Ej8AaAGSP1gaWz8AgP945JFHFBkZqQceeMDsUADAL7HV\nAyyNrR8AwD8sWbJES5cu1caNG9nPDwBaiDl/sDRnWz8w5w8wH22+awK9vPbs2aPBgwfrgw8+UGJi\notnhAIBpmPMHtMKpWz+MGDFC7dq100svvUTiBwA+4uTJk7rjjjs0c+ZMEj8AaCWe/AGnePHFF7V8\n+XJ16dJF5eXlbP0AmIQ23zWBXF4PPfSQvv32Wy1dupThngAsj60eADfasWOH4uLiVFdX5zjGMFDA\n+2jzXROo5fXhhx9qypQp2rhxozp37mx2OABgOoZ9Am7017/+tVHiJ7H1AwCYYf/+/crKytL8+fNJ\n/ADATUj+gFOw9QMAmK+mpkYZGRmaMWOGhg0bZnY4ABAwSP6AUzS19cO2bduUmZkpu93u5YgAwHpm\nzZqljh076pFHHjE7FAAIKMz5A07hbOuHUzH/D/AO2nzXBFJ5FRQU6O6779amTZt0ySWXmB0OAPgU\n5vwBbnTq1g9du3Y97Tzz/wDAc8rLyzVx4kTl5eWR+AGAB5D8Ab8RFRWl3NxcxcbGOj3P/D8AcB+7\n3a7MzEwlJSVp4MCBGjdunEaOHGl2WAAQkNqYHQDgq5qa/xcWFublSAAgMDkbar906VJNmzaN4fUA\n4AE8+QOakJOTo+jo6EbHoqOjlZOTY1JEABBYbDbbaXOsd+/ezfB6APAQkj+gCafO/4uPj9dFF13E\nYi8A4EZsrwMA3sWwT+AMGub/1dbWMtwTANzs2LFjTo/T3gKAZ/DkD2iGNm3a6Oabb9Z7771ndigA\n4Pdqa2s1ffp0HTp0SBEREY3OMbweADyH5A9opttuu02LFy82OwwA8GtVVVVKS0vT9u3btXHjRn36\n6acaP368kpOTNX78eIbXA4AHsck70Ew7d+5UXFycBg4c6Phkmj9QAM+gzXeNv5TXzp07ddNNN+n6\n66/Xc889pzZtmH0CAK5obXtP8gc0g7PlyKOjo/mEGvAQ2nzX+EN5rVy5UuPHj1dOTo4mTZpkdjgA\n4Jda294z7BNoBmfLkZeVlbEcOQCchWEYeuWVV5SZmal33nmHxA8ATETyBzRDU8uRL1u2TJmZmbLb\n7V6OCAB8X01Nje6//369+uqr+vzzz5WUlGR2SABgaR5P/goKCtSnTx/FxMTo2WefPe38t99+q6FD\nh6pdu3Z6/vnnPR0O0CLh4eFOj1dVVSkvL0+pqakkgABwih9++EHXX3+99u/fr3Xr1qlnz55mhwQA\nlufR5K++vl4PPPCAVqxYoW3btmnBggX65ptvGl3TuXNnvfzyy3r44Yc9GQrQKjk5OYqOjm7yPENA\nAeDfduzYocTERF111VXKz89Xx44dzQ4JACAPJ3+lpaXq1auXIiMjFRoaqoyMDOXn5ze65uKLL9ag\nQYNY8Qs+LSoqSoWFhRo/frwuuOACp9dUVFR4OSoA8D0fffSRRo4cqccff1x/+9vfFBISYnZIAID/\nz6PJX3l5eaPNW3v06NHk3CnA10VFRSk3N1dpaWlOz4eFhXk5IgDwHYZh6Pnnn1dWVpaWLFmiiRMn\nmh0SAOA3WPAFcJGzIaDt2rVTWVkZi78AsKQTJ04oKytLb731ltatW6dhw4aZHRIAwAmPjrUMDw/X\nvn37HK8PHDjQ5MIZzZGdne34PikpiVXDYIqGIaAN2z9s2bJFx48fV0lJieOL/f8A1xQXF6u4uNjs\nMNAChw4d0q233qouXbpozZo1Ov/8880OCQDQBI9u8l5XV6fevXurqKhI3bt31+DBg7VgwQLFxsae\ndu3s2bN1/vnn689//rPzQP1gA1tYT2ZmpvLy8k47Pn78eOXm5poQERAYaPNdY1Z5bd26Venp6crM\nzNTs2bMVHMyAIgDwJJ/e5D0kJERz5szRqFGj1K9fP2VkZCg2Nlavvfaa5s6dK0mqrKxURESEXnjh\nBf31r3/VpZdeqqNHj3oyLMBt2P8PgFXl5+fr2muv1VNPPaWcnBwSPwDwAx598udOfAoMX9TUk78G\n0dHRDAEFWoA23zXeLC/DMPTMM8/olVde0ZIlS5SQkOCV9wUA+PiTPyDQNWf/v6uvvpqngABaJCsr\nS127dlX//v3NDkWSVF1drT/84Q9677339MUXX5D4AYCfIfkDWqE5+/8dOnRIeXl56tu3r9LT00kC\nATTbnXfeqRUrVpgdhiTpu+++08iRI1VbW6vVq1e3agE3AIA5SP6AVjrb/n8NqqurtXTpUqWmppIA\nAmiW4cOH68ILLzQ7DG3YsEGJiYm68cYbtWDBAp177rlmhwQAaAGSP8BNzjYEtEFZWZlSUlJIAAH4\nhXfffVe/+93v9MILL8hmsykoKMjskAAALeTRff4AKzl1/7+VK1eqsrKyyWv37Nmj1NRUFoMBCeaM\nZgAAEw5JREFU4Dbu3gu3vr5e//mf/6l58+bp448/1pVXXtm6AAEALnP3Pris9gl4gN1uV2pqqsrK\nys54HfsBAs7R5v/b3r17NWbMGG3durXJa9xdXseOHdPEiRO1f/9+LVmyRN26dXPbvQEALcdqn4AP\nangKmJ6ervbt2zd5HfsBAjgbwzC8mggfOHBA11xzjdq3b69Vq1aR+AFAACH5AzwkKipK77//vrZt\n26bLLrvM6TVVVVXKy8tjERgATv3+97/X0KFDtXPnTl166aWaN2+eR9/viy++UGJiou644w7985//\nVLt27Tz6fgAA72LYJ+AFzRkGetlll+mTTz5hDiAg2nxXuaO8cnNzNWPGDL3xxhsaM2aMmyIDALhT\na9t7kj/AS+x2u2w2m5YtW6aqqiqn10RHR7MIDCDafFe1przq6+v12GOP6Z133tHSpUt1xRVXuDk6\nAIC7kPwBfiYzM1N5eXlNnmcRGIA231UtLa+ff/5ZmZmZqqqq0uLFi3XJJZd4IDoAgLuQ/AF+5mxD\nQLt27ap169bx9A+WRpvvmuaWV8MIhPLycnXq1Ek7duzQiBEj9Morr6ht27ZeiBQA0Bokf4Afstvt\nSklJ0Z49e5yeb9eunUaNGqUXX3yRJBCWRJvvmuaUl7MPnjp37qzS0lL17NnT0yECANyArR4APxQV\nFaVPPvlE0dHRTs9XV1dr6dKlrAIKwG1sNttpIw5++OEHzZo1y6SIAADeRvIHmKRhL8AuXbo0eU1Z\nWZlSUlJIAAG0Wnl5udPjFRUVXo4EAGAWkj/ARFFRUUpNTT3jNXv27FHfvn2Vnp5OEgigxcLDw50e\nDwsL83IkAACzMOcPMFlz9gBswFYQsArafNe0dM4fbQoA+BcWfAECgN1u14wZM/Txxx/r+PHjZ7yW\nzeBhBbT5rnF1tc+KigqFhYUpJyeHtgQA/AjJHxBAzrYKaAM+rUego813DeUFANbAap9AADnbKqAN\nysrKNGPGDC9FBQAAgEBA8gf4mIZVQNPT09W+ffsmr/v4449ZAAYAAADNxrBPwIedbRjoeeedp7i4\nOEVHRzN3BwGFNt81lBcAWANz/oAAZ7fb1bdvX1VXV5/xujZt2iguLk59+/YlEYTfo813DeUFANZA\n8gdYQHp6upYuXdrs688//3wtW7ZMI0aM8GBUgOfQ5ruG8gIAa2DBF8ACXnzxxbMuAnOqo0ePKjk5\nmY3hAQAA4MCTP8BPNHcbiN9iOCj8EW2+aygvALAGhn0CFmK325WamqqysrIW36Nbt2667LLLWCQG\nPo023zWUFwBYA8kfYDF2u102m03btm3T119/rdra2hbfKzg4WCkpKZo7dy5JIHwKbb5rKC8AsAaS\nP8DC3JkI8kQQvoQ23zWUFwBYA8kfAEm/JoIzZszQhx9+qLq6ulbf74ILLtCIESP04osvkgjC62jz\nXUN5AYA1kPwBaGT16tVKS0vT0aNH3XbP0NBQdezYUcOGDSMZhFfQ5ruG8gIAayD5A3CaU4eDbt26\nVfX19W69P8kgPI023zWUFwBYA8kfgDNqGA5aUlKi6upq/fTTTx75XQoJCVFISIg6dOhAUohWo813\nDeUFANZA8gfAJXa7XZMmTVJRUZHXfqeCgoIUGhqqESNGsLIomoU23zWUFwBYA8kfgBbx1hPBswkJ\nCVG3bt00f/58jRgxwuvvD99Em+8aygsArIHkD4BbNCSDxcXF+vHHH80OR9KvcwtHjhzJ00ILos13\nDeUFANZA8gfA7RoSwTVr1ujHH39s1f6B3kKiGFho811DeQGANbS2vQ92YyxOFRQUqE+fPoqJidGz\nzz7r9Jpp06apV69eio+P1+bNmz0dEoCziIqK0vvvv6/Dhw+rpqZGu3fvVnp6ujp37qw2bdqYHZ5T\nNTU1WrlypXr27KmgoCC3fIWEhOill14y+58GAADgFh598ldfX6+YmBgVFRUpLCxMCQkJWrhwofr0\n6eO45qOPPtKcOXO0bNkyffHFF3rwwQdVUlJyeqB8qgn4jIZFY1avXq2TJ0+aHU7AiomJUUFBgSWf\nZNLmu4byAgBr8Oknf6WlperVq5ciIyMVGhqqjIwM5efnN7omPz9fEyZMkCQlJibqxx9/VGVlpSfD\nAtBKUVFRKiws1IkTJ2QYhuPr008/VXh4uEJCQiRJwcEeH1wQ0Hbu3Kn4+HjZ7XazQwEAAAHAo3+Z\nlZeXKyIiwvG6R48eKi8vP+M14eHhp10DwD+MGDFCBw4cUG1trQzDUF1dndOkEM33008/yWazmR0G\nAAAIAHwsD8DjfpsUNnzt3r1b1113nc/OI/QVFRUVZocAAAACgEf/4goPD9e+ffscrw8cOKDw8PDT\nrtm/f/8Zr2mQnZ3t+D4pKUlJSUlujReAdzUMH3Vm9erVuv3223Xo0CEvR+V7wsLCzA7B44qLi1Vc\nXGx2GAAABDSPLvhSV1en3r17q6ioSN27d9fgwYO1YMECxcbGOq5Zvny5XnnlFS1btkwlJSWaPn06\nC74AaLaGxWeKi4v9YksKV3Xs2FGbN2+23KIvtPmuobwAwBp8esGXkJAQzZkzR6NGjVK/fv2UkZGh\n2NhYvfbaa5o7d64k6YYbblBUVJQuv/xy3XvvvXr11Vc9GRKAANPw9LCmpqbRkNLWfC1YsMAnhqLG\nxMRYMvEDAACewSbvAACfQ5vvGsoLAKzBp5/8AQAAAAB8A8kfAAAAAFgAyR8AAD6soKBAffr0UUxM\njJ599lmzwwEA+LGATf5YMtw6qGvroK5hNfX19XrggQe0YsUKbdu2TQsWLNA333zT6vvyu2Qd1LU1\nUM9oLpI/+D3q2jqoa1hNaWmpevXqpcjISIWGhiojI0P5+fmtvi+/S9ZBXVsD9Yzm8vvkz5/+s3sq\n1tbc19Wfbe71Z7vuTOebOkddU9e+yBOxtvaenqjr1l4TCHVthvLyckVERDhe9+jRQ+Xl5c3+eX8q\nX36XWnfeX+qa/rF551t6zpdQ18077+26JvnzIn4Jmnfe3zs2ibpu7nnq2jP3JPmzpuzsbMdXQ7n6\nU/nyu9S68/5S1/SPzTtP8ueZ+/pjXRcXFzdq31vLr/b5AwBYh590Tx5VUlKi7OxsFRQUSJKeeeYZ\nBQUFaebMmY2uo48EAOtoTf/oN8kfAABWU1dXp969e6uoqEjdu3fX4MGDtWDBAsXGxpodGgDAD7Ux\nOwAAAOBcSEiI5syZo1GjRqm+vl5ZWVkkfgCAFuPJHwAAAABYgN8v+AIAAAAAODuSPwAAAACwAMsk\nf8eOHdPEiRN17733av78+WaHAw+y2+26++67NXbsWLNDgYfl5+dr0qRJGjdunAoLC80OBx70zTff\n6L777tPYsWP1P//zP2aHE3DoI62DPtI66COtw5U+0jJz/nJzc3XhhRcqLS1NGRkZWrhwodkhwcPG\njh2rRYsWmR0GvKCqqkoPP/yw/vGPf5gdCjzMMAz98Y9/1FtvvWV2KAGFPtJ66COtgz7SOprTR/rt\nk7+srCx17dpV/fv3b3S8oKBAffr0UUxMjJ599lnH8QMHDigiIkLSr6unwX+4WtfwXy2t6yeffFJT\npkzxVphwg5bU9QcffKAbb7xRN9xwgzdD9Uv0kdZBH2kd9JHW4dE+0vBTn332mbFp0yYjLi7Ocayu\nrs6Ijo429uzZY5w8edIYMGCAsWPHDsMwDCM3N9dYtmyZYRiGMW7cOFNiRsu4WtcNbrvtNm+HilZq\nSV3PnDnTKCoqMiNctEJLf68NwzDS0tK8Gapfoo+0DvpI66CPtA5P9pF+++Rv+PDhuvDCCxsdKy0t\nVa9evRQZGanQ0FBlZGQoPz9fknTLLbdo8eLFmjJlisaMGWNGyGghV+v6yJEjuu+++7R582Y+7fQz\nrtb1yy+/rKKiIi1evFhz5841I2S0kKt1/emnn+rBBx/U5MmTlZaWZkbIfoU+0jroI62DPtI6PNlH\nBtQm7+Xl5Y5hK5LUo0cPlZaWSpLOPfdcvfHGG2aFBjc7U11fdNFF+u///m+zQoObnamup06dqqlT\np5oVGtzsTHU9cuRIjRw50qzQAgJ9pHXQR1oHfaR1uKuP9NsnfwAAAACA5guo5C88PFz79u1zvD5w\n4IDCw8NNjAieQl1bB3VtHdS1Z1G+1kFdWwd1bR3uqmu/Tv4Mw5Bxyk4VCQkJ2rVrl/bu3auTJ09q\n4cKFuummm0yMEO5CXVsHdW0d1LVnUb7WQV1bB3VtHR6r61YvR2OScePGGd27dzfatm1rREREGG+8\n8YZhGIaxfPlyIyYmxrj88suNp59+2uQo4Q7UtXVQ19ZBXXsW5Wsd1LV1UNfW4cm6tswm7wAAAABg\nZX497BMAAAAA0DwkfwAAAABgASR/AAAAAGABJH8AAAAAYAEkfwAAAABgASR/AAAAAGABJH8AAAAA\nYAEkf7C8Dh06tOrnb7/9du3Zs0eS9Msvv2jy5Mm6/PLLlZCQoJSUFH355ZduiNI8W7Zs0UcffeR4\nvWzZMj3xxBMmRgQA8Bb6yDOjj4S/IfmD5QUFBbX4Z7dv3676+npddtllkqS7775bnTt31q5du/Tl\nl19q3rx5Onz4sJsiNcfmzZu1fPlyx+u0tDR9+OGHqq6uNjEqAIA30EeeGX0k/A3JH3CKhx9+WHFx\ncRowYIAWLVokSTIMQ/fff7/69u2r66+/XmlpafrXv/4lScrLy1N6erokaffu3SotLdWTTz7puF9k\nZKRGjx4tSXr++ecVFxen/v3766WXXpIk7d27V7GxsbrzzjvVu3dvZWZmqqioSMOHD1fv3r21fv16\nSdLs2bM1YcIEDR06VL1799brr79+xpg//fRTJScn6/bbb1dsbKz+8Ic/OK7fuHGjkpKSlJCQoNGj\nR6uyslKSlJycrEcffVSJiYnq06eP1q5dq5qaGs2aNUuLFi3SwIED9e6770qSkpKS9OGHH7q/AgAA\nPos+kj4SAcAALK5Dhw6GYRjG4sWLjVGjRhmGYRiVlZXGpZdeahw8eNBYvHixkZaWZhiGYRw8eNC4\n8MILjffee88wDMMYOXKk8fXXXxuGYRhLly41br31VqfvsWHDBqN///7G8ePHjaNHjxr9+vUzNm/e\nbOzZs8cIDQ01tm3bZhiGYQwaNMjIysoyDMMw8vPzjZtvvtkwDMPIzs424uPjjRMnThiHDx82IiIi\njO+++8547733nMZcXFxsXHDBBUZFRYVRX19vDBkyxFi7dq1RU1NjDB061Dh8+LBhGIbxzjvvGHfd\ndZdhGIaRlJRkPPTQQ4ZhGMby5cuN6667zjAMw/jnP/9pTJ06tdG/Jy8vz5g2bVqryh0A4PvoI+kj\nEVjamJ18Ar5i7dq1GjdunCSpS5cuSkpKUmlpqdasWaPbb79dktS1a1clJyc7fua7777TJZdcctZ7\nr1mzRrfccovatWsnSbr11lv12WefacyYMYqKilLfvn0lSf369dO1114rSYqLi9PevXsd90hPT1fb\ntm3VuXNnpaSk6IsvvtCaNWtOi/nLL79Uhw4dNHjwYHXv3l2SFB8frz179qhTp076+uuvlZqaKsMw\nVF9fr7CwMMd73HrrrZKkQYMGNXrv3+rSpYsqKirO+u8GAAQG+kj6SAQGkj+gCYZhnHWuQ/v27R3j\n+vv166ctW7Y06+dOdc455zi+Dw4OdrwODg5WbW2t49yp9zQMQ8HBp4/aNgzD6X1DQkJUW1srwzB0\nxRVXaO3atWeMpeH6plRXV6t9+/Zn+6cBAAIUfSR9JPwTc/5geQ2dwTXXXKN33nlH9fX1+v777/XZ\nZ59p8ODBGjZsmBYvXizDMFRZWani4mLHz8bGxmrXrl2SpJ49e+qqq65qtMrX3r17tXz5cl1zzTV6\n//33VV1drV9++UVLlizRNddc0+j9zyY/P18nT57UDz/8oE8//VQJCQlNxtyU3r176/vvv1dJSYkk\nqba2Vtu3bz9juXTo0EE//fRTo3M7d+7UFVdc0ay4AQD+iz6SPhKBheQPltfwaeEtt9yi/v37a8CA\nAbruuuv097//XV26dNF//Md/KCIiQv369dOECRM0aNAgderUSdKvq3qtWrXKca/XX39dBw8e1OWX\nX67+/fvrzjvvVNeuXXXllVdq4sSJSkhI0JAhQzRp0iQNGDCg0fv/9vvf6t+/v5KSkjR06FDNmjVL\n3bp1azLmpv6NoaGhWrx4sWbOnKn4+HhdeeWVWrdundP3bnidnJys7du3N5rMvmrVKqWlpblW0AAA\nv0MfSR+JwBJkNPcjFcDCfvnlF5133nk6cuSIEhMTtXbtWnXp0kXV1dVKSUnR2rVrW7Uc9tnMnj1b\nHTp00J/+9CePvUdzHTp0SOPHj1dhYaHZoQAAfAB95L/RR8LXMecPaIYbb7xRVVVVjmWdGz45bNeu\nnWbPnq3y8nL16NHD5Ci9Y9++fXruuefMDgMA4CPoI/+NPhK+jid/AAAAAGABzPkDAAAAAAsg+QMA\nAAAACyD5AwAAAAALIPkDAAAAAAsg+QMAAAAACyD5AwAAAAAL+H/lEfNvlF70XQAAAABJRU5ErkJg\ngg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x107525fd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "lam = np.squeeze(np.array(list(emb.item()['orig_lambdas'].flatten())))[1::]\n", "\n", "fig, (ax) = plt.subplots(1,2, figsize=(15,7), facecolor='white')\n", "\n", "vals = lam #/ np.sum(lam)\n", "vals = np.hstack((0,vals))\n", "ax[0].semilogx(vals, marker='o', mfc='k', mec='k', linestyle='-', color='k')\n", "ax[0].set_ylim([-0.025,np.max(vals)+0.025])\n", "ax[0].set_xlim(xmin=.8)\n", "ax[0].set_title('Component Lambdas')\n", "\n", "vals = np.cumsum(lam) #/ np.sum(lam)\n", "vals = np.hstack((0,vals))\n", "ax[1].semilogx(vals, marker='o', mfc='k', mec='k', linestyle='-', color='k')\n", "ax[1].set_ylim([np.min(vals) - 0.025,np.max(vals)+0.025])\n", "ax[1].set_xlim(xmin=.8)\n", "ax[1].set_title('Component Cumulative Lambdas')\n", "\n", "ax[0].set_xlabel('log(Component)')\n", "ax[0].set_ylabel('Lambda')\n", "ax[1].set_xlabel('log(Component)')\n", "ax[1].set_ylabel('Cumulative Lambdas')\n", "\n", "fig.savefig('gradient_data/figures/Fig.supp.human_componentVariance.lambdas.svg', format='svg')\n", "fig.savefig('gradient_data/figures/Fig.supp.human_componentVariance.lambdas.png')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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YwrA2ISEhUkdoEEPlbMp+dd22oevXt96znm/sc6aEn3XDnudnbZj9mtNnbS6fs7kyp+PL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\nTIj2Ql/bB31tD/Szf9G+9kFf2wd9bR9+7es2LY0ToObNm2f69u1roqK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"text/plain": [ "<matplotlib.figure.Figure at 0x115ff6c90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "lam = np.squeeze(np.array(list(emb.item()['lambdas'].flatten())))\n", "\n", "fig, (ax) = plt.subplots(1,2, figsize=(15,7), facecolor='white')\n", "\n", "vals = lam / np.sum(lam)\n", "vals = np.hstack((0,vals))\n", "ax[0].semilogx(vals, marker='o', mfc='k', mec='k', linestyle='-', color='k')\n", "ax[0].set_ylim([-0.025,np.max(vals)+0.025])\n", "ax[0].set_xlim(xmin=.8)\n", "ax[0].set_title('Component variance')\n", "\n", "vals = np.cumsum(lam) / np.sum(lam)\n", "vals = np.hstack((0,vals))\n", "ax[1].semilogx(vals, marker='o', mfc='k', mec='k', linestyle='-', color='k')\n", "ax[1].set_ylim([np.min(vals) - 0.025,np.max(vals)+0.025])\n", "ax[1].set_xlim(xmin=.8)\n", "ax[1].set_title('Component cumulative variance')\n", "\n", "ax[0].set_xlabel('log(Component)')\n", "ax[0].set_ylabel('Variance')\n", "ax[1].set_xlabel('log(Component)')\n", "ax[1].set_ylabel('Cumulative variance')\n", "\n", "fig.savefig('gradient_data/figures/Fig.supp.human_componentVariance.lambdas.svg', format='svg')\n", "fig.savefig('gradient_data/figures/Fig.supp.human_componentVariance.lambdas.png')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(301,)" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.shape(lam)" ] }, { "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
queirozfcom/python-sandbox	python3/notebooks/pandas-columns/main.ipynb	1	65323	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## accompanying blog post: http://queirozf.com/entries/pandas-dataframe-examples-column-operations" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'3.6.7'" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from platform import python_version\n", "python_version()" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'/home/felipe/venv36/bin/python3.6'" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import sys\n", "\n", "sys.executable" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "('0.24.2', '1.16.3')" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "import numpy as np\n", "\n", "pd.__version__, np.__version__" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe tex2jax_ignore\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>name</th>\n", " <th>age</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>alice</td>\n", " <td>25</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>bob</td>\n", " <td>26</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>charlie</td>\n", " <td>27</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " name age\n", "0 alice 25\n", "1 bob 26\n", "2 charlie 27" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.DataFrame({\n", " 'name': ['alice','bob','charlie'],\n", " 'age': [25,26,27]\n", "})\n", "df = df[['name','age']]\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## rename" ] }, { "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 tex2jax_ignore\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>person_name</th>\n", " <th>age</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>alice</td>\n", " <td>25</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>bob</td>\n", " <td>26</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>charlie</td>\n", " <td>27</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " person_name age\n", "0 alice 25\n", "1 bob 26\n", "2 charlie 27" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.rename(columns={'name':'person_name'})" ] }, { "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 tex2jax_ignore\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>person_name</th>\n", " <th>age_in_years</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>alice</td>\n", " <td>25</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>bob</td>\n", " <td>26</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>charlie</td>\n", " <td>27</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " person_name age_in_years\n", "0 alice 25\n", "1 bob 26\n", "2 charlie 27" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.rename(columns={'name':'person_name','age':'age_in_years'})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## apply function to column names" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe tex2jax_ignore\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>name</th>\n", " <th>age</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>alice</td>\n", " <td>25</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>bob</td>\n", " <td>26</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>charlie</td>\n", " <td>27</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " name age\n", "0 alice 25\n", "1 bob 26\n", "2 charlie 27" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe tex2jax_ignore\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>NAME</th>\n", " <th>AGE</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>alice</td>\n", " <td>25</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>bob</td>\n", " <td>26</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>charlie</td>\n", " <td>27</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " NAME AGE\n", "0 alice 25\n", "1 bob 26\n", "2 charlie 27" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df2 = df.copy()\n", "\n", "df2.columns = [col.upper() for col in df2.columns]\n", "\n", "df2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## apply function" ] }, { "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 tex2jax_ignore\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>name</th>\n", " <th>age</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>ALICE</td>\n", " <td>25</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>BOB</td>\n", " <td>26</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>CHARLIE</td>\n", " <td>27</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " name age\n", "0 ALICE 25\n", "1 BOB 26\n", "2 CHARLIE 27" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df2 = df.copy()\n", "\n", "df2['name']= df['name'].map(lambda name: name.upper())\n", "df2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## apply function using information from 2 columns" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe tex2jax_ignore\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>word</th>\n", " <th>text</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <td>0</td>\n", " <td>foo</td>\n", " <td>foo bar</td>\n", " </tr>\n", " <tr>\n", " <td>1</td>\n", " <td>foo</td>\n", " <td>bar baz</td>\n", " </tr>\n", " <tr>\n", " <td>2</td>\n", " <td>bar</td>\n", " <td>baz quux</td>\n", " </tr>\n", " <tr>\n", " <td>3</td>\n", " <td>foo</td>\n", " <td>foo quux</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " word text\n", "0 foo foo bar\n", "1 foo bar baz\n", "2 bar baz quux\n", "3 foo foo quux" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_multiple = pd.DataFrame({\n", " 'text': ['foo bar','bar baz','baz quux','foo quux'],\n", " 'word': ['foo','foo','bar','foo']\n", "})\n", "df_multiple[['word','text']]" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe tex2jax_ignore\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>word</th>\n", " <th>text</th>\n", " <th>word_is_in_text</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <td>0</td>\n", " <td>foo</td>\n", " <td>foo bar</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <td>1</td>\n", " <td>foo</td>\n", " <td>bar baz</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <td>2</td>\n", " <td>bar</td>\n", " <td>baz quux</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <td>3</td>\n", " <td>foo</td>\n", " <td>foo quux</td>\n", " <td>True</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " word text word_is_in_text\n", "0 foo foo bar True\n", "1 foo bar baz False\n", "2 bar baz quux False\n", "3 foo foo quux True" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_multiple['word_is_in_text'] = df_multiple[['text','word']].apply(lambda row: row['word'] in row['text'], axis=1)\n", "df_multiple[['word','text','word_is_in_text']]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## create derived" ] }, { "cell_type": "code", "execution_count": 9, "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 tex2jax_ignore\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>name</th>\n", " <th>age</th>\n", " <th>age_times_two</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>alice</td>\n", " <td>25</td>\n", " <td>50</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>bob</td>\n", " <td>26</td>\n", " <td>52</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>charlie</td>\n", " <td>27</td>\n", " <td>54</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " name age age_times_two\n", "0 alice 25 50\n", "1 bob 26 52\n", "2 charlie 27 54" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df2 = df.copy()\n", "\n", "df2['age_times_two']= df['age'].map(lambda age: age2)\n", "df2" ] }, { "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 tex2jax_ignore\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>name</th>\n", " <th>age</th>\n", " <th>age_times_two</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>alice</td>\n", " <td>25</td>\n", " <td>50</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>bob</td>\n", " <td>26</td>\n", " <td>52</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>charlie</td>\n", " <td>27</td>\n", " <td>54</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " name age age_times_two\n", "0 alice 25 50\n", "1 bob 26 52\n", "2 charlie 27 54" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df2 = df.copy()\n", "\n", "df2['age_times_two']= df['age'] 2\n", "df2" ] }, { "cell_type": "code", "execution_count": 11, "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 tex2jax_ignore\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>name</th>\n", " <th>age</th>\n", " <th>name_uppercase</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>alice</td>\n", " <td>25</td>\n", " <td>ALICE</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>bob</td>\n", " <td>26</td>\n", " <td>BOB</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>charlie</td>\n", " <td>27</td>\n", " <td>CHARLIE</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " name age name_uppercase\n", "0 alice 25 ALICE\n", "1 bob 26 BOB\n", "2 charlie 27 CHARLIE" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df2 = df.copy()\n", "\n", "df2['name_uppercase']= df['name'].map(lambda name: name.upper())\n", "df2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## number of NaN in column" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\n", "1\n", "2\n" ] } ], "source": [ "import pandas as pd\n", "\n", "df = pd.DataFrame({\n", " 'name': ['alice','bob','charlie'],\n", " 'age': [25,26,np.nan],\n", " 'state': ['ak',np.nan,None]\n", "})\n", "\n", "print(df['name'].isnull().sum())\n", "print(df['age'].isnull().sum())\n", "print(df['state'].isnull().sum())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## column names" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['name' 'age' 'state']\n" ] } ], "source": [ "import pandas as pd\n", "\n", "df = pd.DataFrame({\n", " 'name': ['alice','bob','charlie'],\n", " 'age': [25,26,27],\n", " 'state': ['ak','ny','dc']\n", "})\n", "\n", "print(df.columns.values)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## number of columns" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3\n" ] } ], "source": [ "import pandas as pd\n", "\n", "df = pd.DataFrame({\n", " 'name': ['alice','bob','charlie'],\n", " 'age': [25,26,27],\n", " 'state': ['ak','ny','dc']\n", "})\n", "\n", "print(len(df.columns.values))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## change order" ] }, { "cell_type": "code", "execution_count": 15, "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 tex2jax_ignore\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>name</th>\n", " <th>age</th>\n", " <th>state</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>alice</td>\n", " <td>25</td>\n", " <td>ak</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>bob</td>\n", " <td>26</td>\n", " <td>ny</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>charlie</td>\n", " <td>27</td>\n", " <td>dc</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " name age state\n", "0 alice 25 ak\n", "1 bob 26 ny\n", "2 charlie 27 dc" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "\n", "df = pd.DataFrame({\n", " 'name': ['alice','bob','charlie'],\n", " 'age': [25,26,27],\n", " 'state': ['ak','ny','dc']\n", "})\n", "df" ] }, { "cell_type": "code", "execution_count": 16, "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 tex2jax_ignore\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>name</th>\n", " <th>age</th>\n", " <th>state</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>alice</td>\n", " <td>25</td>\n", " <td>ak</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>bob</td>\n", " <td>26</td>\n", " <td>ny</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>charlie</td>\n", " <td>27</td>\n", " <td>dc</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " name age state\n", "0 alice 25 ak\n", "1 bob 26 ny\n", "2 charlie 27 dc" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df2 = df[['name','age','state']]\n", "df2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## dropping" ] }, { "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 tex2jax_ignore\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>name</th>\n", " <th>state</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>alice</td>\n", " <td>ak</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>bob</td>\n", " <td>ny</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>charlie</td>\n", " <td>dc</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " name state\n", "0 alice ak\n", "1 bob ny\n", "2 charlie dc" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df2 = df.copy()\n", "\n", "df2.drop(columns=['age'],inplace=True)\n", "df2" ] }, { "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 tex2jax_ignore\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>state</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>ak</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>ny</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>dc</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " state\n", "0 ak\n", "1 ny\n", "2 dc" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df2 = df.copy()\n", "\n", "df2.drop(columns=['age','name'],inplace=True)\n", "df2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## append new" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe tex2jax_ignore\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>name</th>\n", " <th>age</th>\n", " <th>state</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>alice</td>\n", " <td>25</td>\n", " <td>dc</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>bob</td>\n", " <td>26</td>\n", " <td>ca</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>charlie</td>\n", " <td>27</td>\n", " <td>ny</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " name age state\n", "0 alice 25 dc\n", "1 bob 26 ca\n", "2 charlie 27 ny" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df2 = df.copy()\n", "\n", "states = pd.Series(['dc','ca','ny'])\n", "\n", "df2['state'] = states\n", "\n", "df2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## check if column in dataframe" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\"name\" is a column name\n", "\"age\" is a column name\n" ] } ], "source": [ "df2 = df.copy()\n", "\n", "candidate_names = ['name','gender','age']\n", "\n", "for name in candidate_names:\n", " if name in df2.columns.values:\n", " print('\"{}\" is a column name'.format(name))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## insert column at specific index" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe tex2jax_ignore\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>name</th>\n", " <th>gender</th>\n", " <th>age</th>\n", " <th>state</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>alice</td>\n", " <td>female</td>\n", " <td>25</td>\n", " <td>ak</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>bob</td>\n", " <td>male</td>\n", " <td>26</td>\n", " <td>ny</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>charlie</td>\n", " <td>male</td>\n", " <td>27</td>\n", " <td>dc</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " name gender age state\n", "0 alice female 25 ak\n", "1 bob male 26 ny\n", "2 charlie male 27 dc" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df2 = df.copy()\n", "\n", "col = pd.Series(['female','male','male'])\n", "\n", "df2.insert(1,'gender',col)\n", "df2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## astype" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "int64\n", "object\n", "uint8\n" ] } ], "source": [ "import numpy as np\n", "\n", "df2 = df.copy()\n", "\n", "print(df2['age'].dtype)\n", "\n", "df2['age'] = df2['age'].astype(str)\n", "\n", "print(df2['age'].dtype)\n", "\n", "df2['age'] = df2['age'].astype(np.uint8)\n", "\n", "print(df2['age'].dtype)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## to datetime" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe tex2jax_ignore\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>name</th>\n", " <th>date_of_birth</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>alice</td>\n", " <td>10/25/2005</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>bob</td>\n", " <td>10/29/2002</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>charlie</td>\n", " <td>01/01/2001</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " name date_of_birth\n", "0 alice 10/25/2005\n", "1 bob 10/29/2002\n", "2 charlie 01/01/2001" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df3 = pd.DataFrame({\n", " 'name': ['alice','bob','charlie'],\n", " 'date_of_birth': ['10/25/2005','10/29/2002','01/01/2001']\n", "})[['name','date_of_birth']]\n", "df3" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe tex2jax_ignore\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>name</th>\n", " <th>date_of_birth</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>alice</td>\n", " <td>2005-10-25</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>bob</td>\n", " <td>2002-10-29</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>charlie</td>\n", " <td>2001-01-01</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " name date_of_birth\n", "0 alice 2005-10-25\n", "1 bob 2002-10-29\n", "2 charlie 2001-01-01" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df3['date_of_birth'] = pd.to_datetime(df3['date_of_birth'])\n", "df3" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe tex2jax_ignore\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>name</th>\n", " <th>date_of_birth</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>alice</td>\n", " <td>27/05/2001</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>bob</td>\n", " <td>16/02/1999</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>charlie</td>\n", " <td>25/09/1998</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " name date_of_birth\n", "0 alice 27/05/2001\n", "1 bob 16/02/1999\n", "2 charlie 25/09/1998" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df3 = pd.DataFrame({\n", " 'name': ['alice','bob','charlie'],\n", " 'date_of_birth': ['27/05/2001','16/02/1999','25/09/1998']\n", "})[['name','date_of_birth']]\n", "df3" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe tex2jax_ignore\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>name</th>\n", " <th>date_of_birth</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>alice</td>\n", " <td>2001-05-27</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>bob</td>\n", " <td>1999-02-16</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>charlie</td>\n", " <td>1998-09-25</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " name date_of_birth\n", "0 alice 2001-05-27\n", "1 bob 1999-02-16\n", "2 charlie 1998-09-25" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df3 = pd.DataFrame({\n", " 'name': ['alice','bob','charlie'],\n", " 'date_of_birth': ['27/05/2001','16/02/1999','25/09/1998']\n", "})[['name','date_of_birth']]\n", "df3\n", "df3['date_of_birth'] = pd.to_datetime(df3['date_of_birth'],format='%d/%m/%Y')\n", "df3" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## map example" ] }, { "cell_type": "code", "execution_count": 48, "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 tex2jax_ignore\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>name</th>\n", " <th>age</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>alice</td>\n", " <td>25</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>bob</td>\n", " <td>26</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>charlie</td>\n", " <td>27</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>david</td>\n", " <td>22</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " name age\n", "0 alice 25\n", "1 bob 26\n", "2 charlie 27\n", "3 david 22" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df4 = pd.DataFrame({\n", " 'name': ['alice','bob','charlie','david'],\n", " 'age': [25,26,27,22],\n", "})[['name', 'age']]\n", "df4" ] }, { "cell_type": "code", "execution_count": 49, "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 tex2jax_ignore\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>name</th>\n", " <th>age</th>\n", " <th>name_uppercase</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>alice</td>\n", " <td>25</td>\n", " <td>ALICE</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>bob</td>\n", " <td>26</td>\n", " <td>BOB</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>charlie</td>\n", " <td>27</td>\n", " <td>CHARLIE</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>david</td>\n", " <td>22</td>\n", " <td>DAVID</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " name age name_uppercase\n", "0 alice 25 ALICE\n", "1 bob 26 BOB\n", "2 charlie 27 CHARLIE\n", "3 david 22 DAVID" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df4['name_uppercase'] = df4['name'].map(lambda element: element.upper())\n", "df4" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## apply example" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Series.apply" ] }, { "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 tex2jax_ignore\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>name</th>\n", " <th>age</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>alice</td>\n", " <td>25</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>bob</td>\n", " <td>26</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>charlie</td>\n", " <td>27</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>david</td>\n", " <td>22</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " name age\n", "0 alice 25\n", "1 bob 26\n", "2 charlie 27\n", "3 david 22" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df5 = pd.DataFrame({\n", " 'name': ['alice','bob','charlie','david'],\n", " 'age': [25,26,27,22],\n", "})[['name', 'age']]\n", "df5" ] }, { "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 tex2jax_ignore\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>name</th>\n", " <th>age</th>\n", " <th>name_uppercase</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>alice</td>\n", " <td>25</td>\n", " <td>ALICE</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>bob</td>\n", " <td>26</td>\n", " <td>BOB</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>charlie</td>\n", " <td>27</td>\n", " <td>CHARLIE</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>david</td>\n", " <td>22</td>\n", " <td>DAVID</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " name age name_uppercase\n", "0 alice 25 ALICE\n", "1 bob 26 BOB\n", "2 charlie 27 CHARLIE\n", "3 david 22 DAVID" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df5['name_uppercase'] = df5['name'].apply(lambda element: element.upper())\n", "df5" ] }, { "cell_type": "code", "execution_count": 64, "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 tex2jax_ignore\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>name</th>\n", " <th>age</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>alice</td>\n", " <td>25</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>bob</td>\n", " <td>26</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>charlie</td>\n", " <td>27</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>david</td>\n", " <td>22</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " name age\n", "0 alice 25\n", "1 bob 26\n", "2 charlie 27\n", "3 david 22" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df6 = pd.DataFrame({\n", " 'name': ['alice','bob','charlie','david'],\n", " 'age': [25,26,27,22],\n", "})[['name', 'age']]\n", "df6" ] }, { "cell_type": "code", "execution_count": 66, "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 tex2jax_ignore\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>name</th>\n", " <th>age</th>\n", " <th>age_times_2</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>alice</td>\n", " <td>25</td>\n", " <td>50</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>bob</td>\n", " <td>26</td>\n", " <td>52</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>charlie</td>\n", " <td>27</td>\n", " <td>54</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>david</td>\n", " <td>22</td>\n", " <td>44</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " name age age_times_2\n", "0 alice 25 50\n", "1 bob 26 52\n", "2 charlie 27 54\n", "3 david 22 44" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df6['age_times_2'] = df6[['age']].apply(lambda arr: np.multiply(arr,2))\n", "df6" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## apply vs map" ] }, { "cell_type": "code", "execution_count": 68, "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 tex2jax_ignore\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>name</th>\n", " <th>age</th>\n", " <th>number_of_children</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>alice</td>\n", " <td>25</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>bob</td>\n", " <td>26</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>charlie</td>\n", " <td>27</td>\n", " <td>4</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " name age number_of_children\n", "0 alice 25 1\n", "1 bob 26 3\n", "2 charlie 27 4" ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df7 = pd.DataFrame({\n", " 'name': ['alice','bob','charlie'],\n", " 'age': [25,26,27],\n", " 'number_of_children':[1,3,4]\n", "})[['name','age','number_of_children']]\n", "df4" ] }, { "cell_type": "code", "execution_count": 41, "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 tex2jax_ignore\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>age</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>50</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>52</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>54</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " age\n", "0 50\n", "1 52\n", "2 54" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# returns a new dataframe with a single column\n", "df4[['age']].apply(lambda arr: arr2)" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 50\n", "1 52\n", "2 54\n", "Name: age, dtype: int64" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# returns a new series\n", "df4['age'].map(lambda element: element 2)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 71, "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 tex2jax_ignore\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>x</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>2.126701</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>0.428444</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>-0.084118</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>0.324723</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>1.699269</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " x\n", "0 2.126701\n", "1 0.428444\n", "2 -0.084118\n", "3 0.324723\n", "4 1.699269" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_numeric = pd.DataFrame({\n", " 'x': np.random.normal(loc=0.0, scale=1.0, size=10000000),\n", "})\n", "df_numeric.head()" ] }, { "cell_type": "code", "execution_count": 78, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 80 ms, sys: 120 ms, total: 200 ms\n", "Wall time: 198 ms\n" ] } ], "source": [ "%%time\n", "\n", "def multiply_by_two(arr):\n", " return np.multiply(arr,2)\n", " \n", "df_numeric['2x_apply'] = df_numeric[['x']].apply(multiply_by_two)" ] }, { "cell_type": "code", "execution_count": 79, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 13.8 s, sys: 284 ms, total: 14.1 s\n", "Wall time: 14.1 s\n" ] } ], "source": [ "%%time\n", "def multiply_by_two_map(x):\n", " return x*2\n", " \n", "df_numeric['2x_map'] = df_numeric['x'].map(multiply_by_two)" ] }, { "cell_type": "code", "execution_count": 80, "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 tex2jax_ignore\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>x</th>\n", " <th>2x_apply</th>\n", " <th>2x_map</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>2.126701</td>\n", " <td>4.253402</td>\n", " <td>4.253402</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>0.428444</td>\n", " <td>0.856887</td>\n", " <td>0.856887</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>-0.084118</td>\n", " <td>-0.168235</td>\n", " <td>-0.168235</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>0.324723</td>\n", " <td>0.649445</td>\n", " <td>0.649445</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>1.699269</td>\n", " <td>3.398538</td>\n", " <td>3.398538</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " x 2x_apply 2x_map\n", "0 2.126701 4.253402 4.253402\n", "1 0.428444 0.856887 0.856887\n", "2 -0.084118 -0.168235 -0.168235\n", "3 0.324723 0.649445 0.649445\n", "4 1.699269 3.398538 3.398538" ] }, "execution_count": 80, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_numeric.head()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3.6", "language": "python", "name": "python36" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.7" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
ssunkara1/bqplot	examples/Advanced Plotting/Animations.ipynb	1	36869	{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Animations of marks and axes can be enabled by setting 'animation_duration' property of the figure" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Line Animations" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "import numpy as np\n", "import bqplot.pyplot as plt\n", "from bqplot import " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "axes_options = {'x': {'label': 'x'}, 'y': {'label': 'y'}}" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "x = np.arange(100)\n", "y = np.cumsum(np.random.randn(2, 100), axis=1) #two random walks\n", "\n", "fig = plt.figure(animation_duration=1000)\n", "lines = plt.plot(x=x, y=y, colors=['red', 'green'], axes_options=axes_options)\n", "fig" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "# update data of the line mark\n", "lines.y = np.cumsum(np.random.randn(2, 100), axis=1)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Scatter Animations" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "fig = plt.figure(animation_duration=1000)\n", "x, y = np.random.rand(2, 20)\n", "scatt = plt.scatter(x, y, colors=['blue'], axes_options=axes_options)\n", "fig" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "#data updates\n", "scatt.x = np.random.rand(20) 10\n", "scatt.y = np.random.rand(20)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "deletable": true, "editable": true }, "source": [ "## Pie Animations" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "data = np.random.rand(6)\n", "\n", "fig = plt.figure(animation_duration=1000)\n", "pie = plt.pie(data, radius=180, sort=False, display_labels='outside', display_values=True,\n", " values_format='.0%', labels=list('ABCDEFGHIJ'))\n", "fig" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "pie.sizes = np.random.rand(8)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "pie.sort = True" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "#make pie a donut\n", "with pie.hold_sync():\n", " pie.radius = 180\n", " pie.inner_radius = 120" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Bar animations" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "n = 10\n", "x = list('ABCDEFGHIJ')\n", "y1, y2 = np.random.rand(2, n)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "fig = plt.figure(animation_duration=1000)\n", "bar = plt.bar(x, [y1, y2], padding=0.2, type='grouped')\n", "fig" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "y1, y2 = np.random.rand(2, n)\n", "bar.y = [y1, y2]" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Multiple Mark Animations (using the object model)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "xs = LinearScale()\n", "ys1 = LinearScale()\n", "ys2 = LinearScale()\n", "\n", "x = np.arange(20)\n", "y = np.cumsum(np.random.randn(20))\n", "y1 = np.random.rand(20)\n", "\n", "line = Lines(x=x, y=y, scales={'x': xs, 'y': ys1}, colors=['magenta'], marker='square')\n", "bar = Bars(x=x, y=y1, scales={'x': xs, 'y': ys2}, colorpadding=0.2, colors=['steelblue'])\n", "\n", "xax = Axis(scale=xs, label='x', grid_lines='solid')\n", "yax1 = Axis(scale=ys1, orientation='vertical', tick_format='0.1f', label='y', grid_lines='solid')\n", "yax2 = Axis(scale=ys2, orientation='vertical', side='right', tick_format='0.0%', label='y1', grid_lines='none')\n", "\n", "Figure(marks=[bar, line], axes=[xax, yax1, yax2], animation_duration=1000)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "# update mark data\n", "line.y = np.cumsum(np.random.randn(20))\n", "bar.y = np.random.rand(20)" ] } ], "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" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { "0587719f4cf241c79b5225dc95ee9719": { "model_module": "bqplot", "model_module_version": "^0.2.3", "model_name": "FigureModel", "state": { "_dom_classes": [], "animation_duration": 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marioberges/F16-12-752	lectures/Lecture2.ipynb	1	14519	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Lecture \\#2: Setting up your Development Environment" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here is what I intend to cover today:\n", "\n", "* Python Basics\n", "* What is Interactive Python (IPython)?\n", "* What are IPython or Jupyter Notebooks?\n", "* How do I make my code available to others (git)?\n", "* What is GitHub?\n", "\n", "At the end of this process, I would like for each of you to be able to create an Jupyter Notebook locally on your computer, and then be able to allow anyone else to see it using the online Jupyter Notebook Viewer (https://nbviewer.jupyter.org/).\n", "\n", "This very same file we have on the screen now will make that journey.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Before you begin\n", "\n", "Things you'll need to do ahead of time:\n", "\n", "1. Create an account on [github.com](http://github.com)\n", "2. Install the [Anaconda Python distribution](https://www.continuum.io/downloads) \n", "3. Install git on your computer, which you can get [here](http://git-scm.com/)\n", "\n", "Some references that will be very helpful to ensure you understand what we are doing and how it all works:\n", "\n", "1. Git References\n", " * What it is and what is it used for? \n", " * [Official Documentation](https://git-scm.com/documentation), especially the first three videos on this page.\n", " * [Official Git Tutorial](https://git-scm.com/docs/gittutorial), if you are already familiar with the command line interface to some other version control software and just need to get started.\n", " * How does it work?\n", " * [Visual, interactive explanation](http://www.wei-wang.com/ExplainGitWithD3/#): this is really valuable if you want to wrap your head around the basic of what's happening under the hood.\n", " * [Git from the inside out](https://codewords.recurse.com/issues/two/git-from-the-inside-out): an in-depth discussion of how things really work under the hood.\n", "2. Python References\n", " * Why Python and how is it useful for for Scientific Computing?\n", " * First, a quick intro to [Python for Scientific Computing](http://nbviewer.ipython.org/github/jrjohansson/scientific-python-lectures/blob/master/Lecture-0-Scientific-Computing-with-Python.ipynb)\n", " * Other cool sources:\n", " * [SciPy lecture notes](http://www.scipy-lectures.org/): heavy focus on Python fundamentals.\n", " * [Quantitative Economics with Python](http://lectures.quantecon.org/): the name says it all.\n", " * Online Courses:\n", " * [Introduction to Scientifi Python](https://web.stanford.edu/~arbenson/cme193.html) (Stanford)\n", " * [Practical Data Science](http://datasciencecourse.org/) (CMU)\n", " * [Computational Statistics in Python](http://people.duke.edu/~ccc14/sta-663/index.html) (Duke)\n", " * How to use Python?\n", " * Here's [a tutorial](http://nbviewer.ipython.org/github/jrjohansson/scientific-python-lectures/blob/master/Lecture-1-Introduction-to-Python-Programming.ipynb).\n", " * Two libraries that we are going to be making extensive use of are numpy and matplotlib. The same person who wrote the tutorial above has tutorials for [numpy](http://nbviewer.ipython.org/github/jrjohansson/scientific-python-lectures/blob/master/Lecture-2-Numpy.ipynb) and [matplotlib](http://nbviewer.ipython.org/github/jrjohansson/scientific-python-lectures/blob/master/Lecture-4-Matplotlib.ipynb)\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "OK. Let's get you started." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Cloning the course's git repository\n", "\n", "This file you are currently viewing is part of the course's git repository, which you can find here:\n", "\n", "[http://github.com/marioberges/F16-12-752/](http://github.com/marioberges/F16-12-752/)\n", "\n", "Since it is in a public repository, you should be able to clone that repository into your computer and edit each file locally. For that, you could either clone it using the command line interface to git, or a graphical user interface (whichever you installed on your computer if you chose to install git). From the command line, for instance, you would issue this command to clone it:\n", "\n", "```\n", "git clone http://github.com/marioberges/F16-12-752.git\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Make sure that you can clone the repository into your computer by issuing that command.\n", "\n", "If you are successful, you will be able to see a new folder called `F16-12-752` inside the folder where you issued the command. A copy of this Jupyter Notebook file should be in there as well, and you can view it by opening an IPython Notebook Server as follows:\n", "\n", "```\n", "jupyter notebook\n", "```\n", "\n", "Just make sure you issue this last command on the corresponding folder." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Creating and using your own repositories\n", "\n", "The steps we followed above were for cloning the course's official repository. However, you will want to repeat these steps for any other repository you may be interested in working with, especially the ones that you end up creating under your Github account. Thus, let's practice importing one of your repositories.\n", "\n", "Follow these steps:\n", "\n", "1. Head over to github.com and log in using your credentials.\n", "2. Create a new repository and name it whatever you like.\n", "3. At the end of the process you will be given a checkout string. Copy that.\n", "4. Use the checkout string to replace the one we used earlier that looked like this:\n", " ```\n", " git clone http://github.com/yourusername/yourrepository.git\n", " ```\n", "5. Try issuing that command on your computer (obviously, replacing `yourusername` and `yourrepository` with the right information)\n", "6. If all goes well, you'll have your (empty) repository available for use in your computer." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now it's time for you to practice some of your recently learned git skills. \n", "\n", "Create a new Jupyter notebook, making sure to place it inside the folder of the repository you just cloned. \n", "\n", "Add a couple of Python commands to it, or some comments, and save it.\n", "\n", "Now go back to the terminal and add, commit and push the changes to your repository:\n", "\n", "```\n", "git add yourfile.ipynb\n", "git commit -m \"Made my first commit\"\n", "git push origin master\n", "```\n", "\n", "If this worked, you should be able to see the file added to your repository by simply pointing your browser to:\n", "\n", "`http://github.com/yourusername/yourrepository`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Doing away with the terminal\n", "\n", "Because Jupyter can be used to issue commands to a shell, directly, you can avoid having to switch to a terminal screen if you want to. This means we could have performed all of the above git manipulation directly from this notebook. The trick is to create a Code cell (the default type of cells) in the Jupyter notebook and then issuing the commands preceded by a `!` sign, as follows:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "On branch master\r\n", "Your branch is up-to-date with 'origin/master'.\r\n", "Changes not staged for commit:\r\n", " (use \"git add <file>...\" to update what will be committed)\r\n", " (use \"git checkout -- <file>...\" to discard changes in working directory)\r\n", "\r\n", "\t\u001b[31mmodified: data/surveyresults.csv\u001b[m\r\n", "\r\n", "Untracked files:\r\n", " (use \"git add <file>...\" to include in what will be committed)\r\n", "\r\n", "\t\u001b[31mLecture5_Assignment2-2014-ReDo.ipynb\u001b[m\r\n", "\r\n", "no changes added to commit (use \"git add\" and/or \"git commit -a\")\r\n" ] } ], "source": [ "!git status" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Try running the above cell and see what you get." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "data/surveyresults.csv: text/plain; charset=utf-16le\r\n" ] } ], "source": [ "!file -I data/surveyresults.csv" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "!iconv -f utf-16le -t utf-8 < data/surveyresults.csv > data/surveyresults_fixed.csv" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "data/surveyresults_fixed.csv: text/plain; charset=utf-8\r\n" ] } ], "source": [ "!file -I data/surveyresults_fixed.csv" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "with open('data/surveyresults_fixed.csv') as f:\n", " contents = f.readlines()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "29" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(contents)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sing platform\"\n", "\n", "\"4,6,13,11,21\"\n", "\n", "\"\"14\"\",\"\"25\"\"\"\n", "\n", "\",\"3,2,7,8,15\"\n", "\n", "14,11,15,7,20\"\n", "\n", "5,15,16,18,38\"\n", "\n", ",\"7,18,8,21,1\"\n", "\n", "\",\"8,1,2,12,5\"\n", "\n", ",\"13,12,9,3,2\"\n", "\n", "\"13,15,3,12,6\"\n", "\n", "\"6,10,12,13,7\"\n", "\n", "3,18,22,36,48\"\n", "\n", "0,18,19,20,24\"\n", "\n", "\"15,4,5,22,23\"\n", "\n", "18,26,6,11,13\"\n", "\n", "16,6,22,15,13\"\n", "\n", "\"13,12,3,6,23\"\n", "\n", "\"4,15,23,16,6\"\n", "\n", "\",\"8,6,10,7,3\"\n", "\n", "6,11,14,22,24\"\n", "\n", ",\"3,6,8,11,24\"\n", "\n", "17,24,13,11,7\"\n", "\n", ",\"2,7,14,8,12\"\n", "\n", ">\",\"3,1,5,2,7\"\n", "\n", "\"8,10,6,22,13\"\n", "\n", "\"11,4,13,7,20\"\n", "\n", ",\"6,14,7,24,3\"\n", "\n", ",\"13,14,8,6,5\"\n", "\n", ",\"2,7,8,13,15\"\n", "\n" ] } ], "source": [ "for i in range(len(contents)):\n", " print(contents[i][-15:])" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "couldnt parse: ['Analysing energy usage on a city scale using utility smart meters', 'MotionSync: Personal energy analytics through motion sensing and wearable sensing', 'SunSpot: exposing the location of annymous solar powered homes', 'Manual shade control simulation', ' algorithm and impact', 'AURES: A wide band ultrasonic occupancy sensing platform']\n", "Paper: # 6 Borda score: 43\n", "Paper: # 13 Borda score: 41\n", "Paper: # 8 Borda score: 31\n", "Paper: # 7 Borda score: 28\n", "Paper: # 3 Borda score: 25\n" ] } ], "source": [ "a = contents\n", "out = []\n", "\n", "for l in a:\n", " d = l.split(' </ul>')[1]\n", " nums = d.split('\"')[2]\n", " if len(nums) > 0:\n", " n = nums.split(',')\n", " try:\n", " out.append([int(x) for x in n])\n", " except Exception as e:\n", " print('couldnt parse:', n)\n", "\n", "\n", "#borda count\n", "b_score = {}\n", "for vote in out:\n", " for i,paper in enumerate(vote):\n", " if not paper in b_score:\n", " b_score[paper] = 0\n", " b_score[paper] += 5 - i\n", "\n", "#sorting by boarda score\n", "scores = [(paper, b_score[paper]) for paper in b_score]\n", "scores.sort(key= lambda x: x[1], reverse=True)\n", "\n", "#print the top 5 papers\n", "for paper, b_score in scores[:5]:\n", " print('Paper: #',paper, ' Borda score:', b_score)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }	gpl-3.0
GoogleCloudPlatform/cloudml-samples	notebooks/xgboost/TrainingWithXGBoostInCMLE.ipynb	1	18649	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# XGBoost Training on AI Platform\n", "This notebook uses the [Census Income Data Set](https://archive.ics.uci.edu/ml/datasets/Census+Income) to demonstrate how to train a model on Ai Platform." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# How to bring your model to AI Platform\n", "Getting your model ready for training can be done in 3 steps:\n", "1. Create your python model file\n", " 1. Add code to download your data from [Google Cloud Storage](https://cloud.google.com/storage) so that AI Platform can use it\n", " 1. Add code to export and save the model to [Google Cloud Storage](https://cloud.google.com/storage) once AI Platform finishes training the model\n", "1. Prepare a package\n", "1. Submit the training job" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Prerequisites\n", "Before you jump in, let’s cover some of the different tools you’ll be using to get online prediction up and running on AI Platform. \n", "\n", "[Google Cloud Platform](https://cloud.google.com/) lets you build and host applications and websites, store data, and analyze data on Google's scalable infrastructure.\n", "\n", "[AI Platform](https://cloud.google.com/ml-engine/) is a managed service that enables you to easily build machine learning models that work on any type of data, of any size.\n", "\n", "[Google Cloud Storage](https://cloud.google.com/storage/) (GCS) is a unified object storage for developers and enterprises, from live data serving to data analytics/ML to data archiving.\n", "\n", "[Cloud SDK](https://cloud.google.com/sdk/) is a command line tool which allows you to interact with Google Cloud products. In order to run this notebook, make sure that Cloud SDK is [installed](https://cloud.google.com/sdk/downloads) in the same environment as your Jupyter kernel.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Part 0: Setup\n", "* [Create a project on GCP](https://cloud.google.com/resource-manager/docs/creating-managing-projects)\n", "* [Create a Google Cloud Storage Bucket](https://cloud.google.com/storage/docs/quickstart-console)\n", "* [Enable AI Platform Training and Prediction and Compute Engine APIs](https://console.cloud.google.com/flows/enableapi?apiid=ml.googleapis.com,compute_component&_ga=2.217405014.1312742076.1516128282-1417583630.1516128282)\n", "* [Install Cloud SDK](https://cloud.google.com/sdk/downloads)\n", "* [[Optional] Install XGBoost](http://xgboost.readthedocs.io/en/latest/build.html)\n", "* [[Optional] Install scikit-learn](http://scikit-learn.org/stable/install.html)\n", "* [[Optional] Install pandas](https://pandas.pydata.org/pandas-docs/stable/install.html)\n", "* [[Optional] Install Google API Python Client](https://github.com/google/google-api-python-client)\n", "\n", "These variables will be needed for the following steps.\n", "* `TRAINER_PACKAGE_PATH <./census_training>` - A packaged training application that will be staged in a Google Cloud Storage location. The model file created below is placed inside this package path.\n", "* `MAIN_TRAINER_MODULE <census_training.train>` - Tells AI Platform which file to execute. This is formatted as follows <folder_name.python_file_name>\n", "* `JOB_DIR <gs://$BUCKET_ID/xgb_job_dir>` - The path to a Google Cloud Storage location to use for job output.\n", "* `RUNTIME_VERSION <1.9>` - The version of AI Platform to use for the job. If you don't specify a runtime version, the training service uses the default AI Platform runtime version 1.0. See the list of runtime versions for more information.\n", "* `PYTHON_VERSION <3.5>` - The Python version to use for the job. Python 3.5 is available with runtime version 1.4 or greater. If you don't specify a Python version, the training service uses Python 2.7.\n", "\n", " Replace: \n", "* `PROJECT_ID <YOUR_PROJECT_ID>` - with your project's id. Use the PROJECT_ID that matches your Google Cloud Platform project.\n", "* `BUCKET_ID <YOUR_BUCKET_ID>` - with the bucket id you created above.\n", "* `JOB_DIR <gs://YOUR_BUCKET_ID/xgb_job_dir>` - with the bucket id you created above.\n", "* `REGION <REGION>` - select a region from [here](https://cloud.google.com/ml-engine/docs/regions) or use the default '`us-central1`'. The region is where the model will be deployed." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "env: PROJECT_ID=<YOUR_PROJECT_ID>\n", "env: BUCKET_ID=<YOUR_BUCKET_ID>\n", "env: REGION=<REGION>\n", "env: TRAINER_PACKAGE_PATH=./census_training\n", "env: MAIN_TRAINER_MODULE=census_training.train\n", "env: JOB_DIR=<gs://YOUR_BUCKET_ID/xgb_job_dir>\n", "env: RUNTIME_VERSION=1.9\n", "env: PYTHON_VERSION=3.5\n" ] } ], "source": [ "%env PROJECT_ID <YOUR_PROJECT_ID>\n", "%env BUCKET_ID <YOUR_BUCKET_ID>\n", "%env REGION <REGION>\n", "%env TRAINER_PACKAGE_PATH ./census_training\n", "%env MAIN_TRAINER_MODULE census_training.train\n", "%env JOB_DIR <gs://YOUR_BUCKET_ID/xgb_job_dir>\n", "%env RUNTIME_VERSION 1.9\n", "%env PYTHON_VERSION 3.5\n", "! mkdir census_training" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The data\n", "The [Census Income Data Set](https://archive.ics.uci.edu/ml/datasets/Census+Income) that this sample\n", "uses for training is provided by the [UC Irvine Machine Learning\n", "Repository](https://archive.ics.uci.edu/ml/datasets/). We have hosted the data on a public GCS bucket `gs://cloud-samples-data/ml-engine/census/data/`. \n", "\n", " * Training file is `adult.data.csv`\n", " * Evaluation file is `adult.test.csv` (not used in this notebook)\n", "\n", "Note: Your typical development process with your own data would require you to upload your data to GCS so that AI Platform can access that data. However, in this case, we have put the data on GCS to avoid the steps of having you download the data from UC Irvine and then upload the data to GCS." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Disclaimer\n", "This dataset is provided by a third party. Google provides no representation,\n", "warranty, or other guarantees about the validity or any other aspects of this dataset." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Part 1: Create your python model file\n", "\n", "First, we'll create the python model file (provided below) that we'll upload to AI Platform. This is similar to your normal process for creating a XGBoost model. However, there are two key differences:\n", "1. Downloading the data from GCS at the start of your file, so that AI Platform can access the data.\n", "1. Exporting/saving the model to GCS at the end of your file, so that you can use it for predictions.\n", "\n", "The code in this file loads the data into a pandas DataFrame and pre-processes the data with scikit-learn. This data is then loaded into a DMatrix and used to train a model. Lastly, the model is saved to a file that can be uploaded to [AI Platform's prediction service](https://cloud.google.com/ml-engine/docs/scikit/getting-predictions#deploy_models_and_versions).\n", "\n", "REPLACE Line 18: BUCKET_ID = 'true-ability-192918' with your GCS BUCKET_ID\n", "\n", "Note: In normal practice you would want to test your model locally on a small dataset to ensure that it works, before using it with your larger dataset on AI Platform. This avoids wasted time and costs." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Writing ./census_training/train.py\n" ] } ], "source": [ "%%writefile ./census_training/train.py\n", "# [START setup]\n", "import datetime\n", "import os\n", "import subprocess\n", "\n", "from sklearn.preprocessing import LabelEncoder\n", "import pandas as pd\n", "from google.cloud import storage\n", "import xgboost as xgb\n", "\n", "\n", "# TODO: REPLACE 'BUCKET_CREATED_ABOVE' with your GCS BUCKET_ID\n", "BUCKET_ID = 'torryyang-xgb-models'\n", "# [END setup]\n", "\n", "# ---------------------------------------\n", "# 1. Add code to download the data from GCS (in this case, using the publicly hosted data).\n", "# AI Platform will then be able to use the data when training your model.\n", "# ---------------------------------------\n", "# [START download-data]\n", "census_data_filename = 'adult.data.csv'\n", "\n", "# Public bucket holding the census data\n", "bucket = storage.Client().bucket('cloud-samples-data')\n", "\n", "# Path to the data inside the public bucket\n", "data_dir = 'ml-engine/census/data/'\n", "\n", "# Download the data\n", "blob = bucket.blob(''.join([data_dir, census_data_filename]))\n", "blob.download_to_filename(census_data_filename)\n", "# [END download-data]\n", "\n", "# ---------------------------------------\n", "# This is where your model code would go. Below is an example model using the census dataset.\n", "# ---------------------------------------\n", "\n", "# [START define-and-load-data]\n", "\n", "# these are the column labels from the census data files\n", "COLUMNS = (\n", " 'age',\n", " 'workclass',\n", " 'fnlwgt',\n", " 'education',\n", " 'education-num',\n", " 'marital-status',\n", " 'occupation',\n", " 'relationship',\n", " 'race',\n", " 'sex',\n", " 'capital-gain',\n", " 'capital-loss',\n", " 'hours-per-week',\n", " 'native-country',\n", " 'income-level'\n", ")\n", "# categorical columns contain data that need to be turned into numerical values before being used by XGBoost\n", "CATEGORICAL_COLUMNS = (\n", " 'workclass',\n", " 'education',\n", " 'marital-status',\n", " 'occupation',\n", " 'relationship',\n", " 'race',\n", " 'sex',\n", " 'native-country'\n", ")\n", "\n", "# Load the training census dataset\n", "with open(census_data_filename, 'r') as train_data:\n", " raw_training_data = pd.read_csv(train_data, header=None, names=COLUMNS)\n", "# remove column we are trying to predict ('income-level') from features list\n", "train_features = raw_training_data.drop('income-level', axis=1)\n", "# create training labels list\n", "train_labels = (raw_training_data['income-level'] == ' >50K')\n", "\n", "# [END define-and-load-data]\n", "\n", "# [START categorical-feature-conversion]\n", "# Since the census data set has categorical features, we need to convert\n", "# them to numerical values. \n", "# convert data in categorical columns to numerical values\n", "encoders = {col:LabelEncoder() for col in CATEGORICAL_COLUMNS}\n", "for col in CATEGORICAL_COLUMNS:\n", " train_features[col] = encoders[col].fit_transform(train_features[col])\n", "# [END categorical-feature-conversion]\n", "\n", "# [START load-into-dmatrix-and-train]\n", "# load data into DMatrix object\n", "dtrain = xgb.DMatrix(train_features, train_labels)\n", "# train model\n", "bst = xgb.train({}, dtrain, 20)\n", "# [END load-into-dmatrix-and-train]\n", "\n", "# ---------------------------------------\n", "# 2. Export and save the model to GCS\n", "# ---------------------------------------\n", "# [START export-to-gcs]\n", "# Export the model to a file\n", "model = 'model.bst'\n", "bst.save_model(model)\n", "\n", "# Upload the model to GCS\n", "bucket = storage.Client().bucket(BUCKET_ID)\n", "blob = bucket.blob('{}/{}'.format(\n", " datetime.datetime.now().strftime('census_%Y%m%d_%H%M%S'),\n", " model))\n", "blob.upload_from_filename(model)\n", "# [END export-to-gcs]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Part 2: Create Trainer Package\n", "Before you can run your trainer application with AI Platform, your code and any dependencies must be placed in a Google Cloud Storage location that your Google Cloud Platform project can access. You can find more info [here](https://cloud.google.com/ml-engine/docs/tensorflow/packaging-trainer)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Writing ./census_training/__init__.py\n" ] } ], "source": [ "%%writefile ./census_training/__init__.py\n", "# Note that __init__.py can be an empty file.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Part 3: Submit Training Job\n", "Next we need to submit the job for training on AI Platform. We'll use gcloud to submit the job which has the following flags:\n", "\n", "* `job-name` - A name to use for the job (mixed-case letters, numbers, and underscores only, starting with a letter). In this case: `census_training_$(date +\"%Y%m%d_%H%M%S\")`\n", "* `job-dir` - The path to a Google Cloud Storage location to use for job output.\n", "* `package-path` - A packaged training application that is staged in a Google Cloud Storage location. If you are using the gcloud command-line tool, this step is largely automated.\n", "* `module-name` - The name of the main module in your trainer package. The main module is the Python file you call to start the application. If you use the gcloud command to submit your job, specify the main module name in the --module-name argument. Refer to Python Packages to figure out the module name.\n", "* `region` - The Google Cloud Compute region where you want your job to run. You should run your training job in the same region as the Cloud Storage bucket that stores your training data. Select a region from [here](https://cloud.google.com/ml-engine/docs/regions) or use the default '`us-central1`'.\n", "* `runtime-version` - The version of AI Platform to use for the job. If you don't specify a runtime version, the training service uses the default AI Platform runtime version 1.0. See the list of runtime versions for more information.\n", "* `python-version` - The Python version to use for the job. Python 3.5 is available with runtime version 1.4 or greater. If you don't specify a Python version, the training service uses Python 2.7.\n", "* `scale-tier` - A scale tier specifying the type of processing cluster to run your job on. This can be the CUSTOM scale tier, in which case you also explicitly specify the number and type of machines to use.\n", "\n", "Note: Check to make sure gcloud is set to the current PROJECT_ID" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Updated property [core/project].\r\n" ] } ], "source": [ "! gcloud config set project $PROJECT_ID" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Submit the training job." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Job [census_training_20180719_224106] submitted successfully.\r\n", "Your job is still active. You may view the status of your job with the command\r\n", "\r\n", " $ gcloud ml-engine jobs describe census_training_20180719_224106\r\n", "\r\n", "or continue streaming the logs with the command\r\n", "\r\n", " $ gcloud ml-engine jobs stream-logs census_training_20180719_224106\r\n", "jobId: census_training_20180719_224106\r\n", "state: QUEUED\r\n" ] } ], "source": [ "! gcloud ml-engine jobs submit training census_training_$(date +\"%Y%m%d_%H%M%S\") \\\n", " --job-dir $JOB_DIR \\\n", " --package-path $TRAINER_PACKAGE_PATH \\\n", " --module-name $MAIN_TRAINER_MODULE \\\n", " --region $REGION \\\n", " --runtime-version=$RUNTIME_VERSION \\\n", " --python-version=$PYTHON_VERSION \\\n", " --scale-tier BASIC" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# [Optional] StackDriver Logging\n", "You can view the logs for your training job:\n", "1. Go to https://console.cloud.google.com/\n", "1. Select \"Logging\" in left-hand pane\n", "1. Select \"Cloud ML Job\" resource from the drop-down\n", "1. In filter by prefix, use the value of $JOB_NAME to view the logs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# [Optional] Verify Model File in GCS\n", "View the contents of the destination model folder to verify that model file has indeed been uploaded to GCS.\n", "\n", "Note: The model can take a few minutes to train and show up in GCS." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "! gsutil ls gs://$BUCKET_ID/census_*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Next Steps:\n", "The AI Platform online prediction service manages computing resources in the cloud to run your models. Check out the [documentation pages](https://cloud.google.com/ml-engine/docs/scikit/) that describe the process to get online predictions from these exported models using AI Platform." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 }	apache-2.0
aksp/vrview	examples/orientations/optical-flow-to-important-points.ipynb	1	41091	{ "cells": [ { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "videos/probation-reg-small.mp4\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python2.7/site-packages/ipykernel/__main__.py:44: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.\n" ] }, { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-24-663036bbc57a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 58\u001b[0m \u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'frame'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 59\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 60\u001b[0;31m \u001b[0mk\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwaitKey\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m30\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m&\u001b[0m \u001b[0;36m0xff\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 61\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mk\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m27\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 62\u001b[0m \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ "# visualize important points optical flow\n", "\n", "import os, json, math\n", "from collections import Counter\n", "import cv2\n", "import numpy as np\n", "\n", "root = \"analysis/optical-flow/\"\n", "fns = [root + f for f in os.listdir(root) if f[0] != \".\"][7:]\n", "\n", "for flow_fn in fns:\n", " with open(flow_fn) as f: \n", " j = json.load(f)\n", " \n", " fn = \"videos/\" + flow_fn.split(\".json\")[0].split(\"/\")[-1] + \".mp4\"\n", " print fn\n", " \n", " cap = cv2.VideoCapture(fn)\n", " fps = cap.get(cv2.CAP_PROP_FPS)\n", "\n", " # Create some random colors\n", " color = np.random.randint(0,255,(100,3))\n", " ret, old_frame = cap.read()\n", " # mask = np.zeros_like(old_frame)\n", "\n", " frame_count = 0\n", " unique_frames = sorted(list(set([c[\"frame\"] for c in j])))\n", "\n", " while(1):\n", " frame_count += 1\n", "\n", " ret,frame = cap.read()\n", " \n", " lower_frames = [u for u in unique_frames if u <= frame_count]\n", " upper_frames = [u for u in unique_frames if u > frame_count]\n", " if not lower_frames: \n", " lower_frames = [0]\n", " if not upper_frames:\n", " upper_frames = [10000]\n", " \n", " lower_bound_frame = lower_frames[-1]\n", " upper_bound_frame = upper_frames[0]\n", "\n", " if frame != None and frame.any() and frame_count % 5: \n", " frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n", " rel_feats = [feat for feat in j if feat[\"frame\"] == lower_bound_frame]\n", " for feat in rel_feats:\n", " \n", " a = int(feat[\"b\"][0])\n", " b = int(feat[\"b\"][1])\n", " c = int(feat[\"a\"][0])\n", " d = int(feat[\"a\"][1])\n", " \n", " # mask = cv2.line(mask, (a,b),(c,d), color[i].tolist(), 2)\n", " frame = cv2.circle(frame,(a,b),5,color[int(feat[\"feature\"])].tolist(),-1)\n", " \n", " img = frame\n", " cv2.imshow('frame',img)\n", " \n", " k = cv2.waitKey(30) & 0xff\n", " if k == 27:\n", " break\n", "\n", " cv2.waitKey(1)\n", " cv2.destroyAllWindows()\n", " cv2.waitKey(1)\n", " cap.release()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# find clusters from optical flow files\n", "\n", "import os, json, math\n", "from collections import Counter,defaultdict\n", "import cv2\n", "import numpy as np\n", "\n", "from sklearn.cluster import KMeans\n", "\n", "root = \"analysis/optical-flow/\"\n", "fns = [root + f for f in os.listdir(root) if f[0] != \".\"][1:]\n", "clusters = defaultdict(dict)\n", "\n", "for flow_fn in fns:\n", " with open(flow_fn) as f: \n", " j = json.load(f)\n", " print flow_fn\n", " \n", " unique_frames = sorted(list(set([k[\"frame\"] for k in j])))\n", " \n", " clusters[flow_fn] = []\n", " \n", " for frame in unique_frames: \n", " rel = [k for k in j if k[\"frame\"] == frame]\n", " points = np.array([(k[\"b\"][0], k[\"b\"][1]) for k in rel])\n", " \n", " # print np.array(points[0:20])\n", "\n", " if len(points) > 1:\n", " km = KMeans(n_clusters=2, random_state=0).fit(points)\n", " labels = km.labels_\n", " cl1 = [points[i] for i in range(len(points)) if i in np.where(labels == 0)[0]]\n", " cl2 = [points[i] for i in range(len(points)) if i in np.where(labels == 1)[0]]\n", " \n", " # also want to record which features comprise the clusters\n", " cl_features = [[],[]]\n", " cl_list = [cl1, cl2]\n", " \n", " for i in range(len(cl_list)):\n", " cl = cl_list[i]\n", " for p in cl: \n", " rel_feat = [k for k in rel if k[\"b\"][0] == p[0] and k[\"b\"][1] == p[1]][0][\"feature\"]\n", " cl_features[i].append(rel_feat)\n", " \n", " \n", " clusters[flow_fn].append({\n", " \"cluster-centers\": km.cluster_centers_,\n", " \"scores\": [np.std(cl1), np.std(cl2), km.score(points)],\n", " \"frame\": frame,\n", " \"cluster-features\": cl_features,\n", " }) \n", "\n", "# turns out what we actually want to do is cluster features together for the whole movie\n", "# not to cluster together features in each frame" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[{'cluster-centers': array([[ 409.35868835, 158.5582962 ],\n", " [ 477.42402954, 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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-21-b16386f4665d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 26\u001b[0m \u001b[0mframe_count\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 27\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 28\u001b[0;31m \u001b[0mret\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mframe\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcap\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 29\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 30\u001b[0m \u001b[0mlower_frames\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mu\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mu\u001b[0m \u001b[0;32min\u001b[0m \u001b[0munique_frames\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mu\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0mframe_count\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ "# visualize cluster centers\n", "\n", "root = \"analysis/optical-flow/\"\n", "fns = [root + f for f in os.listdir(root) if f[0] != \".\"][7:]\n", "\n", "for flow_fn in fns:\n", " with open(flow_fn) as f: \n", " flow_fn_root = flow_fn.split(\".json\")[0].split(\"/\")[-1]\n", " \n", " j = clusters[flow_fn]\n", " fn = \"videos/\" + flow_fn_root + \".mp4\"\n", " print fn\n", " \n", " cap = cv2.VideoCapture(fn)\n", " fps = cap.get(cv2.CAP_PROP_FPS)\n", "\n", " # Create some random colors\n", " color = np.random.randint(0,255,(100,3))\n", " ret, old_frame = cap.read()\n", " # mask = np.zeros_like(old_frame)\n", "\n", " frame_count = 0\n", " unique_frames = sorted(list(set([c[\"frame\"] for c in j])))\n", "\n", " while(1):\n", " frame_count += 1\n", "\n", " ret,frame = cap.read()\n", " \n", " lower_frames = [u for u in unique_frames if u <= frame_count]\n", " upper_frames = [u for u in unique_frames if u > frame_count]\n", " if not lower_frames: \n", " lower_frames = [0]\n", " if not upper_frames:\n", " upper_frames = [10000]\n", " \n", " lower_bound_frame = lower_frames[-1]\n", " upper_bound_frame = upper_frames[0]\n", "\n", " if frame != None and frame.any() and frame_count % 5: \n", " frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n", " rel_feats = [feat for feat in j if feat[\"frame\"] == lower_bound_frame]\n", " cluster_centers = []\n", " print len(rel_feats)\n", " if rel_feats: \n", " cluster_centers = rel_feats[0][\"cluster-centers\"]\n", " \n", " cluster_center_count = 0\n", " for cluster_center in cluster_centers:\n", " \n", " a = int(cluster_center[0])\n", " b = int(cluster_center[1])\n", " \n", " # mask = cv2.line(mask, (a,b),(c,d), color[i].tolist(), 2)\n", " frame = cv2.circle(frame,(a,b),5,color[int(cluster_center_count)].tolist(),-1)\n", " cluster_center_count +=1\n", " \n", " img = frame\n", " cv2.imshow('frame',img)\n", " \n", " k = cv2.waitKey(30) & 0xff\n", " if k == 27:\n", " break\n", "\n", " cv2.waitKey(1)\n", " cv2.destroyAllWindows()\n", " cv2.waitKey(1)\n", " cap.release()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "cv2.waitKey(1)\n", "cv2.destroyAllWindows()\n", "cv2.waitKey(1)\n", "cap.release()" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "29.4117647059\n" ] } ], "source": [ "print fps" ] }, { "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": "2.7.8" } }, "nbformat": 4, "nbformat_minor": 0 }	apache-2.0
mdiaz236/DeepLearningFoundations	Untitled.ipynb	1	2115	{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[3.0, 3.5, 4.199999999999999]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "inputs = [6, 14, 3]\n", "weights = [0.5, 0.25, 1.4]\n", "[i * w for i, w in zip(inputs, weights)]" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def _sigmoid(x):\n", " \"\"\"\n", " This method is separate from `forward` because it\n", " will be used later with `backward` as well.\n", "\n", " `x`: A numpy array-like object.\n", "\n", " Return the result of the sigmoid function.\n", "\n", " Your code here!\n", " \"\"\"\n", " return 1 / (1 + np.exp(-1. * x))" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0.73105858, 0.88079708, 0.95257413])" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "_sigmoid(np.array([1., 2., 3.]))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
bollwyvl/nosebook	tests/py3/ipy3/Scrubbing.ipynb	1	3737	{ "cells": [ { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import random\n", "from IPython import display" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "rnd = \"a random number <0x%s>\" % random.random()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'a random number <0x0.8961822563910737>'" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rnd" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "a random number <0x0.8961822563910737>\n", "1234\n" ] }, { "data": { "text/plain": [ "'a random number <0x0.8961822563910737>'" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print(rnd)\n", "print(1234)\n", "rnd" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "rnd2 = \"some other random number <0x%s>\" % random.random()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'some other random number <0x0.17670111352577222>'" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rnd2" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "some other random number <0x0.17670111352577222>\n", "4567\n" ] }, { "data": { "text/plain": [ "'a random number <0x0.8961822563910737>'" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print(rnd2)\n", "print(4567)\n", "rnd" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/markdown": [ "#some other random number <0x0.17670111352577222>" ], "text/plain": [ "<IPython.core.display.Markdown object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display.display(display.Markdown(\"#%s\" % rnd2))" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<__main__.A at 0x7f8d745a1390>" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "class A(object):\n", " pass\n", "A()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.2" } }, "nbformat": 4, "nbformat_minor": 0 }	bsd-3-clause
wrossmorrow/sgsb	baseball.ipynb	1	1580107	null	apache-2.0
google/jax-cfd	notebooks/ml_model_inference_demo.ipynb	1	951934	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "id": "dq5Hou4QzH0Q", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "2dec9716-08bc-457f-c937-1a00e12e13da" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\u001b[K \|████████████████████████████████\| 134 kB 4.7 MB/s \n", "\u001b[K \|████████████████████████████████\| 41 kB 258 kB/s \n", "\u001b[K \|████████████████████████████████\| 287 kB 7.9 MB/s \n", "\u001b[K \|████████████████████████████████\| 280 kB 10.1 MB/s \n", "\u001b[K \|████████████████████████████████\| 280 kB 8.0 MB/s \n", "\u001b[K \|████████████████████████████████\| 280 kB 36.3 MB/s \n", "\u001b[K \|████████████████████████████████\| 280 kB 10.7 MB/s \n", "\u001b[K \|████████████████████████████████\| 279 kB 11.7 MB/s \n", "\u001b[K \|████████████████████████████████\| 279 kB 23.2 MB/s \n", "\u001b[K \|████████████████████████████████\| 40 kB 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This behaviour is the source of the following dependency conflicts.\n", "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\u001b[0m\n", "\u001b[?25h" ] } ], "source": [ "! pip install -U -q jax-cfd[complete]==0.1.0" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "id": "WFXyg2qVtPHM" }, "outputs": [], "source": [ "dataset_name = 'kolmogorov_re_1000' #@param ['kolmogorov_re_1000', 'decaying', 'kolmogorov_re_4000'] {type: \"string\"}" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "id": "kUtRFyDgzyN9", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "651d3d2e-abda-4cc7-fe14-329048f4dcd6" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Copying gs://gresearch/jax-cfd/public_eval_datasets/kolmogorov_re_1000/eval_128x128_64x64.nc...\n", "/ [0/6 files][ 0.0 B/ 2.9 GiB] 0% Done \rCopying gs://gresearch/jax-cfd/public_eval_datasets/kolmogorov_re_1000/eval_1024x1024_64x64.nc...\n", "/ [0/6 files][ 0.0 B/ 2.9 GiB] 0% Done \rCopying gs://gresearch/jax-cfd/public_eval_datasets/kolmogorov_re_1000/eval_2048x2048_64x64.nc...\n", "/ [0/6 files][ 0.0 B/ 2.9 GiB] 0% Done \rCopying gs://gresearch/jax-cfd/public_eval_datasets/kolmogorov_re_1000/eval_64x64_64x64.nc...\n", "/ [0/6 files][ 0.0 B/ 2.9 GiB] 0% Done \rCopying gs://gresearch/jax-cfd/public_eval_datasets/kolmogorov_re_1000/eval_256x256_64x64.nc...\n", "Copying gs://gresearch/jax-cfd/public_eval_datasets/kolmogorov_re_1000/eval_512x512_64x64.nc...\n", "- [6/6 files][ 2.9 GiB/ 2.9 GiB] 100% Done 41.7 MiB/s ETA 00:00:00 \n", "Operation completed over 6 objects/2.9 GiB. \n", "CPU times: user 692 ms, sys: 112 ms, total: 804 ms\n", "Wall time: 52.2 s\n" ] } ], "source": [ "%time ! gsutil -m cp gs://gresearch/jax-cfd/public_eval_datasets/{dataset_name}/eval_.nc /content" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "id": "AiC9-QA4Deni", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "9f0d6b12-5894-4a76-a3a2-b0318287f4ec" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Copying gs://gresearch/jax-cfd/public_models/EPD_ckpt.pkl...\n", "/ [0/3 files][ 0.0 B/ 3.8 MiB] 0% Done \rCopying gs://gresearch/jax-cfd/public_models/LC_ckpt.pkl...\n", "Copying gs://gresearch/jax-cfd/public_models/LI_ckpt.pkl...\n", "/ [3/3 files][ 3.8 MiB/ 3.8 MiB] 100% Done \n", "Operation completed over 3 objects/3.8 MiB. \n", "CPU times: user 62.3 ms, sys: 8.41 ms, total: 70.7 ms\n", "Wall time: 4.38 s\n" ] } ], "source": [ "%time ! gsutil -m cp gs://gresearch/jax-cfd/public_models/.pkl /content" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "id": "MzEWHd5e0Jwf", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "315b1e4d-47c3-46cb-b67f-1dbc96e28506" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "EPD_ckpt.pkl\t\t eval_256x256_64x64.nc\tLI_ckpt.pkl\n", "eval_1024x1024_64x64.nc eval_512x512_64x64.nc\tsample_data\n", "eval_128x128_64x64.nc\t eval_64x64_64x64.nc\n", "eval_2048x2048_64x64.nc LC_ckpt.pkl\n" ] } ], "source": [ "! ls /content" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "cellView": "form", "id": "wTfEmQPZ1YiT" }, "outputs": [], "source": [ "#@title Imports { form-width: \"30%\" }\n", "\n", "import warnings\n", "warnings.simplefilter('ignore')\n", "\n", "import os\n", "import functools\n", "import pickle\n", "\n", "import gin\n", "import jax\n", "import jax.numpy as jnp\n", "import numpy as np\n", "import haiku as hk\n", "\n", "import xarray\n", "import seaborn\n", "import matplotlib.pyplot as plt\n", "\n", "import jax_cfd.base as cfd\n", "import jax_cfd.data as cfd_data\n", "import jax_cfd.ml as ml\n", "\n", "model_builder = ml.model_builder\n", "model_utils = ml.model_utils\n", "optimizer_modules = ml.optimizer_modules\n", "physics_specifications = ml.physics_specifications" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "cellView": "form", "id": "0aVRpqBzEOzS" }, "outputs": [], "source": [ "#@title Helper functions\n", "\n", "shape_structure = lambda tree: jax.tree_map(lambda x: x.shape, tree)\n", "\n", "\n", "def xarray_open(path):\n", " return xarray.open_dataset(path, chunks={'time': '100MB'})\n", "\n", "\n", "def strip_imports(s):\n", " out_lines = []\n", " for line in s.splitlines():\n", " if not line.startswith('import'):\n", " out_lines.append(line)\n", " return '\\n'.join(out_lines)" ] }, { "cell_type": "markdown", "metadata": { "id": "ljEfE4rdVQ3o" }, "source": [ "# Selecting evaluation dataset" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "id": "xiQG5F213nN1" }, "outputs": [], "source": [ "#@title Paths to evaluation datasets\n", "\n", "base_path = '/content/'\n", "\n", "kolmogorov_re_1000 = {\n", " f'baseline_{i}x{i}': os.path.join(base_path, f'eval_{i}x{i}_64x64.nc')\n", " for i in [64, 128, 256, 512, 1024, 2048]\n", "}\n", "decaying = {\n", " f'baseline_{i}x{i}': os.path.join(base_path, f'eval_{i}x{i}_64x64.nc')\n", " for i in [64, 128, 256, 512, 1024, 2048]\n", "}\n", "kolmogorov_re_4000 = {\n", " f'baseline_{i}x{i}': os.path.join(base_path, f'eval_{i}x{i}_128x128.nc')\n", " for i in [128, 256, 512, 1024, 2048, 4096]\n", "}\n", "\n", "all_datasets = {\n", " 'kolmogorov_re_1000': kolmogorov_re_1000,\n", " 'decaying': decaying,\n", " 'kolmogorov_re_4000': kolmogorov_re_4000,\n", "}\n", "\n", "reference_names = {\n", " 'kolmogorov_re_1000': 'baseline_2048x2048',\n", " 'decaying': 'baseline_2048x2048',\n", " 'kolmogorov_re_4000': 'baseline_4096x4096',\n", "}" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "id": "f6osDl1JQb1P", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "ce9bbd0f-7a97-470a-813a-c73e94dee116" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "EPD_ckpt.pkl\t\t eval_256x256_64x64.nc\tLI_ckpt.pkl\n", "eval_1024x1024_64x64.nc eval_512x512_64x64.nc\tsample_data\n", "eval_128x128_64x64.nc\t eval_64x64_64x64.nc\n", "eval_2048x2048_64x64.nc LC_ckpt.pkl\n" ] } ], "source": [ "! ls /content/" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "id": "I_Mvh9yI3XJn" }, "outputs": [], "source": [ "#@title Loading evaluation dataset {run: \"auto\"}\n", "\n", "dataset_paths = all_datasets[dataset_name]\n", "datasets = {k: xarray_open(v) for k, v in dataset_paths.items()}\n", "reference_ds = datasets[reference_names[dataset_name]]\n", "\n", "grid = cfd_data.xarray_utils.grid_from_attrs(reference_ds.attrs)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "cellView": "form", "id": "BcRUrBGb6v-U" }, "outputs": [], "source": [ "#@title Selecting initial conditions and baseline trajectories.\n", "\n", "sample_id = 0\n", "time_id = 0\n", "length = 200 # length of the trajectory.\n", "inner_steps = 10 # since we deal with subsampled datasets\n", "\n", "initial_conditions = tuple(\n", " reference_ds[velocity_name].isel(\n", " sample=slice(sample_id, sample_id + 1),\n", " time=slice(time_id, time_id + 1)\n", " ).values\n", " for velocity_name in cfd_data.xarray_utils.XR_VELOCITY_NAMES[:grid.ndim]\n", ")\n", "\n", "target_ds = reference_ds.isel(\n", " sample=slice(sample_id, sample_id + 1),\n", " time=slice(time_id, time_id + length))\n", "\n", "\n", "datasets = {\n", " k: v.isel(sample=slice(sample_id, sample_id + 1),\n", " time=slice(time_id, time_id + length))\n", " for k, v in datasets.items()\n", "}" ] }, { "cell_type": "markdown", "metadata": { "id": "WASsDE3iVXKs" }, "source": [ "# Selecting model checkpoint to load" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "id": "--a-KrfBHlp-" }, "outputs": [], "source": [ "class CheckpointState:\n", " \"\"\"Object to package up the state we load and restore.\"\"\"\n", "\n", " def __init__(self, kwargs):\n", " for name, value in kwargs.items():\n", " setattr(self, name, value)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "id": "7NFw1ynrVXKt" }, "outputs": [], "source": [ "checkpoint_paths = {\n", " 'LI': \"/content/LI_ckpt.pkl\",\n", " 'LC': \"/content/LC_ckpt.pkl\",\n", " 'EPD': \"/content/EPD_ckpt.pkl\",\n", "}" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "id": "aMBjDP-yVXKt" }, "outputs": [], "source": [ "#@title selecting model to evaluate {run: \"auto\"}\n", "\n", "model_name = \"LI\" #@param ['LI', 'LC', 'EPD',] {type: \"string\"}" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "id": "3rBkCx9OVXKt" }, "outputs": [], "source": [ "#@title Loading the checkpoint\n", "\n", "ckpt_path = checkpoint_paths[model_name]\n", "with open(ckpt_path, 'rb') as f:\n", " ckpt = pickle.load(f)\n", "params = ckpt.eval_params" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "id": "sqZfsPHT65dl", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "2e4de23b-c634-4f75-9a8b-4e941ecba5dc" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "FlatMap({\n", " 'modular_step_model/~/navier_stokes_step_fn/fused_learned_interpolation/periodic_conv_2d/~/conv2_d': FlatMap({'b': (64,), 'w': (3, 3, 2, 64)}),\n", " 'modular_step_model/~/navier_stokes_step_fn/fused_learned_interpolation/periodic_conv_2d_1/~/conv2_d': FlatMap({'b': (64,), 'w': (3, 3, 64, 64)}),\n", " 'modular_step_model/~/navier_stokes_step_fn/fused_learned_interpolation/periodic_conv_2d_2/~/conv2_d': FlatMap({'b': (64,), 'w': (3, 3, 64, 64)}),\n", " 'modular_step_model/~/navier_stokes_step_fn/fused_learned_interpolation/periodic_conv_2d_3/~/conv2_d': FlatMap({'b': (64,), 'w': (3, 3, 64, 64)}),\n", " 'modular_step_model/~/navier_stokes_step_fn/fused_learned_interpolation/periodic_conv_2d_4/~/conv2_d': FlatMap({'b': (64,), 'w': (3, 3, 64, 64)}),\n", " 'modular_step_model/~/navier_stokes_step_fn/fused_learned_interpolation/periodic_conv_2d_5/~/conv2_d': FlatMap({'b': (64,), 'w': (3, 3, 64, 64)}),\n", " 'modular_step_model/~/navier_stokes_step_fn/fused_learned_interpolation/periodic_conv_2d_6/~/conv2_d': FlatMap({'b': (120,), 'w': (3, 3, 64, 120)}),\n", "})" ] }, "metadata": {}, "execution_count": 16 } ], "source": [ "shape_structure(params)" ] }, { "cell_type": "markdown", "metadata": { "id": "oF3_xji9VbsP" }, "source": [ "# Model inference" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "id": "VBk07Syx6v6V" }, "outputs": [], "source": [ "#@title Setting up model configuration from the checkpoint;\n", "\n", "gin.clear_config()\n", "gin.parse_config(ckpt.model_config_str)\n", "gin.parse_config(strip_imports(reference_ds.attrs['physics_config_str']))\n", "dt = ckpt.model_time_step\n", "physics_specs = physics_specifications.get_physics_specs()\n", "model_cls = model_builder.get_model_cls(grid, dt, physics_specs)\n", "\n", "\n", "def compute_trajectory_fwd(x):\n", " solver = model_cls()\n", " x = solver.encode(x)\n", " final, trajectory = solver.trajectory(\n", " x, length, inner_steps, start_with_input=True, post_process_fn=solver.decode)\n", " return trajectory\n", "\n", "\n", "model = hk.without_apply_rng(hk.transform(compute_trajectory_fwd))\n", "trajectory_fn = functools.partial(model.apply, params)\n", "trajectory_fn = jax.vmap(trajectory_fn) # predict a batch of trajectories;" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "id": "8Ns9kfMJ7SD7", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "99b0c0e9-7dde-4fb3-a110-9c92d755c673" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "WARNING:absl:No GPU/TPU found, falling back to CPU. (Set TF_CPP_MIN_LOG_LEVEL=0 and rerun for more info.)\n" ] } ], "source": [ "#@title Running inference;\n", "\n", "prediction = trajectory_fn(initial_conditions)\n", "prediction_ds = cfd_data.xarray_utils.velocity_trajectory_to_xarray(\n", " prediction, grid, samples=True)\n", "\n", "# roundoff error in coordinates sometimes leads to wrong alignment results;\n", "prediction_ds.coords['x'] = target_ds.coords['x']\n", "prediction_ds.coords['y'] = target_ds.coords['y']\n", "prediction_ds.coords['time'] = target_ds.coords['time']\n", "\n", "datasets[model_name] = prediction_ds" ] }, { "cell_type": "markdown", "metadata": { "id": "2TPzWKdWWTpc" }, "source": [ "# Computing summaries\n", "\n", "Note: Evaluations in this notebook are demonstrative and performed over a single sample and shorter times than those used in the paper;" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "id": "oQQUugPn7XbC" }, "outputs": [], "source": [ "summary = xarray.concat([\n", " cfd_data.evaluation.compute_summary_dataset(ds, target_ds)\n", " for ds in datasets.values()\n", "], dim='model')\n", "summary.coords['model'] = list(datasets.keys())\n", "\n", "correlation = summary.vorticity_correlation.compute()\n", "spectrum = summary.energy_spectrum_mean.mean('time').compute()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "id": "PCmgDeDqc4zr" }, "outputs": [], "source": [ "baseline_palette = seaborn.color_palette('YlGnBu', n_colors=7)[1:]\n", "models_color = seaborn.xkcd_palette(['burnt orange', 'taupe', 'greenish blue'])\n", "palette = baseline_palette + models_color[:(len(datasets.keys()) - 6)]" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "cellView": "form", "id": "v0HoNYna9Stp", "colab": { "base_uri": "https://localhost:8080/", "height": 405 }, "outputId": "e801f83f-d470-4e6d-e348-bf3c3b8e96bc" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "(0.0, 15.0)" ] }, "metadata": {}, "execution_count": 21 }, { "output_type": "display_data", "data": { "image/png": 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\n", "text/plain": [ "<Figure size 504x432 with 1 Axes>" ] }, "metadata": { "needs_background": "light" } } ], "source": [ "#@title Vorticity correlation as a function of time\n", "\n", "plt.figure(figsize=(7, 6))\n", "for color, model in zip(palette, summary['model'].data):\n", " style = '-' if 'baseline' in model else '--'\n", " correlation.sel(model=model).plot.line(\n", " color=color, linestyle=style, label=model, linewidth=3);\n", "plt.axhline(y=0.95, xmin=0, xmax=20, color='gray')\n", "plt.legend();\n", "plt.title('')\n", "plt.xlim(0, 15)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "cellView": "form", "id": "-EhXq3cX9SoB", "colab": { "base_uri": "https://localhost:8080/", "height": 392 }, "outputId": "1b8976d4-c268-42e5-aadf-cd206295ece0" }, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x432 with 1 Axes>" ] }, "metadata": { "needs_background": "light" } } ], "source": [ "#@title Energy spectrum\n", "\n", "plt.figure(figsize=(10, 6))\n", "for color, model in zip(palette, summary['model'].data):\n", " style = '-' if 'baseline' in model else '--'\n", " (spectrum.k 5 * spectrum).sel(model=model).plot.line(\n", " color=color, linestyle=style, label=model, linewidth=3);\n", "plt.legend();\n", "plt.yscale('log')\n", "plt.xscale('log')\n", "plt.title('')\n", "plt.xlim(3.5, None)\n", "if dataset_name == 'kolmogorov_re_4000':\n", " plt.ylim(5e8, None)\n", "elif dataset_name == 'kolmogorov_re_1000':\n", " plt.ylim(1e9, None)\n", "elif dataset_name == 'decaying':\n", " plt.ylim(2e8, None)\n", "else:\n", " raise ValueError('Unrecognized dataset')" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "id": "LULDCYOc8FQv" }, "outputs": [], "source": [ "vorticities = xarray.concat(\n", " [cfd_data.xarray_utils.vorticity_2d(ds) for ds in datasets.values()],\n", " dim='model'\n", ").to_dataset()\n", "vorticities.coords['model'] = list(datasets.keys())" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "cellView": "form", "id": "MpYjDWfE9SjC", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "outputId": "83ab0c48-f2a2-49ed-e692-1103ea258863" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "<xarray.plot.facetgrid.FacetGrid at 0x7f884a4aeb10>" ] }, "metadata": {}, "execution_count": 24 }, { "output_type": "display_data", "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1152x1512 with 36 Axes>" ] }, "metadata": { "needs_background": "light" } } ], "source": [ "#@title Visualizing model unrolls { form-width: \"30%\", run: \"auto\"}\n", "time_range = {'min': 0, 'max': vorticities.sizes['time'], 'step': 1}\n", "\n", "last_step_to_plot = 200 #@param {type: \"slider\", min: 1, max: 200 , step: 5}\n", "num_to_show = 5 #@param {type: \"slider\", min: 1, max: 10, step: 1}\n", "time_slice = slice(None, last_step_to_plot, last_step_to_plot // num_to_show)\n", "\n", "(vorticities.isel({'time': time_slice, 'sample': 0})['vorticity']\n", " .plot.imshow(row='model', col='time', cmap=seaborn.cm.icefire, robust=True))" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "id": "Z-JMFbSKODM2" }, "outputs": [], "source": [ "" ] } ], "metadata": { "colab": { "collapsed_sections": [], "name": "ml_model_inference_demo.ipynb", "provenance": [] }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "name": "python" } }, "nbformat": 4, "nbformat_minor": 0 }	apache-2.0
GoogleCloudPlatform/training-data-analyst	courses/machine_learning/deepdive/01_bigquery/labs/c_extract_and_benchmark.ipynb	2	15482	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Extract Datasets and Establish Benchmark\n", "\n", "Learning Objectives\n", "- Divide into Train, Evaluation and Test datasets\n", "- Understand why we need each\n", "- Pull data out of BigQuery and into CSV\n", "- Establish Rules Based Benchmark\n", "\n", "## Introduction \n", "In the previous notebook we demonstrated how to do ML in BigQuery. However BQML is limited to linear models.\n", "\n", "For advanced ML we need to pull the data out of BigQuery and load it into a ML Framework, in our case TensorFlow.\n", "\n", "While TensorFlow [can read from BigQuery directly](https://www.tensorflow.org/api_docs/python/tf/contrib/cloud/BigQueryReader), the performance is slow. The best practice is to first stage the BigQuery files as .csv files, and then read the .csv files into TensorFlow. \n", "\n", "The .csv files can reside on local disk if we're training locally, but if we're training in the cloud we'll need to move the .csv files to the cloud, in our case Google Cloud Storage." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Set up environment variables and load necessary libraries" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "PROJECT = \"cloud-training-demos\" # Replace with your PROJECT\n", "REGION = \"us-central1\" # Choose an available region for Cloud MLE" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import os\n", "os.environ[\"PROJECT\"] = PROJECT\n", "os.environ[\"REGION\"] = REGION" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "!pip freeze \| grep google-cloud-bigquery==1.21.0 \|\| pip install google-cloud-bigquery==1.21.0" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%load_ext google.cloud.bigquery" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Review\n", "\n", "In the [a_sample_explore_clean](a_sample_explore_clean.ipynb) notebook we came up with the following query to extract a repeatable and clean sample: \n", "<pre>\n", "#standardSQL\n", "SELECT\n", " (tolls_amount + fare_amount) AS fare_amount, -- label\n", " pickup_datetime,\n", " pickup_longitude, \n", " pickup_latitude, \n", " dropoff_longitude, \n", " dropoff_latitude\n", "FROM\n", " `nyc-tlc.yellow.trips`\n", "WHERE\n", " -- Clean Data\n", " trip_distance > 0\n", " AND passenger_count > 0\n", " AND fare_amount >= 2.5\n", " AND pickup_longitude > -78\n", " AND pickup_longitude < -70\n", " AND dropoff_longitude > -78\n", " AND dropoff_longitude < -70\n", " AND pickup_latitude > 37\n", " AND pickup_latitude < 45\n", " AND dropoff_latitude > 37\n", " AND dropoff_latitude < 45\n", " -- repeatable 1/5000th sample\n", " AND ABS(MOD(FARM_FINGERPRINT(CAST(pickup_datetime AS STRING)), 5000)) = 1\n", " </pre>\n", " \n", "We will use the same query with one change. Instead of using `pickup_datetime` as is, we will extract `dayofweek` and `hourofday` from it. This is to give us some categorical features in our dataset so we can illustrate how to deal with them when we get to feature engineering. The new query will be:\n", "\n", "<pre>\n", "SELECT\n", " (tolls_amount + fare_amount) AS fare_amount, -- label\n", " EXTRACT(DAYOFWEEK from pickup_datetime) AS dayofweek,\n", " EXTRACT(HOUR from pickup_datetime) AS hourofday,\n", " pickup_longitude, \n", " pickup_latitude, \n", " dropoff_longitude, \n", " dropoff_latitude\n", "-- rest same as before\n", "</pre>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Split into train, evaluation, and test sets\n", "\n", "For ML modeling we need not just one, but three datasets.\n", "\n", "Train: This is what our model learns on\n", "\n", "Evaluation (aka Validation): We shouldn't evaluate our model on the same data we trained on because then we couldn't know whether it was memorizing the input data or whether it was generalizing. Therefore we evaluate on the evaluation dataset, aka validation dataset.\n", "\n", "Test: We use our evaluation dataset to tune our hyperparameters (we'll cover hyperparameter tuning in a future lesson). We need to know that our chosen set of hyperparameters will work well for data we haven't seen before because in production, that will be the case. For this reason, we create a third dataset that we never use during the model development process. We only evaluate on this once our model development is finished. Data scientists don't always create a test dataset (aka holdout dataset), but to be thorough you should.\n", "\n", "We can divide our existing 1/5000th sample three ways 70%/15%/15% (or whatever split we like) with some modulo math demonstrated below.\n", "\n", "Because we are using a hash function these results are deterministic, we'll get the same exact split every time the query is run (assuming the underlying data hasn't changed)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Exercise 1\n", "\n", "The `create_query` function below returns a query string that we will pass to BigQuery to collect our data. It takes as arguments the phase (`TRAIN`, `VALID`, or `TEST`) and the sample_size (relating to the fraction of the data we wish to sample). Complete the code below so that when the phase is set as `VALID` or `TEST` a new 15% split of the data will be created." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def create_query(phase, sample_size):\n", " basequery = \"\"\"\n", " SELECT\n", " (tolls_amount + fare_amount) AS fare_amount,\n", " EXTRACT(DAYOFWEEK from pickup_datetime) AS dayofweek,\n", " EXTRACT(HOUR from pickup_datetime) AS hourofday,\n", " pickup_longitude AS pickuplon,\n", " pickup_latitude AS pickuplat,\n", " dropoff_longitude AS dropofflon,\n", " dropoff_latitude AS dropofflat\n", " FROM\n", " `nyc-tlc.yellow.trips`\n", " WHERE\n", " trip_distance > 0\n", " AND fare_amount >= 2.5\n", " AND pickup_longitude > -78\n", " AND pickup_longitude < -70\n", " AND dropoff_longitude > -78\n", " AND dropoff_longitude < -70\n", " AND pickup_latitude > 37\n", " AND pickup_latitude < 45\n", " AND dropoff_latitude > 37\n", " AND dropoff_latitude < 45\n", " AND passenger_count > 0\n", " AND ABS(MOD(FARM_FINGERPRINT(CAST(pickup_datetime AS STRING)), EVERY_N)) = 1\n", " \"\"\"\n", "\n", " if phase == \"TRAIN\":\n", " subsample = \"\"\"\n", " AND ABS(MOD(FARM_FINGERPRINT(CAST(pickup_datetime AS STRING)), EVERY_N * 100)) >= (EVERY_N * 0)\n", " AND ABS(MOD(FARM_FINGERPRINT(CAST(pickup_datetime AS STRING)), EVERY_N * 100)) < (EVERY_N * 70)\n", " \"\"\"\n", " elif phase == \"VALID\":\n", " subsample = \"\"\"\n", " # TODO: Your code goes here\n", " \"\"\"\n", " elif phase == \"TEST\":\n", " subsample = \"\"\"\n", " # TODO: Your code goes here\n", " \"\"\"\n", "\n", " query = basequery + subsample\n", " return query.replace(\"EVERY_N\", sample_size)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Write to CSV\n", "Now let's execute a query for train/valid/test and write the results to disk in csv format. We use Pandas's `.to_csv()` method to do so." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Exercise 2\n", "\n", "The `for` loop below will generate the TRAIN/VALID/TEST sampled subsets of our dataset. Complete the code in the cell below to 1) create the BigQuery `query_string` using the `create_query` function you completed above, taking our original 1/5000th of the dataset and 2) load the BigQuery results of that `query_string` to a DataFrame labeled `df`. \n", "\n", "The remaining lines of code write that DataFrame to a csv file with the appropriate naming." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from google.cloud import bigquery\n", "bq = bigquery.Client(project=PROJECT)\n", "\n", "for phase in [\"TRAIN\", \"VALID\", \"TEST\"]:\n", " # 1. Create query string\n", " query_string = # TODO: Your code goes here\n", "\n", " # 2. Load results into DataFrame\n", " df = # TODO: Your code goes here\n", "\n", " # 3. Write DataFrame to CSV\n", " df.to_csv(\"taxi-{}.csv\".format(phase.lower()), index_label = False, index = False)\n", " print(\"Wrote {} lines to {}\".format(len(df), \"taxi-{}.csv\".format(phase.lower())))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that even with a 1/5000th sample we have a good amount of data for ML. 150K training examples and 30K validation." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h3> Verify that datasets exist </h3>" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "!ls -l .csv" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Preview one of the files" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "!head taxi-train.csv" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looks good! We now have our ML datasets and are ready to train ML models, validate them and test them." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Establish rules-based benchmark\n", "\n", "Before we start building complex ML models, it is a good idea to come up with a simple rules based model and use that as a benchmark. After all, there's no point using ML if it can't beat the traditional rules based approach!\n", "\n", "Our rule is going to be to divide the mean fare_amount by the mean estimated distance to come up with a rate and use that to predict. \n", "\n", "Recall we can't use the actual `trip_distance` because we won't have that available at prediction time (depends on the route taken), however we do know the users pick up and drop off location so we can use euclidean distance between those coordinates." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Exercise 3\n", "\n", "In the code below, we create a rules-based benchmark and measure the Root Mean Squared Error against the label. The function `euclidean_distance` takes as input a Pandas dataframe and should measure the straight line distance between the pickup location and the dropoff location. Complete the code so that the function returns Euclidean distance between the pickup and dropoff location. \n", "\n", "The `compute_rmse` funciton takes the actual (label) value and the predicted value and computes the Root Mean Squared Error between the the two. Complete the code below for the `compute_rmse` function." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "\n", "def euclidean_distance(df):\n", " return # TODO: Your code goes here\n", "\n", "def compute_rmse(actual, predicted):\n", " return # TODO: Your code goes here\n", "\n", "def print_rmse(df, rate, name):\n", " print(\"{} RMSE = {}\".format(compute_rmse(df[\"fare_amount\"], rate euclidean_distance(df)), name))\n", "\n", "df_train = pd.read_csv(\"taxi-train.csv\")\n", "df_valid = pd.read_csv(\"taxi-valid.csv\")\n", "\n", "rate = df_train[\"fare_amount\"].mean() / euclidean_distance(df_train).mean()\n", "\n", "print_rmse(df_train, rate, \"Train\")\n", "print_rmse(df_valid, rate, \"Valid\") " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The simple distance-based rule gives us an RMSE of <b>$7.70</b> on the validation dataset. We have to beat this, of course, but you will find that simple rules of thumb like this can be surprisingly difficult to beat. \n", "\n", "You don't want to set a goal on the test dataset because you'll want to tweak your hyperparameters and model architecture to get the best validation error. Then, you can evaluate ONCE on the test data." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Challenge exercise\n", "\n", "Let's say that you want to predict whether a Stackoverflow question will be acceptably answered. Using this [public dataset of questions](https://console.cloud.google.com/marketplace/details/stack-exchange/stack-overflow?filter=solution-type:dataset&q=stack%20overflow), create a machine learning dataset that you can use for classification.\n", "<p>\n", "What is a reasonable benchmark for this problem?\n", "What features might be useful?\n", "<p>\n", "If you got the above easily, try this harder problem: you want to predict whether a question will be acceptably answered within 2 days. How would you create the dataset?\n", "<p>\n", "Hint (highlight to see):\n", "<p style='color:white' linkstyle='color:white'> \n", "You will need to do a SQL join with the table of [answers]( https://bigquery.cloud.google.com/table/bigquery-public-data:stackoverflow.posts_answers) to determine whether the answer was within 2 days.\n", "</p>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Copyright 2019 Google Inc.\n", "Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at\n", "http://www.apache.org/licenses/LICENSE-2.0\n", "Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.3" } }, "nbformat": 4, "nbformat_minor": 4 }	apache-2.0
MassimoSchiappa/datascience	FinaleExamUCSD+DSE200x.ipynb	1	8779948	null	apache-2.0
lemonyhermit/CodingYoga	python-for-developers/Chapter1/Chapter1_Introduction.ipynb	1	15763	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "[Python for Developers](http://ricardoduarte.github.io/python-for-developers/#content)\n", "==========================\n", "First edition\n", "-----------------------------------\n", "\n", "Chapter 1\n", "==========\n", "__________\n", "\n", "Introduction\n", "----------\n", "[Python](http://www.python.org) is a Very High Level Language, object-oriented, dynamic and with strong typing, interpreted and interactive.\n", "\n", "Features\n", "---------------\n", "Python has a clear and concise syntax, which favors the readability of source code, and makes the language more productive.\n", "\n", "The language includes several high-level structures (lists, dictionaries, date / time, complex numbers and others) and a vast collection of modules ready for use, plus third-party frameworks that can be added. It also has features found in other modern languages, such as generators, introspection, persistence, metaclasses and unity tests. Multiparadigm, the language supports modular, functional, and object-oriented programming. Even the basic types in Python are objects. The language is interpreted through bytecode by the Python virtual machine, making the code portable. This makes it possible to build applications on one platform and run them on other systems or direct from the source.\n", "\n", "Python is open source software (with license compatible with the General Public License (GPL), but less restrictive, allowing Python to be even incorporated into proprietary products). The language specification is maintained by the [Python Software Foundation](http://www.python.org/psf/) (PSF).\n", "\n", "Besides being used as the main language in the development of systems, Python is also used as a scripting language in various pieces of software, enabling you to automate tasks and add new features, among them: LibreOffice.org, PostgreSQL, Blender, GIMP and Inkscape.\n", "\n", "It is possible to integrate Python with other languages such as C and Fortran. In general terms, the language has many similarities with other dynamic languages such as Perl and Ruby.\n", "\n", "History\n", "---------\n", "The language was created in 1990 by Guido van Rossum, the National Research Institute for Mathematics and Computer Science in the Netherlands (CWI) and had originally focused on users as physicists and engineers. Python was designed from another existing language at the time, called ABC.\n", "\n", "Today, the language is well accepted in the industry for high-tech companies, such as:\n", "\n", "+ Google (Web applications).\n", "+ Yahoo (Web applications).\n", "+ Microsoft (IronPython: Python for. NET)\n", "+ Nokia (available for recent lines of cell phones and PDAs).\n", "+ Disney (3D animations).\n", "\n", "Versions\n", "-------\n", "The official implementation of Python is maintained by the PSF and written in C, and therefore is also known as CPython. The latest stable version is available for download at:\n", "\n", "[http://www.python.org/download/](http://www.python.org/download/)\n", "\n", "For Windows platforms, simply run the installer. For other platforms, such as Linux, Python is usually already part of the system, but in some cases it may be necessary to compile and install the interpreter from the source files.\n", "\n", "There are also implementations of Python for. NET (IronPython), JVM (Jython) and Python (PyPy).\n", "\n", "Running programs\n", "--------------------\n", "\n", "Example of Python program:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Bass\n", "Drums\n", "Guitar\n" ] } ], "source": [ "# the character \"#\" indicates that the rest of the line is a comment\n", "# A list of musical instruments\n", "instruments = ['Bass', 'Drums', 'Guitar']\n", "\n", "# for each name in the list of instruments\n", "for instrument in instruments:\n", " # show the name of the musical instrument\n", " print instrument" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the example, `instruments` is a list containing the items \"Bass\", \"Drums\" and \"Guitar\". Now `instrument` is a name that corresponds to each of the items on the list, as the loop is executed.\n", "\n", "The source files are usually identified by the extension \".py\" and can be run directly by the interpreter:\n", "\n", "`python apl.py`\n", "\n", "Thus `apl.py` will run. On Windows, the file extensions \".py\", \". pyw\", \". pyc\" and \". pyo\" are associated with Python automatically during installation, so just click a the file to run it. The \". pyw\" files run with an alternate version of the interpreter that does not open the console window.\n", "\n", "Dynamic Typing\n", "----------------\n", "Python uses dynamic typing, which means that the type of a variable is inferred by the interpreter at runtime (this is known as Duck Typing). By the time a variable is created by attribution the interpreter defines the type of a variable, along with the operations that can be applied.\n", "\n", "Typing of Python is strong, ie, the interpreter checks whether the transactions are valid and does automatic coercions between incompatible types. In Python, coercions are performed automatically only between types that are clearly related, as integer and long integer. To perform the operation between non-compatible types, you must explicitly convert the type of the variable or variables before the operation.\n", "\n", "Compilation and interpretation\n", "--------------------------\n", "The source code is translated by Python to bytecode, which is a binary format with instructions for the interpreter. The bytecode is cross platform and can be distributed and run without the original source.\n", "\n", "![Compilation, interpretation and packing](files/bpyfd_diags1.png)\n", "\n", "By default, the parser compiles the code and stores the bytecode on disk, so the next time you run it, there is no need to recompile the program, reducing the load time of execution. If the source files are changed, the interpreter will be responsible for regenerating the bytecode automatically, even using the interactive shell. When a program or a module is invoked, the interpreter performs the analysis of the code, converts to symbols, compiles (if there is no updated bytecode on disk) and runs it in the Python virtual machine.\n", "\n", "The bytecode is stored in files with the extension \". pyc\" (normal bytecode) or \". pyo\" (optimized bytecode). The bytecode can also be packaged along with an executable interpreter, to facilitate the distribution of the application, eliminating the need to install Python on each computer.\n", "\n", "Interactive Mode\n", "----------------\n", "The Python interpreter can be used interactively, where lines of code are typed into a prompt (command line) shell similar to the operating system.\n", "\n", "`python`\n", "\n", "It is ready to receive commands after the appearance of the signal `>>>` on the screen:\n", "\n", "`Python 2.6.4 (r264:75706, Nov 3 2009, 13:20:47)`<br/>\n", "`[GCC 4.4.1] on linux2`<br/>\n", "`Type \"help\", \"copyright\", \"credits\" or \"license\" for more information.`<br/>\n", "`>>>`\n", "\n", "On Windows, the interactive mode is also available via the icon \"Python (command line)\".\n", "\n", "The interactive mode is a distinguishing feature of the language, as it is possible to test and modify code snippets before inclusion in programs, to extract and convert data or even analyze the state of the objects in memory, among other possibilities.\n", "\n", "Besides the traditional interactive mode of Python, there are other programs that act as alternatives to more sophisticated interfaces (such as <span class=\"note\" title=\"PyCrust is part of wxPython project (http://www.wxpython.org/\">PyCrust</span>):\n", "![PyCrust](files/pycrust.png)\n", "\n", "Tools\n", "-----------\n", "There are many development tools for Python, such as IDEs, editors and shells (that take advantage of the interactive capabilities of Python).\n", "\n", "Integrated Development Environments (IDEs) are software packages that integrate various development tools in an environment consistent with the goal of increasing developer productivity. Generally, IDEs include such features as syntax highlighting (colorized source code according to the syntax of the language), source browsers, integrated shell and code completion (the editor presents possible ways to complete the text it can identify while typing).\n", "Among Python IDEs, there are:\n", "\n", "+ [PyScripter](http://code.google.com/p/pyscripter/)\n", "+ [SPE](http://pythonide.blogspot.com/) (Stani's Python Editor)\n", "+ [Eric](http://eric-ide.python-projects.org/)\n", "+ [PyDev](http://pydev.org/) (plug-in para a IDE Eclipse)\n", "\n", "![PyScripter](files/pyscripter.png)\n", "\n", "There are also text editors specialized in programming code, which have features like syntax colorization, export to other formats and convert text encoding.\n", "\n", "These editors support multiple programming languages, Python among them:\n", "\n", "+ [SciTE](http://www.scintilla.org/SciTE.html)\n", "+ [Notepad++](http://notepad-plus.sourceforge.net/br/site.htm)\n", "\n", "Shell is the name given to interactive environments for executing commands that can be used to test small pieces of code and for activities like data crunching (extraction of information of interest in masses of data and subsequent translation to other formats).\n", "\n", "Beyond the standard Python Shell, there are others available:\n", "\n", "+ PyCrust \n", "+ IPython \n", "\n", "Packers are utilities that are used to build executables that comprise the bytecode, the interpreter and other dependencies, allowing the application to run on machines without Python installed, which facilitates program distribution.\n", "\n", "Among packers for Python, are available:\n", "\n", "+ py2exe (Windows only)\n", "+ cx_Freeze (portable)\n", "\n", "Frameworks are collections of software components (libraries, utilities and others) that have been designed to be used by other systems.\n", "\n", "Some of the most known frameworks availble are:\n", "\n", "+ Web: Django, TurboGears, Zope and web2py.\n", "+ Graphic interface: wxPython, PyGTK and PyQt.\n", "+ Scientific processing: NumPy and SciPy.\n", "+ Image processing: PIL.\n", "+ 2D: Matplotlib and SVGFig.\n", "+ 3D: Visual Python, PyOpenGL and Python Ogre.\n", "+ Object-relational mapping: SQLAlchemy e SQLObject.\n", "\n", "Culture\n", "-------\n", "The name Python was taken by Guido van Rossum from british TV program Monty Python's Flying Circus, and there are many references to the show in its documentation. For instance, Python's oficial package repository was called Cheese Shop, the name of one of the frames of the program. Currently, the repository name is [Python Package Index](http://pypi.python.org/pypi) (PYPI).\n", "\n", "The goals of the project was summarized by Tim Peters in a text called Zen of Python, which is available in Python itself using the command:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The Zen of Python, by Tim Peters\n", "\n", "Beautiful is better than ugly.\n", "Explicit is better than implicit.\n", "Simple is better than complex.\n", "Complex is better than complicated.\n", "Flat is better than nested.\n", "Sparse is better than dense.\n", "Readability counts.\n", "Special cases aren't special enough to break the rules.\n", "Although practicality beats purity.\n", "Errors should never pass silently.\n", "Unless explicitly silenced.\n", "In the face of ambiguity, refuse the temptation to guess.\n", "There should be one-- and preferably only one --obvious way to do it.\n", "Although that way may not be obvious at first unless you're Dutch.\n", "Now is better than never.\n", "Although never is often better than right now.\n", "If the implementation is hard to explain, it's a bad idea.\n", "If the implementation is easy to explain, it may be a good idea.\n", "Namespaces are one honking great idea -- let's do more of those!\n" ] } ], "source": [ "import this" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The text emphasizes the pragmatic attitude of the Benevolent Dictator for Life (BDFL) as Guido is known in the Python community.\n", "\n", "Proposals for improving the language are called PEPs (Python Enhancement Proposals), which also serve as a reference for new features to be implemented in the language.\n", "\n", "In addition to the official website, other good source of information about the language are: [Python Cookbook](http://aspn.activestate.com/ASPN/Python/Cookbook/) site that stores \"recipes\": small portions of code to accomplish specific tasks." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The Zen of Python, by Tim Peters\n", "\n", "Beautiful is better than ugly.\n", "Explicit is better than implicit.\n", "Simple is better than complex.\n", "Complex is better than complicated.\n", "Flat is better than nested.\n", "Sparse is better than dense.\n", "Readability counts.\n", "Special cases aren't special enough to break the rules.\n", "Although practicality beats purity.\n", "Errors should never pass silently.\n", "Unless explicitly silenced.\n", "In the face of ambiguity, refuse the temptation to guess.\n", "There should be one-- and preferably only one --obvious way to do it.\n", "Although that way may not be obvious at first unless you're Dutch.\n", "Now is better than never.\n", "Although never is often better than right now.\n", "If the implementation is hard to explain, it's a bad idea.\n", "If the implementation is easy to explain, it may be a good idea.\n", "Namespaces are one honking great idea -- let's do more of those!\n" ] } ], "source": [ "import this" ] }, { "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 }	gpl-2.0
zhouqifanbdh/liupengyuan.github.io	chapter1/homework/computer/3-22/201611680345 .ipynb	54	5546	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "请输入你的姓名，以回车结束。lai\n", "请输入你的出生日期，以回车结束。6.04\n", "lai 你是双子座\n" ] } ], "source": [ "name=input('请输入你的姓名，以回车结束。')\n", "d=float(input('请输入你的出生日期，以回车结束。'))\n", "\n", "if d>=3.21 and d<=4.19:\n", " print(name,'你是白羊座')\n", "elif d>=4.20 and d<=5.20:\n", " print(name,'你是金牛座')\n", "elif d>=5.21 and d<=6.21:\n", " print(name,'你是双子座')\n", "elif d>=6.22 and d<=7.22:\n", " print(name,'你是巨蟹座')\n", "elif d>=7.23 and d<=8.22:\n", " print(name,'你是狮子座')\n", "elif d>=8.23 and d<=9.22:\n", " print(name,'你是处女座')\n", "elif d>=9.23 and d<=10.23:\n", " print(name,'你是天秤座')\n", "elif d>=10.24 and d<=11.22:\n", " print(name,'你是天蝎座')\n", "elif d>=11.23 and d<=12.21:\n", " print(name,'你是射手座')\n", "elif d>=12.22 or d<=1.19:\n", " print(name,'你是摩羯座')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "请输入一个整数m，以回车结束。3\n", "请输入一个整数n，以回车结束。6\n", "如果想要求和，输入1；想求积，输入2；想求余数，输入3；以回车结束。3\n", "3\n" ] } ], "source": [ "m=int(input('请输入一个整数m，以回车结束。'))\n", "n=int(input('请输入一个整数n，以回车结束。'))\n", "k=int(input('如果想要求和，输入1；想求积，输入2；想求余数，输入3；以回车结束。'))\n", "he=0;\n", "ji=0;\n", "yu=0;\n", "\n", "if k==1:\n", " he=m+n\n", " print(he)\n", "elif k==2:\n", " ji=m*n\n", " print(ji)\n", "else:\n", " yu=m%n\n", " print(yu)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "请输入pm2.5数值，以回车结束600\n", "应打开空气净化器，戴防霾口罩\n" ] } ], "source": [ "pm2 = int(input('请输入pm2.5数值，以回车结束'))\n", "if pm2>500:\n", " print('应打开空气净化器，戴防霾口罩')\n", "elif pm2>250:\n", " print('应戴防霾口罩')\n", "else:\n", " print('多喝水')" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "请输入要输入的英文单词，回车结束。knife\n", "复数为：把f或fe改为v再加es\n" ] } ], "source": [ "m = str(input('请输入要输入的英文单词，回车结束。'))\n", "if m.endswith('s') or m.endswith('x') or m.endswith('z') or m.endswith('ch') or m.endswith('sh'):\n", " print('复数为:',m,'es')\n", "elif m.endswith('y'):\n", " print('复数为：把y改为i再加es')\n", "elif m.endswith('f') or m.endswith('fe'):\n", " print('复数为：把f或fe改为v再加es')\n", "else:\n", " print('复数为:',m,'s')" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n" ] } ], "source": [ "#尝试性练习\n", "print('\\n')" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "请输入一个整数，以回车结束2\n", "请输入一个整数，以回车结束4\n", "请输入一个整数，以回车结束1\n", "请输入一个整数，以回车结束-9999\n", "第二大的数为： 2\n" ] } ], "source": [ "#挑战性练习\n", "maxn = int(input('请输入一个整数，以回车结束'))\n", "second = int(input('请输入一个整数，以回车结束'))\n", "if second > maxn:\n", " second,maxn = maxn,second\n", " \n", "n = None\n", "while n != -9999:\n", " n = int(input('请输入一个整数，以回车结束'))\n", " if n > maxn:\n", " n,maxn = maxn,n\n", " second,n = n,second\n", " elif n > second:\n", " second,n = n,second\n", " else:\n", " pass\n", " \n", "print('第二大的数为：',second)" ] } ], "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
mne-tools/mne-tools.github.io	0.14/_downloads/plot_sensors_decoding.ipynb	1	5391	{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "execution_count": null, "cell_type": "code", "source": [ "%matplotlib inline" ], "outputs": [], "metadata": { "collapsed": false } }, { "source": [ "\n# Decoding sensor space data\n\n\nDecoding, a.k.a MVPA or supervised machine learning applied to MEG\ndata in sensor space. Here the classifier is applied to every time\npoint.\n\n" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "import numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn.metrics import roc_auc_score\nfrom sklearn.cross_validation import StratifiedKFold\n\nimport mne\nfrom mne.datasets import sample\nfrom mne.decoding import TimeDecoding, GeneralizationAcrossTime\n\ndata_path = sample.data_path()\n\nplt.close('all')" ], "outputs": [], "metadata": { "collapsed": false } }, { "source": [ "Set parameters\n\n" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'\nevent_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw-eve.fif'\ntmin, tmax = -0.2, 0.5\nevent_id = dict(aud_l=1, vis_l=3)\n\n# Setup for reading the raw data\nraw = mne.io.read_raw_fif(raw_fname, preload=True)\nraw.filter(2, None) # replace baselining with high-pass\nevents = mne.read_events(event_fname)\n\n# Set up pick list: EEG + MEG - bad channels (modify to your needs)\nraw.info['bads'] += ['MEG 2443', 'EEG 053'] # bads + 2 more\npicks = mne.pick_types(raw.info, meg='grad', eeg=False, stim=True, eog=True,\n exclude='bads')\n\n# Read epochs\nepochs = mne.Epochs(raw, events, event_id, tmin, tmax, proj=True,\n picks=picks, baseline=None, preload=True,\n reject=dict(grad=4000e-13, eog=150e-6))\n\nepochs_list = [epochs[k] for k in event_id]\nmne.epochs.equalize_epoch_counts(epochs_list)\ndata_picks = mne.pick_types(epochs.info, meg=True, exclude='bads')" ], "outputs": [], "metadata": { "collapsed": false } }, { "source": [ "Temporal decoding\n-----------------\n\nWe'll use the default classifer for a binary classification problem\nwhich is a linear Support Vector Machine (SVM).\n\n" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "td = TimeDecoding(predict_mode='cross-validation', n_jobs=1)\n\n# Fit\ntd.fit(epochs)\n\n# Compute accuracy\ntd.score(epochs)\n\n# Plot scores across time\ntd.plot(title='Sensor space decoding')" ], "outputs": [], "metadata": { "collapsed": false } }, { "source": [ "Generalization Across Time\n--------------------------\n\nThis runs the analysis used in [1]_ and further detailed in [2]_\n\nHere we'll use a stratified cross-validation scheme.\n\n" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# make response vector\ny = np.zeros(len(epochs.events), dtype=int)\ny[epochs.events[:, 2] == 3] = 1\ncv = StratifiedKFold(y=y) # do a stratified cross-validation\n\n# define the GeneralizationAcrossTime object\ngat = GeneralizationAcrossTime(predict_mode='cross-validation', n_jobs=1,\n cv=cv, scorer=roc_auc_score)\n\n# fit and score\ngat.fit(epochs, y=y)\ngat.score(epochs)\n\n# let's visualize now\ngat.plot()\ngat.plot_diagonal()" ], "outputs": [], "metadata": { "collapsed": false } }, { "source": [ "Exercise\n--------\n - Can you improve the performance using full epochs and a common spatial\n pattern (CSP) used by most BCI systems?\n - Explore other datasets from MNE (e.g. Face dataset from SPM to predict\n Face vs. Scrambled)\n\nHave a look at the example\n`sphx_glr_auto_examples_decoding_plot_decoding_csp_space.py`\n\nReferences\n==========\n\n.. [1] Jean-Remi King, Alexandre Gramfort, Aaron Schurger, Lionel Naccache\n and Stanislas Dehaene, \"Two distinct dynamic modes subtend the\n detection of unexpected sounds\", PLOS ONE, 2013,\n http://www.ncbi.nlm.nih.gov/pubmed/24475052\n\n.. [2] King & Dehaene (2014) 'Characterizing the dynamics of mental\n representations: the temporal generalization method', Trends In\n Cognitive Sciences, 18(4), 203-210.\n http://www.ncbi.nlm.nih.gov/pubmed/24593982\n\n" ], "cell_type": "markdown", "metadata": {} } ], "metadata": { "kernelspec": { "display_name": "Python 2", "name": "python2", "language": "python" }, "language_info": { "mimetype": "text/x-python", "nbconvert_exporter": "python", "name": "python", "file_extension": ".py", "version": "2.7.13", "pygments_lexer": "ipython2", "codemirror_mode": { "version": 2, "name": "ipython" } } } }	bsd-3-clause
getmykhan/PythonforDataAnalytics	Machine Learning/Pokemon.ipynb	1	631950	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "D:\\WorkArea\\Anaconda3\\lib\\site-packages\\sklearn\\cross_validation.py:41: 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 pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "pd.set_option('max.columns', None)\n", "from sklearn.cross_validation import train_test_split, cross_val_score, KFold\n", "from sklearn import metrics\n", "from sklearn.metrics import roc_curve, f1_score, accuracy_score, precision_recall_curve, classification_report, confusion_matrix\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.neighbors import KNeighborsClassifier\n", "from sklearn.svm import SVC\n", "\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df = pd.read_csv('Pokemon.csv', low_memory=False)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'pandas.core.frame.DataFrame'>\n", "RangeIndex: 800 entries, 0 to 799\n", "Data columns (total 13 columns):\n", "# 800 non-null int64\n", "Name 800 non-null object\n", "Type 1 800 non-null object\n", "Type 2 414 non-null object\n", "Total 800 non-null int64\n", "HP 800 non-null int64\n", "Attack 800 non-null int64\n", "Defense 800 non-null int64\n", "Sp. Atk 800 non-null int64\n", "Sp. Def 800 non-null int64\n", "Speed 800 non-null int64\n", "Generation 800 non-null int64\n", "Legendary 800 non-null bool\n", "dtypes: bool(1), int64(9), object(3)\n", "memory usage: 75.9+ KB\n" ] } ], "source": [ "df.info()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>#</th>\n", " <th>Name</th>\n", " <th>Type 1</th>\n", " <th>Type 2</th>\n", " <th>Total</th>\n", " <th>HP</th>\n", " <th>Attack</th>\n", " <th>Defense</th>\n", " <th>Sp. Atk</th>\n", " <th>Sp. Def</th>\n", " <th>Speed</th>\n", " <th>Generation</th>\n", " <th>Legendary</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>Bulbasaur</td>\n", " <td>Grass</td>\n", " <td>Poison</td>\n", " <td>318</td>\n", " <td>45</td>\n", " <td>49</td>\n", " <td>49</td>\n", " <td>65</td>\n", " <td>65</td>\n", " <td>45</td>\n", " <td>1</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>Ivysaur</td>\n", " <td>Grass</td>\n", " <td>Poison</td>\n", " <td>405</td>\n", " <td>60</td>\n", " <td>62</td>\n", " <td>63</td>\n", " <td>80</td>\n", " <td>80</td>\n", " <td>60</td>\n", " <td>1</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>3</td>\n", " <td>Venusaur</td>\n", " <td>Grass</td>\n", " <td>Poison</td>\n", " <td>525</td>\n", " <td>80</td>\n", " <td>82</td>\n", " <td>83</td>\n", " <td>100</td>\n", " <td>100</td>\n", " <td>80</td>\n", " <td>1</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>3</td>\n", " <td>VenusaurMega Venusaur</td>\n", " <td>Grass</td>\n", " <td>Poison</td>\n", " <td>625</td>\n", " <td>80</td>\n", " <td>100</td>\n", " <td>123</td>\n", " <td>122</td>\n", " <td>120</td>\n", " <td>80</td>\n", " <td>1</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>4</td>\n", " <td>Charmander</td>\n", " <td>Fire</td>\n", " <td>NaN</td>\n", " <td>309</td>\n", " <td>39</td>\n", " <td>52</td>\n", " <td>43</td>\n", " <td>60</td>\n", " <td>50</td>\n", " <td>65</td>\n", " <td>1</td>\n", " <td>False</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " # Name Type 1 Type 2 Total HP Attack Defense \\\n", "0 1 Bulbasaur Grass Poison 318 45 49 49 \n", "1 2 Ivysaur Grass Poison 405 60 62 63 \n", "2 3 Venusaur Grass Poison 525 80 82 83 \n", "3 3 VenusaurMega Venusaur Grass Poison 625 80 100 123 \n", "4 4 Charmander Fire NaN 309 39 52 43 \n", "\n", " Sp. Atk Sp. Def Speed Generation Legendary \n", "0 65 65 45 1 False \n", "1 80 80 60 1 False \n", "2 100 100 80 1 False \n", "3 122 120 80 1 False \n", "4 60 50 65 1 False " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "8.125%\n" ] } ], "source": [ "print(str(len(df[df['Legendary'] == True]) / len(df) * 100) + '%')" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x178c9d395f8>" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Vu1+XZr9jZsPS+8OAd1uRNhERKWnF6CYD5gDPu/vZubduAqal/6cBN3Z22kREZGGteHbT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\njb7pnKq5c5m2+8mWT/t9TJo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"text/plain": [ "<matplotlib.figure.Figure at 0x178c9d39cc0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.title('Count Plot')\n", "plt.xticks(rotation = 45)\n", "sns.countplot(df['Type 1'])" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x178c9fb76d8>" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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GqUx8qYF8DSW6rt6gEuj3Td/jtURl5Dnyu38GUQn86xOTCX5ADOavUvqORhOV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\nsihgiIhIlv8PqrkV9VX2lR0AAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x178ca026cf8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.title('Count Plot')\n", "plt.xticks(rotation = 45)\n", "sns.countplot(df['Type 2'])" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x178ca042ac8>" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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+YBOf/9163j8nv9/jwtkkUtfazjM7apk5MYU5RUNr9hop5+Ukw45a3qw/PuSmOpFQqIYg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\nO+fmhVJWM1iNcnc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"text/plain": [ "<matplotlib.figure.Figure at 0x178c9d39b70>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.distplot(df['Total'])" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1 166\n", "5 165\n", "3 160\n", "4 121\n", "2 106\n", "6 82\n", "Name: Generation, dtype: int64" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['Generation'].value_counts()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<seaborn.axisgrid.PairGrid at 0x178ca4b1320>" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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g2NMzcFthnjxh4oDNasGcScPavIfxdqYpsWDPIdFd/7YSXLanP5i+d1B+b905\nN+9FozeEZb+pxfxNeyFKEtZVFGPX4QsxfWJtRTFe+ehU3N08f1hql31NBWRfO9amtudcIVHC428e\nwhv7PjPcRd55+AL2/aMxZh4wemAfvFFzFmcafMh22XH7yP5x752Z5s3sLXnfdG/MtOgLidj68T9M\nc1BEv+5Mg69NfcTLD2OxMGQ6rAbzWPNF0nhjg81mQY7bgRfnlqhzDCtj+NeNH2P8j97Gg69c4xjQ\nC3XPOE/M2DHG9nHOJ6S4PUlRWlrK9+7dm5JzD/veH1Jy3mvl9E/v6eom9FQ6beaTSt0mQ0vki5V2\nZXnSiFw1XnbkEzvV0pUAYLMwHHt6BiyMqaUllZXjD49fwne21upiUv1hCW6n/EXRyhj+zeS9olex\nJUlOeHjFF8aQHDc+a/Shr9uOrAy7bpKjbX/ZuIFYfvcodWdw8ZQCfOPW4fBk2OTyrjYL/ILUWu5V\n8/+ZBh9Wv30M1QfOx21XNGlQrq9baDaeztq6xokgSRwBQYQkQdXbrz88hdXvHEfZuIFYdGcBCvp7\n4AvJOgQDnDYLzl0JxOwkD+qbgaAgqefS7vyY7Wq77VZwzg1zXtT8oxELX9ufks/djekU3abCziYa\nc2yk+WVTC/HgbcOR6bThUnMQIUHCwD4u1NW3YNfhC/jm7SNMY6nlMr5yzoslWzUaq5DzqvhCIhZs\nronpY899tQi+kIjC/h4EwiIkzuGK2CvFdrsdVox68o+mtt7sc2+onACrxaLrJz14ASGtbG1H2tTo\nc5WNG4hld43E0Fy3Wu5Uu0PssLeOmYvuLMCuwxfU3XBVy7eNwOWWIPp5HAAAl8OG5kAYHqcNJy55\nsX53HVZ/rVjVFxB/x51yXrSLtNJsIpjp+rX5E3H8Yguqqo8Yah4cmL9Zr+HHpo/Co9sPxthKf1jS\neRgrmlNsYViQVM0HQrLNdDtthjYueh4sSRwPb9mH/Gwnlk6V+5AvGFvuvSUo4OUPTsb2G5MxQKGX\n5LxI6IO0lbCzGa0eF27GWJPm5Jxznt3+9hEE0ZXEc0GL51LnslnQ4IudRP/9x9MRECR10iFKEs5f\nDeHNmrP49pcKE3Z3s1gYsjLssFotYAzol+U0nBhr268sPFSVjUZB/0w1GaP2i+S2v57BunfrsHhK\nAcpvGar7ovns/UXqeRJxw+slg0iHkEpXR6P78Nr8iVj3bh0A+X5WHzgPm4Xh6FMzUPmrj7FydhFc\ndgvsVobKVy1vAAAgAElEQVTX5k+ELyjic38Ib9ScxVcmDAZj0E16NlROQEiUsFij95Wzi7Bq11Fc\nbAqq9z0306FzBXXZrfjO1tqUfG6ia0g0IWK05svGDcSs8YOxYHMN8rOdWD5tFB77rX7hzGWP9VSI\n1vfiKQV4YU4JPBk2tAQEfFR3CSPyslCY70FV2Wg8/16dags/Od2IQX1d+NeNH7faqPJiNAUEPPL6\nAV2C5bbco6M/dyASx714yz6yf11AR9pU7bm0mwDTx+RjxpgBMQuyO/d+hj8evhhZ7HXhnnfrsPqd\n4+r5bBaGRVMK8fibh/CzB8Yh02HFhc/9Opu6rqIYgZCIjDhhqlq7atTfUp2YlOh8zHTtC8oLsJ+c\nbtRtSNTVt8Blt4Axpntd9YHzsDBg49xSuJ1WtAQEvPLRKax7ty7GVrntVjS0yNoz07zLbo1ZVDBM\n0l1RjF9/oxTNQUE3X4i2jS67BbPGD47ZPDEaA7SQ7ltpq1RqFuc8O/Jj0/ydRQsXBNG9ieeC1lbZ\nvOjEcUu21sIfFtUEYb6wiCu+MB7dfhDTxgzQJUXUvpc3KLSr7UbJRKsPnEdV9RH4Qgbt21aLaWMG\nQJA4pkUGp+iQmEV3FuiuQdxr10biJCpn1UoqXR2N7oOZy2hdfQv2nGzAmzVnIUrAstcPqC72HEDF\nLUNhiSxcaM93xRdWq98oxx7dfhALJxe03veQgKAggUU2DRgYQqJELp49kEQSIkZrXluVYeHkghiN\nrXjjoGHoiTck6PS9+p3jeOjVGnUyXvqFHDhtFnAuexP94J4bdYmVzzT4dO/jDYl45PUDumOvfHQq\nKfdoABAlHtMnemviuK6gI22q9lxand5bPMgwCfFXxg/Glm9NVMe2eLb2kdcPgFkY3qzRhxxt/fgM\n6puDccNUFT2Z9bdUJyYlOh8zXVsskG3hlAIsv3uUGjpSVX0EDd4QLjUHY153sSkIQA7pfOjVGqx+\n57ihrdJqz0zzRnbNMEn31lqETWxjICyqpdR9IRE79p9tcwwwgnQvQzXcCKKHkegX57Zi/sziiuMl\nJ1TP7bBiSI5bzXDvcdpiYq2VhG8S5/AFhVbDHhTQ4A3q4h4ve4MQJQktQQGCIOGyN2hYn3tdxXjT\n9hX09wCIzZBe9eWbsL5yAgrzPaj94V3YOLek1XtElNR2NQfCEEVJ/XymyZnaiCGOd3964qJHPJ21\nF+U6uR1WVJWNVr+wAcCad47F5MB49v4iPP+e7I1htHj16PaDcNitGNDHhfxsJ3YtvQMnfjITu5be\noepYi1ZPn5xuhMthRaMvhPmbW3MOeIMCNlROiMn90tsSC/Y0Eum/bocVL8wpwcff/xJO/GQmCvNb\nbU686jgS52gJCAiEZBvodhjbMk+GDfeXDEYwkntg1JM78fibhxAUJayYPkrV2pp3julea6Tlde/W\n6Wz9i3NLkJNphy8s6myW1qaZtcsscWlC9i4Qhi/yt9bWJksiNrS729n22lSjz60k4Ny9fLLqwVM2\nbiCyXfYYW5if7YQnw4ZHXpc9yjzO2LwAWlv7yelGZDqsulwFVdVHMGv8YAzu68LirfvhDRnbce14\n2tH3ymxcJ7oWM11n2OQknPO+OFxdXNMuGIQEKWZ+uaFyAiQuh39Eays/2yknOeYc4JH/AWS77HHn\ntlotxpsHRx/Pz3bCGxKwYHMNRj6xEws21+D+kiExes902tQcHlrNi6KUcB+Ip+3ubve09OrAW4Lo\naSQTztCWC5qywgsALpsF3pCATKctoWogvpCIy5HV8Lr6FjhtFlTXnouEdcjufjv2n8XcLw6Dx2FD\nS0hQw1DeWfYvePzNQzH1tavKRmPX4Qv4xq3DVc8K5XGlPnemw2Ya8lJX3wJAXw2l6ss3YebYAVio\ncX9W3ARF0TiPQW6mA35BMnW1BhBTP3zx1v3ydbZbTe8PYBzf291dsTva1dGsEg4gu4tebJLzCVSV\njUZhvgdnGnxY9aejqju92ZfHLKcNvqCA5dP0sbJmbvXnr/rVv5sDgrqbDij3vBYb55ZifeUEeJw2\nnL3ihyMF1ReIziOefQVi++/K2UV45PVaLJ06UtXQ+at+Qz01BwR4nDY5V0CWE4sjNs+sks51Locu\nzltZhNs4txTP3DcWdqsFI/pl6tpvVplHiQNX3KejP5t2dxKIU1EqKhN/e67Xqt/J4ViKrU2mYkki\n419PCPlrj001+9w5bruagFNrTwMhMcYWrpwtH192lz6/1IY5JcjKsMXYWkUTyhdOoHW8Xl85IbJA\nYcPIJ3bG2HFlTuGPaK+jcq0Yuvu3Q2tExxNP1y1BEVkZxgsGA/u48N3f1Krzy8stQQRFKSa0+edf\nGwd/SERLUMT8zXt1upY4VA9hI3trtTA5VE4zTzV7bvTxFdNvUL0xALkPLN9+AM99tUjXV840+NDP\n49Rp3ijM2cxexdM2Y6zb2z0tKeupjLGXGWP1jLHDmmM5jLG3GWPHI7/7Ro4zxtg6xlgdY+wgYyyt\nEoMSRHch2TrQibigKQZRWTUOCWJMNZBffL0YHFxd0XXZLOjrtmPl7CLsOnwBmQ4r7ivR776UTxyK\nPScuwxsV5hFvp3vamAFxV7yVGMbo3aC15XKmfZuFydVQIhnSZ403dxM0dAuMPBZv5ytuLpE496cn\n1/DuSFdHs0o4i+4sUCvTrNx1FFXVRxAIi+ib6cDPv1aMXUvvwLKphaauzs0BOYQp2qXfH47V+8rZ\nRbAwqDvcWRnGOzYuhxULt+zD+asBrH77GB7esq9H3M/eSrL9VwkvWv32MVVDFmZcTSkQFlUPCn9I\nQH62E8+/VxdbHaS8GC6bFW6niZ1xWjF51W4s3LIP824drnttpsOKX359PHYvn4wTP5mJ3csnY0Pl\nBDUc0OyzRds0o3YZ7fy393rFc9du7/1J5jndgWRtarzPHe3mvuKNgwhLPMYWPrpdPq7d/V79znE8\n/GoNApES1Jeag5hVPBC7l0/Ga/Mnmo7XWRl2dVPByI6vqxgPiwW6NudlOdEcFFo93NpRZSXeuE50\nPWa6VjbQtKHCQOuCwaXmIO5Z9wEqf/UxMmxWw9DmunovLreE8N3fxHpeLrtrJH5fe86wes6b+86i\nvimo6ydam659bs0/GmOO51+XYdgHBvV16TyW1rxzDG6nVad5I09RM3sVT9s9xe4ppNLz4hUAvwSw\nWXPsewD+zDn/KWPse5H/VwCYAaAw8jMRwPrIb4LoVVxrBYtrSeQVnTnZbZdraPvCIrb99Yy6qv2/\nn/uR6bDhmfvGYkiOG5dbgpA4sGBzjW6V2+OwYWAfFx68bThcditCgqQmUFIqlEy6vh88UavpZrt6\nzYEwrs/LNN251GZGd9mtrcnlQiICgoRvf6kQ37xtBD73h1Dzj0Y1uWI8N0HFbVbxFlm/u05eJGFR\nOwSRChRgMFx5V3OJtHF/UpXYMtV0RuUVpaqI2TUszPdgw5wSBMIiVs0e15pUUJc4qxgAx4bKCXhY\nm2ywvBi+kID86zLUxIeAHAOel+XEuSt+rJpdhH+6Tq4OsWrXUaz+WjGeuW8sQoJkuqOtxH4rO41V\n1UdUV2ij62WWcb+Lq9r0eBLVbzL9V0ksV5jvwaI7C/DnTy+q9m+ZZpdQqcIQCEuYOXYAqg+cx+Kt\ntXjmvrGYvGo3AOieu+fEZdxWmAcmMBMPOAG7lt6B9bvr4Im4TGvf54ovpNtlX1dRrParVZEdSKUK\nyvrddeo1+UVFMSZd3w/ZLjua/GGcutyCF+eWqI9nGmTkb+t6TR+Tj/WVE9Rz/r72nC4cSxuKmAiJ\njH+pTCCczhglkX102qg4iwvmx/OznfhoxZ24zuWA22nFuSt+OO0W/OqDM1g1uwgOm6XNHWp/SMSG\nOSUQRBEnfjJT1Vthvgcvzi1RK5v88uvFsFmsyHLZ1FwveVlO/GHx7Sjo78FnjT5kOq1wO1pd++P1\n5ejPq/TTTKdNDvki+5p2SBKPqS7z7P1FKMjLRMXEL6hhR8o8LXpOCcSGemrJz3ain8eJOZOGwRsU\n8NK8UmQ4rGjyh7Fj/znsO3MVcyYNi0kKWvKFPuo8siUgYN8/GvGNV/aibNxAPHPfWAzu44JfEON6\nKx97ega8QQFnGry42BSELyjq3sfMUzTaXinhIa/Nn6i2u+q//qazoz3J7qXM84Jz/j6AxqjD9wLY\nFPl7E4BZmuObucz/AOjDGBuQqrYRRDrSEbXb25vIK9q7YsHmGjR4QxBFuZyoNmZ1+faDEEQJ/TwO\nsEjZSaNV7vrmoHquK74wMuxWeDLkL/5uhxWjB/ZBVoZdXaxQeP69OsMV7T0nLqPRG0K2yxbz+Ma5\nJWgK6GMKr/pCeHXPaTT6Qlj02j5dcsabh+egyR+Om0hUcZvVeossnzYKgci1VMNquFxZpb4pCM7l\ne2CWAE8ZxGLuT1DstjW8O0K3ibxHcyCMRm/IvIZ7xK30mbc+xfmrAVxuCRkkzqrFFW8YIVHCqtlF\nOPrUDKyaXYSQyNUEnlXVR7Bi+g34wT03oqr6CEY+sROP/fYgRAn47m9qMW3N+7jYFMS5K37YrAxv\n1JxFP4/DcMdmz4nLAORJQnaGHcunjUIoLBpeL6UPao83B8Ix+V86+tr2dpLRb7w+qn1Mqdqg6Keq\n+gi+dGM+LBb5HBebgnj+vTqcu+LHwi37cMMP/oj5m/di+d2jUDZuID453YihuW5MGpGLtw5dQFX1\nEZy74seO/ecwemAfLNhcgyd3HIqxgz97YBye3HFYtVX/+7kf09a8j+u//xZKn3oHAYNd9sVbaxEI\nS1j2m1pwAI/99qDe3oVFZFgtKPlCDhZuke3owi37MKiPGy6bFY3esGp3o69dvOsVCouYMWaA7pwz\nxgyALySoz0s2oXM8+5rIPezJROvz8Zk34LHfHsTxiy2G16PZb+yh5guJePL/uxEcUD0gHvvtQTR4\nQzh52YuWoNjmDvXK2UV4cschPPxqDfxhCY+8Xqvq7cJVPxZsrsFlbwjvH6uHKAEPb5H19dCrNai4\nZShWTL9BHZcff/OQWqYykb7s1XjeRfdTsq/piZm35YO3DUeO246AIKGq+giu//5bmLbmfXWDS4uy\nmRA93ywbNxDLp41Stbxgcw1aggI45yh96h3sO3MVy+8eFTPvKBs3EF+6MV+1fQ+9WoMReVlq8nhv\nMIxGX8jUVq+cXQRvSFDfMy8rAy/NK4XFAt37RLdX+SxaexU9f1+4ZR9mjh2Aqi/fpNrRnmb3GOep\n66SMsWEA/ptzPiby/1XOeR/N41c4530ZY/8N4Kec8w8jx/8MYAXnPG7h4FTUcVcY9r0/pOS818rp\nn97T1U3oqXR5Tez21m7X7jQY7za3HdfWHJAnoNHv/eLcEgCIeWzZ1EI8eNtwuB02+EMintxxCDtq\nz6uPK6Upr//+W7pzWS0MGXbZ82LB5ho1j0V02ahffn08bFYLsjJsaPLLu40j8rJQVX0EL80rhchb\na89bGYPIOdwOG+rqW9Rdc6VWvVKTXoktnDQiFy/MKcHn/jAGZGeg0RcbI5idYUNY4qbXRLvLGBBE\nNHpDutjgX3y9GE6bNWY30hcSYp67cnYRcjIdyLCZ58MwuXddrlmg/bpNBl9IQEiQsHDLPuRlOdVy\nfrpY+V1H8fjMG+G0WZCVYTfV5bGnZ+BMgw95WU5s3/sZJl3fz7B+vHbnWzlWVTYaVdVHsGr2ODz7\nx78DkHWW63Hg1x+eiqnbPm3MAExb876quVc+OoUHbxtuqqvo47uXT9blf0nk2ir2wGW3mO6IdzGd\n0ohE5wfx9KuEUyi7uC6bJWYHUJvDwRcSIEgc2S47LjXLuVcUL4Zdhy/gm7eNgNsh93NvUDC8t899\ntQiixDE01w1/SATnHO5IriGAY75GI2XjBsa1c899tQi3P/ee+v9r8ydi5BM7IWi+nCl94sJVP2xW\nCzIdNnU3/Xf7zuKbt40Ah7ktNDq+cW4pPBm2uPkllAR2ZudsTx6CePZVuzPfjtjvtLC114KyAOwL\nici/LkPVC4AYe/rs/UXIdFrgD0vqtfxFRTFuK8xDplMekzf/5bSuROqkEblYXzkBWRl2jHpSr7FZ\nxQPx1KyxcDuthjqtKhut2klFs4rNfOhVY5uYl+VUS2Z+1uhD/ywnJKDNsUibF0Cx56kcu7qQbq1Z\n7byWc466eq86tj7/Xh3eOnQBR5+aAV8kF5u2HOpH35uCsNiq3cVTCjDvi8PhybDh/FU/sl02XPGG\nMbivSx4jHVY0BwV1M23X4QvqOL1qdhHCIsfgvi40eENYus04Nxug1/Khqrt19i3aVnsyrPjRf32q\n6wdK3rbo8tjROS/WVkRyr0VyFZnZ0vWVExASpO6W8yKhxqRL7zRqrOGqCmNsAYAFADB06NBUtokg\nOoxEdNsed1ajidiGygmqe3KiX1raCp+IdrOcNX6wLkxESXikTT6kJMhs/Rw2XPjcL7vcP1CMT043\n4vn36rD87lHYsf+s6vbnDwnwh0Q8/J81usnUwD4ZmD4mHy1BQTXkRoZ91exxcFgZFmuOaROBfXJa\nztb/0Ks16qRMWZDwBgUwAA++shevzZ9oek2UBGPrKsbDZbfGJGv8zn/KyRqVuE2FDLsVq3Yd1bk4\nKiEIMaEoXfyFM1Fbm2o3bKUsbk6mE5+cblQnxa16EfH93x0CAAgix9Jt++Lq8vjFFlRVH8G6imLM\nGj8I2Sb5KobkuGOOFeZ78Mx9Y+Gwyvek+sB5vHXoAo49PQPr3q3TTeZtFoZFUwp1lXVmjR+cVJZy\ns/wvZtdWsQdbP/5HzIJgmk5UOpT2zA/i6dcogWWO227YR0VRkss0b6tFfrYTy6eNwmO/1X8hdDks\nalK6XI/DNA76Xzd+jPxsJx6bfgMeef2ALsRDyYwP6PU3dfX/1X1hVBLZ2SxMfX9z92U5WaiST0Br\nS10OCxhjCWtWybshSTxuAr54/eDFSMWnZBMoxrOvCh2dQLgj6Kx5bUiUsEyjp2fvL8KqPx3Fqj8d\nVRMcK18CF00pxI9fl7/gX98vU91F1r627pJXta1KHosWg7DJi01BXG4JYojDbahTrTv/wD4u9W8j\n9/8hOW7kZzt1SUOVvpGTadyntPbSarUgN9MRN2y0u7rSdyap1Kx2Xpuf7cT3Z8pekNEhI3X1LSjs\n71HnY2srirFoSgEYY5rQvEw0eEN4eEuNbs5YXXsOs8YPxo79Z2PGSmW83lA5ASFRwvLtrfPNDZUl\n8GTYwJhxGEZBf4+cXyhKW1pb3c/jwJM7Dqt9R3mtYUhyZNFc+V+7SKOO6yZjSbbLDi5x1Y6mm927\nFjo7te5FJRwk8rs+cvwsgCGa5w0GcB4GcM5f5JyXcs5L8/LyUtpYgugoEtFte9y6jNzpHt6yD2BI\nKjmi1ySJoTcoxDymrQUfnfBI6xKneEAo56qrb1ETsh2vb8H2h/8ZT80ag8E5cl6M6/tlojkQBgew\nWJN0KC/LCYnLE+GvjB+MbX89EzeZ0fLtB+ANiYYJHZW2XPw8oD6+8LX9WLC5Br6QCMaYurPZEjC/\nJtqER/ES58Xcr4jLuOLKrYQgKPc4nWp4J2prU+2OqCSU07pPVh84j2lr3kflrz7Guat+VB84j0V3\nFmD59gNxdamU8VPc5a/6wvCGjO/zZ42+mGPeoIAhOW54QyJWTB+lO250Dn9IwPrKCRjc14WWoIAd\n+8/G7WvRx5VcGtHPNbu2ij2YNmZATB/tzsm5EqU98wMz/coebLEJzvyCZNhHtcnSFk4uiEl2uOKN\ng7p+bva+Zxp86jkeef1A1PvLlUuM2qo9V9WXb8LeJ6eCMWD/D+/C/185Aav+dNQ0WaiFMVzxhWPa\nvHz7AfhCYlKaVeyrojUzmxbvnFkZ9nZVfmjLviqkk50FOmdea5aYc9GdBag+cB5V1UdwpsEHf1jE\ntDED4NdcS6NEgNoxFWgd4zd9dCo2YXZFMSyMm7rAKxsdNw/TV3EyGoM/a/Rh6dSRBvatNuGxyGqV\nvfN6mit9Z5JKzWrntQsnFxhqb94Xh2PX4QtoCoR14cr+sKSzA96gPiG8MmdUxkijsVK11YzpQ6De\nOY6Ht9SgvikQZ8wXsb5ygunjLUEBHMDPHig2TDwqCJJaPtgbFJBhtcBqtcAT8aB86NUarH7nuG5M\nimdLtXY03ezetdDZixfVAOZF/p4H4Pea43MjVUf+GcDnnPMLndw2guhS2lO7Pd6uYTK1nN12K9ZH\nar0rWejXV06A3cIiidzGqe0ySyA0NNeNo0/NwOoHxsHjtOFSc1D3pXHPicvq7s7gvhkY1MeNh15t\nzVHR6Ashw2ZBptOmqy//43vHoLr2nBpXOGv8YNXgm7XFaNdcWRFfOVteVf9oxZ3qeZRr5nZY1ff2\nOG14YU4Jlk0t1OUxONPg1Z3XZzJwaOOstdc52Xuc7ph9JpfNknBN8Xj1xxWNayscaLPZZ2fYUDZu\noE4LZeMGYtfSO7DlWxPRz+PE0aemo6pstK6Mn6ITl81qmK8iL8sRc+xSc0CNs3bYLFg2tVDVRMw5\nKooRjIS6jHxSjkOdNX4wXHE0EH28r9uOdSb5UwzvReRaJZrki4jTJ5P0KNLutJldf8WTTZI4LAwG\n97YYa945FvccSi4M7Wu0Gv6Pstbyz4ptDQsS/uPe0XBEJsHP3DcWR5+agWfuG4s+LjskDgzNdevs\n7q6ldyA/24lMpw2ZJrbwUnPAsO9c57IjwxZ/eumyGVSFinyWaOLZh4TuZTe2r1oSvQ4KoiihORCG\nxLmpngv6e7BsaiE2zCnB0Fw33A4rBvXJQIbdot7beAkQtToc3NeFoiHXIcflwAtzSnD0qRmoKhuN\nbR+fQYbdBpedxVSpWTm7COt316l/Z0Ts6srZRdh3pjFGI9kuG4bmGnukZTptpvff6Nr1dL10B7Qa\nbQ6E1TxrbdnSLJcNFbcMVT1by8YNRFXZaNk+c+CleaWo/eFdMdpVzhf9O/r8mQ4bPCaeOfnXZeDX\nH57CuvJi3Zx5XXkxntxxSF58ZTBexIMchj3qyZ3YdfgCfnzvGJz8yUy8MKdEziHk0+eea/SFIAiS\nnEzWpA+77MZzmJ6s45SFjTDGtgKYDKAfY+wsgH8H8FMArzPG/g3AGQCzI09/C8BMAHUAfAAeTFW7\nCCJdaY87q7JzEO0GfKbBh6mr/2/C7uKMMYSjar2vrSjG5r+cRsXEoXDamVpdpDlgXAv7+MUWTFvz\nPgA5J4YSv3e8vgXv/v0iptyQr7rm7fvBXepqOgC1pNP6ygkQOGLqy6+aPQ73lwzGP13nwmeNPqyY\nPgrVB86bViYx2jX3h0Q899UiPPfHo7jUHMQz943F8rvl3fNLzfLunIXFvrfiiugLibjUHMA9v/hI\nd16LhWFdRXFMVQujLznp6LJ8rRh9pni5AaI/a1sx6IrGlUWH6Gz2SniIkqTLKCeGUipX66ap7IJY\nGHTVdOrqW7Dtr2fw4G3D1WO+kIBLzQFM+Zmsb2Wn74U5JRA5x40Dr0MwLGLjvFKAQ62o8+sPT+k0\nvuKNg3hxbompBoyOA0hYL8q1MusXvpDYE2K5OxSz6+4Lm4RYmFxDbZWheNffbbfqXKKfuW8shua6\ncfHzAEKChItNQQDmVZd8QTGSW8CGzxrlyk82W6srPKDPUaS1rY3+EOwWIDeSbLl/lhMtIQFLttZi\n1eyiGNu3cnYRLjUHcetP39XZQm9QxCsfncLcLw7D5r+cjuk7c784DILE4XEyU61aLAxOq0UdVz5r\n9MFptSRtHxK5l93Zviokm6tDm9shP9uJ/7h3jKGe/CEB5ROH4uFX9WGgq3YcxrTR+XhxbolpBS1/\nSETtD++GNyTg6T98iotNQawtL4ZfEGNyVew5KVf4euJ3h1XNezW70HX1LerYvKGyBAFBxO2FeWgO\nCDqNOKxWXPw8YNgeb1AwtaFm166n6qU7oNWodqx2O6xt2tIzDT44bBZcbAqoiVeVMV8bTlxVNlr3\nemWeoJw3nq0WJY7FUwp04aDKe9dd8iIkct2cedXscRjRLxP+kIiQIMXYN4fVgpcjcwIl/FoJZ7l5\nWA5enFtiOC9W8gBFfxalPYFIXgtt+HN7wu66E6msNlLBOR/AObdzzgdzzl/inDdwzr/EOS+M/G6M\nPJdzzhdxzq/nnI9tK1EnQfRUknXrMto5WDm7CKvfPpaUu7iRS+mSrbJrncNmxXf+sxaTV+3G9d9/\nCz/8/ZEY1+N1FcXwOK2qJ8O6d+vU0mMnLzVj1vhBGNTXhaqy0Zg5doC6Gq7skJ/4yUxUlY1GVoYN\nzX5j9+WwyHW73rOKB2LX4QuGO9593HbdsWfvL8LLH56ELySqeS+G5Lix4g05rGBdRXFkdwYx771k\nay38IQmZDhuyMhwxuzQZNityM53YOK8Ux56egY3zSpGb6Yw7ae8prnsK0Z/JL0gJ1xRvq/64VuNv\nHbqAsMhjtPro9oPIzXRgTXkxlt0V61K8ZFst5t06PEYTmz46BbfDqku0+fx7dap+lSoPLrsVd//8\nA127lZ2+sCjBFxSw8f2TaGwJ6bKWa72EtK8x04DR8WT0olyrXYcvxOxw0o6iOUbXONldWbuFqbZo\n/e7YiklqxSGN3nfUnsfkVbvxrxs/RlNAwLN/PKret/85eRkb5pSoXhDKbvSTOw5h4ZZ9aPSG4MmQ\n2yJxDr8g2yizOP5slx1LttbCarGqO3v1zUHVpVrisbbv0e0HERIknS2sq/fKO9zv1iHbZce6d+t0\nYRrK8UynLe644wuLeHjLPnVcmbxqNx7esi/mNW3Zh0TuZU8g2esQHca06aNTMTZBDudgMdXClPDO\nnYcvoqElBLdD7yWjeGq4HFZcbgnCAkDirV+24uUzudQchIUxXLjqh9thw4Qfva1qR5uTKhzJIbMw\nSiMuhxXP/vHvBvZN9toxuv/xrl1P1Ut3wCgcacm2WlgYU22vkS199n55jvvI6wdgYUwXyjxz7ADM\n/Y6RowoAACAASURBVOIw9bxaj02bhcFhs2Dl7CJ1jDQaK9VqOFtqUH7LUCybWqjz9rRaGB6dNiom\nTHX59gP4xq3D8f3fHYLFwmLs28It+zBtjFxIMzr8Oi/LCQaGLd+aqAsnUfqN0WfRjitKKJSFsXaH\n3XUnaAuGILoxMTtNQbnCQnQioLbcxbXhEsqXuP85eRmD+rjgdlrx3FeLYGHAP10nZ87/86cXsXFu\nCdyRyUggLGfV//G9YzBhaB/sOnJRzUj+jVuHw+OUPTBOXmrGj+8dI8eTTymISZS0tqIY+ddlxLRl\n/e46DMlxq0beGxSx+mvF8AYF2C1MTVDaHBCQGfmsuqRtfzqKtw5dwKIphQAipeACYWz51kT4QyJs\nFvlaxstf0VZCTWU3lna2k0viGe+5iqtkptOq3mPl8ejnO+1W/PVUA+4Y1T/m8fxsJ6yMYcu3blEr\ncJy74sd1bjsavCHDZGDeoKCGmiy6s8Bkd0ZArtsBZmGYNmaALvFsXX0Lduw/q8aUt74mdd4Pij34\n5u0j4LJbYirj0MQ8cZLdxXfYrfjbqQa8MEdO6OYPierf3qAAl03OCp/ptKGqbLSaE0ipmOAPiVj9\nwDh87g+przvT4MO///6wuqP91qELavWcxVtr8dxXi9DXbcXIJ3Zi8ZQCfOPW4WAMpkk5q8pGw5Mh\nh4BkOq26RJwD+7gM+5WSQFH5vzDfo+7EK6Wmo9+rOSAnPvZk2NAcCBvuAiZqI1KdELi7cK1hTPe8\nW4e6S17VPp2/6ofHYUOGw6rqURtSV9A/E0/ec6MuOfaGOSXwRMbZK94QPE4bXA4rMhxWrCkvxk++\nMhaf+0MImHiEeoOCOpcY0MeFC1f9eGfZv2BIjltdOL7UHJQ9MrhxIvG6+hZcbAqqSUaVaiOKB1JH\nXDuiczBb5MpwWJFht+Klb5RCkgCXQx7LlIpySvinzcLk+eJ18hj/wWN3YlBfF3xBUU1sXH3gPCYM\n7YP1lRPkBJac4/zVAL79pUJ4gwIWTSmAPyTipXmlyHDI1XCe+2NreOmSbbXYOLcE/sgGnzJPMEvo\n7smwofrAeawpLzYNtQIQE+a6/O5RumTJSpJ5pT8o8+HzV/1YNbsIA/q4EAiLkCQATK6apSysaytk\n9dRxn2bZBNHNUXYOAAAMqtuxQiJfmAJhUecyrLjdaY3pytlFeOT1WlxsCmLl7CJwAL/88/HYBYhy\nuYpD7ZkrqLhlKBq9IWQ6bXDaLJh0fT9s+sspFA25Dt+4dbjOtVTZ2ds4t8TQffl/P/fHuAeqLq67\njuJiUxDryosBDmS55Pf77m9qdaWo6upb1HjAzX85rcnYXAyHrW1Xce21pkUKc8zCmYx0aPbcloCg\nVoRR3HyVAdn4+WHcNPA6tR67tkTZ8mmj8PKHJw0Xy7Z9fCYmtOOFOSWwWZgaBgXIiQ6jw4kyHTa1\nBO6Qvi5UTByqm+AoVXKUig9ry4vhaiMXwLWi1WhWhvxepNX2kUx/D4REFPTP0ml25ewi/PD3hzGi\nX2ZMVaR15cUIiRzLt7dWf1g1exycdqY7x5pIydCzV/y466Z8VP3X3wC0LizU1bdg5tgBqLhlKLxB\nAfnXZahleRX7tr5yApqDQswiXZ9Mu9pXzMIBmwNh3f/KovTa8mLsOXEZa8uLY9y+rQyYr6lMYVT2\nNFEbkYwt6ckkex2UBaa8LKea+LL6wHlUHziPsnED8dj0Ufi3TfovSxOG9sGk6/upoXJKcmwAWP3O\ncew52YiX5pWiJVLmV6mqs2CzPoQvLEpYXzkBC7fs02nAbmFgDFi+/SBWzS4CYwyPv6kfyz1OG0TO\n8dhvDxq6ye86fAFryouxdFst7ln3gTo2ZMTxKiMNpSdm4UjeoIBMhw3eoKgL9VESwWs3A7xBAVYL\nM5wzKilhptyQj4Vb9qmVS6KrQO3YfxblE4fCYbMYVsOxMAZvUMSWb01UF9mi5xna9pz4yUzTz9YS\nFLBsaqHaJ/ecbNB5YQCt85Bn7hsLt8MKu4XpbPfK2UUIhcVIUml9uLLDasHDmn7XU6uMMc7bTuaX\nrqSqHjYADPveH1Jy3mvl9E/v6eom9FS6dU1shWTjYpVa2uBcrbJRNm4gfnTvaLXmtTJYRNdjf23+\nRLXspNZATxqRizVfK4YgSWCM6Ur9/eyBceCc49Zn38PJZ2Zi5BP6evA2C0PtD+9S26I953NfLYIv\nJOLkpWZMur4fsl12NPnD2HPiMkbkZeH59+rw2PTYAUxZ2FBqYzcHBMMa9RvnlUbi0YMxA4JRGIgo\nSvCFxWuKMdTWMm/HKnnaajYZHUY/V9lBVrx1FP0tm1qIB28bjkynDc0BAZsiX85+UVGMWwvz4HHa\n0OQPo/bMFYzIy1IXKZR67FVlow21qmhawWZhOPb0DARCom5yv75yAmwWBnekprxyrwKCPImQOOAP\niTG7iPIut02tH//N20eYTpg7QlPJcI36ay+dottU2Vmza9YSCBvaraqy0QAQo73dyyfj8TcPxTz/\nmfvGYvKq3THnUMr7/vi/P1XtseJZ4w+JaA4I+O5vanWLa7mZDniDIkKihO/85/6Y93pxbgk+j4Tp\nvfpvt+D81UBMycCBfTJwww/+qPu/4ImdWDa1EHO/OAwehw0hUYIUlesl2r6+OLcEjDH1ekmSnKDv\nii+sxoT3ddtl1+fI48p1ji4PuKFyAqwWS1LlwK+RLre18WwqELvbyrn8fF9IVMtCam1ide05Xbjc\nyUvNKBmWgyVRi6/aJMfy+Hw35m/eiz0nG7Br6R2GNvWZ+8ain8cBDqi2zBsMw2W34+Etch/54LE7\n8dhvDxpqcoFmLhK9WfGzB8bh7b/9L74yfhAYY+r5XTarqeeFfO0SG9dTQU+2s0D7ba1ZzovcTAf8\ngoT5m/YaakvJ56Y+Nyypmox+blCQVI2a6VWxry/MKYnJ1fLuI3cgLysDmZH5xZ4TlzF6YB+8d/Qi\nZhUP0tmvPm47bBaG4h+9jU+enKqGIiljf/nEochxO+ALi3jlw1Nqn9zyrYkY9WTsXPjY0zMghEXM\neyX2s22YU4KHX40db4zGj43zSg3nHDpdBkVYLHK56S722EjoTWnJkSB6EMm4OmsnQ1u+NVHNQfGD\ne27EVV8YWRl2OG0W/OCeGwEAbx26oLq85Wc74Q0KKMw3ztTcL8sJf0jQTeb3nGzAI68fwMa5Jdi1\n9A6cu+LH4ikFugnUrsMX4DZxJRzYxwUucfR122N2dHIyHXhq1pgYT45Htx/ExrmlkDjHKx+dwsnL\nXqz+WjHWvVsXc34lS7XDIMlSNPEG3US/bCa70NSdSEaH2ue67BY0eEO6nWdlR3DKDflYsFm/I/1/\nJheg0RfCQ1G7vJ4MW0yYiVlWcUXTCjcPy8Gl5iD6eZyqW7I3GF1bvRgOK0OzX4A3KH85qJg4VJe8\nS47LPYpMpw3Xf/8tAPKE5NtfKjS8Zh2hqWToyfpLFfGumZndUvQV/diQHOOKCWaVkpRwuTXlxfjR\nvaNhszC8/KGsSWWBLtqLTZmUm7k4Zzpt+MGOw6gqG41AWNKFPTUHwmp5vr//eDpOXPJix/6zasz2\ndW47rBYGZgFa/ELMl966S15dGEKm04Zf/vm4vDCZYUNAkBdVtH1mXUUxgEhf8IV051SShQYFSd5x\n7AW7i1riJfSNl4yyXxbDuqiQEYDHeKFtqCxRFxbKxg3EojsLMKivC0/NGoPVD4zDiUveyPjcdiWI\nwX1duOIL6RYLVs0eB09G62vNwpS0oQSKfpQqZWcafHA7rPjXW76ARl+srcxxOwwXMDjnsGvG9fqm\nANwOm87dPlXaITtrjtXammg4esHexfShv8+/V4e3Dl3A0Fw3jj09Q/dct4UZamlorlv9GzDXq3Lc\nk2HDs/cXqf3iFxXF8DjtunnH2vJi1PyjEdPHDEDQwH5lZTgwc+wACKKk0//aimLUnG7Ed7bW4uhT\nM9TcQOsrJ8Afx/vUk2HD+soJ2LH/HPaduaqGGTJmHD47NNeNsnEDdbbXKDzKSJc6L+Y012jPzuhB\nEL0EbRkwXzjyRbGNBFTaJFYtERe3FdNHqQZZSY7JLMCPZ43B0admoDkQRtWXb4q4itbg+EXjuu3N\ngbDpZN7ttKGP246+bjvKbxmKquojGPXkTlRVH0F5JMzE7Jx+wTjBk5LHwPj9rGpt7B21503b7A0K\nahK51W8fQ119C4bkuHHFF0ZAiE0iZ9iONhKjml3/ZJKrdheSSYSmPNcflmKSx6144yDuLxmsfrFS\nyu/95q9n4DPRQ1jkKP7Rn/DLPx9X3TOVrOJalAlCdBK781d9qKuXq+d81ugzqK1ei4AgT06G5Lgx\nbcyAmCSiK944iKVTR6KuvkX3ft6gYHgNOkJTydDT9ZcK4l0zxU1Yi6I7I+191ugzfL5RpaTLzUEs\nv3sUHn/zEEY+IZfd9YVEZLvsECRuuhBS0N+DPScbVBfn6PM2+cO42BTEtDXv47c1n6H8lqHYdfgC\nzl3xY+GWfbjhB3/EQ6/W4PzVAHYdvoBZ4wfj+ffqUPXlmzBjzAAs2FyDunqvYZ9ddGeB7r18QQGz\nxg9Wy2PXNwVj+szirbIt94bEmHMu2VoLf1j28Ih9Xe/QbbLJKK1WC3whEYunFKhfeurqW+ANiTFJ\njbVJtFdMv0Edlx96tQYXm4LyzvEtQ3WlwU1tasSdPTqZoRK+AUANU4p5bUDfj6oPnEdV9REcv9ii\nJj00mwf4BfPkpUriz+/+Rh4fvrVJTqw8f9NeNHhDCZWUbw9kZ+NjlGhS+WKtnRsuv3sUFk8pgDco\nwMIYMh1yYnCJc1Pbq+SLOPb0DByumqbOc6Ofp+i4yR/WzTNuLcgz1Nmk6/sh02GLsVGK/Vp0Z4Fh\nAvwReVmYOXYAmgNhLJ5SoIazPLnjUExS0rXlxXjlo1OqvZ81fhAen9naL81s+pkGH5bfPUpN+Gk2\n5zDSpZKotztolBYvCKKboxj6+W0MxtF1zpUkVmXjBsJuYXhhbgmuczl0GefzspwIhCU8/Kpcl3rh\nln2YOXYA3qw5a5r9+Nn7i5AVSVJnZlzllWCmDgwzxw5AVdlo5HqccNmt+NkD42LO+cPfHzFdoPBk\n2EwnUr6giA2VJaj68k0AENPmZVMLsaFSXvkHB6aPycfyu0epg8Tjbx6SwwI01zNeNvVEoSRiscS7\nJrPGD9ZNZmaNH2xahz0rwwZB4pg2ZgBeiWTZN8oq/uz9RfidZrKyvnICtn18BoP6uHH9/2Pv2+Oj\nqM/1n9nZnb0GAiHEBIwQElCBZCEoRdAKxUb0mFJoIKlc1IqXQ0WKKMdK2xwFOQqmwKk/UKyXSAWk\nKsYjiKBSy6VUAhsuUiBcDJA0hISQ7G12Z3Z+f8x+v5nZmQnhapB9Px8/YNjMzM4888473/d5n6eL\nE5tm3onrO+t3CDvYLSjO74vqxoBhRyc9yYEjdc34+zPDcWTePVg2aRBYhoEoRlT3YiQiXRJMnU9c\nKvzF5pXL9RLwfYXy+5H8QByS1k+/AykdrHBwLBwcq6tav2RTpa4rUieHReXgMCQjCX8c70aCzazZ\nhtNq1nXQGTOwOw6/eA/8IeOiHAAWbjyIxTH7kme9T9JjHtKrC7U5jd3XrA9248FhPbFm1wms3VOD\n0QO60dzdWjdTWYj7ePULs9GCi8PKGi9ER89zPG+2ROz5yM9JQ3F+Xyp4bGNNmiaBXt4kz08jF4Wf\nD+yOJ1d6AAB/HG/sqjO/IBsd7BbDXPbnyYOw83d3oYPdgqUTcjFjZJYKk+Q+mjEyi95nSyfkYtvh\n06rtnE+uVH4+1uXhcr+oxfF6fhGJSPBF7Ztjc9ADQ3uCi47ogQGaA2HUNMpuYEsmDMSmmXfi8Iv3\nYNPMO7FsUi5MDKiz0pTSHQiLoibnktpgUZEbLqsZhYNb7hWyoKcM8uw3End3Ws2GjOTMri7M/Gkf\nlG49hsm39aQLJQt+kYOOdgv+MmUwdv3+Lrz1wCCs/GeVqmHS6A9jxqqW+7Jkw0FDFxayeKy8p2LD\nCJdKpmB7xmh8bCQe8bjKQ7mCCoA+jJVzbnoUsdcm5uKWHp3xh/tugpcX8ORKDx0fITF1eCZdzCDb\nJt7ZJRsPaWidzQEBWw/XIRBOwJG6Zl0xt3V7azBxSA9IEujiSexc659+OQAv/yIb3TrZcfJMACZG\nLpiUIkckyIo5WZSIFQ+dvWYPVesHgOJPvkVmshNLJgyEy2pGvS+k8tqOnSUkK+rK89ma0FSCzdK2\n6xYXEdPMXEYkyeC8irqCVq9PyjW8DoBaZX/q8EykJdqo6rhSNyN2vnvbkQYsmTAQz364B4sK3bpe\n74dqoxoWwzKMxbmCAob06qIacyKidGrB2AGwWUwXjanzCT9vgD9ehMvWNvz9UCnRBJdkjImMLxAh\nY2VOm1+QjWBYhBiRVGMXPl5AICzilXFuHG/ww2YxUZeP5qDsOiLxgmpEjWUAQKI/qzzlxcufHcAf\nx+sr17tsZnx98BTc13fSCMqWjM/BvLX/AiCLOFstrEqx/8t/1WJIry7o3ske/TmLe7+sxNQRWYZF\n+djc7pg6IktFWSYvvdo8Jnc85W7mSUwc0kO1XcPf40WcbAwY5kby92s5byqDMCvy+qWiV7IT9b4Q\npq9U09WVwptKJk6sEOaiIje6uKy6179bJ3kR12ZmIURaMHray1N3HD8vwsSAsjO011ZAY1RjRTX+\nNzxTNZa07fBpzX320ths7KxqpN3088mVys8bLbZdrhe1+HO+7UGeJ52dnH6+s5px2str8u9ftn+H\nu26+TjXC8drEXMp+AGTcP/GeBwsL3Vg6MZc22BwcKz/DQwJmrq5ARhennKc5tlWcGT0/q+r94IWI\n7r81B8O0jvnP4bLb3r7qRnRyWDR1cvb1HVXfP3axt6yiGiYGWDZpEOwcq3FhyUpxoTi/L9bsOhHV\n2VJzFYxwSRa82ztG48yLeMTjKg3SEWzLyj5Z4EhOsOLTabdj+cODIQFYOmEgODNLu2ix7IW26AQQ\nWmdNYwBn/CHk9UuFg2OR3T0RK/9ZpaL6r/xnFX7cuyuq6v10IUKvE/Lr93YBAGoaA1SdvM/sddhS\nWafpYC4qdGPNrpMAAKvFhGWTBuHg3FF4+RfZ6OzksMZTTRddRg/ohiEZSZh0Ww8EQrLHeyAkIjnB\n2kKfbaXrR7rKDgurexyOVhTPY8NhYamXeaxn97UQRLDvdDMPSQJOe3lIkGhXj5yThVENC71rYje4\nDiwjvzgTPJdVVCNv4dfIjFIwq+rlF8V8dzccmHM3lkwYiG6d7Jg6PBPF992M4vy+dB61/LsGTLkj\nA57f34Uj8+6B5/d34fVJuegVdZCYUroDH+06odvReXvLUTT6wxpqZqM/jAeG9sTBuTLbY8exelhM\nzEVjipzXtjAhTCbodk1N51EV+MMiVmz/TnWPr9j+Xbumm+qF6pwFBTQHw5jyzg7NSERev1QNjfjp\n1buB6Cn+9U+yYDWb0ODj6bUHAF6IYEvlabAmBjVng3h8+U4crvNRKnuv367FnQs2YdpKD6xmM0aW\n/A29frsWeQu/RllFdasjT7k3dMbjy3fi5c8O0Oswb0x/JFjNWLunhjI6zAwDBycLEq/fW4MRN6ag\nuGwfes9eh0dKy+VFqBGZhvuqa+aRYLeAYeSXwf+N6lPose8WFbXcgyaGwaj+qXSum8SrX2m79ouL\nBsBkgi5LalGRfC9c63kzNuzmFmbF4TofpsfS3Fd48NCwDFms8L6bsX76Hejeya7JV4W3pqOj1Uxf\nypRBFpVu6dEZ3pCgwu3gF7/Ao++W4+SZABp8IfzqnR34YOcJTS6bX5ANE8OomJ3bjtRj+koPguEI\nOtjMeOj2DGR2damYPcrO+4y7elPXEr1cabewujlP+bw2ZGiGLhPzIo7XNgepU42uUVMwrJt/f+bu\nhqfeV7OFjNg5XVxWmZkZFhEMi3j1y0qcbAygi8uK53/WDw/fngFRkhAIi9h8SL/erKr3Ye7a/Zoc\n9cq4HJRsOKifEwvdtLbMz0mDPzq6NaRXF93RlIHp5x4zrG3iIUQimPDGdvqsIOeKNFceGNqTsrCU\n94WMS7fm+JdsqrwqMNo+l1TiEY94tBrKjqeenVjsqqmDY5HSwYoZd6kZDgsKcpCaaKNJPpa9QBKm\n3srzkIyklm5r1PZPaXu2uMiNI6d9GieHX/8kC9NXepCZ7MTCwtY7PSfPBFSK5I//ZReW3D+Adnp8\nvIDNh+rgOd6IZ+7uo+o4zS/IRm1TULXNDnYLFha6ERIjmKFwQSGe2soXBb0VdafVTAUUjYSm2hrn\nI2r5Q4ygIKI5arlHrsMr43LQwWbGvDH9kZ7kgDcod6+NOnnBsAir2UTZFE2BMBgAnNkEs4nB+r01\nWFzk1tiXlmw4gJLxbrgEUdVZJ12Plf+swr2LK2nnkhciGpFYXojQomPbkXrcP/gGlcDYgs8PYO2e\nGkwdoRbo/OZYiyhj7+fW0e2ZLwGmzocJYbOwWLD+gPqY18vnpa1ht5g0wn8vjc2G3XL19EWMhMuS\nE6yaxVu9xdyUDlb4QmrLuqUTBmJs7vUqG9RFhW5IkkRf3gw7wFbWsCsei9MtlXXI65eKb441QIhI\nKleIg3NH4cCcUQiEBAgRiTrnTBuRiQeG9cSjpVqbamqxGnPPkPweK4y75P4BeGKFnMtJTq49GwTH\nmlROPfMLsvHBzuMqdkhdMw+X1Yxlk3LhiAqDkmK5aPANdFGMsFicXEtuvJbzZmwEFHnICFN2jqWa\nFU+u9CClgxWz/+MmtTC12QSWNUGQIhoWz/yCbPhCAl4am40ONv2RkG6d7Lh/2XYkJ1gxvE8KbVxk\ndnXBGxSwxnNCw75RHt+U0h2YX5CN2esP4JVx+kyj9CQHIqIEE8tg3Y7jNO97gwK2VNbhiRUe3Zyn\nfF47OFZzL13OF7Vr/Tl/rlCyL0kjTo9F+9LYbMPGkt6YklEdd7zBj85ODj5ewOodx7X26YVucGYT\nHFYznljhwex7b1LVFy6rmbIkMpOdWDoxFy6rGccb/LBEr2lZRTUGpieqnMbKv2vAsKxkTBshMy7I\n4orRiJUrOj5IjivRYUHJ+BzMWKWuW8non/I7LC5yo5ODQ+HgdI3dvPK+iBWnd3DyyHYgHGn3GI0v\nXsQjHldhKEdF9BJ97MPYHxIxfWRvDfV+5uoKvKag3pPil7w81jQG8KdfDkBzUKAJzsnJ3S9lYg4K\nETzz192U2ZHZ1YXjDX48e89NWOOppsdxS4/OqGviKbXtP4dn0nltPfowoaoqg6g1379sO/70SzeG\n9OqC27O6qqyyyIr8y7/IVm3TGxTQ6A+r7LJIV6c4vy/KKqpVPvJKauvcT/ejrpmn4yMsa0JC9MXS\nyZnlB7CJOa/ihAiwAWi39LzLFZEINCNJT71fgZJxOeCFCIIhEU1BmWK8oCCbFtQpHayYPrI30pMc\n8PECXFYzmqNdBSIsN3loTyowq3xAk0WFumZeXhgJRVSLY7FjUeSlbt6Y/prPxDo4HK7z6dqw6Qkw\nHm+QqaXKTsvrk3KRYLNQTF3IqEhbRsjoZ0MiFWtUHu/5UEX9IeNxngTb1bGAoXfOnl4t54PYAliv\nIJ4+sreGnnzGH8azH+7R5MMuCS0LtUbFdXNQ0CweFA5Ox7cnz9Iimizarttbi2FZyfjXC3fDG6XM\nE9empkAYjy/fidcn5eLx5Tvpfko2HjIcDXFZzcjrlwonZ1bdMyS/x94Dr0/KpaMhWyrrMDQzGU1B\nATOiXdDY8/nyZwfos+VQrRelW4/hwWE9qQAliSQnh4duz6Ave06ORUCIqPLrtZo3Y4M0JtZPv8PQ\ntaDylJeyhpITZBelBJsF9V4vfrPKQ613l0wYiNKtx1B0a7pqLMRuYaNjIQKaDUdCRHxzrAGfTrud\n5gQyakcsHJsCYcPjIzhZUJCN5mAYB+aMUlm1KxsyXl7AZ3tr8YeybzX2l0Y5T/m87uK0XtHFhGv5\nOd9axC4cb5zxY8qUBFrcRvwh2X558tCeuvhR4oo45fRKdmoWqeYXZMPKmvD2lqP49U+ykNcvFbM+\n0NatElrGKos/+RbFn3wLIGo7OmkQbTBU1fvxuzV76fES29WyimoM6dVFY7v67X/nYfLQnnjs3XIU\n5/fFtBGZ8AYFXaz7eIEuCHuDAj7ceQI7qxrpiPah2pYxEeXPq+r9cHJmBIUW8XNAe18QcfrYesXI\nVrW9Rfs/wnjEIx6aUI6KxOpO6D2MHRYW6UkOWuSQTuuSTZVwmFmVNkVdMw8Hx+LdbcdQ/l0jZt19\no6Y7fjYYgtXMgmEAq9mEtES7LrNjcZEbM0Zm0dl+mToqi4plJjujBbdZo41BPmekcXG8wY/FRW4E\nwxFs3F9j2NHp1smOI/PuQVMgDLOJgc3MtiqmNCQjCaMHdMe6PTWqjnQXF4epwzPl88WxsqtLG6zq\n2vPK9fcdRoJXKR1tmPH+diybNIgubqR0sKG2KYg/Tx6k6XLL3YcTGD2gO9bvrcGY3O4IhUX8JlpU\nLLl/AHJv6IwJb2xXdVc+2nUCE36kjxvlWBTp+MXaj8XOvL76VSVeGZeDpxSMnpLxOeBYk6qDQgqo\nkg0HVfu4FMKc5yMOR+jMsbg9nw7klRYZvRzRmnDZb1Z5VAvDRHhTmavSk7TCk9d3dujmQ6IzRBad\nFxTkqNgZi4vcgCRhxXZF15oXYGUZPFxaLrMmolaj2d0Tkd29E97afFS3e3j0tBcvjZXFPlM6WLFl\n1nB0tHNwWFk0BwR9HZeoww7RISL3zIE5owyvM2UPRcc6WsuvZRXVWLunBgfmjELewq8NrYOVL3sO\nCxvPr61EMCxiZl4furAby5ogWj5/HO+mmFSyyAjr0MQAZpMJU0dkIRASYOdYSJJMM1d1bwvdmg7w\nK+NycDYgu4S1Jl5cuvWYbkd9wecHAMgsJs5s0hxfZrITk27rAUgSFc5dOmEgHlu+s1UNCyMbDYQ0\nEQAAIABJREFU1PhiQvuI2IVjIkL59OrdWLunBnXNPF4am41unWzI65eKEw0+VdOMPO8/9pzEK+Ny\n8Ncok4Jop/RKduK1STIzws+LMJuAOm8IU0dkwccLyOrqMqxbzdGxylgGkihFwDLyItjIkr9BiI5i\nkEWTrBQX1k+/A72SnRpcAkBCdASW6LrE2sJnJsvjqFX1PtyY2hG9n1uHg3NHYf2+WlqfL52Yq1qw\nK6uoRl0zj+L8vrh38d9xcO4oANCv96O1wNUuJBu/a+MRj6swYsV2SPIyWjU1mRj4eYEWOcpkHBQi\ncHCsihonRiL4bG8tivP7UuVxoKU7vnRiLqYoKMSvTczVZXZMW+HB0om5mDoii4rP1TXzWFjoBgPQ\nImXaiEwV/c4efYHiWEbzsrC4SJ5tnb1mLzK6kESvP95SVe/HyJK/0YLexDA469Xv/gRCIu08KYt6\nsppeXLYP8wuyUdfMY+j/fEmLaLuFbXO3+1oPJUXUSAyrqt6PbUfq6eJGfk4a6n0hzIx2b41YM7M+\n2I0lEwbi9x/vow/yumYeuTd0RmcHR7sYTQGZjfHZ3lr8KKOL7jF4gwJdrCDHNPOnfQCA/gwMVNjM\nTHbCwbEqGqaZYfBJRTVKxuUgpaONCtq98fcjdCGE7PNSCHOejzjcpaAz/xDE6IyE1443+LF2Tw2l\nBicoxtSUdPjas0HN7x9v8Ovmw7e3HKXdQBMDWC2MCi8MA6zxnES+uxvSkxzyCIbZBIuJxb9euBsN\n/hAtdjfO+DGe/bAF+3qsCJZhEAyJmP0fNyEYjmBK6Q7VAgcAurC8qMiNldurAEDDwDNaRCZdQ1ls\nlMHMv1Zg+sjeht11vb+fC/fnwya6VkKZR8WIpGKwRaSW61ZV76fd2anDM3UxOesDmaHIMFDhY2Gh\nG806LJoVUUeaWLq5y2rGoiK34ajpyTMBFH/yLXZWNeoeH6DPYiJMrkBYxOPL1XXAnx8YZMg2Icyj\n+EJX+43YF2g9Eco1u05g0m098OpXlZh9702ql/1FhW5YzCZM+NENaA4KmHRbD5RuPaZZzF1Y6MbO\n7xow8IbOeOavu9tUt74+KRd2i/p5brOYIEmAjTOpcqKe6PyiIq3QdzMvj/Dd0qOzSuuC7JdgvfJU\nM5ITbPj32QBlK5NnzvEGP6wsoztCsuDzAxT7PqN6PyzCwZkviWD39xlXB68zHvGIhyouRARKVMxb\nExtUQZTgsLKwmBiwZD7UxMDCmrBsUq5hF81lNasEht7eclS3A/nNMdm2Uik+982xBtkjWyFSVLLx\nEB57txyBqGgWa2LkThBrAmeWaf9EiI5hgN9+tBdrPNWUCluy4aCuTWHJhoMqar4Y7dzoCRWeDYRQ\nuvUYCm9N1wgvvvpVJaW1hhR0/2krdhkyCK6WFewrFbGWvm9tPqoRjVtc5MbCjTIjoVkh6Dr9HDPd\n5OcJNgumDs/E8ocHo1uiHX+ePAgOzoz/t6kSA1/YgPuXbUdIjIBjTVhU5KYjQrHX+52tR/Hiz/tj\n08w75U5jjP3YokI3AiFBJUg76bYeugKMd/bpirAoYfpKD6aU7oAoSRiT212DPyJweDFxvnmBdCAJ\nbf98C/wfghidnnDpoiI37ByLA3NGId/dDYh2e53ROei8hV+j8pQXjy0vx7x1/9LkHifH6ubDxV9W\nIsnJYd6Y/pgzuj+eeM+jwssT73kwekB3XN9ZfrFLsJkRiUiwWkyobQrCz4tY/vBgfDrtdqo+b9x5\nNuNX78h48wZFjVDikys9eHBYi2hsXVMQk27rQe0GZ9zVG4kOC9bvrZEFaXWE6z7ceYIe+6/f24XH\n78zUtfAjlrGxf2+LIO3V3iG81BGbRx2cWfMCOLLkb5Ak+dle18xT/R+jZ3S3TnZ8WK62jE5yckjp\naNN8Pq9fqibPPb58J4LhCBycGR1sZpUQ4IyRWXhtYi66JdrlLnB0kTcUFmFiGHp8QzKSDI/PaTVr\nrDOnrfAgIgF2zqRrgU1qlMtpgxqPiwt/SCsOW9vE47SXp7a+hbem42PPScy4q7eusOWpJh6BkKxJ\nlWCz0FEQ5eemr/RgWFYyurjkkal7+qees251cmYAEhIdskhxZyeHRBuHRn8YtU1BlTW2nuj8kys8\nmDy0pwqX8gu3hEWF7lbztoOzILWjHS6bBYsL3Zi7dj/yFn6NXr9di5Elf4PFLIvHLy5y4+BcuS4u\n2SA3BomAfUSCJuc/vXo3IhF5X+cr2N3eLNHb//JKPOIRD03Edk2DYVFOStFRC5NJFuRTdlMJfZjQ\nyLxBAe9sPYqODgtGRRcByArtkgkDERREnPaGdFdn/302oKGjGdujqQuHW3p0Nn7ht7LoLHFo9PNg\nnVZwFnlRJTnBCoYBklwcyr9rwNThmfjjeDcCIREpHaxUV4OsTgdCImav2aPqbpMiyMGZ8dT7Hl2h\nwgeH9YTdzNLzWtMYQESSbVrJ2Ehaol21zUthm3othIYiGu1IkPnR5qAAlmGQ0cWJ9dPvgMsqC6sl\nOWWdgOL7bjY810Q/wBsUUFy2T6WLUdfEY2xud/z6J1nwBgUcrmtGMBRBvS+IB4b2hNPKGgptPvvh\nHiwoyAEgX+usFBeWTBhIrR+PnPbR4zAS3kpPcmD6Sg/VeXFazfhw5wnKdPLzAnwhETaORTAkIByR\nzinYqbKYVdzjV1oc7ocgRqcnXLpuTw3GDrweDAN0SbDS7+TlBWpLmZXiwoKCbEQkILWjTWVD+sKn\n+/HCz/pix+yRlM3mqTqDHl1cYBgGSS7OMAcm2MyYscqD2iYei4vcaObDcFrN4MwmzFytFAXNVWFf\nq50RxvKHByMQEjU2e2RfTqsZJxp86OS04sbUDmjwhVQjgn8c70Z290Rc19EOHx9WCSV/uPMEnQUn\n2yOjIaR76rDKDCuWYVAy3o1gSIQoSSgZ74aPF2A3a7UsYrFjxO7x8bLd7NWIuYuJ2DxqdP0DoQhc\nnJmyhqrq/Tjrl1mHyQlWzLq7Dx0jCoVFTB7aEy4FHX/0gO6wsIxm260JzYbCIiSzCR05lopj1vtC\n6m55kRvfVp/FHb27Ii3RphIoNsrvRE9Ds89ozlFaFDcHw1QXIz8nDWv31MgMFTECf1i8YDHkeFz6\n0B9ddMPJmXFw7ig0BwQIERETftRDZc9M4u5+KUhLtMFhlZnD3mAY3RLtWP7wYJWGBFkU6DN7nWpU\navGXlXhomL6OxsnGAGwWE34dM8JU5jmJwsHpAIAurhaBbaNcrmQ0r9l1EjurGjHr7j6GWm+khlGy\nS0rG5WDW3X0QkYC0RDuag2Fc19GGQDiCiCghycXRnFr+XQOKP/nWcJTaYZXHniMR4Iv9tW0S7G6P\nlujxOzce8bhKg3RNpYgEQZRg51gcqvXizc1H0OALYcYqD6a8swP1vhAiEQnBkDwbW1y2D31mr8Nj\ny8sxekB3jHZ316xoN/rDeHKFPqNhyYSBYBiGbqe4bB9m5vWBiWE0K7n/+0s3JEg4/OI9WD/9DswY\nmYWXxmajronXrLjf0qMzmgNyUWyzmPFIaTl6Pyfb+J3xy99n86E63JzWke57SukOzMzrQyn+eQu/\nxoQ3tiMCWbxRs/2ggMpTXipUSBghtU08/CERCTYLzGYTXFYzgiEREoBn/rpb9T3/fTag2qZyBV7Z\n+blWO4NGoddBXfxlJRyc/LLWHAzj0Kkmavv31PsVMDFAczCMaSMy8fMB3fH2lqO655poEbyz9SiS\nE+QZ1mc/3IPez63D9FUeMIxsieu0mpGRnAAhIqJbogOPvluO6sYg1u+tQeUpLzK7ujB1eCa1jNx2\nRBa1nTo8U0VF3lnVCC8voGS8G1azCU+976GjS8q4pYdsWaa0MPPxAn5yUwoeX74TM1Z5EBREBEIi\nIqKEpqCgwn29LwRRjKi2Gdt5Vd7jwMWzKc43rvT+LnUohUtJPvhsby3AyDafDgsLf1hERJJgAlA4\nWMbnjFUemh9u/N1neKS0HDVnA3j1q0oMTE8EL8rdwN5Re96bu3VEmeckvbaEWaQMMqL0+J2ZtLvc\nxWXDqWae0ulJjt56uA6vTcxFVlcXlk7IxYyRWSpWROnWYzRHenn9fdWeDdJcW3nKp9nHb1Z54OVF\n/GaVB41+AY++K2Oz3huSz1HM9sg4SG2TbHtsYhgk2CxwWM2ABPhCIsX3W5uPosFvjGMSeuyeRYVu\nvLX5aKu/90ON2DyqZ8tI3F1sHIumQBj3L9uOTQdOwcIyeG1ibtR+2oI3Nx/BjFUeNAUFPPZuOX3O\nEd0APZai1wC3fl5AU1AWVqxuDNIF6ZXbqzQd6R5JsoOMlxdQ7w1BkoB6bwgMoGs57QsZ7DO6cFU0\n+Aas31uDk2cCeHz5Ttz4u8/w+PKdmPnTPpg2IhOhsOwqda7cGo8rG8rF74NzR2HZ5EFIcloRAfCn\nLw7h301BJDqsqDzlRb1XXTMW33czRvVLpde0dOsx8II8GkfrtZ/2QfF9N2PjjB+DYYBPp90uL9xF\nWZS39OiMiCTpssoSHRZNPpz1wW6Mze0uj3+aGAgRCW9tPkoFQ5VBnvXKZ8CIG1MAAENf+kqXebow\nWsPEsktqzgZVtejjy3ei3heC3WzCmUBYhevMrgnIz0kztJutqvfLebN0B35yUwpe/apSUwfHhnLB\nVMk6/j4ZTQwR5LkaY9CgQdKOHTsuy7Z7/Nenl2W7FxvH/ufe7/sQfqhxxSruS4XbSERCUBDh5QWV\nojIRMMzrl4q8hV9jxsgsPDisJwDgkVK1+vGQjCT8Zcpg9H5uHRUeAoDDL96DPrPlnxEhIsJoCIQF\nmtSV/+blBUCScMYfpirlDAN6bEqxOW9QQEgU8cR7HlVHZt2eGvzM3U2ljk+Oc0FBNlxWCx5brv0O\n88b0p9oWCwvd2F99FjendVSxSRYVurFubw3O+sPUMq61VWRvMIwpOuerZFwObn/5K5XmxZubjyCv\nXypdwV6/twYP3Z5xuWeyryrMenkBU97ZoTmfxfl9AYB6km+prMOQXl3QwW5Bc1AAAwmAzFjoM3sd\n7umfSjHXHAwjwWZGIBSBnTOhz+zP8Om023VdP5QYWVTkRvmxBmQkJyArRWYhvb3laMv8f6Eba/fU\noPiTb2E2MTgwZxROngngy3/V4t7+qeDFiEYU78t/1eI/clLhDYp0RjbRYUHp1mN0u4uLBsDJsfhV\n9DxsmTUcEmR655IJA3VxT1xIznUeryINgCuC27ZiVq+rtHTCQLAmE+ycCc1Rh6LrOzvQHJQXr5IT\nrHj+Z32puwfp8BGcdXJyeOxdbe54+RfZuP3lrwAAM0ZmYfJtPWi+JE5Oc9fuxyvj3Oj127UUewBo\nPgZgOGOd5OTgD4l4a/NR1az1jJFZmpy3qMiNsBDBzCi1WJnzSRDLVbJoR75Pfk4anrlbO0+9YP0B\n1DbJ1OUONjM4BfvPHxZVuI11iSDnSA/HsVo5sd+PuACAweViYbSbXEvu/+QEK82DDT4eTs4MG8fK\nTC5eRHIHK7xBQRaq5lgEQ9FaYaW6VmAYqJxkgJa8nNnVhafe92DO6P5Ug2Db4dO4t38qfKGWPOey\nsXBYzHjj70d0rZOVuhYEU6GwiNO+kAZDyU4OvCipRBkn3dYDgbComvFXPrMjEQm+kKBb37w2MRcM\no1/7xObWH1i0G8y2FnqMGEAWQSdYnTYiEw8O6wmn1UztSv0hUeXmYZRPlk7MxWMxopglG2Q73pNn\nAuhot+CdrUc19duDw3piwPMbVPlwtDsNz917k0YwPK2jDdVngxrcpyXakPncOtXxFOf3pY5oCTYz\nmgMCXDYWPl52Verzu890c/D9y7br4lcP1/PG9MfCjQd1NS9e/uyAyh2F1EXK+jyWzRaRJM07Ajku\n0yUYd42JNm3wqqh04hGPeKiDFNw+XsCzH+7RFTDM7OpCfk4aRg/ojkdKyzXWjoCsRqxH1TzV1CJC\nV1ZRTQvzl3+RTe1L9QroBQU5sLBy7rFGVcPJIsfoAd1V9NEFBTlYUJCN6zrao6JfZkwc0gOAlh74\nzbEGdLRzsBvMP6cnOXBwzig08wLW7DqBz/bWYtmk3JaRBAX9sPKUF+v21lBasxH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IAAAg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\nfxCiCN3tko0fwgN4h9kEc+jpe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2vmTcGKbVJg0XzoLG698u9QbDNrFprVUmbh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\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gVTD7ol14AoBlW6JSp4oSUzLKSIMrvFh2pw/hiKTzk7LM43xqeYkbl3r7YdtnzTq1p1d2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\nQvPMOiSySbOFs9j9F0+vwsadxzDz2i/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ynRUb8rqZ+guIAED5LUSUZWw9eBZfubIPHIJg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\nOLxYlA0GwJJ8Gxb8qj9O2T3w+DkEeWGwWwwsJSb0+DmB1f48NX6jFRr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TrHhl2mCc9fhR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v+yLhxzO//ZvhePJXGfnYard3vYzJFSPkMEN9dYdUzpHRe7By0TUs19332q9ejxnjhym69q4x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aqfWDxO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\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PFH/XNjG6GgZa57F44mDYLQbsO9WAmX87oHrPs9sOo7ZJFOY6UtOo0LOwmw1ggjw8AVay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\n6LIiHJXjQ4rDqtJGKS6vRHF5Jb0X//jX2wA0LD50c3Aqh4T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"text/plain": [ "<matplotlib.figure.Figure at 0x178ca4b1fd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.pairplot(df[['HP','Attack','Defense','Sp. Atk','Sp. Def','Speed']])" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "corr = df.corr()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x178cbe59cc0>" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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y70g6P8x4CoA7nnyVDdt20bl183qPsvZe0E+jDVw1KpIffs+l7LipFoH+Bnp0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\neYPKOeecc24TeYPKOeecc24T/T+lEmkwWubO3QAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x178cbd65e48>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10,8))\n", "sns.heatmap(corr, cmap='coolwarm', annot=True)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>#</th>\n", " <th>Total</th>\n", " <th>HP</th>\n", " <th>Attack</th>\n", " <th>Defense</th>\n", " <th>Sp. Atk</th>\n", " <th>Sp. Def</th>\n", " <th>Speed</th>\n", " <th>Generation</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>800.000000</td>\n", " <td>800.00000</td>\n", " <td>800.000000</td>\n", " <td>800.000000</td>\n", " <td>800.000000</td>\n", " <td>800.000000</td>\n", " <td>800.000000</td>\n", " <td>800.000000</td>\n", " <td>800.00000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>362.813750</td>\n", " <td>435.10250</td>\n", " <td>69.258750</td>\n", " <td>79.001250</td>\n", " <td>73.842500</td>\n", " <td>72.820000</td>\n", " <td>71.902500</td>\n", " <td>68.277500</td>\n", " <td>3.32375</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>208.343798</td>\n", " <td>119.96304</td>\n", " <td>25.534669</td>\n", " <td>32.457366</td>\n", " <td>31.183501</td>\n", " <td>32.722294</td>\n", " <td>27.828916</td>\n", " <td>29.060474</td>\n", " <td>1.66129</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>1.000000</td>\n", " <td>180.00000</td>\n", " <td>1.000000</td>\n", " <td>5.000000</td>\n", " <td>5.000000</td>\n", " <td>10.000000</td>\n", " <td>20.000000</td>\n", " <td>5.000000</td>\n", " <td>1.00000</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>184.750000</td>\n", " <td>330.00000</td>\n", " <td>50.000000</td>\n", " <td>55.000000</td>\n", " <td>50.000000</td>\n", " <td>49.750000</td>\n", " <td>50.000000</td>\n", " <td>45.000000</td>\n", " <td>2.00000</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>364.500000</td>\n", " <td>450.00000</td>\n", " <td>65.000000</td>\n", " <td>75.000000</td>\n", " <td>70.000000</td>\n", " <td>65.000000</td>\n", " <td>70.000000</td>\n", " <td>65.000000</td>\n", " <td>3.00000</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>539.250000</td>\n", " <td>515.00000</td>\n", " <td>80.000000</td>\n", " <td>100.000000</td>\n", " <td>90.000000</td>\n", " <td>95.000000</td>\n", " <td>90.000000</td>\n", " <td>90.000000</td>\n", " <td>5.00000</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>721.000000</td>\n", " <td>780.00000</td>\n", " <td>255.000000</td>\n", " <td>190.000000</td>\n", " <td>230.000000</td>\n", " <td>194.000000</td>\n", " <td>230.000000</td>\n", " <td>180.000000</td>\n", " <td>6.00000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " # Total HP Attack Defense Sp. Atk \\\n", "count 800.000000 800.00000 800.000000 800.000000 800.000000 800.000000 \n", "mean 362.813750 435.10250 69.258750 79.001250 73.842500 72.820000 \n", "std 208.343798 119.96304 25.534669 32.457366 31.183501 32.722294 \n", "min 1.000000 180.00000 1.000000 5.000000 5.000000 10.000000 \n", "25% 184.750000 330.00000 50.000000 55.000000 50.000000 49.750000 \n", "50% 364.500000 450.00000 65.000000 75.000000 70.000000 65.000000 \n", "75% 539.250000 515.00000 80.000000 100.000000 90.000000 95.000000 \n", "max 721.000000 780.00000 255.000000 190.000000 230.000000 194.000000 \n", "\n", " Sp. Def Speed Generation \n", "count 800.000000 800.000000 800.00000 \n", "mean 71.902500 68.277500 3.32375 \n", "std 27.828916 29.060474 1.66129 \n", "min 20.000000 5.000000 1.00000 \n", "25% 50.000000 45.000000 2.00000 \n", "50% 70.000000 65.000000 3.00000 \n", "75% 90.000000 90.000000 5.00000 \n", "max 230.000000 180.000000 6.00000 " ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.describe()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>#</th>\n", " <th>Name</th>\n", " <th>Type 1</th>\n", " <th>Type 2</th>\n", " <th>Total</th>\n", " <th>HP</th>\n", " <th>Attack</th>\n", " <th>Defense</th>\n", " <th>Sp. Atk</th>\n", " <th>Sp. Def</th>\n", " <th>Speed</th>\n", " <th>Generation</th>\n", " <th>Legendary</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ "Empty DataFrame\n", "Columns: [#, Name, Type 1, Type 2, Total, HP, Attack, Defense, Sp. Atk, Sp. Def, Speed, Generation, Legendary]\n", "Index: []" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[df['Name'].duplicated()] # no dupliactes" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th>Legendary</th>\n", " <th>False</th>\n", " <th>True</th>\n", " </tr>\n", " <tr>\n", " <th>Type 1</th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Bug</th>\n", " <td>69</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>Dark</th>\n", " <td>29</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>Dragon</th>\n", " <td>20</td>\n", " <td>12</td>\n", " </tr>\n", " <tr>\n", " <th>Electric</th>\n", " <td>40</td>\n", " <td>4</td>\n", " </tr>\n", " <tr>\n", " <th>Fairy</th>\n", " <td>16</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>Fighting</th>\n", " <td>27</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>Fire</th>\n", " <td>47</td>\n", " <td>5</td>\n", " </tr>\n", " <tr>\n", " <th>Flying</th>\n", " <td>2</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>Ghost</th>\n", " <td>30</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>Grass</th>\n", " <td>67</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>Ground</th>\n", " <td>28</td>\n", " <td>4</td>\n", " </tr>\n", " <tr>\n", " <th>Ice</th>\n", " <td>22</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>Normal</th>\n", " <td>96</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>Poison</th>\n", " <td>28</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>Psychic</th>\n", " <td>43</td>\n", " <td>14</td>\n", " </tr>\n", " <tr>\n", " <th>Rock</th>\n", " <td>40</td>\n", " <td>4</td>\n", " </tr>\n", " <tr>\n", " <th>Steel</th>\n", " <td>23</td>\n", " <td>4</td>\n", " </tr>\n", " <tr>\n", " <th>Water</th>\n", " <td>108</td>\n", " <td>4</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ "Legendary False True \n", "Type 1 \n", "Bug 69 0\n", "Dark 29 2\n", "Dragon 20 12\n", "Electric 40 4\n", "Fairy 16 1\n", "Fighting 27 0\n", "Fire 47 5\n", "Flying 2 2\n", "Ghost 30 2\n", "Grass 67 3\n", "Ground 28 4\n", "Ice 22 2\n", "Normal 96 2\n", "Poison 28 0\n", "Psychic 43 14\n", "Rock 40 4\n", "Steel 23 4\n", "Water 108 4" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.crosstab(df['Type 1'] , df['Legendary'])" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "# 721\n", "Name 800\n", "Type 1 18\n", "Type 2 19\n", "Total 200\n", "HP 94\n", "Attack 111\n", "Defense 103\n", "Sp. Atk 105\n", "Sp. Def 92\n", "Speed 108\n", "Generation 6\n", "Legendary 2\n" ] } ], "source": [ "for i in df.columns:\n", " print(i, len(df[i].unique()))" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df['Legendary'] = df['Legendary'].apply(lambda x: 1 if x == True else 0)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dataset = df.iloc[:, 2:]" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Type 1</th>\n", " <th>Type 2</th>\n", " <th>Total</th>\n", " <th>HP</th>\n", " <th>Attack</th>\n", " <th>Defense</th>\n", " <th>Sp. Atk</th>\n", " <th>Sp. Def</th>\n", " <th>Speed</th>\n", " <th>Generation</th>\n", " <th>Legendary</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Grass</td>\n", " <td>Poison</td>\n", " <td>318</td>\n", " <td>45</td>\n", " <td>49</td>\n", " <td>49</td>\n", " <td>65</td>\n", " <td>65</td>\n", " <td>45</td>\n", " <td>1</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Grass</td>\n", " <td>Poison</td>\n", " <td>405</td>\n", " <td>60</td>\n", " <td>62</td>\n", " <td>63</td>\n", " <td>80</td>\n", " <td>80</td>\n", " <td>60</td>\n", " <td>1</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Grass</td>\n", " <td>Poison</td>\n", " <td>525</td>\n", " <td>80</td>\n", " <td>82</td>\n", " <td>83</td>\n", " <td>100</td>\n", " <td>100</td>\n", " <td>80</td>\n", " <td>1</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Grass</td>\n", " <td>Poison</td>\n", " <td>625</td>\n", " <td>80</td>\n", " <td>100</td>\n", " <td>123</td>\n", " <td>122</td>\n", " <td>120</td>\n", " <td>80</td>\n", " <td>1</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Fire</td>\n", " <td>NaN</td>\n", " <td>309</td>\n", " <td>39</td>\n", " <td>52</td>\n", " <td>43</td>\n", " <td>60</td>\n", " <td>50</td>\n", " <td>65</td>\n", " <td>1</td>\n", " <td>0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Type 1 Type 2 Total HP Attack Defense Sp. Atk Sp. Def Speed \\\n", "0 Grass Poison 318 45 49 49 65 65 45 \n", "1 Grass Poison 405 60 62 63 80 80 60 \n", "2 Grass Poison 525 80 82 83 100 100 80 \n", "3 Grass Poison 625 80 100 123 122 120 80 \n", "4 Fire NaN 309 39 52 43 60 50 65 \n", "\n", " Generation Legendary \n", "0 1 0 \n", "1 1 0 \n", "2 1 0 \n", "3 1 0 \n", "4 1 0 " ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset.head()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dataset = pd.get_dummies(dataset, dummy_na=True,drop_first=True)\n", "dataset['Target'] = dataset['Legendary']\n", "dataset.drop(['Legendary', 'Total'], inplace=True, axis=1)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true }, "outputs": [], "source": [ "X = dataset.iloc[:, :-1]\n", "y = dataset.iloc[:, -1]" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 0\n", "1 0\n", "Name: Target, dtype: int64" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y.head(2)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>HP</th>\n", " <th>Attack</th>\n", " <th>Defense</th>\n", " <th>Sp. Atk</th>\n", " <th>Sp. Def</th>\n", " <th>Speed</th>\n", " <th>Generation</th>\n", " <th>Type 1_Dark</th>\n", " <th>Type 1_Dragon</th>\n", " <th>Type 1_Electric</th>\n", " <th>Type 1_Fairy</th>\n", " <th>Type 1_Fighting</th>\n", " <th>Type 1_Fire</th>\n", " <th>Type 1_Flying</th>\n", " <th>Type 1_Ghost</th>\n", " <th>Type 1_Grass</th>\n", " <th>Type 1_Ground</th>\n", " <th>Type 1_Ice</th>\n", " <th>Type 1_Normal</th>\n", " <th>Type 1_Poison</th>\n", " <th>Type 1_Psychic</th>\n", " <th>Type 1_Rock</th>\n", " <th>Type 1_Steel</th>\n", " <th>Type 1_Water</th>\n", " <th>Type 1_nan</th>\n", " <th>Type 2_Dark</th>\n", " <th>Type 2_Dragon</th>\n", " <th>Type 2_Electric</th>\n", " <th>Type 2_Fairy</th>\n", " <th>Type 2_Fighting</th>\n", " <th>Type 2_Fire</th>\n", " <th>Type 2_Flying</th>\n", " <th>Type 2_Ghost</th>\n", " <th>Type 2_Grass</th>\n", " <th>Type 2_Ground</th>\n", " <th>Type 2_Ice</th>\n", " <th>Type 2_Normal</th>\n", " <th>Type 2_Poison</th>\n", " <th>Type 2_Psychic</th>\n", " <th>Type 2_Rock</th>\n", " 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<td>1</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " HP Attack Defense Sp. Atk Sp. Def Speed Generation Type 1_Dark \\\n", "0 45 49 49 65 65 45 1 0 \n", "1 60 62 63 80 80 60 1 0 \n", "2 80 82 83 100 100 80 1 0 \n", "3 80 100 123 122 120 80 1 0 \n", "4 39 52 43 60 50 65 1 0 \n", "\n", " Type 1_Dragon Type 1_Electric Type 1_Fairy Type 1_Fighting Type 1_Fire \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 1 \n", "\n", " Type 1_Flying Type 1_Ghost Type 1_Grass Type 1_Ground Type 1_Ice \\\n", "0 0 0 1 0 0 \n", "1 0 0 1 0 0 \n", "2 0 0 1 0 0 \n", "3 0 0 1 0 0 \n", "4 0 0 0 0 0 \n", "\n", " Type 1_Normal Type 1_Poison Type 1_Psychic Type 1_Rock Type 1_Steel \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " Type 1_Water Type 1_nan Type 2_Dark Type 2_Dragon Type 2_Electric \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " Type 2_Fairy Type 2_Fighting Type 2_Fire Type 2_Flying Type 2_Ghost \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " Type 2_Grass Type 2_Ground Type 2_Ice Type 2_Normal Type 2_Poison \\\n", "0 0 0 0 0 1 \n", "1 0 0 0 0 1 \n", "2 0 0 0 0 1 \n", "3 0 0 0 0 1 \n", "4 0 0 0 0 0 \n", "\n", " Type 2_Psychic Type 2_Rock Type 2_Steel Type 2_Water Type 2_nan \n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 1 " ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X.head()" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": true }, "outputs": [], "source": [ "X_train , X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(640, 43)" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_train.shape" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(160, 43)" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_test.shape" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(640,)" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y_train.shape" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(160,)" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y_test.shape" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": true }, "outputs": [], "source": [ "clr = LogisticRegression()" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n", " intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n", " penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n", " verbose=0, warm_start=False)" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clr.fit(X_train, y_train)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": true }, "outputs": [], "source": [ "y_pred = clr.predict(X_test)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.9375" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "accuracy_score(y_test, y_pred)" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " 0 0.94 1.00 0.97 146\n", " 1 1.00 0.29 0.44 14\n", "\n", "avg / total 0.94 0.94 0.92 160\n", "\n" ] } ], "source": [ "print(classification_report(y_test, y_pred))" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": true }, "outputs": [], "source": [ "cm = confusion_matrix(y_test, y_pred)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[146, 0],\n", " [ 10, 4]], dtype=int64)" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cm" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": true }, "outputs": [], "source": [ "probs = clr.predict_proba(X_test)\n", "preds = probs[:,1]\n", "fpr, tpr, threshold = metrics.roc_curve(y_test, preds)\n", "roc_auc = metrics.auc(fpr, tpr)" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": true }, "outputs": [], "source": [ "## SVC" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": true }, "outputs": [], "source": [ "svc = SVC(probability=True)" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", " decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n", " max_iter=-1, probability=True, random_state=None, shrinking=True,\n", " tol=0.001, verbose=False)" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "svc.fit(X_train, y_train)" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": true }, "outputs": [], "source": [ "svc_probs = svc.predict_proba(X_test)\n", "svc_preds = svc_probs[:,1]\n", "svc_fpr, svc_tpr, svc_threshold = metrics.roc_curve(y_test, svc_preds)\n", "svc_roc_auc = metrics.auc(svc_fpr, svc_tpr)" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": true }, "outputs": [], "source": [ "svc_y_pred = svc.predict(X_test)" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.91249999999999998" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "accuracy_score(y_test, svc_y_pred)" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 0.07142857, 0.64285714, 0.64285714, 0.71428571, 0.71428571,\n", " 0.78571429, 0.78571429, 0.85714286, 0.85714286, 0.92857143,\n", " 0.92857143, 1. , 1. ])" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tpr" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 0. , 0. , 0.14285714, 0.14285714, 0.21428571,\n", " 0.21428571, 0.28571429, 0.28571429, 0.5 , 0.5 ,\n", " 0.78571429, 0.78571429, 0.78571429, 0.85714286, 0.85714286,\n", " 0.92857143, 0.92857143, 1. , 1. ])" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "svc_tpr" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[146, 0],\n", " [ 10, 4]], dtype=int64)" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cm" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": true }, "outputs": [], "source": [ "svc_cm = confusion_matrix(y_test, svc_y_pred)" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[146, 0],\n", " [ 14, 0]], dtype=int64)" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "svc_cm" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x178cd4d7748>" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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HUlLg0UehWbNwRxTRLFEYY2JL794wZowr3vf110V3QVQUs0RhjCn+fIv4XXQR1KkDgwdD\nKfsKDEZIxyhEpIOIrBWR9SLyDz/LzxWRWSKyRESWi8jVoYzHGBODNm1yg9Njx7rH/fvDkCGWJPIg\nZIlCREoCbwFXAXWB7iJSN9tqjwEfq2pjoBvwdqjiMcbEmGPH4I033JWq8+ZltSpMnoWyRdEMWK+q\nG1X1CDAR6JhtHQUqePdPA/4XwniMMbFizRpX++b++6F1a1enqXfvcEcVtULZ9qoCbPV5nAw0z7bO\nUOAbEbkPOBW4wt+GRKQ/0B/g3HPPLfRAjTHFzPr1rpDfBx/ArbfGXBG/whbKFoW//5nsbb/uwGhV\njQOuBj4QkRNiUtXhqpqoqolnnnlmCEI1xkS9xYth1Ch3/7rr3NjEbbdZkigEoWxRJANVfR7HcWLX\n0u1ABwBVnSsiZYHKwB8hjMuvV1+FcePc3Oi7dwde99AhOPlkSEwsmth+/RWaZ2+LGWOcQ4fgySfh\n5ZehalU381zZslChQu6vNUEJZaJYCMSLSA1gG26w+pZs62wBLgdGi0gdoCywM4Qx5WjECNi3z33m\nUlMDf8ZKl3ZX+599dtHEdvbZroClMSabOXPchELr1sHtt7tkYUX8Cl3IEoWqponIvcAMoCQwSlVX\nichTwCJVnQYMBt4XkUG4bqneqkV/asKxY65Lc9Agd3IEFN1cE8aYfNq2DS6/3LUivv3W3TchEdIT\niVV1OjA923OP+9xfDVwcyhiCsWWLmxgoPj4rURhjItSKFdCggWvWf/qpq/h66qnhjqpYs6KAuKlG\nAS64ILxxGGMC2LULevSAhg2zivhde60liSJglyZiicKYiKYKkybBvfe6yeOfeMLO7ihilihw42AV\nKsBZZ4U7EmPMCXr1ctdDJCbCd9+5bidTpCxR4FoU8fF2urUxEcO3iF/r1q676YEHrD5TmNgYBS5R\nWLeTMRFi40a44goYPdo9vv12eOghSxJhFPOJ4vBh2LzZEoUxYXfsGLz2mutaWrgQSsT811PEiPkU\nvWGDa+VaojAmjFavhr59Yf58uOYaePddiIsLd1TGE/OJYt06968lCmPCaNMm96tt/Hjo1s0GDCNM\nzCeKjFNj4+PDG4cxMWfhQli6FO64w7UiNm6E8uXDHZXxI+Y7AZOS3Gmxp50W7kiMiREHD7rB6RYt\n4LnnXHE1sCQRwSxR2BlPxhSdH35wp7q+8oprSSxZYkX8okBMdD0NH+66Pv2ZOxcqVYI2bdzjpUsh\nIaHIQjMmdiQnw5VXQrVq8P33rkaTiQox0aIYP94lgOyOHYOjR93cEhkSElw5e2NMIVm2zP0bFwdT\np8Ly5ZYkokxMtCjAJYDspcN/+QWaNnUl7Dt1CktYxhRfO3e6OasnTHB/fK1bw9VXhzsqkw8xkyj8\nsWKAxoSAKkycCAMHutnAnnwSWrYMd1SmAIJKFCJSGjhXVdeHOJ4ilZTkTteuWTPckRhTjPTo4eYV\nbt4cRo6EevXCHZEpoFzHKETkGmAFMNN7nCAin4Y6sKKQlATnnnv8GIUxJh/S07MK+bVtC8OGwc8/\nW5IoJoIZzH4KaA7sBVDVpcD5oQyqqKxbZ91OxhTY+vVuGtL//Mc9vv12N69wyZLhjcsUmmASxVFV\n3ZvtuSKf17qwqWaVFzfG5ENamjsTpEEDdz1E6dLhjsiESDBjFGtE5GaghIjUAO4Hon5m6V27YO9e\na1EYky8rV0KfPrBoEXTsCG+/DeecE+6oTIgE06K4F2gKpAOfAKm4ZBHV7IwnYwpgyxb47Td3dtOn\nn1qSKOaCaVG0V9UhwJCMJ0SkEy5pRC1LFMbk0fz57uK5/v3d9RAbN0K5cuGOyhSBYFoUj/l57tHC\nDqSorVsHJ53kqgkYYwL46y948EF3LcSLL7rZvsCSRAzJsUUhIu2BDkAVERnms6gCrhsqqiUlwXnn\n2eyKxgT0/feueN/GjTBgADz/PJQpE+6oTBEL9DX5B7ASNyaxyuf5/cA/QhlUUbCqscbkIjkZ2reH\nGjVg9mxo1SrcEZkwyTFRqOoSYImIjFPV1CKMKeTS013XU7t24Y7EmAi0ZAk0buyK+H3+uavRZFel\nxrRgxiiqiMhEEVkuIkkZt5BHFkLJyW6uFGtRGOPj99+ha1do0sS1IAA6dLAkYYI662k08AzwMnAV\n0IcwjlGsXZs1d0Swss8xYfNkG+ND1dVmuv9+OHAAnnkGLroo3FGZCBJMi+IUVZ0BoKobVPUxIGzF\n5A8dyvtrss8xYfNkG+PjlltcIb9atdyvqkcfdacEGuMJpkVxWEQE2CAidwHbgLNCG1bOTj75xHkl\n8iopCU45xa4RMjEsPd2VThZxg3UtW8I991h9JuNXMC2KQUA5YCBwMXAH0DeUQYVaxhlPIuGOxJgw\nSEpyFV5HjXKP+/Rxc0dYkjA5yLVFoarzvbv7gR4AIhIXyqBCLSnJjdcZE1PS0lz57yeegLJlbZDa\nBC1gi0JELhSRG0Sksve4noiMJYqLAh49Cps22UC2iTHLl0OLFjBkCFx1FaxebZPDm6DlmChE5Dlg\nHHAr8LWIPArMApYBUfs1u2kTHDtmA9kmxiQnw9atMGkSTJkCf/97uCMyUSRQ11NHoJGqHhKRM4D/\neY/XBrtxEekAvA6UBEao6vN+1rkZGIqb42KZqob0Z44VAzQx47//dS2Ju+7KKuJ36qnhjspEoUBd\nT6mqeghAVXcDv+YxSZQE3sJde1EX6C4idbOtEw88AlysqvWAB/IYf55ZojDF3oED7pqISy6BV17J\nKuJnScLkU6AWxXkiklFKXIDqPo9R1U65bLsZsF5VNwKIyERcK2W1zzp3AG+p6h5vm3/kMf48S0qC\nSpXgjDNCvSdjwuCbb1wZ8C1b3Omu//63FfEzBRYoUXTO9vjNPG67CrDV53Eybu5tXxcAiMjPuO6p\noar6dfYNiUh/oD9AmTIN8xjG8WyebFNsbd0K11wDNWvCnDmuRWFMIQhUFPC7Am7b31UK2efaLgXE\nA22AOOBHEamffY5uVR0ODAcoXz6xQPN1JyXBZZcVZAvGRJjFi6FpU6haFaZPh0svdae/GlNIgrng\nLr+Sgao+j+NwA+LZ15mqqkdVdROwFpc4QuKvv9zJH9aiMMXCjh3QpQskJmYV8bvySksSptCFMlEs\nBOJFpIaIlAa6AdOyrfMZXt0o71qNC4CNoQpo/Xr3ryUKE9VUYcwYqFvXlQH/97+tiJ8JqaDndxOR\nMqp6ONj1VTVNRO4FZuDGH0ap6ioReQpYpKrTvGXtRGQ1cAx4WFVT8vYWgmdnPJlioVs3+PhjuPhi\nGDECatcOd0SmmMs1UYhIM2AkcBpwrog0Avqp6n25vVZVpwPTsz33uM99BR70biGXUV78/POLYm/G\nFCLfIn5XX+3GIe6+G0qEslPAGCeYT9kbwLVACoCqLiOMZcYLIikJqlSx08lNlPn1VzcN6ciR7nGv\nXnDvvZYkTJEJ5pNWQlV/y/bcsVAEE2o2T7aJKkePuvGHRo1cbaZy5cIdkYlRwSSKrV73k4pISRF5\nAIjKqVAtUZiosXQpNGvmJhG6/nqXKLp1C3dUJkYFM5g9ANf9dC7wO/Ct91xU2b0bUlIsUZgosWOH\nu02ZAp1yK4JgTGgFkyjSVDXqf8rYPNkm4v30kyvid/fd0KEDbNjgpmI0JsyC6XpaKCLTRaSXiJQP\neUQhYvNkm4i1f78bnL70UnjttawifpYkTITINVGoak3gGaApsEJEPhORqGthJCW5mR5r1Ah3JMb4\nmDED6teHt992FV9/+cWK+JmIE9T5dar6X1UdCDQB/sRNaBRVkpJckihdOtyRGOPZuhWuvda1HH76\nybUm7MwmE4FyTRQiUk5EbhWRz4EFwE4g6uoF2BlPJiKowoIF7n7VqvDVV7BkiZXgMBEtmBbFSqAF\n8KKqnq+qg1V1fojjKlSqbjDbxidMWG3fDp07Q/PmWUX8rrjCiviZiBfMWU/nqWp6yCMJoe3bXeVY\na1GYsFCF0aPhwQchNRVeeMHVaTImSuSYKETkFVUdDEwRkRPmgAhihruIYcUATVjdfDNMnuzOahox\nwj6IJuoEalF85P2b15ntIo4lClPkjh1zBfxKlIDrrnOzZd15p9VnMlEpx0+tqnojbtRR1e98b0Cd\nogmvcCQluW7guLhwR2Jiwpo1rvWQUcSvZ08YMMCShIlawXxy+/p57vbCDiSU1q1zpcXt79SE1NGj\n8MwzkJAAa9fCaaeFOyJjCkWgMYquuFnpaojIJz6LygN7/b8qchw8CPfdB/v2uXnmbZ5sE1JLlkDv\n3q4ER9eu8MYbcNZZ4Y7KmEIRaIxiAW4OijjgLZ/n9wNLQhlUYVi5EkaNgnPPdV1ON98c7ohMsfb7\n77BrF3z2GXTsGO5ojClUOSYKVd0EbMJVi41a77zjJgQzptDNmQMrVsA997gifuvXw8knhzsqYwpd\njr32IjLb+3ePiOz2ue0Rkd1FF6IxEebPP12F19atXRdTRhE/SxKmmAo0vJsx3Wll4EyfW8ZjY2LP\n9OlQrx689567gM6K+JkYEOj02IyrsasCJVX1GNASuBOwWadN7Nm61Y0/nHYa/Pe/8MorNgG7iQnB\nnDD6GW4a1JrAWNw1FONDGpUxkUIV5s1z96tWhW++ca2I5s3DG5cxRSiYRJGuqkeBTsBrqnofUCW0\nYRkTAf73P7jhBmjZMquIX9u2VqvexJxgEkWaiHQBegBfeM+dFLqQjAkzVVeTqW5d14J4+WUr4mdi\nWjDVY/sCd+PKjG8UkRrAhNCGZUwY3XQTfPKJO6tpxAh3Wb8xMSzXRKGqK0VkIHC+iNQG1qvqs6EP\nzZgi5FvE74YboF07uOMOq/tiDMHNcHcpsB4YCYwCkkTE2uGm+Fi50nUtZRTx69HDKr0a4yOYv4RX\ngatV9WJVvQi4Bng9tGEZUwSOHIEnn4QmTWDDBjj99HBHZExECmaMorSqrs54oKprRMRO+zDRbfFi\nV8Rv5Uq45RZ47TU4064jNcafYBLFLyLyHvCB9/hWoqAooDEBpaTA3r3w+edw7bXhjsaYiBZMorgL\nGAj8P0CAOcD/hTIoY0Ji1ixXxG/gQDdYvW6dm9HKGBNQwEQhIg2AmsCnqvpi0YRkTCHbtw/+3/+D\n4cOhdm03UF2mjCUJY4IUqHrsP3HlO24FZoqIv5nujIlsn3/uLpwbMQIeesiNTVgRP2PyJFCL4lag\noar+JSJnAtNxp8caEx22boXOnV0r4rPP4MILwx2RMVEp0Omxh1X1LwBV3ZnLusZEBlVX2RWyivgt\nWmRJwpgCCPTlf56IfOLdPgVq+jz+JMDrMolIBxFZKyLrReQfAda7SURURBLz+gaMyZScDNdf7y6e\nyyji16aNFfEzpoACdT11zvb4zbxsWERK4ubavhJIBhaKyDTfazK89crjzqqan5ftG5MpPR3efx8e\nfhjS0mDYMLjkknBHZUyxEWjO7O8KuO1muLpQGwFEZCLQEVidbb2ngReBhwq4PxOrOnd2YxCXXeYS\nxnnnhTsiY4qVUI47VAG2+jxOJts8FiLSGKiqql8QgIj0F5FFIrLo6NGjhR+piT5paa4lAS5RvP8+\nfPutJQljQiCUiUL8PKeZC0VK4OpIDc5tQ6o6XFUTVTXxpJNsKoyYt3y5m0zo/ffd49tug379XPVX\nY0yhCzpRiEheTz5Pxs23nSEO+J/P4/JAfeAHEdkMtACm2YC2ydHhw/DEE9C0Kfz2m9VmMqaIBFNm\nvJmIrADWeY8biUgwJTwWAvEiUsMrItgNmJaxUFX3qWplVa2uqtWBecD1qrooP2/EFHMLF7oqr089\nBd27w5o10KlTuKMyJiYE06J4A7gWSAFQ1WVA29xepKppwL3ADGAN8LGqrhKRp0Tk+vyHbGLSnj1w\n4ABMnw5jx0KlSuGOyJiYEUxRwBKq+psc3/97LJiNq+p03BXdvs89nsO6bYLZpokh33/vivjdf78r\n4peUZOU3jAmDYFoUW0WkGaAiUlJEHgCSQhyXiWV797ppSC+/HN57z41NgCUJY8IkmEQxAHgQOBf4\nHTfoPCCUQZkYNnWqK+I3apSr+GpF/IwJu1y7nlT1D9xAtDGhtWULdOkCderAtGmQaCfAGRMJck0U\nIvI+PtdurFkPAAAbM0lEQVQ/ZFDV/iGJyMQWVfjpJ7j0Ujj3XHfRXIsWVp/JmAgSTNfTt8B33u1n\n4CzgcCiDMjFiyxa45hpo1SqriF+rVpYkjIkwwXQ9feT7WEQ+AGaGLCJT/KWnw7vvwpAhrkXxxhtW\nxM+YCBbM6bHZ1QCqFXYgJoZ06uQGra+80k1PWr16uCMyxgQQzBjFHrLGKEoAu4Ec55Ywxq+0NChR\nwt26doWOHaF3b6vPZEwUCJgoxF1l1wjY5j2VrqonDGwbE9CyZdC3r7s24q67XAkOY0zUCDiY7SWF\nT1X1mHezJGGCl5oKjz3mTnNNToazzw53RMaYfAjmrKcFItIk5JGY4mXBAmjcGJ59Fm691RXxu+GG\ncEdljMmHHLueRKSUV9jvEuAOEdkA/IWbZ0JV1ZKHydmff8KhQ/D119C+fbijMcYUQKAxigVAE8B+\nBprgfPMNrFoFgwbBFVfA2rVWfsOYYiBQohAAVd1QRLGYaLVnDzz4IIweDfXqwd13uwRhScKYYiFQ\nojhTRB7MaaGqDgtBPCbafPIJ3HMP7NwJjzwCjz9uCcKYYiZQoigJlMP/3NfGuBIc3bpB/fpuQqHG\njcMdkTEmBAIliu2q+lSRRWKigyrMmQOtW7sift9/D82bw0knhTsyY0yIBDo9NqpaEseOQUpK1m3f\nvnBHVAz99htcdRW0aZNVxO+SSyxJGFPMBWpRXF5kURSCm26Czz478Xn7DisE6enw9tvwD69yy//9\nnysLboyJCTkmClXdXZSBFNTWrW5itLvuynru1FNd1WpTQDfcAJ9/7q6HeO89qGY1IY2JJfmpHhux\natSA++4LdxTFxNGjULKkK+LXvbtrsvXoYUX8jIlBwZTwMLHml1+gWTM3ZwS4RNGzpyUJY2KUJQqT\n5dAhdy1Es2awYwdUrRruiIwxEaBYdT2ZApg3D3r1gqQkVxL85Zfh9NPDHZUxJgJYojDOX3+5cYmZ\nM12dJmOM8ViiiGVff+2K+A0eDJdfDr/+CqVLhzsqY0yEsTGKWJSS4rqZrroKxoyBI0fc85YkjDF+\nWKKIJaowebK74GT8eDf73MKFliCMMQFZ11Ms2bIFbrkFGjZ0c0c0ahTuiIwxUcBaFMWdqivcB+6K\n6h9+cGc4WZIwxgTJEkVxtmkTtGvnBqozivhddBGUsoakMSZ4liiKo2PH4PXX3TwR8+fDO+9YET9j\nTL7ZT8viqGNH+PJLuPpqV4bDrrA2xhRAVCcKVffDeds2Vz327LPDHVEY+Rbx69HD1We65Rarz2SM\nKbCQdj2JSAcRWSsi60XkH36WPygiq0VkuYh8JyJ5ql+9axcMGuSSxYED0KRJ4cUeVRYtgsRE18UE\n0LUr3HqrJQljTKEIWaIQkZLAW8BVQF2gu4jUzbbaEiBRVRsCk4EX87KP9HT37+uvuwoUT8XaxK2H\nDsGQIW4q0p07bZ4IY0xIhLJF0QxYr6obVfUIMBHo6LuCqs5S1YPew3lAXAjjKV7mznWnuL74oivi\nt3o1XHttuKMyxhRDoRyjqAJs9XmcDDQPsP7twFf+FohIf6A/QJkyDQsrvuh26JBrUn37rTv91Rhj\nQiSUicJfB7n6XVHkNiARaO1vuaoOB4YDlC+f6HcbMWH6dFfE7+GH4bLLYM0amxTcGBNyoex6SgZ8\nz8uMA/6XfSURuQJ4FLheVQ+HMJ7otWsX3HYbXHMNjBuXVcTPkoQxpgiEMlEsBOJFpIaIlAa6AdN8\nVxCRxsB7uCTxRwhjiU6qMHEi1KkDH38MTzwBCxZYET9jTJEKWdeTqqaJyL3ADKAkMEpVV4nIU8Ai\nVZ0GvASUAyaJO5Vzi6peH6qYos6WLa4ceKNGMHIkNGgQ7oiMMTEopBfcqep0YHq25x73uW9TqWWn\nCt9952aZq1bN1Wi68EJ3MZ0xxoRBVF+ZXexs2AB33AGzZrkqr61bQ4sW4Y7KFHNHjx4lOTmZ1NTU\ncIdiCkHZsmWJi4vjpEIcw7REEQkyivg99pgboH7vPSviZ4pMcnIy5cuXp3r16ohdzR/VVJWUlBSS\nk5OpUaNGoW3XEkUkuO46+Oord8HcO+9AnF13aIpOamqqJYliQkSoVKkSO3fuLNTtWqIIlyNH3LwQ\nJUpA796ukF+3blafyYSFJYniIxT/lzYfRTgsWABNm8Lbb7vHN9/sqr3aH6sxJgJFbaJIT3dd+1Hl\n4EEYPBhatoQ9e6BmzXBHZExEePbZZ6lXrx4NGzYkISGB+fPnM3ToUB555JHj1lu6dCl16tQB4MCB\nA9x5553UrFmTevXq0apVK+bPn+93+0uWLEFEmDFjRuZzmzdvpn79+setN3ToUF5++eXMxy+//DK1\na9emfv36NGrUiLFjxxb4vY4ZM4b4+Hji4+MZM2aM33WWLVtGy5YtadCgAddddx1//vknADNnzqRp\n06Y0aNCApk2b8n3GNMchFpWJYtYsKFMGqlRxj6PizNGffnLXQQwb5s5sWrUKrroq3FEZE3Zz587l\niy++4JdffmH58uV8++23VK1ale7du/PRRx8dt+7EiRO55ZZbAOjXrx9nnHEG69atY9WqVYwePZpd\nu3b53ceECRO45JJLmDBhQtBxvfvuu8ycOZMFCxawcuVK5syZg2rBKgjt3r2bJ598kvnz57NgwQKe\nfPJJ9uzZc8J6/fr14/nnn2fFihXceOONvPTSSwBUrlyZzz//nBUrVjBmzBh69OhRoHiCFZVjFJs2\nQVoaPPggnHUWdO4c7oiCkDGx0KxZ0KZNuKMxxq8HHoClSwt3mwkJ8NprOS/fvn07lStXpkyZMoD7\nMsxQsWJF5s+fT/Pmrp7oxx9/zIwZM9iwYQPz589n3LhxlCjhfu+ed955nHfeeSdsX1WZPHkyM2fO\n5NJLLyU1NZWyZcvmGve///1vZs2aRYUKFQA47bTT6NWrV9Dv258ZM2Zw5ZVXcsYZZwBw5ZVX8vXX\nX9O9e/fj1lu7di2tWrXKXKd9+/Y8/fTTNG7cOHOdevXqkZqayuHDhzOPXahEZYsiw/33u+kYKlUK\ndyQ5+PxzVwYcoG1bVwrckoQxx2nXrh1bt27lggsu4O6772b27NmZy7p3787EiRMBmDdvHpUqVSI+\nPp5Vq1aRkJBAySC6E37++Wdq1KhBzZo1adOmDdOnT8/1Nfv372f//v3UDKJ7+KWXXiIhIeGE28CB\nA09Yd9u2bVT1mZo4Li6Obdu2nbBe/fr1mTbNVTyaNGkSW7duPWGdKVOm0Lhx45AnCYjSFkXE27nT\nZbEJE9zPqQcecPWZStnhNpEt0C//UClXrhyLFy/mxx9/ZNasWXTt2pXnn3+e3r17061bNy666CJe\neeUVJk6ceMIv72BMmDCBbt26AdCtWzc++OADOnXqlOPZQSKCqgZ99tDDDz/Mww8/HNS6/rqu/O1n\n1KhRDBw4kKeeeorrr7+e0tnqu61atYohQ4bwzTffBLXfgrJvrsKk6pLDwIHw559uyr0hQ6yInzG5\nKFmyJG3atKFNmzY0aNCAMWPG0Lt3b6pWrUr16tWZPXs2U6ZMYe7cuYDrdlm2bBnp6emZXU/+HDt2\njClTpjBt2jSeffbZzAvS9u/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BrpLBABE5A7gRqKeqDYFnfF+sqpOBRbhf/gmqeshn8WSg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\nkCUKY4wxAVmiMMYYE5AlCmOMMQH9f8SjhunzlAs2AAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x178cd70dcc0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.title('Receiver Operating Characteristic')\n", "plt.plot(svc_fpr, svc_tpr, 'b', label = 'SVC AUC = %0.2f' % svc_roc_auc)\n", "plt.plot(fpr, tpr, 'b', label = 'LR AUC = %0.2f' % roc_auc)\n", "plt.legend(loc = 'lower right')\n", "plt.plot([0, 1], [0, 1],'r--')\n", "plt.xlim([0, 1])\n", "plt.ylim([0, 1])\n", "plt.ylabel('True Positive Rate')\n", "plt.xlabel('False Positive Rate')" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.2" } }, "nbformat": 4, "nbformat_minor": 2 }	gpl-3.0
zambzamb/zpic	python/Animation.ipynb	1	5158	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Animation using direct access to simulation data\n", "\n", "In this more advanced example, we show how to create a movie of the particles phasespace using direct access to the simulation data. The initialization of the simulation is done the normal way:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import em1d as zpic\n", "\n", "import numpy as np\n", "\n", "nx = 120\n", "box = 4 * np.pi\n", "dt = 0.1\n", "tmax = 50.0\n", "\n", "ppc = 500\n", "ufl = [0.2, 0.0, 0.0]\n", "uth = [0.001,0.001,0.001]\n", "\n", "right = zpic.Species( \"right\", -1.0, ppc, ufl = ufl, uth = uth )\n", "\n", "ufl[0] = -ufl[0]\n", "left = zpic.Species( \"left\", -1.0, ppc, ufl = ufl, uth = uth )\n", "\n", "# Initialize the simulation without diagnostics\n", "sim = zpic.Simulation( nx, box, dt, species = [right,left] )\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Instead of calling the `run` method as usual, we chose to call the `iter` method instead, that advances the simulation 1 time step. This is done inside an `animate` function that is responsible for updating the movie frames.\n", "\n", "To lower the overall frame count and speed up things, we don't create a frame for every iteration, and automatically skip a few iterations based on the movie parameters.\n", "\n", "Note that for this particular example, the time is mostly spent generating the animation, the simulation time is almost negligible." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "75d798bf5b0a49abbae12cda775310c2", "version_major": 2, "version_minor": 0 }, "text/plain": [ "A Jupyter Widget" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "5ca9f6c0ffb84ae8b56d784f359b643c", "version_major": 2, "version_minor": 0 }, "text/plain": [ "A Jupyter Widget" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "from matplotlib import animation\n", "from IPython.display import display\n", "import ipywidgets\n", "\n", "# Movie parameters\n", "nframes = 200\n", "tmax = 150.0\n", "fps = 16\n", "\n", "# Create progress bar\n", "bar = ipywidgets.FloatProgress( min = 0, max = 200 )\n", "label = ipywidgets.HTML()\n", "prog = ipywidgets.VBox(children=[label, bar])\n", "display(prog)\n", "\n", "# Create plot\n", "x = lambda s : (s.particles['ix'] + s.particles['x']) * s.dx\n", "\n", "fig, ax = plt.subplots()\n", "\n", "plt.rc('font', size=12) \n", "fig.set_size_inches( (10.66,6.0) )\n", "\n", "(p1,) = ax.plot([], [], '.', ms=1,alpha=0.3, label = \"Left\")\n", "(p2,) = ax.plot([], [], '.', ms=1,alpha=0.3, label = \"Right\")\n", "ax.set_xlabel(\"x1\")\n", "ax.set_ylabel(\"u1\")\n", "ax.set_title(\"u1-x1 phasespace\\nt = {:.1f}\".format(sim.t))\n", "ax.grid(True)\n", "\n", "ax.set_xlim( (0,box ))\n", "ax.set_ylim( (-1.5, 1.5) )\n", "\n", "# Function to create each movie frame\n", "skip = np.int32(np.ceil((tmax / dt ) / (nframes-1) ))\n", "\n", "def animate(i):\n", " label.value = \"Generating frame {:d}/200 ...\".format(i+1)\n", " bar.value = i\n", " \n", " if ( i > 0 ):\n", " for j in range(skip):\n", " sim.iter()\n", " \n", " p1.set_xdata(x(left))\n", " p1.set_ydata(left.particles['ux'])\n", "\n", " p2.set_xdata(x(right))\n", " p2.set_ydata(right.particles['ux'])\n", "\n", " ax.set_title(\"u1-x1 phasespace\\nt = {:.1f}\".format(sim.t))\n", "\n", " return (p1,p2)\n", "\n", "# Create the movie\n", "anim = animation.FuncAnimation( fig, animate, frames = nframes, repeat = False, blit = True, interval = 1000.0/fps )\n", "movie = ipywidgets.HTML(anim.to_html5_video())\n", "\n", "# Show the completed movie\n", "label.value = \"Done!\"\n", "bar.bar_style = \"success\"\n", "display(movie)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 }	agpl-3.0
yigong/AY250	hw4/hw4_training.ipynb	1	9498	{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "# import\n", "#!/usr/bin/env python\n", "import matplotlib.pyplot as plt\n", "from glob import glob\n", "import numpy as np\n", "import scipy.ndimage as ndimg\n", "import skimage.filter as filter\n", "import skimage.transform as transform\n", "import re\n", "from os import listdir\n", "from multiprocessing import Pool, cpu_count\n", "from pylab import imread\n", "from time import time\n", "import pickle\n", "from sklearn.ensemble import RandomForestClassifier\n", "import sklearn.cross_validation as cross_v\n", "\n", "\n", "def row(img2d):\n", " return np.shape(img2d)[0]\n", " \n", "def col(img2d):\n", " return np.shape(img2d)[1]\n", "\n", "def layer_mean(img2d):\n", " img1d = img2d[img2d<255]\n", " return img1d.mean()\n", "\n", "def hist_max(img2d):\n", " img1d = img2d[img2d<255]\n", " [n, bins, patches]= plt.hist(img1d, np.arange(255))\n", " return bins[n.argmax()]\n", "\n", "def edge_length(img2d):\n", " \"gray input\"\n", " imgsize = float((img2d.shape[0]img2d.shape[1]))\n", " med_filter = ndimg.median_filter(img2d, size = (5,5))\n", " edges = filter.canny(med_filter,3)\n", " return edges.sum()/imgsize\n", "\n", "def edge_sobel_h(img2d):\n", " \"gray input\"\n", " imgsize = float((img2d.shape[0]img2d.shape[1]))\n", " med_filter = ndimg.median_filter(img2d, size = (5,5))\n", " edges_h = filter.hsobel(med_filter/255.)\n", " return edges_h.sum()/imgsize\n", "\n", "def edge_sobel_v(img2d):\n", " \"gray input\"\n", " imgsize = float((img2d.shape[0]img2d.shape[1]))\n", " med_filter = ndimg.median_filter(img2d, size = (5,5))\n", " edges_v = filter.vsobel(med_filter/255.)\n", " return edges_v.sum()/imgsize\n", "\n", "def edge_sobel(img2d):\n", " \"gray input\"\n", " imgsize = float((img2d.shape[0]img2d.shape[1]))\n", " med_filter = ndimg.median_filter(img2d, size = (5,5))\n", " edges = filter.sobel(med_filter/255.)\n", " return edges.sum()/imgsize\n", "\n", "def houghLine(img2d):\n", " \"gray input\"\n", " med_filter = ndimg.median_filter(img2d, size = (5,5))\n", " edges = filter.sobel(med_filter/255.)\n", " [H,theta,distances] = transform.hough_line(edges);\n", " imgsize = float(len(theta)len(distances))\n", " return H.sum()/imgsize\n", "\n", "def cate_extract(image_path):\n", " cate_temp = image_path.replace(MYDIRECTORY,'')\n", " cate = re.search(r'/.+?/', cate_temp)\n", " return cate_map[cate.group()[1:-1]]\n", "\n", "def features(image_path):\n", " #red mean\n", " img2d = ndimg.imread(image_path)\n", " img2d_gray = ndimg.imread(image_path, flatten= True)\n", " \n", " row_n = row(img2d_gray)\n", " col_n = col(img2d_gray)\n", " \n", " red_mean = layer_mean(img2d[...,0])\n", " green_mean = layer_mean(img2d[...,1])\n", " blue_mean = layer_mean(img2d[...,2])\n", " gray_mean = layer_mean(img2d_gray)\n", " \n", " red_most = hist_max(img2d[...,0])\n", " green_most = hist_max(img2d[...,1])\n", " blue_most = hist_max(img2d[...,2])\n", " gray_most = hist_max(img2d_gray)\n", " \n", " length = edge_length(img2d_gray)\n", " sobel_h = edge_sobel_h(img2d_gray)\n", " sobel_v = edge_sobel_v(img2d_gray)\n", " sobel = edge_sobel(img2d_gray)\n", " hough = houghLine(img2d_gray)\n", " \n", " cate = cate_extract(image_path)\n", " \n", " return list([row_n, col_n, red_mean, green_mean, blue_mean, gray_mean,\\\n", " red_most,green_most,blue_most, gray_most,\\\n", " length, sobel_h, sobel_v, sobel, hough, cate])\n", "\n", "#!/usr/bin/env python\n", "\"\"\"\n", "AY 250 - Scientific Research Computing with Python\n", "Homework Assignment 4 - Parallel Feature Extraction Example\n", "Author: Christopher Klein, Joshua Bloom\n", "\"\"\"\n", "## CHANGE THIS NEXT LINE!\n", "MYDIRECTORY = \"/home/yigong/Documents/Class/AY250/hw4/50_categories\"\n", "\n", "# FUNCTION DEFINITIONS\n", "# Quick function to divide up a large list into multiple small lists, \n", "# attempting to keep them all the same size. \n", "def split_seq(seq, size):\n", " newseq = []\n", " splitsize = 1.0/sizelen(seq)\n", " for i in range(size):\n", " newseq.append(seq[int(round(isplitsize)):\n", " int(round((i+1)splitsize))])\n", " return newseq\n", "# Our simple feature extraction function. It takes in a list of image paths, \n", "# does some measurement on each image, then returns a list of the image paths\n", "# paired with the results of the feature measurement.\n", "def extract_features(image_path_list):\n", " feature_list = []\n", " for image_path in image_path_list:\n", " image_array = imread(image_path)\n", " ft = features(image_path)\n", " \n", " #ft = image_array.shape # This feature is simple. You can modify this\n", " # code to produce more complicated features and to produce multiple\n", " # features in one function call.\n", " feature_list.append(ft)\n", " return feature_list\n", "### Main program starts here ###################################################\n", "# We first collect all the local paths to all the images in one list\n", "image_paths = []\n", "categories = listdir(MYDIRECTORY)\n", "\n", "cate_map = dict(zip(categories, range(len(categories))))\n", "for category in categories:\n", " image_names = listdir(MYDIRECTORY + \"/\" + category)\n", " for name in image_names:\n", " image_paths.append(MYDIRECTORY + \"/\" + category + \"/\" + name)\n", "ip = image_paths \n", "image_paths = image_paths\n", "print (\"There should be 4244 images, actual number is \" + \n", " str(len(image_paths)) + \".\")\n", "# Then, we run the feature extraction function using multiprocessing.Pool so \n", "# so that we can parallelize the process and run it much faster.\n", "#numprocessors = cpu_count() # To see results of parallelizing, set numprocessors\n", "# to less than cpu_count().\n", "\n", "#create an array to store features\n", "#nFeature = 13\n", "#features_array = np.zeros(len(image_paths), nFeature)\n", "\n", "numprocessors = cpu_count()\n", "\n", "# We have to cut up the image_paths list into the number of processes we want to\n", "# run. \n", "split_image_paths = split_seq(image_paths, numprocessors)\n", "\n", "# Ok, this block is where the parallel code runs. We time it so we can get a \n", "# feel for the speed up.\n", "start_time = time()\n", "p = Pool(numprocessors)\n", "result = p.map_async(extract_features, split_image_paths)\n", "poolresult = result.get()\n", "end_time = time()\n", "#DATA = np.vstack([poolresult[i] for i in range(np.shape(poolresult)[0])])\n", "# All done, print timing results.\n", "print (\"Finished extracting features. Total time: \" + \n", " str(round(end_time-start_time, 3)) + \" s, or \" + \n", " str( round( (end_time-start_time)/len(image_paths), 5 ) ) + \" s/image.\")\n", "# This took about 10-11 seconds on my 2.2 GHz, Core i7 MacBook Pro. It may also\n", "# be affected by hard disk read speeds.\n", "\n", "# To tidy-up a bit, we loop through the poolresult to create a final list of\n", "# the feature extraction results for all images.\n", "combined_result = []\n", "for single_proc_result in poolresult:\n", " for single_image_result in single_proc_result:\n", " combined_result.append(single_image_result)\n", "DATA = np.array(combined_result)\n", "\n", "# DATA contains all the data we wanna train. Now is the training part.\n", "X = DATA[:,:-1]\n", "Y = DATA[:,-1]\n", "clf = RandomForestClassifier(n_estimators=50, n_jobs=-1, \\\n", " compute_importances=True)\n", "clf.fit(X,Y)\n", "scores = cross_v.cross_val_score(clf, X, Y, cv = cross_v.KFold(len(Y), 5))\n", "# save the classifier\n", "pickle.dump(X, open('data_X.p'), 'w')\n", "pickle.dump(Y, open('data_Y.p'), 'w')\n", "pickle.dump(clf, open('trained_classifier.p','w')) \n", "pickle.dump(scores, open('score.p'), 'w')" ], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }	mit
bamford/gzhubble	python/creating_debiased_catalog/STEP_1_p_features_thresholds_slope_method.ipynb	1	835447	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Step 1: Analyzing FERENGI Data \n", "## Determine correctable / uncorrectable regions of z/mu space. Ferengi galaxies that are considered 'correctable' are used to compute zeta in Step 2. This also generates low and hi limit values of p_features for the Hubble galaxies, which are useful particularly in cases where the values can't be confidentely debiased. " ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "%pylab inline\n", "from astropy.io import fits\n", "from datetime import datetime\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import matplotlib.gridspec as gridspec\n", "from astropy.io import fits\n", "from astropy.table import Table,Column\n", "from astropy.cosmology import WMAP9 \n", "import os\n", "import warnings\n", "import requests\n", "\n", "mpl.rcParams['text.usetex']=True\n", "mpl.rcParams['axes.linewidth'] = 3\n", "\n", "warnings.filterwarnings('ignore', category=RuntimeWarning, append=True)\n", "warnings.filterwarnings('ignore', category=UserWarning, append=True);" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Load data from Dropbox folder instead of clogging up Github\n", "\n", "def download_from_dropbox(url):\n", " \n", " local_filename = \"../{:}\".format(url.split(\"/\")[-1].split(\"?\")[0])\n", " r = requests.get(url, stream=True)\n", " with open(local_filename, 'wb') as f:\n", " for chunk in r.iter_content(chunk_size=1024): \n", " if chunk: # filter out keep-alive new chunks\n", " f.write(chunk)\n", " f.flush()\n", " \n", " return local_filename" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#Load Data!\n", "ferengi_filename = download_from_dropbox(\"https://www.dropbox.com/s/r88l9u5fcsbppui/ferengi_all_weighted_and_meta.fits?dl=1\")\n", "old_data = Table.read(ferengi_filename) " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 63, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Calculate Surface brightness using equation 3 from Griffith et al. 2012\n", "\n", "def new_sb(mag,r_pix,a,b,pix_scale):\n", " #convert to r from pix to arcsec:\n", " r = r_pixpix_scale\n", " sb = mag + 2.5log10(2b/apir2)\n", " return sb" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#Add new surface brightnesses to FERENGI table\n", "\n", "data = old_data.copy(copy_data=True)\n", "\n", "gz_sb = Table.Column(name='GZ_MU_I',length=len(data))\n", "data.add_columns([gz_sb])\n", "for gal in data:\n", " gal['GZ_MU_I']=new_sb(gal['automag_i'],gal['r50'],gal['a_image'],gal['b_image'],0.03)\n", "b_mag= Table.Column(name='bad_automag',length=len(data),dtype = bool)\n", "idx = Table.Column(name='Index',length=len(data),dtype = int)\n", "data.add_columns([b_mag,idx])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 67, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#cheaply set index\n", "for i,gal in enumerate(data):\n", " data[i]['Index']=i" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Don't include galaxies with bad surface brightness measurements\n", "for e in np.arange(0,4,0.5):\n", " egals = data[data['sim_evolution']==e]\n", " ugals = set(egals['objid']) #unique galaxies with evolution e\n", " for gal in ugals: \n", " this_gal = egals[egals['objid']==gal]\n", " this_gal_abs_mag = this_gal['automag_i'] - WMAP9.distmod(this_gal['sim_redshift']).value\n", " sig = np.std(this_gal_abs_mag)\n", " if sig > 1.: \n", " for row in data[(data['objid']==gal) & (data['sim_evolution']==e)]:\n", " idx = row['Index']\n", " data[idx]['bad_automag'] = True" ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "493 out of 3950 FERENGI images have poorly-measured surface brightnesses by SExtractor.\n" ] } ], "source": [ "print '%i out of %i FERENGI images have poorly-measured surface brightnesses by SExtractor.'%(len(data[data['bad_automag']==True]),len(data))" ] }, { "cell_type": "code", "execution_count": 79, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#redefine data - only includes good sbs. \n", "data = data[data['bad_automag']==False]" ] }, { "cell_type": "code", "execution_count": 81, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 17.83092442, 18.66896745, 19.50701049, 20.34505353,\n", " 21.18309657, 22.0211396 , 22.85918264, 23.69722568,\n", " 24.53526871, 25.37331175])" ] }, "execution_count": 81, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Defining surface brightness bins\n", "SB = 'GZ_MU_I' #set which surface brightness to use\n", "yedges=np.linspace(np.min(data[SB]),np.max(data[SB]),10)\n", "yedges" ] }, { "cell_type": "code", "execution_count": 82, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#define which vote fraction we use\n", "p_x= 't00_smooth_or_features_a1_features_frac_weighted_2' \n" ] }, { "cell_type": "code", "execution_count": 83, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#Pick out unique galaxies\n", "galaxies = set(data['objid'])" ] }, { "cell_type": "code", "execution_count": 84, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#simulated redshifts of ferengi data:\n", "reds=[.3,.4,.5,.6,.7,.8,.9,1]\n", "\n", "#Defining lists of p_features at high and low (z=0.3) redshifts at given SB and redshifts. \n", "scatter_dct={}\n", "for z in reds:\n", " for edge in yedges:\n", " scatter_dct[z,edge,'hi']=[]\n", " scatter_dct[z,edge,'lo']=[]\n", " scatter_dct[z,edge,'subj_id']=[]\n", " \n", "for i,g in enumerate(galaxies):\n", " this_gal=(data['objid']==g)\n", " evos = set(data[this_gal]['sim_evolution'])\n", " for e in evos:\n", " this_evo=(data[this_gal]['sim_evolution']==e)\n", " if len(set(data[this_gal][this_evo]['sim_redshift']))==8: #only want stuff where we have data down to 0.3\n", " p_at_3=(data[this_gal][this_evo]['sim_redshift']==.3)\n", " p_feat_at_3 = data[this_gal][this_evo][p_at_3][p_x][0]#value of p_x at redshift 0.3\n", " for row in data[this_gal][this_evo]:\n", " for y in range(0,len(yedges)-1):\n", " for j,hi_z in enumerate(reds):\n", " if round(row['sim_redshift'],2)==hi_z and row[SB] > yedges[y] and row[SB] < yedges[y+1]: #now look at high redshift data \n", " scatter_dct[hi_z,yedges[y],'hi'].append(row[p_x]) # slap p_x in high list \n", " scatter_dct[hi_z,yedges[y],'lo'].append(p_feat_at_3) # put z=0.3 value in low list \n", " " ] }, { "cell_type": "code", "execution_count": 85, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def derivative_of_poly(x_list,fit_cos,deg):\n", " if deg == 3:\n", " derivative=3fit_cos[3]x_list2+2fit_cos[2]x_list+fit_cos[1]\n", " if deg == 2:\n", " derivative=2fit_cos[2]x_list+fit_cos[1]\n", " if deg ==1:\n", " derivative=fit_cos[1]\n", "\n", "\n", " return derivative" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 87, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/lib/pymodules/python2.7/matplotlib/collections.py:548: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", " if self._edgecolors == 'face':\n" ] }, { "data": { "image/png": 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DLsUN2xQ3bPPGjfvuu48zz0xm3749JCcP4KWXnqZu3bqRHl6Vt3XrjxF77qys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9bW1tzhSBJAlNxo2LAhP/74o7nDkSQQb29v+vXrx759+8iTJ4+5w5EkAHndkrqQMzgkQmIoN8aN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e1pZGhbtmxh2LDfMBjWA86o1WOoWTOGZctSZ5c8ffqUyMhIPDw8/vIqVu/bf2Kc60tKlZrD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\nQIFQMmWqTaZM9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SOMna2AkX37AmnZUpSCrVatGmvWLKFXr4EkJDzDatUgzNQLArWArxFS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\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dRq3WM8UXM5GRkTbp0k0mE93dfQjsNN/TjygIRXjmzJkM22DNn0NXlv8hEYtgf88Qp8Z/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YsVzdY2Zwd/4sFgtnzZrL8uVrs3LlBly5MiVNdk6zpRw+fJiVK9ejyeTHxo3bZ+i9sWPHDgqC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\nrupzp7FHpi9uXLt2jZ6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IIBEKxVaQewAEAVgLaWvibvn7swA6ARgHYAIslnO4dKkPJk78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0ps7AHkYrsJcpLhjawCcTTu79uQXiLHMRrrc+3aufh41VfJxYsXKVeuOtHRm4ByqFQz8fH5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\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NOXvWP4arnQS+AiRUqtL873/b2LhxK4sWPcBkmgkoUCgWUKnSX2zYsOy9dS5dugpPn/6K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BxzS8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\nuKE2CTfUJeGG2qRvsZjyif+/T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"text/plain": [ "<matplotlib.figure.Figure at 0x7f5a2dc38150>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Plot p_features at a given redshift vs p_features at z=0.3 at each redshift, binned by surface brightness\n", "f=figure(figsize=(20,18))\n", "#track 'uncorrectable' regions of z/mu/p space (shaded)\n", "p_range_uncorrectable_dct={}\n", "#track 'correctable regions of z/mu/p space (unshaded and* at least 5 points in bin) \n", "p_range_correctable_dct={}\n", "#store range of spread of data in each bin\n", "interval_dct={}\n", "x_new = np.linspace(0,.95,40)\n", "slope_cut=.4\n", "\n", "gs=gridspec.GridSpec(9,10)\n", "gs.update(wspace=0)\n", "gs.update(hspace=0)\n", "yedge_int=[8,7,6,5,4,3,2,1,0]\n", "y_label=[]\n", "facecolors=['#af8dc3','#999999','#7fbf7b']\n", "for i in range(0,len(yedges)-1):\n", " y_label.append(round((yedges[i]+yedges[i+1])/2,2))\n", "y_label=y_label[::-1]\n", "for j,y in enumerate(yedge_int):\n", " for i,z in enumerate(reds):\n", " if z==1 and y == 3:\n", " plt.rcParams['axes.linewidth'] = 8 #embolden 1 square\n", " ax=plt.subplot(gs[j,i])\n", " xs=scatter_dct[z,yedges[y],'lo']\n", " ys=scatter_dct[z,yedges[y],'hi']\n", " plt.plot(x_new,x_new,c='k')\n", " flat_list=[]\n", " p_range_uncorrectable_dct[z,yedges[y]]=[]\n", " p_range_correctable_dct[z,yedges[y]]=[]\n", " plt.scatter(xs,ys)\n", " if len(xs)>5:\n", " poly_params=np.polynomial.polynomial.polyfit(xs,ys,3)\n", " poly = np.polynomial.Polynomial(poly_params)\n", " drv=derivative_of_poly(x_new,poly_params,3)\n", " if np.min(drv)>-.3: #fit okay, go ahead and plot\n", " plt.plot(x_new,np.polyval(poly_params[::-1],x_new),c='k',lw=3,ls='dashed')\n", " for d,val in enumerate(drv):\n", " if val < slope_cut:\n", " flat_list.append(x_new[d]) #list of x-values for shaded region\n", " if len(flat_list)>0: # if there are bad regions, record stuff:\n", " min_p_at_3=np.min(flat_list) #minimum x value of shaded region \n", " max_p_at_3=np.max(flat_list) #maximum x value of shaded region\n", " \n", " #get list of y values in shaded region\n", " bad_p_at_z_list=[] \n", " for p_int,p in enumerate(xs): \n", " if p>=min_p_at_3 and p < max_p_at_3:\n", " bad_p_at_z_list.append(ys[p_int])\n", " #shade out 3 standard deviations above and below the mean of that area \n", " min_val = 0 #I think the minimum should be 0...because... \n", " if len(bad_p_at_z_list)>0: #need at least 1 pts to get mean and std\n", " max_val = np.mean(bad_p_at_z_list)+1.5np.std(bad_p_at_z_list)\n", " else:\n", " max_val=0\n", " plt.axhspan(0,max_val,alpha=.1)\n", " p_range_uncorrectable_dct[z,yedges[y]].append(0)\n", " p_range_uncorrectable_dct[z,yedges[y]].append(max_val) \n", " p_range_correctable_dct[z,yedges[y]].append(max_val)\n", " p_range_correctable_dct[z,yedges[y]].append(1)\n", " else: #if no bad regions, whole square is correctable. \n", " p_range_uncorrectable_dct[z,yedges[y]].append(0)\n", " p_range_uncorrectable_dct[z,yedges[y]].append(0) \n", " p_range_correctable_dct[z,yedges[y]].append(0)\n", " p_range_correctable_dct[z,yedges[y]].append(1)\n", "\n", "\n", "\n", " else: #if < 0, try 2nd order instead \n", " poly_params=np.polynomial.polynomial.polyfit(xs,ys,2)\n", " poly = np.polynomial.Polynomial(poly_params)\n", " drv=derivative_of_poly(x_new,poly_params,2)\n", " if np.min(drv)>-1: #fit okay, go ahead and plot\n", " plt.plot(x_new,np.polyval(poly_params[::-1],x_new),c='k',lw=3,ls='dashed')\n", " for d,val in enumerate(drv):\n", " if val < slope_cut:\n", " flat_list.append(x_new[d]) #list of x-values for shaded region\n", " if len(flat_list)>0: # if there are bad regions, record stuff:\n", " min_p_at_3=np.min(flat_list) #minimum x value of shaded region \n", " max_p_at_3=np.max(flat_list) #maximum x value of shaded region\n", " \n", " #get list of y values in shaded region\n", " bad_p_at_z_list=[] \n", " for p_int,p in enumerate(xs): \n", " if p>=min_p_at_3 and p < max_p_at_3:\n", " bad_p_at_z_list.append(ys[p_int])\n", " #shade out 3 standard deviations around the mean of that area \n", " min_val=0\n", " #min_val = np.mean(bad_p_at_z_list)-1.5np.std(bad_p_at_z_list)\n", " if len(bad_p_at_z_list)>0: #need at least 1 pts to get mean and std\n", " max_val = np.mean(bad_p_at_z_list)+1.5np.std(bad_p_at_z_list)\n", " else:\n", " max_val=0\n", " plt.axhspan(min_val,max_val,alpha=.1)\n", " p_range_uncorrectable_dct[z,yedges[y]].append(0)\n", " p_range_uncorrectable_dct[z,yedges[y]].append(max_val) \n", " p_range_correctable_dct[z,yedges[y]].append(max_val)\n", " p_range_correctable_dct[z,yedges[y]].append(1)\n", " else: #if no bad regions, whole square is correctable. \n", " p_range_uncorrectable_dct[z,yedges[y]].append(0)\n", " p_range_uncorrectable_dct[z,yedges[y]].append(0) \n", " p_range_correctable_dct[z,yedges[y]].append(0)\n", " p_range_correctable_dct[z,yedges[y]].append(1)\n", " else: #fit still bad, do a linear\n", " poly_params=np.polynomial.polynomial.polyfit(xs,ys,1)\n", " poly = np.polynomial.Polynomial(poly_params)\n", " plt.plot(x_new,np.polyval(poly_params[::-1],x_new),c='k',lw=3,ls='dashed')\n", " drv=derivative_of_poly(x_new,poly_params,1)\n", " if drv < slope_cut:\n", " #plt.axvline(x_new[d],lw=3,alpha=.1)\n", " flat_list=x_new #list of x-values for shaded region\n", " min_p_at_3=np.min(flat_list) #minimum x value of shaded region \n", " max_p_at_3=np.max(flat_list) #maximum x value of shaded region\n", " \n", " #get list of y values in shaded region\n", " bad_p_at_z_list=ys\n", " min_val=0\n", " if len(bad_p_at_z_list)>0: #need at least 1 pts to get mean and std\n", " max_val = np.mean(bad_p_at_z_list)+1.5np.std(bad_p_at_z_list)\n", " else:\n", " max_val=0\n", " plt.axhspan(min_val,max_val,alpha=.1)\n", " p_range_uncorrectable_dct[z,yedges[y]].append(0)\n", " p_range_uncorrectable_dct[z,yedges[y]].append(max_val) \n", " p_range_correctable_dct[z,yedges[y]].append(max_val)\n", " p_range_correctable_dct[z,yedges[y]].append(1)\n", " else: #if no bad regions, whole square is correctable. \n", " p_range_uncorrectable_dct[z,yedges[y]].append(0)\n", " p_range_uncorrectable_dct[z,yedges[y]].append(0) \n", " p_range_correctable_dct[z,yedges[y]].append(0)\n", " p_range_correctable_dct[z,yedges[y]].append(1)\n", "\n", " \n", " plt.xlim(0,1)\n", " plt.ylim(0,1)\n", " \n", " else: #fewer than 5 points in square, so fuck it\n", " plt.axhspan(0,1,alpha=.1,color='k')\n", " plt.xlim(0,1)\n", " plt.ylim(0,1)\n", " p_range_uncorrectable_dct[z,yedges[y]].append(0)\n", " p_range_uncorrectable_dct[z,yedges[y]].append(0) \n", " p_range_correctable_dct[z,yedges[y]].append(0)\n", " p_range_correctable_dct[z,yedges[y]].append(0)\n", " \n", " plt.tick_params(labelleft='off')\n", " plt.tick_params(top='off',right='off',labelbottom='off')\n", " plt.rcParams['axes.linewidth'] = 3\n", "\n", " \n", " if j==8:\n", " plt.xlabel('%s'%reds[i],fontsize=34)\n", " if i==7:\n", " ax.yaxis.set_label_position(\"right\")\n", " plt.ylabel('%s'%str(round(y_label[j],1)).rjust(500),fontsize=34,rotation=270,labelpad=30)\n", "\n", "fs = 55\n", "f.text(.78,.5,r'$\\mathrm{\\mu_{i}~(mag/arcsec^2)}$',rotation=270,fontsize=fs,va='center')\n", "f.text(.45,.91,r'$\\mathrm{redshift}$',fontsize=fs,ha='center')\n", "f.text(.08,.5,r'$\\mathrm{f_{features}}$',rotation=90,fontsize=fs,va='center')\n", "f.text(.45,.05,r'$\\mathrm{f_{features},z=0.3}$',fontsize=fs,ha='center');\n", "\n", "plt.savefig('../../writeup/figures/p_vs_p_SB_redshift.pdf')" ] }, { "cell_type": "code", "execution_count": 88, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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lnHKehIQE+vTpw4EDB4iKiqJGjRpOOY+Yxx2HeQRjdNKxN8OOsyggRSTPLBYL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\nIIcP/0Vs7AlOnjzByZPHadCgCY0ahVK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"text/plain": [ "<matplotlib.figure.Figure at 0x7f5a25c66510>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Subfigure plot:\n", "\n", "xs = scatter_dct[1,yedges[3],'lo']\n", "ys=scatter_dct[1,yedges[3],'hi']\n", "x_new = np.linspace(0,.99,40)\n", "\n", "x_line=np.linspace(0,1,10)\n", "#blue\n", "flat_list=[]\n", "\n", "f=plt.figure(figsize=(7,7))\n", "\n", "poly_params=np.polynomial.polynomial.polyfit(xs,ys,2)\n", "poly = np.polynomial.Polynomial(poly_params)\n", "drv=derivative_of_poly(x_new,poly_params,2)\n", "if np.min(drv)>-3: #fit okay, go ahead and plot\n", " plt.plot(x_new,np.polyval(poly_params[::-1],x_new),c='k',lw=3,ls='dashed')\n", " for d,val in enumerate(drv):\n", " if val < slope_cut:\n", " flat_list.append(x_new[d]) #list of x-values for shaded region\n", " if len(flat_list)>0: # if there are bad regions, record stuff:\n", " min_p_at_3=np.min(flat_list) #minimum x value of shaded region \n", " max_p_at_3=np.max(flat_list) #maximum x value of shaded region\n", " \n", " #get list of y values in shaded region\n", " bad_p_at_z_list=[] \n", " for p_int,p in enumerate(xs): \n", " if p>=min_p_at_3 and p < max_p_at_3:\n", " bad_p_at_z_list.append(ys[p_int])\n", " #shade out 3 standard deviations above and below the mean of that area \n", " min_val = 0 #I think the minimum should be 0...because... \n", " if len(bad_p_at_z_list)>0: #need at least 1 pts to get mean and std\n", " max_val = np.mean(bad_p_at_z_list)+1.5*np.std(bad_p_at_z_list)\n", " else:\n", " max_val=0\n", "plt.axhspan(0,max_val,alpha=.1)\n", "\n", "plt.scatter(xs,ys)\n", "interval_dct[z,yedges[y]]=[]\n", "plt.xlim(0,1)\n", "plt.ylim(0,1)\n", "this_dct={} #stores p_features at z=0.3 value for each bin (xs); use to compute spread in each bin\n", " #stored like: this_dct[lower_bin_edge,higher_bin_edge] = [distribution of xs in bin]\n", "bins_list=np.linspace(0,1,6) #bin measured p_features from 0 to 1\n", "for b in range(0,len(bins_list)-1):\n", " bin_bottom = round(bins_list[b],1)\n", " bin_top = round(bins_list[b+1],1)\n", " this_dct[bin_bottom,bin_top]=[]\n", " for l,val in enumerate(ys): #check p_features at z values\n", " if val >= bin_bottom and val < bin_top: # if it falls inside bin in question:\n", " this_dct[bin_bottom,bin_top].append(xs[l]) #then put corresponding p_features,z=0.3 value in list\n", " \n", " #Now compute the mean and spread of p_features,z=0.3 for each bin\n", "p_features_means=[]\n", "x_error_lo=[]\n", "x_error_hi=[]\n", "bin_centers=[]\n", "\n", "for b in range(0,len(bins_list)-1):\n", " bin_bottom = round(bins_list[b],3)\n", " bin_top = round(bins_list[b+1],3)\n", " try:\n", " p_features_means.append(np.median(this_dct[bin_bottom,bin_top]))\n", " except IndexError:\n", " p_features_means.append(0)\n", " try:\n", " # x_error_lo.append(p_features_means[b] - np.percentile(this_dct[bin_bottom,bin_top],10))\n", " x_error_lo.append(np.percentile(this_dct[bin_bottom,bin_top],50) - np.percentile(this_dct[bin_bottom,bin_top],10))\n", "\n", " except IndexError:\n", " x_error_lo.append(0)\n", " try:\n", " # x_error_hi.append(np.percentile(this_dct[bin_bottom,bin_top],90) - p_features_means[b])\n", " x_error_hi.append(np.percentile(this_dct[bin_bottom,bin_top],90) - np.percentile(this_dct[bin_bottom,bin_top],50))\n", " \n", " except IndexError:\n", " x_error_hi.append(0)\n", " bin_centers.append((bin_top-bin_bottom)/2.+bin_bottom)\n", " try:\n", " interval_dct[z,yedges[y]].append({'bin_bottom':bin_bottom,'bin_top':bin_top,'low_limit':np.percentile(this_dct[bin_bottom,bin_top],10),'hi_limit':np.percentile(this_dct[bin_bottom,bin_top],90)})\n", " except IndexError:\n", " pass\n", "\n", "p_features_means=[p_features_means[0],-1]\n", "center = max_val/2.\n", "bin_centers=[center,-1]\n", "x_error_lo=[x_error_lo[0],-1]\n", "x_error_hi=[x_error_hi[0],-1]\n", "plt.errorbar(p_features_means,bin_centers,xerr=[x_error_lo,x_error_hi],fmt='o',c='#d95f02',markersize=1,elinewidth=5)\n", "plt.xlabel(r'$\\mathrm{f_{features},z=0.3}$',fontsize=25)\n", "plt.ylabel(r'$\\mathrm{f_{features},z=1.0}$',fontsize=25)\n", "\n", "plt.savefig('../../writeup/figures/p_vs_p_SB_redshift_subplot.pdf')" ] }, { "cell_type": "code", "execution_count": 89, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7f5a2c9efb10>" ] }, "execution_count": 89, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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TX+qqeWW6X8pH/uymTp1mPt8xBl8ZrDKfr50NHz46JHn4/X6LjIw2SNv3B35s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imZZs+qL5Bau4CAYDly5IgUKVJabLbuYrO1l8DAUlK6dHWpWrVByv7d1DjnapBLp9pyM83XWYGK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\nNPULarRoX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mODYR1Vq1bwslV3H0WLFmXr1lAiI88SHX2V999/545t9Ho9v/22nGLFRmGx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ISIiAnBcYcHF5BYsXv4bOnTvbydq8I622t3PnTsyY8QUOHjyKe/daAZiL5aFNUNe3YGhnSw7c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oArL0bHjj0oEiof0L6hzSxUqFya43Lz5k1mz56Xev1bBOZSknJQlq0sVqz8A6tNPAiixGdT\n7Xv4jYqSh5s2bUpXW064089ut2uKDmdVmaNUlCAeO3aMXl7BFAoyQQtZjqLBUFWbe5soFKMqgY8p\nlN5GAi9R5Gr4lCLUKYhAAA0Gf86fP5+y7EdRGaUrJWkkg4LCaLHkIrCfwElKUjnqdMUpkmwW5eDB\nwzz6LhRo7grUOVrowRJt3i+iCK26RBEyM5wxMSJp8fr167XvYCdFYsGqlOWg5ypJ9t1zb+nSpVTV\n0hR70RhtbTtMUX42iIBzn3HQaGzHkSNHsVChUhSKsN4EBmvjtpX58pVh7dpNKMuvad96HBXlZU6f\nPsP1/F69XqNIwnqZIhdHHUqSct9qQIMGDdUSO/5FYD8VJTfXr19/z3XXr19nuXLVaTTaqNdb2KfP\nQI91au/evdq6sYZi3/amogRz1qyPPdopXboahaIggGLvKMjAwPBnJlzVnX4ZlB2fR7yBtF3/3ZUA\nYQAOIHOM7JkC986l1amnogS4fPkyc+QIpyRZabH4ccmSJbxw4QLDwgrTZitBiyUfIyOLZ6gUyQs8\nHA+j3759+zhs2Lvs1asX9foINwblBIVA8RqBXfTyCnEteBcvXqRer1DELuamEEbyU1iwxhB4iVZr\nqIfl/uOP52o5AYpRVQPSzST06zeYqhpF4F2qagXWrdsswwzjs4pHmXtXr17l/v37+euvv96TLfzE\niRPcuHEj3357KHv16s+1a9dy586dtFiCKayWtSmy5DqZla0EilCS8jI8vDg3btz4wP4dOXKEsmwj\n8AlFdvl1FDXMGzIysni6rEruOHv2LDdv3szffvuNsbGtebd2PyqqfIbaz2o8iH5z5syh2VxbYwx3\nEjhJi8WHZcpU1pQd2yisVFYKBc0NjYH1p7Ayfa/97XAbk4YUQkV7jWmSmSpgksK6vIyiokBqyTKj\nMYQmk7vAmUJJkmkw+FDEn9ei2Rzgqn3scDj4+eefc9CgIZp1qxKFt4Hz/q9YuXLskx7uTMf96Nen\nTx+KsnDOWOtYiuRvzvdfoK2HuSiE6NaUJF8WLlyGt2/fpkgOF0JggsbkRlPEcteiED5epVAQeFMk\nn/qRwMts0KAFd+3axeDgMFqtpamq+Vi+/CuujNtTp86g2VyaQri4QbO5BgcOfJsNG7akJMnU6Qzs\n0aMvZ86cTV/fHDSbvViqVDkOHTpUK2Pn9OK6TrM5gGvWrGG7dt3YqlVn7tq1i3FxcezZc4BWfu68\n6311uiEcPfr+lTmeBh4094oVq0QR608CVymExFRvDC+vWAYF5aEoVSyOSVJ3Dh36Dm/dukVVDaGo\nZHSboqRmJIE+BArQy6sE9+7dy7x5S7o9g1oFJJlC2E+hU4kj9k5J+yY+pxBI9dTpwujjE0RZfo/A\nPkpSPxqNPlRVf+bMWdCjDnnXrn3oWWFlOwsWfCnNcVm3bh1ttipu1ybQYFAzVC0lODiCqaVaSeCD\nDOd3cqffxYsXNc+ZVGWzl1cjfvXVV1QUP4p8GysI/EyDoQtLl65ASfLS6LKSQgFg0+acpM2xFNf7\ni3k2moJ3USlJEyjW14a02XKwWbO2FB5W57Q5v49WazYWLFiSsbGiTOPSpUs5ZMgQFioUzdy5o2gw\neBMYRmAkjUYfba0nhfAXqPXFSJMpiIGBeVijRh3Wrx/LGjVq0bN2/B/U60XJ0WPHjnHixImcMWNG\nmnlREhISeOrUqQdWDXkSuHvu2e12tmzZkYoSSp0umKlJaElgGiXJQr2+CI3GIgwLi+K1a9fYsWMP\nSpIPhbeGH4EvKcv9GBlZnMLLykahzLlAYCoLFSrNsmVfYceOvfjxxx9r3hpG7deWsmzxkDV+++03\nbt++nRcvXmRYWHEC+9z69BE7dux53/c7fPgwa9duyoIFX2LHjr088uGsW7eOBQpEM2fOwnzzzXfT\nNFKNGTNB679TgZ9Ik6mIR9Lmpwl3+mVQdnwe8QZE7KwDIp7L+bvq9q8Dz5AXAOAp+KelBMhQ8gKk\nQwmQkpJCozGAQBVtcypCwMrvvvuO8fHx3LNnD3/44Yf7ugq+QObhQfRbv349FSWIkjSUen1DjQFJ\noshuHEjBoPajEERkmkxWDh8+mpcvX6bR6EWR/OkERY35YkxNQrclTUvExYsXeeDAgXQzHKnPdbrZ\nJlBVIx5Yp/x5xsPm3oIFi7TxCCVgodlsc9WyHjLkXZrNgfT2LkerNZATJkzQMtvqmD17uJbR3Osu\nhmO+Rvc1BJbSYrk38/LdOHz4MPPkKUxJ0tHHJwebN2/FCRMmZDir7bp166goAfT2rkqLJYTVq9ej\nokRr39wdWiz1OGDAmxl6RlbjQfTr0aOHNv5NKKxCzWkwKLx8+TLr1m1Of//cNBgCKEl5teu8NQvk\nMgqX4oLa8flMVdoFUSjmGlIoCqwao7SUQpkXSZGFvCxFKBYphBcDhYDSgMI6uo+AWbO2LKFe/xLr\n1Wua5jsKq3ZnCotGEgVTXY3e3tmfSBnGrMT96Ld7924KwaEggbYaHVQK699UjcGro83LxhQuoleo\nKHU4e/ZsRkREabT5m0L4nKvRKRdl2cp8+QpQuMLuo/DaKKBdr6PR6EO9XqXR6MfIyCK8dOmSR7/0\nei+mll70oaoGave2oihX5kOj0YuNGrVxMa8HDhygl1dRt3WA9PIqds+6GhvbkmZzIwJ5mCrgOmix\nxHLWrFlZT5DHwIPmXlTUSxSJMZ1CoJGpIU3JtFqLcfjw4VSUEALvU6/vRX//UJ49e5Y3b95kVFS0\nRn+9RuuJFMo4AxUlggcOHGCuXFEUlmPnmI6i0ehNofBxJj6rw0KFyjAxMVGbR85kY3G0Wotx3rx5\nzJ+/BEWYViTNZl8uW7binvfZt2+f5rXxKYGVVJS8nDNnbprjsmHDBtpsFZmqPLxNg0HJUKhWoUJl\nCSx3vavR2JZjxnyQ7vZIT/olJydTUXzcxvMKFSUX9+3bx6pVa2vzrw5FmVuVwrsmm/avQuF6n0ej\nuarNW+f72ykUp39SrK3uoQCJ1OstbNOmPYXVNlCjdz0t4eB71Ok6UZJU6vXVKLzgVAI5aDRGsmzZ\niuzbdyBnzpxJVS1MwSeRwHJaLN48fvw4K1euTYOhgvYNlaFY22OYqpDbyty5i/C7776jqgbQaOxN\ni6UFQ0LCPeb+zp076eUVREUJpaL4cvXqNQ8Y3axFWnPP4XDw8OHDLFAgmqmlmx0EilOWSxEYTZOp\nFJs1a8/NmzdrVWmcCuzBGi0t2vhv0+4dRKAedbrKlOUSBDbQYOipJbwMJVCOIgwnXJuv3syRIy87\nduxBiyUbvb1fptUayLx5i9I9QbJe35eDBqXNW9y6dYvZskVQlocT2E2TqT1jYmo8liEqOTmZQumX\n5HqmxdKFM2bMePjNTwDu9Mug7Pi84SsIAf84gP13/bZovy+RcZk60+HeobTCAWZnsP37bqZpYdeu\nXaxWrZq26Dq1rVcImNmyZass/nxf4G48iH7CWrHJtRlKUiT1+uraJjTObTOcpDEuf1NRCnPx4s/Z\noEErWiy1CHxJg6GLFos3gaLeay4uWpR5ruBOnDx5UnOxTLV+entX4vbt2zP9Wc8C7kc7u93OQ4cO\n0Wz2Y6rL8bcEfKmq/ty2bRsVJTdTqyusp4gz/Fqbk9005uh9CstyP4o8ASpTLUq3CIxio0ZteeXK\nlYducpkZliGYPl8Ka4xwhVaUnGzevC31ehNl2cjGjdt61Bx+FnE/+jkcDlqtgQS+094vnkBe1qjh\nWZmhSZN2lOWGFNaOHyjL/tTpjEx1b22mMaehGnPUSmN2amrMpJlAZQpLSjBFpusAAjGUJG8t+3sO\nigzx8Vp7BbXr87nN/xuUZeM94/3nn38yLKwYJcmXgum2aX2LpcHQgW+9lf4yU88C7ke/Dz/8iLJc\nQBtfi/ZTtPHrRKGYUbXxcLeYv8n33nuPJ06c0KxcE+gUxIRwuIgGQze2bt1ao99hCqWMH4W3jZ3C\nOh1I4ByNxl6sWLE6+/YdyNGj32eFCrUoBMGZFB4cezS6f8JUprstgYI0mdqxTh0RTnP79m36++fU\n7r1F4DP6+eXgL7/8wuPHj9PhcDApKYk6nYFCkFmn9aEHjcYaLFCgVIa9fjIbD5p7Fou3Ni7NKMI6\nrNTrc1GShlJRqrJixVpMSUnhrl272LfvQA4b9q4r1KFPn4FaKMQOiizgTkFlJwEby5QRpUtHjRpL\nRSmhHV9KRQnk5MmTabH402AoQlnOxsjIoq5xW7VqNS2WAFqtrWi1RjE2tqVW796PqSUHf6LF4uuy\nAu/fv5+zZ8/mxo0bNb6rIWNianP+/IU8deoUx48fzwkTJrjyNJAifj8srDANht4EvqKi1GCzZqnl\n0JKTk7l69WrOmzePJ06ceKSx/vbbb6koATSZulNR6jMsrLCrxHBm0c+ZO8jbuybN5mDmyVOUUVHl\nKcsKRRlgUiiIfSmEv0oabc5RZOg3aWvfJ9p8Gqitv50IlKDYF9cSKMVU/uIqZdmklbF1esz9TqF0\nmKL93ZmAe0nJN7VnR7lKnjocDjZv/ipVNT+9vJpQUQK4du1aHjp0SAvNSdDuvUOhxI3W+tiPihLM\nNWvWsESJl+mewd5g6MG33xbr6+3btzUlktMIs4+K4v/UvGzTmnsOh4PHjh3jJ598QovFn5I0lLLc\nWntPp3LkNs3mIHbp0kWLvb9AodwOYKqlfhGFh9U+jV4KdToftzY+plCoOgXsehReF7s0eodTrMtO\nJcs2Wq2BtFj8KcsDaTS+ysDAXDx37hwdDgf/++8/jzCCLVu20Murghu9k2ky+bky/D8qihQpp5Xe\ndhA4RoslJEMVGTIT7vTLoOz4vOHE0+5AelEdol6hF+71CjgAoEQG209zM00Ls2fPpmCGSlJoNZ0T\nxU7Am23btn0Cn/ALuONB9AsOzuu2gZLACFao8DJl2WlxdB5fSZHUiARmsXXrLkxKSuKIEe+zevXG\n7NVrAL/88ktGRBRntmyF+OabWWOhTUlJYXh4EcrySAL/EphLX98cz73F8X5Ii3abN2+mzRZAvd6i\nMSxOGiURCKBOZ2W1atVoNlehqEedi6mWwcnatT0oYrhJEQfXi4BFc+tcRlHSyEohUJppMNgYEJDT\nI0/A5s2bOXjwW5wyZUqaAoDD4eCnn85jbGxrdu3ax4MRfRguXbqkuSff6/6ZnJx839i+Zw1p0e+v\nv/7iuHHjKEky3V2QJak5K1R4mW+9NYwXLlwgSc0F9rTbOLzN/PmLskyZihSCv55CEI1lam3ichQK\nWBtFSabsFEzrQoqcHH8TmMYSJSqwdesO9FT2/U6hFJhMoTxwHo+jLJs83EyvX7/OgIBc1OkmUZQo\nc9ZDPqQxNmKdeJ5xN/1u3rzJbt360s8vjzZHEikUbQPoqTQhgTCazQHU6V6lULAcoSwHujx1Dh48\nSEUJ1GgVTCGMLCRgodEYTEXxpyxbCOgoSYF3tV2VQhkwlJIUTOADGo0daDT6UsRNV2GqcjeEIubc\nee8UCqHnOg0GxfWuhw8fZmRkCer1ZkZEFGVUVDQVJSctluysWLEWb926pSWtdNaz/5VGYz527979\nmSzXeb99z263U5YN2ryaTyFUNNXmko6AjnXqNGV8fDxTUlI4Y8ZMdu3ah9OmTWdycjLLl69D4Snl\noFCeZqckvUSdzkpZtlCvtzA0ND+7d+9Df//cNJuzs0CBaH766acsUKA09XoLQ0LycsyYMWzR4lVW\nqFCX48ZNot1u5x9//MGFCxdy69atdDgc/O677+jtHX3XOhjFgwcPcsaM2VSU7FSUzrRao9iqVSeX\novbIkSO02YJoMPSk0didXl7BPHbsGJctW8ahQ4dx6tSp7NatL6tVa8T33vvA5ZGZlJTEmJgatFqj\nqaptqCgBHrXXH4Q//viDU6ZM4aeffvpY5UIfhX5Tp07l3LlzeeTIES5atIheXsGUpHEUQrH/XXOj\nHIXn6Tdux0TSyTx5ilOELJ4j0IZCcRlKEbqzkMKybKXB0I7AHCpKNDt27EGDQfV4hiQ5k/uRwgNh\njdv5VdoxyaPkqcPh4DfffMOlS5e6lCs//PADvbxKuN3roPDWOkxJGsiiRcvyxx9/pN1up6pmp2dC\n0I/YuXNvN3rn9+ijt3cF7ty5M8N0SA/unnvJycmsW7cZFSUHbbaCDAjIzRw5CjAwMIwmU4G7xjYX\nDQZ/CjnCWUq8uNs1dyj4krwEClCv96HR6M9Uof9NepafLUrPcoOrtDZTx1yWjdy3bx9HjRrNiRMn\n8uLFizx9+jQjI4vTaPSiwaBw+nTh6fT111/TZitBd08ao9HmSvr3qDhz5gwLFChFvd5Co1Hl3Lmf\nZTod0gt3+mVQdnzeMAvO0mbPIZoiNVbB/d+M5gMAHlEJcOfoTm0yrqWw/AdQMEp/UiSZs7lqxjuv\nf4Gsx4Po16vX61rW9+MEvmHF3P6sXbsuhZWnIEUinUMUDO7HFBro7hw8+G2Pdi5cuEB//1AtkdgM\nKkoYZ8yY7XHN/ejtcDi4YMEiNm7cnr17D3ioRvXvv/9mhQq16OUVzCJFYlx1V/8XcTftzp8/T1UN\noNBq/0Vh9TjN6OAtFMl2ClMIhCpTlXH9KKwcxyncHr+lUAB0dtsID1Cn82ZkZBEKgTGIQtPufBYJ\nrKCvb3bGx8dz0qQpVJQwAu/RbG7gqllNptJ5xIj3qShRBBZQlocwICCnRy3fB8Fut2v101doz/6D\nFktQuhNXPS3cTb+DBw/Sag2k0dhDm2Pj6RSoooMV7Vge+vgEccmSJZqFfasbnWIJNKJeb6Us56IQ\nuitTCJDlNJoP0xjJXhr9V1EogAIpLMQfEbBy8uTJHDt2HE2mFm4MzecUVqjplCSVsjyCwBZWzPUS\nGzdu4/FumzZtopeXu6LArjFnJwhcoKIU4/z5C57GsGca3Ol35swZlilTmSZTBwLdKQTtRAIORgc3\n1Mb+F9d8UhQ/rl69WnMbFrXh9foybNo01eoaFxfHQYOG0GwOpUjI6s1Upexy+vuH8tKlSzQabRTK\nG+GVIQSXXymsaB1M3M0AACAASURBVKk14g2GWBoM2Sjckp3ZydtSVBm4Q+AfAmGMDn6dwM/08cmW\n5nv36NFfs3anEEim2dyEQ4a8wzfeGEpFKU7gYxqNXZknT6EMh/1kFR6071WuXJdCaXaW0cFjKazE\nRSiEw1vU6WqxT5+BbNiwNRXlZQKTqSjVWLt2E/bqNUAbGzsBOw2GeqxWrRbNZl8CP2hzaY5Gy90U\nYVUB9PEJoSR9TOHZMUWbX8Mp3Pdj2Lv36/f089y5c5ongPObOEiLxZfnzp2j0ahqc00IRqoawe++\n+453ju5kbGwrSlJqYjqd7n3mzVuMqlqUwHCqagXWq9f8Hu+u+fPnU1UrM9WDczNz5MifZTR6ENzp\nZzZ3o6I0YK5cBThjxgwaDLF0Kr6jg1WmGix+olB+FqRnYj7Bf3bs2Fnbt1YQmEeLxZ++vjmo0zkV\nByKMR6+3sWbNxpw9ew5TUlKoqn5MDdW4SYMhF02mQhTKtc4UnpM3tV8VOquFVKpU64HvGBcXx+zZ\n81KEC/1OYCijg8MoBNplrFKlAUly3rx51OtzU3h4nSdwiDpddq5du5akyAtkNntTJI8lgX9psTy9\nhNt3z72pU6dRUapSeDw4CAxhdHABjQ5emkLnJCVpNMU+FUxgg/Yuu7W19U/t7z4U1v0UiiS2fanX\nB1Cny0tgOvX6aloI3RXtWRF3fQuzKZQAZ7S/FzNHjkiP/l+4cEFTunhT7KErqSg5uG/5dCYmJjIq\nqixNprYE5lFRqrB58w7pHqsbN248c8mt3emXCfLj84auEEb1scgc+fm+kNJ5XwmIopNf3+d8dYgS\nBydxb03D9ML1IYjvwxO3b99Gnz590DxpPfIarz1yo+b8lZDrrZ2Z08MXuC+c9diBe+mXlJSEfv2G\nYNmylVAUK1Y1kWC79nuW9ON+9B416gOMHbsYcXGvQ68/Cj+/FfjttwMICAjIkn48T7ibdtu3b0fT\npu/jxg3nOM4EMBgLa6agbEjyU+nj3fgrORCzblXF9u2bER+/H85SqmZzW0yaFINevXo9Ujv79+9H\nzZoNkZRkQErKVUyfPgVdunTMwp5nPu6mX9Wqsdi5sw6AHgDKQ9QRvgKAWFjTgLIhN55ORx+C8/qc\nKDP1KBRFcR3btWsX6tbtg9u3DwKQAdyAJIVAkuzQ6WS8/vogjB37nscYPG9w7/vs2bPx+uujEBd3\nBkLH3gzAXhgMXvi06hmUDXE8rW4+Nn68kBM9didj5syx6NCh3T3ny5SpgQMHBgKopR1ZjqpVP8f2\n7Ssxb94CbN26G7lyheCttwbB19f3ifb9UXG/fS8hIQELFixAz56jQSZiYc2bz8zamRkw56+EluvM\n+P77XgDqa0fnQpL6g/wHgn1MhKoWwq5dy1CyZEnXvePGjcOwYZeRkjJRO3IVZnMY4uOf/LrkuW4I\n+hmNnVCv3i2sXHkVwCYAX2NhzYb/U/T78UJptN+yBYoSi3feicWbbw5C374DMW2aP4B/IEKSTVBV\nO27fvuS677PPFqBPn0EwGkshKekXjBgxBIMHD3gq73D33OvcuTc++yw/gL7a0UNYWDMGZUPin0r/\n0ouragRemvEnbt26hTFjJuDYsVOoUKEU+vd/DbIsP+3uZRru2rOf3w38GYc+nfe9BWHhd1cCVAOw\nQ/t/Zgn+j4SzZ88iV67CcDiIajXvIG+IOP7ln2LRbpHvxffzLMNoNGLWrMmYNWsyAGBvz0jY7nNt\nVtF03LiJiIvbByACKSnArVtnsWLFCnTv3j1Tn/O8Y+nSr1CsWBEkJR2DEBz9AdSC0TgENpsJwIMZ\noSc1J/+74o11W6oAWAXA4jpOWpCSkvLI7ZQpUwbnz5/Ev//+i6CgINhs9/synx/89981APm1v6wQ\nSueeAAoByANgX7razWraXrh0CVarDWFhUVi79gsULlwYMTExKFQoAIcPN0F8fFUoyudo1aozZs78\nELIs/08xRQAQFBQE0g6hADAAWAAgF5KTzwEwA4hLV7tPY6/Mk1uHLcO+RIUKFdI8HxUViUOH1iIp\nqSYAwmRahyJF8kGSJHTq9Co6dXr1ifU1s2C329GxY0988cVCkASZEyLZc2EIm0n68CzyOs2a1cGv\nv45EXFwBAA6YzRPhcHgjKekwRPLry0hKsuPChQse98XExMBobIWUlO4AwqHXv4/o6LS/kaeBpKTC\n8PY+DqEAyAfADxmVUZ41+knSAchyMFq16oo33hBCfKFCkVCUrxAXtxnALOh0E1GsmCer36lTB7z8\ncgUcO3YMERERKFCgwFPofdooXrwgFGU14uK6AzBCKDIUAOlTAjwtmplMJgCAzWbDBx+890Sf/QJP\nBD8BKPWI14YBmAOgRnofpkvnfdsgzEjueFgn3njI+XSjcuU6cDgaAbgGICarHvMCWYD4+HgPC8kv\nv/yC02f+eeL9SElJhhCIBEgrkpKSnng/nnV06tQNYWFh6N27CxSlFGy2FlCU8hg37gMY9OnVKWYF\nskMIt1Wg0zUGsAOSNB1G41o0aNDgsVoymUyIiIj4n1AAAEDDhjWhKMMhLDqvAhgNYAyAJDxMifM0\nkZSUF2QiTp58DVWr1kNSUhL0ej2+/XYjRo6sgE6d/sC0ad0xZ85UGI3G/zkFAAA0aNAAL71UGhZL\ncwBfQqerBLH1/gsg8el27jGRO0/u+yoAAGDSpNEID98Hm60IrNbCKFjwL4waNewJ9jDzMX78h1ix\n4gTs9stwOK4ByAmgLZ7leZde9O/fB/361YO398vw8amKN95og8BABUAsgMEANiM5uSSmTfvU476K\nFSti4sR3YTSWgE5nQfHiP2PZsnlP4xXuwjUAx6AoM9GoUV3kzp0NQBQEz1766XYtk1GxYkXEx9/G\n3LnTXetoly5dUKmSL1Q1P7y8yiAgYAZGjx6CU6dOefBwERERqFu37jOlAACAnj17oHJlXyhKXqhq\nIUjSNAglwPMF/xfeqf/rCMOj5QTwhgjBD8vIw9LLtUcAGARh8b8OoQb1gTAjpQUJglOZkM7npYmv\nv/4aCxZ8hdOnzwL4DEKnkcr4PSta1Re4Fz/99BPq12+Bixf/hq9vMFat+gIVK1bE0aNHIUneAP5L\n876somnbtu2xZEkbxMUNB/Ab9Pq1qF//+WY4swKkCVeuXMGECaPQtGk9/PXXX7h0KQZvv/0O5rx8\nCwh+8P1Pfk62Qc6cH8DH5z0EBflj0qTtyJ079xPuw7OFd999E1evXsP8+SUgy3o0bNgS69cvwdWr\n8QCOpLvdrKetP8SW1QVxcR/g9OnTyJcvH8xmM954Y1AWP/vZgE6nw8aNyzB69Dj8+ONXOHIkCRcu\ndISwRgYCuPCQFtLGs7hX+vn54dCh73Hw4EHodDoUL14c+mdK0fj42LbtO8TFhUM4ThoAVAEwH8Cl\nB932UDyL9JMkCWPGjMCYMSNcx0wmHd555w+QztzRi7B9ewBIerj/9uzZDd27d0FSUhLMZvOT7fh9\nIEkh0OmM6N27D+rXr48VKzZgwYICEOxtxr7LZ41+kiTBYDB4HNPr9di4cTkOHTqEy5cvY+jQMahX\nry1IB6KjS2LTpuWwWCz3afHponHjdjh+/BTKlCmK2rUr4bvvfoSvbzAk6XS623xqNHvAY8+fP482\nbbrjl19+Rq5cYVi8eBaioqKeXN9eIDPgDeFp/6i4npGHpXflehPAOABzIWL/AaEEaP6Ae7zT+aw0\nsWrVKrRt2xtxcUMABECUTnxUD4oXeJqIj49HjRqxuHbtQwDNceXKZtSt2xSnT/+O/Pnz4w8++di/\nWbMmIyBgNNatG4LAQH9MmbINefLkeeL9eNbhcKQgOFhI+mXLlkVoaCjy5SuG+PjtIHtBFAJ5FnAJ\nwJdQlCGYOnUmYmNjn3aHngns3bsXMTExmD59EqZPnwQA2LNnD5Yv3wCgDoST17NqlbRr/55DcvJ/\n/2/zdZjNZowePRwA0K1bX8yduxxkHWRECfCswmAwIDo6+ml3I9MQH38dwFEAnwC4DeGJEwHhhXb8\n6XXsCSE0NBSK8h3u3CGENHMWJpOaZs4OnU73zCgAAIDsD4cjGLNmjUW3bp1RvXoFLFs2CXFx7fD/\nJXeZJEkoVqwYXnttEA4dyoaEhG0AHPjxx5YYMWIMxo0b9bS7mCbWrcuNlJQuOHq0J4AccDjmAYgF\nCxcGcPgp9y5z4HA4UKVKPfz1V02kpEzD9evbUKlSTZw4cRh+fn5Pu3sv8HiYg7RLBfpDyNwlARyE\nYNgyHVXTed/Yh5z/OJ3tOuGR6bNQoXJumTtPUmQoL87o4GzU6Xz4888/P1IGyhfVAZ4M3OmXVikZ\nmy2agwYN4vLlyzl9cGeazf5UlLwUGabdS+8oWtbWZFdmXiCQrVqJbNeff/45rdbmbtc7qNcrvHHj\nBskX9E4P3Gmn1ysepRC3bdtGL69KBD5mdHAHipJHZorM/laKTO8BWnbd9QTGU6drwA8++MDVxvbt\n2+nt/bIHna3WiDSz8F+6dIkDBgxms2avct68BfdklibJ9evXs8sr5VihQl2uXr06awblOYI7/erU\naU5SlAaaNWsWP/vsM7Zo8SpFOb5fGR1so6jCkZ+iikMo9fqqbrRJJmCk2dyBVms0y5WrzpkzZ3LJ\nkiW8efMmt2zZwhUrVrgqMBw9epQ5c+anXq8QMBJY7jYvI2gy5aS3dw1KksrUkkrxVNWSXL58uesd\nunR5jZXy5KbF0o2KkpujR497KmP5NHD33ueOa9euMTw8ipKUm9HBgQQs2rxTGBFRhDabs8SfiRZL\nfprNwYyNbUJJ0lNkFW9HYKgbfc9RVPUI1zJa52ThwmU4aNAgyvIg7doaFLWz/6Ci5OeSJUvT9V7/\nH9biu2lXsOBLTK2jTgIzKMs+LJtNr82B1FK5ktSIkmTW5k2ARluVorSiH0UZMgNTq2rEa+uuiak1\n30mgEs3mMObLV4JXr159aJ///fdf1qvXgmFhxdmkSXuPsmP//vsv8+QpTJstPy2WYDZu3CbNrOL3\no+3t27eZN29RmkwtCYyhouThlCnT0ze4TwDu9APeIUDK8gCOHPkeHQ4H+/UbTL3ezJeym7R5ZyVg\nYZ48Ubx8+TJ79epPYKQbLbYxf/5o3rx5kxUr1qIsi3VRlv00Hnaedl0izeYQ5slTQGtTpiR5ccyY\n1H3z1Vd7UlULUa/vSMCfZrMfx46dmOae+Lh42NyMjq5BUebQ+V7LWblybIafm5nwpN0+iophVgKX\nKCo45Gd0cE4C2QlYqdNlZ9WqdThz5my2bduROp2RqeVvQwiozJEjgvPmzaOq+mtzMYo6nZGS5F6h\nJpmybGbHjh1pMuXTnkcCcdTpTLRaAyjLBkZEFKbV6sz+P941j729q3Hjxo33fa/70ebvv/+mxRLi\nth6QXl5VuHnz5iwa4ayFJ/3+XyEt4T8tVAUwO7MfPhsi41CXdNz7sDIGGYpbwF2baURESQLfuU28\n91inTix37NjBpKSkp/z5vsDdcKffxYsXaTJ5Ezir0e4KAS+azY1ptZZjTEwN/vnnn5wzZw4VJZyi\nrJRT2aOnEFCcC52DQDYOHjyYpBBKrdbCFGWzSOB3mkzWZ678yfMEd9qZTD4eJfaOHDmilR+rQVEW\nzl9jVpsQaEFRRsyLoqxcIYpa5uGsWLGGqw2xefkTOKLRbBdV1Z+3b99+Gq/7Pwd3+lWt2tBVItBi\n6UBVbUCLJZDAGALXNcYmB4Ey2jyz0GIp7Tbf/iOg54ABA7hy5cpHmlcOh4MXLlygLJu0+8W8VdUy\nnDp1Kjdu3Mj169fTZguit3d1qmo4mzRpR7vd7tHG5s2bOWPGDO7evTsrh+uZw917nzvWrl1Li8Wf\nqlqOJlMQu3fvx0uXLvG///5jVFRZ2mwlabNVYHBwGJcuXcoqVepq62NNilJkfhTlGJ31rRdqTOkm\nAg4qSj3OmTOHn3zyCRWllrau9iJgoyQp/OCDCU9hRJ4fuNNu6dIvGR1dncBXHnyLLPtSUV7V1s1z\nrnNGY0+OHDmSp06dYvv2XWgyVSXwM4EuFCXDHNp66ywbF0+dLphCaXDdNc90umiOGjWKCQkJmfJO\niYmJPHToEE+cOJEugfPGjRscN248Bwx4g5s2bcqUPmUV3OknSi6Ssvw6R4wY6bomPj6es2bNphAw\nXyewmpJUkU2atGXPnv20tdVJ728YGVnade/Vq1d5+/Zt7t+/nzZblAdfo6rh/O2330gyzXF2OBzc\ntGkTZ8yYwe+++y7rB8MNHTv2otHYS+uvgyZTR/bpM/CJ9uFh8KRdGIHaFEak3yiUmF4EPtTG+yyB\nIC5fvpzDhr1HRclDs7kFASv1+jrU6xsRUKiqlagoOTho0FCuXbuWiuJPWa5FoZhL3SP1ehNXrFhB\nm81dOXCHer2FN2/edNHz+vXrNBhUrT8kkEBVjeAPP/zw2O979epVGo1Wtz02iaqan3v37s3soX0i\n8KTf/ytseYxrvZDJ+fbGQVj00xLYH6djWQEPRmjs2IlU1eIU9cZXUlGCM8Qc7ty5M5M+3RftpoW7\n6TdmzAQqSihVtT2FZb+9tnDZqaqVuHjxYjocDrZu3ZmqWpiK0pmKkp3Vq9ehJPlR1NzdRaArDQYv\nl8Bot9tZv34LWq0laLF0oaKE8LPP5qerz1k1FlnZdla06067mJgaHgzJunXraDQWp6hXTQJ13DZW\nEhhCL6/sGoPkZEyv02wOdNUP3rlzJxcuXEyz2Yc2WwGqqj+3bNmS4X4/T2OclW270++rr5YxJqam\ni6EFSL2+GfV6LwITCEQy1XKVQp3uFSpKEIFGBKZozE5dvvRSjcfub79+g6koJQh8RLO5GYsWLcfE\nxETX+YsXL3LTpk3ct28fHQ7Hc0e/J7V2OmG327Xa4T+45pWqhnPPnj188813aDK1cTGmsjycZcpU\noNVakqlW4p0UFuaGFN4fZSksXrMIBNFiacTIyOK8ffs24+PjWbRoOapqNZrNPagogVy/fv0TH4us\nbDur516OHPm5efNmWixBBCZQkt6hEPy3aPToSuH59iOBhVTVAP75558kheDduXNvWq0B9PXNQV/f\nUBqN3Qk0IGClwRBLq7UgK1V6hbLsTaHEm0/gVWbLFsE7d+489bF4Htt1px/QjZI0mVZrII8fP+5x\nnc0WQMDdY+o2dToT9+zZQ0UJIDCXwCoqSiRnzvz4nj4nJCQwIqIoDYY3COyl0fgaCxYszeTk5Mfu\n85OYe1euXGH+/CVpsxWh1VqIUVFlXd6WGWk3M+FJu9saXfpqa9xkAjKBOBfNZLkHR4wYQZPJl8BF\nAq8RGOhG0/EEmhO4QkUJZZky5anTjdbW02gKw8dUKkoJ9us3mLdv32auXAVoMPQnsIIWyyts2rTd\nPf0cNuw9qmokZfkNqmo0y5evmm5vjn79BlNVixB4j4pShdWrx7qU6c/bmuxJvxd4ALpm5Oa7qwMQ\nIt7/VBrX+j9m2/vT1aNHxODBr2P48PbIn/8NFC8+BcuWffbATMMPwzfffJN5nXvR7kPx1luDsHPn\nCkyd+jJk+RqA6doZHZKTi+HixYuQJAmLF3+CZcsm4MMPy+Cbb1Zj69b1GDlyIHx918JsboGyZU+g\nb9+uUFVV3K3TYfXqL/DFFyPx4YelsHv3BnTs2CFdfczKsXhe6bdp03JIkoQvv/wKYWFF0aZND5Ap\nEA5EgChR5p6IpggKFYqCouRGalqQ1UhMdKBMmSoYOnQkdu7ciXbt2uD8+VPYu3cZzp8/hVdeeSXD\nfX0exzir6desWVNcvHgZQFHXsZSUqqhevSpatvwdZvMtpNb1luFwxCI+PgHAXgA/AOgPYBguX77y\n2P2dPHksZs8egM6dj+Odd0pi795tMBqNrvNBQUGoVasWypQpA0mSnjv6Pam1c82aNVDV7NDrfXHn\nTgpS9favwOHIjTNnzuDYsVNITKwOZxYpu706Dhw4iNu3iwAwaS3FQJT5XAaRWPcYhK6/FXS6G5gw\noRoOHtwDVVVhNpsxbty7sNn+hl6/GtWqVUGVKlXu28fncY5kNf3Onz+Hdu16omrVCujQ4QTatj0H\ns1kF4FzrpsNovIiQkPYoWfIzlC5dCjExryAqqhx++eUXzJ07HbduXcbVq//ixIlf8dprvihY8DRG\nj34b8+Y1x5o10/HNN5vx7bcbULq0EaGhE9CypQNHjuyDojxeFvTnbYyfxNzLlesb1KjxDfLnL4Ry\n5aojJuYV/PXXXwDwf+ydd3gUVRfG391Ntswmm0AqEFroEHrvJRRRmhQREFQgoCAKItKUoiihKL0K\nKgoKIkVUBKT7SZMO0rtChDRIb7vv98dswgbSd5PNkvt7nnmSnbnlzLx7Z+feufccxMdHI6PH6bp1\n62LXrq1o3/5XNG26AosXT8YbbwQ9ZbNGo8Gff+5Cly4hqFz5HfToEY0//tiRJ0eYBdH2Up127tq1\nErt3f4mTJ/+AwWCwutz8Q2/+OwRALDSaGVCpXPF4SXU8dLojMBgM0GhKAfAGcB9AXYsy6pr3FYeT\nUx3cuXMLJpM/5PvpHgB6eHrOx7JlozFvXjD0ej3++usABg1KRuvWazB+fGt89136SBgA8PHHH2LT\npkX4+ONiWLVqDAIDW2ToIyMnzJsXjK+/noLx4+Mxf34//PbbJiiV8vfSEe/JgiwJgvzgnZUvvmx5\n8g7jDnmdwV5rCjVTzAZlZIpCocC4cWPSYpgKHI9GjRqhUaNG+PLLH3D06DSkpAQDuASVaiNattwG\nQNa5c+fO6fJ9+OEkfPjhpLTP06ZNS3dcqVSia9euENgeg8GAffv2YfDgMYiLWwv5x/I1AAMAzINS\nmQJgMkymAAAJ0OvnYMCAYbh6NRhxcashz16aBHILwsPdMX/+UDRuLIfec3d3h7t7TiKjCKyhc+d2\nWL36Y8THrwXwEJK0CK+8MhkDBvRHkyaBOHp0BYBlkAd0vgU5FrLTuY8ATIFONxpdu+Y+LK1CocDA\ngQMxcOBAW55OkeLq1at48cUBIL8A0BDAdAB/QH6YPYP4+FdRrNg4tGhRH7t2rUFcXB/ID6mLQTYD\nsAOyA7qKAGZD1lUF2TlWMQBV4eQ0CU2atMXIkSPT6r1w4QJ69RqIuLhVAKrh998n4dVX38TGjWsK\n7uQdHJPJF6GhP2PPnvfw0kvAV1+twO7d+xES8gXkFZh/wtk5AidOnEH//kE4fNgXSUmLERZ2DIGB\nXXDhwgmULl0agNwJmzt3JlxcNJg8eWK6epo3b46//vpfwZ/gM87ixXMxatQE/PtvXxiNAxARsRUt\nWnTEtWtn0apVB+zdewRy6MMWAOYiMPA5SJKE5s2b4/ffm2dbvo+PDzZv/ja/T8NmqNVqNGnSxN5m\n5JAbeNy9mYXExI5QqSZDqRwAV9cmSEm5juefb42goCB8/PFcAOsBtAYwF0B7yPfQTyFH8/gbyclH\n0KBBbURHf4K4uNoAnCBJf2PChLcxaNCgtFq9vb2xatViZEenTp3QqVMnAE8/z+YGhUKB3r17o3fv\n3nkuQ+AwhEOOW2qVX4Anh5v8IQc9vQbZzfdDi3TDAGzIYbkeAHrBMl6f9YgpIQKBQCAQCAQCgUBQ\nNChcMTSfcdwhzw+8DnmqQV4345MFW8n+2rVrP7lORGwOtAn9HHcT2jn2JvRz7E3o57ib0M6xN6Gf\n425CO4ffYgDshyDfyM3oynEADXKRPgKALYNT0oZlCQQCgUAgEAgEAoGg8FJUZwL0AjAc8to/NwCP\nIPfFZwHYbYsKcu91JOfczK+CZceRAkfC0tGJ0M+xENo5NkI/x0bo57gI7RwboZ/jIrRzbPLqHPEZ\nwQ3yjPz25s+EvP7fHUCgedsE2Tngw4wKyClPujPNigm5LLt+LtMLBAKBQCAQCAQCgUBQFNkIeeb9\neAAVIPvX8zD/9Yfso6+OOZ1VONJQS9pQnhjVczzEqKzjIrRzbIR+jo3Qz3ER2jk2Qj/HRWjn2Dwx\nE8CR+qrWEgSgDx7Hjs2KDZDjU67Ma2W5mQkgEKTj7t27OHLkCMLDw+1tikAgEDgkISEhOHPmDGJj\nY+1tikAgEAgEAvvRAfIgQE7oi8dLBvKEGAQQ5Ii4i/vTfV6wYAlebloNzz03CmXLVsVvv/1mH8OK\nKE/qYau0AsclYud8e5tQJLBle1oyfij8/WugZcsBKF26Mk6cOGGzsgW2JTvdxX3Wflh77YV2AoH9\nEM8u6YiA7AAwN+nzTH46BhQ8Q4RtnY6EWYFpnzsDKF69EQbtPAzgEPr06Ybw8LvQaDR2s7Eo8aQe\nWaGt0gplqrXJX4MEdifm5E8o3mm0vc145slN28uOYg/USEi4hYQEHwAb0L17P/z77xWblC2wLdnp\nLu6z9sPaNim0Ewjsh3h2SUduHf0VmGNAQRFm7Z83sOHKk+uqtOa/zUBq8d9//xW0WQIzG64wA30E\nRYUln36AtX/esLcZAjM5b4/FAPiY/++De/duIDk5OR8tE9gCcb91HIRWAkHhRTy72BcxCCCwggTz\n34NQKhPh6+trV2sEAoHAoVA8BJDqU2UbvL3LwNnZ2Z4WCQQCgUAgsA/rAfTK4vguANfN/y8HsMKa\nysRyAEG2zJ49G2VNIWhYOb2DTqXyOAyG2jCZ7mHz5u/EUgA70rdyUXKeKniSkZNm4M7MP+1thsBM\nTttjyRLe0OmqQa0uB+AfbN26JV/tEtgGcb91HIRWAkHhRTy7PMVpyGEAg83bk9P9y5u3HwDMBHDT\nmsrEIIAgU0giKCgIq1evxzedAgCcSXe8SePG2DdjISpUqAA3Nzf7GCkQCAQOin+F8jh/fjXu37+P\natWqwd3d3d4mCQQCgUAgsB+bAewG0BByCEBLKgEwAIiyRUViEECQKSNHjsXq1d8B6IzFp0dh0P02\nAAiVSo/w8BA43zuFCtXq2dnKoolnj6mQcujISHg+Lhq41OtubxOeSZKSknD48GEkJyejSZMmuWp7\n2RF3cT8kEDJRUQAAIABJREFUf3/4+/vbpDxB/pGd7uI+az+sbZNCO4Gg4ElOTsbdu3ehDehsb1MK\nE3UBeJj/J4BAAMeRPmJARQA3YKVTQABwpHlSaZ5dSOHkJb+5ffs2qlZtgISEZAClAJwCoAZwGSpV\nHSQmxkClUuW4PIXi8VdN6OdYCO0KNzdv3sSRI0fg7e2Ndu3apdMLEPpZQ0xMDJo374ibN+OhUOjh\n4nIfR4/ug5+fX4HZIPRzXIR2jo3Qz3ER2hVuTp48iU6deiAujjAaH2HJkoUYMuS1tONPPMc4Ul/V\nWoYCWGn+/ySADebPjyAPEKTGEX4IYCOA4dZUJhwDCjIkIiICarUvgN6QHQA2AjAEQBNMmjQ+VwMA\nAus5d+4cevQYgFatumL58i/Ej5oAALBjxw4EBDTC8OGb0aPHO+jW7WWYTCZ7m/XM8Mkns3H5cnlE\nR59EVNT/cP9+f4wc+b69zRIUQkwmE8LCwmA0Gu1tyjOPyWTC/fv3bRZJ45dffkGtWi1QoUI9fPxx\nsLiHCgT5iMlkwnPPvYiwsDmIi/sHiYl/YdSo8bh48aK9TSsMrILs7K8+gAYA5kAeAHCDvDTgBuSw\nQsUB7ITcScszYhBAkCFVqlSBRhMNoDaA1gBuQaH4HrNmTcRHH02zr3FFjGvXrqFZs0Bs29YAf/zx\nOsaOXYjg4Lk5ynv48GH4+9eCJLmjWbOOuHv3bj5bKyhIXnklCHFxPyI6eiNiYk5i//5r2LZtm73N\nema4dOkmEhM7IPVFhNHYAVevWuWHR+BA3L59G3PmzMHs2bNx69atTNMdOnQInp6l4edXCcWKlcDu\n3bsLzsgixunTp1GiRAWUK1cDbm5e2LjxR6vK+/PPP/HSS0Nx7tz7uHFjCYKDN2HGjFk2slYgEDxJ\nREQEoqKiAPQ176kCJ6cWOHv2rD3NKiyMAzAB8vRrS14C4A5gPB4vDdgM2UlgnhGDAIIMkSQJBw78\nhurV10Kt/gFVq1bC2bPH8P774i1YQfPdd98jPn4AyDEAeiIubh3mzVuWbb6QkBB07NgdN29OQ3z8\ndRw71hjt23cXswieEUwmEyIjQwA0M+9RIyWloRjosSHNm9eDJH0DIA5ACjSaVWjSRPhBKQpcunQJ\nNWs2wuTJN/HBB7dRq1bjDN9UxcbGonPnnoiMXInExEhER/+AHj36ITw8PINSBdZgNBrRqVMPPHjw\nCRISwhAfvx+vvvombtzIOs54dHQ0PvxwOgYMCMIXX6xO9xu4bt1GxMePAdANQFPExS3BV1+tz98T\nEQiKMMWKFYNKpQBwzLwnEkbjcZQvb1V/9lnBA+nX/6fSx/z3SUeBVvkFEIMAgkypVq0a/v77KBIT\nY3Dx4l8ICAiwt0lFkifXeGe270mOHDkCpbIxgJ4APGA0foSbN6+Lh9NnBKVSiRo1GkGpnAvZZcpV\nKJXb0KhRI3ub9swwevQovPBCaajVJaDVlkDduv9iwYJge5tVZNm5cyfq1WuDKlUaYebMuTaZtv3r\nr7+iXr22qFGjGRYvXpbWQfzgg08REzMWyclLkZy8BDEx4zBp0idP5b9+/TpIDwAvmPe0gZNTBVy6\ndMlq2wTpefDgAaKi4gD0N++pA2fnpjhz5kymeRISEtCoUVvMmXMN331XH6NHL8eoUe+lHZckLZRK\ny9/EcGi12nyxXyAQACqVCuvXr4EkvQA3t46QpAC88cYA8ewik9kDen3IPgKsdgZoiUNGBxg7diwA\noGnTpmjWrFk2qQWZER4ejlGjxuPMmTMoWbIU5s//BDVq1Mj3eoV+uSMwsB2Cg7siLs4AwA863WcY\nPLgf7t27l2U+o9GIlJQrAG4DcAYQAqMxAVFRUUhKSsqTLUK7wsWqVfPRr98Q3LkzAwoFMXXqxyhV\nqlSm3w2hX+6ZP38mpkwZh6SkJPj4+CA6OhrR0dE5zp+YmIiIiAh4eXnBycm6n9yirN/x48fRt+9g\nJCQEA/DERx99iMjISIwePTLPZR46dAgDB76JhISZAAwYN+4DPHwYicGDX8O///4HsiUAuS2RxXD3\n7n9PtS2FQoHExH8gv9XyAxCKhIQrT/nNKcra2YrExESQ8ZBfhlUD8AhJSSeg043I9J63e/du/POP\nAomJwQAUiItrhWXL6uLdd0dCq9WiT58XsWJFF8TGxoL0gFa7HO+9N/Op8oR+jovQzn7ExsZi3759\nMBqNaNmyJYoXLw4AqF+/Pg4c+A2XLl1CiRIlUK1atWyfaYsw5SH7Afgig2NFJq4wUzeB9ZhMJtas\n2ZhOTmMI3CDwJd3cfPngwYN8qU/oZx3nzp1jjx4D2Lp1Vy5f/gVNJlO2eYxGIzt06E69vgVVqvcp\nSf785JPZua5baFf4efjwIZOTkzM8JvSzH+vX/0Cdzo06nQ89PPz4119/5boMoZ/MiBGjCXxKgObt\nKMuVq2VVmQMHDiMw36LMPaxevSlJcsmS5ZSk2gQuErhESarDRYuWZljOvHmLKEm+dHXtRUkqxWnT\nPiUptMsP1q37npLkRYOhOyWpDEeNGpdl+h9//JGuri9YaJxEZ2eJUVFRaWlu3LjB0aPHMSjoLe7b\nty9tv9DPcRHa2Z8HDx6wdOkqdHHpQBeXrvTw8OONGzdylNdSv4LqZBYSMppquByACXJ0AEt6AQjK\nd4sKCaJB24hff/2VBoMPASWBBgSuESANhue4bdu2fKlT6GcfkpOT+c0333DGjBnctWtXnsoQ2tmW\nq1evctiwUezXbwi3b9+e7/UJ/R7z6NEjDh48knXqtOYrrwQxNDQ03+q6ceMGJcmTwGlz5+NHenj4\nZTpYkxlCP5mxY8dTqXzfojO3k1WqNLSqzKCgt6hQfGRR5hbWqdOapDxQPm3aJyxWrBSLFSvFKVM+\nznLw9ezZs1y/fj1PnDiRtk9olz9cvXqVP/74Y44G1cLCwli8eCkqFPMIHKNGM4Bt23bJUT1CP/tj\nMpm4adMmBgcH87fffssy3dWrV3nq1CkmJCQI7QoBI0e+S2fnt9Lur0rlp+za9eUc5bXUr6A6mYWE\ncgB2AWgPoB4eDwCkDg64Qe747zLvt8pRmyPFXkz7IlA4NsszN2/eREBAI8TFbQHQFMA8AGsAnIKL\nSz1s27YAbdu2tXm9ImZr3khKSsKKFStw6dINNG1aHwMGDMiRPwBbIrSzHTdu3EDduk0RHT0cpC8k\naSa++GI2+vfvl291Cv1kTCYTGjZsg/PnKyEpqT/U6p9Qrtz/cO7cEajVaqvK/t///odt236Fm5sr\nhg0LgpeXF7Zt24aBA5cjKmp7WjqdrgSuXPkLfn5+OS5b6Cdz8+ZN1KnTFDExQ2Ay+UCSZuHrrxeg\nT5+8R0i6cOECGjVqjbi4t0G6Q5I+xfr1K9G1a1eb2Cy0sy979+7FokVfIT4+BqGhEXj4MBqtWjXB\nokWz4eLikm1+oZ99IYkBA4Zi69bDSEhwh5PTFbz22stYuXJxunQmkwn9+w/Btm2/wcnJA25uKfj3\n3yvpyhEUPF269MOvv74A4BXznr2oXXs6Tp8+kG3eJ55zHamvagvKAZgFoAOACMghA+eYj7kB8Mfj\nPrECT0cSeCYRo3pW8ODBA/r6VqBC4Uqgg8WbDxMBPbXatmzevCNTUlLypX6hX+acO3eOdeq0ZPHi\npRkY2J0hISEkyZSUFDZv3pE63XME5lCnq8NatRpz5MgxPHr0aIHZJ7SzHePHT6JSOc6i/e1mxYr1\n8rVOoZ/M5cuXqdeXIWBMu/e5ugZY3ZY2bvyRkuRLYDqdnYfQ17c8Q0NDefr0aUpSKQJh5vrOUqs1\nMD4+PlflC/0ec/XqVY4cOYavvvoGf//992zTR0VFsV+/ISxZsgrr12/DkydPPpXm/PnzHDp0JAcM\nCOLevXttaq/Qzn7s2LGDOp0PgeUEFlKSPHno0KFclSH0sy9nzpyhRuNDwJPAOAJvE9A9pePXX39N\nvb4JgVgCpEo1U2hXCJg3byElqRmBSAJx1Om6cMiQN3ny5EnGxcWlSxsVFcU///yTFy5coMlkKsoz\nAQSZIBp0Hrl//z4VCncCBgIVCZQkEG9+ML1MlUrLhQsXMjExMd9sEPplTHh4OIsVK0mFYiWBG3Ry\nep/Vqzek0WjkH3/8QReX6gRSzFpFEtARmExJ8uLu3bsLxEahne145533CHxsMQjwF8uUCcjXOoV+\nMteuXaNOV4JAkvnap9DFpTKPHz9uVbllytQgsDdNU7X6dQYHB/P27dssV64mFYpidHLyp1ZbnOvW\nfZ/r8oV+eScwsBs1moEEzhP4kq6u3vz3338LrH6hne159OgRFy5cyBkzZmS5HKBlyxcIrLO41y7i\niy++kqu6hH72Zd++fVSp/MwDOak6fswuXfqkSzd27PsEPrFIc0NoVwgwGo0cNuxtqlRqKpXOLFeu\nBrVaTxoMAfT2LsdLly6RlF+EeXj40WBoSJ2uJAcMGCoGAdLjDvntv80RIQKfcVatWgVf3yogVQCq\nAngPQAyA6lCpXoEktcGyZUswatQoq6fECnLP0aNHYTRWBRkEoDxSUoJx8+ZthISEIDY2FkqlN4BU\nL9NuAFwBvIG4uAWYPHlWjuo4duwY3njjHbz11rv4+++/8+dEBDliwICXIEkLAWwAcACSNBxDhw7I\nMG1ycjJGjXoP3t7+KFeuJjZu/LFAbX3W8Pf3R5Mm9aDT9QHwPbTagahSxRe1a9e2qty4uBjIXuFl\nkpP9EBYWgaZNA/HPP31A7gbQGWXKlEXfvn0yLUdgWxISErB//w4kJq4CUAPA6yDbYM+ePfj88wV4\n8cWBGD/+g3SRHu7evYtXX30D7dr1wOzZn8NoNNrNfsHTPHr0CLVrN8X48X9g6tQotGr1An766acM\n06akGAFYhvrTmvcJHIHQ0FDMmrUYRmMcgDIWR8oiJSV92oCAatDrfwUQBwBQKsVvZWFAqVRixYoF\niIuLxvffr0VoqBIJCdcRFXUOoaHj0KfP6wCAvn2HIDx8KqKijiE+/gq2bj1pZ8sLDUGQlwNEALgG\n2QfAdVjpB8BREaN6GRB7YV+mx8aMGWN+c/wZge0E6hCowUY+TQmUYsOGDdM5McpPhH6PsdRMfttf\njUCyeQQ7nM7OLoyMjGRkZCQ9PPyoUCwicMk8Fa6JeTrzbgYENM+2jv3791OSvCh71Z5Kvd6TZ86c\nyZW9QruMyartZcXu3bvZsGEgq1dvyuDguTQajRmme/vt99mybD2z9nspSSV44MCBXNcn9HtMfHw8\nP/hgOjt27M1x4yYzJiYm07Q51Xf48Heo03Wi7EV+JyXJh8uWLaPB0CjtzVQjn72UpFK8fv16rm0W\n+uWN6PN76OSkJXCXqcs/XFxas2XLQEpSawJfUaMZyOrVGzIhIYERERH08SlHJ6fxBH6kJLXg9MEv\nWmWD0M46nmyD8+bNo1bb1+KN7x6WLl0tw7zr12+gJJUhsJnAerYsU5w7d+7MVf1Cv/wjq/ur0Whk\njRqN6Ow8hsAkAjUInCFwjDpdRa5d+126coxGI3v3HkhJKkmDoRZLlqwotCtkzJgxg41L9LNou2HU\n6dxIkjqdOxv5bE07plSOL+ozAdwAHIfc6Q8H8Jd5u2r+nDoYYPXsAEdytpD2RaBw8pHGnZltkXD5\nYK7yHP1Pi0E7Tbh58yLKlSuXP4Y9gXCw85i8aJZbtFVaoczEfWjVqgv++OMlAIPMR2ajX78r+O67\nVTkuS2iXMQWh49H/6mPQzmPmT59izJiH+Pzz2bkqQ+iXN2yp79H/WiBo/3lcv34WpUqVylVeoV/e\nsIV+x+4DL/+cCCcnpzzlF9pZhy3bYJxnDdSZezZXeYR++YettE191iGJK1euICYmBtWrV4ckSWlp\nhHb2Z9OmTUj88jU08IrL8Pix//wxcOdVAA+h17dEbOx5y8OO1Fe1Bcchv/0fj/RO/8ZBdhBoADAR\nQB8AFa2pSCwHeAbZcIXYcCXzm54CSThwYGeBDQAIrCM7PbMiPj4BgIfFHk/ExsbbxC5B3sidnolp\n/zk53YHBkL1Ha4F1kMTixctw9mzWS2dyo6NKeRbdunXJ9QCAIP+x5v4qKHxkpmdxD48MUgscgZy0\nUYVCgSpVqqB+/frQ6XQFZJkgp/Ts2RNeXm6ZaqlW34Wra1VotRXw6qsd7WBhoWEc5EGAjsjc638U\n5EGAYZCjCOQZMQhQBGnZsjlatWplbzMEBcDQoS9Dkt4DcBDALkjSNAwZ0tfeZglyiEp5CcBUODsH\noXjxHXjzzeH2NumZZ+LEqRg/fhUiI71tVqa/vxe+//5Lm5UnKBh8vH3zPAtAIBAIBDLyIE2lTI83\nbtwQhw9vwrVrZ7FkyWcFaFmhowOAN3KYdi+sXC4hft2eQfpWznrmjEJZ1GbWODbZ6ZkVw4YNQVJS\nEhYufBdOTip88EEwunXrZkPrih4///wzliz5Bu94XEQF59znz42edWrXxITaRri6+mPo0GPw9rZd\nx1SQMYsWLUZc3GkArwK4mGm63OhYslQpqFSq7BMKCpysdKycxUOrIOcYjUZ8/fXX+Pvvy6hVqzoG\nDRoEpTJ/3kFZ83spKJwITZ8dMtNSoVSiRo0aBWxNoeRGQVYmBgEEgmcYhUKBUaNGYNSoEfY25Zlg\n3bp1GDp0LBISJmNAp7Oo4Buar/W5GFwxc+KMfK1DkB55/ajosAvSrwkX5A2S6N17EHbtuoO4uOeh\n16/Ezp0H8d13q8X1FQgEgvQ8zGV693yxohAiPH0+gclk4t6vgqlSuRL4joCvefvSHCf1LarVHkxJ\nSUmXL69eza1B6Cfz7bfrOLRDU3bq1JuHDh1K23/+/HnWrduKxYr5sV27bly4cCFdXCy9IKdQpVIz\nLi6OpOz1f9GiRdyxYwdNJtNT9cRc2Mvw8PC09NYgtJM5efKkua1VJODGRj5BBD5jYOAL1Om8CKwg\nsIo6nQ937NjxVP4tW7ZQp/OiTjeMQF0CzQnoCYSn6azRjODo0aNZv34blikTwBlBvRkfH2+V3UK/\n3DFq1HuUpKZs5DOTCsVsurh40sXFi8BiAgcIlCWw1qJtrmVgoOxF/uLFi/T19adeX45qtSunT59J\n0rp7rtAvb2R1zdev38C6dduwTp3WXLt2XYZpTCYTH57ZxYSEhDzbILQjL1y4QEkqRSDe3F5iqNP5\n5ChSRnbtxmg0cu7c+WzfvidLlqxChWJyWiQI4BUCLejh4cf79+/nqQ0K/WzPkSNH2KJFZ/ZpXIPT\npn3y1PNpbslMV6Fd4SOrNvjkMUv9sugXBgFon8Vxq73n24HgLI6Ne+JzLwDL89GWQoVo0BYYjUa+\n9NKrVKs9CQSaf/R6EGhGoBoBNyqVxXnu3Dl7m0pS3JAPHDjAUqWqEShGoCuBxZQkz0xDNN66dYt6\nvSeBLQTu0dn5bTZu3C5HdT148IB167agWu1KZ2cdJ02aZpXtRV27VEqVqmwebCOBewTKEHiHJUtW\nI7DSolP4Ldu06fZUfg+P0gT+NKcxEmhBwEDgbFperbYL1WoXAt8SOEmttgv79Rtsld1Cv9xhNBo5\nc+ZcNm7ckd279+fUqVOp072WrtMvt+MfKYeSK8WffvopLX9SUhKvXr3K8PBwm9gj9LMtW7ZsoSSV\nJvAzgV8oSWW5YcMP6dLMnv25ecBPRcCJ3bq9nKfBAKEdeezYMRoMtS3aD+nqWjXXoWqz4/r16/T0\nLG1+BmpCoB6BR9RqB3LhwoV5KlPoZ1suXbpkfq75ksB+SlJzjhkzIV/qEto5Npb6ZdEvNJm3SAC9\nnzhWzuK4Vc7zCphyeLqzn8o4APUgd/53QY4g4FYwZtkf0aDNHDlyhEqlgYA3gdcJVCAQSyCRwPsE\nXKjVFrP6DaItKcr6HT16lGq1O4GN5g7fCwSGEfiUw4aNyjTfjh07qNV6E9BRoTCwV68B6UbN79+/\nz3ffHc++fQdz7dp1aTMCOnXqRWfn0eaO5n/U66tyy5Yteba/KGuXSmJiIhUKlXmwLfVh9hU6O7uw\nadOOBFZb7P+erVt3faoMJycNgWiLdEFUKCoR8CHwCdXq/ixevDQ1GssOZxjVar1Vtgv9rOOLL76g\nJPW20OQWAYkKhR99fCpz/fr1+Vq/0M+2tG/fk+lncmxgq1aP2+svv/xCZ2cvc0cykkAsVaoOHDt2\nUq7rEtqRcXFxLFmyIpXKWQSuUaWawdKlq1o1wyIzIiIiqNG4EFjD1JkHavVwfv7553kqT+hnW2bM\n+IRKZW8C4wl8QuAY3d1L5ktdQjvHxlK/LPqFJshvwpeZ/39yIKCnxbH3reh/FjR1IJ/XkzMZduLx\nwMZxAHWtrUhEB3Awjh07hiZN2sNkGgD5O3IPgDOAhgAGQKFYiSZNaiE2NgxardautgqAhw8folOn\nLkhK6gf5/lQTwCoAPwIwZrkm8ocffgbZEUAUyLv47bd/sGjR0rRy69RphkWLYrFhQyMMGzYDM2bI\ng53Hjh1BcvI7kJu3D2Jj++Pw4WOZ1iPIHrVaDS+v0gB+Me+JgFK5D7Nnf4xp08ZCp5sM4DsAGyBJ\n7+Hdd4c+VUbp0pUBTIAc9u8kFIofMHJkJ7zzzssYNSoUH31UG1OmjIVK9cgiVxjUahHuyJ707NkT\nrq4n4OQ0GsCXANpBoTBBqYxATEwShg8fjUOHDtnbTEEO0WicAURb7ImGVqtO+7Rr1z4kJ5cF8Bbk\n5ZYSjMbx2L37z4I19BlBp9Phf//bhaZN98LTMxDNmx/C//63ExqNxuZ1FStWDEFBwyFJqwAcArAM\navVm9OjRw+Z1CXLPhQsXYDLtA+AC4DqA3sJhqsBabgB4E3KH6AekXyO/2XxsBQBHCot1GnKEgCc7\n+SsghwWsD6ABMg8h+ExS5Ef1tm/fTicnHYE6Fm8xYglIBIbSx6csT548aW8zM6So6jd79myqVA0J\nvGSh2WkCnpQkT546dSrTvOXKBRCYRuCMOd9q9uw5iCS5atUqSlJPizKvU6dzJ0kGBDQ1vwmRfQlI\nUicuXbo0z+dQVLV7kt9++41OTgYqFDWoVLpz8OARace2b9/O1q27skWLF7h169an8qakpNDZWUeg\nNQEnAt7UaCqmm0ZOkg8fPmSpUpXo7BxEYD4lqSLnzJlnld1CP+sJCQnhW2+9y549B3HWrDnUaj0I\n/G1uY7/Q3d2XSUlJ+VK30M+2HDp0iJLkSWAugc8pSV48cOBA2vFZs2ZTqaxO4C2L++tH7NatX67r\nEtoVPCkpKZw6dQZr1WrJwMDuPH36dJ7LEvrZFl/figT+sGhXL7F79xfzpS6hnWNjqV8W/cIn3/Bf\nBfB7Bul6mtMKnsCRXLOmfRHk70fRonfvfti06TfIs0RiIM8EAYAEAO7QaCScOXMYVapUsZuNWWH5\nxrso6Td+/CTMnk3Ib/7bAKgCYA5q1iyP1asXoWHDhhnmmzbtU8yYMQ9GY1PIWo+HUnkEPj7HMXJk\nENzcJIwbdxIJCamxxyPg7FwaiYkxOHXqFNq2fR5AQ5hM/6JmTU/s3/8r1Gp1hnVlR1HVzhKSqF+/\nFc6fr4zk5BZQKC7C0/MHXLt2FgaDIdv8CQkJ0OsNMJn+AuADwBcuLv2wdOnzGDhwYLq04eHhmDdv\nIf77LxxdurS3+i2W0M+2/PLLL3jllSV49Oi3tH2SVAoXLx5GmTJlbF6f0M/2HDt2DIsXrwZJjBw5\nGE2aNEk7Fh0djdq1m+HWrbsgawJwgsFwAadPH0L58uVzVY/QzrER+tmW4sX9EBn5BwC5HSkUEzB1\nqoSpU6fYvC6hXcESFRWFzZs3Iz4+Hp07d0a5cuWsKu+JWbKZ9VVNkKdXzjZ/bgdgN+S36Cst0i2H\n7ECwolVGFTy9AAyHPNXbDcAjyB2CWZDP02ocMkTg2LFjAQBNmzZFs2bN7GxN/rN161Zs2rQJwB4A\nvgA6QXaK2RLAKigUahw/fhCurq64d++ePU3NEUVJv8aNG0CrHYGEhHkADkKpXIbmzQOwfv23AJCh\nXnfu3EFw8FwYjfsAeAG4C6AVTCYPhIRMxYwZK9GlS2UolTsg3/uqQqudh+ef74GQkBD4+vri4MGd\n+Ouvv+Di4oJmzZohLCzMJudTlLSzJCQkBBcuXEBy8vcAlCA7IT7+ILZv345WrVplm//s2bMA1AC6\nQL6Pd4bRuBtVqozJ8DswYsTwtP9t2aaLqn45ISkpCaGhofD09MxyqrJer0di4gkAZyC3z3MwmWKQ\nkpKS7/ffoqxfTEwMLl68CDc3N1SqVMmq8HJ+fn4IDp6a9vlJ3X7/fSu2bNmCU6dOoUqVKujZcwE0\nGo1V+hZl7Z4FhH6Zk5ycjOjoaBQrVizLdtmpUwds3ToECQnTANyBRrMKjRuvTdeuIiMjMX/+Ety5\nE4LmzRtg8OBXoVRat3JZaJe/REZGomPH7oiI8AdQHO+99wF+/HEtateuXdCm7IU8ZX455CnzJyA7\n0hsGuTPtKLgB2IjHkQ8I2fmhO4BA87YJckcwtyEFHZYiN7UnLCyMTZoEEtBSDiWW6pQshIA/geIs\nWdKfERER9jY1W4qifql8881aenuXp4uLF/v3H5pt2L4//viDbm6NLabMkUA5An+Z/79Lnc6Np06d\nYosWnVm5ckOOGTOBiYmJ+WJ/UdYulbCwMKrVBgJRacssXFwC+Mcff+Qof4kSFQisT9NPofDk8uXL\nM0wbHx/P4ODZfP31N7ly5Rc0Go1W2V7U9Uu9fpcvX2aXLi+zfv12nDLlYyYnJ6el2bdvHw0Gb0pS\nCer1xfnrr79mWea0aZ9Sp/Ohm1s7SpInf/xxU9qxo0eP8v33J/Kjjz7mvXv3rLa/qOtHyiFUPTxK\n02BoSEkqxX79BmcYGrWwIbRzbIR+2fPVV2uo0bhQrXZjmTLVePny5UzTJiQkcPjwd+jt7c+KFes+\ndZ+NjY1l+fIBVKuHEVhDSWrOIUNG5skuoV3BMXnyFDo7D7F4Xv2aDRvmLJpVZljql0W/0IiMHf7N\nNB/ofKRqAAAgAElEQVQzQfagn5m3/cJKquf/cUidNvOYcgCGIvOlD88sRapBX7hwgX5+1alUDjev\nC/chMJtAinlNlZ7BwbMd4kGIFDdko9HINWvWcOLEyfz++++z1C0sLMwcl3yP+Yb6M+VQcqme5W9T\nkooVmO1FSbvExEQuXbqU778/8am1/YMGDackNSWwiFptDzZs2IbJycn8888/2afPa+zd+1UePHjw\nqTLj4uKoVDrTMrKAXj+Iq1evfiptcnIyGzduR52uO4GFlKQmfO21N6w6p6KknyVffvk1XVw8qFQ6\nsXHjtnRz86VSOZvADkpS27SHy6ioKLq6ehH43azPIer1nrx//36W5V+6dIk7d+7kP//8k7Zv+/bt\nlCRvAlPp5PQGPTz8ePfuXavOo6jqZ0lAQBMqFKlhOGOp1zfIdVSGxMRETpw4lY0bd2Tfvq/z33//\nzSdrHyO0c2yEfllz9uxZSpIPgQsESIViIf39a+aprP/++4+NGrWlQuFN2YfSXQIPqVJp8hTpSmhX\ncLz++psEFlgMApxk2bJ5+x6kYqmfFf3G7NdqFj6CIA8C5IQNkGc5FAmKTIO+ePGiuRNYnMAdymGK\nKpoHApQEpDyHvLEXRUm/JzGZTOzZ8xXq9U0JTKNeX4+DB2c9ur1nzx4aDN50dnalq6sn9fpiVCo/\nJLCeklSXEyZMKSDri452ycnJbNq0PSWpE4GPqNdX5QcffJR23Gg0csWKlXz11Tc4c+YsxsfH8+DB\ng5QkLwILCSymJHlzz5496co1mUz08PAjsN38AxlOvb58hrMIDh48SBeXAPNgHwk8olrtyrCwsDyf\nV1HRz5JDhw5RpytBOSRnPFWqNlQqLR1phtHZWUeTycQzZ87Q1bW6xTHSza1JhgM6TxITE8Nt27Zx\ny5YtfPjwIatVa2wetJPLcXJ6i5MmfWjVuRRF/Z5EkooTuJ92XRWKiZw+fXquyujdeyB1uucI/EqV\nahJ9ff358OHDfLJYRmjn2Aj9ZIxGI8+cOcNjx46lC+345ZdfUq8faHHvNFGlUmc72/FJkpOTWbly\nXapU7xI4TmAygeoEHtLJScuYmJhc2yy0Kzg2btxISapM4DqBh9Rqu3P48HesKtNSvxz2Ed0hh9Rr\nb978kT5SgKPwA+TlALlJXyR45hu0yWTi558vpMFQhvJ0/8oEfjDfXB9Sra7Jl156idevX7e3qbmm\nKOiXGefPn6ck+RGIS+vYabUevHPnTpb5bt26xdKlq9LFpTI1Gk+WKxfA9u17csmS5QU6A6SoaLdr\n1y66uNS16ICH0MlJm+VbiBdeeJnACouHoC/ZoUNPkvKDTWregwcP0tXVm25uTanTefO99yZnWN7O\nnTtpMLSyKM9IrdbLqreWRUU/S2bOnEknp7EW13EBgc4Wn+9RrdbTZDIxNDSUGo0bgavmY/9Qq/Xg\nrVu3sqwjNDSU5cpVp6tra7q6dqCvrz9LlKhM4KRFPcEcOXKMVedSFPV7knr1WlGp/Nx8TSOp19fk\n5s2bc5w/Li6OKpWGcjQdWRtX107ctGlT9pmtQGjn2Aj95Cn8LVs+R72+PF1dA+jvX5MhISEkyd9/\n/516fTWLdnWELi4euX4+OX/+PF1cKvDxbDkTgepUq9vy+ed758luoV3BMnPmHOp07nRy0rJ370F5\nmr1hiaV+2fQNAyG/OTdlsv2Ox2vrHYHl+Zw+HdZ52xDYjMTERHTq1A1jx36KqKh6AIIh+3t4A0Bv\nKBQtUatWcXzzzTfw9/e3r7GCXBEREQEnJ18AqfHeDXByKo6oqKgs87322lu4d68fYmIuIzHxFkJD\nXdG/fxeMGDHcKqdYgoyJioqCQuEHIDVusTeUSmfExcVlmic5OQWPdQUACUlJKZgy5WPodK5wcXFD\nu3ZdUbt2bdy6dRE//zwLZ878D3PmzMiwvCZNmkCjuQ2lci6AM3B2fhtVq1ZGyZIlbXSWRQNvb2+o\n1Wfx+PnBDwrFQchL7L6Hs3NnjBz5FhQKBTw9PbFgwVzodM1gMDwPna4Bpk+fjLJlywIAYmNjceTI\nEVy4cCGdl+kpUz7B3bttER29H9HRuxAaOgCuri6QpNEALgDYB0laiN69uxXsyT+D/PDDl/D1XQpX\n1yrQaitg4MDAPEbNsIwSZRT3UYEgG+bOnYfjx9WIjb2C6Oiz+OefznjzzfcAAIGBgejevRn0+jpw\nde0JSeqCdeu+zHW70mq1MBpjASSa96RAoYhAz56l8eOP39j2hAT5woQJ7yE2NgJJSXHYuHENtFpt\nQVTbC3In/yGA3pCdAPqbt1SHgCbIgwS9C8IgG5BbR3/CMWBhJ/bCviyPJyYmsk2b56lQdCDwC4Gx\nBKoQWESgNTUaPw4ZEsRH534vGIPzAUfWj8xew4zYvXu3eRq4gkqlKxWKBQTuUKmcyRIlKvLQoUN8\n9OhRpvk9PculvZ1s5LOPQDDffnusFWeRNxxdu5wSEhJCg8GHjXwmEbhJZ+fRrFu3RZZvNX7++WdK\nUikCPxLYQkkqzbFjx1KSqhG4RyCRGs0gvvzy4Czrtvx+Xbt2jW3adGHp0jXYs+dAq5YCkEVHv1Ri\nL+xjQkIC69VrSReXVtTphlKjcada7UagI4FAOjuXf2pJzZUrV7ht2zZeuHAhbd/ly5fp5VWWBkN9\nSpIfe/UamOZosEOHXnzs7JFs5DOH9eq15ZgxE+jjU5HlytXi+vUbrD4fR9cvL/fOjEhISOD58+fz\nPCumX7/BlKRAApvo5DSWzwf4MSoqyia2ZYaja2crbPUdKOjyhX5knz6vsZHPexazmw6zUqUGacdN\nJhMPHjzIDRs28Nq1a3mqw2Qy8YUX+lCS2hNYSp2uM9u168rov/fm2W6hXe6xth3Zsh1a6pdFv/A4\n5Dem2bEBcqQARyAn52NN+nQ40jB42heBDhbz887Mtki4fNDqcrRVWqHMxH02sKjgcfSYrbbSMK8c\n/a8F3viDmD9/IIKCggq0bkfXLjecOHECtz9thwDXmAKtNz/bdlHSD7BfWz3+oDhOVAvCZ599atNy\nHV0/e987M0NVvgkqTP0zX+twdO1sRX5/B/Lr/in0A2bNmovSB2aigXfBv3C0RlehXe6xtp3ash0+\nMZsks76qCUBHALuzKW4ogJVwjNnvdQBUgBwCMCN2mY9XgLwUYBaAm3mtzBEuyDPPhivEhiviJvWs\nY53OR5CQcAY6nd6mNgnSU79+fTRoUM8mZYl2XfjIT03ciynxySdT8qVsgYwt9XNWq21SjiB/EfdR\n+/Luu+/AzV2VfcJMEPo9GxRSHX8HMD6bNG6Q11VnN1BQWDgN+aV3MDJ2bFjevP0AYAWsGAAAACdr\nMgsEgoKiNkymVRg2rC06dAiEj4+PvQ1yGDZv3oIvvvgekqTFpEnvoH79+vY2SfAMEhBQvaDWQQoE\nAkGB4OzsjJo1axTK2TyCIs9wyNP8IyF38v/C4zXy7gAaQHYK6A75zbmjsBny+TQEsOeJY5Ughz7M\n2qlYDhGDAIWAvpUdaVWGIK9Yp7MeQC2o1RVw48YNMQiQQ9au/Q7Dh09EXNwMAJHYseM5HDq0G7Vr\n1873unOj97Wr17BvzRoMGjRIOCvLR8S91rER+hU9hOaOjdDv2aCQ6ngL8lvxYZAHBALx+O35QwA3\nAMyEvBTgkR3ss4YoPD0AYHkMkM/VqnU6YjmAQPAEJHH79m1cvnwZRqPR3uZYcAFJSddRvnx5exvi\nMMycuRhxcV8AGAjgbcTFjcbSpauzzPMosuDXPt69p8GIEUswZMhbBV63QCAQCAQCgQMSBWAu5Dfk\nxSH3a5Xm/xsAmAPHGwDIKRHIPvxhBB47UHSkUIlP4bCePmMv7KPJZGL16g0JvEXgNoG1BLypUJQh\noCTgzLZtO2bpiTy/PezmJ46iX3JyMnv2HECt1ot6fVlWq9aADx48yPDaR0VF8datW0xOTs6yvFKl\nKlGhWEwgmcAsAjUsPO2Srq6V0nkkt+T77zdQpyvOthWqUKcrzjVrvrXVqeYYR9EuI6pVa0Jgt8X1\nDmZQ0FtZ5ulRryKBbWnxirXaXpw/f75N7Xr99RFUq9uao4Akm6M/PKJa7cb79+/btC5H1i8vxF7Y\nx+nTP6VeX4FK5Xjq9c343HM907z652e9+YGj65fRdXnxxYEEvrRol/9j1aqNMy1j9eqvKEnlCXxH\nYBElyZOnT5+2uV22xtG1sxUiOoBjk9vrGxoaao7GkpLWxg2G9vzll1/ytV5LhHY54++//+b06R/x\n009n8vbBjbnKe/jwYa5fv56XLl0iaZfoAEUdE+S4x72ySNMT8mwJQJ4pEZjfRuUXDt2gw8PD6exs\nIGC0ePDpSq22PPfs2ZPvD6j2xlH0W7hwESWpDYE4AiY6O49hjx4Dnkq3YMESqtUulKRS9PEpz/Pn\nz2da5qVLl1ipUl0qFEoWL16SGo0vgWjzd+ABNRo3/vfff5nmv3//Pg8fPpxlmvzEUbTLiJUrV1GS\nKhLYTGA1JcmTx44dyzJPsWKlCNxMa6cKxTROnDg5V/VeuHCBQ4eOYJ06Tdi69QucM+dzJicnMz4+\nnqtXr6abmzeBSQRaWNwPTJSkkrxx44Y1p/wUjqxfXoiJiaGzs0Q5RCMJJNHFpRoPHjxob9PyxLOo\n34IFiylJzQk8IpBEjeZlBgW9nWWe9es3MDDwRXbr1p9//fVXjupJSEjgoUOHePTo0XSDtSkpKfzq\nq6/4wQcfcvPmzVkOvlvDs6hdfrB7926+//5EfvbZZ4yOjra3OWkI/fLGo0ePzPfgh+Z7sJEuLvX5\n++8FF+ZaaJc9hw8fpiR5Uql8j87Ob9DdvQRv3rzJc+fOcfv27bxz506meUeMeJeSVI6urr2o03nx\n22/X2dQ2S/2y6Bf+AOB923Y1HQoTgLoA2kEeDMgJOU1X6HC4Bm00Gjlw4Kt0cytNX9+yVKm0BELM\nN8UUAtXo61uO8fHx9jY133EU/QYOHEZgiUXH7ATLlKmZLs2JEycoSSUJ3DCnWc0SJSpyz549Wb7F\nTR3oee21N+niUpNq9dvU6ytywoSp+XlKVuMo2mXGmjXfslmzzgwM7JGjjmDv3oOo0bxiHqg5T0kq\nzT179uS4vr///puS5EGgIoEXCayiRtOG3bu/zNq1m1Gv70h5RpAHAU8CnxO4SKVyDKtVa8CUlBRr\nTvcpHF2/3BISEkKt1oOAyeItVGf+9NNP9jYtTzyL+hmNRr7++pt0ctLR2VnPwMCujImJsWkdoaGh\nrFSpDl1da9HFpRrr1GnO6Ohomkwmdu7ci3p9SwJTqNcHcPTo8TatO5VnUbvMCA8PZ8+eA1mqVDW2\nbNmZV65cyVG+ZctWUpLKEJhOrbYPK1WqY/PvQl4pSvqRZFxcHNesWcMFCxZk+WIjJwwZMpKS1ITA\nUmo0fVmrVlMmJibayNLsKWra5YXmzTsT+Crtd1KpnMTatZtSpytBN7cOlCRPbtmy9al8x44doySV\ntRjkOU+NxpUJCQk2s81Svyz6hUbIIfOKKiYAqeGs6kAOGZgdwflnTv7iUA06OjqaLi4eBLwJ9DF3\nBvTUaCoSmEqFohU9PcszJCTE3qYWCI6i36xZc6jTvUB56j4JTKZW682rV6+mpVm1ahX1+lfTvcEF\nlHR1bU693jPbDqPJZOK2bdv42WefpRsZN5lMBfojmVMcRTtr+PbbdezSpR9ff/1Nnj17ls8/34dO\nThq6uHhy2bKVuSpr8OARBIZRXvaROvMnjk5ObtTpWlh0Tv8iYKBSWZoqlTvbt++eL7M9HEG/O3fu\ncPz4SRwxYrTVb+xNJhMrV65LlWoqgVACP9DV1dtuM2mspTDoFx0dzZdfHkwPjzKsVKlergbFsiIm\nJoaRkZE2KetJXnkliM7Ob5vbm5EazSscO3Yijxw5Qr2+IoFEczsMp1rtyrCwMJvbUBi0KwhMJhPr\n1WtJtfpNAmepVH5OT8/SOdLW1dWLwPm031K9vjO/+uqrLPOsW/cda9RoxvLl6zAgoCGbNn2OixYt\ntfmMjqKiH0nGxsayevWG1Os7UKN5g5Lkyd9++y3P5RmNRi5btoIDBgRx2rSP8zSwExUVxcOHD/Py\n5cu5zluUtMsrAQHNCeyzeJb9kCqVN4GwtGcUSXJnUlJSunybNm2iwdDVIh+p1XratD9jqV9BdTId\nEMtBAEAeCDgOwD+LPA47aOIwDfrvv/+mQuFOwIdAE/M2iEA91qxZjxMnTuayZcsKZYcvvyjM+t25\nc4cvvfQaa9ZsRn//2tTpfAiUIFCHQGUCH9DPr2pa+r1791Kvr0wgynwD3EfAnYCOgJoqlTtDQ0Oz\nrddoNPLGjRsMCQnhzz//TIPBm0qlEytXrstr167l5ynnisKsXV65e/cub926RZPJxDlz5lGSqhD4\nmkrlh3R3L8F///2Xd+7c4fz58zlv3jz+888/OS67b9/BBEYTaGzxI2mks7MX1eqBFvuiqVRquHbt\n2nydAlvY9btz5w7d3UtQpRpDIJiS5MutW7da9UD/zz//sEmT9tTp3OnvX4tHjhyxocUFS2HQr1u3\nl6nRvEzgGoGfKEmemfoxKSzUrduGwO8W7e17tm/fk7t27aLB0DrdIK4kleLNmzdtbkNh0K4guHfv\nnnn2zePljgZD22w7kSaTiU5OWsrLQlI7E8O5aNEimkwmbt68mVOmTOU333yTNkNqy5Yt5pkDOwjs\nIVCOwGjq9bX50UczbXpeRUU/kly6dCl1ui4Wg9Q7Wbp0NbvZc+bMGRYvXopubg2o0/kyKGhUrn4T\nipJ2eeXjj4PNszUuEThOtdqHOl3nbDv3ly9fppOTG4ET5nRr6ONT3qbLmi31y0H/sB6A3kjfIS4K\nPDkIAMgDARGQl0m4W+x3g7x8QswEyE+WLVtGQE95uu9RAuMIBBAoTqAF27d/zt4m2oXCql9ERAS9\nvctSqXzHrNFcAm8S6ErgT8q+AZIIKDlv3kKS8oNLUNAoSlJp6nStzZ3/cgTuUF7mMZQdO/bMst7Q\n0FAGBDSmJJWkWu1GlcqNwP8IGKlQfM7y5QPybZ1qbims2mVETEwMt27dyk2bNmX4FiopKYndu/ej\nRlOcOp0PGzZsQw+PMgTOpv3oOTsHcfz48XRz86VGM4QazVAaDD5pzm+yY9euXdTpfAmUITCZwCGq\nVINZrVoDarWeBA5Sfvs4jIGB3Wx9CZ6isOs3YcJk8wBA6oPHeCqVLlSp1Gzd+gWGhYUxNDSUV69e\nzdIJJ0meP3+eq1ev5q+//vrM+FkpDPo5O+sIRKZppNG8wQULFtjNnqxI7SwOHjyCCkU/8z05kUAn\nVqtWnxERESxWrCSB1QT+pUo1lRUq1LL5MhyycGhXEERGRtLZ2YXp14DX4v79+7PN26VLX2o0/Qlc\nTxtgunjxIkePHk+9vgaBD6jXN2PXrn3NSzleIrDG4n6xhUBnAmfp5VXepudVVPQjyenTP6JSOdHi\nut6ji4tXvtS1b98+BgcHc9GiRTxz5kyGL8QqVqxD4GuzLY+o1wfkaklXUdIurxiNRr7//gf08ChD\nX9+K/PDDadTpvAhcNF/3TfTw8Hvq3vjmm2OoVgcQcDVvem7atMmmtlnql03fcBfkznDqttG8fznk\nznAkgFlW9j8LK0bI4RGfpByAq5Cvx3UA18z/Hy8wy/KBQt+g69evb+4QKii//b9lHlWtTnntr8Zh\nnVNZS2HVb/369XRx6UJgAYEh5hvfzwRqmgcA5BFxwI9ubr7p8p48eZILFy6kSuVCYKrFj+dturuX\nzLLeHj0GWExVjSbQgMCKtDLUaleGh4fn56nnmMKq3ZOEhoaybNlqdHVtS1fX5+jtXZa3b99Ol2bW\nrLnU6dqbtU2hWj2YanUxAlfSrr2T09sMCKhHpfJTC02D6e9fi3v37s2RLZs2bWKVKg2o15eil1cl\nDhgwlJGRkdy6dSu9vctTo3Flx44vMiIiIj8uRToKu34jR44hMNN8nY8T8CJwhMBBqlQ96edXlWq1\ngXp9GZYpUzVTx4kbNvxAnc6Lev0gurjUZpcuLz0TAwGFQT+DwdtioMxESXqBq1evtln5N2/e5KlT\np6zyj7Nnzx56epamQqGkRuNFg8GLst+NUpRn5XWms7MLw8LCePbsWQYENKXB4MMWLZ7L1Uyf3FAY\ntCso5IHxRgTmU6vtwYYN22Q7aEfKS0369n09banJ3r17zd7lDQTCzd+5BOr1/jx+/Dh79RpEYL7F\nvflLAj0InKK3t79Nz6ko6Hfz5k0eOHCAP/30k9nX0SkC0VSrX2fXri/bvD555l1ZKhTvEKhFwJ1a\nrRd3796dLp088BiVprOz8xjOnj07x/UUBe1yS1xcHKdPn8E+fV5Lc1b8JF99tYZarYGS5MfixUtl\n6EjZYPCl7Dw5mUAYlcox/OSTT2xqq6V+WfQLgyF3bnsBMEB2kBcO4ATkAYBxAJaZ0zyLDgTbAQiC\nfJ5uGRzvCfn8lyGTCAKKfDPN9qR9Ed59910AQNOmTdGsWTO7GZQKSfTo0RPHj58DsA5AfQBLAGwH\n8AuARgCiMH9+MPr06WNHS+1HqVKl0v4vTPpt27YN7767HvHxrgCuAHgJwOsAXgZwC3JkjUsAPoNW\n+y6uX7/0VBkDBgzE/v33AayHHJp0GypUWIaDB3/LtN6GDdvi3r3FAGqY93wJ4CLkkKbXoFZ3xtWr\nF+Hk5GSjM8079tIuPj4eM2bMxfHjZ+DvXwbTpo2Hj49PpunHjZuM9evDYDK9C6AylMrP0abNZUye\n/C5Kly4NvV6PwYNHYefOFgD6mnMdg7v7W0hI8EZCwvsAbkOnm4XKlavhzJnXATxvTrcTwBxotRGY\nO3cKXnyxR7q6Y2JicPjwYSiVSjRr1gw6nc7m1yOvFNa2l8qxY8fQr18QEhI+B3AQQCzktvcAQBLk\nwe6dAIpDoViKgIA92LFjU7oySKJixRpISPgBQACAJEjSC1i+fAICAx02Ig6AwqHft99+h2nT5iEh\noT/U6isoWfIqfv99GyRJsqpckhg7djK2bv0ZTk6ekKQEbN68Dv7+WS1pfJqQkBC0bNkB8fFLATQD\nsAbyc48H5BdCKgAloFbXwbFj++Hl5WWV3TmlMGhXUJDE+vXrcfz4OVSo4IfBgwdDq9Xmqazbt28j\nMLA34uOPIfUx1dX1RaxaNRZubm548cV+iI8fCkANYBGAodBqt2Hs2AEYMSKjF2N541nXb+nSlZg7\ndyHU6gpISbmO/v17Y8OGLYiPj0KLFoFYvvxzGAwGm9WXmJiIypWrIyXlIIBSAJIhRyxrBKXyJxw+\nvA9+fn4AgFatOuP69f4ABgJ4CJ2uG1asmJrj+/mzrl1uMRqN6N69Hy5cMCAxsR202p/QqpUHvvpq\n6VNp4+LiEBYWBl9fX6jV6qeO16zZGBERqwDUBACo1WMwYUJVDB8+3Gb2WuqHzPuq1wCsgPzgnMpQ\nACshd8ROmffNhPxwX8FmBgoKnEI5qpeQkMCyZSsRMJhHoy2dxWkJtKJC4WZ1TGNHx176bd68mU3/\nz951hldRtNGzt+7ubemVhNB7CSX0Epr0JghIFQQLIFUFBVSKNBFEQZqiWChKk14jghQVEaVIERHp\nKhgI6bnn+zGb5IYUEkjDj/M8eSCb3ZnZeXdm3v7Wacm6dVtx3br0WU5v3rxJs9mLQF0C8wk8RpEH\nwKbR1EbAi3p9JfbqNTDd8+PGTaQse1GnK0/AQkVpRKvVmwcOHEhzX0xMDH/55RdevnyZJNmkSXvq\ndMnWz0TqdC1oMvnRYulLRfHlhx9+lOb5yMhIvvPOO3z99Td46NChXJyhe6MgaOd0Ohke3pay/ASB\nHTQYXmZQUJlMkwv9/vvvNJs9KZLxBRF4gsAoSpJCm60sbTZv7t69m2PHTtBim5M0q/8r7NDhSU6Z\nMoNVqzZieHh7Hj58mHPnzqOqVqNwUT1HoBqB5wnMYEBA6TR9X7x4kf7+JWizNaHNVp/Fi1fKkyRj\n94uC3DuTk0N169af48e/kWnug/Xr17Ns2TB6eARSpytGkUw1kcAkipCq5H31LyqKW7rnY2JiqNMZ\n6RqTbDA8wXbt2nHevHm8c+dOXr9qCqKjo3nixIlc8+QpLGdfchm3t956i7du3brn/XFxcVy2bBnf\neuutTMtyrlq1ihZL1RSLnyTNYWhogxyPbf369bTb08ayiqS8IQTGEviGZnMv1q/fIl/DrAoL7R42\nJCQkMCSkAvX6yRSlPj+ku3tASojXTz/9xEGDhvLxx59k06at2axZZ3744UePEgPmACdPntTyH/2p\nrZeDVFV3xsTE5Nka+fvvvzUPD6fLOu1A4AsCAzh9+vSUe48fP05v76K02cpRlj04bNhLj3ICPAAO\nHTpEq7Wsdq6KZMWy7HlfXlCiokcIgXdoMLxAb+/gLCtj3Q9c6ZeFXOiEyAXgiibadVd0zuBaQSK/\nLRPFtJ+HGoVuQZ88eZJGowdFzH9DAsWYmnH4VwImenr6FZpyNwWJ3KDfgQMHOHPmTC5btixdtlJX\nREdHc/Xq1RwxYgRl2U87YFZRUQK4ceNGbtu2jbNmzeKmTZt4/vx5TXhMdv1PIOCuuamdJ/AHgWqU\nJHs6QeLQoUNU1SAC15gcNqCqnulc0I8fP04fnxDabGVpNrtx9OhXee7cOfr5FafdXodWa1nWrduc\nX331FZcsWZJOYRQZGclixSpQlrtSpxtDRfHN9firrFAQa+/atWs0m90o8jEkJ5qqx23btmV4f6NG\nbShJk5nsOiqS8lkJnNKu7aLN5s1//vmHoaH1abVWpN0exqCgMrx06VK69pxOJ8eMmaCFetgIFCVQ\nnUADSpI1DY27du1Lvf7VFOWfyfQcn39+RJ7NTU5RkHtnapmohTSbn2SFCmFZlhGKi4ujw1GUqbGg\ny7V5j9F+X8YyZapn+Gzx4pU0pUESgR81ug2gorRn+fI1GR0dnVevmYLvvvuO7u4BtFpL0my28+j+\n4JUAACAASURBVJ135j1wm4Xx7LsX4uPjWbNmY1os4TSZhlFV/bhs2afp7nvttdcpSclrhwSu0GLx\nJEmuW7eOXbv249NPD75notQDBw7QYilO4I4mYGyiUMJvpCRZWa5cbQ4YMCSNEio2NpYvvzyeYWHN\n2b17/wz3gQdFftLu0KFDbNq0I8PCmudJpvyc4PLly1y6dCk//fRTRkZG3lcbf/zxB2vXbk6r1ZsV\nKtTmzz//nMujvDcexrWXXWzYsIEOR8s0ijNVDeT58+fzrE+n08myZatTksZR5Bj5iiL8608Cz3Da\ntGlp7o+OjubRo0fvS1B9GGn3xRdf0sOjCA0GmeHhbXOsSHY6nSllUO/GN998Q7u9hgu9k6iqgfzt\nt9/ua6xfffUV+/V7jqNHj0kxbuUmXOmXhVz4PYQngCuSQwRclQOrULji4Tsj/8ITQpG90oGFHoVq\nQf/222/U6RQCjTSmM0lTBJQl0JeAgzab+38iJjU38KD0W7LkQ6pqAI3G4bRYGrFOnWY8cOAAO3fu\nzdatu3HDhg0kRUmZ0qVDabU2pk7XhiLZ31Ft0/uY/v5labGUpsn0Ai2W8uzeva+WadhVM+1HYI3L\n72sJeKSLWVu2bBmt1u5pDlGDQU3H9JQuXY2StIjJlkyLpRS3b9/O27dvc/fu3Txw4AATExOZkJDA\nb775htu3b0/Txty5cynLXV36+TqdNTovURBrLzUmNFk546TNVpM7d+7k+vXrWaFCXZYoUY1vvjmT\nTqeTvr4lNMVb8hz1pyTVuYvBKcJz584xPj6e3377LSMiIrK0EB89elRTENkJPJnyjUjSBLZt2y3l\nvmrVwglsd+lrBVu0eDw/pilbKKi98/bt2zQYFKYmC3PSZgvLVJGTjIkTp9BkaqYpcxIoSeWp1wfQ\n4WhINzd/HjlyJN0zM2fOocnkoTGUegJmAjNT+rVYmnPZsmV59aokBQPm7R1M4Eut39+pqn48evTo\nA7Vb2M6+7GDlypW0WhtQKFWnEahKnc6De/bsSXPfihUraLFUIxClra33WKVKPX7wwVKtDvVCStIE\n2u2+WWbvdzqd7NGjP1W1AkUOHk+KPCsW9uv3dIbPdOz4pFYOdjMNhlfo71/ivgXWzJBftDt27Bgt\nFi+KvDIbqaqVOHXqzEzvj4+Pv6eSID4+nm+99TZ79x7E2bPfyVZ8f1JSEqdNm0az2Y2y/DgtltYs\nUqR0tqrlZDaGgwcP8sCBAwVSTelhXHvZxdmzZ7UkcMn5cHbSbvfJ83n+888/GRbWlJJkJuAgMIXA\nDFosnpnme7kfPGy0O3LkCFXVh8ABArdoND7P8PB22X5+y5YttNm8aTDIDAwslU5pdufOHQYFlaFe\nP47AAZpMz7Fy5TqFVkZxpV8WcuHTEAL/QgihP1kBMFX7dwdSk+IVtrjAzhBJDXMW+5YzTINQgPwn\nUGgW9I4dO+jrW5JAEwLjXZj/Pyisj0b26tWroIdZqPAg9HM6nVQUB4HjKRpMRalEs9mdIqHfUqpq\nEa5cuYpvvDGJZnNPF6F+IYFmFKVMQggYCCQLjJGUZR+WLFmFRuNwAoep179Oo9GTwEQXuk4mUISy\n7MkPP/w4ZVyHDx+mqgYSuKjd9z4dDt90nh96vYnCQpWc9G8oZ82alfL3tWvXsnbtx2i1BlCWy9Fu\nb0Afn5CUA1Fk7X3ZZTx/0m73vU9K5BwFtfY6dOhBRXmMwHKaTANZunQot23bRlX1o0je+C1VNZRT\np85k06YdqNdP0Oh+h7IcqnnpJLs6HqKiOHLklfPll1/Sbu9AoDlF8qnk+d/LcuVqp9w3fPjLlOVO\nmtAaRVVtyilTpmfRcv6ioOj3zz+i/roQBJO9OYTHS1aIj49nq1aPU5Z9qKpBrFKlLr/++mvu2rUr\nw0SKFy5coCx7UFTnoLZP6CgSbop+ZflZzp07N69elaRrhvRUxZPN9gQ/++yzB2q3MJ1998KtW7d4\n4sQJzp49m4oykCJhahiBPQQ+pap6pfF0cjqd7NVrIBXFj3Z7VToc/gwLa0Kz2YeiWoqYR71+OCdM\neD3Lvp1OJ1977TWaTGVTlArAGhYpUibdvUJBJTNVyUjabM25du3aXJ2P/KLdmDGvUpJecfn2DjMg\nIP17//XXX6xTpzl1OgMVxcGFC5dk2J7T6WTz5h20/XceVbUpW7fukqXiICEhgeXLV9cEu7dTxmI0\nDuawYS9m6z0SExPZs+dTNJkslGUbvb2L0mqtQJutEsuWrZ4viVRd8TCtvfvBokUfUJYdKSFzERER\n+dZ3UlISx44dx8qVG7BNmyd4/PjxXG3/YaPdnDlzaDYPdlnDYo/KDi5evKgpAfdqPNBH9PEJSae4\nu3jxItu1686SJauze/f+hSb5dEZwpd89ZMPREImDkqsDDNSudwawEkIILmwKgGSEQCgppiNtOb8H\nxUCt3Rdzsc0CR6FY0L16PUXASMBEoAxF/PF5ijibgQQc96yN+/+IB6FffHw8dTpDGmFCr6/I1Izi\nJLCBVao0ZP/+z2uKgeTrP2k08iTwGUXs6bsEAggco8NRgwsXLqSHR3Hq9Z709y/DzZs3a27g3Qn0\noMgsfZrAScqyg//++2/K2KZNm0WTyUGDwY+Ag6pagb6+xXjq1KmUe0JCKhL4XBvPLVosFbhx40aS\nyTWPi1BYmjswOaZZp5vGpk07kEwOO/Al8DWBPynLj7N79/4PSJHso6DWXnx8PCdOfJMtWjzOIUNG\n8ebNmxwwYDBFGcdUr4jg4PLcv38/ixYtT6u1FBXFh1279uH06W9TUbxotValJFlpMFioKA6uWvVF\ntvo/fvy4FjM5gcLj5zaBBJrNvdi//+CU+2JiYtiq1eM0Gq00GlV269YvW1az5HccMmQ0vbxCGBxc\ngStWrLyvucoKBUU/p9PJ+vUfo9ncl8B31Olm0MsrKFNG/q+//uKPP/7Imzdv0ul08vz58zxz5sw9\nLRXffvstHY6wNMK3iAd/ksAlAlupql65zmTejaSkJNps3to6FV4/qhr8wDk8CsvZdy+sXr2GiuJO\nm60UZdmuVd3wIvBLCl0kaSxfeWVcmuecTidXrVrFihVrUpI8CYzQ9uyfXOg5ji+//Mo9x5CekY6h\nTmdIJ7xGRUVpSoColHtttvAM88Y8CPKLdq++Op463WiX9z7AoKDyJMW6mj59OseNm8AaNRrTaBxK\nEWZ1kqoayH379qVr79ixY5qHXHJ4YywVxZ+nT5/OdAyLFy+mCMGoqgkjyWP5gJ069c7We/Tt+zSF\nIWWEdv66E7hCEWb1DAcOHHp/E3SfeFjW3oPgn3/+4S+//JJpvpaHFQ8b7T755BNaLOFMNWAdpLt7\nYLae3bRpEx2OFmnOQFUNSBea+jDBlX7ZlBFzL4Nl/uNpiGoGOyBK/t2Pd8DjSC2LuAoZVwt4qJEv\nC/rOiYgMr9++fZuVKtWkKAEYQGEZHEURO64QMNBo9EiJXcysnf9XPCj9wsKa0GAYSeFavIu1AywE\nZrlsetvo4VGCLVq0piyX1xiHOErS4xpTUeUuISGQgJ2K4kY3N39K0rsEjtBk6st69Zrz999/Z/36\n9anTBTPV0k9arSV58uTJNGNbuHAhFaUqky2PkvQuQ0MbpHwDhw8fppubPx2OWlQUfw4cODSFKW3Y\nsC2BFQQGEZjnMr7DDAmpnNLHmjVrGBBQmna7L7t375+vic7ya+0lI6u1M3ToKBeL1xUCJanT+VOW\nvennV4p+fiVZt25T7t69m5GRkTx37hw9PYMJLNae+ZGq6s0zZ85kayxz586nwaBSkjwY5muiwWBj\n/fqPZcgw3bx5M8fuxMOHv0xVbULhmfI1FcU/160xuU2/nOxtt27dYp8+z7BEiVA2bdoh09juDz/8\nmLLsRru9Ei0WT27cuOmebcfGxnLy5Kns0KEHDQYrgTEEdhDYSUlSGRxcgTabD4sWrcCtW7emPOd0\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88cWxadqbNGmyxid9nPJtAPU4dqy4LykpiTqdnkB8SnuKMpDz589/4HdZunQpLZamLmf5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bpg6nTvs3DhcoyIiMiUzXv37lGt9iBww1Rub1Kt9uSdO3es5LXtcST9JkyYRLncfJ/3fQwMLGFv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\nhWrVPqVDhw7aFi+EEEKIbEEaAbKBy5cv8+yzNbl7N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mAwfIuT0x6KFStGyZIVadVqOA0b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"text/plain": [ "<matplotlib.figure.Figure at 0x7f5a278989d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Here we'll compute the scatter in the points for different bins of measured p_values, so we can provide upper limits\n", "#on p_features for the difficult galaxies. \n", "\n", "f=figure(figsize=(20,15))\n", "#store range of spread of data in each bin\n", "interval_dct={}\n", "\n", "\n", "gs=gridspec.GridSpec(9,10)\n", "gs.update(wspace=0)\n", "gs.update(hspace=0)\n", "yedge_int=[8,7,6,5,4,3,2,1,0]\n", "y_label=[]\n", "facecolors=['#af8dc3','#999999','#7fbf7b']\n", "for i in range(0,len(yedges)-1):\n", " y_label.append(round((yedges[i]+yedges[i+1])/2,1))\n", "y_label=y_label[::-1]\n", "for j,y in enumerate(yedge_int):\n", " for i,z in enumerate(reds):\n", " ax=plt.subplot(gs[j,i])\n", " xs=scatter_dct[z,yedges[y],'lo']\n", " ys=scatter_dct[z,yedges[y],'hi']\n", " plt.scatter(xs,ys)\n", " interval_dct[z,yedges[y]]=[]\n", " plt.xlim(0,1)\n", " plt.ylim(0,1)\n", " this_dct={} #stores p_features at z=0.3 value for each bin (xs); use to compute spread in each bin\n", " #stored like: this_dct[lower_bin_edge,higher_bin_edge] = [distribution of xs in bin]\n", "\n", " bins_list=np.linspace(0,1,6) #bin measured p_features from 0 to 1\n", " for b in range(0,len(bins_list)-1):\n", " bin_bottom = round(bins_list[b],1)\n", " bin_top = round(bins_list[b+1],1)\n", " this_dct[bin_bottom,bin_top]=[]\n", " for l,val in enumerate(ys): #check p_features at z values\n", " if val >= bin_bottom and val < bin_top: # if it falls inside bin in question:\n", " this_dct[bin_bottom,bin_top].append(xs[l]) #then put corresponding p_features,z=0.3 value in list\n", " \n", " #Now compute the mean and spread of p_features,z=0.3 for each bin\n", " p_features_means=[]\n", " x_error_lo=[]\n", " x_error_hi=[]\n", " bin_centers=[]\n", "\n", " for b in range(0,len(bins_list)-1):\n", " bin_bottom = round(bins_list[b],3)\n", " bin_top = round(bins_list[b+1],3)\n", " plt.axhline(bin_bottom,c='k',lw=1,alpha=.1)\n", " p_features_means.append(np.median(this_dct[bin_bottom,bin_top]))\n", " try:\n", " # x_error_lo.append(p_features_means[b] - np.percentile(this_dct[bin_bottom,bin_top],10))\n", " x_error_lo.append(np.percentile(this_dct[bin_bottom,bin_top],50) - np.percentile(this_dct[bin_bottom,bin_top],10))\n", "\n", " except IndexError:\n", " x_error_lo.append(0)\n", " try:\n", " # x_error_hi.append(np.percentile(this_dct[bin_bottom,bin_top],90) - p_features_means[b])\n", " x_error_hi.append(np.percentile(this_dct[bin_bottom,bin_top],90) - np.percentile(this_dct[bin_bottom,bin_top],50))\n", " \n", " except IndexError:\n", " x_error_hi.append(0)\n", " bin_centers.append((bin_top-bin_bottom)/2.+bin_bottom)\n", " try:\n", " interval_dct[z,yedges[y]].append({'bin_bottom':bin_bottom,'bin_top':bin_top,'low_limit':np.percentile(this_dct[bin_bottom,bin_top],10),'hi_limit':np.percentile(this_dct[bin_bottom,bin_top],90)})\n", " except IndexError:\n", " pass\n", " plt.errorbar(p_features_means,bin_centers,xerr=[x_error_lo,x_error_hi],fmt='o',c='#d95f02',markersize=1,elinewidth=5)\n", "\n", " \n", "\n", " plt.tick_params(labelleft='off')\n", " plt.tick_params(top='off',right='off',labelbottom='off')\n", "\n", " if j==8:\n", " plt.xlabel('%s'%reds[i],fontsize=16)\n", " if i==7:\n", " ax.yaxis.set_label_position(\"right\")\n", " plt.ylabel('%s'%y_label[j],fontsize=16,rotation=270,labelpad=20)\n", "\n", "f.text(.78,.5,r'$\\mathrm{\\mu_{i}~(mag/arcsec^2)}$',rotation=270,fontsize=30,va='center')\n", "f.text(.45,.91,r'$\\mathrm{redshift}$',fontsize=30,ha='center')\n", "f.text(.1,.5,r'$\\mathrm{f_{features}}$',rotation=90,fontsize=30,va='center')\n", "f.text(.45,.08,r'$\\mathrm{f_{features},z=0.3}$',fontsize=30,ha='center')\n", "\n" ] }, { "cell_type": "code", "execution_count": 78, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{(1,\n", " 17.830924416199601): [{'bin_bottom': 0.0,\n", " 'bin_top': 0.2,\n", " 'hi_limit': 0.49611911955751392,\n", " 'low_limit': 0.080353677038456472}, {'bin_bottom': 0.2,\n", " 'bin_top': 0.4,\n", " 'hi_limit': 0.72627932162015574,\n", " 'low_limit': 0.3080412831730911}, {'bin_bottom': 0.4,\n", " 'bin_top': 0.6,\n", " 'hi_limit': 0.81744707391466798,\n", " 'low_limit': 0.68380097465546164}, {'bin_bottom': 0.6,\n", " 'bin_top': 0.8,\n", " 'hi_limit': 0.95350034742478584,\n", " 'low_limit': 0.80718780572978666}]}" ] }, "execution_count": 78, "metadata": {}, "output_type": "execute_result" } ], "source": [ "interval_dct" ] }, { "cell_type": "code", "execution_count": 90, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#get list of debiasable ferengi galaxies \n", "unique_galaxies = set(data['objid'])\n", "z0ind = np.zeros(len(data),dtype=bool)\n", "for ug in unique_galaxies:\n", " ind = (data['objid'] == ug)\n", " if data[ind]['sim_redshift'].min() < 0.301:\n", " z0ind[ind] = True\n", " \n", "data_z0 = data[z0ind]\n", "category_list_ferengi=[]\n", "for row in data_z0:\n", " if row[SB] > yedges[0] and row[SB] <= yedges[len(yedges)-1] and row['sim_redshift'] > reds[0]-.05 and row['sim_redshift'] <= reds[len(reds)-1] + .05: # if within ferengi space, check where it is. else, consider NEI or uncorrectable.\n", " for y in range(0,len(yedges)-1):\n", " if row['mu_max_i'] > yedges[y] and row['mu_max_i'] <= yedges[y+1]: \n", " for i,z in enumerate(reds): \n", " if row['sim_redshift'] > reds[i]-.05 and row['sim_redshift'] <= reds[i] + .05: # pick out where it is in SB/z and check color\n", " if row[p_x] > p_range_correctable_dct[z,yedges[y]][0] and row[p_x] <= p_range_correctable_dct[z,yedges[y]][1]:# if it's in correctable range::\n", " category_list_ferengi.append({'sdss_id':row['objid'],'subject_id':row['subject_id_1'],'Correctable_Category':'correctable','sim_redshift':row['sim_redshift'],'p_features':row[p_x],'sim_evolution':row['sim_evolution'],'GZ_MU_I':row[SB]})\n", " elif row[p_x] > p_range_uncorrectable_dct[z,yedges[y]][0] and row[p_x] <= p_range_uncorrectable_dct[z,yedges[y]][1]:# if it's in uncorrectable range::\n", " category_list_ferengi.append({'sdss_id':row['objid'],'subject_id':row['subject_id_1'],'Correctable_Category':'uncorrectable','sim_redshift':row['sim_redshift'],'p_features':row[p_x],'sim_evolution':row['sim_evolution'],'GZ_MU_I':row[SB]})\n", " else: #not in correctable or uncorrectable range, so nei\n", " category_list_ferengi.append({'sdss_id':row['objid'],'subject_id':row['subject_id_1'],'Correctable_Category':'nei','sim_redshift':row['sim_redshift'],'p_features':row[p_x],'sim_evolution':row['sim_evolution'],'GZ_MU_I':row[SB]})\n", " else: #galaxies outside ferengi SB and z limits - still need to have meaasureable z and SB to possibly correct. \n", " if row['sim_redshift'] > 0 and row['sim_redshift'] < 9 and row['GZ_MU_I'] >0: #these have measurements for z and SB, put in NEI\n", " category_list_ferengi.append({'sdss_id':row['objid'],'subject_id':row['subject_id_1'],'Correctable_Category':'nei','sim_redshift':row['sim_redshift'],'p_features':row[p_x],'sim_evolution':row['sim_evolution'],'GZ_MU_I':row[SB]})\n", " else: #these have nan or infinite values of z or mu, put in need_redshift_list\n", " category_list_ferengi.append({'sdss_id':row['objid'],'subject_id':row['subject_id_1'],'Correctable_Category':'nei_needs_redshift','sim_redshift':row['sim_redshift'],'p_features':row[p_x],'sim_evolution':row['sim_evolution'],'GZ_MU_I':row[SB]})\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Output time\n", "## write file categorizing Ferengi galaxies. This will be used in Step 2 to compute zeta using the correctable galaxies, and in Step 3 the corretable/uncorrectable regions will be smoothed out to apply to the Hubble data." ] }, { "cell_type": "code", "execution_count": 96, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#create fits file of galaxies with Correctable_Category Label for FERENGI\n", "c0 = Column([x['sdss_id'] for x in category_list_ferengi], name='sdss_id', format='A24') \n", "c00 = Column([x['subject_id'] for x in category_list_ferengi], name='subject_id', format='A24') \n", "c01 = Column([x['Correctable_Category'] for x in category_list_ferengi], name='Correctable_Category',format='A24')\n", "c02 = Column([x['p_features'] for x in category_list_ferengi], name='p_features',format='D')\n", "c03 = Column([x['sim_redshift'] for x in category_list_ferengi], name='sim_redshift',format='D')\n", "c04 = Column([x['sim_evolution'] for x in category_list_ferengi], name='sim_evolution',format='D')\n", "c05 = Column([x['GZ_MU_I'] for x in category_list_ferengi], name='GZ_MU_I',format='D')\n", "\n", "category_table_ferengi = Table() \n", "category_table_ferengi.add_columns([c0,c00,c01,c02,c03,c04,c05])\n", "\n", "fname = '/home/mel/Dropbox/gzhubble/ferengi_files/ferengi_data_with_categories_new_sb.fits'\n", "if os.path.exists(fname):\n", " os.remove(fname)\n", "category_table_ferengi.write(fname,format='fits')" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The number of correctable ferengi galaxies is 1884\n", "The number of uncorrectable ferengi galaxies is 1986\n", "The number of nei galaxies is 80\n", "Total: 3950\n" ] } ], "source": [ "#old:\n", "\n", "print 'The number of correctable ferengi galaxies is %i' %((category_table_ferengi['Correctable_Category'] == 'correctable')).sum()\n", "print 'The number of uncorrectable ferengi galaxies is %i' %((category_table_ferengi['Correctable_Category'] == 'uncorrectable')).sum()\n", "print 'The number of nei galaxies is %i' %((category_table_ferengi['Correctable_Category'] == 'nei')).sum()\n", "print 'Total: %i' %len(category_table_ferengi)" ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The number of correctable ferengi galaxies is 1915\n", "The number of uncorrectable ferengi galaxies is 1939\n", "The number of nei galaxies is 88\n", "Total: 3942\n" ] } ], "source": [ "#newer :\n", "print 'The number of correctable ferengi galaxies is %i' %((category_table_ferengi['Correctable_Category'] == 'correctable')).sum()\n", "print 'The number of uncorrectable ferengi galaxies is %i' %((category_table_ferengi['Correctable_Category'] == 'uncorrectable')).sum()\n", "print 'The number of nei galaxies is %i' %((category_table_ferengi['Correctable_Category'] == 'nei')).sum()\n", "print 'Total: %i' %len(category_table_ferengi)" ] }, { "cell_type": "code", "execution_count": 92, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The number of correctable ferengi galaxies is 1690\n", "The number of uncorrectable ferengi galaxies is 1678\n", "The number of nei galaxies is 81\n", "Total: 3449\n" ] } ], "source": [ "#newest :\n", "print 'The number of correctable ferengi galaxies is %i' %((category_table_ferengi['Correctable_Category'] == 'correctable')).sum()\n", "print 'The number of uncorrectable ferengi galaxies is %i' %((category_table_ferengi['Correctable_Category'] == 'uncorrectable')).sum()\n", "print 'The number of nei galaxies is %i' %((category_table_ferengi['Correctable_Category'] == 'nei')).sum()\n", "print 'Total: %i' %len(category_table_ferengi)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Input time\n", "## Now definte the low and high limits of p_features for all of the Hubble galaxies, depending on where they exist in z/mu space. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "hubble_filename = download_from_dropbox(\"https://www.dropbox.com/s/b55kbq49ep21qow/gzh_t01_mu.fits?dl=1\")\n", "hubble_data = Table.read(hubble_filename) \n" ] }, { "cell_type": "code", "execution_count": 94, "metadata": { "collapsed": false }, "outputs": [], "source": [ "low_hi_limit_list=[]\n", "p_features='t01_smooth_or_features_a02_features_or_disk_weighted_fraction'\n", "Z='Z_BEST'\n", "for row in hubble_data:\n", " if row[SB] > yedges[0] and row[SB] <= yedges[len(yedges)-1] and row[Z] > reds[0]-.05 and row[Z] <= reds[len(reds)-1] + .05: \n", " for y in range(0,len(yedges)-1):\n", " if row[SB] > yedges[y] and row[SB] <= yedges[y+1]: \n", " for i,red in enumerate(reds): \n", " if row[Z] > round(reds[i]-.05,2) and row[Z] <= round(reds[i] + .05,2): #now we have mu,z info:\n", " for bin_range in interval_dct[red,yedges[y]]:\n", " if row[p_features] >= bin_range['bin_bottom'] and row[p_features] < bin_range['bin_top']:\n", " low_hi_limit_list.append({'zooniverse_id':row['zooniverse_id'],'survey_id':row['survey_id'],'OBJNO':row['OBJNO'],'Z_BEST':row[Z],'GZ_MU_I':row[SB],'low_limit':bin_range['low_limit'],'hi_limit':bin_range['hi_limit'],'p_features_weighted':row[p_features],'Table':row['Table']})" ] }, { "cell_type": "code", "execution_count": 95, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#create fits file of lower and upper limits for p_features\n", "c1 = Column([x['zooniverse_id'] for x in low_hi_limit_list],name='zooniverse_id')\n", "c2 = Column([x['survey_id'] for x in low_hi_limit_list],name='survey_id')\n", "c3 = Column([x['OBJNO'] for x in low_hi_limit_list], name='OBJNO')\n", "c4 = Column([x['Z_BEST'] for x in low_hi_limit_list], name='Z_BEST')\n", "c5 = Column([x['GZ_MU_I'] for x in low_hi_limit_list], name='GZ_MU_I')\n", "c6 = Column([x['p_features_weighted'] for x in low_hi_limit_list], name='t01_smooth_or_features_a02_features_or_disk_weighted_fraction')\n", "c7 = Column([x['low_limit'] for x in low_hi_limit_list], name='t01_smooth_or_features_a02_features_or_disk_lower_limit') \n", "c8 = Column([x['hi_limit'] for x in low_hi_limit_list], name='t01_smooth_or_features_a02_features_or_disk_upper_limit') \n", "c9 = Column([x['Table'] for x in low_hi_limit_list], name='Table')\n", "\n", "limit_table = Table() \n", "limit_table.add_columns([c1,c2,c3,c4,c5,c6,c7,c8,c9])\n", "\n", "\n", "#User: Set output path here\n", "fname = '/home/mel/Dropbox/gzhubble/hubble_files/catalog_debiasing_files/new_sb_method/lo_hi_limits.fits'\n", "if os.path.exists(fname):\n", " os.remove(fname)\n", "limit_table.write(fname,format='fits')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
karlstroetmann/Artificial-Intelligence	Python/6 Classification/Polynomial-Logistic-Regression.ipynb	1	22080	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "from IPython.core.display import HTML\n", "with open (\"../style.css\", \"r\") as file:\n", " css = file.read()\n", "HTML(css)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Polynomial Logistic Regression" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The data we want to investigate is stored in the file `'fake-data.csv'`. It is data that I have found somewhere. I am not sure whether this data is real or fake. Therefore, I won't discuss the attributes of the data. The point of the data is that it is a classification problem that can not be solved with \n", "ordinary logistic regression. We will introduce <em style=\"color:blue;\">polynomial logistic regression</em> to solve this problem." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "DF = pd.read_csv('fake-data.csv')\n", "DF.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We extract the features from the data frame and convert it into a `NumPy` <em style=\"color:blue;\">feature matrix</em>." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "X = np.array(DF[['x','y']])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We extract the target column and convert it into a `NumPy` array." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "Y = np.array(DF['class'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In order to plot the instances according to their class we divide the feature matrix $X$ into two parts. $\\texttt{X_pass}$ contains those examples that have class $1$, while $\\texttt{X_fail}$ contains those examples that have class $0$." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "X_pass = X[Y == 1.0]\n", "X_fail = X[Y == 0.0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us plot the data." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import seaborn as sns" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.figure(figsize=(15, 10))\n", "sns.set(style='darkgrid')\n", "plt.title('A Classification Problem')\n", "plt.axvline(x=0.0, c='k')\n", "plt.axhline(y=0.0, c='k')\n", "plt.xlabel('x axis')\n", "plt.ylabel('y axis')\n", "plt.xticks(np.arange(-0.9, 1.1, step=0.1))\n", "plt.yticks(np.arange(-0.8, 1.2, step=0.1))\n", "plt.scatter(X_pass[:,0], X_pass[:,1], color='b') \n", "plt.scatter(X_fail[:,0], X_fail[:,1], color='r') " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We want to split the data into a training set and a test set.\n", "The training set will be used to compute the parameters of our model, while the\n", "testing set is only used to check the accuracy. SciKit-Learn has a predefined method\n", "`train_test_split` that can be used to randomly split data into a training set and a test set." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from sklearn.model_selection import train_test_split" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will split the data at a ratio of $4:1$, i.e. $80\\%$ of the data will be used for training, while the remaining $20\\%$ is used to test the accuracy." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In order to build a <em style=\"color:blue;\">logistic regression</em> classifier, we import the module `linear_model` from SciKit-Learn." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "import sklearn.linear_model as lm" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The function $\\texttt{logistic_regression}(\\texttt{X_train}, \\texttt{Y_train}, \\texttt{X_test}, \\texttt{Y_test})$ takes a feature matrix $\\texttt{X_train}$ and a corresponding vector $\\texttt{Y_train}$ and computes a logistic regression model $M$ that best fits these data. Then, the accuracy of the model is computed using the test data $\\texttt{X_test}$ and $\\texttt{Y_test}$." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def logistic_regression(X_train, Y_train, X_test, Y_test, reg=10000):\n", " M = lm.LogisticRegression(C=reg, tol=1e-6)\n", " M.fit(X_train, Y_train)\n", " train_score = M.score(X_train, Y_train)\n", " yPredict = M.predict(X_test)\n", " accuracy = np.sum(yPredict == Y_test) / len(Y_test)\n", " return M, train_score, accuracy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We use this function to build a model for our data. Initially, we will take all the available data to create the model." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "M, score, accuracy = logistic_regression(X, Y, X, Y)\n", "score, accuracy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Given that there are only two classes, the accuracy of our first model is quite poor. \n", "Let us extract the coefficients so we can plot the <em style=\"color:blue;\">decision boundary</em>." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ϑ0 = M.intercept_[0]\n", "ϑ1, ϑ2 = M.coef_[0]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.figure(figsize=(15, 10))\n", "sns.set(style='darkgrid')\n", "plt.title('A Classification Problem')\n", "plt.axvline(x=0.0, c='k')\n", "plt.axhline(y=0.0, c='k')\n", "plt.xlabel('x axis')\n", "plt.ylabel('y axis')\n", "plt.xticks(np.arange(-0.9, 1.1, step=0.1))\n", "plt.yticks(np.arange(-0.8, 1.2, step=0.1))\n", "plt.scatter(X_pass[:,0], X_pass[:,1], color='b') \n", "plt.scatter(X_fail[:,0], X_fail[:,1], color='r') \n", "H = np.arange(-0.8, 1.0, 0.05)\n", "P = -(ϑ0 + ϑ1 * H)/ϑ2\n", "plt.plot(H, P, color='green')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Clearly, pure logistic regression is not working for this example. The reason is, that a linear decision boundary is not able to separate the positive examples from the negative examples. Let us add polynomial features. This enables us to create more complex decision boundaries." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The function $\\texttt{extend}(X)$ takes a feature matrix $X$ that is supposed to contain two features $x$ and $y$. It creates the new features $x^2$, $y^2$ and $x\\cdot y$ and returns a new feature matrix that also contains these additional features." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def extend(X):\n", " n = len(X)\n", " fx = np.reshape(X[:,0], (n, 1)) # extract first column\n", " fy = np.reshape(X[:,1], (n, 1)) # extract second column\n", " return np.hstack([fx, fy, fxfx, fyfy, fxfy]) # stack everthing horizontally" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "X_train_quadratic = extend(X_train)\n", "X_test_quadratic = extend(X_test)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "M, score, accuracy = logistic_regression(X_train_quadratic, Y_train, X_test_quadratic, Y_test)\n", "score, accuracy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This seems to work better. Let us compute the decision boundary and plot it." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ϑ0 = M.intercept_[0]\n", "ϑ1, ϑ2, ϑ3, ϑ4, ϑ5 = M.coef_[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The decision boundary is now given by the following equation:\n", "$$ \\vartheta_0 + \\vartheta_1 \\cdot x + \\vartheta_2 \\cdot y + \\vartheta_3 \\cdot x^2 + \\vartheta_4 \\cdot y^2 + \\vartheta_5 \\cdot x \\cdot y = 0$$\n", "This is the equation of an ellipse. Let us plot the decision boundary* with the data." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "a = np.arange(-1.0, 1.0, 0.005)\n", "b = np.arange(-1.0, 1.0, 0.005)\n", "A, B = np.meshgrid(a,b)\n", "A" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "B" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "Z = ϑ0 + ϑ1 * A + ϑ2 * B + ϑ3 * A * A + ϑ4 * B * B + ϑ5 * A * B \n", "Z" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.figure(figsize=(15, 10))\n", "sns.set(style='darkgrid')\n", "plt.title('A Classification Problem')\n", "plt.axvline(x=0.0, c='k')\n", "plt.axhline(y=0.0, c='k')\n", "plt.xlabel('x axis')\n", "plt.ylabel('y axis')\n", "plt.xticks(np.arange(-0.9, 1.1, step=0.1))\n", "plt.yticks(np.arange(-0.8, 1.2, step=0.1))\n", "plt.scatter(X_pass[:,0], X_pass[:,1], color='b') \n", "plt.scatter(X_fail[:,0], X_fail[:,1], color='r') \n", "CS = plt.contour(A, B, Z, 0, colors='green')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us try to add <em style=\"color:blue;\">quartic features</em> next. These are features like $x^4$, $x^2\\cdot y^2$, etc.\n", "Luckily, SciKit-Learn has function that can automize this process." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from sklearn.preprocessing import PolynomialFeatures" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "quartic = PolynomialFeatures(4, include_bias=False)\n", "X_train_quartic = quartic.fit_transform(X_train)\n", "X_test_quartic = quartic.fit_transform(X_test)\n", "print(quartic.get_feature_names(['x', 'y']))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us fit the quartic model." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "M, score, accuracy = logistic_regression(X_train_quartic, Y_train, X_test_quartic, Y_test)\n", "score, accuracy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The accuracy on the training set has increased, but we observe that the accuracy on the training set is actually not improving. Again, we proceed to plot the decision boundary." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ϑ0 = M.intercept_[0]\n", "ϑ1, ϑ2, ϑ3, ϑ4, ϑ5, ϑ6, ϑ7, ϑ8, ϑ9, ϑ10, ϑ11, ϑ12, ϑ13, ϑ14 = M.coef_[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plotting the decision boundary starts to get tedious." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "a = np.arange(-1.0, 1.0, 0.005)\n", "b = np.arange(-1.0, 1.0, 0.005)\n", "A, B = np.meshgrid(a,b)\n", "Z = ϑ0 + ϑ1 * A + ϑ2 * B + \\\n", " ϑ3 * A*2 + ϑ4 A * B + ϑ5 * B*2 + \\\n", " ϑ6 A*3 + ϑ7 A*2 B + ϑ8 * A * B*2 + ϑ9 B*3 + \\\n", " ϑ10 A*4 + ϑ11 A*3 B + ϑ12 * A*2 B*2 + ϑ13 A * B*3 + ϑ14 B*4 " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.figure(figsize=(15, 10))\n", "sns.set(style='darkgrid')\n", "plt.title('A Classification Problem')\n", "plt.axvline(x=0.0, c='k')\n", "plt.axhline(y=0.0, c='k')\n", "plt.xlabel('x axis')\n", "plt.ylabel('y axis')\n", "plt.xticks(np.arange(-0.9, 1.1, step=0.1))\n", "plt.yticks(np.arange(-0.8, 1.2, step=0.1))\n", "plt.scatter(X_pass[:,0], X_pass[:,1], color='b') \n", "plt.scatter(X_fail[:,0], X_fail[:,1], color='r') \n", "CS = plt.contour(A, B, Z, 0, colors='green')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The decision boundary looks strange. Let's get bold and try to add features of a higher power. \n", "However, in order to understand what is happening, we will only plot the training data." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "X_pass_train = X_train[Y_train == 1.0]\n", "X_fail_train = X_train[Y_train == 0.0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In order to automatize the process, we define some auxiliary functions." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$\\texttt{polynomial}(n)$ creates a polynomial in the variables `A` and `B` that contains all terms of the form $\\Theta[k] \\cdot A^i \\cdot B^j$ where $i+j \\leq n$." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def polynomial(n):\n", " sum = 'Θ[0]' \n", " cnt = 0\n", " for k in range(1, n+1):\n", " for i in range(0, k+1):\n", " cnt += 1\n", " sum += f' + Θ[{cnt}] A*{k-i} B**{i}'\n", " print('number of features:', cnt)\n", " return sum" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's check this out for $n=4$." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "polynomial(4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The function $\\texttt{polynomial_grid}(n, M)$ takes a number $n$ and a model $M$. It returns a meshgrid that can be used to plot the decision boundary of the model." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def polynomial_grid(n, M):\n", " Θ = [M.intercept_[0]] + list(M.coef_[0])\n", " a = np.arange(-1.0, 1.0, 0.005)\n", " b = np.arange(-1.0, 1.0, 0.005)\n", " A, B = np.meshgrid(a,b)\n", " return eval(polynomial(n))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The function $\\texttt{plot_nth_degree_boundary}(n)$ creates a polynomial logistic regression model of degree $n$. It plots both the training data and the decision boundary." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def plot_nth_degree_boundary(n, C=10000):\n", " poly = PolynomialFeatures(n, include_bias=False)\n", " X_train_poly = poly.fit_transform(X_train)\n", " X_test_poly = poly.fit_transform(X_test)\n", " M, score, accuracy = logistic_regression(X_train_poly, Y_train, X_test_poly, Y_test, C)\n", " print('The accuracy on the training set is:', score)\n", " print('The accuracy on the test set is:', accuracy)\n", " Z = polynomial_grid(n, M)\n", " plt.figure(figsize=(15, 10))\n", " sns.set(style='darkgrid')\n", " plt.title('A Classification Problem')\n", " plt.axvline(x=0.0, c='k')\n", " plt.axhline(y=0.0, c='k')\n", " plt.xlabel('x axis')\n", " plt.ylabel('y axis')\n", " plt.xticks(np.arange(-0.9, 1.11, step=0.1))\n", " plt.yticks(np.arange(-0.8, 1.21, step=0.1))\n", " plt.scatter(X_pass_train[:,0], X_pass_train[:,1], color='b') \n", " plt.scatter(X_fail_train[:,0], X_fail_train[:,1], color='r') \n", " CS = plt.contour(A, B, Z, 0, colors='green')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us test this for the polynomial logistic regression model of degree $4$." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "plot_nth_degree_boundary(4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This seems to be the same shape that we have seen earlier. It looks like the function $\\texttt{plot_nth_degree_boundary}(n)$ is working. Let's try higher degree polynomials." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plot_nth_degree_boundary(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The score on the training set has improved. What happens if we try still higher degrees?" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plot_nth_degree_boundary(6)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We captured one more of the training examples. Let's get bold, we want a $100\\%$ training accuracy." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plot_nth_degree_boundary(14)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The model is getting more complicated, but it is not getting better, as the accuracy on the test set has not improved." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=2)\n", "X_pass_train = X_train[Y_train == 1.0]\n", "X_fail_train = X_train[Y_train == 0.0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us check whether regularization can help. Below, the regularization parameter prevents the decision boundary from becoming to wiggly and thus the accuracy on the test set can increase. The function below plots all the data." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def plot_nth_degree_boundary_all(n, C):\n", " poly = PolynomialFeatures(n, include_bias=False)\n", " X_train_poly = poly.fit_transform(X_train)\n", " X_test_poly = poly.fit_transform(X_test)\n", " M, score, accuracy = logistic_regression(X_train_poly, Y_train, X_test_poly, Y_test, C)\n", " print('The accuracy on the training set is:', score)\n", " print('The accuracy on the test set is:', accuracy)\n", " Z = polynomial_grid(n, M)\n", " plt.figure(figsize=(15, 10))\n", " sns.set(style='darkgrid')\n", " plt.title('A Classification Problem')\n", " plt.axvline(x=0.0, c='k')\n", " plt.axhline(y=0.0, c='k')\n", " plt.xlabel('x axis')\n", " plt.ylabel('y axis')\n", " plt.xticks(np.arange(-0.9, 1.11, step=0.1))\n", " plt.yticks(np.arange(-0.8, 1.21, step=0.1))\n", " plt.scatter(X_pass[:,0], X_pass[:,1], color='b') \n", " plt.scatter(X_fail[:,0], X_fail[:,1], color='r') \n", " CS = plt.contour(A, B, Z, 0, colors='green')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plot_nth_degree_boundary_all(14, 100.0)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plot_nth_degree_boundary_all(20, 100000.0)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.1" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }	gpl-2.0
tpin3694/tpin3694.github.io	machine-learning/.ipynb_checkpoints/loading_scikit-learns_digits-dataset-checkpoint.ipynb	1	8545	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Title: Loading Scikit-Learn's Digits Dataset \n", "Slug: loading_scikit-learns_digits-dataset \n", "Summary: Loading the built-in digits datasets of Scikit-Learn. \n", "Date: 2016-08-31 12:00 \n", "Category: Machine Learning \n", "Tags: Basics \n", "Authors: Chris Albon " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Preliminaries" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Load libraries\n", "from sklearn import datasets\n", "import matplotlib.pyplot as plt " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load Digits Dataset\n", "\n", "Digits is a dataset of handwritten digits. Each feature is the intensity of one pixel of an 8 x 8 image." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 0., 0., 5., 13., 9., 1., 0., 0., 0., 0., 13.,\n", " 15., 10., 15., 5., 0., 0., 3., 15., 2., 0., 11.,\n", " 8., 0., 0., 4., 12., 0., 0., 8., 8., 0., 0.,\n", " 5., 8., 0., 0., 9., 8., 0., 0., 4., 11., 0.,\n", " 1., 12., 7., 0., 0., 2., 14., 5., 10., 12., 0.,\n", " 0., 0., 0., 6., 13., 10., 0., 0., 0.])" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Load digits dataset\n", "digits = datasets.load_digits()\n", "\n", "# Create feature matrix\n", "X = digits.data\n", "\n", "# Create target vector\n", "y = digits.target\n", "\n", "# View the first observation's feature values\n", "X[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The observation's feature values are presented as a vector. However, by using the `images` method we can load the the same feature values as a matrix and then visualize the actual handwritten character:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 0., 0., 5., 13., 9., 1., 0., 0.],\n", " [ 0., 0., 13., 15., 10., 15., 5., 0.],\n", " [ 0., 3., 15., 2., 0., 11., 8., 0.],\n", " [ 0., 4., 12., 0., 0., 8., 8., 0.],\n", " [ 0., 5., 8., 0., 0., 9., 8., 0.],\n", " [ 0., 4., 11., 0., 1., 12., 7., 0.],\n", " [ 0., 2., 14., 5., 10., 12., 0., 0.],\n", " [ 0., 0., 6., 13., 10., 0., 0., 0.]])" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# View the first observation's feature values as a matrix\n", "digits.images[0]" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.figure.Figure at 0x1133b8cf8>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { 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"text/plain": [ "<matplotlib.figure.Figure at 0x113073f98>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Visualize the first observation's feature values as an image\n", "plt.gray() \n", "plt.matshow(digits.images[0]) \n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.3" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
CompPhysics/MachineLearning	doc/Programs/JupyterFiles/Examples/Housing.ipynb	1	631949	{ "cells": [ { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": 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Qj173d36/rmh2CZrJe8EQKLB0yyFc8cfPWFiJwQgAcwwGjLi0e8Dnk2w8Xru9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\nsAY3RqdF2mFtfmQcBvxuo+5rkm08igal47mp+bj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"text/plain": [ "<matplotlib.figure.Figure at 0x1a1dace400>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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crWFGo+pT9x181yLOFfNVn+WmjzOWyJg0hWVjaY7FcQOfoCjfjTNFqjTDNOPD\n3REf7o8YJjkLNe9YqAeOO904U9zZH7HdT7jfjY6kzB1LMt/w2Bsm5ONZFIt1l71BwsEo5Vor4Hqn\nwrVWQDfMTn2+yVrzXJ/bU1HKcpeUPBpP8sTw08CvCiF+CfCBBsUJoiWEsMenhlVg4wmu4Uwus9u0\nhOBKyz+2Mz7JWSGLj3LXelGpaWGKGctxqqj7RSVVL8qoeQ5Zrqj5Nq3AJVOGhbpHnCm2+zGDJCWM\nM4QlWaoF+H4xae7+QcjdwwF/8cEue72EhZpPuthAAhpYaQZHp6iJ093sRtw7jHBtwVqngi3FsUT2\narvCziDBtQSWJbnS8EkyRZLkJLk5el5VzzqS6c6UJkxytgcxSa7oR4r5ukvg2Keea6Y0aa7wbOfc\nZ1VSUnKaJ+YYjDG/CfwmgBDi54B/aIz5u0KI/xv4OxSVSX8P+OdPag0P46zQz6zqnP1hSrU9+3Gd\nZfxnXWejG3H1nP6FWWuZOJyLjsg0Amqexc6g2KVLJJ2Ki2sL8CRf+9wVKq7D3jBh6zDiXi9iqxfy\nx9/f4IP9ARbwb3zhKr/4+Wv4jsXvf+8+/+uf3+Kg6AHEBr76/Bz/8c+9wFLdZ6MXcaNdxbYL5+A7\nVqFdpDT1wDlSb50YZnQxZ3q1FWBZAs8uusZzITgMMyqORcW1iZKM3X7O1UbAZj9m/WDEDzf7CFlo\nRl1p+FgCam37VPXURrcQLzwYpSy3AuyxVHhZulpS8nA+iT6G/wz4HSHEfw28BfzPn8AazuWiO/NJ\nSGOjG+HZ8lSJ5cnr5Npw/7AQopt0PF92dOV5CdeJEzHKMEwUq60Kv/jqCr/7nXsMRoqrbY8fv9Hm\n+xsDBnGOYwnaFZf3t3r8H99a54ODBzOxb/3xOt+90+Mnnuvwf37rzpFTAMiBb9065O//jYxubDNK\ncjCwOlUF5FjFqFOtTVGiOnZiWa65P0jGiWxDbgzaFA2Ai81C0iLMFN0w5TBMcS151DwYpaqQ0dAQ\nK02YKnaGCavtylFIK081t/eHBI7FWqfCVi9ifT9kdS5geZIvKSkpOZePxTEYY/4E+JPx1x8AP/lx\n3PdRucjO/GhCXK7Y7idFuMQ67kSmryOlYKsXHZXIamPOlWs4r6dgVghs2oloY6j5FrmCwJFcmwvw\nr9jcWKgijeT2/oiVpk/Nd9gPE16/vXXMKUBh/N+83cNoRZienpOdAt/b6PFTz/nUfBvPeaASC4Vz\nXax77AySIyc2+f6kBtXkBAXFxLqKa7GZx1ypexiKEab7gxRLciRkaLQmzTSBa8bPVzKIMt6+12Oj\nH1H1bF6+UmetU2UwVl99WEMnnswsAAAgAElEQVRhSUlJwVM98/lReVhy+mQfRJHYLEZvnpTcmFxn\nEGWkuTnatZ6s0T/JeT0FJ/shTiZaPUsyjBWdqoNtCxbqAUuNClXbZn9UdAc7toVAYCFJ1WyDqTTE\nuSawZ/+ZqPHAoPmaV5wOjGGU5qwfhNw9CNkZJCzWvaOeAseWpz7T5HlPD+vJtCHJC+HAxYZP4Npo\nYQBBxZMkSuE5klwpGr6NEIL5qssPNvsErqRT87CE4N3tAWmmcG3rzEqykpKS0zz1khiPynnJ6ZMh\novNE8SbXycay3fb43x8m13DRfMKs9biORafmEqWFTlHNL6pycmPIlaEZFONOD6OcUZozX7FwgZPt\n564NVc/hC6s+Oz88IJv62VfWqtxcaFD3CmnxdCwtst2Px0KDxejQSf5BWqc1o9JMocezHqaf+412\nteg+d4pwVKdiGEQ5Smvc2KJdEVwZNxle61SpunYhbaINnu/QgEI/KcyIMsWN+VoZQiopuQSlYzgH\nKQU6N8RK4Up5lFw9abQfJoonpcCTFsut4EKKqZP3XCSfcF5SOjOajcOIg1GKtAQLVY/rnYClVsAg\nUgS2xK57/LtffZ5ISX7v7d2j2uGqBa9ca/LCUpM4V/z0Cy2+f6/LKIZmBeIc/urDfXpxzmItwHcl\n8zWPbpix1qmQqpztXlyMNx33Qzi2PAon9eOE/WFKp+pyrxsdy7fYtmS1XWGrFxcDhKTkq891yJRm\nc3wys6Vkda5ypPXkyuI0cmd/hGMXTqdTdXmuUzsal1pSUnIxSsdwDgejhL/6cI8wzqlVHL54rU2n\nWtT4d2pF3X2S62OieFobkkyRj8Msvm0dq9Z52DyD6Z+d9fqHJaWLGLxhoxuz2Y3ZGsRordEdzSsr\nDT6/3CoGGCnN5mFE4Nv8J7/wMp9bqfPdO3uEmWK1U+dKq8ZaK+DDgxFJmvO2gRAIQ9gMR9zZH5Hl\nClbbPH+lRitw6EUpH+4MOIwzjAbbFiR5zpvrhyw3fCxLMl912errc+dHn/zsUPRU1H3nyPntDBLW\nHOsoDNWpuvSjjDhTCOBKwz969iUlJRendAxnMIwyfvubH/LW7S5ag8Dwwxs9fv6VZWwpccYGZ6Hu\nHfU3TBq6frTd54PtIRXf5manyms3O7QqLnB2iexZJa8nX3+RpLTWhlu7A+7sDcmN4eZ8lVGsyDXs\nj9KiC9mxUNqwPUyQowTXtvgbL16hXamQC41rWeRKcfsgpBdm7IwShsfz03QT+Pb6QdHJ3PC5S4jR\n8J17PRDQqXrUhMW720MW6x6eayGArX6MgAenGynQuT5V9TX92R/oKM2uFFPGUPMdXrvZJs/10ZyH\nTGmkEaUkRknJJSgdwwy0Nry33eONDw5pVVxSZYizjDdud3lhqU674h9NX5v0N2hdiMrd3hvy9R/t\ncDBMEUg2DyMSpfn5l6/gjQ39yZPBw1RNpzmvlNaxZCGJIYq4fWr0eMIceI6FEYWU+ORUsz2eKz0Z\nMBTGmldW6mjgL27t0x0l3D+MAMNhPz6WY5iwP8y4sx/SrPV4ZalJw7fwLIljC640iqqi27tDlhr+\nA+MsFHGm2NtPEEJgjDk1k+EkZ4XLxFgaXRjQxtAPE3JddJlrBEYbxLiHoZTEKCm5GKVjmIEyhihX\n5AbCNKcb5RhTaPkMI8VcpTCu00YZIEpz3tnostlNqAc2ShVaQt/8YJ/lhs9c1aMZFI1leqwdtNwK\njo0HhfO7dE+WwKZjmYlpoyql4Fq7ylzg0R0qhlGG41i0fJv5mstWPyZT+qjMdqUVoLXhIEzYH6Ts\nDBK2exH3DkPW90b04pQ012hOI4Glho9vWyil2epndGouji3JNCitUMZQ9yw0hnSq9DWbnBZ04ezO\nY1bOpVVxuNeNUErTj1Pe2erz5+/tsdENcS3JWrvCz728xKvXWqc6r0tKSs6mdAwzsISg6bt4lmCz\nH2G0IFE5dc9hsx9ijyUcgGOVQtoYumGOsEAgkNKwN0xwbEE3ytgLU+7vj6gFLp5t0arYpErz/Hyt\nUBXNFEIWu9yHqYre2RuxMyhiO4sNj1RpfPlgN1wLHH7x88t84/1dbu2OEMKwNtZ5kgKqvo0bpmx2\nI66PTz+9MGex7nKvN+L23pB7hyGuY1M3DgOTUrMMI8WRg5jz4Qtrc3ztCyvsjzKUAcYT7/I8x3ck\nwzRnqeGz0Y14Y/0A15K0Aps4MzSrLoNY0akU6qpXmimtwD0z9zIx9mpcyXSvG6G0ZneU8K1be7y5\nfkiS5WgNoVLc2h3hWNu4dtF8F0U5rcCmMeMeJSUlDygdwwwKLZ8qr642+eYtRW40tnZ4aanBYi2g\nGdjcO4i4Oq5EmhiZq60KNd9GasNeP8K2JanKuTpXpTtM+MF2n3uHMT95o0295TCMFZiYG53qpVRF\nXUvi2pJr7eBITmLWbrhVcXlluclLV+p4tkWY5Xx/Y8DKnMJoqHqSXpgziDOkFNQ8i1GmEWOF1iTX\nGBTSkiw0CvmKOVcCiiSH5bkaP3a9zTBWaGUQVrG2wJa8cXufO3shB1HGStPH92ze2+iyO0zIlGa+\nXuEzy3W++vw8B2HhVL93r8dau1LIn49DPmflXjKlUUpzGGUYrRmlCiNhlGiwJFJrLCH4YHfIbj/B\nYLAsm5eX6/zNFxd4dXWuDCuVlJxB6RjOwHcsvnStzWorIAOiKMMIwbV2hRvzNUZJftSxm6nCCNV9\nh89dbeJakq1eRJzrsV6Q5Bu399kfxIySjO2+j+dY1Hy76FTWhm6Yca0doJVBYTgcpTR8Z2ZOQhmD\nASpu8euTiJmhp0mIq+I7CAPDoUJpzd29EXe7EWmmuNqu8MKVGi3f5c1uMcL05nwVz7MwRqNVcfrp\nJwq0YuVqjc+uLZDlgiSHq82AXqLIjWGpVajQfm+jy2YvwXEkLeHw4V6fD7cTYh5I6aYqROUZSab4\nzJUGr661cC2BEBzroD4r92KJQjq8EMorRqpaugjnGVE8UykFwzDBsW3qVZcX5h2Gcc5b64c0Ky4v\nLNTLk0NJyQxKx3AGjiVZGp8G9sOUYZTTClxW5gqD5dpFVc9mLzzazXZqLjc6Ndo1j1GcoQ3cPxjx\ne9/Z5P29IWmmsUSxq/1Kblio+6w93xnPiM7YHsTs9GKEBTXXoe7bVFybnbG20GTH7I5lIaI0Pzox\nTMtnT5xIlms2+zFSFCGvMM2QwO2DCEcKQg3dUcq//M59vnpznkQpBAaD4V+7OU+eGb6/2aXf1Uyk\nktbfHfKH7w55ba3GL/7YNTzP4stLdd7fGdKuOEgE/VFKnCnqns1IKbYPE6ITz7efgh9lbPQitNE4\njsR3HdpVr8hPqKJK6eQ87ulE+0pz0heiWZuv0o1TdgcRG72YOAOtwZdg2aCVYf0wGqvKauKkVFot\nKTmL0jGcwWR+8aQp64WFKpaUGAOZMqd1f5Rmb5DgORYdS4ApNH7u7A14f7eP0uBYkOWwvhtyve1z\nc7HKYZjTC1PeuH3At9f36UU5aE214rBxGHK1FXBzoUYjcI/UWZcaPmmuj+UYrneqpOMd9SSRmyl9\nVHWUZIqtbkyuDRoYpjnNioMlACT9JMMRgr1hwnzVo1FxWJsPuLffY2d0/NnkwF+uD7HFOs6X1ogz\nRZIbvnO3T82THI4ykkwTpglhkjGcVc4EDCKwehE3WgGZhms1h3c2+7x0pcZGN0IZc2oe93TupeLZ\nfHltjo1uVMhza41vCd64s0+SK3Z6KYJC1sNNU7LMsDeMuTpXwffsUmm1pOQMSsdwDr5jcaNTPdZk\nNfl6dtmoplN1+d79HlIUcxGULt7nWBKDwJIKBCw2qryy3CLJNN++e8g7G12++eEB4Tgs0/Rt+mFG\n4Dq8sFhjeS5gseZhCUmU5rQqLo2KQ5oViqO2ENzrRkeOKk5z7vcTXlisEXg2ea4ZxDm9uBCji7UB\nA5k21HyJ0YUWkUFgWQLXtri9F9KPTwvoTXjz7oh6ZZuXl+dYaQUsNz2klLy0VGe7l/CDrS5hpgls\nSGY4h8ADx7ZZqPtUPIlShmGek2lN05bYlkQCG72YZQOWJU91i1c8m+cWikl7giL0dH2+wSBMiBJD\nmKlxuA+wivDS5681WWtffuBSScmzQukYHsLJBrOjr0/o/kx2s64lma+5VFwbpTStqovvOOQ6w7Fd\nhkpRcS1aNadoxLIEwzjlO/e7KAOBa9Ed5ewOcipuyAuLdd5cP6S5O8C3BPWKy4tLdX725SUkhRhf\nlilSrU/pJUFxaglcG2UMFdcu8gmBy1vrB/TClGbFxncEB6PCci80fFaaAXGe0wgcAs+CaLZzSDQk\nWrM/SHAdyShT+LaFYwu+cL1Fw7fYHcbcceF7mzHTVxFA4Ah8WzJMcoIwR+WgjCZXD4T2Zs3jnvU7\n8qTFtbkqniUYRhmxgvm6P5bIEFxv+bxydY4Xl4pu7ixTuBeciVFS8qxROoZLcDIJPJlUNkpyHEsy\nV3XZ7MfsDlIcK2Op4fPySouXl7u8tzUgzQ31wGW55aO1YGeU8HynSm4MKjcEtkUvSsl0Ea7ZG6Z4\ndsj+KCVwCmVUKYrTyfubff7mK8sEdmH0l+r+MUeldRHuMgZGSY4xhnbFwbUkL16pIwRsdEOEEVjS\nolN3ORhmRUOYVfRK3FyosnM45HY3nPk8PAvyTGMwpLlhGCe0qw4LrkcOaKOJU80gSU6916GI+weu\nxZ1uyHwroOY7LLc8bu+FtHyHylie/OQ87rNoVly+8vw8m72IrX4ynn3tMl8P+PKNFmGc8Dvf/IB/\n8cYdlpoVfvmLa3zl+YWyOqmk5ASlY7ggs8om4UGVjTKG7X5MzbOPBsTcPYy43q7wd167zv2DIZv9\nYn7yQt1npeVT8ywOo5zXrrf5Z7UNdgaFQJygMJyOhK1BgtGaw5Ei0ZMeAs3vfGuD/WHMv/OV57na\nCtgbpafmH1yfr+JaklGaszdIkJbg9l5ImmvqnsNPPb/AZjdCWoLVVoW1Obh7GNEdJuwNM1660kAp\nTS/d4Ic7x2NBdRuW5wLqvotlFUM+4yynHxnSTLE/TDiMcu53Q7Z7hpNnDgX4XmHwr7UC5nyHG51i\n0M9WL+HD/RGeYx/lTy6ys5dS8NKVBj92rc0XMCTKsN+POAwzvvXeHt9YHzx4LSPe3hjyn//q5/ip\n5xbLk0NJyRSlY7gAsyQr7h2GGG3wXYuq5xKnOZvdmMaig+/IowEx1ztVbs7XWN+vcWd/SD9W3Fio\nYgtBkmuans1c1efvfvU6/9df3WEQpViBwbMl7YrHZi/BtSEdOwUBOOP8xQ82B/TjhOfdGkmmcWx5\nSnQvzzXbvRjPkbQ9D8+SrB+EXGtXSJWmn+QkqcKWksWax0rLR2nDj11tsn4Y4to2P37jCv/m5y16\no4SN/RH7mcITNtfna9R8B2XAGOhFKT/ajsEUTmKUKpI0IZwRiRIUO/x23cMIi61+CrIHRtIMHF5e\naRSlu6bojbgodd/h1dUWe8OYvWFKGnjcPezzl1NOAYpn+aOdiD/83n2+dLVNNXAe50+kpOSponQM\nF2DWiM57ByHKGFoVtxhUcyKmr7U5GhDjOYKbCzUQMEhyHFkkVreHMR/uDlkcxFQ8h//wbz7Hv3zr\nPsYoNBZKKXIMtjTsjBXsBMVJQhkYxRnfW+9TsW2uzlWPnMEkDxJninuHIfe7ETXfZr7mEbg2lizm\nJxyEGe2Kw74GpTQbvZhXrzbZGyTkxvDB7qiQ9jBwpekReB7PLc2x3Yu5MV9lruKx3Y/pxxmuI3jj\nTkKcFmM5t7oJcZFnnymlEdhwtVnl5mKd3V7M9rhsteE7rM1XkAhc12KU5IzijERraraN75//Jytl\n8awtKUiVwRLwL3bCmWtQQBLnpEZTfbQ/jZKSp5LSMVyAWSM6XVsWlUa6kL1YrHvHYvon5yc4lsR3\nbFxLchhl7PdCvn3nkBcW6mgDgQNxCl97dYW3bh+w1U8IM83nVuYYRAkHg5SNYRG4ShVUXPBch0Tl\nvLc7olPzjq15csrxLEnVs4+vs+GR5JpumBZNeStFv0aWFeZzsxdzrzuiF6ZcbVeQQrDVT/BswVKz\nxkLNYxAplNHUA5tXlmt8d6PL4SBkfTdhcI7skQ/YAuZqNlrArZ0hUsB81Qaj2R1GrO8XQnyLdY+t\nfszvfaeHMkU+4mufW+bGfO3c35fvWDw3X0MAt3YHND0bC06Fs2zg5lKNql3+Nygpmab8H3EBpgXc\n0iQnzQ1rnaLRbXeQ0I8yqo7FWqeK71in5idMktaTHEDdt9kfJlxrV+jUPcJEsdVLEKIQpXvtZhtj\n4P2dAZkCYyost6p889Yu9w5jtIKaZ/HVmx2utqvMVxwOwoxMabyxXlKmNGmuqPsOC3WP3UHCMM5p\n+g6distWP8JoQ5RkbKlx5kJDpHL6Scj72wOGcQxS8NmlBnGmaQYOjhTMVXwsO2V/FPPdDw+4u3PA\n9zdS9s+ubD3Ct6FVlSy3q3QqPgdhyvrBiO9vZGhdiAm+ebfLF5ebBJ7F7f2QhZpHq+YTuBZ/8P1N\n/t5Xbj705GDbRYPi+v6IhUaFq62I9W5+7DU//UKLX/jsKq5rnSosKCl5likdwwWZNaLTtiQt3yZM\nciwh2Bkkp6SdTyatF+seCDDKMIhz7h6MGMQ5caaLzmEJ72wNaVcdmhWPwJEkqshl/MzLizi2oDfM\n6KcZWgju7Yf0Rw6fcexj99zoRmz3Ew5GKcutgCt1j6Ti0Ahs/vRHu0DhsNJM4bqStbkqCsV/9//+\niDc+7JLqImT1/JUhvgUHYY7nSL6/OUBrxbfX9/ir93v0LuAMpqkGIKXFTjdmEKY0AodulJAkCmlZ\nCAGDUcrd3ghLWOwNU2q+i5vkaGOouBbDPMfVpx3wqXu5Ntfmq/zsZ67QqTp8e/2QgzBipVXha69e\n5Xq7jpSSMMlPdZeXlUolzzKlY7gEJ0d0RlnG1iDh6lxw1Jk7LWY3K2m9M0hYbQU4jkXTt+lFGb0o\nJUpyRknK3jAFDEoZlpseQlpcnQuwpGRlrsoozql1bPZuJ+R5RtUvkt17/RiVabQlixCSLY+qo9b3\nQ1bnApbqPq+vH2BLQavi0gsTPtwrKqcOwpivv7vN6x920UDdF0Sp4f3NkNvzh/zM51YRQvD6B3t8\n/YebfNCbFbU/H0HRpKaUwXYFca7o7mfEmUEbqIybAJUunKYtilLYrV5M03fY6cU8v1jDFoL1g/Ch\nhlzKotpqp5/wMy8v8YXrHfb6Ma2qx+pchcWGT54X3eSBaz10FkZJybNC6RgegcnpIc4VwhRyDTB7\nqtisOQtGFJPf7h9GvDBfpR+mVF2Lb6/3qXmCrV4xz+GH2yO+erOJLQV116KfKIZxyq2dITuDGFdK\n2oHLRqaxJPzprV1eu9EeG0yJJWF1rsIoyVlpBYX4ninWH2WKXpIzSjJ2RhE7I8G7O0NyA7aEJC8S\nBQbYCXNsSzBKFH/49n02Rmc9mfOpSnBtF0vAIDH4dpG/8W1BnAs8S9INEzRwOFQsNqtcn6uwM8q4\nfTCi6tr87VcW6Ub5TGE94NQpYlo244ouynSvNDxaFQ9tDNnY9k8a6s6bhQGne1lKSp5GSsfwiEgp\n8G0La9z1PN39PJHPOGvqmCUEVdemU3W4tdNnux+xN4rZ6kc0Ky6jNCfKcvaGKXv9iNW5gNdutOjG\nOX/x/i41zyFwbTKl+avbB1xf8GlXA3pRyg82+izUXHb68dGppTkuxbQRGAPDOGerH/H2vS79KCeK\nc6oVB8tojOFUeel7W33+/L0d7mwNHtkpAMxVIdcKI6Du2tR8myRXzNccNg5TekmKkIK6Z7HUqDBX\nLcah3uj4VGyXL95ssjxXZX+UnnK2ozRnf5jOPEVUPJuVVsBGN2Kh7rE/ykAIAsdmpRmwM0jO/B3C\nA2eQjfWpypBTydNO6Rgeg1lTxaYrkc77+TDKeHerz5+8u82tnRGW0ERxzn4vYpSBBUgJ7w1zfrgb\n80c/Ojy679VGyrW5OoejlK1eQpgpakGKbUlqnk2rYjOxa1mu2ezFhSKrFDQCm4NRwl4/JkxzOlWH\nZtVjEKfM1wLaQcj2lBRqRUK96vPD+wPW94eP/Kyu1m3mmwFKazBFCaslBa+sNKi6FlcaGonGtm18\nx0ZTSHyMYs1KK2Cx4XKzXWWrH43nUT8w5IKiCMCzJbZlkeaKewchNzpVbLvoAt8ZJASuRT1waAQO\nSaZZbQVFkno83W3W73CSI8p1cTJZbvozw4YlJU8TpWN4TCZhpbPCC7N+HiY5f/nBHm/dPmQUZTi2\nYBApDoYZw6kJaafqK8fc72uGUY80A8+FlZaPNoLv3jngSsPjSt1lqVVBK8PWIMbkBscpSmtHieK5\nVsB31vdZqHscDGICVxBliq1+TG6KrusMaDqwMufj2g6OVKTp5Z/P5xYsfvazy1yfb4LU7BxmXO/4\nvLMxYJTm5FpQ8Wwcqyh9TTLDXM0jTHIEmk5FEHgWH+yOOAwLKfOvfXYJIcSRIZ8fV13ZlixmSQ+L\nCixEEUo7OTrVtS0yZTDi/N/hdI7IsYqZD4dhRsW1HxpyKin5NFM6ho+Ak0J75/1ca8OHe0O2+xHd\nKCPRArTkYJASq9nNYLPojRUqmk4hmZ1mhmGc8433dvnhxiFL9Ro3lyocDnPm6i6OlDQrDtv9mG93\nQ751a5+dXkIvh0BAo1qUqzYDGylyDiIYZDCIMhqBoOk7NOoW/UuUIf3MjTpfvLlAw7cZJYVo3VzV\nJVXQCFwWmj5V1yFMczYOIySSlTmPu3uFfMi1OR8BvLs1wJhCj8lozR/8YItf++oNgvGgoknILs0L\np2C0oerZeONE/GorODOkd97vcDpHpI3BdSRJVsyw1urs8aslJZ92SsfwMZMpzcEgpeY5+I5F3I/Y\nGQwZpkWiV/BAf+lhVMZDf9IsJlXgu/8/e28aY2d23nf+znn39+61V5FV3Hvf1JtakiVbljSOt7Hl\nNbYzmMxM5EwcwJnMl0wAA5kZZJZMgHwIEHigABM4AxuJNzmOt8iK5JEtS2r1LqoXsrtJFmuvuvt9\n9+XMh/dWdZGsYhXZrG6xdX9AgeSt+7733GLd85xn+z+glOL5N9tsxm1y4HQVzs6MEZ4tNrSJkslf\nXtjgjeY7wnaeAm9Q6B9N1SxyJFEe40WF1EXJMvjUg3OUL22x3t1in/EKO+jAD97T4Jl7pikZGi0/\nphclkCscU2ey4iKlRAiI45xU5UxWLIQmaA5CHEdH60c0/RhLStp+QpRmKARzDYe2l3BxvcdExUFR\nqNzWXYPNXkjHLybfTVWKbnQvKpL9Nwv57cf1OaKGY7ASZ0RxtqcE+IgRHxRGhuH9QAomyiaaVowD\nrTsWURzQv1GEdF9KQN2F5d3J4AheWhyw+zZv92Cl32KjH3J8ooxBznNX++xFP4Ugipitl2iUDKSC\nz/3gGfJU8MCxOlXboNmP+eaV3r7rMoCGC4kqEt/jZZN+nDFftgiGE+w6fsYDs2VeXevz1lYfW9Ox\nDA2RZ7T8CEvTqboGXpTR8wO6fmFIdU3SC4oQ0aYX0ShZWIbGwItZ7/uUhsN3kmHD3m7PwDBu1JE6\niBtyRFLy+EIDQ5ejqqQRH2hGhuE9xtAkU1WLta7P3JhL3TFoOBoV12Bxq083VHjpwffxAM8r/gMF\nRU5gsE8cKlSw1vYp2Qbnr3b3XxuQIwjjjHrJ4jMPznHvVJ3lTohQUHNMvv++KR44VuXSSouljk9r\nAFkGaBDmhbgfSrDRD7m42mW6YrEw7lKxdJqDmDTLcS2NOM2J0xyhCpXVNM3p+TE9PyXLEpQAy9So\nuBaTiaITpIRJiqkLGo5Jx0u5suXzynKH5bZPP0y4f7bK990zSZaz07sxOxzPCgeH/PbioBzSiBEf\nREaG4T1GSlHISCtYaUf4muD0VIULa11sXSfMYhY3PbYGisPkeiVgGqDn+87TAQrD4Yc+3ZsYnTEH\nPvfxMyxMlAjiHE3TiLKcM5Ml1rsBb6z3sXWNx+Yn+PjZKV682kYA317u8NpSm8gH1wDX1sizhI1+\nTC+KyYKEt+OMmarNZMUGAd0wZaZqUXEM1toBaz2f+bpNrWyy1Y/ZGkSF8cgyLEPnwZrDZNXBMiVB\nlFFzdS5u9nh9vYcuBLqUNL2El6+2+fSDs0RxUc1k3YFy0tsxKCNG3M2MDMP7gG1onJmuUHZ0vnpx\nE00TzNQcHjvZoDuI2fJCXlvpognBc283WQv2v1cMmAryA3LCYQh+tH9qWwd+7plTPLQwhoZAqQRF\nzguXW9Rdg26Q4loax+ouQoAfZYyXbRYaDmemy4UC6tqAVgytdmF92oMWriX50EKDjW5MnGYgBOMl\nkzjN8eMM29Q4OVmi7cVIQzJbsrE0wWY/4FitRNU1cQzBIMo5N1kmRdHsxyAEF9b75JnCsnUsXSvm\nTEc5YZhimcZOWGl00h8x4tYYGYb3CSkF01WHJxfGUBQlj8udgJ5IaZQcfuzRKh0/5tRkhd/7+mVW\nbpJ/8FIwgceOOWx6IcudG9PXHrDc2tt66MDPPD7Np+6fLjSfuhGuLfnqhRYqh7VuxGTZxI9T/ChB\nCMFyO8A1dWxLZxCmZFlK+zoXZy2Ab729xVTNxjF0gjhnrm4jFCy2YgRF+WySFWNELU2jPYhRwD0z\nVWZrNpahY+sa7bUuV9s+nSBhrGQSxhmWJmlnOYYuKFmS5U5ImhpkAkqWxmK7mDw3akYbMeLWODLD\nIISwga8C1vB1flcp9U+EEJ8C/jlFFGQA/G2l1JtHtY7vZqQUHBtzWeuGZFmOEvDAXIUr7YAsF5i6\nzv2zNX7sqXm+8K2rbO5hHErATB2ONcpMVV3umS7zH1/YZC8nY6/2NAt4dN4h8Dx+51uXsLWU9U6K\n0CVT5RKz42Xa/ZDX1rqMlW2CJCPPYKpmM1W1EErxwmKLF5fDPd/jRgDPX9ri8flxDEPHixJKlknN\nNfDDlLItEcLAlIJemFAIb44AACAASURBVFJrGARRRpjkWLrg3pky5692sXWNTOVs9iPiNMMxNQTQ\n9iM6XkSSFXMw5usWr17tUnJ86q7BXN1FHzawvZ/NaCMpjRF3E0fpMUTADyqlBkIIA/grIcSfAr8O\n/IRS6jUhxK8Avwb87SNcx3c1u3WXMgrxuOmKTTdISbIMXdP42NkpOn7Mn720Tu+6aJAHLHWgZGdY\nesTLl7p7GoX9iIBnr25fca3pMGnz6EIVS5MoAVGaM1U2EULy4fk6mpS8vd7jj19Zumn/xZXNECE6\nPHS8gVICS0IQZ0xXbTpBQn04DOjeusNY2UIoCNOMqy0fL0hY7wdUbBPb0Kg6CV0/5rlLTV5f7bDR\nC8lzSDIo2cUkuL9+s8WjCxWePDWFAk5NlMnT/H1rRttrLOzIexnx3cyRGQallOKdncYYfqnhV3X4\neA1YOao13C1s6y5JBHGSU3EMDE1StjSiJINhL8F4WdK73jJQbO6vrAXcNBlxG8TAtxZ7TNowUy/x\n9IkajqET54UMhW1otCOfTf/m9/FTcEyNyYrJRj9gvR8yV3WouSZVx8CLU6ZLFkIKVK4wDA3SYtyo\nrmsIJclyhaFLTCm50vS41OzT93OkKO6fA0JkCJERRAlyKWe6WkYpmC5baMPO5feavRR232/vZcSI\ngzjUMF0hxD1CiP8shDg//PcjQohfO8R1mhDiJWAD+HOl1DeBvwP8iRBiCfivgP9zn2t/WQjxnBDi\nuc3NzcO+n7sWKcWOAmoviMkVjJdMEIJuFLPajQ/dFX2n8WPoBTEvLvV4bbXDRi+i5UV4Ucra9YmF\nPUgo3otraqQZ5GmhLpspRZIroiQnVYosV1xp+Wz1Q1a7IXN1m+mqw7nZCs1BRGsQEWcZUtfIsqIa\nS4pCOUQBgxiiHFJACHh9rcdic8CVdhFW2i11kWQ5eX7YVsLb553u6XfUW3NVhJVGjPhu5bBT1v81\n8I8pPuMopV4B/uZBFymlMqXUY8Bx4GkhxEPAPwR+RCl1HPg3wL/Y59rPK6WeVEo9OTk5echl3t1s\nS0RPVW0ajoGha1RsjfNXuyRpihDvz2ZStinGkfZiWkHCVNWiH2XMVm3umznctOSVbsBGLyKMi7kT\nV9s+E2WTcddAk4KyqVMvmRyrFzIY0xWLkl1s5vfOVHl0vs5czWau7rAwZlO1TXQB8a7y2wxQOSSp\nIs0Vri64f67GmckyHT8hzxVhkrHY8rna8lls+YTJLU4augXyXJHnCkHRbAfsKccxYsR3G4cNJblK\nqWfFtb/Mh2jDKlBKdYQQfwH8MPDo0HMA+PfAnx32Pt8LuJbO6YkymVIIBYMgpuKYTNcdFrf6cKAg\nxZ1lpiQxDJ04j2mUTaarNiWzkMvuhTGGYTBuQPOAZXX9lI4XUbV1cgTfeGuL5bbPeNlikERcXO9Q\nLukcK5dRwFjZxIhTLF1Dl4Jz01WmyhaLbQ8pFG9v9PHiGF3LsdLCKNgGaAJsQ8fWTYSuMVW1sA0d\nL0pJhmGcWw3r3E7iOEwyVjvBjmcSDyf/HVaOY8SI95PDGoYtIcQZhjI+QoifAVZvdoEQYhJIhkbB\nAT4N/DOgJoS4Ryl1AfgM8Nptr/4DynZDVZLlOKbOiYkyszWbkiHZ+NpV9u9dfvecrYDrSLw4x9A1\nDNMiz6Bq68w2HGxD40rTI8ly/r/X1nnuchPd5EB71epExDMxlzYVQVII6nWDkKUtn9fWPNIMEPDY\nfI3vv2+a+XGXsmUU41CrFieG87Rnlct42eHEWIU4zkkqORJFyTapWwbr/YBG2WK26vLQfI3lTshY\nyQIKnao0z3HMvQcr7cXtJI7zXHGl6RW9GdszMVyDY3WnMA4jozDiu5zDGoa/D3weuE8IsQxcAv7W\nAdfMAr8hhNAoQla/rZT6IyHE54DfE0LkQBv4b29v6R98NCEwdI2zkyXe3PQYLzv86JOz/NZzN7XJ\n74qxms2nHpilOUi4sD5AoAgzuH+2im0ZeGGGFxVS4X/8ygrdQ0pxd3L48utt8hwCbhQKdGQh7fHS\n1S62oVD5FJ96YAZDkyRKoQtBmuZs9SMemK1SdXSW2rUiZwFc2fRIcsV4zeKhY2PM1WwGUUYnSOgM\nYmxLZ70bstYLkUBpOFNBCoFQ7NkId7uJ4yTL2ehFVGx9R811qx9zeoLbNgqjctcR7yWHMgxKqbeB\nTwshSoBUSu2twnbtNa8AH9rj8S8AX7jVhX4v8o6IG9w/U+HkmMPj8zWee3uVC62jec1nl0KeXbqE\nLcDSYX7c4WNnJzg7W6NhG2wMIvww5Y9eXt4xChr7jo64hv20nACCHGwBuYItL2XTi7nS9KiVLII4\nI0mHA3kGMa6pMV6yEEKy0gmYKJs8erxOwzV4c2OAZUiCRJGmhWifpgnKVrFJzwpY6YbMALqU1F2D\npU6wp0ew32jWWy17zZViEEastj2myg6uaxz6WhiVu45477mpYRBC/I/7PA6AUmrPxPGIO8f1Im4d\nP2Km6nKhdUCN6LskVeBosNIJeHmpy7GGy1TZxtQ0PBL8qLAK2vArp/ACxk1oxYeXDt9NOLyo2fVZ\nanu8vKTxkTPjuJZOL0xQqsghrHXD4rVKBg/MVjhWd2kHCZmC8bLF62t9DCkwDY37Jyu0/ZRG2RoK\n+OnMVm1m6w6mlCy2fTRRlNNuewjbHsHNRrPeDEOTTFUsOkHMIEo5v9TmxSsdvmSvU3ctfu6pee6d\nre08/2bewKjcdcT7wUEeQ2X4573AU8AfDv/94xRdzSPeA3aLuGkISiUHOFrDoFOcqJMsJ4xTMope\ni6mqRZ5nVOwisZDxjlGo6PCZh2fohzHfeqtFd9gM7erQPmSpggDGaw69IOP5K21OjJd4cK7GQBUn\n72KTFkRpDgI0KekEyXCsp0QTRSnsfN3BGU5a2+z3uLzpYegSpRR1x8TWNbw4ZbkdYBkSTQomytZO\nKalEHDi6dT+kFJyYKKG1BRfXunxnpcvxMZeqa9D1E37/hSX+wSddXNc40Bu4U17LiBG3wk0Ng1Lq\nfwEQQnwReHw7hCSE+J+B3zny1Y24gVQp6q5JgyJBc2SvAyRJEftpuBbPnByn4poIVUx6+5mnTyJf\nuMorV3rkQMWBn378ON93bppU5ZycqhHFGc1+wDff2jy0YVDAUjvg1JSGrRu4lsRLMrI8J8sVlq7h\nWjpRkrMw7hJEGcnQE4DCSLW9BFMTOKZRTKSTRV+DyhWJKmZJ57liqx+hSUApskyx0vGZrjrXeAS3\nK7ttGxrzDZf1rodj6NTLJgAlS6cXxPSSBDvXD/QGbtdrGTHi3XDY5PMCXKMCHQMn7/hqRhyIrWuM\nlRzOzLs8d/VovAZBEUYyDMkDs1V+6SMnqbomuiYJ4hRNSp45M8HCmMtSe0Dbj/nEmQkM3cQyNeI0\nox8U/QL9MOX4WIkVzzvwdbdphzA2iDkxXyKMipGlk8ONda0b4sUZ42WLlXZA1S4m4aVZjpSCjV7I\nVMXCNjSSNGe5EzBVsSiZOmu9EE0I1nohkxWLMMmI05xXNwegiulyJ8Zv7Mu4XdltQ5OMlW0MTdL3\nE0p2UTZrGxpVwziUN3C7XsuIEe+GwxqG/xd4VgjxBYpD3WeBf3tkqxqxL46l85EzY7yy2GTMhtbe\n2nXvihkLfuLxSR47O8fT8xO4jrGzMalcMVYy0EUxR+J4wyVTMFW2eGmpgxTFBmdqgiutAR0vwbVs\nHp6Gb68f3jistmM+85BFxdEReUaUpjiWQaagZEl0TaDUtkqtzUY/Io5S4lRxYqKEqUvyXOHFKSiK\naiQBTT8lijO+vdwlyTKCOGOh4RLGKeu9kFdXunhhykzdwdE1lOC2K4GkFJyaqPCpB6f54vk1Wl5E\nzTH5qceP47oGea729Aaur5La7bUIBUoUuYeRcRhxVBy2Kul/GwrgfXz40H+jlHrx6JY1Yj+kFDw6\nP8YPPThLlEEQxXQGMVcHd66DdzWC//vrm/zkQPHRk1M7G5MXp2z2I6QQXGn5jJdNHENnpmqx0Y+Y\nq9k0vZgwyUhzhaVJNKBRLWSyTkQxAy85sBkOitPHV15d57m3m+RZhm3rPDE/zljVxjL1QhxvrIzU\nJIYuOV53CNMMTQh0WYRfchSmplGzdZbaPr0oxdIk8+MubS9itRMQpQrLSAtNKgFb/Yi2l2Cv9XAs\njdmqM5xTXXgdt7oZ24bGD9wzzRPzDbw4pW6ZO1VJe3kD+1VJSSnwg5TFVmFcHVNntu7s5CNG5awj\n7iSHMgxCiAVgi11lpkKIBaXU4lEtbMT+lEydp05O0AlTTE1gmTrrHY8/fn6RK4c/lB/IH3x7C6m+\nxa999knqjklzEGPpkpKlU7aLOP/xulOcYJVC0yQK0KTANnWON1y8qBDDk6LIjYxXTKbChCjNCaOU\nlX2iYXUX1noRl7Yicopf1PNLA6YrGq6pU7clp6YbfOqhWSqmxmovAFV4K6lSO13Gddeg7RUzHrI0\nZ6LuIAEvyiibBg1XAjkXhuElP8lQWcLzGwOONVyW2z7Hag5XmhrH6g7HG+5OPuOwSCmolSxqw0a7\n3VzvDSx1gj1zDp0g5ovfWaPjJxiaZGHcIc5yzk1ViIfPG5WzjrhTHPY3/I95pwLRAU4BbwAPHsWi\nRtwcKQUnpso8k0zw3OUW/TBlrlHmf/iRRwizmM9/6XUu9e7Ma/3++Q4npt/klz9x7zXxcFPXSDK1\nE2oRwGonwDY0TE0iBJyYKBGlsNb1SXKFRFK3NFSmkLIwFFN1uLjq4+2qb512oFa2aa6HxAwHehjQ\nS6DXytjumvjq5YBvvN3i3pkqpq5Tdw3mx10eOl7jwdk6mhAsdQJMXXJ6sszLQYelVsBM1aLm6pi6\nRp7DRjcgTDIsXWMQJTS9mPVeiBcnbA0S7pksMVMvkeU5G/2IxxcaexqH2z217+503yvnECUZLy+2\n6fnxTuXUaidEl5KFhst6PxqVs464oxw2lPTw7n8LIR4H/u6RrGjEobANjccXxrh/ukqschypcanl\nsTWI+IWPneN3vnmZi607o6v0H19Y5mefPAFAGKeYhrYTH9/eBCcqFkvtAClzNCmYq7vUHZPjdZfV\nbsBba31eutpGSUGYFqEeP8544tQ4j51osN71WG36bHoJJdNEk4VOewwYOoR7vBUFvL0ZkuU5jx4f\nw4szLq736foJJ+olqiVzZ6PVNcmj8/Wiac4xaPkwU7FxLJ3ZioVraui6ZLUfcHGtV5TpRsUs6dV+\nRNmxSDKFbSgWmx5npyro+jsalHeiCW2/CiQvTlnphgySnHwQM142SXNFluWjctYRR8JtzWNQSr0g\nhHjqTi9mxK0hpaDkGJQoNiYpRaEqapr87NMn+cvXV7mwMmD9kLIV+9EcJJxfajNVLdENi1j4VMXi\nxERp51RaMnWONRw0wY7hcEydc1MVzkxVaLgGG4OQi5s+0zUDpQRxnhFEOfeeqDFRdjg7nYFSbHkR\n/SBlqxsS+5AO5y3shQCyXOHFGZouCWNFU0S8eLXNM6cnrtloTV2yMF7iWN3hZK7Y6EcEcUYKzI25\nbPYjgjAlyBQls2iqk1qRxK6XDfwoJU5zkkyh65LjDRfb0A7dhHaQR7FXzmGqYrHaDSiZGnXbIExT\nllo+JUtjsmZhSjkqZx1xxzlsjmF3B7QEHgc++EMS7hK2N6aypXN2sowuBaZe5sOnJ3j+SovzV1t8\n41KTlf7tTXToJPCvvnyRh+cafPjcJGenypi6xNTeOTFvz5NY64YEcbZzatZ1iQ0EiaJRsqn0E2xd\nI8lz7qlb2KbG/bNVqpbJVM1GCsFaL2CrH1Avmbx0pcVKJ0RPFHvl1w0BuhAEcUw/StCFYKpaBaVY\navscb7hsefHORjtXd7CGJ/njmiRMM9Z7IeWSRc02WOn49IOUME3RpEAoRcUxybOcTphw71QFy9CQ\nwPLw/nmuiNMMS99fnO+wHsX1fROZUggheGCuxndWumz2cyJyZmoOptRY6YXUXYOOn4zKWUfcMQ7r\nMVR2/T2lyDn83p1fzojbYXc4IZdq2ACWUXdMToy7DMIUx9D5Dy+t7rm5HkTDgH6keHG5zYYX8cn7\npnn4eGNn49s+CZua3LMZTA27kccrJtlShpdmSF2QZVAyDObqDlIU09tKjsG5qSqTFRsvUjw0U6cb\nR1R1nW8tNvlP5zfxhu+hJOHB+QoTZZtLTZ8wThgrW6z1Qs6v9hGaRNclczUHQ5c7a9ouY93sR6RZ\nznovYmG8ON3XHIsHjlW52gpoDkL8OOPhhRrjbrHZSilI0pyVbshaN6TpRfhhRpLnVGyD2bqzUxW1\nfWq/VVmLa/omcpBCULJ0nj41zsCPWe6FnJkqYxs6aZbT8ZOdIoBRVdKIO8FhDcOrSqlrOp2FED/L\nqPv5u4LrY9MNx+BKkPDm1oCVbkSc5yx1AhxTMAhuTcWoLKDi6GQqJwwzLq7luFoxAW1hzCUztANP\nwpoQuKbO/JjLx+6Z5IXLLXIEli44Pubw+a++xSDI0DTJh0+P88n7pmh7Ma6lU7E0otxCKMHnTk3w\n2aeOc2HNww987p0ZR+qStVZIP1zDKFvYtk7bT9GbA548UUdTcKXlcWa8jDTlzpyEpXaAqQumqjam\nLljtBMyPudQcnY1uwP2zVaCKbQrm6yVOj5dZ6gVsDSJsU2O9FyKE4ttLPSZLJilFee5i0+d4w2G2\n7uxs0O8mD7A7vJQrhWZozNYcbEO/5l5KFA11I0bcCQ5rGP4xNxqBvR4b8T5wfWxaCMFM1cY2JLYp\nWdwc0I8yhJAcTge1+MWYKoOUBrFSxGFMOwBFxlonYjAMDZ2dLlO1jZuehHcnp89NVrB0yWzVRgl4\n9u0mvSBjru6SZYoXrrRwNIlr63S8hGcvNzE1gaFJfvqJ43zk5AyPzKW8sthhy48xMkU+bKqbrlho\nukamQvwkI81yXlhq89ZqkySTfPTeMY7Xaxi6xNCLE/3WIGKqarPUCvCilDHXZH7MoWwXzXRCwaYX\nY1shjZLJei8iTDKiLMeQRfVV2TEI4hTDkDRc85pwFbx7WYu9SlrjtAjXbRvkw9xr1Osw4rAcpK76\nw8CPAMeEEP9y17eq3MIEtxFHz+7NI88Vy50A19KZqcDcuMsLiy1ydTij0DCAHKIY0jQhF9AfXmoJ\n0DR4bbXHudUWpiF45FgDuPlJuGTqTFRMNvshrmUwiHN0CWGcYesSyxAIU9KPYK3vMyNcnr3UJM4y\nglRhS40vfmcNIQUgmKpZtMMEJRRBlDJeNsmFoGJqWFJD0xTfeLvJl19b58JW0R7+r7++xDMnq/zq\np+9jaxAjUERpjqMLJismddeg64NjGgRJRtdPMYalt+SKnp8wWTUxhEAI2OxFmFohNGgMS191KW84\nud8JWYvd4aW6a3B+uUuWKzQpeOhY7cB7jaS7R9wKB3kMK8BzwH8JPL/r8T7F7OYR30Vsbx65eEdq\nwbV07pmu8Mn7Z/jDZ99kKzj4Pu3t0tChMWhYYGRF1YFrgqFL/Fix3olYKnuUDclY2cI2daSQ+55e\nJQJL15goS5qDiBxRSFeojDRTxEkxBtPSdEAxiFLqJYO2n+DYOn6UE4QJW37CwpjLZMXEkJKKbaBL\nwRvrfZIso1EymaqafP3C6o5R0ClOMt+43KP2F6/y0fvnWe2EhHHKK1ebnBgvEyQZMzWLubrDlWZA\nL0w5PVlmrGSyMYhIMsWYa7AVpriWJMkV01WTMM2pWEU39tyuENJubleM73ryXNHxE06MuQhZ5GU6\nfkJ1OB97v2tG0t0jboWD1FVfBl4WQvymUmrkIdwlXBOXTnMmSjbfd26Sb1/Z4EL31jvf2lHxp01R\nNupFOeSw1vFoeRF/+OISFdvg/pkav/CRk3tuNplSGMNy0TxXnJos4YUp5ybL/Mn5NZZaHlJKnjo1\nxpnJMm0/olHWMYSkZutIAZnKWO5FvLjY4fW1HmOuyUTVRkOyMFFmpu6QKbCkpOYa/Pn5FWA4M6Jo\ncCYFLnUjSldbJElK0wt4/YpPTxXfE8C4BScmbE5N13Y8idVuyEzVZqJqUy/lREnOZ+6vsNGPUKpo\n9Juu3PwUfrtifNf/HPOh4N/2m/Oim+crRr0OI26Vg0JJv62U+jngRSHEDVlLpdQjR7ayEe+K60+o\nAP/1J07xny68fFv3syikq4Oo+HO8rNHyM7pBgJKw2Y/Z6EUoMn71Uw9QLZnXXL8dZ89ztRNnN/Vi\njf99o0QnjjGFpOqYxFnO81fanBqr8FZzQJIrVJJxdqrMWjdiqmwRphmXN/pcbfl89MwEJyeqO+qv\nV1s+XpJwdtLhxRW/mBmRv9O6b6uMlxe3eKt1Y2hNAVsRbC2HvLoaMggy7puro0u4f7aCFGKn69ux\ndE7bBl6cstWPaHoxbT850jDN7eQrRtLdI26Vg0JJ/2D4548d9UJG3HmuP6F+9OxxfvHJdX7rubUb\nnwtUTPad4ewYcHbSJkVSMiUKiR/nrPUyHEPHNIrN8rnFLt9e6fCRM5M3JKCnKhYrnQBEoZ+0HWe3\nbZ0Z+51fRV2XPHGiQd3RmWnYREmOoRWJ1pVOiGvrpKHiSisgHTaq/fxTC5yYKKNpkijNaHoJj5yY\n4o2tiFeWBztG4XhVUnIdXlk8WFQqyOHZi1sYmuC+uRrnVzo8cWIcW9eu2Vibgxhdip3QzlGGaW4n\nXzGS7h5xqxwUStqeOv8rSql/tPt7Qoh/BvyjG68a8d3M//4zT/Bjjy3zu9+4RKvfxzBdJuolJksu\nXS9iqeXxn9/q3nBdL4HVVsjJaZdz01WutiN6oUemAAlKCExNoAvYHEQkWY4l3zk1h0nGRj9CUJzK\nt2cm7IeUgrJtcFIvOntnKjYr3QA/yej7Md9Z6mJoGpNVA4ngy69u8PNPWawPIiqOQa1kkmU5f+PB\nKQyVsulFqFhRdy0ubR5eabCVwlff2OTtLY/5mkPPS/nw2XFOjpeBIkwTJClelO0kdkuWRpLlSCWu\n6Z24UxVBt5OvuFM5jhHfGxy2XPUz3GgEfniPx0bcBXz07DGePjlLL0pY7QT89cVlXl3zmHQl5ZLJ\nhAmDGBLeKW7NgeUQVq/49BPB6Ykyll5mEGZ0/BTpShqOyYnxCmXz2l+r3clPxzRIs0KMbmEoJ309\n2883dYlrFU1crSBhYbxElGV88TtrdIOEE+Mus3UbTUrWeiEr/YAsK8T7AC6sd/namy1sy+JD43Va\nXsRaz9tXXmM/ugkk6z6bXR9NV2Qqx48SxssOJVNycb1H3daplx3iJGO1E2JKiRg2um13Jt/JiqDr\nvcHrDc9ehuhO5DhGfG9wUI7h7wG/ApwWQryy61sV4GtHubARR4uuS8Z0i//rT87z756/NrRkAoYE\nld/Y9ZADVzc9fvrJOYTSOT1d5qVLHaQmmK45fOzcJPPjpWtKNm81+bnf8w1d8vCxBlNlCz96m4ql\nYxk6eZZhaYKyodHNM7Isx7F0sjTHMTVKloGlSWxLw9IEjqNDcGu1FLGCzQC+8p0Wb611+KvXXe6b\nG2OtHzIIU2xd8KGTY9w3W0cphaYJHFMnTjLOL3c5Me7i6PqBFUG341lcX4p6FIZoxPcWB3kMvwX8\nKfB/AP/Trsf7SqnWka1qxHvCn75y6QajAIWiabIrWXsDAubqJSZLNlDj+05PsOUnzFRsxiv2NV2/\ncOvJz4OeP1F1+FsfPsEXX11nECWEccYn75tmplGiHCasdEMmyia6oXFivEzbT4gzhR8mVFybOUsj\ni9ssDQ7fBb5tRnzg9WbO680Bz18ecHrKZrxq0/FzXrzcomIaTFZtDFn0NygKkT85XPvNjOLt9Bpc\nX4oap0NDNObimAcbohEj9uKgHEMX6AK/ACCEmKKoWiwLIcqjQT13L2GY8ltfu7Lv92+2ZTqmwNAE\nFzc8ZmoWjmlw74yDQDDfcK+Ro4b9VUMzpQotoOs2rP2SpbsH0mi6xi98eIEwzWh7MWNlGwDX0pks\nmczWHCxdo2zqfPn1DbZ6Ia5p8PipOjXH4tx0na++scS3125fetYHLm6ENP0UTYd2YPCQH1IrmVxp\neeiaJMuK3ow0LwJY+3Uq326vwfXelRSCLFfDRsBRaeqI2+Ow6qo/DvwLYA7YAE4ArzEa1HPX0ksS\nVH7zTVEDGiZs7Xpa3YLPPn6CV1eKuQZBojD1nG6QUrE0vCSlhH6Dcdid/EzSIsdws5PxXuW2iy3/\nmo2zE6TFSM9EkWY5aa5Y6wbEqcI0NBolEyEEn330GP0kQSqYbZQKvaR6QCcIcfQeV7YGtMIip1K0\n1h1WOAQiIExSaoaBFyV4cWHIojjFVwpL0yg7GovNYlznfp3Kt9trcL13tf0aKlegMSpNHXFbHDb5\n/E+BZ4AvKaU+JIT4JEMvYsTdiSM15sbG4Or6vs+5d8JkYbpCnCQ0OyEPLzT4qScWCBJY7gaUbRNQ\ntP2EPMt5K0qZ7obomuShYzXq7rW9DFIKyGH5kBPHdidL95tupgTM1GxWOgHL7WIs5sK4iy7FLtVR\nZ0djCKBsGwjp0wtSbFPn5GSVRpSg8hxNk3R6AWvB/jMgrieMwbGgbhusdyOu2APaXkScCSwdzkxV\nefxEA9PQULmi5cU4hlaMHx2+59vtNdjLu3roWI2On+BF709p6kiT6e7nsIYhUUo1hRBSCCGVUl8Z\nlquOuEspOQY/9Ogc37zc5Er32kSsAzxwzOHYWAXb0LBcm5PjdT52boKJaonFtodr6TRcneYg4Uqz\nT9tLuX+2TMUxiJOUb761yTOnJ6g45jWbw506Ge/eOA1DcqzukGaF9PX2620bDk0IMhRTFYuNfkTP\nD3lxscts1cbRJR0vIVMpqZIYUqM6VUZtDFgdyodYFFIgvfhaT2JbZiNU4PUSyBMurEreWOlzYqrE\n8UYJP8n4xqUmT5xooEmBn2QstYOdBr9tb+nd9BrsVYpatY33ZXMeaTJ9MDisYegIIcrAV4HfFEJs\nMBLRu6uRUvD0+AsQCQAAIABJREFU6Uk+98mzfPGVVVp+hFAZY2WHqYrDTKOEY2qIQkGOhUaZubpL\nkuagBGfGSwySjDjNqJcsJko29ZLFm+t9lloeq92Q19cHfPTMOA8fb+xsDnfyZLx74zS0QrwuSovZ\nzdsbU5LmLO8KW1VtnbYfEWcZmtToRxlvbw3w4pSqrZEYcHE1YHeQrebCbL2MEFA3JBdbA3peviMs\nqCgScd0BGFpAkuYYbcjSnLprESUpW4MQfMlyywcEC2Muhiau8ZbeTa/B9aWo70dp6kiT6YPDYQ3D\nTwAhhXDeLwE14H89qkWNeG8omTofPTPNI8fqrHcjMqGQSvLATIVOmLA5iFFKMVm2ODNVeA+ZUszW\nnaJZTQoaJZNHGg6vrfYJ45RXV7oIBBNlm7GSyStXO9Rck7OTlZ1BN3fyZLxNnOXEw9wFwFTVYr7h\nsrErbBWnGa+u9pgpmaQ5hFlG3TWKiqU4I0yyHV2o3fR8+KVnxqiXHK42A2qu5I9e7ey5xivdIgDV\nDXwGoWKunlN1DNpejGPpSCmZrlq0/Ji5ukOe5jc0w92tSeKRJtMHh0MZBqXU7lbR3ziitYx4j3ln\nHKdAkxqbg4jJikWK4PRkhXPT75zGd5qkKGYjLBhFd6+la5i65N7ZCs9fatELE8Zci4UJF9fU2Ygz\nen5Eyzepmgamqd2Rk3GeF8OCtj2NtW5I2dapugZxkpEp0KQgzjIEEsQ7FTu6qXH/TJmvv7XFG+s9\n0lxRdw3COGV3PZZGETrKgKutiEapjK1Lekl+YHLay2GjF5DnKR87N8ls3aXmmthGiKHJwpAlGUmW\nszzMfdztoZeRJtMHh4Ma3PrsXbkoAKWUqh7Jqka8Z9iGxvG6w+VWzpmJMqahvdOZvCsEcH1CUUqB\nJTVmh3OedSl57HidZFgdZOkaXpTQ9WO+/naTixs+pi752NkJZmrOuzoZ745jB0mCDsSZYqrmFu/J\n1PGilI4f8/LVDuRgWZIzE6XCWCQZCslYycTSNMbLJmGcoduw7ic7r7OdfNaBRknDMgRTDZvnFw9X\ns+QnsNJNuLQ1YBCljJUtpqo2q52AJFPEaV5Io5sapn73h15GmkwfHA7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hk6NsevLilKV2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\nwEsSXKntq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"text/plain": [ "<matplotlib.figure.Figure at 0x1a1dc91ef0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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CtDSd79/Rs99QSBKJQEyMYO4MWLsN/AFIioMZZ0FZmRKRyZMhKUkJ1YIFsGUL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\nd/dbG5fvzXUYX0x8fE4zYSJcpC/kmd6fs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"text/plain": [ "<matplotlib.figure.Figure at 0x1127055f8>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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AI2FZNomAh2+cXObJiTXyNR2vIvGbbx7n8wfmyFU07hyMUdFMZjJlbMfVQS83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USd1V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xfReLnehu414NjWEeWFkzNomsZvv3sjvekQmtDoS4ewXckrQ3P8yQ/OUG7YC+/tSIl0QTYD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\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uzbknHUjCbu/ESxEEcqIxXZ+hRARNVUlHNWKGRtTQGIgbvFhosVQ22ahbskGUbqOtqnJ8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ASZmE7dDZS3WKiBwFgLN1dzAuKWzqPnSnSnLLb3pDiyvYszyw1WGy67BtIslJvYfsSh0Rw/e+c2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\nNvDHYpnelPs9PhXm4nI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"text/plain": [ "<matplotlib.figure.Figure at 0x1a1e40a6d8>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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WOiIl0d01fRQ3z4cRokoEN5rwgpsbOXVjQlFlZ4m5jjuuF+uV6/H5MZ93RzoX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N68cP8oeGA93HibISPJhscEbwatPIlZkyH2dS7v1Xug72NwGGsockmZCFY4Qh8zkqed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\nHPgGsCAi95a3+Rng/9zGfT4KvK1s+oeInBSRo9DB0zgETMIxjNv3fsAFn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"text/plain": [ "<matplotlib.figure.Figure at 0x1a1db787b8>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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hdubaKx93f82y3bzj1uY/EbeeMd6HiPprYmKCUXxdW7G9o61f7XU6myRJkqRaycwHgQng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\nc0nPhWYAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1a1dd70630>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:110: FutureWarning: 'pandas.tools.plotting.scatter_matrix' is deprecated, import 'pandas.plotting.scatter_matrix' instead.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[0 0 4 ..., 1 0 3]\n", "['<1H OCEAN' 'INLAND' 'ISLAND' 'NEAR BAY' 'NEAR OCEAN']\n", "[[1 0 0 0 0]\n", " [1 0 0 0 0]\n", " [0 0 0 0 1]\n", " ..., \n", " [0 1 0 0 0]\n", " [1 0 0 0 0]\n", " [0 0 0 1 0]]\n" ] }, { "ename": "NameError", "evalue": "name 'self' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-15-df779ce82881>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 181\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mc_\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrooms_per_household\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpopulation_per_household\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 182\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 183\u001b[0;31m \u001b[0mattr_adder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mCombinedAttributesAdder\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0madd_bedrooms_per_room\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 184\u001b[0m \u001b[0mhousing_extra_attribs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattr_adder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhousing\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 185\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mNameError\u001b[0m: name 'self' is not defined" ] } ], "source": [ "import os\n", "import tarfile\n", "from six.moves import urllib\n", "import csv\n", "\n", "DOWNLOAD_ROOT=\"https://raw.githubusercontent.com/ageron/handson-ml/master/\"\n", "HOUSING_PATH=\"datasets/housing\"\n", "HOUSING_URL=DOWNLOAD_ROOT+HOUSING_PATH+\"/housing.csv\"\n", "\n", "def fetch_housing_data(housing_url=HOUSING_URL,housing_path=HOUSING_PATH):\n", " if not os.path.isdir(housing_path):\n", " os.makedirs(housing_path)\n", " tgz_path=os.path.join(housing_path, \"housing.tgz\")\n", " urllib.request.urlretrieve(housing_url, tgz_path)\n", " housing_tgz = tarfile.open(tgz_path)\n", " housing_tgz.extractall(path=housing_path)\n", " housing_tgz.close\n", " \n", "import pandas as pd\n", "def load_housing_data(housing_path=HOUSING_PATH):\n", " csv_path=os.path.join(\"https://raw.githubusercontent.com/ageron/handson-ml/master/datasets/housing\", \"housing.csv\")\n", " return pd.read_csv(csv_path)\n", "\n", "housing=load_housing_data()\n", "#print (housing.head())\n", "\n", "#print (housing.info())\n", "\n", "#housing[\"ocean_proximity\"].value_counts()\n", "\n", "#print (housing.describe())\n", "\n", "import matplotlib.pyplot as plt\n", "housing.hist(bins=50, figsize=(20,15))\n", "plt.show()\n", "\n", "import hashlib\n", "import numpy as np\n", "\"\"\"def split_train_test(data, test_ratio):\n", " shuffled_indices=np.random.permutation(len(data))\n", " test_set_size=int(len(data))test_ratio\n", " test_indices=shuffled_indices[:test_set_size]\n", " train_indices=shuffled_indices[test_set_size:]\n", " return data.iloc[train_indices], data.iloc[test_indices]\n", "\n", "train_set, test_set=split_train_test(housing, 0.2)\n", "print(len(train_set), \"train +\", len(test_set), \"test\")\"\"\"\n", "\n", "def test_set_check(identifier, test_ratio, hash):\n", " return hash(np.int64(identifier)).digest()[-1]<256test_ratio\n", "\n", "def split_train_test_by_id(data, test_ratio, id_column, hash=hashlib.md5):\n", " ids=data[id_column]\n", " in_test_set=ids.apply(lambda id_:test_set_check(id_, test_ratio, hash))\n", " return data.loc[~in_test_set], data.loc[in_test_set]\n", "\n", "housing_with_id=housing.reset_index() #adds an index column\n", "train_set,test_set=split_train_test_by_id(housing_with_id, 0.2, \"index\")\n", "\n", "housing_with_id[\"id\"]=housing[\"longitude\"]*1000+housing[\"latitude\"]\n", "train_set,test_set=split_train_test_by_id(housing_with_id, 0.2, \"id\")\n", "\n", "from sklearn.model_selection import train_test_split\n", "train_set, test_set=train_test_split(housing, test_size=0.2, random_state=42)\n", "\n", "\"\"\"stratified sampling: sampling to represent a population at large for how\n", "it is, as opposed to random sampling\"\"\"\n", "\n", "housing[\"income_cat\"]=np.ceil(housing[\"median_income\"]/1.5)\n", "housing[\"income_cat\"].where(housing[\"income_cat\"]<5, 5.0, inplace=True)\n", "\n", "#use Stratified Split to do this\n", "from sklearn.model_selection import StratifiedShuffleSplit\n", "\n", "split=StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=42)\n", "for train_index, test_index in split.split(housing, housing[\"income_cat\"]):\n", " strat_train_set=housing.loc[train_index]\n", " strat_test_set=housing.loc[test_index]\n", " \n", "housing[\"income_cat\"].value_counts()/len(housing)\n", "\n", "for set in (strat_train_set, strat_test_set):\n", " set.drop([\"income_cat\"], axis=1, inplace=True)\n", " \n", "#make a copy of the train set to not harm the original\n", "housing=strat_train_set.copy()\n", "\n", "import matplotlib.pyplot as plt\n", "housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\")\n", "####plt.show()\n", "\n", "#set alpha option to make high-density areas clearer\n", "\n", "housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", alpha=0.1)\n", "#######plt.show()\n", "\n", "housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", alpha=0.4, \n", " s=housing[\"population\"]/100, label=\"population\",\n", " c=\"median_house_value\", cmap=plt.get_cmap(\"jet\"), colorbar=True)\n", "plt.legend()\n", "###########plt.show()\n", "\n", "corr_matrix=housing.corr()\n", "\n", "corr_matrix[\"median_house_value\"].sort_values(ascending=False)\n", "\n", "from pandas.tools.plotting import scatter_matrix\n", "attributes=[\"median_house_value\", \"median_income\", \"total_rooms\",\n", " \"housing_median_age\"]\n", "scatter_matrix(housing[attributes], figsize=(12,8))\n", "\n", "#zoom in on median housing value\n", "housing.plot(kind=\"scatter\", x=\"median_income\", y=\"median_house_value\",\n", " alpha=0.1)\n", "\n", "#comparing other attributes of each house\n", "housing[\"rooms_per_household\"]=housing[\"total_rooms\"]/housing[\"households\"]\n", "housing[\"bedrooms_per_room\"]=housing[\"total_bedrooms\"]/housing[\"total_rooms\"]\n", "housing[\"population_per_household\"]=housing[\"population\"]/housing[\"households\"]\n", "\n", "corr_matrix=housing.corr()\n", "corr_matrix[\"median_house_value\"].sort_values(ascending=False)\n", "\n", "#Using Machine Learning algorithms; revert to clean train set\n", "housing=strat_train_set.drop(\"median_house_value\", axis=1)\n", "housing_labels=strat_train_set[\"median_house_value\"].copy()\n", "\n", "#Get rid of corresponding districts\n", "housing.dropna(subset=[\"total_bedrooms\"])\n", "from sklearn.preprocessing import Imputer\n", "imputer=Imputer(strategy=\"median\")\n", "\n", "#get rid of text attribute, since median can only be computed on numbers\n", "housing_num=housing.drop(\"ocean_proximity\", axis=1)\n", "#fit imputer instance to training data\n", "imputer.fit(housing_num)\n", "\n", "imputer.statistics_\n", "housing_num.median().values\n", "X=imputer.transform(housing_num)\n", "#the result is in numpy, convert to pd DataFrame:\n", "housing_tr=pd.DataFrame(X, columns=housing_num.columns)\n", "\n", "#converting text labels to numbers\n", "from sklearn.preprocessing import LabelEncoder\n", "encoder=LabelEncoder()\n", "housing_cat=housing[\"ocean_proximity\"]\n", "housing_cat_encoded=encoder.fit_transform(housing_cat)\n", "print (housing_cat_encoded)\n", "\n", "print (encoder.classes_)\n", "\n", "#making the text scales a binary option\n", "from sklearn.preprocessing import OneHotEncoder\n", "encoder=OneHotEncoder()\n", "housing_cat_1hot=encoder.fit_transform(housing_cat_encoded.reshape(-1,1))\n", "#storing 1's as a sparse matrix, ignoring 0's\n", "housing_cat_1hot.toarray()\n", "from sklearn.preprocessing import LabelBinarizer\n", "encoder=LabelBinarizer()\n", "housing_cat_1hot=encoder.fit_transform(housing_cat)\n", "print(housing_cat_1hot)\n", "\n", "#creating custom transformers\n", "from sklearn.base import BaseEstimator, TransformerMixin\n", "rooms_ix, bedrooms_ix, population_ix, household_ix=3,4,5,6\n", "\n", "class CombinedAttributesAdder(BaseEstimator, TransformerMixin):\n", " def _init_(self,add_bedrooms_per_room=True):\n", " self.add_bedrooms_per_room=add_bedrooms_per_room\n", " def fit(self, X, y=None):\n", " return self\n", " def transform(self, X, y=None):\n", " rooms_per_household=X[:, rooms_ix]/X[:, household_ix]\n", " population_per_household=X[:, population_ix]/x[:, household_ix]\n", " if self.add_bedrooms_per_room:\n", " bedrooms_per_room=X[:, bedrooms_ix]/ X[:, household_ix]\n", " return np.c_[X, rooms_per_household, population_per_household,\n", " bedrooms_per_room]\n", " else:\n", " return np.c_[X, rooms_per_household, population_per_household]\n", " \n", "attr_adder=CombinedAttributesAdder(add_bedrooms_per_room=False)\n", "housing_extra_attribs=attr_adder.transform(housing.values)\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.3" } }, "nbformat": 4, "nbformat_minor": 2 }	cc0-1.0
Joshuaalbert/IonoTomo	src/ionotomo/notebooks/PropagationPathlengthVar.ipynb	1	44135	{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "ename": "ImportError", "evalue": "No module named 'Logger'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mImportError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-2-48d26ec7239b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mLogger\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mLogger\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mLayer\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mLayer\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mastropy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munits\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mau\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mastropy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcoordinates\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mac\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mImportError\u001b[0m: No module named 'Logger'" ] } ], "source": [ "import numpy as np\n", "from Logger import Logger\n", "from Layer import Layer\n", "import astropy.units as au\n", "import astropy.coordinates as ac\n", "import astropy.time as at\n", "from scipy.integrate import ode\n", "from scipy.interpolate import UnivariateSpline\n", "\n", "def fft(A):\n", " return np.fft.fftshift(np.fft.fftn(np.fft.ifftshift(A)))\n", "\n", "def ifft(A):\n", " return np.fft.fftshift(np.fft.ifftn(np.fft.ifftshift(A)))\n", "\n", "def transformCosines(theta1,phi1,theta2,phi2):\n", " #switch theta and phi for this implementation\n", " cosphi1 = np.cos(theta1)\n", " sinphi1 = np.sin(theta1)\n", " costheta1 = np.cos(phi1)\n", " sintheta1 = np.sin(phi1)\n", " cosphi2 = np.cos(theta2)\n", " sinphi2 = np.sin(theta2)\n", " costheta2 = np.cos(phi2)\n", " sintheta2 = np.sin(phi2)\n", " costheta12 = np.cos(phi1-phi2)\n", " sintheta12 = np.sin(phi1-phi2)\n", " \n", " return np.array([[cosphi1cosphi2 + costheta12sinphi1sinphi2,sinphi1sintheta12,cosphi2costheta12sinphi1 - cosphi1sinphi2],\n", " [cosphi2sinphi1 - cosphi1costheta12sinphi2,-cosphi1sintheta12,-cosphi1cosphi2costheta12 - sinphi1sinphi2],\n", " [sinphi2sintheta12,-costheta12,cosphi2sintheta12]])\n", "\n", "def ITRS2Frame(theta,phi):\n", " s1,s2 = np.sin(theta),np.sin(phi)\n", " c1,c2 = np.cos(theta),np.cos(phi)\n", " return np.array([[s1,c1,0],\n", " [c1,-s1,0],\n", " [0,0,1]]).dot(np.array([[c2,s2,0],\n", " [0,0,1],\n", " [-s2,c2,0]]))\n", "def Frame2ITRS(theta,phi):\n", " s1,s2 = np.sin(theta),np.sin(phi)\n", " c1,c2 = np.cos(theta),np.cos(phi)\n", " return np.array([[c2,s2,0],\n", " [s2,-c2,0],\n", " [0,0,1]]).dot(np.array([[s1,c1,0],\n", " [0,0,-1],\n", " [c1,-s1,0]]))\n", "\n", "def Frame2Frame(theta0,phi0,theta,phi):\n", " '''Rotate frames from those theta, phi to those at theta0, phi0'''\n", " s1,c1 = np.sin(theta0),np.cos(theta0)\n", " s2,c2 = np.sin(phi - phi0),np.cos(phi-phi0)\n", " s3,c3 = np.sin(theta),np.cos(theta)\n", " return np.array([[s1,c1,0],\n", " [c1,-s1,0],\n", " [0,0,1]]).dot(np.array([[c2,s2,0],\n", " [0,0,1],\n", " [s2,-c2,0]])).dot(np.array([[s3,c3,0],[0,0,-1],[c3,-s3,0]]))\n", "\n", "def polarSphericalVars(x):\n", " '''transforms itrs whose lat is from equator'''\n", " theta = np.pi/2. - x.spherical.lat.rad\n", " phi = x.spherical.lon.rad\n", " r = x.spherical.distance.m\n", " return r,theta,phi\n", "\n", "\n", " \n", "class splineFit(object):\n", " def __init__(self,data,x,y,z):\n", " '''creates a class where data is nxmxp and x is 1xn, y is 1xm, and z is 1xp.\n", " Does derivatives using analytic interpolation.\n", " Tried not to do things twice.'''\n", " self.data = data\n", " self.x = x\n", " self.dx = np.abs(x[1] - x[0])\n", " self.y = y\n", " self.dy = np.abs(y[1] - y[0])\n", " self.z = z\n", " self.dz = np.abs(z[1] - z[0])\n", " self.current_x = None\n", " self.current_y = None\n", " self.current_z = None\n", "\n", " def compute_zeroth(self,x,y,z):\n", " '''Return the nearest.'''\n", " if (self.current_x != x or self.current_y != y or self.current_z != z):\n", " self.current_x = x\n", " self.current_y = y\n", " self.current_z = z\n", " self.zerothDone = False\n", " self.onethDone = False\n", " self.twothDone = False\n", " if self.zerothDone:\n", " return self.zero\n", " else:\n", " nx = np.argmin((x - self.x)2)\n", " ny = np.argmin((y - self.y)2)\n", " nz = np.argmin((z - self.z)*2)\n", " self.xsp = UnivariateSpline(self.x,self.data[:,ny,nz] , k=2 , s = 2)\n", " self.ysp = UnivariateSpline(self.y,self.data[nx,:,nz] , k=2 , s = 2)\n", " self.zsp = UnivariateSpline(self.z,self.data[nx,ny,:] , k=2 , s = 2)\n", " self.zerothDone = True\n", " gx = self.xsp(x)\n", " gy = self.ysp(y)\n", " gz = self.zsp(z)\n", " self.zero = (gx+gy+gz)/3.\n", " return self.zero\n", " \n", " def compute_oneth(self,x,y,z):\n", " '''Calculate fourier of dsinc/dx and use that to compute du/dx then nearest.'''\n", " if (self.current_x != x or self.current_y != y or self.current_z != z):\n", " self.current_x = x\n", " self.current_y = y\n", " self.current_z = z\n", " self.zerothDone = False\n", " self.onethDone = False\n", " self.twothDone = False\n", " self.compute_zeroth(x,y,z)\n", " if self.onethDone:\n", " return self.one\n", " else:\n", " self.dxsp = self.xsp.derivative(n=1)\n", " self.dysp = self.ysp.derivative(n=1)\n", " self.dzsp = self.zsp.derivative(n=1)\n", " self.onethDone = True\n", " gx = self.dxsp(x)\n", " gy = self.dysp(y)\n", " gz = self.dzsp(z)\n", " self.one = (gx,gy,gz)\n", " return self.one\n", " def compute_twoth(self,x,y,z):\n", " '''Calculate fourier of dsinc/dx and use that to compute du/dx then nearest.'''\n", " if (self.current_x != x or self.current_y != y or self.current_z != z):\n", " self.current_x = x\n", " self.current_y = y\n", " self.current_z = z\n", " self.zerothDone = False\n", " self.onethDone = False\n", " self.twothDone = False\n", " self.compute_oneth(x,y,z)\n", " if self.twothDone:\n", " \n", " return self.two\n", " else:\n", " #should build xy,xz,yz components but those are second order\n", " self.dxxsp = self.xsp.derivative(n=2)\n", " self.dyysp = self.ysp.derivative(n=2)\n", " self.dzzsp = self.zsp.derivative(n=2)\n", " self.twothDone = True\n", " gxx = self.dxxsp(x)\n", " gxy = 0.\n", " gxz = 0.\n", " gyy = self.dyysp(x)\n", " gyz = 0.\n", " gzz = self.dzzsp(z)\n", " self.two = (gxx,gxy,gxz,gyy,gyz,gzz)\n", " return self.two\n", " \n", "def coordsCart2Sph(x,y,z):\n", " r = np.sqrt(xx+yy+zz)\n", " theta = np.arccos(z/r)\n", " phi = np.arctan2(y/x)\n", " return r,theta,phi\n", "\n", "def coordsSph2Cart(r,theta,phi):\n", " x = rnp.cos(phi)np.sin(theta)\n", " y = rnp.sin(phi)np.sin(theta)\n", " z = rnp.cos(theta)\n", " return x,y,z\n", " \n", "def compCart2SphMatrix(r,theta,phi):\n", " costheta = np.cos(theta)\n", " sintheta = np.sin(theta)\n", " cosphi = np.cos(phi)\n", " sinphi = np.sin(phi)\n", " return np.array([[cosphisintheta,sinphisintheta,costheta],\n", " [-sinphi,cosphi,0],\n", " [cosphicostheta,sinphicostheta,-sintheta]])\n", "\n", "def compSph2CartMatric(r,theta,phi):\n", " return compCart2SphMatrix(r,theta,phi).transpose()\n", "\n", "def compCart2Sph(compCart,r,theta,phi):\n", " '''(ar,atheta,aphi) = M.(ax,ay,az)'''\n", " M = compCart2SphMatrix(r,theta,phi)\n", " return M.dot(compCart)\n", "\n", "def compSph2Cart(compSph,r,theta,phi):\n", " '''(ar,atheta,aphi) = M.(ax,ay,az)'''\n", " M = compSph2CartMatrix(r,theta,phi)\n", " return M.dot(compSph)\n", "\n", "def gradSph2CartMatrix(r,theta,phi):\n", " '''problems at theta = 0\n", " {{Cos[phi]Sin[theta], Cos[phi]Cos[theta]/r,\n", " -Sin[phi]/r/Sin[theta]}, {Sin[phi]Sin[theta],\n", " Sin[phi]Cos[theta]/r,Cos[phi]/r/Sin[theta]}, {Cos[theta],-Sin[theta]/r,0}}\n", " '''\n", " costheta = np.cos(theta)\n", " sintheta = np.sin(theta)\n", " cosphi = np.cos(phi)\n", " sinphi = np.sin(phi)\n", " return np.array([[cosphisintheta, cosphicostheta/r,-sinphi/r/sintheta],\n", " [sinphisintheta,sinphicostheta/r,cosphi/r/sintheta],\n", " [costheta,-sintheta/r,0.]])\n", "\n", "def gradCart2SphMatrix(r,theta,phi):\n", " costheta = np.cos(theta)\n", " sintheta = np.sin(theta)\n", " cosphi = np.cos(phi)\n", " sinphi = np.sin(phi)\n", " return np.array([[cosphisintheta,-rsinthetasinphi,rcosthetacosphi],\n", " [sinthetasinphi,rcosphisintheta,rcosthetasinphi],\n", " [costheta,0,-rsintheta]])\n", " \n", "def gradSph2Cart(gradSph, r,theta,phi):\n", " M = gradSph2CartMatrix(r,theta,phi)\n", " return M.dot(gradSph)\n", "\n", "def gradCart2Sph(gradCart, r,theta,phi):\n", " M = gradCart2SphMatrix(r,theta,phi)\n", " return M.transpose().dot(gradCart)\n", "\n", "def hessianSph2Cart(hessSph,r,theta,phi):\n", " M = gradSph2CartMatrix(r,theta,phi)\n", " return M.dot(hessSph).dot(M.transpose())\n", "\n", "def hessianCart2Sph(hessCart,r,theta,phi):\n", " M = gradCart2SphMatrix(r,theta,phi)\n", " m00 = np.outer(M[:,0],M[:,0])\n", " m01 = np.outer(M[:,0],M[:,1])\n", " m02 = np.outer(M[:,0],M[:,2])\n", " m11 = np.outer(M[:,1],M[:,1])\n", " m12 = np.outer(M[:,1],M[:,2])\n", " m22 = np.outer(M[:,2],M[:,2])\n", " hessSph = np.zeros([3,3])\n", " hessSph[0,0] = np.trace(m00.dot(hessCart))\n", " hessSph[0,1] = np.trace(m01.dot(hessCart))\n", " hessSph[1,0] = hessSph[0,1]\n", " hessSph[0,2] = np.trace(m02.dot(hessCart))\n", " hessSph[2,0] = hessSph[0,2]\n", " hessSph[1,1] = np.trace(m11.dot(hessCart))\n", " hessSph[1,2] = np.trace(m12.dot(hessCart))\n", " hessSph[2,1] = hessSph[1,2]\n", " hessSph[2,2] = np.trace(m22.dot(hessCart))\n", " return hessSph\n", "\n", "def gradAndHessCart2Sph(gradCart,hessCart,r,theta,phi):\n", " M = gradCart2SphMatrix(r,theta,phi)\n", " m00 = np.outer(M[:,0],M[:,0])\n", " m01 = np.outer(M[:,0],M[:,1])\n", " m02 = np.outer(M[:,0],M[:,2])\n", " m11 = np.outer(M[:,1],M[:,1])\n", " m12 = np.outer(M[:,1],M[:,2])\n", " m22 = np.outer(M[:,2],M[:,2])\n", " hessSph = np.zeros([3,3])\n", " gradSph = np.zeros(3)\n", " hessSph[0,0] = np.trace(m00.dot(hessCart))\n", " hessSph[0,1] = np.trace(m01.dot(hessCart))\n", " hessSph[1,0] = hessSph[0,1]\n", " hessSph[0,2] = np.trace(m02.dot(hessCart))\n", " hessSph[2,0] = hessSph[0,2]\n", " hessSph[1,1] = np.trace(m11.dot(hessCart))\n", " hessSph[1,2] = np.trace(m12.dot(hessCart))\n", " hessSph[2,1] = hessSph[1,2]\n", " hessSph[2,2] = np.trace(m22.dot(hessCart))\n", " gradSph[0] = M[:,0].dot(gradCart)\n", " gradSph[1] = M[:,1].dot(gradCart)\n", " gradSph[2] = M[:,2].dot(gradCart)\n", " return gradSph,hessSph\n", "\n", "class gaussianDecomposition(object):\n", " def __init__(self,params):\n", " self.x0 = params[:,0]\n", " self.y0 = params[:,1]\n", " self.z0 = params[:,2]\n", " self.a = params[:,3]\n", " self.bx = params[:,4]\n", " self.by = params[:,5]\n", " self.bz = params[:,6] \n", " self.zeroarray = np.zeros(np.size(self.x0))\n", " self.onearray = np.zeros([3,np.size(self.x0)])\n", " self.current_x = None\n", " self.current_y = None\n", " self.current_z = None\n", "\n", " def fitParameters(self,N):\n", " '''Fit N component Gaussian model to data'''\n", " \n", " data = np.copy(self.data) - 1#zero centered 1-vp^2/v^2\n", " xdata = np.sum(np.sum(data,axis=2),axis=1)\n", " ydata = np.sum(np.sum(data,axis=2),axis=0)\n", " zdata = np.sum(np.sum(data,axis=1),axis=0)\n", " xsp = UnivariateSpline(self.x,xdata , k=5 , s = 2)\n", " ysp = UnivariateSpline(self.y,ydata , k=5 , s = 2)\n", " zsp = UnivariateSpline(self.z,zdata , k=5 , s = 2)\n", " dxsp = xsp.derivative(n=1)\n", " dddxsp = UnivariateSpline(self.x,dxsp(self.x) , k=5 , s = 2).derivative(n=2)\n", " ddxsp = xsp.derivative(n=2)\n", " ddddxsp = xsp.derivative(n=4)\n", " dysp = ysp.derivative(n=1)\n", " dddysp = UnivariateSpline(self.y,dysp(self.y) , k=5 , s = 2).derivative(n=2)\n", " ddysp = ysp.derivative(n=2)\n", " ddddysp = ysp.derivative(n=4)\n", " dzsp = zsp.derivative(n=1)\n", " dddzsp = UnivariateSpline(self.z,dxsp(self.z) , k=5 , s = 2).derivative(n=2)\n", " ddzsp = zsp.derivative(n=2)\n", " ddddzsp = zsp.derivative(n=4)\n", " #find parameters that fit f>ep, ddf<0, dddf=0, ddddf > 0\n", " xroots = dddxsp.roots()\n", " yroots = dddysp.roots()\n", " zroots = dddzsp.roots()\n", " print (xroots,yroots,zroots)\n", " \n", " def compute_zeroth(self,x,y,z):\n", " '''Return the nearest.'''\n", " if (self.current_x != x or self.current_y != y or self.current_z != z):\n", " self.current_x = x\n", " self.current_y = y\n", " self.current_z = z\n", " i = 0\n", " while i < np.size(self.x0):\n", " self.zeroarray[i] = self.a[i]np.exp(-(x-self.x0[i])2/self.bx[i]2-(y-self.y0[i])2/self.by[i]2-(z-self.z0[i])2/self.bz[i]2)\n", " i += 1\n", " self.zero = 1+np.sum(self.zeroarray)\n", " return self.zero\n", " \n", " def compute_oneth(self,x,y,z):\n", " '''Calculate grad of n'''\n", " if (self.current_x != x or self.current_y != y or self.current_z != z):\n", " self.current_x = x\n", " self.current_y = y\n", " self.current_z = z\n", " self.compute_zeroth(x,y,z)\n", " i = 0\n", " while i < np.size(self.x0):\n", " self.onearray[0,i] = -2(x-self.x0[i])/self.bx[i]*2 self.zeroarray[i]\n", " self.onearray[1,i] = -2(y-self.y0[i])/self.by[i]2 self.zeroarray[i]\n", " self.onearray[2,i] = -2(z-self.z0[i])/self.bz[i]2 self.zeroarray[i]\n", " i += 1\n", " self.one = np.sum(self.onearray,axis=1)\n", " #print self.one,(x-self.x0[0])/self.bx[0],(y-self.y0[0])/self.by[0],(z-self.z0[0])/self.bz[0]\n", " return self.one\n", " \n", " def compute_twoth(self,x,y,z):\n", " '''Calculate Hessian of n'''\n", " if (self.current_x != x or self.current_y != y or self.current_z != z):\n", " self.current_x = x\n", " self.current_y = y\n", " self.current_z = z\n", " self.compute_oneth(x,y,z)\n", " nxx,nxy,nxz,nyy,nyz,nzz = 0.,0.,0.,0.,0.,0.\n", " i = 0\n", " while i < np.size(self.x0):\n", " nxx += -2self.zeroarray[i](self.bx[i]*2 - 2(x-self.x0[i])2)/self.bx[i]4\n", " nyy += -2self.zeroarray[i](self.by[i]*2 - 2(y-self.y0[i])2)/self.by[i]4\n", " nzz += -2self.zeroarray[i](self.bz[i]*2 - 2(z-self.z0[i])2)/self.bz[i]4\n", " nxy += self.onearray[0,i]self.onearray[1,i]/self.zeroarray[i]\n", " nxz += self.onearray[0,i]self.onearray[2,i]/self.zeroarray[i]\n", " nyz += self.onearray[1,i]self.onearray[2,i]/self.zeroarray[i]\n", " i += 1\n", " self.two = nxx,nxy,nxz,nyy,nyz,nzz\n", " return self.two\n", "\n", "class numericDiff(object):\n", " def __init__(self,data,x,y,z):\n", " '''creates a class where data is nxmxp and x is 1xn, y is 1xm, and z is 1xp.\n", " Tried not to do things twice.'''\n", " self.data = np.ones([data.shape[0]+2,data.shape[1]+2,data.shape[2]+2])\n", " self.data[:data.shape[0],:data.shape[1],:data.shape[2]] = data\n", " self.x = x\n", " self.dx = np.abs(x[1] - x[0])\n", " self.y = y\n", " self.dy = np.abs(y[1] - y[0])\n", " self.z = z\n", " self.dz = np.abs(z[1] - z[0])\n", " self.current_x = None\n", " self.current_y = None\n", " self.current_z = None\n", " \n", " def compute_zeroth(self,x,y,z):\n", " '''Return the nearest.'''\n", " if (self.current_x != x or self.current_y != y or self.current_z != z):\n", " self.current_x = x\n", " self.current_y = y\n", " self.current_z = z\n", " self.current_nx = np.argmin((self.x - x)2)\n", " self.current_ny = np.argmin((self.y - y)2)\n", " self.current_nz = np.argmin((self.z - z)2)\n", " #check if on edge\n", " self.zerothDone = False\n", " self.onethDone = False\n", " self.twothDone = False\n", " if self.zerothDone:\n", " return self.zero\n", " else: \n", " g = self.data[self.current_nx,self.current_ny,self.current_nz]\n", " self.zerothDone = True\n", " self.zero = g\n", " return self.zero\n", " \n", " def compute_oneth(self,x,y,z):\n", " '''Calculate fourier of dsinc/dx and use that to compute du/dx then nearest.'''\n", " if (self.current_x != x or self.current_y != y or self.current_z != z):\n", " self.current_x = x\n", " self.current_y = y\n", " self.current_z = z\n", " self.zerothDone = False\n", " self.onethDone = False\n", " self.twothDone = False\n", " self.compute_zeroth(x,y,z)\n", " if self.onethDone:\n", " return self.one\n", " else:\n", " gx = self.data[self.current_nx+1,self.current_ny,self.current_nz] - self.zero\n", " gy = self.data[self.current_nx,self.current_ny+1,self.current_nz] - self.zero\n", " gz = self.data[self.current_nx,self.current_ny,self.current_nz+1] - self.zero\n", " self.one = (gx/self.dx,gy/self.dy,gz/self.dz)\n", " self.onethDone = True\n", " return self.one\n", " def compute_twoth(self,x,y,z):\n", " '''Calculate fourier of dsinc/dx and use that to compute du/dx then nearest.'''\n", " if (self.current_x != x or self.current_y != y or self.current_z != z):\n", " self.current_x = x\n", " self.current_y = y\n", " self.current_z = z\n", " self.zerothDone = False\n", " self.onethDone = False\n", " self.twothDone = False\n", " self.compute_oneth(x,y,z)\n", " if self.twothDone:\n", " return self.two\n", " else:\n", " nx,ny,nz = self.current_nx,self.current_ny,self.current_nz\n", " gxx = (self.data[nx+2,ny,nz] - 2self.data[nx+1,ny,nz] + self.data[nx,ny,nz])/self.dx*2\n", " gxy = ((self.data[nx+1,ny+1,nz] - self.data[nx,ny+1,nz])/self.dx - self.one[0])/self.dy\n", " gxz = ((self.data[nx+1,ny,nz+1] - self.data[nx,ny,nz+1])/self.dx - self.one[0])/self.dz\n", " gyy = (self.data[nx,ny+2,nz] - 2self.data[nx,ny+1,nz] + self.data[nx,ny,nz])/self.dy*2\n", " gyz = ((self.data[nx,ny+1,nz+1] - self.data[nx,ny,nz+1])/self.dy - self.one[1])/self.dz\n", " gzz = (self.data[nx,ny,nz+2] - 2self.data[nx,ny,nz+1] + self.data[nx,ny,nz])/self.dz*2\n", " self.two = (gxx,gxy,gxz,gyy,gyz,gzz)\n", " return self.two\n", "\n", "#class NObject(splineFit):\n", "#class NObject(numericDiff):\n", "class NObject(gaussianDecomposition):\n", " def __init__(self,params):\n", " super(NObject,self).__init__(params)\n", "# def __init__(self,data,x,y,z):\n", "# '''data is cartesian, but compute will be in spherical'''\n", "# super(NObject,self).__init__(data,x,y,z)\n", " def compute_n(self,r,theta,phi):\n", " #convert r,theta,phi to x,y,z\n", " x,y,z = coordsSph2Cart(r,theta,phi)\n", " return self.compute_zeroth(x,y,z)\n", " def compute_dn(self,r,theta,phi):\n", " x,y,z = coordsSph2Cart(r,theta,phi)\n", " nx,ny,nz = self.compute_oneth(x,y,z)\n", " nr,ntheta,nphi = gradCart2Sph(np.array([nx,ny,nz]),r,theta,phi)\n", " return nr,ntheta,nphi\n", " def compute_ddn(self,r,theta,phi):\n", " x,y,z = coordsSph2Cart(r,theta,phi)\n", " nxx,nxy,nxz,nyy,nyz,nzz = self.compute_twoth(x,y,z)\n", " H = np.array([[nxx,nxy,nxz],\n", " [nxy,nyy,nyz],\n", " [nxz,nyz,nzz]])\n", " Hsph = hessianCart2Sph(H,r,theta,phi)\n", " return Hsph[0,0],Hsph[0,1],Hsph[0,2],Hsph[1,1],Hsph[1,2],Hsph[2,2]\n", " \n", "def eulerEqns(t,p, nObj):\n", " pr = p[0]\n", " ptheta = p[1]\n", " pphi = p[2]\n", " r = p[3]\n", " theta = p[4]\n", " phi = p[5]\n", " phase = p[6]\n", "\n", " n = nObj.compute_n(r,theta,phi)\n", " nr,ntheta,nphi = nObj.compute_dn(r,theta,phi)\n", " \n", " sintheta = np.sin(theta)\n", " costheta = np.cos(theta)\n", " n_r = nr\n", " r2 = rr\n", " \n", " prdot = (ptheta2 + pphi2/sintheta2)/(n_rr2) + nr\n", " pthetadot = costheta * pphi*2/(n_rrsintheta3) + ntheta\n", " pphidot = nphi\n", " rdot = pr/n\n", " thetadot = ptheta/(n_rr)\n", " phidot = pphi/(n_rrsintheta*2)\n", " phasedot = nnp.sqrt(rdot2 + thetadot2r2 + r2sintheta*2phidot*2)\n", " #ar,atheta,aphi = transformCosines(theta0,phi0,theta,phi).dot(np.array([pr/n,ptheta/n/r,pphi/n/r/np.sin(theta)]))\n", " #phasedot = np.cos(np.arcsin(ar) - alt_s)n2np.pi\n", " return [prdot,pthetadot,pphidot,rdot,thetadot,phidot,phasedot]\n", " \n", "def eulerJac(t,p,nObj):\n", " pr = p[0]\n", " ptheta = p[1]\n", " pphi = p[2]\n", " r = p[3]\n", " theta = p[4]\n", " phi = p[5]\n", " phase = p[6]\n", " \n", " n = nObj.compute_n(r,phi,theta)\n", " nr,ntheta,nphi = nObj.compute_dn(r,theta,phi)\n", " nrr,nrtheta,nrphi,nthetatheta,nthetaphi,nphiphi = nObj.compute_ddn(r,theta,phi)\n", " \n", " sintheta = np.sin(theta)\n", " sintheta2 = sinthetasintheta\n", " #costheta = np.cos(theta)\n", " \n", " pphi2 = pphipphi\n", " \n", " csctheta2 = 1./np.sin(theta)2\n", " cottheta = 1./np.tan(theta)\n", " jac = np.zeros([6,6])\n", " r2 = 1/r2\n", " n2 = 1/n*2\n", " nr2 = 1/nr2\n", " nr3 = nr2/r\n", " n2r2 = n2r2\n", " n2r3 = n2r2/r\n", " A0 = pphicsctheta2\n", " A1 = pphiA0\n", " A2 = ptheta2 + A1\n", " #col pr\n", " \n", " jac[:,0]=np.array([0,0,0,1/n,0,0])\n", " #col ptheta\n", " jac[:,1]=np.array([(2ptheta)nr3,\n", " 0,\n", " 0,\n", " 0,\n", " 0,\n", " nr2])\n", " #col pphi\n", " jac[:,2] = np.array([(2A0)nr3 ,\n", " (2A0cottheta)nr2,\n", " 0,\n", " 0,\n", " csctheta2nr2,\n", " 0])\n", " #col r\n", " jac[:,3] = np.array([-((A2(3n + rnr))n2r2r2) + nrr,\n", " -((A1cottheta(2n + rnr))n2r3) + nrtheta,\n", " nrphi,\n", " ((prnr)n2),\n", " -((A0(2n + rnr))n2r3),\n", " -((ptheta(2n + rnr))n2r3)])\n", "\n", " #col theta\n", " jac[:,4] = np.array([-((2nA1cottheta + A2ntheta)n2r3) + nrtheta,\n", " - (nthetatheta/n2r2 - (A1csctheta2(2n(2 + np.cos(2theta)) + nthetanp.sin(2theta)))/2.)n2r2,\n", " nthetaphi,\n", " -((prntheta)n2),\n", " ((A0(2ncottheta + ntheta))n2r2),\n", " -((pthetantheta)n2r2)])\n", " #col phi\n", " jac[:,5] = np.array([-((A2nphi)n2r3) + nrphi,\n", " -((A1cotthetanphi)n2r2) + nthetaphi,\n", " nphiphi,\n", " ((prnphi)n2),\n", " -((A0nphi)n2r2),\n", " -((pthetanphi)n2r2)])\n", " return jac\n", "\n", "def LM2DiffAltAz(l,m):\n", " dAlt = np.arccos(np.sqrt(1-l2-m2))\n", " dAz = np.arctan2(l,m)\n", " return dAlt,dAz\n", "\n", "def getKzComp(r,theta,phi,n,alt_s,theta0,phi0):\n", " #ar = sin(as+da)\n", " \n", " #cos(da) = sqrt(1-l2-m2)\n", " #kz = 2pin/lambdasqrt(1-l2-m2)\n", " cosphi1 = np.cos(theta0)\n", " sinphi1 = np.sin(theta0)\n", " #costheta1 = np.cos(phi0)\n", " sintheta1 = np.sin(phi0)\n", " cosphi2 = np.cos(theta)\n", " sinphi2 = np.sin(theta)\n", " costheta2 = np.cos(phi)\n", " sintheta2 = np.sin(phi)\n", " costheta12 = np.cos(phi-phi)\n", " sintheta12 = np.sin(phi-phi)\n", " \n", " ar = (cosphi1cosphi2 + costheta12sinphi1sinphi2)pr/n+\\\n", " (sinphi1sintheta12)ptheta/n/r+\\\n", " (cosphi2costheta12sinphi1 - cosphi1sinphi2)pphi/n/r/np.sin(theta)\n", " da = np.arcsin(ar)-alt_s\n", " kz = np.cos(np.arcsin(ar) - alt_s)2np.pi\n", " return kz\n", " \n", "def zDist(r,theta,phi,s,x0):\n", " phis = s.spherical.lon.rad\n", " thetas = np.pi/2. - s.spherical.lat.rad\n", " r0,theta0,phi0 = polarSphericalVars(x0)\n", " \n", " costhetas = np.cos(thetas)\n", " sinthetas = np.sin(thetas)\n", " \n", " zDist = -r(costhetasnp.cos(theta)+np.cos(phis-phi)sinthetasnp.sin(theta)) + r0(costhetasnp.cos(theta0)+np.cos(phis-phi0)sinthetasnp.sin(theta0))\n", " return zDist\n", " \n", "def zDot(r,theta,phi,s,pr,ptheta,pphi,n):\n", " phis = s.spherical.lon.rad\n", " thetas = np.pi/2. - s.spherical.lat.rad\n", " \n", " costhetas = np.cos(thetas)\n", " costheta = np.cos(theta)\n", " cosphis1 = np.cos(phis - phi)\n", " sintheta = np.sin(theta)\n", " sinthetas = np.sin(thetas)\n", " zdot = (costhetascostheta+cosphis1sinthetassintheta)pr/n +(-costhetassintheta+cosphis1sinthetascostheta)ptheta/n/r +(-np.sin(phi-phis)sinthetassintheta)pphi/n/r/sintheta2\n", " return zdot\n", " \n", "def propagateBackwards(l,m,s,x0,xi,obstime,NObj,rmaxRatio,plot=False,ax=None):\n", " '''Propagate a ray from observer to source plane using numerical integration.\n", " l - direction cosine pointing East\n", " m - direction cosine pointing West\n", " s - ponting center of field, ICRS object\n", " x0 - Location of observer coordinate system origin, ITRS object\n", " obstime - ISOT or time object\n", " rmaxRatio - multiple of earth radius to propagate to in plane perp to s pointing\n", " '''\n", " r2d = 180./np.pi\n", " #initial location\n", " r,theta,phi = polarSphericalVars(xi)\n", " #center where frame defined\n", " r0,theta0,phi0 = polarSphericalVars(x0) \n", " #transform matrix cosines from ray location to center\n", " \n", " #direction cosines on pointing at center\n", " frame = ac.AltAz(location = x0, obstime = obstime, pressure=None, copy = True) \n", " s_ = s.transform_to(frame)\n", " #for stopping criterion\n", " theta_s = np.pi/2. - s_.spherical.lat.rad\n", " phi_s = s_.spherical.lon.rad\n", " \n", " alt_s = s_.alt.rad#of pointing\n", " az_s = s_.az.rad\n", " #l,m alt/az relative to s pointing\n", " dAlt,dAz = LM2DiffAltAz(l,m)\n", " #alt,az of s+l,m\n", " alt = alt_s + dAlt\n", " az = az_s + dAz\n", " #direction cosines of s+l,m at center\n", " ar0 = np.sin(alt)\n", " atheta0 = np.cos(alt)np.cos(az)\n", " aphi0 = np.cos(alt)np.sin(az)\n", " #transform to xi\n", " M = transformCosines(theta0,phi0,theta,phi)\n", " ar,atheta,aphi = M.transpose().dot(np.array([ar0,atheta0,aphi0]))\n", " if plot:\n", " print(\"----\")\n", " print(\"Obs. location (aperture center): lon: {0}, lat: {1}, radial: {2}\".format(x0.earth_location.geodetic[0].deg,\n", " x0.earth_location.geodetic[1].deg,\n", " x0.earth_location.geodetic[2]))\n", " print(\"Obs. offset (ray emitter): lon: {0}, lat: {1}, radial: {2}\".format(xi.earth_location.geodetic[0].deg-x0.earth_location.geodetic[0].deg,\n", " xi.earth_location.geodetic[1].deg-x0.earth_location.geodetic[1].deg,\n", " xi.earth_location.geodetic[2]-x0.earth_location.geodetic[2]))\n", " print(\"Obs. time: {0}\".format(obstime.isot))\n", " print(\"Pointing center: ra = {0}, dec = {1}\".format(s.ra.deg,s.dec.deg))\n", " print(\"\\talt = {0}, az = {1}\".format(alt_sr2d,az_sr2d))\n", " print(\"Image plane cosines: l = {0}, m = {1}\".format(l,m))\n", " print(\"Ray initial direction: alt = {0}, az = {1}\".format(altr2d,azr2d))\n", " print(\"Ray initial cosines: ar = {0}, atheta = {1}, aphi = {2}\".format(ar,atheta,aphi))\n", " print(\"----\")\n", " #define parameters\n", " n = NObj.compute_n(r,theta,phi)\n", " #print(n)\n", " #for analytic radial profile\n", " #C = n0r0np.cos(alt)\n", " pr = nar\n", " ptheta = nratheta\n", " pphi = nrnp.sin(theta)aphi\n", " rmax = r0rmaxRatio\n", " cosx0s = s_.cartesian.xyz.value.dot(x0.cartesian.xyz.value)\n", " rNum = np.sqrt(cosx0s2 - r02 + rmax*2)\n", " #ODE = ode(eulerEqns, eulerJac).set_integrator('vode',method='adams').set_jac_params(NObj)\n", " ODE = ode(eulerEqns).set_integrator('vode', method='adams')\n", " phase = 0\n", " ODE.set_initial_value([pr,ptheta,pphi,r,theta,phi,phase], 0)#set initit and time=0\n", " ODE.set_f_params(NObj)\n", " zMax = rmax - r0\n", " #one go\n", " if not plot:\n", " pr,ptheta,pphi,r,theta,phi,phase = ODE.integrate(rmax)\n", " M = transformCosines(theta0,phi0,theta,phi)\n", " n = NObj.compute_n(r,theta,phi)\n", " ar,atheta,aphi = M.dot(np.array([pr/n,ptheta/n/r,pphi/n/r/np.sin(theta)]))\n", " xf=rnp.cos(phi)np.sin(theta)\n", " yf=rnp.sin(phi)np.sin(theta)\n", " zf=rnp.cos(theta)\n", " xs = np.cos(phi_s)np.sin(theta_s)\n", " ys = np.sin(phi_s)np.sin(theta_s)\n", " zs = np.cos(theta_s)\n", " x0=r0np.cos(phi0)np.sin(theta0)\n", " y0=r0np.sin(phi0)np.sin(theta0)\n", " z0=r0np.cos(theta0)\n", " #isoplanDiff = xfxs+yfys+zfzs - (x0xs+y0ys+z0zs)\n", " #phase += np.cos(np.arcsin(ar) - alt_s)n2np.piisoplanDiff\n", " \n", " return phase\n", " zMax = rmax-r0\n", " \n", " if plot:\n", " sols = []\n", " X,Y,Z,N = [],[],[],[]\n", " X.append(rnp.cos(phi)np.sin(theta))\n", " Y.append(rnp.sin(phi)np.sin(theta))\n", " Z.append(rnp.cos(theta))\n", " #while r < rNum/np.abs(np.sin(theta_s)np.sin(theta)np.cos(phi_s - phi) + np.cos(theta_s)np.cos(theta)) and ODE.successful():\n", " z = zDist(r,theta,phi,s,x0)\n", " print (zDot(r,theta,phi,s,pr,ptheta,pphi,n))\n", " while r < rmax:#zMax:\n", " dt = zMax/100.\n", " #dt = max(zMax/10000,(zMax - z)/zDot(r,theta,phi,s,pr,ptheta,pphi,n)/10.)#sections of arc.\n", " pr,ptheta,pphi,r,theta,phi,phase = ODE.integrate(ODE.t + dt)\n", " #print zDot(r,theta,phi,s,pr,ptheta,pphi,n),dt\n", " M = transformCosines(theta0,phi0,theta,phi)\n", " n = NObj.compute_n(r,theta,phi)\n", " ar,atheta,aphi = M.dot(np.array([pr/n,ptheta/n/r,pphi/n/r/np.sin(theta)]))\n", " z = zDist(r,theta,phi,s,x0)\n", " #print z,zMax,dt\n", " #ar,atheta,aphi = r/n,ptheta/n/r,pphi/n/r/np.sin(theta)\n", " #print ar, atheta, aphi\n", " if plot: \n", " pathlength = ODE.t\n", " X.append(rnp.cos(phi)np.sin(theta))\n", " Y.append(rnp.sin(phi)np.sin(theta))\n", " Z.append(rnp.cos(theta))\n", " N.append(n)\n", " #print (ar,ar_)\n", " #psi = -np.arccos(C/r/NObj.compute_n(r,theta,phi))#+(alt+alt_)\n", " sols.append([pathlength,ar,atheta,aphi,r,theta,phi,dt,phase])\n", " #print(pathlength,pr,ptheta,pphi,r,theta,phi)\n", " if plot:\n", " import pylab as plt\n", " ax.plot(X,Y,Z)\n", " #plt.gcf().savefig('figs/axes_{0:04d}'.format(num))\n", " sols= np.array(sols)\n", " f = plt.figure()\n", " plt.subplot(131)\n", " plt.plot(sols[:,4]-xi.spherical.distance.m,sols[:,8])\n", " plt.xlabel('r (m)')\n", " plt.ylabel('pathlength (m)')\n", " plt.subplot(132)\n", " plt.plot(sols[:,4]-xi.spherical.distance.m,N)\n", " #plt.scatter(sols[:,4],sols[:,2])\n", " plt.xlabel('r (m)')\n", " plt.ylabel('n')\n", " plt.subplot(133)\n", " plt.plot(sols[:,4]-xi.spherical.distance.m,sols[:,1])\n", " plt.xlabel('r (m)')\n", " plt.ylabel('ar Sqrt(1-l^2-m^2)')\n", " plt.show()\n", " \n", " #isoplanDiff = xfxs+yfys+zfzs - (x0xs+y0ys+z0zs)\n", " #phase += np.cos(np.arcsin(ar) - alt_s)n2np.piisoplanDiff\n", " return phase\n", "\n", "def plotPathLength(lvec,mvec,s,x0,xi,obstime,NObj,rmaxRatio,num=0):\n", " pl = np.zeros([np.size(lvec),np.size(mvec)])\n", " i = 0\n", " while i < len(lvec):\n", " j = 0\n", " while j < len(mvec):\n", " pl[i,j] = propagateBackwards(lvec[i],mvec[j],s,x0,xi,obstime,NObj,rmaxRatio)np.pi2\n", " j += 1\n", " i += 1\n", " pl = np.angle(ifft(np.abs(fft(pl/3e8))*2))\n", " import pylab as plt\n", " f=plt.figure()\n", " plt.imshow((pl.transpose()-pl[0,0]),origin='lower',extent=(lvec[0],lvec[-1],mvec[0],mvec[-1]),interpolation='nearest')\n", " plt.colorbar(label='rad')\n", " plt.xlabel('l')\n", " plt.ylabel('m')\n", " frame = ac.AltAz(location = x0, obstime = obstime, pressure=None, copy = True) \n", " s_ = s.transform_to(frame) \n", " alt_s = s_.alt.deg#of pointing\n", " az_s = s_.az.deg\n", " plt.title(\"Time: {0}, Alt: {1:.0f}, Az: {2:.0f}\".format(obstime.isot,alt_s,az_s))\n", " f.savefig(\"figs/fig_{0:04d}\".format(num))\n", " plt.close()\n", " \n", " \n", "if __name__=='__main__':\n", " l=0.0\n", " m=0.0\n", " obstime = at.Time('2000-01-01T00:00:00.000',format='isot',scale='utc')\n", " c0 = ac.ITRS(ac.EarthLocation(lon=0au.deg,lat=0au.deg,height=0au.m).geocentric)\n", " xi = ac.ITRS(ac.EarthLocation(lon=0au.deg,lat=0.001au.deg,height=0au.m).geocentric)\n", " s = ac.SkyCoord(ra=90au.deg,dec=0au.deg,frame='icrs')\n", " xvec = np.linspace(c0.cartesian.x.value,c0.cartesian.x.value2,100)\n", " yvec = np.linspace(-c0.cartesian.x.value/2.,c0.cartesian.x.value/2.,100)\n", " zvec = np.linspace(-c0.cartesian.x.value/2.,c0.cartesian.x.value/2.,100)\n", " X,Y,Z = np.meshgrid(xvec,yvec,zvec)\n", " R = np.sqrt(X2 + Y2 + Z*2)\n", " #ndata = 1 + 0.1np.cos(R/60000.)\n", " frame = ac.AltAz(location = c0, obstime = obstime, pressure=None, copy = True) \n", " s_ = s.transform_to(frame) \n", " x0 = [(c0.cartesian.x.value+s_.cartesian.x.value350000)]#np.cos(c0.spherical.lon.rad+0.1)np.sin(np.pi/2-c0.spherical.lat.rad)]\n", " y0 = [(c0.cartesian.y.value+s_.cartesian.y.value350000)]#np.sin(c0.spherical.lon.rad)np.sin(np.pi/2-c0.spherical.lat.rad)]\n", " z0 = [(c0.cartesian.z.value+s_.cartesian.z.value350000)]#np.cos(np.pi/2-c0.spherical.lat.rad)]\n", " a = [1.]\n", " bx=[3500000]\n", " by=[3500000]\n", " bz=[3500000]\n", " params = np.array([x0,y0,x0,a,bx,by,bz]).transpose()\n", " NObj = NObject(params)\n", " rvec = np.linspace(xi.spherical.distance.m,4xi.spherical.distance.m,10)\n", " thetavec = np.linspace(np.pi/2.-xi.spherical.lat.rad-0.5,np.pi/2.-xi.spherical.lat.rad+0.5,10)\n", " phivec = np.linspace(xi.spherical.lon.rad-.5,xi.spherical.lon.rad+.5,10)\n", " R,Theta,Phi = np.meshgrid(rvec,thetavec,phivec)\n", " X = Rnp.cos(Phi)np.sin(Theta)\n", " Y = Rnp.sin(Phi)np.sin(Theta)\n", " Z = Rnp.cos(Theta)\n", " dnu = np.zeros_like(X)\n", " dnv = np.zeros_like(X)\n", " dnw = np.zeros_like(X)\n", " n = np.ones_like(X)\n", " i = 0\n", " while i < X.shape[0]:\n", " j = 0\n", " while j < X.shape[1]:\n", " k = 0\n", " while k < X.shape[2]:\n", " n[i,j,k] = NObj.compute_zeroth(X[i,j,k],Y[i,j,k],Z[i,j,k])\n", " dnu[i,j,k],dnv[i,j,k],dnw[i,j,k] = NObj.compute_oneth(X[i,j,k],Y[i,j,k],Z[i,j,k])\n", " #print dnu[i,j,k]\n", " k += 1\n", " j += 1\n", " i += 1\n", " from mpl_toolkits.mplot3d import axes3d\n", " import pylab as plt\n", " fig = plt.figure()\n", " ax = fig.gca(projection='3d')\n", " #ax.quiver(X,Y,Z,dnu/np.max(dnu),dnv/np.max(dnu),dnw/np.max(dnu),length=1e7/4.)\n", " ax.scatter(X,Y,Z,c=n)\n", " ax.set_xlabel('x')\n", " ax.set_ylabel('y')\n", " ax.set_zlabel('z')\n", " #ndata = 0.95+0.1np.random.uniform(size=[100,100,100])\n", " #NObj = NObject(ndata,xvec,yvec,zvec)\n", " \n", " propagateBackwards(l,m,s,c0,xi,obstime,NObj,3,plot=True,ax=ax)\n", " lvec = np.linspace(-0.5,0.5,10)\n", " mvec = np.linspace(-0.5,0.5,10)\n", " import os\n", " try:\n", " os.mkdir('./figs')\n", " except:\n", " pass\n", " obstimes = at.Time(np.linspace(obstime.gps,obstime.gps+160*60,10),format='gps',scale='utc')\n", " c = 0\n", " for obstime in obstimes:\n", " plotPathLength(lvec,mvec,s,c0,xi,obstime,NObj,100,num=c)\n", " c += 1\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " " ] }, { "cell_type": "code", "execution_count": 147, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'0004'" ] }, "execution_count": 147, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"{0:04d}\".format(4)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }	apache-2.0
georgetown-analytics/yelp-classification	Yelp_web_scrapper/Review_Scrapper.ipynb	1	38663	{ "cells": [ { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from bs4 import BeautifulSoup\n", "import requests\n", "import re\n", "import json\n", "import sys\n", "sys.path.append('/Users/robertsonwang/Desktop/Python/Yelp/Yelp_scrapper')\n", "import os\n", "#import scrapping_functions as sf\n", "#reload(sf)\n", "\n", "base_url = 'https://www.yelp.com'" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'/Users/robertsonwang/Desktop/Python/Yelp_class/yelp-classification'" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "os.getcwd()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "test = json.load(open('/Users/robertsonwang/Desktop/Python/Yelp_class/yelp-classification/Yelp_scrapper/dc_reviews.json'))" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "biz_test = json.load(open('/Users/robertsonwang/Desktop/Python/Yelp_class/yelp-classification/Yelp_scrapper/biz_details.json'))" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{u'city': u'Washington', u'rating': u'4.5', u'category_aliases': u'foodtrucks,korean,tradamerican', u'biz_name': u'Rolling Cow', u'city_state': u'Washington, DC', u'longitude': -77.0413344, u'state': u'DC', u'biz_id': u'ynuv5y74SB2gEXFPjMYb_Q', u'latitude': 38.897244}\n" ] } ], "source": [ "for key in biz_test.keys():\n", " print biz_test[key]\n", " break" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{u'servesCuisine': u'Latin American', u'name': u'Panas', u'review': [{u'reviewRating': {u'ratingValue': 5}, u'datePublished': u'2017-03-01', u'description': u'Amazing place with fresh cooked empanadas!\\nVegetarians should be impressed, they have decent selection there!\\nDelicious food for a reasonable price!', u'author': u'Artem M.'}, {u'reviewRating': {u'ratingValue': 3}, u'datePublished': u'2017-02-28', u'description': u'Not bad. Not stunning.\\nI grew up on empanadas in South America - these ones are nowhere near as good.\\nBut, when you need an empanada fix, these just might have to do.\\nThey do sell them frozen in the event you want to home prepare or impress your friends.', u'author': u'Hank M.'}, {u'reviewRating': {u'ratingValue': 5}, u'datePublished': u'2017-01-23', u'description': u\"The empanadas were amazing! It's a cute, small shop so it's not sufficient for a big crowd or group. But its location in Dupont is spectacular.\\n\\nMy friend and I were able to each get 4 empanadas for a great price (which is hard to find in DC). Definitely would recommend!\", u'author': u'Blessing I.'}, {u'reviewRating': {u'ratingValue': 5}, u'datePublished': u'2017-01-08', u'description': u'Their empanadas are super fresh! I ordered 12 with another friend and I thought I needed to order more. But it made me very full. The insides were really full of meat and rich flavor.\\nI also ordered the mango juice which is perfect in size and goes well with the empanadas.\\nI ordered Beef(CA), 2 Chicken Steak(CS), Ham and Cheese(JQ) 2Chicken Pesto(CP) 2Chicken Mushrooms(CM), Pepperoni Empanada(PE), Spinach Empanada(PY), Brie Cheese Empanada(BA), Four Cheeses and Onions(FC)\\nI personally like Aji(spicy) and Chimi(mild) sauce', u'author': u'Brian L.'}, {u'reviewRating': {u'ratingValue': 5}, u'datePublished': u'2016-12-19', u'description': u\"My visit to Panas is all thanks to fate. I actually tried to eat here the one time I visited the District before moving here, as I went to Urbana and was super unimpressed. I remember walking up to Panas about ten minutes after they closed and being crushed. I then came to the Icy Code opening, smelled the empanadas and thought I should make the trip back but didn't pay much attention to where it was. Suddenly, I got off at the Dupont Metro one day and was roaming around trying to find some lunch. As I walked up to Panas, both of these memories hit me, and I realized I had ended up back here. So my friend and I finally stopped in, and I will definitely be back.\\n\\nWhen you walk into Panas, you walk up to the counter and order by letters of the things you want. This comes in handy when you get the little pockets of joy because they print the letters on the dough so you know which one you are about to bite into!\\n\\nWe ordered the Empanadas Combo x4 ($9), which was supposed to come with chips and two drips, aka sauces. The owner was super nice, though, and gave us an extra empanada and drip for free. We tried the Chipotle Steak (CS), Chicken Pesto (CP), BBQ Sauce Chicken (CB), Pepperoni Empanada (PE), and were given a free Ham and Cheese (JQ). The Chipotle Steak and BBQ Chicken were hands down the best empanadas, and these are the two must have ones. The others were good but not special. We also enjoyed the Salsa Verde, Aji, and Pimenton drips. The Salsa Verde was mild but very good, and I loved the Pimenton the most with our empanadas. The Aji tasted more mustard-y to me, which went with the ham and cheese but wasn't my top choice. Once we finished stuffing ourselves with these pockets of joy, we ate the interestingly spiced chips, which were just OK, and we polished off a Mango Juice ($2.50). It was very mango-y and delicious, and the price was right!\\n\\nThe shop was very warm on a cold day, and there are probably ten or so small tables to choose from. The man who was taking care of us seemed to be the owner, and he was just the nicest. Conversational, helpful, and generous. It was just one of the only places that I have found in DC that delivered in all ways, which was really nice at a reasonable price.\\n\\nAll in all, fate may have kept bringing me to Panas, but their wonderful food and service will keep bringing me back.\", u'author': u'Amanda J.'}, {u'reviewRating': {u'ratingValue': 5}, u'datePublished': u'2016-12-02', u'description': u\"Best empanadas right here! They make them to order and they are so flavorful and by themselves but come with different sauces for dipping. They have savory and sweet empanadas to choose from. We got the savory ones. They were out of the beef when we were there on a Sunday for lunch. You can choose from cheese, pork and chicken. They do offer vegetarian ones too. The fruit drinks are made fresh to order and are very sweet and tasty. Very reasonably priced. It's a small place with not too many tables to sit at. Would highly recommend this place.\", u'author': u'Krys G.'}, {u'reviewRating': {u'ratingValue': 4}, u'datePublished': u'2016-10-23', u'description': u\"I've been to Panas a few times now and haven't been disappointed yet. I like that they have a wide range of empanada fillings to choose from. (I do wish it were easier to figure out which one is which though. I know you have the letters stamped on the edge, but how am I supposed to remember what PY and CA mean?) \\n\\nI particularly like the spinach with goat cheese and raisins (PY). The aji (yellow) sauce is my favorite. Their plantain chips are thinly sliced and not greasy--far and away better than the stuff in the bag at Trader Joe's. \\n\\nIf you're more than one person, order a canoa! You can get all of your empanadas, sauces, and chips served on an impressive-looking long wooden platter.\", u'author': u'Calvin H.'}, {u'reviewRating': {u'ratingValue': 3}, u'datePublished': u'2017-01-09', u'description': u'Delicious Empanadas and great service. I used to come here for lunch when I was a little hung over from the night before and these things do the trick.', u'author': u'Ron C.'}, {u'reviewRating': {u'ratingValue': 4}, u'datePublished': u'2016-09-21', u'description': u\"I had bookmarked this place for a long time but today was the first time I had the chance to try it out. After all, it lived up to my expectations. \\n\\nThey have Very tasty empanadas with a great variety, both for vegetarians and meat lovers. You have the option to choose 3 ($7.5) or 4 ($9) with two sauces + plantain chips or go for big platter (6 or 8) to share, which comes with 4 sauces. I got beef with olives, chicken pesto, chicken mushroom, and eggplant. They were so damn hot so i burned my tongue. But when they got colder, they tasted better. \\n\\nThe location is casual but cute, especially the outdoor sitting. \\n\\nAlthough the empanadas are very tasty and fresh, i'm afraid they are half size the ones we are used to see. For me, with a small appetite, 3 or 4 is more than enough, but for a guy, I dont think 3-4 would make for a meal, so definitely you need a side. In that case you'll end up paying $15 for empanadas. I think they can be a good option if you want to share a fun snack with a friend or you want to oder for your parties.\\n\\nI think they need to knock down the prices or make the empanadas 1.5 times bigger.\", u'author': u'Sou M.'}, {u'reviewRating': {u'ratingValue': 5}, u'datePublished': u'2016-10-11', u'description': u\"Has a variety of options you can choose from. Love that they serve it to you after you order them. The flavors are so good. The juices are also good to try, such as the mango one. Horchata was also really yum. \\n\\nIf you get the 8 empanadas, it's enough to share for 2ppl. Not bad for the price.\", u'author': u'Jun Li Z.'}, {u'reviewRating': {u'ratingValue': 5}, u'datePublished': u'2016-09-14', u'description': u'I love and adore Panas! It\\'s one of my absolute favorite counter service places in DC. I can\\'t believe I haven\\'t reviewed it before! I come here as often as I can, and I\\'ve made it a life goal to try every single empanada here at least once (they have over a dozen), but it\\'s sort of hard because I love the ones I\\'ve already had and keep getting them. That\\'s a good problem to have though right? \\n\\nThe empanadas are delicious, baked, warm pockets of goodness. I\\'ve tried almost every variety at this point and my favorites are the Carne (traditional ground beef), the Pepperoni (it\\'s like a much better version of a hot pocket), and the Spinach (formerly known as the Popeye). The two I don\\'t really like here are the corn and the chicken pesto, but that\\'s more of a personal preference thing. The empanadas are the perfect size-- not too big, not too small-- and GREAT for practicing portion size. I usually have 1 for a snack or 3 for a meal, but it depends on your appetite. They feel kind of decadent but they\\'re small and baked, so they\\'re reasonsably healthy for what they are (notice that I did say REASONABLY) as long as you don\\'t have a million in one seating (tempting). \\n\\nOverall, the food is a really solid 4. The reason for the extra star is the amazing, above-and-beyond, service with a smile I always receive when I\\'m here. They are just so SO nice here! All the guys here (I\\'ve only ever had male cashiers) are super helpful and happy to give recommendations if it\\'s your first time. One time, I got a take out order on a really hot day and I was standing there drinking from a water bottle I brought. One of the guys there saw me and was like \"you should refill your water bottle at the drink station before you go back outside\". It was a small suggestion, but it was super considerate and the kind of thing that makes a business standout. I\\'m always happy when I leave here because they\\'re just so darn friendly. \\n\\nTo me, going to Panas is like happiness, rainbows, and baked pastries all rolled into a cute little store right off of the Circle. :)\\n\\nTip: When I\\'m in MD and can\\'t go to Panas (sad), I get their empanadas at Balducci\\'s or Harris Teeter. They stock a decent selection of their empanadas in local grocery stores all around the DMV. They come in packs of 3 and they\\'re super convenient for snacks or meals. I even eat them for breakfast!', u'author': u'Alyssa B.'}, {u'reviewRating': {u'ratingValue': 5}, u'datePublished': u'2017-02-18', u'description': u'Amazing selection of baked empanadas. Friendly family owned place. Meat, veggie, cheese, and sweet empanadas. Fried plantain chips were good too.', u'author': u'Katie B.'}, {u'reviewRating': {u'ratingValue': 4}, u'datePublished': u'2016-09-13', u'description': u'The best little empanadas I\\'ve had in DC! We had a large order at work today for a birthday lunch for a member of our team, and they were a huge hit! I tried all 4 \"drips\" or sauces, and my favorite was mixing them together for incredible flavor. For empanadas I tried the beef and the chipotle steak, both of which were de-licious! It\\'s cool that they stamp the two letter code on the pastry of each one, so that people could figure out which was which. The plantain were also great. \\n\\nMy colleagues said there was an issue with picking up the order, but it was resolved without too much hassle. \\n\\nI\\'m looking forward to coming up to the store sometime and trying more flavors!', u'author': u'Kristi C.'}, {u'reviewRating': {u'ratingValue': 5}, u'datePublished': u'2016-07-19', u'description': u\"Very good, very cheap food. Huge variety of empanadas, and they're reasonably healthy too since they're baked instead of fried. Everything will be under $10 and very filling. The sauces could be a little spicier, but honestly that's a relatively minor complaint. The empanadas are good enough on their own without sauce. \\n\\nI'm a big fan of the spinach and feta empanada - definitely not authentic but hey it's yummy.\", u'author': u'Nicholas W.'}, {u'reviewRating': {u'ratingValue': 3}, u'datePublished': u'2017-02-09', u'description': u'Panas is a fun little place with a wide variety of tiny empanadas. They have at least 12 fillings, but only a couple could be considered even vaguely authentic to any country that serves empanadas. I would have liked to see some more classic flavors, and I would have preferred a corn flour empanada to wheat flour. I had 4 empanadas and chips, but left a little hungry.', u'author': u'John H.'}, {u'reviewRating': {u'ratingValue': 4}, u'datePublished': u'2016-07-09', u'description': u\"Yum!!! Great casual place to grab a bite! There are so many flavours of baked empanadas to choose from - chicken, beef, pork and lots of veggie options too. Just order by the initial letters from the menu for the fillings you want. \\n\\nwe opted for the two person share option which is 6 empanadas of any flavours with an ounce of plantain chips and four dipping sauces from spicy to mild (the spicy wasn't really spicy for me but all very good flavours). Very ingenious to have the dough show the initials of the filling so you know what you're eating. We tried many of the protein and veggie ones and they were all delicious. Can't go wrong with any fillings!\\n\\nThe place is brightly decorated with very friendly service. It's just steps away from Dupont Circle and a couple of blocks from the Phillip Collection.\", u'author': u'DineoutGal A.'}, {u'reviewRating': {u'ratingValue': 4}, u'datePublished': u'2016-10-23', u'description': u\"If you like empanadas... this is the place for you! You're a long way from the typical ground beef empanadas. They have savory. They have sweet. Bottom line you've got options! You can pick from beef, pork, chicken, seafood, cheese and vegetarian. They've got combos 3, 4, family pack comes with plantain chips and your choice of sauce - mild, medium, hot and oh la la.\\nThey're not big; an average person can eat 3 or 4 no problem. The sweet ones cost more and aren't included in the combos. \\nPlus they have passion fruit juice.\", u'author': u'Pascale A.'}, {u'reviewRating': {u'ratingValue': 5}, u'datePublished': u'2016-05-02', u'description': u'Great empanadas @ Panas! Absolutely live the Brie one! (French taste!) My empanadas to go place on a Monday night. \\nTried all of the empanadas! (Literally!) and they are all very delicious. \\nStaff is very friendly ;-)', u'author': u'Marina H.'}, {u'reviewRating': {u'ratingValue': 5}, u'datePublished': u'2017-01-16', u'description': u\"Loved this place so much and can't wait to come back. My sister and I ordered 6 empanadas. I got the Brie, mushroom and artichoke and the steak chipotle and the beef and olives. All 3 were amazing! It came with?4 sauces and plantain chips! The empanadas were baked; light and fresh! Will bring other friends.\", u'author': u'Yasmin H.'}, {u'reviewRating': {u'ratingValue': 4}, u'datePublished': u'2016-09-29', u'description': u'I have been very skeptical to visit again, a couple of times that I visited their now closed Bethesda store they were out of something or my selections never were fulfilling. I came to the DuPont location and I honestly can say that I love it, especially their Cholados that bring memories from of my childhood in Colombia.', u'author': u'Indir S.'}], u'telephone': u'+12022232964', u'aggregateRating': {u'reviewCount': 439, u'@type': u'AggregateRating', u'ratingValue': 4.10933940774487}, u'priceRange': u'Under $10', u'image': u'https://s3-media4.fl.yelpcdn.com/bphoto/AAwxypGa753fHGLs_RFCVw/ls.jpg', u'address': {u'addressLocality': u'Washington, DC', u'addressRegion': None, u'streetAddress': u'2029 P St NW', u'postalCode': u'20036', u'addressCountry': u'US'}, u'@context': u'http://schema.org/', u'@type': u'Restaurant'}\n" ] } ], "source": [ "for key in test.keys():\n", " print test[key]\n", " break" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "link_file = open(\"cleanbiz_links_2.txt\", \"r\")\n", "link_list = link_file.read().split('\\n')\n", "link_list = list(set(link_list))" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'/biz/the-codmother-washington?osq=Restaurants'" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "link_list[33]" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "json_dict = {}\n", "for link in link_list:\n", " reviews = sf.scrap_reviews(link, base_url)\n", " if type(reviews) == dict:\n", " json_dict[link] = reviews\n", " else:\n", " print reviews\n", " break" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "with open('dc_reviews_2.json', 'w') as outfile:\n", " json.dump(json_dict, outfile)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Load in Review Data" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "review_json = json.load(open(\"/Users/robertsonwang/Desktop/Python/Yelp/dc_reviews.json\"))" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{u'@context': u'http://schema.org/',\n", " u'@type': u'Restaurant',\n", " u'address': {u'addressCountry': u'US',\n", " u'addressLocality': u'Washington, DC',\n", " u'addressRegion': None,\n", " u'postalCode': u'20036',\n", " u'streetAddress': u'2029 P St NW'},\n", " u'aggregateRating': {u'@type': u'AggregateRating',\n", " u'ratingValue': 4.10933940774487,\n", " u'reviewCount': 439},\n", " u'image': u'https://s3-media4.fl.yelpcdn.com/bphoto/AAwxypGa753fHGLs_RFCVw/ls.jpg',\n", " u'name': u'Panas',\n", " u'priceRange': u'Under $10',\n", " u'review': [{u'author': u'Artem M.',\n", " u'datePublished': u'2017-03-01',\n", " u'description': u'Amazing place with fresh cooked empanadas!\\nVegetarians should be impressed, they have decent selection there!\\nDelicious food for a reasonable price!',\n", " u'reviewRating': {u'ratingValue': 5}},\n", " {u'author': u'Hank M.',\n", " u'datePublished': u'2017-02-28',\n", " u'description': u'Not bad. Not stunning.\\nI grew up on empanadas in South America - these ones are nowhere near as good.\\nBut, when you need an empanada fix, these just might have to do.\\nThey do sell them frozen in the event you want to home prepare or impress your friends.',\n", " u'reviewRating': {u'ratingValue': 3}},\n", " {u'author': u'Blessing I.',\n", " u'datePublished': u'2017-01-23',\n", " u'description': u\"The empanadas were amazing! It's a cute, small shop so it's not sufficient for a big crowd or group. But its location in Dupont is spectacular.\\n\\nMy friend and I were able to each get 4 empanadas for a great price (which is hard to find in DC). Definitely would recommend!\",\n", " u'reviewRating': {u'ratingValue': 5}},\n", " {u'author': u'Brian L.',\n", " u'datePublished': u'2017-01-08',\n", " u'description': u'Their empanadas are super fresh! I ordered 12 with another friend and I thought I needed to order more. But it made me very full. The insides were really full of meat and rich flavor.\\nI also ordered the mango juice which is perfect in size and goes well with the empanadas.\\nI ordered Beef(CA), 2 Chicken Steak(CS), Ham and Cheese(JQ) 2Chicken Pesto(CP) 2Chicken Mushrooms(CM), Pepperoni Empanada(PE), Spinach Empanada(PY), Brie Cheese Empanada(BA), Four Cheeses and Onions(FC)\\nI personally like Aji(spicy) and Chimi(mild) sauce',\n", " u'reviewRating': {u'ratingValue': 5}},\n", " {u'author': u'Amanda J.',\n", " u'datePublished': u'2016-12-19',\n", " u'description': u\"My visit to Panas is all thanks to fate. I actually tried to eat here the one time I visited the District before moving here, as I went to Urbana and was super unimpressed. I remember walking up to Panas about ten minutes after they closed and being crushed. I then came to the Icy Code opening, smelled the empanadas and thought I should make the trip back but didn't pay much attention to where it was. Suddenly, I got off at the Dupont Metro one day and was roaming around trying to find some lunch. As I walked up to Panas, both of these memories hit me, and I realized I had ended up back here. So my friend and I finally stopped in, and I will definitely be back.\\n\\nWhen you walk into Panas, you walk up to the counter and order by letters of the things you want. This comes in handy when you get the little pockets of joy because they print the letters on the dough so you know which one you are about to bite into!\\n\\nWe ordered the Empanadas Combo x4 ($9), which was supposed to come with chips and two drips, aka sauces. The owner was super nice, though, and gave us an extra empanada and drip for free. We tried the Chipotle Steak (CS), Chicken Pesto (CP), BBQ Sauce Chicken (CB), Pepperoni Empanada (PE), and were given a free Ham and Cheese (JQ). The Chipotle Steak and BBQ Chicken were hands down the best empanadas, and these are the two must have ones. The others were good but not special. We also enjoyed the Salsa Verde, Aji, and Pimenton drips. The Salsa Verde was mild but very good, and I loved the Pimenton the most with our empanadas. The Aji tasted more mustard-y to me, which went with the ham and cheese but wasn't my top choice. Once we finished stuffing ourselves with these pockets of joy, we ate the interestingly spiced chips, which were just OK, and we polished off a Mango Juice ($2.50). It was very mango-y and delicious, and the price was right!\\n\\nThe shop was very warm on a cold day, and there are probably ten or so small tables to choose from. The man who was taking care of us seemed to be the owner, and he was just the nicest. Conversational, helpful, and generous. It was just one of the only places that I have found in DC that delivered in all ways, which was really nice at a reasonable price.\\n\\nAll in all, fate may have kept bringing me to Panas, but their wonderful food and service will keep bringing me back.\",\n", " u'reviewRating': {u'ratingValue': 5}},\n", " {u'author': u'Krys G.',\n", " u'datePublished': u'2016-12-02',\n", " u'description': u\"Best empanadas right here! They make them to order and they are so flavorful and by themselves but come with different sauces for dipping. They have savory and sweet empanadas to choose from. We got the savory ones. They were out of the beef when we were there on a Sunday for lunch. You can choose from cheese, pork and chicken. They do offer vegetarian ones too. The fruit drinks are made fresh to order and are very sweet and tasty. Very reasonably priced. It's a small place with not too many tables to sit at. Would highly recommend this place.\",\n", " u'reviewRating': {u'ratingValue': 5}},\n", " {u'author': u'Calvin H.',\n", " u'datePublished': u'2016-10-23',\n", " u'description': u\"I've been to Panas a few times now and haven't been disappointed yet. I like that they have a wide range of empanada fillings to choose from. (I do wish it were easier to figure out which one is which though. I know you have the letters stamped on the edge, but how am I supposed to remember what PY and CA mean?) \\n\\nI particularly like the spinach with goat cheese and raisins (PY). The aji (yellow) sauce is my favorite. Their plantain chips are thinly sliced and not greasy--far and away better than the stuff in the bag at Trader Joe's. \\n\\nIf you're more than one person, order a canoa! You can get all of your empanadas, sauces, and chips served on an impressive-looking long wooden platter.\",\n", " u'reviewRating': {u'ratingValue': 4}},\n", " {u'author': u'Ron C.',\n", " u'datePublished': u'2017-01-09',\n", " u'description': u'Delicious Empanadas and great service. I used to come here for lunch when I was a little hung over from the night before and these things do the trick.',\n", " u'reviewRating': {u'ratingValue': 3}},\n", " {u'author': u'Sou M.',\n", " u'datePublished': u'2016-09-21',\n", " u'description': u\"I had bookmarked this place for a long time but today was the first time I had the chance to try it out. After all, it lived up to my expectations. \\n\\nThey have Very tasty empanadas with a great variety, both for vegetarians and meat lovers. You have the option to choose 3 ($7.5) or 4 ($9) with two sauces + plantain chips or go for big platter (6 or 8) to share, which comes with 4 sauces. I got beef with olives, chicken pesto, chicken mushroom, and eggplant. They were so damn hot so i burned my tongue. But when they got colder, they tasted better. \\n\\nThe location is casual but cute, especially the outdoor sitting. \\n\\nAlthough the empanadas are very tasty and fresh, i'm afraid they are half size the ones we are used to see. For me, with a small appetite, 3 or 4 is more than enough, but for a guy, I dont think 3-4 would make for a meal, so definitely you need a side. In that case you'll end up paying $15 for empanadas. I think they can be a good option if you want to share a fun snack with a friend or you want to oder for your parties.\\n\\nI think they need to knock down the prices or make the empanadas 1.5 times bigger.\",\n", " u'reviewRating': {u'ratingValue': 4}},\n", " {u'author': u'Jun Li Z.',\n", " u'datePublished': u'2016-10-11',\n", " u'description': u\"Has a variety of options you can choose from. Love that they serve it to you after you order them. The flavors are so good. The juices are also good to try, such as the mango one. Horchata was also really yum. \\n\\nIf you get the 8 empanadas, it's enough to share for 2ppl. Not bad for the price.\",\n", " u'reviewRating': {u'ratingValue': 5}},\n", " {u'author': u'Alyssa B.',\n", " u'datePublished': u'2016-09-14',\n", " u'description': u'I love and adore Panas! It\\'s one of my absolute favorite counter service places in DC. I can\\'t believe I haven\\'t reviewed it before! I come here as often as I can, and I\\'ve made it a life goal to try every single empanada here at least once (they have over a dozen), but it\\'s sort of hard because I love the ones I\\'ve already had and keep getting them. That\\'s a good problem to have though right? \\n\\nThe empanadas are delicious, baked, warm pockets of goodness. I\\'ve tried almost every variety at this point and my favorites are the Carne (traditional ground beef), the Pepperoni (it\\'s like a much better version of a hot pocket), and the Spinach (formerly known as the Popeye). The two I don\\'t really like here are the corn and the chicken pesto, but that\\'s more of a personal preference thing. The empanadas are the perfect size-- not too big, not too small-- and GREAT for practicing portion size. I usually have 1 for a snack or 3 for a meal, but it depends on your appetite. They feel kind of decadent but they\\'re small and baked, so they\\'re reasonsably healthy for what they are (notice that I did say REASONABLY) as long as you don\\'t have a million in one seating (tempting). \\n\\nOverall, the food is a really solid 4. The reason for the extra star is the amazing, above-and-beyond, service with a smile I always receive when I\\'m here. They are just so SO nice here! All the guys here (I\\'ve only ever had male cashiers) are super helpful and happy to give recommendations if it\\'s your first time. One time, I got a take out order on a really hot day and I was standing there drinking from a water bottle I brought. One of the guys there saw me and was like \"you should refill your water bottle at the drink station before you go back outside\". It was a small suggestion, but it was super considerate and the kind of thing that makes a business standout. I\\'m always happy when I leave here because they\\'re just so darn friendly. \\n\\nTo me, going to Panas is like happiness, rainbows, and baked pastries all rolled into a cute little store right off of the Circle. :)\\n\\nTip: When I\\'m in MD and can\\'t go to Panas (sad), I get their empanadas at Balducci\\'s or Harris Teeter. They stock a decent selection of their empanadas in local grocery stores all around the DMV. They come in packs of 3 and they\\'re super convenient for snacks or meals. I even eat them for breakfast!',\n", " u'reviewRating': {u'ratingValue': 5}},\n", " {u'author': u'Katie B.',\n", " u'datePublished': u'2017-02-18',\n", " u'description': u'Amazing selection of baked empanadas. Friendly family owned place. Meat, veggie, cheese, and sweet empanadas. Fried plantain chips were good too.',\n", " u'reviewRating': {u'ratingValue': 5}},\n", " {u'author': u'Kristi C.',\n", " u'datePublished': u'2016-09-13',\n", " u'description': u'The best little empanadas I\\'ve had in DC! We had a large order at work today for a birthday lunch for a member of our team, and they were a huge hit! I tried all 4 \"drips\" or sauces, and my favorite was mixing them together for incredible flavor. For empanadas I tried the beef and the chipotle steak, both of which were de-licious! It\\'s cool that they stamp the two letter code on the pastry of each one, so that people could figure out which was which. The plantain were also great. \\n\\nMy colleagues said there was an issue with picking up the order, but it was resolved without too much hassle. \\n\\nI\\'m looking forward to coming up to the store sometime and trying more flavors!',\n", " u'reviewRating': {u'ratingValue': 4}},\n", " {u'author': u'Nicholas W.',\n", " u'datePublished': u'2016-07-19',\n", " u'description': u\"Very good, very cheap food. Huge variety of empanadas, and they're reasonably healthy too since they're baked instead of fried. Everything will be under $10 and very filling. The sauces could be a little spicier, but honestly that's a relatively minor complaint. The empanadas are good enough on their own without sauce. \\n\\nI'm a big fan of the spinach and feta empanada - definitely not authentic but hey it's yummy.\",\n", " u'reviewRating': {u'ratingValue': 5}},\n", " {u'author': u'John H.',\n", " u'datePublished': u'2017-02-09',\n", " u'description': u'Panas is a fun little place with a wide variety of tiny empanadas. They have at least 12 fillings, but only a couple could be considered even vaguely authentic to any country that serves empanadas. I would have liked to see some more classic flavors, and I would have preferred a corn flour empanada to wheat flour. I had 4 empanadas and chips, but left a little hungry.',\n", " u'reviewRating': {u'ratingValue': 3}},\n", " {u'author': u'DineoutGal A.',\n", " u'datePublished': u'2016-07-09',\n", " u'description': u\"Yum!!! Great casual place to grab a bite! There are so many flavours of baked empanadas to choose from - chicken, beef, pork and lots of veggie options too. Just order by the initial letters from the menu for the fillings you want. \\n\\nwe opted for the two person share option which is 6 empanadas of any flavours with an ounce of plantain chips and four dipping sauces from spicy to mild (the spicy wasn't really spicy for me but all very good flavours). Very ingenious to have the dough show the initials of the filling so you know what you're eating. We tried many of the protein and veggie ones and they were all delicious. Can't go wrong with any fillings!\\n\\nThe place is brightly decorated with very friendly service. It's just steps away from Dupont Circle and a couple of blocks from the Phillip Collection.\",\n", " u'reviewRating': {u'ratingValue': 4}},\n", " {u'author': u'Pascale A.',\n", " u'datePublished': u'2016-10-23',\n", " u'description': u\"If you like empanadas... this is the place for you! You're a long way from the typical ground beef empanadas. They have savory. They have sweet. Bottom line you've got options! You can pick from beef, pork, chicken, seafood, cheese and vegetarian. They've got combos 3, 4, family pack comes with plantain chips and your choice of sauce - mild, medium, hot and oh la la.\\nThey're not big; an average person can eat 3 or 4 no problem. The sweet ones cost more and aren't included in the combos. \\nPlus they have passion fruit juice.\",\n", " u'reviewRating': {u'ratingValue': 4}},\n", " {u'author': u'Marina H.',\n", " u'datePublished': u'2016-05-02',\n", " u'description': u'Great empanadas @ Panas! Absolutely live the Brie one! (French taste!) My empanadas to go place on a Monday night. \\nTried all of the empanadas! (Literally!) and they are all very delicious. \\nStaff is very friendly ;-)',\n", " u'reviewRating': {u'ratingValue': 5}},\n", " {u'author': u'Yasmin H.',\n", " u'datePublished': u'2017-01-16',\n", " u'description': u\"Loved this place so much and can't wait to come back. My sister and I ordered 6 empanadas. I got the Brie, mushroom and artichoke and the steak chipotle and the beef and olives. All 3 were amazing! It came with?4 sauces and plantain chips! The empanadas were baked; light and fresh! Will bring other friends.\",\n", " u'reviewRating': {u'ratingValue': 5}},\n", " {u'author': u'Indir S.',\n", " u'datePublished': u'2016-09-29',\n", " u'description': u'I have been very skeptical to visit again, a couple of times that I visited their now closed Bethesda store they were out of something or my selections never were fulfilling. I came to the DuPont location and I honestly can say that I love it, especially their Cholados that bring memories from of my childhood in Colombia.',\n", " u'reviewRating': {u'ratingValue': 4}}],\n", " u'servesCuisine': u'Latin American',\n", " u'telephone': u'+12022232964'}" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "review_json['/biz/panas-washington']" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "agg_dict = dict(review_json, **json_dict)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [], "source": [ "with open('dc_reviews_agg.json', 'w') as outfile:\n", " json.dump(agg_dict, outfile)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### TESTING IS GOING ON BEYOND THIS POINT" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "test_link = '/biz/the-codmother-washington?osq=Restaurants'\n", "\n", "articles = articles.find_all(\"div\", class_=\"biz-listing-large\")\n", "for article in articles:\n", " match = re.search(r'href=[\\'\"]?([^\\'\" >]+)', str(article.find_all(href = True)))\n", "\n", " if match:\n", " link = match.group(0)\n", " link = link[6:]\n", " link_list.append(link)" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "py3", "language": "python", "name": "py3" }, "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
tensorflow/docs-l10n	site/en-snapshot/tfx/tutorials/tfx/penguin_simple.ipynb	1	23515	{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "DjUA6S30k52h" }, "source": [ "##### Copyright 2021 The TensorFlow Authors." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "SpNWyqewk8fE" }, "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": "6x1ypzczQCwy" }, "source": [ "# Simple TFX Pipeline Tutorial using Penguin dataset\n", "\n", "*A Short tutorial to run a simple TFX pipeline." ] }, { "cell_type": "markdown", "metadata": { "id": "HU9YYythm0dx" }, "source": [ "Note: We recommend running this tutorial in a Colab notebook, with no setup required! Just click \"Run in Google Colab\".\n", "\n", "<div class=\"devsite-table-wrapper\"><table class=\"tfo-notebook-buttons\" align=\"left\">\n", "<td><a target=\"_blank\" href=\"https://www.tensorflow.org/tfx/tutorials/tfx/penguin_simple\">\n", "<img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\"/>View on TensorFlow.org</a></td>\n", "<td><a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/tfx/blob/master/docs/tutorials/tfx/penguin_simple.ipynb\">\n", "<img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\">Run in Google Colab</a></td>\n", "<td><a target=\"_blank\" href=\"https://github.com/tensorflow/tfx/tree/master/docs/tutorials/tfx/penguin_simple.ipynb\">\n", "<img width=32px src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\">View source on GitHub</a></td>\n", "<td><a href=\"https://storage.googleapis.com/tensorflow_docs/tfx/docs/tutorials/tfx/penguin_simple.ipynb\"><img src=\"https://www.tensorflow.org/images/download_logo_32px.png\" />Download notebook</a></td>\n", "</table></div>" ] }, { "cell_type": "markdown", "metadata": { "id": "_VuwrlnvQJ5k" }, "source": [ "In this notebook-based tutorial, we will create and run a TFX pipeline\n", "for a simple classification model.\n", "The pipeline will consist of three essential TFX components: ExampleGen,\n", "Trainer and Pusher. The pipeline includes the most minimal ML workflow like\n", "importing data, training a model and exporting the trained model.\n", "\n", "Please see\n", "[Understanding TFX Pipelines](https://www.tensorflow.org/tfx/guide/understanding_tfx_pipelines)\n", "to learn more about various concepts in TFX." ] }, { "cell_type": "markdown", "metadata": { "id": "Fmgi8ZvQkScg" }, "source": [ "## Set Up\n", "We first need to install the TFX Python package and download\n", "the dataset which we will use for our model.\n", "\n", "### Upgrade Pip\n", "\n", "To avoid upgrading Pip in a system when running locally,\n", "check to make sure that we are running in Colab.\n", "Local systems can of course be upgraded separately." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "as4OTe2ukSqm" }, "outputs": [], "source": [ "try:\n", " import colab\n", " !pip install --upgrade pip\n", "except:\n", " pass" ] }, { "cell_type": "markdown", "metadata": { "id": "MZOYTt1RW4TK" }, "source": [ "### Install TFX\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "iyQtljP-qPHY" }, "outputs": [], "source": [ "!pip install -U tfx" ] }, { "cell_type": "markdown", "metadata": { "id": "EwT0nov5QO1M" }, "source": [ "### Did you restart the runtime?\n", "\n", "If you are using Google Colab, the first time that you run\n", "the cell above, you must restart the runtime by clicking\n", "above \"RESTART RUNTIME\" button or using \"Runtime > Restart\n", "runtime ...\" menu. This is because of the way that Colab\n", "loads packages." ] }, { "cell_type": "markdown", "metadata": { "id": "BDnPgN8UJtzN" }, "source": [ "Check the TensorFlow and TFX versions." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "6jh7vKSRqPHb" }, "outputs": [], "source": [ "import tensorflow as tf\n", "print('TensorFlow version: {}'.format(tf.__version__))\n", "from tfx import v1 as tfx\n", "print('TFX version: {}'.format(tfx.__version__))" ] }, { "cell_type": "markdown", "metadata": { "id": "aDtLdSkvqPHe" }, "source": [ "### Set up variables\n", "\n", "There are some variables used to define a pipeline. You can customize these\n", "variables as you want. By default all output from the pipeline will be\n", "generated under the current directory." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "EcUseqJaE2XN" }, "outputs": [], "source": [ "import os\n", "\n", "PIPELINE_NAME = \"penguin-simple\"\n", "\n", "# Output directory to store artifacts generated from the pipeline.\n", "PIPELINE_ROOT = os.path.join('pipelines', PIPELINE_NAME)\n", "# Path to a SQLite DB file to use as an MLMD storage.\n", "METADATA_PATH = os.path.join('metadata', PIPELINE_NAME, 'metadata.db')\n", "# Output directory where created models from the pipeline will be exported.\n", "SERVING_MODEL_DIR = os.path.join('serving_model', PIPELINE_NAME)\n", "\n", "from absl import logging\n", "logging.set_verbosity(logging.INFO) # Set default logging level." ] }, { "cell_type": "markdown", "metadata": { "id": "8F2SRwRLSYGa" }, "source": [ "### Prepare example data\n", "We will download the example dataset for use in our TFX pipeline. The dataset we\n", "are using is\n", "[Palmer Penguins dataset](https://allisonhorst.github.io/palmerpenguins/articles/intro.html)\n", "which is also used in other\n", "[TFX examples](https://github.com/tensorflow/tfx/tree/master/tfx/examples/penguin).\n", "\n", "There are four numeric features in this dataset:\n", "\n", "- culmen_length_mm\n", "- culmen_depth_mm\n", "- flipper_length_mm\n", "- body_mass_g\n", "\n", "All features were already normalized to have range [0,1]. We will build a\n", "classification model which predicts the `species` of penguins." ] }, { "cell_type": "markdown", "metadata": { "id": "11J7XiCq6AFP" }, "source": [ "Because TFX ExampleGen reads inputs from a directory, we need to create a\n", "directory and copy dataset to it." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "4fxMs6u86acP" }, "outputs": [], "source": [ "import urllib.request\n", "import tempfile\n", "\n", "DATA_ROOT = tempfile.mkdtemp(prefix='tfx-data') # Create a temporary directory.\n", "_data_url = 'https://raw.githubusercontent.com/tensorflow/tfx/master/tfx/examples/penguin/data/labelled/penguins_processed.csv'\n", "_data_filepath = os.path.join(DATA_ROOT, \"data.csv\")\n", "urllib.request.urlretrieve(_data_url, _data_filepath)" ] }, { "cell_type": "markdown", "metadata": { "id": "ASpoNmxKSQjI" }, "source": [ "Take a quick look at the CSV file." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "-eSz28UDSnlG" }, "outputs": [], "source": [ "!head {_data_filepath}" ] }, { "cell_type": "markdown", "metadata": { "id": "OTtQNq1DdVvG" }, "source": [ "You should be able to see five values. `species` is one of 0, 1 or 2, and all\n", "other features should have values between 0 and 1." ] }, { "cell_type": "markdown", "metadata": { "id": "nH6gizcpSwWV" }, "source": [ "## Create a pipeline\n", "\n", "TFX pipelines are defined using Python APIs. We will define a pipeline which\n", "consists of following three components.\n", "- CsvExampleGen: Reads in data files and convert them to TFX internal format\n", "for further processing. There are multiple\n", "[ExampleGen](https://www.tensorflow.org/tfx/guide/examplegen)s for various\n", "formats. In this tutorial, we will use CsvExampleGen which takes CSV file input.\n", "- Trainer: Trains an ML model.\n", "[Trainer component](https://www.tensorflow.org/tfx/guide/trainer) requires a\n", "model definition code from users. You can use TensorFlow APIs to specify how to\n", "train a model and save it in a _saved_model_ format.\n", "- Pusher: Copies the trained model outside of the TFX pipeline.\n", "[Pusher component](https://www.tensorflow.org/tfx/guide/pusher) can be thought\n", "of as a deployment process of the trained ML model.\n", "\n", "Before actually define the pipeline, we need to write a model code for the\n", "Trainer component first." ] }, { "cell_type": "markdown", "metadata": { "id": "lOjDv93eS5xV" }, "source": [ "### Write model training code\n", "\n", "We will create a simple DNN model for classification using TensorFlow Keras\n", "API. This model training code will be saved to a separate file.\n", "\n", "In this tutorial we will use\n", "[Generic Trainer](https://www.tensorflow.org/tfx/guide/trainer#generic_trainer)\n", "of TFX which support Keras-based models. You need to write a Python file\n", "containing `run_fn` function, which is the entrypoint for the `Trainer`\n", "component." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "aES7Hv5QTDK3" }, "outputs": [], "source": [ "_trainer_module_file = 'penguin_trainer.py'" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Gnc67uQNTDfW" }, "outputs": [], "source": [ "%%writefile {_trainer_module_file}\n", "\n", "from typing import List\n", "from absl import logging\n", "import tensorflow as tf\n", "from tensorflow import keras\n", "from tensorflow_transform.tf_metadata import schema_utils\n", "\n", "from tfx import v1 as tfx\n", "from tfx_bsl.public import tfxio\n", "from tensorflow_metadata.proto.v0 import schema_pb2\n", "\n", "_FEATURE_KEYS = [\n", " 'culmen_length_mm', 'culmen_depth_mm', 'flipper_length_mm', 'body_mass_g'\n", "]\n", "_LABEL_KEY = 'species'\n", "\n", "_TRAIN_BATCH_SIZE = 20\n", "_EVAL_BATCH_SIZE = 10\n", "\n", "# Since we're not generating or creating a schema, we will instead create\n", "# a feature spec. Since there are a fairly small number of features this is\n", "# manageable for this dataset.\n", "_FEATURE_SPEC = {\n", " *{\n", " feature: tf.io.FixedLenFeature(shape=[1], dtype=tf.float32)\n", " for feature in _FEATURE_KEYS\n", " },\n", " _LABEL_KEY: tf.io.FixedLenFeature(shape=[1], dtype=tf.int64)\n", "}\n", "\n", "\n", "def _input_fn(file_pattern: List[str],\n", " data_accessor: tfx.components.DataAccessor,\n", " schema: schema_pb2.Schema,\n", " batch_size: int = 200) -> tf.data.Dataset:\n", " \"\"\"Generates features and label for training.\n", "\n", " Args:\n", " file_pattern: List of paths or patterns of input tfrecord files.\n", " data_accessor: DataAccessor for converting input to RecordBatch.\n", " schema: schema of the input data.\n", " batch_size: representing the number of consecutive elements of returned\n", " dataset to combine in a single batch\n", "\n", " Returns:\n", " A dataset that contains (features, indices) tuple where features is a\n", " dictionary of Tensors, and indices is a single Tensor of label indices.\n", " \"\"\"\n", " return data_accessor.tf_dataset_factory(\n", " file_pattern,\n", " tfxio.TensorFlowDatasetOptions(\n", " batch_size=batch_size, label_key=_LABEL_KEY),\n", " schema=schema).repeat()\n", "\n", "\n", "def _build_keras_model() -> tf.keras.Model:\n", " \"\"\"Creates a DNN Keras model for classifying penguin data.\n", "\n", " Returns:\n", " A Keras Model.\n", " \"\"\"\n", " # The model below is built with Functional API, please refer to\n", " # https://www.tensorflow.org/guide/keras/overview for all API options.\n", " inputs = [keras.layers.Input(shape=(1,), name=f) for f in _FEATURE_KEYS]\n", " d = keras.layers.concatenate(inputs)\n", " for _ in range(2):\n", " d = keras.layers.Dense(8, activation='relu')(d)\n", " outputs = keras.layers.Dense(3)(d)\n", "\n", " model = keras.Model(inputs=inputs, outputs=outputs)\n", " model.compile(\n", " optimizer=keras.optimizers.Adam(1e-2),\n", " loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n", " metrics=[keras.metrics.SparseCategoricalAccuracy()])\n", "\n", " model.summary(print_fn=logging.info)\n", " return model\n", "\n", "\n", "# TFX Trainer will call this function.\n", "def run_fn(fn_args: tfx.components.FnArgs):\n", " \"\"\"Train the model based on given args.\n", "\n", " Args:\n", " fn_args: Holds args used to train the model as name/value pairs.\n", " \"\"\"\n", "\n", " # This schema is usually either an output of SchemaGen or a manually-curated\n", " # version provided by pipeline author. A schema can also derived from TFT\n", " # graph if a Transform component is used. In the case when either is missing,\n", " # `schema_from_feature_spec` could be used to generate schema from very simple\n", " # feature_spec, but the schema returned would be very primitive.\n", " schema = schema_utils.schema_from_feature_spec(_FEATURE_SPEC)\n", "\n", " train_dataset = _input_fn(\n", " fn_args.train_files,\n", " fn_args.data_accessor,\n", " schema,\n", " batch_size=_TRAIN_BATCH_SIZE)\n", " eval_dataset = _input_fn(\n", " fn_args.eval_files,\n", " fn_args.data_accessor,\n", " schema,\n", " batch_size=_EVAL_BATCH_SIZE)\n", "\n", " model = _build_keras_model()\n", " model.fit(\n", " train_dataset,\n", " steps_per_epoch=fn_args.train_steps,\n", " validation_data=eval_dataset,\n", " validation_steps=fn_args.eval_steps)\n", "\n", " # The result of the training should be saved in `fn_args.serving_model_dir`\n", " # directory.\n", " model.save(fn_args.serving_model_dir, save_format='tf')" ] }, { "cell_type": "markdown", "metadata": { "id": "blaw0rs-emEf" }, "source": [ "Now you have completed all preparation steps to build a TFX pipeline." ] }, { "cell_type": "markdown", "metadata": { "id": "w3OkNz3gTLwM" }, "source": [ "### Write a pipeline definition\n", "\n", "We define a function to create a TFX pipeline. A `Pipeline` object\n", "represents a TFX pipeline which can be run using one of the pipeline\n", "orchestration systems that TFX supports.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "M49yYVNBTPd4" }, "outputs": [], "source": [ "def _create_pipeline(pipeline_name: str, pipeline_root: str, data_root: str,\n", " module_file: str, serving_model_dir: str,\n", " metadata_path: str) -> tfx.dsl.Pipeline:\n", " \"\"\"Creates a three component penguin pipeline with TFX.\"\"\"\n", " # Brings data into the pipeline.\n", " example_gen = tfx.components.CsvExampleGen(input_base=data_root)\n", "\n", " # Uses user-provided Python function that trains a model.\n", " trainer = tfx.components.Trainer(\n", " module_file=module_file,\n", " examples=example_gen.outputs['examples'],\n", " train_args=tfx.proto.TrainArgs(num_steps=100),\n", " eval_args=tfx.proto.EvalArgs(num_steps=5))\n", "\n", " # Pushes the model to a filesystem destination.\n", " pusher = tfx.components.Pusher(\n", " model=trainer.outputs['model'],\n", " push_destination=tfx.proto.PushDestination(\n", " filesystem=tfx.proto.PushDestination.Filesystem(\n", " base_directory=serving_model_dir)))\n", "\n", " # Following three components will be included in the pipeline.\n", " components = [\n", " example_gen,\n", " trainer,\n", " pusher,\n", " ]\n", "\n", " return tfx.dsl.Pipeline(\n", " pipeline_name=pipeline_name,\n", " pipeline_root=pipeline_root,\n", " metadata_connection_config=tfx.orchestration.metadata\n", " .sqlite_metadata_connection_config(metadata_path),\n", " components=components)" ] }, { "cell_type": "markdown", "metadata": { "id": "mJbq07THU2GV" }, "source": [ "## Run the pipeline\n", "\n", "TFX supports multiple orchestrators to run pipelines.\n", "In this tutorial we will use `LocalDagRunner` which is included in the TFX\n", "Python package and runs pipelines on local environment.\n", "We often call TFX pipelines \"DAGs\" which stands for directed acyclic graph.\n", "\n", "`LocalDagRunner` provides fast iterations for development and debugging.\n", "TFX also supports other orchestrators including Kubeflow Pipelines and Apache\n", "Airflow which are suitable for production use cases.\n", "\n", "See\n", "[TFX on Cloud AI Platform Pipelines](https://www.tensorflow.org/tfx/tutorials/tfx/cloud-ai-platform-pipelines)\n", "or\n", "[TFX Airflow Tutorial](https://www.tensorflow.org/tfx/tutorials/tfx/airflow_workshop)\n", "to learn more about other orchestration systems." ] }, { "cell_type": "markdown", "metadata": { "id": "7mp0AkmrPdUb" }, "source": [ "Now we create a `LocalDagRunner` and pass a `Pipeline` object created from the\n", "function we already defined.\n", "\n", "The pipeline runs directly and you can see logs for the progress of the pipeline including ML model training." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "fAtfOZTYWJu-" }, "outputs": [], "source": [ "tfx.orchestration.LocalDagRunner().run(\n", " _create_pipeline(\n", " pipeline_name=PIPELINE_NAME,\n", " pipeline_root=PIPELINE_ROOT,\n", " data_root=DATA_ROOT,\n", " module_file=_trainer_module_file,\n", " serving_model_dir=SERVING_MODEL_DIR,\n", " metadata_path=METADATA_PATH))" ] }, { "cell_type": "markdown", "metadata": { "id": "ppERq0Mj6xvW" }, "source": [ "You should see \"INFO:absl:Component Pusher is finished.\" at the end of the\n", "logs if the pipeline finished successfully. Because `Pusher` component is the\n", "last component of the pipeline.\n", "\n", "The pusher component pushes the trained model to the `SERVING_MODEL_DIR` which\n", "is the `serving_model/penguin-simple` directory if you did not change the\n", "variables in the previous steps. You can see the result from the file browser\n", "in the left-side panel in Colab, or using the following command:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "NTHROkqX6yHx" }, "outputs": [], "source": [ "# List files in created model directory.\n", "!find {SERVING_MODEL_DIR}" ] }, { "cell_type": "markdown", "metadata": { "id": "08R8qvweThRf" }, "source": [ "## Next steps\n", "\n", "You can find more resources on https://www.tensorflow.org/tfx/tutorials.\n", "\n", "Please see\n", "[Understanding TFX Pipelines](https://www.tensorflow.org/tfx/guide/understanding_tfx_pipelines)\n", "to learn more about various concepts in TFX.\n" ] } ], "metadata": { "colab": { "collapsed_sections": [ "DjUA6S30k52h" ], "name": "penguin_simple.ipynb", "toc_visible": true }, "kernelspec": { "display_name": "Python 3", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 0 }	apache-2.0
c22n/ion-channel-ABC	docs/examples/crn/crn_ina_testing.ipynb	1	204814	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Create and test ion channel model" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:myokit:Loading Myokit version 1.28.3\n", "WARNING:myokit._config:Sundials version not set in myokit.ini. Continuing with detected version (20700). For a tiny performance boost, please set this version in /Users/charles/.config/myokit/myokit.ini\n" ] } ], "source": [ "from experiments.crn_ina import (sakakibara_act,\n", " sakakibara_inact,\n", " schneider_taum,\n", " sakakibara_tauh,\n", " sakakibara_tauh_depol)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from ionchannelABC.experiment import setup" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "modelfile = 'models/ina_markov.mmt'" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "observations, model, summary_statistics = setup(modelfile,\n", " sakakibara_act,\n", " sakakibara_inact,\n", " schneider_taum,\n", " sakakibara_tauh,\n", " sakakibara_tauh_depol)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "assert len(observations)==len(summary_statistics(model({})))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Set limits and generate uniform initial priors" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "from pyabc import Distribution, RV\n", "limits = {'log_ina.p_1': (-7., 3.),\n", " 'ina.p_2': (1e-7, 0.4),\n", " 'log_ina.p_3': (-7., 3.),\n", " 'ina.p_4': (1e-7, 0.4),\n", " 'log_ina.p_5': (-7., 3.),\n", " 'ina.p_6': (1e-7, 0.4),\n", " 'log_ina.p_7': (-7., 3.),\n", " 'ina.p_8': (1e-7, 0.4)}\n", "prior = Distribution(*{key: RV(\"uniform\", a, b - a)\n", " for key, (a,b) in limits.items()})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Run ABC calibration" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "import os, tempfile\n", "db_path = (\"sqlite:///\" +\n", " os.path.join(tempfile.gettempdir(), \"crn_ina_testing.db\"))" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "import logging\n", "logging.basicConfig()\n", "abc_logger = logging.getLogger('ABC')\n", "abc_logger.setLevel(logging.DEBUG)\n", "eps_logger = logging.getLogger('Epsilon')\n", "eps_logger.setLevel(logging.DEBUG)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Theoretical minimum population size is 256 particles\n" ] } ], "source": [ "from pyabc.populationstrategy import ConstantPopulationSize\n", "from ionchannelABC import theoretical_population_size\n", "pop_size = theoretical_population_size(2, len(limits))\n", "print(\"Theoretical minimum population size is {} particles\".format(pop_size))" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "DEBUG:ABC:ion channel weights: {'0': 1.5585327376534017, '1': 1.5585327376534017, '2': 1.5585327376534017, '3': 1.5585327376534017, '4': 1.5585327376534017, '5': 0.5922502978635686, '6': 0.48479560247060055, '7': 0.6880033400331257, '8': 0.9191724622842619, '9': 1.5585327376534017, '10': 1.5585327376534017, '11': 1.5585327376534017, '12': 1.5585327376534017, '13': 1.6660177540432917, '14': 1.6660177540432917, '15': 1.6660177540432917, '16': 1.6660177540432917, '17': 1.6660177540432917, '18': 0.6141022916123275, '19': 0.4000666394868585, '20': 0.46000171656354155, '21': 0.7676278645154099, '22': 1.6660177540432917, '23': 1.6660177540432917, '24': 0.02906218941217941, '25': 0.03367374906920667, '26': 0.03457895737751867, '27': 0.046999392320876986, '28': 0.08141374774960093, '29': 0.10610649611527939, '30': 0.20902979734710037, '31': 0.07785094873262584, '32': 0.2188793689498433, '33': 0.05927794792597441, '34': 0.11670345997926211, '35': 0.16237003127549512, '36': 0.2667507656668848, '37': 1.988472803212961, '38': 1.988472803212961, '39': 1.988472803212961, '40': 1.988472803212961, '41': 1.988472803212961}\n", "DEBUG:Epsilon:init quantile_epsilon initial_epsilon=20, quantile_multiplier=1\n" ] } ], "source": [ "from pyabc import ABCSMC\n", "from pyabc.epsilon import MedianEpsilon\n", "from pyabc.sampler import MulticoreEvalParallelSampler, SingleCoreSampler\n", "from ionchannelABC import IonChannelDistance, EfficientMultivariateNormalTransition, IonChannelAcceptor\n", "\n", "abc = ABCSMC(models=model,\n", " parameter_priors=prior,\n", " distance_function=IonChannelDistance(\n", " exp_id=list(observations.exp_id),\n", " variance=list(observations.variance),\n", " delta=0.05),\n", " population_size=ConstantPopulationSize(25), # Small pop to test\n", " summary_statistics=summary_statistics,\n", " transitions=EfficientMultivariateNormalTransition(),\n", " eps=MedianEpsilon(initial_epsilon=20),\n", " sampler=MulticoreEvalParallelSampler(n_procs=6),\n", " acceptor=IonChannelAcceptor())" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "obs = observations.to_dict()['y']\n", "obs = {str(k): v for k, v in obs.items()}" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:History:Start <ABCSMC(id=1, start_time=2019-07-17 14:00:17.435795, end_time=None)>\n" ] } ], "source": [ "abc_id = abc.new(db_path, obs)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "history = abc.run(minimum_epsilon=0.1, max_nr_populations=100, min_acceptance_rate=0.005)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Results analysis" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "from pyabc import History" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<ABCSMC(id=1, start_time=2019-07-16 15:16:46.691662, end_time=None)>,\n", " <ABCSMC(id=2, start_time=2019-07-16 15:20:21.065814, end_time=2019-07-16 15:21:23.319129)>]" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "history = History(db_path)\n", "history.all_runs()" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "history.id = 2" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "df, w = history.get_distribution(m=0)" ] }, { "cell_type": "code", "execution_count": 26, "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>name</th>\n", " <th>ina.p_2</th>\n", " <th>ina.p_4</th>\n", " <th>ina.p_6</th>\n", " <th>ina.p_8</th>\n", " <th>log_ina.p_1</th>\n", " <th>log_ina.p_3</th>\n", " <th>log_ina.p_5</th>\n", " <th>log_ina.p_7</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>20.000000</td>\n", " <td>20.000000</td>\n", " <td>20.000000</td>\n", " <td>20.000000</td>\n", " <td>20.000000</td>\n", " <td>20.000000</td>\n", " <td>20.000000</td>\n", " <td>20.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>0.152418</td>\n", " <td>0.124618</td>\n", " <td>0.204145</td>\n", " <td>0.212663</td>\n", " <td>1.497692</td>\n", " <td>-1.892742</td>\n", " <td>-1.361832</td>\n", " <td>-2.912250</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>0.109698</td>\n", " <td>0.089901</td>\n", " <td>0.090097</td>\n", " <td>0.088270</td>\n", " <td>1.240786</td>\n", " <td>1.883950</td>\n", " <td>2.443193</td>\n", " <td>2.446290</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>0.005179</td>\n", " <td>0.011122</td>\n", " <td>0.063843</td>\n", " <td>0.090318</td>\n", " <td>-1.421610</td>\n", " <td>-5.524446</td>\n", " <td>-6.200844</td>\n", " <td>-6.660549</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>0.076660</td>\n", " <td>0.057064</td>\n", " <td>0.113290</td>\n", " <td>0.154365</td>\n", " <td>0.610072</td>\n", " <td>-3.246264</td>\n", " <td>-2.897067</td>\n", " <td>-4.863269</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>0.133123</td>\n", " <td>0.103693</td>\n", " <td>0.191261</td>\n", " <td>0.174066</td>\n", " <td>1.876010</td>\n", " <td>-1.755545</td>\n", " <td>-0.905199</td>\n", " <td>-3.106732</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>0.191822</td>\n", " <td>0.189799</td>\n", " <td>0.283577</td>\n", " <td>0.292748</td>\n", " <td>2.358829</td>\n", " <td>-0.426004</td>\n", " <td>0.316332</td>\n", " <td>-1.976666</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>0.395849</td>\n", " <td>0.267499</td>\n", " <td>0.366606</td>\n", " <td>0.373880</td>\n", " <td>2.985568</td>\n", " <td>2.103943</td>\n", " <td>2.592736</td>\n", " <td>2.292265</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ "name ina.p_2 ina.p_4 ina.p_6 ina.p_8 log_ina.p_1 log_ina.p_3 \\\n", "count 20.000000 20.000000 20.000000 20.000000 20.000000 20.000000 \n", "mean 0.152418 0.124618 0.204145 0.212663 1.497692 -1.892742 \n", "std 0.109698 0.089901 0.090097 0.088270 1.240786 1.883950 \n", "min 0.005179 0.011122 0.063843 0.090318 -1.421610 -5.524446 \n", "25% 0.076660 0.057064 0.113290 0.154365 0.610072 -3.246264 \n", "50% 0.133123 0.103693 0.191261 0.174066 1.876010 -1.755545 \n", "75% 0.191822 0.189799 0.283577 0.292748 2.358829 -0.426004 \n", "max 0.395849 0.267499 0.366606 0.373880 2.985568 2.103943 \n", "\n", "name log_ina.p_5 log_ina.p_7 \n", "count 20.000000 20.000000 \n", "mean -1.361832 -2.912250 \n", "std 2.443193 2.446290 \n", "min -6.200844 -6.660549 \n", "25% -2.897067 -4.863269 \n", "50% -0.905199 -3.106732 \n", "75% 0.316332 -1.976666 \n", "max 2.592736 2.292265 " ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.describe()" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/scratch/cph211/miniconda3/envs/ionchannelABC/lib/python3.7/site-packages/matplotlib/tight_layout.py:211: UserWarning: Tight layout not applied. tight_layout cannot make axes height small enough to accommodate all axes decorations\n", " warnings.warn('Tight layout not applied. '\n", "/scratch/cph211/miniconda3/envs/ionchannelABC/lib/python3.7/site-packages/matplotlib/tight_layout.py:211: UserWarning: Tight layout not applied. tight_layout cannot make axes height small enough to accommodate all axes decorations\n", " warnings.warn('Tight layout not applied. '\n", "/scratch/cph211/miniconda3/envs/ionchannelABC/lib/python3.7/site-packages/matplotlib/tight_layout.py:211: UserWarning: Tight layout not applied. tight_layout cannot make axes height small enough to accommodate all axes decorations\n", " warnings.warn('Tight layout not applied. '\n", "/scratch/cph211/miniconda3/envs/ionchannelABC/lib/python3.7/site-packages/matplotlib/tight_layout.py:211: UserWarning: Tight layout not applied. tight_layout cannot make axes height small enough to accommodate all axes decorations\n", " warnings.warn('Tight layout not applied. '\n", "/scratch/cph211/miniconda3/envs/ionchannelABC/lib/python3.7/site-packages/matplotlib/tight_layout.py:211: UserWarning: Tight layout not applied. tight_layout cannot make axes height small enough to accommodate all axes decorations\n", " warnings.warn('Tight layout not applied. '\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 518.4x345.6 with 8 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from ionchannelABC import plot_parameters_kde\n", "g = plot_parameters_kde(df, w, limits, aspect=12,height=0.6)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Samples for quantitative analysis" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "# Generate parameter samples\n", "n_samples = 100\n", "df, w = history.get_distribution(m=0)\n", "th_samples = df.sample(n=n_samples, weights=w, replace=True).to_dict(orient='records')" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "plotting_obs = observations.copy()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "plotting_obs.rename({'exp_id': 'exp', 'variance': 'errs'}, axis=1, inplace=True)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "plotting_obs['errs'] = np.sqrt(plotting_obs['errs'])" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "# Generate sim results samples\n", "import pandas as pd\n", "samples = pd.DataFrame({})\n", "for i, th in enumerate(th_samples):\n", " results = summary_statistics(log_model(th))\n", " output = pd.DataFrame({'x': observations.x, 'y': list(results.values()),\n", " 'exp_id': observations.exp_id})\n", " #output = model.sample(pars=th, n_x=50)\n", " output['sample'] = i\n", " output['distribution'] = 'post'\n", " samples = samples.append(output, ignore_index=True)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1888.53x360 with 5 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from ionchannelABC import plot_sim_results\n", "import seaborn as sns\n", "sns.set_context('talk')\n", "g = plot_sim_results(samples, obs=observations)\n", "\n", "# Set axis labels\n", "#xlabels = [\"voltage, mV\", \"voltage, mV\", \"voltage, mV\", \"time, ms\", \"time, ms\",\"voltage, mV\"]\n", "#ylabels = [\"current density, pA/pF\", \"activation\", \"inactivation\", \"recovery\", \"normalised current\",\"current density, pA/pF\"]\n", "#for ax, xl in zip(g.axes.flatten(), xlabels):\n", "# ax.set_xlabel(xl)\n", "#for ax, yl in zip(g.axes.flatten(), ylabels):\n", "# ax.set_ylabel(yl)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "#g.savefig('results/icat-generic/icat_sim_results.pdf')" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [], "source": [ "def plot_sim_results_all(samples: pd.DataFrame):\n", " with sns.color_palette(\"gray\"):\n", " grid = sns.relplot(x='x', y='y',\n", " col='exp',\n", " units='sample',\n", " kind='line',\n", " data=samples,\n", " estimator=None, lw=0.5,\n", " alpha=0.5,\n", " #estimator=np.median,\n", " facet_kws={'sharex': 'col',\n", " 'sharey': 'col'})\n", " return grid" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1440x360 with 4 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "grid2 = plot_sim_results_all(samples)" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "#grid2.savefig('results/icat-generic/icat_sim_results_all.pdf')" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "import numpy as np" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-0.9792263129382246\n", "0.060452038127623814\n" ] } ], "source": [ "# Mean current density\n", "print(np.mean(samples[samples.exp=='0'].groupby('sample').min()['y']))\n", "# Std current density\n", "print(np.std(samples[samples.exp=='0'].groupby('sample').min()['y']))" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [], "source": [ "import scipy.stats as st\n", "peak_current = samples[samples['exp']=='0'].groupby('sample').min()['y'].tolist()\n", "rv = st.rv_discrete(values=(peak_current, [1/len(peak_current),]len(peak_current)))" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "median: -0.9929750589235674\n", "95% CI: (-1.0714884415582595, -0.8489199437971181)\n" ] } ], "source": [ "print(\"median: {}\".format(rv.median()))\n", "print(\"95% CI: {}\".format(rv.interval(0.95)))" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "mean: -20.1\n", "STD: 0.7\n" ] } ], "source": [ "# Voltage of peak current density\n", "idxs = samples[samples.exp=='0'].groupby('sample').idxmin()['y']\n", "print(\"mean: {}\".format(np.mean(samples.iloc[idxs]['x'])))\n", "print(\"STD: {}\".format(np.std(samples.iloc[idxs]['x'])))" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "median: -20.0\n", "95% CI: (-20.0, -20.0)\n" ] } ], "source": [ "voltage_peak = samples.iloc[idxs]['x'].tolist()\n", "rv = st.rv_discrete(values=(voltage_peak, [1/len(voltage_peak),]len(voltage_peak)))\n", "print(\"median: {}\".format(rv.median()))\n", "print(\"95% CI: {}\".format(rv.interval(0.95)))" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [], "source": [ "# Half activation potential\n", "# Fit of activation to Boltzmann equation\n", "from scipy.optimize import curve_fit\n", "grouped = samples[samples['exp']=='1'].groupby('sample')\n", "def fit_boltzmann(group):\n", " def boltzmann(V, Vhalf, K):\n", " return 1/(1+np.exp((Vhalf-V)/K))\n", " guess = (-30, 10)\n", " popt, _ = curve_fit(boltzmann, group.x, group.y)\n", " return popt\n", "output = grouped.apply(fit_boltzmann).apply(pd.Series)" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 -33.399071\n", "1 5.739255\n", "dtype: float64\n", "0 0.823473\n", "1 0.366996\n", "dtype: float64\n" ] } ], "source": [ "print(np.mean(output))\n", "print(np.std(output))" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "median: -33.407394098238164\n", "95% CI: (-34.93130871417603, -31.973122716861205)\n" ] } ], "source": [ "Vhalf = output[0].tolist()\n", "rv = st.rv_discrete(values=(Vhalf, [1/len(Vhalf),]len(Vhalf)))\n", "print(\"median: {}\".format(rv.median()))\n", "print(\"95% CI: {}\".format(rv.interval(0.95)))" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "median: 5.728938366573993\n", "95% CI: (5.117385157850234, 6.485585591389819)\n" ] } ], "source": [ "slope = output[1].tolist()\n", "rv = st.rv_discrete(values=(slope, [1/len(slope),]len(slope)))\n", "print(\"median: {}\".format(rv.median()))\n", "print(\"95% CI: {}\".format(rv.interval(0.95)))" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [], "source": [ "# Half activation potential\n", "grouped = samples[samples['exp']=='2'].groupby('sample')\n", "def fit_boltzmann(group):\n", " def boltzmann(V, Vhalf, K):\n", " return 1-1/(1+np.exp((Vhalf-V)/K))\n", " guess = (-100, 10)\n", " popt, _ = curve_fit(boltzmann, group.x, group.y,\n", " bounds=([-100, 1], [0, 30]))\n", " return popt\n", "output = grouped.apply(fit_boltzmann).apply(pd.Series)" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 -49.011222\n", "1 4.399126\n", "dtype: float64\n", "0 0.613833\n", "1 0.306758\n", "dtype: float64\n" ] } ], "source": [ "print(np.mean(output))\n", "print(np.std(output))" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "median: -49.01404281457659\n", "95% CI: (-50.06478757419054, -47.57952101705519)\n" ] } ], "source": [ "Vhalf = output[0].tolist()\n", "rv = st.rv_discrete(values=(Vhalf, [1/len(Vhalf),]len(Vhalf)))\n", "print(\"median: {}\".format(rv.median()))\n", "print(\"95% CI: {}\".format(rv.interval(0.95)))" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "median: 4.420440009120772\n", "95% CI: (3.7821747606540193, 4.959106709731536)\n" ] } ], "source": [ "slope = output[1].tolist()\n", "rv = st.rv_discrete(values=(slope, [1/len(slope),]len(slope)))\n", "print(\"median: {}\".format(rv.median()))\n", "print(\"95% CI: {}\".format(rv.interval(0.95)))" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [], "source": [ "# Recovery time constant\n", "grouped = samples[samples.exp=='3'].groupby('sample')\n", "def fit_single_exp(group):\n", " def single_exp(t, I_max, tau):\n", " return I_max(1-np.exp(-t/tau))\n", " guess = (1, 50)\n", " popt, _ = curve_fit(single_exp, group.x, group.y, guess)\n", " return popt[1]\n", "output = grouped.apply(fit_single_exp)" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "114.50830523453935\n", "5.781251582667316\n" ] } ], "source": [ "print(np.mean(output))\n", "print(np.std(output))" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "median: 113.75533911706513\n", "95% CI: (104.11137902797657, 125.98102619971708)\n" ] } ], "source": [ "tau = output.tolist()\n", "rv = st.rv_discrete(values=(tau, [1/len(tau),]*len(tau)))\n", "print(\"median: {}\".format(rv.median()))\n", "print(\"95% CI: {}\".format(rv.interval(0.95)))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 2 }	gpl-3.0
mdastro/Challenge	Coding/SanityCheckAll.ipynb	1	170767	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/mldantas/miniconda2/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n", " warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n" ] } ], "source": [ "import numpy as np\n", "import os\n", "import pysynphot as s\n", "import pandas as pd\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Paths ------------------------------------------------------------------------------------------------------------\n", "filters_path = '/home/mldantas/Dropbox/DoutoradoIAG/Challenge/Filters' # where are your filters\n", "jpas_filters_folder = 'JPAS_filters' # folder where the filters are\n", "jplus_filters_folder = 'JPLUS_SDSS_filters' # folder where the filters are\n", "jpas_filters_list = 'jpas_filters_list.txt' # file w/ a list of filters' files\n", "jplus_filters_list = 'jplus_filters_list.txt' # file w/ a list of filters' files\n", "test_specs = '/home/mldantas/Dropbox/DoutoradoIAG/Challenge/Sanity_Check/Specs' # where your spectra are\n", "results_path = '/home/mldantas/Dropbox/DoutoradoIAG/Challenge/Sanity_Check/Results' # where do you wish to\n", " # save your results\n", "specs_list_path = '/home/mldantas/Dropbox/DoutoradoIAG/Challenge/Sanity_Check' # folder w/ a list of spec files\n", "specs_list_file = 'specslist2.txt' # file w/ a list of spec files " ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Reading your files -----------------------------------------------------------------------------------------------\n", "jpas_filters_list = np.loadtxt(os.path.join(filters_path, jpas_filters_list), dtype=str)\n", "jplus_filters_list = np.loadtxt(os.path.join(filters_path, jplus_filters_list), dtype=str)\n", "specs_list = np.loadtxt(os.path.join(specs_list_path, specs_list_file), dtype=str)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Setting the T80 M1 effective area in cm^2 ------------------------------------------------------------------------\n", "s.setref(area=4400)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0443.51873.152.txt\n" ] }, { "data": { "image/png": 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LL7m3yZU0tLTYCckfKOwNyFNP6R8AeOQR9zZhCnomrhjGhhOSP1DYG5BPPrGO\nN25M/Ly42NqbNAxhpTgT0jigsDcQd98NLFrk/XmvXjpbo/GLh+Efz6SP4mJdDhuW+TgIIdmFwt5A\njBuX/HNjXYcp7JlgxtOpU8OOgxCSGgp7juIU9DAmL8NwxZhc7D//eeZ9EUKyA4W9AfCzWbURUCPG\nuSbsmW64TQjJHhT2EFAKePhh/+2dE6W7dye26d5dl8Zi79w5vbHZCcOdY4TdvlE2ISS3oLCHxC23\n+G/rXIw0YEBim9//Xpdm0vKkk9IbV9gcPqxLtzETQnIDCnuI+HV1OIV95crENma3pJ/+FFi2LLNx\nGcJ0xRBCchcKewgEFUynsLtx2mm6LCwMlsExGZkIu1koRWEnJPehsIdIuhZ7fZGJsN96K/DWW5Yr\nhhCSu/hO2ysizQC8CaAo9jNTKTU2WwOLKvv3p97ibsWK7Nw7E2Fv2RL45jdpsROSD/i22JVShwAM\nVkr1BdALwPkiMjBrI8sjjGC6CeeHHwJDhljnfja6OOOMcMblhD52QhoHgVwxSqlYElk0i127K/QR\n5TFuwvnaa/H7lTZtwK1NwhD2Tz/NvA9CSHYJJOwiUiAiVQA2A6hUSi3PzrByl/vuS4wpTyaYJu1u\nLli6YQh7TU3mfRBCsksg+1EpVQegr4i0AjBbRL6tlJrnbDd+/Phjx2VlZSgrK8twmLnDvHnAZ5+5\nf+YmnMZCnz0buOSS5BOnJSVAixaZjzGb3HADxZ2QMKisrERlZWVW+haVphknIuUADiilHnDUq3T7\nzAeGDwdeeSVexI8c0ZZ5ba1loQM6/nzyZOAPf9DnSgHr1wNdurj3XVMDHH+8/skGJi1BhP88hOQt\nIgKlVChb2gSJimkL4LBSareIHAdgKIAJYQwin0i2qbRTMM86K/58//7kFnurVjr6hBBCMiGIj/2r\nAP4v5mOfD2CWUmpuimsihzMZ19q1iQm7vJg2LV7YL7ss/vMw9jVNBbe4IyT6+LbYlVJLAfTL4ljy\nkq5d48+feAIYOhQ49VT39nZhdwp5fexrSmEnJPpw5WmIKAXceCPw0EPun2/bXo3bfjcS6DQYKBmJ\nXV9Ux31Oi50QEgYNGFWdn7z9duo27gJdjUdeG4rtg9YA1wGoBeb9Yz6AOQBKAdBiJ4SEAy32AIwd\nC2zbpo+XLElcRWp87AUFLrnPS8q1qBfFzouAusvWACXlx5rUh8VOCIk+tNgDMHOmddy7N3DppfGf\n23c7euneUc6uAAAREElEQVQlx8UtayxRNxQBaLkR8JFmICxosRMSfWgjBuDgwfjzffviz2fN0uWm\nTS67Iu3rANQ66moB7Gsf4ghT06xZvd6OENIAUNgDsHZt/Llz38+rr9bliy+6xKvvrACmd7XEvRZo\nNberrkd4m2kkY9w4oKIi+/chhDQsdMVkwObN3p8lLkQqBVbNQe/55ajethF7atrj8uEV+PP7euI0\nrM00kkFRJ6RxQGHPAKcrxs6BA261pfjRJdMwcybw1lKg9YnZGhkhpDGTdq4Yzw4jnCsmjInHCy/U\nOWVOPBG47Ta9eQXA/C2ENHbCzBVDYfdJsuRdQbngAuD11/UxE3MRQoBwhZ2Tpz5Zv97rk2qgxFpN\nClQntLjrrvjz+liIRAhpvFDYffLKK2611UD3ocCNzwPXVeqy+1A4xf2mm+Kvsgv7xIkhD5QQ0uih\nsPvkggtcKkvKgSviV5PiivjVpEDyZF+33w7Mnx/mSAkhjR0Ku08SUgQAyVeT2igoAIqLrXO7sBcV\nAV//elijJIQQCrtvmjd3qfS5mrSgID6ihj52Qkg2obD7xDXU0WU1KaZbq0kNBQXx7hgKOyEkm3CB\nkk/ct7TTq0kxuVy7X/a1j4l6aVwrEVrshJD6g8Luk1df9fqkFNg5LWmGRgo7IaQ+oSvGJw88kP61\nIkBT2yvUdSKWEEJCgsIekBYt0rvuvfeAZ57Rx3v2hDceQghxQldMQNJZ+t+0KXD66VbSsMLCcMdE\nCCF2aLEHJB1hNzHsxs/OLfAIIdmEEhMQu3/8yy+BOXPc2ymVuPCIib4IIfUBXTEBsQt78+bJMz46\nUwVw0pQQUh/QYvfgwAH32PV27eLPTztNl2PHpu6zUydd2jfFJoSQsKGwe9CiBVBenlj/7rvu7fv0\n0eXjj3v3edJJunRf7EQIIeFAYU/CqlXx508+CZSU6GO7v7xZM6BnT308ahR96YSQhoU+dp/07Qv0\n6+f+2cGD1nEY2+cRQkgm+LbYRaSjiLwhIstEZKmIjM7mwHKBw4et4w0bdDx6GNa4se4JISQbBLHY\njwC4VSm1SERaAvhIRGYrpVZmaWwNjtk1afVqYPv21AuLPvnEXx4Y+tgJIdnEt8WulNqslFoUO94H\nYAWADtkaWC6xbZsumzZN7mo5/XR//dXUZD4mQgjxIq3JUxHpAqAPgPfDHEwuMmQIcN55+rigIHNX\nTMuWwMiRmY+LEEK8CDx5GnPD/A3AzTHLPYHx48cfOy4rK0NZWVmaw/PHgw8CVVXAc8+F3/fcudbx\n4cM6AiaTtLs7dzJtLyEEqKysRGVlZVb6FhXABBWRpgD+CeBfSqlHPNqoIH2my7JlwFNPAQ89BHTv\nrv3bbredMEGHIHZwcRp98QVw4onu/bu5XFau1PcihJCwEREopUKJqwvqinkGwHIvUQ+Ldu2ABQuS\nt5k6FXj4YX2c7D0yfjzw0kuJ9R9+CLRuHWxcXi8BQgjJJYKEOw4EMALA+SJSJSILRWRYNga1ZUti\nAq1k+PmCsHkzsGmTdW4mRINw8snBryGEkPrGt49dKfUOgHr3DtfVpU5zmyq5llJ6cdGBA9r94uca\nQgjJV3I6pcDy5f4mGlNZ7HV1wNatwO7dwOzZuu7IEV3aFyEZ3PzrZ5yRehyEEJIL5LSwb9wYfp8L\nF+rSpAGorU3e/uyzdcnNMQgh+UKjkKs77rBWexrr3vjbN25MbvF37KhLCjshJF/IabnyWuW5Z4/O\n3WLwEmZn7nR721tu0WW3bsD06e7X9+1r+eKZ3IsQki/kdHZHL8G+5hrgH/9Iff2WLYl1y5Yl1m3d\n6n790aOWsK9dm/p+hBCSC+S0xe7Fjh3pX/vCC8C6dfF1H37o3rauzhJ2umIIIflCTstVJu6Pd97x\n/mz//vjzZ5/VoZCffx5f//HHVtQMhZ0Qki/ktFz5WXg0caJ7/Te/6X3N0aPAgAHxdaNHA6ecktj2\n9dd1SWEnhOQLOS1Xd9/tXm+35N32JU2FiM7aaMf44/fuNTXVQMlIoNNgoGQkjtZVB78RIYQ0ADk9\neeqV+OzNN+PPnZa9WXzkxaRJwJQp7n0sXQoA1UD3ocAVa4AiALXAvn/MR/W6OSjtUupv8IQQ0kDk\ntMUeFOMjnzw5ebuPPkqsM5OkdXUASsotUQeAIqDusjUofzCNrweEEFLP5J2w//nPiXVGlHft0uWe\nPcn7cEtTYPpQCkDLGkvUDUXAxj1ZWApLCCEhk3fC/pOfJNaZxUpGnH/5y+R9fPBBYp1xxdTVAdjX\nAXCmGqgF2rdqH2CkhBDSMOSVsDvDEZ1ksr+HeSkcPQpgZwUwvasl7rVAk5ldUXFrRfo3IISQeiKn\nJ08NBw8CzZu7hyPa2bkzcfGRXzZv1qXOKVMKrJqDZn8qx+Gijajb0x5tiyo4cUoIyQsCbY3nq8MQ\ntsZzLkx6/HHgxhv9LVh67z3gG99I/97t2mmR791bh0Bu366jbDp2jM9PQwghYRLm1nh5YbE/+mjq\nCVFDpsm6jOW+aJEWcxM62TQvflOEEJInPvYVK4A77/TX1t/OSPGLjwD3xUf26JnCQn/3J4SQhiZy\ndui116Zqkbj4CNPnA6vmAIj3oTONACEkH4mcdK1alaKBy+IjXLFG1zuwW+zMx04IyRciJ+wp8Vh8\nhJaJi4/87LdKCCG5RuMTdo/FR9iXuPjI7oqhxU4IyRcan7C7LD7C9K663gFdMYSQfCRCk6fV2k/e\nskZb5Tsr4JwM1ejFR5hcrt0v+9p7tqWwE0LykYgIe/JIl7Zt9UIji1Jg5zQsfB3o18+715DXbhFC\nSL0QDVdMikgXrxj0vn2Td2sX9hUrMh4lIYTUC9EQ9hSRLkGjWx56SJe02Akh+Ug0hD1FpMtNNwHn\nnOO/O7N6ddmyUEZHCCH1SiBhF5GnRWSLiCzJ1oDSIkWkyx13AE8+GX/JHXd4d6czPBJCSH4S1GJ/\nFsBF2RhIZphIlxHAM4N16UgR4HSrfPe7unTbMJupBAgh+UygqBil1Nsi0jlbg8kMHemCnf5aG3eL\nW4735s112awZcOhQOKMjhJD6IpK2adu2urzkEu82W7bo0m2CdNQoXRY5J2QJISQPiKSwT5oETJ4M\nlJV5t1m8WJdOYe/SRVvqALB3bzZGRwgh2SUrC5TGjx9/7LisrAxlyRQ2C5xwAnDVVcD991t1XqGL\nQ4f66zNIVA0hhKSisrISlZWVWek7HWGX2I8ndmFvCMzkZ5s23m2+8hVdtnfk/vJKHWDaE0JIGDiN\n3gkTJoTWd9BwxxcAvAugm4h8JiIpt7UIShiLgsyCpGuuAdav18d9+gDjxlltLr002P2ZK4YQki8E\njYr592wNxBBGDLmZGC0oADp10sdNmgAVFcCXX+pUAqeemvl9CCEkF8m5ydOgwm4iYOzs2uXd/v77\ngREj4uvsljzTCBBC8p2cy+7obzNqi23bEt0kQcMUjevmo4/0xCshhOQzOSfsYbhi0hX2ZCl8CSEk\nX8h7Vwyg/eZ2ggo7UwgQQqJEzklaUFcMoFMAbN1qnZsFRn5hxAshJErknLD7sdiHD0+sO+kk67hp\nSA4mCj4hJB/JS2Hv0MG9fuB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"text/plain": [ "<matplotlib.figure.Figure at 0xa9c0eb8c>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "1019.52707.261.txt\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0xa9be28cc>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "1180.52995.637.txt\n", "Warning, 9 of 6001 bins contained negative fluxes; they have been set to zero.\n", "Warning, 9 of 6001 bins contained negative fluxes; they have been set to zero.\n", "Warning, 9 of 6001 bins contained negative fluxes; they have been set to zero.\n", "Warning, 9 of 6001 bins contained negative fluxes; they have been set to zero.\n", "Warning, 9 of 6001 bins contained negative fluxes; they have been set to zero.\n", "Warning, 9 of 6001 bins contained negative fluxes; they have been set to zero.\n", "Warning, 9 of 6001 bins contained negative fluxes; they have been set to zero.\n", "Warning, 9 of 6001 bins contained negative fluxes; they have been set to zero.\n", "Warning, 9 of 6001 bins contained negative fluxes; they have been set to zero.\n", "Warning, 9 of 6001 bins contained negative fluxes; they have been set to zero.\n", "Warning, 9 of 6001 bins contained negative fluxes; they have been set to zero.\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0xa9c67c4c>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "1665.52976.514.txt\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0xa9c2f7cc>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "2231.53816.545.txt\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0xa9b7770c>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Simulating photometry for your spectra using the selected survey filters -----------------------------------------\n", "all_jplus = []\n", "for each_spectrum in specs_list:\n", " print each_spectrum\n", " filter_name = []\n", " photometry = []\n", " photometry_temp = []\n", " photometry_flam = []\n", " lambda_eff = []\n", " for jplus_filters in jplus_filters_list:\n", " # saving an array with the filters names -------------------------------------------------------------------\n", " filter_name_i = jplus_filters.split('.')[0]\n", " filter_name.append(filter_name_i)\n", " \n", " # convolution ----------------------------------------------------------------------------------------------\n", " jplus_filter_bandpass = s.FileBandpass(os.path.join(jplus_filters_path, jplus_filters))\n", " sdss_spectrum = s.FileSpectrum(os.path.join(test_specs, each_spectrum)) # the entire spectrum\n", " index = np.where(sdss_spectrum.flux > 0) # selecting only the positive part of the spectrum\n", " sdss_spectrum2 = s.ArraySpectrum(wave=sdss_spectrum.wave[index], flux=sdss_spectrum.flux[index], \n", " fluxunits=sdss_spectrum.fluxunits, \n", " waveunits=sdss_spectrum.waveunits)\n", " binset = jplus_filter_bandpass.wave # this is very very important! \n", " # don't change this unless \n", " # you are absolutely sure of what you are doing!\n", " ## convolved photometry ------------------------------------------------------------------------------------\n", " photometry_i = s.Observation(sdss_spectrum2, jplus_filter_bandpass, binset=binset, force='extrap')\n", " \n", " ## effective wavelength ------------------------------------------------------------------------------------\n", " lambda_eff_i = photometry_i.efflam()\n", " lambda_eff.append(lambda_eff_i)\n", " \n", " ## checking if the simulated photometry is \"virtual\" and letting those away --------------------------------\n", " if lambda_eff_i >= sdss_spectrum2.wave.max():\n", " new_photometry_i = -999\n", " photometry.append(new_photometry_i)\n", " photometry_flam.append(new_photometry_i)\n", " \n", " elif lambda_eff_i <= sdss_spectrum2.wave.min():\n", " new_photometry_i = -999\n", " photometry.append(new_photometry_i)\n", " photometry_flam.append(new_photometry_i)\n", " \n", " else:\n", " photometry.append(photometry_i.effstim('abmag'))\n", " photometry_flam_i = photometry_i.effstim('flam')\n", " photometry_flam.append(photometry_flam_i)\n", " \n", "\n", " # putting the iterated items into arrays ----------------------------------------------------------------------- \n", " filter_name = np.array(filter_name) # name of each filter\n", " photometry = np.array(photometry) # in magnitudes\n", " lambda_eff = np.array(lambda_eff) # effective wavelengths of the filters\n", " photometry_flam = np.array(photometry_flam) # in flux of lambda\n", " photometry_fnu = 10*(-0.4(photometry + 48.60)) # in flux of nu\n", " \n", " # plots --------------------------------------------------------------------------------------------------------\n", " plot01 = plt.plot(sdss_spectrum2.wave, sdss_spectrum2.flux, '-')\n", " plot02 = plt.plot(lambda_eff[[photometry_flam!=-999]], photometry_flam[[photometry_flam!=-999]], 'o')\n", " plt.savefig(os.path.join(results_path, os.path.split(each_spectrum)[-1][0:14]+'_JPLUS.png'), dpi = 100)\n", " plt.show()\n", " \n", " # saving the newley calculated photometry ----------------------------------------------------------------------\n", " galaxy_simulation_abmag = np.vstack((filter_name, photometry))\n", " galaxy_simulation_abmag = pd.DataFrame(galaxy_simulation_abmag)\n", " galaxy_simulation_abmag.to_csv(os.path.join(results_path, os.path.split(each_spectrum)[-1][0:14]\n", " +'_JPLUS_abmag.csv'), sep=',', header=None, index=False)\n", " galaxy_simulation_fnu = np.vstack((filter_name, photometry_fnu))\n", " galaxy_simulation_fnu = pd.DataFrame(galaxy_simulation_fnu)\n", " galaxy_simulation_fnu.to_csv(os.path.join(results_path, os.path.split(each_spectrum)[-1][0:14]\n", " +'_JPLUS_fnu.csv'), sep=',', header=None, index=False)\n", " " ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0443.51873.152.txt\n" ] }, { "data": { "image/png": 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f//J6p8jsWxq1j+ndZXfHhEz2eN/0I5UrVP0iUTGCEHzy9C7gaiDAZmVCMnjq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\nIAhClvH/AYV+VTNSN/3eAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0xa9c826cc>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "1019.52707.261.txt\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0xa9b7710c>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "1180.52995.637.txt\n", "Warning, 9 of 6001 bins contained negative fluxes; they have been set to zero.\n", "Warning, 9 of 6001 bins contained negative fluxes; they have been set to zero.\n", "Warning, 9 of 6001 bins contained negative fluxes; they have been set to zero.\n", "Warning, 9 of 6001 bins contained negative fluxes; they have been set to zero.\n", "Warning, 9 of 6001 bins contained negative fluxes; they have been set to zero.\n", "Warning, 9 of 6001 bins contained negative fluxes; they have been set to zero.\n", "Warning, 9 of 6001 bins contained negative fluxes; they have been set to zero.\n", "Warning, 9 of 6001 bins contained negative fluxes; they have been set to zero.\n", "Warning, 9 of 6001 bins contained negative fluxes; they have been set to zero.\n", "Warning, 9 of 6001 bins contained negative fluxes; they have been set to zero.\n", "Warning, 9 of 6001 bins contained negative fluxes; they have been set to zero.\n", "Warning, 9 of 6001 bins contained negative fluxes; they have been set to zero.\n", "Warning, 9 of 6001 bins contained negative fluxes; they have been set to zero.\n", "Warning, 9 of 6001 bins contained negative fluxes; they have been set to zero.\n", "Warning, 9 of 6001 bins contained negative fluxes; they have been set to zero.\n", "Warning, 9 of 6001 bins contained negative fluxes; they have been set to zero.\n", "Warning, 9 of 6001 bins contained negative fluxes; they have been set to zero.\n", "Warning, 9 of 6001 bins contained negative fluxes; they have been set to zero.\n", "Warning, 9 of 6001 bins contained negative fluxes; they have been set to zero.\n", "Warning, 9 of 6001 bins contained negative fluxes; they have been set to zero.\n", "Warning, 9 of 6001 bins contained negative fluxes; they have been set to zero.\n", "Warning, 9 of 6001 bins contained negative fluxes; they have been set to zero.\n", "Warning, 9 of 6001 bins contained negative fluxes; they have been set to zero.\n", "Warning, 9 of 6001 bins contained negative fluxes; they have been set to zero.\n", "Warning, 9 of 6001 bins contained negative fluxes; they have been set to zero.\n", "Warning, 9 of 6001 bins contained negative fluxes; they have been set to zero.\n", "Warning, 9 of 6001 bins contained negative fluxes; they have been set to zero.\n", "Warning, 9 of 6001 bins contained negative fluxes; they have been set to zero.\n", "Warning, 9 of 6001 bins contained negative fluxes; they have been set to zero.\n", "Warning, 9 of 6001 bins contained negative fluxes; they have been set to zero.\n", "Warning, 9 of 6001 bins contained negative fluxes; they have been set to zero.\n", "Warning, 9 of 6001 bins contained negative fluxes; they have been set to zero.\n", "Warning, 9 of 6001 bins contained negative fluxes; they have been set to zero.\n", "Warning, 9 of 6001 bins contained negative fluxes; they have been set to zero.\n", "Warning, 9 of 6001 bins contained negative fluxes; they have been set to zero.\n", "Warning, 9 of 6001 bins contained negative fluxes; they have been set to zero.\n", "Warning, 9 of 6001 bins contained negative fluxes; they have been set to zero.\n", "Warning, 9 of 6001 bins contained negative fluxes; they have been set to zero.\n", "Warning, 9 of 6001 bins contained negative fluxes; they have been set to zero.\n", "Warning, 9 of 6001 bins contained negative fluxes; they have been set to zero.\n", "Warning, 9 of 6001 bins contained negative fluxes; they have been set to zero.\n", "Warning, 9 of 6001 bins contained negative fluxes; they have been set to zero.\n", "Warning, 9 of 6001 bins contained negative fluxes; they have been set to zero.\n", "Warning, 9 of 6001 bins contained negative fluxes; they have been set to zero.\n", "Warning, 9 of 6001 bins contained negative fluxes; they have been set to zero.\n", "Warning, 9 of 6001 bins contained negative fluxes; they have been set to zero.\n", "Warning, 9 of 6001 bins contained negative fluxes; they have been set to zero.\n", "Warning, 9 of 6001 bins contained negative fluxes; they have been set to zero.\n", "Warning, 9 of 6001 bins contained negative fluxes; they have been set to zero.\n", "Warning, 9 of 6001 bins contained negative fluxes; they have been set to zero.\n", "Warning, 9 of 6001 bins contained negative fluxes; they have been set to zero.\n", "Warning, 9 of 6001 bins contained negative fluxes; they have been set to zero.\n", "Warning, 9 of 6001 bins contained negative fluxes; they have been set to zero.\n", "Warning, 9 of 6001 bins contained negative fluxes; they have been set to zero.\n", "Warning, 9 of 6001 bins contained negative fluxes; they have been set to zero.\n", "Warning, 9 of 6001 bins contained negative fluxes; they have been set to zero.\n", "Warning, 9 of 6001 bins contained negative fluxes; they have been set to zero.\n", "Warning, 9 of 6001 bins contained negative fluxes; they have been set to zero.\n", "Warning, 9 of 6001 bins contained negative fluxes; they have been set to zero.\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0xa9c87eac>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "1665.52976.514.txt\n" ] }, { "data": { "image/png": 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ZOJJdIwhCqiIi72fbtsBaNBbl5XBfF9OjWOEa8eQFQUg1ROT9BHvy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\nIvKCIAgZjIi8IAhCBvP/AdBazPU5oZasAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0xa9b3138c>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "2231.53816.545.txt\n" ] }, { "data": { "image/png": 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RCnh0crBNML8dD5/EKI1cSTCog1vZaVg1ZTPKlLbxGAV2kQzRoQNs2ACHHQZd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\n/H8xSny0kbF3RQAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0xa9c0ba4c>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Simulating photometry for your spectra using the selected survey filters -----------------------------------------\n", "all_jpas = []\n", "for each_spectrum in specs_list:\n", " print each_spectrum\n", " filter_name = []\n", " photometry = []\n", " photometry_temp = []\n", " photometry_flam = []\n", " lambda_eff = []\n", " for jpas_filters in jpas_filters_list:\n", " # saving an array with the filters names -------------------------------------------------------------------\n", " filter_name_i = jpas_filters.split('.')[0]\n", " filter_name.append(filter_name_i)\n", " \n", " # convolution ----------------------------------------------------------------------------------------------\n", " jpas_filter_bandpass = s.FileBandpass(os.path.join(jpas_filters_path, jpas_filters))\n", " sdss_spectrum = s.FileSpectrum(os.path.join(test_specs, each_spectrum)) # the entire spectrum\n", " index = np.where(sdss_spectrum.flux > 0) # selecting only the positive part of the spectrum\n", " sdss_spectrum2 = s.ArraySpectrum(wave=sdss_spectrum.wave[index], flux=sdss_spectrum.flux[index], \n", " fluxunits=sdss_spectrum.fluxunits, \n", " waveunits=sdss_spectrum.waveunits)\n", " binset = jpas_filter_bandpass.wave # this is very very important! \n", " # don't change this unless \n", " # you are absolutely sure of what you are doing!\n", " ## convolved photometry ------------------------------------------------------------------------------------\n", " photometry_i = s.Observation(sdss_spectrum2, jpas_filter_bandpass, binset=binset, force='extrap')\n", " \n", " ## effective wavelength ------------------------------------------------------------------------------------\n", " lambda_eff_i = photometry_i.efflam()\n", " lambda_eff.append(lambda_eff_i)\n", " \n", " ## checking if the simulated photometry is \"virtual\" and letting those away --------------------------------\n", " if lambda_eff_i >= sdss_spectrum2.wave.max():\n", " new_photometry_i = -999\n", " photometry.append(new_photometry_i)\n", " photometry_flam.append(new_photometry_i)\n", " \n", " elif lambda_eff_i <= sdss_spectrum2.wave.min():\n", " new_photometry_i = -999\n", " photometry.append(new_photometry_i)\n", " photometry_flam.append(new_photometry_i)\n", " \n", " else:\n", " photometry.append(photometry_i.effstim('abmag'))\n", " photometry_flam_i = photometry_i.effstim('flam')\n", " photometry_flam.append(photometry_flam_i)\n", " \n", "\n", " # putting the iterated items into arrays ----------------------------------------------------------------------- \n", " filter_name = np.array(filter_name) # name of each filter\n", " photometry = np.array(photometry) # in magnitudes\n", " lambda_eff = np.array(lambda_eff) # effective wavelengths of the filters\n", " photometry_flam = np.array(photometry_flam) # in flux of lambda\n", " photometry_fnu = 10*(-0.4(photometry + 48.60)) # in flux of nu\n", " \n", " # plots --------------------------------------------------------------------------------------------------------\n", " plot01 = plt.plot(sdss_spectrum2.wave, sdss_spectrum2.flux, '-')\n", " plot02 = plt.plot(lambda_eff[[photometry_flam!=-999]], photometry_flam[[photometry_flam!=-999]], 'o')\n", " plt.savefig(os.path.join(results_path, os.path.split(each_spectrum)[-1][0:14]+'_JPAS.png'), dpi = 100)\n", " plt.show()\n", " \n", " # saving the newley calculated photometry ----------------------------------------------------------------------\n", " galaxy_simulation_abmag = np.vstack((filter_name, photometry))\n", " galaxy_simulation_abmag = pd.DataFrame(galaxy_simulation_abmag)\n", " galaxy_simulation_abmag.to_csv(os.path.join(results_path, os.path.split(each_spectrum)[-1][0:14]\n", " +'_JPAS_abmag.csv'), sep=',', header=None, index=False)\n", " galaxy_simulation_fnu = np.vstack((filter_name, photometry_fnu))\n", " galaxy_simulation_fnu = pd.DataFrame(galaxy_simulation_fnu)\n", " galaxy_simulation_fnu.to_csv(os.path.join(results_path, os.path.split(each_spectrum)[-1][0:14]\n", " +'_JPAS_fnu.csv'), sep=',', header=None, index=False)\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": 2 }	mit
davidvilla/python-course	iteradores.ipynb	1	9956	{ "metadata": { "name": "", "signature": "sha256:aecedc9996af45fc57408c5a0e8e47ebf9a40244087ac323522ec0b9556afa64" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Creando un iterador" ] }, { "cell_type": "code", "collapsed": false, "input": [ "class ThingIterator(object):\n", " def __init__(self, thing):\n", " self.thing = thing\n", " self.index = 0\n", " \n", " def __iter__(self):\n", " return self\n", " \n", " def next(self):\n", " if self.index >= len(self.thing.data):\n", " raise StopIteration\n", " \n", " retval = self.thing.data[self.index]\n", " self.index += 1\n", " return retval 2\n", " \n", " # python3 way\n", " __next__ = next \n", "\n", "class Thing(object):\n", " def __init__(self, data):\n", " self.data = data\n", " \n", " def __iter__(self):\n", " return ThingIterator(self)\n", " \n", " def iter_squares(self):\n", " return ThingIterator(self)\n", " \n", "def iter_squares(x):\n", " return x.iter_squares()\n", " \n", "t = Thing([1, 2, 3])\n", "for x in t.iter_squares():\n", " print(x)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1\n", "4\n", "9\n" ] } ], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Generadores\n", "\n", "Los generadores son funciones que se comportan como iteradores, evitando al programador escribir el c\u00f3digo de soporte que hemos visto en la secci\u00f3n anterior. Esto es gracias a la palabra reservada `yield` que permite al generador emitir un valor sin terminar." ] }, { "cell_type": "code", "collapsed": false, "input": [ "def more():\n", " yield 20\n", " yield 300\n", " yield 4000\n", " \n", "for x in more():\n", " print(x)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "20\n", "300\n", "4000\n" ] } ], "prompt_number": 24 }, { "cell_type": "code", "collapsed": false, "input": [ "def get_squares(seq):\n", " for x in seq:\n", " yield x 2\n", " \n", "for x in get_squares([1, 2, 3]):\n", " print(x)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1\n", "4\n", "9\n" ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "for x in get_squares(xrange(1, 4)):\n", " print(x)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1\n", "4\n", "9\n" ] } ], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "gen = get_squares([1, 2, 3])\n", "print(gen)\n", "print(gen.next())\n", "print(gen.next())" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "<generator object get_squares at 0x7f4db07f54b0>\n", "1\n", "4\n" ] } ], "prompt_number": 14 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### _generator expressions_\n", "\n", "Son equivalentes la comprensi\u00f3n de listas para generadores." ] }, { "cell_type": "code", "collapsed": false, "input": [ "gen = (x 2 for x in [1, 2, 3])\n", "print(gen)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "<generator object <genexpr> at 0x7f4db07f54b0>\n" ] } ], "prompt_number": 16 }, { "cell_type": "code", "collapsed": false, "input": [ "sum(x2 for x in xrange(100) if x % 2)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 19, "text": [ "166650" ] } ], "prompt_number": 19 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## `itertools`\n", "\n", "Es un m\u00f3dulo con unas cuantas utilidades interesantes implementadas como iteradores. Aqu\u00ed vemos algunas:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import itertools as it" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "x = it.count(5, 3)\n", "print(x.next())\n", "print(x.next())\n", "print(x.next())\n", "print(x.next())" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "5\n", "8\n", "11\n", "14\n" ] } ], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "x = it.cycle([2, 4, 8])\n", "print(x.next())\n", "print(x.next())\n", "print(x.next())\n", "print(x.next())" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "2\n", "4\n", "8\n", "2\n" ] } ], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "for x in it.repeat(\"a\", 5):\n", " print(x)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "a\n", "a\n", "a\n", "a\n", "a\n" ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "for x in it.chain(\"foo\", \"bar\", \"bizz\"):\n", " print(x)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "f\n", "o\n", "o\n", "b\n", "a\n", "r\n", "b\n", "i\n", "z\n", "z\n" ] } ], "prompt_number": 8 }, { "cell_type": "markdown", "metadata": {}, "source": [ "`imap()` e `ifilter()` son versiones _lazy_ de `map()` y `filter()`." ] }, { "cell_type": "code", "collapsed": false, "input": [ "for x in it.takewhile(lambda x: x<5, [1,4,6,4,1]):\n", " print(x)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1\n", "4\n" ] } ], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "for x in it.dropwhile(lambda x: x<5, [1,4,6,4,1]):\n", " print(x)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "6\n", "4\n", "1\n" ] } ], "prompt_number": 12 }, { "cell_type": "code", "collapsed": false, "input": [ "print([str.join('', list(x)) for x in it.product('ABCD', repeat=2)])" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "['AA', 'AB', 'AC', 'AD', 'BA', 'BB', 'BC', 'BD', 'CA', 'CB', 'CC', 'CD', 'DA', 'DB', 'DC', 'DD']\n" ] } ], "prompt_number": 16 }, { "cell_type": "code", "collapsed": false, "input": [ "for i,j in it.product(range(5), repeat=2):\n", " print(i, j)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "(0, 0)\n", "(0, 1)\n", "(0, 2)\n", "(0, 3)\n", "(0, 4)\n", "(1, 0)\n", "(1, 1)\n", "(1, 2)\n", "(1, 3)\n", "(1, 4)\n", "(2, 0)\n", "(2, 1)\n", "(2, 2)\n", "(2, 3)\n", "(2, 4)\n", "(3, 0)\n", "(3, 1)\n", "(3, 2)\n", "(3, 3)\n", "(3, 4)\n", "(4, 0)\n", "(4, 1)\n", "(4, 2)\n", "(4, 3)\n", "(4, 4)\n" ] } ], "prompt_number": 17 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }	gpl-2.0
mne-tools/mne-tools.github.io	0.17/_downloads/d25fdfa446b06c82b756855681845935/plot_mne_dspm_source_localization.ipynb	1	10262	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\nSource localization with MNE/dSPM/sLORETA/eLORETA\n=================================================\n\nThe aim of this tutorial is to teach you how to compute and apply a linear\ninverse method such as MNE/dSPM/sLORETA/eLORETA on evoked/raw/epochs data.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# sphinx_gallery_thumbnail_number = 10\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nimport mne\nfrom mne.datasets import sample\nfrom mne.minimum_norm import make_inverse_operator, apply_inverse" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Process MEG data\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data_path = sample.data_path()\nraw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'\n\nraw = mne.io.read_raw_fif(raw_fname) # already has an average reference\nevents = mne.find_events(raw, stim_channel='STI 014')\n\nevent_id = dict(aud_l=1) # event trigger and conditions\ntmin = -0.2 # start of each epoch (200ms before the trigger)\ntmax = 0.5 # end of each epoch (500ms after the trigger)\nraw.info['bads'] = ['MEG 2443', 'EEG 053']\npicks = mne.pick_types(raw.info, meg=True, eeg=False, eog=True,\n exclude='bads')\nbaseline = (None, 0) # means from the first instant to t = 0\nreject = dict(grad=4000e-13, mag=4e-12, eog=150e-6)\n\nepochs = mne.Epochs(raw, events, event_id, tmin, tmax, proj=True, picks=picks,\n baseline=baseline, reject=reject)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compute regularized noise covariance\n------------------------------------\n\nFor more details see `tut_compute_covariance`.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "noise_cov = mne.compute_covariance(\n epochs, tmax=0., method=['shrunk', 'empirical'], rank=None, verbose=True)\n\nfig_cov, fig_spectra = mne.viz.plot_cov(noise_cov, raw.info)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compute the evoked response\n---------------------------\nLet's just use MEG channels for simplicity.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "evoked = epochs.average().pick_types(meg=True)\nevoked.plot(time_unit='s')\nevoked.plot_topomap(times=np.linspace(0.05, 0.15, 5), ch_type='mag',\n time_unit='s')\n\n# Show whitening\nevoked.plot_white(noise_cov, time_unit='s')\n\ndel epochs # to save memory" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Inverse modeling: MNE/dSPM on evoked and raw data\n-------------------------------------------------\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Read the forward solution and compute the inverse operator\nfname_fwd = data_path + '/MEG/sample/sample_audvis-meg-oct-6-fwd.fif'\nfwd = mne.read_forward_solution(fname_fwd)\n\n# make an MEG inverse operator\ninfo = evoked.info\ninverse_operator = make_inverse_operator(info, fwd, noise_cov,\n loose=0.2, depth=0.8)\ndel fwd\n\n# You can write it to disk with::\n#\n# >>> from mne.minimum_norm import write_inverse_operator\n# >>> write_inverse_operator('sample_audvis-meg-oct-6-inv.fif',\n# inverse_operator)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compute inverse solution\n------------------------\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "method = \"dSPM\"\nsnr = 3.\nlambda2 = 1. / snr ** 2\nstc, residual = apply_inverse(evoked, inverse_operator, lambda2,\n method=method, pick_ori=None,\n return_residual=True, verbose=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Visualization\n-------------\nView activation time-series\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plt.figure()\nplt.plot(1e3 * stc.times, stc.data[::100, :].T)\nplt.xlabel('time (ms)')\nplt.ylabel('%s value' % method)\nplt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Examine the original data and the residual after fitting:\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fig, axes = plt.subplots(2, 1)\nevoked.plot(axes=axes)\nfor ax in axes:\n ax.texts = []\n for line in ax.lines:\n line.set_color('#98df81')\nresidual.plot(axes=axes)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we use peak getter to move visualization to the time point of the peak\nand draw a marker at the maximum peak vertex.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "vertno_max, time_max = stc.get_peak(hemi='rh')\n\nsubjects_dir = data_path + '/subjects'\nsurfer_kwargs = dict(\n hemi='rh', subjects_dir=subjects_dir,\n clim=dict(kind='value', lims=[8, 12, 15]), views='lateral',\n initial_time=time_max, time_unit='s', size=(800, 800), smoothing_steps=5)\nbrain = stc.plot(surfer_kwargs)\nbrain.add_foci(vertno_max, coords_as_verts=True, hemi='rh', color='blue',\n scale_factor=0.6, alpha=0.5)\nbrain.add_text(0.1, 0.9, 'dSPM (plus location of maximal activation)', 'title',\n font_size=14)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Morph data to average brain\n---------------------------\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# setup source morph\nmorph = mne.compute_source_morph(\n src=inverse_operator['src'], subject_from=stc.subject,\n subject_to='fsaverage', spacing=5, # to ico-5\n subjects_dir=subjects_dir)\n# morph data\nstc_fsaverage = morph.apply(stc)\n\nbrain = stc_fsaverage.plot(surfer_kwargs)\nbrain.add_text(0.1, 0.9, 'Morphed to fsaverage', 'title', font_size=20)\ndel stc_fsaverage" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Dipole orientations\n-------------------\nThe ``pick_ori`` parameter of the\n:func:`mne.minimum_norm.apply_inverse` function controls\nthe orientation of the dipoles. One useful setting is ``pick_ori='vector'``,\nwhich will return an estimate that does not only contain the source power at\neach dipole, but also the orientation of the dipoles.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "stc_vec = apply_inverse(evoked, inverse_operator, lambda2,\n method=method, pick_ori='vector')\nbrain = stc_vec.plot(surfer_kwargs)\nbrain.add_text(0.1, 0.9, 'Vector solution', 'title', font_size=20)\ndel stc_vec" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that there is a relationship between the orientation of the dipoles and\nthe surface of the cortex. For this reason, we do not use an inflated\ncortical surface for visualization, but the original surface used to define\nthe source space.\n\nFor more information about dipole orientations, see\n`sphx_glr_auto_tutorials_plot_dipole_orientations.py`.\n\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's look at each solver:\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "for mi, (method, lims) in enumerate((('dSPM', [8, 12, 15]),\n ('sLORETA', [3, 5, 7]),\n ('eLORETA', [0.75, 1.25, 1.75]),)):\n surfer_kwargs['clim']['lims'] = lims\n stc = apply_inverse(evoked, inverse_operator, lambda2,\n method=method, pick_ori=None)\n brain = stc.plot(figure=mi, surfer_kwargs)\n brain.add_text(0.1, 0.9, method, 'title', font_size=20)\n del stc" ] } ], "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": 0 }	bsd-3-clause
PyPSA/PyPSA	examples/notebooks/scigrid-sclopf.ipynb	2	3565	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Security-Constrained Optimisation\n", "\n", "In this example, the dispatch of generators is optimised using the security-constrained linear OPF, to guaranteed that no branches are overloaded by certain branch outages." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import pypsa, os\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "network = pypsa.examples.scigrid_de(from_master=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are some infeasibilities without line extensions." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for line_name in [\"316\", \"527\", \"602\"]:\n", " network.lines.loc[line_name, \"s_nom\"] = 1200\n", "\n", "now = network.snapshots[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Performing security-constrained linear OPF" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "branch_outages = network.lines.index[:15]\n", "network.sclopf(now, branch_outages=branch_outages, solver_name=\"cbc\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For the PF, set the P to the optimised P." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "network.generators_t.p_set = network.generators_t.p_set.reindex(\n", " columns=network.generators.index\n", ")\n", "network.generators_t.p_set.loc[now] = network.generators_t.p.loc[now]\n", "\n", "network.storage_units_t.p_set = network.storage_units_t.p_set.reindex(\n", " columns=network.storage_units.index\n", ")\n", "network.storage_units_t.p_set.loc[now] = network.storage_units_t.p.loc[now]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Check no lines are overloaded with the linear contingency analysis" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "p0_test = network.lpf_contingency(now, branch_outages=branch_outages)\n", "p0_test" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Check loading as per unit of s_nom in each contingency" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "max_loading = (\n", " abs(p0_test.divide(network.passive_branches().s_nom, axis=0)).describe().loc[\"max\"]\n", ")\n", "max_loading" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "np.allclose(max_loading, np.ones((len(max_loading))))" ] } ], "metadata": { "@webio": { "lastCommId": null, "lastKernelId": null }, "kernelspec": { "display_name": "", "language": "python", "name": "" }, "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": 1 }	mit
mementum/backtrader	samples/pyfoliotest/backtrader-pyfolio.ipynb	1	1470926	null	gpl-3.0
oresat/oresat-design	SatReports.ipynb	1	34317	{ "metadata": { "name": "", "signature": "sha256:e09322625ea94dd3a92b5256503244c5520f56dc10befd459de92aa0247a46b4" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import ephem\n", "import numpy as np\n", "import datetime\n", "import matplotlib.pyplot as plt\n", "\n", "%matplotlib inline\n", "plt.style.use('ggplot')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "iss = \"ISS (ZARYA)\", \"1 25544U 98067A 16145.69686492 .00003239 00000-0 55059-4 0 9992\", \"2 25544 51.6424 175.2139 0001726 113.6054 353.3802 15.54685316 1333\"\n", "\n", "start = datetime.datetime.now()\n", "end = start + datetime.timedelta(30) #end time: +30 days\n", "step = datetime.timedelta(0,5) #time step in s\n", "\n", "sat = ephem.readtle(iss)\n", "pdx = ephem.Observer()\n", "pdx.lat = '45.5231'\n", "pdx.lon = '122.6765'\n", "pdx.elevation = 30\n", "\n", "critical_az_angle = ephem.degrees(30)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "pass_happening = False\n", "now = start\n", "\n", "pass_list = []\n", "elevations = []\n", "\n", "while now < end:\n", " pdx.date = now\n", " sat.compute(pdx)\n", " alt = np.degrees(sat.alt)\n", " \n", " if pass_happening:\n", " if alt < critical_az_angle:\n", " pass_happening = False\n", " pass_end = now\n", " pass_list.append(((pass_end-pass_start).total_seconds(), pass_start, pass_end))\n", " else:\n", " if alt > critical_az_angle:\n", " pass_happening = True\n", " pass_start = now\n", " \n", " elevations.append(alt)\n", " now += step" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "pass_lengths, pass_starts, pass_ends = zip(pass_list)\n", "pass_lengths = [x/60.0 for x in pass_lengths]\n", "\n", "\n", "plt.figure(figsize=(15,10))\n", "n, bins, patches = plt.hist(pass_lengths, 10)\n", "plt.xlabel(\"Length of pass (minutes)\")\n", "plt.ylabel(\"Number of passes\")\n", "plt.title(u\"ISS pass lengths over PDX for %g days above critical elevation angle = %g\u00b0\"%((end-start).days, critical_az_angle))\n", "plt.show()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x7efe22ef3d10>" ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }	gpl-3.0
mne-tools/mne-tools.github.io	0.12/_downloads/plot_interpolate_bad_channels.ipynb	1	2308	{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "execution_count": null, "cell_type": "code", "source": [ "%matplotlib inline" ], "outputs": [], "metadata": { "collapsed": false } }, { "source": [ "\n=============================================\nInterpolate bad channels for MEG/EEG channels\n=============================================\n\nThis example shows how to interpolate bad MEG/EEG channels\n\n - Using spherical splines as described in [1]_ for EEG data.\n - Using field interpolation for MEG data.\n\nThe bad channels will still be marked as bad. Only the data in those channels\nis removed.\n\nReferences\n----------\n.. [1] Perrin, F., Pernier, J., Bertrand, O. and Echallier, JF. (1989)\n Spherical splines for scalp potential and current density mapping.\n Electroencephalography and Clinical Neurophysiology, Feb; 72(2):184-7.\n\n" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Authors: Denis A. Engemann <[email protected]>\n# Mainak Jas <[email protected]>\n#\n# License: BSD (3-clause)\n\nimport mne\nfrom mne.datasets import sample\n\nprint(__doc__)\n\ndata_path = sample.data_path()\n\nfname = data_path + '/MEG/sample/sample_audvis-ave.fif'\nevoked = mne.read_evokeds(fname, condition='Left Auditory',\n baseline=(None, 0))\n\n# plot with bads\nevoked.plot(exclude=[])\n\n# compute interpolation (also works with Raw and Epochs objects)\nevoked.interpolate_bads(reset_bads=False)\n\n# plot interpolated (previous bads)\nevoked.plot(exclude=[])" ], "outputs": [], "metadata": { "collapsed": false } } ], "metadata": { "kernelspec": { "display_name": "Python 2", "name": "python2", "language": "python" }, "language_info": { "mimetype": "text/x-python", "nbconvert_exporter": "python", "name": "python", "file_extension": ".py", "version": "2.7.11", "pygments_lexer": "ipython2", "codemirror_mode": { "version": 2, "name": "ipython" } } } }	bsd-3-clause
dominikgrimm/ridge_and_svm	Toy-Example-Solution.ipynb	1	225315	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Toy Example: Ridge Regression vs. SVM\n", "<p></p>\n", " \n", "<div style=\"text-align:justify\">\n", " In this toy example we will compare two machine learning models: <em>Ridge Regression</em> and <em>C-SVM</em>. The data is generated <em>in silico</em> and is only used to illustrate how to use <em>Ridge Regression</em> and <em>C-SVM</em>.\n", "</div>\n", "\n", "## Problem Description of the Toy Example\n", "<p></p>\n", " \n", "<div style=\"text-align:justify\">\n", "A new cancer drug was developed for therapy. During the clinical trail the researchers releaized that the drug had a faster response for a certain subgroup of the patients, while it was less responsive in the others. In addition, the researchers recognized that the drug leads to severe side-effects the longer the patient is treated with the drug. The goal should be to reduce the side effects by treating only those patients that are predicted to have a fast response when taking the drug.\n", "</div>\n", "<br>\n", "<div style=\"text-align:justify\">\n", "The researches believe that different genetic mutations in the genomes of the individual patients might play a role for the differences in response times.\n", "</div>\n", "<br>\n", "<div style=\"text-align:justify\">\n", " The researches contacted the <em>machine learning</em> lab to build a predictive model. The model should predict the individual response time of the drug based on the individual genetic backgrounds of a patient.\n", "</div>\n", "<br>\n", "<div style=\"text-align:justify\">\n", "For this purpose, we get a dataset of 400 patients. For each patient a panel of 600 genetic mutations was measured. In addition, the researchers measured how many days it took until the drug showed a positive response.\n", "</div>\n", "\n", "\n", "## 1. Using Ridge Regression to predict the response time\n", "<div style=\"text-align:justify\">\n", " To predict the response time of the drug for new patients, we will train a <em>Ridge Regression</em> model. The target variable for this task is the response time in days. The features are the 600 genetic mutations measured for each of the 400 patients. To avoid overfitting we will use a nested-crossvalidation to determine the optimal hyperparamter.\n", "</div>\n", "### 1.1 Data Preprocessing" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Orginal Data\n", "Number Patients:\t400\n", "Number Features:\t600\n", "\n", "Training Data\n", "Number Patients:\t320\n", "Number Features:\t600\n", "\n", "Testing Data\n", "Number Patients:\t80\n", "Number Features:\t600\n" ] } ], "source": [ "%matplotlib inline\n", "import scipy as sp\n", "import matplotlib\n", "import pylab as pl\n", "matplotlib.rcParams.update({'font.size': 15})\n", "\n", "from sklearn.linear_model import Ridge\n", "from sklearn.svm import SVC\n", "from sklearn.model_selection import KFold, StratifiedKFold, GridSearchCV,StratifiedShuffleSplit\n", "from sklearn.model_selection import cross_val_score, train_test_split\n", "from sklearn.metrics import accuracy_score, mean_squared_error, mean_absolute_error\n", "from sklearn.metrics import roc_curve, auc\n", "\n", "def visualized_variance_bias_tradeoff(hyperp, line_search, optimal_hyperp,classification=False):\n", " pl.figure(figsize=(18,7))\n", " if classification:\n", " factor=1\n", " else:\n", " factor=-1\n", " pl.plot(hyperp,line_search.cv_results_['mean_train_score']factor,label=\"Training Error\",color=\"#e67e22\")\n", " pl.fill_between(hyperp,\n", " line_search.cv_results_['mean_train_score']factor-line_search.cv_results_['std_train_score'],\n", " line_search.cv_results_['mean_train_score']factor+line_search.cv_results_['std_train_score'],\n", " alpha=0.3,color=\"#e67e22\")\n", " pl.plot(hyperp,line_search.cv_results_['mean_test_score']factor,label=\"Validation Error\",color=\"#2980b9\")\n", " pl.fill_between(hyperp,\n", " line_search.cv_results_['mean_test_score']factor-line_search.cv_results_['std_test_score'],\n", " line_search.cv_results_['mean_test_score']factor+line_search.cv_results_['std_test_score'],\n", " alpha=0.3,color=\"#2980b9\")\n", " pl.xscale(\"log\")\n", " if classification:\n", " pl.ylabel(\"Accuracy\")\n", " else:\n", " pl.ylabel(\"Mean Squared Error\")\n", " pl.xlabel(\"Hyperparameter\")\n", " pl.legend(frameon=True)\n", " pl.grid(True)\n", " pl.axvline(x=optimal_hyperp,color='r',linestyle=\"--\")\n", " pl.title(\"Training- vs. Validation-Error (Optimal Hyperparameter = %.1e)\"%optimal_hyperp);\n", "\n", "random_state = 42\n", "\n", "#Load Data\n", "data = sp.loadtxt(\"data/X.txt\")\n", "binary_target = sp.loadtxt(\"data/y_binary.txt\")\n", "continuous_target = sp.loadtxt(\"data/y.txt\")\n", "\n", "#Summary of the Data\n", "print(\"Orginal Data\")\n", "print(\"Number Patients:\\t%d\"%data.shape[0])\n", "print(\"Number Features:\\t%d\"%data.shape[1])\n", "print()\n", "\n", "#Split Data into Training and Testing data\n", "train_test_data = train_test_split(data,\n", " continuous_target,\n", " test_size=0.2,\n", " random_state=random_state)\n", "training_data = train_test_data[0]\n", "testing_data = train_test_data[1]\n", "training_target = train_test_data[2]\n", "testing_target = train_test_data[3]\n", "\n", "print(\"Training Data\")\n", "print(\"Number Patients:\\t%d\"%training_data.shape[0])\n", "print(\"Number Features:\\t%d\"%training_data.shape[1])\n", "print()\n", "print(\"Testing Data\")\n", "print(\"Number Patients:\\t%d\"%testing_data.shape[0])\n", "print(\"Number Features:\\t%d\"%testing_data.shape[1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1.2 Train Ridge Regression on training data\n", "\n", "The first step is to train the ridge regression model on the training data with a 5-fold cross-validation with an internal line-search to find the optimal hyperparameter $\\alpha$. We will plot the training errors against the validation errors, to illustrate the effect of different $\\alpha$ values." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5-fold nested cross-validation\n", "Mean-Squared-Error:\t\t587.09 (-+ 53.45)\n", "\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1296x504 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Initialize different alpha values for the Ridge Regression model\n", "alphas = sp.logspace(-2,8,11)\n", "param_grid = dict(alpha=alphas)\n", "\n", "#5-fold cross-validation (outer-loop)\n", "outer_cv = KFold(n_splits=5,shuffle=True,random_state=random_state)\n", "\n", "#Line-search to find the optimal alpha value (internal-loop)\n", "#Model performance is measured with the negative mean squared error\n", "line_search = GridSearchCV(Ridge(random_state=random_state,solver=\"cholesky\"),\n", " param_grid=param_grid,\n", " scoring=\"neg_mean_squared_error\",\n", " return_train_score=True)\n", "\n", "#Execute nested cross-validation and compute mean squared error\n", "score = cross_val_score(line_search,X=training_data,y=training_target,cv=outer_cv,scoring=\"neg_mean_squared_error\")\n", "\n", "print(\"5-fold nested cross-validation\")\n", "print(\"Mean-Squared-Error:\\t\\t%.2f (-+ %.2f)\"%(score.mean()*(-1),score.std()))\n", "print()\n", "\n", "#Estimate optimal alpha on the full training data\n", "line_search.fit(training_data,training_target)\n", "optimal_alpha = line_search.best_params_['alpha']\n", "\n", "#Visualize training and validation error for different alphas\n", "visualized_variance_bias_tradeoff(alphas, line_search, optimal_alpha)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1.3 Train Ridge Regression with optimal $\\alpha$ and evaluate model in test data\n", "Next we retrain the ridge regresssion model with the optimal $\\alpha$ (from the last section). After re-training we will test the model on the not used test data to evaluate the model performance on unseen data." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Prediction results on test data\n", "MSE (test data, alpha=optimal):\t699.56 \n", "Optimal Alpha:\t\t\t100000.00\n", "\n" ] } ], "source": [ "#Train Ridge Regression on the full training data with optimal alpha\n", "model = Ridge(alpha=optimal_alpha,solver=\"cholesky\")\n", "model.fit(training_data,training_target)\n", "\n", "#Use trained model the predict new instances in test data\n", "predictions = model.predict(testing_data)\n", "print(\"Prediction results on test data\")\n", "print(\"MSE (test data, alpha=optimal):\\t%.2f \"%(mean_squared_error(testing_target,predictions)))\n", "print(\"Optimal Alpha:\\t\\t\\t%.2f\"%optimal_alpha)\n", "print()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "<div style=\"text-align:justify\">\n", " Using 5-fold cross-validation on the training data leads to a mean squared error (MSE) of $MSE=587.09 \\pm 53.54$. On the test data we get an error of $MSE=699.56$ ($\\sim 26.5$ days). That indicates that the ridge regression model performs rather mediocre (even with hyperparameter optimization).\n", " One reason might be that the target variable (number of days until the drug shows a positive response) is insufficently described by the given features (genetic mutations).\n", "</div>\n", "\n", "\n", "## 2. Prediction of patients with slow and fast response times using a Support-Vector-Machine \n", "\n", "<div style=\"text-align:justify\">\n", " Due to the rather bad results with the ridge regession model the machine learning lab returned to the researchers to discuss potential issues. The researches than mentioned that it might not be necessarily important to predict the exact number of days. It might be even better to only predict if a patient reacts fast or slowly on the drug. Based on some prior experiments the researchers observed, that most of the patients showed severe side-effects after 50 days of treatment. Thus we can binarise the data, such that all patients below 50 days are put into class 0 and all others into class 1. This leads to a classical classification problem for which a support vector machine could be used. \n", "</div>\n", "\n", "### 2.1 Data Preprocessing" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training Data\n", "Number Patients:\t\t320\n", "Number Features:\t\t600\n", "Number Patients Class 0:\t160\n", "Number Patients Class 1:\t160\n", "\n", "Testing Data\n", "Number Patients:\t\t80\n", "Number Features:\t\t600\n", "Number Patients Class 0:\t40\n", "Number Patients Class 1:\t40\n" ] } ], "source": [ "#Split data into training and testing splits, stratified by class-ratios\n", "stratiefied_splitter = StratifiedShuffleSplit(n_splits=1,test_size=0.2,random_state=42)\n", "for train_index,test_index in stratiefied_splitter.split(data,binary_target):\n", " training_data = data[train_index,:]\n", " training_target = binary_target[train_index]\n", " testing_data = data[test_index,:]\n", " testing_target = binary_target[test_index]\n", "\n", "print(\"Training Data\")\n", "print(\"Number Patients:\\t\\t%d\"%training_data.shape[0])\n", "print(\"Number Features:\\t\\t%d\"%training_data.shape[1])\n", "print(\"Number Patients Class 0:\\t%d\"%(training_target==0).sum())\n", "print(\"Number Patients Class 1:\\t%d\"%(training_target==1).sum())\n", "print()\n", "print(\"Testing Data\")\n", "print(\"Number Patients:\\t\\t%d\"%testing_data.shape[0])\n", "print(\"Number Features:\\t\\t%d\"%testing_data.shape[1])\n", "print(\"Number Patients Class 0:\\t%d\"%(testing_target==0).sum())\n", "print(\"Number Patients Class 1:\\t%d\"%(testing_target==1).sum())" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### 2.2 Classification with a linear SVM" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5-fold nested cross-validation on training data\n", "Average(Accuracy):\t\t\t0.80 (-+ 0.02)\n", "\n", "Prediction with optimal C\n", "Accuracy (Test data, C=Optimal):\t0.82 \n", "Optimal C:\t\t\t\t1.00e-04\n", "\n" ] }, { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x11388deb8>" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1296x504 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 576x576 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "Cs = sp.logspace(-7, 1, 9)\n", "param_grid = dict(C=Cs)\n", "\n", "grid = GridSearchCV(SVC(kernel=\"linear\",random_state=random_state),\n", " param_grid=param_grid,\n", " scoring=\"accuracy\",\n", " n_jobs=4,\n", " return_train_score=True)\n", "outer_cv = StratifiedKFold(n_splits=5,shuffle=True,random_state=random_state)\n", "\n", "#Perform 5 Fold cross-validation with internal line-search and report average Accuracy\n", "score = cross_val_score(grid,X=training_data,y=training_target,cv=outer_cv,scoring=\"accuracy\")\n", "\n", "print(\"5-fold nested cross-validation on training data\")\n", "print(\"Average(Accuracy):\\t\\t\\t%.2f (-+ %.2f)\"%(score.mean(),score.std()))\n", "print()\n", "grid.fit(training_data,training_target)\n", "optimal_C = grid.best_params_['C']\n", "\n", "#Plot variance bias tradeoff\n", "visualized_variance_bias_tradeoff(Cs, grid, optimal_C,classification=True)\n", "\n", "#retrain model with optimal C and evaluate on test data\n", "model = SVC(C=optimal_C,random_state=random_state,kernel=\"linear\")\n", "model.fit(training_data,training_target)\n", "predictions = model.predict(testing_data)\n", "print(\"Prediction with optimal C\")\n", "print(\"Accuracy (Test data, C=Optimal):\\t%.2f \"%(accuracy_score(testing_target,predictions)))\n", "print(\"Optimal C:\\t\\t\\t\\t%.2e\"%optimal_C)\n", "print()\n", "\n", "#Compute ROC FPR, TPR and AUC\n", "fpr, tpr, _ = roc_curve(testing_target, model.decision_function(testing_data))\n", "roc_auc = auc(fpr, tpr)\n", "\n", "#Plot ROC Curve\n", "pl.figure(figsize=(8,8))\n", "pl.plot(fpr, tpr, color='darkorange',\n", " lw=3, label='ROC curve (AUC = %0.2f)' % roc_auc)\n", "pl.plot([0, 1], [0, 1], color='navy', lw=3, linestyle='--')\n", "pl.xlim([-0.01, 1.0])\n", "pl.ylim([0.0, 1.05])\n", "pl.xlabel('False Positive Rate (1-Specificity)',fontsize=18)\n", "pl.ylabel('True Positive Rate (Sensitivity)',fontsize=18)\n", "pl.title('Receiver Operating Characteristic (ROC) Curve',fontsize=18)\n", "pl.legend(loc=\"lower right\",fontsize=18)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.3 Classification with SVM and RBF kernel\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5-fold nested cross-validation on training data\n", "Average(Accuracy):\t\t\t0.86 (-+ 0.02)\n", "\n", "Prediction with optimal C and Gamma\n", "Accuracy (Test Data, C=Optimal):\t0.93 \n", "Optimal C:\t\t\t\t1.00e+01\n", "Optimal Gamma:\t\t\t\t1.00e-05\n", "\n" ] }, { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x107d69eb8>" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 576x576 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "Cs = sp.logspace(-4, 4, 9)\n", "gammas = sp.logspace(-7, 1, 9)\n", "param_grid = dict(C=Cs,gamma=gammas)\n", "\n", "grid = GridSearchCV(SVC(kernel=\"rbf\",random_state=42),\n", " param_grid=param_grid,\n", " scoring=\"accuracy\",\n", " n_jobs=4,\n", " return_train_score=True)\n", "\n", "outer_cv = StratifiedKFold(n_splits=5,shuffle=True,random_state=random_state)\n", "\n", "#Perform 5 Fold cross-validation with internal line-search and report average Accuracy\n", "score = cross_val_score(grid,X=training_data,y=training_target,cv=outer_cv,scoring=\"accuracy\")\n", "\n", "print(\"5-fold nested cross-validation on training data\")\n", "print(\"Average(Accuracy):\\t\\t\\t%.2f (-+ %.2f)\"%(score.mean(),score.std()))\n", "print()\n", "\n", "grid.fit(training_data,training_target)\n", "optimal_C = grid.best_params_['C']\n", "optimal_gamma = grid.best_params_['gamma']\n", "\n", "#Retrain and test\n", "model = SVC(C=optimal_C,gamma=optimal_gamma,random_state=42,kernel=\"rbf\")\n", "model.fit(training_data,training_target)\n", "predictions = model.predict(testing_data)\n", "print(\"Prediction with optimal C and Gamma\")\n", "print(\"Accuracy (Test Data, C=Optimal):\\t%.2f \"%(accuracy_score(testing_target,predictions)))\n", "print(\"Optimal C:\\t\\t\\t\\t%.2e\"%optimal_C)\n", "print(\"Optimal Gamma:\\t\\t\\t\\t%.2e\"%optimal_gamma)\n", "print()\n", "\n", "#Compute ROC FPR, TPR and AUC\n", "fpr, tpr, _ = roc_curve(testing_target, model.decision_function(testing_data))\n", "roc_auc = auc(fpr, tpr)\n", "\n", "#Plot ROC Curve\n", "pl.figure(figsize=(8,8))\n", "pl.plot(fpr, tpr, color='darkorange',\n", " lw=3, label='ROC curve (AUC = %0.2f)' % roc_auc)\n", "pl.plot([0, 1], [0, 1], color='navy', lw=3, linestyle='--')\n", "pl.xlim([-0.01, 1.0])\n", "pl.ylim([0.0, 1.05])\n", "pl.xlabel('False Positive Rate (1-Specificity)',fontsize=18)\n", "pl.ylabel('True Positive Rate (Sensitivity)',fontsize=18)\n", "pl.title('Receiver Operating Characteristic (ROC) Curve',fontsize=18)\n", "pl.legend(loc=\"lower right\",fontsize=18)" ] }, { "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": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
erfannoury/TextClassification	sklearn-text classification.ipynb	2	112878	{ "metadata": { "name": "", "signature": "sha256:b120c5bed47d95f008362ba2a2a459b1463e2594f34b1a08044be28d28fa31df" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "import scipy as sc\n", "import matplotlib.pyplot as plt\n", "from prettyprint import pp\n", "import os, re\n", "from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer\n", "from sklearn.naive_bayes import BernoulliNB, GaussianNB, MultinomialNB\n", "from sklearn.metrics import confusion_matrix, f1_score, accuracy_score, precision_score, recall_score, classification_report\n", "from sklearn.neighbors import KNeighborsClassifier\n", "from sklearn.svm import LinearSVC, NuSVC, SVC\n", "from sklearn.grid_search import GridSearchCV\n", "from datetime import datetime as dt\n", "from ipy_table import \n", "%matplotlib inline" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "###Files directory" ] }, { "cell_type": "code", "collapsed": false, "input": [ "root_path = 'E:/University Central/Modern Information Retrieval/Project/Project Phase 2/20_newsgroup/'\n", "#top_view folders\n", "folders = [root_path + folder + '/' for folder in os.listdir(root_path)]\n", "\n", "\n", "#there are only 4 classes\n", "class_titles = os.listdir(root_path)\n", "\n", "\n", "#list of all the files belonging to each class\n", "files = {}\n", "for folder, title in zip(folders, class_titles):\n", " files[title] = [folder + f for f in os.listdir(folder)]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "###Split documents to test and train sets" ] }, { "cell_type": "code", "collapsed": false, "input": [ "train_test_ratio = 0.75\n", "\n", "def train_test_split(ratio, classes, files):\n", " \"\"\"\n", " this method will split the input list of files to train and test sets.\n", " Note: currently this method uses the simplest way an array can be split in two parts.\n", " Parameters\n", " ----------\n", " ratio: float\n", " ratio of total documents in each class assigned to the training set\n", " classes: list\n", " list of label classes\n", " files: dictionary\n", " a dictionary with list of files for each class\n", " \n", " Returns\n", " -------\n", " train_dic: dictionary\n", " a dictionary with lists of documents in the training set for each class\n", " test_dict: dictionary\n", " a dictionary with lists of documents in the testing set for each class\n", " \"\"\"\n", " train_dict = {}\n", " test_dict = {}\n", " for cl in classes:\n", " train_cnt = int(ratio * len(files[cl]))\n", " train_dict[cl] = files[cl][:train_cnt]\n", " test_dict[cl] = files[cl][train_cnt:]\n", " return train_dict, test_dict" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "train_path, test_path = train_test_split(train_test_ratio, class_titles, files)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "###Cleanup text" ] }, { "cell_type": "code", "collapsed": false, "input": [ "pattern = re.compile(r'([a-zA-Z]+\|[0-9]+(\\.[0-9]+)?)')\n", "\n", "def cleanupText(path):\n", " \"\"\"\n", " this method will read in a text file and try to cleanup its text.\n", " \n", " Parameters\n", " ----------\n", " path: str\n", " path to the document file\n", " Returns\n", " -------\n", " text_translated: str\n", " cleaned up version of the raw text in the input file\n", " \"\"\"\n", " from string import punctuation, digits\n", " text_translated = ''\n", " try:\n", " f = open(path)\n", " raw = f.read().lower()\n", " text = pattern.sub(r' \\1 ', raw.replace('\\n', ' '))\n", " text_translated = text.translate(None, punctuation + digits)\n", " text_translated = ' '.join([word for word in text_translated.split(' ') if (word and len(word) > 1)])\n", " finally:\n", " f.close()\n", " return text_translated\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "###Create arrays of documents and their corresponding labels" ] }, { "cell_type": "code", "collapsed": false, "input": [ "train_arr = []\n", "test_arr = []\n", "train_lbl = []\n", "test_lbl = []\n", "for cl in class_titles:\n", " for path in train_path[cl]:\n", " train_arr.append(cleanupText(path))\n", " train_lbl.append(cl)\n", " for path in test_path[cl]:\n", " test_arr.append(cleanupText(path))\n", " test_lbl.append(cl)\n", " \n", "print len(train_arr)\n", "print len(test_arr)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "600\n", "200\n" ] } ], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": {}, "source": [ "###Create text vectorizer" ] }, { "cell_type": "code", "collapsed": false, "input": [ "vectorizer = CountVectorizer()\n", "vectorizer.fit(train_arr)\n", "train_mat = vectorizer.transform(train_arr)\n", "print train_mat.shape\n", "test_mat = vectorizer.transform(test_arr)\n", "print test_mat.shape" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "(600, 19420)\n", "(200, 19420)" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] } ], "prompt_number": 7 }, { "cell_type": "markdown", "metadata": {}, "source": [ "###Create Tfidf Transformer" ] }, { "cell_type": "code", "collapsed": false, "input": [ "tfidf = TfidfTransformer()\n", "tfidf.fit(train_mat)\n", "train_tfmat = tfidf.transform(train_mat)\n", "print train_tfmat.shape\n", "test_tfmat = tfidf.transform(test_mat)\n", "print test_tfmat.shape" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "(600, 19420)\n", "(200, 19420)\n" ] } ], "prompt_number": 8 }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Test Classifier" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def testClassifier(x_train, y_train, x_test, y_test, clf):\n", " \"\"\"\n", " this method will first train the classifier on the training data\n", " and will then test the trained classifier on test data.\n", " Finally it will report some metrics on the classifier performance.\n", " \n", " Parameters\n", " ----------\n", " x_train: np.ndarray\n", " train data matrix\n", " y_train: list\n", " train data label\n", " x_test: np.ndarray\n", " test data matrix\n", " y_test: list\n", " test data label\n", " clf: sklearn classifier object implementing fit() and predict() methods\n", " \n", " Returns\n", " -------\n", " metrics: list\n", " [training time, testing time, recall and precision for every class, macro-averaged F1 score]\n", " \"\"\"\n", " metrics = []\n", " start = dt.now()\n", " clf.fit(x_train, y_train)\n", " end = dt.now()\n", " print 'training time: ', (end - start)\n", " \n", " # add training time to metrics\n", " metrics.append(end-start)\n", " \n", " start = dt.now()\n", " yhat = clf.predict(x_test)\n", " end = dt.now()\n", " print 'testing time: ', (end - start)\n", " \n", " # add testing time to metrics\n", " metrics.append(end-start)\n", " \n", " print 'classification report: '\n", "# print classification_report(y_test, yhat)\n", " pp(classification_report(y_test, yhat))\n", " \n", " print 'f1 score'\n", " print f1_score(y_test, yhat, average='macro')\n", " \n", " print 'accuracy score'\n", " print accuracy_score(y_test, yhat)\n", " \n", " precision = precision_score(y_test, yhat, average=None)\n", " recall = recall_score(y_test, yhat, average=None)\n", " \n", " # add precision and recall values to metrics\n", " for p, r in zip(precision, recall):\n", " metrics.append(p)\n", " metrics.append(r)\n", " \n", " \n", " #add macro-averaged F1 score to metrics\n", " metrics.append(f1_score(y_test, yhat, average='macro'))\n", " \n", " print 'confusion matrix:'\n", " print confusion_matrix(y_test, yhat)\n", " \n", " # plotting the confusion matrix\n", " plt.imshow(confusion_matrix(y_test, yhat), interpolation='nearest')\n", " plt.show()\n", " \n", " return metrics" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 9 }, { "cell_type": "markdown", "metadata": {}, "source": [ "###Metrics list" ] }, { "cell_type": "code", "collapsed": false, "input": [ "metrics_dict = []\n", "#'name', 'metrics'" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 10 }, { "cell_type": "markdown", "metadata": {}, "source": [ "##First, classifiers with default settings will be tested" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###Naive Bayes classifiers" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "####Bernoulli Naive Bayes classifier" ] }, { "cell_type": "code", "collapsed": false, "input": [ "bnb = BernoulliNB()\n", "bnb_me = testClassifier(train_tfmat, train_lbl, test_tfmat, test_lbl, bnb)\n", "metrics_dict.append({'name':'BernoulliNB', 'metrics':bnb_me})" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "training time: 0:00:00.007000\n", "testing time: 0:00:00.004000\n", "classification report: \n", "\" precision recall f1-score support\n", "\n", "comp.os.ms-windows.misc 1.00 0.38 0.55 50\n", "rec.sport.baseball 1.00 1.00 1.00 50\n", "sci.electronics 0.60 0.98 0.74 50\n", "soc.religion.christian 0.98 0.96 0.97 50\n", "\n", "avg / total 0.89 0.83 0.82 200\n", "\"\n", "f1 score\n", "0.815711462451\n", "accuracy score\n", "0.83\n", "confusion matrix:\n", "[[19 0 31 0]\n", " [ 0 50 0 0]\n", " [ 0 0 49 1]\n", " [ 0 0 2 48]]\n" ] }, { "metadata": {}, "output_type": "display_data", "png": "iVBORw0KGgoAAAANSUhEUgAAAQ8AAAEACAYAAACtefPrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nlHQ5wp9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"text": [ "<matplotlib.figure.Figure at 0x215bba8>" ] } ], "prompt_number": 11 }, { "cell_type": "markdown", "metadata": {}, "source": [ "####Gaussian Naive Bayes classifier" ] }, { "cell_type": "code", "collapsed": false, "input": [ "gnb = GaussianNB()\n", "gnb_me = testClassifier(train_tfmat.toarray(), train_lbl, test_tfmat.toarray(), test_lbl, gnb)\n", "metrics_dict.append({'name':'GaussianNB', 'metrics':gnb_me})" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "training time: 0:00:00.404000\n", "testing time: " ] }, { "output_type": "stream", "stream": "stdout", "text": [ " 0:00:00.354000\n", "classification report: \n", "\" precision recall f1-score support\n", "\n", "comp.os.ms-windows.misc 0.83 0.86 0.84 50\n", "rec.sport.baseball 0.94 0.96 0.95 50\n", "sci.electronics 0.89 0.80 0.84 50\n", "soc.religion.christian 0.96 1.00 0.98 50\n", "\n", "avg / total 0.90 0.91 0.90 200\n", "\"\n", "f1 score\n", "0.904032431107\n", "accuracy score\n", "0.905\n", "confusion matrix:\n", "[[43 2 4 1]\n", " [ 1 48 1 0]\n", " [ 8 1 40 1]\n", " [ 0 0 0 50]]\n" ] }, { "metadata": {}, "output_type": "display_data", "png": "iVBORw0KGgoAAAANSUhEUgAAAQ8AAAEACAYAAACtefPrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAADdVJREFUeJzt3W2MXVW9x/FvgRqBGmvFlIepd6KFxPpE1ZQGvHr0irHV\n1PuiuWJiNJgoQRuMLxQfMJZEI5eYqFVK+kINagImGEkjNIo32osaq0hbQUEoMgkgVAy0Vqqx6Phi\n7XFO95yn+a919jkz/X6SnbPPOWvOf6+Z9tf91PmDJEmSJEmSJEmSJKlhSzK+dgXwbeA/gCngf4BD\nHcZNAX8G/gEcA9Zl1JS0CFwLfLRavxK4psu4h0hBI0kA3AesrNbPrJ538hDw/Ea2SNKC8FTb+pLa\n83a/B/YCdwLvG/ZGSWrGKX3ev520V1H3ydrz6Wrp5CLgMeAF1efdB9wxj22UNIb6hcfFPd47SAqW\nx4GzgD92GfdY9fgE8F3SCdM54fHipUw/eKzP1kgagjOAP8374km/8OhlJ/Ae4H+rx1s6jDkNOBk4\nApwOvBm4utOHPXgMpl+WsTXzsPUgbF3Zf1zJelc/cXNzBfk28I6G6322wXrbgCsarremwXrfA97W\nYL0PhL7qpIyK15D2TO4H3sjs1ZazgVur9TNJexn7gD2k78oPMmpKGhM5ex5PAm/q8PofgLdW678H\nzs+oIWlM5ex5LFit0xd3PXjpIq93wSKvd17D9WJOzPBYtrjrQUMnj0ZWz/AYBydkeEjKZ3hICjE8\nJIUYHpJCDA9JIYaHpBDDQ1KI4SEpxPCQFGJ4SAoxPCSFGB6SQgwPSSGGh6QQw0NSiOEhKcTwkBRS\nIjzeQurF8gCp7WQn26r39wNrC9SUNGK54XEy8BVSgKwB3gm8pDZmI7AaOBd4P3B9Zk1JYyA3PNYB\nB4Ap4BhwE/D22phNwA3V+h5gObM9biUtULnhcQ7wcNvzR6rX+o2ZyKwracRyw6Nbf9q6eiu7Qb9O\n0pjKafoE8Ciwqu35KtKeRa8xE9Vrc2w9OLveOn0ULQukE8H91ZInNzzuJJ0InSR1insH6aRpu53A\nFtL5kPXAIVKT7Dma7B8rnbjO4/jeMLeFPiU3PJ4hBcP3SVdevgrcC1xWvb+j2rKNpBOrTwOXZtaU\nNAZywwNgV7W021F7vqVAHUljxDtMJYUYHpJCDA9JIYaHpBDDQ1KI4SEpxPCQFGJ4SAoxPCSFGB6S\nQgwPSSGGh6QQw0NSiOEhKcTwkBRieEgKMTwkhRgekkIMD0khTfSqbQGHgb3VclWBmpJGLPcXIM/0\nqn0TqRfLL0mtFu6tjdtNajspaZFoolctzO0YJ2mBa6JX7TRwIbCf1MNlTWZNSWMg97BlkJ6zd5Ha\nTR4FNgC3cHy7qn9bcs/Gtmf1rlYL281sHvUmDNVmto96E4asY5PDBWqqWvI00av2SNv6LmA7sAJ4\ncu7HvS1zcyT1N1ktM3aHPiX3sKW9V+2zSL1qd9bGrGT2nMe6ar1DcEhaSJroVbsZuLwaexS4JLOm\npDHQRK/a66pF0iLiHaaSQgwPSSGGh6QQw0NSiOEhKcTwkBRieEgKMTwkhRgekkIMD0khhoekEMND\nUojhISnE8JAUYnhICjE8JIUYHpJCDA9JIYaHpJDc8PgaqaHF3T3GbCP1sd0PrM2sJ2lM5IbH10mN\nrrvZCKwmtWd4P3B9Zj1JYyI3PO4Anurx/ibghmp9D7Cc1MdF0gI37HMenXrZTgy5pqQGlOjb0s+S\n2vMe/W2/17a+uHrVSuNjinHoVdtPvZftRPVaF/aqlYZvknHoVdvPTuDd1fp64BCLq924dMLK3fO4\nEXg9cAbp3MangaXVezuA20hXXA4ATwOXZtaTNCZyw+OdA4zZkllD0hjyDlNJIYaHpBDDQ1KI4SEp\nxPCQFGJ4SAoxPCSFGB6SQgwPSSGGh6QQw0NSiOEhKcTwkBRieEgKMTwkhRgekkIMD0khhoekEMND\nUkiJ8OjXr7YFHAb2VstVBWpKGrESfVu+DnwZ+EaPMbtJrSclLRIl9jz69auFuV3jJC1wTZzzmAYu\nBPaT+risaaCmpCFrolftXaSWk0eBDcAtdGtC+9y2ZnLPbsGprWFvW2M2T416C4Zr+rIPjHoThmrJ\nju2j3oSC7q+WPE2Ex5G29V3AdmAF8OSckc/b2sDmSCe6ehP520Kf0sRhy0pmz3msq9bnBoekBaXE\nnke/frWbgcuBZ0iHLpcUqClpxEqER79+tddVi6RFxDtMJYUYHpJCDA9JIYaHpBDDQ1KI4SEpxPCQ\nFGJ4SAoxPCSFGB6SQgwPSSGGh6QQw0NSiOEhKcTwkBRieEgKMTwkhRgekkIMD0khueGxCvgR8Bvg\nHuCKLuO2AQ+QGj+tzawpaQzk/gLkY8CHgX3AMuBXwO3AvW1jNgKrgXOBC4DrgfWZdSWNWO6ex+Ok\n4AD4Cyk0zq6N2QTcUK3vAZaTerlIWsBKnvOYJB2S7Km9fg6pn8uMR4CJgnUljUCpdpPLgJuBD5H2\nQOqW1J5Pd/yUp7bOri+yXrXS+BifXrVLge8A3yI1sa57lHRidcZE9dpc9qqVGjAevWqXAF8Ffgt8\nscuYncC7q/X1wCHgYGZdSSOWu+dxEfAu4NfA3uq1TwAvrNZ3kGJtI3AAeBq4NLOmpDGQGx4/YbC9\nly2ZdSSNGe8wlRRieEgKMTwkhRgekkIMD0khhoekEMNDUojhISnE8JAUYnhICjE8JIUYHpJCDA9J\nIYaHpBDDQ1KI4SEpxPCQFGJ4SAoxPCSFNNGrtgUcJv2C5L3AVZk1JY2BJnrVAuwmtZ2UtEg00asW\n5naMk7TANdGrdhq4ENhP6uGypmBNSSNSao9gGfBj4DPMbTn5HOAfwFFgA/Alju91N2MaXt/2dLJa\npNH7NFePehOKmaqWGbvTw7yzoIletUfa1ncB24EVwJNzh7YKbI6kXiY5/p/l3cHPaaJX7UpmU21d\ntd4hOCQtJE30qt0MXA48Qzp0uSSzpqQx0ESv2uuqRdIi4h2mkkIMD0khhoekEMNDUojhISnE8JAU\nYnhICjE8JIUYHpJCDA9JIYaHpBDDQ1KI4SEpxPCQFGJ4SAoxPCSFGB6SQgwPSSG54fFsUp+WfaRf\ngvy5LuO2AQ+QereszawpaQzk/g7TvwFvIP1i41NIv9P0tdXjjI3AauBc4ALgemB9Zl1JI1bisOVo\n9fgs4GTmtlXYBNxQre8BlpPaMUhawEqEx0mkw5aDwI9Ihy/tzgEebnv+CDBRoK6kESoRHv8EzicF\nwuvo3Pat3spuukBdSSNUot3kjMPArcBrSH1rZzwKrGp7PlG91kH7l01ir1qpvCmO71UblRseZ5A6\nwR0CTgUuhjkdgXcCW4CbSCdKD5EOcTpoZW6OpH4mKdOrNjc8ziKdDD2pWr4J/B9wWfX+DuA20hWX\nA8DTwKWZNSWNgdzwuBt4VYfXd9Seb8msI2nMeIeppBDDQ1KI4SEpxPCQFGJ4SAoxPCSFGB6SQgwP\nSSGGh6QQw0NSiOEhKcTwkBRieEgKMTwkhRgekkIMD0khhoekEMNDUojhISmkiV61LVJbhr3VclVm\nTUljIDc8ZnrVng+8olp/bYdxu0kNrtcCn8msWcCU9aw3tvWarRbXRK9amNsxbsSmrGe9sa3XbLW4\nJnrVTgMXAvtJPVzWFKgpacSa6FV7F6nd5CuBLwO3FKgpacRKH058Cvgr8PkeYx4CXs3cw5sDwIsL\nb4+k/h4EVjdd9AxgebV+KvD/wH/VxqxkNqTWsXAO6ST10ESv2s3A5aSG2EeBSzJrSpIkzc8K4Hbg\nfuAHzB761E0BvybdXPaLQJ23APcBDwBXdhmzrXp/P+k+lBz96rUod8Pc10hXuO7uMabk3PrVa1H2\nZsBVpKt3vwHuAa7oMq7UHAep16LcHAe5wRLKzW/R3NB5LfDRav1K4Jou4x4iBU3EyaSTsJPAUtI3\n7SW1MRtJl48BLgB+Hqw1aL0WsDOjRrv/JP1h6vaXueTcBqnXotzcAM4kXcUDWAb8juH+/Aap16Ls\nHE+rHk8hbXv9BsvSP8N+9VrMY36j+r8tm0jnSqge/7vH2OgVoXWkv8xTwDHgJuDtPbZjD2kPaOUQ\n60G5K1x3AE/1eL/k3AapB2Wv3j1OCmCAvwD3AmfXxpSc4yD1oOwc+91gWfpnWPSGzlGFx0rSLjDV\nY7dvyDTwQ+BO4H3zrHEO8HDb80eq1/qNmZhnnfnUa/KGuZJzG8Qw5zZJ2uvZU3t9WHPsVq/0HPvd\nYFl6fkVv6My92tLL7aRdwbpP1p5PV0snFwGPAS+oPu8+0r+Ag+j2mXX1pB306yL1Zm6YOwpsIN0w\nd16w3iBKzW0Qw5rbMuBm4EOkPYK60nPsVa/0HGdusHwu8H3SYcOPa2NKzq9fvXnNb5h7HhcDL++w\n7CQl30ywnAX8sctnPFY9PgF8l3RoMKhHSd+IGatIyd1rzET1WsQg9Y4wu+u4i3RuJHpOZ77bkzO3\nQQxjbkuB7wDfovOdyaXn2K/esH5+h4FbgdfUXh/Wz7BbvSb/fIZdy+zViI/R+YTpacBzqvXTgZ8C\nb55HjVNId85Nko7x+p0wXU/eCalB6pW+YW6SwU6Y5s5tkHql57YE+AbwhR5jSs5xkHol5zjIDZYl\n57dobuhcQTqXUb9UezYpEQFeRPoLuI906ezjgTobSGfND7R9/WXM3sQG8JXq/f3AqwI15lPvg6S5\n7AN+RvoDEXUj8Afg76Tj4vcy3Ln1q1dybpCuBPyz+ryZS4cbGN4cB6lXco4vJx0m7CPdjvCR6vVh\nzW+QeqV/hpIkSZIkSZIkSZIkSZKkhepfxPLWgakjLWoAAAAASUVORK5CYII=\n", "text": [ "<matplotlib.figure.Figure at 0x215bcc0>" ] } ], "prompt_number": 12 }, { "cell_type": "markdown", "metadata": {}, "source": [ "####Multinomial Naive Bayes classifier" ] }, { "cell_type": "code", "collapsed": false, "input": [ "mnb = MultinomialNB()\n", "mnb_me = testClassifier(train_tfmat.toarray(), train_lbl, test_tfmat.toarray(), test_lbl, mnb)\n", "metrics_dict.append({'name':'MultinomialNB', 'metrics':mnb_me})" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "training time: 0:00:00.178000\n", "testing time: 0:00:00.015000\n", "classification report: \n", "\" precision recall f1-score support\n", "\n", "comp.os.ms-windows.misc 0.98 0.98 0.98 50\n", "rec.sport.baseball 0.98 0.98 0.98 50\n", "sci.electronics 0.98 0.92 0.95 50\n", "soc.religion.christian 0.94 1.00 0.97 50\n", "\n", "avg / total 0.97 0.97 0.97 200\n", "\"\n", "f1 score\n", "0.969831848664\n", "accuracy score\n", "0.97\n", "confusion matrix:\n", "[[49 0 0 1]\n", " [ 0 49 1 0]\n", " [ 1 1 46 2]\n", " [ 0 0 0 50]]\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x215bb00>" ] } ], "prompt_number": 13 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### K Nearest Neighbors classifier" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# for nn in [5, 10, 15]:\n", "for nn in [5]:\n", " print 'knn with ', nn, ' neighbors'\n", " knn = KNeighborsClassifier(n_neighbors=nn)\n", " knn_me = testClassifier(train_tfmat, train_lbl, test_tfmat, test_lbl, knn)\n", " metrics_dict.append({'name':'5NN', 'metrics':knn_me})\n", " print ' '" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "knn with 5 neighbors\n", "training time: 0:00:00.001000\n", "testing time: 0:00:00.039000\n", "classification report: \n", "\" precision recall f1-score support\n", "\n", "comp.os.ms-windows.misc 0.74 0.90 0.81 50\n", "rec.sport.baseball 0.80 0.74 0.77 50\n", "sci.electronics 0.84 0.62 0.71 50\n", "soc.religion.christian 0.89 1.00 0.94 50\n", "\n", "avg / total 0.82 0.81 0.81 200\n", "\"\n", "f1 score\n", "0.80942101218\n", "accuracy score\n", "0.815\n", "confusion matrix:\n", "[[45 0 3 2]\n", " [10 37 3 0]\n", " [ 6 9 31 4]\n", " [ 0 0 0 50]]\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x2e98fd0>" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " \n" ] } ], "prompt_number": 14 }, { "cell_type": "markdown", "metadata": {}, "source": [ "###SVM classifier" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "####Linear SVM" ] }, { "cell_type": "code", "collapsed": false, "input": [ "lsvm = LinearSVC()\n", "lsvm_me = testClassifier(train_tfmat, train_lbl, test_tfmat, test_lbl, lsvm)\n", "metrics_dict.append({'name':'LinearSVM', 'metrics':lsvm_me})" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "training time: 0:00:00.038000\n", "testing time: 0:00:00\n", "classification report: \n", "\" precision recall f1-score support\n", "\n", "comp.os.ms-windows.misc 1.00 1.00 1.00 50\n", "rec.sport.baseball 1.00 0.98 0.99 50\n", "sci.electronics 0.98 1.00 0.99 50\n", "soc.religion.christian 1.00 1.00 1.00 50\n", "\n", "avg / total 1.00 0.99 0.99 200\n", "\"\n", "f1 score\n", "0.99499949995\n", "accuracy score\n", "0.995\n", "confusion matrix:\n", "[[50 0 0 0]\n", " [ 0 49 1 0]\n", " [ 0 0 50 0]\n", " [ 0 0 0 50]]\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x13ecf390>" ] } ], "prompt_number": 15 }, { "cell_type": "markdown", "metadata": {}, "source": [ "####$\\nu$-SVM" ] }, { "cell_type": "code", "collapsed": false, "input": [ "nusvm = NuSVC()\n", "nusvm_me = testClassifier(train_tfmat, train_lbl, test_tfmat, test_lbl, nusvm)\n", "metrics_dict.append({'name':'nuSVM', 'metrics':nusvm_me})" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "training time: 0:00:00.383000\n", "testing time: " ] }, { "output_type": "stream", "stream": "stdout", "text": [ " 0:00:00.079000\n", "classification report: \n", "\" precision recall f1-score support\n", "\n", "comp.os.ms-windows.misc 1.00 0.84 0.91 50\n", "rec.sport.baseball 0.92 0.98 0.95 50\n", "sci.electronics 0.85 0.92 0.88 50\n", "soc.religion.christian 0.98 1.00 0.99 50\n", "\n", "avg / total 0.94 0.94 0.93 200\n", "\"\n", "f1 score\n", "0.934803545864\n", "accuracy score\n", "0.935\n", "confusion matrix:\n", "[[42 1 7 0]\n", " [ 0 49 1 0]\n", " [ 0 3 46 1]\n", " [ 0 0 0 50]]\n" ] }, { "metadata": {}, "output_type": "display_data", "png": "iVBORw0KGgoAAAANSUhEUgAAAQ8AAAEACAYAAACtefPrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n91CSJEmSJEmSJEmSJEmStFz9EcG61Z4eH+nzAAAAAElFTkSuQmCC\n", "text": [ "<matplotlib.figure.Figure at 0x215bba8>" ] } ], "prompt_number": 16 }, { "cell_type": "markdown", "metadata": {}, "source": [ "####SVM with RBF kernel" ] }, { "cell_type": "code", "collapsed": false, "input": [ "rbfsvm = SVC()\n", "rbfsvm_me = testClassifier(train_tfmat, train_lbl, test_tfmat, test_lbl, rbfsvm)\n", "metrics_dict.append({'name':'SVM with RBF kernel', 'metrics':rbfsvm_me})" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "training time: 0:00:00.743000\n", "testing time: " ] }, { "output_type": "stream", "stream": "stdout", "text": [ " 0:00:00.153000\n", "classification report: \n", "\" precision recall f1-score support\n", "\n", "comp.os.ms-windows.misc 1.00 0.52 0.68 50\n", "rec.sport.baseball 1.00 0.92 0.96 50\n", "sci.electronics 0.64 1.00 0.78 50\n", "soc.religion.christian 1.00 1.00 1.00 50\n", "\n", "avg / total 0.91 0.86 0.86 200\n", "\"\n", "f1 score\n", "0.855948464912\n", "accuracy score\n", "0.86\n", "confusion matrix:\n", "[[26 0 24 0]\n", " [ 0 46 4 0]\n", " [ 0 0 50 0]\n", " [ 0 0 0 50]]\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x215bba8>" ] } ], "prompt_number": 17 }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Second, best classifier of each kind will be found using 5 fold cross validation with a search on parameter grid" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###Naive Bayes classifiers" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "####Best Bernoulli Naive Bayes classifier\n", "\n", "####Parameters\n", "alpha : float, optional (default=1.0)\n", "\n", " Additive (Laplace/Lidstone) smoothing parameter\n", " (0 for no smoothing).\n", "\n", "binarize : float or None, optional\n", "\n", " Threshold for binarizing (mapping to booleans) of sample features.\n", " If None, input is presumed to already consist of binary vectors.\n", "\n", "fit_prior : boolean\n", "\n", " Whether to learn class prior probabilities or not.\n", " If false, a uniform prior will be used.\n", "\n", "class_prior : array-like, size=[n_classes,]\n", "\n", " Prior probabilities of the classes. If specified the priors are not\n", " adjusted according to the data.\n", " \n", "__Note__: since classes are balanced, their priors are equal." ] }, { "cell_type": "code", "collapsed": false, "input": [ "bnb_params = {'alpha': [a0.1 for a in range(0,11)]}\n", "bnb_clf = GridSearchCV(BernoulliNB(), bnb_params, cv=10)\n", "bnb_clf.fit(train_tfmat, train_lbl)\n", "print 'best parameters'\n", "print bnb_clf.best_params_\n", "best_bnb = BernoulliNB(alpha=bnb_clf.best_params_['alpha'])\n", "best_bnb_me = testClassifier(train_tfmat, train_lbl, test_tfmat, test_lbl, best_bnb)\n", "metrics_dict.append({'name':'Best BernoulliNB', 'metrics':best_bnb_me})" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "best parameters\n", "{'alpha': 0.1}\n", "training time: 0:00:00.004000\n", "testing time: 0:00:00.003000\n", "classification report: \n", "\" precision recall f1-score support\n", "\n", "comp.os.ms-windows.misc 1.00 0.74 0.85 50\n", "rec.sport.baseball 1.00 1.00 1.00 50\n", "sci.electronics 0.79 1.00 0.88 50\n", "soc.religion.christian 1.00 1.00 1.00 50\n", "\n", "avg / total 0.95 0.94 0.93 200\n", "\"\n", "f1 score\n", "0.933882616214\n", "accuracy score\n", "0.935\n", "confusion matrix:\n", "[[37 0 13 0]\n", " [ 0 50 0 0]\n", " [ 0 0 50 0]\n", " [ 0 0 0 50]]\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x14721748>" ] } ], "prompt_number": 21 }, { "cell_type": "markdown", "metadata": {}, "source": [ "####Best Gaussian Naive Bayes classifier\n", "__Note__: this classifier doesn't have any paramters, so no cross validation and grid search on its parameters can be performed." ] }, { "cell_type": "code", "collapsed": false, "input": [ "best_gnb = GaussianNB()\n", "best_gnb_me = testClassifier(train_tfmat.toarray(), train_lbl, test_tfmat.toarray(), test_lbl, best_gnb)\n", "metrics_dict.append({'name':'Best GaussianNB', 'metrics':best_gnb_me})" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "training time: 0:00:00.393000\n", "testing time: " ] }, { "output_type": "stream", "stream": "stdout", "text": [ " 0:00:00.271000\n", "classification report: \n", "\" precision recall f1-score support\n", "\n", "comp.os.ms-windows.misc 0.83 0.86 0.84 50\n", "rec.sport.baseball 0.94 0.96 0.95 50\n", "sci.electronics 0.89 0.80 0.84 50\n", "soc.religion.christian 0.96 1.00 0.98 50\n", "\n", "avg / total 0.90 0.91 0.90 200\n", "\"\n", "f1 score\n", "0.904032431107\n", "accuracy score\n", "0.905\n", "confusion matrix:\n", "[[43 2 4 1]\n", " [ 1 48 1 0]\n", " [ 8 1 40 1]\n", " [ 0 0 0 50]]\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x35e9630>" ] } ], "prompt_number": 22 }, { "cell_type": "markdown", "metadata": {}, "source": [ "####Best Multinomial Bayes classifier\n", "####Parameters\n", "\n", "alpha : float, optional (default=1.0)\n", "\n", " Additive (Laplace/Lidstone) smoothing parameter\n", " (0 for no smoothing).\n", "\n", "fit_prior : boolean\n", "\n", " Whether to learn class prior probabilities or not.\n", " If false, a uniform prior will be used.\n", "\n", "class_prior : array-like, size (n_classes,)\n", "\n", " Prior probabilities of the classes. If specified the priors are not\n", " adjusted according to the data." ] }, { "cell_type": "code", "collapsed": false, "input": [ "mbn_params = {'alpha': [a0.1 for a in range(0,11)]}\n", "mbn_clf = GridSearchCV(MultinomialNB(), mbn_params, cv=10)\n", "mbn_clf.fit(train_tfmat, train_lbl)\n", "print 'best parameters'\n", "print mbn_clf.best_params_\n", "best_mbn = MultinomialNB(alpha=mbn_clf.best_params_['alpha'])\n", "best_mbn_me = testClassifier(train_tfmat, train_lbl, test_tfmat, test_lbl, best_mbn)\n", "metrics_dict.append({'name':'Best MultinomialNB', 'metrics':best_mbn_me})" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "best parameters\n", "{'alpha': 0.2}\n", "training time: 0:00:00.003000\n", "testing time: 0:00:00.001000\n", "classification report: \n", "\" precision recall f1-score support\n", "\n", "comp.os.ms-windows.misc 0.98 0.98 0.98 50\n", "rec.sport.baseball 0.98 1.00 0.99 50\n", "sci.electronics 0.98 0.96 0.97 50\n", "soc.religion.christian 1.00 1.00 1.00 50\n", "\n", "avg / total 0.98 0.98 0.98 200\n", "\"\n", "f1 score\n", "0.984948994899\n", "accuracy score\n", "0.985\n", "confusion matrix:\n", "[[49 0 1 0]\n", " [ 0 50 0 0]\n", " [ 1 1 48 0]\n", " [ 0 0 0 50]]\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x2118a58>" ] } ], "prompt_number": 25 }, { "cell_type": "markdown", "metadata": {}, "source": [ "####Best KNN classifier\n", "####Parameters\n", "\n", "n_neighbors : int, optional (default = 5)\n", "\n", " Number of neighbors to use by default for :meth:`k_neighbors` queries.\n", "\n", "weights : str or callable\n", "\n", " weight function used in prediction. Possible values:\n", "\n", " - 'uniform' : uniform weights. All points in each neighborhood\n", " are weighted equally.\n", " - 'distance' : weight points by the inverse of their distance.\n", " in this case, closer neighbors of a query point will have a\n", " greater influence than neighbors which are further away.\n", " - [callable] : a user-defined function which accepts an\n", " array of distances, and returns an array of the same shape\n", " containing the weights.\n", "\n", " Uniform weights are used by default.\n", "\n", "algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, optional\n", "\n", " Algorithm used to compute the nearest neighbors:\n", "\n", " - 'ball_tree' will use :class:`BallTree`\n", " - 'kd_tree' will use :class:`KDTree`\n", " - 'brute' will use a brute-force search.\n", " - 'auto' will attempt to decide the most appropriate algorithm\n", " based on the values passed to :meth:`fit` method.\n", "\n", " Note: fitting on sparse input will override the setting of\n", " this parameter, using brute force.\n", "\n", "leaf_size : int, optional (default = 30)\n", "\n", " Leaf size passed to BallTree or KDTree. This can affect the\n", " speed of the construction and query, as well as the memory\n", " required to store the tree. The optimal value depends on the\n", " nature of the problem.\n", "\n", "metric : string or DistanceMetric object (default='minkowski')\n", "\n", " the distance metric to use for the tree. The default metric is\n", " minkowski, and with p=2 is equivalent to the standard Euclidean\n", " metric. See the documentation of the DistanceMetric class for a\n", " list of available metrics.\n", "\n", "p : integer, optional (default = 2)\n", "\n", " Power parameter for the Minkowski metric. When p = 1, this is\n", " equivalent to using manhattan_distance (l1), and euclidean_distance\n", " (l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used." ] }, { "cell_type": "code", "collapsed": false, "input": [ "knn_params = {'n_neighbors': range(1,21), 'weights': ['uniform', 'distance'], 'algorithm': ['ball_tree', 'kd_tree'],\n", " 'leaf_size': [15, 30, 50, 100], 'p': [1,2]}\n", "knn_clf = GridSearchCV(KNeighborsClassifier(), knn_params, cv=10)\n", "knn_clf.fit(train_tfmat, train_lbl)\n", "print 'best parameters'\n", "print knn_clf.best_params_\n", "best_knn = KNeighborsClassifier(n_neighbors=knn_clf.best_params_['n_neighbors'], weights=knn_clf.best_params_['weights'],\n", " algorithm=knn_clf.best_params_['algorithm'], leaf_size=knn_clf.best_params_['leaf_size'])\n", "best_knn_me = testClassifier(train_tfmat, train_lbl, test_tfmat, test_lbl, best_knn)\n", "metrics_dict.append({'name':'Best KNN', 'metrics':best_knn_me})" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "best parameters\n", "{'n_neighbors': 4, 'weights': 'distance', 'leaf_size': 15, 'algorithm': 'ball_tree', 'p': 1}\n", "training time: 0:00:00\n", "testing time: 0:00:00.031000\n", "classification report: \n", "\" precision recall f1-score support\n", "\n", "comp.os.ms-windows.misc 0.71 0.84 0.77 50\n", "rec.sport.baseball 0.80 0.70 0.74 50\n", "sci.electronics 0.80 0.66 0.73 50\n", "soc.religion.christian 0.89 1.00 0.94 50\n", "\n", "avg / total 0.80 0.80 0.80 200\n", "\"\n", "f1 score\n", "0.795998501147\n", "accuracy score\n", "0.8\n", "confusion matrix:\n", "[[42 2 4 2]\n", " [11 35 4 0]\n", " [ 6 7 33 4]\n", " [ 0 0 0 50]]\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x2118a20>" ] } ], "prompt_number": 28 }, { "cell_type": "markdown", "metadata": {}, "source": [ "####Best Linear SVM classifier\n", "####Parameters\n", "\n", "C : float, optional (default=1.0)\n", "\n", " Penalty parameter C of the error term.\n", "\n", "loss : string, 'l1' or 'l2' (default='l2')\n", "\n", " Specifies the loss function. 'l1' is the hinge loss (standard SVM)\n", " while 'l2' is the squared hinge loss.\n", "\n", "penalty : string, 'l1' or 'l2' (default='l2')\n", "\n", " Specifies the norm used in the penalization. The 'l2'\n", " penalty is the standard used in SVC. The 'l1' leads to `coef_`\n", " vectors that are sparse.\n", "\n", "dual : bool, (default=True)\n", "\n", " Select the algorithm to either solve the dual or primal\n", " optimization problem. Prefer dual=False when n_samples > n_features.\n", "\n", "tol : float, optional (default=1e-4)\n", "\n", " Tolerance for stopping criteria\n", "\n", "multi_class: string, 'ovr' or 'crammer_singer' (default='ovr')\n", "\n", " Determines the multi-class strategy if `y` contains more than\n", " two classes.\n", " `ovr` trains n_classes one-vs-rest classifiers, while `crammer_singer`\n", " optimizes a joint objective over all classes.\n", " While `crammer_singer` is interesting from an theoretical perspective\n", " as it is consistent it is seldom used in practice and rarely leads to\n", " better accuracy and is more expensive to compute.\n", " If `crammer_singer` is chosen, the options loss, penalty and dual will\n", " be ignored.\n", "\n", "fit_intercept : boolean, optional (default=True)\n", "\n", " Whether to calculate the intercept for this model. If set\n", " to false, no intercept will be used in calculations\n", " (e.g. data is expected to be already centered).\n", "\n", "intercept_scaling : float, optional (default=1)\n", "\n", " when self.fit_intercept is True, instance vector x becomes\n", " [x, self.intercept_scaling],\n", " i.e. a \"synthetic\" feature with constant value equals to\n", " intercept_scaling is appended to the instance vector.\n", " The intercept becomes intercept_scaling * synthetic feature weight\n", " Note! the synthetic feature weight is subject to l1/l2 regularization\n", " as all other features.\n", " To lessen the effect of regularization on synthetic feature weight\n", " (and therefore on the intercept) intercept_scaling has to be increased\n", "\n", "class_weight : {dict, 'auto'}, optional\n", "\n", " Set the parameter C of class i to class_weight[i]*C for\n", " SVC. If not given, all classes are supposed to have\n", " weight one. The 'auto' mode uses the values of y to\n", " automatically adjust weights inversely proportional to\n", " class frequencies.\n", "\n", "verbose : int, default: 0\n", "\n", " Enable verbose output. Note that this setting takes advantage of a\n", " per-process runtime setting in liblinear that, if enabled, may not work\n", " properly in a multithreaded context.\n", "\n", "random_state : int seed, RandomState instance, or None (default)\n", "\n", " The seed of the pseudo random number generator to use when\n", " shuffling the data." ] }, { "cell_type": "code", "collapsed": false, "input": [ "lsvm_params = {'C':[1,10,100,1000], 'loss':['l1', 'l2']}\n", "lsvm_clf = GridSearchCV(LinearSVC(), lsvm_params, cv=5)\n", "lsvm_clf.fit(train_tfmat, train_lbl)\n", "print 'best parameters'\n", "print lsvm_clf.best_params_\n", "best_lsvm = LinearSVC(C=lsvm_clf.best_params_['C'], loss=lsvm_clf.best_params_['loss'])\n", "best_lsvm_me = testClassifier(train_tfmat, train_lbl, test_tfmat, test_lbl, best_lsvm)\n", "metrics_dict.append({'name':'Best Linear SVM', 'metrics':best_lsvm_me})" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "best parameters\n", "{'loss': 'l1', 'C': 10}\n", "training time: " ] }, { "output_type": "stream", "stream": "stdout", "text": [ " 0:00:00.096000\n", "testing time: 0:00:00\n", "classification report: \n", "\" precision recall f1-score support\n", "\n", "comp.os.ms-windows.misc 1.00 1.00 1.00 50\n", "rec.sport.baseball 1.00 0.98 0.99 50\n", "sci.electronics 0.98 1.00 0.99 50\n", "soc.religion.christian 1.00 1.00 1.00 50\n", "\n", "avg / total 1.00 0.99 0.99 200\n", "\"\n", "f1 score\n", "0.99499949995\n", "accuracy score\n", "0.995\n", "confusion matrix:\n", "[[50 0 0 0]\n", " [ 0 49 1 0]\n", " [ 0 0 50 0]\n", " [ 0 0 0 50]]\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x144ee550>" ] } ], "prompt_number": 29 }, { "cell_type": "markdown", "metadata": {}, "source": [ "###Creating a table summarizing metrics of the classifiers\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "metrics_table = []\n", "metrics_table.append(['', 'name', 'training time', 'testing time',\n", " 'p_1', 'r_1',\n", " 'p_2', 'r_2',\n", " 'p_3', 'r_3',\n", " 'p_4', 'r_4',\n", " 'macro-averaged F1 score'\n", " ])\n", "i = 0\n", "for me in metrics_dict:\n", " i += 1\n", " metric = []\n", " metric.append(i)\n", " metric.append(me['name'])\n", " for m in me['metrics']:\n", " metric.append(m)\n", " metrics_table.append(metric)\n", "make_table(metrics_table)\n", "\n", "# styling\n", "apply_theme('basic_both')\n", "set_column_style(12, align='center')" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<table border=\"1\" cellpadding=\"3\" cellspacing=\"0\" style=\"border:1px solid black;border-collapse:collapse;\"><tr><td style=\"background-color:White;border-left: 1px solid transparent;border-top: 1px solid transparent;\"><b></b></td><td style=\"background-color:LightGray;\"><b>name</b></td><td style=\"background-color:LightGray;\"><b>training&nbsptime</b></td><td style=\"background-color:LightGray;\"><b>testing&nbsptime</b></td><td style=\"background-color:LightGray;\"><b>p_1</b></td><td style=\"background-color:LightGray;\"><b>r_1</b></td><td style=\"background-color:LightGray;\"><b>p_2</b></td><td style=\"background-color:LightGray;\"><b>r_2</b></td><td style=\"background-color:LightGray;\"><b>p_3</b></td><td style=\"background-color:LightGray;\"><b>r_3</b></td><td style=\"background-color:LightGray;\"><b>p_4</b></td><td style=\"background-color:LightGray;\"><b>r_4</b></td><td style=\"background-color:LightGray;text-align:center;\"><b>macro-averaged&nbspF1&nbspscore</b></td></tr><tr><td style=\"background-color:LightGray;\"><b>1</b></td><td style=\"background-color:Ivory;\">BernoulliNB</td><td style=\"background-color:Ivory;\">0:00:00.007000</td><td style=\"background-color:Ivory;\">0:00:00.004000</td><td style=\"background-color:Ivory;\">1.0000</td><td style=\"background-color:Ivory;\">0.3800</td><td style=\"background-color:Ivory;\">1.0000</td><td style=\"background-color:Ivory;\">1.0000</td><td style=\"background-color:Ivory;\">0.5976</td><td style=\"background-color:Ivory;\">0.9800</td><td style=\"background-color:Ivory;\">0.9796</td><td style=\"background-color:Ivory;\">0.9600</td><td style=\"background-color:Ivory;text-align:center;\">0.8157</td></tr><tr><td style=\"background-color:LightGray;\"><b>2</b></td><td style=\"background-color:AliceBlue;\">GaussianNB</td><td style=\"background-color:AliceBlue;\">0:00:00.404000</td><td style=\"background-color:AliceBlue;\">0:00:00.354000</td><td style=\"background-color:AliceBlue;\">0.8269</td><td style=\"background-color:AliceBlue;\">0.8600</td><td style=\"background-color:AliceBlue;\">0.9412</td><td style=\"background-color:AliceBlue;\">0.9600</td><td style=\"background-color:AliceBlue;\">0.8889</td><td style=\"background-color:AliceBlue;\">0.8000</td><td style=\"background-color:AliceBlue;\">0.9615</td><td style=\"background-color:AliceBlue;\">1.0000</td><td style=\"background-color:AliceBlue;text-align:center;\">0.9040</td></tr><tr><td style=\"background-color:LightGray;\"><b>3</b></td><td style=\"background-color:Ivory;\">MultinomialNB</td><td style=\"background-color:Ivory;\">0:00:00.178000</td><td style=\"background-color:Ivory;\">0:00:00.015000</td><td style=\"background-color:Ivory;\">0.9800</td><td style=\"background-color:Ivory;\">0.9800</td><td style=\"background-color:Ivory;\">0.9800</td><td style=\"background-color:Ivory;\">0.9800</td><td style=\"background-color:Ivory;\">0.9787</td><td style=\"background-color:Ivory;\">0.9200</td><td style=\"background-color:Ivory;\">0.9434</td><td style=\"background-color:Ivory;\">1.0000</td><td style=\"background-color:Ivory;text-align:center;\">0.9698</td></tr><tr><td style=\"background-color:LightGray;\"><b>4</b></td><td style=\"background-color:AliceBlue;\">5NN</td><td style=\"background-color:AliceBlue;\">0:00:00.001000</td><td style=\"background-color:AliceBlue;\">0:00:00.039000</td><td style=\"background-color:AliceBlue;\">0.7377</td><td style=\"background-color:AliceBlue;\">0.9000</td><td style=\"background-color:AliceBlue;\">0.8043</td><td style=\"background-color:AliceBlue;\">0.7400</td><td style=\"background-color:AliceBlue;\">0.8378</td><td style=\"background-color:AliceBlue;\">0.6200</td><td style=\"background-color:AliceBlue;\">0.8929</td><td style=\"background-color:AliceBlue;\">1.0000</td><td style=\"background-color:AliceBlue;text-align:center;\">0.8094</td></tr><tr><td style=\"background-color:LightGray;\"><b>5</b></td><td style=\"background-color:Ivory;\">LinearSVM</td><td style=\"background-color:Ivory;\">0:00:00.038000</td><td style=\"background-color:Ivory;\">0:00:00</td><td style=\"background-color:Ivory;\">1.0000</td><td style=\"background-color:Ivory;\">1.0000</td><td style=\"background-color:Ivory;\">1.0000</td><td style=\"background-color:Ivory;\">0.9800</td><td style=\"background-color:Ivory;\">0.9804</td><td style=\"background-color:Ivory;\">1.0000</td><td style=\"background-color:Ivory;\">1.0000</td><td style=\"background-color:Ivory;\">1.0000</td><td style=\"background-color:Ivory;text-align:center;\">0.9950</td></tr><tr><td style=\"background-color:LightGray;\"><b>6</b></td><td style=\"background-color:AliceBlue;\">nuSVM</td><td style=\"background-color:AliceBlue;\">0:00:00.383000</td><td style=\"background-color:AliceBlue;\">0:00:00.079000</td><td style=\"background-color:AliceBlue;\">1.0000</td><td style=\"background-color:AliceBlue;\">0.8400</td><td style=\"background-color:AliceBlue;\">0.9245</td><td style=\"background-color:AliceBlue;\">0.9800</td><td style=\"background-color:AliceBlue;\">0.8519</td><td style=\"background-color:AliceBlue;\">0.9200</td><td style=\"background-color:AliceBlue;\">0.9804</td><td style=\"background-color:AliceBlue;\">1.0000</td><td style=\"background-color:AliceBlue;text-align:center;\">0.9348</td></tr><tr><td style=\"background-color:LightGray;\"><b>7</b></td><td style=\"background-color:Ivory;\">SVM&nbspwith&nbspRBF&nbspkernel</td><td style=\"background-color:Ivory;\">0:00:00.743000</td><td style=\"background-color:Ivory;\">0:00:00.153000</td><td style=\"background-color:Ivory;\">1.0000</td><td style=\"background-color:Ivory;\">0.5200</td><td style=\"background-color:Ivory;\">1.0000</td><td style=\"background-color:Ivory;\">0.9200</td><td style=\"background-color:Ivory;\">0.6410</td><td style=\"background-color:Ivory;\">1.0000</td><td style=\"background-color:Ivory;\">1.0000</td><td style=\"background-color:Ivory;\">1.0000</td><td style=\"background-color:Ivory;text-align:center;\">0.8559</td></tr><tr><td style=\"background-color:LightGray;\"><b>8</b></td><td style=\"background-color:AliceBlue;\">Best&nbspBernoulliNB</td><td style=\"background-color:AliceBlue;\">0:00:00.004000</td><td style=\"background-color:AliceBlue;\">0:00:00.003000</td><td style=\"background-color:AliceBlue;\">1.0000</td><td style=\"background-color:AliceBlue;\">0.7400</td><td style=\"background-color:AliceBlue;\">1.0000</td><td style=\"background-color:AliceBlue;\">1.0000</td><td style=\"background-color:AliceBlue;\">0.7937</td><td style=\"background-color:AliceBlue;\">1.0000</td><td style=\"background-color:AliceBlue;\">1.0000</td><td style=\"background-color:AliceBlue;\">1.0000</td><td style=\"background-color:AliceBlue;text-align:center;\">0.9339</td></tr><tr><td style=\"background-color:LightGray;\"><b>9</b></td><td style=\"background-color:Ivory;\">Best&nbspGaussianNB</td><td style=\"background-color:Ivory;\">0:00:00.393000</td><td style=\"background-color:Ivory;\">0:00:00.271000</td><td style=\"background-color:Ivory;\">0.8269</td><td style=\"background-color:Ivory;\">0.8600</td><td style=\"background-color:Ivory;\">0.9412</td><td style=\"background-color:Ivory;\">0.9600</td><td style=\"background-color:Ivory;\">0.8889</td><td style=\"background-color:Ivory;\">0.8000</td><td style=\"background-color:Ivory;\">0.9615</td><td style=\"background-color:Ivory;\">1.0000</td><td style=\"background-color:Ivory;text-align:center;\">0.9040</td></tr><tr><td style=\"background-color:LightGray;\"><b>10</b></td><td style=\"background-color:AliceBlue;\">Best&nbspMultinomialNB</td><td style=\"background-color:AliceBlue;\">0:00:00.003000</td><td style=\"background-color:AliceBlue;\">0:00:00.001000</td><td style=\"background-color:AliceBlue;\">0.9800</td><td style=\"background-color:AliceBlue;\">0.9800</td><td style=\"background-color:AliceBlue;\">0.9804</td><td style=\"background-color:AliceBlue;\">1.0000</td><td style=\"background-color:AliceBlue;\">0.9796</td><td style=\"background-color:AliceBlue;\">0.9600</td><td style=\"background-color:AliceBlue;\">1.0000</td><td style=\"background-color:AliceBlue;\">1.0000</td><td style=\"background-color:AliceBlue;text-align:center;\">0.9849</td></tr><tr><td style=\"background-color:LightGray;\"><b>11</b></td><td style=\"background-color:Ivory;\">Best&nbspKNN</td><td style=\"background-color:Ivory;\">0:00:00</td><td style=\"background-color:Ivory;\">0:00:00.031000</td><td style=\"background-color:Ivory;\">0.7119</td><td style=\"background-color:Ivory;\">0.8400</td><td style=\"background-color:Ivory;\">0.7955</td><td style=\"background-color:Ivory;\">0.7000</td><td style=\"background-color:Ivory;\">0.8049</td><td style=\"background-color:Ivory;\">0.6600</td><td style=\"background-color:Ivory;\">0.8929</td><td style=\"background-color:Ivory;\">1.0000</td><td style=\"background-color:Ivory;text-align:center;\">0.7960</td></tr><tr><td style=\"background-color:LightGray;\"><b>12</b></td><td style=\"background-color:AliceBlue;\">Best&nbspLinear&nbspSVM</td><td style=\"background-color:AliceBlue;\">0:00:00.096000</td><td style=\"background-color:AliceBlue;\">0:00:00</td><td style=\"background-color:AliceBlue;\">1.0000</td><td style=\"background-color:AliceBlue;\">1.0000</td><td style=\"background-color:AliceBlue;\">1.0000</td><td style=\"background-color:AliceBlue;\">0.9800</td><td style=\"background-color:AliceBlue;\">0.9804</td><td style=\"background-color:AliceBlue;\">1.0000</td><td style=\"background-color:AliceBlue;\">1.0000</td><td style=\"background-color:AliceBlue;\">1.0000</td><td style=\"background-color:AliceBlue;text-align:center;\">0.9950</td></tr></table>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 31, "text": [ "<ipy_table.IpyTable at 0x14513a20>" ] } ], "prompt_number": 31 } ], "metadata": {} } ] }	mit
ziky5/F4500_Python_pro_fyziky	lekce_05/B.ipynb	1	2557	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "def B(t, r):\n", " \"\"\"Returns magnetic field of a current loop in point r = (r1, r2, r3) and time t\n", " The expressions for the magnatic field of the current loop are taken from \n", " [J. Simpson et al. Simple Analytic Expressions for the Magnetic Field of a Circular Current Loop. \n", " Transactions on Magnetics (2001)]\n", " \n", " Arguments:\n", " r -- vector of position\n", " t -- time\n", " \n", " Returns:\n", " B -- vector of magnetic field components\n", " \"\"\"\n", " x,y,z = r\n", " \n", " a = 0.03 # diameter of the solenoid in meters\n", " mu0 = 4np.pi1e-7 # vacuum permeability\n", " I = 10 # current in Amps\n", " #D = 0.1 # distance of two current loops\n", " C = mu0 * I / np.pi\n", " \n", " rho2 = np.power(x,2) + np.power(y,2)\n", " rho = np.sqrt(rho2)\n", " r2 = np.power(x,2) + np.power(y,2) + np.power(z,2)\n", " a2 = np.power(a, 2)\n", " alpha2 = a2 + r2 - 2arho\n", " beta2 = a2 + r2 + 2arho\n", " beta = np.sqrt(beta2)\n", " k2 = 1 - alpha2/beta2\n", " \n", " #print(ellipk(k2))\n", " #print(2 * alpha2 * beta * rho2)\n", " \n", " Bx = (C * x * z) / (2 * alpha2 * beta * rho2) * ( (a2 + r2) * ellipe(k2) - alpha2 * ellipk(k2) )\n", " By = Bx * y / x\n", " Bz = C / ( 2 * alpha2 * beta ) * ( (a2 - r2) * ellipe(k2) + alpha2 * ellipk(k2) )\n", " \n", " # Function diverges if x = y = 0, in which case we fix it.\n", " if x == 0 or y == 0:\n", " Bx, By = (0, 0)\n", " \n", " return (Bx, By, Bz)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
jasag/Phytoliths-recognition-system	code/notebooks/Prototypes/Image_Labeler_Prototypes/Image_Labeler_prototype2.ipynb	1	17921	{ "cells": [ { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from __future__ import print_function\n", "import ipywidgets as widgets\n", "from traitlets import Unicode, validate, Bool, Any\n", "from ipywidgets import Color\n" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class ImageLabelerWidget(widgets.DOMWidget):\n", " _view_name = Unicode('ImageLabelerView').tag(sync=True)\n", " _view_module = Unicode('ImageLabeler').tag(sync=True)\n" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "application/javascript": [ "var isClicked = false;\n", "var rect, startX, startY, endX, endY;\n", "\n", "//Cargamos CSS\n", "var cssId = 'Style';\n", "if (!document.getElementById(cssId))\n", "{\n", " var head = document.getElementsByTagName('head')[0];\n", " var link = document.createElement('link');\n", " link.id = cssId;\n", " link.rel = 'stylesheet';\n", " link.type = 'text/css';\n", " link.href = 'Style.css';\n", " head.appendChild(link);\n", "}\n", "\n", "function updateRect(rect) {\n", "// rect.attr(\"x\",endX - startX > 0 ? startX : endX)\n", "// .attr(\"y\",y: endY - startY > 0 ? startY : endY)\n", "// .attr(\"width\",Math.abs(endX - startX))\n", "// .attr(\"height\",Math.abs(endY - startY));\n", " rect.attr({\n", " x: endX - startX > 0 ? startX : endX,\n", " y: endY - startY > 0 ? startY : endY,\n", " width: Math.abs(endX - startX),\n", " height: Math.abs(endY - startY)\n", " });\n", "}\n", "\n", "require.undef('ImageLabeler');\n", "define('ImageLabeler', [\"jupyter-js-widgets\"], function(widgets) {\n", "\n", " var ImageLabelerView = widgets.DOMWidgetView.extend({\n", " // Renderizar vista\n", " render: function() {\n", " require.config({\n", " paths: {\n", " d3: '//cdnjs.cloudflare.com/ajax/libs/d3/3.4.8/d3.min'\n", " }\n", " });\n", " require(['d3'], function(d3){\n", " //Añadimos un contenedor\n", " var canvas = element.append(\"<div id='canvas'></div>\");\n", "// var canvas = createElement('div');\n", "// d3.select(canvas)\n", "// .attr(\"id\",\"canvas\")\n", "// .attr(\"width\",\"960px\")\n", "// .attr(\"height\",\"600px\");\n", " \n", " $(\"#canvas\").width(\"960px\");\n", " $(\"#canvas\").height(\"600px\");\n", " \n", " var margin = {top: 20, right: 20, bottom: 30, left: 40};\n", " var width = 880 - margin.left - margin.right;\n", " var height = 500 - margin.top - margin.bottom;\n", "\n", " //Creamos SVG\n", " var w = 600, h = 500;\n", " var svg = d3.select('#canvas').append('svg').attr({width: w, height: h});\n", "\n", " // Añadimos un rectángulo\n", " //var svg = svg.append(\"rect\").attr(\"x\",10).attr(\"y\",10).attr(\"width\",10).attr(\"height\",10)\n", " \n", " //Añadimos la imagen\n", " \n", " svg.append(\"image\")\n", " .attr(\"xlink:href\", \"http://www.google.com/intl/en_ALL/images/logo.gif\")//\"@Url.Content(\"~/Content/images/icons/refresh.png\")\")\n", " .attr(\"x\", \"0\")\n", " .attr(\"y\", \"0\")\n", " .attr(\"width\", \"200\")\n", " .attr(\"height\", \"200\");\n", " \n", " //Añadimos listeners\n", " d3.select(\"#canvas\").on(\"click\", function(eve){\n", " console.log(d3.mouse(this));\n", " \n", " if(isClicked){\n", " //Finalizamos rectangulo\n", " isClicked = false;\n", " //Inicializamos coords.\n", " endX = d3.mouse(this)[0];\n", " endY = d3.mouse(this)[1]; \n", " \n", " //Cambiamos ref al rectangulo\n", " rect = null;\n", " \n", " //logger\n", " console.log(\"Finalizamos rectangulo\");\n", " console.log(endX);\n", " console.log(endY);\n", " \n", " //Inicializamos coords.\n", " endX = d3.mouse(this)[0];\n", " endY = d3.mouse(this)[1]; \n", " \n", " }else{\n", " //Inicializamos rectangulo\n", " isClicked = true;\n", " //Inicializamos coords.\n", " startX = d3.mouse(this)[0];\n", " startY = d3.mouse(this)[1]; \n", " \n", " //Creamos rectangulo\n", " rect = svg.append(\"rect\")\n", " .attr(\"x\",startX)\n", " .attr(\"y\",startY)\n", " .attr(\"width\",0)\n", " .attr(\"height\",0)\n", " .attr(\"class\",\"rectangle\");\n", "// .attr(\"fill\",\"transparent\")\n", "// .attr(\"stroke\",\"green\")\n", "// .attr(\"stroke-width\",\"5px\");\n", " \n", " console.log(\"Inicializamos rectangulo\");\n", " console.log(startX);\n", " console.log(startY);\n", " \n", " rect = svg.append(\"rect\");\n", " //updateRect(rect);\n", " }\n", " });\n", " \n", " d3.select(\"#canvas\").on(\"mousemove\", function(eve){\n", " if(isClicked){\n", " endX = d3.mouse(this)[0];\n", " endY = d3.mouse(this)[1];\n", "// updateRect(rect);\n", " \n", " //Actualizamos valores\n", " if (endX - startX > 0){\n", " var x = startX\n", " }else{\n", " var x = endX;\n", " }\n", " \n", " if (endY - startY > 0){\n", " var y = startY\n", " }else{\n", " var y = endY;\n", " }\n", " var width = Math.abs(endX - startX);\n", " var height = Math.abs(endY - startY);\n", " \n", " rect.attr(\"x\", x)\n", " .attr(\"y\", y)\n", " .attr(\"width\", width)\n", " .attr(\"height\", height)\n", " .attr(\"class\",\"rectangle\");\n", " }\n", " });\n", " \n", " });\n", " },\n", " });\n", "\n", " return {\n", " ImageLabelerView: ImageLabelerView\n", " };\n", "});" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%javascript\n", "var isClicked = false;\n", "var rect, startX, startY, endX, endY;\n", "\n", "//Cargamos CSS\n", "var cssId = 'Style';\n", "if (!document.getElementById(cssId))\n", "{\n", " var head = document.getElementsByTagName('head')[0];\n", " var link = document.createElement('link');\n", " link.id = cssId;\n", " link.rel = 'stylesheet';\n", " link.type = 'text/css';\n", " link.href = 'Style.css';\n", " head.appendChild(link);\n", "}\n", "\n", "function updateRect(rect) {\n", "// rect.attr(\"x\",endX - startX > 0 ? startX : endX)\n", "// .attr(\"y\",y: endY - startY > 0 ? startY : endY)\n", "// .attr(\"width\",Math.abs(endX - startX))\n", "// .attr(\"height\",Math.abs(endY - startY));\n", " rect.attr({\n", " x: endX - startX > 0 ? startX : endX,\n", " y: endY - startY > 0 ? startY : endY,\n", " width: Math.abs(endX - startX),\n", " height: Math.abs(endY - startY)\n", " });\n", "}\n", "\n", "require.undef('ImageLabeler');\n", "define('ImageLabeler', [\"jupyter-js-widgets\"], function(widgets) {\n", "\n", " var ImageLabelerView = widgets.DOMWidgetView.extend({\n", " // Renderizar vista\n", " render: function() {\n", " require.config({\n", " paths: {\n", " d3: '//cdnjs.cloudflare.com/ajax/libs/d3/3.4.8/d3.min'\n", " }\n", " });\n", " require(['d3'], function(d3){\n", " //Añadimos un contenedor\n", " var canvas = element.append(\"<div id='canvas'></div>\");\n", "// var canvas = createElement('div');\n", "// d3.select(canvas)\n", "// .attr(\"id\",\"canvas\")\n", "// .attr(\"width\",\"960px\")\n", "// .attr(\"height\",\"600px\");\n", " \n", " $(\"#canvas\").width(\"960px\");\n", " $(\"#canvas\").height(\"600px\");\n", " \n", " var margin = {top: 20, right: 20, bottom: 30, left: 40};\n", " var width = 880 - margin.left - margin.right;\n", " var height = 500 - margin.top - margin.bottom;\n", "\n", " //Creamos SVG\n", " var w = 600, h = 500;\n", " var svg = d3.select('#canvas').append('svg').attr({width: w, height: h});\n", "\n", " // Añadimos un rectángulo\n", " //var svg = svg.append(\"rect\").attr(\"x\",10).attr(\"y\",10).attr(\"width\",10).attr(\"height\",10)\n", " \n", " //Añadimos la imagen\n", " \n", " svg.append(\"image\")\n", " .attr(\"xlink:href\", \"http://www.google.com/intl/en_ALL/images/logo.gif\")//\"@Url.Content(\"~/Content/images/icons/refresh.png\")\")\n", " .attr(\"x\", \"0\")\n", " .attr(\"y\", \"0\")\n", " .attr(\"width\", \"200\")\n", " .attr(\"height\", \"200\");\n", " \n", " //Añadimos listeners\n", " d3.select(\"#canvas\").on(\"click\", function(eve){\n", " console.log(d3.mouse(this));\n", " \n", " if(isClicked){\n", " //Finalizamos rectangulo\n", " isClicked = false;\n", " //Inicializamos coords.\n", " endX = d3.mouse(this)[0];\n", " endY = d3.mouse(this)[1]; \n", " \n", " //Cambiamos ref al rectangulo\n", " rect = null;\n", " \n", " //logger\n", " console.log(\"Finalizamos rectangulo\");\n", " console.log(endX);\n", " console.log(endY);\n", " \n", " //Inicializamos coords.\n", " endX = d3.mouse(this)[0];\n", " endY = d3.mouse(this)[1]; \n", " \n", " }else{\n", " //Inicializamos rectangulo\n", " isClicked = true;\n", " //Inicializamos coords.\n", " startX = d3.mouse(this)[0];\n", " startY = d3.mouse(this)[1]; \n", " \n", " //Creamos rectangulo\n", " rect = svg.append(\"rect\")\n", " .attr(\"x\",startX)\n", " .attr(\"y\",startY)\n", " .attr(\"width\",0)\n", " .attr(\"height\",0)\n", " .attr(\"class\",\"rectangle\");\n", "// .attr(\"fill\",\"transparent\")\n", "// .attr(\"stroke\",\"green\")\n", "// .attr(\"stroke-width\",\"5px\");\n", " \n", " console.log(\"Inicializamos rectangulo\");\n", " console.log(startX);\n", " console.log(startY);\n", " \n", " rect = svg.append(\"rect\");\n", " //updateRect(rect);\n", " }\n", " });\n", " \n", " d3.select(\"#canvas\").on(\"mousemove\", function(eve){\n", " if(isClicked){\n", " endX = d3.mouse(this)[0];\n", " endY = d3.mouse(this)[1];\n", "// updateRect(rect);\n", " \n", " //Actualizamos valores\n", " if (endX - startX > 0){\n", " var x = startX\n", " }else{\n", " var x = endX;\n", " }\n", " \n", " if (endY - startY > 0){\n", " var y = startY\n", " }else{\n", " var y = endY;\n", " }\n", " var width = Math.abs(endX - startX);\n", " var height = Math.abs(endY - startY);\n", " \n", " rect.attr(\"x\", x)\n", " .attr(\"y\", y)\n", " .attr(\"width\", width)\n", " .attr(\"height\", height)\n", " .attr(\"class\",\"rectangle\");\n", " }\n", " });\n", " \n", " });\n", " },\n", " });\n", "\n", " return {\n", " ImageLabelerView: ImageLabelerView\n", " };\n", "});" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ImageLabelerWidget()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" }, "widgets": { "state": { "7471755e5af441d290008281c6d74569": { "views": [ { "cell_index": 3 } ] } }, "version": "1.2.0" } }, "nbformat": 4, "nbformat_minor": 0 }	bsd-3-clause
balarsen/pymc_learning	combine_counts/Combine counts and resample.ipynb	1	521123	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Idea for spectrogram error bars\n", "\n", "1. For each pixel get the dist that go into thwt ais plotted. \n", "1. Resample each based on the dist\n", "1. replot\n", "1. repeat and see which are still there" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import numpy as np\n", "import pymc3 as pm\n", "\n", "\n", "sns.set(font_scale=1.5)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Auto-assigning NUTS sampler...\n", "Initializing NUTS using jitter+adapt_diag...\n", "Multiprocess sampling (2 chains in 2 jobs)\n", "NUTS: [p_means, c_means]\n", "Sampling 2 chains, 0 divergences: 100%\|██████████\| 21000/21000 [00:12<00:00, 1617.47draws/s]\n" ] } ], "source": [ "with pm.Model() as model:\n", " c_means = pm.Uniform('c_means', 0, 100, shape=5)\n", " corrections = pm.Normal('corrs', c_means, sd=1, shape=5, observed=[3,4,5,4,3])\n", " p_means = pm.Uniform('p_means', 0, 100, shape=5)\n", " p = pm.Poisson('p', p_means, shape=5, observed=[30, 35, 20, 45, 16])\n", " avg = pm.Deterministic('avg', pm.math.sum(c_means, axis=0) + pm.math.sum(p_means, axis=0)/5)\n", " \n", " trace = pm.sample(10000)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>mean</th>\n", " <th>sd</th>\n", " <th>hpd_3%</th>\n", " <th>hpd_97%</th>\n", " <th>mcse_mean</th>\n", " <th>mcse_sd</th>\n", " <th>ess_mean</th>\n", " <th>ess_sd</th>\n", " <th>ess_bulk</th>\n", " <th>ess_tail</th>\n", " <th>r_hat</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>c_means[0]</th>\n", " <td>3.013</td>\n", " <td>0.981</td>\n", " <td>1.083</td>\n", " <td>4.793</td>\n", " <td>0.007</td>\n", " <td>0.005</td>\n", " <td>17991.0</td>\n", " <td>17991.0</td>\n", " <td>17367.0</td>\n", " <td>8356.0</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>c_means[1]</th>\n", " <td>3.997</td>\n", " <td>0.989</td>\n", " <td>2.127</td>\n", " <td>5.830</td>\n", " <td>0.007</td>\n", " <td>0.005</td>\n", " <td>21108.0</td>\n", " <td>21108.0</td>\n", " <td>20809.0</td>\n", " <td>10844.0</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>c_means[2]</th>\n", " <td>5.000</td>\n", " <td>1.006</td>\n", " <td>3.148</td>\n", " <td>6.914</td>\n", " <td>0.006</td>\n", " <td>0.005</td>\n", " <td>24783.0</td>\n", " <td>24783.0</td>\n", " <td>24903.0</td>\n", " <td>11266.0</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>c_means[3]</th>\n", " <td>4.002</td>\n", " <td>1.009</td>\n", " <td>2.122</td>\n", " <td>5.904</td>\n", " <td>0.007</td>\n", " <td>0.005</td>\n", " <td>23110.0</td>\n", " <td>23110.0</td>\n", " <td>23302.0</td>\n", " <td>10057.0</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>c_means[4]</th>\n", " <td>3.008</td>\n", " <td>0.987</td>\n", " <td>1.142</td>\n", " <td>4.839</td>\n", " <td>0.007</td>\n", " <td>0.005</td>\n", " <td>20088.0</td>\n", " <td>20088.0</td>\n", " <td>19526.0</td>\n", " <td>10649.0</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>p_means[0]</th>\n", " <td>30.958</td>\n", " <td>5.532</td>\n", " <td>21.147</td>\n", " <td>41.582</td>\n", " <td>0.033</td>\n", " <td>0.024</td>\n", " <td>27546.0</td>\n", " <td>25524.0</td>\n", " <td>27984.0</td>\n", " <td>14584.0</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>p_means[1]</th>\n", " <td>36.014</td>\n", " <td>5.992</td>\n", " <td>25.148</td>\n", " <td>47.323</td>\n", " <td>0.036</td>\n", " <td>0.027</td>\n", " <td>27775.0</td>\n", " <td>25399.0</td>\n", " <td>28560.0</td>\n", " <td>14025.0</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>p_means[2]</th>\n", " <td>21.028</td>\n", " <td>4.580</td>\n", " <td>12.783</td>\n", " <td>29.626</td>\n", " <td>0.026</td>\n", " <td>0.020</td>\n", " <td>30479.0</td>\n", " <td>26678.0</td>\n", " <td>31297.0</td>\n", " <td>14131.0</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>p_means[3]</th>\n", " <td>45.977</td>\n", " <td>6.881</td>\n", " <td>33.130</td>\n", " <td>58.873</td>\n", " <td>0.042</td>\n", " <td>0.031</td>\n", " <td>27470.0</td>\n", " <td>24821.0</td>\n", " <td>28676.0</td>\n", " <td>12313.0</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>p_means[4]</th>\n", " <td>16.986</td>\n", " <td>4.167</td>\n", " <td>9.177</td>\n", " <td>24.567</td>\n", " <td>0.024</td>\n", " <td>0.018</td>\n", " <td>29977.0</td>\n", " <td>26330.0</td>\n", " <td>30326.0</td>\n", " <td>13785.0</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>avg</th>\n", " <td>49.212</td>\n", " <td>3.332</td>\n", " <td>43.048</td>\n", " <td>55.576</td>\n", " <td>0.021</td>\n", " <td>0.015</td>\n", " <td>25465.0</td>\n", " <td>25224.0</td>\n", " <td>25513.0</td>\n", " <td>15294.0</td>\n", " <td>1.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " mean sd hpd_3% hpd_97% mcse_mean mcse_sd ess_mean \\\n", "c_means[0] 3.013 0.981 1.083 4.793 0.007 0.005 17991.0 \n", "c_means[1] 3.997 0.989 2.127 5.830 0.007 0.005 21108.0 \n", "c_means[2] 5.000 1.006 3.148 6.914 0.006 0.005 24783.0 \n", "c_means[3] 4.002 1.009 2.122 5.904 0.007 0.005 23110.0 \n", "c_means[4] 3.008 0.987 1.142 4.839 0.007 0.005 20088.0 \n", "p_means[0] 30.958 5.532 21.147 41.582 0.033 0.024 27546.0 \n", "p_means[1] 36.014 5.992 25.148 47.323 0.036 0.027 27775.0 \n", "p_means[2] 21.028 4.580 12.783 29.626 0.026 0.020 30479.0 \n", "p_means[3] 45.977 6.881 33.130 58.873 0.042 0.031 27470.0 \n", "p_means[4] 16.986 4.167 9.177 24.567 0.024 0.018 29977.0 \n", "avg 49.212 3.332 43.048 55.576 0.021 0.015 25465.0 \n", "\n", " ess_sd ess_bulk ess_tail r_hat \n", "c_means[0] 17991.0 17367.0 8356.0 1.0 \n", "c_means[1] 21108.0 20809.0 10844.0 1.0 \n", "c_means[2] 24783.0 24903.0 11266.0 1.0 \n", "c_means[3] 23110.0 23302.0 10057.0 1.0 \n", "c_means[4] 20088.0 19526.0 10649.0 1.0 \n", "p_means[0] 25524.0 27984.0 14584.0 1.0 \n", "p_means[1] 25399.0 28560.0 14025.0 1.0 \n", "p_means[2] 26678.0 31297.0 14131.0 1.0 \n", "p_means[3] 24821.0 28676.0 12313.0 1.0 \n", "p_means[4] 26330.0 30326.0 13785.0 1.0 \n", "avg 25224.0 25513.0 15294.0 1.0 " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pm.summary(trace)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/balarsen/miniconda3/envs/python3/lib/python3.7/site-packages/arviz/plots/backends/matplotlib/distplot.py:38: UserWarning: Argument backend_kwargs has not effect in matplotlib.plot_distSupplied value won't be used\n", " \"Argument backend_kwargs has not effect in matplotlib.plot_dist\"\n", "/Users/balarsen/miniconda3/envs/python3/lib/python3.7/site-packages/arviz/plots/backends/matplotlib/distplot.py:38: UserWarning: Argument backend_kwargs has not effect in matplotlib.plot_distSupplied value won't be used\n", " \"Argument backend_kwargs has not effect in matplotlib.plot_dist\"\n", "/Users/balarsen/miniconda3/envs/python3/lib/python3.7/site-packages/arviz/plots/backends/matplotlib/distplot.py:38: UserWarning: Argument backend_kwargs has not effect in matplotlib.plot_distSupplied value won't be used\n", " \"Argument backend_kwargs has not effect in matplotlib.plot_dist\"\n" ] }, { "data": { "text/plain": [ "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1285ae6d0>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x129128dd0>],\n", " [<matplotlib.axes._subplots.AxesSubplot object at 0x129654b50>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x12968d910>],\n", " [<matplotlib.axes._subplots.AxesSubplot object at 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\n", "text/plain": [ "<Figure size 864x432 with 6 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pm.traceplot(trace)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([<matplotlib.axes._subplots.AxesSubplot object at 0x12a4ebad0>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x12a5bb850>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x12a5f4890>],\n", " dtype=object)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1490.4x331.2 with 3 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pm.plot_posterior(trace)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "29.2\n" ] } ], "source": [ "print(np.average([30, 35, 20, 45, 16]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Can we do multi-d together?" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO (theano.gof.compilelock): Refreshing lock /Users/balarsen/.theano/compiledir_Darwin-18.7.0-x86_64-i386-64bit-i386-3.7.6-64/lock_dir/lock\n", "Auto-assigning NUTS sampler...\n", "Initializing NUTS using jitter+adapt_diag...\n", "Multiprocess sampling (2 chains in 2 jobs)\n", "NUTS: [p_means]\n", "Sampling 2 chains, 0 divergences: 100%\|██████████\| 21000/21000 [00:11<00:00, 1892.77draws/s]\n" ] } ], "source": [ "with pm.Model() as model:\n", " p_means = pm.Uniform('p_means', 0, 100, shape=(5, 16)) # time, pixel, sector\n", " p = pm.Poisson('p', p_means, shape=5, observed=np.random.poisson(30, size=(5, 16)))\n", " avg = pm.Deterministic('avg', pm.math.sum(p_means)/(5*16))\n", " \n", " trace = pm.sample(10000)" ] }, { "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>mean</th>\n", " <th>sd</th>\n", " <th>hpd_3%</th>\n", " <th>hpd_97%</th>\n", " <th>mcse_mean</th>\n", " <th>mcse_sd</th>\n", " <th>ess_mean</th>\n", " <th>ess_sd</th>\n", " <th>ess_bulk</th>\n", " <th>ess_tail</th>\n", " <th>r_hat</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>p_means[0,0]</th>\n", " <td>31.019</td>\n", " <td>5.606</td>\n", " <td>20.973</td>\n", " <td>41.886</td>\n", " <td>0.029</td>\n", " <td>0.022</td>\n", " <td>36484.0</td>\n", " <td>31995.0</td>\n", " <td>37615.0</td>\n", " <td>13528.0</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>p_means[0,1]</th>\n", " <td>28.996</td>\n", " <td>5.423</td>\n", " <td>18.878</td>\n", " <td>38.954</td>\n", " <td>0.031</td>\n", " <td>0.023</td>\n", " <td>30850.0</td>\n", " <td>27753.0</td>\n", " <td>31984.0</td>\n", " <td>15189.0</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>p_means[0,2]</th>\n", " <td>34.018</td>\n", " <td>5.821</td>\n", " <td>23.702</td>\n", " <td>45.180</td>\n", " <td>0.030</td>\n", " <td>0.023</td>\n", " <td>37132.0</td>\n", " <td>31559.0</td>\n", " <td>39285.0</td>\n", " <td>13392.0</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>p_means[0,3]</th>\n", " <td>30.992</td>\n", " <td>5.596</td>\n", " <td>20.520</td>\n", " <td>41.353</td>\n", " <td>0.032</td>\n", " <td>0.024</td>\n", " <td>31291.0</td>\n", " <td>27784.0</td>\n", " <td>32416.0</td>\n", " <td>13568.0</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>p_means[0,4]</th>\n", " <td>25.959</td>\n", " <td>5.054</td>\n", " <td>17.050</td>\n", " <td>35.843</td>\n", " <td>0.027</td>\n", " <td>0.020</td>\n", " <td>35055.0</td>\n", " <td>30526.0</td>\n", " <td>36394.0</td>\n", " <td>14481.0</td>\n", " <td>1.0</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", " </tr>\n", " <tr>\n", " <th>p_means[4,12]</th>\n", " <td>33.979</td>\n", " <td>5.854</td>\n", " <td>23.257</td>\n", " <td>44.853</td>\n", " <td>0.031</td>\n", " <td>0.023</td>\n", " <td>36571.0</td>\n", " <td>32177.0</td>\n", " <td>37791.0</td>\n", " <td>13523.0</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>p_means[4,13]</th>\n", " <td>28.967</td>\n", " <td>5.379</td>\n", " <td>19.189</td>\n", " <td>39.285</td>\n", " <td>0.027</td>\n", " <td>0.021</td>\n", " <td>39670.0</td>\n", " <td>33861.0</td>\n", " <td>40855.0</td>\n", " <td>12167.0</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>p_means[4,14]</th>\n", " <td>30.939</td>\n", " <td>5.483</td>\n", " <td>20.897</td>\n", " <td>41.374</td>\n", " <td>0.028</td>\n", " <td>0.021</td>\n", " <td>37143.0</td>\n", " <td>32625.0</td>\n", " <td>38408.0</td>\n", " <td>14148.0</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>p_means[4,15]</th>\n", " <td>32.014</td>\n", " <td>5.639</td>\n", " <td>21.755</td>\n", " <td>42.671</td>\n", " <td>0.031</td>\n", " <td>0.023</td>\n", " <td>34111.0</td>\n", " <td>29706.0</td>\n", " <td>35870.0</td>\n", " <td>14018.0</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>avg</th>\n", " <td>31.309</td>\n", " <td>0.621</td>\n", " <td>30.171</td>\n", " <td>32.494</td>\n", " <td>0.003</td>\n", " <td>0.002</td>\n", " <td>35823.0</td>\n", " <td>35823.0</td>\n", " <td>35773.0</td>\n", " <td>14234.0</td>\n", " <td>1.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>81 rows × 11 columns</p>\n", "</div>" ], "text/plain": [ " mean sd hpd_3% hpd_97% mcse_mean mcse_sd ess_mean \\\n", "p_means[0,0] 31.019 5.606 20.973 41.886 0.029 0.022 36484.0 \n", "p_means[0,1] 28.996 5.423 18.878 38.954 0.031 0.023 30850.0 \n", "p_means[0,2] 34.018 5.821 23.702 45.180 0.030 0.023 37132.0 \n", "p_means[0,3] 30.992 5.596 20.520 41.353 0.032 0.024 31291.0 \n", "p_means[0,4] 25.959 5.054 17.050 35.843 0.027 0.020 35055.0 \n", "... ... ... ... ... ... ... ... \n", "p_means[4,12] 33.979 5.854 23.257 44.853 0.031 0.023 36571.0 \n", "p_means[4,13] 28.967 5.379 19.189 39.285 0.027 0.021 39670.0 \n", "p_means[4,14] 30.939 5.483 20.897 41.374 0.028 0.021 37143.0 \n", "p_means[4,15] 32.014 5.639 21.755 42.671 0.031 0.023 34111.0 \n", "avg 31.309 0.621 30.171 32.494 0.003 0.002 35823.0 \n", "\n", " ess_sd ess_bulk ess_tail r_hat \n", "p_means[0,0] 31995.0 37615.0 13528.0 1.0 \n", "p_means[0,1] 27753.0 31984.0 15189.0 1.0 \n", "p_means[0,2] 31559.0 39285.0 13392.0 1.0 \n", "p_means[0,3] 27784.0 32416.0 13568.0 1.0 \n", "p_means[0,4] 30526.0 36394.0 14481.0 1.0 \n", "... ... ... ... ... \n", "p_means[4,12] 32177.0 37791.0 13523.0 1.0 \n", "p_means[4,13] 33861.0 40855.0 12167.0 1.0 \n", "p_means[4,14] 32625.0 38408.0 14148.0 1.0 \n", "p_means[4,15] 29706.0 35870.0 14018.0 1.0 \n", "avg 35823.0 35773.0 14234.0 1.0 \n", "\n", "[81 rows x 11 columns]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pm.summary(trace)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/balarsen/miniconda3/envs/python3/lib/python3.7/site-packages/arviz/plots/backends/matplotlib/distplot.py:38: UserWarning: Argument backend_kwargs has not effect in matplotlib.plot_distSupplied value won't be used\n", " \"Argument backend_kwargs has not effect in matplotlib.plot_dist\"\n", "/Users/balarsen/miniconda3/envs/python3/lib/python3.7/site-packages/arviz/plots/backends/matplotlib/distplot.py:38: UserWarning: Argument backend_kwargs has not effect in matplotlib.plot_distSupplied value won't be used\n", " \"Argument backend_kwargs has not effect in matplotlib.plot_dist\"\n" ] }, { "data": { "text/plain": [ "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x133c4d710>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x131d6c6d0>],\n", " [<matplotlib.axes._subplots.AxesSubplot object at 0x129e9c710>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x12adc0a50>]],\n", " dtype=object)" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x288 with 4 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pm.traceplot(trace)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([<matplotlib.axes._subplots.AxesSubplot object at 0x128799a50>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x1283f5690>],\n", " dtype=object)" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 993.6x331.2 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pm.plot_posterior(trace)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.5" } }, "nbformat": 4, "nbformat_minor": 4 }	bsd-3-clause
kimlaborg/NGSKit	notebooks/1-PWM_basics.ipynb	1	54824	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# PWMs\n", "\n", "This notebook is a short example of how to generate a PWM from a MSA, and a few analyses you can do using Ngskit. " ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "ExecuteTime": { "end_time": "2020-03-02T20:34:01.000413Z", "start_time": "2020-03-02T20:34:00.995663Z" } }, "outputs": [], "source": [ "import ngskit.analysis as nk\n", "from IPython.display import Image\n", "import seaborn as sns" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2020-03-02T20:20:31.796192Z", "start_time": "2020-03-02T20:20:31.789060Z" } }, "outputs": [], "source": [ "# Here a prealigned results from PxL paper.\n", "\n", "Pep_DATA = ['FTDVPALNY',\n", " 'FDSTPYLAL',\n", " 'FGSWFSWLT',\n", " 'LRAIYAYFM',\n", " 'LEDKPILDF',\n", " 'LGRIPEIFA',\n", " 'SSTTPKLKP',\n", " 'WPNLPFQAL',\n", " 'LFQAPLIVL',\n", " 'LSSVPPLPY',\n", " 'TFSTPRLIY',\n", " 'SKNIPSLSP',\n", " 'GNKVENLGF',\n", " 'LRIWSAIVH',\n", " 'LKDLLKYFI',\n", " 'LELAPQLSF',\n", " 'LKSTQPLLS',\n", " 'MALVEAMLT',\n", " 'FLANTPYLS',\n", " 'FAEKLRNLI',\n", " 'DISDVPSLK',\n", " 'YDLNIPKPT',\n", " 'SFKNAPLLA']" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2020-03-02T20:20:31.977742Z", "start_time": "2020-03-02T20:20:31.799600Z" } }, "outputs": [ { "data": { "text/plain": [ "{'F': 5, 'L': 9, 'S': 3, 'W': 1, 'T': 1, 'G': 1, 'M': 1, 'D': 1, 'Y': 1}" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# raw frequency matrix returns a dict\n", "pfm = nk.get_pfm(Pep_DATA)\n", "# Values for position 1\n", "pfm[0]" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2020-03-02T20:20:32.278860Z", "start_time": "2020-03-02T20:20:31.980946Z" } }, "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>0</th>\n", " <th>1</th>\n", " <th>2</th>\n", " <th>3</th>\n", " <th>4</th>\n", " <th>5</th>\n", " <th>6</th>\n", " <th>7</th>\n", " <th>8</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>R</th>\n", " <td>0.041667</td>\n", " <td>0.125000</td>\n", " <td>0.083333</td>\n", " <td>0.041667</td>\n", " <td>0.041667</td>\n", " <td>0.125000</td>\n", " <td>0.041667</td>\n", " <td>0.041667</td>\n", " <td>0.041667</td>\n", " </tr>\n", " <tr>\n", " <th>H</th>\n", " <td>0.041667</td>\n", " <td>0.041667</td>\n", " <td>0.041667</td>\n", " <td>0.041667</td>\n", " <td>0.041667</td>\n", " <td>0.041667</td>\n", " <td>0.041667</td>\n", " <td>0.041667</td>\n", " <td>0.083333</td>\n", " </tr>\n", " <tr>\n", " <th>K</th>\n", " <td>0.041667</td>\n", " <td>0.166667</td>\n", " <td>0.125000</td>\n", " <td>0.125000</td>\n", " <td>0.041667</td>\n", " <td>0.125000</td>\n", " <td>0.083333</td>\n", " <td>0.083333</td>\n", " <td>0.083333</td>\n", " </tr>\n", " <tr>\n", " <th>D</th>\n", " <td>0.083333</td>\n", " <td>0.125000</td>\n", " <td>0.166667</td>\n", " <td>0.083333</td>\n", " <td>0.041667</td>\n", " <td>0.041667</td>\n", " <td>0.041667</td>\n", " <td>0.083333</td>\n", " <td>0.041667</td>\n", " </tr>\n", " <tr>\n", " <th>E</th>\n", " <td>0.041667</td>\n", " <td>0.125000</td>\n", " <td>0.083333</td>\n", " <td>0.041667</td>\n", " <td>0.125000</td>\n", " <td>0.083333</td>\n", " <td>0.041667</td>\n", " <td>0.041667</td>\n", " <td>0.041667</td>\n", " </tr>\n", " <tr>\n", " <th>S</th>\n", " <td>0.166667</td>\n", " <td>0.125000</td>\n", " <td>0.291667</td>\n", " <td>0.041667</td>\n", " <td>0.083333</td>\n", " <td>0.125000</td>\n", " <td>0.083333</td>\n", " <td>0.125000</td>\n", " <td>0.125000</td>\n", " </tr>\n", " <tr>\n", " <th>T</th>\n", " <td>0.083333</td>\n", " <td>0.083333</td>\n", " <td>0.083333</td>\n", " <td>0.208333</td>\n", " <td>0.083333</td>\n", " <td>0.041667</td>\n", " <td>0.041667</td>\n", " <td>0.041667</td>\n", " <td>0.166667</td>\n", " </tr>\n", " <tr>\n", " <th>N</th>\n", " <td>0.041667</td>\n", " <td>0.083333</td>\n", " <td>0.125000</td>\n", " <td>0.166667</td>\n", " <td>0.041667</td>\n", " <td>0.083333</td>\n", " <td>0.083333</td>\n", " <td>0.083333</td>\n", " <td>0.041667</td>\n", " </tr>\n", " <tr>\n", " <th>Q</th>\n", " <td>0.041667</td>\n", " <td>0.041667</td>\n", " <td>0.083333</td>\n", " <td>0.041667</td>\n", " <td>0.083333</td>\n", " <td>0.083333</td>\n", " <td>0.083333</td>\n", " <td>0.041667</td>\n", " <td>0.041667</td>\n", " </tr>\n", " <tr>\n", " <th>C</th>\n", " <td>0.041667</td>\n", " <td>0.041667</td>\n", " <td>0.041667</td>\n", " <td>0.041667</td>\n", " <td>0.041667</td>\n", " <td>0.041667</td>\n", " <td>0.041667</td>\n", " <td>0.041667</td>\n", " <td>0.041667</td>\n", " </tr>\n", " <tr>\n", " <th>G</th>\n", " <td>0.083333</td>\n", " <td>0.125000</td>\n", " <td>0.041667</td>\n", " <td>0.041667</td>\n", " <td>0.041667</td>\n", " <td>0.041667</td>\n", " <td>0.041667</td>\n", " <td>0.083333</td>\n", " <td>0.041667</td>\n", " </tr>\n", " <tr>\n", " <th>P</th>\n", " <td>0.041667</td>\n", " <td>0.083333</td>\n", " <td>0.041667</td>\n", " <td>0.041667</td>\n", " <td>0.500000</td>\n", " <td>0.291667</td>\n", " <td>0.041667</td>\n", " <td>0.125000</td>\n", " <td>0.125000</td>\n", " </tr>\n", " <tr>\n", " <th>A</th>\n", " <td>0.041667</td>\n", " <td>0.125000</td>\n", " <td>0.125000</td>\n", " <td>0.125000</td>\n", " <td>0.083333</td>\n", " <td>0.208333</td>\n", " <td>0.041667</td>\n", " <td>0.125000</td>\n", " <td>0.125000</td>\n", " </tr>\n", " <tr>\n", " <th>V</th>\n", " <td>0.041667</td>\n", " <td>0.041667</td>\n", " <td>0.041667</td>\n", " <td>0.208333</td>\n", " <td>0.083333</td>\n", " <td>0.041667</td>\n", " <td>0.041667</td>\n", " <td>0.125000</td>\n", " <td>0.041667</td>\n", " </tr>\n", " <tr>\n", " <th>I</th>\n", " <td>0.041667</td>\n", " <td>0.083333</td>\n", " <td>0.083333</td>\n", " <td>0.166667</td>\n", " <td>0.083333</td>\n", " <td>0.083333</td>\n", " <td>0.166667</td>\n", " <td>0.083333</td>\n", " <td>0.125000</td>\n", " </tr>\n", " <tr>\n", " <th>L</th>\n", " <td>0.416667</td>\n", " <td>0.083333</td>\n", " <td>0.166667</td>\n", " <td>0.125000</td>\n", " <td>0.125000</td>\n", " <td>0.083333</td>\n", " <td>0.500000</td>\n", " <td>0.333333</td>\n", " <td>0.166667</td>\n", " </tr>\n", " <tr>\n", " <th>M</th>\n", " <td>0.083333</td>\n", " <td>0.041667</td>\n", " <td>0.041667</td>\n", " <td>0.041667</td>\n", " <td>0.041667</td>\n", " <td>0.041667</td>\n", " <td>0.083333</td>\n", " <td>0.041667</td>\n", " <td>0.083333</td>\n", " </tr>\n", " <tr>\n", " <th>F</th>\n", " <td>0.250000</td>\n", " <td>0.166667</td>\n", " <td>0.041667</td>\n", " <td>0.041667</td>\n", " <td>0.083333</td>\n", " <td>0.083333</td>\n", " <td>0.041667</td>\n", " <td>0.166667</td>\n", " <td>0.166667</td>\n", " </tr>\n", " <tr>\n", " <th>Y</th>\n", " <td>0.083333</td>\n", " <td>0.041667</td>\n", " <td>0.041667</td>\n", " <td>0.041667</td>\n", " <td>0.083333</td>\n", " <td>0.083333</td>\n", " <td>0.166667</td>\n", " <td>0.041667</td>\n", " <td>0.166667</td>\n", " </tr>\n", " <tr>\n", " <th>W</th>\n", " <td>0.083333</td>\n", " <td>0.041667</td>\n", " <td>0.041667</td>\n", " <td>0.125000</td>\n", " <td>0.041667</td>\n", " <td>0.041667</td>\n", " <td>0.083333</td>\n", " <td>0.041667</td>\n", " <td>0.041667</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " 0 1 2 3 4 5 6 \\\n", "R 0.041667 0.125000 0.083333 0.041667 0.041667 0.125000 0.041667 \n", "H 0.041667 0.041667 0.041667 0.041667 0.041667 0.041667 0.041667 \n", "K 0.041667 0.166667 0.125000 0.125000 0.041667 0.125000 0.083333 \n", "D 0.083333 0.125000 0.166667 0.083333 0.041667 0.041667 0.041667 \n", "E 0.041667 0.125000 0.083333 0.041667 0.125000 0.083333 0.041667 \n", "S 0.166667 0.125000 0.291667 0.041667 0.083333 0.125000 0.083333 \n", "T 0.083333 0.083333 0.083333 0.208333 0.083333 0.041667 0.041667 \n", "N 0.041667 0.083333 0.125000 0.166667 0.041667 0.083333 0.083333 \n", "Q 0.041667 0.041667 0.083333 0.041667 0.083333 0.083333 0.083333 \n", "C 0.041667 0.041667 0.041667 0.041667 0.041667 0.041667 0.041667 \n", "G 0.083333 0.125000 0.041667 0.041667 0.041667 0.041667 0.041667 \n", "P 0.041667 0.083333 0.041667 0.041667 0.500000 0.291667 0.041667 \n", "A 0.041667 0.125000 0.125000 0.125000 0.083333 0.208333 0.041667 \n", "V 0.041667 0.041667 0.041667 0.208333 0.083333 0.041667 0.041667 \n", "I 0.041667 0.083333 0.083333 0.166667 0.083333 0.083333 0.166667 \n", "L 0.416667 0.083333 0.166667 0.125000 0.125000 0.083333 0.500000 \n", "M 0.083333 0.041667 0.041667 0.041667 0.041667 0.041667 0.083333 \n", "F 0.250000 0.166667 0.041667 0.041667 0.083333 0.083333 0.041667 \n", "Y 0.083333 0.041667 0.041667 0.041667 0.083333 0.083333 0.166667 \n", "W 0.083333 0.041667 0.041667 0.125000 0.041667 0.041667 0.083333 \n", "\n", " 7 8 \n", "R 0.041667 0.041667 \n", "H 0.041667 0.083333 \n", "K 0.083333 0.083333 \n", "D 0.083333 0.041667 \n", "E 0.041667 0.041667 \n", "S 0.125000 0.125000 \n", "T 0.041667 0.166667 \n", "N 0.083333 0.041667 \n", "Q 0.041667 0.041667 \n", "C 0.041667 0.041667 \n", "G 0.083333 0.041667 \n", "P 0.125000 0.125000 \n", "A 0.125000 0.125000 \n", "V 0.125000 0.041667 \n", "I 0.083333 0.125000 \n", "L 0.333333 0.166667 \n", "M 0.041667 0.083333 \n", "F 0.166667 0.166667 \n", "Y 0.041667 0.166667 \n", "W 0.041667 0.041667 " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# probabilty matrix, returns a df (pandas), adding pseudocounts\n", "ppm = nk.get_ppm(Pep_DATA, pseudocounts=1)\n", "ppm" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2020-03-02T20:20:32.407619Z", "start_time": "2020-03-02T20:20:32.282290Z" } }, "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>0</th>\n", " <th>1</th>\n", " <th>2</th>\n", " <th>3</th>\n", " <th>4</th>\n", " <th>5</th>\n", " <th>6</th>\n", " <th>7</th>\n", " <th>8</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>R</th>\n", " <td>-0.263034</td>\n", " <td>1.321928</td>\n", " <td>0.736966</td>\n", " <td>-0.263034</td>\n", " <td>-0.263034</td>\n", " <td>1.321928</td>\n", " <td>-0.263034</td>\n", " <td>-0.263034</td>\n", " <td>-0.263034</td>\n", " </tr>\n", " <tr>\n", " <th>H</th>\n", " <td>-0.263034</td>\n", " <td>-0.263034</td>\n", " <td>-0.263034</td>\n", " <td>-0.263034</td>\n", " <td>-0.263034</td>\n", " <td>-0.263034</td>\n", " <td>-0.263034</td>\n", " <td>-0.263034</td>\n", " <td>0.736966</td>\n", " </tr>\n", " <tr>\n", " <th>K</th>\n", " <td>-0.263034</td>\n", " <td>1.736966</td>\n", " <td>1.321928</td>\n", " <td>1.321928</td>\n", " <td>-0.263034</td>\n", " <td>1.321928</td>\n", " <td>0.736966</td>\n", " <td>0.736966</td>\n", " <td>0.736966</td>\n", " </tr>\n", " <tr>\n", " <th>D</th>\n", " <td>0.736966</td>\n", " <td>1.321928</td>\n", " <td>1.736966</td>\n", " <td>0.736966</td>\n", " <td>-0.263034</td>\n", " <td>-0.263034</td>\n", " <td>-0.263034</td>\n", " <td>0.736966</td>\n", " <td>-0.263034</td>\n", " </tr>\n", " <tr>\n", " <th>E</th>\n", " <td>-0.263034</td>\n", " <td>1.321928</td>\n", " <td>0.736966</td>\n", " <td>-0.263034</td>\n", " <td>1.321928</td>\n", " <td>0.736966</td>\n", " <td>-0.263034</td>\n", " <td>-0.263034</td>\n", " <td>-0.263034</td>\n", " </tr>\n", " <tr>\n", " <th>S</th>\n", " <td>1.736966</td>\n", " <td>1.321928</td>\n", " <td>2.544321</td>\n", " <td>-0.263034</td>\n", " <td>0.736966</td>\n", " <td>1.321928</td>\n", " <td>0.736966</td>\n", " <td>1.321928</td>\n", " <td>1.321928</td>\n", " </tr>\n", " <tr>\n", " <th>T</th>\n", " <td>0.736966</td>\n", " <td>0.736966</td>\n", " <td>0.736966</td>\n", " <td>2.058894</td>\n", " <td>0.736966</td>\n", " <td>-0.263034</td>\n", " <td>-0.263034</td>\n", " <td>-0.263034</td>\n", " <td>1.736966</td>\n", " </tr>\n", " <tr>\n", " <th>N</th>\n", " <td>-0.263034</td>\n", " <td>0.736966</td>\n", " <td>1.321928</td>\n", " <td>1.736966</td>\n", " <td>-0.263034</td>\n", " <td>0.736966</td>\n", " <td>0.736966</td>\n", " <td>0.736966</td>\n", " <td>-0.263034</td>\n", " </tr>\n", " <tr>\n", " <th>Q</th>\n", " <td>-0.263034</td>\n", " <td>-0.263034</td>\n", " <td>0.736966</td>\n", " <td>-0.263034</td>\n", " <td>0.736966</td>\n", " <td>0.736966</td>\n", " <td>0.736966</td>\n", " <td>-0.263034</td>\n", " <td>-0.263034</td>\n", " </tr>\n", " <tr>\n", " <th>C</th>\n", " <td>-0.263034</td>\n", " <td>-0.263034</td>\n", " <td>-0.263034</td>\n", " <td>-0.263034</td>\n", " <td>-0.263034</td>\n", " <td>-0.263034</td>\n", " <td>-0.263034</td>\n", " <td>-0.263034</td>\n", " <td>-0.263034</td>\n", " </tr>\n", " <tr>\n", " <th>G</th>\n", " <td>0.736966</td>\n", " <td>1.321928</td>\n", " <td>-0.263034</td>\n", " <td>-0.263034</td>\n", " <td>-0.263034</td>\n", " <td>-0.263034</td>\n", " <td>-0.263034</td>\n", " <td>0.736966</td>\n", " <td>-0.263034</td>\n", " </tr>\n", " <tr>\n", " <th>P</th>\n", " <td>-0.263034</td>\n", " <td>0.736966</td>\n", " <td>-0.263034</td>\n", " <td>-0.263034</td>\n", " <td>3.321928</td>\n", " <td>2.544321</td>\n", " <td>-0.263034</td>\n", " <td>1.321928</td>\n", " <td>1.321928</td>\n", " </tr>\n", " <tr>\n", " <th>A</th>\n", " <td>-0.263034</td>\n", " <td>1.321928</td>\n", " <td>1.321928</td>\n", " <td>1.321928</td>\n", " <td>0.736966</td>\n", " <td>2.058894</td>\n", " <td>-0.263034</td>\n", " <td>1.321928</td>\n", " <td>1.321928</td>\n", " </tr>\n", " <tr>\n", " <th>V</th>\n", " <td>-0.263034</td>\n", " <td>-0.263034</td>\n", " <td>-0.263034</td>\n", " <td>2.058894</td>\n", " <td>0.736966</td>\n", " <td>-0.263034</td>\n", " <td>-0.263034</td>\n", " <td>1.321928</td>\n", " <td>-0.263034</td>\n", " </tr>\n", " <tr>\n", " <th>I</th>\n", " <td>-0.263034</td>\n", " <td>0.736966</td>\n", " <td>0.736966</td>\n", " <td>1.736966</td>\n", " <td>0.736966</td>\n", " <td>0.736966</td>\n", " <td>1.736966</td>\n", " <td>0.736966</td>\n", " <td>1.321928</td>\n", " </tr>\n", " <tr>\n", " <th>L</th>\n", " <td>3.058894</td>\n", " <td>0.736966</td>\n", " <td>1.736966</td>\n", " <td>1.321928</td>\n", " <td>1.321928</td>\n", " <td>0.736966</td>\n", " <td>3.321928</td>\n", " <td>2.736966</td>\n", " <td>1.736966</td>\n", " </tr>\n", " <tr>\n", " <th>M</th>\n", " <td>0.736966</td>\n", " <td>-0.263034</td>\n", " <td>-0.263034</td>\n", " <td>-0.263034</td>\n", " <td>-0.263034</td>\n", " <td>-0.263034</td>\n", " <td>0.736966</td>\n", " <td>-0.263034</td>\n", " <td>0.736966</td>\n", " </tr>\n", " <tr>\n", " <th>F</th>\n", " <td>2.321928</td>\n", " <td>1.736966</td>\n", " <td>-0.263034</td>\n", " <td>-0.263034</td>\n", " <td>0.736966</td>\n", " <td>0.736966</td>\n", " <td>-0.263034</td>\n", " <td>1.736966</td>\n", " <td>1.736966</td>\n", " </tr>\n", " <tr>\n", " <th>Y</th>\n", " <td>0.736966</td>\n", " <td>-0.263034</td>\n", " <td>-0.263034</td>\n", " <td>-0.263034</td>\n", " <td>0.736966</td>\n", " <td>0.736966</td>\n", " <td>1.736966</td>\n", " <td>-0.263034</td>\n", " <td>1.736966</td>\n", " </tr>\n", " <tr>\n", " <th>W</th>\n", " <td>0.736966</td>\n", " <td>-0.263034</td>\n", " <td>-0.263034</td>\n", " <td>1.321928</td>\n", " <td>-0.263034</td>\n", " <td>-0.263034</td>\n", " <td>0.736966</td>\n", " <td>-0.263034</td>\n", " <td>-0.263034</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " 0 1 2 3 4 5 6 \\\n", "R -0.263034 1.321928 0.736966 -0.263034 -0.263034 1.321928 -0.263034 \n", "H -0.263034 -0.263034 -0.263034 -0.263034 -0.263034 -0.263034 -0.263034 \n", "K -0.263034 1.736966 1.321928 1.321928 -0.263034 1.321928 0.736966 \n", "D 0.736966 1.321928 1.736966 0.736966 -0.263034 -0.263034 -0.263034 \n", "E -0.263034 1.321928 0.736966 -0.263034 1.321928 0.736966 -0.263034 \n", "S 1.736966 1.321928 2.544321 -0.263034 0.736966 1.321928 0.736966 \n", "T 0.736966 0.736966 0.736966 2.058894 0.736966 -0.263034 -0.263034 \n", "N -0.263034 0.736966 1.321928 1.736966 -0.263034 0.736966 0.736966 \n", "Q -0.263034 -0.263034 0.736966 -0.263034 0.736966 0.736966 0.736966 \n", "C -0.263034 -0.263034 -0.263034 -0.263034 -0.263034 -0.263034 -0.263034 \n", "G 0.736966 1.321928 -0.263034 -0.263034 -0.263034 -0.263034 -0.263034 \n", "P -0.263034 0.736966 -0.263034 -0.263034 3.321928 2.544321 -0.263034 \n", "A -0.263034 1.321928 1.321928 1.321928 0.736966 2.058894 -0.263034 \n", "V -0.263034 -0.263034 -0.263034 2.058894 0.736966 -0.263034 -0.263034 \n", "I -0.263034 0.736966 0.736966 1.736966 0.736966 0.736966 1.736966 \n", "L 3.058894 0.736966 1.736966 1.321928 1.321928 0.736966 3.321928 \n", "M 0.736966 -0.263034 -0.263034 -0.263034 -0.263034 -0.263034 0.736966 \n", "F 2.321928 1.736966 -0.263034 -0.263034 0.736966 0.736966 -0.263034 \n", "Y 0.736966 -0.263034 -0.263034 -0.263034 0.736966 0.736966 1.736966 \n", "W 0.736966 -0.263034 -0.263034 1.321928 -0.263034 -0.263034 0.736966 \n", "\n", " 7 8 \n", "R -0.263034 -0.263034 \n", "H -0.263034 0.736966 \n", "K 0.736966 0.736966 \n", "D 0.736966 -0.263034 \n", "E -0.263034 -0.263034 \n", "S 1.321928 1.321928 \n", "T -0.263034 1.736966 \n", "N 0.736966 -0.263034 \n", "Q -0.263034 -0.263034 \n", "C -0.263034 -0.263034 \n", "G 0.736966 -0.263034 \n", "P 1.321928 1.321928 \n", "A 1.321928 1.321928 \n", "V 1.321928 -0.263034 \n", "I 0.736966 1.321928 \n", "L 2.736966 1.736966 \n", "M -0.263034 0.736966 \n", "F 1.736966 1.736966 \n", "Y -0.263034 1.736966 \n", "W -0.263034 -0.263034 " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# probabilty wieght, returns a df (pandas)\n", "pwm = nk.get_pwm(Pep_DATA, pseudocounts=1)\n", "pwm" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2020-03-02T20:20:33.750306Z", "start_time": "2020-03-02T20:20:32.412236Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/ccorbi/anaconda3/envs/libs/lib/python3.6/site-packages/ngskit/analysis.py:396: RuntimeWarning: divide by zero encountered in log\n", " r = prob_ij* ( np.log(prob_ij/(prob_aiprob_aj)))\n", "/home/ccorbi/anaconda3/envs/libs/lib/python3.6/site-packages/ngskit/analysis.py:396: RuntimeWarning: invalid value encountered in double_scalars\n", " r = prob_ij ( np.log(prob_ij/(prob_aiprob_aj)))\n", "/home/ccorbi/anaconda3/envs/libs/lib/python3.6/site-packages/seaborn/matrix.py:603: ClusterWarning: scipy.cluster: The symmetric non-negative hollow observation matrix looks suspiciously like an uncondensed distance matrix\n", " metric=self.metric)\n", "/home/ccorbi/anaconda3/envs/libs/lib/python3.6/site-packages/seaborn/matrix.py:603: ClusterWarning: scipy.cluster: The symmetric non-negative hollow observation matrix looks suspiciously like an uncondensed distance matrix\n", " metric=self.metric)\n" ] }, { "data": { "text/plain": [ "<seaborn.matrix.ClusterGrid at 0x7fb0574c2c50>" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x720 with 4 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Quickly Plot Mutual information Matrix\n", "sns.clustermap(nk.mi_matrix(Pep_DATA).astype(float))" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "ExecuteTime": { "end_time": "2020-03-02T20:37:25.587987Z", "start_time": "2020-03-02T20:37:24.587700Z" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nk.generate_logo(Pep_DATA, filename='pepdata', *{'fineprint':'PhageRound_3', 'title':'EXAMPLE'})\n", "Image('pepdata.PNG')" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2020-03-02T20:20:33.760606Z", "start_time": "2020-03-02T20:20:33.754676Z" } }, "outputs": [ { "data": { "text/plain": [ "20" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# score target seqeunce with the pwm\n", "target = 'GLLPPPLPYGGGGLLLEWER'\n", "len(target)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2020-03-02T20:20:33.980884Z", "start_time": "2020-03-02T20:20:33.763711Z" } }, "outputs": [ { "data": { "text/plain": [ "{'GLLPPPLPY': 15.194932771716926,\n", " 'LLPPPLPYG': 6.539580943104372,\n", " 'LPPPLPYGG': 9.346935865161978,\n", " 'PPPLPYGGG': 5.802615348938165,\n", " 'PPLPYGGGG': 2.6326903474958545,\n", " 'PLPYGGGGL': 1.632690347495854,\n", " 'LPYGGGGLL': 6.954618442383218,\n", " 'PYGGGGLLL': 6.217652848217011,\n", " 'YGGGGLLLE': 7.802615348938167,\n", " 'GGGGLLLEW': 6.387577849659323,\n", " 'GGGLLLEWE': 4.387577849659322,\n", " 'GGLLLEWER': 7.387577849659322}" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nk.scan_binding_score(target, pwm)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2020-03-02T20:20:34.140672Z", "start_time": "2020-03-02T20:20:33.984026Z" } }, "outputs": [ { "data": { "text/plain": [ "'RWGGVSFFYNDYGINNSNCW'" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# generate a random peptide\n", "random_target = nk.rand_peptide(n=20)\n", "random_target" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2020-03-02T20:20:34.276089Z", "start_time": "2020-03-02T20:20:34.143686Z" } }, "outputs": [ { "data": { "text/plain": [ "{'RWGGVSFFY': 4.217652848217011,\n", " 'WGGVSFFYN': 4.539580943104371,\n", " 'GGVSFFYND': 5.217652848217011,\n", " 'GVSFFYNDY': 7.440045269553458,\n", " 'VSFFYNDYG': 1.2176528482170106,\n", " 'SFFYNDYGI': 6.2176528482170115,\n", " 'FFYNDYGIN': 6.217652848217009,\n", " 'FYNDYGINN': 6.802615348938165,\n", " 'YNDYGINNS': 6.217652848217011,\n", " 'NDYGINNSN': 3.802615348938167,\n", " 'DYGINNSNC': 3.6326903474958545,\n", " 'YGINNSNCW': 5.802615348938166}" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nk.scan_binding_score(random_target, pwm)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2020-03-02T20:20:41.116117Z", "start_time": "2020-03-02T20:20:34.279119Z" } }, "outputs": [ { "data": { "text/plain": [ "11.393858404176381" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# let's calc a threshold for a FDR < .01\n", "pwm_distr = nk.ScoreDistribution(pwm = pwm)\n", "pwm_distr.threshold_fpr(.01)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2020-03-02T20:20:41.127909Z", "start_time": "2020-03-02T20:20:41.120931Z" } }, "outputs": [ { "data": { "text/plain": [ "'GLLPPPLPY'" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "'GLLPPPLPY'\n", "# Best target !\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Environment (conda_libs)", "language": "python", "name": "conda_libs" }, "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" }, "nav_menu": {}, "toc": { "navigate_menu": true, "number_sections": true, "sideBar": true, "threshold": 6, "toc_cell": false, "toc_section_display": "block", "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }	mit
catalystcomputing/DSIoT-Python-sessions	Session3/code/03 Supervised Learning - 00 Python basics and Logistic Regression.ipynb	1	14363	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Here we introduce Data science by starting with a common regression model(logistic regression). The example uses the Iris Dataset\n", "# We also introduce Python as we develop the model. (The Iris dataset section is adatped from an example from Analyics Vidhya) \n", "# Python uses some libraries which we load first. \n", "# numpy is used for Array operations\n", "# mathplotlib is used for visualization\n", "\n", "import numpy as np\n", "import matplotlib as mp\n", "from sklearn import datasets\n", "from sklearn import metrics\n", "from sklearn.linear_model import LogisticRegression" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dataset = datasets.load_iris()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Display the data\n", "dataset" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# first we need to understand the data\n", "\n", "from IPython.display import Image\n", "from IPython.core.display import HTML\n", "Image(\"https://upload.wikimedia.org/wikipedia/commons/5/56/Kosaciec_szczecinkowaty_Iris_setosa.jpg\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "Image(\"http://www.opengardensblog.futuretext.com/wp-content/uploads/2016/01/iris-dataset-sample.jpg\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# In statistics, linear regression is an approach for modeling the relationship between a scalar dependent variable y \n", "# and one or more explanatory variables (or independent variables) denoted X. There are differnt types of regressions that model the\n", "# relationship between the independent and the dependent variables \n", "\n", "# In linear regression, the relationships are modeled using linear predictor functions whose unknown model \n", "# parameters are estimated from the data. Such models are called linear models.\n", "\n", "# In mathematics, a linear combination is an expression constructed from a set of terms by multiplying \n", "# each term by a constant and adding the results (e.g. a linear combination of x and y would be any expression of the \n", "# form ax + by, where a and b are constants)\n", "\n", "# Linear regression\n", "Image(\"https://www.biomedware.com/files/documentation/spacestat/Statistics/Multivariate_Modeling/Regression/regression_line.png\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "Image(url=\"http://31.media.tumblr.com/e00b481257fac723638b32271e611a2f/tumblr_inline_ntui2ohGy41sfzcxh_500.gif\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We use the <b> Iris dataset </b> \n", "\n", "https://en.m.wikipedia.org/wiki/Iris_flower_data_set\n", "\n", "The data set consists of 50 samples from each of three species of Iris (Iris setosa, Iris virginica and Iris versicolor). Four features were measured from each sample: the length and the width of the sepals and petals, in centimetres. Based on the combination of these four features, Fisher developed a linear discriminant model to distinguish the species from each other.\n", "\n", "logistic regression\n", "\n", "While logistic regression gives each predictor (independent variable) a coefficient ‘b’ which measures its independent contribution to variations in the dependent variable, the dependent variable can only take on one of the two values: 0 or 1. What we want to predict from knowledge of relevant independent variables and coefficients is therefore not a numerical value of a dependent variable as in linear regression, but rather the probability (p) that it is 1 rather than 0 (belonging to one group rather than the other). \n", "\n", "The outcome of the regression is not a prediction of a Y value, as in linear regression, but a probability of belonging to one of two conditions of Y, which can take on any value between 0 and 1 rather than just 0 and 1. \n", "\n", "The crucial limitation of linear regression is that it cannot deal with dependent variable’s that are dichotomous and categorical. Many interesting variables are dichotomous: for example, consumers make a decision to buy or not buy, a product may pass or fail quality control, there are good or poor credit risks, an employee may be promoted or not. A range of regression techniques have been developed for analysing data with categorical dependent variables, including logistic regression and discriminant analysis. \n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "model = LogisticRegression()\n", "model.fit(dataset.data, dataset.target)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "expected = dataset.target\n", "predicted = model.predict(dataset.data)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# classification metrics report builds a text report showing the main classification metrics\n", "# In pattern recognition and information retrieval with binary classification, \n", "# precision (also called positive predictive value) is the fraction of retrieved instances that are relevant, \n", "# while recall (also known as sensitivity) is the fraction of relevant instances that are retrieved. \n", "# Both precision and recall are therefore based on an understanding and measure of relevance. \n", "# Suppose a computer program for recognizing dogs in scenes from a video identifies 7 dogs in a scene containing 9 dogs \n", "# and some cats. If 4 of the identifications are correct, but 3 are actually cats, the program's precision is 4/7 \n", "# while its recall is 4/9.\n", "\n", "# In statistical analysis of binary classification, the F1 score (also F-score or F-measure) is a measure of a test's accuracy. \n", "# It considers both the precision p and the recall r of the test to compute the score: \n", "# p is the number of correct positive results divided by the number of all positive results, \n", "# and r is the number of correct positive results divided by the number of positive results that should have been returned. \n", "# The F1 score can be interpreted as a weighted average of the precision and recall\n", "\n", "print(metrics.classification_report(expected, predicted))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Confusion matrix \n", "# https://en.wikipedia.org/wiki/Confusion_matrix\n", "# In the field of machine learning, a confusion matrix is a table layout that allows visualization of the performance \n", "# of an algorithm, typically a supervised learning one. \n", "# Each column of the matrix represents the instances in a predicted class \n", "# while each row represents the instances in an actual class (or vice-versa)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# If a classification system has been trained to distinguish between cats, dogs and rabbits, \n", "# a confusion matrix will summarize the results of testing the algorithm for further inspection. \n", "# Assuming a sample of 27 animals — 8 cats, 6 dogs, and 13 rabbits, the resulting confusion matrix \n", "# could look like the table below:\n", "\n", "Image(\"http://www.opengardensblog.futuretext.com/wp-content/uploads/2016/01/confusion-matrix.jpg\")\n", "\n", "# In this confusion matrix, of the 8 actual cats, the system predicted that three were dogs, \n", "# and of the six dogs, it predicted that one was a rabbit and two were cats. \n", "# We can see from the matrix that the system in question has trouble distinguishing between cats and dogs, \n", "# but can make the distinction between rabbits and other types of animals pretty well. \n", "# All correct guesses are located in the diagonal of the table, so it's easy to visually \n", "# inspect the table for errors, as they will be represented by values outside the diagonal." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print (metrics.confusion_matrix(expected, predicted))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We typically need the following libraries:\n", "\n", "<b> NumPy </b> Numerical Python - mainly used for n-dimensional array(which is absent in traditional Python).\n", "Also contains basic linear algebra functions, Fourier transforms, advanced random number capabilities and tools for integration with other low level languages like Fortran, C and C++\n", "\n", "<b>SciPy</b> Scientific Python (built on NumPy). Contains a variety of high level science and engineering modules like discrete Fourier transform, Linear Algebra, Optimization and Sparse matrices.\n", "\n", "<b> Matplotlib </b> for plotting vast variety of graphs ex histograms, line plots and heat maps.\n", "\n", "<b> Pandas </b> for structured data operations and data manipulation. It is extensively used for pre processing. \n", "\n", "<b> Scikit Learn </b> for machine learning. Built on NumPy, SciPy and matplotlib, this library contains a lot of effiecient tools for machine learning and statistical modeling including classification, regression, clustering and dimensionality reduction.\n", "Statsmodels for statistical modeling. Statsmodels is a Python module that allows users to explore data, estimate statistical models, and perform statistical tests. An extensive list of descriptive statistics, statistical tests, plotting functions, and result statistics are available for different types of data and each estimator.\n", "Seaborn for statistical data visualization. Seaborn is a library for making attractive and informative statistical graphics in Python. It is based on matplotlib. Seaborn aims to make visualization a central part of exploring and understanding data.\n", " \n", "\n", "<b>Additional libraries, you might need:</b>\n", "\n", "urllib for web based operations like opening URLs and performing operations\n", "os for Operating system and file operations\n", "networkx and igraph for graph based data manipulations\n", "regular expressions for finding patterns in text data\n", "BeautifulSoup for scrapping web" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "integers_list = [1,3,5,7,9] # lists are seperated by square brackets\n", "print(integers_list)\n", "tuple_integers = 1,3,5,7,9 #tuples are seperated by commas and are immutable\n", "print(tuple_integers)\n", "tuple_integers[0] = 11" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Python strings can be in single or double quotes\n", "string_ds = \"Data Science\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "string_iot = \"Internet of Things\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "string_dsiot = string_ds + \" for \" + string_iot" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print (string_dsiot)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "len(string_dsiot)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# sets are unordered collections with no duplicate elements\n", "prog_languages = set(['Python', 'Java', 'Scala'])\n", "prog_languages" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Dictionaies are comma seperated key value pairs seperated by braces\n", "dict_marks = {'John':95, 'Mark': 100, 'Anna': 99}" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dict_marks['John']" ] }, { "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
helgako/cms-dqm	notebooks/CMS-AE.ipynb	1	1070552	null	mit
kmunve/APS	aps/notebooks/ml_varsom/linear_regression.ipynb	1	35332	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# LINEAR REGRESSION\n", "\n", "- is the simplest machine learning model\n", "- is used for finding linear relationship between target and one or more predictors\n", "- there are two types of linear regression:\n", " - Simple (one feature)\n", " - Multiple (two or more features) \n", "- The main idea of linear regression is to obtain a line that best fits the data. \n", "- That means finding the one line for which total prediction error (for all data points) are as small as possible. (Error is the distance between actual values and values predicted using regression line.)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### First linear regression model " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First we'll create a simple linear regression model - we saw that LSTAT and RM are two variables that are highly correlated with target. We will see how good predicteions we can get with just one feature - and how to decide which one of these features is better for estimating median house price? \n", "\n", "Step one is to divide our dataset into training and testing part - it is important to test our model against data that has never been used for training – that tells us how the model might perform against data that it has not yet seen and it is meant to be representative of how the model might perform in the real world.\n", "\n", "That's why we will use only 70% of our data to train the model and then we'll use the rest of data (30%) to evaluate our model. " ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import json\n", "import graphviz\n", "import matplotlib.pyplot as plt\n", "from sklearn import linear_model\n", "\n", "pd.set_option(\"display.max_rows\",6)\n", "\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "df_data = pd.read_csv('varsom_ml_preproc.csv', index_col=0)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "X = df_data.filter(['mountain_weather_wind_speed_num', 'mountain_weather_precip_most_exposed'])#, 'ZN', 'INDUS', 'CHAS', 'RM', 'AGE', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT'])\n", "y = df_data['danger_level']" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state = 222, test_size = 0.3) # split the data\n", "\n", "lm = linear_model.LinearRegression()\n", "model_lr = lm.fit(X_train, y_train) # train the model\n", "\n", "predictions_lr = model_lr.predict(X_test) # predict values for test dataset" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.96, [-0.00599321 0.02980983]\n" ] } ], "source": [ "print(f'{model_lr.intercept_:.2f}, {model_lr.coef_}')" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.collections.PathCollection at 0x241b1324390>" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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yZzayoXUaRWVx1BP6umM84mzlTWfM863OvDyjV1Xu3fZN+txuEl4/Hi4Jr5/t\nva+xovXRoMObkH6/4XnaOsvSc91oepiHqkNvdzHnPXxL0OFNSCfc+hUibWEcHMLx9I+jDpH2EMf8\n8D+CDm9CuvOlF3i1uQnPc+jtKqavuwgQfrzq7zR2d/tWb14m+vZkEx2JwX2MSU2wqu2pACIyX12Z\n/QNWPYe+pH3DCkJf99Bn7skeWxw8CL9Y/dKQ2257oc63evOz60Y1+0g+2G8tRjOODnJ9xFokKDas\n9XCje65flaSgKDMqqi8M/Q7e4bo4eFAqo9Moj1QNKo9IlJMq3xxAROb6U96atVwcj+LI4DUyjf8i\nxT1DjqN3im0d3yBctuQ4qEpAmYtEFIkqlCehPMWHTjrZt3rzMtGLCFfM/Swxp4SIpK8yRZ0iZhQv\n5PQpbw84uonphBkLmTq7DUhSOb2dmtmthKL9lFb0cc5i/y4ymaG948SlJKcn0QP+S01NctaSxUGH\nNyFNq47hhAEFjTtoQkAgXOLhOf7d2zDqrhsRmQPcBUwnPXBruap+T0SqgPuB+cAW4J9UtX3soe5v\nVskirjv6J6zu+CtdyTbmlR7ForITcGx1+0A8vutlUglh4XH75rUpr+wj3hvitf5tAUY2cf1px6Yh\ne2+eb6of32AMAH/ZtRmvX6ArOnCmEKhMsaplO3PKJvtS71j66FPAZ1X1BREpB1aJyOPAB4EnVfVm\nEfk88HnSywvmXFGolGXV2bsMzPja2NDIpJo+AGRAcomVuDQ22SJjQWhv301kZ+l+C1EDhBsj9ET6\nAopqYtve2gFdEUD2XbxSSLWFCWdbPCBHRp3oVbUBaMg83i0i64BZwKXAOZnd7gSexqdEv6JlDfdu\ne4LWeCfHTz6CD8x/KzOKp/hRlRnGX1peo6J6/yS/R1mV9QcHQZqGHlkTbvbvdnsztNe2tmbfoPDA\nqhe5eMExvtSbk1E3IjIfOAlYCUzLfAigqg0iMnWI51wLXAswd+7cQ67z4fq/cNum39HvpScIerKx\njr+1vMKttZ9jRnH1aA7DjMFQc6eIgPWmBUO8A8/lM+WAuDYiJwiplGY/G1LoiPt3QjTmt6CIlAG/\nAj6lqiOeZ1NVl6tqrarW1tQc2mo3CS/F7Zt/vzfJA3gofW6Cu7f+8ZBey+RGb3f2PyVVcJOW6YNQ\nWjP0DTgH22b8M7OojCwrCQLCu4483rd6x/QOFJEI6SR/t6o+lCluFJEZme0zgKaxhThYQ1/2rz8e\nHqs7Nua6OjMC4diAVe09IeU6aObkJWGJPhDT57RTUtYHKOooGkqP4S4qjjNjnk1qFoTjymYhSfYl\ne00/DvdAdci/qSlG/Q4UEQHLrS7DAAAMg0lEQVRuB9ap6rcHbHoEuCrz+Crg4dGHl11ltIykm31x\n45qYP1etzcGpFyKVcmhor2BrcxXbWyrZ3lJJbzyCm8rP+/LyXV8iwoKTtuLtuTHHAy+mzFu6jX7X\n7lYOQjKSwukj3X824P4oJw7lpf5dNxnLO/AM4APAKyKy577eLwA3Aw+IyDXANuDysYU4WEWkFEmF\n0ZC7X3eXKkzT6bmuzoxARwP0OhUkvRAgKJDyQjR2VBDpdoMOb0La2lDJrqYZSEr2jbyJw/oVi0lO\n7w02uAnq733bcCcxaNhrogpW7N7Im1nkS72jPqNX1WdVVVT1eFU9MfPzqKq2qup5qro4829bLgMG\n2N7VRsKJD7qmIQLP7Ho119WZEYiFoyRTYWLhFFPKdzNtUhdlRf2oKm58qEVJjJ8cDYEn+w2vFCTd\nbeDat6wgtLYPXl1qT/P89tXXfas3L1t7S0cTeAJZVi6KOzY+OAiqDuXFPVRP6kmP6hAojiaoKA7T\nsLsy6PAmJEk6WS/8iQpOwq6bBEGRIW9ii8ezd0fnQl629onT5iFZl6dTqtSSSiDCSaon9eDIvtFj\njgPRcIpym1clGK5mTSoqerA56IyPws7QyzsuqvYvHedloi+PFbOorAVnv9MVxUF56wL/VlI3QwuX\nplAdnFUcB4on2Z2xQXA9F6/YQ2VfZlFRNObhenbdJAhFB5nL9azZM3yrNy8TfWPfq0wO76ahrZxN\njdVsaqxmc2M1XgJa3HVBhzchqYSQLH/D6XH0ofEPyEClQ2JeHK/I2zelWcwjPj8OVXbDVBB6UkO/\nF26v82/+obxM9Emvj4c3nkBPMr06S3qUh8Om9hr+1mCjboLQ3xvB82RQl4Cq0NVSGkxQE5yqEttc\nhNPvIHv+63eIbSqy9ZUDojBkH73r+Td7ZV4m+sc3xEl4mYmBBlCFNa0zgwlqgpMuaNg8BTfl4LqC\n6wqeB227yoknbMx2EEJdISQhiO4/6kZSQqgzL8dh5D1xNZCVePKytX/22l9RlSzDKwXPTlQCIZEQ\niX5h69rpFJclcEIefd1RvGQIKrIMKTO+k9QQo248QRLWdRMEjUj2RJ+EHh9HIeflGX1NKHtXgCrp\nYZdm3LnJVHpRcHHo6ymip6sEzwtDCJz+vPwzy3sq+0bd7Omj31OuWUetGd+ldE9v8z4CRKDEx9n/\n8vKMvr4nDiHQcJaJ4Lrtwl8QIsVCSjXLyBvBsZkSA+EBXlhxZ8bxJnnpJQQ7HcI7o7j22RuIqIZJ\npjwiXYKT+aLrFkOyXDl1yhzf6s3LRO94AkN1+9qZSiAiEZdkIvt3Tyfm30UmcxDlLom5CSQ9KwUA\n3iSPRHk/tNsJURAi4uC0pvvp99yxHOpTJCXoNP/qzcvP9QWTK9KRK2hvCN0dRuNOuu+r1JJKENxw\nKv3H60J4N4S7BCcOqCJFtjh4ELTEhQFJHtKP1QFKbRx9IFLufkke0o+dFBSX+/fhm5dn9KvaWyAi\n0B6BsKaTfn84/W+lXfgLQqIvitMH0Y4BWaVXcGMQL8nLP7O8J8jefvn9yg/sIzbjJqEu4SHS7kvN\nO32rNy/fgcl2F2IRqEqmr8AmgDIg4cAQC2AYf6kbItbp7Esukp5TJRQHt9OGVwYiIVAyONGrYqNu\nAqIlHrpb9xvyunebj12ceZnoSQhUpYi+XoQzoFcgNdklVWldN0Fwkg4qSnKS4maWKpWUEu0Uwr2W\nVIIQ6nRITc7+fgh12AlREMqmRelvVtTVvd03KopX4nHmgoW+1ZuXrS0pIbaxCFTRcLrP0Yuk/7BD\nXZZUAqFKvJJ0ks90DWgE4lWKF7YL5IHodLKP2VZgt12MDcKXas8nOa+fkOulv1p5iiMuyflxvrrs\nLb7V69sZvYhcAHyP9OWg21T15ly9tqKoKM6ABRXUS69bF26zP+AgeK5AMYP7fgVSMfvwDUKqSqEt\nilYm9rWLAu1RUrYQWyAumv0mfvL4H4h3JHEy3TdeSDmKORSH/btjypczehEJAf8NXAgsAd4rIkty\n9vr9IRw3y4IKChqypBKIbCvbA3snpzfjThyBlAMtMWiLQns0/dh1rE0C8sSTa3A73b1JHsBxhY11\nDWyvz/kaTfvq8Ol1lwFvqOomVU0A9wGX5urFoz3ZywVBrIs+EE7yIInDem4CIfE9t9tLOrmnnPRj\nD5x+S/RBeOLJNXhZ5mnxPGXVqi2+1etXop8FbB/we32mLCfCPQeZGCgvrzrkvyE/YJX9LpibcaSC\nuHDAsg3ptrIP30Ds7h56EZ7Wtt2+1etXWsx2urDfn5aIXCsidSJS19zcfEgv7niKF2bQGGFFceJ2\nSh8EzwVJkTWBOLbAVCC8CETbINRHZj4ECPVCtDW9zYy/RQunZi0XgSMX+zfFul+Jvh4YOHHDbGC/\nuwFUdbmq1qpqbU1NzSG9+Mu334DT7e5N9irpHw+FlH0lDYL2vUS0jfT8HUpmAWqItGMTaAUk0tpH\nogrCvUJRY/on3CMkKiHcY1+zgvDud9USiQweMBKLRVh2Sv4Nr/w7sFhEFohIFLgCeCSXFUS6XUJJ\noMdFel1IuoQ8oe8V/+4uM0Pb9F+/JBXziLYLsSaItkCsCTQEG77yuaDDm5Beu/lLaEqJT1HiNZmf\nKen1Yl//6ueDDm9CetNRM/jgVWcRiYSIxcIUF0UoLo7wja9fRizm39cs8WulGRG5CPgu6eGVd6jq\n/xlq39raWq2rqzvkOhZe+GnKFs3CiwihnhQvL79+9AGbnFh4w2cJlcxARNBEgg033RB0SBPeUZ/6\nKqmZ5QBEGrtZf8uNAUdkWlu7qVu1maKi9Jl8cfHohlaKyCpVrR12v8NhSbHRJnpjjJnIRprobYyK\nMcYUOEv0xhhT4CzRG2NMgbNEb4wxBc4SvTHGFLjDYtSNiDQDW8fwElOAlhyFE7RCOZZCOQ4onGOx\n4zj8jPVY5qnqsHecHhaJfqxEpG4kQ4zyQaEcS6EcBxTOsdhxHH7G61is68YYYwqcJXpjjClwhZLo\nlwcdQA4VyrEUynFA4RyLHcfhZ1yOpSD66I0xxgytUM7ojTHGDCGvEr2I3CEiTSLy6hDbRUS+LyJv\niMhqEVk63jGOxAiO4xwR6RSRlzI/XxnvGEdCROaIyJ9EZJ2IrBGRf8+yz2HfJiM8jnxpkyIReV5E\nXs4cy9ey7BMTkfszbbJSROaPf6QHN8Lj+KCINA9okw8HEetIiEhIRF4Ukd9l2eZ/e6hq3vwAZwNL\ngVeH2H4R8BjpFa5OBVYGHfMoj+Mc4HdBxzmC45gBLM08LgdeB5bkW5uM8DjypU0EKMs8jgArgVMP\n2OdjwI8zj68A7g867lEexweBHwYd6wiP5zPAPdn+hsajPfLqjF5VnwEOtlT6pcBdmrYCmCwiM8Yn\nupEbwXHkBVVtUNUXMo93A+sYvDbwYd8mIzyOvJD5/9yd+TWS+TnwQtylwJ2Zxw8C54nIYbU02wiP\nIy+IyGzg7cBtQ+zie3vkVaIfAV8XJR9np2W+tj4mIscEHcxwMl83TyJ95jVQXrXJQY4D8qRNMt0E\nLwFNwOOqOmSbqGoK6ASqxzfK4Y3gOADek+kSfFBE5mTZfjj4LnAd+y/TPpDv7VFoiX7YRcnzxAuk\nb20+AfgB8JuA4zkoESkDfgV8SlW7Dtyc5SmHZZsMcxx50yaq6qrqiaTXal4mIscesEtetMkIjuO3\nwHxVPR54gn1nxYcNEbkYaFLVVQfbLUtZTtuj0BL9sIuS5wNV7drztVVVHwUiIjIl4LCyEpEI6eR4\nt6o+lGWXvGiT4Y4jn9pkD1XtAJ4GLjhg0942EZEwMInDuCtxqONQ1VZVjWd+/Slw8jiHNhJnAJeI\nyBbgPuBcEfnlAfv43h6FlugfAa7MjPQ4FehU1YaggzpUIjJ9Tx+diCwj3U6twUY1WCbG24F1qvrt\nIXY77NtkJMeRR21SIyKTM4+LgfOB9Qfs9ghwVebxZcBTmrkSeLgYyXEccK3nEtLXVg4rqnqDqs5W\n1fmkL7Q+parvP2A339sjnMsX85uI3Et69MMUEakHbiR9kQZV/THwKOlRHm8AvcDVwUR6cCM4jsuA\nj4pICugDrjjc3ogZZwAfAF7J9KUCfAGYC3nVJiM5jnxpkxnAnSISIv1h9ICq/k5EbgLqVPUR0h9q\nvxCRN0ifOV4RXLhDGslxfFJELgFSpI/jg4FFe4jGuz3szlhjjClwhdZ1Y4wx5gCW6I0xpsBZojfG\nmAJnid4YYwqcJXpjjClwluiNMabAWaI3xpgCZ4neGGMK3P8Hi5Rm528vBK8AAAAASUVORK5CYII=\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(y, X['mountain_weather_precip_most_exposed'], c=X['mountain_weather_wind_speed_num'])" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "ename": "IndexError", "evalue": "index 1 is out of bounds for axis 0 with size 1", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-12-f36db2a37fb8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m\" + {0:.2f}\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel_lr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcoef_\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m\" * AGE\"\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m\" + {0:.2f}\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel_lr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcoef_\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;34m\" * RAD\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m\"\\n {0:.2f}\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel_lr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcoef_\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m7\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m\" * TAX\"\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m\" {0:.2f}\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel_lr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcoef_\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m8\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m\" * PTRATIO\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m + \" + {0:.2f}\".format(model_lr.coef_[9]) + \" * B\" + \" {0:.2f}\".format(model_lr.coef_[10]) + \" * LSTAT\")\n\u001b[0m", "\u001b[0;31mIndexError\u001b[0m: index 1 is out of bounds for axis 0 with size 1" ] } ], "source": [ "print(\"Our third model: \\n \\ny = {0:.2f}\".format(model_lr.intercept_) + \" {0:.2f}\".format(model_lr.coef_[0]) + \" * CRIM\"\n", " + \" + {0:.2f}\".format(model_lr.coef_[1]) + \" * ZN\" + \" + {0:.2f}\".format(model_lr.coef_[2]) + \" * INDUS\"\n", " + \" + {0:.2f}\".format(model_lr.coef_[3]) + \" + * CHAS\" + \" {0:.2f}\".format(model_lr.coef_[4]) + \" * RM\" \n", " + \" + {0:.2f}\".format(model_lr.coef_[5]) + \" * AGE\" + \" + {0:.2f}\".format(model_lr.coef_[6]) + \" * RAD\"\n", " + \"\\n {0:.2f}\".format(model_lr.coef_[7]) + \" * TAX\" + \" {0:.2f}\".format(model_lr.coef_[8]) + \" * PTRATIO\"\n", " + \" + {0:.2f}\".format(model_lr.coef_[9]) + \" * B\" + \" {0:.2f}\".format(model_lr.coef_[10]) + \" * LSTAT\")" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "ename": "ValueError", "evalue": "not enough values to unpack (expected 4, got 2)", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-4-4694af095757>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodel_selection\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mX_train_1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX_test_1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train_1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_test_1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf_data\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrandom_state\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m222\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest_size\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0.3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mValueError\u001b[0m: not enough values to unpack (expected 4, got 2)" ] } ], "source": [ "from sklearn.model_selection import train_test_split\n", "\n", "X_train_1, X_test_1, y_train_1, y_test_1 = train_test_split(df_data, random_state = 222, test_size = 0.3)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ " # we are importing machine learning model we'll use\n", "\n", "lm1 = linear_model.LinearRegression()\n", "\n", "model_1 = lm1.fit(X_train_1, y_train_1) # we have just created a model! :) " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# as we said before, the model in this simple case is a line that has two parameters\n", "\n", "# so we ask: what are our estimated parameters? (alpha and beta?)\n", "\n", "print(\"Our first model: y = {0:.2f}\".format(model_1.intercept_) + \" {0:.2f}\".format(model_1.coef_[0]) + \" * x\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(\"Intercept: {0:.2f}\".format(model_1.intercept_))\n", "print(\"Extra price per extra unit of LSTAT: {0:.2f}\".format(model_1.coef_[0]))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# now we'd like is to predict house price for test data (data that model hasn't seen yet)\n", "\n", "predictions_1 = model_1.predict(X_test_1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "predictions_1[0:5]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# let's visualize our regression line\n", "\n", "plt.plot(X_test_1, y_test_1, 'o')\n", "plt.plot(X_test_1, predictions_1, color = 'red')\n", "plt.xlabel('% of lower status of the population')\n", "plt.ylabel('Median home value in $1000s')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Evaluation of your model\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# let's try to visualize the estimated and real house values for all data points in test dataset\n", "\n", "\n", "fig, ax = plt.subplots(figsize=(15, 5))\n", "\n", "plt.subplot(1, 2, 1)\n", "plt.plot(X_test_1,predictions_1, 'o')\n", "plt.xlabel('% of lower status of the population')\n", "plt.ylabel('Estimated home value in $1000s')\n", "\n", "\n", "plt.subplot(1, 2, 2)\n", "plt.plot(X_test_1,y_test_1, 'o')\n", "plt.xlabel('% of lower status of the population')\n", "plt.ylabel('Median home value in $1000s')\n", "\n", "plt.tight_layout()\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To evaulate the performance of the model, we can compute the error between the real house value (`y_test_1`) and the predicted values we got form our model (`predictions_1`).\n", "\n", "One such metric is called the residual sum of squares (RSS): \n", "\n", "![title](pictures/rss.png)\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# first we define our RSS function\n", "\n", "def RSS(y, p):\n", " return sum((y - p)2)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# then we calculate RSS: \n", "\n", "RSS_model_1 = RSS(y_test_1, predictions_1)\n", "\n", "RSS_model_1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This number doesn't tell us much - is 7027 good? Is it bad? \n", "\n", "Unfortunatelly, there is no right answer - it depends on the data. Sometimes RSS of 7000 indicates very bad model, and sometimes 7000 is as good as it gets. \n", "\n", "That's why we use RSS when comparing models - the model with lowest RSS is the best. \n", "\n", "The other metrics we can use to evaluate our model is called coefficient of determination*. \n", "\n", "It's denoted as $R^{2}$ and it is the proportion of the variance in the dependent variable that is predictable from the independent variable(s).\n", "\n", "To calculate it, we use .score* function in Python." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "lm1.score(X_test_1,y_test_1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This means that only 51% of variability is explained by our model. \n", "\n", "In general, $R^{2}$ is a number between 0 and 1 - the closer it is to 1, the better the model is. \n", "\n", "Since we got only 0.51, we can conclude that this is not a very good model. \n", "\n", "But we can try to build a model with second variable - RM - and check if we can get better result. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### More linear regression models" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# we just repeat everything as before \n", "\n", "X_train_2, X_test_2, y_train_2, y_test_2 = train_test_split(boston_data[['RM']], boston_data.MEDV, \n", " random_state = 222, test_size = 0.3) # split the data\n", "\n", "lm = linear_model.LinearRegression()\n", "model_2 = lm.fit(X_train_2, y_train_2) # train the model\n", "\n", "predictions_2 = model_2.predict(X_test_2) # predict values for test dataset\n", "\n", "print(\"Our second model: y = {0:.2f}\".format(model_2.intercept_) + \" + {0:.2f}\".format(model_2.coef_[0]) + \" * x\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# let's visualize our regression line\n", "\n", "plt.plot(X_test_2, y_test_2, 'o')\n", "plt.plot(X_test_2, predictions_2, color = 'red')\n", "plt.xlabel('Average number of rooms')\n", "plt.ylabel('Median home value in $1000s')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# let's calculate RSS and R^2\n", "\n", "print (RSS(y_test_2, predictions_2)) \n", "\n", "print (lm.score(X_test_2, y_test_2))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# now we can compare our models \n", "\n", "print(\"RSS for first model is {0:.2f}\".format(RSS(y_test_1, predictions_1)) \n", " + \", and RSS for second model is {0:.2f}\".format(RSS(y_test_2, predictions_2)) + '\\n' + '\\n' \n", " + \"R^2 for first model is {0:.2f}\".format(lm1.score(X_test_1, y_test_1)) \n", " + \", and R^2 for second model is {0:.2f}\".format(lm.score(X_test_2, y_test_2)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since RSS is lower for second modell (and lower the RSS, better the model) and $R^{2}$ is higher for second modell (and we want $R^{2}$ as close to 1 as possible), both measures tells us that second model is better.\n", "\n", "However, difference is not big - out second model performs slightly better, but we still can't say it fits our data well. \n", "\n", "Next thing we can try is to build a model with all features we have available and see if using multiple features improves performace of the model. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "X = boston_data[['CRIM', 'ZN', 'INDUS', 'CHAS', 'RM', 'AGE', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT']]\n", "y = boston_data[\"MEDV\"]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state = 222, test_size = 0.3) # split the data\n", "\n", "lm = linear_model.LinearRegression()\n", "model_lr = lm.fit(X_train, y_train) # train the model\n", "\n", "predictions_lr = model_lr.predict(X_test) # predict values for test dataset\n", "\n", "print(\"Our third model: \\n \\ny = {0:.2f}\".format(model_lr.intercept_) + \" {0:.2f}\".format(model_lr.coef_[0]) + \" * CRIM\"\n", " + \" + {0:.2f}\".format(model_lr.coef_[1]) + \" * ZN\" + \" + {0:.2f}\".format(model_lr.coef_[2]) + \" * INDUS\"\n", " + \" + {0:.2f}\".format(model_lr.coef_[3]) + \" + * CHAS\" + \" {0:.2f}\".format(model_lr.coef_[4]) + \" * RM\" \n", " + \" + {0:.2f}\".format(model_lr.coef_[5]) + \" * AGE\" + \" + {0:.2f}\".format(model_lr.coef_[6]) + \" * RAD\"\n", " + \"\\n {0:.2f}\".format(model_lr.coef_[7]) + \" * TAX\" + \" {0:.2f}\".format(model_lr.coef_[8]) + \" * PTRATIO\"\n", " + \" + {0:.2f}\".format(model_lr.coef_[9]) + \" * B\" + \" {0:.2f}\".format(model_lr.coef_[10]) + \" * LSTAT\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# let's evaluate the model\n", "\n", "print(\"RSS for the third model is {0:.2f}\".format(RSS(y_test, predictions_lr)) + '\\n' + '\\n' \n", " + \"R^2 for the third model is {0:.2f}\".format(lm.score(X_test, y_test)) )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can see improvement - RSS is 2000 less than for second model, and $R^{2}$ is 0.24 higher than for second model.\n", "\n", "So out of the three models we tested, we can see that third one (with multiple features) is performing the best. \n", "\n", "Of course, linear regression is not the only method we can use to solve this problems - there are more advanced methods like decision trees, random forests and gradient boosted trees. " ] } ], "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
alexjj/money-scripts	UK Index Prices.ipynb	1	5161	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "ename": "ModuleNotFoundError", "evalue": "No module named 'selenium'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-1-e4744d28687f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdatetime\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mselenium\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mwebdriver\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtime\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msleep\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[1;32m 5\u001b[0m '''\n", "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'selenium'" ] } ], "source": [ "import datetime\n", "from selenium import webdriver\n", "from time import sleep\n", "\n", "'''\n", "Index fund scraper.\n", "\n", "This python script will take a list of funds and look up their price from\n", "iii.co.uk, then write them to a pricesdb file for ledger-cli's usage.\n", "\n", "Run cron daily/weekly/monthly\n", "\n", "python ukfundprices.py\n", "'''\n", "__author__ = \"Alex Johnstone <[email protected]>\"\n", "\n", "# List of funds to look up.\n", "funds = ('FIAAGY',\n", " 'VVDVWE',\n", " 'VVFUSI',\n", " 'MYKAAS',\n", " 'VIUKGO',\n", " 'VIGSCA',\n", " 'VVUILG',\n", " 'VVLSRE')\n", "\n", "# FIAAGY and MYKAAS prices are in p\n", "penny_funds = ('FIAAGY', 'MYKAAS')\n", "\n", "base_url = 'http://www.iii.co.uk/investment/detail?code=mex:'\n", "end_url = '&it=ukut'\n", "\n", "pricedb_file = '/home/alex/finance/prices.beancount'\n", "\n", "# ledger or beancount\n", "\n", "program = 'beancount'\n", "#program = 'ledger'\n", "\n", "# Make the ledger string\n", "\n", "def make_ledger_str(fund, price):\n", " now = datetime.datetime.today()\n", "\n", " if program == 'ledger':\n", " timestamp = now.strftime(\"%Y/%m/%d %H:%M:%S\")\n", " string = \"P {} {} {} GBP\".format(timestamp, fund, price)\n", " elif program == 'beancount':\n", " timestamp = now.strftime(\"%Y-%m-%d\")\n", " string = \"{} price {} {} GBP\".format(timestamp, fund, price)\n", " else:\n", " print(\"Only ledger or beancount\")\n", " quit()\n", " return string\n", "\n", "\n", "def get_prices():\n", " price_list = []\n", " for fund in funds:\n", " browser = webdriver.PhantomJS()\n", " browser.get(base_url + fund + end_url)\n", " price = browser.find_element_by_class_name('price').text\n", " if fund in penny_funds:\n", " price = float(price) / 100\n", " string = make_ledger_str(fund, price)\n", " price_list.append(string)\n", " browser.quit()\n", " sleep(1) # Kept getting connection refused\n", " return price_list\n", "\n", "\n", "def write_prices(price_list):\n", " with open(pricedb_file, 'a') as text_file:\n", " for string in price_list:\n", " print(string, file=text_file)\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "browser = webdriver.PhantomJS()\n", "site = base_url + 'VVDVWE' + end_url\n", "browser.get(site)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "http://www.iii.co.uk/investment/detail?code=mex:VVDVWE&it=ukut\n" ] } ], "source": [ "print (site)\n", "price = browser.find_element_by_class_name('price').text" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "325.25\n" ] } ], "source": [ "print (price)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 }	bsd-2-clause
ComputationalModeling/spring-2017-danielak	past-semesters/spring_2016/day-by-day/day18-kinematics-terminal-velocity-of-a-skydiver/video_stuff/example_function.ipynb	2	1544	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook calculates an example function that we'll be using in our videos of numerical integration and differentiation!" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "x = np.linspace(-10.0,10.0,1000)\n", "y = 1.0 + 3.0x + 2.0x*2np.sin(8.0*x)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plt.plot([0,2],[0,0],'k-')\n", "plt.plot(x,y,linewidth=3)\n", "plt.xlim(0,2.0)\n", "plt.ylim(0.0,15.0)\n", "plt.xticks(np.linspace(0.0,2.0,11),fontsize=12)\n", "plt.xlabel('x',fontsize=18)\n", "plt.ylabel('f(x)',fontsize=18)\n", "plt.text(0.2,12,r'$f(x) = 1 + 3\\mathrm{x}+ 2\\mathrm{x}^2\\sin(\\mathrm{8x})$',fontsize=14)\n", "plt.savefig('example_function.png',dpi=300)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }	agpl-3.0
hdesmond/StatisticalMethods	lessons/2.UnderstandingFromData.ipynb	1	1543	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# PHYS366: Statistical Methods in Astrophysics\n", "\n", "\n", "# Lesson 2: Understanding From Data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Goals for this session:\n", "\n", "* ... \n", "* ...\n", "* ...\n", "\n", "#### Related reading:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }	gpl-2.0
leewujung/trex_fish	Split-window normalizer and total energy variation.ipynb	1	528171	{ "cells": [ { "cell_type": "code", "execution_count": 142, "metadata": { "collapsed": false }, "outputs": [], "source": [ "if isunix\n", " addpath('~/internal_2tb/Dropbox/0_CODE/MATLAB/saveSameSize');\n", " addpath(['~/internal_2tb/Dropbox/0_CODE/trex_fish/Triplet_processing_toolbox'])\n", "else\n", " addpath('F:\\Dropbox\\0_CODE\\MATLAB\\saveSameSize');\n", " addpath('F:\\Dropbox\\0_CODE\\trex_fish\\Triplet_processing_toolbox')\n", "end\n", "\n", "base_data_path='~/internal_2tb/trex/figs_results/';\n", "base_save_path='~/internal_2tb/trex/figs_results/';\n", "data_path='subset_beamform_cardioid_coherent_run131';\n", "ping_num = 500;\n", "plot_show_opt=1;\n", "\n", "% Set up various paths\n", "ss = strsplit(data_path,'_');\n", "run_num = str2double(ss{end}(4:end));\n", "\n", "[~,script_name,~] = fileparts(mfilename('fullpath'));\n", "script_name = script_name(1:end-4);\n", "save_path = fullfile(base_save_path,sprintf('%s_run%03d',script_name,run_num));\n", "if ~exist(save_path,'dir')\n", " mkdir(save_path);\n", "end\n", "\n", "% Ping range\n", "if isempty(ping_num)\n", " data_files = dir(fullfile(base_data_path,data_path,'.mat'));\n", " ping_len = length(data_files);\n", "else\n", " ping_len = length(ping_num);\n", "end\n", "\n", "% Set params\n", "cmap = 'jet';\n", "sm_len = 200;\n", "axis_lim = [-4 -2 -4 -2];\n", "color_axis = [180 210];\n", "norm_param.sm_len = sm_len; % smooth length\n", "norm_param.aux_m = 200; % length of auxiliary band in [m]\n", "norm_param.guard_num_bw = 2; % 2/BW\n" ] }, { "cell_type": "code", "execution_count": 143, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "ping_len =\n", "\n", " 1\n", "\n" ] } ], "source": [ "ping_len" ] }, { "cell_type": "code", "execution_count": 144, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "iP =\n", "\n", " 1\n", "\n", "Processing subset_beamform_cardioid_coherent_run131_ping0500.mat\n" ] } ], "source": [ "iP=1\n", "% Load file and set filename\n", " if isempty(ping_num) % if processing all files in the folder\n", " fname = data_files(iP).name;\n", " ping_num_curr = str2double(fname(end-7:end-4));\n", " else\n", " fname = sprintf('%s_ping%04d.mat',...\n", " data_path,ping_num(iP));\n", " ping_num_curr = ping_num(iP);\n", " end\n", " disp(['Processing ',fname])\n", " A = load(fullfile(base_data_path,data_path,fname));\n", "\n", " save_fname = sprintf('%s_run%03d_ping%04d',script_name,run_num,ping_num_curr);\n", "\n", " % Get normalizer output\n", " [beamform_norm,meta] = normalizer_split_window(A,norm_param);\n", "\n", " % Plotting\n", " title_text = sprintf('Run %d, Ping %d, %02d:%02d:%02d',...\n", " run_num,ping_num_curr,A.data.time_hh_local,...\n", " A.data.time_mm_local,A.data.time_ss_local);\n" ] }, { "cell_type": "code", "execution_count": 145, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "title_text =\n", "\n", "Run 131, Ping 500, 01:14:14\n", "\n" ] } ], "source": [ "title_text" ] }, { "cell_type": "code", "execution_count": 146, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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I5MyomPFzwSnIH3JEkNsAWJAQsQaaeAhmhHR50RCAIQpF5DUJaAcLOpEdKnVI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\n8ENAQMDIkSPt7n/r1q233XZbdHS09eb9+vXbsmVLc3OzenQb9fX1QghfX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"text/plain": [ "<IPython.core.display.Image object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "% Set up figure\n", "fig = figure('position',[280 60 1000 500]);\n", "corder = get(gca,'colororder');\n", "if ~plot_show_opt % if not showing figure\n", "set(fig,'visible','off');\n", "end\n", "\n", "figure(fig)\n", "h_ori = plot_small_echogram(subplot(121),A,sm_len,color_axis,axis_lim);\n", "\n", "subplot(122)\n", "cla\n", "h_norm = pcolor(meta.X/1e3,meta.Y/1e3,10log10(beamform_norm));\n", "hold on\n", "set(h_norm,'edgecolor','none');\n", "axis equal\n", "colormap(jet)\n", "colorbar('location','southoutside');\n", "caxis([10,20])\n", "axis(axis_lim)\n", "xlabel('Distance (km)','fontsize',14)\n", "ylabel('Distance (km)','fontsize',14)\n", "set(gca,'fontsize',12)\n", "\n", "mtit(title_text,'fontsize',16);\n", "saveSameSize_150(gcf,'file',fullfile(save_path,[save_fname,'.png']),...\n", " 'format','png');" ] }, { "cell_type": "code", "execution_count": 84, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "ans =\n", "\n", " 26.3218 -22.9546\n", "\n" ] } ], "source": [ "[max(max(10log10(beamform_norm))), min(min(10log10(beamform_norm(~isinf(10log10(beamform_norm))))))]" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<IPython.core.display.Image object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "hist(10log10(beamform_norm(:)),-30:1:30)" ] }, { "cell_type": "code", "execution_count": 159, "metadata": { "collapsed": true }, "outputs": [], "source": [ "base_data_path='~/internal_2tb/trex/figs_results/';\n", "base_save_path='~/internal_2tb/trex/figs_results/';\n", "data_path='subset_beamform_cardioid_coherent_run087';\n", "plot_show_opt=1;\n", "\n", "% Set params\n", "cmap = 'jet';\n", "sm_len = 300;\n", "axis_lim = [-4 -2 -4 -2];\n", "norm_caxis = [10 15];\n", "norm_param.sm_len = sm_len; % smooth length\n", "norm_param.aux_m = 200; % length of auxiliary band in [m]\n", "norm_param.guard_num_bw = 2; % 2/BW" ] }, { "cell_type": "code", "execution_count": 160, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Processing subset_beamform_cardioid_coherent_run087_ping0100.mat\n" ] }, { "data": { "image/png": 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Nu6RS/Tr2bWZGlDNaMXPs6WGOxPNlcu2sS9NQ5tRWCl1IYcLN0MbkGHuzXLCY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pGI9RZBN1aYLnZLVhJqBUaD+pdO9gUJW1E7YHpPT91AzcvEKOzhhcTmYoqLd0Y6VciN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\ndZRer4+JiYmJiZk4cWJaWtonn3ySlZVVW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5PSVKYLM1DxqF9h4Pi6BvOTaux1kJ7Dyo4IINUQDHI2rgeaBCFnSQoQQymS5m\nNgI4IjNbEAMA38J3GZ3AyyPFQIA0BD2TxV0l6hp71gwPBIyvJ96MY7G6wMeaWOqyAnl+hIlJXu5A\nlAgsyiaFEmfIoLNlNUIGFCSBrQ51GnIa3SCTgTKlCA1RLm1G9SEOPiSxo4gJKIFIvkA0yrY0SgPx\nGNFGtpi0m+7/biCRpFhGtZlusCdNxEGIIsqYm3BK0MWEekwHoch4le9tYEozazvx84zWiagcNBln\noCb4g8cf87RnhI/HhH0uG9PMWcgDndRE6FjD6w5+FLEueHQ5S59xv7sC1xE+sVg4XyCqscx+t7rl\nSc+P75/9nombTrQWJxehTTomTmmDVKdCJTCl3eMgp1j4aQKRZzPSLJlmmcAiGkGIsNwiojFLobPI\ndLH31JFGBuIGWJAg6OPqKscQxR73Nt9GNPA7EGXkZPnPpXH1RTCJiKhN7PcZYSA54ILTY3ICE9FA\nrvgdzOKNOIoLRbBABQcUsHAsHA8ADYIe/3IMcCnm2F0GcGPEEiAhGwB70+QVkPEqs7GKkjLI2B3M\n1MGHGHaBQMZWiM4nEcX3OcNjg+mXRGSNfJHAxfcR4GULqwA+xSyFDFhICm4bnRKqhyz1zB1/l6Ws\nckUTIkRrmbsIBFAJLIiBTj6LGMUu4mTJK5xpYFm8bLG+Fb9MNMr2dnwJbQFKgFJArGVmCqvI9lZO\na6chxroymogn02oyagZZcGLsLXBhM1vSTExi5wHSbvBYRvi/MYJW/rSSqU3UaYi1SCUSMfwyCNg2\nK21+sDz4XlGoSwhXJFh4Em6KVP7jJ4Ltz77bXvIhfw2cygZJvjIFldG88vArkrWIAPXyFCbIOdZp\njJyPLpLQyORoEIkpdIKhodeBygGLrElEo74e2vFzby5yuhV/s8rPsoDvE2QZb5BfXW4L1v64gaSC\nV2aSQWkVRhTbBR0UZBlBR/FBBQk9ChmECAVIzQMLyeidD/motTgWuFCGoMdzQdRgARh0mNQZeGny\nEqKOrzJ5IbpFrYZkgNt7iYAYRa7D6kSrzJYsULBsRgTUGKRdVnpcHGPfavdXZVICig4CXoHApCix\nraunDUUNBGoMXJdMkUtnMSyGqOMXQMCxeTjLJQtxJRydxiR6GTkCAmggkk8jJqGI4LG1nbMbsU0U\ng840mQw2dG1gOJymMUnHLWI6JJqgESdNV0BjA0HA3ARAdytbi1gWBZ9ftHH+QnbIiHFqk3gqBcn/\nL/g/C9EcnniGvRkun80BA6mEaiDoKAGJOLZKabX/UJnSpl6n/FOJd9BmdK4IDVLI4HMqGySxMd77\nm/QgAhayyahp+BlGdcRn54lraBFKErbJLlhRxAZd4TwZOQI2dKIhNvnMbUY1ULsKkgAAIABJREFU\nesf36g3kXvtkERTBJRVAAUHiuS78JHYRKcBKIpWYdB6iDipuHtlgRAo83BJyAttEkAiiOD6kkCpT\nCqn3LgrI4IEFq1AUCKAd6inliCSgyMYs6gz8Mq+ncUC3CDSkAL0OJAjw04g66PxuA4kGBIeRSV42\nUUAU2evhSaz0EUR2FxAVunWEcs+JK6UTaQbE0ZoQ6pCnsTfNqHp2ZHjcIRqhxoC0+K8669t5OY9q\nEVGRZS6McJaGqsMaWAE+fmXQVxAEygFlkDQCEBLUNUKE3SZWlvZl1LhINnvaEB0UAaeB13Jk24nF\n6dKZF+X0FL7N9k34edwcXSaTm5igMy7gQJaYi2rRaXB7IzObWa2zbRnNUSbWI3sI9USa8WUEkGIU\n24IXijza96DxSYKoHfFr/x21SSEhg86pbJDcTbHeM6FAFlzcgMCFiLuKkh0x5pmIEuPi2HkmNKLa\nZMGEFS5jLfBwuyiZAgGajF38C1cZNYZXmeXIYBO4CCo7TeINBBadnVzuQ4R8QCpGKsYbIlOakBXQ\n0UR8EEVEFUCFmEY0RiQOOp6J2rt8ZKd74gwBFEAGEV1FsJF1gJwFcYIOVAVBo+wzyWBXG34JIcHY\nKLIMKggEPkIKs5MxGoFPIFDsQDQY1sHwGAmVhMek2RQ7eNFk1wYcvUcHJ0eiACpulBFRkAnydJfw\nIuzqAIVUDKCcFi5owFJ5sAMnz84iSpwZ9YzPQmXcVAD8Dkjit4PMllbGN+KqDI8zrJZSJ+40dncx\ntomsSySGp1AsM6YBpUzgs62VUhZDoixSJzIzggCaytQZvNJBeTUBbJWZnuDZLG6ZrjQrdK6QuaCO\nPRrPL2drB1MMlC5GJzmnnoiIa1F3HrbKGe98jxx03CMbUf9k9FoK+WvmVDZI/ooOGqK9o3kFCbsA\nJjnVWmNNvrgLDV6XmDmbXRaaSCe0KKyDht5nz71W0CkQkzmjuWf5iwiALRCtQ4z0PIT6JqjkTcYr\nBC6FOHflQMRSyHvEBFKg+ohxUHCLqCJyAqmeUTp6koKJDWM04i6egui8+a8RtT4zMxknh2aAwkgH\nRMx2tBiUwSUSB5+xAkWXiI8o81qWiXVITTAfx0SoA4U9JnKUfR1gsc3CshgRsMpHVNhXhoAgQIqC\n01M1x2KcgO7iBUgJYgK+hV8J5BODAtEoasx/oGPMjSKT6hhROWzk83ArEZX6FqRe29azpdcFPuQA\nig4IdDvIPp7MGyKBzL48pQKSzehanAK72hktIugIOtuKtHWCgurSEEetxY8R15g/i90Wr3YgdbC2\nwLg4L+R4SsbO8gzEXZobGNVE3uHFDUxvxvdJ2VxYy/QWdJjZgvGuhmkYHPyjLjO674YaISGDxKls\nkHjFZOoho4zHcAs5ghTbn1csGealkT0SDZwVY22WZihDUsbqPZXiWsHjGSGSxRcZtaB3hhSFPIqI\n0GfOJOqQQEgR2AQSXhkEDmbwk+RNRou85HO6QSSGHkVMcHaUwMKTGJNEs3BBUrAU0HHNv3i2lY1e\nL508FHFyBBoHCshJACSQyNxPMQ3QUeSCRuLgOsTrGB7FK0Ftz6IlEi4EeZQEiDjtKAb5DWxcxRMO\nw1TQcV0ECSGHGAUdYFuaydOQCwQCp9UTNXBtjDzJKNs2UAwQppFXxD8V1RabOo1IlLLDHouChecy\nrOLv50ADJHuDOYkg8kYJ3WeixEGb6S3s62JKM24GL0fWRgHJwGsDEbUNDWSTOQ5rsmSg3WQS+BKC\nyGg4fwaCRq6IW+AlnxGzEDIsLeFJdAg4JnUVl8VGnl1DzUochS4Lv8Atqfjf51n4jvTBE0149iXk\nZOKUNkhjjbc8IHoUHEYbTJLtn2T1VMAGeG+ELhtZpEsgCx2wUGahTtQAKDvBs7mgS0BVCCq2weqd\nJJUIZIiBQryZoIhtIciIOkEBVIDAo0ZlR1ZcIvBZgVkx6hKcoRAIKAKo7CuTL7PfRMyTUDi3nmgS\nv4Sc6lHZLeD6fzGy2BniCeyunkyrHQAdihAlZxFV6MoSD5iQwC7RICHl0evxW6HM+EaEOFLFwqnk\nfHwHdx2BhaSAgLsGKyCwEHq9NgoBpTI4CBa7SiiNUKTNZnIRJ8ruNTg+yK//2IKA/Wmm6PgBqRS/\nfAZk4jEEHdnojQyk9+7qqVhdJOqwJU7TOFjEt9jbzuRZAHTyaiuBh2iwfSWjG/HhjAXMbRImmawo\ngIqtY9tsK+LFkGCMhqAiuygJhstEGvFMnl+FI4DE+i78HIEDKTal2dxKfYr9Fqscpc05/6IV70AX\nDAkJ6QentEFSwK+MqhVU8AiKBA478lanJrgBI9IkI+zNogrEomo0x+oAF77r9c6unJ5ADHsK+A5o\nvZs6Evk0fhQKoIFCYCGq7C6jxMAmUEnoGDGSFplOUsi+SwJGaux12VWiAONqKepINsUyvo+ZZ5iM\nY+KDoPWYtB7N++CLOEUo41bCr1XiRCR6XkJBhE6RqIqmsDWNLnAgSZPMuTpqAkR2pzkjRakMUTDx\nPcQ4NMAadjyMXAmL54BBYCFOgyj47O4k0sDrObwCjkbUQCmw2SIm46ioFoLuKXHb1LA0VJnRPmNj\nyBFefBrV7TlTLNuggg0KiGgLkBKY7QwzsXMcULmwmQkqgYjUiKiBjZ8nkEBmVyt2mVc2kAvEm2Yx\nvI3XRHb5iFH2a0giqsxej+EtTJqN3s5lCnPjzJqGnWbLkzzfRTJGKQ2dICEvYEOGbVmubGBbW6FZ\nLbs6ISEhJ5RT2iDtyhEYDK+u2lW8G+Ls72Ciga2+9ngQnx6hq8wYhUVwR1y9WJATNmsgbuNWDnn4\n4PCaSaPISFB80KCA2AAFCHrelmTniWkIIsU8ioYooOQYF0F0iEexFH9lXmzyiZmg4QYYBcwsMZG4\ni6Qi17EjTdlmhIA2DVQ8euZheOBBvNcsRZDnIYJYPYMSQBayMK9nw2xtmclJzBxOhjnTcHXaTKIw\nphEg30WDiuZABMpEXNwMgJ8HH7lyCLcTLPw8ggUJcLFFcq3oKsNkillEkckJCjnGR7ECAoFgA1aO\nNSajddb6qDqyzfsayEm8lmF8C6R6lumoTPhcFAV1HpbAfoNdJrZGVxTdYFQGL4/cDArECSwQcCyw\nKXby54zXEeW98zgNVBgb44DAU624AefXs99jq8LfNeBn9fo2/X0GzdNwc9idpHNEZiDqyAFqExcs\nIVNkTZo50+z3r7GfKb/T/TEkJOTInNIGySoRF0kZvWN375kkx2RHgdGR3COKW1fHIoOkTA5apPw9\n6tgrSuRgjMROhZQBPgTcv4HuWpIKcbEnDEHPxCsLCbCx80gxvDSSgV8iUHAFdvuMiuGUkWM8lXbj\nMVYK7CnhiJwfZbgDBcYm6LYRBcixz8OxmRxDT+D7iJHemlReEhiHOHiUO5HUPjO/ypsAbcj1DvpZ\n2iNYEq5DschoF1HCMVlgoCvgsrWDKbFe21AmpUFlwqRgFcCCoOfkUFAGB/TelwrCyDJ65ZSriKGx\n+UnIYQVQh9mFprD2GSbX0m3xbI64hFZHcQMHTNB6wlKggwt5ihsI8hBhfxFfwxOxfF4scUEj8QBM\npjSBDxKCjhDtcRc0V7KpFSfO2HZGuRxczVkGYpylj1DuYK7OGTor9ZEfUJxsAtWM/10CfQZIiC6i\nS1RCd5mYRSpydhzJ4DubeE/Ty7ec0r+FkJCTgVP6R2iV+NkmWqoDt9ezXhTITJJoTlp/Ft3fdOnx\nMrPipMvIErMkUQso+XxYZaTPGxVL5kCetWXicdJptAgEeJ0IUWSr13PMY2oSVOw0YxsJAgSh5yWn\n+zJocUzBRyTmErPJ6+wR8EVeLyAEFLPYOVyZgoWiEvGRNcgixKHitlDxCChCHdhQwBHwsn+5lOej\nOkw2GBGnmEZykAwQ+HMrYyXeBy+kiUJqFkC6jZfVnmlKKQMaIqi1EEOc0ePIgInciO++eYoLsNoQ\n8qCgJNiY5vSGXhtTBh80duWJxcQPFoWpCrbJk12cUwBwLMQu5s5iig6VVg3w0wQ5pBLBBgIDKU2T\ni+fwW4vT52OXCFSiIuQIypAEAUQmNfGCiZRlhMKOgDPqeL0VOYFQywtdBCJiiZy4ryPKIrX862n5\nFfDJJMj4Fr7Me+uYmaLbwg4Q49guLQYv5pFj72hnDAkJOSqntEHCpzGJGCBWj8dWZhIyokLRYXTM\nzcVk12UGlFxWwAxv96aoPNvlnzdxo1w5GAou6Lgd7FfBwFBBRYhw+gxkwO55ct/VwTQdzyQw0HX8\nAuU8w0sUA86Nk87RZjEvylifqEzBwnUouHS3EZWIehDHNuk0yTs4ESgSlBGSPbOiysvrhIrrXQ7X\nQ9XfPDwLoKPKdJvEDICDOeQESJQVUjavakQtyLFPg2mIEQomsgBxECgVmb6Y4QpqlCCPGIEIaOg6\nOMgestzr8g6ShrCOM5NQ4rU80cbeNz8VQcKWef8s/xdp8eNx3ltPUy2iSsrAsxmdZEc7LS1QguYe\nv0HLwrMIHPw0u0E3iEYYN42NPnWN7PGZcjEkwSHI9bxm8KV2ptbxVBt1OjGNnMaYFKUMikqdytZ2\nZseoKfL9Lm+dxA1gyqwr0dSIJGF38bscMYVrpjEKnl/DFpeXTS7VKIQe0iEhJ5hT2yAFmGVcmffP\n+Mt8hz0ZbIdEhNfLbmuxdlOXfgPy6vzUIGMLivhhmCggQ8JANnpiwbkduC7vXchUDRKICbRGXAud\nnjcstJl49WDxSoZynsADl3USnooqgMEmC1tk6Toyy9gf0KwxXqNQ5nQDRQUdVeGlEusfwfJAx+9A\nSEG0N5Crh1BAMAC8HFJt7yKk9GZ9swV0CyFB2UcRIAUOr9gEAufO5t6AQCSq4zsIDn6ZiVcgzqCU\nxy1xwGJUBMFj4jywQMPtIBrlrBQiSFEQsS1yHqU8bzhE68luoGYWogEZiKBbjHZoE5mcEltL4vwU\nMyQ0XbzmIsQIO1uZrLI8zZTKrl5l5mr2BvQr45tIJh+rJy4wOUlRodyJXqRuBlRez+H1xFv6cytz\nZ/Db1Uy0adSRIowBex15kxgsN5keIaHzo1V8bR2yhhMjm2VKE5Mvxu/kJ6v4QYY5Bp9sRnUwLf7g\ncn7qHe6NJ4xUKnT+fkeRji4Scmyc2gYpgZXlOZn6AmrfMJQOZRdXod6L3RlduuTaW6/977FNtpuX\nlXmOort+AWINrDZp1nrPHpYhSWcXMY3nShhRggKuiawha5BCSuKqFCRQ8dJQCwFeDkVlXIrtJlGD\n51v5tYUVAOxUUKDGwvYRBYoWdEAlSpCBWOrZmgpyQG+4VRE/3TuOZ7FNUHsdH1RwyedQdTYuJ6jH\n7ngzQuuGgDOSlBIkJTAYAWoLYhN+nPIGlDiCxJ4M58/j4loCCbvI6BZkECR0jVeyTNSYMBsthtEI\neYjRrnJGE77A7jWI9WBAQNlkn8mWNLXNzoOlGsMVV9unX+EEsi68ZwbROl7tYLKJ76B29Il/bBJP\nImjg89TTpC3iFotFRsTRE2zt4r1RjCTnLIQIEkgyUoInVjK5kS1dbJMxokhJRixiTgt5k1dEfmER\n1TkjwasdPPVDNrci1/NSGtXmmk8RU8mt4+tPkna5bAZntTAnhZN7V/rkCSCjpkKb9E7iHeoHGzJQ\nTm2DJIJE1sRMIml/mW+RikK0UKxPJ1pmOOuDLkGt83c8osmO56cFudZlfYEGrXfDw4U8e/K4RU6L\no8kEPkWLSY1MNPiYAbNAwu3ofStrHGw8GzFPkCEtcbpOu4/uoxhgky2Sh5yDK+KBJkMctQQFAhWh\nEhrcI6gE+U+A92bcPAAHrwvyEOnxQcdHVTgr1vMydVTsLhIq6jwkmY4ynsAFKrjsakctoFiIcfZb\nnG0weQZmJ+0mDkyKcGA13WVclwn15DuhGSWFnet9ZbsCLqPi7LSY2YK7iTOjMA/K0ICjUSPwc5P3\nGPu+lfYlkQJRr6B8OSlNrCdXxxkGr2ewK7WovFTQwgoQBEQdK6CcRobA5maZUo5uDa3MLQ3oJT5y\nMUoSRUFUicxgaw47h/kkq30+FGdyB7Up4zYDbROFNp7swHV6V2sF3C6is1ibZZ3JnIXEdFyPJ0x+\nkacD8kX0U9fLrs1cMLbzRCtxyhMeGxgETmmD1GCAR5CmLoJXjTIpQAyvSL6MDfX81r6qQWmfkVw3\napHnmoxvyvtrXD8vsi1KyfyLCP+BQtahPs57E4w2kDXijew0aYkxRkKI4IJcCxZ0QQxUPJO9RawS\nkg9RXkuTSqEICCLjE1gSosx6h6lNEGD7qAG0460DE6JQeQGr2Ku5TnBI+DIPVEQFsRnbIrAxaqEd\nBOxO3gBJp7SJbUWiAct8FBlFI28STVHr8Z6F+HliOtEYBzrRHBbHsQs9b0Df6SA24ZZYb3ORgeAw\nTkGrBZeDaRwNSwaV3W1Eg563FAY++OxZgS0yUsItv/L/8mNnFQMX9WYYleDBTt6fQkqA23vaN4Jj\ngoqfAZWn1lDweaCTgsTfzGNXhPYonstFMV7ZxEfPIwqCTmBx1gz0Otw8r64g53P5DLK5YF5y+rcC\npgm8YbLDY0wccQGuR73GNXHOTbKlledXoyeojRAr461mz2/YbvJ8/h3ujieSTO1QeUH7KUr4Mq3B\n4ZQ2SCMNRKDE3UXqor25Qc/r73Z0AmxgXXaGpYxqTm3Q4lbZbUCFnOhvzCEn2ZxlStUgeQQ+De7E\nvzdxRZoMsCibBFCA2TAyBT7lDojABiiAiJ1hmAZFdmwiFmVDngkGY1sQFV7LMbcRQcBxKtF/cHXs\nfO9altPrv1d5X5/Wc5j0zZBClf9dFiL4JoIGLq/ZBEUwe94o6DmMsBHqcdtpNblWYKfJuBlQYnSR\nyWCAAHURzqil7PB8B7rHBbPoXIeRwu5CrEN0EAPKNrvTiBGMOFoCWed9BoGF2kIuTbyLeBQsMIhD\nxOCpVsQWHv09O83t/1GSflMIYj4LFfSF/K6TEfOQKrVTQQIV0UOUwIUYqy0uiPLLVlyV2QppE2zy\nApckWbWGufOJFbB1nBwXNDOlmffO4rlNsioqS6K5hzWxIXrJ9/IkIvgwFhSPKR+kU2ZpGx+Pc+ls\nygGiy+SUeNts8TOzic2ilMPp+zrgk4TokaN9v0luq9WsnMoWdwjwrs+wBa3P2Y9ThFPZIInzfSZM\ngzylNJP6/ufKEFDsAmhjdF05//CEeYXnaxu7jBlmoRTTpgE29dAh0aL1HEqVNPQ6DhYPupJ6lY8s\n4ULJYrLBSpMY7C+Dh0ufVnUBAp85UYoegocnkC8i1zIsoEuk0WCYhlBEVzkjBgJirM8rAxwQwYMc\nCD1rZW8WLvVJqwQmUj15FyXWa7R8Xs+wu4saHbmWl9vIK3gCroAeY1eOnQFSmaTBugJGnDqPGpes\ng1ELInYeLNw8PiQVcgHnN9JuMszBCyh00WESSyCVETXa1jE1hqQg+KgxRBfXpeQzvhnwDihlX3Ef\nLusLykzoouyxr4zUBElwkUWiTQQeYhREyPFqK88rWA5tXbxeQHG5N4sU0Blngc7PNjB5AVGHV0zy\nJjMSROCWGe7vC6NEJ7mosOWexkykcc4vPWUGoHF2nGSZSbUMS/Gv7byY4/IGykXWbvK/VxBsSf2S\nIrTUo85/Z7rhO8kxG6T3R3871XnxHdUl5N0msEKDdFKxuV2eUZkYFbHl3t0gwAOdaC0GWF5dqfOR\n5ssval1eR+eE2bl9K9TxH8gzSsctYXs8aCLWgo2nMDzgMd9yGNdUohk0DUdne44/t/LnPHol0p0H\nTs8LyEWJKR9gVyeL4vhlXIkxDbzeheyjQ0T+/+y9f3wU93nv+2Y0GoZhGIZlWJbVsCxiJZZFFrIQ\noGCCMSa2Y7tOcuO4cdOTpm6S+raJ0ySvvHr622lOzj05be9NcpK4SV6t7ZvkJHWSxnEcxyY2YIyx\nwLIQQiyLWMSyLMuyLMswDKPRaDS6f0ikTpvrgBNsx9X7P2lnvjvSPrPPfL/f5/l86PMYtxE0tg/S\nbhLkEWKXXAOES89c1iUp0hpCAoxLVT3ypY/PAoFAR9JAx/FQY6Cw8ia0DjyfsQoTCTTI9ZOEMzkW\n3YRbY5FHpcIGUKEQsDTBMZ0BgbpPLI1VAggGQGK4BzTKChfynCmzshtkTpWIi4QhooZg8NwQ8xIE\n/RwtsqibRV2c2kW8EzwWimyveGXR+aeqkEyyMkW4F2cXqKDi1QhEot14LqoJBtjIWZatZ6TArBoT\nNtel+KlLwqcIvy3xTA8rVOIJ9lQZtKmHNMPvJk//QzCnVFmyaejw3zYfriaVTxrKPAU09lviWlna\n4hOqaBL797LAJGglL/lPaN7O2Jy/MqT3/QbeCzWb9GWZxu5nzdpkdnrv/c1G8Bto4vWK/AbehJdN\n0Ff2zCiiCRYnqnT8TEMohAimwnAJhQvfE/Ym11KiNT9EEq8qX6jLHN7LSwO4FSoWwuR2pcpIDrHV\nczVpwGVnTdgSx4fKEIhUh1liIGpQBwE5DQZBlYyB0MozJdpVAh9Jp1JBk/lOhmUipx3mmwgylRyy\nQsIgqIF/yapcuFTtPakbpCMql/RJAQ8kmPyNCxZ+GUmkUsYzp4yg3EkpII+xLHPTHPZYHhJqnB3E\n1lEk8j5WnW6TSp2zcEN6yma9KYXYjmfjFacEHYzJYOmgUuJsiXc0Y3lYOZa1Eag0dhECCiJYFY7u\nRkvh1UkUkdvY3sfHYpTVYMc2BstsMbgxg65BbirXBlnmx0gm8S1mdSOa5HqJBQgqtYCWCAWXFo3e\nOhhUfJoltudYFicW5UiW7UN8NcSE/5I4+IA+Y4cXSwxb/8Wt9Rruxijn6qw0gwHL36aKvyXgOFga\nL+3A6YPdnHok+O6u8x/V/Y7m1yNIfzVq9mVOknbsSBUKapLi5Y6cfLM9ek/zG8GbOSFRt+TmArMy\n4JHfSfblZmUViHGoSlLa1btZUMOeTHe0Xi3LcTnhVB6PYKqXunwEQmFqxuNbrI7w5f7RAWQ1IKiI\n3bWpfRh7CEkiAFSa1rOyDbEVNByHpjg7sly7lrDCcpfQpdRPEeIabxGJNNOgI8ps38uizYST+ngB\nRCEEA0QEYUqQNJzMjpNrejK4MFmPp0EBmiEg1k5YR4hxMsu6GNEECw38IWxoinCiTAesUPEFni2Q\nEnkmSzN0KOyuI5ZQTQ7mOVwBBaEVwBuEKPt7aEuiSOBT2AFJFJEXdYIKQZ7xfjA4PYTvQwXHYlZR\n/WyG/XnenqC9m2/v4j6NxRvCg730wAaTpsxU+xERPJ+gwsIuVsQYyyEYIPFMH0vbsHVkFcUlKOF6\n7LJ5XmBmnKY0R0qsaCUGSpG9Wf6xhyQsjuR+LHs9tv5R+Ho/WxXmqewbpOiiyMETHosN3JDutdy+\nlqAVJQCL4Uf5h6dfw9D89VG7XOf1MBl5h/jUFYy86bLmXtNM82vkTZ2QiDQnbO2uyWc9g7D8slW7\nGnttfJkASnYyX/hO+rc3929fL+4Wt4QMB7w1A3WQEauEIXIa8vgSWpWaUviJMW9jED5XFW5XQSW0\naLpdvl0WW2KQ4mwPGZGZCYBnS6R1InFOiby1k5UihsZomb8ucbuJEHJ6kNE+RINaDlkAecpIItqK\n0oXcBQrBEEIMPIIKSgHjZ+o7k5lpUr4oiirhV0EjdEDBKhEVsaoYOmKE83nQaEwyoeD5pBRqHpLK\nwBDbemiWUSLYMv8VlkTxXQQfKYEYn6oBsWxCm0hsaoPqh1v5vXYUCcdkeTeBgziZvxVQoUL/3mCv\nGfktHTmgEea08b/6+PM24mn27KZso5ms7p5aeBSbOd9Ls4vYjerh5yFEKfFsD7clGahSFcgHWAoN\nAYgIoGvMiRPCW+6gXcPP8RR8poebIpi29XzF+qHP76dRSoyUWGhwsZdTu+lMMxwQlBmJcc4hVuba\nLSxJQp3ib+TqR7IysCl5WSXd+wtrPiDsvdxxC/b0JGma1543d0Ii+4TGZg0jChKhjfizVTsPBsX3\nSewscd47+TiFVNIo1vSCNavNQxOJRIiDrBGWCZ9AmVwl0zhSYEWMsKrd4omiEBDHgM+9k6jp9avi\nrRqigWczXGKDgaqgukgV3tZGX5EtJj8scWMrjsukVrjoszSO7IOEa1PJEU2DhyawKsUcGVG+1Prq\ngYKYwrVRJr8prEvZK5zqgXD6URO4VdanCSsgsj/Pug7OZVnVjhpw0kLV8CQsuD4FJfrLrMnwXBYC\n3qMTeJRs7swg2cxI4gfIm0FFGcKI4drcpqF3QwTKfGMbH1I4VUcykduggtw25VuBACWv6i7+sKzV\nKqTj7PGZb9Kb57Ykf3ALX+8hCad9Uu0gojss6eKlHqjh3gE6RFBUhDoPD5COogucFznvsVAm9KlV\niE1+IhFkWHcTnRpeD/0lHsgzM4Vu0LeXR3L4KnNEMjHm30q5xvZvMuahJ9i/lUMWTSlaoTXNgluQ\nfiO17ApWJKlfVn3gDlIP+akOYbohaZo3Lm/uhOTyXNU08izIQJWwjDBZNj1ZcFygbgmGxVLjyYc7\nA0Hckdz04eGvj4uimPL4QT/lGopFOPngP9nrWiDnEREpS6ceSgTXtodFl+tNnszSBsPQBjNViHFq\nyLjHYm43OBzM0maCj1JhlilmNGIxbtWoQ4fGuTpKBK+EaHI4T7UHLOwQr4Kk4Q0T0UEkrIBAUEM0\ncasYMrKJfMnWdtLQwQ8QfKgxR0ZtBYV8P8cDZpmoIoGOU6fsIcc4LkFIsoNTWaoaSjM9JXyBdoOn\nbQyHVILxvTTqBA5mBtdhcZJnejBtrmlG34QYo1LkhT6aypyxWGgSxJnnIylQgiTI7Kwe2ZFo+fMa\nz5SJe9RkfmTz/SJ2wHVb2CszLwo2kRgVgaM9jHbjVFnnQxy5GUtgTYakw1MDdOislvBsanXCvagR\nAomMge3ihQwGXLOFG7rBI+dw3CUiIkQ4VuN0lmrAya3cpdKyFkOiVuLn3W25AAAgAElEQVQdCTbc\ngRXhpQLVCKJOOnnJfvc3j4IV+eUHAXCcNe8QL7vW7qHsq7yg/4S86ardXi9ei4TkOE4+n9+3b19f\nX9+BAwdOnDgxPj7+GrwvRKjbuKK2frKJR+NmA3FSrhQIgh8MhzGBUCInyQPeI63vQZxw/14IChLl\nAkaMWh4lClDPIsXBQdTY38vMZntYY8jmJ9Bp8MIQGfBYEc/e+Il+5ncwPEzZpj1BzadcwbJpSVCU\n+e8Zagoxn902KiQ1FoCrIEYIDFBAAhkCjuWICMgBy5RL7g8WiIQBtRKiggeyPVWoRgECsHFz+DYH\nbVarIEDIUD9iiqrNylYkhcNbkSSWJXm2zI3NyDEO2DSYvJinWOHQgLze49ESq5IEMhMW3uMEEWI6\nJ4osNMlmuUlGUZi7kflreTpLFKwShkJKo1FmQTtKFKqQ4mLefShiqYk1f5PDsdj/OG6IX+GZYZJx\n5oVUJfKD1PtApJICm3qKI0UiUbwci9t4IcsyEyp8+XuYEe42kaIsXsvRJyBHpc6JEr11PJsTHiWT\nmW2oNa6LEajE42jtFA3OGVQSfG4baZHr76BNp8dlocCHRJZlyMnkAoZlmoxXCqVX5PWLcJicJLVd\nljJQPynr9pZfudbucvPffyJEczon/Vq4iglpbGzsgQceWL9+/erVq2+77bb3vve9d99995133rll\ny5ZMJrN58+Y9e/ZcvXcHQKM+nP1X2TbMKW+FE1UWt/6bHXi9LrYrBA5J+dnvdDwaf6eadGLJCm0a\ngjq1MjZpZI6CYSBsoEGiUEYs44ZIoLqkVEzdEIev//sdRrx2++2Po5iIRu2bttLtEUuBwJefplNE\nEHEIioirmqnaBCX64aSHm0WIX6pQuFRBVxK56OPohB7m5M6NgygRDAF4dWQPNGQNVSW2EWoQELgQ\n4tg0x2hLQBKGOZzH0DjjY8sICQ7tRQ5xfSSRxe0IIbZMvJPjJU6G2K6oyGREliXx+yFJpZ/QpOYi\nmmwvESuxUsUT8dtR0zyfZVmco3tZqONWiEZoap8SNBJbqdtH/zzlb2kmpYCHkwUBG7aWmC9ztoq0\nASQowzDHa1wYwBtmlowU4dgAi2/iqT6uz6DCl7+JUOd3Fbo7WXYnz9oYDqtlDlc5VGEiy0Q/82o4\nQzy8lbfFuT7K/BqRASwXMcTMsK+A7zOjldE6Aza7TPEeU7jVwariOBy64mb7N0CEAxSsSCF9WUIM\nBSIs8TalfsUOWf9XO/3NiD89m/z1cLUS0mc/+9m2trbPf/7zsizfe++9P/7xj7dv3759+/ann376\n0UcfXb9+ved573//+zs6OrZu3XqVrgGyIPFi1kjkWBoHgf5ezOi/9Qmhh9tz4vxdLPBrT1mCGPYq\nXbd1PYnj0dKK6wDUBkEHBd8mDEBCTHFhiCBE0O54y2Mppchb1tb8mLGxtifXbbaWmjtKCHEODcmB\nxyoJt47n8tIgvoMLGxXOaEzYtCGmApp0MAldxBBiyJPmdR5UcATUkMN1lrVPJdFgCFEDsErocawK\n3jBOYWr+BxDilbGy5ALeOmkg1Aw9jPTwNhnFBAV3iIMhikbWZqPMHIULIWWbkktE835Q5g6NP8vh\n6wiTNn01/AKGwks96Ck+VWeDhBEQesw3kcHRkB2OusgJjmeJRVm8FlHAs6gH5O39f9lh/kWCRCuC\ngRcjKKKLvDTMXJ3AJhUj1Qk+9LEwimdwdi++CwEne7hmIz/Js6obQeYLvQxADG6Kcs0GnnIoy1yr\nU+/neB1ZISJgmEgqD/fja7xX5oZumlUqBktsZsQ5n2e9hGIwUufEQPAPQ6LsSB+uog8y58pi640R\n4ZcoXW6t3bP7Wq+PXEmHbNt/rLVzpvWtfwHh5X4E07wCVyUhvetd7/r+979///33Dw4Obtu27eMf\n/3gqlYrH4/F4fPHixStWrHjwwQd37979wgsv3HzzzR/96EePHDlyNS4DAJlanYJrZIQpEwfVRlZR\nJzcMomFfWbh7LaMl6v7YLunzwZ9sie9ACTBMaiXmvp9Qmip7c2sYEuM+DQaVEp3OLX//5Gc+8tVr\nugbwIOuerscMpfaYfscf3/U1Ziv4Ufe7df134ohx8BmsoapqtfLW9h7hOp2qRsEWgwIZFTVBkGN2\nJ3M1PA+9CwCLSgkVnAq1YdTJdRJpKiGhU9MgOiUm5GQxJ+s1winloRf7ETTekQaN2d30lpDqLBMI\nQ9CQeymZBA6VgG6DOZOTgyT7BzidCB4SSStYFooJVejEKjFXR9fIy7gW/2rRLaBXOb6Xld0c72HN\neuSQsI6SZp+NYjK3FaHAxRq+zaBtP6nzsY0IEgQs2cjFEokYjRZhH/kqaoUV3Ygix7fSIiF4EEId\nz+VIEbGLPVlaulFMfjhIrs6xEu0+WowDBQ4UcOtUPRoN6hauiG8RDjMwzEMGgc0qWCZTi7OoyuoI\nP9rBXJ+ladw09aL/d9/zH66yMcPGK7gX3kgRDkDJpvuyqrQLBUO/xrqyVbvo9GLUNK8RVyUhfepT\nn+rr67v77rsbGxtf4bBIJPK5z31uYGDANK9mx0PNrg0KblcERUNMILq8tfWSKNwwNSksOtRtfMfZ\nGuR3Jb7ad6+QKrBv0oS0QmN6yqDP9VEkRBvZZcHvMljurXfVlXkfEB+KdPoU7F3/M70yMlguxu9o\n/VHypjpi4OWqMh5zMuCg6mobs2YI5pbS4tYiN2d41g4CV96oszIFKUYHkOKI3su+LApUhsDlWJaV\nW6aeTL0aTK7PVBESEKAkcYZZJpP82b8xQHDZbmFGiOlcdBBNdtZJ2bQozFuLlUWsc8DFUlkvktQQ\n4+Bh21wos7vEda2MFHAqUAUFYigyCzMEJcIMVZtcDdUntp6TQ6TXovjIHqpArZ+5AWUPIQptBP0E\nOlbN9gWiKmtSrIwS1WjM4Azw7iRqJ9Tp78X0aLkJReLALlZ30N4JBtTxHHyfUCFQWddMIsKQxFiM\nY1X0XqIiQR4Eghj1BEIbQivouOs5G0Gy6LfZX0Rw0cvMUhmwiei4CvGA98WIpKGDSsA3dnPgCuwn\n3lgRDpRszMtKG1ZB+PTJP74CMdDBX1gNP71qN81VoeH+++//tQ+6ePHiyz9YFMVXvqtfNZ/+dM+U\nP6zljTkj1EdhDvVGPpBh+2EkBf80xBDONVyfDE8LYwfGR7ePlre0z3t7w8VvHUHKYPWg3oB/kHAB\n2hwWyEyEMJPGCnnNvUaNrzzb5h3MJjouRudJsh19/AjLtOjy6pxz9T0HNnDxgnhxwlvUxLJVmPP8\ndkOoTYwwa0nyeDVY6D9rhZLI7EhYmEO1jnuBWQsRZc5XkXWCOngwHzzGLqLP4kwUcQ6ITAhQIhyn\nYR6hQWIdiFSKLG/ieAkUpEXg4M+lUSKhc/gQi1dAkoU+1fM4DURNqj4zNEZdLog0hRTPM3oSLjJR\nhXksamRkjPPHAYRGEluwy8gSowGSwGge0WTeGJUzBE3MOs7RGHcpDFykJU71IIvjnM0Tphg/zbiL\nvJLjWearRCMcakAeoVFBVNCrLGpl6BiMMFygpR1hMaOnOXqU21cir6BkwWmAxoVYZ5lpkJhJCOdV\n2g3mLsIKGI8yWsdzWRijRWToMA0ijc2o82hZSLFC9QSVC4zW8WcSX8TSmQweZcTmyCyWG/gjXLgI\nI8Tk+/94yWWG1hspwi+RPXOZZ3n5Cxga7ujlvs3SBaIShNbYFV7dNG9E7r+/+/W+hFfizV32DUiI\nJjWbA1XWJMCmbmPBdSrS5DaSH+5wgicFTmVpkTltuXtD2fLEd8UQwbcZHaA9CQ5KklMhcgVJZlRi\nImSnva/cGR0+/d/Ev2yIyzPX6rlavDvR88/Ze35345PJTSF6xXrqadY7+CotJgWbZZx6ON6Z6lsR\nyzKhcbwWTorALmsFhQt7GalDiLr2UtOrMzVBOVjETBIECBpUIEXCYHUK2eBCBSOD43AqIJmCCL6P\nFMUfpuYRiKRaOW1hwCDEPH5HJW2iixBQs3gpoAhvSaIaYCImCMo8n6czgiijthMGuAEzYyga6zqQ\nSsxJc6rEsTi2xXiJQoBRYyjKHxlUXfwU54d5RzthCb0ZD9xBrChPVVkvkhFRNVICVpStAZEsN20G\nDQJeeBzfZ3Enaox/epKMy/s2QRzJRaoi+JzL4QssUFB9dgZ4Gtf6tBgsXg8CQ1tJBeIfdqMXaehH\nkrFguYkaIknEMxgRTlexYJXJcThfpGSzSmd1mrd0UP3P5Gdz2aqsQGxwYN2mVyFlLf/yQ6aZ5ud5\n0yckCF3ECNUANcApE9jsLnFLBicADUroMUSBUEL3wWP78MWKKtyZ5nwVOY3dT9Ok556IIIPASJ1A\nIGKxwXxSvSVvpFK9+VZjSCQYuS3qbqvXByK7Y+vXrX+CdRmQeT5LBKowbKudjr7Oos49bf+s32rQ\n44X5ihAPUGXiLn4NPzulRK4kAQhAmbKfKA1jJiFAiqFFqUqsDdFlqjamIL0nybDDbAMsUHFq+Arn\nyjzVyyIDT+T4EJ0GJ22eGWCTynKdtItXwq1zOMQJUVMYCgs7AepD1GOot+D7qGnODKDoxKOYIks7\nUV2uaWOWCAmcKoFKIYdfp2qSNrBViirVIi1RWnTEkPMFRmBQ4B8LJCQGQxQRrc5ohW87zK8TNSAC\nFice52SRlhRagn/ahuLy9g4kHd9CCqhVObgTy0eRkeo8W2ZQpl2jNcLdW4gl+RdbDBT1zzajObg9\nxCu48Nt3cV2afI5CmdEUJx1ch09luNFkrMzZkJSD5/Lu5OsXo685ly2CB1SIGIWSmFSu8D3EX37I\nNNP8PK9FQnrggQeuu+66lStXLv95MpnMLz/5V8Un9KZaBI4XMEzwKJQY0AirU4d4NoeqyM2cKBHT\nGHRqj2iCpZKCmTr4HK+SilMZIBBxZYQKFz2sEM/G5fvenZI4dp/6xdGyHN6S+LZ+byaT/ZtH/3Zj\nc7/cqYHK01laKyihfHtkSSU/9/+wdu3dEItVlnXkWZZiz5D/eBWnwIpWCEGAKgSggnxJ7U2GAKoI\nPqvW09SMXWdmhH1D3BxFlhEs6SOdxAXOekRjEEdK49vUSighRz2MOqVh6j5LupmnUayihSwQadYI\nCtgBB3to8qmFjFaQTWSDwxaLY8gKvo0YUKuRD8gzVRZhSszwWBRHNKGAnWTfLvoCOuK024ghh+sk\ndXyJ96RZlGRkJ6LPs3vZNsDtAt/fSyTG3DZEl28XWdGFGGX+BhBwchzLsjKFpvL1Ps4P02aAgVuC\nAMvh6FZeKuBYaBUO9/CDbSR1NI0/2MTSiNfjO08mhffdIdyaphrQpsiFPJLGojaak+KsPC0qbSZf\n7aFU4jqFFpXhjNCdFjuv9Av333hdI/xVUbMvXwQPOL4jXJ28Uot3b3qSNM2VctWfYv70T//00Ucf\nnTdvXiqVamhoePlL/+7HyyGfz//oRz8aHBw8ePBgS0vLunXr7rnnHkV55a8Sn9AGh702tEGRE1Ec\nmxUZDmSnLH/kNuZFGOhnSYJA4Keu16yLt5nBP9jIzQz0c2Mn+Tyo+BqOTVBGauaJEqnMjo5N5Uh8\n0/eeFbuCJV6pkokFmihv8wpb2m/uf+yHre3kQ74/zFdN4kIhlzT1ysm6OUjbe7u+85J2Lz/JYgi8\nLU1eIBmlUEOA0MetQhysKV8+3cUSKe6kMUK0lTN1xj0cd+Hm4ukfC+yp+f2y9AdR/wsOjgICgYIo\n4spoHp7MzPXwEC8OsKKTUzJ+iS3t9Ais0fGHkSuUJE7nWZbhTI3VXZTyXMxxXGFBFFnhaMi5MqHI\nsQorI1yEvMRSmXyAauA4hCUqIeP9RDuZFycoULFgiFiCUpnb2+j12FcGiYODjNQRFPb0siSJL0CN\nl4bQEoxnWbyeEzuxShyCZes5tpfdDqaKbyNAUAUdqwaP4m0iWkP1sOr8Sy/v66TPIhlSHsLSwqBV\n2KIR1+kt6rcH3q5e60QCR5PvkNASzm6FlhhijqcK1HZzR0qJp/wdOh++0niEN0SEX3X6Sd3Nrj1s\nuJKTLqtXd5ppXs5VT0iPPvrozTff/MUvfvHXMtrf/d3fHT16dP369W9/+9sHBga+8pWvPPPMM488\n8sgrbho7BEAC6qBCQCAybJM2OdADJl4JXA4PgI8X4AM1BhE/YAZPFTgUxatS8TFVajIoaBq2j+9S\nVqlSqpnfr9/5yfrfvy21dV9Pp/deuWew+5rmgadKt6xu7eFja/nnOPv7yVU8IyYGmrirMNYlPrLj\nrvVdu+ObhPJzMVwbz8aXWJKm8DRiG/4QpMAGFyIQMj85ZVA03kN4J00RDue5MXnu0Yr0nrX+A33+\nA9Xmb2vDz1fJypQcwhgkYRc2zG/HVZndjV3mSJF6QODjBHTATo/bOvj+DnQVS2ZhjRkqloVscNYn\n7VEo40aYK3LW4mwR3aASp8HiwG6a7iBawU9xsIbsEmqcHuR5g8UiZpSSw0oHJeA5jYksm7po3Mpe\nGRSGh8h0kS1weJBZW6AXholEKUURaizv5nAPns3BJ1l5C0cL1CT0BNZu4N98OYNdlCViUcbbcHro\nT3GnQR1+ZKP4jOwMrQ4kjVsTtYojdxp6omL/sOp8VUUdkN7T6icM+iK8Q6ca58lB57Fs7DMZWP8q\nYvINEOGvBYd2SO3J4YHCFZl0XI2cZMJvpAzuNJfDVamy+xnj4+Nf/vKXv/jFL0Yivx65kXQ6/fGP\nf/yGG27IZDI33HBDIpH41re+1dLS0tLS8h8PflkNkgCzJxU/oZHQIxxnzTKO7cMdhVECIAI2Yy7z\nljAzwBVpU4WSH144Tr3OWZFEM+UKNDBHwAsYP8PYIgiFG+XhYvMf3fjAsueP/pP0wVlxzx+beWP3\nMy99Oxg+Mo+z+uiqJnYMc/gi71miRi/mfzCm/bZc75s/d/n5G5c9u7v/RnIvUhxDnMEMH2kC6zwN\ncwhFGIHzIEGUBhF5BiOjWDUamnjbUg6f4XBpfLk2a6nnHztP8fg5f4n5rlH7p+ewA6gxMRsUqDHh\nILeii4xUOD+E0M6541SPo0c5cprWhYizuVBnRowxm9RMDlWY8GEOpQqrEmT7Sa/h5Gmogcx8OF6G\nUS7YzNGw+nEkxBjjR5m/kvo8dIWZZ6jP5oKDoSLN58hpqmPcei1+yMkiRDhTmVI2CobQGvFHcUts\n6SC7jPEaxgbCUcbPU8qzYD1BldHjGIsJJggCGEfUCEdgHOciYsiiDUw0cRRWw1s1KlX2H2DkIgPN\n+IS6JARCOArv1ScanImDJ8f7ephxipsXcyFG40zkDuKznN3y/fctuNKAfMNE+FWnQtOWjpcOXFlC\nuhpMbn1ddongND/Pf+oqu4aGBk3Tcrncr2vAf3db3nLLLcDBgwd/2XkBOJc8WGNQ5GyNb2+jZsPk\nSvqkmIqIX0OSyA9xuhr8xc6gJKOa4BDaOCEMIabxQkQbHPw6Px0IesViMrGb9c2FY6s7e9XHa8vT\nOUn1V/+eW/q2bWZqxAOuzTArIu3y5oV1OhOzduTH24VCTzIeK4sdGnoHbhHHpr/K4hRhFSQYvqTk\n7YFLrc7ySQGkkFM99MDNCSqCoAlOj8X72vAqPGkjm1KbSDoKw6BAG2gsMlmQY0Mc1UBWCQYgybCI\n4CCHPD0sbpCZpyM6NBict4mDrTIywOwU+SGu3Yzv0dEGOp6FmGJlCoAejjwKHjERsc6SZqw88x3q\nMmYG0aIe4WQPGZdrWxns57sD6ElWpcAiZlx6fPaxSugmy7ro7eGWEK0DIWBWG3ocXaT0HTSTBQkq\nedCIdUAEOYVsTqU0r8KJx7m4kzS4UJGFm9uFGzNUbRq38mIPT9jekBRUNGmXEnYY/J+b6dhItsYn\nH2P/NvI2voWoIr2aTvs3TIS/Fpwo6Il0+HpfRelSTprmTchVL2r43Oc+98lPftKyXkXZ6C+nr68P\nuPbaa3/ZgZMJaXKXNQ8mQQ3f+/kVSwfAiHJiAEoQULfRPfwIugYOJ+roMYIS1l4CDXyCIr7EjpoQ\nD/+vbX/Y9+FrKVEeiMt4JcwNyQHzwxKRGjt88fNJbkn5PxECQTSa/Xwuqqack9tif/U/7pVTAXNN\nMKjlSCS56BFJEwYIUSiBCQ7YUOfQELd1opoEJYayiCYrOniqIqQFqh6ZFKeypY+W1etbmZ8gEocs\nuKCjxKna5CyWdRCoEIdegG/08JYYF2phX0lYm6TRIXA5qzLHwCtAKxNPsyqKnUMRWaYQawaZwztQ\nU1xrTunHVMrg4ZS5WGJxKxcGSQuE8JYMYYVawMGtbJHRWxny2bMXMcmNXThVWtJIk1sjUSpZRJEF\nbTy2i+YSMxQSAUtSJNvQk5SKXGxl/iY8i8ogi7pAQlJJtIOKrAIUB9izlzpECYuCEEtLf3U78Qyf\nzJAuUXZ8FKeioZt4NVwD7S6uv4m+gOcsDpXIC8ivsgDhjRHhrwU7C13CvPTrfRVML9m9iZkxMTFx\ntd/jueee++AHP9jU1DRnzs/phQmC8IMf/OBVDzsyMvLud79bEITHHntMEH5BZp0x45tQAyAGtUs1\nbLzMpq98qedcAh8StMYYtgkcVImFCSZsFmQ4nafQDx3EWqkNQQAVQptQREgSi3BdEqH20XsfuUl8\n7j2l7y5xcqkPltoYrBSUhz//AWxBfL+MQ/C/A6mLxVtKR/9fQwwrwXASFbmtFPxrJTgiYe0m3kl5\niKXrOTaMGBDUYFLazp+SaXj3TeRt9u8AWHMnzSaPZ8V31cKqG55PsmcrUpe0XmWh4RdtXtiGvIWg\ngAQtbYguaooDOeoW1KBCVwLLRotRdMQ/6w6+NcTAMIHKXJMFAfkcokgswhINx2S9yb4cgw5OnmiS\nv+rmG1vZW0QS8G1IQYWVa/Hb8XMkmpGr+DoHB4k7LDdJJ/lMAWmAMM6idpIlXuyjqZMTOeIqRCjs\n4h03YSV4Nsu6NBWZRBVRouQz0o+XYlGMI7vxquCgrsVRoYChEarMlzghElisNugw0SSG6jxtC3+i\n02yH/QHNEb4DoU4rRCECmk9eElNB2FcJfxwyW0SuTeQuS6L0P/L6RfjnX/Xgr45NG6o7BjuntjOn\n+Q1kYuJPXu9LeCWu+gxp27ZtH/zgB4GTJ08e+XmGhoZe9bBhGN533331ev1LX/rSL7xXgUu+R8Ck\n7fdkQgqm5BsAyaSr7VJxqgpFhvLM10HFKSLKOBo2rDExUmDhBMggaAQlBA0kRJHGGD/uw0xu/05r\nTZ1/Y+zp04Mxt67EqHQlh1Z+pEhAsCMUYiEm/k+tc8MRtUTwXYRWgeHAG5LlDzez3CSWYTyKnuRM\nkSaDQEaYrPmWYFLt2+fHO7mrmfQmgINb8aErE/zverimjRd7ERMEOX9XXdJc9DTpVrzdyK24dQyZ\nzTKhzeI0Uh0lhprEEWgymS+yLB48UOBaA1GHChdDhj0EjSCgFiDrJEv02NyQJiEj6NRsvpLjt29i\nWRtN3cgalEDmoEM4iBrlVI6sR0fIwk58lQGXco2bJSQBYYiTvRwokW7lZB9KnKoCNh+6hx/aCEWu\nb+alGpJD2eZCnm6DWR3YZQ7kWdWFKoGKUwUHWaPWj1CllqQlyvI4sseDe6n7ZERiavj/lMVsXfyv\nCaFL5iM6KSiF7IYSVAU2IdwazvpbXXw4Trt6yfjqinldI/y1ZseuaHLpdPncNFeLq15l94lPfEIU\nxaeffnrRokVXdOLu3bt///d//2c/Hj58+OWv3nffffv27XvwwQeTyeT//xhlEC9lIA2cKeFRLDCI\nuPgq12xBhl2DlxTkVHSVMxaSyuGnIYVrMqPOymae3YtTJhajPgSdBHmw8Q3OOuDQ7wwaHX3WULEe\ns7ZZ29XWjX+tx6VyV6L3YGsbBScckrlDouTVHxEit4v0G8LRoTCZ5oWs8yMZ6izv4qUe9FYqA8w1\nkV084dIEzpsSePVshoZZZ5KL4VYY6CGWJGLSW+KjKb5cI3TBd75eosmku41cAS+PoqD5eCaRLLEM\nR9twtrKkjVyOhWkO1VnVSlzgqQF+K8kPFRYonKwieKgmK+PoIbEoJ3sob+Atbdh5qnEODfA/A+7p\n4Os7WdrNaQtbJBjifAozQlDHlvhqH6tTFLuZuZWeOKM1VqQ5UAERS6Tg4TXjDWM0MxFF0fhUt/DT\nnnDC4a2tvJAlrECClwb5nU522mzv4bDDjXdxbC8DLkbIkiQHbRyJcBd0s8VkW4XbdHp8btG4M8sT\nvv+kieVLd/pSIvA2y8HWkDgYIjtDqqFfk3xTUjNOw9+HFx/vuKL4/Bmva4S/HpwvTTe9TnOVuLpP\nXmNjYyMjIw8//PCV3qtAJpP52st4+Usf+9jHdu/e/fWvf/2aa655xTGCS3+gDZFL7aUqWjNYRCSc\nYXJV3j65eeCDCnXOOFyXRJ70xCuARVYiJmGYYOFKRCRwoRWAGr7FgrXsz1Gyn81vORUkhdkeu/hJ\n5dYQoUvqXfE72YV3OUIlpAprTY7mHVVVO8RgJzyTI5el5lKzQEDX8AKMdk6XkDXQLnkFiWitU0re\nD/eAxYYOJIMTWWyFa02eAzXDZC99mAWH0V6cGO/ejC5jGLzYS9blnSYbPJYIkOFiieVtPFfnunZe\nyhMTkJLsKTJHw1PQokRjOIN4Ici8WOP6DJUybo0/2ILgQoSzeZ4o8qFuRkosiyNaKGmsMo6HkmRm\nFSwO99BuUzYYH8YxOJrDsxEMBLCGQYAMyEgaj5VEoT7nf6WFlEY+x6wMisnRAuMhDxaw46zejJXn\nh//MYp0PJfESnHFZtZl4K4LK6T4e6mG9woUK76xJYp2eJB9ppyNgR91/COdffTFqie8XECUeF9gk\nsc/hTyvstJ1B9fyALm/xrjREef0j/HWgMDydjaa5WlzdhNTY2NjQ0DB37txXca6u69e/jJ/9/hOf\n+MSzzz77ta997fJ2eienRDZYqM0AYju2i5acev1YH3qcRPRSGXvWptoAACAASURBVJ5MvYwhXvrP\neDg5JIUDddYkMEzsKnMMqF5yVKrg1zhVRW9lXzbfm1yzca9wc5K8s/dr8eF68+5wvZkonXs0svDD\nFbKwQaQl7v9pr+8WsfM4EQQD8tDMvm00dRIIIIAIHroBEXQT6tgaZnrqkiyHCRFdZV47R7dSVYmJ\nfHYryzPQOlXdHsKRraSbWZikWMELpbZhCY+Ix2aDhVEsgyAgHuWZItfI0p7eyF/IFH3GS/gWazIk\nmlnVxckcHnxoLb0hAYgeOxyWbkKGwOTgXn6wk1UZzg6hNbMwCs3s30m7y1yZDgNL5Me9CBGGNZQi\ngQ4VgiJKBBIwADpyjIsWbVrwhHXxS6F8X4IujSDHuAE+h6vgcahCCLEMuPx4K89JdMeZ30qrzLVx\nlnaypINEnJ9YNEbJKn51SO0elna7xCPcY+LJlBTvH61ga40OeCd8u8CMIjfofD3H/xhgl+08/Wp8\nVN8AET7NNG8erm4fEnDmzJlvfvOb733ve38to332s5/93ve+d/fddxuGMXyJ0dHRBQt+QQfJpz/d\nc2nfaAIRZqdp8PFHaGikMc6pMhGBM2e42MLKBQzmYQQiMIaosXg2Z2wCDwIWypxfSNMsyjUQGJ2N\nDk4NInAWoozPRJvLRDB25OT8DXNGVF24UPWPxwVPWrT61JjYOD634eKjs8ccadxtYL/I3j3jQpo5\nZcZm4euEBaRrWflWEFEc6h7iLJzjNK3EPokyh/A8gYK2DE4yOsrJOunlzIlTHGbsHEGANYY/xNhy\nzHGqF2A+YyH6XPIluiOMNnA8GD9SmvuHC0ae8FgcoKnkZnChwBKTusjw+Lg0o2HivLRlqb+9xDXN\nFE+w2aTYSIMDCxA1Fo8hyBRLqApnbZRWpFNccKlVmGEz3kJwhrpCbCHnhjk0xMZWjl1gdki9hi/S\nHOX8MWanoIHRLIKP3IIvQBVbY9kEUoVl8fD5E2PPVvmrDK5D75OIUbiIOk5rksNHGB1h1jJGLeyZ\nxCdY18AJGGugTSQHjadZZ1DyKMlMzPJ3BNEPOeE6dayhEV1EhQmDYZULNsYo9yzE1RmdwJzPqUae\n3s/JI/ffu+xVxOTrHeHTTHMFvMH7kK56Qmptbe3p6fnCF77Q1dUVBMGFn+ffVSX9Uh588METJ04M\nDAw88TLGx8c3b978Hw/+9KcHYAIaQSS8SIPGvDbO70ZsRnRY0MScMeo+ExdoXU2+gn9uquqhKtNl\nkj9DYMMoM0TmpymdJKnhNFA/w6I49cMkruH8EbAhjVUj3kJt37k16z/ye18ujHedOzBetNq6NvZe\nkOfMbrpY3rFIuce9+NcqHRKHG6n2sXQt+R6WruXcBRpCPJNoAHPxq3gXkOaxcBkN41in0Bfg5LDH\nWRyjXsYfZSbMWoB3jgmRixU0BSSqh1jazshcGubj5YgsZ/QIRpzkQvSQnDR+8tzM+xZPZOeF82ZT\nzVFXqB0jOpuxOg2mX3a0jYESnHMPRFk2h6ETdMZ4ZzNbjyDMRDOYETAywWgDuk3FZq7BuIQ3k9ox\nzo0RxJnrcdHBXEj5CEerfCDDkTNos6hPMMOnK8O+FxhdDecITuJPQAhjJAIaz7EoQr3C2hm8NM7W\nInI75lxO5JkRxTqBV+Wm1RypM8ejKcJpn9Gz+CFrI8QbmAvLRH46g1IewWCmzCmfxbOc743MjMyS\n1gR+g8RLPoGNNEZR46Ht7HNIz2PNLEpV1oWsW0nPmfs/cQWmEm+MCJ9OSNNcGW/whHTVy767u7vP\nnTv3C19qaGjIZq+iF/2MGf8MMlRBBQc1w6wkIwN4ClKcv2nlG/14kN/LqjsRXV56EkIwQUX3kUzs\nIbwKGCxvo2awNuTFOnPiHO9B8NEN3BJuDdrAQDe4NqRZ++O//GY5GX/qd9pcL7bh9oHZ661hqbW8\nLTpDB090/lEl4fAvj7G4meP9xFP4cWo51LWYUcZd5DrHSjg2aoqVBnt2sTTGhEchT6oDKuTzAKu7\nOV6iVkZPYNmk1pLfgSghdSLXcSL4ea7r5mAPt91JYLGnQDGvfCY5e2PizH+LkizxrSzjSRoGkBOo\n4Gto5TWfsfZ/SvRHNtLlELFpzqA7fMmhO4YIlRLnSvRBk4HgoDbj2xzZSmhDO9SQBeQYEZnhXRgm\nf9DNP2WpuSAz3+D2Zp4aoJKCHmIaqFSqoHJdlNBDUVEFuiT+bgdKnGWbObGXOgQi3gBIKGmooxnE\nTVydcwVabejGc0hX6EjxtTqVIQw4GpKK0ljnpC0uauWd8WCtyE54NEvMBjgmUZVJBSzP4FYwRDZr\nE/e+Gsm41zXCX+uy72l+03mDl31f9YT0yubNv1AQ5dfFjBnfAy61InkAcpqmOGeytNyK6LIh4Lk6\ne3tIJHnrTfzwUZwCQjNAaNOU4qKH1Q8KWpRr7mDPYwQ1lrdx1EUI8YdJbKL4BOigg8NH72Kgbn4i\n+LM7/vs/PnHvga9EyJir1vbsz3fM/qNg5P8W9fe79T+K4Ae82I+zC1TwWXMHL+YBxHZWKxzPMzfg\nuIOosiJNxCdfJxbwfA9AKkV+GCRMmGlyvkQg4oWIAkg4Q8hpZA0M5klcgKYKHihJTJ2dOWb1Gd/d\n5A1EnW/4zCixr8iMVmYP4EZ5SzPHhrUWb/kHnRf/dhO3RtjdQ0Yjk8GBR6AbttU48iSA1oqok7Bo\naMbzONxHUIE0FBEF9FaoUcvRluFtGb6Rw3ZBIKNycycP9GAXQSemcb3G9ipVketMGiqc1zgRsE7l\n+T5CjSBBUGZ5KxdCiv3gYkSoWWibud6k4nO8SnOe+SnwSOukVZ6o89MhGqJcrBJkueYmTpfkm7x5\n72o9+1DCNxRKWV7sQTSxDVSJOQaqijhMIE4Mvpre2Nc1wqcT0jRXxhs8IV31Jbv5r8hVfetPfyGL\np8AsqKFojI0SBEzMYNZctHFOzOaORv71FE06xw5gRJm/mtIRJk7RsITQZpZIwzwax/HOMyoQjjDD\nwgsZP83SDVSP09BIw0XcycbVFBQoqNwctweEpe2nFlx7puDM98bkuUvGG09fuDDfkCL+aL6xMTIe\n/H/svWt8G/d55/sFMBgMAXAIgiAIgiAIXkRRFEVRskzTsiwriRzbie04ieMk2ziXNttNe5o2vTdp\ns7FPjru9pE3bbZJmu+3ppp9c3NzbxI6b2JEdW6ZlXSiKoiiKF5AEQRAAgcFgAAwGg8G+kN32sz0n\nGythpGz0fQlg+Pxf/MAf5pnn8u1j5Dqxl7Gy0ISqgER9CrGJgsrebiyLYgm1jhrn0E423BiLhEOk\nLpBLIe6jvoYq4pfBRz1P0YGhE+3GKFNexXRRtuOJUjxP2y7Wp1EN8gF6W1nMls8VbL/Vazu9ZTW3\n4qhSqrPloZZnK8Fte6urjXJpX+imLeUpN2+K8dhZChLhZm6AL1os17DcsERVpXIWvZnGJkKQNh9a\nCbOEvA9XllwWdwBvmLU6aprbd3F+HXeA9TzlVe69gQubVBW0Kja4u49clLQDwY27RqHIBSeCm+IG\nZgkpRNaJWCPYSslOUcZeQF/FaOFdIaKtLPYwXaTsxF3h1Co+gaNdLMbpmWB3O8kkStE8qzNpj/1C\nqql5tfy9utU+ikulvIaySGEBdy+6H6P5oQ9cybqEq6nwq5uyu/ydus5PFNd4ym7bG+5++Zd/+f/v\nrctzurYRXwDKoCPISBMv1W2rWVpiXFqgTeQ7JkNBcmmA7z5GVMU3AWCpCH7SacwcOi81pW7E6Xkt\nGCgW5hJ2CUsmuwBu0EGDPtJTLJmSzDHtSJD0nvtypBPzJwZ7jybNb5l1ryialsuv0zWMmXi5b9dO\nOYeRADfmLJod02JFpykEIprGyTRjAuUwqoAQARNrCXsAYHGGepn2GMRBYuEEUh8cwjQhzfosnYOs\nJekaQ1lAS+CAXYeZ1esfO+n8g6D9ggYybgl0MFHdPHqSe0eVSc0RIHrvKt8yuW2c8ydZUsnCfguP\nGyEEQy9NWlJmMfysPEU5S8cAviBqDs8QUpnsSWwCnQPMCXw1RdsBHHb6hzht58vf4c2Xm36yzOR4\ndJq3yziDlCUSUTqj2GdRX+4Mk8KI8yhTtAWI+CFB+zgtQ6ye4KPHOaYzIDAYIrPARpmKnceXeH6e\nBw8SgjmZNx/l/3oTv3631ilfeERsiwp7f6Xs33WS2QSvOcLt7+SGV7N1GiNN4Aqrma+mwq8ugcj/\n/jPXuc4rYdsN6cSJEx/60If+/ev33HPP6urq9sa2ADeYmCqGijsCYBloKSw3co6nRRxhcjkATC7N\nc0Mf+LCWsLwwiKqC/FJizTLJzuLzQYSLCWIHsC7PULjcwDQDPjC5pOh+7/pSJKmHRwPTsbEcWuLC\nlwbtJ+zGf0tpSa8gm/ZoGL+B5cO+HyyEGGQhgKnCHOsp2qFhIVswwvnTrGr47Sgingm4PCfCjjwM\nQVIJCjq+GEIZX5CyiduCAKiQoDpJu59IhOAA2hQnFrBkXH3mZ1P1Z1K8UebMsyhpIl6QkSzQ+Ppx\nhr2Lf2eVdcM9WGbey979fPk7fHESWWDCjjAPCRh4aX9gch53gMwCBZGWGP4I6wqBe/HtZ3OSaoqd\n43igkKIm4MqxK0ZS4usnGB98aTBSOsVH/5a7FIb7kEyWL28I9EEaUuSeommIpmFe/Ee67NwxyGac\nsJdb7kY0KRwnsYplEBRJ25kWqfopBfmSSiLHgMhTGorOkJcPjPH60an/Hjk3M7zrt4N7/j4oFE+S\nOcbP+vngndxpUp65MpVdTYVfXbKJ6550nR8t256y6+/v/+M//mObzTY+Pv4vL77tbW87f/78Zz7z\nma6uru0L/fCvfQ15B1UdCthBGqaeoFalVMbfSyZFUyt1D0IeLQuwscV4P8smtS0aA3hb0IsIFUzr\npUdQpTq/+CaSBgUXYhavhCbBKngRRVplyhk8PixRb/Gv+7veFPjKRd+NyumqdrLN+dp6/bidw9SN\nJrHVqM0KbJ2nO0LBolHA0YJVAQErg7+d5nYyOXydFFZoCrOxQcsQriSWE70JS8HawlGmaRfVMlqK\nziFUL80ybODqhgqNGO4g2TP4ZFSBVw9QWWNLJ2OwZzcltf5cvVGN0OcnsUQjQriT4hKCTCKN3eRA\nW/mriZaf97DRqM0HaaqwuUahGdVNfhbz8k3hbtDBoqwTGyM5S8FB0CK2F0FA6saqs3WGho/IAFaW\nzTSpOp4CY8NcSLK+BhGIQB00TqmM+NjbxaxAKUfrKOgYChiURQQvaCxvYPPS2cLSFJrKviFeO4qy\nzppOuZXhTsIOygmWVqgXafdQaKaQYUnhqSpJF10u+sT6BcfaVEyp9UT/o1CptJqfOId/0fvmqPjG\n7g/FxCuQ2dVU+NVN2dWquGXKVzIl/TpXi2s8ZbfthtTb2yvL8p/8yZ+Ew+HLG53f9a53nTx58m/+\n5m9uvvnmbQ398MOTtMuoXlAxt2jI9PSTWaRRgSplEdli00V3mM15MBHcQw/Uhf6odnIFyhg+RB/1\nDGILZg1qNOyMC7h3kYDcLKFBclsvZQWb2lAyBEIk42wGOCA3ENai3YWzLa4Wij6h8U9Vxz1t1lSx\nvuW0NTnq61sUnWxOExiitAYijQCsgUQuzugwUoVhF40SnUNsLWEIhDpIXqRyBrzgpLaJHEXwUL2A\nVKP5IPk0bREq53C2UHJhryCEyUxSEuh3cmsvpy9i6jRk3J3U1qlLjEZZTFB20TyIqFIDK0RqlmAQ\nifI/qbVAGyoY3cglVnJsJJHDNLJI7RgqdICKPEDMT7mZyiIVmeYU3d3UaoSrNLWROI4OvTKWTrGE\nHsVcYfdeMgbmCpQQBmk5ipVhZoaqnZ0RXDtIPEldoKWHSgqyGF7CR6HBRorKTpo7yM2yFCdfZyLG\nW9uo5Vlw4gwhONgS0VOsLdKlsX8nbwjQMDm3zNNNaA3sEovZeiafJyp1SKF7PLUXvaW/TJmq8pE3\nveJpC1x1hb9SxiLsHWbxRzQd9bob/aTx025IwNjYmKqqf/qnfzo+Pv7II49873vf+8QnPnHkyJHt\njvvww5NQpX0nahkK7InQ2kNlmZKJkacpTEPH7qOplZ0u9odYbsomO2/83dX4V1rRV6CLpib0Itiw\nVkAGlRU7b25ntUoGxHXEdioVKCJKIOCSoESbH8Fjtns2lVB/cFFpCTqXlIrUytlsY3MDyVlPNXhH\nhFNZNBtiDaED/QIcgLWXjm6z88DNlFx4U2yAGqE4R95B8RSYL4+faGCmuf0Qa4vkM3gMtCCeAtFW\nVuYQBfRuqBKso5aYy9DRza1Bnp+mdYhslZZuzHkUk8xZqj3UCvhipFYQa5idLE4RHWdxi3mTN7uZ\nrdLUQ1mhmqIaQMhjqyC6MTQwqXrQHAx5CXRQiLNlQ8/S3U1XO3ZweEnOoZWJ9lAyKF1A2kdmHbsT\nmjAzWG7MAt5+2mNs5LiwSO55alXqOg4XHQNYKkYCYxF5ACtIfROaqJuYabbcXHCREznYyZDFVI6m\nAO09CE1oPpIJZs5wycVwp/CeDqtyiXNn2MzQ1c7pTb51yegMFQ4F629tbRzobuQ6H7rjCpV2VRX+\nCkmpHBrm7OI2HOc6PwFcNySAw4cPz8zMfPzjH19eXv7Yxz521113/RiCPvw/EmxmoIVqAgxqILcj\nukivA4gOFJ2wzJ4SH9iFL8ILk2Q90h5J6Oh6+SZJx++nVqHeAAVktCojHsJ9WBbZaWzNVJyQwyjQ\nEmFrk5obPcvNu6gLDNAyoEbMRF6OltfNxtdPI7dS3sTfjWzS3GBOQFsh1klGgRJ0QQJ7gC0VT4yA\nA2eEmXkmYigeamWMl/f1XZ5yVK+S3+Dm21iYpSzS6UWrsquTTQWzhLmJtUJogGoCw8FCmbEhwnVO\nzdDRw/o6o0PU2+gPMv9P1GV29WBp5OxIbqR+NidxTmBb43SVQopygd0jZFYx8hhZDBtiETlAOQFb\n6F6qXezuoNlHKU69h8V1gu24ZBLgdJGPkykhxRCc5M6xp50mB+U6ughpLB0C2BO0uKhpVHOgQAXD\nQt2gdzdtMpsJtEWC7fiiZC8QupUmH9pJ9GWWNlgQWGnlXeDMklB4Vy+tdUp+ymm2LrHQZGW89EXF\n9/XWKzrPTdNlMhpm6jyf3nLu97lipu1S48NHHVcstquj8CtL2dmqP8qbpOv8RPHTaEjJZLL47zhy\n5Mjjjz/+4IMP3n777Vfcx/6KePhrZVaSVLdgGJKUVfwttA2ycg4cGFXQiI6wtMFEBA3aHExls4uB\nfb+bXP2cA3MFLBwGTT4qDri8yFVhw8dtLcyKBN2sTdPUidGAMu19OAXKFUwTr04wgChkWwP1b9kL\n52QhpZntInNZbg7SsHNsixdnUKJ4gyjncMWoLiP20Oqm2gQSWo6DvZyp+H5T0CcN7vXyYhpDhzw0\nQw2KEKCUxCXjj6CoaB30BllfJNaDWqRShiy5GtEethTa91IvMjFMNcuqRZNGYJOuLpRm3E7SJ8nW\nGb0JRcdewDtKWYVF7P0U/ommMLUKgoMdXcRPQRDWMUT8XpCoqrBCeZ5SJ91BBkX0BPUIF55DqRLo\npwJVAzOOkUEKUb3EeoLeCLEoy3EIQp4WD7W9ZON4QlgS9QboUIEGW+1EmgmGSScoJtAMQjHUcyhV\nGnugCBVsYOWZ6SRgo6YwvU40QG8rtkEqMqUMyytUjPqCm76A8J6d1rSX5+wMwJHB+rEtR8MV/qXU\nr3haf8IUfmWGlFLplFnN/KiPc52fAK5xQ9qWxti77777+3cLXmbb+9j3/jNlg4UFGIMF0AhEaDuA\nLcfccZDAjaTTM8EhmfsjLMBHP4c1HH3Rn/tFQXv8HwAI0DFGPomRfunv2kXeMoJ3mLjC4jGyErqP\nQIryKN0G5ychgJDmP91NyiIUIgkp0/dLmvI5lfNzBIPY7KxYpGawDyC5seeIQlpGWWDXflZOokcx\n5rjtTqLhjt9L5X/fbnhl5hROKShzLw8vT4IFEijsfYDF4+gR7AL9fiqnCRzg5LMggIJ/jAGY1XCG\n2B8iEOCbp2kLkpnktmFmvIg69SzxE3TfidtHY5rSMGKE/CwemeIs6izuMcoL3HQnJZWZZ8AHIpSJ\nxLAEkidfqmL3D9Dt52iUJ+M0hzj1DQIT6DrpLIKKmYQg8NLO+JFhpBAns0TcqEsQwuNmY5L216IK\n1E5i5ZDGEQy0LAE/rSJrs+hBsDMchSSzuZcTmBKCncMhukf552kCsJzjUIxWmbRCKow9gWrH0ikk\n6B3mHVF0ib9XaJcYlohBnMZ/4wfkWlH49cbY67xCrvHG2G0xpB/ku3qZ7e1jb/lbbtzPk8dAghjM\n4Q6yYxwpwAvfAOWlTbI7D6Or/D8Tbxz8Sm5efvpBL++K9L9XX3zNEsYsBPCGMFIYJigwBHMIQ/z6\nCI95EVKcXcUZ49UGj68yOEIuTnYJLHpHGRXwBtAiBEAxfUNx5ekczx5nYIhajKpGagbhCIKBPkto\nFGUWS2XXKOfmsEs0BTg0IYya7e9Ib9zvtv+sbP3nEwg+SguQAv/La3B1CLB3grPfoOU/4JIIzFJW\nEAKs5iCJ0ceAG0wUuC2AkkWPcEHHVmbrKV5/P09rdJUprJLSCQVwBfDrGDIjwzyTQJTJPEt5HmEI\nM8uN93LpGZQF8EEY7LxmnMVZ4iexj8AScpBggFtHWTap6Vx4hmwKfBB8+bQg2DGXAEIxekIsakh9\nkCJt0SxQUWgeQRNwnSAnwiBBFTNLTiHgR01gqIQP4PGyNgs6ugJ+pMOQRk+z4xApheKzkAaBWw9g\nv1ymPigm7IbmY3WOtTh3jPBAhLkUSR8TEiaN9/2g0rpWFP4KDel1H8499lH/Nh3mOj8RXOOGtC0p\nu+/fu/7j62P/o0WCbpwSuTRIoOLqxCFh+mh1k4u/tB6pFOdo/w3R537znk/Ho31rX5T0qVrzz1Nf\n7jEWzkMZI0dde3lLngZeMKGKusryedqGUTXMALEM52vsaGFzCUooaXwRVstEQ8yBUtbbs3jaqbuZ\nWyISxgpQSPCRfnoEzpVxb9IySjkPMu4tDJOGjIWV81n9Dtd7bdWvuYRx05rO4vBjqgg92MtYPlCg\nTLOE8yZiMnkNez/mOXSdSglvH5VT5NrZFyWj0h2jPUNAZlPmLQYvFCnN8aabODZFIQs5NANPkHtv\nZKHKOPS2U6jg30WuTD2Bs4eNU/TeTi2DnoYKWCznuGkMxUn5JI596HGydc6tc6SXhkR4lNIqxSw4\nIQutCBYC0IKloNnZqrKvl/U53jZIcy/r86hZnBojboYi+BwUt7BHGOxFzrAyQ0sv9QbKRTQJ32Gi\nu7CbaJ2Y07jD+FtZ/2dpwhL2TlC1WcoGqwlsAyQCTG+03LzedLPVGIyY/UOcyHFMIOwTh+qN0/aG\nvfHQEdsPKK1rReGvMGU38ZHG9FIvq9dmaZzw8s3udbaRazxl92MqargqPPznCdYWGbuNhRlQAYwc\nYoBWL3Ib66ugIXRiuBnyFNPuwVvzPptS6YqufL5c0Lu9r2mqfPMs8HL7cAMuDzXoo7HMapaSQCWD\npx29jGrR6bN77A1vgJYmsmWosAkjbUgSG8usCNTy3NpFSmB5iRYRtY0BBxfX+a0xTgisqbgszBCS\niOyntIijnfQsLnftm6e89wYrdre94rULq9ZKGtso3h2g01CxPFAkt8lgL/lmOkVWcjwwzumLmCUM\naAohpaiNcKOHb5/jyKhw6nuW5WXVzT6BMyqWRbFGNQWtkJdu7zdb24m28LWM/ajlCEuWX6DSQ34L\no4HloLhCx6vJr0MeGmCS9LCjD8tFcZKOmzDXMGqcXWHCQm6jeYSCQmkF2iCHvYTohwqCH7OACRsF\n7jjAmUU8ZR48xGKZ1CqJJV7VQa2fQ/24C1xaJ9HDrgiVVVQNvNRTlE7R5KLYTPU8NFPfwlcl+Crz\nomaemxbfs8fxlq7GvN6INyO34OupfLuv8q2FQO+S94hlHuo0+9x8s17/h4RNVxoXhId+wXX11Hol\nvFJDSq423/Xh3IW/v/KIcoyqcuWXf18ubwK77knby0+jIX3nO9/p6+v7AT9cLpdLpZIkXckYse/P\nw7/5T1Sr1AKEQmy9XFNkc+NwYPqwCZSXcfdgFNmoGPfsz63W33nwa/FoX2W6KfelqcrECJcyqCpY\n4IQ66C89gUeHBqaGd4j8GTpGsUrBiUTLO9qLn9cZ62RtHbOCVcbRy4U1XFAqorfg28T0kC+zpiHv\nwNXG6guUWrk/yjdn8KQR27GLlG24PSiz0ENmFvoqLxb8PyeWznhIBRvpJPoSdOANYRaIevAIqCqp\nBP292O3sLzBiIA5y6SKiC6CqIK1RG6fX5GLe/Tv7jcmzWBEqJv9hlO9WcLlweHB6MRpmzSGFRVP3\n+t9dLz/Wan8QjtcaowI5yM5CKyb4bdT6MHJQgCrmMjmJyCAZBe0s3p2gsTvKZApXhiEfuWFMi8oq\nrYewp9BWEGQEH6Ifq4ocYTlLSx3Dxtk53jqBPUIuz/EZe2/D0esh2t5o9eB2sQmd7bS5SSvQBQbF\nDFYLjUEaC1h5igZKHrkJW4/5zGR9zml755HGq8qcX8PmpRdaItq5mPbEamBEEIZN621CfTjQmN/g\n1ORDD/2gw1WvFYW/QkMqx80D7y6rcZuycoUjWvrvIzN1ZZf+b7H+zS+/62wX17ghbcvooE9+8pN7\n9uz5i7/4i3K5/H0+trm5+Z73vGffvn2ZzHYW/KzPEvg3/2i0BdJZVnK4QgB6FsmPYnE6eeap/XO5\noSP6sd6ftZDcPDrJe1778mWXvy1e6AP/vy5HNzUEP6UZvOn0i1JbIBe6SWFOobcPRLxjLKgIBopI\nv0RK5Hk73XacfuwWW7PkRHZO8OTxjiMp4Q4fKQkU1jWEEEU7pkXAjRSjNMtM0vhckqWUpSTYdxhR\nQJ+iqrFzP7ksngiyDDpnjjGoczjKE/McsXPHEbQFtDkQbaxzOwAAIABJREFUSKvkJxmI4Ba1309J\nv7qf+iQtQUJeXhfEsMDNGyLsPcJCUv/uzIH7T3rHtbYPZM3/W+ABkbLKmyK0H4QkqKzP01HGO4rw\nOvCDQfUEF1cR+2AYZQrdy9QqPhklzPemuVNlYALc5GfoOkpwHD2LmUNXMA0KczgDqHaiUTxe/nCG\nqJe3vo4dB63nkuajT7GkMihzv8weH2k3ZR83HyIUhP00H0LUEKZxm6BjGQh2yhLmEoRIuK1HjvN1\nO288QjhJOYGsMaASjaX+Mq788pzxmE4Q8Q8G+Pb9P7imri2FvxK+/HPBW+JPXPHliWNEjvzoTvO/\nYl6/Q/opZ7vWT3zyk5/88z//cyAYDA4ODv7Gb/yG1+t1OByGYczPz3/iE59YX18vFouyLH/qU586\ncODAdpzhXx/59hxB0rk4iRjBMjH9NPuxInjjbE7ScoTCEuEgHx4/EJp+dPitfxb9lW+/rW/u6wv8\nyv1MJjg1i/kvafdREF6q2cOOXcYuY6YYHkXT8LH34eDZd3sZENk8gRKAEAKgEJTR4uwb5eIs3cMc\nT1CNIx1md4DFY+57wq3/2bd+wzRqktAhUnFCAVJJyBHaT+ok7jBmkugoO0MkBHSLlUkI0hLEa5CZ\nozfC2QQkEMb47WFMQdCTHe/15X9jvvzENIDox3AzfphOkZm48KaY6S7zidPIQzwYYsEg7ObT3+Lt\nB3gSjOTgHWr/Lxqz3uHqjJR61uRghM8oBCW+NE3+OASQvESGSFiYbszjkIYI9gGkMuUTAIRA57YD\nhAZ4epruYc6cwFRAIBDDbWf1WYQhzDi4Icve/RR1sjl2DZGB1hCSjJRgIUVYwFPmrsPMgAQzGoJC\nH3xXZCmNlKbZTS2BAFoQPYRboyOGpZGZw4xiV2mzGB1GdfOixYBGsxdDxRRZnmRQ4vYxDsqNe1+B\nuq4thb8SbmNqC+8MA1cWNHKExLEru/Q6V59rvKhhG/ch1ev1z3/+85/61Key2ey/f7e7u/vjH//4\nnj17tik6//brKgTpPcSlryBEcI1R+g4M0xTDL7D1LUwfUgBL467D3O396KHfmzAn/0j7rW/fmiMs\nSO8/wKdn9YXUy54kwhAYsABeYiESOmYKBCIRWnwH37naHU4/+uABIpCYxHsATUdKExslO4cQoEMi\nCFmZ8ynkEK8aZjZLfq7906HqrKF+eAYxDCm8AYBUAp8fvChZvBYh6DmEpZO2EHTOnkAaoiWEa5VU\nFkeYSgIBvFHfIwHlK33SL+k+r5J6/zRzcSiDCFEGDrDLTsLiAS9fnWdpFc8g74wRdyMqPD859Nex\n+MfK+lLolgcmXQd8W4f9m19wpyZNYsN8+gS7hzk+Q34S7HhDhEZJWBDDfBZzFiK4g0g6uVnsPoQ+\njNMM7OeuCT5jRxbIfAc9C1EwQXt5ldQ82EElFIQQ+jw9w5RlLBlVRJzFHsSvsp4jDLuHSLrRTOwq\ncoJLMloQQ6NJRF7FhKrFjjBhP6tlpBCrJ9lcwDVKNc3uMdxw9iQ5jcFBdBUHOCx0nZC/8eLYKxLY\nNaTwV4IP6z/x//4hP3fFccdYmuIHzVhe55rip9eQ/oVyuZzJZDRNMwzD7XbLstzR0WG3b/ugcZvt\nM5B7uShuBAHMkzTfR2US049bwBqkV+XCM/giKBIB+ODhA2PTX4y85fd9H/zqu4az30kRtvseDCp/\nkkL/l872cchCGVLERlCSlMMYcaIDCH6M1M99d+bRh+7VPgvDWWYX2DXChSzEuWWC56a4YQIzy9EI\nfzKJMMJYhGY36pJgWxV/NVL+hWcRIhhutJMMHGUhBav0jLBpoM/jldkfPPhw+fhDB9ESKCrrGpJJ\ns49ammKQKpizCH4OuX1vHldO+9p/Le2cNJL/MQsnoAwC4kFEkf4IQxKDZT47R9mNpvIzw4QCCPiW\nlL2/N/X0hwYw5bfu+Yr6+mBqLLT+Z3L6MQN7jMUZdg7zxFdAAwNvGCGCqcEE+ixkX6pFDHpJZsFE\n8GGqjAxzi59n/UhZznwHS4cYzEAAIYRgoi+Bj+Ews2VCB8iu4tNpFrDLIFBTUXT2uZHCPKFBmUiQ\nRpLNJW6NIA0zraIL1E/QHkT3sa7hDtCZ5tVhyjKyzPFJ0mAfxhA5KKEscOZZFIXgMFkZElj+RuP/\nY034D8LVU/gV9iHdxmQI5VGucDuGDx3cyral14706/G1cty4XqH+o+caN6QfR5Wd0+n0+XzBYLCz\nszMQCDQ3N9tsP2h97Q/Dw3/tpLj18p0B2GNYlzCTSK+hNkmtgKnT5aJSp6ojtGCWyHUkh/qzPYHX\na9/8uu8d+lfnKTmkQz5JFPWVMpYDauDgciMlIkqOoQEqRVzdbNkZaEZ3VTYLe96rLP5jJ/kMzi06\neygVqHrwFNkZJq9i87O1xINjfO8YG04OyRR81lymtqLy/gN8exG8iGFSU6BBC9UcPf1smeBGLbS8\nyT8wtrD29G7aXVg60gCpWcI7sVYwBIQadicJP2Oqt6/JeHJTerWrpgbMWUBBcFHTENuo1XE3429m\ntcraKpKLssgeB+0u3SvZvsFNvzi7eGn3+Y3hG7Ym02KXcbCZk65qYZOWEE9+Df8AlQqYGAp6E5YN\nLuAN09CpZ0GmYBEIgUU1C62kV6l6eHczJ5209lErUG2ADHncOxDtuGJUl8goRHZhJmg0MJ2kz1Fz\nEerCmUPJsihQstgdIb/J1jzFM1gW8SX0DFEwBTYq5DKIm/jDNDpIJkiUyDToLDMR4eZ+CoukV7gk\nU+1k1xi9MsUEyiyNcbyehz4UvDKlXTWFX+m07xUGxh5IXSwN1BXzCi7XEYZYTNEFtSs7wPcnnhfG\nYrZ4/go3VF3n+3CNFzX8H132/bEimg2SEIANrGaEMPVlol3YMlQq0KAI+25l6QSBdvI2yhu4xExi\n4PDrnvH5tPW2Qe1YUl9t8r3OriVayW2BE1RogxK0QJ5gM227iDaz4UHXsYrZBWn/W1MEOzPPJggF\nWTzP6DAJk+wlDozy3HeJBjhvMKFjb2e9zlqZm/3UXcwpDJhIbtaSaDloAwMKmEHqG+gGdQ9lX/pM\nfuy3a77W9OY7R6y8j4UzdO5j9QxtMSwH6ireVrSsOe20frO39mTKEbZsgy22dpd5Ssc9grGAUcco\nQoitDIdjGDprad44zgsZ3LDTpQheZ5d148ETC+eGzq8MTzSdKFqy7QGRE7Xqd6cwClRyeCMYbeCD\nS1gCpkpljs4Bqg1cNZw5lDrNLTg6MVYgir7BrMQbHKw18DkJNbHZRMhPbhIhzIhMwgs51CVu8uFs\nJZGGJEaOzRVqXnZ009rGaoiNTUajKDuoiQgN7O0oyyRS5GXkIG2d1CQqlzAyeAaphKjlOaUzdZ4S\n3NzC0SHqi8ydJ6PDDiIj7B+m5kH2PPR+99XW7Cvjh1g/YfbbkuJBT2rqSjZuADoNCVNnuzwj7ood\n6UjH85fTG9f5kXHdkK4aD3/423hvQY9DHgJggEmjQK2dw33ML4NBrYXWBk4/VpEbBvFmqbdrOa81\n3PR2+fPfi96tfWvD1P1isOySnbreQLFBEeqwF84jHCC/xbv7eCxPb4Nlg93tJPX5J4Ye+L1vTi/d\nYy4uYlUxG9hMzA7Ka9yyk8UEu8f4apI7RnjxLIYBRXb1YnPypTh37uG5c3ibMbLQBgXI4t5PpQgq\nuEnbcob5ybd9bHUkuto52DgrkU/gtLO8QneMSoDiCoNRjHB93u76vY7Sf123xttsn0+aVQ+FJaRh\nUPD1k5tlZx/Ti7ymmyMjLCTojvBixr7D7vQYm+sdgr/RN7a0vha9WB66xXy2aMjFWriq1VnLQxXD\nDnHoAS9sQBvIFM7jacFm4G6CLIUeRIumfqrHqDrw2JiXGawiDlAx6WljaBdSg401du1hvwulm6KT\n5QLpGUYi0E1JgQK6QqMLXzs2E2+AuTwuA7kHrYK7g6YBukbJr1NdpkXGaRHYjz+EOk1Npa+DYJB6\nkekZnrlIXedwP6/dSTHN1AskVojLHGrhzNJDHwpdbc2+Mn6YfUhz6a4333L6gmfEiOtXcLlO0xCn\nU2zbtidN9dXiIcNIEdim+7CfTq4b0lXj4YfXEBV0GZJ4w/hcmBqmguGgPYarSE4BldYwbxhlq0Je\nZjzAXIJKNZkcG3zLfHbKc+nJXTRVKol6x1G9MOVFiUMA0rABFawawg7mZ7ijn1QNvUZiFgkrmS52\n7b7h7vlLkwPUK6xrNA1gbaE0GItw6RJuF3IEscwBgfMuUilGA/jbSGqsr+KPoWnQoK7hv5GKA0cG\n0Y+hg4kcKJ9YdN/meYv3iaWhvuKOiPHpNA8EWFBJzNM3TGuEnx3l4iYLJUtoFd4eNH43Y+6UUErk\n7ERk3n+Ii2voBvEVjg6TTpCoMiQzvSi9q8vdltTFZqdpbK512Hy2mBjfpOPi5lCXlSi3esqt/Xia\nWNgAESIwAzdBG8TBDe3oZSQPgpOdg+irKM3YtjAdoKGW2dVCcxB7kbJFQUczGRpk3MaT85xXpY8F\nhf5mc0bD7GQzT68Nby/5KihoBfwSrVkCDcbdqBblMp111AylIrsFduzH382Wk5SGrwnqDA7S6WP2\nFNh5exe3TbBW5sx5nlonI/ML/fzMTjxV5l/k9DGa3Q/9dv/V1uwr44dc0Oep6GW3pMavMLuYoivG\ngsJ2PelJGYGb+MYmR3SK2xTip5DrhnTVePi/KFhpHK2YDowCoT0ULxfLaeRldjUTXycaw9VL2EGz\nk4rBhSw9dbJVoySe+EKX8rrhUsLDuTnc4VJW676PQtyLZmEfxkqCDVRML4abIYOayNw65hLtAwgt\n2Seco+9dWl7aa25mqUSoTCOHqcL6KocmeP4SvVFSKUIBCvOUdnJ2Eq2XiRinTiIHSWk0+RB62RWk\n4qGwjNdP3UZ9hfowbvfZ58VbX7X87tLnB8YW5qSj6ueWeEcvz6WppAnu4EKRn4lxaqmxYKvvd2H3\ncaFEXxOxMFYMFwzLzGSIBflgBKtKWuWiGvkVKbfokG7yuJS0Hu1oPHpRrfQ7u2t7pOmNxeBWJRj2\nrNsClE87cLS9NKnBO4wxC63QCRVQwINeQu5lZZ1QC21bbNphA3yQYq2DXJHhLr6r0QS+DU5uEunh\nni5sefMZO10t3rukhlqrZ71sqhhF/F5oxYCMwkaK4CCGjCAwuEnVTf8e0hmWFJYvsMvG67ppj5BN\nk6ghOnG5Geql5uDr85R0fsHP2A57ItW4cJrncnRXaYrQ2MOOFpSLD/3aK6uyu+r8kIYUT/uv2I0u\n4yOHT9b1bSrfaFxk70082X6fnJy7wtTidf4XrhvSVePhj6cwNPSz4AOF5m4cOtUqVgWjQuct7Oxm\n6gUGx0iXuLGLuRSBHQQEVrJ4Xcax2WpgMHB0o3S2k9SslfY79oUrl+roaRwBDA1qUAcV76spbfDA\nKN9Ogk5dpyVEfunC2XDoXlU9345ToFLDAluRkohp0NyGoBCOMG8yHmJ6BZsL5QK+YYIy5yYZGyG3\nTslO1WRAZh0qi9QbkMXaoh61qubzm8O/tffzfnuu+lrXxaXXV0/FuSVCHDbncA0iIb6zs/6oiqbx\n8zIlmdN21lq528Y0+Jzc100yw6Uqb4pAlaY2NdcWuTGROeeLHlTbs9nszbt4LKFWIgW0RqFRXxQL\nzrZapmSOtfF0mo4QVZm2IPVejCl8TeDE9EEcDBpeWnpIryF6GXGydvlhQBZWKbWw6iJoUdeYTSC3\nM18kW+VgD1LVmtOqoV7HfZ11Z4XkAHoHShlRxUhDFSlIZhM37PeT6WSPj81NXM34WimUWcpxfIYR\nF28ZQPaTXSaXx++h4MXTxabOl+F4yXnfuHBft62kWf/jFN87ySE7ozH72/d+ZMR5tTX7yrjKK8xB\nwa+HoijbNxzPsnn9N0kviPf3r79wfYLDj4DrhnTVePh31pCd1MpYOYhQmGHgCPkUpgl7aFTxhbDp\nZOKEQtQg4kJRccBuBxdFqFiLOfl9EetCsbacp22gMhXmBge6g81L2AewNgDwQDuvDbKUZ4/MeSfG\nPJUi9WZSF+ShoByuqYtVjCCl81hOLJVciugIlxbwhXF7OT9NZhXHPmxrpEr0DZOyaPMQ9FBcQvFR\nrXFQYrUTCjSaYQNLpLNfl5rjVtP9wvcGO+Y33tC5/IUDZsROo8hmHcqsRaWxsm1Pa/3zSdq8jDdR\nWSLhJw2vt6FBL4y08/gsl9R9v17ItnRbMzbV5R8cXtha64j2r3hy5exIH3+2UE3WzI6q2EJtMmnr\na65vVbgzwot59uwgruOr0WijpYm6CFuYMuQxyjibcQ1QLZHcItJKVaLmhSwYeFqQQgQEHC42XkCS\nSawSTzIxSMjDZLG+1SL1eR135xurmYYrRL4NS4cC5iaBGI0gpxR21NHdtHtpKZDJobsRRWoWF9ZY\nWMce4NYIbe08t0ggzbgX3U2mSkiun2uqn25yv7dVPNBvHW63vlHhsYLT4//wG/4Pn2W3LSgqscj2\neZJiCEpl50TPZLHVn4tfr7v7YbluSP9KpVJxOn98P0If/q8r2JvBiZEEGRrYmujykylCjOJF/D20\nN5Gco2cH6qY92t148TwHu8mqFMqE+zl7rqx0iRNe4/lm1k+yo59cK7UMFZWGAD7sBlYVM0slRnad\nu3u5FEcpUlcgAn71hZT8jgE15ae2glHBuFzyV2dznVtuYqlA3seNnSTylFdpuonykxS9uGMsFgg2\naPaSyeB00yfj1NgKYZWgjNdBNML81sVE2Dws3711zOgV04fCqUei1s4qNhsbKRxq7Zke8cEqPZ3W\nPzyHEGB/hHqSKY/vF9WO16YKkz56oDPCC+vCUnXvzy+tV6LWht07WtnpuXjmxP62zqxo1tRqjLNp\nKnWzIjfK5fqlLaHJb80nqOyivMBAJ6smQoKSHadBYAfFFaw2qFJZwqiyfx+uMPEVvJ1g0PBhKVQW\nKGdI7qLZRSTK2hr1EKUakxnO6Rzpx6aaSWe90GYLdDQkHa8PM4phYmlocZQkjgppG2dLnHPiCxBt\nQ8hh1Sieh3ZyMvFNputcEvEHaIRYihNZ4i4fboNqESNpPC8ZMx73fof1yzut8e76+fxDb/X+kJL7\ncSv8WjAkwCfjk7fRkyp1/4XVtr7AYipArbpNUX5KuG5IJJPJN77xjY888shf/dVfHTp0qLOzE7jn\nnnvOnTt39OjR7Yv78BfrLK7R3kXNxExADC1NbBw1j6FAO/Y1hndhmZw/w703ODuT9doApxdoayWS\n4xJ4vY3nnjD0ZminKiHksXno8pBIUIoj7MYlYJWwLLZq7N1DNodPYmEebFCBIOTUTR0rSqUNv4tK\nkVoaopAhFaZbRiyTcDPoYEVB1BHbsSUQ2rAJrCn4AxR06hp6njePMjePzYeZoVrEkvAHEaQXT4/F\nBi8d8Z00e5zasLz+QRthOyU75U0ku/mPHukDCZvuqC/Ok27n9nY2F3xd1dKu1mA4XXzB2fhMgXv7\n1BeznlV1/P4LC0uDylIr/Qy3zE49sc/TWpIjquoY4PGLLCr0DyFp1lwaVwTPRXx7WFxFNsi5GJJJ\nZ/A2aNqBUcdcgipWlsQ6Upn+fazO4tXRlpF2ANR8NE5SdrPlxL2G6EUfwpOnmGdGwrGEAev1RsNN\nDPxbVJK0tlN0Y9bBR22NchHHBvZl1raoWYSCiFHqEtUVrEVwYZSxbCjNVIqUVphxs1igW2E0jNTJ\nQorAhtFwWy/4yM/yM20P7bjCO6SrpvBrxJAuW5G+jVaxXI9OKE8XQzuUStN1T/phuMYNadubyS9c\nuPCqV70qm83u27cvEAj8y+tHjx59/PHHtzl4Cr+bqoHPDj5IgsL5FLuHQAeRpEo6zc+MMyKwsGok\nRWFCRLy8ZzXCbX46fCDx9CQhcITYTKCnSIh0x0DCmEUXEWRQIMniAhLsjxEOv7SqlQWkGFNzZGaQ\nveQNAn1gx5tiYAh9iosJhDLNBu4BAl6wQEDRsC3hkRnQCJg4hjCzJOGZOW71Iqu0TYBOaoY2wd6u\nEgn94eTvzKfC757/u8CQIj0yyIsKvW7cAfKT3Bgvf2Re/EBI7IyizJJMsMtMfTQVmEuUQu7296nC\nTSn+QWVscP6zQubP9De8+mt2wVp5KnZeHNnx6vlELkIfsm8Wt46e5rmTBPsI55ibxxrAeAYhjaHS\nH2IliZUjtYArjmyBj8tNKlaa1Sle/AadfWgBENBn8IfoHoARzDTmM2g6IZHYPOEwXWHsKc6ofPcY\nZpylFF+HSYGUm/U0IR3/KOIw9CG4MXWEECSIH2dlhnqO/lF2301gP0IaFjCOYT2FXaF5mFY7g1G0\nQdIpUqvcFOaWMV5UefxLPJ/gM1e42vWqKvyaYRsfI13G+mzuzjusf4z5c9sc6DpXk203pPe9731t\nbW1TU1Nf+MIXBOFfU8BvectbKpWKpmnbGPt8nJ4Q5QQ3DYMGFngpT6P5CcVgiQfvRDGZgdsn+PIk\nUoDUrH1ilC9P4/UyK9MwGRgDOD/JBDDKRZ2VeQYmCEVAA3BE6DwIYbIacyozWV7/WrxuglFQIIUU\nIz6FmaJlBN2OewjNREsT8qKnWFexNJ58jJ6DaDroeAdJzdF+goUFyNKZQjiImSUFm3b2BqgptE0g\niIzH7HcdENSlRG7w/X/7B8+EDgtes/U+TfjVcRYVQl7sg5xbYOew9pE579sstixUmbDMG30LP6ce\nnf67oJ7u/oRbuCnFU1lhl3TyUXnj78x7Jv6RxxLZvwxseMO7B2cSf2Sp8wIfHCYQQs/xxVl2HeJW\nmJsm6San4bSzcpK0jqZjqKRSuLPERiHMvzZOZln5BqTwHgY/6Tlc8+zS2TmEPwQaC1mEQdoUfNA/\nQvsQ/lfz4iqbczQlufgsyjyIxHP4E9wo0n0Ydxi7DAkwwEcqx9xxjNNETG4dZe+99B/EHUEyMXRK\nAgMBnCF2iEJ00P7ePg55GbJzxzC330fxCF+9wq7Yq6nwny7MTyceiGuxq32M62wj22tIlmWlUqm/\n/uu//vdvybIMlEqlbQyvq7jTyGGyJkMRALLg5tI8TT7EHL4UyGQVrAhjPr47Y876JDGFGWVlgbxO\nbxBBxutHVd13Cu7XiPhENIWZSXaNAZhTuCz8YXwqmkTBQpAICAyGwaL7f7L3vuFRnOe9/0ej0Wg0\nGo1Wy7JaltWyiEWIRQghZCEbjDEm2E78N3ac1HFPTtM0Oc3JSdr8aZr2pDHnNG18ktRtTtMkjdPa\nrevWieO4DnYItgnYgIXAIIRYhBBiWa2W1Wq1Wo1Go9FoNPt7AfyS1ElPTUOgjT6XXu0zO89c2u9e\n997Pcz/fu+OSA57IiU6CNgubUQSUejLZi8+TL1A0WNvOiZdZ04Kto4DoozdO1MP+XlZLiAncNPiI\nZ5h0aJBRIogBnu90kqLw9nrheE8q1falp97zAfWxenWw9vcNAl56erFc9ChHj1DuzW8fVD8W5JVe\nJG3FFmPh73p++InGGwo7PWZh2eddodWyegVxbejw09q5/6vd+MFXGUzl/9J7qquReoW9Bk9muC+C\nZuMk+WY/QpTVQTIii4OM5SmYUAAVW8FIkRiAARoj8JNNgFysAYwDoECMgSxncwRE1sZY3ISYYOCf\ncGThoUaqUgRl1CweAzfNmU5kFdGD0QMCAzb7X2T8VSr91IbxNOKpR85iZbESnCqwtwdjkHaJu0Jc\nfwMbonjj5DuRspjdHNCdv+0TD+1TVV0SbBrhnSIPSNzdfBkSu8oK/8Uih5BDV/sh/lWsPICsXe3n\nmOdKcWUD0gXn1oqKijcPOY4DXOEdYJP9nVwXZH8/axvAAhkEsBlOoDbyj7toFTmb54DOu7eR7ieN\n3ZcVr4uyL8V1Jv0yCxS0esyA+XcF3z06/iBykKE8gsY7WgDyXQwnqAxAgoECR/tIpFgeIJshoiFq\nWAnkIOQ52ktAZnEMRQEP2TzRRoQQPSlkhyUByLC8gTWNKE2IIQCvzP5BNkTBw5mXWdzB4RSmSIfC\nqi0Mp4in7LwmvDcmWImjOzZ/4Y/vejDw1PjvDVIawSvBIJaDXs+5BEHJ+Ite9WNBoTPhtqptn0yU\n/Vb90/c1byvs+qj6ldDvy8JdYSsv8KH27t3G+AuBGz/6Kj+MO19JkvXxa42cs/nGDrRWNHD62Z2h\nKcLtXs45rKgnIIIAysWsEUgk6OvC40X281MGMwUwLnql4+NYjpxDLMwt2/D76d7Hc3FxeVhotFnc\nwMJNrGijJYBsY+ShATQYBDDinH+W831kXJBZdhtrHkRrhQRGnNcL/H0f/XGa4YEYv72N/xGhvECD\nzdY0Ycfujhm7ZftLrjeb8w7mpT+x+ePLUdjVVvgvlB/bB1/DFFIwH5P+03JlA1JpaWlFRcXTTz/9\n5qHnn38e8HqvqKGvBDDUTTjC0QyNragBlrVg60h+DBFLIx1nWYQhgZ06tzZwYp8zGJK3JZA38UIX\nmk5YYYkPBE4M5F5Euc/Fo2Gb7H2Rxig+DS58SQIsjiKYVProd7AVNrfy2i6u7wAB2Y8ngt7N2SQx\nP4pGbRNLt1DQ8CQRG9nbhaeJmQSyTCrDugj4GNRxBByXswPUt4HD8V38xh2k4WScqMziTbzWSYfj\nKK54B7yR3L/z7Z9+7L9azRGGjrBqK4BgUx4gFSZrss7HoFX/AffMx30DiUjHJwdWfNHzt195fy7r\n+3TgC6EPO9GPSKs+mWT75t7dxrkXgtJvygyb7E6TdzBdMEjtQGsgFGPU5TtHkMLU2xzvY2UjUS9I\n/PjcvgwyhfxFx6afIgcikoI1QKGHYZP9Gl1QFmZRk/tGl/PCbrXRVT8CtSopHyMakoto4KQgB0EI\ngAwiZGEPhV6GD3Cul3Xt/Nr7ue5CWMrwispfHObRTnZDn8EShXc24lG5WeR9DvVZGrrzXXI+4ZX+\nzOCxzGUo7Gor/BeNlbrWkyTA0rGu9JbVPFeHK74cqY1QAAAgAElEQVSH9N73vvfxxx8/cODAT77Y\n1dX1+c9//pZbbrmycyv1AN1xVoboi7MmiCMzlUENYphIEgTYG6clzzqZMw6BKBs1GDD2yPJ7JbIC\nhxP0ZJmSCDmYHvOltNLssM7PojAuPJfi3RsByDCVp72FeoWTcRTQFdZECfiZcWhpp9CNHEb2Mrib\nwQJLvKDgCVEiQwjvAKqfvl7eto2hI0gKZo5AALEe048mkc1hZanfxk0RUl18qIk+P4k4m2XCGp/a\nR4PHtlXx3TIHk8Zv9YlNjvCxGIe6WHEHisFIilIv4xHOO4apZgb99R9Mn/m4380KH7nrL1dujn/n\nuQdyWd8btN4dfW7qn9Q1Dw1GtkeSz2TtU4b4kMhwH08fwPIjtyFqOC7lUdZqpGS6eriukeX1/OgI\nOZP1GvigATwQBhFkLOHiL4OfIoek420FyB1mZY4WkTGHCh8bOpB0/Q+fMf48qaySxDoZfyvVHch+\nZBFRBj/4oRlaoAk0MChkKXRy9FWeSeHr4BP/hTv9qF2IEv0CrzzHS/2cFvlMgqzClnrsPK93MqVz\nU4JQzujROOx700P+m7iaCr9CiP9p8w9582V+yvP8cvhl9EN65zvfeeLEiZqamvHx8aVLlxYKhfHx\n8Zqams7OK1u0WuIbYGwXeFnTgZNhKsX6Dp7uZtlGRhIAYp4yhWY/d9TzLQc7ya9JbN9BaAv1CeYa\n2L+PyigdKpbK/l5UjQ0dbBTYrdOVYKaPNS1UpejzUJBYEkV0OPkqis3KGJsDvAoHDTZ42b8TdAKN\n5PoRPbyjjT6RgoXfy0CKeoHhDOYgddvYDN94mes2ofp4rQtRxEoRiuDIOLCqgYlOVmmIbXyvi5tF\n2oI8laLc4mubeRU6e/l+llBO/MFW569FbI0dKUa7ccKsCaIXaDEJeQOtZp0/MbIvcvuHd2/073us\n6wPDhD4Xefiu/uf/l/pHe/KbP7rlK1/8wu/1/mmKmJflfv5+F3ihHhEcl8VBRI3oICcUpCzvaCKe\nYe9OPPXIIkI9JQVGkjgelCRuE46N0/fj1Ty8IIMLGWo6cDxM7sMX4N5NdKU4OciKDqwsZzvxhNT3\nNbFRNb6eowfMPMU8ehxUCIF06S8HabDBBhDrkRu4PkqriBnn+ykyDo4XTJweiHJ3Ex+MoBcoyLw6\nSEDnhzp1TcWdwctT2lVT+OX2Q/pVRt7ss/b8jIaKvyLM90Pi3e9+d01NzbFjx2zbnpiYqKioeN/7\n3vetb33rMm41NDT0yCOPPPXUU4888sjzzz9/6tSpZcuWVVdX/8yLt29PMlsNpxlxaG/j2FFWL2Pc\nYEZg+WJMEXmaOY1knGU+ViicgdkS6qoY0DndR1hlXGMmjRpFExgyWdbA4CRBD1MlzMwxPoxdxu2L\nmfAypTI6iubDU0G+yOw0yWpi5UwrnDG4LsjQWYxShIW4aZxK/DbjIhMGd1YyYOLxYs8wcozStawu\nsv8Qa2NoVSR6kBtwT5FTWV7HmSHqNnK8U32PhyV1c4930RphbRU/GGTKw30qk37cKU6Wu08lqahG\nncPvx1RYO8b1YSyBvjkU3TiPdpOg+Au9z6wuj809EP32hL/6a10fXho7+5HzX82/7v1n8e4Pv+ev\nBqZvzz5/ltQE629k6iyzZZTXYLtMTlGvUhKgNk+2iuPnuT9KSTmnehCrqIDKCB6NQorFWxDOMTuN\nc6GVxgVj6WqognLEIvYIniLqcrIOR/bRVs1NDcSP4MLaLYyO2y/vsV8xsRVWOUzOMl5EXE5xhOIw\njIAHQaBYAUtgHIog4OawBxgc5FCGXIh1a7m+Do9JzqUsRmUN8Tf4xx6K49jljC5gUQ3D5ymVHn7f\nwv9oCr82ziH9h8JJmNdH+vOe2tnClWoweC1zjZ9D+mVkSL9Avv/97z/zzDPLly9vbGw8ffr0jh07\nbNt+9tln6+rq3nxxSdnLuDHcFwEiMSo8nO/kXdvokgmoDMjocWZTlGkEZN7ZwG6Hk93c1MDRJFkb\n4zD3vZ3vxqkM0KSiRujtwxfAAw+E2GVwIk9OZuEgbw/yXZFCloDM4hAn9lCAWBAlyBKVvQbLJCYO\n0JeFKLKBNcj6rYwb9B9hwx1EXHpd3AITOkmdNTcw8TIZuPt+DvQwa3OLwj/sRtzMGj+nszwS5XOD\n6iM++1nT/v4e7tuGx+SlDO+KsDHEDvjhHgo5VrQhS7R4qZfRM+gFCiHyDnERb4Go3vjnQmlPbuox\n8aav9N3lef7b7gP/3HnP5/wP/96+L/5J7jO7Nm97oOPbz21veeVhkYCP2zfxUheFIJIHJ8uKenwa\nrTCY4qUkOfgtPzmXF/Zh64QaKQkieBnPIEtUmQynEOsxuuHC6r8MAQigqpDGiCMGoRknQcjg1m1k\nspg+oh76MhzqxEqjRkHFyWHpCD7cLOR/3HpR0HB1XB10sPjJZqZCPa5Gs58KQCerMZGGEKKN6bDR\nU/mHfnefMDdgzjx29ddz3prC5zOkyySwbnPfyYTPTPzKNVuaz5B+kaxYseLee+/dtGlTLBa78cYb\nt23b9s1vflMQhBtvvPHNF2//3/2UVjK3EPopzLF2OclRpmBlA1mD1TIny1FhboaJPP0F7l9E3sPR\nLFtrOJmDInoOT5TZMc4O8WtL0W2sPDmVKo1DR7i/jngW3UfuJLeHSCgsEin3EfAzMcjQCJ4g5gyF\nPEYNty5hKMXUEE4AWeBcL8UlTJcxNEBrA4U0JUH6RiHDiMr6dUwcYWKCuaVMzDJlE5FJvcF4LQ8t\nJV3B/WX2V0aqP1IxPeBh7wCLYzSoPN2LtpDScrweEllGM1RWUVfJVglRZc5haJCOemZtch4mZnNp\ndcG7SsTx1JmXY1MtNQ9WPKUuNZ7ofZ8TE39/4JGMEvhWw2/+xubvWSWBszsMMuP81gZOnmHW5Lol\nHD3Nwioy5QQkWiuYOMcunTXlbGiiJ8X4MPYUciUlNjOz5ARql2IdxG68aJGOAzE4i63iyFQuxh2m\nvIhdgj7L0QPMlBCrozDNapXWJpwaRk5hjiMvp6oR8zWYvrQ1pVI0oQxhAUINuBSnYA4EKAIUxyHD\nyBmGzzBcwC2l4LKwSHqEgMRo9Wz5QufVsuqbrE+v/xnFcr9k3prC5zOky8Q4n4h03KML3Vewfca1\nyTWeIV3xooaPfexjb3/729/8+kMPPfTQQw/9O29eV1cniuLPPerhJHDTiCEIIKtoKus7ON6Jk8KF\n3gJRDUsjEuD6JmSd7yUJi0jQJxMQqGsm5WfIYaGGILC3k80hThkEVcwB7onxXJy31TNVYKyR1ID4\nWS9bQrgWlsp17SChx0mZBETMBPs07uwACQwsFxTy+1CDAE/3oPo43w0uROEIP0zjayWVYriXco1U\ngILMrVuwOzn2KkFwPPxWJN/vkT/aQETgB/24Ku9t54hOn47i4foOVJWlfnwih6ERQgG8QRJxWiQ0\nHUtlZ67v/wRoDckduVe+1PH7vV/w5XL3bH7u0e6P/0/5Mx/PfPGTf/nIV/jo5s8kbv5bmUyS7Tt4\nRxRZIq2xoYVzaVydPodXRN7WQofE03lOWXzofgL1WDbDnRRV9DBOmqFeFmxFdUGFRlAgBTKkcAew\noOouyoOQAR00knH+8W8YSPNPKWzYEuW9D7G8ASPBXB65HpRLm0Z5yCOqCAKuheBFbEZoRFBBBA9c\n2KK/EL0M9CxkSZoQIKmR7OKJnZTvyndepnHn1VT4PJdPZjIhro+YLXRd7SeZ58dc8YC0d+/e2267\n7c2vf/aznz106NDc3Ny/5+ZPPPGE4zjvete7fs64CAZCPxhYOXoNVJXaDl7pJOLBgXqHrSH8IgEv\ngsRcgQaZxginBqn0UJZikYzrkPPjgy6BzjS3tiM57AevQ71GIcuWEJN5xA4x7yJDTEQGw08sQipM\nJonro9wkE6c3xO1NMAAmqCDDAFI9Vg+vHKCmgaAMEgRRjtCbIhDD6cMsUKWwsJmWGDd10NlHoZsk\nykYUr+CERHX7DfgyfO8wvQk8CrrOgE7Iwzs6mMiTzJGz2A0pEIK4ITID3KHygAdPjN1G318FBFeT\nQtkzf+x/NvVOXy63+v6eL/Ph3zC+8aD81Cf/+ouHM23SXb6b/9YnRmy+8TJ3KyzPkDK4z8epHVhJ\nFqq8YGAqtEt0ZzmQ4c52vCJArhv6IQAu53ZgW6heJB28YEIWvNCMk2Kih0kvlc1IIogXjy6d2MXo\nIAfS6DoxuL+dlR1IA3h8LHknajsoAIKGfRh7Hx4BrQlBQcwhhpBaEd2LEa52K1IEPGBADhKQgBy+\nEIbEHpnv7Lk8EV5Vhc9z+XTvcU5FGlZE5PmYdO1wZfeQ5ubmYrHYD37wg/r6+n8xpOv6ddddt2/f\nvoUL3/JO8sc//vEDBw5MTU2Vl5d//etfb2tr+5mXlZQ8Cfal7QQDmljTRkzmu0+xJoa3A8fAEhFz\n5CwiPo51syCE3+VQnkKadj+WTdYl60UzWOTD1viAj30GBRhL8ptRvtrJ3Vvo7mUgQpNH3mhZhky/\nRXeeyQxI5FIoCv4o5mFKb2Cbj9eeYTANKnghj6oBGCkCbSwKc94g08XSdkaOYGoEJDIphHoaXKo3\ncYPEq50c7+d37+EhzWfkcjt8YpvDM7bzDy8SCLIugO3BhLMZ7g0haxxJEPESUBBFDLDB1Mkk2diE\nDXtAKBDyVLcWqsV4dpdfuV+pC2TVTcahx+rvcl/eLj2STIUf+W+fLlHc3A6OfyxHIc9/38IRhRaN\nFpFP7yDoZ0UHAxZKltNHMCVEP7d4OZoiKYMBBQDy4ELgYnsqJHDAgjDYkAID6qmK4vqY7sPtv2T4\n5CJo3NDIjU04ClmRI3GGUvhioJLpxhYokSimsFMA3hg1zdh5RrpxARfHBh1vDFHE9lLIQgMUIA8F\n1I2s8CILxX1v+ffZ1Vb4/B7Sv5dt/zVV3DNoJ8y9/IxfFf/5uMb3kH4ZAemFF16IRqP/YujC13X/\n/v0/6Uf5b+TgwYOZTGZoaOi5554TBOHv//7va2tr33xZSclfgReMiwtlKITuYKVD2uCkw4YGTBuf\ngyzjZMCH288b/SiwpINjGYQMGKxr5UdZvCKRGOUa3hRtPp408HmojvO+ZvIO/fCdbha0iDFoxsmI\nDBj8qBcFTAWzgCpSL9CTI9bKBpWnn0E3EXy4PrBRTbgUkwwbO4EDtR2cP4AaAAlDgUEag6zaStDC\nGuTbOn8UY6PmM3NWr0NANf5Qp383gRAhjdIYYzqFbm7tQNUI6CgahgMiBuyzMPOoee5qEkKu+0SB\nWglR1ep1icHc1xx+u2lVaMD3ntxrjzXEhMSfbvsTcefcFzd9qnunT3CF3Jf7KaTY2MR0kFCQ92v8\n3yO8PMh17Rg+TBchy/gRhDB1PkayZHKofuw4tgkCOHDhCK0CCbhg8eADH+Qv5kxyA2UuRQEngZVG\nrEeQcAZwTbQYN7YQ1MiK5OKc1FkaxoJzcTSBKoUxHbOAmQOVQIhSP2P9WDYEwISBi5aGtVsZl7Et\nPA6yiWuQ9ReL/1Kl17zC5wPSL4CmzYU1dKX2uL8KMekaD0hX3KmhsrLyy1/+8puHLhgh19TU/Lz3\nHjhwYMVP8JND69evv/vuuz/ykY88++yzMzMzjzzyyM+5hwTmJZsAFQqkuzEcbvCjZDlVwJHRoWCh\nhrCytDXjhQIUUlSr5EWiYY724aunPMpgJyqkYF+KqEq9SJ+HfBaviOhwa4Az3Y5uOoM5FIiorG4k\nJ6LoeAOU+JgS8YjEE/QJ3L4NHEQvQhIUDJA08JI5jDGI5EOE0TjRRgwXIwkFiNI3gDmIYhAOsjLE\nF1L06qLfUZ2M6s+on9fwt5FxSCkcTbDIi6eFH/bgd9A18qCKZBy+7RAVsRQML9894L7cJ35Gw3UY\nyusZLdfXwn0BvnbkRFdo/99sFNr88e7QZ3b+Qe8dq74of+rejV2u7PI/thIMsK+XqT6sNJ/axU1R\n/rCNQ/uY7SSYwNWwPOTjnOyjNkRAQ02ysh3pgp25c8nQIQnKpciUvmiFhwNJrJeZ7IQs+JDDiEns\nBGITS9sgwQvP8s3d7I7jhLmuCa2Am2VdEzV+hpL4RG65gfWbURwyPQwfRvRBBCmHIlO9BdELBiPP\nofZAikI/GQtJRrP+Ayp8nl8AvXs8x2ifjYR+s2VnJDDvJn41ueJVdvl8/nvf+57f729qavr/Xzx4\n8OBHP/rRlpaWBx544Oe9saqqqr29/c5LRCKRN19TXl7e3d3d2dn5gQ984M2j27d3QQloMAESzFEy\nhVlORTnVNQyex+dFKKPW5tUzVHsoEVjlpSfFhE5AYkbkzGnMIqEJZhtYUUbiFNEYGYtYFa5ASzV/\nN8ymMiKVjEpYBfb2symCO40ho8GMxGQWtQLFR14mCEaawQEiS5moQO+mdCFuP7RQ7McJQhEK2LNU\nLEa08BfZ1kiPACPg4r+eWJidOcISkSKzZZSWG+4C9R3wSl5qsKlw7QMpWMziUuLTRKrIhJhx8UNR\nIA91NsMuB1xuljlXSrqCni43bUq/F57bn8OXR6thQqOlmm+94o5Mu55Q6Z2V2ceqdh26+/jbVv+2\n+rW1oTNdUy1mVSPnxzk7xJkRxmRODhNroOghfQKlkvIi1SrFUibTjKRxKtEtMnGiq/AtYiwFOpTg\nC8M4s1MwCRVgwBCEoRRMmMUewcnhhqhaTbXETALLS2M70jSFOPYU5/MkEiTLWBrEySGJrFhJVuLQ\nj/BBoJ3KaswsJqAzt4CSxTgTyAtQQ8yBPcncOJyHMvQxZs48/PDa/2gKn6+y+8WQTchDBf+5jPeh\n6A/TLC4YV7/e8gpxjVfZ/TLOId155539/f2VlZUXfL0mJiZ0Xa+oqOju7v6F3LysrOzZZ59981BJ\nydcv7o3LCpZ00dZTirE2RhN0Wpx0uVUjbjCVRwiwSqIjQLyTvSkwKQigggEWN25lQCU6gBSiKUR3\nhs0BTEhYDPXyiQZ5IGs94+fsAWQvdzcggSFwMENvnCUBxBC6jJjFL3LsRRpDLNvCocNk44ghlCAe\nSMahEeJg4qnH52Ugz3tDjCrs6kbQUBoIKHg9ZDOsccXNAfpzTosXWVvyQGLsj5wlH7SGP9NXeEZA\nbWEFDBtUehjxsxb8JiMp7m4irbNPps8SH07xfY/T7WIdYF1U/v0m5+u9jijT3oQD/2Rzeh9rNB5o\nk4Om9XcSEfFdD31bE/V0ffDEQFMyE+ZrXew+AiKEWeKjKYxqc6yXlEWFnwUWOZWCgWCDBAHsV9nS\nRJnGj7qwdZDwNaJJZPsx9EueCzZEQbxU+iEAiEGIsjiIXGAoyQKNgMusTncSvGAjCLg5GmKU+hEh\n61B0yfqhD48X2aSQwjIgCikEDXcAtrAoiJNnuoAp4GYhUSx++LJFeJUUPr9kN89b4xpfsvtlnEN6\n8MEHNU3r6enJ5/OTk5OyLD/44INPPvnkZdzqm9/85ujoaFlZmSRJR48efeSRR7q6uj71qU81Nja+\n+eLt2ztBglLUCmQZywKLuVFyGq6GX2R0hvoIuT5qvExmmfajzrAyypFu8hY4YIMAMmYaXzVOEb9C\nQMMHvTarJEZFweMpnnDEgIk86g4swTrFtIfNHl4aZi6Pt4EzB9HKqdQwyohWY48xmGJ8lIbbOH8e\nj8Z0jooleEQmRiAIJpQgSKgVvD5ERzWBekyV/GnMWcoqWeelb8pdX6Gsr3R3nHVX1k7tE8X71JEv\nBJZuZ/aNM9YZE2pYrpEZpkYgV8k9FViTvJhifZi6OQ72uzsOCp9YyGhV0YiQsJzeOenzS0kW3DNz\nNKvESknX0ZsiOSC218n32fYzQvz80pNnWiqjU8sWnpmUtcqt1VPxAENJOM+ExYxMZZhlGv1jWKPM\neFmtUr2A4QFKLCrTWHOcTXGmSPst2CJTKcxZxsuRQkgO9gTYUA0OpKAEqmEKynCncM8y7VBwWdxA\n9TLOGYhQ56e8lIkpihVgMZYmd46CwkwV+knI0bgCj042Tl0rJT7MHghSXgYeXAerE6MXqQplKeXL\n8TQ9/En5zSq65hU+zzxvgfkM6RfJo48++thjj10w9gf8fv/v/M7v3HfffT/z4pLmfo6/CH5w8cgU\nChcbuYohFjfhURFlMiLtEoe6WRbkdZfrZLweXotTuPDbVgARTPAAqB5WeQiEaA5xJINfpUHFROzP\nOQFXbsDpzDidXk7v45aNvMfDH/cRcDgnow+w5DZ0A6BD5KXn0XUa2qht5mwf1UFO9bC6mfP9ZPIX\nm9t6NEQXTwRb4d1hduVIpJmwkBXhNr94u9f+bpYNfrlZsnuybtgrDjryg6L153Ldx5MjHyiYJ0Rq\nPAQFhnQSArcrbA1zuJ+jpvTbjaTT9l/ksQbETzQ4QgO7ReIpvJb4Z0Hn6QEm6nmnDxGe0tm/E5/M\n3Ztp0/jTDOsMnKh4g65uca2s7CYFDqfsbyYhAS6NDSyNkcowEidvoDbhCPhz6ApLQowdJpEHGUzu\n3YTl8oOXAVDwRHBkrAEcHcRLZnfZi950F8oQACykBkqaiEoEPWT6cXRMgWmJbPZi314AES4c+cqw\nUaXSyxsJlkfB5PgARg5M1BYqWxh7lgUxJuJYOkKsOLftCun2385bU/h8hjTPW+Qaz5D+gwWkt0RJ\nh8nQi6SzF1feAk1kkkgydgHVQ2WEaIDzeaojhHN0u0wdxoDazdzg4R9fhgw4l3r5XEiYAqgiq0LE\nYoQ19qQJq/g1sdFx/y7rxkx5q2Z91iKTpdDPh+4gYPL5TgJ+Ur2s3YxzobgA6k1+8DzEuKmevAc7\nh+UynOC+dr7bhXNhddHEE2KFl/EcUoTbPeyA4UFWBjg2IH6iGVVx/jnFqnrtw6bxZMFt9squ7TRo\nzrfz4Xus3MM584RDjZeAnxNxCn5usLmvkcdTlOTFOxsFJ2X/xSCrAmwJIkrsEhhOsq5AR4xcmm4J\nT4A2eEph+ABymvXb2KzxqoGeQ1URzcD7JatVdp/PloxYEw/rMAD13NXACoGCh3iWN9KoLkUfFQI5\nB4+FmqOQIyeCTiBIhcZIHPOSmZDaipXEMS8VhTeDAf0XK78BFHDARWqAGGs0ghqpOAM6mkgp5GX0\nLFwqAwloGAZGkrZ6DmvcG0FW2GtTl+FEBtNGcHAcAveQeZzlLcX+1qsm1stiPiDN81aZD0gcPHjw\nc5/7nK7rtm3/1NwlJYcOHbpy85aEB2lUeP1FDAkkZAMngOxieKELNUJliJCPBHRE6E9yuh8SeKIs\nq8ej8MoOMC8VhqkXzarVNlaISOCL0abxZIpNIVSkmC0+lTNfz/CuIFmR17qwXP70Dq3Qpz+qYqfg\nMDfdz4jNtIrHxRjgTCeEWN/IkMhCheO9XB/Gq/GDXhwXXKIBwgESKSokQiFaoFfktX6WKpwyxE80\nuj7N3e2Gt6QLm3zGzoKrehEsDItuK/xBKfkHJkM2usPyKGc7KUTFD9lyU8h4TEVOyHf5FzYlh/7U\npEKlTcWWGDAYN1kGqkY+hymRVWlXeEFg0IQuVjfyBzH+l06hwB1+RMPTIMqNhcI/WpU3C2NfNknk\nMZIQ5O313BxhMMd3syz34WqYMJSh2iIqMJShb/BiqrS8meEkZvqnPzoJ0YfjuVgnKWZxLpxk8l0M\nSBdXU2MoTdwo4vPSE+f4EWIhqho5nsA0IQcm0RCGB9sgnwCZ8BaQMWW2SzwGqTiKiptlUqFgF4st\nl6e0q6bw+YA0z1vkGg9IV3wP6Utf+tJnP/vZyclJj8dTUVEh/wSKovz6r//6lZt6+98OkZhmWR3p\nUyDgFFDqMGy8i5ieQ3JQPMga43nScNNiRgymyrGOMb2I+ipmHQpcOtTpXvSqsV0mfIw5iBaynzaN\nnhSaNjdYuuhd4xM/CiGmWehBqiQ/zPCk9keLtPIJo9MDNkNxOlbRb2HMEgnhlGPEmZZZWotapKSW\n3vM0mihLOD8K5dgWcw4VMtMFsqdZsYK5KWZLGbdo8ruvTxRDAerFiV1lVQtm5A1l00/0kbWp9+Do\nEzuhrYHqBYyMcT7HsmYMy5Vk128oG0X7Vb9cHF1wz0zpXbVzb+Sds1Us9nPKoL6CwzpFmYPl3LqA\nBQ4nbRYWqLLJVJM1+GEnN5cj27w0hle2lvsM3SO+V53987POe7xU5jhbBg59Zzk2RXkttyiYBVbC\nsVFMlemzTE0TqKWyFEfC8pIfY1aGMBjggzmYhTncSeRyUHEF3ApEECXm8uAHGUGlCGSZPcKZcoaL\nRBViGxBNBvaieal2mPUw6yE/g9HHdA3ta6lTGZnk/EEEkdML2QD6Qu6rZjiIWUKT/+EPvLlv0/+b\nq6nw+T2ked4iv+p7SGvWrFm0aNHOnTuv6Cw/k5Lql1kUxu9lYICRJG4aUUMMIcrYInY3ip+gxmw7\n57uo66BF5ns9uIMICm6Gm5rYq0PqJwyqFXybyO1BiuD1skKkKYah44N+jQZ85HJ/47AkxZ0tPN/P\nsS5u37b4cXfm/ZncCxp0EvJwy128kKKoE41x+hlkWL+RNAgiRpYTCdZEQOdoEgQCIQyHiEw2gU/j\n3g56dHICp/q5z8+P4L/HsOCwvqKlczIUTn8ryfVR6gN0OyRMtgUYdPheEqOb9ds4ZLHGogO11WMk\nDI/mLNhoTWY1u1svZIKEFR5P0WCzt4/bm3gNrlc8jXbhSZ0QGCJHU1SpjOdZr6FpvPAq3hbe1oSi\n0g69/dgmix3+LkM2jGHSBDe28JDM13NYCl6F112cAQo9LNxCyOZElkTy0sZP4FLUNxAKuDqAN4Yq\nkZMxHaQ8ko2tY+tIMVBxCrhZ0EFBjeLorA3Q3EAhzUtxdA9uEte9ZBcURBC5wcc+CV+KGh9jIVZ7\nCIo4BZI6zWrxry/HZ/NqKnw+Q5rnLfIrnSHNzs5+5StfefLJJxcsWHDlZvl5bP+D5ymWMHyaG0NM\nqBhp3GnEcuwSyqcILmM0jllOhc3UcqYTBC93CqsAACAASURBVOooU9FTMIPgo1ggUkZGg1EAHCgF\nmbI67CQzAuYMCZ2pKhaW4MyQL1eaTKal2UNZfAJV5cw6HOqenFxT89Gy6UKlu0Ci9xgemaYACR17\nhimNsaNUqsgKTjk1LrnTnBvk3lasSbKVGCOsjVIsoHk4fQagLcSBBOMe8klurebRM9wW4pbyse8u\nClYfL98cnnz+HN1TLI+wRGVfhnUaWg3DZQweoX0ZR0SxQ3MXiBXhUn3ImT6ntK89OllWW1aWm96F\n9G517jsjbI2w9wQN5SREKhf4bjeNznIqi2RHmT7P0gaOZxnwU1WLfYzuEyyuoM/H8gUsdHgiwaZG\nrCRVNfRPcugIe8t4Vx2BEfpS3OvntUpGBHIncQQchSkLYQ5Xg1kIQAGxhNJFlEi4E0yPMpFhUZBA\nBYbAVCkVXoQy5kaZHaSmlurlOCrODPZZ5ABTCgcHmHAJrgOT0noYQ6zCmYJxyqoYNigZxwgQm6K6\nSO9hxCmqVconcT0P3/+WM6SrrPD5DGmet8g1niFdWaeGsrKy0tLSKzrFv4pGvp/yEK+nCDoIXhCx\nUogOhgc0Ik0AuRR+kxk4nCGksbwVJwUWiRzVHiIghn78jzJ7UCTkIG6BrMBknjd6SKg4OpDr8+ET\nWBagK0NS55YmpCBPdI0+5636ksitbdQ3sncfepLVGhM9TCYQO3ijEzMNeTQVNQbwjVdZ3kGDAVmO\ndVMe4rzNqg5ej7Mnw7oGltik/Pwww43wVz3sgI8opw5srEylmm5WeKOX5/oYhPYAT3XTE+e+KFoD\nL73I22wnITsDol2Q1GZVaTHPpuo3duwLt5kL7snZ/yep/nZISuRZHsaNkU5ZnXbmxXql2VQKDqUx\nNA8n96F50fJMWFRtA50XdmJ3koYjAivDPNHH8gZWmFTJVN/FmTSf6mK3n3tj7E4RyfA2BU+EpImR\npbaBmq3IYfBACkycAG4UsQEhiqABJLs41YNPJizjgiUhhhD95AfJ7MbjEOsgtBXDZGQQj4gVxnKZ\nyFNlIESxDAJhgmFEH3YSZxApT1eAwwfQbSZ1DiZ5KU6i/zIUdrUVPs88/6m44ntI09PTX/3qV//9\nPvyXwfbt+0Gg6CIuRANlCRNTFCdxTLAp1OKpJjeIKOH0U7uU8y+Rq+D6ZYyC0YsY5uwpbtmA2MHY\nKdxpsEHDzLCogfFJilUwjWYRn6KtATdFPjUr+fD6GElgByg6rIpwwpw7Wi6WVAgbXKc8wrksPWnC\nLaAxdhJClFYwfJT3dPDaKE0Rpmz0DP2nedsNJMYxU4wZbFjJqREqaxmpRa6mXmJ0hKxIapiGOo6n\nmK3lv5Tln9KXRvbXNK3OvnycqXKGHPEmH5nx4uAYt63ArCY7TXN5MSnNTZd6hfxo6WJbltLDi2+K\n7T0zHjNiC+xvnK94T0SoqnIGcqyLcfQoJfrsTYvL7jXKjozN5pYyp2ENscjFW8noBL4FGAXO5ijk\nyLvkHVpaODKE6ydiIw1T9DEzzclp3hBZ5WN6irlzvG0xiRQjlUyeZ9EEkytwJsGLWIqbxvXgVFC1\nCKWRWRFsirNMnGNijvLai8dnnRqoBAtjmNFT6GfwRanZwKiCfojJFEEJRaKkCruVyTz6ANVFTI3F\nzZhgD1BegVNgdDETB3HGmKt8+JPLLkNmV1Xh8xnSPG+NazxDuuIBKRKJdHd3P/roo21tbY7jTP40\nVVVVV27q7V92sdM4BYpQ0GlcgF3L5DhMwTSUU8gRqSE3wEwZty/DrSZ1BH0ZG5YxlMLOU7KUdJyO\n5ei1TFyoP55GCpD30xgml2BWQ6lGNjijUyOzsBw9w2wVi5ZwqBPvMs6VUDnHWNI+5VM3O6wTneoo\n+UGOGaxcijvLxDlEH0WHA6e4t4F4jrbVjA/jVHB0P7d2kBhlZpQhgQ3XMdJH5QwZDUUjUMZ4DqOU\n0XPcUMfrKfFsUfmQ7+zTwtKVh2taN2T365RZru2Uti4S5Gl3zzghhTnom6C9XFpbnDCqPSMFu1Jy\nfOKJvU1rV3aP47feVmd/54wg2OKDtXPPZVgcQtR5IT4baaz+epUwnLMPnwUZI0j1GTSF5FnkBYgW\n4yFyQ9QHSPVTu5i0yolJrg/jnIKFqIsY7uVsDeJCKpew6whlReRSpl2MKSqSeBbh1KBqzFYi9OHm\nmfFSJrJgIcUiMxc8WCexE9gGkh9Hu9TrqAossDHTTB6lvoTrN+HUkMoyqSIWqZ+jtZmJHONjCLNM\npKhuZTqD5kXbiDhJZRlzChOZhx++nLLvq6nw+YA0z1vkGg9IV7yooaOjY3x8/GcOlZaWxuPxKzd1\nyaIjZLov+qt6I0gyikZKwj586cRlOxEdUWcgDX5+dxtP7CNvc9P9ZAc4P4CqkTfxO6y5jT2HmTgA\nImioLcgBSJEzQGWZwLkjqI2s9+AUaGzglILTz6hLPkqZjdtPSmZxVP22Y2uKvdviD59DbOC6Zt7o\nIZ9GimEfxhfgN9v5pzwbVfYeIS9g9rK+g2MpLIdAO00++vZR7iXWgQyDac4NULCQdcL1lIhiwCt/\nRDX+omfd5p6p8q19/+DgOCxWpXBYEHRrd56wilTA51XvV20ke1DyiAVLlj1ywZDV68JdEwn1yPPt\n9HRKHXZJa/vM44OEDDxBnt7Fza01fxZwOo2p7Vk3kaKuHlzGM2BBBEtAUHFzqAUcUMOUh6mwqHYQ\nU/TDVAHXA/XUerESjB/GziEGcZKoUSSN6iATGewMth9XgjiOB8KERDSTlISehhxYoIAfFBQRRYIg\nuR7IwQVnzDDtfmpj2AaZMOkUkwXqDc6AouGITCURNSpcpppwdhOVqYlRyBT7f3aXh3+dq6nw+aKG\ned4i13hRwxUPSKdPn/5XRpcvX37lpi4p+RuiNzDwMsTAJKphagC5AnY3iAh+BImIQ14nbxKIcV8r\nX/020lZW+zndheLgKOQyrG9EbOLgDpwcuKhhbB+aDwbJmawMIXo4/hyRDmpdwkEKIi+9DBLXt3Hc\nwxKZoS6UJm5RxfdTKjHzYo6vdlGzkaEs7iDoiC04B2iNsbqR103WK/zoCJZEIU1lB1PdOA6BLTRJ\n9HSzNEprkBxks5ztY9bD+QFCXqq9YsDLBwLOZ3eveWhQXtFy8PM+FsgYyCFJjLnGt3UaPWh+LNQP\nGnZKsl1J7rVoIBRMGZIqGG4+Z1qPyzi64C24W27gxQxyioYG9qRo8Vf/T1fMpcfu7MXIcVszo17O\n50j3IoQRwqz04bd5YxAHbBlNY7WX8yZLNM7HyYrgxcizoA1bYKYfO4/t4qRwTWIxCJLVsRysLHIA\nwIzjquAjKGJZGBJYYGGnwQsaWJCBJrwa+QQAedBBJNDK2hY0hd4kNowl0Bvw6ZAm4+KNUS5RalCp\nM6RjHi4WP3oZMruqCp8PSPO8NX7VA9JVpKRkNys0ThWgAE3QRwDECKU+zvWiKhj9iD6cFNEAAynQ\naGnn1xr5ZieTTYQFjh/G4+Bo5OKsv59zBpkdIICA2oCt0ODS2w0q17WSzDOSIuSnRkXSKJoc6QEf\nq5pIe6l4EdPDgg62iuUPUxwQ7GfT7DTRBc6ruHtAIRRAT7M+hsfPMZMbBfYUmM2TdqiMMPkqrsJ9\nWxnTiWdYGqY1SAH2p3AHmPNejEmGyLoQG708H4+s6am8+4YTf+NB1sjrYkGRN9tGnxclixhiEPXj\nhuMVLUsOxDOWJDsDKalJkyzJku3C41kOKqgFPt1EQuBAL00x9uvMCvL7Lautni/uoitOwMu9W/hh\nJ4Mp8BBo485GChle2o2rYAM+loRoDNOdRsownqZggQ8tRrmfInAEPQXNmAOQRRNY0EZGR5SYTCOF\nwcDpwzUBvI0YOqqPsgDFHHoflh+8kAKR9gaWRXglTzYNNgyCQaQZT5Sb/GREunVOxVFdjAR4IQcu\nsRhlQaDYHbyKcr0M5gPSPG+Vazwg/TLMVa8W2//3ayy8CXOY2elLXcNdrFFmypnx4V2OMYKbQQzh\nTlAhMa2T0VkWIaxwIoUQYtECxoaobSWfwj5DbD1WESMFZQig1VNZwgKBXJb0NI2rmClQ1BgrUqMw\nZ9K8hMEEMzm2eclVMhlnZpJccM4RYq2nsvISRkZJTVKmMROieBw5yvoAr59AcVizhJzMagjVkR8l\nb6KuolSgrgFVpgpOn8coYVUVssYZh/IR5CqmDQrHOS9TKOd94cIzzE6PqTfXmscsFM2VBfu1aXnR\nuDOziKnzhLHNcrVmWlasnN/n7DTdjbXGo6eptgXbb5kWp/qww7x2Fo/EXAn7drGpHHnCmS3FmuHe\nZVgLOdpLSmfjNsrK+f/Ye//oNs7z3vPD4XA4HA6HQxAEIRAESYiCKIiiaZmWaVmR5V+y49ix87Px\nj2zSNHuztzfZ3rTN3mTT7SY9e3eb9uY0ac/dtNtNt7dtkjZ1rh07dRxVsSVZlilaoimKgiiKgkAQ\nBEEQBIfD4XA4HA72D9l3021vT+VbxW7Cz18Dzhw84DlfnAfP+37f5ykUsCYYSbNNU//Dfl+K+XYr\n6zZLaRYWeXA7rk1zFTcNslLP6jTlIm6FtSBKgKY6vAaqqpG3sZDFPU/jNuoUnFU2bKpkKjLYrM2z\nabG2yepVOm7m7jtRPUqjeCVYY3aJvMUt63S30rDO2l1s1mHYzOU5PU5DA/0NDMYQPJY7WZ+HWlhg\n4SrzF5i/8A5fXv/7bO0hbXG9vMNF/lNKSIZh5PP5QqFQ+ru8henO/3S+/KVXMZZJ7KJYgAxEcCu0\n7MROs+Fi1RMOYKVhE6catZ5QEmOSsw4fTlK3yukcHV2YU2grMEjxderXUfaxVkCUqY1RVUEUiHXR\n6FBYZTnD7T2kFmmQKYkIPnW1bJP5UB/n5lBUtBjTJ9nUSTdIl/J7nszNtOxhPEfJpmaJ9RVW55Bb\niYS5PIXsk6/FiRAqsbedmTkaNFoUsia9QZDRqsnMIIVIVlMf4IqHKFAVRhMxzrMkM7NOUnW+s+iv\n5ps/HVo941Opo7fZO70qt8wT9/w9GhNrTk2NjK/WWtZAc+V5s+ae1rUfmb6Z3UzXUC9DAU+kXMDw\nifdzZZaVWnLrOCbVtVjtVLeSLjJ+jFvj3HoTlww2l7jquyMNwidr/T0bzFnUNrGU59Rp3r+TXRJ/\nfJYaGUI4NWymqZtnrQnVo2kdT8G9iXqdOpnlHBuzBBPUg5fBW4BtUAsrYMMmC5c4v0QwymfvpFXD\ntDEKrLlccbhyjkAdGnx8D/OrrGtUrWPWcuZ1XjPZVk1HM/XbaLCp1rHnr43L+m/5rr49Ct9KSFtc\nJ+/whPTTWLL76Ec/Ojw8/Pf/fuO3fJ+BDM33ojtcyaEqtPYzb9KhcWEYAoQTMEFhFOJgsruT6RxW\njughvtDLs2niCY4MMZti5yCTGmtPcesHuZjHGiE4gCjgWezsQ3c4N0YuRThKRzdnHXQHQyQc5PYo\nlye5NcLpNB1Jzo2Sd2jVCUWj8VTHnyqvfHeA/2WcbUEupbBsEOntY8NkPUNVDDVCMoyfQy7wcor6\nu8EBXTqgYRvu0SJVNjf3s0/ihMnZYcQe3CJiiXIKoCfK7jgv23Kf7ew+RMpAUNEUxlM8BI6JHsPT\nRK8sqiEEvCdF7/MWPpTzeCYZleUsdx4gLXD1CIToDLBD4OUschQ5R1cveRkKTJ/BEjnQy44EJ7PM\nSVDEQv6g3fSp8NJ4p/N8jqtZ8jkejXHPPv73Ca4M0XkIw8VIgQARiNAVpFphDroF1l2MSRwbY4Rw\nD7Uq81M4/6XRqgDXzrEadPewq4cHguQn+XGOiRyG8EbPp74kmkxjLxmNcgHXZMWkTmN1Es8lGYAA\nVSpTZTZSlc1PvDWlvX0K31qy2+L6+HlfsvvN3/zNI0eOfP7zn//0pz/91FNPvfTSSx/60IdeeeWV\nSqVy9OjR+vr6Gxf6y18+AxJraUL7qeRYTtPcRb3HnE9Ew7iCBbf1UVnCmIEeFkbYsZNlk6UZCqs8\ndgsylDRqNMaPcPMgxVpKQ0gKTh2KhVVDXYDyItFuKuv4LcynCIdp8snXojVRybLaQqKRisN2iZVV\n+X/a7k3q5LJIsmlEG3O5rruM2ZEk53O0Kay7uIt4jSQ6WK+wkcV2cRqQZE5f4a52Lg8j7SCmbs6v\nNz5etenVbV4xuTKNLbLp0RYgP05NG+seio5TpGSysMT7d3kvSfirvDvC6VmEWnqDnBgjVEtuho5G\nfyoIBanW5VgtH5b9XAZHQ9Pw02yL8+IL7GigKYFpsZgivUHbAHae7Bzpafo3ad7NTJH1TbJ1jBa4\nuYvAInUSjbJ3RrFea5TuXVce0Nb37kLp4ESZPz+mPtrb/Kjmjo5vhqEQgTnYABPjNNIKwRCbS0y7\n3BSkqQ27wmIR4zLhGC29rK+zWYXURXWQik1FojzH5AQnlghE+YWbuf9mqnRm8mw0MD9PdobLF2lb\n46YwhHFgpQN5GRcWgiykWLhAtQp86TeTb0Fmb6vCtyqkLa6Pn/cKqb+///777//KV74yNzd36NCh\nVCp17WT75z73uYmJieeee+7Gha6qOgPjIKFHuKmb48+Cwu4Hmc1Qn2R9iNI46iAfSPK9H2C5EKTT\nwdPIZcDmiUHCg6hw0uTCEGqIpigXnkXSoBvjDIoGKsEwVd0kNYo5Lg1h5bhpEBE2gsz5NHooOjtF\n9YCt+qXCDzz58/3Ov01T8FEtZO22x04sdx2a+L/CXMrRJnI5j52h9V52y9TkuZTB9AlpNPWwkuce\nnb9NcfujuGAb6iHRHbbdp1MIabYfQNXwPS6M0LWP11PoPk4Wx0QIsusA8xKOwS+FGZFxHCIS5XES\nsGRyS4KRIBhSXPRGNT+i0JPjeRM/xGtn6IqynBd3K+zr9v5oDMlFVblpALK8NERZIBjC6MMfwzdR\nBEIRbkoSCJCCbkhbvJ7jbod/E8fXeKbAqwVyEm6g6xtlc7q0/KeTXqYTLFAAMMBkX4yGIOcFyh69\nEmhkshg25InGqE6yUsSyaYyhhJgbxs1CESCQ5LYAT/YgqTw7ydMWVh4KbzRrJ0D3XgiyqOGPs2rh\nXTPpKeBUKh9/CzJ7WxW+VSFtcX28wyukG9s6aHNzc21t7VOf+hQgCALwXyaPfeELX5icnLQs60bG\n99E6CfdiqBg5btoLNtOjtCZZGqb9AGoUa4TjBd5zGCwQyQi0hhkYAIVvDeHn8EDVqEqQE5jx2HUY\nI4dsIEdwFbbFIY5gMG7SrbEtiRbl3ARTMkKaiMKSTTmLp1hikEhIDVrOH6bV34nRLVMJ4nD6D+ON\nV0/2PJkjFGbFZWcMJcj8US640EljN5JELs/SBA0Rflzg5gTHhrBB061jntivineE8cJcOYll4onc\nspf5STo68WTkBEIUMc6F52ktowT4oyKaCBIGiAmOaVQ0MgXC4xxS3NfHhLsNYdZEj/JAJ7kce/pZ\nyNPY6Z11xNSE+s39xBKkixz1eTnJE5/gQITSJN5TiGHEBHYBq0BxkskSPuQgqfIZnZUQHxriVJoH\nwtzWz0e76Sxc/e1AZVuy6Vce0j6uggBTkAIdOhnO8OMTBIvc5jGZYzQDGsgQpiAyOwoGTQk8A3+C\n5jhtB5EPQ4Jylh+e5Ik/49eOEQjyhb18cT/xfsROUMFh6ihToyydojFERKExhpR8c87IdfN2K3yL\nLX6muLEJ6SepqakB1tbWrr2UJAlYXV29kTEz+Aa+ha5yrkQ4SjiMNcniCAGZ4ijN+5BkMqcowJ0H\noIh8gNcdmoJ0J0DhO0ewTeKm+P4oooPjYClsH6QwiqziGcyVaHHZ0NBMxqFT5tZBoirLo1zVqMpQ\nG2Qdyh7HpIIdYGCvGsw7Pyjt+R8n2SZTgdro6a8FG+eH1C6L9Diuw84kSCwNMeXTILOtGzXwRk5K\nT/HcEEmN0SFKJoJiHy2IBzrF9/XihXn9eRZTvHKGriAbOVrjyGXEJO4EYpwLJ4iOsTvCSwUkCUUk\nKVOt8kqUa2dbj4p0Jb2/HRdvGRWPF/mPo3xCYyHN9kHmUgRl57Rk/esR7TFF+dcD1J1iPcUzsPMw\n9xxG13BP4J0BkVKBWZN8DstEBBP8AA9F+Oxh/go+l6Fgokp8tJ9+vfyUuvCXirgrzq/dzZ2PI+6D\naw6UIPSQSvHKEDGR22TsEbAhijeF6GErLA6zWkINEjbwPNqjbH8Q/Unk/SCSHeXr3+bfP8+rFk8M\n8MVHuWM/PZ2gQgY9RDZNlca6gRqBAVr/u/9Gtb0dCt9ii58pbmxCqq6urq+vP3fuHBAIBERR/P73\nv3/t1jPPPAPc0BV2xBhWDtuAKQhz/AxdPeBRGsHyWS8huLQeBIPjJ3F7UCJ4L6IkealIh04gTMHk\nOycwTd+3xMe6scaYLxLai7YfIw/g5piyqQV0vBxFhfESdxxGV1nNMxOgwyQRIJenKJCSrW4pfCAi\nr4wsnQps//AU7VHWoTFx+rfvtdYkxD6mM0gSO5O4DvMjFALUi2/kpAWNtkE8eO4IUbgwTCkPqvN8\nRuwJiU/sRdGYmiSscnaSriDhMu/qRh9CTOLlUZOkZTZf5C6dV3NMmIgeH4rxLpmxTs5ohEpkTaqj\n7nHHW09zn8b/muHdvSQnURKUTAoF0MzfG1XUnPKLfbQWmB1nuIDezbsOo0fAvtZmjmyGqxZzw4RT\nKPAUHHMx4GNx7gjyWppvjvBMmqtTqCWcUvm78EKGRIlf6UO5G7GHN8orFWAixekU3T3cESCcg304\nKs4YnodrMDPKxSkaJhHBNhFH2d7Djn+FchgU7DwvHuMrI/xREaGHJw7z7z/J/QfZJRHRmM6xs48d\nAneGacy8BYm9zQrfYoufLW74HtLjjz8+Nzf30ksvAb/1W7/1rW99q62tDZidnd2xY8cPfvCDGxe6\nqmEUqwiTqJ2IIkYYrYgChRToRPpwsrTsZyVFPo2apLWP6RcQA9CJanKrzg/HYIqbHyDsi4cSjOS8\n72XY1ctlH1I4FpSQO2nqJBKhUyKTZkVkXSABLx+h/TDTE9zZzZiBJhFMiJ/0RcHXOw3rL8d1PeLa\nxeKZvbyexwFk+iQmLKRJ7jrESJ7ZU6hRwnsJF7kqMD9FUyc6zA7hqqhRxCJ7+pEVvLKo+d6EwIUh\ncOjswVVo91m12RXh+ClKgyhQayLILJzkQ49y1KfK4BGd7hgpm+fHWCnzbw5SMvB0XskSydGu8coU\njXsJwRUT28MooASwJ4Mf1KQnDuX/T5/X0tySJBbkmMX0OP44WKCBCXH6k3To6BKTAlNwt8OBIDoc\nsxgtcnkS22RvlFQIy2RXkBWDog1xcNBKNMaYy2KPvNnqyWPHPlyYByeIPIVn4jls62XdwMrgRiHJ\nThMX1kIYQSopNs7gm6Aj70XU2RYkJNHjsg/GJQyX87Au0i9Uvv1WZPZ2KnxrD2mL6+Qdvod0w112\nH/jAB97znvdc+5145513rq+vnzt3bm1tbWBg4Lvf/e4NDf3lLx5D3YG7hgR+DHEVO4hWS+0mdpGN\nWhrbWEijdaGalAyqaojspXgKOYpXRX6ZXR0UchTSNO7yq2zhUMTPu1wepmsXK0H8afxavAyST6vA\nYoBINTMZwq2UF3lwL3MCLTt4bYInQ0xUqK34y4K427PGGvVbVPPI6/XBDumOkl3QmS9QqWO+lkP1\nBDxmprhlD6bG4gXcVSQFZwa1m9IVqnSiceavQojaVuZTNDdjuf5r4zTX0bydhQJGmdYIszPUKWzC\nLbtp2eCSSXWUqjItMc6bdMSoWuK8RMXk3iBdUZY3OHqFlm1oDivr1DVwYYGWnczmKJr0tpBbhlqc\nqwTjdrFp5cSE+r4YUmLz1CzjBvUFnDAo+IuwF2phCkPggsfVPMkV4hov2xhF6iTqVHY20dcNXbQE\nqZ1mcYEljU0ZuR1vls0i1iZLM7TWcvsB5GaWC2z6lBdYXWNbJzU+po6/DmVYQ2um806cPN4IC6CY\nuA1sjLO+Sbgft5FNGy+NO0t5gWp4LcAVk3NVKGeJuXxQa+xf+Xyv/BZk9nYqfMtlt8V18vPusnsb\nqar6LnKEgEi5hN+DXGIzzGqGNpmlFLYJEeQw1SE6HWbSmALbD2CUWTzGnr1c0egSWLAongGRxH4c\ngce6+eZRGlRqkmRtvBR6iNII3TG6eiiFUXKUHFZzdEWIBTjjYAnMj/OxAX6coS1Gvywf8L0pJbw/\nk/udUnAgZAc9+y8UXs/iB1AdvhjjaJqST3cfJ7MsjKMGiEoYFlVx5nPsDiAKnB1B7qFeYTGNGmBH\nJ1dP0dtJ1qXg447T3Ue5RCDGcjf7HPQS3zFRNIQM2iBOkW06ywYFk6a9PC4ShOcnuJCi4V5iRWIq\nkyqLkxg2no3j4SVRJSSH8gT4IEM28GthN7TP+ncyssAdHkmNvx56owaVA5AGIEIwBgrvDlOUOJWh\nW6Q7iKYQhE6YguEcCxOM2kQToGOamMOgQJigQVilvZ9cjvNDYIKE2kd1L6sTeAUwCPSCRytseKxo\nLBbwckhRGpJYOVRYzCB64OGZEAcJZIJ9NAqIDtM5ApnK7PvfXsVeL1sV0hbXy897hfSxj32sp6fn\n759Xv3z58ic/+cmPfOQjNy70l798EdljsczuQYrDbMYRp/BbWXepqSd+O9YlHI9qGyNIZy0Li6zU\nU22xXqF4gcQ2rkq0eHgV1ldp2smCQ97i0V5ePkuzQ5VAjYHiUxMlN4UgUCMiqQRqWDHYsNlcQRTx\nq0nWM+PysQj/eQw/4JVrBMk3LtXKCd98Ot3YGuSO9Y0ZhYU5KHLG56NdFAwKBWI9FHTsDI5IqA5n\nnuU15msIaURbmLlEsI/6DoxLrNRw004m5ph7ncoq1RFKU+xJMjtBg0e5DauWexRGj9Pbi5ujqp1i\nmftk9E2mpzkjEa5HD7JUw9LfstiK3sKUye5uhA2sBQQbt4BrsraG3oS0zvoydKy9Wu+WctxWxSq8\nXs/IOEoeNYRl4xVoS6K2sFki0ErZH+NOYQAAIABJREFUwoBd1SSrubrBpRKmjdnIMkSgXeOOOKvV\nrE8wPUNrI/oAlQrrafY20dbB0AV8j/AhGlowCrhXWR9HUfGr8T3WiqxFcEIUXmNDpOc2goPYxzHm\n2Zxms57qIJU2ZA23iNRCJUclgxzCsSm6aI3Q8aVfV9+CzN5WhW9VSFtcH+/wCumGu+wuXbr0Fm79\nM1FGdQBmxtjWjXsGN0G9hVvCzHDVZddhKOHYVNtccunspDWGEEPVIcz0GLtULgMFCDI1hBpm1mCi\nxGcOc6FErYQtY5s06yhJpnJQwsqhBtndjwtXDHSfoERPhDqbH7g81sNsAdvzbB9FcYyQ/Cs9pR9N\nKY5ILERAw1VwTX43o/cHdEfBynJ7GHkvlknOxRdhCnKcy7IksrOHmUk0hfa7sYucTlPyoR/fxJtC\njDCeQ4rj5Fk8BSrfFbjt48znqBFoniJo8iMHU+NhHWGS4SxajgMhdr8fY4SXh9gJtTlMhZ1JRBvy\nMA4qRoqOKLEI5MBh1Of7p3gkwy/m8CYohAHiEspBpj3WTbYnsdJ0+kgW/0+GpzXiUVoVZiUSPj4c\nhSkQ4de7eey93NNLbZqZozRJdDzEVJzvD7NX4qM9tKbxDd5zmPccJqBgpfAmoAwWpLCehSiCTDZH\ndohAktoYt38QGdwy3jiugNiLqyD2ApRNyidx09SlUN6iP/ttVfgWW/xM8dOzff//cF23qqrqxsZ4\npI9CjrBG2WTdob0HP8NKGb8AHs4ZijLt/WBh5dmugsaqRWMAEshBLJg9yV0hHGAKPArHUDv52zzP\nuDTv4+oZGsLYPmt5FAE1yrkhMEmnkBSao3gebhACnHC4N85qhiI8EqNg4fgYLgHFSWnyw5HSH0+h\nCezsJRDAEnBN46tHm58s6ZbI3Bi3h5GT+C6FEnrnG6O+MxkyIhGHS6cQoH0/rggp8OAgSHg+DVFs\nEUslKHDhJD1Z0iZt9+LCnEmdj1qk5POSyc4E8zm+k8e3eUhl+yAKHD/B8xkCEtM693yYvXtBhpMQ\n5XyagsOOBIEw+KDz9TQ/znBXH8o4hSLpMsE0sSAFgUsuwRi1PhOjCCVqSrxoMxVGhG/kSVtEIVpg\n2OFPEMNe44NB8ZEHuaefzSlmnmGzROuDDGn83gl2++InusWVAsc9dg8SG0TSwAUHitdWEXHOsJnD\n08j5CCpjNu0qtSG0CFIA1UNOI/hw+I3TuIJGQWX61D+v+n4aCv8XjnpIlzrfyr7dFj+riDfofaen\np3/yWlGUn7y7sbHxxBNP3NBhmgBE6Y2SyaFGKZpEVG6ROV0CQACb2SH23E2NSTbBcgZVhxLLHvU6\nThzXpligOUNbktkUbgHCFE6yo4fLE+zswzvEwkk69nN1CBU6urnSw5UsN/UykaIzia1TtogpSDp/\nXuCXBvjOGEYPcZ0JA00DF1dxRj32BHhtiHcdhl4mJ1gcAefKF0Zv+XPjym/3G3Np7AhhE1WjkEbv\nxMjiu2xCKUibz9VhunoJxCkLMAxRCBIUWCkjBqiOMDUOFtMBdmpcyrA9xsIw8xZdPVycwI9SGmZn\nH5c8vjVBV5m9YAd5ycadYtphVy/jJnqS22TOTuCNQxBX4PIY7zrIQoAJD1yyBbJT9MXJiZSnyOYh\nDQ5qJ1aSKZOufQhBHIP6Is0hesJMWFwoI0MyjFVAx8uGly1dPWRVR4Kr2oB3pshamvlhAv3UHeBb\nOc8cUn+vj9/VnFM57/+YgjjIMAnWm+0YwEpBCkBOgsJFB9+mcoBYmpkQ8W6cMiuTiD5WJ56CJ9Nw\nfbMn3hkK/5eNdcxQD+mAm3He7s+yxTuCG2VqeOSRRyYmJv7xZ/7gD/7g8OHDNyL6NarCQ7w3yfef\nwoa2QS4d4bZHmR0n54ABHoiEBnm4lx8PUdDR07T2sezghNgh83KW2hIbKXYlAS6ncH3UPmoi1NkU\nYVeE6QL2JL6An0GN0hpnOkPQRg2xcBDJJQTRMqaOIFIp89EQv3uGrl4iHmNZklEMkExm07RJTEJn\nkh9NEBAojYAD/vZv9k//ca+XkSn4dBcppLFK4L5x6EeMI0TAxLNo6WZDo1yEU+CDQlDH1tjWi2mw\nPIFbBoebBpiLYWeQHMrj7D7MVbCLUGB3P1GF08fYNkhQRipSULk8BB6hXtp6UEW8AhcnKKZBhSCY\nqFEiMYolDB8MyIIOiTeyUfggtVnmJ4l20iRRBa1JMiaREKr0xujXrEna5HCUboSMj4+vCbiohy2G\nDWdK9v7oFI4LHvognohj0uvKT6huTse0/NfSjBuggkEghCXgTv5/yQlAQEpCFNdFtKiPEgozk0KU\n6fCZGSYawVQqMwf+6ep6Ryj8Z8LUoB7S3YyzlZN+OrzDTQ03KiGl09fsVTz++OO/8Ru/kUz+nbaV\nNTU14XD42sn2G0dV0w9oirMXvneEWwYpwOw4t9zLlREMBTLggce7P4FvctbEEthpMK/TqqGo+BKv\nTVIrsnaEOw6TN7k6hNwLEZp1Vsq4LtuSzJxCsPEERAs1SIPCbA5PgzANfQQ8RJNIGTOAarER4BdV\nfnec3d2EbMYCRHKUNIIms2nqJcaiuAVWXYISpREkCRT5M73eKwEvo1DwiZQQHHJnQAIDXISD+JOI\nMr6CroFM+RiIb+SkWJKuMKU45RyLY7gl1EE6gkgar59EtbCKBPuxNewiTILAIweZLpF3qSgEouAx\n/SJOCaWPpgFEB0rM53Cu7SqFAdQQu/tYLzA6iuSDg2tCJ1zLnSrhbjpCLJao9nChP4qikLYRQgxI\n9IMBp0ww+XgUB2HU9xMCOgRRIrZXEL3fL/o/KlLKgIXajSPgZUiGMCXhXWF/tcgVgcspXInAQcpT\n6BIUsEw8E2TohDxI0A0i5AkHqe/GEKgYWHnc9HX1sntHKPxnIiFt8dPkHZ6QbpTLrulNxsbGDh8+\nnEgkmn6CxsbGaw0obyhf/sIw4goXmqluYWaIgUHKJTJZ7riNq+cJ6yDiehQK9CRpg3SRRYd3hVlp\nJLtMUqQSorRBTSNXXyEUZrUKZwqxjrUKgSCuSUuI1Q0q64ghNpYRfJYtNnZCGUq4QAiphnKRkI9b\nT8zjlM+HovzpSa4u0bBOpZPoAldBrrC8hqpjqNTMs7xOsJOVAnVR79Wryq83+J7s18lMv0x9EC2O\nOf/G/1mZhji+T7XDmoPtQDMUoRo8lleoW2S/zFoLfpQVA8FiCSr1xKIsXsabx15nW4yaNfxtiHBh\njHAL3TLlMuUKqsmB+3CrKM5QLxENIkbojhBowaxmowDLuBazGUo6SjeVDWoE1Aib82wuQQU2sFYQ\nw1RXU2nEW8fyMXVitWyWOLFMup6QwH21xDWeNrmyUHlvI8vwEtSzsVCzSXXNk3LVwbBfrGI2i53G\ntwAW6jCrKuMnWGjk/pspqPhZNtO44M7S1k7bAYQAkshaDuqpjaMl8I4j9GMtYIH9GpFGNm0SyS/9\nD83/dHW9IxS+5bLb4jp5h7vsfqbPIe05QyZNWGcmyeYQusmew7x6BD1BRGfkDGGVggEu4RDv+SA/\nPEVXkrOneM8BLgisZRlMctyh1aCcYa7EjjAzZawU6t0INmYGPcHuJK9lkQwcE7+Ib0MIumEcfOhF\nNPHSEOf+biwBXASFeJH/dJJgD+0htG4iOU6ZbBbBprGPtElVAdshmSB1AjUOjvzRiJMWAV4aIZRA\nC5MagSJ44EMfCAhpfAMiEIQUYgghjjtCUOSR/YgRThmcn0ANUq2yPcS8gD/O3BBA670gsFymVmR5\nnO5ebg/xwyGsOAjs6UEzeeUIjsb7HiCgcspgdhzLpUZhI4WvA0idNAZRZeZGiPQjp0kNgwwDILJT\no18nLIq6Qtr0pmwMDSPHhoCosC3MoxIHIA8TJoLJoShDMAZJuNaZSIMMDKU4e4aS9OYY8tIbrVEl\nDdekO4llURLx8tQ+SO04UZX6JBkTT8R2qC7RpSBGmD6DG8QvoIcx8pW1R982sb4ltiqkLa6Xn9MK\n6R/h4sWLx48fb2pqutFbvl/+v7PEdlDIUAcbSawr1C6w607GjxLvIxHl/ARhHasWK08sirXEtMTg\ndk6Oc4fOTAPLi/RKXLXRBCp55iTiAZbKdGsI7VSKLM9RXU9zgMVl6h02NvBLsAEr0AHLUEVkO9oa\n5gxX6oirFKFRZ6OO/U28OoJXB0tMtMAGG+tUV7GeY1uMYD2rzcyOEe2jNI7a5aXrWFpkdZGenVwa\nw/DRulmrgAg21zqkVtbAf3N7P4FvsnkBMYltMDLN5QoPBWgJkSkjrpNsQ6vDC1Krsb6IOU2NQOMO\nljK034agc+wy99zK9mUm1ykU8FbZ+zCezWIWvYnbG9kew2zBsKhJIFhsTrFZwTYxy9R7zFkstKC1\n0SBgr4DOosd0FbLr36ZLd1cFYl5tlDVLx6lgrZK5Qm6THytC23rNIb9Saa786UWkBQZqebUWG4ZM\nXqhBqnB3iMGbCAjMZ7FF2AYRWGVzBaC8gLWMvwQCmxm230UqRXsY4wL1Nk0xalrQ2ykuEAtzew++\nh7PB+sqXvtjzzyK8n57CtyqkLa6Tn/cK6cEHH3Rd9+jRo9dePvTQQ5cvX752/dWvfvWhhx66caGr\nqr7GPYMsexg2RY2NKJUpbutkukBmiDufZHacQg5VpqAhG/zCIf4mjR7G9ylPcssBzpXQTW6N81KW\nHQqvvkigB8XCiNPuYMVYeZGSS2cvbQqiyOkhPPCudQXV3/Dy0Um0E3MM0wINMYiscGsCxSGY5z+d\nRBGxNdoOUVVmo0iNgy3QG0DUOWXhpIgOYBsINvU6VSVkm5ZOTp/ET6LEMUfBfXNLLAhA+U1Dfyd4\nkEdIENqPcxJV5aYggQDflzCz7IrQF6Do8tIIjIOMnqC1k5lTRHRqB7kyTp9KncekxZKHL6DGwad8\nhu0hPjiI6fGyz1QRv0SDw2oKxwQNMYl3bVhqN3f0IeS5WKIkEFFpj2NoJEzeq+17aJgsU38RKp+w\nuSDiOVDmtiiOIB30/Ecj3hlVODLuBy0Gk4ybWBpjLpenGFS5NUpC5z+kWEhh2qi9uDnc4t+1M1yb\nKhsAi8c+zPd+wPsO8NJR7HuxUuwZYLNMQ5iDGlm/8pdv5RTE26zwLba4Ht7hFdINT0h79uz5+te/\nfvfddwOZTOb+++//zGc+8+STT37uc5977bXXRkdHb1zoquo/wTe551EuDhMOkhaoDbLocE+cVyax\nMrzvIMdT7NIwQlzIoJd45F7+Jo/u40MxzC6R1zLcFyIU5IcTtKvMFbhpL6ePIfSyEwqw8CKuyvZB\n+n1ycC6FY+LlIARF0EGCENEezGFMC0yke5F83pPAcTh/ivQkAFHaDiEbzI3gBxB19vqIIU7ZuHm6\nIqwpuDlaQmyWhE91+i8IXBohryH24pz6CeuzAmGkHvxxvDxEQEaVsTz0PuQMokdHjLjO6znGPcIh\nHg5TyvB0DhwYJ3ovbT3MDJPPcucAistLJe7rZfoMWR1nCieI1AspIi4fOEBcI+XxrQmMCbZFAZZS\nOCbyYTwTLw8qsW7uimOmOGWy5JKA9n5sEOFx7cDdJ1XBGv39eOEZl+kyYQtCdIUoGBwUeSgq6opw\ndNzzRD8UQ9L4kxQXR3BMlDBalPZeJIWzz6MoEMDKIRWwTAAUiIADZfDBQQpw536mc/QEODMBEfJl\nCNIfrbweewsyezsVvpWQtrhOfq4T0ubmZjKZPHLkSEdHB/Dkk0+OjIykUilgYWHhwIEDr776aiAQ\nuEHRq5QUa0eQNZ64l28eYSBKTsXScDTuiHL2GeQQ//1Bvj1Fj885h0KZ3TKH4nxzhN59ZLMISeQ8\nGZd7JIQgi+M0Jzhn0RVgdhQpQYfLFRvrJKZGvJcOnwJcTuG4IIAMBdDBRwjS2svSCzg+OIj3Itjs\niNPpsDzJyTEAooSTGCM4DmIAOcZen3qZlzxcly6NNQ2zhC5I77O8zk7/WYerE+QdPAck0CEPDiQg\ng9iLUMKdRBlE0ylkwIABenzWolgT7AthuMyXaUwSFcHl9DjFYdBJxqlOYBgsvEg8yq2DvDqErJIX\nEIM4WQwXchAAhR6VO/uIaHzvCGM5xCSiBimcHGqS6iirWTwH4rSHuTtEeoiLFobGwxJykCxIGge0\nBz7yAiqjvx8o/HWZbBrCPBKhoDFhsl+jLyo9KjNietmAL1r4Gi+keH0ULBAIdlK6lvvTINC9F3IU\nclhFcCECHiTAgDx44BJI4kW5P8JMmlCQMz+ozH7iejX2Nit8KyFtcZ28wxPSje3U4Ps+bw4uA65e\nvRqNRq9dX+uOvLGxcQPDb6SoHcQR+H6W+wbAQ8ijGogu8zn2HKbk4jokOjlv0a4QinKhxFia23Sm\nhoklKaQRIuySeAUCBi29XDlFu8JilrYEpWGUIBGNwL1oJulxph26NW7rBUADB8JgoITwUyxM0vQA\nsgAy3lHcKa6MkJFpTHCg743PXDiJHEaW8co4WYZ8LijcpyJJXC1RZ7GnlzXcP1eF4+PiJ3Vu7acp\nBiEQwIA4COBCEG8Yt0DHAPUumwLhON0HkMfIDKNP4Hdzssh8jl2dCEXGihQk7kjQsx8sUmmmh4lK\ndBwg7/D0U+yM0q5jpfBc3AhBHVmAPEwxkeJbRzia4t2HeXgQz8KZwAmjD+LkWD6CLCCHUbMsneA7\npyglaeljW4LnQ/xVhmAJPcep3Auf3v/C//ZA/8HyA2cJf2M/CZHvD1HJMQhRjTOm+zuSOyn5rouj\nMZQCh50PEfwgSi+lPJyBEyBCmqmnmBpC1LjjveweJCyADyehE3Rq70ZIUPYwn+evn2E0w7FJ2h94\nCxJ7mxW+xRY/W9zYhFRTUyPL8rVJZYZhlEqlJ5544tot27b5iW/yW+Dpp5/+1V/91W9/+78+xMbL\nsWGBRqmArVIEBIQicp72IGjsUPmDKfaDlORqgS4FUeDlcbqj7NaZGmYgTHoUN4A3yY8Mgj71vVSN\nUKNgwbv6eWWIbUE8kcC9eDmyBU4LBILcthcKoIFLMElTlIZBvFEWJlH2o+p0PQQSTporI0zJNCbY\nl4AcdGKkkcPoIbw87knyU5wTeHeErghXRnn1CN1RajTvSsL/a4NDAfRumoMggArXzqtmwYV+5CTr\nfUSibEyyYVH0uOUhdg9yboTYKIO9VCn8zQu4DuESboZynro4tx5Gs/F8yi5+jMZ7aejnuRPMFnnk\nMMEcyiSlcZqTtA280ezDgVdT/OUQSpg7B1GjYGEUkQdQB3ByOENYOSyVgM3MU1waZ96jXqGtl3SQ\nl7PIRfQcKV74nx944eMPhCV6nksEv3mIisbxFKsp/i18GCZM/vMo3x5CDfKRKA/adCnc3A/dIIAG\nBgTemOxnpHjlKS4MIQW56zA7DhIaBZH1Ar6ApCAdQNBwHcwznH4royLeZoVvscXPFjd8D+lzn/vc\ns88+29PTk06nXdc9e/asqqrA888//9nPfvYtd5+cnp5+5JFHXNd9+OGHv/KVr/yDz1RV/SE40A0l\nCHFzJzPjdGo4IdoiDIQZhounMGJ8Ksr3CggSVXmmp/DyfOy9HB/CSaCJbMgs+9gGMYV7okxaLGcw\nNHZFMSzO57lnL1dK2BnyMaodKjK/oDOW5fQp1AhhifooJZ/lLFYKcRCxRHWQUDdXT4KNHEbU2K2w\nlmNsEmJQQg+Dj5EBDzFJJMYukdky46cgyq2HsUyWU9i9/FKApz1Wi6wcw/ahEwyQCJj8wns55rPk\n0REkN4QTQVCJB1jMkTuGGKJ9EBymJwhApZ8Q9Nq8atEVp5jn3AhKkB5o6+bloxg+qso9vWQcLgzj\n+ajd1MssZ3EcsJC62TOI6KFozDlMvAi94KEKOFN4EyCBRiLGngCvTmIGcFUkkzsGUG2On6I9gpoE\njVIOp8SDUf5VUM1Z1l9k+eE4RonHu8UOhSnNG7ZZzuGIHIqz3slFA8vAGn2zMDVAAwsksMADIEAy\nCXFyOTCxdMQAnoEQwnsWOiuVu/+lKXxryW6L6+MdvmR3w23fhw8fvnjx4vnz5xVF+epXv9rT84az\n9hOf+ERjY+PHP/7xt/a2v/zLv/zQQw+Njo7u2LHjvvvu+wef+fKXh6EGitAJsxTWCLZzcY6925j2\nCNWzKUI7K2NYnvrJJvdyPRs+gSYWZ5jO8diDXLbYnsS6wgZUSxQK1IDcjCmzAFaOm7qQq3l5lB0R\nFqrYucmcjO+Q8rijFdOheJ5AGxsGeiO+RuXa7O1WNnJsVEjuZT6Pvot2iZFNOgN0NzF9Aa2byjxV\nVehxrAX8eao8ZqPsEAluJ58mP8N2jd2DzL3K+SYercFZpmBQ47IxAyGoYi3HmfPcohJqZ2yF7m6q\nFzAWsZroq6fzZvJXyA8hddK0g+VVVstIK6xKtNg4BSbX6XkX5izmJqUGhF2stbC+yKUp5lV23UH1\nKuVZVq8QjNIcYb2K9UUsn/U68ml2VGjcpDAMi7g60VU2qtgQoMxiNRfL3BUiEcAus1Bk6nVmPJ7o\n41Z48UU25/Ha0eOMZnhuwX19tft9TvtnmleVm9wTgv+srexblrqW/E+917ejXMyTniFQzc3bqYpR\nvggBMEGHRjBBgSpohg0WplgYZX2T7VU4NXRWUedRVyB0M1Ldl379Og7GvjMUvmX73uL6+Hm3fd8I\n/uzP/uzb3/72c889t3fv3gcffPC/+vux7i9wrjmARYijm9QPMluCM/QPogp0xJBlRgucO6Z+IOSG\nD7jHQLe5OEoxSzBE434iGgq8dozaOEt5AiLvjvK8TMhitkxcpCWOlebVHLuiGBJ1HjkN30awuKOf\n80fIp+jei2XSkcAKsJ5hahgSUKQ+zt5ezo9R6aWpSMYm4bLpcWWIaC9mAddF76YwARK6hnyIuM2G\nxWvHQCYZZ89BXj6C18nDMj/O4YvYWUo5gndTbTM/BAL7+njkEKMQgDGLTIENmz1h4iFGUrx+BLWH\n6h7CImsmBZv6EPt1/BTDIvEkAx5HNFZzNOVQVc6N4Xog0T9AxsDIQAk9RHMPS0XKGcQQ2/oImiyk\nEMKYPsYYqoqi4kMpB4AEEQSRT8URozyf4uoUfolwlMeSgiTwVxm/pFATQ45gT7E+xYCm7kxY/RFG\nXc57TGTV9ztW2RKbBe+szliOnT2IHnKQnMOij3fqzS7gCbhmwXDfcGFQAA9s8CDJrj5KGUJaZTz5\nDwrpp88/VeFbFdIW18nPe4X0z04+n//0pz/9ta99rb29/Rvf+MY/9vvxP5axCiCBAxuoGqqE2owp\nQwZJYabMg81EPE6vuBcK1TGh+lBwM1VLtYK1iXmJaouNblRIdDJ3mZYIJYFyFfdWyIbQNnjdImDS\nk6S1hdMpWupZqdBiE+hk02DG5OZbWTOZHiMYYn2JaCfBBuRmFkYgxsYUVRX238TCFOUobQbT1Sgd\n1CoURmjpxHNYvEL4dqwZHB8vw1oPyiLNEVbnyBtUTHYMUC9weYN/tx3DI2cSbGNuiG0RGrazWmZm\ngXINT7QyA45ER4C5TcaL+Jtsj7MeZbnA6jQrPusCHSGql5mfpySSqGPVpbjCIzJ6M4Uol8pUGqm2\n2DQp2DiLEIQYTo61WWoUtDbWV1mykDbZO8iOVnyTugCrKyyV0RqQ6qlpYN2CMpUwY/2cPc+HNN57\nDx5kcrycqrwyVfOoXn37zZVMuVJ6mVWJbftY3ukeO80PcyxKtNZTrbqzITKOf0Lg3dU81k3TNibP\nMX0OSfh/2Xv/6CjOM0H3UVEqFa2iKJqiaZqiaZpGiEYIWQhZxjJg4gE7jp04cSaZJBvbSXbvzcye\nmdmT3DM3dze/djM53pO7u7NzM5O9mfEkntls4p04Ezv+KRMbMMYyxkII0YimaTVN0zRNqykVpaJU\nlEr3D4kkm8kP42sCiXn+k7pO13f6vFVPfV997/uyLUpCoPE8NR9FwDvB4s1cmpkTz4FzyEvxHeZ3\nMjVG9RBOkfMTX/rC2t9wMP9CriDCb8yQbnCFvBNnSDN1J5PJJD9Tg/IXMnPMFfHQQw8tXbr0K1/5\nCrBu3bpf9fz4pxUey1AZJnQnXh1/hHiSKYkLMawKHVVqIW5uY3uEZ3I8X8TNKl/c6skJ7/82sU2m\ncvgVlncid5IAp8TLBVIp6j7LfBIS+3ViWS6aNCToMSiW2J1hRZoLVRYYiCInRghF2JjiYB/lEq1b\n8cusb8f2KFU4sgdUgOUpNvewZxA5QbrMMxZ6Cq/Aub20d1LKUzfRNuHmcV0ALYIssTzB4T04AuEY\nS3uQqlgu/3sykqjUv1Xy+y2cKmqMJSmO7cHRIMGfpVBUSlCGTIkT+0nFubmLis8bfZgOKJCjt4UL\nnRzJ0axiOMg+R2t8NC3/fszdKfLICJaL5GP3gzArJATIo2m4VUIGdRvaoUYMlqYJAQ71EqMOUQOK\n+C6mg1kFEdIs6SQE22VS8GKG3f0zWUTC+1qDxZsolNjdT7gVMUHNxSmABS70gA7PsbiL8zk8h2iE\nHSGeN1EE0GlW6Y1QzHI0S85EMQiK6F04AbU6AGWwCKdAwjOnL7zZ0kHXS4TfmCHd4Aq5zmdIV0VI\nM8nqTz311KpVq3p6es6fP/8LD5szZ85Mxsab57HHHvv617/+7LPPzrw3/jWX64MmVZODRcwIDVEu\n7ccvoYeZ18YZETeP4uOa7NjExw2+meOVEUST/+1exIC/sxiv4OcIHBb3ILZxB+zt50SIlErFYWVA\nWOBwhQ3tHNnP4na6DDIlDg4iuihx5id+6qRFCY72ETi0dqJVWNZGzSPjc3YPeADLU7T2cHSIFTHC\nVfZaSCkuDlEfIpWAgFwRrQPPxAmBiaYghliicmwYJYpnsaQHXeRchU8ba99fOff3dvX/MXHKhHQ2\nbuVoP4UCbOKTbdwJRVDhh/D0Tgj4vR5cgRNZrJHL+aQeG+7AhIrDZJnVUTBYE8h/Irm1CN+v86MR\nrPJsuXHC4IEHGtFWlticyGAIDbpWAAAgAElEQVQZEAcHsUazSkjFUHm9BDKIdCeQc1RsqhZmERTC\naRZGmQhxn0YEDmR4aVZLdKRZnyYU4gUPT6KWBRvHAgUEqIFLeBP1ATABOuIMqqwPc6KfSBo/zSKV\nbTCQ4ZiHY1HPEm4DC1sFB2+m8JL95q/V6yXC36lC0hIBYBauWX/R317eiUKa6V02kyr4s33M/jkz\nx/xC9u3b99BDD/3kz2PHjtVqte3bt3/lK1/5yQpGZ2fnnXfe+dWvfnXOnDmC8PPR2TB/LzeHcWMc\nzOCpNLVy8SmkgLBCc5pTKs4uZA9N4ZZNvF/l8y9SsJB9vrgdK+Bva5yvEGQJWhAVViXpUtnVz0SU\nlMApkaRJ2eKCy609HD/A4nbSKnmbl/sQg1knIXJkH7EIjZ2c6iNw6O2iVmayDdWjctlJgoEYZnkL\nkwOsSJAKeNIiHuHYMPYgqQRhhf3D6CkcAccGBU3EhgVdTA4SmEgKEixpIyyTMNd+wbH2W6f+T5WT\nVaQyt91LMcPRIq13slHl/VCFfrBsXslQKZBuY3GC0WHcMpUKiKDRrTKnhyMDOA4Ldc7beJb8MU38\ndItrRvw/z7LPhJlGRAqUwAQBJNJRcMl4s70nBBUZvDK+ACLoCB6CwJYU0xWqHmMBZzMEFmonU2kM\nnQ4QIJfhXIZCCUKkEtxuEDbYp1AfIYAJAbsKEnYJPFIGmQHUFnCxTQKVqEpdxXcIROIuUg+LVIBi\nnmiSYy9iK2BBHnQITU+/2VSk6yXC36lC4oaT3irvRCG9LZimeejQoZ/8uWXLlp+7gH+Wb37zm1u2\nbPm5fzZsy3N4hA3tFB1OVZhOQojJXSQMXFjYymGB4EkUHUNkcRvdEf5hJ5UKssw37mfI5a8zXKoQ\nSABijJUG7Sr9/UQ0nBj1Oj0ib2SYG6arjZcLTMd4t0rpJ06KUIogi7hDxA0aOxntA5mEjl2geSuq\nh+tzfB+BjdgBLssTyFXmuCxMcLrOkjDZHGf6CYeJRslkiKdxBGougkdIwQ9o6kIsMLaPRCf1Ios2\n0R6l0+vt3b9QKu9+OGk+L+ANktyMGcMeojvOeII7hdm6d6bPd7MU9rL2w0gebo1LVapV7CgBkGVN\nD56P63HRxKxDgs2q/EWVTWH3CzLfGqHqQH12+VFwCezZiYvqExjYNahCBwSQhxCEZitKiAZE2aYw\nz2U/+JdrDklp5rTRLhCN0gJmhh+XKAr4PhRR0ixtp02lDIHNa0+idLIsRjjH2RK5AptamZdmuMRU\nickeJsogQo0AdAMfUhJFkSYFVeFoGV+GIoH55oX0tvA2RPg7WEhAYqsvL9RHng1wrKvw9TNVEL2r\n8M3XkhtCetv4uQsY+MM//MOurq5PfOIT69at++cFWhoWPcXaFGdyrNlMrshJn+UtWAFndqHq6BEm\ndU5ZBM+BjWyQupP1AU/sxPZoCfO57fz1MK/3X26NGiDqrEzh+pwZZl0EN4ZpkrA5WcKBpZs5mWNe\nnI+pDNm8/CT2TKpmFFmdddJkKxNl3BpxFbtM8ybiDq/uBRWvjtiKIDBf42IepcbqXs5YrArzWpZ6\nhaCKoWNZSCpinHqAn0FSEVW8KL6IVGV5gjMvErQhxWmXO75YuLml/7F/12Y+GhAMkNpGYxunhpA9\netNM+CQUoiJRGHT5m350iVVt1E1O70IKYQYEMbBJCSzo4GyBShYhjCuAw4MJ+YuGNCS7X6h5x20c\nCy2geTN2mQvDBB7EYRDFBxVfw/MIdHBg5vbhgAkBGODRazBXQAkj1nmjSL4CIuEUK5PIOl0qZZ9C\nlsNV5Ch2CUlGEvAirI2RKvNKmMIAYZWNSVSbl8tUhrkjwXyDSsBZmWVJ3BHK4Fgzubz0qLzhcFfA\nGzBo4tenpz/8GwnkX8oVR/g7W0hANOFEN4Qrb9QrhdCvP/qtMDMDC67Ol18D3ulCOn78+Oc///la\nrTY1NdXY2LhmzZrPfvazy5Yte1u+/NessDfuxJDYkOLQAAvaOWdxXqK1hZLJ6V2kWsCnoZ3pYXI7\nAZQuEl3cXOOxAew6Spg/28rOIrtnnBQBmYRPwaOpGylPe5iCBiaywOk9yFGWbZp10vtUDti80Yfv\nXXaSgbsLUUBIIoVmnVQfwVERoghVfPDKCGkWB5zpRzbQYMN28hY9Os8WqOYJirNOckTkVlwHwcPL\ngATt0AJlbopxPkMhD920Gfyeu+NPB0f/Ust+I8AZQomzvpc3hnEd3rsVxSchIniYIYISjw1hCyzr\n5GSOmMnCBEWLuTKNLiczyHFCKprJApWjVSwLFP5ti/alWOMPvfGHy149RCRKAJaK1c/5Om4EPDQX\n16FBB5is09zOxRq+BSaEwIQ6+CDQnWaBQruKaPFSjeEqtg0y0RhLenAFRmvcWuFICSfKChEHTtm4\nIe61eb6TO+H1CuVB4gkWR2mTGbF4rZ/tBkVY1UNMJQ9+hlSaV/soxQnKyBq3wWl9+pW3Ulz1Wkb4\nO15IM3R8QK5Uo5WXC9d6IL8FvHOFNDU1ddttt42Njf3zj9auXfuDH/zg//8pfs3l2pPhqM9qkZDA\niTwlFz3M3HYSYTIFxgZItFODDSm8fl7tB5A7uKsXq8iP94KHEuEzW/nvRU7sgTiiwrpW3AJHLZp6\naBxkfYKyzJkKi8Kc28O8FqJtnBxC9vjoJl50OPITJ4UgBy5iFLEdMcBzUErUKyAhdyBW8cCrk0oz\n7XCiH9lAhhXbcBU2CbySI5shsAgb1APIIkZAJAgT5GCmW107VNnQBjne2AOtbOthnpDorDQnrJNf\ndO3CEOiIKYIqQYjeOGtFvjvAyjRdcQzoL/KayTwBQeH0i6gqahTHZ36C81mkEPNjiC4XSzgW09tw\nFVZZ4r8VFm11/R86574ewlLRXDap5Fze6KfkgQ5VFBUCFkaJqpTBlrhYpClgwseXoAhRZrYAdm9i\nlcGdYUzYmefHNWwLKihx7ChYbEvTJDKcQYUxi6Uq4yXqFrbBfWn6NObb+HlKVeJJ7olgqwwUGM/g\ng6iyII2vklDpChjJI0R56Slq3dOXrmxH3LWP8BtCukw04Vy1SdLvFO9cIa1fv9513Ztuuunhhx/W\ndX3OnDmXLl06e/bspz/96VOnTrW0tPzoRz+6SqeeoeH9GfIqlRpzTdYneGIvoSh6mLlxZJ1KkXNZ\njBjlgLvaGO1jOAMgt+BWIQ4joKBoPLSVp4fJ7wcVuZf1MaxhjgY0taHViYiYMufKs06Skggui2Qu\n+NzeQ8blSB/4YONfLmMz4yRNp5bDz4IDHmI75PBDANFWml1OZQhEfJdwF8s62QqDLlkLipwvQgx3\nBFHDr4N+uWtqGGSwuDmNHOGNPdgCqsH6tN7irfh4/ui/i9mvuXgDSG0g4A2Dyl2dIHHBYZ3KiISb\n5YSPqDBX4fQg81TmWITirI1gS7zcj2ijR3BqqHGakvgwIXCzrXyYRqrTOcH8dgTHp0MmrJDNMVqn\nNtNqwgYd6myIIUZQBIoV6gGTNm6YIEA08CWkIv5+egyWdhCIDHvEKpytMDyCqAL4GihQ4c4EuIwa\nhC06DXIlzjpkZCSbD/Wwx6dV4vUMlQJKnB0GIZX9OS7VKJeQVUwZWSTRwkKZXnX6YeWKAuzaR/gN\nId3gCrnOhXS1EmMfeeSR3bt3f+lLX/ryl7+saZokSY2NjU1NTQsXLvz4xz8+Pj7+0ksv3XPPPZqm\nXY2zz/DlPznBNpVxn9HDBA3oYc7UaRQ4X0cOMzmXRR6nTaJwzOXmLiZKjAv4U1CDSVgKVTyX6iSb\nFjJ2jgsW/lnGFrBqJc3nqLzERIbGJAummW5mbIxGhQslXInAZkEzx4p0J5iYy9gBpEXgECyEiwQO\n/jnsBpZEudhE4MIldJXJcabGQMEu0BQnvIz6KARcPM2Ew5GFbAmxspkzYaYamS4gtcNp/ClohDCU\n4SJUQeR0Aw1T3NTO+BnOX6J42qkvL7+cvPWDf7/uU1726HbOHGNqEmw4Sc6mdz7zzvG9l7AmOb+e\nxREiDuMTXBxn3Mb30TTMGsECYmu4IOOfQZBxL7LUJDyHpMpNmvfDiltoZL1OYlLIHg+yFxn1CS2n\newXCJEEVZxJGIcSZJk4foyVgwRyU+czxmWfQNIdJkwafcJTGFKcnOfwyZRtpIbLOtqUk5zOV4KJK\n4zSLVC6cpCRSWkAyxlCItjFeLRBbyIPL0Js5WsCqk8vxsSQXl9O5ht07ec1nbJyFDkoKQ6EpRFDj\n3DFGB3hl+Etf2vjmo+u6iPAbibG/iNSDUn1w6lqP4jrlnZgYC9x+++2e573yyiu/7IC1a9euXr36\nbVnW+GU0dOdpLrGjg6/1Y5dItjE34GCZUBhHZnkL4yLiAEGYqE8xxntDvLKfggku1ECDBOTRYog2\n70rzcj9lC1SUXu6LcXIfezIgsvTdKC6WxlgGbwSiIKGJLAwz7rAkzbEc3gGUNG4JXwIRFDChheUx\nzlewhpBhYZpzGTwLosQ0FJ1qgDkIAQhIHSxWaEkTgbxLvsbFvSgxXB+zBoAOJhQBUCCKnuSmVg4O\nUKuDRNJAdMJ3ldZ8ihN/0Vl5oopdxAWqYEEr96U5MYIDczoRbcYcghCNCudqBDmW64zlWJNmUyc2\n9GXAQk9SqSA6RNO0qUQhX2JfIN8rE3b8RzP+hI7UiqSSgsEyE2XsMpTRDRbqnCrRqdIcpSZBGBPO\nmbgWYQVsLomMF8BGjbDCoOzyHpXvymgV3Dy1KvO7SInMtTilMiZxTys7Jd6d4aUMKwzeZ5BX6S8x\nXmGJhmogyYyUOAWuhZcFgXgUL8BS0Zk+eQXX6nUR4TdmSL8EJSEAduF3ZzPC28V1PkO6Wrv4y+Xy\njh07fsUByWSyXC5fpbPPkhVYYPDSMHelWZBmZB+6TszAKYHAyWGaJOZ2o1hMhFGyPL+HDa1owuUG\n5DMv21OYFr7CjzPc1kNMBQt7Dz+qc8t2NqfB5/QzeCEmKwgtiEmogIfpc7bOdIhTg7QkiHRhZxBV\nQgGyjOhBFLKczCGFiPTgWpzuZ1Ea1QCfchmhzgoVuR05BlG8BGfDZAvkLdIyPQYf+DAK4BBNAFCB\n6kxKDdhgUXuRF17kpjSdSbCZEJmI1A9sfeUeYWXPk3/2V8+oOzqIRCEKEaQ6/zREIspKlVNPcrRI\ntYy1n8kCKxR0iRP9rEjhKnzjOQ6V+Fiarh6yeS44BC0cHuLxPvpMPmzw7bhr4n5b5FaV329FHOHC\nTooW6CxpZ3EXC9+Nk+R4CV2lqHKohmdBlTaXNToLWqmJ2DJjw/gF5BjTCQ7ZnMnwoyJKDsunNUXv\nZpY4HPbZm+FUnpsEJvJ0VzgU4+77yfewx+KZErLFfVE2pdACzo4Q0wjV+UCaNV0oHVREKjJOhuKV\nZbBeFxF+g1+CXQiiW0Uj4cGNF0u/TVwVIV28eBH44Ac/+CuO2bhx48TExNU4+09pyrETGlXO5vl9\nA8XghT00BmhRKEDAWJ5AZKqDOXWWbMbR2D3IxlY0CWSIQBEGITrrpN0ZbuuZfRdi9vFo8adOGn2S\neSqhGmIbYhKqiApigokqczSqRSIGi7fhltCjCA6igGhBFHLMsfEhcj+yyul+Fhts64AUIyUmi6xV\nLzvGxxM4E1CuUzTJO1g+N21muUElw/IOYjrIIFzO9dGhE3K88By1EBs6mDyAX6Fg0dDzyvfvP70n\n8h8/8e/bPyCxuhVZBQ1F5rkDPF/A2IoSRtQIUgR1TpeYk2DFdo5mOFJgy52cs/jzfuoWf9jDWoOJ\nEQIRP8ZrO3mgn28GfDzCw4ZvRfmHJ9ke8IE4F/pp2IXggs40yBG091E1MCtEQY1RE9hf4eABOk3e\nFWFBAt4P27ALTBUQTIQ4lSo1nwVt7B3iwBCFOkEJqRNsXq3xZJ6ns8Q96lU6QUjzvjS1bh5X+Fae\nV3NIJvGAT6YRPOZ4rIWVSRaEaO5BvILKqtdLhN/gl5P7tpeKFh/oOaBd5a5vN3gbuSpLdrZtb9iw\n4dlnn/0VhbwOHjz40Y9+9EoLq1wRDX+a4a/KLEuyWUA0JTnqPe5SeZGlnZwv41igI8v4IaIufoQl\nKQ7uJClxU5Kn9+MKYEMNROiGMpqI6RGNUhmcrUEQ284DcXJZXspTG2H5e7BhcqbHRJaFW5mScfPM\nN5iu4kvUy1BD10HG9gH8AL/M4m6kGJNg9YHKe9MIEt+1oYjh8YE0j+cozeztVgi7zJWIyFgBHRJh\nkaEih3NoBmKFYhYECMNMGQUDPFItCDEcj9KLRAyWtxGNUaSndd9nvvqffzjyvmcf7q4fqmOVCMfB\npR4mrUCZTBEhQlCAAKmF5R1cHGEqIJFGVRnsZ57FPT3YKnsyWA7nogQmQYVwigfCdKqI8L0Muw+g\np2hSGS/jqizowckx7iFFUATqNlqBsM9EGCFEWaIjhCtzNI8bIdDgAOQQBIKZneJhxCjNIcQEk1nc\nDKKBmwUfJEiBRTjGPUm8EAkVFfrAszk0jJ0Hh0iCjQqkiYagzCt1JsXpfNubDK3rJcJvLNn9OhJG\nfYsxfLJk7DI7LpfFekfzTlyym+nrfO3ZXaZX4FSRYYkijYtGxHfpaNs4vYvlrYQkBBfXxB+kZCHW\nCEqs7iZf4/Ui4RSyAzro4MN+0DB9NJ2KTbRjtvNbuY8n86RbuLsDvZWTT6HAXBu/CAnGdjHHRU4x\nlsH2qDsgg06tBi5KCF9BUhBjnB3CKzMP1C6weCJDocYfKKDhGPzXPnZEMDzYCxkW6CQVRh0u2ey2\n2VdFjnFrO2aBusTSLrSZAueABjWQqbmUcoRK/N7H0VUO76Ns0Ua/tOmDX/pe46D/xfu/3nm3xIpN\nODXqVaI1igUyVdQWUKAL4njDHP8flALGVQ7tZSjLHT00pnm0j0MZtqZJtLK0xtwAqQ2zxH/5O/6P\nPkYsPpVmx8eZkhgdQFbA5XwVP8GSNPM9KgOQxYwyv4euOBerNFWRTM6OoIK6n3lDCEWIEsQgPGsd\nv8b4MMtK2EmULhYaCN0QAaACOvUWHt3Dd/v5Tj8v7Md1+H2FjT18YBvv+jCOy9P9PP13PPJ1HulD\nrbPCefORdb1E+A1+HYVS+NH+zVC7W9mnRW8s313vXJUZkmVZGzdu3LBhw68+bHBw8Co/P36PDoUV\nBvt0khCuNW8KLu5sDV4dwD1AZBu1ImIL3hBYkMQQkHTmqxx8Cr2VeVFGd0Fstnzngs2ct6GCFsZ0\niSpURsBD0VnfyXuSDOR4ehCnhN7GtMLY8OyK3MKtxBMcGcQrQxQCMKFGIsaibg6ZiEU8G99Ej9OQ\n4pIPGVyPhMpdvRywyBU5s48/6uG0xg+LNPVgqBhQ8fEdTnwPsYWbemkReWUYX2ZdlKO7KJQgCh6Y\nJOKsb8OWOF5DtrFqVMosbaO7hwSU2aru+g9//Pknh+752n/8GEPDkCW1CRLUStgWAvgKQQAZECEK\nOhGZdSm0mWpwGRpUVkVxLAoevsW5DF4dArBpS3NXD6aKBftHGD0AdeKt0MXaMA7ki5zN41VQYvxB\nB55KMUsI5CgFkyODuFXwLjd+lSAKFZChCwQocns7WoyBHJNQrRPMfBoiJTC/jRP9mDIrNUI6LTKm\nih0gitQyuAInM1DiSh4er5sIvzFDerP0kLtVOfaactNeu/WdPFW6zmdIV1FIv/awt1AL+YpoaPgL\nxAjv7WC0xOk07RZ4zduVi9+LBGdqVPoJIogSRPAPgAQGrTHmaEgjHBxGTyCFKQ+ACjW0DhrUnzrJ\n9kDG9yA766TeKAWLlwapVtDjTIcY65+9dXakWbqNF17Eq83uIBBNBAHFQ4+RD2gOmKjjHyCeZirG\nZAQxg6cS8XmglT6HoSITw7zPYFknXxtAMmhK0wF2wJhFsR8cVvcS1jhcQQtYGcUd5LV+xDb8mZqn\nNlqMljhNPgmfcxGeO4AsY/SwRUEBm3tjT55LyUdeSlrftmEYBOQAWcZ1CZIEdXwZ5NkMLbkVw2eO\nQTyKDqN9BGmmoyQhX6TsEgSM5fCc2RypzjTv6qHf4jwMVyEzq6U1acIGVShnOFPBrJIyWNlCPULY\nQ7Z5o8hChTGbUh5ql80UhSrIABhIERZ1YRV4TxRPZrjIBY9KlSAPPmmDxWksHVfiTIZpnUSeTJqW\nEFMHOAsXDChNT77ZXXbXUYTf4EroZX/J2Foo/eTh5h3HO1FI1wkNTX14GZQ0t6qMWlzoodsSpcrK\nP1ZO/2XEzosc6sPXEAMCnSADFnhE72CFgTTC7kH0FA1Rzu0EAUS0bhpkzrtQAhlZxw/w7VknLW+h\nLUlc4el9ZLJE47g+5uX7UUea5i5eH8HLIsbAJBARFGSX5QbjNpUq6PhZ9DAL0py28cOoEKrzIYOa\nwH6fE31sMliT4OkKJRGxnVtBUjhkYw7h5MFH2cScEI11liSYzlHI4SgAQQngrl42xYlIfL/G4QxN\nvZwZwJPZ0kkPDJQS6tDaPzZP5o3hL6coPAM2WpzFaVyTsxk8BaIEwWyGL2FkDSNgYQdt4GaoWwRJ\nFA1ESjY1C8fl3B78mda9AZ1p1vZQtzgJwwNQBJ94mpvTGAZlKJXI+5yzUTXsKjtSXFJZnSMHgUbN\n50gBpwgmRKEIGngoCQiYUhAU2tuomtyvsEsgXeLpENVdoIDENpGKxsZWLAkN9meIqrgWVQtdnd53\nvXSMfZPcENJbQVQRY/h1/Oq1Hso14IaQrhkNnSUO9RFYyAZbDEbANVhWDS8QN/7n8vMP30F/hdE+\ngjZEkyBKkIUSqETvYIWKNMLuA2hRpgwu9P+Mk1TO5yEPaWTlspNyaDGWt6HobLrsJFnFVeCykxIG\na+/gpX6cEeQO3DKAqBOT+FCc7wxTsRDTeAcIichteA6ejmeiB9yeIhHjABzvwyvxR3fwrSqFKsom\nVknEJMpweA/+EIDcRtNmJsvsCCM77B6gLhMEiCGoIoZY3UKLTlBElTgr81QWFKQ4S0K4KkKp4z2D\n5ra2yhfq7vFBAgtE5FY0FUxqFkIYrw51CCHLRNqZG8WBqEoPlKs4HkEYRQSR4n7yKsB4P/7lpZLu\nNMvaOWiTt2EEatCCZnALdBuEVHZCNsPpLL4EcGcEr43FIkeymAJLQowGzNeZtHDy1JzL3codjCRy\nwJhCOEUoIAiRFiHArnMwi2tjBlBEauEuhf2t/GuVKmjwXGa6/3dESEqHxnLDfmL4Nzye3yaUNL6H\nm7vW4/hNc0NI14yGniKe8FMnfTDN4zUUET2SWlVa/K/FV/6yh/4KY30EMijQCkM/ddLSMFODDB5A\nM/BV7Myskxa9h/Mm/sxtdMZJPmIMfwTRYnUXUZ12hX96kVyR2R4PFbABUgb3bOfZYUb2I3eAjxow\n1+BCiU8m+E6OcgHBIMiDhtTOghDnTSQBSmzsYFOSIcj0c2KED3RwJsG+XYhJVqeJuVQVgkEO7728\nolWHgHdtQ5Y4UadYR9WwLXwPv8rtPWxLo8A/jFBSWRzDy3CkRKvIui4qKq1E768wQOXf15kcIHBB\nAQ9ZxAXawIYhCCFqrIsQilEW8FV0lU4I6lR8TgasF5ArDDtkFcZn8nYvP5n2djMvxJkI+RGsAriQ\nQrRYbZBOo6i4UB/mDYG6SOBAlW0t7AhTrdKnUS9BknGBpQIJD9/ltRq2PbsvPxVDqXNGIRqn6iHL\nqCIJkRELscLZCrUieCATTnGrRs6YzqjXJFDfMr9ihpT6E71SiN5w0g1+jhtCumY0NPwt99xLzuPo\n9xFViLI8xnmLeWGa5dSqjHR/KvP3KSouh/vAhjC0Qj/UQUbuQoljZP9XJwGw8P2M/4yTlBBBksYQ\nE3tnnRQOEcjUMxweBAMkKJLQKJQIqzxwP7tyHOxHbsPVWAtCmGqJT6gccnlmP5oOYNaQulFEVoo4\nAuMWqslD26nAiMWrJrrJkgjHhym74LGuFSVNtci5vVgymGCDgNHKbW2URDIVmh08B9NHE/EEVnXQ\nrmJnCDy0NJk6B/P4ESImcxW2xdisaoL53p1/+8Rom7mzAjpEEB18G0KgQQZqiAkElbURVB3HRogS\nVmgBAWTYWSUFcoWDMBiGKhQvb6kP09vBygTFEq8PY89sRKyDx+pP0AueT2uYvQFvDFLNgwI1hBRb\nWgh8Omo85XHWpjmJq5JSaDFRXXZ7ZE0Ck6YuOrK8ZnNrL16GEYl4jNEaTTpROGsxr8q4i5lDFKYv\nXeP2E1fKr16yiz6QsE8G9q7ib2w8v8XEZzo3/u5vdrjOhXS1atldD3z5azVGh+jpYkKEpQgCEzUS\nSeoNMFk/3Tzfrd/8Z8dyOY2LrZg5mIApaINxmMKvETQhtxBvpDCC0IC2DqcOl7h4lHkrmVpI4MBx\nlHn45wkk5FYmz3LmKIUTnDzP0nUsaqQ6BHNhDmaZjjSjJfoPcttqmuKUTsJyztksClAX8fI5bpvH\nll5ezMM4+nLGD9C4jlAIrY7UjKfxfJm588X2JlrnTw9JjJ4kkeb8OfxJqh6CSbPBosWoFxibA9Pg\nYNWonGd9C8pC6gJNc9ADvAaESU4fZE4TN7eyLsqzp2j0uXMdTRajFuUyuRx+oKyZ2711+E97v392\nfrpwfB6sYt5yGi5xyYPztK6mNY5d5kIeS6M+F6UJz8SrczTMXp+6x23z0Zt54gIrLnLrOA0hvPlM\nNTM1Dy5SPM3RSWwffT4rdbwwjg8qY69y1GX4DJLLtIwcoXslqsvFlThZCic5o3BuCcsUGkQ2TVB4\nmZN1qnPJN7NuEZ+MojfhHafcwPR8qkOUBG6K0zjMgvkIxzk7QaNPvYQDi+PMXfylz0audcxeGb+6\nlp19yAxvWaCfqZkTNzJDfw2J8SPr7x4bn7/ELV661mO5urxDa9ldDzTE+rlgEZi8azP7FKayOBUk\nm5VdVCyUgBPB9q8NzL0j8sQXWhiOMfoU+BCCmc7WJsgonUQN/AEKPiTRBWovggMw/w6miuBiV1F0\ngjCkaNS5uBOvACrEWMT3NvEAACAASURBVN2FXuGVPlDABZ+EgWlhWrz3I1RqvJaBTrCIS6xR6S9x\nr0bE4D/tgSyRFlwDz2VpjJRDxkLzGTe5eZMYCwUIwZDDsRyBi+tiZiFMSMaRaZFZInLIxgQur9ts\nSnNrJ30lZJ/TNlZASAKfisdNPcRVShmabTa0Egi8lGGoRjRMRY48KMz9nPLVoc97VenRkY/uemYz\nFQffQQm4mMFVCWvoFiMV0AiHaWwh6lCXkQTCEhUfL8v740QUvpUjbXLRRzXI1hkN8AMaY1waQFQQ\nq6wwEGHcoirg+Jcb+jlEP8btAYKAG+LxAW4OcTSHZaGFWZiivRWnwstV1mkc3YkVJxphjkxPjBaF\nARshz+vgWtiDzN2GBikDK4NoUCgxHWD705Obr1mwviXezKaGB+4eOXRcH8zqv4Hx/LbzXr5rxpfv\n5s7f4anSdT5D+p0W0qo+Jg3OVXFH2HAHxyM0FRgvEVbxAiZbuTSApz3wj/vMVPKJr3TxWojSkyCC\nCUnwZpNd5E7cMgggQYy4RLEPHJCRk2getjfrpDkJJqPMizL+It4IKGCwooU5eXLDl5siuyQMIRwE\nAzK3dqP6vNCPnwKJ90YIueyzWGlzTwePZRgZQEliGVgmss8OjZBNXuHYEMs7pH+VEITAdWSerHBw\nBMHDLeHO3MQ7UBKschgdQU5gD87mXugGO7bzPDR4RCpMexRswh5WkSDNQz14MFLEc+mJkyuxu4o1\n8zskVn2L/6vlv/UMvdbv9nw5/7nC3wgsEDGiWDKlfuaEWCEJUyPBsAIqK2P4ETwLUSEu4EM+i+fw\nYSNk2MGP97tmhNvTBAb/s845F8GEGkEOETSDtT1YUMxw3saTwLycxC0Si/B7cRyVasDLGeQ8gYVr\nEU2zJoaeomqyO4+i4WdwBaIGtsmaNBGDwMeCQpbJDLaB24+yHdVicZp6ZrpwXT88/nPe5C67L963\n79Hz3YVd4tUez+8AHey/m+fLpJ5mR5Wfb9H7O8ANIV0zGhp+yE3dHOxHiWAPs+E95DUuHcBz8CzY\nRLM846QP/ePAmVTbnn+V5myB4jAE4EAaLLCgDmmwQQERDOIS5X34VZBR2lkqc7qAXUXWIYoQoTnJ\nxf3Yg7O30fUdIHJoH8yU+raFO7pYIAT/aJNuZaHHq0Os387ZKitjdNTYZSGEeDDG3iov9BEzsOKU\nHUIqn/awHQYDxk3m6FJXSooGnhryBh3+qR8B3JHLP4AOIVIxUGYbMuUOgAw+d3djxDhQQhQZszAd\nIjGsMiWR7jgrUhRdRrOs0oknMPO8XMKqIwnd/0Zbfm/lPdZT2wsvPMedX/67zxUOySwOiEXxAspV\nwr4Q8zgbBAvawKJi4UU4X8FwWabhRRgeZNwKfyTamCiO76q4hSRLdFybEz6Oj6zhDrC4hSmTBXHO\nl2kIEcB5E2+mo1IIvNls5biGEOe+CC+6jOS4NEJggUVHmlvTlHyOVbmocLaA6wFQRduMXae5jVaB\n0QILRTCZsKiHsEempz91jUL1LfLmt30/cPfIE0dazMKNtbs3RQf77+P5MqnH2GH+bmnphpCuGQ0N\n30TuJSpTGESWcYvs+Ah1h9Mj1PO4Iehins6lYVzhvicyZa/ltS+0MTFCcRhEcMAAH2QoQ+Kyk3z0\nTlbCG3vwyyAjp9Al7BJmgCgiJwk05hpM53BL6ArFLKk4iOSGIQIhyEsPtgVzFP+REtHNVHJQYv27\nMWtMBuwIs3cEJ8lHNAyJv+mjVGLNHRw2UeCjcbw8QwFVHwG6WxRN8PSQF5L4eonRHGRg5hasX952\noYKL4IOHZUGNRJxbtnJ4kMURzhapBDgSgQs2bkBXnPltHC2yyKU9jeLz6jCDBYihGN0PlFd/thDf\nV/xU6ZFHSw9++8C/KLxhs0QhJCKFqAQkxFBKpoLjhcBCsKiEOFknGSAETEY5M4AXCn8k0viZkHOg\nNvHlTBCKUpRwgtld5iGNm6IcKxOUUVTGW5ko4888HCiQg57L7c/D9IapKDwQ4hswleE8eHvBpbcd\nfOa1ErX4kUNtAJTZ6k04iDqU8DWUEFKVVGr6tdZrGrBXzBXlIX3u7n3/75HueuHGPOnN0sH+LTw/\nyMbd3Hmtx/K2cUNI14yGhp2QR+5lqcTJArJMUOTW+zE8nj9APY8bAYPmMBO7SLTd/aNibST8M06S\nEEOQwK+DBLnLhdRiyHEUkdUqx/ZQy4GI2EEbFLKY0qyT/BCygB4iN4zuU6sR1ViRYHSIigoSVMSt\nEeFdKe8bWbReMhXYT+9mLuhYZd7fxoFh3nC5r5UOhRdzPD3Myji0cLJEm8e/CfONDJcMjpVY0hH+\nuOttDtkZhR/k2e3g5WGm94EODjikeiiYtOhgkxkBkDSSMeaG8CHkUxQZMwlLNKmczeMJJFtYmMIr\n4Tqko9QLvJ7BlsAg2tb6L0vzP2Hf/z++r+fGnrcWf+/xrSCyPImqIumUoNUKGaLo+Va3StliX4mK\nBibnioQ0EKlV2ZgI389kJD5dzDt/U6WagBB+QDACFlIEv8bt7Ygy2RCj/xMxejmZyYQ0jEALhMFC\niXGLhqzSJtNvkq0xUcQsgko4SUuYWyIMCRzPYJUxyxBBFhGTYCKJ2KXpyfddo1B9i1xpYuynHxx8\nbFfbDSddEVt4LsGJ3ewokLrWY3kbuCGka0ZDw3+HEDhEulksMFqCABlW99JR55kSo3uhBQxkD3c/\niZ6fcVKeYhE0ZBdfxy+BCnmkdsjgJVESNIu0qhzZRy0PQAsJDxwKLjgQhgSEiEIlT6hKJIEd4pZO\nXt9FpQQ6VKREi/IHUv3RCnYnhLB2cvu9xODFEu+LUa3wqs+qOHGFWpFDw2gaTUnKAg1VPilQs9gJ\nQNgghazW/U0xP6/z7RJuifEMrgVxcGbTkkSDQEYBAiwLJcq6BIZCtkgiRrnKGxkIIYVwq4gGGwx0\nA1tmdCc1k0SCZoGjGewoxFmTSH2kZLcHf5T9aouY/y/f2dZ/oBtCKALLOgiplOCmGhvDqmxbispw\nnuEQUphijjGTkM1tLVTAiKFK9IBZ4XGPikgug1QFF8+CMGiEW0DnoSrfrVO2Zks8oM+2FqQCCXDB\nQk1yi8Y2laJCDqo5jmbAxAujSSzvIaxi+lgiZ4r4Vdw6eBBMT997zYL1LfEWKjXccNJbIEFuVaJE\nQjteMAqF3+7tITeEdM1oaPhvl4uZOizpQlc4lcF1UUJs2YxR5x8zVAos6ea0jSz+xEnVjP76X/dy\neB/18mUnKfh1hBiUCbXi9eMlkcOIDu/qYPeLmGXwfsZJCRgCAVpBIerg14V33YEQBM9nuSnKkRwV\nC7UFu0YgKP8p4r064vVFQMTJs2EbcZHhMp1hHIsflVDTbFCJO3xnF4bMqjivWSxK8rGAWpbn4GyF\nxla0QL43oF13D4g8U8N2mapSy4AKSShCHSkNCuEo5KhkAZQ4t6WwNMISdo6DNlaVIAQCkkKDRiqF\nY+GXkOG8yYJOfIfT/Xhhol00KGyWUpsGHvD+UvWc/+o+nH9EopxBkVnXgahyxKO7zMa40VMueQY/\nrFMFVUGSqEGLRd3GkTilsEzmw6DADzweH4YIYhV7EGemurZLopP2GOMxzuWw8pTqYIEALZf9lAUd\nypAm7NPmsroLV6UN/qlC1sIuQBXZYJEMaWIqhRK2yrg9PRW7huH6FnhrpYM+tHX4hULrDSddKeEH\no+vZLxbqr++K//a+WLohpGtGw8JB6jPlTV3wWdmDInOsH0QUhS2bSdb4q0E8hxWtHLdRZOx9JLau\ne9w7W4xV/yLyM06CsIoLlkhQJNQy6yRJQYuxReOFA5hZ8KCD+QpTFewojIADGiS5ReKsK25p9fMi\nr2X4gM64SX+Bee2cLBGgfCbmeQXvZTAFilVW30GvynMjxGO4Kif68Q1uMTB8PHiyQBIEmaMessnN\nMnWL4xUaYqgKbdCrYqvsqpG1kF3GPJwAIpCFIszc3xU0A7Mwu1MgGkEK44OSoOZQH7r8wkYi3gYp\nillWgWdhghYhFMHKcy6HJyJ1sVLnPdL7ne9/Pvnng+GO/5D/fP4RiTUWByxaDASDc3nayukHXKKx\n0l7D+p7MJokIFGA4S2eAq3BIZDDEZ9TZZOJ8hW+UcQKostrg1CCOCz5E6EqTUFkY4bUKtQylOjiz\n2xrFCL4LBZAvV4WwCbdye4qtKoMCLQr/MELBxHX+v/buPbCp+v7/+JP0kIYQjzGEEkuoXQmxhALl\nVirjIgjIEJwO9+XrhpepX7d5mQ75zq8699PNy3ROx9f53byg4h23eZtVVFRQxFIRSymhlFJCCTWG\nkMYYQkhPT39/BDrk0oFAe4rvxz8k5+TyavMJr9OTk8+BML3zyY5zsru18gSZOujfmn519I2F+d/m\nSa+/McelrrOWPh0OtKyhpCvWkhRSp+mWXYHNTHQ5jIF6SNF/CIrCFj9aCi3FjDEMVHmo6uudtJTc\nkv7/dH7VYP9XJ1ks9M1Hh+1RUjb0aqxeCNJrONsawc35Obyzilhgz4xqFjeKSiJzKvEAagGEOaOQ\nrWZloAevWft7iL4aZ9v541KyCtieRNfM1+WnE1HeD6PmULma7JH8pojllYStJLxEytkBw0ppbKRY\nIRhH0bAprNFJmfBF6AbbQiRV0iNxwag46SDWPF6to0ceLRqJWkhCHtRDCAAX9pGkqkk1gg2Pj1Eu\nFAuak81QW020AksOpNDsmL1YcolXcIoF0qQh10PSzOc1pGvBjqUYDZzOS8c9d13pgzW6Zfm0cxbV\n/izyf2FWxTmzGEuStX48undKLJVbEFlRkMy1Zs5qSyUsb8ANY3PIsWTmfyAPAhCEzQ0EEhREqDdh\ni0GUROYsGEnGDsGlk2Vjo4lwPcEgig2TBTxoCtkhmoPoEXQdogAWNy6VwT5KVWrC5Hh43U/aTdjf\nmjgxD/s+qNLZlJep0knfTDH+oXwSoM+XLk9lqCt9tiSF1Gm6dVuCRSdVBzoUQy2Y6T8EPcnmVZAL\nYf5rMnYLD1WQ1snx0BjFppHwf62TQqv5KoWeQLHRXWV7kJQNPc6oXNaHOaWIr8IkchhnZVM5MQ00\nYoE9nZRyoYRJVeMuIt7IGUWYnYrDkv+reN2f0jxt4moHCyvIysWZg6Zwaoqz4IHVqF5cOmcVEjMT\nrqRWwzOSsjdI2elXBFHyNRIWWuvp5WRlCK2EoTEK4ryhQJTdoLg5K06+g7fTbFmFzU6PQrbXkWsm\n4SLUANV7DsazqFjyidXvmXDPquLyMcxHnkpFkDUfkEhjyyVLpcXEgEKCSZr89M3jq3qiIfqWYs5n\nWyXEQQE7uhOn85fXXjny/X80unMjN8wqi84JvGFJrLaRayfXRMNq3vLnzdCTZ01K1uYkrTYSOlYT\nCtRDTCNHQzWbatCDJi6GODQkKVtBQx148VhJ5RAJQpyUjjIEgozPxQGtZka6efANdpaw8w1O/gE7\n68m2szuNniTt3/tNWwAUNyaVc9zobhzx1sfdnTZYv5GjnO279HK1fBHSSd9YMf4J/CPm8qxhVFep\nJSmkTtOt25PYC0k1kMrstymFKrDjNIOFSM3+naTqDBzL+34sUVLBTCdted2trUqwroFYHLO2p5N2\nRdDyiCUYZ6M+jKmApgYSLr7rZPsHNKaxWAlVYXFzkhezSlOQ5Cp8w4knGORSznRrqx2uiyOJqlji\nd3FmFRNNELWjRmk00V1jioV336ZRwWnjJ+OJmIk0UBXGrrEySgwcY+kZ56QoAzxUVJELup3PFCxB\nUjVYCunp5st6KEYrx11Ea5pIitYoPQtI57F7CU4rCTuJaogBkMLuJhUnlQITpHHmYvIxwYcW59Ny\nGuohhWfInq/39hpCfZLmKiwKsQA2J6cWo+URKEeH1hQFOVyXP6m0fEL1kyNf+4c1kUzMGPvK8EsW\nvXJmIubkF3Ya4HU/i8oZ7uY0n3lMjvairgcS/MaMWWUFvJSmWDdfCjVoNrO+ykQSvFBZy8YINRUw\nBpcNLIRrwYHihgCKgp5ipIWZXiobaLQSS1APihmLypcRTvLwpZ9Uw56jPPacWhcM/1490NGffkI6\n6ehljg4P4FnDKOMfiWfwQX5iF9JrEMfuIdVAKgI6jIVVoGF3o+zTSRdOJmjmw7dx2b/WSeoQbvJi\ntlOp81EtsQhmE2YzPSx8GUTzEUsxwkQshakAYqxPMMFHopJ19dhdhKpw+Ug76abQmiK6GreHfip2\nVRnu1pPWnPHhRMSWuMePJ86pZ7LcRG6QTToOC1kam9/DYseWzw+dJHIxhQnVY1EpCxDRcTjpn8+a\nGibmYUrREEeDLSE0wIymcHI+2VHCtQBuL0OHUBNn0xuQw8ljaA6RG+ckG2sq0DNzoMYoKuAklQ1+\noinQcXjp5sOr4lOp8rM2TCoMKWxObFZ6+9AsbKlG1SFNFApy6KmSzCNQTm87JBhsY1LerDFv3Lvi\nxvhf62NJEleMrZz63VcdF6xiJHUQQamv0stqdLvd/L0crSFff6ORQjv5FvIchNOETFRrJp+uO8yM\nUQhBBFZDMkg8zqZGEtWcPJ50mt0J0FCs9ClAC/KlCaWOgnxcKXbZGTuct1az3UHCTDqBZqO5CpOL\ntB8SmbmgDP5ePdCxOR+STXbcHQPF5poJvEU6XskoI3+8ZPBBfmIX0utghjCuIcRqSIXBjeJBKwcN\nlw8tTaQGCqCR0UVoLj5dgstO/7F85McaJRkEuPsCzHaWplgXIFSHaiam08fN7iBaHgkHIxJsD2N3\n85XO+jglhbTU8mk9+V5iVSgq2WNIJLFq7EqjKfRPYdGUaT79A1POD8IhVz63reKkRgaNodxOTozP\nk7hstDhIlPPZKlwF9ks8J7v1LyrtqXiAOGiwLMopJeSaiSbJjjEwj7XLaXWzM0jKhhYh1cB3R4LK\nR+WgQw6qk4SO3gBhzJOw5ZGsQY3TTWW7H8WOzUbUT66PLJXP/ZCHVo8tn25uznNjtbCkjs2N6ClI\n48nlZA+qnXiK9VVYbGAikabATC83EZ1NteTmkAy4zs/tMzc53b74vLdf0Z5cF3o/6Z1E+XWX/mPM\n7MpocSjs6pGfpDa++8WI/nycQjf93ESTrH0bp4OLhqOp+KNUhVCsjLRjttGgMBaC4IfGejanUOKk\nynEMx+QksgJsWApwxdit4sphSxUksLmZ6KE6iLeUT6vYoZKysbMe8qACLK2tUzt7zB4ZOUGfsTh8\nxVRMSCywpwOVlCwz5CwPUkidplu3J8EJ+p6/k+IN6I3gQSlEW7qnkxJREgGUIrQ6vltMam8n9R3D\nZ36sMdJx0nGunUp+HuWwuoZtddjNxHT6+9gdJGanOY/CCMkEfVW2mFkfZlI+/hihBvLziFWjmDhl\nPE4bLgsVMXY0MsiCHlOm5vFx0jYuZbnCHvp1lI9rufxMnjQzxkqORixBtZVwHZtW47DZv1/Y52K2\nLHelog00WElEqIYdGqfnkU7TpHGWm0A9VUms8GUIRSOdxubilJE0+YlWgAOlGJOKFkIPQDEuK9kJ\nTopxksrGBiI1mFWAdJwfT0WL87FGWEOr3zMzwg12zHae8bO5HoZAlBFmXLlErKyNk1yNwwUmBuTi\nsBKzEI2yqRJbLliY5XBdk7iz8NeTn/xbwxOJBj/WEvJnuxZPvmS+dn1CsZ3sjDVVOtLPhfjYr1ms\nfD6cnXHSteQpJNNMLCUn84daDIuNYp3PvVitlEB9lNWQtrH5FVDIywMziQR6hHgcPYrNx2AbfgdD\nND6PkUqgqZyuEo5j8bE1QNJJMtHa6u3UAXvEpJCMyOYudlYMjb2bH1tdSYnR9uNJIXWabt1ehOSe\nP4Awo+Sh16KHoQglH20ppGDPpAl7OqmogGwvny7BaaffVDatRo/s6aRLxoOT8gRmExsrsZtJ6fTy\nocf5Mkm3YobHCYXIgp4qW3T0FFYndbXk5xALEIsyYSouF1YTq2tYH+dUBwUxy81DWJVK1Wr2OVps\nscYfPmD0EOwekgoujVydhJWXKmmqw2Nj4PDTpkdbAnrInau/W6dnO9lUS7WV0324NEIptsBgJ1uX\nE7KRatwzjWwiyOwxKCaeLYc4WFCGoOaiVxMzQw6WNIqCzwIpKlZhs2O2kW6kxE1vle1x1qu4HayP\nkMql2MolNirCvOUnrEMxpghnm8l1U2VhXZx0Nd3teD3k22hMUWAhUMenUTQzqOY73WfPWDyv8D7f\n8vXBOyJVH6Glyb3UVXnR7Fdd/9lozW0w5fV2hvXyVNNTuv6BHaeJhM7GcrQazD76+cgHRSUUY5sf\ns5PCKB+VcoWKDh9UU+ji4/dotIKCz0RdIcNipCxsqSaVJBXF5mN0LqvcjIyzI04kQbaDZjM99VZ/\nficP2SMkhWRkxVScXOwqDrwciOVsoX8AjxH+YJJC6jTdur0O8T0HkpnHk65HcaFXoYcxDUFxka4A\ny96bh1BGolWTn0MgBGmcbgZMZV0NegPpOGknLjOjCqlNoqqs/QC7hZiGZsFhQU/SPJzvaPQM8wVM\n8fGBwrYanCp1dRS7CMSIxRnqwe3BYiIWYl2E73iwxJRzrZY8U+IlzTJdSS3XWRTEBX1cqDnoKRIW\nbBDws74BLcIQt++mnB56bFM4LxHQtIBGKEXShcmEx0oswroU4/JIJShbhZ6ECDY7p+UzeSTpFK9W\n0pg5G4UNCrCkIYXmpWeCnibiJswKRInW4ZlEzzCbKihxM8hHSKUhTjzEdp2TCphhxxRjQ4TFmZkd\nCiHGWWZy3SyLEWyEMN3yKCxgqhVNIQWpOt6N05hmoM98sWVK4dtzip+Zuurt2ItfVX+ohRvIL8Fx\nievtkkvmO683p9PYSTZav6qk+cGw3s1MzEoyxhfl6GlI4fDR30fQxo4gWg0mKLTQkMvpHvItVDdS\nYGdbDaE0oXpwUFxAtgPCmNxsriBhJlGPxceYJNt8DFGJBFvf+7Z8D0l0nGJfMRUTAgvsMUPsx5NC\n6jTduv0JPNAAaUx5KMWkG1HsezvJg+IiXQsa6GCBEKbh6FWggwJprCpDL2B9HVqEtEo6gUtj0BDS\nGmkTa5ZgsZHS93SSRWFnLg4LPZNUlnN+KTU5bGrAoeAyEcjBEiSUwm1ngouImSIbgRBBBRJMcih5\nJu0vq5lYQF4O99RhTjMwl9VJhuRTADaogY/Lcds4za16TaePr9nUkLfTaWmer+ndUuxooLcbdGwa\nkTSFbqwWPqykLg012ArpWYRPYZKNWJBnlxCKgQd82BW+EydqIu0mBwigQzLJdj/xHDwlpGoI1nBu\nPoNLqYBgiJYAcehTyngrepgN1SzxgxezDz3Id+148qgMsimInqbfcEY6iSuYTPwHVMEiPw2Q76aX\nOmRG1ZRL377S9Eju3zfVPaavWI8pTc6leZEzJj1sujbgyo/mOfp767bVuVMvWni7johOc4pIhGQa\nAqCDBzyYHWhV6LUA2HB4Sah0U5nmIBQlx0RDjE1hEjGUKAM9OFRScWxF1NaS1PgqjtnW+tW36HtI\nokMV+4oDLw3lk7b9eDEcndJMUkidplu3xyAXQpAE7V+dZDJhSZAKottRctFqwLR38oIYuCEEGiig\nYVWxTSfciC1OOk3ajMvEaB9VETYv3Xu0tI5mwWoBG0kbOuSZCFRwjg/Ng6qzup4dCfBiiRMKY7Xy\nYxVzLmYLjTHqFdKNlCiMzee9EJsD/NckXqjhQz89JpFOgMpwFUdm5oE4eTpfxBiWP2L4qkQjm0J5\nelTTP0jicLBRY6iKy0IgThJKVAIJljSwqxzdjuJkUC7Xe9ATPF/Bkuo9Bzug4/PRkiahYlJxpIhF\nsduwxFnvp99knA42+2mo5IJ8ziqlVmV5kK3ldHeT46NUJRlkq58lfiw5pFRQKFYZUcSqIBvLSdo5\nYywulZDGEAslCmF4w0+DGzNYVdfFoUtKn/zPgkWFFetDf232b9C1evRJ2C/xvZJzyTu106I15tCl\nHotXo4rU2xaW+NmY4GSVbVFOzeOLt0lZAQjBSKiEIqgDMOeRToEF3Exz0MdBJIizgI9WscvEV7X0\nz2dXnD55JLXWiqJOG6zfiBRS1/P1P5g6/hMmKaRO063vCrqb2JKC+n06aTjmAIkoPXPZ5Ue3o9jR\nGjBZ0Rv33jUfMpOkmfeex2EapLElSCdIOzHp5KVIQCyM1oDdDTbiUfQwyhiwoKuoCWikRGVQEVUQ\naWB7mKSHVD2p1djcjHYzshCzhbogATPmBGqcq0r5IEKthlvj3Qi6zi4n4TQ2D/YYJSppE6uD5MWJ\n5eKzuGbE+uYFNyxxp+229KMJknVk5+HMIZ7CGiOWxJ1DnpPlEChnVznmsVgLmK1ypsLyOv5RTigC\ndlDAzGmFKG7SOuYoaRO6FavCZgu2OMNUMLG2gWgDV7twFBBQWeGnyc+pblQfxSqxIJv9rA5iUYlZ\nIEJuLqeXEg2ysZwiD2cVsxxiJi6zUKgQhXJY4UePk/ChqZee++Sp8xqvtD+S997Wqmf0UBnJJMMv\nYPUlk16Jzv5Heo7po/LE2SVKroU02hKFt/1oEHNCnBwnO1YTSENm3iAbuAAI730d05APEXAw1kV/\nOyELZ+awKkVDPU6l9Q3ZZSc6RLGvuPLJodYNdqKxpBLD0WG78qSQOk23MX4a4ih8rZNQsU2GWhJx\nTipklx8thcmOHkFxo+092zfuvZ8/tX1xcjwoOEzodZwyiS3V5GmkLESCaA1Yckg5IAQxlGKy3LQo\nqA3E6tDdzB5O1EQsiRLE6mXDCoJ12NwMdeN0YFFxpqlLYFLZ7ueSUlZFeDZGXgq7HRL08LA6Smsl\nKQsTxlOqUg7OOC5wqXjp76xjdXRTyEM0zVtBTnFgceE2o+jUB0mluayACjOL/ewqh1xMxfSy8wsL\nXnjSz7Jy4nHIhVxc0M/L1gRmG/0t2HQiFuqCNFlwwgQXVoW3o3xewdUu8oqph/f87PBzqptcH3YV\nD1SXsz6OYiMYx/uP9AAAIABJREFUIB4mz0ffUmrr2R2kOA+c1KSZqOBwkm/BB+Xwup+vVPqq6Kqj\n1H/Jua+dm784PxZQKyOBpbuq/qnnx3Bfoiwu/vGzlsnlqybbmv2JAZPIATNUwZt+ommiboYq6FG2\nWtHqiaahHnIhByrBjRIEB1pk77kWNUhQUow1wWhP6++/XTM1iM6X5yumYmjw5Xx9dSUlwPFuJimk\nTtPNXo5P3dtJZqjcO2fM1ztpt590cM8crF/rJOfe05m3ddKZEKevjdYI2T62xBiiEUrv6SQskA8x\nCKF4IR8thNmG3oAGs6eSYzIpCmmTnkrycRX+1Th82CDbTYvG94tIJYjDZ+UMLSapUh5mVw0eF8EU\nJ1tRi2gs5/Na8koY4aWhkU8bON+O10cKz/S6SIMz1qiQgOcCtDRyci4JBzYLLhubahjsRM8hoBOv\nY3sAkxs9n/82c66FJUFe9rPaD549f0l4PJxiZUOSAU6GuAnFaFTYWElvC7YCih0oCkvjoFOaoNhN\nA7zhZ4cfh8rppeSo2DWqG0gn0VOsbyDegNkHLrQICgzz8Fkt5/iYlMP/NlDiYrwVK1TAcj82qLBy\nmtN+Tux75772H2e+Pbxytd0fq3ypW/3aeH4Wzgtsr5sueTh0TaDRjtNOnmXPHLZ/hul+ypIk0rgV\ndjrIc1PfgJZgRwJi6BZsKik/FjdaBKx7JvEjAbS2XtXRY/ToSCGdOMy+YiomaAvseuC4fsgkhXQs\nrV+//tFHH913SXZ29t13333QG3fr9idUH4PcBMEU53MT6VV7d8FlOqmSRIRhpWz1EwmCHUBxozeg\nx/ee2k6FxJ5J3tr0HYtVI6HylQ1nDX1G8kUFgcy5w+1ghygON2krCR3FhClJ2s+UH5FnV0o1U00q\nvSwFQWqrMalYcunj5CuFKXZMSQIKDguNKXLtvJUiHeV8FQ22NHJqPpVBNpej2ZlVCGbetTBZ5yQ7\nw1VMmGw6/jo9YgInr4bIcmIxg4bVjClM1ESpg1wb5UE21xBVMeXSW+dcFzMsvFDJ8+Vg2XOCcGKY\nPegeTCpDTbitaBC3UFlOS4rTfBTkkA9LdeJxShNoCrqL+iDb/Fjh5FI0MwUQrYcku/IJNBBbjWJH\n08CBo4BEkIl2xruZ38ggG/ku8qyZ2WhJxYmpmMtJ2dik2X5smXnuB57xobGx5e6KbZHngvUb4vkj\niE0ufiV2yZrXfVWxEt1mxWHCrvC3BEPhqzA1dZh0WgsoMBEATy6bK9ll5ksgwEkj6RamZw476+id\nQ1OgdXvnnw/piEe4OLHkU2cnOoG37EQrKdlB/1o84WPXTFJIx9KyZcuuvPLK0tLSXr16ZZa093bt\n/jhaHNVHfzfbNfITlMfR6g/opEZOL2ZXnAY/2MGC1YnesPfUdhFQQYPI1x7dPASnTosNZzFffECf\nkXSvobIC2HsouQubFbOLaDUomEzoeYzII9+Mw6zkKPq7YV2JUVNNJIrdRY9CVDPuXNIpzEnG5LE0\nRomFpTo7GzBZGRDlk0Z6FeHKY+PbBPx4fJxTykcxVtXx0zxsHiyYi9JESIeS2My8GGKTH91JPx/J\nEG4rTfWk4TtFYGdtDaEAiodsO7lm0xyVUEJ/t4raarBhyyERgCSmAihEATXBGV4sNkJx1pSjwcml\nFKt44D2o92NNMtKN6qI+xlo/2SZyfeSoWOGT93DYiBRAkNhqMO9tPvDkMtPNq7V814k5TVUEUx7n\n2kmZqIXFfjxuciFUz6aQZ0bY8WPn2ZNXpU1mEnhe+Lu2cF3JGZb3pv/klWXTNy91hXq49aCNkSYW\nm8hPEEuztYq+BXwRoKcdiw27woYIfT2EGmhppKePnX6yVHabWneVHI9Be0SObIRLIZ248qnLJXoW\nn+RR9zoltjNPKwuUxgKmo3xYKaRjKfN2ffbZZ0eOHPlvb9ytx2toEbQ4FpW+peyKUmD6eieBrQiz\nQrRmn06yYC7AGicVJ5X5OwlQcIwnWg+Z6aIzy/IZ4OFLDydbiJfTYqVvkk/LwQaACRwQxeYlHSMd\nhiQMx1bAWVbMmvofil5sSrwAz66ipgaLmdOKmTaEijSYSaT5ASyPk1BYEyc/TNrGYDtfRdlZgMdG\nnZ/PyjHDWaVETXwWoMRB/0KKVZLw+zTNAYanONnFmhCN9aTNYMIcQ3ViyqG3lUQ+XypofhKNqF7c\nunJRjilX0Sri+ssVmMxk5bGjlkTjnh/ZpGJyotn57nDyzMTDfOInASeX4gvidrNKpd6PNY4LNA8p\nlVAt5gTNKqc6IcIXlSgjOc2GPcaaBPE4BPf0tyeXkJlxueTaebmW//LxQQSTCV3Fo1IHm1fTYsaX\nR9TPtohtctp6Tn72dOe5ztdq6gpHLllVaKkq3bJ6xfrR9cMH/EM7r8bvI6hhjhIAdD6PojiJ19M3\nH0sYxU62hV1WvkwSa0Cxkappbf7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HP/jBd8IHQRCef/75L37xi6NGjTrsq8mTJz/55JOf/OQnD8vvfy7yF7/4xaMUXt3W0383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hVRjqY+0a4l3lTCoSYyOryF8XA7dh/NopYo7NE8BC65dyhCTZb1RQedQ8xbfy3eqG4i0c0UCp\nGQZMUpByhuHnahNBVjop8O0Nrd6LUZO6Ss/jFTF1gBs0iIvZiwbxfNcjtkeixVEjMBIaOosyz8TF\nZTDG/tlEPYtSiIIXNAZXsHsDiXoccEiA3QmiCVHAKQEJnC6c+SRiJKLYAmhW1DiE25ausAsp1fM1\n6AF7uvz4RA66FBYbWNFS4ALFIEtFWFxodRSU0L+QHbWkGttGbRjPwpseC7oCtLhoDVLs4ogAZT7n\n7MSl/nvmv3l30/Phqo9QmygdT/z88dUnT389MjuGO1Vsj6Tyd/zSpW2KEQujBPFZ+Z6LAhfvriEW\nF7fM0A8AETdvE7Hjqhi96VcgWzInk/9WOyIFyfzMnhc8jletKz+LN2hm8OOZvJF/RwQpg1GTOk3k\nU2BIBRRI12JoT6nIMoAhS6kXvNAg5pwaAaylWBwoG8DbwYGmzwAVQ0pkJOoUNziFi88JLrxOBnjZ\nWo0WxOOnxcVXEciHKDjSS25tLiBdqcjqQ9UX7hijOYwlKuxQBHquWKsYOUXBgtWHxYkaQkOMDuN7\n92BxoTXiLeFQF5urScYNUtRplVgoCKSjFKNBynzcMsFRav1J/NmLm/7hWVmfeC6y8gvsKtZ5Y/2n\nWN6c+cP36ibVUapusMaLXLsf1bTlFiz1fN/KrggH23E7eTszZvKIzEBa21tWBElwi8y5qkgspAeY\n+AyZZJXW1nld3AWTIgWpr3D2vKr/rAyPTqwlEtX9eDE8OVEmKUg5o4ufayY2jA6aZBeetIxfS0//\n3Ok4SRXftYnFqk6R+M6wZ0sBmg2C4snY7nAq5INbrFXqlMwjVUlHIvgcDB3J2mq0OnBBkRjfxMXE\nEjh92Fwkgihx4bTMTAsZz0JfbGSHEogZJIp05Q4sOMbS38au9ahWkfhcjDMsfjQb1FNSzPcyspTB\nxuET2F5Fqq2oFwU4NEAyzmdrONHHaQECvtKyuvM3PPrL1fcHn4pUbSEVxTsJ+/kVtRPGrbFMCwZ8\nVcsqtEZLc3VS2WinFnYFGQ47nQyOYbfyL12Z9KtRh2cSiQ0obXO0UyqyLoVFBV6byPFqhaLWVll+\nQtKzzJtUOYoPqKmLRcjJDJMUpJyRl/dYW4dYp7Sr/mA3zO6kAKw+ALWjJrWLTXAa5pYSYrUQYs/u\ndDqczOBp7+GsopRcqqeEh1YAACAASURBVE2e1jY4oAi7jUEOdoWwewlt4PCx/MdJdD1EoQAcQiNt\nokp6EqcPRUXJyEwAiwoxNEV47UpFFEAy/XS2JEXecWNshRvnaIiR0FdNZZb36puVgwp1FBczwMX2\nILHM5XLiDYBCrJ4U7YebzgCHutgZZI6LY31M9JWWhidF3ru44R/Frzc0vRJe+RlulQknU3/++KrZ\nxy2rnlM1vmLXo04tFKYOqpwkLaQaaEnxPRdOO5Y4m2uJNEIhtgKcxRBFDaOGSbZb46y7ZH3C3ZcE\nX2vrhC6uv0mRgtRHmTepsuo9TnEudxOtSpTG8PSaK08KUs7Iy3sULN3QpKK2BcIRIcIinZ21sG0q\n7gx24QtqR0aT9OWc7QLzOqJrSQFYDHLVcU2rF3sBgwrZUQn5oOBwQAXRGtggVo9GhD0OcUYO8TqZ\nDsSwuMCKZjGk+LOLIA4FazEWF2q9KChuyLuDF6sHVRVLrDrKkgJ1lJakZSmSiXZTKQkw2MWOOHW0\nd4QGArSWsDVMooZ5RZxZxkRfkaXxfOXRk95can1nnWultnIzToXRZ1iqr5j74qRT31k2JTIn/z9X\nuFAa+RC+ysenEmsgasXnwwb1QXYHiQVRNJxFeLzs9uBM8Z9GUk0k4mJOUTfeD01Q3No6rbO7Y16k\nIPV15pVWjuKDWGMUJVrFeKCnlUkKUs7Iy7tHDEqypK1DTJwobXvQbhHDHQENi7NtKm4j3qya5DX4\nADuN7tNxioRyDrG4xyWy1RmdTgGwMcQFCv0VUglC9dh8kI/SJNILZXyS+UIC1fYOQ6sPnGhRbBoo\nwqXmFkuUYtjKyXPRXIUW7zAhVCSqTIUMGVpFvAMlEIZ6ikrY4+KrIKmYqOcUwxYgz8X3XBQoNFST\njIhwfHA6SLjxeomHGO+g0ML/TMDjcqYS58cfvbLpL/EV/44u11JB7AqB823Lzvuvvwau+Oy5stRo\nOw9AOIbHSp2VL5IcHsIToL6OqBOLSjTO7mqUOJoDrw9nEa027Co7aknGiesjJAtYWlvPy9pOTIcU\npAOGeZ7KUXxAoi6m0KOTTFKQ9ieffvrpgw8+aHxnwIABt912W6cb5+X9RYRrf+04Sc+x3U6TiqER\nCsRkvobFjdbpUtNOyUhRpwuPOuIRBuit0A5OqBEDFGMYmw9XEYM1tgdx5pNowpafXnIbrRVZJBDi\nZ0jz02b+DBwBLDasCex67p+MLDnTSmYLoIVRG4AOq1OLxGVJid0aisxSAhsgiidAPx/JOLuq9m7p\nLGFugAIfL71HuIlkXNSMSIBKUQBsRGIkFc4p4IelOF3ku852P2VLKMfHlxUuec2yPG5JYR2EOi/w\n3MTT1jjn/Ov5CmqSJJLke6jSCLvwQxHURrA4Caqk7GhN7Iii1qZz3BU5sXoZVkgoiUdhc2Pr1twn\nV933Fi45oJhmrTzF/gGpuphKFeP3e7y4FKT9yapVqy666KIJEyYMGjRIf6cbP1crOHB7idV2upnA\nLooMGTWpBKJi/BQT3fmOZcW7wihFna69zW87q2QTQcz5UAAN4ACLePQbkg/ZZqA1MaKInTFskIgQ\nqgc9YagFRQ+ZKwMr1ImEsLoOieVB6dPUsBaCnXwPToW6DcISPQwvAnYsHkiIVNztyKzNchhyIGVk\nibQv1FaIoieACINbpCqPM6OUPcXkFxKr5+MwMb3GRCFshARFAVQv0TpSGmcXcFwpTS4KnOM9lYHi\n6qE0lm98Z3bDv2pfSTorWH3FvMXW84PJ4oY3C7h/HS0emsMMGksizkgnLjculXpQrDRCXEFr4D/1\nKFERjeKgqLR1y+ju3dYe5Bu1cMkByDRr5Sn5H7jVuoYI+9GbJwVpf6L/XJ988smxY8d+7cZtf675\neIsJrcv6jU4zZBenxcCeFGOIfdIkDLNHXX2xXWBFBj/EcJdDhFijYVWNBRQsfiwuBltxWNkexG0l\nHiYexxMAJ9FGCIFXlARsF+BuT9csB1CwurD6UR04GiBBPKM9+sAxJRb3KJ0FwSNGVHpNPCs0GDbT\nXYWZgIJMdKIueCo4KcpnejGjfdxbRzgEKWJuCEMUi4LLRSzG4TPZHiYVZk4h0wtpcrLaafemJlas\nHj6huiAePOnHrxefvMlzl3Lfc/PqJw1/Yd2P6za6qY6zqZKUG+wUFaI1cWwpTh/5UC3KSMWTfBki\nFUZJtLbO7PY97Sm+RQuXHJjMK6ocxQcRS9xNNNjgaqB0i29cDE9D8JuIkxSk/ck+/lzvaRsXkI/b\nR6wq65fsnc3xFEMYdwmpxm+qSZlSSZ1VKoK2IXkGLCVYXNis2CykgqRC6QJCmee7pRCbDTfgwJMi\n3kgwCA6sVtCr7emrgAOwoa3b0AYFhkpLNpx++hWwqxGvRsxCotFQXQmR6c5pSCHRBY5iHHaSEZLt\nVNDZYQCq408PnqYU8+Voil2kUvw7TEM9tgCEUOvRmvAGCCkcPoHtIVIRAl6GFxC2E3Sw7QnG+zzn\ne+ZOWOmPh+LlruJwQyl1fw1eURcq3bysUK1qYkuCWHW6EFRpgBQcHsDtwu7KJIRqfSfbafUOUpAk\nXXH6vOpRVKZWfhErG1k6Rl25dPC0qVsW/t/x+6RMUpD2J/rPdcaMGf379x84cOD06dPnzJnT1cZ5\neY92CGzLaFLGOdZN3JDCGyAVJ6avNOpKWrJgNdR66Phc1n1cjR3ez8dSghbB7oMgKQvUgVMsflJw\nBujnIg/sdhL1uG3EbcSjOH1gI9EEYVBgCiRhfdtD+8VyWt3tpuEs4WAvRaXEomxdTaKjPYWQMFTU\n7QoP2HCobWXJ08Vknh434QUHXifjionZ2aYwOMynduIprG7UEDRACG8AxcbQchIqW+MUOEnaUeLQ\nhBLE68Wb8vzIf3Jg5XPW088vezTo9Pnza1+pOal1SbKuIV9dG6OqSVznFPk+NBfNPpyu1iZf1jPq\nDfaxhUtB+o5STF2sbOTUyP8y6fuxTb6Pt/jR4jHNKtZsdIkUpP3JqlWrfvvb344ZM6Zfv36NjY1V\nVVWTJk168MEHLRZLx41PPPGvlZUfdxjxdOUf+1q8EMPtIxVuv9JzH+iqMKBeHL3r3Vr9aCE8hSSj\nJJNCSsVQye4DSMXxuog6KHUStBFPQD1WK2pK5GD1QwKa2mqJA0qgAZS9iQy8fvq5cMHW+rYFWDNk\nT0drOC+fm3is6xx0HSlKq4UtwGEu8uHDKK4QcU2EttemnaheXzr0I6GRiIMdZ4KkCim0OKQoCtDP\nRQEVE6oiWv4Jltci5Nfa/Z/Fj1RfqgU3TXHRZbGNH3/0q69e0W0je4p9auFSkCRAxTyl6lHbKfOq\nkzvL3E2RVcnxyufhmKqnDWuPFKT9SXNzc//+/TN/PvHEE3/4wx9uuOGGc845J4dWSST7i31q4eee\ne25lZWUvWifp24wfP/7xxx/PtRXZMK8grV69+oILLsj8+dlnn3W62dSpU4866qh77rmnt+ySSPYP\nsoVLJO2wfv0mOSIQCDzwwANfu9mQIUOam5t7wR6JZP8iW7hE0g7zCpLb7Z46dWr2bb788suPP/74\nlFNO6R2TJJL9iGzhEkk7zCtInbJo0aIhQ4aMGDHikEMOqaysvPPOOy0Wi9HvIZH0aWQLl3yX6WOC\nFAqF/vznP7e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eNNWhtB+cW9YAd2up32FLRKmGKJRZ6pfrfiE3EWGexsJ0ParukjIPrz+ysJCu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"text/plain": [ "<IPython.core.display.Image object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_total_energy_fcn(131,1:1:1000)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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ZCfxAV9AKTJ0UqTP5WjjOIeTLf3Xjl0LAV7WKsxLYOgHRCB+7BdQquJw7jh5A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C8Vm4NgZTI2CqKwGGzRYnI+Zh5/7J1gZfDGuXfE7Zizl4QL3hF7K9j/AIAuNds32UXw7UUrCl+h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\nIAhBQAqJIAiCEASkkAiCIAhB8P8Bb48yhgycltYAAAAASUVORK5CYII=\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_total_energy_fcn(124,1:1:973)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Processing echo_info_run103_ping0003.mat\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[0;31mError using load\n", "Unable to read file '/home/wu-jung/internal_2tb/trex/figs_results/echo_info_run103/echo_info_run103_ping0003.mat'. No such file or directory.\n", "\n", "Error in plot_total_energy (line 44)\n", " S = load(fullfile(base_data_path,data_path,scat_fname));\r\n", "\n", "\u001b[0m" ] } ], "source": [ "plot_total_energy" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Matlab", "language": "matlab", "name": "matlab" }, "language_info": { "codemirror_mode": "octave", "file_extension": ".m", "help_links": [ { "text": "MetaKernel Magics", "url": "https://github.com/calysto/metakernel/blob/master/metakernel/magics/README.md" } ], "mimetype": "text/x-matlab", "name": "matlab", "version": "0.13.0" } }, "nbformat": 4, "nbformat_minor": 2 }	gpl-3.0
scottgigante/m-phate	examples/classification_keras.ipynb	1	16352780	null	gpl-3.0
DJCordhose/ai	notebooks/unsupervised/pca-intuition.ipynb	1	42188	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import warnings\n", "warnings.filterwarnings('ignore')" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "from sklearn.decomposition import PCA" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2], [4, 3], [4, -1]])\n", "# X = np.array([[-1, 1], [-2, 2], [-3, 3], [1, 1], [2, 2], [3, 3], [4, 4]])" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[-1, -1],\n", " [-2, -1],\n", " [-3, -2],\n", " [ 1, 1],\n", " [ 2, 1],\n", " [ 3, 2],\n", " [ 4, 3],\n", " [ 4, -1]])" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.collections.PathCollection at 0x1a74bfce4a8>" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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GiCoAgAGiCgBggKgCABggqgAABogqAIABogoAYICoAgAYIKoAAAaIKgCAAaIKAGCAqAIAGCCqAAAGiCoAgAGiCgBggKgCABggqgAABogqAIABogoAYICoAgAYIKoAAAaIKgCAAaIKAGCAqAIAGCCqAAAGiCoAgAGiCgBggKgCABggqgAABogqAIABogoAYICoAgAYIKoAAAaIKgCAAaIKAGCAqAIAGCCqAAAGiCoAgAGiCgBggKgCABggqgAABogqAIABogoAYICoAgAYIKoAAAaIKgCAAaIKAGCAqAIAGCCqAAAGiCoAgAGiCgBggKgCABggqgAABogqAIABogoAYICoAgAYIKoAAAaIKgCAAaIKAGCAqAIAGCCqAAAGiCoAgAGiCgBggKgCABggqgAABogqAIABogoAYICoAgAYIKoAAAaIKgCAAaIKAGCAqAIAGCCqAAAGiCoAgAGiCgBggKgCABggqgAABogqAIABogoAYICoAgAYIKoAAAYsFFVVdaaqvl5VX62q36mq9anBAAD2kkWfqXo2ybu6+z1J/iTJ6cVHWtz5S5t57GOfz9s/8qk89rHP5/ylzWWPBADscwtFVXd/trtf2b75xSQPLz7SYs5f2szpc5ezeX0rnWTz+lZOn7ssrACAN9XkNVU/k+Qzg+t9T85cuJKtGzfvOLZ142bOXLiypIkAgAfBQzvdoao+l+Rtd/nR0939u9v3eTrJK0meucc6TyV5KkkeffTR72nYN+Lq9a37Og4AMGHHqOrux+/186r66SQfSPIj3d33WOdskrNJsrGx8f+936IOr69l8y4BdXh97c06JQDAwu/+eyLJLyb5YHf/9cxIizl14ljWDh6449jawQM5deLYkiYCAB4EOz5TtYOPJ/n+JM9WVZJ8sbv/5cJTLeDk8SNJbl1bdfX6Vg6vr+XUiWOvHgcAeDMsFFXd/Q+mBpl08vgREQUA7CqfqA4AMEBUAQAMEFUAAANEFQDAAFEFADBAVAEADBBVAAADRBUAwABRBQAwQFQBAAwQVQAAA0QVAMAAUQUAMEBUAQAMEFUAAANEFQDAAFEFADBAVAEADBBVAAADRBUAwIDq7t0/adW1JH+2C6d6a5K/2IXz7FX2597sz87s0b3Zn3uzPzuzR/e2W/vz97v70E53WkpU7ZaqutjdG8ueY1XZn3uzPzuzR/dmf+7N/uzMHt3bqu2Pl/8AAAaIKgCAAfs9qs4ue4AVZ3/uzf7szB7dm/25N/uzM3t0byu1P/v6mioAgN2y35+pAgDYFfs6qqrq31fVV6vq+ar6bFUdXvZMq6aqzlTV17f36Xeqan3ZM62SqvrxqvpaVX27qlbmHSbLVlVPVNWVqvpGVX1k2fOsmqr6RFW9XFUvLHuWVVRVj1TV71fVi9v/ff3csmdaJVX1lqr646r639v78++WPdOqqqoDVXWpqj657FmSfR5VSc5093u6+71JPpnk3y57oBX0bJJ3dfd7kvxJktNLnmfVvJDkySRfWPYgq6KqDiT5lSQ/muSdSX6yqt653KlWzq8leWLZQ6ywV5L8Qnf/wyQ/nORf+R26w98keX93/6Mk703yRFX98JJnWlU/l+TFZQ9x276Oqu7+y9fc/LtJXED2Ot392e5+ZfvmF5M8vMx5Vk13v9jdV5Y9x4p5X5JvdPefdvffJvnNJB9a8kwrpbu/kOT/LnuOVdXdf97dX9n+/q9y64/ikeVOtTr6lm9t3zy4/eXv1+tU1cNJ/lmS/7bsWW7b11GVJFX1H6rqm0n+eTxTtZOfSfKZZQ/ByjuS5Juvuf1S/EHke1RVR5McT/Kl5U6yWrZf1no+yctJnu1u+/Pd/mOSf53k28se5LY9H1VV9bmqeuEuXx9Kku5+ursfSfJMkg8vd9rl2GmPtu/zdG49Jf/M8iZdjjeyP9yh7nLM/0Vz36rqB5L8dpKff90rCw+87r65fenKw0neV1XvWvZMq6SqPpDk5e5+btmzvNZDyx5gUd39+Bu86/9M8qkkv/QmjrOSdtqjqvrpJB9I8iP9AH7Gxn38DnHLS0keec3th5NcXdIs7FFVdTC3guqZ7j637HlWVXdfr6o/yK1r9Lzx4TseS/LBqvqnSd6S5O9V1f/o7n+xzKH2/DNV91JV73jNzQ8m+fqyZllVVfVEkl9M8sHu/utlz8Oe8OUk76iqt1fV9yX5iSS/t+SZ2EOqqpL8apIXu/uXlz3PqqmqQ7ffiV1Va0kej79fd+ju0939cHcfza3HoM8vO6iSfR5VST62/TLOV5P8k9x6lwB3+niSH0zy7PZHT/yXZQ+0Sqrqx6rqpST/OMmnqurCsmdatu03Nnw4yYXcusD4t7r7a8udarVU1W8k+aMkx6rqpar62WXPtGIeS/JTSd6//bjz/PYzDtzyQ0l+f/tv15dz65qqlfjIAO7NJ6oDAAzY789UAQDsClEFADBAVAEADBBVAAADRBUAwABRBQAwQFQBAAwQVQAAA/4fRZAWvuHtn/oAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1a74c334ba8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10,10))\n", "plt.scatter(X[:, 0], X[:, 1])\n", "# plt.savefig('original.png')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Scale" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "from sklearn.preprocessing import StandardScaler" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "scaler = StandardScaler()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "StandardScaler(copy=True, with_mean=True, with_std=True)" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "scaler.fit(X)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([1. , 0.25])" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "scaler.mean_" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([6.5 , 2.6875])" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "scaler.var_" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "X_scaled = scaler.transform(X)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.collections.PathCollection at 0x1a74c3e99e8>" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1a74c17e2b0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(X_scaled[:, 0], X_scaled[:, 1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Find linear correlations" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "df = pd.DataFrame(X_scaled)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "corr_mat = df.corr()" ] }, { "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>0</th>\n", " <th>1</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1.000000</td>\n", " <td>0.717778</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>0.717778</td>\n", " <td>1.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " 0 1\n", "0 1.000000 0.717778\n", "1 0.717778 1.000000" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "corr_mat" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "import seaborn as sns" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x2087012cd68>" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x20870132d30>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10,10))\n", "sns.heatmap(corr_mat, annot=True)\n", "# plt.savefig('correlation.png')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Eigenvectors on the correlation matrix will be the principal components" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "PCA(copy=True, iterated_power='auto', n_components=2, random_state=None,\n", " svd_solver='auto', tol=0.0, whiten=False)" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pca = PCA(n_components=2)\n", "pca.fit(X)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([9.3121762, 1.1878238])" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pca.explained_variance_" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0.88687392, 0.11312608])" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# sum is 1, first pc has a very high variance, i.e. is very good, second could be deleted\n", "pca.explained_variance_ratio_" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "X_reduced = pca.transform(X)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.collections.PathCollection at 0x2086fa26518>" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x2086f7b0e48>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10,10))\n", "plt.scatter(X_reduced[:, 0], X_reduced[:, 1])\n", "# plt.savefig('reduced.png')" ] }, { "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": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 1 }	mit
ncfausti/machine_learning	Week 2.ipynb	1	2202	{ "metadata": { "name": "", "signature": "sha256:3af4550ebf00deb5c2ca529d7bc510bbd041151233f037119e7831e9ee591e6a" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "#Week 2\n", "\n", "####Matrix Multiplication in Python w/NumPy\n", "\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "x = np.matrix( \"2 4 5; 3 4 5\") # Can use either format\n", "y = np.matrix( ((5,10), (10,20), (20,50)) )" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "x" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 15, "text": [ "matrix([[2, 4, 5],\n", " [3, 4, 5]])" ] } ], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [ "y" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 16, "text": [ "matrix([[ 5, 10],\n", " [10, 20],\n", " [20, 50]])" ] } ], "prompt_number": 16 }, { "cell_type": "code", "collapsed": false, "input": [ "x * y" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 17, "text": [ "matrix([[150, 350],\n", " [155, 360]])" ] } ], "prompt_number": 17 }, { "cell_type": "markdown", "metadata": {}, "source": [ "__Some new notation__" ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }	gpl-3.0
djangogo/CoCo-Matlab-API-on-Windows	PythonAPI/pycocoEvalDemo.ipynb	2	517049	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "from pycocotools.coco import COCO\n", "from pycocotools.cocoeval import COCOeval\n", "import numpy as np\n", "import skimage.io as io\n", "import pylab\n", "pylab.rcParams['figure.figsize'] = (10.0, 8.0)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Running demo for bbox results.\n" ] } ], "source": [ "annType = ['segm','bbox']\n", "annType = annType[1] #specify type here\n", "print 'Running demo for %s results.'%(annType)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "loading annotations into memory...\n", "Done (t=6.49s)\n", "creating index...\n", "index created!\n" ] } ], "source": [ "#initialize COCO ground truth api\n", "dataDir='../'\n", "dataType='val2014'\n", "annFile = '%s/annotations/instances_%s.json'%(dataDir,dataType)\n", "cocoGt=COCO(annFile)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading and preparing results... \n", "DONE (t=0.03s)\n", "creating index...\n", "index created!\n" ] } ], "source": [ "#initialize COCO detections api\n", "resFile='%s/results/instances_%s_fake%s100_results.json'\n", "resFile = resFile%(dataDir, dataType, annType)\n", "cocoDt=cocoGt.loadRes(resFile)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# visialuze gt and dt side by side\n", "imgIds=sorted(cocoGt.getImgIds())\n", "imgIds=imgIds[0:100]\n", "imgId = imgIds[np.random.randint(100)]\n", "img = cocoGt.loadImgs(imgId)[0]\n", "I = io.imread('%s/images/val2014/%s'%(dataDir,img['file_name']))" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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IKxS1qvwJiPM1OLNEMaWkqiL6EAjRk3vFecGJq0QpirhqmRGROphSw5WzfPeo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kbBn2gRQiOd9nkVW+o66FtuUsjCFwPA6kWDBeTo+UM9EUmlVLd3mK+eIZYRwkXBU5gCtfkUuF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\nBhWqN6nBTV5Ssu1ts4/dab8eIOvW4lHcOTYIVWFujdC9MdCZrsWNDwo0ZTO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ZE/0sGVJv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\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d2hJQ1iBDm19i+ytL2YhlBXOy3sU+BGIXSRTmNBE7YZ6SKVj6QEqFXFtCiURTkHYBq9+t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tqYXbWopFI9Faj57BFXKOI1Qxl1ZKrEvsNbCrlA1AVO+4MlA4SaSzFhQlLR+5vkSM4qc54lcxwT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fZ5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\n7qM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AV6+2QrcDFssWf1xkndogCkUdlLEmU8SEEWPEZk1mo3XbunlCGxaPrXS9cttHtdNM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npJDB10hdWUkiuIoAJ6ff/IBWa35LcplGZXxhAWerd/RJuc+cWcnjeLu46NjvaQB0QQlhwP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\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Pg69vf8TsndGd29c3fvTDH7PSl3W2X4FJoCwuQSYQq5QbxMTgoSS3Crs4C7XUeN9LYVYC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uZu6CkanWmoI7sBif8YmawZCc6Wkli871LcbwLz96FPF/Xuua0eqeSLSo99mbv4a+TOPDemU7UH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\nV65Xp12/MMbkuElveas3ZtfUkgh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8TAUHs0+tk6NGIkBZOEddVvgSqquhkG23z8ktVczq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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10b6dbb50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# visialuze gt and dt side by side\n", "fig = plt.figure(figsize=[15,10])\n", "\n", "# ground truth\n", "plt.subplot(121)\n", "plt.imshow(I); plt.axis('off'); plt.title('ground truth')\n", "annIds = cocoGt.getAnnIds(imgIds=imgId)\n", "anns = cocoGt.loadAnns(annIds)\n", "cocoGt.showAnns(anns)\n", "\n", "# detections\n", "plt.subplot(122)\n", "plt.imshow(I); plt.axis('off'); plt.title('detections')\n", "annIds = cocoDt.getAnnIds(imgIds=imgId)\n", "anns = cocoDt.loadAnns(annIds)\n", "cocoDt.showAnns(anns)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Running per image evaluation... \n", "DONE (t=0.68s).\n", "Accumulating evaluation results... \n", "DONE (t=0.31s).\n", " Average Precision (AP) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.505\n", " Average Precision (AP) @[ IoU=0.50 \| area= all \| maxDets=100 ] = 0.697\n", " Average Precision (AP) @[ IoU=0.75 \| area= all \| maxDets=100 ] = 0.573\n", " Average Precision (AP) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = 0.586\n", " Average Precision (AP) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.519\n", " Average Precision (AP) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.501\n", " Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 1 ] = 0.387\n", " Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 10 ] = 0.594\n", " Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.595\n" ] } ], "source": [ "# running evaluation\n", "cocoEval = COCOeval(cocoGt,cocoDt)\n", "cocoEval.params.imgIds = imgIds\n", "cocoEval.params.useSegm = (annType == 'segm')\n", "cocoEval.evaluate()\n", "cocoEval.accumulate()\n", "cocoEval.summarize()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }	bsd-2-clause
eds-uga/cbio4835-sp17	lectures/Lecture23.ipynb	1	2013925	null	mit
aswolf/xmeos	examples/test-RTpress-MgSiO3/06-compare-miegruneisen-EOS.ipynb	1	12414	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "%matplotlib notebook\n", "import numpy as np\n", "import pandas as pd\n", "import pickle\n", "\n", "import xmeos\n", "from xmeos import models\n", "from xmeos import datamod\n", "CONSTS = models.CONSTS\n", "import copy\n", "\n", "from mpltools import annotation\n", "\n", "from collections import OrderedDict" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "analysis_file = 'data/analysis.pkl'\n", "with open(analysis_file, 'rb') as f:\n", " analysis = pickle.load(f)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "datamodel = analysis['datamodel']\n", "data = datamodel['data']\n", "eos_mod = datamodel['eos_mod']" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "T0 = eos_mod.get_refstate()['T0']\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Extract reference isotherm and roughly estimate \n", "# compression parameters using interpolation\n", "\n", "S0 = 0 # Ignore absolute entropy for simplicity\n", "\n", "kind_thermal='Debye'\n", "kind_thermal='ConstHeatCap'\n", "kind_gamma = 'GammaFiniteStrain'\n", "kind_compress = 'Vinet'\n", "compress_path_const = 'S'\n", "# compress_path_const = 'T'\n", "ref_energy_type='E0'\n", "# ref_energy_type='F0'\n", "natom = 1\n", "molar_mass = (24.31+28.09+316.0)/5.0 # g/(mol atom)\n", "\n", "eos_mod_mgd = models.MieGruneisenEos(\n", " kind_thermal=kind_thermal, kind_gamma=kind_gamma, \n", " kind_compress=kind_compress, compress_path_const=compress_path_const,\n", " natom=natom, ref_energy_type=ref_energy_type)\n", "\n", "eos_mod_mgd.ndof=3\n", "\n", "eos_mod_mgd.refstate.ref_state['T0'] = T0\n", "eos_mod_mgd.set_param_values(S0, param_names='S0')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "datamodel_mgd = datamod.init_datamodel(data, eos_mod_mgd)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "datamodel_mgd['eos_mod']" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "compress_param_names0 = ['V0','K0','KP0','E0']\n", "compress_param_names = ['V0','K0','KP0','E0']\n", "gamma_param_names = ['gamma0', 'gammap0']\n", "\n", "compress_param_vals = eos_mod.get_param_values(\n", " param_names=compress_param_names0)\n", "gamma_param_vals = eos_mod.get_param_values(\n", " param_names=gamma_param_names)\n", "\n", "eos_mod_mgd.set_param_values(param_names=compress_param_names,\n", " param_values=compress_param_vals)\n", "eos_mod_mgd.set_param_values(param_names=gamma_param_names,\n", " param_values=gamma_param_vals)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "tbl = datamodel_mgd['data']['table']\n", "err_scale = datamodel['err_scale']\n", "# tbl['E']" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "resid=datamod.calc_resid(datamodel_mgd)\n", "residP = resid[0:79]\n", "residE = resid[79:]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "residP_a = (eos_mod_mgd.press(tbl['V'],tbl['T'])-tbl['P'])/datamodel['err_scale']['P']\n", "residE_a = (eos_mod_mgd.internal_energy(tbl['V'],tbl['T'])-tbl['E'])/datamodel['err_scale']['E']\n", "# residE_a-residE\n", "# residP_a-residP" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "datamodel_mgd['eos_mod']" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# datamodel_mgd" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "eos_mod_mgd.set_param_values(param_names=['Cvlimfac'],param_values=[1.55])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "display(eos_mod_mgd.get_params())\n", "display(eos_mod_mgd.get_refstate())" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "T0 = eos_mod_mgd.get_refstate()['T0']\n", "V0 = eos_mod_mgd.get_params()['V0']\n", "eos_mod_mgd.internal_energy(V0,T0)\n", "# eos_mod_mgd.press(V0,T0)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# datamodel_mgd['data']['exp_constraint']=None" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fit_calcs = ['compress','gamma','refstate','thermal']\n", "fix_params = ['S0','theta0']" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "datamod.select_fit_params(datamodel_mgd, fit_calcs, fix_params=fix_params)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "datamodel_mgd['fit_params']" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "datamod.fit(datamodel_mgd)\n", "datamod.fit(datamodel_mgd, apply_bulk_mod_wt=True, wt_vol=.5)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "display('R2fit = ', datamodel_mgd['posterior']['R2fit'])\n", "display('Model Residual Error = ', datamodel_mgd['posterior']['fit_err'])\n", "display(datamodel_mgd['posterior']['param_tbl'])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def fit_param_symbols():\n", " param_tex_sym = OrderedDict()\n", " param_tex_sym['V0'] = r\"$V_0$\"\n", " param_tex_sym['K0'] = r\"$K_0$\"\n", " param_tex_sym['KP0'] = r\"$K'_0$\"\n", " param_tex_sym['E0'] = r\"$E_0$\"\n", " param_tex_sym['gamma0'] = r\"$\\gamma_0$\"\n", " param_tex_sym['gammap0'] = r\"$\\gamma'_0$\"\n", " param_tex_sym['Cvlimfac'] = r\"$C_V^\\textrm{lim}$\"\n", " \n", " return param_tex_sym\n", "\n", "\n", "param_tex_sym = fit_param_symbols()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "tbl = datamodel_mgd['data']['table']\n", "\n", "Tlbl = data['T_labels']\n", "delT = Tlbl[1]-Tlbl[0]\n", "clims = [Tlbl[0]-delT/2,Tlbl[-1]+delT/2]\n", "cmap = plt.get_cmap('coolwarm',len(Tlbl))\n", "\n", "T0 = eos_mod_mgd.get_refstate()['T0']\n", "V0 = eos_mod_mgd.get_params()['V0']\n", "Vmod = V0np.linspace(.3,1.2,1001)\n", "\n", "plt.figure()\n", "for iT in data['T_avg']:\n", " icol = cmap((iT-clims[0])/(clims[1]-clims[0]))\n", " plt.plot(Vmod/V0, eos_mod_mgd.internal_energy(Vmod,iT), '-', color=icol)\n", " \n", "plt.scatter(tbl['V']/V0,tbl['E'],c=tbl['T'], cmap=cmap)\n", "plt.xlabel(r'$V$ / $V_0$')\n", "plt.ylabel(r'Energy [eV/atom]')\n", "cbar = plt.colorbar()\n", "plt.clim(clims)\n", "cbar.set_ticks(Tlbl)\n", "plt.ylim(-21,-19)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.figure()\n", "for iT in data['T_avg']:\n", " icol = cmap((iT-clims[0])/(clims[1]-clims[0]))\n", " plt.plot(Vmod/V0, eos_mod_mgd.press(Vmod,iT), '-', color=icol)\n", "\n", "\n", "Tbnd = 1773\n", "Tbnd = 1673\n", "Pbnd = eos_mod_mgd.press(Vmod,Tbnd)\n", "# indbnd = np.argmin(Pbnd)\n", "indbnd = np.argmin(Pbnd*2)\n", "\n", "\n", "plt.plot(Vmod[:indbnd]/V0, Pbnd[:indbnd],'-.',color=[.5,.5,.5])\n", " \n", "plt.scatter(tbl['V']/V0,tbl['P'],c=tbl['T'], cmap=cmap)\n", "plt.clim(clims)\n", "plt.xlabel(r'$V$ / $V_0$')\n", "plt.ylabel(r'Pressure [GPa]')\n", "cbar = plt.colorbar(label='Temperature [K]')\n", "cbar.set_ticks(Tlbl)\n", "\n", "#plt.ylim(-2,15);\n", "plt.plot(Vmod/V0,0Vmod,'k-')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "Tlbl = [2000,2250,2500,2750,3000,3250,3500]\n", "delT = Tlbl[1]-Tlbl[0]\n", "clims = [Tlbl[0]-delT/2,Tlbl[-1]+delT/2]\n", "Tfoot_grid = Tlbl\n", "Pgrid = np.arange(0,550.1,1)\n", "eos_mod.apply_electronic = False\n", "Vad_grid, Tad_grid = eos_mod.adiabatic_path_grid(Tfoot_grid, Pgrid)\n", "\n", "Vad_grid_mgd, Tad_grid_mgd = eos_mod_mgd.adiabatic_path_grid(Tfoot_grid, Pgrid)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.figure()\n", "\n", "for Tad, Tad_mgd in zip(Tad_grid, Tad_grid_mgd):\n", " plt.plot(Pgrid,Tad,'-')\n", " plt.plot(Pgrid,Tad_mgd,':')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "eos_mod_mgd.gamma(Vmod,3000)\n", "eos_mod_mgd.heat_capacity(Vmod,3000)\n", "eos_mod_mgd." ] }, { "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
sansbacon/nba	examples/machine_learning.ipynb	1	49100	{ "metadata": { "name": "", "signature": "sha256:28944676c90a8f3613d515070149629e59524ed4f81fa592b84f48df6a911150" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "%load_ext sql\n", "%matplotlib inline" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "import logging\n", "import os\n", "import sys\n", "\n", "from configparser import ConfigParser\n", "import pandas as pd\n", "import numpy as np\n", "import matplotlib\n", "import matplotlib.pyplot as plt" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": true, "input": [ "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n", "config = ConfigParser()\n", "configfn = os.path.join(os.path.expanduser('~'), '.pgcred')\n", "config.read(configfn)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 3, "text": [ "['/home/sansbacon/.nbadb']" ] } ], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "user=config['nbadb']['username']\n", "password=config['nbadb']['password']\n", "db=config['nbadb']['database']" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "connection_string = \"postgresql://{user}:{password}@localhost/{db}\".format(user=user, password=password, db=db)\n", "%sql $connection_string" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 5, "text": [ "u'Connected: nbadb@nbadb'" ] } ], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "from tpot import TPOTClassifier\n", "from sklearn.model_selection import train_test_split\n", "from sklearn import preprocessing" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 25 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2017 Player Gamelogs" ] }, { "cell_type": "code", "collapsed": false, "input": [ "result = %sql SELECT * FROM tmpmodel" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "5586 rows affected.\n" ] } ], "prompt_number": 55 }, { "cell_type": "code", "collapsed": false, "input": [ "df = result.DataFrame()\n", "df.head(10)" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>salary</th>\n", " <th>minema</th>\n", " <th>dkema</th>\n", " <th>consensus_game_ou</th>\n", " <th>dtot</th>\n", " <th>back_to_back</th>\n", " <th>three_in_four</th>\n", " <th>def_rating_ema</th>\n", " <th>pace_ema</th>\n", " <th>y</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>5000</td>\n", " <td>23.3</td>\n", " <td>22.8</td>\n", " <td>197.0</td>\n", " <td>-4.44</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>98.2</td>\n", " <td>98.3</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>5200</td>\n", " <td>25.0</td>\n", " <td>22.6</td>\n", " <td>196.5</td>\n", " <td>-7.08</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>107.8</td>\n", " <td>93.0</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>5800</td>\n", " <td>26.5</td>\n", " <td>27.2</td>\n", " <td>207.5</td>\n", " <td>-1.12</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>110.6</td>\n", " <td>96.0</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>6000</td>\n", " <td>27.4</td>\n", " <td>28.0</td>\n", " <td>198.25</td>\n", " <td>-4.65</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>97.1</td>\n", " <td>97.2</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>6100</td>\n", " <td>27.5</td>\n", " <td>29.6</td>\n", " <td>197.5</td>\n", " <td>-4.98</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>106.1</td>\n", " <td>98.1</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>5900</td>\n", " <td>27.4</td>\n", " <td>29.5</td>\n", " <td>200.5</td>\n", " <td>0.75</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>105.7</td>\n", " <td>101.6</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>5400</td>\n", " <td>27.8</td>\n", " <td>29.6</td>\n", " <td>188.5</td>\n", " <td>-7.69</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>101.9</td>\n", " <td>95.4</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>5500</td>\n", " <td>27.1</td>\n", " <td>26.1</td>\n", " <td>207.5</td>\n", " <td>2.06</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>106.2</td>\n", " <td>102.1</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>5500</td>\n", " <td>25.3</td>\n", " <td>25.8</td>\n", " <td>201.0</td>\n", " <td>-3.61</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>107.3</td>\n", " <td>98.4</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>5000</td>\n", " <td>25.2</td>\n", " <td>27.6</td>\n", " <td>195.5</td>\n", " <td>-15.79</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>109.1</td>\n", " <td>99.4</td>\n", " <td>1</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 56, "text": [ " salary minema dkema consensus_game_ou dtot back_to_back three_in_four def_rating_ema pace_ema y\n", "0 5000 23.3 22.8 197.0 -4.44 0 0 98.2 98.3 0\n", "1 5200 25.0 22.6 196.5 -7.08 0 0 107.8 93.0 1\n", "2 5800 26.5 27.2 207.5 -1.12 0 0 110.6 96.0 1\n", "3 6000 27.4 28.0 198.25 -4.65 0 0 97.1 97.2 1\n", "4 6100 27.5 29.6 197.5 -4.98 0 0 106.1 98.1 0\n", "5 5900 27.4 29.5 200.5 0.75 0 0 105.7 101.6 1\n", "6 5400 27.8 29.6 188.5 -7.69 1 1 101.9 95.4 0\n", "7 5500 27.1 26.1 207.5 2.06 0 1 106.2 102.1 0\n", "8 5500 25.3 25.8 201.0 -3.61 0 0 107.3 98.4 1\n", "9 5000 25.2 27.6 195.5 -15.79 0 0 109.1 99.4 1" ] } ], "prompt_number": 56 }, { "cell_type": "code", "collapsed": false, "input": [ "df = df.dropna(how='any')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 57 }, { "cell_type": "code", "collapsed": false, "input": [ "X_train, X_test, y_train, y_test = train_test_split(df.ix[:,:-1], df.ix[:,-1],\n", " train_size=0.75, test_size=0.25)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 58 }, { "cell_type": "code", "collapsed": false, "input": [ "X_train_scale = preprocessing.scale(X_train)\n", "X_test_scale = preprocessing.scale(X_test)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 59 }, { "cell_type": "code", "collapsed": false, "input": [ "tpot = TPOTClassifier(generations=5, population_size=20, verbosity=2)\n", "tpot.fit(X_train_scale, y_train)\n", "print(tpot.score(X_test_scale, y_test))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "\r", "Optimization Progress: 0%\| \| 0/120 [00:00<?, ?pipeline/s]" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "\r", "Optimization Progress: 1%\| \| 1/120 [00:00<00:16, 7.03pipeline/s]" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "\r", "Optimization Progress: 2%\|\u258e \| 3/120 [00:01<00:31, 3.68pipeline/s]" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "\r", "Optimization Progress: 3%\|\u258e \| 4/120 [00:10<05:32, 2.87s/pipeline]" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "\r", "Optimization Progress: 6%\|\u258c \| 7/120 [00:19<05:26, 2.89s/pipeline]" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "\r", "Optimization Progress: 7%\|\u258b \| 8/120 [00:19<03:51, 2.07s/pipeline]" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "\r", "Optimization Progress: 8%\|\u258a \| 10/120 [00:23<03:57, 2.16s/pipeline]" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "\r", "Optimization Progress: 11%\|\u2588 \| 13/120 [00:24<02:43, 1.53s/pipeline]" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "\r", "Optimization Progress: 12%\|\u2588\u258e \| 15/120 [00:32<04:10, 2.38s/pipeline]" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "\r", "Optimization Progress: 14%\|\u2588\u258d \| 17/120 [00:34<03:24, 1.99s/pipeline]" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "\r", "Optimization Progress: 17%\|\u2588\u258b \| 20/120 [00:35<02:20, 1.40s/pipeline]" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Version 0.6.8 of tpot is outdated. Version 0.7.0 was released Wednesday March 22, 2017.\n", "Generation 1 - Current best internal CV score: 0.531110408573" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "\r", " \r", "\r", " \r", "Optimization Progress: 17%\|\u2588\u258b \| 20/120 [00:35<02:20, 1.40s/pipeline]\r", "Optimization Progress: 18%\|\u2588\u258a \| 22/120 [00:46<04:24, 2.70s/pipeline]" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "\r", "Optimization Progress: 19%\|\u2588\u2589 \| 23/120 [00:55<07:21, 4.56s/pipeline]" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "\r", "Optimization Progress: 20%\|\u2588\u2588 \| 24/120 [00:57<06:05, 3.81s/pipeline]" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "\r", "Optimization Progress: 21%\|\u2588\u2588 \| 25/120 [01:39<24:01, 15.17s/pipeline]" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "\r", "Optimization Progress: 22%\|\u2588\u2588\u258f \| 26/120 [01:48<20:48, 13.28s/pipeline]" ] }, { 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"stream", "stream": "stdout", "text": [ "\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Best pipeline: ExtraTreesClassifier(input_matrix, 43, 0.82000000000000006)\n", "0.512391465097" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "\r" ] } ], "prompt_number": 60 }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.ensemble import ExtraTreesClassifier\n", "\n", "forest = ExtraTreesClassifier(n_estimators=250, random_state=0)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 61 }, { "cell_type": "code", "collapsed": false, "input": [ "forest.fit(X_train_scale, y_train)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 62, "text": [ "ExtraTreesClassifier(bootstrap=False, class_weight=None, criterion='gini',\n", " max_depth=None, max_features='auto', max_leaf_nodes=None,\n", " min_impurity_split=1e-07, min_samples_leaf=1,\n", " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", " n_estimators=250, n_jobs=1, oob_score=False, random_state=0,\n", " verbose=0, warm_start=False)" ] } ], "prompt_number": 62 }, { "cell_type": "code", "collapsed": false, "input": [ "importances = forest.feature_importances_\n", "std = np.std([tree.feature_importances_ for tree in forest.estimators_],\n", " axis=0)\n", "indices = np.argsort(importances)[::-1]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 63 }, { "cell_type": "code", "collapsed": false, "input": [ "print(\"Feature ranking:\")\n", "\n", "for f in range(X_train_scale.shape[1]):\n", " print(\"%d. feature %d (%f)\" % (f + 1, indices[f], importances[indices[f]]))\n", "\n", "plt.figure()\n", "plt.title(\"Feature importances\")\n", "plt.bar(range(X_train_scale.shape[1]), importances[indices],\n", " color=\"r\", yerr=std[indices], align=\"center\")\n", "plt.xticks(range(X_train_scale.shape[1]), indices)\n", "plt.xlim([-1, X_train_scale.shape[1]])\n", "plt.show()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Feature ranking:\n", "1. feature 2 (0.159057)\n", "2. feature 1 (0.158966)\n", "3. feature 0 (0.148447)\n", "4. feature 4 (0.131073)\n", "5. feature 7 (0.128373)\n", "6. feature 8 (0.127443)\n", "7. feature 3 (0.126365)\n", "8. feature 6 (0.010289)\n", "9. feature 5 (0.009987)\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x7fbceeacda90>" ] } ], "prompt_number": 66 }, { "cell_type": "code", "collapsed": false, "input": [ "df.columns" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 68, "text": [ "Index([u'salary', u'minema', u'dkema', u'consensus_game_ou', u'dtot', u'back_to_back', u'three_in_four', u'def_rating_ema', u'pace_ema', u'y'], dtype='object')" ] } ], "prompt_number": 68 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }	mit
nikken1/patentprocessor	notebooks/MySQL.ipynb	6	841125	{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "from sqlalchemy import create_engine, MetaData, Table, inspect\n", "from sqlalchemy.sql import select\n", "from sqlalchemy.orm import sessionmaker\n", "from matplotlib import pyplot as plt\n", "from scipy.stats import mode\n", "import pandas as pd\n", "from unidecode import unidecode\n", "from lib.alchemy.schema import GrantBase, ApplicationBase\n", "from lib.alchemy import session_generator\n", "sessiongen = session_generator(dbtype='grant')\n", "session = sessiongen()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "def printstats(series):\n", " print 'mean',series.mean()\n", " print 'median',series.median()\n", " print 'mode',mode(series)[0][0]\n", " print 'std',series.std()\n", " print 'min',series.min()\n", " print 'max',series.max()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 10 }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Table Counts" ] }, { "cell_type": "code", "collapsed": false, "input": [ "counts =[]\n", "tablekeys = []\n", "tables = GrantBase.metadata.tables\n", "rawtables = tables.keys()\n", "for table in rawtables:\n", " res = session.execute('select count() from {0}'.format(table)).fetchone()[0]\n", " if res:\n", " counts.append(res)\n", " tablekeys.append(table)\n", "d = pd.DataFrame.from_dict({'tables': tablekeys, 'counts': map(lambda x: int(x), counts)})\n", "d.index = d['tables']\n", "h = d.plot(kind='bar',figsize=(16,10))\n", "h.set_xlabel('Table')\n", "h.set_ylabel('Record Count')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 12, "text": [ "<matplotlib.text.Text at 0x10b503610>" ] }, { "metadata": {}, "output_type": 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ffvpUggAAniibUIB+nvAXJnrTm95Uz3/+8+uSSy6pd77znZMfiZyvXlxSS1VW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LxKn1uhNpQGrq6usLe3h5eXFwghOHDgAGbOnElNDytkZmaqLAyMGTOG6u975syZUFdXh5+fnxgU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\n", "text": [ "<matplotlib.figure.Figure at 0x10b500a50>" ] } ], "prompt_number": 12 }, { "cell_type": "code", "collapsed": false, "input": [ "patent_count = session.execute('select count() from patent;').fetchone()[0]\n", "app_count = session.execute('select count() from application;').fetchone()[0]\n", "\n", "rawinventor_count = session.execute('select count() from rawinventor;').fetchone()[0]\n", "disambiginventor_count = session.execute('select count() from inventor;').fetchone()[0]\n", "\n", "rawassignee_count = session.execute('select count() from rawassignee;').fetchone()[0]\n", "disambigassignee_count = session.execute('select count() from assignee;').fetchone()[0]\n", "\n", "rawlawyer_count = session.execute('select count() from rawlawyer;').fetchone()[0]\n", "disambiglawyer_count = session.execute('select count() from lawyer;').fetchone()[0]\n", "\n", "rawlocation_count = session.execute('select count() from rawlocation;').fetchone()[0]\n", "disambiglocation_count = session.execute('select count() from location;').fetchone()[0]\n", "\n", "d = pd.DataFrame.from_dict({'raw': [patent_count,'',rawinventor_count,rawassignee_count,rawlawyer_count,rawlocation_count],\n", " 'disambig': ['', app_count, disambiginventor_count, disambigassignee_count, disambiglawyer_count, disambiglocation_count],\n", " 'labels': ['patent','application','inventor','assignee','lawyer','location']})\n", "d[['labels','raw','disambig']]" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>labels</th>\n", " <th>raw</th>\n", " <th>disambig</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> patent</td>\n", " <td> 5072245</td>\n", " <td> </td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> application</td>\n", " <td> </td>\n", " <td> 5002322</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> inventor</td>\n", " <td> 11546565</td>\n", " <td> 3780422</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> assignee</td>\n", " <td> 6738097</td>\n", " <td> 368684</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> lawyer</td>\n", " <td> 3648399</td>\n", " <td> 121268</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td> location</td>\n", " <td> 403853</td>\n", " <td> 69640</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 4, "text": [ " labels raw disambig\n", "0 patent 5072245 \n", "1 application 5002322\n", "2 inventor 11546565 3780422\n", "3 assignee 6738097 368684\n", "4 lawyer 3648399 121268\n", "5 location 403853 69640" ] } ], "prompt_number": 4 }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Inventor" ] }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "Raw Inventor" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# inventors per patent\n", "res = session.execute('select count() from rawinventor group by patent_id;')\n", "inventor_counts = [x[0] for x in res.fetchall()]\n", "d = pd.DataFrame.from_dict({'counts': inventor_counts})\n", "h = d[d['counts'] < 20].hist(bins=20, figsize=(16,10))[0][0]\n", "h.set_xlabel('Number of Inventors')\n", "h.set_ylabel('Patent Count')\n", "h.set_title('Inventors per Patent')\n", "printstats(d['counts'])\n", "print 'Total:', session.execute('select count() from rawinventor;').fetchone()[0]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "mean 2.27642144187\n", "median " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "2.0\n", "mode " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "1.0\n", "std " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "1.67215348567\n", "min " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "1\n", "max " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "76\n", "Total: " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "11546565\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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bfxLYl9bLly9LMmjb7hds+/PkPrT+ebbrC++Xdd9dbz/WV/Zjbb23rE8++eQ+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\nM+g9wAx6D3AOAjMAAAAAABYIzACMY5Y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"text": [ "<matplotlib.figure.Figure at 0x139ecc650>" ] } ], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ "# patents per inventor\n", "session = sessiongen()\n", "res = session.execute('select count() from patent_inventor group by inventor_id;')\n", "patent_counts = [x[0] for x in res.fetchall()]\n", "d = pd.DataFrame.from_dict({'counts': patent_counts})\n", "h = d[d['counts'] < 20].hist(bins=20, figsize=(16,10))[0][0]\n", "h.set_xlabel('Patents')\n", "h.set_ylabel('Inventors')\n", "h.set_title('Patents per Inventor')\n", "printstats(d['counts'])\n", "print 'Total:', session.execute('select count() from rawinventor;').fetchone()[0]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "mean 3.01963405144\n", "median " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "1.0\n", "mode " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "1.0\n", "std " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "7.50657935128\n", "min 1\n", "max " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "4582\n", "Total: 11546565\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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BGbIHZcgeVEevF+aPf/zjGTBgwNuO7yzCP+/hhx/O+eefn379+qW9vT3HHHNM\nlixZkrVr12bTpk0ZNWpUkuSiiy7KQw89lCSZP39+pk6dmiSZNGlSHn/88STJwoULM378+LS0tKSl\npSXjxo3LY489lnq9nieffDLnnntukmTq1KmNawEAAMDu9N1XX+juu+/Ovffem4997GP54he/mJaW\nlqxZsyadnZ2NzxkyZEhWr16dfv36ZciQIY3jbW1tWb16dZJk9erVGTp06PbN9+2bww8/PK+88krW\nrFmzyzk7r7Vu3bq0tLSkT58+b7vWL5o2bVra29uTJC0tLRk5cmRjxmTnTwL3p3VPz8okA3fs/qkd\nf4/ej9bfy077w/fLev9d7zy2v+zH2vpAWY8ePXq/2o+1tbW1tXVvrp9//vls2LAhSbJixYq8E7V6\ns0e//0YrVqzImWeeme985ztJkh//+Mc54ogjkiQ33HBD1q5dm66urlxxxRXp7OzMBRdckCS59NJL\nc9ppp6W9vT3XXHNNFi9enCR5+umnc+edd+aRRx7JiBEjsnDhwhx11FFJ0ngqfc899+T111/Pdddd\nlyS59dZbc8ghh2Tq1Knp7OxMd3d3kqSnpyenn356Y2+Nb0St1vQp+P5sxoyZueuugUlmlt7Kbtyf\niRMX5OGH7y+9EQAA4AD3Tjpfn720l10ceeSRqdVqqdVqufTSS7N06dIk25/29vT0ND5v1apVGTJk\nSNra2rJq1aq3Hd95zsqVK5MkW7duzcaNG9Pa2vq2a/X09KStrS0DBw7Mhg0b8tZbbzWu1dbWttfv\nGfjV7fyJILBvyR6UIXtQHfukMK9du7bx77/7u79rvIP2xIkTM2fOnLz55ptZvnx5uru7M2rUqAwe\nPDj9+/fPkiVLUq/Xc9999+Wss85qnDN79uwkybx58zJ27Ngkyfjx47No0aJs2LAh69evz+LFi3Pq\nqaemVqtlzJgxmTt3bpLt76R99tln74vbBgAAoMJ6fYb5/PPPzze+8Y389Kc/zdChQ3PzzTc3Xj9e\nq9Vy9NFH5y//8i+TJMOHD8/kyZMzfPjw9O3bN7NmzUqtVkuSzJo1K9OmTcvmzZtz+umnZ8KECUmS\n6dOn58ILL0xHR0daW1szZ86cJMnAgQNzww035MQTT0yS3HjjjWlpaUmS3HHHHZkyZUquv/76nHDC\nCZk+fXpv3zbwb7Bz1gTYt2QPypA9qI69MsNcRWaY9wYzzAAAwP5hv51hBvhlzHJBGbIHZcgeVIfC\nDAAAAE10F1F4AAAgAElEQVQozEBxZrmgDNmDMmQPqkNhBgAAgCYUZqA4s1xQhuxBGbIH1aEwAwAA\nQBMKM1CcWS4oQ/agDNmD6lCYAQAAoAmFGSjOLBeUIXtQhuxBdSjMAAAA0ITCDBRnlgvKkD0oQ/ag\nOhRmAAAAaEJhBoozywVlyB6UIXtQHQozAAAANKEwA8WZ5YIyZA/KkD2oDoUZAAAAmlCYgeLMckEZ\nsgdlyB5Uh8IMAAAATSjMQHFmuaAM2YMyZA+qQ2EGAACAJhRmoDizXFCG7EEZsgfVoTADAABAEwoz\nUJxZLihD9qAM2YPqUJgBAACgCYUZKM4sF5Qhe1CG7EF1KMwAAADQhMIMFGeWC8qQPShD9qA6FGYA\nAABoYo+F+bXXXsu2bduSJP/yL/+S+fPnZ8uWLXt9Y8CBwywXlCF7UIbsQXXssTB/4hOfyBtvvJHV\nq1fn1FNPzX333Zdp06btg60BAABAOXsszPV6PYccckj+9m//Npdffnnmzp2b7373u/tib8ABwiwX\nlCF7UIbsQXX8SjPM//AP/5C//uu/zn/6T/8pSfLWW2/t1U0BAABAaXsszP/tv/233HbbbTnnnHNy\n3HHH5cUXX8yYMWP2xd6AA4RZLihD9qAM2YPq6PvLPrht27bMnz8/8+fPbxz78Ic/nK985St7fWMA\nAABQ0i99wnzQQQflmWeeSb1e31f7AQ5AZrmgDNmDMmQPquOXPmFOkpEjR+ass87Kpz71qRxyyCFJ\nklqtlk9+8pN7fXMAAABQyh5nmF9//fUMHDgwTzzxRB599NE8+uijeeSRR/bF3oADhFkuKEP2oAzZ\ng+rY4xPme+65Zx9sAwAAAPYve3zC3NPTk3POOSdHHHFEjjjiiEyaNCmrVq3aF3sDDhBmuaAM2YMy\nZA+qY4+F+eKLL87EiROzZs2arFmzJmeeeWYuvvjifbE3AAAAKGaPhfknP/lJLr744vTr1y/9+vXL\ntGnT8uMf/3hf7A04QJjlgjJkD8qQPaiOPRbm1tbW3Hfffdm2bVu2bt2a+++/P+9///v3xd4AAACg\nmD0W5r/6q7/Kgw8+mMGDB+cDH/hA5s6dm//1v/7XvtgbcIAwywVlyB6UIXtQHXt8l+zVq1e/7ddI\nPfPMM/ngBz+41zYFAAAApe3xCfPnPve5X+kYwDtllgvKkD0oQ/agOnb7hPkf/uEf8s1vfjM/+clP\nctddd6VerydJNm3alLfeemufbRAAAABK2G1hfvPNN7Np06Zs27YtmzZtahzv379/5s2bt082BxwY\nzHJBGbIHZcgeVMduC/Mpp5ySU045JdOmTUt7e/s+3BIAAACUt8cZ5jfeeCOf/exnM27cuIwZMyZj\nxozJ7/7u7+6LvQEHCLNcUIbsQRmyB9Wxx3fJ/tSnPpXLLrssl156aQ466KAkSa1W2+sbAwAAgJL2\nWJj79euXyy67bF/sBThAmeWCMmQPypA9qI49viT7zDPPzFe/+tWsXbs269ata/wBAACAd7M9FuZ7\n7rknX/jCF3LyySfnd37ndxp/AHqLWS4oQ/agDNmD6tjjS7JXrFixD7YBAAAA+5c9PmH+2c9+lj/9\n0z/NZz/72SRJd3d3Hn300b2+MeDAYZYLypA9KEP2oDr2WJgvvvjivOc978k3v/nNJMlRRx2V6667\nbq9vDAAAAEraY2F+8cUXM3PmzLznPe9JkvzGb/zGXt8UcGAxywVlyB6UIXtQHXsszAcffHA2b97c\nWL/44os5+OCD9+qmAAAAoLQ9vunXTTfdlAkTJmTVqlX59Kc/nWeeeSb33HPPPtgacKAwywVlyB6U\nIXtQHXsszOPHj88JJ5yQb33rW0mSL3/5yzniiCP2+sYAAACgpD2+JPvMM8/MokWLMmbMmJxxxhnK\nMtDrzHJBGbIHZcgeVMceC/OMGTPy9NNPZ/jw4Tn33HMzb968vP766/tibwAAAFDMHl+SPXr06Iwe\nPTpbt27Nk08+mf/xP/5HLrnkkrz66qv7Yn/AAcAsF5Qhe1CG7EF17LEwJ8nmzZszf/78PPjgg/mn\nf/qnTJ06dW/vCwAAAIra40uyJ0+enGOPPTZPPPFEPve5z+UHP/hB7r777n2xN+AAYZYLypA9KEP2\noDr2+IT5kksuyde+9rUcdNBB+2I/AAAAsF/YY2GeMGFCvvnNb2b58uXZunVrkqRWq+Wiiy7a65sD\nDgxmuaAM2YMyZA+qY4+F+TOf+Ux++MMfZuTIkbs8ZVaYAQAAeDfbY2F+9tlns2zZstRqtX2xH+AA\n9NRTT/lpOxQge1CG7EF17PFNvz7ykY9k7dq1+2IvAAAAsN/Y4xPmn/zkJxk+fHhGjRqVgw8+OMn2\nGeb58+fv9c0BBwY/ZYcyZA/KkD2ojj0W5ptuumkfbAMAAAD2L3sszH4CBuxtZrmgDNmDMmQPqmO3\nhfnQQw/d7Rt91Wq1vPrqq3ttUwAAAFDabgvza6+9ti/3ARzA/JQdypA9KEP2oDr2+C7ZAAAAcCBS\nmIHinnrqqdJbgAOS7EEZsgfVoTADAABAEwozUJxZLihD9qAM2YPqUJgBAACgCYUZKM4sF5Qhe1CG\n7EF1KMwAAADQhMIMFGeWC8qQPShD9qA6FGYAAABoQmEGijPLBWXIHpQhe1AdCjMAAAA0oTADxZnl\ngjJkD8qQPagOhRkAAACaUJiB4sxyQRmyB2XIHlSHwgwAAABNKMxAcWa5oAzZgzJkD6pDYQYAAIAm\nFGagOLNcUIbsQRmyB9WhMAMAAEATCjNQnFkuKEP2oAzZg+pQmAEAAKAJhRkoziwXlCF7UIbsQXUo\nzAAAANCEwgwUZ5YLypA9KEP2oDoUZgAAAGhCYQaKM8sFZcgelCF7UB0KMwAAADShMAPFmeWCMmQP\nypA9qI5eL8yXXHJJBg0alBEjRjSOrVu3LuPGjcuwYcMyfvz4bNiwofGx2267LR0dHTn22GOzaNGi\nxvFnn302I0aMSEdHR6688srG8TfeeCPnnXdeOjo60tnZmZdeeqnxsdmzZ2fYsGEZNmxY7r333sbx\n5cuX56STTkpHR0emTJmSLVu29PZtAwAA8C7T64X54osvzoIFC3Y5dvvtt2fcuHF54YUXMnbs2Nx+\n++1JkmXLluWBBx7IsmXLsmDBglx++eWp1+tJkssuuyxdXV3p7u5Od3d345pdXV1pbW1Nd3d3rr76\n6sycOTPJ9lJ+yy23ZOnSpVm6dGluvvnmbNy4MUkyc+bMzJgxI93d3RkwYEC6urp6+7aBfwOzXFCG\n7EEZsgfV0euF+eMf/3gGDBiwy7H58+dn6tSpSZKpU6fmoYceSpI8/PDDOf/889OvX7+0t7fnmGOO\nyZIlS7J27dps2rQpo0aNSpJcdNFFjXN+/lqTJk3K448/niRZuHBhxo8fn5aWlrS0tGTcuHF57LHH\nUq/X8+STT+bcc89929cHAACA3em7L77Iyy+/nEGDBiVJBg0alJdffjlJsmbNmnR2djY+b8iQIVm9\nenX69euXIUOGNI63tbVl9erVSZLVq1dn6NCh2zfft28OP/zwvPLKK1mzZs0u5+y81rp169LS0pI+\nffq87Vq/aNq0aWlvb0+StLS0ZOTIkY0Zk50/Cdyf1j09K5MM3LH7p3b8PXo/Wn8vO+0P3y/r/Xe9\n89j+sh9r6wNlPXr06P1qP9bW1tbW1r25fv755xvjwCtWrMg7UavvfA10L1qxYkXOPPPMfOc730mS\nDBgwIOvXr298fODAgVm3bl2uuOKKdHZ25oILLkiSXHrppTnttNPS3t6ea665JosXL06SPP3007nz\nzjvzyCOPZMSIEVm4cGGOOuqoJGk8lb7nnnvy+uuv57rrrkuS3HrrrTnkkEMyderUdHZ2pru7O0nS\n09OT008/vbG3xjeiVste+FbsVTNmzMxddw1MMrP0Vnbj/kycuCAPP3x/6Y0AAAAHuHfS+frspb3s\nYtCgQfnRj36UJFm7dm2OPPLIJNuf9vb09DQ+b9WqVRkyZEja2tqyatWqtx3fec7KlSuTJFu3bs3G\njRvT2tr6tmv19PSkra0tAwcOzIYNG/LWW281rtXW1rZ3bxj4tez8iSCwb8kelCF7UB37pDBPnDgx\ns2fPTrL9nazPPvvsxvE5c+bkzTffzPLly9Pd3Z1Ro0Zl8ODB6d+/f5YsWZJ6vZ777rsvZ5111tuu\nNW/evIwdOzZJMn78+CxatCgbNmzI+vXrs3jx4px66qmp1WoZM2ZM5s6d+7avDwAAALvT6zPM559/\nfr7xjW/kpz/9aYYOHZpbbrkl11xzTSZPnpyurq60t7fnwQcfTJIMHz48kydPzvDhw9O3b9/MmjUr\ntVotSTJr1qxMmzYtmzdvzumnn54JEyYkSaZPn54LL7wwHR0daW1tzZw5c5Jsf5n3DTfckBNPPDFJ\ncuONN6alpSVJcscdd2TKlCm5/vrrc8IJJ2T69Om9fdvAv8HOWRNg35I9KEP2oDr2ygxzFZlh3hvM\nMAMAAPuH/XaGGeCXMcsFZcgelCF7UB0KMwAAADShMAPFmeWCMmQPypA9qA6FGQAAAJpQmIHizHJB\nGbIHZcgeVIfCDAAAAE0ozEBxZrmgDNmDMmQPqkNhBgAAgCYUZqA4s1xQhuxBGbIH1aEwAwAAQBMK\nM1CcWS4oQ/agDNmD6lCYAQAAoAmFGSjOLBeUIXtQhuxBdSjMAAAA0ITCDBRnlgvKkD0oQ/agOhRm\nAAAAaEJhBoozywVlyB6UIXtQHQozAAAANKEwA8WZ5YIyZA/KkD2oDoUZAAAAmlCYgeLMckEZsgdl\nyB5Uh8IMAAAATSjMQHFmuaAM2YMyZA+qQ2EGAACAJhRmoDizXFCG7EEZsgfVoTADAABAEwozUJxZ\nLihD9qAM2YPqUJgBAACgCYUZKM4sF5Qhe1CG7EF1KMwAAADQhMIMFGeWC8qQPShD9qA6FGYAAABo\nQmEGijPLBWXIHpQhe1AdCjMAAAA0oTADxZnlgjJkD8qQPagOhRkAAACaUJiB4sxyQRmyB2XIHlSH\nwgwAAABNKMxAcWa5oAzZgzJkD6pDYQYAAIAmFGagOLNcUIbsQRmyB9WhMAMAAEATCjNQnFkuKEP2\noAzZg+pQmAEAAKAJhRkoziwXlCF7UIbsQXUozAAAANCEwgwUZ5YLypA9KEP2oDoUZgAAAGhCYQaK\nM8sFZcgelCF7UB0KMwAAADShMAPFmeWCMmQPypA9qA6FGQAAAJpQmIHizHJBGbIHZcgeVIfCDAAA\nAE0ozEBxZrmgDNmDMmQPqkNhBgAAgCYUZqA4s1xQhuxBGbIH1aEwAwAAQBMKM1CcWS4oQ/agDNmD\n6lCYAQAAoAmFGSjOLBeUIXtQhuxBdSjMAAAA0ITCDBRnlgvKkD0oQ/agOhRmAAAAaEJhBoozywVl\nyB6UIXtQHQozAAAANKEwA8WZ5YIyZA/KkD2oDoUZAAAAmlCYgeLMckEZsgdlyB5Uh8IMAAAATSjM\nQHFmuaAM2YMyZA+qQ2EGAACAJhRmoDizXFCG7EEZsgfVoTADAABAEwozUJxZLihD9qAM2YPqUJgB\nAACgCYUZKM4sF5Qhe1CG7EF1KMwAAADQhMIMFGeWC8qQPShD9qA6FGYAAABoQmEGijPLBWXIHpQh\ne1AdfUtvgHezyzN//qbUan9deiNNHXbYgLz66rrS2wAAAPZTCjN70aYk9dKb2K1Nm2qlt8AOZrmg\nDNmDMmQPqsNLsgEAAKAJhRkoziwXlCF7UIbsQXUozAAAANCEwgwUZ5YLypA9KEP2oDoUZgAAAGhC\nYQaKM8sFZcgelCF7UB0KMwAAADShMAPFmeWCMmQPypA9qA6FGQAAAJpQmIHizHJBGbIHZcgeVIfC\nDAAAAE0ozEBxZrmgDNmDMmQPqkNhBgAAgCYUZqA4s1xQhuxBGbIH1aEwAwAAQBMKM1CcWS4oQ/ag\nDNmD6lCYAQAAoAmFGSjOLBeUIXtQhuxBdSjMAAAA0MQ+Lczt7e356Ec/muOPPz6jRo1Kkqxbty7j\nxo3LsGHDMn78+GzYsKHx+bfddls6Ojpy7LHHZtGiRY3jzz77bEaMGJGOjo5ceeWVjeNvvPFGzjvv\nvHR0dKSzszMvvfRS42OzZ8/OsGHDMmzYsNx777374G6BX5VZLihD9qAM2YPq2KeFuVar5amnnspz\nzz2XpUuXJkluv/32jBs3Li+88ELGjh2b22+/PUmybNmyPPDAA1m2bFkWLFiQyy+/PPV6PUly2WWX\npaurK93d3enu7s6CBQuSJF1dXWltbU13d3euvvrqzJw5M8n2Un7LLbdk6dKlWbp0aW6++eZdijkA\nAAD8on3+kuydpXen+fPnZ+rUqUmSqVOn5qGHHkqSPPzwwzn//PPTr1+/tLe355hjjsmSJUuydu3a\nbNq0qfGE+qKLLmqc8/PXmjRpUh5//PEkycKFCzN+/Pi0tLSkpaUl48aNa5RsoDyzXFCG7EEZsgfV\n0XdffrFarZb/+B//Yw466KD83u/9Xj772c/m5ZdfzqBBg5IkgwYNyssvv5wkWbNmTTo7OxvnDhky\nJKtXr06/fv0yZMiQxvG2trasXr06SbJ69eoMHTo0SdK3b98cfvjheeWVV7JmzZpdztl5rV80bdq0\ntLe3J0laWloycuTIxktmdv4P2/607ulZmWTgjt0/tePv0fvZOnv4eOn1jtV+8N/zQF4///zz+9V+\nrK2tra2t9+Z6p/1lP9bW79b1888/33hl8YoVK/JO1Oq/+Mh3L1q7dm0+8IEP5Cc/+UnGjRuXu+++\nOxMnTsz69esbnzNw4MCsW7cuV1xxRTo7O3PBBRckSS699NKcdtppaW9vzzXXXJPFixcnSZ5++unc\neeedeeSRRzJixIgsXLgwRx11VJI0nkrfc889ef3113PdddclSW699da8733vy4wZM/7fN6JWe9vT\n7/3djBkzc9ddA5PMLL2V3agl2Z+/p9X7bw4AALwz76Tz9dlLe2nqAx/4QJLkiCOOyDnnnJOlS5dm\n0KBB+dGPfpRke6E+8sgjk2x/ctzT09M4d9WqVRkyZEja2tqyatWqtx3fec7KlSuTJFu3bs3GjRvT\n2tr6tmv19PTs8sQZAAAAftE+K8z/+q//mk2bNiVJfvazn2XRokUZMWJEJk6cmNmzZyfZ/k7WZ599\ndpJk4sSJmTNnTt58880sX7483d3dGTVqVAYPHpz+/ftnyZIlqdfrue+++3LWWWc1ztl5rXnz5mXs\n2LFJkvHjx2fRokXZsGFD1q9fn8WLF+fUU0/dV7cO7MEvvkQN2DdkD8qQPaiOfTbD/PLLL+ecc85J\nsv3p7wUXXJDx48fnYx/7WCZPnpyurq60t7fnwQcfTJIMHz48kydPzvDhw9O3b9/MmjUrtVotSTJr\n1qxMmzYtmzdvzumnn54JEyYkSaZPn54LL7wwHR0daW1tzZw5c5Jsf5n3DTfckBNPPDFJcuONN6al\npWVf3ToAAAAVtE9nmPdnZpj3BjPMAADA/mG/n2EGAACAqlCYgeLMckEZsgdlyB5Uh8IMAAAATSjM\nQHE7f8E8sG/JHpQhe1AdCjMAAAA0oTADxZnlgjJkD8qQPagOhRkAAACaUJiB4sxyQRmyB2XIHlSH\nwgwAAABNKMxAcWa5oAzZgzJkD6pDYQYAAIAmFGagOLNcUIbsQRmyB9WhMAMAAEATCjNQnFkuKEP2\noAzZg+pQmAEAAKAJhRkoziwXlCF7UIbsQXUozAAAANCEwgwUZ5YLypA9KEP2oDoUZgAAAGhCYQaK\nM8sFZcgelCF7UB0KMwAAADShMAPFmeWCMmQPypA9qA6FGQAAAJpQmIHizHJBGbIHZcgeVIfCDAAA\nAE0ozEBxZrmgDNmDMmQPqkNhBgAAgCYUZqA4s1xQhuxBGbIH1aEwAwAA8P+3d28hVtVvH8CfjY5Q\nOKF/0ck0sNO/zDyM6BSIkpRmZaZYdkIsx5uoyCw6XY3dZBBJmpXURV5F3lghKCE4olZKMIbkdDAU\nTXQqtGZnho7Me9HbkLX0fW1m91tr9ucDG9x79t7z7C0Pa75rrWf9yCAwA8mZ5YI09B6kofegOARm\nAAAAyCAwA8mZ5YI09B6kofegOARmAAAAyCAwA8mZ5YI09B6kofegOARmAAAAyCAwA8mZ5YI09B6k\nofegOARmAAAAyCAwA8mZ5YI09B6kofegOARmAAAAyCAwA8mZ5YI09B6kofegOARmAAAAyCAwA8mZ\n5YI09B6kofegOPqmLgDS6RulUil1EWdVWzsw2tuPpi4DAACqlsBMFeuIiM7URZxVuZzfMN/TzHJB\nGnoP0tB7UBxOyQYAAIAMAjOQnFkuSEPvQRp6D4pDYAYAAIAMAjOQnFkuSEPvQRp6D4pDYAYAAIAM\nAjOQnFkuSEPvQRp6D4pDYAYAAIAMAjOQnFkuSEPvQRp6D4pDYAYAAIAMAjOQnFkuSEPvQRp6D4pD\nYAYAAIAMAjOQnFkuSEPvQRp6D4pDYAYAAIAMAjOQnFkuSEPvQRp6D4pDYAYAAIAMAjOQnFkuSEPv\nQRp6D4pDYAYAAIAMAjOQnFkuSEPvQRp6D4pDYAYAAIAMAjOQnFkuSEPvQRp6D4pDYAYAAIAMAjOQ\nnFkuSEPvQRp6D4pDYAYAAIAMAjOQnFkuSEPvQRp6D4qjb+oCgLPpG6VSKXUR51RbOzDa24+mLgMA\nACrCEWbIrY6I6Mz1rVw+1iOf1CwXpKH3IA29B8UhMAMAAEAGgRlIziwXpKH3IA29B8UhMAMAAEAG\ngRlIziwXpKH3IA29B8UhMAMAAEAGgRlIziwXpKH3IA29B8UhMAMAAEAGgRlIziwXpKH3IA29B8Uh\nMAMAAEAGgRlIziwXpKH3IA29B8UhMAMAAEAGgRnohr5RKpVye7voov+k/oIg18xRQhp6D4pDYAa6\noSMiOnvgtrmH3ufMW7l8rIKfHQCA3k5gBnLgxtQFQFUyRwlp6D0oDoEZAAAAMgjMQA40py4AqpI5\nSkhD70FxCMwAAACQQWAGcuDG1AVAVTJHCWnoPSgOgRkAAAAyCMxADjRX6H2tEw3nYo4S0tB7UBx9\nUxcAUDl/rBOdT+VyKXUJAACcgyPMQA7cmLoAqErmKCENvQfFITADAABABoEZyIHm1AUkYsaatMxR\nQhp6D4rDDDNAMmasAQDyzBFmIAduTF0AVCVzlJCG3oPicIQZgLP4/ZTxvKqtHRjt7UdTlwEA9GKO\nMAM50Jy6ADL9ccp4Pm/l8rEKfvbqYI4S0tB7UByOMANQUPk+Ah7hKDgAFJ3ADOTAjakLoJDyfdG0\niPxfOM0cJaSh96A4BGYAqFIXXfSfXJ/a7gg9AKmZYQZyoDl1AVCVfg/L6efRzz6nXk6+Hrm1yqkE\nM8xQHAIzkAO7UhcA5FLeLzyX70BfKvXLQQ12OGTZtct2D4qiqgLzxo0b45prromrrroqXnrppdTl\nAF1+Sl0AwD+Q70AfcSoHNRR5h0Pldjo88cQTdjpAQVTNDPPp06fj0UcfjU2bNsWwYcNi4sSJMWvW\nrBg5cmTq0gAAqlD+L9wXUYrK1Nj0v7fuKZdrolTK88UFa+L3HTd5pb7uqJbrTFRNYN65c2dceeWV\nMWLEiIiIuPfee+ODDz4QmCEX9qcuAAD+Rft76H3yvtOhUjsceor6uiPvK0H0lKoJzIcOHYpLL720\n6/7w4cNjx44dZzwn33vozuXZ1AWcQ96/U/V1X0/VuKaH3uev8v4dqq978l5fEbYt6use9XVP3uuL\nqFyNPbXdy/t3qL7uyXd9+d/GdV/VBOb/6z+zszO/e28AAAD491XNRb+GDRsWBw8e7Lp/8ODBGD58\neMKKAAAAyLOqCcwTJkyIb775Jvbv3x8nT56M9957L2bNmpW6LAAAAHKqak7J7tu3b7z22mtxyy23\nxOnTp6OxsdEFvwAAADirqjnCHBFx6623xldffRV79+6N5557rutx6zNDOiNGjIgxY8ZEfX19NDQ0\npC4Heq2FCxdGXV1djB49uuuxo0ePxrRp0+K///1vTJ8+PX76yZro0NOyeq+pqSmGDx8e9fX1UV9f\nHxs3bkxYIfROBw8ejKlTp8aoUaPiuuuuixUrVkTE+W/7qiowZ/ljfeaNGzfGnj174t13343W1tbU\nZUHVKJVK0dzcHC0tLbFz587U5UCv9dBDD/3tj/Jly5bFtGnT4uuvv46bbropli1blqg66L2yeq9U\nKsWSJUuipaUlWlpaYsaMGYmqg96rpqYmli9fHl988UV8+umnsWrVqmhtbT3vbV/VB+Y/r89cU1PT\ntT4z8O9xlXqovMmTJ8fAgQPPeOzDDz+MBQsWRETEggUL4v33309RGvRqWb0XYdsHlXbxxRfHuHHj\nIiKif//+MXLkyDh06NB5b/uqPjBnrc986NChhBVBdSmVSnHzzTfHhAkT4q233kpdDlSVtra2qKur\ni4iIurq6aGtrS1wRVI+VK1fG2LFjo7Gx0TgEVNj+/fujpaUlrr/++vPe9lV9YK6GxbYhz7Zv3x4t\nLS2xYcOGWLVqVWzdujV1SVCVSqWSbSL8Sx5++OHYt29f7Nq1K4YOHRpPPvlk6pKg1/rll19i7ty5\n8eqrr0Ztbe0ZP/v/bPuqPjBbnxnSGjp0aEREDB48OObMmWOOGf5FdXV1ceTIkYiIOHz4cAwZMiRx\nRVAdhgwZ0vWH+qJFi2z7oEJOnToVc+fOjfnz58fs2bMj4vy3fVUfmK3PDOn8+uuvUS6XIyLi+PHj\n8dFHH51xFVGgsmbNmhVr1qyJiIg1a9Z0/TEBVNbhw4e7/r1u3TrbPqiAzs7OaGxsjGuvvTYWL17c\n9fj5bvtKna44EBs2bIjFixd3rc/85yWngMrZt29fzJkzJyIiOjo64oEHHtB/UCH33XdfbNmyJX78\n8ceoq6uLF154Ie68886YN29eHDhwIEaMGBFr166NAQMGpC4VepW/9t7SpUujubk5du3aFaVSKS67\n7H2+V9MAAANoSURBVLJYvXp110wl0DO2bdsWU6ZMiTFjxnSddv3iiy9GQ0PDeW37BGYAAADIUPWn\nZAMAAEAWgRkAAAAyCMwAAACQQWAGAACADAIzABRMnz59or6+PkaPHh3z5s2LEydOnPW5W7ZsiU8+\n+eQf/66ff/453njjjX/8egAoMoEZAArmwgsvjJaWlti9e3f069cv3nzzzbM+d/PmzfHxxx//4991\n7NixeP311//x6wGgyARmACiwyZMnx969e2P9+vVxww03xPjx42PatGnx/fffx/79+2P16tWxfPny\nqK+vj+3bt8cPP/wQd911VzQ0NERDQ0NXmG5qaoqFCxfG1KlT44orroiVK1dGRMSzzz4b3377bdTX\n18czzzwTR44ciSlTpnQd4d62bVvKjw8AFWUdZgAomNra2iiXy9HR0RFz586N2267Le65554YMGBA\nRES8/fbb8eWXX8bLL78cS5cujdra2liyZElERNx///3xyCOPxKRJk+LAgQMxY8aM2LNnTzQ1NcWm\nTZti8+bN0d7eHldffXW0tbXFd999FzNnzozdu3dHRMQrr7wSv/32Wzz//PPR2dkZx48fj/79+yf7\nLgCgkvqmLgAAOD8nTpyI+vr6iIiYMmVKNDY2Rmtra8ybNy+OHDkSJ0+ejMsvv7zr+X/eN75p06Zo\nbW3tul8ul+P48eNRKpXi9ttvj5qamhg0aFAMGTIk2tra4q/71SdOnBgLFy6MU6dOxezZs2Ps2LEV\n/rQAkI7ADAAFc8EFF0RLS8sZjz322GPx1FNPxcyZM2PLli3R1NSU+drOzs7YsWNH9OvX728/+/Nj\nffr0iY6Ojr89Z/LkybF169ZYv359PPjgg7FkyZKYP39+9z4QAOSUGWYA6AXa29vjkksuiYiId955\np+vxP07f/sP06dNjxYoVXfc///zzc77vX19/4MCBGDx4cCxatCgWLVr0t+AOAL2JwAwABVMqlf72\nWFNTU9x9990xYcKEGDx4cNdz7rjjjli3bl3XRb9WrFgRn332WYwdOzZGjRoVq1evPuf7Dho0KCZN\nmhSjR4+Op59+Opqbm2PcuHExfvz4WLt2bTz++OOV+6AAkJiLfgEAAEAGR5gBAAAgg8AMAAAAGQRm\nAAAAyCAwAwAAQAaBGQAAADIIzAAAAJDhfwDgLHPhrYeuWQAAAABJRU5ErkJggg==\n", "text": [ "<matplotlib.figure.Figure at 0x139ecc410>" ] } ], "prompt_number": 14 }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Location" ] }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "RawLocation" ] }, { "cell_type": "code", "collapsed": false, "input": [ "res = session.execute('select location.state, count() from rawinventor \\\n", " left join rawlocation on rawinventor.rawlocation_id = rawlocation.id \\\n", " left join location on location.id = rawlocation.location_id \\\n", " where location.country = \"US\" and length(location.state) = 2 group by location.state ')\n", "data = res.fetchall()\n", "inventor_counts = [int(x[1]) for x in data]\n", "inventor_states = [unidecode(x[0]) for x in data]\n", "d = pd.DataFrame.from_dict({'counts': inventor_counts, 'states': inventor_states})\n", "d.index = d['states']\n", "h = d.plot(kind='bar',figsize=(16,10))\n", "h.set_xlabel('U.S. States')\n", "h.set_ylabel('Raw Inventors')\n", "h.set_title('Raw Inventors per Reported State')\n", "printstats(d['counts'])\n", "print len(d['states']), 'identified states'" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "mean 106914.854545\n", "median 41123.0\n", "mode 7.0\n", "std 187392.226124\n", "min 7\n", "max 1244222\n", "55 identified states\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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0qV544QXNnj1bkZGRvs1tWlqapk2bpi5dumjAgAHavHmzJCk7O1urVq3S008/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"text": [ "<matplotlib.figure.Figure at 0x1654f2dd0>" ] } ], "prompt_number": 15 }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "Location" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# cities by state\n", "res = session.execute('select rawlocation.state, count(distinct rawlocation.city) from rawlocation \\\n", " where rawlocation.country = \"US\" and length(rawlocation.state) = 2 \\\n", " group by rawlocation.state')\n", "data = res.fetchall()\n", "d = pd.DataFrame.from_records(data)\n", "d.columns = ['state','count']\n", "d.index = d['state']\n", "h = d.plot(kind='bar',figsize=(16,10))\n", "h.set_xlabel('U.S. State')\n", "h.set_ylabel('Number of Unique Cities')\n", "h.set_title('Cities per State (raw)')\n", "print \"Raw Locations\"\n", "printstats(d['count'])\n", "print sum(d['count']), 'raw total cities'\n", "# disambiguated\n", "res = session.execute('select location.state, count(distinct location.city) from location \\\n", " where location.country = \"US\" and length(location.state) = 2 \\\n", " group by location.state')\n", "data = res.fetchall()\n", "d = pd.DataFrame.from_records(data)\n", "d.columns = ['state','count']\n", "d.index = d['state']\n", "h = d[['count','state']].plot(kind='bar',figsize=(16,10))\n", "h.set_xlabel('U.S. State')\n", "h.set_ylabel('Number of Unique Cities')\n", "h.set_title('Cities per State (disambig)')\n", "print \"Disambiguated Locations:\"\n", "printstats(d['count'])\n", "print sum(d['count']), 'total cities'" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Raw Locations\n", "mean 983.229508197\n", "median 621.0\n", "mode 1.0\n", "std 1165.26165007\n", "min 1\n", "max 5809\n", "59977 raw total cities\n", "Disambiguated Locations:" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "mean 284.018181818\n", "median 195.0\n", "mode 1.0\n", "std 263.929809055\n", "min 1\n", "max 1073\n", "15621 total cities\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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WTjOzdJqZdaXTS5ZOM7N0mpk1ZcY2KSlVCQkJvr0lJaXGba3xlp6ernfeeSfuX9fX32ML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"text": [ "<matplotlib.figure.Figure at 0x1654ca590>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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8vJUrV6pNmzbKycnRypUrK7w/JyenPlsGAAAAADisXhe1ffr00cSJEyVJEydO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"text": [ "<matplotlib.figure.Figure at 0x14f7aa810>" ] } ], "prompt_number": 16 }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "Patents per State" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# patents per state\n", "res = session.execute(\"select location.state, count() from patent \\\n", " left join rawinventor on rawinventor.patent_id = patent.id \\\n", " left join rawlocation on rawlocation.id = rawinventor.rawlocation_id \\\n", " right join location on location.id = rawlocation.location_id \\\n", " where length(location.state) = 2 and rawinventor.sequence = 0 \\\n", " and location.country = 'US' \\\n", " group by location.state\")\n", "data = res.fetchall()\n", "d = pd.DataFrame.from_records(data)\n", "d.columns = ['state','count']\n", "d.index = d['state']\n", "h = d.plot(kind='bar',figsize=(16,10))\n", "h.set_xlabel('U.S. State')\n", "h.set_ylabel('Patents')\n", "h.set_title('Patents per State')\n", "printstats(d['count'])\n", "print sum(d['count'])" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "mean 18535.2222222\n", "median 7598.0\n", "mode 1.0\n", "std 36240.9846594\n", "min 1\n", "max 252941\n", "1000902\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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98sVco/ENjV6vQUPZGqqwBw0aytZQhT1o0FDkeg0aytZgBhgAAADawQwwScwA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"text": [ "<matplotlib.figure.Figure at 0x14f7cc1d0>" ] } ], "prompt_number": 17 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Patents" ] }, { "cell_type": "code", "collapsed": false, "input": [ "res = session.execute('select year(date), count() from patent group by year(date);')\n", "year_counts = map(lambda x: (str(int(x[0])), int(x[1])), res.fetchall())\n", "d = pd.DataFrame.from_dict({'dates': [x[0] for x in year_counts], 'counts': [x[1] for x in year_counts]})\n", "d.index = d['dates']\n", "h = d.plot(kind='bar',figsize=(16,10))\n", "h.set_xlabel('Grant Year')\n", "h.set_ylabel('Patent Count')\n", "h.set_title('Granted Patents by Grant year')\n", "printstats(d['counts'])\n", "print sum(d['counts']), 'total patents'" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "mean 130057.564103\n", "median 101419.0\n", "mode 48854.0\n", "std 67968.1111903\n", "min 48854\n", "max 303478\n", "5072245 total patents\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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QMbfrEanHoWNcj4qPQ7geZY9DnP78RfJx+e51mR86Vts+Uuhx9mEeZ1fz/bK8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\n9tel7a9L21+XdiRugAEAAAAASYEzwBGP87p5222Lm622LW622ra42Wr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"text": [ "<matplotlib.figure.Figure at 0x14f921290>" ] } ], "prompt_number": 18 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Granted Applications" ] }, { "cell_type": "code", "collapsed": false, "input": [ "res = session.execute('select year(application.date), count() from patent \\\n", " left join application on application.patent_id = patent.id \\\n", " where year(application.date) != \"\" \\\n", " group by year(application.date);')\n", "year_counts = map(lambda x: (str(int(x[0])), int(x[1])), res.fetchall())\n", "d = pd.DataFrame.from_dict({'dates': [x[0] for x in year_counts], 'counts': [x[1] for x in year_counts]})\n", "d.index = d['dates']\n", "h = d.plot(kind='bar',figsize=(16,10))\n", "h.set_xlabel('App Year')\n", "h.set_ylabel('Patent Count')\n", "h.set_title('Granted Patents by Application year')\n", "printstats(d['counts'])\n", "print sum(d['counts']), 'total granted applications'" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "mean 54373.0326087\n", "median 283.5\n", "mode 1.0\n", "std 74219.9389947\n", "min 1\n", "max 228149\n", "5002319 total granted applications\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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AAAAAAABbYAYYAAAAQFgKxAxwMDftgpdBAgAAAADYHhvgYqF+bTvN8GqaXUeT\nJk2aVtbRpEmTZqCbZtd5uxms87jMAPt2nTs2wAAAAAAAW2AGGAAAAEBYCuZ5XGaAfYcZYAAAAACA\n7bEBLhbq17bTDK+m2XU0adKkaWUdTZo0aQa6aXadt5vBOo/LDLBv17ljAwwAAAAAsAVmgAEAAACE\npWCex2UG2HeYAQYAAAAA2B4b4GKhfm07zfBqml1HkyZNmlbW0aRJk2agm2bXebsZrPO4zAD7dp07\nNsAAAAAAAFtgBhgAAABAWArmeVxmgH2HGWAAAAAAgO2xAS4W6te20wyvptl1NGnSpGllHU2aNGkG\numl2nbebwTqPywywb9e5YwMMAAAAALAFZoABAAAAhKVgnsdlBth3mAEGAAAAAJtzOmPlcDjKfXM6\nYwN9en7BBrhYqF/bTjO8mmbX0aRJk6aVdTRp0qQZ6KbZdd5uBus8rr+aBQV5Knqk2JC0ye19o/hz\n5RwpiO9P8/f7RWyAAQAAAAC2wAwwAAAAgLAUzPO4wdoMB8wAAwAAAABsjw1wsVC/tp1meDXNrqNJ\nkyZNK+to0qRJM9BNs+u83QymedxQaQbz/Wn+fr+IDTAAAAAAwBaYAQYAAAAQlkJ9HpcZYGuYAQYA\nAAAA2B4b4GKhfm07zfBqml1HkyZNmlbW0aRJk2agm2bXebsZ6vO4zABbW+eODTAAAAAAwBaYAQYA\nAAAQUpzOWBUU5Hn8fFRUjE6ezA35eVxmgK2paM8X6edzAQAAAIBLUrT59bxZKyhw+O9kEFK4BLpY\nqF/bTjO8mmbX0aRJk6aVdTRp0qQZ6KbZdWaPZZd5XGaAra1zxwYYAAAAAGALzAADAAAACCl2mcdl\nBtgav74OcHZ2trp06aIrr7xSV111lebPny9Jys3NVffu3dWqVSv16NFD+fn5rq+ZPXu2WrZsqdat\nW2vDhg2u23fu3Km2bduqZcuWuu+++1y3nzlzRkOGDFHLli2VkpKigwcPuj63ePFitWrVSq1atdKS\nJUu8/e0BAAAAAEKU1zfA1atX11NPPaWvvvpK27dv13PPPaevv/5ajz/+uLp3765vvvlG3bp10+OP\nPy5J2rt3r5YvX669e/dq3bp1Gj9+vGu3Pm7cOC1cuFCZmZnKzMzUunXrJEkLFy5UXFycMjMzNWnS\nJE2ePFlS0Sb7scceU0ZGhjIyMjRjxowSG+2KhPq17TTDq2l2HU2aNGlaWUeTJk2agW6aXWf2WHaZ\nx2UG2NrixP6oAAAgAElEQVQ6d17fADds2FDt27eXJNWpU0f//d//rZycHL377rtKS0uTJKWlpent\nt9+WJL3zzju64447VL16dSUnJ6tFixbasWOHjh49qoKCAnXq1EmSNGLECNfXuB9r4MCB+vDDDyVJ\n69evV48ePRQdHa3o6Gh1797dtWkGAAAAANibT18GKSsrS7t379Z1112nY8eOKT4+XpIUHx+vY8eO\nSZKOHDmilJQU19ckJiYqJydH1atXV2Jiouv2hIQE5eTkSJJycnLUpEmTom8gMlJ169bV8ePHdeTI\nkRJfc+FYpY0cOVLJycmSpOjoaLVv316pqamSLv5XBE8fX7itsvXuaz0dLzU1tdJeVY7nzfM387G3\nz9/M8bx5/lU5nrfOn5+H9eN58/z5/fbN+XN/+u78vX08O/08vHX+/H777vyrcjxvnT/3p+/O39vH\nq+j83X4CJj9OLX4r+fmyx7vwNake1rgfr7xe2a93/9h752/2eCXvj8rOPxR/v/fs2eO68jcrK0sV\n8dmTYP3000/q3LmzHnnkEfXv318xMTHKy7v4YtWxsbHKzc3VhAkTlJKSomHDhkmSxowZo969eys5\nOVlTpkzRxo0bJUkff/yx5syZo/fee09t27bV+vXr1bhxY0lyPWq8aNEinT59Wg899JAkaebMmapV\nq5YeeOCBi98wT4IFAAAAhDS7PCEVT4JljV+fBEuSfv31Vw0cOFDDhw9X//79JRU96vvdd99Jko4e\nPaoGDRpIKnpkNzs72/W1hw8fVmJiohISEnT48OEyt1/4mkOHDkmSCgsLdeLECcXFxZU5VnZ2dolH\nhCtS/n/5sbbOm8eiac+m2XU0adKkaWUdTZo0aQa6aXad2WOV/yis1XU0S6wKg981d17fABuGodGj\nR6tNmzaaOHGi6/Z+/fpp8eLFkoqeqfnCxrhfv35KT0/X2bNndeDAAWVmZqpTp05q2LChnE6nduzY\nIcMwtHTpUt12221ljrVq1Sp169ZNktSjRw9t2LBB+fn5ysvL08aNG9WzZ09vf4sAAAAAgBDk9Uug\nP/nkE918881q165d8UPsRS9z1KlTJw0ePFiHDh1ScnKyVqxYoejoaEnSrFmz9PLLLysyMlJPP/20\na9O6c+dOjRw5UqdOnVKfPn1cL6l05swZDR8+XLt371ZcXJzS09NdM72vvPKKZs2aJUl6+OGHXU+W\n5fqGuQQaAAAACGl2uRyZS6CtqWjP57MZ4GDFBhgAAAAIbXbZjLIBtsbvM8ChKNSvbacZXk2z62jS\npEnTyjqaNGnSDHTT7Dqzx7LLPC4zwNbWuWMDDAAAAACwBS6BBgAAABBS7HI5MpdAW8Ml0AAAAAAA\n22MDXCzUr22nGV5Ns+to0qRJ08o6mjRp0gx00+w6s8eyyzwuM8DW1rljAwwAAAAAsAVmgAEAAACE\nFLvM4zIDbA0zwAAAAAAA22MDXCzUr22nGV5Ns+to0qRJ08o6mjRp0gx00+w6s8eyyzwuM8DW1rlj\nAwwAAAAAsAVmgAEAAACEFLvM4zIDbA0zwAAAAAAA22MDXCzUr22nGV5Ns+to0qRJ08o6mjRp0gx0\n0+w6s8eyyzwuM8DW1rljAwwAAAAAsAVmgAEAAACEFLvM4zIDbA0zwAAAAAAA22MDXCzUr22nGV5N\ns+to0qRJ08o6mjRp0gx00+w6s8eyyzwuM8DW1rljAwwAAAAAsAVmgAEAAACEFLvM4zIDbA0zwAAA\nAAAA22MDXCzUr22nGV5Ns+to0qRJ08o6mjRp0gx00+w6s8eyyzwuM8DW1rljAwwAAAAAsAVmgAEA\nAACEFLvM4zIDbA0zwAAAAAAA22MDXCzUr22nGV5Ns+to0qRJ08o6mjRp0gx00+w6s8eyyzwuM8DW\n1rljAwwAAAAAsAVmgAEAAACEFLvM4zIDbA0zwAAAAAAA22MDXCzUr22nGV5Ns+to0qRJ08o6mjRp\n0gx00+w6s8eyyzwuM8DW1rljAwwAAAAAsAVmgAEAAAAEDaczVgUFeeV+LioqRidP5tpmHpcZYGsq\n2vNF+vlcAAAAAMCjos1v+ZuXggKHf08GYYdLoIuF+rXtNMOraXYdTZo0aVpZR5MmTZqBbppfZ+5Y\ndpnHZQbY2jp3bIABAAAAALbADDAAAACAoBGss7F2aYYDXgcYAAAAAGB7bICLhfq17TTDq2l2HU2a\nNGlaWUeTJk2agW6aX2fuWHaZx2UG2No6dzwLNAAAAADLzLxsEUJHRfenFPr3KTPAAAAAACzz9lxp\nsM7G0ix7vGDFDDAAAAAAwPbYABcL9WvbaYZX0+w6mjRp0rSyjiZNmjR92fTubKl3m6E+jxvMzWD9\n/S6NDTAAAAAAwBaYAQYAAABgGTPA9mwGM2aAAQAAAAC2xwa4WDDPUdC0X9PsOpo0adK0so4mTZo0\nfdlkBtiezWD9/S6NDTAAAAAAwBaYAQYAAABgGTPA9mwGM2aAAQAAAAC2xwa4WDDPUdC0X9PsOpo0\nadK0so4mTZo0fdlkBtiezWD9/S6NDTAAAAAAwBaYAQYAAABgGTPA9mwGM2aAAQAAAAC2xwa4WDDP\nUdC0X9PsOpo0adK0so4mTZo0fdlkBtiezWD9/S6NDTAAAAAAwBaYAQYAAABgGTPA9mwGM2aAAQAA\nAAC2xwa4WDDPUdC0X9PsOpo0adK0so4mTZo0fdlkBtiezWD9/S6NDTAAAAAAwBaYAQYAAABgGTPA\n9mwGM2aAAQAAAAC2xwa4WDDPUdC0X9PsOpo0adK0so4mTZo0fdlkBtiezWD9/S6NDTAAAAAAwBaY\nAQYAAABgGTPA9mwGM2aAAQAAAAC2xwa4WDDPUdC0X9PsOpo0adK0so4mTZo0fdlkBtiezWD9/S6N\nDTAAAAAAwBaYAQYAAABgGTPA9mwGM2aAAQAAAAC2xwa4WDDPUdC0X9PsOpo0adK0so4mTZo0fdlk\nBtiezWD9/S6NDTAAAAAAwBaYAQYAAABgGTPA9mwGM2aAAQAAAAC2xwa4WDDPUdC0X9PsOpo0adK0\nso4mTZo0fdlkBtiezWD9/S6NDTAAAAAAwBaYAQYAAABgGTPA9mwGM2aAAQAAAAC2xwa4WDDPUdC0\nX9PsOpo0adK0so4mTZo0fdlkBtiezWD9/S6NDTAAAAAAwBaYAQYAAABgGTPA9mwGM2aAAQAAAAC2\nxwa4WDDPUdC0X9PsOpo0adK0so4mTZo0fdlkBtiezWD9/S6NDTAAAAAAwBaYAQYAAABgGTPA9mwG\nM2aAAQAAAAC2xwa4WDDPUdC0X9PsOpo0adK0so4mTZo0fdlkBtiezWD9/S6NDTAAAAAAwBaYAQYA\nAABgGTPA9mwGM2aAAQAAAAC2xwa4WDDPUdC0X9PsOpo0adK0so4mTZo0fdlkBtiezWD9/S6NDTAA\nAAAAwBaYAQYAAABgGTPA9mwGM2aAAQAAAAC2xwa4WDDPUdC0X9PsOpo0adK0so4mTZo0fdlkBtge\nTaczVg6Ho9w3pzO2/CMxAwwAAAAACDUFBXkqulT6wtsm1/tFnwtOzAADAAAAsIwZYJoVNQOBGWAA\nAAAAgO2xAS4WzHMUNO3XNLuOJk2aNK2so0mTJk1fNpkBpulxRRDMAEdW+SsAAAAAoIqczliPs6FR\nUTE6eTLXz2cEO2IGGAAAAIBl3pw/NbsuHGZj7dIMBGaAAQAAAAC2xwa4WDDPUdC0X9PsOpo0adK0\nso4mTZo0fdkM9TlVmr5bF6i/je7YAAMAAAAAbIEZYAAAAACWMQNMs6JmIPh1Bvjuu+9WfHy82rZt\n67pt+vTpSkxMVIcOHdShQwetXbvW9bnZs2erZcuWat26tTZs2OC6fefOnWrbtq1atmyp++67z3X7\nmTNnNGTIELVs2VIpKSk6ePCg63OLFy9Wq1at1KpVKy1ZssTb3xoAAAAAIIR5fQM8atQorVu3rsRt\nDodD999/v3bv3q3du3erd+/ekqS9e/dq+fLl2rt3r9atW6fx48e7durjxo3TwoULlZmZqczMTNcx\nFy5cqLi4OGVmZmrSpEmaPHmyJCk3N1ePPfaYMjIylJGRoRkzZig/P9/0eQfzHAVN+zXNrqNJkyZN\nK+to0qRJ0+wapzNWDoej3DenM9bTEU2dW7DOqdL03bpA/W105/UN8E033aSYmJgyt5f3EPQ777yj\nO+64Q9WrV1dycrJatGihHTt26OjRoyooKFCnTp0kSSNGjNDbb78tSXr33XeVlpYmSRo4cKA+/PBD\nSdL69evVo0cPRUdHKzo6Wt27dy+zEQcAAABgXtHr9hrFb5vc3jc8vqYvEMwi/RV65plntGTJEnXs\n2FHz5s1TdHS0jhw5opSUFNeaxMRE5eTkqHr16kpMTHTdnpCQoJycHElSTk6OmjRpUnTykZGqW7eu\njh8/riNHjpT4mgvHKs/IkSOVnJwsSYqOjlb79u2Vmpoq6eJ/RfD08YXbKlvvvtbT8VJTUyvtVeV4\n3jx/Mx97+/zNHM+b51+V43nr/Pl5WD+eN8+f32/fnD/3p+/O39vHs9PPw1vnz++3786/Ksfz1vlz\nf1bt/KULvdIflzgLt8+X/Lj0+VbteKll1ns+XmUflz6ePBzPvV/empLn472fR2Ufp8raz6Pk/Xup\n51+2WfHx/PH7vWfPHtfVv1lZWeV8Pxf55EmwsrKy1LdvX3355ZeSpO+//17169eXJD3yyCM6evSo\nFi5cqAkTJiglJUXDhg2TJI0ZM0a9e/dWcnKypkyZoo0bN0qSPv74Y82ZM0fvvfee2rZtq/Xr16tx\n48aS5HrUeNGiRTp9+rQeeughSdLMmTNVq1YtPfDAAyW/YZ4ECwAAADDl0p80iSfBsnszEPz6JFjl\nadCggWtWYMyYMcrIyJBU9Mhudna2a93hw4eVmJiohIQEHT58uMztF77m0KFDkqTCwkKdOHFCcXFx\nZY6VnZ1d4hHhypT/X36srfPmsWjas2l2HU2aNGlaWUeTJk2aVo5V/iOFvl5HM5yagfrb6M4vG+Cj\nR4+63n/rrbdczxDdr18/paen6+zZszpw4IAyMzPVqVMnNWzYUE6nUzt27JBhGFq6dKluu+0219cs\nXrxYkrRq1Sp169ZNktSjRw9t2LBB+fn5ysvL08aNG9WzZ09/fHsAAAAAgBDg9Uug77jjDm3ZskU/\n/vij4uPjNWPGDG0uvi7b4XCoWbNmWrBggeLj4yVJs2bN0ssvv6zIyEg9/fTTrk3rzp07NXLkSJ06\ndUp9+vTR/PnzJRW9DNLw4cO1e/duxcXFKT093TXP+8orr2jWrFmSpIcfftj1ZFklvmEugQYAAABM\n4RJompfaDISK9nw+mQEOZmyAAQAAAHPYANO81GYgBHwGOBTYZXaDZmg0za6jSZMmTSvraNKkSdPK\nsewyp0rTd+sC9bfRHRtgAAAAAIAtcAk0AAAAgHJxCTTNS20GApdAAwAAAHBxOmNdL1Na+s3pjA30\n6QE+wwa4mF1mN2iGRtPsOpo0adK0so4mTZo0CwryVPTonSFpk9v7RvHnyj2aqWaoz6nS9N26QP1t\ndMcGGAAAAABgC8wAAwAAADbjv5lRZoDt3gwEZoABAAAAALbHBrhYKM5u0Azfptl1NGnSpGllHU2a\nNGmWWmXqWHaZU6Xpu3WB+tvojg0wAAAAAMAWmAEGAAAAbIYZYJr+agYCM8AAAAAAANtjA1ws1Gc3\naIZX0+w6mjRp0rSyjiZNmjRLrTJ1LLvMqdL03bpA/W10xwYYAAAAAGALzAADAAAANsMMME1/NQOB\nGWAAAAAAgO2xAS4W6rMbNMOraXYdTZo0aVpZR5MmTZqlVpk6ll3mVGn6bl2g/ja6YwMMAAAAALAF\nZoABAAAAm2EGmKa/moFwSTPAn3zySZnbtm7deulnBQAAAACAH1W6AZ4wYUKZ2+69916fnEwghfrs\nBs3wappdR5MmTZpW1tGkSZNmqVWmjmWXOVWavlsXqL+N7iI9feLTTz/Vtm3b9MMPP+jJJ590PYRc\nUFCg8+fPVzkEAAAAAEAgeZwB3rJlizZt2qQFCxZo7NixrtujoqLUt29ftWzZ0m8n6U3MAAMAAMDu\nmAGm6a9mIFS056v0SbCysrKUnJzsi/MKCDbAAAAAsDs2wDT91QyES3oSrDNnzuiee+5R9+7d1aVL\nF3Xp0kVdu3b1+kkGWqjPbtAMr6bZdTRp0qRpZR1NmjRpllpl6lh2mVOl6bt1gfrb6M7jDPAFgwYN\n0rhx4zRmzBhVq1ZN0oX/cgAAAAAAQOio9BLoa6+9Vjt37vTX+fgcl0ADAADA7rgEmqa/moFwSZdA\n9+3bV88995yOHj2q3Nxc1xsAAAAAAKGk0g3wokWLNHfuXF1//fW69tprXW/hJtRnN2iGV9PsOpo0\nadK0so4mTZo0S60ydSy7zKnS9N26QP1tdFfpDHBWVlaVDwoAAAAAQLCpdAZ48eLF5T7p1YgRI3x2\nUr7EDDAAAADsjhlgmv5qBkJFe75KHwH+7LPPXBvgU6dO6aOPPtI111wTshtgAAAAAIA9VToD/Oyz\nz+qZZ57RM888o3/84x/atWuXCgoK/HFufhXqsxs0w6tpdh1NmjRpWllHkyZNmqVWmTqWXeZUafpu\nXaD+NrqrdANcWu3atXXgwIEqhwAAAAAACKRKZ4D79u3rev/8+fPau3evBg8erCeeeMLnJ+cLzAAD\nAADA7pgBpumvZiBUtOerdAN84WFlh8OhyMhIJSUlqUmTJl4/SX9hAwwAAAC7YwNM01/NQKhoz1fp\nJdCpqalq3bq1Tp48qby8PNWsWdPrJxgMQn12g2Z4Nc2uo0mTJk0r62jSpEmz1CpTx7LLnCpN360L\n1N9Gd5VugFesWKHrrrtOK1eu1IoVK9SpUyetXLmyyiEAAAAAAAKp0kug27Vrpw8++EANGjSQJP3w\nww/q1q2bvvjiC7+coLdxCTQAAADsjkugafqrGQiXdAm0YRiqX7++6+O4uDg2kAAAAACAkFPpBrhX\nr17q2bOnFi1apFdeeUV9+vRR7969/XFufhXqsxs0w6tpdh1NmjRpWllHkyZNmqVWmTqWXeZUafpu\nXaD+NrqLrGzB3/72N73xxhvaunWrJOkPf/iDbr/99iqHAAAAAAAIJI8zwJmZmTp27JhuvPHGErd/\n8sknatSokZo3b+6XE/Q2ZoABAABgd8wA0/RXMxAszQBPnDhRTqezzO1Op1MTJ0703tkBAAAAAOAH\nHjfAx44dU7t27crc3q5dOx04cMCnJxUIoT67QTO8mmbX0aRJk6aVdTRp0qRZapWpY9llTpWm79YF\n6m+jO48b4Pz8fI9fdPr06SqHAAAAAAAIJI8zwEOHDlXXrl31+9//vsTtL730kj744AMtX77cLyfo\nbcwAAwAAwO6YAabpr2YgVLTn87gB/u6773T77berRo0auvbaayVJO3fu1JkzZ/TWW2+pUaNGvjtj\nH2IDDAAAALtjA0zTX81AsPQkWA0bNtS2bds0bdo0JScnq1mzZpo2bZq2b98espvfioT67AbN8Gqa\nXUeTJk2aVtbRpEmTZqlVpo5llzlVmr5bF6i/je4qfB1gh8Ohrl27qmvXrlU+MAAAAAAAwcTjJdDh\nikugAQAAYHdcAk3TX81AsHQJNAAAAAAA4aTSDfDkyZNN3RbqQn12g2Z4Nc2uo0mTJk0r62jSpEmz\n1CpTx7LLnCpN360L1N9Gd5VugDds2FDmtvfff7/KIQAAAAC+5XTGyuFwuN66dOniet/pjA306QEB\n53EG+IUXXtDzzz+v/fv3q3nz5q7bCwoKdMMNN2jZsmV+O0lvYgYYAAAA4Sr45nGZAbZ7MxAsvQ7w\niRMnlJeXpylTpuiJJ55wHSAqKkpxcXG+O1sfYwMMAACAcBV8m1E2wHZvBoKlJ8GqW7eukpOTlZ6e\nrsTERNWoUUMRERH6+eefdejQIZ+dbKCE+uwGzfBqml1HkyZNmlbW0aRJ0z7NYJ0FNb+OZjg1A/W3\n0V2FrwMsSc8884xmzJihBg0aqFq1aq7bv/zyyyrHAAAAAAAIlEpfB7h58+bKyMgI6cue3XEJNAAA\nAMJV8F2OzCXQdm8GwiW9DnBSUpKcTqfXTwoAAAAAAH+qdAPcrFkzdenSRbNnz9a8efM0b948Pfnk\nk/44N78K5tkNmvZrml1HkyZNmlbW0aRJ0z7NYJ0FNb+OZjg1A/W30V2lM8BJSUlKSkrS2bNndfbs\n2SoHAAAAAAAIBpXOAF/w888/6/LLL/f1+fgcM8AAAAAIV8E3j8sMsN2bgXBJM8Dbtm1TmzZt1Lp1\na0nS559/rvHjx3v3DAEAAAAA8LFKN8ATJ07UunXrVK9ePUnS1VdfrS1btvj8xPwtmGc3aNqvaXYd\nTZo0aVpZR5MmTfs0g3UW1Pw6muHUDNTfRneVboClojlgd5GRlY4OAwAAAAAQVCqdAf7d736nSZMm\n6d5779WOHTs0f/58/etf/1J6erq/ztGrmAEGAABAuAq+eVxmgO3eDIRLmgF+4YUX9NxzzyknJ0cJ\nCQnavXu3nnvuOa+fJAAAAAAAvlTpBvibb77Ra6+9pu+//14//PCDli1bpn379vnj3PwqmGc3aNqv\naXYdTZo0aVpZR5MmTfs0g3UW1Pw6muHUDNTfRneVboDvvfdeU7cBAAAAABDMPM4Af/rpp9q2bZue\neuop3X///a5rqAsKCvTWW2/p888/9+uJegszwAAAAAhXwTePywyw3ZuBUNGez+PTOZ89e1YFBQU6\nd+6cCgoKXLc7nU6tWrXK+2cJAAAAAIAPebwEunPnzpo+fbo+/fRTTZs2zfV2//33q2XLlv48R78I\n5tkNmvZrml1HkyZNmlbW0aRJ0z7NYJ0FNb+OZjg1A/W30V2lL+hbu3Zt/elPf9LevXt16tQpSUUP\nKX/00UdVjgEAAAAAECiVvg5w9+7dNWTIEM2dO1cLFizQokWLVL9+fc2ZM8df5+hVzAADAAAgXAXf\nPC4zwHZvBkJFe75KN8DXXHONdu3apXbt2umLL76QJHXs2FH/+te/vH+mfsAGGAAAAOEq+DajbIDt\n3gyEivZ8lb4MUo0aNSRJDRs21OrVq7Vr1y7l5eV59wyDQDDPbtC0X9PsOpo0adK0so4mTZr2aQbr\nLKj5dTTDqRmov43uKp0Bfvjhh5Wfn6958+ZpwoQJOnnypJ566qkqhwAAAAAACCSPl0CfOnVKL774\nor799lu1a9dOo0ePVmRkpfvloMcl0AAAAAhXwXc5MpdA270ZCJYugU5LS9POnTvVrl07vf/++3rg\ngQd8doIAAAAAAPiaxw3w119/rVdffVV/+MMf9MYbb+if//ynP8/L74J5doOm/Zpm19GkSZOmlXU0\nadK0TzNYZ0HNr6MZTs1A/W1053ED7H65czhc+gwAAAAAsDePM8DVqlVT7dq1XR+fOnVKtWrVKvoi\nh0MnT570zxl6GTPAAAAACFfBN4/LDLDdm4FQ0Z7P40O7586d89kJAQAAAADgb5W+DrBdBPPsBk37\nNc2uo0mTJk0r62jSpGmfZrDOgppfRzOcmoH62+iODTAAAAAAwBY8zgCHK2aAAQAAEK6Cbx6XGWC7\nN53OWBUU5JW7KioqRidP5lbQssbSDDAAAAAAAJeiaPNb/ma0oMDh35MRl0C7BPPsBk37Nc2uo0mT\nJk0r62jSpBmaTaczVg6Ho9w3pzPW0xHNnJmJNYFaR9OOTW//23PHBhgAAAAIARcfSTMkbXJ73/B4\niSmAkpgBBgAAAEJAaM7jMgNM0/+vF1zRno9HgAEAAAAAtsAGuFg4z4vQDL2m2XU0adKkaWUdTZo0\nQ78Z+rOgZtfRtGPT2//23LEBBgAAAADYAjPAAAAAQAgIzXlcZoBpMgMMAAAAAIDfsQEuZpd5EZqh\n0TS7jiZNmjStrKNJk2boN0N/FtTsOpp2bHr73567yCp/BQAAAACvcTpjPb6Ob1RUjE6ezPXzGQHh\nixlgAAAAIIDCex6XGWCazAADAAAAAOB3bICL2WVehGZoNM2uo0mTJk0r62jSpBm8zWCey7TPudEM\ndNPb//bcsQEGAAAAANiC12eA7777bq1Zs0YNGjTQl19+KUnKzc3VkCFDdPDgQSUnJ2vFihWKjo6W\nJM2ePVsvv/yyqlWrpvnz56tHjx6SpJ07d2rkyJE6ffq0+vTpo6efflqSdObMGY0YMUK7du1SXFyc\nli9frqZNm0qSFi9erP/5n/+RJD388MMaMWJE2W+YGWAAAAAEkfCex2UGmGaYzwCPGjVK69atK3Hb\n448/ru7du+ubb75Rt27d9Pjjj0uS9u7dq+XLl2vv3r1at26dxo8f7zrRcePGaeHChcrMzFRmZqbr\nmAsXLlRcXJwyMzM1adIkTZ48WVLRJvuxxx5TRkaGMjIyNGPGDOXn53v72wMAAAAAhCivb4Bvuukm\nxcTElLjt3XffVVpamiQpLS1Nb7/9tiTpnXfe0R133KHq1asrOTlZLVq00I4dO3T06FEVFBSoU6dO\nkqQRI0a4vsb9WAMHDtSHH34oSVq/fr169Oih6OhoRUdHq3v37mU24hUJ9XkRmuHVNLuOJk2aNK2s\no0mTZvA2g3ku0z7nRjPQTW//23Pnl9cBPnbsmOLj4yVJ8fHxOnbsmCTpyJEjSklJca1LTExUTk6O\nqlevrsTERNftCQkJysnJkSTl5OSoSZMmRScfGam6devq+PHjOnLkSImvuXCs8owcOVLJycmSpOjo\naLVv3971uQs/xNTU1HI/3rNnT4Wf37x5s/bs2VPh56v6sZnjefP8q3I8b52/2Y+9ff7Ben/y8/Dd\n+VfleNyfVf+Y+zOw92dVjmeHnwe/39yfgb4/+/Ub4PH1fWvVqqNffilwr0hKdXtfro9Ln0/R5/eU\ns16lPi7/82WPt6fU+qI1F74fz8cvfduF9SWPV/75V+V4Fz5f0fF8//MoezxPH1+47cLXm/l5WDl/\nswq01/YAACAASURBVMfz7v1ZtlnV4134fGqJY13Kv/c9e/a4rv7Nysoqp3+RT14HOCsrS3379nXN\nAMfExCgv7+I//tjYWOXm5mrChAlKSUnRsGHDJEljxoxR7969lZycrClTpmjjxo2SpI8//lhz5szR\ne++9p7Zt22r9+vVq3LixJLkeNV60aJFOnz6thx56SJI0c+ZM1apVSw888EDJb5gZYAAAAPhB8M3G\n2qXpm3Oj6bumtwX8dYDj4+P13XffSZKOHj2qBg0aSCp6ZDc7O9u17vDhw0pMTFRCQoIOHz5c5vYL\nX3Po0CFJUmFhoU6cOKG4uLgyx8rOzi7xiDAAAADgDU5nrBwOh8c3pzM20KcIwAO/bID79eunxYsX\nSyp6pub+/fu7bk9PT9fZs2d14MABZWZmqlOnTmrYsKGcTqd27NghwzC0dOlS3XbbbWWOtWrVKnXr\n1k2S1KNHD23YsEH5+fnKy8vTxo0b1bNnT9PnWPbSAevrvHksmvZsml1HkyZNmlbW0aRJ89KaRZc1\nG25vm0p87Omy5/IvDbWyxtvrAtE0u46mHZve/vfuzuszwHfccYe2bNmiH3/8UU2aNNFjjz2mKVOm\naPDgwVq4cKHrZZAkqU2bNho8eLDatGmjyMhIPf/888UPkUvPP/+8Ro4cqVOnTqlPnz7q1auXJGn0\n6NEaPny4WrZsqbi4OKWnp0squqz6kUce0W9+8xtJ0rRp01wvtQQAAAAAgE9mgIMZM8AAAAC4FIGa\nkQzNeVxmgGnacAYYAAAAAIBAYwNcLFxnVGiGZtPsOpo0adK0so4mTZrebdplLtM+50Yz0E1v/3t3\nxwYYAAAAAGALzAADAAAAxZzOWI/P4hwVFaOTJ3OZAQ76pm/Ojabvmt5W0Z7P688CDQAAAISqiy9x\nVN7nHP49GQBexyXQxewyo0IzNJpm19GkSZOmlXU0adI0u87csewyl2mfc6MZ6Ka3/727YwMMAAAA\nALAFZoABAACAYsE8Ixma87jMANMMrhlgHgEGAAAAANgCG+BioT6jQjO8mmbX0aRJk6aVdTRp0mQG\nOPBNs+to2rHp7X/v7tgAAwAAAABsgRlgAAAAoFgwz0iG5jwuM8A0mQEGAAAAAMDv2AAXC/UZFZrh\n1TS7jiZNmjStrKNJkyYzwIFvml1H045Nb/97d8cGGAAAAABgC8wAAwAAAMWCeUYyNOdxmQGmyQww\nAAAA4FdOZ6wcDke5b05nbKBPD4CfsAEuFuozKjTDq2l2HU2aNGlaWUeTph2bBQV5KnoU6sLbJtf7\nRZ8r92immnaZy7TPudEMdNPb/xvjjg0wAAAAAMAWmAEGAABA2AuHGcnQnMdlBpgmM8AAAAAAAPgd\nG+BiwTSjQpOm2XU0adKkaWUdTZo0pVCfkQz9ptl1NO3Y9P6/94vYAAMAAAAAbIEZYAAAAIS9cJiR\nDM15XGaAaTIDDAAAAACA37EBLhbMMyo07dc0u44mTZo0rayjSZOmFOozkqHfNLuOph2b3v/3fhEb\nYAAAAACALTADDAAAgLAXDjOSoTmPywwwTWaAAQAAAADwOzbAxYJ5RoWm/Zpm19GkSZOmlXU0adKU\nQn1GMvSbZtfRtGPT+//eL2IDDAAAAACwBWaAAQAAEPbCYUYyNOdxmQGmyQwwAAAAAAB+xwa4WDDP\nqNC0X9PsOpo0adK0so4mTZpSqM9Ihn7T7Dqadmx6/9/7RWyAAQAAAAC2wAwwAAAAwl44zEiG5jwu\nM8A0mQEGAAAAvMLpjJXD4fD45nTGBvoUAQQRNsDFgnlGhab9mmbX0aRJk6aVdTRphlOzoCBPRY8u\nXXjbVOLjos+Xe0QzZ2ZijbfX2aVpdh1NOza9/b8x7tgAAwAAAABsgRlgAAD+f3v3Hh11eedx/DMh\nQSAhJkGuCRC5RiACcpV2Cy0gqBXLsqJUF13ZVm3tKj3HU9jtWtxWUVvX42Xtbl1QlAqotSCcU4QF\nadUeEkVZhACyQLiJoEQkXBOSZ/8gM2bIJHky+U3mN/O8X+dwDjBffu9n5hdm8oM8EwAJy6U9kom5\nH5c9wDTZAwwAAAAAQIvjArhGsu6LoZmYTds5mjRp0oxmjibNZG66skcy8Zu2czRdbHr9HFMbF8AA\nAAAAACewBxgAAAAJy6U9kom5H5c9wDTZAwwAAAAAQIvjAriGK/tiaCZG03aOJk2aNKOZo0kzmZuu\n7JFM/KbtHE0Xm14/x9TGBTAAAAAAwAnsAQYAAEDCcmmPZGLux2UPME32AAMAAAAA0OK4AK7hyr4Y\nmonRtJ2jSZMmzWjmaNJM5qYreyQTv2k7R9PFptfPMbWlNvlPAAAAAC0gMzNH5eVfRrytfftsnThR\n1sIrApDo2AMMAAAAX/LrfkX2APu9GZu10Yxd02vsAQYAAAAAOI8L4Bqu7IuhmRhN2zmaNGnSjGaO\nJs1EbPp5vyLNWM7RdLHp9XNMbVwAAwAAAACcwB5gAAAA+JJf9yuyB9jvzdisjWbsml5jDzAAAAAA\nwHlcANdI9H0xNJOraTtHkyZNmtHM0aSZiE0/71ekGcs5mi42vX6OqY3vAwwAAIAWxff3BRAv7AEG\nAABAi0r0/YrsAfZ7MzZroxm7ptfYAwwAAAAAcB4XwDUSfV8MzeRq2s7RpEmTZjRzNGn6rZno+xVp\nxnKOpotN759jvsYFMAAAAADACewBBgAAQItK9P2K7AH2ezM2a6MZu6bX2AMMAAAAAHAeF8A1/Lwv\nhqZ7Tds5mjRp0oxmjiZNvzUTfb8izVjO0XSx6f1zzNe4AAYAAAAAOIE9wAAAAGhRib5fkT3Afm/G\nZm00Y9f0GnuAAQAAEHOZmTkKBAL1/sjMzIn3EgE4jgvgGn7eF0PTvabtHE2aNGlGM0cz8ZsNXWjW\nd5HZEvezvPxLXfifnuCPt8N+feH2iEe0WZnFjNdzNOM/R9PFZiz3AKc2+U8AAAAgJDMzp94Lu/bt\ns3XiRJnnza8vNKULn1COq3VbwPMeACQL9gADAICkYXMx2tCM7VztC1ub/W3xaMaDK/sV2QPs92Zs\n1kYzdk2vNXTNxwUwAABIGn795C4ZPqG04cpjywWw35uxWRvN2DW9xptgWUjE/T80k7dpO0eTJk2a\n0cy50vTz/jY/7amL5X5iVx5bmrGco+liM5Z7gLkABgAAcFhDb1zV0JdtA0Ai4kugAQBA0vDrl/fF\no2m779jLL0905bHlS6D93ozN2mjGrum1hq75eBdoAACAJBT+TtGRbufdogG4hy+BrpHo+5xoJlfT\ndo4mTZo0o5lzpenn/W2J3mQPMM2Wm6PpYpM9wAAAAAAANBN7gAEAQNLw6/62ZGjaSIb76demN2tz\npRmbtdGMXdNrfBskAAAANEs03y4JAPyGC+Aaib7PiWZyNW3naNKkSTOaOVeaft7flojN8G+X9Hat\nnzf07ZK8XJuXx6IZ/6btHE0Xm+wBBgAAAACgmdgDDAAAkoZf97fRpNncpjdrc6UZm7XRjF3Ta+wB\nBgAAAAA4jwvgGom+z4lmcjVt52jSpEkzmjlXmn7e30aTJk3bOZouNtkDDAAAAABAM7EHGAAAJA2/\n7m+jSbO5TW/W5kozNmujGbum19gDDAAAAABwHhfANRJ9nxPN5GraztGkSZNmNHOuNP28v40mTZq2\nczRdbLIHGAAAAACAZmIPMAAASBp+3d9Gk2Zzm96szZVmbNZGM3ZNr7EHGAAAAADgPC6AayT6Piea\nydW0naNJkybNaOZcafp5fxtNmjRt52i62GQPMAAAAAAAzcQeYAAAkDT8ur+NJs3mNr1ZmyvN2KyN\nZuyaXmMPMAAAAADAeVwA10j0fU40k6tpO0eTJk2a0cy50vTz/jaaNGnaztF0sRnLPcCpTf4TAAAA\nAAB4JDMzR+XlX9Z7e/v22TpxosyTFnuAAQBA0vDr/jaaNJvb9GZtrjRjszaa8W/aYg8wAAAAAMB5\nXADXSPR9TjSTq2k7R5MmTZrRzLnS9PP+Npo0adrO0aTZwFQUe4C5AAYAAAAAOKFF9wDn5+crMzNT\nrVq1UlpamoqLi1VWVqabb75Z+/btU35+vl599VVlZWVJkubPn6+FCxeqVatWevrpp3XNNddIkjZt\n2qQ77rhDZ8+e1XXXXaennnpKknTu3DnNnDlTH374oTp06KBly5apZ8+e4XeYPcAAACStRN/fRpNm\nbNfmSjM2a6MZ/6Yt3+wBDgQC2rBhgz766CMVFxdLkh599FFNnDhRn3zyicaPH69HH31UklRSUqJl\ny5appKREq1ev1o9+9KPQnbjnnnu0YMEC7dq1S7t27dLq1aslSQsWLFCHDh20a9cuzZ49Wz/72c9a\n8u4BAAAAAHysxb8E+uIr8TfffFO33367JOn222/X8uXLJUkrVqzQjBkzlJaWpvz8fPXp00dFRUU6\nfPiwysvLNXLkSEnSzJkzQ3+m9rGmTZumdevWWa8r0fc50Uyupu0cTZo0aUYz50ozGfa30aSZvE3b\nOZo0G5iKYg9wi34f4EAgoAkTJqhVq1a666679IMf/EBHjhxR586dJUmdO3fWkSNHJEmffvqpRo8e\nHfqzeXl5OnTokNLS0pSXlxf6/dzcXB06dEiSdOjQIXXv3l2SlJqaqksvvVRlZWXKyckJW8cdd9yh\n/Px8SVJWVpaGDBkSui34II4bNy7irzdv3tzg7Rs2bNDmzZsbvL2pv7Y5npfrb8rxvFq/7a+9Xr9f\nzyePR+zW35TjcT6b/mvOZ3zPZ1OOl6yPR60jSNosaVytX198uyLcbnu8CzPBfv3Hv/j3gvObL+pf\n3Gzq8YK3j4twrODt0TwekdYmeb/+8ONxPoO3j4twrODtsT+fXj0ekdfflOMFb2/oeHx813+85Pr4\nru/16vjx45Kk0tLSCP2vtege4MOHD6tr1676/PPPNXHiRD3zzDOaMmWKvvzy6296nJOTo7KyMv3k\nJz/R6NGjdeutt0qS/vEf/1HXXnut8vPzNWfOHK1du1aS9M477+jxxx/XypUrVVhYqLfeekvdunWT\nJPXp00fFxcVhF8DsAQYAIHkl+v42mjRjuzZXmrFZG834N235Zg9w165dJUkdO3bU1KlTVVxcrM6d\nO+uzzz6TdOECuVOnTpIu/M/ugQMHQn/24MGDysvLU25urg4ePFjn94N/Zv/+/ZKk8+fP66uvvqrz\nv78AAAAAADe12AXw6dOnVV5eLkk6deqU1qxZo8LCQk2ZMkWLFi2SJC1atEjf+973JElTpkzR0qVL\nVVFRob1792rXrl0aOXKkunTposzMTBUVFckYo5dfflk33nhj6M8Ej/X6669r/Pjx1uur+1/v0c95\neSyabjZt52jSpEkzmjlXmpG/DC/WczRp0kz8tdFMlKbta0ZtLbYH+MiRI5o6daqkC/87e+utt+qa\na67R8OHDNX36dC1YsCD0bZAkacCAAZo+fboGDBig1NRUPffcczX/NS4999xzuuOOO3TmzBldd911\nmjx5siRp1qxZ+vu//3v17dtXHTp00NKlS1vq7gEAAAAAfK5F9wD7AXuAAQBIXom+v40mzdiuzZVm\nbNZGM/5NW77ZAwwAAAAAQLxwAVwj0fc50Uyupu0cTZo0aUYz50ozGfa30aSZvE3bOZo0G5iKYg8w\nF8AAAAAAACewBxgAACSNRN/fRpNmbNfmSjM2a6MZ/6Yt9gADAAAAAJzHBXCNRN/nRDO5mrZzNGnS\npBnNnCvNZNjfRpNm8jZt52jSbGCKPcAAAAAAAETGHmAAAJA0En1/G02asV2bK83YrI1m/Ju22AMM\nAAAAAHAeF8A1En2fE83katrO0aRJk2Y0c640k2F/G02aydu0naNJs4Ep9gADAAAAABAZe4ABAEDS\nSPT9bTRpxnZtrjRjszaa8W/aYg8wAAAAAMB5XADXSPR9TjSTq2k7R5MmTZrRzCViMzMzR4FAIOKP\nzMyc+o5m1fTz/jaaNJO3aTtHk2YDU+wBBgAAyai8/Etd+PK44I+3Qz+/cBsAAI1jDzAAAPA9V/a3\n0aQZ27W50ozN2mjGv2mLPcAAAAAAAOdxAVzDT/ucaNK0naNJkybNaOaSoenK/jaaNJO3aTtHk2YD\nU+wBBgAAAAAgMvYAAwAA33NlfxtNmrFdmyvN2KyNZvybttgDDAAAAABwHhfANfy8z4mme03bOZo0\nadKMZi4Zmq7sb6NJM3mbtnM0aTYwxR5gAAAAAAAiYw8wAADwPVf2t9GkGdu1udKMzdpoxr9piz3A\nAAAAAADncQFcw8/7nGi617Sdo0mTJs1o5pKh6cr+Npo0k7dpO0eTZgNT7AEGAAAAACAy9gADAADf\nc2V/G02asV2bK83YrI1m/Ju22AMMAAAAAHAeF8A1/LzPiaZ7Tds5mjRp0oxmLhmaruxvo0kzeZu2\nczRpNjDFHmAAAAAAACJjDzAAAPA9V/a30aQZ27W50ozN2mjGv2mLPcAAAAAAAOdxAVzDz/ucaLrX\ntJ2jSZMmzWjmkqHpyv42mjSTt2k7R5NmA1PsAQYAAAAAIDL2AAMAAN9zZX8bTZqxXZsrzdisjWb8\nm7bYAwwAAAAAcB4XwDX8vM+JpntN2zmaNGnSjGYuGZqu7G+jSTN5m7ZzNGk2MMUeYAAAAAAAImMP\nMAAA8D1X9rfRpBnbtbnSjM3aaMa/aYs9wAAAAAAA53EBXMPP+5xoute0naNJkybNaOaSoenK/jaa\nNJO3aTtHk2YDU+wBBgAAAAAgMvYAAwAA33NlfxtNmrFdmyvN2KyNZvybttgDDAAAAABwHhfANfy8\nz4mme03bOZo0adKMZi4Zmq7sb6NJM3mbtnM0aTYwxR5gAAAAAAAiYw8wAADwPVf2t9GkGdu1udKM\nzdpoxr9piz3AAAAAAADncQFcw8/7nGi617Sdo0mTJs1o5pKh6cr+Npo0k7dpO0eTZgNT7AEGAAAA\nACAy9gADAADfc2V/G02asV2bK83YrI1m/Ju22AMMAAAAAHAeF8A1/LzPiaZ7Tds5mjRp0oxmLhma\nruxvo0kzeZu2czRpNjDFHmAAAAAAACJjDzAAAPA9V/a30aQZ27W50ozN2mjGv2mLPcAAAAAAAOdx\nAVzDz/ucaLrXtJ2jSZMmzWjmkqHpyv42mjSTt2k7R5NmA1PsAQYAAAAAIDL2AAMAAN9zZX8bTZqx\nXZsrzdisjWb8m7bYAwwAAAAAcB4XwDX8vM+JpntN2zmaNGnSjGYuGZqu7G+jSTN5m7ZzNGk2MMUe\nYAAAAAAAImMPMAAA8D1X9rfRpBnbtbnSjM3aaMa/aYs9wAAAAAAA53EBXMPP+5xoute0naNJkybN\naOaSoenK/jaaNJO3aTtHk2YDU+wBBgAAAAAgMvYAAwAA33NlfxtNmrFdmyvN2KyNZvybttgDDAAA\nAABwHhfANfy8z4mme03bOZo0adKMZi4Zmq7sb6NJM3mbtnM0aTYwxR5gAAAAAAAiYw8wAADwPVf2\nt9GkGdu1udKMzdpoxr9piz3AAAAAAADncQFcw8/7nGi617Sdo0mTJs1o5pKh6cr+Npo0k7dpO0eT\nZgNT7AEGAAAAACAy9gADAADfc2V/G02asV2bK83YrI1m/Ju22AMMAAAAAHAeF8A1/LzPiaZ7Tds5\nmjRp0oxmLhmaruxvo0kzeZu2czRpNjDFHmAAAAAAACJjDzAAAPA9V/a30aQZ27W50ozN2mjGv2mL\nPcAAAAAAAOdxAVzDz/ucaLrXtJ2jSZMmzWjmkqHpyv42mjSTt2k7R5NmA1PsAQYAAAAAIDL2AAMA\nAN9zZX8bTZqxXZsrzdisjWb8m7bYAwwAAAAAcB4XwDX8vM+JpntN2zmaNGnSjGbOT83MzBwFAoF6\nf2Rm5tR3RJuVWcx4PUeTJs3EXxvNRGmyBxgAACSU8vIvdeHL3oI/3g779YXbAQDwBnuAAQBA3Ph5\nrxlNmn5qerM2V5qxWRvN+DdtsQcYAAAAAOA8LoBrJOveKpqJ2bSdo0mTJs1o5vzc9PNeM5o0acZj\njibNBqbYAwwAAAAAQGTsAQYAAHHj571mNGn6qenN2lxpxmZtNOPftMUeYAAAAACA87gAruHK3iqa\nidG0naNJkybNaOb83PTzXjOaNGnGY44mzQamotgDnNrkPwEAAAAAQAvLzMyp9/vDt2+frRMnyho9\nBnuAAQBA3Ph5rxlNmn5qerM2V5qxWRvNxGqyBxgAAAAA4DQugGu4sreKZmI0bedo0qRJM5o5Pzf9\nvNeMJk2a8ZijSdOLua9xAQwAAAAAcAJ7gAEAQNwky14zmjRj3fRmba40Y7M2monVZA8wAABoUZmZ\nOQoEAhF/ZGbmxHt5AAAHcQFcw5W9VTQTo2k7R5MmTZrRzLVU88K3qjA1P96u9XNT77exSPy9ZjRp\n0kz8tdFMrmY4LoBrbN682bM5L49F082m7RxNmjRpRjMXn7XZHcvbOZo0afq3aTtHk6YXc19Lugvg\n1atXq6CgQH379tVjjz1m/eeOHz/u2ZyXx6LpZtN2jiZNmjSjmWvusS7+0ubZs2dbfGmzXdPbOZo0\nafq3aTtHk6YXc19Lqgvgqqoq3XvvvVq9erVKSkq0ZMkSbd++Pd7LAgAgIVx8YfvQQw9FvLAN/9Jm\nI+kXavxLmwEAiL+kugAuLi5Wnz59lJ+fr7S0NN1yyy1asWKF1Z8tLS31bM7LY9F0s2k7R5Mmzfrn\nal/M1b6Qq30x19AFn+1c7QtDm6YXa6vvWM1dW90L29tld2Frc65sZryeo0mTpn+btnM0aXox97Wk\n+jZIr7/+ut566y09//zzkqTFixerqKhIzzzzTGjmwltnAwAAAACSVX2XuaktvI6Ysrm4TaLrfQAA\nAABAEyTVl0Dn5ubqwIEDoV8fOHBAeXl5cVwRAAAAAMAvkuoCePjw4dq1a5dKS0tVUVGhZcuWacqU\nKfFeFgAAAADAB5LqS6BTU1P17LPPatKkSaqqqtKsWbN0xRVXxHtZAAAAAAAfSKo3wQIAAACAplq9\nerUOHTqk8ePHKz8/P/T7Cxcu1J133hm/hTmkpc5BUn0JtBf+7d/+LezXq1ev1oIFC+p8m42FCxeG\nfl5ZWanFixdr9erVkqRFixbp3nvv1YIFCxp9063vfOc7Yb/+4osvwn798ssv6yc/+Yl+97vfhR3r\n2LFjeuihh/Tf//3fqq6u1sMPP6zrr79eDzzwgL78MvxbVaxfv14//vGPNWXKFE2dOlVz5szR//3f\n/8W0WZ/aj6/NY2u7Npv7GWzefffduuGGG3TDDTfo7rvvDp23pq7/jTfe0LFjxyRJR48e1cyZMzVo\n0CDdfPPNOnjwoCRvz5MX96ElP74v/tiW7M+nzWM7e/Zsvfvuu43eZ6/PgZcfQ8HjcQ6+5sdz4OVz\nvGR3Dmwef8n7c9ASz0NS018L4vE66+U58NtrgcQ5iPc54LXAX68Fc+fO1SOPPKKPP/5Y48eP19NP\nPx26LfjdZHgtiO3zUKzPQW38D/BFunfvHnojrblz5+q9997TVVddpZUrV+q+++7TP/3TP0mShg4d\nqo8++kiSNGvWLH311VeqqKhQ27Ztde7cOU2bNk2rVq1Sjx499Otf/1qSVFhYqEAgEHaCPvnkE/Xr\n10+BQEBbtmwJO+6vfvUrvfPOO/r+97+vlStXqnv37nryySclSddee62uvPJKnThxQtu3b1dhYaFu\nuukmrV27Vlu2bAl9/+M5c+bos88+0/jx47V8+XJdfvnl6tevn377299q7ty5mj59uudNm8fX9rG1\nXZvN/bzvvvu0a9cuzZw5U7m5uZKkgwcP6uWXX1afPn3C/qLZfHxcccUV2r59uyRp+vTpuvrqq/V3\nf/d3WrdunX7/+99r7dq1np4nSc2+D7H6+Lb52G7K+bR5bDt27KiePXvq6NGjuuWWWzRjxgwNHTq0\nzn328hx4/THEOUiMc+Dlc7ztObB5/CV5eg5a6nmo9jnw8+usl+fAb68FnIP4nwNeC/z1WjBo0CB9\n9NFHSktL0/HjxzVjxgz1799fTz75pK666ipeC2L8POT1OWiUcVBGRka9P1q1ahWaGzhwoKmoqDDG\nGPPll1+ayZMnm/vuu89UV1ebIUOGhOYGDBhgjDGmoqLCZGdnm7NnzxpjjKmsrDSFhYWhuRtuuMF8\n//vfNyUlJaa0tNTs3bvX5OXlhX5ujAk77pAhQ0x5eXno2AMHDgzdduWVVxpjjKmurjZdu3YNu3/B\n24L3IaiystJcffXVxhhjysrKQuv2umnz+No+trZrs7mfffr0MZFUV1eb3r17N2n9xhjTr1+/0M+v\nuuqqiI+Hl+fJ9j7E4+Pb5mPbGPvzafPYBo+1c+dO89BDD5kBAwaYfv36mXnz5pmdO3fWmffiHHj9\nMcQ5SIxz4OVzfO3HLfjzSOfA5vGvfSwvzoGXz0PGePtaEI/XWS/PQTxeC4zhHMT7HPBakDivBQUF\nBWHHqaysNP/wD/9gpk2bFmryWhC75yFjvD0HjXHyS6Czs7O1a9culZeX1/nRtWvX0FxVVZXS0tIk\nSVlZWVq5cqVOnDihm266SRUVFaG54ExaWppGjBihSy65RNKFN+Wq/b2J33zzTU2bNk0//OEPtXnz\nZuXn5ys1NVU9e/YMfZ37mTNn9OGHH2rTpk2qrKxURkZG6NitWrUKHau6ulplZWU6cOCATp48qb17\n90q68OUU1dXVoblWrVqFvnTi0KFDoduys7NDM143bR5f28fWdm0297NNmzYqLi7WxYqLi9W2bdsm\nrV+Sxo4dqwcffFBnzpzRuHHj9MYbb0iS3n77bWVlZXl+nmzvQzw+vm0+tiX782nz2Ab169dPDz74\noLZt26ZXX31VZ86c0bXXXhu63ctz4PXHEOcgMc6Bl8/xtuegKY+/V+fAy+ch23Pg59dZL89BPF4L\nOAfxPwe8FiTOa0GvXr305z//OfTr1NRULVy4UAUFBaH/geW1IHbPQ16fg0ZZXyonkX/+5382cSM8\n8gAAFedJREFURUVFEW974IEHQj+/7rrrzIYNG+rM/Mu//IsJBAKhX0+aNCn0rza1ffrpp2bEiBF1\nfr+8vNzcf//9ZsqUKaZbt25ht40dO9aMGzcu9OPQoUPGGGM+//xzM2zYsNDcwoULTU5Ojundu7dZ\ntWqV6dWrlxk/frzJzc01ixYtCs0tXbrU9OjRw4wfP97k5eWZlStXGmOMOXLkiJkxY0ZMmjaPr+1j\na7s2m/v5wQcfmBEjRpiCggIzYcIEM2HCBFNQUGBGjhxpPvjggyat3xhjzp07Zx588EHTvXt30717\ndxMIBEx6erq55ZZbzL59+zw/T7b3IZ4f3w19bBtjfz5tHtva/3LaEC/PgdcfQ5yDxDgHXj7HG2N3\nDmwef2O8PQdePg8Z4+1rQTxeZ708B/F4LTDmwjnYuHFjxDVFcw5OnDhRZ45zwOtxMrwWnD592pw+\nfTri3IEDB4wxvBbE8rXAmNicg/qwB7gBZ86ckaSwf8UIOnjwoPLy8hr886dOndKpU6fUqVOniLdv\n3rxZGzdu1N13393oWqqqqnT27Fmlp6eHfq+iokKpqalKSUkJfS1/r1691LFjx7A/e+zYMe3Zs0d9\n+/aN+K9FDTXPnTundu3aNbnZmOY+tpEej+D97NOnT51/part8OHDOnTokCQpLy9PXbp0adLaIzl+\n/LjOnz+vDh061PkXqKaep8bW79V9OH36tAKBQLM+vk+ePKnOnTvXua0pH9tS5PMZVN9jW15ervbt\n21sd/+JzsGPHDl1++eVRn4Paj39ubm7Yv2A2hcvnoLl/D4LnIBAIKDc3N+q/x805Bw09/pJ35yD4\n+F922WV1/kxz/x706tUr7LjRPg9F+/fAb6+zF7/mBTX0HG97Dlx8LZCa/7lOUCzOQXNfC6Tw56Ju\n3br57u9Bor8WNPa56+HDh/Xpp59KUrNeCyTJGKOioqKw15aRI0c2+j+LXj4PSY2/FjTn74Bkdw6a\ncu3g1edEDdmxY4cKCgrqvb2xc3CxVvPmzZvn0doSSvCDvKioSDt27NCJEyeUm5sb9gGVlpam1NTU\niHOXXnppo8fLz88PfTlDUHV1tYqKilRcXKyysjL179+/Tjd4rOLiYm3fvl0nTpxQXl6eWrduHXas\nlJQUvf/++yoqKtLevXuVmZmpvn371vlL0bZtWx04cEBbtmwJHe/iZiQ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"text": [ "<matplotlib.figure.Figure at 0x14f7a2d90>" ] } ], "prompt_number": 19 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Raw Assignees" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# patents per assignee\n", "res = session.execute('select count() from rawassignee group by organization;')\n", "data = res.fetchall()\n", "d = pd.DataFrame.from_dict({'count': [int(x[0]) for x in data]})\n", "printstats(d['count'])\n", "h = d[d['count'] <= 20].hist(bins=20)[0][0]\n", "h.set_xlabel('RawAssignees')\n", "h.set_ylabel('Patents')\n", "h.set_title('Patents per RawAssignee')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "mean 16.016432176\n", "median 2.0\n", "mode " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "1.0\n", "std 1077.23710111\n", "min 1\n", "max 661694\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 20, "text": [ "<matplotlib.text.Text at 0x1642918d0>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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AgJiYGGRnZ6O6uhpVVVXYtGkTxo4d2xWHR0REFyIqmzZtmvj6+opWqxWDwSCrVq2Sb775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\n8Xr16oXQ0FAMHToUEydOxMmTJ1XZ7uu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"text": [ "<matplotlib.figure.Figure at 0x14f7a2950>" ] } ], "prompt_number": 20 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Raw Lawyers" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# patents per lawyer\n", "res = session.execute('select count() from rawlawyer group by organization;')\n", "data = res.fetchall()\n", "d = pd.DataFrame.from_dict({'count': [int(x[0]) for x in data]})\n", "printstats(d['count'])\n", "h = d[d['count'] <= 20].hist(bins=20)[0][0]\n", "h.set_xlabel('RawLawyers')\n", "h.set_ylabel('Patents')\n", "h.set_title('Patents per RawLawyer')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "mean 70.0483641809\n", "median 1.0\n", "mode " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "1.0\n", "std 5569.59634035\n", "min 1\n", "max 1266613\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 21, "text": [ "<matplotlib.text.Text at 0x14f9e8a50>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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j/PjxXTE26iZ4nb5czFMu5qk9i4Xk2rVrmD9/Pvr164fQ0FC8++67PBohIiKV\nxamtfv36AQDc3Nzwt7/9DY8//jgqKyst9KLehFfEyMU85WKe2rNYSH75y1+iqqoK69atw0svvYSa\nmhps2LChK8ZGREQ9QLtTW3V1ddiwYQP27NmD7du3Y/To0cjJycFnn32G6OjorhwjaYxz0HIxT7mY\np/baLSTx8fE4ceIExo4di48++givvPJKV46LiIh6iHants6cOYNTp04BaPmixCeffLLLBkXdC+eg\n5WKecjFP7bV7RNL6N0ce9PdHiIjo4dFuIfniiy9gZ2en3k6dOqX+bW9v35VjJI1xDlou5ikX89Re\nu4caTU1NXTkOIiLqoe7rp3bp4cY5aLmYp1zMU3ssJEREZBUWErKIc9ByMU+5mKf2WEiIiMgqLCRk\nEeeg5WKecjFP7bGQEBGRVVhIyCLOQcvFPOVintpjISEiIquwkJBFnIOWi3nKxTy1x0JCRERWYSEh\nizgHLRfzlIt5ao+FhIiIrMJCQhZxDlou5ikX89QeCwkREVlFeiGZN28eXF1d4evrq7Zdv34d4eHh\n8PLyQkREBKqqqtT7Vq9eDYPBgFGjRmHv3r1q+4kTJ+Dr6wuDwYDk5GS1/fbt25g1axYMBgOCg4Nx\n4cIF2btA38I5aLmYp1zMU3vSC8ncuXORnZ3dpi0lJQXh4eE4e/YswsLCkJKSAgAoKirC9u3bUVRU\nhOzsbCxYsABCCABAUlIS0tLSYDQaYTQa1XWmpaXB2dkZRqMRS5YswdKlS2XvAhERPQDpv6E7adIk\nlJSUtGnLysrCwYMHAQDx8fGYMmUKUlJSsHv3bsyePRu2trbQ6/Xw9PREXl4eRowYAbPZjKCgIADA\nnDlzsGvXLkRGRiIrKwurVq0CAMTGxmLRokXtjiUhIQF6vR4A4ODgAH9/f3U+9c67mPaWjx8/jsbG\nG63WlvPNv1Puc/lzmM2V/+xtYXvdeXnKlCndajw9fZl5Ms/usnzn72+/Zj8w0QmKi4uFj4+Puuzg\n4KD+3dzcrC4vWrRIbNu2Tb0vMTFRZGZmivz8fDF16lS1PTc3V0RFRQkhhPDx8REmk0m9z8PDQ1y7\ndu2uMVi7a6dPnxb29t4CEB28fSwCA8OsGgMRUVfq6Otml59sVxQFiqJ09WbJCq3fvZD1mKdczFN7\nXVJIXF1dcfnyZQBAeXk5XFxcAADu7u4oLS1VH1dWVgadTgd3d3eUlZXd1X6nz8WLFwEAjY2NqK6u\nhpOTU1czQyU0AAAPyUlEQVTsBhER3UOXFJLo6Gikp6cDANLT0zFjxgy1PSMjA/X19SguLobRaERQ\nUBDc3Nxgb2+PvLw8CCGwdetWxMTE3LWuzMxMhIWFdcUuPNTuzKuSHMxTLuapPekn22fPno2DBw/i\n6tWrGDZsGP7rv/4Ly5Ytw8yZM5GWlga9Xo8dO3YAALy9vTFz5kx4e3vDxsYGqamp6rRXamoqEhIS\nUFdXh+nTpyMyMhIAkJiYiLi4OBgMBjg7OyMjI0P2LhAR0QNQvjnB0usoigJrdq2wsBAhITNRU1PY\nwTXsQ2BgCvLz93V4DN1FTk4O3/VJxDzlYp7ydPR1k59sJyIiq7CQkEV8tycX85SLeWqPhYSIiKzC\nQkIW8Tp9uZinXMxTeywkRERkFRYSsohz0HIxT7mYp/ZYSIiIyCosJGQR56DlYp5yMU/tsZAQEZFV\nWEjIIs5By8U85WKe2mMhISIiq7CQkEWcg5aLecrFPLXHQkJERFZhISGLOActF/OUi3lqj4WEiIis\nwkJCFnEOWi7mKRfz1B4LCRERWYWFhCziHLRczFMu5qk9FhIiIrIKCwlZxDlouZinXMxTeywkRERk\nFRYSsohz0HIxT7mYp/ZYSIiIyCosJGQR56DlYp5yMU/t2Wg9gN4rFidO1EBRlA71trNzRE3Ndclj\nIiKSTxFCCK0H0RkURYE1u1ZYWIiQkJmoqSns6AgAWBOtdeMnInpQHX3d5NQWERFZhYWELOIctFzM\nUy7mqb0uLSR6vR5jx45FQEAAgoKCAADXr19HeHg4vLy8EBERgaqqKvXxq1evhsFgwKhRo7B37161\n/cSJE/D19YXBYEBycnJX7gIREX1LlxYSRVGQk5ODgoICHDt2DACQkpKC8PBwnD17FmFhYUhJSQEA\nFBUVYfv27SgqKkJ2djYWLFigzt0lJSUhLS0NRqMRRqMR2dnZXbkbDx1epy8X85SLeWqvy6e2vn0i\nJysrC/Hx8QCA+Ph47Nq1CwCwe/duzJ49G7a2ttDr9fD09EReXh7Ky8thNpvVI5o5c+aofYiIqOt1\n6eW/iqJg6tSp6Nu3L37605/ihRdeQEVFBVxdXQEArq6uqKioAABcunQJwcHBal+dTgeTyQRbW1vo\ndDq13d3dHSaT6Z7bS0hIgF6vBwA4ODjA399fffdyZ161veXjx4+jsfFGq7XlfPPvlPtcvtN2v4+/\nu39OTs59j7czl1vPQXeH8fT0ZebJPLvL8p2/S0pKYBXRhS5duiSEEOL//u//hJ+fn8jNzRUODg5t\nHuPo6CiEEGLRokVi27ZtantiYqLIzMwU+fn5YurUqWp7bm6uiIqKumtb1u7a6dOnhb29twBEB2+w\noq/145fpwIEDWg+hV2GecjFPeTr6utOlU1tDhw4FADz22GP44Q9/iGPHjsHV1RWXL18GAJSXl8PF\nxQVAy5FGaWmp2resrAw6nQ7u7u4oKytr0+7u7t6Fe/HwufMuhuRgnnIxT+11WSG5efMmzGYzAODG\njRvYu3cvfH19ER0djfT0dABAeno6ZsyYAQCIjo5GRkYG6uvrUVxcDKPRiKCgILi5ucHe3h55eXkQ\nQmDr1q1qHyIi6npdVkgqKiowadIk+Pv7Y8KECYiKikJERASWLVuGjz/+GF5eXvjkk0+wbNkyAIC3\ntzdmzpwJb29vTJs2DampqerXjaSmpmL+/PkwGAzw9PREZGRkV+3GQ6n1fCpZj3nKxTy112Un20eO\nHImTJ0/e1e7k5IR9+/bds8+KFSuwYsWKu9oDAwNx6tQp6WMkIqIHx+/aage/a4uIHjb8ri0iItIE\nCwlZxDlouZinXMxTeywkRERkFZ4jaQfPkRDRw4bnSIiISBMsJGQR56DlYp5yMU/tsZAQEZFVeI6k\nHTxHQkQPG54jISIiTbCQkEWcg5aLecrFPLXHQtJt2UBRlA7f7O2dtN4BInpI8BxJO7rDORKeYyGi\nrsRzJEREpAkWErKIc9ByMU+5mKf2WEiIiMgqPEfSDp4jIaKHDc+REBGRJlhIyCLOQcvFPOVintpj\nIem1+DkUIuoaPEfSjt5wjoTnWIjoQfAcCRERaYKFhNrBqbHOwjl9uZin9lhIqB2NaJkaEwAOtPr7\n/m5mc6UGYyYiLfAcSTt4jsTa/rZoKUYdY2fniJqa61Zsn4geVEdfN206YSxE+OcRTceYzYq8oRBR\np+LUFt2HHA22ad05GkXp123P8XBOXy7mqT0WEroPJzXYZutzNB25NVjV32w2d1oROnlSizx7L+ap\nvR5bSLKzszFq1CgYDAasWbNG6+H0clVaD0ADHS9klorQkiVLOvVoqjsfjXWGqqqH8fnZzYgeqLGx\nUXh4eIji4mJRX18v/Pz8RFFRUZvHWLtrp0+fFvb23gIQHbzBir7drf+vevj4u7q/pb6W8tR6322+\nWUdHb7bsb0V/OztHq167rNHR180eeURy7NgxeHp6Qq/Xw9bWFs899xx2796t9bB6sRKtB9DLlGg9\nAAu0nVZ88P7xGm9fbn9rplWtPRrtqB551ZbJZMKwYcPUZZ1Oh7y8vLseZ00wrdaiUd/u1j9d4+33\ntP6W+lrKsyfvuxb9v51nTxu/LA2abLVHFpL7KRAtR2lERNTZeuTUlru7O0pLS9Xl0tJS6HQ6DUdE\nRPTw6pGFZPz48TAajSgpKUF9fT22b9+O6OhorYdFRPRQ6pFTWzY2Nvj973+Pf/3Xf0VTUxMSExMx\nevRorYdFRPRQ6pFHJAAwbdo0fPXVVzh37hyWL1/e5j5+xkQevV6PsWPHIiAgAEFBQVoPp8eZN28e\nXF1d4evrq7Zdv34d4eHh8PLyQkREBD8H8QDulefKlSuh0+kQEBCAgIAAZGdnazjCnqO0tBRPPfUU\nxowZAx8fH2zcuBFAx56fPbaQtKepqQmLFi1CdnY2ioqK8MEHH+DMmTNaD6vHUhQFOTk5KCgowLFj\nx7QeTo8zd+7cu17YUlJSEB4ejrNnzyIsLAwpKSkaja7nuVeeiqLg5ZdfRkFBAQoKChAZGanR6HoW\nW1tbbNiwAYWFhfj000+xefNmnDlzpkPPz15XSPgZE/l4BVzHTZo0CY6Ojm3asrKyEB8fDwCIj4/H\nrl27tBhaj3SvPAE+RzvCzc0N/v7+AIDBgwdj9OjRMJlMHXp+9rpCcq/PmJhMJg1H1LMpioKpU6di\n/PjxeOutt7QeTq9QUVEBV1dXAICrqysqKio0HlHPt2nTJvj5+SExMZFThR1QUlKCgoICTJgwoUPP\nz15XSOR8CJHuOHLkCAoKCrBnzx5s3rwZhw4d0npIvYq1nygmICkpCcXFxTh58iSGDh2KV155Resh\n9Si1tbWIjY3F7373O9jZ2bW5736fn72ukPAzJnINHToUAPDYY4/hhz/8Ic+TSODq6orLly8DAMrL\ny+Hi4qLxiHo2FxcX9QVv/vz5fI4+gIaGBsTGxiIuLg4zZswA0LHnZ68rJPyMiTw3b96E2WwGANy4\ncQN79+5tc7UMdUx0dDTS01u+0iM9PV39H5g6pry8XP17586dfI7eJyEEEhMT4e3tjcWLF6vtHXp+\nSvziyG7jo48+El5eXsLDw0O88cYbWg+nx/r666+Fn5+f8PPzE2PGjGGWHfDcc8+JoUOHCltbW6HT\n6cQ777wjrl27JsLCwoTBYBDh4eGisrJS62H2GN/OMy0tTcTFxQlfX18xduxYERMTIy5fvqz1MHuE\nQ4cOCUVRhJ+fn/D39xf+/v5iz549HXp+9trfbCcioq7R66a2iIioa7GQEBGRVVhIiIjIKiwkRERk\nFRYSemj17dsXAQEBGDt2LH70ox+htrbWqvWVlJTw0lN6KLGQ0ENr4MCBKCgowBdffAF7e3ts2bJF\n6yFJ09jYqPUQ6CHCQkIE4Pvf/z7Onz8PoOWLP0NCQjBu3Dj84Ac/wNmzZwEAUVFROHXqFAAgICAA\nr7/+OgDgtddew9tvv93uV0m89dZbCAoKgr+/P3784x+jrq4OTU1N+N73vgcAqKqqQt++fXH48GEA\nwOTJk3H27Fl4eXnh6tWrAIDm5mZ4enri2rVruHLlCn784x8jKCgIQUFBOHr0KICWr1OPi4vDxIkT\nER8fj8LCQgQFBSEgIAB+fn44d+5cJ6VHDzsWEnroNTU1Ye/evfDx8QEAjB49GocOHcJnn32GVatW\nYcWKFQBavnn20KFDqKmpga2trfoCfvjwYYSGhrb7DbSxsbE4duwYTp48idGjRyMtLQ19+/bFE088\ngaKiIhw+fBiBgYHIzc3F7du3UVZWBi8vLzz//PN47733AAD79u1DQEAAnJ2dkZycjCVLluDYsWPI\nzMzE/Pnz1W19+eWX2L9/P9577z1s2bIFixcvRkFBAU6cOMGvCqJO0yN/IZFIhrq6OgQEBMBkMkGv\n1+PFF18E0HKEMGfOHJw7dw6KoqChoQFASyHZuHEjRo4ciaeffhr79u1DXV0diouLYTAYUFJScs/t\nnDp1Cr/85S9RXV2N2tpa9fcyJk2ahNzcXBQXF2P58uV46623EBoaiieffBJAy484xcTEIDk5Ge+8\n8w7mzp0LoKWotP6NHbPZjBs3bkBRFERHR6N///4AWo6yfvOb36CsrAw/+tGP4Onp2Sk5EvGIhB5a\nAwYMQEFBAS5cuIBHHnlE/d2a//zP/0RYWBhOnTqFv/71r7h16xaAlu9xy8/Px6FDhzB58mT4+/vj\nj3/8I8aPH/+d20lISEBqaiq++OIL/OpXv0JdXR2Alims3NxcHDt2DNOnT0dVVRVycnIwadIkAC0/\ngeDq6opPPvkEx48fx7Rp0wC0fEdSXl6e+kNOpaWlGDRoEICW8z53zJ49G3/9618xYMAATJ8+HQcO\nHJAbINE3WEjooTdgwABs3LgRr776KoQQqKmpweOPPw4AePfdd9XH9evXDzqdDh9++CFCQkIwadIk\nrF27FpMnT/7O9dfW1sLNzQ0NDQ3Ytm2b2v7kk0/i6NGj6Nu3L/r37w8/Pz9s2bIFoaGh6mPmz5+P\n559/HjNnzlTPwURERKg/iwoAn3/++T23W1xcjJEjR+Kll15CTEyMen6HSDYWEnpotT457u/vD09P\nT+zYsQO/+MUvsHz5cowbNw5NTU1tHjd58mS4urqif//+mDhxIi5duqQeQQDAV199hWHDhqm3zMxM\nvP7665gwYQImTpyI0aNHq+vr378/hg8fjuDgYHXdtbW1bS4hfuaZZ3Djxg11WgsANm7ciPz8fPj5\n+WHMmDFtrjZrPdYdO3bAx8cHAQEBKCwsxJw5cySmR/RP/NJGom4sPz8fr7zyCg4ePKj1UIjaxZPt\nRN1USkoK/vCHP+D999/XeihE34lHJEREZBWeIyEiIquwkBARkVVYSIiIyCosJEREZBUWEiIisgoL\nCRERWeX/AcrV1KFMZe07AAAAAElFTkSuQmCC\n", "text": [ "<matplotlib.figure.Figure at 0x15ac7cf10>" ] } ], "prompt_number": 21 }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Citations" ] }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "US Patent Citation" ] }, { "cell_type": "code", "collapsed": false, "input": [ "res = session.execute('select count() from uspatentcitation group by patent_id;')\n", "cit_counts = [x[0] for x in res.fetchall()]\n", "d = pd.DataFrame.from_dict({'counts': cit_counts})\n", "print \"Forward Citation Stats\"\n", "printstats(d['counts'])\n", "res = session.execute('select count() from uspatentcitation group by citation_id;')\n", "cit_counts = [x[0] for x in res.fetchall()]\n", "d = pd.DataFrame.from_dict({'counts': cit_counts})\n", "print \"Backward Citation Stats\"\n", "printstats(d['counts'])" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Forward Citation Stats\n", "mean 12.7942816581\n", "median " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "7.0\n", "mode " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "4.0\n", "std 31.1585783602\n", "min " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "1\n", "max 3036\n", "Backward Citation Stats" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "mean 10.5262394974\n", "median " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "4.0\n", "mode " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "1.0\n", "std " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "22.0138044268\n", "min 1\n", "max " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "2910\n" ] } ], "prompt_number": 22 }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "Foreign Citation" ] }, { "cell_type": "code", "collapsed": false, "input": [ "res = session.execute('select count() from foreigncitation group by patent_id;')\n", "cit_counts = [x[0] for x in res.fetchall()]\n", "d = pd.DataFrame.from_dict({'counts': cit_counts})\n", "print \"Forward Citation Stats\"\n", "printstats(d['counts'])\n", "res = session.execute('select count() from foreigncitation group by number;')\n", "cit_counts = [x[0] for x in res.fetchall()]\n", "d = pd.DataFrame.from_dict({'counts': cit_counts})\n", "print \"Backward Citation Stats\"\n", "printstats(d['counts'])" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Forward Citation Stats\n", "mean 7.96402179982\n", "median 4.0\n", "mode " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "1.0\n", "std 17.5793650342\n", "min 1\n", "max 954\n", "Backward Citation Stats" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "mean 2.38540606228\n", "median " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "1.0\n", "mode " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "1.0\n", "std 5.55979417389\n", "min " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "1\n", "max 814\n" ] } ], "prompt_number": 23 }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "Other References" ] }, { "cell_type": "code", "collapsed": false, "input": [ "res = session.execute('select count() from otherreference group by patent_id;')\n", "cit_counts = [x[0] for x in res.fetchall()]\n", "d = pd.DataFrame.from_dict({'counts': cit_counts})\n", "print \"Forward Citation Stats\"\n", "printstats(d['counts'])" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Forward Citation Stats\n", "mean 13.6768728825\n", "median 3.0\n", "mode " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "1.0\n", "std 43.0066034322\n", "min 1\n", "max 2964\n" ] } ], "prompt_number": 24 }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "Application Citation" ] }, { "cell_type": "code", "collapsed": false, "input": [ "res = session.execute('select count() from usapplicationcitation group by patent_id;')\n", "cit_counts = [x[0] for x in res.fetchall()]\n", "d = pd.DataFrame.from_dict({'counts': cit_counts})\n", "print \"Forward Citation Stats\"\n", "printstats(d['counts'])" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Forward Citation Stats\n", "mean 8.10105988436\n", "median 3.0\n", "mode " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "1.0\n", "std 22.1858136802\n", "min 1\n", "max 1168\n" ] } ], "prompt_number": 25 }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Application Data" ] }, { "cell_type": "code", "collapsed": false, "input": [ "sessiongen = session_generator(dbtype='application')\n", "session = sessiongen()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Table Counts" ] }, { "cell_type": "code", "collapsed": false, "input": [ "counts =[]\n", "tablekeys = []\n", "tables = ApplicationBase.metadata.tables\n", "rawtables = tables.keys()\n", "for table in rawtables:\n", " res = session.execute('select count() from {0}'.format(table)).fetchone()[0]\n", " if res:\n", " counts.append(res)\n", " tablekeys.append(table)\n", "d = pd.DataFrame.from_dict({'tables': tablekeys, 'counts': map(lambda x: int(x), counts)})\n", "d.index = d['tables']\n", "h = d.plot(kind='bar',figsize=(16,10))\n", "h.set_xlabel('Table')\n", "h.set_ylabel('Record Count')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 3, "text": [ "<matplotlib.text.Text at 0x114452110>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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AgMaYCS2cmVAAAKBJZkIBAADoDIfQPpV4F7t+VdsL6Dz7KKdRTqPe9MlplNMop1FOo5xG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\nQmCC\n", "text": [ "<matplotlib.figure.Figure at 0x114091f10>" ] } ], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "patent_count = session.execute('select count() from application;').fetchone()[0]\n", "\n", "rawinventor_count = session.execute('select count() from rawinventor;').fetchone()[0]\n", "disambiginventor_count = session.execute('select count() from inventor;').fetchone()[0]\n", "\n", "rawassignee_count = session.execute('select count() from rawassignee;').fetchone()[0]\n", "disambigassignee_count = session.execute('select count() from assignee;').fetchone()[0]\n", "\n", "rawlocation_count = session.execute('select count() from rawlocation;').fetchone()[0]\n", "disambiglocation_count = session.execute('select count() from location;').fetchone()[0]\n", "\n", "d = pd.DataFrame.from_dict({'raw': [patent_count,rawinventor_count,rawassignee_count,rawlocation_count],\n", " 'disambig': ['N/A',disambiginventor_count, disambigassignee_count, disambiglocation_count],\n", " 'labels': ['application','inventor','assignee','location']})\n", "d[['labels','raw','disambig']]" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>labels</th>\n", " <th>raw</th>\n", " <th>disambig</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> application</td>\n", " <td> 3422423</td>\n", " <td> N/A</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> inventor</td>\n", " <td> 9009611</td>\n", " <td> 4392105</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> assignee</td>\n", " <td> 1925136</td>\n", " <td> 368685</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> location</td>\n", " <td> 222423</td>\n", " <td> 121331</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 8, "text": [ " labels raw disambig\n", "0 application 3422423 N/A\n", "1 inventor 9009611 4392105\n", "2 assignee 1925136 368685\n", "3 location 222423 121331" ] } ], "prompt_number": 8 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Inventor" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# inventors per patent\n", "res = session.execute('select count() from rawinventor group by application_id;')\n", "inventor_counts = [x[0] for x in res.fetchall()]\n", "d = pd.DataFrame.from_dict({'counts': inventor_counts})\n", "h = d[d['counts'] < 20].hist(bins=20, figsize=(16,10))[0][0]\n", "h.set_xlabel('Number of Inventors')\n", "h.set_ylabel('Application Count')\n", "h.set_title('Inventors per Application')\n", "printstats(d['counts'])\n", "print 'Total:', session.execute('select count() from rawinventor;').fetchone()[0]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "mean 2.63268640283\n", "median " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "2.0\n", "mode " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "1.0\n", "std " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "1.93344233411\n", "min 1\n", "max " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "100\n", "Total: 9009611\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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Nufjiiyv7t27dmrPOOivNzc0ZNWpUnn766crXZs+enaFDh2bo0KG54YYbOuYF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\nISFJp061mI4BAABc4npmvjiHsgAAuo122TzYt7Z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"text": [ "<matplotlib.figure.Figure at 0x133dab7d0>" ] } ], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "# patents per inventor\n", "session = sessiongen()\n", "res = session.execute('select count() from application_inventor group by inventor_id;')\n", "patent_counts = [x[0] for x in res.fetchall()]\n", "d = pd.DataFrame.from_dict({'counts': patent_counts})\n", "h = d[d['counts'] < 20].hist(bins=20, figsize=(16,10))[0][0]\n", "h.set_xlabel('Applications')\n", "h.set_ylabel('Inventors')\n", "h.set_title('Applications per Inventor')\n", "printstats(d['counts'])\n", "print 'Total:', session.execute('select count() from rawinventor;').fetchone()[0]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "mean 1.01828553734\n", "median " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "1.0\n", "mode " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "1.0\n", "std " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "0.822763437723\n", "min 1\n", "max " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "990\n", "Total: 9009611\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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GhsQl8O9+97u88cYbRCIRLr30Up588kkA8vLyGDFiBHl5ebRs2ZJZs2Yljpk1axZFRUXs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\nYfakeLE0S5IkSZIUwdIsKThnu6QwzJ4UhtmT4sX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"text": [ "<matplotlib.figure.Figure at 0x156ce6590>" ] } ], "prompt_number": 12 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Location" ] }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "RawLocation" ] }, { "cell_type": "code", "collapsed": false, "input": [ "res = session.execute('select location.state, count() from rawinventor \\\n", " left join rawlocation on rawinventor.rawlocation_id = rawlocation.id \\\n", " left join location on location.id = rawlocation.location_id \\\n", " where location.country = \"US\" and length(location.state) = 2 group by location.state ')\n", "data = res.fetchall()\n", "inventor_counts = [int(x[1]) for x in data]\n", "inventor_states = [unidecode(x[0]) for x in data]\n", "d = pd.DataFrame.from_dict({'counts': inventor_counts, 'states': inventor_states})\n", "d.index = d['states']\n", "h = d.plot(kind='bar',figsize=(16,10))\n", "h.set_xlabel('U.S. States')\n", "h.set_ylabel('Raw Inventors')\n", "h.set_title('Raw Inventors per Reported State')\n", "printstats(d['counts'])\n", "print len(d['states']), 'identified states'" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "mean 72594.862069\n", "median 24226.5\n", "mode 15.0\n", "std 150918.82239\n", "min 1\n", "max 1074626\n", "58 identified states\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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kyBBlZWWVuS8tLU3du3fXF198obvvvltpaWmSpB07dmjx4sXasWOHsrKyNGLE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"text": [ "<matplotlib.figure.Figure at 0x13aa63050>" ] } ], "prompt_number": 13 }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "Disambiguated Location" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# cities by state\n", "res = session.execute('select rawlocation.state, count(distinct rawlocation.city) from rawlocation \\\n", " where rawlocation.country = \"US\" and length(rawlocation.state) = 2 \\\n", " group by rawlocation.state')\n", "data = res.fetchall()\n", "d = pd.DataFrame.from_records(data)\n", "d.columns = ['state','count']\n", "d.index = d['state']\n", "h = d.plot(kind='bar',figsize=(16,10))\n", "h.set_xlabel('U.S. State')\n", "h.set_ylabel('Number of Unique Cities')\n", "h.set_title('Cities per State (raw)')\n", "print \"Raw Locations\"\n", "printstats(d['count'])\n", "print sum(d['count']), 'raw total cities'\n", "# disambiguated\n", "res = session.execute('select location.state, count(distinct location.city) from location \\\n", " where location.country = \"US\" and length(location.state) = 2 \\\n", " group by location.state')\n", "data = res.fetchall()\n", "d = pd.DataFrame.from_records(data)\n", "d.columns = ['state','count']\n", "d.index = d['state']\n", "h = d[['count','state']].plot(kind='bar',figsize=(16,10))\n", "h.set_xlabel('U.S. State')\n", "h.set_ylabel('Number of Unique Cities')\n", "h.set_title('Cities per State (disambig)')\n", "print \"Disambiguated Locations:\"\n", "printstats(d['count'])\n", "print sum(d['count']), 'total cities'" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Raw Locations\n", "mean 390.46875\n", "median 5.0\n", "mode 1.0\n", "std 813.071051292\n", "min 1\n", "max 5636\n", "49980 raw total cities\n", "Disambiguated Locations:" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "mean 424.93220339\n", "median 308.0\n", "mode 2.0\n", "std 412.873649555\n", "min 1\n", "max 1724\n", "25071 total cities\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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3klnpEUTGjK2bmZUeZGR+ZVZ6kJH5lVnpQUbmV2alR9gzL84ubAEAAAAA4cCM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"text": [ "<matplotlib.figure.Figure at 0x16115c210>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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JWdte/207Xq4lsl7L+lkzqQUAAAAAeA89tZajvwQAAPvQU+u4t/HHC3gNPbUA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"text": [ "<matplotlib.figure.Figure at 0x13217a210>" ] } ], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "# applications per state\n", "res = session.execute(\"select location.state, count() from application \\\n", " left join rawinventor on rawinventor.application_id = application.id \\\n", " left join rawlocation on rawlocation.id = rawinventor.rawlocation_id \\\n", " right join location on location.id = rawlocation.location_id \\\n", " where length(location.state) = 2 and rawinventor.sequence = 0 \\\n", " and location.country = 'US' \\\n", " group by location.state\")\n", "data = res.fetchall()\n", "d = pd.DataFrame.from_records(data)\n", "d.columns = ['state','count']\n", "d.index = d['state']\n", "h = d.plot(kind='bar',figsize=(16,10))\n", "h.set_xlabel('U.S. State')\n", "h.set_ylabel('Applications')\n", "h.set_title('Applications per State')\n", "printstats(d['count'])\n", "print sum(d['count'])" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "mean 28035.3508772\n", "median 10315.0\n", "mode 1.0\n", "std 56035.2411388\n", "min 1\n", "max 396320\n", "1598015\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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zVFRUJEkHPaFXXnlFffr0UY0aNZSenq4WLVpo0aJFKikp0bZt29Sx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HI8TZqIiCibKFCgAEqWLImYmBgAQEJCAlatWoUGDRqkeW9MTAxu3LgBILke+MiRIyhXrly6075+\n/XqqQe3OnTvh5+cHAChYsCCuXr1qf+3atWsoUaIE7t69i++++84+AE75Pm9vb/j7+2PBggUAkn8x\n3r17t+cfnoiIKIt5iZTnRREREZGlDhw4gDfeeMP+C/E777yD8PBwAMBXX30FAOjbty8mTpyIOXPm\nIE+ePLh//z569OiBQYMGAQCqV6+e5hfba9euoVOnTjhy5Ajy58+PAgUKYOrUqahRowY2b96M3r17\n4+GHH8aPP/6I1atX49NPP0XRokVRp04dXLt2DbNnz071vgULFsDLywuvv/46zp49i7t37yI8PBzv\nvfdeFvYWERGR5zgYJiIiIiIiIu3wNGkiIiIiIiLSDgfDREREREREpB0OhomIiIiIiEg7HAwTERER\nERGRdjgYJiIiIiIiIu1wMExERERERETa+X8jqkZiIKZkHwAAAABJRU5ErkJggg==\n", "text": [ "<matplotlib.figure.Figure at 0x13239d210>" ] } ], "prompt_number": 15 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Applications" ] }, { "cell_type": "code", "collapsed": false, "input": [ "res = session.execute('select year(date), count() from application group by year(date);')\n", "year_counts = map(lambda x: (str(int(x[0])), int(x[1])), res.fetchall())\n", "d = pd.DataFrame.from_dict({'dates': [x[0] for x in year_counts], 'counts': [x[1] for x in year_counts]})\n", "d.index = d['dates']\n", "h = d.plot(kind='bar',figsize=(16,10))\n", "h.set_xlabel('App Year')\n", "h.set_ylabel('Application Count')\n", "h.set_title('Applications by year')\n", "printstats(d['counts'])\n", "print sum(d['counts']), 'total patents'" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "mean 171121.15\n", "median 159806.5\n", "mode 1.0\n", "std 157077.904978\n", "min 1\n", "max 404099\n", "3422423 total patents\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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HxrcEvWbRaxa9ZtnWK9nXbFsvM8Nm0WsWvWbRa5ZtvVJkNbMZBgAAAADEHGaG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\ngxSw4gAAAABJRU5ErkJggg==\n", "text": [ "<matplotlib.figure.Figure at 0x11ed72490>" ] } ], "prompt_number": 16 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Assignees" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# patents per assignee\n", "res = session.execute('select count(*) from rawassignee group by organization;')\n", "data = res.fetchall()\n", "d = pd.DataFrame.from_dict({'count': [int(x[0]) for x in data]})\n", "printstats(d['count'])\n", "h = d[d['count'] <= 20].hist(bins=20)[0][0]\n", "h.set_xlabel('RawAssignees')\n", "h.set_ylabel('Applications')\n", "h.set_title('Applications per RawAssignee')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "mean 8.78443826915\n", "median 1.0\n", "mode " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "1.0\n", "std 216.518254708\n", "min 1\n", "max 51561\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 17, "text": [ "<matplotlib.text.Text at 0x13e535210>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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VuHz5Mpqbm3H58mW4uLhg165d0kUGMTEx2LFjBwBg586diIqKglKphEajgYeH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\nYYJUf/DgQWlbPj4+Qq/XS8v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ES-DOC/esdoc-jupyterhub	notebooks/cas/cmip6/models/sandbox-3/ocean.ipynb	1	164407	{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "source": [ "# ES-DOC CMIP6 Model Properties - Ocean \n", "MIP Era: CMIP6 \n", "Institute: CAS \n", "Source ID: SANDBOX-3 \n", "Topic: Ocean \n", "Sub-Topics: Timestepping Framework, Advection, Lateral Physics, Vertical Physics, Uplow Boundaries, Boundary Forcing. \n", "Properties: 133 (101 required) \n", "Model descriptions: [Model description details](https://specializations.es-doc.org/cmip6/ocean?client=jupyter-notebook) \n", "Initialized From: -- \n", "\n", "Notebook Help: [Goto notebook help page](https://es-doc.org/cmip6-models-documenting-with-ipython) \n", "Notebook Initialised: 2018-02-15 16:53:45" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### Document Setup \n", "IMPORTANT: to be executed each time you run the notebook " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# DO NOT EDIT ! \n", "from pyesdoc.ipython.model_topic import NotebookOutput \n", "\n", "# DO NOT EDIT ! \n", "DOC = NotebookOutput('cmip6', 'cas', 'sandbox-3', 'ocean')" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Authors \n", "Set document authors" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set as follows: DOC.set_author(\"name\", \"email\") \n", "# TODO - please enter value(s)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Contributors \n", "Specify document contributors " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set as follows: DOC.set_contributor(\"name\", \"email\") \n", "# TODO - please enter value(s)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Publication \n", "Specify document publication status " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set publication status: \n", "# 0=do not publish, 1=publish. \n", "DOC.set_publication_status(0)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Table of Contents \n", "[1. Key Properties](#1.-Key-Properties) \n", "[2. Key Properties --> Seawater Properties](#2.-Key-Properties--->-Seawater-Properties) \n", "[3. Key Properties --> Bathymetry](#3.-Key-Properties--->-Bathymetry) \n", "[4. Key Properties --> Nonoceanic Waters](#4.-Key-Properties--->-Nonoceanic-Waters) \n", "[5. Key Properties --> Software Properties](#5.-Key-Properties--->-Software-Properties) \n", "[6. Key Properties --> Resolution](#6.-Key-Properties--->-Resolution) \n", "[7. Key Properties --> Tuning Applied](#7.-Key-Properties--->-Tuning-Applied) \n", "[8. Key Properties --> Conservation](#8.-Key-Properties--->-Conservation) \n", "[9. Grid](#9.-Grid) \n", "[10. Grid --> Discretisation --> Vertical](#10.-Grid--->-Discretisation--->-Vertical) \n", "[11. Grid --> Discretisation --> Horizontal](#11.-Grid--->-Discretisation--->-Horizontal) \n", "[12. Timestepping Framework](#12.-Timestepping-Framework) \n", "[13. Timestepping Framework --> Tracers](#13.-Timestepping-Framework--->-Tracers) \n", "[14. Timestepping Framework --> Baroclinic Dynamics](#14.-Timestepping-Framework--->-Baroclinic-Dynamics) \n", "[15. Timestepping Framework --> Barotropic](#15.-Timestepping-Framework--->-Barotropic) \n", "[16. Timestepping Framework --> Vertical Physics](#16.-Timestepping-Framework--->-Vertical-Physics) \n", "[17. Advection](#17.-Advection) \n", "[18. Advection --> Momentum](#18.-Advection--->-Momentum) \n", "[19. Advection --> Lateral Tracers](#19.-Advection--->-Lateral-Tracers) \n", "[20. Advection --> Vertical Tracers](#20.-Advection--->-Vertical-Tracers) \n", "[21. Lateral Physics](#21.-Lateral-Physics) \n", "[22. Lateral Physics --> Momentum --> Operator](#22.-Lateral-Physics--->-Momentum--->-Operator) \n", "[23. Lateral Physics --> Momentum --> Eddy Viscosity Coeff](#23.-Lateral-Physics--->-Momentum--->-Eddy-Viscosity-Coeff) \n", "[24. Lateral Physics --> Tracers](#24.-Lateral-Physics--->-Tracers) \n", "[25. Lateral Physics --> Tracers --> Operator](#25.-Lateral-Physics--->-Tracers--->-Operator) \n", "[26. Lateral Physics --> Tracers --> Eddy Diffusity Coeff](#26.-Lateral-Physics--->-Tracers--->-Eddy-Diffusity-Coeff) \n", "[27. Lateral Physics --> Tracers --> Eddy Induced Velocity](#27.-Lateral-Physics--->-Tracers--->-Eddy-Induced-Velocity) \n", "[28. Vertical Physics](#28.-Vertical-Physics) \n", "[29. Vertical Physics --> Boundary Layer Mixing --> Details](#29.-Vertical-Physics--->-Boundary-Layer-Mixing--->-Details) \n", "[30. Vertical Physics --> Boundary Layer Mixing --> Tracers](#30.-Vertical-Physics--->-Boundary-Layer-Mixing--->-Tracers) \n", "[31. Vertical Physics --> Boundary Layer Mixing --> Momentum](#31.-Vertical-Physics--->-Boundary-Layer-Mixing--->-Momentum) \n", "[32. Vertical Physics --> Interior Mixing --> Details](#32.-Vertical-Physics--->-Interior-Mixing--->-Details) \n", "[33. Vertical Physics --> Interior Mixing --> Tracers](#33.-Vertical-Physics--->-Interior-Mixing--->-Tracers) \n", "[34. Vertical Physics --> Interior Mixing --> Momentum](#34.-Vertical-Physics--->-Interior-Mixing--->-Momentum) \n", "[35. Uplow Boundaries --> Free Surface](#35.-Uplow-Boundaries--->-Free-Surface) \n", "[36. Uplow Boundaries --> Bottom Boundary Layer](#36.-Uplow-Boundaries--->-Bottom-Boundary-Layer) \n", "[37. Boundary Forcing](#37.-Boundary-Forcing) \n", "[38. Boundary Forcing --> Momentum --> Bottom Friction](#38.-Boundary-Forcing--->-Momentum--->-Bottom-Friction) \n", "[39. Boundary Forcing --> Momentum --> Lateral Friction](#39.-Boundary-Forcing--->-Momentum--->-Lateral-Friction) \n", "[40. Boundary Forcing --> Tracers --> Sunlight Penetration](#40.-Boundary-Forcing--->-Tracers--->-Sunlight-Penetration) \n", "[41. Boundary Forcing --> Tracers --> Fresh Water Forcing](#41.-Boundary-Forcing--->-Tracers--->-Fresh-Water-Forcing) \n", "\n" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "# 1. Key Properties \n", "Ocean key properties" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 1.1. Model Overview\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Overview of ocean model." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.model_overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.2. Model Name\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Name of ocean model code (NEMO 3.6, MOM 5.0,...)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.model_name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.3. Model Family\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Type of ocean model." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.model_family') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"OGCM\" \n", "# \"slab ocean\" \n", "# \"mixed layer ocean\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.4. Basic Approximations\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### Basic approximations made in the ocean." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.basic_approximations') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Primitive equations\" \n", "# \"Non-hydrostatic\" \n", "# \"Boussinesq\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.5. Prognostic Variables\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### List of prognostic variables in the ocean component." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.prognostic_variables') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Potential temperature\" \n", "# \"Conservative temperature\" \n", "# \"Salinity\" \n", "# \"U-velocity\" \n", "# \"V-velocity\" \n", "# \"W-velocity\" \n", "# \"SSH\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 2. Key Properties --> Seawater Properties \n", "Physical properties of seawater in ocean" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 2.1. Eos Type\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Type of EOS for sea water" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.seawater_properties.eos_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Linear\" \n", "# \"Wright, 1997\" \n", "# \"Mc Dougall et al.\" \n", "# \"Jackett et al. 2006\" \n", "# \"TEOS 2010\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 2.2. Eos Functional Temp\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Temperature used in EOS for sea water" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.seawater_properties.eos_functional_temp') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Potential temperature\" \n", "# \"Conservative temperature\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 2.3. Eos Functional Salt\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Salinity used in EOS for sea water" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.seawater_properties.eos_functional_salt') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Practical salinity Sp\" \n", "# \"Absolute salinity Sa\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 2.4. Eos Functional Depth\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Depth or pressure used in EOS for sea water ?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.seawater_properties.eos_functional_depth') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Pressure (dbars)\" \n", "# \"Depth (meters)\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 2.5. Ocean Freezing Point\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Equation used to compute the freezing point (in deg C) of seawater, as a function of salinity and pressure" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.seawater_properties.ocean_freezing_point') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"TEOS 2010\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 2.6. Ocean Specific Heat\n", "Is Required: TRUE    Type: FLOAT    Cardinality: 1.1\n", "### Specific heat in ocean (cpocean) in J/(kg K)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.seawater_properties.ocean_specific_heat') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 2.7. Ocean Reference Density\n", "Is Required: TRUE    Type: FLOAT    Cardinality: 1.1\n", "### Boussinesq reference density (rhozero) in kg / m3" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.seawater_properties.ocean_reference_density') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 3. Key Properties --> Bathymetry \n", "Properties of bathymetry in ocean" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 3.1. Reference Dates\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Reference date of bathymetry" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.bathymetry.reference_dates') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Present day\" \n", "# \"21000 years BP\" \n", "# \"6000 years BP\" \n", "# \"LGM\" \n", "# \"Pliocene\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.2. Type\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Is the bathymetry fixed in time in the ocean ?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.bathymetry.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.3. Ocean Smoothing\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Describe any smoothing or hand editing of bathymetry in ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.bathymetry.ocean_smoothing') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.4. Source\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Describe source of bathymetry in ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.bathymetry.source') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 4. Key Properties --> Nonoceanic Waters \n", "Non oceanic waters treatement in ocean" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 4.1. Isolated Seas\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe if/how isolated seas is performed" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.nonoceanic_waters.isolated_seas') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 4.2. River Mouth\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe if/how river mouth mixing or estuaries specific treatment is performed" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.nonoceanic_waters.river_mouth') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 5. Key Properties --> Software Properties \n", "Software properties of ocean code" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 5.1. Repository\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Location of code for this component." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.software_properties.repository') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.2. Code Version\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Code version identifier." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.software_properties.code_version') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.3. Code Languages\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.N\n", "### Code language(s)." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.software_properties.code_languages') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 6. Key Properties --> Resolution \n", "Resolution in the ocean grid" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 6.1. Name\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### This is a string usually used by the modelling group to describe the resolution of this grid, e.g. ORCA025, N512L180, T512L70 etc." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.resolution.name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.2. Canonical Horizontal Resolution\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Expression quoted for gross comparisons of resolution, eg. 50km or 0.1 degrees etc." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.resolution.canonical_horizontal_resolution') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.3. Range Horizontal Resolution\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Range of horizontal resolution with spatial details, eg. 50(Equator)-100km or 0.1-0.5 degrees etc." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.resolution.range_horizontal_resolution') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.4. Number Of Horizontal Gridpoints\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### Total number of horizontal (XY) points (or degrees of freedom) on computational grid." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.resolution.number_of_horizontal_gridpoints') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.5. Number Of Vertical Levels\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### Number of vertical levels resolved on computational grid." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.resolution.number_of_vertical_levels') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.6. Is Adaptive Grid\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Default is False. Set true if grid resolution changes during execution." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.resolution.is_adaptive_grid') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.7. Thickness Level 1\n", "Is Required: TRUE    Type: FLOAT    Cardinality: 1.1\n", "### Thickness of first surface ocean level (in meters)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.resolution.thickness_level_1') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 7. Key Properties --> Tuning Applied \n", "Tuning methodology for ocean component" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 7.1. Description\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### General overview description of tuning: explain and motivate the main targets and metrics retained. &Document the relative weight given to climate performance metrics versus process oriented metrics, &and on the possible conflicts with parameterization level tuning. In particular describe any struggle &with a parameter value that required pushing it to its limits to solve a particular model deficiency." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.tuning_applied.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 7.2. Global Mean Metrics Used\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.N\n", "### List set of metrics of the global mean state used in tuning model/component" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.tuning_applied.global_mean_metrics_used') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 7.3. Regional Metrics Used\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.N\n", "### List of regional metrics of mean state (e.g THC, AABW, regional means etc) used in tuning model/component" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.tuning_applied.regional_metrics_used') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 7.4. Trend Metrics Used\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.N\n", "### List observed trend metrics used in tuning model/component" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.tuning_applied.trend_metrics_used') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 8. Key Properties --> Conservation \n", "Conservation in the ocean component" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 8.1. Description\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Brief description of conservation methodology" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.conservation.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.2. Scheme\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### Properties conserved in the ocean by the numerical schemes" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.conservation.scheme') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Energy\" \n", "# \"Enstrophy\" \n", "# \"Salt\" \n", "# \"Volume of ocean\" \n", "# \"Momentum\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.3. Consistency Properties\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Any additional consistency properties (energy conversion, pressure gradient discretisation, ...)?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.conservation.consistency_properties') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.4. Corrected Conserved Prognostic Variables\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Set of variables which are conserved by more* than the numerical scheme alone." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.conservation.corrected_conserved_prognostic_variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.5. Was Flux Correction Used\n", "Is Required:* FALSE    Type: BOOLEAN    Cardinality: 0.1\n", "### Does conservation involve flux correction ?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.conservation.was_flux_correction_used') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 9. Grid \n", "Ocean grid" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 9.1. Overview\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Overview of grid in ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.grid.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 10. Grid --> Discretisation --> Vertical \n", "Properties of vertical discretisation in ocean" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 10.1. Coordinates\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Type of vertical coordinates in ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.grid.discretisation.vertical.coordinates') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Z-coordinate\" \n", "# \"Z-coordinate\" \n", "# \"S-coordinate\" \n", "# \"Isopycnic - sigma 0\" \n", "# \"Isopycnic - sigma 2\" \n", "# \"Isopycnic - sigma 4\" \n", "# \"Isopycnic - other\" \n", "# \"Hybrid / Z+S\" \n", "# \"Hybrid / Z+isopycnic\" \n", "# \"Hybrid / other\" \n", "# \"Pressure referenced (P)\" \n", "# \"P\" \n", "# \"Z\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 10.2. Partial Steps\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality:** 1.1\n", "### Using partial steps with Z or Z vertical coordinate in ocean ?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.grid.discretisation.vertical.partial_steps') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 11. Grid --> Discretisation --> Horizontal \n", "Type of horizontal discretisation scheme in ocean" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 11.1. Type\n", "Is Required:* TRUE    Type: ENUM    Cardinality: 1.1\n", "### Horizontal grid type" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.grid.discretisation.horizontal.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Lat-lon\" \n", "# \"Rotated north pole\" \n", "# \"Two north poles (ORCA-style)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.2. Staggering\n", "Is Required: FALSE    Type: ENUM    Cardinality: 0.1\n", "### Horizontal grid staggering type" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.grid.discretisation.horizontal.staggering') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Arakawa B-grid\" \n", "# \"Arakawa C-grid\" \n", "# \"Arakawa E-grid\" \n", "# \"N/a\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.3. Scheme\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Horizontal discretisation scheme in ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.grid.discretisation.horizontal.scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Finite difference\" \n", "# \"Finite volumes\" \n", "# \"Finite elements\" \n", "# \"Unstructured grid\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 12. Timestepping Framework \n", "Ocean Timestepping Framework" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 12.1. Overview\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Overview of time stepping in ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.timestepping_framework.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 12.2. Diurnal Cycle\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Diurnal cycle type" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.timestepping_framework.diurnal_cycle') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"None\" \n", "# \"Via coupling\" \n", "# \"Specific treatment\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 13. Timestepping Framework --> Tracers \n", "Properties of tracers time stepping in ocean" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 13.1. Scheme\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Tracers time stepping scheme" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.timestepping_framework.tracers.scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Leap-frog + Asselin filter\" \n", "# \"Leap-frog + Periodic Euler\" \n", "# \"Predictor-corrector\" \n", "# \"Runge-Kutta 2\" \n", "# \"AM3-LF\" \n", "# \"Forward-backward\" \n", "# \"Forward operator\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.2. Time Step\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### Tracers time step (in seconds)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.timestepping_framework.tracers.time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 14. Timestepping Framework --> Baroclinic Dynamics \n", "Baroclinic dynamics in ocean" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 14.1. Type\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Baroclinic dynamics type" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.timestepping_framework.baroclinic_dynamics.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Preconditioned conjugate gradient\" \n", "# \"Sub cyling\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.2. Scheme\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Baroclinic dynamics scheme" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.timestepping_framework.baroclinic_dynamics.scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Leap-frog + Asselin filter\" \n", "# \"Leap-frog + Periodic Euler\" \n", "# \"Predictor-corrector\" \n", "# \"Runge-Kutta 2\" \n", "# \"AM3-LF\" \n", "# \"Forward-backward\" \n", "# \"Forward operator\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.3. Time Step\n", "Is Required: FALSE    Type: INTEGER    Cardinality: 0.1\n", "### Baroclinic time step (in seconds)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.timestepping_framework.baroclinic_dynamics.time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 15. Timestepping Framework --> Barotropic \n", "Barotropic time stepping in ocean" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 15.1. Splitting\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Time splitting method" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.timestepping_framework.barotropic.splitting') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"None\" \n", "# \"split explicit\" \n", "# \"implicit\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.2. Time Step\n", "Is Required: FALSE    Type: INTEGER    Cardinality: 0.1\n", "### Barotropic time step (in seconds)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.timestepping_framework.barotropic.time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 16. Timestepping Framework --> Vertical Physics \n", "Vertical physics time stepping in ocean" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 16.1. Method\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Details of vertical time stepping in ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.timestepping_framework.vertical_physics.method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 17. Advection \n", "Ocean advection" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 17.1. Overview\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Overview of advection in ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.advection.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 18. Advection --> Momentum \n", "Properties of lateral momemtum advection scheme in ocean" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 18.1. Type\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Type of lateral momemtum advection scheme in ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.advection.momentum.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Flux form\" \n", "# \"Vector form\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 18.2. Scheme Name\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Name of ocean momemtum advection scheme" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.advection.momentum.scheme_name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 18.3. ALE\n", "Is Required: FALSE    Type: BOOLEAN    Cardinality: 0.1\n", "### Using ALE for vertical advection ? (if vertical coordinates are sigma)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.advection.momentum.ALE') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 19. Advection --> Lateral Tracers \n", "Properties of lateral tracer advection scheme in ocean" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 19.1. Order\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### Order of lateral tracer advection scheme in ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.advection.lateral_tracers.order') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 19.2. Flux Limiter\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Monotonic flux limiter for lateral tracer advection scheme in ocean ?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.advection.lateral_tracers.flux_limiter') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 19.3. Effective Order\n", "Is Required: TRUE    Type: FLOAT    Cardinality: 1.1\n", "### Effective order of limited lateral tracer advection scheme in ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.advection.lateral_tracers.effective_order') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 19.4. Name\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Descriptive text for lateral tracer advection scheme in ocean (e.g. MUSCL, PPM-H5, PRATHER,...)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.advection.lateral_tracers.name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 19.5. Passive Tracers\n", "Is Required: FALSE    Type: ENUM    Cardinality: 0.N\n", "### Passive tracers advected" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.advection.lateral_tracers.passive_tracers') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Ideal age\" \n", "# \"CFC 11\" \n", "# \"CFC 12\" \n", "# \"SF6\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 19.6. Passive Tracers Advection\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Is advection of passive tracers different than active ? if so, describe." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.advection.lateral_tracers.passive_tracers_advection') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 20. Advection --> Vertical Tracers \n", "Properties of vertical tracer advection scheme in ocean" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 20.1. Name\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Descriptive text for vertical tracer advection scheme in ocean (e.g. MUSCL, PPM-H5, PRATHER,...)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.advection.vertical_tracers.name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 20.2. Flux Limiter\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Monotonic flux limiter for vertical tracer advection scheme in ocean ?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.advection.vertical_tracers.flux_limiter') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 21. Lateral Physics \n", "Ocean lateral physics" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 21.1. Overview\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Overview of lateral physics in ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 21.2. Scheme\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Type of transient eddy representation in ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"None\" \n", "# \"Eddy active\" \n", "# \"Eddy admitting\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 22. Lateral Physics --> Momentum --> Operator \n", "Properties of lateral physics operator for momentum in ocean" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 22.1. Direction\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Direction of lateral physics momemtum scheme in the ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.momentum.operator.direction') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Horizontal\" \n", "# \"Isopycnal\" \n", "# \"Isoneutral\" \n", "# \"Geopotential\" \n", "# \"Iso-level\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 22.2. Order\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Order of lateral physics momemtum scheme in the ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.momentum.operator.order') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Harmonic\" \n", "# \"Bi-harmonic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 22.3. Discretisation\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Discretisation of lateral physics momemtum scheme in the ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.momentum.operator.discretisation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Second order\" \n", "# \"Higher order\" \n", "# \"Flux limiter\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 23. Lateral Physics --> Momentum --> Eddy Viscosity Coeff \n", "Properties of eddy viscosity coeff in lateral physics momemtum scheme in the ocean" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 23.1. Type\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Lateral physics momemtum eddy viscosity coeff type in the ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.momentum.eddy_viscosity_coeff.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Constant\" \n", "# \"Space varying\" \n", "# \"Time + space varying (Smagorinsky)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 23.2. Constant Coefficient\n", "Is Required: FALSE    Type: INTEGER    Cardinality: 0.1\n", "### If constant, value of eddy viscosity coeff in lateral physics momemtum scheme (in m2/s)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.momentum.eddy_viscosity_coeff.constant_coefficient') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 23.3. Variable Coefficient\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### If space-varying, describe variations of eddy viscosity coeff in lateral physics momemtum scheme" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.momentum.eddy_viscosity_coeff.variable_coefficient') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 23.4. Coeff Background\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Describe background eddy viscosity coeff in lateral physics momemtum scheme (give values in m2/s)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.momentum.eddy_viscosity_coeff.coeff_background') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 23.5. Coeff Backscatter\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Is there backscatter in eddy viscosity coeff in lateral physics momemtum scheme ?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.momentum.eddy_viscosity_coeff.coeff_backscatter') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 24. Lateral Physics --> Tracers \n", "Properties of lateral physics for tracers in ocean" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 24.1. Mesoscale Closure\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Is there a mesoscale closure in the lateral physics tracers scheme ?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.tracers.mesoscale_closure') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 24.2. Submesoscale Mixing\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Is there a submesoscale mixing parameterisation (i.e Fox-Kemper) in the lateral physics tracers scheme ?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.tracers.submesoscale_mixing') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 25. Lateral Physics --> Tracers --> Operator \n", "Properties of lateral physics operator for tracers in ocean" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 25.1. Direction\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Direction of lateral physics tracers scheme in the ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.tracers.operator.direction') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Horizontal\" \n", "# \"Isopycnal\" \n", "# \"Isoneutral\" \n", "# \"Geopotential\" \n", "# \"Iso-level\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 25.2. Order\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Order of lateral physics tracers scheme in the ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.tracers.operator.order') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Harmonic\" \n", "# \"Bi-harmonic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 25.3. Discretisation\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Discretisation of lateral physics tracers scheme in the ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.tracers.operator.discretisation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Second order\" \n", "# \"Higher order\" \n", "# \"Flux limiter\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 26. Lateral Physics --> Tracers --> Eddy Diffusity Coeff \n", "Properties of eddy diffusity coeff in lateral physics tracers scheme in the ocean" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 26.1. Type\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Lateral physics tracers eddy diffusity coeff type in the ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.tracers.eddy_diffusity_coeff.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Constant\" \n", "# \"Space varying\" \n", "# \"Time + space varying (Smagorinsky)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 26.2. Constant Coefficient\n", "Is Required: FALSE    Type: INTEGER    Cardinality: 0.1\n", "### If constant, value of eddy diffusity coeff in lateral physics tracers scheme (in m2/s)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.tracers.eddy_diffusity_coeff.constant_coefficient') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 26.3. Variable Coefficient\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### If space-varying, describe variations of eddy diffusity coeff in lateral physics tracers scheme" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.tracers.eddy_diffusity_coeff.variable_coefficient') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 26.4. Coeff Background\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### Describe background eddy diffusity coeff in lateral physics tracers scheme (give values in m2/s)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.tracers.eddy_diffusity_coeff.coeff_background') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 26.5. Coeff Backscatter\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Is there backscatter in eddy diffusity coeff in lateral physics tracers scheme ?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.tracers.eddy_diffusity_coeff.coeff_backscatter') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 27. Lateral Physics --> Tracers --> Eddy Induced Velocity \n", "Properties of eddy induced velocity (EIV) in lateral physics tracers scheme in the ocean" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 27.1. Type\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Type of EIV in lateral physics tracers in the ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.tracers.eddy_induced_velocity.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"GM\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 27.2. Constant Val\n", "Is Required: FALSE    Type: INTEGER    Cardinality: 0.1\n", "### If EIV scheme for tracers is constant, specify coefficient value (M2/s)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.tracers.eddy_induced_velocity.constant_val') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 27.3. Flux Type\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Type of EIV flux (advective or skew)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.tracers.eddy_induced_velocity.flux_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 27.4. Added Diffusivity\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Type of EIV added diffusivity (constant, flow dependent or none)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.tracers.eddy_induced_velocity.added_diffusivity') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 28. Vertical Physics \n", "Ocean Vertical Physics" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 28.1. Overview\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Overview of vertical physics in ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 29. Vertical Physics --> Boundary Layer Mixing --> Details \n", "Properties of vertical physics in ocean" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 29.1. Langmuir Cells Mixing\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Is there Langmuir cells mixing in upper ocean ?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.boundary_layer_mixing.details.langmuir_cells_mixing') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 30. Vertical Physics --> Boundary Layer Mixing --> Tracers \n", "Properties of boundary layer (BL) mixing on tracers in the ocean " ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 30.1. Type\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Type of boundary layer mixing for tracers in ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.boundary_layer_mixing.tracers.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Constant value\" \n", "# \"Turbulent closure - TKE\" \n", "# \"Turbulent closure - KPP\" \n", "# \"Turbulent closure - Mellor-Yamada\" \n", "# \"Turbulent closure - Bulk Mixed Layer\" \n", "# \"Richardson number dependent - PP\" \n", "# \"Richardson number dependent - KT\" \n", "# \"Imbeded as isopycnic vertical coordinate\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.2. Closure Order\n", "Is Required: FALSE    Type: FLOAT    Cardinality: 0.1\n", "### If turbulent BL mixing of tracers, specific order of closure (0, 1, 2.5, 3)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.boundary_layer_mixing.tracers.closure_order') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.3. Constant\n", "Is Required: FALSE    Type: INTEGER    Cardinality: 0.1\n", "### If constant BL mixing of tracers, specific coefficient (m2/s)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.boundary_layer_mixing.tracers.constant') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.4. Background\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Background BL mixing of tracers coefficient, (schema and value in m2/s - may by none)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.boundary_layer_mixing.tracers.background') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 31. Vertical Physics --> Boundary Layer Mixing --> Momentum \n", "Properties of boundary layer (BL) mixing on momentum in the ocean " ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 31.1. Type\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Type of boundary layer mixing for momentum in ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.boundary_layer_mixing.momentum.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Constant value\" \n", "# \"Turbulent closure - TKE\" \n", "# \"Turbulent closure - KPP\" \n", "# \"Turbulent closure - Mellor-Yamada\" \n", "# \"Turbulent closure - Bulk Mixed Layer\" \n", "# \"Richardson number dependent - PP\" \n", "# \"Richardson number dependent - KT\" \n", "# \"Imbeded as isopycnic vertical coordinate\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 31.2. Closure Order\n", "Is Required: FALSE    Type: FLOAT    Cardinality: 0.1\n", "### If turbulent BL mixing of momentum, specific order of closure (0, 1, 2.5, 3)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.boundary_layer_mixing.momentum.closure_order') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 31.3. Constant\n", "Is Required: FALSE    Type: INTEGER    Cardinality: 0.1\n", "### If constant BL mixing of momentum, specific coefficient (m2/s)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.boundary_layer_mixing.momentum.constant') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 31.4. Background\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Background BL mixing of momentum coefficient, (schema and value in m2/s - may by none)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.boundary_layer_mixing.momentum.background') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 32. Vertical Physics --> Interior Mixing --> Details \n", "Properties of interior mixing in the ocean " ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 32.1. Convection Type\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Type of vertical convection in ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.details.convection_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Non-penetrative convective adjustment\" \n", "# \"Enhanced vertical diffusion\" \n", "# \"Included in turbulence closure\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 32.2. Tide Induced Mixing\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Describe how tide induced mixing is modelled (barotropic, baroclinic, none)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.details.tide_induced_mixing') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 32.3. Double Diffusion\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Is there double diffusion" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.details.double_diffusion') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 32.4. Shear Mixing\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Is there interior shear mixing" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.details.shear_mixing') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 33. Vertical Physics --> Interior Mixing --> Tracers \n", "Properties of interior mixing on tracers in the ocean " ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 33.1. Type\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Type of interior mixing for tracers in ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.tracers.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Constant value\" \n", "# \"Turbulent closure / TKE\" \n", "# \"Turbulent closure - Mellor-Yamada\" \n", "# \"Richardson number dependent - PP\" \n", "# \"Richardson number dependent - KT\" \n", "# \"Imbeded as isopycnic vertical coordinate\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 33.2. Constant\n", "Is Required: FALSE    Type: INTEGER    Cardinality: 0.1\n", "### If constant interior mixing of tracers, specific coefficient (m2/s)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.tracers.constant') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 33.3. Profile\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Is the background interior mixing using a vertical profile for tracers (i.e is NOT constant) ?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.tracers.profile') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 33.4. Background\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Background interior mixing of tracers coefficient, (schema and value in m2/s - may by none)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.tracers.background') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 34. Vertical Physics --> Interior Mixing --> Momentum \n", "Properties of interior mixing on momentum in the ocean " ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 34.1. Type\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Type of interior mixing for momentum in ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.momentum.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Constant value\" \n", "# \"Turbulent closure / TKE\" \n", "# \"Turbulent closure - Mellor-Yamada\" \n", "# \"Richardson number dependent - PP\" \n", "# \"Richardson number dependent - KT\" \n", "# \"Imbeded as isopycnic vertical coordinate\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 34.2. Constant\n", "Is Required: FALSE    Type: INTEGER    Cardinality: 0.1\n", "### If constant interior mixing of momentum, specific coefficient (m2/s)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.momentum.constant') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 34.3. Profile\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Is the background interior mixing using a vertical profile for momentum (i.e is NOT constant) ?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.momentum.profile') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 34.4. Background\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Background interior mixing of momentum coefficient, (schema and value in m2/s - may by none)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.momentum.background') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 35. Uplow Boundaries --> Free Surface \n", "Properties of free surface in ocean" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 35.1. Overview\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Overview of free surface in ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.uplow_boundaries.free_surface.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 35.2. Scheme\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Free surface scheme in ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.uplow_boundaries.free_surface.scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Linear implicit\" \n", "# \"Linear filtered\" \n", "# \"Linear semi-explicit\" \n", "# \"Non-linear implicit\" \n", "# \"Non-linear filtered\" \n", "# \"Non-linear semi-explicit\" \n", "# \"Fully explicit\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 35.3. Embeded Seaice\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Is the sea-ice embeded in the ocean model (instead of levitating) ?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.uplow_boundaries.free_surface.embeded_seaice') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 36. Uplow Boundaries --> Bottom Boundary Layer \n", "Properties of bottom boundary layer in ocean" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 36.1. Overview\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Overview of bottom boundary layer in ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.uplow_boundaries.bottom_boundary_layer.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 36.2. Type Of Bbl\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Type of bottom boundary layer in ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.uplow_boundaries.bottom_boundary_layer.type_of_bbl') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Diffusive\" \n", "# \"Acvective\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 36.3. Lateral Mixing Coef\n", "Is Required: FALSE    Type: INTEGER    Cardinality: 0.1\n", "### If bottom BL is diffusive, specify value of lateral mixing coefficient (in m2/s)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.uplow_boundaries.bottom_boundary_layer.lateral_mixing_coef') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 36.4. Sill Overflow\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Describe any specific treatment of sill overflows" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.uplow_boundaries.bottom_boundary_layer.sill_overflow') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 37. Boundary Forcing \n", "Ocean boundary forcing" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 37.1. Overview\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Overview of boundary forcing in ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.boundary_forcing.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 37.2. Surface Pressure\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Describe how surface pressure is transmitted to ocean (via sea-ice, nothing specific,...)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.boundary_forcing.surface_pressure') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 37.3. Momentum Flux Correction\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe any type of ocean surface momentum flux correction and, if applicable, how it is applied and where." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.boundary_forcing.momentum_flux_correction') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 37.4. Tracers Flux Correction\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe any type of ocean surface tracers flux correction and, if applicable, how it is applied and where." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.boundary_forcing.tracers_flux_correction') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 37.5. Wave Effects\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Describe if/how wave effects are modelled at ocean surface." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.boundary_forcing.wave_effects') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 37.6. River Runoff Budget\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Describe how river runoff from land surface is routed to ocean and any global adjustment done." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.boundary_forcing.river_runoff_budget') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 37.7. Geothermal Heating\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Describe if/how geothermal heating is present at ocean bottom." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.boundary_forcing.geothermal_heating') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 38. Boundary Forcing --> Momentum --> Bottom Friction \n", "Properties of momentum bottom friction in ocean" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 38.1. Type\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Type of momentum bottom friction in ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.boundary_forcing.momentum.bottom_friction.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Linear\" \n", "# \"Non-linear\" \n", "# \"Non-linear (drag function of speed of tides)\" \n", "# \"Constant drag coefficient\" \n", "# \"None\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 39. Boundary Forcing --> Momentum --> Lateral Friction \n", "Properties of momentum lateral friction in ocean" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 39.1. Type\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Type of momentum lateral friction in ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.boundary_forcing.momentum.lateral_friction.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"None\" \n", "# \"Free-slip\" \n", "# \"No-slip\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 40. Boundary Forcing --> Tracers --> Sunlight Penetration \n", "Properties of sunlight penetration scheme in ocean" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 40.1. Scheme\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Type of sunlight penetration scheme in ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.boundary_forcing.tracers.sunlight_penetration.scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"1 extinction depth\" \n", "# \"2 extinction depth\" \n", "# \"3 extinction depth\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 40.2. Ocean Colour\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Is the ocean sunlight penetration scheme ocean colour dependent ?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.boundary_forcing.tracers.sunlight_penetration.ocean_colour') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 40.3. Extinction Depth\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe and list extinctions depths for sunlight penetration scheme (if applicable)." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.boundary_forcing.tracers.sunlight_penetration.extinction_depth') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 41. Boundary Forcing --> Tracers --> Fresh Water Forcing \n", "Properties of surface fresh water forcing in ocean" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 41.1. From Atmopshere\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Type of surface fresh water forcing from atmos in ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.boundary_forcing.tracers.fresh_water_forcing.from_atmopshere') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Freshwater flux\" \n", "# \"Virtual salt flux\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 41.2. From Sea Ice\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Type of surface fresh water forcing from sea-ice in ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.boundary_forcing.tracers.fresh_water_forcing.from_sea_ice') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Freshwater flux\" \n", "# \"Virtual salt flux\" \n", "# \"Real salt flux\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 41.3. Forced Mode Restoring\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Type of surface salinity restoring in forced mode (OMIP)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.boundary_forcing.tracers.fresh_water_forcing.forced_mode_restoring') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### \u00a92017 [ES-DOC](https://es-doc.org) \n" ], "cell_type": "markdown", "metadata": {} } ], "metadata": { "kernelspec": { "display_name": "Python 2", "name": "python2", "language": "python" }, "language_info": { "mimetype": "text/x-python", "nbconvert_exporter": "python", "name": "python", "file_extension": ".py", "version": "2.7.10", "pygments_lexer": "ipython2", "codemirror_mode": { "version": 2, "name": "ipython" } } } }	gpl-3.0
jepegit/cellpy	dev_utils/new_formats/tweaking_pec_OLD.ipynb	1	7322	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# PEC" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%load_ext autoreload\n", "%autoreload 2" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from pathlib import Path\n", "import os\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "\n", "import cellpy" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# C:\\scripts\\cellpy\\dev_data\\PEC\n", "filename = \"Test1058_2.csv\"\n", "raw_file_path = (Path(\"../../../../Eksempelfiler\") / filename).resolve()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "raw_file_path.is_file()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pec = cellpy.get(\n", " instrument=\"pec_csv\", filename=raw_file_path, cycle_mode=\"cathode\", mass=50_000\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import holoviews as hv\n", "\n", "hv.extension(\"bokeh\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pec.get_cycle_numbers()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "steptable = pec.cell.steps\n", "steptable.head(10)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "summary = pec.cell.summary\n", "summary.head(5)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "c, v = pec.get_dcap(3)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fig = plt.figure(figsize=(8, 5))\n", "curve1 = plt.scatter(c, v) # hv.Scatter((c,v))\n", "plt.xlabel(\"Discharge capacity [mAh/g]\")\n", "plt.ylabel(\"Voltage [V]\")\n", "plt.show()\n", "# curve1" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from cellpy import log\n", "\n", "log.setup_logging(default_level=\"DEBUG\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from cellpy.utils import ica" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dv1, dq1 = ica.dqdv(\n", " v,\n", " c,\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "converter = ica.Converter()\n", "converter.pre_smoothing = False\n", "converter.post_smoothing = False\n", "converter.smoothing = False\n", "converter.normalise = True\n", "converter.voltage_fwhm = 0.01\n", "converter.max_points = 400\n", "converter.capacity_resolution = 5.0\n", "converter.voltage_resolution = 0.01\n", "converter.set_data(c, v)\n", "converter.inspect_data()\n", "converter.pre_process_data()\n", "converter.increment_data()\n", "converter.post_process_data()\n", "\n", "fig = plt.figure(figsize=(8, 5))\n", "curve00 = plt.plot(c, v, label=\"as is\")\n", "curve01 = plt.plot(\n", " converter.capacity_preprocessed,\n", " converter.voltage_preprocessed,\n", " label=\"preprocessed\",\n", ")\n", "plt.xlabel(\"Discharge capacity [mAh/g]\")\n", "plt.ylabel(\"Voltage [V]\")\n", "plt.legend()\n", "plt.show()\n", "\n", "# curve00 = hv.Scatter((c, v), label=\"as is\").opts(width=800, height=500)\n", "# curve01 = hv.Scatter((converter.capacity_preprocessed, converter.voltage_preprocessed), label=\"preprocessed\").opts(width=800, height=500)\n", "# curve00 * curve01" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(len(converter._voltage_processed))\n", "print(len(converter._incremental_capacity))\n", "fig = plt.figure(figsize=(8, 5))\n", "curve1 = plt.plot(\n", " dv1,\n", " dq1,\n", " label=\"as is\",\n", ")\n", "curve2 = plt.plot(\n", " converter.voltage_processed, converter.incremental_capacity, label=\"new\"\n", ")\n", "curve3 = plt.plot(\n", " converter._voltage_processed, converter._incremental_capacity, label=\"pre\"\n", ")\n", "curve4 = plt.plot(\n", " converter.voltage_processed, converter._incremental_capacity, label=\"shifted\"\n", ")\n", "plt.xlabel(\"Voltage [V]\")\n", "plt.ylabel(\"dQdV [mAh/$g^{-1}V^{-1}$]\")\n", "plt.legend()\n", "plt.show()\n", "\n", "# curve1 = hv.Curve((dv1, dq1), label=\"as is\").opts(width=800, height=500)\n", "# curve2 = hv.Curve((converter.voltage_processed, converter.incremental_capacity), label=\"new\").opts(width=800, height=500)\n", "# curve3 = hv.Curve((converter._voltage_processed, converter._incremental_capacity), label=\"pre\").opts(width=800, height=500)\n", "# curve4 = hv.Curve((converter.voltage_processed, converter._incremental_capacity), label=\"shifted\").opts(width=800, height=500)\n", "# curve1 * curve2 * curve3 * curve4" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dv1, dq1 = ica.dqdv(\n", " v,\n", " c,\n", " voltage_fwhm=0.01,\n", ")\n", "\n", "\n", "dv2, dq2 = ica.dqdv(\n", " v,\n", " c,\n", " voltage_fwhm=0.01,\n", " max_points=200,\n", ")\n", "\n", "fig = plt.figure(figsize=(8, 5))\n", "curve2 = plt.plot(dv1, dq1, label=\"as is\")\n", "scatter2 = plt.plot(dv2, dq2, label=\"1000 points\")\n", "plt.xlabel(\"Voltage [V]\")\n", "plt.ylabel(\"dQdV [mAh/$g^{-1}V^{-1}$]\")\n", "plt.legend()\n", "plt.show()\n", "\n", "# curve2 = hv.Curve((dv1, dq1), label=\"as is\").opts(width=800, height=500)\n", "# scatter2 = hv.Curve((dv2, dq2), label=\"1000 points\").opts(width=800, height=500)\n", "# curve2 * scatter2" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "?ica.dqdv" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "cellpy", "language": "python", "name": "cellpy" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.7" } }, "nbformat": 4, "nbformat_minor": 4 }	mit
BartKeulen/drl	notebooks/RandomExploration.ipynb	1	2055627	null	mit
EmuKit/emukit	notebooks/Emukit-tutorial-bayesian-optimization-external-objective-evaluation.ipynb	1	35713	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# External objective function evaluation in Bayesian optimization with Emukit" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Overview" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Bayesian optimization component of Emukit allows for objective functions to be evaluated externally. If users opt for this approach, they can use Emukit to suggest the next point for evaluation, and then evaluate the objective function themselves as well as decide on the stopping criteria of the evaluation loop. This notebook shall demonstrate how to carry out this procedure. The main benefit of using Emukit in this manner is that you can externally manage issues such as parallelizing the computation of the objective function, which is convenient in many scenarios." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "### General imports\n", "%matplotlib inline\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from matplotlib import colors as mcolors\n", "%pylab inline\n", "\n", "### --- Figure config\n", "colors = dict(mcolors.BASE_COLORS, *mcolors.CSS4_COLORS)\n", "LEGEND_SIZE = 15\n", "TITLE_SIZE = 25\n", "AXIS_SIZE = 15" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Navigation\n", "\n", "1. [Handling the loop yourself](#1.-Handling-the-loop-yourself)\n", "\n", "2. [Comparing with the high level API](#2.-Comparing-with-the-high-level-API)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Handling the loop yourself" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For the purposes of this notebook we are going to use one of the predefined objective functions that come with GPyOpt. However, the key thing to realize is that the function could be anything (e.g., the results of a physical experiment). As long as users are able to externally evaluate the suggested points and provide GPyOpt with the results, the library has options for setting the objective function's origin." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from emukit.test_functions import forrester_function\n", "from emukit.core.loop import UserFunctionWrapper\n", "\n", "target_function, space = forrester_function()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First we are going to run the optimization loop outside of Emukit, and only use the library to get the next point at which to evaluate our function.\n", "\n", "There are two things to pay attention to when creating the main optimization object:\n", "\n", " Since we recreate the object anew for each iteration, we need to pass data about all previous iterations to it.\n", "\n", "* The model gets optimized from the scratch in every iteration but the parameters of the model could be saved and used to update the state (TODO).\n", "\n", "We start with three initial points at which we evaluate the objective function." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "X = np.array([[0.1],[0.6],[0.9]])\n", "Y = target_function(X)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And we run the loop externally." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "from emukit.examples.gp_bayesian_optimization.single_objective_bayesian_optimization import GPBayesianOptimization\n", "from emukit.core.loop import UserFunctionResult\n", "\n", "num_iterations = 10\n", "\n", "bo = GPBayesianOptimization(variables_list=space.parameters, X=X, Y=Y)\n", "results = None\n", "\n", "for _ in range(num_iterations):\n", " X_new = bo.get_next_points(results)\n", " Y_new = target_function(X_new)\n", " results = [UserFunctionResult(X_new[0], Y_new[0])]\n", "\n", "X = bo.loop_state.X\n", "Y = bo.loop_state.Y" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's visualize the results. The size of the marker denotes the order in which the point was evaluated - the bigger the marker the later was the evaluation." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "x = np.arange(0.0, 1.0, 0.01)\n", "y = target_function(x)\n", "\n", "plt.figure()\n", "plt.plot(x, y)\n", "for i, (xs, ys) in enumerate(zip(X, Y)):\n", " plt.plot(xs, ys, 'ro', markersize=10 + 10 * (i+1)/len(X))" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[0.1 ],\n", " [0.6 ],\n", " [0.9 ],\n", " [0.0549192 ],\n", " [0.24163332],\n", " [0.44706868],\n", " [0.15537258],\n", " [0.13687813],\n", " [0.6888019 ],\n", " [0.71922108],\n", " [0.73967583],\n", " [0.75168362]])" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Comparing with the high level API" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To compare the results, let's now execute the whole loop with Emukit." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "X = np.array([[0.1],[0.6],[0.9]])\n", "Y = target_function(X)\n", "\n", "bo_loop = GPBayesianOptimization(variables_list=space.parameters, X=X, Y=Y)\n", "bo_loop.run_optimization(target_function, num_iterations)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's print the results of this optimization and compare it to the previous external evaluation run. As before, the size of the marker corresponds to its evaluation order." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "x = np.arange(0.0, 1.0, 0.01)\n", "y = target_function(x)\n", "\n", "plt.figure()\n", "plt.plot(x, y)\n", "for i, (xs, ys) in enumerate(zip(bo_loop.model.model.X, bo_loop.model.model.Y)):\n", " plt.plot(xs, ys, 'ro', markersize=10 + 10 * (i+1)/len(bo_loop.model.model.X))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It can be observed that we obtain the same result as before but now the objective function is handled internally." ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.6" } }, "nbformat": 4, "nbformat_minor": 1 }	apache-2.0
mdeff/ntds_2016	project/reports/breast_cancer/report.ipynb	1	12202209	null	mit
cdt15/lingam	examples/VARMALiNGAM.ipynb	1	49086	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# VARMALiNGAM" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Import and settings\n", "In this example, we need to import `numpy`, `pandas`, and `graphviz` in addition to `lingam`." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['1.16.2', '0.24.2', '0.11.1', '1.5.2']\n" ] } ], "source": [ "import numpy as np\n", "import pandas as pd\n", "import graphviz\n", "import lingam\n", "from lingam.utils import make_dot, print_causal_directions, print_dagc\n", "\n", "import warnings\n", "warnings.filterwarnings('ignore')\n", "\n", "print([np.__version__, pd.__version__, graphviz.__version__, lingam.__version__])\n", "\n", "np.set_printoptions(precision=3, suppress=True)\n", "np.random.seed(0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Test data\n", "We create test data consisting of 5 variables." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "psi0 = np.array([\n", " [ 0. , 0. , -0.25, 0. , 0. ],\n", " [-0.38, 0. , 0.14, 0. , 0. ],\n", " [ 0. , 0. , 0. , 0. , 0. ],\n", " [ 0.44, -0.2 , -0.09, 0. , 0. ],\n", " [ 0.07, -0.06, 0. , 0.07, 0. ]\n", "])\n", "phi1 = np.array([\n", " [-0.04, -0.29, -0.26, 0.14, 0.47],\n", " [-0.42, 0.2 , 0.1 , 0.24, 0.25],\n", " [-0.25, 0.18, -0.06, 0.15, 0.18],\n", " [ 0.22, 0.39, 0.08, 0.12, -0.37],\n", " [-0.43, 0.09, -0.23, 0.16, 0.25]\n", "])\n", "theta1 = np.array([\n", " [ 0.15, -0.02, -0.3 , -0.2 , 0.21],\n", " [ 0.32, 0.12, -0.11, 0.03, 0.42],\n", " [-0.07, -0.5 , 0.03, -0.27, -0.21],\n", " [-0.17, 0.35, 0.25, 0.24, -0.25],\n", " [ 0.09, 0.4 , 0.41, 0.24, -0.31]\n", "])\n", "causal_order = [2, 0, 1, 3, 4]\n", "\n", "# data generated from psi0 and phi1 and theta1, causal_order\n", "X = np.loadtxt('data/sample_data_varma_lingam.csv', delimiter=',')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Causal Discovery\n", "To run causal discovery, we create a `VARMALiNGAM` object and call the `fit` method." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<lingam.varma_lingam.VARMALiNGAM at 0x21589a465f8>" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model = lingam.VARMALiNGAM(order=(1, 1), criterion=None)\n", "model.fit(X)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using the `causal_order_` properties, we can see the causal ordering as a result of the causal discovery." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[2, 0, 1, 3, 4]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.causal_order_" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Also, using the `adjacency_matrices_` properties, we can see the adjacency matrix as a result of the causal discovery." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 0. , 0. , -0.238, 0. , 0. ],\n", " [-0.392, 0. , 0.182, 0. , 0. ],\n", " [ 0. , 0. , 0. , 0. , 0. ],\n", " [ 0.523, -0.149, 0. , 0. , 0. ],\n", " [ 0. , 0. , 0. , 0. , 0. ]])" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# psi0\n", "model.adjacency_matrices_[0][0]" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[-0.145, -0.288, -0.418, 0.041, 0.592],\n", " [-0.324, 0.027, 0.024, 0.231, 0.379],\n", " [-0.249, 0.191, -0.01 , 0.136, 0.261],\n", " [ 0.182, 0.698, 0.21 , 0.197, -0.815],\n", " [-0.486, 0.063, -0.263, 0.112, 0.26 ]])" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# psi1\n", "model.adjacency_matrices_[0][1]" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 0.247, -0.12 , -0.128, -0.124, 0.037],\n", " [ 0.378, 0.319, -0.12 , -0.023, 0.573],\n", " [-0.107, -0.624, 0.012, -0.303, -0.246],\n", " [-0.22 , 0.26 , 0.313, 0.227, -0.057],\n", " [ 0.255, 0.405, 0.41 , 0.256, -0.286]])" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# omega0\n", "model.adjacency_matrices_[1][0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using `DirectLiNGAM` for the `residuals_` properties, we can calculate psi0 matrix." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 0. , 0. , -0.238, 0. , 0. ],\n", " [-0.392, 0. , 0.182, 0. , 0. ],\n", " [ 0. , 0. , 0. , 0. , 0. ],\n", " [ 0.523, -0.149, 0. , 0. , 0. ],\n", " [ 0. , 0. , 0. , 0. , 0. ]])" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dlingam = lingam.DirectLiNGAM()\n", "dlingam.fit(model.residuals_)\n", "dlingam.adjacency_matrix_" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can draw a causal graph by utility funciton" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\r\n", "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n", " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\r\n", "<!-- Generated by graphviz version 2.38.0 (20140413.2041)\r\n", " -->\r\n", "<!-- Title: %3 Pages: 1 -->\r\n", "<svg width=\"469pt\" height=\"218pt\"\r\n", " viewBox=\"0.00 0.00 469.04 218.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\r\n", "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 214)\">\r\n", "<title>%3</title>\r\n", "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-214 465.044,-214 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Roman,serif\" font-size=\"14.00\">-0.81</text>\r\n", "</g>\r\n", "</g>\r\n", "</svg>\r\n" ], "text/plain": [ "<graphviz.dot.Digraph at 0x21589c9a4e0>" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "labels = ['y0(t)', 'y1(t)', 'y2(t)', 'y3(t)', 'y4(t)', 'y0(t-1)', 'y1(t-1)', 'y2(t-1)', 'y3(t-1)', 'y4(t-1)']\n", "make_dot(np.hstack(model.adjacency_matrices_[0]), lower_limit=0.3, ignore_shape=True, labels=labels)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Independence between error variables\n", "To check if the LiNGAM assumption is broken, we can get p-values of independence between error variables. The value in the i-th row and j-th column of the obtained matrix shows the p-value of the independence of the error variables $e_i$ and $e_j$." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[0. 0.517 0.793 0.004 0.001]\n", " [0.517 0. 0.09 0.312 0.071]\n", " [0.793 0.09 0. 0.058 0.075]\n", " [0.004 0.312 0.058 0. 0.011]\n", " [0.001 0.071 0.075 0.011 0. ]]\n" ] } ], "source": [ "p_values = model.get_error_independence_p_values()\n", "print(p_values)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Bootstrap" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Bootstrapping\n", "We call `bootstrap()` method instead of `fit()`. Here, the second argument specifies the number of bootstrap sampling." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "model = lingam.VARMALiNGAM()\n", "result = model.bootstrap(X, n_sampling=100)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Causal Directions\n", "Since `BootstrapResult` object is returned, we can get the ranking of the causal directions extracted by `get_causal_direction_counts()` method. In the following sample code, `n_directions` option is limited to the causal directions of the top 8 rankings, and `min_causal_effect` option is limited to causal directions with a coefficient of 0.4 or more." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "cdc = result.get_causal_direction_counts(n_directions=8, min_causal_effect=0.4, split_by_causal_effect_sign=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can check the result by utility function." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "y0(t) <--- y2(t-1) (b<0) (100.0%)\n", "y0(t) <--- y4(t-1) (b>0) (100.0%)\n", "y1(t) <--- e4(t-1) (b>0) (100.0%)\n", "y2(t) <--- e1(t-1) (b<0) (100.0%)\n", "y3(t) <--- y0(t) (b>0) (100.0%)\n", "y3(t) <--- y1(t-1) (b>0) (100.0%)\n", "y3(t) <--- y4(t-1) (b<0) (100.0%)\n", "y4(t) <--- y0(t-1) (b<0) (100.0%)\n" ] } ], "source": [ "labels = ['y0(t)', 'y1(t)', 'y2(t)', 'y3(t)', 'y4(t)', 'y0(t-1)', 'y1(t-1)', 'y2(t-1)', 'y3(t-1)', 'y4(t-1)', 'e0(t-1)', 'e1(t-1)', 'e2(t-1)', 'e3(t-1)', 'e4(t-1)']\n", "print_causal_directions(cdc, 100, labels=labels)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Directed Acyclic Graphs\n", "Also, using the `get_directed_acyclic_graph_counts()` method, we can get the ranking of the DAGs extracted. In the following sample code, `n_dags` option is limited to the dags of the top 3 rankings, and `min_causal_effect` option is limited to causal directions with a coefficient of 0.3 or more." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "dagc = result.get_directed_acyclic_graph_counts(n_dags=3, min_causal_effect=0.3, split_by_causal_effect_sign=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can check the result by utility function." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "DAG[0]: 40.0%\n", "\ty0(t) <--- y2(t-1) (b<0)\n", "\ty0(t) <--- y4(t-1) (b>0)\n", "\ty1(t) <--- y0(t) (b<0)\n", "\ty1(t) <--- y0(t-1) (b<0)\n", "\ty1(t) <--- y4(t-1) (b>0)\n", "\ty1(t) <--- e0(t-1) (b>0)\n", "\ty1(t) <--- e1(t-1) (b>0)\n", "\ty1(t) <--- e4(t-1) (b>0)\n", "\ty2(t) <--- e1(t-1) (b<0)\n", "\ty2(t) <--- e3(t-1) (b<0)\n", "\ty3(t) <--- y0(t) (b>0)\n", "\ty3(t) <--- y1(t-1) (b>0)\n", "\ty3(t) <--- y4(t-1) (b<0)\n", "\ty3(t) <--- e2(t-1) (b>0)\n", "\ty4(t) <--- y0(t-1) (b<0)\n", "\ty4(t) <--- e1(t-1) (b>0)\n", "\ty4(t) <--- e2(t-1) (b>0)\n", "DAG[1]: 19.0%\n", "\ty0(t) <--- y2(t-1) (b<0)\n", "\ty0(t) <--- y4(t-1) (b>0)\n", "\ty1(t) <--- y0(t) (b<0)\n", "\ty1(t) <--- y0(t-1) (b<0)\n", "\ty1(t) <--- y4(t-1) (b>0)\n", "\ty1(t) <--- e0(t-1) (b>0)\n", "\ty1(t) <--- e4(t-1) (b>0)\n", "\ty2(t) <--- e1(t-1) (b<0)\n", "\ty2(t) <--- e3(t-1) (b<0)\n", "\ty3(t) <--- y0(t) (b>0)\n", "\ty3(t) <--- y1(t-1) (b>0)\n", "\ty3(t) <--- y4(t-1) (b<0)\n", "\ty3(t) <--- e2(t-1) (b>0)\n", "\ty4(t) <--- y0(t-1) (b<0)\n", "\ty4(t) <--- e1(t-1) (b>0)\n", "\ty4(t) <--- e2(t-1) (b>0)\n", "DAG[2]: 7.0%\n", "\ty0(t) <--- y2(t) (b<0)\n", "\ty0(t) <--- y2(t-1) (b<0)\n", "\ty0(t) <--- y4(t-1) (b>0)\n", "\ty1(t) <--- y0(t) (b<0)\n", "\ty1(t) <--- y0(t-1) (b<0)\n", "\ty1(t) <--- y4(t-1) (b>0)\n", "\ty1(t) <--- e0(t-1) (b>0)\n", "\ty1(t) <--- e1(t-1) (b>0)\n", "\ty1(t) <--- e4(t-1) (b>0)\n", "\ty2(t) <--- e1(t-1) (b<0)\n", "\ty2(t) <--- e3(t-1) (b<0)\n", "\ty3(t) <--- y0(t) (b>0)\n", "\ty3(t) <--- y1(t-1) (b>0)\n", "\ty3(t) <--- y4(t-1) (b<0)\n", "\ty3(t) <--- e2(t-1) (b>0)\n", "\ty4(t) <--- y0(t-1) (b<0)\n", "\ty4(t) <--- e1(t-1) (b>0)\n", "\ty4(t) <--- e2(t-1) (b>0)\n" ] } ], "source": [ "print_dagc(dagc, 100, labels=labels)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Probability\n", "Using the `get_probabilities()` method, we can get the probability of bootstrapping." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Probability of psi0:\n", " [[0. 0. 1. 0. 0. ]\n", " [1. 0. 0.95 0. 0. ]\n", " [0. 0. 0. 0. 0. ]\n", " [1. 0.96 0.24 0. 0. ]\n", " [0.16 0.03 0.1 0.04 0. ]]\n", "Probability of psi1:\n", " [[1. 1. 1. 0. 1. ]\n", " [1. 0. 0. 1. 1. ]\n", " [1. 1. 0. 1. 1. ]\n", " [1. 1. 1. 1. 1. ]\n", " [1. 0.19 1. 0.96 1. ]]\n", "Probability of omega1:\n", " [[1. 0.77 1. 0.96 0. ]\n", " [1. 1. 1. 0. 1. ]\n", " [1. 1. 0. 1. 1. ]\n", " [1. 1. 1. 1. 0.04]\n", " [1. 1. 1. 1. 1. ]]\n" ] } ], "source": [ "prob = result.get_probabilities(min_causal_effect=0.1)\n", "print('Probability of psi0:\\n', prob[0])\n", "print('Probability of psi1:\\n', prob[1])\n", "print('Probability of omega1:\\n', prob[2])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Total Causal Effects\n", "Using the `get_total causal_effects()` method, we can get the list of total causal effect. The total causal effects we can get are dictionary type variable.\n", "We can display the list nicely by assigning it to pandas.DataFrame. Also, we have replaced the variable index with a label below." ] }, { "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>from</th>\n", " <th>to</th>\n", " <th>effect</th>\n", " <th>probability</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>y4(t-1)</td>\n", " <td>y2(t)</td>\n", " <td>0.377029</td>\n", " <td>1.00</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>y2(t)</td>\n", " <td>y3(t)</td>\n", " <td>-0.238642</td>\n", " <td>1.00</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>y1(t)</td>\n", " <td>y3(t)</td>\n", " <td>-0.213468</td>\n", " <td>1.00</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>y0(t)</td>\n", " <td>y3(t)</td>\n", " <td>0.563522</td>\n", " <td>1.00</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>y3(t-1)</td>\n", " <td>y4(t)</td>\n", " <td>0.343541</td>\n", " <td>1.00</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>y0(t-1)</td>\n", " <td>y2(t)</td>\n", " <td>-0.254723</td>\n", " <td>1.00</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>y4(t-1)</td>\n", " <td>y1(t)</td>\n", " <td>0.438051</td>\n", " <td>1.00</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>y3(t-1)</td>\n", " <td>y1(t)</td>\n", " <td>0.266735</td>\n", " <td>1.00</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>y1(t-1)</td>\n", " <td>y1(t)</td>\n", " <td>0.312631</td>\n", " <td>1.00</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>y0(t-1)</td>\n", " <td>y4(t)</td>\n", " <td>-0.531720</td>\n", " <td>1.00</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>y1(t-1)</td>\n", " <td>y4(t)</td>\n", " <td>0.226082</td>\n", " <td>1.00</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>y2(t)</td>\n", " <td>y1(t)</td>\n", " <td>0.231064</td>\n", " <td>1.00</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>y0(t)</td>\n", " <td>y1(t)</td>\n", " <td>-0.310366</td>\n", " <td>1.00</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>y4(t-1)</td>\n", " <td>y0(t)</td>\n", " <td>0.210816</td>\n", " <td>1.00</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>y3(t-1)</td>\n", " <td>y0(t)</td>\n", " <td>0.375119</td>\n", " <td>1.00</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>y2(t-1)</td>\n", " <td>y0(t)</td>\n", " <td>-0.377158</td>\n", " <td>1.00</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>y2(t-1)</td>\n", " <td>y4(t)</td>\n", " <td>-0.368007</td>\n", " <td>1.00</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>y0(t-1)</td>\n", " <td>y1(t)</td>\n", " <td>-0.419723</td>\n", " <td>1.00</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>y1(t-1)</td>\n", " <td>y2(t)</td>\n", " <td>0.329416</td>\n", " <td>0.99</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>y0(t-1)</td>\n", " <td>y0(t)</td>\n", " <td>-0.188156</td>\n", " <td>0.99</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>y1(t-1)</td>\n", " <td>y3(t)</td>\n", " <td>0.120133</td>\n", " <td>0.98</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>y0(t-1)</td>\n", " <td>y3(t)</td>\n", " <td>0.217037</td>\n", " <td>0.98</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>y4(t-1)</td>\n", " <td>y3(t)</td>\n", " <td>-0.186410</td>\n", " <td>0.97</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>y3(t-1)</td>\n", " <td>y2(t)</td>\n", " <td>0.184045</td>\n", " <td>0.97</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>y4(t-1)</td>\n", " <td>y4(t)</td>\n", " <td>0.287224</td>\n", " <td>0.92</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td>y2(t)</td>\n", " <td>y0(t)</td>\n", " <td>-0.147135</td>\n", " <td>0.91</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td>y3(t)</td>\n", " <td>y4(t)</td>\n", " <td>0.056672</td>\n", " <td>0.73</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td>y3(t-1)</td>\n", " <td>y3(t)</td>\n", " <td>-0.139039</td>\n", " <td>0.63</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td>y0(t)</td>\n", " <td>y4(t)</td>\n", " <td>0.086335</td>\n", " <td>0.46</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td>y2(t-1)</td>\n", " <td>y1(t)</td>\n", " <td>0.081208</td>\n", " <td>0.41</td>\n", " </tr>\n", " <tr>\n", " <th>30</th>\n", " <td>y1(t-1)</td>\n", " <td>y0(t)</td>\n", " <td>-0.040277</td>\n", " <td>0.26</td>\n", " </tr>\n", " <tr>\n", " <th>31</th>\n", " <td>y2(t)</td>\n", " <td>y4(t)</td>\n", " <td>-0.088182</td>\n", " <td>0.20</td>\n", " </tr>\n", " <tr>\n", " <th>32</th>\n", " <td>y2(t-1)</td>\n", " <td>y2(t)</td>\n", " <td>-0.052064</td>\n", " <td>0.19</td>\n", " </tr>\n", " <tr>\n", " <th>33</th>\n", " <td>y1(t)</td>\n", " <td>y4(t)</td>\n", " <td>-0.056033</td>\n", " <td>0.05</td>\n", " </tr>\n", " <tr>\n", " <th>34</th>\n", " <td>y4(t)</td>\n", " <td>y3(t)</td>\n", " <td>0.057538</td>\n", " <td>0.04</td>\n", " </tr>\n", " <tr>\n", " <th>35</th>\n", " <td>y2(t-1)</td>\n", " <td>y3(t)</td>\n", " <td>-0.261473</td>\n", " <td>0.02</td>\n", " </tr>\n", " <tr>\n", " <th>36</th>\n", " <td>y4(t)</td>\n", " <td>y1(t)</td>\n", " <td>0.013746</td>\n", " <td>0.01</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " from to effect probability\n", "0 y4(t-1) y2(t) 0.377029 1.00\n", "1 y2(t) y3(t) -0.238642 1.00\n", "2 y1(t) y3(t) -0.213468 1.00\n", "3 y0(t) y3(t) 0.563522 1.00\n", "4 y3(t-1) y4(t) 0.343541 1.00\n", "5 y0(t-1) y2(t) -0.254723 1.00\n", "6 y4(t-1) y1(t) 0.438051 1.00\n", "7 y3(t-1) y1(t) 0.266735 1.00\n", "8 y1(t-1) y1(t) 0.312631 1.00\n", "9 y0(t-1) y4(t) -0.531720 1.00\n", "10 y1(t-1) y4(t) 0.226082 1.00\n", "11 y2(t) y1(t) 0.231064 1.00\n", "12 y0(t) y1(t) -0.310366 1.00\n", "13 y4(t-1) y0(t) 0.210816 1.00\n", "14 y3(t-1) y0(t) 0.375119 1.00\n", "15 y2(t-1) y0(t) -0.377158 1.00\n", "16 y2(t-1) y4(t) -0.368007 1.00\n", "17 y0(t-1) y1(t) -0.419723 1.00\n", "18 y1(t-1) y2(t) 0.329416 0.99\n", "19 y0(t-1) y0(t) -0.188156 0.99\n", "20 y1(t-1) y3(t) 0.120133 0.98\n", "21 y0(t-1) y3(t) 0.217037 0.98\n", "22 y4(t-1) y3(t) -0.186410 0.97\n", "23 y3(t-1) y2(t) 0.184045 0.97\n", "24 y4(t-1) y4(t) 0.287224 0.92\n", "25 y2(t) y0(t) -0.147135 0.91\n", "26 y3(t) y4(t) 0.056672 0.73\n", "27 y3(t-1) y3(t) -0.139039 0.63\n", "28 y0(t) y4(t) 0.086335 0.46\n", "29 y2(t-1) y1(t) 0.081208 0.41\n", "30 y1(t-1) y0(t) -0.040277 0.26\n", "31 y2(t) y4(t) -0.088182 0.20\n", "32 y2(t-1) y2(t) -0.052064 0.19\n", "33 y1(t) y4(t) -0.056033 0.05\n", "34 y4(t) y3(t) 0.057538 0.04\n", "35 y2(t-1) y3(t) -0.261473 0.02\n", "36 y4(t) y1(t) 0.013746 0.01" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "causal_effects = result.get_total_causal_effects(min_causal_effect=0.01)\n", "df = pd.DataFrame(causal_effects)\n", "\n", "df['from'] = df['from'].apply(lambda x : labels[x])\n", "df['to'] = df['to'].apply(lambda x : labels[x])\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can easily perform sorting operations with pandas.DataFrame." ] }, { "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>from</th>\n", " <th>to</th>\n", " <th>effect</th>\n", " <th>probability</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>3</th>\n", " <td>y0(t)</td>\n", " <td>y3(t)</td>\n", " <td>0.563522</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>y4(t-1)</td>\n", " <td>y1(t)</td>\n", " <td>0.438051</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>0</th>\n", " <td>y4(t-1)</td>\n", " <td>y2(t)</td>\n", " <td>0.377029</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>y3(t-1)</td>\n", " <td>y0(t)</td>\n", " <td>0.375119</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>y3(t-1)</td>\n", " <td>y4(t)</td>\n", " <td>0.343541</td>\n", " <td>1.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " from to effect probability\n", "3 y0(t) y3(t) 0.563522 1.0\n", "6 y4(t-1) y1(t) 0.438051 1.0\n", "0 y4(t-1) y2(t) 0.377029 1.0\n", "14 y3(t-1) y0(t) 0.375119 1.0\n", "4 y3(t-1) y4(t) 0.343541 1.0" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.sort_values('effect', ascending=False).head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And with pandas.DataFrame, we can easily filter by keywords. The following code extracts the causal direction towards y2(t)." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>from</th>\n", " <th>to</th>\n", " <th>effect</th>\n", " <th>probability</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>y4(t-1)</td>\n", " <td>y2(t)</td>\n", " <td>0.377029</td>\n", " <td>1.00</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>y0(t-1)</td>\n", " <td>y2(t)</td>\n", " <td>-0.254723</td>\n", " <td>1.00</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>y1(t-1)</td>\n", " <td>y2(t)</td>\n", " <td>0.329416</td>\n", " <td>0.99</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>y3(t-1)</td>\n", " <td>y2(t)</td>\n", " <td>0.184045</td>\n", " <td>0.97</td>\n", " </tr>\n", " <tr>\n", " <th>32</th>\n", " <td>y2(t-1)</td>\n", " <td>y2(t)</td>\n", " <td>-0.052064</td>\n", " <td>0.19</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " from to effect probability\n", "0 y4(t-1) y2(t) 0.377029 1.00\n", "5 y0(t-1) y2(t) -0.254723 1.00\n", "18 y1(t-1) y2(t) 0.329416 0.99\n", "23 y3(t-1) y2(t) 0.184045 0.97\n", "32 y2(t-1) y2(t) -0.052064 0.19" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[df['to']=='y2(t)'].head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Because it holds the raw data of the causal effect (the original data for calculating the median), it is possible to draw a histogram of the values of the causal effect, as shown below." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(array([ 1., 5., 5., 7., 24., 13., 17., 11., 13., 4.]),\n", " array([-0.359, -0.34 , -0.322, -0.303, -0.285, -0.266, -0.248, -0.229,\n", " -0.21 , -0.192, -0.173]),\n", " <a list of 10 Patch objects>)" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXgAAAEBCAYAAABysL6vAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAEMhJREFUeJzt3X2MZXV9x/H3zO46s7qzKZ0OAqJig/sFFYHo4gOPDUgqQldigaitGuUptS2YRWtTEDW1JDVLaU03tKYgkSBEloesSKtlqeHBUm19aFG+pQWklG27GYjsEnbZ3Zn+cc9kL+vcnft87/z2/Uom3HvmnPP7zo/ffObs75x7zsjs7CySpPKMDroASVJvGPCSVCgDXpIKZcBLUqEMeEkqlAEvSYUy4CWpUAa8JBXKgJekQhnwklSopc2sFBFXAudWb+/KzE9FxPXACcDz1fLPZebtPahRktSGBQM+Ik4DTgeOBWaBv42Is4G3Aidl5uYW2xwDVgObgd0tbitJ+6slwMHA94AdzWzQzBH8ZmBtZr4IEBE/BV5TfV0XEa8Cbqd2BD/TxP5WA/c1U5wk6RecCNzfzIoLBnxmPjz3OiJeT22q5kTgFOB3gJ8D3wA+Bny5iTY3Azz77PPMzPT3TpaTkyuYnt7W1zZbZY3dYY3dYY3d0Y0aR0dHOOCAV0CVoc0YafZ2wRHxRuAu4MrMvGGv750NfCgzz25iV4cBjzdboCTpJV4HPNHMis2eZD0e2ABcmpk3R8RRwKrM3FCtMgLsbKXC6eltfT+Cn5qaYMuWrX1ts1XW2B3W2B3W2B3dqHF0dITJyRUtbdPMSdZXA3cA52XmpmrxCHBNRGwCtgEXAjc02IUkaQCaOYK/DBgHro6IuWXXAlcBDwDLgA2Z+bWeVChJakszJ1kvAS5p8O313S1HktQtfpJVkgplwEtSoQx4SSpUU5dJSv02sXI542OdDc+pqYmWt9m+Yxdbn3uho3alYWHAayiNjy3lrLV39r3djevWMNxXVEvNc4pGkgplwEtSoQx4SSqUAS9JhTLgJalQBrwkFcqAl6RCGfCSVCgDXpIKZcBLUqEMeEkqlAEvSYUy4CWpUAa8JBXKgJekQhnwklQoA16SCmXAS1KhDHhJKpQBL0mFMuAlqVAGvCQVyoCXpEIZ8JJUKANekgplwEtSoQx4SSqUAS9JhTLgJalQS5tZKSKuBM6t3t6VmZ+KiNOAq4HlwC2ZeXmPapQktWHBI/gqyE8HjgWOAd4SEe8HrgPWAEcCqyPi3b0sVJLUmmamaDYDazPzxczcCfwUWAU8mpmPZ+Yu4EbgnB7WKUlq0YJTNJn58NzriHg9tamaL1EL/jmbgUO7Xp0kqW1NzcEDRMQbgbuATwK7qB3FzxkBZlppeHJyRSurd83U1MRA2m2FNQ5WP3+2xdCP1tgdg6ix2ZOsxwMbgEsz8+aIOBk4uG6Vg4CnW2l4enobMzOzrWzSsampCbZs2drXNltljXvaGJR+9b//r7tjf6lxdHSk5QPjBQM+Il4N3AGcl5mbqsUP1b4VhwOPAx+gdtJVkjQkmjmCvwwYB66OiLll1wIfoXZUPw58E7i1B/VJktrUzEnWS4BLGnz76O6WI0nqFj/JKkmFMuAlqVAGvCQVyoCXpEIZ8JJUKANekgplwEtSoQx4SSqUAS9JhTLgJalQBrwkFcqAl6RCGfCSVCgDXpIKZcBLUqEMeEkqlAEvSYUy4CWpUAa8JBXKgJekQhnwklSopYMuQFLNxMrljI/1/1dy+45dbH3uhb63q94z4KUhMT62lLPW3tn3djeuW8PWvreqfnCKRpIKZcBLUqEMeEkqlAEvSYUy4CWpUAa8JBXKgJekQhnwklQoA16SCmXAS1KhDHhJKlTT96KJiJXAg8CZmflERFwPnAA8X63yucy8vQc1SpLa0FTAR8TbgC8Dq+oWvxU4KTM396IwSVJnmp2iuQD4OPA0QES8HHgNcF1E/DgiPhcRTvdI0hBpKpQz8/zMvK9u0UHAJuCjwNuBE4GPdb88SVK72roffGY+Bpw99z4ivgR8iNo0TlMmJ1e003THpqYmBtJuK6xxsPr5sw1LP+6rjmGpcV+scX5tBXxEHAWsyswN1aIRYGcr+5ie3sbMzGw7zbdtamqCLVuG+9EG1rinjUHpV//v3Y/D+DM7HrujGzWOjo60fGDc7hOdRoBrImITsA24ELihzX1JknqgrROjmflj4CrgAeAnwA8z82vdLEyS1JmWjuAz87C61+uB9d0uSJLUHV7aKEmFMuAlqVAGvCQVyoCXpEIZ8JJUKANekgplwEtSoQx4SSqUAS9JhTLgJalQBrwkFcqAl6RCtXu7YKlIL+7cvV8+8GNQJlYuZ3ys8xhqtR+379jF1ude6LjdYWfAS3VetmwJZ629cyBtb1y3ZiDtDtL42NKB9PfGdWsY7keEdIdTNJJUKANekgplwEtSoQx4SSqUAS9JhTLgJalQBrwkFcqAl6RCGfCSVCgDXpIKZcBLUqEMeEkqlAEvSYUy4CWpUAa8JBXK+8FL+7mFHnKyvz+UZDEz4KX9nA85KZdTNJJUKANekgplwEtSoZqag4+IlcCDwJmZ+UREnAZcDSwHbsnMy3tYoySpDQsewUfE24D7gVXV++XAdcAa4EhgdUS8u5dFSpJa18wUzQXAx4Gnq/fHAY9m5uOZuQu4ETinR/VJktq04BRNZp4PEBFziw4BNtetshk4tOuVSZI60s518KPAbN37EWCm1Z1MTq5oo+nOLYYPbVij1Hv9HsOD+J1pJ+CfAg6ue38Qe6ZvmjY9vY2ZmdmFV+yiqakJtmzZ2tc2W2WNe9qQeqmfv2fd+J0ZHR1p+cC4nYB/CIiIOBx4HPgAtZOukqQh0vJ18Jm5HfgIsAH4CfAIcGt3y5IkdarpI/jMPKzu9T3A0b0oSJLUHX6SVZIKZcBLUqEMeEkqlAEvSYUy4CWpUAa8JBXKgJekQhnwklQoA16SCmXAS1KhDHhJKpQBL0mFMuAlqVAGvCQVyoCXpEIZ8JJUKANekgplwEtSoQx4SSqUAS9JhTLgJalQSwddgIbXxMrljI/NP0Smpib6XI2kVhnwamh8bClnrb1zIG1vXLdmIO1KJXGKRpIKZcBLUqEMeEkqlAEvSYUy4CWpUAa8JBXKgJekQhnwklQoA16SCmXAS1KhDHhJKlRH96KJiHuBA4Gd1aKLMvOhjquSJHWs7YCPiBFgFfDazNzVvZIkSd3QyRRNVP/9VkT8KCJ+txsFSZK6o5OAPwC4BzgbOBW4OCLe1ZWqJEkda3uKJjO/C3x37n1E/A1wBvDtZrafnFzRbtMd6eRBFS/u3M3Lli3pYjXz27vGHTt3M9aHdqX9Sb8fWjOIh+R0Mgd/AjCWmfdUi0bYc7J1QdPT25iZmW23+bZMTU2wZcvWjrYfxAMwNq5bM7B2pVJ1kgWt6jR7AEZHR1o+MO7kKppfAj4fEe8ElgEfBi7uYH+SpC5qew4+M78B3AX8APhn4Lpq2kaSNAQ6ug4+M68AruhSLZKkLvKTrJJUKANekgplwEtSoQx4SSpURydZJWkxenHn7oF90Gn7jl1sfe6FvrRpwEva77xs2ZKBfHgQah8g7NdHrJyikaRCGfCSVCgDXpIKZcBLUqEMeEkqlAEvSYUy4CWpUAa8JBXKgJekQhnwklQoA16SCmXAS1KhDHhJKpQBL0mFMuAlqVAGvCQVyoCXpEIZ8JJUKANekgplwEtSoQx4SSqUAS9JhTLgJalQBrwkFWrpoAto1cTK5YyPtV/21NREF6uRpOG16AJ+fGwpZ629cyBtb1y3ZiDtSlI7nKKRpEIZ8JJUKANekgrV0Rx8RHwAuBxYBlyTmX/ZlaokSR1r+wg+Il4FfAE4ATgGuDAi3tCtwiRJnenkCP40YFNmPgMQEbcCvwl8foHtlgCMjo603fCBByxve9tODart/a3dQbbtz7x/tD3In7md/KvbZkmz24zMzs623BBARPwh8IrMvLx6fz5wXGZeuMCmJwD3tdWoJOlE4P5mVuzkCH4UqP/rMALMNLHd96gVuBnY3UH7krQ/WQIcTC1Dm9JJwD9FLajnHAQ83cR2O2jyr48k6SX+s5WVOwn4vwc+GxFTwPPA+4CFpmckSX3S9lU0mfnfwB8B9wI/BG7KzH/qVmGSpM60fZJVkjTc/CSrJBXKgJekQhnwklQoA16SCrXoHvixt4h4DXAjcCCQwAczc9te6xwMfBWYArYDF2XmDyNiGTANPFa3+luofWDri8CZ1esLMvOBAdW4ArgOOILah8m+kJk3V9s8BjxXt5uzMvO/BlDjCA36KyLWAhdQO5j4dGbe1k59LdZ4PbXPZcwAl2Xmpoi4Fnh73apHAedl5q0D6sf5ahy28ThfjcM2HuercWjGY92676raO7V635fxWMIR/HpgfWYeAXwfuGKedf4EuDUzjwaurLYBeDPw3cw8pu5rN7Vr+o8E3gC8F/hKRHTyx7CTGj8NPJmZbwZOBa6OiFdGxCTw4l61tzUIulDjvP0VEauB36J2M7oTgC9GxC/3uMYvAhsz8xjg/cBNEbEkMy+e6yfgL4BvAxsG1I/z1shwjcdGNQ7TeGxU49CMx4gYrf6o3EzdPWT6NR4XdcBXRzwnAbdWi74CnDPPqh8D/rp6/Trg2er1amAqIr4fEf8YESdXy98D3JyZM5n578CTwDsHVON3qA0AMvP/gGeoHbGsBkYi4oGI+JeImG+f/aqxUX+dAdyWmdur2v+B2lFVL2u8Hbipev0fwDiwom4/k9RuiHdRZs4ymH5sVOMwjcdGNQ7TeGxU4zCNxyOrrwsa7Kdn4xEW/xTNrwDPZeau6v1m4NC9V8rMGYCIeAQ4DJh7uOoscAdwFfAm4O6IeBNwSLWvOfPutx81Zua359aJiHOBMeBhagH7d8AfAK8E7ouIf8vMn/a7Rhr31yG89L4Z/ejHDXVvLwN+kJk/r1v2CWq//D+r3o/T/36ct8aIGKbx2Kgfh2k8NurHYRqPDwPnR8QpDfbTy/G4eAK++kv2Z3stfpSX3vAM9nHDs8w8IiKOAb4VEUdk5l/VffsHEfEQcDxt3kitRzXO3Y75HODPgV+vBtUd1RfAExFxG3A6sM+B0IsaadxfA+vHiLgUuAg4uW7ZKPBR4Li6n2Vg/bh3jcM4Hufrx7p9D8V4nKfGoRuPDfbbtfHYyKIJ+Mz8OvD1+mVzJ6WqOdbd1O609gs3PIuI9wDfycxt1UnBnwG/Wi1/MDPnbuAzAuykdiO1g+t20dSN1HpRI/BMRPwe8Eng9Mz812r9M4H/yczv71X7IGps1F/zLc9e1lit+6fU/pl+UmY+VfetdwCP1i8bRD82qjEifpshGY+NaqyWD8V43EeNQzUe96Fr47GRRT0Hn5k7qd1b/rxq0YeAu+dZ9cNUN0KL2lOnDgIeAY4G1lbLAzi22t83gQ9GxJKIOBxYRQu36OxmjRHxXmr/jDt+7pepchhwZXUS55XAbwB3DaJGGvfX3cD7IuLlUbsp3anAPb2ssTqa+zVq/fXUXt9+B7/4LILD6HM/7qPGoRmPjWocpvG4j34cmvG4gJ6ORyjgXjQR8VrgBmqXKj0JvD8zn42Ii4FDMvMz1Zzc3OVU24G1mXl/RKxkzyVfs8DvZ+a9secyq3dXzXwiM781oBp/VG33v3W7PJ/aDd7WU7saYBS4MjNvGVCNDfsralcQfJTavxb/ODO/2qsaqV3Z8wy1S8yerdv0jMx8OiLWAz/OzGvr9rmUPvbjvmoEtjEE43GBGu9mCMbjAjVuZgjGY2Z+pm7dU4DPZuYpdct6Ph4XfcBLkua3qKdoJEmNGfCSVCgDXpIKZcBLUqEMeEkqlAEvSYUy4CWpUAa8JBXq/wFFIXQfzF+JagAAAABJRU5ErkJggg==\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "sns.set()\n", "%matplotlib inline\n", "\n", "from_index = 5 # index of y0(t-1). (index:0)+(n_features:5)(lag:1) = 5\n", "to_index = 2 # index of y2(t). (index:2)+(n_features:5)(lag:0) = 2\n", "plt.hist(result.total_effects_[:, to_index, from_index])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 4 }	mit
mdiaz236/DeepLearningFoundations	tensorboard/Anna_KaRNNa.ipynb	1	37430	{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Anna KaRNNa\n", "\n", "In this notebook, I'll build a character-wise RNN trained on Anna Karenina, one of my all-time favorite books. It'll be able to generate new text based on the text from the book.\n", "\n", "This network is based off of Andrej Karpathy's [post on RNNs](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) and [implementation in Torch](https://github.com/karpathy/char-rnn). Also, some information [here at r2rt](http://r2rt.com/recurrent-neural-networks-in-tensorflow-ii.html) and from [Sherjil Ozair](https://github.com/sherjilozair/char-rnn-tensorflow) on GitHub. Below is the general architecture of the character-wise RNN.\n", "\n", "<img src=\"assets/charseq.jpeg\" width=\"500\">" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "import time\n", "from collections import namedtuple\n", "\n", "import numpy as np\n", "import tensorflow as tf" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "First we'll load the text file and convert it into integers for our network to use." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "with open('anna.txt', 'r') as f:\n", " text=f.read()\n", "vocab = set(text)\n", "vocab_to_int = {c: i for i, c in enumerate(vocab)}\n", "int_to_vocab = dict(enumerate(vocab))\n", "chars = np.array([vocab_to_int[c] for c in text], dtype=np.int32)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "'Chapter 1\\n\\n\\nHappy families are all alike; every unhappy family is unhappy in its own\\nway.\\n\\nEverythin'" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "text[:100]" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "array([25, 57, 17, 2, 49, 67, 56, 47, 58, 32, 32, 32, 22, 17, 2, 2, 76,\n", " 47, 54, 17, 14, 30, 52, 30, 67, 11, 47, 17, 56, 67, 47, 17, 52, 52,\n", " 47, 17, 52, 30, 65, 67, 55, 47, 67, 33, 67, 56, 76, 47, 53, 41, 57,\n", " 17, 2, 2, 76, 47, 54, 17, 14, 30, 52, 76, 47, 30, 11, 47, 53, 41,\n", " 57, 17, 2, 2, 76, 47, 30, 41, 47, 30, 49, 11, 47, 19, 45, 41, 32,\n", " 45, 17, 76, 66, 32, 32, 37, 33, 67, 56, 76, 49, 57, 30, 41], dtype=int32)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "chars[:100]" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Now I need to split up the data into batches, and into training and validation sets. I should be making a test set here, but I'm not going to worry about that. My test will be if the network can generate new text.\n", "\n", "Here I'll make both input and target arrays. The targets are the same as the inputs, except shifted one character over. I'll also drop the last bit of data so that I'll only have completely full batches.\n", "\n", "The idea here is to make a 2D matrix where the number of rows is equal to the number of batches. Each row will be one long concatenated string from the character data. We'll split this data into a training set and validation set using the `split_frac` keyword. This will keep 90% of the batches in the training set, the other 10% in the validation set." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "def split_data(chars, batch_size, num_steps, split_frac=0.9):\n", " \"\"\" \n", " Split character data into training and validation sets, inputs and targets for each set.\n", " \n", " Arguments\n", " ---------\n", " chars: character array\n", " batch_size: Size of examples in each of batch\n", " num_steps: Number of sequence steps to keep in the input and pass to the network\n", " split_frac: Fraction of batches to keep in the training set\n", " \n", " \n", " Returns train_x, train_y, val_x, val_y\n", " \"\"\"\n", " \n", " \n", " slice_size = batch_size * num_steps\n", " n_batches = int(len(chars) / slice_size)\n", " \n", " # Drop the last few characters to make only full batches\n", " x = chars[: n_batchesslice_size]\n", " y = chars[1: n_batchesslice_size + 1]\n", " \n", " # Split the data into batch_size slices, then stack them into a 2D matrix \n", " x = np.stack(np.split(x, batch_size))\n", " y = np.stack(np.split(y, batch_size))\n", " \n", " # Now x and y are arrays with dimensions batch_size x n_batchesnum_steps\n", " \n", " # Split into training and validation sets, keep the virst split_frac batches for training\n", " split_idx = int(n_batchessplit_frac)\n", " train_x, train_y= x[:, :split_idxnum_steps], y[:, :split_idxnum_steps]\n", " val_x, val_y = x[:, split_idxnum_steps:], y[:, split_idxnum_steps:]\n", " \n", " return train_x, train_y, val_x, val_y" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "train_x, train_y, val_x, val_y = split_data(chars, 10, 200)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "(10, 178400)" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_x.shape" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "array([[25, 57, 17, 2, 49, 67, 56, 47, 58, 32],\n", " [48, 41, 72, 47, 57, 67, 47, 14, 19, 33],\n", " [47, 82, 17, 49, 82, 57, 30, 41, 18, 47],\n", " [19, 49, 57, 67, 56, 47, 45, 19, 53, 52],\n", " [47, 49, 57, 67, 47, 52, 17, 41, 72, 5],\n", " [47, 15, 57, 56, 19, 53, 18, 57, 47, 52],\n", " [49, 47, 49, 19, 32, 72, 19, 66, 32, 32],\n", " [19, 47, 57, 67, 56, 11, 67, 52, 54, 20],\n", " [57, 17, 49, 47, 30, 11, 47, 49, 57, 67],\n", " [67, 56, 11, 67, 52, 54, 47, 17, 41, 72]], dtype=int32)" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_x[:,:10]" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "I'll write another function to grab batches out of the arrays made by split data. Here each batch will be a sliding window on these arrays with size `batch_size X num_steps`. For example, if we want our network to train on a sequence of 100 characters, `num_steps = 100`. For the next batch, we'll shift this window the next sequence of `num_steps` characters. In this way we can feed batches to the network and the cell states will continue through on each batch." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "def get_batch(arrs, num_steps):\n", " batch_size, slice_size = arrs[0].shape\n", " \n", " n_batches = int(slice_size/num_steps)\n", " for b in range(n_batches):\n", " yield [x[:, bnum_steps: (b+1)num_steps] for x in arrs]" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "def build_rnn(num_classes, batch_size=50, num_steps=50, lstm_size=128, num_layers=2,\n", " learning_rate=0.001, grad_clip=5, sampling=False):\n", " \n", " if sampling == True:\n", " batch_size, num_steps = 1, 1\n", "\n", " tf.reset_default_graph()\n", " \n", " # Declare placeholders we'll feed into the graph\n", " \n", " inputs = tf.placeholder(tf.int32, [batch_size, num_steps], name='inputs')\n", " x_one_hot = tf.one_hot(inputs, num_classes, name='x_one_hot')\n", "\n", "\n", " targets = tf.placeholder(tf.int32, [batch_size, num_steps], name='targets')\n", " y_one_hot = tf.one_hot(targets, num_classes, name='y_one_hot')\n", " y_reshaped = tf.reshape(y_one_hot, [-1, num_classes])\n", " \n", " keep_prob = tf.placeholder(tf.float32, name='keep_prob')\n", " \n", " # Build the RNN layers\n", " \n", " lstm = tf.contrib.rnn.BasicLSTMCell(lstm_size)\n", " drop = tf.contrib.rnn.DropoutWrapper(lstm, output_keep_prob=keep_prob)\n", " cell = tf.contrib.rnn.MultiRNNCell([drop] * num_layers)\n", "\n", " initial_state = cell.zero_state(batch_size, tf.float32)\n", "\n", " # Run the data through the RNN layers\n", " outputs, state = tf.nn.dynamic_rnn(cell, x_one_hot, initial_state=initial_state)\n", " final_state = state\n", " \n", " # Reshape output so it's a bunch of rows, one row for each cell output\n", " \n", " seq_output = tf.concat(outputs, axis=1,name='seq_output')\n", " output = tf.reshape(seq_output, [-1, lstm_size], name='graph_output')\n", " \n", " # Now connect the RNN putputs to a softmax layer and calculate the cost\n", " softmax_w = tf.Variable(tf.truncated_normal((lstm_size, num_classes), stddev=0.1),\n", " name='softmax_w')\n", " softmax_b = tf.Variable(tf.zeros(num_classes), name='softmax_b')\n", " logits = tf.matmul(output, softmax_w) + softmax_b\n", "\n", " preds = tf.nn.softmax(logits, name='predictions')\n", " \n", " loss = tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=y_reshaped, name='loss')\n", " cost = tf.reduce_mean(loss, name='cost')\n", "\n", " # Optimizer for training, using gradient clipping to control exploding gradients\n", " tvars = tf.trainable_variables()\n", " grads, _ = tf.clip_by_global_norm(tf.gradients(cost, tvars), grad_clip)\n", " train_op = tf.train.AdamOptimizer(learning_rate)\n", " optimizer = train_op.apply_gradients(zip(grads, tvars))\n", "\n", " # Export the nodes \n", " export_nodes = ['inputs', 'targets', 'initial_state', 'final_state',\n", " 'keep_prob', 'cost', 'preds', 'optimizer']\n", " Graph = namedtuple('Graph', export_nodes)\n", " local_dict = locals()\n", " graph = Graph([local_dict[each] for each in export_nodes])\n", " \n", " return graph" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Hyperparameters\n", "\n", "Here I'm defining the hyperparameters for the network. The two you probably haven't seen before are `lstm_size` and `num_layers`. These set the number of hidden units in the LSTM layers and the number of LSTM layers, respectively. Of course, making these bigger will improve the network's performance but you'll have to watch out for overfitting. If your validation loss is much larger than the training loss, you're probably overfitting. Decrease the size of the network or decrease the dropout keep probability." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "batch_size = 100\n", "num_steps = 100\n", "lstm_size = 512\n", "num_layers = 2\n", "learning_rate = 0.001" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Write out the graph for TensorBoard" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "model = build_rnn(len(vocab),\n", " batch_size=batch_size,\n", " num_steps=num_steps,\n", " learning_rate=learning_rate,\n", " lstm_size=lstm_size,\n", " num_layers=num_layers)\n", "\n", "with tf.Session() as sess:\n", " \n", " sess.run(tf.global_variables_initializer())\n", " file_writer = tf.summary.FileWriter('./logs/1', sess.graph)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Training\n", "\n", "Time for training which is is pretty straightforward. Here I pass in some data, and get an LSTM state back. Then I pass that state back in to the network so the next batch can continue the state from the previous batch. And every so often (set by `save_every_n`) I calculate the validation loss and save a checkpoint." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "!mkdir -p checkpoints/anna" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false, "deletable": true, "editable": true, "scrolled": true }, "outputs": [ { "ename": "ValueError", "evalue": "Expected state to be a tuple of length 2, but received: Tensor(\"initial_state:0\", shape=(2, 2, 100, 512), dtype=float32)", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-20-4190d11347ea>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0mlearning_rate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlearning_rate\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0mlstm_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlstm_size\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m num_layers=num_layers)\n\u001b[0m\u001b[1;32m 11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0msaver\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSaver\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmax_to_keep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m<ipython-input-19-a7e675cc0f3d>\u001b[0m in \u001b[0;36mbuild_rnn\u001b[0;34m(num_classes, batch_size, num_steps, lstm_size, num_layers, learning_rate, grad_clip, sampling)\u001b[0m\n\u001b[1;32m 25\u001b[0m \u001b[0;31m# Run the data through the RNN layers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 26\u001b[0m \u001b[0mrnn_inputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqueeze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msqueeze_dims\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_one_hot\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_steps\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 27\u001b[0;31m \u001b[0moutputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontrib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatic_rnn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcell\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrnn_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minitial_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minitial_state\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 28\u001b[0m \u001b[0mfinal_state\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0midentity\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'final_state'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 29\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/home/mat/miniconda3/envs/tf-gpu/lib/python3.5/site-packages/tensorflow/contrib/rnn/python/ops/core_rnn.py\u001b[0m in \u001b[0;36mstatic_rnn\u001b[0;34m(cell, inputs, initial_state, dtype, sequence_length, scope)\u001b[0m\n\u001b[1;32m 195\u001b[0m state_size=cell.state_size)\n\u001b[1;32m 196\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 197\u001b[0;31m \u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcall_cell\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 198\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 199\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/home/mat/miniconda3/envs/tf-gpu/lib/python3.5/site-packages/tensorflow/contrib/rnn/python/ops/core_rnn.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 182\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtime\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mvarscope\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreuse_variables\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 183\u001b[0m \u001b[0;31m# pylint: disable=cell-var-from-loop\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 184\u001b[0;31m \u001b[0mcall_cell\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mcell\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 185\u001b[0m \u001b[0;31m# pylint: enable=cell-var-from-loop\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 186\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0msequence_length\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/home/mat/miniconda3/envs/tf-gpu/lib/python3.5/site-packages/tensorflow/contrib/rnn/python/ops/core_rnn_cell_impl.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs, state, scope)\u001b[0m\n\u001b[1;32m 647\u001b[0m raise ValueError(\n\u001b[1;32m 648\u001b[0m \u001b[0;34m\"Expected state to be a tuple of length %d, but received: %s\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 649\u001b[0;31m % (len(self.state_size), state))\n\u001b[0m\u001b[1;32m 650\u001b[0m \u001b[0mcur_state\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 651\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mValueError\u001b[0m: Expected state to be a tuple of length 2, but received: Tensor(\"initial_state:0\", shape=(2, 2, 100, 512), dtype=float32)" ] } ], "source": [ "epochs = 1\n", "save_every_n = 200\n", "train_x, train_y, val_x, val_y = split_data(chars, batch_size, num_steps)\n", "\n", "model = build_rnn(len(vocab), \n", " batch_size=batch_size,\n", " num_steps=num_steps,\n", " learning_rate=learning_rate,\n", " lstm_size=lstm_size,\n", " num_layers=num_layers)\n", "\n", "saver = tf.train.Saver(max_to_keep=100)\n", "\n", "with tf.Session() as sess:\n", " sess.run(tf.global_variables_initializer())\n", " \n", " # Use the line below to load a checkpoint and resume training\n", " #saver.restore(sess, 'checkpoints/anna20.ckpt')\n", " \n", " n_batches = int(train_x.shape[1]/num_steps)\n", " iterations = n_batches epochs\n", " for e in range(epochs):\n", " \n", " # Train network\n", " new_state = sess.run(model.initial_state)\n", " loss = 0\n", " for b, (x, y) in enumerate(get_batch([train_x, train_y], num_steps), 1):\n", " iteration = e*n_batches + b\n", " start = time.time()\n", " feed = {model.inputs: x,\n", " model.targets: y,\n", " model.keep_prob: 0.5,\n", " model.initial_state: new_state}\n", " batch_loss, new_state, _ = sess.run([model.cost, model.final_state, model.optimizer], \n", " feed_dict=feed)\n", " loss += batch_loss\n", " end = time.time()\n", " print('Epoch {}/{} '.format(e+1, epochs),\n", " 'Iteration {}/{}'.format(iteration, iterations),\n", " 'Training loss: {:.4f}'.format(loss/b),\n", " '{:.4f} sec/batch'.format((end-start)))\n", " \n", " \n", " if (iteration%save_every_n == 0) or (iteration == iterations):\n", " # Check performance, notice dropout has been set to 1\n", " val_loss = []\n", " new_state = sess.run(model.initial_state)\n", " for x, y in get_batch([val_x, val_y], num_steps):\n", " feed = {model.inputs: x,\n", " model.targets: y,\n", " model.keep_prob: 1.,\n", " model.initial_state: new_state}\n", " batch_loss, new_state = sess.run([model.cost, model.final_state], feed_dict=feed)\n", " val_loss.append(batch_loss)\n", "\n", " print('Validation loss:', np.mean(val_loss),\n", " 'Saving checkpoint!')\n", " saver.save(sess, \"checkpoints/anna/i{}_l{}_{:.3f}.ckpt\".format(iteration, lstm_size, np.mean(val_loss)))" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "model_checkpoint_path: \"checkpoints/anna/i3560_l512_1.122.ckpt\"\n", "all_model_checkpoint_paths: \"checkpoints/anna/i200_l512_2.432.ckpt\"\n", "all_model_checkpoint_paths: \"checkpoints/anna/i400_l512_1.980.ckpt\"\n", "all_model_checkpoint_paths: \"checkpoints/anna/i600_l512_1.750.ckpt\"\n", "all_model_checkpoint_paths: \"checkpoints/anna/i800_l512_1.595.ckpt\"\n", "all_model_checkpoint_paths: \"checkpoints/anna/i1000_l512_1.484.ckpt\"\n", "all_model_checkpoint_paths: \"checkpoints/anna/i1200_l512_1.407.ckpt\"\n", "all_model_checkpoint_paths: \"checkpoints/anna/i1400_l512_1.349.ckpt\"\n", "all_model_checkpoint_paths: \"checkpoints/anna/i1600_l512_1.292.ckpt\"\n", "all_model_checkpoint_paths: \"checkpoints/anna/i1800_l512_1.255.ckpt\"\n", "all_model_checkpoint_paths: \"checkpoints/anna/i2000_l512_1.224.ckpt\"\n", "all_model_checkpoint_paths: \"checkpoints/anna/i2200_l512_1.204.ckpt\"\n", "all_model_checkpoint_paths: \"checkpoints/anna/i2400_l512_1.187.ckpt\"\n", "all_model_checkpoint_paths: \"checkpoints/anna/i2600_l512_1.172.ckpt\"\n", "all_model_checkpoint_paths: \"checkpoints/anna/i2800_l512_1.160.ckpt\"\n", "all_model_checkpoint_paths: \"checkpoints/anna/i3000_l512_1.148.ckpt\"\n", "all_model_checkpoint_paths: \"checkpoints/anna/i3200_l512_1.137.ckpt\"\n", "all_model_checkpoint_paths: \"checkpoints/anna/i3400_l512_1.129.ckpt\"\n", "all_model_checkpoint_paths: \"checkpoints/anna/i3560_l512_1.122.ckpt\"" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tf.train.get_checkpoint_state('checkpoints/anna')" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Sampling\n", "\n", "Now that the network is trained, we'll can use it to generate new text. The idea is that we pass in a character, then the network will predict the next character. We can use the new one, to predict the next one. And we keep doing this to generate all new text. I also included some functionality to prime the network with some text by passing in a string and building up a state from that.\n", "\n", "The network gives us predictions for each character. To reduce noise and make things a little less random, I'm going to only choose a new character from the top N most likely characters.\n", "\n" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "def pick_top_n(preds, vocab_size, top_n=5):\n", " p = np.squeeze(preds)\n", " p[np.argsort(p)[:-top_n]] = 0\n", " p = p / np.sum(p)\n", " c = np.random.choice(vocab_size, 1, p=p)[0]\n", " return c" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "def sample(checkpoint, n_samples, lstm_size, vocab_size, prime=\"The \"):\n", " prime = \"Far\"\n", " samples = [c for c in prime]\n", " model = build_rnn(vocab_size, lstm_size=lstm_size, sampling=True)\n", " saver = tf.train.Saver()\n", " with tf.Session() as sess:\n", " saver.restore(sess, checkpoint)\n", " new_state = sess.run(model.initial_state)\n", " for c in prime:\n", " x = np.zeros((1, 1))\n", " x[0,0] = vocab_to_int[c]\n", " feed = {model.inputs: x,\n", " model.keep_prob: 1.,\n", " model.initial_state: new_state}\n", " preds, new_state = sess.run([model.preds, model.final_state], \n", " feed_dict=feed)\n", "\n", " c = pick_top_n(preds, len(vocab))\n", " samples.append(int_to_vocab[c])\n", "\n", " for i in range(n_samples):\n", " x[0,0] = c\n", " feed = {model.inputs: x,\n", " model.keep_prob: 1.,\n", " model.initial_state: new_state}\n", " preds, new_state = sess.run([model.preds, model.final_state], \n", " feed_dict=feed)\n", "\n", " c = pick_top_n(preds, len(vocab))\n", " samples.append(int_to_vocab[c])\n", " \n", " return ''.join(samples)" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Farlathit that if had so\n", "like it that it were. He could not trouble to his wife, and there was\n", "anything in them of the side of his weaky in the creature at his forteren\n", "to him.\n", "\n", "\"What is it? I can't bread to those,\" said Stepan Arkadyevitch. \"It's not\n", "my children, and there is an almost this arm, true it mays already,\n", "and tell you what I have say to you, and was not looking at the peasant,\n", "why is, I don't know him out, and she doesn't speak to me immediately, as\n", "you would say the countess and the more frest an angelembre, and time and\n", "things's silent, but I was not in my stand that is in my head. But if he\n", "say, and was so feeling with his soul. A child--in his soul of his\n", "soul of his soul. He should not see that any of that sense of. Here he\n", "had not been so composed and to speak for as in a whole picture, but\n", "all the setting and her excellent and society, who had been delighted\n", "and see to anywing had been being troed to thousand words on them,\n", "we liked him.\n", "\n", "That set in her money at the table, he came into the party. The capable\n", "of his she could not be as an old composure.\n", "\n", "\"That's all something there will be down becime by throe is\n", "such a silent, as in a countess, I should state it out and divorct.\n", "The discussion is not for me. I was that something was simply they are\n", "all three manshess of a sensitions of mind it all.\"\n", "\n", "\"No,\" he thought, shouted and lifting his soul. \"While it might see your\n", "honser and she, I could burst. And I had been a midelity. And I had a\n", "marnief are through the countess,\" he said, looking at him, a chosing\n", "which they had been carried out and still solied, and there was a sen that\n", "was to be completely, and that this matter of all the seconds of it, and\n", "a concipation were to her husband, who came up and conscaously, that he\n", "was not the station. All his fourse she was always at the country,,\n", "to speak oft, and though they were to hear the delightful throom and\n", "whether they came towards the morning, and his living and a coller and\n", "hold--the children. \n" ] } ], "source": [ "checkpoint = \"checkpoints/anna/i3560_l512_1.122.ckpt\"\n", "samp = sample(checkpoint, 2000, lstm_size, len(vocab), prime=\"Far\")\n", "print(samp)" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Farnt him oste wha sorind thans tout thint asd an sesand an hires on thime sind thit aled, ban thand and out hore as the ter hos ton ho te that, was tis tart al the hand sostint him sore an tit an son thes, win he se ther san ther hher tas tarereng,.\n", "\n", "Anl at an ades in ond hesiln, ad hhe torers teans, wast tar arering tho this sos alten sorer has hhas an siton ther him he had sin he ard ate te anling the sosin her ans and\n", "arins asd and ther ale te tot an tand tanginge wath and ho ald, so sot th asend sat hare sother horesinnd, he hesense wing ante her so tith tir sherinn, anded and to the toul anderin he sorit he torsith she se atere an ting ot hand and thit hhe so the te wile har\n", "ens ont in the sersise, and we he seres tar aterer, to ato tat or has he he wan ton here won and sen heren he sosering, to to theer oo adent har herere the wosh oute, was serild ward tous hed astend..\n", "\n", "I's sint on alt in har tor tit her asd hade shithans ored he talereng an soredendere tim tot hees. Tise sor and \n" ] } ], "source": [ "checkpoint = \"checkpoints/anna/i200_l512_2.432.ckpt\"\n", "samp = sample(checkpoint, 1000, lstm_size, len(vocab), prime=\"Far\")\n", "print(samp)" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fard as astice her said he celatice of to seress in the raice, and to be the some and sere allats to that said to that the sark and a cast a the wither ald the pacinesse of her had astition, he said to the sount as she west at hissele. Af the cond it he was a fact onthis astisarianing.\n", "\n", "\n", "\"Or a ton to to be that's a more at aspestale as the sont of anstiring as\n", "thours and trey.\n", "\n", "The same wo dangring the\n", "raterst, who sore and somethy had ast out an of his book. \"We had's beane were that, and a morted a thay he had to tere. Then to\n", "her homent andertersed his his ancouted to the pirsted, the soution for of the pirsice inthirgest and stenciol, with the hard and and\n", "a colrice of to be oneres,\n", "the song to this anderssad.\n", "The could ounterss the said to serom of\n", "soment a carsed of sheres of she\n", "torded\n", "har and want in their of hould, but\n", "her told in that in he tad a the same to her. Serghing an her has and with the seed, and the camt ont his about of the\n", "sail, the her then all houg ant or to hus to \n" ] } ], "source": [ "checkpoint = \"checkpoints/anna/i600_l512_1.750.ckpt\"\n", "samp = sample(checkpoint, 1000, lstm_size, len(vocab), prime=\"Far\")\n", "print(samp)" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Farrat, his felt has at it.\n", "\n", "\"When the pose ther hor exceed\n", "to his sheant was,\" weat a sime of his sounsed. The coment and the facily that which had began terede a marilicaly whice whether the pose of his hand, at she was alligated herself the same on she had to\n", "taiking to his forthing and streath how to hand\n", "began in a lang at some at it, this he cholded not set all her. \"Wo love that is setthing. Him anstering as seen that.\"\n", "\n", "\"Yes in the man that say the mare a crances is it?\" said Sergazy Ivancatching. \"You doon think were somether is ifficult of a mone of\n", "though the most at the countes that the\n", "mean on the come to say the most, to\n", "his feesing of\n", "a man she, whilo he\n", "sained and well, that he would still at to said. He wind at his for the sore in the most\n", "of hoss and almoved to see him. They have betine the sumper into at he his stire, and what he was that at the so steate of the\n", "sound, and shin should have a geest of shall feet on the conderation to she had been at that imporsing the dre\n" ] } ], "source": [ "checkpoint = \"checkpoints/anna/i1000_l512_1.484.ckpt\"\n", "samp = sample(checkpoint, 1000, lstm_size, len(vocab), prime=\"Far\")\n", "print(samp)" ] } ], "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
andypetrella/spark-notebook	notebooks/viz/Simple Plotly Chart.snb.ipynb	3	60770	{ "metadata" : { "id" : "c7f8e137-ece9-4383-b2c9-6604fe3b072c", "name" : "Simple Plotly Chart", "user_save_timestamp" : "1111-10-12T09:00:00.000Z", "auto_save_timestamp" : "1970-01-01T03:00:00.000Z", "language_info" : { "name" : "scala", "file_extension" : "scala", "codemirror_mode" : "text/x-scala" }, "trusted" : true, "sparkNotebook" : null, "customLocalRepo" : null, "customRepos" : null, "customDeps" : null, "customImports" : null, "customArgs" : null, "customSparkConf" : null, "customVars" : null }, "cells" : [ { "metadata" : { "id" : "DC16BF67890349F580549428C0848BB2" }, "cell_type" : "markdown", "source" : "# Bar Charts" }, { "metadata" : { "id" : "B569E588AE474C348E5DC84731FF8372" }, "cell_type" : "markdown", "source" : "## Basic Bar Chart" }, { "metadata" : { "trusted" : true, "input_collapsed" : false, "collapsed" : false, "id" : "D3BAD6370D1B4EB98988845E1A09F323" }, "cell_type" : "code", "source" : [ "case class Species(name: String, amount: Int)\n" ], "outputs" : [ { "name" : "stdout", "output_type" : "stream", "text" : "defined class Species\n" }, { "metadata" : { }, "data" : { "text/html" : "" }, "output_type" : "execute_result", "execution_count" : 1, "time" : "Took: 1.153s, at 2017-07-13 16:00" } ] }, { "metadata" : { "trusted" : true, "input_collapsed" : false, "collapsed" : false, "id" : "37B690E0CA6D41F380EA548312EFB490" }, "cell_type" : "code", "source" : [ "val bar = CustomPlotlyChart(Seq(Species(\"giraffes\", 20), Species(\"orangutans\", 14), Species(\"monkeys\", 23)), \n", " dataOptions=\"{type: 'bar'}\",\n", " dataSources=\"{x: 'name', y: 'amount'}\")\n", "bar\n" ], "outputs" : [ { "name" : "stdout", "output_type" : "stream", "text" : "bar: notebook.front.widgets.charts.CustomPlotlyChart[Seq[Species]] = <CustomPlotlyChart widget>\nres2: notebook.front.widgets.charts.CustomPlotlyChart[Seq[Species]] = <CustomPlotlyChart widget>\n" }, { "metadata" : { }, "data" : { "text/html" : "<div>\n <script data-this=\"{"dataId":"anondd714328a46da563dee3980eef451880","dataInit":[{"name":"giraffes","amount":20},{"name":"orangutans","amount":14},{"name":"monkeys","amount":23}],"genId":"1951807630"}\" type=\"text/x-scoped-javascript\">/<![CDATA[/req(['../javascripts/notebook/playground','../javascripts/notebook/magic/customPlotlyChart'], \n function(playground, _magiccustomPlotlyChart) {\n // data ==> data-this (in observable.js's scopedEval) ==> this in JS => { dataId, dataInit, ... }\n // this ==> scope (in observable.js's scopedEval) ==> this.parentElement ==> div.container below (toHtml)\n\n playground.call(data,\n this\n ,\n {\n \"f\": _magiccustomPlotlyChart,\n \"o\": {\"js\":\"var layout = {}; var dataSources={x: 'name', y: 'amount'}; var dataOptions = {type: 'bar'}; var extraOptions = {}\",\"headers\":[\"name\",\"amount\"],\"height\":400}\n }\n \n \n \n );\n }\n );/]]>/</script>\n <div>\n <span class=\"chart-total-item-count\"><p data-bind=\"text: 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" ] }, "output_type" : "execute_result", "execution_count" : 2, "time" : "Took: 1.566s, at 2017-07-13 16:00" } ] }, { "metadata" : { "trusted" : true, "input_collapsed" : false, "collapsed" : true, "id" : "3E8C3458A74E471486763AA3C991260E" }, "cell_type" : "code", "source" : [ ], "outputs" : [ ] } ], "nbformat" : 4 }	apache-2.0
SarahWooller/Using_NetSurfP	Using_NetSurfP/NetsurfP pipeline.ipynb	2	24406	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# NetsurfP pipeline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Intention is to take a list of mutations and\n", "make a file for netsurfp\n", "\n", "for each of the mutations\n", "- check whether ENST or Uniprot\n", "- for ENST \n", "- get the ENST codes\n", "- for Uniprot - get the ENSTs first\n", "- then get the codes\n", "- check that the wild is where it should be and if so \n", "- add to the list of ENSTs\n", "- construct the section to add to the file for netsurfp\n", "- add to the file\n", "\n", "split the file into files of a reasonable length\n", "Do the same for all the ENSTs in the list\n", "\n", "call netsurfp with each of the files - saving them to somewhere sensible\n", "change each of the responses into a csv file.\n", "\n", "Concatenate the csv files into two - one for the mutations and one for the wild ENSTs\n", "\n", "for each of the mutations take out the right line from the mutations and from the wild ENSTs\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "import json\n", "import re\n", "import sys\n", "import subprocess\n", "\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import os" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ENST_codes = get_ENST_codes()\n", "\n", "ENST_Uniprot = get_ENST_Uniprot()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def get_ENST_codes():\n", "\twith open(os.path.abspath('./data/ENST_codes.json'), 'r') as file:\n", "\t\treturn json.load(file)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def get_ENST_Uniprot(): \n", " return pd.DataFrame.from_csv(os.path.abspath('./data/ENST_Uniprot.csv'))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def clean_directories():\n", " subprocess.Popen(['rm','-rf', os.path.abspath('./temp_questions/')])\n", " subprocess.Popen(['mkdir', 'temp_questions'])\n", " subprocess.Popen(['rm','-rf', os.path.abspath('./temp_answers/')])\n", " subprocess.Popen(['mkdir', 'temp_answers'])" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def Split(string,n):\n", "\t\"\"\"Split a string into lines of length n with \\n in between them\"\"\"\n", "\tN =len(string)//n\n", "\treturn '\\n'.join([string[in:(i+1)n] for i in range(N)]+[string[N*n:]])" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class Mut:\n", " \n", " def __init__(self,mut):\n", " \n", " self.mut = mut\n", " self.messages = {'ok':(True,'no problems encountered' ),\n", " 'no ENST':(False,'no ENSTs correspond to this Uniprot code'),\n", " 'too short':(False, \"none of the corresponding codes were long enough to encorporate this \"+\n", " \"mutation\"),\n", " 'wrong wild type': (False, \"whilst at least one of the corresponding codes was long enough to\"+\n", " \"encorporate this mutation the AA did not correspond to the wild type given\")\n", " \n", " }\n", "\n", " \n", " parts = mut.split('_')\n", " self.name = parts[0]\n", " self.mutation = parts[1]\n", " self.wild = self.mutation[0]\n", " self.change = self.mutation[-1]\n", " self.pos = int(self.mutation[1:-1])\n", " \n", " self.valid,self.ENSTs = self.get_ENSTs()\n", " self.valid,self.ENST,self.wild_code = self.get_code()\n", " self.mutant_code = self.mutate_code()\n", " \n", " \n", " def get_ENSTs(self):\n", " i=self.name\n", " if i[:4]=='ENST':\n", " return [i]\n", " elif i in set(ENST_Uniprot['UniProtKB/Swiss-Prot ID']):\n", " Uni = 'UniProtKB/Swiss-Prot ID'\n", " elif i in set(ENST_Uniprot['UniProtKB/TrEMBL ID']):\n", " Uni = 'UniProtKB/TrEMBL ID'\n", " else:\n", " return (self.messages['no ENST'],'')\n", " return (self.messages['ok'],list(ENST_Uniprot[ENST_Uniprot[Uni]==i].index))\n", " \n", " def get_code(self):\n", " length_ok = False\n", " pos_ok = False\n", " \n", " codes = [ENST_codes.get(m,'') for m in self.ENSTs]\n", " C = len(codes)\n", " \n", " for i in range(C):\n", " if len(codes[i])>=self.pos:\n", " length_ok = True\n", " if codes[i][self.pos-1]==self.wild:\n", " pos_ok = True\n", " return (self.messages['ok'],self.ENSTs[i],codes[i])\n", " if not length_ok:\n", " return (self.messages['too short'],'','')\n", " else:\n", " return (self.messages['wrong wild type'],'','')\n", " \n", " def mutate_code(self):\n", " return self.wild_code[:self.pos-1]+self.change+self.wild_code[self.pos:]\n", " \n", " def for_printing(self):\n", " return ('>{0}_{1}'.format(self.ENST,self.mut),Split(self.mutant_code,61))" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [], "source": [ "self = Mut('P00519_M244V')" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def main():\n", " ENST_codes = get_ENST_codes()\n", " print('got ENST codes')\n", " ENST_Uniprot = get_ENST_Uniprot()\n", " print('got ENST Uniprot')\n", " clean_directories()\n", " print('directories cleaned')\n", " file_number = make_NetSurfP_query()\n", " print('queries made')\n", " do_netsurfp(file_number)\n", " print('netsurfp completed')" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "collapsed": false }, "outputs": [], "source": [ "Questions = [q+'.fsa' for q in os.listdir('./temp_questions') if 'json' not in q]" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "collapsed": true }, "outputs": [], "source": [ "for q in os.listdir('./temp_questions'):\n", " if 'json' not in q:\n", " p=subprocess.Popen(['cp','./temp_questions/'+q,'./temp_questions/'+q+'.fsa'])\n", " p.communicate()" ] }, { "cell_type": "code", "execution_count": 69, "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-69-723ede3bcf54>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m7566\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[1;34m'questions{}.fsa'\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlistdir\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'./temp_questions'\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 3\u001b[0m \u001b[0mprint\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;34m'number {} is not there'\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\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;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ "for i in range(7566):\n", " if 'questions{}.fsa'.format(i) not in os.listdir('./temp_questions'):\n", " print ('number {} is not there'.format(i))" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "7566" ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(Questions)" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [], "source": [ "mokca = pd.DataFrame.from_csv('~/Downloads/MoKCA.csv')" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "64064" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mokca.size" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false }, "outputs": [], "source": [ "codes = [mokca.index[i].strip()+'_'+mokca.ix[i]['Substitution'] for i in range(mokca.size)]" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "collapsed": false }, "outputs": [], "source": [ "with open('./data/codes.txt','w') as file:\n", " file.write('')\n", "with open('./data/codes.txt','a') as file:\n", " for f in codes:\n", " file.write(f+'\\n')" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['O15504_S64P',\n", " 'O15504_S177F',\n", " 'O15504_D123Y',\n", " 'O15504_R13C',\n", " 'O15504_R159G',\n", " 'O15504_S177Y',\n", " 'O15504_Q36R',\n", " 'O15504_S66F',\n", " 'O15504_V57G',\n", " 'O15504_R180H']" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "codes[:10]" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def make_NetSurfP_query():\n", " muts = get_query()\n", " mutations =[Mut(l) for l in muts]\n", " validity = dict(zip([m.name for m in mutations],[m.valid for m in mutations]))\n", " for_printing = [m.for_printing() for m in mutations]\n", " temp_lists = dont_exceed_max(10000,for_printing)\n", " make_questions('./temp_questions/','questions', temp_lists)\n", " mutations_listed=[[i[0] for i in j] for j in temp_lists]\n", " fine, too_short,wrong_wild = split_validity(validity)\n", " \n", " query = {'fine':fine,\n", " 'too short': too_short,\n", " 'wrong wild': wrong_wild,\n", " 'mutations for netsurfp': mutations_listed}\n", " \n", " with open('./temp_answers/query.json','w') as file:\n", " json.dump(query,file)\n", " return len(temp_lists)\n" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def split_validity(validity):\n", " too_short=[]\n", " wrong_wild=[]\n", " fine = []\n", " for v in validity:\n", " a,b = validity[v]\n", " if a:\n", " fine.append(v)\n", " elif b=='none of the corresponding codes were long enough to encorporate this mutation':\n", " too_short.append(v)\n", " else:\n", " wrong_wild.append(v)\n", " " ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def make_questions(pathname, filename, temp_lists):\n", " for t in range(len(temp_lists)):\n", " name = pathname+filename+str(t)+'.fsa'\n", " with open(name,'w') as file:\n", " file.write('')\n", " with open(name,'a') as file:\n", " for i in temp_lists[t]:\n", " a,b = i\n", " file.write(a+'\\n')\n", " file.write(b+'\\n')" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def do_netsurfp(file_number):\n", "\n", " for j in range(file_number):\n", " input_file = 'temp_questions/questions{}.fsa'.format(j)\n", " output_file = 'temp_answers/answers{}.rsa'.format(j)\n", " p = subprocess.Popen(['netsurfp', '-i',input_file, '-o', output_file])\n", " p.communicate()\n", " print('{} completed'.format(j))\n", " " ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [], "source": [ "mutations_listed=[[i[0] for i in j] for j in temp_lists]" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['>ENST00000318560_P00519_M244V',\n", " '>ENST00000318560_P00519_F359I',\n", " '>ENST00000318560_P00519_F317L',\n", " '>ENST00000318560_P00519_Q252H',\n", " '>ENST00000318560_P00519_F359V',\n", " '>ENST00000372348_P00519_V299L',\n", " '>ENST00000318560_P00519_E355G',\n", " '>ENST00000318560_P00519_L248V']" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mutations_listed[0]" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "To use this program you need to supply a file with a list of mutation codes\n", " These codes should be in the form of identifier_M244V where here\n", " M is the wild type 244 is the position and V is the mutant amino acid\n", " Your file should contain one mutation code per line and no other information\n", "\n", "please type the full path of the file that contains your mutation codes here without quotations marks./data/codes.txt\n", "Your query has been found\n", "9203\n", "9658\n", "9678\n", "8864\n", "9503\n", "9516\n", "9595\n", "9382\n", "8849\n", "9212\n", "9769\n", "9805\n", "9666\n", "9910\n", "9556\n", "8844\n", "8844\n", "9808\n", "9831\n", "9765\n", "9765\n", "9992\n", "9901\n", "8774\n", "9837\n", "9471\n", "9268\n", "8427\n", "9564\n", "9952\n", "8912\n", "9586\n", "8984\n", "9834\n", "9218\n", "9844\n", "9911\n", "9112\n", "9851\n", "9596\n", "5455\n", "6728\n", "6728\n", "6728\n", "6728\n", "9947\n", "9943\n", "9480\n", "9962\n", "9821\n", "9564\n", "7298\n", "5287\n", "9855\n" ] } ], "source": [ "muts = get_query()\n", "mutations =[Mut(l) for l in muts]\n", "validity = dict(zip([m.name for m in mutations],[m.valid for m in mutations]))\n", "for_printing = [m.for_printing() for m in mutations]\n", "temp_lists = dont_exceed_max(10000,for_printing)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "f[:2]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "os.listdir()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "os.system(\"netsurfp -h\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "\n", "subprocess.Popen(['netsurfp','-i','./temp_questions/questions0','-o','./temp_answers/answers'])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fine, too_short,wrong_wild = split_validity(validity)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "subprocess.Popen(['pwd'])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "validity = make_NetSurfP_query()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def get_query():\n", " print('To use this program you need to supply a file with a list of mutation codes\\n',\n", " 'These codes should be in the form of identifier_M244V where here\\n',\n", " 'M is the wild type 244 is the position and V is the mutant amino acid\\n',\n", " 'Your file should contain one mutation code per line and no other information\\n')\n", " query_file = input('please type the full path of the file that contains your mutation codes here without quotations marks')\n", " try:\n", " \n", " with open(query_file,'r') as file:\n", " tmp = file.readlines()\n", " print('Your query has been found')\n", " return [t.strip('\\n') for t in tmp]\n", " except FileNotFoundError:\n", " print('file not found, quit and try again')\n", " return []" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def dont_exceed_max(Max,code_list):\n", " \n", " C = len(code_list)\n", " temp_list=[]\n", " for_inclusion=[]\n", " limit = 0\n", " for i in range(C):\n", " a,b = code_list[i]\n", " B = len(b)\n", " if limit+B<Max:\n", " for_inclusion.append(code_list[i])\n", " limit+=B\n", " else:\n", " temp_list.append(for_inclusion)\n", " limit=B\n", " for_inclusion=[code_list[i]]\n", " temp_list.append(for_inclusion)\n", " return temp_list" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "To use this program you need to supply a file with a list of mutation codes\n", " These codes should be in the form of identifier_M244V where here\n", " M is the wild type 244 is the position and V is the mutant amino acid\n", " Your file should contain one mutation code per line and no other information\n", "\n", "please type the full path of the file that contains your mutation codes here./data/codes.txt\n" ] } ], "source": [ "muts = get_query()\n", "\n", "mutations =[Mut(l) for l in muts]\n", "\n", "Validity = dict(zip([m.name for m in mutations],[m.valid for m in mutations]))\n", "\n", "for_printing = [m.for_printing() for m in mutations]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "temp_lists = dont_exceed_max(100000,for_printing)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "temp_lists[0]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "self.for_printing()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "bit of codes to give me something to play with " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "codes = pd.DataFrame.from_csv('OGvNeutral.csv')\n", "\n", "codes['codes'] = codes['Uniprot ID']+'_'+codes['Substitution']\n", "\n", "\n", "L = list(codes['codes'])\n", "\n", "L1 = [i for i in L if type(i)==str]\n", "\n", "with open('./data/codes.txt','w') as file:\n", " file.write('')\n", "\n", "with open('./data/codes.txt','a') as file:\n", " for l in L1:\n", " file.write(l+'\\n')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "for_printing[:10]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "self.mutant_code[243]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "self.ENST" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "self.code" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.4" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
tensorflow/docs-l10n	site/ja/lite/performance/post_training_integer_quant_16x8.ipynb	1	19418	{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "c8Cx-rUMVX25" }, "source": [ "##### Copyright 2020 The TensorFlow Authors." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "I9sUhVL_VZNO" }, "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": "6Y8E0lw5eYWm" }, "source": [ "# int16 アクティベーションによるトレーニング後の整数量子化" ] }, { "cell_type": "markdown", "metadata": { "id": "CGuqeuPSVNo-" }, "source": [ "<table class=\"tfo-notebook-buttons\" align=\"left\">\n", " <td> <a target=\"_blank\" href=\"https://www.tensorflow.org/lite/performance/post_training_integer_quant_16x8\"><img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\">View on TensorFlow.org</a> </td>\n", " <td> <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs-l10n/blob/master/site/ja/lite/performance/post_training_integer_quant_16x8.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\">Run in Google Colab</a> </td>\n", " <td> <a target=\"_blank\" href=\"https://github.com/tensorflow/docs-l10n/blob/master/site/ja/lite/performance/post_training_integer_quant_16x8.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\">View source on GitHub</a> </td>\n", " <td><a href=\"https://storage.googleapis.com/tensorflow_docs/docs-l10n/site/ja/lite/performance/post_training_integer_quant_16x8.ipynb\"><img src=\"https://www.tensorflow.org/images/download_logo_32px.png\"> ノートブックをダウンロード</a></td>\n", "</table>" ] }, { "cell_type": "markdown", "metadata": { "id": "BTC1rDAuei_1" }, "source": [ "## 概要\n", "\n", "現在、[TensorFlow Lite](https://www.tensorflow.org/lite/) はモデルを TensorFlow から TensorFlow Lite のフラットバッファ形式に変換する際に、アクティベーションを 16 ビット整数値に、重みを 8 ビット整数値に変換することをサポートしています。このモードは「16x8 量子化モード」と呼ばれています。このモードでは、アクティベーションが量子化の影響を受けやすい場合に、量子化モデルの精度を大幅に向上させ、モデルサイズを約 1/3〜1/4 に縮小することができます。また、この完全に量子化されたモデルは、整数のみのハードウェアアクセラレータで使用できます。\n", "\n", "次のようなモデルでは、このモードのトレーニング後の量子化が有用です。\n", "\n", "- 超解像\n", "- ノイズキャンセリングやビームフォーミングなどのオーディオ信号処理\n", "- 画像ノイズ除去\n", "- 単一の画像からの HDR 再構成\n", "\n", "このチュートリアルでは、MNIST モデルを新規にトレーニングし、TensorFlow でその精度を確認してから、このモードを使用してモデルを Tensorflow Lite フラットバッファに変換します。最後に、変換されたモデルの精度を確認し、元の float32 モデルと比較します。この例は、このモードの使用法を示しており、TensorFlow Liteで利用可能な他の量子化手法と比較した場合の利点は示していません。" ] }, { "cell_type": "markdown", "metadata": { "id": "2XsEP17Zelz9" }, "source": [ "## MNIST モデルの構築" ] }, { "cell_type": "markdown", "metadata": { "id": "dDqqUIZjZjac" }, "source": [ "### セットアップ" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "gyqAw1M9lyab" }, "outputs": [], "source": [ "import logging\n", "logging.getLogger(\"tensorflow\").setLevel(logging.DEBUG)\n", "\n", "import tensorflow as tf\n", "from tensorflow import keras\n", "import numpy as np\n", "import pathlib" ] }, { "cell_type": "markdown", "metadata": { "id": "srTSFKjn1tMp" }, "source": [ "16x8 量子化モードが使用可能であることを確認します " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "c6nb7OPlXs_3" }, "outputs": [], "source": [ "tf.lite.OpsSet.EXPERIMENTAL_TFLITE_BUILTINS_ACTIVATIONS_INT16_WEIGHTS_INT8" ] }, { "cell_type": "markdown", "metadata": { "id": "eQ6Q0qqKZogR" }, "source": [ "### モデルをトレーニングしてエクスポートする" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "hWSAjQWagIHl" }, "outputs": [], "source": [ "# Load MNIST dataset\n", "mnist = keras.datasets.mnist\n", "(train_images, train_labels), (test_images, test_labels) = mnist.load_data()\n", "\n", "# Normalize the input image so that each pixel value is between 0 to 1.\n", "train_images = train_images / 255.0\n", "test_images = test_images / 255.0\n", "\n", "# Define the model architecture\n", "model = keras.Sequential([\n", " keras.layers.InputLayer(input_shape=(28, 28)),\n", " keras.layers.Reshape(target_shape=(28, 28, 1)),\n", " keras.layers.Conv2D(filters=12, kernel_size=(3, 3), activation=tf.nn.relu),\n", " keras.layers.MaxPooling2D(pool_size=(2, 2)),\n", " keras.layers.Flatten(),\n", " keras.layers.Dense(10)\n", "])\n", "\n", "# Train the digit classification model\n", "model.compile(optimizer='adam',\n", " loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n", " metrics=['accuracy'])\n", "model.fit(\n", " train_images,\n", " train_labels,\n", " epochs=1,\n", " validation_data=(test_images, test_labels)\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "5NMaNZQCkW9X" }, "source": [ "この例では、モデルを 1 エポックでトレーニングしたので、トレーニングの精度は 96％以下になります。" ] }, { "cell_type": "markdown", "metadata": { "id": "xl8_fzVAZwOh" }, "source": [ "### TensorFlow Lite モデルに変換する\n", "\n", "Python [TFLiteConverter](https://www.tensorflow.org/lite/convert/python_api) を使用して、トレーニング済みモデルを TensorFlow Lite モデルに変換できるようになりました。\n", "\n", "次に、`TFliteConverter`を使用してモデルをデフォルトの float32 形式に変換します。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "_i8B2nDZmAgQ" }, "outputs": [], "source": [ "converter = tf.lite.TFLiteConverter.from_keras_model(model)\n", "tflite_model = converter.convert()" ] }, { "cell_type": "markdown", "metadata": { "id": "F2o2ZfF0aiCx" }, "source": [ "`.tflite`ファイルに書き込みます。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "vptWZq2xnclo" }, "outputs": [], "source": [ "tflite_models_dir = pathlib.Path(\"/tmp/mnist_tflite_models/\")\n", "tflite_models_dir.mkdir(exist_ok=True, parents=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Ie9pQaQrn5ue" }, "outputs": [], "source": [ "tflite_model_file = tflite_models_dir/\"mnist_model.tflite\"\n", "tflite_model_file.write_bytes(tflite_model)" ] }, { "cell_type": "markdown", "metadata": { "id": "7BONhYtYocQY" }, "source": [ "モデルを 16x8 量子化モードに量子化するには、最初に`optimizations`フラグを設定してデフォルトの最適化を使用します。次に、16x8 量子化モードがターゲット仕様でサポートされる必要な演算であることを指定します。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "HEZ6ET1AHAS3" }, "outputs": [], "source": [ "converter.optimizations = [tf.lite.Optimize.DEFAULT]\n", "converter.target_spec.supported_ops = [tf.lite.OpsSet.EXPERIMENTAL_TFLITE_BUILTINS_ACTIVATIONS_INT16_WEIGHTS_INT8]" ] }, { "cell_type": "markdown", "metadata": { "id": "zLxQwZq9CpN7" }, "source": [ "int8 トレーニング後の量子化の場合と同様に、コンバーターオプション`inference_input(output)_type`を tf.int16 に設定することで、完全整数量子化モデルを生成できます。" ] }, { "cell_type": "markdown", "metadata": { "id": "yZekFJC5-fOG" }, "source": [ "キャリブレーションデータを設定します。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Y3a6XFqvHbYM" }, "outputs": [], "source": [ "mnist_train, _ = tf.keras.datasets.mnist.load_data()\n", "images = tf.cast(mnist_train[0], tf.float32) / 255.0\n", "mnist_ds = tf.data.Dataset.from_tensor_slices((images)).batch(1)\n", "def representative_data_gen():\n", " for input_value in mnist_ds.take(100):\n", " # Model has only one input so each data point has one element.\n", " yield [input_value]\n", "converter.representative_dataset = representative_data_gen" ] }, { "cell_type": "markdown", "metadata": { "id": "xW84iMYjHd9t" }, "source": [ "最後に、通常どおりにモデルを変換します。デフォルトでは、変換されたモデルは呼び出しの便宜上、浮動小数点の入力と出力を引き続き使用します。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "yuNfl3CoHNK3" }, "outputs": [], "source": [ "tflite_16x8_model = converter.convert()\n", "tflite_model_16x8_file = tflite_models_dir/\"mnist_model_quant_16x8.tflite\"\n", "tflite_model_16x8_file.write_bytes(tflite_16x8_model)" ] }, { "cell_type": "markdown", "metadata": { "id": "PhMmUTl4sbkz" }, "source": [ "生成されるファイルのサイズが約`1/3`であることに注目してください。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "JExfcfLDscu4" }, "outputs": [], "source": [ "!ls -lh {tflite_models_dir}" ] }, { "cell_type": "markdown", "metadata": { "id": "L8lQHMp_asCq" }, "source": [ "## TensorFlow Lite モデルを実行する" ] }, { "cell_type": "markdown", "metadata": { "id": "-5l6-ciItvX6" }, "source": [ "Python TensorFlow Lite インタープリタを使用して TensorFlow Lite モデルを実行します。" ] }, { "cell_type": "markdown", "metadata": { "id": "Ap_jE7QRvhPf" }, "source": [ "### モデルをインタープリタに読み込む" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Jn16Rc23zTss" }, "outputs": [], "source": [ "interpreter = tf.lite.Interpreter(model_path=str(tflite_model_file))\n", "interpreter.allocate_tensors()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "J8Pztk1mvNVL" }, "outputs": [], "source": [ "interpreter_16x8 = tf.lite.Interpreter(model_path=str(tflite_model_16x8_file))\n", "interpreter_16x8.allocate_tensors()" ] }, { "cell_type": "markdown", "metadata": { "id": "2opUt_JTdyEu" }, "source": [ "### 1 つの画像でモデルをテストする" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "AKslvo2kwWac" }, "outputs": [], "source": [ "test_image = np.expand_dims(test_images[0], axis=0).astype(np.float32)\n", "\n", "input_index = interpreter.get_input_details()[0][\"index\"]\n", "output_index = interpreter.get_output_details()[0][\"index\"]\n", "\n", "interpreter.set_tensor(input_index, test_image)\n", "interpreter.invoke()\n", "predictions = interpreter.get_tensor(output_index)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "XZClM2vo3_bm" }, "outputs": [], "source": [ "import matplotlib.pylab as plt\n", "\n", "plt.imshow(test_images[0])\n", "template = \"True:{true}, predicted:{predict}\"\n", "_ = plt.title(template.format(true= str(test_labels[0]),\n", " predict=str(np.argmax(predictions[0]))))\n", "plt.grid(False)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "3gwhv4lKbYZ4" }, "outputs": [], "source": [ "test_image = np.expand_dims(test_images[0], axis=0).astype(np.float32)\n", "\n", "input_index = interpreter_16x8.get_input_details()[0][\"index\"]\n", "output_index = interpreter_16x8.get_output_details()[0][\"index\"]\n", "\n", "interpreter_16x8.set_tensor(input_index, test_image)\n", "interpreter_16x8.invoke()\n", "predictions = interpreter_16x8.get_tensor(output_index)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "CIH7G_MwbY2x" }, "outputs": [], "source": [ "plt.imshow(test_images[0])\n", "template = \"True:{true}, predicted:{predict}\"\n", "_ = plt.title(template.format(true= str(test_labels[0]),\n", " predict=str(np.argmax(predictions[0]))))\n", "plt.grid(False)" ] }, { "cell_type": "markdown", "metadata": { "id": "LwN7uIdCd8Gw" }, "source": [ "### モデルを評価する" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "05aeAuWjvjPx" }, "outputs": [], "source": [ "# A helper function to evaluate the TF Lite model using \"test\" dataset.\n", "def evaluate_model(interpreter):\n", " input_index = interpreter.get_input_details()[0][\"index\"]\n", " output_index = interpreter.get_output_details()[0][\"index\"]\n", "\n", " # Run predictions on every image in the \"test\" dataset.\n", " prediction_digits = []\n", " for test_image in test_images:\n", " # Pre-processing: add batch dimension and convert to float32 to match with\n", " # the model's input data format.\n", " test_image = np.expand_dims(test_image, axis=0).astype(np.float32)\n", " interpreter.set_tensor(input_index, test_image)\n", "\n", " # Run inference.\n", " interpreter.invoke()\n", "\n", " # Post-processing: remove batch dimension and find the digit with highest\n", " # probability.\n", " output = interpreter.tensor(output_index)\n", " digit = np.argmax(output()[0])\n", " prediction_digits.append(digit)\n", "\n", " # Compare prediction results with ground truth labels to calculate accuracy.\n", " accurate_count = 0\n", " for index in range(len(prediction_digits)):\n", " if prediction_digits[index] == test_labels[index]:\n", " accurate_count += 1\n", " accuracy = accurate_count * 1.0 / len(prediction_digits)\n", "\n", " return accuracy" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "T5mWkSbMcU5z" }, "outputs": [], "source": [ "print(evaluate_model(interpreter))" ] }, { "cell_type": "markdown", "metadata": { "id": "Km3cY9ry8ZlG" }, "source": [ "16x8 量子化モデルで評価を繰り返します。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "-9cnwiPp6EGm" }, "outputs": [], "source": [ "# NOTE: This quantization mode is an experimental post-training mode,\n", "# it does not have any optimized kernels implementations or\n", "# specialized machine learning hardware accelerators. Therefore,\n", "# it could be slower than the float interpreter.\n", "print(evaluate_model(interpreter_16x8))" ] }, { "cell_type": "markdown", "metadata": { "id": "L7lfxkor8pgv" }, "source": [ "この例では、モデルの精度を低下することなく、16x8 に量子化しました。サイズは 1/3 に縮小されました。\n" ] } ], "metadata": { "colab": { "collapsed_sections": [], "name": "post_training_integer_quant_16x8.ipynb", "toc_visible": true }, "kernelspec": { "display_name": "Python 3", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 0 }	apache-2.0
dadavidson/Python_Lab	Complete-Python-Bootcamp/Lambda expressions.ipynb	2	8317	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "#lambda expressions\n", "\n", "One of Pythons most useful (and for beginners, confusing) tools is the lambda expression. lambda expressions allow us to create \"anonymous\" functions. This basically means we can quickly make ad-hoc functions without needing to properly define a function using def.\n", "\n", "Function objects returned by running lambda expressions work exactly the same as those created and assigned by defs. There is key difference that makes lambda useful in specialized roles:\n", "\n", "lambda's body is a single expression, not a block of statements.\n", "\n", "* The lambda's body is similar to what we would put in a def body's return statement. We simply type the result as an expression instead of explicitly returning it. Because it is limited to an expression, a lambda is less general that a def. We can only squeeze design, to limit program nesting. lambda is designed for coding simple functions, and def handles the larger tasks.\n", "\n", "Lets slowly break down a lambda expression by deconstructing a function:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def square(num):\n", " result = num2\n", " return result" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "4" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "square(2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Continuing the breakdown:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def square(num):\n", " return num2" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "4" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "square(2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can actually write this in one line (although it would be bad style to do so)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def square(num): return num2" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "4" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "square(2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is the form a function that a lambda expression intends to replicate. A lambda expression can then be written as:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<function __main__.<lambda>>" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lambda num: num2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note how we get a function back. We can assign this function to a label:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "square = lambda num: num**2" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "4" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "square(2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And there you have it! The breakdown of a function into a lambda expression!\n", "Lets see a few more examples:\n", "\n", "##Example 1\n", "Check it a number is even" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "even = lambda x: x%2==0" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "even(3)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "even(4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Example 2\n", "Grab first character of a string:" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": true }, "outputs": [], "source": [ "first = lambda s: s[0]" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'h'" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "first('hello')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Example 3\n", "Reverse a string:" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": true }, "outputs": [], "source": [ "rev = lambda s: s[::-1]" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'olleh'" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rev('hello')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Example 4\n", "Just like a normal function, we can accept more than one function into a lambda expression:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "adder = lambda x,y : x+y" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "5" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "adder(2,3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "lambda expressions really shine when used in conjunction with map(),filter() and reduce(). Each of those functions has its own lecture, so feel free to explore them if you're very interested in lambda." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I highly recommend reading this blog post at [Python Conquers the Universe](https://pythonconquerstheuniverse.wordpress.com/2011/08/29/lambda_tutorial/) for a great breakdown on lambda expressions and some explanations of common confusions! " ] } ], "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
ipython/ipywidgets	docs/source/examples/Index.ipynb	1	2449	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Back to the main [Index](../Index.ipynb)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Interactive Widgets" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "IPython includes an architecture for interactive widgets that tie together Python code running in the kernel and JavaScript/HTML/CSS running in the browser. These widgets enable users to explore their code and data interactively." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Tutorials" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- [Using Interact](Using%20Interact.ipynb)\n", "- [Widget Basics](Widget%20Basics.ipynb) \n", "- [Widget Events](Widget%20Events.ipynb) \n", "- [Widget List](Widget%20List.ipynb) \n", "- [Widget Styling](Widget%20Styling.ipynb) \n", "- [Widget Custom](Widget%20Custom.ipynb)\n", "- [Widget Asynchronous](Widget%20Asynchronous.ipynb): how to pause and listen in the kernel for widget changes in the frontend." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Examples of custom widgets" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- [Variable Inspector](Variable%20Inspector.ipynb) \n", "- [Export As (nbconvert)](Export As (nbconvert%29.ipynb)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Examples using `interact`/`interactive`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* [Beat Frequencies](Beat%20Frequencies.ipynb)\n", "* [Exploring Graphs](Exploring%20Graphs.ipynb)\n", "* [Factoring](Factoring.ipynb)\n", "* [Image Browser](Image%20Browser.ipynb)\n", "* [Image Processing](Image%20Processing.ipynb)\n", "* [Lorenz Differential Equations](Lorenz%20Differential%20Equations.ipynb)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.2" } }, "nbformat": 4, "nbformat_minor": 1 }	bsd-3-clause
GoogleCloudPlatform/vertex-ai-samples	notebooks/community/migration/UJ2,12 Custom Training Prebuilt Container TF Keras.ipynb	1	78486	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "id": "copyright" }, "outputs": [], "source": [ "# Copyright 2021 Google LLC\n", "#\n", "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", "# you may not use this file except in compliance with the License.\n", "# You may obtain a copy of the License at\n", "#\n", "# https://www.apache.org/licenses/LICENSE-2.0\n", "#\n", "# Unless required by applicable law or agreed to in writing, software\n", "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", "# See the License for the specific language governing permissions and\n", "# limitations under the License." ] }, { "cell_type": "markdown", "metadata": { "id": "title:migration,new" }, "source": [ "# Vertex SDK: Train & deploy a TensorFlow model with hosted runtimes (aka pre-built containers)\n" ] }, { "cell_type": "markdown", "metadata": { "id": "install_aip" }, "source": [ "## Installation\n", "\n", "Install the latest (preview) version of Vertex SDK.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "7RCTGVIN_5rI" }, "outputs": [], "source": [ "! pip3 install -U google-cloud-aiplatform --user" ] }, { "cell_type": "markdown", "metadata": { "id": "install_storage" }, "source": [ "Install the Google cloud-storage library as well.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "AsKFgsNV_5rJ" }, "outputs": [], "source": [ "! pip3 install google-cloud-storage" ] }, { "cell_type": "markdown", "metadata": { "id": "restart" }, "source": [ "### Restart the Kernel\n", "\n", "Once you've installed the Vertex SDK and Google cloud-storage, you need to restart the notebook kernel so it can find the packages.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "I22lCeuV_5rJ" }, "outputs": [], "source": [ "import os\n", "\n", "if not os.getenv(\"AUTORUN\"):\n", " # Automatically restart kernel after installs\n", " import IPython\n", "\n", " app = IPython.Application.instance()\n", " app.kernel.do_shutdown(True)" ] }, { "cell_type": "markdown", "metadata": { "id": "before_you_begin" }, "source": [ "## Before you begin\n", "\n", "### GPU run-time\n", "\n", "Make sure you're running this notebook in a GPU runtime if you have that option. In Colab, select Runtime > Change Runtime Type > GPU\n", "\n", "### Set up your GCP project\n", "\n", "The following steps are required, regardless of your notebook environment.\n", "\n", "1. [Select or create a GCP project](https://console.cloud.google.com/cloud-resource-manager). When you first create an account, you get a $300 free credit towards your compute/storage costs.\n", "\n", "2. [Make sure that billing is enabled for your project.](https://cloud.google.com/billing/docs/how-to/modify-project)\n", "\n", "3. [Enable the Vertex APIs and Compute Engine APIs.](https://console.cloud.google.com/flows/enableapi?apiid=ml.googleapis.com,compute_component)\n", "\n", "4. [Google Cloud SDK](https://cloud.google.com/sdk) is already installed in Google Cloud Notebooks.\n", "\n", "5. Enter your project ID in the cell below. Then run the cell to make sure the\n", "Cloud SDK uses the right project for all the commands in this notebook.\n", "\n", "Note: Jupyter runs lines prefixed with `!` as shell commands, and it interpolates Python variables prefixed with `$` into these commands.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "set_project_id" }, "outputs": [], "source": [ "PROJECT_ID = \"[your-project-id]\" # @param {type:\"string\"}" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "autoset_project_id" }, "outputs": [], "source": [ "if PROJECT_ID == \"\" or PROJECT_ID is None or PROJECT_ID == \"[your-project-id]\":\n", " # Get your GCP project id from gcloud\n", " shell_output = !gcloud config list --format 'value(core.project)' 2>/dev/null\n", " PROJECT_ID = shell_output[0]\n", " print(\"Project ID:\", PROJECT_ID)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "set_gcloud_project_id" }, "outputs": [], "source": [ "! gcloud config set project $PROJECT_ID" ] }, { "cell_type": "markdown", "metadata": { "id": "region" }, "source": [ "#### Region\n", "\n", "You can also change the `REGION` variable, which is used for operations\n", "throughout the rest of this notebook. Below are regions supported for Vertex AI. We recommend when possible, to choose the region closest to you.\n", "\n", "- Americas: `us-central1`\n", "- Europe: `europe-west4`\n", "- Asia Pacific: `asia-east1`\n", "\n", "You cannot use a Multi-Regional Storage bucket for training with Vertex. Not all regions provide support for all Vertex services. For the latest support per region, see [Region support for Vertex AI services](https://cloud.google.com/vertex-ai/docs/general/locations)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "A-UGGt5N_5rM" }, "outputs": [], "source": [ "REGION = \"us-central1\" # @param {type: \"string\"}" ] }, { "cell_type": "markdown", "metadata": { "id": "timestamp" }, "source": [ "#### Timestamp\n", "\n", "If you are in a live tutorial session, you might be using a shared test account or project. To avoid name collisions between users on resources created, you create a timestamp for each instance session, and append onto the name of resources which will be created in this tutorial.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "dWAPNQs4_5rN" }, "outputs": [], "source": [ "from datetime import datetime\n", "\n", "TIMESTAMP = datetime.now().strftime(\"%Y%m%d%H%M%S\")" ] }, { "cell_type": "markdown", "metadata": { "id": "gcp_authenticate" }, "source": [ "### Authenticate your GCP account\n", "\n", "If you are using Google Cloud Notebooks, your environment is already\n", "authenticated. Skip this step.\n", "\n", "Note: If you are on an Vertex notebook and run the cell, the cell knows to skip executing the authentication steps.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "nBegxspu_5rN" }, "outputs": [], "source": [ "import os\n", "import sys\n", "\n", "# If you are running this notebook in Colab, run this cell and follow the\n", "# instructions to authenticate your Google Cloud account. This provides access\n", "# to your Cloud Storage bucket and lets you submit training jobs and prediction\n", "# requests.\n", "\n", "# If on Vertex, then don't execute this code\n", "if not os.path.exists(\"/opt/deeplearning/metadata/env_version\"):\n", " if \"google.colab\" in sys.modules:\n", " from google.colab import auth as google_auth\n", "\n", " google_auth.authenticate_user()\n", "\n", " # If you are running this tutorial in a notebook locally, replace the string\n", " # below with the path to your service account key and run this cell to\n", " # authenticate your Google Cloud account.\n", " else:\n", " %env GOOGLE_APPLICATION_CREDENTIALS your_path_to_credentials.json\n", "\n", " # Log in to your account on Google Cloud\n", " ! gcloud auth login" ] }, { "cell_type": "markdown", "metadata": { "id": "bucket:batch_prediction" }, "source": [ "### Create a Cloud Storage bucket\n", "\n", "The following steps are required, regardless of your notebook environment.\n", "\n", "This tutorial is designed to use training data that is in a public Cloud Storage bucket and a local Cloud Storage bucket for your batch predictions. You may alternatively use your own training data that you have stored in a local Cloud Storage bucket.\n", "\n", "Set the name of your Cloud Storage bucket below. It must be unique across all Cloud Storage buckets.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "bucket" }, "outputs": [], "source": [ "BUCKET_NAME = \"[your-bucket-name]\" # @param {type:\"string\"}" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "autoset_bucket" }, "outputs": [], "source": [ "if BUCKET_NAME == \"\" or BUCKET_NAME is None or BUCKET_NAME == \"[your-bucket-name]\":\n", " BUCKET_NAME = PROJECT_ID + \"aip-\" + TIMESTAMP" ] }, { "cell_type": "markdown", "metadata": { "id": "create_bucket" }, "source": [ "Only if your bucket doesn't already exist: Run the following cell to create your Cloud Storage bucket.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "N3slQeys_5rP" }, "outputs": [], "source": [ "! gsutil mb -l $REGION gs://$BUCKET_NAME" ] }, { "cell_type": "markdown", "metadata": { "id": "validate_bucket" }, "source": [ "Finally, validate access to your Cloud Storage bucket by examining its contents:\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "PnmUK9Si_5rP" }, "outputs": [], "source": [ "! gsutil ls -al gs://$BUCKET_NAME" ] }, { "cell_type": "markdown", "metadata": { "id": "setup_vars" }, "source": [ "### Set up variables\n", "\n", "Next, set up some variables used throughout the tutorial.\n", "### Import libraries and define constants\n" ] }, { "cell_type": "markdown", "metadata": { "id": "import_aip" }, "source": [ "#### Import Vertex SDK\n", "\n", "Import the Vertex SDK into our Python environment.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "JhQ4R80M_5rQ" }, "outputs": [], "source": [ "import time\n", "\n", "from google.cloud.aiplatform import gapic as aip\n", "from google.protobuf import json_format\n", "from google.protobuf.json_format import MessageToJson, ParseDict\n", "from google.protobuf.struct_pb2 import Value" ] }, { "cell_type": "markdown", "metadata": { "id": "aip_constants" }, "source": [ "#### Vertex AI constants\n", "\n", "Setup up the following constants for Vertex AI:\n", "\n", "- `API_ENDPOINT`: The Vertex AI API service endpoint for dataset, model, job, pipeline and endpoint services.\n", "- `API_PREDICT_ENDPOINT`: The Vertex AI API service endpoint for prediction.\n", "- `PARENT`: The Vertex AI location root path for dataset, model and endpoint resources.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Ut9grfWV_5rQ" }, "outputs": [], "source": [ "# API Endpoint\n", "API_ENDPOINT = \"{}-aiplatform.googleapis.com\".format(REGION)\n", "\n", "# Vertex AI location root path for your dataset, model and endpoint resources\n", "PARENT = \"projects/\" + PROJECT_ID + \"/locations/\" + REGION" ] }, { "cell_type": "markdown", "metadata": { "id": "clients" }, "source": [ "## Clients\n", "\n", "The Vertex SDK works as a client/server model. On your side (the Python script) you will create a client that sends requests and receives responses from the server (Vertex).\n", "\n", "You will use several clients in this tutorial, so set them all up upfront.\n", "\n", "- Dataset Service for managed datasets.\n", "- Model Service for managed models.\n", "- Pipeline Service for training.\n", "- Endpoint Service for deployment.\n", "- Job Service for batch jobs and custom training.\n", "- Prediction Service for serving. Note: Prediction has a different service endpoint.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "r1e3E3ua_5rR" }, "outputs": [], "source": [ "# client options same for all services\n", "client_options = {\"api_endpoint\": API_ENDPOINT}\n", "\n", "\n", "def create_model_client():\n", " client = aip.ModelServiceClient(client_options=client_options)\n", " return client\n", "\n", "\n", "def create_endpoint_client():\n", " client = aip.EndpointServiceClient(client_options=client_options)\n", " return client\n", "\n", "\n", "def create_prediction_client():\n", " client = aip.PredictionServiceClient(client_options=client_options)\n", " return client\n", "\n", "\n", "def create_job_client():\n", " client = aip.JobServiceClient(client_options=client_options)\n", " return client\n", "\n", "\n", "clients = {}\n", "clients[\"model\"] = create_model_client()\n", "clients[\"endpoint\"] = create_endpoint_client()\n", "clients[\"prediction\"] = create_prediction_client()\n", "clients[\"job\"] = create_job_client()\n", "\n", "for client in clients.items():\n", " print(client)" ] }, { "cell_type": "markdown", "metadata": { "id": "ZX-ma7Xj_5rS" }, "source": [ "## Prepare a trainer script" ] }, { "cell_type": "markdown", "metadata": { "id": "lwO1GXK0_5rS" }, "source": [ "### Package assembly" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "yZmd22dN_5rS" }, "outputs": [], "source": [ "! rm -rf cifar\n", "! mkdir cifar\n", "! touch cifar/README.md\n", "\n", "setup_cfg = \"[egg_info]\\n\\\n", "tag_build =\\n\\\n", "tag_date = 0\"\n", "! echo \"$setup_cfg\" > cifar/setup.cfg\n", "\n", "setup_py = \"import setuptools\\n\\\n", "# Requires TensorFlow Datasets\\n\\\n", "setuptools.setup(\\n\\\n", " install_requires=[\\n\\\n", " 'tensorflow_datasets==1.3.0',\\n\\\n", " ],\\n\\\n", " packages=setuptools.find_packages())\"\n", "! echo \"$setup_py\" > cifar/setup.py\n", "\n", "pkg_info = \"Metadata-Version: 1.0\\n\\\n", "Name: Custom Training CIFAR-10\\n\\\n", "Version: 0.0.0\\n\\\n", "Summary: Demonstration training script\\n\\\n", "Home-page: www.google.com\\n\\\n", "Author: Google\\n\\\n", "Author-email: [email protected]\\n\\\n", "License: Public\\n\\\n", "Description: Demo\\n\\\n", "Platform: Vertex AI\"\n", "! echo \"$pkg_info\" > cifar/PKG-INFO\n", "\n", "! mkdir cifar/trainer\n", "! touch cifar/trainer/__init__.py" ] }, { "cell_type": "markdown", "metadata": { "id": "7agySmGx_5rS" }, "source": [ "### Task.py contents" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "h4XYVUTB_5rS" }, "outputs": [], "source": [ "%%writefile cifar/trainer/task.py\n", "import tensorflow_datasets as tfds\n", "import tensorflow as tf\n", "from tensorflow.python.client import device_lib\n", "import argparse\n", "import os\n", "import sys\n", "\n", "tfds.disable_progress_bar()\n", "\n", "parser = argparse.ArgumentParser()\n", "parser.add_argument('--model-dir', dest='model_dir',\n", " default='/tmp/saved_model', type=str, help='Model dir.')\n", "parser.add_argument('--lr', dest='lr',\n", " default=0.01, type=float,\n", " help='Learning rate.')\n", "parser.add_argument('--epochs', dest='epochs',\n", " default=10, type=int,\n", " help='Number of epochs.')\n", "parser.add_argument('--steps', dest='steps',\n", " default=200, type=int,\n", " help='Number of steps per epoch.')\n", "parser.add_argument('--distribute', dest='distribute', type=str, default='single',\n", " help='distributed training strategy')\n", "args = parser.parse_args()\n", "\n", "print('Python Version = {}'.format(sys.version))\n", "print('TensorFlow Version = {}'.format(tf.__version__))\n", "print('TF_CONFIG = {}'.format(os.environ.get('TF_CONFIG', 'Not found')))\n", "print('DEVICES', device_lib.list_local_devices())\n", "\n", "if args.distribute == 'single':\n", " if tf.test.is_gpu_available():\n", " strategy = tf.distribute.OneDeviceStrategy(device=\"/gpu:0\")\n", " else:\n", " strategy = tf.distribute.OneDeviceStrategy(device=\"/cpu:0\")\n", "elif args.distribute == 'mirror':\n", " strategy = tf.distribute.MirroredStrategy()\n", "elif args.distribute == 'multi':\n", " strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy()\n", "\n", "print('num_replicas_in_sync = {}'.format(strategy.num_replicas_in_sync))\n", "\n", "BUFFER_SIZE = 10000\n", "BATCH_SIZE = 64\n", "\n", "def make_datasets_unbatched():\n", " def scale(image, label):\n", " image = tf.cast(image, tf.float32)\n", " image /= 255.0\n", " return image, label\n", "\n", " datasets, info = tfds.load(name='cifar10',\n", " with_info=True,\n", " as_supervised=True)\n", " return datasets['train'].map(scale).cache().shuffle(BUFFER_SIZE).repeat()\n", "\n", "def build_and_compile_cnn_model():\n", " model = tf.keras.Sequential([\n", " tf.keras.layers.Conv2D(32, 3, activation='relu', input_shape=(32, 32, 3)),\n", " tf.keras.layers.MaxPooling2D(),\n", " tf.keras.layers.Conv2D(32, 3, activation='relu'),\n", " tf.keras.layers.MaxPooling2D(),\n", " tf.keras.layers.Flatten(),\n", " tf.keras.layers.Dense(10, activation='softmax')\n", " ])\n", " model.compile(\n", " loss=tf.keras.losses.sparse_categorical_crossentropy,\n", " optimizer=tf.keras.optimizers.SGD(learning_rate=args.lr),\n", " metrics=['accuracy'])\n", " return model\n", "\n", "NUM_WORKERS = strategy.num_replicas_in_sync\n", "GLOBAL_BATCH_SIZE = BATCH_SIZE * NUM_WORKERS\n", "train_dataset = make_datasets_unbatched().batch(GLOBAL_BATCH_SIZE)\n", "\n", "with strategy.scope():\n", " model = build_and_compile_cnn_model()\n", "\n", "model.fit(x=train_dataset, epochs=args.epochs, steps_per_epoch=args.steps)\n", "model.save(args.model_dir)\n" ] }, { "cell_type": "markdown", "metadata": { "id": "7NYujrjG_5rT" }, "source": [ "### Store training script on your Cloud Storage bucket" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "7vc09ylR_5rT" }, "outputs": [], "source": [ "! rm -f cifar.tar cifar.tar.gz\n", "! tar cvf cifar.tar cifar\n", "! gzip cifar.tar\n", "! gsutil cp cifar.tar.gz gs://$BUCKET_NAME/trainer_cifar.tar.gz" ] }, { "cell_type": "markdown", "metadata": { "id": "text_create_and_deploy_model:migration" }, "source": [ "## Train a model" ] }, { "cell_type": "markdown", "metadata": { "id": "0oqIBOSnJjkW" }, "source": [ "### [projects.locations.customJobs.create](https://cloud.google.com/vertex-ai/docs/reference/rest/v1beta1/projects.locations.trainingPipelines/create)" ] }, { "cell_type": "markdown", "metadata": { "id": "_-NJrAth_5rU" }, "source": [ "#### Request" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "B2TRiVhq_5rU" }, "outputs": [], "source": [ "JOB_NAME = \"custom_job_TF_\" + TIMESTAMP\n", "\n", "TRAIN_IMAGE = \"gcr.io/cloud-aiplatform/training/tf-gpu.2-1:latest\"\n", "TRAIN_NGPU = 1\n", "TRAIN_GPU = aip.AcceleratorType.NVIDIA_TESLA_K80\n", "\n", "worker_pool_specs = [\n", " {\n", " \"replica_count\": 1,\n", " \"machine_spec\": {\n", " \"machine_type\": \"n1-standard-4\",\n", " \"accelerator_type\": TRAIN_GPU,\n", " \"accelerator_count\": TRAIN_NGPU,\n", " },\n", " \"python_package_spec\": {\n", " \"executor_image_uri\": TRAIN_IMAGE,\n", " \"package_uris\": [\"gs://\" + BUCKET_NAME + \"/trainer_cifar.tar.gz\"],\n", " \"python_module\": \"trainer.task\",\n", " \"args\": [\n", " \"--model-dir=\" + \"gs://{}/{}\".format(BUCKET_NAME, JOB_NAME),\n", " \"--epochs=\" + str(20),\n", " \"--steps=\" + str(100),\n", " \"--distribute=\" + \"single\",\n", " ],\n", " },\n", " }\n", "]\n", "\n", "training_job = {\n", " \"display_name\": JOB_NAME,\n", " \"job_spec\": {\"worker_pool_specs\": worker_pool_specs},\n", "}\n", "\n", "print(\n", " MessageToJson(\n", " aip.CreateCustomJobRequest(parent=PARENT, custom_job=training_job).__dict__[\n", " \"_pb\"\n", " ]\n", " )\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "datasets_import:migration,new,request" }, "source": [ "Example output:\n", "```\n", "{\n", " \"parent\": \"projects/migration-ucaip-training/locations/us-central1\",\n", " \"customJob\": {\n", " \"displayName\": \"custom_job_TF_20210227173057\",\n", " \"jobSpec\": {\n", " \"workerPoolSpecs\": [\n", " {\n", " \"machineSpec\": {\n", " \"machineType\": \"n1-standard-4\",\n", " \"acceleratorType\": \"NVIDIA_TESLA_K80\",\n", " \"acceleratorCount\": 1\n", " },\n", " \"replicaCount\": \"1\",\n", " \"pythonPackageSpec\": {\n", " \"executorImageUri\": \"gcr.io/cloud-aiplatform/training/tf-gpu.2-1:latest\",\n", " \"packageUris\": [\n", " \"gs://migration-ucaip-trainingaip-20210227173057/trainer_cifar.tar.gz\"\n", " ],\n", " \"pythonModule\": \"trainer.task\",\n", " \"args\": [\n", " \"--model-dir=gs://migration-ucaip-trainingaip-20210227173057/custom_job_TF_20210227173057\",\n", " \"--epochs=20\",\n", " \"--steps=100\",\n", " \"--distribute=single\"\n", " ]\n", " }\n", " }\n", " ]\n", " }\n", " }\n", "}\n", "```\n" ] }, { "cell_type": "markdown", "metadata": { "id": "BRxBp8pz_5rU" }, "source": [ "#### Call" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "HKdIlqNn_5rU" }, "outputs": [], "source": [ "request = clients[\"job\"].create_custom_job(parent=PARENT, custom_job=training_job)" ] }, { "cell_type": "markdown", "metadata": { "id": "9NnOt5N6_5rX" }, "source": [ "#### Response" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "2RvB7ep5_5rX" }, "outputs": [], "source": [ "print(MessageToJson(request.__dict__[\"_pb\"]))" ] }, { "cell_type": "markdown", "metadata": { "id": "4Gvzcf0j_5rY" }, "source": [ "Example output:\n", "```\n", "{\n", " \"name\": \"projects/116273516712/locations/us-central1/customJobs/2970106362064797696\",\n", " \"displayName\": \"custom_job_TF_20210227173057\",\n", " \"jobSpec\": {\n", " \"workerPoolSpecs\": [\n", " {\n", " \"machineSpec\": {\n", " \"machineType\": \"n1-standard-4\",\n", " \"acceleratorType\": \"NVIDIA_TESLA_K80\",\n", " \"acceleratorCount\": 1\n", " },\n", " \"replicaCount\": \"1\",\n", " \"diskSpec\": {\n", " \"bootDiskType\": \"pd-ssd\",\n", " \"bootDiskSizeGb\": 100\n", " },\n", " \"pythonPackageSpec\": {\n", " \"executorImageUri\": \"gcr.io/cloud-aiplatform/training/tf-gpu.2-1:latest\",\n", " \"packageUris\": [\n", " \"gs://migration-ucaip-trainingaip-20210227173057/trainer_cifar.tar.gz\"\n", " ],\n", " \"pythonModule\": \"trainer.task\",\n", " \"args\": [\n", " \"--model-dir=gs://migration-ucaip-trainingaip-20210227173057/custom_job_TF_20210227173057\",\n", " \"--epochs=20\",\n", " \"--steps=100\",\n", " \"--distribute=single\"\n", " ]\n", " }\n", " }\n", " ]\n", " },\n", " \"state\": \"JOB_STATE_PENDING\",\n", " \"createTime\": \"2021-02-27T17:31:04.494716Z\",\n", " \"updateTime\": \"2021-02-27T17:31:04.494716Z\"\n", "}\n", "```\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "training_pipeline_id:migration,new,response" }, "outputs": [], "source": [ "# The full unique ID for the custom training job\n", "custom_training_id = request.name\n", "# The short numeric ID for the custom training job\n", "custom_training_short_id = custom_training_id.split(\"/\")[-1]\n", "\n", "print(custom_training_id)" ] }, { "cell_type": "markdown", "metadata": { "id": "zqfTUu82_5rY" }, "source": [ "### [projects.locations.customJobs.get](https://cloud.google.com/vertex-ai/docs/reference/rest/v1beta1/projects.locations.trainingPipelines/get)" ] }, { "cell_type": "markdown", "metadata": { "id": "-xTV4OhZ_5rY" }, "source": [ "#### Call" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Tll5SwUs_5rZ" }, "outputs": [], "source": [ "request = clients[\"job\"].get_custom_job(name=custom_training_id)" ] }, { "cell_type": "markdown", "metadata": { "id": "9dNrHBTK_5rZ" }, "source": [ "#### Response" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "3txyKNv3_5rZ" }, "outputs": [], "source": [ "print(MessageToJson(request.__dict__[\"_pb\"]))" ] }, { "cell_type": "markdown", "metadata": { "id": "BN1yzKND_5ra" }, "source": [ "Example output:\n", "```\n", "{\n", " \"name\": \"projects/116273516712/locations/us-central1/customJobs/2970106362064797696\",\n", " \"displayName\": \"custom_job_TF_20210227173057\",\n", " \"jobSpec\": {\n", " \"workerPoolSpecs\": [\n", " {\n", " \"machineSpec\": {\n", " \"machineType\": \"n1-standard-4\",\n", " \"acceleratorType\": \"NVIDIA_TESLA_K80\",\n", " \"acceleratorCount\": 1\n", " },\n", " \"replicaCount\": \"1\",\n", " \"diskSpec\": {\n", " \"bootDiskType\": \"pd-ssd\",\n", " \"bootDiskSizeGb\": 100\n", " },\n", " \"pythonPackageSpec\": {\n", " \"executorImageUri\": \"gcr.io/cloud-aiplatform/training/tf-gpu.2-1:latest\",\n", " \"packageUris\": [\n", " \"gs://migration-ucaip-trainingaip-20210227173057/trainer_cifar.tar.gz\"\n", " ],\n", " \"pythonModule\": \"trainer.task\",\n", " \"args\": [\n", " \"--model-dir=gs://migration-ucaip-trainingaip-20210227173057/custom_job_TF_20210227173057\",\n", " \"--epochs=20\",\n", " \"--steps=100\",\n", " \"--distribute=single\"\n", " ]\n", " }\n", " }\n", " ]\n", " },\n", " \"state\": \"JOB_STATE_PENDING\",\n", " \"createTime\": \"2021-02-27T17:31:04.494716Z\",\n", " \"updateTime\": \"2021-02-27T17:31:04.494716Z\"\n", "}\n", "```\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "trainingpipelines_get:migration,new,wait" }, "outputs": [], "source": [ "while True:\n", " response = clients[\"job\"].get_custom_job(name=custom_training_id)\n", " if response.state != aip.PipelineState.PIPELINE_STATE_SUCCEEDED:\n", " print(\"Training job has not completed:\", response.state)\n", " if response.state == aip.PipelineState.PIPELINE_STATE_FAILED:\n", " break\n", " else:\n", " print(\"Training Time:\", response.end_time - response.start_time)\n", " break\n", " time.sleep(20)\n", "\n", "# model artifact output directory on Google Cloud Storage\n", "model_artifact_dir = (\n", " response.job_spec.worker_pool_specs[0].python_package_spec.args[0].split(\"=\")[-1]\n", ")\n", "print(\"artifact location \" + model_artifact_dir)" ] }, { "cell_type": "markdown", "metadata": { "id": "wvWy83_C_5rb" }, "source": [ "## Deploy the model" ] }, { "cell_type": "markdown", "metadata": { "id": "1SRF-O7V_5rb" }, "source": [ "### Load the saved model" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "3yubnL3r_5rb" }, "outputs": [], "source": [ "import tensorflow as tf\n", "\n", "model = tf.keras.models.load_model(model_artifact_dir)" ] }, { "cell_type": "markdown", "metadata": { "id": "_KAnkwnV_5rc" }, "source": [ "### Serving function for image data" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "HrAjZhPa_5rc" }, "outputs": [], "source": [ "CONCRETE_INPUT = \"numpy_inputs\"\n", "\n", "\n", "def _preprocess(bytes_input):\n", " decoded = tf.io.decode_jpeg(bytes_input, channels=3)\n", " decoded = tf.image.convert_image_dtype(decoded, tf.float32)\n", " resized = tf.image.resize(decoded, size=(32, 32))\n", " rescale = tf.cast(resized / 255.0, tf.float32)\n", " return rescale\n", "\n", "\n", "@tf.function(input_signature=[tf.TensorSpec([None], tf.string)])\n", "def preprocess_fn(bytes_inputs):\n", " decoded_images = tf.map_fn(\n", " _preprocess, bytes_inputs, dtype=tf.float32, back_prop=False\n", " )\n", " return {\n", " CONCRETE_INPUT: decoded_images\n", " } # User needs to make sure the key matches model's input\n", "\n", "\n", "m_call = tf.function(model.call).get_concrete_function(\n", " [tf.TensorSpec(shape=[None, 32, 32, 3], dtype=tf.float32, name=CONCRETE_INPUT)]\n", ")\n", "\n", "\n", "@tf.function(input_signature=[tf.TensorSpec([None], tf.string)])\n", "def serving_fn(bytes_inputs):\n", " images = preprocess_fn(bytes_inputs)\n", " prob = m_call(*images)\n", " return prob\n", "\n", "\n", "tf.saved_model.save(\n", " model,\n", " model_artifact_dir,\n", " signatures={\n", " \"serving_default\": serving_fn,\n", " },\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "SQdpI3ok_5rc" }, "source": [ "### Get the serving function signature" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "9JxBIHY7_5rc" }, "outputs": [], "source": [ "loaded = tf.saved_model.load(model_artifact_dir)\n", "\n", "input_name = list(\n", " loaded.signatures[\"serving_default\"].structured_input_signature[1].keys()\n", ")[0]\n", "\n", "print(\"Serving function input:\", input_name)" ] }, { "cell_type": "markdown", "metadata": { "id": "uSLjjGyy_5rd" }, "source": [ "Example output:\n", "```\n", "Serving function input: bytes_inputs\n", "```\n" ] }, { "cell_type": "markdown", "metadata": { "id": "COwVZtxhJjkW" }, "source": [ "### [projects.locations.models.upload](https://cloud.google.com/vertex-ai/docs/reference/rest/v1beta1/projects.locations.models/upload)" ] }, { "cell_type": "markdown", "metadata": { "id": "QJ0VwMc1_5rd" }, "source": [ "#### Request" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "mZp4UlTb_5rd" }, "outputs": [], "source": [ "model = {\n", " \"display_name\": \"custom_job_TF\" + TIMESTAMP,\n", " \"metadata_schema_uri\": \"\",\n", " \"artifact_uri\": model_artifact_dir,\n", " \"container_spec\": {\n", " \"image_uri\": \"gcr.io/cloud-aiplatform/prediction/tf2-cpu.2-1:latest\"\n", " },\n", "}\n", "\n", "print(MessageToJson(aip.UploadModelRequest(parent=PARENT, model=model).__dict__[\"_pb\"]))" ] }, { "cell_type": "markdown", "metadata": { "id": "uSLjjGyy_5rd" }, "source": [ "Example output:\n", "```\n", "{\n", " \"parent\": \"projects/migration-ucaip-training/locations/us-central1\",\n", " \"model\": {\n", " \"displayName\": \"custom_job_TF20210227173057\",\n", " \"containerSpec\": {\n", " \"imageUri\": \"gcr.io/cloud-aiplatform/prediction/tf2-cpu.2-1:latest\"\n", " },\n", " \"artifactUri\": \"gs://migration-ucaip-trainingaip-20210227173057/custom_job_TF_20210227173057\"\n", " }\n", "}\n", "\n", "```\n" ] }, { "cell_type": "markdown", "metadata": { "id": "CxTMT98g_5re" }, "source": [ "#### Call" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "3q-pqGTN_5re" }, "outputs": [], "source": [ "request = clients[\"model\"].upload_model(parent=PARENT, model=model)" ] }, { "cell_type": "markdown", "metadata": { "id": "I3Z3mGDv_5re" }, "source": [ "#### Response" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "r_hEBXn2_5re" }, "outputs": [], "source": [ "result = request.result()\n", "\n", "print(MessageToJson(result.__dict__[\"_pb\"]))" ] }, { "cell_type": "markdown", "metadata": { "id": "dQak2NW3_5re" }, "source": [ "Example output:\n", "```\n", "{\n", " \"model\": \"projects/116273516712/locations/us-central1/models/8844102097923211264\"\n", "}\n", "```\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "zOvD5qZK_5re" }, "outputs": [], "source": [ "# The full unique ID for the model\n", "model_id = result.model\n", "\n", "print(model_id)" ] }, { "cell_type": "markdown", "metadata": { "id": "make_batch_predictions:migration" }, "source": [ "## Make batch predictions\n" ] }, { "cell_type": "markdown", "metadata": { "id": "make_batch_file:automl,image" }, "source": [ "### Make the batch input file\n", "\n", "Let's now make a batch input file, which you will store in your local Cloud Storage bucket. The batch input file can be JSONL.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "_X3Dlg4X_5rf" }, "outputs": [], "source": [ "import base64\n", "import json\n", "\n", "import cv2\n", "import numpy as np\n", "import tensorflow as tf\n", "\n", "(_, _), (x_test, y_test) = tf.keras.datasets.cifar10.load_data()\n", "\n", "\n", "test_image_1, test_label_1 = x_test[0], y_test[0]\n", "test_image_2, test_label_2 = x_test[1], y_test[1]\n", "\n", "cv2.imwrite(\"tmp1.jpg\", (test_image_1).astype(np.uint8))\n", "cv2.imwrite(\"tmp2.jpg\", (test_image_2).astype(np.uint8))\n", "\n", "gcs_input_uri = \"gs://\" + BUCKET_NAME + \"/\" + \"test.jsonl\"\n", "with tf.io.gfile.GFile(gcs_input_uri, \"w\") as f:\n", " bytes = tf.io.read_file(\"tmp1.jpg\")\n", " b64str = base64.b64encode(bytes.numpy()).decode(\"utf-8\")\n", " f.write(json.dumps({input_name: {\"b64\": b64str}}) + \"\\n\")\n", "\n", " bytes = tf.io.read_file(\"tmp2.jpg\")\n", " b64str = base64.b64encode(bytes.numpy()).decode(\"utf-8\")\n", " f.write(json.dumps({input_name: {\"b64\": b64str}}) + \"\\n\")\n", "\n", "! gsutil cat $gcs_input_uri" ] }, { "cell_type": "markdown", "metadata": { "id": "N-mZZ59W_5rg" }, "source": [ "Example output:\n", "```\n", "{\"bytes_inputs\": {\"b64\": \"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\"}}\n", "{\"bytes_inputs\": {\"b64\": \"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\"}}\n", "```\n" ] }, { "cell_type": "markdown", "metadata": { "id": "batchpredictionjobs_create:migration,new" }, "source": [ "### [projects.locations.batchPredictionJobs.create](https://cloud.google.com/vertex-ai/docs/reference/rest/v1beta1/projects.locations.batchPredictionJobs/create)\n" ] }, { "cell_type": "markdown", "metadata": { "id": "request:migration" }, "source": [ "#### Request\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "batchpredictionjobs_create:migration,new,request,icn" }, "outputs": [], "source": [ "batch_prediction_job = aip.BatchPredictionJob(\n", " display_name=\"custom_job_TF\" + TIMESTAMP,\n", " model=model_id,\n", " input_config={\n", " \"instances_format\": \"jsonl\",\n", " \"gcs_source\": {\"uris\": [gcs_input_uri]},\n", " },\n", " model_parameters=ParseDict(\n", " {\"confidenceThreshold\": 0.5, \"maxPredictions\": 2}, Value()\n", " ),\n", " output_config={\n", " \"predictions_format\": \"jsonl\",\n", " \"gcs_destination\": {\n", " \"output_uri_prefix\": \"gs://\" + f\"{BUCKET_NAME}/batch_output/\"\n", " },\n", " },\n", " dedicated_resources={\n", " \"machine_spec\": {\"machine_type\": \"n1-standard-2\", \"accelerator_type\": 0},\n", " \"starting_replica_count\": 1,\n", " \"max_replica_count\": 1,\n", " },\n", ")\n", "\n", "print(\n", " MessageToJson(\n", " aip.CreateBatchPredictionJobRequest(\n", " parent=PARENT, batch_prediction_job=batch_prediction_job\n", " ).__dict__[\"_pb\"]\n", " )\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "pX4e-aNR_5rg" }, "source": [ "Example output:\n", "```\n", "{\n", " \"parent\": \"projects/migration-ucaip-training/locations/us-central1\",\n", " \"batchPredictionJob\": {\n", " \"displayName\": \"custom_job_TF_TF20210227173057\",\n", " \"model\": \"projects/116273516712/locations/us-central1/models/8844102097923211264\",\n", " \"inputConfig\": {\n", " \"instancesFormat\": \"jsonl\",\n", " \"gcsSource\": {\n", " \"uris\": [\n", " \"gs://migration-ucaip-trainingaip-20210227173057/test.jsonl\"\n", " ]\n", " }\n", " },\n", " \"modelParameters\": {\n", " \"maxPredictions\": 10000.0,\n", " \"confidenceThreshold\": 0.5\n", " },\n", " \"outputConfig\": {\n", " \"predictionsFormat\": \"jsonl\",\n", " \"gcsDestination\": {\n", " \"outputUriPrefix\": \"gs://migration-ucaip-trainingaip-20210227173057/batch_output/\"\n", " }\n", " },\n", " \"dedicatedResources\": {\n", " \"machineSpec\": {\n", " \"machineType\": \"n1-standard-2\"\n", " },\n", " \"startingReplicaCount\": 1,\n", " \"maxReplicaCount\": 1\n", " }\n", " }\n", "}\n", "```\n" ] }, { "cell_type": "markdown", "metadata": { "id": "call:migration" }, "source": [ "#### Call\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "batchpredictionjobs_create:migration,new,call" }, "outputs": [], "source": [ "request = clients[\"job\"].create_batch_prediction_job(\n", " parent=PARENT, batch_prediction_job=batch_prediction_job\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "response:migration" }, "source": [ "#### Response\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "print:migration,new,request" }, "outputs": [], "source": [ "print(MessageToJson(request.__dict__[\"_pb\"]))" ] }, { "cell_type": "markdown", "metadata": { "id": "batchpredictionjobs_create:migration,new,response,icn" }, "source": [ "Example output:\n", "```\n", "{\n", " \"name\": \"projects/116273516712/locations/us-central1/batchPredictionJobs/659759753223733248\",\n", " \"displayName\": \"custom_job_TF_TF20210227173057\",\n", " \"model\": \"projects/116273516712/locations/us-central1/models/8844102097923211264\",\n", " \"inputConfig\": {\n", " \"instancesFormat\": \"jsonl\",\n", " \"gcsSource\": {\n", " \"uris\": [\n", " \"gs://migration-ucaip-trainingaip-20210227173057/test.jsonl\"\n", " ]\n", " }\n", " },\n", " \"modelParameters\": {\n", " \"maxPredictions\": 10000.0,\n", " \"confidenceThreshold\": 0.5\n", " },\n", " \"outputConfig\": {\n", " \"predictionsFormat\": \"jsonl\",\n", " \"gcsDestination\": {\n", " \"outputUriPrefix\": \"gs://migration-ucaip-trainingaip-20210227173057/batch_output/\"\n", " }\n", " },\n", " \"dedicatedResources\": {\n", " \"machineSpec\": {\n", " \"machineType\": \"n1-standard-2\"\n", " },\n", " \"startingReplicaCount\": 1,\n", " \"maxReplicaCount\": 1\n", " },\n", " \"manualBatchTuningParameters\": {},\n", " \"state\": \"JOB_STATE_PENDING\",\n", " \"createTime\": \"2021-02-27T18:00:30.887438Z\",\n", " \"updateTime\": \"2021-02-27T18:00:30.887438Z\"\n", "}\n", "```\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "batch_job_id:migration,new,response" }, "outputs": [], "source": [ "# The fully qualified ID for the batch job\n", "batch_job_id = request.name\n", "# The short numeric ID for the batch job\n", "batch_job_short_id = batch_job_id.split(\"/\")[-1]\n", "\n", "print(batch_job_id)" ] }, { "cell_type": "markdown", "metadata": { "id": "batchpredictionjobs_get:migration,new" }, "source": [ "### [projects.locations.batchPredictionJobs.get](https://cloud.google.com/vertex-ai/docs/reference/rest/v1beta1/projects.locations.batchPredictionJobs/get)\n" ] }, { "cell_type": "markdown", "metadata": { "id": "oW7MtyrH_5ri" }, "source": [ "#### Call\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "batchpredictionjobs_get:migration,new,call" }, "outputs": [], "source": [ "request = clients[\"job\"].get_batch_prediction_job(name=batch_job_id)" ] }, { "cell_type": "markdown", "metadata": { "id": "Ewx0qI1l_5ri" }, "source": [ "#### Response\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "FYSggc9c_5ri" }, "outputs": [], "source": [ "print(MessageToJson(request.__dict__[\"_pb\"]))" ] }, { "cell_type": "markdown", "metadata": { "id": "batchpredictionjobs_get:migration,new,response,icn" }, "source": [ "Example output:\n", "```\n", "{\n", " \"name\": \"projects/116273516712/locations/us-central1/batchPredictionJobs/659759753223733248\",\n", " \"displayName\": \"custom_job_TF_TF20210227173057\",\n", " \"model\": \"projects/116273516712/locations/us-central1/models/8844102097923211264\",\n", " \"inputConfig\": {\n", " \"instancesFormat\": \"jsonl\",\n", " \"gcsSource\": {\n", " \"uris\": [\n", " \"gs://migration-ucaip-trainingaip-20210227173057/test.jsonl\"\n", " ]\n", " }\n", " },\n", " \"modelParameters\": {\n", " \"confidenceThreshold\": 0.5,\n", " \"maxPredictions\": 10000.0\n", " },\n", " \"outputConfig\": {\n", " \"predictionsFormat\": \"jsonl\",\n", " \"gcsDestination\": {\n", " \"outputUriPrefix\": \"gs://migration-ucaip-trainingaip-20210227173057/batch_output/\"\n", " }\n", " },\n", " \"dedicatedResources\": {\n", " \"machineSpec\": {\n", " \"machineType\": \"n1-standard-2\"\n", " },\n", " \"startingReplicaCount\": 1,\n", " \"maxReplicaCount\": 1\n", " },\n", " \"manualBatchTuningParameters\": {},\n", " \"state\": \"JOB_STATE_RUNNING\",\n", " \"createTime\": \"2021-02-27T18:00:30.887438Z\",\n", " \"startTime\": \"2021-02-27T18:00:30.938444Z\",\n", " \"updateTime\": \"2021-02-27T18:00:30.938444Z\"\n", "}\n", "```\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "batchpredictionjobs_get:migration,new,wait" }, "outputs": [], "source": [ "def get_latest_predictions(gcs_out_dir):\n", " \"\"\" Get the latest prediction subfolder using the timestamp in the subfolder name\"\"\"\n", " folders = !gsutil ls $gcs_out_dir\n", " latest = \"\"\n", " for folder in folders:\n", " subfolder = folder.split(\"/\")[-2]\n", " if subfolder.startswith(\"prediction-\"):\n", " if subfolder > latest:\n", " latest = folder[:-1]\n", " return latest\n", "\n", "\n", "while True:\n", " response = clients[\"job\"].get_batch_prediction_job(name=batch_job_id)\n", " if response.state != aip.JobState.JOB_STATE_SUCCEEDED:\n", " print(\"The job has not completed:\", response.state)\n", " if response.state == aip.JobState.JOB_STATE_FAILED:\n", " break\n", " else:\n", " folder = get_latest_predictions(\n", " response.output_config.gcs_destination.output_uri_prefix\n", " )\n", " ! gsutil ls $folder/prediction\n", "\n", " ! gsutil cat $folder/prediction\n", " break\n", " time.sleep(60)" ] }, { "cell_type": "markdown", "metadata": { "id": "batchpredictionjobs_get:migration,new,wait,icn" }, "source": [ "Example output:\n", "```\n", "gs://migration-ucaip-trainingaip-20210227173057/batch_output/prediction-custom_job_TF_TF20210227173057-2021_02_27T10_00_30_820Z/prediction.errors_stats-00000-of-00001\n", "gs://migration-ucaip-trainingaip-20210227173057/batch_output/prediction-custom_job_TF_TF20210227173057-2021_02_27T10_00_30_820Z/prediction.results-00000-of-00001\n", "{\"instance\": {\"bytes_inputs\": {\"b64\": \"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\"}}, \"prediction\": [0.0407731421, 0.125140116, 0.118551917, 0.100501947, 0.128865793, 0.089787662, 0.157575116, 0.121281914, 0.0312845968, 0.0862377882]}\n", "{\"instance\": {\"bytes_inputs\": {\"b64\": \"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\"}}, \"prediction\": [0.0406896845, 0.125281364, 0.118567884, 0.100639313, 0.12864624, 0.0898737088, 0.157521054, 0.121037535, 0.0313298739, 0.0864133239]}\n", "```\n" ] }, { "cell_type": "markdown", "metadata": { "id": "make_online_predictions:migration" }, "source": [ "## Make online predictions\n" ] }, { "cell_type": "markdown", "metadata": { "id": "endpoints_create:migration,new" }, "source": [ "### [projects.locations.endpoints.create](https://cloud.google.com/vertex-ai/docs/reference/rest/v1beta1/projects.locations.endpoints/create)\n" ] }, { "cell_type": "markdown", "metadata": { "id": "O_IkMU4i_5rj" }, "source": [ "#### Request\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "endpoints_create:migration,new,request" }, "outputs": [], "source": [ "endpoint = {\"display_name\": \"custom_job_TF\" + TIMESTAMP}\n", "\n", "print(\n", " MessageToJson(\n", " aip.CreateEndpointRequest(parent=PARENT, endpoint=endpoint).__dict__[\"_pb\"]\n", " )\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "GD2ezZB1_5rk" }, "source": [ "Example output:\n", "```\n", "{\n", " \"parent\": \"projects/migration-ucaip-training/locations/us-central1\",\n", " \"endpoint\": {\n", " \"displayName\": \"custom_job_TF_TF20210227173057\"\n", " }\n", "}\n", "```\n" ] }, { "cell_type": "markdown", "metadata": { "id": "sqoAv87L_5rk" }, "source": [ "#### Call\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "endpoints_create:migration,new,call" }, "outputs": [], "source": [ "request = clients[\"endpoint\"].create_endpoint(parent=PARENT, endpoint=endpoint)" ] }, { "cell_type": "markdown", "metadata": { "id": "s70F_62P_5rk" }, "source": [ "#### Response\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "print:migration,new,response" }, "outputs": [], "source": [ "result = request.result()\n", "\n", "print(MessageToJson(result.__dict__[\"_pb\"]))" ] }, { "cell_type": "markdown", "metadata": { "id": "endpoints_create:migration,new,response" }, "source": [ "Example output:\n", "```\n", "{\n", " \"name\": \"projects/116273516712/locations/us-central1/endpoints/6810814827095654400\"\n", "}\n", "```\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "endpoint_id:migration,new,response" }, "outputs": [], "source": [ "# The full unique ID for the endpoint\n", "endpoint_id = result.name\n", "# The short numeric ID for the endpoint\n", "endpoint_short_id = endpoint_id.split(\"/\")[-1]\n", "\n", "print(endpoint_id)" ] }, { "cell_type": "markdown", "metadata": { "id": "endpoints_deploymodel:migration,new" }, "source": [ "### [projects.locations.endpoints.deployModel](https://cloud.google.com/vertex-ai/docs/reference/rest/v1beta1/projects.locations.endpoints/deployModel)\n" ] }, { "cell_type": "markdown", "metadata": { "id": "EN_sldlj_5rl" }, "source": [ "#### Request\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "endpoints_deploymodel:migration,new,request" }, "outputs": [], "source": [ "deployed_model = {\n", " \"model\": model_id,\n", " \"display_name\": \"custom_job_TF\" + TIMESTAMP,\n", " \"dedicated_resources\": {\n", " \"min_replica_count\": 1,\n", " \"machine_spec\": {\"machine_type\": \"n1-standard-4\", \"accelerator_count\": 0},\n", " },\n", "}\n", "\n", "print(\n", " MessageToJson(\n", " aip.DeployModelRequest(\n", " endpoint=endpoint_id,\n", " deployed_model=deployed_model,\n", " traffic_split={\"0\": 100},\n", " ).__dict__[\"_pb\"]\n", " )\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "0kJRbqBm_5rl" }, "source": [ "Example output:\n", "```\n", "{\n", " \"endpoint\": \"projects/116273516712/locations/us-central1/endpoints/6810814827095654400\",\n", " \"deployedModel\": {\n", " \"model\": \"projects/116273516712/locations/us-central1/models/8844102097923211264\",\n", " \"displayName\": \"custom_job_TF_TF20210227173057\",\n", " \"dedicatedResources\": {\n", " \"machineSpec\": {\n", " \"machineType\": \"n1-standard-4\"\n", " },\n", " \"minReplicaCount\": 1\n", " }\n", " },\n", " \"trafficSplit\": {\n", " \"0\": 100\n", " }\n", "}\n", "```\n" ] }, { "cell_type": "markdown", "metadata": { "id": "cZlTImIm_5rl" }, "source": [ "#### Call\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "endpoints_deploymodel:migration,new,call" }, "outputs": [], "source": [ "request = clients[\"endpoint\"].deploy_model(\n", " endpoint=endpoint_id, deployed_model=deployed_model, traffic_split={\"0\": 100}\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "bg7Dd8XM_5rm" }, "source": [ "#### Response\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "CQSG7JM0_5rm" }, "outputs": [], "source": [ "result = request.result()\n", "\n", "print(MessageToJson(result.__dict__[\"_pb\"]))" ] }, { "cell_type": "markdown", "metadata": { "id": "endpoints_deploymodel:migration,new,response" }, "source": [ "Example output:\n", "```\n", "{\n", " \"deployedModel\": {\n", " \"id\": \"2064302294823862272\"\n", " }\n", "}\n", "```\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "deployed_model_id:migration,new,response" }, "outputs": [], "source": [ "# The unique ID for the deployed model\n", "deployed_model_id = result.deployed_model.id\n", "\n", "print(deployed_model_id)" ] }, { "cell_type": "markdown", "metadata": { "id": "endpoints_predict:migration,new" }, "source": [ "### [projects.locations.endpoints.predict](https://cloud.google.com/vertex-ai/docs/reference/rest/v1beta1/projects.locations.endpoints/predict)\n" ] }, { "cell_type": "markdown", "metadata": { "id": "DA2OeN6e_5rn" }, "source": [ "### Prepare file for online prediction" ] }, { "cell_type": "markdown", "metadata": { "id": "tPDV6rxh_5ro" }, "source": [ "#### Request\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "endpoints_predict:migration,new,request,icn" }, "outputs": [], "source": [ "import base64\n", "\n", "import cv2\n", "import tensorflow as tf\n", "\n", "(_, _), (x_test, y_test) = tf.keras.datasets.cifar10.load_data()\n", "test_image, test_label = x_test[0], y_test[0]\n", "\n", "cv2.imwrite(\"tmp.jpg\", (test_image 255).astype(np.uint8))\n", "bytes = tf.io.read_file(\"tmp.jpg\")\n", "b64str = base64.b64encode(bytes.numpy()).decode(\"utf-8\")\n", "\n", "instances_list = [{\"bytes_inputs\": {\"b64\": b64str}}]\n", "\n", "prediction_request = aip.PredictRequest(endpoint=endpoint_id)\n", "prediction_request.instances.append(instances_list)\n", "\n", "print(MessageToJson(prediction_request.__dict__[\"_pb\"]))" ] }, { "cell_type": "markdown", "metadata": { "id": "9aOWkkN-_5ro" }, "source": [ "Example output:\n", "```\n", "{\n", " \"endpoint\": \"projects/116273516712/locations/us-central1/endpoints/6810814827095654400\",\n", " \"instances\": [\n", " [\n", " {\n", " \"bytes_inputs\": {\n", " \"b64\": \"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\"\n", " }\n", " }\n", " ]\n", " ]\n", "}\n", "\n", "```\n" ] }, { "cell_type": "markdown", "metadata": { "id": "-c1HT8Bw_5ro" }, "source": [ "#### Call\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "endpoints_predict:migration,new,call" }, "outputs": [], "source": [ "request = clients[\"prediction\"].predict(endpoint=endpoint_id, instances=instances_list)" ] }, { "cell_type": "markdown", "metadata": { "id": "koz-wcHo_5ro" }, "source": [ "#### Response\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "9zRSZ3DM_5ro" }, "outputs": [], "source": [ "print(MessageToJson(request.__dict__[\"_pb\"]))" ] }, { "cell_type": "markdown", "metadata": { "id": "endpoints_predict:migration,new,response,icn" }, "source": [ "Example output:\n", "```\n", "{\n", " \"predictions\": [\n", " [\n", " 0.0406113081,\n", " 0.125313938,\n", " 0.118626907,\n", " 0.100714684,\n", " 0.128500372,\n", " 0.0899592042,\n", " 0.157601,\n", " 0.121072263,\n", " 0.0312432405,\n", " 0.0863570943\n", " ]\n", " ],\n", " \"deployedModelId\": \"2064302294823862272\"\n", "}\n", "```\n" ] }, { "cell_type": "markdown", "metadata": { "id": "endpoints_undeploymodel:migration,new" }, "source": [ "### [projects.locations.endpoints.undeployModel](https://cloud.google.com/vertex-ai/docs/reference/rest/v1beta1/projects.locations.endpoints/undeployModel)\n" ] }, { "cell_type": "markdown", "metadata": { "id": "x8bg9Xyj_5rp" }, "source": [ "#### Call\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "endpoints_undeploymodel:migration,new,call" }, "outputs": [], "source": [ "request = clients[\"endpoint\"].undeploy_model(\n", " endpoint=endpoint_id, deployed_model_id=deployed_model_id, traffic_split={}\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "a3_S-AC6_5rp" }, "source": [ "#### Response\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "gWdonkAJ_5rq" }, "outputs": [], "source": [ "result = request.result()\n", "\n", "print(MessageToJson(result.__dict__[\"_pb\"]))" ] }, { "cell_type": "markdown", "metadata": { "id": "endpoints_undeploymodel:migration,new,response" }, "source": [ "Example output:\n", "```\n", "{}\n", "```\n" ] }, { "cell_type": "markdown", "metadata": { "id": "cleanup:migration,new" }, "source": [ "# Cleaning up\n", "\n", "To clean up all GCP resources used in this project, you can [delete the GCP\n", "project](https://cloud.google.com/resource-manager/docs/creating-managing-projects#shutting_down_projects) you used for the tutorial.\n", "\n", "Otherwise, you can delete the individual resources you created in this tutorial.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "ND2Y2TnN_5rq" }, "outputs": [], "source": [ "delete_model = True\n", "delete_endpoint = True\n", "delete_custom_job = True\n", "delete_batchjob = True\n", "delete_bucket = True\n", "\n", "# Delete the model using the Vertex AI fully qualified identifier for the model\n", "try:\n", " if delete_model:\n", " clients[\"model\"].delete_model(name=model_id)\n", "except Exception as e:\n", " print(e)\n", "\n", "# Delete the endpoint using the Vertex AI fully qualified identifier for the endpoint\n", "try:\n", " if delete_endpoint:\n", " clients[\"endpoint\"].delete_endpoint(name=endpoint_id)\n", "except Exception as e:\n", " print(e)\n", "\n", "# Delete the custom training using the Vertex AI fully qualified identifier for the custom training\n", "try:\n", " if delete_custom_job:\n", " clients[\"job\"].delete_custom_job(name=custom_training_id)\n", "except Exception as e:\n", " print(e)\n", "\n", "# Delete the batch job using the Vertex AI fully qualified identifier for the batch job\n", "try:\n", " if delete_batchjob:\n", " clients[\"job\"].delete_batch_prediction_job(name=batch_job_id)\n", "except Exception as e:\n", " print(e)\n", "\n", "if delete_bucket and \"BUCKET_NAME\" in globals():\n", " ! gsutil rm -r gs://$BUCKET_NAME" ] } ], "metadata": { "colab": { "name": "UJ2,12 unified Custom Training Prebuilt Container TF Keras.ipynb", "toc_visible": true }, "kernelspec": { "display_name": "Python 3", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 0 }	apache-2.0
telescopeuser/uat_shl	rnd03/shl_sm_NoOCR_v010.ipynb	1	433077	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### SHL Project\n", "\n", "* simulation module: shl_sm\n", "\n", "### shl_sm required data feeds:\n", "\n", "* live bidding price, per second, time series\n", "\n", "### prediction module parameters/csv\n", "\n", "* parm_si.csv (seasonality index per second)\n", "\n", "* parm_month.csv (parameter like alpha, beta, gamma, etc. per month)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# SHL Simulation Module: shl_sm" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Import useful reference packages" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Import SHL Prediction Module: shl_pm" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+-----------------------------------------------+\n", "\| Loaded SHL Prediction Module \|\n", "\| Version 0.0.0.1 \|\n", "+-----------------------------------------------+\n", "\n", "+-----------------------------------------------+\n", "\| SHL Prediction Module User Guide \|\n", "+-----------------------------------------------+\n", "\n", "+-----------------------------------------------+\n", "\| Key Function I: \n", "\| shl_initialize(in_ccyy_mm='2017-07')\n", "+-----------------------------------------------+\n", "\n", "This function takes one input. Run this funciton once, before calling shl_predict_price_k_step()\n", "\n", "Inputs:\n", "(1) in_ccyy_mm: the (current) year month for predicting bidding price\n", " string, i.e. '2017-07'\n", " \n", "Outputs: N.A.\n", "\n", "+-----------------------------------------------+\n", "\| Key Function II: \n", "\| shl_predict_price_k_step(in_current_time, in_current_price, in_k_seconds=1, return_value='f_1_step_pred_set_price_rounded')\n", "+-----------------------------------------------+\n", "\n", "This function takes four inputs then returns prediciton values in a python list.\n", "\n", "Ensure this function is called 'once and only once' for EVERY second with price, starting from '11:29:00'! \n", "This is to ensure prediction module could capture all actual prices for internal prediction calculation.\n", "\n", "Inputs:\n", "(1) in_current_time: current time/second of bidding price\n", " string, i.e. '11:29:50'\n", "(2) in_current_price : current bidding price\n", " number/integer/float, i.e. 89400\n", "(3) in_k_seconds : forecast price in the next k seconds\n", " integer, default value = 1, i.e. 7\n", "(4) return_value : return result of predicted price, or predicted set price = predicted price + dynamic increment\n", " string, i.e. 89600 predicted price (return_value = 'f_1_step_pred_price_rounded')\n", " string, i.e. 89800 predicted set price (return_value = 'f_1_step_pred_set_price_rounded')\n", "\n", "Outputs:\n", "(1) Returned restuls in python list\n", " list of integer , i.e. [89800] (in_k_seconds = 1)\n", " list of integers, i.e. [89800, 89900, 89900, 90000, 90100, 90100, 90200] (in_k_seconds = 7)\n", "\n" ] } ], "source": [ "import shl_pm" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### shl_sm parameters:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### shl_sm simulated real time per second price ata, fetch from csv:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>ccyy-mm</th>\n", " <th>time</th>\n", " <th>bid-price</th>\n", " <th>ref-price</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>2015-01</td>\n", " <td>11:29:00</td>\n", " <td>74000</td>\n", " <td>74000</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2015-01</td>\n", " <td>11:29:01</td>\n", " <td>74000</td>\n", " <td>74000</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2015-01</td>\n", " <td>11:29:02</td>\n", " <td>74000</td>\n", " <td>74000</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>2015-01</td>\n", " <td>11:29:03</td>\n", " <td>74000</td>\n", " <td>74000</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>2015-01</td>\n", " <td>11:29:04</td>\n", " <td>74000</td>\n", " <td>74000</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>2015-01</td>\n", " <td>11:29:05</td>\n", " <td>74000</td>\n", " <td>74000</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>2015-01</td>\n", " <td>11:29:06</td>\n", " <td>74000</td>\n", " <td>74000</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>2015-01</td>\n", " <td>11:29:07</td>\n", " <td>74000</td>\n", " <td>74000</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>2015-01</td>\n", " <td>11:29:08</td>\n", " <td>74000</td>\n", " <td>74000</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>2015-01</td>\n", " <td>11:29:09</td>\n", " <td>74000</td>\n", " <td>74000</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>2015-01</td>\n", " <td>11:29:10</td>\n", " <td>74000</td>\n", " <td>74000</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>2015-01</td>\n", " <td>11:29:11</td>\n", " <td>74000</td>\n", " <td>74000</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>2015-01</td>\n", " <td>11:29:12</td>\n", " <td>74000</td>\n", " <td>74000</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>2015-01</td>\n", " <td>11:29:13</td>\n", " <td>74000</td>\n", " <td>74000</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>2015-01</td>\n", " <td>11:29:14</td>\n", " <td>74000</td>\n", " <td>74000</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>2015-01</td>\n", " <td>11:29:15</td>\n", " <td>74000</td>\n", " <td>74000</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>2015-01</td>\n", " <td>11:29:16</td>\n", " <td>74000</td>\n", " <td>74000</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>2015-01</td>\n", " <td>11:29:17</td>\n", " <td>74000</td>\n", " <td>74000</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>2015-01</td>\n", " <td>11:29:18</td>\n", " <td>74000</td>\n", " <td>74000</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>2015-01</td>\n", " <td>11:29:19</td>\n", " <td>74000</td>\n", " <td>74000</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>2015-01</td>\n", " <td>11:29:20</td>\n", " <td>74000</td>\n", " <td>74000</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>2015-01</td>\n", " <td>11:29:21</td>\n", " <td>74000</td>\n", " <td>74000</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>2015-01</td>\n", " <td>11:29:22</td>\n", " <td>74000</td>\n", " <td>74000</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>2015-01</td>\n", " <td>11:29:23</td>\n", " <td>74000</td>\n", " <td>74000</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>2015-01</td>\n", " <td>11:29:24</td>\n", " <td>74000</td>\n", " <td>74000</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td>2015-01</td>\n", " <td>11:29:25</td>\n", " <td>74000</td>\n", " <td>74000</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td>2015-01</td>\n", " <td>11:29:26</td>\n", " <td>74000</td>\n", " <td>74000</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td>2015-01</td>\n", " <td>11:29:27</td>\n", " <td>74000</td>\n", " <td>74000</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td>2015-01</td>\n", " <td>11:29:28</td>\n", " <td>74000</td>\n", " <td>74000</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td>2015-01</td>\n", " <td>11:29:29</td>\n", " <td>74000</td>\n", " <td>74000</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>1861</th>\n", " <td>2017-07</td>\n", " <td>11:29:31</td>\n", " <td>90700</td>\n", " <td>89800</td>\n", " </tr>\n", " <tr>\n", " <th>1862</th>\n", " <td>2017-07</td>\n", " <td>11:29:32</td>\n", " <td>90700</td>\n", " <td>89800</td>\n", " </tr>\n", " <tr>\n", " <th>1863</th>\n", " <td>2017-07</td>\n", " <td>11:29:33</td>\n", " <td>90700</td>\n", " <td>89800</td>\n", " </tr>\n", " <tr>\n", " <th>1864</th>\n", " <td>2017-07</td>\n", " <td>11:29:34</td>\n", " <td>90700</td>\n", " <td>89800</td>\n", " </tr>\n", " <tr>\n", " <th>1865</th>\n", " <td>2017-07</td>\n", " <td>11:29:35</td>\n", " <td>90800</td>\n", " <td>89800</td>\n", " </tr>\n", " <tr>\n", " <th>1866</th>\n", " <td>2017-07</td>\n", " <td>11:29:36</td>\n", " <td>90800</td>\n", " <td>89800</td>\n", " </tr>\n", " <tr>\n", " <th>1867</th>\n", " <td>2017-07</td>\n", " <td>11:29:37</td>\n", " <td>90900</td>\n", " <td>89800</td>\n", " </tr>\n", " <tr>\n", " <th>1868</th>\n", " <td>2017-07</td>\n", " <td>11:29:38</td>\n", " <td>91000</td>\n", " <td>89800</td>\n", " </tr>\n", " <tr>\n", " <th>1869</th>\n", " <td>2017-07</td>\n", " <td>11:29:39</td>\n", " <td>91000</td>\n", " <td>89800</td>\n", " </tr>\n", " <tr>\n", " <th>1870</th>\n", " <td>2017-07</td>\n", " <td>11:29:40</td>\n", " <td>91000</td>\n", " <td>89800</td>\n", " </tr>\n", " <tr>\n", " <th>1871</th>\n", " <td>2017-07</td>\n", " <td>11:29:41</td>\n", " <td>91000</td>\n", " <td>89800</td>\n", " </tr>\n", " <tr>\n", " <th>1872</th>\n", " <td>2017-07</td>\n", " <td>11:29:42</td>\n", " <td>91000</td>\n", " <td>89800</td>\n", " </tr>\n", " <tr>\n", " <th>1873</th>\n", " <td>2017-07</td>\n", " <td>11:29:43</td>\n", " <td>91000</td>\n", " <td>89800</td>\n", " </tr>\n", " <tr>\n", " <th>1874</th>\n", " <td>2017-07</td>\n", " <td>11:29:44</td>\n", " <td>91100</td>\n", " <td>89800</td>\n", " </tr>\n", " <tr>\n", " <th>1875</th>\n", " <td>2017-07</td>\n", " <td>11:29:45</td>\n", " <td>91100</td>\n", " <td>89800</td>\n", " </tr>\n", " <tr>\n", " <th>1876</th>\n", " <td>2017-07</td>\n", " <td>11:29:46</td>\n", " <td>91200</td>\n", " <td>89800</td>\n", " </tr>\n", " <tr>\n", " <th>1877</th>\n", " <td>2017-07</td>\n", " <td>11:29:47</td>\n", " <td>91300</td>\n", " <td>89800</td>\n", " </tr>\n", " <tr>\n", " <th>1878</th>\n", " <td>2017-07</td>\n", " <td>11:29:48</td>\n", " <td>91400</td>\n", " <td>89800</td>\n", " </tr>\n", " <tr>\n", " <th>1879</th>\n", " <td>2017-07</td>\n", " <td>11:29:49</td>\n", " <td>91400</td>\n", " <td>89800</td>\n", " </tr>\n", " <tr>\n", " <th>1880</th>\n", " <td>2017-07</td>\n", " <td>11:29:50</td>\n", " <td>91500</td>\n", " <td>89800</td>\n", " </tr>\n", " <tr>\n", " <th>1881</th>\n", " <td>2017-07</td>\n", " <td>11:29:51</td>\n", " <td>91600</td>\n", " <td>89800</td>\n", " </tr>\n", " <tr>\n", " <th>1882</th>\n", " <td>2017-07</td>\n", " <td>11:29:52</td>\n", " <td>91700</td>\n", " <td>89800</td>\n", " </tr>\n", " <tr>\n", " <th>1883</th>\n", " <td>2017-07</td>\n", " <td>11:29:53</td>\n", " <td>91800</td>\n", " <td>89800</td>\n", " </tr>\n", " <tr>\n", " <th>1884</th>\n", " <td>2017-07</td>\n", " <td>11:29:54</td>\n", " <td>91900</td>\n", " <td>89800</td>\n", " </tr>\n", " <tr>\n", " <th>1885</th>\n", " <td>2017-07</td>\n", " <td>11:29:55</td>\n", " <td>92000</td>\n", " <td>89800</td>\n", " </tr>\n", " <tr>\n", " <th>1886</th>\n", " <td>2017-07</td>\n", " <td>11:29:56</td>\n", " <td>92100</td>\n", " <td>89800</td>\n", " </tr>\n", " <tr>\n", " <th>1887</th>\n", " <td>2017-07</td>\n", " <td>11:29:57</td>\n", " <td>92100</td>\n", " <td>89800</td>\n", " </tr>\n", " <tr>\n", " <th>1888</th>\n", " <td>2017-07</td>\n", " <td>11:29:58</td>\n", " <td>92100</td>\n", " <td>89800</td>\n", " </tr>\n", " <tr>\n", " <th>1889</th>\n", " <td>2017-07</td>\n", " <td>11:29:59</td>\n", " <td>92200</td>\n", " <td>89800</td>\n", " </tr>\n", " <tr>\n", " <th>1890</th>\n", " <td>2017-07</td>\n", " <td>11:30:00</td>\n", " <td>92200</td>\n", " <td>89800</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>1891 rows × 4 columns</p>\n", "</div>" ], "text/plain": [ " ccyy-mm time bid-price ref-price\n", "0 2015-01 11:29:00 74000 74000\n", "1 2015-01 11:29:01 74000 74000\n", "2 2015-01 11:29:02 74000 74000\n", "3 2015-01 11:29:03 74000 74000\n", "4 2015-01 11:29:04 74000 74000\n", "5 2015-01 11:29:05 74000 74000\n", "6 2015-01 11:29:06 74000 74000\n", "7 2015-01 11:29:07 74000 74000\n", "8 2015-01 11:29:08 74000 74000\n", "9 2015-01 11:29:09 74000 74000\n", "10 2015-01 11:29:10 74000 74000\n", "11 2015-01 11:29:11 74000 74000\n", "12 2015-01 11:29:12 74000 74000\n", "13 2015-01 11:29:13 74000 74000\n", "14 2015-01 11:29:14 74000 74000\n", "15 2015-01 11:29:15 74000 74000\n", "16 2015-01 11:29:16 74000 74000\n", "17 2015-01 11:29:17 74000 74000\n", "18 2015-01 11:29:18 74000 74000\n", "19 2015-01 11:29:19 74000 74000\n", "20 2015-01 11:29:20 74000 74000\n", "21 2015-01 11:29:21 74000 74000\n", "22 2015-01 11:29:22 74000 74000\n", "23 2015-01 11:29:23 74000 74000\n", "24 2015-01 11:29:24 74000 74000\n", "25 2015-01 11:29:25 74000 74000\n", "26 2015-01 11:29:26 74000 74000\n", "27 2015-01 11:29:27 74000 74000\n", "28 2015-01 11:29:28 74000 74000\n", "29 2015-01 11:29:29 74000 74000\n", "... ... ... ... ...\n", "1861 2017-07 11:29:31 90700 89800\n", "1862 2017-07 11:29:32 90700 89800\n", "1863 2017-07 11:29:33 90700 89800\n", "1864 2017-07 11:29:34 90700 89800\n", "1865 2017-07 11:29:35 90800 89800\n", "1866 2017-07 11:29:36 90800 89800\n", "1867 2017-07 11:29:37 90900 89800\n", "1868 2017-07 11:29:38 91000 89800\n", "1869 2017-07 11:29:39 91000 89800\n", "1870 2017-07 11:29:40 91000 89800\n", "1871 2017-07 11:29:41 91000 89800\n", "1872 2017-07 11:29:42 91000 89800\n", "1873 2017-07 11:29:43 91000 89800\n", "1874 2017-07 11:29:44 91100 89800\n", "1875 2017-07 11:29:45 91100 89800\n", "1876 2017-07 11:29:46 91200 89800\n", "1877 2017-07 11:29:47 91300 89800\n", "1878 2017-07 11:29:48 91400 89800\n", "1879 2017-07 11:29:49 91400 89800\n", "1880 2017-07 11:29:50 91500 89800\n", "1881 2017-07 11:29:51 91600 89800\n", "1882 2017-07 11:29:52 91700 89800\n", "1883 2017-07 11:29:53 91800 89800\n", "1884 2017-07 11:29:54 91900 89800\n", "1885 2017-07 11:29:55 92000 89800\n", "1886 2017-07 11:29:56 92100 89800\n", "1887 2017-07 11:29:57 92100 89800\n", "1888 2017-07 11:29:58 92100 89800\n", "1889 2017-07 11:29:59 92200 89800\n", "1890 2017-07 11:30:00 92200 89800\n", "\n", "[1891 rows x 4 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# which month to predictsimulate?\n", "\n", "# shl_sm_parm_ccyy_mm = '2017-04'\n", "# shl_sm_parm_ccyy_mm_offset = 1647\n", "\n", "# shl_sm_parm_ccyy_mm = '2017-05'\n", "# shl_sm_parm_ccyy_mm_offset = 1708\n", "\n", "# shl_sm_parm_ccyy_mm = '2017-06'\n", "# shl_sm_parm_ccyy_mm_offset = 1769\n", "\n", "shl_sm_parm_ccyy_mm = '2017-07'\n", "shl_sm_parm_ccyy_mm_offset = 1830\n", "\n", "#----------------------------------\n", "\n", "shl_sm_data = pd.read_csv('shl_sm_data/history_ts.csv') \n", "shl_sm_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### shl_pm Initialization" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "+-----------------------------------------------+\n", "\| shl_initialize() \|\n", "+-----------------------------------------------+\n", "\n", "shl_global_parm_ccyy_mm : 2017-07\n", "-------------------------------------------------\n", "shl_global_parm_alpha : 0.636279780099081\n", "shl_global_parm_beta : 0.237518711616408\n", "shl_global_parm_gamma : 0.223562510966253\n", "shl_global_parm_short_weight : 0.1250000000\n", "shl_global_parm_short_weight_ratio: 0.0000000000\n", "shl_global_parm_sec57_weight : 0.5000000000\n", "shl_global_parm_month_weight : 0.9000000000\n", "shl_global_parm_dynamic_increment : 300\n", "-------------------------------------------------\n", "\n", "prediction results dataframe: shl_data_pm_1_step\n", "Empty DataFrame\n", "Columns: []\n", "Index: []\n", "\n", "prediction results dataframe: shl_data_pm_k_step\n", "Empty DataFrame\n", "Columns: []\n", "Index: []\n" ] } ], "source": [ "shl_pm.shl_initialize(shl_sm_parm_ccyy_mm)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "<<<< Record No.: 1830 >>>>\n", "2017-07\n", "11:29:00\n", "90400\n", "==>> Forecasting 1 out of next 1 seconds/steps... \n", "\n", "+-----------------------------------------------+\n", "\| shl_predict_price_1_step() \|\n", "+-----------------------------------------------+\n", "current_ccyy_mm : 2017-07\n", "in_current_time : 11:29:00\n", "in_current_price : 90400.000000\n", "INFO At time [ 11:29:00 ] Set shl_global_parm_base_price : 90399 \n", "INFO f_current_datetime : 2017-07 11:29:00 \n", "INFO f_current_si : 0.0023669570 \n", "INFO f_current_price4pm : 1 \n", "INFO f_current_price4pmsi : 422.4833826724 \n", "INFO f_1_step_time : 11:29:01\n", "INFO f_1_step_si : 0.0223882810 \n", "-------------------------------------------------\n", "==>> Prediction Restuls in Python List : [90700]\n", "[90700]\n", "\n", "<<<< Record No.: 1831 >>>>\n", "2017-07\n", "11:29:01\n", "90400\n", "==>> Forecasting 1 out of next 1 seconds/steps... \n", "\n", "+-----------------------------------------------+\n", "\| shl_predict_price_1_step() \|\n", "+-----------------------------------------------+\n", "current_ccyy_mm : 2017-07\n", "in_current_time : 11:29:01\n", "in_current_price : 90400.000000\n", "INFO f_current_datetime : 2017-07 11:29:01 \n", "INFO f_current_si : 0.0223882810 \n", "INFO f_current_price4pm : 1 \n", "INFO f_current_price4pmsi : 44.6662251559 \n", "INFO f_1_step_time : 11:29:02\n", "INFO f_1_step_si : 0.0309107700 \n", " previous_pred_les_level : 422.4833826724\n", " previous_pred_les_trend : 0.0000000000\n", " f_1_step_pred_les_level : 182.0859647701\n", " f_1_step_pred_les_trend : -57.0988849760\n", " f_1_step_pred_les : 124.9870797941\n", " f_1_step_pred_adj_misc : 0.0000000000\n", " pred_les + pred_adj_misc : 124.9870797941\n", " f_1_step_pred_price_inc : 3.8634468765\n", " f_1_step_pred_price : 90402.8634468765\n", " f_1_step_pred_price_rounded : 90400\n", " f_1_step_pred_set_price_rounded : 90700\n", "-------------------------------------------------\n", "==>> Prediction Restuls in Python List : [90700]\n", "[90700]\n", "\n", "<<<< Record No.: 1832 >>>>\n", "2017-07\n", "11:29:02\n", "90400\n", "==>> Forecasting 1 out of next 1 seconds/steps... \n", "\n", "+-----------------------------------------------+\n", "\| shl_predict_price_1_step() \|\n", "+-----------------------------------------------+\n", "current_ccyy_mm : 2017-07\n", "in_current_time : 11:29:02\n", "in_current_price : 90400.000000\n", "INFO f_current_datetime : 2017-07 11:29:02 \n", "INFO f_current_si : 0.0309107700 \n", "INFO f_current_price4pm : 1 \n", "INFO f_current_price4pmsi : 32.3511837460 \n", "INFO f_1_step_time : 11:29:03\n", "INFO f_1_step_si : 0.0377696020 \n", " previous_pred_les_level : 182.0859647701\n", " previous_pred_les_trend : -57.0988849760\n", " f_1_step_pred_les_level : 66.0447322273\n", " f_1_step_pred_les_trend : -71.0987954298\n", " f_1_step_pred_les : -5.0540632024\n", " f_1_step_pred_adj_misc : 0.0000000000\n", " pred_les + pred_adj_misc : -5.0540632024\n", " f_1_step_pred_price_inc : -0.1908899556\n", " f_1_step_pred_price : 90398.8091100444\n", " f_1_step_pred_price_rounded : 90400\n", " f_1_step_pred_set_price_rounded : 90700\n", "-------------------------------------------------\n", "==>> Prediction Restuls in Python List : [90700]\n", "[90700]\n", "\n", "<<<< Record No.: 1833 >>>>\n", "2017-07\n", "11:29:03\n", "90400\n", "==>> Forecasting 1 out of next 1 seconds/steps... \n", "\n", "+-----------------------------------------------+\n", "\| shl_predict_price_1_step() \|\n", "+-----------------------------------------------+\n", "current_ccyy_mm : 2017-07\n", "in_current_time : 11:29:03\n", "in_current_price : 90400.000000\n", "INFO f_current_datetime : 2017-07 11:29:03 \n", "INFO f_current_si : 0.0377696020 \n", "INFO f_current_price4pm : 1 \n", "INFO f_current_price4pmsi : 26.4763181778 \n", "INFO f_1_step_time : 11:29:04\n", "INFO f_1_step_si : 0.0457052300 \n", " previous_pred_les_level : 66.0447322273\n", " previous_pred_les_trend : -71.0987954298\n", " f_1_step_pred_les_level : 15.0080809286\n", " f_1_step_pred_les_trend : -66.3336608035\n", " f_1_step_pred_les : -51.3255798749\n", " f_1_step_pred_adj_misc : 0.0000000000\n", " pred_les + pred_adj_misc : -51.3255798749\n", " f_1_step_pred_price_inc : -2.3458474331\n", " f_1_step_pred_price : 90396.6541525669\n", " f_1_step_pred_price_rounded : 90400\n", " f_1_step_pred_set_price_rounded : 90700\n", "-------------------------------------------------\n", "==>> Prediction Restuls in Python List : [90700]\n", "[90700]\n", "\n", "<<<< Record No.: 1834 >>>>\n", "2017-07\n", "11:29:04\n", "90400\n", "==>> Forecasting 1 out of next 1 seconds/steps... \n", "\n", "+-----------------------------------------------+\n", "\| shl_predict_price_1_step() \|\n", "+-----------------------------------------------+\n", "current_ccyy_mm : 2017-07\n", "in_current_time : 11:29:04\n", "in_current_price : 90400.000000\n", "INFO f_current_datetime : 2017-07 11:29:04 \n", "INFO f_current_si : 0.0457052300 \n", "INFO f_current_price4pm : 1 \n", "INFO f_current_price4pmsi : 21.8793341594 \n", "INFO f_1_step_time : 11:29:05\n", "INFO f_1_step_si : 0.0452799070 \n", " previous_pred_les_level : 15.0080809286\n", " previous_pred_les_trend : -66.3336608035\n", " f_1_step_pred_les_level : -4.7467732710\n", " f_1_step_pred_les_trend : -55.2703226703\n", " f_1_step_pred_les : -60.0170959413\n", " f_1_step_pred_adj_misc : 0.0000000000\n", " pred_les + pred_adj_misc : -60.0170959413\n", " f_1_step_pred_price_inc : -2.7175685226\n", " f_1_step_pred_price : 90396.2824314774\n", " f_1_step_pred_price_rounded : 90400\n", " f_1_step_pred_set_price_rounded : 90700\n", "-------------------------------------------------\n", "==>> Prediction Restuls in Python List : [90700]\n", "[90700]\n", "\n", "<<<< Record No.: 1835 >>>>\n", "2017-07\n", "11:29:05\n", "90400\n", "==>> Forecasting 1 out of next 1 seconds/steps... \n", "\n", "+-----------------------------------------------+\n", "\| shl_predict_price_1_step() \|\n", "+-----------------------------------------------+\n", "current_ccyy_mm : 2017-07\n", "in_current_time : 11:29:05\n", "in_current_price : 90400.000000\n", "INFO f_current_datetime : 2017-07 11:29:05 \n", "INFO f_current_si : 0.0452799070 \n", "INFO f_current_price4pm : 1 \n", "INFO f_current_price4pmsi : 22.0848510135 \n", "INFO f_1_step_time : 11:29:06\n", "INFO f_1_step_si : 0.0807556680 \n", " previous_pred_les_level : -4.7467732710\n", " previous_pred_les_trend : -55.2703226703\n", " f_1_step_pred_les_level : -7.7772871872\n", " f_1_step_pred_les_trend : -42.8623905999\n", " f_1_step_pred_les : -50.6396777871\n", " f_1_step_pred_adj_misc : 0.0000000000\n", " pred_les + pred_adj_misc : -50.6396777871\n", " f_1_step_pred_price_inc : -4.0894410070\n", " f_1_step_pred_price : 90394.9105589930\n", " f_1_step_pred_price_rounded : 90400\n", " f_1_step_pred_set_price_rounded : 90700\n", "-------------------------------------------------\n", "==>> Prediction Restuls in Python List : [90700]\n", "[90700]\n", "\n", "<<<< Record No.: 1836 >>>>\n", "2017-07\n", "11:29:06\n", "90400\n", "==>> Forecasting 1 out of next 1 seconds/steps... \n", "\n", "+-----------------------------------------------+\n", "\| shl_predict_price_1_step() \|\n", "+-----------------------------------------------+\n", "current_ccyy_mm : 2017-07\n", "in_current_time : 11:29:06\n", "in_current_price : 90400.000000\n", "INFO f_current_datetime : 2017-07 11:29:06 \n", "INFO f_current_si : 0.0807556680 \n", "INFO f_current_price4pm : 1 \n", "INFO f_current_price4pmsi : 12.3830317396 \n", "INFO f_1_step_time : 11:29:07\n", "INFO f_1_step_si : 0.0985017130 \n", " previous_pred_les_level : -7.7772871872\n", " previous_pred_les_trend : -42.8623905999\n", " f_1_step_pred_les_level : -10.5396020282\n", " f_1_step_pred_les_trend : -33.3378722700\n", " f_1_step_pred_les : -43.8774742981\n", " f_1_step_pred_adj_misc : 0.0000000000\n", " pred_les + pred_adj_misc : -43.8774742981\n", " f_1_step_pred_price_inc : -4.3220063805\n", " f_1_step_pred_price : 90394.6779936195\n", " f_1_step_pred_price_rounded : 90400\n", " f_1_step_pred_set_price_rounded : 90700\n", "-------------------------------------------------\n", "==>> Prediction Restuls in Python List : [90700]\n", "[90700]\n", "\n", "<<<< Record No.: 1837 >>>>\n", "2017-07\n", "11:29:07\n", "90400\n", "==>> Forecasting 1 out of next 1 seconds/steps... \n", "\n", "+-----------------------------------------------+\n", "\| shl_predict_price_1_step() \|\n", "+-----------------------------------------------+\n", "current_ccyy_mm : 2017-07\n", "in_current_time : 11:29:07\n", "in_current_price : 90400.000000\n", "INFO f_current_datetime : 2017-07 11:29:07 \n", "INFO f_current_si : 0.0985017130 \n", "INFO f_current_price4pm : 1 \n", "INFO f_current_price4pmsi : 10.1521077100 \n", "INFO f_1_step_time : 11:29:08\n", "INFO f_1_step_si : 0.1361543100 \n", " previous_pred_les_level : -10.5396020282\n", " previous_pred_les_trend : -33.3378722700\n", " f_1_step_pred_les_level : -9.4995437391\n", " f_1_step_pred_les_trend : -25.1724704955\n", " f_1_step_pred_les : -34.6720142347\n", " f_1_step_pred_adj_misc : 0.0000000000\n", " pred_les + pred_adj_misc : -34.6720142347\n", " f_1_step_pred_price_inc : -4.7207441744\n", " f_1_step_pred_price : 90394.2792558256\n", " f_1_step_pred_price_rounded : 90400\n", " f_1_step_pred_set_price_rounded : 90700\n", "-------------------------------------------------\n", "==>> Prediction Restuls in Python List : [90700]\n", "[90700]\n", "\n", "<<<< Record No.: 1838 >>>>\n", "2017-07\n", "11:29:08\n", "90400\n", "==>> Forecasting 1 out of next 1 seconds/steps... \n", "\n", "+-----------------------------------------------+\n", "\| shl_predict_price_1_step() \|\n", "+-----------------------------------------------+\n", "current_ccyy_mm : 2017-07\n", "in_current_time : 11:29:08\n", "in_current_price : 90400.000000\n", "INFO f_current_datetime : 2017-07 11:29:08 \n", "INFO f_current_si : 0.1361543100 \n", "INFO f_current_price4pm : 1 \n", "INFO f_current_price4pmsi : 7.3446077469 \n", "INFO f_1_step_time : 11:29:09\n", "INFO f_1_step_si : 0.2041642360 \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " previous_pred_les_level : -9.4995437391\n", " previous_pred_les_trend : -25.1724704955\n", " f_1_step_pred_les_level : -7.9376872397\n", " f_1_step_pred_les_trend : -18.8225675918\n", " f_1_step_pred_les : -26.7602548315\n", " f_1_step_pred_adj_misc : 0.0000000000\n", " pred_les + pred_adj_misc : -26.7602548315\n", " f_1_step_pred_price_inc : -5.4634869828\n", " f_1_step_pred_price : 90393.5365130172\n", " f_1_step_pred_price_rounded : 90400\n", " f_1_step_pred_set_price_rounded : 90700\n", "-------------------------------------------------\n", "==>> Prediction Restuls in Python List : [90700]\n", "[90700]\n", "\n", "<<<< Record No.: 1839 >>>>\n", "2017-07\n", "11:29:09\n", "90400\n", "==>> Forecasting 1 out of next 1 seconds/steps... \n", "\n", "+-----------------------------------------------+\n", "\| shl_predict_price_1_step() \|\n", "+-----------------------------------------------+\n", "current_ccyy_mm : 2017-07\n", "in_current_time : 11:29:09\n", "in_current_price : 90400.000000\n", "INFO f_current_datetime : 2017-07 11:29:09 \n", "INFO f_current_si : 0.2041642360 \n", "INFO f_current_price4pm : 1 \n", "INFO f_current_price4pmsi : 4.8980174961 \n", "INFO f_1_step_time : 11:29:10\n", "INFO f_1_step_si : 0.2310771670 \n", " previous_pred_les_level : -7.9376872397\n", " previous_pred_les_trend : -18.8225675918\n", " f_1_step_pred_les_level : -6.6167362766\n", " f_1_step_pred_les_trend : -14.0381050172\n", " f_1_step_pred_les : -20.6548412938\n", " f_1_step_pred_adj_misc : 0.0000000000\n", " pred_les + pred_adj_misc : -20.6548412938\n", " f_1_step_pred_price_inc : -4.7728622110\n", " f_1_step_pred_price : 90394.2271377890\n", " f_1_step_pred_price_rounded : 90400\n", " f_1_step_pred_set_price_rounded : 90700\n", "-------------------------------------------------\n", "==>> Prediction Restuls in Python List : [90700]\n", "[90700]\n", "\n", "<<<< Record No.: 1840 >>>>\n", "2017-07\n", "11:29:10\n", "90500\n", "==>> Forecasting 1 out of next 1 seconds/steps... \n", "\n", "+-----------------------------------------------+\n", "\| shl_predict_price_1_step() \|\n", "+-----------------------------------------------+\n", "current_ccyy_mm : 2017-07\n", "in_current_time : 11:29:10\n", "in_current_price : 90500.000000\n", "INFO f_current_datetime : 2017-07 11:29:10 \n", "INFO f_current_si : 0.2310771670 \n", "INFO f_current_price4pm : 101 \n", "INFO f_current_price4pmsi : 437.0834267671 \n", "INFO f_1_step_time : 11:29:11\n", "INFO f_1_step_si : 0.2910254840 \n", " previous_pred_les_level : -6.6167362766\n", " previous_pred_les_trend : -14.0381050172\n", " f_1_step_pred_les_level : 270.5947632509\n", " f_1_step_pred_les_trend : 55.1391258131\n", " f_1_step_pred_les : 325.7338890640\n", " f_1_step_pred_adj_misc : 0.0000000000\n", " pred_les + pred_adj_misc : 325.7338890640\n", " f_1_step_pred_price_inc : 94.7968627201\n", " f_1_step_pred_price : 90493.7968627201\n", " f_1_step_pred_price_rounded : 90500\n", " f_1_step_pred_set_price_rounded : 90800\n", "-------------------------------------------------\n", "==>> Prediction Restuls in Python List : [90800]\n", "[90800]\n", "\n", "<<<< Record No.: 1841 >>>>\n", "2017-07\n", "11:29:11\n", "90500\n", "==>> Forecasting 1 out of next 1 seconds/steps... \n", "\n", "+-----------------------------------------------+\n", "\| shl_predict_price_1_step() \|\n", "+-----------------------------------------------+\n", "current_ccyy_mm : 2017-07\n", "in_current_time : 11:29:11\n", "in_current_price : 90500.000000\n", "INFO f_current_datetime : 2017-07 11:29:11 \n", "INFO f_current_si : 0.2910254840 \n", "INFO f_current_price4pm : 101 \n", "INFO f_current_price4pmsi : 347.0486454032 \n", "INFO f_1_step_time : 11:29:12\n", "INFO f_1_step_si : 0.3431273480 \n", " previous_pred_les_level : 270.5947632509\n", " previous_pred_les_trend : 55.1391258131\n", " f_1_step_pred_les_level : 339.2960375404\n", " f_1_step_pred_les_trend : 58.3603898459\n", " f_1_step_pred_les : 397.6564273863\n", " f_1_step_pred_adj_misc : 0.0000000000\n", " pred_les + pred_adj_misc : 397.6564273863\n", " f_1_step_pred_price_inc : 136.4467953442\n", " f_1_step_pred_price : 90535.4467953442\n", " f_1_step_pred_price_rounded : 90500\n", " f_1_step_pred_set_price_rounded : 90800\n", "-------------------------------------------------\n", "==>> Prediction Restuls in Python List : [90800]\n", "[90800]\n", "\n", "<<<< Record No.: 1842 >>>>\n", "2017-07\n", "11:29:12\n", "90500\n", "==>> Forecasting 1 out of next 1 seconds/steps... \n", "\n", "+-----------------------------------------------+\n", "\| shl_predict_price_1_step() \|\n", "+-----------------------------------------------+\n", "current_ccyy_mm : 2017-07\n", "in_current_time : 11:29:12\n", "in_current_price : 90500.000000\n", "INFO f_current_datetime : 2017-07 11:29:12 \n", "INFO f_current_si : 0.3431273480 \n", "INFO f_current_price4pm : 101 \n", "INFO f_current_price4pmsi : 294.3513555206 \n", "INFO f_1_step_time : 11:29:13\n", "INFO f_1_step_si : 0.3510740950 \n", " previous_pred_les_level : 339.2960375404\n", " previous_pred_les_trend : 58.3603898459\n", " f_1_step_pred_les_level : 331.9254989765\n", " f_1_step_pred_les_trend : 42.7480644167\n", " f_1_step_pred_les : 374.6735633932\n", " f_1_step_pred_adj_misc : 0.0000000000\n", " pred_les + pred_adj_misc : 374.6735633932\n", " f_1_step_pred_price_inc : 131.5381821887\n", " f_1_step_pred_price : 90530.5381821887\n", " f_1_step_pred_price_rounded : 90500\n", " f_1_step_pred_set_price_rounded : 90800\n", "-------------------------------------------------\n", "==>> Prediction Restuls in Python List : [90800]\n", "[90800]\n", "\n", "<<<< Record No.: 1843 >>>>\n", "2017-07\n", "11:29:13\n", "90600\n", "==>> Forecasting 1 out of next 1 seconds/steps... \n", "\n", "+-----------------------------------------------+\n", "\| shl_predict_price_1_step() \|\n", "+-----------------------------------------------+\n", "current_ccyy_mm : 2017-07\n", "in_current_time : 11:29:13\n", "in_current_price : 90600.000000\n", "INFO f_current_datetime : 2017-07 11:29:13 \n", "INFO f_current_si : 0.3510740950 \n", "INFO f_current_price4pm : 201 \n", "INFO f_current_price4pmsi : 572.5287136324 \n", "INFO f_1_step_time : 11:29:14\n", "INFO f_1_step_si : 0.3706555480 \n", " previous_pred_les_level : 331.9254989765\n", " previous_pred_les_trend : 42.7480644167\n", " f_1_step_pred_les_level : 500.5647948788\n", " f_1_step_pred_les_trend : 72.6495875230\n", " f_1_step_pred_les : 573.2143824018\n", " f_1_step_pred_adj_misc : 0.0000000000\n", " pred_les + pred_adj_misc : 573.2143824018\n", " f_1_step_pred_price_inc : 212.4650910306\n", " f_1_step_pred_price : 90611.4650910306\n", " f_1_step_pred_price_rounded : 90600\n", " f_1_step_pred_set_price_rounded : 90900\n", "-------------------------------------------------\n", "==>> Prediction Restuls in Python List : [90900]\n", "[90900]\n", "\n", "<<<< Record No.: 1844 >>>>\n", "2017-07\n", "11:29:14\n", "90600\n", "==>> Forecasting 1 out of next 1 seconds/steps... \n", "\n", "+-----------------------------------------------+\n", "\| shl_predict_price_1_step() \|\n", "+-----------------------------------------------+\n", "current_ccyy_mm : 2017-07\n", "in_current_time : 11:29:14\n", "in_current_price : 90600.000000\n", "INFO f_current_datetime : 2017-07 11:29:14 \n", "INFO f_current_si : 0.3706555480 \n", "INFO f_current_price4pm : 201 \n", "INFO f_current_price4pmsi : 542.2824535733 \n", "INFO f_1_step_time : 11:29:15\n", "INFO f_1_step_si : 0.4011467510 \n", " previous_pred_les_level : 500.5647948788\n", " previous_pred_les_trend : 72.6495875230\n", " f_1_step_pred_les_level : 553.5330215288\n", " f_1_step_pred_les_trend : 67.9748960455\n", " f_1_step_pred_les : 621.5079175743\n", " f_1_step_pred_adj_misc : 0.0000000000\n", " pred_les + pred_adj_misc : 621.5079175743\n", " f_1_step_pred_price_inc : 249.3158818557\n", " f_1_step_pred_price : 90648.3158818557\n", " f_1_step_pred_price_rounded : 90600\n", " f_1_step_pred_set_price_rounded : 90900\n", "-------------------------------------------------\n", "==>> Prediction Restuls in Python List : [90900]\n", "[90900]\n", "\n", "<<<< Record No.: 1845 >>>>\n", "2017-07\n", "11:29:15\n", "90600\n", "==>> Forecasting 1 out of next 1 seconds/steps... \n", "\n", "+-----------------------------------------------+\n", "\| shl_predict_price_1_step() \|\n", "+-----------------------------------------------+\n", "current_ccyy_mm : 2017-07\n", "in_current_time : 11:29:15\n", "in_current_price : 90600.000000\n", "INFO f_current_datetime : 2017-07 11:29:15 \n", "INFO f_current_si : 0.4011467510 \n", "INFO f_current_price4pm : 201 \n", "INFO f_current_price4pmsi : 501.0635122905 \n", "INFO f_1_step_time : 11:29:16\n", "INFO f_1_step_si : 0.4120902590 \n", " previous_pred_les_level : 553.5330215288\n", " previous_pred_les_trend : 67.9748960455\n", " f_1_step_pred_les_level : 544.8715778662\n", " f_1_step_pred_les_trend : 49.7723313751\n", " f_1_step_pred_les : 594.6439092413\n", " f_1_step_pred_adj_misc : 0.0000000000\n", " pred_les + pred_adj_misc : 594.6439092413\n", " f_1_step_pred_price_inc : 245.0469625720\n", " f_1_step_pred_price : 90644.0469625720\n", " f_1_step_pred_price_rounded : 90600\n", " f_1_step_pred_set_price_rounded : 90900\n", "-------------------------------------------------\n", "==>> Prediction Restuls in Python List : [90900]\n", "[90900]\n", "\n", "<<<< Record No.: 1846 >>>>\n", "2017-07\n", "11:29:16\n", "90700\n", "==>> Forecasting 1 out of next 1 seconds/steps... \n", "\n", "+-----------------------------------------------+\n", "\| shl_predict_price_1_step() \|\n", "+-----------------------------------------------+\n", "current_ccyy_mm : 2017-07\n", "in_current_time : 11:29:16\n", "in_current_price : 90700.000000\n", "INFO f_current_datetime : 2017-07 11:29:16 \n", "INFO f_current_si : 0.4120902590 \n", "INFO f_current_price4pm : 301 \n", "INFO f_current_price4pmsi : 730.4225067839 \n", "INFO f_1_step_time : 11:29:17\n", "INFO f_1_step_si : 0.4535685080 \n", " previous_pred_les_level : 544.8715778662\n", " previous_pred_les_trend : 49.7723313751\n", " f_1_step_pred_les_level : 681.0370854278\n", " f_1_step_pred_les_trend : 70.2923272754\n", " f_1_step_pred_les : 751.3294127032\n", " f_1_step_pred_adj_misc : 0.0000000000\n", " pred_les + pred_adj_misc : 751.3294127032\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " f_1_step_pred_price_inc : 340.7793607363\n", " f_1_step_pred_price : 90739.7793607363\n", " f_1_step_pred_price_rounded : 90700\n", " f_1_step_pred_set_price_rounded : 91000\n", "-------------------------------------------------\n", "==>> Prediction Restuls in Python List : [91000]\n", "[91000]\n", "\n", "<<<< Record No.: 1847 >>>>\n", "2017-07\n", "11:29:17\n", "90700\n", "==>> Forecasting 1 out of next 1 seconds/steps... \n", "\n", "+-----------------------------------------------+\n", "\| shl_predict_price_1_step() \|\n", "+-----------------------------------------------+\n", "current_ccyy_mm : 2017-07\n", "in_current_time : 11:29:17\n", "in_current_price : 90700.000000\n", "INFO f_current_datetime : 2017-07 11:29:17 \n", "INFO f_current_si : 0.4535685080 \n", "INFO f_current_price4pm : 301 \n", "INFO f_current_price4pmsi : 663.6263203705 \n", "INFO f_1_step_time : 11:29:18\n", "INFO f_1_step_si : 0.4836754840 \n", " previous_pred_les_level : 681.0370854278\n", " previous_pred_les_trend : 70.2923272754\n", " f_1_step_pred_les_level : 695.5257083998\n", " f_1_step_pred_les_trend : 57.0379033258\n", " f_1_step_pred_les : 752.5636117256\n", " f_1_step_pred_adj_misc : 0.0000000000\n", " pred_les + pred_adj_misc : 752.5636117256\n", " f_1_step_pred_price_inc : 363.9965691421\n", " f_1_step_pred_price : 90762.9965691421\n", " f_1_step_pred_price_rounded : 90800\n", " f_1_step_pred_set_price_rounded : 91100\n", "-------------------------------------------------\n", "==>> Prediction Restuls in Python List : [91100]\n", "[91100]\n", "\n", "<<<< Record No.: 1848 >>>>\n", "2017-07\n", "11:29:18\n", "90700\n", "==>> Forecasting 1 out of next 1 seconds/steps... \n", "\n", "+-----------------------------------------------+\n", "\| shl_predict_price_1_step() \|\n", "+-----------------------------------------------+\n", "current_ccyy_mm : 2017-07\n", "in_current_time : 11:29:18\n", "in_current_price : 90700.000000\n", "INFO f_current_datetime : 2017-07 11:29:18 \n", "INFO f_current_si : 0.4836754840 \n", "INFO f_current_price4pm : 301 \n", "INFO f_current_price4pmsi : 622.3180830062 \n", "INFO f_1_step_time : 11:29:19\n", "INFO f_1_step_si : 0.5045423610 \n", " previous_pred_les_level : 695.5257083998\n", " previous_pred_les_trend : 57.0379033258\n", " f_1_step_pred_les_level : 669.6910153531\n", " f_1_step_pred_les_trend : 37.3541110071\n", " f_1_step_pred_les : 707.0451263602\n", " f_1_step_pred_adj_misc : 0.0000000000\n", " pred_les + pred_adj_misc : 707.0451263602\n", " f_1_step_pred_price_inc : 356.7342173873\n", " f_1_step_pred_price : 90755.7342173873\n", " f_1_step_pred_price_rounded : 90800\n", " f_1_step_pred_set_price_rounded : 91100\n", "-------------------------------------------------\n", "==>> Prediction Restuls in Python List : [91100]\n", "[91100]\n", "\n", "<<<< Record No.: 1849 >>>>\n", "2017-07\n", "11:29:19\n", "90700\n", "==>> Forecasting 1 out of next 1 seconds/steps... \n", "\n", "+-----------------------------------------------+\n", "\| shl_predict_price_1_step() \|\n", "+-----------------------------------------------+\n", "current_ccyy_mm : 2017-07\n", "in_current_time : 11:29:19\n", "in_current_price : 90700.000000\n", "INFO f_current_datetime : 2017-07 11:29:19 \n", "INFO f_current_si : 0.5045423610 \n", "INFO f_current_price4pm : 301 \n", "INFO f_current_price4pmsi : 596.5802344196 \n", "INFO f_1_step_time : 11:29:20\n", "INFO f_1_step_si : 0.5273150370 \n", " previous_pred_les_level : 669.6910153531\n", " previous_pred_les_trend : 37.3541110071\n", " f_1_step_pred_les_level : 636.7585492076\n", " f_1_step_pred_les_trend : 20.6597337579\n", " f_1_step_pred_les : 657.4182829655\n", " f_1_step_pred_adj_misc : 0.0000000000\n", " pred_les + pred_adj_misc : 657.4182829655\n", " f_1_step_pred_price_inc : 346.6665462064\n", " f_1_step_pred_price : 90745.6665462064\n", " f_1_step_pred_price_rounded : 90700\n", " f_1_step_pred_set_price_rounded : 91000\n", "-------------------------------------------------\n", "==>> Prediction Restuls in Python List : [91000]\n", "[91000]\n", "\n", "<<<< Record No.: 1850 >>>>\n", "2017-07\n", "11:29:20\n", "90700\n", "==>> Forecasting 1 out of next 1 seconds/steps... \n", "\n", "+-----------------------------------------------+\n", "\| shl_predict_price_1_step() \|\n", "+-----------------------------------------------+\n", "current_ccyy_mm : 2017-07\n", "in_current_time : 11:29:20\n", "in_current_price : 90700.000000\n", "INFO f_current_datetime : 2017-07 11:29:20 \n", "INFO f_current_si : 0.5273150370 \n", "INFO f_current_price4pm : 301 \n", "INFO f_current_price4pmsi : 570.8162651921 \n", "INFO f_1_step_time : 11:29:21\n", "INFO f_1_step_si : 0.5666965740 \n", " previous_pred_les_level : 636.7585492076\n", " previous_pred_les_trend : 20.6597337579\n", " f_1_step_pred_les_level : 602.3151701405\n", " f_1_step_pred_les_trend : 7.5717133936\n", " f_1_step_pred_les : 609.8868835342\n", " f_1_step_pred_adj_misc : 0.0000000000\n", " pred_les + pred_adj_misc : 609.8868835342\n", " f_1_step_pred_price_inc : 345.6208074263\n", " f_1_step_pred_price : 90744.6208074263\n", " f_1_step_pred_price_rounded : 90700\n", " f_1_step_pred_set_price_rounded : 91000\n", "-------------------------------------------------\n", "==>> Prediction Restuls in Python List : [91000]\n", "[91000]\n", "\n", "<<<< Record No.: 1851 >>>>\n", "2017-07\n", "11:29:21\n", "90700\n", "==>> Forecasting 1 out of next 1 seconds/steps... \n", "\n", "+-----------------------------------------------+\n", "\| shl_predict_price_1_step() \|\n", "+-----------------------------------------------+\n", "current_ccyy_mm : 2017-07\n", "in_current_time : 11:29:21\n", "in_current_price : 90700.000000\n", "INFO f_current_datetime : 2017-07 11:29:21 \n", "INFO f_current_si : 0.5666965740 \n", "INFO f_current_price4pm : 301 \n", "INFO f_current_price4pmsi : 531.1484378234 \n", "INFO f_1_step_time : 11:29:22\n", "INFO f_1_step_si : 0.5783832890 \n", " previous_pred_les_level : 602.3151701405\n", " previous_pred_les_trend : 7.5717133936\n", " f_1_step_pred_les_level : 559.7872026120\n", " f_1_step_pred_les_trend : -4.3278982714\n", " f_1_step_pred_les : 555.4593043406\n", " f_1_step_pred_adj_misc : 0.0000000000\n", " pred_les + pred_adj_misc : 555.4593043406\n", " f_1_step_pred_price_inc : 321.2683793502\n", " f_1_step_pred_price : 90720.2683793502\n", " f_1_step_pred_price_rounded : 90700\n", " f_1_step_pred_set_price_rounded : 91000\n", "-------------------------------------------------\n", "==>> Prediction Restuls in Python List : [91000]\n", "[91000]\n", "\n", "<<<< Record No.: 1852 >>>>\n", "2017-07\n", "11:29:22\n", "90700\n", "==>> Forecasting 1 out of next 1 seconds/steps... \n", "\n", "+-----------------------------------------------+\n", "\| shl_predict_price_1_step() \|\n", "+-----------------------------------------------+\n", "current_ccyy_mm : 2017-07\n", "in_current_time : 11:29:22\n", "in_current_price : 90700.000000\n", "INFO f_current_datetime : 2017-07 11:29:22 \n", "INFO f_current_si : 0.5783832890 \n", "INFO f_current_price4pm : 301 \n", "INFO f_current_price4pmsi : 520.4161422444 \n", "INFO f_1_step_time : 11:29:23\n", "INFO f_1_step_si : 0.5903581650 \n", " previous_pred_les_level : 559.7872026120\n", " previous_pred_les_trend : -4.3278982714\n", " f_1_step_pred_les_level : 533.1620488681\n", " f_1_step_pred_les_trend : -9.6239136638\n", " f_1_step_pred_les : 523.5381352042\n", " f_1_step_pred_adj_misc : 0.0000000000\n", " pred_les + pred_adj_misc : 523.5381352042\n", " f_1_step_pred_price_inc : 309.0750128067\n", " f_1_step_pred_price : 90708.0750128067\n", " f_1_step_pred_price_rounded : 90700\n", " f_1_step_pred_set_price_rounded : 91000\n", "-------------------------------------------------\n", "==>> Prediction Restuls in Python List : [91000]\n", "[91000]\n", "\n", "<<<< Record No.: 1853 >>>>\n", "2017-07\n", "11:29:23\n", "90700\n", "==>> Forecasting 1 out of next 1 seconds/steps... \n", "\n", "+-----------------------------------------------+\n", "\| shl_predict_price_1_step() \|\n", "+-----------------------------------------------+\n", "current_ccyy_mm : 2017-07\n", "in_current_time : 11:29:23\n", "in_current_price : 90700.000000\n", "INFO f_current_datetime : 2017-07 11:29:23 \n", "INFO f_current_si : 0.5903581650 \n", "INFO f_current_price4pm : 301 \n", "INFO f_current_price4pmsi : 509.8599762739 \n", "INFO f_1_step_time : 11:29:24\n", "INFO f_1_step_si : 0.6203383340 \n", " previous_pred_les_level : 533.1620488681\n", " previous_pred_les_trend : -9.6239136638\n", " f_1_step_pred_les_level : 514.8349992479\n", " f_1_step_pred_les_trend : -11.6910713032\n", " f_1_step_pred_les : 503.1439279447\n", " f_1_step_pred_adj_misc : 0.0000000000\n", " pred_les + pred_adj_misc : 503.1439279447\n", " f_1_step_pred_price_inc : 312.1194660234\n", " f_1_step_pred_price : 90711.1194660234\n", " f_1_step_pred_price_rounded : 90700\n", " f_1_step_pred_set_price_rounded : 91000\n", "-------------------------------------------------\n", "==>> Prediction Restuls in Python List : [91000]\n", "[91000]\n", "\n", "<<<< Record No.: 1854 >>>>\n", "2017-07\n", "11:29:24\n", "90700\n", "==>> Forecasting 1 out of next 1 seconds/steps... \n", "\n", "+-----------------------------------------------+\n", "\| shl_predict_price_1_step() \|\n", "+-----------------------------------------------+\n", "current_ccyy_mm : 2017-07\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "in_current_time : 11:29:24\n", "in_current_price : 90700.000000\n", "INFO f_current_datetime : 2017-07 11:29:24 \n", "INFO f_current_si : 0.6203383340 \n", "INFO f_current_price4pm : 301 \n", "INFO f_current_price4pmsi : 485.2190869120 \n", "INFO f_1_step_time : 11:29:25\n", "INFO f_1_step_si : 0.6624022500 \n", " previous_pred_les_level : 514.8349992479\n", " previous_pred_les_trend : -11.6910713032\n", " f_1_step_pred_les_level : 491.7387140341\n", " f_1_step_pred_les_trend : -14.4000230169\n", " f_1_step_pred_les : 477.3386910172\n", " f_1_step_pred_adj_misc : 0.0000000000\n", " pred_les + pred_adj_misc : 477.3386910172\n", " f_1_step_pred_price_inc : 316.1902229418\n", " f_1_step_pred_price : 90715.1902229418\n", " f_1_step_pred_price_rounded : 90700\n", " f_1_step_pred_set_price_rounded : 91000\n", "-------------------------------------------------\n", "==>> Prediction Restuls in Python List : [91000]\n", "[91000]\n", "\n", "<<<< Record No.: 1855 >>>>\n", "2017-07\n", "11:29:25\n", "90700\n", "==>> Forecasting 1 out of next 1 seconds/steps... \n", "\n", "+-----------------------------------------------+\n", "\| shl_predict_price_1_step() \|\n", "+-----------------------------------------------+\n", "current_ccyy_mm : 2017-07\n", "in_current_time : 11:29:25\n", "in_current_price : 90700.000000\n", "INFO f_current_datetime : 2017-07 11:29:25 \n", "INFO f_current_si : 0.6624022500 \n", "INFO f_current_price4pm : 301 \n", "INFO f_current_price4pmsi : 454.4066690595 \n", "INFO f_1_step_time : 11:29:26\n", "INFO f_1_step_si : 0.6803182270 \n", " previous_pred_les_level : 491.7387140341\n", " previous_pred_les_trend : -14.4000230169\n", " f_1_step_pred_les_level : 462.7475091287\n", " f_1_step_pred_les_trend : -17.8657017400\n", " f_1_step_pred_les : 444.8818073887\n", " f_1_step_pred_adj_misc : 0.0000000000\n", " pred_les + pred_adj_misc : 444.8818073887\n", " f_1_step_pred_price_inc : 302.6612024272\n", " f_1_step_pred_price : 90701.6612024272\n", " f_1_step_pred_price_rounded : 90700\n", " f_1_step_pred_set_price_rounded : 91000\n", "-------------------------------------------------\n", "==>> Prediction Restuls in Python List : [91000]\n", "[91000]\n", "\n", "<<<< Record No.: 1856 >>>>\n", "2017-07\n", "11:29:26\n", "90700\n", "==>> Forecasting 1 out of next 1 seconds/steps... \n", "\n", "+-----------------------------------------------+\n", "\| shl_predict_price_1_step() \|\n", "+-----------------------------------------------+\n", "current_ccyy_mm : 2017-07\n", "in_current_time : 11:29:26\n", "in_current_price : 90700.000000\n", "INFO f_current_datetime : 2017-07 11:29:26 \n", "INFO f_current_si : 0.6803182270 \n", "INFO f_current_price4pm : 301 \n", "INFO f_current_price4pmsi : 442.4400053594 \n", "INFO f_1_step_time : 11:29:27\n", "INFO f_1_step_si : 0.7013944910 \n", " previous_pred_les_level : 462.7475091287\n", " previous_pred_les_trend : -17.8657017400\n", " f_1_step_pred_les_level : 443.3281381305\n", " f_1_step_pred_les_trend : -18.2347272605\n", " f_1_step_pred_les : 425.0934108699\n", " f_1_step_pred_adj_misc : 0.0000000000\n", " pred_les + pred_adj_misc : 425.0934108699\n", " f_1_step_pred_price_inc : 298.1581765446\n", " f_1_step_pred_price : 90697.1581765446\n", " f_1_step_pred_price_rounded : 90700\n", " f_1_step_pred_set_price_rounded : 91000\n", "-------------------------------------------------\n", "==>> Prediction Restuls in Python List : [91000]\n", "[91000]\n", "\n", "<<<< Record No.: 1857 >>>>\n", "2017-07\n", "11:29:27\n", "90700\n", "==>> Forecasting 1 out of next 1 seconds/steps... \n", "\n", "+-----------------------------------------------+\n", "\| shl_predict_price_1_step() \|\n", "+-----------------------------------------------+\n", "current_ccyy_mm : 2017-07\n", "in_current_time : 11:29:27\n", "in_current_price : 90700.000000\n", "INFO f_current_datetime : 2017-07 11:29:27 \n", "INFO f_current_si : 0.7013944910 \n", "INFO f_current_price4pm : 301 \n", "INFO f_current_price4pmsi : 429.1450871974 \n", "INFO f_1_step_time : 11:29:28\n", "INFO f_1_step_si : 0.7261122680 \n", " previous_pred_les_level : 443.3281381305\n", " previous_pred_les_trend : -18.2347272605\n", " f_1_step_pred_les_level : 427.6714105926\n", " f_1_step_pred_les_trend : -17.6224040878\n", " f_1_step_pred_les : 410.0490065048\n", " f_1_step_pred_adj_misc : 0.0000000000\n", " pred_les + pred_adj_misc : 410.0490065048\n", " f_1_step_pred_price_inc : 297.7416141043\n", " f_1_step_pred_price : 90696.7416141043\n", " f_1_step_pred_price_rounded : 90700\n", " f_1_step_pred_set_price_rounded : 91000\n", "-------------------------------------------------\n", "==>> Prediction Restuls in Python List : [91000]\n", "[91000]\n", "\n", "<<<< Record No.: 1858 >>>>\n", "2017-07\n", "11:29:28\n", "90700\n", "==>> Forecasting 1 out of next 1 seconds/steps... \n", "\n", "+-----------------------------------------------+\n", "\| shl_predict_price_1_step() \|\n", "+-----------------------------------------------+\n", "current_ccyy_mm : 2017-07\n", "in_current_time : 11:29:28\n", "in_current_price : 90700.000000\n", "INFO f_current_datetime : 2017-07 11:29:28 \n", "INFO f_current_si : 0.7261122680 \n", "INFO f_current_price4pm : 301 \n", "INFO f_current_price4pmsi : 414.5364474134 \n", "INFO f_1_step_time : 11:29:29\n", "INFO f_1_step_si : 0.7412284280 \n", " previous_pred_les_level : 427.6714105926\n", " previous_pred_les_trend : -17.6224040878\n", " f_1_step_pred_les_level : 412.9042744193\n", " f_1_step_pred_les_trend : -16.9442245315\n", " f_1_step_pred_les : 395.9600498879\n", " f_1_step_pred_adj_misc : 0.0000000000\n", " pred_les + pred_adj_misc : 395.9600498879\n", " f_1_step_pred_price_inc : 293.4968453292\n", " f_1_step_pred_price : 90692.4968453292\n", " f_1_step_pred_price_rounded : 90700\n", " f_1_step_pred_set_price_rounded : 91000\n", "-------------------------------------------------\n", "==>> Prediction Restuls in Python List : [91000]\n", "[91000]\n", "\n", "<<<< Record No.: 1859 >>>>\n", "2017-07\n", "11:29:29\n", "90700\n", "==>> Forecasting 1 out of next 1 seconds/steps... \n", "\n", "+-----------------------------------------------+\n", "\| shl_predict_price_1_step() \|\n", "+-----------------------------------------------+\n", "current_ccyy_mm : 2017-07\n", "in_current_time : 11:29:29\n", "in_current_price : 90700.000000\n", "INFO f_current_datetime : 2017-07 11:29:29 \n", "INFO f_current_si : 0.7412284280 \n", "INFO f_current_price4pm : 301 \n", "INFO f_current_price4pmsi : 406.0826442021 \n", "INFO f_1_step_time : 11:29:30\n", "INFO f_1_step_si : 0.7848751150 \n", " previous_pred_les_level : 412.9042744193\n", " previous_pred_les_trend : -16.9442245315\n", " f_1_step_pred_les_level : 402.4008519721\n", " f_1_step_pred_les_trend : -15.4144135186\n", " f_1_step_pred_les : 386.9864384535\n", " f_1_step_pred_adj_misc : 0.0000000000\n", " pred_les + pred_adj_misc : 386.9864384535\n", " f_1_step_pred_price_inc : 303.7360253846\n", " f_1_step_pred_price : 90702.7360253846\n", " f_1_step_pred_price_rounded : 90700\n", " f_1_step_pred_set_price_rounded : 91000\n", "-------------------------------------------------\n", "==>> Prediction Restuls in Python List : [91000]\n", "[91000]\n", "\n", "<<<< Record No.: 1860 >>>>\n", "2017-07\n", "11:29:30\n", "90700\n", "==>> Forecasting 1 out of next 1 seconds/steps... \n", "\n", "+-----------------------------------------------+\n", "\| shl_predict_price_1_step() \|\n", "+-----------------------------------------------+\n", "current_ccyy_mm : 2017-07\n", "in_current_time : 11:29:30\n", "in_current_price : 90700.000000\n", "INFO f_current_datetime : 2017-07 11:29:30 \n", "INFO f_current_si : 0.7848751150 \n", "INFO f_current_price4pm : 301 \n", "INFO f_current_price4pmsi : 383.5005012231 \n", "INFO f_1_step_time : 11:29:31\n", "INFO f_1_step_si : 0.7883406290 \n", " previous_pred_les_level : 402.4008519721\n", " previous_pred_les_trend : -15.4144135186\n", " f_1_step_pred_les_level : 384.7684070791\n", " f_1_step_pred_les_trend : -15.9412374730\n", " f_1_step_pred_les : 368.8271696061\n", " f_1_step_pred_adj_misc : 0.0000000000\n", " pred_les + pred_adj_misc : 368.8271696061\n", " f_1_step_pred_price_inc : 290.7614428795\n", " f_1_step_pred_price : 90689.7614428795\n", " f_1_step_pred_price_rounded : 90700\n", " f_1_step_pred_set_price_rounded : 91000\n", "-------------------------------------------------\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "==>> Prediction Restuls in Python List : [91000]\n", "[91000]\n", "\n", "<<<< Record No.: 1861 >>>>\n", "2017-07\n", "11:29:31\n", "90700\n", "==>> Forecasting 1 out of next 1 seconds/steps... \n", "\n", "+-----------------------------------------------+\n", "\| shl_predict_price_1_step() \|\n", "+-----------------------------------------------+\n", "current_ccyy_mm : 2017-07\n", "in_current_time : 11:29:31\n", "in_current_price : 90700.000000\n", "INFO f_current_datetime : 2017-07 11:29:31 \n", "INFO f_current_si : 0.7883406290 \n", "INFO f_current_price4pm : 301 \n", "INFO f_current_price4pmsi : 381.8146482972 \n", "INFO f_1_step_time : 11:29:32\n", "INFO f_1_step_si : 0.8143918490 \n", " previous_pred_les_level : 384.7684070791\n", " previous_pred_les_trend : -15.9412374730\n", " f_1_step_pred_les_level : 377.0908396917\n", " f_1_step_pred_les_trend : -13.9784612011\n", " f_1_step_pred_les : 363.1123784906\n", " f_1_step_pred_adj_misc : 0.0000000000\n", " pred_les + pred_adj_misc : 363.1123784906\n", " f_1_step_pred_price_inc : 295.7157613138\n", " f_1_step_pred_price : 90694.7157613138\n", " f_1_step_pred_price_rounded : 90700\n", " f_1_step_pred_set_price_rounded : 91000\n", "-------------------------------------------------\n", "==>> Prediction Restuls in Python List : [91000]\n", "[91000]\n", "\n", "<<<< Record No.: 1862 >>>>\n", "2017-07\n", "11:29:32\n", "90700\n", "==>> Forecasting 1 out of next 1 seconds/steps... \n", "\n", "+-----------------------------------------------+\n", "\| shl_predict_price_1_step() \|\n", "+-----------------------------------------------+\n", "current_ccyy_mm : 2017-07\n", "in_current_time : 11:29:32\n", "in_current_price : 90700.000000\n", "INFO f_current_datetime : 2017-07 11:29:32 \n", "INFO f_current_si : 0.8143918490 \n", "INFO f_current_price4pm : 301 \n", "INFO f_current_price4pmsi : 369.6009486952 \n", "INFO f_1_step_time : 11:29:33\n", "INFO f_1_step_si : 0.8351005610 \n", " previous_pred_les_level : 377.0908396917\n", " previous_pred_les_trend : -13.9784612011\n", " f_1_step_pred_les_level : 367.2409245135\n", " f_1_step_pred_les_trend : -12.9978542688\n", " f_1_step_pred_les : 354.2430702447\n", " f_1_step_pred_adj_misc : 0.0000000000\n", " pred_les + pred_adj_misc : 354.2430702447\n", " f_1_step_pred_price_inc : 295.8285866917\n", " f_1_step_pred_price : 90694.8285866917\n", " f_1_step_pred_price_rounded : 90700\n", " f_1_step_pred_set_price_rounded : 91000\n", "-------------------------------------------------\n", "==>> Prediction Restuls in Python List : [91000]\n", "[91000]\n", "\n", "<<<< Record No.: 1863 >>>>\n", "2017-07\n", "11:29:33\n", "90700\n", "==>> Forecasting 1 out of next 1 seconds/steps... \n", "\n", "+-----------------------------------------------+\n", "\| shl_predict_price_1_step() \|\n", "+-----------------------------------------------+\n", "current_ccyy_mm : 2017-07\n", "in_current_time : 11:29:33\n", "in_current_price : 90700.000000\n", "INFO f_current_datetime : 2017-07 11:29:33 \n", "INFO f_current_si : 0.8351005610 \n", "INFO f_current_price4pm : 301 \n", "INFO f_current_price4pmsi : 360.4356338111 \n", "INFO f_1_step_time : 11:29:34\n", "INFO f_1_step_si : 0.8670445380 \n", " previous_pred_les_level : 367.2409245135\n", " previous_pred_les_trend : -12.9978542688\n", " f_1_step_pred_les_level : 358.1832732289\n", " f_1_step_pred_les_trend : -12.0619823325\n", " f_1_step_pred_les : 346.1212908964\n", " f_1_step_pred_adj_misc : 0.0000000000\n", " pred_les + pred_adj_misc : 346.1212908964\n", " f_1_step_pred_price_inc : 300.1025747573\n", " f_1_step_pred_price : 90699.1025747573\n", " f_1_step_pred_price_rounded : 90700\n", " f_1_step_pred_set_price_rounded : 91000\n", "-------------------------------------------------\n", "==>> Prediction Restuls in Python List : [91000]\n", "[91000]\n", "\n", "<<<< Record No.: 1864 >>>>\n", "2017-07\n", "11:29:34\n", "90700\n", "==>> Forecasting 1 out of next 1 seconds/steps... \n", "\n", "+-----------------------------------------------+\n", "\| shl_predict_price_1_step() \|\n", "+-----------------------------------------------+\n", "current_ccyy_mm : 2017-07\n", "in_current_time : 11:29:34\n", "in_current_price : 90700.000000\n", "INFO f_current_datetime : 2017-07 11:29:34 \n", "INFO f_current_si : 0.8670445380 \n", "INFO f_current_price4pm : 301 \n", "INFO f_current_price4pmsi : 347.1563302784 \n", "INFO f_1_step_time : 11:29:35\n", "INFO f_1_step_si : 0.9216129500 \n", " previous_pred_les_level : 358.1832732289\n", " previous_pred_les_trend : -12.0619823325\n", " f_1_step_pred_les_level : 346.7798655268\n", " f_1_step_pred_les_trend : -11.9055585348\n", " f_1_step_pred_les : 334.8743069920\n", " f_1_step_pred_adj_misc : 0.0000000000\n", " pred_les + pred_adj_misc : 334.8743069920\n", " f_1_step_pred_price_inc : 308.6244979461\n", " f_1_step_pred_price : 90707.6244979461\n", " f_1_step_pred_price_rounded : 90700\n", " f_1_step_pred_set_price_rounded : 91000\n", "-------------------------------------------------\n", "==>> Prediction Restuls in Python List : [91000]\n", "[91000]\n", "\n", "<<<< Record No.: 1865 >>>>\n", "2017-07\n", "11:29:35\n", "90800\n", "==>> Forecasting 1 out of next 1 seconds/steps... \n", "\n", "+-----------------------------------------------+\n", "\| shl_predict_price_1_step() \|\n", "+-----------------------------------------------+\n", "current_ccyy_mm : 2017-07\n", "in_current_time : 11:29:35\n", "in_current_price : 90800.000000\n", "INFO f_current_datetime : 2017-07 11:29:35 \n", "INFO f_current_si : 0.9216129500 \n", "INFO f_current_price4pm : 401 \n", "INFO f_current_price4pmsi : 435.1067332550 \n", "INFO f_1_step_time : 11:29:36\n", "INFO f_1_step_si : 0.9539289700 \n", " previous_pred_les_level : 346.7798655268\n", " previous_pred_les_trend : -11.9055585348\n", " f_1_step_pred_les_level : 398.6501731334\n", " f_1_step_pred_les_trend : 3.2424030233\n", " f_1_step_pred_les : 401.8925761567\n", " f_1_step_pred_adj_misc : 0.0000000000\n", " pred_les + pred_adj_misc : 401.8925761567\n", " f_1_step_pred_price_inc : 383.3769712238\n", " f_1_step_pred_price : 90782.3769712238\n", " f_1_step_pred_price_rounded : 90800\n", " f_1_step_pred_set_price_rounded : 91100\n", "-------------------------------------------------\n", "==>> Prediction Restuls in Python List : [91100]\n", "[91100]\n", "\n", "<<<< Record No.: 1866 >>>>\n", "2017-07\n", "11:29:36\n", "90800\n", "==>> Forecasting 1 out of next 1 seconds/steps... \n", "\n", "+-----------------------------------------------+\n", "\| shl_predict_price_1_step() \|\n", "+-----------------------------------------------+\n", "current_ccyy_mm : 2017-07\n", "in_current_time : 11:29:36\n", "in_current_price : 90800.000000\n", "INFO f_current_datetime : 2017-07 11:29:36 \n", "INFO f_current_si : 0.9539289700 \n", "INFO f_current_price4pm : 401 \n", "INFO f_current_price4pmsi : 420.3667281433 \n", "INFO f_1_step_time : 11:29:37\n", "INFO f_1_step_si : 0.9779660700 \n", " previous_pred_les_level : 398.6501731334\n", " previous_pred_les_trend : 3.2424030233\n", " f_1_step_pred_les_level : 413.6473055203\n", " f_1_step_pred_les_trend : 6.0343711971\n", " f_1_step_pred_les : 419.6816767174\n", " f_1_step_pred_adj_misc : 0.0000000000\n", " pred_les + pred_adj_misc : 419.6816767174\n", " f_1_step_pred_price_inc : 410.4344400303\n", " f_1_step_pred_price : 90809.4344400303\n", " f_1_step_pred_price_rounded : 90800\n", " f_1_step_pred_set_price_rounded : 91100\n", "-------------------------------------------------\n", "==>> Prediction Restuls in Python List : [91100]\n", "[91100]\n", "\n", "<<<< Record No.: 1867 >>>>\n", "2017-07\n", "11:29:37\n", "90900\n", "==>> Forecasting 1 out of next 1 seconds/steps... \n", "\n", "+-----------------------------------------------+\n", "\| shl_predict_price_1_step() \|\n", "+-----------------------------------------------+\n", "current_ccyy_mm : 2017-07\n", "in_current_time : 11:29:37\n", "in_current_price : 90900.000000\n", "INFO f_current_datetime : 2017-07 11:29:37 \n", "INFO f_current_si : 0.9779660700 \n", "INFO f_current_price4pm : 501 \n", "INFO f_current_price4pmsi : 512.2877115767 \n", "INFO f_1_step_time : 11:29:38\n", "INFO f_1_step_si : 0.9935136330 \n", " previous_pred_les_level : 413.6473055203\n", " previous_pred_les_trend : 6.0343711971\n", " f_1_step_pred_les_level : 478.6050242136\n", " f_1_step_pred_les_trend : 20.0297687786\n", " f_1_step_pred_les : 498.6347929921\n", " f_1_step_pred_adj_misc : 0.0000000000\n", " pred_les + pred_adj_misc : 498.6347929921\n", " f_1_step_pred_price_inc : 495.4004647258\n", " f_1_step_pred_price : 90894.4004647258\n", " f_1_step_pred_price_rounded : 90900\n", " f_1_step_pred_set_price_rounded : 91200\n", "-------------------------------------------------\n", "==>> Prediction Restuls in Python List : [91200]\n", "[91200]\n", "\n", "<<<< Record No.: 1868 >>>>\n", "2017-07\n", "11:29:38\n", "91000\n", "==>> Forecasting 1 out of next 1 seconds/steps... \n", "\n", "+-----------------------------------------------+\n", "\| shl_predict_price_1_step() \|\n", "+-----------------------------------------------+\n", "current_ccyy_mm : 2017-07\n", "in_current_time : 11:29:38\n", "in_current_price : 91000.000000\n", "INFO f_current_datetime : 2017-07 11:29:38 \n", "INFO f_current_si : 0.9935136330 \n", "INFO f_current_price4pm : 601 \n", "INFO f_current_price4pmsi : 604.9237574982 \n", "INFO f_1_step_time : 11:29:39\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO f_1_step_si : 1.0325517050 \n", " previous_pred_les_level : 478.6050242136\n", " previous_pred_les_trend : 20.0297687786\n", " f_1_step_pred_les_level : 566.2643119550\n", " f_1_step_pred_les_trend : 36.0930449899\n", " f_1_step_pred_les : 602.3573569448\n", " f_1_step_pred_adj_misc : 0.0000000000\n", " pred_les + pred_adj_misc : 602.3573569448\n", " f_1_step_pred_price_inc : 621.9651159327\n", " f_1_step_pred_price : 91020.9651159327\n", " f_1_step_pred_price_rounded : 91000\n", " f_1_step_pred_set_price_rounded : 91300\n", "-------------------------------------------------\n", "==>> Prediction Restuls in Python List : [91300]\n", "[91300]\n", "\n", "<<<< Record No.: 1869 >>>>\n", "2017-07\n", "11:29:39\n", "91000\n", "==>> Forecasting 1 out of next 1 seconds/steps... \n", "\n", "+-----------------------------------------------+\n", "\| shl_predict_price_1_step() \|\n", "+-----------------------------------------------+\n", "current_ccyy_mm : 2017-07\n", "in_current_time : 11:29:39\n", "in_current_price : 91000.000000\n", "INFO f_current_datetime : 2017-07 11:29:39 \n", "INFO f_current_si : 1.0325517050 \n", "INFO f_current_price4pm : 601 \n", "INFO f_current_price4pmsi : 582.0531766978 \n", "INFO f_1_step_time : 11:29:40\n", "INFO f_1_step_si : 1.0762695320 \n", " previous_pred_les_level : 566.2643119550\n", " previous_pred_les_trend : 36.0930449899\n", " f_1_step_pred_les_level : 589.4382176022\n", " f_1_step_pred_les_trend : 33.0245076580\n", " f_1_step_pred_les : 622.4627252602\n", " f_1_step_pred_adj_misc : 0.0000000000\n", " pred_les + pred_adj_misc : 622.4627252602\n", " f_1_step_pred_price_inc : 669.9376660032\n", " f_1_step_pred_price : 91068.9376660032\n", " f_1_step_pred_price_rounded : 91100\n", " f_1_step_pred_set_price_rounded : 91400\n", "-------------------------------------------------\n", "==>> Prediction Restuls in Python List : [91400]\n", "[91400]\n", "\n", "<<<< Record No.: 1870 >>>>\n", "2017-07\n", "11:29:40\n", "91000\n", "==>> Forecasting 1 out of next 1 seconds/steps... \n", "\n", "+-----------------------------------------------+\n", "\| shl_predict_price_1_step() \|\n", "+-----------------------------------------------+\n", "current_ccyy_mm : 2017-07\n", "in_current_time : 11:29:40\n", "in_current_price : 91000.000000\n", "INFO f_current_datetime : 2017-07 11:29:40 \n", "INFO f_current_si : 1.0762695320 \n", "INFO f_current_price4pm : 601 \n", "INFO f_current_price4pmsi : 558.4103072055 \n", "INFO f_1_step_time : 11:29:41\n", "INFO f_1_step_si : 1.1032848210 \n", " previous_pred_les_level : 589.4382176022\n", " previous_pred_les_trend : 33.0245076580\n", " f_1_step_pred_les_level : 581.7074667855\n", " f_1_step_pred_les_trend : 23.3443711735\n", " f_1_step_pred_les : 605.0518379590\n", " f_1_step_pred_adj_misc : 0.0000000000\n", " pred_les + pred_adj_misc : 605.0518379590\n", " f_1_step_pred_price_inc : 667.5445087383\n", " f_1_step_pred_price : 91066.5445087383\n", " f_1_step_pred_price_rounded : 91100\n", " f_1_step_pred_set_price_rounded : 91400\n", "-------------------------------------------------\n", "==>> Prediction Restuls in Python List : [91400]\n", "[91400]\n", "\n", "<<<< Record No.: 1871 >>>>\n", "2017-07\n", "11:29:41\n", "91000\n", "==>> Forecasting 1 out of next 1 seconds/steps... \n", "\n", "+-----------------------------------------------+\n", "\| shl_predict_price_1_step() \|\n", "+-----------------------------------------------+\n", "current_ccyy_mm : 2017-07\n", "in_current_time : 11:29:41\n", "in_current_price : 91000.000000\n", "INFO f_current_datetime : 2017-07 11:29:41 \n", "INFO f_current_si : 1.1032848210 \n", "INFO f_current_price4pm : 601 \n", "INFO f_current_price4pmsi : 544.7369424110 \n", "INFO f_1_step_time : 11:29:42\n", "INFO f_1_step_si : 1.1629896100 \n", " previous_pred_les_level : 581.7074667855\n", " previous_pred_les_trend : 23.3443711735\n", " f_1_step_pred_les_level : 566.6746894830\n", " f_1_step_pred_les_trend : 14.2290803120\n", " f_1_step_pred_les : 580.9037697950\n", " f_1_step_pred_adj_misc : 0.0000000000\n", " pred_les + pred_adj_misc : 580.9037697950\n", " f_1_step_pred_price_inc : 675.5850486814\n", " f_1_step_pred_price : 91074.5850486814\n", " f_1_step_pred_price_rounded : 91100\n", " f_1_step_pred_set_price_rounded : 91400\n", "-------------------------------------------------\n", "==>> Prediction Restuls in Python List : [91400]\n", "[91400]\n", "\n", "<<<< Record No.: 1872 >>>>\n", "2017-07\n", "11:29:42\n", "91000\n", "==>> Forecasting 1 out of next 1 seconds/steps... \n", "\n", "+-----------------------------------------------+\n", "\| shl_predict_price_1_step() \|\n", "+-----------------------------------------------+\n", "current_ccyy_mm : 2017-07\n", "in_current_time : 11:29:42\n", "in_current_price : 91000.000000\n", "INFO f_current_datetime : 2017-07 11:29:42 \n", "INFO f_current_si : 1.1629896100 \n", "INFO f_current_price4pm : 601 \n", "INFO f_current_price4pmsi : 516.7715986732 \n", "INFO f_1_step_time : 11:29:43\n", "INFO f_1_step_si : 1.2717913130 \n", " previous_pred_les_level : 566.6746894830\n", " previous_pred_les_trend : 14.2290803120\n", " f_1_step_pred_les_level : 540.0977660563\n", " f_1_step_pred_les_trend : 4.5368908778\n", " f_1_step_pred_les : 544.6346569341\n", " f_1_step_pred_adj_misc : 0.0000000000\n", " pred_les + pred_adj_misc : 544.6346569341\n", " f_1_step_pred_price_inc : 692.6616254475\n", " f_1_step_pred_price : 91091.6616254475\n", " f_1_step_pred_price_rounded : 91100\n", " f_1_step_pred_set_price_rounded : 91400\n", "-------------------------------------------------\n", "==>> Prediction Restuls in Python List : [91400]\n", "[91400]\n", "\n", "<<<< Record No.: 1873 >>>>\n", "2017-07\n", "11:29:43\n", "91000\n", "==>> Forecasting 1 out of next 1 seconds/steps... \n", "\n", "+-----------------------------------------------+\n", "\| shl_predict_price_1_step() \|\n", "+-----------------------------------------------+\n", "current_ccyy_mm : 2017-07\n", "in_current_time : 11:29:43\n", "in_current_price : 91000.000000\n", "INFO f_current_datetime : 2017-07 11:29:43 \n", "INFO f_current_si : 1.2717913130 \n", "INFO f_current_price4pm : 601 \n", "INFO f_current_price4pmsi : 472.5618062151 \n", "INFO f_1_step_time : 11:29:44\n", "INFO f_1_step_si : 1.3866613510 \n", " previous_pred_les_level : 540.0977660563\n", " previous_pred_les_trend : 4.5368908778\n", " f_1_step_pred_les_level : 498.7761593275\n", " f_1_step_pred_les_trend : -6.3553603904\n", " f_1_step_pred_les : 492.4207989371\n", " f_1_step_pred_adj_misc : 0.0000000000\n", " pred_les + pred_adj_misc : 492.4207989371\n", " f_1_step_pred_price_inc : 682.8208903146\n", " f_1_step_pred_price : 91081.8208903146\n", " f_1_step_pred_price_rounded : 91100\n", " f_1_step_pred_set_price_rounded : 91400\n", "-------------------------------------------------\n", "==>> Prediction Restuls in Python List : [91400]\n", "[91400]\n", "\n", "<<<< Record No.: 1874 >>>>\n", "2017-07\n", "11:29:44\n", "91100\n", "==>> Forecasting 1 out of next 1 seconds/steps... \n", "\n", "+-----------------------------------------------+\n", "\| shl_predict_price_1_step() \|\n", "+-----------------------------------------------+\n", "current_ccyy_mm : 2017-07\n", "in_current_time : 11:29:44\n", "in_current_price : 91100.000000\n", "INFO f_current_datetime : 2017-07 11:29:44 \n", "INFO f_current_si : 1.3866613510 \n", "INFO f_current_price4pm : 701 \n", "INFO f_current_price4pmsi : 505.5307840624 \n", "INFO f_1_step_time : 11:29:45\n", "INFO f_1_step_si : 1.4370894140 \n", " previous_pred_les_level : 498.7761593275\n", " previous_pred_les_trend : -6.3553603904\n", " f_1_step_pred_les_level : 500.7624173897\n", " f_1_step_pred_les_trend : -4.3740699228\n", " f_1_step_pred_les : 496.3883474669\n", " f_1_step_pred_adj_misc : 0.0000000000\n", " pred_les + pred_adj_misc : 496.3883474669\n", " f_1_step_pred_price_inc : 713.3544393777\n", " f_1_step_pred_price : 91112.3544393777\n", " f_1_step_pred_price_rounded : 91100\n", " f_1_step_pred_set_price_rounded : 91400\n", "-------------------------------------------------\n", "==>> Prediction Restuls in Python List : [91400]\n", "[91400]\n", "\n", "<<<< Record No.: 1875 >>>>\n", "2017-07\n", "11:29:45\n", "91100\n", "==>> Forecasting 1 out of next 1 seconds/steps... \n", "\n", "+-----------------------------------------------+\n", "\| shl_predict_price_1_step() \|\n", "+-----------------------------------------------+\n", "current_ccyy_mm : 2017-07\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "in_current_time : 11:29:45\n", "in_current_price : 91100.000000\n", "INFO f_current_datetime : 2017-07 11:29:45 \n", "INFO f_current_si : 1.4370894140 \n", "INFO f_current_price4pm : 701 \n", "INFO f_current_price4pmsi : 487.7914993813 \n", "INFO f_1_step_time : 11:29:46\n", "INFO f_1_step_si : 1.5686206330 \n", " previous_pred_les_level : 500.7624173897\n", " previous_pred_les_trend : -4.3740699228\n", " f_1_step_pred_les_level : 490.9183468574\n", " f_1_step_pred_les_trend : -5.6732974201\n", " f_1_step_pred_les : 485.2450494374\n", " f_1_step_pred_adj_misc : 0.0000000000\n", " pred_les + pred_adj_misc : 485.2450494374\n", " f_1_step_pred_price_inc : 761.1653966086\n", " f_1_step_pred_price : 91160.1653966086\n", " f_1_step_pred_price_rounded : 91200\n", " f_1_step_pred_set_price_rounded : 91500\n", "-------------------------------------------------\n", "==>> Prediction Restuls in Python List : [91500]\n", "[91500]\n", "\n", "<<<< Record No.: 1876 >>>>\n", "2017-07\n", "11:29:46\n", "91200\n", "==>> Forecasting 1 out of next 1 seconds/steps... \n", "\n", "+-----------------------------------------------+\n", "\| shl_predict_price_1_step() \|\n", "+-----------------------------------------------+\n", "current_ccyy_mm : 2017-07\n", "in_current_time : 11:29:46\n", "in_current_price : 91200.000000\n", "INFO f_current_datetime : 2017-07 11:29:46 \n", "INFO f_current_si : 1.5686206330 \n", "INFO f_current_price4pm : 801 \n", "INFO f_current_price4pmsi : 510.6397194764 \n", "INFO f_1_step_time : 11:29:47\n", "INFO f_1_step_si : 1.6413910300 \n", " previous_pred_les_level : 490.9183468574\n", " previous_pred_les_trend : -5.6732974201\n", " f_1_step_pred_les_level : 501.4031645055\n", " f_1_step_pred_les_trend : -1.8354427469\n", " f_1_step_pred_les : 499.5677217585\n", " f_1_step_pred_adj_misc : 0.0000000000\n", " pred_les + pred_adj_misc : 499.5677217585\n", " f_1_step_pred_price_inc : 819.9859773720\n", " f_1_step_pred_price : 91218.9859773720\n", " f_1_step_pred_price_rounded : 91200\n", " f_1_step_pred_set_price_rounded : 91500\n", "-------------------------------------------------\n", "==>> Prediction Restuls in Python List : [91500]\n", "[91500]\n", "\n", "<<<< Record No.: 1877 >>>>\n", "2017-07\n", "11:29:47\n", "91300\n", "==>> Forecasting 1 out of next 1 seconds/steps... \n", "\n", "+-----------------------------------------------+\n", "\| shl_predict_price_1_step() \|\n", "+-----------------------------------------------+\n", "current_ccyy_mm : 2017-07\n", "in_current_time : 11:29:47\n", "in_current_price : 91300.000000\n", "INFO f_current_datetime : 2017-07 11:29:47 \n", "INFO f_current_si : 1.6413910300 \n", "INFO f_current_price4pm : 901 \n", "INFO f_current_price4pmsi : 548.9246520374 \n", "INFO f_1_step_time : 11:29:48\n", "INFO f_1_step_si : 1.7490712830 \n", " previous_pred_les_level : 501.4031645055\n", " previous_pred_les_trend : -1.8354427469\n", " f_1_step_pred_les_level : 530.9725385027\n", " f_1_step_pred_les_trend : 5.6237888647\n", " f_1_step_pred_les : 536.5963273674\n", " f_1_step_pred_adj_misc : 0.0000000000\n", " pred_les + pred_adj_misc : 536.5963273674\n", " f_1_step_pred_price_inc : 938.5452267616\n", " f_1_step_pred_price : 91337.5452267616\n", " f_1_step_pred_price_rounded : 91300\n", " f_1_step_pred_set_price_rounded : 91600\n", "-------------------------------------------------\n", "==>> Prediction Restuls in Python List : [91600]\n", "[91600]\n", "\n", "<<<< Record No.: 1878 >>>>\n", "2017-07\n", "11:29:48\n", "91400\n", "==>> Forecasting 1 out of next 1 seconds/steps... \n", "\n", "+-----------------------------------------------+\n", "\| shl_predict_price_1_step() \|\n", "+-----------------------------------------------+\n", "current_ccyy_mm : 2017-07\n", "in_current_time : 11:29:48\n", "in_current_price : 91400.000000\n", "INFO f_current_datetime : 2017-07 11:29:48 \n", "INFO f_current_si : 1.7490712830 \n", "INFO f_current_price4pm : 1001 \n", "INFO f_current_price4pmsi : 572.3037189674 \n", "INFO f_1_step_time : 11:29:49\n", "INFO f_1_step_si : 1.7897347710 \n", " previous_pred_les_level : 530.9725385027\n", " previous_pred_les_trend : 5.6237888647\n", " f_1_step_pred_les_level : 559.3162186426\n", " f_1_step_pred_les_trend : 11.0201881684\n", " f_1_step_pred_les : 570.3364068110\n", " f_1_step_pred_adj_misc : 0.0000000000\n", " pred_les + pred_adj_misc : 570.3364068110\n", " f_1_step_pred_price_inc : 1020.7508984368\n", " f_1_step_pred_price : 91419.7508984368\n", " f_1_step_pred_price_rounded : 91400\n", " f_1_step_pred_set_price_rounded : 91700\n", "-------------------------------------------------\n", "==>> Prediction Restuls in Python List : [91700]\n", "[91700]\n", "\n", "<<<< Record No.: 1879 >>>>\n", "2017-07\n", "11:29:49\n", "91400\n", "==>> Forecasting 1 out of next 1 seconds/steps... \n", "\n", "+-----------------------------------------------+\n", "\| shl_predict_price_1_step() \|\n", "+-----------------------------------------------+\n", "current_ccyy_mm : 2017-07\n", "in_current_time : 11:29:49\n", "in_current_price : 91400.000000\n", "INFO f_current_datetime : 2017-07 11:29:49 \n", "INFO f_current_si : 1.7897347710 \n", "INFO f_current_price4pm : 1001 \n", "INFO f_current_price4pmsi : 559.3007501557 \n", "INFO f_1_step_time : 11:29:50\n", "INFO f_1_step_si : 1.9329318490 \n", " previous_pred_les_level : 559.3162186426\n", " previous_pred_les_trend : 11.0201881684\n", " f_1_step_pred_les_level : 563.3146416211\n", " f_1_step_pred_les_trend : 9.3523875472\n", " f_1_step_pred_les : 572.6670291683\n", " f_1_step_pred_adj_misc : 0.0000000000\n", " pred_les + pred_adj_misc : 572.6670291683\n", " f_1_step_pred_price_inc : 1106.9263395517\n", " f_1_step_pred_price : 91505.9263395517\n", " f_1_step_pred_price_rounded : 91500\n", " f_1_step_pred_set_price_rounded : 91800\n", "-------------------------------------------------\n", "==>> Prediction Restuls in Python List : [91800]\n", "[91800]\n" ] } ], "source": [ "# Upon receiving 11:29:00 second price, to predict till 11:29:49 <- one-step forward price forecasting\n", "\n", "for i in range(shl_sm_parm_ccyy_mm_offset, shl_sm_parm_ccyy_mm_offset+50): # use csv data as simulatino\n", "# for i in range(shl_sm_parm_ccyy_mm_offset, shl_sm_parm_ccyy_mm_offset+55): # use csv data as simulatino\n", " print('\\n<<<< Record No.: %5d >>>>' % i)\n", " print(shl_sm_data['ccyy-mm'][i]) # format: ccyy-mm\n", " print(shl_sm_data['time'][i]) # format: hh:mm:ss\n", " print(shl_sm_data['bid-price'][i]) # format: integer\n", " \n", "###################################################################################################################### \n", "# call prediction function, returned result is in 'list' format, i.e. [89400] \n", " shl_sm_prediction_list_local_1 = shl_pm.shl_predict_price_k_step(shl_sm_data['time'][i], shl_sm_data['bid-price'][i],1) # <- one-step forward price forecasting\n", " print(shl_sm_prediction_list_local_1)\n", "###################################################################################################################### \n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "<<<< Record No.: 1880 >>>>\n", "2017-07\n", "11:29:50\n", "91500\n", "==>> Forecasting 1 out of next 10 seconds/steps... \n", "\n", "+-----------------------------------------------+\n", "\| shl_predict_price_1_step() \|\n", "+-----------------------------------------------+\n", "current_ccyy_mm : 2017-07\n", "in_current_time : 11:29:50\n", "in_current_price : 91500.000000\n", "INFO f_current_datetime : 2017-07 11:29:50 \n", "INFO f_current_si : 1.9329318490 \n", "INFO f_current_price4pm : 1101 \n", "INFO f_current_price4pmsi : 569.6010444288 \n", "INFO f_1_step_time : 11:29:51\n", "INFO f_1_step_si : 2.0011852710 \n", "INFO sec50_error : 5.9263395517\n", "INFO sec46_49_error : -163.5525008210\n", "INFO shl_global_parm_short_weight_misc : -31.5252322539\n", "INFO shl_global_parm_short_weight_ratio : 1\n", " previous_pred_les_level : 563.3146416211\n", " previous_pred_les_trend : 9.3523875472\n", " f_1_step_pred_les_level : 570.7162050725\n", " f_1_step_pred_les_trend : 8.8890303214\n", " f_1_step_pred_les : 579.6052353939\n", " f_1_step_pred_adj_misc : -0.8809825102\n", " pred_les + pred_adj_misc : 578.7242528837\n", " f_1_step_pred_price_inc : 1158.1344508413\n", " f_1_step_pred_price : 91557.1344508413\n", " f_1_step_pred_price_rounded : 91600\n", " f_1_step_pred_set_price_rounded : 91900\n", "-------------------------------------------------\n", "==>> Forecasting 2 out of next 10 seconds/steps... \n", "\n", "+-----------------------------------------------+\n", "\| shl_predict_price_1_step() \|\n", "+-----------------------------------------------+\n", "current_ccyy_mm : 2017-07\n", "in_current_time : 11:29:51\n", "in_current_price : 91557.134451\n", "INFO f_current_datetime : 2017-07 11:29:51 \n", "INFO f_current_si : 2.0011852710 \n", "INFO f_current_price4pm : 1158 \n", "INFO f_current_price4pmsi : 578.7242528837 \n", "INFO f_1_step_time : 11:29:52\n", "INFO f_1_step_si : 2.0661036070 \n", "INFO shl_global_parm_short_weight_ratio : 2\n", " previous_pred_les_level : 570.7162050725\n", " previous_pred_les_trend : 8.8890303214\n", " f_1_step_pred_les_level : 579.0446840360\n", " f_1_step_pred_les_trend : 8.7558888851\n", " f_1_step_pred_les : 587.8005729211\n", " f_1_step_pred_adj_misc : -1.7619650204\n", " pred_les + pred_adj_misc : 586.0386079007\n", " f_1_step_pred_price_inc : 1210.8164816249\n", " f_1_step_pred_price : 91609.8164816249\n", " f_1_step_pred_price_rounded : 91600\n", " f_1_step_pred_set_price_rounded : 91900\n", "-------------------------------------------------\n", "==>> Forecasting 3 out of next 10 seconds/steps... \n", "\n", "+-----------------------------------------------+\n", "\| shl_predict_price_1_step() \|\n", "+-----------------------------------------------+\n", "current_ccyy_mm : 2017-07\n", "in_current_time : 11:29:52\n", "in_current_price : 91609.816482\n", "INFO f_current_datetime : 2017-07 11:29:52 \n", "INFO f_current_si : 2.0661036070 \n", "INFO f_current_price4pm : 1210 \n", "INFO f_current_price4pmsi : 586.0386079007 \n", "INFO f_1_step_time : 11:29:53\n", "INFO f_1_step_si : 2.1682095660 \n", "INFO shl_global_parm_short_weight_ratio : 3\n", " previous_pred_les_level : 579.0446840360\n", " previous_pred_les_trend : 8.7558888851\n", " f_1_step_pred_les_level : 586.6794702054\n", " f_1_step_pred_les_trend : 8.4896060125\n", " f_1_step_pred_les : 595.1690762179\n", " f_1_step_pred_adj_misc : -2.6429475306\n", " pred_les + pred_adj_misc : 592.5261286873\n", " f_1_step_pred_price_inc : 1284.7208203248\n", " f_1_step_pred_price : 91683.7208203248\n", " f_1_step_pred_price_rounded : 91700\n", " f_1_step_pred_set_price_rounded : 92000\n", "-------------------------------------------------\n", "==>> Forecasting 4 out of next 10 seconds/steps... \n", "\n", "+-----------------------------------------------+\n", "\| shl_predict_price_1_step() \|\n", "+-----------------------------------------------+\n", "current_ccyy_mm : 2017-07\n", "in_current_time : 11:29:53\n", "in_current_price : 91683.720820\n", "INFO f_current_datetime : 2017-07 11:29:53 \n", "INFO f_current_si : 2.1682095660 \n", "INFO f_current_price4pm : 1284 \n", "INFO f_current_price4pmsi : 592.5261286873 \n", "INFO f_1_step_time : 11:29:54\n", "INFO f_1_step_si : 2.2903489060 \n", "INFO shl_global_parm_short_weight_ratio : 4\n", " previous_pred_les_level : 586.6794702054\n", " previous_pred_les_trend : 8.4896060125\n", " f_1_step_pred_les_level : 593.4874221443\n", " f_1_step_pred_les_trend : 8.0901817035\n", " f_1_step_pred_les : 601.5776038478\n", " f_1_step_pred_adj_misc : -3.5239300407\n", " pred_les + pred_adj_misc : 598.0536738071\n", " f_1_step_pred_price_inc : 1369.7515775334\n", " f_1_step_pred_price : 91768.7515775334\n", " f_1_step_pred_price_rounded : 91800\n", " f_1_step_pred_set_price_rounded : 92100\n", "-------------------------------------------------\n", "==>> Forecasting 5 out of next 10 seconds/steps... \n", "\n", "+-----------------------------------------------+\n", "\| shl_predict_price_1_step() \|\n", "+-----------------------------------------------+\n", "current_ccyy_mm : 2017-07\n", "in_current_time : 11:29:54\n", "in_current_price : 91768.751578\n", "INFO f_current_datetime : 2017-07 11:29:54 \n", "INFO f_current_si : 2.2903489060 \n", "INFO f_current_price4pm : 1369 \n", "INFO f_current_price4pmsi : 598.0536738071 \n", "INFO f_1_step_time : 11:29:55\n", "INFO f_1_step_si : 2.4136021070 \n", "INFO shl_global_parm_short_weight_ratio : 5\n", " previous_pred_les_level : 593.4874221443\n", " previous_pred_les_trend : 8.0901817035\n", " f_1_step_pred_les_level : 599.3353984164\n", " f_1_step_pred_les_trend : 7.5576159583\n", " f_1_step_pred_les : 606.8930143747\n", " f_1_step_pred_adj_misc : -4.4049125509\n", " pred_les + pred_adj_misc : 602.4881018238\n", " f_1_step_pred_price_inc : 1454.1665520044\n", " f_1_step_pred_price : 91853.1665520044\n", " f_1_step_pred_price_rounded : 91900\n", " f_1_step_pred_set_price_rounded : 92200\n", "-------------------------------------------------\n", "==>> Forecasting 6 out of next 10 seconds/steps... \n", "\n", "+-----------------------------------------------+\n", "\| shl_predict_price_1_step() \|\n", "+-----------------------------------------------+\n", "current_ccyy_mm : 2017-07\n", "in_current_time : 11:29:55\n", "in_current_price : 91853.166552\n", "INFO f_current_datetime : 2017-07 11:29:55 \n", "INFO f_current_si : 2.4136021070 \n", "INFO f_current_price4pm : 1454 \n", "INFO f_current_price4pmsi : 602.4881018238 \n", "INFO f_1_step_time : 11:29:56\n", "INFO f_1_step_si : 2.5506970550 \n", "INFO shl_global_parm_short_weight_ratio : 6\n", " previous_pred_les_level : 599.3353984164\n", " previous_pred_les_trend : 7.5576159583\n", " f_1_step_pred_les_level : 604.0902575855\n", " f_1_step_pred_les_trend : 6.8919087767\n", " f_1_step_pred_les : 610.9821663622\n", " f_1_step_pred_adj_misc : -5.2858950611\n", " pred_les + pred_adj_misc : 605.6962713011\n", " f_1_step_pred_price_inc : 1544.9476954322\n", " f_1_step_pred_price : 91943.9476954322\n", " f_1_step_pred_price_rounded : 91900\n", " f_1_step_pred_set_price_rounded : 92200\n", "-------------------------------------------------\n", "==>> Forecasting 7 out of next 10 seconds/steps... \n", "\n", "+-----------------------------------------------+\n", "\| shl_predict_price_1_step() \|\n", "+-----------------------------------------------+\n", "current_ccyy_mm : 2017-07\n", "in_current_time : 11:29:56\n", "in_current_price : 91943.947695\n", "INFO f_current_datetime : 2017-07 11:29:56 \n", "INFO f_current_si : 2.5506970550 \n", "INFO f_current_price4pm : 1544 \n", "INFO f_current_price4pmsi : 605.6962713011 \n", "INFO f_1_step_time : 11:29:57\n", "INFO f_1_step_si : 2.7053908880 \n", "INFO shl_global_parm_short_weight_ratio : 7\n", " previous_pred_les_level : 604.0902575855\n", " previous_pred_les_trend : 6.8919087767\n", " f_1_step_pred_les_level : 607.6188582151\n", " f_1_step_pred_les_trend : 6.0930601589\n", " f_1_step_pred_les : 613.7119183739\n", " f_1_step_pred_adj_misc : -6.1668775713\n", " pred_les + pred_adj_misc : 607.5450408027\n", " f_1_step_pred_price_inc : 1643.6468174371\n", " f_1_step_pred_price : 92042.6468174371\n", " f_1_step_pred_price_rounded : 92000\n", " f_1_step_pred_set_price_rounded : 92300\n", "-------------------------------------------------\n", "==>> Forecasting 8 out of next 10 seconds/steps... \n", "\n", "+-----------------------------------------------+\n", "\| shl_predict_price_1_step() \|\n", "+-----------------------------------------------+\n", "current_ccyy_mm : 2017-07\n", "in_current_time : 11:29:57\n", "in_current_price : 92042.646817\n", "INFO f_current_datetime : 2017-07 11:29:57 \n", "INFO f_current_si : 2.7053908880 \n", "INFO f_current_price4pm : 1643 \n", "INFO f_current_price4pmsi : 607.5450408027 \n", "INFO f_1_step_time : 11:29:58\n", "INFO f_1_step_si : 2.7745487590 \n", "INFO shl_global_parm_short_weight_ratio : 8\n", " previous_pred_les_level : 607.6188582151\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " previous_pred_les_trend : 6.0930601589\n", " f_1_step_pred_les_level : 609.7880588690\n", " f_1_step_pred_les_trend : 5.1610701047\n", " f_1_step_pred_les : 614.9491289737\n", " f_1_step_pred_adj_misc : -7.0478600815\n", " pred_les + pred_adj_misc : 607.9012688922\n", " f_1_step_pred_price_inc : 1686.6517111994\n", " f_1_step_pred_price : 92085.6517111994\n", " f_1_step_pred_price_rounded : 92100\n", " f_1_step_pred_set_price_rounded : 92400\n", "-------------------------------------------------\n", "==>> Forecasting 9 out of next 10 seconds/steps... \n", "\n", "+-----------------------------------------------+\n", "\| shl_predict_price_1_step() \|\n", "+-----------------------------------------------+\n", "current_ccyy_mm : 2017-07\n", "in_current_time : 11:29:58\n", "in_current_price : 92085.651711\n", "INFO f_current_datetime : 2017-07 11:29:58 \n", "INFO f_current_si : 2.7745487590 \n", "INFO f_current_price4pm : 1686 \n", "INFO f_current_price4pmsi : 607.9012688922 \n", "INFO f_1_step_time : 11:29:59\n", "INFO f_1_step_si : 2.9291830210 \n", "INFO shl_global_parm_short_weight_ratio : 9\n", " previous_pred_les_level : 609.7880588690\n", " previous_pred_les_trend : 5.1610701047\n", " f_1_step_pred_les_level : 610.4647181109\n", " f_1_step_pred_les_trend : 4.0959386142\n", " f_1_step_pred_les : 614.5606567251\n", " f_1_step_pred_adj_misc : -7.9288425917\n", " pred_les + pred_adj_misc : 606.6318141334\n", " f_1_step_pred_price_inc : 1776.9356099580\n", " f_1_step_pred_price : 92175.9356099580\n", " f_1_step_pred_price_rounded : 92200\n", " f_1_step_pred_set_price_rounded : 92500\n", "-------------------------------------------------\n", "==>> Forecasting 10 out of next 10 seconds/steps... \n", "\n", "+-----------------------------------------------+\n", "\| shl_predict_price_1_step() \|\n", "+-----------------------------------------------+\n", "current_ccyy_mm : 2017-07\n", "in_current_time : 11:29:59\n", "in_current_price : 92175.935610\n", "INFO f_current_datetime : 2017-07 11:29:59 \n", "INFO f_current_si : 2.9291830210 \n", "INFO f_current_price4pm : 1776 \n", "INFO f_current_price4pmsi : 606.6318141334 \n", "INFO f_1_step_time : 11:30:00\n", "INFO f_1_step_si : 3.0710424510 \n", "INFO shl_global_parm_short_weight_ratio : 10\n", " previous_pred_les_level : 610.4647181109\n", " previous_pred_les_trend : 4.0959386142\n", " f_1_step_pred_les_level : 609.5156945044\n", " f_1_step_pred_les_trend : 2.8976656874\n", " f_1_step_pred_les : 612.4133601918\n", " f_1_step_pred_adj_misc : -8.8098251018\n", " pred_les + pred_adj_misc : 603.6035350899\n", " f_1_step_pred_price_inc : 1853.6920798349\n", " f_1_step_pred_price : 92252.6920798349\n", " f_1_step_pred_price_rounded : 92300\n", " f_1_step_pred_set_price_rounded : 92600\n", "-------------------------------------------------\n", "==>> Prediction Restuls in Python List : [91900, 91900, 92000, 92100, 92200, 92200, 92300, 92400, 92500, 92600]\n", "[91900, 91900, 92000, 92100, 92200, 92200, 92300, 92400, 92500, 92600]\n" ] } ], "source": [ "# Upon receiving 11:29:50 second price, to predict till 11:30:00 <- ten-step forward price forecasting\n", "\n", "for i in range(shl_sm_parm_ccyy_mm_offset+50, shl_sm_parm_ccyy_mm_offset+51): # use csv data as simulation\n", " print('\\n<<<< Record No.: %5d >>>>' % i)\n", " print(shl_sm_data['ccyy-mm'][i]) # format: ccyy-mm\n", " print(shl_sm_data['time'][i]) # format: hh:mm:ss\n", " print(shl_sm_data['bid-price'][i]) # format: integer/boost-trap-float\n", " \n", "###################################################################################################################### \n", "# call prediction function, returned result is in 'list' format, i.e. [89400, 89400, 89400, 89500, 89500, 89500, 89500, 89600, 89600, 89600] \n", " shl_sm_prediction_list_local_k = shl_pm.shl_predict_price_k_step(shl_sm_data['time'][i], shl_sm_data['bid-price'][i],10) # <- ten-step forward price forecasting\n", " print(shl_sm_prediction_list_local_k)\n", "###################################################################################################################### \n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>ccyy-mm</th>\n", " <th>f_1_step_pred_adj_misc</th>\n", " <th>f_1_step_pred_les</th>\n", " <th>f_1_step_pred_les_level</th>\n", " <th>f_1_step_pred_les_trend</th>\n", " <th>f_1_step_pred_price</th>\n", " <th>f_1_step_pred_price_inc</th>\n", " <th>f_1_step_pred_price_rounded</th>\n", " <th>f_1_step_pred_set_price_rounded</th>\n", " <th>f_1_step_si</th>\n", " <th>f_1_step_time</th>\n", " <th>f_current_bid</th>\n", " <th>f_current_datetime</th>\n", " <th>f_current_price4pm</th>\n", " <th>f_current_price4pmsi</th>\n", " <th>f_current_si</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>422.483383</td>\n", " <td>422.483383</td>\n", " <td>0.000000</td>\n", " <td>90408.458677</td>\n", " <td>9.458677</td>\n", " <td>90400.0</td>\n", " <td>90700.0</td>\n", " <td>0.022388</td>\n", " <td>11:29:01</td>\n", " <td>90400.0</td>\n", " <td>2017-07 11:29:00</td>\n", " <td>1.0</td>\n", " <td>422.483383</td>\n", " <td>0.002367</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>124.987080</td>\n", " <td>182.085965</td>\n", " <td>-57.098885</td>\n", " <td>90402.863447</td>\n", " <td>3.863447</td>\n", " <td>90400.0</td>\n", " <td>90700.0</td>\n", " <td>0.030911</td>\n", " <td>11:29:02</td>\n", " <td>90400.0</td>\n", " <td>2017-07 11:29:01</td>\n", " <td>1.0</td>\n", " <td>44.666225</td>\n", " <td>0.022388</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>-5.054063</td>\n", " <td>66.044732</td>\n", " <td>-71.098795</td>\n", " <td>90398.809110</td>\n", " <td>-0.190890</td>\n", " <td>90400.0</td>\n", " <td>90700.0</td>\n", " <td>0.037770</td>\n", " <td>11:29:03</td>\n", " <td>90400.0</td>\n", " <td>2017-07 11:29:02</td>\n", " <td>1.0</td>\n", " <td>32.351184</td>\n", " <td>0.030911</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>-51.325580</td>\n", " <td>15.008081</td>\n", " <td>-66.333661</td>\n", " <td>90396.654153</td>\n", " <td>-2.345847</td>\n", " <td>90400.0</td>\n", " <td>90700.0</td>\n", " <td>0.045705</td>\n", " <td>11:29:04</td>\n", " <td>90400.0</td>\n", " <td>2017-07 11:29:03</td>\n", " <td>1.0</td>\n", " <td>26.476318</td>\n", " <td>0.037770</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>-60.017096</td>\n", " <td>-4.746773</td>\n", " <td>-55.270323</td>\n", " <td>90396.282431</td>\n", " <td>-2.717569</td>\n", " <td>90400.0</td>\n", " <td>90700.0</td>\n", " <td>0.045280</td>\n", " <td>11:29:05</td>\n", " <td>90400.0</td>\n", " <td>2017-07 11:29:04</td>\n", " <td>1.0</td>\n", " <td>21.879334</td>\n", " <td>0.045705</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>-50.639678</td>\n", " <td>-7.777287</td>\n", " <td>-42.862391</td>\n", " <td>90394.910559</td>\n", " <td>-4.089441</td>\n", " <td>90400.0</td>\n", " <td>90700.0</td>\n", " <td>0.080756</td>\n", " <td>11:29:06</td>\n", " <td>90400.0</td>\n", " <td>2017-07 11:29:05</td>\n", " <td>1.0</td>\n", " <td>22.084851</td>\n", " <td>0.045280</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>-43.877474</td>\n", " <td>-10.539602</td>\n", " <td>-33.337872</td>\n", " <td>90394.677994</td>\n", " <td>-4.322006</td>\n", " <td>90400.0</td>\n", " <td>90700.0</td>\n", " <td>0.098502</td>\n", " <td>11:29:07</td>\n", " <td>90400.0</td>\n", " <td>2017-07 11:29:06</td>\n", " <td>1.0</td>\n", " <td>12.383032</td>\n", " <td>0.080756</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>-34.672014</td>\n", " <td>-9.499544</td>\n", " <td>-25.172470</td>\n", " <td>90394.279256</td>\n", " <td>-4.720744</td>\n", " <td>90400.0</td>\n", " <td>90700.0</td>\n", " <td>0.136154</td>\n", " <td>11:29:08</td>\n", " <td>90400.0</td>\n", " <td>2017-07 11:29:07</td>\n", " <td>1.0</td>\n", " <td>10.152108</td>\n", " <td>0.098502</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>-26.760255</td>\n", " <td>-7.937687</td>\n", " <td>-18.822568</td>\n", " <td>90393.536513</td>\n", " <td>-5.463487</td>\n", " <td>90400.0</td>\n", " <td>90700.0</td>\n", " <td>0.204164</td>\n", " <td>11:29:09</td>\n", " <td>90400.0</td>\n", " <td>2017-07 11:29:08</td>\n", " <td>1.0</td>\n", " <td>7.344608</td>\n", " <td>0.136154</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>-20.654841</td>\n", " <td>-6.616736</td>\n", " <td>-14.038105</td>\n", " <td>90394.227138</td>\n", " <td>-4.772862</td>\n", " <td>90400.0</td>\n", " <td>90700.0</td>\n", " <td>0.231077</td>\n", " <td>11:29:10</td>\n", " <td>90400.0</td>\n", " <td>2017-07 11:29:09</td>\n", " <td>1.0</td>\n", " <td>4.898017</td>\n", " <td>0.204164</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>325.733889</td>\n", " <td>270.594763</td>\n", " <td>55.139126</td>\n", " <td>90493.796863</td>\n", " <td>94.796863</td>\n", " <td>90500.0</td>\n", " <td>90800.0</td>\n", " <td>0.291025</td>\n", " <td>11:29:11</td>\n", " <td>90500.0</td>\n", " <td>2017-07 11:29:10</td>\n", " <td>101.0</td>\n", " <td>437.083427</td>\n", " <td>0.231077</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>397.656427</td>\n", " <td>339.296038</td>\n", " <td>58.360390</td>\n", " <td>90535.446795</td>\n", " <td>136.446795</td>\n", " <td>90500.0</td>\n", " <td>90800.0</td>\n", " <td>0.343127</td>\n", " <td>11:29:12</td>\n", " <td>90500.0</td>\n", " <td>2017-07 11:29:11</td>\n", " <td>101.0</td>\n", " <td>347.048645</td>\n", " <td>0.291025</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>374.673563</td>\n", " <td>331.925499</td>\n", " <td>42.748064</td>\n", " <td>90530.538182</td>\n", " <td>131.538182</td>\n", " <td>90500.0</td>\n", " <td>90800.0</td>\n", " <td>0.351074</td>\n", " <td>11:29:13</td>\n", " <td>90500.0</td>\n", " <td>2017-07 11:29:12</td>\n", " <td>101.0</td>\n", " <td>294.351356</td>\n", " <td>0.343127</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>573.214382</td>\n", " <td>500.564795</td>\n", " <td>72.649588</td>\n", " <td>90611.465091</td>\n", " <td>212.465091</td>\n", " <td>90600.0</td>\n", " <td>90900.0</td>\n", " <td>0.370656</td>\n", " <td>11:29:14</td>\n", " <td>90600.0</td>\n", " <td>2017-07 11:29:13</td>\n", " <td>201.0</td>\n", " <td>572.528714</td>\n", " <td>0.351074</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>621.507918</td>\n", " <td>553.533022</td>\n", " <td>67.974896</td>\n", " <td>90648.315882</td>\n", " <td>249.315882</td>\n", " <td>90600.0</td>\n", " <td>90900.0</td>\n", " <td>0.401147</td>\n", " <td>11:29:15</td>\n", " <td>90600.0</td>\n", " <td>2017-07 11:29:14</td>\n", " <td>201.0</td>\n", " <td>542.282454</td>\n", " <td>0.370656</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>594.643909</td>\n", " <td>544.871578</td>\n", " <td>49.772331</td>\n", " <td>90644.046963</td>\n", " <td>245.046963</td>\n", " <td>90600.0</td>\n", " <td>90900.0</td>\n", " <td>0.412090</td>\n", " <td>11:29:16</td>\n", " <td>90600.0</td>\n", " <td>2017-07 11:29:15</td>\n", " <td>201.0</td>\n", " <td>501.063512</td>\n", " <td>0.401147</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>751.329413</td>\n", " <td>681.037085</td>\n", " <td>70.292327</td>\n", " <td>90739.779361</td>\n", " <td>340.779361</td>\n", " <td>90700.0</td>\n", " <td>91000.0</td>\n", " <td>0.453569</td>\n", " <td>11:29:17</td>\n", " <td>90700.0</td>\n", " <td>2017-07 11:29:16</td>\n", " <td>301.0</td>\n", " <td>730.422507</td>\n", " <td>0.412090</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>752.563612</td>\n", " <td>695.525708</td>\n", " <td>57.037903</td>\n", " <td>90762.996569</td>\n", " <td>363.996569</td>\n", " <td>90800.0</td>\n", " <td>91100.0</td>\n", " <td>0.483675</td>\n", " <td>11:29:18</td>\n", " <td>90700.0</td>\n", " <td>2017-07 11:29:17</td>\n", " <td>301.0</td>\n", " <td>663.626320</td>\n", " <td>0.453569</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>707.045126</td>\n", " <td>669.691015</td>\n", " <td>37.354111</td>\n", " <td>90755.734217</td>\n", " <td>356.734217</td>\n", " <td>90800.0</td>\n", " <td>91100.0</td>\n", " <td>0.504542</td>\n", " <td>11:29:19</td>\n", " <td>90700.0</td>\n", " <td>2017-07 11:29:18</td>\n", " <td>301.0</td>\n", " <td>622.318083</td>\n", " <td>0.483675</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>657.418283</td>\n", " <td>636.758549</td>\n", " <td>20.659734</td>\n", " <td>90745.666546</td>\n", " <td>346.666546</td>\n", " <td>90700.0</td>\n", " <td>91000.0</td>\n", " <td>0.527315</td>\n", " <td>11:29:20</td>\n", " <td>90700.0</td>\n", " <td>2017-07 11:29:19</td>\n", " <td>301.0</td>\n", " <td>596.580234</td>\n", " <td>0.504542</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>609.886884</td>\n", " <td>602.315170</td>\n", " <td>7.571713</td>\n", " <td>90744.620807</td>\n", " <td>345.620807</td>\n", " <td>90700.0</td>\n", " <td>91000.0</td>\n", " <td>0.566697</td>\n", " <td>11:29:21</td>\n", " <td>90700.0</td>\n", " <td>2017-07 11:29:20</td>\n", " <td>301.0</td>\n", " <td>570.816265</td>\n", " <td>0.527315</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>555.459304</td>\n", " <td>559.787203</td>\n", " <td>-4.327898</td>\n", " <td>90720.268379</td>\n", " <td>321.268379</td>\n", " <td>90700.0</td>\n", " <td>91000.0</td>\n", " <td>0.578383</td>\n", " <td>11:29:22</td>\n", " <td>90700.0</td>\n", " <td>2017-07 11:29:21</td>\n", " <td>301.0</td>\n", " <td>531.148438</td>\n", " <td>0.566697</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>523.538135</td>\n", " <td>533.162049</td>\n", " <td>-9.623914</td>\n", " <td>90708.075013</td>\n", " <td>309.075013</td>\n", " <td>90700.0</td>\n", " <td>91000.0</td>\n", " <td>0.590358</td>\n", " <td>11:29:23</td>\n", " <td>90700.0</td>\n", " <td>2017-07 11:29:22</td>\n", " <td>301.0</td>\n", " <td>520.416142</td>\n", " <td>0.578383</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>503.143928</td>\n", " <td>514.834999</td>\n", " <td>-11.691071</td>\n", " <td>90711.119466</td>\n", " <td>312.119466</td>\n", " <td>90700.0</td>\n", " <td>91000.0</td>\n", " <td>0.620338</td>\n", " <td>11:29:24</td>\n", " <td>90700.0</td>\n", " <td>2017-07 11:29:23</td>\n", " <td>301.0</td>\n", " <td>509.859976</td>\n", " <td>0.590358</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>477.338691</td>\n", " <td>491.738714</td>\n", " <td>-14.400023</td>\n", " <td>90715.190223</td>\n", " <td>316.190223</td>\n", " <td>90700.0</td>\n", " <td>91000.0</td>\n", " <td>0.662402</td>\n", " <td>11:29:25</td>\n", " <td>90700.0</td>\n", " <td>2017-07 11:29:24</td>\n", " <td>301.0</td>\n", " <td>485.219087</td>\n", " <td>0.620338</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>444.881807</td>\n", " <td>462.747509</td>\n", " <td>-17.865702</td>\n", " <td>90701.661202</td>\n", " <td>302.661202</td>\n", " <td>90700.0</td>\n", " <td>91000.0</td>\n", " <td>0.680318</td>\n", " <td>11:29:26</td>\n", " <td>90700.0</td>\n", " <td>2017-07 11:29:25</td>\n", " <td>301.0</td>\n", " <td>454.406669</td>\n", " <td>0.662402</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>425.093411</td>\n", " <td>443.328138</td>\n", " <td>-18.234727</td>\n", " <td>90697.158177</td>\n", " <td>298.158177</td>\n", " <td>90700.0</td>\n", " <td>91000.0</td>\n", " <td>0.701394</td>\n", " <td>11:29:27</td>\n", " <td>90700.0</td>\n", " <td>2017-07 11:29:26</td>\n", " <td>301.0</td>\n", " <td>442.440005</td>\n", " <td>0.680318</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>410.049007</td>\n", " <td>427.671411</td>\n", " <td>-17.622404</td>\n", " <td>90696.741614</td>\n", " <td>297.741614</td>\n", " <td>90700.0</td>\n", " <td>91000.0</td>\n", " <td>0.726112</td>\n", " <td>11:29:28</td>\n", " <td>90700.0</td>\n", " <td>2017-07 11:29:27</td>\n", " <td>301.0</td>\n", " <td>429.145087</td>\n", " <td>0.701394</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>395.960050</td>\n", " <td>412.904274</td>\n", " <td>-16.944225</td>\n", " <td>90692.496845</td>\n", " <td>293.496845</td>\n", " <td>90700.0</td>\n", " <td>91000.0</td>\n", " <td>0.741228</td>\n", " <td>11:29:29</td>\n", " <td>90700.0</td>\n", " <td>2017-07 11:29:28</td>\n", " <td>301.0</td>\n", " <td>414.536447</td>\n", " <td>0.726112</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>386.986438</td>\n", " <td>402.400852</td>\n", " <td>-15.414414</td>\n", " <td>90702.736025</td>\n", " <td>303.736025</td>\n", " <td>90700.0</td>\n", " <td>91000.0</td>\n", " <td>0.784875</td>\n", " <td>11:29:30</td>\n", " <td>90700.0</td>\n", " <td>2017-07 11:29:29</td>\n", " <td>301.0</td>\n", " <td>406.082644</td>\n", " <td>0.741228</td>\n", " </tr>\n", " <tr>\n", " <th>30</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>368.827170</td>\n", " <td>384.768407</td>\n", " <td>-15.941237</td>\n", " <td>90689.761443</td>\n", " <td>290.761443</td>\n", " <td>90700.0</td>\n", " <td>91000.0</td>\n", " <td>0.788341</td>\n", " <td>11:29:31</td>\n", " <td>90700.0</td>\n", " <td>2017-07 11:29:30</td>\n", " <td>301.0</td>\n", " <td>383.500501</td>\n", " <td>0.784875</td>\n", " </tr>\n", " <tr>\n", " <th>31</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>363.112378</td>\n", " <td>377.090840</td>\n", " <td>-13.978461</td>\n", " <td>90694.715761</td>\n", " <td>295.715761</td>\n", " <td>90700.0</td>\n", " <td>91000.0</td>\n", " <td>0.814392</td>\n", " <td>11:29:32</td>\n", " <td>90700.0</td>\n", " <td>2017-07 11:29:31</td>\n", " <td>301.0</td>\n", " <td>381.814648</td>\n", " <td>0.788341</td>\n", " </tr>\n", " <tr>\n", " <th>32</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>354.243070</td>\n", " <td>367.240925</td>\n", " <td>-12.997854</td>\n", " <td>90694.828587</td>\n", " <td>295.828587</td>\n", " <td>90700.0</td>\n", " <td>91000.0</td>\n", " <td>0.835101</td>\n", " <td>11:29:33</td>\n", " <td>90700.0</td>\n", " <td>2017-07 11:29:32</td>\n", " <td>301.0</td>\n", " <td>369.600949</td>\n", " <td>0.814392</td>\n", " </tr>\n", " <tr>\n", " <th>33</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>346.121291</td>\n", " <td>358.183273</td>\n", " <td>-12.061982</td>\n", " <td>90699.102575</td>\n", " <td>300.102575</td>\n", " <td>90700.0</td>\n", " <td>91000.0</td>\n", " <td>0.867045</td>\n", " <td>11:29:34</td>\n", " <td>90700.0</td>\n", " <td>2017-07 11:29:33</td>\n", " <td>301.0</td>\n", " <td>360.435634</td>\n", " <td>0.835101</td>\n", " </tr>\n", " <tr>\n", " <th>34</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>334.874307</td>\n", " <td>346.779866</td>\n", " <td>-11.905559</td>\n", " <td>90707.624498</td>\n", " <td>308.624498</td>\n", " <td>90700.0</td>\n", " <td>91000.0</td>\n", " <td>0.921613</td>\n", " <td>11:29:35</td>\n", " <td>90700.0</td>\n", " <td>2017-07 11:29:34</td>\n", " <td>301.0</td>\n", " <td>347.156330</td>\n", " <td>0.867045</td>\n", " </tr>\n", " <tr>\n", " <th>35</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>401.892576</td>\n", " <td>398.650173</td>\n", " <td>3.242403</td>\n", " <td>90782.376971</td>\n", " <td>383.376971</td>\n", " <td>90800.0</td>\n", " <td>91100.0</td>\n", " <td>0.953929</td>\n", " <td>11:29:36</td>\n", " <td>90800.0</td>\n", " <td>2017-07 11:29:35</td>\n", " <td>401.0</td>\n", " <td>435.106733</td>\n", " <td>0.921613</td>\n", " </tr>\n", " <tr>\n", " <th>36</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>419.681677</td>\n", " <td>413.647306</td>\n", " <td>6.034371</td>\n", " <td>90809.434440</td>\n", " <td>410.434440</td>\n", " <td>90800.0</td>\n", " <td>91100.0</td>\n", " <td>0.977966</td>\n", " <td>11:29:37</td>\n", " <td>90800.0</td>\n", " <td>2017-07 11:29:36</td>\n", " <td>401.0</td>\n", " <td>420.366728</td>\n", " <td>0.953929</td>\n", " </tr>\n", " <tr>\n", " <th>37</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>498.634793</td>\n", " <td>478.605024</td>\n", " <td>20.029769</td>\n", " <td>90894.400465</td>\n", " <td>495.400465</td>\n", " <td>90900.0</td>\n", " <td>91200.0</td>\n", " <td>0.993514</td>\n", " <td>11:29:38</td>\n", " <td>90900.0</td>\n", " <td>2017-07 11:29:37</td>\n", " <td>501.0</td>\n", " <td>512.287712</td>\n", " <td>0.977966</td>\n", " </tr>\n", " <tr>\n", " <th>38</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>602.357357</td>\n", " <td>566.264312</td>\n", " <td>36.093045</td>\n", " <td>91020.965116</td>\n", " <td>621.965116</td>\n", " <td>91000.0</td>\n", " <td>91300.0</td>\n", " <td>1.032552</td>\n", " <td>11:29:39</td>\n", " <td>91000.0</td>\n", " <td>2017-07 11:29:38</td>\n", " <td>601.0</td>\n", " <td>604.923757</td>\n", " <td>0.993514</td>\n", " </tr>\n", " <tr>\n", " <th>39</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>622.462725</td>\n", " <td>589.438218</td>\n", " <td>33.024508</td>\n", " <td>91068.937666</td>\n", " <td>669.937666</td>\n", " <td>91100.0</td>\n", " <td>91400.0</td>\n", " <td>1.076270</td>\n", " <td>11:29:40</td>\n", " <td>91000.0</td>\n", " <td>2017-07 11:29:39</td>\n", " <td>601.0</td>\n", " <td>582.053177</td>\n", " <td>1.032552</td>\n", " </tr>\n", " <tr>\n", " <th>40</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>605.051838</td>\n", " <td>581.707467</td>\n", " <td>23.344371</td>\n", " <td>91066.544509</td>\n", " <td>667.544509</td>\n", " <td>91100.0</td>\n", " <td>91400.0</td>\n", " <td>1.103285</td>\n", " <td>11:29:41</td>\n", " <td>91000.0</td>\n", " <td>2017-07 11:29:40</td>\n", " <td>601.0</td>\n", " <td>558.410307</td>\n", " <td>1.076270</td>\n", " </tr>\n", " <tr>\n", " <th>41</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>580.903770</td>\n", " <td>566.674689</td>\n", " <td>14.229080</td>\n", " <td>91074.585049</td>\n", " <td>675.585049</td>\n", " <td>91100.0</td>\n", " <td>91400.0</td>\n", " <td>1.162990</td>\n", " <td>11:29:42</td>\n", " <td>91000.0</td>\n", " <td>2017-07 11:29:41</td>\n", " <td>601.0</td>\n", " <td>544.736942</td>\n", " <td>1.103285</td>\n", " </tr>\n", " <tr>\n", " <th>42</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>544.634657</td>\n", " <td>540.097766</td>\n", " <td>4.536891</td>\n", " <td>91091.661625</td>\n", " <td>692.661625</td>\n", " <td>91100.0</td>\n", " <td>91400.0</td>\n", " <td>1.271791</td>\n", " <td>11:29:43</td>\n", " <td>91000.0</td>\n", " <td>2017-07 11:29:42</td>\n", " <td>601.0</td>\n", " <td>516.771599</td>\n", " <td>1.162990</td>\n", " </tr>\n", " <tr>\n", " <th>43</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>492.420799</td>\n", " <td>498.776159</td>\n", " <td>-6.355360</td>\n", " <td>91081.820890</td>\n", " <td>682.820890</td>\n", " <td>91100.0</td>\n", " <td>91400.0</td>\n", " <td>1.386661</td>\n", " <td>11:29:44</td>\n", " <td>91000.0</td>\n", " <td>2017-07 11:29:43</td>\n", " <td>601.0</td>\n", " <td>472.561806</td>\n", " <td>1.271791</td>\n", " </tr>\n", " <tr>\n", " <th>44</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>496.388347</td>\n", " <td>500.762417</td>\n", " <td>-4.374070</td>\n", " <td>91112.354439</td>\n", " <td>713.354439</td>\n", " <td>91100.0</td>\n", " <td>91400.0</td>\n", " <td>1.437089</td>\n", " <td>11:29:45</td>\n", " <td>91100.0</td>\n", " <td>2017-07 11:29:44</td>\n", " <td>701.0</td>\n", " <td>505.530784</td>\n", " <td>1.386661</td>\n", " </tr>\n", " <tr>\n", " <th>45</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>485.245049</td>\n", " <td>490.918347</td>\n", " <td>-5.673297</td>\n", " <td>91160.165397</td>\n", " <td>761.165397</td>\n", " <td>91200.0</td>\n", " <td>91500.0</td>\n", " <td>1.568621</td>\n", " <td>11:29:46</td>\n", " <td>91100.0</td>\n", " <td>2017-07 11:29:45</td>\n", " <td>701.0</td>\n", " <td>487.791499</td>\n", " <td>1.437089</td>\n", " </tr>\n", " <tr>\n", " <th>46</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>499.567722</td>\n", " <td>501.403165</td>\n", " <td>-1.835443</td>\n", " <td>91218.985977</td>\n", " <td>819.985977</td>\n", " <td>91200.0</td>\n", " <td>91500.0</td>\n", " <td>1.641391</td>\n", " <td>11:29:47</td>\n", " <td>91200.0</td>\n", " <td>2017-07 11:29:46</td>\n", " <td>801.0</td>\n", " <td>510.639719</td>\n", " <td>1.568621</td>\n", " </tr>\n", " <tr>\n", " <th>47</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>536.596327</td>\n", " <td>530.972539</td>\n", " <td>5.623789</td>\n", " <td>91337.545227</td>\n", " <td>938.545227</td>\n", " <td>91300.0</td>\n", " <td>91600.0</td>\n", " <td>1.749071</td>\n", " <td>11:29:48</td>\n", " <td>91300.0</td>\n", " <td>2017-07 11:29:47</td>\n", " <td>901.0</td>\n", " <td>548.924652</td>\n", " <td>1.641391</td>\n", " </tr>\n", " <tr>\n", " <th>48</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>570.336407</td>\n", " <td>559.316219</td>\n", " <td>11.020188</td>\n", " <td>91419.750898</td>\n", " <td>1020.750898</td>\n", " <td>91400.0</td>\n", " <td>91700.0</td>\n", " <td>1.789735</td>\n", " <td>11:29:49</td>\n", " <td>91400.0</td>\n", " <td>2017-07 11:29:48</td>\n", " <td>1001.0</td>\n", " <td>572.303719</td>\n", " <td>1.749071</td>\n", " </tr>\n", " <tr>\n", " <th>49</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>572.667029</td>\n", " <td>563.314642</td>\n", " <td>9.352388</td>\n", " <td>91505.926340</td>\n", " <td>1106.926340</td>\n", " <td>91500.0</td>\n", " <td>91800.0</td>\n", " <td>1.932932</td>\n", " <td>11:29:50</td>\n", " <td>91400.0</td>\n", " <td>2017-07 11:29:49</td>\n", " <td>1001.0</td>\n", " <td>559.300750</td>\n", " <td>1.789735</td>\n", " </tr>\n", " <tr>\n", " <th>50</th>\n", " <td>2017-07</td>\n", " <td>-0.880983</td>\n", " <td>579.605235</td>\n", " <td>570.716205</td>\n", " <td>8.889030</td>\n", " <td>91557.134451</td>\n", " <td>1158.134451</td>\n", " <td>91600.0</td>\n", " <td>91900.0</td>\n", " <td>2.001185</td>\n", " <td>11:29:51</td>\n", " <td>91500.0</td>\n", " <td>2017-07 11:29:50</td>\n", " <td>1101.0</td>\n", " <td>569.601044</td>\n", " <td>1.932932</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " ccyy-mm f_1_step_pred_adj_misc f_1_step_pred_les \\\n", "0 2017-07 0.000000 422.483383 \n", "1 2017-07 0.000000 124.987080 \n", "2 2017-07 0.000000 -5.054063 \n", "3 2017-07 0.000000 -51.325580 \n", "4 2017-07 0.000000 -60.017096 \n", "5 2017-07 0.000000 -50.639678 \n", "6 2017-07 0.000000 -43.877474 \n", "7 2017-07 0.000000 -34.672014 \n", "8 2017-07 0.000000 -26.760255 \n", "9 2017-07 0.000000 -20.654841 \n", "10 2017-07 0.000000 325.733889 \n", "11 2017-07 0.000000 397.656427 \n", "12 2017-07 0.000000 374.673563 \n", "13 2017-07 0.000000 573.214382 \n", "14 2017-07 0.000000 621.507918 \n", "15 2017-07 0.000000 594.643909 \n", "16 2017-07 0.000000 751.329413 \n", "17 2017-07 0.000000 752.563612 \n", "18 2017-07 0.000000 707.045126 \n", "19 2017-07 0.000000 657.418283 \n", "20 2017-07 0.000000 609.886884 \n", "21 2017-07 0.000000 555.459304 \n", "22 2017-07 0.000000 523.538135 \n", "23 2017-07 0.000000 503.143928 \n", "24 2017-07 0.000000 477.338691 \n", "25 2017-07 0.000000 444.881807 \n", "26 2017-07 0.000000 425.093411 \n", "27 2017-07 0.000000 410.049007 \n", "28 2017-07 0.000000 395.960050 \n", "29 2017-07 0.000000 386.986438 \n", "30 2017-07 0.000000 368.827170 \n", "31 2017-07 0.000000 363.112378 \n", "32 2017-07 0.000000 354.243070 \n", "33 2017-07 0.000000 346.121291 \n", "34 2017-07 0.000000 334.874307 \n", "35 2017-07 0.000000 401.892576 \n", "36 2017-07 0.000000 419.681677 \n", "37 2017-07 0.000000 498.634793 \n", "38 2017-07 0.000000 602.357357 \n", "39 2017-07 0.000000 622.462725 \n", "40 2017-07 0.000000 605.051838 \n", "41 2017-07 0.000000 580.903770 \n", "42 2017-07 0.000000 544.634657 \n", "43 2017-07 0.000000 492.420799 \n", "44 2017-07 0.000000 496.388347 \n", "45 2017-07 0.000000 485.245049 \n", "46 2017-07 0.000000 499.567722 \n", "47 2017-07 0.000000 536.596327 \n", "48 2017-07 0.000000 570.336407 \n", "49 2017-07 0.000000 572.667029 \n", "50 2017-07 -0.880983 579.605235 \n", "\n", " f_1_step_pred_les_level f_1_step_pred_les_trend f_1_step_pred_price \\\n", "0 422.483383 0.000000 90408.458677 \n", "1 182.085965 -57.098885 90402.863447 \n", "2 66.044732 -71.098795 90398.809110 \n", "3 15.008081 -66.333661 90396.654153 \n", "4 -4.746773 -55.270323 90396.282431 \n", "5 -7.777287 -42.862391 90394.910559 \n", "6 -10.539602 -33.337872 90394.677994 \n", "7 -9.499544 -25.172470 90394.279256 \n", "8 -7.937687 -18.822568 90393.536513 \n", "9 -6.616736 -14.038105 90394.227138 \n", "10 270.594763 55.139126 90493.796863 \n", "11 339.296038 58.360390 90535.446795 \n", "12 331.925499 42.748064 90530.538182 \n", "13 500.564795 72.649588 90611.465091 \n", "14 553.533022 67.974896 90648.315882 \n", "15 544.871578 49.772331 90644.046963 \n", "16 681.037085 70.292327 90739.779361 \n", "17 695.525708 57.037903 90762.996569 \n", "18 669.691015 37.354111 90755.734217 \n", "19 636.758549 20.659734 90745.666546 \n", "20 602.315170 7.571713 90744.620807 \n", "21 559.787203 -4.327898 90720.268379 \n", "22 533.162049 -9.623914 90708.075013 \n", "23 514.834999 -11.691071 90711.119466 \n", "24 491.738714 -14.400023 90715.190223 \n", "25 462.747509 -17.865702 90701.661202 \n", "26 443.328138 -18.234727 90697.158177 \n", "27 427.671411 -17.622404 90696.741614 \n", "28 412.904274 -16.944225 90692.496845 \n", "29 402.400852 -15.414414 90702.736025 \n", "30 384.768407 -15.941237 90689.761443 \n", "31 377.090840 -13.978461 90694.715761 \n", "32 367.240925 -12.997854 90694.828587 \n", "33 358.183273 -12.061982 90699.102575 \n", "34 346.779866 -11.905559 90707.624498 \n", "35 398.650173 3.242403 90782.376971 \n", "36 413.647306 6.034371 90809.434440 \n", "37 478.605024 20.029769 90894.400465 \n", "38 566.264312 36.093045 91020.965116 \n", "39 589.438218 33.024508 91068.937666 \n", "40 581.707467 23.344371 91066.544509 \n", "41 566.674689 14.229080 91074.585049 \n", "42 540.097766 4.536891 91091.661625 \n", "43 498.776159 -6.355360 91081.820890 \n", "44 500.762417 -4.374070 91112.354439 \n", "45 490.918347 -5.673297 91160.165397 \n", "46 501.403165 -1.835443 91218.985977 \n", "47 530.972539 5.623789 91337.545227 \n", "48 559.316219 11.020188 91419.750898 \n", "49 563.314642 9.352388 91505.926340 \n", "50 570.716205 8.889030 91557.134451 \n", "\n", " f_1_step_pred_price_inc f_1_step_pred_price_rounded \\\n", "0 9.458677 90400.0 \n", "1 3.863447 90400.0 \n", "2 -0.190890 90400.0 \n", "3 -2.345847 90400.0 \n", "4 -2.717569 90400.0 \n", "5 -4.089441 90400.0 \n", "6 -4.322006 90400.0 \n", "7 -4.720744 90400.0 \n", "8 -5.463487 90400.0 \n", "9 -4.772862 90400.0 \n", "10 94.796863 90500.0 \n", "11 136.446795 90500.0 \n", "12 131.538182 90500.0 \n", "13 212.465091 90600.0 \n", "14 249.315882 90600.0 \n", "15 245.046963 90600.0 \n", "16 340.779361 90700.0 \n", "17 363.996569 90800.0 \n", "18 356.734217 90800.0 \n", "19 346.666546 90700.0 \n", "20 345.620807 90700.0 \n", "21 321.268379 90700.0 \n", "22 309.075013 90700.0 \n", "23 312.119466 90700.0 \n", "24 316.190223 90700.0 \n", "25 302.661202 90700.0 \n", "26 298.158177 90700.0 \n", "27 297.741614 90700.0 \n", "28 293.496845 90700.0 \n", "29 303.736025 90700.0 \n", "30 290.761443 90700.0 \n", "31 295.715761 90700.0 \n", "32 295.828587 90700.0 \n", "33 300.102575 90700.0 \n", "34 308.624498 90700.0 \n", "35 383.376971 90800.0 \n", "36 410.434440 90800.0 \n", "37 495.400465 90900.0 \n", "38 621.965116 91000.0 \n", "39 669.937666 91100.0 \n", "40 667.544509 91100.0 \n", "41 675.585049 91100.0 \n", "42 692.661625 91100.0 \n", "43 682.820890 91100.0 \n", "44 713.354439 91100.0 \n", "45 761.165397 91200.0 \n", "46 819.985977 91200.0 \n", "47 938.545227 91300.0 \n", "48 1020.750898 91400.0 \n", "49 1106.926340 91500.0 \n", "50 1158.134451 91600.0 \n", "\n", " f_1_step_pred_set_price_rounded f_1_step_si f_1_step_time f_current_bid \\\n", "0 90700.0 0.022388 11:29:01 90400.0 \n", "1 90700.0 0.030911 11:29:02 90400.0 \n", "2 90700.0 0.037770 11:29:03 90400.0 \n", "3 90700.0 0.045705 11:29:04 90400.0 \n", "4 90700.0 0.045280 11:29:05 90400.0 \n", "5 90700.0 0.080756 11:29:06 90400.0 \n", "6 90700.0 0.098502 11:29:07 90400.0 \n", "7 90700.0 0.136154 11:29:08 90400.0 \n", "8 90700.0 0.204164 11:29:09 90400.0 \n", "9 90700.0 0.231077 11:29:10 90400.0 \n", "10 90800.0 0.291025 11:29:11 90500.0 \n", "11 90800.0 0.343127 11:29:12 90500.0 \n", "12 90800.0 0.351074 11:29:13 90500.0 \n", "13 90900.0 0.370656 11:29:14 90600.0 \n", "14 90900.0 0.401147 11:29:15 90600.0 \n", "15 90900.0 0.412090 11:29:16 90600.0 \n", "16 91000.0 0.453569 11:29:17 90700.0 \n", "17 91100.0 0.483675 11:29:18 90700.0 \n", "18 91100.0 0.504542 11:29:19 90700.0 \n", "19 91000.0 0.527315 11:29:20 90700.0 \n", "20 91000.0 0.566697 11:29:21 90700.0 \n", "21 91000.0 0.578383 11:29:22 90700.0 \n", "22 91000.0 0.590358 11:29:23 90700.0 \n", "23 91000.0 0.620338 11:29:24 90700.0 \n", "24 91000.0 0.662402 11:29:25 90700.0 \n", "25 91000.0 0.680318 11:29:26 90700.0 \n", "26 91000.0 0.701394 11:29:27 90700.0 \n", "27 91000.0 0.726112 11:29:28 90700.0 \n", "28 91000.0 0.741228 11:29:29 90700.0 \n", "29 91000.0 0.784875 11:29:30 90700.0 \n", "30 91000.0 0.788341 11:29:31 90700.0 \n", "31 91000.0 0.814392 11:29:32 90700.0 \n", "32 91000.0 0.835101 11:29:33 90700.0 \n", "33 91000.0 0.867045 11:29:34 90700.0 \n", "34 91000.0 0.921613 11:29:35 90700.0 \n", "35 91100.0 0.953929 11:29:36 90800.0 \n", "36 91100.0 0.977966 11:29:37 90800.0 \n", "37 91200.0 0.993514 11:29:38 90900.0 \n", "38 91300.0 1.032552 11:29:39 91000.0 \n", "39 91400.0 1.076270 11:29:40 91000.0 \n", "40 91400.0 1.103285 11:29:41 91000.0 \n", "41 91400.0 1.162990 11:29:42 91000.0 \n", "42 91400.0 1.271791 11:29:43 91000.0 \n", "43 91400.0 1.386661 11:29:44 91000.0 \n", "44 91400.0 1.437089 11:29:45 91100.0 \n", "45 91500.0 1.568621 11:29:46 91100.0 \n", "46 91500.0 1.641391 11:29:47 91200.0 \n", "47 91600.0 1.749071 11:29:48 91300.0 \n", "48 91700.0 1.789735 11:29:49 91400.0 \n", "49 91800.0 1.932932 11:29:50 91400.0 \n", "50 91900.0 2.001185 11:29:51 91500.0 \n", "\n", " f_current_datetime f_current_price4pm f_current_price4pmsi f_current_si \n", "0 2017-07 11:29:00 1.0 422.483383 0.002367 \n", "1 2017-07 11:29:01 1.0 44.666225 0.022388 \n", "2 2017-07 11:29:02 1.0 32.351184 0.030911 \n", "3 2017-07 11:29:03 1.0 26.476318 0.037770 \n", "4 2017-07 11:29:04 1.0 21.879334 0.045705 \n", "5 2017-07 11:29:05 1.0 22.084851 0.045280 \n", "6 2017-07 11:29:06 1.0 12.383032 0.080756 \n", "7 2017-07 11:29:07 1.0 10.152108 0.098502 \n", "8 2017-07 11:29:08 1.0 7.344608 0.136154 \n", "9 2017-07 11:29:09 1.0 4.898017 0.204164 \n", "10 2017-07 11:29:10 101.0 437.083427 0.231077 \n", "11 2017-07 11:29:11 101.0 347.048645 0.291025 \n", "12 2017-07 11:29:12 101.0 294.351356 0.343127 \n", "13 2017-07 11:29:13 201.0 572.528714 0.351074 \n", "14 2017-07 11:29:14 201.0 542.282454 0.370656 \n", "15 2017-07 11:29:15 201.0 501.063512 0.401147 \n", "16 2017-07 11:29:16 301.0 730.422507 0.412090 \n", "17 2017-07 11:29:17 301.0 663.626320 0.453569 \n", "18 2017-07 11:29:18 301.0 622.318083 0.483675 \n", "19 2017-07 11:29:19 301.0 596.580234 0.504542 \n", "20 2017-07 11:29:20 301.0 570.816265 0.527315 \n", "21 2017-07 11:29:21 301.0 531.148438 0.566697 \n", "22 2017-07 11:29:22 301.0 520.416142 0.578383 \n", "23 2017-07 11:29:23 301.0 509.859976 0.590358 \n", "24 2017-07 11:29:24 301.0 485.219087 0.620338 \n", "25 2017-07 11:29:25 301.0 454.406669 0.662402 \n", "26 2017-07 11:29:26 301.0 442.440005 0.680318 \n", "27 2017-07 11:29:27 301.0 429.145087 0.701394 \n", "28 2017-07 11:29:28 301.0 414.536447 0.726112 \n", "29 2017-07 11:29:29 301.0 406.082644 0.741228 \n", "30 2017-07 11:29:30 301.0 383.500501 0.784875 \n", "31 2017-07 11:29:31 301.0 381.814648 0.788341 \n", "32 2017-07 11:29:32 301.0 369.600949 0.814392 \n", "33 2017-07 11:29:33 301.0 360.435634 0.835101 \n", "34 2017-07 11:29:34 301.0 347.156330 0.867045 \n", "35 2017-07 11:29:35 401.0 435.106733 0.921613 \n", "36 2017-07 11:29:36 401.0 420.366728 0.953929 \n", "37 2017-07 11:29:37 501.0 512.287712 0.977966 \n", "38 2017-07 11:29:38 601.0 604.923757 0.993514 \n", "39 2017-07 11:29:39 601.0 582.053177 1.032552 \n", "40 2017-07 11:29:40 601.0 558.410307 1.076270 \n", "41 2017-07 11:29:41 601.0 544.736942 1.103285 \n", "42 2017-07 11:29:42 601.0 516.771599 1.162990 \n", "43 2017-07 11:29:43 601.0 472.561806 1.271791 \n", "44 2017-07 11:29:44 701.0 505.530784 1.386661 \n", "45 2017-07 11:29:45 701.0 487.791499 1.437089 \n", "46 2017-07 11:29:46 801.0 510.639719 1.568621 \n", "47 2017-07 11:29:47 901.0 548.924652 1.641391 \n", "48 2017-07 11:29:48 1001.0 572.303719 1.749071 \n", "49 2017-07 11:29:49 1001.0 559.300750 1.789735 \n", "50 2017-07 11:29:50 1101.0 569.601044 1.932932 " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "shl_pm.shl_data_pm_1_step\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>ccyy-mm</th>\n", " <th>f_1_step_pred_adj_misc</th>\n", " <th>f_1_step_pred_les</th>\n", " <th>f_1_step_pred_les_level</th>\n", " <th>f_1_step_pred_les_trend</th>\n", " <th>f_1_step_pred_price</th>\n", " <th>f_1_step_pred_price_inc</th>\n", " <th>f_1_step_pred_price_rounded</th>\n", " <th>f_1_step_pred_set_price_rounded</th>\n", " <th>f_1_step_si</th>\n", " <th>f_1_step_time</th>\n", " <th>f_current_bid</th>\n", " <th>f_current_datetime</th>\n", " <th>f_current_price4pm</th>\n", " <th>f_current_price4pmsi</th>\n", " <th>f_current_si</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>422.483383</td>\n", " <td>422.483383</td>\n", " <td>0.000000</td>\n", " <td>90408.458677</td>\n", " <td>9.458677</td>\n", " <td>90400.0</td>\n", " <td>90700.0</td>\n", " <td>0.022388</td>\n", " <td>11:29:01</td>\n", " <td>90400.000000</td>\n", " <td>2017-07 11:29:00</td>\n", " <td>1.000000</td>\n", " <td>422.483383</td>\n", " <td>0.002367</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>124.987080</td>\n", " <td>182.085965</td>\n", " <td>-57.098885</td>\n", " <td>90402.863447</td>\n", " <td>3.863447</td>\n", " <td>90400.0</td>\n", " <td>90700.0</td>\n", " <td>0.030911</td>\n", " <td>11:29:02</td>\n", " <td>90400.000000</td>\n", " <td>2017-07 11:29:01</td>\n", " <td>1.000000</td>\n", " <td>44.666225</td>\n", " <td>0.022388</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>-5.054063</td>\n", " <td>66.044732</td>\n", " <td>-71.098795</td>\n", " <td>90398.809110</td>\n", " <td>-0.190890</td>\n", " <td>90400.0</td>\n", " <td>90700.0</td>\n", " <td>0.037770</td>\n", " <td>11:29:03</td>\n", " <td>90400.000000</td>\n", " <td>2017-07 11:29:02</td>\n", " <td>1.000000</td>\n", " <td>32.351184</td>\n", " <td>0.030911</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>-51.325580</td>\n", " <td>15.008081</td>\n", " <td>-66.333661</td>\n", " <td>90396.654153</td>\n", " <td>-2.345847</td>\n", " <td>90400.0</td>\n", " <td>90700.0</td>\n", " <td>0.045705</td>\n", " <td>11:29:04</td>\n", " <td>90400.000000</td>\n", " <td>2017-07 11:29:03</td>\n", " <td>1.000000</td>\n", " <td>26.476318</td>\n", " <td>0.037770</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>-60.017096</td>\n", " <td>-4.746773</td>\n", " <td>-55.270323</td>\n", " <td>90396.282431</td>\n", " <td>-2.717569</td>\n", " <td>90400.0</td>\n", " <td>90700.0</td>\n", " <td>0.045280</td>\n", " <td>11:29:05</td>\n", " <td>90400.000000</td>\n", " <td>2017-07 11:29:04</td>\n", " <td>1.000000</td>\n", " <td>21.879334</td>\n", " <td>0.045705</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>-50.639678</td>\n", " <td>-7.777287</td>\n", " <td>-42.862391</td>\n", " <td>90394.910559</td>\n", " <td>-4.089441</td>\n", " <td>90400.0</td>\n", " <td>90700.0</td>\n", " <td>0.080756</td>\n", " <td>11:29:06</td>\n", " <td>90400.000000</td>\n", " <td>2017-07 11:29:05</td>\n", " <td>1.000000</td>\n", " <td>22.084851</td>\n", " <td>0.045280</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>-43.877474</td>\n", " <td>-10.539602</td>\n", " <td>-33.337872</td>\n", " <td>90394.677994</td>\n", " <td>-4.322006</td>\n", " <td>90400.0</td>\n", " <td>90700.0</td>\n", " <td>0.098502</td>\n", " <td>11:29:07</td>\n", " <td>90400.000000</td>\n", " <td>2017-07 11:29:06</td>\n", " <td>1.000000</td>\n", " <td>12.383032</td>\n", " <td>0.080756</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>-34.672014</td>\n", " <td>-9.499544</td>\n", " <td>-25.172470</td>\n", " <td>90394.279256</td>\n", " <td>-4.720744</td>\n", " <td>90400.0</td>\n", " <td>90700.0</td>\n", " <td>0.136154</td>\n", " <td>11:29:08</td>\n", " <td>90400.000000</td>\n", " <td>2017-07 11:29:07</td>\n", " <td>1.000000</td>\n", " <td>10.152108</td>\n", " <td>0.098502</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>-26.760255</td>\n", " <td>-7.937687</td>\n", " <td>-18.822568</td>\n", " <td>90393.536513</td>\n", " <td>-5.463487</td>\n", " <td>90400.0</td>\n", " <td>90700.0</td>\n", " <td>0.204164</td>\n", " <td>11:29:09</td>\n", " <td>90400.000000</td>\n", " <td>2017-07 11:29:08</td>\n", " <td>1.000000</td>\n", " <td>7.344608</td>\n", " <td>0.136154</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>-20.654841</td>\n", " <td>-6.616736</td>\n", " <td>-14.038105</td>\n", " <td>90394.227138</td>\n", " <td>-4.772862</td>\n", " <td>90400.0</td>\n", " <td>90700.0</td>\n", " <td>0.231077</td>\n", " <td>11:29:10</td>\n", " <td>90400.000000</td>\n", " <td>2017-07 11:29:09</td>\n", " <td>1.000000</td>\n", " <td>4.898017</td>\n", " <td>0.204164</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>325.733889</td>\n", " <td>270.594763</td>\n", " <td>55.139126</td>\n", " <td>90493.796863</td>\n", " <td>94.796863</td>\n", " <td>90500.0</td>\n", " <td>90800.0</td>\n", " <td>0.291025</td>\n", " <td>11:29:11</td>\n", " <td>90500.000000</td>\n", " <td>2017-07 11:29:10</td>\n", " <td>101.000000</td>\n", " <td>437.083427</td>\n", " <td>0.231077</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>397.656427</td>\n", " <td>339.296038</td>\n", " <td>58.360390</td>\n", " <td>90535.446795</td>\n", " <td>136.446795</td>\n", " <td>90500.0</td>\n", " <td>90800.0</td>\n", " <td>0.343127</td>\n", " <td>11:29:12</td>\n", " <td>90500.000000</td>\n", " <td>2017-07 11:29:11</td>\n", " <td>101.000000</td>\n", " <td>347.048645</td>\n", " <td>0.291025</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>374.673563</td>\n", " <td>331.925499</td>\n", " <td>42.748064</td>\n", " <td>90530.538182</td>\n", " <td>131.538182</td>\n", 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<td>730.422507</td>\n", " <td>0.412090</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>752.563612</td>\n", " <td>695.525708</td>\n", " <td>57.037903</td>\n", " <td>90762.996569</td>\n", " <td>363.996569</td>\n", " <td>90800.0</td>\n", " <td>91100.0</td>\n", " <td>0.483675</td>\n", " <td>11:29:18</td>\n", " <td>90700.000000</td>\n", " <td>2017-07 11:29:17</td>\n", " <td>301.000000</td>\n", " <td>663.626320</td>\n", " <td>0.453569</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>707.045126</td>\n", " <td>669.691015</td>\n", " <td>37.354111</td>\n", " <td>90755.734217</td>\n", " <td>356.734217</td>\n", " <td>90800.0</td>\n", " <td>91100.0</td>\n", " <td>0.504542</td>\n", " <td>11:29:19</td>\n", " <td>90700.000000</td>\n", " <td>2017-07 11:29:18</td>\n", " <td>301.000000</td>\n", " <td>622.318083</td>\n", " <td>0.483675</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>657.418283</td>\n", " <td>636.758549</td>\n", " <td>20.659734</td>\n", " <td>90745.666546</td>\n", " <td>346.666546</td>\n", " <td>90700.0</td>\n", " <td>91000.0</td>\n", " <td>0.527315</td>\n", " <td>11:29:20</td>\n", " <td>90700.000000</td>\n", " <td>2017-07 11:29:19</td>\n", " <td>301.000000</td>\n", " <td>596.580234</td>\n", " <td>0.504542</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>609.886884</td>\n", " <td>602.315170</td>\n", " <td>7.571713</td>\n", " <td>90744.620807</td>\n", " <td>345.620807</td>\n", " <td>90700.0</td>\n", " <td>91000.0</td>\n", " <td>0.566697</td>\n", " <td>11:29:21</td>\n", " <td>90700.000000</td>\n", " <td>2017-07 11:29:20</td>\n", " <td>301.000000</td>\n", " <td>570.816265</td>\n", " <td>0.527315</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>555.459304</td>\n", " <td>559.787203</td>\n", " 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<td>0.701394</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>395.960050</td>\n", " <td>412.904274</td>\n", " <td>-16.944225</td>\n", " <td>90692.496845</td>\n", " <td>293.496845</td>\n", " <td>90700.0</td>\n", " <td>91000.0</td>\n", " <td>0.741228</td>\n", " <td>11:29:29</td>\n", " <td>90700.000000</td>\n", " <td>2017-07 11:29:28</td>\n", " <td>301.000000</td>\n", " <td>414.536447</td>\n", " <td>0.726112</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>386.986438</td>\n", " <td>402.400852</td>\n", " <td>-15.414414</td>\n", " <td>90702.736025</td>\n", " <td>303.736025</td>\n", " <td>90700.0</td>\n", " <td>91000.0</td>\n", " <td>0.784875</td>\n", " <td>11:29:30</td>\n", " <td>90700.000000</td>\n", " <td>2017-07 11:29:29</td>\n", " <td>301.000000</td>\n", " <td>406.082644</td>\n", " <td>0.741228</td>\n", " </tr>\n", " <tr>\n", " <th>30</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>368.827170</td>\n", " <td>384.768407</td>\n", " <td>-15.941237</td>\n", " <td>90689.761443</td>\n", " <td>290.761443</td>\n", " <td>90700.0</td>\n", " <td>91000.0</td>\n", " <td>0.788341</td>\n", " <td>11:29:31</td>\n", " <td>90700.000000</td>\n", " <td>2017-07 11:29:30</td>\n", " <td>301.000000</td>\n", " <td>383.500501</td>\n", " <td>0.784875</td>\n", " </tr>\n", " <tr>\n", " <th>31</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>363.112378</td>\n", " <td>377.090840</td>\n", " <td>-13.978461</td>\n", " <td>90694.715761</td>\n", " <td>295.715761</td>\n", " <td>90700.0</td>\n", " <td>91000.0</td>\n", " <td>0.814392</td>\n", " <td>11:29:32</td>\n", " <td>90700.000000</td>\n", " <td>2017-07 11:29:31</td>\n", " <td>301.000000</td>\n", " <td>381.814648</td>\n", " <td>0.788341</td>\n", " </tr>\n", " <tr>\n", " <th>32</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>354.243070</td>\n", " <td>367.240925</td>\n", " <td>-12.997854</td>\n", 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11:29:36</td>\n", " <td>401.000000</td>\n", " <td>420.366728</td>\n", " <td>0.953929</td>\n", " </tr>\n", " <tr>\n", " <th>37</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>498.634793</td>\n", " <td>478.605024</td>\n", " <td>20.029769</td>\n", " <td>90894.400465</td>\n", " <td>495.400465</td>\n", " <td>90900.0</td>\n", " <td>91200.0</td>\n", " <td>0.993514</td>\n", " <td>11:29:38</td>\n", " <td>90900.000000</td>\n", " <td>2017-07 11:29:37</td>\n", " <td>501.000000</td>\n", " <td>512.287712</td>\n", " <td>0.977966</td>\n", " </tr>\n", " <tr>\n", " <th>38</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>602.357357</td>\n", " <td>566.264312</td>\n", " <td>36.093045</td>\n", " <td>91020.965116</td>\n", " <td>621.965116</td>\n", " <td>91000.0</td>\n", " <td>91300.0</td>\n", " <td>1.032552</td>\n", " <td>11:29:39</td>\n", " <td>91000.000000</td>\n", " <td>2017-07 11:29:38</td>\n", " <td>601.000000</td>\n", " <td>604.923757</td>\n", " <td>0.993514</td>\n", " </tr>\n", " <tr>\n", " <th>39</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>622.462725</td>\n", " <td>589.438218</td>\n", " <td>33.024508</td>\n", " <td>91068.937666</td>\n", " <td>669.937666</td>\n", " <td>91100.0</td>\n", " <td>91400.0</td>\n", " <td>1.076270</td>\n", " <td>11:29:40</td>\n", " <td>91000.000000</td>\n", " <td>2017-07 11:29:39</td>\n", " <td>601.000000</td>\n", " <td>582.053177</td>\n", " <td>1.032552</td>\n", " </tr>\n", " <tr>\n", " <th>40</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>605.051838</td>\n", " <td>581.707467</td>\n", " <td>23.344371</td>\n", " <td>91066.544509</td>\n", " <td>667.544509</td>\n", " <td>91100.0</td>\n", " <td>91400.0</td>\n", " <td>1.103285</td>\n", " <td>11:29:41</td>\n", " <td>91000.000000</td>\n", " <td>2017-07 11:29:40</td>\n", " <td>601.000000</td>\n", " <td>558.410307</td>\n", " <td>1.076270</td>\n", " </tr>\n", " <tr>\n", " <th>41</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>580.903770</td>\n", " <td>566.674689</td>\n", " <td>14.229080</td>\n", " <td>91074.585049</td>\n", " <td>675.585049</td>\n", " <td>91100.0</td>\n", " <td>91400.0</td>\n", " <td>1.162990</td>\n", " <td>11:29:42</td>\n", " <td>91000.000000</td>\n", " <td>2017-07 11:29:41</td>\n", " <td>601.000000</td>\n", " <td>544.736942</td>\n", " <td>1.103285</td>\n", " </tr>\n", " <tr>\n", " <th>42</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>544.634657</td>\n", " <td>540.097766</td>\n", " <td>4.536891</td>\n", " <td>91091.661625</td>\n", " <td>692.661625</td>\n", " <td>91100.0</td>\n", " <td>91400.0</td>\n", " <td>1.271791</td>\n", " <td>11:29:43</td>\n", " <td>91000.000000</td>\n", " <td>2017-07 11:29:42</td>\n", " <td>601.000000</td>\n", " <td>516.771599</td>\n", " <td>1.162990</td>\n", " </tr>\n", " <tr>\n", " <th>43</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>492.420799</td>\n", " <td>498.776159</td>\n", " <td>-6.355360</td>\n", " <td>91081.820890</td>\n", " <td>682.820890</td>\n", " <td>91100.0</td>\n", " <td>91400.0</td>\n", " <td>1.386661</td>\n", " <td>11:29:44</td>\n", " <td>91000.000000</td>\n", " <td>2017-07 11:29:43</td>\n", " <td>601.000000</td>\n", " <td>472.561806</td>\n", " <td>1.271791</td>\n", " </tr>\n", " <tr>\n", " <th>44</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>496.388347</td>\n", " <td>500.762417</td>\n", " <td>-4.374070</td>\n", " <td>91112.354439</td>\n", " <td>713.354439</td>\n", " <td>91100.0</td>\n", " <td>91400.0</td>\n", " <td>1.437089</td>\n", " <td>11:29:45</td>\n", " <td>91100.000000</td>\n", " <td>2017-07 11:29:44</td>\n", " <td>701.000000</td>\n", " <td>505.530784</td>\n", " <td>1.386661</td>\n", " </tr>\n", " <tr>\n", " <th>45</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>485.245049</td>\n", " <td>490.918347</td>\n", " <td>-5.673297</td>\n", " <td>91160.165397</td>\n", " <td>761.165397</td>\n", " <td>91200.0</td>\n", " <td>91500.0</td>\n", " <td>1.568621</td>\n", " <td>11:29:46</td>\n", " <td>91100.000000</td>\n", " <td>2017-07 11:29:45</td>\n", " <td>701.000000</td>\n", " <td>487.791499</td>\n", " <td>1.437089</td>\n", " </tr>\n", " <tr>\n", " <th>46</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>499.567722</td>\n", " <td>501.403165</td>\n", " <td>-1.835443</td>\n", " <td>91218.985977</td>\n", " <td>819.985977</td>\n", " <td>91200.0</td>\n", " <td>91500.0</td>\n", " <td>1.641391</td>\n", " <td>11:29:47</td>\n", " <td>91200.000000</td>\n", " <td>2017-07 11:29:46</td>\n", " <td>801.000000</td>\n", " <td>510.639719</td>\n", " <td>1.568621</td>\n", " </tr>\n", " <tr>\n", " <th>47</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>536.596327</td>\n", " <td>530.972539</td>\n", " <td>5.623789</td>\n", " <td>91337.545227</td>\n", " <td>938.545227</td>\n", " <td>91300.0</td>\n", " <td>91600.0</td>\n", " <td>1.749071</td>\n", " <td>11:29:48</td>\n", " <td>91300.000000</td>\n", " <td>2017-07 11:29:47</td>\n", " <td>901.000000</td>\n", " <td>548.924652</td>\n", " <td>1.641391</td>\n", " </tr>\n", " <tr>\n", " <th>48</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>570.336407</td>\n", " <td>559.316219</td>\n", " <td>11.020188</td>\n", " <td>91419.750898</td>\n", " <td>1020.750898</td>\n", " <td>91400.0</td>\n", " <td>91700.0</td>\n", " <td>1.789735</td>\n", " <td>11:29:49</td>\n", " <td>91400.000000</td>\n", " <td>2017-07 11:29:48</td>\n", " <td>1001.000000</td>\n", " <td>572.303719</td>\n", " <td>1.749071</td>\n", " </tr>\n", " <tr>\n", " <th>49</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>572.667029</td>\n", " <td>563.314642</td>\n", " <td>9.352388</td>\n", " <td>91505.926340</td>\n", " <td>1106.926340</td>\n", " <td>91500.0</td>\n", " <td>91800.0</td>\n", " <td>1.932932</td>\n", " <td>11:29:50</td>\n", " <td>91400.000000</td>\n", " <td>2017-07 11:29:49</td>\n", " <td>1001.000000</td>\n", " <td>559.300750</td>\n", " <td>1.789735</td>\n", " </tr>\n", " <tr>\n", " <th>50</th>\n", " <td>2017-07</td>\n", " <td>-0.880983</td>\n", " <td>579.605235</td>\n", " <td>570.716205</td>\n", " <td>8.889030</td>\n", " <td>91557.134451</td>\n", " <td>1158.134451</td>\n", " <td>91600.0</td>\n", " <td>91900.0</td>\n", " <td>2.001185</td>\n", " <td>11:29:51</td>\n", " <td>91500.000000</td>\n", " <td>2017-07 11:29:50</td>\n", " <td>1101.000000</td>\n", " <td>569.601044</td>\n", " <td>1.932932</td>\n", " </tr>\n", " <tr>\n", " <th>51</th>\n", " <td>2017-07</td>\n", " <td>-1.761965</td>\n", " <td>587.800573</td>\n", " <td>579.044684</td>\n", " <td>8.755889</td>\n", " <td>91609.816482</td>\n", " <td>1210.816482</td>\n", " <td>91600.0</td>\n", " <td>91900.0</td>\n", " <td>2.066104</td>\n", " <td>11:29:52</td>\n", " <td>91557.134451</td>\n", " <td>2017-07 11:29:51</td>\n", " <td>1158.134451</td>\n", " <td>578.724253</td>\n", " <td>2.001185</td>\n", " </tr>\n", " <tr>\n", " <th>52</th>\n", " <td>2017-07</td>\n", " <td>-2.642948</td>\n", " <td>595.169076</td>\n", " <td>586.679470</td>\n", " <td>8.489606</td>\n", " <td>91683.720820</td>\n", " <td>1284.720820</td>\n", " <td>91700.0</td>\n", " <td>92000.0</td>\n", " <td>2.168210</td>\n", " <td>11:29:53</td>\n", " <td>91609.816482</td>\n", " <td>2017-07 11:29:52</td>\n", " <td>1210.816482</td>\n", " <td>586.038608</td>\n", " <td>2.066104</td>\n", " </tr>\n", " <tr>\n", " <th>53</th>\n", " <td>2017-07</td>\n", " <td>-3.523930</td>\n", " <td>601.577604</td>\n", " <td>593.487422</td>\n", " <td>8.090182</td>\n", " <td>91768.751578</td>\n", " <td>1369.751578</td>\n", " <td>91800.0</td>\n", " <td>92100.0</td>\n", " <td>2.290349</td>\n", " <td>11:29:54</td>\n", " <td>91683.720820</td>\n", " <td>2017-07 11:29:53</td>\n", " <td>1284.720820</td>\n", " <td>592.526129</td>\n", " <td>2.168210</td>\n", " </tr>\n", " <tr>\n", " <th>54</th>\n", " <td>2017-07</td>\n", " <td>-4.404913</td>\n", " <td>606.893014</td>\n", " <td>599.335398</td>\n", " <td>7.557616</td>\n", " <td>91853.166552</td>\n", " <td>1454.166552</td>\n", " <td>91900.0</td>\n", " <td>92200.0</td>\n", " <td>2.413602</td>\n", " <td>11:29:55</td>\n", " <td>91768.751578</td>\n", " <td>2017-07 11:29:54</td>\n", " <td>1369.751578</td>\n", " <td>598.053674</td>\n", " <td>2.290349</td>\n", " </tr>\n", " <tr>\n", " <th>55</th>\n", " <td>2017-07</td>\n", " <td>-5.285895</td>\n", " <td>610.982166</td>\n", " <td>604.090258</td>\n", " <td>6.891909</td>\n", " <td>91943.947695</td>\n", " <td>1544.947695</td>\n", " <td>91900.0</td>\n", " <td>92200.0</td>\n", " <td>2.550697</td>\n", " <td>11:29:56</td>\n", " <td>91853.166552</td>\n", " <td>2017-07 11:29:55</td>\n", " <td>1454.166552</td>\n", " <td>602.488102</td>\n", " <td>2.413602</td>\n", " </tr>\n", " <tr>\n", " <th>56</th>\n", " <td>2017-07</td>\n", " <td>-6.166878</td>\n", " <td>613.711918</td>\n", " <td>607.618858</td>\n", " <td>6.093060</td>\n", " <td>92042.646817</td>\n", " <td>1643.646817</td>\n", " <td>92000.0</td>\n", " <td>92300.0</td>\n", " <td>2.705391</td>\n", " <td>11:29:57</td>\n", " <td>91943.947695</td>\n", " <td>2017-07 11:29:56</td>\n", " <td>1544.947695</td>\n", " <td>605.696271</td>\n", " <td>2.550697</td>\n", " </tr>\n", " <tr>\n", " <th>57</th>\n", " <td>2017-07</td>\n", " <td>-7.047860</td>\n", " <td>614.949129</td>\n", " <td>609.788059</td>\n", " <td>5.161070</td>\n", " <td>92085.651711</td>\n", " <td>1686.651711</td>\n", " <td>92100.0</td>\n", " <td>92400.0</td>\n", " <td>2.774549</td>\n", " <td>11:29:58</td>\n", " <td>92042.646817</td>\n", " <td>2017-07 11:29:57</td>\n", " <td>1643.646817</td>\n", " <td>607.545041</td>\n", " <td>2.705391</td>\n", " </tr>\n", " <tr>\n", " <th>58</th>\n", " <td>2017-07</td>\n", " <td>-7.928843</td>\n", " <td>614.560657</td>\n", " <td>610.464718</td>\n", " <td>4.095939</td>\n", " <td>92175.935610</td>\n", " <td>1776.935610</td>\n", " <td>92200.0</td>\n", " <td>92500.0</td>\n", " <td>2.929183</td>\n", " <td>11:29:59</td>\n", " <td>92085.651711</td>\n", " <td>2017-07 11:29:58</td>\n", " <td>1686.651711</td>\n", " <td>607.901269</td>\n", " <td>2.774549</td>\n", " </tr>\n", " <tr>\n", " <th>59</th>\n", " <td>2017-07</td>\n", " <td>-8.809825</td>\n", " <td>612.413360</td>\n", " <td>609.515695</td>\n", " <td>2.897666</td>\n", " <td>92252.692080</td>\n", " <td>1853.692080</td>\n", " <td>92300.0</td>\n", " <td>92600.0</td>\n", " <td>3.071042</td>\n", " <td>11:30:00</td>\n", " <td>92175.935610</td>\n", " <td>2017-07 11:29:59</td>\n", " <td>1776.935610</td>\n", " <td>606.631814</td>\n", " <td>2.929183</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " ccyy-mm f_1_step_pred_adj_misc f_1_step_pred_les \\\n", "0 2017-07 0.000000 422.483383 \n", "1 2017-07 0.000000 124.987080 \n", "2 2017-07 0.000000 -5.054063 \n", "3 2017-07 0.000000 -51.325580 \n", "4 2017-07 0.000000 -60.017096 \n", "5 2017-07 0.000000 -50.639678 \n", "6 2017-07 0.000000 -43.877474 \n", "7 2017-07 0.000000 -34.672014 \n", "8 2017-07 0.000000 -26.760255 \n", "9 2017-07 0.000000 -20.654841 \n", "10 2017-07 0.000000 325.733889 \n", "11 2017-07 0.000000 397.656427 \n", "12 2017-07 0.000000 374.673563 \n", "13 2017-07 0.000000 573.214382 \n", "14 2017-07 0.000000 621.507918 \n", "15 2017-07 0.000000 594.643909 \n", "16 2017-07 0.000000 751.329413 \n", "17 2017-07 0.000000 752.563612 \n", "18 2017-07 0.000000 707.045126 \n", "19 2017-07 0.000000 657.418283 \n", "20 2017-07 0.000000 609.886884 \n", "21 2017-07 0.000000 555.459304 \n", "22 2017-07 0.000000 523.538135 \n", "23 2017-07 0.000000 503.143928 \n", "24 2017-07 0.000000 477.338691 \n", "25 2017-07 0.000000 444.881807 \n", "26 2017-07 0.000000 425.093411 \n", "27 2017-07 0.000000 410.049007 \n", "28 2017-07 0.000000 395.960050 \n", "29 2017-07 0.000000 386.986438 \n", "30 2017-07 0.000000 368.827170 \n", "31 2017-07 0.000000 363.112378 \n", "32 2017-07 0.000000 354.243070 \n", "33 2017-07 0.000000 346.121291 \n", "34 2017-07 0.000000 334.874307 \n", "35 2017-07 0.000000 401.892576 \n", "36 2017-07 0.000000 419.681677 \n", "37 2017-07 0.000000 498.634793 \n", "38 2017-07 0.000000 602.357357 \n", "39 2017-07 0.000000 622.462725 \n", "40 2017-07 0.000000 605.051838 \n", "41 2017-07 0.000000 580.903770 \n", "42 2017-07 0.000000 544.634657 \n", "43 2017-07 0.000000 492.420799 \n", "44 2017-07 0.000000 496.388347 \n", "45 2017-07 0.000000 485.245049 \n", "46 2017-07 0.000000 499.567722 \n", "47 2017-07 0.000000 536.596327 \n", "48 2017-07 0.000000 570.336407 \n", "49 2017-07 0.000000 572.667029 \n", "50 2017-07 -0.880983 579.605235 \n", "51 2017-07 -1.761965 587.800573 \n", "52 2017-07 -2.642948 595.169076 \n", "53 2017-07 -3.523930 601.577604 \n", "54 2017-07 -4.404913 606.893014 \n", "55 2017-07 -5.285895 610.982166 \n", "56 2017-07 -6.166878 613.711918 \n", "57 2017-07 -7.047860 614.949129 \n", "58 2017-07 -7.928843 614.560657 \n", "59 2017-07 -8.809825 612.413360 \n", "\n", " f_1_step_pred_les_level f_1_step_pred_les_trend f_1_step_pred_price \\\n", "0 422.483383 0.000000 90408.458677 \n", "1 182.085965 -57.098885 90402.863447 \n", "2 66.044732 -71.098795 90398.809110 \n", "3 15.008081 -66.333661 90396.654153 \n", "4 -4.746773 -55.270323 90396.282431 \n", "5 -7.777287 -42.862391 90394.910559 \n", "6 -10.539602 -33.337872 90394.677994 \n", "7 -9.499544 -25.172470 90394.279256 \n", "8 -7.937687 -18.822568 90393.536513 \n", "9 -6.616736 -14.038105 90394.227138 \n", "10 270.594763 55.139126 90493.796863 \n", "11 339.296038 58.360390 90535.446795 \n", "12 331.925499 42.748064 90530.538182 \n", "13 500.564795 72.649588 90611.465091 \n", "14 553.533022 67.974896 90648.315882 \n", "15 544.871578 49.772331 90644.046963 \n", "16 681.037085 70.292327 90739.779361 \n", "17 695.525708 57.037903 90762.996569 \n", "18 669.691015 37.354111 90755.734217 \n", "19 636.758549 20.659734 90745.666546 \n", "20 602.315170 7.571713 90744.620807 \n", "21 559.787203 -4.327898 90720.268379 \n", "22 533.162049 -9.623914 90708.075013 \n", "23 514.834999 -11.691071 90711.119466 \n", "24 491.738714 -14.400023 90715.190223 \n", "25 462.747509 -17.865702 90701.661202 \n", "26 443.328138 -18.234727 90697.158177 \n", "27 427.671411 -17.622404 90696.741614 \n", "28 412.904274 -16.944225 90692.496845 \n", "29 402.400852 -15.414414 90702.736025 \n", "30 384.768407 -15.941237 90689.761443 \n", "31 377.090840 -13.978461 90694.715761 \n", "32 367.240925 -12.997854 90694.828587 \n", "33 358.183273 -12.061982 90699.102575 \n", "34 346.779866 -11.905559 90707.624498 \n", "35 398.650173 3.242403 90782.376971 \n", "36 413.647306 6.034371 90809.434440 \n", "37 478.605024 20.029769 90894.400465 \n", "38 566.264312 36.093045 91020.965116 \n", "39 589.438218 33.024508 91068.937666 \n", "40 581.707467 23.344371 91066.544509 \n", "41 566.674689 14.229080 91074.585049 \n", "42 540.097766 4.536891 91091.661625 \n", "43 498.776159 -6.355360 91081.820890 \n", "44 500.762417 -4.374070 91112.354439 \n", "45 490.918347 -5.673297 91160.165397 \n", "46 501.403165 -1.835443 91218.985977 \n", "47 530.972539 5.623789 91337.545227 \n", "48 559.316219 11.020188 91419.750898 \n", "49 563.314642 9.352388 91505.926340 \n", "50 570.716205 8.889030 91557.134451 \n", "51 579.044684 8.755889 91609.816482 \n", "52 586.679470 8.489606 91683.720820 \n", "53 593.487422 8.090182 91768.751578 \n", "54 599.335398 7.557616 91853.166552 \n", "55 604.090258 6.891909 91943.947695 \n", "56 607.618858 6.093060 92042.646817 \n", "57 609.788059 5.161070 92085.651711 \n", "58 610.464718 4.095939 92175.935610 \n", "59 609.515695 2.897666 92252.692080 \n", "\n", " f_1_step_pred_price_inc f_1_step_pred_price_rounded \\\n", "0 9.458677 90400.0 \n", "1 3.863447 90400.0 \n", "2 -0.190890 90400.0 \n", "3 -2.345847 90400.0 \n", "4 -2.717569 90400.0 \n", "5 -4.089441 90400.0 \n", "6 -4.322006 90400.0 \n", "7 -4.720744 90400.0 \n", "8 -5.463487 90400.0 \n", "9 -4.772862 90400.0 \n", "10 94.796863 90500.0 \n", "11 136.446795 90500.0 \n", "12 131.538182 90500.0 \n", "13 212.465091 90600.0 \n", "14 249.315882 90600.0 \n", "15 245.046963 90600.0 \n", "16 340.779361 90700.0 \n", "17 363.996569 90800.0 \n", "18 356.734217 90800.0 \n", "19 346.666546 90700.0 \n", "20 345.620807 90700.0 \n", "21 321.268379 90700.0 \n", "22 309.075013 90700.0 \n", "23 312.119466 90700.0 \n", "24 316.190223 90700.0 \n", "25 302.661202 90700.0 \n", "26 298.158177 90700.0 \n", "27 297.741614 90700.0 \n", "28 293.496845 90700.0 \n", "29 303.736025 90700.0 \n", "30 290.761443 90700.0 \n", "31 295.715761 90700.0 \n", "32 295.828587 90700.0 \n", "33 300.102575 90700.0 \n", "34 308.624498 90700.0 \n", "35 383.376971 90800.0 \n", "36 410.434440 90800.0 \n", "37 495.400465 90900.0 \n", "38 621.965116 91000.0 \n", "39 669.937666 91100.0 \n", "40 667.544509 91100.0 \n", "41 675.585049 91100.0 \n", "42 692.661625 91100.0 \n", "43 682.820890 91100.0 \n", "44 713.354439 91100.0 \n", "45 761.165397 91200.0 \n", "46 819.985977 91200.0 \n", "47 938.545227 91300.0 \n", "48 1020.750898 91400.0 \n", "49 1106.926340 91500.0 \n", "50 1158.134451 91600.0 \n", "51 1210.816482 91600.0 \n", "52 1284.720820 91700.0 \n", "53 1369.751578 91800.0 \n", "54 1454.166552 91900.0 \n", "55 1544.947695 91900.0 \n", "56 1643.646817 92000.0 \n", "57 1686.651711 92100.0 \n", "58 1776.935610 92200.0 \n", "59 1853.692080 92300.0 \n", "\n", " f_1_step_pred_set_price_rounded f_1_step_si f_1_step_time f_current_bid \\\n", "0 90700.0 0.022388 11:29:01 90400.000000 \n", "1 90700.0 0.030911 11:29:02 90400.000000 \n", "2 90700.0 0.037770 11:29:03 90400.000000 \n", "3 90700.0 0.045705 11:29:04 90400.000000 \n", "4 90700.0 0.045280 11:29:05 90400.000000 \n", "5 90700.0 0.080756 11:29:06 90400.000000 \n", "6 90700.0 0.098502 11:29:07 90400.000000 \n", "7 90700.0 0.136154 11:29:08 90400.000000 \n", "8 90700.0 0.204164 11:29:09 90400.000000 \n", "9 90700.0 0.231077 11:29:10 90400.000000 \n", "10 90800.0 0.291025 11:29:11 90500.000000 \n", "11 90800.0 0.343127 11:29:12 90500.000000 \n", "12 90800.0 0.351074 11:29:13 90500.000000 \n", "13 90900.0 0.370656 11:29:14 90600.000000 \n", "14 90900.0 0.401147 11:29:15 90600.000000 \n", "15 90900.0 0.412090 11:29:16 90600.000000 \n", "16 91000.0 0.453569 11:29:17 90700.000000 \n", "17 91100.0 0.483675 11:29:18 90700.000000 \n", "18 91100.0 0.504542 11:29:19 90700.000000 \n", "19 91000.0 0.527315 11:29:20 90700.000000 \n", "20 91000.0 0.566697 11:29:21 90700.000000 \n", "21 91000.0 0.578383 11:29:22 90700.000000 \n", "22 91000.0 0.590358 11:29:23 90700.000000 \n", "23 91000.0 0.620338 11:29:24 90700.000000 \n", "24 91000.0 0.662402 11:29:25 90700.000000 \n", "25 91000.0 0.680318 11:29:26 90700.000000 \n", "26 91000.0 0.701394 11:29:27 90700.000000 \n", "27 91000.0 0.726112 11:29:28 90700.000000 \n", "28 91000.0 0.741228 11:29:29 90700.000000 \n", "29 91000.0 0.784875 11:29:30 90700.000000 \n", "30 91000.0 0.788341 11:29:31 90700.000000 \n", "31 91000.0 0.814392 11:29:32 90700.000000 \n", "32 91000.0 0.835101 11:29:33 90700.000000 \n", "33 91000.0 0.867045 11:29:34 90700.000000 \n", "34 91000.0 0.921613 11:29:35 90700.000000 \n", "35 91100.0 0.953929 11:29:36 90800.000000 \n", "36 91100.0 0.977966 11:29:37 90800.000000 \n", "37 91200.0 0.993514 11:29:38 90900.000000 \n", "38 91300.0 1.032552 11:29:39 91000.000000 \n", "39 91400.0 1.076270 11:29:40 91000.000000 \n", "40 91400.0 1.103285 11:29:41 91000.000000 \n", "41 91400.0 1.162990 11:29:42 91000.000000 \n", "42 91400.0 1.271791 11:29:43 91000.000000 \n", "43 91400.0 1.386661 11:29:44 91000.000000 \n", "44 91400.0 1.437089 11:29:45 91100.000000 \n", "45 91500.0 1.568621 11:29:46 91100.000000 \n", "46 91500.0 1.641391 11:29:47 91200.000000 \n", "47 91600.0 1.749071 11:29:48 91300.000000 \n", "48 91700.0 1.789735 11:29:49 91400.000000 \n", "49 91800.0 1.932932 11:29:50 91400.000000 \n", "50 91900.0 2.001185 11:29:51 91500.000000 \n", "51 91900.0 2.066104 11:29:52 91557.134451 \n", "52 92000.0 2.168210 11:29:53 91609.816482 \n", "53 92100.0 2.290349 11:29:54 91683.720820 \n", "54 92200.0 2.413602 11:29:55 91768.751578 \n", "55 92200.0 2.550697 11:29:56 91853.166552 \n", "56 92300.0 2.705391 11:29:57 91943.947695 \n", "57 92400.0 2.774549 11:29:58 92042.646817 \n", "58 92500.0 2.929183 11:29:59 92085.651711 \n", "59 92600.0 3.071042 11:30:00 92175.935610 \n", "\n", " f_current_datetime f_current_price4pm f_current_price4pmsi f_current_si \n", "0 2017-07 11:29:00 1.000000 422.483383 0.002367 \n", "1 2017-07 11:29:01 1.000000 44.666225 0.022388 \n", "2 2017-07 11:29:02 1.000000 32.351184 0.030911 \n", "3 2017-07 11:29:03 1.000000 26.476318 0.037770 \n", "4 2017-07 11:29:04 1.000000 21.879334 0.045705 \n", "5 2017-07 11:29:05 1.000000 22.084851 0.045280 \n", "6 2017-07 11:29:06 1.000000 12.383032 0.080756 \n", "7 2017-07 11:29:07 1.000000 10.152108 0.098502 \n", "8 2017-07 11:29:08 1.000000 7.344608 0.136154 \n", "9 2017-07 11:29:09 1.000000 4.898017 0.204164 \n", "10 2017-07 11:29:10 101.000000 437.083427 0.231077 \n", "11 2017-07 11:29:11 101.000000 347.048645 0.291025 \n", "12 2017-07 11:29:12 101.000000 294.351356 0.343127 \n", "13 2017-07 11:29:13 201.000000 572.528714 0.351074 \n", "14 2017-07 11:29:14 201.000000 542.282454 0.370656 \n", "15 2017-07 11:29:15 201.000000 501.063512 0.401147 \n", "16 2017-07 11:29:16 301.000000 730.422507 0.412090 \n", "17 2017-07 11:29:17 301.000000 663.626320 0.453569 \n", "18 2017-07 11:29:18 301.000000 622.318083 0.483675 \n", "19 2017-07 11:29:19 301.000000 596.580234 0.504542 \n", "20 2017-07 11:29:20 301.000000 570.816265 0.527315 \n", "21 2017-07 11:29:21 301.000000 531.148438 0.566697 \n", "22 2017-07 11:29:22 301.000000 520.416142 0.578383 \n", "23 2017-07 11:29:23 301.000000 509.859976 0.590358 \n", "24 2017-07 11:29:24 301.000000 485.219087 0.620338 \n", "25 2017-07 11:29:25 301.000000 454.406669 0.662402 \n", "26 2017-07 11:29:26 301.000000 442.440005 0.680318 \n", "27 2017-07 11:29:27 301.000000 429.145087 0.701394 \n", "28 2017-07 11:29:28 301.000000 414.536447 0.726112 \n", "29 2017-07 11:29:29 301.000000 406.082644 0.741228 \n", "30 2017-07 11:29:30 301.000000 383.500501 0.784875 \n", "31 2017-07 11:29:31 301.000000 381.814648 0.788341 \n", "32 2017-07 11:29:32 301.000000 369.600949 0.814392 \n", "33 2017-07 11:29:33 301.000000 360.435634 0.835101 \n", "34 2017-07 11:29:34 301.000000 347.156330 0.867045 \n", "35 2017-07 11:29:35 401.000000 435.106733 0.921613 \n", "36 2017-07 11:29:36 401.000000 420.366728 0.953929 \n", "37 2017-07 11:29:37 501.000000 512.287712 0.977966 \n", "38 2017-07 11:29:38 601.000000 604.923757 0.993514 \n", "39 2017-07 11:29:39 601.000000 582.053177 1.032552 \n", "40 2017-07 11:29:40 601.000000 558.410307 1.076270 \n", "41 2017-07 11:29:41 601.000000 544.736942 1.103285 \n", "42 2017-07 11:29:42 601.000000 516.771599 1.162990 \n", "43 2017-07 11:29:43 601.000000 472.561806 1.271791 \n", "44 2017-07 11:29:44 701.000000 505.530784 1.386661 \n", "45 2017-07 11:29:45 701.000000 487.791499 1.437089 \n", "46 2017-07 11:29:46 801.000000 510.639719 1.568621 \n", "47 2017-07 11:29:47 901.000000 548.924652 1.641391 \n", "48 2017-07 11:29:48 1001.000000 572.303719 1.749071 \n", "49 2017-07 11:29:49 1001.000000 559.300750 1.789735 \n", "50 2017-07 11:29:50 1101.000000 569.601044 1.932932 \n", "51 2017-07 11:29:51 1158.134451 578.724253 2.001185 \n", "52 2017-07 11:29:52 1210.816482 586.038608 2.066104 \n", "53 2017-07 11:29:53 1284.720820 592.526129 2.168210 \n", "54 2017-07 11:29:54 1369.751578 598.053674 2.290349 \n", "55 2017-07 11:29:55 1454.166552 602.488102 2.413602 \n", "56 2017-07 11:29:56 1544.947695 605.696271 2.550697 \n", "57 2017-07 11:29:57 1643.646817 607.545041 2.705391 \n", "58 2017-07 11:29:58 1686.651711 607.901269 2.774549 \n", "59 2017-07 11:29:59 1776.935610 606.631814 2.929183 " ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "shl_pm.shl_data_pm_k_step\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[91800]\n" ] } ], "source": [ "print(shl_sm_prediction_list_local_1)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[91900, 91900, 92000, 92100, 92200, 92200, 92300, 92400, 92500, 92600]\n" ] } ], "source": [ "print(shl_sm_prediction_list_local_k)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>ccyy-mm</th>\n", " <th>f_1_step_pred_adj_misc</th>\n", " <th>f_1_step_pred_les</th>\n", " <th>f_1_step_pred_les_level</th>\n", " <th>f_1_step_pred_les_trend</th>\n", " <th>f_1_step_pred_price</th>\n", " <th>f_1_step_pred_price_inc</th>\n", " <th>f_1_step_pred_price_rounded</th>\n", " <th>f_1_step_pred_set_price_rounded</th>\n", " <th>f_1_step_si</th>\n", " <th>f_1_step_time</th>\n", " <th>f_current_bid</th>\n", " <th>f_current_datetime</th>\n", " <th>f_current_price4pm</th>\n", " <th>f_current_price4pmsi</th>\n", " <th>f_current_si</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>40</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>605.051838</td>\n", " <td>581.707467</td>\n", " <td>23.344371</td>\n", " <td>91066.544509</td>\n", " <td>667.544509</td>\n", " <td>91100.0</td>\n", " <td>91400.0</td>\n", " <td>1.103285</td>\n", " <td>11:29:41</td>\n", " <td>91000.0</td>\n", " <td>2017-07 11:29:40</td>\n", " <td>601.0</td>\n", " <td>558.410307</td>\n", " <td>1.076270</td>\n", " </tr>\n", " <tr>\n", " <th>41</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>580.903770</td>\n", " <td>566.674689</td>\n", " <td>14.229080</td>\n", " <td>91074.585049</td>\n", " <td>675.585049</td>\n", " <td>91100.0</td>\n", " <td>91400.0</td>\n", " <td>1.162990</td>\n", " <td>11:29:42</td>\n", " <td>91000.0</td>\n", " <td>2017-07 11:29:41</td>\n", " <td>601.0</td>\n", " <td>544.736942</td>\n", " <td>1.103285</td>\n", " </tr>\n", " <tr>\n", " <th>42</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>544.634657</td>\n", " <td>540.097766</td>\n", " <td>4.536891</td>\n", " <td>91091.661625</td>\n", " <td>692.661625</td>\n", " <td>91100.0</td>\n", " <td>91400.0</td>\n", " <td>1.271791</td>\n", " <td>11:29:43</td>\n", " <td>91000.0</td>\n", " <td>2017-07 11:29:42</td>\n", " <td>601.0</td>\n", " <td>516.771599</td>\n", " <td>1.162990</td>\n", " </tr>\n", " <tr>\n", " <th>43</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>492.420799</td>\n", " <td>498.776159</td>\n", " <td>-6.355360</td>\n", " <td>91081.820890</td>\n", " <td>682.820890</td>\n", " <td>91100.0</td>\n", " <td>91400.0</td>\n", " <td>1.386661</td>\n", " <td>11:29:44</td>\n", " <td>91000.0</td>\n", " <td>2017-07 11:29:43</td>\n", " <td>601.0</td>\n", " <td>472.561806</td>\n", " <td>1.271791</td>\n", " </tr>\n", " <tr>\n", " <th>44</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>496.388347</td>\n", " <td>500.762417</td>\n", " <td>-4.374070</td>\n", " <td>91112.354439</td>\n", " <td>713.354439</td>\n", " <td>91100.0</td>\n", " <td>91400.0</td>\n", " <td>1.437089</td>\n", " <td>11:29:45</td>\n", " <td>91100.0</td>\n", " <td>2017-07 11:29:44</td>\n", " <td>701.0</td>\n", " <td>505.530784</td>\n", " <td>1.386661</td>\n", " </tr>\n", " <tr>\n", " <th>45</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>485.245049</td>\n", " <td>490.918347</td>\n", " <td>-5.673297</td>\n", " <td>91160.165397</td>\n", " <td>761.165397</td>\n", " <td>91200.0</td>\n", " <td>91500.0</td>\n", " <td>1.568621</td>\n", " <td>11:29:46</td>\n", " <td>91100.0</td>\n", " <td>2017-07 11:29:45</td>\n", " <td>701.0</td>\n", " <td>487.791499</td>\n", " <td>1.437089</td>\n", " </tr>\n", " <tr>\n", " <th>46</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>499.567722</td>\n", " <td>501.403165</td>\n", " <td>-1.835443</td>\n", " <td>91218.985977</td>\n", " <td>819.985977</td>\n", " <td>91200.0</td>\n", " <td>91500.0</td>\n", " <td>1.641391</td>\n", " <td>11:29:47</td>\n", " <td>91200.0</td>\n", " <td>2017-07 11:29:46</td>\n", " <td>801.0</td>\n", " <td>510.639719</td>\n", " <td>1.568621</td>\n", " </tr>\n", " <tr>\n", " <th>47</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>536.596327</td>\n", " <td>530.972539</td>\n", " <td>5.623789</td>\n", " <td>91337.545227</td>\n", " <td>938.545227</td>\n", " <td>91300.0</td>\n", " <td>91600.0</td>\n", " <td>1.749071</td>\n", " <td>11:29:48</td>\n", " <td>91300.0</td>\n", " <td>2017-07 11:29:47</td>\n", " <td>901.0</td>\n", " <td>548.924652</td>\n", " <td>1.641391</td>\n", " </tr>\n", " <tr>\n", " <th>48</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>570.336407</td>\n", " <td>559.316219</td>\n", " <td>11.020188</td>\n", " <td>91419.750898</td>\n", " <td>1020.750898</td>\n", " <td>91400.0</td>\n", " <td>91700.0</td>\n", " <td>1.789735</td>\n", " <td>11:29:49</td>\n", " <td>91400.0</td>\n", " <td>2017-07 11:29:48</td>\n", " <td>1001.0</td>\n", " <td>572.303719</td>\n", " <td>1.749071</td>\n", " </tr>\n", " <tr>\n", " <th>49</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>572.667029</td>\n", " <td>563.314642</td>\n", " <td>9.352388</td>\n", " <td>91505.926340</td>\n", " <td>1106.926340</td>\n", " <td>91500.0</td>\n", " <td>91800.0</td>\n", " <td>1.932932</td>\n", " <td>11:29:50</td>\n", " <td>91400.0</td>\n", " <td>2017-07 11:29:49</td>\n", " <td>1001.0</td>\n", " <td>559.300750</td>\n", " <td>1.789735</td>\n", " </tr>\n", " <tr>\n", " <th>50</th>\n", " <td>2017-07</td>\n", " <td>-0.880983</td>\n", " <td>579.605235</td>\n", " <td>570.716205</td>\n", " <td>8.889030</td>\n", " <td>91557.134451</td>\n", " <td>1158.134451</td>\n", " <td>91600.0</td>\n", " <td>91900.0</td>\n", " <td>2.001185</td>\n", " <td>11:29:51</td>\n", " <td>91500.0</td>\n", " <td>2017-07 11:29:50</td>\n", " <td>1101.0</td>\n", " <td>569.601044</td>\n", " <td>1.932932</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " ccyy-mm f_1_step_pred_adj_misc f_1_step_pred_les \\\n", "40 2017-07 0.000000 605.051838 \n", "41 2017-07 0.000000 580.903770 \n", "42 2017-07 0.000000 544.634657 \n", "43 2017-07 0.000000 492.420799 \n", "44 2017-07 0.000000 496.388347 \n", "45 2017-07 0.000000 485.245049 \n", "46 2017-07 0.000000 499.567722 \n", "47 2017-07 0.000000 536.596327 \n", "48 2017-07 0.000000 570.336407 \n", "49 2017-07 0.000000 572.667029 \n", "50 2017-07 -0.880983 579.605235 \n", "\n", " f_1_step_pred_les_level f_1_step_pred_les_trend f_1_step_pred_price \\\n", "40 581.707467 23.344371 91066.544509 \n", "41 566.674689 14.229080 91074.585049 \n", "42 540.097766 4.536891 91091.661625 \n", "43 498.776159 -6.355360 91081.820890 \n", "44 500.762417 -4.374070 91112.354439 \n", "45 490.918347 -5.673297 91160.165397 \n", "46 501.403165 -1.835443 91218.985977 \n", "47 530.972539 5.623789 91337.545227 \n", "48 559.316219 11.020188 91419.750898 \n", "49 563.314642 9.352388 91505.926340 \n", "50 570.716205 8.889030 91557.134451 \n", "\n", " f_1_step_pred_price_inc f_1_step_pred_price_rounded \\\n", "40 667.544509 91100.0 \n", "41 675.585049 91100.0 \n", "42 692.661625 91100.0 \n", "43 682.820890 91100.0 \n", "44 713.354439 91100.0 \n", "45 761.165397 91200.0 \n", "46 819.985977 91200.0 \n", "47 938.545227 91300.0 \n", "48 1020.750898 91400.0 \n", "49 1106.926340 91500.0 \n", "50 1158.134451 91600.0 \n", "\n", " f_1_step_pred_set_price_rounded f_1_step_si f_1_step_time f_current_bid \\\n", "40 91400.0 1.103285 11:29:41 91000.0 \n", "41 91400.0 1.162990 11:29:42 91000.0 \n", "42 91400.0 1.271791 11:29:43 91000.0 \n", "43 91400.0 1.386661 11:29:44 91000.0 \n", "44 91400.0 1.437089 11:29:45 91100.0 \n", "45 91500.0 1.568621 11:29:46 91100.0 \n", "46 91500.0 1.641391 11:29:47 91200.0 \n", "47 91600.0 1.749071 11:29:48 91300.0 \n", "48 91700.0 1.789735 11:29:49 91400.0 \n", "49 91800.0 1.932932 11:29:50 91400.0 \n", "50 91900.0 2.001185 11:29:51 91500.0 \n", "\n", " f_current_datetime f_current_price4pm f_current_price4pmsi f_current_si \n", "40 2017-07 11:29:40 601.0 558.410307 1.076270 \n", "41 2017-07 11:29:41 601.0 544.736942 1.103285 \n", "42 2017-07 11:29:42 601.0 516.771599 1.162990 \n", "43 2017-07 11:29:43 601.0 472.561806 1.271791 \n", "44 2017-07 11:29:44 701.0 505.530784 1.386661 \n", "45 2017-07 11:29:45 701.0 487.791499 1.437089 \n", "46 2017-07 11:29:46 801.0 510.639719 1.568621 \n", "47 2017-07 11:29:47 901.0 548.924652 1.641391 \n", "48 2017-07 11:29:48 1001.0 572.303719 1.749071 \n", "49 2017-07 11:29:49 1001.0 559.300750 1.789735 \n", "50 2017-07 11:29:50 1101.0 569.601044 1.932932 " ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "shl_pm.shl_data_pm_1_step.tail(11)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>ccyy-mm</th>\n", " <th>f_1_step_pred_adj_misc</th>\n", " <th>f_1_step_pred_les</th>\n", " <th>f_1_step_pred_les_level</th>\n", " <th>f_1_step_pred_les_trend</th>\n", " <th>f_1_step_pred_price</th>\n", " <th>f_1_step_pred_price_inc</th>\n", " <th>f_1_step_pred_price_rounded</th>\n", " <th>f_1_step_pred_set_price_rounded</th>\n", " <th>f_1_step_si</th>\n", " <th>f_1_step_time</th>\n", " <th>f_current_bid</th>\n", " <th>f_current_datetime</th>\n", " <th>f_current_price4pm</th>\n", " <th>f_current_price4pmsi</th>\n", " <th>f_current_si</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>40</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>605.051838</td>\n", " <td>581.707467</td>\n", " <td>23.344371</td>\n", " <td>91066.544509</td>\n", " <td>667.544509</td>\n", " <td>91100.0</td>\n", " <td>91400.0</td>\n", " <td>1.103285</td>\n", " <td>11:29:41</td>\n", " <td>91000.000000</td>\n", " <td>2017-07 11:29:40</td>\n", " <td>601.000000</td>\n", " <td>558.410307</td>\n", " <td>1.076270</td>\n", " </tr>\n", " <tr>\n", " <th>41</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>580.903770</td>\n", " <td>566.674689</td>\n", " <td>14.229080</td>\n", " <td>91074.585049</td>\n", " <td>675.585049</td>\n", " <td>91100.0</td>\n", " <td>91400.0</td>\n", " <td>1.162990</td>\n", " <td>11:29:42</td>\n", " <td>91000.000000</td>\n", " <td>2017-07 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<td>11:29:51</td>\n", " <td>91500.000000</td>\n", " <td>2017-07 11:29:50</td>\n", " <td>1101.000000</td>\n", " <td>569.601044</td>\n", " <td>1.932932</td>\n", " </tr>\n", " <tr>\n", " <th>51</th>\n", " <td>2017-07</td>\n", " <td>-1.761965</td>\n", " <td>587.800573</td>\n", " <td>579.044684</td>\n", " <td>8.755889</td>\n", " <td>91609.816482</td>\n", " <td>1210.816482</td>\n", " <td>91600.0</td>\n", " <td>91900.0</td>\n", " <td>2.066104</td>\n", " <td>11:29:52</td>\n", " <td>91557.134451</td>\n", " <td>2017-07 11:29:51</td>\n", " <td>1158.134451</td>\n", " <td>578.724253</td>\n", " <td>2.001185</td>\n", " </tr>\n", " <tr>\n", " <th>52</th>\n", " <td>2017-07</td>\n", " <td>-2.642948</td>\n", " <td>595.169076</td>\n", " <td>586.679470</td>\n", " <td>8.489606</td>\n", " <td>91683.720820</td>\n", " <td>1284.720820</td>\n", " <td>91700.0</td>\n", " <td>92000.0</td>\n", " <td>2.168210</td>\n", " <td>11:29:53</td>\n", " <td>91609.816482</td>\n", " <td>2017-07 11:29:52</td>\n", " <td>1210.816482</td>\n", " <td>586.038608</td>\n", " <td>2.066104</td>\n", " </tr>\n", " <tr>\n", " <th>53</th>\n", " <td>2017-07</td>\n", " <td>-3.523930</td>\n", " <td>601.577604</td>\n", " <td>593.487422</td>\n", " <td>8.090182</td>\n", " <td>91768.751578</td>\n", " <td>1369.751578</td>\n", " <td>91800.0</td>\n", " <td>92100.0</td>\n", " <td>2.290349</td>\n", " <td>11:29:54</td>\n", " <td>91683.720820</td>\n", " <td>2017-07 11:29:53</td>\n", " <td>1284.720820</td>\n", " <td>592.526129</td>\n", " <td>2.168210</td>\n", " </tr>\n", " <tr>\n", " <th>54</th>\n", " <td>2017-07</td>\n", " <td>-4.404913</td>\n", " <td>606.893014</td>\n", " <td>599.335398</td>\n", " <td>7.557616</td>\n", " <td>91853.166552</td>\n", " <td>1454.166552</td>\n", " <td>91900.0</td>\n", " <td>92200.0</td>\n", " <td>2.413602</td>\n", " <td>11:29:55</td>\n", " <td>91768.751578</td>\n", " <td>2017-07 11:29:54</td>\n", " <td>1369.751578</td>\n", " <td>598.053674</td>\n", " <td>2.290349</td>\n", " </tr>\n", " <tr>\n", " <th>55</th>\n", " <td>2017-07</td>\n", " <td>-5.285895</td>\n", " <td>610.982166</td>\n", " <td>604.090258</td>\n", " <td>6.891909</td>\n", " <td>91943.947695</td>\n", " <td>1544.947695</td>\n", " <td>91900.0</td>\n", " <td>92200.0</td>\n", " <td>2.550697</td>\n", " <td>11:29:56</td>\n", " <td>91853.166552</td>\n", " <td>2017-07 11:29:55</td>\n", " <td>1454.166552</td>\n", " <td>602.488102</td>\n", " <td>2.413602</td>\n", " </tr>\n", " <tr>\n", " <th>56</th>\n", " <td>2017-07</td>\n", " <td>-6.166878</td>\n", " <td>613.711918</td>\n", " <td>607.618858</td>\n", " <td>6.093060</td>\n", " <td>92042.646817</td>\n", " <td>1643.646817</td>\n", " <td>92000.0</td>\n", " <td>92300.0</td>\n", " <td>2.705391</td>\n", " <td>11:29:57</td>\n", " <td>91943.947695</td>\n", " <td>2017-07 11:29:56</td>\n", " <td>1544.947695</td>\n", " <td>605.696271</td>\n", " <td>2.550697</td>\n", " </tr>\n", " <tr>\n", " <th>57</th>\n", " <td>2017-07</td>\n", " <td>-7.047860</td>\n", " <td>614.949129</td>\n", " <td>609.788059</td>\n", " <td>5.161070</td>\n", " <td>92085.651711</td>\n", " <td>1686.651711</td>\n", " <td>92100.0</td>\n", " <td>92400.0</td>\n", " <td>2.774549</td>\n", " <td>11:29:58</td>\n", " <td>92042.646817</td>\n", " <td>2017-07 11:29:57</td>\n", " <td>1643.646817</td>\n", " <td>607.545041</td>\n", " <td>2.705391</td>\n", " </tr>\n", " <tr>\n", " <th>58</th>\n", " <td>2017-07</td>\n", " <td>-7.928843</td>\n", " <td>614.560657</td>\n", " <td>610.464718</td>\n", " <td>4.095939</td>\n", " <td>92175.935610</td>\n", " <td>1776.935610</td>\n", " <td>92200.0</td>\n", " <td>92500.0</td>\n", " <td>2.929183</td>\n", " <td>11:29:59</td>\n", " <td>92085.651711</td>\n", " <td>2017-07 11:29:58</td>\n", " <td>1686.651711</td>\n", " <td>607.901269</td>\n", " <td>2.774549</td>\n", " </tr>\n", " <tr>\n", " <th>59</th>\n", " <td>2017-07</td>\n", " <td>-8.809825</td>\n", " <td>612.413360</td>\n", " <td>609.515695</td>\n", " <td>2.897666</td>\n", " <td>92252.692080</td>\n", " <td>1853.692080</td>\n", " <td>92300.0</td>\n", " <td>92600.0</td>\n", " <td>3.071042</td>\n", " <td>11:30:00</td>\n", " <td>92175.935610</td>\n", " <td>2017-07 11:29:59</td>\n", " <td>1776.935610</td>\n", " <td>606.631814</td>\n", " <td>2.929183</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " ccyy-mm f_1_step_pred_adj_misc f_1_step_pred_les \\\n", "40 2017-07 0.000000 605.051838 \n", "41 2017-07 0.000000 580.903770 \n", "42 2017-07 0.000000 544.634657 \n", "43 2017-07 0.000000 492.420799 \n", "44 2017-07 0.000000 496.388347 \n", "45 2017-07 0.000000 485.245049 \n", "46 2017-07 0.000000 499.567722 \n", "47 2017-07 0.000000 536.596327 \n", "48 2017-07 0.000000 570.336407 \n", "49 2017-07 0.000000 572.667029 \n", "50 2017-07 -0.880983 579.605235 \n", "51 2017-07 -1.761965 587.800573 \n", "52 2017-07 -2.642948 595.169076 \n", "53 2017-07 -3.523930 601.577604 \n", "54 2017-07 -4.404913 606.893014 \n", "55 2017-07 -5.285895 610.982166 \n", "56 2017-07 -6.166878 613.711918 \n", "57 2017-07 -7.047860 614.949129 \n", "58 2017-07 -7.928843 614.560657 \n", "59 2017-07 -8.809825 612.413360 \n", "\n", " f_1_step_pred_les_level f_1_step_pred_les_trend f_1_step_pred_price \\\n", "40 581.707467 23.344371 91066.544509 \n", "41 566.674689 14.229080 91074.585049 \n", "42 540.097766 4.536891 91091.661625 \n", "43 498.776159 -6.355360 91081.820890 \n", "44 500.762417 -4.374070 91112.354439 \n", "45 490.918347 -5.673297 91160.165397 \n", "46 501.403165 -1.835443 91218.985977 \n", "47 530.972539 5.623789 91337.545227 \n", "48 559.316219 11.020188 91419.750898 \n", "49 563.314642 9.352388 91505.926340 \n", "50 570.716205 8.889030 91557.134451 \n", "51 579.044684 8.755889 91609.816482 \n", "52 586.679470 8.489606 91683.720820 \n", "53 593.487422 8.090182 91768.751578 \n", "54 599.335398 7.557616 91853.166552 \n", "55 604.090258 6.891909 91943.947695 \n", "56 607.618858 6.093060 92042.646817 \n", "57 609.788059 5.161070 92085.651711 \n", "58 610.464718 4.095939 92175.935610 \n", "59 609.515695 2.897666 92252.692080 \n", "\n", " f_1_step_pred_price_inc f_1_step_pred_price_rounded \\\n", "40 667.544509 91100.0 \n", "41 675.585049 91100.0 \n", "42 692.661625 91100.0 \n", "43 682.820890 91100.0 \n", "44 713.354439 91100.0 \n", "45 761.165397 91200.0 \n", "46 819.985977 91200.0 \n", "47 938.545227 91300.0 \n", "48 1020.750898 91400.0 \n", "49 1106.926340 91500.0 \n", "50 1158.134451 91600.0 \n", "51 1210.816482 91600.0 \n", "52 1284.720820 91700.0 \n", "53 1369.751578 91800.0 \n", "54 1454.166552 91900.0 \n", "55 1544.947695 91900.0 \n", "56 1643.646817 92000.0 \n", "57 1686.651711 92100.0 \n", "58 1776.935610 92200.0 \n", "59 1853.692080 92300.0 \n", "\n", " f_1_step_pred_set_price_rounded f_1_step_si f_1_step_time f_current_bid \\\n", "40 91400.0 1.103285 11:29:41 91000.000000 \n", "41 91400.0 1.162990 11:29:42 91000.000000 \n", "42 91400.0 1.271791 11:29:43 91000.000000 \n", "43 91400.0 1.386661 11:29:44 91000.000000 \n", "44 91400.0 1.437089 11:29:45 91100.000000 \n", "45 91500.0 1.568621 11:29:46 91100.000000 \n", "46 91500.0 1.641391 11:29:47 91200.000000 \n", "47 91600.0 1.749071 11:29:48 91300.000000 \n", "48 91700.0 1.789735 11:29:49 91400.000000 \n", "49 91800.0 1.932932 11:29:50 91400.000000 \n", "50 91900.0 2.001185 11:29:51 91500.000000 \n", "51 91900.0 2.066104 11:29:52 91557.134451 \n", "52 92000.0 2.168210 11:29:53 91609.816482 \n", "53 92100.0 2.290349 11:29:54 91683.720820 \n", "54 92200.0 2.413602 11:29:55 91768.751578 \n", "55 92200.0 2.550697 11:29:56 91853.166552 \n", "56 92300.0 2.705391 11:29:57 91943.947695 \n", "57 92400.0 2.774549 11:29:58 92042.646817 \n", "58 92500.0 2.929183 11:29:59 92085.651711 \n", "59 92600.0 3.071042 11:30:00 92175.935610 \n", "\n", " f_current_datetime f_current_price4pm f_current_price4pmsi f_current_si \n", "40 2017-07 11:29:40 601.000000 558.410307 1.076270 \n", "41 2017-07 11:29:41 601.000000 544.736942 1.103285 \n", "42 2017-07 11:29:42 601.000000 516.771599 1.162990 \n", "43 2017-07 11:29:43 601.000000 472.561806 1.271791 \n", "44 2017-07 11:29:44 701.000000 505.530784 1.386661 \n", "45 2017-07 11:29:45 701.000000 487.791499 1.437089 \n", "46 2017-07 11:29:46 801.000000 510.639719 1.568621 \n", "47 2017-07 11:29:47 901.000000 548.924652 1.641391 \n", "48 2017-07 11:29:48 1001.000000 572.303719 1.749071 \n", "49 2017-07 11:29:49 1001.000000 559.300750 1.789735 \n", "50 2017-07 11:29:50 1101.000000 569.601044 1.932932 \n", "51 2017-07 11:29:51 1158.134451 578.724253 2.001185 \n", "52 2017-07 11:29:52 1210.816482 586.038608 2.066104 \n", "53 2017-07 11:29:53 1284.720820 592.526129 2.168210 \n", "54 2017-07 11:29:54 1369.751578 598.053674 2.290349 \n", "55 2017-07 11:29:55 1454.166552 602.488102 2.413602 \n", "56 2017-07 11:29:56 1544.947695 605.696271 2.550697 \n", "57 2017-07 11:29:57 1643.646817 607.545041 2.705391 \n", "58 2017-07 11:29:58 1686.651711 607.901269 2.774549 \n", "59 2017-07 11:29:59 1776.935610 606.631814 2.929183 " ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "shl_pm.shl_data_pm_k_step.tail(20)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### MISC - Validation" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>ccyy-mm</th>\n", " <th>f_1_step_pred_adj_misc</th>\n", " <th>f_1_step_pred_les</th>\n", " <th>f_1_step_pred_les_level</th>\n", " <th>f_1_step_pred_les_trend</th>\n", " <th>f_1_step_pred_price</th>\n", " <th>f_1_step_pred_price_inc</th>\n", " <th>f_1_step_pred_price_rounded</th>\n", " <th>f_1_step_pred_set_price_rounded</th>\n", " <th>f_1_step_si</th>\n", " <th>f_1_step_time</th>\n", " <th>f_current_bid</th>\n", " <th>f_current_datetime</th>\n", " <th>f_current_price4pm</th>\n", " <th>f_current_price4pmsi</th>\n", " <th>f_current_si</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>422.483383</td>\n", " <td>422.483383</td>\n", " <td>0.000000</td>\n", " <td>90408.458677</td>\n", " <td>9.458677</td>\n", " <td>90400.0</td>\n", " <td>90700.0</td>\n", " <td>0.022388</td>\n", " <td>11:29:01</td>\n", " <td>90400.000000</td>\n", " <td>2017-07 11:29:00</td>\n", " <td>1.000000</td>\n", " <td>422.483383</td>\n", " <td>0.002367</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>124.987080</td>\n", " <td>182.085965</td>\n", " <td>-57.098885</td>\n", " <td>90402.863447</td>\n", " <td>3.863447</td>\n", " <td>90400.0</td>\n", " <td>90700.0</td>\n", " <td>0.030911</td>\n", " <td>11:29:02</td>\n", " <td>90400.000000</td>\n", " <td>2017-07 11:29:01</td>\n", " <td>1.000000</td>\n", " <td>44.666225</td>\n", " <td>0.022388</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>-5.054063</td>\n", " <td>66.044732</td>\n", " <td>-71.098795</td>\n", " <td>90398.809110</td>\n", " <td>-0.190890</td>\n", " <td>90400.0</td>\n", " <td>90700.0</td>\n", " <td>0.037770</td>\n", " <td>11:29:03</td>\n", " <td>90400.000000</td>\n", " <td>2017-07 11:29:02</td>\n", " <td>1.000000</td>\n", " <td>32.351184</td>\n", " <td>0.030911</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>-51.325580</td>\n", " <td>15.008081</td>\n", " <td>-66.333661</td>\n", " <td>90396.654153</td>\n", " <td>-2.345847</td>\n", " <td>90400.0</td>\n", " <td>90700.0</td>\n", " <td>0.045705</td>\n", " <td>11:29:04</td>\n", " <td>90400.000000</td>\n", " <td>2017-07 11:29:03</td>\n", " <td>1.000000</td>\n", " <td>26.476318</td>\n", " <td>0.037770</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>-60.017096</td>\n", " <td>-4.746773</td>\n", " <td>-55.270323</td>\n", " <td>90396.282431</td>\n", " <td>-2.717569</td>\n", " <td>90400.0</td>\n", " <td>90700.0</td>\n", " <td>0.045280</td>\n", " <td>11:29:05</td>\n", " <td>90400.000000</td>\n", " <td>2017-07 11:29:04</td>\n", " <td>1.000000</td>\n", " <td>21.879334</td>\n", " <td>0.045705</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>-50.639678</td>\n", " <td>-7.777287</td>\n", " <td>-42.862391</td>\n", " <td>90394.910559</td>\n", " <td>-4.089441</td>\n", " <td>90400.0</td>\n", " <td>90700.0</td>\n", " <td>0.080756</td>\n", " <td>11:29:06</td>\n", " <td>90400.000000</td>\n", " <td>2017-07 11:29:05</td>\n", " <td>1.000000</td>\n", " <td>22.084851</td>\n", " <td>0.045280</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>-43.877474</td>\n", " <td>-10.539602</td>\n", " <td>-33.337872</td>\n", " <td>90394.677994</td>\n", " <td>-4.322006</td>\n", " <td>90400.0</td>\n", " <td>90700.0</td>\n", " <td>0.098502</td>\n", " <td>11:29:07</td>\n", " <td>90400.000000</td>\n", " <td>2017-07 11:29:06</td>\n", " <td>1.000000</td>\n", " <td>12.383032</td>\n", " <td>0.080756</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>-34.672014</td>\n", " <td>-9.499544</td>\n", " <td>-25.172470</td>\n", " <td>90394.279256</td>\n", " <td>-4.720744</td>\n", " <td>90400.0</td>\n", " <td>90700.0</td>\n", " <td>0.136154</td>\n", " <td>11:29:08</td>\n", " <td>90400.000000</td>\n", " <td>2017-07 11:29:07</td>\n", " <td>1.000000</td>\n", " <td>10.152108</td>\n", " <td>0.098502</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>-26.760255</td>\n", " <td>-7.937687</td>\n", " <td>-18.822568</td>\n", " <td>90393.536513</td>\n", " <td>-5.463487</td>\n", " <td>90400.0</td>\n", " <td>90700.0</td>\n", " <td>0.204164</td>\n", " <td>11:29:09</td>\n", " <td>90400.000000</td>\n", " <td>2017-07 11:29:08</td>\n", " <td>1.000000</td>\n", " <td>7.344608</td>\n", " <td>0.136154</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>-20.654841</td>\n", " <td>-6.616736</td>\n", " <td>-14.038105</td>\n", " <td>90394.227138</td>\n", " <td>-4.772862</td>\n", " <td>90400.0</td>\n", " <td>90700.0</td>\n", " <td>0.231077</td>\n", " <td>11:29:10</td>\n", " <td>90400.000000</td>\n", " <td>2017-07 11:29:09</td>\n", " <td>1.000000</td>\n", " <td>4.898017</td>\n", " <td>0.204164</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>325.733889</td>\n", " <td>270.594763</td>\n", " <td>55.139126</td>\n", " <td>90493.796863</td>\n", " <td>94.796863</td>\n", " <td>90500.0</td>\n", " <td>90800.0</td>\n", " <td>0.291025</td>\n", " <td>11:29:11</td>\n", " <td>90500.000000</td>\n", " <td>2017-07 11:29:10</td>\n", " <td>101.000000</td>\n", " <td>437.083427</td>\n", " <td>0.231077</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>397.656427</td>\n", " <td>339.296038</td>\n", " <td>58.360390</td>\n", " <td>90535.446795</td>\n", " <td>136.446795</td>\n", " <td>90500.0</td>\n", " <td>90800.0</td>\n", " <td>0.343127</td>\n", " <td>11:29:12</td>\n", " <td>90500.000000</td>\n", " <td>2017-07 11:29:11</td>\n", " <td>101.000000</td>\n", " <td>347.048645</td>\n", " <td>0.291025</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>374.673563</td>\n", " <td>331.925499</td>\n", " <td>42.748064</td>\n", " <td>90530.538182</td>\n", " <td>131.538182</td>\n", " <td>90500.0</td>\n", " <td>90800.0</td>\n", " <td>0.351074</td>\n", " <td>11:29:13</td>\n", " <td>90500.000000</td>\n", " <td>2017-07 11:29:12</td>\n", " <td>101.000000</td>\n", " <td>294.351356</td>\n", " <td>0.343127</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>573.214382</td>\n", " <td>500.564795</td>\n", " <td>72.649588</td>\n", " <td>90611.465091</td>\n", " <td>212.465091</td>\n", " <td>90600.0</td>\n", " <td>90900.0</td>\n", " <td>0.370656</td>\n", " <td>11:29:14</td>\n", " <td>90600.000000</td>\n", " <td>2017-07 11:29:13</td>\n", " <td>201.000000</td>\n", " <td>572.528714</td>\n", " <td>0.351074</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>621.507918</td>\n", " <td>553.533022</td>\n", " <td>67.974896</td>\n", " <td>90648.315882</td>\n", " <td>249.315882</td>\n", " <td>90600.0</td>\n", " <td>90900.0</td>\n", " <td>0.401147</td>\n", " <td>11:29:15</td>\n", " <td>90600.000000</td>\n", " <td>2017-07 11:29:14</td>\n", " <td>201.000000</td>\n", " <td>542.282454</td>\n", " <td>0.370656</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>594.643909</td>\n", " <td>544.871578</td>\n", " <td>49.772331</td>\n", " <td>90644.046963</td>\n", " <td>245.046963</td>\n", " <td>90600.0</td>\n", " <td>90900.0</td>\n", " <td>0.412090</td>\n", " <td>11:29:16</td>\n", " <td>90600.000000</td>\n", " <td>2017-07 11:29:15</td>\n", " <td>201.000000</td>\n", " <td>501.063512</td>\n", " <td>0.401147</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>751.329413</td>\n", " <td>681.037085</td>\n", " <td>70.292327</td>\n", " <td>90739.779361</td>\n", " <td>340.779361</td>\n", " <td>90700.0</td>\n", " 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<td>2017-07 11:29:18</td>\n", " <td>301.000000</td>\n", " <td>622.318083</td>\n", " <td>0.483675</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>657.418283</td>\n", " <td>636.758549</td>\n", " <td>20.659734</td>\n", " <td>90745.666546</td>\n", " <td>346.666546</td>\n", " <td>90700.0</td>\n", " <td>91000.0</td>\n", " <td>0.527315</td>\n", " <td>11:29:20</td>\n", " <td>90700.000000</td>\n", " <td>2017-07 11:29:19</td>\n", " <td>301.000000</td>\n", " <td>596.580234</td>\n", " <td>0.504542</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>609.886884</td>\n", " <td>602.315170</td>\n", " <td>7.571713</td>\n", " <td>90744.620807</td>\n", " <td>345.620807</td>\n", " <td>90700.0</td>\n", " <td>91000.0</td>\n", " <td>0.566697</td>\n", " <td>11:29:21</td>\n", " <td>90700.000000</td>\n", " <td>2017-07 11:29:20</td>\n", " <td>301.000000</td>\n", " <td>570.816265</td>\n", " <td>0.527315</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>555.459304</td>\n", " <td>559.787203</td>\n", " <td>-4.327898</td>\n", " <td>90720.268379</td>\n", " <td>321.268379</td>\n", " <td>90700.0</td>\n", " <td>91000.0</td>\n", " <td>0.578383</td>\n", " <td>11:29:22</td>\n", " <td>90700.000000</td>\n", " <td>2017-07 11:29:21</td>\n", " <td>301.000000</td>\n", " <td>531.148438</td>\n", " <td>0.566697</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>523.538135</td>\n", " <td>533.162049</td>\n", " <td>-9.623914</td>\n", " <td>90708.075013</td>\n", " <td>309.075013</td>\n", " <td>90700.0</td>\n", " <td>91000.0</td>\n", " <td>0.590358</td>\n", " <td>11:29:23</td>\n", " <td>90700.000000</td>\n", " <td>2017-07 11:29:22</td>\n", " <td>301.000000</td>\n", " <td>520.416142</td>\n", " <td>0.578383</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>503.143928</td>\n", " <td>514.834999</td>\n", " <td>-11.691071</td>\n", " <td>90711.119466</td>\n", " <td>312.119466</td>\n", " <td>90700.0</td>\n", " <td>91000.0</td>\n", " <td>0.620338</td>\n", " <td>11:29:24</td>\n", " <td>90700.000000</td>\n", " <td>2017-07 11:29:23</td>\n", " <td>301.000000</td>\n", " <td>509.859976</td>\n", " <td>0.590358</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>477.338691</td>\n", " <td>491.738714</td>\n", " <td>-14.400023</td>\n", " <td>90715.190223</td>\n", " <td>316.190223</td>\n", " <td>90700.0</td>\n", " <td>91000.0</td>\n", " <td>0.662402</td>\n", " <td>11:29:25</td>\n", " <td>90700.000000</td>\n", " <td>2017-07 11:29:24</td>\n", " <td>301.000000</td>\n", " <td>485.219087</td>\n", " <td>0.620338</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>444.881807</td>\n", " <td>462.747509</td>\n", " <td>-17.865702</td>\n", 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11:29:29</td>\n", " <td>301.000000</td>\n", " <td>406.082644</td>\n", " <td>0.741228</td>\n", " </tr>\n", " <tr>\n", " <th>31</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>368.827170</td>\n", " <td>384.768407</td>\n", " <td>-15.941237</td>\n", " <td>90689.761443</td>\n", " <td>290.761443</td>\n", " <td>90700.0</td>\n", " <td>91000.0</td>\n", " <td>0.788341</td>\n", " <td>11:29:31</td>\n", " <td>90700.000000</td>\n", " <td>2017-07 11:29:30</td>\n", " <td>301.000000</td>\n", " <td>383.500501</td>\n", " <td>0.784875</td>\n", " </tr>\n", " <tr>\n", " <th>32</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>363.112378</td>\n", " <td>377.090840</td>\n", " <td>-13.978461</td>\n", " <td>90694.715761</td>\n", " <td>295.715761</td>\n", " <td>90700.0</td>\n", " <td>91000.0</td>\n", " <td>0.814392</td>\n", " <td>11:29:32</td>\n", " <td>90700.000000</td>\n", " <td>2017-07 11:29:31</td>\n", " <td>301.000000</td>\n", " <td>381.814648</td>\n", " <td>0.788341</td>\n", " </tr>\n", " <tr>\n", " <th>33</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>354.243070</td>\n", " <td>367.240925</td>\n", " <td>-12.997854</td>\n", " <td>90694.828587</td>\n", " <td>295.828587</td>\n", " <td>90700.0</td>\n", " <td>91000.0</td>\n", " <td>0.835101</td>\n", " <td>11:29:33</td>\n", " <td>90700.000000</td>\n", " <td>2017-07 11:29:32</td>\n", " <td>301.000000</td>\n", " <td>369.600949</td>\n", " <td>0.814392</td>\n", " </tr>\n", " <tr>\n", " <th>34</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>346.121291</td>\n", " <td>358.183273</td>\n", " <td>-12.061982</td>\n", " <td>90699.102575</td>\n", " <td>300.102575</td>\n", " <td>90700.0</td>\n", " <td>91000.0</td>\n", " <td>0.867045</td>\n", " <td>11:29:34</td>\n", " <td>90700.000000</td>\n", " <td>2017-07 11:29:33</td>\n", " <td>301.000000</td>\n", " <td>360.435634</td>\n", " <td>0.835101</td>\n", " </tr>\n", " <tr>\n", " <th>35</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " 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<td>11:29:39</td>\n", " <td>91000.000000</td>\n", " <td>2017-07 11:29:38</td>\n", " <td>601.000000</td>\n", " <td>604.923757</td>\n", " <td>0.993514</td>\n", " </tr>\n", " <tr>\n", " <th>40</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>622.462725</td>\n", " <td>589.438218</td>\n", " <td>33.024508</td>\n", " <td>91068.937666</td>\n", " <td>669.937666</td>\n", " <td>91100.0</td>\n", " <td>91400.0</td>\n", " <td>1.076270</td>\n", " <td>11:29:40</td>\n", " <td>91000.000000</td>\n", " <td>2017-07 11:29:39</td>\n", " <td>601.000000</td>\n", " <td>582.053177</td>\n", " <td>1.032552</td>\n", " </tr>\n", " <tr>\n", " <th>41</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>605.051838</td>\n", " <td>581.707467</td>\n", " <td>23.344371</td>\n", " <td>91066.544509</td>\n", " <td>667.544509</td>\n", " <td>91100.0</td>\n", " <td>91400.0</td>\n", " <td>1.103285</td>\n", " <td>11:29:41</td>\n", " <td>91000.000000</td>\n", " <td>2017-07 11:29:40</td>\n", " <td>601.000000</td>\n", " <td>558.410307</td>\n", " <td>1.076270</td>\n", " </tr>\n", " <tr>\n", " <th>42</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>580.903770</td>\n", " <td>566.674689</td>\n", " <td>14.229080</td>\n", " <td>91074.585049</td>\n", " <td>675.585049</td>\n", " <td>91100.0</td>\n", " <td>91400.0</td>\n", " <td>1.162990</td>\n", " <td>11:29:42</td>\n", " <td>91000.000000</td>\n", " <td>2017-07 11:29:41</td>\n", " <td>601.000000</td>\n", " <td>544.736942</td>\n", " <td>1.103285</td>\n", " </tr>\n", " <tr>\n", " <th>43</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>544.634657</td>\n", " <td>540.097766</td>\n", " <td>4.536891</td>\n", " <td>91091.661625</td>\n", " <td>692.661625</td>\n", " <td>91100.0</td>\n", " <td>91400.0</td>\n", " <td>1.271791</td>\n", " <td>11:29:43</td>\n", " <td>91000.000000</td>\n", " <td>2017-07 11:29:42</td>\n", " <td>601.000000</td>\n", " <td>516.771599</td>\n", " <td>1.162990</td>\n", " </tr>\n", " <tr>\n", " <th>44</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>492.420799</td>\n", " <td>498.776159</td>\n", " <td>-6.355360</td>\n", " <td>91081.820890</td>\n", " <td>682.820890</td>\n", " <td>91100.0</td>\n", " <td>91400.0</td>\n", " <td>1.386661</td>\n", " <td>11:29:44</td>\n", " <td>91000.000000</td>\n", " <td>2017-07 11:29:43</td>\n", " <td>601.000000</td>\n", " <td>472.561806</td>\n", " <td>1.271791</td>\n", " </tr>\n", " <tr>\n", " <th>45</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>496.388347</td>\n", " <td>500.762417</td>\n", " <td>-4.374070</td>\n", " <td>91112.354439</td>\n", " <td>713.354439</td>\n", " <td>91100.0</td>\n", " <td>91400.0</td>\n", " <td>1.437089</td>\n", " <td>11:29:45</td>\n", " <td>91100.000000</td>\n", " <td>2017-07 11:29:44</td>\n", " <td>701.000000</td>\n", " <td>505.530784</td>\n", " <td>1.386661</td>\n", " </tr>\n", " <tr>\n", " <th>46</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>485.245049</td>\n", " <td>490.918347</td>\n", " <td>-5.673297</td>\n", " <td>91160.165397</td>\n", " <td>761.165397</td>\n", " <td>91200.0</td>\n", " <td>91500.0</td>\n", " <td>1.568621</td>\n", " <td>11:29:46</td>\n", " <td>91100.000000</td>\n", " <td>2017-07 11:29:45</td>\n", " <td>701.000000</td>\n", " <td>487.791499</td>\n", " <td>1.437089</td>\n", " </tr>\n", " <tr>\n", " <th>47</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>499.567722</td>\n", " <td>501.403165</td>\n", " <td>-1.835443</td>\n", " <td>91218.985977</td>\n", " <td>819.985977</td>\n", " <td>91200.0</td>\n", " <td>91500.0</td>\n", " <td>1.641391</td>\n", " <td>11:29:47</td>\n", " <td>91200.000000</td>\n", " <td>2017-07 11:29:46</td>\n", " <td>801.000000</td>\n", " <td>510.639719</td>\n", " <td>1.568621</td>\n", " </tr>\n", " <tr>\n", " <th>48</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>536.596327</td>\n", " <td>530.972539</td>\n", " <td>5.623789</td>\n", " <td>91337.545227</td>\n", " <td>938.545227</td>\n", " <td>91300.0</td>\n", " <td>91600.0</td>\n", " <td>1.749071</td>\n", " <td>11:29:48</td>\n", " <td>91300.000000</td>\n", " <td>2017-07 11:29:47</td>\n", " <td>901.000000</td>\n", " <td>548.924652</td>\n", " <td>1.641391</td>\n", " </tr>\n", " <tr>\n", " <th>49</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>570.336407</td>\n", " <td>559.316219</td>\n", " <td>11.020188</td>\n", " <td>91419.750898</td>\n", " <td>1020.750898</td>\n", " <td>91400.0</td>\n", " <td>91700.0</td>\n", " <td>1.789735</td>\n", " <td>11:29:49</td>\n", " <td>91400.000000</td>\n", " <td>2017-07 11:29:48</td>\n", " <td>1001.000000</td>\n", " <td>572.303719</td>\n", " <td>1.749071</td>\n", " </tr>\n", " <tr>\n", " <th>50</th>\n", " <td>2017-07</td>\n", " <td>0.000000</td>\n", " <td>572.667029</td>\n", " <td>563.314642</td>\n", " <td>9.352388</td>\n", " <td>91505.926340</td>\n", " <td>1106.926340</td>\n", " <td>91500.0</td>\n", " <td>91800.0</td>\n", " <td>1.932932</td>\n", " <td>11:29:50</td>\n", " <td>91400.000000</td>\n", " <td>2017-07 11:29:49</td>\n", " <td>1001.000000</td>\n", " <td>559.300750</td>\n", " <td>1.789735</td>\n", " </tr>\n", " <tr>\n", " <th>51</th>\n", " <td>2017-07</td>\n", " <td>-0.880983</td>\n", " <td>579.605235</td>\n", " <td>570.716205</td>\n", " <td>8.889030</td>\n", " <td>91557.134451</td>\n", " <td>1158.134451</td>\n", " <td>91600.0</td>\n", " <td>91900.0</td>\n", " <td>2.001185</td>\n", " <td>11:29:51</td>\n", " <td>91500.000000</td>\n", " <td>2017-07 11:29:50</td>\n", " <td>1101.000000</td>\n", " <td>569.601044</td>\n", " <td>1.932932</td>\n", " </tr>\n", " <tr>\n", " <th>52</th>\n", " <td>2017-07</td>\n", " <td>-1.761965</td>\n", " <td>587.800573</td>\n", " <td>579.044684</td>\n", " <td>8.755889</td>\n", " <td>91609.816482</td>\n", " <td>1210.816482</td>\n", " <td>91600.0</td>\n", " <td>91900.0</td>\n", " <td>2.066104</td>\n", " <td>11:29:52</td>\n", " <td>91557.134451</td>\n", " <td>2017-07 11:29:51</td>\n", " <td>1158.134451</td>\n", " <td>578.724253</td>\n", " <td>2.001185</td>\n", " </tr>\n", " <tr>\n", " <th>53</th>\n", " <td>2017-07</td>\n", " <td>-2.642948</td>\n", " <td>595.169076</td>\n", " <td>586.679470</td>\n", " <td>8.489606</td>\n", " <td>91683.720820</td>\n", " <td>1284.720820</td>\n", " <td>91700.0</td>\n", " <td>92000.0</td>\n", " <td>2.168210</td>\n", " <td>11:29:53</td>\n", " <td>91609.816482</td>\n", " <td>2017-07 11:29:52</td>\n", " <td>1210.816482</td>\n", " <td>586.038608</td>\n", " <td>2.066104</td>\n", " </tr>\n", " <tr>\n", " <th>54</th>\n", " <td>2017-07</td>\n", " <td>-3.523930</td>\n", " <td>601.577604</td>\n", " <td>593.487422</td>\n", " <td>8.090182</td>\n", " <td>91768.751578</td>\n", " <td>1369.751578</td>\n", " <td>91800.0</td>\n", " <td>92100.0</td>\n", " <td>2.290349</td>\n", " <td>11:29:54</td>\n", " <td>91683.720820</td>\n", " <td>2017-07 11:29:53</td>\n", " <td>1284.720820</td>\n", " <td>592.526129</td>\n", " <td>2.168210</td>\n", " </tr>\n", " <tr>\n", " <th>55</th>\n", " <td>2017-07</td>\n", " <td>-4.404913</td>\n", " <td>606.893014</td>\n", " <td>599.335398</td>\n", " <td>7.557616</td>\n", " <td>91853.166552</td>\n", " <td>1454.166552</td>\n", " <td>91900.0</td>\n", " <td>92200.0</td>\n", " <td>2.413602</td>\n", " <td>11:29:55</td>\n", " <td>91768.751578</td>\n", " <td>2017-07 11:29:54</td>\n", " <td>1369.751578</td>\n", " <td>598.053674</td>\n", " <td>2.290349</td>\n", " </tr>\n", " <tr>\n", " <th>56</th>\n", " <td>2017-07</td>\n", " <td>-5.285895</td>\n", " <td>610.982166</td>\n", " <td>604.090258</td>\n", " <td>6.891909</td>\n", " <td>91943.947695</td>\n", " <td>1544.947695</td>\n", " <td>91900.0</td>\n", " <td>92200.0</td>\n", " <td>2.550697</td>\n", " <td>11:29:56</td>\n", " <td>91853.166552</td>\n", " <td>2017-07 11:29:55</td>\n", " <td>1454.166552</td>\n", " <td>602.488102</td>\n", " <td>2.413602</td>\n", " </tr>\n", " <tr>\n", " <th>57</th>\n", " <td>2017-07</td>\n", " <td>-6.166878</td>\n", " <td>613.711918</td>\n", " <td>607.618858</td>\n", " <td>6.093060</td>\n", " <td>92042.646817</td>\n", " <td>1643.646817</td>\n", " <td>92000.0</td>\n", " <td>92300.0</td>\n", " <td>2.705391</td>\n", " <td>11:29:57</td>\n", " <td>91943.947695</td>\n", " <td>2017-07 11:29:56</td>\n", " <td>1544.947695</td>\n", " <td>605.696271</td>\n", " <td>2.550697</td>\n", " </tr>\n", " <tr>\n", " <th>58</th>\n", " <td>2017-07</td>\n", " <td>-7.047860</td>\n", " <td>614.949129</td>\n", " <td>609.788059</td>\n", " <td>5.161070</td>\n", " <td>92085.651711</td>\n", " <td>1686.651711</td>\n", " <td>92100.0</td>\n", " <td>92400.0</td>\n", " <td>2.774549</td>\n", " <td>11:29:58</td>\n", " <td>92042.646817</td>\n", " <td>2017-07 11:29:57</td>\n", " <td>1643.646817</td>\n", " <td>607.545041</td>\n", " <td>2.705391</td>\n", " </tr>\n", " <tr>\n", " <th>59</th>\n", " <td>2017-07</td>\n", " <td>-7.928843</td>\n", " <td>614.560657</td>\n", " <td>610.464718</td>\n", " <td>4.095939</td>\n", " <td>92175.935610</td>\n", " <td>1776.935610</td>\n", " <td>92200.0</td>\n", " <td>92500.0</td>\n", " <td>2.929183</td>\n", " <td>11:29:59</td>\n", " <td>92085.651711</td>\n", " <td>2017-07 11:29:58</td>\n", " <td>1686.651711</td>\n", " <td>607.901269</td>\n", " <td>2.774549</td>\n", " </tr>\n", " <tr>\n", " <th>60</th>\n", " <td>2017-07</td>\n", " <td>-8.809825</td>\n", " <td>612.413360</td>\n", " <td>609.515695</td>\n", " <td>2.897666</td>\n", " <td>92252.692080</td>\n", " <td>1853.692080</td>\n", " <td>92300.0</td>\n", " <td>92600.0</td>\n", " <td>3.071042</td>\n", " <td>11:30:00</td>\n", " <td>92175.935610</td>\n", " <td>2017-07 11:29:59</td>\n", " <td>1776.935610</td>\n", " <td>606.631814</td>\n", " <td>2.929183</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " ccyy-mm f_1_step_pred_adj_misc f_1_step_pred_les \\\n", "1 2017-07 0.000000 422.483383 \n", "2 2017-07 0.000000 124.987080 \n", "3 2017-07 0.000000 -5.054063 \n", "4 2017-07 0.000000 -51.325580 \n", "5 2017-07 0.000000 -60.017096 \n", "6 2017-07 0.000000 -50.639678 \n", "7 2017-07 0.000000 -43.877474 \n", "8 2017-07 0.000000 -34.672014 \n", "9 2017-07 0.000000 -26.760255 \n", "10 2017-07 0.000000 -20.654841 \n", "11 2017-07 0.000000 325.733889 \n", "12 2017-07 0.000000 397.656427 \n", "13 2017-07 0.000000 374.673563 \n", "14 2017-07 0.000000 573.214382 \n", "15 2017-07 0.000000 621.507918 \n", "16 2017-07 0.000000 594.643909 \n", "17 2017-07 0.000000 751.329413 \n", "18 2017-07 0.000000 752.563612 \n", "19 2017-07 0.000000 707.045126 \n", "20 2017-07 0.000000 657.418283 \n", "21 2017-07 0.000000 609.886884 \n", "22 2017-07 0.000000 555.459304 \n", "23 2017-07 0.000000 523.538135 \n", "24 2017-07 0.000000 503.143928 \n", "25 2017-07 0.000000 477.338691 \n", "26 2017-07 0.000000 444.881807 \n", "27 2017-07 0.000000 425.093411 \n", "28 2017-07 0.000000 410.049007 \n", "29 2017-07 0.000000 395.960050 \n", "30 2017-07 0.000000 386.986438 \n", "31 2017-07 0.000000 368.827170 \n", "32 2017-07 0.000000 363.112378 \n", "33 2017-07 0.000000 354.243070 \n", "34 2017-07 0.000000 346.121291 \n", "35 2017-07 0.000000 334.874307 \n", "36 2017-07 0.000000 401.892576 \n", "37 2017-07 0.000000 419.681677 \n", "38 2017-07 0.000000 498.634793 \n", "39 2017-07 0.000000 602.357357 \n", "40 2017-07 0.000000 622.462725 \n", "41 2017-07 0.000000 605.051838 \n", "42 2017-07 0.000000 580.903770 \n", "43 2017-07 0.000000 544.634657 \n", "44 2017-07 0.000000 492.420799 \n", "45 2017-07 0.000000 496.388347 \n", "46 2017-07 0.000000 485.245049 \n", "47 2017-07 0.000000 499.567722 \n", "48 2017-07 0.000000 536.596327 \n", "49 2017-07 0.000000 570.336407 \n", "50 2017-07 0.000000 572.667029 \n", "51 2017-07 -0.880983 579.605235 \n", "52 2017-07 -1.761965 587.800573 \n", "53 2017-07 -2.642948 595.169076 \n", "54 2017-07 -3.523930 601.577604 \n", "55 2017-07 -4.404913 606.893014 \n", "56 2017-07 -5.285895 610.982166 \n", "57 2017-07 -6.166878 613.711918 \n", "58 2017-07 -7.047860 614.949129 \n", "59 2017-07 -7.928843 614.560657 \n", "60 2017-07 -8.809825 612.413360 \n", "\n", " f_1_step_pred_les_level f_1_step_pred_les_trend f_1_step_pred_price \\\n", "1 422.483383 0.000000 90408.458677 \n", "2 182.085965 -57.098885 90402.863447 \n", "3 66.044732 -71.098795 90398.809110 \n", "4 15.008081 -66.333661 90396.654153 \n", "5 -4.746773 -55.270323 90396.282431 \n", "6 -7.777287 -42.862391 90394.910559 \n", "7 -10.539602 -33.337872 90394.677994 \n", "8 -9.499544 -25.172470 90394.279256 \n", "9 -7.937687 -18.822568 90393.536513 \n", "10 -6.616736 -14.038105 90394.227138 \n", "11 270.594763 55.139126 90493.796863 \n", "12 339.296038 58.360390 90535.446795 \n", "13 331.925499 42.748064 90530.538182 \n", "14 500.564795 72.649588 90611.465091 \n", "15 553.533022 67.974896 90648.315882 \n", "16 544.871578 49.772331 90644.046963 \n", "17 681.037085 70.292327 90739.779361 \n", "18 695.525708 57.037903 90762.996569 \n", "19 669.691015 37.354111 90755.734217 \n", "20 636.758549 20.659734 90745.666546 \n", "21 602.315170 7.571713 90744.620807 \n", "22 559.787203 -4.327898 90720.268379 \n", "23 533.162049 -9.623914 90708.075013 \n", "24 514.834999 -11.691071 90711.119466 \n", "25 491.738714 -14.400023 90715.190223 \n", "26 462.747509 -17.865702 90701.661202 \n", "27 443.328138 -18.234727 90697.158177 \n", "28 427.671411 -17.622404 90696.741614 \n", "29 412.904274 -16.944225 90692.496845 \n", "30 402.400852 -15.414414 90702.736025 \n", "31 384.768407 -15.941237 90689.761443 \n", "32 377.090840 -13.978461 90694.715761 \n", "33 367.240925 -12.997854 90694.828587 \n", "34 358.183273 -12.061982 90699.102575 \n", "35 346.779866 -11.905559 90707.624498 \n", "36 398.650173 3.242403 90782.376971 \n", "37 413.647306 6.034371 90809.434440 \n", "38 478.605024 20.029769 90894.400465 \n", "39 566.264312 36.093045 91020.965116 \n", "40 589.438218 33.024508 91068.937666 \n", "41 581.707467 23.344371 91066.544509 \n", "42 566.674689 14.229080 91074.585049 \n", "43 540.097766 4.536891 91091.661625 \n", "44 498.776159 -6.355360 91081.820890 \n", "45 500.762417 -4.374070 91112.354439 \n", "46 490.918347 -5.673297 91160.165397 \n", "47 501.403165 -1.835443 91218.985977 \n", "48 530.972539 5.623789 91337.545227 \n", "49 559.316219 11.020188 91419.750898 \n", "50 563.314642 9.352388 91505.926340 \n", "51 570.716205 8.889030 91557.134451 \n", "52 579.044684 8.755889 91609.816482 \n", "53 586.679470 8.489606 91683.720820 \n", "54 593.487422 8.090182 91768.751578 \n", "55 599.335398 7.557616 91853.166552 \n", "56 604.090258 6.891909 91943.947695 \n", "57 607.618858 6.093060 92042.646817 \n", "58 609.788059 5.161070 92085.651711 \n", "59 610.464718 4.095939 92175.935610 \n", "60 609.515695 2.897666 92252.692080 \n", "\n", " f_1_step_pred_price_inc f_1_step_pred_price_rounded \\\n", "1 9.458677 90400.0 \n", "2 3.863447 90400.0 \n", "3 -0.190890 90400.0 \n", "4 -2.345847 90400.0 \n", "5 -2.717569 90400.0 \n", "6 -4.089441 90400.0 \n", "7 -4.322006 90400.0 \n", "8 -4.720744 90400.0 \n", "9 -5.463487 90400.0 \n", "10 -4.772862 90400.0 \n", "11 94.796863 90500.0 \n", "12 136.446795 90500.0 \n", "13 131.538182 90500.0 \n", "14 212.465091 90600.0 \n", "15 249.315882 90600.0 \n", "16 245.046963 90600.0 \n", "17 340.779361 90700.0 \n", "18 363.996569 90800.0 \n", "19 356.734217 90800.0 \n", "20 346.666546 90700.0 \n", "21 345.620807 90700.0 \n", "22 321.268379 90700.0 \n", "23 309.075013 90700.0 \n", "24 312.119466 90700.0 \n", "25 316.190223 90700.0 \n", "26 302.661202 90700.0 \n", "27 298.158177 90700.0 \n", "28 297.741614 90700.0 \n", "29 293.496845 90700.0 \n", "30 303.736025 90700.0 \n", "31 290.761443 90700.0 \n", "32 295.715761 90700.0 \n", "33 295.828587 90700.0 \n", "34 300.102575 90700.0 \n", "35 308.624498 90700.0 \n", "36 383.376971 90800.0 \n", "37 410.434440 90800.0 \n", "38 495.400465 90900.0 \n", "39 621.965116 91000.0 \n", "40 669.937666 91100.0 \n", "41 667.544509 91100.0 \n", "42 675.585049 91100.0 \n", "43 692.661625 91100.0 \n", "44 682.820890 91100.0 \n", "45 713.354439 91100.0 \n", "46 761.165397 91200.0 \n", "47 819.985977 91200.0 \n", "48 938.545227 91300.0 \n", "49 1020.750898 91400.0 \n", "50 1106.926340 91500.0 \n", "51 1158.134451 91600.0 \n", "52 1210.816482 91600.0 \n", "53 1284.720820 91700.0 \n", "54 1369.751578 91800.0 \n", "55 1454.166552 91900.0 \n", "56 1544.947695 91900.0 \n", "57 1643.646817 92000.0 \n", "58 1686.651711 92100.0 \n", "59 1776.935610 92200.0 \n", "60 1853.692080 92300.0 \n", "\n", " f_1_step_pred_set_price_rounded f_1_step_si f_1_step_time f_current_bid \\\n", "1 90700.0 0.022388 11:29:01 90400.000000 \n", "2 90700.0 0.030911 11:29:02 90400.000000 \n", "3 90700.0 0.037770 11:29:03 90400.000000 \n", "4 90700.0 0.045705 11:29:04 90400.000000 \n", "5 90700.0 0.045280 11:29:05 90400.000000 \n", "6 90700.0 0.080756 11:29:06 90400.000000 \n", "7 90700.0 0.098502 11:29:07 90400.000000 \n", "8 90700.0 0.136154 11:29:08 90400.000000 \n", "9 90700.0 0.204164 11:29:09 90400.000000 \n", "10 90700.0 0.231077 11:29:10 90400.000000 \n", "11 90800.0 0.291025 11:29:11 90500.000000 \n", "12 90800.0 0.343127 11:29:12 90500.000000 \n", "13 90800.0 0.351074 11:29:13 90500.000000 \n", "14 90900.0 0.370656 11:29:14 90600.000000 \n", "15 90900.0 0.401147 11:29:15 90600.000000 \n", "16 90900.0 0.412090 11:29:16 90600.000000 \n", "17 91000.0 0.453569 11:29:17 90700.000000 \n", "18 91100.0 0.483675 11:29:18 90700.000000 \n", "19 91100.0 0.504542 11:29:19 90700.000000 \n", "20 91000.0 0.527315 11:29:20 90700.000000 \n", "21 91000.0 0.566697 11:29:21 90700.000000 \n", "22 91000.0 0.578383 11:29:22 90700.000000 \n", "23 91000.0 0.590358 11:29:23 90700.000000 \n", "24 91000.0 0.620338 11:29:24 90700.000000 \n", "25 91000.0 0.662402 11:29:25 90700.000000 \n", "26 91000.0 0.680318 11:29:26 90700.000000 \n", "27 91000.0 0.701394 11:29:27 90700.000000 \n", "28 91000.0 0.726112 11:29:28 90700.000000 \n", "29 91000.0 0.741228 11:29:29 90700.000000 \n", "30 91000.0 0.784875 11:29:30 90700.000000 \n", "31 91000.0 0.788341 11:29:31 90700.000000 \n", "32 91000.0 0.814392 11:29:32 90700.000000 \n", "33 91000.0 0.835101 11:29:33 90700.000000 \n", "34 91000.0 0.867045 11:29:34 90700.000000 \n", "35 91000.0 0.921613 11:29:35 90700.000000 \n", "36 91100.0 0.953929 11:29:36 90800.000000 \n", "37 91100.0 0.977966 11:29:37 90800.000000 \n", "38 91200.0 0.993514 11:29:38 90900.000000 \n", "39 91300.0 1.032552 11:29:39 91000.000000 \n", "40 91400.0 1.076270 11:29:40 91000.000000 \n", "41 91400.0 1.103285 11:29:41 91000.000000 \n", "42 91400.0 1.162990 11:29:42 91000.000000 \n", "43 91400.0 1.271791 11:29:43 91000.000000 \n", "44 91400.0 1.386661 11:29:44 91000.000000 \n", "45 91400.0 1.437089 11:29:45 91100.000000 \n", "46 91500.0 1.568621 11:29:46 91100.000000 \n", "47 91500.0 1.641391 11:29:47 91200.000000 \n", "48 91600.0 1.749071 11:29:48 91300.000000 \n", "49 91700.0 1.789735 11:29:49 91400.000000 \n", "50 91800.0 1.932932 11:29:50 91400.000000 \n", "51 91900.0 2.001185 11:29:51 91500.000000 \n", "52 91900.0 2.066104 11:29:52 91557.134451 \n", "53 92000.0 2.168210 11:29:53 91609.816482 \n", "54 92100.0 2.290349 11:29:54 91683.720820 \n", "55 92200.0 2.413602 11:29:55 91768.751578 \n", "56 92200.0 2.550697 11:29:56 91853.166552 \n", "57 92300.0 2.705391 11:29:57 91943.947695 \n", "58 92400.0 2.774549 11:29:58 92042.646817 \n", "59 92500.0 2.929183 11:29:59 92085.651711 \n", "60 92600.0 3.071042 11:30:00 92175.935610 \n", "\n", " f_current_datetime f_current_price4pm f_current_price4pmsi f_current_si \n", "1 2017-07 11:29:00 1.000000 422.483383 0.002367 \n", "2 2017-07 11:29:01 1.000000 44.666225 0.022388 \n", "3 2017-07 11:29:02 1.000000 32.351184 0.030911 \n", "4 2017-07 11:29:03 1.000000 26.476318 0.037770 \n", "5 2017-07 11:29:04 1.000000 21.879334 0.045705 \n", "6 2017-07 11:29:05 1.000000 22.084851 0.045280 \n", "7 2017-07 11:29:06 1.000000 12.383032 0.080756 \n", "8 2017-07 11:29:07 1.000000 10.152108 0.098502 \n", "9 2017-07 11:29:08 1.000000 7.344608 0.136154 \n", "10 2017-07 11:29:09 1.000000 4.898017 0.204164 \n", "11 2017-07 11:29:10 101.000000 437.083427 0.231077 \n", "12 2017-07 11:29:11 101.000000 347.048645 0.291025 \n", "13 2017-07 11:29:12 101.000000 294.351356 0.343127 \n", "14 2017-07 11:29:13 201.000000 572.528714 0.351074 \n", "15 2017-07 11:29:14 201.000000 542.282454 0.370656 \n", "16 2017-07 11:29:15 201.000000 501.063512 0.401147 \n", "17 2017-07 11:29:16 301.000000 730.422507 0.412090 \n", "18 2017-07 11:29:17 301.000000 663.626320 0.453569 \n", "19 2017-07 11:29:18 301.000000 622.318083 0.483675 \n", "20 2017-07 11:29:19 301.000000 596.580234 0.504542 \n", "21 2017-07 11:29:20 301.000000 570.816265 0.527315 \n", "22 2017-07 11:29:21 301.000000 531.148438 0.566697 \n", "23 2017-07 11:29:22 301.000000 520.416142 0.578383 \n", "24 2017-07 11:29:23 301.000000 509.859976 0.590358 \n", "25 2017-07 11:29:24 301.000000 485.219087 0.620338 \n", "26 2017-07 11:29:25 301.000000 454.406669 0.662402 \n", "27 2017-07 11:29:26 301.000000 442.440005 0.680318 \n", "28 2017-07 11:29:27 301.000000 429.145087 0.701394 \n", "29 2017-07 11:29:28 301.000000 414.536447 0.726112 \n", "30 2017-07 11:29:29 301.000000 406.082644 0.741228 \n", "31 2017-07 11:29:30 301.000000 383.500501 0.784875 \n", "32 2017-07 11:29:31 301.000000 381.814648 0.788341 \n", "33 2017-07 11:29:32 301.000000 369.600949 0.814392 \n", "34 2017-07 11:29:33 301.000000 360.435634 0.835101 \n", "35 2017-07 11:29:34 301.000000 347.156330 0.867045 \n", "36 2017-07 11:29:35 401.000000 435.106733 0.921613 \n", "37 2017-07 11:29:36 401.000000 420.366728 0.953929 \n", "38 2017-07 11:29:37 501.000000 512.287712 0.977966 \n", "39 2017-07 11:29:38 601.000000 604.923757 0.993514 \n", "40 2017-07 11:29:39 601.000000 582.053177 1.032552 \n", "41 2017-07 11:29:40 601.000000 558.410307 1.076270 \n", "42 2017-07 11:29:41 601.000000 544.736942 1.103285 \n", "43 2017-07 11:29:42 601.000000 516.771599 1.162990 \n", "44 2017-07 11:29:43 601.000000 472.561806 1.271791 \n", "45 2017-07 11:29:44 701.000000 505.530784 1.386661 \n", "46 2017-07 11:29:45 701.000000 487.791499 1.437089 \n", "47 2017-07 11:29:46 801.000000 510.639719 1.568621 \n", "48 2017-07 11:29:47 901.000000 548.924652 1.641391 \n", "49 2017-07 11:29:48 1001.000000 572.303719 1.749071 \n", "50 2017-07 11:29:49 1001.000000 559.300750 1.789735 \n", "51 2017-07 11:29:50 1101.000000 569.601044 1.932932 \n", "52 2017-07 11:29:51 1158.134451 578.724253 2.001185 \n", "53 2017-07 11:29:52 1210.816482 586.038608 2.066104 \n", "54 2017-07 11:29:53 1284.720820 592.526129 2.168210 \n", "55 2017-07 11:29:54 1369.751578 598.053674 2.290349 \n", "56 2017-07 11:29:55 1454.166552 602.488102 2.413602 \n", "57 2017-07 11:29:56 1544.947695 605.696271 2.550697 \n", "58 2017-07 11:29:57 1643.646817 607.545041 2.705391 \n", "59 2017-07 11:29:58 1686.651711 607.901269 2.774549 \n", "60 2017-07 11:29:59 1776.935610 606.631814 2.929183 " ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "shl_data_pm_k_step_local = shl_pm.shl_data_pm_k_step.copy()\n", "shl_data_pm_k_step_local.index = shl_data_pm_k_step_local.index + 1\n", "shl_data_pm_k_step_local" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7fad6f6a42b0>]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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fCvW7Fn+BIkchrEFKzrnRzrluzrnewC5g2ZH6m1kDYCIw2Dm3Mtd1kv19CjAO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\n1dHAs8AFQDvgSjNr5/cfBTzhnGsB7AK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"text/plain": [ "<matplotlib.figure.Figure at 0x7fad7175ff28>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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s3JfF458somerBlx7QquDT6ZMhB9ehN7DoOsV4SlQqj0FaxEREYkIf/94Efuy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\nXagRXcT/Xe5eA/PfgZ43arZ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"text/plain": [ "<matplotlib.figure.Figure at 0x7fad6f74b6d8>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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ERKqF0jIPY6el0zgpnkeHdQ8cPO8BKC6AK1+CGJU7Eh76P01ERESqhb99uYWM\nbQd44soeNKlXx3/gutmwdgZcOBaadw5fglLrqbAWERGRqLd5Tz6TFm5k8OktuaxnK/+Bh/fD3N9B\nq55w7n3hS1AE3bwoIiIiUc7jcYz7KIM6cTE8ObwHFmh3jwWPQEEu3PABxMaHL0kRtGItIiIiUe4f\ny7eyfMs+Hr28Oy0aJvoP3LQYVr8D594LrfuEL0ERHxXWIiIiErW25R3mmXnrOC+1GdemtfUfWJQP\ns++F5FS4cFz4EhQpR60gIiIiEpWcczw8PQOPgwkjegZuAVk03nvK4u3zIT7AqrZIFdKKtYiIiESl\nGWu2sWTDHh4c0oV2TZP8B25dBsteg/6joP3Z4UtQ5BgqrEVERCTq5OYX8cTstfRNacwtZ3fwH1hy\nBGaNhkZt4Wd/CFt+IsejVhARERGJOo/NyqKwqIxnR/YiNiZAC8gXz0HuRrhpGiQ0CF+CIsehFWsR\nERGJKguydjI3fQf3/iyV1BYBiuWdGfDVn6D3DZA6KHwJivihwlpERESixoHDJTwyI5NupzTkNxee\n5j+wrBRm3g11m8Lgp8KXoEgAagURERGRqPH03HXsLSjmjdv6ER8bYP3vm5dgx3fw87cgqWn4EhQJ\nIKgVazO7z8wyzSzLzMb4xq71vfaYWVq52EvMbJWZZfgeLy733hIz22Bma3xfLXzjCWb2vpllm9ky\nM+sQ2o8pIiIi0e6r7FzeX5nDqPNPpUebRv4Dc7Nh8QTodgV0Hx6+BEUqUOGKtZn1AEYB/YFi4GMz\nmwNkAiOA/znmklzgCufcdt+1nwBtyr1/o3Nu5THX/ArY75xLNbPrgInAL07mA4mIiEj1U1hcyriP\n0jm1WT3GDOrkP9Dj8e4CEp8Ilz0fvgRFghDMinU3YJlzrtA5Vwp8Doxwzq1zzm04Ntg5t9o5t933\nMguoa2YJFXyP4cCbvucfAj+zgLvAi4iISE3y/Ccbydl3mGeu6UVifKz/wJVTYOs3MHgCNGgVvgRF\nghBMYZ0JnG9myWaWBFwGtAty/muAb51zReXGpvraQB4tVzy3AXIAfMX7ASA5yO8hIiIi1diqH/cz\n9est3DygPf07BuiXztsKnz4Op10MfW4IW34iwaqwFcQ5t87MJgILgAJgDVBW0XVmdjrelo5Lyw3f\n6JzbZmYNgGnAzcBbwSZrZncAdwCkpKQEe5mIiIhEqaLSMsZOS+eUhomMHdrVf6BzMOd+7+OwP4F+\nsS1RKKiMCiADAAAgAElEQVSbF51zU5xzZzrnLgD2AxsDxZtZW2A6cItzblO5ebb5Hg8B7+Lt2wbY\nhm8V3MzigEbA3uPk8bpzLs05l9a8efNgUhcREZEo9sqibLJ35/P0iJ7UTwiw3pf+PmR/CoMegybt\nw5egyAkIdleQo7t3pOC9YfHdALGNgbnAOOfcV+XG48ysme95PDAMb5sJwCzgVt/zkcAi55w7sY8i\nIiIi1cna7QeZvGQTI/q24aIuLfwH5u+Gj8dBu7Og36jwJShygoLdx3qamSUDJcDdzrk8M7saeAlo\nDsw1szXOucHAaCAV+IOZ/cF3/aV420g+8RXVscCnwF99708B3jazbGAfcF0IPpuIiIhEqdIyD2On\npdM4KZ5HL+8eOHjeA1BcAFe+DDE6206iV1CFtXPu/OOMTcfb7nHs+HhgvJ+pzvQz/xHg2mByERER\nkervb19uIWPbAV65oS9N6tXxH7huNqydARc/Cs07hy9BkZOgH/tEREQkrDbvyWfSwo0MPr0ll/UM\nsGXe4f0w93fQqiece1/4EhQ5STrSXERERMLG43GMm5ZBQlwMTw7vQcBjKxY8AgW5cMMHEBsfviRF\nTpIKaxEREQmbz+Z9wLU/vUNah6a0WDTTf2BpEWR+COfdD637hC9BkUpQYS0iIiJhsWvr95yz4l6I\njyPpUGM4VMFe1J0uhQvHhSc5kRBQYS0iIiJVznk87Hz3v+iEI+/WRdTr0CXSKYmEnG5eFBERkSq3\nas5r9D6ygvSu99FaRbXUUCqsRUREpErl7swh9dunWB/XjX7Xjo10OiJVRoW1iIiIVKmt79xNkjtC\n3ZGTiY1TF6rUXCqsRUREpMqsXvA2ffM/Z1XHUbTv2jfS6YhUKRXWIiIiUiUO7NtDu68fYVNsR9Ju\neCLS6YhUORXWIiIiUiU2vHUvjd1B3JUvE18nIdLpiFQ5FdYiIiISchlfzKR/3jxWtL6J1N7nRTod\nkbBQYS0iIiIhVXAoj+TFvyfHWnPGzRMinY5I2KiwFhERkZDKePsBWrvdHBo8icSk+pFORyRsVFiL\niIhIyKxfvpD+u/7F0mYj6D5gSKTTEQkrFdYiIiISEkcOF1D34zHstmR63PJipNMRCTsV1iIiIhIS\nq995mPaen9h94UTqN2wS6XREwk7HH4mIiERSWWmkMwjI4SjzuArjfli7nLSf3mJF48H0GzgyDJmJ\nRB8V1iIiIpHy+bOw+KlIZxGQEVyxkArstUZ0uvkvVZyRSPRSYS0iIhIJ21bBkgmQOgjaDYh0Nsf1\nSdZOsrYfoH/HpsTGWIXxrfqPoGOzVmHITCQ6qbAWEREJt9JimHkP1G8JI9+AxEaRzug/fPl9Lr+Z\nv4w7LzyN84Z2jXQ6ItWCCmsREZFw+3IS7M6C69+LyqK6sLiUcR+lc2qzeowZ1CnS6YhUGyqsRURE\nwmnXWvjiOehxDXQZGulsjuv5Tzby0/7DfPCbs0mMj410OiLVhrbbExERCRdPGcwaDQkNYOizkc7m\nuFb9uJ+pX2/h5gHt6d+xaaTTEalWtGItIiISLste8960OOJvUK9ZpLP5D0WlZYydls4pDRMZq75q\nkROmwlpERCQc9m2Gz56EzkOgZ3Tu8/zyomyyd+fz99v7UT9BJYLIiVIriIiISFVzDmbdCzFxcPmL\nYBVvXRdua7cf5NUlmxjRtw0XdWkR6XREqiX9OCoiIlLVvn0Tfvg3DJsEjdpEOpv/UFrmYey0dBon\nxfPo5d0jnY5ItaXCWkREpCod3A4LHoUO50Pf2yKdzXH97cstZGw7wCs39KVJvTqRTkek2lIriIiI\nSFVxDub8FspK4Io/Q0z0/bO7eU8+kxZuZPDpLbmsp05NFKmMoP6Em9l9ZpZpZllmNsY3dq3vtcfM\n0o6Jf8jMss1sg5kNLjc+xDeWbWbjyo13NLNlvvH3zUw/LouISPWXOQ02zoeLH4bk0yKdzX/weBzj\npmWQEBfDk8N7YFHY+y1SnVRYWJtZD2AU0B/oDQwzs1QgExgBfHFMfHfgOuB0YAgw2cxizSwWeAUY\nCnQHrvfFAkwEJjnnUoH9wK9C8NlEREQip2AvzH8QWveFs/4r0tkc1z+Wb2X5D/t4ZFh3WjRMjHQ6\nItVeMCvW3YBlzrlC51wp8Dkwwjm3zjm34Tjxw4H3nHNFzrktQDbeorw/kO2c2+ycKwbeA4ab98fj\ni4EPfde/CVxVuY8lIiISYR+PhSMHYfgrEBt9tzRtyzvMM/PWcV5qM649s22k0xGpEYL5k54JPGVm\nycBh4DJgZYD4NsDScq9/8o0B5BwzfhaQDOT5ivZj40VERKJG5rYDrM7JqzCuze7PuTjjX3x32p2k\nb6kHW34MQ3YnZs532/E4mDCip1pAREKkwsLaObfOzCYCC4ACYA1QVtWJHY+Z3QHcAZCSkhKJFERE\npJbaureQa1/7hsMlgf8JbEAhCxIeY4Nry8iscyjJygxThifGDJ4c3oN2TZMinYpIjRHU76acc1OA\nKQBm9jTeVWV/tgHtyr1u6xvDz/heoLGZxflWrcvHH5vH68DrAGlpaS6Y3EVERCrLOce4j9KJjTE+\nGXMBTQNsSVd/4e9JzMgj8fp3+fqUvmHM8sTUiY2hUVJ8pNMQqVGCKqzNrIVzbreZpeC9YXFAgPBZ\nwLtm9iLQGugELAcM6GRmHfEWztcBNzjnnJktBkbi7bu+FZh5sh9IREQk1D5YmcPXm/by1NU96NKq\ngf/ALf+G9Lfg7NE06Xx2+BIUkagQ7N0U03w91iXA3c65PDO7GngJaA7MNbM1zrnBzrksM/sAWAuU\n+uLLAMxsNPAJEAu84ZzL8s0/FnjPzMYDq/GtjouIiETaroNHGD93HWd1bMr1/QK0IRYXwqx7oEkH\nGPhw2PITkegRbCvI+ccZmw5M9xP/FPDUccbnAfOOM74Z764hIiIiUcM5x8PTMyku9TDxml7ExAS4\nyW/J07B/C9w6G+qob1mkNoq+I6BERESixJz0HXy6bhe/u7QzHZrV8x+4bRV88wqceRt0vCBs+YlI\ndFFhLSIichz7Cop5fFYWvdo24pfndvQfWFoMM0dD/VZwyR/Dl6CIRJ3o27FeREQkCvxxdhYHDpfw\nj1FnERcbYB3qyxdh91q4/j1IbBS+BEUk6mjFWkRE5BiL1u9ixprt3DUwla6tGvoP3LUWvngeeoyE\nLkPDl6CIRCUV1iIiIuUcOlLCw9Mz6dyyPncPPM1/oKcMZo2GxIYwdGL4EhSRqKVWEBERkXKemb+e\nnQePMPnGc0iIi/UfuPRV702L10yBes3Cl6CIRC2tWIuIiPgs3byXfyzbyi/P7cgZKU38B+7bDIvG\nQ+ch0OOa8CUoIlFNhbWIiAhwuLiMcdPSSWmaxO8u7ew/0DmYdS/ExsPlL4IF2NtaRGoVtYKIiIgA\nf/p0Iz/sLeTdX59FUp0A/zx++yb88G8Y9ido1CZ8CYpI1NOKtYiI1HrpP+Xx139v5vr+7TgnNUC/\n9MHtsOBR6HC+9zAYEZFyVFiLiEitVlzq4cEP02neIIFxQ7v5D3QO5vwWykrgij+rBURE/oMKaxER\nqdVe+3wT63ceYvxVPWlUN95/YOY02DgfLn4YkgNswycitZYKaxERqbU27jrES4u+54rerbmke0v/\ngQW5MP9BaHMmDLgrfAmKSLWiwlpERGqlMo/jwQ/TqZ8Qx+NXdA8c/PE4OHIQrnwZYgLsbS0itZoK\naxERqZWmfrWFNTl5PH7l6STXT/AfuOFjyPgXXPB7aFlBAS4itZoKaxERqXW27i3k+QUbuLhrC67s\n3dp/4JEDMOd+aNEdzvtt+BIUkWpJ+1iLiEit4pxj3EfpxMXE8NTVPbBAu3ss/APk74RfvANxdcKX\npIhUS1qxFhGRWuX9FTl8vWkvD13WlVMa1fUfuOULWPV3782Kbc8MW34iUn2psBYRkVpj54EjPDV3\nHQNObcr1/VL8BxYXeo8tb9IRBj4cvgRFpFpTK4iIiNQKzjkemZFJicfDMyN6ERMToAVk8VOwfwvc\nOhvqJIUvSRGp1rRiLSIitcKc9B18um4Xv7ukCx2a1fMf+NMqWDrZe2R5xwvClp+IVH8qrEVEpMbb\nV1DM47Oy6N22Ebef28F/YGkxzBoN9VvBJX8MW34iUjOoFURERGq8P87O4uCREp4dOYC42ABrSl++\nCLvXwvXvQWKj8CUoIjWCVqxFRKRGW7R+FzPWbOeui1Lp0qqB/8Bda+GL56HHSOgyNHwJikiNocJa\nRERqrENHSvjvjzLp0rIBdw9M9R/oKYOZd0NiQxg6MXwJikiNolYQEfFuLZa7IbjYJh2gbpMqTUek\nIltyC8g/Ulph3IwvltEifwPPDe1Dnd3f+Q9cPw+2fwvXTIF6zUKYqYjUJiqsRWq70iL42yDYnRVc\nfFIzuGsp1G9etXmJ+PHByhwe/DC9wrizbB3/qPMUcXU8MDOIiTsPhR7XVD5BEam1VFiL1HZfPO8t\nqodMhCbtA8cWHfL+unz+g3Dt1PDkJ1LOroNHeHLOWvp1aMJvLjjNb1xM6WEGLHiIEtpilz9DbEwF\nnY8W691aL9Dx5iIiFVBhLVKb7czw7oLQ+3oYcGdw1+T9CIvGQ8+R0PXyqs1PpJyjB7wUl3p4bmTv\nwHtRL3gE8n/0HvCivahFJEx086JIbVVWCjNHe/ulBz8d/HXnjoGWPWHOb+FwXtXlJ3KMuRk7WLh2\nF7+7tHPgonrbKvjmFeh7q4pqEQmroAprM7vPzDLNLMvMxvjGmprZQjP73vfYxDf+gJmt8X1lmlmZ\nmTX1vfeDmWX43ltZbv7jziUiVeibl2HHGrjseUhqGvx1sfEw/GUo2ONdFRQJg30FxTw203vAyy/P\n7eg/sLQYZt4D9VvCpU+GL0EREYIorM2sBzAK6A/0BoaZWSowDvjMOdcJ+Mz3Gufcc865Ps65PsBD\nwOfOuX3lphzoez+t3Nhx5xKRKpKbDUsmQNdh0H34iV/fug+ccw+sfhs2Lwl5eiLHenLOWg4cLmHi\nyF4VHPAyyXvPwLBJOuBFRMIumBXrbsAy51yhc64U+BwYAQwH3vTFvAlcdZxrrwf+GcT3CGYuEQkF\njwdm3wtxCXD5Cyd/s9ZF4yA5FWbdC8UFoc1RpJzF63czffU27hqYStdWDf0H7l4HXzynA15EJGKC\nKawzgfPNLNnMkoDLgHZAS+fcDl/MTqBl+Yt8sUOAaeWGHbDAzFaZ2R3lxgPOJSIhtOoN+PErb191\ng1YnP098Xbjypf+7mVGkChw6UsJ/T8+gc8v63D3Q/y4g3gNeRuuAFxGJqAoLa+fcOmAisAD4GFgD\nlB0T4/AWzeVdAXx1TBvIec65vsBQ4G4z+4+7SvzMBYCZ3WFmK81s5Z49eypKXUSOlZcDCx+DUwdC\nnxsrP1/7c6DfKFj6KuQsr/x8IseY+PF6dh08wsRrepEQF+s/cNlrsG0lDH1WB7yISMQEdfOic26K\nc+5M59wFwH5gI7DLzE4B8D3uPuay6zimDcQ5t833uBuYjrdvmyDmOnr96865NOdcWvPmOpxC5IQ4\nB3Pu9z5e8efQ7dc76DFo1Na7WlhaFJo5RYClm/fyztKt/PLcjpyREuCe9n2b4bMnofMQHfAiIhEV\n7K4gLXyPKXj7q98FZgG3+kJupdy5VmbWCLjwmLF6Ztbg6HPgUrxtJgSaS0RCJP0DyF4IP/tDxQfB\nnIiEBjDsT94j0b94LnTzSq12pKSMcdPSSWmaxG8v7ew/0Dlvn39sPFz+og54EZGICvaAmGlmlgyU\nAHc75/LM7BngAzP7FfAj8PNy8VcDC5xz5e9oaglMN+9fenHAu865j33vBZpLRCorfw98PBba9of+\no0I/f6dB3kNmvpzk3WWkVc/Qfw+pVSYt3MgPewt599dnkVQnwD9V374JP/zb+8NdozbhS1BE5DjM\n29Jc/aSlpbmVK1dWHCgi8K/bYP1cuPNLaN6lar5H4T54pT80bAO//gxidbCrnJz0n/K46pWv+EW/\ndkwY0ct/4MHt8MpZcEpvuGUWVHRsuYjISTKzVcdsFX1c+ltIpKZbNweypsOFD1ZdUQ3eQ2Yue957\n6Mw3L1fd95EarbjUw4MfptO8QQLjhnbzH+ic9/TPshLvPQMqqkUkCuhvIpGa7HAezP2d9wjyc8dU\n/ffrPtx76MySCd5DaERO0Gufb2L9zkOMv6onjerG+w/MnAYb58PFD0NygG34RETCSL+rFalmPB7H\n377czA97CyuMvTrnGfrm7+HVU55i+6z1YcgOGsT8mjFuCQf/NpIf6gX4Nb7IsRw035PPOy2TOG/T\nQtgUIHbtTGhzJgy4K2zpiYhURIW1SDXz7vKtPD1vPU3r1SEmwA4I/T3f0c8zh7/bVfx9S2NgV9hy\n3O/u5LdH/s5pRz4P2/eUmqFznNGoJA7WV7C7R/0WMPwViAmwt7WISJjp5kWRamR73mEunfQFfdo1\n5u1f9cf8FdbFBTB5AMTW8d6wGF83vImKiIjUIMHevKgVa5FqwjnHw9MzKPM4Jozo6b+oBu8R43lb\n4fb5KqpFRETCRDcvilQTM9dsZ/GGPTwwuAvtmib5D8xZ7j1ivN+vvUeOi4iISFiosBapBnLzi3hi\ndhZ9Uxpz6zkd/AeWFnmPFm/YBgY9HqbsREREBNQKIlItPD4ri4KiMiZe04vYmAAtIF885z1a/MZp\n3qPGRUREJGy0Yi0S5RZk7WRO+g7uuTiVTi0DFMs7M7xHive+3nvEuIiIiISVCmuRKHbgcAmPzMik\na6sG3HlRgEMwykq9LSB1m8Dgp8OXoIiIiPwvtYKIRLGn565jb0ExU27tR3xsgJ+Dv3nZe5T4tW96\njxYXERGRsNOKtUiU+io7l/dX5jDq/FPp2baR/8DcbO8R4l2HeY8UFxERkYhQYS0ShQqLSxn3UTod\nm9VjzKBO/gM9Hph1D8QlwOUvQKC9rUVERKRKqRVEJAo9/8lGcvYd5oPfnE1ifIAjm1e9AVu/9h7t\n3KBV+BIUERGR/6AVa5Eos+rH/Uz9egs3D2hP/44B+qXzcmDhY3DqQOhzY/gSFBERkeNSYS0SRYpK\nyxg7LZ1TGiYydmhX/4HOwZz7vY9X/FktICIiIlFArSAiUeTlRdlk785n6u39qJ8Q4I9n+vuQvRCG\nTIQm7cOXoIiIiPilwlrkJL27bCuPz86itMwTMO4C+46X4v9CEkcqnHMMcH+iEfNeBYHOA237Q/9R\nwScsIiIiVUqFtchJ+CG3gD/OyaJnm0acc1qy37iE0kPcuuYNimNbkpl8cYXz1omNoUebRiTEVdCl\nFRMPfW+BmAA3NoqIiEhYqbAWOUEej2PcR+nEx8Yw+ca+tGyY6D941j1Qshdu+xdntz4jfEmKiIhI\n2OnmRZET9N6KHJZu3sfDl3ULXFRvXgLfvgXn3AMqqkVERGo8FdYiJ2DHgcNMmLeOc05L5hf92vkP\nLC6A2fdB09PgoofCl6CIiIhEjFpBRILknOOR6ZmUeDw8M6IXFmiLu0VPwf4f4LZ5EF83bDmKiIhI\n5GjFWiRIs77bzmfrd/P7S7uQkpzkPzBnBSydDGm/gg7nhi9BERERiSgV1iJB2JtfxBOz19KnXWNu\nP7ej/8DSIpg1Ghq2gUGPhys9ERERiQJqBREJwhOz13LoSAnPjuxFbEyAFpB/vwB71sMN/4LEhuFL\nUERERCJOK9YiFfh07S5mfbed0QM70bllA/+BOzO9hXWvX0DnS8OXoIiIiEQFFdYiARw8UsLDMzLo\n2qoB/3XRaf4Dy0q9LSCJjWHIM+FLUERERKKGWkFEApgwbz17DhXx+s1p1Al0GuLSybB9NYycCklN\nw5egiIiIRI2gVqzN7D4zyzSzLDMb4xtramYLzex732MT3/hFZnbAzNb4vv5Qbp4hZrbBzLLNbFy5\n8Y5mtsw3/r6Z1Qn1BxU5UV9vyuWfy7cy6vxT6d2usf/AvZtg8VPQdRicfnX4EhQREZGoUmFhbWY9\ngFFAf6A3MMzMUoFxwGfOuU7AZ77XR/3bOdfH9/VH3zyxwCvAUKA7cL2ZdffFTwQmOedSgf3Ar0Ly\n6URO0uHiMsZNy6BDchJjBnX2H+jxwKx7ITYBLnseAu1tLSIiIjVaMCvW3YBlzrlC51wp8DkwAhgO\nvOmLeRO4qoJ5+gPZzrnNzrli4D1guHlP2bgY+PAE5hKpUi8s2MDWfYU8c00v6taJ9R+4air8+CUM\nHg8NTwlfgiIiIhJ1gumxzgSeMrNk4DBwGbASaOmc2+GL2Qm0LHfN2Wb2HbAd+L1zLgtoA+SUi/kJ\nOAtIBvJ8RfvR8TYn+XmkFsrcdoC12w9WGFe/YCvN962sMO5wSRmH0nfwYmpTBuTtg2/9BHpKYeFj\n0PFCOOPmE8xaREREapoKC2vn3DozmwgsAAqANUDZMTHOzJzv5bdAe+dcvpldBswAOoUiWTO7A7gD\nICUlJRRTSjW3cdchrp78FSVlLmBcEw7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"text/plain": [ "<matplotlib.figure.Figure at 0x7fad6f69f2b0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# bid is predicted bid-price from shl_pm\n", "plt.figure(figsize=(12,6))\n", "plt.plot(shl_pm.shl_data_pm_k_step['f_current_bid'])\n", "# plt.plot(shl_data_pm_1_step_k_step['f_1_step_pred_price'].shift(1))\n", "plt.plot(shl_data_pm_k_step_local['f_1_step_pred_price'])\n", "\n", "# bid is actual bid-price from raw dataset\n", "shl_data_actual_bid_local = shl_sm_data[shl_sm_parm_ccyy_mm_offset:shl_sm_parm_ccyy_mm_offset+61].copy()\n", "shl_data_actual_bid_local.reset_index(inplace=True)\n", "plt.figure(figsize=(12,6))\n", "plt.plot(shl_data_actual_bid_local['bid-price'])\n", "plt.plot(shl_data_pm_k_step_local['f_1_step_pred_price'])\n", "\n", "plt.figure(figsize=(12,6))\n", "plt.plot(shl_data_actual_bid_local['bid-price'])\n", "plt.plot(shl_data_pm_k_step_local['f_1_step_pred_price_rounded'])\n" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>bid-price</th>\n", " <th>f_1_step_pred_price</th>\n", " <th>0</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>50</th>\n", " <td>91500</td>\n", " <td>91505.926340</td>\n", " <td>5.926340</td>\n", " </tr>\n", " <tr>\n", " <th>51</th>\n", " <td>91600</td>\n", " <td>91557.134451</td>\n", " <td>-42.865549</td>\n", " </tr>\n", " <tr>\n", " <th>52</th>\n", " <td>91700</td>\n", " <td>91609.816482</td>\n", " <td>-90.183518</td>\n", " </tr>\n", " <tr>\n", " <th>53</th>\n", " <td>91800</td>\n", " <td>91683.720820</td>\n", " <td>-116.279180</td>\n", " </tr>\n", " <tr>\n", " <th>54</th>\n", " <td>91900</td>\n", " <td>91768.751578</td>\n", " <td>-131.248422</td>\n", " </tr>\n", " <tr>\n", " <th>55</th>\n", " <td>92000</td>\n", " <td>91853.166552</td>\n", " <td>-146.833448</td>\n", " </tr>\n", " <tr>\n", " <th>56</th>\n", " <td>92100</td>\n", " <td>91943.947695</td>\n", " <td>-156.052305</td>\n", " </tr>\n", " <tr>\n", " <th>57</th>\n", " <td>92100</td>\n", " <td>92042.646817</td>\n", " <td>-57.353183</td>\n", " </tr>\n", " <tr>\n", " <th>58</th>\n", " <td>92100</td>\n", " <td>92085.651711</td>\n", " <td>-14.348289</td>\n", " </tr>\n", " <tr>\n", " <th>59</th>\n", " <td>92200</td>\n", " <td>92175.935610</td>\n", " <td>-24.064390</td>\n", " </tr>\n", " <tr>\n", " <th>60</th>\n", " <td>92200</td>\n", " <td>92252.692080</td>\n", " <td>52.692080</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " bid-price f_1_step_pred_price 0\n", "50 91500 91505.926340 5.926340\n", "51 91600 91557.134451 -42.865549\n", "52 91700 91609.816482 -90.183518\n", "53 91800 91683.720820 -116.279180\n", "54 91900 91768.751578 -131.248422\n", "55 92000 91853.166552 -146.833448\n", "56 92100 91943.947695 -156.052305\n", "57 92100 92042.646817 -57.353183\n", "58 92100 92085.651711 -14.348289\n", "59 92200 92175.935610 -24.064390\n", "60 92200 92252.692080 52.692080" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# pd.concat([shl_data_actual_bid_local['bid-price'], shl_data_pm_k_step_local['f_1_step_pred_price'], shl_data_pm_k_step_local['f_1_step_pred_price'] - shl_data_actual_bid_local['bid-price']], axis=1, join='inner')\n", "pd.concat([shl_data_actual_bid_local['bid-price'].tail(11), shl_data_pm_k_step_local['f_1_step_pred_price'].tail(11), shl_data_pm_k_step_local['f_1_step_pred_price'].tail(11) - shl_data_actual_bid_local['bid-price'].tail(11)], axis=1, join='inner')\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The End" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "---" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 1 }	mit
joosthub/pytorch-nlp-tutorial-sf2017	day_1/4_Chinese_document_classification.ipynb	1	58545	{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Chinese document Classification" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import pandas as pd\n", "import numpy as np\n", "plt.style.use('fivethirtyeight')\n", "plt.rcParams['figure.figsize'] = (14, 6)\n", "\n", "from local_settings import settings, datautils" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Overview of Task" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## 1. Load data" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>label</th>\n", " <th>title</th>\n", " <th>content</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>ti3 ca1o shi4 jie4 be1i : che2ng fe1i na2 pi2...</td>\n", " <td>su4 du4 : ( shuo1 mi2ng : dia3n ji1 zi4 do4ng ...</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>4</td>\n", " <td>da3o ha2ng</td>\n", " <td>du2 jia1 ti2 go1ng me3i ri4 ba4o jia4 \\n re4 ...</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1</td>\n", " <td>wa3ng yi4 ti3 yu4</td>\n", " <td>gu3n do4ng tu2 ji2 \\n be3n tu2 ji2 go4ng 7 zh...</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>3</td>\n", " <td>zi1 lia4o tu2 pia4n : dia4n shi4 ju4 < fu2 gu...</td>\n", " <td>wa3ng ye4 \\n bu4 zhi1 chi2 Flash\\n xi1n la4n...</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>2</td>\n", " <td>niu3 yua2n dui4 me3i yua2n : ku4 lu2n jia3ng ...</td>\n", " <td>xi1n xi1 la2n ca2i cha2ng ku4 lu2n fa1 bia3o j...</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " label title \\\n", "0 1 ti3 ca1o shi4 jie4 be1i : che2ng fe1i na2 pi2... \n", "1 4 da3o ha2ng \n", "2 1 wa3ng yi4 ti3 yu4 \n", "3 3 zi1 lia4o tu2 pia4n : dia4n shi4 ju4 < fu2 gu... \n", "4 2 niu3 yua2n dui4 me3i yua2n : ku4 lu2n jia3ng ... \n", "\n", " content \n", "0 su4 du4 : ( shuo1 mi2ng : dia3n ji1 zi4 do4ng ... \n", "1 du2 jia1 ti2 go1ng me3i ri4 ba4o jia4 \\n re4 ... \n", "2 gu3n do4ng tu2 ji2 \\n be3n tu2 ji2 go4ng 7 zh... \n", "3 wa3ng ye4 \\n bu4 zhi1 chi2 Flash\\n xi1n la4n... \n", "4 xi1n xi1 la2n ca2i cha2ng ku4 lu2n fa1 bia3o j... " ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = pd.read_csv(settings.ZHNEWS_CSV, names=['label', 'title', 'content'])\n", "data.head()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f8cd8ae3828>" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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VFgAAgOIpecTu3r07d955Z77yla/k6KOPTvLye1vXrFmTJFm9enWmTp2aiRMn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\nyQAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f8d0f3b5c88>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.countplot(data['label'])" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## 2. Build vocab" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "chars = 'abcdefghijklmnopqrstuvwxyz-,;!?:\\'\\\\\|_@#$%ˆ&*˜‘+-=<>()[]{} '\n", "char_to_index = {char:i for i, char in enumerate(chars)}\n", "index_to_char = {i: char for i, char in enumerate(chars)}" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## 3. Find max sequence length" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "207" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "maxlen = int(max(data['title'].apply(len)))\n", "maxlen" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## 4. Convert sequences to Tensors" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "def encode_input(title, maxlen=207):\n", " title = title.lower().strip()\n", " encoding = np.zeros((len(chars), maxlen), dtype=np.int64)\n", " for i, char in enumerate(title[:maxlen]):\n", " index = char_to_index.get(char, 'unknown')\n", " if index is not 'unknown':\n", " encoding[index,i] = 1\n", " return encoding" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "import torch\n", "import torch.nn as nn\n", "from torch.autograd import Variable\n", "import torch.nn.functional as F" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "array([[0, 0, 0, ..., 0, 0, 0],\n", " [1, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0],\n", " ..., \n", " [0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0]])" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "encode_input('Brian')" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "(57, 207)" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "encode_input('Brian').shape" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## 5. Build PyTorch Dataset and DataLoader" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "from torch.utils.data import Dataset, DataLoader" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "class SogouNews(Dataset):\n", " \"\"\"Sogou News dataset\"\"\"\n", " \n", " def __init__(self, data_path):\n", " self.data = pd.read_csv(data_path, names=['label', 'title', 'content']).dropna()\n", " del self.data['content']\n", " self.X = self.data['title']\n", " self.y = self.data['label']\n", " \n", " def __len__(self):\n", " return len(self.data)\n", " \n", " def __getitem__(self, index):\n", " content = torch.from_numpy(encode_input(self.data['title'][index])).float()\n", " label = self.data['label'][index] - 1\n", " sample = {'X': content, 'y': label}\n", " return sample" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "sogou_dataset = SogouNews(settings.ZHNEWS_CSV)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "dataloader = DataLoader(sogou_dataset, batch_size=32, shuffle=True, num_workers=0)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "\n", " 0 0 0 ... 0 0 0\n", " 0 0 0 ... 0 0 0\n", " 0 0 0 ... 0 0 0\n", " ... ⋱ ... \n", " 0 0 0 ... 0 0 0\n", " 0 0 0 ... 0 0 0\n", " 0 0 0 ... 0 0 0\n", "[torch.FloatTensor of size 57x207]" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_batch = next(iter(dataloader))\n", "test_batch['X'][0]" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Define Model" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "class CharCNN(nn.Module): \n", " def __init__(self, n_classes, vocab_size, max_seq_length, channel_size=128, pool_size=5):\n", " \n", " super(CharCNN, self).__init__()\n", " \n", " self.conv_stack = nn.ModuleList([nn.Conv1d(vocab_size, channel_size, 7), \n", " nn.ReLU(),\n", " nn.BatchNorm1d(num_features=channel_size),\n", " nn.MaxPool1d(pool_size),\n", " nn.Conv1d(channel_size, channel_size, 3, padding=1),\n", " nn.ReLU(),\n", " nn.BatchNorm1d(num_features=channel_size),\n", " nn.MaxPool1d(pool_size)])\n", " self.dropout1 = nn.Dropout(p=0.5)\n", " self.output = nn.Linear(1024, n_classes)\n", " \n", " \n", " def forward(self, x):\n", " for op in self.conv_stack:\n", " x = op(x)\n", " \n", " x = x.view(x.size(0),-1)\n", " x = self.dropout1(x)\n", " x = self.output(x)\n", " return x" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Define loss" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "criterion = nn.CrossEntropyLoss()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "from tqdm import tqdm_notebook" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "def train(model, dataloader, num_epochs, loss_history):\n", " cuda = torch.cuda.is_available()\n", " if cuda:\n", " model.cuda()\n", " optimizer = torch.optim.Adam(model.parameters())\n", " bar = tqdm_notebook(total=len(dataloader))\n", " for i in range(num_epochs):\n", " per_epoch_losses = []\n", " for batch in dataloader:\n", " X = Variable(batch['X'])\n", " y = Variable(batch['y'])\n", " if cuda:\n", " X = X.cuda()\n", " y = y.cuda()\n", " model.zero_grad()\n", " outputs = model(X)\n", " loss = criterion(outputs, y)\n", " loss.backward()\n", " optimizer.step()\n", " per_epoch_losses.append(loss.data[0])\n", " bar.set_postfix(loss=loss.data[0])\n", " bar.update(1)\n", " loss_history.append(np.mean(per_epoch_losses))\n", " print('epoch[%d] loss: %.4f' % (i, loss.data[0]))\n", " return loss_history" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "charcnn = CharCNN(n_classes=5, vocab_size=len(chars), max_seq_length=maxlen)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "loss_history = []" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "d4893c16067043d8b6732581b4552092" } }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "epoch[0] loss: 1.0125\n", "epoch[1] loss: 0.6316\n", "epoch[2] loss: 0.3102\n", "epoch[3] loss: 0.3798\n", "epoch[4] loss: 0.4475\n", "epoch[5] loss: 0.4318\n", "epoch[6] loss: 0.4616\n", "epoch[7] loss: 0.2947\n", "epoch[8] loss: 0.3853\n", "epoch[9] loss: 0.5907\n", "epoch[10] loss: 0.2385\n", "epoch[11] loss: 0.3859\n", "epoch[12] loss: 0.5880\n", "epoch[13] loss: 0.0821\n", "epoch[14] loss: 0.1574\n", "epoch[15] loss: 0.2979\n", "epoch[16] loss: 0.1919\n", "epoch[17] loss: 0.0421\n", "epoch[18] loss: 0.2789\n", "epoch[19] loss: 0.0759\n", "epoch[20] loss: 0.2457\n", "epoch[21] loss: 0.1539\n", "epoch[22] loss: 0.2495\n", "epoch[23] loss: 0.2738\n", "epoch[24] loss: 0.1787\n", "epoch[25] loss: 0.1818\n", "epoch[26] loss: 0.1908\n", "epoch[27] loss: 0.1909\n", "epoch[28] loss: 0.1800\n", "epoch[29] loss: 0.2165\n", "epoch[30] loss: 0.1303\n", "epoch[31] loss: 0.0821\n", "epoch[32] loss: 0.1988\n", "epoch[33] loss: 0.0734\n", "epoch[34] loss: 0.0995\n", "epoch[35] loss: 0.3799\n", "epoch[36] loss: 0.1245\n", "epoch[37] loss: 0.2139\n", "epoch[38] loss: 0.3832\n", "epoch[39] loss: 0.0434\n", "epoch[40] loss: 0.1631\n", "epoch[41] loss: 0.1988\n", "epoch[42] loss: 0.0680\n", "epoch[43] loss: 0.1036\n", "epoch[44] loss: 0.1608\n", "epoch[45] loss: 0.0424\n", "epoch[46] loss: 0.1204\n", "epoch[47] loss: 0.0638\n", "epoch[48] loss: 0.0178\n", "epoch[49] loss: 0.0133\n", "epoch[50] loss: 0.0336\n", "epoch[51] loss: 0.2700\n", "epoch[52] loss: 0.2089\n", "epoch[53] loss: 0.2930\n", "epoch[54] loss: 0.0989\n", "epoch[55] loss: 0.0273\n", "epoch[56] loss: 0.0835\n", "epoch[57] loss: 0.2447\n", "epoch[58] loss: 0.1637\n", "epoch[59] loss: 0.1404\n", "epoch[60] loss: 0.2227\n", "epoch[61] loss: 0.0680\n", "epoch[62] loss: 0.2013\n", "epoch[63] loss: 0.1672\n", "epoch[64] loss: 0.1086\n", "epoch[65] loss: 0.1863\n", "epoch[66] loss: 0.2671\n", "epoch[67] loss: 0.0799\n", "epoch[68] loss: 0.1660\n", "epoch[69] loss: 0.0298\n", "epoch[70] loss: 0.0633\n", "epoch[71] loss: 0.0100\n", "epoch[72] loss: 0.1455\n", "epoch[73] loss: 0.0962\n", "epoch[74] loss: 0.1906\n", "epoch[75] loss: 0.0872\n", "epoch[76] loss: 0.1409\n", "epoch[77] loss: 0.0295\n", "epoch[78] loss: 0.0506\n", "epoch[79] loss: 0.2864\n", "epoch[80] loss: 0.0231\n", "epoch[81] loss: 0.0760\n", "epoch[82] loss: 0.0249\n", "epoch[83] loss: 0.0694\n", "epoch[84] loss: 0.3208\n", "epoch[85] loss: 0.4952\n", "epoch[86] loss: 0.1414\n", "epoch[87] loss: 0.1518\n", "epoch[88] loss: 0.0397\n", "epoch[89] loss: 0.0941\n", "epoch[90] loss: 0.1205\n", "epoch[91] loss: 0.0815\n", "epoch[92] loss: 0.0767\n", "epoch[93] loss: 0.0212\n", "epoch[94] loss: 0.0082\n", "epoch[95] loss: 0.0798\n", "epoch[96] loss: 0.1038\n", "epoch[97] loss: 0.1353\n", "epoch[98] loss: 0.0666\n", "epoch[99] loss: 0.0283\n", "187500it [24:21, 150.17it/s, loss=0.0283]" ] } ], "source": [ "try:\n", " train(charcnn, dataloader, 100, loss_history)\n", "except KeyboardInterrupt:\n", " print(\"...\")" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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AACDfECCRptRt6q/OK9HLH6nRze8oktcx9jXbumO6+jedOhgkRAIAAAD5hACJ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\np5Y4dXqJU6eVOFVdYLLza47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"text/plain": [ "<matplotlib.figure.Figure at 0x7f8ca6ea7e10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(loss_history);" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
jnarhan/Breast_Cancer	src/models/JN_BC_Threshold_Diagnosis.ipynb	1	282739	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<hr>\n", "<h1>Predicting Benign and Malignant Classes in Mammograms Using Thresholded Data</h1>\n", "\n", "<p>Jay Narhan</p>\n", "June 2017\n", "\n", "This is an application of the best performing models but using thresholded data instead of differenced data. See JN_DC_Diff_Diagnosis.ipynb for more background and details on the problem.\n", "\n", "<hr>" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using Theano backend.\n" ] } ], "source": [ "import os\n", "import sys\n", "import time\n", "import numpy as np\n", "\n", "from tqdm import tqdm\n", "\n", "import sklearn.metrics as skm\n", "from sklearn import metrics\n", "from sklearn.svm import SVC\n", "from sklearn.model_selection import train_test_split\n", "\n", "from skimage import color\n", "\n", "import keras.callbacks as cb\n", "import keras.utils.np_utils as np_utils\n", "from keras import applications\n", "from keras import regularizers\n", "from keras.models import Sequential\n", "from keras.constraints import maxnorm\n", "from keras.preprocessing.image import ImageDataGenerator\n", "from keras.layers.convolutional import Convolution2D, MaxPooling2D\n", "from keras.layers import Activation, Dense, Dropout, Flatten, GaussianNoise\n", "\n", "from matplotlib import pyplot as plt\n", "%matplotlib inline\n", "\n", "plt.rcParams['figure.figsize'] = (10,10)\n", "np.set_printoptions(precision=2)\n", "\n", "sys.path.insert(0, '../helper_modules/')\n", "import jn_bc_helper as bc" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h2>Reproducible Research</h2>" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "scipy: 0.19.0\n", "numpy: 1.12.1\n", "matplotlib: 2.0.0\n", "sklearn: 0.18.1\n", "skimage: 0.13.0\n", "theano: 0.9.0.dev-c697eeab84e5b8a74908da654b66ec9eca4f1291\n", "tensorflow: 0.10.0\n", "keras: 2.0.3\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Python 2.7.13 :: Continuum Analytics, Inc.\n", "Using Theano backend.\n" ] } ], "source": [ "%%python\n", "import os\n", "os.system('python -V')\n", "os.system('python ../helper_modules/Package_Versions.py')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%\|██████████\| 2/2 [00:00<00:00, 13421.77it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Number of entries in incoming meta_data: 5822\n", "Images found: 5251\n", "Images missing: 571\n", "Number of entries of outgoing meta_data: 5251\n", "------------\n", "Loading data\n", "------------\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%\|██████████\| 2/2 [00:00<00:00, 14169.95it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "----------------\n", "Before Balancing\n", "----------------\n", "malignant : 948\n", "benign : 732\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "SEED = 7\n", "np.random.seed(SEED)\n", "\n", "CURR_DIR = os.getcwd()\n", "DATA_DIR = '/Users/jnarhan/Dropbox/Breast_Cancer_Data/Data_Thresholded/ALL_IMGS/'\n", "AUG_DIR = '/Users/jnarhan/Dropbox/Breast_Cancer_Data/Data_Thresholded/AUG_DIAGNOSIS_IMGS/'\n", "meta_file = '../../Meta_Data_Files/meta_data_all.csv'\n", "PATHO_INX = 6 # Column number of pathology label in meta_file\n", "FILE_INX = 1 # Column number of File name in meta_file\n", "\n", "meta_data, _ = tqdm( bc.load_meta(meta_file, patho_idx=PATHO_INX, file_idx=FILE_INX,\n", " balanceByRemoval=False, verbose=False) )\n", "\n", "# Minor addition to reserve records in meta data for which we actually have images:\n", "meta_data = bc.clean_meta(meta_data, DATA_DIR)\n", "\n", "# Only work with benign and malignant classes:\n", "for k,v in meta_data.items():\n", " if v not in ['benign', 'malignant']:\n", " del meta_data[k]\n", "\n", "bc.pprint('Loading data')\n", "cats = bc.bcLabels(['benign', 'malignant'])\n", "\n", "# For smaller images supply tuple argument for a parameter 'imgResize':\n", "# X_data, Y_data = bc.load_data(meta_data, DATA_DIR, cats, imgResize=(150,150)) \n", "X_data, Y_data = tqdm( bc.load_data(meta_data, DATA_DIR, cats) )\n", "\n", "cls_cnts = bc.get_clsCnts(Y_data, cats)\n", "bc.pprint('Before Balancing')\n", "for k in cls_cnts:\n", " print '{0:10}: {1}'.format(k, cls_cnts[k])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Class Balancing\n", "\n", "Here - I look at a modified version of SMOTE, growing the under-represented class via synthetic augmentation, until there is a balance among the categories:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "datagen = ImageDataGenerator(rotation_range=5, width_shift_range=.01, height_shift_range=0.01,\n", " data_format='channels_first')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "---------------\n", "After Balancing\n", "---------------\n", "malignant : 948\n", "benign : 948\n" ] } ], "source": [ "X_data, Y_data = bc.balanceViaSmote(cls_cnts, meta_data, DATA_DIR, AUG_DIR, cats, \n", " datagen, X_data, Y_data, seed=SEED, verbose=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create the Training and Test Datasets" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Size of X_train: 1516\n", "Size of X_test: 380\n", "Size of Y_train: 1516\n", "Size of Y_test: 380\n", "(1516, 255, 255)\n", "(380, 255, 255)\n", "(1516, 1)\n", "(380, 1)\n" ] } ], "source": [ "X_train, X_test, Y_train, Y_test = train_test_split(X_data, Y_data,\n", " test_size=0.20, # deviation given small data set\n", " random_state=SEED,\n", " stratify=zip(Y_data)[0])\n", "\n", "print 'Size of X_train: {:>5}'.format(len(X_train))\n", "print 'Size of X_test: {:>5}'.format(len(X_test))\n", "print 'Size of Y_train: {:>5}'.format(len(Y_train))\n", "print 'Size of Y_test: {:>5}'.format(len(Y_test))\n", "\n", "print X_train.shape\n", "print X_test.shape\n", "print Y_train.shape\n", "print Y_test.shape\n", "\n", "data = [X_train, X_test, Y_train, Y_test]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h2>Support Vector Machine Model</h2>" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "X_train_svm = X_train.reshape( (X_train.shape[0], -1)) \n", "X_test_svm = X_test.reshape( (X_test.shape[0], -1))" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/jnarhan/miniconda2/envs/bc_venv/lib/python2.7/site-packages/sklearn/utils/validation.py:526: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n" ] }, { "data": { "text/plain": [ "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", " decision_function_shape=None, degree=3, gamma=0.001, kernel='rbf',\n", " max_iter=-1, probability=False, random_state=None, shrinking=True,\n", " tol=0.001, verbose=False)" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "SVM_model = SVC(gamma=0.001)\n", "SVM_model.fit( X_train_svm, Y_train)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SVM Accuracy: 66.32%\n", "SVM Error: 33.68%\n" ] } ], "source": [ "predictOutput = SVM_model.predict(X_test_svm)\n", "svm_acc = metrics.accuracy_score(y_true=Y_test, y_pred=predictOutput)\n", "\n", "print 'SVM Accuracy: {: >7.2f}%'.format(svm_acc 100)\n", "print 'SVM Error: {: >10.2f}%'.format(100 - svm_acc * 100)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Normalized confusion matrix\n", "[[ 0.68 0.32]\n", " [ 0.36 0.64]]\n" ] }, { "data": { "image/png": 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Pjs3MzDrJ3IgY2cZtZktaNSJmSloVmJPOnwHkxsPB6bwW5dXmKulwST9O3w+W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\n+JSoqY2vHMu6poefe+8rHY6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"text/plain": [ "<matplotlib.figure.Figure at 0x116168b90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "svm_matrix = skm.confusion_matrix(y_true=Y_test, y_pred=predictOutput)\n", "numBC = bc.reverseDict(cats)\n", "class_names = numBC.values()\n", "\n", "plt.figure(figsize=(8,6))\n", "bc.plot_confusion_matrix(svm_matrix, classes=class_names, normalize=True, \n", " title='SVM Normalized Confusion Matrix Using Thresholded \\n')\n", "plt.tight_layout()\n", "plt.savefig('../../figures/jn_SVM_Diagnosis_CM_Threshold_20170609.png', dpi=100)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Confusion matrix, without normalization\n", "[[130 60]\n", " [ 68 122]]\n" ] }, { "data": { "image/png": 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MzMrMmauZmVk5Vf+tONVdezMzsxxy5mpmZvnSMHB/FXNwNTOz/KnyZmEHVzMzy5nqv+bq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\nMqBPV0QWaN//YCmLloj+vbK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"text/plain": [ "<matplotlib.figure.Figure at 0x14a4b5b50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8,6))\n", "bc.plot_confusion_matrix(svm_matrix, classes=class_names, normalize=False, \n", " title='SVM Normalized Confusion Matrix Using Thresholded \\n')\n", "plt.tight_layout()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'Accuracy': 66.32,\n", " 'F1': 0.66,\n", " 'NPV': 65.66,\n", " 'PPV': 67.03,\n", " 'Sensitivity': 64.21,\n", " 'Specificity': 68.42}" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bc.cat_stats(svm_matrix)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h2>CNN Modelling Using VGG16 in Transfer Learning</h2>" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def VGG_Prep(img_data):\n", " \"\"\"\n", " :param img_data: training or test images of shape [#images, height, width]\n", " :return: the array transformed to the correct shape for the VGG network\n", " shape = [#images, height, width, 3] transforms to rgb and reshapes\n", " \"\"\"\n", " images = np.zeros([len(img_data), img_data.shape[1], img_data.shape[2], 3])\n", " for i in range(0, len(img_data)):\n", " im = (img_data[i] * 255) # Original imagenet images were not rescaled\n", " im = color.gray2rgb(im)\n", " images[i] = im\n", " return(images)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def vgg16_bottleneck(data, modelPath, fn_train_feats, fn_train_lbls, fn_test_feats, fn_test_lbls):\n", " # Loading data\n", " X_train, X_test, Y_train, Y_test = data\n", " \n", " print('Preparing the Training Data for the VGG_16 Model.')\n", " X_train = VGG_Prep(X_train)\n", " print('Preparing the Test Data for the VGG_16 Model')\n", " X_test = VGG_Prep(X_test)\n", " \n", " print('Loading the VGG_16 Model')\n", " # \"model\" excludes top layer of VGG16:\n", " model = applications.VGG16(include_top=False, weights='imagenet') \n", " \n", " # Generating the bottleneck features for the training data\n", " print('Evaluating the VGG_16 Model on the Training Data')\n", " bottleneck_features_train = model.predict(X_train)\n", " \n", " # Saving the bottleneck features for the training data\n", " featuresTrain = os.path.join(modelPath, fn_train_feats)\n", " labelsTrain = os.path.join(modelPath, fn_train_lbls)\n", " print('Saving the Training Data Bottleneck Features.')\n", " np.save(open(featuresTrain, 'wb'), bottleneck_features_train)\n", " np.save(open(labelsTrain, 'wb'), Y_train)\n", "\n", " # Generating the bottleneck features for the test data\n", " print('Evaluating the VGG_16 Model on the Test Data')\n", " bottleneck_features_test = model.predict(X_test)\n", " \n", " # Saving the bottleneck features for the test data\n", " featuresTest = os.path.join(modelPath, fn_test_feats)\n", " labelsTest = os.path.join(modelPath, fn_test_lbls)\n", " print('Saving the Test Data Bottleneck Feaures.')\n", " np.save(open(featuresTest, 'wb'), bottleneck_features_test)\n", " np.save(open(labelsTest, 'wb'), Y_test)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Preparing the Training Data for the VGG_16 Model.\n", "Preparing the Test Data for the VGG_16 Model\n", "Loading the VGG_16 Model\n", "Evaluating the VGG_16 Model on the Training Data\n", "Saving the Training Data Bottleneck Features.\n", "Evaluating the VGG_16 Model on the Test Data\n", "Saving the Test Data Bottleneck Feaures.\n" ] } ], "source": [ "# Locations for the bottleneck and labels files that we need\n", "train_bottleneck = '2Class_Lesions_VGG16_bottleneck_features_train_threshold.npy'\n", "train_labels = '2Class_Lesions_VGG16_labels_train_threshold.npy'\n", "test_bottleneck = '2Class_Lesions_VGG16_bottleneck_features_test_threshold.npy'\n", "test_labels = '2Class_Lesions_VGG16_labels_test_threshold.npy'\n", "modelPath = os.getcwd()\n", "\n", "top_model_weights_path = './weights/'\n", "\n", "np.random.seed(SEED)\n", "vgg16_bottleneck(data, modelPath, train_bottleneck, train_labels, test_bottleneck, test_labels)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def train_top_model(train_feats, train_lab, test_feats, test_lab, model_path, model_save, epoch = 50, batch = 64):\n", " start_time = time.time()\n", " \n", " train_bottleneck = os.path.join(model_path, train_feats)\n", " train_labels = os.path.join(model_path, train_lab)\n", " test_bottleneck = os.path.join(model_path, test_feats)\n", " test_labels = os.path.join(model_path, test_lab)\n", " \n", " history = bc.LossHistory()\n", " \n", " X_train = np.load(train_bottleneck)\n", " Y_train = np.load(train_labels)\n", " Y_train = np_utils.to_categorical(Y_train, num_classes=2)\n", " \n", " X_test = np.load(test_bottleneck)\n", " Y_test = np.load(test_labels)\n", " Y_test = np_utils.to_categorical(Y_test, num_classes=2)\n", "\n", " model = Sequential()\n", " model.add(Flatten(input_shape=X_train.shape[1:]))\n", " model.add( Dropout(0.7))\n", " \n", " model.add( Dense(256, activation='relu', kernel_constraint= maxnorm(3.)) )\n", " model.add( Dropout(0.5))\n", " \n", " # Softmax for probabilities for each class at the output layer\n", " model.add( Dense(2, activation='softmax'))\n", " \n", " model.compile(optimizer='rmsprop', # adadelta\n", " loss='binary_crossentropy', \n", " metrics=['accuracy'])\n", "\n", " model.fit(X_train, Y_train,\n", " epochs=epoch,\n", " batch_size=batch,\n", " callbacks=[history],\n", " validation_data=(X_test, Y_test),\n", " verbose=2)\n", " \n", " print \"Training duration : {0}\".format(time.time() - start_time)\n", " score = model.evaluate(X_test, Y_test, batch_size=16, verbose=2)\n", "\n", " print \"Network's test score [loss, accuracy]: {0}\".format(score)\n", " print 'CNN Error: {:.2f}%'.format(100 - score[1] * 100)\n", " \n", " bc.save_model(model_save, model, \"jn_VGG16_Diagnosis_top_weights_threshold.h5\")\n", " \n", " return model, history.losses, history.acc, score" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train on 1516 samples, validate on 380 samples\n", "Epoch 1/100\n", "4s - loss: 6.5469 - acc: 0.5660 - val_loss: 5.6614 - val_acc: 0.6342\n", "Epoch 2/100\n", "4s - loss: 6.0066 - acc: 0.6022 - val_loss: 5.6908 - val_acc: 0.6158\n", "Epoch 3/100\n", "4s - loss: 5.6912 - acc: 0.6266 - val_loss: 5.6555 - val_acc: 0.6289\n", "Epoch 4/100\n", "4s - loss: 5.6275 - acc: 0.6392 - val_loss: 6.4929 - val_acc: 0.5816\n", "Epoch 5/100\n", "4s - loss: 5.7286 - acc: 0.6313 - val_loss: 6.1990 - val_acc: 0.5974\n", "Epoch 6/100\n", "4s - loss: 5.3939 - acc: 0.6524 - val_loss: 5.4737 - val_acc: 0.6421\n", "Epoch 7/100\n", "4s - loss: 5.1540 - acc: 0.6669 - val_loss: 5.3344 - val_acc: 0.6579\n", "Epoch 8/100\n", "4s - loss: 4.7835 - acc: 0.6893 - val_loss: 5.5056 - val_acc: 0.6474\n", "Epoch 9/100\n", "4s - loss: 5.2417 - acc: 0.6623 - val_loss: 4.9187 - val_acc: 0.6816\n", "Epoch 10/100\n", "4s - loss: 4.9289 - acc: 0.6801 - val_loss: 5.5650 - val_acc: 0.6474\n", "Epoch 11/100\n", "4s - loss: 5.3987 - acc: 0.6544 - val_loss: 5.9577 - val_acc: 0.6237\n", "Epoch 12/100\n", "4s - loss: 5.2896 - acc: 0.6609 - val_loss: 5.3941 - val_acc: 0.6553\n", "Epoch 13/100\n", "4s - loss: 5.1168 - acc: 0.6748 - val_loss: 5.4289 - val_acc: 0.6500\n", "Epoch 14/100\n", "4s - loss: 4.9755 - acc: 0.6814 - val_loss: 5.0208 - val_acc: 0.6711\n", "Epoch 15/100\n", "4s - loss: 5.0132 - acc: 0.6774 - val_loss: 5.0259 - val_acc: 0.6737\n", "Epoch 16/100\n", "4s - loss: 4.8202 - acc: 0.6880 - val_loss: 5.4721 - val_acc: 0.6474\n", "Epoch 17/100\n", "4s - loss: 5.2363 - acc: 0.6629 - val_loss: 5.3822 - val_acc: 0.6605\n", "Epoch 18/100\n", "4s - loss: 5.0052 - acc: 0.6788 - val_loss: 5.1784 - val_acc: 0.6658\n", "Epoch 19/100\n", "4s - loss: 4.8893 - acc: 0.6860 - val_loss: 5.2198 - val_acc: 0.6632\n", "Epoch 20/100\n", "4s - loss: 4.5577 - acc: 0.7104 - val_loss: 5.0272 - val_acc: 0.6842\n", "Epoch 21/100\n", "4s - loss: 5.0399 - acc: 0.6755 - val_loss: 5.2006 - val_acc: 0.6684\n", "Epoch 22/100\n", "4s - loss: 4.8440 - acc: 0.6906 - val_loss: 5.2305 - val_acc: 0.6658\n", "Epoch 23/100\n", "4s - loss: 4.5396 - acc: 0.7111 - val_loss: 5.0844 - val_acc: 0.6789\n", "Epoch 24/100\n", "4s - loss: 5.0124 - acc: 0.6814 - val_loss: 5.1196 - val_acc: 0.6711\n", "Epoch 25/100\n", "4s - loss: 4.9945 - acc: 0.6840 - val_loss: 5.2788 - val_acc: 0.6658\n", "Epoch 26/100\n", "4s - loss: 5.0184 - acc: 0.6781 - val_loss: 5.2492 - val_acc: 0.6632\n", "Epoch 27/100\n", "4s - loss: 4.9916 - acc: 0.6821 - val_loss: 4.9630 - val_acc: 0.6842\n", "Epoch 28/100\n", "4s - loss: 5.0391 - acc: 0.6781 - val_loss: 5.0281 - val_acc: 0.6789\n", "Epoch 29/100\n", "4s - loss: 5.2092 - acc: 0.6682 - val_loss: 5.3984 - val_acc: 0.6553\n", "Epoch 30/100\n", "4s - loss: 4.5759 - acc: 0.7038 - val_loss: 5.1468 - val_acc: 0.6737\n", "Epoch 31/100\n", "4s - loss: 4.7959 - acc: 0.6959 - val_loss: 5.3088 - val_acc: 0.6605\n", "Epoch 32/100\n", "4s - loss: 4.5480 - acc: 0.7124 - val_loss: 5.3218 - val_acc: 0.6658\n", "Epoch 33/100\n", "5s - loss: 4.7692 - acc: 0.6966 - val_loss: 5.1056 - val_acc: 0.6789\n", "Epoch 34/100\n", "5s - loss: 4.5239 - acc: 0.7111 - val_loss: 5.0627 - val_acc: 0.6737\n", "Epoch 35/100\n", "5s - loss: 4.6604 - acc: 0.7012 - val_loss: 4.7975 - val_acc: 0.6974\n", "Epoch 36/100\n", "5s - loss: 4.7272 - acc: 0.6999 - val_loss: 4.9157 - val_acc: 0.6842\n", "Epoch 37/100\n", "5s - loss: 4.6800 - acc: 0.7038 - val_loss: 5.3457 - val_acc: 0.6579\n", "Epoch 38/100\n", "5s - loss: 4.5138 - acc: 0.7157 - val_loss: 5.6073 - val_acc: 0.6474\n", "Epoch 39/100\n", "5s - loss: 4.7464 - acc: 0.6972 - val_loss: 5.7346 - val_acc: 0.6395\n", "Epoch 40/100\n", "5s - loss: 4.6725 - acc: 0.7045 - val_loss: 5.4678 - val_acc: 0.6579\n", "Epoch 41/100\n", "5s - loss: 4.4495 - acc: 0.7197 - val_loss: 5.2702 - val_acc: 0.6658\n", "Epoch 42/100\n", "5s - loss: 4.4709 - acc: 0.7144 - val_loss: 5.5002 - val_acc: 0.6526\n", "Epoch 43/100\n", "5s - loss: 4.6214 - acc: 0.7065 - val_loss: 5.1930 - val_acc: 0.6737\n", "Epoch 44/100\n", "5s - loss: 4.9412 - acc: 0.6887 - val_loss: 5.3257 - val_acc: 0.6632\n", "Epoch 45/100\n", "5s - loss: 4.6813 - acc: 0.7032 - val_loss: 5.2463 - val_acc: 0.6684\n", "Epoch 46/100\n", "5s - loss: 4.6106 - acc: 0.7065 - val_loss: 5.0304 - val_acc: 0.6789\n", "Epoch 47/100\n", "5s - loss: 4.4477 - acc: 0.7157 - val_loss: 5.2086 - val_acc: 0.6658\n", "Epoch 48/100\n", "5s - loss: 4.5315 - acc: 0.7117 - val_loss: 5.0881 - val_acc: 0.6789\n", "Epoch 49/100\n", "5s - loss: 4.6902 - acc: 0.7018 - val_loss: 4.9868 - val_acc: 0.6816\n", "Epoch 50/100\n", "5s - loss: 4.3165 - acc: 0.7236 - val_loss: 5.2611 - val_acc: 0.6684\n", "Epoch 51/100\n", "6s - loss: 4.3830 - acc: 0.7216 - val_loss: 5.2189 - val_acc: 0.6658\n", "Epoch 52/100\n", "6s - loss: 4.5965 - acc: 0.7084 - val_loss: 5.2869 - val_acc: 0.6684\n", "Epoch 53/100\n", "6s - loss: 4.3635 - acc: 0.7249 - val_loss: 5.2562 - val_acc: 0.6684\n", "Epoch 54/100\n", "6s - loss: 4.3947 - acc: 0.7190 - val_loss: 4.8756 - val_acc: 0.6895\n", "Epoch 55/100\n", "6s - loss: 4.4371 - acc: 0.7170 - val_loss: 4.9916 - val_acc: 0.6789\n", "Epoch 56/100\n", "6s - loss: 4.4655 - acc: 0.7177 - val_loss: 4.8863 - val_acc: 0.6895\n", "Epoch 57/100\n", "6s - loss: 4.4223 - acc: 0.7177 - val_loss: 4.8820 - val_acc: 0.6895\n", "Epoch 58/100\n", "6s - loss: 4.3193 - acc: 0.7269 - val_loss: 5.3663 - val_acc: 0.6632\n", "Epoch 59/100\n", "6s - loss: 4.6521 - acc: 0.7051 - val_loss: 5.2130 - val_acc: 0.6684\n", "Epoch 60/100\n", "6s - loss: 4.7777 - acc: 0.6979 - val_loss: 5.0796 - val_acc: 0.6816\n", "Epoch 61/100\n", "6s - loss: 4.6053 - acc: 0.7098 - val_loss: 5.4579 - val_acc: 0.6553\n", "Epoch 62/100\n", "6s - loss: 4.6242 - acc: 0.7071 - val_loss: 5.2278 - val_acc: 0.6711\n", "Epoch 63/100\n", "6s - loss: 4.2114 - acc: 0.7335 - val_loss: 4.8966 - val_acc: 0.6868\n", "Epoch 64/100\n", "6s - loss: 4.2805 - acc: 0.7289 - val_loss: 4.8276 - val_acc: 0.6895\n", "Epoch 65/100\n", "6s - loss: 4.3478 - acc: 0.7256 - val_loss: 5.4464 - val_acc: 0.6500\n", "Epoch 66/100\n", "6s - loss: 4.4381 - acc: 0.7197 - val_loss: 5.0427 - val_acc: 0.6737\n", "Epoch 67/100\n", "6s - loss: 4.4075 - acc: 0.7203 - val_loss: 4.8604 - val_acc: 0.6921\n", "Epoch 68/100\n", "6s - loss: 4.3979 - acc: 0.7203 - val_loss: 5.1457 - val_acc: 0.6711\n", "Epoch 69/100\n", "6s - loss: 4.5156 - acc: 0.7137 - val_loss: 5.0389 - val_acc: 0.6816\n", "Epoch 70/100\n", "6s - loss: 4.6944 - acc: 0.7032 - val_loss: 4.9084 - val_acc: 0.6921\n", "Epoch 71/100\n", "6s - loss: 4.1835 - acc: 0.7348 - val_loss: 5.0378 - val_acc: 0.6789\n", "Epoch 72/100\n", "6s - loss: 4.1175 - acc: 0.7368 - val_loss: 4.8405 - val_acc: 0.6947\n", "Epoch 73/100\n", "6s - loss: 4.4767 - acc: 0.7177 - val_loss: 4.7564 - val_acc: 0.6974\n", "Epoch 74/100\n", "6s - loss: 4.4478 - acc: 0.7177 - val_loss: 4.9730 - val_acc: 0.6842\n", "Epoch 75/100\n", "6s - loss: 4.4775 - acc: 0.7170 - val_loss: 5.5075 - val_acc: 0.6553\n", "Epoch 76/100\n", "6s - loss: 4.7588 - acc: 0.6985 - val_loss: 5.3006 - val_acc: 0.6658\n", "Epoch 77/100\n", "6s - loss: 4.3499 - acc: 0.7230 - val_loss: 4.9313 - val_acc: 0.6895\n", "Epoch 78/100\n", "6s - loss: 4.3849 - acc: 0.7249 - val_loss: 5.1165 - val_acc: 0.6789\n", "Epoch 79/100\n", "6s - loss: 4.4016 - acc: 0.7223 - val_loss: 5.0017 - val_acc: 0.6789\n", "Epoch 80/100\n", "6s - loss: 4.1910 - acc: 0.7361 - val_loss: 4.9296 - val_acc: 0.6842\n", "Epoch 81/100\n", "6s - loss: 4.0718 - acc: 0.7427 - val_loss: 4.9459 - val_acc: 0.6895\n", "Epoch 82/100\n", "6s - loss: 4.3695 - acc: 0.7236 - val_loss: 5.0753 - val_acc: 0.6789\n", "Epoch 83/100\n", "6s - loss: 4.4822 - acc: 0.7157 - val_loss: 5.3169 - val_acc: 0.6658\n", "Epoch 84/100\n", "6s - loss: 4.1877 - acc: 0.7355 - val_loss: 4.9930 - val_acc: 0.6842\n", "Epoch 85/100\n", "6s - loss: 3.9650 - acc: 0.7500 - val_loss: 5.1749 - val_acc: 0.6737\n", "Epoch 86/100\n", "6s - loss: 4.1216 - acc: 0.7381 - val_loss: 5.0517 - val_acc: 0.6789\n", "Epoch 87/100\n", "6s - loss: 4.0317 - acc: 0.7460 - val_loss: 4.9654 - val_acc: 0.6895\n", "Epoch 88/100\n", "6s - loss: 4.1285 - acc: 0.7375 - val_loss: 4.9228 - val_acc: 0.6842\n", "Epoch 89/100\n", "6s - loss: 4.4787 - acc: 0.7170 - val_loss: 5.4681 - val_acc: 0.6553\n", "Epoch 90/100\n", "6s - loss: 4.3391 - acc: 0.7256 - val_loss: 5.3659 - val_acc: 0.6605\n", "Epoch 91/100\n", "6s - loss: 3.9544 - acc: 0.7480 - val_loss: 5.5113 - val_acc: 0.6447\n", "Epoch 92/100\n", "6s - loss: 4.0209 - acc: 0.7447 - val_loss: 5.0409 - val_acc: 0.6816\n", "Epoch 93/100\n", "6s - loss: 4.2773 - acc: 0.7296 - val_loss: 4.9986 - val_acc: 0.6842\n", "Epoch 94/100\n", "6s - loss: 4.2028 - acc: 0.7355 - val_loss: 5.0763 - val_acc: 0.6737\n", "Epoch 95/100\n", "6s - loss: 4.0720 - acc: 0.7421 - val_loss: 5.0638 - val_acc: 0.6816\n", "Epoch 96/100\n", "6s - loss: 3.9683 - acc: 0.7493 - val_loss: 5.3062 - val_acc: 0.6632\n", "Epoch 97/100\n", "6s - loss: 4.3139 - acc: 0.7243 - val_loss: 5.0445 - val_acc: 0.6816\n", "Epoch 98/100\n", "6s - loss: 3.9886 - acc: 0.7480 - val_loss: 5.0594 - val_acc: 0.6842\n", "Epoch 99/100\n", "6s - loss: 4.1417 - acc: 0.7375 - val_loss: 5.1458 - val_acc: 0.6763\n", "Epoch 100/100\n", "6s - loss: 4.0623 - acc: 0.7401 - val_loss: 5.0523 - val_acc: 0.6816\n", "Training duration : 549.902695894\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Network's test score [loss, accuracy]: [5.0522738255952531, 0.68157894705471245]\n", "CNN Error: 31.84%\n", "Model and Weights Saved to Disk\n" ] }, { "data": { "text/plain": [ "<matplotlib.figure.Figure at 0x117a7ce50>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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lO2/d/jA1Zbo+WSE0pIUsd7QN4nU5WFI7dlPyfNQaQTY9+Dz6jyiU/gef7pA5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\nlBWBCjJFWQTuunot5wZHs0Xwc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"text/plain": [ "<matplotlib.figure.Figure at 0x1163a8890>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "np.random.seed(SEED)\n", "(trans_model, loss_cnn, acc_cnn, test_score_cnn) = train_top_model(train_feats=train_bottleneck,\n", " train_lab=train_labels, \n", " test_feats=test_bottleneck, \n", " test_lab=test_labels,\n", " model_path=modelPath, \n", " model_save=top_model_weights_path,\n", " epoch=100)\n", "plt.figure(figsize=(10,10))\n", "bc.plot_losses(loss_cnn, acc_cnn)\n", "plt.savefig('../../figures/epoch_figures/jn_Transfer_Diagnosis_Threshold_20170609.png', dpi=100)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Transfer Learning CNN Accuracy: 68.16%\n", "Transfer Learning CNN Error: 31.84%\n", "Normalized confusion matrix\n", "[[ 0.77 0.23]\n", " [ 0.41 0.59]]\n" ] }, { "data": { "image/png": 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LWywzM2vL2kmNfuVBncFV0jXAl4BvpoMWA38uZqHMzKxtUxNeeVCfzHW3iPgW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\np85fujqLtbbtuQ/n8tyHcys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"text/plain": [ "<matplotlib.figure.Figure at 0x11a4653d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print 'Transfer Learning CNN Accuracy: {: >7.2f}%'.format(test_score_cnn[1] * 100)\n", "print 'Transfer Learning CNN Error: {: >10.2f}%'.format(100 - test_score_cnn[1] * 100)\n", "\n", "predictOutput = bc.predict(trans_model, np.load(test_bottleneck))\n", "trans_matrix = skm.confusion_matrix(y_true=Y_test, y_pred=predictOutput)\n", "\n", "plt.figure(figsize=(8,6))\n", "bc.plot_confusion_matrix(trans_matrix, classes=class_names, normalize=True,\n", " title='Transfer CNN Normalized Confusion Matrix Using Thresholded \\n')\n", "plt.tight_layout()\n", "plt.savefig('../../figures/TMP_jn_Transfer_Diagnosis_CM_Threshold_20170609.png', dpi=100)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Confusion matrix, without normalization\n", "[[147 43]\n", " [ 78 112]]\n" ] }, { "data": { "image/png": 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sz5y5mplZ5hR54urgamZm2SL5VhwzM7O8a1bcsdXB1czMsqfYM1d3aDIzM8sz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\nq4GHRcS+kn4FDI6IH1ZUAEl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"text/plain": [ "<matplotlib.figure.Figure at 0x11a3ab550>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8,6))\n", "bc.plot_confusion_matrix(trans_matrix, classes=class_names, normalize=False,\n", " title='Transfer CNN Normalized Confusion Matrix Using Thresholded \\n')\n", "plt.tight_layout()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'Accuracy': 68.16,\n", " 'F1': 0.65,\n", " 'NPV': 65.33,\n", " 'PPV': 72.26,\n", " 'Sensitivity': 58.95,\n", " 'Specificity': 77.37}" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bc.cat_stats(trans_matrix)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "<h2>Core CNN Modelling</h2>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Prep and package the data for Keras processing:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Prep data for NNs ...\n", "Data Prepped for Neural Nets.\n", "(1516, 1, 255, 255)\n", "(380, 1, 255, 255)\n", "(1516, 2)\n", "(380, 2)\n" ] } ], "source": [ "data = [X_train, X_test, Y_train, Y_test]\n", "X_train, X_test, Y_train, Y_test = bc.prep_data(data, cats)\n", "data = [X_train, X_test, Y_train, Y_test]\n", "\n", "print X_train.shape\n", "print X_test.shape\n", "print Y_train.shape\n", "print Y_test.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Heavy Regularization" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def diff_model_v7_reg(numClasses, input_shape=(3, 150,150), add_noise=False, noise=0.01, verbose=False):\n", " model = Sequential()\n", " if (add_noise):\n", " model.add( GaussianNoise(noise, input_shape=input_shape))\n", " model.add( Convolution2D(filters=16, \n", " kernel_size=(5,5), \n", " data_format='channels_first',\n", " padding='same',\n", " activation='relu'))\n", " else:\n", " model.add( Convolution2D(filters=16, \n", " kernel_size=(5,5), \n", " data_format='channels_first',\n", " padding='same',\n", " activation='relu',\n", " input_shape=input_shape))\n", " # model.add( Dropout(0.7))\n", " model.add( Dropout(0.5))\n", " \n", " model.add( Convolution2D(filters=32, kernel_size=(3,3), \n", " data_format='channels_first', padding='same', activation='relu'))\n", " model.add( MaxPooling2D(pool_size= (2,2), data_format='channels_first'))\n", " # model.add( Dropout(0.4))\n", " model.add( Dropout(0.25))\n", " model.add( Convolution2D(filters=32, kernel_size=(3,3), \n", " data_format='channels_first', activation='relu'))\n", " \n", " model.add( Convolution2D(filters=64, kernel_size=(3,3), \n", " data_format='channels_first', padding='same', activation='relu',\n", " kernel_regularizer=regularizers.l2(0.01)))\n", " model.add( MaxPooling2D(pool_size= (2,2), data_format='channels_first'))\n", " model.add( Convolution2D(filters=64, kernel_size=(3,3), \n", " data_format='channels_first', activation='relu',\n", " kernel_regularizer=regularizers.l2(0.01)))\n", " #model.add( Dropout(0.4))\n", " model.add( Dropout(0.25))\n", " \n", " model.add( Convolution2D(filters=128, kernel_size=(3,3), \n", " data_format='channels_first', padding='same', activation='relu',\n", " kernel_regularizer=regularizers.l2(0.01)))\n", " model.add( MaxPooling2D(pool_size= (2,2), data_format='channels_first'))\n", " \n", " model.add( Convolution2D(filters=128, kernel_size=(3,3), \n", " data_format='channels_first', activation='relu',\n", " kernel_regularizer=regularizers.l2(0.01)))\n", " #model.add(Dropout(0.4))\n", " model.add( Dropout(0.25))\n", " \n", " model.add( Flatten())\n", " \n", " model.add( Dense(128, activation='relu', kernel_constraint= maxnorm(3.)) )\n", " # model.add( Dropout(0.4))\n", " model.add( Dropout(0.25))\n", " \n", " model.add( Dense(64, activation='relu', kernel_constraint= maxnorm(3.)) )\n", " # model.add( Dropout(0.4))\n", " model.add( Dropout(0.25))\n", " \n", " # Softmax for probabilities for each class at the output layer\n", " model.add( Dense(numClasses, activation='softmax'))\n", " \n", " if verbose:\n", " print( model.summary() )\n", " \n", " model.compile(loss='binary_crossentropy',\n", " optimizer='rmsprop',\n", " metrics=['accuracy'])\n", " return model" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "gaussian_noise_2 (GaussianNo (None, 1, 255, 255) 0 \n", "_________________________________________________________________\n", "conv2d_8 (Conv2D) (None, 16, 255, 255) 416 \n", "_________________________________________________________________\n", "dropout_9 (Dropout) (None, 16, 255, 255) 0 \n", "_________________________________________________________________\n", "conv2d_9 (Conv2D) (None, 32, 255, 255) 4640 \n", "_________________________________________________________________\n", "max_pooling2d_4 (MaxPooling2 (None, 32, 127, 127) 0 \n", "_________________________________________________________________\n", "dropout_10 (Dropout) (None, 32, 127, 127) 0 \n", "_________________________________________________________________\n", "conv2d_10 (Conv2D) (None, 32, 125, 125) 9248 \n", "_________________________________________________________________\n", "conv2d_11 (Conv2D) (None, 64, 125, 125) 18496 \n", "_________________________________________________________________\n", "max_pooling2d_5 (MaxPooling2 (None, 64, 62, 62) 0 \n", "_________________________________________________________________\n", "conv2d_12 (Conv2D) (None, 64, 60, 60) 36928 \n", "_________________________________________________________________\n", "dropout_11 (Dropout) (None, 64, 60, 60) 0 \n", "_________________________________________________________________\n", "conv2d_13 (Conv2D) (None, 128, 60, 60) 73856 \n", "_________________________________________________________________\n", "max_pooling2d_6 (MaxPooling2 (None, 128, 30, 30) 0 \n", "_________________________________________________________________\n", "conv2d_14 (Conv2D) (None, 128, 28, 28) 147584 \n", "_________________________________________________________________\n", "dropout_12 (Dropout) (None, 128, 28, 28) 0 \n", "_________________________________________________________________\n", "flatten_3 (Flatten) (None, 100352) 0 \n", "_________________________________________________________________\n", "dense_6 (Dense) (None, 128) 12845184 \n", "_________________________________________________________________\n", "dropout_13 (Dropout) (None, 128) 0 \n", "_________________________________________________________________\n", "dense_7 (Dense) (None, 64) 8256 \n", "_________________________________________________________________\n", "dropout_14 (Dropout) (None, 64) 0 \n", "_________________________________________________________________\n", "dense_8 (Dense) (None, 2) 130 \n", "=================================================================\n", "Total params: 13,144,738\n", "Trainable params: 13,144,738\n", "Non-trainable params: 0\n", "_________________________________________________________________\n", "None\n" ] } ], "source": [ "diff_model7_noise_reg = diff_model_v7_reg(len(cats),\n", " input_shape=(X_train.shape[1], X_train.shape[2], X_train.shape[3]),\n", " add_noise=True, verbose=True)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training model...\n", "Train on 1516 samples, validate on 380 samples\n", "Epoch 1/50\n", "462s - loss: 2.5185 - acc: 0.5165 - val_loss: 1.6954 - val_acc: 0.5000\n", "Epoch 2/50\n", "440s - loss: 1.3317 - acc: 0.5343 - val_loss: 1.0157 - val_acc: 0.5737\n", "Epoch 3/50\n", "429s - loss: 0.8887 - acc: 0.5594 - val_loss: 0.7735 - val_acc: 0.5737\n", "Epoch 4/50\n", "431s - loss: 0.7237 - acc: 0.6214 - val_loss: 0.7004 - val_acc: 0.6211\n", "Epoch 5/50\n", "433s - loss: 0.6887 - acc: 0.6135 - val_loss: 0.6824 - val_acc: 0.5921\n", "Epoch 6/50\n", "433s - loss: 0.6751 - acc: 0.5996 - val_loss: 0.6897 - val_acc: 0.6053\n", "Epoch 7/50\n", "447s - loss: 0.6721 - acc: 0.6141 - val_loss: 0.6731 - val_acc: 0.6158\n", "Epoch 8/50\n", "433s - loss: 0.6587 - acc: 0.6141 - val_loss: 0.6705 - val_acc: 0.6158\n", "Epoch 9/50\n", "433s - loss: 0.6546 - acc: 0.6293 - val_loss: 0.6658 - val_acc: 0.6184\n", "Epoch 10/50\n", "431s - loss: 0.6442 - acc: 0.6234 - val_loss: 0.6630 - val_acc: 0.6079\n", "Epoch 11/50\n", "430s - loss: 0.6440 - acc: 0.6398 - val_loss: 0.6743 - val_acc: 0.5947\n", "Epoch 12/50\n", "430s - loss: 0.6453 - acc: 0.6266 - val_loss: 0.6606 - val_acc: 0.6263\n", "Epoch 13/50\n", "448s - loss: 0.6325 - acc: 0.6379 - val_loss: 0.7320 - val_acc: 0.5184\n", "Epoch 14/50\n", "446s - loss: 0.6388 - acc: 0.6365 - val_loss: 0.6646 - val_acc: 0.6158\n", "Epoch 00013: early stopping\n", "Training duration : 6141.59421992\n", "Network's test score [loss, accuracy]: [0.66461233841745482, 0.61578947399791917]\n", "CNN Error: 38.42%\n" ] }, { "data": { "text/plain": [ "<matplotlib.figure.Figure at 0x11d69e550>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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OCIQMt2fNVoQaY2q5wdWCOseeqPP+PuC+OseabPvMEYe2fkWxxtC9/1hISHVy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\nb7A9ikXkIhFpFJE33ce33eOVIvJnEdkqIltE5OZYx7rnwGF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"text/plain": [ "<matplotlib.figure.Figure at 0x11d6219d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "np.random.seed(SEED)\n", "\n", "(cnn_model, loss_cnn, acc_cnn, test_score_cnn) = bc.run_network(model=diff_model7_noise_reg, earlyStop=True,\n", " data=data, \n", " epochs=50, batch=64)\n", "plt.figure(figsize=(10,10))\n", "bc.plot_losses(loss_cnn, acc_cnn)\n", "plt.savefig('../../figures/epoch_figures/jn_Core_CNN_Diagnosis_Threshold_20170609.png', dpi=100)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model and Weights Saved to Disk\n" ] } ], "source": [ "bc.save_model(dir_path='./weights/', model=cnn_model, name='jn_Core_CNN_Diagnosis_Threshold_20170609')" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Core CNN Accuracy: 61.58%\n", "Core CNN Error: 38.42%\n", "Normalized confusion matrix\n", "[[ 0.67 0.33]\n", " [ 0.44 0.56]]\n" ] }, { "data": { "image/png": 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EG7UtY2Zm1mj1vAB/ntX3Ck0VwGKypmFfj9jMzEqqzGNrvUYL/wy4gezk2bXJ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\n9J/c8sZKSsOVT4zlyifGNr6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"text/plain": [ "<matplotlib.figure.Figure at 0x1200dddd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print 'Core CNN Accuracy: {: >7.2f}%'.format(test_score_cnn[1] * 100)\n", "print 'Core CNN Error: {: >10.2f}%'.format(100 - test_score_cnn[1] * 100)\n", "\n", "predictOutput = bc.predict(cnn_model, X_test)\n", "\n", "cnn_matrix = skm.confusion_matrix(y_true=[val.argmax() for val in Y_test], y_pred=predictOutput)\n", "\n", "plt.figure(figsize=(8,6))\n", "bc.plot_confusion_matrix(cnn_matrix, classes=class_names, normalize=True,\n", " title='CNN Normalized Confusion Matrix Using Thresholded \\n')\n", "plt.tight_layout()\n", "plt.savefig('../../figures/jn_Core_CNN_Diagnosis_Threshold_201706090.png', dpi=100)" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Confusion matrix, without normalization\n", "[[127 63]\n", " [ 83 107]]\n" ] }, { "data": { "image/png": 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IN0vPqxoHNEnahmTC4h2AlhXlEbFtAetlZmaNWInH1loNaLoduI0kSz8IuB+4\nr4B1MjMzK2m1Ca6tI2IkQER8GBEXkQRZMzOzgmgM17kuTyfu/1DSt4DJQLvCVsvMzBqzjMTIOqtN\ncP0B0Ab4Lknfawfg1EJWyszMGi+hTX9AU0S8nD5dyNobppuZmRWGNuHMVdLDpPdw3ZCI+FpBamRm\nZo1eVvpO66q6zPWGBqtFidp6qx789s6Lil0Na2T2vuJfxa6CNTLjpy4sdhVKTnWTSDzbkBUxMzOr\nUOr3Nq3t/VzNzMwahNi0m4XNzMyKotRvOVfr4CqpRUQsL2RlzMzMoPSDa43N2pL2kPQ2MCFd3lnS\n9QWvmZmZWYmqTZ/xdcAhwGyAiHgT+FIhK2VmZo2X1DimP2wSEZ9UqnB5gepjZmZW8s3CtQmun0na\nAwhJZcB3gA8KWy0zM2vMMpKA1lltgutZJE3DfYDpwD/TMjMzs7wTjeBm6RExAxjRAHUxMzPbJNQY\nXCXdygbmGI6IMwtSIzMza/QawwxN/8x53hI4AvisMNUxMzNrBH2uEXFf7rKkvwAvFKxGZmbWqEmN\n4H6uG7AV0C3fFTEzM6tQ4rG1Vn2uc1nb59oEmAOcX8hKmZlZ47ZJX+eqZOaInYHJadHqiKjyBupm\nZmZWQ3CNiJD0j4gY1FAVMjOzxm1TuM61NqOdx0oaXPCamJmZpZL5hev2yIIqM1dJTSNiFTAYeEXS\nh8Bikh8VERG7NlAdzcysMdGm3ec6BtgVOKyB6mJmZgaAKO3oWl1wFUBEfNhAdTEzM9skVBdcu0j6\nYVUrI+K3BaiPmZk1csmApgIeX/oecEZ6qlsj4hpJmwH3AX2Bj4FjImJuXc9R3YCmMqAt0K6Kh5mZ\nWUE0Ud0f1ZE0iCSw7kFyqekhkvqRzN/wbET0B56lnvM5VJe5To2IS+tzcDMzs7pQ4Yb9bg+8HBFL\n0vM8B3wNGA4MTbe5AxgF/KSuJ6kucy3t3mQzMytJFc3C9chcO0t6NeeRexe3ccC+kjaX1Br4KrAF\n0C0ipqbbTKOe0/xWl7l+pT4HNjMzK5JZETFkQysi4j1JVwNPk1xeOhYor7RNSKrXbIRVZq4RMac+\nBzYzM6uTekwgUZvW5Ij4U0TsFhFfAOYCHwDTJfUASP+dUZ+XUOr3ozUzs01Qk/S2c3V51ERS1/Tf\nPiT9rX8FHgNOSjc5CXi0PvWvyy3nzMzMCqbQl+IAf5O0ObASODsi5km6Crhf0mnAJ8Ax9TmBg6uZ\nmWVOIedPah77AAAZ0UlEQVQIjoh9N1A2mzyONXJwNTOzjBFNSvyCFfe5mpmZ5ZkzVzMzyxSRnVvH\n1ZWDq5mZZcsmfss5MzOzoqjNJTVZ5uBqZmaZsik0C3tAk5mZWZ45czUzs8xxs7CZmVmelXhsdXA1\nM7NsEaXfZ+ngamZm2aKC3iy9QZT6jwMzM7PMceZqZmaZU9p5q4OrmZllTHLLudIOrw6uZmaWOaUd\nWh1czcwsg0o8cfWAJjMzs3xz5mpmZhmjkr8Ux8HVzMwyxZNImJmZFYAzVzMzszwr7dDq4GpmZlnj\n6Q/NzMysMmeuZmaWKR7QZGZmVgCl3izs4GpmZplT2qHVwdXMzDKoxBPXkm/WNjMzyxxnrmZmlinJ\ngKbSTl0dXM3MLHNKvVnYwdXMzDJGyJmrmZlZfpV65uoBTWZmZnnmzNXMzDLFA5rMzMzyTaXfLOzg\namZmmePgamZmlmceLWxmZpZHApqUdmz1aGEzM7N8c+ZqZmaZU+rNws5czcwsc6S6P2o+tn4g6R1J\n4yTdI6mlpM0kPSNpQvpvp/rU35mr1drWm7emz2atAFiwbBVjJ82nf9e29GjXggCWr1rNG5Pms3zV\n6vX27dK2OTv2bI+AT+YuZeLMxQA0KxNDtuhIq+ZlLF1RzqufzmPl6gCgX5c2bNmpFQG8PWUBMxet\nAKBDy6YM3qIDZRLTFy5n3NSFDfHyrYFcfNh2fGHbzsxZvIKjfz8GgPYtm3L1UYPo2bElU+Yt47wH\nx7Fw2SoO2rEbJ+3TZ82+/bu15bg/vMIH0xetc8yq9gc49fNbMnxwD1avDn711ARe+nAOANv3aMcl\nw7enRbMmvDhhNr96agKQfGYvO3wHtu/ZjvlLVvKTB99h6vxlDfHWNCqFylwl9QK+C+wQEUsl3Q+M\nAHYAno2IqySdD5wP/KSu53HmarXSsmkTturcmucnzmbUhNkI6NWhFR/OXMyoibN5buJspi9czoCu\nbTe4/0492zP6o7n8a8IsenVoSdsWZQD079KGmYtX8K8PZjFz8Qr6dW0DQNsWZfTq0JJ/T5jF6I/m\nslPP9muP1as9b05awLMfzKJNi6Z0bdu84K/fGs7jY6dx9l1j1yk75fNbMuajuQy/YTRjPprLKZ/f\nEoAn357OiD+8wog/vMJFD7/L5LnL1gus1e2/defWHDCwK0fd9DJn3/0mF3x1wJqBND89eACXPf4+\nw68fTZ/NWvO5fpsBcPjgnixctorh14/m7tGf8b39tingu9E4VQxoquujFpoCrSQ1BVoDU4DhwB3p\n+juAw+vzGhxcrdaaIMqaJL8ny5qIZavKWZVmmaRlG9KpdTMWryhnycpyImDy/GV0b98SgO7tW/LZ\n3KUAfDZ3KT1yyifPX8bqgCUry1m8opxOrZvRomkTmjZpwtylKwGYNHfpmmPZpuH1T+cxf+mqdcqG\nDujM429OBeDxN6fypQGd19vvwEHdGPnO9A0es6r9h27XhZHvzGBleTBl3jI+m7OEQb3a07ltc9q0\nKOPtyQsAeOKtaQzdrst6x/rnuzPZY+t6tR5aYXSW9GrO48yKFRExGfgN8CkwFZgfEU8D3SJiarrZ\nNKBbfSrgZmGrlWWrVjNx1mL2H9CF8oCZi5avaabdrltbtujYipWrV/Pf/81Zb9+WTZuwdGX52mOt\nTAIlQIumTdY0Iy9ftZoWTZPfe62aNWHukpVr9lm6spyWTZuwOoJlq8rXLW/m34ibus3bNmdW+nmb\ntWgFm2+gtWLYwG784N63Nmr/Lu1a8Pak+Wu2m7FwOV3btWBVeTBjwfI15dMXLKNruxYAdG3fgmnz\nk3XlESxaVk7HVs2Yt3Tt59Xqq953xZkVEUM2eOSkL3U4sBUwD3hA0gm520RESIoN7V9bmfxWkjRU\n0hPp88PS9u+GOvcukr7aUOcrFc2aiO7tW/DP8TN5+r0ZlEn07phkjO9PX8Qz42cyad4yttq8Tb3O\nU69PszUaUemDMqhXe5atLOfDtC9/Y/e3jKnHYKZaDGjaD/goImZGxErgIWAfYLqkHgDpvzPq8xIy\nGVxzRcRjEXFVA55yF8DBtZLObZuzZEU5K8qDAKYuWEan1utmD5PnLaVHhxbr7bts1WpaNStbs9yy\nWRlLV66frbZo2oQVaRa7dOVqWubs06pZGctWrWbZytW0bFqpfOX6A6hs0zJ70Qo6p9lm57bNmbN4\nxTrrDxjUlafGbbhJuLr9Zy5cTvcOa7sVurZrwYyFy5MMtv3az3K39i2ZsTDJVmcsWE739HNeJtG2\nZZmz1gJQPR41+BTYS1JrSQK+ArwHPAaclG5zEvBofepfsOAqqa+k9yXdLukDSXdL2k/Si+lQ5z3S\nx0uS3pD0X0kDNnCckyXdkD7fRtJoSW9LulzSorR8qKRRkh5Mz3l3+qYh6eeSXkmHXN+SUz5K0tWS\nxqT121dSc+BS4FhJYyUdW6j3p9QsXbmaTq2bUZZ+cru0bcGi5ato03xtoOveviWLlpevt++8JStp\n06KM1s3KkKBXh5ZMT5vcpi1YzhadkhHIW3RqxbQFyajL6QuW06tDS5oIWjcro02LMuYuWcnyVatZ\ntXo1nVolzcq9O7Vi2kKP1NzUPffBLA7duQcAh+7cg1HjZ61ZJ2DYDt0YWU1wrWr/UeNnccDArjQr\nEz07tqTP5q0ZN3kBsxatYPHycnbslQykO2Sn7jz3/qz1jrXfDl145aO5eX+9jV0yoEl1flQnIl4G\nHgReB94miYO3AFcB+0uaQJLd1iupK3Sfaz/gaOBU4BXg68DngcOAnwInAvtGxCpJ+wG/BI6s5njX\nAtdGxD2SvlVp3WBgIMmorxeBzwEvADdExKUAkv4CHAI8nu7TNCL2SJuBL46I/ST9HBgSEedsqAJp\nx/iZAF169Kr9O1Hi5i1dydT5y/lCv84Ewfylq/hkzhJ23aJjMvI3HXj0VjoApEXTJuzSuwMvfzx3\nzaU0e23VCQGfzl3KwuXJgJUJMxcxpE9H+nRqxdKVyaU4AAuXr2LK/GV8qX/nZP/0uABvTVnA4N7J\npTgzFi1nxsIV2Kbjyq8NZLe+HenYuhlP/WAfbh71Ebe98AlXHzWIwwf3YOr8ZZz3wLg12++6ZUem\nLVjG5Hnr/sj6+aHb8eCrk3l36sIq9//fzMU8/e4M/vbtvShfvZqr/jGeijF6V/59PJccvj0tmpbx\n4sTZvDBxNgCPvD6Vy4/YgUe/sxcLlq7i/AfHYaUlIi4GLq5UvJwki80LRYE6HyT1BZ6JiP7p8p3A\nyIi4W9LWJO3chwLXAf1JutuaRcR2koYC50bEIZJOJg12kmaTjOhaJak9MCUi2qbbXxgR+6fn+j3w\nYkTcJelI4DyS4dabAden1zGNSvd5UVK3dPt+ueer6TX2G7hz/Pbekfl4u8xq7eKH3i12FayRGf+H\ns1gyZXyDTZm0/Y6D47aH/13n/ffu3+m1qgY0NZRC97kuz3m+Omd5NUnWfBnw74gYRBJo63NNRe65\nyoGmkloCNwFHRcSOwK2VzrE8d/t6nNvMzPKpgJ2uDaHYA5o6AJPT5yfXYvvRrG02HlGL7SsC6SxJ\nbYGjarHPQqBdLbYzM7MCUT3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"text/plain": [ "<matplotlib.figure.Figure at 0x11f9c0590>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8,6))\n", "bc.plot_confusion_matrix(cnn_matrix, classes=class_names, normalize=False,\n", " title='CNN Raw Confusion Matrix Using Thresholded \\n')\n", "plt.tight_layout()" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'Accuracy': 61.58,\n", " 'F1': 0.59,\n", " 'NPV': 60.48,\n", " 'PPV': 62.94,\n", " 'Sensitivity': 56.32,\n", " 'Specificity': 66.84}" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bc.cat_stats(cnn_matrix)" ] }, { "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
chapmanbe/RadNLP	notebooks/radnlp_demo.ipynb	1	18601	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# RadNLP\n", "## Radiology NLP or\n", "## Rad (as in cool) NLP or\n", "## [Fill in the Blank] NLP\n", "#### © Brian E. Chapman, PhD\n", "\n", "RadNLP is a package that builds upon the [pyConTextNLP]() algorithm's sentence-level text processing to perform simple document-level classification. The package also contains a number of functions for identifying sections of reports, identifing and eliminating boiler-plate text, etc.\n", "\n", "In this notebook I will demonstrate radnlp's most basic functionality: given a text of interest create an overall document classification. The classifiction uses a simple maximum function: for each concept marked in a report the maximal schema value occurance is selected to characterize the report for that concept.\n", "\n", "## Report Schema and maximal value\n", "\n", "The document classification is based on schema that combines multiple concepts (e.g. existence, certitude, severity) into a single ordinal scale. The RadNLP GitHub repository includes a knowledge base directory (KBs) contains the schema we ahve previously developed for critical findings projects. It is included below:\n", "\n", "```Python\n", "# Lines that start with the # symbol are comments and are ignored\n", "# The schema consists of a numeric value, followed by a label (e.g. \"AMBIVALENT\"), \n", "# followed by a Python express that can evaluate to True or False\n", "# The Python expression uses LABELS from the rules. processReports.py will substitute \n", "# the LABEL with any matched values identified from \n", "# the corresponding rules\n", "1,AMBIVALENT,DISEASE_STATE == 2\n", "2,Negative/Certain/Acute,DISEASE_STATE == 0 and CERTAINTY_STATE == 1\n", "3,Negative/Uncertain/Chronic,DISEASE_STATE == 0 and CERTAINTY_STATE == 0 and ACUTE_STATE == 0\n", "4,Positive/Uncertain/Chronic,DISEASE_STATE == 1 and CERTAINTY_STATE == 0 and ACUTE_STATE == 0 \n", "5,Positive/Certain/Chronic,DISEASE_STATE == 1 and CERTAINTY_STATE == 1 and ACUTE_STATE == 0 \n", "6,Negative/Uncertain/Acute,DISEASE_STATE == 0 and CERTAINTY_STATE == 0 \n", "7,Positive/Uncertain/Acute,DISEASE_STATE == 1 and CERTAINTY_STATE == 0 and ACUTE_STATE == 1 \n", "8,Positive/Certain/Acute,DISEASE_STATE == 1 and CERTAINTY_STATE == 1 and ACUTE_STATE == 1 \n", "```\n", "\n", "A key idea is \"a Python express that can evaluate to True or False\".\n", "\n", "The ``radnlp.schema`` subpackage contains functions for reading schema and applying the schema to the pyConTextNLP findings given ``rules`` specified by the user.\n", "\n", "There are two key functions in ``radnlp.schema``:\n", "\n", "```Python\n", "def instantiate_schema(values, rule):\n", " \"\"\"\n", " evaluates rule by substituting values into rule and evaluating the resulting literal.\n", " This is currently insecure\n", " * \"For security the ast.literal_eval() method should be used.\"\n", " \"\"\"\n", " r = rule\n", " for k in values.keys():\n", " r = r.replace(k, values[k].__str__())\n", " #return ast.literal_eval(r)\n", " return eval(r)\n", "\n", "def assign_schema(values, rules):\n", " \"\"\"\n", " \"\"\"\n", " for k in rules.keys():\n", " if instantiate_schema(values, rules[k][1]):\n", " return k\n", "```\n", "\n", "For any given category (e.g. ``pulmonary_embolism``), the maximal schema score encountered in the report is selected to characterize that report." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## ``radnlp`` Rules\n", "\n", "``radnlp`` uses rule files to specify rules that define how particular pyConTextNLP findings relate to radnlp concepts. For example, in the ``classificationRules3.csv`` provided in KBs, we provide a rules that state:\n", "\n", "* The default disease state is 1. \n", "* ``PROBABLE_EXISTENCE`` AND ``DEFINITE_EXISTENCE`` map to a disease state of 1\n", "\n", "Rules as currently indicated are not quite general and reflect paraticular use cases we were working on.\n", "\n", "### Types of Rules\n", "\n", "#### We currently support three rules:\n", "\n", "1. ``CLASSIFICAITON_RULE``: these are the rules that relate to disease, temporality, and certainty\n", "1. ``CATEGORY_RULE``: these are only partially developed concepts that attempt to address combinatorics problems in pyConTextNLP by making default findings more general (e.g. ``infaract``) and then tries to create more specific findings by attaching anatomic locations to the findings (e.g. an ``infarct`` becomes a critical finding when attached to an anatomic concept like ``brain_anatomy`` or ``heart_anatomy``.\n", "1. ``SEVERITY_RULE``: Again, not fully developed but relates to extracting severity concepts (e.g. ``large`` or 4.3 cm).\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```Python\n", "# Lines that start with the # symbol are comments and are ignored,,,,,,,,,,,,,\n", "# processReport current has three types of rules: @CLASSIFICATION_RULE, @CATEGORY_RULE, and @SEVERITY_RULE,,,,,,,,,,,\n", "# classification rules would be for things like disease_state, certainty_state, temporality state,,,,,,,,,,,\n", "# For each classification_rule set,\" there is a rule label (e.g. \"\"DISEASE_STATE\"\". This must match\",,,,,,,,,,,,\n", "# the terms used in the schema file,,,,,,,,,,,,,\n", "# Each rule set requires a DEFAULT which is the schema value to be returned if no rule conditions are satisifed,,,,,,,,,,,,,\n", "# Each rule set has zero or more rules consisting of a schema value to be returned if the rule evaluates to true,,,,,,,,,,,,,\n", "# A rule evalutes to true if the target is modified by one or more of the ConText CATEGORIES listed following,,,,,,,,,,,,,\n", "@CLASSIFICATION_RULE,DISEASE_STATE,RULE,0,DEFINITE_NEGATED_EXISTENCE,PROBABLE_NEGATED_EXISTENCE,FUTURE,INDICATION,PSEUDONEG,,,,,\n", "@CLASSIFICATION_RULE,DISEASE_STATE,RULE,2,AMBIVALENT_EXISTENCE,,,,,,,,,\n", "@CLASSIFICATION_RULE,DISEASE_STATE,RULE,1,PROBABLE_EXISTENCE,DEFINITE_EXISTENCE,,,,,,,,\n", "@CLASSIFICATION_RULE,DISEASE_STATE,DEFAULT,1,,,,,,,,,,\n", "@CLASSIFICATION_RULE,CERTAINTY_STATE,RULE,0,PROBABLE_NEGATED_EXISTENCE,AMBIVALENT_EXISTENCE,PROBABLE_EXISTENCE,,,,,,,\n", "@CLASSIFICATION_RULE,CERTAINTY_STATE,DEFAULT,1,,,,,,,,,,\n", "@CLASSIFICATION_RULE,ACUTE_STATE,RULE,0,HISTORICAL,,,,,,,,,\n", "@CLASSIFICATION_RULE,ACUTE_STATE,DEFAULT,1,,,,,,,,,,\n", "#CATEGORY_RULE rules specify what Findings (e.g. DVT) can have the category modified by the following ANATOMIC modifies,,,,,,,,,,,,,\n", "@CATEGORY_RULE,DVT,LOWER_DEEP_VEIN,UPPER_DEEP_VEIN,HEPATIC_VEIN,PORTAL_SYSTEM_VEIN,PULMONARY_VEIN,RENAL_VEIN,SINUS_VEIN,LOWER_SUPERFICIAL_VEIN,UPPER_SUPERFICIAL_VEIN,VARICOCELE,ARTERIAL,NON_VASCULAR\n", "@CATEGORY_RULE,INFARCT,BRAIN_ANATOMY,HEART_ANATOMY,OTHER_CRITICAL_ANATOMY,,,,,,,,,\n", "@CATEGORY_RULE,ANEURYSM,AORTIC_ANATOMY,,,,,,,,,,,\n", "#SEVERITY_RUlE specifiy which targets to try to obtain severity measures for,,,,,,,,,,,,,\n", "@SEVERITY_RULE,AORTIC_ANATOMY_ANEURYSM,SEVERITY,,,,,,,,,,,\n", "```" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Licensing\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](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." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Program Description" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import radnlp.rules as rules\n", "import radnlp.schema as schema\n", "import radnlp.utils as utils\n", "import radnlp.classifier as classifier\n", "import radnlp.split as split\n", "\n", "from IPython.display import clear_output, display, HTML\n", "from IPython.html.widgets import interact, interactive, fixed\n", "import io\n", "from IPython.html import widgets # Widget definitions\n", "import pyConTextNLP.itemData as itemData\n", "\n", "from pyConTextNLP.display.html import mark_document_with_html" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Example Data\n", "\n", "Below are two example radiology reports pulled from the MIMIC2 demo data set." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "reports = [\"\"\"1. Pulmonary embolism with filling defects noted within the upper and lower\n", " lobar branches of the right main pulmonary artery.\n", " 2. Bilateral pleural effusions, greater on the left.\n", " 3. Ascites.\n", " 4. There is edema of the gallbladder wall, without any evidence of\n", " distention, intra- or extra-hepatic biliary dilatation. This, along with\n", " stranding within the mesentery, likely represents third spacing of fluid.\n", " 5. There are several wedge shaped areas of decreased perfusion within the\n", " spleen, which may represent splenic infarcts.\n", " \n", " Results were discussed with Dr. [First Name8 (NamePattern2) 15561] [Last Name (NamePattern1) 13459] \n", " at 8 pm on [3099-11-6].\"\"\",\n", " \n", " \"\"\"1. Filling defects within the subsegmental arteries in the region\n", " of the left lower lobe and lingula and within the right lower lobe consistent\n", " with pulmonary emboli.\n", " \n", " 2. Small bilateral pleural effusions with associated bibasilar atelectasis.\n", " \n", " 3. Left anterior pneumothorax.\n", " \n", " 4. No change in the size of the thoracoabdominal aortic aneurysm.\n", " \n", " 5. Endotracheal tube 1.8 cm above the carina. NG tube within the stomach,\n", " although the tip is pointed superiorly toward the fundus.\"\"\",\n", " \n", " \"\"\"1. There are no pulmonary emboli observed.\n", " \n", " 2. Small bilateral pleural effusions with associated bibasilar atelectasis.\n", " \n", " 3. Left anterior pneumothorax.\n", " \n", " 4. No change in the size of the thoracoabdominal aortic aneurysm.\n", " \n", " 5. Endotracheal tube 1.8 cm above the carina. NG tube within the stomach,\n", " although the tip is pointed superiorly toward the fundus.\"\"\"\n", "]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#!python -m textblob.download_corpora" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Define locations of knowledge, schema, and rules files" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def getOptions():\n", " \"\"\"Generates arguments for specifying database and other parameters\"\"\"\n", " options = {}\n", " options['lexical_kb'] = [\"https://raw.githubusercontent.com/chapmanbe/pyConTextNLP/master/KB/lexical_kb_04292013.tsv\", \n", " \"https://raw.githubusercontent.com/chapmanbe/pyConTextNLP/master/KB/criticalfinder_generalized_modifiers.tsv\"]\n", " options['domain_kb'] = [\"https://raw.githubusercontent.com/chapmanbe/pyConTextNLP/master/KB/pe_kb.tsv\"]#[os.path.join(DATADIR2,\"pe_kb.tsv\")]\n", " options[\"schema\"] = \"https://raw.githubusercontent.com/chapmanbe/RadNLP/master/KBs/schema2.csv\"#\"file specifying schema\"\n", " options[\"rules\"] = \"https://raw.githubusercontent.com/chapmanbe/RadNLP/master/KBs/classificationRules3.csv\" # \"file specifying sentence level rules\")\n", " return options\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Define report analysis\n", "\n", "For every report we do two steps\n", "\n", "1. Markup all the sentences in the report based on the provided targets and modifiers\n", "1. Given this markup we apply our rules and schema to generate a document classification.\n", "\n", "``radnlp`` provides functions to do both of these steps:\n", "\n", "1. ``radnlp.utils.mark_report`` takes lists of modifiers and targets and generates a pyConTextNLP document graph\n", "1. ``radnlp.classify.classify_document_targets`` takes the document graph, rules, and schema and generates document classification for each identified concept.\n", "\n", "Because pyConTextNLP operates on sentences we split the report into sentences. In this function we use ``radnlp.split.get_sentences`` which is simply a wrapper around ``textblob`` for splitting the sentences.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def analyze_report(report, modifiers, targets, rules, schema):\n", " \"\"\"\n", " given an individual radiology report, creates a pyConTextGraph\n", " object that contains the context markup\n", " report: a text string containing the radiology reports\n", " \"\"\"\n", " \n", " markup = utils.mark_report(split.get_sentences(report),\n", " modifiers,\n", " targets)\n", " return classifier.classify_document_targets(markup,\n", " rules[0],\n", " rules[1],\n", " rules[2],\n", " schema)\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def process_report(report):\n", " \n", " options = getOptions()\n", "\n", " _radnlp_rules = rules.read_rules(options[\"rules\"])\n", " _schema = schema.read_schema(options[\"schema\"])\n", " #_schema = readSchema(options[\"schema\"])\n", " modifiers = itemData.itemData()\n", " targets = itemData.itemData()\n", " for kb in options['lexical_kb']:\n", " modifiers.extend( itemData.instantiateFromCSVtoitemData(kb) )\n", " for kb in options['domain_kb']:\n", " targets.extend( itemData.instantiateFromCSVtoitemData(kb) )\n", " return analyze_report(report, modifiers, targets, _radnlp_rules, _schema)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "rslt_0 = process_report(reports[0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "``radnlp.classifier.classify_document_targets`` returns a dictionary with keys equal to the target category (e.g. ``pulmonary_embolism``) and the values a 3-tuple with the following values:\n", "\n", "1. The schema category (e.g. 8 or 2).\n", "1. The XML representation of the maximal schema node\n", "1. A list (usually empty (not really implemented yet)) of severity values." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "for key, value in rslt_0.items():\n", " print((\"%s\"%key).center(42,\"-\"))\n", " for v in value:\n", " print(v)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "rslt_1 = main(reports[1])\n", "\n", "for key, value in rslt_1.items():\n", " print((\"%s\"%key).center(42,\"-\"))\n", " for v in value:\n", " print(v)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Negative Report\n", "\n", "For the third report I simply rewrote one of the findings to be negative for PE. We now see a change in the schema classification." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "rslt_2 = main(reports[2])\n", "\n", "for key, value in rslt_2.items():\n", " print((\"%s\"%key).center(42,\"-\"))\n", " for v in value:\n", " print(v)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "keys = list(pec.markups.keys())\n", "keys.sort()\n", "\n", "pec.reports.insert(pec.reports.columns.get_loc(u'markup')+1,\n", " \"ConText Coding\",\n", " [codingKey.get(pec.markups[k][1].get(\"pulmonary_embolism\",[None])[0],\"NA\") for k in keys])" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }	apache-2.0
sains1/UTRonsProject	Notebooks/11_microRNAOverlap.ipynb	1	33611	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Finding the overlap between novel UTRons and microRNA sites predicted from TargetScan" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Statement to find overlap: \n", "\n", "bedtools intersect \n", " -c\n", " -a novel_utrons.bed\n", " -b TS_predictions_hg38_liftover.bed\n", " > overlap.txt\n", " \n", "File in misc_files microRNAoverlap.txt has the number of overlaps with microRNAs for these genes\n", " (done for both novel and known)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pandas as pd\n", "import sqlite3\n", "import pandas as pd\n", "import numpy as np\n", "\n", "cnx = sqlite3.connect('/shared/sudlab1/General/projects/utrons_project/BladderCancerUtrons/431BladderUtrons.db')\n", "cnx.execute(\"ATTACH '/shared/sudlab1/General/annotations/hg38_noalt_ensembl85/csvdb' as annotations\")\n", "\n", "systematicUtrons = \"/shared/sudlab1/General/projects/utrons_project/misc_files/systematicUtronGenes.txt\"\n", "systematicUtrons = pd.read_csv(systematicUtrons, sep=\"\\t\", header=None)" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [], "source": [ "novelOverlapFile = \"/shared/sudlab1/General/projects/utrons_project/misc_files/microRNAOverlap.txt\"\n", "allOverlapFile = \"/shared/sudlab1/General/projects/utrons_project/misc_files/allUtronsMicroRnaOverlap.txt\"\n", "\n", "novelOverlap = pd.read_csv(novelOverlapFile, sep=\" \|\\t\", engine=\"python\", header=None)\n", "allOverlap = pd.read_csv(allOverlapFile, sep=\" \|\\t\", engine=\"python\", header=None)\n", "\n", "def getId(row):\n", " Id = row[3][:-16]\n", " return Id\n", "\n", "novelOverlap[\"GeneId\"] = novelOverlap.apply(getId, axis=1)\n", "allOverlap[\"GeneId\"] = allOverlap[3]\n", "\n", "novelOverlap = novelOverlap[[\"GeneId\", 7]]\n", "allOverlap = allOverlap[[\"GeneId\", 12]]" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [], "source": [ "query_text1 = '''\n", " SELECT uid.transcript_id AS Name, ti.gene_name AS Gene\n", " FROM novel_utrons_ids AS uid\n", " INNER JOIN annotations.transcript_info AS ti\n", " ON ti.transcript_id = uid.match_transcript_id\n", " WHERE uid.track='agg-agg-agg' AND uid.transcript_id like \"MSTRG%\"\n", " ORDER BY uid.transcript_id\n", " '''\n", "novelIds = pd.read_sql_query(query_text1, cnx)\n", "novelIds = novelIds[~novelIds[\"Gene\"].isin(systematicUtrons[0])]\n", "\n", "\n", "query_text1 = '''\n", " SELECT uid.transcript_id AS Name, ti.gene_name AS Gene\n", " FROM all_utrons_ids AS uid\n", " INNER JOIN transcript_class AS tc\n", " ON tc.transcript_id = uid.transcript_id\n", " INNER JOIN annotations.transcript_info AS ti\n", " ON ti.transcript_id = tc.match_transcript_id \n", " WHERE uid.track='agg-agg-agg' AND uid.transcript_id like \"ENS%\"\n", " ORDER BY uid.transcript_id\n", " '''\n", "knownIds = pd.read_sql_query(query_text1, cnx)\n", "knownIds = knownIds[~knownIds[\"Gene\"].isin(systematicUtrons[0])]" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "424 3068\n", "1199 26449\n" ] } ], "source": [ "novelOverlap = novelOverlap[novelOverlap[\"GeneId\"].isin(novelIds[\"Name\"])]\n", "allOverlap = allOverlap[allOverlap[\"GeneId\"].isin(knownIds[\"Name\"])]\n", "\n", "\n", "print len(novelOverlap[novelOverlap[7]>0]), len(novelOverlap[novelOverlap[7]==0])\n", "print len(allOverlap[allOverlap[12]>0]), len(allOverlap[allOverlap[12]==0])" ] }, { "cell_type": "code", "execution_count": 166, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] }, { "data": { "image/png": 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w98P4OZAG/A6o3l/u7oujC639tLy5iEj7RHGL1knBv99PKHPgvPYEJiIi3VfY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\nGCIiEsr/B671/ixrW6K+AAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f3f11e61b10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "lengthInfo = pd.read_csv(\"/shared/sudlab1/General/projects/utrons_project/misc_files/SpliceSite/novelLengths.txt\", sep=\"\\t\")\n", "\n", "def getPercents(length):\n", " shortIds = lengthInfo[lengthInfo[\"Length\"]<=length][\"transcript_id\"]\n", "\n", " shortNovel = novelOverlap[novelOverlap[\"GeneId\"].isin(shortIds)]\n", " longNovel = novelOverlap[~novelOverlap[\"GeneId\"].isin(shortIds)]\n", "\n", " shortWith = len(shortNovel[shortNovel[7]>0])\n", " shortNone = len(shortNovel[shortNovel[7]==0])\n", " longWith = len(longNovel[longNovel[7]>0])\n", " longNone = len(longNovel[longNovel[7]==0])\n", " \n", " percent1 = shortWith / float(shortWith + shortNone)\n", " percent2 = longWith / float(longWith + longNone)\n", " \n", " #print percent1 * 100, (1 - percent1) * 100\n", " #print percent2 * 100, (1 - percent2) * 100\n", " \n", " return percent1, percent2\n", " \n", "\n", "%pylab inline\n", "\n", "shortList = []\n", "longList = []\n", "lengthList = []\n", "for num in range(25,2000,10):\n", " a, b = getPercents(num)\n", " shortList.append(a)\n", " longList.append(b)\n", " lengthList.append(num)\n", "\n", "pylab.plot(lengthList, longList)\n", "pylab.ylim(0,0.15)\n", "pylab.xlabel(\"Length\")\n", "pylab.ylabel(\"Proportion\")\n", "pylab.savefig(\"./images/LengthVsMicroRNA\", dpi=300)\n" ] }, { "cell_type": "code", "execution_count": 108, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0.12158808933002481, 0.12386156648451731, 0.13311421528348397, 0.13479052823315119, 0.13636363636363635, 0.1312240663900415, 0.13743815283122596, 0.13908450704225353, 0.14081885856079404, 0.14018087855297157, 0.1415282392026578, 0.1412894375857339, 0.14285714285714285, 0.14296134208606856, 0.14157303370786517, 0.1426403641881639, 0.14340786430223593, 0.1410756040530008, 0.1422924901185771, 0.14170692431561996, 0.14193025141930252, 0.14133986928104575, 0.14262159934047816, 0.14250207125103562, 0.14309623430962343, 0.14370748299319727, 0.14432989690721648, 0.14507772020725387, 0.14298169136878813, 0.1436077057793345, 0.14386584289496912, 0.14658273381294964, 0.14660633484162897, 0.1468721668177697, 0.1479779411764706, 0.14838709677419354, 0.1467038068709378, 0.14684014869888476, 0.1455223880597015, 0.1455223880597015, 0.14553990610328638, 0.14366729678638943, 0.14312796208530806, 0.14435946462715105, 0.1424446583253128, 0.13627450980392156, 0.13681102362204725, 0.13663366336633664, 0.13690476190476192, 0.13755020080321284, 0.13420787083753785, 0.13149847094801223, 0.12833675564681724, 0.12980269989615784, 0.12839248434237996, 0.12866108786610878, 0.12906610703043023, 0.12842105263157894, 0.1285563751317176, 0.12882787750791974, 0.12937433722163308, 0.1276595744680851, 0.12740899357601712, 0.12701829924650163, 0.12701829924650163, 0.1271551724137931, 0.12742980561555076, 0.12689804772234273, 0.12663755458515283, 0.12806236080178174, 0.1282051282051282, 0.12598425196850394, 0.12641083521444696, 0.12655367231638417, 0.12669683257918551, 0.12300683371298406, 0.11507479861910241, 0.11600928074245939, 0.11529411764705882, 0.1154299175500589, 0.11556603773584906, 0.11575178997613365, 0.11589008363201912, 0.11473429951690821, 0.11515151515151516, 0.1120584652862363, 0.11233211233211234, 0.11274509803921569, 0.11288343558282209, 0.11288343558282209, 0.11372064276885044, 0.11400247831474597, 0.1141439205955335, 0.115, 0.115, 0.11586901763224182, 0.11586901763224182, 0.11504424778761062, 0.11518987341772152, 0.11518987341772152, 0.11518987341772152, 0.11518987341772152, 0.11533586818757921, 0.11421319796954314, 0.11464968152866242, 0.11508951406649616, 0.11553273427471117, 0.11673151750972763, 0.11780104712041885, 0.11795543905635648, 0.11330698287220026, 0.11345646437994723, 0.11273209549071618, 0.11303191489361702, 0.11378848728246319, 0.10326086956521739, 0.10326086956521739, 0.10326086956521739, 0.10231923601637108, 0.10027472527472528, 0.09793103448275862, 0.09681881051175657, 0.09695290858725762, 0.09596662030598054, 0.09596662030598054, 0.09596662030598054, 0.09563994374120956, 0.09563994374120956, 0.09590973201692525, 0.09590973201692525, 0.0950354609929078, 0.09557774607703282, 0.09557774607703282, 0.09571428571428571, 0.09585121602288985, 0.09339080459770115, 0.09221902017291066, 0.09248554913294797, 0.09130434782608696, 0.09143686502177069, 0.09170305676855896, 0.09197080291970802, 0.08944281524926687, 0.08957415565345081, 0.08970588235294118, 0.08970588235294118, 0.08836524300441827, 0.08915304606240713, 0.08915304606240713, 0.08779761904761904, 0.08779761904761904, 0.08832335329341318, 0.08858858858858859, 0.0887218045112782, 0.0887218045112782, 0.0887218045112782, 0.0889894419306184, 0.0891238670694864, 0.0891238670694864, 0.08925869894099848, 0.08966565349544073, 0.08841463414634146, 0.08841463414634146, 0.08841463414634146, 0.08868501529051988, 0.08868501529051988, 0.08895705521472393, 0.0890937019969278, 0.08923076923076922, 0.08923076923076922, 0.08936825885978428, 0.08950617283950617, 0.08950617283950617, 0.08950617283950617, 0.08950617283950617, 0.09006211180124224, 0.09034267912772585, 0.09034267912772585, 0.0892018779342723, 0.08948194662480377, 0.0880503144654088, 0.08832807570977919, 0.08846761453396525, 0.08860759493670886, 0.08860759493670886, 0.08860759493670886, 0.08874801901743265, 0.08888888888888889, 0.08888888888888889, 0.0875796178343949, 0.0875796178343949, 0.08771929824561403, 0.08814102564102565, 0.08667736757624397, 0.08520900321543408, 0.08520900321543408, 0.08562197092084006, 0.08441558441558442]\n" ] } ], "source": [ "print longList" ] }, { "cell_type": "code", "execution_count": 131, "metadata": { "collapsed": false }, "outputs": [], "source": [ "tpm = 20\n", "supDf = pd.read_csv(\"/shared/sudlab1/General/projects/utrons_project/misc_files/cancerUtrons/suppresor_%dTPM.txt\" % tpm,sep=\"\\t\", header=None)\n", "oncDf = pd.read_csv(\"/shared/sudlab1/General/projects/utrons_project/misc_files/cancerUtrons/oncogenes_%dTPM.txt\" % tpm,sep=\"\\t\", header=None)\n", "unkDf = pd.read_csv(\"/shared/sudlab1/General/projects/utrons_project/misc_files/cancerUtrons/unknown_%dTPM.txt\" % tpm,sep=\"\\t\", header=None)" ] }, { "cell_type": "code", "execution_count": 134, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "oncogenes\n", "known 0.0953757225434\n", "novel 0.311111111111\n", "\n", "Suppressors\n", "known 0.0454545454545\n", "novel 0.125\n", "\n", "Unknown\n", "known 0.0833333333333\n", "novel 0.0\n" ] } ], "source": [ "allOncOverlap = allOverlap[allOverlap[\"GeneId\"].isin(oncDf[0])]\n", "novelOncOverlap = novelOverlap[novelOverlap[\"GeneId\"].isin(oncDf[0])]\n", "\n", "print \"oncogenes\"\n", "print \"known\", len(allOncOverlap[allOncOverlap[12]>0])/ float(len(allOncOverlap))\n", "print \"novel\", len(novelOncOverlap[novelOncOverlap[7]>0])/float(len(novelOncOverlap))\n", "\n", "\n", "allSupOverlap = allOverlap[allOverlap[\"GeneId\"].isin(supDf[0])]\n", "novelSupOverlap = novelOverlap[novelOverlap[\"GeneId\"].isin(supDf[0])]\n", "\n", "print \"\\nSuppressors\"\n", "print \"known\", len(allSupOverlap[allSupOverlap[12]>0])/ float(len(allSupOverlap))\n", "print \"novel\", len(novelSupOverlap[novelSupOverlap[7]>0])/float(len(novelSupOverlap))\n", "\n", "\n", "allUnkOverlap = allOverlap[allOverlap[\"GeneId\"].isin(unkDf[0])]\n", "novelUnkOverlap = novelOverlap[novelOverlap[\"GeneId\"].isin(unkDf[0])]\n", "\n", "print \"\\nUnknown\"\n", "print \"known\", len(allUnkOverlap[allUnkOverlap[12]>0])/ float(len(allSupOverlap))\n", "print \"novel\", len(novelUnkOverlap[novelUnkOverlap[7]>0])/float(len(novelSupOverlap))\n" ] }, { "cell_type": "code", "execution_count": 168, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Known Oncogenes removing microRNAs\n", "[u'ACSL3', u'ADAM10', u'AKT1', u'AKT2', u'ALDH2', u'ASPSCR1', u'ATF1', u'ATIC', u'BCL11A', u'BCLAF1', u'BCR', u'BRAF', u'BRD4', u'CALR', u'CAMTA1', u'CANT1', u'CARM1', u'CARS', u'CCNB1IP1', u'CD74', u'CDK4', u'CHCHD7', u'CIITA', u'CLTC', u'CLTCL1', u'CMC4', u'COX6C', u'CREB3L2', u'CTNNB1', u'CTTN', u'DDX6', u'DEK', u'EIF4A2', u'ELN', u'ERBB2', u'ETV5', u'FGFR1', u'FIP1L1', u'GNAS', u'GOT2', u'HERPUD1', u'HNRNPA2B1', u'HOXA9', u'HRAS', u'HSP90AA1', u'IDH2', u'IL7R', u'KEAP1', u'KIF5B', u'KLK2', u'KRAS', u'KTN1', u'LASP1', u'LMO2', u'LZTR1', u'MDM2', u'MED17', u'METTL14', u'MLF1', u'MMP2', u'MUC1', u'MYH11', u'NACA', u'NDRG1', u'NIN', u'NRAS', u'NUMA1', u'NUP214', u'PAX8', u'PBX1', u'PCSK7', u'PDE4DIP', u'PDGFB', u'PICALM', u'PLCG1', u'PML', u'PPARG', u'PPP2R1A', u'PTPN11', u'RAF1', u'RHEB', u'RHOA', u'RPN1', u'SEPT5', u'SEPT9', u'SETDB1', u'SF3B1', u'SFPQ', u'SMC1A', u'SMO', u'SRSF2', u'SRSF3', u'SS18', u'SS18L1', u'SSX1', u'SSX2', u'SSX4', u'STAT3', u'TAF15', u'TCEA1', u'TCL1A', u'TFDP1', u'TFG', u'TFPT', u'TFRC', u'TPM3', u'TPM4', u'TRAF7', u'TRIP11', u'U2AF1', u'WHSC1', u'WWTR1', u'XPO1', u'YWHAE', u'ZNF814']\n", "\n", "\n", "Novel Oncogenes removing microRNAs\n", "[u'AKT2', u'CCND1', u'COL1A1', u'COX6C', u'H3F3B', u'HSP90AA1', u'JUN', u'LASP1', u'MAFB', u'MYCL', u'NDRG1', u'NEDD4L', u'NONO', u'PIM1', u'RAF1', u'RHOA', u'SDC4', u'SET', u'SRSF3', u'TPM3', u'TRAF7', u'YWHAE']\n" ] } ], "source": [ "oncGenes1 = pd.merge(knownIds, allOncOverlap, left_on=\"Name\", right_on=\"GeneId\")\n", "print \"Known Oncogenes removing microRNAs\"\n", "print sorted(oncGenes1[\"Gene\"].unique())\n", "\n", "\n", "oncGenes2 = pd.merge(novelIds, novelOncOverlap, left_on=\"Name\", right_on=\"GeneId\")\n", "print \"\\n\\nNovel Oncogenes removing microRNAs\"\n", "print sorted(oncGenes2[\"Gene\"].unique())" ] }, { "cell_type": "code", "execution_count": 169, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Known suppressors removing microRNAs\n", "[u'ASXL1', u'ATRX', u'B2M', u'BAP1', u'BMPR1A', u'CASP8', u'CAST', u'CBLC', u'CCAR1', u'CDH1', u'CDK12', u'CDKN1B', u'CDKN2A', u'CIC', u'CYLD', u'DDB2', u'EPHB6', u'ERCC3', u'EXT1', u'FANCA', u'FANCG', u'FAS', u'FBXO11', u'FUBP1', u'HLA-B', u'KDM5C', u'MAP2K4', u'MLH1', u'MSH6', u'MUTYH', u'NF1', u'NF2', u'PALB2', u'PIK3R1', u'RB1', u'RECQL4', u'SBDS', u'SDHC', u'SDHD', u'SMAD4', u'STK11', u'TBL1XR1', u'TOM1', u'TP53BP1', u'TSC2', u'XPC']\n", "\n", "\n", "Novel suppressors removing microRNAs\n", "[u'B2M', u'CDH1', u'CDKN1A', u'EXT1', u'PIK3R1', u'SDHC', u'ZFP36L2']\n" ] } ], "source": [ "supGenes1 = pd.merge(knownIds, allSupOverlap, left_on=\"Name\", right_on=\"GeneId\")\n", "print \"Known suppressors removing microRNAs\"\n", "print sorted(supGenes1[\"Gene\"].unique())\n", "\n", "\n", "supGenes2 = pd.merge(novelIds, novelSupOverlap, left_on=\"Name\", right_on=\"GeneId\")\n", "print \"\\n\\nNovel suppressors removing microRNAs\"\n", "print sorted(supGenes2[\"Gene\"].unique())" ] }, { "cell_type": "code", "execution_count": 157, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\t\tTranscripts \tGenes\n", "known Oncogenes \t 692 \t 115\n", "novel Oncogenes \t 59 \t 22\n", "known suppressors \t 264 \t 46\n", "novel suppressors \t 34 \t 7\n" ] } ], "source": [ "print \"\\t\\tTranscripts\", \"\\tGenes\"\n", "print \"known Oncogenes \\t %s \\t %s\" % (len(oncGenes1), len(oncGenes1[\"Gene\"].unique()))\n", "print \"novel Oncogenes \\t %s \\t %s\" % (len(oncGenes2), len(oncGenes2[\"Gene\"].unique()))\n", "print \"known suppressors \\t %s \\t %s\" % ( len(supGenes1), len(supGenes1[\"Gene\"].unique()))\n", "print \"novel suppressors \\t %s \\t %s\"% ( len(supGenes2), len(supGenes2[\"Gene\"].unique()))" ] }, { "cell_type": "code", "execution_count": 165, "metadata": { "collapsed": false }, "outputs": [], "source": [ "supjoined = pd.concat([supGenes1, supGenes2])\n", "supjoined.to_csv(\"/shared/sudlab1/General/projects/utrons_project/misc_files/cancerUtrons/suppressorMicroRNA.txt\", sep=\"\\t\", header=None, index=None)\n", "\n", "oncjoined = pd.concat([oncGenes1, oncGenes2])\n", "oncjoined.to_csv(\"/shared/sudlab1/General/projects/utrons_project/misc_files/cancerUtrons/oncogeneMicroRNA.txt\", sep=\"\\t\", header=None, index=None)" ] } ], "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
asazo/ANN	tarea3/pregunta1.ipynb	2	1107276	null	mit
DistrictDataLabs/03-censusables	censusables/data/starbucks-location-frequencies.ipynb	1	68483	{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "-" } }, "source": [ "A look at Starbucks Location Distribution\n", "======================\n", "\n", "When looking at block data, I noticed that very few geographic units have more than one Starbucks. \n" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x109398990>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%pylab inline\n", "import pandas as pd\n", "import json\n", "\n", "def summarize(fname, title):\n", " d = pd.DataFrame(json.loads(l) for l in open(fname))\n", " counts = pd.DataFrame(d.GISJOIN.value_counts())\n", " counts.columns = ['counts']\n", " ax = counts['counts'].value_counts().plot(kind='barh')\n", " ax.set_title(title)\n", " ax.set_xscale('log')\n", " ax.set_xlabel('number of geographic units')\n", " ax.set_ylabel('Starbucks per unit')\n", " \n", "summarize('sb_block.json', 'Frequency of starbucks counts for census blocks')\n" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x109398110>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "summarize('sb_track.json', 'Frequency of starbucks counts for census tracks')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For county data, there's a much greater distribution of starbucks counts." ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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tJD0k6WPAV4B3SboXeHt53WcP4EHbszrZr4iIaE/KfUREjAK9eBoqIiJ6TMeC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\nOML2422L3k8rizsiImqgsjMLSesAZwPftf3jtvlrAwcAu4z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"text/plain": [ "<matplotlib.figure.Figure at 0x108565050>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "summarize('sb_county.json', 'Frequency of starbucks counts for census counties')\n", "import pylab\n", "pylab.gcf().set_size_inches(6., 16.)" ] } ], "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
tpin3694/tpin3694.github.io	python/csv_to_python_code.ipynb	1	33214	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Title: Convert A CSV Into Python Code To Recreate It \n", "Slug: csv_to_python_code \n", "Summary: Convert A CSV Into Python Code To Recreate It \n", "Date: 2016-05-01 12:00 \n", "Category: Python \n", "Tags: Data Wrangling \n", "Authors: Chris Albon \n", "\n", "This might seem like a strange bit of code, but it serves a very valuable (though niche) function. I prefer to the code in my tutorials to not rely on outside data to run. That is, dieally the data is created within the code itself, rather than requiring loading an data from a csv file. Obviously this is not reasonable for real analyses, but for tutorials it can make everything simpler and easier.\n", "\n", "However, this preference to embed the generation of data in the snippets themselves becomes a problem when I want to use data found in existing datasets. So, I created this script to complete one simple task: To take a dataset and generate the python code required to recreate it." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Preliminaries" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Import the pandas package\n", "import pandas as pd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load the external dataset" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Load the csv file as a pandas dataframe\n", "df_original = pd.read_csv('http://vincentarelbundock.github.io/Rdatasets/csv/datasets/iris.csv')\n", "df = pd.read_csv('http://vincentarelbundock.github.io/Rdatasets/csv/datasets/iris.csv')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Print the code required to create that dataset" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "==============================\n", "RUN THE CODE BELOW THIS LINE\n", "==============================\n", "raw_data = {'Sepal.Length': [5.0999999999999996, 4.9000000000000004, 4.7000000000000002, 4.5999999999999996, 5.0, 5.4000000000000004, 4.5999999999999996, 5.0, 4.4000000000000004, 4.9000000000000004, 5.4000000000000004, 4.7999999999999998, 4.7999999999999998, 4.2999999999999998, 5.7999999999999998, 5.7000000000000002, 5.4000000000000004, 5.0999999999999996, 5.7000000000000002, 5.0999999999999996, 5.4000000000000004, 5.0999999999999996, 4.5999999999999996, 5.0999999999999996, 4.7999999999999998, 5.0, 5.0, 5.2000000000000002, 5.2000000000000002, 4.7000000000000002, 4.7999999999999998, 5.4000000000000004, 5.2000000000000002, 5.5, 4.9000000000000004, 5.0, 5.5, 4.9000000000000004, 4.4000000000000004, 5.0999999999999996, 5.0, 4.5, 4.4000000000000004, 5.0, 5.0999999999999996, 4.7999999999999998, 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7.2999999999999998, 6.7000000000000002, 7.2000000000000002, 6.5, 6.4000000000000004, 6.7999999999999998, 5.7000000000000002, 5.7999999999999998, 6.4000000000000004, 6.5, 7.7000000000000002, 7.7000000000000002, 6.0, 6.9000000000000004, 5.5999999999999996, 7.7000000000000002, 6.2999999999999998, 6.7000000000000002, 7.2000000000000002, 6.2000000000000002, 6.0999999999999996, 6.4000000000000004, 7.2000000000000002, 7.4000000000000004, 7.9000000000000004, 6.4000000000000004, 6.2999999999999998, 6.0999999999999996, 7.7000000000000002, 6.2999999999999998, 6.4000000000000004, 6.0, 6.9000000000000004, 6.7000000000000002, 6.9000000000000004, 5.7999999999999998, 6.7999999999999998, 6.7000000000000002, 6.7000000000000002, 6.2999999999999998, 6.5, 6.2000000000000002, 5.9000000000000004], 'Petal.Width': [0.20000000000000001, 0.20000000000000001, 0.20000000000000001, 0.20000000000000001, 0.20000000000000001, 0.40000000000000002, 0.29999999999999999, 0.20000000000000001, 0.20000000000000001, 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1.5, 1.0, 1.5, 1.1000000000000001, 1.8, 1.3, 1.5, 1.2, 1.3, 1.3999999999999999, 1.3999999999999999, 1.7, 1.5, 1.0, 1.1000000000000001, 1.0, 1.2, 1.6000000000000001, 1.5, 1.6000000000000001, 1.5, 1.3, 1.3, 1.3, 1.2, 1.3999999999999999, 1.2, 1.0, 1.3, 1.2, 1.3, 1.3, 1.1000000000000001, 1.3, 2.5, 1.8999999999999999, 2.1000000000000001, 1.8, 2.2000000000000002, 2.1000000000000001, 1.7, 1.8, 1.8, 2.5, 2.0, 1.8999999999999999, 2.1000000000000001, 2.0, 2.3999999999999999, 2.2999999999999998, 1.8, 2.2000000000000002, 2.2999999999999998, 1.5, 2.2999999999999998, 2.0, 2.0, 1.8, 2.1000000000000001, 1.8, 1.8, 1.8, 2.1000000000000001, 1.6000000000000001, 1.8999999999999999, 2.0, 2.2000000000000002, 1.5, 1.3999999999999999, 2.2999999999999998, 2.3999999999999999, 1.8, 1.8, 2.1000000000000001, 2.3999999999999999, 2.2999999999999998, 1.8999999999999999, 2.2999999999999998, 2.5, 2.2999999999999998, 1.8999999999999999, 2.0, 2.2999999999999998, 1.8], 'Petal.Length': [1.3999999999999999, 1.3999999999999999, 1.3, 1.5, 1.3999999999999999, 1.7, 1.3999999999999999, 1.5, 1.3999999999999999, 1.5, 1.5, 1.6000000000000001, 1.3999999999999999, 1.1000000000000001, 1.2, 1.5, 1.3, 1.3999999999999999, 1.7, 1.5, 1.7, 1.5, 1.0, 1.7, 1.8999999999999999, 1.6000000000000001, 1.6000000000000001, 1.5, 1.3999999999999999, 1.6000000000000001, 1.6000000000000001, 1.5, 1.5, 1.3999999999999999, 1.5, 1.2, 1.3, 1.3999999999999999, 1.3, 1.5, 1.3, 1.3, 1.3, 1.6000000000000001, 1.8999999999999999, 1.3999999999999999, 1.6000000000000001, 1.3999999999999999, 1.5, 1.3999999999999999, 4.7000000000000002, 4.5, 4.9000000000000004, 4.0, 4.5999999999999996, 4.5, 4.7000000000000002, 3.2999999999999998, 4.5999999999999996, 3.8999999999999999, 3.5, 4.2000000000000002, 4.0, 4.7000000000000002, 3.6000000000000001, 4.4000000000000004, 4.5, 4.0999999999999996, 4.5, 3.8999999999999999, 4.7999999999999998, 4.0, 4.9000000000000004, 4.7000000000000002, 4.2999999999999998, 4.4000000000000004, 4.7999999999999998, 5.0, 4.5, 3.5, 3.7999999999999998, 3.7000000000000002, 3.8999999999999999, 5.0999999999999996, 4.5, 4.5, 4.7000000000000002, 4.4000000000000004, 4.0999999999999996, 4.0, 4.4000000000000004, 4.5999999999999996, 4.0, 3.2999999999999998, 4.2000000000000002, 4.2000000000000002, 4.2000000000000002, 4.2999999999999998, 3.0, 4.0999999999999996, 6.0, 5.0999999999999996, 5.9000000000000004, 5.5999999999999996, 5.7999999999999998, 6.5999999999999996, 4.5, 6.2999999999999998, 5.7999999999999998, 6.0999999999999996, 5.0999999999999996, 5.2999999999999998, 5.5, 5.0, 5.0999999999999996, 5.2999999999999998, 5.5, 6.7000000000000002, 6.9000000000000004, 5.0, 5.7000000000000002, 4.9000000000000004, 6.7000000000000002, 4.9000000000000004, 5.7000000000000002, 6.0, 4.7999999999999998, 4.9000000000000004, 5.5999999999999996, 5.7999999999999998, 6.0999999999999996, 6.4000000000000004, 5.5999999999999996, 5.0999999999999996, 5.5999999999999996, 6.0999999999999996, 5.5999999999999996, 5.5, 4.7999999999999998, 5.4000000000000004, 5.5999999999999996, 5.0999999999999996, 5.0999999999999996, 5.9000000000000004, 5.7000000000000002, 5.2000000000000002, 5.0, 5.2000000000000002, 5.4000000000000004, 5.0999999999999996], 'Species': ['setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica'], 'Sepal.Width': [3.5, 3.0, 3.2000000000000002, 3.1000000000000001, 3.6000000000000001, 3.8999999999999999, 3.3999999999999999, 3.3999999999999999, 2.8999999999999999, 3.1000000000000001, 3.7000000000000002, 3.3999999999999999, 3.0, 3.0, 4.0, 4.4000000000000004, 3.8999999999999999, 3.5, 3.7999999999999998, 3.7999999999999998, 3.3999999999999999, 3.7000000000000002, 3.6000000000000001, 3.2999999999999998, 3.3999999999999999, 3.0, 3.3999999999999999, 3.5, 3.3999999999999999, 3.2000000000000002, 3.1000000000000001, 3.3999999999999999, 4.0999999999999996, 4.2000000000000002, 3.1000000000000001, 3.2000000000000002, 3.5, 3.6000000000000001, 3.0, 3.3999999999999999, 3.5, 2.2999999999999998, 3.2000000000000002, 3.5, 3.7999999999999998, 3.0, 3.7999999999999998, 3.2000000000000002, 3.7000000000000002, 3.2999999999999998, 3.2000000000000002, 3.2000000000000002, 3.1000000000000001, 2.2999999999999998, 2.7999999999999998, 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3.2000000000000002, 2.7999999999999998, 2.7999999999999998, 2.7000000000000002, 3.2999999999999998, 3.2000000000000002, 2.7999999999999998, 3.0, 2.7999999999999998, 3.0, 2.7999999999999998, 3.7999999999999998, 2.7999999999999998, 2.7999999999999998, 2.6000000000000001, 3.0, 3.3999999999999999, 3.1000000000000001, 3.0, 3.1000000000000001, 3.1000000000000001, 3.1000000000000001, 2.7000000000000002, 3.2000000000000002, 3.2999999999999998, 3.0, 2.5, 3.0, 3.3999999999999999, 3.0], 'Unnamed: 0': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150]}\n", "df = pd.DataFrame(raw_data, columns = ['Unnamed: 0', 'Sepal.Length', 'Sepal.Width', 'Petal.Length', 'Petal.Width', 'Species'])\n" ] } ], "source": [ "# Print the code to create the dataframe\n", "print('==============================')\n", "print('RUN THE CODE BELOW THIS LINE')\n", "print('==============================')\n", "print('raw_data =', df.to_dict(orient='list'))\n", "print('df = pd.DataFrame(raw_data, columns = ' + str(list(df_original)) + ')')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## If you want to check the results...\n", "\n", "### 1. Enter the code produced from the cell above in this cell" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "raw_data = {'Petal.Width': [0.20000000000000001, 0.20000000000000001, 0.20000000000000001, 0.20000000000000001, 0.20000000000000001, 0.40000000000000002, 0.29999999999999999, 0.20000000000000001, 0.20000000000000001, 0.10000000000000001, 0.20000000000000001, 0.20000000000000001, 0.10000000000000001, 0.10000000000000001, 0.20000000000000001, 0.40000000000000002, 0.40000000000000002, 0.29999999999999999, 0.29999999999999999, 0.29999999999999999, 0.20000000000000001, 0.40000000000000002, 0.20000000000000001, 0.5, 0.20000000000000001, 0.20000000000000001, 0.40000000000000002, 0.20000000000000001, 0.20000000000000001, 0.20000000000000001, 0.20000000000000001, 0.40000000000000002, 0.10000000000000001, 0.20000000000000001, 0.20000000000000001, 0.20000000000000001, 0.20000000000000001, 0.10000000000000001, 0.20000000000000001, 0.20000000000000001, 0.29999999999999999, 0.29999999999999999, 0.20000000000000001, 0.59999999999999998, 0.40000000000000002, 0.29999999999999999, 0.20000000000000001, 0.20000000000000001, 0.20000000000000001, 0.20000000000000001, 1.3999999999999999, 1.5, 1.5, 1.3, 1.5, 1.3, 1.6000000000000001, 1.0, 1.3, 1.3999999999999999, 1.0, 1.5, 1.0, 1.3999999999999999, 1.3, 1.3999999999999999, 1.5, 1.0, 1.5, 1.1000000000000001, 1.8, 1.3, 1.5, 1.2, 1.3, 1.3999999999999999, 1.3999999999999999, 1.7, 1.5, 1.0, 1.1000000000000001, 1.0, 1.2, 1.6000000000000001, 1.5, 1.6000000000000001, 1.5, 1.3, 1.3, 1.3, 1.2, 1.3999999999999999, 1.2, 1.0, 1.3, 1.2, 1.3, 1.3, 1.1000000000000001, 1.3, 2.5, 1.8999999999999999, 2.1000000000000001, 1.8, 2.2000000000000002, 2.1000000000000001, 1.7, 1.8, 1.8, 2.5, 2.0, 1.8999999999999999, 2.1000000000000001, 2.0, 2.3999999999999999, 2.2999999999999998, 1.8, 2.2000000000000002, 2.2999999999999998, 1.5, 2.2999999999999998, 2.0, 2.0, 1.8, 2.1000000000000001, 1.8, 1.8, 1.8, 2.1000000000000001, 1.6000000000000001, 1.8999999999999999, 2.0, 2.2000000000000002, 1.5, 1.3999999999999999, 2.2999999999999998, 2.3999999999999999, 1.8, 1.8, 2.1000000000000001, 2.3999999999999999, 2.2999999999999998, 1.8999999999999999, 2.2999999999999998, 2.5, 2.2999999999999998, 1.8999999999999999, 2.0, 2.2999999999999998, 1.8], 'Sepal.Width': [3.5, 3.0, 3.2000000000000002, 3.1000000000000001, 3.6000000000000001, 3.8999999999999999, 3.3999999999999999, 3.3999999999999999, 2.8999999999999999, 3.1000000000000001, 3.7000000000000002, 3.3999999999999999, 3.0, 3.0, 4.0, 4.4000000000000004, 3.8999999999999999, 3.5, 3.7999999999999998, 3.7999999999999998, 3.3999999999999999, 3.7000000000000002, 3.6000000000000001, 3.2999999999999998, 3.3999999999999999, 3.0, 3.3999999999999999, 3.5, 3.3999999999999999, 3.2000000000000002, 3.1000000000000001, 3.3999999999999999, 4.0999999999999996, 4.2000000000000002, 3.1000000000000001, 3.2000000000000002, 3.5, 3.6000000000000001, 3.0, 3.3999999999999999, 3.5, 2.2999999999999998, 3.2000000000000002, 3.5, 3.7999999999999998, 3.0, 3.7999999999999998, 3.2000000000000002, 3.7000000000000002, 3.2999999999999998, 3.2000000000000002, 3.2000000000000002, 3.1000000000000001, 2.2999999999999998, 2.7999999999999998, 2.7999999999999998, 3.2999999999999998, 2.3999999999999999, 2.8999999999999999, 2.7000000000000002, 2.0, 3.0, 2.2000000000000002, 2.8999999999999999, 2.8999999999999999, 3.1000000000000001, 3.0, 2.7000000000000002, 2.2000000000000002, 2.5, 3.2000000000000002, 2.7999999999999998, 2.5, 2.7999999999999998, 2.8999999999999999, 3.0, 2.7999999999999998, 3.0, 2.8999999999999999, 2.6000000000000001, 2.3999999999999999, 2.3999999999999999, 2.7000000000000002, 2.7000000000000002, 3.0, 3.3999999999999999, 3.1000000000000001, 2.2999999999999998, 3.0, 2.5, 2.6000000000000001, 3.0, 2.6000000000000001, 2.2999999999999998, 2.7000000000000002, 3.0, 2.8999999999999999, 2.8999999999999999, 2.5, 2.7999999999999998, 3.2999999999999998, 2.7000000000000002, 3.0, 2.8999999999999999, 3.0, 3.0, 2.5, 2.8999999999999999, 2.5, 3.6000000000000001, 3.2000000000000002, 2.7000000000000002, 3.0, 2.5, 2.7999999999999998, 3.2000000000000002, 3.0, 3.7999999999999998, 2.6000000000000001, 2.2000000000000002, 3.2000000000000002, 2.7999999999999998, 2.7999999999999998, 2.7000000000000002, 3.2999999999999998, 3.2000000000000002, 2.7999999999999998, 3.0, 2.7999999999999998, 3.0, 2.7999999999999998, 3.7999999999999998, 2.7999999999999998, 2.7999999999999998, 2.6000000000000001, 3.0, 3.3999999999999999, 3.1000000000000001, 3.0, 3.1000000000000001, 3.1000000000000001, 3.1000000000000001, 2.7000000000000002, 3.2000000000000002, 3.2999999999999998, 3.0, 2.5, 3.0, 3.3999999999999999, 3.0], 'Species': ['setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica'], 'Unnamed: 0': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150], 'Sepal.Length': [5.0999999999999996, 4.9000000000000004, 4.7000000000000002, 4.5999999999999996, 5.0, 5.4000000000000004, 4.5999999999999996, 5.0, 4.4000000000000004, 4.9000000000000004, 5.4000000000000004, 4.7999999999999998, 4.7999999999999998, 4.2999999999999998, 5.7999999999999998, 5.7000000000000002, 5.4000000000000004, 5.0999999999999996, 5.7000000000000002, 5.0999999999999996, 5.4000000000000004, 5.0999999999999996, 4.5999999999999996, 5.0999999999999996, 4.7999999999999998, 5.0, 5.0, 5.2000000000000002, 5.2000000000000002, 4.7000000000000002, 4.7999999999999998, 5.4000000000000004, 5.2000000000000002, 5.5, 4.9000000000000004, 5.0, 5.5, 4.9000000000000004, 4.4000000000000004, 5.0999999999999996, 5.0, 4.5, 4.4000000000000004, 5.0, 5.0999999999999996, 4.7999999999999998, 5.0999999999999996, 4.5999999999999996, 5.2999999999999998, 5.0, 7.0, 6.4000000000000004, 6.9000000000000004, 5.5, 6.5, 5.7000000000000002, 6.2999999999999998, 4.9000000000000004, 6.5999999999999996, 5.2000000000000002, 5.0, 5.9000000000000004, 6.0, 6.0999999999999996, 5.5999999999999996, 6.7000000000000002, 5.5999999999999996, 5.7999999999999998, 6.2000000000000002, 5.5999999999999996, 5.9000000000000004, 6.0999999999999996, 6.2999999999999998, 6.0999999999999996, 6.4000000000000004, 6.5999999999999996, 6.7999999999999998, 6.7000000000000002, 6.0, 5.7000000000000002, 5.5, 5.5, 5.7999999999999998, 6.0, 5.4000000000000004, 6.0, 6.7000000000000002, 6.2999999999999998, 5.5999999999999996, 5.5, 5.5, 6.0999999999999996, 5.7999999999999998, 5.0, 5.5999999999999996, 5.7000000000000002, 5.7000000000000002, 6.2000000000000002, 5.0999999999999996, 5.7000000000000002, 6.2999999999999998, 5.7999999999999998, 7.0999999999999996, 6.2999999999999998, 6.5, 7.5999999999999996, 4.9000000000000004, 7.2999999999999998, 6.7000000000000002, 7.2000000000000002, 6.5, 6.4000000000000004, 6.7999999999999998, 5.7000000000000002, 5.7999999999999998, 6.4000000000000004, 6.5, 7.7000000000000002, 7.7000000000000002, 6.0, 6.9000000000000004, 5.5999999999999996, 7.7000000000000002, 6.2999999999999998, 6.7000000000000002, 7.2000000000000002, 6.2000000000000002, 6.0999999999999996, 6.4000000000000004, 7.2000000000000002, 7.4000000000000004, 7.9000000000000004, 6.4000000000000004, 6.2999999999999998, 6.0999999999999996, 7.7000000000000002, 6.2999999999999998, 6.4000000000000004, 6.0, 6.9000000000000004, 6.7000000000000002, 6.9000000000000004, 5.7999999999999998, 6.7999999999999998, 6.7000000000000002, 6.7000000000000002, 6.2999999999999998, 6.5, 6.2000000000000002, 5.9000000000000004], 'Petal.Length': [1.3999999999999999, 1.3999999999999999, 1.3, 1.5, 1.3999999999999999, 1.7, 1.3999999999999999, 1.5, 1.3999999999999999, 1.5, 1.5, 1.6000000000000001, 1.3999999999999999, 1.1000000000000001, 1.2, 1.5, 1.3, 1.3999999999999999, 1.7, 1.5, 1.7, 1.5, 1.0, 1.7, 1.8999999999999999, 1.6000000000000001, 1.6000000000000001, 1.5, 1.3999999999999999, 1.6000000000000001, 1.6000000000000001, 1.5, 1.5, 1.3999999999999999, 1.5, 1.2, 1.3, 1.3999999999999999, 1.3, 1.5, 1.3, 1.3, 1.3, 1.6000000000000001, 1.8999999999999999, 1.3999999999999999, 1.6000000000000001, 1.3999999999999999, 1.5, 1.3999999999999999, 4.7000000000000002, 4.5, 4.9000000000000004, 4.0, 4.5999999999999996, 4.5, 4.7000000000000002, 3.2999999999999998, 4.5999999999999996, 3.8999999999999999, 3.5, 4.2000000000000002, 4.0, 4.7000000000000002, 3.6000000000000001, 4.4000000000000004, 4.5, 4.0999999999999996, 4.5, 3.8999999999999999, 4.7999999999999998, 4.0, 4.9000000000000004, 4.7000000000000002, 4.2999999999999998, 4.4000000000000004, 4.7999999999999998, 5.0, 4.5, 3.5, 3.7999999999999998, 3.7000000000000002, 3.8999999999999999, 5.0999999999999996, 4.5, 4.5, 4.7000000000000002, 4.4000000000000004, 4.0999999999999996, 4.0, 4.4000000000000004, 4.5999999999999996, 4.0, 3.2999999999999998, 4.2000000000000002, 4.2000000000000002, 4.2000000000000002, 4.2999999999999998, 3.0, 4.0999999999999996, 6.0, 5.0999999999999996, 5.9000000000000004, 5.5999999999999996, 5.7999999999999998, 6.5999999999999996, 4.5, 6.2999999999999998, 5.7999999999999998, 6.0999999999999996, 5.0999999999999996, 5.2999999999999998, 5.5, 5.0, 5.0999999999999996, 5.2999999999999998, 5.5, 6.7000000000000002, 6.9000000000000004, 5.0, 5.7000000000000002, 4.9000000000000004, 6.7000000000000002, 4.9000000000000004, 5.7000000000000002, 6.0, 4.7999999999999998, 4.9000000000000004, 5.5999999999999996, 5.7999999999999998, 6.0999999999999996, 6.4000000000000004, 5.5999999999999996, 5.0999999999999996, 5.5999999999999996, 6.0999999999999996, 5.5999999999999996, 5.5, 4.7999999999999998, 5.4000000000000004, 5.5999999999999996, 5.0999999999999996, 5.0999999999999996, 5.9000000000000004, 5.7000000000000002, 5.2000000000000002, 5.0, 5.2000000000000002, 5.4000000000000004, 5.0999999999999996]}\n", "df = pd.DataFrame(raw_data, columns = ['Unnamed: 0', 'Sepal.Length', 'Sepal.Width', 'Petal.Length', 'Petal.Width', 'Species'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2. Compare the original and recreated dataframes" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Unnamed: 0</th>\n", " <th>Sepal.Length</th>\n", " <th>Sepal.Width</th>\n", " <th>Petal.Length</th>\n", " <th>Petal.Width</th>\n", " <th>Species</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>5.1</td>\n", " <td>3.5</td>\n", " <td>1.4</td>\n", " <td>0.2</td>\n", " <td>setosa</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>4.9</td>\n", " <td>3.0</td>\n", " <td>1.4</td>\n", " <td>0.2</td>\n", " <td>setosa</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>3</td>\n", " <td>4.7</td>\n", " <td>3.2</td>\n", " <td>1.3</td>\n", " <td>0.2</td>\n", " <td>setosa</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>4</td>\n", " <td>4.6</td>\n", " <td>3.1</td>\n", " <td>1.5</td>\n", " <td>0.2</td>\n", " <td>setosa</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5</td>\n", " <td>5.0</td>\n", " <td>3.6</td>\n", " <td>1.4</td>\n", " <td>0.2</td>\n", " <td>setosa</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Unnamed: 0 Sepal.Length Sepal.Width Petal.Length Petal.Width Species\n", "0 1 5.1 3.5 1.4 0.2 setosa\n", "1 2 4.9 3.0 1.4 0.2 setosa\n", "2 3 4.7 3.2 1.3 0.2 setosa\n", "3 4 4.6 3.1 1.5 0.2 setosa\n", "4 5 5.0 3.6 1.4 0.2 setosa" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Look at the top few rows of the original dataframe\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Unnamed: 0</th>\n", " <th>Sepal.Length</th>\n", " <th>Sepal.Width</th>\n", " <th>Petal.Length</th>\n", " <th>Petal.Width</th>\n", " <th>Species</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>5.1</td>\n", " <td>3.5</td>\n", " <td>1.4</td>\n", " <td>0.2</td>\n", " <td>setosa</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>4.9</td>\n", " <td>3.0</td>\n", " <td>1.4</td>\n", " <td>0.2</td>\n", " <td>setosa</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>3</td>\n", " <td>4.7</td>\n", " <td>3.2</td>\n", " <td>1.3</td>\n", " <td>0.2</td>\n", " <td>setosa</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>4</td>\n", " <td>4.6</td>\n", " <td>3.1</td>\n", " <td>1.5</td>\n", " <td>0.2</td>\n", " <td>setosa</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5</td>\n", " <td>5.0</td>\n", " <td>3.6</td>\n", " <td>1.4</td>\n", " <td>0.2</td>\n", " <td>setosa</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Unnamed: 0 Sepal.Length Sepal.Width Petal.Length Petal.Width Species\n", "0 1 5.1 3.5 1.4 0.2 setosa\n", "1 2 4.9 3.0 1.4 0.2 setosa\n", "2 3 4.7 3.2 1.3 0.2 setosa\n", "3 4 4.6 3.1 1.5 0.2 setosa\n", "4 5 5.0 3.6 1.4 0.2 setosa" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Look at the top few rows of the dataframe created with our code\n", "df_original.head()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
google/jax	docs/notebooks/Common_Gotchas_in_JAX.ipynb	1	61852	{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "hjM_sV_AepYf" }, "source": [ "# 🔪 JAX - The Sharp Bits 🔪\n", "\n", "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/google/jax/blob/main/docs/notebooks/Common_Gotchas_in_JAX.ipynb)" ] }, { "cell_type": "markdown", "metadata": { "id": "4k5PVzEo2uJO" }, "source": [ "levskaya@ mattjj@\n", "\n", "When walking about the countryside of [Italy](https://iaml.it/blog/jax-intro), the people will not hesitate to tell you that __JAX__ has _\"una anima di pura programmazione funzionale\"_.\n", "\n", "__JAX__ is a language for __expressing__ and __composing__ __transformations__ of numerical programs. __JAX__ is also able to __compile__ numerical programs for CPU or accelerators (GPU/TPU). \n", "JAX works great for many numerical and scientific programs, but __only if they are written with certain constraints__ that we describe below." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "id": "GoK_PCxPeYcy" }, "outputs": [], "source": [ "import numpy as np\n", "from jax import grad, jit\n", "from jax import lax\n", "from jax import random\n", "import jax\n", "import jax.numpy as jnp\n", "import matplotlib as mpl\n", "from matplotlib import pyplot as plt\n", "from matplotlib import rcParams\n", "rcParams['image.interpolation'] = 'nearest'\n", "rcParams['image.cmap'] = 'viridis'\n", "rcParams['axes.grid'] = False" ] }, { "cell_type": "markdown", "metadata": { "id": "gX8CZU1g2agP" }, "source": [ "## 🔪 Pure functions" ] }, { "cell_type": "markdown", "metadata": { "id": "2oHigBkW2dPT" }, "source": [ "JAX transformation and compilation are designed to work only on Python functions that are functionally pure: all the input data is passed through the function parameters, all the results are output through the function results. A pure function will always return the same result if invoked with the same inputs. \n", "\n", "Here are some examples of functions that are not functionally pure for which JAX behaves differently than the Python interpreter. Note that these behaviors are not guaranteed by the JAX system; the proper way to use JAX is to use it only on functionally pure Python functions." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "A6R-pdcm4u3v", "outputId": "25dcb191-14d4-4620-bcb2-00492d2f24e1" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Executing function\n", "First call: 4.0\n", "Second call: 5.0\n", "Executing function\n", "Third call, different type: [5.]\n" ] } ], "source": [ "def impure_print_side_effect(x):\n", " print(\"Executing function\") # This is a side-effect \n", " return x\n", "\n", "# The side-effects appear during the first run \n", "print (\"First call: \", jit(impure_print_side_effect)(4.))\n", "\n", "# Subsequent runs with parameters of same type and shape may not show the side-effect\n", "# This is because JAX now invokes a cached compilation of the function\n", "print (\"Second call: \", jit(impure_print_side_effect)(5.))\n", "\n", "# JAX re-runs the Python function when the type or shape of the argument changes\n", "print (\"Third call, different type: \", jit(impure_print_side_effect)(jnp.array([5.])))" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "-N8GhitI2bhD", "outputId": "fd3624c9-197d-42cb-d97f-c5e0ef885467" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "First call: 4.0\n", "Second call: 5.0\n", "Third call, different type: [14.]\n" ] } ], "source": [ "g = 0.\n", "def impure_uses_globals(x):\n", " return x + g\n", "\n", "# JAX captures the value of the global during the first run\n", "print (\"First call: \", jit(impure_uses_globals)(4.))\n", "g = 10. # Update the global\n", "\n", "# Subsequent runs may silently use the cached value of the globals\n", "print (\"Second call: \", jit(impure_uses_globals)(5.))\n", "\n", "# JAX re-runs the Python function when the type or shape of the argument changes\n", "# This will end up reading the latest value of the global\n", "print (\"Third call, different type: \", jit(impure_uses_globals)(jnp.array([4.])))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "RTB6iFgu4DL6", "outputId": "16697bcd-3623-49b1-aabb-c54614aeadea" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "First call: 4.0\n", "Saved global: Traced<ShapedArray(float32[], weak_type=True)>with<DynamicJaxprTrace(level=0/1)>\n" ] } ], "source": [ "g = 0.\n", "def impure_saves_global(x):\n", " global g\n", " g = x\n", " return x\n", "\n", "# JAX runs once the transformed function with special Traced values for arguments\n", "print (\"First call: \", jit(impure_saves_global)(4.))\n", "print (\"Saved global: \", g) # Saved global has an internal JAX value" ] }, { "cell_type": "markdown", "metadata": { "id": "Mlc2pQlp6v-9" }, "source": [ "A Python function can be functionally pure even if it actually uses stateful objects internally, as long as it does not read or write external state:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "TP-Mqf_862C0", "outputId": "78d55886-54de-483c-e7c4-bafd1d2c7219" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "50.0\n" ] } ], "source": [ "def pure_uses_internal_state(x):\n", " state = dict(even=0, odd=0)\n", " for i in range(10):\n", " state['even' if i % 2 == 0 else 'odd'] += x\n", " return state['even'] + state['odd']\n", "\n", "print(jit(pure_uses_internal_state)(5.))" ] }, { "cell_type": "markdown", "metadata": { "id": "cDpQ5u63Ba_H" }, "source": [ "It is not recommended to use iterators in any JAX function you want to `jit` or in any control-flow primitive. The reason is that an iterator is a python object which introduces state to retrieve the next element. Therefore, it is incompatible with JAX functional programming model. In the code below, there are some examples of incorrect attempts to use iterators with JAX. Most of them return an error, but some give unexpected results." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "w99WXa6bBa_H", "outputId": "52d885fd-0239-4a08-f5ce-0c38cc008903" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "45\n", "0\n" ] } ], "source": [ "import jax.numpy as jnp\n", "import jax.lax as lax\n", "from jax import make_jaxpr\n", "\n", "# lax.fori_loop\n", "array = jnp.arange(10)\n", "print(lax.fori_loop(0, 10, lambda i,x: x+array[i], 0)) # expected result 45\n", "iterator = iter(range(10))\n", "print(lax.fori_loop(0, 10, lambda i,x: x+next(iterator), 0)) # unexpected result 0\n", "\n", "# lax.scan\n", "def func11(arr, extra):\n", " ones = jnp.ones(arr.shape) \n", " def body(carry, aelems):\n", " ae1, ae2 = aelems\n", " return (carry + ae1 * ae2 + extra, carry)\n", " return lax.scan(body, 0., (arr, ones)) \n", "make_jaxpr(func11)(jnp.arange(16), 5.)\n", "# make_jaxpr(func11)(iter(range(16)), 5.) # throws error\n", "\n", "# lax.cond\n", "array_operand = jnp.array([0.])\n", "lax.cond(True, lambda x: x+1, lambda x: x-1, array_operand)\n", "iter_operand = iter(range(10))\n", "# lax.cond(True, lambda x: next(x)+1, lambda x: next(x)-1, iter_operand) # throws error" ] }, { "cell_type": "markdown", "metadata": { "id": "oBdKtkVW8Lha" }, "source": [ "## 🔪 In-Place Updates" ] }, { "cell_type": "markdown", "metadata": { "id": "JffAqnEW4JEb" }, "source": [ "In Numpy you're used to doing this:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "om4xV7_84N9j", "outputId": "88b0074a-4440-41f6-caa7-031ac2d1a96f" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "original array:\n", "[[0. 0. 0.]\n", " [0. 0. 0.]\n", " [0. 0. 0.]]\n", "updated array:\n", "[[0. 0. 0.]\n", " [1. 1. 1.]\n", " [0. 0. 0.]]\n" ] } ], "source": [ "numpy_array = np.zeros((3,3), dtype=np.float32)\n", "print(\"original array:\")\n", "print(numpy_array)\n", "\n", "# In place, mutating update\n", "numpy_array[1, :] = 1.0\n", "print(\"updated array:\")\n", "print(numpy_array)" ] }, { "cell_type": "markdown", "metadata": { "id": "go3L4x3w4-9p" }, "source": [ "If we try to update a JAX device array in-place, however, we get an __error__! (☉_☉)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "2AxeCufq4wAp", "outputId": "fa4a87ad-1a84-471a-a3c5-a1396c432c85", "tags": [ "raises-exception" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Exception '<class 'jaxlib.xla_extension.DeviceArray'>' object does not support item assignment. JAX arrays are immutable. Instead of ``x[idx] = y``, use ``x = x.at[idx].set(y)`` or another .at[] method: https://jax.readthedocs.io/en/latest/jax.ops.html\n" ] } ], "source": [ "jax_array = jnp.zeros((3,3), dtype=jnp.float32)\n", "\n", "# In place update of JAX's array will yield an error!\n", "try:\n", " jax_array[1, :] = 1.0\n", "except Exception as e:\n", " print(\"Exception {}\".format(e))" ] }, { "cell_type": "markdown", "metadata": { "id": "7mo76sS25Wco" }, "source": [ "Allowing mutation of variables in-place makes program analysis and transformation difficult. JAX requires that programs are pure functions.\n", "\n", "Instead, JAX offers a _functional_ array update using the [`.at` property on JAX arrays](https://jax.readthedocs.io/en/latest/_autosummary/jax.numpy.ndarray.at.html#jax.numpy.ndarray.at)." ] }, { "cell_type": "markdown", "metadata": { "id": "hfloZ1QXCS_J" }, "source": [ "️⚠️ inside `jit`'d code and `lax.while_loop` or `lax.fori_loop` the __size__ of slices can't be functions of argument _values_ but only functions of argument _shapes_ -- the slice start indices have no such restriction. See the below __Control Flow__ Section for more information on this limitation." ] }, { "cell_type": "markdown", "metadata": { "id": "X2Xjjvd-l8NL" }, "source": [ "### Array updates: `x.at[idx].set(y)`" ] }, { "cell_type": "markdown", "metadata": { "id": "SHLY52KQEiuX" }, "source": [ "For example, the update above can be written as:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "PBGI-HIeCP_s", "outputId": "de13f19a-2066-4df1-d503-764c34585529" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "updated array:\n", " [[0. 0. 0.]\n", " [1. 1. 1.]\n", " [0. 0. 0.]]\n" ] } ], "source": [ "updated_array = jax_array.at[1, :].set(1.0)\n", "print(\"updated array:\\n\", updated_array)" ] }, { "cell_type": "markdown", "metadata": { "id": "zUANAw9sCmgu" }, "source": [ "JAX's array update functions, unlike their NumPy versions, operate out-of-place. That is, the updated array is returned as a new array and the original array is not modified by the update." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "dbB0UmMhCe8f", "outputId": "55d46fa1-d0de-4c43-996c-f3bbc87b7175" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "original array unchanged:\n", " [[0. 0. 0.]\n", " [0. 0. 0.]\n", " [0. 0. 0.]]\n" ] } ], "source": [ "print(\"original array unchanged:\\n\", jax_array)" ] }, { "cell_type": "markdown", "metadata": { "id": "eM6MyndXL2NY" }, "source": [ "However, inside __jit__-compiled code, if the __input value__ `x` of `x.at[idx].set(y)` is not reused, the compiler will optimize the array update to occur _in-place_." ] }, { "cell_type": "markdown", "metadata": { "id": "7to-sF8EmC_y" }, "source": [ "### Array updates with other operations" ] }, { "cell_type": "markdown", "metadata": { "id": "ZY5l3tAdDmsJ" }, "source": [ "Indexed array updates are not limited simply to overwriting values. For example, we can perform indexed addition as follows:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "tsw2svao8FUp", "outputId": "3c62a3b1-c12d-46f0-da74-791ec4b61e0b" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "original array:\n", "[[1. 1. 1. 1. 1. 1.]\n", " [1. 1. 1. 1. 1. 1.]\n", " [1. 1. 1. 1. 1. 1.]\n", " [1. 1. 1. 1. 1. 1.]\n", " [1. 1. 1. 1. 1. 1.]]\n", "new array post-addition:\n", "[[1. 1. 1. 8. 8. 8.]\n", " [1. 1. 1. 1. 1. 1.]\n", " [1. 1. 1. 8. 8. 8.]\n", " [1. 1. 1. 1. 1. 1.]\n", " [1. 1. 1. 8. 8. 8.]]\n" ] } ], "source": [ "print(\"original array:\")\n", "jax_array = jnp.ones((5, 6))\n", "print(jax_array)\n", "\n", "new_jax_array = jax_array.at[::2, 3:].add(7.)\n", "print(\"new array post-addition:\")\n", "print(new_jax_array)" ] }, { "cell_type": "markdown", "metadata": { "id": "sTjJ3WuaDyqU" }, "source": [ "For more details on indexed array updates, see the [documentation for the `.at` property](https://jax.readthedocs.io/en/latest/_autosummary/jax.numpy.ndarray.at.html#jax.numpy.ndarray.at)." ] }, { "cell_type": "markdown", "metadata": { "id": "oZ_jE2WAypdL" }, "source": [ "## 🔪 Out-of-Bounds Indexing" ] }, { "cell_type": "markdown", "metadata": { "id": "btRFwEVzypdN" }, "source": [ "In Numpy, you are used to errors being thrown when you index an array outside of its bounds, like this:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "5_ZM-BJUypdO", "outputId": "c9c41ae8-2653-4219-e6dc-09b03faa3b95", "tags": [ "raises-exception" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Exception index 11 is out of bounds for axis 0 with size 10\n" ] } ], "source": [ "try:\n", " np.arange(10)[11]\n", "except Exception as e:\n", " print(\"Exception {}\".format(e))" ] }, { "cell_type": "markdown", "metadata": { "id": "eoXrGARWypdR" }, "source": [ "However, raising an error from code running on an accelerator can be difficult or impossible. Therefore, JAX must choose some non-error behavior for out of bounds indexing (akin to how invalid floating point arithmetic results in `NaN`). When the indexing operation is an array index update (e.g. `index_add` or `scatter`-like primitives), updates at out-of-bounds indices will be skipped; when the operation is an array index retrieval (e.g. NumPy indexing or `gather`-like primitives) the index is clamped to the bounds of the array since __something__ must be returned. For example, the last value of the array will be returned from this indexing operation:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "cusaAD0NypdR", "outputId": "af1708aa-b50b-4da8-f022-7f2fa67030a8" }, "outputs": [ { "data": { "text/plain": [ "DeviceArray(9, dtype=int32)" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "jnp.arange(10)[11]" ] }, { "cell_type": "markdown", "metadata": { "id": "J8uO8yevBa_M" }, "source": [ "Note that due to this behavior for index retrieval, functions like `jnp.nanargmin` and `jnp.nanargmax` return -1 for slices consisting of NaNs whereas Numpy would throw an error.\n", "\n", "Note also that, as the two behaviors described above are not inverses of each other, reverse-mode automatic differentiation (which turns index updates into index retrievals and vice versa) [will not preserve the semantics of out of bounds indexing](https://github.com/google/jax/issues/5760). Thus it may be a good idea to think of out-of-bounds indexing in JAX as a case of [undefined behavior](https://en.wikipedia.org/wiki/Undefined_behavior)." ] }, { "cell_type": "markdown", "metadata": { "id": "LwB07Kx5sgHu" }, "source": [ "## 🔪 Non-array inputs: NumPy vs. JAX\n", "\n", "NumPy is generally happy accepting Python lists or tuples as inputs to its API functions:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "sErQES14sjCG", "outputId": "601485ff-4cda-48c5-f76c-2789073c4591" }, "outputs": [ { "data": { "text/plain": [ "6" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.sum([1, 2, 3])" ] }, { "cell_type": "markdown", "metadata": { "id": "ZJ1Wt1bTtrSA" }, "source": [ "JAX departs from this, generally returning a helpful error:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "DFEGcENSsmEc", "outputId": "08535679-6c1f-4dd9-a414-d8b59310d1ee" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "TypeError: sum requires ndarray or scalar arguments, got <class 'list'> at position 0.\n" ] } ], "source": [ "try:\n", " jnp.sum([1, 2, 3])\n", "except TypeError as e:\n", " print(f\"TypeError: {e}\")" ] }, { "cell_type": "markdown", "metadata": { "id": "QPliLUZztxJt" }, "source": [ "This is a deliberate design choice, because passing lists or tuples to traced functions can lead to silent performance degradation that might otherwise be difficult to detect.\n", "\n", "For example, consider the following permissive version of `jnp.sum` that allows list inputs:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "jhe-L_TwsvKd", "outputId": "ab2ee183-d9ec-45cc-d6be-5009347e1bc5" }, "outputs": [ { "data": { "text/plain": [ "DeviceArray(45, dtype=int32)" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def permissive_sum(x):\n", " return jnp.sum(jnp.array(x))\n", "\n", "x = list(range(10))\n", "permissive_sum(x)" ] }, { "cell_type": "markdown", "metadata": { "id": "m0XZLP7nuYdE" }, "source": [ "The output is what we would expect, but this hides potential performance issues under the hood. In JAX's tracing and JIT compilation model, each element in a Python list or tuple is treated as a separate JAX variable, and individually processed and pushed to device. This can be seen in the jaxpr for the ``permissive_sum`` function above:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "k81u6DQ7vAjQ", "outputId": "869fc3b9-feda-4aa9-d2e5-5b5107de102d" }, "outputs": [ { "data": { "text/plain": [ "{ lambda ; a b c d e f g h i j.\n", " let k = broadcast_in_dim[ broadcast_dimensions=( )\n", " shape=(1,) ] a\n", " l = broadcast_in_dim[ broadcast_dimensions=( )\n", " shape=(1,) ] b\n", " m = broadcast_in_dim[ broadcast_dimensions=( )\n", " shape=(1,) ] c\n", " n = broadcast_in_dim[ broadcast_dimensions=( )\n", " shape=(1,) ] d\n", " o = broadcast_in_dim[ broadcast_dimensions=( )\n", " shape=(1,) ] e\n", " p = broadcast_in_dim[ broadcast_dimensions=( )\n", " shape=(1,) ] f\n", " q = broadcast_in_dim[ broadcast_dimensions=( )\n", " shape=(1,) ] g\n", " r = broadcast_in_dim[ broadcast_dimensions=( )\n", " shape=(1,) ] h\n", " s = broadcast_in_dim[ broadcast_dimensions=( )\n", " shape=(1,) ] i\n", " t = broadcast_in_dim[ broadcast_dimensions=( )\n", " shape=(1,) ] j\n", " u = concatenate[ dimension=0 ] k l m n o p q r s t\n", " v = convert_element_type[ new_dtype=int32\n", " weak_type=False ] u\n", " w = reduce_sum[ axes=(0,) ] v\n", " in (w,) }" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "make_jaxpr(permissive_sum)(x)" ] }, { "cell_type": "markdown", "metadata": { "id": "C0_dpCfpvCts" }, "source": [ "Each entry of the list is handled as a separate input, resulting in a tracing & compilation overhead that grows linearly with the size of the list. To prevent surprises like this, JAX avoids implicit conversions of lists and tuples to arrays.\n", "\n", "If you would like to pass a tuple or list to a JAX function, you can do so by first explicitly converting it to an array:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "nFf_DydixG8v", "outputId": "e31b43b3-05f7-4300-fdd2-40e3896f6f8f" }, "outputs": [ { "data": { "text/plain": [ "DeviceArray(45, dtype=int32)" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "jnp.sum(jnp.array(x))" ] }, { "cell_type": "markdown", "metadata": { "id": "MUycRNh6e50W" }, "source": [ "## 🔪 Random Numbers" ] }, { "cell_type": "markdown", "metadata": { "id": "O8vvaVt3MRG2" }, "source": [ "> _If all scientific papers whose results are in doubt because of bad \n", "> `rand()`s were to disappear from library shelves, there would be a \n", "> gap on each shelf about as big as your fist._ - Numerical Recipes" ] }, { "cell_type": "markdown", "metadata": { "id": "Qikt9pPW9L5K" }, "source": [ "### RNGs and State\n", "You're used to _stateful_ pseudorandom number generators (PRNGs) from numpy and other libraries, which helpfully hide a lot of details under the hood to give you a ready fountain of pseudorandomness:" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "rr9FeP41fynt", "outputId": "df0ceb15-96ec-4a78-e327-c77f7ea3a745" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.07022903604194575\n", "0.11575983097278075\n", "0.15620432311959775\n" ] } ], "source": [ "print(np.random.random())\n", "print(np.random.random())\n", "print(np.random.random())" ] }, { "cell_type": "markdown", "metadata": { "id": "ORMVVGZJgSVi" }, "source": [ "Underneath the hood, numpy uses the [Mersenne Twister](https://en.wikipedia.org/wiki/Mersenne_Twister) PRNG to power its pseudorandom functions. The PRNG has a period of $2^{19937}-1$ and at any point can be described by __624 32bit unsigned ints__ and a __position__ indicating how much of this \"entropy\" has been used up." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "id": "7Pyp2ajzfPO2" }, "outputs": [], "source": [ "np.random.seed(0)\n", "rng_state = np.random.get_state()\n", "#print(rng_state)\n", "# --> ('MT19937', array([0, 1, 1812433255, 1900727105, 1208447044,\n", "# 2481403966, 4042607538, 337614300, ... 614 more numbers..., \n", "# 3048484911, 1796872496], dtype=uint32), 624, 0, 0.0)" ] }, { "cell_type": "markdown", "metadata": { "id": "aJIxHVXCiM6m" }, "source": [ "This pseudorandom state vector is automagically updated behind the scenes every time a random number is needed, \"consuming\" 2 of the uint32s in the Mersenne twister state vector:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "id": "GAHaDCYafpAF" }, "outputs": [], "source": [ "_ = np.random.uniform()\n", "rng_state = np.random.get_state()\n", "#print(rng_state) \n", "# --> ('MT19937', array([2443250962, 1093594115, 1878467924,\n", "# ..., 2648828502, 1678096082], dtype=uint32), 2, 0, 0.0)\n", "\n", "# Let's exhaust the entropy in this PRNG statevector\n", "for i in range(311):\n", " _ = np.random.uniform()\n", "rng_state = np.random.get_state()\n", "#print(rng_state) \n", "# --> ('MT19937', array([2443250962, 1093594115, 1878467924,\n", "# ..., 2648828502, 1678096082], dtype=uint32), 624, 0, 0.0)\n", "\n", "# Next call iterates the RNG state for a new batch of fake \"entropy\".\n", "_ = np.random.uniform()\n", "rng_state = np.random.get_state()\n", "# print(rng_state) \n", "# --> ('MT19937', array([1499117434, 2949980591, 2242547484, \n", "# 4162027047, 3277342478], dtype=uint32), 2, 0, 0.0)" ] }, { "cell_type": "markdown", "metadata": { "id": "N_mWnleNogps" }, "source": [ "The problem with magic PRNG state is that it's hard to reason about how it's being used and updated across different threads, processes, and devices, and it's _very easy_ to screw up when the details of entropy production and consumption are hidden from the end user.\n", "\n", "The Mersenne Twister PRNG is also known to have a [number](https://cs.stackexchange.com/a/53475) of problems, it has a large 2.5Kb state size, which leads to problematic [initialization issues](https://dl.acm.org/citation.cfm?id=1276928). It [fails](http://www.pcg-random.org/pdf/toms-oneill-pcg-family-v1.02.pdf) modern BigCrush tests, and is generally slow." ] }, { "cell_type": "markdown", "metadata": { "id": "Uvq7nV-j4vKK" }, "source": [ "### JAX PRNG" ] }, { "cell_type": "markdown", "metadata": { "id": "COjzGBpO4tzL" }, "source": [ "JAX instead implements an _explicit_ PRNG where entropy production and consumption are handled by explicitly passing and iterating PRNG state. JAX uses a modern [Threefry counter-based PRNG](https://github.com/google/jax/blob/main/design_notes/prng.md) that's __splittable__. That is, its design allows us to __fork__ the PRNG state into new PRNGs for use with parallel stochastic generation.\n", "\n", "The random state is described by two unsigned-int32s that we call a __key__:" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "yPHE7KTWgAWs", "outputId": "ae8af0ee-f19e-474e-81b6-45e894eb2fc3" }, "outputs": [ { "data": { "text/plain": [ "DeviceArray([0, 0], dtype=uint32)" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from jax import random\n", "key = random.PRNGKey(0)\n", "key" ] }, { "cell_type": "markdown", "metadata": { "id": "XjYyWYNfq0hW" }, "source": [ "JAX's random functions produce pseudorandom numbers from the PRNG state, but __do not__ change the state! \n", "\n", "Reusing the same state will cause __sadness__ and __monotony__, depriving the end user of __lifegiving chaos__:" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "7zUdQMynoE5e", "outputId": "23a86b72-dfb9-410a-8e68-22b48dc10805" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[-0.20584226]\n", "[0 0]\n", "[-0.20584226]\n", "[0 0]\n" ] } ], "source": [ "print(random.normal(key, shape=(1,)))\n", "print(key)\n", "# No no no!\n", "print(random.normal(key, shape=(1,)))\n", "print(key)" ] }, { "cell_type": "markdown", "metadata": { "id": "hQN9van8rJgd" }, "source": [ "Instead, we __split__ the PRNG to get usable __subkeys__ every time we need a new pseudorandom number:" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ASj0_rSzqgGh", "outputId": "2f13f249-85d1-47bb-d503-823eca6961aa" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "old key [0 0]\n", " \\---SPLIT --> new key [4146024105 967050713]\n", " \\--> new subkey [2718843009 1272950319] --> normal [-1.2515389]\n" ] } ], "source": [ "print(\"old key\", key)\n", "key, subkey = random.split(key)\n", "normal_pseudorandom = random.normal(subkey, shape=(1,))\n", "print(\" \\---SPLIT --> new key \", key)\n", "print(\" \\--> new subkey\", subkey, \"--> normal\", normal_pseudorandom)" ] }, { "cell_type": "markdown", "metadata": { "id": "tqtFVE4MthO3" }, "source": [ "We propagate the __key__ and make new __subkeys__ whenever we need a new random number:" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "jbC34XLor2Ek", "outputId": "4059a2e2-0205-40bc-ad55-17709d538871" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "old key [4146024105 967050713]\n", " \\---SPLIT --> new key [2384771982 3928867769]\n", " \\--> new subkey [1278412471 2182328957] --> normal [-0.58665055]\n" ] } ], "source": [ "print(\"old key\", key)\n", "key, subkey = random.split(key)\n", "normal_pseudorandom = random.normal(subkey, shape=(1,))\n", "print(\" \\---SPLIT --> new key \", key)\n", "print(\" \\--> new subkey\", subkey, \"--> normal\", normal_pseudorandom)" ] }, { "cell_type": "markdown", "metadata": { "id": "0KLYUluz3lN3" }, "source": [ "We can generate more than one __subkey__ at a time:" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "lEi08PJ4tfkX", "outputId": "1f280560-155d-4c04-98e8-c41d72ee5b01" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[-0.37533438]\n", "[0.98645043]\n", "[0.14553197]\n" ] } ], "source": [ "key, subkeys = random.split(key, 4)\n", "for subkey in subkeys:\n", " print(random.normal(subkey, shape=(1,)))" ] }, { "cell_type": "markdown", "metadata": { "id": "rg4CpMZ8c3ri" }, "source": [ "## 🔪 Control Flow" ] }, { "cell_type": "markdown", "metadata": { "id": "izLTvT24dAq0" }, "source": [ "### ✔ python control_flow + autodiff ✔\n", "\n", "If you just want to apply `grad` to your python functions, you can use regular python control-flow constructs with no problems, as if you were using [Autograd](https://github.com/hips/autograd) (or Pytorch or TF Eager)." ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "aAx0T3F8lLtu", "outputId": "383b7bfa-1634-4d23-8497-49cb9452ca52" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "12.0\n", "-4.0\n" ] } ], "source": [ "def f(x):\n", " if x < 3:\n", " return 3. x ** 2\n", " else:\n", " return -4 * x\n", "\n", "print(grad(f)(2.)) # ok!\n", "print(grad(f)(4.)) # ok!" ] }, { "cell_type": "markdown", "metadata": { "id": "hIfPT7WMmZ2H" }, "source": [ "### python control flow + JIT\n", "\n", "Using control flow with `jit` is more complicated, and by default it has more constraints.\n", "\n", "This works:" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "OZ_BJX0CplNC", "outputId": "60c902a2-eba1-49d7-c8c8-2f68616d660c" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "24\n" ] } ], "source": [ "@jit\n", "def f(x):\n", " for i in range(3):\n", " x = 2 * x\n", " return x\n", "\n", "print(f(3))" ] }, { "cell_type": "markdown", "metadata": { "id": "22RzeJ4QqAuX" }, "source": [ "So does this:" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "pinVnmRWp6w6", "outputId": "25e06cf2-474f-4782-af7c-4f5514b64422" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "6.0\n" ] } ], "source": [ "@jit\n", "def g(x):\n", " y = 0.\n", " for i in range(x.shape[0]):\n", " y = y + x[i]\n", " return y\n", "\n", "print(g(jnp.array([1., 2., 3.])))" ] }, { "cell_type": "markdown", "metadata": { "id": "TStltU2dqf8A" }, "source": [ "But this doesn't, at least by default:" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "9z38AIKclRNM", "outputId": "38dd2075-92fc-4b81-fee0-b9dff8da1fac" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Exception Abstract tracer value encountered where concrete value is expected: Traced<ShapedArray(bool[], weak_type=True)>with<DynamicJaxprTrace(level=0/1)>\n", "The problem arose with the `bool` function. \n", "While tracing the function f at <ipython-input-30-b42e45c0293f>:1 for jit, this concrete value was not available in Python because it depends on the value of the argument 'x'.\n", "\n", "See https://jax.readthedocs.io/en/latest/errors.html#jax.errors.ConcretizationTypeError\n" ] } ], "source": [ "@jit\n", "def f(x):\n", " if x < 3:\n", " return 3. * x ** 2\n", " else:\n", " return -4 * x\n", "\n", "# This will fail!\n", "try:\n", " f(2)\n", "except Exception as e:\n", " print(\"Exception {}\".format(e))" ] }, { "cell_type": "markdown", "metadata": { "id": "pIbr4TVPqtDN" }, "source": [ "__What gives!?__\n", "\n", "When we `jit`-compile a function, we usually want to compile a version of the function that works for many different argument values, so that we can cache and reuse the compiled code. That way we don't have to re-compile on each function evaluation.\n", "\n", "For example, if we evaluate an `@jit` function on the array `jnp.array([1., 2., 3.], jnp.float32)`, we might want to compile code that we can reuse to evaluate the function on `jnp.array([4., 5., 6.], jnp.float32)` to save on compile time.\n", "\n", "To get a view of your Python code that is valid for many different argument values, JAX traces it on _abstract values_ that represent sets of possible inputs. There are [multiple different levels of abstraction](https://github.com/google/jax/blob/main/jax/_src/abstract_arrays.py), and different transformations use different abstraction levels.\n", "\n", "By default, `jit` traces your code on the `ShapedArray` abstraction level, where each abstract value represents the set of all array values with a fixed shape and dtype. For example, if we trace using the abstract value `ShapedArray((3,), jnp.float32)`, we get a view of the function that can be reused for any concrete value in the corresponding set of arrays. That means we can save on compile time.\n", "\n", "But there's a tradeoff here: if we trace a Python function on a `ShapedArray((), jnp.float32)` that isn't committed to a specific concrete value, when we hit a line like `if x < 3`, the expression `x < 3` evaluates to an abstract `ShapedArray((), jnp.bool_)` that represents the set `{True, False}`. When Python attempts to coerce that to a concrete `True` or `False`, we get an error: we don't know which branch to take, and can't continue tracing! The tradeoff is that with higher levels of abstraction we gain a more general view of the Python code (and thus save on re-compilations), but we require more constraints on the Python code to complete the trace.\n", "\n", "The good news is that you can control this tradeoff yourself. By having `jit` trace on more refined abstract values, you can relax the traceability constraints. For example, using the `static_argnums` argument to `jit`, we can specify to trace on concrete values of some arguments. Here's that example function again:" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "-Tzp0H7Bt1Sn", "outputId": "f7f664cb-2cd0-4fd7-c685-4ec6ba1c4b7a" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "12.0\n" ] } ], "source": [ "def f(x):\n", " if x < 3:\n", " return 3. * x ** 2\n", " else:\n", " return -4 * x\n", "\n", "f = jit(f, static_argnums=(0,))\n", "\n", "print(f(2.))" ] }, { "cell_type": "markdown", "metadata": { "id": "MHm1hIQAvBVs" }, "source": [ "Here's another example, this time involving a loop:" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "iwY86_JKvD6b", "outputId": "48f9b51f-bd32-466f-eac1-cd23444ce937" }, "outputs": [ { "data": { "text/plain": [ "DeviceArray(5., dtype=float32)" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def f(x, n):\n", " y = 0.\n", " for i in range(n):\n", " y = y + x[i]\n", " return y\n", "\n", "f = jit(f, static_argnums=(1,))\n", "\n", "f(jnp.array([2., 3., 4.]), 2)" ] }, { "cell_type": "markdown", "metadata": { "id": "nSPTOX8DvOeO" }, "source": [ "In effect, the loop gets statically unrolled. JAX can also trace at _higher_ levels of abstraction, like `Unshaped`, but that's not currently the default for any transformation" ] }, { "cell_type": "markdown", "metadata": { "id": "wWdg8LTYwCW3" }, "source": [ "️⚠️ functions with argument-__value__ dependent shapes\n", "\n", "These control-flow issues also come up in a more subtle way: numerical functions we want to __jit__ can't specialize the shapes of internal arrays on argument _values_ (specializing on argument __shapes__ is ok). As a trivial example, let's make a function whose output happens to depend on the input variable `length`." ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Tqe9uLmUI_Gv", "outputId": "989be121-dfce-4bb3-c78e-a10829c5f883" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[4. 4. 4. 4. 4.]\n", "Exception Shapes must be 1D sequences of concrete values of integer type, got (Traced<ShapedArray(int32[], weak_type=True)>with<DynamicJaxprTrace(level=0/1)>,).\n", "If using `jit`, try using `static_argnums` or applying `jit` to smaller subfunctions.\n", "[4. 4. 4. 4. 4. 4. 4. 4. 4. 4.]\n", "[4. 4. 4. 4. 4.]\n" ] } ], "source": [ "def example_fun(length, val):\n", " return jnp.ones((length,)) * val\n", "# un-jit'd works fine\n", "print(example_fun(5, 4))\n", "\n", "bad_example_jit = jit(example_fun)\n", "# this will fail:\n", "try:\n", " print(bad_example_jit(10, 4))\n", "except Exception as e:\n", " print(\"Exception {}\".format(e))\n", "# static_argnums tells JAX to recompile on changes at these argument positions:\n", "good_example_jit = jit(example_fun, static_argnums=(0,))\n", "# first compile\n", "print(good_example_jit(10, 4))\n", "# recompiles\n", "print(good_example_jit(5, 4))" ] }, { "cell_type": "markdown", "metadata": { "id": "MStx_r2oKxpp" }, "source": [ "`static_argnums` can be handy if `length` in our example rarely changes, but it would be disastrous if it changed a lot! \n", "\n", "Lastly, if your function has global side-effects, JAX's tracer can cause weird things to happen. A common gotcha is trying to print arrays inside __jit__'d functions:" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "m2ABpRd8K094", "outputId": "4f7ebe17-ade4-4e18-bd8c-4b24087c33c3" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Traced<ShapedArray(int32[], weak_type=True)>with<DynamicJaxprTrace(level=0/1)>\n", "Traced<ShapedArray(int32[], weak_type=True)>with<DynamicJaxprTrace(level=0/1)>\n" ] }, { "data": { "text/plain": [ "DeviceArray(4, dtype=int32, weak_type=True)" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "@jit\n", "def f(x):\n", " print(x)\n", " y = 2 * x\n", " print(y)\n", " return y\n", "f(2)" ] }, { "cell_type": "markdown", "metadata": { "id": "uCDcWG4MnVn-" }, "source": [ "### Structured control flow primitives\n", "\n", "There are more options for control flow in JAX. Say you want to avoid re-compilations but still want to use control flow that's traceable, and that avoids un-rolling large loops. Then you can use these 4 structured control flow primitives:\n", "\n", " - `lax.cond` _differentiable_\n", " - `lax.while_loop` __fwd-mode-differentiable__\n", " - `lax.fori_loop` __fwd-mode-differentiable__ in general; __fwd and rev-mode differentiable__ if endpoints are static.\n", " - `lax.scan` _differentiable_" ] }, { "cell_type": "markdown", "metadata": { "id": "Sd9xrLMXeK3A" }, "source": [ "#### cond\n", "python equivalent:\n", "\n", "```python\n", "def cond(pred, true_fun, false_fun, operand):\n", " if pred:\n", " return true_fun(operand)\n", " else:\n", " return false_fun(operand)\n", "```" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "SGxz9JOWeiyH", "outputId": "942a8d0e-5ff6-4702-c499-b3941f529ca3" }, "outputs": [ { "data": { "text/plain": [ "DeviceArray([-1.], dtype=float32)" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from jax import lax\n", "\n", "operand = jnp.array([0.])\n", "lax.cond(True, lambda x: x+1, lambda x: x-1, operand)\n", "# --> array([1.], dtype=float32)\n", "lax.cond(False, lambda x: x+1, lambda x: x-1, operand)\n", "# --> array([-1.], dtype=float32)" ] }, { "cell_type": "markdown", "metadata": { "id": "xkOFAw24eOMg" }, "source": [ "#### while_loop\n", "\n", "python equivalent:\n", "```\n", "def while_loop(cond_fun, body_fun, init_val):\n", " val = init_val\n", " while cond_fun(val):\n", " val = body_fun(val)\n", " return val\n", "```" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "jM-D39a-c436", "outputId": "552fe42f-4d32-4e25-c8c2-b951160a3f4e" }, "outputs": [ { "data": { "text/plain": [ "DeviceArray(10, dtype=int32, weak_type=True)" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "init_val = 0\n", "cond_fun = lambda x: x<10\n", "body_fun = lambda x: x+1\n", "lax.while_loop(cond_fun, body_fun, init_val)\n", "# --> array(10, dtype=int32)" ] }, { "cell_type": "markdown", "metadata": { "id": "apo3n3HAeQY_" }, "source": [ "#### fori_loop\n", "python equivalent:\n", "```\n", "def fori_loop(start, stop, body_fun, init_val):\n", " val = init_val\n", " for i in range(start, stop):\n", " val = body_fun(i, val)\n", " return val\n", "```" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "dt3tUpOmeR8u", "outputId": "7819ca7c-1433-4d85-b542-f6159b0e8380" }, "outputs": [ { "data": { "text/plain": [ "DeviceArray(45, dtype=int32, weak_type=True)" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "init_val = 0\n", "start = 0\n", "stop = 10\n", "body_fun = lambda i,x: x+i\n", "lax.fori_loop(start, stop, body_fun, init_val)\n", "# --> array(45, dtype=int32)" ] }, { "cell_type": "markdown", "metadata": { "id": "SipXS5qiqk8e" }, "source": [ "#### Summary\n", "\n", "$$\n", "\\begin{array} {r\|rr} \n", "\\hline \\\n", "\\textrm{construct} \n", "& \\textrm{jit} \n", "& \\textrm{grad} \\\\\n", "\\hline \\\n", "\\textrm{if} & ❌ & ✔ \\\\\n", "\\textrm{for} & ✔* & ✔\\\\\n", "\\textrm{while} & ✔* & ✔\\\\\n", "\\textrm{lax.cond} & ✔ & ✔\\\\\n", "\\textrm{lax.while_loop} & ✔ & \\textrm{fwd}\\\\\n", "\\textrm{lax.fori_loop} & ✔ & \\textrm{fwd}\\\\\n", "\\textrm{lax.scan} & ✔ & ✔\\\\\n", "\\hline\n", "\\end{array}\n", "$$\n", "\n", "<center>\n", "\n", "$\\ast$ = argument-<b>value</b>-independent loop condition - unrolls the loop\n", "\n", "</center>" ] }, { "cell_type": "markdown", "metadata": { "id": "DKTMw6tRZyK2" }, "source": [ "## 🔪 NaNs" ] }, { "cell_type": "markdown", "metadata": { "id": "ncS0NI4jZrwy" }, "source": [ "### Debugging NaNs\n", "\n", "If you want to trace where NaNs are occurring in your functions or gradients, you can turn on the NaN-checker by:\n", "\n", "* setting the `JAX_DEBUG_NANS=True` environment variable;\n", "\n", "* adding `from jax.config import config` and `config.update(\"jax_debug_nans\", True)` near the top of your main file;\n", "\n", "* adding `from jax.config import config` and `config.parse_flags_with_absl()` to your main file, then set the option using a command-line flag like `--jax_debug_nans=True`;\n", "\n", "This will cause computations to error-out immediately on production of a NaN. Switching this option on adds a nan check to every floating point type value produced by XLA. That means values are pulled back to the host and checked as ndarrays for every primitive operation not under an `@jit`. For code under an `@jit`, the output of every `@jit` function is checked and if a nan is present it will re-run the function in de-optimized op-by-op mode, effectively removing one level of `@jit` at a time.\n", "\n", "There could be tricky situations that arise, like nans that only occur under a `@jit` but don't get produced in de-optimized mode. In that case you'll see a warning message print out but your code will continue to execute.\n", "\n", "If the nans are being produced in the backward pass of a gradient evaluation, when an exception is raised several frames up in the stack trace you will be in the backward_pass function, which is essentially a simple jaxpr interpreter that walks the sequence of primitive operations in reverse. In the example below, we started an ipython repl with the command line `env JAX_DEBUG_NANS=True ipython`, then ran this:" ] }, { "cell_type": "markdown", "metadata": { "id": "p6ZtDHPbBa_W" }, "source": [ "```\n", "In [1]: import jax.numpy as jnp\n", "\n", "In [2]: jnp.divide(0., 0.)\n", "---------------------------------------------------------------------------\n", "FloatingPointError Traceback (most recent call last)\n", "<ipython-input-2-f2e2c413b437> in <module>()\n", "----> 1 jnp.divide(0., 0.)\n", "\n", ".../jax/jax/numpy/lax_numpy.pyc in divide(x1, x2)\n", " 343 return floor_divide(x1, x2)\n", " 344 else:\n", "--> 345 return true_divide(x1, x2)\n", " 346\n", " 347\n", "\n", ".../jax/jax/numpy/lax_numpy.pyc in true_divide(x1, x2)\n", " 332 x1, x2 = _promote_shapes(x1, x2)\n", " 333 return lax.div(lax.convert_element_type(x1, result_dtype),\n", "--> 334 lax.convert_element_type(x2, result_dtype))\n", " 335\n", " 336\n", "\n", ".../jax/jax/lax.pyc in div(x, y)\n", " 244 def div(x, y):\n", " 245 r\"\"\"Elementwise division: :math:`x \\over y`.\"\"\"\n", "--> 246 return div_p.bind(x, y)\n", " 247\n", " 248 def rem(x, y):\n", "\n", "... stack trace ...\n", "\n", ".../jax/jax/interpreters/xla.pyc in handle_result(device_buffer)\n", " 103 py_val = device_buffer.to_py()\n", " 104 if np.any(np.isnan(py_val)):\n", "--> 105 raise FloatingPointError(\"invalid value\")\n", " 106 else:\n", " 107 return DeviceArray(device_buffer, result_shape)\n", "\n", "FloatingPointError: invalid value\n", "```" ] }, { "cell_type": "markdown", "metadata": { "id": "_NCnVt_GBa_W" }, "source": [ "The nan generated was caught. By running `%debug`, we can get a post-mortem debugger. This also works with functions under `@jit`, as the example below shows." ] }, { "cell_type": "markdown", "metadata": { "id": "pf8RF6eiBa_W" }, "source": [ "```\n", "In [4]: from jax import jit\n", "\n", "In [5]: @jit\n", " ...: def f(x, y):\n", " ...: a = x y\n", " ...: b = (x + y) / (x - y)\n", " ...: c = a + 2\n", " ...: return a + b * c\n", " ...:\n", "\n", "In [6]: x = jnp.array([2., 0.])\n", "\n", "In [7]: y = jnp.array([3., 0.])\n", "\n", "In [8]: f(x, y)\n", "Invalid value encountered in the output of a jit function. Calling the de-optimized version.\n", "---------------------------------------------------------------------------\n", "FloatingPointError Traceback (most recent call last)\n", "<ipython-input-8-811b7ddb3300> in <module>()\n", "----> 1 f(x, y)\n", "\n", " ... stack trace ...\n", "\n", "<ipython-input-5-619b39acbaac> in f(x, y)\n", " 2 def f(x, y):\n", " 3 a = x * y\n", "----> 4 b = (x + y) / (x - y)\n", " 5 c = a + 2\n", " 6 return a + b * c\n", "\n", ".../jax/jax/numpy/lax_numpy.pyc in divide(x1, x2)\n", " 343 return floor_divide(x1, x2)\n", " 344 else:\n", "--> 345 return true_divide(x1, x2)\n", " 346\n", " 347\n", "\n", ".../jax/jax/numpy/lax_numpy.pyc in true_divide(x1, x2)\n", " 332 x1, x2 = _promote_shapes(x1, x2)\n", " 333 return lax.div(lax.convert_element_type(x1, result_dtype),\n", "--> 334 lax.convert_element_type(x2, result_dtype))\n", " 335\n", " 336\n", "\n", ".../jax/jax/lax.pyc in div(x, y)\n", " 244 def div(x, y):\n", " 245 r\"\"\"Elementwise division: :math:`x \\over y`.\"\"\"\n", "--> 246 return div_p.bind(x, y)\n", " 247\n", " 248 def rem(x, y):\n", "\n", " ... stack trace ...\n", "```" ] }, { "cell_type": "markdown", "metadata": { "id": "6ur2yArDBa_W" }, "source": [ "When this code sees a nan in the output of an `@jit` function, it calls into the de-optimized code, so we still get a clear stack trace. And we can run a post-mortem debugger with `%debug` to inspect all the values to figure out the error.\n", "\n", "⚠️ You shouldn't have the NaN-checker on if you're not debugging, as it can introduce lots of device-host round-trips and performance regressions!\n", "\n", "⚠️ The NaN-checker doesn't work with `pmap`. To debug nans in `pmap` code, one thing to try is replacing `pmap` with `vmap`." ] }, { "cell_type": "markdown", "metadata": { "id": "YTktlwTTMgFl" }, "source": [ "## 🔪 Double (64bit) precision\n", "\n", "At the moment, JAX by default enforces single-precision numbers to mitigate the Numpy API's tendency to aggressively promote operands to `double`. This is the desired behavior for many machine-learning applications, but it may catch you by surprise!" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "CNNGtzM3NDkO", "outputId": "b422bb23-a784-44dc-f8c9-57f3b6c861b8" }, "outputs": [ { "data": { "text/plain": [ "dtype('float32')" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x = random.uniform(random.PRNGKey(0), (1000,), dtype=jnp.float64)\n", "x.dtype" ] }, { "cell_type": "markdown", "metadata": { "id": "VcvqzobxNPbd" }, "source": [ "To use double-precision numbers, you need to set the `jax_enable_x64` configuration variable __at startup__. \n", "\n", "There are a few ways to do this:\n", "\n", "1. You can enable 64bit mode by setting the environment variable `JAX_ENABLE_X64=True`.\n", "\n", "2. You can manually set the `jax_enable_x64` configuration flag at startup:\n", "\n", " ```python\n", " # again, this only works on startup!\n", " from jax.config import config\n", " config.update(\"jax_enable_x64\", True)\n", " ```\n", "\n", "3. You can parse command-line flags with `absl.app.run(main)`\n", "\n", " ```python\n", " from jax.config import config\n", " config.config_with_absl()\n", " ```\n", "\n", "4. If you want JAX to run absl parsing for you, i.e. you don't want to do `absl.app.run(main)`, you can instead use\n", "\n", " ```python\n", " from jax.config import config\n", " if __name__ == '__main__':\n", " # calls config.config_with_absl() and runs absl parsing\n", " config.parse_flags_with_absl()\n", " ```\n", "\n", "Note that #2-#4 work for _any_ of JAX's configuration options.\n", "\n", "We can then confirm that `x64` mode is enabled:" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "HqGbBa9Rr-2g", "outputId": "5aa72952-08cc-4569-9b51-a10311ae9e81" }, "outputs": [ { "data": { "text/plain": [ "dtype('float32')" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import jax.numpy as jnp\n", "from jax import random\n", "x = random.uniform(random.PRNGKey(0), (1000,), dtype=jnp.float64)\n", "x.dtype # --> dtype('float64')" ] }, { "cell_type": "markdown", "metadata": { "id": "6Cks2_gKsXaW" }, "source": [ "### Caveats\n", "⚠️ XLA doesn't support 64-bit convolutions on all backends!" ] }, { "cell_type": "markdown", "metadata": { "id": "WAHjmL0E2XwO" }, "source": [ "## 🔪 Miscellaneous Divergences from NumPy\n", "\n", "While `jax.numpy` makes every attempt to replicate the behavior of numpy's API, there do exist corner cases where the behaviors differ.\n", "Many such cases are discussed in detail in the sections above; here we list several other known places where the APIs diverge.\n", "\n", "- For binary operations, JAX's type promotion rules differ somewhat from those used by NumPy. See [Type Promotion Semantics](https://jax.readthedocs.io/en/latest/type_promotion.html) for more details.\n", "- When performing unsafe type casts (i.e. casts in which the target dtype cannot represent the input value), JAX's behavior may be backend dependent, and in general may diverge from NumPy's behavior. Numpy allows control over the result in these scenarios via the `casting` argument (see [`np.ndarray.astype`](https://numpy.org/devdocs/reference/generated/numpy.ndarray.astype.html)); JAX does not provide any such configuration, instead directly inheriting the behavior of [XLA:ConvertElementType](https://www.tensorflow.org/xla/operation_semantics#convertelementtype).\n", "\n", " Here is an example of an unsafe cast with differing results between NumPy and JAX:\n", " ```python\n", " >>> np.arange(254.0, 258.0).astype('uint8') \n", " array([254, 255, 0, 1], dtype=uint8)\n", "\n", " >>> jnp.arange(254.0, 258.0).astype('uint8') \n", " DeviceArray([254, 255, 255, 255], dtype=uint8)\n", " ```\n", " This sort of mismatch would typically arise when casting extreme values from floating to integer types or vice versa.\n", "\n", "\n", "## Fin.\n", "\n", "If something's not covered here that has caused you weeping and gnashing of teeth, please let us know and we'll extend these introductory _advisos_!" ] } ], "metadata": { "accelerator": "GPU", "colab": { "collapsed_sections": [], "name": "Common Gotchas in JAX", "provenance": [], "toc_visible": true }, "jupytext": { "formats": "ipynb,md:myst" }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }	apache-2.0
jhamilius/chain	notebooks/chain-clustering.ipynb	1	241148	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Clustering and outlier detection\n", "<hr>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<u>Objectives</u>\n", "- Test clustering methods on the features extracted from the graph for nodes and transactions\n", "- Test outlier detection methds on the features extracted from the graph for nodes and transactions\n", "- Detect if the clustering is splitting some publicy known groups of addresses (exchange, pool, smart contracts etc.)\n", "- Plot Clustering and outlier detection\n", "\n", "<hr>\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 0 - Data preparation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Importing librairies" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", "from time import time\n", "from joblib import Parallel, delayed\n", "import multiprocessing\n", "import time\n", "import matplotlib.pyplot as plt\n", "import warnings\n", "warnings.filterwarnings('ignore')\n", "from sklearn.cluster import MiniBatchKMeans, KMeans\n", "from sklearn.metrics.pairwise import pairwise_distances_argmin\n", "from sklearn.datasets.samples_generator import make_blobs\n", "from scipy.spatial.distance import cdist, pdist\n", "from sklearn import metrics\n", "from sklearn.cluster import KMeans\n", "from sklearn.datasets import load_digits\n", "from sklearn.decomposition import PCA\n", "from sklearn.preprocessing import scale\n", "from sklearn.covariance import EmpiricalCovariance, MinCovDet" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "loading different datasets :\n", "- Publicy known adresses\n", "- Features dataframe from the graph features generators" ] }, { "cell_type": "code", "execution_count": 121, "metadata": { "collapsed": false }, "outputs": [], "source": [ "known = pd.read_csv('../data/known.csv')\n", "rogues = pd.read_csv('../data/rogues.csv')\n", "transactions = pd.read_csv('../data/edges.csv').drop('Unnamed: 0',1)\n", "#Dropping features and fill na with 0\n", "df = pd.read_csv('../data/features_full.csv').drop('Unnamed: 0',1).fillna(0)\n", "df = df.set_index(['nodes'])\n", "#build normalize values\n", "data = scale(df.values)\n", "n_sample = 10000" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# I - Clustering Nodes\n", "<hr>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Exploring clustering methods on the nodes featured dataset" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## A - k-means" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First a very simple kmeans method" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Define estimator / by default clusters = 6 an init = 10\n", "kmeans = KMeans(init='k-means++', n_clusters=6, n_init=10)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "KMeans(copy_x=True, init='k-means++', max_iter=300, n_clusters=6, n_init=10,\n", " n_jobs=1, precompute_distances='auto', random_state=None, tol=0.0001,\n", " verbose=0)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "kmeans.fit(data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1 - Parameters Optimization\n", "\n", "#### a - Finding the best k \n", "code from http://www.slideshare.net/SarahGuido/kmeans-clustering-with-scikitlearn#notes-panel)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%time\n", "#Determine your k range\n", "k_range = range(1,14)\n", "# Fit the kmeans model for each n_clusters = k\n", "k_means_var = [KMeans(n_clusters=k).fit(data) for k in k_range]\n", "# Pull out the centroids for each model\n", "centroids = [X.cluster_centers_ for X in k_means_var]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%time\n", "# Caluculate the Euclidean distance from each pont to each centroid\n", "k_euclid=[cdist(data, cent, 'euclidean') for cent in centroids]\n", "dist = [np.min(ke,axis=1) for ke in k_euclid]\n", "\n", "# Total within-cluster sum of squares\n", "wcss = [sum(d2) for d in dist]\n", "\n", "# The total sum of squares\n", "tss = sum(pdist(data)2)/data.shape[0]\n", "\n", "#The between-cluster sum of squares\n", "bss = tss - wcss" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%time\n", "plt.plot(k_range,bss/tss,'-bo')\n", "plt.xlabel('number of cluster')\n", "plt.ylabel('% of variance explained')\n", "plt.title('Variance explained vs k')\n", "plt.grid(True)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Difficult to find an elbow criteria\n", "- Other heuristic criteria k = sqrt(n/2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### b - Other heuristic method \n", "$k=\\sqrt{\\frac{n}{2}}$" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "np.sqrt(data.shape[0]/2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "-> Weird" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### c - Silhouette Metrics for supervised ?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2 - Visualize with PCA reduction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "code from scikit learn" ] }, { "cell_type": "code", "execution_count": 85, "metadata": { "collapsed": true }, "outputs": [], "source": [ "##############################################################################\n", "# Generate sample data\n", "\n", "\n", "batch_size = 10\n", "#centers = [[1, 1], [-1, -1], [1, -1]]\n", "n_clusters = 6\n", "#X, labels_true = make_blobs(n_samples=3000, centers=centers, cluster_std=0.7)\n", "X = PCA(n_components=2).fit_transform(data)\n", "\n", "##############################################################################\n", "# Compute clustering with Means\n", "\n", "k_means = KMeans(init='k-means++', n_clusters=6, n_init=10,random_state=2)\n", "t0 = time.time()\n", "k_means.fit(X)\n", "t_batch = time.time() - t0\n", "k_means_labels = k_means.labels_\n", "k_means_cluster_centers = k_means.cluster_centers_\n", "k_means_labels_unique = np.unique(k_means_labels)\n", "\n", "##############################################################################\n", "# Compute clustering with MiniBatchKMeans\n", "\n", "mbk = MiniBatchKMeans(init='k-means++', n_clusters=6, batch_size=batch_size,\n", " n_init=10, max_no_improvement=10, verbose=0,random_state=2)\n", "t0 = time.time()\n", "mbk.fit(X)\n", "t_mini_batch = time.time() - t0\n", "mbk_means_labels = mbk.labels_\n", "mbk_means_cluster_centers = mbk.cluster_centers_\n", "mbk_means_labels_unique = np.unique(mbk_means_labels)" ] }, { "cell_type": "code", "execution_count": 86, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "image/png": 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0/HQAGg+f5viOelqOt5BTnMux8mNUr61m1tICsq7LJtAe4PCmWvat2sdVi69i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\nQLFYjEuXLsWBAwdi9+7dsby8HCsrK1mPdUeEJyB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"text/plain": [ "<matplotlib.figure.Figure at 0x7fc923f6e950>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "##############################################################################\n", "# Plot result\n", "\n", "fig = plt.figure(figsize=(15, 5))\n", "colors = ['#4EACC5', '#FF9C34', '#4E9A06','#FF0000','#800000','purple']\n", "#fig.subplots_adjust(left=0.02, right=0.98, bottom=0.05, top=0.9)\n", "\n", "# We want to have the same colors for the same cluster from the\n", "# MiniBatchKMeans and the KMeans algorithm. Let's pair the cluster centers per\n", "# closest one.\n", "\n", "order = pairwise_distances_argmin(k_means_cluster_centers,\n", " mbk_means_cluster_centers)\n", "\n", "# KMeans\n", "ax = fig.add_subplot(1, 3, 1)\n", "for k, col in zip(range(n_clusters), colors):\n", " my_members = k_means_labels == k\n", " cluster_center = k_means_cluster_centers[k]\n", " ax.plot(X[my_members, 0], X[my_members, 1], 'w',\n", " markerfacecolor=col, marker='.',markersize=10)\n", " ax.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor=col,\n", " markeredgecolor='k', markersize=6)\n", "ax.set_title('KMeans')\n", "ax.set_xticks(())\n", "ax.set_yticks(())\n", "#plt.text(10,10, 'train time: %.2fs\\ninertia: %f' % (\n", " #t_batch, k_means.inertia_))\n", "\n", "# Plot result\n", "\n", "\n", "# MiniBatchKMeans\n", "ax = fig.add_subplot(1, 3, 2)\n", "for k, col in zip(range(n_clusters), colors):\n", " my_members = mbk_means_labels == order[k]\n", " cluster_center = mbk_means_cluster_centers[order[k]]\n", " ax.plot(X[my_members, 0], X[my_members, 1], 'w',\n", " markerfacecolor=col, marker='.', markersize=10)\n", " ax.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor=col,\n", " markeredgecolor='k', markersize=6)\n", "ax.set_title('MiniBatchKMeans')\n", "ax.set_xticks(())\n", "ax.set_yticks(())\n", "#plt.text(-5, 10, 'train time: %.2fs\\ninertia: %f' %\n", " #(t_mini_batch, mbk.inertia_))\n", "\n", "# Plot result\n", "\n", "\n", "\n", "# Initialise the different array to all False\n", "different = (mbk_means_labels == 4)\n", "ax = fig.add_subplot(1, 3, 3)\n", "\n", "for l in range(n_clusters):\n", " different += ((k_means_labels == k) != (mbk_means_labels == order[k]))\n", "\n", "identic = np.logical_not(different)\n", "ax.plot(X[identic, 0], X[identic, 1], 'w',\n", " markerfacecolor='#bbbbbb', marker='.')\n", "ax.plot(X[different, 0], X[different, 1], 'w',\n", " markerfacecolor='m', marker='.')\n", "ax.set_title('Difference')\n", "ax.set_xticks(())\n", "ax.set_yticks(())\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## B - Mini batch" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# II - Outlier Detection \n", "<hr>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Objectives : \n", "- Perform outlier detection on node data\n", "- Test different methods (with perf metrics) \n", "- Plot outlier detection\n", "- Tag transaction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Explain : Mahalanobis Distance" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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Amr/SnYKiSCmFDbvqMGloblw0WT/EWwvUrQcCPt1JyMaafAZOmzQIFx41VHcU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\nVPd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"text/plain": [ "<matplotlib.figure.Figure at 0x7fc91a691bd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "X = PCA(n_components=2).fit_transform(data)\n", "# compare estimators learnt from the full data set with true parameters\n", "emp_cov = EmpiricalCovariance().fit(X)\n", "robust_cov = MinCovDet().fit(X)\n", "\n", "\n", "###############################################################################\n", "# Display results\n", "fig = plt.figure(figsize=(15, 8))\n", "plt.subplots_adjust(hspace=-.1, wspace=.4, top=.95, bottom=.05)\n", "\n", "# Show data set\n", "subfig1 = plt.subplot(1, 1, 1)\n", "inlier_plot = subfig1.scatter(X[:, 0], X[:, 1],\n", " color='black', label='points')\n", "subfig1.set_xlim(subfig1.get_xlim()[0], 11.)\n", "subfig1.set_title(\"Mahalanobis distances of a contaminated data set:\")\n", "\n", "# Show contours of the distance functions\n", "xx, yy = np.meshgrid(np.linspace(plt.xlim()[0], plt.xlim()[1], 100),\n", " np.linspace(plt.ylim()[0], plt.ylim()[1], 100))\n", "zz = np.c_[xx.ravel(), yy.ravel()]\n", "\n", "mahal_emp_cov = emp_cov.mahalanobis(zz)\n", "mahal_emp_cov = mahal_emp_cov.reshape(xx.shape)\n", "emp_cov_contour = subfig1.contour(xx, yy, np.sqrt(mahal_emp_cov),\n", " cmap=plt.cm.PuBu_r,\n", " linestyles='dashed')\n", "\n", "mahal_robust_cov = robust_cov.mahalanobis(zz)\n", "mahal_robust_cov = mahal_robust_cov.reshape(xx.shape)\n", "robust_contour = subfig1.contour(xx, yy, np.sqrt(mahal_robust_cov),\n", " cmap=plt.cm.YlOrBr_r, linestyles='dotted')\n", "\n", "\n", "plt.xticks(())\n", "plt.yticks(())\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<hr>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# III - Look at the clusters" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>total_degree</th>\n", " <th>degree_in</th>\n", " <th>degree_out</th>\n", " <th>unique_successors</th>\n", " <th>unique_predecessors</th>\n", " <th>mean_value_in</th>\n", " <th>mean_value_out</th>\n", " <th>std_value_in</th>\n", " <th>std_value_out</th>\n", " <th>ratio_in_timestamp</th>\n", " <th>...</th>\n", " <th>frequency_out</th>\n", " <th>balance</th>\n", " <th>mean_velocity_in</th>\n", " <th>mean_velocity_out</th>\n", " <th>std_velocity_in</th>\n", " <th>std_velocity_out</th>\n", " <th>mean_acceleration_in</th>\n", " <th>mean_acceleration_out</th>\n", " <th>min_path_to_rogue</th>\n", " <th>min_path_from_rogue</th>\n", " </tr>\n", " <tr>\n", " <th>nodes</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>0</th>\n", " <td>58793</td>\n", " <td>58793</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>9161</td>\n", " <td>2.025492e+20</td>\n", " <td>0.000000e+00</td>\n", " <td>4.908332e+22</td>\n", " <td>0.0</td>\n", " <td>1.476135</td>\n", " <td>...</td>\n", " <td>0.0</td>\n", " <td>1.190847e+25</td>\n", " <td>2.934858e+13</td>\n", " <td>0.0</td>\n", " <td>3.698298e+15</td>\n", " <td>0.0</td>\n", " <td>8.548336e+10</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>2.0</td>\n", " </tr>\n", " <tr>\n", " <th>0xaeeb08aca66b2346006cd83cbb2c7d10c39e3301</th>\n", " <td>2</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>5.689887e+18</td>\n", " <td>5.688457e+18</td>\n", " <td>0.000000e+00</td>\n", " <td>0.0</td>\n", " <td>1.000000</td>\n", " <td>...</td>\n", " <td>0.0</td>\n", " <td>1.429602e+15</td>\n", " <td>0.000000e+00</td>\n", " <td>0.0</td>\n", " <td>0.000000e+00</td>\n", " <td>0.0</td>\n", " <td>0.000000e+00</td>\n", " <td>0.0</td>\n", " <td>2347.0</td>\n", " <td>1130.0</td>\n", " </tr>\n", " <tr>\n", " <th>0x8dcca392c5d80d5ed8a566af8821002d12cd2f65</th>\n", " <td>3</td>\n", " <td>2</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>2</td>\n", " <td>1.500095e+21</td>\n", " <td>3.000000e+21</td>\n", " <td>3.000050e+20</td>\n", " <td>0.0</td>\n", " <td>1.000000</td>\n", " <td>...</td>\n", " <td>0.0</td>\n", " <td>1.900000e+17</td>\n", " <td>0.000000e+00</td>\n", " <td>0.0</td>\n", " <td>0.000000e+00</td>\n", " <td>0.0</td>\n", " <td>0.000000e+00</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>2.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>3 rows × 22 columns</p>\n", "</div>" ], "text/plain": [ " total_degree degree_in \\\n", "nodes \n", "0 58793 58793 \n", "0xaeeb08aca66b2346006cd83cbb2c7d10c39e3301 2 1 \n", "0x8dcca392c5d80d5ed8a566af8821002d12cd2f65 3 2 \n", "\n", " degree_out unique_successors \\\n", "nodes \n", "0 0 0 \n", "0xaeeb08aca66b2346006cd83cbb2c7d10c39e3301 1 1 \n", "0x8dcca392c5d80d5ed8a566af8821002d12cd2f65 1 1 \n", "\n", " unique_predecessors \\\n", "nodes \n", "0 9161 \n", "0xaeeb08aca66b2346006cd83cbb2c7d10c39e3301 1 \n", "0x8dcca392c5d80d5ed8a566af8821002d12cd2f65 2 \n", "\n", " mean_value_in mean_value_out \\\n", "nodes \n", "0 2.025492e+20 0.000000e+00 \n", "0xaeeb08aca66b2346006cd83cbb2c7d10c39e3301 5.689887e+18 5.688457e+18 \n", "0x8dcca392c5d80d5ed8a566af8821002d12cd2f65 1.500095e+21 3.000000e+21 \n", "\n", " std_value_in std_value_out \\\n", "nodes \n", "0 4.908332e+22 0.0 \n", "0xaeeb08aca66b2346006cd83cbb2c7d10c39e3301 0.000000e+00 0.0 \n", "0x8dcca392c5d80d5ed8a566af8821002d12cd2f65 3.000050e+20 0.0 \n", "\n", " ratio_in_timestamp \\\n", "nodes \n", "0 1.476135 \n", "0xaeeb08aca66b2346006cd83cbb2c7d10c39e3301 1.000000 \n", "0x8dcca392c5d80d5ed8a566af8821002d12cd2f65 1.000000 \n", "\n", " ... \\\n", "nodes ... \n", "0 ... \n", "0xaeeb08aca66b2346006cd83cbb2c7d10c39e3301 ... \n", "0x8dcca392c5d80d5ed8a566af8821002d12cd2f65 ... \n", "\n", " frequency_out balance \\\n", "nodes \n", "0 0.0 1.190847e+25 \n", "0xaeeb08aca66b2346006cd83cbb2c7d10c39e3301 0.0 1.429602e+15 \n", "0x8dcca392c5d80d5ed8a566af8821002d12cd2f65 0.0 1.900000e+17 \n", "\n", " mean_velocity_in \\\n", "nodes \n", "0 2.934858e+13 \n", "0xaeeb08aca66b2346006cd83cbb2c7d10c39e3301 0.000000e+00 \n", "0x8dcca392c5d80d5ed8a566af8821002d12cd2f65 0.000000e+00 \n", "\n", " mean_velocity_out \\\n", "nodes \n", "0 0.0 \n", "0xaeeb08aca66b2346006cd83cbb2c7d10c39e3301 0.0 \n", "0x8dcca392c5d80d5ed8a566af8821002d12cd2f65 0.0 \n", "\n", " std_velocity_in std_velocity_out \\\n", "nodes \n", "0 3.698298e+15 0.0 \n", "0xaeeb08aca66b2346006cd83cbb2c7d10c39e3301 0.000000e+00 0.0 \n", "0x8dcca392c5d80d5ed8a566af8821002d12cd2f65 0.000000e+00 0.0 \n", "\n", " mean_acceleration_in \\\n", "nodes \n", "0 8.548336e+10 \n", "0xaeeb08aca66b2346006cd83cbb2c7d10c39e3301 0.000000e+00 \n", "0x8dcca392c5d80d5ed8a566af8821002d12cd2f65 0.000000e+00 \n", "\n", " mean_acceleration_out \\\n", "nodes \n", "0 0.0 \n", "0xaeeb08aca66b2346006cd83cbb2c7d10c39e3301 0.0 \n", "0x8dcca392c5d80d5ed8a566af8821002d12cd2f65 0.0 \n", "\n", " min_path_to_rogue \\\n", "nodes \n", "0 0.0 \n", "0xaeeb08aca66b2346006cd83cbb2c7d10c39e3301 2347.0 \n", "0x8dcca392c5d80d5ed8a566af8821002d12cd2f65 0.0 \n", "\n", " min_path_from_rogue \n", "nodes \n", "0 2.0 \n", "0xaeeb08aca66b2346006cd83cbb2c7d10c39e3301 1130.0 \n", "0x8dcca392c5d80d5ed8a566af8821002d12cd2f65 2.0 \n", "\n", "[3 rows x 22 columns]" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head(3)" ] }, { "cell_type": "code", "execution_count": 132, "metadata": { "collapsed": true }, "outputs": [], "source": [ "k_means = KMeans(init='random', n_clusters=6, n_init=10, random_state=2)\n", "clusters = k_means.fit_predict(data)" ] }, { "cell_type": "code", "execution_count": 131, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>total_degree</th>\n", " <th>degree_in</th>\n", " <th>degree_out</th>\n", " <th>unique_successors</th>\n", " <th>unique_predecessors</th>\n", " <th>mean_value_in</th>\n", " <th>mean_value_out</th>\n", " <th>std_value_in</th>\n", " <th>std_value_out</th>\n", " <th>ratio_in_timestamp</th>\n", " <th>...</th>\n", " <th>frequency_out</th>\n", " <th>balance</th>\n", " <th>mean_velocity_in</th>\n", " <th>mean_velocity_out</th>\n", " <th>std_velocity_in</th>\n", " <th>std_velocity_out</th>\n", " <th>mean_acceleration_in</th>\n", " <th>mean_acceleration_out</th>\n", " <th>min_path_to_rogue</th>\n", " <th>min_path_from_rogue</th>\n", " </tr>\n", " <tr>\n", " <th>clusters</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>0</th>\n", " <td>19</td>\n", " <td>19</td>\n", " <td>19</td>\n", " <td>19</td>\n", " <td>19</td>\n", " <td>19</td>\n", " <td>19</td>\n", " <td>19</td>\n", " <td>19</td>\n", " <td>19</td>\n", " <td>...</td>\n", " <td>19</td>\n", " <td>19</td>\n", " <td>19</td>\n", " <td>19</td>\n", " <td>19</td>\n", " <td>19</td>\n", " <td>19</td>\n", " <td>19</td>\n", " <td>19</td>\n", " <td>19</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>93065</td>\n", " <td>93065</td>\n", " <td>93065</td>\n", " <td>93065</td>\n", " <td>93065</td>\n", " <td>93065</td>\n", " <td>93065</td>\n", " <td>93065</td>\n", " <td>93065</td>\n", " <td>93065</td>\n", " <td>...</td>\n", " <td>93065</td>\n", " <td>93065</td>\n", " <td>93065</td>\n", " <td>93065</td>\n", " <td>93065</td>\n", " <td>93065</td>\n", " <td>93065</td>\n", " <td>93065</td>\n", " <td>93065</td>\n", " <td>93065</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>...</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>67</td>\n", " <td>67</td>\n", " <td>67</td>\n", " <td>67</td>\n", " <td>67</td>\n", " <td>67</td>\n", " <td>67</td>\n", " <td>67</td>\n", " <td>67</td>\n", " <td>67</td>\n", " <td>...</td>\n", " <td>67</td>\n", " <td>67</td>\n", " <td>67</td>\n", " <td>67</td>\n", " <td>67</td>\n", " <td>67</td>\n", " <td>67</td>\n", " <td>67</td>\n", " <td>67</td>\n", " <td>67</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>16531</td>\n", " <td>16531</td>\n", " <td>16531</td>\n", " <td>16531</td>\n", " <td>16531</td>\n", " <td>16531</td>\n", " <td>16531</td>\n", " <td>16531</td>\n", " <td>16531</td>\n", " <td>16531</td>\n", " <td>...</td>\n", " <td>16531</td>\n", " <td>16531</td>\n", " <td>16531</td>\n", " <td>16531</td>\n", " <td>16531</td>\n", " <td>16531</td>\n", " <td>16531</td>\n", " <td>16531</td>\n", " <td>16531</td>\n", " <td>16531</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>343472</td>\n", " <td>343472</td>\n", " <td>343472</td>\n", " <td>343472</td>\n", " <td>343472</td>\n", " <td>343472</td>\n", " <td>343472</td>\n", " <td>343472</td>\n", " <td>343472</td>\n", " <td>343472</td>\n", " <td>...</td>\n", " <td>343472</td>\n", " <td>343472</td>\n", " <td>343472</td>\n", " <td>343472</td>\n", " <td>343472</td>\n", " <td>343472</td>\n", " <td>343472</td>\n", " <td>343472</td>\n", " <td>343472</td>\n", " <td>343472</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>6 rows × 22 columns</p>\n", "</div>" ], "text/plain": [ " total_degree degree_in degree_out unique_successors \\\n", "clusters \n", "0 19 19 19 19 \n", "1 93065 93065 93065 93065 \n", "2 1 1 1 1 \n", "3 67 67 67 67 \n", "4 16531 16531 16531 16531 \n", "5 343472 343472 343472 343472 \n", "\n", " unique_predecessors mean_value_in mean_value_out std_value_in \\\n", "clusters \n", "0 19 19 19 19 \n", "1 93065 93065 93065 93065 \n", "2 1 1 1 1 \n", "3 67 67 67 67 \n", "4 16531 16531 16531 16531 \n", "5 343472 343472 343472 343472 \n", "\n", " std_value_out ratio_in_timestamp ... \\\n", "clusters ... \n", "0 19 19 ... \n", "1 93065 93065 ... \n", "2 1 1 ... \n", "3 67 67 ... \n", "4 16531 16531 ... \n", "5 343472 343472 ... \n", "\n", " frequency_out balance mean_velocity_in mean_velocity_out \\\n", "clusters \n", "0 19 19 19 19 \n", "1 93065 93065 93065 93065 \n", "2 1 1 1 1 \n", "3 67 67 67 67 \n", "4 16531 16531 16531 16531 \n", "5 343472 343472 343472 343472 \n", "\n", " std_velocity_in std_velocity_out mean_acceleration_in \\\n", "clusters \n", "0 19 19 19 \n", "1 93065 93065 93065 \n", "2 1 1 1 \n", "3 67 67 67 \n", "4 16531 16531 16531 \n", "5 343472 343472 343472 \n", "\n", " mean_acceleration_out min_path_to_rogue min_path_from_rogue \n", "clusters \n", "0 19 19 19 \n", "1 93065 93065 93065 \n", "2 1 1 1 \n", "3 67 67 67 \n", "4 16531 16531 16531 \n", "5 343472 343472 343472 \n", "\n", "[6 rows x 22 columns]" ] }, "execution_count": 131, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['clusters'] = clusters\n", "df.groupby('clusters').count()" ] }, { "cell_type": "code", "execution_count": 133, "metadata": { "collapsed": false }, "outputs": [], "source": [ "tagged = pd.merge(known,df,left_on='id',how='inner',right_index=True)" ] }, { "cell_type": "code", "execution_count": 134, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "clusters\n", "0 11.827957\n", "1 61.290323\n", "2 1.075269\n", "3 1.075269\n", "5 24.731183\n", "Name: id, dtype: float64" ] }, "execution_count": 134, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tagged.groupby('clusters').count().apply(lambda x: 100x/float(x.sum()))['id']" ] }, { "cell_type": "code", "execution_count": 135, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "clusters\n", "0 0.004193\n", "1 20.537123\n", "2 0.000221\n", "3 0.014785\n", "4 3.647979\n", "5 75.795699\n", "Name: total_degree, dtype: float64" ] }, "execution_count": 135, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.groupby('clusters').count().apply(lambda x: 100x/float(x.sum()))['total_degree']" ] }, { "cell_type": "code", "execution_count": 136, "metadata": { "collapsed": false }, "outputs": [], "source": [ "rogues_tag = pd.merge(rogues,df,left_on='id',how='inner',right_index=True)" ] }, { "cell_type": "code", "execution_count": 137, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "clusters\n", "1 6\n", "5 3\n", "Name: total_degree, dtype: int64" ] }, "execution_count": 137, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rogues_tag.groupby('clusters').count()['total_degree']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Rogues and tagged are overrepresnetated in cluster 1" ] }, { "cell_type": "code", "execution_count": 138, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>total_degree</th>\n", " <th>degree_in</th>\n", " <th>degree_out</th>\n", " <th>unique_successors</th>\n", " <th>unique_predecessors</th>\n", " <th>mean_value_in</th>\n", " <th>mean_value_out</th>\n", " <th>std_value_in</th>\n", " <th>std_value_out</th>\n", " <th>ratio_in_timestamp</th>\n", " <th>...</th>\n", " <th>frequency_out</th>\n", " <th>balance</th>\n", " <th>mean_velocity_in</th>\n", " <th>mean_velocity_out</th>\n", " <th>std_velocity_in</th>\n", " <th>std_velocity_out</th>\n", " <th>mean_acceleration_in</th>\n", " <th>mean_acceleration_out</th>\n", " <th>min_path_to_rogue</th>\n", " <th>min_path_from_rogue</th>\n", " </tr>\n", " <tr>\n", " <th>clusters</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>0</th>\n", " <td>2.612900e+05</td>\n", " <td>4.042695e+04</td>\n", " <td>220863.052632</td>\n", " <td>7042.631579</td>\n", " <td>7918.052632</td>\n", " <td>7.212737e+20</td>\n", " <td>1.098110e+22</td>\n", " <td>5.273081e+21</td>\n", " <td>2.178159e+22</td>\n", " <td>1.890468</td>\n", " 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" <td>1.243209e+16</td>\n", " <td>3.253731</td>\n", " <td>2.791045</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>2.000000e+00</td>\n", " <td>1.000000e+00</td>\n", " <td>1.000000</td>\n", " <td>1.000000</td>\n", " <td>1.000000</td>\n", " <td>9.173332e+18</td>\n", " <td>9.172241e+18</td>\n", " <td>0.000000e+00</td>\n", " <td>0.000000e+00</td>\n", " <td>1.000000</td>\n", " <td>...</td>\n", " <td>0.000000</td>\n", " <td>1.091282e+15</td>\n", " <td>0.000000e+00</td>\n", " <td>0.000000e+00</td>\n", " <td>0.000000e+00</td>\n", " <td>0.000000e+00</td>\n", " <td>0.000000e+00</td>\n", " <td>0.000000e+00</td>\n", " <td>4317.845744</td>\n", " <td>4316.450729</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>3.097724e+01</td>\n", " <td>1.625887e+01</td>\n", " <td>14.718373</td>\n", " <td>2.183575</td>\n", " <td>1.622426</td>\n", " <td>1.340362e+20</td>\n", " <td>2.098518e+20</td>\n", " <td>7.264903e+19</td>\n", " <td>1.237454e+20</td>\n", " <td>0.967353</td>\n", " <td>...</td>\n", " <td>0.001317</td>\n", " <td>-2.875345e+20</td>\n", " <td>6.179641e+16</td>\n", " <td>2.648585e+17</td>\n", " <td>2.996784e+16</td>\n", " <td>1.146132e+17</td>\n", " <td>2.842660e+14</td>\n", " <td>3.994207e+14</td>\n", " <td>21.917123</td>\n", " <td>22.265763</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>6 rows × 22 columns</p>\n", "</div>" ], "text/plain": [ " total_degree degree_in degree_out unique_successors \\\n", "clusters \n", "0 2.612900e+05 4.042695e+04 220863.052632 7042.631579 \n", "1 2.138136e+01 2.118231e+01 0.199044 0.052243 \n", "2 1.228371e+06 1.084279e+06 144092.000000 42729.000000 \n", "3 3.786418e+02 2.353881e+02 143.253731 71.447761 \n", "4 2.000000e+00 1.000000e+00 1.000000 1.000000 \n", "5 3.097724e+01 1.625887e+01 14.718373 2.183575 \n", "\n", " unique_predecessors mean_value_in mean_value_out std_value_in \\\n", "clusters \n", "0 7918.052632 7.212737e+20 1.098110e+22 5.273081e+21 \n", "1 2.076097 7.417071e+19 3.937509e+19 5.389531e+19 \n", "2 23113.000000 5.009805e+19 4.865686e+20 2.017054e+21 \n", "3 181.477612 9.070842e+22 2.818395e+23 8.254031e+22 \n", "4 1.000000 9.173332e+18 9.172241e+18 0.000000e+00 \n", "5 1.622426 1.340362e+20 2.098518e+20 7.264903e+19 \n", "\n", " std_value_out ratio_in_timestamp ... \\\n", "clusters ... \n", "0 2.178159e+22 1.890468 ... \n", "1 4.038152e+19 1.156968 ... \n", "2 7.598143e+21 5.043979 ... \n", "3 9.752651e+22 4.250715 ... \n", "4 0.000000e+00 1.000000 ... \n", "5 1.237454e+20 0.967353 ... \n", "\n", " frequency_out balance mean_velocity_in mean_velocity_out \\\n", "clusters \n", "0 0.011008 3.381983e+24 8.516933e+16 2.825936e+20 \n", "1 0.000140 4.895816e+20 1.127456e+17 3.659001e+16 \n", "2 0.004264 -1.579038e+25 2.566505e+15 3.213480e+17 \n", "3 0.004012 7.059440e+22 6.472854e+20 1.240381e+19 \n", "4 0.000000 1.091282e+15 0.000000e+00 0.000000e+00 \n", "5 0.001317 -2.875345e+20 6.179641e+16 2.648585e+17 \n", "\n", " std_velocity_in std_velocity_out mean_acceleration_in \\\n", "clusters \n", "0 4.798054e+18 3.794037e+20 1.967291e+14 \n", "1 6.598585e+16 4.025345e+15 1.157953e+15 \n", "2 7.026051e+17 3.929951e+19 3.696645e+11 \n", "3 2.959118e+20 2.150052e+19 1.432339e+19 \n", "4 0.000000e+00 0.000000e+00 0.000000e+00 \n", "5 2.996784e+16 1.146132e+17 2.842660e+14 \n", "\n", " mean_acceleration_out min_path_to_rogue min_path_from_rogue \n", "clusters \n", "0 6.388873e+18 2.315789 2.526316 \n", "1 3.678747e+13 0.111728 4.103121 \n", "2 1.529833e+16 2.000000 1.000000 \n", "3 1.243209e+16 3.253731 2.791045 \n", "4 0.000000e+00 4317.845744 4316.450729 \n", "5 3.994207e+14 21.917123 22.265763 \n", "\n", "[6 rows x 22 columns]" ] }, "execution_count": 138, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.groupby('clusters').mean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# IV - Tag transactions" ] }, { "cell_type": "code", "execution_count": 139, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ 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<td>0.000000e+00</td>\n", " <td>1.000000</td>\n", " <td>...</td>\n", " <td>4.200000e+14</td>\n", " <td>0.000000e+00</td>\n", " <td>0.000000e+00</td>\n", " <td>0.000000e+00</td>\n", " <td>0.000000e+00</td>\n", " <td>0.000000e+00</td>\n", " <td>0.000000e+00</td>\n", " <td>3.0</td>\n", " <td>4.0</td>\n", " <td>5</td>\n", " </tr>\n", " <tr>\n", " <th>0xa1fdb22f7a951f91db11373bef001b1f3577b811</th>\n", " <td>4</td>\n", " <td>4</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>3.625000e+18</td>\n", " <td>0.000000e+00</td>\n", " <td>2.102825e+18</td>\n", " <td>0.000000e+00</td>\n", " <td>1.000000</td>\n", " <td>...</td>\n", " <td>1.450000e+19</td>\n", " <td>6.531940e+12</td>\n", " <td>0.000000e+00</td>\n", " <td>8.084199e+11</td>\n", " <td>0.000000e+00</td>\n", " <td>0.000000e+00</td>\n", " <td>0.000000e+00</td>\n", " <td>0.0</td>\n", " <td>3.0</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>0x8344b50a8cc5ae9014ecc43cb1413e44da34376d</th>\n", " <td>1320</td>\n", " 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<td>0.0</td>\n", " <td>0.0</td>\n", " <td>5</td>\n", " </tr>\n", " <tr>\n", " <th>0xe2d5790bafa507de7b1870abcdd020a7348bbcb8</th>\n", " <td>3</td>\n", " <td>2</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>2</td>\n", " <td>2.049895e+19</td>\n", " <td>4.099748e+19</td>\n", " <td>1.950000e+19</td>\n", " <td>0.000000e+00</td>\n", " <td>2.000000</td>\n", " <td>...</td>\n", " <td>4.200000e+14</td>\n", " <td>0.000000e+00</td>\n", " <td>0.000000e+00</td>\n", " <td>0.000000e+00</td>\n", " <td>0.000000e+00</td>\n", " <td>0.000000e+00</td>\n", " <td>0.000000e+00</td>\n", " <td>5.0</td>\n", " <td>3.0</td>\n", " <td>5</td>\n", " </tr>\n", " <tr>\n", " <th>0xc840f256b0f0ff9069e918d060063057aaaa6b36</th>\n", " <td>2</td>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>2.712494e+15</td>\n", " <td>0.000000e+00</td>\n", " <td>7.624935e+14</td>\n", " <td>0.000000e+00</td>\n", " <td>2.000000</td>\n", " <td>...</td>\n", " <td>5.424987e+15</td>\n", " <td>0.000000e+00</td>\n", " <td>0.000000e+00</td>\n", " <td>0.000000e+00</td>\n", " <td>0.000000e+00</td>\n", " <td>0.000000e+00</td>\n", " <td>0.000000e+00</td>\n", " <td>0.0</td>\n", " <td>7.0</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>0xd3d8d934eef334d9b14511d312aab53e2cf6c850</th>\n", " <td>20</td>\n", " <td>11</td>\n", " <td>9</td>\n", " <td>9</td>\n", " <td>1</td>\n", " <td>6.011655e+19</td>\n", " <td>7.347534e+19</td>\n", " <td>2.048643e+19</td>\n", " <td>1.845453e+19</td>\n", " <td>1.375000</td>\n", " <td>...</td>\n", " <td>3.969000e+15</td>\n", " <td>4.349216e+16</td>\n", " <td>1.666741e+16</td>\n", " <td>4.870549e+16</td>\n", " <td>2.715391e+16</td>\n", " <td>1.824059e+14</td>\n", " <td>1.312211e+13</td>\n", " <td>4.0</td>\n", " <td>3.0</td>\n", " <td>5</td>\n", " </tr>\n", " <tr>\n", " <th>0x197a8bf6f97dc3fce54535563232f5df61da4322</th>\n", " <td>12</td>\n", " <td>8</td>\n", " <td>4</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>2.602602e+17</td>\n", " <td>5.201290e+17</td>\n", " <td>9.552195e+16</td>\n", " <td>2.186886e+17</td>\n", " <td>1.000000</td>\n", " <td>...</td>\n", " <td>1.565466e+15</td>\n", " <td>1.418978e+12</td>\n", " <td>1.211334e+12</td>\n", " <td>8.789384e+11</td>\n", " <td>1.602692e+11</td>\n", " <td>4.545106e+06</td>\n", " <td>0.000000e+00</td>\n", " <td>5.0</td>\n", " <td>5.0</td>\n", " <td>5</td>\n", " </tr>\n", " <tr>\n", " <th>0x035f15ee36264cbadcee4569634c98931460cfe1</th>\n", " <td>21</td>\n", " <td>13</td>\n", " <td>8</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>9.984083e+16</td>\n", " <td>1.618161e+17</td>\n", " <td>7.543433e+15</td>\n", " <td>4.384597e+16</td>\n", " <td>1.000000</td>\n", " <td>...</td>\n", " <td>3.402200e+15</td>\n", " <td>9.851619e+10</td>\n", " <td>3.428645e+11</td>\n", " <td>7.148373e+10</td>\n", " <td>2.846631e+11</td>\n", " <td>4.978643e+05</td>\n", " <td>3.240746e+06</td>\n", " <td>4.0</td>\n", " <td>0.0</td>\n", " <td>5</td>\n", " </tr>\n", " <tr>\n", " <th>0xa6a16d6afc64838721a4f40b68260d51ba7a3933</th>\n", " <td>2</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1.000000e+16</td>\n", " <td>9.580000e+15</td>\n", " <td>0.000000e+00</td>\n", " <td>0.000000e+00</td>\n", " <td>1.000000</td>\n", " <td>...</td>\n", " <td>4.200000e+14</td>\n", " <td>0.000000e+00</td>\n", " <td>0.000000e+00</td>\n", " <td>0.000000e+00</td>\n", " <td>0.000000e+00</td>\n", " <td>0.000000e+00</td>\n", " <td>0.000000e+00</td>\n", " <td>5.0</td>\n", " <td>4.0</td>\n", " <td>5</td>\n", " </tr>\n", " <tr>\n", " <th>0x2cc89b50432b5d96e2a2ce198127976a9af5daa6</th>\n", " <td>2</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1.630000e+18</td>\n", " <td>1.629480e+18</td>\n", " <td>0.000000e+00</td>\n", " <td>0.000000e+00</td>\n", " <td>1.000000</td>\n", " <td>...</td>\n", " <td>5.201000e+14</td>\n", " <td>0.000000e+00</td>\n", " <td>0.000000e+00</td>\n", " <td>0.000000e+00</td>\n", " <td>0.000000e+00</td>\n", " <td>0.000000e+00</td>\n", " <td>0.000000e+00</td>\n", " <td>3.0</td>\n", " <td>4.0</td>\n", " <td>5</td>\n", " </tr>\n", " <tr>\n", " <th>0xbc4be2bcaf0d0d29172b97939f34167dd455bef8</th>\n", " <td>11</td>\n", " <td>7</td>\n", " <td>4</td>\n", " <td>3</td>\n", " <td>3</td>\n", " <td>7.803258e+17</td>\n", " <td>1.365150e+18</td>\n", " <td>9.522350e+17</td>\n", " <td>1.145508e+18</td>\n", " <td>1.000000</td>\n", " <td>...</td>\n", " <td>1.680000e+15</td>\n", " <td>2.210971e+13</td>\n", " <td>1.150399e+14</td>\n", " <td>1.696559e+13</td>\n", " <td>1.116297e+14</td>\n", " <td>1.412889e+09</td>\n", " <td>0.000000e+00</td>\n", " <td>6.0</td>\n", " <td>5.0</td>\n", " <td>5</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>20 rows × 23 columns</p>\n", "</div>" ], "text/plain": [ " total_degree degree_in \\\n", "nodes \n", "0 58793 58793 \n", "0xaeeb08aca66b2346006cd83cbb2c7d10c39e3301 2 1 \n", "0x8dcca392c5d80d5ed8a566af8821002d12cd2f65 3 2 \n", "0x945ac1e3ed6ae8270da0169e79621ad278b6e079 2 1 \n", "0x4c6df0a7c40b8746e6f23b14aca9aa4e8a7538d7 4 2 \n", "0x4ade66eff13240f425e4ecc51659dad06f644044 2 1 \n", "0xb02aac17b3484e2b6af054dd90b0ba756790fbad 2 1 \n", "0x0b9b2631c2e43028b82e3c5ac7f634c25bc1098a 2 1 \n", "0x961df528c61262878460cee1df19cfc7b21e5810 2 1 \n", "0xa1fdb22f7a951f91db11373bef001b1f3577b811 4 4 \n", "0x8344b50a8cc5ae9014ecc43cb1413e44da34376d 1320 665 \n", "0x0f09baddb0a09890f41a60d204bed420fef8410c 3 0 \n", "0xe2d5790bafa507de7b1870abcdd020a7348bbcb8 3 2 \n", "0xc840f256b0f0ff9069e918d060063057aaaa6b36 2 2 \n", "0xd3d8d934eef334d9b14511d312aab53e2cf6c850 20 11 \n", "0x197a8bf6f97dc3fce54535563232f5df61da4322 12 8 \n", "0x035f15ee36264cbadcee4569634c98931460cfe1 21 13 \n", "0xa6a16d6afc64838721a4f40b68260d51ba7a3933 2 1 \n", "0x2cc89b50432b5d96e2a2ce198127976a9af5daa6 2 1 \n", "0xbc4be2bcaf0d0d29172b97939f34167dd455bef8 11 7 \n", "\n", " degree_out unique_successors \\\n", "nodes \n", "0 0 0 \n", "0xaeeb08aca66b2346006cd83cbb2c7d10c39e3301 1 1 \n", "0x8dcca392c5d80d5ed8a566af8821002d12cd2f65 1 1 \n", "0x945ac1e3ed6ae8270da0169e79621ad278b6e079 1 1 \n", "0x4c6df0a7c40b8746e6f23b14aca9aa4e8a7538d7 2 2 \n", "0x4ade66eff13240f425e4ecc51659dad06f644044 1 1 \n", "0xb02aac17b3484e2b6af054dd90b0ba756790fbad 1 1 \n", "0x0b9b2631c2e43028b82e3c5ac7f634c25bc1098a 1 1 \n", "0x961df528c61262878460cee1df19cfc7b21e5810 1 1 \n", "0xa1fdb22f7a951f91db11373bef001b1f3577b811 0 0 \n", "0x8344b50a8cc5ae9014ecc43cb1413e44da34376d 655 2 \n", "0x0f09baddb0a09890f41a60d204bed420fef8410c 3 1 \n", "0xe2d5790bafa507de7b1870abcdd020a7348bbcb8 1 1 \n", "0xc840f256b0f0ff9069e918d060063057aaaa6b36 0 0 \n", "0xd3d8d934eef334d9b14511d312aab53e2cf6c850 9 9 \n", "0x197a8bf6f97dc3fce54535563232f5df61da4322 4 1 \n", "0x035f15ee36264cbadcee4569634c98931460cfe1 8 1 \n", "0xa6a16d6afc64838721a4f40b68260d51ba7a3933 1 1 \n", "0x2cc89b50432b5d96e2a2ce198127976a9af5daa6 1 1 \n", "0xbc4be2bcaf0d0d29172b97939f34167dd455bef8 4 3 \n", "\n", " unique_predecessors \\\n", "nodes \n", "0 9161 \n", "0xaeeb08aca66b2346006cd83cbb2c7d10c39e3301 1 \n", "0x8dcca392c5d80d5ed8a566af8821002d12cd2f65 2 \n", "0x945ac1e3ed6ae8270da0169e79621ad278b6e079 1 \n", "0x4c6df0a7c40b8746e6f23b14aca9aa4e8a7538d7 2 \n", "0x4ade66eff13240f425e4ecc51659dad06f644044 1 \n", "0xb02aac17b3484e2b6af054dd90b0ba756790fbad 1 \n", "0x0b9b2631c2e43028b82e3c5ac7f634c25bc1098a 1 \n", "0x961df528c61262878460cee1df19cfc7b21e5810 1 \n", "0xa1fdb22f7a951f91db11373bef001b1f3577b811 1 \n", "0x8344b50a8cc5ae9014ecc43cb1413e44da34376d 4 \n", "0x0f09baddb0a09890f41a60d204bed420fef8410c 0 \n", "0xe2d5790bafa507de7b1870abcdd020a7348bbcb8 2 \n", "0xc840f256b0f0ff9069e918d060063057aaaa6b36 2 \n", "0xd3d8d934eef334d9b14511d312aab53e2cf6c850 1 \n", "0x197a8bf6f97dc3fce54535563232f5df61da4322 1 \n", "0x035f15ee36264cbadcee4569634c98931460cfe1 1 \n", "0xa6a16d6afc64838721a4f40b68260d51ba7a3933 1 \n", "0x2cc89b50432b5d96e2a2ce198127976a9af5daa6 1 \n", "0xbc4be2bcaf0d0d29172b97939f34167dd455bef8 3 \n", "\n", " mean_value_in mean_value_out \\\n", "nodes \n", "0 2.025492e+20 0.000000e+00 \n", "0xaeeb08aca66b2346006cd83cbb2c7d10c39e3301 5.689887e+18 5.688457e+18 \n", "0x8dcca392c5d80d5ed8a566af8821002d12cd2f65 1.500095e+21 3.000000e+21 \n", "0x945ac1e3ed6ae8270da0169e79621ad278b6e079 1.000000e+18 9.993354e+17 \n", "0x4c6df0a7c40b8746e6f23b14aca9aa4e8a7538d7 6.558730e+17 6.554530e+17 \n", "0x4ade66eff13240f425e4ecc51659dad06f644044 2.136258e+17 2.125758e+17 \n", "0xb02aac17b3484e2b6af054dd90b0ba756790fbad 2.832805e+17 2.782805e+17 \n", "0x0b9b2631c2e43028b82e3c5ac7f634c25bc1098a 1.599000e+19 1.598958e+19 \n", "0x961df528c61262878460cee1df19cfc7b21e5810 1.000000e+18 9.995800e+17 \n", "0xa1fdb22f7a951f91db11373bef001b1f3577b811 3.625000e+18 0.000000e+00 \n", "0x8344b50a8cc5ae9014ecc43cb1413e44da34376d 5.230388e+18 5.309541e+18 \n", "0x0f09baddb0a09890f41a60d204bed420fef8410c 0.000000e+00 1.000000e+00 \n", "0xe2d5790bafa507de7b1870abcdd020a7348bbcb8 2.049895e+19 4.099748e+19 \n", "0xc840f256b0f0ff9069e918d060063057aaaa6b36 2.712494e+15 0.000000e+00 \n", "0xd3d8d934eef334d9b14511d312aab53e2cf6c850 6.011655e+19 7.347534e+19 \n", "0x197a8bf6f97dc3fce54535563232f5df61da4322 2.602602e+17 5.201290e+17 \n", "0x035f15ee36264cbadcee4569634c98931460cfe1 9.984083e+16 1.618161e+17 \n", "0xa6a16d6afc64838721a4f40b68260d51ba7a3933 1.000000e+16 9.580000e+15 \n", "0x2cc89b50432b5d96e2a2ce198127976a9af5daa6 1.630000e+18 1.629480e+18 \n", "0xbc4be2bcaf0d0d29172b97939f34167dd455bef8 7.803258e+17 1.365150e+18 \n", "\n", " std_value_in std_value_out \\\n", "nodes \n", "0 4.908332e+22 0.000000e+00 \n", "0xaeeb08aca66b2346006cd83cbb2c7d10c39e3301 0.000000e+00 0.000000e+00 \n", "0x8dcca392c5d80d5ed8a566af8821002d12cd2f65 3.000050e+20 0.000000e+00 \n", "0x945ac1e3ed6ae8270da0169e79621ad278b6e079 0.000000e+00 0.000000e+00 \n", "0x4c6df0a7c40b8746e6f23b14aca9aa4e8a7538d7 6.386689e+17 3.445470e+17 \n", "0x4ade66eff13240f425e4ecc51659dad06f644044 0.000000e+00 0.000000e+00 \n", "0xb02aac17b3484e2b6af054dd90b0ba756790fbad 0.000000e+00 0.000000e+00 \n", "0x0b9b2631c2e43028b82e3c5ac7f634c25bc1098a 0.000000e+00 0.000000e+00 \n", "0x961df528c61262878460cee1df19cfc7b21e5810 0.000000e+00 0.000000e+00 \n", "0xa1fdb22f7a951f91db11373bef001b1f3577b811 2.102825e+18 0.000000e+00 \n", "0x8344b50a8cc5ae9014ecc43cb1413e44da34376d 2.397235e+18 2.420706e+18 \n", "0x0f09baddb0a09890f41a60d204bed420fef8410c 0.000000e+00 0.000000e+00 \n", "0xe2d5790bafa507de7b1870abcdd020a7348bbcb8 1.950000e+19 0.000000e+00 \n", "0xc840f256b0f0ff9069e918d060063057aaaa6b36 7.624935e+14 0.000000e+00 \n", "0xd3d8d934eef334d9b14511d312aab53e2cf6c850 2.048643e+19 1.845453e+19 \n", "0x197a8bf6f97dc3fce54535563232f5df61da4322 9.552195e+16 2.186886e+17 \n", "0x035f15ee36264cbadcee4569634c98931460cfe1 7.543433e+15 4.384597e+16 \n", "0xa6a16d6afc64838721a4f40b68260d51ba7a3933 0.000000e+00 0.000000e+00 \n", "0x2cc89b50432b5d96e2a2ce198127976a9af5daa6 0.000000e+00 0.000000e+00 \n", "0xbc4be2bcaf0d0d29172b97939f34167dd455bef8 9.522350e+17 1.145508e+18 \n", "\n", " ratio_in_timestamp ... \\\n", "nodes ... \n", "0 1.476135 ... \n", "0xaeeb08aca66b2346006cd83cbb2c7d10c39e3301 1.000000 ... \n", "0x8dcca392c5d80d5ed8a566af8821002d12cd2f65 1.000000 ... \n", "0x945ac1e3ed6ae8270da0169e79621ad278b6e079 1.000000 ... \n", "0x4c6df0a7c40b8746e6f23b14aca9aa4e8a7538d7 2.000000 ... \n", "0x4ade66eff13240f425e4ecc51659dad06f644044 1.000000 ... \n", "0xb02aac17b3484e2b6af054dd90b0ba756790fbad 1.000000 ... \n", "0x0b9b2631c2e43028b82e3c5ac7f634c25bc1098a 1.000000 ... \n", "0x961df528c61262878460cee1df19cfc7b21e5810 1.000000 ... \n", "0xa1fdb22f7a951f91db11373bef001b1f3577b811 1.000000 ... \n", "0x8344b50a8cc5ae9014ecc43cb1413e44da34376d 1.003017 ... \n", "0x0f09baddb0a09890f41a60d204bed420fef8410c 0.000000 ... \n", "0xe2d5790bafa507de7b1870abcdd020a7348bbcb8 2.000000 ... \n", "0xc840f256b0f0ff9069e918d060063057aaaa6b36 2.000000 ... \n", "0xd3d8d934eef334d9b14511d312aab53e2cf6c850 1.375000 ... \n", "0x197a8bf6f97dc3fce54535563232f5df61da4322 1.000000 ... \n", "0x035f15ee36264cbadcee4569634c98931460cfe1 1.000000 ... \n", "0xa6a16d6afc64838721a4f40b68260d51ba7a3933 1.000000 ... \n", "0x2cc89b50432b5d96e2a2ce198127976a9af5daa6 1.000000 ... \n", "0xbc4be2bcaf0d0d29172b97939f34167dd455bef8 1.000000 ... \n", "\n", " balance mean_velocity_in \\\n", "nodes \n", "0 1.190847e+25 2.934858e+13 \n", "0xaeeb08aca66b2346006cd83cbb2c7d10c39e3301 1.429602e+15 0.000000e+00 \n", "0x8dcca392c5d80d5ed8a566af8821002d12cd2f65 1.900000e+17 0.000000e+00 \n", "0x945ac1e3ed6ae8270da0169e79621ad278b6e079 6.645832e+14 0.000000e+00 \n", "0x4c6df0a7c40b8746e6f23b14aca9aa4e8a7538d7 8.400000e+14 0.000000e+00 \n", "0x4ade66eff13240f425e4ecc51659dad06f644044 1.050000e+15 0.000000e+00 \n", "0xb02aac17b3484e2b6af054dd90b0ba756790fbad 5.000000e+15 0.000000e+00 \n", "0x0b9b2631c2e43028b82e3c5ac7f634c25bc1098a 4.200000e+14 0.000000e+00 \n", "0x961df528c61262878460cee1df19cfc7b21e5810 4.200000e+14 0.000000e+00 \n", "0xa1fdb22f7a951f91db11373bef001b1f3577b811 1.450000e+19 6.531940e+12 \n", "0x8344b50a8cc5ae9014ecc43cb1413e44da34376d 4.585454e+17 3.309423e+13 \n", "0x0f09baddb0a09890f41a60d204bed420fef8410c -3.000000e+00 0.000000e+00 \n", "0xe2d5790bafa507de7b1870abcdd020a7348bbcb8 4.200000e+14 0.000000e+00 \n", "0xc840f256b0f0ff9069e918d060063057aaaa6b36 5.424987e+15 0.000000e+00 \n", "0xd3d8d934eef334d9b14511d312aab53e2cf6c850 3.969000e+15 4.349216e+16 \n", "0x197a8bf6f97dc3fce54535563232f5df61da4322 1.565466e+15 1.418978e+12 \n", "0x035f15ee36264cbadcee4569634c98931460cfe1 3.402200e+15 9.851619e+10 \n", "0xa6a16d6afc64838721a4f40b68260d51ba7a3933 4.200000e+14 0.000000e+00 \n", "0x2cc89b50432b5d96e2a2ce198127976a9af5daa6 5.201000e+14 0.000000e+00 \n", "0xbc4be2bcaf0d0d29172b97939f34167dd455bef8 1.680000e+15 2.210971e+13 \n", "\n", " mean_velocity_out \\\n", "nodes \n", "0 0.000000e+00 \n", "0xaeeb08aca66b2346006cd83cbb2c7d10c39e3301 0.000000e+00 \n", "0x8dcca392c5d80d5ed8a566af8821002d12cd2f65 0.000000e+00 \n", "0x945ac1e3ed6ae8270da0169e79621ad278b6e079 0.000000e+00 \n", "0x4c6df0a7c40b8746e6f23b14aca9aa4e8a7538d7 0.000000e+00 \n", "0x4ade66eff13240f425e4ecc51659dad06f644044 0.000000e+00 \n", "0xb02aac17b3484e2b6af054dd90b0ba756790fbad 0.000000e+00 \n", "0x0b9b2631c2e43028b82e3c5ac7f634c25bc1098a 0.000000e+00 \n", "0x961df528c61262878460cee1df19cfc7b21e5810 0.000000e+00 \n", "0xa1fdb22f7a951f91db11373bef001b1f3577b811 0.000000e+00 \n", "0x8344b50a8cc5ae9014ecc43cb1413e44da34376d 2.273164e+13 \n", "0x0f09baddb0a09890f41a60d204bed420fef8410c 0.000000e+00 \n", "0xe2d5790bafa507de7b1870abcdd020a7348bbcb8 0.000000e+00 \n", "0xc840f256b0f0ff9069e918d060063057aaaa6b36 0.000000e+00 \n", "0xd3d8d934eef334d9b14511d312aab53e2cf6c850 1.666741e+16 \n", "0x197a8bf6f97dc3fce54535563232f5df61da4322 1.211334e+12 \n", "0x035f15ee36264cbadcee4569634c98931460cfe1 3.428645e+11 \n", "0xa6a16d6afc64838721a4f40b68260d51ba7a3933 0.000000e+00 \n", "0x2cc89b50432b5d96e2a2ce198127976a9af5daa6 0.000000e+00 \n", "0xbc4be2bcaf0d0d29172b97939f34167dd455bef8 1.150399e+14 \n", "\n", " std_velocity_in std_velocity_out \\\n", "nodes \n", "0 3.698298e+15 0.000000e+00 \n", "0xaeeb08aca66b2346006cd83cbb2c7d10c39e3301 0.000000e+00 0.000000e+00 \n", "0x8dcca392c5d80d5ed8a566af8821002d12cd2f65 0.000000e+00 0.000000e+00 \n", "0x945ac1e3ed6ae8270da0169e79621ad278b6e079 0.000000e+00 0.000000e+00 \n", "0x4c6df0a7c40b8746e6f23b14aca9aa4e8a7538d7 0.000000e+00 0.000000e+00 \n", "0x4ade66eff13240f425e4ecc51659dad06f644044 0.000000e+00 0.000000e+00 \n", "0xb02aac17b3484e2b6af054dd90b0ba756790fbad 0.000000e+00 0.000000e+00 \n", "0x0b9b2631c2e43028b82e3c5ac7f634c25bc1098a 0.000000e+00 0.000000e+00 \n", "0x961df528c61262878460cee1df19cfc7b21e5810 0.000000e+00 0.000000e+00 \n", "0xa1fdb22f7a951f91db11373bef001b1f3577b811 8.084199e+11 0.000000e+00 \n", "0x8344b50a8cc5ae9014ecc43cb1413e44da34376d 2.788704e+14 3.888878e+13 \n", "0x0f09baddb0a09890f41a60d204bed420fef8410c 0.000000e+00 0.000000e+00 \n", "0xe2d5790bafa507de7b1870abcdd020a7348bbcb8 0.000000e+00 0.000000e+00 \n", "0xc840f256b0f0ff9069e918d060063057aaaa6b36 0.000000e+00 0.000000e+00 \n", "0xd3d8d934eef334d9b14511d312aab53e2cf6c850 4.870549e+16 2.715391e+16 \n", "0x197a8bf6f97dc3fce54535563232f5df61da4322 8.789384e+11 1.602692e+11 \n", "0x035f15ee36264cbadcee4569634c98931460cfe1 7.148373e+10 2.846631e+11 \n", "0xa6a16d6afc64838721a4f40b68260d51ba7a3933 0.000000e+00 0.000000e+00 \n", "0x2cc89b50432b5d96e2a2ce198127976a9af5daa6 0.000000e+00 0.000000e+00 \n", "0xbc4be2bcaf0d0d29172b97939f34167dd455bef8 1.696559e+13 1.116297e+14 \n", "\n", " mean_acceleration_in \\\n", "nodes \n", "0 8.548336e+10 \n", "0xaeeb08aca66b2346006cd83cbb2c7d10c39e3301 0.000000e+00 \n", "0x8dcca392c5d80d5ed8a566af8821002d12cd2f65 0.000000e+00 \n", "0x945ac1e3ed6ae8270da0169e79621ad278b6e079 0.000000e+00 \n", "0x4c6df0a7c40b8746e6f23b14aca9aa4e8a7538d7 0.000000e+00 \n", "0x4ade66eff13240f425e4ecc51659dad06f644044 0.000000e+00 \n", "0xb02aac17b3484e2b6af054dd90b0ba756790fbad 0.000000e+00 \n", "0x0b9b2631c2e43028b82e3c5ac7f634c25bc1098a 0.000000e+00 \n", "0x961df528c61262878460cee1df19cfc7b21e5810 0.000000e+00 \n", "0xa1fdb22f7a951f91db11373bef001b1f3577b811 0.000000e+00 \n", "0x8344b50a8cc5ae9014ecc43cb1413e44da34376d 2.604020e+10 \n", "0x0f09baddb0a09890f41a60d204bed420fef8410c 0.000000e+00 \n", "0xe2d5790bafa507de7b1870abcdd020a7348bbcb8 0.000000e+00 \n", "0xc840f256b0f0ff9069e918d060063057aaaa6b36 0.000000e+00 \n", "0xd3d8d934eef334d9b14511d312aab53e2cf6c850 1.824059e+14 \n", "0x197a8bf6f97dc3fce54535563232f5df61da4322 4.545106e+06 \n", "0x035f15ee36264cbadcee4569634c98931460cfe1 4.978643e+05 \n", "0xa6a16d6afc64838721a4f40b68260d51ba7a3933 0.000000e+00 \n", "0x2cc89b50432b5d96e2a2ce198127976a9af5daa6 0.000000e+00 \n", "0xbc4be2bcaf0d0d29172b97939f34167dd455bef8 1.412889e+09 \n", "\n", " mean_acceleration_out \\\n", "nodes \n", "0 0.000000e+00 \n", "0xaeeb08aca66b2346006cd83cbb2c7d10c39e3301 0.000000e+00 \n", "0x8dcca392c5d80d5ed8a566af8821002d12cd2f65 0.000000e+00 \n", "0x945ac1e3ed6ae8270da0169e79621ad278b6e079 0.000000e+00 \n", "0x4c6df0a7c40b8746e6f23b14aca9aa4e8a7538d7 0.000000e+00 \n", "0x4ade66eff13240f425e4ecc51659dad06f644044 0.000000e+00 \n", "0xb02aac17b3484e2b6af054dd90b0ba756790fbad 0.000000e+00 \n", "0x0b9b2631c2e43028b82e3c5ac7f634c25bc1098a 0.000000e+00 \n", "0x961df528c61262878460cee1df19cfc7b21e5810 0.000000e+00 \n", "0xa1fdb22f7a951f91db11373bef001b1f3577b811 0.000000e+00 \n", "0x8344b50a8cc5ae9014ecc43cb1413e44da34376d 6.841447e+08 \n", "0x0f09baddb0a09890f41a60d204bed420fef8410c 0.000000e+00 \n", "0xe2d5790bafa507de7b1870abcdd020a7348bbcb8 0.000000e+00 \n", "0xc840f256b0f0ff9069e918d060063057aaaa6b36 0.000000e+00 \n", "0xd3d8d934eef334d9b14511d312aab53e2cf6c850 1.312211e+13 \n", "0x197a8bf6f97dc3fce54535563232f5df61da4322 0.000000e+00 \n", "0x035f15ee36264cbadcee4569634c98931460cfe1 3.240746e+06 \n", "0xa6a16d6afc64838721a4f40b68260d51ba7a3933 0.000000e+00 \n", "0x2cc89b50432b5d96e2a2ce198127976a9af5daa6 0.000000e+00 \n", "0xbc4be2bcaf0d0d29172b97939f34167dd455bef8 0.000000e+00 \n", "\n", " min_path_to_rogue \\\n", "nodes \n", "0 0.0 \n", "0xaeeb08aca66b2346006cd83cbb2c7d10c39e3301 2347.0 \n", "0x8dcca392c5d80d5ed8a566af8821002d12cd2f65 0.0 \n", "0x945ac1e3ed6ae8270da0169e79621ad278b6e079 4.0 \n", "0x4c6df0a7c40b8746e6f23b14aca9aa4e8a7538d7 4.0 \n", "0x4ade66eff13240f425e4ecc51659dad06f644044 4.0 \n", "0xb02aac17b3484e2b6af054dd90b0ba756790fbad 6.0 \n", "0x0b9b2631c2e43028b82e3c5ac7f634c25bc1098a 3.0 \n", "0x961df528c61262878460cee1df19cfc7b21e5810 3.0 \n", "0xa1fdb22f7a951f91db11373bef001b1f3577b811 0.0 \n", "0x8344b50a8cc5ae9014ecc43cb1413e44da34376d 3.0 \n", "0x0f09baddb0a09890f41a60d204bed420fef8410c 0.0 \n", "0xe2d5790bafa507de7b1870abcdd020a7348bbcb8 5.0 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"metadata": {}, "output_type": "execute_result" } ], "source": [ "get_cluster('0x037dd056e7fdbd641db5b6bea2a8780a83fae180',df)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 1 }	mit
ES-DOC/esdoc-jupyterhub	notebooks/csiro-bom/cmip6/models/sandbox-1/landice.ipynb	1	37256	{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "source": [ "# ES-DOC CMIP6 Model Properties - Landice \n", "MIP Era: CMIP6 \n", "Institute: CSIRO-BOM \n", "Source ID: SANDBOX-1 \n", "Topic: Landice \n", "Sub-Topics: Glaciers, Ice. \n", "Properties: 30 (21 required) \n", "Model descriptions: [Model description details](https://specializations.es-doc.org/cmip6/landice?client=jupyter-notebook) \n", "Initialized From: -- \n", "\n", "Notebook Help: [Goto notebook help page](https://es-doc.org/cmip6-models-documenting-with-ipython) \n", "Notebook Initialised: 2018-02-15 16:53:55" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### Document Setup \n", "IMPORTANT: to be executed each time you run the notebook " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# DO NOT EDIT ! \n", "from pyesdoc.ipython.model_topic import NotebookOutput \n", "\n", "# DO NOT EDIT ! \n", "DOC = NotebookOutput('cmip6', 'csiro-bom', 'sandbox-1', 'landice')" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Authors \n", "Set document authors" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set as follows: DOC.set_author(\"name\", \"email\") \n", "# TODO - please enter value(s)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Contributors \n", "Specify document contributors " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set as follows: DOC.set_contributor(\"name\", \"email\") \n", "# TODO - please enter value(s)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Publication \n", "Specify document publication status " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set publication status: \n", "# 0=do not publish, 1=publish. \n", "DOC.set_publication_status(0)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Table of Contents \n", "[1. Key Properties](#1.-Key-Properties) \n", "[2. Key Properties --> Software Properties](#2.-Key-Properties--->-Software-Properties) \n", "[3. Grid](#3.-Grid) \n", "[4. Glaciers](#4.-Glaciers) \n", "[5. Ice](#5.-Ice) \n", "[6. Ice --> Mass Balance](#6.-Ice--->-Mass-Balance) \n", "[7. Ice --> Mass Balance --> Basal](#7.-Ice--->-Mass-Balance--->-Basal) \n", "[8. Ice --> Mass Balance --> Frontal](#8.-Ice--->-Mass-Balance--->-Frontal) \n", "[9. Ice --> Dynamics](#9.-Ice--->-Dynamics) \n", "\n" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "# 1. Key Properties \n", "Land ice key properties" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 1.1. Overview\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Overview of land surface model." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.key_properties.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.2. Model Name\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Name of land surface model code" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.key_properties.model_name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.3. Ice Albedo\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### Specify how ice albedo is modelled" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.key_properties.ice_albedo') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prescribed\" \n", "# \"function of ice age\" \n", "# \"function of ice density\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.4. Atmospheric Coupling Variables\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Which variables are passed between the atmosphere and ice (e.g. orography, ice mass)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.key_properties.atmospheric_coupling_variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.5. Oceanic Coupling Variables\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Which variables are passed between the ocean and ice" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.key_properties.oceanic_coupling_variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.6. Prognostic Variables\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### Which variables are prognostically calculated in the ice model" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.key_properties.prognostic_variables') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"ice velocity\" \n", "# \"ice thickness\" \n", "# \"ice temperature\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 2. Key Properties --> Software Properties \n", "Software properties of land ice code" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 2.1. Repository\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Location of code for this component." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.key_properties.software_properties.repository') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 2.2. Code Version\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Code version identifier." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.key_properties.software_properties.code_version') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 2.3. Code Languages\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.N\n", "### Code language(s)." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.key_properties.software_properties.code_languages') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 3. Grid \n", "Land ice grid" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 3.1. Overview\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Overview of the grid in the land ice scheme" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.grid.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.2. Adaptive Grid\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Is an adative grid being used?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.grid.adaptive_grid') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.3. Base Resolution\n", "Is Required: TRUE    Type: FLOAT    Cardinality: 1.1\n", "### The base resolution (in metres), before any adaption" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.grid.base_resolution') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.4. Resolution Limit\n", "Is Required: FALSE    Type: FLOAT    Cardinality: 0.1\n", "### If an adaptive grid is being used, what is the limit of the resolution (in metres)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.grid.resolution_limit') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.5. Projection\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### The projection of the land ice grid (e.g. albers_equal_area)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.grid.projection') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 4. Glaciers \n", "Land ice glaciers" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 4.1. Overview\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Overview of glaciers in the land ice scheme" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.glaciers.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 4.2. Description\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Describe the treatment of glaciers, if any" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.glaciers.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 4.3. Dynamic Areal Extent\n", "Is Required: FALSE    Type: BOOLEAN    Cardinality: 0.1\n", "### Does the model include a dynamic glacial extent?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.glaciers.dynamic_areal_extent') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 5. Ice \n", "Ice sheet and ice shelf" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 5.1. Overview\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Overview of the ice sheet and ice shelf in the land ice scheme" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.ice.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.2. Grounding Line Method\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Specify the technique used for modelling the grounding line in the ice sheet-ice shelf coupling" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.ice.grounding_line_method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"grounding line prescribed\" \n", "# \"flux prescribed (Schoof)\" \n", "# \"fixed grid size\" \n", "# \"moving grid\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.3. Ice Sheet\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Are ice sheets simulated?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.ice.ice_sheet') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.4. Ice Shelf\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Are ice shelves simulated?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.ice.ice_shelf') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 6. Ice --> Mass Balance \n", "Description of the surface mass balance treatment" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 6.1. Surface Mass Balance\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Describe how and where the surface mass balance (SMB) is calulated. Include the temporal coupling frequeny from the atmosphere, whether or not a seperate SMB model is used, and if so details of this model, such as its resolution" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.ice.mass_balance.surface_mass_balance') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 7. Ice --> Mass Balance --> Basal \n", "Description of basal melting" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 7.1. Bedrock\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe the implementation of basal melting over bedrock" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.ice.mass_balance.basal.bedrock') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 7.2. Ocean\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe the implementation of basal melting over the ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.ice.mass_balance.basal.ocean') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 8. Ice --> Mass Balance --> Frontal \n", "Description of claving/melting from the ice shelf front" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 8.1. Calving\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe the implementation of calving from the front of the ice shelf" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.ice.mass_balance.frontal.calving') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.2. Melting\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe the implementation of melting from the front of the ice shelf" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.ice.mass_balance.frontal.melting') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 9. Ice --> Dynamics \n", "" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 9.1. Description\n", "Is Required: TRUE    Type: STRING    Cardinality:** 1.1\n", "### General description if ice sheet and ice shelf dynamics" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.ice.dynamics.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.2. Approximation\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### Approximation type used in modelling ice dynamics" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.ice.dynamics.approximation') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"SIA\" \n", "# \"SAA\" \n", "# \"full stokes\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.3. Adaptive Timestep\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Is there an adaptive time scheme for the ice scheme?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.ice.dynamics.adaptive_timestep') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.4. Timestep\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### Timestep (in seconds) of the ice scheme. If the timestep is adaptive, then state a representative timestep." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.ice.dynamics.timestep') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### \u00a92017 [ES-DOC](https://es-doc.org) \n" ], "cell_type": "markdown", "metadata": {} } ], "metadata": { "kernelspec": { "display_name": "Python 2", "name": "python2", "language": "python" }, "language_info": { "mimetype": "text/x-python", "nbconvert_exporter": "python", "name": "python", "file_extension": ".py", "version": "2.7.10", "pygments_lexer": "ipython2", "codemirror_mode": { "version": 2, "name": "ipython" } } } }	gpl-3.0
landlab/landlab	notebooks/tutorials/agent_based_modeling/groundwater/landlab_mesa_groundwater_pumping.ipynb	1	17944	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Coupling a Landlab groundwater with a Mesa agent-based model\n", "\n", "This notebook shows a toy example of how one might couple a simple groundwater model (Landlab's `GroundwaterDupuitPercolator`, by [Litwin et al. (2020)](https://joss.theoj.org/papers/10.21105/joss.01935)) with an agent-based model (ABM) written using the [Mesa](https://mesa.readthedocs.io/en/latest/) Agent-Based Modeling (ABM) package.\n", "\n", "The purpose of this tutorial is to demonstrate the technical aspects of creating an integrated Landlab-Mesa model. The example is deliberately very simple in terms of the processes and interactions represented, and not meant to be a realistic portrayal of water-resources decision making. But the example does show how one might build a more sophisticated and interesting model using these basic ingredients.\n", "\n", "(Greg Tucker, November 2021; created from earlier notebook example used in May 2020\n", "workshop)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Running the groundwater model\n", "\n", "The following section simply illustrates how to create a groundwater model using the `GroundwaterDupuitPercolator` component." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Imports:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from landlab import RasterModelGrid, imshow_grid\n", "from landlab.components import GroundwaterDupuitPercolator\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Set parameters:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "base_depth = 22.0 # depth of aquifer base below ground level, m\n", "initial_water_table_depth = 2.0 # starting depth to water table, m\n", "dx = 100.0 # cell width, m\n", "pumping_rate = 0.001 # pumping rate, m3/s\n", "well_locations = [800, 1200]\n", "K = 0.001 # hydraulic conductivity, (m/s)\n", "n = 0.2 # porosity, (-)\n", "dt = 3600.0 # time-step duration, s\n", "background_recharge = 0.1 / (3600 * 24 * 365.25) # recharge rate from infiltration, m/s" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create a grid and add fields:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Raster grid with closed boundaries\n", "# boundaries = {'top': 'closed','bottom': 'closed','right':'closed','left':'closed'}\n", "grid = RasterModelGrid((41, 41), xy_spacing=dx) # , bc=boundaries)\n", "\n", "# Topographic elevation field (meters)\n", "elev = grid.add_zeros(\"topographic__elevation\", at=\"node\")\n", "\n", "# Field for the elevation of the top of an impermeable geologic unit that forms\n", "# the base of the aquifer (meters)\n", "base = grid.add_zeros(\"aquifer_base__elevation\", at=\"node\")\n", "base[:] = elev - base_depth\n", "\n", "# Field for the elevation of the water table (meters)\n", "wt = grid.add_zeros(\"water_table__elevation\", at=\"node\")\n", "wt[:] = elev - initial_water_table_depth\n", "\n", "# Field for the groundwater recharge rate (meters per second)\n", "recharge = grid.add_zeros(\"recharge__rate\", at=\"node\")\n", "recharge[:] = background_recharge\n", "recharge[well_locations] -= pumping_rate / (\n", " dx * dx\n", ") # pumping rate, in terms of recharge" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Instantiate the component (note use of an array/field instead of a scalar constant for `recharge_rate`):" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "gdp = GroundwaterDupuitPercolator(\n", " grid,\n", " hydraulic_conductivity=K,\n", " porosity=n,\n", " recharge_rate=recharge,\n", " regularization_f=0.01,\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Define a couple of handy functions to run the model for a day or a year:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def run_for_one_day(gdp, dt):\n", " num_iter = int(3600.0 * 24 / dt)\n", " for _ in range(num_iter):\n", " gdp.run_one_step(dt)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def run_for_one_year(gdp, dt):\n", " num_iter = int(365.25 * 3600.0 * 24 / dt)\n", " for _ in range(num_iter):\n", " gdp.run_one_step(dt)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run for a year and plot the water table:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "run_for_one_year(gdp, dt)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "imshow_grid(grid, wt, colorbar_label=\"Water table elevation (m)\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Aside: calculating a pumping rate in terms of recharge\n", "\n", "The pumping rate at a particular grid cell (in volume per time, representing pumping from a well at that location) needs to be given in terms of a recharge rate (depth of water equivalent per time) in a given grid cell. Suppose for example you're pumping 16 gallons/minute (horrible units of course). That equates to:\n", "\n", "16 gal/min x 0.00378541 m3/gal x (1/60) min/sec =" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "Qp = 16.0 * 0.00378541 / 60.0\n", "print(Qp)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "...equals about 0.001 m$^3$/s. That's $Q_p$. The corresponding negative recharge in a cell of dimensions $\\Delta x$ by $\\Delta x$ would be\n", "\n", "$R_p = Q_p / \\Delta x^2$" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "Rp = Qp / (dx * dx)\n", "print(Rp)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## A very simple ABM with farmers who drill wells into the aquifer\n", "\n", "For the sake of illustration, our ABM will be extremely simple. There are $N$ farmers, at random locations, who each pump at a rate $Q_p$ as long as the water table lies above the depth of their well, $d_w$. Once the water table drops below their well, the well runs dry and they switch from crops to pasture." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Check that Mesa is installed\n", "\n", "For the next step, we must verify that Mesa is available. If it is not, use one of the installation commands below to install, then re-start the kernel (Kernel => Restart) and continue." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "try:\n", " from mesa import Model\n", "except ModuleNotFoundError:\n", " print(\n", " \"\"\"\n", "Mesa needs to be installed in order to run this notebook.\n", "\n", "Normally Mesa should be pre-installed alongside the Landlab notebook collection. \n", "But it appears that Mesa is not already installed on the system on which you are\n", "running this notebook. You can install Mesa from a command prompt using either:\n", "\n", "`conda install -c conda-forge mesa`\n", "\n", "or\n", "\n", "`pip install mesa`\n", " \"\"\"\n", " )\n", " raise" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Defining the ABM\n", "\n", "In Mesa, an ABM is created using a class for each Agent and a class for the Model. Here's the Agent class (a Farmer). Farmers have a grid location and an attribute: whether they are actively pumping their well or not. They also have a well depth: the depth to the bottom of their well. Their action consists of checking whether their well is wet or dry; if wet, they will pump, and if dry, they will not." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from mesa import Agent, Model\n", "from mesa.space import MultiGrid\n", "from mesa.time import RandomActivation" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "class FarmerAgent(Agent):\n", " \"\"\"An agent who pumps from a well if it's not dry.\"\"\"\n", "\n", " def __init__(self, unique_id, model, well_depth=5.0):\n", " super().__init__(unique_id, model)\n", " self.pumping = True\n", " self.well_depth = well_depth\n", "\n", " def step(self):\n", " x, y = self.pos\n", " print(f\"Farmer {self.unique_id}, ({x}, {y})\")\n", " print(f\" Depth to the water table: {self.model.wt_depth_2d[x,y]}\")\n", " print(f\" Depth to the bottom of the well: {self.well_depth}\")\n", " if self.model.wt_depth_2d[x, y] >= self.well_depth: # well is dry\n", " print(\" Well is dry.\")\n", " self.pumping = False\n", " else:\n", " print(\" Well is pumping.\")\n", " self.pumping = True" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, define the model class. The model will take as a parameter a reference to a 2D array (with the same dimensions as the grid) that contains the depth to water table at each grid location. This allows the Farmer agents to check whether their well has run dry." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "class FarmerModel(Model):\n", " \"\"\"A model with several agents on a grid.\"\"\"\n", "\n", " def __init__(self, N, width, height, well_depth, depth_to_water_table):\n", " self.num_agents = N\n", " self.grid = MultiGrid(width, height, True)\n", " self.depth_to_water_table = depth_to_water_table\n", " self.schedule = RandomActivation(self)\n", "\n", " # Create agents\n", " for i in range(self.num_agents):\n", " a = FarmerAgent(i, self, well_depth)\n", " self.schedule.add(a)\n", " # Add the agent to a random grid cell (excluding the perimeter)\n", " x = self.random.randrange(self.grid.width - 2) + 1\n", " y = self.random.randrange(self.grid.width - 2) + 1\n", " self.grid.place_agent(a, (x, y))\n", "\n", " def step(self):\n", " self.wt_depth_2d = self.depth_to_water_table.reshape(\n", " (self.grid.width, self.grid.height)\n", " )\n", " self.schedule.step()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Setting up the Landlab grid, fields, and groundwater simulator" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "base_depth = 22.0 # depth of aquifer base below ground level, m\n", "initial_water_table_depth = 2.8 # starting depth to water table, m\n", "dx = 100.0 # cell width, m\n", "pumping_rate = 0.004 # pumping rate, m3/s\n", "well_depth = 3 # well depth, m\n", "background_recharge = 0.002 / (365.25 * 24 * 3600) # recharge rate, m/s\n", "K = 0.001 # hydraulic conductivity, (m/s)\n", "n = 0.2 # porosity, (-)\n", "dt = 3600.0 # time-step duration, s\n", "num_agents = 12 # number of farmer agents\n", "run_duration_yrs = 15 # run duration in years" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "grid = RasterModelGrid((41, 41), xy_spacing=dx)\n", "\n", "elev = grid.add_zeros(\"topographic__elevation\", at=\"node\")\n", "\n", "base = grid.add_zeros(\"aquifer_base__elevation\", at=\"node\")\n", "base[:] = elev - base_depth\n", "\n", "wt = grid.add_zeros(\"water_table__elevation\", at=\"node\")\n", "wt[:] = elev - initial_water_table_depth\n", "\n", "depth_to_wt = grid.add_zeros(\"water_table__depth_below_ground\", at=\"node\")\n", "depth_to_wt[:] = elev - wt\n", "\n", "recharge = grid.add_zeros(\"recharge__rate\", at=\"node\")\n", "recharge[:] = background_recharge\n", "recharge[well_locations] -= pumping_rate / (\n", " dx * dx\n", ") # pumping rate, in terms of recharge" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "gdp = GroundwaterDupuitPercolator(\n", " grid,\n", " hydraulic_conductivity=K,\n", " porosity=n,\n", " recharge_rate=recharge,\n", " regularization_f=0.01,\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Set up the Farmer model" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "nc = grid.number_of_node_columns\n", "nr = grid.number_of_node_rows\n", "farmer_model = FarmerModel(\n", " num_agents, nc, nr, well_depth, depth_to_wt.reshape((nr, nc))\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Check the spatial distribution of wells:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "\n", "\n", "def get_well_count(model):\n", " well_count = np.zeros((nr, nc), dtype=int)\n", " pumping_well_count = np.zeros((nr, nc), dtype=int)\n", " for cell in model.grid.coord_iter():\n", " cell_content, x, y = cell\n", " well_count[x][y] = len(cell_content)\n", " for agent in cell_content:\n", " if agent.pumping:\n", " pumping_well_count[x][y] += 1\n", " return well_count, pumping_well_count\n", "\n", "\n", "well_count, p_well_count = get_well_count(farmer_model)\n", "imshow_grid(grid, well_count.flatten())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Set the initial recharge field" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "recharge[:] = -(pumping_rate / (dx * dx)) * p_well_count.flatten()\n", "imshow_grid(grid, -recharge * 3600 * 24, colorbar_label=\"Pumping rate (m/day)\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Run the model" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for i in range(run_duration_yrs):\n", "\n", " # Run the groundwater simulator for one year\n", " run_for_one_year(gdp, dt)\n", "\n", " # Update the depth to water table\n", " depth_to_wt[:] = elev - wt\n", "\n", " # Run the farmer model\n", " farmer_model.step()\n", "\n", " # Count the number of pumping wells\n", " well_count, pumping_well_count = get_well_count(farmer_model)\n", " total_pumping_wells = np.sum(pumping_well_count)\n", " print(f\"In year {i + 1} there are {total_pumping_wells} pumping wells\")\n", " print(f\" and the greatest depth to water table is {np.amax(depth_to_wt)} meters.\")\n", "\n", " # Update the recharge field according to current pumping rate\n", " recharge[:] = (\n", " background_recharge - (pumping_rate / (dx * dx)) * pumping_well_count.flatten()\n", " )\n", " print(f\"Total recharge: {np.sum(recharge)}\")\n", " print(\"\")\n", "\n", " plt.figure()\n", " imshow_grid(grid, wt)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "imshow_grid(grid, wt)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Display the area of water table that lies below the well depth\n", "depth_to_wt[:] = elev - wt\n", "too_deep = depth_to_wt > well_depth\n", "imshow_grid(grid, too_deep)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This foregoing example is very simple, and leaves out many aspects of the complex problem of water extraction as a \"tragedy of the commons\". But it does illustrate how one can build a model that integrates agent-based dynamics with continuum dynamics by combining Landlab grid-based model code with Mesa ABM code." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.1" } }, "nbformat": 4, "nbformat_minor": 4 }	mit
joshnsolomon/phys202-2015-work	assignments/assignment05/InteractEx04.ipynb	1	18375	{ "cells": [ { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "# Interact Exercise 4" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "## Imports" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false, "nbgrader": {} }, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false, "nbgrader": {} }, "outputs": [], "source": [ "from IPython.html.widgets import interact, interactive, fixed\n", "from IPython.display import display" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "## Line with Gaussian noise" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "Write a function named `random_line` that creates `x` and `y` data for a line with y direction random noise that has a normal distribution $N(0,\\sigma^2)$:\n", "\n", "$$\n", "y = m x + b + N(0,\\sigma^2)\n", "$$\n", "\n", "Be careful about the `sigma=0.0` case." ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false, "nbgrader": { "checksum": "f1fccd14526477d1457886a737404055", "solution": true } }, "outputs": [], "source": [ "def random_line(m, b, sigma, size=10):\n", " \"\"\"Create a line y = mx + b + N(0,sigma2) between x=[-1.0,1.0]\n", " \n", " Parameters\n", " ----------\n", " m : float\n", " The slope of the line.\n", " b : float\n", " The y-intercept of the line.\n", " sigma : float\n", " The standard deviation of the y direction normal distribution noise.\n", " size : int\n", " The number of points to create for the line.\n", " \n", " Returns\n", " -------\n", " x : array of floats\n", " The array of x values for the line with `size` points.\n", " y : array of floats\n", " The array of y values for the lines with `size` points.\n", " \"\"\"\n", " x = np.linspace(-1, 1, size)\n", " n = np.random.randn(size)\n", " y = np.zeros(size)\n", " for a in range(size):\n", " y[a] = mx[a] + b + (sigma * n[a]) \n", " # formula for normal sitribution found on SciPy.org\n", " return x, y\n", " \n", " \n" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false, "deletable": false, "nbgrader": { "checksum": "085b717fea11f553f5549a88b1090e24", "grade": true, "grade_id": "interactex04a", "points": 2 } }, "outputs": [], "source": [ "m = 0.0; b = 1.0; sigma=0.0; size=3\n", "x, y = random_line(m, b, sigma, size)\n", "assert len(x)==len(y)==size\n", "assert list(x)==[-1.0,0.0,1.0]\n", "assert list(y)==[1.0,1.0,1.0]\n", "sigma = 1.0\n", "m = 0.0; b = 0.0\n", "size = 500\n", "x, y = random_line(m, b, sigma, size)\n", "assert np.allclose(np.mean(y-mx-b), 0.0, rtol=0.1, atol=0.1)\n", "assert np.allclose(np.std(y-mx-b), sigma, rtol=0.1, atol=0.1)" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "Write a function named `plot_random_line` that takes the same arguments as `random_line` and creates a random line using `random_line` and then plots the `x` and `y` points using Matplotlib's `scatter` function:\n", "\n", "* Make the marker color settable through a `color` keyword argument with a default of `red`.\n", "* Display the range $x=[-1.1,1.1]$ and $y=[-10.0,10.0]$.\n", "* Customize your plot to make it effective and beautiful." ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": true, "nbgrader": {} }, "outputs": [], "source": [ "def ticks_out(ax):\n", " \"\"\"Move the ticks to the outside of the box.\"\"\"\n", " ax.get_xaxis().set_tick_params(direction='out', width=1, which='both')\n", " ax.get_yaxis().set_tick_params(direction='out', width=1, which='both')" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false, "nbgrader": { "checksum": "701a9529400e32449715b0090b912d11", "solution": true } }, "outputs": [], "source": [ "def plot_random_line(m, b, sigma, size=10, color='red'):\n", " \"\"\"Plot a random line with slope m, intercept b and size points.\"\"\"\n", " x, y = random_line(m, b, sigma, size)\n", " plt.scatter(x,y,color=color)\n", " plt.xlim(-1.1,1.1)\n", " plt.ylim(-10.0,10.0)\n", " \n", " " ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false, "nbgrader": { "solution": false } }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f419714ee48>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_random_line(5.0, -1.0, 2.0, 50)" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": true, "deletable": false, "nbgrader": { "checksum": "b079fa9a413c8bc761692d3bfd9eb813", "grade": true, "grade_id": "interactex04b", "points": 4 } }, "outputs": [], "source": [ "assert True # use this cell to grade the plot_random_line function" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "Use `interact` to explore the `plot_random_line` function using:\n", "\n", "* `m`: a float valued slider from `-10.0` to `10.0` with steps of `0.1`.\n", "* `b`: a float valued slider from `-5.0` to `5.0` with steps of `0.1`.\n", "* `sigma`: a float valued slider from `0.0` to `5.0` with steps of `0.01`.\n", "* `size`: an int valued slider from `10` to `100` with steps of `10`.\n", "* `color`: a dropdown with options for `red`, `green` and `blue`." ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false, "deletable": false, "nbgrader": { "checksum": "6cff4e8e53b15273846c3aecaea84a3d", "solution": true } }, "outputs": [ { "data": { "image/png": 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IBIEOAJkg0AEgEwQ6AGTi/+RnT9L5sc7gAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f4197097940>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "interact(plot_random_line, m=(-10.0,10.0,0.1),b=(-5.0,5.0,.1),sigma=(0.0,5.0,.01),size=(10,100,10),color = ['red','green','blue']);" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": true, "deletable": false, "nbgrader": { "checksum": "49bbb321697a88612357059cba486cd3", "grade": true, "grade_id": "interactex04c", "points": 4 } }, "outputs": [], "source": [ "#### assert True # use this cell to grade the plot_random_line interact" ] } ], "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

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