{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Other OTUs as input\n", "\n", "### (1) Lab_Pathogens (2) Microbiome_Pathogens (3) Farm Practices and (4) Physicochemicals as Targets" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "#Import necessary libraries\n", "%matplotlib inline\n", "import scipy as sp\n", "import matplotlib.pyplot as plt\n", "from sklearn.inspection import partial_dependence\n", "import pandas as pd\n", "import numpy as np\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn import metrics\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.inspection import plot_partial_dependence\n", "from sklearn.metrics import roc_curve, roc_auc_score\n", "from sklearn.model_selection import cross_val_score\n", "import matplotlib.ticker as mtick\n", "import seaborn as sns\n", "import warnings\n", "from warnings import simplefilter\n", "simplefilter(action='ignore', category=FutureWarning)\n", "simplefilter(action='ignore', category=UserWarning)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "#Ignore unncessary warnings\n", "import warnings\n", "from warnings import simplefilter\n", "simplefilter(action='ignore', category=FutureWarning)\n", "warnings.filterwarnings('ignore')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "#Set seed to ensure possible consistent model prediction\n", "np.random.seed(1)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of duplicates SampleID in raw microbiome sheet1: 0\n", "Rows vs columns in sheet1: (364, 1319)\n" ] }, { "data": { "text/html": [ "
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IndexSampleIDUnassigned;Other;Other;Other;Other;Otherk__Archaea;p__Crenarchaeota;c__MBGA;o__NRP-J;f__;g__k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Cenarchaeales;f__Cenarchaeaceae;g__k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Cenarchaeales;f__SAGMA-X;g__k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Nitrososphaerales;f__Nitrososphaeraceae;g__k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Nitrososphaerales;f__Nitrososphaeraceae;g__Candidatus Nitrososphaerak__Archaea;p__Euryarchaeota;c__Methanobacteria;o__Methanobacteriales;f__Methanobacteriaceae;g__Methanobacteriumk__Archaea;p__Euryarchaeota;c__Methanobacteria;o__Methanobacteriales;f__Methanobacteriaceae;g__Methanobrevibacterk__Archaea;p__Euryarchaeota;c__Methanobacteria;o__Methanobacteriales;f__Methanobacteriaceae;g__Methanosphaera...k__Bacteria;p__WS3;c__PRR-12;o__Sediment-1;f__;g__k__Bacteria;p__WS3;c__PRR-12;o__Sediment-1;f__PRR-10;g__k__Bacteria;p__WS4;c__;o__;f__;g__k__Bacteria;p__WS5;c__;o__;f__;g__k__Bacteria;p__ZB3;c__BS119;o__;f__;g__k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Deinococcaceae;g__Deinococcusk__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Trueperaceae;g__k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Trueperaceae;g__Trueperak__Bacteria;p__[Thermi];c__Deinococci;o__Thermales;f__Thermaceae;g__Meiothermusk__Bacteria;p__[Thermi];c__Deinococci;o__Thermales;f__Thermaceae;g__Thermus
0A1-10.0036950.00.00.00.00.0000000.00.0006470.0...0.00.0000000.00.00.0000000.000000.00.00.00.0
1A1-100.0241690.00.00.00.00.0737360.00.0002150.0...0.00.0000590.00.00.0000590.000000.00.00.00.0
2A1-110.0021780.00.00.00.00.0001020.00.0001170.0...0.00.0000000.00.00.0000000.000000.00.00.00.0
3A1-120.0085000.00.00.00.00.0001400.00.0000600.0...0.00.0000000.00.00.0000000.000040.00.00.00.0
4A1-130.0870240.00.00.00.00.0000000.00.0002610.0...0.00.0000000.00.00.0000000.000000.00.00.00.0
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5 rows × 1319 columns

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" ], "text/plain": [ "Index SampleID Unassigned;Other;Other;Other;Other;Other \\\n", "0 A1-1 0.003695 \n", "1 A1-10 0.024169 \n", "2 A1-11 0.002178 \n", "3 A1-12 0.008500 \n", "4 A1-13 0.087024 \n", "\n", "Index k__Archaea;p__Crenarchaeota;c__MBGA;o__NRP-J;f__;g__ \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Cenarchaeales;f__Cenarchaeaceae;g__ \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Cenarchaeales;f__SAGMA-X;g__ \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Nitrososphaerales;f__Nitrososphaeraceae;g__ \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Nitrososphaerales;f__Nitrososphaeraceae;g__Candidatus Nitrososphaera \\\n", "0 0.000000 \n", "1 0.073736 \n", "2 0.000102 \n", "3 0.000140 \n", "4 0.000000 \n", "\n", "Index k__Archaea;p__Euryarchaeota;c__Methanobacteria;o__Methanobacteriales;f__Methanobacteriaceae;g__Methanobacterium \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Archaea;p__Euryarchaeota;c__Methanobacteria;o__Methanobacteriales;f__Methanobacteriaceae;g__Methanobrevibacter \\\n", "0 0.000647 \n", "1 0.000215 \n", "2 0.000117 \n", "3 0.000060 \n", "4 0.000261 \n", "\n", "Index k__Archaea;p__Euryarchaeota;c__Methanobacteria;o__Methanobacteriales;f__Methanobacteriaceae;g__Methanosphaera \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index ... k__Bacteria;p__WS3;c__PRR-12;o__Sediment-1;f__;g__ \\\n", "0 ... 0.0 \n", "1 ... 0.0 \n", "2 ... 0.0 \n", "3 ... 0.0 \n", "4 ... 0.0 \n", "\n", "Index k__Bacteria;p__WS3;c__PRR-12;o__Sediment-1;f__PRR-10;g__ \\\n", "0 0.000000 \n", "1 0.000059 \n", "2 0.000000 \n", "3 0.000000 \n", "4 0.000000 \n", "\n", "Index k__Bacteria;p__WS4;c__;o__;f__;g__ k__Bacteria;p__WS5;c__;o__;f__;g__ \\\n", "0 0.0 0.0 \n", "1 0.0 0.0 \n", "2 0.0 0.0 \n", "3 0.0 0.0 \n", "4 0.0 0.0 \n", "\n", "Index k__Bacteria;p__ZB3;c__BS119;o__;f__;g__ \\\n", "0 0.000000 \n", "1 0.000059 \n", "2 0.000000 \n", "3 0.000000 \n", "4 0.000000 \n", "\n", "Index k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Deinococcaceae;g__Deinococcus \\\n", "0 0.00000 \n", "1 0.00000 \n", "2 0.00000 \n", "3 0.00004 \n", "4 0.00000 \n", "\n", "Index k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Trueperaceae;g__ \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Trueperaceae;g__Truepera \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Bacteria;p__[Thermi];c__Deinococci;o__Thermales;f__Thermaceae;g__Meiothermus \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Bacteria;p__[Thermi];c__Deinococci;o__Thermales;f__Thermaceae;g__Thermus \n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "[5 rows x 1319 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Preprocess microbiome sheet 1\n", "microbiome_1 = pd.read_excel(\"PP Genus Level QIIME1.9 taxa tables.xlsx\", sheet_name=0,skiprows = 1)\n", "microbiome_1.dropna(how='all', axis=1, inplace=True)\n", "microbiome_1.rename(columns={\"#OTU ID\":\"Index\"}, inplace = True)\n", "microbiome_1 = microbiome_1.set_index('Index').transpose()\n", "microbiome_1.reset_index(inplace = True)\n", "microbiome_1.rename(columns={\"index\":\"SampleID\"}, inplace = True)\n", "microbiome_1.replace({'\\.': '-','01': '1','02': '2','03': '3','04': '4','05': '5','06': '6','07': '7','08': '8','09': '9'}, regex=True, inplace=True)\n", "print(\"Number of duplicates SampleID in raw microbiome sheet1:\", microbiome_1.SampleID.duplicated().sum()) \n", "print(\"Rows vs columns in sheet1:\", microbiome_1.shape)\n", "microbiome_1.head()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of duplicates SampleID in raw microbiome sheet2: 0\n", "Rows vs columns in sheet2: (314, 1396)\n" ] }, { "data": { "text/html": [ "
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IndexSampleIDUnassigned;Other;Other;Other;Other;Otherk__Archaea;p__Crenarchaeota;c__MCG;o__pGrfC26;f__;g__k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Cenarchaeales;f__SAGMA-X;g__k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Nitrososphaerales;f__Nitrososphaeraceae;g__Candidatus Nitrososphaerak__Archaea;p__Euryarchaeota;c__Methanobacteria;o__Methanobacteriales;f__Methanobacteriaceae;g__Methanobacteriumk__Archaea;p__Euryarchaeota;c__Methanobacteria;o__Methanobacteriales;f__Methanobacteriaceae;g__Methanobrevibacterk__Archaea;p__Euryarchaeota;c__Methanobacteria;o__Methanobacteriales;f__Methanobacteriaceae;g__Methanosphaerak__Archaea;p__Euryarchaeota;c__Methanomicrobia;o__Methanocellales;f__Methanocellaceae;g__Methanocellak__Archaea;p__Euryarchaeota;c__Methanomicrobia;o__Methanomicrobiales;f__;g__...k__Bacteria;p__WS4;c__;o__;f__;g__k__Bacteria;p__WWE1;c__[Cloacamonae];o__[Cloacamonales];f__;g__k__Bacteria;p__WWE1;c__[Cloacamonae];o__[Cloacamonales];f__[Cloacamonaceae];Otherk__Bacteria;p__ZB3;c__;o__;f__;g__k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Deinococcaceae;g__Deinococcusk__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Deinococcaceae;g__R18-435k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Trueperaceae;g__k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Trueperaceae;g__B-42k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Trueperaceae;g__Trueperak__Bacteria;p__[Thermi];c__Deinococci;o__Thermales;f__Thermaceae;g__Thermus
0A2-10.0014050.00.00.0000000.00.0000640.00.00.0...0.00.00.00.00.00.00.00.00.00.0
1A2-100.0118490.00.00.0316240.00.0000000.00.00.0...0.00.00.00.00.00.00.00.00.00.0
2A2-110.0046210.00.00.0000750.00.0001490.00.00.0...0.00.00.00.00.00.00.00.00.00.0
3A2-120.0020960.00.00.0000620.00.0000620.00.00.0...0.00.00.00.00.00.00.00.00.00.0
4A2-130.0002230.00.00.0000500.00.0000000.00.00.0...0.00.00.00.00.00.00.00.00.00.0
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5 rows × 1396 columns

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" ], "text/plain": [ "Index SampleID Unassigned;Other;Other;Other;Other;Other \\\n", "0 A2-1 0.001405 \n", "1 A2-10 0.011849 \n", "2 A2-11 0.004621 \n", "3 A2-12 0.002096 \n", "4 A2-13 0.000223 \n", "\n", "Index k__Archaea;p__Crenarchaeota;c__MCG;o__pGrfC26;f__;g__ \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Cenarchaeales;f__SAGMA-X;g__ \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Nitrososphaerales;f__Nitrososphaeraceae;g__Candidatus Nitrososphaera \\\n", "0 0.000000 \n", "1 0.031624 \n", "2 0.000075 \n", "3 0.000062 \n", "4 0.000050 \n", "\n", "Index k__Archaea;p__Euryarchaeota;c__Methanobacteria;o__Methanobacteriales;f__Methanobacteriaceae;g__Methanobacterium \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Archaea;p__Euryarchaeota;c__Methanobacteria;o__Methanobacteriales;f__Methanobacteriaceae;g__Methanobrevibacter \\\n", "0 0.000064 \n", "1 0.000000 \n", "2 0.000149 \n", "3 0.000062 \n", "4 0.000000 \n", "\n", "Index k__Archaea;p__Euryarchaeota;c__Methanobacteria;o__Methanobacteriales;f__Methanobacteriaceae;g__Methanosphaera \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Archaea;p__Euryarchaeota;c__Methanomicrobia;o__Methanocellales;f__Methanocellaceae;g__Methanocella \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Archaea;p__Euryarchaeota;c__Methanomicrobia;o__Methanomicrobiales;f__;g__ \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index ... k__Bacteria;p__WS4;c__;o__;f__;g__ \\\n", "0 ... 0.0 \n", "1 ... 0.0 \n", "2 ... 0.0 \n", "3 ... 0.0 \n", "4 ... 0.0 \n", "\n", "Index k__Bacteria;p__WWE1;c__[Cloacamonae];o__[Cloacamonales];f__;g__ \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Bacteria;p__WWE1;c__[Cloacamonae];o__[Cloacamonales];f__[Cloacamonaceae];Other \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Bacteria;p__ZB3;c__;o__;f__;g__ \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Deinococcaceae;g__Deinococcus \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Deinococcaceae;g__R18-435 \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Trueperaceae;g__ \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Trueperaceae;g__B-42 \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Trueperaceae;g__Truepera \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Bacteria;p__[Thermi];c__Deinococci;o__Thermales;f__Thermaceae;g__Thermus \n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "[5 rows x 1396 columns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Preprocess microbiome sheet 2\n", "microbiome_2 = pd.read_excel(\"PP Genus Level QIIME1.9 taxa tables.xlsx\", sheet_name=1,skiprows = 1)\n", "microbiome_2.dropna(how='all', axis=1, inplace=True)\n", "microbiome_2.rename(columns={\"#OTU ID\":\"Index\"}, inplace = True)\n", "microbiome_2 = microbiome_2.set_index('Index').transpose()\n", "microbiome_2.reset_index(inplace = True)\n", "microbiome_2.rename(columns={\"index\":\"SampleID\"}, inplace = True)\n", "microbiome_2.replace({'\\.': '-','01': '1','02': '2','03': '3','04': '4','05': '5','06': '6','07': '7','08': '8','09': '9'}, regex=True, inplace=True)\n", "print(\"Number of duplicates SampleID in raw microbiome sheet2:\", microbiome_2.SampleID.duplicated().sum()) \n", "print(\"Rows vs columns in sheet2:\", microbiome_2.shape)\n", "microbiome_2.head()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of duplicates SampleID in raw microbiome sheet3: 0\n", "Rows vs columns in sheet3: (331, 1421)\n" ] }, { "data": { "text/html": [ "
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IndexSampleIDUnassigned;Other;Other;Other;Other;Otherk__Archaea;p__Crenarchaeota;c__;o__;f__;g__k__Archaea;p__Crenarchaeota;c__MBGA;o__NRP-J;f__;g__k__Archaea;p__Crenarchaeota;c__MBGB;o__;f__;g__k__Archaea;p__Crenarchaeota;c__MCG;o__;f__;g__k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Cenarchaeales;f__Cenarchaeaceae;g__k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Cenarchaeales;f__Cenarchaeaceae;g__Nitrosopumilusk__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Cenarchaeales;f__SAGMA-X;g__k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Nitrososphaerales;f__Nitrososphaeraceae;g__Candidatus Nitrososphaera...k__Bacteria;p__[Caldithrix];c__KSB1;o__GW-22;f__;g__k__Bacteria;p__[Caldithrix];c__KSB1;o__Ucn15732;f__;g__k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Deinococcaceae;g__Deinobacteriumk__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Deinococcaceae;g__Deinococcusk__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Trueperaceae;Otherk__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Trueperaceae;g__k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Trueperaceae;g__B-42k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Trueperaceae;g__Trueperak__Bacteria;p__[Thermi];c__Deinococci;o__Thermales;f__Thermaceae;g__Meiothermusk__Bacteria;p__[Thermi];c__Deinococci;o__Thermales;f__Thermaceae;g__Thermus
0A6-10.0082090.00.00.00.00.00.00.00.000019...0.00.00.00.0000190.00.00.00.00.00.0
1A6-100.0102100.00.00.00.00.00.00.00.002054...0.00.00.00.0000000.00.00.00.00.00.0
2A6-110.0002320.00.00.00.00.00.00.00.000000...0.00.00.00.0000000.00.00.00.00.00.0
3A6-120.0081970.00.00.00.00.00.00.00.000087...0.00.00.00.0000000.00.00.00.00.00.0
4A6-130.0017970.00.00.00.00.00.00.00.000143...0.00.00.00.0000000.00.00.00.00.00.0
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5 rows × 1421 columns

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" ], "text/plain": [ "Index SampleID Unassigned;Other;Other;Other;Other;Other \\\n", "0 A6-1 0.008209 \n", "1 A6-10 0.010210 \n", "2 A6-11 0.000232 \n", "3 A6-12 0.008197 \n", "4 A6-13 0.001797 \n", "\n", "Index k__Archaea;p__Crenarchaeota;c__;o__;f__;g__ \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Archaea;p__Crenarchaeota;c__MBGA;o__NRP-J;f__;g__ \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Archaea;p__Crenarchaeota;c__MBGB;o__;f__;g__ \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Archaea;p__Crenarchaeota;c__MCG;o__;f__;g__ \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Cenarchaeales;f__Cenarchaeaceae;g__ \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Cenarchaeales;f__Cenarchaeaceae;g__Nitrosopumilus \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Cenarchaeales;f__SAGMA-X;g__ \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Nitrososphaerales;f__Nitrososphaeraceae;g__Candidatus Nitrososphaera \\\n", "0 0.000019 \n", "1 0.002054 \n", "2 0.000000 \n", "3 0.000087 \n", "4 0.000143 \n", "\n", "Index ... k__Bacteria;p__[Caldithrix];c__KSB1;o__GW-22;f__;g__ \\\n", "0 ... 0.0 \n", "1 ... 0.0 \n", "2 ... 0.0 \n", "3 ... 0.0 \n", "4 ... 0.0 \n", "\n", "Index k__Bacteria;p__[Caldithrix];c__KSB1;o__Ucn15732;f__;g__ \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Deinococcaceae;g__Deinobacterium \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Deinococcaceae;g__Deinococcus \\\n", "0 0.000019 \n", "1 0.000000 \n", "2 0.000000 \n", "3 0.000000 \n", "4 0.000000 \n", "\n", "Index k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Trueperaceae;Other \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Trueperaceae;g__ \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Trueperaceae;g__B-42 \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Trueperaceae;g__Truepera \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Bacteria;p__[Thermi];c__Deinococci;o__Thermales;f__Thermaceae;g__Meiothermus \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Bacteria;p__[Thermi];c__Deinococci;o__Thermales;f__Thermaceae;g__Thermus \n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "[5 rows x 1421 columns]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Preprocess microbiome sheet 3\n", "microbiome_3 = pd.read_excel(\"PP Genus Level QIIME1.9 taxa tables.xlsx\", sheet_name=2,skiprows = 1)\n", "microbiome_3.dropna(how='all', axis=1, inplace=True)\n", "microbiome_3.rename(columns={\"#OTU ID\":\"Index\"}, inplace = True)\n", "microbiome_3 = microbiome_3.set_index('Index').transpose()\n", "microbiome_3.reset_index(inplace = True)\n", "microbiome_3.rename(columns={\"index\":\"SampleID\"}, inplace = True)\n", "microbiome_3.replace({'\\.': '-','01': '1','02': '2','03': '3','04': '4','05': '5','06': '6','07': '7','08': '8','09': '9'}, regex=True, inplace=True)\n", "print(\"Number of duplicates SampleID in raw microbiome sheet3:\", microbiome_3.SampleID.duplicated().sum()) \n", "print(\"Rows vs columns in sheet3:\", microbiome_3.shape)\n", "microbiome_3.head()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of duplicates SampleID in raw microbiome sheet4: 0\n", "Rows vs columns in sheet4: (333, 1454)\n" ] }, { "data": { "text/html": [ "
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IndexSampleIDUnassigned;Other;Other;Other;Other;Otherk__Archaea;p__Crenarchaeota;c__MBGA;o__NRP-J;f__;g__k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Cenarchaeales;f__Cenarchaeaceae;g__k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Cenarchaeales;f__Cenarchaeaceae;g__Nitrosopumilusk__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Cenarchaeales;f__SAGMA-X;g__k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Nitrososphaerales;f__Nitrososphaeraceae;g__Candidatus Nitrososphaerak__Archaea;p__Euryarchaeota;c__Halobacteria;o__Halobacteriales;f__Halobacteriaceae;g__Haladaptatusk__Archaea;p__Euryarchaeota;c__Methanobacteria;o__Methanobacteriales;f__Methanobacteriaceae;g__k__Archaea;p__Euryarchaeota;c__Methanobacteria;o__Methanobacteriales;f__Methanobacteriaceae;g__Methanobacterium...k__Bacteria;p__WS3;c__PRR-12;o__Sediment-1;f__;g__k__Bacteria;p__WS3;c__PRR-12;o__Sediment-1;f__PRR-10;g__k__Bacteria;p__WS4;c__;o__;f__;g__k__Bacteria;p__WS5;c__;o__;f__;g__k__Bacteria;p__[Caldithrix];c__KSB1;o__MSB-5B5;f__;g__k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Deinococcaceae;g__Deinobacteriumk__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Deinococcaceae;g__Deinococcusk__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Trueperaceae;g__B-42k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Trueperaceae;g__Trueperak__Bacteria;p__[Thermi];c__Deinococci;o__Thermales;f__Thermaceae;g__Thermus
0A9-10.0029770.00.00.00.00.0000000.00.00.0...0.00.00.00.00.00.00.0000490.00.00.0
1A9-20.0027820.00.00.00.00.0000000.00.00.0...0.00.00.00.00.00.00.0000000.00.00.0
2A9-30.0018570.00.00.00.00.0000550.00.00.0...0.00.00.00.00.00.00.0000000.00.00.0
3A9-40.0003880.00.00.00.00.0000260.00.00.0...0.00.00.00.00.00.00.0000000.00.00.0
4A9-50.0003890.00.00.00.00.0000350.00.00.0...0.00.00.00.00.00.00.0000000.00.00.0
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5 rows × 1454 columns

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" ], "text/plain": [ "Index SampleID Unassigned;Other;Other;Other;Other;Other \\\n", "0 A9-1 0.002977 \n", "1 A9-2 0.002782 \n", "2 A9-3 0.001857 \n", "3 A9-4 0.000388 \n", "4 A9-5 0.000389 \n", "\n", "Index k__Archaea;p__Crenarchaeota;c__MBGA;o__NRP-J;f__;g__ \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Cenarchaeales;f__Cenarchaeaceae;g__ \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Cenarchaeales;f__Cenarchaeaceae;g__Nitrosopumilus \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Cenarchaeales;f__SAGMA-X;g__ \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Nitrososphaerales;f__Nitrososphaeraceae;g__Candidatus Nitrososphaera \\\n", "0 0.000000 \n", "1 0.000000 \n", "2 0.000055 \n", "3 0.000026 \n", "4 0.000035 \n", "\n", "Index k__Archaea;p__Euryarchaeota;c__Halobacteria;o__Halobacteriales;f__Halobacteriaceae;g__Haladaptatus \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Archaea;p__Euryarchaeota;c__Methanobacteria;o__Methanobacteriales;f__Methanobacteriaceae;g__ \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Archaea;p__Euryarchaeota;c__Methanobacteria;o__Methanobacteriales;f__Methanobacteriaceae;g__Methanobacterium \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index ... k__Bacteria;p__WS3;c__PRR-12;o__Sediment-1;f__;g__ \\\n", "0 ... 0.0 \n", "1 ... 0.0 \n", "2 ... 0.0 \n", "3 ... 0.0 \n", "4 ... 0.0 \n", "\n", "Index k__Bacteria;p__WS3;c__PRR-12;o__Sediment-1;f__PRR-10;g__ \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Bacteria;p__WS4;c__;o__;f__;g__ k__Bacteria;p__WS5;c__;o__;f__;g__ \\\n", "0 0.0 0.0 \n", "1 0.0 0.0 \n", "2 0.0 0.0 \n", "3 0.0 0.0 \n", "4 0.0 0.0 \n", "\n", "Index k__Bacteria;p__[Caldithrix];c__KSB1;o__MSB-5B5;f__;g__ \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Deinococcaceae;g__Deinobacterium \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Deinococcaceae;g__Deinococcus \\\n", "0 0.000049 \n", "1 0.000000 \n", "2 0.000000 \n", "3 0.000000 \n", "4 0.000000 \n", "\n", "Index k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Trueperaceae;g__B-42 \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Trueperaceae;g__Truepera \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Bacteria;p__[Thermi];c__Deinococci;o__Thermales;f__Thermaceae;g__Thermus \n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "[5 rows x 1454 columns]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Preprocess microbiome sheet 4\n", "microbiome_4 = pd.read_excel(\"PP Genus Level QIIME1.9 taxa tables.xlsx\", sheet_name=3,skiprows = 1)\n", "microbiome_4.dropna(how='all', axis=1, inplace=True)\n", "microbiome_4.rename(columns={\"#OTU ID\":\"Index\"}, inplace = True)\n", "microbiome_4 = microbiome_4.set_index('Index').transpose()\n", "microbiome_4.reset_index(inplace = True)\n", "microbiome_4.rename(columns={\"index\":\"SampleID\"}, inplace = True)\n", "microbiome_4.replace({'\\.': '-','01': '1','02': '2','03': '3','04': '4','05': '5','06': '6','07': '7','08': '8','09': '9'}, regex=True, inplace=True)\n", "print(\"Number of duplicates SampleID in raw microbiome sheet4:\", microbiome_4.SampleID.duplicated().sum()) \n", "print(\"Rows vs columns in sheet4:\", microbiome_4.shape)\n", "microbiome_4.head()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of duplicates SampleID in raw microbiome sheet5: 0\n", "Rows vs columns in sheet5: (336, 1443)\n" ] }, { "data": { "text/html": [ "
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IndexSampleIDUnassigned;Other;Other;Other;Other;Otherk__Archaea;p__Crenarchaeota;c__MBGA;o__NRP-J;f__;g__k__Archaea;p__Crenarchaeota;c__MCG;o__pGrfC26;f__;g__k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Cenarchaeales;f__SAGMA-X;g__k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Nitrososphaerales;f__Nitrososphaeraceae;g__k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Nitrososphaerales;f__Nitrososphaeraceae;g__Candidatus Nitrososphaerak__Archaea;p__Euryarchaeota;c__DSEG;o__104A5;f__;g__k__Archaea;p__Euryarchaeota;c__Methanobacteria;o__Methanobacteriales;f__Methanobacteriaceae;g__k__Archaea;p__Euryarchaeota;c__Methanobacteria;o__Methanobacteriales;f__Methanobacteriaceae;g__Methanobacterium...k__Bacteria;p__WS4;c__;o__;f__;g__k__Bacteria;p__WS5;c__;o__;f__;g__k__Bacteria;p__ZB3;c__;o__;f__;g__k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Deinococcaceae;g__Deinococcusk__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Deinococcaceae;g__R18-435k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Trueperaceae;g__k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Trueperaceae;g__B-42k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Trueperaceae;g__Trueperak__Bacteria;p__[Thermi];c__Deinococci;o__Thermales;f__Thermaceae;g__Meiothermusk__Bacteria;p__[Thermi];c__Deinococci;o__Thermales;f__Thermaceae;g__Thermus
0B5-10.0019800.00.00.00.00.0001130.00.00.0...0.00.00.00.0000000.00.00.00.00.00.0
1B5-20.0014380.00.00.00.00.0001330.00.00.0...0.00.00.00.0000120.00.00.00.00.00.0
2B5-30.0013990.00.00.00.00.0001820.00.00.0...0.00.00.00.0000000.00.00.00.00.00.0
3B5-40.0012060.00.00.00.00.0001010.00.00.0...0.00.00.00.0000000.00.00.00.00.00.0
4B5-50.0018230.00.00.00.00.0007200.00.00.0...0.00.00.00.0000000.00.00.00.00.00.0
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5 rows × 1443 columns

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" ], "text/plain": [ "Index SampleID Unassigned;Other;Other;Other;Other;Other \\\n", "0 B5-1 0.001980 \n", "1 B5-2 0.001438 \n", "2 B5-3 0.001399 \n", "3 B5-4 0.001206 \n", "4 B5-5 0.001823 \n", "\n", "Index k__Archaea;p__Crenarchaeota;c__MBGA;o__NRP-J;f__;g__ \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Archaea;p__Crenarchaeota;c__MCG;o__pGrfC26;f__;g__ \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Cenarchaeales;f__SAGMA-X;g__ \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Nitrososphaerales;f__Nitrososphaeraceae;g__ \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Nitrososphaerales;f__Nitrososphaeraceae;g__Candidatus Nitrososphaera \\\n", "0 0.000113 \n", "1 0.000133 \n", "2 0.000182 \n", "3 0.000101 \n", "4 0.000720 \n", "\n", "Index k__Archaea;p__Euryarchaeota;c__DSEG;o__104A5;f__;g__ \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Archaea;p__Euryarchaeota;c__Methanobacteria;o__Methanobacteriales;f__Methanobacteriaceae;g__ \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Archaea;p__Euryarchaeota;c__Methanobacteria;o__Methanobacteriales;f__Methanobacteriaceae;g__Methanobacterium \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index ... k__Bacteria;p__WS4;c__;o__;f__;g__ \\\n", "0 ... 0.0 \n", "1 ... 0.0 \n", "2 ... 0.0 \n", "3 ... 0.0 \n", "4 ... 0.0 \n", "\n", "Index k__Bacteria;p__WS5;c__;o__;f__;g__ k__Bacteria;p__ZB3;c__;o__;f__;g__ \\\n", "0 0.0 0.0 \n", "1 0.0 0.0 \n", "2 0.0 0.0 \n", "3 0.0 0.0 \n", "4 0.0 0.0 \n", "\n", "Index k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Deinococcaceae;g__Deinococcus \\\n", "0 0.000000 \n", "1 0.000012 \n", "2 0.000000 \n", "3 0.000000 \n", "4 0.000000 \n", "\n", "Index k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Deinococcaceae;g__R18-435 \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Trueperaceae;g__ \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Trueperaceae;g__B-42 \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Trueperaceae;g__Truepera \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Bacteria;p__[Thermi];c__Deinococci;o__Thermales;f__Thermaceae;g__Meiothermus \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Bacteria;p__[Thermi];c__Deinococci;o__Thermales;f__Thermaceae;g__Thermus \n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "[5 rows x 1443 columns]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Preprocess microbiome sheet 5\n", "microbiome_5 = pd.read_excel(\"PP Genus Level QIIME1.9 taxa tables.xlsx\", sheet_name=4,skiprows = 1)\n", "microbiome_5.dropna(how='all', axis=1, inplace=True)\n", "microbiome_5.rename(columns={\"#OTU ID\":\"Index\"}, inplace = True)\n", "microbiome_5 = microbiome_5.set_index('Index').transpose()\n", "microbiome_5.reset_index(inplace = True)\n", "microbiome_5.rename(columns={\"index\":\"SampleID\"}, inplace = True)\n", "microbiome_5.replace({'\\.': '-','01': '1','02': '2','03': '3','04': '4','05': '5','06': '6','07': '7','08': '8','09': '9'}, regex=True, inplace=True)\n", "print(\"Number of duplicates SampleID in raw microbiome sheet5:\", microbiome_5.SampleID.duplicated().sum()) \n", "print(\"Rows vs columns in sheet5:\", microbiome_5.shape)\n", "microbiome_5.head()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of duplicates SampleID in raw microbiome sheet6: 0\n", "Rows vs columns in sheet6: (318, 1380)\n" ] }, { "data": { "text/html": [ "
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IndexSampleIDUnassigned;Other;Other;Other;Other;Otherk__Archaea;p__Crenarchaeota;c__MBGA;o__NRP-J;f__;g__k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Cenarchaeales;f__Cenarchaeaceae;g__k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Cenarchaeales;f__SAGMA-X;g__k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Nitrososphaerales;f__Nitrososphaeraceae;g__Candidatus Nitrososphaerak__Archaea;p__Euryarchaeota;c__Methanobacteria;o__Methanobacteriales;f__Methanobacteriaceae;Otherk__Archaea;p__Euryarchaeota;c__Methanobacteria;o__Methanobacteriales;f__Methanobacteriaceae;g__k__Archaea;p__Euryarchaeota;c__Methanobacteria;o__Methanobacteriales;f__Methanobacteriaceae;g__Methanobacteriumk__Archaea;p__Euryarchaeota;c__Methanobacteria;o__Methanobacteriales;f__Methanobacteriaceae;g__Methanobrevibacter...k__Bacteria;p__WS3;c__PRR-12;o__Sediment-1;f__PRR-10;g__k__Bacteria;p__WS4;c__;o__;f__;g__k__Bacteria;p__WWE1;c__[Cloacamonae];o__[Cloacamonales];f__SHA-116;g__k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Deinococcaceae;g__Deinobacteriumk__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Deinococcaceae;g__Deinococcusk__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Deinococcaceae;g__R18-435k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Trueperaceae;g__k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Trueperaceae;g__B-42k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Trueperaceae;g__Trueperak__Bacteria;p__[Thermi];c__Deinococci;o__Thermales;f__Thermaceae;g__Meiothermus
0E1-260.0077630.00.00.00.0032620.00.00.00.000015...0.00.00.00.00.0003320.00.0000450.00.0000760.0
1J1-10.0007430.00.00.00.0000000.00.00.00.000000...0.00.00.00.00.0000000.00.0000000.00.0000000.0
2J1-20.0018130.00.00.00.0000250.00.00.00.000124...0.00.00.00.00.0000000.00.0000000.00.0000000.0
3J1-30.0014370.00.00.00.0000340.00.00.00.000103...0.00.00.00.00.0000000.00.0000000.00.0000000.0
4J1-40.0009230.00.00.00.0001420.00.00.00.000071...0.00.00.00.00.0000000.00.0000000.00.0000000.0
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5 rows × 1380 columns

\n", "
" ], "text/plain": [ "Index SampleID Unassigned;Other;Other;Other;Other;Other \\\n", "0 E1-26 0.007763 \n", "1 J1-1 0.000743 \n", "2 J1-2 0.001813 \n", "3 J1-3 0.001437 \n", "4 J1-4 0.000923 \n", "\n", "Index k__Archaea;p__Crenarchaeota;c__MBGA;o__NRP-J;f__;g__ \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Cenarchaeales;f__Cenarchaeaceae;g__ \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Cenarchaeales;f__SAGMA-X;g__ \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Archaea;p__Crenarchaeota;c__Thaumarchaeota;o__Nitrososphaerales;f__Nitrososphaeraceae;g__Candidatus Nitrososphaera \\\n", "0 0.003262 \n", "1 0.000000 \n", "2 0.000025 \n", "3 0.000034 \n", "4 0.000142 \n", "\n", "Index k__Archaea;p__Euryarchaeota;c__Methanobacteria;o__Methanobacteriales;f__Methanobacteriaceae;Other \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Archaea;p__Euryarchaeota;c__Methanobacteria;o__Methanobacteriales;f__Methanobacteriaceae;g__ \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Archaea;p__Euryarchaeota;c__Methanobacteria;o__Methanobacteriales;f__Methanobacteriaceae;g__Methanobacterium \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Archaea;p__Euryarchaeota;c__Methanobacteria;o__Methanobacteriales;f__Methanobacteriaceae;g__Methanobrevibacter \\\n", "0 0.000015 \n", "1 0.000000 \n", "2 0.000124 \n", "3 0.000103 \n", "4 0.000071 \n", "\n", "Index ... k__Bacteria;p__WS3;c__PRR-12;o__Sediment-1;f__PRR-10;g__ \\\n", "0 ... 0.0 \n", "1 ... 0.0 \n", "2 ... 0.0 \n", "3 ... 0.0 \n", "4 ... 0.0 \n", "\n", "Index k__Bacteria;p__WS4;c__;o__;f__;g__ \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Bacteria;p__WWE1;c__[Cloacamonae];o__[Cloacamonales];f__SHA-116;g__ \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Deinococcaceae;g__Deinobacterium \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Deinococcaceae;g__Deinococcus \\\n", "0 0.000332 \n", "1 0.000000 \n", "2 0.000000 \n", "3 0.000000 \n", "4 0.000000 \n", "\n", "Index k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Deinococcaceae;g__R18-435 \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Trueperaceae;g__ \\\n", "0 0.000045 \n", "1 0.000000 \n", "2 0.000000 \n", "3 0.000000 \n", "4 0.000000 \n", "\n", "Index k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Trueperaceae;g__B-42 \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "Index k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;f__Trueperaceae;g__Truepera \\\n", "0 0.000076 \n", "1 0.000000 \n", "2 0.000000 \n", "3 0.000000 \n", "4 0.000000 \n", "\n", "Index k__Bacteria;p__[Thermi];c__Deinococci;o__Thermales;f__Thermaceae;g__Meiothermus \n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "\n", "[5 rows x 1380 columns]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Preprocess microbiome sheet 6\n", "microbiome_6 = pd.read_excel(\"PP Genus Level QIIME1.9 taxa tables.xlsx\", sheet_name=5,skiprows = 1)\n", "microbiome_6.dropna(how='all', axis=1, inplace=True)\n", "microbiome_6.rename(columns={\"#OTU ID\":\"Index\"}, inplace = True)\n", "microbiome_6 = microbiome_6.set_index('Index').transpose()\n", "microbiome_6.reset_index(inplace = True)\n", "microbiome_6.rename(columns={\"index\":\"SampleID\"}, inplace = True)\n", "microbiome_6.replace({'\\.': '-','01': '1','02': '2','03': '3','04': '4','05': '5','06': '6','07': '7','08': '8','09': '9'}, regex=True, inplace=True)\n", "print(\"Number of duplicates SampleID in raw microbiome sheet6:\", microbiome_6.SampleID.duplicated().sum()) \n", "print(\"Rows vs columns in sheet6:\", microbiome_6.shape)\n", "microbiome_6.head()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of duplicates SampleID after merging all sheets: 6\n", "Number of duplicates SampleID after drop function: 0\n", "Microbiome Rows (SampleID) vs Columns (OTUS): 1990 vs 1823\n" ] } ], "source": [ "#Merged the 6 processed sheets\n", "microbiome = pd.concat([microbiome_1,microbiome_2,microbiome_3,microbiome_4,microbiome_5,microbiome_6], axis=0, ignore_index=True)\n", "print(\"Number of duplicates SampleID after merging all sheets:\", microbiome.SampleID.duplicated().sum()) \n", "microbiome.drop_duplicates(subset=\"SampleID\", inplace=True)\n", "print(\"Number of duplicates SampleID after drop function:\", microbiome.SampleID.duplicated().sum()) \n", "microbiome.fillna(0, inplace=True)\n", "print(\"Microbiome Rows (SampleID) vs Columns (OTUS):\", microbiome.shape[0],\"vs\",microbiome.shape[1]-2)\n", "microbiome.head()\n", "\n", "#drop listeria at genus level as it contains no information\n", "microbiome.drop('k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacillales;f__Listeriaceae;g__Listeria', axis=1, inplace=True)\n", "#Listeria detectection was at the family level, rename as genus for analysis purpose\n", "microbiome.rename(columns = {'k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacillales;f__Listeriaceae;Other':\n", " 'k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacillales;f__Listeriaceae;g__Listeria'},inplace=True)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1990" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "microbiome.SampleID.nunique()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Unique Taxonomy\n" ] }, { "data": { "text/plain": [ "Index\n", "Kingdom 3\n", "Phylum 63\n", "Class 176\n", "Order 305\n", "Family 389\n", "Genus 877\n", "dtype: int64" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Check number of unique taxonomy at each of the 6 levels\n", "microbiome.rename(columns={\"SampleID\":\"Index\"}, inplace = True)\n", "microbiome = microbiome.set_index('Index').transpose()\n", "microbiome.reset_index(inplace = True)\n", "microbiome.rename(columns={\"Index\":\"OTU\"}, inplace = True)\n", "\n", "microbiomecopy = microbiome.copy()\n", "microbiomecopy[['Kingdom','Phylum','Class','Order','Family','Genus']] = microbiomecopy[\"OTU\"].str.split(pat=\";\", expand=True)\n", "microbiomecopy = microbiomecopy[['Kingdom','Phylum','Class','Order','Family','Genus']]\n", "print(\"Unique Taxonomy\")\n", "microbiomecopy.nunique()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Unique Kingdom to Genus= 1823\n", "Unique Kingdom to Family= 842\n", "Unique Kingdom to Order= 479\n", "Unique Kingdom to Class= 176\n" ] } ], "source": [ "#Check uniques from kingdom down to lower levels\n", "microbiomecopy[\"Genus\"] = microbiomecopy[\"Kingdom\"]+\";\"+microbiomecopy[\"Phylum\"]+\";\"+ microbiomecopy[\"Class\"]+\";\"+microbiomecopy[\"Order\"]+\";\"+microbiomecopy[\"Family\"]+\";\"+microbiomecopy[\"Genus\"]\n", "print(\"Unique Kingdom to Genus=\", microbiomecopy.Genus.nunique())\n", "\n", "microbiomecopy[\"Family\"] = microbiomecopy[\"Kingdom\"]+\";\"+microbiomecopy[\"Phylum\"]+\";\"+ microbiomecopy[\"Class\"]+\";\"+microbiomecopy[\"Order\"]+\";\"+microbiomecopy[\"Family\"]\n", "print(\"Unique Kingdom to Family=\", microbiomecopy.Family.nunique())\n", "\n", "microbiomecopy[\"Order\"] = microbiomecopy[\"Kingdom\"]+\";\"+microbiomecopy[\"Phylum\"]+\";\"+ microbiomecopy[\"Class\"]+\";\"+microbiomecopy[\"Order\"]\n", "print(\"Unique Kingdom to Order=\", microbiomecopy.Order.nunique())\n", "\n", "microbiomecopy[\"Order\"] = microbiomecopy[\"Kingdom\"]+\";\"+microbiomecopy[\"Phylum\"]+\";\"+ microbiomecopy[\"Class\"]\n", "print(\"Unique Kingdom to Class=\", microbiomecopy.Class.nunique())" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Out of 1823 genera, unique ones = 877 , the rest are just repeated g_ or Other\n" ] } ], "source": [ "#Compute unique genera\n", "microbiomecopy[['Kingdom','Phylum','Class','Order','Family','Genus']] = microbiomecopy[\"Genus\"].str.split(pat=\";\", expand=True)\n", "microbiome[\"Genus\"] = microbiomecopy[\"Genus\"]\n", "print(\"Out of 1823 genera, unique ones =\",microbiome.Genus.nunique(),\", the rest are just repeated g_ or Other\")" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "
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GenusSampleIDOtherg__g__02d06g__1-68g__4-29g__5-7N15g__A17g__Acaryochlorisg__Acetobacter...g__cc_115g__gutg__heteroC45_4Wg__p-75-a5g__ph2g__rc4-4g__u114g__vadinCA02g__vadinCA11g__vadinHB04
0A1-10.0224480.1206470.00.00.0000000.00.0000000.00.0...0.0002770.00.0000000.0000000.0000000.00.00.00.0000920.0
1A1-100.0521810.5339220.00.00.0000000.00.0010920.00.0...0.0000200.00.0001950.0000200.0000000.00.00.00.0000000.0
2A1-110.0064760.0232130.00.00.0000150.00.0000000.00.0...0.0000000.00.0000000.0000000.0000150.00.00.00.0000290.0
3A1-120.1899010.0880240.00.00.0000000.00.0000200.00.0...0.0002210.00.0000200.0000000.0000400.00.00.00.0000800.0
4A1-130.1060450.3439290.00.00.0000000.00.0000000.00.0...0.0018240.00.0000000.0002610.0000000.00.00.00.0007820.0
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5 rows × 878 columns

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" ], "text/plain": [ "Genus SampleID Other g__ g__02d06 g__1-68 g__4-29 g__5-7N15 \\\n", "0 A1-1 0.022448 0.120647 0.0 0.0 0.000000 0.0 \n", "1 A1-10 0.052181 0.533922 0.0 0.0 0.000000 0.0 \n", "2 A1-11 0.006476 0.023213 0.0 0.0 0.000015 0.0 \n", "3 A1-12 0.189901 0.088024 0.0 0.0 0.000000 0.0 \n", "4 A1-13 0.106045 0.343929 0.0 0.0 0.000000 0.0 \n", "\n", "Genus g__A17 g__Acaryochloris g__Acetobacter ... g__cc_115 g__gut \\\n", "0 0.000000 0.0 0.0 ... 0.000277 0.0 \n", "1 0.001092 0.0 0.0 ... 0.000020 0.0 \n", "2 0.000000 0.0 0.0 ... 0.000000 0.0 \n", "3 0.000020 0.0 0.0 ... 0.000221 0.0 \n", "4 0.000000 0.0 0.0 ... 0.001824 0.0 \n", "\n", "Genus g__heteroC45_4W g__p-75-a5 g__ph2 g__rc4-4 g__u114 g__vadinCA02 \\\n", "0 0.000000 0.000000 0.000000 0.0 0.0 0.0 \n", "1 0.000195 0.000020 0.000000 0.0 0.0 0.0 \n", "2 0.000000 0.000000 0.000015 0.0 0.0 0.0 \n", "3 0.000020 0.000000 0.000040 0.0 0.0 0.0 \n", "4 0.000000 0.000261 0.000000 0.0 0.0 0.0 \n", "\n", "Genus g__vadinCA11 g__vadinHB04 \n", "0 0.000092 0.0 \n", "1 0.000000 0.0 \n", "2 0.000029 0.0 \n", "3 0.000080 0.0 \n", "4 0.000782 0.0 \n", "\n", "[5 rows x 878 columns]" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#make new dataframe with only unique microbiome\n", "microbiome = microbiome.groupby([\"Genus\"]).sum()\n", "microbiome = microbiome.transpose()\n", "microbiome.reset_index(inplace = True)\n", "microbiome.rename(columns={\"Index\":\"SampleID\"}, inplace = True)\n", "microbiome.head()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "'Microbiome SampleID vs OTUs'" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "(1990, 878)" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Sum of rows in microbiome\n", " 0 1.0\n", "1 1.0\n", "2 1.0\n", "3 1.0\n", "4 1.0\n", "5 1.0\n", "6 1.0\n", "7 1.0\n", "8 1.0\n", "9 1.0\n", "dtype: float64\n" ] } ], "source": [ "#Check number of samples vs microbiome\n", "display(\"Microbiome SampleID vs OTUs\", microbiome.shape)\n", "#check to see if sum of microbiome rows equal\n", "print(\"Sum of rows in microbiome\\n\",microbiome.sum(axis=1).round(2).head(10))" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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GenusPathogen_SalmonellaPathogen_CampyPathogen_ListeriaPathogen_EcoliProbiotic_BacillusProbiotic_BifidobacteriumProbiotic_ClostridiumProbiotic_EnterococcusProbiotic_LactobacillusProbiotic_PediococcusProbiotic_PropionibacteriumProbiotic_Streptococcus
00.0000000.0010160.00.00.0001850.0000000.0002770.0036950.7444800.0000000.00.000370
10.0000780.0006830.00.00.0277390.0000000.0058910.0004680.0213600.0000000.00.000156
20.0000000.0032890.00.00.0006140.0000000.0003360.0019880.9238120.0001020.00.001579
30.0000000.0003210.00.00.0006620.0000000.0004610.0032480.6458980.0000000.00.000321
40.0000000.0005210.00.00.0000000.0000000.0015630.0013030.1886400.0000000.00.001563
.......................................
19850.0000000.0000000.00.00.0620760.0000000.0028070.0000510.0020920.0000260.00.000077
19860.0000000.0000000.00.00.0314840.0000300.0032450.0000910.0016750.0000000.00.000060
19870.0000000.0000130.00.00.0293800.0000380.0010130.0001390.0014440.0000250.00.000025
19880.0000000.0000000.00.00.0252660.0000820.0023580.0000820.0012840.0000000.00.000070
19890.0000000.0000000.00.00.0428470.0000560.0024490.0001970.0045040.0000000.00.207702
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1990 rows × 12 columns

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" ], "text/plain": [ "Genus Pathogen_Salmonella Pathogen_Campy Pathogen_Listeria Pathogen_Ecoli \\\n", "0 0.000000 0.001016 0.0 0.0 \n", "1 0.000078 0.000683 0.0 0.0 \n", "2 0.000000 0.003289 0.0 0.0 \n", "3 0.000000 0.000321 0.0 0.0 \n", "4 0.000000 0.000521 0.0 0.0 \n", "... ... ... ... ... \n", "1985 0.000000 0.000000 0.0 0.0 \n", "1986 0.000000 0.000000 0.0 0.0 \n", "1987 0.000000 0.000013 0.0 0.0 \n", "1988 0.000000 0.000000 0.0 0.0 \n", "1989 0.000000 0.000000 0.0 0.0 \n", "\n", "Genus Probiotic_Bacillus Probiotic_Bifidobacterium Probiotic_Clostridium \\\n", "0 0.000185 0.000000 0.000277 \n", "1 0.027739 0.000000 0.005891 \n", "2 0.000614 0.000000 0.000336 \n", "3 0.000662 0.000000 0.000461 \n", "4 0.000000 0.000000 0.001563 \n", "... ... ... ... \n", "1985 0.062076 0.000000 0.002807 \n", "1986 0.031484 0.000030 0.003245 \n", "1987 0.029380 0.000038 0.001013 \n", "1988 0.025266 0.000082 0.002358 \n", "1989 0.042847 0.000056 0.002449 \n", "\n", "Genus Probiotic_Enterococcus Probiotic_Lactobacillus Probiotic_Pediococcus \\\n", "0 0.003695 0.744480 0.000000 \n", "1 0.000468 0.021360 0.000000 \n", "2 0.001988 0.923812 0.000102 \n", "3 0.003248 0.645898 0.000000 \n", "4 0.001303 0.188640 0.000000 \n", "... ... ... ... \n", "1985 0.000051 0.002092 0.000026 \n", "1986 0.000091 0.001675 0.000000 \n", "1987 0.000139 0.001444 0.000025 \n", "1988 0.000082 0.001284 0.000000 \n", "1989 0.000197 0.004504 0.000000 \n", "\n", "Genus Probiotic_Propionibacterium Probiotic_Streptococcus \n", "0 0.0 0.000370 \n", "1 0.0 0.000156 \n", "2 0.0 0.001579 \n", "3 0.0 0.000321 \n", "4 0.0 0.001563 \n", "... ... ... \n", "1985 0.0 0.000077 \n", "1986 0.0 0.000060 \n", "1987 0.0 0.000025 \n", "1988 0.0 0.000070 \n", "1989 0.0 0.207702 \n", "\n", "[1990 rows x 12 columns]" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#rename microbiome pathogens and probiotics for quick identification during analysis\n", "microbiome.rename(columns = {'g__Salmonella':'Pathogen_Salmonella',\n", " 'g__Campylobacter':'Pathogen_Campy',\n", " 'g__Listeria':'Pathogen_Listeria',\n", " 'g__Escherichia':'Pathogen_Ecoli',\n", " \n", " 'g__Bacillus': 'Probiotic_Bacillus',\n", " 'g__Bifidobacterium': 'Probiotic_Bifidobacterium',\n", " 'g__Clostridium':'Probiotic_Clostridium',\n", " 'g__Enterococcus':'Probiotic_Enterococcus',\n", " 'g__Lactobacillus':'Probiotic_Lactobacillus',\n", " 'g__Pediococcus':'Probiotic_Pediococcus',\n", " 'g__Propionibacterium':'Probiotic_Propionibacterium', \n", " 'g__Streptococcus':'Probiotic_Streptococcus'}, inplace=True)\n", "\n", "#Check that pathogens and probiotics columns are found and rename was correctly done\n", "microbiome[['Pathogen_Salmonella', 'Pathogen_Campy','Pathogen_Listeria','Pathogen_Ecoli',\n", " \n", " 'Probiotic_Bacillus','Probiotic_Bifidobacterium','Probiotic_Clostridium','Probiotic_Enterococcus',\n", " 'Probiotic_Lactobacillus','Probiotic_Pediococcus','Probiotic_Propionibacterium','Probiotic_Streptococcus']]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'Number of duplicates SampleID in raw poultry data:'" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "3" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "'Number of duplicates SampleID in poultry data now:'" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "0" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "'SampleType in the raw file '" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "Feces 818\n", "Soil 817\n", "WCR-F 210\n", "Ceca 210\n", "WCR-P 210\n", "WCR-P* 25\n", "WCR-F* 20\n", "Name: SampleType, dtype: int64" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "'SampleType count after * was removed'" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "Feces 818\n", "Soil 817\n", "WCR-P 235\n", "WCR-F 230\n", "Ceca 210\n", "Name: SampleType, dtype: int64" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Number of EcoliLog10CFU/mL cells with missing value in raw data= 9\n", "Number of EcoliLog10CFU/mL cells with missing value now= 0\n" ] }, { "data": { "text/html": [ "
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SampleIDFarmAvgNumBirdsAvgNumFlocksYearsFarmingEggSourceBroodBeddingBroodFeedBrGMOFreeBrSoyFree...MnMoNaNiPPbSSiZnEcoli
0E2-1E600414MMWSSSNN...173.721.29642.0211.5633977.071.8591091.660863.766128.0391
1E2-2E600414MMWSSSNN...128.7631.00477.2421.1562696.481.739822.626534.392.8691
2E2-3E600414MMWSSSNN...162.2491.278629.541.3843385.392.3041068.750546.466136.0831
3E2-4E600414MMWSSSNN...155.9332.225602.2152.5752872.7013.079878.725242.178103.21
4E2-5E600414MMWSSSNN...131.5561.001473.751.732175.495.5221015.680157.706125.8681
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5 rows × 162 columns

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" ], "text/plain": [ " SampleID Farm AvgNumBirds AvgNumFlocks YearsFarming EggSource \\\n", "0 E2-1 E 600 4 14 MM \n", "1 E2-2 E 600 4 14 MM \n", "2 E2-3 E 600 4 14 MM \n", "3 E2-4 E 600 4 14 MM \n", "4 E2-5 E 600 4 14 MM \n", "\n", " BroodBedding BroodFeed BrGMOFree BrSoyFree ... Mn Mo Na \\\n", "0 WS SS N N ... 173.72 1.29 642.021 \n", "1 WS SS N N ... 128.763 1.00 477.242 \n", "2 WS SS N N ... 162.249 1.278 629.54 \n", "3 WS SS N N ... 155.933 2.225 602.215 \n", "4 WS SS N N ... 131.556 1.00 1473.75 \n", "\n", " Ni P Pb S Si Zn Ecoli \n", "0 1.563 3977.07 1.859 1091.660 863.766 128.039 1 \n", "1 1.156 2696.48 1.739 822.626 534.3 92.869 1 \n", "2 1.384 3385.39 2.304 1068.750 546.466 136.083 1 \n", "3 2.575 2872.70 13.079 878.725 242.178 103.2 1 \n", "4 1.73 2175.49 5.522 1015.680 157.706 125.868 1 \n", "\n", "[5 rows x 162 columns]" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#read-in the raw poultry dataset\n", "poultry = pd.read_excel(\"Pastured Poultry Summary DB 03162020.xlsx\")\n", "\n", "\n", "#SOME PREPROCESSING STEPS\n", "\n", "#Find number duplicates\n", "display(\"Number of duplicates SampleID in raw poultry data:\", poultry.SampleID.duplicated().sum()) \n", "\n", "#Drop the duplicates\n", "poultry.drop_duplicates(subset=\"SampleID\", inplace=True)\n", "\n", "# #Confirm no duplicates after drop function\n", "display(\"Number of duplicates SampleID in poultry data now:\", poultry.SampleID.duplicated().sum()) \n", "\n", "# save_SampleID = poultry[\"SampleID\"]\n", "\n", "#SampleType in the original file\n", "display(\"SampleType in the raw file \",poultry.SampleType.value_counts())\n", "\n", "#remove special characters * and < found in poultry file \n", "poultry.replace({'\\*': '','\\<': '','\\>': ''}, regex=True, inplace=True)\n", "\n", "#SampleType after special character was removed\n", "display(\"SampleType count after * was removed\",poultry.SampleType.value_counts())\n", "\n", "#Count missing values in E coli CFU\n", "missing_values=pd.isnull(poultry['EcoliLog10CFU/mL']).sum().sum()\n", "print(\"Number of EcoliLog10CFU/mL cells with missing value in raw data=\", missing_values)\n", "\n", "#Replace the E coli CFU * and na values with 0, and convert it to numeric 0\n", "poultry['EcoliLog10CFU/mL'].replace({'\\*': '0'}, regex=True, inplace = True)\n", "poultry['EcoliLog10CFU/mL'] = pd.to_numeric(poultry['EcoliLog10CFU/mL'])\n", "poultry['EcoliLog10CFU/mL'].fillna(value=0, inplace = True)\n", "\n", "#Double check the missing values are gone now\n", "missing_values=pd.isnull(poultry['EcoliLog10CFU/mL']).sum().sum()\n", "print(\"Number of EcoliLog10CFU/mL cells with missing value now=\", missing_values)\n", "\n", "#Convert anywhere E coli CFU could be enumerated to presence (1), otherwise 0\n", "poultry['Ecoli'] = poultry['EcoliLog10CFU/mL'].values[poultry['EcoliLog10CFU/mL'] > 0] = 1\n", "\n", "#convert presence and absence of Campy from + and - to 1 and 0 respectively\n", "poultry.rename(columns = {'CampyCapetown': 'Campy'}, inplace=True)\n", "poultry['Campy'].replace({'\\+': '1', '\\-':'0'}, regex=True, inplace = True)\n", "poultry['Campy'] = pd.to_numeric(poultry['Campy'])\n", "\n", "#convert presence and absence of Salmonella from + and - to 1 and 0 respectively\n", "poultry['Salmonella'].replace({'\\+': '1', '\\-':'0'}, regex=True, inplace = True)\n", "poultry['Salmonella'] = pd.to_numeric(poultry['Salmonella'])\n", "\n", "#convert presence and absence of Listeria from + and - to 1 and 0 respectively\n", "poultry['Listeria'].replace({'\\+': '1', '\\-':'0'}, regex=True, inplace = True)\n", "poultry['Listeria'] = pd.to_numeric(poultry['Listeria'])\n", "\n", "#Check the top five rows of the poultry file now\n", "poultry.head()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(2310, 162)" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "poultry.shape" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(1990, 878)" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "microbiome.shape" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(1981, 880)\n", "SAMPLE DISTRIBUTION \n", "\n", "Feces_Start (200, 880)\n", "Feces_Mid (313, 880)\n", "Feces_End (185, 880)\n", "Soil_Start (199, 880) \n", "\n", "Soil_Mid (313, 880) \n", "\n", "Soil_End (183, 880) \n", "\n", "Ceca (185, 880)\n", "WCR-P (208, 880)\n", "WCR-F (195, 880) \n", "\n" ] } ], "source": [ "sample = pd.merge(microbiome, poultry[['SampleID','SampleType','PastureTime']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "print(sample.shape)\n", "#sample = sample[~sample.isin([np.nan, np.inf, -np.inf]).any(1)]\n", "\n", "feces1=sample[(sample.SampleType=='Feces') & (sample.PastureTime=='Start')]\n", "feces2=sample[(sample.SampleType=='Feces') & (sample.PastureTime=='Mid')]\n", "feces3=sample[(sample.SampleType=='Feces') & (sample.PastureTime=='End')]\n", "\n", "soil1=sample[(sample.SampleType=='Soil') & (sample.PastureTime=='Start')]\n", "soil2=sample[(sample.SampleType=='Soil') & (sample.PastureTime=='Mid')]\n", "soil3=sample[(sample.SampleType=='Soil') & (sample.PastureTime=='End')]\n", "\n", "ceca=sample[sample.SampleType=='Ceca']\n", "wcrp=sample[sample.SampleType=='WCR-P']\n", "wcrf=sample[sample.SampleType=='WCR-F']\n", "\n", "\n", "\n", "print ('SAMPLE DISTRIBUTION \\n')\n", "\n", "print('Feces_Start', feces1.shape)\n", "print('Feces_Mid', feces2.shape)\n", "print('Feces_End', feces3.shape)\n", "\n", "print('Soil_Start', soil1.shape,'\\n')\n", "print('Soil_Mid', soil2.shape,'\\n')\n", "print('Soil_End', soil3.shape,'\\n')\n", "\n", "print('Ceca', ceca.shape)\n", "print('WCR-P', wcrp.shape)\n", "print('WCR-F', wcrf.shape,'\\n')" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['I1-19',\n", " 'E1-30',\n", " 'I3-13',\n", " 'J1-9A',\n", " 'STANDARD',\n", " 'J1-9B',\n", " 'E1-26a',\n", " 'E1-26b',\n", " 'I1-15']" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Sample ID in microbiome only in microbiome = 9\n", "list(set(microbiome.SampleID) - set(sample.SampleID))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Relative Importance/Partial Dependency Plots & Pearson Correlation (Slope)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Poultry Pathogens as Targets in different sample-time points:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (1) Salmonella" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SAMPLE DISTRIBUTION \n", "\n", "Feces_Start (200, 881)\n", "Feces_Mid (313, 881)\n", "Feces_End (185, 881)\n", "Soil_Start (199, 881) \n", "\n", "Soil_Mid (313, 881) \n", "\n", "Soil_End (183, 881) \n", "\n", "Ceca (185, 881)\n", "WCR-P (208, 881)\n", "WCR-F (195, 881) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "0.0 171\n", "1.0 29\n", "Name: Salmonella, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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PearsonRResult(statistic=0.8432124039105072, pvalue=3.63916158059388e-28)\n", "Slope and P-value = PearsonRResult(statistic=0.6430401943760713, pvalue=3.317425402258954e-06)\n", "0.0 268\n", "1.0 45\n", "Name: Salmonella, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.7637856403545966, pvalue=0.0101270785427047)\n", "Slope and P-value = PearsonRResult(statistic=0.9082253662554973, pvalue=7.41945700441336e-39)\n", "Slope and P-value = PearsonRResult(statistic=0.9637179689955493, pvalue=3.524962699890068e-18)\n", "Slope and P-value = PearsonRResult(statistic=0.5311447135572541, pvalue=7.964669780009005e-07)\n", "Slope and P-value = PearsonRResult(statistic=0.8229549791513283, pvalue=8.354498044542441e-26)\n", "Slope and P-value = PearsonRResult(statistic=0.7723609765562451, pvalue=4.996367097851647e-21)\n", "Slope and P-value = PearsonRResult(statistic=0.6668926383984088, pvalue=3.5974810242581717e-14)\n", "Slope and P-value = PearsonRResult(statistic=0.8615340821561507, pvalue=3.474055299538197e-23)\n", "Slope and P-value = PearsonRResult(statistic=0.41672489835138526, pvalue=0.02197245132862409)\n", "Slope and P-value = PearsonRResult(statistic=0.8934936894083023, pvalue=3.0305976951909393e-30)\n", "0.0 149\n", "1.0 36\n", "Name: Salmonella, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.45027911114312397, pvalue=0.06971938670421836)\n", "Slope and P-value = PearsonRResult(statistic=0.4333887740065622, pvalue=0.06378844846677144)\n", "Slope and P-value = PearsonRResult(statistic=0.4102631925008734, pvalue=3.641503678932944e-05)\n", "Slope and P-value = PearsonRResult(statistic=0.96536467380658, pvalue=9.340277423601251e-11)\n", "Slope and P-value = PearsonRResult(statistic=0.19239969463693138, pvalue=0.05514070509036656)\n", "Slope and P-value = PearsonRResult(statistic=-0.555723285862609, pvalue=1.953584211481409e-09)\n", "Slope and P-value = PearsonRResult(statistic=-0.23210947634016854, pvalue=0.02013922112910719)\n", "Slope and P-value = PearsonRResult(statistic=0.9560964119318598, pvalue=4.840984330048458e-54)\n", "Slope and P-value = PearsonRResult(statistic=0.7544779840781648, pvalue=1.2648079878719866e-19)\n", "Slope and P-value = PearsonRResult(statistic=0.803611274701947, pvalue=8.15712871573271e-24)\n", "0.0 176\n", "1.0 23\n", "Name: Salmonella, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.6520078501432621, pvalue=5.7130372162972316e-11)\n", "Slope and P-value = PearsonRResult(statistic=0.5184466993906633, pvalue=0.48155330060933665)\n", "Slope and P-value = PearsonRResult(statistic=0.7687516732606686, pvalue=0.23124832673933138)\n", "Slope and P-value = PearsonRResult(statistic=0.8120889199764765, pvalue=1.1691320501759304e-24)\n", "Slope and P-value = PearsonRResult(statistic=0.37989236045882685, pvalue=0.02668440091208231)\n", "Slope and P-value = PearsonRResult(statistic=0.8289876680337185, pvalue=0.003027355655242649)\n", "Slope and P-value = PearsonRResult(statistic=0.8808434373772753, pvalue=3.027620442144688e-13)\n", "Slope and P-value = PearsonRResult(statistic=0.9109602167713751, pvalue=9.685910811076833e-09)\n", "Slope and P-value = PearsonRResult(statistic=0.9362088414042002, pvalue=2.673980651767495e-46)\n", "Slope and P-value = PearsonRResult(statistic=0.8032658217491508, pvalue=7.550863884766368e-10)\n", "0.0 280\n", "1.0 33\n", "Name: Salmonella, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.7866879087607923, pvalue=3.0124903759612453e-22)\n", "Slope and P-value = PearsonRResult(statistic=0.4242359769106053, pvalue=0.002655293851414895)\n", "Slope and P-value = PearsonRResult(statistic=0.9271506291183136, pvalue=1.4358009463872155e-43)\n", "Slope and P-value = PearsonRResult(statistic=0.5641099290193333, pvalue=9.852861502006064e-10)\n", "Slope and P-value = PearsonRResult(statistic=0.8974393425825371, pvalue=1.5944791464904406e-30)\n", "Slope and P-value = PearsonRResult(statistic=0.48777018866457805, pvalue=2.6428875687228695e-07)\n", "Slope and P-value = PearsonRResult(statistic=0.9321029716072249, pvalue=8.5360061574569545e-19)\n", "Slope and P-value = PearsonRResult(statistic=0.7040926724757459, pvalue=3.04140398390872e-16)\n", "Slope and P-value = PearsonRResult(statistic=0.8976866652278237, pvalue=1.1768866375561785e-36)\n", "Slope and P-value = PearsonRResult(statistic=0.6051894976366778, pvalue=0.012991416925788889)\n", "0.0 156\n", "1.0 27\n", "Name: Salmonella, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.7385754468830212, pvalue=2.711665222268551e-07)\n", "Slope and P-value = PearsonRResult(statistic=0.9058632589066028, pvalue=1.205942145839184e-17)\n", "Slope and P-value = PearsonRResult(statistic=0.9542795480502717, pvalue=3.379724658001682e-53)\n", "Slope and P-value = PearsonRResult(statistic=0.777513516150184, pvalue=7.945342978300049e-20)\n", "Slope and P-value = PearsonRResult(statistic=0.9199926636668188, pvalue=1.190391186099998e-41)\n", "Slope and P-value = PearsonRResult(statistic=0.9373512188196275, pvalue=9.10979268985099e-40)\n", "Slope and P-value = PearsonRResult(statistic=0.9999756336885137, pvalue=1.443832979673971e-07)\n", "Slope and P-value = PearsonRResult(statistic=0.893761179871595, pvalue=3.2970533127657785e-22)\n", "Slope and P-value = PearsonRResult(statistic=0.49150447405763353, pvalue=2.3774966507462708e-06)\n", "Slope and P-value = PearsonRResult(statistic=0.9321606503389035, pvalue=4.940787713558651e-45)\n", "0.0 161\n", "1.0 24\n", "Name: Salmonella, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.6620465079075036, pvalue=0.22349874956879578)\n", "Slope and P-value = PearsonRResult(statistic=0.7981833881265542, pvalue=4.553927893202823e-19)\n", "Slope and P-value = PearsonRResult(statistic=0.8288935789933363, pvalue=0.0008600773994795488)\n", "Slope and P-value = PearsonRResult(statistic=0.8545798293830703, pvalue=1.2148309255651068e-29)\n", "Slope and P-value = PearsonRResult(statistic=0.8839184708733402, pvalue=4.0647660654991124e-34)\n", "Slope and P-value = PearsonRResult(statistic=0.8430410256516877, pvalue=3.411614786240466e-08)\n", "Slope and P-value = PearsonRResult(statistic=0.7285946522886216, pvalue=0.00012037748314514237)\n", "Slope and P-value = PearsonRResult(statistic=0.8535062305335837, pvalue=1.6953336219378928e-29)\n", "Slope and P-value = PearsonRResult(statistic=0.7320326744628745, pvalue=0.061416755273565084)\n", "Slope and P-value = PearsonRResult(statistic=0.7013131569609854, pvalue=0.0035756582469645523)\n", "0.0 154\n", "1.0 54\n", "Name: Salmonella, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.9457840142294264, pvalue=1.1662561934941531e-49)\n", "Slope and P-value = PearsonRResult(statistic=0.9560894378870131, pvalue=4.8779912069180024e-54)\n", "Slope and P-value = PearsonRResult(statistic=0.8433664031351318, pvalue=1.1996045794919704e-24)\n", "Slope and P-value = PearsonRResult(statistic=0.8826420131647421, pvalue=1.3203252175097883e-29)\n", "Slope and P-value = PearsonRResult(statistic=0.9174496028044905, pvalue=5.182473432218809e-41)\n", "Slope and P-value = PearsonRResult(statistic=0.950897627592926, pvalue=2.3483176633917238e-36)\n", "Slope and P-value = PearsonRResult(statistic=-0.8796811714186706, pvalue=2.118873572326609e-33)\n", "Slope and P-value = PearsonRResult(statistic=0.9264610821084949, pvalue=4.17228570812111e-05)\n", "Slope and P-value = PearsonRResult(statistic=0.7756583242674369, pvalue=8.688612826332558e-13)\n", "Slope and P-value = PearsonRResult(statistic=0.9012538830674097, pvalue=2.5076394336509526e-29)\n", "0.0 157\n", "1.0 38\n", "Name: Salmonella, dtype: int64\n" ] }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.7421611055046116, pvalue=4.593368367547926e-10)\n", "Slope and P-value = PearsonRResult(statistic=0.867727899955035, pvalue=1.6282938289952575e-31)\n", "Slope and P-value = PearsonRResult(statistic=0.35833316699720275, pvalue=0.34367680705868964)\n", "Slope and P-value = PearsonRResult(statistic=0.7726573251837574, pvalue=2.5035955502158044e-05)\n", "Slope and P-value = PearsonRResult(statistic=0.7637472745064742, pvalue=2.4550785974950844e-20)\n", "Slope and P-value = PearsonRResult(statistic=0.7515358277129924, pvalue=2.3031054449964062e-05)\n", "Slope and P-value = PearsonRResult(statistic=0.9182989036678336, pvalue=3.396532068065827e-13)\n", "Slope and P-value = PearsonRResult(statistic=0.9153817531719779, pvalue=3.4362515225738484e-24)\n", "Slope and P-value = PearsonRResult(statistic=-0.6953282326619143, pvalue=9.993536101218266e-16)\n", "Slope and P-value = PearsonRResult(statistic=0.8645484007092862, pvalue=2.4692191276646663e-28)\n" ] } ], "source": [ "sample = pd.merge(microbiome, poultry[['SampleID', 'Salmonella','SampleType','PastureTime']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "#sample = sample[~sample.isin([np.nan, np.inf, -np.inf]).any(1)]\n", "\n", "feces1=sample[(sample.SampleType=='Feces') & (sample.PastureTime=='Start')]\n", "feces2=sample[(sample.SampleType=='Feces') & (sample.PastureTime=='Mid')]\n", "feces3=sample[(sample.SampleType=='Feces') & (sample.PastureTime=='End')]\n", "\n", "soil1=sample[(sample.SampleType=='Soil') & (sample.PastureTime=='Start')]\n", "soil2=sample[(sample.SampleType=='Soil') & (sample.PastureTime=='Mid')]\n", "soil3=sample[(sample.SampleType=='Soil') & (sample.PastureTime=='End')]\n", "\n", "ceca=sample[sample.SampleType=='Ceca']\n", "wcrp=sample[sample.SampleType=='WCR-P']\n", "wcrf=sample[sample.SampleType=='WCR-F']\n", "\n", "\n", "\n", "print ('SAMPLE DISTRIBUTION \\n')\n", "\n", "print('Feces_Start', feces1.shape)\n", "print('Feces_Mid', feces2.shape)\n", "print('Feces_End', feces3.shape)\n", "\n", "print('Soil_Start', soil1.shape,'\\n')\n", "print('Soil_Mid', soil2.shape,'\\n')\n", "print('Soil_End', soil3.shape,'\\n')\n", "\n", "print('Ceca', ceca.shape)\n", "print('WCR-P', wcrp.shape)\n", "print('WCR-F', wcrf.shape,'\\n')\n", "\n", "mylist = []\n", "mylist.append([\"Target/SampleType\", \"Important_Feature\", \"Correlation\", \"P-Value\", \"AUC\"])\n", "\n", "# mylist2 = []\n", "# mylist2.append([\"Target/SampleType\", \"AUC\"])\n", "\n", "sampletypes = [feces1,feces2,feces3, soil1,soil2,soil3,ceca, wcrp, wcrf]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES_START\", 1: \"FECES_MID\", 2:\"FECES_END\",\n", " 3: \"SOIL_START\", 4: \"SOIL_MID\", 5: \"SOIL_END\",\n", " 6:\"CECA\", 7: \"WCR-P\", 8: \"WCR-F\"\n", " }\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType', 'Salmonella','PastureTime',\n", " 'Pathogen_Salmonella','Pathogen_Campy','Pathogen_Listeria'],axis='columns'),sample.Salmonella,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1]\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " \n", "# mylist2.append([f\"Lab_Salmonella_{sample_name[indexing]}\", rf_auc_normal])\n", " \n", " \n", " print(pd.value_counts(sample['Salmonella']))\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " plt.title(f\"Salmonella in {sample_name[indexing]} Model\")\n", " prelim3_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','Salmonella','SampleType','PastureTime',\n", " 'Pathogen_Salmonella','Pathogen_Campy','Pathogen_Listeria'],axis='columns').columns)\n", " prelim3_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", " \n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim3_plot.max(), decimals=None, symbol=''))\n", " \n", " xmax=prelim3_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " \n", " plt.show()\n", " \n", " prelim3_plot.nlargest(10).to_csv(\"prelim3.csv\")\n", " top10 = pd.read_csv('prelim3.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", "# plt.plot(pdp[1][0], pdp[0][0],'.')\n", "# plt.ylabel('Partial dependence'), plt.xlabel(top10[feature])\n", "# plt.show()\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"Salmonella_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " \n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (2) Campylobacter" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SAMPLE DISTRIBUTION \n", "\n", "Feces_Start (200, 881)\n", "Feces_Mid (313, 881)\n", "Feces_End (185, 881)\n", "Soil_Start (199, 881) \n", "\n", "Soil_Mid (313, 881) \n", "\n", "Soil_End (183, 881) \n", "\n", "Ceca (185, 881)\n", "WCR-P (208, 881)\n", "WCR-F (195, 881) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "1.0 129\n", "0.0 71\n", "Name: Campy, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
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PearsonRResult(statistic=-0.7624802695528531, pvalue=3.085250145509203e-20)\n", "Slope and P-value = PearsonRResult(statistic=-0.5480953587437781, pvalue=3.5822031275564146e-09)\n", "1.0 173\n", "0.0 140\n", "Name: Campy, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
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PearsonRResult(statistic=0.43301837695476314, pvalue=6.804268874925762e-06)\n", "Slope and P-value = PearsonRResult(statistic=-0.7208308683850085, pvalue=2.7677758416229358e-17)\n", "1.0 137\n", "0.0 48\n", "Name: Campy, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.5378672826892038, pvalue=4.57933633735516e-08)\n", "Slope and P-value = PearsonRResult(statistic=-0.8204621709943767, pvalue=1.3032187902552366e-21)\n", "Slope and P-value = PearsonRResult(statistic=-0.5914463821220651, pvalue=9.216831296939196e-11)\n", "Slope and P-value = PearsonRResult(statistic=-0.9140352205760219, pvalue=3.4726291370653857e-40)\n", "Slope and P-value = PearsonRResult(statistic=-0.838757079157229, pvalue=1.4960506254083414e-18)\n", "Slope and P-value = PearsonRResult(statistic=-0.46521421432381144, pvalue=7.291363650024606e-05)\n", "Slope and P-value = PearsonRResult(statistic=-0.5131058654537883, pvalue=0.0246591455380623)\n", "Slope and P-value = PearsonRResult(statistic=-0.890474798829155, pvalue=1.8329271660929576e-30)\n", "Slope and P-value = PearsonRResult(statistic=-0.6179108497116296, pvalue=1.0205482578853594e-09)\n", "Slope and P-value = PearsonRResult(statistic=-0.9352009872318847, pvalue=1.5121852048263322e-20)\n", "0.0 139\n", "1.0 60\n", "Name: Campy, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
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PearsonRResult(statistic=0.4899140066312604, pvalue=0.00040876248759662796)\n", "Slope and P-value = PearsonRResult(statistic=0.6752626415139226, pvalue=4.1465945219499625e-11)\n", "0.0 253\n", "1.0 60\n", "Name: Campy, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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PearsonRResult(statistic=-0.7642741716083531, pvalue=2.231613293840706e-20)\n", "Slope and P-value = PearsonRResult(statistic=0.8762074389415025, pvalue=3.470012811040461e-16)\n", "0.0 134\n", "1.0 49\n", "Name: Campy, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.3669701031250062, pvalue=0.0001730170710590243)\n", "Slope and P-value = PearsonRResult(statistic=0.8539578474503516, pvalue=5.499896578486003e-29)\n", "Slope and P-value = PearsonRResult(statistic=-0.6933765767879027, pvalue=1.2949778360086866e-15)\n", "Slope and P-value = PearsonRResult(statistic=0.41766746722745524, pvalue=0.003488120186898599)\n", "Slope and P-value = PearsonRResult(statistic=0.7515705965437424, pvalue=4.837531847783275e-19)\n", "Slope and P-value = PearsonRResult(statistic=0.9177180402960523, pvalue=4.447126961750719e-41)\n", "Slope and P-value = PearsonRResult(statistic=0.6410626104321697, pvalue=8.721653463584258e-11)\n", "Slope and P-value = PearsonRResult(statistic=0.45247396441529, pvalue=3.195437911714434e-05)\n", "Slope and P-value = PearsonRResult(statistic=-0.40801530568245775, pvalue=2.5116146314510683e-05)\n", "Slope and P-value = PearsonRResult(statistic=0.9006847540982531, pvalue=2.9513186686947293e-37)\n", "1.0 174\n", "0.0 11\n", "Name: Campy, dtype: int64\n" ] }, { "data": { "image/png": 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nl1tcJoCjgPWBnSJiiaSZaZvltLkK8G4D5/5LxWpmZtYirXUO/SHgQEk1qcfa0rsnvAxsLWl1Sd2Bfcqo8yywkaSdAdL581IfYA5Kca4LDAQmAN2BOSmZ7w1slsreCxyWyiKpR1o+H+gGy0YCZkj6eiojSds3fZfNzMzK1yo99HTOegwwhSwZ1wHNvsorIl6VdB0wFXgBeLKMOh9KOhw4X9IaZOfPS32d7QmyIfZNgf8XEbMlXQXcJqkOmEz24YCIeErS74EHJC1NcRwLXANcIulEYDBZD/8iSb8BOqf1U5qyz9tu3J0630XKzMzKpIjWufZKUteIWCCpCzAeGBIRk1plY1WotrY26uoq/e06MzNbkUmaGBG1pda15vfQR6SbrdQAo5zMzczMWk+rJfSIODI/L+lCYPeiYn3IhtDzzo2Iy1srLjMzs2rUZneKK9wBzszMzCqvw93L3czMzJrOCd3MzKwKOKGbmZlVASd0MzOzKuCEbmZmVgWc0M3MzKpAm31tzZpm2mvz6DXs9vYOw1YAM32LYDPDPXQzM7Oq4IRuZmZWBVbIhC7pJEnPSpouaYqkoxspf7+k2jQ9U9J6afqRCsd1rKQL0vTpkk6qZPtmZmb1WeESuqTvA18EdomIvsCegJrTVkTsVsnYzMzM2ku7JHRJp6Qe9jhJo5vYk/0V8MOIeA8gIuZFxKjU7j6SnpQ0TdI/JK3eSBwL0u+BqRd/Q4rrKklK64ZLelrSVElnp2XrS7pR0oT0U/zQmeLtfDeVm5LqdWnC/pqZmTWqzRN6Gvo+FNgBOAQo+VzXeup2A7pFxIsl1tUAI4HDI2Jbsiv4f9CE0HYAhgJbA58FdpfUAzgY2CYitgPOSGXPBf4SETunfbm0kbZvioidI2J74BngO/Xs3xBJdZLqli6c14TQzcxsZdcePfQBwK0RsSgi5gO3NaGugKhn3eeAGRHxfJofRTYcX64nImJWRHwMTAZ6Ae8Bi4FLJR0CLExl9wUukDQZGAOslT5s1KevpAclTQOOArYpVSgiRkREbUTUdurSvQmhm5nZyq49EnqzzncDpGH29yV9tpLtJh/kppcCq0bER8AuwI3AIODOtH4VoH9E9Es/G6cPJ/UZCfwojRz8FqhpYaxmZmbLaY+E/hBwoKQaSV2Bpt4V44/AhZLWApC0lqQhwLNAL0m9U7lvAg+0JNAUX/eI+BfZcHy/tOpu4Ee5cv2K6xbpBrwuqTNZD93MzKyi2vxOcRExQdIYYArwMlAHNOWE8UVAV2CCpCXAEuDPEbFY0reA6yWtCkwA/t7CcLsBt6bz8wJ+kpafSPahYirZMRwPfL+Bdk4BHifb32mpXTMzs4pRRH2npFtxo1LXiFiQrvYeDwyJiEltHkgHVltbG3V1de0dhpmZdSCSJkZEyYvJ2+te7iMkbU12LnmUk7mZmVnLtEtCj4gj8/OSLgSKv8vdB3ihaNm5EXF5a8ZmZma2IuoQT1uLiOPbOwYzM7MV2Qp361czMzP7NCd0MzOzKuCEbmZmVgWc0M3MzKqAE7qZmVkVcEI3MzOrAk7oZmZmVaBDfA/dPm3aa/PoNez29g7DOqiZw5v6TCMzq3buoZuZmVUBJ3QzM7Mq0O4JXdJISTMkTZb0rKTTKtj2vyStLamXpOkVaG8jSTek6YGSxqbpYyVd0NL2zczMmqvdE3pyckT0A/oBx0javLiApE5NbTQivhIR77Y4uk/amx0RgyvVnpmZWaVUJKFLOiX1rsdJGi3ppGY2VZN+v5/anSnpVEkPAV+X9CVJj0qaJOl6SV0l7S/pulwsAyXdlqu/Xlq1qqRRkqZKuiE9i53U/gRJ0yWNkKS0vLekeyRNSdvbopyefhpxGJybX5B+95Q0Po1ETJe0R4m6QyTVSapbunBeMw+hmZmtjFqc0CXVAocCOwCHACUfvN6IsyRNBmYB10TEnNy6xRExALgH+A2wb0TsCNQBPwXGAbtKWjOVPxy4tsQ2PgeMiIjtgPeAH6blF0TEzhHRF1gDOCAtvwq4MCK2B3YDXm/GfuUdCdyVRiK2ByYXF4iIERFRGxG1nbp0b+HmzMxsZVKJHvoA4NaIWBQR84HbmtFGYcj9f4B9JO2WW1dIzrsCWwMPp+R/DLBZRHwE3AkcKGlV4KvArSW28WpEPJym/5niBthb0uOSpgH/C2wjqRuwcUTcDBARiyNiYTP2K28C8C1JpwPbpmNlZmZWEZVI6KpAGwBExALgfj5JtpCG39N2xkVEv/SzdUR8J627FjiMLCFPqCdZRvG8pBrgb8DgiNgWuIRs2L8l+/QR6bim4fvV0r6NB/YEXgOulHR0C7ZhZma2nEok9IfIesc1krqS9ZCbJfWwvwC8WGL1Y8Duknqnsl0kbZnW3Q/sCHyX0sPtAJtK6p+mj0hxF87Zv5ViHwwQEe8BsyQNSttavXDOvQwzgZ3S9EFA59TGZsCciLgEuCzFa2ZmVhEtTugRMQEYA0wBbiI7t93UK7oK59CnAtNSO8XbeRM4FhgtaSpZgt8qrVsKjAX2T79LeYbsCvqpQA/gonQF/CVpm7eQDYsXfBM4MZV/hOx0QDkuAfaS9ATZh5PCCMNAYLKkJ8muOTi3zPbMzMwapYjikehmNCJ1jYgFqRc7HhgSEZNa3PBKrLa2Nurq6to7DDMz60AkTYyIkhefV+pe7iMkbU02hD3KydzMzKxtVSShR8SR+XlJFwK7FxXrA7xQtOzciLi8EjGYmZmtzFrlaWsRcXxrtGtmZmaldZRbv5qZmVkLOKGbmZlVASd0MzOzKuCEbmZmVgWc0M3MzKqAE7qZmVkVcEI3MzOrAq3yPXRruWmvzaPXsNvbOwzrYGYOb/azj8ysyrmHbmZmVgWc0M3MzKpAWQld0lJJkyVNl3R9E54NjqRjJV1Qz7pHGqnbS9KRuflaSeeVu+1cvZmSpqV9mCbpoKa2kdr5mqRhafp0SSel6ZGSBjenTTMzs0oot4e+KCL6RURf4EPg+/mVkjo1Z+MRsVsjRXoByxJ6RNRFxInN2Rawd0T0AwYDTf5QkLY/JiKGN3P7ZmZmraY5Q+4PAr0lDZR0n6SrgWmSaiRdnnrAT0raO1fnM5LulPScpNMKCyUtSL8l6aw0AjBN0uGpyHBgj9Sz/kna5thUp2tue1MlHVpm/GsB7+RiuEXSRElPSRqSW76fpEmSpki6Ny2rd7QhV2+mpPXSdK2k+9P0Xmk/Jqfj061E3SGS6iTVLV04r8zdMTMza+JV7pJWBfYH7kyLdgH6RsQMST8DiIhtJW0F3C1py3w5YCEwQdLtEVGXa/oQoB+wPbBeKjMeGAacFBEHpO0PzNU5BZgXEdumdes0Ev59kgR8Fjgst/zbETFX0hppuzeSfdC5BNgz7VuPMg5PY04Cjo+IhyV1BRYXF4iIEcAIgNV79okKbNPMzFYS5fbQ15A0GagDXgEuS8ufiIgZaXoAcCVARDwLvAwUEvq4iHg7IhYBN6WyeQOA0RGxNCLeAB4Adm4kpn2BCwszEfFOA2UhG3LvC2wLXJCSKsCJkqYAjwGfIXtu+67A+MK+RcTcRtoux8PAOZJOBNaOiI8q0KaZmRlQfg99UTr/vEzW2eX9/KIG6hf3NovnG6pbH5Vop1ER8aKkN4Ct08V9+wL9I2JhGh6vaW7byUd88kGpJrfd4ZJuB74CPCZp3/TBx8zMrMUq+bW18cBRAGmofVPgubTui5J6pGHtQWS91eK6h0vqJGl9YE/gCWA+8KlzzcndwI8KM2UMuRfKbQBsTjaC0B14JyXzrch65gCPAntJ2jzVacqQ+0xgpzS97Ly+pC0iYlpEnEk20rFVE9o0MzNrUCUT+t+ATpKmAdcCx0bEB2ndQ2TD8ZOBG4vOnwPcDEwFpgD/Bn4eEf9Nyz5KF6b9pKjOGcA66UK6KcDeNOy+dNrgPmBYGtq/E1hV0lTg/5ENuxMRbwJDgJtS29c24Tj8FjhX0oPA0tzyoblYFwF3NKFNMzOzBinC1151RLW1tVFXV/y5x8zMVmaSJkZEbal1vlOcmZlZFaiqh7NIehxYvWjxNyNiWnvEY2Zm1laqKqFHxBfaOwYzM7P24CF3MzOzKuCEbmZmVgWc0M3MzKqAE7qZmVkVcEI3MzOrAk7oZmZmVcAJ3czMrApU1ffQq8m01+bRa9jt7R2GdQAzh3+1vUMwsxWAe+hmZmZVwAndzMysCnSohC5ppKQZkianR6buk1t3qaStG6h7uqSTmrCtX+Wm15b0wzLq/EvS2o2UmSlpvTS9oNx4zMzMWqJDJfTk5IjoBwwF/l5YGBHHRcTTFdzOr3LTawONJvSI+EpEvFvBGMzMzCqi4gld0imSnpU0TtLopvSaizwKbJxr935JtWl6P0mTUi/+3hIxfFfSHZLWkPR/kp5Ivf6LJXWSNBxYIy27ChgObJHmz5LUU9L4ND9d0h6p3Xzv+1PtNnBMukq6N8U8TdJB9ZQbIqlOUt3ShfOaedjMzGxlVNGr3FPCPRTYIbU9CZjYzOb2A24psY31gUuAPSNihqQeRet/BHwJGAR8Fjgc2D0ilkj6G3BURAyT9KM0EoCkXkDf3PzPgLsi4vcpUXcp2sbnS7ULXFHPviwGDo6I99IHgsckjYmIyBeKiBHACIDVe/aJEu2YmZmVVOmvrQ0Abo2IRQCSbmtGG2dJ+hOwAbBrifW7AuMjYgZARMzNrfsmMAsYlBLtPsBOwARJAGsAc8qIYQLwD0mdgVsiYnLR+qa2K+APkvYEPiYbedgQ+G8ZsZiZmTWq0gldFWjjZOAm4ERgFFniLN5Gfb3X6UA/YBNgRio7KiJ+2ZQAImJ8Sr5fBa6UdFZE5HvfTW33KGB9YKf0QWMmUNOUmMzMzBpS6XPoDwEHSqqR1JUsITZZRHwMnAusIunLRasfBfaStDlA0ZD7k8D3gDGSNgLuBQZL2qBQVtJmqeyS1AMHmA90KzSSysyJiEuAy4Adi2JoqN1Suqf2lkjaG2iorJmZWZNVtIceERMkjQGmAC8DdUCzru6KiJB0BvBz4K7c8jclDQFukrQK2VD3F3PrH0oX4t2elv8GuDuVXQIcn2IbAUyVNCkijpL0sKTpwB1kPf2TJS0BFgBHF8X2tKT62i3lKuA2SXXAZODZxvZ/2427U+c7hJmZWZlUdF1WyxuUukbEAkldgPHAkIiYVNGNrARqa2ujrq6uvcMwM7MORNLEiKgtta417uU+It0ApobsPLOTuZmZWSureEKPiCPz85IuBHYvKtYHeKFo2bkRcXml4zEzM1sZtPrT1iLi+NbehpmZ2cquI9761czMzJrICd3MzKwKOKGbmZlVASd0MzOzKuCEbmZmVgWc0M3MzKpAq39tzZpn2mvz6DXs9vYOw9rYTN/u18yayT10MzOzKuCEbmZmVgU6REKXtKqktyT9sZFyx6bHohbmZ0parwXbHSlpcHPr1xeXmZlZW+sQCR34EvAccJgklSogqRNwLNCkxCmpLa4TOJaOGZeZma0kKpLQJZ0i6VlJ4ySNTs8jb4ojgHOBV4Bdc+3OlHSqpIdSmVrgKkmTJa2Rip0gaZKkaZK2SvVOlzRC0t3AFZI2k3SvpKnp96a5be8r6UFJz0s6INXvlZZNSj+75WL6edrWFEnDUw9/ubgk7STpAUkTJd0lqWeqe7+kP0h6APhxE4+RmZlZvVrcS5RUCxwK7JDamwRMbEL9NYB9gO8Ba5Ml7kdzRRZHxIBU9jjgpIioS/MAb0XEjpJ+CJwEHJfq7QQMiIhFkm4DroiIUZK+DZwHDErlegF7AVsA90nqDcwBvhgRiyX1AUYDtZL2T/W+EBELJfWIiLmSflSIS1Jn4HzgoIh4U9LhwO+Bb6ftrR0Re9VzLIYAQwA6rbV+uYfQzMysIj30AcCtEbEoIuYDtzWx/gHAfRGxELgRODgNrxdc20j9m9LviWTJuWBMRCxK0/2Bq9P0lSnmgusi4uOIeAF4CdgK6AxcImkacD2wdSq7L3B5ipWImFsins8BfYFxkiYDvwE2KWd/ImJERNRGRG2nLt3r32MzM7MilTiPW/KcdxMcAewuaWaaXxfYG7gnzb/fSP0P0u+lLL8/DdWLeqYL8z8B3gC2J/vQszitU4nyxQQ8FRH961nf2P6YmZk1WSV66A8BB0qqkdQVKPvOGJLWIustbxoRvSKiF3A8WZIvZT7QrRkxPgJ8I00flWIu+LqkVSRtAXyW7OK87sDrEfEx8E2gMGJwN/BtSV1S/D1KxPUcsL6k/qlMZ0nbNCNmMzOzsrU4oUfEBGAMMIVs+LsOmFdm9UOAf0fEB7lltwJfk7R6ifIjgb8XXRRXjhOBb0maSpag8xekPQc8ANwBfD8iFgN/A46R9BiwJalXHRF3ku1rXRpOL1z8tywusuQ/GDhT0hRgMrDsojozM7PWoIjGRpDLaETqGhELUs91PDAkIia1uOGVWG1tbdTV1bV3GGZm1oFImhgRtaXWVeq70CMkbQ3UAKOczM3MzNpWRRJ6RByZn5d0IbB7UbE+wAtFy86NiMsrEYOZmdnKrFXuVhYRx7dGu2ZmZlZaR7n1q5mZmbWAE7qZmVkVcEI3MzOrAk7oZmZmVcAJ3czMrAo4oZuZmVUBJ3QzM7Mq0CrfQ7eWm/baPHoNu729w7A2NHN42c81MjP7FPfQzczMqoATupmZWRVYqRO6pJ9KelbSNElTJJ2Tnl/+Y0l/zZW7WNI9ufkTJJ1Xor3LUjtTJd2Qng+fX7+zpKWSBrfqjpmZ2UpnpU3okr4PfAnYNSK2BXYG5gBrAI+w/DPM+wHdJXVK87sBD5do9icRsX1EbAe8Avwot71OwJnAXRXeFTMzsxU7oUs6JfWwx0kaLemkJlT/NfCDiHgXICI+jIjhEfEe8CSwpaQ1JHUHFgKTgW1T3d3Ikv5yUl0kieyDQf5h8ycAN5J9aKhvf4ZIqpNUt3ThvCbsipmZrexW2IQuqRY4FNgBOAQo+cD3eup2A7pGxIxS6yPiI7IEvjOwK/A48Biwm6SNAEXEq/W0fTnwX2Ar4Py0bGPgYODvDcUVESMiojYiajt16V7u7piZma24CR0YANwaEYsiYj5wWxPqilzvWdKXJU2WNFNSYaj9YbKe+G7Ao+lnN7LnvH+qd14QEd8CNgKeAQ5Pi/8K/CIiljYhRjMzs7KtyAldza2Yhsbfl7R5mr8rIvoB04HVUrHCefT+ZMn8GWBrcufPJd2VPghcWtT+UuBashEEyEYPrpE0ExgM/E3SoObGb2ZmVmxFTugPAQdKqklXkzf1rhx/BC6StDYsO+9dk1v/CNlw+/oRMSciAngTOCitIyK+HBH9IuI4ZXrn2joQeDaV2zwiekVEL+AG4IcRcUtzdtrMzKyUFfZOcRExQdIYYArwMlAHNOVKsouALsDjkj4AFpD1vJ9M7b8j6U3gqVydR8mG3KeUaE/AKElrpekpwA+atFNmZmbNpKzjuWKS1DUiFkjqAowHhkTEpPaOqxJqa2ujrq6uvcMwM7MORNLEiCh5EfgK20NPRkjammyofFS1JHMzM7OmWqETekQcmZ+XdCHZkHheH+CFomXnRsTlrRmbmZlZW1qhE3qxiDi+vWMwMzNrDyvyVe5mZmaWOKGbmZlVASd0MzOzKuCEbmZmVgWc0M3MzKqAE7qZmVkVcEI3MzOrAlX1PfRqMu21efQadnt7h2FtZObwpj5byMxsee6hm5mZVQEndDMzsyrQ6gld0khJMyRNljRF0j4VardW0nmVaMvMzGxF11bn0E+OiBsk7Q2MIHtgSotERB3ZM9DNzMxWemX10CWdIulZSeMkjZZ0UjO39yiwcWrzWEkX5LYxVtLANL1A0pmSJkq6R9Iuku6X9JKkr6UyAyWNTdOnS/pHrsyJuXZ/Kml6+hmaW360pKlp1ODKtGwzSfem5fdK2jQt31DSzansFEm7NdDGSEmDc9tZkH73lDQ+jVRMl7RHieM8RFKdpLqlC+c18xCbmdnKqNEeuqRa4FBgh1R+EjCxmdvbD7iljHJrAvdHxC8k3QycAXwR2BoYBYwpUWcrYG+gG/CcpIuA7YBvAV8ABDwu6QHgQ+DXwO4R8ZakHqmNC4ArImKUpG8D5wGD0u8HIuJgSZ2ArpK2qaeN+hwJ3BURv09tdCkuEBEjyEYwWL1nn2j8MJmZmWXKGXIfANwaEYsAJN3WjO2cJelPwAbArmWU/xC4M01PAz6IiCWSpgG96qlze0R8AHwgaQ6wYYr95oh4P8V+E7AHEMANEfEWQETMTW30Bw5J01cCf0rT/wscncouBeZJOrqeNuozAfiHpM7ALRExudGjYGZmVqZyhtxVge2cDPQGfkPWwwb4qGj7NbnpJRFR6KF+DHwAEBEfU/+HkA9y00tTufpiF1lSb0xDZeprY9l+SRKwGkBEjAf2BF4DrkwfCMzMzCqinIT+EHCgpBpJXYFm3QEjJeNzgVUkfRmYCfSTtIqkzwC7NKfdRowHBknqImlN4GDgQeBe4DBJ6wLkhssfAb6Rpo8i23dS+R+ksp0krdVAGzOBndL0QUDntH4zYE5EXAJcBuxY8b01M7OVVqND7hExQdIYYArwMtmV5c26YisiQtIZwM+BfYEZZEPq08nOzVdUREySNBJ4Ii26NCKeBJD0e+ABSUuBJ4FjgRPJhsVPBt4kO/8O8GNghKTvkPX+fxARj9bTxiXArZKeIEv676c2BgInS1oCLCAN4ZuZmVWCPhnZbqCQ1DUiFkjqQtbrHRIRFU/A9ona2tqoq/O38szM7BOSJkZEbal15X4PfYSkrcnOc49yMjczM+tYykroEXFkfl7ShcDuRcX6AC8ULTs3Ii5vfnhmZmZWjmbdKS4ijq90IGZmZtZ8fnyqmVkVWrJkCbNmzWLx4sXtHYo1Q01NDZtssgmdO3cuu44TuplZFZo1axbdunWjV69eZLfEsBVFRPD2228za9YsNt9887Lr+fGpZmZVaPHixay77rpO5isgSay77rpNHl1xQjczq1JO5iuu5rx2TuhmZmZVwOfQzcxWAr2G3V7R9mYOL+8u4DfffDOHHHIIzzzzDFtttRUA999/P2effTZjx45dVu7YY4/lgAMOYPDgwQwcOJDXX3+dmpoaVlttNS655BL69esHwLx58zjhhBN4+OGHAdh99905//zz6d69OwDPP/88Q4cO5fnnn6dz585su+22nH/++Wy44YbN3te5c+dy+OGHM3PmTHr16sV1113HOuuss1yZ5557jsMPP3zZ/EsvvcTvfvc7hg4dWm/9adOm8ec//5mRI0c2O7Y8J/QOatpr8yr+B2gdU7n/GM1WRKNHj2bAgAFcc801nH766WXXu+qqq6itreXyyy/n5JNPZty4cQB85zvfoW/fvlxxxRUAnHbaaRx33HFcf/31LF68mK9+9aucc845HHjggQDcd999vPnmmy1K6MOHD2efffZh2LBhDB8+nOHDh3PmmWcuV+Zzn/sckydPBmDp0qVsvPHGHHzwwQ3W33bbbZk1axavvPIKm266abPjK/CQu5mZtYoFCxbw8MMPc9lll3HNNdc0q43+/fvz2muvAfCf//yHiRMncsoppyxbf+qpp1JXV8eLL77I1VdfTf/+/Zclc4C9996bvn37tmg/br31Vo455hgAjjnmGG655ZYGy997771sscUWbLbZZo3WP/DAA5t9bIo5oZuZWau45ZZb2G+//dhyyy3p0aMHkyY1/a7hd955J4MGDQLg6aefpl+/fnTq1GnZ+k6dOtGvXz+eeuoppk+fzk477VRPS5+YP38+/fr1K/nz9NNPf6r8G2+8Qc+ePQHo2bMnc+bMabD9a665hiOOOKKs+rW1tTz44IONxlwOD7mbmVmrGD16NEOHDgXgG9/4BqNHj2bHHXes9wru/PKjjjqK999/n6VLly77IBARJevWt7w+3bp1WzY8XmkffvghY8aM4Y9//GNZ5TfYYANmz55dkW23ekKXdD/QEzg5Isakx5nuxSePYF0YEbs1UL8XMDYiWjRmktrZLSKubkbdRxqKsQntbAHcCPSOiK4tbc/MrKN6++23+fe//8306dORxNKlS5HEn/70J9Zdd13eeeed5crPnTuX9dZbb9n8VVddxfbbb8+wYcM4/vjjuemmm9hmm2148skn+fjjj1lllWyA+eOPP2bKlCl8/vOfZ86cOTzwwAONxjZ//nz22GOPkuuuvvpqtt566+WWbbjhhrz++uv07NmT119/nQ022KDetu+44w523HHH5c7ZN1R/8eLFrLHGGo3GXI62GnI/KiLG5OZPjoh+6afFibJMvYAjGyuUJ6kTQKVijIgXI6JfJdoyM+vIbrjhBo4++mhefvllZs6cyauvvsrmm2/OQw89RJ8+fZg9ezbPPPMMAC+//DJTpkxZdiV7QefOnTnjjDN47LHHeOaZZ+jduzc77LADZ5xxxrIyZ5xxBjvuuCO9e/fmyCOP5JFHHuH22z+5oPjOO+9k2rRpy7Vb6KGX+ilO5gBf+9rXGDVqFACjRo3ioIMOqne/R48evdxwe2P1n3/++Raf4y8oq4cu6RTgKOBV4C1gYkScXZEIPtnG6cCCQruSpgMHFOKUNArYAXgeODoiFko6FTgQWAN4BPheRISk3sDfgfWBpcDXgeHA5yVNBkYB56VlA4HVgQsj4mJJA4HTgNeBfsDWkhZERNe07qSIOCDFeAFQFxEjJc0Ergb2BjoDQ4A/Ar2BsyLi72UcgyGpHp3WWr9Jx8/MrCFt/W2K0aNHM2zYsOWWHXrooVx99dXsscce/POf/+Rb3/oWixcvpnPnzlx66aXLvnqWt8Yaa/Czn/2Ms88+m8suu4zLLruME044gd69exMR9O/fn8suu2xZ2bFjxzJ06FCGDh1K586d2W677Tj33HNbtC/Dhg3jsMMO47LLLmPTTTfl+uuvB2D27Nkcd9xx/Otf/wJg4cKFjBs3josvvris+pBdhf/Vr1bmtVFENFxAqgUuBfqTfQCYBFxcbkJPQ+4nRURdmh/J8kPuT0XEUY0k9BnAgIh4WNI/gKcj4mxJPSJibip/JXBdRNwm6XFgeETcLKmGbCRiF5ZPxkOADSLiDEmrAw+TJf7NgNuBvhExI5UtN6GfGREXSfoLsA/ZI2Zr0j4uG2MptNfQcVu9Z5/oecxfyznEtoLz19asNTzzzDN8/vOfb+8wrAEffPABe+21Fw899BCrrvrp/nWp11DSxIioLdVeOT30AcCtEbEoNXZb08P+lJMj4oYmlH81Ih5O0/8ETgTOBvaW9HOgC9ADeCp9gNg4Im4GiIjFKe7iNr8EbCdpcJrvTvZM9w+BJwrJvIkKpxWmAV0jYj4wX9JiSWtHxLvNaNPMzKrQK6+8wvDhw0sm8+Yop5W2uhnwRyx/Tr8mN108jBCp5/03oDYiXk09/BrKj1fACRFx13ILs174+82IEeCD9Pvj3HRh3t8oMDOzZfr06UOfPn0q1l45F8U9BBwoqUZSV6C1xgdnAjsCSNoRyD8zblNJ/dP0ESmmQjJ9K8U1GCAi3gNmSRqU2lpdUhdgPtAt1+ZdwA8kdU7ltpS0ZiMxvkx2Tn11Sd3JhtXNzDqkxk6pWsfVnNeu0V5jREyQNAaYQpbQ6vjk/HdznSXpN7n5Xci+znV0umhtAtnFbwXPAMdIuhh4AbgoXRR3Cdnw9sxUp+CbwMWSfgcsITs3PhX4SNIUYCRwLtmV75OUjce/CQxqKOg0EnBdausF4Mmm7ni5tt24O3U+t2pmzVRTU8Pbb7/tR6iugArPQ6+pKR4EblijF8UBSOoaEQtST3c8MCQiyrrlT/FFcVbeRXG1tbVRV+dDZmbNs2TJEmbNmtXkZ2pbx1BTU8Mmm2xC586dl1ve0oviAEZI2ppsmHtUuck8mQuMlPSrou+ir3RyN5Z5o71jMbPq1rlzZzbffPPGC1rVKCuhR8RyN2SRdCHZV7Ly+pANQ+edGxGHND+86hIRL5J9t93MzKyimnXldUQcX+lAzMzMrPn8tDUzM7MqUNZFcdb2JM0HnmvvOFYy65Hd2tjajo952/Mxb3uVPOabRUTJe4P7Zicd13P1XclorUNSnY952/Ixb3s+5m2vrY65h9zNzMyqgBO6mZlZFXBC77hGtHcAKyEf87bnY972fMzbXpscc18UZ2ZmVgXcQzczM6sCTuhmZmZVwAm9A5K0n6TnJP1H0rD2jqcaSfqMpPskPSPpKUk/TstPl/SapMnp5yvtHWs1kTRT0rR0bOvSsh6Sxkl6If1ep73jrBaSPpd7L0+W9J6koX6fV5akf0iaI2l6blm972tJv0z/35+T9OWKxeFz6B2LpE5kj479IjCL7LGwR0TE0+0aWJWR1BPoGRGTJHUDJpI9PvcwYEFEnN2e8VUrSTOB2oh4K7fsT8DciBiePsCuExG/aK8Yq1X63/Ia8AXgW/h9XjGS9gQWAFdERN+0rOT7Oj3obDTZY8M3Au4BtoyIpS2Nwz30jmcX4D8R8VJEfAhcAxzUzjFVnYh4vfDUwIiYDzwDbNy+Ua20DgJGpelRZB+srPL2AV6MiJfbO5BqExHjyZ4smlff+/og4JqI+CAiZgD/Ifu/32JO6B3PxsCruflZONG0Kkm9gB2Ax9OiH0mamobRPPxbWQHcLWmipCFp2YYR8TpkH7SADdotuur2DbKeYYHf562rvvd1q/2Pd0LveFRimc+LtBJJXcmeUT80It4DLgK2IHvM7evAn9svuqq0e0TsCOwPHJ+GKq2VSVoN+BpwfVrk93n7abX/8U7oHc8s4DO5+U2A2e0US1WT1JksmV8VETcBRMQbEbE0Ij4GLqFCQ2GWiYjZ6fcc4Gay4/tGuqahcG3DnPaLsGrtD0yKiDfA7/M2Ut/7utX+xzuhdzwTgD6SNk+fqr8BjGnnmKqOJAGXAc9ExDm55T1zxQ4GphfXteaRtGa6ABFJawJfIju+Y4BjUrFjgFvbJ8KqdgS54Xa/z9tEfe/rMcA3JK0uaXOgD/BEJTboq9w7oPQVkr8CnYB/RMTv2zei6iNpAPAgMA34OC3+Fdk/vn5kQ2Azge8VzoNZy0j6LFmvHLInPV4dEb+XtC5wHbAp8Arw9YgovsDImklSF7Jztp+NiHlp2ZX4fV4xkkYDA8kek/oGcBpwC/W8ryX9Gvg28BHZ6b47KhKHE7qZmdmKz0PuZmZmVcAJ3czMrAo4oZuZmVUBJ3QzM7Mq4IRuZmZWBZzQbaUmaWl62tR0SbdJWruR8qdLOqmRMoPSAxgK87+TtG8FYh0paXBL22niNoemrz11GJK2Sq/Zk5K2KFo3U9KDRcsmF56CJalW0nkViKFX/slaResuzb/+rU3ShpKulvRSuqXuo5IObqvtW8fhhG4ru0UR0S89IWkucHwF2hwELPuHHhGnRsQ9FWi3TaWncw0FOlRCJzu+t0bEDhHxYon13SR9BkDS5/MrIqIuIk4sd0PpGDRJRBzXVk9HTDdIugUYHxGfjYidyG5GtUkrb3fV1mzfmscJ3ewTj5IekiBpC0l3ph7Pg5K2Ki4s6buSJkiaIulGSV0k7UZ2z+yzUs9wi0LPWtL+kq7L1R8o6bY0/aXUs5ok6fp0j/l6pZ7oH1KdOkk7SrpL0ouSvp9rf7ykmyU9LenvklZJ645Q9lzy6ZLOzLW7II0oPA78muzxjvdJui+tvyht7ylJvy2K57cp/mmF4yWpq6TL07Kpkg4td38l9ZP0WKp3s6R10k2XhgLHFWIq4Trg8DRdfIe0gZLGNhJb/hj0l/TTdJymSxqa286qkkalujcURjIk3S+ptozjfGZ6f90jaZdU7yVJX0tlOkk6K73Hpkr6Xol9/V/gw4j4e2FBRLwcEec31EY6DvenuJ+VdFX6cICknSQ9kGK7S5/cvvT+9J57APixpAMlPa5spOQeSRvW83pYW4kI//hnpf0heyY0ZHflux7YL83fC/RJ018A/p2mTwdOStPr5to5AzghTY8EBufWjQQGk90d7RVgzbT8IuD/yO4uNT63/BfAqSViXdYu2d29fpCm/wJMBboB6wNz0vKBwGLgs2n/xqU4NkpxrJ9i+jcwKNUJ4LDcNmcC6+Xme+SO1/3Adrlyhf3/IXBpmj4T+Guu/jpN2N+pwF5p+neFdvKvQYk6M4EtgUfS/JNkoyXTc8dkbH2xFR8DYCeyuwmuCXQFniJ7Ml+vVG73VO4ffPK+uB+oLeM475+mbwbuBjoD2wOT0/IhwG/S9OpAHbB50f6eCPylgfd3yTbScZhH1pNfhezD7IAUwyPA+qnO4WR3qyzs19+KXsvCzcmOA/7c3n/PK/uPh01sZbeGpMlk/6AnAuNSb3E34PrUaYHsn2GxvpLOANYm+2d/V0MbioiPJN0JHCjpBuCrwM+BvciSzsNpe6uR/YNtTOEe/9OArpE9132+pMX65FqAJyLiJVh2e8oBwBLg/oh4My2/CtiTbOh2KdkDa+pzmLLHnq4K9ExxT03rbkq/JwKHpOl9yYaAC8fgHUkHNLa/kroDa0fEA2nRKD55Ulhj5gLvSPoG2XPuF9ZT7lOxpcn8MRgA3BwR76e4bgL2IDv2r0bEw6ncP8mS69m59nem/uP8IXBnKjcN+CAilkiaRvZehOxe99vpk+smupPd93tGfTsu6cIU84cRsXMDbXxI9t6YlepNTtt9F+hL9ncA2Qe3/C1hr81NbwJcm3rwqzUUl7UNJ3Rb2S2KiH4pgYwlO4c+Eng3Ivo1UnckWY9riqRjyXo9jbk2bWMuMCEi5qehznERcUQTY/8g/f44N12YL/xtF9/bOSj9+MaCxRGxtNQKZQ+SOAnYOSXmkUBNiXiW5ravEjE0d3+b4lrgQuDYBsqUig2WPwYNHatSx7a4/fosidS1Jff6RcTH+uT8tMhGPRr6oPgUcOiyACKOl7QeWU+83jYkDWT590zhNRPwVET0r2d77+emzwfOiYgxqb3TG4jT2oDPoZsBkT204kSyhLUImCHp65BdeCRp+xLVugGvK3sM61G55fPTulLuB3YEvssnvZ3HgN0l9U7b6yJpy5bt0TK7KHty3ypkw6cPAY8De0laT9lFX0cAD9RTP78va5H9Q5+XzpfuX8b27wZ+VJiRtA5l7G96Pd6RtEda9M0GYizlZuBPNDxqUiq2YuOBQSnGNcmeTFa4in5TSYXEdwTZsc1rynEu5S7gB+n9haQtUwx5/wZqJP0gtyx/EWM5beQ9B6xf2C9JnSVtU0/Z7sBrafqYespYG3JCN0si4klgCtkw7FHAdyRNIesFHVSiyilk/7THAc/mll8DnKwSX6tKPb+xZMlwbFr2JllPcrSkqWQJ71MX4TXTo8BwssdjziAbPn4d+CVwH9n+ToqI+h5ZOgK4Q9J9ETGF7Jz0U2TnjB+up07eGcA66aKwKcDeTdjfY8guLpxK9mSw35WxPQAiYn5EnBkRHzYlthLtTCIbiXmC7LW+NL1PIBvOPybF14Psmoh83aYc51IuBZ4GJin7itzFFI2qpl7+ILIPDjMkPUF2euIX5bZR1N6HZNdZnJmOyWSy00+lnE52WupB4K0m7Je1Ej9tzaxKpWHQkyLigHYOxczagHvoZmZmVcA9dDMzsyrgHrqZmVkVcEI3MzOrAk7oZmZmVcAJ3czMrAo4oZuZmVWB/w+gj5XYrFi63AAAAABJRU5ErkJggg==\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.18337207434414304, pvalue=0.06782253132769497)\n", "Slope and P-value = PearsonRResult(statistic=-0.9414587150086534, pvalue=6.669705240918645e-42)\n", "Slope and P-value = PearsonRResult(statistic=-0.5847844020172487, pvalue=0.05880896082298956)\n", "Slope and P-value = PearsonRResult(statistic=-0.41641168744936174, pvalue=0.12259801320318853)\n", "Slope and P-value = PearsonRResult(statistic=-0.4457791226629286, pvalue=3.353565939427117e-06)\n", "Slope and P-value = PearsonRResult(statistic=-0.5456886932733891, pvalue=0.12857575096772286)\n", "Slope and P-value = PearsonRResult(statistic=-0.45307594937507056, pvalue=2.208727253277947e-06)\n", "Slope and P-value = PearsonRResult(statistic=-0.8939420093654451, pvalue=0.1060579906345549)\n", "Slope and P-value = PearsonRResult(statistic=-0.7938111044896152, pvalue=6.871403323197119e-23)\n", "Slope and P-value = PearsonRResult(statistic=-0.8946834668989918, pvalue=1.7061434143790024e-25)\n", "0.0 172\n", "1.0 36\n", "Name: Campy, dtype: int64\n" ] }, { "data": { "image/png": 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tHBEPAttL2gQ4BrgtIj5OqydExDu5vu+PiPkRsRh4BvhMHbFeAjwQEXeTJR6TIuLN1O8NwIGp3vJc7NeTHaem+GdELAWqgTLgnlReDVQ0sS8zM7M6tYdkQy3VUUQsBCax6on5g9zyKOCH6RP+Oay87AIQdXR7HVli8l3gmjr6BViSW15Gkfkykk4gS0LOqS2qY5vFFIvvY1a+xuUF65YARMRyYGlE1LZfXiw2MzOz5moPycbDwOGSyiV1p8gQf2OlyZqfA16qo0oPYF6as3BsrvwR4Oi0fGxBm1HAUICIeHo1YtsTOB34dkoAAJ4AvpDmdJSRjZ48mNZ1AQam5cFkx6lQDbBnWj6qubGZmZmtjjX+E2xETJE0FpgBzAGqgKbOULxQ0m+AtYH7gdvrqHcm2Ql+DtnlhB6p/MfAjZJ+DNxWEN/rkp4F7mxiTIV+CGwITJQEUBURJ0n6JTCRbJTjHxFxV6r/AbCLpKlkx2NQkT7PAa6W9Ku0Xy2i7xY9qWqHv2BnZmZtQytHz9dckrpHxML07ZDJwJCImNbWcQGkmKqBPSKiU3xNo7KyMqqqqto6DDMzW4NImhoRRb/E0R4uowCMTN8mmUY2CXNNSTQOBZ4DLu0siYaZmVlTrfGXUQAiYnD+uaTLyb4hktcbeLGg7JKIuIYSiYj7gK1L1b+ZmVlH0C6SjUIRcWpbx2BmZmaN014uo5iZmVk75WTDzMzMSsrJhpmZmZWUkw0zMzMrKScbZmZmVlJONszMzKyk2uVXX61tVb86n4ph49s6DDOzNlPjWzY0iUc2zMzMrKScbJiZmVlJOdlogKRRkgam5Q0lPSXpu/XU31zSrfWsX1/SDxqx3f6SxjUv6lX66Sfpq6vbj5mZWXM52WgkST2BfwEj67vfSkS8FhED6+lqfaDBZKMF9QOalGxI8lweMzNrMZ0i2ZB0pqTnJE2QNEbS6U3sojvwT+DGiLgi9Vkh6SFJ09LfvrnyWWl5F0lPSpouaaak3sAIYLtUdqEyF0qaJala0qDcdteTdIekZyT9VVKX1O8VkqokPS3pnNx+7iXpUUkz0nZ7AucCg9L2BklaV9LfJE1JozRHpLYnSLpF0t3Avc060GZmZkV0+E+wkiqBo4DdyfZ3GjC1id38AbgqIv6YK3sD+GJELE5JxBigsqDdyWR3nr1B0tpAGTAM6BMR/VJ8R5GNPuwG9AKmSJqc2u8N7AzMAe4BjgRuBX4dEe9IKgPul7Qr2a3ubwYGRcQUSesBHwJnAZUR8cO0vf8FHoiI70laH3hS0n1pe/sAu0bEO4UHQNIQYAhA2XobN+3omZlZp9YZRjb2B+6KiEURsQC4uxl9PAAcIWmTXFlX4EpJ1cAtZElBoceAX0n6BfCZiFhUR3xjImJZRLwOPAjsldY9GREvR8QysmRm/1T+LUnTgKeAXdK2PwvMi4gpABHxfkR8XGR7/wMMkzQdmASUA1undROKJRqpv5ERURkRlWXdeharYmZmVlSHH9kA1AJ93AQ8DPxD0kEpafkJ8DrZiEQXYHFho4i4UdITwGHAvySdBLzchPii8LmkbYDTgb0i4l1Jo8gSBhWpX4yAoyLi+VUKpc8BHzSivZmZWZN0hpGNh4HDJZVL6k524m+yiPgTcD9wR7ok0pNsJGE58B2ySySrkLQt8HJE/BkYC+wKLAB65KpNJptTUSZpY+BA4Mm0bm9J26S5GoPSvqxHlhTMl7Qp8JVU9zlgc0l7pW33SBM9C7f3L+BHkpTq7d6c42FmZtZYHT7ZSJcVxgIzgNuBKmB+M/v6BfAKcB3wV+B4SY8DO1B8VGAQMCtdstgRuDYi3gYeSRNCLwTuAGam+B4Afh4R/03tHyObUDoLmA3cEREzyC6fPA38DXgkxfZR2t6lkmYAE8hGPCYCO9dOEAV+S3YJaGaayPrb5hwLMzOzxlJEY0be2zdJ3SNioaRuZCMJQyJiWlvH1V5VVlZGVVVVW4dhZmZrEElTI6LwixJA55izATBS0s5kn/RHO9EwMzNrPZ0i2YiIwfnnki4H9iuo1ht4saDskvp+wMvMzMwa1imSjUIRcWpbx2BmZtZZdPgJomZmZta2nGyYmZlZSTnZMDMzs5JysmFmZmYl5WTDzMzMSsrJhpmZmZWUkw0zMzMrqU75Oxu2eqpfnU/FsPFtHYaZWauqGdGs+3gaHtkwMzOzEnOyYWZmZiXlZKMISadLei7dBn6GpONSeY2kXkXqf13SsLQ8IN30rXbduZIObeH4+ksa15J9mpmZlYrnbBSQdDLwRWDviHhfUk9gQH1tImIsMDY9HQCMA55J684qWbBmZmbtQIcc2ZB0ZhqZmCBpjKTTm9D8V8APIuJ9gIiYHxGjc+t/JGmapGpJO6btnSDpMkn7Al8HLpQ0XdJ2kkZJGpjq1Ug6p0j7DSXdKWmmpMcl7ZrKh0u6TtIDkl6U9P1cHN0l3Zr28wZJSm3OkjQljcqMzJXvlfp/TNKFkmal8gpJD6WYpqV9KHZMh0iqklS17MP5TTicZmbW2XW4ZENSJXAUsDtwJFDZhLY9gB4R8VI91d6KiD2AK4BVkpiIeJRshOOMiOhXRz/F2p8DPBURu5IlO9fm6u8KHAbsA5wlafNUvjswFNgZ2BbYL5VfFhF7RUQfYB3ga6n8GuDkiNgHWJbr/w3giymmQcCfi+10RIyMiMqIqCzr1rOOQ2NmZvZJHS7ZAPYH7oqIRRGxALi7CW0FRAN1bk+PU4GKpodXtP3+wHUAEfEAsFG6fAMr9+UtYCKwdyp/MiLmRsRyYHqur4MkPSGpGjgY2EXS+mRJ1KOpzo25eLoCV6b6t5AlL2ZmZi2mI87ZUHMbpjkaH0jaNiJerqPakvS4jOYdv2Lti8UcBY+F5UtyZcuAtSSVA38BKiPiFUnDgfI6+q/1E+B1YDey5HNxI/bBzMys0TriyMbDwOGSyiV1J7sE0RS/By6XtB6ApPUkDWlC+wVAjyZuczJwbNpef7JLLe+ndUekfdkI6A9Mqaef8vT4Vtr3gQAR8S6wQNLn0/qjc216AvPSCMl3gLImxm5mZlavDjeyERFTJI0FZgBzgCqgKTMarwC6A1MkLQWWAhc3of1NZJclTiOd7BthOHCNpJnAh8DxuXVPAuOBrYHfRsRrknYo1klEvCfpSqAaqGHVxOTEFNcHwCRWHpO/ALdJ+ibZZZoPGhmzmZlZoyiioSkK7Y+k7hGxUFI3slGDIRExra3jaqp0GWRhRFzUAn11j4iFaXkYsFlE/Lg5fVVWVkZVVdXqhmRmZh2IpKkRUfRLGR1uZCMZmX5YqxwY3R4TjRI4TNIvyV7zOcAJbRuOmZl1Fh0y2YiIwfnnki5n5VdDa/UGXiwouyQirillbE0REcNbsK+bgZtbqj8zM7PG6pDJRqGIOLWtYzAzM+usOuK3UczMzGwN4mTDzMzMSsrJhpmZmZWUkw0zMzMrKScbZmZmVlJONszMzKyknGyYmZlZSXWK39mwllX96nwqho1v6zDMrJ2rGdHU+2Rae+WRDTMzMyspJxtmZmZWUq2SbEg6XdJzkmZJmiHpuBbqd5KkyrT8D0nrF6kzXNLpDfQzIN24rSViqpHUKy3vKWm2pN2b0P6q2lgkLWyBeNaX9IPV7cfMzKy5Sp5sSDoZ+CKwd0T0AQ4E1NLbiYivRsR7zWw+AGiRZKOWpF2BW4FBEfFUI9uURcRJEfFMC4ayPtCkZEMZj3qZmVmLaPCEIunMNCoxQdKYhkYJivgV8IOIeB8gIuZHxOjU91mSpqQRj5GSlMonSTpf0pOSXpB0QCpfR9JNkmZKuhlYJxdnfkTh15Kel3Qf8Nlcne+n7c2QdJukbpL2Bb4OXChpuqTtCkZMekmqScu7pJimpxh617HPOwF3At+JiCdT22MkVad9PT8X00JJ50p6Atgnv+20/mJJ0yTdL2njuvYjlW8q6Y5UPiPt2whguxTzhaneGan9TEnnpLIKSc9K+gswDdgqv0OShkiqklS17MP5jXrhzczMoIFkI530jgJ2B44EKuurX6R9D6BHRLxUR5XLImKvNOKxDvC13Lq1ImJvYChwdio7BfgwInYFfgfsWWSbewJH52LeK7f69rS93YBngRMj4lFgLHBGRPSrJ1aAk8luQ9+P7FjMraPeXcAPI+LhFNPmwPnAwUA/YC9JA1LddYFZEfG52vo56wLTImIP4MHccfjEfqTyPwMPpvI9gKeBYcBLad/OkPQ/QG9g7xTLnpIOTO0/C1wbEbtHxJx8IBExMiIqI6KyrFvPeg6RmZnZqhoa2dgfuCsiFkXEAuDuJvYvIOpZf5CkJyRVk52Id8mtuz09TgUq0vKBwPUAETETmFmkzwOAOyLiwzSaMja3ro+kh9L2ji3YXmM8BvxK0i+Az0TEojrq3QecJKksPd8LmBQRb0bEx8ANaV8AlgG31dHPcuDmtHw92etR334cDFwBEBHLIqLYEMT/pL+nyEYwdiRLPgDmRMTjdcRiZmbWLA0lG6s1tyKd7D+QtO0nOpbKgb8AAyOiL3AlUJ6rsiQ9LmPV3wOpL3lpqM4oshGHvsA5BdvL+5iVx2ZFnYi4keySyyLgX5IOrqP9D9PjX9JjfcdxcUQsq2d9Xu1+jaJx+1GMgN+nkY5+EbF9RFyd1n3QhH7MzMwapaFk42HgcEnlkroDzfkFlt8Dl0taD0DSepKGsPIE+Vbqe2Aj+ppM9kkeSX2AXeuo8400v6MHcHhuXQ9gnqSutf0kC9K6WjWsvESzIq6UNL0cEX8mGzEptn3IRiSOAT4r6VzgCeALaf5HWVr3YMO7S5fc9geTvR717cf9ZJeakFSWjnnhvv0L+F465kjaQtImjYjFzMysWer9BdGImCJpLDADmANUAU2dHXgF0B2YImkpsBS4OCLek3QlUE12cp/SyL6ukTQTmA48WSTmaWny6PQU80O51WeSnfjnpO3WnoRvAq6UdBrZyf0i4O+SvgM8kGs/CPh22o//AufWFWhELJF0BFlS8TrwS2Ai2cjCPyLirkbs7wfALpKmkh33QQ3sx4+BkZJOJBsROiUiHpP0iKRZwD/TvI2dgMfSfNyFwLdTfTMzsxaniPqvSkjqHhEL0zceJgNDImJaq0Rna6TKysqoqqpq6zDMzGwNImlqRBT9Iklj7o0yUtmPTJUDo51omJmZWVM0mGxExODCMkmXA/sVFPcGXiwouyQirml+eGZmZtbeNeuurxFxaksHYmZmZh2Tf5LazMzMSsrJhpmZmZWUkw0zMzMrKScbZmZmVlJONszMzKyknGyYmZlZSTnZMDMzs5Jq1u9sWOdW/ep8KoaNb+swzKydqhnRnHt6WnvmkQ0zMzMrKScbZmZmVlLtKtmQNErSwGa27Sfpq7nnwyWdXkfdR1cjxhpJvZrbPtfPr5rZ7qp04zwzM7M1QrtKNlZTP+CrDVUCiIh9SxtKozQ52ZBUFhEnRcQzpQjIzMysOVo92ZB0pqTnJE2QNKau0YVG9HOWpCmSZkkaKUmpfJKk8yU9KekFSQdIWhs4FxgkabqkQambnVP9lyWdlut7YXrsn9bfmmK+IbedQyQ9Jala0t8kfSoX3hlp+09K2j7VP1zSE6nNfZI2TeXdJV2T+pkp6ShJI4B1Uqw3pHrfTv1Nl/R/kspqY5V0rqQngH1SvJX5/UjLAyWNSsujJF0haWLa9y+kfXi2tk6R4z1EUpWkqmUfzm/OS2ZmZp1UqyYb6SR4FLA7cCRQuRrdXRYRe0VEH2Ad4Gu5dWtFxN7AUODsiPgIOAu4OSL6RcTNqd6OwJeAvYGzJXUtsp3dUz87A9sC+0kqB0YBgyKiL9m3ek7JtXk/bf8y4E+p7GHg8xGxO3AT8PNUfiYwPyL6RsSuwAMRMQxYlGI9VtJOwCBgv4joBywDjk3t1wVmRcTnIuLhxh48YAPgYOAnwN3AH4FdgL6S+hVWjoiREVEZEZVl3Xo2YTNmZtbZtfbIxv7AXRGxKCIWkJ3kmuugNFJQTXbS3CW37vb0OBWoqKeP8RGxJCLeAt4ANi1S58mImBsRy4Hpqb/PArMj4oVUZzRwYK7NmNzjPml5S+BfKd4zcvEeClxe2zAi3i0SwyHAnsAUSdPT823TumXAbfXsY13ujogAqoHXI6I67ePT1H/MzMzMmqS1f2dDLdJJNrLwF6AyIl6RNBwoz1VZkh6XUf8+Lskt11W3WJ2G9iOKLF8K/CEixkrqDwxP5SqoX4yA0RHxyyLrFkfEskbEUV6wrna/lrPqPi7Hv79iZmYtqLVHNh4GDpdULqk70Nxfdqk9cb6V+mnMN1QWAD2aub1CzwEVtfMxgO8AD+bWD8o9PpaWewKvpuXjc3XvBX5Y+0TSBmlxae6yzv3AQEmbpDobSvpMI+J8XdJOkroA32hEfTMzsxbXqp9gI2KKpLHADGAOUAU0ZbbhWsCSiHhP0pVklwBqgCmNaDsRGJYuQ/y+KXEXiojFkr4L3CJprbT9v+aqfCpN2OwCHJPKhqf6rwKPA9uk8vOAyyXNIhs5OYfsMtBIYKakaWnexm+Ae1PisBQ4lewY1mcYMA54BZgFdF+N3V6h7xY9qfIvAJqZWSMpu2zfihuUukfEQkndgMnAkIiY1oh2XchO6sdFxNOljtPqVllZGVVVVW0dhpmZrUEkTY2Iol/8aItr8yPTj06Vk81DaEyisTlwHzDRiYaZmVn70urJRkQMzj+XdDmwX0G13sCLBWUXRsQ1pYzNzMzMWl6bf+sgIk5t6xjMzMysdDrTz5WbmZlZG3CyYWZmZiXlZMPMzMxKysmGmZmZlZSTDTMzMyspJxtmZmZWUm3+1Vdrf6pfnU/FsPFtHYaZtbEa37bAGskjG2ZmZlZSTjbMzMyspEqWbEhaJmm6pFmSbkk3Xmts2xMkXVbHukcbaFshaXDueaWkPzc+8hXtaiT1amq7gj6GNma/JS1cne3k+nk0PVaku8giqb+kcS3Rv5mZWXOUcmRjUUT0i4g+wEfAyfmVksqa02lE7NtAlQpgRbIREVURcVpzttUChgKNTrJWVyOOjZmZWatrrcsoDwHbp0/ZEyXdCFRLKpd0jaRqSU9JOijXZitJ90h6XtLZtYW1owDKXJhGTqolDUpVRgAHpFGVn+Q/2UvqntveTElHNWUnJO0t6dEU66OSPpvKyyRdlOv3R5JOAzYHJkqamOodk+rMknR+Qd8XS5om6X5JG6ey70uaImmGpNtqR0kkbSrpjlQ+Q9K++WNTT/zDJZ2eez4rjYKsK2l86mtW7liamZmttpJ/G0XSWsBXgHtS0d5An4iYLelnABHRV9KOwL2SdsjXAz4EpkgaHxFVua6PBPoBuwG9Up3JwDDg9Ij4Wtp+/1ybM4H5EdE3rdugibvzHHBgRHws6VDgf4GjgCHANsDuad2GEfGOpJ8CB0XEW5I2B84H9gTeTfs6ICLuBNYFpkXEzySdBZwN/BC4PSKuTLGeB5wIXAr8GXgwIr6RRoi6N3E/Cn0ZeC0iDkvb6llYQdKQtJ+Urbfxam7OzMw6k1KObKwjaTpQBfwHuDqVPxkRs9Py/sB1ABHxHDAHqE02JkTE2xGxCLg91c3bHxgTEcsi4nXgQWCvBmI6FLi89klEvNvEfeoJ3JLmQ/wR2CXX718j4uPU7ztF2u4FTIqIN1O9G4AD07rlwM1p+XpW7msfSQ9JqgaOzW3vYOCKtK1lETG/iftRqBo4VNL5kg4o1l9EjIyIyoioLOv2iVzEzMysTqUc2VgUEf3yBZIAPsgX1dM+GnheX9u6qEg/TfFbYGIaUagAJjWh36bEW9vXKGBARMyQdALQvwl9FPMxqyaY5QAR8YKkPYGvAr+XdG9EnLua2zIzMwPa/quvk8k+sZMun2wNPJ/WfVHShpLWAQYAjxRpOyjNl9iYbJTgSWAB0KOO7d1LdnmCtM2mXkbpCbyalk8o6PfkdMkISRum8nwsTwBfkNQrXfo4hmw0BrLXYWBaHgw8nJZ7APMkdSUdp+R+4JS0rTJJ6zUy/hpgj9RuD7JLP6RLPB9GxPXARbV1zMzMWkJbJxt/AcrSZYKbgRMiYkla9zDZJZbpwG0F8zUA7gBmAjOAB4CfR8R/U9nHabLjTwranAdskCZBzgAOon4zJc1Nf38ALiD75P8IkP82zVVkl4pmpn5rvw0zEvinpIkRMQ/4JTAxxTwtIu5K9T4AdpE0lewSSe2owplkScoEsvkitX4MHJSO21RWXl5pyG3Ahuny1inAC6m8L/BkKv812XEyMzNrEYpYnasK1hlVVlZGVVVh7mdmZp2ZpKkRUVlsXVuPbJiZmVkH1+lvxCbpCeBTBcXfiYjqtojHzMyso+n0yUZEfK6tYzAzM+vIfBnFzMzMSsrJhpmZmZWUkw0zMzMrKScbZmZmVlJONszMzKyknGyYmZlZSTnZMDMzs5Lq9L+zYU1X/ep8KoaNb+swzKwN1Yw4rK1DsHbEIxtmZmZWUk42zMzMrKQ6dbIhaZSk2ZKmp1vSH9JC/VZK+nNL9GVmZtbeec4GnBERt0o6CBgJ9F7dDiOiCvA92M3MzOgAIxuSzpT0nKQJksZIOr2ZXT0GbJH6PEHSZbltjJPUPy0vlHS+pKmS7pO0t6RJkl6W9PVUp7+kcWl5uKS/5eqcluv3p5Jmpb+hufLjJM1Moy3XpbLPSLo/ld8vaetUvqmkO1LdGZL2raePUZIG5razMD1uJmlyGuGZJemAIsd5iKQqSVXLPpzfzENsZmadUbse2ZBUCRwF7E62L9OAqc3s7svAnY2oty4wKSJ+IekO4Dzgi8DOwGhgbJE2OwIHAT2A5yVdAewKfBf4HCDgCUkPAh8Bvwb2i4i3JG2Y+rgMuDYiRkv6HvBnYEB6fDAiviGpDOguaZc6+qjLYOBfEfG71Ee3wgoRMZJs5IdPbdY7Gj5MZmZmmXadbAD7A3dFxCIASXc3o48LJV0AbAJ8vhH1PwLuScvVwJKIWCqpGqioo834iFgCLJH0BrBpiv2OiPggxX47cAAQwK0R8RZARLyT+tgHODItXwdckJYPBo5LdZcB8yUdV0cfdZkC/E1SV+DOiJje4FEwMzNrpPZ+GUUt0McZwPbAb8hGJgA+ZtVjU55bXhoRtZ/slwNLACJiOXUnb0tyy8tSvbpiF1nC0ZD66tTVx4r9kiRgbYCImAwcCLwKXJeSFTMzsxbR3pONh4HDJZVL6g4061dmUqJwCdBF0peAGqCfpC6StgL2bqmAcyYDAyR1k7Qu8A3gIeB+4FuSNgLIXQJ5FDg6LR9Ltu+k+qekumWS1qunjxpgz7R8BNA1rf8M8EZEXAlcDezR4ntrZmadVru+jBIRUySNBWYAc8i+AdKs2YsREZLOA34OHArMJrtMMotsLkiLiohpkkYBT6aiqyLiKQBJvwMelLQMeAo4ATiN7FLHGcCbZPM9AH4MjJR0ItmoySkR8VgdfVwJ3CXpSbKE5IPUR3/gDElLgYWkyzJmZmYtQSuvCLRPkrpHxEJJ3chGC4ZERIsnB7ZSZWVlVFX5m71mZraSpKkRUVlsXbse2UhGStqZbF7FaCcaZmZma5Z2n2xExOD8c0mXA/sVVOsNvFhQdklEXFPK2MzMzKwDJBuFIuLUto7BzMzMVmrv30YxMzOzNZyTDTMzMyspJxtmZmZWUk42zMzMrKScbJiZmVlJOdkwMzOzknKyYWZmZiXV4X5nw0qv+tX5VAwb39ZhmFmJ1Yxo1r0tzT7BIxtmZmZWUk42zMzMrKScbJiZmVlJec5GBybpTOBY4BXgLWBqRFzUtlGZmVln42Sjg5JUCRwF7E72Ok8Dpq5Gf0OAIQBl623cEiGamVkn4csoHdf+wF0RsSgiFgB3r05nETEyIiojorKsW8+WidDMzDoFJxsdl9o6ADMzM3Cy0ZE9DBwuqVxSd8BfmDczszbhORsdVERMkTQWmAHMAaqA+W0blZmZdUYe2ejYLoqIzwIDgM+yGhNEzczMmssjGx3bSEk7A+XA6IiY1hKd9t2iJ1X+GWMzM2skJxsdWEQMzj+XdDmwX0G13sCLBWWXRMQ1pYzNzMw6DycbnUhEnNrWMZiZWefjORtmZmZWUh7ZMDOzVrV06VLmzp3L4sWL2zoUa4by8nK23HJLunbt2ug2TjbMzKxVzZ07lx49elBRUYHk3x9sTyKCt99+m7lz57LNNts0up0vo5iZWatavHgxG220kRONdkgSG220UZNHpZxsmJlZq3Oi0X4157VzsmFmZmYl5TkbZmbWpiqGjW/R/moa+aODd9xxB0ceeSTPPvssO+64IwCTJk3ioosuYty4cSvqnXDCCXzta19j4MCB9O/fn3nz5lFeXs7aa6/NlVdeSb9+/QCYP38+P/rRj3jkkUcA2G+//bj00kvp2TO7U/YLL7zA0KFDeeGFF+jatSt9+/bl0ksvZdNNN232vr7zzjsMGjSImpoaKioq+Pvf/84GG2xQtO6yZcuorKxkiy22WGX/AC666CLOOOMM3nzzTXr16kV1dTUXX3wxo0aNanZseU42rMmqX53f4v85mNmapbEn7PZszJgx7L///tx0000MHz680e1uuOEGKisrueaaazjjjDOYMGECACeeeCJ9+vTh2muvBeDss8/mpJNO4pZbbmHx4sUcdthh/OEPf+Dwww8HYOLEibz55purlWyMGDGCQw45hGHDhjFixAhGjBjB+eefX7TuJZdcwk477cT777+/Svkrr7zChAkT2HrrrVeU9e3bl7lz5/Kf//xnlfLm8mUUMzPrdBYuXMgjjzzC1VdfzU033dSsPvbZZx9effVVAP79738zdepUzjzzzBXrzzrrLKqqqnjppZe48cYb2WeffVYkGgAHHXQQffr0Wa39uOuuuzj++OMBOP7447nzzjuL1ps7dy7jx4/npJNO+sS6n/zkJ1xwwQWfmItx+OGHN/vYFHKyYWZmnc6dd97Jl7/8ZXbYYQc23HBDpk1r+q2j7rnnHgYMGADAM888Q79+/SgrK1uxvqysjH79+vH0008za9Ys9txzzwb7XLBgAf369Sv698wzz3yi/uuvv85mm20GwGabbcYbb7xRtN+hQ4dywQUX0KXLqqf9sWPHssUWW7Dbbrt9ok1lZSUPPfRQgzE3hi+jmJlZpzNmzBiGDh0KwNFHH82YMWPYY4896vymRb782GOP5YMPPmDZsmUrkpSIKNq2rvK69OjRg+nTpzd+Rxph3LhxbLLJJuy5555MmjRpRfmHH37I7373O+69996i7TbZZBNee+21Fomhw4xsSBolabak6ZJmSDqkrWMCkDRc0ulp+VxJh6blq9IdWZG0sIW29Wh6rJA0Ky33lzSu/pZmZp3H22+/zQMPPMBJJ51ERUUFF154ITfffDMRwUYbbcS77767Sv133nmHXr16rXh+ww03MHv2bAYPHsypp2a3nNpll1146qmnWL58+Yp6y5cvZ8aMGey0007ssssuTJ06tcHYmjqysemmmzJv3jwA5s2bxyabbPKJOo888ghjx46loqKCo48+mgceeIBvf/vbvPTSS8yePZvddtuNiooK5s6dyx577MF///tfIPs9lHXWWacRR7RhHSbZSM6IiH7AUOCvbRvKJ0XEWRFxX1o+KSI++c5Zvf73bcn+zMw6oltvvZXjjjuOOXPmUFNTwyuvvMI222zDww8/TO/evXnttdd49tlnAZgzZw4zZsxY8Y2TWl27duW8887j8ccf59lnn2X77bdn991357zzzltR57zzzmOPPfZg++23Z/DgwTz66KOMH79ycv0999xDdXX1Kv3WjmwU+9t5550/sS9f//rXGT16NACjR4/miCOO+ESd3//+98ydO5eamhpuuukmDj74YK6//nr69u3LG2+8QU1NDTU1NWy55ZZMmzaNT3/600D27ZnVnVNSa426jCLpTOBY4BXgLWBqRFzUjK4eA7ZIfZ4AVEbED9PzccBFETEpjShcDhwKvAv8CrgA2BoYGhFjU/sBQBnQB7gYWBv4DrAE+GpEvCNpu9TXxsCHwPcj4rmC/RsFjIuIWyVNAk6PiKq07mLgoBTH0RHxpqTvA0PS9v4NfCciPpS0KVkytW3q+pSIeFTSwojoXtdBkTQcWFh7TNPox9eAN4G/A1um/fxtRNxc0HZIioWy9Tau+8ibmTVRa3/zZcyYMQwbNmyVsqOOOoobb7yRAw44gOuvv57vfve7LF68mK5du3LVVVet+Ppq3jrrrMPPfvYzLrroIq6++mquvvpqfvSjH7H99tsTEeyzzz5cffXVK+qOGzeOoUOHMnToULp27cquu+7KJZdcslr7MmzYML71rW9x9dVXs/XWW3PLLbcA8Nprr3HSSSfxj3/8o9l9T5w4kcMOa5nXZo1JNiRVAkcBu5PFNQ1oeMypuC8Ddzai3rrApIj4haQ7gPOALwI7A6OBsalenxRXOdlJ/xcRsbukPwLHAX8CRgInR8SLkj4H/AU4uJHxrgtMi4ifSToLOBv4IXB7RFwJIOk84ETgUuDPwIMR8Q1JZUCdCUYjfRl4LSIOS9v6xL+qiBhJto98arPesZrbMzNrM/l5C7VOO+20Fcv77bcfjz/+eKPa/uxnP1uxvMEGG3D99dfXud0dd9yRe+65p2nBNmCjjTbi/vvv/0T55ptvXjTR6N+/P/379y/aV01NzYrlJUuWUFVVxZ/+9KcWiXONSTaA/YG7ImIRgKS7m9HHhZIuADYBPt+I+h8Bta98NbAkIpZKqgYqcvUmRsQCYIGk+cDduTa7SuoO7AvckpsI9KkmxL0cqB1JuB64PS33SUnG+mQJxb9S+cFkSQ4RsQyY34RtFVMNXCTpfLKRl5aZfmxmZu3Sf/7zH0aMGMFaa7VMmrAmJRst8UP5Z5CdqE8jG5nYE/iYVeemlOeWl0ZE7af05WSXRYiI5ZLyx2ZJbnl57vlysmPYBXgvzRdpCbUxjQIGRMSMdDmn/2r2W/RYRMQLkvYEvgr8XtK9EXHuam7LzMzaqd69e9O7d+8W629NmiD6MHC4pPI0UtCsC0URsRy4BOgi6UtADdBPUhdJWwF7t1TAuW2+D8yW9E0AZT75peW6dQEGpuXBZMcCoAcwT1JXsrkste4HTknbKpO0XiO3UwPskdrtAWyTljcHPoyI64GLauuYmZXKys951t4057VbY0Y2ImKKpLHADGAOUEUzLw9ERKTLDz8nm/w5m+xSwSyyuSClcCxwhaTfAF2Bm8j2pTE+AHaRNJVsnwel8jOBJ8iORzVZ8gHwY2CkpBOBZWSJx2ON2M5twHGSpgNTgBdSeV+yS1DLgaWpvzr13aInVZ3gp4zNrDTKy8t5++23fZv5digiePvttykvL2+4co7WpOxSUveIWCipGzAZGBIRpUoOrJkqKyujqqqqrcMws3Zq6dKlzJ07l8WLF7d1KNYM5eXlbLnllnTt2nWVcklTI6KyWJs1ZmQjGZl+6KocGO1Ew8ys4+natSvbbLNNW4dhrWiNSjYiYnD+uaTLgf0KqvUGXiwouyQirillbGZmZtY8a1SyUSgiTm3rGMzMzGz1rEnfRjEzM7MOaI2aIGrtg6QFwPNtHUcn04vsJ/yt9fiYtz4f89bXksf8MxFR9H4Wa/RlFFtjPV/XjGMrDUlVPuaty8e89fmYt77WOua+jGJmZmYl5WTDzMzMSsrJhjXHyLYOoBPyMW99Puatz8e89bXKMfcEUTMzMyspj2yYmZlZSTnZMDMzs5JysmFNIunLkp6X9G9Jw9o6no5I0laSJkp6VtLTkn6cyodLelXS9PT31baOtSORVCOpOh3bqlS2oaQJkl5Mjxu0dZwdhaTP5t7L0yW9L2mo3+ctS9LfJL0haVaurM73taRfpv/fn5f0pRaLw3M2rLEklZHdlv6LwFyy29QfExHPtGlgHYykzYDNImKapB7AVGAA8C1gYURc1JbxdVSSaoDKiHgrV3YB8E5EjEjJ9QYR8Yu2irGjSv+3vAp8Dvgufp+3GEkHAguBayOiTyor+r5ON0IdA+wNbA7cB+wQEctWNw6PbFhT7A38OyJejoiPgJuAI9o4pg4nIubV3vE4IhYAzwJbtG1UndYRwOi0PJos6bOWdwjwUkTMaetAOpqImAy8U1Bc1/v6COCmiFgSEbOBf5P9v7/anGxYU2wBvJJ7PhefBEtKUgWwO/BEKvqhpJlpaNRD+i0rgHslTZU0JJVtGhHzIEsCgU3aLLqO7WiyT9S1/D4vrbre1yX7P97JhjWFipT5OlyJSOoO3AYMjYj3gSuA7YB+wDzg4raLrkPaLyL2AL4CnJqGn63EJK0NfB24JRX5fd52SvZ/vJMNa4q5wFa551sCr7VRLB2apK5kicYNEXE7QES8HhHLImI5cCUtNLxpmYh4LT2+AdxBdnxfT3NoaufSvNF2EXZYXwGmRcTr4Pd5K6nrfV2y/+OdbFhTTAF6S9omfRo5GhjbxjF1OJIEXA08GxF/yJVvlqv2DWBWYVtrHknrpsm4SFoX+B+y4zsWOD5VOx64q20i7NCOIXcJxe/zVlHX+3oscLSkT0naBugNPNkSG/S3UaxJ0tfQ/gSUAX+LiN+1bUQdj6T9gYeAamB5Kv4V2X/K/ciGNWuA/1d73dVWj6RtyUYzILsb9o0R8TtJGwF/B7YG/gN8MyIKJ9tZM0nqRjZHYNuImJ/KrsPv8xYjaQzQn+xW8q8DZwN3Usf7WtKvge8BH5Ndwv1ni8ThZMPMzMxKyZdRzMzMrKScbJiZmVlJOdkwMzOzknKyYWZmZiXlZMPMzMxKysmG2RpK0rJ018tZku6WtH4D9YdLOr2BOgPSzZZqn58r6dAWiHWUpIGr208Ttzk0fXVyjSFpx/SaPSVpu4J1NZIeKiibXns3TkmVkv7cAjFU5O/wWbDuqvzrX2qSNpV0o6SX08/APybpG621fVtzONkwW3Mtioh+6U6N7wCntkCfA4AVJ5uIOCsi7muBfltVukvoUGCNSjbIju9dEbF7RLxUZH0PSVsBSNopvyIiqiLitMZuKB2DJomIk1rrLs3px+nuBCZHxLYRsSfZDwFuWeLtrlXK/q15nGyYtQ+PkW6IJGk7SfekT4oPSdqxsLKk70uaImmGpNskdZO0L9k9KC5Mn6i3qx2RkPQVSX/Pte8v6e60/D/pE+k0Sbeke7bUKX2C/9/UpkrSHpL+JeklSSfn+p8s6Q5Jz0j6q6Quad0xkqrTiM75uX4XppGYJ4Bfk90Ce6KkiWn9FWl7T0s6pyCec1L81bXHS1J3SdekspmSjmrs/krqJ+nx1O4OSRukH7wbCpxUG1MRfwcGpeXCX87sL2lcA7Hlj8E+kn6ajtMsSUNz21lL0ujU9tbaESBJkyRVNuI4n5/eX/dJ2ju1e1nS11OdMkkXpvfYTEn/r8i+Hgx8FBF/rS2IiDkRcWl9faTjMCnF/ZykG1LigqQ9JT2YYvuXVv7k9qT0nnsQ+LGkwyU9oWyE6T5Jm9bxelhriQj/+c9/a+AfsDA9lpHdpOrL6fn9QO+0/DnggbQ8HDg9LW+U6+c84EdpeRQwMLduFDCQ7Fcz/wOsm8qvAL5N9quDk3PlvwDOKhLrin7JfvXxlLT8R2Am0APYGHgjlfcHFgPbpv2bkOLYPMWxcYrpAWBAahPAt3LbrAF65Z5vmDtek4Bdc/Vq9/8HwFVp+XzgT7n2GzRhf2cCX0jL59b2k38NirSpAXYAHk3PnyIbZZqVOybj6oqt8BgAe5L9yuy6QHfgabI7BFekevulen9j5ftiElDZiOP8lbR8B3Av0BXYDZieyocAv0nLnwKqgG0K9vc04I/1vL+L9pGOw3yyEZAuZIn2/imGR4GNU5tBZL9iXLtffyl4LWt/tPIk4OK2/vfc2f883GS25lpH0nSyk8dUYEL6lL0vcEv6sAfZf9SF+kg6D1if7ET0r/o2FBEfS7oHOFzSrcBhwM+BL5CdEB9J21ub7D//htTeM6ca6B4RC4AFkhZr5dyTJyPiZVjxk8r7A0uBSRHxZiq/ATiQbDh+GdnN6eryLWW3hl8L2CzFPTOtuz09TgWOTMuHkg3r1x6DdyV9raH9ldQTWD8iHkxFo1l5x9KGvAO8K+lo4FngwzrqfSK2tJg/BvsDd0TEBymu24EDyI79KxHxSKp3PdmJ/6Jc/3tR93H+CLgn1asGlkTEUknVZO9FyO4ds6tWztPpSXYfjdl17biky1PMH0XEXvX08RHZe2Nuajc9bfc9oA/ZvwPIksr8z5jfnFveErg5jXysXV9c1jqcbJituRZFRL90chtHNmdjFPBeRPRroO0osk+qMySdQPZpsSE3p228A0yJiAVp+HpCRBzTxNiXpMflueXa57X/7xTeKyEofovrWosjYlmxFcpuGnU6sFdKGkYB5UXiWZbbvorE0Nz9bYqbgcuBE+qpUyw2WPUY1Hesih3bwv7rsjTSkAC51y8ilmvlfAiRjRbVl8Q+DRy1IoCIUyX1IhvBqLMPSf1Z9T1T+5oJeDoi9qljex/kli8F/hARY1N/w+uJ01qB52yYreEiu0HVaWQn00XAbEnfhGwSnqTdijTrAcxTdqv6Y3PlC9K6YiYBewDfZ+WnxMeB/SRtn7bXTdIOq7dHK+yt7A7CXciGxB8GngC+IKmXsgmQxwAP1tE+vy/rkZ1s5qfr819pxPbvBX5Y+0TSBjRif9Pr8a6kA1LRd+qJsZg7gAuof7SpWGyFJgMDUozrkt0htfbbLltLqj0pH0N2bPOacpyL+RdwSnp/IWmHFEPeA0C5pFNyZfkJvY3pI+95YOPa/ZLUVdIuddTtCbyalo+vo461IicbZu1ARDwFzCAbWj8WOFHSDLJPj0cUaXIm2QllAvBcrvwm4AwV+Wpm+sQ8juxEPS6VvUn2CXyMpJlkJ+NPTEhtpseAEWS3EJ9NdklgHvBLYCLZ/k6LiLpu6z4S+KekiRExg2wOxNNkcxQeqaNN3nnABmmC5AzgoCbs7/FkE21nkt2h9NxGbA+AiFgQEedHxEdNia1IP9PIRrCeJHutr0rvE8gu0Ryf4tuQbA5Ovm1TjnMxVwHPANOUfc32/ygYKU+jIwPIkprZkp4ku+T0i8b2UdDfR2Tzes5Px2Q62SXFYoaTXWp8CHirCftlJeK7vppZq0tD26dHxNfaOBQzawUe2TAzM7OS8siGmZmZlZRHNszMzKyknGyYmZlZSTnZMDMzs5JysmFmZmYl5WTDzMzMSur/A2wKJ7z+nGJWAAAAAElFTkSuQmCC\n", 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PearsonRResult(statistic=0.8038402097744288, pvalue=7.749911555901578e-24)\n", "Slope and P-value = PearsonRResult(statistic=0.6566421262155423, pvalue=1.1916395157114658e-13)\n", "0.0 190\n", "1.0 5\n", "Name: Campy, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.878065624386801, pvalue=0.05016675220166733)\n", "Slope and P-value = PearsonRResult(statistic=0.9157696996255134, pvalue=0.010343319063672363)\n", "Slope and P-value = PearsonRResult(statistic=1.0, pvalue=1.0)\n", "Slope and P-value = PearsonRResult(statistic=0.7859782288191052, pvalue=3.479625465914393e-22)\n", "Slope and P-value = PearsonRResult(statistic=1.0, pvalue=1.0)\n", "Slope and P-value = PearsonRResult(statistic=0.2557402360502642, pvalue=0.06456045652042677)\n", "Slope and P-value = PearsonRResult(statistic=0.20726412283880818, pvalue=0.3945408677197756)\n", "Slope and P-value = PearsonRResult(statistic=0.4822010956914042, pvalue=0.13308046991744768)\n", "Slope and P-value = PearsonRResult(statistic=0.9617448135067693, pvalue=0.0001359772828004556)\n", "Slope and P-value = PearsonRResult(statistic=-0.12488695660159681, pvalue=0.2157080658824904)\n" ] } ], "source": [ "sample = pd.merge(microbiome, poultry[['SampleID', 'Campy','SampleType','PastureTime']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "#sample = sample[~sample.isin([np.nan, np.inf, -np.inf]).any(1)]\n", "\n", "feces1=sample[(sample.SampleType=='Feces') & (sample.PastureTime=='Start')]\n", "feces2=sample[(sample.SampleType=='Feces') & (sample.PastureTime=='Mid')]\n", "feces3=sample[(sample.SampleType=='Feces') & (sample.PastureTime=='End')]\n", "\n", "soil1=sample[(sample.SampleType=='Soil') & (sample.PastureTime=='Start')]\n", "soil2=sample[(sample.SampleType=='Soil') & (sample.PastureTime=='Mid')]\n", "soil3=sample[(sample.SampleType=='Soil') & (sample.PastureTime=='End')]\n", "\n", "ceca=sample[sample.SampleType=='Ceca']\n", "wcrp=sample[sample.SampleType=='WCR-P']\n", "wcrf=sample[sample.SampleType=='WCR-F']\n", "\n", "\n", "\n", "print ('SAMPLE DISTRIBUTION \\n')\n", "\n", "print('Feces_Start', feces1.shape)\n", "print('Feces_Mid', feces2.shape)\n", "print('Feces_End', feces3.shape)\n", "\n", "print('Soil_Start', soil1.shape,'\\n')\n", "print('Soil_Mid', soil2.shape,'\\n')\n", "print('Soil_End', soil3.shape,'\\n')\n", "\n", "print('Ceca', ceca.shape)\n", "print('WCR-P', wcrp.shape)\n", "print('WCR-F', wcrf.shape,'\\n')\n", "\n", "sampletypes = [feces1,feces2,feces3, soil1,soil2,soil3,ceca, wcrp, wcrf]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES_START\", 1: \"FECES_MID\", 2:\"FECES_END\",\n", " 3: \"SOIL_START\", 4: \"SOIL_MID\", 5: \"SOIL_END\",\n", " 6:\"CECA\", 7: \"WCR-P\", 8: \"WCR-F\"\n", " }\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType', 'Campy','PastureTime',\n", " 'Pathogen_Salmonella','Pathogen_Campy','Pathogen_Listeria'],axis='columns'),sample.Campy,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1]\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " \n", "# mylist2.append([f\"Lab_Campy_{sample_name[indexing]}\", rf_auc_normal])\n", " \n", " \n", " print(pd.value_counts(sample['Campy']))\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " \n", " plt.title(f\"Campylobacter in {sample_name[indexing]} Model\")\n", " prelim3_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','Campy','SampleType','PastureTime',\n", " 'Pathogen_Salmonella','Pathogen_Campy','Pathogen_Listeria'],axis='columns').columns)\n", " prelim3_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", "\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim3_plot.max(), decimals=None, symbol=''))\n", "\n", " xmax=prelim3_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " \n", " plt.show()\n", " \n", " prelim3_plot.nlargest(10).to_csv(\"prelim3.csv\")\n", " top10 = pd.read_csv('prelim3.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", "# plt.plot(pdp[1][0], pdp[0][0],'.')\n", "# plt.ylabel('Partial dependence'), plt.xlabel(top10[feature])\n", "# plt.show()\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"Campylobacter_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (3) Listeria" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SAMPLE DISTRIBUTION \n", "\n", "Feces_Start (200, 881)\n", "Feces_Mid (313, 881)\n", "Feces_End (185, 881)\n", "Soil_Start (199, 881) \n", "\n", "Soil_Mid (313, 881) \n", "\n", "Soil_End (183, 881) \n", "\n", "Ceca (185, 881)\n", "WCR-P (208, 881)\n", "WCR-F (195, 881) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "0.0 156\n", "1.0 44\n", "Name: Listeria, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.6065608623854871, pvalue=2.254945256704019e-11)\n", "Slope and P-value = PearsonRResult(statistic=0.7709254183317802, pvalue=9.58192409930254e-08)\n", "Slope and P-value = PearsonRResult(statistic=0.6614838891694641, pvalue=3.595970987654534e-09)\n", "Slope and P-value = PearsonRResult(statistic=0.891313957257391, pvalue=1.2597636172216732e-22)\n", "Slope and P-value = PearsonRResult(statistic=0.6004718100509662, pvalue=0.030007304871575914)\n", "Slope and P-value = PearsonRResult(statistic=0.9639494972888563, pvalue=3.7400290148542025e-58)\n", "Slope and P-value = PearsonRResult(statistic=0.894770496023378, pvalue=4.339554302205558e-36)\n", "Slope and P-value = PearsonRResult(statistic=0.562723787060244, pvalue=0.03617443368434883)\n", "Slope and P-value = PearsonRResult(statistic=0.8486050467775887, pvalue=7.510390631845634e-29)\n", "Slope and P-value = PearsonRResult(statistic=0.7787381439359171, pvalue=2.4853024337896578e-20)\n", "0.0 265\n", "1.0 48\n", "Name: Listeria, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.3996809204187486, pvalue=0.1980085035855334)\n", "Slope and P-value = PearsonRResult(statistic=0.5590951322540724, pvalue=3.8915258859967236e-09)\n", "Slope and P-value = PearsonRResult(statistic=0.75573249907464, pvalue=1.017479057937902e-19)\n", "Slope and P-value = PearsonRResult(statistic=0.8501498170704136, pvalue=7.381762501869829e-10)\n", "Slope and P-value = PearsonRResult(statistic=0.4670557250027938, pvalue=0.01614761898682641)\n", "Slope and P-value = PearsonRResult(statistic=0.7419448447542005, pvalue=3.386233070310527e-07)\n", "Slope and P-value = PearsonRResult(statistic=0.9626495606657584, pvalue=9.67066688838465e-40)\n", "Slope and P-value = PearsonRResult(statistic=0.9406331529396316, pvalue=1.9957952544925911e-28)\n", "Slope and P-value = PearsonRResult(statistic=0.9156653846379036, pvalue=6.494855924531964e-07)\n", "Slope and P-value = PearsonRResult(statistic=0.8791917154531752, pvalue=1.5805628420866206e-05)\n", "0.0 165\n", "1.0 20\n", "Name: Listeria, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.7484211643702747, pvalue=3.5515132054580064e-19)\n", "Slope and P-value = PearsonRResult(statistic=0.7656829692058976, pvalue=1.7269074578439264e-20)\n", "Slope and P-value = PearsonRResult(statistic=0.5418360643142345, pvalue=0.2667837317715566)\n", "Slope and P-value = PearsonRResult(statistic=0.7805750676665507, pvalue=1.024539586522675e-21)\n", "Slope and P-value = PearsonRResult(statistic=0.8576864673002877, pvalue=1.435605435597756e-19)\n", "Slope and P-value = PearsonRResult(statistic=0.9997352217395542, pvalue=1.2108949221293606e-55)\n", "Slope and P-value = PearsonRResult(statistic=0.8941188767382766, pvalue=0.00020507989428539348)\n", "Slope and P-value = PearsonRResult(statistic=0.8946637494879799, pvalue=2.623334774720858e-34)\n", "Slope and P-value = PearsonRResult(statistic=-0.32643321643953843, pvalue=0.0009178069353403366)\n", "Slope and P-value = PearsonRResult(statistic=0.8707115875685979, pvalue=9.314291052146152e-11)\n", "0.0 155\n", "1.0 44\n", "Name: Listeria, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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PearsonRResult(statistic=-0.6449405892915401, pvalue=4.424952946482671e-13)\n", "Slope and P-value = PearsonRResult(statistic=0.8408608503130183, pvalue=7.1091093119917105e-28)\n", "0.0 259\n", "1.0 54\n", "Name: Listeria, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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PearsonRResult(statistic=0.4671190082062586, pvalue=2.3910767990969057e-05)\n", "Slope and P-value = PearsonRResult(statistic=0.24249923832469827, pvalue=0.015061129313889192)\n", "0.0 166\n", "1.0 17\n", "Name: Listeria, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.6620221793522646, pvalue=6.392497556075227e-14)\n", "Slope and P-value = PearsonRResult(statistic=0.7359771110690502, pvalue=2.7092314816693484e-18)\n", "Slope and P-value = PearsonRResult(statistic=0.932982751039528, pvalue=0.0002401864129485062)\n", "Slope and P-value = PearsonRResult(statistic=nan, pvalue=nan)\n", "Slope and P-value = PearsonRResult(statistic=0.783700949937234, pvalue=1.1300413407301687e-07)\n", "Slope and P-value = PearsonRResult(statistic=0.8589054811680983, pvalue=3.085659473911857e-30)\n", "Slope and P-value = PearsonRResult(statistic=0.773594675157927, pvalue=3.0283726632576286e-09)\n", "Slope and P-value = PearsonRResult(statistic=0.991611991103586, pvalue=1.4933859998001185e-83)\n", "Slope and P-value = PearsonRResult(statistic=0.837385772298509, pvalue=1.8751997703239362e-27)\n", "Slope and P-value = PearsonRResult(statistic=0.6072830071377204, pvalue=0.0002914973682202079)\n", "0.0 177\n", "1.0 8\n", "Name: Listeria, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.9109528923791592, pvalue=1.809077721387858e-39)\n", "Slope and P-value = PearsonRResult(statistic=0.5743320125744794, pvalue=4.167081089519893e-10)\n", "Slope and P-value = PearsonRResult(statistic=0.9374443796302816, pvalue=4.262475479666583e-42)\n", "Slope and P-value = PearsonRResult(statistic=0.34843339595871825, pvalue=0.1704961222808112)\n", "Slope and P-value = PearsonRResult(statistic=0.9447337758543872, pvalue=9.429842546575233e-31)\n", "Slope and P-value = PearsonRResult(statistic=-0.5093007374818255, pvalue=6.260065034767463e-08)\n", "Slope and P-value = PearsonRResult(statistic=0.6914230964101151, pvalue=0.008852358319361984)\n", "Slope and P-value = PearsonRResult(statistic=-0.2022405159937081, pvalue=0.04360363769682316)\n", "Slope and P-value = PearsonRResult(statistic=0.3747848683458144, pvalue=0.002688998290412974)\n", "Slope and P-value = PearsonRResult(statistic=0.8374474383276436, pvalue=1.42514001768826e-20)\n", "0.0 192\n", "1.0 16\n", "Name: Listeria, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.7612983520571626, pvalue=3.060293621236688e-18)\n", "Slope and P-value = PearsonRResult(statistic=0.9556127401119107, pvalue=4.459335150359204e-12)\n", "Slope and P-value = PearsonRResult(statistic=1.0, pvalue=1.0)\n", "Slope and P-value = PearsonRResult(statistic=0.9622352555075906, pvalue=4.5735137369431184e-27)\n", "Slope and P-value = PearsonRResult(statistic=0.9361505353021697, pvalue=8.516460171354676e-07)\n", "Slope and P-value = PearsonRResult(statistic=0.8607168507865138, pvalue=0.0006699165662070135)\n", "Slope and P-value = PearsonRResult(statistic=1.0, pvalue=1.0)\n", "Slope and P-value = PearsonRResult(statistic=-0.19541753688927624, pvalue=0.051362867900031944)\n", "Slope and P-value = PearsonRResult(statistic=0.5288265795086644, pvalue=4.2183996621234635e-06)\n", "Slope and P-value = PearsonRResult(statistic=0.9685639644330851, pvalue=3.30049664279757e-42)\n", "0.0 158\n", "1.0 37\n", "Name: Listeria, dtype: int64\n" ] }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.4902079307627581, pvalue=2.2562874936497443e-07)\n", "Slope and P-value = PearsonRResult(statistic=0.7753903093162635, pvalue=2.8071003405335823e-21)\n", "Slope and P-value = PearsonRResult(statistic=-0.2276014136499097, pvalue=0.022763781370586333)\n", "Slope and P-value = PearsonRResult(statistic=0.8273803750871044, pvalue=2.707521348261582e-26)\n", "Slope and P-value = PearsonRResult(statistic=0.8857860818925095, pvalue=1.9233900952889541e-34)\n", "Slope and P-value = PearsonRResult(statistic=-0.4727398467273555, pvalue=6.823598281319766e-07)\n", "Slope and P-value = PearsonRResult(statistic=0.8684908915015603, pvalue=2.0821722274682521e-22)\n", "Slope and P-value = PearsonRResult(statistic=0.6257608573111444, pvalue=0.03946027648251976)\n", "Slope and P-value = PearsonRResult(statistic=-0.781051436778632, pvalue=9.326419549945965e-22)\n", "Slope and P-value = PearsonRResult(statistic=0.7590927152453285, pvalue=5.643912353640014e-20)\n" ] } ], "source": [ "sample = pd.merge(microbiome, poultry[['SampleID', 'Listeria','SampleType','PastureTime']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "#sample = sample[~sample.isin([np.nan, np.inf, -np.inf]).any(1)]\n", "\n", "feces1=sample[(sample.SampleType=='Feces') & (sample.PastureTime=='Start')]\n", "feces2=sample[(sample.SampleType=='Feces') & (sample.PastureTime=='Mid')]\n", "feces3=sample[(sample.SampleType=='Feces') & (sample.PastureTime=='End')]\n", "\n", "soil1=sample[(sample.SampleType=='Soil') & (sample.PastureTime=='Start')]\n", "soil2=sample[(sample.SampleType=='Soil') & (sample.PastureTime=='Mid')]\n", "soil3=sample[(sample.SampleType=='Soil') & (sample.PastureTime=='End')]\n", "\n", "ceca=sample[sample.SampleType=='Ceca']\n", "wcrp=sample[sample.SampleType=='WCR-P']\n", "wcrf=sample[sample.SampleType=='WCR-F']\n", "\n", "\n", "\n", "print ('SAMPLE DISTRIBUTION \\n')\n", "\n", "print('Feces_Start', feces1.shape)\n", "print('Feces_Mid', feces2.shape)\n", "print('Feces_End', feces3.shape)\n", "\n", "print('Soil_Start', soil1.shape,'\\n')\n", "print('Soil_Mid', soil2.shape,'\\n')\n", "print('Soil_End', soil3.shape,'\\n')\n", "\n", "print('Ceca', ceca.shape)\n", "print('WCR-P', wcrp.shape)\n", "print('WCR-F', wcrf.shape,'\\n')\n", "\n", "\n", "sampletypes = [feces1,feces2,feces3, soil1,soil2,soil3,ceca, wcrp, wcrf]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES_START\", 1: \"FECES_MID\", 2:\"FECES_END\",\n", " 3: \"SOIL_START\", 4: \"SOIL_MID\", 5: \"SOIL_END\",\n", " 6:\"CECA\", 7: \"WCR-P\", 8: \"WCR-F\"\n", " }\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType', 'Listeria','PastureTime',\n", " 'Pathogen_Salmonella','Pathogen_Campy','Pathogen_Listeria'],axis='columns'),sample.Listeria,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1]\n", " \n", " try:\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " except ValueError:\n", " pass\n", "# mylist2.append([f\"Lab_Listeria_{sample_name[indexing]}\", rf_auc_normal])\n", " \n", " \n", " print(pd.value_counts(sample['Listeria']))\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", " \n", " plt.title(f\"Lab_Listeria in {sample_name[indexing]} Model\")\n", " prelim3_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','Listeria','SampleType','PastureTime',\n", " 'Pathogen_Salmonella','Pathogen_Campy','Pathogen_Listeria'],axis='columns').columns)\n", " prelim3_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", "\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim3_plot.max(), decimals=None, symbol=''))\n", "\n", " xmax=prelim3_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " \n", " plt.show()\n", " \n", " prelim3_plot.nlargest(10).to_csv(\"prelim3.csv\")\n", " top10 = pd.read_csv('prelim3.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", "# plt.plot(pdp[1][0], pdp[0][0],'.')\n", "# plt.ylabel('Partial dependence'), plt.xlabel(top10[feature])\n", "# plt.show()\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"Lab_Listeria_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "#Converting microbiome pathogens' relative abundance to binary targets\n", "microbiome.loc[microbiome['Pathogen_Salmonella'] == 0, 'new_Pathogen_Salmonella'] = 0 \n", "microbiome.loc[microbiome['Pathogen_Salmonella'] > 0 , 'new_Pathogen_Salmonella'] = 1 \n", "\n", "microbiome.loc[microbiome['Pathogen_Campy'] == 0, 'new_Pathogen_Campy'] = 0 \n", "microbiome.loc[microbiome['Pathogen_Campy'] > 0 , 'new_Pathogen_Campy'] = 1 \n", "\n", "microbiome.loc[microbiome['Pathogen_Listeria'] == 0, 'new_Pathogen_Listeria'] = 0 \n", "microbiome.loc[microbiome['Pathogen_Listeria'] > 0 , 'new_Pathogen_Listeria'] = 1 \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Farm Management Practises as Targets:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (4) BroodBedding" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Feces_Start (200, 878)\n", "Feces_Mid (313, 878)\n", "Feces_End (185, 878) \n", "\n", "Soil_Start (199, 878)\n", "Soil_Mid (313, 878)\n", "Soil_End (183, 878) \n", "\n", "Ceca (185, 878)\n", "WCR-P (208, 878)\n", "WCR-F (195, 878) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "WS 185\n", "PB 10\n", "SDSP 5\n", "Name: BroodBedding, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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PearsonRResult(statistic=-0.17692854039279216, pvalue=0.07824486005851003)\n", "Slope and P-value = PearsonRResult(statistic=-0.2229619941853378, pvalue=0.025764988336005483)\n", "WS 278\n", "PB 30\n", "SDSP 5\n", "Name: BroodBedding, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.6258519380996901, pvalue=3.298970548881988e-06)\n", "Slope and P-value = PearsonRResult(statistic=-0.11980468633149736, pvalue=0.23512265469705973)\n", "Slope and P-value = PearsonRResult(statistic=0.7059843296966082, pvalue=2.3393022936434845e-16)\n", "Slope and P-value = PearsonRResult(statistic=0.40962978378286075, pvalue=2.3156697104561935e-05)\n", "Slope and P-value = PearsonRResult(statistic=0.8662021726926856, pvalue=1.633979836242057e-18)\n", "Slope and P-value = PearsonRResult(statistic=0.7746261495349214, pvalue=0.00042602176133272023)\n", "Slope and P-value = PearsonRResult(statistic=0.7769920706198841, pvalue=8.392709940357122e-21)\n", "Slope and P-value = PearsonRResult(statistic=0.6122494861206109, pvalue=2.6876872857440482e-05)\n", "Slope and P-value = PearsonRResult(statistic=0.3385281445127059, pvalue=0.0005710047160334018)\n", "Slope and P-value = PearsonRResult(statistic=0.2759230347278408, pvalue=0.11420921861586256)\n", "WS 170\n", "PB 10\n", "SDSP 5\n", "Name: BroodBedding, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.7289397336635803, pvalue=8.132356444188561e-18)\n", "Slope and P-value = PearsonRResult(statistic=0.43078144737967045, pvalue=0.3937984574755498)\n", "Slope and P-value = PearsonRResult(statistic=-0.5653235867230504, pvalue=3.467842909275833e-05)\n", "Slope and P-value = PearsonRResult(statistic=0.9292194096636937, pvalue=5.482109013434297e-42)\n", "Slope and P-value = PearsonRResult(statistic=0.6891146314580808, pvalue=0.5159992769183442)\n", "Slope and P-value = PearsonRResult(statistic=0.8944835491491234, pvalue=2.6959790163136613e-11)\n", "Slope and P-value = PearsonRResult(statistic=0.9745262411642712, pvalue=0.14400179292559812)\n", "Slope and P-value = PearsonRResult(statistic=0.8928378108851112, pvalue=1.388170976515436e-32)\n", "Slope and P-value = PearsonRResult(statistic=0.9086339254256053, pvalue=4.605695136448217e-14)\n", "Slope and P-value = PearsonRResult(statistic=0.6223397971604352, pvalue=4.780549648084127e-12)\n", "WS 184\n", "PB 10\n", "SDSP 5\n", "Name: BroodBedding, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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PearsonRResult(statistic=0.8403051283660689, pvalue=1.8222956777652624e-26)\n", "Slope and P-value = PearsonRResult(statistic=0.9189770883735535, pvalue=7.456972222414425e-33)\n", "WS 278\n", "PB 30\n", "SDSP 5\n", "Name: BroodBedding, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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PearsonRResult(statistic=0.8366965792931438, pvalue=2.266792521058733e-27)\n", "Slope and P-value = PearsonRResult(statistic=-0.1824926493533383, pvalue=0.06917493845329764)\n", "WS 168\n", "PB 10\n", "SDSP 5\n", "Name: BroodBedding, dtype: int64\n" ] }, { "data": { "image/png": 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j+RkiYnBElEVEWZt2HWsozszM7AvN1fM/CBgTEcsiYjHwYD3LuQ84Dfgq8FRO+hFAT2CKpOnp/Q5VlPFGRExPy1OB0rQ8BDhHUhuyE4k78rbbDxgfEe9FxOfACOAQ4HVgB0k3SDoa+LiatufXWZUK4EhJ10g6OCLctTczs0bTXMFfjVTOncCvgXERsSqv/OGph989InaJiMurKOPTnOWVfHGvg3uBY4DjgKkR8UHedgX3ISI+BPYBxgP9yU4iqqs3t87PWfMzKEllvkJ2MlMB/FbSZVWUaWZmVmfNFfwnAsdLKpHUHqjX3SYi4k3gl8Bf81Y9BvSRtCWApM0lbZfWrZDUthZlLwf+TTYsX2jo/jngUEmd0+jA6cCT6dcG60XEvcClwL512KU5lfkl7Qtsn5a7Aksj4nbgujqWaWZmVq1mucNfREyR9AAwA5gLlAP1GsqOiL8XSJst6RJgbPolwAqyXvhcYDAwM03o+2UNxY8gm5MwtkAdCyT9HHiCbBTgnxExRtI+wNBUL8DP67A79wLfTZcqpgCvpPS9gGslrUr78qPqCtlr646U++5dZmZWS4qI5qlIah8RSyS1AyYA/SJiWk3bNac0875jRFza0m2pi7KysigvL2/pZpiZWSsiaWpEFJxg35z39h8saXey69rDW2HgHw3sSDab38zMbJ3VbME/Is7IfS/pRqBXXrZuZD/py3V9RFT187lGExEnN3UdZmZmrUGLPdUvIvq3VN1mZmbFbG26va+ZmZk1Agd/MzOzIuPgb2ZmVmQc/M3MzIqMg7+ZmVmRcfA3MzMrMi32Uz9rPBXzF1E68OGWboaZtQJzfKtvqwX3/M3MzIqMg7+ZmVmRadHgL2l9Se9L+m1e+nhJZWn5n5I2baT6SiXNqiHP2ZL+0gh1nSBpYFq+PD00CEnDJPVpaPlmZmb11dI9/6OAl4FTJalQhoj4ZkR81KytagQR8UBEDGrpdpiZmeVrUPCXdKmklySNkzSysndbB6cD1wNvAgdUUcccSZ3T8nclzZQ0Q9JtkjpIekNS27R+k5S/raSdJD2a8k6TtGNeuSWShkqqkPS8pMNzVn9F0iOSXpb0q5xt7pc0VdILkvrlpB+d6pgh6bGUVuMIQt6+lUkan5YPlTQ9vZ6X1KHWR9TMzKwG9Z7tn4blewM9UjnTgKl12H4j4Ajgh8CmZCcCz1STfw/gl0CviHhf0uYRsTgFzGOB+4HTgHsjYoWkEcCgiBgtqYTsRGfLnCL7A0TEXpJ2BcZK2jmt2x/YE1gKTJH0cESUA9+LiIWp7VMk3ZvK/QdwSES8IWnz2h6DalwE9I+ISZLaA8sLHI9+QD+ANpts0QhVmplZsWhIz/8gYExELIuIxcCDddz+OOCJiFgK3AucLKlNNfm/DtwTEe8DRMTClD4EOCctnwMMTT3lrSNidMq7PNWT3/7b0vqXgLlAZfAfFxEfRMQy4L6UF+BCSTOAZ4GvkD2C+ABgQkS8kdeuhpgE/EHShcCmEfF5foaIGBwRZRFR1qZdx0ao0szMikVDgn/Ba/R1cDpwpKQ5ZCMGnYDDq8kvIPITI2ISUCrpUKBNRMyqZduqy5NfT0g6DDgSODAi9gGeB0qqalctfc4Xn0HJ6sqyuQLnAhsBz6aRCTMzs0bRkOA/ETg+XTtvTzb0XiuSNiHrTW8bEaURUUo2DH96NZs9RjYxsFMqI3d4/VZgJDAUICI+BuZJOinl3VBSu7zyJgBnpvU7A9uSTT4E+IakzdPw/klkPfGOwIcRsTQF48o5Cs8Ah0ravkC7ajIH6JmWe1cmStoxIioi4hqgHHDwNzOzRlPv4B8RU4AHgBlkQ+PlwKJabn4K8HhEfJqTNgY4QdKGVdT3AvAb4Mk09P6HnNUjgM3ITgAqfYdsmH4m8DTwX3lF/hVoI6kCuAs4O6c9E8kuCUwnm0NQDjwCrJ/K+zXZ0D8R8R7Ztff7UrvuquUxALgCuF7SU8DKnPQBkmal8pYB/6pDmWZmZtVSRH1HrEFS+4hYknrVE4B+ETGt0VpX+3b0AU6MiO80d92tQVlZWZSXl7d0M8zMrBWRNDUiygqta+i9/QdL2p3sevXwFgr8NwDHAN9s7rrNzMzWRg0K/hFxRu57STcCvfKydQNezUu7PiKGNqTunDZc0BjlmJmZFYtGfapfRPRvzPLMzMys8bX07X3NzMysmTn4m5mZFRkHfzMzsyLj4G9mZlZkHPzNzMyKjIO/mZlZkXHwNzMzKzKN+jt/axkV8xdROvDhlm6GmTWTOYNq/Rw1s4Lc8zczMysyDv5mZmZFZp0N/pKGSXpD0nRJMyQdUYttltSy7PMkfbeGPEPSQ4/MzMxalXX9mv/FEXGPpMOBwWQPGWoQSetHxE015YuIcxtal5mZWVNo1T1/SZdKeknSOEkjJV1Uz6KeAbZOZZ4t6S85dTwk6bCc97+XNE3SY5K2SGnjJV0t6Ungx5Iul3SRpN0kTc7ZtlTSzJxtyiT9SNLvcvKcnR5DjKT7JU2V9IKkfiltO0mvSuosaT1JT0k6qsCx6SepXFL5yqWL6nlYzMysGLXa4C+pDOgN9ABOAcoaUNzRwP21yLcxMC0i9gWeBH6Vs27TiDg0In5fmRARLwIbSNohJfUFRuWVeQ9Z+8nJc1da/l5E9CTbtwsldYqIucA1wE3AT4DZETE2v6ERMTgiyiKirE27jrXYNTMzs0yrDf7AQcCYiFgWEYuBB+tRxrWSXgduB66uRf5VfBGYb09tqHTXl7MDWbA/NS33zc8XEe8Br0s6QFInYBdgUlp9oaQZwLPAV0iXJSJiCNABOA+o72iHmZlZQa05+KsRyrgY2Am4BBie0j5nzf0uqWb7yFn+pIo8dwGnStoZiIh4tao8ZCMZoyMi0qWGI4EDI2If4PnKtkhqB2yTtm1fTfvMzMzqrDUH/4nA8ZJKJLUH6nVXi4hYBVwPrCfpv4E5QPd0Pf0rwP452dcD+qTlM1Ibair/NWAlcClVjw7cB5wEnJ6TpyPwYUQslbQrcEBO/muAEcBlwD9qaoOZmVldtNrZ/hExRdIDwAxgLlAO1GtmW+ppXwX8lKy3/QZQAcwCpuVk/QTYQ9LUVFffWlZxF3AtsH0V9X8oaTawe0RUThB8BDgvTRB8mWzoH0mHAvsBvSJipaTeks6JiKG132MzM7OqKSJqztVCJLWPiCVpGHwC0C8iptW0XbEpKyuL8vLylm6GmZm1IpKmRkTByfKttuefDE43yikBhjvwm5mZNVyrDv4RcUbue0k3Ar3ysnUD8ifZXe9hcjMzs8JadfDPFxH9W7oNZmZma7vWPNvfzMzMmoCDv5mZWZFx8DczMysyDv5mZmZFxsHfzMysyDj4m5mZFRkHfzMzsyKzVv3O3wqrmL+I0oEPt3QzzKwB5gyq17PLzOrFPX8zM7Mi4+BvZmZWZNaa4C/pRknTJc2WtCwtT5fUJy/f0zWUM15SwaccmZmZFYO15pp/5X39JZUCD0VE99z1ktpExMqI+FoLNM/MzGyt0ew9f0mXSnpJ0jhJIyVd1ICyDpP0hKQ7gIqUtiRn/U8lVUiaIWlQzqbfkjRZ0iuSDk55SyQNTfmfl3R4Sm8j6bqUPlPSBSn9iJSvQtItkjZM6ftJejrVOVlSh2rKmCOpc1oukzQ+LR+aM7LxvKQOBfa9n6RySeUrly6q7yE0M7Mi1Kw9/zTc3hvokeqeBkxtYLH7A3tGxBt5dR0DnAR8NSKWSto8Z/X6EbG/pG8CvwKOBPoDRMReknYFxkraGTgH2B7oERGfS9pcUgkwDDgiIl6RdCvwI0l/Be4C+kbEFEmbAMuAfvll1LBPFwH9I2KSpPbA8vwMETEYGAywYZduUduDZWZm1tw9/4OAMRGxLCIWAw82QpmT8wN/ciQwNCKWAkTEwpx196V/pwKlOW27LeV9CZgL7JzKuSkiPs8pZxfgjYh4JW07HDgkpS+IiCkp78dpu0JlVGcS8AdJFwKbVm5nZmbWGJo7+KsJyvykmrqq6hF/mv5dyRejH1W1rVA5dclbXfrnfPEZlFQmRsQg4FxgI+DZNBJhZmbWKJo7+E8Ejk/X19sDTXlXi7HA9yS1A6jFUPsE4MyUd2dgW+DlVM55ktbPKecloFTSTmnb7wBPpvSukvZLeTuk7QqVATAH6JmWe1c2RNKOEVEREdcA5YCDv5mZNZpmDf5pOPwBYAbZ0Hs50CSz1SLikVRXuaTpZNfRq/NXoI2kCrLr9mdHxKfAEOBNYKakGcAZEbGcbC7A3Sn/KrJh/c+AvsANKe84sh79l8pIdV4BXC/pKbJRiEoDJM1KeZcB/2rIsTAzM8uliOadKyapfUQsST3yCUC/iJjWrI1Yx5SVlUV5eXlLN8PMzFoRSVMjouB9bVrid/6DJe1O1iMe7sBvZmbWvJo9+EfEGbnvJd0I9MrL1g14NS/t+ogY2pRtMzMzKwYtfoe/yjv3mZmZWfNYa+7tb2ZmZo3Dwd/MzKzIOPibmZkVGQd/MzOzIuPgb2ZmVmQc/M3MzIqMg7+ZmVmRafHf+VvDVcxfROnAh1u6GWZWD3MGNeXzzcwKc8/fzMysyDj4m5mZFZkmD/6SDpD0nKTpkl6UdHk9yymVdEbNOWtd3mGSHqrjNnMkdW6Eun/R0DLMzMzqqzl6/sPJHtvbHdgTGFXPckqBgsFf0to2d6HOwV9Sm6ZoiJmZFZ9aBX9Jl0p6SdI4SSMlXVSHOrYEFgBExMqImC1pPUmvStoilb+epP9I6ixpmKQ/S3pa0uuS+qRyBgEHpxGE/5V0tqS7JT0IjJW0uaT7Jc2U9KykvVPZl0u6TdLjqc4f5LStvaR70r6NkKS0zRGSnpdUIekWSRvmbHOxpMnptVPKf3wa3Xhe0qOStkrp7SUNTeXMlNRb0iBgo7QfI1K+b6fypkv6e2Wgl7RE0pWSngMOzPtM+kkql1S+cumiOnwcZmZW7GoM/pLKgN5AD+AUoKyOdfwReFnSaEk/lFQSEauA24EzU54jgRkR8X563wU4CDiOLOgDDASeiojuEfHHlHYgcFZEfB24Ang+IvYm61nfmtOGvYFjU/7LJHVN6T2AAcDuwA5AL0klwDCgb0TsRfaLiB/llPVxROwP/AX4U0qbCBwQET2AO4GfpvRLgUURsVdq1+MRMRBYlvbjTEm7AX2BXml0ZGXOcdkYmBURX42IibkHNSIGR0RZRJS1adex4IE3MzMrpDY9/4OAMRGxLCIWAw/WpYKIuJLshGEs2bD9I2nVLcB30/L3gKE5m90fEasiYjawVTXFj4uIhTntvC3V+TjQSVJlVKxs//vAE8D+KX1yRMxLJyPTyS4t7AK8ERGvpDzDgUNy6hyZ829lb3wb4N+SKoCLgT1S+pHAjTnH4sMC+3AE0BOYIml6er9DWrcSuLea/TczM6uz2gR/NbSSiHgtIv5GFtj2kdQpIt4C3pH0deCrwL9yNvm0lvV/UkO+yPs3Pz23npVkvfya9jcKLN8A/CWNFPwQKMlpU37d+QQMTyMB3SNil4i4PK1bHhEra9jezMysTmoT/CcCx0sqkdSebPi81iQdW3ktHehGFmQ/Su+HkA3/j6pFkFsMdKhm/QTScLmkw4D3I+LjtO7E1P5OwGHAlGrKeQkorbyeD3wHeDJnfd+cf59Jyx2B+Wn5rJy8Y4HzK99I2iwtrpDUNi0/BvSRtGXKs7mk7appn5mZWYPUOEs+IqZIegCYAcwFyoG6zDD7DvBHSUuBz4EzcwL9A2TD/UOr2jjHTOBzSTPIrsnnD6FfDgyVNBNYyppBeDLwMLAt8OuIeFvSzoUqiYjlks4B7k6/IpgC3JSTZcM0AW894PScuu+WNB94Ftg+pV8F3ChpFtlJzxXAfcBgYKakaem6/yVkkxbXA1YA/cmOda3stXVHyn2XMDMzqyVF1DQqnc1aj4glktqR9bD7RcS0BleeTSb8Y0Qc3NCyqqnjcmBJRFzXVHW0tLKysigvL2/pZpiZWSsiaWpEFJykX9vfxw+WtDvZtezhjRT4B5LNoj+zprxmZmbWeGrV8//SRtKNQK+85G7Aq3lp10dEbYb0rQHc8zczs3yN0fNfQ0T0b1iTzMzMrKX4wT5mZmZFxsHfzMysyDj4m5mZFRkHfzMzsyLj4G9mZlZkHPzNzMyKTL1+6metS8X8RZQOfLilm2FWVOb4ltq2FnPP38zMrMg4+JuZmRWZVhn8Ja0v6X1Jv60mz9mS/pKWh0nq00RtGZKea4CkJY1Q3qaS/qfhLTMzM6ufVhn8gaOAl4FTJamlGiGpTUScGxGzG7HYTYE6BX9lWutnZWZma5kmCSiSLpX0kqRxkkZKuqiORZwOXA+8CRyQU+45kl6R9CRffrDQIZKelvR65SiApPaSHpM0TVKFpBNzyvq2pMmSpkv6u6Q2KX2JpCslPQccKGl8evRw5Xa/T+U9JmmLlPYDSVMkzZB0b3r0MZK2kjQ6pc+Q9DVgELBjqvfalO/itP1MSVektFJJL0r6KzAN+Eodj6GZmVlBjR78U6DsDfQATgEKPlGomu03Ao4AHgJGkp0IIKkLcAVZ0P8GsHvepl2Ag4DjyAIswHLg5IjYFzgc+H3qRe8G9AV6RUR3YCVfPFp4Y2BWRHw1Iibm1bExMC2V9yTwq5R+X0TsFxH7AC8C30/pfwaeTOn7Ai8AA4HXIqJ7RFws6SiyJyLuD3QHeko6JG2/C3BrRPSIiLl5x6mfpHJJ5SuXLqrhqJqZmX2hKXr+BwFjImJZRCwGHqzj9scBT0TEUuBe4OTUK/8qMD4i3ouIz4C78ra7PyJWpSH6rVKagKslzQQeBbZO644AegJTJE1P73dI26xM9RayKqfe29O+Auwp6SlJFWQnEXuk9K8DfwOIiJURUShKH5Vez5P18HclOxkAmBsRzxZqSEQMjoiyiChr065jFc01MzP7sqb4nX9Dr9GfDvSSNCe970TWaweIarb7tEAbzgS2AHpGxIpUZklaPzwifl6gnOURsbKWba1szzDgpIiYIels4LBabl/Z1t9GxN/XSJRKgU/qUI6ZmVmtNEXPfyJwvKQSSe2BWt8JQ9ImZL3pbSOiNCJKgf5kJwTPAYdJ6iSpLfCtWhTZEXg3Bf7Dge1S+mNAH0lbpno3l7RdVYXkWA+o/FXBGWT7CtABWJDadWZO/seAH6U62qT9W5zyV/o38L10rJC0dWW7zMzMmkKj9/wjYoqkB4AZwFygHKjtRelTgMcjIrcXPwb4HdkM+cuBZ4AFZEPkbWoobwTwoKRyYDrwUmrjbEmXAGPTLPoVZCcZc6sqKPkE2EPS1LRPfVP6pWQnJ3OBCr4I7j8GBkv6PtnlhB9FxDOSJkmaBfwrXfffDXgm/bBhCfDtlN/MzKzRKaK6kfR6Fiq1j4gladb7BKBfRExr9IoMgLKysigvL2/pZpiZWSsiaWpEFJx031T39h+cboxTQnZt3YHfzMyslWiS4B8RZ+S+l3QjX/5dfjfg1by06yNiaFO0yczMzDLN8lS/iOjfHPWYmZlZzXzLWDMzsyLj4G9mZlZkHPzNzMyKjIO/mZlZkXHwNzMzKzIO/mZmZkXGwd/MzKzINMvv/K1pVcxfROnAh1u6GWZFZc6gWj+zzKzVcc/fzMysyDj4m5mZFZm1KvhLGiZpvqQN0/vOkuak5a6S7knL3SV9s4XaeIKkgTnt7VOLbVa33czMrKmtVcE/WQl8Lz8xIt6OiMpA2x0oGPwlNek8h4h4ICIG1Ta/pPXz2m5mZtakmj34S7pU0kuSxkkaKemiOhbxJ+B/84O4pFJJsyRtAFwJ9JU0XVJfSZdLGixpLHCrpO0kPSZpZvp321TGt1IZMyRNSGklkoZKqpD0vKTDU/pzkvbIqX+8pJ6Szpb0l5ymHSnpKUmvSDou5T1b0t2SHgTGVra9uvoKHMd+ksolla9cuqiOh9DMzIpZswZ/SWVAb6AHcApQVo9i3gQmAt8ptDIiPgMuA+6KiO4RcVda1RM4MT1u+C/ArRGxNzAC+HPKcxnw3xGxD3BCSuufyt0LOB0YLqkEuBM4Ne1XF6BrREwt0KRS4FDgWOCmtC3AgcBZEfH1vPxV1Ze/n4Mjoiwiytq061joUJiZmRXU3D3/g4AxEbEsIhYDD9aznKuBi6lb+x+IiGVp+UDgjrR8W2oXwCRgmKQfAG1y2nwbQES8BMwFdgZGAd9KeU4F7q6i3lERsSoiXgVeB3ZN6eMiYmGB/FXVZ2Zm1iiaO/irMQqJiP8A00k971r6pLoiU7nnAZcAXwGmS+pEFW2OiPnAB5L2BvqSjQRUWXaB91W1p1GOkZmZWVWaO/hPBI5P17Xbkw2F19dvgKrmCywGOlSz7dPAaWn5zNQuJO0YEc9FxGXA+2QnARNSHiTtDGwLvJy2vRP4KdAxIiqqqOtbktaTtCOwQ862VamuPjMzswZr1uAfEVOAB4AZwH1AOVCv2WoR8QIwrYrVTwC7V074K7D+QuAcSTPJ5g78OKVfmybazSILwjOAvwJtJFUAdwFnR8SnKf89ZCcRo6pp6svAk8C/gPMiYnkNu1ZdfWZmZg2miPxR6SauUGofEUsktSMLsP0ioqogbrVQVlYW5eXlLd0MMzNrRSRNjYiCE+tb4t7+gyXtDpQAwx34zczMmlezB//0U7vVJN0I9MrL1g14NS/t+ogY2pRtMzMzKwYt/lS/iOjf0m0wMzMrJmvj7X3NzMysARz8zczMioyDv5mZWZFx8DczMysyDv5mZmZFxsHfzMysyDj4m5mZFZkW/52/NVzF/EWUDny4pZth1mrMGdSQZ4aZrfvc8zczMysyDv5mZmZFZq0I/pKGSXojPaJ3mqQDG7n88yR9Ny2Pl1TwKUh1LHNIeoARkuZI6pyWlzS0bDMzs4ZYm675XxwR90g6Cvg7sHdjFRwRNzVWWTllntvYZZqZmTWGZuv5S7pU0kuSxkkaKemiehY1AdhJUntJj6WRgApJJ+bU9W1Jk9NIwd8ltUnpSyT9RtIMSc9K2iqlX57Xnm9LelrSLEn7pzz7p7Tn07+7pPQ2kq5LbZgp6YKUXu0IgqTDJD2U8/4vks5Oy4MkzU7lXVfF9v0klUsqX7l0UT0PpZmZFaNmCf4pCPYGegCnAA0ZVj8eqACWAydHxL7A4cDvldkN6Av0iojuwErgzLTtxsCzEbEP2UnED6qoY+OI+BrwP8AtKe0l4JCI6AFcBlyd0vsB2wM9ImJvYEQD9g1JmwMnA3uk8q4qlC8iBkdEWUSUtWnXsSFVmplZkWmuYf+DgDERsQxA0oP1KONaSZcA7wHfBwRcLekQYBWwNbAVcATQE5giCWAj4N1UxmdAZW97KvCNKuoaCRAREyRtImlToAMwXFI3IIC2Ke+RwE0R8XnaZmE99i3Xx2QnNkMkPZzTXjMzs0bRXMFfjVDGxRFxz+oCsyHyLYCeEbFC0hygJNU1PCJ+XqCMFRERaXklVe9/FHj/a+CJiDhZUikwvrIpBfLXxuesOfJSAhARn6dLDUcApwHnA1+vR/lmZmYFNdc1/4nA8ZJKJLUHGuMOHB2Bd1PgPxzYLqU/BvSRtCVkw+iStquqkCr0TdseBCyKiEWpvvlp/dk5eccC50lav7K+WtYxF9hd0oaSOpIFe9Lx6RgR/wQGAN3r2HYzM7NqNUvPPyKmSHoAmEEW9MqBhs5SGwE8KKkcmE52TZ6ImJ0uD4yVtB6wAuif6q2tDyU9DWwCfC+l/Y5s2P//AY/n5B0C7AzMlLQC+Afwl5oqiIi3JI0CZgKvAs+nVR2AMZIqRzH+tw7tNjMzq5G+GAVv4oqk9hGxRFI7ssl2/SJiWrNUvo4rKyuL8vLylm6GmZm1IpKmRkTBCfbN+Tv/wemmNyVk1+Qd+M3MzFpAswX/iDgj972kG4Feedm6kQ2B57o+IoY2ZdvMzMyKSYvd4S8i+rdU3WZmZsVsbbq9r5mZNYEVK1Ywb948li9f3tJNsXooKSlhm222oW3btjVnThz8zcyK3Lx58+jQoQOlpaWkm6PZWiIi+OCDD5g3bx7bb799rbdbK57qZ2ZmTWf58uV06tTJgX8tJIlOnTrVedTGwd/MzBz412L1+ewc/M3MzIqMr/mbmdkaSgc+3KjlzRlUuzu6jx49mlNOOYUXX3yRXXfdFYDx48dz3XXX8dBDXzzj7Oyzz+a4446jT58+HHbYYSxYsICSkhI22GAD/vGPf9C9e3cAFi1axAUXXMCkSZMA6NWrFzfccAMdO2ZPQn3llVcYMGAAr7zyCm3btmWvvfbihhtuYKuttqr3vi5cuJC+ffsyZ84cSktLGTVqFJttttmX8pWWltKhQwfatGnD+uuvT+WN2vr27cvLL78MwEcffcSmm27K9OnTqaio4Pe//z3Dhg2rd9tyOfivAyrmL2r0/6xmTam2wcCKy8iRIznooIO48847ufzyy2u93YgRIygrK2Po0KFcfPHFjBs3DoDvf//77Lnnntx6660A/OpXv+Lcc8/l7rvvZvny5Rx77LH84Q9/4PjjjwfgiSee4L333mtQ8B80aBBHHHEEAwcOZNCgQQwaNIhrrrmmYN4nnniCzp07r5F21113rV7+yU9+svpEZa+99mLevHm8+eabbLvttvVuXyUP+5uZWYtbsmQJkyZN4uabb+bOO++sVxkHHngg8+dnz1/7z3/+w9SpU7n00ktXr7/ssssoLy/ntdde44477uDAAw9cHfgBDj/8cPbcc88G7ceYMWM466yzADjrrLO4//7761VORDBq1ChOP/301WnHH398vY9NPgd/MzNrcffffz9HH300O++8M5tvvjnTptX9DvCPPPIIJ510EgCzZ8+me/futGnTZvX6Nm3a0L17d1544QVmzZpFz549ayxz8eLFdO/eveBr9uzZX8r/zjvv0KVLFwC6dOnCu+++W7BcSRx11FH07NmTwYMHf2n9U089xVZbbUW3bt1Wp5WVlfHUU0/V2Oba8LC/mZm1uJEjRzJgwAAATjvtNEaOHMm+++5b5Uz23PQzzzyTTz75hJUrV64+aYiIgttWlV6VDh06MH369NrvSC1NmjSJrl278u677/KNb3yDXXfdlUMOOWT1+pEjR67R6wfYcsstefvttxul/hYJ/pKGAYcCHwMbAc8CP4+I+fUo62ygLCLOL7BuTlr3fkPaa2ZmTeeDDz7g8ccfZ9asWUhi5cqVSOJ3v/sdnTp14sMPP1wj/8KFC9e4Vj5ixAj22WcfBg4cSP/+/bnvvvvYY489eP7551m1ahXrrZcNcq9atYoZM2aw22678e677/Lkk0/W2LbFixdz8MEHF1x3xx13sPvuu6+RttVWW7FgwQK6dOnCggUL2HLLLQtu27VrVyAL6CeffDKTJ09eHfw///xz7rvvPqZOnbrGNsuXL2ejjTaqsc210ZLD/hdHxD7ALmTPsn9C0gYt1RhlfBnEzKyZ3XPPPXz3u99l7ty5zJkzh7feeovtt9+eiRMn0q1bN95++21efPFFAObOncuMGTNWz+iv1LZtW6666iqeffZZXnzxRXbaaSd69OjBVVddtTrPVVddxb777stOO+3EGWecwdNPP83DD38xWfqRRx6hoqJijXIre/6FXvmBH+CEE05g+PDhAAwfPpwTTzzxS3k++eQTFi9evHp57Nixa8w1ePTRR9l1113ZZptt1tjulVdeafCchEr17vlLuhQ4E3gLeB+YGhHX1bWciAjgj5JOBo4Bxkg6CrgC2BB4DTgnIpZI2g+4HtgY+BQ4IhXTVdIjwI7A6Ij4aYH2/j/ge+ntkIj4k6RS4F/AE8CBwEmSzk/tCOCqiLhL0mGpPe8A3YH7gArgx2QjFydFxGuStgNuAbYA3kvtfjONdHwMlAH/Bfw0Iu6R1B4YA2wGtAUuiYgxkjYGRgHbAG2AX0fEF1NAs/3pB/QDaLPJFnU44mZm1WvuX2OMHDmSgQMHrpHWu3dv7rjjDg4++GBuv/12zjnnHJYvX07btm0ZMmTI6lnwuTbaaCN+8pOfcN1113HzzTdz8803c8EFF7DTTjsRERx44IHcfPPNq/M+9NBDDBgwgAEDBtC2bVv23ntvrr/++gbty8CBAzn11FO5+eab2Xbbbbn77rsBePvttzn33HP55z//yTvvvMPJJ58MZL38M844g6OPPnp1GXfeeeeXhvwh+3XAscc2zmejLPbWcSOpDBhCFjDXB6YBf69t8E/B8KGIuCcn7U/AAuBmsuB6TER8IulnZCcBg4CXgL4RMUXSJsBS4NvAZUAPshOCl4GDIuKtymF/YDtgGHAAIOC5tN2HwOvA1yLiWUm9gfOAo4HOwBTgq2SjE/cDuwEL0zZDIuJXkn4MbB8RAyQ9CNwTEcMlfQ84ISJOSvu7MdAX2BV4ICJ2krQ+0C4iPpbUmezyRzfgFODoiPhBOjYdI2JRVcdzwy7dostZf6rNoTdrFfxTv9blxRdfZLfddmvpZlg1Pv30Uw499FAmTpzI+ut/ud9e6DOUNDUiygqVV99h7oOAMRGxLCIWAw/Ws5xclTMwDgB2ByZJmg6cRRa8dwEWRMQUgIj4OCI+T9s8FhGLImI5MDvlz2/v6Ij4JCKWkJ1cVF7EmRsRz+bkGxkRKyPiHeBJYL+0bkpELIiIT8lGI8am9AqgNC0fCNyRlm9L5VW6PyJWRcRsoPJHpAKuljQTeBTYOq2rAI6UdI2kg6sL/GZmtu578803GTRoUMHAXx/1LaUpbgLdA3gslT0uItYY85C0N9lQfCGf5iyv5Mv7VV17P6llvtw6VuW8X1Wgvkq57c3dvrKeM8kuEfSMiBVppKIkIl6R1BP4JvBbSWMj4spq2mZmZuuwbt26rfGzv4aqb89/InC8pJJ03breY3hpot2FQBfgEbKh716Sdkrr20namWzIv2u67o+kDmnYvDYmkF3Pb5eup58MFPqx5ASgr6Q2krYADgEm12F3ngZOS8tnkh2n6nQE3k2B/3DSiIWkrsDSiLgduA7Ytw5tMDOrs/pcArbWoT6fXb16/uma+wPADGAuUA7UdWj62jRpsB1ZwD88Ij4D3ks/3xspacOU95LUG+4L3CBpI2AZcGQt2zstXXevDORDIuL5NOEv12iyofsZZL32n0bE/0natZb7dCFwi6SLSRP+asg/AnhQUjkwnewEB2AvsuOzClgB/Ki6QvbauiPlvoZqZvVUUlLCBx984Mf6roUigg8++ICSkpI6bVevCX8AktqnGfjtyHrM/SKi7rdksgYrKyuLyodCmJnV1YoVK5g3b16dnwlvrUNJSQnbbLMNbdu2XSO9ugl/DZk5MFjS7kAJMNyB38xs7dS2bVu23377lm6GNaN6B/+IOCP3vaQbgV552boBr+alXR8RQ+tbr5mZmTVMo93eNyL6N1ZZZmZm1nR8O1szM7MiU+8Jf9Z6SFpMdmdDaz6dyW5rbc3Hx7z5+Zg3v8Y85ttFRMH7v/uRvuuGl6ua0WlNQ1K5j3nz8jFvfj7mza+5jrmH/c3MzIqMg7+ZmVmRcfBfNwxu6QYUIR/z5udj3vx8zJtfsxxzT/gzMzMrMu75m5mZFRkHfzMzsyLj4L+Wk3S0pJcl/UfSwJZuz7pI0lckPSHpRUkvSPpxSr9c0nxJ09Prmy3d1nWJpDmSKtKxLU9pm0saJ+nV9O9mLd3OdYWkXXK+y9MlfSxpgL/njUvSLZLelTQrJ63K77Wkn6e/7y9L+u9Ga4ev+a+9JLUBXgG+AcwDpgCnR8TsFm3YOkZSF6BLejR0B2AqcBJwKrAkIq5ryfatqyTNAcoi4v2ctN8BCyNiUDrZ3SwiftZSbVxXpb8t84Gvkj2a3N/zRiLpEGAJcGtE7JnSCn6v08PzRgL7A12BR4GdI2JlQ9vhnv/abX/gPxHxekR8BtwJnNjCbVrnRMSCyqdWRsRi4EVg65ZtVdE6ERieloeTnYRZ4zsCeC0i5rZ0Q9Y1ETEBWJiXXNX3+kTgzoj4NCLeAP5D9ne/wRz8125bA2/lvJ+Hg1KTklQK9ACeS0nnS5qZhvI8BN24Ahgraaqkfiltq4hYANlJGbBli7Vu3XYaWY+zkr/nTauq73WT/Y138F+7qUCar+M0EUntgXuBARHxMfA3YEegO7AA+H3LtW6d1Csi9gWOAfqn4VJrYpI2AE4A7k5J/p63nCb7G+/gv3abB3wl5/02wNst1JZ1mqS2ZIF/RETcBxAR70TEyohYBfyDRhqOs0xEvJ3+fRcYTXZ830lzMCrnYrzbci1cZx0DTIuId8Df82ZS1fe6yf7GO/iv3aYA3SRtn87WTwMeaOE2rXMkCbgZeDEi/pCT3iUn28nArPxtrX4kbZwmVyJpY+AosuP7AHBWynYWMKZlWrhOO52cIX9/z5tFVd/rB4DTJG0oaXugGzC5MSr0bP+1XPrZzZ+ANsAtEfGblm3RukfSQcBTQAWwKiX/guyPZHeyYbg5wA8rr9tZw0jagay3D9nTR++IiN9I6gSMArYF3gS+FRH5k6esniS1I7vGvENELEppt+HveaORNBI4jOzRve8AvwLup4rvtaRfAt8DPie75PivRmmHg7+ZmVlx8bC/mZlZkXHwNzMzKzIO/mZmZkXGwd/MzKzIOPibmZkVGQd/s1qStDI91WyWpAclbVpD/sslXVRDnpPSwzsq318p6chGaOswSX0aWk4d6xyQfirWakjaNX1mz0vaMW/dHElP5aVNr3zamqQySX9uhDaU5j7BLW/dkNzPv6lJ2krSHZJeT7dNfkbSyc1Vv7UeDv5mtbcsIrqnJ3EtBPo3QpknAav/+EfEZRHxaCOU26zSU+AGAK0q+JMd3zER0SMiXiuwvoOkrwBI2i13RUSUR8SFta0oHYM6iYhzm+spnOlmVfcDEyJih4joSXZjsG2auN71m7J8qx8Hf7P6eYb0gA1JO0p6JPWknpK0a35mST+QNEXSDEn3Smon6Wtk91C/NvU4d6zssUs6RtKonO0Pk/RgWj4q9dimSbo7PXOgSqmHe3XaplzSvpL+Lek1SefllD9B0mhJsyXdJGm9tO50SRVpxOOanHKXpJGK54Bfkj1y9AlJT6T1f0v1vSDpirz2XJHaX1F5vCS1lzQ0pc2U1Lu2+yupu6Rn03ajJW2WboA1ADi3sk0FjAL6puX8O9sdJumhGtqWewwOlPT/0nGaJWlATj3rSxqetr2ncoRE0nhJZbU4ztek79ejkvZP270u6YSUp42ka9N3bKakHxbY168Dn0XETZUJETE3Im6orox0HMandr8kaUQ6kUBST0lPprb9W1/conZ8+s49CfxY0vGSnlM2AvOopK2q+DysuUSEX375VYsX2TPNIbub4t3A0en9Y0C3tPxV4PG0fDlwUVrulFPOVcAFaXkY0Cdn3TCgD9ld7d4ENk7pfwO+TXZXsAk56T8DLivQ1tXlkt2V7Udp+Y/ATKADsAXwbko/DFgO7JD2b1xqR9fUji1Smx4HTkrbBHBqTp1zgM457zfPOV7jgb1z8lXu//8AQ9LyNcCfcrbfrA77OxM4NC1fWVlO7mdQYJs5wM7A0+n982SjMLNyjslDVbUt/xgAPcnuArkx0B54gewJkKUpX6+U7xa++F6MB8pqcZyPScujgbFAW2AfYHpK7wdckpY3BMqB7fP290Lgj9V8vwuWkY7DIrIRgvXITnwPSm14GtgibdOX7C6jlfv117zPsvKmcucCv2/p/8/F/vJwjFntbSRpOtkf86nAuNQL/Rpwd+oMQfaHM9+ekq4CNiULDP+urqKI+FzSI8Dxku4BjgV+ChxKFqAmpfo2IPtjXJPKZz5UAO0jYjGwWNJyfTF3YXJEvA6rb0F6ELACGB8R76X0EcAhZMPHK8kedlSVU5U9ind9oEtq98y07r7071TglLR8JNkwdOUx+FDScTXtr6SOwKYR8WRKGs4XT6SryULgQ0mnAS8CS6vI96W2pcXcY3AQMDoiPkntug84mOzYvxURk1K+28kC8XU55e9H1cf5M+CRlK8C+DQiVkiqIPsuQvbsg731xTyPjmT3gX+jqh2XdGNq82cRsV81ZXxG9t2Yl7abnur9CNiT7P8BZCd5ubf9vStneRvgrjQysEF17bLm4eBvVnvLIqJ7CjYPkV3zHwZ8FBHda9h2GFlPboaks8l6UzW5K9WxEJgSEYvTcOu4iDi9jm3/NP27Kme58n3l34H8e30HhR8pWml5RKwstELZQ0guAvZLQXwYUFKgPStz6leBNtR3f+viLuBG4Oxq8hRqG6x5DKo7VoWObX75VVkRqctMzucXEav0xfV0kY2mVHdS+QLQe3UDIvpL6kzWw6+yDEmHseZ3pvIzE/BCRBxYRX2f5CzfAPwhIh5I5V1eTTutGfiav1kdRfbAkwvJgtsy4A1J34JsUpWkfQps1gFYoOzRwGfmpC9O6woZD+wL/IAvelHPAr0k7ZTqaydp54bt0Wr7K3tC5HpkQ7gTgeeAQyV1Vjah7XTgySq2z92XTcj++C9K13ePqUX9Y4HzK99I2oxa7G/6PD6UdHBK+k41bSxkNPA7qh+NKdS2fBOAk1IbNyZ7Al7lrwm2lVQZJE8nO7a56nKcC/k38KP0/ULSzqkNuR4HSiT9KCctd4JmbcrI9TKwReV+SWoraY8q8nYE5qfls6rIY83Iwd+sHiLieWAG2VDwmcD3Jc0g612dWGCTS8n+wI8DXspJvxO4WAV+ipZ6lA+RBc6HUtp7ZD3UkZJmkgXHL00wrKdngEFkj2x9g2wIewHwc+AJsv2dFhFVPUZ3MPAvSU9ExAyya+gvkF3jnlTFNrmuAjZLE95mAIfXYX/PIps4OZPsCXRX1qI+ACJicURcExGf1aVtBcqZRjbCM5nssx6SvieQXVI4K7Vvc7I5HLnb1uU4FzIEmA1MU/azwr+TN7KbRg9OIjvJeEPSZLJLJD+rbRl55X1GNi/kmnRMppNdAivkcrJLY08B79dhv6yJ+Kl+ZlY5tHtRRBzXwk0xs2bgnr+ZmVmRcc/fzMysyLjnb2ZmVmQc/M3MzIqMg7+ZmVmRcfA3MzMrMg7+ZmZmReb/A679Cf++QXmLAAAAAElFTkSuQmCC\n", "text/plain": [ "
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PearsonRResult(statistic=0.5285250644235735, pvalue=1.5863856290580273e-08)\n", "Slope and P-value = PearsonRResult(statistic=0.7566915021957021, pvalue=1.5213329007082043e-11)\n", "WS 170\n", "PB 10\n", "SDSP 5\n", "Name: BroodBedding, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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PearsonRResult(statistic=0.9305731456983598, pvalue=1.4766332787182251e-44)\n", "Slope and P-value = PearsonRResult(statistic=0.9461890610055816, pvalue=8.155656597687935e-50)\n", "WS 193\n", "PB 10\n", "SDSP 5\n", "Name: BroodBedding, dtype: int64\n" ] }, { "data": { "image/png": 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0EXBdrvh0SfuQjeo8A/w7Ij6RtC3weFqvuxD4fkRMlTQMeCode01EPC2pB/AscJykfwAvAlel0aCryaZ05gCTc/3+APiHpN+l8/keMANYKmk62TTYpWTTZlPTwuF3gP61nOPrkm5Nbb1INlXWJHbYvBOVbejbK83MrOEUUdsSkdIjqUNELEwjEBOAQRExdTXHsBkwDtgmIpavzr5bg4qKiqisrGzpMMzMbA0iaUpErPLBntY4zQQwNC1knQrc0QKJzLFki2jPciJjZmbWvFrdNBNA+nj0CpKuBPYoqNaTbGok79KIuI5Giojrgesb246ZmZnVrlUmM4Ui4uSWjsHMzMyaR2udZjIzM7M2wsmMmZmZlTQnM2ZmZlbSnMyYmZlZSXMyY2ZmZiXNyYyZmZmVtDbx0WwrPTPfmE+Pwfe0dBhmZk1qjm/T0iw8MmNmZmYlzcmMmZmZlTQnMy1I0jBJr0iaJmm6pP1y++ZI6tqItht8vKRxkla5kZeZmdmayMlMyzszIsqB04G/t2woZmZmpcfJTCNJOlvSc5LGShoh6YwGNvU4sHlB2amSpkqaKWmb1N9Gku6SNEPSE5J2TOVdJD0g6WlJ/wCUi/F/Jc1KP6ensh4p7uGprdslrVfk/K6SVClptqTzc+VzJJ1fJL71Jf1T0uQUyyGpfHtJT6VRqBmSejbwOpmZma3EyUwjpKmYw4HewGFAY6ZmvgXcVVD2bkTsDFwFVCVJ5wNPR8SOwG/4/O7c5wKTIqI3MBronmLcBTgB2B34GvBjSb3TMV8Fhqa2PgJ+ViSusyKiAtgR2LsqeaohvrOAhyNiV2Af4CJJ6wM/JbsreTnZdZpb2JGkQSlxqly2aH71V8rMzCzHyUzj9AVGRcTiiFgA3N2ANi6S9DJwA/CHgn13pt9TgB65Pv8FEBEPA10kdQL2Sm0QEfcAH+Tqj4yIjyNiYWpzz7Tv9Yh4NG3fkOoWOkLSVOBpYHtgu1riOwAYLGkaMA4oI0usHgd+I+lXwJciYnFhRxExNCIqIqKi3XqdioRiZma2KiczjaPaq9TqTGBr4LfA8IJ9n6Tfy/j8O4GK9RkFv/NqirGw/kqPJW1JNuKyXxq9uYcsOaktvsMjojz9dI+IZyPiJuBgYDFwv6R9a4jLzMyszpzMNM4k4CBJZZI6AA36NqSIWA5cCqwl6Zu1VJ8AHAMgqR/ZVM9HBeXfBjbM1e8vab003XMoMDHt6y6pT9o+Kp1P3gbAx8B8SZsC367D6dxPttZHKZbe6feXgZcj4jKyabAdq2/CzMys7pzMNEJETCZ7Y55ONuVSCTRosUdEBHAB8Mtaqp4HVEiaAQwBjkvl5wN7pSmhA4DXUrtTgWHAU8CTwDUR8XQ65lnguNTWRmRrX/IxTSebXpoN/BN4lNr9H9AemCFpVnoMMBCYlaaftuHztT5mZmaNouw91BpKUoeIWJg+CTQBGJQSiDWapB7AmIjo1dKxFFNRURGVlZUtHYaZma1BJE1JH0pZie/N1HhDJW1HtpZkeCkkMmZmZq2Jk5lGioij848lXQnsUVCtJ/BiQdmlEXFdc8ZWk4iYA6yRozJmZmb14WSmiUXEyS0dg5mZWVviBcBmZmZW0pzMmJmZWUlzMmNmZmYlzcmMmZmZlTQnM2ZmZlbSnMyYmZlZSXMyY2ZmZiXN3zNja6SZb8ynx+B7WjoMM6vFnCENur+uWZPyyIyZmZmVNCczZmZmVtKczNSRpDMkPSdplqTpko5toTiOl3RFE7TTT9LXG3BchaTLGtu/mZlZU/GamTqQ9FPgG8BuEfGRpE5A/3oc3y4iljVXfA3UD1gIPFbXAyStHRGVQGVzBWVmZlZfbWZkRtLZaWRlrKQRks6ox+G/AX4WER8BRMT8iBie2t1P0tOSZkr6p6R1UvkcSedImgQMljQ1F0tPSVNy9c6XNDW1sU0qXz+1Nzm1f0guni9Kuk/S85LOzbV7l6QpkmZLGpQr/1Zqf7qkhyT1AH4K/I+kaZL2lLSxpDtSf5Ml7ZGOPU/SUEkPANenEZ0xuX1n5PqZJalH+nlO0jWp7EZJ+0t6VNKLknar5jkaJKlSUuWyRfPr8fSYmVlb1iZGZiRVAIcDvcnOeSowpY7HdgQ6RsRLRfaVAcOA/SLiBUnXAycBf01VlkRE31R3f0nlETENOCEdV+XdiNhZ0s+AM4ATgbOAhyPih5I6A09JejDV3w3oBSwCJku6J42Y/DAi3pe0biq/gyxhvRrYKyJekbRRqvN3YGFEXJziuwn4S0RMktQduB/YNvW3C9A3IhZL6leX6wZsDXwPGARMBo4G+gIHkyWH/QsPiIihwFCAdbr1jDr2Y2ZmbVxbGZnpC4yKiMURsQC4ux7HCqjujfWrwCsR8UJ6PBzYK7f/ltz2NcAJktoBA4GbcvvuTL+nAD3S9gFkIzrTgHFAGdA97RsbEe9FxOJ0bN9Ufpqk6cATwBeBnsDXgAkR8QpARLxfzbnsD1yR+hsNbJASOYDRqa/6eCUiZkbEcmA28FBEBDAzd45mZmaN1iZGZsgSkgZJa2Q+lvTliHi5nu1+nNu+AzgXeBiYEhHv5fZ9kn4v4/PnRMDhEfH8Sh1Ku7NqchVpxGR/oE9ELJI0jiwBqikZy1srHbtS0iKp8DzylrJyQlyW2/4kt70893g5bed1Z2Zmq0FbGZmZBBwkqUxSB6C+3/L0R+BKSRsASNogrUl5DughaetU7wfA+GINRMQSsqmbq4Dr6tDn/cCpStmEpN65fd+QtFGaTuoPPAp0Aj5Iicw2ZCMyAI8De0vaMrWzUSpfAHTMtfkAcErVA0nldYhxDrBzqr8zsGUdjjEzM2tSbSKZiYjJZFMn08mmZSqB+qwwvQp4hGwdyiyyhGVRSlBOAG6TNJNs1OHvNbRzI9koyQN16PP/gPbAjNTn/+X2TQL+BUwD7kjrZe4D1pY0I9V9AiAi3iFbt3JnmoKqmvq6Gzi0agEwcBpQIWmGpGfIFgjX5g5gozQ1dRLwQs3VzczMmp6yZQytn6QOEbFQ0nrABGBQREyt7bgmjuEMoFNEnL06+y1FFRUVUVnpT4CbmdnnJE2JiIrC8ra0dmGopO3I1nUMb4FEZiSwFbDv6uzXzMystWszyUxEHJ1/LOlKYI+Caj2BFwvKLo2Iuqxxqa3/QxvbhpmZma2qzSQzhSLi5JaOwczMzBqvTSwANjMzs9bLyYyZmZmVNCczZmZmVtKczJiZmVlJczJjZmZmJc3JjJmZmZU0JzNmZmZW0trs98zYmm3mG/PpMfielg7DrEXNGVLfe+KatU0emTEzM7OS5mTGzMzMStpqSWYknSHpOUmzJE2XdGwTtTtOUkXavldS5yJ1zkt3q66pnf7pJpRNEdMcSXfkHg+QNCxtHyxpcFP32RQk9ZA0q6XjMDMzq69mT2Yk/RT4BrBbRPQC9gLU1P1ExHci4sMGHt4faMrEokLS9oWFETE6IobU1qekVrOWqTWdi5mZrZlqTWYknZ1GVcZKGlHbKEcRvwF+FhEfAUTE/IgYnto+R9LkNGIzVJJS+ThJF0p6StILkvZM5etKulnSDEm3AOvm4pwjqWvaPkvS85IeBL6aq/Pj1N90SXdIWk/S14GDgYskTZO0VcGIT1dJc9L29immaSmGntWc88XpvAuv5fGSrqihzz9IGg/8XNJ+kp6WNFPSPyWtk9oYIumZ1P/FqexLkh5KZQ9J6p7Kv5cbDZuQi2GUpPvSNTo3F2I7SVdLmi3pAUnrVnfdUvnG6fHk9LNHKj8vPZ8PANenUZ+Jkqamn68Xu2iSBkmqlFS5bNH8ai6tmZnZympMZtIb+uFAb+AwoKI+jUvqCHSMiJeqqXJFROyaRmzWBQ7M7Vs7InYDTgeq3nBPAhZFxI7A74FdivS5C3BkLuZdc7vvTP3tBDwL/CgiHgNGA2dGRHkNsQL8FLg0IsrJrsXcaurdCuwsaetiO2vos3NE7A1cCQwDBkbEDmSfOjtJ0kbAocD26RpckI67Arg+ld0IXJbKzwG+mc734FwIuwHHAOXA96oSN6AncGVEbA98SPbcQ5HrlsovBf4SEbumutfk+tgFOCQijgbeBr4RETsDA3PxFV6XoRFREREV7dbrVKyKmZnZKmobmekLjIqIxRGxALi7nu0LiBr27yPpSUkzgX2B/NTMnen3FKBH2t4LuAEgImYAM4q0uScwMiIWpdGg0bl9vdIIwUyyN/NVpoJq8TjwG0m/Ar4UEYurqbcMuAj4dT3bvyX9/irwSkS8kB4PJzv3j4AlwDWSDgMWpf19gJvS9r/InjeAR4Fhkn4MtMv1MzYi3kvx35mr/0pETEvb+ete3XXbH7hC0jSy67xBSmABRueuT3vg6nT8bTTtlJ6ZmbVxtSUzjVrbkpKJjyV9eZWGpTLgb8CANPpwNVCWq/JJ+r2Mlb8Pp6bkqLY6w4BTUn/nF/SXt5TPr82KOhFxE9kIx2Lgfkn71hDDv8gSkO51iLfKx+l30eseEUvJRlXuIFtzc1817USq/1Pgt8AXgWmSuuT3F9bn82sOK1/3YRS/bmsBfdLoUnlEbJ6S3vy5APwP8BawE9mI1heqidvMzKzeaktmJgEHSSqT1AFoyDc4/RG4UtIGAJI2kDSIz98Q301tD6hDWxPIRgaQ1AvYsZo6h6b1NR2Bg3L7OgLzJLWvaidZkPZVmcPnU1gr4kpJ2csRcRnZSESx/gGIiM+Av5BNkxVT2Gfec0CP3DTVD4Dx6Tp1ioh7U7vlaf9jZFNrpPOalOLdKiKejIhzgHfJkhqAb0jaKK2J6U82glOT6q7bA8ApVQ8klVNcJ2BeRCxP59KumnpmZmb1VmMyExGTyd60p5NNR1QC9V2ZeRXwCDBZ2Ud/x5Ote/mQbDRmJnAXMLmObXWQNAP4JfBUkZinkk3XTCMbwZiY23028CQwlixhqHIzcGZacLsV2QLekyQ9BnTN1RsIzErTKtsA19cS77VU/y3LhX3mz2EJcAJwW5qaWQ78nSypGJPOfzzZiAfAacAJqfwHwM9T+UVpAfEssiRveiqfRDZyNA24IyIqazmP6q7baWSf3Joh6RmyNUXF/A04TtITwFdYedTGzMysURRR86yNpA4RsTB9gmUCMCglDFaCJB0PVETEKbXVbUkVFRVRWVlbjmVmZm2JpCkRscqHkeryHSBDlX25Wxkw3ImMmZmZrUlqTWbSR2tXIulKYI+C4p7AiwVll0bEdQ0Pz5paRAwjW9BrZmbWKjTo21kj4uSmDsTMzMysIXyjSTMzMytpTmbMzMyspDmZMTMzs5LmZMbMzMxKmpMZMzMzK2lOZszMzKykOZkxMzOzktag75kxa24z35hPj8H3tHQYZk1qzpCG3KvXzGrjkRkzMzMraU5mzMzMrKQ5mVmNJK0t6V1Jf6xD3YMlDW5gP50l/awO9fpJGtOQPgraKZf0nca2Y2Zm1hBOZlavA4DngSMkqaaKETE6IoY0sJ/OQK3JTBMqB+qVzEjyei0zM2sSTmbqQdLZkp6TNFbSCEln1LOJo4BLgdeAr+Xa/ZakqZKmS3oolR0v6Yq0PUzSZZIek/SypAG5Y8+UNFnSDEnnp+IhwFaSpkm6SJmLJM2SNFPSwFxMG0gaKekZSX+XtFZq9ypJlZJm59pF0q4pjumSnpLUCfgdMDD1N1DS+pL+meJ6WtIhuXO6TdLdwANFru+g1GflskXz63lpzcysrfJfx3UkqQI4HOhNdt2mAlPqcfy6wH7AT8hGTo4CHpe0MXA1sFdEvCJpo2qa6Ab0BbYBRgO3SzoA6AnsBggYLWkvYDDQKyLKU9+Hk42e7AR0BSZLmpDa3Q3YDngVuA84DLgdOCsi3pfUDnhI0o7Ac8AtwMCImCxpA2ARcA5QERGnpP7+ADwcET+U1Bl4StKDqb8+wI4R8X7hCUbEUGAowDrdekadLqyZmbV5Hpmpu77AqIhYHBELgLvrefyBwCMRsQi4Azg0JQpfAyZExCsAxd7kk7siYnlEPANsmsoOSD9PkyVX25AlN8ViHxERyyLiLWA8sGva91REvBwRy4ARqS5kU2FTU9vbkyU8XwXmRcTkFOtHEbG0SH8HAIMlTQPGAWVA97RvbA3naGZmVm8emam7Gte41MFRwB6S5qTHXYB9Urt1GYX4pEgsAv4YEf/IV5TUo+DYmmIv7DskbQmcAewaER9IGkaWkNQ1VgGHR8TzBXHtDnxch+PNzMzqzCMzdTcJOEhSmaQOQJ2//SpNx/QFukdEj4joAZxMmmoC9k4JBDVMMxVzP/DDFA+SNpe0CbAA6JirN4FsTUu7NK21F/BU2rebpC3TWpmB6Tw3IEs65kvaFPh2qvscsJmkXVN/HdNC3sL+7gdOrVrkLKl3Pc7JzMysXjwyU0dpjchoYDrZ+pJKoK6rVA8jW0OSH10ZBfyJ7FNHg4A7U0LxNvCNOsb0gKRtydbeACwEvh8RL0l6VNIs4N/AL8nWqkwnG1n5ZUT8V9I2ZMnUEGAHsqRnZEQsl/Q0MBt4GXg09fdpWjx8eVoDtBjYH3iEz6eV/gj8H/BXYEZKaOaQTbPV2Q6bd6LS35ZqZmZ1oAivs6wrSR0iYqGk9cje+AdFxNSWjqs1qqioiMrKypYOw8zM1iCSpkRERWG5R2bqZ6ik7cjWjwx3ImNmZtbynMzUQ0QcnX8s6Upgj4JqPYEXC8oujYjrmjM2MzOztsrJTCNExMktHYOZmVlb508zmZmZWUlzMmNmZmYlzcmMmZmZlTQnM2ZmZlbSnMyYmZlZSXMyY2ZmZiXNH822NdLMN+bTY/A9LR2GWYPN8e04zFYbj8yYmZlZSXMyY2ZmZiXNyUwbIuk8SW9ImpZ+vlNNvVtydeaku2EjqZ+kkHRQru4YSf3S9imS/pPqdM3V6Sdpfq7Nc5r1RM3MrE3xmpm25y8RcXFNFSJiYNW2pEuA+bndc4GzgLuLHPooMAYYV2TfxIg4sN7RmpmZ1cLJTImRdDZwDPA68C4wpbbkpBF9CTgC2DdXPB1oL+kbETE2Xz8ink7HNUc4ZmZmRXmaqYRIqgAOB3oDhwEVDWjmFEkzJP1T0oa11N0TeCsiCu8CfgHw23r220fSdEn/lrR9sQqSBkmqlFS5bNH8YlXMzMxW4WSmtPQFRkXE4ohYQPGpnppcBWwFlAPzgEtqqX8UMKKwMCImAkjas479TgW+FBE7AZcDdxWrFBFDI6IiIirardepjk2bmVlb52SmtDRq/iYi3oqIZRGxHLga2A1A0nVpYe69KzqS1iYb/bmlmuZ+T7Z2pi79fhQRC9P2vWTTVF1rOczMzKxOnMyUlknAQZLKJHUA6vWtXJK65R4eCswCiIgTIqI8IvKfbtofeC4i5hZrKyIeADYEdqpDv/8vrb9B0m5kr7v36hO7mZlZdbwAuIRExGRJo8kW4b4KVLLyJ41q8ydJ5UAAc4Cf1FD3SIpMMRX4PTCq6oGk04BfAv8PmCHp3og4ERgAnCRpKbAYODIioh5xm5mZVUt+TyktkjpExEJJ6wETgEERMbWl42pqFRUVUVlZ2dJhmJnZGkTSlIhY5cMvHpkpPUMlbQeUAcNbYyJjZmZWH05mSkxEHJ1/LOlKYI+Caj2Bwo9TXxoR1zVnbGZmZi3ByUyJi4iTWzoGMzOzluRPM5mZmVlJczJjZmZmJc3JjJmZmZU0JzNmZmZW0pzMmJmZWUlzMmNmZmYlzcmMmZmZlTR/z4ytkWa+MZ8eg+9p6TDM6mXOkHrd+9XMmohHZszMzKykOZkxMzOzkuZkZg0haZikAY1so3+6CaWZmVmb4WSmdekPOJkxM7M2xclME5J0tqTnJI2VNELSGY1sr4OkhyRNlTRT0iG5fcdKmiFpuqR/Sfo6cDBwkaRpkraSVC7piVRvpKQN07FbS3owHTs11ZWkiyTNSn0NzPX1y1Q2XdKQGtroJ2lM7rgrJB2ftodIeibFcnE15ztIUqWkymWL5jfm0pmZWRviTzM1EUkVwOFAb7LrOhWY0shmlwCHRsRHkroCT0gaTTb6chawR0S8K2mjiHg/7RsTEbenmGYAp0bEeEm/A84FTgduBIZExEhJZWRJ7WFAObAT0BWYLGlCKusP7B4RiyRtlGIr1sYXq7k2GwGHAttEREjqXKxeRAwFhgKs061nNOySmZlZW+Nkpun0BUZFxGIASXc3QZsC/iBpL2A5sDmwKbAvcHtEvAsQEe+vcqDUCegcEeNT0XDgNkkdgc0jYmQ6dkmq3xcYERHLgLckjQd2BfYGrouIRVV91dBGdefxEVlido2ke4Ax1VU0MzOrL08zNZ1q38kb4RhgY2CXiCgH3gLKUl8NHbmoLs6aygv7qq7uUlZ+TZUBRMRSYDfgDrJRnvvqEqiZmVldOJlpOpOAgySVSeoANMW3Z3UC3o6IzyTtA3wplT8EHCGpC6yYxgFYAHQEiIj5wAeS9kz7fgCMj4iPgLmS+qdj15G0HjABGCipnaSNgb2Ap4AHgB+mOqQpreraeBXYLj3uBOyX9ncAOkXEvWTTXOVNcG3MzMwATzM1mYiYnNasTCd7U68E6ruK9R+S/pq2XwcOAu6WVAlMA55Lfc2W9HtgvKRlwNPA8cDNwNWSTgMGAMcBf0+JxsvACantH6S+fgd8BnwPGAn0SfEH8MuI+C9wn6RyoFLSp8C9wG+KtRERL0u6FZgBvJjigizBGpXW1gj4n3peFzMzs2opwussm4qkDhGxMDfSMSgiprZ0XKWooqIiKisrWzoMMzNbg0iaEhEVheUemWlaQ9OX1pUBw53ImJmZNT8nM00oIo7OP5Z0JbBHQbWeZFMweZdGxHXNGZuZmVlr5WSmGUXEyS0dg5mZWWvnTzOZmZlZSXMyY2ZmZiXNyYyZmZmVNCczZmZmVtKczJiZmVlJczJjZmZmJc3JjJmZmZU0f8+MrZFmvjGfHoPvaekwzOpszpCmuLesmTWER2bMzMyspDmZMTMzs5LmZKaOJA2T9IqkaZKmS9qvpWMCkHSepDPS9u8k7Z+2r0k3vUTSwibq67H0u4ekWWm7n6QxTdG+mZlZQ3jNTP2cGRG3S9oHGEp208g1RkSck9s+sRna/3pTt2lmZtZYbWpkRtLZkp6TNFbSiKoRjQZ4HNg8tXm8pCtyfYyR1C9tL5R0oaQpkh6UtJukcZJelnRw7vi7JN2dRn5OkfS/kp6W9ISkjVK9rSTdl9qaKGmbIuc3TNKAtD1OUkVu3yWSpkp6SNLGqezHkiankaY7JK2XyjeVNDKVT5f09arzqeX6rhglSo9npVGc9SXdk9qaJWlgNccPklQpqXLZovl1eBrMzMzaUDKT3tgPB3oDhwEVNR9Ro28Bd9Wh3vrAuIjYBVgAXAB8AzgU+F2uXi/gaGA34PfAoojoTZY0HZvqDAVOTW2dAfytHvGuD0yNiJ2B8cC5qfzOiNg1InYCngV+lMovA8an8p2B2fXoq5hvAW9GxE4R0Qu4r1iliBgaERURUdFuvU6N7NLMzNqKtjTN1BcYFRGLASTd3YA2LpL0J2AT4Gt1qP8pn79xzwQ+iYjPJM0EeuTqPRIRC4AFkuYDd+eO2VFSB+DrwG2Sqo5Zpx5xLwduSds3AHem7V6SLgA6Ax2A+1P5vqQkKiKWAY0dJpkJXCzpQmBMRExsZHtmZmYrtJmRGUC1V6nVmcDWwG+B4alsKStfx7Lc9mcREWl7OfAJQEQsZ+VE8pPc9vLc46p6awEfRkR57mfbRpxHVUzDgFMiYgfg/ILYG6LotYiIF4BdyJKaP0o6p8ixZmZmDdKWkplJwEGSytJIR4O+4SolIpcCa0n6JjAHKJe0lqQvkk0VNamI+Ah4RdL3AJTZqR5NrAUMSNtHk10LgI7APEntgWNy9R8CTkp9tZO0QR37mUM2LYWknYEt0/ZmZFNnNwAXV9UxMzNrCm1mmikiJksaDUwHXgUqaeD0SUREmp75JbA/8ArZqMMsYGrTRLyKY4CrJP0WaA/cTHYudfExsL2kKWTnXLUA92zgSbLrMZMsuQH4OTBU0o+AZWSJzeN16OcO4FhJ04DJwAupfAeyKbrlwGepPTMzsyahz2dBWj9JHSJiYfrUzgRgUEQ0V/JhjVBRURGVlZUtHYaZma1BJE2JiFU+wNNmRmaSoemL5MqA4U5kzMzMSl+bSmYi4uj8Y0lXAnsUVOsJvFhQdmlEXNecsZmZmVnDtKlkplBEnNzSMZiZmVnjtOlkxszMWqfPPvuMuXPnsmTJkpYOxRqgrKyMLbbYgvbt29epvpMZMzNrdebOnUvHjh3p0aMHuS8btRIQEbz33nvMnTuXLbfcsk7HtKXvmTEzszZiyZIldOnSxYlMCZJEly5d6jWq5mTGzMxaJScypau+z52TGTMzMytpXjNjZmatXo/B9zRpe3OG1O2OOCNHjuSwww7j2WefZZtttgFg3LhxXHzxxYwZM2ZFveOPP54DDzyQAQMG0K9fP+bNm0dZWRlf+MIXuPrqqykvLwdg/vz5nHrqqTz66KMA7LHHHlx++eV06tQJgBdeeIHTTz+dF154gfbt27PDDjtw+eWXs+mmmzb4XN9//30GDhzInDlz6NGjB7feeisbbrjhKvX+8pe/cM011yCJHXbYgeuuu46ysjIGDhzI888/D8CHH35I586dmTZtGjNnzuSSSy5h2LBhDY6tipMZWyPNfGN+k//nY1ZXdX2jMqvNiBEj6Nu3LzfffDPnnXdenY+78cYbqaio4LrrruPMM89k7NixAPzoRz+iV69eXH/99QCce+65nHjiidx2220sWbKE7373u/z5z3/moIMOAuCRRx7hnXfeaVQyM2TIEPbbbz8GDx7MkCFDGDJkCBdeeOFKdd544w0uu+wynnnmGdZdd12OOOIIbr75Zo4//nhuueWWFfV+8YtfrEi8dthhB+bOnctrr71G9+7dGxwfeJrJzMysWSxcuJBHH32Ua6+9lptvvrlBbfTp04c33ngDgP/85z9MmTKFs88+e8X+c845h8rKSl566SVuuukm+vTpsyKRAdhnn33o1atXo85j1KhRHHfccQAcd9xx3HXXXUXrLV26lMWLF7N06VIWLVrEZpttttL+iODWW2/lqKOOWlF20EEHNfja5DmZMTMzawZ33XUX3/rWt/jKV77CRhttxNSp9b+Dzn333Uf//v0BeOaZZygvL6ddu3Yr9rdr147y8nJmz57NrFmz2GWXXWptc8GCBZSXlxf9eeaZZ1ap/9Zbb9GtWzcAunXrxttvv71Knc0335wzzjiD7t27061bNzp16sQBBxywUp2JEyey6aab0rNnzxVlFRUVTJw4sU7XoiaeZjIzM2sGI0aM4PTTTwfgyCOPZMSIEey8887VflInX37MMcfw8ccfs2zZshVJUEQUPba68up07NiRadOm1f1E6uCDDz5g1KhRvPLKK3Tu3Jnvfe973HDDDXz/+99fUWfEiBErjcoAbLLJJrz55puN7t/JTAuTtDbwX+DqiPh1PY/dDLgsIgY0sO+FEdGhIcfm2ugMHB0Rf2tMO2Zmrcl7773Hww8/zKxZs5DEsmXLkMSf/vQnunTpwgcffLBS/ffff5+uXbuueHzjjTey0047MXjwYE4++WTuvPNOtt9+e55++mmWL1/OWmtlEyvLly9n+vTpbLvttrz99tuMHz++1tgWLFjAnnvuWXTfTTfdxHbbbbdS2aabbsq8efPo1q0b8+bNY5NNNlnluAcffJAtt9ySjTfeGIDDDjuMxx57bEUys3TpUu68806mTJmy0nFLlixh3XXXrTXm2niaqeUdADwPHKF6frA+It5saCLThDoDP6vPAcr4tWdmrdbtt9/Osccey6uvvsqcOXN4/fXX2XLLLZk0aRI9e/bkzTff5NlnnwXg1VdfZfr06Ss+sVSlffv2XHDBBTzxxBM8++yzbL311vTu3ZsLLrhgRZ0LLriAnXfema233pqjjz6axx57jHvu+fzDE/fddx8zZ85cqd2qkZliP4WJDMDBBx/M8OHDARg+fDiHHHLIKnW6d+/OE088waJFi4gIHnroIbbddtsV+x988EG22WYbtthii5WOe+GFFxq9pgc8MtNoks4GjgFeB94FpkTExfVo4ijgUuAk4GvA46ndOcBNwD5Ae2AQ8Edga+CiiPi7pB7AmIjoJel44GBgPWArYGRE/DK1dRTwG0DAPRHxq1z8l6Q+PgCOjIh3JP049fcF4D/ADyJikaRNgb8DX06HnwScBmwlaRowNiLOlHQmcASwTorj3BTrv4FHgD5Af+DVgms5KPVLuw02rsclNDOr2er+hNqIESMYPHjwSmWHH344N910E3vuuSc33HADJ5xwAkuWLKF9+/Zcc801Kz7lk7fuuuvyi1/8gosvvphrr72Wa6+9llNPPZWtt96aiKBPnz5ce+21K+qOGTOG008/ndNPP5327duz4447cumllzbqXAYPHswRRxzBtddeS/fu3bntttsAePPNNznxxBO599572X333RkwYAA777wza6+9Nr1792bQoEEr2rj55ptXmWKC7NNW3/1u458bRUSjG2mrJFUA15C9Oa8NTAX+UddkRtK6wEtkCcr3gV4RcVraNwe4MCKukvQXYD9gD6AMmB0RmxRJZs4BegOfkI329AWWAU8Au5AlLA+QTU3dJSmA70fEjZLOATaJiFMkdYmI91IcFwBvRcTlkm4BHo+Iv0pqB3QANqyKIdU/ABgA/IQseRoN/Al4DXgZ+HpEPFHbtVmnW8/odtxf63IZzZqcP5pd+p599tmVRgZszfPJJ5+w9957M2nSJNZee9WxlWLPoaQpEVFRWNdD/Y3TFxgVEYsjYgFwdz2PPxB4JCIWAXcAh6Ykocro9Hsm8GRELIiId4Alaa1KoYciYn5ELAGeAb4E7AqMi4h3ImIpcCOwV6q/HKj6AoAb0vkA9JI0UdJMslGn7VP5vsBVABGxLCLmF4nhgPTzNFlytw1QtXT91bokMmZm1vq99tprDBkypGgiU1+eZmqcxt744yhgjzQKA9CFbMrnwfT4k/R7eW676nGx5y5fZ1mqU58Yq4bphgH9I2J6GvHpV482BPwxIv6xUmE2ivRxPdoxM7NWrGfPnit9TLsxPDLTOJOAgySVSeoA1HlsWtIGZCMh3SOiR0T0AE4mS3Ca0pPA3pK6plGfo4Cq5e5rkU0JARxNdj4AHYF5ktqTjcxUeYhsnQyS2qVzWJDqV7kf+GG6HkjaXNKqS9/NzJqZl1GUrvo+dx6ZaYSImCxpNDCdbDFrJVBs6qWYw4CHIyI/mjIK+JOkdZowxnmSfk228FbAvRExKu3+GNhe0pQU98BUfjZZEvQq2RRXVbLyc2CopB+RjfycFBGPS3pU0izg32kB8LbA4+nDWQvJ1gMtq0/cO2zeiUqvWzCzBiorK+O9996jS5cuvnt2iYkI3nvvPcrKyup8jBcAN5KkDhGxUNJ6wARgUETU/2sebSUVFRVRWVnZ0mGYWYn67LPPmDt3LkuWLGnpUKwBysrK2GKLLWjfvv1K5dUtAPbITOMNlbQd2aeMhjuRMTNree3bt2fLLbds6TBsNXEy00gRcXT+saQryT5CndcTeLGg7NKIuK45YzMzM2sLnMw0sYg4uaVjMDMza0v8aSYzMzMraV4AbGskSQvIvsXYVp+uZLfksNXH13z18zVf/Zrymn8pIla5342nmWxN9XyxFevWfCRV+pqvXr7mq5+v+eq3Oq65p5nMzMyspDmZMTMzs5LmZMbWVENbOoA2yNd89fM1X/18zVe/Zr/mXgBsZmZmJc0jM2ZmZlbSnMyYmZlZSXMyY2scSd+S9Lyk/0ga3NLxtDaSvijpEUnPSpot6eep/DxJb0ialn6+09KxtjaS5kiama5vZSrbSNJYSS+m3xu2dJythaSv5l7P0yR9JOl0v9ablqR/Snpb0qxcWbWva0m/Tv+/Py/pm00Sg9fM2JpEUjvgBeAbwFxgMnBURDzTooG1IpK6Ad0iYqqkjsAUoD9wBLAwIi5uyfhaM0lzgIqIeDdX9ifg/YgYkpL3DSPiVy0VY2uV/m95A9gdOAG/1puMpL2AhcD1EdErlRV9XacbM48AdgM2Ax4EvhIRyxoTg0dmbE2zG/CfiHg5Ij4FbgYOaeGYWpWImFd1d/eIWAA8C2zeslG1aYcAw9P2cLLE0prefsBLEfFqSwfS2kTEBOD9guLqXteHADdHxCcR8QrwH7L/9xvFyYytaTYHXs89novfaJuNpB5Ab+DJVHSKpBlp2NjTHU0vgAckTZE0KJVtGhHzIEs0gU1aLLrW7UiyEYEqfq03r+pe183yf7yTGVvTqEiZ50KbgaQOwB3A6RHxEXAVsBVQDswDLmm56FqtPSJiZ+DbwMlpeN6amaQvAAcDt6Uiv9ZbTrP8H+9kxtY0c4Ev5h5vAbzZQrG0WpLakyUyN0bEnQAR8VZELIuI5cDVNMHQr60sIt5Mv98GRpJd47fSOqaq9Uxvt1yErda3gakR8Rb4tb6aVPe6bpb/453M2JpmMtBT0pbpr6kjgdEtHFOrIknAtcCzEfHnXHm3XLVDgVmFx1rDSVo/LbhG0vrAAWTXeDRwXKp2HDCqZSJs1Y4iN8Xk1/pqUd3rejRwpKR1JG0J9ASeamxn/jSTrXHSxyT/CrQD/hkRv2/ZiFoXSX2BicBMYHkq/g3Zf/jlZEO+c4CfVM15W+NJ+jLZaAzA2sBNEfF7SV2AW4HuwGvA9yKicDGlNZCk9cjWaHw5Iuansn/h13qTkTQC6Ad0Bd4CzgXuoprXtaSzgB8CS8mmuf/d6BiczJiZmVkp8zSTmZmZlTQnM2ZmZlbSnMyYmZlZSXMyY2ZmZiXNyYyZmZmVNCczZm2YpGXprsGzJN0tqXMt9c+TdEYtdfqnm8lVPf6dpP2bINZhkgY0tp169nl6+mjvGkPSNuk5e1rSVgX75kiaWFA2repuxpIqJF3WBDH0yN8huWDfNfnnv7lJ2lTSTZJeTreJeFzSoaurf1szOJkxa9sWR0R5utPt+8DJTdBmf2DFm1lEnBMRDzZBu6tVusvy6cAalcyQXd9REdE7Il4qsr+jpC8CSNo2vyMiKiPitLp2lK5BvUTEiavrLvfpCyDvAiZExJcjYheyL9rcopn7Xbs527f6czJjZlUeJ93wTdJWku5Lf+lOlLRNYWVJP5Y0WdJ0SXdIWk/S18nugXNRGhHYqmpERdK3Jd2aO76fpLvT9gHpL+qpkm5L942qVhqB+EM6plLSzpLul/SSpJ/m2p8gaaSkZyT9XdJaad9RkmamEakLc+0uTCNJTwJnAZsBj0h6JO2/KvU3W9L5BfGcn+KfWXW9JHWQdF0qmyHp8Lqer6RySU+k40ZK2jB9oeTpwIlVMRVxKzAwbRd+820/SWNqiS1/DfpI+t90nWZJOj3Xz9qShqdjb68awZI0TlJFHa7zhen19aCk3dJxL0s6ONVpJ+mi9BqbIeknRc51X+DTiPh7VUFEvBoRl9fURroO41Lcz0m6MSVGSNpF0vgU2/36/Cv5x6XX3Hjg55IOkvSkshGyByVtWs3zYatDRPjHP/5poz/AwvS7HdlN+L6VHj8E9EzbuwMPp+3zgDPSdpdcOxcAp6btYcCA3L5hwACyb719DVg/lV8FfJ/sW0Mn5Mp/BZxTJNYV7ZJ9a+tJafsvwAygI7Ax8HYq7wcsAb6czm9simOzFMfGKaaHgf7pmACOyPU5B+iae7xR7nqNA3bM1as6/58B16TtC4G/5o7fsB7nOwPYO23/rqqd/HNQ5Jg5wFeAx9Ljp8lGyWblrsmY6mIrvAbALmTfFL0+0AGYTXaX9R6p3h6p3j/5/HUxDqiow3X+dtoeCTwAtAd2Aqal8kHAb9P2OkAlsGXB+Z4G/KWG13fRNtJ1mE82grMWWSLfN8XwGLBxOmYg2beQV53X3wqey6ovnj0RuKSl/z235R8PlZm1betKmkb25jQFGJtGCb4O3Jb+WIXsjaBQL0kXAJ3J3ujur6mjiFgq6T7gIEm3A98FfgnsTfaG+2jq7wtkby61qbpn10ygQ0QsABZIWqLP1/48FREvw4qvXO8LfAaMi4h3UvmNwF5k0xXLyG7AWZ0jJA0ie3PuluKekfbdmX5PAQ5L2/uTTXtUXYMPJB1Y2/lK6gR0jojxqWg4n9/xuTbvAx9IOhJ4FlhUTb1VYkub+WvQFxgZER+nuO4E9iS79q9HxKOp3g1kicXFufZ3pfrr/ClwX6o3E/gkIj6TNJPstQjZvat21OfrpDqR3cfnlepOXNKVKeZPI2LXGtr4lOy1MTcdNy31+yHQi+zfAWRJa/42B7fktrcAbkkjN1+oKS5rfk5mzNq2xRFRnt48x5CtmRkGfBgR5bUcO4zsL+3pko4n+2u3NrekPt4HJkfEgjS8PzYijqpn7J+k38tz21WPq/5vK7xfSwCieksiYlmxHcpuincGsGtKSoYBZUXiWZbrX0ViaOj51sctwJXA8TXUKRYbrHwNarpWxa5tYfvV+SzSkAa55y8iluvz9SgiG+2qKUmeDRy+IoCIkyV1JRuBqbYNSf1Y+TVT9ZwJmB0Rfarp7+Pc9uXAnyNidGrvvBritGbmNTNmRmQ34DuN7M16MfCKpO9BtshS0k5FDusIzJPUHjgmV74g7StmHLAz8GM+/yv3CWAPSVun/taT9JXGndEKuym7A/taZFMGk4Angb0ldVW2wPUoYHw1x+fPZQOyN7P5aX3Et+vQ/wPAKVUPJG1IHc43PR8fSNozFf2ghhiLGQn8iZpHy4rFVmgC0D/FuD7ZHaarPi3VXVLVm/5RZNc2rz7XuZj7gZPS6wtJX0kx5D0MlEk6KVeWX7Bdlzbyngc2rjovSe0lbV9N3U7AG2n7uGrq2GriZMbMAIiIp4HpZFMPxwA/kjSd7K/fQ4occjbZG9ZY4Llc+c3AmSry0eH0F/8YskRgTCp7h2wEYYSkGWRv9qssOG6gx4EhwCyyaYCRkd0d+dfAI2TnOzUiRlVz/FDg35IeiYjpZGtQZpOtEXm0mmPyLgA2TAtgpwP71ON8jyNbSD2D7A7Pv6tDfwBExIKIuDAiPq1PbEXamUo2AvcU2XN9TXqdQDaFdVyKbyOyNVD5Y+tznYu5BngGmKrsY+D/oGA2IY3u9CdLml6R9BTZlNyv6tpGQXufkq2rujBdk2lkU67FnEc2FTsReLce52XNwHfNNrNWKQ39nxERB7ZwKGbWzDwyY2ZmZiXNIzNmZmZW0jwyY2ZmZiXNyYyZmZmVNCczZmZmVtKczJiZmVlJczJjZmZmJe3/AztKvgbShDR7AAAAAElFTkSuQmCC\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.8950713037519827, pvalue=3.799809429769476e-36)\n", "Slope and P-value = PearsonRResult(statistic=0.9644199141531858, pvalue=1.955818347264721e-40)\n", "Slope and P-value = PearsonRResult(statistic=0.8193243810617624, pvalue=8.906174118070206e-21)\n", "Slope and P-value = PearsonRResult(statistic=-0.27243487093449004, pvalue=0.0061034778806438655)\n", "Slope and P-value = PearsonRResult(statistic=-0.4776114578025363, pvalue=3.8128864814028513e-05)\n", "Slope and P-value = PearsonRResult(statistic=0.021588310647995892, pvalue=0.8311758401571453)\n", "Slope and P-value = PearsonRResult(statistic=0.9269869238240557, pvalue=4.042830735684092e-05)\n", "Slope and P-value = PearsonRResult(statistic=-0.36557251505107935, pvalue=0.00018395695785034585)\n", "Slope and P-value = PearsonRResult(statistic=-0.2687752188211074, pvalue=0.006852968531241462)\n", "Slope and P-value = PearsonRResult(statistic=0.9999386673171932, pvalue=5.765909278206761e-07)\n", "WS 170\n", "PB 20\n", "SDSP 5\n", "Name: BroodBedding, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.887860538162381, pvalue=8.249089970482903e-35)\n", "Slope and P-value = PearsonRResult(statistic=0.677573249703149, pvalue=1.1392978149088491e-06)\n", "Slope and P-value = PearsonRResult(statistic=0.7127796887964049, pvalue=8.95508483357982e-17)\n", "Slope and P-value = PearsonRResult(statistic=0.7202209069308838, pvalue=3.029604351888914e-17)\n", "Slope and P-value = PearsonRResult(statistic=0.4252375928971543, pvalue=1.0330773505138368e-05)\n", "Slope and P-value = PearsonRResult(statistic=-0.6961096212229908, pvalue=9.003447203232702e-16)\n", "Slope and P-value = PearsonRResult(statistic=0.8654623951229147, pvalue=9.37295517169427e-19)\n", "Slope and P-value = PearsonRResult(statistic=-0.16650858587975031, pvalue=0.09777614888215118)\n", "Slope and P-value = PearsonRResult(statistic=0.7657187289269917, pvalue=3.907598420460161e-19)\n", "Slope and P-value = PearsonRResult(statistic=0.9327668327711088, pvalue=4.8569820892943264e-08)\n" ] } ], "source": [ "sample = pd.merge(microbiome, poultry[['SampleID', 'BroodBedding','PastureTime','SampleType']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "#sample = sample[~sample.isin([np.nan, np.inf, -np.inf]).any(1)]\n", "sample = sample.drop(['Pathogen_Salmonella', 'new_Pathogen_Salmonella',\n", " 'Pathogen_Campy','new_Pathogen_Campy',\n", " 'Pathogen_Listeria','new_Pathogen_Listeria'],axis='columns')\n", "\n", "\n", "feces1=sample[(sample.SampleType=='Feces') & (sample.PastureTime=='Start')]\n", "feces2=sample[(sample.SampleType=='Feces') & (sample.PastureTime=='Mid')]\n", "feces3=sample[(sample.SampleType=='Feces') & (sample.PastureTime=='End')]\n", "\n", "soil1=sample[(sample.SampleType=='Soil') & (sample.PastureTime=='Start')]\n", "soil2=sample[(sample.SampleType=='Soil') & (sample.PastureTime=='Mid')]\n", "soil3=sample[(sample.SampleType=='Soil') & (sample.PastureTime=='End')]\n", "\n", "ceca=sample[sample.SampleType=='Ceca']\n", "wcrp=sample[sample.SampleType=='WCR-P']\n", "wcrf=sample[sample.SampleType=='WCR-F']\n", "\n", "\n", "print('Feces_Start', feces1.shape)\n", "print('Feces_Mid', feces2.shape)\n", "print('Feces_End', feces3.shape,'\\n')\n", "\n", "print('Soil_Start', soil1.shape)\n", "print('Soil_Mid', soil2.shape)\n", "print('Soil_End', soil3.shape,'\\n')\n", "\n", "print('Ceca', ceca.shape)\n", "print('WCR-P', wcrp.shape)\n", "print('WCR-F', wcrf.shape,'\\n')\n", "\n", "\n", "sampletypes = [feces1,feces2,feces3, soil1,soil2,soil3,ceca, wcrp, wcrf]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES_START\", 1: \"FECES_MID\", 2:\"FECES_END\",\n", " 3: \"SOIL_START\", 4: \"SOIL_MID\", 5: \"SOIL_END\",\n", " 6:\"CECA\", 7: \"WCR-P\", 8: \"WCR-F\"\n", " }\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType', 'PastureTime','BroodBedding'],axis='columns'),sample.BroodBedding,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, :] #multiclass\n", " \n", " try:\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs, multi_class='ovo')\n", " except ValueError:\n", " pass\n", " \n", " \n", " \n", " \n", "# mylist2.append([f\"BroodBedding_{sample_name[indexing]}\", rf_auc_normal])\n", " \n", " \n", " print(pd.value_counts(sample['BroodBedding']))\n", "\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", " \n", " plt.title(f\"BroodBedding in {sample_name[indexing]} Model\")\n", " prelim3_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','BroodBedding','PastureTime','SampleType'],axis='columns').columns)\n", " prelim3_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", "\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim3_plot.max(), decimals=None, symbol=''))\n", "\n", " xmax=prelim3_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " \n", " plt.show()\n", " \n", " \n", " prelim3_plot.nlargest(10).to_csv(\"prelim3.csv\")\n", " top10 = pd.read_csv('prelim3.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", "# plt.plot(pdp[1][0], pdp[0][0],'.')\n", "# plt.ylabel('Partial dependence'), plt.xlabel(top10[feature])\n", "# plt.show()\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " #mylist.append([f\"BroodBedding_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " \n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (5) BrGMOFree" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Feces_Start (200, 878)\n", "Feces_Mid (313, 878)\n", "Feces_End (185, 878) \n", "\n", "Soil_Start (199, 878)\n", "Soil_Mid (313, 878)\n", "Soil_End (183, 878) \n", "\n", "Ceca (185, 878)\n", "WCR-P (208, 878)\n", "WCR-F (195, 878) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "1 125\n", "0 75\n", "Name: BrGMOFree, dtype: int64\n" ] }, { "data": { 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\n", 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PearsonRResult(statistic=-0.2952310698892764, pvalue=0.009622813142323298)\n", "Slope and P-value = PearsonRResult(statistic=-0.6307657445645424, pvalue=4.499917173663595e-11)\n", "1 218\n", "0 95\n", "Name: BrGMOFree, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.7900034979966178, pvalue=1.5249053490077229e-22)\n", "Slope and P-value = PearsonRResult(statistic=-0.7349267554251772, pvalue=3.839498044250891e-15)\n", "Slope and P-value = PearsonRResult(statistic=0.836842872781358, pvalue=2.1775118218024564e-27)\n", "Slope and P-value = PearsonRResult(statistic=-0.6660052299221388, pvalue=3.997941468728996e-14)\n", "Slope and P-value = PearsonRResult(statistic=-0.1881020623206761, pvalue=0.06091143189933994)\n", "Slope and P-value = PearsonRResult(statistic=-0.6638213708358328, pvalue=5.175828361923326e-14)\n", "Slope and P-value = PearsonRResult(statistic=-0.44328416354346495, pvalue=3.859802506758021e-06)\n", "Slope and P-value = PearsonRResult(statistic=-0.8654546370748288, pvalue=3.5444528182097485e-31)\n", "Slope and P-value = PearsonRResult(statistic=-0.9449518180908935, pvalue=2.4111808835956412e-49)\n", "Slope and P-value = PearsonRResult(statistic=-0.9358774391512291, pvalue=3.419274007403162e-46)\n", "1 125\n", "0 60\n", "Name: BrGMOFree, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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PearsonRResult(statistic=0.4233623225308486, pvalue=1.1407105140919147e-05)\n", "Slope and P-value = PearsonRResult(statistic=0.6574402238309187, pvalue=1.4502492557161817e-13)\n", "1 124\n", "0 75\n", "Name: BrGMOFree, dtype: int64\n" ] }, { "data": { "image/png": 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I5RHxHPACWZIwGjhUUlfgW8CIVLcnMFrSLOD3wE5F7h8zM7NGtdd9AtRGbZ4VEfetUij1AyKvbv77YmL6K3AicBzZQbreBznTy8j2ZVNtfiKuiHhf0gPAYcAxrOwl+T/g4Yg4XFIVMK6Z8ZqZmTVLe/UETAT6S+omqQdZd3hr3Qeckc6gkbStpLXSvD0lbSXpM2Rd9hNT+Uf19YHxwABJ3dNyhwMTCqxnBDAYICKeaiKmycCBktaTtBrZJZBcR6fxCVsDnwP+ncqHA38ApkTEW6msJzA/TQ9sYr1mZmZFa5eegIiYIukOYAYwF6gFih3B9mdJl6fpl4F9ya6xT5Mk4HVgQJr/GDCU7Hr9eGBsKh8GzJQ0LY0LGEF24AYYHhFPFoj9VUmzgduaCjAi5kv6DdnliVfILhPkbue/gUeATYDvpEsJRMRUSe8C1+fUvRgYKelHwENNrbvezpv2pNZ39DIzs2ZQRKGe8jZYkdQjIhZJ6k52YB4UEdPaYD39gLMj4tAStdcdqAN2i4gmE5ec7VyNLPn4S0SMbWKZz5J1928fEctbE29NTU3U1ta2pgkzM+tEJE2NiIID8tvzPgHD0iC+aWQD7EqeAJSapIPIRulf2ZwEILkgbecs4EWa6EGQdBJZz8HPWpsAmJmZFaPdegI+sWLpKrIu/Vy9gefyyq6IiOuxZnFPgJmZ5WqsJ6BsTxGMiDPLtW4zMzOrvNsGm5mZWeIkwMzMrEI5CTAzM6tQTgLMzMwqlJMAMzOzCuUkwMzMrEKV7SeC1jbq5i+kasjd5Q7DzDqYOb6duBXgngAzM7MK5STAzMysQnW6JEDSCEkvSpouaZqkvVvYzsD0YJ/698Ml7ZimF5UgznUlfbe17ZiZmbVUp0sCknMiohoYAvy5hW0MBFYkARFxWkQ83frQVlgXKCoJUKazfmZmZtbOOuQBRdJ5kp6R9ICkUZLObmFT44FtUps/kjQrvQansipJsyVdK+kpSfdLWlPSUUANcGPqUVhT0jhJKx7AIOmy1NPwoKSNUtnpkqZImiHp1vQYYiRtImlsKp8haR9gKLB1av+SVO+ctPxMSb/Mi/FPZE9g3LyF+8LMzGwVHS4JSAfaI4G+wBFkB+OW6g/USdodOAX4PLAXcLqkvqlOb+CqiNgJeAc4MiJuAWqBEyOiOiKW5LW7FjAtInYDHgHOT+VjImKPiNgVmA2cmsr/ADySyncDniLrpXg+tX+OpINTLHsC1cDukg5Iy28H3BARfSNibv5GShokqVZS7bL3m/vEYzMzq3QdLgkA9gNuj4glEfEecGcL2rhE0nRgENmBeD9gbEQsjohFwBhg/1T3xYiYnqanAlXNaH85cHOa/ltqH6CPpAmS6oATgZ1S+ReBqwEiYllEFDpSH5xeT5Kd8W9PlhQAzI2IxxsKJiKGRURNRNR06d6zGeGbmZl1zPsEqARtnJPO5rMGpYMaqftBzvQyYM0WrC/S3xHAgIiYIWkg0K+INgT8NiJWGcMgqQpY3IKYzMzMGtURewImAv0ldZPUAyjFHS7GAwMkdZe0FnA4MKGJZd4D1m5g3meAo9L0CSlmUv0FkrqS9QTUexA4A0BSF0nrFGj/PuBbaZuRtKmkjZuzcWZmZi3R4XoCImKKpDuAGcBcsmvzrbrQHRHTJI0AJqei4RHxZDrLbsgI4BpJS4D8nxkuBnaSNDXFdmwqPw94IsVdx8qD/A+AYZJOJettOCMiHpM0SdIs4J9pXMAOwGOSABYB30j1zczMSk4R0XStdiapR0QsSqPrxwODImJaueP6NKipqYna2tpyh2FmZh2EpKkRUXCQfYfrCUiGpRvzdANGOgEwMzMrvQ6ZBETECbnvJV0F7JtXrTfwXF7ZFRFxfVvGZmZm1ll0yCQgX0ScWe4YzMzMOpuO+OsAMzMzawdOAszMzCqUkwAzM7MK5STAzMysQjkJMDMzq1BOAszMzCqUkwAzM7MK9am4T4A1X938hVQNubvcYZhZA+YMLcUz0cxKwz0BZmZmFcpJgJmZWYVyElAESSMkzZe0Rnq/oaQ5LWzrpyUNzszMrEhOAoq3DPhWCdpxEmBmZmVVcUmApPMkPSPpAUmjJJ1dZBOXAz+UtMqgSmUukTRLUp2kY1N5L0njJU1P8/aXNBRYM5XdmOr9KM2fJWlwTrsnSZopaYakvzawTYMk1UqqXfb+wiI3x8zMKlVF/TpAUg1wJNCXbNunAVOLbOYlYCLwTeDOnPIjgGpgV2BDYIqk8cAJwH0R8WtJXYDuETFB0vciojrFtTtwCvB5QMATkh4BPgR+BuwbEW9IWr9QQBExDBgGsEav3lHk9piZWYWqqCQA2A+4PSKWAEi6s4n6DfkNcAeQ+1u8/YBREbEMeDUdxPcApgB/kdQVuC0ipjcQ19iIWJziGgPsDwRwS0S8ARARb7UwXjMzs0+otMsBKkUjEfEfYDpwTFNtR8R44ABgPvBXSScVEZfIEgEzM7OSq7QkYCLQX1I3ST2A1ty149dA7niC8cCxkrpI2ojswD9Z0pbAaxFxLXAdsFuq/1HqHahfdoCk7pLWAg4HJgAPAsdI2gCgocsBZmZmLVFRlwMiYoqkO4AZwFygFmjRSLqIeErSNFYe1McCe6e2A/hJRPxX0snAOZI+AhYB9T0Bw4CZkqZFxImSRgCT07zhEfEkgKRfA49IWgY8CQxsSbxmZmb5FFFZvc2SekTEIkndyc7AB0XEtHLHVSo1NTVRW1tb7jDMzKyDkDQ1ImoKzauonoBkmKQdgW7AyM6UAJiZmRWj4pKAiDgh972kq4B986r1Bp7LK7siIq5vy9jMzMzaU8UlAfki4sxyx2BmZlYOlfbrADMzM0ucBJiZmVUoJwFmZmYVykmAmZlZhXISYGZmVqGcBJiZmVUoJwFmZmYVquLvE9DZ1M1fSNWQu5uuaGYlM2doa55FZlY+7gkwMzOrUE4CzMzMKtSnNgmQtKuk6Tnvj5f0vqSu6f3Okma2sO2flihMJH1H0klN1LlA0tlpeoSko0q1fjMzs4Z8apMAoA7YUtLa6f0+wDNA35z3k1rYdtFJgKQuhcoj4pqIuKGFcZiZmbWZsicBks6T9IykBySNqj8jbkpELAemAJ9PRbsDV5Ed/El/H5W0p6RHJT2Z/m6X1jtQ0h9z4rhLUj9JQ4E1JU2XdGOa9w1Jk1PZn+sP+JIWSfqVpCeAvSUNlfS0pJmSLk11cs/yt5Z0r6SpkiZI2r6JffMLSVMkzZI0TJIaqDdIUq2k2mXvL2zO7jMzMytvEiCpBjiS7Oz9CKCmyCYeBfaRtBawHBjHqknAJLLegQMioi/wC+A3jTUYEUOAJRFRHREnStoBOBbYNyKqgWXAian6WsCsiPg88DRwOLBTROwCXFig+WHAWRGxO3A28Kcmtu+PEbFHRPQB1gQObSDmYRFRExE1Xbr3bKJJMzOzTLl/IrgfcHtELAGQdGeRy08CfgxMAKZExPOStpG0EdAjIl6QtDkwUlJvIICuRa7jS2S9DFPSifiawGtp3jLg1jT9LrAUGC7pbuCu3EYk9SBLTEbnnNCv0cS6vyDpJ0B3YH3gKaDYfWRmZlZQuZOAgt3bRXgc2IMsmXgslc0DjiPrJQD4P+DhiDhcUhVZbwHAx6zaE9KtkRhHRsT/Fpi3NCKWAUTEx5L2JEsajgO+B3wxp+5ngHdSb0KTJHUj6ymoiYiXJV3QSIxmZmZFK/eYgIlAf0nd0plyUXfciIj3gJeBgaxMAh4DBrMyCegJzE/TA3MWnwNUS/pM6i3YM2feR/W/MgAeBI6StDGApPUlbZkfS4q/Z0Tck9ZfnRfru8CLko5O9SVp10Y2r/6A/0Zq278YMDOzkiprEhARU4A7gBnAGKAWKHZk2yRgjYh4Ob1/DPgcK5OAi4HfSpoEdMlb7kWyXxlcCkzLmTcMmCnpxoh4Gvg5cH/6yeEDQK8CcawN3JXqPAL8sECdE4FTJc0g69o/rKGNioh3gGtTfLeRDYI0MzMrGUVEeQOQekTEIkndgfHAoIiY1tRyVlhNTU3U1taWOwwzM+sgJE2NiIID78s9JgBgmKQdybq/RzoBMDMzax9lTwIi4oTc95KuAvbNq9YbeC6v7IqIuL4tYzMzM+vMyp4E5IuIM8sdg5mZWSUo968DzMzMrEycBJiZmVUoJwFmZmYVykmAmZlZhXISYGZmVqGcBJiZmVUoJwFmZmYVqsPdJ8Bap27+QqqG3F3uMMw6rTlDi3rOmVmH5p4AMzOzCuUkoEQkbSbpdknPSXpe0hWSVpdULel/cupdIOnscsZqZmYGTgJKQpLIHoV8W0T0BrYFegC/BqqB/2l46aLX1aXpWmZmZk1zElAaXwSW1j/QKCKWAT8ETgMuBo6VNF3Ssan+jpLGSXpB0vfrG5H0DUmTU90/1x/wJS2S9CtJTwB7t+uWmZlZp+UkoDR2AqbmFkTEu8Ac4ELg5oiojoib0+ztga8AewLnS+oqaQfgWGDfiKgGlgEnpvprAbMi4vMRMTF/5ZIGSaqVVLvs/YWl3zozM+uU/OuA0hAQRZTfHREfAB9Ieg3YBPgSsDswJbu6wJrAa6n+MuDWhlYeEcOAYQBr9OpdaH1mZmaf4CSgNJ4CjswtkLQOsDnZATzfBznTy8g+BwEjI+J/C9Rfmi4xmJmZlYwvB5TGg0B3SSfBisF7lwEjgFeBtZvZxlGSNk5trC9py7YJ18zMzElASUREAIcDR0t6DngWWAr8FHiYbCBg7sDAQm08DfwcuF/STOABoFebB29mZhVL2fHLOouampqora0tdxhmZtZBSJoaETWF5rknwMzMrEI5CTAzM6tQTgLMzMwqlJMAMzOzCuUkwMzMrEI5CTAzM6tQTgLMzMwqlJMAMzOzCuUkwMzMrEI5CTAzM6tQfopgJ1M3fyFVQ+4udxhmndKcoYeUOwSzknJPgJmZWYVyEmBmZlah2jwJkDRC0lF5ZYta2FaVpFmliewTbT9aonbmSNqwQPl3JJ1UinWYmZmVgscEJBGxT3PrSlotIj4usv1rio/KzMys7TSrJ0DSeZKekfSApFGSzi7FyiX9VdJhOe9vlPR1SQMl3S7pXkn/lnR+zmJdJF0r6SlJ90taMy1bLelxSTMljZW0XiofJ+n3ksZLmi1pD0ljJD0n6cKcdS/Kmf6JpDpJMyQNzWnnN5IeAX4g6UuSnkz1/iJpjZwYz5E0Ob22SctfUL/fUls1aXpDSXPS9EBJt0m6U9KLkr4n6UdpPY9LWr8U+93MzAyakQSkg9WRQF/gCKCmBeu5RNL0+ldO+XDglLSensA+wD1p3p7AiUA1cHT9QRPoDVwVETsB76TYAG4Azo2IXYA6IDdx+DAiDgCuAW4HzgT6AAMlbZC3vV8DBgCfj4hdgYtzZq8bEQcCVwEjgGMjYmeyHpUzcuq9GxF7An8ELm9696yiD3BC2v5fA+9HRF/gMaDg5QRJgyTVSqpd9v7CIldnZmaVqjk9AfsBt0fEkoh4D7izBes5JyKq61/1hRHxCLCNpI2B44Fbc7rZH4iINyNiCTAmxQHwYkRMT9NTgaqUQKyb2gMYCRyQs/470t864KmIWBARHwAvAJvnxXoQcH1EvJ9ifCtn3s3p73YpjmcbWN+onL97N7hXCns4It6LiNeBhazc33VAVaEFImJYRNRERE2X7j2LXJ2ZmVWq5owJUBvH8FeyM/7jgG/llEdevfr3H+SULQPWbMY66pdZnrf8cj65D1Rg3fUW59RpTDQwXe9jViZg3fLm5ceXG7vHcJiZWck0pydgItBfUjdJPYBS3y1jBDAYICKeyin/sqT10zX/AcCkhhqIiIXA25L2T0XfBB5pqH4T7ge+Jak7QAPX4Z8h64HYpoH1HZvz97ECy88Bdk/TRxWYb2Zm1uaaPLOMiCmS7gBmAHOBWrJu6pKIiFclzQZuy5s1kayXYBvg7xFRK6mqkaZOBq5JB+8XSGMNWhDPvZKqgVpJH5KNUfhpXp2lkk4BRktaDZhCNt6g3hqSniBLso4vsJpLgX9I+ibwUEviNDMzay1FNNTznVNJ6hERi9IBdjwwKCKmlSSArM06YLd0Ro+kgUBNRHyvFOuoJDU1NVFbW1vuMMzMrIOQNDUiCg7qb+7NgoalUf3TyAbvlSoBOIisa/3K+gTAzMzM2kezBppFxAm57yVdBeybV6038Fxe2RURcX0j7f4L2KJA+QiysQJmZmbWRlo02jwizix1IGZmZta+/AAhMzOzCuUkwMzMrEI5CTAzM6tQTgLMzMwqlJMAMzOzCuUkwMzMrEI5CTAzM6tQfipdJ1M3fyFVQ+4udxhmHdqcoaV+DprZp5N7AszMzCqUkwAzM7MK5SSgCJJGSHpR0gxJz0q6QdKmeXUOlxSSts8rv1fSO5LuyivfStITkp6TdLOk1VP59pIek/SBpLPbfuvMzKzSOAko3jkRsSuwHfAk8HD9gTs5HpgIHJe33CXANwu0dxHw+4joDbwNnJrK3wK+D1xawtjNzMxWqLgkQNJ5kp6R9ICkUS09y47M74H/Al9Lbfcge7riqeQlARHxIPBeXiwCvgjckopGAgNS/dciYgrwUTO2aZCkWkm1y973E5nNzKx5KioJkFQDHAn0BY4AakrQ7DSgvut/AHBvRDwLvCVptyaW3QB4JyI+Tu/nAZs2Ur+giBgWETURUdOle89iFzczswpVUUkAsB9we0QsiYj3gDtL0KZypo8HbkrTN6X3zV22XpQgJjMzsyZV2n0CCh10W6sv8KCkDci69vtICqALEJJ+EhENHdjfANaVtFrqDdgMeKUNYjQzM/uESusJmAj0l9QtXb9v8R1DlPk+0Au4FzgKuCEitoyIqojYHHiRrPehoJQcPJyWBTgZuL2lMZmZmRWjopKANNDuDmAGMAaoBYodSXeJpBnAs8AewBci4kOyrv+xeXVvBU4AkDQBGA18SdI8SV9Jdc4FfiTpP2RjBK5L9f+fpHnAj4Cfp2XWKTJWMzOzBqnhnurOSVKPiFgkqTswHhgUEdPKHVep1NTURG1tbbnDMDOzDkLS1IgoOBC+0sYEAAyTtCPQDRjZmRIAMzOzYlRcEhARJ+S+l3QV2W/7c/UGnssruyIirm/L2MzMzNpTxSUB+SLizHLHYGZmVg4VNTDQzMzMVnISYGZmVqGcBJiZmVUoJwFmZmYVykmAmZlZhXISYGZmVqGcBJiZmVWoir9PQGdTN38hVUPuLncYZh3OnKEtfl6YWaflngAzM7MK5STAzMysQnWKJEDS2ZKekTRL0gxJJ6XyOZI2LFB/oKQ/tn+kZmZmHcenPgmQ9B3gy8CeEdEHOABQidfhsRNmZtbpdIgkQNJ56Uz+AUmjJJ1dxOI/Bb4bEe8CRMTCiBiZM/8sSdMk1UnavsC6t5T0oKSZ6e8WqXyEpN9Jehi4SFK1pMdTvbGS1kv1xkm6SNJkSc9K2j+Vd5F0iaQpaZlvp/J+kh6R9I9Uf6ikE9PydZK2TvX6S3pC0pOS/iVpk0b23yBJtZJql72/sIhdZ2ZmlazsSYCkGuBIoC9wBFBTxLJrA2tHxPONVHsjInYDrgYKJRd/BG6IiF2AG4E/5MzbFjgoIn4M3ACcm+rVAefn1FstIvYEBueUnwosjIg9gD2A0yVtlebtCvwA2Bn4JrBtWn44cFaqMxHYKyL6AjcBP2loAyNiWETURERNl+49G9kVZmZmK3WEbu79gNsjYgmApDuLWFZANFFnTPo7lSzJyLd3TvlfgYtz5o2OiGWSegLrRsQjqXwkMLqBdVSl6YOBXSQdld73BHoDHwJTImIBgKTngftTnTrgC2l6M+BmSb2A1YEXm9hOMzOzopS9J4BWXL9PlwAWS/pcI9U+SH+X0bykJzepWNzMUAqtQ8BZEVGdXltFxP159QGW57xfnrP8lcAfI2Jn4NtAt2bGYmZm1iwdIQmYCPSX1E1SD6DYO3r8FrhK0joAktaRNKiI5R8FjkvTJ6Z4VhERC4G366/3k3XhP5JfL899wBmSuqa4tpW0VhFx9QTmp+mTi1jOzMysWcp+OSAipki6A5gBzAVqgWJGt10N9ACmSPoI+Ai4rIjlvw/8RdI5wOvAKQ3UOxm4RlJ34IVG6tUbTnZpYJokpbYHFBHXBcBoSfOBx4GtGq9uZmZWHEU0dUm9HYKQekTEonSAHQ8Miohp5Y7r06impiZqa2vLHYaZmXUQkqZGRMFB92XvCUiGSdqR7Lr3SCcAZmZmba9DJAERcULue0lXAfvmVesNPJdXdkVEXN+WsZmZmXVWHSIJyBcRZ5Y7BjMzs86uQyYBZmZWHh999BHz5s1j6dKl5Q7FitStWzc222wzunbt2uxlnASYmdkK8+bNY+2116aqqorsh032aRARvPnmm8ybN4+ttmr+j8k6wn0CzMysg1i6dCkbbLCBE4BPGUlssMEGRffgOAkwM7NVOAH4dGrJ5+YkwMzMrEJ5TICZmTWoasjdJW1vztDm3Rl+7NixHHHEEcyePZvtt8+eAj9u3DguvfRS7rrrrhX1Bg4cyKGHHspRRx1Fv379WLBgAd26dWP11Vfn2muvpbq6GoCFCxdy1llnMWnSJAD23XdfrrzySnr2zJ68+uyzzzJ48GCeffZZunbtys4778yVV17JJps0+BT3Jr311lsce+yxzJkzh6qqKv7xj3+w3nrrfaLeFVdcwbXXXktEcPrppzN48GAARo8ezQUXXMDs2bOZPHkyNTXZ/X7q6uq47LLLGDFiRItjq+ckoJOpm7+w5P9ozcqtuQcO6zxGjRrFfvvtx0033cQFF1zQ7OVuvPFGampquP766znnnHN44IEHADj11FPp06cPN9xwAwDnn38+p512GqNHj2bp0qUccsgh/O53v6N///4APPzww7z++uutSgKGDh3Kl770JYYMGcLQoUMZOnQoF1100Sp1Zs2axbXXXsvkyZNZffXV+epXv8ohhxxC79696dOnD2PGjOHb3/72KsvsvPPOzJs3j5deeoktttiixfGBLweYmVkHs2jRIiZNmsR1113HTTfd1KI29t57b+bPz57B9p///IepU6dy3nnnrZj/i1/8gtraWp5//nn+/ve/s/fee69IAAC+8IUv0KdPn1Ztx+23387JJ2fPfzv55JO57bbbPlFn9uzZ7LXXXnTv3p3VVluNAw88kLFjxwKwww47sN122xVsu3///i3eN7mcBJiZWYdy22238dWvfpVtt92W9ddfn2nTir+T/L333suAAQMAePrpp6murqZLly4r5nfp0oXq6mqeeuopZs2axe67795km++99x7V1dUFX08//fQn6r/66qv06tULgF69evHaa699ok6fPn0YP348b775Ju+//z733HMPL7/8cpOx1NTUMGHChCbrNcWXA8zMrEMZNWrUiuvixx13HKNGjWK33XZrcPR7bvmJJ57I4sWLWbZs2YrkISIKLttQeUPWXnttpk+f3vwNaYYddtiBc889ly9/+cv06NGDXXfdldVWa/rQvPHGG/PKK6+0ev2dpidA0mqS3pD02zLH0U/SXWn665KGlDMeM7NPkzfffJOHHnqI0047jaqqKi655BJuvvlmIoINNtiAt99+e5X6b731FhtuuOGK9zfeeCMvvvgiJ5xwAmeemd2BfqedduLJJ59k+fLlK+otX76cGTNmsMMOO7DTTjsxderUJmMrtidgk002YcGCBQAsWLCAjTfeuGC7p556KtOmTWP8+PGsv/769O7du8lYli5dypprrtlkvaZ0miQAOBj4N3CM2vhHrpK6NF0LIuKOiBjalrGYmXUmt9xyCyeddBJz585lzpw5vPzyy2y11VZMnDiR3r1788orrzB79mwA5s6dy4wZM1b8AqBe165dufDCC3n88ceZPXs222yzDX379uXCCy9cUefCCy9kt912Y5tttuGEE07g0Ucf5e67Vw6qvvfee6mrq1ul3fqegEKvHXfc8RPb8vWvf52RI0cCMHLkSA477LCC21x/meCll15izJgxHH/88U3up2effbbVYxagA10OkHQecCLwMvAGMDUiLi2iieOBK4AzgL2Ax1K7c4CRQH+gK3B0RDwjaU/gcmBNYAlwSkT8Ox3ghwL9gDWAqyLiz5L6AecDC4BqSbsBVwM1wMfAjyLi4bxtGgjURMT3JI0A3k31/x/wk4i4JdU7BzgmrW9sRJyfym8DNid7xPIVETGsgX03CBgE0GWdjYrYZWZmjWvvX2aMGjWKIUNW7UA98sgj+fvf/87+++/P3/72N0455RSWLl1K165dGT58+Iqf+eVac801+fGPf8yll17Kddddx3XXXcdZZ53FNttsQ0Sw9957c911162oe9dddzF48GAGDx5M165d2WWXXbjiiitatS1DhgzhmGOO4brrrmOLLbZg9OjRALzyyiucdtpp3HPPPSu2780336Rr165cddVVK35GOHbsWM466yxef/11DjnkEKqrq7nvvvuA7NcLhxzS+s9GEdHqRlodhFQDDAf2JktMpgF/bm4SIGlN4HlgG+AbQJ+I+H6aNwe4LCKulPRdYLeIOE3SOsD7EfGxpIOAMyLiyHRA3TgiLpS0BjAJOBrYErg7tf2ipB+n6VMkbQ/cD2xLloCcHRGHFkgC1gKOBbYH7oiIbSQdDBwFfBsQcAdwcUSMl7R+RLyVtm8KcGBEvNnYvlijV+/odfLlzdltZp8a/olg+5k9ezY77LBDucOwRnzwwQcceOCBTJw48RPjBwp9fpKmRkRNobY6Sk/AfsDtEbEEQNKdRS5/KPBwRLwv6VbgPEk/jIhlaf6Y9HcqcESa7gmMlNQbCLJeAsguK+wi6aicer2BD4HJEfFiTsxXAqSehblkSUBjbouI5cDTkup/fHpwej2Z3vdI6xsPfF/S4al881TeaBJgZmad20svvcTQoUObNYCwKR0lCWjtNfzjgX3TWT/ABsAXgH+l9x+kv8tYuc3/R5Y4HC6pChiXE8tZEXHfKgFmlwMWtzLmD3KmlfP3txHx5wLrOwjYOyU348guC5iZWQXr3bt3swYPNkdHGRg4EegvqZukHkCz+/5St/5+wBYRURURVcCZZIlBY3oC89P0wJzy+4AzJHVN7W8raa0Cy48nG8OApG2BLcgGJhbrPuBbabuRtKmkjVN8b6cEYHuyywxmZm2uI1wmtuK15HPrED0BETFF0h3ADGAuUAssbObiRwAPRUTuWfbtwMXpmn5DLia7HPAj4KGc8uFAFTAt/crgdWBAgeX/BFwjqY5sYODAiPig2B8mRMT9knYAHkvLLiIb13Av8B1JM8mSi8eb097Om/ak1tdPzayFunXrxptvvunHCX/KRARvvvkm3boV12HcIQYGAkjqERGLJHUnO8seFBHF3yaqwtXU1ERtbW25wzCzT6mPPvqIefPmFf1ceiu/bt26sdlmm9G1a9dVyj8NAwMBhknakey690gnAGZm7a9r165stdVW5Q7D2kmHSQIi4oTc95KuAvbNq9YbeC6v7IqIuL4tYzMzM+uMOkwSkC8izix3DGZmZp1ZR/l1gJmZmbWzDjMw0EpD0nu07KeK1nIbkt3q2tqP93n78z5vf6Xa51tGRMF7ynfYywHWYv9uaBSotQ1Jtd7n7cv7vP15n7e/9tjnvhxgZmZWoZwEmJmZVSgnAZ1PwccNW5vyPm9/3uftz/u8/bX5PvfAQDMzswrlngAzM7MK5STAzMysQjkJ6CQkfVXSvyX9R9KQcsfTGUnaXNLDkmZLekrSD1L5BZLmS5qeXv9T7lg7E0lzJNWlfVubytaX9ICk59Lf9codZ2chabuc7/J0Se9KGuzveWlJ+ouk1yTNyilr8Hst6X/T/+//lvSVksXhMQGffpK6AM8CXwbmAVOA4yPi6bIG1slI6gX0iohpktYGppI9ZvoYYFFEXFrO+DorSXOAmoh4I6fsYuCtiBiakt71IuLccsXYWaX/W+YDnwdOwd/zkpF0ANmj42+IiD6prOD3Oj1cbxSwJ/BZ4F/AthGxrLVxuCegc9gT+E9EvBARHwI3AYeVOaZOJyIW1D/dMiLeA2YDm5Y3qop1GDAyTY8kS8as9L4EPB8Rc8sdSGcTEeOBt/KKG/peHwbcFBEfRMSLwH/I/t9vNScBncOmwMs57+fhg1ObklQF9AWeSEXfkzQzdfG5a7q0Arhf0lRJg1LZJhGxALLkDNi4bNF1bseRnYHW8/e8bTX0vW6z/+OdBHQOKlDm6zxtRFIP4FZgcES8C1wNbA1UAwuAy8oXXae0b0TsBnwNODN1o1obk7Q68HVgdCry97x82uz/eCcBncM8YPOc95sBr5Qplk5NUleyBODGiBgDEBGvRsSyiFgOXEuJuuksExGvpL+vAWPJ9u+raYxG/ViN18oXYaf1NWBaRLwK/p63k4a+1232f7yTgM5hCtBb0lYpez8OuKPMMXU6kgRcB8yOiN/llPfKqXY4MCt/WWsZSWulQZhIWgs4mGz/3gGcnKqdDNxengg7tePJuRTg73m7aOh7fQdwnKQ1JG0F9AYml2KF/nVAJ5F+rnM50AX4S0T8urwRdT6S9gMmAHXA8lT8U7L/LKvJuufmAN+uv65nrSPpc2Rn/5A99fTvEfFrSRsA/wC2AF4Cjo6I/EFW1kKSupNdg/5cRCxMZX/F3/OSkTQK6Ef2uOBXgfOB22jgey3pZ8C3gI/JLkX+syRxOAkwMzOrTL4cYGZmVqGcBJiZmVUoJwFmZmYVykmAmZlZhXISYGZmVqGcBJgVSdKy9BS1WZLulLRuE/UvkHR2E3UGpIeE1L//laSDShDrCElHtbadItc5OP3ErMOQtH36zJ6UtHXevDmSJuSVTa9/upukGkl/KEEMVblPjMubNzz3829rkjaR9HdJL6TbMT8m6fD2Wr91HE4CzIq3JCKq05O/3gLOLEGbA4AVB4GI+EVE/KsE7bar9NS5wUCHSgLI9u/tEdE3Ip4vMH9tSZsDSNohd0ZE1EbE95u7orQPihIRp7XXUz/TTa9uA8ZHxOciYneyG4xt1sbrXa0t27eWcRJg1jqPkR7kIWlrSfemM6sJkrbPryzpdElTJM2QdKuk7pL2IbtH+yXpDHTr+jN4SV+T9I+c5ftJujNNH5zO4KZJGp2eadCgdMb7m7RMraTdJN0n6XlJ38lpf7yksZKelnSNpM+kecdLqks9IBfltLso9Vw8AfyM7FGnD0t6OM2/Oq3vKUm/zIvnlyn+uvr9JamHpOtT2UxJRzZ3eyVVS3o8LTdW0nrpRlqDgdPqYyrgH8CxaTr/Tnn9JN3VRGy5+2BvST9K+2mWpME561lN0si07C31PSaSxkmqacZ+vih9v/4lac+03AuSvp7qdJF0SfqOzZT07QLb+kXgw4i4pr4gIuZGxJWNtZH2w7gU9zOSbkwJBZJ2l/RIiu0+rbz17bj0nXsE+IGk/pKeUNYj8y9JmzTweVh7iQi//PKriBfZM9UhuzvjaOCr6f2DQO80/XngoTR9AXB2mt4gp50LgbPS9AjgqJx5I4CjyO6S9xKwViq/GvgG2V3GxueUnwv8okCsK9olu8vbGWn698BMYG1gI+C1VN4PWAp8Lm3fAymOz6Y4NkoxPQQMSMsEcEzOOucAG+a8Xz9nf40DdsmpV7/93wWGp+mLgMtzll+viO2dCRyYpn9V307uZ1BgmTnAtsCj6f2TZL0ys3L2yV0NxZa/D4Ddye4quRbQA3iK7ImTVanevqneX1j5vRgH1DRjP38tTY8F7ge6ArsC01P5IODnaXoNoBbYKm97vw/8vpHvd8E20n5YSNZj8BmyBHi/FMOjwEZpmWPJ7lpav11/yvss629SdxpwWbn/PVf6y90zZsVbU9J0sv/UpwIPpLPSfYDR6eQIsv9A8/WRdCGwLtkB4r7GVhQRH0u6F+gv6RbgEOAnwIFkB6pJaX2rk/2n3JT6Z0rUAT0i4j3gPUlLtXJsw+SIeAFW3Np0P+AjYFxEvJ7KbwQOIOtWXkb2UKWGHKPsEcCrAb1S3DPTvDHp71TgiDR9EFn3dP0+eFvSoU1tr6SewLoR8UgqGsnKJ+A15S3gbUnHAbOB9xuo94nY0mTuPtgPGBsRi1NcY4D9yfb9yxExKdX7G9kB+dKc9veg4f38IXBvqlcHfBARH0mqI/suQvZshV20chxIT7L7zL/Y0IZLuirF/GFE7NFIGx+SfTfmpeWmp/W+A/Qh+3cAWbKXezvhm3OmNwNuTj0FqzcWl7UPJwFmxVsSEdXpoHMX2ZiAEcA7EVHdxLIjyM7sZkgaSHZ21ZSb0zreAqZExHupG/aBiDi+yNg/SH+X50zXv6///yD/XuJB4UeZ1lsaEcsKzVD2sJOzgT3SwXwE0K1APMty1q8CMbR0e4txM3AVMLCROoVig1X3QWP7qtC+zW+/IR9FOoUm5/OLiOVaeb1dZL0rjSWXTwFHrggg4kxJG5Kd8TfYhqR+rPqdqf/MBDwVEXs3sL7FOdNXAr+LiDtSexc0Eqe1A48JMGuhyB6s8n2yg9wS4EVJR0M2+ErSrgUWWxtYoOyRxCfmlL+X5hUyDtgNOJ2VZ1WPA/tK2iatr7ukbVu3RSvsqeyJlJ8h69qdCDwBHChpQ2UD344HHmlg+dxtWYfsILAwXf/9WjPWfz/wvfo3ktajGdubPo+3Je2fir7ZSIyFjAUupvHemUKx5RsPDEgxrkX2xL36Xx9sIan+YHk82b7NVcx+LuQ+4Iz0/ULStimGXA8B3SSdkVOWO5CzOW3k+jewUf12SeoqaacG6vYE5qfpkxuoY+3ISYBZK0TEk8AMsi7iE4FTJc0gO9s6rMAi55H9R/8A8ExO+U3AOSrwE7Z0hnkX2QH0rlT2OtkZ6yhJM8kOkp8YiNhCjwFDyR4V+yJZ1/YC4H+Bh8m2d1pENPT43mHAPyU9HBEzyK6xP0V2DXxSA8vkuhBYLw2MmwF8oYjtPZlsgOVMsife/aoZ6wMgIt6LiIsi4sNiYivQzjSyHp/JZJ/18PQ9gexSw8kpvvXJxnjkLlvMfi5kOPA0ME3ZzxH/TF6Pb+pNGECWbLwoaTLZpZNzm9tGXnsfko0buSjtk+lkl8YKuYDsktkE4I0itsvaiJ8iaGYrpC7asyPi0DKHYmbtwD0BZmZmFco9AWZmZhXKPQFmZmYVykmAmZlZhXISYGZmVqGcBJiZmVUoJwFmZmYV6v8DnSAXKi1feeMAAAAASUVORK5CYII=\n", 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PearsonRResult(statistic=-0.3694141197063736, pvalue=0.15907387634085648)\n", "Slope and P-value = PearsonRResult(statistic=-0.7353287397186885, pvalue=2.452330227676146e-06)\n", "1 219\n", "0 94\n", "Name: BrGMOFree, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.7837604120718673, pvalue=5.440815471850448e-22)\n", "Slope and P-value = PearsonRResult(statistic=-0.5038130387257052, pvalue=9.122472984736849e-08)\n", "Slope and P-value = PearsonRResult(statistic=-0.8958562206870048, pvalue=2.6817396083583336e-36)\n", "Slope and P-value = PearsonRResult(statistic=-0.46647432689245827, pvalue=4.6971439721776414e-05)\n", "Slope and P-value = PearsonRResult(statistic=-0.4166162130851542, pvalue=0.004416211971968835)\n", "Slope and P-value = PearsonRResult(statistic=0.781444204591535, pvalue=8.629528417292871e-22)\n", "Slope and P-value = PearsonRResult(statistic=-0.9330132524655704, pvalue=2.7142515787320045e-45)\n", "Slope and P-value = PearsonRResult(statistic=-0.8708412504835519, pvalue=5.480051653246185e-32)\n", "Slope and P-value = PearsonRResult(statistic=0.7381093846736984, pvalue=1.928395210871702e-18)\n", "Slope and P-value = PearsonRResult(statistic=-0.7189650081254114, pvalue=3.646578857799313e-17)\n", "1 125\n", "0 58\n", "Name: BrGMOFree, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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PearsonRResult(statistic=0.8026234842348042, pvalue=1.0166344815801895e-23)\n", "Slope and P-value = PearsonRResult(statistic=0.5183743531135103, pvalue=3.309715330785949e-08)\n", "1 125\n", "0 60\n", "Name: BrGMOFree, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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PearsonRResult(statistic=-0.8955385921575328, pvalue=3.0889133697994686e-36)\n", "Slope and P-value = PearsonRResult(statistic=0.5597428527171877, pvalue=1.4105445451300535e-09)\n", "1 149\n", "0 59\n", "Name: BrGMOFree, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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PearsonRResult(statistic=-0.8142325535902767, pvalue=7.224250440309474e-20)\n", "Slope and P-value = PearsonRResult(statistic=-0.5361010904345861, pvalue=2.4606192569825054e-05)\n", "1 135\n", "0 60\n", "Name: BrGMOFree, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.5041401247059343, pvalue=8.92166031449161e-08)\n", "Slope and P-value = PearsonRResult(statistic=0.03467109599755961, pvalue=0.732008554899858)\n", "Slope and P-value = PearsonRResult(statistic=-0.7776092373514674, pvalue=2.092197722370077e-18)\n", "Slope and P-value = PearsonRResult(statistic=-0.5819130267217192, pvalue=0.01804045759979379)\n", "Slope and P-value = PearsonRResult(statistic=0.779034424815573, pvalue=1.3862200807438224e-21)\n", "Slope and P-value = PearsonRResult(statistic=-0.6661658669064534, pvalue=1.145565350990657e-11)\n", "Slope and P-value = PearsonRResult(statistic=-0.9162508071236062, pvalue=1.603926103996668e-37)\n", "Slope and P-value = PearsonRResult(statistic=0.4693040881191594, pvalue=8.422691183200817e-07)\n", "Slope and P-value = PearsonRResult(statistic=-0.6481138683705693, pvalue=3.1177450034131304e-13)\n", "Slope and P-value = PearsonRResult(statistic=-0.5550155077538645, pvalue=2.067970266510285e-09)\n" ] } ], "source": [ "sample = pd.merge(microbiome, poultry[['SampleID', 'BrGMOFree','PastureTime','SampleType']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "#sample = sample[~sample.isin([np.nan, np.inf, -np.inf]).any(1)]\n", "sample = sample.drop(['Pathogen_Salmonella', 'new_Pathogen_Salmonella',\n", " 'Pathogen_Campy','new_Pathogen_Campy',\n", " 'Pathogen_Listeria','new_Pathogen_Listeria'],axis='columns')\n", "\n", "sample.BrGMOFree.replace({'Y': 1,'N':0}, regex=True, inplace=True)\n", "\n", "feces1=sample[(sample.SampleType=='Feces') & (sample.PastureTime=='Start')]\n", "feces2=sample[(sample.SampleType=='Feces') & (sample.PastureTime=='Mid')]\n", "feces3=sample[(sample.SampleType=='Feces') & (sample.PastureTime=='End')]\n", "\n", "soil1=sample[(sample.SampleType=='Soil') & (sample.PastureTime=='Start')]\n", "soil2=sample[(sample.SampleType=='Soil') & (sample.PastureTime=='Mid')]\n", "soil3=sample[(sample.SampleType=='Soil') & (sample.PastureTime=='End')]\n", "\n", "ceca=sample[sample.SampleType=='Ceca']\n", "wcrp=sample[sample.SampleType=='WCR-P']\n", "wcrf=sample[sample.SampleType=='WCR-F']\n", "\n", "\n", "print('Feces_Start', feces1.shape)\n", "print('Feces_Mid', feces2.shape)\n", "print('Feces_End', feces3.shape,'\\n')\n", "\n", "print('Soil_Start', soil1.shape)\n", "print('Soil_Mid', soil2.shape)\n", "print('Soil_End', soil3.shape,'\\n')\n", "\n", "print('Ceca', ceca.shape)\n", "print('WCR-P', wcrp.shape)\n", "print('WCR-F', wcrf.shape,'\\n')\n", "\n", "\n", "sampletypes = [feces1,feces2,feces3, soil1,soil2,soil3,ceca, wcrp, wcrf]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES_START\", 1: \"FECES_MID\", 2:\"FECES_END\",\n", " 3: \"SOIL_START\", 4: \"SOIL_MID\", 5: \"SOIL_END\",\n", " 6:\"CECA\", 7: \"WCR-P\", 8: \"WCR-F\"\n", " }\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType', 'PastureTime','BrGMOFree'],axis='columns'),sample.BrGMOFree,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1] #multiclass\n", " \n", " try:\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " except ValueError:\n", " pass\n", " \n", " print(pd.value_counts(sample['BrGMOFree'])) \n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", " \n", " plt.title(f\"BrGMOFree in {sample_name[indexing]} Model\")\n", " prelim3_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','BrGMOFree','PastureTime','SampleType'],axis='columns').columns)\n", " prelim3_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", "\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim3_plot.max(), decimals=None, symbol=''))\n", "\n", " xmax=prelim3_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " \n", " plt.show()\n", " \n", " prelim3_plot.nlargest(10).to_csv(\"prelim3.csv\")\n", " top10 = pd.read_csv('prelim3.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", "# plt.plot(pdp[1][0], pdp[0][0],'.')\n", "# plt.ylabel('Partial dependence'), plt.xlabel(top10[feature])\n", "# plt.show()\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"BrGMOFree_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (6) BrSoyFree" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Feces_Start (200, 878)\n", "Feces_Mid (313, 878)\n", "Feces_End (185, 878) \n", "\n", "Soil_Start (199, 878)\n", "Soil_Mid (313, 878)\n", "Soil_End (183, 878) \n", "\n", "Ceca (185, 878)\n", "WCR-P (208, 878)\n", "WCR-F (195, 878) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "0 165\n", "1 35\n", "Name: BrSoyFree, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.6359457981225567, pvalue=0.00027571205353562543)\n", "Slope and P-value = PearsonRResult(statistic=-0.5366817918543227, pvalue=8.630817944257701e-09)\n", "Slope and P-value = PearsonRResult(statistic=0.37187510757241626, pvalue=0.0005816645548498669)\n", "Slope and P-value = PearsonRResult(statistic=0.9163234997723249, pvalue=9.792398470852193e-41)\n", "Slope and P-value = PearsonRResult(statistic=0.9538252415269773, pvalue=5.426849592722056e-53)\n", "Slope and P-value = PearsonRResult(statistic=-0.3754048450406875, pvalue=0.00011879339756960552)\n", "Slope and P-value = PearsonRResult(statistic=0.7354934262450092, pvalue=2.4331566136178118e-06)\n", "Slope and P-value = PearsonRResult(statistic=0.8398649152817578, pvalue=8.93817068458601e-05)\n", "Slope and P-value = PearsonRResult(statistic=0.9033471363310668, pvalue=1.6746726783657856e-21)\n", "Slope and P-value = PearsonRResult(statistic=0.5570321103531951, pvalue=1.7578504637320626e-09)\n", "0 268\n", "1 45\n", "Name: BrSoyFree, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.4278883605764026, pvalue=0.000377350722540569)\n", "Slope and P-value = PearsonRResult(statistic=0.5875015669612548, pvalue=0.165431210232407)\n", "Slope and P-value = PearsonRResult(statistic=0.97525936130438, pvalue=4.770818679795005e-66)\n", "Slope and P-value = PearsonRResult(statistic=0.47466588450187386, pvalue=0.040022939515744496)\n", "Slope and P-value = PearsonRResult(statistic=0.4660862007162413, pvalue=1.023745655869459e-06)\n", "Slope and P-value = PearsonRResult(statistic=-0.13455161897027634, pvalue=0.18198185099845085)\n", "Slope and P-value = PearsonRResult(statistic=0.01161588710106725, pvalue=0.9086810736742192)\n", "Slope and P-value = PearsonRResult(statistic=0.5219118576188639, pvalue=1.6059029071079572e-05)\n", "Slope and P-value = PearsonRResult(statistic=0.9484116190197851, pvalue=6.975806234805905e-09)\n", "Slope and P-value = PearsonRResult(statistic=0.706142219399983, pvalue=2.2883975397068776e-16)\n", "0 150\n", "1 35\n", "Name: BrSoyFree, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.6230818457980012, pvalue=0.0019505795366310083)\n", "Slope and P-value = PearsonRResult(statistic=0.7925381854076237, pvalue=5.275250017802757e-11)\n", "Slope and P-value = PearsonRResult(statistic=0.5128298990766169, pvalue=0.035285473981857066)\n", "Slope and P-value = PearsonRResult(statistic=0.9004227071753163, pvalue=1.993466078900025e-06)\n", "Slope and P-value = PearsonRResult(statistic=0.5875105626265885, pvalue=1.9439098604204272e-08)\n", "Slope and P-value = PearsonRResult(statistic=0.5461284612539059, pvalue=0.015559617887643037)\n", "Slope and P-value = PearsonRResult(statistic=0.7837460786074995, pvalue=5.45646302809468e-22)\n", "Slope and P-value = PearsonRResult(statistic=0.8423233771979266, pvalue=2.7096044289792582e-18)\n", "Slope and P-value = PearsonRResult(statistic=0.26467955813450444, pvalue=0.022672810726820678)\n", "Slope and P-value = PearsonRResult(statistic=0.4737662829825652, pvalue=6.404757829590522e-07)\n", "0 164\n", "1 35\n", "Name: BrSoyFree, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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PearsonRResult(statistic=0.5773059728107245, pvalue=3.2260783041930973e-10)\n", "Slope and P-value = PearsonRResult(statistic=0.57428747579261, pvalue=9.108464108874801e-09)\n", "0 268\n", "1 45\n", "Name: BrSoyFree, dtype: int64\n" ] }, { "data": { "image/png": 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UJGxN1gVf6R7gMOBYsqAB4BngMElrSloL+BFZFz5Ad0l7pOVjU9srT9KfpPYfARARXwCzJB0GIGmNNKdhPtA5pw2PAqdJ6pDybZm2W513gW1TnV2A/Ys7FGZmZvWrwa8+I2KSpDHANLITYDlQn7PpHgFOlTQdeINs+KFy259JehXYNiImprQpkkYCE1O26yPiJUmlwGvAiZL+CbwFXJMCkOvIuvlnApNytv0T4J+SLgC+AY4EpgNL0lDISOBysl9CTEm9GR+TBS8FpZ6LO1JdbwEv1f6wFLbDJl0o9x3KzMysCIqo2tveABuROkXEgnTF/QwwKCKmNPiGLa+ysrIoLy9v6maYmVkzIWlyROT9sUFjjWcPl7QtWTf+KAcJZmZmLUOjBAoRsdKdCiVdBexVJVtPsm72XJdHxIiGbJuZmZkV1iQz5CPi9KbYrpmZmdVOs3/Wg5mZmTUdBwpmZmZWkAMFMzMzK8iBgpmZmRXkQMHMzMwKcqBgZmZmBfkBQm1Qxex5lA55qKmbYWYt0Ezf/r3NcY+CmZmZFeRAwczMzApyoFCFpJGSjqhj2TJJ/0jLQyUNrof2HJaek2FmZtboHCjUo4goj4iz6rnaw4BaBQqSPPfEzMzqRasMFCSdJ+l1SY9LGl3XK3tJv5c0SdLLkoZLUkofJ+kiSRMlvSlp75TeT9KDOVXsJOkpSW9J+lnK00nSk5KmSKqQdGjO9k6QNF3SNEk3SdoTOAS4WNJUSZun1yOSJkt6VtLWqexISX+XNBa4qG5HzszMbGWt7spTUhkwAOhNtn9TgMl1rO7KiLgg1XsTcDDwQFrXPiL6SPoBcD5wQJ7yOwK7A2sBL0l6CPgI+FFEfCFpfeAFSWPIeg1+C+wVEZ9I6hoRc9O6ByPirtSOJ4FTI+ItSbsBVwP/nba3JXBARCzNc1wGAYMA2q29QR0Ph5mZtTWtLlAA+gL3R8QiAEkP1JC/OvtJOhdYE+gKvMKKQOGe9HcyUFqgfGU7FqUr/T7AQ8CfJe0DLAM2ATYiO9nfFRGfAETE3KqVSeoE7AncmTo3ANbIyXJnviAh1TccGA6wRreeUf1um5mZZVpjoKCasxRRiVRCdrVeFhHvSxoKlORk+Sr9XUrh41j1hBzA8cAGwC4R8Y2kmale5clf1WrA5xHRq8D6L2sob2ZmViutcY7CeKC/pJJ0BV7Xu4NUBgWfpHrq8kuIQ1M71gP6AZOALsBHKUjYD/ivlPdJ4KiUF0ldU/p8oDNARHwBzJB0ZMojSTvVoV1mZmZFaXWBQkRMAsYA08iGB8qBebWooj3wVUR8DlwHVAD3kZ3ka2si2VDDC8AfI+ID4BagTFI5We/C66ndrwB/Ap6WNA34e6rjNuAcSS9J2jyVOTnleQU4FDMzswaiiNY3XC2pU0QskLQm8AwwKCKmFFFuNbKA4IR04m6VysrKory8vKmbYWZmzYSkyRFRlm9da5yjADA83aSoBBhVZJCwMfAEMLY1BwlmZma10SoDhYg4Lve9pKuAvapk6wm8VSXt4ogY0ZBtMzMza0laZaBQVUSc3tRtMDMza4la3WRGMzMzqz8OFMzMzKwgBwpmZmZWkAMFMzMzK8iBgpmZmRXkQMHMzMwKcqBgZmZmBbWJ+yjYyipmz6N0yENN3QwzayFmDqvrs/WsNXCPgpmZmRXkQMHMzMwKavBAQdLukl6UNFXSa5KG1rL8OEllaflhSevUouwhkobUkGdjSXel5YGSrqxN+wrU2U/SnnUoVybpH6u6fTMzs/rSGHMURgFHRcQ0Se2ArepaUUT8oJb5xwBjasjzAXBEXdtUQD9gAfBcsQUktY+IcsDPfzYzs2ajqB4FSedJel3S45JGSxpci21sCMwBiIilEfFqqnMtSf+SNEnSS5IOTekdJd0mabqk24GOOe2YKWl9SaWpPddLelnSLZIOkDRB0luS+qT8y3sIJI2U9A9Jz0l6R9IRKb1U0ss57d1M0iOS3pB0fs6275M0WdIrkgblpB8kaYqkaZKelFQKnAr8MvWi7C1pA0l3p32dJGmvVHaopOGSHgNuTD0RD+asG5yznZdTW4va9zyf4SBJ5ZLKly6cV4uPz8zM2rIaexRSt/8AoHfKPwWYXIttXAq8IWkc8AgwKiIWA78FnoqIn6bhhImSngB+DiyMiB0l7Zi2l88WwJHAIGAScBzQFzgE+D/gsDxluqU8W5P1NNyVJ08fYHtgITBJ0kPpSv+nETFXUseUfjdZoHUdsE9EzJDUNeW5FlgQEZcASLoVuDQixkvqDjwKbJO2twvQNyIWSepX7ZFchX2PiOHAcIA1uvWMIrdjZmZtXDFDD32B+yNiEYCkB2qzgYi4QNItwIFkJ7RjybrmDwQOyblqLgG6A/sA/0hlp0uaXqDqGRFRkdr0CvBkRISkCqC0QJn7ImIZ8KqkjQrkeTwiPk313kO2/+XAWZJ+lPJsBvQENgCeiYgZqb1zC9R5ALCtpMr3a0vqnJbHVB7bWqjLvpuZmdVaMYGCas5SvYh4G7hG0nXAx5LWS/UOiIg3VtpYdjIt5or3q5zlZTnvl1F4v3LLFNqvqtuOdKV/ALBHRCxMvSMlqY5i2rpaKrtSQJD29csCZZaw8tBQSc5yXfbdzMys1oqZozAe6C+pRFInoFZ33pD0Q624lO4JLAU+J+t+P7NynaTeKc8zwPEpbXtgx9psrx58V1LXNMRwGDAB6AJ8loKErYHdU97ngX0l9Ujt7ZrS5wOdc+p8DDij8o2kXkW0Yyawc8q/M9CjjvtjZmZWZzUGChExiWw8fxpwD1k3fG1mw/2EbI7CVOAm4PiIWAr8EegATE+TCf+Y8l8DdEpDDucCE2uxrfowPrVzKnB3mp/wCNA+temPwAsAEfEx2TyBeyRNA25PdTwA/KhyMiNwFlCWJmi+SjbZsSZ3A13TcTsNeLOe9s/MzKxoiqi551xSp4hYIGlNsiv+QRFRaJKhNXNlZWVRXu5fYZqZWUbS5Igoy7eu2PHs4ZK2JRsnH+UgwczMrG0oKlCIiONy30u6CtirSraewFtV0i6PiBF1b56ZmZk1pTrNkI+I0+u7IWZmZtb8+KFQZmZmVpADBTMzMyvIgYKZmZkV5EDBzMzMCnKgYGZmZgU5UDAzM7OCHCiYmZlZQX7SYBtUMXsepUMeaupmmFkTmjmsVs/3szbMPQpmZmZWkAMFMzMzK6jFBArK/E7SW5LelDRW0nbNoF0zJa1fD/U8LGmdtLwg/S1Nj+A2MzNrEi1pjsLpwJ7AThGxUNKBwBhJ20XE4iZu2yqLiB80dRvMzMyqatQeBUnnSXpd0uOSRksaXIvivwHOjIiFABHxGPAccHyq+yBJUyRNk/RkSltL0r8kTZL0kqRDU3qppGdT/imS9kzp/SSNk3RXauctkpTW7Z/qqEh1rpHTtnMkTUyvLVL+/pJeTGWekLRRSu8kaUSqZ7qkASm92p4JSQMlXZnz/sHU3naSRkp6OdX5ywLlB0kql1S+dOG8Whx2MzNryxqtR0FSGTAA6J22OwWYXGTZtYG1IuLtKqvKge0kbQBcB+wTETMkdU3rfws8FRE/Td36EyU9AXwEfDciFkvqCYwGylKZ3sB2wAfABGAvSeXASGD/iHhT0o3AacBlqcwXEdFH0gkp7WBgPLB7RISkU4BzgV8D5wHzImKHtG/rFnMMqtEL2CQitk/1rZMvU0QMB4YDrNGtZ6ziNs3MrI1ozKGHvsD9EbEIQNID9VCngAB2B56JiBkAETE3rT8QOCSn56IE6E4WBFwpqRewFNgyp86JETErtXEqUArMB2ZExJspzyiyoZDL0vvROX8vTcubArdL6gasDsxI6QcAx1RuLCI+q9Oer/AO8B1JVwAPAY+tYn1mZmbLNebQg+paMCK+AL6U9J0qq3YGXmVFwJBvmwMiold6dY+I14BfAh8CO5H1JKyeU+arnOWlZMFUTW2PPMtXAFemnoOfkwUplW2qyxX9Elb+vEpgeaCxEzCOLHi5vg51m5mZ5dWYgcJ4oL+kEkmdgNre7eNi4B+SOgJIOoCsl+JW4HlgX0k90rrKoYdHgTNz5hn0TuldgDkRsQz4CdCuhm2/DpRWzj9IZZ7OWX90zt/nc7YxOy2fmJP3MeCMyje1GHqYCfSStJqkzYA+qfz6wGoRcTfZsMbORdZnZmZWo0YbeoiISZLGANOAd8nmF9RmVt0VwLpAhaSlwP8HHJqGMhZJGgTcI2k10hwE4I9kwwPTU7Awk2z+wNXA3ZKOBMYCX9bQ9sWSTgLulNQemARcm5NlDUkvkgVex6a0oSn/bOAFoEdKvxC4Kv3scSnwB+CeIvZ/AtnwRQXwMtkcD4BNgBFpvwH+t4i6zMzMiqKIxpvXJqlTRCyQtCbwDDAoIqbUVM7qV1lZWZSXlzd1M8zMrJmQNDkiyvKta+z7KAyXtC3Z+PooBwlmZmbNW6MGChFxXO57SVcBe1XJ1hN4q0ra5RExoiHbZmZmZt/WpHdmjIjTm3L7ZmZmVr0W86wHMzMza3wOFMzMzKwgBwpmZmZWkAMFMzMzK8iBgpmZmRXkQMHMzMwKcqBgZmZmBTXpfRSsaVTMnkfpkIeauhlm1oBmDqvtc/fM8nOPgpmZmRXkQMHMzMwKatWBgqSRko6okragiHIL0t+NJd1Vx23PlLR+XcpWqef/VrUOMzOzumrVgcKqiogPIuKImnM2qFoHCpLaNURDzMys7Wn2gYKk8yS9LulxSaMlDa7Hus+RNEnSdEl/yLO+VNLLabmdpEskVaT8Z6b0/SW9lNL/JWmNnCrOkTQxvbZI+ftLejGVeULSRim9k6QROfUPkDQM6ChpqqRbUr4fp/qmSvpnZVAgaYGkCyS9COyRZ18GSSqXVL504bz6OoRmZtbKNetAQVIZMADoDRwOlNWhmovTSXWqpKk5dR9I9kjrPkAvYBdJ+1RTzyCgB9A7InYEbpFUAowEjo6IHch+RXJaTpkvIqIPcCVwWUobD+weEb2B24BzU/p5wLyI2CHV/1REDAEWRUSviDhe0jbA0cBeEdELWAocn8qvBbwcEbtFxPiqjY+I4RFRFhFl7dbsUtMxMzMzA5r/zyP7AvdHxCIASQ/UoY5zImL5PIOcOQoHptdL6X0nssDhmQL1HABcGxFLACJirqSdgBkR8WbKMwo4nRVBweicv5em5U2B2yV1A1YHZuTUf0zlxiLiszxt2B/YBZgkCaAj8FFatxS4u0DbzczM6qS5Bwpq4Lr/EhH/rEX+yJNWncizfAXw94gYI6kfMLSa+vO1YVRE/G+edYsjYmkN5c3MzGqlWQ89kHXT95dUIqkTUJ93EHkU+GmqF0mbSNqwmvyPAadKap/ydwVeB0or5x8APwGezilzdM7f59NyF2B2Wj6xSv1nVL6RtG5a/EZSh7T8JHBEZTsldZX0X8XsrJmZWV006x6FiJgkaQwwDXgXKAfqZSZeRDyWxvyfT934C4Afs6Irv6rrgS2B6ZK+Aa6LiCslnQTcmQKIScC1OWXWSJMLVwOOTWlDU/7ZwAtk8x4ALgSuSpMnlwJ/AO4BhqdtTknzFH4HPCZpNeAbsqGOd2uz7zts0oVy37XNzMyKoIiaerublqROEbFA0ppk8wcGRcSUpm5XS1ZWVhbl5eVN3QwzM2smJE2OiLw/GGjWPQrJcEnbAiVk4/MOEszMzBpJsw8UIuK43PeSrgL2qpKtJ/BWlbTLI2JEQ7bNzMystWv2gUJVEXF6U7fBzMysrWjuv3owMzOzJuRAwczMzApyoGBmZmYFOVAwMzOzghwomJmZWUEOFMzMzKygFvfzSFt1FbPnUTrkoaZuhpnlmOnbqlsz5R4FMzMzK8iBgpmZmRXU6gMFSSMlHVElbUENZfpJerBhW1Y7kkrTkyWbZfvMzKx1avWBgpmZmdVdiwgUJJ0n6XVJj0saLWlwPdV7k6RDc97fIumQKnnWkvQvSZMkvVSZX9JASfdJekDSDElnSPpVyvOCpK4pX6/0frqkeyWtm9LHSbpI0kRJb0raO6W3k3Rx2t50ST+vYR/6SHoubfc5SVvVx7ExMzODFhAoSCoDBgC9gcOBvM/LrsHFkqZWvnLSrwdOStvpAuwJPFyl7G+BpyJiV2C/VNdaad32wHFAH+BPwMKI6A08D5yQ8twI/CYidgQqgPNz6m4fEX2As3PSTwbmpe3tCvxMUo9q9u11YJ+03d8Df86XSdIgSeWSypcunFdNdWZmZiu0hJ9H9gXuj4hFAJIeqEMd50TEXZVvKucoRMTTkq6StCFZEHJ3RCyRlFv2QOCQnF6MEqB7Wh4bEfOB+ZLmAZVtqwB2TMHHOhHxdEofBdyZU/c96e9koDRnezvmzKvoQvYY7TcL7FsXYJSknkAAHfJliojhwHCANbr1jAJ1mZmZraQlBAqqOcsquQk4HjgG+GmB7Q+IiDdWSpR2A77KSVqW834ZxR3byvxLc/ILODMiHq2yvdICdfyRLGD5UcozrojtmpmZFaXZDz0A44H+kkokdQLq+64kI8m6/omIV/KsfxQ4U6mbQVLvYiuOiHnAZ5XzD4CfAE9XU6Rye6dJ6pC2t2XOUEc+XYDZaXlgsW0zMzMrRrPvUYiISZLGANOAd4FyoN4G2SPiQ0mvAfcVyPJH4DJgegoWZgIH12ITJwLXSloTeIc0J6Ia15MNQ0xJ2/sYOKya/H8lG3r4FfBULdplZmZWI0U0/+FqSZ0iYkE62T4DDIqIKfVU95pkcwp2Tj0ArV5ZWVmUl5c3dTPMzKyZkDQ5IvL+WKAlDD0ADE+/VphCNuGwvoKEA8h+NXBFWwkSzMzMaqPZDz0ARMRxue8lXQXsVSVbT+CtKmmXR8SIaup9ghW/YDAzM7MqWkSgUFVEnN7UbTAzM2sLWsrQg5mZmTUBBwpmZmZWkAMFMzMzK8iBgpmZmRXkQMHMzMwKcqBgZmZmBTlQMDMzs4Ja5H0UbNVUzJ5H6ZCHmroZZm3OzGH1/Uw7s4bnHgUzMzMryIGCmZmZFdQmAgVJIyUd0cjbHCjpynqop5+kPeujTWZmZrXVJgKFFq4fUKtAQZLnnpiZWb1oMYGCpPMkvS7pcUmjJQ1exfraSbpY0iRJ0yX9PKX3k/RgTr4rJQ1My7tKek7SNEkTJXWWVCJphKQKSS9J2i9nM5tJekTSG5LOz6nzPkmTJb0iaVBO+kGSpqT6n5RUCpwK/FLSVEl7S9pA0t2p3ZMk7ZXKDpU0XNJjwI159neQpHJJ5UsX+onaZmZWnBZx5SmpDBgA9CZr8xRg8ipWezIwLyJ2lbQGMCGdZAu1YXXgduDoiJgkaW1gEfALgIjYQdLWwGOStkzF+gDbAwuBSZIeiohy4KcRMVdSx5R+N1nQdh2wT0TMkNQ15bkWWBARl6R23ApcGhHjJXUHHgW2SdvbBegbEYuqtj8ihgPDAdbo1jPqfNTMzKxNaRGBAtAXuL/yBCjpgXqo80Bgx5y5C12AnsDXBfJvBcyJiEkAEfFFaktf4IqU9rqkd4HKQOHxiPg05bsn7Uc5cJakH6U8m6XtbgA8ExEzUl1zC7TjAGBbSZXv15bUOS2PyRckmJmZ1VVLCRRUc5Y61XlmRDy6UmJ24s8dkinJyZ/vSry6tlXNH5L6kZ3s94iIhZLGpW0Uqr+q1VLZlQKCFDh8WUR5MzOzorWUOQrjgf5pPkAnoD7uWvIocJqkDgCStpS0FvAu2RX7GpK6APun/K8DG0vaNeXvnCYNPgMcX1kH0B14I5X5rqSuaYjhMGACWc/FZylI2BrYPeV9HthXUo9UV9eUPh+o7DEAeAw4o/KNpF71cCzMzMzyahE9CmlOwBhgGtmJvByo7Yy8f0q6LC2/D+wFlAJTlF2OfwwcFhHvS7oDmA68BbyU2vC1pKOBK9KJfxFZz8DVwLWSKoAlwMCI+Cpd4Y8HbgK2AG6NiPKU71RJ08kCihdS/R+niY33SFoN+Aj4LvAAcJekQ4EzgbOAq1L5ykDl1FoeCzMzs6IoomXMa5PUKSIWSFqT7OQ4KCKmNHW7WqKysrIoLy9v6maYmVkzIWlyRJTlW9ciehSS4ZK2JRvPH+UgwczMrOG1mEAhIo7LfS/pKrLhg1w9yYYLcl0eESMasm1mZmatVYsJFKqKiNObug1mZmatXUv51YOZmZk1AQcKZmZmVpADBTMzMyvIgYKZmZkV5EDBzMzMCnKgYGZmZgU5UDAzM7OCWux9FKzuKmbPo3TIQ03dDLNWb+aw+nh+nVnTco+CmZmZFeRAwczMzApqNoGCpN0lvShpqqTXJA1N6f0k7VmP2+kl6QerUP769HCqqukDJV2Zlk+VdEIN9QyVNLjAuufS31JJL9e1rWZmZquqOc1RGAUcFRHTJLUDtkrp/YAFwHNVC0hqHxFLarmdXkAZ8HBdGhkRpxSR59q61J1Tvt4CIzMzs1VRrz0Kks6T9LqkxyWNLnTFXMCGwByAiFgaEa9KKgVOBX6Zehr2ljRS0t8ljQUukrS5pEckTZb0rKStU1tGSro2pb0p6WBJqwMXAEen+o6W1EfSc5JeSn+3SuXbSbpEUoWk6ZLOTOnjJJWl5ZNS3U+T8yTL3N6ClP+yVPfLkvrk7PO2af07ks7KKb8gz7Fd3mOR3j+YelvapX19ObX1lwU+m0GSyiWVL104rxYfi5mZtWX11qOQTp4DgN6p3inA5FpUcSnwhqRxwCPAqIiYKelaYEFEXJK2czKwJXBARCyV9CRwakS8JWk34Grgv1OdpcC+wObAWGAL4PdAWUSckepbG9gnIpZIOgD4c9qPQUAPoHda17XK/nYD/gDsAsxL9b9UYN/Wiog9Je0D/AvYPqVvDewHdE77fk1EfFOLYwZZD8kmEbF9atc6+TJFxHBgOMAa3XpGLbdhZmZtVH0OPfQF7o+IRQCSHqhN4Yi4QNItwIHAccCxZMMO+dyZgoROwJ7AnZIq162Rk++OiFgGvCXpHbITc1VdgFGSegIBdEjpBwDXVg5tRMTcKuV2A8ZFxMcAkm4nC2DyGZ3qeEbS2jkn84ci4ivgK0kfARsBswrUUcg7wHckXQE8BDxWy/JmZmYF1efQg2rOUr2IeDsirgH2B3aStF6BrF+mv6sBn0dEr5zXNrlVVt1Enrr+CIxNV+T9gZKUrgL5a6qvmHyV77/KSVtK9YHbElb+vEoAIuIzYCdgHHA6cH2RbTIzM6tRfQYK44H+kkrSlX6t7jQi6Yda0S3Qk+zE+Tkwn6xr/lsi4gtghqQjUx2StFNOliMlrSZpc+A7wBt56usCzE7LA3PSHwNOldQ+1b3S0APwItBP0nqSOgBHVrN7R6c6+gLzIqIukwRmAr3S/mwG9El1rg+sFhF3A+cBO9ehbjMzs7zqLVCIiEnAGGAacA9QTjZ2X6yfkI3TTwVuAo6PiKXAA8CPKicz5il3PHCypGnAK8ChOeveAJ4G/k02j2Ex2VyCbSsnMwJ/Bf4iaQLQLqfs9cB7wPRU93FV9ncOMBR4HniCbE5GIZ+lnzxeC5xc45HIbwIwA6gALsnZ3ibAuHTcRgL/W8f6zczMvkUR9TevTVKniFggaU3gGWBQRFR3Am0wkkYCD0bEXU2x/Zx2jAMGR0R5U7YjV1lZWZSXN5vmmJlZE5M0OSLK8q2r7/soDFd2M6ISsl8tNEmQYGZmZvWjXgOFiFipe17SVeTcXyDpCbxVJe3yiBhRz20ZWJ/11VVE9GvqNpiZmdVVg96ZMSJOb8j6zczMrGE1p1s4m5lZC/DNN98wa9YsFi9e3NRNsVoqKSlh0003pUOHDjVnThwomJlZrcyaNYvOnTtTWlpKzs3urJmLCD799FNmzZpFjx49ii7XbJ4eaWZmLcPixYtZb731HCS0MJJYb731at0T5EDBzMxqzUFCy1SXz82BgpmZmRXkOQpmZrZKSoc8VK/1zRxW3BMA7r33Xg4//HBee+01tt46e+bfuHHjuOSSS3jwwQeX5xs4cCAHH3wwRxxxBP369WPOnDmUlJSw+uqrc91119GrVy8A5s2bx5lnnsmECRMA2Guvvbjiiivo0qULAG+++SZnn302b775Jh06dGCHHXbgiiuuYKONNqrzvs6dO5ejjz6amTNnUlpayh133MG66677rXyXXnop119/PZLYYYcdGDFiBCUlJUydOpVTTz2VxYsX0759e66++mr69OlDRUUFf/vb3xg5cmSd21bJgUIbVDF7Xr3/wzZri4o9oVnDGD16NH379uW2225j6NChRZe75ZZbKCsrY8SIEZxzzjk8/vjjAJx88slsv/323HjjjQCcf/75nHLKKdx5550sXryYH/7wh/z973+nf//+AIwdO5aPP/54lQKFYcOGsf/++zNkyBCGDRvGsGHDuOiii1bKM3v2bP7xj3/w6quv0rFjR4466ihuu+02Bg4cyLnnnsv555/P97//fR5++GHOPfdcxo0bxw477MCsWbN477336N69e53bBx56MDOzFmjBggVMmDCBG264gdtuu61Odeyxxx7Mnp09E/A///kPkydP5rzzzlu+/ve//z3l5eW8/fbb3Hrrreyxxx7LgwSA/fbbj+23336V9uP+++/nxBNPBODEE0/kvvvuy5tvyZIlLFq0iCVLlrBw4UI23nhjIJtz8MUXXwBZj0hlOkD//v3rfGxyuUfBzMxanPvuu4+DDjqILbfckq5duzJlyhR23rl2D8995JFHOOywwwB49dVX6dWrF+3arXg2YLt27ejVqxevvPIKL7/8MrvsskuNdc6fP5+99873/EK49dZb2XbbbVdK+/DDD+nWrRsA3bp146OPPvpWuU022YTBgwfTvXt3OnbsyIEHHsiBBx4IwGWXXcb3vvc9Bg8ezLJly3juueeWlysrK2PYsGGce+65Nba7Og4UzMysxRk9ejRnn302AMcccwyjR49m5513LjirPzf9+OOP58svv2Tp0qVMmZI9kigi8pYtlF5I586dmTp1avE7UoTPPvuM+++/nxkzZrDOOutw5JFHcvPNN/PjH/+Ya665hksvvZQBAwZwxx13cPLJJ/PEE08AsOGGG/LBBx+s8vbb7NCDpPaSPpH0lyLy9pO0Z877UyWdUMft9pL0gyK3+WBN+czM2ppPP/2Up556ilNOOYXS0lIuvvhibr/9diKC9dZbj88++2yl/HPnzmX99ddf/v6WW25hxowZHHfccZx+evakge22246XXnqJZcuWLc+3bNkypk2bxjbbbMN2223H5MmTa2zb/Pnz6dWrV97Xq6+++q38G220EXPmzAFgzpw5bLjhht/K88QTT9CjRw822GADOnTowOGHH76852DUqFEcfvjhABx55JFMnDhxebnFixfTsWPHGttckzYbKAAHAm8AR6nmcLEfsDxQiIhrI+LGOm63F1BjoGBmZvndddddnHDCCbz77rvMnDmT999/nx49ejB+/Hh69uzJBx98wGuvvQbAu+++y7Rp05b/sqFShw4duPDCC3nhhRd47bXX2GKLLejduzcXXnjh8jwXXnghO++8M1tssQXHHXcczz33HA89tGIi+COPPEJFRcVK9Vb2KOR7VR12ADjkkEMYNWoUkJ30Dz300G/l6d69Oy+88AILFy4kInjyySfZZpttANh44415+umnAXjqqafo2bPn8nJvvvnmKs+hgBY89CDpPOB44H3gE2ByRFxSiyqOBS4HTgN2B55P9R4E/Blol+o9GTgVWCrpx8CZwP7Agoi4RNI44EVgP2Ad4OSIeFZSCXANUAYsAX4FTAAuADpK6gv8BZgBXAZ0BBYBJ0XEG1X2dS3gCmAHss9saETcL6kUuAlYK2U9IyKeIw9Jg4BBAO3W3qAWh8nMrHqN/euP0aNHM2TIkJXSBgwYwK233sree+/NzTffzEknncTixYvp0KED119//fKfOObq2LEjv/71r7nkkku44YYbuOGGGzjzzDPZYostiAj22GMPbrjhhuV5H3zwQc4++2zOPvtsOnTowI477sjll1++SvsyZMgQjjrqKG644Qa6d+/OnXfeCcAHH3zAKaecwsMPP8xuu+3GEUccwc4770z79u3p3bs3gwYNAuC6667jF7/4BUuWLKGkpIThw4cvr3vs2LH88Ier/tkoIla5ksYmqQy4HtiD7MQ5BfhnsYGCpI7A28AWwI+B7SPiLEkbpLr2iYgZkrpGxFxJQ0mBQSq//H0KFCZHxK/TkMKvIuIASb9O9Z4kaWvgMWBL4BigLCLOSHWtDSyMiCWSDgBOi4gBkvoBgyPiYEl/Bl6NiJslrQNMBHoDASyLiMWSegKjI6Kspv1fo1vP6HbiZcUcKjOrRlv9eeRrr722/IrWmqevvvqKfffdl/Hjx9O+/cp9Avk+P0mTC50/WmqPQl/g/ohYBCDpgVqWPxgYGxELJd0NnCfpl2Q9C89ExAyAiJhbZH33pL+TgdKcNl6R6nld0rtkgUJVXYBR6UQfQL5Heh0IHCJpcHpfAnQHPgCulNQLWFqgfjMza2Pee+89hg0b9q0goS5aaqCwqjcZPxbYS9LM9H49sqEDkZ2sa+ur9HcpK45psW38I1nQ8qM0lDAuTx4BA/IMSQwFPgR2Iptv4me+mpkZPXv2XGm+wqpoqZMZxwP9JZVI6gQU3f+Xuvr7At0jojQiSoHTyYKH54F9JfVIebumYvOBzrVs4zNkcyiQtCVZD8AbeerqAsxOywML1PUocGblpEtJvXPKzomIZcBPyOZVmJk1uJY4bG11+9xaZI9CREySNAaYBrwLlAPziix+OPBURHyVk3Y/8Ffgf8gm/N0jaTXgI+C7wAPAXZIOJZvMWIyrgWslVZBNZhwYEV9JGgsMkTSVbDLjX8mGHn4FPFWgrj+STXicnoKFmWTDJ1cDd0s6EhgLfFlMw3bYpAvlbXRs1cxWXUlJCZ9++qkfNd3CRASffvopJSUltSrXIiczAkjqFBELJK1JdvU+KCKmNHW7WoKysrIoLy9v6maYWQv1zTffMGvWLBYv9mhnS1NSUsKmm25Khw4rT4drjZMZAYZL2pZsYt8oBwlmZo2jQ4cO9OjRo6mbYY2kxQYKEXFc7ntJVwF7VcnWE3irStrlETGiIdtmZmbWWrTYQKGqiDi9qdtgZmbW2rTUXz2YmZlZI2ixkxmt7iTNJ/uppjWe9cluCW6Nx8e88fmYN776Oub/FRF57+/faoYerFbeKOZWz1Z/JJX7mDcuH/PG52Pe+BrjmHvowczMzApyoGBmZmYFOVBom4bXnMXqmY954/Mxb3w+5o2vwY+5JzOamZlZQe5RMDMzs4IcKJiZmVlBDhTaEEkHSXpD0n8kDWnq9rRGkjaTNFbSa5JekfSLlD5U0mxJU9PrB03d1tZE0kxJFenYlqe0rpIel/RW+rtuU7eztZC0Vc53eaqkLySd7e95/ZL0L0kfSXo5J63g91rS/6b/39+Q9L16a4fnKLQNktoBb5I9NnsWMAk4NiJebdKGtTKSugHdImKKpM7AZOAw4ChgQURc0pTta60kzQTKIuKTnLS/AnMjYlgKjNeNiN80VRtbq/R/y2xgN+Ak/D2vN5L2ARYAN0bE9ikt7/c6PSRxNNAH2Bh4AtgyIpauajvco9B29AH+ExHvRMTXwG3AoU3cplYnIuZUPsk0IuYDrwGbNG2r2qxDgVFpeRRZwGb1b3/g7Yh4t6kb0tpExDPA3CrJhb7XhwK3RcRXETED+A/Z//urzIFC27EJ8H7O+1n4BNagJJUCvYEXU9IZkqan7kR3g9evAB6TNFnSoJS2UUTMgSyAAzZssta1bseQXclW8ve8YRX6XjfY//EOFNoO5UnzuFMDkdQJuBs4OyK+AK4BNgd6AXOAvzVd61qlvSJiZ+D7wOmpy9YamKTVgUOAO1OSv+dNp8H+j3eg0HbMAjbLeb8p8EETtaVVk9SBLEi4JSLuAYiIDyNiaUQsA66jnroELRMRH6S/HwH3kh3fD9Ockcq5Ix81XQtbre8DUyLiQ/D3vJEU+l432P/xDhTajklAT0k90lXAMcCYJm5TqyNJwA3AaxHx95z0bjnZfgS8XLWs1Y2ktdLEUSStBRxIdnzHACembCcC9zdNC1u1Y8kZdvD3vFEU+l6PAY6RtIakHkBPYGJ9bNC/emhD0k+VLgPaAf+KiD81bYtaH0l9gWeBCmBZSv4/sv9Qe5F1Bc4Efl45zmirRtJ3yHoRIHsi7q0R8SdJ6wF3AN2B94AjI6LqxDCrI0lrko2Jfyci5qW0m/D3vN5IGg30I3uU9IfA+cB9FPheS/ot8FNgCdmw57/rpR0OFMzMzKwQDz2YmZlZQQ4UzMzMrCAHCmZmZlaQAwUzMzMryIGCmZmZFeRAwawBSFqanp73sqQHJK1TQ/6hkgbXkOew9OCXyvcXSDqgHto6UtIRq1pPLbd5dvp5XbMhaev0mb0kafMq62ZKerZK2tTKp/pJKpP0j3poQ2nukwKrrLs+9/NvaJI2knSrpHfSrbGfl/Sjxtq+NR8OFMwaxqKI6JWe+DYXOL0e6jwMWH6iiIjfR8QT9VBvo0pPGzwbaFaBAtnxvT8iekfE23nWd5a0GYCkbXJXRER5RJxV7IbSMaiViDilsZ72mm4cdh/wTER8JyJ2IbtJ26YNvN32DVm/1Y0DBbOG9zzp4SySNpf0SLpCe1bS1lUzS/qZpEmSpkm6W9KakvYku6f+xelKdvPKngBJ35d0R075fpIeSMsHpivBKZLuTM+gKChdOf85lSmXtLOkRyW9LenUnPqfkXSvpFclXStptbTuWEkVqSflopx6F6QekBeB35I9BnespLFp/TVpe69I+kOV9vwhtb+i8nhJ6iRpREqbLmlAsfsrqZekF1K5eyWtm25GdjZwSmWb8rgDODotV70jYT9JD9bQttxjsIekX6Xj9LKks3O2017SqFT2rsqeF0njJJUVcZwvSt+vJyT1SeXekXRIytNO0sXpOzZd0s/z7Ot/A19HxLWVCRHxbkRcUV0d6TiMS+1+XdItKehA0i6Snk5te1QrbkM8Ln3nngZ+Iam/pBeV9ew8IWmjAp+HNZaI8Msvv+r5BSxIf9uRPTDnoPT+SaBnWt4NeCotDwUGp+X1cuq5EDgzLY8EjshZNxI4guxuhO8Ba6X0a4Afk93N7Zmc9N8Av8/T1uX1kt1N77S0fCkwHegMbAB8lNL7AYuB76T9ezy1Y+PUjg1Sm54CDktlAjgqZ5szgfVz3nfNOV7jgB1z8lXu//8A16fli4DLcsqvW4v9nQ7sm5YvqKwn9zPIU2YmsCXwXHr/Elnvzss5x+TBQm2regyAXcju3rkW0Al4hexJo6Up314p379Y8b0YB5QVcZy/n5bvBR4DOgA7AVNT+iDgd2l5DaAc6FFlf88CLq3m+523jnQc5pH1PKxGFiT3TW14DtgglTma7O6wlft1dZXPsvJmgKcAf2vqf89t/eVuHrOG0VHSVLL/+CcDj6er2z2BO9NFFmT/yVa1vaQLgXXITiKPVrehiFgi6RGgv6S7gB8C5wL7kp3MJqTtrU72H3dNKp8BUgF0ioj5wHxJi7VirsXEiHgHlt9mti/wDTAuIj5O6bcA+5B1YS8le1BWIUcpezx0e6Bbavf0tO6e9HcycHhaPoCsK7zyGHwm6eCa9ldSF2CdiHg6JY1ixZMPazIX+EzSMcBrwMIC+b7VtrSYewz6AvdGxJepXfcAe5Md+/cjYkLKdzPZSfuSnPp3pfBx/hp4JOWrAL6KiG8kVZB9FyF7FsaOWjEvpQvZcwFmFNpxSVelNn8dEbtWU8fXZN+NWanc1LTdz4Htyf4dQBYQ5t7a+fac5U2B21OPw+rVtcsahwMFs4axKCJ6pRPTg2RzFEYCn0dErxrKjiS7QpwmaSDZVVpNbk/bmAtMioj5qcv38Yg4tpZt/yr9XZazXPm+8v+Mqvd+D/I/5rbS4ohYmm+FsgfYDAZ2TSf8kUBJnvYszdm+8rShrvtbG7cDVwEDq8mTr22w8jGo7ljlO7ZV6y/km0iX4uR8fhGxTCvG/0XWS1NdAPoKMGB5AyJOl7Q+Wc9BwTok9WPl70zlZybglYjYo8D2vsxZvgL4e0SMSfUNraad1gg8R8GsAUX2sJyzyE6Ei4AZko6EbMKYpJ3yFOsMzFH2uOrjc9Lnp3X5jAN2Bn7GiquzF4C9JG2RtrempC1XbY+W66PsSaSrkXUjjwdeBPaVtL6yyXrHAk8XKJ+7L2uTnSjmpfHo7xex/ceAMyrfSFqXIvY3fR6fSdo7Jf2kmjbmcy/wV6rv5cnXtqqeAQ5LbVyL7EmLlb+q6C6p8oR6LNmxzVWb45zPo8Bp6fuFpC1TG3I9BZRIOi0nLXfyaTF15HoD2KByvyR1kLRdgbxdgNlp+cQCeawROVAwa2AR8RIwjaw7+njgZEnTyK7aDs1T5Dyyk8HjwOs56bcB5yjPz/fSleqDZCfZB1Pax2RXvqMlTSc7kX5r8mQdPQ8MI3uM8AyybvQ5wP8CY8n2d0pEFHq083Dg35LGRsQ0sjH/V8jG5CcUKJPrQmDdNJlvGrBfLfb3RLJJodPJnnR4QRHbAyAi5kfERRHxdW3alqeeKWQ9RxPJPuvr0/cEsmGNE1P7upLNOcktW5vjnM/1wKvAFGU/xfwnVXqXU6/EYWQByQxJE8mGaX5TbB1V6vuabB7LRemYTCUbhstnKNnw3LPAJ7XYL2sgfnqkmdVK6g4eHBEHN3FTzKwRuEfBzMzMCnKPgpmZmRXkHgUzMzMryIGCmZmZFeRAwczMzApyoGBmZmYFOVAwMzOzgv5/3flTNeGhE6IAAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.9683528099014279, pvalue=8.937029984290362e-49)\n", "Slope and P-value = PearsonRResult(statistic=0.976335984532427, pvalue=5.532465944195124e-67)\n", "Slope and P-value = PearsonRResult(statistic=0.7308380590256235, pvalue=1.496485288413663e-05)\n", "Slope and P-value = PearsonRResult(statistic=0.9276320000280324, pvalue=1.04979939966244e-43)\n", "Slope and P-value = PearsonRResult(statistic=0.6975402802216013, pvalue=3.603758011735015e-08)\n", "Slope and P-value = PearsonRResult(statistic=0.8913999014185117, pvalue=3.677103759232453e-19)\n", "Slope and P-value = PearsonRResult(statistic=0.6401529481817338, pvalue=7.447308719296993e-13)\n", "Slope and P-value = PearsonRResult(statistic=-0.7307893668204732, pvalue=6.112239360901677e-18)\n", "Slope and P-value = PearsonRResult(statistic=0.8104738681900706, pvalue=6.991182493929134e-10)\n", "Slope and P-value = PearsonRResult(statistic=0.9025976073569076, pvalue=3.4009981948164706e-19)\n", "0 148\n", "1 35\n", "Name: BrSoyFree, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.4182283737630509, pvalue=0.0016493329069268983)\n", "Slope and P-value = PearsonRResult(statistic=0.8836762529665134, pvalue=4.4747958637378664e-34)\n", "Slope and P-value = PearsonRResult(statistic=0.5738855785885612, pvalue=3.765197642488793e-06)\n", "Slope and P-value = PearsonRResult(statistic=0.9279011570957542, pvalue=3.593767542150569e-19)\n", "Slope and P-value = PearsonRResult(statistic=0.7406163930729665, pvalue=1.2875212658119568e-18)\n", "Slope and P-value = PearsonRResult(statistic=0.4752103954965228, pvalue=5.856598456271739e-07)\n", "Slope and P-value = PearsonRResult(statistic=0.8285590970702077, pvalue=1.9947248229758338e-26)\n", "Slope and P-value = PearsonRResult(statistic=0.3862840763385229, pvalue=0.024036306247560438)\n", "Slope and P-value = PearsonRResult(statistic=0.8960041412506479, pvalue=2.510497745397603e-36)\n", "Slope and P-value = PearsonRResult(statistic=0.653564561649383, pvalue=4.7478615221993496e-07)\n", "0 150\n", "1 35\n", "Name: BrSoyFree, dtype: int64\n" ] }, { "data": { "image/png": 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NJulApWyAbMq+hmy6fgHQNafqOOC0nHbl9XQ9CThMUmdJXwG+DzxWoN9C7Y5PY2wNbA48n7Z9W1KPtCbhMLJ3+gVFxHzgQ0m1swgnABOL1W9E/HXpCsyT1LF2H9J+bBkRT0fEucB7ZDMeZmZmzaJJMxIRMVXSWKCK7DJAJdCYlXonAJdKWgR8ARwfETWS7gXukHQocDpwBjBc0swU8ySyBZnF4pouaSQwJRVdGxHPAEh6XNlHNv8J5H9941+Bq9PlgS+AQRHxacp1JpNd9tgKuCUiKtPC0GIGpr46k12yOKlBR6SO+OsZ7xzgabLXoZrlCdPFknqRzXI8TPZa1WmHTbpR6W91MzOzBlBE/ox9IzuQukTEwnTCnET2cc7pzRLdaiJdKqiIiNPqq9sWVFRURGVlZUuHYWZmqwlJ0yKi4AcqmuNeGyMkbQ90Aka1tSTCzMzMimtyIhERx+U+lzQc2DOvWi/gxbyyyyPihqaOvypExEiyxYxmZmaWo9nv/hkRpzZ3n2ZmZrZ6aumvyDYzM7NWzImEmZmZlcyJhJmZmZXMiYSZmZmVzImEmZmZlcyJhJmZmZWs2T/+aa1f9ZvzKRua/+3hZmbNY66/gr9N8YyEmZmZlcyJhJmZmZXMiUQzkTRS0iuSZkh6TtJ5DWgzSNKV6fH5ks6sp/4pkk4sUF6W7miKpApJfyl1P8zMzBrDaySa11kRcYekTsCzkm6MiFeaq/OIuLoBdSrJbuduZma20nlGIoekc9JswnhJo+ubIahDp/T7k9TvXEkbpMcVkibUE8eWkh6UNE3SY5K2TeXLZi0k7SqpStKTwKk5bftLui897iPpCUnPpN/blLg/ZmZmBTmRSCRVAEcAOwOHAwXvu16PiyXNAN4Abo2Id0oMZwRwekTsCpwJ/LVAnRuAMyKibx39PAfsHRE7A+cCvy9WUdJgSZWSKmsWzS8xbDMza298aWO5fsA9EbEYQNK9JfRRe2mjC/CwpD0i4onGdJDa7gHcLqm2eO28Ot2A7hExMRXdBHyvQHfdgFGSegEBdCw2bkSMIEtgWLtnr2hMzGZm1n45kVhO9VdpmIhYmC5f9AOeAL5g+exPp2LtkjWAjyKivI46IksM6vNb4NGI+L6kMmBCA9qYmZk1mC9tLDcZOFhSpzQrUPI3pkhaE9gdeCkVzQV2TY+PqKttRHwMvCLpqNSXJO2UV+cjYL6kfqno+CLddQPeTI8HNXwPzMzMGsaJRBIRU4GxQBVwF9knHxq7WKB2jcRMoDr1A/Ab4HJJjwE1DejneOBHkqqA2cChBeqcBAxPiy0XF+nnj8AfJD0OdGjwXpiZmTWQInw5vJakLumyRGdgEjA4Iqa3dFyrWkVFRVRW+hOkZmaWkTQtIgp+CMFrJFY0QtL2ZOsYRrXHJMLMzKwxnEjkiIjjcp9LGg7smVetF/BiXtnlEXHDyozNzMxsdeREog4RcWr9tczMzNovL7Y0MzOzkjmRMDMzs5I5kTAzM7OSOZEwMzOzkjmRMDMzs5I5kTAzM7OSOZEwMzOzkvl7JOxLqt+cT9nQ+1s6DDNbReYOK/kehWaekTAzM7PSOZEwMzOzkrW7RELSSElHNqH9A5K611PnAkn7p8dzJW1Q6niFxpW0MP0ukzSrqX2bmZmVymskGiki/qsBdc5tiXHNzMxWtVY5IyHpHEnPSRovabSkM5vQ128l/XfO899JOkNST0mTJM2QNEvSXmn7XEkbpNmAOZKukTRb0jhJ66Q6+bMeZ0makn62SnUOlvS0pGck/UvSxqm8i6QbJFVLminpiNxx69iPQZKuzHl+n6T+kjqkeGalPn9epP1gSZWSKmsWzS/1cJqZWTvT6hIJSRXAEcDOwOFARRO7vA4YmPpeAzgGuBk4DngoIsqBnYAZBdr2AoZHxDeAj1JchXwcEX2AK4HLUtlk4JsRsTNwK3B2Kj8HmB8RO0TEjsAjTdg3gHJgk4joHRE7AAVvdx4RIyKiIiIqOnTu1sQhzcysvWiNlzb6AfdExGIASfc2pbOImCvpfUk7AxsDz0TE+5KmAtdL6gjcHREzCjR/Jad8GlBWZJjROb8vTY83Bf4hqSewFvBKKt+fLJmpje/DknZsuZeBr0u6ArgfGNfE/szMzJZpdTMSgFZCn9cCg4CTgOsBImISsDfwJnCTpBMLtPs053ENxROzKPD4CuDKNEvwE6BTKlde/Yb6ghVfz06wLBHZCZgAnEq2r2ZmZs2iNSYSk4GDJXWS1AVojm9SGQMcAOwGPAQg6WvAOxFxDdnlj12a0P+AnN9PpsfdyJIUSJdWknHAabVPJK3XwDHmAuWS1pC0GdAntd8AWCMi7iS7bNKU/TAzM1tBq7u0ERFTJY0FqoBXgUqgsasD/ybpsvT49YjoK+lR4KOIqEnl/ckWSX4OLAQKzUg01NqSniZL3I5NZecDt0t6E3gK2CKVXwgMTx/rrAF+A9zVgDEeJ7s8Ug3MAqan8k2AG9L6D4D/bcJ+mJmZrUARpcyityxJXSJioaTOwCRgcERMr69dHf2tQXbiPSoiXmyuOFurioqKqKysbOkwzMxsNSFpWkQU/HBDa7y0ATBC0gyyk/+dTUwitgf+DTzsJMLMzKxxWt2lDYCIOC73uaThwJ551XoB+YnB5RGxwscfI+JZ4OvNHqSZmVk70CoTiXwRcWpLx2BmZtYetdZLG2ZmZrYacCJhZmZmJXMiYWZmZiVzImFmZmYlcyJhZmZmJXMiYWZmZiVzImFmZmYlaxPfI2HNq/rN+ZQNvb+lwzCzOswd1hz3KzRrOs9ImJmZWcmcSJiZmVnJWk0iIembkp6WNEPSHEnnl9hPmaTj6q/Z4P76S7qvufozMzNrTVrTGolRwNERUSWpA7BNif2UAccBt+RvkLRmRHxReohmZmbtyyqdkZB0jqTnJI2XNFrSmY1ovhEwDyAiaiLiWUlrSHpR0oap/zUk/VvSBpJGSvqLpCckvSzpyNTPMGCvNLPxc0mDJN0u6V5gnKQeku6WNFPSU5J2TH2fL+kmSY+kMX+cE1sXSXekfbtZklKb/SQ9I6la0vWS1k7lcyX9XtKTkiol7SLpIUkvSTol1ZGkiyXNSu0HpPL+kiYUGe9cSVNTmxE55WdIejbt061FXpvBKZbKmkXzG/GymJlZe7bKEglJFcARwM7A4UBFI7u4FHhe0hhJP5HUKSKWAn8Hjk919geqIuK99Lwn0A84iCyBABgKPBYR5RFxaSrrCwyMiG8BvwGeiYgdgV8BN+bEsCNwYKp/rqSvpvKdgSHA9mS3JN9TUidgJDAgInYgm/35aU5fr0dEX+CxVO9I4JvABWn74UA5sFPar4sl9Sw2Xiq/MiJ2i4jewDppv2v3eee0T6cUOrgRMSIiKiKiokPnboWqmJmZfcmqnJHoB9wTEYsjYgFwb2MaR8QFZMnHOLJLEw+mTdcDJ6bHPwRuyGl2d0QsjYhngY3r6H58RHyQE+dNacxHgPUl1Z5Za+N/D3gU6JPKp0TEGymxmUF2+WQb4JWIeCHVGQXsnTPm2PS7Gng6IhZExLvAEkndUxyj0+zL28BEYLc6xgPYN60jqQa+BXwjlc8Ebpb0A8CXbszMrNmsykRCTe0gIl6KiKuA/YCdJK0fEa8Db0v6FrA78M+cJp82cPxP6qkXeb/zy3PHqSGbfahvf2vbLM1rv7QB7b80XpoB+StwZJoBuQbolOocCAwHdgWmSWpNa2PMzGw1tioTicnAwZI6SepCdnJrMEkH1l7zB3qRnUA/Ss+vJbvEcVtE1NTT1QKgax3bJ5EulUjqD7wXER+nbYem+NcH+gNT6+jnOaBM0lbp+QlkswoNNQkYIKlDWgOyNzCljvq1ScN76fgemfZhDWCziHgUOBvoDnRpRBxmZmZFrbJ3phExVdJYoAp4FagEGrOq7wTgUkmLyKbnj89JGsaSXdK4oVjjHDOBLyRVka1N+DBv+/nADZJmAouAgTnbpgD3A5sDv42ItyRtXWiQiFgi6STg9jQDMBW4ugHx1RpDthajimzm4+yI+I+kbYuM95Gka8gulcxleZLTAfh7ujwj4NKI+KgRcZiZmRWliPzZ+pU4mNQlIhZK6kz2jntwRExvhn4ryE6QezU5yOJjnA8sjIhLVtYYq4uKioqorKxs6TDMzGw1IWlaRBT8kMSqvlY+QtL2ZNPwo5opiRhK9mmI4+ura2ZmZs1rlc5IfGlwaTjLP7pYqxfwYl7Z5RHRkMsW1gw8I2FmZrlWpxmJFUTEqS05vpmZmTVNq7nXhpmZma1+nEiYmZlZyZxImJmZWcmcSJiZmVnJnEiYmZlZyZxImJmZWcmcSJiZmVnJfBdI+5LqN+dTNvT+lg7DzHLMHdao+xyarTKekTAzM7OSOZEwMzOzkrV4IiHpTEnPSZolqUrSifXUHynpyPR4QrrzZ131z5d0ZglxHZJuCNaYNg9I6t7YsQr0U3C/SonJzMxsZWrRNRKSTgG+DfSJiI8ldQMOa8mYakXEWGBsI9v8V0PrSlozIr5Y2TGZmZmtTE2ekZB0TppRGC9pdCPf/f8K+FlEfAwQEfMjYlTqd1dJEyVNk/SQpJ71xLEw5/GRkkYWqFMu6SlJMyWNkbReKj9D0rOp/NZUNkjSlenxSElXSXpU0suS9pF0vaQ5ueNImitpg/T4xNRflaSbcvr5s6RHgYuKxZP8QNITaaamT5GYjszff0n903G7TdILkoZJOl7SFEnVkrYscvwGS6qUVFmzaH5dh9rMzGyZJiUSafr9CGBn4HCgzssMeW27Al0j4qUC2zoCVwBHRsSuwPXA75oSa3Ij8MuI2BGoBs5L5UOBnVP5KUXargd8C/g5cC9wKfANYAdJ5XnxfwP4NfCtiNgJ+O+czVsD+0fEL+qIB+ArEbEH8DOy/W+M2jF3AE4Ato6IPsC1wOmFGkTEiIioiIiKDp27NXI4MzNrr5o6I9EPuCciFkfEArITbEMJiCLbtgF6A+MlzQD+D9i0KYGmyybdI2JiKhoF7J0ezwRulvQDoNjlhnsjIshO+G9HRHVELAVmA2V5db8F3BER7wFExAc5226PiJp64gEYndpOAtZt5NqLqRExLyI+BV4CxqXy6gKxmpmZlaypayRUasO0JuITSV+PiJcL9Ds7Ivo2psucx50aGc6BZCfxQ4Bz0oxCvk/T76U5j2uf5x/HupKkTxoYU377/OdfkBJBSQLWKhBrbXy5sfu7Q8zMrNk0dUZiMnCwpE6SupCdkBvjD8BwSesCSFpX0mDgeWBDSX1TecciJ/dcb0vaTtIawPfzN0bEfOBDSXulohOAian+ZhHxKHA20B3o0sj9yPcwcLSk9VP8PRoaT06VAaltP2B+qp9rLrBrenwo0LGJMZuZmTVak96dRsRUSWOBKuBVoBJozEq9q8hO2lMlfQ58DvwpIj5LCwn/ki4BrAlcRnYZoZihwH3A68AsCicDA4GrJXUGXgZOAjoAf0/jCLg0Ij7K3uSXJiJmS/odWaJSAzwDDGpgPLU+lPQEsC7wwwJtrwHukTSFLHFp6ExHvXbYpBuV/hY9MzNrAGWX/ZvQgdQlIhamk+EkYHBETG+W6KxFVFRURGVlZUuHYWZmqwlJ0yKi4AcqmuN6+QhJ25OtSxjlJMLMzKz9aHIiERHH5T6XNBzYM69aL+DFvLLLI+KGpo5vZmZmLafZV/BHxKnN3aeZmZmtnlr8XhtmZmbWejmRMDMzs5I5kTAzM7OSOZEwMzOzkjmRMDMzs5I5kTAzM7OS+QZO9iXVb86nbOj9LR2GWZs3119Fb22AZyTMzMysZE4kzMzMrGTtPpGQdKak5yTNklQl6cSWjsnMzKy1aNeJhKRTgG8DfSKiN7A32a3Em9qv156YmVm70OoTCUnnpBmF8ZJGSzqzEc1/BfwsIj4GiIj5ETEq9bufpGckVUu6XtLaqXyupIskTUk/W6XykZL+LOlR4CJJ5ZKekjRT0hhJ66V6W0n6V5r9mC5pS2UuTrMi1ZIG5Ozf2amsStKwOvroL+m+nHZXShqUHg+T9GyK5ZKmHG8zM7Ncrfqds6QK4AhgZ7J9mQ5Ma2DbrkDXiHipwLZOwEhgv4h4QdKNwE+By1KVjyOiT7oMchlwUCrfGtg/ImokzQROj4iJki4AzgOGADcDwyJiTBpnDeBwoBzYCdgAmCppUio7DNg9IhZJ6pHGKdTHZkX2swfwfWDbiAhJ3YvUGwwMBuiw7oZ1HDkzM7PlWvuMRD/gnohYHBELgHsb0VZAFNm2DfBKRLyQno8iu+xRa3TO77455benJKIb0D0iJua2T8nLJhExBiAilkTEorQfoyOiJiLeBiYCuwH7AzekOkTEB3X0UczHwBLgWkmHAwXrRsSIiKiIiIoOnbvV0Z2ZmdlyrT2RKHk9Q7qc8Ymkr5fQbxR5/Ek97Yr1W1d5frJTrO4XrPh6dgKIiC+APsCdZLMbD9YTo5mZWYO19kRiMnCwpE6SugCN/XaXPwDDJa0LIGndNMX/HFBWu/4BOIFslqDWgJzfT+Z3GhHzgQ8l7ZXbPiUvb0g6LI23tqTOwCRggKQOkjYkm/2YAowDfpjqIKlHHX28CmyfnncD9kvbuwDdIuIBsksr5Y08RmZmZkW16jUSETFV0ligiuxEWgnMb0QXVwFdyNYkfA58DvwpIpZIOgm4PX0CYypwdU67tSU9TZaIHVuk74HA1ekk/zJwUio/AfhbWjfxOXAUMIbsEkkV2QzE2RHxH+BBSeVApaTPgAfIFoh+qY+IeFnSbcBM4EXgmTReV+CetJZCwM8bcXzMzMzqpIhiywRaB0ldImJhzjv7wRExfSWONxeoiIj3VtYYLa2ioiIqKytbOgwzM1tNSJoWERWFtrXqGYlkhKTtydYEjFqZSYSZmZmtqNUnEhFxXO5zScOBPfOq9SKb7s91eUTcUMJ4ZY1tY2Zm1la1+kQiX0Sc2tIxmJmZtRet/VMbZmZm1oKcSJiZmVnJnEiYmZlZyZxImJmZWcmcSJiZmVnJnEiYmZlZyZxImJmZWcna3PdIWNNVvzmfsqH3t3QYZi1i7rDG3vvPrH3zjISZmZmVzImEmZmZlazeREJSjaQZkmZJuj3dZbNBJA2SdGWRbU/U07ZM0nE5zysk/aWhY+e0+6Gkakkz0z4cmhPbVxvbXx3jHJZuHmZmZtZuNGRGYnFElEdEb+Az4JTcjZI6lDJwROxRT5UyYFkiERGVEXFGY8aQtCnwa6BfROwIfBOYmTYPAgomEiXu02GAEwkzM2tXGntp4zFgK0n9JT0q6RagWlInSTekd/7PSNo3p81mkh6U9Lyk82oLJS1MvyXp4jRbUC1pQKoyDNgrzYb8PI15X2rTJWe8mZKOKBLvRsACYCFARCyMiFckHQlUADen/teRNFfSuZImA0dJ+o6kJyVNTzMxXdLYcyVdJGlK+tlK0h7AIcDFqb8tJZVLeirFN0bSeqn9VpL+Jakq9b1lHccASWensipJw+roY9nxSXWulDQoPR4m6dkUyyWFDpSkwZIqJVXWLJpf/1+CmZkZjfjUhqQ1ge8BD6aiPkDvdGL+BUBE7CBpW2CcpK1z6wGLgKmS7o+IypyuDwfKgZ2ADVKdScBQ4MyIOCiN3z+nzTnA/IjYIW1br0jYVcDbwCuSHgbuioh7I+IOSael/itTHwBLIqKfpA2Au4D9I+ITSb8E/ge4IPX7cUT0kXQicFlEHCRpLHBfRNyR+psJnB4REyVdAJwHDAFuBoZFxBhJnciSuWLHoJxspmP3iFgkqUcav1AfmxU6AKnN94FtIyIkdS9ULyJGACMA1u7ZK4ocTzMzsxU0ZEZiHUkzgErgNeC6VD4lIl5Jj/sBNwFExHPAq0BtIjE+It6PiMVkJ+d+ef33A0ZHRE1EvA1MBHarJ6b9geG1TyLiw0KVIqIGOAA4EngBuFTS+XX0+4/0+5tklykeT/s+EPhaTr3ROb/75nciqRvQPSImpqJRwN6SugKbRMSYFN+SiFhE8WOwP3BDqkNEfFBHH8V8DCwBrpV0OFlCZ2Zm1iwaMiOxOCLKcwvSu/dPcovqaJ//7jb/eV1ti1GBfgoPHhHAFGCKpPHADcD5RarX7pPIEqBji3Vb5HF9iu1rXeUNPV5fsGJi2AkgIr6Q1AfYDzgGOA34VoOiNTMzq0dzffxzEnA8QLqksTnwfNr2bUk9JK1DNk3/eIG2AyR1kLQhsDfZiX8B0LXIeOPIToikMQte2pD0VUm75BSVk82WUE//TwF7Stoq9dM551INwICc30/m9xcR84EPJe2Vtp0ATIyIj4E3JB2W+l1b2adgih2DccAPUx0k9aijj1eB7dPzbmSJA2ltR7eIeIDs0kp5kX02MzNrtOb6Zsu/AldLqiZ7ZzwoIj5NMxeTyS57bAXckrc+AmAM2eWBKrJ332dHxH8kvQ98IakKGAk8k9PmQmC4pFlADfAbsssm+ToClyj7mOcS4F2Wf+pkZIp5MXmXJyLi3bRQcbSktVPx/5FdHgFYW9LTZIlY7azFrcA1ks4gu5QyMPXfGXgZOCnVOwH4W1o38TlwVLFjADwoqRyolPQZ8ADwq0J9RMTLkm4j+1TKiznHqytwT1pLIeDnBY6TmZlZSZTN/FtDSZoLVETEey0dy8pSUVERlZX5+Z6ZmbVXkqZFREWhbf5mSzMzMytZm7lpV7rUsHZe8QkRUd2c40REWXP2Z2Zm1pq1mUQiInZv6RjMzMzaG1/aMDMzs5I5kTAzM7OSOZEwMzOzkjmRMDMzs5I5kTAzM7OSOZEwMzOzkjmRMDMzs5K1me+RsOZT/eZ8yobe39JhmK0Sc4cd2NIhmLVqnpEwMzOzkjmRMDMzs5K1+0RC0khJR67iMQdJurIZ+ukvaY/miMnMzKwU7T6RaOX6A41KJCR5XYyZmTWbNpFISDpH0nOSxksaLenMJvbXQdLFkqZKminpJ6m8v6T7cupdKWlQerybpCckVUmaIqmrpE6SbpBULekZSfvmDLOZpAclPS/pvJw+75Y0TdJsSYNzyg+QND31/7CkMuAU4OeSZkjaS9KGku5McU+VtGdqe76kEZLGATcW2efBkiolVdYsmt+Uw2dmZu1Iq393KqkCOALYmWx/pgPTmtjtj4D5EbGbpLWBx9NJuFgMawH/AAZExFRJ6wKLgf8GiIgdJG0LjJO0dWrWB+gNLAKmSro/IiqBH0bEB5LWSeV3kiV81wB7R8QrknqkOlcDCyPikhTHLcClETFZ0ubAQ8B2abxdgX4RsbjQPkTECGAEwNo9e0VJR83MzNqdVp9IAP2Ae2pPkJLubYY+vwPsmLN2ohvQC/isSP1tgHkRMRUgIj5OsfQDrkhlz0l6FahNJMZHxPup3l1pPyqBMyR9P9XZLI27ITApIl5JfX1QJI79ge0l1T5fV1LX9HhssSTCzMysVG0hkVD9VUrq8/SIeGiFwiwxyL0c1CmnfqF38XXFll8/JPUnSwb6RsQiSRPSGMX6z7dGartCwpASi08a0N7MzKxR2sIaicnAwWk9QhegOb5d5iHgp5I6AkjaWtJXgFfJ3vGvLakbsF+q/xzwVUm7pfpd06LGScDxtX0AmwPPpzbfltQjXcI4DHicbObjw5REbAt8M9V9EthH0haprx6pfAFQO+MAMA44rfaJpPJmOBZmZmZFtfoZibQmYSxQRXairwQau1rwb5IuS49fB/YEyoDpyt7OvwscFhGvS7oNmAm8CDyTYvhM0gDgipQYLCabWfgrcLWkauALYFBEfJpmCCYDNwFbAbdERGWqd4qkmWQJx1Op/3fTwsu7JK0BvAN8G7gXuEPSocDpwBnA8NS+NpE5pZHHwszMrMEU0frX1UnqEhELJXUmO3kOjojpLR1Xa1VRURGVlZUtHYaZma0mJE2LiIpC21r9jEQyQtL2ZOsJRjmJMDMzWzXaRCIREcflPpc0nOzyRK5eZJcjcl0eETeszNjMzMzasjaRSOSLiFNbOgYzM7P2oE0mEmZm1nI+//xz3njjDZYsWdLSoVgjderUiU033ZSOHTs2uI0TCTMza1ZvvPEGXbt2paysrPZ7bKwViAjef/993njjDbbYYosGt2sL3yNhZmarkSVLlrD++us7iWhlJLH++us3eibJiYSZmTU7JxGtUymvmxMJMzMzK5nXSJiZ2UpVNvT+Zu1v7rCG3QlhzJgxHH744cyZM4dtt90WgAkTJnDJJZdw3333Las3aNAgDjroII488kj69+/PvHnz6NSpE2uttRbXXHMN5eXlAMyfP5/TTz+dxx9/HIA999yTK664gm7dugHwwgsvMGTIEF544QU6duzIDjvswBVXXMHGG29c8r5+8MEHDBgwgLlz51JWVsZtt93Geuutt0Kd559/ngEDBix7/vLLL3PBBRcwZMgQqqqqOOWUU1i4cCFlZWXcfPPNrLvuulRXV/OnP/2JkSNHlhxbLScS9iXVb85v9n/4Zi2toScfaztGjx5Nv379uPXWWzn//PMb3O7mm2+moqKCG264gbPOOovx48cD8KMf/YjevXtz4403AnDeeedx8sknc/vtt7NkyRIOPPBA/vznP3PwwQcD8Oijj/Luu+82KZEYNmwY++23H0OHDmXYsGEMGzaMiy66aIU622yzDTNmzACgpqaGTTbZhO9/P7uJ9Mknn8wll1zCPvvsw/XXX8/FF1/Mb3/7W3bYYQfeeOMNXnvtNTbffPOS4wNf2jAzszZo4cKFPP7441x33XXceuutJfXRt29f3nzzTQD+/e9/M23aNM4555xl288991wqKyt56aWXuOWWW+jbt++yJAJg3333pXfv3k3aj3vuuYeBAwcCMHDgQO6+++466z/88MNsueWWfO1rXwOy2Yq9994bgG9/+9vceeedy+oefPDBJR+bXE4kzMyszbn77rs54IAD2HrrrenRowfTpzf+zgkPPvgghx12GADPPvss5eXldOjQYdn2Dh06UF5ezuzZs5k1axa77rprvX0uWLCA8vLygj/PPvvsl+q//fbb9OzZE4CePXvyzjvv1Nn/rbfeyrHHHrvsee/evRk7diwAt99+O6+//vqybRUVFTz22GP1xlwfX9owM7M2Z/To0QwZMgSAY445htGjR7PLLrsU/VRCbvnxxx/PJ598Qk1NzbIEJCIKti1WXkzXrl2XXYZobp999hljx47lD3/4w7Ky66+/njPOOIMLLriAQw45hLXWWmvZto022oi33nqryeO260RC0k5kN/kqT8+PBa4DukXE55J2AG6OiB1L6PtXEfH7ZorzFGBRRNxYR53zgYURcYmkkcB9EXFHc4xvZtaavP/++zzyyCPMmjULSdTU1CCJP/7xj6y//vp8+OGHK9T/4IMP2GCDDZY9v/nmm9lpp50YOnQop556KnfddRff+MY3eOaZZ1i6dClrrJFN5i9dupSqqiq222473nnnHSZOnFhvbAsWLGCvvfYquO2WW25h++23X6Fs4403Zt68efTs2ZN58+ax0UYbFe37n//8J7vssssKazK23XZbxo0bB2SLQe+/f/n6tyVLlrDOOuvUG3N92vuljWrga5K6pud7AM8BO+c8f7zEvn/V2AaSOhQqj4ir60oizMxsuTvuuIMTTzyRV199lblz5/L666+zxRZbMHnyZHr16sVbb73FnDlzAHj11Vepqqpa9smMWh07duTCCy/kqaeeYs6cOWy11VbsvPPOXHjhhcvqXHjhheyyyy5stdVWHHfccTzxxBMrnKgffPBBqqurV+i3dkai0E9+EgFwyCGHMGrUKABGjRrFoYceWnS/R48evcJlDWDZpZClS5dy4YUXcsoppyzb9sILLzR5DQe0kRkJSecAxwOvA+8B0yLikvraRcRSSVOB3YF/AbsCw8kSiCnp978k9QEuA9YBFgMnRcTzkgYBFRFxWorjPuAS4ABgHUkzgNkRcbykHwBnAGsBTwM/i4gaSQuBPwPfBX4h6SDgEOALYFxEnJk327BlinFDYBHw44h4ro5jcy5wcIr9CeAnEREF6g0GBgN0WHfD+g6dmVmDrepPzIwePZqhQ4euUHbEEUdwyy23sNdee/H3v/+dk046iSVLltCxY0euvfbaZR/hzLXOOuvwi1/8gksuuYTrrruO6667jtNPP52tttqKiKBv375cd911y+red999DBkyhCFDhtCxY0d23HFHLr/88ibty9ChQzn66KO57rrr2Hzzzbn99tsBeOuttzj55JN54IEHAFi0aBHjx4/nb3/725eOxfDhwwE4/PDDOemkk5Zte/TRRznwwKa/NipwTmlVJFUA1wJ9yRKj6cDfGpJIpPbnA0uBPwEPAQOBP0TE0ZJeJDvBv0d2aeELSfsDP42II4olEhExQdLCiOiSyrcD/ggcni6Z/BV4KiJulBTAgIi4TVIP4Elg24gISd0j4qO8ROJh4JSIeFHS7inWbxW7tCGpR0R8kOK4CbgtIu6t65is3bNX9Bx4WUMOn1mr4Y9/rjpz5sxhu+22a+kwrA6ffvop++yzD5MnT2bNNVecUyj0+kmaFhEVhfpqCzMS/YB7ImIxgKQ6T5IFPA78AngMmBoRL0naStKGQJeIeFnSZsAoSb2AABp+W7TMfmSzHVPTopx1gNqltzVA7edxPgaWANdKuh+4L7cTSV3IZkluz1ncs3Y9Y+8r6WygM9ADmA009hiZmVkb8tprrzFs2LAvJRGlaAuJRFO/0P0pYDeyhOTJVPYGcAzZpQCA3wKPRsT3JZUBE1L5F6y4zqRTHTGOioj/LbBtSUTUAKQZjz5kiccxwGnAt3LqrgF8VLs4tD6SOgF/JZs1eT3NWhSL0czM2olevXrRq1evZumrLSy2nAwcLKlTesfeqPnLiFhAtrZiEMsTiSeBISxPJLoBb6bHg3KazwXKJa2RZi365Gz7XFLtzMXDwJGSNgKQ1EPS1/JjSfF3i4gH0vjlebF+DLwi6ahUX+mTJ8XUJg3vpb6PrKOumVmzae2XzdurUl63Vj8jERFTJY0FqoBXgUpgfiO7eRw4NCJqv6njSeD3LE8k/kh2aeN/gEfy2r1C9umPWWTrM2qNAGZKmp4WW/4fME7SGsDnwKkp3lxdgXvSTIKAnxeI9XjgqtRfR+DWtO9fktZXXJPimwtMrfMoJDts0o1KX082sxJ16tSJ999/37cSb2Uigvfff59OnRo3cd3qF1tC9k4+IhZK6gxMAgZHROO/xswAqKioiMrKypYOw8xaqc8//5w33niDJUuWtHQo1kidOnVi0003pWPHFZcCtvXFlgAjJG1PNpU/ykmEmVnL6dixI1tssUVLh2GrSJtIJCLiuNznkoYDe+ZV6wW8mFd2eUTcsDJjMzMza8vaRCKRLyJObekYzMzM2oO28KkNMzMzayFtYrGlNS9JC4DnWzqOdmYDsm9QtVXHx3zV8zFf9ZrrmH8tIgreP6FNXtqwJnu+2OpcWzkkVfqYr1o+5quej/mqtyqOuS9tmJmZWcmcSJiZmVnJnEhYISNaOoB2yMd81fMxX/V8zFe9lX7MvdjSzMzMSuYZCTMzMyuZEwkzMzMrmRMJW0bSAZKel/RvSUNbOp62SNJmkh6VNEfSbEn/ncrPl/SmpBnp579aOta2RNJcSdXp2Famsh6Sxkt6Mf1er6XjbCskbZPztzxD0seShvjvvHlJul7SO5Jm5ZQV/buW9L/p//fnJX232eLwGgkDkNQBeAH4NvAG2S3Hj42IZ1s0sDZGUk+gZ0RMl9QVmAYcBhwNLIyIS1oyvrZK0lygIiLeyyn7I/BBRAxLifN6EfHLloqxrUr/t7wJ7A6chP/Om42kvYGFwI0R0TuVFfy7Tje2HA30Ab4K/AvYOiJqmhqHZySsVh/g3xHxckR8BtwKHNrCMbU5ETGv9u60EbEAmANs0rJRtVuHAqPS41FkCZ01v/2AlyLi1ZYOpK2JiEnAB3nFxf6uDwVujYhPI+IV4N9k/+83mRMJq7UJ8HrO8zfwCW6lklQG7Aw8nYpOkzQzTVd6mr15BTBO0jRJg1PZxhExD7IED9ioxaJr244heydcy3/nK1exv+uV9n+8EwmrpQJlvu61kkjqAtwJDImIj4GrgC2BcmAe8KeWi65N2jMidgG+B5yapoRtJZO0FnAIcHsq8t95y1lp/8c7kbBabwCb5TzfFHirhWJp0yR1JEsibo6IuwAi4u2IqImIpcA1NNOUo2Ui4q30+x1gDNnxfTutWaldu/JOy0XYZn0PmB4Rb4P/zleRYn/XK+3/eCcSVmsq0EvSFuldxDHA2BaOqc2RJOA6YE5E/DmnvGdOte8Ds/LbWmkkfSUtbEXSV4DvkB3fscDAVG0gcE/LRNimHUvOZQ3/na8Sxf6uxwLHSFpb0hZAL2BKcwzoT23YMumjWJcBHYDrI+J3LRtR2yOpH/AYUA0sTcW/IvsPt5xsqnEu8JPa65zWNJK+TjYLAdkdj2+JiN9JWh+4DdgceA04KiLyF65ZiSR1Jrsm//WImJ/KbsJ/581G0migP9mtwt8GzgPupsjftaRfAz8EviC7rPrPZonDiYSZmZmVypc2zMzMrGROJMzMzKxkTiTMzMysZE4kzMzMrGROJMzMzKxkTiTMWoCkmnT3w1mS7pXUvZ7650s6s546h6Ub89Q+v0DS/s0Q60hJRza1n0aOOSR9fHC1IWnb9Jo9I2nLvG1zJT2WVzaj9q6Mkiok/aUZYijLvdNj3rZrc1//lU3SxpJukfRy+urxJyV9f1WNb6sPJxJmLWNxRJSnO/Z9AJzaDH0eBiw7kUTEuRHxr2bod5VKd4scAqxWiQTZ8b0nInaOiJcKbO8qaTMASdvlboiIyog4o6EDpWPQKBFx8qq6W2/6YrW7gUkR8fWI2JXsS+w2Xcnjrrky+7fSOJEwa3lPkm6eI2lLSQ+md3iPSdo2v7KkH0uaKqlK0p2SOkvag+yeBhend8Jb1s4kSPqepNty2veXdG96/J30TnK6pNvTPUCKSu+8f5/aVEraRdJDkl6SdEpO/5MkjZH0rKSrJa2Rth0rqTrNxFyU0+/CNIPyNPBrstscPyrp0bT9qjTebEm/yYvnNyn+6trjJamLpBtS2UxJRzR0fyWVS3oqtRsjab30ZW1DgJNrYyrgNmBAepz/jY79Jd1XT2y5x6CvpP9Jx2mWpCE546wpaVRqe0ftzI2kCZIqGnCcL0p/X/+S1Ce1e1nSIalOB0kXp7+xmZJ+UmBfvwV8FhFX1xZExKsRcUVdfaTjMCHF/Zykm1NSgqRdJU1MsT2k5V/zPCH9zU0E/lvSwZKeVjYz9C9JGxd5PWxViQj/+Mc/q/gHWJh+dyC7odEB6fnDQK/0eHfgkfT4fODM9Hj9nH4uBE5Pj0cCR+ZsGwkcSfZtjq8BX0nlVwE/IPs2vEk55b8Ezi0Q67J+yb6N8Kfp8aXATKArsCHwTirvDywBvp72b3yK46spjg1TTI8Ah6U2ARydM+ZcYIOc5z1yjtcEYMecerX7/zPg2vT4IuCynPbrNWJ/ZwL7pMcX1PaT+xoUaDMX2Bp4Ij1/hmx2aFbOMbmvWGz5xwDYlezbT78CdAFmk90ptizV2zPVu57lfxcTgIoGHOfvpcdjgHFAR2AnYEYqHwz8X3q8NlAJbJG3v2cAl9bx912wj3Qc5pPNXKxBlkT3SzE8AWyY2gwg+3bd2v36a95rWftliicDf2rpf8/t/cfTRGYtYx1JM8hODNOA8end8R7A7elNGmT/CefrLelCoDvZSeahugaKiC8kPQgcLOkO4EDgbGAfspPd42m8tcj+Y69P7T1YqoEuEbEAWCBpiZav9ZgSES/Dsq/x7Qd8DkyIiHdT+c3A3mRT5DVkNzIr5mhlt/9eE+iZ4p6Ztt2Vfk8DDk+P9yebaq89Bh9KOqi+/ZXUDegeERNT0SiW37myPh8AH0o6BpgDLCpS70uxpYe5x6AfMCYiPklx3QXsRXbsX4+Ix1O9v5Od1C/J6X83ih/nz4AHU71q4NOI+FxSNdnfImT3ItlRy9fFdCO7L8MrxXZc0vAU82cRsVsdfXxG9rfxRmo3I437EdCb7N8BZAlj7ldn/yPn8abAP9KMxVp1xWWrhhMJs5axOCLK04nrPrI1EiOBjyKivJ62I8neYVZJGkT2Lq8+/0hjfABMjYgFaUp5fEQc28jYP02/l+Y8rn1e+39K/nfvB4VvY1xrSUTUFNqg7AZDZwK7pYRgJNCpQDw1OeOrQAyl7m9j/AMYDgyqo06h2GDFY1DXsSp0bPP7L+bzSG/lyXn9ImKplq8/ENksT10J6mzgiGUBRJwqaQOymYeifUjqz4p/M7WvmYDZEdG3yHif5Dy+AvhzRIxN/Z1fR5y2CniNhFkLiuxmRmeQnSgXA69IOgqyBW2SdirQrCswT9ntyI/PKV+QthUyAdgF+DHL3909Bewpaas0XmdJWzdtj5bpo+xOsmuQTVNPBp4G9pG0gbLFhMcCE4u0z92XdclOJPPT9fDvNWD8ccBptU8krUcD9je9Hh9K2isVnVBHjIWMAf5I3bNEhWLLNwk4LMX4FbI7ZdZ+KmRzSbUn3GPJjm2uxhznQh4Cfpr+vpC0dYoh1yNAJ0k/zSnLXRzbkD5yPQ9sWLtfkjpK+kaRut2AN9PjgUXq2CrkRMKshUXEM0AV2XT38cCPJFWRves7tECTc8hOFuOB53LKbwXOUoGPJ6Z3uveRnYTvS2Xvkr1zHi1pJtmJ9kuLO0v0JDCM7DbRr5BN088D/hd4lGx/p0dEsVt3jwD+KenRiKgiW3Mwm2xNwONF2uS6EFgvLTasAvZtxP4OJFu0OpPsTpUXNGA8ACJiQURcFBGfNSa2Av1MJ5t5mkL2Wl+b/k4gu2wyMMXXg2zNS27bxhznQq4FngWmK/uo6d/Im71OsxqHkSUsr0iaQnYZ6JcN7SOvv8/I1tFclI7JDLLLfIWcT3b57zHgvUbsl60kvvunmTWrNN18ZkQc1MKhmNkq4BkJMzMzK5lnJMzMzKxknpEwMzOzkjmRMDMzs5I5kTAzM7OSOZEwMzOzkjmRMDMzs5L9f/j0sWcOMWAVAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.727355346469284, pvalue=2.194681413718141e-09)\n", "Slope and P-value = PearsonRResult(statistic=0.9354373760645842, pvalue=1.6246497820732905e-36)\n", "Slope and P-value = PearsonRResult(statistic=0.9697981852622246, pvalue=3.2586444722653675e-16)\n", "Slope and P-value = PearsonRResult(statistic=-0.4992215053339385, pvalue=1.2438054843554205e-07)\n", "Slope and P-value = PearsonRResult(statistic=0.9381019214587246, pvalue=5.156828333296247e-13)\n", "Slope and P-value = PearsonRResult(statistic=0.8996880190247403, pvalue=1.6019925190611667e-14)\n", "Slope and P-value = PearsonRResult(statistic=0.9259417935496176, pvalue=3.122604026434567e-43)\n", "Slope and P-value = PearsonRResult(statistic=0.5187461261255114, pvalue=3.22312187054129e-08)\n", "Slope and P-value = PearsonRResult(statistic=0.618659747796518, pvalue=0.0001605361091985671)\n", "Slope and P-value = PearsonRResult(statistic=0.7388951124514609, pvalue=5.308899965758477e-11)\n", "0 174\n", "1 34\n", "Name: BrSoyFree, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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PearsonRResult(statistic=0.8202682183296679, pvalue=0.04555228600996817)\n", "Slope and P-value = PearsonRResult(statistic=0.9540981815113749, pvalue=5.036519037436793e-22)\n", "0 160\n", "1 35\n", "Name: BrSoyFree, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.9593682794278231, pvalue=1.7575838935919086e-22)\n", "Slope and P-value = PearsonRResult(statistic=0.8271121462608751, pvalue=3.835908684526777e-16)\n", "Slope and P-value = PearsonRResult(statistic=0.9089251107597944, pvalue=6.111900310375114e-18)\n", "Slope and P-value = PearsonRResult(statistic=0.8170604134579269, pvalue=3.572762348509473e-25)\n", "Slope and P-value = PearsonRResult(statistic=0.45773459906101166, pvalue=0.09980400372678701)\n", "Slope and P-value = PearsonRResult(statistic=0.8701342690055307, pvalue=4.2218921114819433e-10)\n", "Slope and P-value = PearsonRResult(statistic=0.506291228506226, pvalue=4.8811045907522395e-06)\n", "Slope and P-value = PearsonRResult(statistic=0.6858556089522057, pvalue=2.873923166716501e-05)\n", "Slope and P-value = PearsonRResult(statistic=0.8623158734130516, pvalue=2.866543153513807e-23)\n", "Slope and P-value = PearsonRResult(statistic=0.6280883951553147, pvalue=0.00525159123443443)\n" ] } ], "source": [ "sample = pd.merge(microbiome, poultry[['SampleID', 'BrSoyFree','PastureTime','SampleType']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "#sample = sample[~sample.isin([np.nan, np.inf, -np.inf]).any(1)]\n", "sample = sample.drop(['Pathogen_Salmonella', 'new_Pathogen_Salmonella',\n", " 'Pathogen_Campy','new_Pathogen_Campy',\n", " 'Pathogen_Listeria','new_Pathogen_Listeria'],axis='columns')\n", "\n", "sample.BrSoyFree.replace({'Y': 1,'N':0}, regex=True, inplace=True)\n", "\n", "feces1=sample[(sample.SampleType=='Feces') & (sample.PastureTime=='Start')]\n", "feces2=sample[(sample.SampleType=='Feces') & (sample.PastureTime=='Mid')]\n", "feces3=sample[(sample.SampleType=='Feces') & (sample.PastureTime=='End')]\n", "\n", "soil1=sample[(sample.SampleType=='Soil') & (sample.PastureTime=='Start')]\n", "soil2=sample[(sample.SampleType=='Soil') & (sample.PastureTime=='Mid')]\n", "soil3=sample[(sample.SampleType=='Soil') & (sample.PastureTime=='End')]\n", "\n", "ceca=sample[sample.SampleType=='Ceca']\n", "wcrp=sample[sample.SampleType=='WCR-P']\n", "wcrf=sample[sample.SampleType=='WCR-F']\n", "\n", "\n", "print('Feces_Start', feces1.shape)\n", "print('Feces_Mid', feces2.shape)\n", "print('Feces_End', feces3.shape,'\\n')\n", "\n", "print('Soil_Start', soil1.shape)\n", "print('Soil_Mid', soil2.shape)\n", "print('Soil_End', soil3.shape,'\\n')\n", "\n", "print('Ceca', ceca.shape)\n", "print('WCR-P', wcrp.shape)\n", "print('WCR-F', wcrf.shape,'\\n')\n", "\n", "\n", "sampletypes = [feces1,feces2,feces3, soil1,soil2,soil3,ceca, wcrp, wcrf]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES_START\", 1: \"FECES_MID\", 2:\"FECES_END\",\n", " 3: \"SOIL_START\", 4: \"SOIL_MID\", 5: \"SOIL_END\",\n", " 6:\"CECA\", 7: \"WCR-P\", 8: \"WCR-F\"\n", " }\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType', 'PastureTime','BrSoyFree'],axis='columns'),sample.BrSoyFree,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1] #multiclass\n", " \n", " try:\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " except ValueError:\n", " pass\n", " \n", " \n", " print(pd.value_counts(sample['BrSoyFree']))\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " plt.title(f\"BrSoyFree in {sample_name[indexing]} Model\")\n", " prelim3_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','BrSoyFree','PastureTime','SampleType'],axis='columns').columns)\n", " prelim3_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", "\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim3_plot.max(), decimals=None, symbol=''))\n", " \n", " xmax=prelim3_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " \n", " plt.show()\n", " \n", " prelim3_plot.nlargest(10).to_csv(\"prelim3.csv\")\n", " top10 = pd.read_csv('prelim3.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", "# plt.plot(pdp[1][0], pdp[0][0],'.')\n", "# plt.ylabel('Partial dependence'), plt.xlabel(top10[feature])\n", "# plt.show()\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"BrSoyFree_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " \n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (7) BrMedicated" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Feces_Start (200, 878)\n", "Feces_Mid (313, 878)\n", "Feces_End (185, 878) \n", "\n", "Soil_Start (199, 878)\n", "Soil_Mid (313, 878)\n", "Soil_End (183, 878) \n", "\n", "Ceca (185, 878)\n", "WCR-P (208, 878)\n", "WCR-F (195, 878) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "0 195\n", "1 5\n", "Name: BrMedicated, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
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PearsonRResult(statistic=0.7187809682581335, pvalue=1.6840605827523654e-06)\n", "Slope and P-value = PearsonRResult(statistic=0.7163471514907178, pvalue=0.4916254203219513)\n", "0 308\n", "1 5\n", "Name: BrMedicated, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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PearsonRResult(statistic=0.6934476586926818, pvalue=1.2828592241340175e-15)\n", "Slope and P-value = PearsonRResult(statistic=0.9002864942875033, pvalue=5.422591957125561e-12)\n", "0 180\n", "1 5\n", "Name: BrMedicated, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
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PearsonRResult(statistic=0.7761722850480124, pvalue=2.415396472070557e-21)\n", "Slope and P-value = PearsonRResult(statistic=0.8087206379378057, pvalue=0.09749117609039891)\n", "0 194\n", "1 5\n", "Name: BrMedicated, dtype: int64\n" ] }, { "data": { "image/png": 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eA7YgSwbOkvRLYONUF+CNiBidlm9M29e6I/0eR5bUFHNXRCyMiOeA9XIx35bK3wZGpPIvA1Mj4sX0egiwW66tYbnfO5fYXykjImJWRLwHzGTxe1BTR+xmZmaN5iRoSWqJRiLiZWACcHh9bUfEPGAk8E2yHqGb66l/E3AQMBd4QNJetasKq+aWP0u/F1B6HthnuWUV/C5U33mKEsu1PmfxtVdRRxwLc68X4jlsZmbWgpwELelx4MA0H6cT0JxvqLqAJYeeRgH9JHWQtC5Zz8nTad3NwAnArsADufpHA0jaAtgIeEHSpsCrEXEZ2cTmbVL9jSTV9rocmY6luR4HDk1zg9YD9kjlzwOVtfN9yIb+Hs1t1y/3+8ki7U4DdkjLfVsgTjMzs0bzX9Y5ETFW0nBgItnwUzXZkExT2npW0nhg+1R0J9nQ0ESy3pFfpCEmgAeBG4DhEfG/VPZ34CpJNWQ9J8dHxGdpQvX3JM0H3gbOA9YEpgDHSfoH8BItc3fav8jmKE0GXgSeAmZGxDxJJwC3pQngY4H83V+rSnqKLMk+ski7FwO3SjoG+G8LxLnI1ht0odrfrmpmZg2giGKjFeVLUqeImJ3urhoF9I+I8a0dV10kVQL3RkTPZdB27fnoStZztUsueWtzqqqqorq6urXDMDOzNkTSuIhY6mYn9wQtbZCkr5LNVRnS1hOg5eBeSWsBqwC/b8sJkJmZWWM4CSoQEUflX0u6AtiloFoPsiGnvEsj4vplGVspETGN7NbyZdH2HsuiXTMzs9bmJKgeEXFya8dgZmZmLc93h5mZmVlZchJkZmZmZclJkJmZmZUlJ0FmZmZWlpwEmZmZWVlyEmRmZmZlybfIW7tS8+ZMKs+8r7XDMDMraZof7dNmuCfIzMzMypKTIDMzMytLyyUJknS6pOclTZY0UdKxLdTuSElVafnf6RlXhXXOlXR6Pe30Sc8La4mYpkmqkTRJ0qOSNk7llZImN7KtwZL6NqDeovPQyPZLnhtJsxvbXnPU7k/S+pJuX577NjOz8rTMkyBJA4B9gZ3SU853A9TS+4mI70TEx03cvA/QIklQsmdEbAOMBH7Tgu22exHxVkTUm/iZmZk1V71JkKSzUy/OQ5KG1derUsRZwI8j4hOAiJgZEUNS2+dIGpt6iAZJUiofKelCSU9LelHSrql8NUk3p16WW4DVcnFOk9QtLf9a0guSHga+nKvzw7S/iZL+JWl1Sd8ADgIukjRB0mYFPUzdJE1Ly1ulmCakGHrUc+xPAhvkXneQdLWkZyU9KGm1UnHlttlH0mPpPBzQgPNwZOqJmizpwlz5tySNT/t4JNf+V9Pxvirp1MIDkNRJ0iNp2xpJB6fyynRdXJP2NVTSPpJGS3pJ0k6SVkrL66ZtVpL0cjqnm0h6Mh3373P7W9RjlpYfS/sen94rMzOzFlFnEpQSgUOB7YBDgEYNuUjqDHSOiFdKVLk8InZMPUSrAQfk1q0cETsBpwG/TWUnAXNSL8sFwA5F9rkDcEQu5h1zq+9I+9sWmAL8ICKeAIYDZ0RErzpiBRhA9rT4XmTnYnoddQG+BdyVe90DuCIitgI+Jju3RePKbVMJ7A7sD1wlqaLUeZC0PnAhsBfQC9hR2VDfusDVwKFpH4fl2t8S+CawE/BbSR0LjmEe8N2I2B7YE7ikNlkFNgcuBbZJ7RwF9AZOB86KiIXAjcDRqf4+wMSIeD9td2VE7Ai8XeL8vQvsm/bdD7isWCVJ/SVVS6peMGdmiabMzMyWVF9PUG/g7oiYGxGzgHsa2b6AqGP9npKeklRD9sG9VW7dHen3OLJEALKhtBsBImISMKlIm7sCd0bEnNT7NDy3rmfqWagh+2Deqsj2dXkSOEvSL4GNI2JuiXojJL1L9qF/U658akRMKHJcdcV1a0QsjIiXgFfJko1S52FHYGREvBcRnwNDU92vA6MiYmra5sNc+/dFxGcpMXkXWK/gWAT8QdIk4GGynq3aOlMjoiYlO88Cj0READW5Y7sOqJ0D9n3g+rS8CzAsLf+T4joCV6fzchslhiwjYlBEVEVEVYfVu5RoyszMbEn1JUHNmruTkpBPJW26VMNZj8bfgb4RsTVZT0VFrspn6fcClvw+o7qSqvrqDAZ+kvb3u4L95X3O4nOzqE5E3EQ2dDYXeEDSXiW23xPYmCwxOC9X/lluOX9cdcVVeCxRohxKv191JaOlYqp1NLAusEPqAXsnF19+24W51wtr24mIN4B30rn6GvCfIsdSyv+l/W1L1vO2Sj31zczMGqy+JOhx4EBJFZI6kQ3JNNYfgSskrQkgaU1J/Vn8Qfp+arshk2FHkYZWJPUkG4YpVue7ad5MZ+DA3LrOwIw05HN0rnxWWldrGouH2hbFlZK5VyPiMrIepmL7ByD1Ep0GHCtpnXqOq1RcAIeluTSbAZsCL1D6PDwF7J7m3HQAjgQeJevB2l3SJmmb+uLJ6wK8GxHzJdUmd411DVnP1a0RsSCVjSYbtoSljzm/7xmpp+kYoEMT9m1mZlZUnUlQRIwl+7CfSDY8VQ00dtLFlcAIYGya8Poo2XyWj8l6f2rI5s2MbWBbndLQzC+Ap4vEPB64BZgA/At4LLf6bLJE4SHg+Vz5zcAZkp5JycbFwEmSngC65er1AyZLmkA2LHVDXcFGxAyyIZ+T6zmuUnFBlvQ8StaDMiAi5lHiPKT9/YrsfE8ExkfE3RHxHtAfuEPSRLLz01BDgSpJ1WTJSmF8DTEc6MTioTCAnwInSxpLluwU83fgOEljgC2AT5uwbzMzs6KUTeGoo4LUKSJmpzuWRgH9U6Jh1iBpgv1fImLXZb2vqqqqqK6uXta7MTOzFYikcRGx1M1dDXl22CBlXyRYAQxxAmSNIelMsrvZSg15mZmZtYp6k6CIOKqwTNIVZHf35PUAXioouzQirsfKVkQMBAa2dhxmZmaFmvQU+Yiob46LmZmZWZvmB6iamZlZWXISZGZmZmXJSZCZmZmVJSdBZmZmVpacBJmZmVlZchJkZmZmZclJkJmZmZWlJn1PkFlbVfPmTCrPvK+1wzCzdmbawKY8P9zaOvcEmZmZWVlyEmRmZmZlyUlQGZA0WNJUSRMkTZS0dwO2md3AtgdIOraeOtekh/CamZm1GZ4TVD7OiIjbJe0JDCJ74G2zSFo5Iq6qr15EnNjcfZmZmbU09wStICSdLel5SQ9JGibp9CY29SSwQWrzeEmX5/Zxr6Q9cq8vkTRe0iOS1k1lIyX9QdKjwE8lnSvpdElfkfR0bttKSZNy21RJOknSn3J1jpf0t7R8l6Rxkp6V1D+VbSzpJUndJK0k6TFJ+xU5N/0lVUuqXjBnZhNPi5mZlRsnQSsASVXAocB2wCFAVTOa+xZwVwPqrQGMj4jtgUeB3+bWrRURu0fEJbUFETEFWEXSpqmoH3BrQZu3k8VPrs4tafn7EbED2bGdKqlrRLwGXAhcBfwceC4iHiwMNCIGRURVRFR1WL1LAw7NzMzMSdCKojdwd0TMjYhZwD1NaOMiSa8CNwJ/aED9hSxOUG5MMdS6ZenqQJb0HJ6W+xXWi4j3gFclfV1SV+DLwOi0+lRJE4ExwJdIw3URcQ3QGRgANLX3y8zMbClOglYMaoE2zgA2B34DDElln7PkNVBRx/aRW/60RJ1bgMMlbQFERLxUqg5Zz9adERFpCG4fYOeI2BZ4pjYWSasDG6ZtO9URn5mZWaM4CVoxPA4cKKlCUiegSd/aFRELgUuBlSR9E5gG9Erzbb4E7JSrvhLQNy0flWKor/1XgAXA2ZTuLboD6AMcmavTBfgoIuZI2hL4eq7+hcBQ4Bzg6vpiMDMzayjfHbYCiIixkoYDE4HXgGqgSTOAU8/L+cAvyHpfpgI1wGRgfK7qp8BWksalffVr4C5uAS4CNimx/48kPQd8NSJqJ1LfDwxIE6lfIBsSQ9LuwI7ALhGxQNKhkk6IiOsbfsRmZmbFKSLqr2WtTlKniJidhodGAf0jYnx925WbqqqqqK6ubu0wzMysDZE0LiKWuqnIPUErjkHpCwcrgCFOgMzMzJrHSdAKIiKOyr+WdAWwS0G1HkDhZORLPXxkZma2NCdBK6iIOLm1YzAzM1uR+e4wMzMzK0tOgszMzKwsOQkyMzOzsuQkyMzMzMqSkyAzMzMrS06CzMzMrCw5CTIzM7Oy5O8Jsnal5s2ZVJ55X2uHYWYruGkDm/ScalvBuCfIzMzMypKTIDMzMytLToKWM0lfl/SUpAmSpkg6t7VjypN0VjO3nyapW0vFY2Zmtqw4CVr+hgD9I6IX0BO4tbkNSurQ3DZympUEmZmZrSicBDWBpLMlPS/pIUnDJJ3eiM2/AMwAiIgFEfFcavNcSf+U9F9JL0n6YSqXpIskTZZUI6lfKt9D0ghJNwE1kiokXZ/qPCNpz1Svg6SLU/kkSadI2lvSnbnj2VfSHZIGAqulXqqhad3P0r4nSzotlVWm4x+S2rxd0uq5YzxF0vi0zy3TNjtJeiLF9oSkL6fy1SXdmtq5JfWSVaV1V0qqlvSspN/V8X70T/WqF8yZ2Yi3wszMypnvDmuk9AF9KLAd2fkbD4xrRBN/AV6QNBK4HxgSEfPSum2ArwNrAM9Iug/YGegFbAt0A8ZKGpXq7wT0jIipkn4OEBFbp8TjQUlbACcAmwDbRcTnktYBPgKukLRuRLyX6lwfEfdI+knqpULSDmnd1wABT0l6NG3/ZeAHETFa0nXAj4GLU1zvR8T2kn4MnA6cCDwP7JZi2Af4QzqPPwY+iohtJPUEJuTO1a8j4sPU0/WIpG0iYlLhCY2IQcAggFW794iGvxVmZlbO3BPUeL2BuyNibkTMAu5pzMYRcR5QBTwIHEWWCNWqbfd9YARZktMbGJZ6jd4BHgV2TPWfjoipubj+mfbxPPAasAWwD3BVRHye1n0YEZHqfk/SWmSJ1n9KHOudEfFpRMwG7gB2TeveiIjRafnGVLfWHen3OKAyLXcBbpM0mSwR3Cq3j5tTbJOBfJJzuKTxwDOp/leLxGhmZtYk7glqPDW3gYh4BbhS0tXAe5K61q4qrFrP/j5tQFwq0i7A9WQJ3Dzgttokqci2pRSLtdZn6fcCFl9jvwdGRMR3JVUCI+vah6RNyHqRdoyIjyQNBirqiMfMzKxR3BPUeI8DB6Y5OJ2ARn2jlqT9JdV+8PcgSxQ+Tq8PTu12BfYAxgKjgH5pbs+6wG7A00WaHgUcnfaxBbAR8AJZj9MASSundesARMRbwFvAb4DBuXbmS+qYa7NPmrezBvBd4LG0biNJO6flI9N5qUsX4M20fHyu/HHg8BTbV4GtU/maZEneTEnrAd+up30zM7NGcRLUSBExFhgOTCQb9qkGGjMb9xiyOUETyIakjo6IBWnd08B9wBjg9ylRuZNsiGgi8F/gFxHxdpF2/w50kFQD3AIcHxGfAdcArwOTJE0kG4KrNZRsWOu5XNmgVHdoRIwnS5CeBp4CromIZ1K9KcBxkiYB6wBX1nPcfwL+KGk0kL+b7e/AuqmdX6ZjnRkRE8mGwZ4FrgNGY2Zm1oKUTQ+xxpDUKSJmpzuiRpHd8j6+mW2eC8yOiIvrq9tSJF0OPBMR1zZyu0rg3ojo2QIxdAA6RsQ8SZsBjwBbRMT/mtJeVVVVVFdXNzcsMzNrRySNi4iqwnLPCWqaQWnopoLs7q5mJUCtQdI4suGmn7dyKKsDI9IQnICTmpoAmZmZNYZ7glqApCuAXQqKewAvFZRdGhHXL5+oypN7gszMrJB7gpahiDi5tWMwMzOzxvHEaDMzMytLToLMzMysLDkJMjMzs7LkJMjMzMzKkpMgMzMzK0tOgszMzKwsOQkyMzOzsuTvCbJ2pebNmVSeeV9rh2HWbk0b2KhnRpu1ae4JMjMzs7LkJMjMzMzKUrtKgiSdLul5SZMlTZR0bAu1O1JSVVr+t6S1itQ5V9Lp9bTTJz14tSVimiapJh3ng5K+mMpnN7Kd4yWt34w46j1uMzOztqjdJEGSBgD7AjtFRE9gN7KnkreoiPhORHzcxM37AC2SBCV7RsS2QDVwVhPbOB5ochJkZma2omozSZCks1MvzkOShjWhd+Es4McR8QlARMyMiCGp7XMkjU09RIMkKZWPlHShpKclvShp11S+mqSbJU2SdAuwWi7OaZK6peVfS3pB0sPAl3N1fpj2N1HSvyStLukbwEHARZImSNqsoIepm6RpaXmrFNOEFEOPeo59FLB5bv8XpH2PkbSepM6SpkrqmNavmY7jMKAKGJr2tZqkvSU9k3qZrpO0au64a8/V05I2Lwyi2HGn8sNyvXOjUtnxku6SdE+K7SeSfpb2PUbSOnW1WbDf/pKqJVUvmDOznlNlZmaWaRNJUEoEDgW2Aw4h+2BuzPadgc4R8UqJKpdHxI6ph2g14IDcupUjYifgNOC3qewkYE5EbANcAOxQZJ87AEfkYt4xt/qOtL9tgSnADyLiCWA4cEZE9KojVoABwKUR0YvsXEyvoy7peGrS8hrAmLTvUcAPI2IWMBKova3jCOBfEXEbWS/S0WlfAQwG+kXE1mR3D56U288n6VxdDvy1SBxLHXcqPwf4Zio/KFe/J3AUsBPZeZ4TEdsBTwLH1tPmIhExKCKqIqKqw+pd6jxRZmZmtdpEEgT0Bu6OiLnpA/ueRm4vsg/wUvaU9JSkGmAvYKvcujvS73FAZVreDbgRICImAZOKtLkrcGdEzEm9T8Nz63pKeizt7+iC/TXEk8BZkn4JbBwRc0vUGyFpArAm8MdU9j/g3iLHdA1wQlo+Abi+SHtfBqZGxIvp9RCyc1FrWO73zkW2L3Xco4HBkn4IdMjHHxGzIuI9YCaL3/eaXNzNPZdmZmZFtZUkqFlzd1IS8qmkTZdqWKoA/g70Tb0bVwMVuSqfpd8LWPJ7k+pKquqrMxj4Sdrf7wr2l/c5i9+DRXUi4iayHpO5wAOS9iqx/Z6pV+nY3Dyl+RFRG9eiY4qI0UClpN2BDhExuUh79b0PUWK51mCKHHdEDAB+A3wJmCCpa6r/WW7bhbnXC1n8XhRt08zMrLnaShL0OHCgpApJnVg8bNMYfwSukLQmLJr30p/FH5rvp7b7NqCtUWS9DkjqCWxTos530zyazsCBuXWdgRlpDs7RufJZaV2taSwealsUV0rmXo2Iy8h6mIrtvyluIOvFyfcC5WN6nixRqp3vcwzwaK5uv9zvJ4u0X/S4JW0WEU9FxDnA+2TJUEOVOpdmZmbN0ia+MToixkoaDkwEXiObp9LYGa5XAp2AsZLmA/OBSyLiY0lXkw2xTAPGNrCt6yVNAiYATxeJeXyaND0hxfxYbvXZwFOpvIbFScbNwNWSTiVLei4GbpV0DPDf3Pb9gO+l43gbOK8BMTfEUOB8Fg9rQdbTcpWkuWRDXCcAt0lamexcXZWru6qkp8iS5yOLtF/quC9Kk7sFPEL2PvdqYMyl2ixq6w26UO1vtDUzswbQ4pGT1iWpU0TMTnf/jAL6R8T41o6rPZHUFzg4Io5pwrbTgKqIeL/FA2tBVVVVUV1d3dphmJlZGyJpXEQsddNVm+gJSgYp+yLBCmCIE6CWJelvwLeB77R2LGZmZm1Bm0mCIuKowjJJVwC7FBT3AF4qKLs0Iord7WRJRJzSzO0rWygUMzOzNqHNJEHFRMTJrR2DmZmZtU9t5e4wMzMzs+XKSZCZmZmVJSdBZmZmVpacBJmZmVlZchJkZmZmZclJkJmZmZWlNn2LvFlj1bw5k8oz72vtMMxWKNP8qBkrU+4JMjMzs7LkJMjMzMzKkpOgZUjSYElzJHXOlV0qKSR1W04xrCXpx8ug3fMk7VPfOkkjJVWl5WnL67jNzMzq4yRo2XsZOBhA0krAnsCby3H/awEtngRFxDkR8XBhuaQOpdaZmZm1JU6C6iHpbEnPS3pI0jBJpzeyiWFAv7S8BzAa+DzX/l2Sxkl6VlL/XPkPJL2YelKulnR5Kh8s6TJJT0h6VVLf3DZnSBoraZKk36XigcBmkiZIukiZiyRNllQjqV/atrukUaneZEm7pvLZki6RNF7SI5LWzcXRNy1Pk3SOpMeBw/LrSpzTSkmTc69Pl3RuWh4p6S8plimSdpR0h6SXJJ3fyHNvZmZWkpOgOqRhnEOB7YBDgKomNPMSsK6ktYEjgZsL1n8/InZIbZ8qqauk9YGzga8D+wJbFmzTHegNHECW5CBpP6AHsBPQC9hB0m7AmcArEdErIs5Ix9EL2BbYB7hIUnfgKOCBiKhdNyHtaw1gfERsDzwK/LbEcc6LiN4RUXh8TfG/iNgNuAq4GzgZ6AkcL6lrYWVJ/SVVS6peMGdmC+zezMzKgW+Rr1tv4O6ImAsg6Z4mtnMHcATwNeBHBetOlfTdtPwlskTmi8CjEfFh2u9twBa5be6KiIXAc5LWS2X7pZ9n0utOqa3XixzTsIhYALwj6VFgR2AscJ2kjqn9Can+QuCWtHxjOpZibilR3hTD0+8a4NmImAEg6VWyc/RBvnJEDAIGAazavUe0YBxmZtaOuSeobmqhdm4Gfg88lJKXrHFpD7LemJ0jYluyBKaiAfv9rEiMAv6Yenx6RcTmEXFtkW2Lth0Ro4DdyOYr/VPSsSX2XSrJ+LSemPM+Z8lrr6Jgfe3xLWTJY12IE3czM2shToLq9jhwoKQKSZ2AJn2jWES8Dvwa+HvBqi7ARxExR9KWZMNfAE8Du0taW9LKZENy9XkA+H6KE0kbSPoCMAvonKs3CugnqUOa37Mb8LSkjYF3I+Jq4Fpg+1R/JaB2fs9RZOekud4BvpCG/lYlG9YzMzNbrvxXdR0iYqyk4cBE4DWgGmjSpJOI+EeR4vuBAZImAS8AY1LdNyX9AXgKeAt4rr79RsSDkr4CPCkJYDbwvYh4RdLoNBH5P8AvgJ3TMQXwi4h4W9JxwBmS5qdta3uCPgW2kjQuxdCPZoqI+ZLOS8c3FXi+uW2amZk1liI8haIukjpFxGxJq5P1ovSPiPHLcb8rA3cC10XEnct6v0XimB0RnZb3fpuqqqoqqqurWzsMMzNrQySNi4ilbm5yT1D9Bkn6Ktm8lSHLIwFKzk1fOFgBPAjctZz2a2ZmVhacBNUjIo7Kv5Z0BbBLQbUeZLfC510aEdc3Y7+N/T6iZWJF6gUyMzNrDCdBjRQRJ7d2DGZmZtZ8vjvMzMzMypKTIDMzMytLToLMzMysLDkJMjMzs7LkJMjMzMzKkpMgMzMzK0tOgszMzKws+XuCrF2peXMmlWfe19phmC1T0wY26VnOZlbAPUFmZmZWlpwEmZmZWVlyElTmJA2WNFXShPTzRCO3P03S6rnX/5a0Vh31B0g6to7160u6vTExmJmZNYXnBBnAGRHR1MTjNOBGYA5ARHynrsoRcVU9698C+jYxFjMzswZzT1A7IOlsSc9LekjSMEnNfgK9pHMlDZH0oKRpkg6R9CdJNZLul9RR0qnA+sAISSPSdtMkdUvLx0qaJGmipH/m2j09LW8u6eG0frykzSRVSpqc1ldKeiytGy/pGyVi7S+pWlL1gjkzm3voZmZWJpwEreAkVQGHAtsBhwBVTWjmotxw2NBc+WbA/sDBZL09IyJia2AusH9EXAa8BewZEXsWxLUV8Gtgr4jYFvhpkf0OBa5I678BzChY/y6wb0RsD/QDLisWfEQMioiqiKjqsHqXxh25mZmVLQ+Hrfh6A3dHxFwASfc0oY1Sw2H/iYj5kmqADsD9qbwGqKynzb2A2yPifYCI+DC/UlJnYIOIuDOtn5fK89U6ApdL6gUsALZoxDGZmZnVyUnQik/1V2myzwAiYqGk+RERqXwh9V87AqKe9fX5P+AdYFuyXst5DdjGzMysQTwctuJ7HDhQUoWkTmTDV8vTLKBzkfJHgMMldQWQtE5+ZUR8AkyX1CetXzV/l1nSBZgREQuBY8h6o8zMzFqEk6AVXESMBYYDE4E7gGqgsbOD83OCJkhapRHbDgL+UzsxOhfXs8AFwKOSJgJ/LrLtMcCpkiYBTwBfLFj/d+A4SWPIhsI+bURcZmZmddLiEQ5bUUnqFBGzU0/KKKB/RIxv7bhaQ1VVVVRXV7d2GGZm1oZIGhcRS9045DlB7cMgSV8FKoAh5ZoAmZmZNYaToHYgIo7Kv5Z0BbBLQbUewEsFZZdGxPXLMjYzM7O2yklQOxQRJ7d2DGZmZm2dJ0abmZlZWXISZGZmZmXJSZCZmZmVJSdBZmZmVpacBJmZmVlZchJkZmZmZclJkJmZmZUlf0+QtSs1b86k8sz7WjsMs2Vm2sDl/Yxks/bLPUFmZmZWlpwEmZmZWVlyEtQEkgZL6ltQNrsB281Ov9eXdPuyim9Zk3SupNNboJ0+6cGvZmZmy52ToFYQEW9FRN/6a7Z7fYBGJUGSPI/NzMxaRNkmQZLOlvS8pIckDWuJno3UbidJj0gaL6lG0sFF6lRKmpyWt5L0tKQJkiZJ6pHKj02vJ0r6ZyrbOLU9Kf3eKJUPlnSlpBGSXpW0u6TrJE2RNDi33/0kPZliu01Sp1Q+TdLvcjFvmcrXkXRX2t8YSdvkDmNbSf+V9JKkH9Z37IXHI+kbwEHARenYN0s/90saJ+mxXByDJf1Z0gjgwiLns7+kaknVC+bMbNb7Z2Zm5aMs/6qWVAUcCmxHdg7GA+Ma2cxFkn5TpHwe8N2I+ERSN2CMpOERESXaGQBcGhFDJa0CdJC0FfBrYJeIeF/SOqnu5cANETFE0veBy8h6UwDWBvYiSyzuAXYBTgTGSuoFTAd+A+wTEZ9K+iXwM+C8tP37EbG9pB8Dp6dtfwc8ExF9JO0F3AD0SvW3Ab4OrAE8I+k+4N1ix07W27PE8UTEh2ndvRFxO4CkR4ABEfGSpK8Bf0/HBLBFin1B4QmMiEHAIIBVu/codZ7NzMyWUJZJENAbuDsi5gJIuqcJbZxR++Gd2qidEyTgD5J2AxYCGwDrAW+XaOdJ4NeSNgTuSAnAXsDtEfE+QER8mOruDBySlv8J/CnXzj0REZJqgHcioibF9SxQCWxIloyMlgSwStp3rTvS73G5ffQmSxaJiP9K6iqpS1pXe/7mph6anYD7Shx7qeNZJPVKfQO4LcUHsGquym3FEiAzM7OmKtckSPVXabKjgXWBHSJivqRpQEWpyhFxk6SngP2BBySdmOJrSI9Gvs5n6ffC3HLt65WBBcBDEXFkibZqt1nA4uui2HmKgt/58lLH3pDjWQn4OCJ6lVj/aT3bm5mZNUq5zgl6HDhQUkXqgWjJbx/rArybkoA9gY3rqixpU+DViLgMGE42zPQIcLikrqlO7XDYE8ARafnodBwNNQbYRdLmqc3VJW1Rzzaj0n6QtAfZkNknad3B6fx1BfYAxlL62EsdzyygM0Bqd6qkw1IdSdq2EcdnZmbWKGWZBEXEWLKEYyLZMFA10FIzaocCVZKqyRKI5+up3w+YLGkCsCXZnJ9ngQuARyVNBP6c6p4KnCBpEnAM8NOGBhUR7wHHA8PS9mPS/upybjqWScBA4LjcuqfJhr/GAL+PiLcocex1HM/NwBmSnpG0WdrmB6nOs8BSk8rNzMxaikrP123fJHWKiNmSVifr8egfEeNbOy5rnqqqqqiurm7tMMzMrA2RNC4iqgrLy3VOEMAgZV/UVwEMcQJkZmZWXso2CYqIo/KvJV1Bdlt5Xg/gpYKySyPi+mUZm5mZmS17ZZsEFYqIk1s7BjMzM1t+nASZmZkl8+fPZ/r06cybN6+1Q7EmqKioYMMNN6Rjx44Nqu8kyMzMLJk+fTqdO3emsrKS3Be32gogIvjggw+YPn06m2yySYO2Kctb5M3MzIqZN28eXbt2dQK0ApJE165dG9WL5yTIzMwsxwnQiqux752TIDMzMytLnhNkZmZWQuWZ97Voe9MGNuwpTXfeeSeHHHIIU6ZMYcstsy/3HzlyJBdffDH33nvvonrHH388BxxwAH379mWPPfZgxowZVFRUsMoqq3D11VfTq1cvAGbOnMkpp5zC6NGjAdhll13429/+Rpcu2TOxX3zxRU477TRefPFFOnbsyNZbb83f/vY31ltvvSYf64cffki/fv2YNm0alZWV3Hrrray99tpL1fvLX/7CNddcgyS23nprrr/+eioqKpg4cSIDBgxg9uzZVFZWMnToUNZcc01qamq45JJLGDx4cJNjq+UkyNqVmjdntvh/WmZ5Df0QM2uOYcOG0bt3b26++WbOPffcBm83dOhQqqqquP766znjjDN46KGHAPjBD35Az549ueGGGwD47W9/y4knnshtt93GvHnz2H///fnzn//MgQceCMCIESN47733mpUEDRw4kL333pszzzyTgQMHMnDgQC688MIl6rz55ptcdtllPPfcc6y22mocfvjh3HzzzRx//PGceOKJXHzxxey+++5cd911XHTRRfz+979n6623Zvr06bz++utstNFGTY4PPBxmZmbWpsyePZvRo0dz7bXXcvPNNzepjZ133pk333wTgJdffplx48Zx9tlnL1p/zjnnUF1dzSuvvMJNN93EzjvvvCgBAthzzz3p2bNns47j7rvv5rjjskdOHnfccdx1111F633++efMnTuXzz//nDlz5rD++usD8MILL7DbbrsBsO+++/Kvf/1r0TYHHnhgk89NnpMgMzOzNuSuu+7iW9/6FltssQXrrLMO48c3/qlO999/P3369AHgueeeo1evXnTo0GHR+g4dOtCrVy+effZZJk+ezA477FBvm7NmzaJXr15Ff5577rml6r/zzjt0794dgO7du/Puu+8uVWeDDTbg9NNPZ6ONNqJ79+506dKF/fbbD4CePXsyfPhwAG677TbeeOONRdtVVVXx2GOPNfyElODhMDMzszZk2LBhnHbaaQAcccQRDBs2jO23377knU/58qOPPppPP/2UBQsWLEqeIqLotqXKS+ncuTMTJkxo+IE0wEcffcTdd9/N1KlTWWuttTjssMO48cYb+d73vsd1113HqaeeynnnncdBBx3EKqussmi7L3zhC7z11lvN3r97gtoASadLel7SZEkTJR2byqdJ6lak/h6SvpF7PVhS3xaK5Zr0YNm66sxugf2sJenHzW3HzKw9+eCDD/jvf//LiSeeSGVlJRdddBG33HILEUHXrl356KOPlqj/4Ycf0q3b4o+JoUOHMnXqVI466ihOPjl7GtRWW23FM888w8KFCxfVW7hwIRMnTuQrX/kKW221FePGjas3tsb2BK233nrMmDEDgBkzZvCFL3xhqToPP/wwm2yyCeuuuy4dO3bkkEMO4YknngBgyy235MEHH2TcuHEceeSRbLbZZou2mzdvHquttlq9MdfHSVArkzQA2BfYKSJ6ArsB9aXmewDfqKdOk0TEiRGx9NXc8tYCGpUEKeNr1szardtvv51jjz2W1157jWnTpvHGG2+wySab8Pjjj9OjRw/eeustpkyZAsBrr73GxIkTF90BVqtjx46cf/75jBkzhilTprD55puz3Xbbcf755y+qc/7557P99tuz+eabc9RRR/HEE09w332Lbyq5//77qampWaLd2p6gYj9f/erSfzsfdNBBDBkyBIAhQ4Zw8MEHL1Vno402YsyYMcyZM4eI4JFHHuErX/kKwKLhs4ULF3L++eczYMCARdu9+OKLzZ6zBB4OaxGSzgaOBt4A3gfGRcTFDdz8LGDPiPgEICJmAkNy60+RdCDQETgMmAcMABZI+h5wSqq3m6SfAV8EfhERtyvr5/wT8G0ggPMj4hZJewDnplh7AuOA70VESBoJnB4R1ZKOTPEJuC8ifpk75kuAPYGPgCMi4j1JPwT6A6sALwPHRMQcSesBVwGbps1PAk4FNpM0AXgoIs6QdAZwOLAqcGdE/FZSJfAfYASwM9AHeK3g/PdP+6XDmus28LSbmdVved8NOGzYMM4888wlyg499FBuuukmdt11V2688UZOOOEE5s2bR8eOHbnmmmsW3eaet9pqq/Hzn/+ciy++mGuvvZZrr72WU045hc0335yIYOedd+baa69dVPfee+/ltNNO47TTTqNjx45ss802XHrppc06ljPPPJPDDz+ca6+9lo022ojbbrsNgLfeeosTTzyRf//733zta1+jb9++bL/99qy88spst9129O/ff9G5uOKKKwA45JBDOOGEExa1PWLECPbfv/nvjSKi2Y2UM0lVwDVkH9ArA+OBfzQkCZLUGXg9Ipb+4oRs/TTgkoj4Wxo62j4iTpR0LjC7dh+SBgNrAP2ALYHhEbG5pEPJEqZvAd2AscDXgC8DdwNbAW8Bo4EzIuLx2iQolY8BdiBLdB4ELouIuyQFWdI0VNI5wBci4ieSukbEBymm84F3Uuy3AE9GxF8ldQA6AWsD96beLyTtB/QFfkSWdA0nS+BeB14FvhERY+o7p6t27xHdj/trfdXMmsy3yLdvU6ZMWdQTYW3TZ599xu67787jjz/Oyisv3ZdT7D2UNC4iqgrremih+XoDd0fE3IiYBdzTiG1F1kNTlzvS73FAZR317oqIhWkoq/aLHXoDwyJiQUS8AzwK7JjWPR0R0yNiITChSNs7AiMj4r2I+BwYSjZUB7AQuCUt35j2A9BT0mOSash6xrZK5XsBVwKkWGYWiX+/9PMMWSK5JdAjrXutIQmQmZm1f6+//joDBw4smgA1lofDmq/JD5mJiE8kfSpp04h4tUS1z9LvBdT9fn2WW1bB7/rqF2u7McdVm8gNBvpExERJx5PNXWooAX+MiH8sUZgNh33aiHbMzKwd69GjBz169Ki/YgO4J6j5HgcOlFQhqRPQ2L7yPwJXSFoTQNKaaY5LXWYBnRvQ9iign6QOktYl68l5uoFxPQXsLqlbGsI6kqwnCbLrpvZutKPIzgEpphmSOpL1BNV6hGweECmWNYscwwPA99M5RNIGkpa+lcDMbBnzNJEVV2PfO/cENVNEjJU0HJhINmG3Gig23FPKlWRzZMZKmg/MBy6pZ5t7gNslHcziidHF3Ek2V2kiWW/NLyLibUlb1hdURMyQ9CuyCckC/h0Rd6fVnwJbSRpHdqz9UvnZZMnTa0ANi5OcnwKDJP2ArNfppIh4UtJoSZOB/6SJ0V8BnkzfWzEb+F6q32Bbb9CFas/ZMLMmqqio4IMPPqBr165+mvwKJiL44IMPqKioaPA2nhjdAiR1iojZklYn633pHxGN/4pPa7aqqqqorq5u7TDMbAU1f/58pk+fzrx581o7FGuCiooKNtxwQzp27LhEeamJ0e4JahmD0hcMVgBDnACZma2YOnbsyCabbNLaYdhy4iSoBUTEUfnXkq4Adimo1gN4qaDs0oi4flnGZmZmZsU5CVoGIuLk1o7BzMzM6ua7w8zMzKwseWK0tSuSZgEvtHYcZaYb2SNYbPnxOV/+fM6Xv5Y85xtHxFLPVfJwmLU3LxS7A8CWHUnVPufLl8/58udzvvwtj3Pu4TAzMzMrS06CzMzMrCw5CbL2ZlBrB1CGfM6XP5/z5c/nfPlb5ufcE6PNzMysLLknyMzMzMqSkyAzMzMrS06CrN2Q9C1JL0h6WdKZrR1PeyPpS5JGSJoi6VlJP03l50p6U9KE9POd1o61vZE0TVJNOr/VqWwdSQ9Jein9Xru142wvJH05dz1PkPSJpNN8rbcsSddJelfS5FxZyeta0q/S/+8vSPpmi8TgOUHWHkjqALwI7AtMB8YCR0bEc60aWDsiqTvQPSLGS+oMjAP6AIcDsyPi4taMrz2TNA2oioj3c2V/Aj6MiIEp6V87In7ZWjG2V+n/ljeBrwEn4Gu9xUjaDZgN3BARPVNZ0es6PaR8GLATsD7wMLBFRCxoTgzuCbL2Yifg5Yh4NSL+B9wMHNzKMbUrETEjIsan5VnAFGCD1o2qrB0MDEnLQ8gSUmt5ewOvRMRrrR1IexMRo4APC4pLXdcHAzdHxGcRMRV4mez//WZxEmTtxQbAG7nX0/EH9DIjqRLYDngqFf1E0qTUve1hmZYXwIOSxknqn8rWi4gZkCWowBdaLbr27QiyHohavtaXrVLX9TL5P95JkLUXKlLmsd5lQFIn4F/AaRHxCXAlsBnQC5gBXNJ60bVbu0TE9sC3gZPTMIItY5JWAQ4CbktFvtZbzzL5P95JkLUX04Ev5V5vCLzVSrG0W5I6kiVAQyPiDoCIeCciFkTEQuBqWqCL2pYUEW+l3+8Cd5Kd43fSPK3a+Vrvtl6E7da3gfER8Q74Wl9OSl3Xy+T/eCdB1l6MBXpI2iT99XYEMLyVY2pXJAm4FpgSEX/OlXfPVfsuMLlwW2s6SWukiehIWgPYj+wcDweOS9WOA+5unQjbtSPJDYX5Wl8uSl3Xw4EjJK0qaROgB/B0c3fmu8Os3Ui3q/4V6ABcFxEXtG5E7Yuk3sBjQA2wMBWfRfZB0Yusa3oa8KPaMX1rPkmbkvX+AKwM3BQRF0jqCtwKbAS8DhwWEYWTTK2JJK1ONgdl04iYmcr+ia/1FiNpGLAH0A14B/gtcBclrmtJvwa+D3xONhz/n2bH4CTIzMzMypGHw8zMzKwsOQkyMzOzsuQkyMzMzMqSkyAzMzMrS06CzMzMrCw5CTKzRpO0ID1Fe7KkeyStVU/9cyWdXk+dPukhibWvz5O0TwvEOlhS3+a208h9npZusW4zJG2Z3rNnJG1WsG6apMcKyibUPt1bUpWky1oghsr8E8ML1l2Tf/+XNUnrSbpJ0qvpcSRPSvru8tq/tQ1OgsysKeZGRK/05OcPgZNboM0+wKIPwYg4JyIeboF2l6v01PHTgDaVBJGd37sjYruIeKXI+s6SvgQg6Sv5FRFRHRGnNnRH6Rw0SkScGBHPNXa7pkhf/HkXMCoiNo2IHci+YHXDZbzflZdl+9Z4ToLMrLmeJD3IUNJmku5Pf1k/JmnLwsqSfihprKSJkv4laXVJ3yB7RtNFqQdis9oeHEnflnRrbvs9JN2TlvdLf8GPl3Rbeq5ZSanH4w9pm2pJ20t6QNIrkgbk2h8l6U5Jz0m6StJKad2RkmpSD9iFuXZnp56rp4BfA+sDIySNSOuvTPt7VtLvCuL5XYq/pvZ8Seok6fpUNknSoQ09Xkm9JI1J290pae30RaKnASfWxlTErUC/tFz4Tcl7SLq3ntjy52BnST9L52mypNNy+1lZ0pC07e21PWaSRkqqasB5vjBdXw9L2ilt96qkg1KdDpIuStfYJEk/KnKsewH/i4iragsi4rWI+FtdbaTzMDLF/bykoSmhQtIOkh5NsT2gxY9+GJmuuUeBn0o6UNJTynrkHpa0Xon3w5aHiPCPf/zjn0b9ALPT7w5kD5f8Vnr9CNAjLX8N+G9aPhc4PS13zbVzPnBKWh4M9M2tGwz0JfuW5NeBNVL5lcD3yL5ldlSu/JfAOUViXdQu2bf8npSW/wJMAjoD6wLvpvI9gHnApun4HkpxrJ/iWDfF9F+gT9omgMNz+5wGdMu9Xid3vkYC2+Tq1R7/j4Fr0vKFwF9z26/diOOdBOyels+rbSf/HhTZZhqwBfBEev0MWa/c5Nw5ubdUbIXnANiB7JvF1wA6Ac8C2wGVqd4uqd51LL4uRgJVDTjP307LdwIPAh2BbYEJqbw/8Ju0vCpQDWxScLynAn+p4/ou2kY6DzPJeoxWIvsDoHeK4Qlg3bRNP7Jvra89rr8XvJe1X1R8InBJa/97Lucfd82ZWVOsJmkC2YfaOOCh1CvxDeC29McxZB8ghXpKOh9Yi+wD8oG6dhQRn0u6HzhQ0u3A/sAvgN3JPqhHp/2tQvahVJ/aZ8rVAJ0iYhYwS9I8LZ7b9HREvAqLvtq/NzAfGBkR76XyocBuZMMqC8geLFvK4ZL6k32od09xT0rr7ki/xwGHpOV9yIZnas/BR5IOqO94JXUB1oqIR1PREBY/Ab0+HwIfSToCmALMKVFvqdjSYv4c9AbujIhPU1x3ALuSnfs3ImJ0qncjWUJyca79HSl9nv8H3J/q1QCfRcR8STVk1yJkz1bbRovngXUhe87U1FIHLumKFPP/ImLHOtr4H9m1MT1tNyHt92OgJ9m/A8iS3fzjNG7JLW8I3JJ6ilapKy5b9pwEmVlTzI2IXulD916yOUGDgY8jolc92w4m+8t+oqTjyf66rs8taR8fAmMjYlYahngoIo5sZOyfpd8Lc8u1r2v/Tyx8nlAAorR5EbGg2AplD3s8HdgxJTODgYoi8SzI7V9FYmjq8TbGLcAVwPF11CkWGyx5Duo6V8XObWH7pcyP1IVC7v2LiIVaPN9GZL1rdSXXzwKHLgog4mRJ3ch6fEq2IWkPlrxmat8zAc9GxM4l9vdpbvlvwJ8jYnhq79w64rRlzHOCzKzJInuw5KlkH/JzgamSDoNs8qmkbYts1hmYIakjcHSufFZaV8xIYHvghyz+q3oMsIukzdP+Vpe0RfOOaJGdJG2ibC5QP+Bx4Clgd0ndlE38PRJ4tMT2+WNZk+xDcGaa//HtBuz/QeAntS8krU0Djje9Hx9J2jUVHVNHjMXcCfyJunvnisVWaBTQJ8W4BtkT12vvPttIUm2ycCTZuc1rzHku5gHgpHR9IWmLFEPef4EKSSflyvIT2RvSRt4LwLq1xyWpo6StStTtAryZlo8rUceWEydBZtYsEfEMMJFsiORo4AeSJpL9tX1wkU3OJvugewh4Pld+M3CGitzCnXoY7iVLIO5NZe+R9VgMkzSJLElYaiJ2Ez0JDAQmkw1X3BnZ08J/BYwgO97xEXF3ie0HAf+RNCIiJpLNsXmWbA7M6BLb5J0PrJ0mBk8E9mzE8R5HNsF8EtkTz89rwP4AiIhZEXFhRPyvMbEVaWc8WY/f02Tv9TXpOoFsqO24FN86ZHO88ts25jwXcw3wHDBe2e34/6Bg1CP1JvUhS7amSnqabOjwlw1to6C9/5HNG7swnZMJZEPDxZxLNmT8GPB+I47LlgE/Rd7MLCcNUZweEQe0cihmtoy5J8jMzMzKknuCzMzMrCy5J8jMzMzKkpMgMzMzK0tOgszMzKwsOQkyMzOzsuQkyMzMzMrS/wOvfGBDXAECCwAAAABJRU5ErkJggg==\n", 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PearsonRResult(statistic=-0.2424992383246979, pvalue=0.015061129313889266)\n", "0 308\n", "1 5\n", "Name: BrMedicated, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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PearsonRResult(statistic=0.5359755797757132, pvalue=9.103702333134289e-09)\n", "Slope and P-value = PearsonRResult(statistic=0.9671445001180137, pvalue=8.649690269360092e-05)\n", "0 178\n", "1 5\n", "Name: BrMedicated, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.9599366430858234, pvalue=4.074873969794364e-05)\n", "Slope and P-value = PearsonRResult(statistic=0.8563052935151658, pvalue=1.1433003594879565e-15)\n", "Slope and P-value = PearsonRResult(statistic=-0.8660687083024938, pvalue=2.876775827484165e-31)\n", "Slope and P-value = PearsonRResult(statistic=0.8471937861931441, pvalue=0.00025681121378746003)\n", "Slope and P-value = PearsonRResult(statistic=0.7690085560813391, pvalue=7.06885833296266e-06)\n", "Slope and P-value = PearsonRResult(statistic=nan, pvalue=nan)\n", "Slope and P-value = PearsonRResult(statistic=0.7829976715592422, pvalue=6.337270024196139e-22)\n", "Slope and P-value = PearsonRResult(statistic=0.9109012982540194, pvalue=1.0418098585206925e-10)\n", "Slope and P-value = PearsonRResult(statistic=0.6957908406639431, pvalue=0.0006574195245828318)\n", "Slope and P-value = PearsonRResult(statistic=0.9972755608420365, pvalue=1.1123741919723147e-05)\n", "0 180\n", "1 5\n", "Name: BrMedicated, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
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pvalue=0.24548192514391157)\n", "Slope and P-value = PearsonRResult(statistic=0.7129248198365279, pvalue=9.831782166882376e-06)\n", "0 203\n", "1 5\n", "Name: BrMedicated, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
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"Slope and P-value = PearsonRResult(statistic=0.720626192128741, pvalue=3.472305317633808e-08)\n", "0 190\n", "1 5\n", "Name: BrMedicated, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.5550548021127502, pvalue=0.07632511326723554)\n", "Slope and P-value = PearsonRResult(statistic=0.3993938268136288, pvalue=0.007972019728537144)\n", "Slope and P-value = PearsonRResult(statistic=0.9013362476676574, pvalue=0.2851747697069022)\n", "Slope and P-value = PearsonRResult(statistic=0.7769823688924666, pvalue=0.004899857248136292)\n", "Slope and P-value = PearsonRResult(statistic=0.9112581199469084, pvalue=0.03130831626922885)\n", "Slope and P-value = PearsonRResult(statistic=0.7820459304017794, pvalue=2.9176158844813727e-12)\n", "Slope and P-value = PearsonRResult(statistic=1.0, pvalue=1.0)\n", "Slope and P-value = PearsonRResult(statistic=0.75977478621798, pvalue=5.001693618465522e-20)\n", "Slope and P-value = PearsonRResult(statistic=0.8475005503776165, pvalue=6.440709137044065e-07)\n", "Slope and P-value = PearsonRResult(statistic=0.8521957837065623, pvalue=1.403540973689055e-05)\n" ] } ], "source": [ "sample = pd.merge(microbiome, poultry[['SampleID', 'BrMedicated','PastureTime','SampleType']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "#sample = sample[~sample.isin([np.nan, np.inf, -np.inf]).any(1)]\n", "sample = sample.drop(['Pathogen_Salmonella', 'new_Pathogen_Salmonella',\n", " 'Pathogen_Campy','new_Pathogen_Campy',\n", " 'Pathogen_Listeria','new_Pathogen_Listeria'],axis='columns')\n", "\n", "sample.BrMedicated.replace({'Bac': 1,'N':0}, regex=True, inplace=True)\n", "\n", "\n", "feces1=sample[(sample.SampleType=='Feces') & (sample.PastureTime=='Start')]\n", "feces2=sample[(sample.SampleType=='Feces') & (sample.PastureTime=='Mid')]\n", "feces3=sample[(sample.SampleType=='Feces') & (sample.PastureTime=='End')]\n", "\n", "soil1=sample[(sample.SampleType=='Soil') & (sample.PastureTime=='Start')]\n", "soil2=sample[(sample.SampleType=='Soil') & (sample.PastureTime=='Mid')]\n", "soil3=sample[(sample.SampleType=='Soil') & (sample.PastureTime=='End')]\n", "\n", "ceca=sample[sample.SampleType=='Ceca']\n", "wcrp=sample[sample.SampleType=='WCR-P']\n", "wcrf=sample[sample.SampleType=='WCR-F']\n", "\n", "\n", "print('Feces_Start', feces1.shape)\n", "print('Feces_Mid', feces2.shape)\n", "print('Feces_End', feces3.shape,'\\n')\n", "\n", "print('Soil_Start', soil1.shape)\n", "print('Soil_Mid', soil2.shape)\n", "print('Soil_End', soil3.shape,'\\n')\n", "\n", "print('Ceca', ceca.shape)\n", "print('WCR-P', wcrp.shape)\n", "print('WCR-F', wcrf.shape,'\\n')\n", "\n", "\n", "sampletypes = [feces1,feces2,feces3, soil1,soil2,soil3,ceca, wcrp, wcrf]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES_START\", 1: \"FECES_MID\", 2:\"FECES_END\",\n", " 3: \"SOIL_START\", 4: \"SOIL_MID\", 5: \"SOIL_END\",\n", " 6:\"CECA\", 7: \"WCR-P\", 8: \"WCR-F\"\n", " }\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType', 'PastureTime','BrMedicated'],axis='columns'),sample.BrMedicated,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1] #multiclass\n", " \n", " try:\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " except ValueError:\n", " pass\n", " \n", " \n", " \n", " \n", "# mylist2.append([f\"BrMedicated_{sample_name[indexing]}\", rf_auc_normal])\n", " \n", " \n", " print(pd.value_counts(sample['BrMedicated']))\n", "\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", " \n", " \n", " plt.title(f\"BrMedicated in {sample_name[indexing]} Model\")\n", " prelim3_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','BrMedicated','PastureTime','SampleType'],axis='columns').columns)\n", " prelim3_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", "\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim3_plot.max(), decimals=None, symbol=''))\n", "\n", " xmax=prelim3_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " \n", " plt.show()\n", " \n", " prelim3_plot.nlargest(10).to_csv(\"prelim3.csv\")\n", " top10 = pd.read_csv('prelim3.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", "# plt.plot(pdp[1][0], pdp[0][0],'.')\n", "# plt.ylabel('Partial dependence'), plt.xlabel(top10[feature])\n", "# plt.show()\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"BrMedicated_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (8) BroodCleanFrequency" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Feces_Start (190, 878)\n", "Feces_Mid (283, 878)\n", "Feces_End (175, 878) \n", "\n", "Soil_Start (189, 878)\n", "Soil_Mid (283, 878)\n", "Soil_End (173, 878) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "DLM 110\n", "3Days 40\n", "AIAO 25\n", "Yearly 5\n", "Weekly 5\n", "Daily 5\n", "Name: BroodCleanFrequency, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.3585933036281646, pvalue=0.00038673974535124707)\n", "Slope and P-value = PearsonRResult(statistic=0.9774752199891895, pvalue=5.0666389521013315e-68)\n", "Slope and P-value = PearsonRResult(statistic=-0.8946986379782881, pvalue=4.4791838093056594e-36)\n", "Slope and P-value = PearsonRResult(statistic=0.8078437016680858, pvalue=3.1301782125979255e-24)\n", "Slope and P-value = PearsonRResult(statistic=0.8967714834605973, pvalue=1.77994418947861e-36)\n", "Slope and P-value = PearsonRResult(statistic=-0.6325763763958482, pvalue=1.6665079870577332e-12)\n", "Slope and P-value = PearsonRResult(statistic=0.6857123183377614, pvalue=1.6298147179509167e-12)\n", "Slope and P-value = PearsonRResult(statistic=0.42158674418638337, pvalue=1.3875101037051728e-05)\n", "Slope and P-value = PearsonRResult(statistic=0.42869505536275143, pvalue=5.863746756044611e-05)\n", "Slope and P-value = PearsonRResult(statistic=-0.1643403543104117, pvalue=0.10228591596727729)\n", "DLM 178\n", "AIAO 40\n", "3Days 40\n", "Daily 15\n", "Yearly 5\n", "Weekly 5\n", "Name: BroodCleanFrequency, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.6607582455412195, pvalue=7.408139346291809e-14)\n", "Slope and P-value = PearsonRResult(statistic=0.7695291407365551, pvalue=8.496990211539007e-21)\n", "Slope and P-value = PearsonRResult(statistic=0.10117859559020738, pvalue=0.31651821304092026)\n", "Slope and P-value = PearsonRResult(statistic=0.5520548361081926, pvalue=2.619952489013323e-09)\n", "Slope and P-value = PearsonRResult(statistic=0.41003778863673773, pvalue=0.0001303938864507006)\n", "Slope and P-value = PearsonRResult(statistic=-0.8951111617069393, pvalue=3.733408068216461e-36)\n", "Slope and P-value = PearsonRResult(statistic=0.8112025024543649, pvalue=1.4389972020868318e-24)\n", "Slope and P-value = PearsonRResult(statistic=0.8893368338441384, pvalue=4.470167503346287e-35)\n", "Slope and P-value = PearsonRResult(statistic=0.8429583489054663, pvalue=3.914259967736627e-28)\n", "Slope and P-value = PearsonRResult(statistic=0.7395163697159658, pvalue=5.906452955991162e-13)\n", "DLM 110\n", "AIAO 25\n", "3Days 25\n", "Yearly 5\n", "Weekly 5\n", "Daily 5\n", "Name: BroodCleanFrequency, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
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PearsonRResult(statistic=0.8956072991380386, pvalue=2.9960100497510998e-36)\n", "Slope and P-value = PearsonRResult(statistic=-0.5239910828126232, pvalue=5.964353818527495e-08)\n", "DLM 109\n", "3Days 40\n", "AIAO 25\n", "Yearly 5\n", "Weekly 5\n", "Daily 5\n", "Name: BroodCleanFrequency, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.27552389168032143, pvalue=0.14799524089461233)\n", "Slope and P-value = PearsonRResult(statistic=0.44714103951566353, pvalue=0.002989700755523641)\n", "Slope and P-value = PearsonRResult(statistic=0.6789340525967695, pvalue=3.259583851106699e-12)\n", "Slope and P-value = PearsonRResult(statistic=-0.5301598141583541, pvalue=1.4060026856337083e-08)\n", "Slope and P-value = PearsonRResult(statistic=-0.8602186954376826, pvalue=2.017152234569041e-30)\n", "Slope and P-value = PearsonRResult(statistic=-0.8225425583288837, pvalue=9.264697467141245e-26)\n", "Slope and P-value = PearsonRResult(statistic=0.53399494401161, pvalue=1.929553078492289e-06)\n", "Slope and P-value = PearsonRResult(statistic=0.9381760827690365, pvalue=6.043425930285854e-47)\n", "Slope and P-value = PearsonRResult(statistic=0.8506207316325652, pvalue=4.098443095645975e-29)\n", "Slope and P-value = PearsonRResult(statistic=0.9264439100991619, pvalue=2.2649627328125637e-43)\n", "DLM 179\n", "3Days 40\n", "AIAO 39\n", "Daily 15\n", "Yearly 5\n", "Weekly 5\n", "Name: BroodCleanFrequency, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.8317564904573312, pvalue=8.606087914290222e-27)\n", "Slope and P-value = PearsonRResult(statistic=0.6139169903336986, pvalue=1.1059388538482634e-11)\n", "Slope and P-value = PearsonRResult(statistic=-0.6539925714048821, pvalue=1.611921673069787e-13)\n", "Slope and P-value = PearsonRResult(statistic=0.20829436148942526, pvalue=0.03756001225962677)\n", "Slope and P-value = PearsonRResult(statistic=-0.5878117013475029, pvalue=1.279213218015423e-10)\n", "Slope and P-value = PearsonRResult(statistic=-0.7978036370218435, pvalue=2.9250992663664857e-23)\n", "Slope and P-value = PearsonRResult(statistic=0.6750461195777611, pvalue=1.3409660934847303e-14)\n", "Slope and P-value = PearsonRResult(statistic=0.15883245264988416, pvalue=0.11447540795388131)\n", "Slope and P-value = PearsonRResult(statistic=0.38020231257228376, pvalue=0.004192918104859056)\n", "Slope and P-value = PearsonRResult(statistic=0.5735155849196674, pvalue=4.4684149466058486e-10)\n", "DLM 110\n", "AIAO 25\n", "3Days 23\n", "Yearly 5\n", "Weekly 5\n", "Daily 5\n", "Name: BroodCleanFrequency, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.6229083197922032, pvalue=0.00014030975677507273)\n", "Slope and P-value = PearsonRResult(statistic=-0.4798314662880097, pvalue=4.3863359442708097e-07)\n", "Slope and P-value = PearsonRResult(statistic=0.7747928160765075, pvalue=2.285838975458943e-14)\n", "Slope and P-value = PearsonRResult(statistic=0.8738760645002267, pvalue=1.8442328138172057e-32)\n", "Slope and P-value = PearsonRResult(statistic=0.6625733536416972, pvalue=5.993054852667033e-14)\n", "Slope and P-value = PearsonRResult(statistic=0.9456647406824114, pvalue=7.968972106710217e-39)\n", "Slope and P-value = PearsonRResult(statistic=0.8934183048849412, pvalue=2.774188342242002e-31)\n", "Slope and P-value = PearsonRResult(statistic=-0.8642113998655526, pvalue=6.711597331608079e-05)\n", "Slope and P-value = PearsonRResult(statistic=0.9210136708544052, pvalue=5.672131592332867e-18)\n", "Slope and P-value = PearsonRResult(statistic=0.7597131208283081, pvalue=5.0567023302492257e-20)\n" ] } ], "source": [ "sample = pd.merge(microbiome, poultry[['SampleID', 'BroodCleanFrequency','PastureTime','SampleType']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "sample = sample[~sample.isin([np.nan, np.inf, -np.inf]).any(1)]\n", "sample = sample.drop(['Pathogen_Salmonella', 'new_Pathogen_Salmonella',\n", " 'Pathogen_Campy','new_Pathogen_Campy',\n", " 'Pathogen_Listeria','new_Pathogen_Listeria'],axis='columns')\n", "\n", "sample.BroodCleanFrequency.replace({4: 1,3:0}, regex=True, inplace=True)\n", "\n", "\n", "feces1=sample[(sample.SampleType=='Feces') & (sample.PastureTime=='Start')]\n", "feces2=sample[(sample.SampleType=='Feces') & (sample.PastureTime=='Mid')]\n", "feces3=sample[(sample.SampleType=='Feces') & (sample.PastureTime=='End')]\n", "\n", "soil1=sample[(sample.SampleType=='Soil') & (sample.PastureTime=='Start')]\n", "soil2=sample[(sample.SampleType=='Soil') & (sample.PastureTime=='Mid')]\n", "soil3=sample[(sample.SampleType=='Soil') & (sample.PastureTime=='End')]\n", "\n", "ceca=sample[sample.SampleType=='Ceca']\n", "wcrp=sample[sample.SampleType=='WCR-P']\n", "wcrf=sample[sample.SampleType=='WCR-F']\n", "\n", "\n", "print('Feces_Start', feces1.shape)\n", "print('Feces_Mid', feces2.shape)\n", "print('Feces_End', feces3.shape,'\\n')\n", "\n", "print('Soil_Start', soil1.shape)\n", "print('Soil_Mid', soil2.shape)\n", "print('Soil_End', soil3.shape,'\\n')\n", "\n", "\n", "\n", "sampletypes = [feces1,feces2,feces3, soil1,soil2,soil3]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES_START\", 1: \"FECES_MID\", 2:\"FECES_END\",\n", " 3: \"SOIL_START\", 4: \"SOIL_MID\", 5: \"SOIL_END\"}\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType', 'PastureTime','BroodCleanFrequency'],axis='columns'),sample.BroodCleanFrequency,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, :] #multiclass\n", " \n", " try:\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs, multi_class='ovo')\n", " except ValueError:\n", " pass\n", " \n", " \n", " \n", " \n", "# mylist2.append([f\"BroodCleanFrequency_{sample_name[indexing]}\", rf_auc_normal])\n", " \n", " \n", " print(pd.value_counts(sample['BroodCleanFrequency']))\n", "\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", " \n", " \n", " plt.title(f\"BroodCleanFrequency in {sample_name[indexing]} Model\")\n", " prelim3_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','BroodCleanFrequency','PastureTime','SampleType'],axis='columns').columns)\n", " prelim3_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", "\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim3_plot.max(), decimals=None, symbol=''))\n", "\n", " xmax=prelim3_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " \n", " plt.show()\n", " \n", " prelim3_plot.nlargest(10).to_csv(\"prelim3.csv\")\n", " top10 = pd.read_csv('prelim3.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", "# plt.plot(pdp[1][0], pdp[0][0],'.')\n", "# plt.ylabel('Partial dependence'), plt.xlabel(top10[feature])\n", "# plt.show()\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"BroodCleanFrequency_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (9) AvgAgeToPasture" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Feces_Start (200, 878)\n", "Feces_Mid (313, 878)\n", "Feces_End (185, 878) \n", "\n", "Soil_Start (199, 878)\n", "Soil_Mid (313, 878)\n", "Soil_End (183, 878) \n", "\n", "Ceca (185, 878)\n", "WCR-P (208, 878)\n", "WCR-F (195, 878) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "0 125\n", "1 75\n", "Name: AvgAgeToPasture, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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PearsonRResult(statistic=-0.34601069884737695, pvalue=0.0004215984256618983)\n", "Slope and P-value = PearsonRResult(statistic=-0.301627657486314, pvalue=0.004071783013903483)\n", "0 190\n", "1 123\n", "Name: AvgAgeToPasture, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
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PearsonRResult(statistic=-0.6535754040699215, pvalue=1.6899831571653477e-13)\n", "Slope and P-value = PearsonRResult(statistic=0.1284157532842854, pvalue=0.20291458034222506)\n", "0 110\n", "1 75\n", "Name: AvgAgeToPasture, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
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PearsonRResult(statistic=0.6456465807427159, pvalue=1.1092420649293273e-11)\n", "Slope and P-value = PearsonRResult(statistic=-0.6045203525459196, pvalue=5.4999534800879746e-11)\n", "0 124\n", "1 75\n", "Name: AvgAgeToPasture, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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PearsonRResult(statistic=0.8440409187112115, pvalue=2.8668122338581716e-28)\n", "Slope and P-value = PearsonRResult(statistic=0.776981964015657, pvalue=2.065993474411249e-21)\n", "0 190\n", "1 123\n", "Name: AvgAgeToPasture, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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PearsonRResult(statistic=-0.8803186026092534, pvalue=1.659485867645008e-33)\n", "Slope and P-value = PearsonRResult(statistic=-0.5348720290649809, pvalue=9.892630159487327e-09)\n", "0 108\n", "1 75\n", "Name: AvgAgeToPasture, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.5974467311824594, pvalue=5.316809789467364e-11)\n", "Slope and P-value = PearsonRResult(statistic=-0.5254153366802108, pvalue=1.298176601016486e-06)\n", "Slope and P-value = PearsonRResult(statistic=0.9524703500715107, pvalue=2.1668488810614777e-52)\n", "Slope and P-value = PearsonRResult(statistic=-0.7043703510900263, pvalue=1.90617675894323e-14)\n", "Slope and P-value = PearsonRResult(statistic=0.7277152303063129, pvalue=9.853089716500042e-10)\n", "Slope and P-value = PearsonRResult(statistic=0.9662992287865524, pvalue=1.4554014497669386e-59)\n", "Slope and P-value = PearsonRResult(statistic=0.7492226224297354, pvalue=7.146109315768942e-19)\n", "Slope and P-value = PearsonRResult(statistic=0.6911164492211299, pvalue=9.394425546525565e-12)\n", "Slope and P-value = PearsonRResult(statistic=0.4590551171531588, pvalue=1.5573071293822393e-06)\n", "Slope and P-value = PearsonRResult(statistic=0.6387869838078537, pvalue=8.625434235682338e-13)\n", "0 110\n", "1 75\n", "Name: AvgAgeToPasture, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
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PearsonRResult(statistic=0.6233004402614625, pvalue=2.9329080096798227e-08)\n", "Slope and P-value = PearsonRResult(statistic=0.45344566515030127, pvalue=3.4928882821842022e-06)\n", "0 133\n", "1 75\n", "Name: AvgAgeToPasture, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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PearsonRResult(statistic=0.8425498741913631, pvalue=6.068896505054226e-20)\n", "Slope and P-value = PearsonRResult(statistic=0.742706099940505, pvalue=9.161526633575717e-19)\n", "0 110\n", "1 85\n", "Name: AvgAgeToPasture, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.6479948326660627, pvalue=3.159201024007539e-13)\n", "Slope and P-value = PearsonRResult(statistic=-0.3074886394370463, pvalue=0.1348541518738852)\n", "Slope and P-value = PearsonRResult(statistic=0.37181988667748866, pvalue=0.0001395546060955936)\n", "Slope and P-value = PearsonRResult(statistic=-0.9344888266904933, pvalue=9.44529578197358e-46)\n", "Slope and P-value = PearsonRResult(statistic=0.9604053115423241, pvalue=3.4006121462305118e-56)\n", "Slope and P-value = PearsonRResult(statistic=-0.4150537488902402, pvalue=0.0050866515316930015)\n", "Slope and P-value = PearsonRResult(statistic=-0.7627522792648835, pvalue=2.93792126449757e-20)\n", "Slope and P-value = PearsonRResult(statistic=-0.9441674669288573, pvalue=4.7322973449886145e-49)\n", "Slope and P-value = PearsonRResult(statistic=0.8724754841395995, pvalue=3.059110843198129e-32)\n", "Slope and P-value = PearsonRResult(statistic=-0.5877923761372938, pvalue=7.967601396468482e-09)\n" ] } ], "source": [ "sample = pd.merge(microbiome, poultry[['SampleID', 'AvgAgeToPasture','PastureTime','SampleType']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "#sample = sample[~sample.isin([np.nan, np.inf, -np.inf]).any(1)]\n", "sample = sample.drop(['Pathogen_Salmonella', 'new_Pathogen_Salmonella',\n", " 'Pathogen_Campy','new_Pathogen_Campy',\n", " 'Pathogen_Listeria','new_Pathogen_Listeria'],axis='columns')\n", "\n", "sample.AvgAgeToPasture.replace({4: 1,3:0}, regex=True, inplace=True)\n", "\n", "\n", "feces1=sample[(sample.SampleType=='Feces') & (sample.PastureTime=='Start')]\n", "feces2=sample[(sample.SampleType=='Feces') & (sample.PastureTime=='Mid')]\n", "feces3=sample[(sample.SampleType=='Feces') & (sample.PastureTime=='End')]\n", "\n", "soil1=sample[(sample.SampleType=='Soil') & (sample.PastureTime=='Start')]\n", "soil2=sample[(sample.SampleType=='Soil') & (sample.PastureTime=='Mid')]\n", "soil3=sample[(sample.SampleType=='Soil') & (sample.PastureTime=='End')]\n", "\n", "ceca=sample[sample.SampleType=='Ceca']\n", "wcrp=sample[sample.SampleType=='WCR-P']\n", "wcrf=sample[sample.SampleType=='WCR-F']\n", "\n", "\n", "print('Feces_Start', feces1.shape)\n", "print('Feces_Mid', feces2.shape)\n", "print('Feces_End', feces3.shape,'\\n')\n", "\n", "print('Soil_Start', soil1.shape)\n", "print('Soil_Mid', soil2.shape)\n", "print('Soil_End', soil3.shape,'\\n')\n", "\n", "print('Ceca', ceca.shape)\n", "print('WCR-P', wcrp.shape)\n", "print('WCR-F', wcrf.shape,'\\n')\n", "\n", "\n", "sampletypes = [feces1,feces2,feces3, soil1,soil2,soil3,ceca, wcrp, wcrf]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES_START\", 1: \"FECES_MID\", 2:\"FECES_END\",\n", " 3: \"SOIL_START\", 4: \"SOIL_MID\", 5: \"SOIL_END\",\n", " 6:\"CECA\", 7: \"WCR-P\", 8: \"WCR-F\"\n", " }\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType', 'PastureTime','AvgAgeToPasture'],axis='columns'),sample.AvgAgeToPasture,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, :] #multiclass\n", " \n", " try:\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs, multi_class='ovo')\n", " except ValueError:\n", " pass\n", " \n", " \n", " \n", " \n", "# mylist2.append([f\"AvgAgeToPasture_{sample_name[indexing]}\", rf_auc_normal])\n", " \n", " \n", " print(pd.value_counts(sample['AvgAgeToPasture']))\n", "\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", " \n", " \n", " plt.title(f\"AvgAgeToPasture in {sample_name[indexing]} Model\")\n", " prelim3_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','AvgAgeToPasture','PastureTime','SampleType'],axis='columns').columns)\n", " prelim3_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", "\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim3_plot.max(), decimals=None, symbol=''))\n", "\n", " xmax=prelim3_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " \n", " plt.show()\n", " \n", " prelim3_plot.nlargest(10).to_csv(\"prelim3.csv\")\n", " top10 = pd.read_csv('prelim3.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", "# plt.plot(pdp[1][0], pdp[0][0],'.')\n", "# plt.ylabel('Partial dependence'), plt.xlabel(top10[feature])\n", "# plt.show()\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"AvgAgeToPasture_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (10) PastureHousing" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Feces_Start (200, 878)\n", "Feces_Mid (313, 878)\n", "Feces_End (185, 878) \n", "\n", "Soil_Start (199, 878)\n", "Soil_Mid (313, 878)\n", "Soil_End (183, 878) \n", "\n", "Ceca (185, 878)\n", "WCR-P (208, 878)\n", "WCR-F (195, 878) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "CT 105\n", "CTF 70\n", "CTFR 25\n", "Name: PastureHousing, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
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PearsonRResult(statistic=0.27934447897906206, pvalue=0.010072857927229863)\n", "Slope and P-value = PearsonRResult(statistic=0.8957145935339406, pvalue=2.8563656837889563e-36)\n", "CT 158\n", "CTF 115\n", "CTFR 40\n", "Name: PastureHousing, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
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PearsonRResult(statistic=-0.6426993259357306, pvalue=5.652557131659413e-13)\n", "Slope and P-value = PearsonRResult(statistic=0.023690177194205193, pvalue=0.8150190184107323)\n", "CT 90\n", "CTF 70\n", "CTFR 25\n", "Name: PastureHousing, dtype: int64\n" ] }, { "data": { "image/png": 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UR0R1h05di2y6mZlZcaWM2A8EHoiIxRGxAHioMSuIiAvJvgw8BpzM6oks1+/7wNPAPql8XETMjoiVwGSgR5GuPwNyx+In5NXZD7g7Pb69YDtGpVmDd4AxwN5p2fiImBcRS4E3UqwAtQX95vr7c+ov5/6IWJlmI7ZKZQJ+J2kq8ASwTVpWCxwm6WJJB0WEh+RmZlY2pSR2NXclEfFGRFwHHArsLmmz3KLCqunfpXllKyh+LsCyiIgG6uSrbzvy17cy7/nKevrNjz2/fW49pwBbAHtFRF/gHaAqIl4D9iJL8P8n6fwG4jYzMytZKYl9LNBfUpWkzmRT5yWTdETuGDnQiywJf5yeH5X63QzoB4xvTN91eJHs0AHAiXnlzwIDJHWQtAXwNWBcI/p9Ia+/U8j2S326Au9GxDJJhwBfBkiHGxZFxK3ApcCejYjBzMysXg2eFR8R4yU9CEwB3gJqgMZMH38PuFzSImA5cEpErEi5fhzwMNAd+E1EzJW0QyO3odAg4FZJP0t952K9j2w6fQrZaPsXEfEvSTuV2O85wJ8k/Rx4Dzi9gfq3AQ9JqiE7nDAjlfchO76/ElgG/KS+Tvps05WadnbVIzMzaz1aNZtdTyWpc0QslNSJbOQ7MCImNmvF2e/ZF0bEpc3pp0i/nYDFERGSTgROioijyrmONam6ujpqampaOwwzM2tDJE2IiKIns5f6O/Zh6cIwVcDI5ib1FrYXcHWa/v8Y+EHrhmNmZrbmlJTYI+Lk/OeSruGLP0HrBbxeUHZFRAyvo88hJcbYKBHxHNnP18zMzNY6TbryXEScVe5AzMzMrPl8ExgzM7MK4sRuZmZWQZzYzczMKogTu5mZWQVxYjczM6sgTuxmZmYVpEk/d7M1p3bOfHoMfri1wzAzazNm+TLb9fKI3czMrII4sZuZmVWQdp/YJY2QNFPS5PR3TiPb95M0ukj5aZKuLlOMN6Vr7ZuZmbWoSjnG/vOIuKe1g6hLRJzR2jGYmdnaoU2M2CWdJ2mGpMcljZJ0bjP7O1/SeEnTJA1Ld3pD0vaSnpA0RdJESdsVtNtb0iRJXyko7y/ppbTsCUlbpfIhkkZKekzSLEnHSLpEUq2kRyR1TPWekVSdHl8nqUbSy5J+3ZztNDMzK9TqiT0lvGOBPYBjgKL3l23A7/Om4vsAV0fE3hHRG9gAODLVuw24JiJ2B/YH5uXFsT9wPXBURLxZ0P9Y4KsRsQdwB/CLvGXbAUcARwG3Ak9HRB9gcSov9Kt0D93dgIMl7VZYQdLAlPxrViya38hdYWZma7O2MBV/IPBARCwGkPRQE/pYbSpe0rGSfgF0AjYFXpb0DLBNRNwHEBFLUl2AnYFhwOERMbdI/9sCd0rqBqwHzMxb9reIWCapFugAPJLKa4EeRfo6QdJAsn3fDdgFmJpfISKGpXhYv1uvKHEfmJmZtf6IHVBZO5OqgGuB49LI+UagqoH1zAOWkM0aFHMV2SxAH+DHqb+cpQARsRJYFhG5RLySgi9OknoC5wKHRsRuwMMFfZmZmTVLW0jsY4H+kqokdab49HVj5BLl+6m/4wAi4hNgtqSjASStL6lTqvtxWu/vJPUr0mdXYE56fGozYtsI+BSYn47Tf7MZfZmZmX1Bq0/FR8R4SQ8CU4C3gBqgyQeWI+JjSTeSTYXPAsbnLf4ecIOkC4FlwPF57d6R1B/4m6QfFHQ7BLhb0hzgRaBnE2ObImkS8DLwJvB8U/oxMzOri1bNHLdiEFLniFiYRtDPAgMjYmJrx9UWVFdXR01NTWuHYWZmbYikCelE7C9o9RF7MixdwKUKGOmkbmZm1jRtIrFHxMn5zyVdAxxQUK0X8HpB2RURMbwlYzMzM2tP2kRiLxQRZ7V2DGZmZu1RWzgr3szMzMrEid3MzKyCOLGbmZlVECd2MzOzCuLEbmZmVkGc2M3MzCqIE7uZmVkFaZO/Y7dVaufMp8fgh1s7DDOzRpk1tLn387Km8ojdzMysgjixm5mZVRAndjMzswrixG5mZlZBfPJcGUk6DzgFeBt4H5gQEZc2oZ+BwECADhttUdYYzcyssnnEXiaSqoFjgT2AY4DqpvYVEcMiojoiqjt06lquEM3MbC3gEXv5HAg8EBGLASQ91MrxmJnZWsgj9vJRawdgZmbmxF4+Y4H+kqokdQZ8dQYzM1vjPBVfJhExXtKDwBTgLaAGmN+6UZmZ2dpGEdHaMVQMSZ0jYqGkTsCzwMCImNicPqurq6OmpqY8AZqZWUWQNCEiip6k7RF7eQ2TtAtQBYxsblI3MzNrLCf2MoqIk/OfS7oGOKCgWi/g9YKyKyJieEvGZmZmawcn9hYUEWe1dgxmZrZ28VnxZmZmFcSJ3czMrII4sZuZmVUQJ3YzM7MK4sRuZmZWQZzYzczMKogTu5mZWQXx79jbuNo58+kx+OHWDsPMrFFmDfV9sFqLR+xmZmYVxIndzMysgqyViV3SCEnHpcebSpok6fQWXN+Fkg5rqf7NzMxy1upj7JK6Ao8Cw1rqJiySOkTE+S3Rt5mZWaF2O2KXdJ6kGZIelzRK0rmN7KIz8Dfg9oi4LvXZV9KLkqZKuk/SJql8e0lPSJoiaaKk7ST1kzQ6L56rJZ2WHs+SdL6kscDxBTME50saL2mapGGSVGTbBkqqkVSzYtH8Ju0fMzNbO7XLxC6pGjgW2AM4Bih6s/kGXAaMjYjL88puAX4ZEbsBtcAFqfw24JqI2B3YH5hXQv9LIuLAiLijoPzqiNg7InoDGwBHFjaMiGERUR0R1R06dW3kZpmZ2dqsXSZ24EDggYhYHBELgIea0MdTwFGStoTPp+U3jogxaflI4GuSugDbRMR9ABGxJCIWldD/nXWUHyLpJUm1wNeBXZsQu5mZWVHt9Rj7F6avm+AOYCzwV0mHNGFdy1n9i1FVwfJPv9CRVAVcC1RHxNuShhRpZ2Zm1mTtdcQ+FugvqUpSZ6BJV0KIiD8CTwL3AYuBjyQdlBZ/DxgTEZ8AsyUdDSBpfUmdgLeAXdLzrsChJawyl8TfT3Ef15S4zczM6tIuR+wRMV7Sg8AUsgRbAzTpLLOI+KWk4cCfgdOBa1PifjM9hyzJ3yDpQmAZcHxEvCnpLmAq8DowqYR1fSzpRrLj97OA8U2J2czMrC6KiNaOoUkkdY6IhSkJPwsMjIiJrR1XuVVXV0dNTU1rh2FmZm2IpAkRUfTE8XY5Yk+GSdqFbHp7ZCUmdTMzs8Zqt4k9Ik7Ofy7pGuCAgmq9yKbJ813RUhejMTMza23tNrEXioizWjsGMzOz1tZez4o3MzOzIpzYzczMKogTu5mZWQVxYjczM6sgTuxmZmYVxIndzMysgjixm5mZVZCK+R17paqdM58egx9u7TDMzACYNbRJ99yyNcgjdjMzswrixG5mZlZB1khil/RfkmZIqpU0RdJlkjo20OYZSdXp8SxJm6fHL6R/+0kaXYbY+kr6VhPabS3pnuau38zMrJxaPLFLOhM4HPhqRPQB9gbeBTZoSn8RsX8ZwwPoCzQqsUtaNyLmRsRxZY7FzMysWUpK7JLOSyPuxyWNknRuI9bxK+AnEfExQER8FhFDI+KT1Pd1kmokvSzp1yXEsjDv6UaS7pP0iqTrJa1TX5+S9pb0Qpo1GCepK3AhMEDSZEkDJG0o6U+SxkuaJOmo1PY0SXdLegh4TFIPSdPyll2dt57Rkvrl4pV0saQJkp6QtE+ajXhT0rfr2MaBKf6aFYvml76nzcxsrdfgWfFpOvxYYI9UfyIwoZTOJXUBOkfEzHqq/SoiPpTUAXhS0m4RMbWU/oF9gF2At4BHgGOAe4r1CcwA7gQGRMR4SRsBi4DzgeqI+GmK+XfAUxHxA0kbA+MkPZHWtx+wW+q7R4kxbgg8ExG/lHQfcBHwbynukcCDhQ0iYhgwDGD9br2ixPWYmZmVNGI/EHggIhZHxALgoUb0L+DzxCTp39PIeJak3JT6CZImApOAXckSXqnGRcSbEbECGJViravPHYF5ETEeICI+iYjlRfo8HBgsaTLwDFAFdE/LHo+IDxsRH8BnZF86AGqBMRGxLD3u0ci+zMzM6lXK79jV1M4j4hNJn0rqGREzI+JR4NF00tt6knoC5wJ7R8RHkkaQJdKSV1H4vJ4+VaR+MQKOjYhXVyuU9gU+raPNclb/kpS/DcsiIrfelcBSgIhYKcnXETAzs7IqZcQ+FugvqUpSZ6CxVyf4P+C6NK2NJLEq8W1EliznS9oK+GYj+95HUs90bH1AirWuPmcAW0vaO8XRJSXWBUCXvD4fBc5OcSJpjxLimAX0lbSOpC+RHSIwMzNb4xocMabj0Q8CU8iOZdcAjTmj6zqgE/CSpKXAQuB5YFJEzJc0CXgZeDOVN8bfgaFAH+BZ4L40Ev5CnxHxmaQBwFWSNgAWA4cBT7Nq6v3/gN8AfwSmpuQ+CziygTieB2aSTa9PIzsPoSz6bNOVGl/pyczMSqRVs8T1VJI6R8RCSZ3IEujAiChb8rK6VVdXR01NTWuHYWZmbYikCRFRXWxZqcd4h0nahWwKfaSTupmZWdtUUmKPiJPzn0u6BjigoFov4PWCsisiYnjTwzMzM7PGaNJZ2RFxVrkDMTMzs+bzTWDMzMwqiBO7mZlZBXFiNzMzqyBO7GZmZhXEid3MzKyCOLGbmZlVEN+EpI2rnTOfHoMfbu0wzGwtMsuXsW7XPGI3MzOrIE7sZmZmFaRVErukr0p6SdJkSdMlDUnl/STtX8b19JX0rXL1Z2Zm1ta11jH2kcAJETFFUgdgx1Tej+y2ri8UNpC0bkQsb+R6+gLVwF+bHqqZmVn70eQRu6TzJM2Q9LikUZLObUTzLYF5ABGxIiJekdQDOBP4/9JI/iBJIyRdJulp4GJJ20l6RNIESc9J2inFMkLS9ansNUlHSloPuBAYkPobIGlTSfdLmirpRUm7pfZDJI2U9JikWZKOkXSJpNq0vo6SDpV0X972/5uke9Pjb0iaKGmKpCdT2YaS/iRpvKRJko5K5btKGpdimiqpV1NfAzMzs0JNGrFLqgaOBfZIfUwEJjSii8uBVyU9AzxCdivYWZKuBxZGxKVpPT8EdgAOi4gVKWmeGRGvS9oXuBb4euqzB3AwsB3wNLA9cD5QHRE/Tf1dBUyKiKMlfR24hWxUT2p3CLAL8Hfg2Ij4RUrmRwAPANdI2iIi3gNOB4ZL2gK4EfhaRMyUtGnq71fAUxHxA0kbA+MkPUH25eWKiLgtffnoUGT/DgQGAnTYaItG7FYzM1vbNXXEfiDwQEQsjogFwEONaRwRF5JNkT8GnEyW3Otyd0rqnYH9gbslTQZuALrl1bsrIlZGxOvAm8BOdcT95xTDU8BmkrqmZX+LiGVALVmyzcVUC/SIiEhtv5sS9X7A34CvAs9GxMzU74ep3eHA4BTrM2T3su9O9qXhfyT9EvhyRCwusn+GRUR1RFR36NS1cLGZmVmdmnqMXc1dcUS8AVwn6UbgPUmb1VH10/TvOsDHEdG3ri4beA7F487VW5riWilpWUrkACtZtZ+Gk32JWUL2hWO5JNWzrmMj4tWC8umSXiKbBXhU0hnpS4aZmVmzNXXEPhboL6kqjaQbdTUDSUekhAjQC1gBfAwsALoUaxMRnwAzJR2f+pCk3fOqHC9pHUnbAV8BXi3S37PAKal9P+D91G9JImIuMBf4X2BEKv47cLCknqnf3FT8o8DZue2UtEf69yvAmxFxJfAgsFup6zczM2tIkxJ7RIwnS0pTgHuBGmB+I7r4Htkx9slk09unRMQKstHwd3InzxVpdwrwQ0lTgJeBo/KWvQqMIZsePzMilpAda98ld/IcMASoljQVGAqc2oiYc24D3o6IVwDS8faBwL0prjtTvd8AHYGpkqal5wADgGlp23ciO85vZmZWFlo149zIhlLniFgoqRPZSHhgREwsa3SlxzICGB0R96yBdV1NdgLezS29LoDq6uqoqalZE6syM7N2QtKEiKgutqw5v2MfJmkXspPCRrZWUl+TJE0gO+b/s9aOxczMrJgmJ/aIODn/uaRrgAMKqvUCXi8ouyIihjd1vXXEclo5+6tnPXutifWYmZk1VdmuPBcRZ5WrLzMzM2sa3wTGzMysgjixm5mZVRAndjMzswrixG5mZlZBnNjNzMwqiBO7mZlZBXFiNzMzqyBl+x27tYzaOfPpMfjh1g7DzCrUrKGNuoeXtQMesZuZmVUQJ3YzM7MKstYldkkjJM1Mt3KdKGm/Rrb/n5aKzczMrLnWusSe/Dwi+gKDgRsKF0rqUE9bJ3YzM2uz2mVil3SepBmSHpc0StK5TezqWWD71OcsSedLGgscL+kkSbWSpkm6ONUZCmyQRvu3pbLvShqXym7IfSmQtDAv3uPSPeORdHzqc4qkZ+vYvoGSaiTVrFg0v4mbZmZma6N2d1a8pGrgWGAPsvgnAhOa2F1/oDbv+ZKIOFDS1sCLwF7AR8Bjko6OiMGSfppG+0jaGRgAHBARyyRdC5wC3FLPOs8H/j0i5kjauFiFiBgGDANYv1uvaOK2mZnZWqg9jtgPBB6IiMURsQB4qAl9/F7SZGAg8MO88jvTv3sDz0TEexGxHLgN+FqRfg4lS/7jU3+HAl9pYN3PAyMk/Qiob8rfzMys0drdiB1QGfr4eUTcU6T800auQ8DIiPjvIsvyR9pVnxdGnClpX+AIYLKkvhHxQYnrMzMzq1d7HLGPBfpLqpLUmSxBlttLwMGSNk/HzE8CxqRlyyR1TI+fBI6TtCWApE0lfTkte0fSzpLWAb6T61jSdhHxUkScD7wPfKkF4jczs7VUuxuxR8R4SQ8CU4C3gBqgrGeYRcQ8Sf8NPE02Kv9rRDyQFg8DpkqaGBGnSPpfsmPw6wDLgLNSXIOB0cDbwDSgc2r/e0m9Ur9Ppu0wMzMrC0W0v3OzJHWOiIWSOpGd2T4wIia2dlwtobq6Ompqalo7DDMza0MkTYiI6mLL2t2IPRkmaReyY9cjKzWpm5mZNVa7TOwRcXL+c0nXAAcUVOsFvF5QdkVEDG/J2MzMzFpTu0zshSLirNaOwczMrC1oj2fFm5mZWR2c2M3MzCqIE7uZmVkFcWI3MzOrIE7sZmZmFcSJ3czMrII4sZuZmVWQivgdeyWrnTOfHoMfbu0wzKyNmDW0Je57ZZXEI3YzM7MK4sRuZmZWQdpEYpd0jaTJkl6RtDg9nizpuIJ6LzTQzzOSit7txszMbG3QJo6x5671LqkHMDoi+uYvl9QhIlZExP6tEJ6ZmVm7UdYRu6TzJM2Q9LikUZLObUZf/SQ9Lel2oDaVLcxb/gtJtZKmSBqa1/R4SeMkvSbpoFS3StLwVH+SpENSeQdJl6byqZLOTuWHpnq1kv4kaf1UvrekF9I6x0nqUk8fsyRtnh5XS3omPT44b0ZikqQuRbZ9oKQaSTUrFs1v6i40M7O1UNlG7GkK/Fhgj9TvRGBCM7vdB+gdETML1vVN4Ghg34hYJGnTvMXrRsQ+kr4FXAAcBpwFEBF9JO0EPCZpB+B0oCewR0Qsl7SppCpgBHBoRLwm6RbgJ5KuBe4EBkTEeEkbAYuBgYV9NLBN5wJnRcTzkjoDSworRMQwYBjA+t16Rak7y8zMrJwj9gOBByJicUQsAB4qQ5/jCpN6chgwPCIWAUTEh3nL7k3/TgB65MX251R3BvAWsEPq5/qIWJ7Xz47AzIh4LbUdCXwtlc+LiPGp7iepXbE+6vM8cJmkc4CNc+3MzMzKoZyJXWXsK+fTetZV10h2afp3BatmJOqKrVg/jalbX/lyVu3fqlxhRAwFzgA2AF5MMwhmZmZlUc7EPhbon45ndwZa8ioKjwE/kNQJoITp72eBU1LdHYDuwKupnzMlrZvXzwygh6TtU9vvAWNS+daS9k51u6R2xfoAmAXslR4fmwtE0nYRURsRFwM1gBO7mZmVTdkSe5qifhCYQjYdXgO0yJlfEfFIWleNpMlkx63rcy3QQVIt2XHy0yJiKXAT8E9gqqQpwMkRsYTs2Pvdqf5Ksqn2z4ABwFWp7uNkI/Ev9JHW+WvgCknPkc0e5AySNC3VXQz8rTn7wszMLJ8iyndulqTOEbEwjaSfBQZGxMSyrWAtVF1dHTU1Na0dhpmZtSGSJkRE0eu2lPt37MMk7UI2kh3ppG5mZrZmlTWxR8TJ+c8lXQMcUFCtF/B6QdkVETG8nLGYmZmtjVr0ynO5K8qZmZnZmtEmLilrZmYtY9myZcyePZslS75wLSxrB6qqqth2223p2LFjyW2c2M3MKtjs2bPp0qULPXr0QGqJy41YS4kIPvjgA2bPnk3Pnj1Lbtcm7u5mZmYtY8mSJWy22WZO6u2QJDbbbLNGz7Y4sZuZVTgn9farKa+dE7uZmVkF8TF2M7O1SI/BD5e1v1lDS7t6+H333ccxxxzD9OnT2Wmn7ErazzzzDJdeeimjR4/+vN5pp53GkUceyXHHHUe/fv2YN28eVVVVrLfeetx444307dsXgPnz53P22Wfz/PPPA3DAAQdw1VVX0bVrVwBee+01Bg0axGuvvUbHjh3p06cPV111FVtttVWTt/XDDz9kwIABzJo1ix49enDXXXexySabfKHe5Zdfzk033YQk+vTpw/Dhw6mqquLuu+9myJAhTJ8+nXHjxlFdnV1fpra2lj/84Q+MGDGiybHlc2Jv42rnzC/7f0Qza59KTaJt0ahRozjwwAO54447GDJkSMntbrvtNqqrqxk+fDg///nPefzxxwH44Q9/SO/evbnlllsAuOCCCzjjjDO4++67WbJkCUcccQSXXXYZ/fv3B+Dpp5/mvffea1ZiHzp0KIceeiiDBw9m6NChDB06lIsvvni1OnPmzOHKK6/klVdeYYMNNuCEE07gjjvu4LTTTqN3797ce++9/PjHP16tTZ8+fZg9ezb//Oc/6d69e5Pjy/FUvJmZtaiFCxfy/PPPc/PNN3PHHXc0qY/99tuPOXPmAPCPf/yDCRMmcN55532+/Pzzz6empoY33niD22+/nf322+/zpA5wyCGH0Lt372ZtxwMPPMCpp54KwKmnnsr9999ftN7y5ctZvHgxy5cvZ9GiRWy99dYA7Lzzzuy4445F2/Tv37/J+6aQE7uZmbWo+++/n2984xvssMMObLrppkyc2PirjT/yyCMcffTRALzyyiv07duXDh06fL68Q4cO9O3bl5dffplp06ax11571dHTKgsWLKBv375F/1555ZUv1H/nnXfo1q0bAN26dePdd9/9Qp1tttmGc889l+7du9OtWze6du3K4Ycf3mAs1dXVPPfccw3WK4Wn4s3MrEWNGjWKQYMGAXDiiScyatQo9txzzzrP+M4vP+WUU/j0009ZsWLF518IIqJo27rK69KlSxcmT55c+oaU4KOPPuKBBx5g5syZbLzxxhx//PHceuutfPe736233ZZbbsncuXPLEkOrJPZ07/J/ATdGxH+3RgxmZtbyPvjgA5566immTZuGJFasWIEkLrnkEjbbbDM++uij1ep/+OGHbL755p8/v+2229h9990ZPHgwZ511Fvfeey+77rorkyZNYuXKlayzTjbxvHLlSqZMmcLOO+/Mu+++y5gxYxqMbcGCBRx00EFFl91+++3ssssuq5VttdVWzJs3j27dujFv3jy23HLLL7R74okn6NmzJ1tssQUAxxxzDC+88EKDiX3JkiVssMEGDcZcitaaij8ceBU4QS38A0tJHRquZWZmLeGee+7h+9//Pm+99RazZs3i7bffpmfPnowdO5ZevXoxd+5cpk+fDsBbb73FlClTPj/zPadjx45cdNFFvPjii0yfPp3tt9+ePfbYg4suuujzOhdddBF77rkn22+/PSeffDIvvPACDz+86sTjRx55hNra2tX6zY3Yi/0VJnWAb3/724wcORKAkSNHctRRR32hTvfu3XnxxRdZtGgREcGTTz7Jzjvv3OB+eu2115p9DkBOk0bsks4DTgHeBt4HJkTEpY3o4iTgCuAnwFeBv6d+ZwEjgf5AR+D4iJghaR/gj8AGwGLg9Ih4NSXtoUA/YH3gmoi4QVI/4AJgHtBX0p7AdUA1sBz4r4h4OrW/GPh3IMhmEK6SdChwKdn+GQ/8JCKWSto7xb0hsBQ4FFhURx+zgOqIeF9SNXBpRPSTdHDqg1T/axGxoGD/DgQGAnTYaItG7FYzs/qt6TPrR40axeDBg1crO/bYY7n99ts56KCDuPXWWzn99NNZsmQJHTt25Kabbvr8J2v5NthgA372s59x6aWXcvPNN3PzzTdz9tlns/322xMR7Lffftx8882f1x09ejSDBg1i0KBBdOzYkd12240rrrjiC/02xuDBgznhhBO4+eab6d69O3fffTcAc+fO5YwzzuCvf/0r++67L8cddxx77rkn6667LnvssQcDBw4Esp/8nX322bz33nscccQR9O3bl0cffRTIzto/4ojyvDaKiMY1yJLUTcB+ZIlvInBDqYld0gbAG8D2wHeB3hFxTlo2C/hDSoz/AewZEWdI2ghYFBHLJR1GlmiPTQlwy4i4SNL6wPPA8cCXgYdT3zMl/Sw9Pl3STsBjwA7A6cBhwIDU96Zkifp14NCIeE3SLWkbrwVmpLrjczEBPyrsIyI+rCexPwQMjYjnJXUGlkTE8rr21/rdekW3U/9Yyq41swrXlKQ8ffr0kkaM1nqWLl3KwQcfzNixY1l33S+Ot4u9hpImRER1sf6aMhV/IPBARCxOI82HGtn+SODpiFgE/AX4TsF0+b3p3wlAj/S4K3C3pGnA5cCuqfxw4PuSJgMvAZuR3e8dYFxEzMyL+c8AETEDeIsssR8GXJ9LrBHxIbAjMDMiXkttRwJfS+XzImJ8qvtJalesj/o8D1wm6Rxg4/qSupmZVb5//vOfDB06tGhSb4qmJPbmHhM/CTgsjWgnkCXjQ/KWL03/rmDVoYLfkH0Z6E02TV+VF8vZEdE3/fWMiMfSsk9LiFlk0+GFZaXWra98Oav2by5eImIocAbZYYUX0wyCmZmtpXr16kW/fv3K1l9TEvtYoL+kqjSVXPLcUJq+PhDoHhE9IqIHcBZZsq9PV2BOenxaXvmjwE8kdUz97yBpwyLtnyU7JwBJOwDdyU7eeww4M52lT5qKnwH0kLR9avs9YEwq3zodZ0dSl9SuWB8As4DcDymPzdsH20VEbURcDNQATuxm1qIae8jV2o6mvHaNHven48sPAlPIprRrgPklNj8GeCoiluaVPQBcko6R1+USYKSk/wKeyiu/iWy6fmI6u/494Ogi7a8FrpdUSzaSPi2dDHcT2ZT8VEnLyE58u1rS6WRT/7mT566PiM8kDQCuSucJLCabhv9CH8DVwK+BmyX9D9lhgpxBkg4hm5F4BfhbfTuszzZdqWnHl5E0s9ZVVVXFBx984Fu3tkO5+7FXVVU1XDlPo0+eA5DUOSIWSupENhoeGBGNv5SQNai6ujpqampaOwwza6eWLVvG7NmzG31Pb2sbqqqq2HbbbenYseNq5fWdPNfUI/XDJO1Cdux4pJO6mVnb1LFjR3r27NnaYdga1KTEHhEn5z+XdA1wQEG1XmQ/G8t3RUQMb8o6zczMrGFlObc+Is4qRz9mZmbWPL67m5mZWQVp0slztuZIWkD20zxbczYnu1SyrTne52ue9/maV859/uWIKHrNcd+2te17ta4zH61lSKrxPl+zvM/XPO/zNW9N7XNPxZuZmVUQJ3YzM7MK4sTe9g1r7QDWQt7na573+Zrnfb7mrZF97pPnzMzMKohH7GZmZhXEid3MzKyCOLG3YZK+IelVSf+QNLi146lEkr4k6WlJ0yW9LOk/U/kQSXMkTU5/32rtWCuJpFmSatO+rUllm0p6XNLr6d9NWjvOSiFpx7z38mRJn0ga5Pd5eUn6k6R3JU3LK6vzfS3pv9Pn+6uS/r1scfgYe9skqQPwGvBvwGyy28eeFBGvtGpgFUZSN6BbREyU1AWYQHbr3xOAhRFxaWvGV6kkzQKqI+L9vLJLgA8jYmj6IrtJRPyytWKsVOmzZQ6wL3A6fp+XjaSvAQuBWyKidyor+r5ON1IbBewDbA08AewQESuaG4dH7G3XPsA/IuLNiPgMuAM4qpVjqjgRMS93d8KIWABMB7Zp3ajWWkcBI9PjkWRfsKz8DgXeiIi3WjuQShMRzwIfFhTX9b4+CrgjIpZGxEzgH2Sf+83mxN52bQO8nfd8Nk44LUpSD2AP4KVU9FNJU9P0mqeFyyuAxyRNkDQwlW0VEfMg+8IFbNlq0VW2E8lGijl+n7esut7XLfYZ78TedqlImY+btBBJnYG/AIMi4hPgOmA7oC8wD/hD60VXkQ6IiD2BbwJnpSlMa2GS1gO+Ddydivw+bz0t9hnvxN52zQa+lPd8W2BuK8VS0SR1JEvqt0XEvQAR8U5ErIiIlcCNlGmKzDIRMTf9+y5wH9n+fSed85A79+Hd1ouwYn0TmBgR74Df52tIXe/rFvuMd2Jvu8YDvST1TN+yTwQebOWYKo4kATcD0yPisrzybnnVvgNMK2xrTSNpw3SiIpI2BA4n278PAqemaqcCD7ROhBXtJPKm4f0+XyPqel8/CJwoaX1JPYFewLhyrNBnxbdh6acnfwQ6AH+KiN+2bkSVR9KBwHNALbAyFf8P2QdgX7KpsVnAj3PHyax5JH2FbJQO2R0mb4+I30raDLgL6A78Ezg+IgpPRLImktSJ7JjuVyJifir7M36fl42kUUA/stuzvgNcANxPHe9rSb8CfgAsJzsM+LeyxOHEbmZmVjk8FW9mZlZBnNjNzMwqiBO7mZlZBXFiNzMzqyBO7GZmZhXEid0MkLQi3d1qmqSHJG3cQP0hks5toM7R6UYPuecXSjqsDLGOkHRcc/tp5DoHpZ9LtRmSdkqv2SRJ2xUsmyXpuYKyybm7bkmqlnRlGWLokX8nr4JlN+W//i1N0laSbpf0ZrpU798lfWdNrd/aDid2s8ziiOib7sj0IXBWGfo8Gvj8gz0izo+IJ8rQ7xqV7gY2CGhTiZ1s/z4QEXtExBtFlneR9CUASTvnL4iImog4p9QVpX3QKBFxxpq6G2O60NL9wLMR8ZWI2IvsolbbtvB6123J/q1pnNjNvujvpJsxSNpO0iNpBPScpJ0KK0v6kaTxkqZI+oukTpL2J7sm9+/TSHG73Ehb0jcl3ZXXvp+kh9Ljw9NIa6Kku9M17OuURqa/S21qJO0p6VFJb0g6M6//ZyXdJ+kVSddLWictO0nZfdGnSbo4r9+FaYbhJeBXZLeVfFrS02n5dWl9L0v6dUE8v07x1+b2l6TOkoansqmSji11eyX1lfRianefpE3SxZsGAWfkYiriLmBAelx4xbV+kkY3EFv+PthP0n+l/TRN0qC89awraWRqe09uZkPSM5KqS9jPF6f31xOS9knt3pT07VSng6Tfp/fYVEk/LrKtXwc+i4jrcwUR8VZEXFVfH2k/PJPiniHptvQlAUl7SRqTYntUqy6L+kx6z40B/lNSf0kvKZs5eULSVnW8HramRIT//LfW/5Hdkxqyq/zdDXwjPX8S6JUe7ws8lR4PAc5NjzfL6+ci4Oz0eARwXN6yEcBxZFdb+yewYSq/Dvgu2dWqns0r/yVwfpFYP++X7GphP0mPLwemAl2ALYB3U3k/YAnwlbR9j6c4tk5xbJFiego4OrUJ4IS8dc4CNs97vmne/noG2C2vXm77/wO4KT2+GPhjXvtNGrG9U4GD0+MLc/3kvwZF2swCdgBeSM8nkc2eTMvbJ6Priq1wHwB7kV2dcEOgM/Ay2Z0Ae6R6B6R6f2LV++IZoLqE/fzN9Pg+4DGgI7A7MDmVDwT+Nz1eH6gBehZs7znA5fW8v4v2kfbDfLKR/TpkX2oPTDG8AGyR2gwgu/plbruuLXgtcxc7OwP4Q2v/f17b/zyNYpbZQNJksg/qCcDjafS4P3B3GsRA9qFYqLeki4CNyT70H61vRRGxXNIjQH9J9wBHAL8ADiZLPs+n9a1H9kHbkNw9BGqBzpHdV36BpCVada7AuIh4Ez6/7OWBwDLgmYh4L5XfBnyNbEp3BdmNcepygrLbra4LdEtxT03L7k3/TgCOSY8PI5sazu2DjyQd2dD2SuoKbBwRY1LRSFbdmawhHwIfSToRmA4sqqPeF2JLD/P3wYHAfRHxaYrrXuAgsn3/dkQ8n+rdSpZkL83rf2/q3s+fAY+kerXA0ohYJqmW7L0I2bX0d9Oq8yq6kl1XfGZdGy7pmhTzZxGxdz19fEb23pid2k1O6/0Y6E32/wCyL3D5l5q9M+/xtsCdaUS/Xn1x2ZrhxG6WWRwRfVMiGU12jH0E8HFE9G2g7QiyEdgUSaeRjYIacmdax4fA+IhYkKZAH4+IkxoZ+9L078q8x7nnuf/jhdeODorfNjJnSUSsKLZA2Q0rzgX2Tgl6BFBVJJ4VeetXkRiaur2NcSdwDXBaPXWKxQar74P69lWxfVvYf12WRRrqkvf6RcRKrTp+LbJZkPq+ML4MHPt5ABFnSdqcbGReZx+S+rH6eyb3mgl4OSL2q2N9n+Y9vgq4LCIeTP0NqSdOWwN8jN0sT2Q3xziHLHEtBmZKOh6yE5Qk7V6kWRdgnrLbv56SV74gLSvmGWBP4EesGv28CBwgafu0vk6SdmjeFn1uH2V3ClyHbFp1LPAScLCkzZWdHHYSMKaO9vnbshHZB/v8dDz1myWs/zHgp7knkjahhO1Nr8dHkg5KRd+rJ8Zi7gMuof5ZlGKxFXoWODrFuCHZndByZ913l5RLgCeR7dt8jdnPxTwK/CS9v5C0Q4oh31NAlaSf5JXln+xYSh/5XgW2yG2XpI6Sdq2jbldgTnp8ah11bA1yYjcrEBGTgClk07OnAD+UNIVsVHRUkSbnkX14Pw7MyCu/A/i5ivwcK40ER5MlxdGp7D2ykeUoSVPJEt8XTtZror8DQ8luyzmTbFp5HvDfwNNk2zsxIuq6Veow4G+Sno6IKWTHrF8mO6b8fB1t8l0EbJJOHpsCHNKI7T2V7CTEqWR3IruwhPUBEBELIuLiiPisMbEV6Wci2czMOLLX+qb0PoFsmv/UFN+mZOdM5LdtzH4u5ibgFWCisp/W3UDBbGsa9R9N9gVipqRxZIctfllqHwX9fUZ2HsbFaZ9MJjssVcwQssNVzwHvN2K7rIX47m5mFS5Nj54bEUe2cihmtgZ4xG5mZlZBPGI3MzOrIB6xm5mZVRAndjMzswrixG5mZlZBnNjNzMwqiBO7mZlZBfn/AXO3zdqPCnFVAAAAAElFTkSuQmCC\n", "text/plain": [ "
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PearsonRResult(statistic=-0.9489256919725506, pvalue=6.751479973142354e-51)\n", "Slope and P-value = PearsonRResult(statistic=-0.4187514421107005, pvalue=0.00015054007083373773)\n", "CT 104\n", "CTF 70\n", "CTFR 25\n", "Name: PastureHousing, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
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PearsonRResult(statistic=-0.9189628249659609, pvalue=2.1722855774738586e-41)\n", "Slope and P-value = PearsonRResult(statistic=-0.743280529678865, pvalue=8.338609932785747e-19)\n", "CT 159\n", "CTF 115\n", "CTFR 39\n", "Name: PastureHousing, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
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PearsonRResult(statistic=-0.9395520158455749, pvalue=2.073971597657583e-47)\n", "Slope and P-value = PearsonRResult(statistic=0.6356247045459268, pvalue=1.208438929158218e-12)\n", "CT 88\n", "CTF 70\n", "CTFR 25\n", "Name: PastureHousing, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
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PearsonRResult(statistic=-0.8584183180331958, pvalue=3.608771928118085e-30)\n", "Slope and P-value = PearsonRResult(statistic=0.8080487874525688, pvalue=2.1465715240381585e-22)\n", "CT 90\n", "CTF 70\n", "CTFR 25\n", "Name: PastureHousing, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
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PearsonRResult(statistic=0.7197055091855116, pvalue=3.269447729360697e-17)\n", "Slope and P-value = PearsonRResult(statistic=0.7870037869955656, pvalue=2.82476782412772e-22)\n", "CTF 94\n", "CT 89\n", "CTFR 25\n", "Name: PastureHousing, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
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PearsonRResult(statistic=0.05288792189407085, pvalue=0.6012555137128829)\n", "Slope and P-value = PearsonRResult(statistic=0.17188143621816637, pvalue=0.08727415632613796)\n", "CT 100\n", "CTF 70\n", "CTFR 25\n", "Name: PastureHousing, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.4136158953178346, pvalue=1.891466824813796e-05)\n", "Slope and P-value = PearsonRResult(statistic=-0.8976326595403696, pvalue=9.746610879978756e-24)\n", "Slope and P-value = PearsonRResult(statistic=0.1087387387797125, pvalue=0.28151902901123305)\n", "Slope and P-value = PearsonRResult(statistic=-0.8092156611030575, pvalue=5.832488190223816e-12)\n", "Slope and P-value = PearsonRResult(statistic=-0.43152640191090497, pvalue=7.3774016839281865e-06)\n", "Slope and P-value = PearsonRResult(statistic=-0.8735913356836971, pvalue=2.3522924498363853e-15)\n", "Slope and P-value = PearsonRResult(statistic=-0.6337353673123295, pvalue=2.4753414286296504e-08)\n", "Slope and P-value = PearsonRResult(statistic=-0.654089821545854, pvalue=1.5942315251128332e-13)\n", "Slope and P-value = PearsonRResult(statistic=0.9141204019272533, pvalue=3.3148889146312077e-40)\n", "Slope and P-value = PearsonRResult(statistic=-0.12616783302774442, pvalue=0.32050228574690187)\n" ] } ], "source": [ "sample = pd.merge(microbiome, poultry[['SampleID', 'PastureHousing','PastureTime','SampleType']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "#sample = sample[~sample.isin([np.nan, np.inf, -np.inf]).any(1)]\n", "sample = sample.drop(['Pathogen_Salmonella', 'new_Pathogen_Salmonella',\n", " 'Pathogen_Campy','new_Pathogen_Campy',\n", " 'Pathogen_Listeria','new_Pathogen_Listeria'],axis='columns')\n", "\n", "\n", "feces1=sample[(sample.SampleType=='Feces') & (sample.PastureTime=='Start')]\n", "feces2=sample[(sample.SampleType=='Feces') & (sample.PastureTime=='Mid')]\n", "feces3=sample[(sample.SampleType=='Feces') & (sample.PastureTime=='End')]\n", "\n", "soil1=sample[(sample.SampleType=='Soil') & (sample.PastureTime=='Start')]\n", "soil2=sample[(sample.SampleType=='Soil') & (sample.PastureTime=='Mid')]\n", "soil3=sample[(sample.SampleType=='Soil') & (sample.PastureTime=='End')]\n", "\n", "ceca=sample[sample.SampleType=='Ceca']\n", "wcrp=sample[sample.SampleType=='WCR-P']\n", "wcrf=sample[sample.SampleType=='WCR-F']\n", "\n", "\n", "print('Feces_Start', feces1.shape)\n", "print('Feces_Mid', feces2.shape)\n", "print('Feces_End', feces3.shape,'\\n')\n", "\n", "print('Soil_Start', soil1.shape)\n", "print('Soil_Mid', soil2.shape)\n", "print('Soil_End', soil3.shape,'\\n')\n", "\n", "print('Ceca', ceca.shape)\n", "print('WCR-P', wcrp.shape)\n", "print('WCR-F', wcrf.shape,'\\n')\n", "\n", "\n", "sampletypes = [feces1,feces2,feces3, soil1,soil2,soil3,ceca, wcrp, wcrf]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES_START\", 1: \"FECES_MID\", 2:\"FECES_END\",\n", " 3: \"SOIL_START\", 4: \"SOIL_MID\", 5: \"SOIL_END\",\n", " 6:\"CECA\", 7: \"WCR-P\", 8: \"WCR-F\"\n", " }\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType', 'PastureTime','PastureHousing'],axis='columns'),sample.PastureHousing,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, :] #multiclass\n", " \n", " try:\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs, multi_class='ovo')\n", " except ValueError:\n", " pass\n", " \n", " \n", " \n", " \n", "# mylist2.append([f\"PastureHousing_{sample_name[indexing]}\", rf_auc_normal])\n", " \n", " \n", " print(pd.value_counts(sample['PastureHousing']))\n", "\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", " \n", " \n", " plt.title(f\"PastureHousing in {sample_name[indexing]} Model\")\n", " prelim3_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','PastureHousing','PastureTime','SampleType'],axis='columns').columns)\n", " prelim3_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", "\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim3_plot.max(), decimals=None, symbol=''))\n", "\n", " xmax=prelim3_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " \n", " plt.show()\n", " \n", " prelim3_plot.nlargest(10).to_csv(\"prelim3.csv\")\n", " top10 = pd.read_csv('prelim3.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", "# plt.plot(pdp[1][0], pdp[0][0],'.')\n", "# plt.ylabel('Partial dependence'), plt.xlabel(top10[feature])\n", "# plt.show()\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " #mylist.append([f\"PastureHousing_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (11) FreqHousingMove" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Feces_Start (200, 878)\n", "Feces_Mid (313, 878)\n", "Feces_End (185, 878) \n", "\n", "Soil_Start (199, 878)\n", "Soil_Mid (313, 878)\n", "Soil_End (183, 878) \n", "\n", "Ceca (185, 878)\n", "WCR-P (208, 878)\n", "WCR-F (195, 878) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "1 195\n", "0 5\n", "Name: FreqHousingMove, dtype: int64\n" ] }, { "data": { "image/png": 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fS38PSJok6RFJ26ayQyX9XtIY4OIi29lPUq2k2vqP5rR0d5mZWRWq+MhfUg1wNLArWX8mA5OWYZPzI2Lf1PZX0r+rAbcBx0XERElrAR8XKbsz8HmgM/CUpFHAW8CREfGBpO7AE5LuA7YHfgbsExHvSFovIt5L60ZGxJ2p7YeA70XEi5L2BK4GDkrtbQ18MSLqCzsSEUOAIQCr9+gVbbFjzMysOlQ8+AP7AiMi4mMASfcv4/ZuK5K2DTA7IiYCRMQHqS+F+Rr6+XEake8BjAJ+LekLwCJgY2BDsgB+Z0S8k+p8r7AySV2AvYE7cm2tnstyR7HAb2Zm1horQvBfKsK20qcseTqjU8H6DxvpQymj58I8AZwErA/sHhELJc1IbZZS5yrA+01cq1Csr2ZmZq2yIpzzHw8cJqlTGgm39s4grwLbS1pd0trAwSWUeQ7YSFIfgHS+v9iB0RGpn92AA4CJwNrAWynwHwhsnvI+BByb8iJpvZQ+F+gKi2cYXpH0jZRHknYpf5PNzMxKV/GRfzrHfh8wlSxw1wItvoItIl6XdDvwNPAi8FQJZT6RdBxwhaQ1yM73F/sJ4JNk0/ybAf8XEW9Iuhm4X1ItMIXsQIKIeEbSr4BxkupTP04FbgWulXQOcAzZzME1kn4OdEzrp5azzTttvDa1vpuamZmVSBGVv1ZMUpeImCdpTeBhoF9ETK50v1YWNTU1UVvb1r9INDOzlZmkSRFRU2xdxUf+yZB045tOwDAHfjMzs2VnhQj+EXFi/rWkq4B9CrL1IpvGz7ssIm5Yln0zMzNrb1aI4F+o4c59ZmZm1vZWhKv9zczMbDly8DczM6syDv5mZmZVxsHfzMysyjj4m5mZVRkHfzMzsyqzQv7Uz8pTN2sOPQeOqnQ3rArM8G2kzdoFj/zNzMyqjIO/mZlZlXHwb4Kkz0uaIGmKpOmSLkzpp0q6soV1ziuStpGkO1vZXTMzs5L4nH/ThgHHRsRUSR2AbZZFIxHxBtnjfc3MzJa5dj/yl3S+pOckjZY0XNK5ZRTfAJgNEBH1EfFskfoPS7MDT0n6p6QNU3oXSTdIqpP0tKSjC8p1l/S4pEMl9ZQ0LaX3lPSIpMnpb++Wb72ZmdnS2vXIX1INcDSwK9m2TgYmlVHFH4DnJY0FHiB73PD8gjzjgc9HREg6Hfgx8CPgfGBOROyU+rJurl8bAvcBP4+I0ZJ65up7C/hSRMyX1AsYDiz1PGZJ/YB+AB3WWr+MTTIzs2rXroM/sC8wIiI+BpB0fzmFI+IiSTcDXwZOBE4ADijItglwm6QewGrAKyn9i8Dxubr+mxY7Ag8B/SNiXJFmOwJXSuoN1ANbN9K3IcAQgNV79IpytsvMzKpbe5/2V2sriIiXIuIa4GBgF0ndCrJcAVyZRvjfBTrl2i4WlD8lm304pJEm/wd4E9iFbMS/Wuu2wMzMbEntPfiPBw6T1ElSF6CsO5Sk8/ENBxC9yEbi7xdkWxuYlZZPyaX/AzgrV1fDtH8ApwHbShpYpNm1gdkRsQj4FtChnD6bmZk1p10H/4iYSHZufSpwN1ALzCmjim+RnfOfAtwInBQR9QV5LgTukPQI8E4ufRCwrqRpkqYCB+b6VU92SuBASd8vqO9q4BRJT5BN+X9YRn/NzMyapYj2fbpYUpeImCdpTeBhoF9ETK50v9pSTU1N1NbWVrobZma2ApE0KSKWumAc2v8FfwBDJG1Pdi5+WHsL/GZmZuVq98E/Ik7Mv5Z0FbBPQbZewIsFaZdFxA3Lsm9mZmaV0O6Df6GI6F/pPpiZmVVSu77gz8zMzJbm4G9mZlZlHPzNzMyqjIO/mZlZlXHwNzMzqzIO/mZmZlXGwd/MzKzKVN3v/Nujullz6DlwVKW7Ye3AjMFlPfvKzFZSHvmbmZlVGQf/VpC0iaQRkl6U9JKkyyStJqm3pK/l8l0o6dxK9tXMzKyBg38LSRLZY4LvjYheZI/f7QL8CugNfK3x0mW31aGt6jIzM3Pwb7mDgPkND/+JiHrgf4DTgUuA4yRNkXRcyr+9pLGSXpZ0TkMlkr4p6cmU988NgV7SPEkXSZoA7LVct8zMzNo1B/+W2wGYlE+IiA+AGcAg4LaI6B0Rt6XV2wKHAHsAv5DUUdJ2wHHAPhHRG6gHTkr5OwPTImLPiBhf2LikfpJqJdXWfzSn7bfOzMzaLV/t33ICooz0URGxAFgg6S1gQ+BgYHdgYnYWgTWAt1L+euCuxhqPiCHAEIDVe/Qq1p6ZmVlRDv4t9wxwdD5B0lrApmSBu9CC3HI92b4XMCwiflIk//x0KsHMzKxNedq/5R4C1pR0Miy+KO93wFDgTaBriXUcI2mDVMd6kjZfNt01MzPLOPi3UEQEcCTwDUkvAi8A84GfAmPILvDLX/BXrI5ngZ8D/5D0NDAa6LHMO29mZlXN0/6tEBGvA4cVWbUA6NNEuR1zy7cBtxXJ06Ut+mhmZlbIwb8d2Gnjtan1bVnNzKxEnvY3MzOrMg7+ZmZmVcbB38zMrMo4+JuZmVUZB38zM7Mq4+BvZmZWZRz8zczMqoyDv5mZWZVx8DczM6syDv5mZmZVxrf3bQfqZs2h58BRle6GrcBm+PbPZpbjkb+ZmVmVcfA3MzOrMssl+EtaVdI7kn7TgrIbSbqzFW3Pa2nZXB3rSPp+C8v+TdI6re2DmZlZW1leI/8vA88Dx0pSOQUj4o2IOGbZdKtk6wBlBX9lVomIr0XE+8ukV2ZmZi1QUvCXdL6k5ySNljRc0rlltnMCcBnwGvD5XL0zJP1a0uOSaiXtJulBSS9J+l7K01PStLR8qqS7JT0g6UVJl+TqOkFSnaRpki4u6P/vJE2W9JCk9VPaGZImSpoq6S5Ja6b0DSXdk9KnStobGAx8TtIUSb9N+c5L5Z+W9MtcX6dLuhqYDGyatrF7fjtS3nMlXZiWx0r6g6SHU/k+aTtflDSokfekX9pntfUfzSnz7TAzs2rWbPCXVAMcDewKHAXUlNOApDWAg4GRwHCyA4G81yNiL+ARYChwDNkBwkWNVNkbOA7YCThO0qaSNgIuBg5K6/tI6pvydwYmR8RuwDjgFyn97ojoExG7ANOB76T0y4FxKX034BlgIPBSRPSOiPMkfRnoBeyR2ttd0hdS+W2Av0bErhHxaqn7CfgkIr4A/AkYAfQHdgROldStMHNEDImImoio6bDm2mU0Y2Zm1a6Ukf++wIiI+Dgi5gL3l9nG14ExEfERcBdwpKQOufX3pX/rgAkRMTci3gbmN3Ku/KGImBMR84Fngc2BPsDYiHg7Ij4FbgYagvEi4La0fFPaHoAdJT0iqQ44CdghpR8EXAMQEfURUWxY/eX09xTZCH9bsoMBgFcj4olm98rS8vvhmYiYHRELgJeBTVtQn5mZWVGl/M6/rHP0RZwA7CNpRnrdDTgQ+Gd6vSD9uyi33PC6WP/yeepTnnL6GOnfoUDfiJgq6VTggDLqEPCbiPjzEolST+DDRsp8ypIHW50K1pe7H8zMzFqklJH/eOAwSZ0kdQFKvluIpLXIRtqbRUTPiOhJNp1dOPXfWhOA/dO59Q6p/nFp3SpkpxIATiTbHoCuwGxJHclG/g0eAs5M/e+QtmFuyt/gQeC0tD+QtLGkDZrp45vABpK6SVqdbEbEzMxsuWt2RBkREyXdB0wFXgVqgVKvMDsK+Feavm4wArgkBcA2ERGzJf0EGEM2Kv9bRIxIqz8EdpA0KfX7uJR+PtlBw6tkU+0Nwf0HwBBJ3yGbWTgzIh6X9Gi6YO/v6bz/dsDj6ccL84BvpvyN9XGhpItSm68Az7XR5puZmZVFEdF8JqlLRMxLV8Q/DPSLiMnLvHdWkpqamqitra10N8zMbAUiaVJEFL1Iv9RzyUMkbU92nnqYA7+ZmdnKq6TgHxEn5l9LugrYpyBbL+DFgrTLIuKGlnfPzMzM2lqLriKPiP5t3REzMzNbPvxgHzMzsyrj4G9mZlZlHPzNzMyqjIO/mZlZlXHwNzMzqzIO/mZmZlXGwd/MzKzK+Glx7UDdrDn0HDiq0t2wFdiMwSU/j8vMqoBH/mZmZlXGwd/MzKzKLNfgL2mspOclHZ5eD5X0iqQpkqZKOriN2qmRdHlb1FVJksZImiep6FOZzMzMWqIS5/xPioj882fPi4g7JR0IDCF7QFCrpPpX+mfcRsSBksZWuh9mZta+lD3yl3S+pOckjZY0XNK5bdSXx4GNUxunSroy1+ZISQek5XmSLpY0SdI/Je2RZhRezs0oHCBpZFq+UNJfcnnOydX7Q0nT0t+AXPrJkp5OsxE3prTNJT2U0h+StFlK31DSPSnvVEl7N1HHUEnH5NqZl/7tIenhNAMyTdJ+ze0sSf0k1Uqqrf9oTsv2uJmZVaWyRv5p+vloYNdUdjIwqY368hXg3hLydQbGRsT/SroHGAR8CdgeGAbcV6TMtsCBQFfgeUnXADsD3wb2BARMkDQO+AT4GbBPRLwjab1Ux5XAXyNimKTTgMuBvunfcRFxpKQOQBdJOzRSR2NOBB6MiF+lOtZsbidExBCymRJW79ErmstvZmbWoNxp/32BERHxMYCk+9ugD7+VdAmwAfD5EvJ/AjyQluuABRGxUFId0LORMqMiYgGwQNJbwIZk23JPRHwIIOluYD8ggDsj4h2AiHgv1bEXcFRavhG4JC0fBJyc8tYDcySd3EgdjZkI/EVSR+DeiJjS7F4wMzNroXKn/bUM+nAesBXwc7KRO8CnLNm3TrnlhRHRMNJdBCwAiIhFNH4wsyC3XJ/yNbYtIjsAaE5TeRqrY/F2SRKwGkBEPAx8AZgF3JgOHszMzJaJcoP/eOAwSZ0kdQHa5M4hKXBfBqwi6RBgBtBb0iqSNgX2aIt2CjwM9JW0pqTOwJHAI8BDwLGSugHkpuwfA45PyyeR7QtS/jNT3g6S1mqijhnA7mn5CKBjWr858FZEXAtcD+zW5ltrZmaWlDXtHxETJd0HTAVeJbuivk2uNouIkDQI+DHwReAVsmn9aWTXFrSpiJgsaSjwZEq6LiKeApD0K2CcpHrgKeBU4ByyqfnzgLfJrhcA+AEwRNJ3yGYVzoyIxxup41pghKQnyQ4QPkx1HACcJ2khMI90GqFUO228NrW+g5uZmZVIn82gl1hA6hIR8yStSTZ67hcRJQXn9LO1cwt+6mdNKGWf1dTURG2td6mZmX1G0qSIKHqfmJbc5GeIpClko/G7Sg38yXvA0Iaf5FnTJI0BtgQWVrovZmbWfpR9k5+IODH/WtJVwD4F2XoBLxakXRYRR2Eli4gDK90HMzNrf1p9h7+I6N8WHTEzM7Plww/2MTMzqzIO/mZmZlXGwd/MzKzKOPibmZlVGQd/MzOzKuPgb2ZmVmVa/VM/q7y6WXPoOXBUpbthFTTDt3c2szJ45G9mZlZlHPzNzMyqzHIP/pKGSpolafX0urukGc2UWUfS95dT/+a1UT2PpX97SpqWlg+QNLIt6jczM2upSo3864HTysi/DlA0+Evq0BYdamsRsXel+2BmZlZMi4K/pPMlPSdptKThks4ts4o/Av8jaYkLDiV1kfSQpMmS6iQdkVYNBj4naYqk36YR9BhJtwB1kjpJuiGVeUrSgam+UyXdLekBSS9KuiTX1gkp/zRJFxf043epDw9JWj+lnSFpoqSpku5KjzRG0oaS7knpUyXtndKbnEGQdGF+v6V+9JTUWdKoVNc0SceVuW/NzMyaVHbwl1QDHA3sChwFFH1WcDNeA8YD3ypInw8cGRG7AQcCv5MkYCDwUkT0jojzUt49gJ9FxPZAf4CI2Ak4ARgmqVPK1xs4DtgJOE7SppI2Ai4GDkrr+0jqm/J3BianPowDfpHS746IPhGxCzAd+E5KvxwYl9J3A55pwf7I+wrwRkTsEhE7Ag8UyySpn6RaSbX1H81pZZNmZlZNWjLy3xcYEREfR8Rc4P4Wtv1r4LyCPgj4taSngX8CGwMbNlL+yYh4JdenGwEi4jngVWDrtO6hiJgTEfOBZ4HNgT7A2Ih4OyI+BW4GvpDyLwJuS8s3pboBdpT0iKQ64CRgh5R+EHBNars+IlobieuAL0q6WNJ+jdUXEUMioiYiajqsuXYrmzQzs2rSkuCvtmg4Iv4NTAGOzSWfBKwP7B4RvYE3gU5LFc58WGKfFuSW68nubVDONkT6dyhwVppd+GUT/SrVpyy5/zsBRMQLwO5kBwG/kXRBK9sxMzNbQkuC/3jgsHSevQvQmruL/ArIXy+wNvBWRCxM5+03T+lzga5N1PMw2YEDkrYGNgOebyL/BGD/9EuDDmSnCsaldasAx6TlE8m2l9T+bEkdG9pKHgLOTG13kLRWE+3mzSA7TYCk3YAt0vJGwEcRcRNwaUMeMzOztlL2Hf4iYqKk+4CpZNPrtUCLproj4hlJk/kswN0M3C+plmxW4LmU711Jj6afzP0dKLyd3dXAn9KU/KfAqRGxILtcoGi7syX9BBhDNgvwt4gYkVZ/COwgaVLaroYL7s4nO2h4lWxU3nAw8gNgiKTvkM0snAk8XsLm3wWcLGkKMBF4IaXvBPxW0iJgYarPzMyszSgims9VWEjqEhHz0hXvDwP9ImJym/fOSlJTUxO1tbWV7oaZma1AJE2KiKIX5bf03v5DJG1Pdp56mAO/mZnZyqNFwT8iTsy/lnQVsE9Btl7AiwVpl0XEDS1p08zMzNpGmzzVLyL6t0U9ZmZmtuz5wT5mZmZVxsHfzMysyjj4m5mZVRkHfzMzsyrj4G9mZlZlHPzNzMyqjIO/mZlZlWmT3/lbZdXNmkPPgYWPO7BqMGNwa56rZWbVyiN/MzOzKuPgb2ZmVmVWyuAv6VxJz0maJmmqpJObyT9U0jHLq39NkTQgPQ3RzMysIla64C/pe8CXgD0iYkfgC4DauI0ObVlfgQFAWcF/GffHzMyqTEWCv6Tz08h9tKThks4to/hPge9HxAcAETEnIoalei+QNDHNCAyRtNRBgaSDJT0lqU7SXyStntJnpPLjgW9IOiPVNVXSXQ2j9TSLcI2kMZJelrR/qme6pKG5dq6RVCvpGUm/TGnnABsBYySNSWknpL5Mk3Rxrvw8SRdJmgDsVWQ7+qX6a+s/mlPG7jMzs2q33IO/pBrgaGBX4CigpoyyXYGuEfFSI1mujIg+aUZgDeDrBeU7AUOB4yJiJ7JfO5yZyzI/IvaNiFuBu1NduwDTge/k8q0LHAT8D3A/8AdgB2AnSb1Tnp9FRA2wM7C/pJ0j4nLgDeDAiDhQ0kbAxamu3kAfSX1T+c7AtIjYMyLGF25oRAyJiJqIqOmw5tpN7TYzM7MlVGLkvy8wIiI+joi5ZMGzVAKiifUHSpogqY4soO5QsH4b4JWIeCG9HkZ22qDBbbnlHSU9kuo6qaCu+yMigDrgzYioi4hFwDNAz5TnWEmTgadS2e2L9LcPMDYi3o6IT4Gbc/2pB+5qYlvNzMxapBLBv8Xn59NU/4eStlyq0mxUfzVwTBrVXwt0KrPtD3PLQ4GzUl2/LKhrQfp3UW654fWqkrYAzgUOjoidgVFF+tJcf+ZHRH0z/TUzMytbJYL/eOAwSZ0kdQHKvUvJb4CrJK0FIGktSf34LLi+k+otdnX/c0BPSVul198CxjXSTldgtqSOZCP/cqxFdiAxR9KGwFdz6+amugEmkJ0S6J4u6juhif6YmZm1ieV+h7+ImCjpPmAq8CpQC5Rzxdo1QBdgoqSFwELgdxHxvqRryabiZwATi7Q9X9K3gTskrZry/KmRds4nC86vpjq7NpJvKRExVdJTZKcBXgYeza0eAvxd0ux03v8nwBiyWYC/RcSIUtsxMzNrCWWnrpdzo1KXiJiXrqB/GOgXEZOXe0faiZqamqitra10N8zMbAUiaVK68Hwplbq3/xBJ25NN1Q9z4DczM1t+KhL8I+LE/GtJVwH7FGTrBbxYkHZZRNywLPtmZmbW3q0QT/WLiP6V7oOZmVm1WOlu72tmZmat4+BvZmZWZRz8zczMqoyDv5mZWZVx8DczM6syDv5mZmZVxsHfzMysyqwQv/O31qmbNYeeA0dVuhu2jMwYXO6zr8zMmuaRv5mZWZVx8DczM6syyzz4S7pK0hRJz0r6OC1PkXRMmfU81kj60Ia6JF2XHhhUap01ki4vpx+N1NNT0rRG1pXVJzMzs2VtmZ/zb7hvv6SewMiI6N3CevYuIc/pZdZZC5T8LFxJHSKivsw2yuqTmZnZslbyyF/S+ZKekzRa0nBJ57a0UUmdJf1F0kRJT0k6IqWfKmmEpAckPS/pF7ky89K/knRlmkkYBWyQyzNWUk1DfkkXS5ok6Z+S9kjrX5Z0eMpzgKSRabmLpBsk1Ul6WtLRuXoukjQB2EvSDyVNS38Dcpu1qqRhqeydktYs1qdcX4+RNDQtD5V0jaQxqX/7p/0zvSFPkX3YT1KtpNr6j+a09K0wM7MqVFLwT8HraGBX4CigppXt/gz4V0T0AQ4Efiupc1q3B3AS0Bv4RkPgzDkS2AbYCTgDaGxGoDMwNiJ2B+YCg4AvpfIXFcl/PjAnInaKiJ2Bf+XqmRYRewIfA98G9gQ+D5whadeUbxtgSCr7AfD9UnZEzrrAQcD/APcDfwB2AHaS1Lswc0QMiYiaiKjpsObaZTZlZmbVrNSR/77AiIj4OCLmkgWn1vgyMFDSFGAs0AnYLK0bHRHvRsTHwN2p7bwvAMMjoj4i3uCzIF3oE+CBtFwHjIuIhWm5Z5H8XwSuangREf9Ni/XAXWl5X+CeiPgwIual/u2X1r0eEY+m5ZuK9Ls590dEpP69GRF1EbEIeKaR/pqZmbVIqef81cbtCjg6Ip5fIlHaE4iCvIWvG0srtDAFU4BFwAKAiFgkqdh2q5F65+fO8ze1H8rtd6eCdQsK+5p77fsxmJlZmyl15D8eOExSJ0ldgNbedeRB4GxJAshNnQN8SdJ6ktYA+gKPFpR9GDheUgdJPchOG7SFfwBnNbyQtG6RPA8DfSWtmU5THAk8ktZtJmmvtHwC2T4r9Kak7SStksqamZktdyUF/4iYCNwHTCWb6q4FWnOV2f8BHYGn00/k/i+3bjxwIzAFuCtdkZ93D/Ai2fT4NcC4VvQjbxCwbrqQbypFDioiYjIwFHgSmABcFxFPpdXTgVMkPQ2sl/pWaCAwkuxUxew26reZmVlZ9NnMeDMZpS4RMS9dxf4w0C8Fw7brjHQqUBMRZzWX1z5TU1MTtbUl/2LRzMyqgKRJEVH0Av1yziUPSTer6QQMa+vAb2ZmZstHycE/Ik7Mv5Z0FbBPQbZeZFPyeZdFxA0ltjGUbFrdzMzMlpEWX0XecOc+MzMzW7n4J2RmZlVu4cKFzJw5k/nz51e6K9YCnTp1YpNNNqFjx44ll3HwNzOrcjNnzqRr16707NmT9AtsW0lEBO+++y4zZ85kiy22KLmcH+lrZlbl5s+fT7du3Rz4V0KS6NatW9mzNg7+ZmbmwL8Sa8l75+BvZmZWZXzO38zMltBz4Kg2rW/G4NLuCH/PPfdw1FFHMX36dLbddlsAxo4dy6WXXsrIkSMX5zv11FP5+te/zjHHHMMBBxzA7Nmz6dSpE6utthrXXnstvXv3BmDOnDmcffbZPPpodpf4ffbZhyuuuIK1186ehPrCCy8wYMAAXnjhBTp27MhOO+3EFVdcwYYbbtjibb3jjju48MILmT59Ok8++SQ1NcUfgvvAAw/wgx/8gPr6ek4//XQGDhwIwHvvvcdxxx3HjBkz6NmzJ7fffjvrrrsudXV1/O53v2Po0KEt7lueg387UDdrTpv/Z7XKKPVL0qw9Gj58OPvuuy+33norF154Ycnlbr75Zmpqarjhhhs477zzGD16NADf+c532HHHHfnrX/8KwC9+8QtOP/107rjjDubPn8+hhx7K73//ew477DAAxowZw9tvv92q4L/jjjty9913893vfrfRPPX19fTv35/Ro0ezySab0KdPHw4//HC23357Bg8ezMEHH8zAgQMZPHgwgwcP5uKLL2annXZi5syZvPbaa2y22WaN1l0qT/ubmVnFzZs3j0cffZTrr7+eW2+9tUV17LXXXsyaNQuAf//730yaNInzzz9/8foLLriA2tpaXnrpJW655Rb22muvxYEf4MADD2THHXds1XZst912bLPNNk3mefLJJ9lqq63YcsstWW211Tj++OMZMWIEACNGjOCUU04B4JRTTuHee+9dXO6www5r8b4p5OBvZmYVd++99/KVr3yFrbfemvXWW4/Jk8u/g/wDDzxA3759AXj22Wfp3bs3HTp0WLy+Q4cO9O7dm2eeeYZp06ax++67N1vn3Llz6d27d9G/Z599tuw+AsyaNYtNN9108etNNtlk8UHLm2++SY8ePQDo0aMHb7311uJ8NTU1PPLII7QFT/ubmVnFDR8+nAEDBgBw/PHHM3z4cHbbbbdGr2TPp5900kl8+OGH1NfXLz5oiIiiZRtLb0zXrl2ZMmVK6RtSgmIP1CulTxtssAFvvPFGm/RhpQ7+koYC+5M9XrgTMDwiftmCenoCe0fELW3Urxrg5Ig4p8i6GWRPLnynLdoyM1vZvfvuu/zrX/9i2rRpSKK+vh5JXHLJJXTr1o3//ve/S+R/77336N69++LXN998M7vssgsDBw6kf//+3H333eywww489dRTLFq0iFVWySa5Fy1axNSpU9luu+146623GDeu+SfCz507l/3226/oultuuYXtt9++7O3dZJNNeP311xe/njlzJhtttBEAG264IbNnz6ZHjx7Mnj2bDTbYYHG++fPns8Yaa5TdXjHtYdr/vIjoDfQGTpFU+i2OPtMTOLG5THmSOjS2LiJqiwV+MzNb2p133snJJ5/Mq6++yowZM3j99dfZYostGD9+PL169eKNN95g+vTpALz66qtMnTp18RX9DTp27MigQYN44oknmD59OltttRW77rorgwYNWpxn0KBB7Lbbbmy11VaceOKJPPbYY4wa9dnF0g888AB1dXVL1Nsw8i/215LAD9CnTx9efPFFXnnlFT755BNuvfVWDj/8cAAOP/xwhg0bBsCwYcM44ogjFpd74YUXWn1NQoOKj/wlnQ+cBLwOvANMiohLW1BVp/Tvh6neC4DDgDWAx4DvRkRI2gr4E7A+UA98AxgMbCdpCjAMuDylHQCsDlwVEX+WdADwC2A20FvSbsA1QA3wKfDDiBiT8p0bEV+X1A0Yntp7Elg8tyPpm8A5wGrABOD7adX1qc4A/hIRfyiy3/oB/QA6rLV+C3aXmVlxy/tXJ8OHD1/8U7cGRx99NLfccgv77bcfN910E9/+9reZP38+HTt25Lrrrlv8c728NdZYgx/96EdceumlXH/99Vx//fWcffbZbLXVVkQEe+21F9dff/3ivCNHjmTAgAEMGDCAjh07svPOO3PZZZe1alvuuecezj77bN5++20OPfRQevfuzYMPPsgbb7zB6aefzt/+9jdWXXVVrrzySg455BDq6+s57bTT2GGHHQAYOHAgxx57LNdffz2bbbYZd9xxx+K6x4wZw6GHts17o2LnHpaXND1+HbAX2YHIZODPpQb/gmn/rYDLI+Knad16EfFeWr4RuD0i7pc0ARgcEfdI6kQ2+7EHKVin/P2ADSJikKTVgUfJDhI2B0YBO0bEK5J+lJa/LWlb4B/A1sDn+Sz4Xw68ExEXSToUGEl2ILA+cAlwVEQslHQ18ATwTOrfl1Jf1omI95vaD6v36BU9TvljKbvMVnD+qZ9VwvTp09luu+0q3Q1rwoIFC9h///0ZP348q6669Li92HsoaVJEFL3RQKWn/fcFRkTExxExF7i/BXU0TPv/P+BgSXun9AMlTZBUBxwE7CCpK7BxRNwDEBHzI+KjInV+GTg5zQRMALoBvdK6JyPilVz/b0x1PQe8Shb8874A3JTyjAIaTl4dDOwOTEztHAxsCbwMbCnpCklfAT4of5eYmVl78tprrzF48OCigb8lKj3t32Y3k46IeZLGAvtKmgxcTXZh3euSLiQ7LVBqewLOjogHl0jMpvM/LMhXUvcaaWNYRPxkqRXSLsAhQH/gWOC0EtsxM7N2qFevXvTq1av5jCWq9Mh/PHCYpE6SugAtnvOUtCqwJ/ASn53/fyfVewxARHwAzJTUN5VZXdKawFyga666B4EzJXVM+baW1LlIsw+TXa+ApK2BzYDnm8jzVWDdlP4QcIykDdK69SRtLqk7sEpE3AWcD+zWgt1hZlaWSp4CttZpyXtX0ZF/REyUdB8wlWzKvJbs/H05fivp52QXzT0E3J0u7LsWqANmABNz+b8F/FnSRcBCsnP5TwOfSpoKDAUuI/sFwGRlP758G+hbpO2rgT+lUwufAqdGxIKC32v+EhieZiPGAa+lbX829fsfklZJfekPfAzckNIAlpoZKLTTxmtT63PFZtZCnTp14t133/VjfVdCEcG7775Lp06dms+cU9EL/gAkdUlT9muSjZL7RUT5t3aqYjU1NVFbW1vpbpjZSmrhwoXMnDmz7GfC24qhU6dObLLJJnTs2HGJ9KYu+Kv0OX+AIZK2J5uqH+bAb2a2fHXs2JEttmjJLVJsZVXx4B8RS9xcR9JVwD4F2XoBLxakXRYRNyzLvpmZmbVHFQ/+hSKif6X7YGZm1p5V+mp/MzMzW84qfsGftZ6kuSz9E0NbtrqT3Y7alh/v8+XP+3z5a8t9vnlEFL3/+wo37W8t8nxjV3TasiGp1vt8+fI+X/68z5e/5bXPPe1vZmZWZRz8zczMqoyDf/swpNIdqELe58uf9/ny532+/C2Xfe4L/szMzKqMR/5mZmZVxsHfzMysyjj4r+QkfUXS85L+LWlgpfvTHknaVNIYSdMlPSPpByn9QkmzJE1Jf1+rdF/bE0kzJNWlfVub0taTNFrSi+nfdZurx0ojaZvcZ3mKpA8kDfDnvG1J+ouktyRNy6U1+rmW9JP0/f68pEParB8+57/yktQBeAH4EjCT7NHFJ0TEsxXtWDsjqQfQIyImS+oKTCJ7xPOxwLyIuLSS/WuvJM0AaiLinVzaJcB7ETE4HeyuGxH/W6k+tlfpu2UWsCfwbfw5bzOSvgDMA/4aETumtKKf6/TQu+HAHsBGwD+BrSOivrX98Mh/5bYH8O+IeDkiPgFuBY6ocJ/anYiY3fC0yYiYC0wHNq5sr6rWEcCwtDyM7CDM2t7BwEsR8WqlO9LeRMTDwHsFyY19ro8Abo2IBRHxCvBvsu/9VnPwX7ltDLyeez0TB6VlSlJPYFdgQko6S9LTaSrPU9BtK4B/SJokqV9K2zAiZkN2UAZsULHetW/Hk404G/hzvmw19rleZt/xDv4rNxVJ83mcZURSF+AuYEBEfABcA3wO6A3MBn5Xud61S/tExG7AV4H+abrUljFJqwGHA3ekJH/OK2eZfcc7+K/cZgKb5l5vArxRob60a5I6kgX+myPiboCIeDMi6iNiEXAtbTQdZ5mIeCP9+xZwD9n+fTNdg9FwLcZblethu/VVYHJEvAn+nC8njX2ul9l3vIP/ym0i0EvSFulo/Xjgvgr3qd2RJOB6YHpE/D6X3iOX7UhgWmFZaxlJndPFlUjqDHyZbP/eB5ySsp0CjKhMD9u1E8hN+ftzvlw09rm+Dzhe0uqStgB6AU+2RYO+2n8ll35280egA/CXiPhVZXvU/kjaF3gEqAMWpeSfkn1J9iabhpsBfLfhvJ21jqQtyUb7kD199JaI+JWkbsDtwGbAa8A3IqLw4ilrIUlrkp1j3jIi5qS0G/HnvM1IGg4cQPbo3jeBXwD30sjnWtLPgNOAT8lOOf69Tfrh4G9mZlZdPO1vZmZWZRz8zczMqoyDv5mZWZVx8DczM6syDv5mZmZVxsHfrESS6tNTzaZJul/SOs3kv1DSuc3k6Zse3tHw+iJJX2yDvg6VdExr6ymzzQHpp2IrDEnbpvfsKUmfK1g3Q9IjBWlTGp62JqlG0uVt0Iee+Se4Fay7Lv/+L2uSNpR0i6SX022TH5d05PJq31YcDv5mpfs4InqnJ3G9B/Rvgzr7Aou//CPigoj4ZxvUu1ylp8ANAFao4E+2f0dExK4R8VKR9V0lbQogabv8ioiojYhzSm0o7YOyRMTpy+spnOlmVfcCD0fElhGxO9mNwTZZxu2uuizrt5Zx8DdrmcdJD9iQ9DlJD6SR1COSti3MLOkMSRMlTZV0l6Q1Je1Ndg/136YR5+caRuySvirp9lz5AyTdn5a/nEZskyXdkZ450Kg0wv11KlMraTdJD0p6SdL3cvU/LOkeSc9K+pOkVdK6EyTVpRmPi3P1zkszFROAn5E9cnSMpDFp/TWpvWck/bKgP79M/a9r2F+Suki6IaU9LenoUrdXUm9JT6Ry90haN90AawBwekOfirgdOC4tF97Z7gBJI5vpW34f7CXph2k/TZM0INfOqpKGpbJ3NsyQSBorqaaE/Xxx+nz9U9IeqdzLkg5PeTpI+m36jD0t6btFtvUg4JOI+FNDQkS8GhFXNFVH2g9jU7+fk3RzOpBA0u6SxqW+PajPblE7Nn3mxgE/kHSYpAnKZmD+KWnDRt4PW14iwn/+818Jf2TPNIfsbop3AF9Jrx8CeqXlPYF/peULgXPTcrdcPYOAs9PyUOCY3LqhwDFkd7V7Deic0q8Bvkl2V7CHc+n/C1xQpK+L6yW7K9uZafkPwNNAV2B94K2UfgAwH9gybd/o1I+NUj/WT336F9A3lQng2FybM4Duudfr5fbXWGDnXL6G7f8+cF1avhj4Y678umVs79PA/mn5ooZ68u9BkTIzgK2Bx9Lrp8hmYabl9snIxvpWuA+A3cnuAtkZ6AI8Q/YEyJ4p3z4p31/47HMxFqgpYT9/NS3fA/wD6AjsAkxJ6f2An6fl1YFaYIuC7T0H+EMTn++idaT9MIdshmAVsgPffVMfHgPWT2WOI7vLaMN2XV3wXjbcVO504HeV/v9c7X+ejjEr3RqSppB9mU8CRqdR6N7AHWkwBNkXZ6EdJQ0C1iELDA821VBEfCrpAeAwSXcChwI/BvYnC1CPpvZWI/sybk7DMx/qgC4RMReYK2m+Prt24cmIeBkW34J0X2AhMDYi3k7pNwNfIJs+rid72FFjjlX2KN5VgR6p30+ndXenfycBR6XlL5JNQzfsg/9K+npz2ytpbWCdiBiXkobx2RPpmvMe8F9JxwPTgY8aybdU39Jifh/sC9wTER+mft0N7Ee271+PiEdTvpvIAvGlufr70Ph+/gR4IOWrAxZExEJJdWSfRciefbCzPrvOY22y+8C/0tiGS7oq9fmTiOjTRB2fkH02ZqZyU1K77wM7kv0/gOwgL3/b39tyy5sAt6WZgdWa6pctHw7+ZqX7OCJ6p2Azkuyc/1Dg/Yjo3UzZoWQjuamSTiUbTTXnttTGe8DEiJibpltHR8QJZfZ9Qfp3UW654XXD90Dhvb6D4o8UbTA/IuqLrVD2EJJzgT4piA8FOhXpT32ufRXpQ0u3txy3AVcBpzaRp1jfYMl90NS+KrZvC+tvzMJIQ2Zy719ELNJn59NFNpvS1EHlM8DRizsQ0V9Sd7IRfqN1SDqAJT8zDe+ZgGciYq9G2vswt3wF8PuIuC/Vd2ET/bTlwOf8zcoU2QNPziELbh8Dr0j6BmQXVUnapUixrsBsZY8GPimXPjetK2YssBtwBp+Nop4A9pG0VWpvTUlbt26LFttD2RMiVyGbwh0PTAD2l9Rd2QVtJwDjGimf35a1yL7856Tzu18tof1/AGc1vJC0LiVsb3o//itpv5T0rSb6WMw9wCU0PRtTrG+FHgb6pj52JnsCXsOvCTaT1BAkTyDbt3nl7OdiHgTOTJ8vJG2d+pD3L6CTpDNzafkLNEupI+95YP2G7ZLUUdIOjeRdG5iVlk9pJI8tRw7+Zi0QEU8BU8mmgk8CviNpKtno6ogiRc4n+4IfDTyXS78VOE9FfoqWRpQjyQLnyJT2NtkIdbikp8mC41IXGLbQ48Bgske2vkI2hT0b+Akwhmx7J0dEY4/RHQL8XdKYiJhKdg79GbJz3I82UiZvELBuuuBtKnBgGdt7CtmFk0+TPYHuohLaAyAi5kbExRHxSTl9K1LPZLIZnifJ3uvr0ucEslMKp6T+rUd2DUe+bDn7uZjrgGeBycp+VvhnCmZ20+xBX7KDjFckPUl2iuR/S62joL5PyK4LuTjtkylkp8CKuZDs1NgjwDtlbJctI36qn5k1TO2eGxFfr3BXzGw58MjfzMysynjkb2ZmVmU88jczM6syDv5mZmZVxsHfzMysyjj4m5mZVRkHfzMzsyrz/wEKRnTnDS9jlQAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=nan, pvalue=nan)\n", "Slope and P-value = PearsonRResult(statistic=-0.881737436653327, pvalue=9.584173637930333e-34)\n", "Slope and P-value = PearsonRResult(statistic=-0.8021004864440553, pvalue=8.853828831912122e-13)\n", "Slope and P-value = PearsonRResult(statistic=-0.8662984061558627, pvalue=2.6600205226864353e-31)\n", "Slope and P-value = PearsonRResult(statistic=-0.8665640378607296, pvalue=0.011610829080539316)\n", "Slope and P-value = PearsonRResult(statistic=-0.8738949056268536, pvalue=1.831645220789357e-32)\n", "Slope and P-value = PearsonRResult(statistic=-0.3291134311919496, pvalue=0.14517626644201734)\n", "Slope and P-value = PearsonRResult(statistic=-0.8109331922406183, pvalue=1.448549811315006e-11)\n", "Slope and P-value = PearsonRResult(statistic=-0.8291820118633157, pvalue=1.8478554975852036e-06)\n", "Slope and P-value = PearsonRResult(statistic=0.17507374373073287, pvalue=0.08147188893325288)\n", "1 308\n", "0 5\n", "Name: FreqHousingMove, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
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pvalue=0.027810195132715813)\n", "Slope and P-value = PearsonRResult(statistic=-0.7397666696145124, pvalue=1.4772150128563515e-18)\n", "1 180\n", "0 5\n", "Name: FreqHousingMove, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.8726646323309659, pvalue=2.858023780762996e-32)\n", "Slope and P-value = PearsonRResult(statistic=0.6654654644896629, pvalue=4.262271962549621e-14)\n", "Slope and P-value = PearsonRResult(statistic=-0.9846844941476357, pvalue=5.027569214221995e-67)\n", "Slope and P-value = PearsonRResult(statistic=-0.9938193391453496, pvalue=2.571077070594274e-74)\n", "Slope and P-value = PearsonRResult(statistic=-0.8689057364770213, pvalue=0.055844397751495405)\n", "Slope and P-value = PearsonRResult(statistic=-0.7802872077893653, pvalue=2.176279259385503e-12)\n", "Slope and P-value = PearsonRResult(statistic=-0.9362455409675392, pvalue=6.689608592180346e-05)\n", "Slope and P-value = PearsonRResult(statistic=nan, pvalue=nan)\n", "Slope and P-value = PearsonRResult(statistic=nan, pvalue=nan)\n", "Slope and P-value = PearsonRResult(statistic=-0.9401289804751114, pvalue=1.3145537194275014e-47)\n", "1 194\n", "0 5\n", "Name: FreqHousingMove, dtype: int64\n" ] }, { "data": { "image/png": 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8HYaZmbWadkv8ks6R9ISkCZJGSzqzFdocJunvkiZJek7S99Oi4cA2aXbgwlT3LElTJM2U9L+prIekOZL+DEwDtpR0oaTZkmZJOjbX3XqSxkp6XNJfCgcJki6TVCvpsUK7qbyvpAclzZA0WVJX4Dzg2BTXsZLWSfFPkfSopMPTuqdKul7SzcAdJbZ7UOqztu7dRS3djWZmVkXaZTQpqQY4Ctg19TkNmFpmMxdKOju9fiwiTkyvPwscCKwLPCnpMmAosGNE9En9f4Hscb97AALGS9ofeBHYDvh6RHxX0lFko/JdgG7AFEn3pn72AHoDLwC3AUcCY4CfR8QbkjoAd0naGXgCuBY4NiKmSFoPeBc4F6iJiO+luH4N3B0R35C0PjBZ0p2pv37AzhHxRvGOiIgRwAiAtbr3ijL3o5mZVbH2mkbeFxgXEUsB0ki2XGdFxJgS5bdGxHvAe5JeATYtUecL6efR9L4L2YHAi8ALEfFwLs7REVEHvCzpHqAv8DYwOSKeS/GPTnXHAMdIGkS2L7uTHRwEsDAipgBExNtpvVJxHZab/egEbJVeTyiV9M3MzFqivRL/JzJeK3ov97qO0tsk4P8i4q8fK5R6AO8U1atP8cg6JG0NnAn0jYg3JY0kS94qUb8UAUdFxJNFce1ZFJeZmVmraK9z/PcDAyR1ktQFaOu7Iiwmm/ovuB34RuobSZtL2qTEeveSnYPvIGljYH9gclq2h6St07n9Y8m2aT2yBL1I0qbAl1PdJ4DNJPVN/a2bLtIrFdcZSlMBknZt6YabmZk1pF1G/Ok893hgBtk58lqg3KvS8uf4ITvnXl9/r0t6QNJs4D8RcZak7YGHUo5dAnyNbIYgbyzZufUZZCP2/4mI/0r6LPAQ2UWDO5EdIIyNiOWSHgUeA54DHkj9v58uDLxE0trAUuBzwERgqKTpwP8BvwT+AMxMyX8ucGg5O2WnzbtSu5reXcrMzFqfItrn2jBJXSJiiaTOZIlzUERMa5fOV2M1NTVRW1tb6TDMzGwlImlqRNSUWtae3xEfIak32TnwUU76ZmZm7a/dEn9EnJB/L+lSYJ+iar2Ap4vKLo6IK9syNjMzs2pRsbvCRcTgSvVtZmZWrValW/aamZlZCznxm5mZVREnfjMzsyrixG9mZlZFnPjNzMyqiBO/mZlZFanY1/msdcyav4geQ2+tdBhmtpqb61uDrzY84jczM6siTvxmZmZVZJVM/JJGSjq60m00s98aSX9s737NzMzA5/jbXUTUkj2W+GMkrRERH1YgJDMzqyIVG/FLOkfSE5ImSBot6cwWttdF0l2SpkmaJenw3LKTJc2UNEPSP3Or7S/pQUnPFUb/kvpLmiRpTIrvKklKyw6W9Ghq/++S1krlwyU9nvq4KJWNlPQXSfdJekrSobn2b0mvh0kaIekO4B+SeqT609LP3i3ZJ2ZmZsUqMuKXVAMcBeyaYpgGTG1hs8uAIyLibUndgIcljQd6Az8H9omI1yRtmFunO7Av8FlgPDAmle8K7AAsAB4A9pFUC4wEDo6IpyT9A/hO+vcI4LMREZLWz7XfAzgA2AaYKKlnibh3B/aNiKWSOgOfj4hlknoBo4FPPE9Z0iBgEECH9TYuayeZmVl1q9SIf19gXEQsjYjFwM2t0KaAX0uaCdwJbA5sChwEjImI1wAi4o3cOjdFxPKIeDzVLZgcEfMiYjkwnSyBbwc8HxFPpTqjgP2Bt8kOOi6XdCTwbq6d61L7TwPPkR1gFBsfEUvT647A3yTNAq4nO2j5hIgYERE1EVHToXPXRneMmZlZQaUSv9qgzROBjYHdI6IP8DLQKfUV9azzXj0x5cvryGYlSsaczsvvAdwADARuyy8url6iiXdyr3+Y4t6FbKS/Zj1xm5mZNUulEv/9wABJnSR1AVrjzhBdgVci4gNJBwKfTuV3AcdI2gigaKq/HE8APXLT9ScB96T4u0bEv4EhQJ/cOl+V9ClJ2wCfAZ5swjYsTDMNJwEdmhmrmZlZSRU5xx8RU9L59xnAC2RXuS8qs5m/SvpDev0SMAC4OZ2Ln06WqImIxyT9iixJ1wGPAqc2I+Zlkr4OXC9pDWAK8BdgQ2CcpMLswg9zqz0J3EN2GuHbqY2GuvkzcIOkrwIT+fhsgJmZWYspor5Z8DbuWOoSEUvSBW33AoMiYlpFgmkDkkYCt0TEmMbqtkRNTU3U1n7i24FmZlbFJE2NiE9cHA6V/R7/CEm9yc7Dj1qdkr6ZmdnKqmKJPyJOyL+XdCmwT1G1XsDTRWUXR8SVbRlba4iIUysdg5mZWbGV5s59ETG40jGYmZmt7lbJe/WbmZlZ8zjxm5mZVREnfjMzsyrixG9mZlZFnPjNzMyqiBO/mZlZFXHiNzMzqyIrzff4rXlmzV9Ej6G3VjoMM2sHc4e3xvPMrNp5xG9mZlZFnPjNzMyqyEqd+CWNlPS8pOnp58EWtDVJUsknFTVh3fMkfa4Z6zU7XjMzs7awKpzjP6utH23bmIg4t5nr7d3asZiZmbVEm4/4JZ0j6QlJEySNlnRmK7Q5TNI/Jd0t6WlJp6fy/pJuydX7k6RTS6x/vKRZkmZLuiBXvkTSbyVNk3SXpI1T+UhJR6fXwyU9LmmmpItS2aaSxkqakX72LrTXWFyS5kr6taSHJNVK2k3S7ZKelfTterZ/UKpbW/fuopbuTjMzqyJtmvjT1PpRwK7AkUBzptovzE31X5Ur3xk4BOgHnCtpsybGtBlwAXAQ0AfoK2lgWrwOMC0idgPuAX5RtO6GwBHADhGxM3B+WvRH4J6I2AXYDXiszG18KSL6AfcBI4Gjgb2A80pVjogREVETETUdOnctsyszM6tmbT3Vvy8wLiKWAki6uRlt1DfVX2h3qaSJwB7AW01ory8wKSJeTTFdBewP3AQsB65N9f4F3Fi07tvAMuBySbcChVH8QcDJABFRB5Q7DB+f/p0FdImIxcBiScskrR8RTdkuMzOzRrX1VL/asO0o8f5DPr5NnUqsV05MH+sjIj4kO8C4ARgI3NbEdhqL67307/Lc68L7VeE6DDMzW0W0deK/HxggqZOkLmRT863l8NTuRkB/YArwAtBb0lqSugIHl1jvEeAASd0kdQCOJ5vWh2x/HJ1en5DiXyFtQ9eI+DcwhOxUAcBdwHdSnQ6S1ivqsylxmZmZtbk2HU1GxBRJ44EZZMmvlvKnwS+UdHbu/R7p38nArcBWwC8jYgGApOuAmcDTwKMlYloo6afARLLR/78jYlxa/A6wg6SpKc5ji1ZfFxgnqVNa94ep/AfACEnfBOrIDgIeyvX5UmNxmZmZtQdFFM+Yt3IHUpeIWCKpM3AvMCgiprWwzWHAkoi4qDVizLW7JCK6tGabba2mpiZqa2srHYaZma1EJE2NiJIX1LfH+eMRknqTndce1dKkb2ZmZs3X5ok/Ik7Iv5d0KbBPUbVeZFPgeRdHxJX1tDms1QL8eLur1GjfzMysXO1+xXhEDG7vPs3MzCyzUt+r38zMzFqXE7+ZmVkVceI3MzOrIk78ZmZmVcSJ38zMrIo48ZuZmVURJ34zM7Mq4ie/reJmzV9Ej6G3VjoMM0vmDm/NZ5GZtT6P+M3MzKqIE7+ZmVkVceJvgKS9JD0iabqkOempgEg6VVJIOjhX94hUdnR6P0nSk7l1B+Xq5pdNl7RJKv+2pFmp7P70cCMzM7NW43P8DRsFHBMRMyR1ALbLLZsFHA/cld4fB8woWv/EiKiVtCHwrKSREfF+fllR/asj4i8Akg4Dfgd8qRW3x8zMqtxqn/glnQOcCLwEvAZMjYiLmrj6JsBCgIioAx7PLbsP2E9SR2AtoCcwvZ52ugDvAHUNdRYRb+fergNEqXpp9mAQQIf1Nm5sG8zMzFZYrRO/pBrgKGBXsm2dBkwto4nfA09KmgTcBoyKiGVpWQB3Al8EugLjga2L1r9K0ntkjx0ekg4eCq6UVAfcAJwfEZFiHgz8CFgTOKhUUBExAhgBsFb3XiUPDszMzEpZ3c/x7wuMi4ilEbEYuLmclSPiPKAGuAM4gSz5511DNsV/HDC6RBMnRsTOwFbAmZI+nSvfCdgv/ZyU6/PSiNgG+AlwdjnxmpmZNWZ1T/xqaQMR8WxEXAYcDOwiaaPcssnAjkC3iHiqgTZeJZtt2DO9n5/+XQxcDexRYrVrgIEtjd/MzCxvdU/89wMDJHWS1AUo684akg6RVDh46EV2jv6tomo/BX7WSDudyU43PCtpDUndUnlH4FBgdnrfK7faIcDT5cRrZmbWmNX6HH9ETJE0nuxq+xeAWmBRGU2cBPxe0rvAh2RT9HUfHQtARPyngfWvkrSU7OK/kRExVdI6wO0p6Xcgu07gb6n+9yR9DvgAeBM4pYxYzczMGqV0TdlqS1KXiFiSRt33AoMiYlql42otNTU1UVtb/K1AMzOrZpKmRkRNqWWr9Yg/GZFuhNOJ7Kr81Sbpm5mZlWu1T/wRcUL+vaRLgX2KqvXik+fTL46IK9syNjMzs/a22if+YhExuNIxmJmZVcrqflW/mZmZ5Tjxm5mZVREnfjMzsyrixG9mZlZFnPjNzMyqiBO/mZlZFXHiNzMzqyJV9z3+1c2s+YvoMfTWSodhtlKbO7ys53OZrdY84jczM6siTvxmZmZVZLVP/JJGSnpe0nRJ0yT1a+P+ftbEenMldUuvl7RlTGZmZgWrfeJPzoqIPsBQ4K/FCyV1aMW+mpT4zczMKmGVSPySzpH0hKQJkkZLOrOZTd0L9ExtzpV0rqT7ga9K+oKkh9KswPWSukj6sqTrcnH0l3Rzen28pFmSZku6IJUNB9ZOswtXpbKvSZqcyv7a0EFG6vOuFMMsSYfXU2+QpFpJtXXvLmrmrjAzs2q00id+STXAUcCuwJFATQuaGwDMyr1fFhH7AncCZwOfi4jdgFrgR8AEYC9J66T6xwLXStoMuAA4COgD9JU0MCKGAksjok9EnChp+7TOPmnGoQ44sYH4lgFHpBgOBH4rScWVImJERNRERE2Hzl2btyfMzKwqrQpf59sXGBcRSwEKI+4yXSjpbOBV4Ju58mvTv3sBvYEHUp5dE3goIj6UdBswQNIY4BDgf8gS/qSIeDXFdBWwP3BTUb8HA7sDU1K7awOvNBCngF9L2h9YDmwObAr8txnbbGZm9gmrQuL/xIi3Gc6KiDElyt/J9TEhIo4vUedaYDDwBjAlIhaXGoXXQ8CoiPhpE+ufCGwM7B4RH0iaC3Rq4rpmZmaNWumn+oH7yUbcnSR1IRt1t7aHgX0kFc7/d5a0bVo2CdgNOJ2PZggeAQ6Q1C2dsz8euCct+0BSx/T6LuBoSZukdjeU9OkG4ugKvJKS/oFAQ3XNzMzKttKP+CNiiqTxwAzgBbLz7616RVtEvCrpVGC0pLVS8dnAUxFRJ+kW4FTglFR/oaSfAhPJRvX/johxab0RwExJ09J5/rOBOyR9CviAbPbghXpCuQq4WVItMB14orHYd9q8K7W+K5mZmTWRIqLSMTRKUpeIWCKpM9mV+YMiYlql41oZ1NTURG1tbaXDMDOzlYikqRFR8mL4lX7En4yQ1JvsfPcoJ30zM7PmWSUSf0SckH8v6VJgn6JqvYCni8oujogr2zI2MzOzVckqkfiLRcTgSsdgZma2KloVruo3MzOzVuLEb2ZmVkWc+M3MzKqIE7+ZmVkVceI3MzOrIk78ZmZmVWSV/DqffWTW/EX0GHprpcMwq6i5vm21WZN5xG9mZlZFnPjNzMyqiBO/mZlZFXHiNzMzqyK+uK+dSDoHOBF4CXgNmBoRF1U2KjMzqzZO/O1AUg1wFLAr2T6fBkxtQXuDgEEAHdbbuDVCNDOzKuGp/vaxLzAuIpZGxGLg5pY0FhEjIqImImo6dO7aOhGamVlVcOJvH6p0AGZmZuDE317uBwZI6iSpC+C7jZiZWUX4HH87iIgpksYDM4AXgFpgUWWjMjOzaqSIqHQMVUFSl4hYIqkzcC8wKCKmtbTdmpqaqK2tbXmAZma22pA0NSJqSi3ziL/9jJDUG+gEjGqNpG9mZlYuJ/52EhEn5N9LuhTYp6haL+DporKLI+LKtozNzMyqhxN/hUTE4ErHYGZm1cdX9ZuZmVURJ34zM7Mq4sRvZmZWRZz4zczMqogTv5mZWRVx4jczM6siTvxmZmZVxN/jX8XNmr+IHkNvrXQYZk02d7ifUWVWSR7xm5mZVREnfjMzsyrS7olf0l6SHpE0XdIcScNSeX9Je7eg3f6SbilznbmSujW3TzMzs1VNJc7xjwKOiYgZkjoA26Xy/sAS4MEKxGRmZlYVmjXil3SOpCckTZA0WtKZZay+CbAQICLqIuJxST2AbwM/TDMB+0kakGYGHpV0p6RNU9/DJP1T0t2SnpZ0eq7tLpLGpNiuUuZgSWNzsX9e0o0ltulHkmannyG58pMlzZQ0Q9I/U9mnJd2Vyu+StFUq31TS2FR3RmEGo542Rko6OtfPkvRvd0n3pv0wW9J+JWIdJKlWUm3du4vK2PVmZlbtyh7xS6oBjgJ2TetPA6aW0cTvgSclTQJuI3s2/VxJfwGWRMRFqZ8NgL0iIiSdBvwP8OPUxs7AXsA6wKOSCpe17wrsACwAHiB77O3dwKWSNo6IV4GvAx97zK2k3VP5noCARyTdA7wP/BzYJyJek7RhWuVPwD8iYpSkbwB/BAamf++JiCPSbEYXSTvU00Z9TgBuj4hfpTY6F1eIiBHACIC1uveKRtozMzNboTkj/n2BcRGxNCIWAzeXs3JEnAfUAHeQJbnb6qm6BXC7pFnAWWQJvaDQ/2vARGCPVD45IuZFxHJgOtAjIgL4J/A1SesD/YD/lNimsRHxTkQsAW4E9gMOAsakfoiIN1L9fsDV6fU/0/qk+pelunURsaiBNuozBfh6uvZhp7SPzczMWkVzEr9a2mlEPBsRlwEHA7tI2qhEtUuAP0XETsC3gE75JoqbTP++lyur46MZjSuBrwHHA9dHxIdF69e3TSrRVykN1amvjQ9J+1+SgDUBIuJeYH9gPvBPSSc3oX8zM7MmaU7ivx8YIKmTpC5AWXfjkHRISnQAvcgS9FvAYmDdXNWuZMkP4JSiZg5P/W9EdlHglIb6jIgFZNP/ZwMjS1S5FxgoqbOkdYAjgPuAu4BjCgcmuWn6B4Hj0usTyfYJqf53Ut0OktZroI25wO6F7QE6puWfBl6JiL8BVwC7NbRtZmZm5Sj7HH9ETJE0HpgBvADUAuVcYXYS8HtJ75KNek+MiDpJNwNjJB0OnAEMA66XNB94GNg618Zk4FZgK+CXEbFA0raN9HsVsHFEPF5im6ZJGpnaBbg8Ih4FkPQr4B5JdcCjwKnA94G/SzoLKFw3APADYISkb5Id0HwnIh6qp42/AeMkTSY7OHgntdEfOEvSB2TfcvCI38zMWo2yU+BlriR1iYglkjqTjZYHRcS0Vo+udN/DyF0EWMZ6fwIejYgr2iSwCqmpqYna2tpKh2FmZisRSVMjoqbUsuZ+j3+EpN5k591HtVfSby5JU8lG1D9urK6ZmdnqrFmJPyJOyL+XdCnZV+fyegFPF5VdHBFX0gIRMawZ6+zeeC0zM7PVX6vcuS8iBrdGO2ZmZta2/JAeMzOzKuLEb2ZmVkWc+M3MzKqIE7+ZmVkVceI3MzOrIk78ZmZmVcSJ38zMrIq0yvf4rXJmzV9Ej6G3VjoMq3Jzh5f1rC4zqyCP+M3MzKqIE7+ZmVkVadPEL+lSSdMlPS5paXo9XdLRZbbzYD3lIwttSbo8PTioqW3WSPpjOXHU004PSbPrWVZWTGZmZm2tTc/xF+7hL6kHcEtE9GlmO3s3oc5pZbZZCzT5ebaSOkREXZl9lBWTmZlZW2vSiF/SOZKekDRB0mhJZza3Q0nrSPq7pCmSHpV0eCo/VdI4SbdJelLSL3LrLEn/StKf0gzCrcAmuTqTJNUU6ku6QNJUSXdK2iMtf07SYalOf0m3pNddJF0paZakmZKOyrVznqRHgH6SfiRpdvoZktusNSSNSuuOkdS5VEy5WI+WNDK9HinpMkkTU3wHpP0zp1CnxD4cJKlWUm3du4ua+6swM7Mq1GjiT4nrKGBX4EigpoV9/hy4OyL6AgcCF0paJy3bAzgR6AN8tZA0c44AtgN2Ak4H6psJWAeYlB7Huxg4H/h8Wv+8EvXPARZFxE4RsTNwd66d2RGxJ7AU+DqwJ7AXcLqkXVO97YARad23ge82ZUfkbAAcBPwQuBn4PbADsJOkPsWVI2JERNRERE2Hzl3L7MrMzKpZU0b8+wLjImJpRCwmS0wt8QVgqKTpwCSgE7BVWjYhIl6PiKXAjanvvP2B0RFRFxEL+ChBF3sfuC29ngXcExEfpNc9StT/HHBp4U1EvJle1gE3pNf7AmMj4p2IWJLi2y8teykiHkiv/1Ui7sbcHBGR4ns5ImZFxHLgsXriNTMza5amnONXK/cp4KiIePJjhdKeQBTVLX5fX1mxD1IiBVgOvAcQEcslldpm1dPustx5/Yb2Q7lxdypa9l5xrLn3vteCmZm1mqaM+O8HBkjqJKkL0NI7ddwOnCFJALnpcoDPS9pQ0trAQOCBonXvBY6T1EFSd7JTBa3hDuB7hTeSNihR515goKTO6dTEEcB9adlWkvql18eT7bNiL0vaXtKn0rpmZmbtrtHEHxFTgPHADLLp7VqgJVeU/RLoCMxMX4P7ZW7Z/cA/genADenK+7yxwNNkU+KXAfe0II6884EN0kV7MyhxQBER04CRwGTgEeDyiHg0LZ4DnCJpJrBhiq3YUOAWstMTC1spbjMzs7LooxnxBipJXSJiSbpa/V5gUEqErReIdCpQExHfa6yufaSmpiZqa5v8rUQzM6sCkqZGRMmL8Zt6/nhEuhFNJ2BUayd9MzMzax9NSvwRcUL+vaRLgX2KqvUim4bPuzgirmxiHyPJptLNzMysjTTrivHCHfnMzMxs1eKvipmZVbEPPviAefPmsWzZskqHYs3QqVMntthiCzp27NjkdZz4zcyq2Lx581h33XXp0aMH6VvWtoqICF5//XXmzZvH1ltv3eT1/FheM7MqtmzZMjbaaCMn/VWQJDbaaKOyZ2uc+M3MqpyT/qqrOb87J34zM7Mq4nP8Zma2Qo+ht7Zqe3OHN+0u72PHjuXII49kzpw5fPaznwVg0qRJXHTRRdxyyy0r6p166qkceuihHH300fTv35+FCxfSqVMn1lxzTf72t7/Rp08fABYtWsQZZ5zBAw9kd37fZ599uOSSS+jaNXui6VNPPcWQIUN46qmn6NixIzvttBOXXHIJm266abO39frrr2fYsGHMmTOHyZMnU1NT+mG2t912Gz/4wQ+oq6vjtNNOY+jQoQC88cYbHHvsscydO5cePXpw3XXXscEGGzBr1ix++9vfMnLkyGbHlufEv4qbNX9Rq/9HNatPU/+Im5Vr9OjR7LvvvlxzzTUMGzasyetdddVV1NTUcOWVV3LWWWcxYcIEAL75zW+y44478o9//AOAX/ziF5x22mlcf/31LFu2jEMOOYTf/e53DBgwAICJEyfy6quvtijx77jjjtx4441861vfqrdOXV0dgwcPZsKECWyxxRb07duXww47jN69ezN8+HAOPvhghg4dyvDhwxk+fDgXXHABO+20E/PmzePFF19kq622qrftpvJUv5mZVdSSJUt44IEHuOKKK7jmmmua1Ua/fv2YP38+AM888wxTp07lnHPOWbH83HPPpba2lmeffZarr76afv36rUj6AAceeCA77rhji7Zj++23Z7vttmuwzuTJk+nZsyef+cxnWHPNNTnuuOMYN24cAOPGjeOUU04B4JRTTuGmm25asd6AAQOavW+KOfGbmVlF3XTTTXzpS19i2223ZcMNN2TatPLvCn/bbbcxcOBAAB5//HH69OlDhw4dVizv0KEDffr04bHHHmP27Nnsvvvujba5ePFi+vTpU/Ln8ccfLztGgPnz57PllluueL/FFlusOGB5+eWX6d69OwDdu3fnlVdeWVGvpqaG++67j9bgqX4zM6uo0aNHM2TIEACOO+44Ro8ezW677VbvFev58hNPPJF33nmHurq6FQcMEVFy3frK67Puuusyffr0pm9IE5R6MF5TYtpkk01YsGBBq8Sw0iV+SSOBA8ge/bscGBwRD7VRX8OAJRFxUVu0b2ZmDXv99de5++67mT17NpKoq6tDEr/5zW/YaKONePPNNz9W/4033qBbt24r3l911VXssssuDB06lMGDB3PjjTeyww478Oijj7J8+XI+9alsYnv58uXMmDGD7bffnldeeYV77mn8qe6LFy9mv/32K7ns6quvpnfv3mVv7xZbbMFLL7204v28efPYbLPNANh0001ZuHAh3bt3Z+HChWyyySYr6i1btoy111677P5KWVmn+s+KiD5kz7D/a4VjMTOzNjJmzBhOPvlkXnjhBebOnctLL73E1ltvzf3330+vXr1YsGABc+bMAeCFF15gxowZK67cL+jYsSPnn38+Dz/8MHPmzKFnz57suuuunH/++SvqnH/++ey222707NmTE044gQcffJBbb/3owujbbruNWbNmfazdwoi/1E9zkj5A3759efrpp3n++ed5//33ueaaazjssMMAOOywwxg1ahQAo0aN4vDDD1+x3lNPPdXiaxAK2mTEL+kc4ETgJeA1YGozR9X3Aj1Tm8OBw4APgTsi4kxJGwN/AQqXOQ6JiAeKR/KSZgOHRsRcST8HTk6xvQpMTXX6pLY6A88C34iINyVNAh4Fdgc2Tuv+FNgJuDYizk7r/wj4Rorj8oj4g6QewH+A+4G9gfnA4RGxVNLpwCBgTeAZ4KSIeFfSV4FfAHXAoojYv8T+HZTWpcN6Gzdjt5qZldbe39wYPXr0iq+zFRx11FFcffXV7LfffvzrX//i61//OsuWLaNjx45cfvnlK76Sl7f22mvz4x//mIsuuogrrriCK664gjPOOIOePXsSEfTr148rrrhiRd1bbrmFIUOGMGTIEDp27MjOO+/MxRdf3KJtGTt2LGeccQavvvoqhxxyCH369OH2229nwYIFnHbaafz73/9mjTXW4E9/+hNf/OIXqaur4xvf+AY77LADAEOHDuWYY47hiiuuYKuttuL6669f0fbEiRM55JDW+d2o1PmGFjUo1QCXA/3IDiymAX9tauJPU/23RMSYlATPBL4MPAR8NiJC0voR8Zakq4E/R8T9krYCbo+I7etL/MBGZI/+3TMX218i4iJJM4EzIuIeSecB60XEkJT4H4mIn0j6AfATsoOAN8gOEHYBeqR29wIEPAJ8DXiTLKnXRMR0SdcB4yPiX5I2iojXU3znAy9HxCWSZgFfioj5he1saH+t1b1XdD/lD03ZtWYt5q/zrX7mzJnD9ttvX+kwrAHvvfceBxxwAPfffz9rrPHJ8Xqp36GkqRFR8kYCbTHi3xcYFxFLU+c3N6ONCyWdTTYi/ybwNrAMuFzSrUDhbg6fA3rnLoxYT9K6DbS7HzA2It5NsY1P/3YF1o+IwkmfUcD1ufXGp39nAY9FxMK03nPAlmmbx0bEO6n8xtTXeOD5iJie1p9KdpAAsGNK+OsDXYDbU/kDwMh0kHBjA9tiZmZV4MUXX2T48OElk35ztEXib42bPp8VEWM+1qi0B3AwcBzwPeAgsmsU+hUOMnJ1P+Tj1y90yr1uzhTHe+nf5bnXhfdr0PA25+vXAYWrM0YCAyNihqRTgf4AEfFtSXsChwDTJfUpzAyYmVn16dWrF7169Wq19tri4r77gQGSOknqQpbAWiS10zUi/g0MAfqkRXeQHQQU6hXK5wK7pbLdgMLzCu8FjpC0dpoZGAAQEYuANyUVLt88CWj8ks+P3AsMlNRZ0jrAEUBjX7hcF1goqSPZ9RCFbdgmIh6JiHPJro/Ysr4GzMxaQ2uf8rX205zfXauP+CNiSppCnwG8ANSSfTWvJdYFxknqRDa6/mEq/z5waTo/vwZZAv42cANwsqTpwBTgqRTbNEnXAtNTbPnkfArwF0mdgeeArzc1uNTuSGByKro8Ih5NF/fV5xyyawFeIDuFUDhFcaGkXmk77yLbj/XaafOu1Pq8q5k1U6dOnXj99df9aN5VUETw+uuv06lTp8Yr57T6xX2QjdAjYklKovcCgyKi/FsxWaNqamqitra20mGY2Srqgw8+YN68eWU/091WDp06dWKLLbagY8eOHytv74v7AEZI6k12bn2Uk76Z2cqpY8eObL311o1XtNVGmyT+iDgh/17SpcA+RdV6AU8XlV0cEVe2RUxmZmbWTrfsjYjB7dGPmZmZNWxlvWWvmZmZtYE2ubjP2o+kxcCTlY6jynQj+6qltR/v8/bnfd7+WnOffzoiSt7TfaV7Op+V7cn6rty0tiGp1vu8fXmftz/v8/bXXvvcU/1mZmZVxInfzMysijjxr/pGVDqAKuR93v68z9uf93n7a5d97ov7zMzMqohH/GZmZlXEid/MzKyKOPGvwiR9SdKTkp6RNLTS8ayOJG0paaKkOZIek/SDVD5M0nxJ09PPVyod6+pE0lxJs9K+rU1lG0qaIOnp9O8GlY5zdSFpu9xnebqktyUN8ee8dUn6u6RXJM3OldX7uZb00/T3/UlJX2y1OHyOf9UkqQPZ44Y/D8wje/zw8RHxeEUDW81I6g50T49eXheYCgwEjgGWRMRFlYxvdSVpLlATEa/lyn4DvBERw9OB7gYR8ZNKxbi6Sn9b5gN7kj2e3J/zViJpf2AJ8I+I2DGVlfxcpwfdjQb2ADYD7gS2jYi6lsbhEf+qaw/gmYh4LiLeB64BDq9wTKudiFhYeLpkRCwG5gCbVzaqqnU4MCq9HkV2AGat72Dg2Yh4odKBrG4i4l7gjaLi+j7XhwPXRMR7EfE88AzZ3/0Wc+JfdW0OvJR7Pw8npDYlqQewK/BIKvqepJlp+s7Tzq0rgDskTZU0KJVtGhELITsgAzapWHSrt+PIRpoF/py3rfo+1232N96Jf9WlEmU+b9NGJHUBbgCGRMTbwGXANkAfYCHw28pFt1raJyJ2A74MDE5TpNbGJK0JHAZcn4r8Oa+cNvsb78S/6poHbJl7vwWwoEKxrNYkdSRL+ldFxI0AEfFyRNRFxHLgb7TSFJxlImJB+vcVYCzZ/n05XXNRuPbilcpFuNr6MjAtIl4Gf87bSX2f6zb7G+/Ev+qaAvSStHU6Sj8OGF/hmFY7kgRcAcyJiN/lyrvnqh0BzC5e15pH0jrpQkokrQN8gWz/jgdOSdVOAcZVJsLV2vHkpvn9OW8X9X2uxwPHSVpL0tZAL2Bya3Toq/pXYemrNX8AOgB/j4hfVTai1Y+kfYH7gFnA8lT8M7I/kH3Ipt7mAt8qnKezlpH0GbJRPmRPEL06In4laSPgOmAr4EXgqxFRfKGUNZOkzmTnlD8TEYtS2T/x57zVSBoN9Cd7/O7LwC+Am6jncy3p58A3gA/JTjP+p1XicOI3MzOrHp7qNzMzqyJO/GZmZlXEid/MzKyKOPGbmZlVESd+MzOzKuLEb9YEkurS08lmS7pZ0vqN1B8m6cxG6gxMD+IovD9P0udaIdaRko5uaTtl9jkkfR1spSHps+l39qikbYqWzZV0X1HZ9MJT0yTVSPpjK8TQI/8ktqJll+d//21N0qaSrpb0XLoV8kOSjmiv/m3l4cRv1jRLI6JPeqLWG8DgVmhzILDiD39EnBsRd7ZCu+0qPc1tCLBSJX6y/TsuInaNiGdLLF9X0pYAkrbPL4iI2oj4flM7SvugLBFxWns9TTPdiOom4N6I+ExE7E52068t2rjfNdqyfWseJ36z8j1EeliGpG0k3ZZGUPdJ+mxxZUmnS5oiaYakGyR1lrQ32T3RL0wjzW0KI3VJX5Z0XW79/pJuTq+/kEZq0yRdn54hUK80sv11WqdW0m6Sbpf0rKRv59q/V9JYSY9L+oukT6Vlx0ualWY6Lsi1uyTNUDwC/JzssaETJU1Myy9L/T0m6X+L4vnfFP+swv6S1EXSlalspqSjmrq9kvpIejitN1bSBunmVkOA0woxlXAdcGx6XXzHuv6Sbmkktvw+6CfpR2k/zZY0JNfPGpJGpXXHFGZGJE2SVNOE/XxB+nzdKWmPtN5zkg5LdTpIujB9xmZK+laJbT0IeD8i/lIoiIgXIuKShtpI+2FSivsJSVelgwgk7S7pnhTb7frotrOT0mfuHuAHkgZIekTZzMudkjat5/dh7SUi/OMf/zTyQ/ZMcsjukng98KX0/i6gV3q9J3B3ej0MODO93ijXzvnAGen1SODo3LKRwNFkd6t7EVgnlV8GfI3sbl/35sp/ApxbItYV7ZLdbe076fXvgZnAusDGwCupvD+wDPhM2r4JKY7NUhwbp5juBgamdQI4JtfnXKBb7v2Guf01Cdg5V6+w/d8FLk+vLwD+kFt/gzK2dyZwQHp9XqGd/O+gxDpzgW2BB9P7R8lmX2bn9skt9cVWvA+A3cnu7rgO0AV4jOxJjj1SvX1Svb/z0ediElDThP385fR6LHAH0BHYBZieygcBZ6fXawG1wNZF2/t94PcNfL5LtpH2wyKymYFPkR307ptieBDYOK1zLNndQwvb9eei32XhZnGnAb+t9P/nav/xNIxZ06wtaTrZH/KpwIQ0+twbuD4NgiD7o1lsR0nnA+uTJYXbG+ooIj6UdBswQNIY4BDgf4ADyJLTA6m/Ncn+EDem8AyHWUCXiFgMLJa0TB9dqzA5Ip6DFbcV3Rf4AJgUEa+m8quA/cmmjOvIHlxUn2OUPU53DaB7intmWnZj+ncqcGR6/TmyqefCPnhT0qGNba+krsD6EXFPKhrFR0+Wa8wbwJuSjgPmAO/WU+8TsaWX+X2wLzA2It5Jcd0I7Ee271+KiAdSvX+RJeGLcu33pf79/D5wW6o3C3gvIj6QNIvsswjZswx21kfXdXQlu6/78/VtuKRLU8zvR0TfBtp4n+yzMS+tNz31+xawI9n/A8gO8PK38r0293oL4No0I7BmQ3FZ+3DiN2uapRHRJyWaW8jO8Y8E3oqIPo2sO5JsBDdD0qlko6jGXJv6eAOYEhGL0xTrhIg4vszY30v/Ls+9Lrwv/A0ovnd3UPqxoAXLIqKu1AJlDxQ5E+ibEvhIoFOJeOpy/atEDM3d3nJcC1wKnNpAnVKxwcf3QUP7qtS+LW6/Ph9EGiqT+/1FxHJ9dP5cZLMoDR1QPgYctSKAiMGSupGN7OttQ1J/Pv6ZKfzOBDwWEf3q6e+d3OtLgN9FxPjU3rAG4rR24HP8ZmWI7OEl3ydLbEuB5yV9FbILqCTtUmK1dYGFyh7ve2KufHFaVsokYDfgdD4aPT0M7COpZ+qvs6RtW7ZFK+yh7EmPnyKbtr0feAQ4QFI3ZRevHQ/cU8/6+W1Zj+wP/6J0PvfLTej/DuB7hTeSNqAJ25t+H29K2i8VndRAjKWMBX5Dw7MwpWIrdi8wMMW4DtmT7ArfGthKUiFBHk+2b/PK2c+l3A58J32+kLRtiiHvbqCTpO/kyvIXYzaljbwngY0L2yWpo6Qd6qnbFZifXp9STx1rR078ZmWKiEeBGWTTvycC35Q0g2xUdXiJVc4h++M+AXgiV34NcJZKfN0sjSRvIUuat6SyV8lGpqMlzSRLjJ+4mLCZHgKGkz129XmyaeuFwE+BiWTbOy0i6nsU7gjgP5ImRsQMsnPmj5Gd036gnnXyzgc2SBe3zQAOLGN7TyG7SHIm2ZPkzmtCfwBExOKIuCAi3i8nthLtTCOb2ZlM9ru+PH1OIDuNcEqKb0Oyazby65azn0u5HHgcmKbsq4N/pWg2N80aDCQ7wHhe0mSy0yI/aWobRe29T3YdyAVpn0wnO+1VyjCy02H3Aa+VsV3WRvx0PrMql6Zfz4yIQyscipm1A4/4zczMqohH/GZmZlXEI34zM7Mq4sRvZmZWRZz4zczMqogTv5mZWRVx4jczM6si/x/iPRPXEYOxwQAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.7383523400245697, pvalue=9.150169011786022e-18)\n", "Slope and P-value = PearsonRResult(statistic=-0.8590224009013778, pvalue=2.4724166430965153e-22)\n", "Slope and P-value = PearsonRResult(statistic=-0.804761838297989, pvalue=0.015987237462269023)\n", "Slope and P-value = PearsonRResult(statistic=-0.8251406726360351, pvalue=8.639409672812017e-23)\n", "Slope and P-value = PearsonRResult(statistic=-0.8960911181757188, pvalue=1.9438266180561655e-32)\n", "Slope and P-value = PearsonRResult(statistic=-0.9571321197158901, pvalue=4.1896779393770256e-42)\n", "Slope and P-value = PearsonRResult(statistic=-0.8598104020031041, pvalue=2.3032253884706526e-30)\n", "Slope and P-value = PearsonRResult(statistic=0.17234549688642783, pvalue=0.08641085291486883)\n", "Slope and P-value = PearsonRResult(statistic=-0.9528091675663062, pvalue=2.1259563831851993e-13)\n", "Slope and P-value = PearsonRResult(statistic=-0.8409374005393568, pvalue=6.9570117132383115e-28)\n", "1 308\n", "0 5\n", "Name: FreqHousingMove, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=nan, pvalue=nan)\n", "Slope and P-value = PearsonRResult(statistic=-0.8122341108677402, pvalue=2.399201432265239e-05)\n", "Slope and P-value = PearsonRResult(statistic=-0.8261123854701152, pvalue=0.0002713480001791625)\n", "Slope and P-value = PearsonRResult(statistic=-0.8492054636107931, pvalue=4.7091844596325044e-23)\n", "Slope and P-value = PearsonRResult(statistic=-0.6578701861880946, pvalue=3.3529376517360794e-11)\n", "Slope and P-value = PearsonRResult(statistic=-0.9848683548601268, pvalue=0.0022293326756648935)\n", "Slope and P-value = PearsonRResult(statistic=-0.8119729556897999, pvalue=6.045502102524444e-15)\n", "Slope and P-value = PearsonRResult(statistic=nan, pvalue=nan)\n", "Slope and P-value = PearsonRResult(statistic=-0.49570613518217194, pvalue=1.5722072055295422e-07)\n", "Slope and P-value = PearsonRResult(statistic=-0.9081820566546839, pvalue=4.6752892641237177e-07)\n", "1 178\n", "0 5\n", "Name: FreqHousingMove, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.9920798708845633, pvalue=2.421979295713789e-10)\n", "Slope and P-value = PearsonRResult(statistic=-0.9354248196542257, pvalue=4.773609143884169e-46)\n", "Slope and P-value = PearsonRResult(statistic=-0.8412596481720662, pvalue=8.39128307226357e-23)\n", "Slope and P-value = PearsonRResult(statistic=0.44580086672532093, pvalue=3.3494430126164492e-06)\n", "Slope and P-value = PearsonRResult(statistic=0.41365651913482593, pvalue=1.887545284553647e-05)\n", "Slope and P-value = PearsonRResult(statistic=nan, pvalue=nan)\n", "Slope and P-value = PearsonRResult(statistic=-0.904229939951475, pvalue=0.000814132848194051)\n", "Slope and P-value = PearsonRResult(statistic=-0.9919025977839616, pvalue=1.862664518941179e-08)\n", "Slope and P-value = PearsonRResult(statistic=-0.6928549666419951, pvalue=1.3873642648859557e-15)\n", "Slope and P-value = PearsonRResult(statistic=0.5559698313141714, pvalue=1.915183554799317e-09)\n", "1 180\n", "0 5\n", "Name: FreqHousingMove, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
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PearsonRResult(statistic=-0.33999389009882297, pvalue=0.01937438607632704)\n", "Slope and P-value = PearsonRResult(statistic=-0.6488445012434314, pvalue=0.012057165198183444)\n", "1 203\n", "0 5\n", "Name: FreqHousingMove, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.6882831292518795, pvalue=0.013336871843902054)\n", "Slope and P-value = PearsonRResult(statistic=-0.9510139142028637, pvalue=3.5602337735435754e-28)\n", "Slope and P-value = PearsonRResult(statistic=-0.9240874809564849, pvalue=1.0026259199375734e-42)\n", "Slope and P-value = PearsonRResult(statistic=-0.7454619843303005, pvalue=6.443567596281641e-07)\n", "Slope and P-value = PearsonRResult(statistic=-0.22678526536740837, pvalue=0.38139854156476816)\n", "Slope and P-value = PearsonRResult(statistic=-0.8589087920873444, pvalue=3.79768376731494e-21)\n", "Slope and P-value = PearsonRResult(statistic=-0.9877753871753321, pvalue=6.571840157250055e-07)\n", "Slope and P-value = PearsonRResult(statistic=0.1723454968864279, pvalue=0.08641085291486883)\n", "Slope and P-value = PearsonRResult(statistic=-0.8132538069158717, pvalue=1.302869187900127e-05)\n", "Slope and P-value = PearsonRResult(statistic=-0.7936056035722974, pvalue=6.261523970663321e-12)\n", "1 190\n", "0 5\n", "Name: FreqHousingMove, dtype: int64\n" ] }, { "data": { "image/png": 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tf1ai/oXAoojYPSL2AB7NtTMrIvYHlgJnAvsDnwHOkbRXqrcjMCxt+y7wrXJORM6mwGeB7wIPAL8DdgV2l9SnuHJEDIuI6oio7rRB9wp3ZWZmHVlL9zT0A+6LiKUR8R7ZRW11fB4YImk6MA7oCmyT1o2JiLcjYikwKu0772BgZETURcRrrLq4F/sQeCgt1wLjI+KjtFxVov4A4JrCi4h4Jy3WAXen5X7APRHxfkQsTvEdlNa9GhGT0vJfSsTdmAciIlJ8r0dEbUSsAGbXE6+ZmVmTtPScBrVAeydExLMfK5T2B6KobvHr+sqKfZQuwgArgA8AImKFpFLnS/W0uyw3j6Gh81Bp3F2L1n1QHGvu9Ro7Z8XMzNqflu5pmAgcJamrpG7A6n7by8PAeZIEkOviB/icpB6S1geOBSYVbTsBOEVSJ0k9yYY3msMjwLcLLyRtWqLOBOBYSRuk4ZTjgMfSum0k9U3Lp5Kds2KvS9pZ0jppWzMzs1bXoneiETFF0v3ADOBlsk8crM7su58DvwdmpsRhLnBkWjcR+DOwPXBb+oRD3j1kY/+1wHPA+NWII+9S4Jr00ck64Kdkww8rRcS0NDFxciq6MSKeSp8MmQOcIemPwPPAdSX2MQQYDbwKzAK6NUfgu3+yOzX+1j4zMyuTVvXEt9AOpG4RsTh9KmACMCgipjXzPgYC1RHx7cbq2irV1dVRU1P2J0fNzKwDkDQ1Ikp+cKE1xryHpS8p6gqMaO6EwczMzFpHiycNEXFa/rWka4ADi6r1Juuaz7syIm4ucx/DgeFNDNHMzMzK0Oqz6wvf9GhmZmbtyxr77AkzMzNbszhpMDMzs7I4aTAzM7OyOGkwMzOzsjhpMDMzs7I4aTAzM7Oy+IFGHVjt/EVUDXmwrcOw1TTXXwVuZq3EPQ1mZmZWFicNZmZmVpYOnTRIukbSdElPS1qalqdLOrHM7RdXuL/+kkY3sH4rSXc10kZVeqJmo+2ZmZk1pw49p6HwldbpEdWjI6JPfr2kThFR14rxvAaUlbCYmZm1trWip0HShZKekTRG0khJF6xGW/0ljZV0G1Cbyu6VNFXSbEmDiur/VtI0Sf+QtEUqGyepOi1vLmluif0ckuvZeErSRkW9CJ0k/UbSFEkzJX29kbj3k/R4autxSTs29RyYmZmV0u57GtLF+QRgL7LjmQZMXc1m9wN2i4iX0uuzImKhpPWBKZLujoi3gQ2BaRHxfUkXARcD3y5zHxcA50bEJEndgGVF678GLIqIfSWtB0yS9AgQ9bT3DHBwRCyXNAD4Jdl5+ZiU9AwC6LTxFmWGamZmthYkDUA/4L6IWAog6YFmaHNyLmEAOF/ScWl5a7JHeb8NrADuSOV/AUZVsI9JwBWSbgVGRcQ8Sfn1nwf2yM2v6J72+1w97XUHRkjqTZZYdClVKSKGAcMA1uvZu74ExMzM7D+sDcMTarxKxd5f2bjUHxgA9I2IPYGngK71bFe4CC9n1bktWTcihgJnA+sDT0jaqaiKgPMiok/62TYiHmkg5p8DYyNiN+CoBmI0MzNrkrUhaZgIHCWpa+rmb+5vuukOvBMRS9KF/TO5deuwauLiaSkWgLnAPmm55MRGSdtFRG1EXAbUAMVJw8PANyV1SfV3kLRhI3HOT8sDGzsoMzOzSrX7pCEipgD3AzPIhgdqgEXNuIuHgM6SZpLdzT+RW/c+sKukqcBngZ+l8svJLviPA5vX0+5gSbMkzQCWAv9btP5G4GlgWpoc+UcaHk76NfArSZOATmUfnZmZWZkU0f6HtSV1i4jFkjYAJgCDImJaW8e1pquuro6ampq2DsPMzNYgkqZGRHWpdWvDREiAYZJ2IRvHH+GEwczMrPmtFUlDRJyWfy3pGuDAomq9geeLyq6MiJtbMjYzM7O1xVqRNBQrfNOjmZmZNZ92PxHSzMzMWoeTBjMzMyuLkwYzMzMri5MGMzMzK4uTBjMzMyuLkwYzMzMri5MGMzMzK8ta+T0NVp7a+YuoGvJgW4dhTTB3aHM/l83MrHHuaTAzM7OyOGkwMzOzsjhpACR1lvSWpF+VUbe/pANyr78h6atN3G8fSV8qc5+jm7IPMzOz5uKkIfN54FngJElqpG5/YGXSEBHXR8QtTdxvH6DRpMHMzGxNsFYkDZIulPSMpDGSRkq6oMImTgWuBF4BPpNr94uSpkmaIekfkqqAbwDflTRd0kGSLinsT9I4SZdJmizpOUkHpfKukm6WVCvpKUmHSloX+BlwcmrrZEn7SXo81Xlc0o4ljnVDSX+SNCXVOyaVV0l6LMU7Ld8bUrT9IEk1kmrqliyq8DSZmVlH1u4/PSGpGjgB2IvseKYBUyvYfn3gMODrwCZkCcQ/JW0B3AAcHBEvSeoREQslXQ8sjojL0/aHFTXZOSL2S8MOFwMDgHMBImJ3STsBjwA7ABcB1RHx7dTWxml/yyUNAH6Zji3vx8CjEXGWpE2AyZL+DrwBfC4ilknqDYwEqouPNyKGAcMA1uvZO8o9T2ZmZu0+aQD6AfdFxFIASQ9UuP2RwNiIWCLpbuBCSd8l63GYEBEvAUTEwjLbG5V+TwWqcjFeldp5RtLLZElDse7AiHTRD6BLiTqfB47O9aZ0BbYBXgOultQHqKunfTMzsyZbG5KGxuYgNOZU4EBJc9PrzYBDU7tNuRP/IP2uY9X5LTfGn5MlMMeloZBxJeoIOCEinv1YoXQJ8DqwJ9mw07KKojYzM2vE2jCnYSJwVJo30A0o+1tv0nBAP2CbiKiKiCqyoYRTgX8Ch0jaNtXtkTZ7D9iowhgnAKendnYg6xl4tkRb3YH5aXlgPW09DJxXmLApaa/ctgsiYgXwFaBThTGamZk1qN0nDRExBbgfmEE2NFADlDvD73iy+QEf5MruA44G3gUGAaMkzQDuSOsfAI4rTIQscz/XAp0k1aZ2BqZ9jgV2KUyEBH4N/ErSJOq/6P+cbNhipqRZ6XVhH2dIeoJsaOL9MmMzMzMriyLa/1w4Sd0iYrGkDcju6gdFxLS2jmtNV11dHTU1NW0dhpmZrUEkTY2I/5hID2vHnAaAYZJ2IZsUOMIJg5mZWfNbK5KGiDgt/1rSNcCBRdV6A88XlV0ZETe3ZGxmZmZri7UiaSgWEee2dQxmZmZrm3Y/EdLMzMxah5MGMzMzK4uTBjMzMyuLkwYzMzMri5MGMzMzK4uTBjMzMyuLkwYzMzMry1r5PQ1Wntr5i6ga8mBbh2EVmDu07OexmZk1O/c0mJmZWVmcNJiZmVlZ2kXSIOkCSc9ImiVphqSvtvD+Bkq6uhna6S/pgCZsVy3pD6u7fzMzs+a0xs9pkPQN4HPAfhHxrqTuwLEVbN85Ipa3VHyN6A8sBh4vd4MUbw3gZ1abmdkapVV6GiRdmHoKxkgaKemCCjb/EfCtiHgXICIWRcSI1O5FkqakHohhkpTKx0n6paTxwHfS68skTZb0nKSDUr2ukm6WVCvpKUmH5va7taSHJD0r6eLcsdwraaqk2ZIG5cq/KGla6gn5h6Qq4BvAdyVNl3SQpC0k3Z1iniLpwLTtJSn+R4BbUg/F6Ny6C3L7mSWpKv08I+nGVHarpAGSJkl6XtJ+9fwtBkmqkVRTt2RRBX8GMzPr6Fq8p0FSNXACsFfa3zRgapnbbgRsFBEv1FPl6oj4War7Z+BI4IG0bpOIOCStOwroHBH7SfoScDEwADgXICJ2l7QT8IikHdL2+wG7AUuAKZIeTD0AZ0XEQknrp/K7yZKvG4CDI+IlST1SneuBxRFxeYrjNuB3ETFR0jbAw8DOaX/7AP0iYqmk/uWcH2B74MvAIGAKcBrQDziaLNk6tniDiBgGDANYr2fvKHM/ZmZmrTI80Q+4LyKWAkh6oJH6eQIaurAdKumHwAZAD2A2q5KGO4rqjkq/pwJVudiuAoiIZyS9DBSShjER8XaKeVSqWwOcL+m4VGdroDewBTAhIl5KbS2sJ94BwC6pQwRg45QYAdxfOEcVeCkialOMs4F/RERIqs0do5mZWbNojaRBjVcpLc1heF/SpyPixY81KnUFrgWqI+JVSZcAXXNV3i9q7oP0u45Vx91QbMXJSqQegAFA34hYImlc2mdjyU3BOmnbjyUHKYkojrdgOR8fRsof4we55RW51ytoB/NVzMysfWmNOQ0TgaPS/IFuQKXfTvMr4BpJGwNI2jjNJShcPN9K7Z7YhNgmAKendncAtgGeTes+J6lHGoY4FpgEdAfeSQnDTsBnUt1/AodI2ja11SOVvwcUehIAHgG+XXghqU8ZMc4F9k719wa2regIzczMmkmLJw0RMQW4H5hBNkRQA1QyA+86YCzZ/IFZwHhgSUT8m2weQS1wL9mYfqWuBTql7vw7gIERUbhbnwj8GZgO3J3mMzwEdJY0E/g58EQ6xjfJ5hWMkjSDVUMjDwDHFSZCAucD1ZJmSnqabKJkY+4GekiaDnwTeK4Jx2lmZrbaFNHyc+EkdYuIxZI2ILu7HxQR01p8x9ag6urqqKnxJzvNzGwVSVMjorrUutYa9x4maReyIYURThjMzMzan1ZJGiLitPxrSdcABxZV6w08X1R2ZUTc3JKxmZmZWXnaZIZ9RJzbFvs1MzOzpmsXz54wMzOztuekwczMzMripMHMzMzK4qTBzMzMyuKkwczMzMripMHMzMzK4qTBzMzMyuInIXZgtfMXUTXkwbYOw3LmDq30eW5mZq3HPQ1mZmZWFicNZmZmVpZWSxokXSDpGUmzJM2Q9NVG6g+XdGJaHiep5BO3cvUvkXRBE+I6WtKQCrf5m6RNKt1XiXZKHldTYjIzM2tprTKnQdI3gM8B+0XEu5K6A8e2xr4bExH3A/dXuM2Xyq0rqXNELG/pmMzMzFpa2T0Nki5MPQVjJI2s8K7+R8C3IuJdgIhYFBEjUrv7SBovaaqkhyX1bCSOxbnlEyUNL1Gnj6QnJM2UdI+kTVP5+ZKeTuW3p7KBkq5Oy8MlXSdprKQXJR0i6U+S5uT3I2mupM3T8ldTezMk/TnXzhWSxgKX1RdP8l+SHk89MPvVE9OJxccvqX86b3+V9JykoZJOlzRZUq2k7eo5f4Mk1UiqqVuyqKFTbWZm9jFlJQ2pC/0EYC/geKDBoYKibTcCNoqIF0qs6wJcBZwYEfsAfwJ+UW7bDbgF+O+I2AOoBS5O5UOAvVL5N+rZdlPgs8B3gQeA3wG7ArtL6lMU/67Aj4HPRsSewHdyq3cABkTE9xuIB2DDiDgA+BbZ8VeisM/dga8AO0TEfsCNwHmlNoiIYRFRHRHVnTboXuHuzMysIyu3p6EfcF9ELI2I98gupuUSEPWs2xHYDRgjaTrwE6BXBW3/586yoY9NImJ8KhoBHJyWZwK3SvovoL4hgwciIsgu7q9HRG1ErABmA1VFdT8L3BURbwFExMLcujsjoq6ReABGpm0nABtXOFdiSkQsiIgPgBeAR1J5bYlYzczMVku5cxrU1B2kOQzvS/p0RLxYot3ZEdG3kiZzy10rDOcIsgv20cCFqaeg2Afp94rccuF18flqKCF6v8yYircvfr2clNxJErBuiVgL8eVj93dwmJlZsyq3p2EicJSkrpK6kV18K/Er4BpJGwNI2ljSIOBZYAtJfVN5l3ou5HmvS9pZ0jrAccUrI2IR8I6kg1LRV4Dxqf7WETEW+CGwCdCtwuMo9g/gJEmbpfh7lBtPrsrJadt+wKJUP28usE9aPgbospoxm5mZNUlZd6MRMUXS/cAM4GWgBqhkFt11ZBfoKZI+Aj4CfhsRH6ZJfn9I3fidgd+TDQXUZwgwGngVmEXpC/8ZwPWSNgBeBM4EOgF/SfsR8LuI+Hd28940ETFb0i/IkpI64ClgYJnxFLwj6XFgY+CsEtveANwnaTJZklJuD0ajdv9kd2r8DYRmZlYmZcP3ZVSUukXE4nThmwAMiohpLRqdtajq6uqoqalp6zDMzGwNImlqRJT8wEMl497DJO1CNo9ghBMGMzOzjqXspCEiTsu/lnQNcGBRtd7A80VlV0bEzU0Lz8zMzNYUTZ5hHxHnNmcgZmZmtmbzA6vMzMysLE4azMzMrCxOGszMzKwsThrMzMysLE4azMzMrCxOGszMzKwsfqhRB1Y7fxFVQx5s6zDWSnP99dxmthZyT4OZmZmVxUmDmZmZlWWNShokDU9PvcyXLa6wjXGSSj5oY01U6fGZmZm1lTUqaWhJkjq1dQxmZmbtWbMnDZIulPSMpDGSRkq6oJna/bOkY3Kvb5V0tKT1Jd0uaaakO4D1c3UWS/qZpCeBvpK+J2lW+hncWMyStpP0kKSpkh6TtFMqHy7pD5Iel/RioXdEmd+k9mslnZzK+0uaIOkeSU9Lul7SOrn9/0LSDElPSNoylR0l6UlJT0n6e658ixTnNEl/lPSypM3TuntTrLMlDWqO825mZlbQrElDGhY4AdgLOB5oyjDBbyRNL/zkym8Ezkz76Q4cAPwN+CawJCL2AH4B7JPbZkNgVkTsDyxN2+8PfAY4R9JejcQ8DDgvIvYBLgCuza3rCfQDjgSGprLjgT7AnsCAdCw907r9gO8DuwPbpbqFGJ+IiD2BCcA5qXwi8JmI2Au4HfhhKr8YeDQi9gbuAbbJxXRWirUaOF/SZhSRNEhSjaSauiWLilebmZnVq7k/ctkPuC8ilgJIeqAJbfwgIu4qvCiM+UfEeEnXSPoE2QX37ohYLulg4A+pzkxJM3Nt1QF352K7JyLeT+2OAg4iS5z+I2ZJ3cgSkzslFdpbL9f2vRGxAni60AuQ9jEyIuqA1yWNB/YF3gUmR8SLqe2Rqe5dwIfA6LT9VOBzabkXcEdKOtYFXsrt47h0vA9JeicX0/mSjkvLW5M9qvzt/MmNiGFkyRDr9ewdmJmZlam5kwY1XmW1/Bk4HTgFOCtXXt/Fb1m6gEP9sdVXvg7w74joU8/6D0q00dDxF8dYeP1RRBSW61j1N7kKuCIi7pfUH7ikoX2kOgOAvhGxRNI4oGsD8ZiZmVWkuec0TASOktQ13ak39zfcDAcGA0TE7FQ2gSyRQNJuwB71bDsBOFbSBpI2JLtbf6y+mCPiXeAlSV9ObUvSno3ENwE4WVInSVsABwOT07r9JG2b5jKcnPbbkO7A/LR8Rq58InBSiunzwKa5+u+khGEnsiEYMzOzZtOsSUNETAHuB2YAo4AaoNkGziPidWAOcHOu+DqgWxqW+CGrLtLF204jSzomA08CN0bEU43EfDrwNUkzgNnAMcXtFrkHmJnaehT4YUT8X1r3T7K5D7PIhhruaaStS8iGRh4D3sqV/xT4vKRpwOHAAuA94CGgczoPPweeaKR9MzOzimhVz3gzNSh1i4jFkjYgu/MelC7YzdH2BkAtsHdENFsy0pIxp/b7AxdExJHN0NZ6QF2az9EXuK6BIZQGVVdXR01NzeqGZGZmaxFJUyOi5AcZWuLZE8Mk7UI2nj6iGROGAcCfyMb5m3vaf4vE3EK2Af6ahjk+ZNWnLczMzFpUs/c0/McOpGuAA4uKewPPF5VdGRE3Y63GPQ1mZlastXsaPiYizm3pfZiZmVnL6zBfI21mZmarx0mDmZmZlcVJg5mZmZXFSYOZmZmVxUmDmZmZlcVJg5mZmZXFSYOZmZmVpcW/p8HWXLXzF1E15MG2DqNdmju0uZ/FZma25nNPg5mZmZXFSYOZmZmVpdGkQVKdpOmSZkm6Mz0JsiySBkq6up51jzeybZWk03KvqyX9odx957abK2nzSrcramNwOcctafHq7CfXzuPpd5WkWWm5v6TRzdG+mZlZU5TT07A0IvpExG5kT1X8Rn6lpE5N2XFEHNBIlSpgZdIQETURcX5T9tUMBgNlJ0urq4xzY2Zm1uoqHZ54DNg+3fWOlXQbUCupq6SbJdVKekrSoblttpb0kKRnJV1cKCzclSvzm9STUSvp5FRlKHBQ6uX4bv5OW1K33P5mSjqhkoOQtJ+kx1Osj0vaMZV3knR5rt3zJJ0PbAWMlTQ21Ts11Zkl6bKitn8raZqkf0jaIpWdI2mKpBmS7i70WkjaUtI9qXyGpAPy56aB+C+RdEHu9azUK7GhpAdTW7Ny5zK/7SBJNZJq6pY09xPGzcxsbVb2pyckdQYOBx5KRfsBu0XES5K+DxARu0vaCXhE0g75esASYIqkByMi/zzm44E+wJ7A5qnOBGAIcEFEHJn23z+3zYXAoojYPa3btOwjzjwDHBwRyyUNAH4JnAAMArYF9krrekTEQknfAw6NiLckbQVcBuwDvJOO9diIuBfYEJgWEd+XdBFwMfBtYFRE3JBivRT4GnAV8AdgfEQcl3psulV4HMW+CLwWEUekfXUvrhARw4BhAOv17N2yz0U3M7O1Sjk9DetLmg7UAK8AN6XyyRHxUlruB/wZICKeAV4GCknDmIh4OyKWAqNS3bx+wMiIqIuI14HxwL6NxDQAuKbwIiLeKeM48roDd6b5Ar8Dds21e31ELE/tLiyx7b7AuIh4M9W7FTg4rVsB3JGW/8KqY91N0mOSaoHTc/v7LHBd2lddRKzurX8tMEDSZZIOaob2zMzMViqnp2FpRPTJF0gCeD9f1MD2xXezxa8b2rY+KtFOJX4OjE13+FXAuArarSTeQlvDgWMjYoakgUD/CtooZTkfT/i6AkTEc5L2Ab4E/ErSIxHxs9Xcl5mZGdB8H7mcQHYHTRqW2AZ4Nq37nKQektYHjgUmldj25DSfYAuyu/bJwHvARvXs7xGybn/SPisdnugOzE/LA4va/UYaikFSj1Sej+VJ4BBJm6chhVPJekcgO58npuXTgIlpeSNggaQupPOU/AP4ZtpXJ0kblxn/XGDvtN3eZEMqpKGTJRHxF+DyQh0zM7Pm0FxJw7VAp9T9fgcwMCI+SOsmkg1dTAfuLprPAHAPMBOYATwK/DAi/i+VLU+T+r5btM2lwKZpst8M4FAaNlPSvPRzBfBrsjvxSUD+0x83kg3BzEztFj69MQz4X0ljI2IB8D/A2BTztIi4L9V7H9hV0lSyoYfCXf6FZMnGGLL5FAXfAQ5N520qq4YtGnM30CMNG30TeC6V7w5MTuU/JjtPZmZmzUIRngvXUVVXV0dNTXEOZ2ZmHZmkqRFRXWqdvxHSzMzMyrLWPLBK0pPAekXFX4mI2raIx8zMbG2z1iQNEbF/W8dgZma2NvPwhJmZmZXFSYOZmZmVxUmDmZmZlcVJg5mZmZXFSYOZmZmVxUmDmZmZlcVJg5mZmZVlrfmeBqtc7fxFVA15sK3DWGPNHXpEW4dgZrZGcU+DmZmZlcVJg5mZmZXFSUOFJHWW9JakX61mO1tJuquJ2w6XdGJaHiep5NPIzMzMmpOThsp9HngWOEmSmtpIRLwWESc2X1hmZmYtq8MlDZIulPSMpDGSRkq6oMImTgWuBF4BPpNr90xJz0kaL+kGSVen8pW9Aun14vS7StKstNxJ0uWSaiXNlHReKt8ntTdV0sOSejZybNdJqpE0W9JP66kzKNWpqVuyqMJDNzOzjqxDfXoideOfAOxFduzTgKkVbL8+cBjwdWATsgTin+li/lNgH2ARMBZ4qoLQBgHbAntFxHJJPSR1Aa4CjomINyWdDPwCOKuBdn4cEQsldQL+IWmPiJiZrxARw4BhAOv17B0VxGhmZh1cR+tp6AfcFxFLI+I94IEKtz8SGBsRS4C7gePSBXp/YFxEvBkRHwJ3VNjuAOD6iFgOEBELgR2B3YAxkqYDPwF6NdLOSZKmkSUsuwK7VBiHmZlZvTpUTwPQ5DkIyanAgZLmptebAYem5fru2peTkrM0B2LdeuIq3l7A7IjoW05gkrYFLgD2jYh3JA0HupazrZmZWTk6Wk/DROAoSV0ldQPK/vYeSRuT9VRsExFVEVEFnEuWSDwJ9Je0WRpW+HJu07lkwxYAxwBdSjT/CPANSZ3TvnqQTbbcQlLfVNZF0q4NhLgx8D6wSNKWwOHlHpuZmVk5OlTSEBFTgPuBGcAooIZsDkI5jgcejYgPcmX3AUcDC4FLgH8CfyebK1FwA3CIpMlkwxjvl2j7RrKJlTMlzQBOS8McJwKXpbLpwAENHNsMsmGJ2cCfgEllHpeZmVlZFNGx5sJJ6hYRiyVtAEwABkXEtMa2q3AfA4HqiPh2c7bb3Kqrq6OmpqatwzAzszWIpKkRUfL7fzranAaAYZJ2IRvvH9HcCYOZmdnaqsMlDRFxWv61pGuAA4uq9QaeLyq7MiJuLnMfw4HhTQzRzMxsjdThkoZiEXFuW8dgZmbWHnT4pMHMzJrmo48+Yt68eSxbtqytQ7Em6Nq1K7169aJLl1If6ivNSYOZmTXJvHnz2GijjaiqqmI1HsVjbSAiePvtt5k3bx7bbrtt2dt1qI9cmplZ81m2bBmbbbaZE4Z2SBKbbbZZxb1EThrMzKzJnDC0X0352zlpMDMzs7J4ToOZmTWLqiEPNmt7c4eW903/99xzD8cffzxz5sxhp512AmDcuHFcfvnljB49emW9gQMHcuSRR3LiiSfSv39/FixYQNeuXVl33XW54YYb6NOnDwCLFi3ivPPOY9Kk7It1DzzwQK666iq6d+8OwHPPPcfgwYN57rnn6NKlC7vvvjtXXXUVW265ZZOP9c477+SSSy5hzpw5TJ48merqkt+txEMPPcR3vvMd6urqOPvssxkyZAgACxcu5OSTT2bu3LlUVVXx17/+lU033ZTa2lp++9vfMnz48CbHluekoQOrnb+o2f+Rt0fl/sdkZmumkSNH0q9fP26//XYuueSSsre79dZbqa6u5uabb+YHP/gBY8aMAeBrX/sau+22G7fccgsAF198MWeffTZ33nkny5Yt44gjjuCKK67gqKOOAmDs2LG8+eabq5U07LbbbowaNYqvf/3r9dapq6vj3HPPZcyYMfTq1Yt9992Xo48+ml122YWhQ4dy2GGHMWTIEIYOHcrQoUO57LLL2H333Zk3bx6vvPIK22yzTZPjK/DwhJmZtVuLFy9m0qRJ3HTTTdx+++1NaqNv377Mnz8fgH/9619MnTqVCy+8cOX6iy66iJqaGl544QVuu+02+vbtuzJhADj00EPZbbfdVus4dt55Z3bccccG60yePJntt9+eT3/606y77rqccsop3HfffQDcd999nHHGGQCcccYZ3HvvvSu3O+qoo5p8boo5aTAzs3br3nvv5Ytf/CI77LADPXr0YNq0yp8M8NBDD3HssccC8PTTT9OnTx86deq0cn2nTp3o06cPs2fPZtasWeyzzz71tLTKe++9R58+fUr+PP300xXHCDB//ny23nrrla979eq1Mtl5/fXX6dmzJwA9e/bkjTfeWFmvurqaxx57rEn7LObhCTMza7dGjhzJ4MGDATjllFMYOXIke++9d72fDMiXn3766bz//vvU1dWtTDYiouS29ZXXZ6ONNmL69OnlH0gZSj1gspyYPvGJT/Daa681SwxrfdKQe7bEusC2wLNp1aURcVeu3uMRUe+jpyWNAy6ICD8W0sxsDfD222/z6KOPMmvWLCRRV1eHJH7961+z2Wab8c4773ys/sKFC9l8881Xvr711lvZc889GTJkCOeeey6jRo1i11135amnnmLFihWss07WGb9ixQpmzJjBzjvvzBtvvMH48eMbje29997joIMOKrnutttuY5dddqn4eHv16sWrr7668vW8efPYaqutANhyyy1ZsGABPXv2ZMGCBXziE59YWW/ZsmWsv/76Fe+vlLV+eCIizo2IPsCXgBciok/6uQtAUqdUr96EwczM1jx33XUXX/3qV3n55ZeZO3cur776Kttuuy0TJ06kd+/evPbaa8yZMweAl19+mRkzZqz8hERBly5duPTSS3niiSeYM2cO22+/PXvttReXXnrpyjqXXnope++9N9tvvz2nnXYajz/+OA8+uGoS+UMPPURtbe3H2i30NJT6aUrCALDvvvvy/PPP89JLL/Hhhx9y++23c/TRRwNw9NFHM2LECABGjBjBMcccs3K75557brXnXBS0m54GSRcCpwOvAm8BUyPi8ia21R+4GFgA9AF2kbQ4Irql9T8EvgKsAP43IoakTb8s6VpgE+BrEfGYpK7AdUA1sBz4XkSMTcnIZcAXgABuiIirJB0GXE527qcA34yIDyTtC1wJbAh8ABwGLKmnjblAdUS8JakauDwi+ks6JLVBqn9wRLxXdOyDgEEAnTbeoimnz8yspNb+JNLIkSNXfuSw4IQTTuC2227joIMO4i9/+Qtnnnkmy5Yto0uXLtx4440rPzaZt/766/P973+fyy+/nJtuuombbrqJ8847j+23356IoG/fvtx0000r644ePZrBgwczePBgunTpwh577MGVV175H+1W4p577uG8887jzTff5IgjjqBPnz48/PDDvPbaa5x99tn87W9/o3Pnzlx99dV84QtfoK6ujrPOOotdd90VgCFDhnDSSSdx0003sc0223DnnXeubHvs2LEccUTz/G1UaoxkTZMujDcCfckuttOAP1aSNEiqAkZHxG4paXgQ2C0iXkrrF0dEN0mHAxcCAyJiiaQeEbEwDU9MjYjvS/oSWXIwQNL3UztnStoJeATYATgTGACcHBHLJfUgSwKeBw6LiOck3ZKO5VrgmVR3iqSNU91zittIscyldNLwADA0IiZJ6gYsi4jl9Z2T9Xr2jp5n/L7cU7jW8kcuzZpmzpw57Lzzzm0dhjXggw8+4JBDDmHixIl07vyf/QSl/oaSpkZEyS+KaC/DE/2A+yJiabpzfqAZ2pxcSBiKDABujoglABGxMLduVPo9FajKxfbnVPcZ4GWypGEAcH3hop3a2RF4KSKeS9uOAA5O5QsiYkqq+27arlQbDZkEXCHpfGCThhIGMzNb+73yyisMHTq0ZMLQFO0laWiJLzd/v4F91df98kH6XceqoZ36YivVTiV1Gypfzqq/XddCYUQMBc4G1geeSD0fZmbWQfXu3Zv+/fs3W3vtJWmYCBwlqWvqdm/J/uRHgLMkbQCQhhUaMoFsrgWSdgC2IfuExiPANyR1zrXzDFAlafu07VeA8al8qzSvAUkbpe1KtQEwFyh8UPiEQiCStouI2oi4DKgBnDSYWYtqD0PcVlpT/nbtYiJkGue/H5hB1v1fAyxqoX09JKkPUCPpQ+BvwI8a2ORa4HpJtWQ9AAPTxMYbyYYpZkr6iGwS49WSzgTuTInAFLLhhw8lnQxcJWl9YCnZ0MR/tAFcDfwUuEnSj4Anc7EMlnQoWU/I08D/NnSsu3+yOzUezzezJuratStvv/22H4/dDkUEb7/9Nl27dm28ck67mAgJIKlbRCxOPQATgEERUflXf9lK1dXVUVPjr50ws6b56KOPmDdvHsuWLWvrUKwJunbtSq9evejSpcvHyhuaCNkuehqSYZJ2IRvDH+GEwcysbXXp0oVtt922rcOwVtRukoaIOC3/OvdNj3m9yT7SmHdlRNzckrGZmZl1BO0maSgWEee2dQxmZmYdSXv59ISZmZm1sXYzEdKan6T3WPUAL2sdm5N9Dbq1Hp/z1udz3vqa85x/KiJKPmeg3Q5PWLN4tr4ZstYyJNX4nLcun/PW53Pe+lrrnHt4wszMzMripMHMzMzK4qShYxvW1gF0QD7nrc/nvPX5nLe+VjnnnghpZmZmZXFPg5mZmZXFSYOZmZmVxUlDByXpi5KelfQvSUPaOp61kaStJY2VNEfSbEnfSeWXSJovaXr6+VJbx7o2kTRXUm06tzWprIekMZKeT783bes41xaSdsy9l6dLelfSYL/Pm5ekP0l6Q9KsXFm972tJ/5P+f39W0heaLQ7Paeh4JHUCngM+B8wje0T3qRHxdJsGtpaR1BPoGRHTJG0ETAWOBU4CFkfE5W0Z39pK0lygOiLeypX9GlgYEUNTkrxpRPx3W8W4tkr/t8wH9gfOxO/zZiPpYGAxcEtE7JbKSr6v08MdRwL7AVsBfwd2iIi61Y3DPQ0d037AvyLixYj4ELgdOKaNY1rrRMSCwtNYI+I9YA7wybaNqsM6BhiRlkeQJW/W/A4DXoiIl9s6kLVNREwAFhYV1/e+Pga4PSI+iIiXgH+R/b+/2pw0dEyfBF7NvZ6HL2YtSlIVsBfwZCr6tqSZqcvRXeXNK4BHJE2VNCiVbRkRCyBL5oBPtFl0a7dTyO5wC/w+b1n1va9b7P94Jw0dk0qUeZyqhUjqBtwNDI6Id4HrgO2APsAC4LdtF91a6cCI2Bs4HDg3detaC5O0LnA0cGcq8vu87bTY//FOGjqmecDWude9gNfaKJa1mqQuZAnDrRExCiAiXo+IuohYAdxAM3UbWiYiXku/3wDuITu/r6c5JoW5Jm+0XYRrrcOBaRHxOvh93krqe1+32P/xTho6pilAb0nbpruDU4D72zimtY4kATcBcyLiilx5z1y144BZxdta00jaME06RdKGwOfJzu/9wBmp2hnAfW0T4VrtVHJDE36ft4r63tf3A6dIWk/StkBvYHJz7NCfnuig0seffg90Av4UEb9o24jWPpL6AY8BtcCKVPwjsv9c+5B1F84Fvl4Yl7TVI+nTZL0LkD3F97aI+IWkzYC/AtsArwBfjojiSWXWRJI2IBtD/3RELEplf8bv82YjaSTQn+wR2K8DFwP3Us/7WtKPgbOA5WRDo//bLHE4aTAzM7NyeHjCzMzMyuKkwczMzMripMHMzMzK4qTBzMzMyuKkwczMzMripMGshUmqS0/5myXpAUmbNFL/EkkXNFLn2PRQmsLrn0ka0AyxDpd04uq2U+E+B6eP7K0xJO2U/mZPSdquaN1cSY8VlU0vPH1QUrWkPzRDDFX5JxoWrbsx//dvaZK2lHSbpBfT13P/U9JxrbV/W3M4aTBreUsjok96Mt1C4NxmaPNYYOVFIyIuioi/N0O7rSo9FXEwsEYlDWTn976I2CsiXiixfiNJWwNI2jm/IiJqIuL8cneUzkFFIuLs1noqbfqSsnuBCRHx6YjYh+wL4Xq18H47t2T71jROGsxa1z9JD46RtJ2kh9Kd22OSdiquLOkcSVMkzZB0t6QNJB1A9h3/v0l3uNsVeggkHS7pr7nt+0t6IC1/Pt0hTpN0Z3omRr3SHfUv0zY1kvaW9LCkFyR9I9f+BEn3SHpa0vWS1knrTpVUm3pYLsu1uzj1jDwJ/Jjs0b1jJY1N669L+5st6adF8fw0xV9bOF+Sukm6OZXNlHRCuccrqY+kJ9J290jaNH3x2WDg7EJMJfwVODktF38TYn9JoxuJLX8O+kr6XjpPsyQNzu2ns6QRadu7Cj0yksZJqi7jPF+W3l9/l7Rf2u5FSUenOp0k/Sa9x2ZK+nqJY/0s8GFEXF8oiIiXI+KqhtpI52FcivsZSbemBARJ+0gan2J7WKu+Cnlces+NB74j6ShJTyrr8fm7pC3r+XtYa4kI//jHPy34AyxOvzuRPczni+n1P4DeaXl/4NG0fAlwQVreLNfOpcB5aXk4cGJu3XDgRLJvQXwF2DCVXwf8F9m3yE3Ilf83cFGJWFe2S/Ytft9My78DZgIbAVsAb6Ty/sAy4NPp+MakOLZKcWyRYnoUODZtE8BJuX3OBTbPve6RO1/jgD1y9QrH/y3gxrR8GfD73PabVnC8M4FD0vLPCu3k/wYltpkL7AA8nl4/RdbrMyt3TkbXF1vxOQD2IfvW0A2BbsBssieiVqV6B6Z6f2LV+2IcUF3GeT48Ld8DPAJ0AfYEpqfyQcBP0vJ6QA2wbdHxng/8roH3d8k20nlYRNYjsQ5ZwtwvxfA4sEXa5mSyb6UtHNe1RX/LwpcQng38tq3/PXf0H3f/mLW89SVNJ7sITAXGpLveA4A7080XZP/hFttN0qXAJmQXlIcb2lFELJf0EHCUpLuAI4AfAoeQXdgmpf2tS/afeGMKzySpBbpFxHvAe5KWadXcjMkR8SKs/KrbfsBHwLiIeDOV3wocTNbNXUf2EK/6nKTskdadgZ4p7plp3aj0eypwfFoeQNZdXjgH70g6srHjldQd2CQixqeiEax6QmNjFgLvSDoFmAMsqafef8SWFvPnoB9wT0S8n+IaBRxEdu5fjYhJqd5fyC7gl+fa35f6z/OHwEOpXi3wQUR8JKmW7L0I2bM59tCqeSzdyZ5T8FJ9By7pmhTzhxGxbwNtfEj23piXtpue9vtvYDeyfweQJYf5r5e+I7fcC7gj9USs21Bc1jqcNJi1vKUR0SddpEaTzWkYDvw7Ivo0su1wsjvHGZIGkt29NeaOtI+FwJSIeC91C4+JiFMrjP2D9HtFbrnwuvD/R/F30QelH81bsCwi6kqtUPZwnQuAfdPFfzjQtUQ8dbn9q0QMTT3eStwBXAMMbKBOqdjg4+egoXNV6twWt1+fjyLdopP7+0XECq2aLyCy3puGktHZwAkrA4g4V9LmZD0K9bYhqT8ff88U/mYCZkdE33r2935u+Srgioi4P7V3SQNxWivwnAazVhLZg3zOJ7soLgVekvRlyCabSdqzxGYbAQuUPWL79Fz5e2ldKeOAvYFzWHXX9gRwoKTt0/42kLTD6h3RSvspe2LqOmRdzROBJ4FDJG2ubKLfqcD4erbPH8vGZBeNRWn8+vAy9v8I8O3CC0mbUsbxpr/HO5IOSkVfaSDGUu4Bfk3DvT+lYis2ATg2xbgh2RMhC5/O2EZS4eJ6Ktm5zavkPJfyMPDN9P5C0g4phrxHga6Svpkry09cLaeNvGeBLQrHJamLpF3rqdsdmJ+Wz6injrUiJw1mrSgingJmkHVZnw58TdIMsru5Y0psciHZhWEM8Eyu/HbgByrxkcB0Bzua7II7OpW9SXZHPFLSTLKL6n9MvGyifwJDyR59/BJZV/sC4H+AsWTHOy0i6nsc9TDgfyWNjYgZZHMEZpON4U+qZ5u8S4FN00TAGcChFRzvGWQTSmeSPZHxZ2XsD4CIeC8iLouIDyuJrUQ708h6lCaT/a1vTO8TyIY+zkjx9SCbo5LftpLzXMqNwNPANGUf7/wjRT3QqbfiWLLk5CVJk8mGcv673DaK2vuQbN7LZemcTCcbqivlErIhvMeAtyo4LmshfsqlmTVZ6jK+ICKObONQzKwVuKfBzMzMyuKeBjMzMyuLexrMzMysLE4azMzMrCxOGszMzKwsThrMzMysLE4azMzMrCz/HwzePZJcV+mmAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.7837176015508249, pvalue=0.004313310640251876)\n", "Slope and P-value = PearsonRResult(statistic=-0.7611094135439926, pvalue=0.0009815581537189243)\n", "Slope and P-value = PearsonRResult(statistic=-0.5497405487807161, pvalue=0.0001968895094276593)\n", "Slope and P-value = PearsonRResult(statistic=-0.9293995245088191, pvalue=1.2259939249889205e-05)\n", "Slope and P-value = PearsonRResult(statistic=-0.7186906206030035, pvalue=0.02914281380159365)\n", "Slope and P-value = PearsonRResult(statistic=-0.9197083338975269, pvalue=1.536437626903441e-14)\n", "Slope and P-value = PearsonRResult(statistic=-0.6432410891769251, pvalue=0.061629388349267816)\n", "Slope and P-value = PearsonRResult(statistic=0.35175189903987547, pvalue=0.0003323380120479654)\n", "Slope and P-value = PearsonRResult(statistic=-0.9319859082116402, pvalue=2.8818835853219964e-10)\n", "Slope and P-value = PearsonRResult(statistic=-0.6834951631463811, pvalue=8.894380928231662e-09)\n" ] } ], "source": [ "sample = pd.merge(microbiome, poultry[['SampleID', 'FreqHousingMove','PastureTime','SampleType']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "#sample = sample[~sample.isin([np.nan, np.inf, -np.inf]).any(1)]\n", "sample = sample.drop(['Pathogen_Salmonella', 'new_Pathogen_Salmonella',\n", " 'Pathogen_Campy','new_Pathogen_Campy',\n", " 'Pathogen_Listeria','new_Pathogen_Listeria'],axis='columns')\n", "\n", "sample.FreqHousingMove.replace({'Daily': 1,'2Days':0}, regex=True, inplace=True)\n", "\n", "\n", "\n", "feces1=sample[(sample.SampleType=='Feces') & (sample.PastureTime=='Start')]\n", "feces2=sample[(sample.SampleType=='Feces') & (sample.PastureTime=='Mid')]\n", "feces3=sample[(sample.SampleType=='Feces') & (sample.PastureTime=='End')]\n", "\n", "soil1=sample[(sample.SampleType=='Soil') & (sample.PastureTime=='Start')]\n", "soil2=sample[(sample.SampleType=='Soil') & (sample.PastureTime=='Mid')]\n", "soil3=sample[(sample.SampleType=='Soil') & (sample.PastureTime=='End')]\n", "\n", "ceca=sample[sample.SampleType=='Ceca']\n", "wcrp=sample[sample.SampleType=='WCR-P']\n", "wcrf=sample[sample.SampleType=='WCR-F']\n", "\n", "\n", "print('Feces_Start', feces1.shape)\n", "print('Feces_Mid', feces2.shape)\n", "print('Feces_End', feces3.shape,'\\n')\n", "\n", "print('Soil_Start', soil1.shape)\n", "print('Soil_Mid', soil2.shape)\n", "print('Soil_End', soil3.shape,'\\n')\n", "\n", "print('Ceca', ceca.shape)\n", "print('WCR-P', wcrp.shape)\n", "print('WCR-F', wcrf.shape,'\\n')\n", "\n", "\n", "sampletypes = [feces1,feces2,feces3, soil1,soil2,soil3,ceca, wcrp, wcrf]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES_START\", 1: \"FECES_MID\", 2:\"FECES_END\",\n", " 3: \"SOIL_START\", 4: \"SOIL_MID\", 5: \"SOIL_END\",\n", " 6:\"CECA\", 7: \"WCR-P\", 8: \"WCR-F\"\n", " }\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType', 'PastureTime','FreqHousingMove'],axis='columns'),sample.FreqHousingMove,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1] #multiclass\n", " \n", " try:\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " except ValueError:\n", " pass\n", " \n", " \n", " \n", " \n", "# mylist2.append([f\"FreqHousingMove_{sample_name[indexing]}\", rf_auc_normal])\n", " \n", " \n", " print(pd.value_counts(sample['FreqHousingMove']))\n", "\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", " \n", " \n", " plt.title(f\"FreqHousingMove in {sample_name[indexing]} Model\")\n", " prelim3_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','FreqHousingMove','PastureTime','SampleType'],axis='columns').columns)\n", " prelim3_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", "\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim3_plot.max(), decimals=None, symbol=''))\n", "\n", " xmax=prelim3_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " \n", " plt.show()\n", " \n", " prelim3_plot.nlargest(10).to_csv(\"prelim3.csv\")\n", " top10 = pd.read_csv('prelim3.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", "# plt.plot(pdp[1][0], pdp[0][0],'.')\n", "# plt.ylabel('Partial dependence'), plt.xlabel(top10[feature])\n", "# plt.show()\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"FreqHousingMove_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (12) AlwaysNewPasture" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Feces_Start (200, 878)\n", "Feces_Mid (313, 878)\n", "Feces_End (185, 878) \n", "\n", "Soil_Start (199, 878)\n", "Soil_Mid (313, 878)\n", "Soil_End (183, 878) \n", "\n", "Ceca (185, 878)\n", "WCR-P (208, 878)\n", "WCR-F (195, 878) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "1 175\n", "0 25\n", "Name: AlwaysNewPasture, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.8764432261387946, pvalue=1.8945001764182336e-07)\n", "Slope and P-value = PearsonRResult(statistic=-0.6865453510134749, pvalue=5.825619650554886e-14)\n", "Slope and P-value = PearsonRResult(statistic=-0.7316068975162973, pvalue=5.531124911125108e-17)\n", "Slope and P-value = PearsonRResult(statistic=-0.8170547743521895, pvalue=4.603817185226698e-13)\n", "Slope and P-value = PearsonRResult(statistic=-0.7912757640913287, pvalue=3.253651251833401e-05)\n", "Slope and P-value = PearsonRResult(statistic=-0.951793075317402, pvalue=4.515998636899081e-35)\n", "Slope and P-value = PearsonRResult(statistic=-0.4696380549478513, pvalue=8.25289800820292e-07)\n", "Slope and P-value = PearsonRResult(statistic=-0.9209551780139502, pvalue=6.735172660239042e-42)\n", "Slope and P-value = PearsonRResult(statistic=-0.8432189448309565, pvalue=3.6323340065518415e-28)\n", "Slope and P-value = PearsonRResult(statistic=0.7062379588934468, pvalue=2.258055526580783e-16)\n", "1 273\n", "0 40\n", "Name: AlwaysNewPasture, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
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PearsonRResult(statistic=-0.8663888457532856, pvalue=2.5791255606699692e-31)\n", "Slope and P-value = PearsonRResult(statistic=-0.7079043941384244, pvalue=1.7883889975505242e-16)\n", "1 160\n", "0 25\n", "Name: AlwaysNewPasture, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.906435232015135, pvalue=1.829490179448124e-38)\n", "Slope and P-value = PearsonRResult(statistic=-0.8252377246232876, pvalue=1.5059597672218947e-22)\n", "Slope and P-value = PearsonRResult(statistic=-0.8849247456107818, pvalue=2.7204447423479173e-34)\n", "Slope and P-value = PearsonRResult(statistic=0.3430330540073856, pvalue=0.0006229277513493144)\n", "Slope and P-value = PearsonRResult(statistic=-0.6946780475964686, pvalue=0.012169566012568921)\n", "Slope and P-value = PearsonRResult(statistic=-0.9385674495617168, pvalue=4.469532526708397e-47)\n", "Slope and P-value = PearsonRResult(statistic=-0.9397001841523408, pvalue=4.685524041736244e-46)\n", "Slope and P-value = PearsonRResult(statistic=-0.5040092397608007, pvalue=9.001505522257097e-08)\n", "Slope and P-value = PearsonRResult(statistic=-0.5294233120118508, pvalue=8.77559057704878e-07)\n", "Slope and P-value = PearsonRResult(statistic=-0.8546141907089143, pvalue=1.2018884932929093e-29)\n", "1 174\n", "0 25\n", "Name: AlwaysNewPasture, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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PearsonRResult(statistic=-0.9768428593874714, pvalue=6.082972535205392e-06)\n", "Slope and P-value = PearsonRResult(statistic=-0.7445730195328044, pvalue=2.4876735047400457e-10)\n", "1 274\n", "0 39\n", "Name: AlwaysNewPasture, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.9193230183579376, pvalue=1.7617602562210562e-41)\n", "Slope and P-value = PearsonRResult(statistic=0.28934363849696465, pvalue=0.0035027222288801273)\n", "Slope and P-value = PearsonRResult(statistic=-0.8632096670597694, pvalue=7.536491435692983e-31)\n", "Slope and P-value = PearsonRResult(statistic=-0.8310656948271694, pvalue=1.0335383081464753e-26)\n", "Slope and P-value = PearsonRResult(statistic=0.4192655984727438, pvalue=1.4135772449339826e-05)\n", "Slope and P-value = PearsonRResult(statistic=-0.9659796899878748, pvalue=3.837527224748176e-22)\n", "Slope and P-value = PearsonRResult(statistic=0.4242645836145535, pvalue=1.087672275341217e-05)\n", "Slope and P-value = PearsonRResult(statistic=-0.625341679885958, pvalue=7.185973825130265e-07)\n", "Slope and P-value = PearsonRResult(statistic=-0.860646079117824, pvalue=5.108904794184167e-21)\n", "Slope and P-value = PearsonRResult(statistic=-0.8749206165545732, pvalue=1.2594821264481436e-32)\n", "1 158\n", "0 25\n", "Name: AlwaysNewPasture, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.9109471111795523, pvalue=1.8145840082980626e-39)\n", "Slope and P-value = PearsonRResult(statistic=-0.7290122102798512, pvalue=2.09856615531808e-07)\n", "Slope and P-value = PearsonRResult(statistic=-0.9790851788106116, pvalue=5.219091191573979e-55)\n", "Slope and P-value = PearsonRResult(statistic=-0.9220295241674511, pvalue=3.536174901223166e-42)\n", "Slope and P-value = PearsonRResult(statistic=-0.761536784479908, pvalue=3.654087044859555e-20)\n", "Slope and P-value = PearsonRResult(statistic=-0.801862551788382, pvalue=0.00018697251444561882)\n", "Slope and P-value = PearsonRResult(statistic=-0.5619323392627867, pvalue=3.9548359851876285e-05)\n", "Slope and P-value = PearsonRResult(statistic=-0.8063228630495286, pvalue=4.428088106378035e-24)\n", "Slope and P-value = PearsonRResult(statistic=-0.8768946053744157, pvalue=3.3129968811211004e-17)\n", "Slope and P-value = PearsonRResult(statistic=0.6636641462209953, pvalue=5.2725106511062336e-14)\n", "1 160\n", "0 25\n", "Name: AlwaysNewPasture, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.8617699337765182, pvalue=1.2140407387981724e-30)\n", "Slope and P-value = PearsonRResult(statistic=-0.6178228814356422, pvalue=0.0009986696496111314)\n", "Slope and P-value = PearsonRResult(statistic=-0.9355962888310462, pvalue=3.498405709846943e-30)\n", "Slope and P-value = PearsonRResult(statistic=-0.9620167976078632, pvalue=1.0816427778565655e-32)\n", "Slope and P-value = PearsonRResult(statistic=-0.736876699334766, pvalue=0.03702808380303745)\n", "Slope and P-value = PearsonRResult(statistic=-0.9475033464250306, pvalue=6.732520370043022e-16)\n", "Slope and P-value = PearsonRResult(statistic=-0.9735844663638373, pvalue=9.75729792519875e-34)\n", "Slope and P-value = PearsonRResult(statistic=0.7637575941897405, pvalue=2.450499813778387e-20)\n", "Slope and P-value = PearsonRResult(statistic=-0.9190194602984436, pvalue=1.1637839598582725e-13)\n", "Slope and P-value = PearsonRResult(statistic=-0.7876641250518942, pvalue=3.684821155572464e-10)\n", "1 183\n", "0 25\n", "Name: AlwaysNewPasture, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.4671683145872598, pvalue=0.004071477500730663)\n", "Slope and P-value = PearsonRResult(statistic=-0.9046391316141336, pvalue=4.4437251107013007e-38)\n", "Slope and P-value = PearsonRResult(statistic=-0.8796390380914346, pvalue=1.508195780516317e-06)\n", "Slope and P-value = PearsonRResult(statistic=-0.8776956007836824, pvalue=8.488896051097158e-22)\n", "Slope and P-value = PearsonRResult(statistic=-0.8562283385234541, pvalue=1.038293499235691e-28)\n", "Slope and P-value = PearsonRResult(statistic=-0.8659277340403029, pvalue=2.856289751005637e-17)\n", "Slope and P-value = PearsonRResult(statistic=-0.7822498716471078, pvalue=3.3713664060335195e-15)\n", "Slope and P-value = PearsonRResult(statistic=-0.8711235051573609, pvalue=4.957951027517001e-32)\n", "Slope and P-value = PearsonRResult(statistic=-0.7962840719412292, pvalue=3.2523970946468408e-06)\n", "Slope and P-value = PearsonRResult(statistic=-0.8206715150303054, pvalue=1.4761543314880301e-25)\n", "1 170\n", "0 25\n", "Name: AlwaysNewPasture, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.4271938787306602, pvalue=9.31015349738477e-06)\n", "Slope and P-value = PearsonRResult(statistic=-0.8295488493350571, pvalue=3.206698232357169e-13)\n", "Slope and P-value = PearsonRResult(statistic=-0.8847800059348242, pvalue=2.8828653108474697e-34)\n", "Slope and P-value = PearsonRResult(statistic=0.3581366876497625, pvalue=0.0002537233432494587)\n", "Slope and P-value = PearsonRResult(statistic=-0.8411228144891503, pvalue=4.986624727508073e-07)\n", "Slope and P-value = PearsonRResult(statistic=-0.32527453594101763, pvalue=0.0009595343291410585)\n", "Slope and P-value = PearsonRResult(statistic=-0.8608903420234638, pvalue=1.6202882310332337e-30)\n", "Slope and P-value = PearsonRResult(statistic=-0.8581333922473451, pvalue=3.953844652166726e-30)\n", "Slope and P-value = PearsonRResult(statistic=-0.9125794942934788, pvalue=5.092128512848681e-23)\n", "Slope and P-value = PearsonRResult(statistic=-0.7707338688932994, pvalue=2.6045468101794627e-19)\n" ] } ], "source": [ "sample = pd.merge(microbiome, poultry[['SampleID', 'AlwaysNewPasture','PastureTime','SampleType']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "#sample = sample[~sample.isin([np.nan, np.inf, -np.inf]).any(1)]\n", "sample = sample.drop(['Pathogen_Salmonella', 'new_Pathogen_Salmonella',\n", " 'Pathogen_Campy','new_Pathogen_Campy',\n", " 'Pathogen_Listeria','new_Pathogen_Listeria'],axis='columns')\n", "\n", "sample.AlwaysNewPasture.replace({'Y': 1,'N':0}, regex=True, inplace=True)\n", "\n", "\n", "\n", "feces1=sample[(sample.SampleType=='Feces') & (sample.PastureTime=='Start')]\n", "feces2=sample[(sample.SampleType=='Feces') & (sample.PastureTime=='Mid')]\n", "feces3=sample[(sample.SampleType=='Feces') & (sample.PastureTime=='End')]\n", "\n", "soil1=sample[(sample.SampleType=='Soil') & (sample.PastureTime=='Start')]\n", "soil2=sample[(sample.SampleType=='Soil') & (sample.PastureTime=='Mid')]\n", "soil3=sample[(sample.SampleType=='Soil') & (sample.PastureTime=='End')]\n", "\n", "ceca=sample[sample.SampleType=='Ceca']\n", "wcrp=sample[sample.SampleType=='WCR-P']\n", "wcrf=sample[sample.SampleType=='WCR-F']\n", "\n", "\n", "print('Feces_Start', feces1.shape)\n", "print('Feces_Mid', feces2.shape)\n", "print('Feces_End', feces3.shape,'\\n')\n", "\n", "print('Soil_Start', soil1.shape)\n", "print('Soil_Mid', soil2.shape)\n", "print('Soil_End', soil3.shape,'\\n')\n", "\n", "print('Ceca', ceca.shape)\n", "print('WCR-P', wcrp.shape)\n", "print('WCR-F', wcrf.shape,'\\n')\n", "\n", "\n", "sampletypes = [feces1,feces2,feces3, soil1,soil2,soil3,ceca, wcrp, wcrf]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES_START\", 1: \"FECES_MID\", 2:\"FECES_END\",\n", " 3: \"SOIL_START\", 4: \"SOIL_MID\", 5: \"SOIL_END\",\n", " 6:\"CECA\", 7: \"WCR-P\", 8: \"WCR-F\"\n", " }\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType', 'PastureTime','AlwaysNewPasture'],axis='columns'),sample.AlwaysNewPasture,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1] #multiclass\n", " \n", " try:\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " except ValueError:\n", " pass\n", " \n", " \n", " \n", " \n", "# mylist2.append([f\"AlwaysNewPasture_{sample_name[indexing]}\", rf_auc_normal])\n", " \n", " \n", " print(pd.value_counts(sample['AlwaysNewPasture']))\n", "\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", " \n", " \n", " plt.title(f\"AlwaysNewPasture in {sample_name[indexing]} Model\")\n", " prelim3_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','AlwaysNewPasture','PastureTime','SampleType'],axis='columns').columns)\n", " prelim3_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", "\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim3_plot.max(), decimals=None, symbol=''))\n", "\n", " xmax=prelim3_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " \n", " plt.show()\n", " \n", " prelim3_plot.nlargest(10).to_csv(\"prelim3.csv\")\n", " top10 = pd.read_csv('prelim3.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", "# plt.plot(pdp[1][0], pdp[0][0],'.')\n", "# plt.ylabel('Partial dependence'), plt.xlabel(top10[feature])\n", "# plt.show()\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"AlwaysNewPasture_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (13) PaGMOFree" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Feces_Start (200, 878)\n", "Feces_Mid (313, 878)\n", "Feces_End (185, 878) \n", "\n", "Soil_Start (199, 878)\n", "Soil_Mid (313, 878)\n", "Soil_End (183, 878) \n", "\n", "Ceca (185, 878)\n", "WCR-P (208, 878)\n", "WCR-F (195, 878) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "1 125\n", "0 75\n", "Name: PaGMOFree, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.659656861214326, pvalue=8.419031284168506e-14)\n", "Slope and P-value = PearsonRResult(statistic=0.1919467917807008, pvalue=0.060997738819631754)\n", "Slope and P-value = PearsonRResult(statistic=0.8060887376627807, pvalue=4.669703874153007e-24)\n", "Slope and P-value = PearsonRResult(statistic=-0.7896579263899087, pvalue=1.6379803766817747e-22)\n", "Slope and P-value = PearsonRResult(statistic=-0.7038140957215826, pvalue=3.1607119410062763e-16)\n", "Slope and P-value = PearsonRResult(statistic=-0.9721041578141428, pvalue=1.5858089316826395e-63)\n", "Slope and P-value = PearsonRResult(statistic=-0.9158891102939445, pvalue=1.24868538344471e-40)\n", "Slope and P-value = PearsonRResult(statistic=-0.8065037686669726, pvalue=4.249780171489782e-24)\n", "Slope and P-value = PearsonRResult(statistic=0.3354494658422294, pvalue=0.0006455034709690358)\n", "Slope and P-value = PearsonRResult(statistic=-0.35190348249685155, pvalue=0.1395367771604781)\n", "1 218\n", "0 95\n", "Name: PaGMOFree, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.8053591278976323, pvalue=5.5079725229591195e-24)\n", "Slope and P-value = PearsonRResult(statistic=-0.928427006501219, pvalue=6.229391343114959e-44)\n", "Slope and P-value = PearsonRResult(statistic=-0.7986606906847702, pvalue=2.429055947095168e-23)\n", "Slope and P-value = PearsonRResult(statistic=-0.8156380995861584, pvalue=5.033677587410231e-25)\n", "Slope and P-value = PearsonRResult(statistic=0.2187676346443161, pvalue=0.028762028523679643)\n", "Slope and P-value = PearsonRResult(statistic=-0.5665972415128848, pvalue=1.296544374780519e-08)\n", "Slope and P-value = PearsonRResult(statistic=0.0589591716014462, pvalue=0.5601044780752562)\n", "Slope and P-value = PearsonRResult(statistic=-0.806387403599256, pvalue=4.363653445797566e-24)\n", "Slope and P-value = PearsonRResult(statistic=-0.8081273247709527, pvalue=2.9330962851463766e-24)\n", "Slope and P-value = PearsonRResult(statistic=-0.755663629350837, pvalue=1.0297410039536009e-19)\n", "1 125\n", "0 60\n", "Name: PaGMOFree, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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PearsonRResult(statistic=-0.9574775612369675, pvalue=1.0450329466956526e-54)\n", "Slope and P-value = PearsonRResult(statistic=-0.5171114301155418, pvalue=3.620770335218928e-08)\n", "1 124\n", "0 75\n", "Name: PaGMOFree, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.7127394608281816, pvalue=9.006867950672883e-17)\n", "Slope and P-value = PearsonRResult(statistic=0.25574249287141515, pvalue=0.010224883674989567)\n", "Slope and P-value = PearsonRResult(statistic=-0.8851406700358524, pvalue=2.494632835649074e-34)\n", "Slope and P-value = PearsonRResult(statistic=-0.5324750527352065, pvalue=1.1914579689589367e-05)\n", "Slope and P-value = PearsonRResult(statistic=-0.2687098836518864, pvalue=0.07096601270633124)\n", "Slope and P-value = PearsonRResult(statistic=-0.3502740744191509, pvalue=0.00035347808069217896)\n", "Slope and P-value = PearsonRResult(statistic=-0.8956082310600574, pvalue=2.994768897176296e-36)\n", "Slope and P-value = PearsonRResult(statistic=-0.8490009209319288, pvalue=6.672741169844551e-29)\n", "Slope and P-value = PearsonRResult(statistic=0.8210501082014161, pvalue=1.3439644178374217e-25)\n", "Slope and P-value = PearsonRResult(statistic=-0.9624636285089097, pvalue=2.611749256449652e-57)\n", "1 219\n", "0 94\n", "Name: PaGMOFree, dtype: int64\n" ] }, { "data": { "image/png": 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gWc7zZTntrwKujoiewI+BsnrGYmZmVi9tJdEYA/SXVCapI1Dsr7dcDFwjqTOApM6SBhbR/gXg2LR8QopnBRExB/i0ev4F2eWOZ/Lr5fkXcLqk9imu7SStW0RcXYCZafmkItqZmZnVS5u4dBIR4yQ9AEwCpgOVQDGzGq8DOgLjJC0BlgCXF9H+LODvks4GPgJ+UEO9k4C/SuoAvFNLvWo3kF1GmSBJqe8BRcR1PnCXpJnAWKBb7dWh5+ZdqPSv7JmZWT0poq7pB62DpI4RMS+dxJ8FBkbEhKaOq6WpqKiIysrKpg7DzMyaEUnjI6LgFy3axIhGMkzSjmTzEEY4yTAzMyu9NpNoRMTxuc8lXQPsmVetO/BWXtmVEXFTKWMzMzNrrdpMopEvIs5o6hjMzMxau7byrRMzMzNrAk40zMzMrGScaJiZmVnJONEwMzOzknGiYWZmZiXjRMPMzMxKps1+vdUapmrmHMqHPNzUYZhZI5vmWwtYiXhEw8zMzErGiYaZmZmVjBONBpK0pqTZki5u6ljMzMyaKycaDXcQ8G/g6HSL9nqT1K40IZmZmTUvbTbRkHSupDckPS5ppKTBRXZxHHAl8C7w9Zx+D5L0oqQJku6S1DGVT5P0O0ljgKMkHSepStIUSZfktD84tZ0k6clUtoGk+yVNljRW0s6pvKOkm1I/kyUdUUsf5+fuY9puuaR1JT2c6k6RdEyDDqiZmVkBbfJbJ5IqgCOA3mTHYAIwvoj26wD7Az8G1iNLOl6U1BX4LXBARMyX9Evg58CFqemiiOgraTNgLLAr8CnwmKQBwPPA9cDeETFV0gap3QXAKxExQNJ+wM1AL+BcYE5E9ExxrS9poxr6qMnBwPsR8e3UR5cC+zsQGAjQrvNG9T1MZmZmbXZEoy8wKiIWRsRc4MEi2x8KPB0RC4B7gO+myyFfB3YEnpc0ETgJ2Cqn3R3p727A6Ij4KCK+AG4F9k7tn42IqQAR8UlOvP9IZU8BG6aE4ADgmurOI+LTWvqoSRVwgKRLJO0VEXPyK0TEsIioiIiKdh1WykPMzMxq1CZHNICi5lQUcBywp6Rp6fmGwL6p38cj4rga2s2vY/sCoobyfFFD/Zr6+IIVE8sygIh4U9KuwLeAiyU9FhEXFmhvZmZWtLY6ojEG6C+pLM2hqPcv1UjqTDbCsGVElEdEOXAGWfIxliwB2TbV7SBpuwLdvATsI6lrGgk5DngGeDGVd0vtqy97PAuckMr6AbMj4jPgMeCnObGtX0sf04A+qawPUL1+M2BBRNwCXFZdx8zMrDG0yRGNiBgn6QFgEjAdqARWumRQg8OBpyJicU7ZKOCPwE+Ak4GRktZO634LvJm3/VmSfgU8TTYC8UhEjILl8yHulbQG8CFwIHA+cJOkycACsksyABcB10iaAiwFLoiIe2vo4x7gxHRJZ1xOTD2BSyUtA5YAp9fzOJiZmdVJEYVG2Vs/SR0jYp6kDmQjBgMjYkJTx9XcVVRURGVlZVOHYWZmzYik8RFRUWhdmxzRSIZJ2pFsrsIIJxlmZmaNr80mGhFxfO5zSdcAe+ZV6w68lVd2ZUTcVMrYzMzMWos2m2jki4gzmjoGMzOz1qatfuvEzMzMVgMnGmZmZlYyTjTMzMysZJxomJmZWck40TAzM7OScaJhZmZmJeNEw8zMzErGv6NhRamaOYfyIQ83dRhmrcq0ofW+r6NZi+MRDTMzMysZJxpmZmZWMq0m0ZA0WNIbkqZImiTpxDrqD5d0ZIHyfpIeSsvfkTSkVDHXRtK8RuhjPUk/aYx4zMzMGqJVJBqSTgMOBHaPiB7A3oBWtd+IeCAihq5qPzWR1K5UfSfrAUUlGsq0iveFmZk1vWZzQpF0bhqReFzSSEmDi2j+a+AnEfEZQETMiYgRqd/fSRqXRjqGSVopAZF0cNr2GODwnPKTJV2dljeRdF8aLZkk6Rup/Oep7ymSBqWy8tTfCEmTJd0tqUNaNy3FNAY4StJxkqpS+0vy4rpc0gRJT0raKJX9KO3PJEn35PRbKL6hwDaSJkq6NNU7O7WfLOmCnHhfl3QtMAH4Sl4cAyVVSqpcumBOES+LmZm1dc0i0ZBUARwB9CY70VcU0bYT0Cki3q6hytURsVsa6VgHODSvfRlwPdAf2Av4fzX08xfgmYjYBegDvCppV+AHwNeArwM/ktQ71f8qMCwidgY+Y8WRhUUR0Rd4FrgE2A/oBewmaUCqsy4wISL6AM8A56Xye9P+7AK8DpxSU3zAEODtiOgVEWdLOgjoDuyetrerpL1z4r05InpHxPTcHY+IYRFREREV7Tp0qeHwmJmZraxZJBpAX2BURCyMiLnAg0W0FRC1rN9X0kuSqshO6Dvlrd8emBoRb0VEALfU0M9+wHUAEbE0IuakuO+LiPkRMQ+4lyxZAXgvIp5Py7ekutXuSH93A0ZHxEcR8QVwK9llH4BlOfVy2/eQ9FzanxNy9qdQfPkOSo9XyEYutidLPACmR8TYGvbdzMysQZrL72g0eD5FRHwmab6krSPinRU6zUYrrgUqIuI9SecDZYW6aeDma4s7v8/c5/Pr0b6m/oYDAyJikqSTgX5F9CHg4oj42wqFUnlOTGZmZo2muYxojAH6SyqT1BEo9tdrLgaukdQZQFJnSQP5MqmYnfpd6VsmwBtAN0nbpOfH1bCNJ4HTU//t0raeBQZI6iBpXeC7wHOp/paS9sjpc0yBPl8C9pHUNU0MPY7sMglkr011vMfntO8EzJLUnmxEo7b45qb61f4F/DAdCyRtLmnjGvbXzMxslTWLRCMixgEPAJPILj9UAsXMOrwOeBoYJ2kK2cl6QUT8l2z+RRVwPzCuwLYXAQOBh9MEzen5dZKfkV2GqQLGAztFxASyEYaXyZKGGyLilVT/deAkSZOBDVKM+dueBfwqxT6JbE7GqLR6PrCTpPFkl0UuTOXnpm09TpYk1Rbfx8DzaaLppRHxGHAb8GKqdzcrJiJmZmaNStm0hKYnqWNEzEvfongWGJhO5C1OuhTxUJqA2qpUVFREZWVlU4dhZmbNiKTxEVHwixzNZY4GwDBJO5Jd7hjRUpMMMzMz+1KzSTQi4vjc55KuAfbMq9YdeCuv7MqIuKmUsRUrIqYBrW40w8zMrFjNJtHIFxFnNHUMZmZmtmqaxWRQMzMza52caJiZmVnJONEwMzOzknGiYWZmZiXjRMPMzMxKxomGmZmZlYwTDTMzMyuZZvs7GtY8Vc2cQ/mQh5s6DLPVatrQYu/zaGbVPKJhZmZmJeNEowWRtIWkUZLekvS2pCslrSWpl6Rv5dQ7X9LgpozVzMwMnGi0GJIE3AvcHxHdge2AjsDvgV7At2puXfS22jVWX2Zm1rY50Wg59gMWVd9ALiKWAv8DnAr8EThG0kRJx6T6O0oaLekdSWdVdyLpe5JeTnX/Vp1USJon6UJJLwF7rNY9MzOzVsuJRsuxEzA+tyAiPgOmARcBd0REr4i4I63eHvgmsDtwnqT2knYAjgH2jIhewFLghFR/XWBKRHwtIsbkbkfSQEmVkiqXLphTmr0zM7NWyd86aTkERBHlD0fEYmCxpA+BTYD9gV2BcdmVGNYBPkz1lwL3FNpwRAwDhgGsvWn3QtsyMzMryIlGy/EqcERugaTOwFfIkoR8i3OWl5K91gJGRMSvCtRflC7HmJmZNRpfOmk5ngQ6SDoRlk/YvBwYDnwAdKpnH0dK2jj1sYGkrUoTrpmZmRONFiMiAvgucJSkt4A3gUXAr4GnySZ/5k4GLdTHa8BvgcckTQYeBzYtefBmZtZm+dJJCxIR7wH9C6xaDOxWS7seOct3AHcUqNOxMWI0MzPL5UTDitJz8y5U+ueYzcysnnzpxMzMzErGiYaZmZmVjBMNMzMzKxknGmZmZlYyTjTMzMysZJxomJmZWck40TAzM7OScaJhZmZmJeNEw8zMzErGiYaZmZmVjH+C3IpSNXMO5UMebuowzOo0zT+Vb9YseETDzMzMSsaJhpmZmZVMm0w0JK0pabaki5s4jpMlXd0I/XxH0pC0fL6kwWl5uKQjV7V/MzOzhmqTiQZwEPBv4GhJKuWGJLUrZf8AEfFARAwt9XbMzMyK1SITDUnnSnpD0uOSRlZ/gi/CccCVwLvA13P6nSbpAkkTJFVJ2j6V7y7pBUmvpL9fTeXtJF0qaZykyZJ+nMr7SXpa0m1AlaQySTelPl+RtG9OLF+R9Kikf0s6LyeW+yWNl/SqpIE55Qen+CZJejKV1Tkykvata1qukDQ6Le8jaWJ6vCKpU4G2AyVVSqpcumBOcUfazMzatBb3rRNJFcARQG+y+CcA44tovw6wP/BjYD2ypOPFnCqzI6KPpJ8Ag4FTgTeAvSPiC0kHAH9IMZwCzImI3SStDTwv6bHUz+5Aj4iYKukXABHRMyUvj0naLrcesAAYJ+nhiKgEfhgRn6R4x0m6hywxvD7FMlXSBvU+cDUbDJwREc9L6ggsyq8QEcOAYQBrb9o9GmGbZmbWRrTEEY2+wKiIWBgRc4EHi2x/KPB0RCwA7gG+m3d54970dzxQnpa7AHdJmgJcAeyUyg8CTpQ0EXgJ2BDonta9HBFTc2L+B0BEvAFMB6oTjccj4uOIWJi23TeVnyVpEjAW+Erq9+vAs9X9RsQnRe57Ic8Df5J0FrBeRHzRCH2amZkBLTPRWNU5FccBB0iaRpZMbAjkXspYnP4u5csRn/8lS056AP2BspxYzoyIXunRLSKqRzTm1zPm/BGCkNQPOADYIyJ2AV5J21SB+vX1BV++3tXxk+Z2nAqsA4ytvlxkZmbWGFpiojEG6J/mPXQE6v2rPJI6k40YbBkR5RFRDpxBlnzUpgswMy2fnFP+L+B0Se1T/9tJWrdA+2eBE6rrAFuSTUYFOFDSBukSyQCyEYYuwKcRsSCd+KvnkbwI7COpW+qrmEsn04Bd0/IR1YWStomIqoi4BKgEnGiYmVmjaXFzNCJinKQHgElklyAqgfrOUDwceCoiFueUjQL+mOZY1OSPwAhJPweeyim/gezyyoT07ZWPyJKFfNcCf5VURTaycHJELE5feBlDdlllW+C2iKhM9U6TNJksIRmb9v2jNDH0XklrAB8CB9Zz3y8AbpT0a7LLPNUGpcmpS4HXgH/W1knPzbtQ6V9cNDOzelJEy5vbJ6ljRMyT1IFstGBgRExo6rjagoqKiqisrGzqMMzMrBmRND4iKgqta3EjGskwSTuSzTUY4STDzMyseWqRiUZEHJ/7XNI1wJ551boDb+WVXRkRN5UyNjMzM/tSi0w08kXEGU0dg5mZma2sJX7rxMzMzFoIJxpmZmZWMk40zMzMrGScaJiZmVnJONEwMzOzknGiYWZmZiXTKr7eaqtP1cw5lA95uKnDsFZqmn/e3qzV8YiGmZmZlYwTDTMzMyuZVpNoSDpZ0si8sq6SPpK0tqQXGtDnDemeKs2epO9IGtLUcZiZmeVqTXM07gUuk9QhIhaksiOBB9Jt4b+R30BSu4hYWlOHEXFqaUIFSWtGxBeN1V9EPAA80Fj9mZmZNYZmN6Ih6VxJb0h6XNJISYPr0y4iPiO7ZXz/nOJjgZGp33npbz9JT0u6DaiStIakayW9KukhSY9IOjLVHS2pQlI7ScMlTZFUJel/ctb/WdILad3uqXwDSfdLmixprKSdU/n5koZJegy4WVK5pOckTUiPb+TEOFrS3elY3CpJad23UtkYSX+R9FAqP1nS1Wl5ePU+FNj3ZyTdKelNSUMlnSDp5bRf2zTsVTMzMyusWY1oSKoAjgB6k8U2ARhfRBcjgeOBOyRtBmwHPF2g3u5Aj4iYmk7I5UBPYGPgdeDvefV7AZtHRI8U53o569aNiG9I2ju16wFcALwSEQMk7QfcnPoA2BXoGxELJXUADoyIRZK6p/grUr3ewE7A+8DzwJ6SKoG/AXun2Fe4VFRPuwA7AJ8A7wA3RMTukn4GnAkMym8gaSAwEKBd540asEkzM2urmtuIRl9gVEQsjIi5wINFtn8I6CupM3A0cHcNl0ZejoipOdu8KyKWRcT/UTgxeQfYWtJVkg4GPstZNxIgIp4FOqckpC/wj1T+FLChpC6p/gMRsTAttweul1QF3AXkzgd5OSJmRMQyYCJZMrQ98E5O7A1JNMZFxKx0Oelt4LFUXpW2sZKIGBYRFRFR0a5Dl0JVzMzMCmpuiYZWpXE6gT8KfJecyyYFzC9mmxHxKdlIwGjgDOCG3NX51Wvos7pe7rb/B/gg9V0BrJWzbnHO8lKyEZ76Hp8vSK9tuuRSU7/Lcp4vo5mNcJmZWcvX3BKNMUB/SWWSOgIN+fWekcDPgU2AsfXc5hFprsYmQL/8CpK6AmtExD3AuUCfnNXHpDp9gTkRMYdsrsgJqbwfMDvNIcnXBZiVRi2+D7SrI9Y3yEZWynO3XcA0sks0AIeRjZyYmZmtds3qE2xEjJP0ADAJmA5UAnOK7OYxYARwY0TkjzYUcg+wPzAFeBN4qcA2NwduklSdmP0qZ92n6auznYEfprLzU/3JwALgpBq2fS1wj6SjyC7ZzK+hHpCN2Ej6CfCopNnAyzVUvR4YJell4Mm6+jUzMysV1e9cvPpI6hgR89JEyWeBgRExYTVtc0Oyk/eeab5GXe1GA4MjorKU8eVtszpWAdcAb0XEFatr+xUVFVFZudp218zMWgBJ4yOiotC6ZjWikQxLP5JVBowodZKRPJQmca4F/G99kowm9CNJJ5HF+grZt1DMzMyapWY3opFP0jXAnnnF3YG38squjIibVk9UbZdHNMzMLF9LG9FYQUSc0dQxmJmZWcM0t2+dmJmZWSviRMPMzMxKxomGmZmZlYwTDTMzMysZJxpmZmZWMk40zMzMrGScaJiZmVnJNPvf0bDmpWrmHMqHPNzUYVgrMG1oQ+6ZaGYtjUc0zMzMrGScaJiZmVnJONFoZJKGS5oqaWJ6nFVk+36SHipQfrKkqxspxhvSjevMzMxKynM0SuPsiLi7qYOoSUSc2tQxmJlZ2+ARjQIknSvpDUmPSxopafAq9vc7SeMkTZE0TJJS+baSnpA0SdIESdvktdtN0iuSts4r7y/ppbTuCUmbpPLzJY2Q9JikaZIOl/RHSVWSHpXUPtUbLakiLV8nqVLSq5IuqCH+galO5dIFc1blUJiZWRvjRCNPOgEfAfQGDgcK3va2DpfmXDrpCVwdEbtFRA9gHeDQVO9W4JqI2AX4BjArJ45vAH8FDouId/L6HwN8PSJ6A7cD5+Ss2wb4NnAYcAvwdET0BBam8ny/Sbf23RnYR9LO+RUiYlhEVERERbsOXYo8FGZm1pb50snK+gKjImIhgKQHG9DHCpdOJB0h6RygA7AB8Kqk0cDmEXEfQEQsSnUBdgCGAQdFxPsF+t8CuEPSpsBawNScdf+MiCWSqoB2wKOpvAooL9DX0ZIGkr0XNgV2BCY3YJ/NzMxW4hGNlalRO5PKgGuBI9PIwvVAWR3bmQUsIhtVKeQqslGSnsCPU3/VFgNExDJgSUREKl9GXmIpqRswGNg/InYGHs7ry8zMbJU40VjZGKC/pDJJHSl8uaEY1Sfu2am/IwEi4jNghqQBAJLWltQh1f1v2u4fJPUr0GcXYGZaPmkVYusMzAfmpHkeh6xCX2ZmZivxpZM8ETFO0gPAJGA6UAk0eAZkRPxX0vVkly6mAeNyVn8f+JukC4ElwFE57T6Q1B/4p6Qf5nV7PnCXpJnAWKBbA2ObJOkV4FXgHeD5hvRjZmZWE305sm7VJHWMiHlphOFZYGBETGjquJqDioqKqKysbOowzMysGZE0Pn2xYCUe0ShsWPpBqzJghJMMMzOzhnGiUUBEHJ/7XNI1wJ551boDb+WVXRkRN5UyNjMzs5bEiUY9RMQZTR2DmZlZS+RvnZiZmVnJONEwMzOzknGiYWZmZiXjRMPMzMxKxomGmZmZlYwTDTMzMysZJxpmZmZWMv4dDStK1cw5lA95uKnDsBZq2tBVvUehmbU0HtEwMzOzknGiYWZmZiXjRGM1kjRY0huSpkiaJOnEOupPk9S1kba9vC9J8xqjTzMzs7o40VhNJJ0GHAjsHhE9gL0BNW1UZmZmpeVEowiSzk0jEo9LGilpcBHNfw38JCI+A4iIORExIvW7v6RXJFVJ+ruktXPanSlpQlq3fap/fqo3WtI7ks7KifF7kl6WNFHS3yS1q2V/Okp6Mqf/w2qoN1BSpaTKpQvmFLHLZmbW1jnRqCdJFcARQG/gcKCiiLadgE4R8XaBdWXAcOCYiOhJ9k2g03OqzI6IPsB1QG5isz3wTWB34DxJ7SXtABwD7BkRvYClwAm1hLYI+G7qf1/gckkrjbJExLCIqIiIinYdutR3t83MzJxoFKEvMCoiFkbEXODBItoKiBrWfRWYGhFvpucjyC6rVLs3/R0PlOeUPxwRiyNiNvAhsAmwP7ArME7SxPR86zri+oOkycATwOapHzMzs0bh39GovwbPp4iIzyTNl7R1RLxTZL+L09+lrPh6Lc5Zrl4nYERE/KqeoZ0AbATsGhFLJE0DyurZ1szMrE4e0ai/MUB/SWWSOgLF/vLQxcA1kjoDSOosaSDwBlAuadtU7/vAMw2M8UngSEkbp21sIGmrWup3AT5MSca+QG11zczMiuYRjXqKiHGSHgAmAdOBSqCYmZHXAR3JLmssAZYAl0fEIkk/AO6StCYwDvhrA2N8TdJvgcckrZG2cUaKt5BbgQclVQITyZIeMzOzRqOImqYOWD5JHSNinqQOwLPAwIiY0NRxrU4VFRVRWVnZ1GGYmVkzIml8RBT8koRHNIozTNKOZPMYRrS1JMPMzKxYTjSKEBHH5z6XdA2wZ1617sBbeWVXRsRNpYzNzMysOXKisQoi4oymjsHMzKw5c6JhZmarzZIlS5gxYwaLFi1q6lCsAcrKythiiy1o3759vds40TAzs9VmxowZdOrUifLycgr8ELE1YxHBxx9/zIwZM+jWrVu92/l3NMzMbLVZtGgRG264oZOMFkgSG264YdGjUU40zMxstXKS0XI15LVzomFmZmYl4zkaZmbWZMqHPNyo/U0bWr+7Q9x3330cfvjhvP7662y//fYAjB49mssuu4yHHnpoeb2TTz6ZQw89lCOPPJJ+/foxa9YsysrKWGuttbj++uvp1asXAHPmzOHMM8/k+eefB2DPPffkqquuokuX7I7Xb775JoMGDeLNN9+kffv29OzZk6uuuopNNmn4fSw/+eQTjjnmGKZNm0Z5eTl33nkn66+//kr1rrjiCm644QYk0bNnT2666SbKysqYOHEip512GosWLWLNNdfk2muvZffdd6eqqorLL7+c4cOHNzi2XE40rChVM+c0+n8M1vrV9z9/s9Vl5MiR9O3bl9tvv53zzz+/3u1uvfVWKioquOmmmzj77LN5/PHHATjllFPo0aMHN998MwDnnXcep556KnfddReLFi3i29/+Nn/605/o378/AE8//TQfffTRKiUaQ4cOZf/992fIkCEMHTqUoUOHcskll6xQZ+bMmfzlL3/htddeY5111uHoo4/m9ttv5+STT+acc87hvPPO45BDDuGRRx7hnHPOYfTo0fTs2ZMZM2bw7rvvsuWWWzY4vmq+dGJmZm3KvHnzeP7557nxxhu5/fbbG9THHnvswcyZMwH4z3/+w/jx4zn33HOXr//d735HZWUlb7/9Nrfddht77LHH8iQDYN9996VHjx6rtB+jRo3ipJNOAuCkk07i/vvvL1jviy++YOHChXzxxRcsWLCAzTbbDMjmW3z22WdANiJTXQ7Qv3//Bh+bfB7RMDOzNuX+++/n4IMPZrvttmODDTZgwoQJ9OnTp6g+Hn30UQYMGADAa6+9Rq9evWjXrt3y9e3ataNXr168+uqrTJkyhV133bXOPufOnctee+1VcN1tt93GjjvuuELZBx98wKabbgrApptuyocffrhSu80335zBgwez5ZZbss4663DQQQdx0EEHAfDnP/+Zb37zmwwePJhly5bxwgsvLG9XUVHB0KFDOeecc+qMuy5ONMzMrE0ZOXIkgwYNAuDYY49l5MiR9OnTp8ZvVOSWn3DCCcyfP5+lS5cyYUJ2u6uIKNi2pvKadOrUiYkTJ9Z/R+rh008/ZdSoUUydOpX11luPo446iltuuYXvfe97XHfddVxxxRUcccQR3HnnnZxyyik88cQTAGy88ca8//77jRLDarl0ImmwpDckTZE0SdKJjdTvaEkVafkRSesVqHO+pMF19DMg3SytMWL6oaQqSZPT/h5WR/3hko5MyzdUxyFpXiPEsp6kn6xqP2ZmrcXHH3/MU089xamnnkp5eTmXXnopd9xxBxHBhhtuyKeffrpC/U8++YSuXbsuf37rrbcydepUjj/+eM44I7sLxU477cQrr7zCsmXLltdbtmwZkyZNYocddmCnnXZi/PjxdcY2d+5cevXqVfDx2muvrVR/k002YdasWQDMmjWLjTfeeKU6TzzxBN26dWOjjTaiffv2HH744ctHLkaMGMHhhx8OwFFHHcXLL7+8vN2iRYtYZ5116oy5PkqeaEg6DTgQ2D0iegB7A43+JeqI+FZE/LeBzQcAq5xoSNoC+A3QNyJ2Br4OTK5v+4g4NSJWfjc13HpAUYmGMp67Y2at0t13382JJ57I9OnTmTZtGu+99x7dunVjzJgxdO/enffff5/XX38dgOnTpzNp0qTl3yyp1r59ey666CLGjh3L66+/zrbbbkvv3r256KKLlte56KKL6NOnD9tuuy3HH388L7zwAg8//OVE+kcffZSqqqoV+q0e0Sj0yL9sAvCd73yHESNGAFnScNhhK3+u3XLLLRk7diwLFiwgInjyySfZYYcdANhss8145plnAHjqqafo3r378nZvvvnmKs8hqVbnpRNJ5wInAO8Bs4HxEXFZEdv4NbBvRHwGEBFzgBGp798B/YF1gBeAH0dESBoNvATsS3ayPCUinpO0DnATWVLwempXHec0oCIiZkv6DXBiivkjYHyq8yNgILAW8B/g+0Av4DvAPpJ+CxwB3AgMjohKSV2Byogol7RT2v5aZEnaERGRe6fWjYG5wLy0r/OqlyX1Av4KdADeBn4YESukzmm/B0dEZXp+eToGnwLHRsRHhfYhIhZI2iT1v3Xq7nTgLGAbSROBxyPibElnA0cDawP3RcR5ksqBfwJPA3uQJV7Tc+IamLZJu84bYWbWWFb3N5JGjhzJkCFDVig74ogjuO2229hrr7245ZZb+MEPfsCiRYto3749N9xww/KvqOZaZ511+MUvfsFll13GjTfeyI033siZZ57JtttuS0Swxx57cOONNy6v+9BDDzFo0CAGDRpE+/bt2XnnnbnyyitXaV+GDBnC0UcfzY033siWW27JXXfdBcD777/PqaeeyiOPPMLXvvY1jjzySPr06cOaa65J7969GThwIADXX389P/vZz/jiiy8oKytj2LBhy/t++umn+fa3G+e1UUTUvDK7LHED2clnTWAC8Lf6JhqSOgHvRsTKX+zN1m8QEZ+k5X8Ad0bEg+mEOz4ifiHpW8DPI+IAST8HekTEDyXtnOL5ekoIpgEVwFbAcOBrOTH/NSIuk7RhRHyctncR8EFEXCVpOPBQRNyd1o2mcKJxFTA2Im6VtBbQLiIW5uxPO+ARYAfgSeDeiHgwrZsMnBkRz0i6EOgcEYNyt5233QC+l7b1O2DjiPhpLftwB/BiRPw5xdERWD/13SPVPwg4Evgx2ajSA8AfgXeBd4BvRMTY2l7TtTftHpue9OfaqpitxF9vtWqvv/768k/U1jwtXryYffbZhzFjxrDmmiuPRxR6DSWNj4iKQv3VNUTeFxgVEQsjYi7wYJHxCqg5k4F9Jb0kqQrYD9gpZ9296e94oDwt7w3cAhARkyl8WWIvsk/qC9IoygM563pIei5t74S87dXHi8CvJf0S2Co3yUgxLQUOJjuZvwlckeaIdAHWi4hnUtURaV9qswy4Iy3fQvZa1LYP+wHXVceRRo7yHZQer5AlYNsD1WNl0+tKMszMrPV79913GTp0aMEkoyHqSjRWaS5FOtHPl7R1/jpJZcC1wJER0RO4HijLqbI4/V3Kipd4aktc6qozHPhp2t4FedvL9QVfHpvldSLiNrLLLAuBf0nab6UNZ16OiIuBY8kuxTSG6n0aTv32oRABF0dEr/TYNiJuTOvmN1KcZmbWgnXv3p1+/fo1Wn91JRpjgP6SyiR1BBoy/nkxcI2kzgCSOqdr/tUnyNmp7yPr0dezZJ/ikdQD2LmGOt+VtE66dNM/Z10nYJak9tX9JHPTumrTgOovPS+PKyVM70TEX8hGSlbYvqTNJOV+GbsX2UjBHOBTSdVfkP4+8Ay1WyNn28eTvRa17cOTZPMykNQuHe/8/foX8MN0vJG0uaSVpymbmZVQbZfsrXlryGtX67hIRIyT9AAwiWxyYCVQaEi+NteRzRcYJ2kJsAS4PCL+K+l6oIrsxD6unn3dlOY7TARezq8QERPSfIWJKebnclafSzbJdHrabvVJ+HbgeklnkZ3cLwPulPR94Kmc9scA30v78X/AhXmbbw9cJmkzYBHZRNTT0rqTgL9K6kA2H+IHdezrfGAnSePJjvkxdezDz4Bhkk4hGwU6PSJelPS8pCnAP9Nk0B2AF5V9t3se8L1Uv156bt6FSl9vN7MGKisr4+OPP/at4lugiODjjz+mrKyYgfQ6JoMCSOoYEfPSCfJZYGBETGh4qNaSVVRURGVlZVOHYWYt1JIlS5gxYwaLFi1q6lCsAcrKythiiy1o3779CuW1TQatz0yPYelHpMqAEU4yzMysodq3b0+3bt2aOgxbjepMNCLi+PwySdcAe+YVdwfeyiu7MiJuanh4ZmZm1pI16LsrEXFGYwdiZmZmrY9/atrMzMxKps7JoGa5JM0F/t3UcbQxXcl+/t9WHx/z1c/HfPVrzGO+VUQUvEeFbxNvxfp3TTOLrTQkVfqYr14+5qufj/nqt7qOuS+dmJmZWck40TAzM7OScaJhxRpWdxVrZD7mq5+P+ernY776rZZj7smgZmZmVjIe0TAzM7OScaJhZmZmJeNEw+pN0sGS/i3pP5KGNHU8rZGkr0h6WtLrkl6V9LNUfr6kmZImpse3mjrW1kTSNElV6dhWprINJD0u6a30d/2mjrO1kPTVnPfyREmfSRrk93njkvR3SR+mO3hXl9X4vpb0q/T/+78lfbPR4vAcDasPSe2AN4EDgRnAOOC4iHitSQNrZSRtCmwaERMkdQLGAwOAo4F5EXFZU8bXWkmaBlRExOycsj8Cn0TE0JRYrx8Rv2yqGFur9H/LTOBrwA/w+7zRSNobmAfcHBE9UlnB93W6eepIYHdgM+AJYLuIWLqqcXhEw+prd+A/EfFORHwO3A4c1sQxtToRMav6DskRMRd4Hdi8aaNqsw4DRqTlEWQJnzW+/YG3I2J6UwfS2kTEs8AnecU1va8PA26PiMURMRX4D9n/+6vMiYbV1+bAeznPZ+ATYElJKgd6Ay+lop9KmpyGQz2M37gCeEzSeEkDU9kmETELsgQQ2LjJomvdjiX7JF3N7/PSqul9XbL/451oWH2pQJmvu5WIpI7APcCgiPgMuA7YBugFzAIub7roWqU9I6IPcAhwRhpythKTtBbwHeCuVOT3edMp2f/xTjSsvmYAX8l5vgXwfhPF0qpJak+WZNwaEfcCRMQHEbE0IpYB19NIQ5qWiYj3098PgfvIju8Hac5M9dyZD5suwlbrEGBCRHwAfp+vJjW9r0v2f7wTDauvcUB3Sd3Sp5BjgQeaOKZWR5KAG4HXI+JPOeWb5lT7LjAlv601jKR108RbJK0LHER2fB8ATkrVTgJGNU2Erdpx5Fw28ft8tajpff0AcKyktSV1A7oDLzfGBv2tE6u39FWzPwPtgL9HxO+bNqLWR1Jf4DmgCliWin9N9h9yL7KhzGnAj6uvs9qqkbQ12SgGZHe0vi0ifi9pQ+BOYEvgXeCoiMifWGcNJKkD2ZyArSNiTir7B36fNxpJI4F+ZLeD/wA4D7ifGt7Xkn4D/BD4guyy7T8bJQ4nGmZmZlYqvnRiZmZmJeNEw8zMzErGiYaZmZmVjBMNMzMzKxknGmZmZlYyTjTMmiFJS9PdK6dIelDSenXUP1/S4DrqDEg3Tqp+fqGkAxoh1uGSjlzVforc5qD09chmQ9L26TV7RdI2eeumSXour2xi9V01JVVI+ksjxFCee6fOvHU35L7+pSZpE0m3SXon/bT7i5K+u7q2b82HEw2z5mlhRPRKd1z8BDijEfocACw/0UTE7yLiiUbod7VKd/scBDSrRIPs+I6KiN4R8XaB9Z0kfQVA0g65KyKiMiLOqu+G0jEoSkScurrutpx+eO5+4NmI2DoidiX7kb8tSrzdNUvZvzWMEw2z5u9F0s2NJG0j6dH0CfE5SdvnV5b0I0njJE2SdI+kDpK+QXZPiUvTJ+ltqkciJB0i6c6c9v0kPZiWD0qfRCdIuivdg6VG6ZP7H1KbSkl9JP1L0tuSTsvp/1lJ90l6TdJfJa2R1h0nqSqN5FyS0++8NALzEvAbsttYPy3p6bT+urS9VyVdkBfPBSn+qurjJamjpJtS2WRJR9R3fyX1kjQ2tbtP0vrpx+wGAadWx1TAncAxaTn/FzH7SXqojthyj8Eekn6ejtMUSYNytrOmpBGp7d3VIz+SRkuqqMdxviS9v56QtHtq946k76Q67SRdmt5jkyX9uMC+7gd8HhF/rS6IiOkRcVVtfaTjMDrF/YakW1PSgqRdJT2TYvuXvvwZ7dHpPfcM8DNJ/SW9pGxk6QlJm9TwetjqEhF++OFHM3sA89LfdmQ3nDo4PX8S6J6WvwY8lZbPBwan5Q1z+rkIODMtDweOzFk3HDiS7Ncw3wXWTeXXAd8j+zXBZ3PKfwn8rkCsy/sl+zXH09PyFcBkoBOwEfBhKu8HLAK2Tvv3eIpjsxTHRimmp4ABqU0AR+dscxrQNef5BjnHazSwc0696v3/CXBDWr4E+HNO+/WL2N/JwD5p+cLqfnJfgwJtpgHbAS+k56+QjS5NyTkmD9UUW/4xAHYl+/XYdYGOwKtkd/otT/X2TPX+zpfvi9FART2O8yFp+T7gMaA9sAswMZUPBH6bltcGKoFueft7FnBFLe/vgn2k4zCHbORjDbIku2+K4QVgo9TmGLJfJ67er2vzXsvqH6M8Fbi8qf89t/WHh5nMmqd1JE0kO3GMBx5Pn66/AdyVPuRB9p90vh6SLgLWIzsJ/au2DUXEF5IeBfpLuhv4NnAOsA/ZyfD5tL21yP7jr0v1PXCqgI4RMReYK2mRvpxr8nJEvAPLfya5L7AEGB0RH6XyW4G9yYbgl5LdaK4mRyu7vfuawKYp7slp3b3p73jg8LR8ANlQfvUx+FTSoXXtr6QuwHoR8UwqGsGXdx6tyyfAp5KOBV4HFtRQb6XY0mLuMegL3BcR81Nc9wJ7kR379yLi+VTvFrKT/mU5/e9Gzcf5c+DRVK8KWBwRSyRVkb0XIbsXzM76cl5OF7L7YkytacclXZNi/jwidqulj8/J3hszUruJabv/BXqQ/TuALKHM/WnyO3KWtwDuSCMea9UWl60eTjTMmqeFEdErndgeIpujMRz4b0T0qqPtcLJPqJMknUz2KbEud6RtfAKMi4i5acj68Yg4rsjYF6e/y3KWq59X/5+Tf++DoPBtqqstioilhVYouwHUYGC3lDAMB8oKxLM0Z/sqEEND97cYdwDXACfXUqdQbLDiMajtWBU6tvn912RJpKEAcl6/iFimL+c/iGyUqLYE9lXgiOUBRJwhqSvZyEWNfUjqx4rvmerXTMCrEbFHDdubn7N8FfCniHgg9Xd+LXHaauA5GmbNWGQ3mzqL7ES6EJgq6SjIJtxJ2qVAs07ALGW3mz8hp3xuWlfIaKAP8CO+/HQ4FthT0rZpex0kbbdqe7Tc7sruBLwG2TD4GOAlYB9JXZVNdjwOeKaG9rn70pnsRDMnXY8/pB7bfwz4afUTSetTj/1Nr8enkvZKRd+vJcZC7gP+SO2jTIViy/csMCDFuC7ZnU6rv9WypaTqE/JxZMc2VzHHuZB/Aaen9xeStksx5HoKKJN0ek5Z7uTd+vSR69/ARtX7Jam9pJ1qqNsFmJmWT6qhjq1GTjTMmrmIeAWYRDacfgJwiqRJZJ8aDyvQ5Fyyk8njwBs55bcDZ6vA1y/TJ+WHyE7SD6Wyj8g+eY+UNJnsRLzS5NMGehEYSnYb8KlklwFmAb8Cnibb3wkRUdOt2YcB/5T0dERMIpvz8CrZnITna2iT6yJg/TQZchKwbxH7exLZpNrJZHcavbAe2wMgIuZGxCUR8XkxsRXoZwLZyNXLZK/1Del9AtllmZNSfBuQzbnJbVvMcS7kBuA1YIKyr9L+jbzR8TQqMoAsoZkq6WWyy0y/rG8fef19TjaP55J0TCaSXUYs5Hyyy4vPAbOL2C8rEd+91cxWqzScPTgiDm3iUMxsNfCIhpmZmZWMRzTMzMysZDyiYWZmZiXjRMPMzMxKxomGmZmZlYwTDTMzMysZJxpmZmZWMv8fo9DhHQW8E4EAAAAASUVORK5CYII=\n", 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PearsonRResult(statistic=0.7153502208050327, pvalue=6.18255516786751e-17)\n", "Slope and P-value = PearsonRResult(statistic=0.5865982115684907, pvalue=1.4258726006099913e-10)\n", "1 125\n", "0 58\n", "Name: PaGMOFree, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.7155136660006429, pvalue=6.03778155264298e-17)\n", "Slope and P-value = PearsonRResult(statistic=0.47682679788958526, pvalue=5.295911838967147e-07)\n", "Slope and P-value = PearsonRResult(statistic=-0.9459789398578758, pvalue=9.82176752790173e-50)\n", "Slope and P-value = PearsonRResult(statistic=0.39723074542232684, pvalue=4.275512523916789e-05)\n", "Slope and P-value = PearsonRResult(statistic=-0.9178932389114964, pvalue=4.0233056203029225e-41)\n", "Slope and P-value = PearsonRResult(statistic=-0.5626232357614358, pvalue=0.0005330765080200022)\n", "Slope and P-value = PearsonRResult(statistic=0.6724473339496534, pvalue=1.8428615065038086e-14)\n", "Slope and P-value = PearsonRResult(statistic=0.559463180648065, pvalue=3.1190090228184762e-09)\n", "Slope and P-value = PearsonRResult(statistic=-0.9654562650400587, pvalue=9.586294245477228e-27)\n", "Slope and P-value = PearsonRResult(statistic=0.874067527314548, pvalue=1.7201562776320383e-32)\n", "1 125\n", "0 60\n", "Name: PaGMOFree, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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PearsonRResult(statistic=-0.904800401070367, pvalue=5.216570549899459e-34)\n", "Slope and P-value = PearsonRResult(statistic=-0.9322741841570142, pvalue=4.5640544280051256e-45)\n", "1 149\n", "0 59\n", "Name: PaGMOFree, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.9001726099432511, pvalue=3.7495417635835437e-37)\n", "Slope and P-value = PearsonRResult(statistic=-0.846293868103613, pvalue=2.4747966870431876e-18)\n", "Slope and P-value = PearsonRResult(statistic=-0.9326604340223688, pvalue=3.481093923721365e-45)\n", "Slope and P-value = PearsonRResult(statistic=-0.8540371798398159, pvalue=1.4381987806566134e-29)\n", "Slope and P-value = PearsonRResult(statistic=-0.9217179741229072, pvalue=3.352029627300464e-39)\n", "Slope and P-value = PearsonRResult(statistic=-0.797606444548189, pvalue=2.3540834944732525e-16)\n", "Slope and P-value = PearsonRResult(statistic=-0.4903340076074775, pvalue=0.004385706760532185)\n", "Slope and P-value = PearsonRResult(statistic=-0.5840940761691253, pvalue=5.46470382669413e-09)\n", "Slope and P-value = PearsonRResult(statistic=-0.9548076606791903, pvalue=1.9371234149353776e-53)\n", "Slope and P-value = PearsonRResult(statistic=-0.6919011077847494, pvalue=8.51376056540204e-13)\n", "1 135\n", "0 60\n", "Name: PaGMOFree, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.7113541458237707, pvalue=1.0978574635676406e-16)\n", "Slope and P-value = PearsonRResult(statistic=-0.1479429941142125, pvalue=0.14185283609090255)\n", "Slope and P-value = PearsonRResult(statistic=-0.9174653461957636, pvalue=3.735088167584527e-32)\n", "Slope and P-value = PearsonRResult(statistic=-0.9469606159670361, pvalue=4.094631132137997e-50)\n", "Slope and P-value = PearsonRResult(statistic=-0.8853795385521914, pvalue=2.266138912593742e-34)\n", "Slope and P-value = PearsonRResult(statistic=-0.9439137060092564, pvalue=3.366717670070492e-26)\n", "Slope and P-value = PearsonRResult(statistic=0.5143109521272843, pvalue=4.413077309733914e-08)\n", "Slope and P-value = PearsonRResult(statistic=0.5296387214310349, pvalue=1.4612567643138489e-08)\n", "Slope and P-value = PearsonRResult(statistic=0.20137425518044424, pvalue=0.04453119658158938)\n", "Slope and P-value = PearsonRResult(statistic=0.7048952122206718, pvalue=2.721599841603967e-16)\n" ] } ], "source": [ "sample = pd.merge(microbiome, poultry[['SampleID', 'PaGMOFree','PastureTime','SampleType']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "#sample = sample[~sample.isin([np.nan, np.inf, -np.inf]).any(1)]\n", "sample = sample.drop(['Pathogen_Salmonella', 'new_Pathogen_Salmonella',\n", " 'Pathogen_Campy','new_Pathogen_Campy',\n", " 'Pathogen_Listeria','new_Pathogen_Listeria'],axis='columns')\n", "\n", "sample.PaGMOFree.replace({'Y': 1,'N':0}, regex=True, inplace=True)\n", "\n", "\n", "feces1=sample[(sample.SampleType=='Feces') & (sample.PastureTime=='Start')]\n", "feces2=sample[(sample.SampleType=='Feces') & (sample.PastureTime=='Mid')]\n", "feces3=sample[(sample.SampleType=='Feces') & (sample.PastureTime=='End')]\n", "\n", "soil1=sample[(sample.SampleType=='Soil') & (sample.PastureTime=='Start')]\n", "soil2=sample[(sample.SampleType=='Soil') & (sample.PastureTime=='Mid')]\n", "soil3=sample[(sample.SampleType=='Soil') & (sample.PastureTime=='End')]\n", "\n", "ceca=sample[sample.SampleType=='Ceca']\n", "wcrp=sample[sample.SampleType=='WCR-P']\n", "wcrf=sample[sample.SampleType=='WCR-F']\n", "\n", "\n", "print('Feces_Start', feces1.shape)\n", "print('Feces_Mid', feces2.shape)\n", "print('Feces_End', feces3.shape,'\\n')\n", "\n", "print('Soil_Start', soil1.shape)\n", "print('Soil_Mid', soil2.shape)\n", "print('Soil_End', soil3.shape,'\\n')\n", "\n", "print('Ceca', ceca.shape)\n", "print('WCR-P', wcrp.shape)\n", "print('WCR-F', wcrf.shape,'\\n')\n", "\n", "\n", "sampletypes = [feces1,feces2,feces3, soil1,soil2,soil3,ceca, wcrp, wcrf]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES_START\", 1: \"FECES_MID\", 2:\"FECES_END\",\n", " 3: \"SOIL_START\", 4: \"SOIL_MID\", 5: \"SOIL_END\",\n", " 6:\"CECA\", 7: \"WCR-P\", 8: \"WCR-F\"\n", " }\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType', 'PastureTime','PaGMOFree'],axis='columns'),sample.PaGMOFree,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1] #multiclass\n", " \n", " try:\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " except ValueError:\n", " pass\n", " \n", " \n", " \n", " \n", "# mylist2.append([f\"PaGMOFree_{sample_name[indexing]}\", rf_auc_normal])\n", " \n", " \n", " print(pd.value_counts(sample['PaGMOFree']))\n", "\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", " \n", " \n", " plt.title(f\"PaGMOFree in {sample_name[indexing]} Model\")\n", " prelim3_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','PaGMOFree','PastureTime','SampleType'],axis='columns').columns)\n", " prelim3_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", "\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim3_plot.max(), decimals=None, symbol=''))\n", "\n", " xmax=prelim3_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " \n", " plt.show()\n", " \n", " prelim3_plot.nlargest(10).to_csv(\"prelim3.csv\")\n", " top10 = pd.read_csv('prelim3.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", "# plt.plot(pdp[1][0], pdp[0][0],'.')\n", "# plt.ylabel('Partial dependence'), plt.xlabel(top10[feature])\n", "# plt.show()\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"PaGMOFree_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (14) PaSoyFree" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Feces_Start (200, 878)\n", "Feces_Mid (313, 878)\n", "Feces_End (185, 878) \n", "\n", "Soil_Start (199, 878)\n", "Soil_Mid (313, 878)\n", "Soil_End (183, 878) \n", "\n", "Ceca (185, 878)\n", "WCR-P (208, 878)\n", "WCR-F (195, 878) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "0 130\n", "1 70\n", "Name: PaSoyFree, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.27721184346574745, pvalue=0.011690692292325276)\n", "Slope and P-value = PearsonRResult(statistic=0.23271152191556577, pvalue=0.07876106993365557)\n", "Slope and P-value = PearsonRResult(statistic=0.6302492709791812, pvalue=2.519335362691599e-07)\n", "Slope and P-value = PearsonRResult(statistic=-0.823453220486303, pvalue=7.370697712730971e-26)\n", "Slope and P-value = PearsonRResult(statistic=0.6497689222445122, pvalue=2.593078221403028e-13)\n", "Slope and P-value = PearsonRResult(statistic=0.6032530835288753, pvalue=6.259420408425945e-10)\n", "Slope and P-value = PearsonRResult(statistic=0.36074069104721085, pvalue=0.03916800573085157)\n", "Slope and P-value = PearsonRResult(statistic=-0.48959296423028215, pvalue=2.3483974914495552e-07)\n", "Slope and P-value = PearsonRResult(statistic=-0.3209362318329071, pvalue=0.0011315913675162663)\n", "Slope and P-value = PearsonRResult(statistic=-0.7330143283367571, pvalue=4.321731317686096e-18)\n", "0 188\n", "1 125\n", "Name: PaSoyFree, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.1772128645815739, pvalue=0.07775936714445983)\n", "Slope and P-value = PearsonRResult(statistic=-0.5808819075981021, pvalue=2.363385642649446e-10)\n", "Slope and P-value = PearsonRResult(statistic=0.7858511761636323, pvalue=3.570391360680734e-22)\n", "Slope and P-value = PearsonRResult(statistic=-0.2535322715138948, pvalue=0.010922233914911834)\n", "Slope and P-value = PearsonRResult(statistic=-0.7759251132604962, pvalue=2.533072468102508e-21)\n", "Slope and P-value = PearsonRResult(statistic=-0.7466687294062196, pvalue=4.761860586156008e-19)\n", "Slope and P-value = PearsonRResult(statistic=-0.8417856626630101, pvalue=5.470292840361699e-28)\n", "Slope and P-value = PearsonRResult(statistic=0.7433309188241798, pvalue=8.269951974733878e-19)\n", "Slope and P-value = PearsonRResult(statistic=-0.45889500902841046, pvalue=1.5720841626606043e-06)\n", "Slope and P-value = PearsonRResult(statistic=-0.6326644284507597, pvalue=1.6511901206698802e-12)\n", "0 115\n", "1 70\n", "Name: PaSoyFree, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
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PearsonRResult(statistic=-0.9444304000126253, pvalue=3.779014913205116e-49)\n", "Slope and P-value = PearsonRResult(statistic=-0.5372832849437957, pvalue=8.246676490771289e-09)\n", "0 129\n", "1 70\n", "Name: PaSoyFree, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.9630889899718544, pvalue=1.1638955819038213e-57)\n", "Slope and P-value = PearsonRResult(statistic=0.4044268776676667, pvalue=3.3001104038653314e-05)\n", "Slope and P-value = PearsonRResult(statistic=0.7278416613243623, pvalue=9.624121773156212e-18)\n", "Slope and P-value = PearsonRResult(statistic=-0.3403687037338479, pvalue=0.001175548975010412)\n", "Slope and P-value = PearsonRResult(statistic=0.8628367559993578, pvalue=2.652958994890122e-29)\n", "Slope and P-value = PearsonRResult(statistic=0.9581912395662078, pvalue=4.6385034293633626e-55)\n", "Slope and P-value = PearsonRResult(statistic=-0.6755707976567394, pvalue=1.5004061311412694e-13)\n", "Slope and P-value = PearsonRResult(statistic=-0.8786437204301026, pvalue=3.1445622990606956e-33)\n", "Slope and P-value = PearsonRResult(statistic=0.9015791741174861, pvalue=1.936824822382132e-37)\n", "Slope and P-value = PearsonRResult(statistic=0.9765745856060878, pvalue=3.3863130455505714e-67)\n", "0 188\n", "1 125\n", "Name: PaSoyFree, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.8436795387234607, pvalue=3.1817215050501356e-28)\n", "Slope and P-value = PearsonRResult(statistic=0.9199196267321799, pvalue=1.2426002039790491e-41)\n", "Slope and P-value = PearsonRResult(statistic=-0.6886570172553892, pvalue=2.40309829278562e-15)\n", "Slope and P-value = PearsonRResult(statistic=-0.6098749822523327, pvalue=1.639559962931325e-11)\n", "Slope and P-value = PearsonRResult(statistic=0.7779395676973854, pvalue=2.165609653557281e-09)\n", "Slope and P-value = PearsonRResult(statistic=-0.9395412570913577, pvalue=2.091591432448767e-47)\n", "Slope and P-value = PearsonRResult(statistic=0.8075771686282197, pvalue=6.135446763442415e-07)\n", "Slope and P-value = PearsonRResult(statistic=-0.35871801806394354, pvalue=0.0008682128813484374)\n", "Slope and P-value = PearsonRResult(statistic=-0.304264265335362, pvalue=0.010441676226117679)\n", "Slope and P-value = PearsonRResult(statistic=0.8176964879839771, pvalue=3.061990680319793e-25)\n", "0 113\n", "1 70\n", "Name: PaSoyFree, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.8643604745208315, pvalue=5.128112236822208e-31)\n", "Slope and P-value = PearsonRResult(statistic=0.9006977238415739, pvalue=2.933431739598103e-37)\n", "Slope and P-value = PearsonRResult(statistic=0.7476193495853471, pvalue=1.11921153691082e-17)\n", "Slope and P-value = PearsonRResult(statistic=0.4767892953809732, pvalue=0.0008082511167192218)\n", "Slope and P-value = PearsonRResult(statistic=-0.9464100132628861, pvalue=6.702186347764046e-50)\n", "Slope and P-value = PearsonRResult(statistic=0.9379747580188539, pvalue=7.052598960277367e-47)\n", "Slope and P-value = PearsonRResult(statistic=-0.92144806883166, pvalue=5.0172986797271456e-42)\n", "Slope and P-value = PearsonRResult(statistic=0.9141700849279044, pvalue=3.2261427743515216e-40)\n", "Slope and P-value = PearsonRResult(statistic=-0.6769067195678785, pvalue=2.9236514283433004e-12)\n", "Slope and P-value = PearsonRResult(statistic=0.1630960791414235, pvalue=0.14069017147515495)\n", "0 115\n", "1 70\n", "Name: PaSoyFree, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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PearsonRResult(statistic=-0.921005677668105, pvalue=6.53559386443689e-42)\n", "Slope and P-value = PearsonRResult(statistic=-0.15539520772865664, pvalue=0.1861517740085703)\n", "0 114\n", "1 94\n", "Name: PaSoyFree, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.14435765040086793, pvalue=0.15187367314984124)\n", "Slope and P-value = PearsonRResult(statistic=0.6824389485355772, pvalue=1.6894908537575086e-05)\n", "Slope and P-value = PearsonRResult(statistic=-0.6384826310711262, pvalue=8.911461521417843e-13)\n", "Slope and P-value = PearsonRResult(statistic=0.8070052341067555, pvalue=3.791307215587071e-24)\n", "Slope and P-value = PearsonRResult(statistic=0.3864739767833522, pvalue=0.0007320429838329578)\n", "Slope and P-value = PearsonRResult(statistic=-0.16015605342750294, pvalue=0.11144812798552899)\n", "Slope and P-value = PearsonRResult(statistic=0.02638443548384702, pvalue=0.7944221719044945)\n", "Slope and P-value = PearsonRResult(statistic=0.06457283963942453, pvalue=0.5232936265787294)\n", "Slope and P-value = PearsonRResult(statistic=0.46374603155908656, pvalue=2.212007930941058e-06)\n", "Slope and P-value = PearsonRResult(statistic=0.6204314957359058, pvalue=2.5670396790543102e-11)\n", "0 125\n", "1 70\n", "Name: PaSoyFree, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.9052558040908565, pvalue=1.3762111972989873e-17)\n", "Slope and P-value = PearsonRResult(statistic=0.21265741730830212, pvalue=0.10900392058453387)\n", "Slope and P-value = PearsonRResult(statistic=0.42308065808101597, pvalue=0.00017299167473159853)\n", "Slope and P-value = PearsonRResult(statistic=0.5641887955586044, pvalue=4.450927204159653e-07)\n", "Slope and P-value = PearsonRResult(statistic=0.718666862852674, pvalue=9.393149543947761e-10)\n", "Slope and P-value = PearsonRResult(statistic=-0.7185834064397559, pvalue=3.857081952041175e-17)\n", "Slope and P-value = PearsonRResult(statistic=0.8776594863331583, pvalue=4.558026867536206e-33)\n", "Slope and P-value = PearsonRResult(statistic=0.6747050795440649, pvalue=1.821316493344024e-09)\n", "Slope and P-value = PearsonRResult(statistic=0.7312814731305196, pvalue=1.1624105635526434e-08)\n", "Slope and P-value = PearsonRResult(statistic=0.6331079222620495, pvalue=1.576081932032667e-12)\n" ] } ], "source": [ "sample = pd.merge(microbiome, poultry[['SampleID', 'PaSoyFree','PastureTime','SampleType']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "#sample = sample[~sample.isin([np.nan, np.inf, -np.inf]).any(1)]\n", "sample = sample.drop(['Pathogen_Salmonella', 'new_Pathogen_Salmonella',\n", " 'Pathogen_Campy','new_Pathogen_Campy',\n", " 'Pathogen_Listeria','new_Pathogen_Listeria'],axis='columns')\n", "\n", "sample.PaSoyFree.replace({'Y': 1,'N':0}, regex=True, inplace=True)\n", "\n", "\n", "feces1=sample[(sample.SampleType=='Feces') & (sample.PastureTime=='Start')]\n", "feces2=sample[(sample.SampleType=='Feces') & (sample.PastureTime=='Mid')]\n", "feces3=sample[(sample.SampleType=='Feces') & (sample.PastureTime=='End')]\n", "\n", "soil1=sample[(sample.SampleType=='Soil') & (sample.PastureTime=='Start')]\n", "soil2=sample[(sample.SampleType=='Soil') & (sample.PastureTime=='Mid')]\n", "soil3=sample[(sample.SampleType=='Soil') & (sample.PastureTime=='End')]\n", "\n", "ceca=sample[sample.SampleType=='Ceca']\n", "wcrp=sample[sample.SampleType=='WCR-P']\n", "wcrf=sample[sample.SampleType=='WCR-F']\n", "\n", "\n", "print('Feces_Start', feces1.shape)\n", "print('Feces_Mid', feces2.shape)\n", "print('Feces_End', feces3.shape,'\\n')\n", "\n", "print('Soil_Start', soil1.shape)\n", "print('Soil_Mid', soil2.shape)\n", "print('Soil_End', soil3.shape,'\\n')\n", "\n", "print('Ceca', ceca.shape)\n", "print('WCR-P', wcrp.shape)\n", "print('WCR-F', wcrf.shape,'\\n')\n", "\n", "\n", "sampletypes = [feces1,feces2,feces3, soil1,soil2,soil3,ceca, wcrp, wcrf]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES_START\", 1: \"FECES_MID\", 2:\"FECES_END\",\n", " 3: \"SOIL_START\", 4: \"SOIL_MID\", 5: \"SOIL_END\",\n", " 6:\"CECA\", 7: \"WCR-P\", 8: \"WCR-F\"\n", " }\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType', 'PastureTime','PaSoyFree'],axis='columns'),sample.PaSoyFree,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1] #multiclass\n", " \n", " try:\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " except ValueError:\n", " pass\n", " \n", " \n", " \n", " \n", "# mylist2.append([f\"PaSoyFree_{sample_name[indexing]}\", rf_auc_normal])\n", " \n", " \n", " print(pd.value_counts(sample['PaSoyFree']))\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim3_plot.max(), decimals=None, symbol=''))\n", " \n", " \n", " plt.title(f\"PaSoyFree in {sample_name[indexing]} Model\")\n", " prelim3_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','PaSoyFree','PastureTime','SampleType'],axis='columns').columns)\n", " prelim3_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", "\n", " xmax=prelim3_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " \n", " plt.show()\n", " \n", " prelim3_plot.nlargest(10).to_csv(\"prelim3.csv\")\n", " top10 = pd.read_csv('prelim3.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", "# plt.plot(pdp[1][0], pdp[0][0],'.')\n", "# plt.ylabel('Partial dependence'), plt.xlabel(top10[feature])\n", "# plt.show()\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"PaSoyFree_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": { "scrolled": false }, "source": [ "# (15) PaMedicated" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Feces_Start (200, 878)\n", "Feces_Mid (313, 878)\n", "Feces_End (185, 878) \n", "\n", "Soil_Start (199, 878)\n", "Soil_Mid (313, 878)\n", "Soil_End (183, 878) \n", "\n", "Ceca (185, 878)\n", "WCR-P (208, 878)\n", "WCR-F (195, 878) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "0 298\n", "1 15\n", "Name: PaMedicated, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
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pvalue=6.985977754099467e-16)\n", "Slope and P-value = PearsonRResult(statistic=0.8519418685716716, pvalue=2.742233783609193e-29)\n", "0 298\n", "1 15\n", "Name: PaMedicated, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.8080007961188236, pvalue=3.0194764040969498e-24)\n", "Slope and P-value = PearsonRResult(statistic=0.3340287358638358, pvalue=0.0006827977296842582)\n", "Slope and P-value = PearsonRResult(statistic=0.8600809830611607, pvalue=8.937121748877485e-09)\n", "Slope and P-value = PearsonRResult(statistic=0.822049789713171, pvalue=1.0479479050496713e-25)\n", "Slope and P-value = PearsonRResult(statistic=0.8626311757257566, pvalue=9.133777366527531e-31)\n", "Slope and P-value = PearsonRResult(statistic=0.7864663902306506, pvalue=2.122881394891068e-11)\n", "Slope and P-value = PearsonRResult(statistic=0.5419680552738269, pvalue=5.766890424961642e-09)\n", "Slope and P-value = PearsonRResult(statistic=1.0, pvalue=1.0)\n", "Slope and P-value = PearsonRResult(statistic=0.8763279064636749, pvalue=7.493622824421634e-33)\n", "Slope and P-value = PearsonRResult(statistic=0.7258410735108288, pvalue=1.3053161789332821e-17)\n" ] } ], "source": [ "sample = pd.merge(microbiome, poultry[['SampleID', 'PaMedicated','PastureTime','SampleType']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "#sample = sample[~sample.isin([np.nan, np.inf, -np.inf]).any(1)]\n", "sample = sample.drop(['Pathogen_Salmonella', 'new_Pathogen_Salmonella',\n", " 'Pathogen_Campy','new_Pathogen_Campy',\n", " 'Pathogen_Listeria','new_Pathogen_Listeria'],axis='columns')\n", "\n", "sample.PaMedicated.replace({'Bac': 1,'N':0}, regex=True, inplace=True)\n", "\n", "\n", "feces1=sample[(sample.SampleType=='Feces') & (sample.PastureTime=='Start')]\n", "feces2=sample[(sample.SampleType=='Feces') & (sample.PastureTime=='Mid')]\n", "feces3=sample[(sample.SampleType=='Feces') & (sample.PastureTime=='End')]\n", "\n", "soil1=sample[(sample.SampleType=='Soil') & (sample.PastureTime=='Start')]\n", "soil2=sample[(sample.SampleType=='Soil') & (sample.PastureTime=='Mid')]\n", "soil3=sample[(sample.SampleType=='Soil') & (sample.PastureTime=='End')]\n", "\n", "\n", "print('Feces_Start', feces1.shape)\n", "print('Feces_Mid', feces2.shape)\n", "print('Feces_End', feces3.shape,'\\n')\n", "\n", "print('Soil_Start', soil1.shape)\n", "print('Soil_Mid', soil2.shape)\n", "print('Soil_End', soil3.shape,'\\n')\n", "\n", "print('Ceca', ceca.shape)\n", "print('WCR-P', wcrp.shape)\n", "print('WCR-F', wcrf.shape,'\\n')\n", "\n", "\n", "sampletypes = [feces2,soil2]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES_MID\", 1:\"SOIL_MID\"}\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType', 'PastureTime','PaMedicated'],axis='columns'),sample.PaMedicated,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1] #multiclass\n", " \n", " try:\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " except IndexError:\n", " pass\n", " \n", " \n", " \n", " \n", "# mylist2.append([f\"PaMedicated_{sample_name[indexing]}\", rf_auc_normal])\n", " \n", " \n", " print(pd.value_counts(sample['PaMedicated']))\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim3_plot.max(), decimals=None, symbol=''))\n", " \n", " plt.title(f\"PaMedicated in {sample_name[indexing]} Model\")\n", " prelim3_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','PaMedicated','PastureTime','SampleType'],axis='columns').columns)\n", " prelim3_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", "\n", " xmax=prelim3_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " \n", " plt.show()\n", " \n", " prelim3_plot.nlargest(10).to_csv(\"prelim3.csv\")\n", " top10 = pd.read_csv('prelim3.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", "# plt.plot(pdp[1][0], pdp[0][0],'.')\n", "# plt.ylabel('Partial dependence'), plt.xlabel(top10[feature])\n", "# plt.show()\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"PaMedicated_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (16) LayerOnFarm" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Feces_Start (200, 878)\n", "Feces_Mid (313, 878)\n", "Feces_End (185, 878) \n", "\n", "Soil_Start (199, 878)\n", "Soil_Mid (313, 878)\n", "Soil_End (183, 878) \n", "\n", "Ceca (185, 878)\n", "WCR-P (208, 878)\n", "WCR-F (195, 878) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "1 190\n", "0 10\n", "Name: LayersOnFarm, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.23954052474239615, pvalue=0.01637958111034288)\n", "Slope and P-value = PearsonRResult(statistic=0.3398395475389963, pvalue=0.0005417348842896379)\n", "Slope and P-value = PearsonRResult(statistic=0.2001523764523745, pvalue=0.045867351127102586)\n", "Slope and P-value = PearsonRResult(statistic=0.20167355235818712, pvalue=0.04420888464370577)\n", "Slope and P-value = PearsonRResult(statistic=-0.18644261668588014, pvalue=0.06326756049960176)\n", "Slope and P-value = PearsonRResult(statistic=0.13448057616779524, pvalue=0.1822148257488678)\n", "Slope and P-value = PearsonRResult(statistic=-0.8598892402870565, pvalue=2.245065039468741e-30)\n", "Slope and P-value = PearsonRResult(statistic=0.232803551513606, pvalue=0.01975917514014753)\n", "Slope and P-value = PearsonRResult(statistic=-0.9308154168316657, pvalue=1.2515342397421926e-44)\n", "Slope and P-value = PearsonRResult(statistic=0.3394282268051896, pvalue=0.0005507637449807767)\n", "1 303\n", "0 10\n", "Name: LayersOnFarm, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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pvalue=5.671101690876403e-06)\n", "Slope and P-value = PearsonRResult(statistic=-0.5816866121644133, pvalue=2.8919184134129333e-08)\n", "1 175\n", "0 10\n", "Name: LayersOnFarm, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.9200187443392622, pvalue=2.4407998171460202e-20)\n", "Slope and P-value = PearsonRResult(statistic=-0.8691796412423306, pvalue=1.364144970786336e-06)\n", "Slope and P-value = PearsonRResult(statistic=-0.6594668360065518, pvalue=5.1857583289410156e-11)\n", "Slope and P-value = PearsonRResult(statistic=-0.60604355469003, pvalue=2.369171231897009e-11)\n", "Slope and P-value = PearsonRResult(statistic=-0.4113598445305147, pvalue=2.1216448526921653e-05)\n", "Slope and P-value = PearsonRResult(statistic=-0.866100888803974, pvalue=1.6443178852187332e-06)\n", "Slope and P-value = PearsonRResult(statistic=0.7582359308262231, pvalue=6.565023127697024e-20)\n", "Slope and P-value = PearsonRResult(statistic=-0.7871431354632233, pvalue=2.745622275456922e-22)\n", "Slope and P-value = PearsonRResult(statistic=-0.9125785387583292, pvalue=2.0887680758870463e-10)\n", "Slope and P-value = PearsonRResult(statistic=-0.9378532854474829, pvalue=0.0017880717380181655)\n", "1 189\n", "0 10\n", "Name: LayersOnFarm, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.9744924311550592, pvalue=8.415045309271846e-63)\n", "Slope and P-value = PearsonRResult(statistic=-0.5758942447442841, pvalue=0.00010095823482619254)\n", "Slope and P-value = PearsonRResult(statistic=0.22603418200456035, pvalue=0.023742299401765834)\n", "Slope and P-value = PearsonRResult(statistic=-0.7313903234997057, pvalue=4.2258168028059343e-14)\n", "Slope and P-value = PearsonRResult(statistic=-0.8544220655306064, pvalue=3.6964767412538697e-09)\n", "Slope and P-value = PearsonRResult(statistic=-0.8368098476601087, pvalue=0.004908774474193135)\n", "Slope and P-value = PearsonRResult(statistic=-0.5658236231253323, pvalue=0.08821240533883687)\n", "Slope and P-value = PearsonRResult(statistic=-0.8184588091202027, pvalue=1.1240266053526103e-21)\n", "Slope and P-value = PearsonRResult(statistic=-0.8109754584004873, pvalue=1.0055659170291772e-21)\n", "Slope and P-value = PearsonRResult(statistic=-0.8045847848364658, pvalue=0.016028487664026564)\n", "1 303\n", "0 10\n", "Name: LayersOnFarm, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.5458983390373846, pvalue=0.2624927077684263)\n", "Slope and P-value = PearsonRResult(statistic=-0.6057345858638477, pvalue=0.002811371225601678)\n", "Slope and P-value = PearsonRResult(statistic=-0.601029220265006, pvalue=3.807393572971131e-11)\n", "Slope and P-value = PearsonRResult(statistic=-0.8308068811996505, pvalue=0.00023275126343759702)\n", "Slope and P-value = PearsonRResult(statistic=-0.9595509948923885, pvalue=2.1689968163031772e-16)\n", "Slope and P-value = PearsonRResult(statistic=-0.7126667984978841, pvalue=2.7632648813635368e-08)\n", "Slope and P-value = PearsonRResult(statistic=-0.8579621793076193, pvalue=3.8896470785465827e-13)\n", "Slope and P-value = PearsonRResult(statistic=-0.7348775082863908, pvalue=1.0494618253709617e-17)\n", "Slope and P-value = PearsonRResult(statistic=-0.4705314920870517, pvalue=7.814472057714008e-07)\n", "Slope and P-value = PearsonRResult(statistic=nan, pvalue=nan)\n", "1 173\n", "0 10\n", "Name: LayersOnFarm, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.6654654644896629, pvalue=4.262271962549621e-14)\n", "Slope and P-value = PearsonRResult(statistic=-0.7014596417305434, pvalue=0.18679452008009473)\n", "Slope and P-value = PearsonRResult(statistic=-0.8316269522385213, pvalue=8.907277752720434e-27)\n", "Slope and P-value = PearsonRResult(statistic=0.27803712375529815, pvalue=0.005096574388599641)\n", "Slope and P-value = PearsonRResult(statistic=-0.918938670378832, pvalue=2.2029379808367974e-41)\n", "Slope and P-value = PearsonRResult(statistic=-0.7859782288191052, pvalue=3.479625465914393e-22)\n", "Slope and P-value = PearsonRResult(statistic=-0.7304283192903871, pvalue=6.463807558899728e-18)\n", "Slope and P-value = PearsonRResult(statistic=-0.8107919587124796, pvalue=2.5470282678299305e-05)\n", "Slope and P-value = PearsonRResult(statistic=-0.7540250204510337, pvalue=0.001837475341084936)\n", "Slope and P-value = PearsonRResult(statistic=-0.16886376967684835, pvalue=0.09305635639198861)\n", "1 175\n", "0 10\n", "Name: LayersOnFarm, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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PearsonRResult(statistic=0.7677442306640392, pvalue=1.182873647188857e-20)\n", "Slope and P-value = PearsonRResult(statistic=-0.7617399712039146, pvalue=1.226001561587974e-09)\n", "1 198\n", "0 10\n", "Name: LayersOnFarm, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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PearsonRResult(statistic=-0.8840737299122895, pvalue=5.5243509806051835e-06)\n", "Slope and P-value = PearsonRResult(statistic=-0.8815988299655687, pvalue=0.00014974231838668316)\n", "1 185\n", "0 10\n", "Name: LayersOnFarm, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.7661796587294029, pvalue=0.0036599418267211424)\n", "Slope and P-value = PearsonRResult(statistic=-0.8640712325444417, pvalue=0.3358131025778489)\n", "Slope and P-value = PearsonRResult(statistic=-0.6150776278302268, pvalue=0.10458734282567189)\n", "Slope and P-value = PearsonRResult(statistic=-0.8798156414189465, pvalue=2.0126398571795535e-33)\n", "Slope and P-value = PearsonRResult(statistic=-0.8811144827567351, pvalue=6.310301635954152e-07)\n", "Slope and P-value = PearsonRResult(statistic=-0.7766723526626673, pvalue=0.000659878060310961)\n", "Slope and P-value = PearsonRResult(statistic=-0.9013362476676574, pvalue=0.2851747697069022)\n", "Slope and P-value = PearsonRResult(statistic=-0.5737913896699037, pvalue=0.00016572479051921843)\n", "Slope and P-value = PearsonRResult(statistic=-0.9306379846903741, pvalue=9.307862962584492e-05)\n", "Slope and P-value = PearsonRResult(statistic=-0.942415020827197, pvalue=1.2464804841933889e-20)\n" ] } ], "source": [ "sample = pd.merge(microbiome, poultry[['SampleID', 'LayersOnFarm','PastureTime','SampleType']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "#sample = sample[~sample.isin([np.nan, np.inf, -np.inf]).any(1)]\n", "sample = sample.drop(['Pathogen_Salmonella', 'new_Pathogen_Salmonella',\n", " 'Pathogen_Campy','new_Pathogen_Campy',\n", " 'Pathogen_Listeria','new_Pathogen_Listeria'],axis='columns')\n", "\n", "sample.LayersOnFarm.replace({'Y': 1,'N':0}, regex=True, inplace=True)\n", "\n", "\n", "\n", "feces1=sample[(sample.SampleType=='Feces') & (sample.PastureTime=='Start')]\n", "feces2=sample[(sample.SampleType=='Feces') & (sample.PastureTime=='Mid')]\n", "feces3=sample[(sample.SampleType=='Feces') & (sample.PastureTime=='End')]\n", "\n", "soil1=sample[(sample.SampleType=='Soil') & (sample.PastureTime=='Start')]\n", "soil2=sample[(sample.SampleType=='Soil') & (sample.PastureTime=='Mid')]\n", "soil3=sample[(sample.SampleType=='Soil') & (sample.PastureTime=='End')]\n", "\n", "ceca=sample[sample.SampleType=='Ceca']\n", "wcrp=sample[sample.SampleType=='WCR-P']\n", "wcrf=sample[sample.SampleType=='WCR-F']\n", "\n", "\n", "print('Feces_Start', feces1.shape)\n", "print('Feces_Mid', feces2.shape)\n", "print('Feces_End', feces3.shape,'\\n')\n", "\n", "print('Soil_Start', soil1.shape)\n", "print('Soil_Mid', soil2.shape)\n", "print('Soil_End', soil3.shape,'\\n')\n", "\n", "print('Ceca', ceca.shape)\n", "print('WCR-P', wcrp.shape)\n", "print('WCR-F', wcrf.shape,'\\n')\n", "\n", "\n", "sampletypes = [feces1,feces2,feces3, soil1,soil2,soil3,ceca, wcrp, wcrf]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES_START\", 1: \"FECES_MID\", 2:\"FECES_END\",\n", " 3: \"SOIL_START\", 4: \"SOIL_MID\", 5: \"SOIL_END\",\n", " 6:\"CECA\", 7: \"WCR-P\", 8: \"WCR-F\"\n", " }\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType', 'PastureTime','LayersOnFarm'],axis='columns'),sample.LayersOnFarm,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1] #multiclass\n", " \n", " try:\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " except ValueError:\n", " pass\n", " \n", " \n", " \n", " \n", "# mylist2.append([f\"LayersOnFarm_{sample_name[indexing]}\", rf_auc_normal])\n", " \n", " \n", " print(pd.value_counts(sample['LayersOnFarm']))\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim3_plot.max(), decimals=None, symbol=''))\n", " \n", " \n", " plt.title(f\"LayersOnFarm in {sample_name[indexing]} Model\")\n", " prelim3_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','LayersOnFarm','PastureTime','SampleType'],axis='columns').columns)\n", " prelim3_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", "\n", " xmax=prelim3_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " \n", " plt.show()\n", " \n", " prelim3_plot.nlargest(10).to_csv(\"prelim3.csv\")\n", " top10 = pd.read_csv('prelim3.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", "# plt.plot(pdp[1][0], pdp[0][0],'.')\n", "# plt.ylabel('Partial dependence'), plt.xlabel(top10[feature])\n", "# plt.show()\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"LayersOnFarm_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (17) CattleOnFarm" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Feces_Start (200, 878)\n", "Feces_Mid (313, 878)\n", "Feces_End (185, 878) \n", "\n", "Soil_Start (199, 878)\n", "Soil_Mid (313, 878)\n", "Soil_End (183, 878) \n", "\n", "Ceca (185, 878)\n", "WCR-P (208, 878)\n", "WCR-F (195, 878) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "1 110\n", "0 90\n", "Name: CattleOnFarm, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.29814593768415265, pvalue=0.005581596294268978)\n", "Slope and P-value = PearsonRResult(statistic=-0.26603003770740336, pvalue=0.023901929630580538)\n", "Slope and P-value = PearsonRResult(statistic=0.21917635199939944, pvalue=0.084369889891924)\n", "Slope and P-value = PearsonRResult(statistic=-0.6646730459275694, pvalue=4.681195673838551e-14)\n", "Slope and P-value = PearsonRResult(statistic=0.36847026748941314, pvalue=0.0013387817520822654)\n", "Slope and P-value = PearsonRResult(statistic=-0.7634167757260437, pvalue=3.702085010517267e-19)\n", "Slope and P-value = PearsonRResult(statistic=-0.18489116032444808, pvalue=0.06553692733746169)\n", "Slope and P-value = PearsonRResult(statistic=0.2472156716110547, pvalue=0.14017252223865365)\n", "Slope and P-value = PearsonRResult(statistic=-0.8513884653447592, pvalue=3.2464707277852256e-29)\n", "Slope and P-value = PearsonRResult(statistic=0.2720594722018367, pvalue=0.0061768614381782425)\n", "1 175\n", "0 138\n", "Name: CattleOnFarm, dtype: int64\n" ] }, { "data": { "image/png": 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y7wHclrZvTuVJ+a9NeRdHxOw66qjNWODUtJZlh3SOzczMGsWqEmxoRSuIiDcj4lpgf2BHSR0KZLsKuDoidgB+ClTkV1GzyvR7YV7aYpaNFt0I/Ag4AbgrIhbVKF/bMalAW4XUlae2OhaR/uaSBHwDICJGA/sAM4GbJZ1cRPtmZmZFWVWCjWeAnpIqJLUFSvr2FkkHp4srQBeyoOAzYA7QLi9re7ILLsApNao5LLXfgWxh6di62oyI98imVn4DDCmQZTRwuKS1Ja0DHAE8DTwJHJsLhvKmQJ4Fjk/bJ5KdE1L+M1PeVpLWraOO6cDOueMB2qT93wQ+iIjrgMFAt7qOzczMrBSrxJqNiBgr6UFgEvA2UA2UskLxJOBySfPIPt2fGBGLJT0E3C3pMOBsYABwl6SZwPNA57w6XgQeBjoBf4yI9yRtVU+7twIbRcTLBY5pvKQhqV6A6yNiAoCkC4FRkhYDE4DewDnADZLOBXLrQAB+DgySdBpZEHVmRDxXSx3XAQ9IepEsIPki1dEDOFfSV2R359Q7srHDZu2p9jf2mZlZEZQtL2j+JLWNiLmS1iYbFegTEeNXUtsDyFtIWkK5q4EJETG4LB1rQlVVVVFdXd3U3TAzs2ZC0riIqCq0b5UY2UgGSdqWbB3F0JUVaDSUpHFkIwe/qi+vmZlZS7bKBBsR0Sv/taRryG4zzdcFeKNG2hURcSMrICIGNKDMzvXnMjMza/lWmWCjpog4q6n7YGZmZvVbVe5GMTMzs1WUgw0zMzMrKwcbZmZmVlYONszMzKysHGyYmZlZWTnYMDMzs7JaZW99taY1ZeZsKvs/3NTdMFttTPfjAWwV5pENMzMzKysHG2ZmZlZWDjbKSNKOkibmvT5B0jxJuUe77yBpch3lqyRd2Yj9GSmpKm1Pl7RhY9VtZmZWGwcb5TUF+Kakdun1nsCrwE55r8fUVjgiqiPinPJ20czMrLwcbBRB0vmSXpX0uKRhkvoVUy4ilgBjgd1S0s7ANWRBBun3s5LWkXSDpLGSJkg6LLXbQ9LwtL2vpInpZ4KkdpI6Shqd0qZK2jvlPVDSc5LGS7pLUtt6ju9+SeMkvSSpT8knyMzMrA4ONuqRph2OIhuNOBKoKrGKZ4E9Ja0DLAFGsnywMQb4NfBUROwC7AdckvLn6wecFRFdgb2B+UAv4NGUtiMwMU2N/AY4ICK6AdXAL+vp44/TU2qrgHMkdSiUSVIfSdWSqhfPm13s8ZuZ2WrOt77WrzvwQETMB5D0UInlxwC/Ap4GxkbEm5K2lLQR0DYi3pJ0IHBo3ohJBdCpQD2XSboVuDciZkgaC9yQ1oDcHxETJe0LbAuMkQTwDeC5evp4jqQj0vYWQBfg45qZImIQMAhgzY5dopSTYGZmqy8HG/XTCpZ/HtiFLGjJXfRnAMeTjXrk2jgqIl5brmFpk9x2RAyU9DDwX8Dzkg6IiNGS9gEOBm6WdAnwKfB4RJxQTOck9QAOAPaIiHmSRpIFO2ZmZo3C0yj1ewboKakirX0o6Zt1ImIO8C7Qm2XBxnNAX5YFG48CZysNRUjaiRokfTsipkTERWRTI9tI+ibwQURcBwwGupEFN3tJ2jKVW1vSVnV0sT3waQo0tgF2L+X4zMzM6uNgox4RMRZ4EJgE3Et2oS91wcIYYM2IeDe9fg74FsuCjT8CbYDJkqam1zX1TYtAJ5Gt1/gX0INsncYEsnUlV0TEh2SBzbB0W+3zwDZ19O0RoHXK+8eU38zMrNEowlPv9ZHUNiLmSlobGA30iYjxTd2vplRVVRXV1dVN3Q0zM2smJI2LiII3UXjNRnEGSdqWbC3D0NU90DAzMyuFg40iRESv/NeSrgH2qpGtC/BGjbQrIuLGcvbNzMysuXOw0QARcVZT98HMzGxV4QWiZmZmVlYONszMzKysHGyYmZlZWTnYMDMzs7JysGFmZmZl5WDDzMzMysrBhpmZmZWVv2fDGmTKzNlU9n+4qbthtkqaPrCk5zmarfI8smFmZmZl5WDDzMzMymq1DzYkDZF0dAPL9pa06Qq03UPS8LR9qKT+Da3LzMysufKajRXTG5gKvLeiFUXEg8CDK1qPmZlZc9MiRjYknS/pVUmPSxomqV8D69lZ0ihJ4yQ9KqljSu8q6XlJkyXdJ2n9NBpSBdwqaaKktST9V+rHM5KuzBu12FXSs5ImpN9bF2i7t6Sr0/YmqZ1J6WfPlP5LSVPTT9+8sienvk2SdHNtdUiqlDQ1r1w/SQPS9jmSXk713F7L+ekjqVpS9eJ5sxtyis3MbDW0yo9sSKoCjgJ2Ijue8cC4BtTTBrgKOCwiPpR0HHAh8GPgJuDsiBgl6Q/A7yKir6SfAf0iolpSBfAPYJ+ImCZpWF71r6b0RZIOAP6U+lybK4FREXGEpFZAW0k7A6cCuwECXpA0CvgS+DWwV0R8JGmD2uoA1q+jzf5A54hYKGm9QhkiYhAwCGDNjl2ijrrMzMyWWuWDDaA78EBEzAeQ9FAD69ka2B54XBJAK2CWpPbAehExKuUbCtxVoPw2wFsRMS29Hgb0SdvtgaGSugABtKmnL98DTgaIiMXAbEndgfsi4gsASfcCe6f67o6Ij1L+T+qoo65gYzLZKM39wP319M/MzKxoLWEaRY1Yz0sR0TX97BARBzZSP/4IjIiI7YGeQEUD+1dberGjDItY/m+e34+DgWuAnYFxklpCIGpmZs1ASwg2ngF6SqqQ1JbsotkQrwEbSdoDsmkVSdtFxGzgU0l7p3wnAblRjjlAu7T9KvAtSZXp9XF5dbcHZqbt3kX05UngzNSPVpLWBUYDh0taW9I6wBHA0ynvsZI6pPwb1FHH+8DGkjpIWhM4JO1fA9giIkYA5wHrkU27mJmZrbBVPtiIiLFkd3FMAu4FqoFSVi+2BhZGxJfA0cBFkiYBE4E9U55TgEskTQa6An9I6UOAv0uamF7/N/CIpGfILuy5flwM/FnSGLLpmfr8HNhP0hSy9SfbRcT41N6LwAvA9RExISJeIltbMir1+7I66vgq9f0FYDhZgETq0y0p7wTg8oj4rIh+mpmZ1UsRq/46P0ltI2KupLXJRgD6pItzfeXWAMYCJ6eLdmP1Q2RTEm9ExOUrWm9zVFVVFdXV1U3dDTMzayYkjYuIqkL7VvmRjWRQGl0YD9xTZKCxKdl3ZDzfGIFG8pPUj5fIpk7+0Uj1mpmZrbJaxCLAiOiV/1rSNcBeNbJ1Ad6okXZJRNzYiP24HGiRIxlmZmYN1SKCjZoi4qym7oOZmZllWso0ipmZmTVTDjbMzMysrBxsmJmZWVk52DAzM7OycrBhZmZmZeVgw8zMzMrKwYaZmZmVVYv8ng0rvykzZ1PZ/+Gm7oZZk5o+sKHPfTRbvXhkw8zMzMrKwYaZmZmVlYONRiKptaSPJP25Rvr/lam96ZI2LJB+hqSTy9GmmZlZQzjYaDwHAq8Bx6ZHzOeUJdioTUT8PSJuWpltmpmZ1cXBRiLpfEmvSnpc0jBJ/Uqs4gTgCuAdYPdU50BgLUkTJd2a0n4paWr66ZvSKlPbQyVNlnS3pLXTvv0lTZA0RdINktbMa/NcSS+mny1T/gG5vksaKakqbW8oaXra7i3pfkkPSZom6WepXxMkPS9pg1rOUR9J1ZKqF8+bXeLpMTOz1ZWDDSBdkI8CdgKOBKpKLL8WsD8wHBhGFngQEf2B+RHRNSJOlLQzcCqwG1lA8hNJO6VqtgYGRcR3gc+B/5ZUAQwBjouIHcjuHjozr+nPI2JX4GrgryUe9vZAL2BX4EJgXkTsBDwHFJyGiYhBEVEVEVWt1m5fYnNmZra6crCR6Q48EBHzI2IO8FCJ5Q8BRkTEPOAe4AhJrWpp576I+CIi5gL3Anunfe9GxJi0fUvKuzUwLSJeT+lDgX3y6huW93uPEvs8IiLmRMSHwGyWHfMUoLLEuszMzGrlYCOj+rPU6QTggDRNMQ7oAOxXYjtR4HV9/YpatnMWsexvXFFj38K87SV5r5fg718xM7NG5GAj8wzQU1KFpLZA0d/UI2ldslGIThFRGRGVwFmkqRTgK0lt0vZo4HBJa0taBzgCeDrt6yQpNzpxQurTq0Blbj0GcBIwKq/54/J+P1ege9OBndP20cUek5mZWWPyJ1ggIsZKehCYBLwNVJNNLRTjSOCpiMgfKXgAuDgt5hwETJY0Pq3bGAK8mPJdHxETJFUCrwCnSPoH8AZwbUQskHQqcJek1sBY4O957awp6QWyoPEEvu5S4E5JJwFPFXk8ZmZmjUoRhUbfVz+S2kbE3HQXyGigT0SMX0ltVwLDI2L7ldFeY6iqqorq6uqm7oaZmTUTksZFRMEbLDyyscwgSduSrW0YurICDTMzs5bOwUYSEb3yX0u6BtirRrYuZFMc+a6IiBtXsO3pZLeimpmZtTgONmoREWc1dR/MzMxaAt+NYmZmZmXlYMPMzMzKysGGmZmZlZWDDTMzMysrBxtmZmZWVg42zMzMrKwcbJiZmVlZ+Xs2rEGmzJxNZf+Hm7obZk1i+sCin9VoZnhkw8zMzMrMwYaZmZmVVYsONiSNlPSapEPT6yGSpkmaKGmSpP0bqZ0qSVc2Rl1NSdIISXMlFXxqn5mZWUOsDms2ToyI/GehnxsRd0vaDxhE9nC1FZLqX+Wftx4R+0ka2dT9MDOzlqXZj2xIOl/Sq5IelzRMUr9Gqvo5YLPURm9JV+e1OVxSj7Q9V9JFksZJekLSrmnE5K28EZMekoan7QGSbsjLc05evb+UNDX99M1LP1nS5DTacnNK+6akJ1P6k5I6pfRNJN2X8k6StGcddQyRdHReO3PT746SRqcRnqmS9i7mhEnqI6laUvXiebNLP+NmZrZaatYjG2k4/yhgJ7K+jgfGNVL1PwTuLyLfOsDIiPgfSfcBFwDfB7YFhgIPFiizDbAf0A54TdK1wHeBU4HdAAEvSBoFfAn8GtgrIj6StEGq42rgpogYKunHwJXA4en3qIg4QlIroK2k7Wqpoza9gEcj4sJUx9pFnAciYhDZaBBrduwSxZQxMzNr1sEG0B14ICLmA0h6qBHqvETSxcDGwO5F5P8SeCRtTwEWRsRXkqYAlbWUeTgiFgILJX0AbEJ2LPdFxBcAku4F9gYCuDsiPgKIiE9SHXsAR6btm4GL0/b3gJNT3sXAbEkn11JHbcYCN0hqA9wfERPrPQtmZmYN1NynUVSGOs8FtgR+QzYyAbCI5c9FRd72VxGR+xS/BFgIEBFLqD1YW5i3vTjlq+1YRBZw1KeuPLXVsfS4JAn4BkBEjAb2AWYCN6dgxczMrCyae7DxDNBTUoWktkCjfJNOChSuANaQ9ANgOtBV0hqStgB2bYx2ahgNHC5pbUnrAEcATwNPAsdK6gCQNwXyLHB82j6R7FyQ8p+Z8raStG4ddUwHdk7bhwFt0v5vAh9ExHXAYKBbox+tmZlZ0qynUSJirKQHgUnA22R3fDTKysSICEkXAOcBBwDTyKZJppKtDWlUETFe0hDgxZR0fURMAJB0ITBK0mJgAtAbOIdsquNc4EOy9R4APwcGSTqNbNTkzIh4rpY6rgMekPQiWUDyRaqjB3CupK+AuaRpmVLssFl7qv0timZmVgQtmyFoniS1jYi5ktYmGx3oExFFBQPpNs5+NW59tToUe86qqqqiutqn1czMMpLGRUTB72lq7tMokH2Kn0g22nBPsYFG8gkwJHeLqtVN0gjgW8BXTd0XMzNrOZr1NApARPTKfy3pGmCvGtm6AG/USLsiIo7EihYR+zV1H8zMrOVp9sFGTRFxVlP3wczMzIq3KkyjmJmZ2SrMwYaZmZmVlYMNMzMzKysHG2ZmZlZWDjbMzMysrBxsmJmZWVmtcre+WvMwZeZsKvs/3NTdMCu76f5afrMV5pENMzMzKysHG2ZmZlZWLSbYkDRE0jRJEyVNkrR/U/cJQNIASf3S9h8kHZC2r5e0bdqe20htPZt+V0qamrZ7SBreGPWbmZk1REtbs3FuRNwtaT9gENkzU5qNiPht3vbpZah/z8au08zMbEU1q5ENSedLelXS45KG5UYEGuA5YLNUZ29JV+e1MVxSj7Q9V9JFksZJekLSrpJGSnor96TYVP5+SQ+lkZOfSfqlpAmSnpe0Qcr3bUmPpLqelrRNgeMbIunotD1SUlXevr9IGi/pSUkbpbSfSBqbRmrukbR2St9E0n0pfZKkPXPHU8/5XTrKkl5PTaMg60h6ONU1VdJxDTrrZmZmBTSbYCNdeI8CdgKOBKrqLlGnHwL3F5FvHWBkROwMzAEuAL4PHAH8IS/f9kAvYFfgQmBeROxEFtScnPIMAs5OdfUD/lZCf9cBxkdEN2AU8LuUfm9E7BIROwKvAKel9CuBUSm9G/BSCW0V8kPgvYjYMSK2Bx4plElSH0nVkqoXz5u9gk2amdnqojlNo3QHHoiI+QCSHmpAHZdIuhjYGNi9iPxfsuzCOgVYGBFfSZoCVOblGxERc4A5kmYDD+WV+a6ktsCewF2ScmXWLKHfS4A70vYtwL1pe3tJFwDrAW2BR1P690hBTkQsBlb0yj8FuFTSRcDwiHi6UKaIGEQWVLFmxy6xgm2amdlqotmMbACqP0u9zgW2BH4DDE1pi1j+OCvytr+KiNxFcwmwECAilrB8ILYwb3tJ3utcvjWAzyKia97Pd1bgOHJ9GgL8LCJ2AH5fo+8NUfBcRMTrwM5kQcefJf22QFkzM7MGaU7BxjNAT0kVaaSgQd+kkwKFK4A1JP0AmA50lbSGpC3IpkIaVUR8DkyTdAyAMjuWUMUawNFpuxfZuQBoB8yS1AY4MS//k8CZqa1WktYtsp3pZNMuSOoGdE7bm5JNDd0CXJrLY2Zm1hiazTRKRIyV9CAwCXgbqKaB0wMREWn64TzgAGAa2af2qcD4xunx15wIXCvpN0Ab4HayYynGF8B2ksaRHXNugeb5wAtk52MKWfAB8HNgkKTTgMVkgcdzRbRzD3CypInAWOD1lL4D2RTUEuCrVJ+ZmVmj0LJZhKYnqW1EzE13XYwG+kREuYIDWwFVVVVRXV3d1N0wM7NmQtK4iCh4c0ezGdlIBqUvuqoAhjrQMDMzW/U1q2AjInrlv5Z0DbBXjWxdgDdqpF0RETeWs29mZmbWMM0q2KgpIs5q6j6YmZnZimlOd6OYmZlZC+Rgw8zMzMrKwYaZmZmVlYMNMzMzKysHG2ZmZlZWDjbMzMysrBxsmJmZWVk16+/ZsOZryszZVPZ/uKm7YVaS6QMb9HxHM1tBHtkwMzOzsnKwYWZmZmXlYCOR1FrSR5L+3NR9MTMza0kcbCxzIPAacKwklbMhSa3KWb+ZmVlz0mKCDUnnS3pV0uOShknqV2IVJwBXAO8Au+fVO13S7yWNlzRF0jYpfVdJz0qakH5vndJbSbpE0lhJkyX9NKX3kDRC0m3AFEkVkm5MdU6QtF9e+UtT+mRJZ6f0/VO+KZJukLRmSt8ltT9J0ouS2tVRx3RJG6btKkkj0/a+kiamnwmS2tVyjvtIqpZUvXje7BJPr5mZra5axN0okqqAo4CdyI5pPDCuhPJrAfsDPwXWIws8nsvL8lFEdJP030A/4HTgVWCfiFgk6QDgT6kPpwGzI2KXFBCMkfRYqmdXYPuImCbpVwARsUMKYB6TtBVwKtAZ2CnVvYGkCmAIsH9EvC7pJuBMSX8D7gCOi4ixktYF5gN9atZRzynoB5wVEWMktQUWFMoUEYOAQQBrduwS9Z1XMzMzaDkjG92BByJifkTMAR4qsfwhwIiImAfcAxxRY6rj3vR7HFCZttsDd0maClwObJfSDwROljQReAHoAHRJ+16MiGl5fb4ZICJeBd4GtgIOAP4eEYvSvk+ArYFpEfF6KjsU2Celz4qIsSnv56lcoTrqMga4TNI5wHq5cmZmZo2hpQQbK7rG4gTgAEnTyQKKDsB+efsXpt+LWTYa9EeyAGV7oCdQkdeXsyOia/rpHBG5kY0viuizgJqjBqXkrSt9Ecv+5rn+EhEDyUZr1gKez00VmZmZNYaWEmw8A/RM6yDaAkV/c0+aeugOdIqIyoioBM4iC0Dq0h6YmbZ756U/SjbF0SbVv5WkdQqUHw2cmMsDdCJboPoYcIak1mnfBmRTNpWStkxlTwJGpfRNJe2S8rZL5QrVATAd2DltH5V3Dr4dEVMi4iKgGnCwYWZmjaZFBBtpGuFBYBLZlEc1UOwKxiOBpyJiYV7aA8ChuUWYtbgY+LOkMUD+lMv1wMvA+DTF8g8Kr435G9BK0hSydRe9Ux+uJ1ukOlnSJKBXRCwgW8txV8q/hGya5EvgOOCqlPdxshGLr9WR2vw9cIWkp8lGaXL6Spqa8s4H/lXHcZuZmZVEES1jnZ+kthExV9LaZKMGfSJifFP3q6WqqqqK6urqpu6GmZk1E5LGRURVoX0t4m6UZJCkbck+2Q91oGFmZtY8tJhgIyJ65b+WdA2wV41sXYA3aqRdERE3lrNvZmZmq7MWE2zUFBFnNXUfzMzMrIUsEDUzM7Pmy8GGmZmZlZWDDTMzMysrBxtmZmZWVg42zMzMrKwcbJiZmVlZOdgwMzOzsmqx37Nh5TVl5mwq+z/c1N0wq9P0gUU/k9HMysgjG2ZmZlZWDjbMzMysrOoNNiQtljQxPYL8rvRU1aJI6i3p6lr2PVtP2UpJvfJeV0m6sti288pNlzQlHcMUSYcV6oOkSyS9lH6fIenkWvo0tZ72aj3mEvvdQ9KeDSjXoPNkZmZWLsWs2ZgfEV0BJN0KnAFcltspqVVELC614Yio70JaCfQCbkv5q4GGPtN8v4j4SNLWwGPAAwX68FNgo4hY2MA2GlsPYC5QZ1CWT1LrFTxPZmZmja7UaZSngS3Tp+4Rkm4DpkiqkHRjGjmYIGm/vDJbSHpE0muSfpdLlDQ3/VYaTZiayh+XsgwE9k4jEr9IbQ5PZdrmtTdZ0lFF9n9d4NMCfXgQWAd4QdJxkgZI6pf27SxpkqTngLPyyjbkmO+XNC6NoPTJS/+hpPGpnSclVZIFdb9Ix7+3pI0k3SNpbPrZK5UdIGmQpMeAm2qcp6XHkV5PTaMzlZJelXR9SrtV0gGSxkh6Q9KuhU6epD6SqiVVL543u8hTbmZmq7ui70aR1Bo4CHgkJe0KbB8R0yT9CiAidpC0DfCYpK3y8wHzgLGSHk6fvnOOBLoCOwIbpjyjgf5Av4g4JLXfI6/M+cDsiNgh7Vu/nu6PkCTgW8CxNXdGxKGS5uaN4AzI230jcHZEjJJ0SV76WQ045h9HxCeS1krp95AFfNcB+6RzuUHK83dgbkRcmvp0G3B5RDwjqRPwKPCd1N7OQPeImF/jPNVlS+AYoA8wlmwUqTtwKPB/wOEFztMgYBDAmh27RJHtmJnZaq6YYGMtSRPT9tPAYGBP4MWImJbSuwNXAUTEq5LeBnIX3scj4mMASfemvPnBRndgWJqKeV/SKGAX4PM6+nQAcHzuRUR8WkdeWDaN8m3gSUkjI2JuPWWQ1B5YLyJGpaSbyQKuXL9LPeZzJB2R8mwBdAE2AkbnzmVEfFLHMW+bxUwArCupXdp+MCLm13c8NUyLiCmpjy8BT0ZESJpCNoVlZmbWKEpas5GTLnhf5CfVUb7mJ+Car+sqWxsVqKdeEfGmpPeBbYEXV7Cdko45jTgcAOwREfMkjQQq6mkj3xqp7HJBRYG/Rb5FLD9VVpG3nb82ZUne6yX4+1fMzKwRNdatr6OBEwHSVEIn4LW07/uSNkhTB4cDYwqUPU5SK0kbAfuQBQJzgHYU9hjws9yLIqZRcvk2BjoDbxeTPyI+A2ZL6p6STqzR71KOuT3waQo0tgF2T3mfA/aV1DnVtUFKr3n8NY+5axGHMB3olvJ3Izt2MzOzlaqxgo2/Aa3SEPwdQO+8uzqeIZt+mAjcU2O9BsB9wGRgEvAUcF5E/CelLUqLJn9Ro8wFwPppceMkYD/qNiJNBY0A+kfE+yUc26nANWmBaP6oQqnH/AjQWtJk4I/A8wAR8SHZuol707Hckep4CDgit0AUOAeoUrYg9mWyBaT1uQfYIB37mcDrJRy3mZlZo1CE1/lZ6aqqqqK62nfYmplZRtK4iKgqtM/fIGpmZmZl1WIWAkp6AVizRvJJuTsuzMzMrGm0mGAjInZr6j6YmZnZ17WYYMPMzFYdX331FTNmzGDBggVN3RUrUUVFBZtvvjlt2rQpuoyDDTMzW+lmzJhBu3btqKyszH1fkK0CIoKPP/6YGTNm0Llz8d+m4AWiZma20i1YsIAOHTo40FjFSKJDhw4lj0g52DAzsybhQGPV1JC/m4MNMzMzKyuv2TAzsyZX2f/hRq1v+sCDi8p33333ceSRR/LKK6+wzTbbADBy5EguvfRShg8fvjRf7969OeSQQzj66KPp0aMHs2bNoqKigm984xtcd911dO3aFYDZs2dz9tlnM2ZM9mSOvfbai6uuuor27dsD8Prrr9O3b19ef/112rRpww477MBVV13FJpts0uBj/eSTTzjuuOOYPn06lZWV3Hnnnay//tef4nH55Zdz/fXXI4kddtiBG2+8kYqKCiZOnMgZZ5zBggULaN26NX/729/YddddmTJlCn/5y18YMmRIg/uW42DDGmTKzNmN/p+DWUMUe1ExK2TYsGF0796d22+/nQEDBhRd7tZbb6Wqqoobb7yRc889l8cffxyA0047je23356bbroJgN/97necfvrp3HXXXSxYsICDDz6Yyy67jJ49ewIwYsQIPvzwwxUKNgYOHMj+++9P//79GThwIAMHDuSiiy5aLs/MmTO58sorefnll1lrrbU49thjuf322+nduzfnnXcev/vd7zjooIP45z//yXnnncfIkSPZYYcdmDFjBu+88w6dOnVqcP/A0yhmZraamjt3LmPGjGHw4MHcfvvtDapjjz32YObMmQD8+9//Zty4cZx//vlL9//2t7+lurqaN998k9tuu4099thjaaABsN9++7H99tuv0HE88MADnHLKKQCccsop3H///QXzLVq0iPnz57No0SLmzZvHpptuCmRrMD7//HMgG5nJpQP07Nmzwecmn0c2zMxstXT//ffzwx/+kK222ooNNtiA8ePH061bt5LqeOSRRzj88MMBePnll+natSutWrVaur9Vq1Z07dqVl156ialTp7LzzjvXW+ecOXPYe++9C+677bbb2HbbbZdLe//99+nYsSMAHTt25IMPPvhauc0224x+/frRqVMn1lprLQ488EAOPPBAAP7617/ygx/8gH79+rFkyRKeffbZpeWqqqoYOHAg5513Xr39rouDDTMzWy0NGzaMvn37AnD88cczbNgwunXrVuvdFvnpJ554Il988QWLFy9m/PjxQPYdFIXK1pZem3bt2jFx4sTiD6QIn376KQ888ADTpk1jvfXW45hjjuGWW27hRz/6Eddeey2XX345Rx11FHfeeSennXYaTzzxBAAbb7wx77333gq372mUOkgaImmepHZ5aVdICkkbNmXfzMys4T7++GOeeuopTj/9dCorK7nkkku44447iAg6dOjAp59+ulz+Tz75hA03XPbf/q233sq0adPo1asXZ511FgDbbbcdEyZMYMmSJUvzLVmyhEmTJvGd73yH7bbbjnHjxtXbtzlz5tC1a9eCPy+//PLX8m+yySbMmjULgFmzZrHxxht/Lc8TTzxB586d2WijjWjTpg1HHnnk0hGMoUOHcuSRRwJwzDHH8OKLLy4tt2DBAtZaa616+1wfBxv1+zdwGICkNYD9gJkrWqkkjyqZmTWRu+++m5NPPpm3336b6dOn8+6779K5c2eeeeYZunTpwnvvvccrr7wCwNtvv82kSZOW3nGS06ZNGy644AKef/55XnnlFbbcckt22mknLrjggqV5LrjgArp168aWW25Jr169ePbZZ3n44WWL6x955BGmTFn+eaG5kY1CPzWnUAAOPfRQhg4dCmSBw2GHHfa1PJ06deL5559n3rx5RARPPvkk3/nOdwDYdNNNGTVqFABPPfUUXbp0WVru9ddfX+E1JbAaTKNIOh84EXgX+AgYFxGXllDFMOA44BagBzAGOCiv/vuBLYAK4IqIGJTSfwj8CWgFfBQR+0saAGwKVAIfSfpf4AZgI+BD4NSIeEfSJsDfgW+lZs6MiGcl/RL4cUq7PiL+mto6GegHBDA5Ik4qVAfwHjA8IrZP5foBbSNigKRzgDOARcDLEXF8gXPZB+gD0GrdjUo4hWZmdVvZdxUNGzaM/v37L5d21FFHcdttt7H33ntzyy23cOqpp7JgwQLatGnD9ddfv/T21XxrrbUWv/rVr7j00ksZPHgwgwcP5uyzz2bLLbckIthjjz0YPHjw0rzDhw+nb9++9O3blzZt2vDd736XK664YoWOpX///hx77LEMHjyYTp06cddddwHw3nvvcfrpp/PPf/6T3XbbjaOPPppu3brRunVrdtppJ/r06QPAddddx89//nMWLVpERUUFgwYNWlr3iBEjOPjgFf/bKCJWuJLmSlIVcD2wB1lgNR74R7HBhqQhwHCyC/lBwMVkQcdQoCoiPpK0QUR8ImktYCywL9mI0Xhgn4iYlpdnANAT6B4R8yU9BNwdEUMl/Rg4NCIOl3QH8FxE/FVSK6AtsCUwBNgdEPAC8CPgS+BeYK8a/SlUx/rUHmy8B3SOiIWS1ouIz+o6N2t27BIdT/lrMafRrKx86+uq6ZVXXln6ydqap4ULF7LvvvvyzDPP0Lr18mMThf5+ksZFRFWhulr6NEp34IGImB8Rc4CHGljPvcDxwG7A0zX2nSNpEvA82QhHF7KAYHRETAOIiE/y8j8YEfPT9h7AbWn75tRfgO8B16ayiyNidtp3X0R8ERFzU5/2TnnvjoiParRVqI66TAZulfQjstENMzNbjb3zzjsMHDjwa4FGQ7T0aZTG+uL928lGKoZGxJLcqmJJPYADgD0iYp6kkWTTKSKb0ijkizraqWuYqbZjqautmhaxfIBZkbd9MLAPcChwvqTtIsJBh5nZaqpLly7Lrd9YES19ZOMZoKekCkltyS6oJYuId4BfA3+rsas98GkKNLYhG9EAeA7YV1JnAEkb1FL1s2QjJpCtK3kmbT9JtsYCSa0krQuMBg6XtLakdYAjyEZZngSOldShRluF6ngf2FhSB0lrAoek/WsAW0TECOA8YD2yaRczs7JpydP4LVlD/m4temQjIsZKehCYBLwNVAP1TSfUVtc/CiQ/ApwhaTLwGtlUChHxYVpMeW+6kH8AfL9A+XOAGySdS1ogmtJ/DgySdBqwmGyB6HNpDUnunqTrI2ICgKQLgVGSFgMTgN511PEHsvUe04BXU12tgFsktScbKbm8vjUbO2zWnmrPlZtZA1VUVPDxxx/7MfOrmIjg448/pqKiov7MeVr0AlEASW0jYq6ktclGB/pExPim7teqrqqqKqqrq5u6G2a2ivrqq6+YMWMGCxYsaOquWIkqKirYfPPNadOmzXLpdS0QbdEjG8kgSduSrU8Y6kDDzKzptWnThs6dOzd1N2wlafHBRkT0yn8t6RpgrxrZugBv1Ei7IiJuLGffzMzMVgctPtioKSLOauo+mJmZrU5a+t0oZmZm1sRa/AJRKw9Jc8juwLGVZ0Oyr9y3lcfnfOXzOV/5GuucfzMiCj7LYrWbRrFG81ptq46tPCRV+5yvXD7nK5/P+cq3Ms65p1HMzMysrBxsmJmZWVk52LCGGlR/FmtkPucrn8/5yudzvvKV/Zx7gaiZmZmVlUc2zMzMrKwcbJiZmVlZOdiwkkj6oaTXJP1bUv+m7k9LJGkLSSMkvSLpJUk/T+kDJM2UNDH9/FdT97UlkTRd0pR0bqtT2gaSHpf0Rvq9flP3s6WQtHXee3mipM8l9fX7vHFJukHSB5Km5qXV+r6W9L/p//fXJP2g0frhNRtWLEmtgNeB7wMzgLHACRHxcpN2rIWR1BHoGBHjJbUDxgGHA8cCcyPi0qbsX0slaTpQFREf5aVdDHwSEQNTcL1+RPxPU/WxpUr/t8wEdgNOxe/zRiNpH2AucFNEbJ/SCr6v00NLhwG7ApsCTwBbRcTiFe2HRzasFLsC/46ItyLiS+B24LAm7lOLExGzck8njog5wCvAZk3bq9XWYcDQtD2ULOizxrc/8GZEvN3UHWlpImI08EmN5Nre14cBt0fEwoiYBvyb7P/9FeZgw0qxGfBu3usZ+CJYVpIqgZ2AF1LSzyRNTkOjHtJvXAE8JmmcpD4pbZOImAVZEAhs3GS9a9mOJ/tEneP3eXnV9r4u2//xDjasFCqQ5nm4MpHUFrgH6BsRnwPXAt8GugKzgL80Xe9apL0iohtwEHBWGn62MpP0DeBQ4K6U5Pd50ynb//EONqwUM4At8l5vDrzXRH1p0SS1IQs0bo2IewEi4v2IWBwRS4DraKThTctExHvp9wfAfWTn9/20hia3luaDputhi3UQMD4i3ge/z1eS2t7XZfs/3sGGlWIs0EVS5/Rp5HjgwSbuU4sjScBg4JWIuCwvvWNetiOAqTXLWsNIWictxkXSOsCBZOf3QeCUlO0U4IGm6WGLdgJ5Uyh+n68Utb2vHwSOl7SmpM5AF+DFxmjQd6NYSdJtaH8FWgE3RMSFTdujlkdSd+BpYAqwJCX/H9l/yl3JhjWnAz/NzbvaipH0LbLRDMiehn1bRFwoqQNwJ9AJeAc4JiJqLrazBpK0NtkagW9FxOyUdjN+nzcaScOAHmSPkX8f+B1wP7W8ryX9GvgxsIhsCvdfjdIPBxtmZmZWTp5GMTMzs7JysGFmZmZl5WDDzMzMysrBhpmZmZWVgw0zMzMrKwcbZs2UpMXpqZdTJT0kab168g+Q1K+ePIenhy3lXv9B0gGN0Nchko5e0XpKbLNvunWy2ZC0TfqbTZD07Rr7pkt6ukbaxNzTOCVVSbqyEfpQmf+Ezxr7rs//+5ebpE0k3SbprfQ18M9JOmJltW/Nh4MNs+ZrfkR0TU9q/AQ4qxHqPBxYerGJiN9GxBONUO9KlZ4S2hdoVsEG2fl9ICJ2iog3C+xvJ2kLAEnfyd8REdURcU6xDaVzUJKIOH1lPaU5fTnd/cDoiPhWROxM9kWAm5e53dblrN8axsGG2arhOdIDkSR9W9Ij6ZPi05K2qZlZ0k8kjZU0SdI9ktaWtCfZMyguSZ+ov50bkZB0kKQ788r3kPRQ2j4wfSIdL+mu9MyWWqVP8H9KZaoldZP0qKQ3JZ2RV/9oSfdJelnS3yWtkfadIGlKGtG5KK/euWkk5gXg12SPwB4haUTaf21q7yVJv6/Rn9+n/k/JnS9JbSXdmNImSzqq2OOV1FXS86ncfZLWT1941xc4PdenAu4EjkvbNb85s4ek4fX0Lf8c7CHpl+k8TZXUN6+d1pKGprJ350aAJI2UVFXEeb4ovb+ekLRrKveWpENTnlaSLknvscmSflrgWL8HfBkRf88lRMTbEXFVXXWk8zAy9ftVSbemwAVJO0salfr2qJZ95fbI9J4bBfxcUk9JLygbYXpC0ia1/D1sZYkI//jHP83wB5ibfrcie0jVD9PrJ4EuaXs34Km0PQDol7Y75NVzAXB22h4CHJ23bwhwNNm3Zr4DrJPSrwV+RPatg6Pz0v8H+G2Bvi6tl+xbH89M25cDk4F2wEbABym9B7AA+FY6vsdTPzZN/dgo9ekp4PBUJoBj89qcDmyY93qDvPM1EvhuXr7c8f83cH3avgj4a1759Us43snAvmn7D7l68v8GBcpMB7YCnk2vJ5CNMk3NOyfDa+tbzXMA7Ez2LbPrAG2Bl8ieEFyZ8u2V8t3AsvfFSKCqiPN8UNq+D3gMaAPsCExM6X2A36TtNYFqoHON4z0HuLyO93fBOtJ5mE02ArIGWaDdPfXhWWCjVOY4sm8xzh3X32r8LXNfWnk68Jem/ve8uv94uMms+VpL0kSyi8c44PH0KXtP4K70YQ+y/6hr2l7SBcB6ZBeiR+tqKCIWSXoE6CnpbuBg4DxgX7IL4pjU3jfI/vOvT+6ZOVOAthExB5gjaYGWrT15MSLegqVfqdwd+AoYGREfpvRbgX3IhuMXkz2crjbHKns0fGugY+r35LTv3vR7HHBk2j6AbFg/dw4+lXRIfccrqT2wXkSMSklDWfbE0vp8Anwq6XjgFWBeLfm+1re0mX8OugP3RcQXqV/3AnuTnft3I2JMyncL2YX/0rz6d6H28/wl8EjKNwVYGBFfSZpC9l6E7Nkx39WydTrtyZ6jMa22A5d0TerzlxGxSx11fEn23piRyk1M7X4GbE/27wCyoDL/a8zvyNveHLgjjXx8o65+2crhYMOs+ZofEV3TxW042ZqNIcBnEdG1nrJDyD6pTpLUm+zTYn3uSG18AoyNiDlp+PrxiDihxL4vTL+X5G3nXuf+36n5rISg8COucxZExOJCO5Q9NKofsEsKGoYAFQX6szivfRXoQ0OPtxR3ANcAvevIU6hvsPw5qOtcFTq3NeuvzVeRhgTI+/tFxBItWw8hstGiuoLYl4CjlnYg4ixJG5KNYNRah6QeLP+eyf3NBLwUEXvU0t4XedtXAZdFxIOpvgF19NNWAq/ZMGvmIntA1TlkF9P5wDRJx0C2CE/SjgWKtQNmKXtU/Yl56XPSvkJGAt2An7DsU+LzwF6StkztrS1pqxU7oqV2VfYE4TXIhsSfAV4A9pW0obIFkCcAo2opn38s65JdbGan+fmDimj/MeBnuReS1qeI401/j08l7Z2STqqjj4XcB1xM3aNNhfpW02jg8NTHdciekJq726WTpNxF+QSyc5uvlPNcyKPAmen9haStUh/yPQVUSDozLy1/QW8xdeR7Ddgod1yS2kjarpa87YGZafuUWvLYSuRgw2wVEBETgElkQ+snAqdJmkT26fGwAkXOJ7ugPA68mpd+O3CuCtyamT4xDye7UA9PaR+SfQIfJmky2cX4awtSG+g5YCDZI8SnkU0JzAL+FxhBdrzjI6K2x7oPAv4laURETCJbA/ES2RqFMbWUyXcBsH5aIDkJ2K+E4z2FbKHtZLInlP6hiPYAiIg5EXFRRHxZSt8K1DOebATrRbK/9fXpfQLZFM0pqX8bkK3ByS9bynku5HrgZWC8stts/0GNkfI0OnI4WVAzTdKLZFNO/1NsHTXq+5JsXc9F6ZxMJJtSLGQA2VTj08BHJRyXlYmf+mpmK10a2u4XEYc0cVfMbCXwyIaZmZmVlUc2zMzMrKw8smFmZmZl5WDDzMzMysrBhpmZmZWVgw0zMzMrKwcbZmZmVlb/H8tC6TpSpBW+AAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.5129942425707541, pvalue=4.840407802702763e-08)\n", "Slope and P-value = PearsonRResult(statistic=-0.49094622879013317, pvalue=2.1502418100451344e-07)\n", "Slope and P-value = PearsonRResult(statistic=0.5764683334545228, pvalue=3.468133168500771e-10)\n", "Slope and P-value = PearsonRResult(statistic=-0.8424942676480413, pvalue=4.470096804125943e-28)\n", "Slope and P-value = PearsonRResult(statistic=0.25850661880556286, pvalue=0.1120622708892286)\n", "Slope and P-value = PearsonRResult(statistic=-0.19746619846112057, pvalue=0.048921890221772714)\n", "Slope and P-value = PearsonRResult(statistic=-0.6336643460827626, pvalue=1.4865044908999162e-12)\n", "Slope and P-value = PearsonRResult(statistic=0.4183815361452615, pvalue=0.00011248165556282476)\n", "Slope and P-value = PearsonRResult(statistic=-0.18043410495494056, pvalue=0.0724259942301618)\n", "Slope and P-value = PearsonRResult(statistic=-0.603292279269045, pvalue=7.737934466264525e-05)\n", "1 95\n", "0 90\n", "Name: CattleOnFarm, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.16115265073373908, pvalue=0.15868312029605927)\n", "Slope and P-value = PearsonRResult(statistic=-0.90514585279732, pvalue=3.465699344772834e-38)\n", "Slope and P-value = PearsonRResult(statistic=-0.4427645653939527, pvalue=0.0003963503009756025)\n", "Slope and P-value = PearsonRResult(statistic=-0.7408015946691724, pvalue=1.2494358275566488e-18)\n", "Slope and P-value = PearsonRResult(statistic=-0.8787474924285686, pvalue=3.0232979608837348e-33)\n", "Slope and P-value = PearsonRResult(statistic=-0.701065918542343, pvalue=4.609008347097378e-16)\n", "Slope and P-value = PearsonRResult(statistic=-0.9091389101962073, pvalue=4.6477217908254027e-39)\n", "Slope and P-value = PearsonRResult(statistic=-0.2849621636373937, pvalue=0.010406496024535609)\n", "Slope and P-value = PearsonRResult(statistic=0.5251359750261982, pvalue=2.0333369232266108e-08)\n", "Slope and P-value = PearsonRResult(statistic=-0.46240079258785954, pvalue=1.2769979434729437e-06)\n", "1 110\n", "0 89\n", "Name: CattleOnFarm, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.15339190303277306, pvalue=0.3201770875464142)\n", "Slope and P-value = PearsonRResult(statistic=0.9435160585704472, pvalue=8.223925176300837e-49)\n", "Slope and P-value = PearsonRResult(statistic=0.624364784214145, pvalue=3.892860843708217e-12)\n", "Slope and P-value = PearsonRResult(statistic=-0.9230230095026217, pvalue=1.9327702732811685e-42)\n", "Slope and P-value = PearsonRResult(statistic=0.8737349782689658, pvalue=1.9412140593396285e-32)\n", "Slope and P-value = PearsonRResult(statistic=0.8160797202285816, pvalue=4.526852516283175e-25)\n", "Slope and P-value = PearsonRResult(statistic=0.9264817138932474, pvalue=2.2106579957280935e-43)\n", "Slope and P-value = PearsonRResult(statistic=-0.4165868049545151, pvalue=1.6239503967360006e-05)\n", "Slope and P-value = PearsonRResult(statistic=0.6982671829561904, pvalue=1.865020530718049e-15)\n", "Slope and P-value = PearsonRResult(statistic=0.32809496841753716, pvalue=0.0021735720863892533)\n", "1 175\n", "0 138\n", "Name: CattleOnFarm, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
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PearsonRResult(statistic=-0.30277771810182574, pvalue=0.003531998939716159)\n", "Slope and P-value = PearsonRResult(statistic=0.3810567256451232, pvalue=0.0001942140316088484)\n", "1 93\n", "0 90\n", "Name: CattleOnFarm, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.8940515116399919, pvalue=5.951328226807146e-36)\n", "Slope and P-value = PearsonRResult(statistic=-0.7379568778206949, pvalue=1.448447882894399e-17)\n", "Slope and P-value = PearsonRResult(statistic=-0.5483939273248404, pvalue=3.9461701067154025e-06)\n", "Slope and P-value = PearsonRResult(statistic=0.6162609418545182, pvalue=8.779134261500469e-12)\n", "Slope and P-value = PearsonRResult(statistic=-0.4864492866210972, pvalue=1.1676259694870966e-06)\n", "Slope and P-value = PearsonRResult(statistic=-0.7031826129995584, pvalue=3.891845991273681e-15)\n", "Slope and P-value = PearsonRResult(statistic=0.5652787867798783, pvalue=7.612276139784501e-07)\n", "Slope and P-value = PearsonRResult(statistic=-0.3067963669332995, pvalue=0.0019052131513022984)\n", "Slope and P-value = PearsonRResult(statistic=-0.8228482498501483, pvalue=8.581315686388573e-26)\n", "Slope and P-value = PearsonRResult(statistic=0.42484764188310203, pvalue=2.997693844943306e-05)\n", "1 95\n", "0 90\n", "Name: CattleOnFarm, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
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PearsonRResult(statistic=0.9301949121941163, pvalue=1.9093934660257194e-44)\n", "Slope and P-value = PearsonRResult(statistic=-0.5980305598907182, pvalue=3.742016531660646e-09)\n", "1 118\n", "0 90\n", "Name: CattleOnFarm, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.7369654135347804, pvalue=2.3151986926251195e-18)\n", "Slope and P-value = PearsonRResult(statistic=-0.6045589902941219, pvalue=2.7287846358581833e-11)\n", "Slope and P-value = PearsonRResult(statistic=-0.7411432210426436, pvalue=1.1820346296479243e-18)\n", "Slope and P-value = PearsonRResult(statistic=0.45514706400924554, pvalue=8.518964872937671e-05)\n", "Slope and P-value = PearsonRResult(statistic=-0.8771889917520415, pvalue=5.43692881236284e-33)\n", "Slope and P-value = PearsonRResult(statistic=0.3649502037149026, pvalue=0.00018903128809035944)\n", "Slope and P-value = PearsonRResult(statistic=-0.7717066869636872, pvalue=5.652367803431642e-21)\n", "Slope and P-value = PearsonRResult(statistic=-0.8490757688080006, pvalue=9.54245804272759e-16)\n", "Slope and P-value = PearsonRResult(statistic=-0.9342413215992643, pvalue=2.927125018051811e-31)\n", "Slope and P-value = PearsonRResult(statistic=-0.6344654060289756, pvalue=1.3661259708944718e-12)\n", "1 105\n", "0 90\n", "Name: CattleOnFarm, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.978906274945651, pvalue=2.102015308846599e-69)\n", "Slope and P-value = PearsonRResult(statistic=0.7937873063849027, pvalue=3.117036016704644e-22)\n", "Slope and P-value = PearsonRResult(statistic=0.521844985337216, pvalue=2.5808678557353774e-08)\n", "Slope and P-value = PearsonRResult(statistic=0.04147948451138972, pvalue=0.6819847439343925)\n", "Slope and P-value = PearsonRResult(statistic=-0.23147436928146373, pvalue=0.020492464475747093)\n", "Slope and P-value = PearsonRResult(statistic=0.7763198555963126, pvalue=1.1801188310033957e-14)\n", "Slope and P-value = PearsonRResult(statistic=-0.799987315376445, pvalue=1.8186445262736236e-23)\n", "Slope and P-value = PearsonRResult(statistic=-0.733533956404184, pvalue=3.9836650115066626e-18)\n", "Slope and P-value = PearsonRResult(statistic=0.48498613749220865, pvalue=0.0009804433669976145)\n", "Slope and P-value = PearsonRResult(statistic=0.6516346641185482, pvalue=2.1038303282903161e-13)\n" ] } ], "source": [ "sample = pd.merge(microbiome, poultry[['SampleID', 'CattleOnFarm','PastureTime','SampleType']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "#sample = sample[~sample.isin([np.nan, np.inf, -np.inf]).any(1)]\n", "sample = sample.drop(['Pathogen_Salmonella', 'new_Pathogen_Salmonella',\n", " 'Pathogen_Campy','new_Pathogen_Campy',\n", " 'Pathogen_Listeria','new_Pathogen_Listeria'],axis='columns')\n", "\n", "sample.CattleOnFarm.replace({'Y': 1,'N':0}, regex=True, inplace=True)\n", "\n", "\n", "feces1=sample[(sample.SampleType=='Feces') & (sample.PastureTime=='Start')]\n", "feces2=sample[(sample.SampleType=='Feces') & (sample.PastureTime=='Mid')]\n", "feces3=sample[(sample.SampleType=='Feces') & (sample.PastureTime=='End')]\n", "\n", "soil1=sample[(sample.SampleType=='Soil') & (sample.PastureTime=='Start')]\n", "soil2=sample[(sample.SampleType=='Soil') & (sample.PastureTime=='Mid')]\n", "soil3=sample[(sample.SampleType=='Soil') & (sample.PastureTime=='End')]\n", "\n", "ceca=sample[sample.SampleType=='Ceca']\n", "wcrp=sample[sample.SampleType=='WCR-P']\n", "wcrf=sample[sample.SampleType=='WCR-F']\n", "\n", "\n", "print('Feces_Start', feces1.shape)\n", "print('Feces_Mid', feces2.shape)\n", "print('Feces_End', feces3.shape,'\\n')\n", "\n", "print('Soil_Start', soil1.shape)\n", "print('Soil_Mid', soil2.shape)\n", "print('Soil_End', soil3.shape,'\\n')\n", "\n", "print('Ceca', ceca.shape)\n", "print('WCR-P', wcrp.shape)\n", "print('WCR-F', wcrf.shape,'\\n')\n", "\n", "\n", "sampletypes = [feces1,feces2,feces3, soil1,soil2,soil3,ceca, wcrp, wcrf]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES_START\", 1: \"FECES_MID\", 2:\"FECES_END\",\n", " 3: \"SOIL_START\", 4: \"SOIL_MID\", 5: \"SOIL_END\",\n", " 6:\"CECA\", 7: \"WCR-P\", 8: \"WCR-F\"\n", " }\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType', 'PastureTime','CattleOnFarm'],axis='columns'),sample.CattleOnFarm,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1] #multiclass\n", " \n", " try:\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " except ValueError:\n", " pass\n", " \n", " \n", " \n", " \n", "# mylist2.append([f\"CattleOnFarm_{sample_name[indexing]}\", rf_auc_normal])\n", " \n", " \n", " print(pd.value_counts(sample['CattleOnFarm']))\n", "\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim3_plot.max(), decimals=None, symbol=''))\n", " \n", " \n", " plt.title(f\"CattleOnFarm in {sample_name[indexing]} Model\")\n", " prelim3_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','CattleOnFarm','PastureTime','SampleType'],axis='columns').columns)\n", " prelim3_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", "\n", " xmax=prelim3_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " \n", " plt.show()\n", " \n", " prelim3_plot.nlargest(10).to_csv(\"prelim3.csv\")\n", " top10 = pd.read_csv('prelim3.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", "# plt.plot(pdp[1][0], pdp[0][0],'.')\n", "# plt.ylabel('Partial dependence'), plt.xlabel(top10[feature])\n", "# plt.show()\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"CattleOnFarm_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (18) SwineOnFarm" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Feces_Start (200, 878)\n", "Feces_Mid (313, 878)\n", "Feces_End (185, 878) \n", "\n", "Soil_Start (199, 878)\n", "Soil_Mid (313, 878)\n", "Soil_End (183, 878) \n", "\n", "Ceca (185, 878)\n", "WCR-P (208, 878)\n", "WCR-F (195, 878) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "1 165\n", "0 35\n", "Name: SwineOnFarm, dtype: int64\n" ] }, { "data": { "image/png": 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k6S5Jz0m6TtI6qd4/SqqS9GyuXiTtmfoxTdJESZ2AnwMnpfZOkrShpD+nfj0j6ZjcMd0u6R/A6HrO8YDUblXtwnklnl4zM1tbrfZXp5IqgeOA3cj6OwWYXEL59sDBwHfJRgxOAZ6UtBlwPXBARMyU1LmeKroCfYAdgHuBv0s6lOxR5HsBAu6VdAAwGOgVERWp7ePIRg12BboAkySNT/XuBewEvAo8CBwL/B34aUS8K6kN8IikXYAXgNuAkyJikqSNgIXAhUBlRHw/tfcr4F8R8U1JGwMTJT2c2tsH2CUi3i12kBExDBgGsH7XntHoiTUzM2PNGJHoA9wTEYsiYj7wjxLLHwmMjYiFwB3AV9OH9BeA8RExE6C+D1jg7oj4JCKeA7ZIaYemn2fIApsdyAKLYn0fFRG1EfEmMA7YM+2bGBH/iYhaYFTKC9n0y5RU985kwcbngTkRMSn19f2IWFKkvUOBwZKmAo8C7YBt074xDRyjmZlZWVb7EQmyK/4VcQqwn6Sa9HpT4KBUb1OuvD8s0hcBv46IP+UzSupWULahvhe2HZK6A4OAPSPiPUkjyIKBpvZVwHER8WJBv/YGPmhCeTMzs5KsCSMSE4CjJLWT1AFo8rempCmAPsC2EdEtIroBZ5GmN4AD04c3DUxtFPMQ8M3UHyRtJWlzYD7QMZdvPNkahjZpKuUAYGLat5ek7mltxEnpODci+8CfJ2kL4PCU9wVgS0l7pvY6pkWThe09BJxdt6BU0m4lHJOZmVnJVvsRibQm4F5gGtl6giqgqasBjyVbM5AfVbgHuIzs7ooBwJ3pw/wt4EtN7NNoSTuSrbUAWAB8LSJekfS4pBnAP4HzydYmTCMbUTg/Iv4raQeyQGYo0Jss4LgrIj6R9AzwLPAf4PHU3kdpoeY1ac3HIuAQYCyfTmX8GvgF8DtgegomasimdkrSe6tOVPlb7szMrAkUsfqvq5PUISIWSNqA7EN3QERMael+tVaVlZVRVVXV0t0wM7PVhKTJEVFZbN9qPyKRDJO0E9l6gZEOIszMzFYPa0QgERGn5l9LuhbYryBbT+DlgrSrImL4yuybmZnZ2myNCCQKRcRZLd0HMzMzWzPu2jAzM7PVlAMJMzMzK5sDCTMzMyubAwkzMzMrmwMJMzMzK5sDCTMzMyvbGnn7p61c1bPn0W3w/S3dDbNVpsZfCW9WNo9ImJmZWdkcSJiZmVnZygokJNVKmipphqTb08O0mlq2v6Tf17PviUbKdpN0au51paSrm97zpeVqJHUptVxBHQObctySFqxIO7l6nki/u6WniyKpr6T7mqN+MzOzcpQ7IrEoIioiohfwEXBmfqekNuVUGhH7NpKlG7A0kIiIqog4p5y2msFAoMkB1IpqwrkxMzNb5ZpjauMxoEe6Oh4r6RagWlI7ScMlVUt6RtJBuTLbSHpQ0ouSLqpLrLt6V+byNOJRLemklGUosH8aDTk3f0UuqUOuvemSjivlICTtJemJ1NcnJH0+pbeRdEWu3rMlnQNsCYyVNDblOyXlmSHp0oK6fyNpiqRHJG2W0r4jaZKkaZLuqBvdkLSFpLtS+jRJ++bPTQP9v1jSoNzrGWn0YkNJ96e6ZuTOpZmZ2Qpbobs2JK0LHA48mJL2AnpFxExJPwSIiN6SdgBGS9o+nw9YCEySdH9EVOWqPhaoAHYFuqQ844HBwKCIODK13zdX5gJgXkT0Tvs2KfFwXgAOiIglkg4BfgUcBwwAugO7pX2dI+JdST8ADoqIdyRtCVwK7AG8l461X0TcDWwITImIH0q6ELgI+D5wZ0Rcn/o6BPgWcA1wNTAuIr6aRnY6lHgchQ4D3oiII1JbnYplkjQgHSttNtpsBZs0M7O1RbkjEu0lTQWqgNeAG1P6xIiYmbb7AH8FiIgXgFeBukBiTETMjYhFwJ0pb14fYFRE1EbEm8A4YM9G+nQIcG3di4h4r8Rj6gTcntYf/BbYOVfvdRGxJNX7bpGyewKPRsTbKd/NwAFp3yfAbWn7Jj491l6SHpNUDZyWa++LwB9TW7URMa/E4yhUDRwi6VJJ+9dXX0QMi4jKiKhss0HRWMPMzGw55Y5ILIqIinyCJIAP8kkNlI9GXjdUtj4qUk8pfgGMTSMB3YBHS6i3lP7W1TUC6BcR0yT1B/qWUEcxS1g2MGwHEBEvSdoD+Arwa0mjI+LnK9iWmZkZsHJv/xxPdqVNmtLYFngx7fuSpM6S2gP9gMeLlD0prU/YjOzqfiIwH+hYT3ujyaYMSG2WOrXRCZidtvsX1HtmmsZBUueUnu/L08CBkrqk6YhTyEZRIDvHx6ftU4EJabsjMEdSW9J5Sh4BvpfaaiNpoyb2vwbYPZXbnWw6hjTtsjAibgKuqMtjZmbWHFZmIPEHoE0aur8N6B8RH6Z9E8imPaYCdxSsjwC4C5gOTAP+BZwfEf9NaUvSwsFzC8oMATZJCwqnAQfRsOmSZqWfK4HLyK7YHwfyd53cQDZ9Mz3VW3fXyDDgn5LGRsQc4MfA2NTnKRFxT8r3AbCzpMlk0xZ1owEXkAUgY8jWZ9T5f8BB6bxN5tMpj8bcAXROU07fA15K6b2BiSn9p2TnyczMrFkoYkVmA6w1qqysjKqqwtjOzMzWVpImR0RlsX3+ZkszMzMrW6t+aJekp4H1C5JPj4jqluiPmZlZa9OqA4mI2Lul+2BmZtaaeWrDzMzMyuZAwszMzMrmQMLMzMzK5kDCzMzMyuZAwszMzMrmQMLMzMzK5kDCzMzMytaqv0fCylM9ex7dBt/f0t2wtUDN0CNaugtmtoI8ImFmZmZlcyBhZmZmZXMgAUgaIWmmpKnpEeUHN6HMghVss0ZSlxWpI9XzkxWtw8zMrFwOJD51XkRUAAOB65qjQkmrYg1KyYGEpDYroyNmZrb2aTWBhKQLJL0gaYykUZIGlVnVk8BWqc7+kn6fa+M+SX1zr38jaYqkRyRtltIelfQrSeOA/yfpYEnPSKqW9GdJ+aeRnidpYvrpkcofJenpVOZhSVuk9A6Shqd6pks6TtJQoH0aSbk55ftaqm+qpD/VBQ2SFkj6eXoi6j5Fzt8ASVWSqmoXzivz1JmZ2dqmVQQSkiqB44DdgGOByhWo7jDg7ibk2xCYEhG7A+OAi3L7No6IA4FrgRHASRHRm+wume/l8r0fEXsBvwd+l9ImAF+IiN2AW4HzU/oFwLyI6B0RuwD/iojBwKKIqIiI0yTtCJwE7JdGV2qB03L9nRERe0fEhMKDiYhhEVEZEZVtNujUhMM3MzNrPbd/9gHuiYhFAJL+UUYdl0u6DNgc+EIT8n8C3Ja2bwLuzO2rS/88MDMiXkqvRwJn8WnQMCr3+7dpe2vgNkldgfWAmSn9EODkugYi4r0ifToY2AOYJAmgPfBW2lcL3NGE4zIzM2uyVjEiAagZ6jgP6AH8jOwDH2AJy56jdg2Uj9z2B03sVxTZvgb4fRrB+G6uTRXkL0bAyDRCURERn4+Ii9O+xRFR20h5MzOzkrSWQGICcJSkdpI6AGV9y01EfAJcBawj6ctADVAhaR1J2wB75bKvAxyftk9NfSj0AtCtbv0DcDrZNEidk3K/n0zbnYDZafuMXN7RwPfrXkjaJG1+LKlt2n4EOF7S5ilPZ0mfbfCgzczMVkCrmNqIiEmS7gWmAa8CVUBZKwYjIiQNIVubcAjZ1EI1MAOYksv6AbCzpMmprZOK1LVY0jeA29MdHJNY9o6Q9dPix3WAU1LaxSn/bOApoHtKHwJcK2kG2TTFJWTTKcOA6ZKmpHUSPwNGS1oH+JhsKuXVcs6FmZlZYxTR2Gj5mkFSh4hYIGkDYDwwICKmNFbOlldZWRlVVVUt3Q0zM1tNSJocEUVvZGgVIxLJMEk7ka0pGOkgwszMbOVrNYFERJyafy3pWmC/gmw9gZcL0q6KiOErs29mZmatVasJJApFxFkt3QczM7PWrrXctWFmZmYtwIGEmZmZlc2BhJmZmZXNgYSZmZmVzYGEmZmZlc2BhJmZmZXNgYSZmZmVrdV+j4SVr3r2PLoNvr+lu2GtSM3Qsp6jZ2ZrAI9ImJmZWdkcSJiZmVnZHEisQSSdIOlZSZ9IqsylbypprKQFkn5fUOZRSS9Kmpp+Nl/1PTczs9bKayTWLDOAY4E/FaQvBi4AeqWfQqdFhJ8LbmZmzc4jEiWSdIGkFySNkTRK0qASyvaQ9LCkaZKmSNoupZ8vqTqlD62vfEQ8HxEvFkn/ICImkAUUZZE0QFKVpKrahfPKrcbMzNYyHpEoQZpOOA7YjezcTQEml1DFzcDQiLhLUjtgHUmHA/2AvSNioaTOzdxtgOGSaoE7gCEREYUZImIYMAxg/a49l9tvZmZWjEckStMHuCciFkXEfOAfTS0oqSOwVUTcBRARiyNiIXAIMDxtExHvNnOfT4uI3sD+6ef0Zq7fzMzWYg4kSqOVUFbAShsBiIjZ6fd84BZgr5XVlpmZrX0cSJRmAnCUpHaSOgBN/padiHgfmCWpH4Ck9SVtAIwGvpm2ac6pDUnrSuqSttsCR5It2DQzM2sWDiRKEBGTgHuBacCdQBVQysrE04FzJE0HngA+ExEPpjqrJE0F6l28KemrkmYB+wD3S3oot68GuBLoL2mWpJ2A9YGHUntTgdnA9SX018zMrEEqsu7OGiCpQ0QsSCMI44EBETGlpfvVnCorK6OqyneLmplZRtLkiKgsts93bZRuWLrabweMbG1BhJmZWSkcSJQoIk7Nv5Z0LbBfQbaewMsFaVdFxPCmtFFPnU0ub2Zmtqo4kFhBEXHWmlCnmZnZyuDFlmZmZlY2BxJmZmZWNgcSZmZmVjYHEmZmZlY2BxJmZmZWNgcSZmZmVjYHEmZmZlY2f4+ELad69jy6Db6/pbthq6maoU1+Vp2ZrQU8ImFmZmZlcyBhZmZmZVvtAwlJtZKmSpoh6fb01M2mlu0v6ff17HuikbLdJJ2ae10p6eqm93xpuRpJ1ekYqiUdU2odqZ6jJQ1O2xdLGpS2R0g6vpw6zczMVtRqH0gAiyKiIiJ6AR8BZ+Z3SmpTTqURsW8jWboBSwOJiKiKiHPKaQs4KCIqgOOBkoOR1P69ETG0zPbNzMxWijUhkMh7DOghqa+ksZJuAaoltZM0PF3xPyPpoFyZbSQ9KOlFSRfVJUpakH5L0uVpxKNa0kkpy1Bg/zSScG5q875UpkOuvemSjmti/zcC3sv14W5JkyU9K2lALv0wSVMkTZP0SEqrd3QlV65GUpe0XSnp0bR9YDqOqen8dCxSdoCkKklVtQvnNfFwzMxsbbfG3LUhaV3gcODBlLQX0CsiZkr6IUBE9Ja0AzBa0vb5fMBCYJKk+yOiKlf1sUAFsCvQJeUZDwwGBkXEkan9vrkyFwDzIqJ32rdJI90fK0nA54ATc+nfjIh3JbVP7d5BFtxdDxyQjq1zE05PYwYBZ0XE45I6AIsLM0TEMGAYwPpde0YztGlmZmuBNWFEor2kqUAV8BpwY0qfGBEz03Yf4K8AEfEC8CpQF0iMiYi5EbEIuDPlzesDjIqI2oh4ExgH7NlInw4Brq17ERHvNZAXsqmNXkBv4PfpwxzgHEnTgKeAbYCewBeA8XXHFhHvNlJ3UzwOXCnpHGDjiFjSDHWamZmtESMSi9L6gqWyi3s+yCc1UL7w6rrwdUNl66Mi9TQqIl6R9CawU1o0egiwT0QsTNMQ7cqtO1nCp8Fhu1y7QyXdD3wFeErSISngMjMzWyFrwohEU4wHTgNIUxrbAi+mfV+S1DlNH/QjuzovLHuSpDaSNgMOACYC84Hl1hIko4Hv171owtRGXb7Nge5kIyadgPdSELED2UgEwJPAgZK6pzKlTG3UAHuk7aXrNiRtFxHVEXEp2cjODiXUaWZmVq81YUSiKf4AXCepmuyqvH9EfJhGLiaQTXv0AG4pWB8BcBewDzCNbCTg/Ij4r6S5wJI09TACeCZXZghwraQZQC1wCdm0SX3GSqoF2gKDI+JNSQ8CZ0qaThb0PAUQEW+nhZd3SloHeAv4UhPPwyXAjZJ+AjydSx+YFqDWAs8B/2yokt5bdaLK315oZmZNoAivq7NlVVZWRlVVYbxlZmZrK0mTI6Ky2L7WMrVhZmZmLaC1TG20OElPA+sXJJ8eEdUt0R8zM7NVwYFEM4mIvVu6D2ZmZquapzbMzMysbA4kzMzMrGwOJMzMzKxsDiTMzMysbA4kzMzMrGwOJMzMzKxsvv3TllM9ex7dBt/f0t2w1VCNvzrdzAp4RMLMzMzK5kDCzMzMyrbWBhKS1pX0jqRfF6TXSOpSkNYtPelztSdpS0l/b+l+mJnZ2mGtDSSAQ8ke332i0vPGVwZJq3QdSkS8ERHHr8o2zcxs7bXGBhKSLpD0gqQxkkZJGlRiFacAVwGvAV8oUn97SQ9K+k5B+uckPSNpT0nbpTyTJT0maYeUZ4SkKyWNBS5Nr/8oaayk/0g6UNKfJT0vaUSu7j9KqpL0rKRLcuk1ki6RNEVSda6dAyVNTT/PSOqYHz1J24+lclMk7VviOTIzM2vQGnnXhqRK4DhgN7JjmAJMLqF8e+Bg4LvAxmRBxZO5LB2AW4G/RMRfJHVL5T6f0r8REVMlPQKcGREvS9ob+APwxVTH9sAhEVGbgoVN0r6jgX8A+wHfBiZJqoiIqcBPI+JdSW2ARyTtEhHTU33vRMTukv4PGJTKDgLOiojHJXUAFhcc6lvAlyJisaSewCig6PPkJQ0ABgC02Wizpp5KMzNby62pIxJ9gHsiYlFEzCf7YC7FkcDYiFgI3AF8NX1417kHGB4Rf8mlbZbSv5aCiA7AvsDtkqYCfwK65vLfHhG1udf/iIgAqoE3I6I6Ij4BngW6pTwnSpoCPAPsDOyUK39n+j05l/9x4EpJ5wAbR8SSguNsC1wvqRq4vaC+ZUTEsIiojIjKNht0qi+bmZnZMtbUQGJF1zScAhwiqYbsg3lT4KDc/seBwwvWTswDXicbSYDs3P0vIipyPzvm8n9Q0OaH6fcnue261+tK6k42wnBwROwC3A+0K1K+ljSSFBFDyUYm2gNP1U155JwLvAnsSjYSsV6Rc2FmZla2NTWQmAAcJaldGhlo8rfkSNqIbERj24joFhHdgLPIgos6FwJzyaYq6nwE9AO+LunUiHgfmCnphFSvJO26Ase0EVnwMU/SFsDhTTiW7dLIxqVAFVAYSHQC5qSRj9OBNoV1mJmZrYg1MpCIiEnAvcA0siH/KrIRg6Y4FvhXRORHBe4Bjpa0fi5tINBO0mW5dj8gmxY5V9IxwGnAtyRNI5uiOKa8I4KImEY2pfEs8GeyUZHGDJQ0I7W/CPhnwf4/AGdIeopszUbhKImZmdkKUTZtv+aR1CEiFkjaABgPDIiIKS3dr9agsrIyqqqqWrobZma2mpA0OSKKLtZfI+/aSIZJ2olsHcFIBxFmZmar3hobSETEqfnXkq7l04WQdXoCLxekXRURw1dm38zMzNYWa2wgUSgizmrpPpiZma1t1sjFlmZmZrZ6cCBhZmZmZXMgYWZmZmVzIGFmZmZlcyBhZmZmZXMgYWZmZmVzIGFmZmZlazXfI2HNp3r2PLoNvr+lu2GrgZqhTX4enpmtpTwiYWZmZmVzIGFmZmZlcyCxCkn6gqSnJU2V9Lyki1N6f0kh6eBc3q+mtOPT60clvZgrOyCXN79vqqTNU/qZkqpT2oT0kDMzM7Nm4zUSq9ZI4MSImCapDfD53L5q4BTgkfT6ZGBaQfnTIqJKUmfgFUkjIuKj/L6C/LdExHUAko4GrgQOa8bjMTOztZwDiRJJugA4DXgdeAeYHBFXNLH45sAcgIioBZ7L7XsM2F9SW2B9oAcwtZ56OgAfALUNNRYR7+debghEfXnTCMcAgDYbbdZQtWZmZks5kCiBpErgOGA3snM3BZhcQhW/BV6U9CjwIDAyIhanfQE8DHwZ6ATcC3QvKH+zpA/JHo8+MAUjdYZLqgXuAIZERKQ+nwX8AFgP+GJ9HYuIYcAwgPW79qw34DAzM8vzGonS9AHuiYhFETEf+EcphSPi50AlMBo4lSyYyLuVbErjZGBUkSpOi4hdgG2BQZI+m0vvDeyffk7PtXltRGwH/Aj4WSn9NTMza4wDidJoRSuIiFci4o/AwcCukjbN7ZsI9AK6RMRLDdTxNtloyN7p9ez0ez5wC7BXkWK3Av1WtP9mZmZ5DiRKMwE4SlI7SR2Akr6tR9IRkuqCkZ5kaxz+V5Dtx8BPGqlnA7LplVckrSupS0pvCxwJzEive+aKHQG8XEp/zczMGuM1EiWIiEmS7iW7m+JVoAqYV0IVpwO/lbQQWEI2JVH7aWwBEfHPBsrfLGkR2WLMERExWdKGwEMpiGhDts7i+pT/+5IOAT4G3gPOKKGvZmZmjVJak2dNJKlDRCxIowLjgQERMaWl+9WcKisro6qq8E5SMzNbW0maHBGVxfZ5RKJ0w9IXO7Uju+uiVQURZmZmpXAgUaKIODX/WtK1wH4F2Xqy/HqEqyJi+Mrsm5mZ2armQGIFRcRZLd0HMzOzluK7NszMzKxsDiTMzMysbA4kzMzMrGwOJMzMzKxsDiTMzMysbA4kzMzMrGwOJMzMzKxs/h4JW0717Hl0G3x/S3fDVrGaoSU9g87MDPCIhJmZma0ABxJmZmZWtkYDCUm1kqZKmiHp9vTUyyaR1F/S7+vZ90QjZbtJOjX3ulLS1U1tO1fum5KqJU1Px3BMrm9bllpfA+30Sw/zMjMzW2s0ZURiUURUREQv4CPgzPxOSW3KaTgi9m0kSzdgaSAREVURcU4pbUjaGvgp0CcidgG+AExPu/sDRQOJMo+pH+BAwszM1iqlTm08BvSQ1FfSWEm3ANWS2kkanq78n5F0UK7MNpIelPSipIvqEiUtSL8l6fI0WlAt6aSUZSiwfxoNOTe1eV8q0yHX3nRJx9XT382B+cACgIhYEBEzJR0PVAI3p/rbS6qRdKGkCcAJkg6V9KSkKWkkpkNqu0bSpZImpp8ekvYFjgYuT/VtJ6lC0lOpf3dJ2iSV7yHpYUnTUt3bNXAOkHR+SpsmaWgDdSw9PynP7yX1T9tDJT2X+nJFsRMlaYCkKklVtQvnNf5OMDMzo4S7NiStCxwOPJiS9gJ6pQ/mHwJERG9JOwCjJW2fzwcsBCZJuj8iqnJVHwtUALsCXVKe8cBgYFBEHJna75srcwEwLyJ6p32b1NPtacCbwExJjwB3RsQ/IuLvkr6f6q9KdQAsjog+kroAdwKHRMQHkn4E/AD4ear3/YjYS9LXgd9FxJGS7gXui4i/p/qmA2dHxDhJPwcuAgYCNwNDI+IuSe3Igrn6zkEF2UjH3hGxUFLn1H6xOrYpdgJSma8CO0RESNq4WL6IGAYMA1i/a8+o53yamZktoykjEu0lTQWqgNeAG1P6xIiYmbb7AH8FiIgXgFeBukBiTETMjYhFZB/OfQrq7wOMiojaiHgTGAfs2UifDgGurXsREe8VyxQRtcBhwPHAS8BvJV3cQL23pd9fIJumeDwd+xnAZ3P5RuV+71NYiaROwMYRMS4ljQQOkNQR2Coi7kr9WxwRC6n/HBwCDE95iIh3G6ijPu8Di4EbJB1LFtCZmZk1i6aMSCyKiIp8Qrp6/yCf1ED5wqvbwtcNla2PitRTvPGIACYCEyWNAYYDF9eTve6YRBYAnVJftfVsN6a+Y20ovannawnLBobtACJiiaS9gIOBk4HvA19sUm/NzMwa0Vy3f44HTgNIUxrbAi+mfV+S1FlSe7Jh+seLlD1JUhtJmwEHkH3wzwc61tPeaLIPRFKbRac2JG0pafdcUgXZaAmN1P8UsJ+kHqmeDXJTNQAn5X4/WVhfRMwD3pO0f9p3OjAuIt4HZknql+pdX9ldMPWdg9HAN1MeJHVuoI5XgZ3S605kgQNpbUeniHiAbGqlop5jNjMzK1lzfbPlH4DrJFWTXRn3j4gP08jFBLJpjx7ALQXrIwDuIpsemEZ29X1+RPxX0lxgiaRpwAjgmVyZIcC1kmYAtcAlZNMmhdoCVyi7zXMx8Daf3nUyIvV5EQXTExHxdlqoOErS+in5Z2TTIwDrS3qaLBCrG7W4Fbhe0jlkUylnpPo3AP4DfCPlOx34U1o38TFwQn3nAHhQUgVQJekj4AHgJ8XqiIj/SPob2V0pL+fOV0fgnrSWQsC5Rc6TmZlZWZSN/FtTSaoBKiPinZbuy8pSWVkZVVWF8Z6Zma2tJE2OiMpi+/zNlmZmZla2VvPQrjTVsH5B8ukRUd2c7UREt+asz8zMbE3WagKJiNi7pftgZma2tmk1gYSZma0ePv74Y2bNmsXixYtbuitWonbt2rH11lvTtm3bJpdxIGFmZs1q1qxZdOzYkW7dutV975CtASKCuXPnMmvWLLp3797kcl5saWZmzWrx4sVsuummDiLWMJLYdNNNSx5JciBhZmbNzkHEmqmcv5sDCTMzMyub10iYmdlK1W3w/c1aX83QI5qU76677uLYY4/l+eefZ4cddgDg0Ucf5YorruC+++5bmq9///4ceeSRHH/88fTt25c5c+bQrl071ltvPa6//noqKioAmDdvHmeffTaPP5496WG//fbjmmuuoVOnTgC89NJLDBw4kJdeeom2bdvSu3dvrrnmGrbYYouyj/Xdd9/lpJNOoqamhm7duvG3v/2NTTZZ/qkQv/3tb7nhhhuQRO/evRk+fDjt2rVbuv+KK67gvPPO4+2336ZLly5UV1fzm9/8hhEjRpTdtzoOJGw51bPnNfs/fFt9NfU/ZbM1zahRo+jTpw+33norF198cZPL3XzzzVRWVjJ8+HDOO+88xowZA8C3vvUtevXqxV/+8hcALrroIr797W9z++23s3jxYo444giuvPJKjjrqKADGjh3L22+/vUKBxNChQzn44IMZPHgwQ4cOZejQoVx66aXL5Jk9ezZXX301zz33HO3bt+fEE0/k1ltvpX///gC8/vrrjBkzhm233XZpmd69ezNr1ixee+21ZdLL4akNMzNrdRYsWMDjjz/OjTfeyK233lpWHfvssw+zZ88G4N///jeTJ0/mggsuWLr/wgsvpKqqildeeYVbbrmFffbZZ2kQAXDQQQfRq1evFTqOe+65hzPOOAOAM844g7vvvrtoviVLlrBo0SKWLFnCwoUL2XLLLZfuO/fcc7nsssuWW/9w1FFHlX1u8hxImJlZq3P33Xdz2GGHsf3229O5c2emTJlSch0PPvgg/fr1A+C5556joqKCNm3aLN3fpk0bKioqePbZZ5kxYwZ77LFHo3XOnz+fioqKoj/PPffccvnffPNNunbtCkDXrl156623lsuz1VZbMWjQILbddlu6du1Kp06dOPTQQwG499572Wqrrdh1112XK1dZWcljjz3WpHPREE9tmJlZqzNq1CgGDhwIwMknn8yoUaPYfffd670rIZ9+2mmn8cEHH1BbW7s0AImIomXrS69Px44dmTp1atMPpAnee+897rnnHmbOnMnGG2/MCSecwE033cSxxx7LL3/5S0aPHl203Oabb84bb7yxwu2XHEhIqgWqU9nngTMiYmETy/Yne3Lm94vseyIi9m2gbDdg34i4Jb2uBL4eEeeU2P8aYD7Z48cBxjdUR3qM95YR8UAp7ZiZWcuYO3cu//rXv5gxYwaSqK2tRRKXXXYZm266Ke+9994y+d999126dOmy9PXNN9/MrrvuyuDBgznrrLO488472XnnnXnmmWf45JNPWGedbDD/k08+Ydq0aey444689dZbjBs3rtG+zZ8/n/3337/ovltuuYWddtppmbQtttiCOXPm0LVrV+bMmcPmm2++XLmHH36Y7t27s9lmmwFw7LHH8sQTT7Drrrsyc+bMpaMRs2bNYvfdd2fixIl85jOfYfHixbRv377RPjemnKmNRRFRERG9gI+AM/M7JbUpXqxhDQURSTfg1Fz+qlKDiJyD0jFUNKGOCuArpVQuySM9ZmYt5O9//ztf//rXefXVV6mpqeH111+ne/fuTJgwgZ49e/LGG2/w/PPPA/Dqq68ybdq0pXdm1Gnbti1Dhgzhqaee4vnnn6dHjx7stttuDBkyZGmeIUOGsPvuu9OjRw9OPfVUnnjiCe6//9OF6g8++CDV1cs+N7JuRKLYT2EQAXD00UczcuRIAEaOHMkxxxyzXJ5tt92Wp556ioULFxIRPPLII+y444707t2bt956i5qaGmpqath6662ZMmUKn/nMZ4DsLpMVXcMBKz618Riwi6S+wEXAHKBC0u7AH4FKYAnwg4gYm8psI+lBoDtwS0RcAiBpQUR0UDZGdBlwOBDAkIi4DRgK7ChpKjASeAYYFBFHSuoAXJPaC+CSiLijlAOR9CjwNHAQsDHwrfT650B7SX2AXwP3pbZ6k52/iyPinjTacgTQDthQ0vHAn4HPAQuBARExvb6+SjoF+Akg4P6I+FHq12HAr4A2wDsRcXADdSyIiA6p3PHAkRHRX9IJ6e9TC8yLiAOKHP8AYABAm402K+XUmZk1aFXfGTRq1CgGDx68TNpxxx3HLbfcwv77789NN93EN77xDRYvXkzbtm254YYblt7Cmde+fXt++MMfcsUVV3DjjTdy4403cvbZZ9OjRw8ign322Ycbb7xxad777ruPgQMHMnDgQNq2bcsuu+zCVVddtULHMnjwYE488URuvPFGtt12W26//XYA3njjDb797W/zwAMPsPfee3P88cez++67s+6667LbbrsxYMCARuseO3YsRxyx4n8bRURpBT79wF8XuAN4kGyK436gV0TMlPTDtP0NSTsAo4HtgZPJPox7kX24TgL6R0RVrt7jyEY5DgO6pDx7A58nBQ6pH335NJC4FFg/IgamfZtExLJjV5/2v4ZlpzZGRsRvUyAxOSJ+KOkrZMHPIYXTMZJ+BTwXETdJ2hiYCOwGnAAMAXaJiHclXUP2wX+JpC8CV0ZERbG+Au2Bp4A9gPfS+boaeByYAhyQzmvnVHfR420gkKgGDouI2ZI2joj/NfQ3Xr9rz+h6xu8aymKtiG//tOb2/PPPs+OOO7Z0N6wBH374IQceeCATJkxg3XWXHVMo9veTNDkiKovVVc6IRPs0KgDZiMSNwL7AxIiYmdL7kF0xExEvSHqVLJAAGBMRc1PH7kx5q3L19wFGRUQt8KakccCewPsN9OkQsiCF1GbRICLnoIh4p0j6nen3ZLKplGIOBY6WNCi9bgfU3YQ7JiLezR3Hcak//5K0qaROxfoq6QDg0Yh4G0DSzcABZMHO+Lrzmqu71ON9HBgh6W+5YzQzs7XUa6+9xtChQ5cLIspRTg2LIqIin5BWrH6QT2qgfOEQSOHrcr6gXUXqKceH6Xct9Z8bAcdFxIvLJEp70/g5CIr3tb5jru+46kvPpy39SrOIODP17whgqqSKumDOzMzWPj179qRnz57NUtfK+h6J8cBpAJK2J7tir/vg/ZKkzpLaA/3IrpYLy54kqY2kzciuzCeSTUd0rKe90cDSO0HSdEFzKWz3IeDstJYDSbvVUy5/DvqSTXO8X09fnwYOlNQlLVY9BRgHPJnSu6e8nVOx+o73TUk7SloH+Gpu/3YR8XREXAi8A2xT6kkwMytFqdPmtnoo5++2su4u+ANwXZqbX0K2DuLD9Nk7Afgr0INssWVVQdm7gH2AaWRX2OdHxH8lzQWWSJoGjCBbbFlnCHCtpBlkowmX0PAQ/th0GyvA9Ij4ekN5gcFpOufXwC+A3wHTUzBRAxxZpNzFwHBJ08nWg5xRX18j4k5JP05tCXggIu6BpYsg70zBwVvAlxo43sFki0FfB2YAHVKbl0vqmep+hOzc1qv3Vp2o8ry5mZWpXbt2zJ07148SX8NEBHPnzl3mGR1NUfJiS2v9Kisro6qqML4zM2uajz/+mFmzZrF48eKW7oqVqF27dmy99da0bdt2mfTmXmxpZmZWr7Zt29K9e/eW7oatIq02kJD0NLB+QfLpEVFdLL+ZmZmVrtUGEhGxd0v3wczMrLXz0z/NzMysbF5sacuRNJ9Pb9e1VaML2a25tur4nK96PuerXnOd889GRNHnJ7TaqQ1bIS/WtzrXVg5JVT7nq5bP+arnc77qrYpz7qkNMzMzK5sDCTMzMyubAwkrZlhLd2At5HO+6vmcr3o+56veSj/nXmxpZmZmZfOIhJmZmZXNgYSZmZmVzYGELSXpMEkvSvq3pMEt3Z/WSNI2ksZKel7Ss5L+X0q/WNJsSVPTz1dauq+tiaQaSdXp3FaltM6Sxkh6Of3epKX72VpI+nzuvTxV0vuSBvp93rwk/VnSW+lJ0HVp9b6vJf04/f/+oqQvN1s/vEbCACS1AV4ie0z5LGAScEpEPNeiHWtlJHUFukbEFEkdgclAP+BEYEFEXNGS/WutJNUAlRHxTi7tMuDdiBiaAudNIuJHLdXH1ir93zIb2Bv4Bn6fNxtJBwALgL9ERK+UVvR9LWknYBSwF7Al8DCwfUTUrmg/PCJhdfYC/h0R/4mIj4BbgWNauE+tTkTMiYgpaXs+8DywVcv2aq11DDAybY8kC+is+R0MvBIRr7Z0R1qbiBgPvFuQXN/7+hjg1oj4MCJmAv8m+39/hTmQsDpbAa/nXs/CH3ArlaRuwG7A0ynp+5Kmp+FKD7M3rwBGS5osaUBK2yIi5kAW4AGbt1jvWreTya6E6/h9vnLV975eaf/HO5CwOiqS5nmvlURSB+AOYGBEvA/8EdgOqADmAL9pud61SvtFxO7A4cBZaUjYVjJJ6wFHA7enJL/PW85K+z/egYTVmQVsk3u9NfBGC/WlVZPUliyIuDki7gSIiDcjojYiPgGup5mGHC0TEW+k328Bd5Gd3zfTmpW6tStvtVwPW63DgSkR8Sb4fb6K1Pe+Xmn/xzuQsDqTgJ6SuqeriJOBe1u4T62OJAE3As9HxJW59K65bF8FZhSWtfJI2jAtbEXShsChZOf3XuCMlO0M4J6W6WGrdgq5aQ2/z1eJ+t7X9wInS1pfUnegJzCxORr0XRu2VLoV63dAG+DPEfHLlu1R6yOpD/AYUA18kpJ/QvYfbgXZUGMN8N26eU5bMZI+RzYKAdkTj2+JiF9K2hT4G7At8BpwQkQULlyzMknagGxO/nMRMS+l/RW/z5uNpFFAX7JHhb8JXATcTT3va0k/Bb4JLCGbVv1ns/TDgYSZmZmVy1MbZmZmVjYHEmZmZlY2BxJmZmZWNgcSZmZmVjYHEmZmZlY2BxJmLUBSbXr64QxJ/5C0cSP5L5Y0qJE8/dKDeepe/1zSIc3Q1xGSjl/Rekpsc2C6fXC1IWmH9Dd7RtJ2BftqJD1WkDa17qmMkiolXd0MfeiWf9Jjwb4b8n//lU3SFpJukfSf9NXjT0r66qpq31YfDiTMWsaiiKhIT+x7FzirGersByz9IImICyPi4Waod5VKT4scCKxWgQTZ+b0nInaLiFeK7O8oaRsASTvmd0REVUSc09SG0jkoSUR8e1U9rTd9sdrdwPiI+FxE7EH2JXZbr+R2112Z9Vt5HEiYtbwnSQ/PkbSdpAfTFd5jknYozCzpO5ImSZom6Q5JG0jal+yZBpenK+Ht6kYSJB0u6W+58n0l/SNtH5quJKdIuj09A6Re6cr7V6lMlaTdJT0k6RVJZ+bqHy/pLknPSbpO0jpp3ymSqtNIzKW5ehekEZSngZ+SPeZ4rKSxaf8fU3vPSrqkoD+XpP5X150vSR0kDU9p0yUd19TjlVQh6alU7i5Jm6QvaxsIfLuuT0X8DTgpbRd+o2NfSfc10rf8OdhH0g/SeZohaWCunXUljUxl/143ciPpUUmVTTjPl6b318OS9krl/iPp6JSnjaTL03tsuqTvFjnWLwIfRcR1dQkR8WpEXNNQHek8PJr6/YKkm1NQgqQ9JI1LfXtIn37N86PpPTcO+H+SjpL0tLKRoYclbVHP38NWlYjwj3/8s4p/gAXpdxuyBxodll4/AvRM23sD/0rbFwOD0vamuXqGAGen7RHA8bl9I4Djyb7N8TVgw5T+R+BrZN+GNz6X/iPgwiJ9XVov2bcRfi9t/xaYDnQENgPeSul9gcXA59LxjUn92DL1Y7PUp38B/VKZAE7MtVkDdMm97pw7X48Cu+Ty1R3//wE3pO1Lgd/lym9SwvFOBw5M2z+vqyf/NyhSpgbYHngivX6GbHRoRu6c3Fdf3wrPAbAH2befbgh0AJ4le1Jst5Rvv5Tvz3z6vngUqGzCeT48bd8FjAbaArsCU1P6AOBnaXt9oAroXnC85wC/beD9XbSOdB7mkY1crEMWRPdJfXgC2CyVOYns23XrjusPBX/Lui9T/Dbwm5b+97y2/3iYyKxltJc0leyDYTIwJl0d7wvcni7SIPtPuFAvSUOAjck+ZB5qqKGIWCLpQeAoSX8HjgDOBw4k+7B7PLW3Htl/7I2pewZLNdAhIuYD8yUt1qdrPSZGxH9g6df49gE+Bh6NiLdT+s3AAWRD5LVkDzKrz4nKHv+9LtA19Xt62ndn+j0ZODZtH0I21F53Dt6TdGRjxyupE7BxRIxLSSP59MmVjXkXeE/SycDzwMJ68i3Xt7SZPwd9gLsi4oPUrzuB/cnO/esR8XjKdxPZh/oVufr3pP7z/BHwYMpXDXwYER9LqiZ7L0L2LJJd9Om6mE5kz2WYWd+BS7o29fmjiNizgTo+IntvzErlpqZ2/wf0Ivt3AFnAmP/q7Nty21sDt6URi/Ua6petGg4kzFrGooioSB9c95GtkRgB/C8iKhopO4LsCnOapP5kV3mNuS218S4wKSLmpyHlMRFxSol9/zD9/iS3Xfe67v+Uwu/eD4o/xrjO4oioLbZD2QOGBgF7poBgBNCuSH9qc+2rSB/KPd5S3AZcC/RvIE+xvsGy56Chc1Xs3BbWX5+PI13Kk/v7RcQn+nT9gchGeRoKUJ8FjlvagYizJHUhG3motw5JfVn2PVP3NxPwbETsU097H+S2rwGujIh7U30XN9BPWwW8RsKsBUX2MKNzyD4oFwEzJZ0A2YI2SbsWKdYRmKPsceSn5dLnp33FPArsDnyHT6/ungL2k9QjtbeBpO1X7IiW2kvZk2TXIRumngA8DRwoqYuyxYSnAOPqKZ8/lo3IPkjmpfnww5vQ/mjg+3UvJG1CE443/T3ek7R/Sjq9gT4WcxdwGQ2PEhXrW6HxQL/Uxw3JnpRZd1fItpLqPnBPITu3eaWc52IeAr6X3l9I2j71Ie9fQDtJ38ul5RfHNqWOvBeBzeqOS1JbSTvXk7cTMDttn1FPHluFHEiYtbCIeAaYRjbcfRrwLUnTyK76jilS5AKyD4sxwAu59FuB81Tk9sR0pXsf2YfwfSntbbIr51GSppN90C63uLNMTwJDyR4TPZNsmH4O8GNgLNnxTomI+h7dPQz4p6SxETGNbM3Bs2RrAh6vp0zeEGCTtNhwGnBQCcd7Btmi1elkT6r8eRPaAyAi5kfEpRHxUSl9K1LPFLKRp4lkf+sb0vsEsmmTM1L/OpOtecmXLeU8F3MD8BwwRdmtpn+iYPQ6jWr0IwtYZkqaSDYN9KOm1lFQ30dk62guTedkKtk0XzEXk03/PQa8U8Jx2Urip3+aWbNKw82DIuLIFu6Kma0CHpEwMzOzsnlEwszMzMrmEQkzMzMrmwMJMzMzK5sDCTMzMyubAwkzMzMrmwMJMzMzK9v/B3aDRBy70aLqAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.31835243137224895, pvalue=0.0012469602911310347)\n", "Slope and P-value = PearsonRResult(statistic=0.30961038021647364, pvalue=0.001721015563807063)\n", "Slope and P-value = PearsonRResult(statistic=-0.3763361281537645, pvalue=0.00011389040192405272)\n", "Slope and P-value = PearsonRResult(statistic=-0.6578854537863636, pvalue=0.002200603550991907)\n", "Slope and P-value = PearsonRResult(statistic=-0.5730088449183406, pvalue=7.015692589717088e-10)\n", "Slope and P-value = PearsonRResult(statistic=0.30752423817048685, pvalue=0.0018559336653081713)\n", "Slope and P-value = PearsonRResult(statistic=-0.8049978795575038, pvalue=3.7328776071433343e-13)\n", "Slope and P-value = PearsonRResult(statistic=-0.6334907296559519, pvalue=1.5139132811067502e-12)\n", "Slope and P-value = PearsonRResult(statistic=-0.8733369499960989, pvalue=2.242448760886184e-32)\n", "Slope and P-value = PearsonRResult(statistic=-0.5797900279656425, pvalue=2.5999897376910987e-10)\n", "1 243\n", "0 70\n", "Name: SwineOnFarm, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.646812754361641, pvalue=2.2120203000969043e-07)\n", "Slope and P-value = PearsonRResult(statistic=-0.9694255291900977, pvalue=1.3293947673269183e-61)\n", "Slope and P-value = PearsonRResult(statistic=-0.6255448963807725, pvalue=9.441577987894826e-06)\n", "Slope and P-value = PearsonRResult(statistic=-0.9455139464896188, pvalue=1.4780975139102393e-49)\n", "Slope and P-value = PearsonRResult(statistic=-0.9203993052726644, pvalue=9.366430706131286e-42)\n", "Slope and P-value = PearsonRResult(statistic=-0.1405255394032081, pvalue=0.1631611860843119)\n", "Slope and P-value = PearsonRResult(statistic=0.3216620619571615, pvalue=0.0032108141107265726)\n", "Slope and P-value = PearsonRResult(statistic=-0.5935523980454279, pvalue=4.617487270169543e-07)\n", "Slope and P-value = PearsonRResult(statistic=-0.9278792877187626, pvalue=8.930616605913745e-44)\n", "Slope and P-value = PearsonRResult(statistic=-0.5846772595158377, pvalue=0.0017075957766836453)\n", "1 150\n", "0 35\n", "Name: SwineOnFarm, dtype: int64\n" ] }, { "data": { "image/png": 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YA+yc9j0aEQsjYhnwArB9Kj9Z0mTgeWB3YLfcGKNyv/s2IvZDJD0rqQb4cuqvvnifAX4k6YfA9ikpMTMzaxGtOaHQWhjjw9z2ytzjlXx6fUlt+YpceX3x5ftdAawvaQdgMHBoROwJPAhU5OpFHdurSTMhvwf6pxmSGwr6Wi3eiLgNOAZYCvxd0pfrG8PMzKwpWnNC8SRwdFqr0AVYO9/M0XjjgTMAJO0M9ABeqqf+xsAHwEJJWwFHFOw/Jff7mSLtFwFd03Zt8vBOOjcNLjSV9DngtYi4GriP7LKLmZlZi2i1n/KIiImS7gOmkl1OqCa7LNEUf5R0Zdp+nWytQ0v5PXB9uuTwMTAgIj5MayNXExFTJT0PzABeA54qqLKhpGfJkrxicY5I4y0luyRyA9mlmdnAxEbEewrwNUnLgf8DflZf5T226Ub1Wvp2NTMza/sUUe/sellJ6hIRiyV1JpsRGBgRk8sd17qgqqoqqquryx2GmZm1IpImpQ8XrKbVzlAkwyTtRjbFP9LJhJmZWevUqhOKiDg9/1jSdcABBdV6Aq8UlF0VEcPXZGxmZma2SqtOKApFxKByx2BmZmara82f8jAzM7M2wgmFmZmZlcwJhZmZmZXMCYWZmZmVzAmFmZmZlcwJhZmZmZWsTX1s1NaemjcWUjnkwXKHYWbWpsxeh29Z4BkKMzMzK5kTCjMzMyvZOp1QSBohaZakKZJmSrq4kW0avF14HW2rJF3dnLZmZmatmddQwAURcZekCuAFSTdHxKw1MVBEVJPdht3MzKxdafMzFJIuTLMLD0saJWlwM7uqSL8/SP1eJGmipOmShklSkbFnS/qppMmSaiTtmso3kvSn1P55Scem8n6SHkjbl0gaKemh1M8Jkn6V+hkrqWOqd2jqoyb1uWEDY+8n6enU5mlJu6Ty3SU9l2Zjpknq2czzZGZmtpo2nVBIqgJOBPoAJwBF79HegCskTQHmArdHxFup/NqI2DciegGdgKPqaP9OROwN/AGoTWZ+DPwjIvYFDkljbFSk7Y7AkcCxwC3AYxGxB7AUODLNmowATknl6wPfaWDsmcCXIqIPcBHwi1R+DtldWHuTnae5hcFIGiipWlL1iiUL6zhcMzOz1bXphAI4EBgTEUsjYhFwfzP6uCC9yH4WOFTSF1P5IZKelVQDfBnYvY7296Tfk4DKtH04MCQlKuPIZj96FGn7t4hYDtQAHYCxqbwm9bULMCsiXk7lI4EvNTB2N+BOSdOB3+Xifgb4kaQfAttHxNLCYCJiWERURURVh87d6jhcMzOz1bX1hGK1yxDNFRGLyV78D0wzA78H+qeZgRtYdUmk0Ifp9wpWrUkRcGJE9E4/PSLixbraRsRKYHlERCpfmfpq6PiKjf1zspmOXsDRtXFHxG3AMWSzH3+X9OUG+jYzM2u0tp5QPAkcLalCUheyywfNIml9YH/gVVYlD++kfpv6qY6/A+fWrruQ1KeZYc0EKiXtlB5/HXi8gTbdgDfS9oDaQkmfA16LiKuB+4A9mxmTmZnZatp0QhERE8leHKeSTf9XA029+F+7hmIa2aWGeyLiPbJZiRpgNDCxiX3+HOgITEuXHn7exPYARMQy4CyySxg1ZDMX1zfQ7FfALyU9RXYZpdYpwPR0rLsCNzcnJjMzs2K0apa9bZLUJSIWS+oMjAcGRsTkcsfV1lVVVUV1tT/hamZmq0iaFBFFPwDRHr6HYpik3cguU4x0MmFmZrb2tfmEIiJOzz+WdB1wQEG1nsArBWVXRcTwNRmbmZnZuqLNJxSFImJQuWMwMzNb17TpRZlmZmbWOjihMDMzs5I5oTAzM7OSOaEwMzOzkjmhMDMzs5I5oTAzM7OSOaEwMzOzkrW776GwllHzxkIqhzxY7jDMzMpq9tBm33NyneMZCjMzMyuZEwozMzMrmROKOijzE0mvSHpZ0mOSdm9GP1tLuitt95P0QNoeIOnaFo756Zbsz8zMrLG8hqJug4AvAntFxBJJhwP3Sdo9IpY1tpOIeBPov6aCLBjri4VlkjpExIq1Mb6Zma272vUMhaQLJc2U9LCkUZIGN6H5D4FzI2IJQEQ8BDwNnCGpg6QRkqZLqpH0P2m8nSQ9ImmqpMmSdpRUKWl6A3FuL+lRSdPS7x6p/KQ0xlRJ41PZAEljJI2V9JKki3P9LE6/+6UZlduAmlQ2WtIkSTMkDawjjoGSqiVVr1iysAmnyszM1nXtdoZCUhVwItCH7DgnA5Ma2XZjYKOIeLVgVzWwO9Ab2CYieqX6m6T9twJDI+JeSRVkCdtnGjHktcDNETFS0jeBq4HjgIuA/4iIN3JjAOwH9AKWABMlPRgR1QV97gf0iohZ6fE3I+JdSZ1Sm7sjYn6+QUQMA4YBbNi9ZzQibjMzM6B9z1AcCIyJiKURsQi4vwX6FBDAa8DnJF0j6avA+5K6kiUZ9wJExLLa2Y1G6Avclrb/nGIHeAoYIenbQIdc/YcjYn5ELAXuydXPey6XTACcJ2kqMAHYDujZyNjMzMwa1J4TCjW3YUS8D3wg6XMFu/YGXoiIBcBewDiytRY3ljJesRBSHOcAPyFLAKZI2jy/v7B+gQ9qNyT1Aw4D+kbEXsDzQEULxmtmZuu49pxQPAkcLalCUhegqd9OcgVwdbpEgKTDyGYCbpO0BbBeRNwNXAjsnZKQuZKOS/U3lNS5kWM9DZyats9IsSNpx4h4NiIuAt4hSywAviJpsxTbcWQzGfXpBixIi0t3Bb7QyLjMzMwapd2uoYiIiZLuA6YCc8jWPzRlpeE1wKZAjaQVwP8Bx0bEUkk7A8Ml1SZk/5t+fx34o6SfAcuBk4CVjRjrPOBPki4A3gbOSuVXSOpJNvvxaDqW3mQJx5+BnYDbiqyfKDQWOEfSNOAlssseZmZmLUYR7XftnaQuEbE4zRSMBwZGxORyx1UKSQOAqoj47pocp6qqKqqrG8pTzMxsXSJpUkRUFdvXbmcokmGSdiNbLzCyrScTZmZmrVW7Tigi4vT8Y0nXAQcUVOsJvFJQdlVEDF+TsTVXRIwARpQ5DDMzs09p1wlFoYgYVO4YzMzM2qP2/CkPMzMzW0ucUJiZmVnJnFCYmZlZyZxQmJmZWcmcUJiZmVnJnFCYmZlZyZxQmJmZWcnWqe+hsMareWMhlUMeLHcYZtYEs4c29R6IZi3HMxRmZmZWMicUZmZmVrI1nlBIGiFplqQpkiZL6rsGx7pE0uA11X/BWMelG4/VPh4nqegd2MzMzNq7tTVDcUFE9AaGAH9cS2OuaccBuzVUyczMbF3QqIRC0oWSZkp6WNKoEmYBxgM7pT6HSnpB0jRJv05lW0q6W9LE9HNAKv/UzIOk6ZIq0/aPJb0k6RFgl1yd3pImpP7vlbSppO0lvSJpC0nrSXpC0uGp/mhJkyTNkDQw189iSZdJmpr620rSF4FjgCvSzMuOqfpJkp6T9LKkg1L7CknDJdVIel7SIal8QBrz/jSD811J30t1JkjarK7jSOXjJF1eZLzKdFyT088XU3l3SeNTvNNr6xf8nQdKqpZUvWLJwmb+ic3MbF3UYEKRpvFPBPoAJwClTOsfDdSkF8vjgd0jYk/g0rT/KuB3EbFvGvPGBmLbBzg1F9u+ud03Az9M/dcAF0fEHOBy4Hrg+8ALEfFQqv/NiNgnHd95kjZP5RsBEyJiL7KE6NsR8TRwH2nmJSJeTXXXj4j9gPOBi1PZIICI2AM4DRgpqSLt6wWcDuwHXAYsiYg+wDPAN+o6jtwxFhvvLeArEbE3cApwdSo/Hfh7minaC5hSeD4jYlhEVEVEVYfO3Qp3m5mZ1akxHxs9EBgTEUsBJN3fjHGukPQT4G3gW8D7wDLgRkkPAg+keocBu0mqbbexpK719HsQcG9ELEmx3Zd+dwM2iYjHU72RwJ0AEXGjpJOAc4Deub7Ok3R82t4O6AnMBz7KxTcJ+Eo98dyTq1eZtg8Erkljz5Q0B9g57XssIhYBiyQtBGrPbQ2wZ33HUc94HYFrJfUGVuTGmgj8SVJHYHRETKnnOMzMzJqkMQmFGq7SoAsi4q5PdSrtBxxKNsPwXeDLZDMmfWuTl1zdj/n0bEpFbjuaEoikzsC26WEXshfzfmTJTN+IWCJpXG6M5RFRO8YK6j9nHxapV9/5+zC3vTL3eGUD49Q33v8A/yabhViPLHEjIsZL+hJwJPBnSVdExM2NGMPMzKxBjVlD8SRwdFoL0IXsBakkqZ9uEfFXsun63mnXQ2TJRW292vLZwN6pbG9gh1Q+HjheUqc0k3E0QEQsBBbk1gl8Hah9l385cCtwEXBDKusGLEjJxK7AFxpxGIuA+mZPao0Hzkix7wz0AF5qRLuGjqMu3YB5EbEy1e+Qxt4eeCsibgBuIp1PMzOzltDgu+CImJguJUwF5gDVQKkr9roCY9JaApG9qwY4D7hO0rQU23iySxN3A9+QNIVs6v7lFNtkSXeQrQeYAzyRG+NM4Po0I/EacJakg8nWWRwQESsknSjpLOA24Jw07kvAhEYcw+3ADZLOA/rXU+/3KY4a4GNgQER8mLus05DVjqOB+r8H7k6XdR4DPkjl/YALJC0HFrNqjYaZmVnJtGo2v55KUpeIWJxe1MYDAyNi8hqPzsqmqqoqqquryx2GmZm1IpImRUTRD2c09l4ew5R9iVMFMNLJhJmZmeU1KqGIiNPzjyVdBxxQUK0n8EpB2VURMbz54ZmZmVlb0Ky7jUbEoJYOxMzMzNou3xzMzMzMSuaEwszMzErmhMLMzMxK5oTCzMzMSuaEwszMzErmhMLMzMxK5oTCzMzMStas76Gw9q/mjYVUDnmw3GGYWTJ7aMn3ZTRbozxDYWZmZiVzQmFmZmYlWycTCkmDJc2UNF3SVEn13spb0ghJ/dP2OElF77SWq3+JpMEtGbOZmVlrts4lFJLOAb4C7BcRvYAvASpvVGZmZm1bm0woJF2YZhgeljSqibMBPwL+OyLeB4iIhRExMvW7j6THJU2S9HdJ3RuIY3Fuu7+kEUXq9JY0QdI0SfdK2jSVj5P0O0njJb0oaV9J90h6RdKlufbfSzMp0yWdn8oqU5sbJM2Q9JCkTmnftyVNTDMvd0vqnMpPys3IjK/jeAZKqpZUvWLJwiacUjMzW9e1uYQiXW44EegDnADUe/mhoG1XoGtEvFpkX0fgGqB/ROwD/Am4rAVCvhn4YUTsCdQAF+f2fRQRXwKuB8YAg4BewABJm0vaBzgL2B/4AvBtSX1S257AdRGxO/Ae2TkBuCci9o2IvYAXgW+l8ouA/0jlxxQLNCKGRURVRFR16NytBQ7dzMzWFW3xY6MHAmMiYimApPub0FZA1LFvF7IX84clAXQA5pUQJ5K6AZtExOOpaCRwZ67Kfel3DTAjIualdq8B25Ed670R8UEqvwc4KLWbFRFTUvtJQGXa7pVmODYBugB/T+VPASMk/QW4p5TjMjMzK9QWE4pmr3eIiPclfSDpcxHxWpF+Z0RE36Z0mduuaEZIH6bfK3PbtY/Xp/5jzddfAXRK2yOA4yJiqqQBQD+AiDhH0v7AkcAUSb0jYn4zYjYzM1tNm7vkATwJHC2pQlIXshfIpvglcJ2kjQEkbSxpIPASsKWkvqm8o6TdG+jr35I+L2k94PjCnRGxEFgg6aBU9HXg8cJ69RgPHCeps6SN0hhPNNCmKzAvXcI5o7ZQ0o4R8WxEXAS8QzYDYmZm1iLa3AxFREyUdB8wFZgDVANNWUH4B7JLARMlLQeWA7+JiI/SR0OvTpcq1geuBGbU09cQ4AHgdWB66rfQmcD1aXHka2RrIholIianhZ7PpaIbI+J5SZX1NLsQeJbs3NSQJRgAV0jqSTbr8SjZ+avTHtt0o9rfzGdmZo2kiLqWFLRekrpExOL0Ij0eGBgRk8sdV3tSVVUV1dXV5Q7DzMxaEUmTIqLohyHa3AxFMkzSbmTrFkY6mTAzMyuvNplQRMTp+ceSrgMOKKjWE3iloOyqiBi+JmMzMzNbF7XJhKJQRAwqdwxmZmbrsrb4KQ8zMzNrZZxQmJmZWcmcUJiZmVnJnFCYmZlZyZxQmJmZWcmcUJiZmVnJ2sXHRq3l1byxkMohD5Y7DLN11mx/9b21MZ6hMDMzs5I5oTAzM7OStemEQtIISbMkTZE0OXfr8RHpzqHliOlnkg5rgX4ukTS4SPnWku4qtX8zM7OW1B7WUFwQEXdJOhz4I7BnOYOJiIsaW1fS+hHxcRP7fxMoS7JkZmZWl7LPUEi6UNJMSQ9LGlXsXXkjjQd2KtL/RZImSpouaZgkpfJxki6X9JyklyUdlMorJA2XVCPpeUmHpPIBkkZLuj/NinxX0vdSnQmSNkv1PpkdkbSvpKclTU3jdE393CnpfuAhSZulfqelfvIJ0V6S/iHpFUnfTn1WSpqei+na3LE+IKlf2l6cjm+SpEck7ZeO+TVJxzTzHJuZmRVV1oRCUhVwItAHOAEoeo/1RjoaqClSfm1E7BsRvYBOwFG5fetHxH7A+cDFqWwQQETsAZwGjJRUkfb1Ak4H9gMuA5ZERB/gGeAbBce2AXAH8P8iYi/gMGBp2t0XODMivgz8FHg+IvYEfgTcnOtmT+DIVP8iSVs3/nSwETAuIvYBFgGXAl8Bjgd+VqyBpIGSqiVVr1iysAlDmZnZuq7cMxQHAmMiYmlELALub0YfV0iaAgwEvlVk/yGSnpVUA3wZ2D237570exJQmYvpzwARMROYA+yc9j0WEYsi4m1gYS7emlz7WrsA8yJiYurr/dzljYcj4t0i4/0D2FxSt7Sv9ty8AzxGlsg01kfA2Fx8j0fE8jpiJY0/LCKqIqKqQ+duxaqYmZkVVe41FGqBPi6IiKKLFNPMwu+Bqoh4XdIlQEWuyofp9wpWnYv6Yvowt70y93glq59LAVFHPx8U1CsUBb8Ly2t9zKeTwvyxLY+I2vqfxBoRKyWV++9uZmbtTLlnKJ4Ejk7rFrqQTe+3pNoX2HdS/41ZzDgeOANA0s5AD+ClZow9E9ha0r6pr651vJDnx+sHvBMR76d9x6ZzsznQD5hY0HY20FvSepK2o2kzGGZmZi2mrO9UI2KipPuAqWSXFqrJLiW0VP/vSbqBbJp/Nqu/IBfze+D6dInkY2BARHyY1nI2ZeyPJJ0CXCOpE9n6iWIfJ70EGC5pGrAEODO37zngQbKk5ucR8aakytz+p4BZZMc3HZjcpCDNzMxaiFbNipcpAKlLRCyW1Jns3frAiPALY5lVVVVFdXV1ucMwM7NWRNKkiCj6AYrWcC19mKTdyC5PjHQyYWZm1vaUPaGIiNPzjyVdBxxQUK0n8EpB2VURMXxNxmZmZmaNU/aEolBEDCp3DGZmZtY05f6Uh5mZmbUDTijMzMysZE4ozMzMrGROKMzMzKxkTijMzMysZE4ozMzMrGROKMzMzKxkre57KKx1qHljIZVDHix3GGbtzuyhLX0PRLPWwTMUZmZmVjInFGZmZlYyJxQlkDRCUv8S2i8uUra1pLtK6HOcpKq0PVvSFs3ty8zMrLG8hqIMJAlQsX0R8SbQ7CTFzMysHNb5GQpJF0qaKelhSaMkDS6xvy6SHpU0WVKNpGNTeaWkFyX9HpgMbJdrs4WkZyQdmepNT+UDJN0jaaykVyT9Ktfm8NRmsqQ7JXVpIK7RkiZJmiFpYB11BkqqllS9YsnCUk6DmZmtY9bphCJdGjgR6AOcAFS1QLfLgOMjYm/gEOA3aUYCYBfg5ojoExFzUgxbAQ8CF0VEsY9V9AZOAfYATpG0XbqM8RPgsDRONfC9BuL6ZkTsQ3aM50navLBCRAyLiKqIqOrQuVsTD9vMzNZl6/oljwOBMRGxFEDS/S3Qp4BfSPoSsBLYBtgq7ZsTERNydTsCjwKDIuLxOvp7NCIWpvheALYHNgF2A55KucoGwDMNxHWepOPT9nZAT2B+E47LzMysTut6QlF0HUOJzgC2BPaJiOWSZgMVad8HBXU/BiYB/wHUlVB8mNteQfY3E/BwRJzWmIAk9QMOA/pGxBJJ43IxmZmZlWydvuQBPAkcLakirUFoiW+c6Qa8lZKJQ8hmFOoSwDeBXSUNacIYE4ADJO0EIKmzpJ0biGlBSiZ2Bb7QhLHMzMwatE7PUETEREn3AVOBOWRrEZq6GvGPkq5M268DRwP3S6oGpgAzG4hhhaRTU5v3gb82Iu63JQ0ARknaMBX/BHi5jiZjgXMkTQNeIktIzMzMWowiotwxlJWkLhGxWFJnYDwwMCImlzuucquqqorq6upyh2FmZq2IpEkRUfQDDOv0DEUyTNJuZGsKRjqZMDMza7p1PqGIiNPzjyVdBxxQUK0n8EpB2VURMXxNxmZmZtZWrPMJRaGIGFTuGMzMzNqadf1THmZmZtYCnFCYmZlZyZxQmJmZWcmcUJiZmVnJnFCYmZlZyZxQmJmZWcmcUJiZmVnJ/D0UVlTNGwupHPJgucMwa/NmD22Jew6atX6eoTAzM7OSOaEwMzOzkrW7hELS+pLekfTLgvJxkqrS9l8lbdJC41VKmt5AnQGSrm2BsY6RNCRtXyJpcNoeIal/qf2bmZk1V7tLKIDDgZeAkyWpWIWI+M+IeG+tRtUCIuK+iBha7jjMzMwKtbqEQtKFkmZKeljSqNp34U1wGnAV8C/gC3WMMVvSFmn7G5KmSZoq6c+SukqaJalj2r9xqt9R0k6SHkl1J0vasaDfCknDJdVIel7SIbnd20kaK+klSRfn2oyWNEnSDEkDc+VfTWNMlfRoKmtwpqPg2KokjUvbB0uakn6el9S1SNuBkqolVa9YsrC+YczMzD6lVX3KI12SOBHoQxbbZGBSE9p3Ag4F/gvYhCy5eKae+rsDPwYOiIh3JG0WEYvSi/CRwGjgVODuiFgu6VZgaETcK6mCLCH7TK7LQQARsYekXYGHJO2c9u0H9AKWABMlPRgR1cA3I+LdFPtESXenfm8AvhQRsyRt1thzUI/BwKCIeEpSF2BZYYWIGAYMA9iwe89ogTHNzGwd0dpmKA4ExkTE0ohYBNzfxPZHAY9FxBLgbuB4SR3qqf9l4K6IeAcgIt5N5TcCZ6Xts4Dh6R39NhFxb6q7LI1TGP+f0/6ZwBygNqF4OCLmR8RS4J5UF+A8SVOBCcB2QE+ymZXxETGrIK5SPAX8VtJ5wCYR8XEL9GlmZga0voSi6JqHJjgNOEzSbLKZjc2BQ+qpL2C1d+IR8RRQKelgoENETG9kbPXVKRwnJPUDDgP6RsRewPNARV1xNdLHrPq7VnwyWLb24mygEzAhzaCYmZm1iNaWUDwJHJ3WInQhu+zQKJI2JnvX3yMiKiOikuwSxGn1NHuUbPHm5qmP/KWFm4FRwHCAiHgfmCvpuFR3Q0mdC/obD5yR9u8M9CBbIArwFUmbpUsbx5HNGHQDFkTEkvQCX7vm4xngYEk7FImrIbOBfdL2ibWFknaMiJqIuByoBpxQmJlZi2lVCUVETATuA6aSXRaoBhq7OvAE4B8R8WGubAxwjKQN6xhvBnAZ8Hi67PDb3O5bgU3JkopaXye7RDENeBr4bEGXvwc6SKoB7gAG5OJ5kuxyyBSyNRnVwFhg/dTfz8kuexARbwMDgXtSXHc08hwA/BS4StITwIpc+fmSpqf+lgJ/a0KfZmZm9VJE61p7J6lLRCxO7/7HAwMjYnIZ4ugPHBsRX1/bY7cGVVVVUV1dXe4wzMysFZE0KSKqiu1rVZ/ySIZJ2o3s+v/IMiUT1wBHAP+5tsc2MzNri1pdQhERp+cfS7oOOKCgWk/glYKyqyJieAvFcG5L9GNmZrauaHUJRaGIGFTuGMzMzKx+rT6hMDOztmf58uXMnTuXZctW+w49awMqKirYdttt6dixY6PbOKEwM7MWN3fuXLp27UplZSV13FbJWqmIYP78+cydO5cddtih0e1a1cdGzcysfVi2bBmbb765k4k2SBKbb755k2eXnFCYmdka4WSi7WrO384JhZmZmZXMayjMzGyNqxzyYIv2N3to4+7McO+993LCCSfw4osvsuuu2R0Hxo0bx69//WseeOCBT+oNGDCAo446iv79+9OvXz/mzZtHRUUFG2ywATfccAO9e/cGYOHChZx77rk89dRTABxwwAFcc801dOvWDYCXX36Z888/n5dffpmOHTuyxx57cM0117DVVls1+1jfffddTjnlFGbPnk1lZSV/+ctf2HTTTVer97vf/Y4bb7wRSeyxxx4MHz6cioqKOtvX1NTwm9/8hhEjRjQ7tjwnFFZUzRsLW/w/ALP2rrEvcrb2jBo1igMPPJDbb7+dSy65pNHtbr31Vqqqqhg+fDgXXHABDz/8MADf+ta36NWrFzfffDMAF198MWeffTZ33nkny5Yt48gjj+S3v/0tRx99NACPPfYYb7/9dkkJxdChQzn00EMZMmQIQ4cOZejQoVx++eWfqvPGG29w9dVX88ILL9CpUydOPvlkbr/9dgYMGFBn+z322IO5c+fyr3/9ix49ejQ7vlq+5GFmZu3S4sWLeeqpp7jpppu4/fbbm9VH3759eeONNwD45z//yaRJk7jwwgs/2X/RRRdRXV3Nq6++ym233Ubfvn0/SSYADjnkEHr16lXScYwZM4YzzzwTgDPPPJPRo0cXrffxxx+zdOlSPv74Y5YsWcLWW2/dYPujjz662eemkBMKMzNrl0aPHs1Xv/pVdt55ZzbbbDMmT276nRzGjh3LcccdB8ALL7xA79696dChwyf7O3ToQO/evZkxYwbTp09nn332qaOnVRYtWkTv3r2L/rzwwgur1f/3v/9N9+7dAejevTtvvfXWanW22WYbBg8eTI8ePejevTvdunXj8MMPb7B9VVUVTzzxRONPSD18ycPMzNqlUaNGcf755wNw6qmnMmrUKPbee+86P8GQLz/jjDP44IMPWLFixSeJSEQUbVtXeV26du3KlClTGn8gjbBgwQLGjBnDrFmz2GSTTTjppJO45ZZb+NrXvlZvu8985jO8+eabLRJDu56hkDRC0ixJU9LPeal8tqQtmtnn081s98mYtX1I6ifpgfpbNqrv3pJ8IzMzs2T+/Pn84x//4Oyzz6ayspIrrriCO+64g4hg8803Z8GCBZ+q/+6777LFFqteFm699VZmzZrF6aefzqBB2R0gdt99d55//nlWrlz5Sb2VK1cydepUPv/5z7P77rszadKkBmNr6gzFVlttxbx58wCYN28en/nMZ1ar88gjj7DDDjuw5ZZb0rFjR0444QSefvrpBtsvW7aMTp06NRhzY7TrhCK5ICJ6p5+rS+0sIr7YGvoo0Jsm3hlVkmenzKzduuuuu/jGN77BnDlzmD17Nq+//jo77LADTz75JD179uTNN9/kxRdfBGDOnDlMnTr1k09y1OrYsSOXXnopEyZM4MUXX2SnnXaiT58+XHrppZ/UufTSS9l7773ZaaedOP3003n66ad58MFVC9rHjh1LTU3Np/qtnaEo9rPbbrutdizHHHMMI0eOBGDkyJEce+yxq9Xp0aMHEyZMYMmSJUQEjz76KJ///OcbbP/yyy+XvMajVqt/UZF0IXAG8DrwDjApIn7dgv2PBrYju136VRExTNJ3gB0i4gepzgBgn4g4V9LiiOgiqTtwB7Ax2Xn8TkQ8Iek04EeAgAcj4odFxlwcEV3Sw40l3QvsAowH/jsiVkr6A7Av0Am4KyIuTm33Ba4CNgI+BL4C/AzoJOlA4JfAA8A1wB4ptksiYkw6jiPTsW4EfLkgroHAQIAOG2/Z7HNqZlZobX8CZtSoUQwZMuRTZSeeeCK33XYbBx10ELfccgtnnXUWy5Yto2PHjtx4442ffPQzr1OnTnz/+9/n17/+NTfddBM33XQT5557LjvttBMRQd++fbnppps+qfvAAw9w/vnnc/7559OxY0f23HNPrrrqqpKOZciQIZx88sncdNNN9OjRgzvvvBOAN998k7PPPpu//vWv7L///vTv35+9996b9ddfnz59+jBw4MB620P2KZQjj2yZv40iokU6WhMkVQE3An3JXhgnA39sbEIhaQRwMLAwFX09ImokzQaqIuIdSZtFxLuSOgETU/31gGciYqfUz9+AyyLiyVxC8X2gIiIuk9QB6Ax0BSYA+wALgIeAqyNidMGYtX30A8YCuwFz0vYfI+KuXFwdgEeB84CZ6eeUiJgoaWNgCfC11Pd3U7y/AF6IiFskbQI8B/QBTgIuBfaMiHfrO3cbdu8Z3c+8sjGn2cwSf2x0lRdffPGTd8jWOn344YccfPDBPPnkk6y//urzC8X+hpImRURVsf5a+wzFgcCYiFgKIOn+ZvRxQUTcVc/+8yQdn7a3A3pGxARJr0n6AvAK2ezBUwXtJgJ/ktQRGB0RUyR9GRgXEW+neG8FvgSMrmf85yLitVR/FNkx3wWcnGYM1ge6kyUdAcyLiIkAEfF+alfY5+HAMZIGp8cVQO2HjB9uKJkwM7P271//+hdDhw4tmkw0R2tPKNboF8GnGYLDgL4RsUTSOLIXX8guZ5xMNiNwbxRM5UTEeElfIruE8GdJVwDvNyOMwimikLQDMBjYNyIWpJmWCrLz0ZgpJQEnRsRLnyqU9gc+aEaMZmbWzvTs2ZOePXu2WH+tfVHmk8DRkiokdSF78W5J3YAFKZnYFfhCbt89wHHAaWTJxadI2h54KyJuAG4C9gaeBQ6WtEW6VHEa8HgDMewnaQdJ6wGnkB3zxmQv/AslbQUckerOBLZO6yiQ1DUtrlxEdrml1t+Bc5WmLiT1adTZMDNrQa35krrVrzl/u1Y9Q5HWCdwHTCVbY1DNqvUQLWEscI6kacBLZOsfasdeIOkFYLeIeK5I237ABZKWA4uBb0TEPEn/CzxGNkvw14gY00AMzwBDyRZQjiebDVkp6XlgBvAa6XJLRHwk6RTgmrTmYynZDMtjwBBJU8gWZf4cuBKYlpKK2cBRTTkxe2zTjWpfDzazZqqoqGD+/Pm+hXkbFBHMnz+fioqKhivntOpFmQCSukTEYkmdyV5wB0ZE07/uzJqkqqoqqquryx2GmbVRy5cvZ+7cuSxbtqzcoVgzVFRUsO2229KxY8dPlbflRZkAwyTtRraGYKSTCTOz1q9jx47ssMMO5Q7D1qJWn1BExOn5x5KuAw4oqNaT7NMYeVdFxPA1GZuZmZllWn1CUSgiBpU7BjMzM/u01v4pDzMzM2sDWv2iTCsPSYvIPvlia88WZF8vb2uPz/na53O+9rXkOd8+Iorem6HNXfKwtealulby2pohqdrnfO3yOV/7fM7XvrV1zn3Jw8zMzErmhMLMzMxK5oTC6jKs3AGsg3zO1z6f87XP53ztWyvn3IsyzczMrGSeoTAzM7OSOaEwMzOzkjmhsNVI+qqklyT9U9KQcsfTHknaTtJjkl6UNEPS/0vll0h6Q9KU9POf5Y61PZE0W1JNOrfVqWwzSQ9LeiX93rTccbYXknbJPZenSHpf0vl+nrcsSX+S9Jak6bmyOp/Xkv43/f/+kqT/aLE4vIbC8iR1AF4GvgLMBSYCp0XEC2UNrJ2R1B3oHhGTJXUFJgHHAScDiyPi1+WMr72SNBuoioh3cmW/At6NiKEpgd40In5Yrhjbq/R/yxvA/sBZ+HneYiR9CVgM3BwRvVJZ0ed1utnmKGA/YGvgEWDniFhRahyeobBC+wH/jIjXIuIj4Hbg2DLH1O5ExLzaO+dGxCLgRWCb8ka1zjoWGJm2R5IldtbyDgVejYg55Q6kvYmI8cC7BcV1Pa+PBW6PiA8jYhbwT7L/90vmhMIKbQO8nns8F7/QrVGSKoE+wLOp6LuSpqVpTE+/t6wAHpI0SdLAVLZVRMyDLNEDPlO26Nq3U8neGdfy83zNqut5vcb+j3dCYYVUpMzXxdYQSV2Au4HzI+J94A/AjkBvYB7wm/JF1y4dEBF7A0cAg9JUsa1hkjYAjgHuTEV+npfPGvs/3gmFFZoLbJd7vC3wZpliadckdSRLJm6NiHsAIuLfEbEiIlYCN9BCU5GWiYg30++3gHvJzu+/05qW2rUtb5UvwnbrCGByRPwb/DxfS+p6Xq+x/+OdUFihiUBPSTukdxWnAveVOaZ2R5KAm4AXI+K3ufLuuWrHA9ML21rzSNooLYBF0kbA4WTn9z7gzFTtTGBMeSJs104jd7nDz/O1oq7n9X3AqZI2lLQD0BN4riUG9Kc8bDXpI1xXAh2AP0XEZeWNqP2RdCDwBFADrEzFPyL7j7c32RTkbOC/aq+DWmkkfY5sVgKyOy3fFhGXSdoc+AvQA/gXcFJEFC5ws2aS1Jnsmv3nImJhKvszfp63GEmjgH5ktyn/N3AxMJo6nteSfgx8E/iY7HLr31okDicUZmZmVipf8jAzM7OSOaEwMzOzkjmhMDMzs5I5oTAzM7OSOaEwMzOzkjmhMCsjSSvS3RanS7pf0iYN1L9E0uAG6hyXbgBU+/hnkg5rgVhHSOpfaj9NHPP89LHDVkPSrulv9rykHQv2zZb0REHZlNq7QEqqknR1C8RQmb+zZMG+G/N//zVN0laSbpP0WvpK82ckHb+2xrfWwwmFWXktjYje6Q6B7wKDWqDP44BPXlAi4qKIeKQF+l2r0t0pzwdaVUJBdn7HRESfiHi1yP6ukrYDkPT5/I6IqI6I8xo7UDoHTRIRZ6+tuwOnL2gbDYyPiM9FxD5kX4a37Roed/012b81jxMKs9bjGdJNeiTtKGlsesf3hKRdCytL+rakiZKmSrpbUmdJXyS7Z8IV6Z3xjrUzC5KOkPSXXPt+ku5P24end5aTJd2Z7jFSp/RO/BepTbWkvSX9XdKrks7J9T9e0r2SXpB0vaT10r7TJNWkmZnLc/0uTjMqzwI/Jru98mOSHkv7/5DGmyHppwXx/DTFX1N7viR1kTQ8lU2TdGJjj1dSb0kTUrt7JW2avvTtfODs2piK+AtwStou/IbIfpIeaCC2/DnoK+l76TxNl3R+bpz1JY1Mbe+qncmRNE5SVSPO8+Xp+fWIpP1Su9ckHZPqdJB0RXqOTZP0X0WO9cvARxFxfW1BRMyJiGvq6yOdh3Ep7pmSbk3JCZL2kfR4iu3vWvX10ePSc+5x4P9JOlrSs8pmih6RtFUdfw9bWyLCP/7xT5l+gMXpdweyGyd9NT1+FOiZtvcH/pG2LwEGp+3Nc/1cCpybtkcA/XP7RgD9yb4d8l/ARqn8D8DXyL5db3yu/IfARUVi/aRfsm83/E7a/h0wDegKbAm8lcr7AcuAz6XjezjFsXWKY8sU0z+A41KbAE7OjTkb2CL3eLPc+RoH7JmrV3v8/w3cmLYvB67Mtd+0Ccc7DTg4bf+stp/836BIm9nAzsDT6fHzZLNF03Pn5IG6Yis8B8A+ZN+muhHQBZhBdmfaylTvgFTvT6x6XowDqhpxno9I2/cCDwEdgb2AKal8IPCTtL0hUA3sUHC85wG/q+f5XbSPdB4Wks1krEeWTB+YYnga2DK1OYXs23prj+v3BX/L2i9nPBv4Tbn/Pa/rP542MiuvTpKmkL1ATAIeTu+Wvwjcmd60QfafcaFeki4FNiF7sfl7fQNFxMeSxgJHS7oLOBL4AXAw2YveU2m8Dcj+g29I7T1eaoAuEbEIWCRpmVatBXkuIl6DT74e+EBgOTAuIt5O5bcCXyKbOl9BdsO0upys7Lbj6wPdU9zT0r570u9JwAlp+zCyKfjac7BA0lENHa+kbsAmEfF4KhrJqjtlNuRdYIGkU4EXgSV11FsttrSZPwcHAvdGxAcprnuAg8jO/esR8VSqdwvZi/uvc/3vS93n+SNgbKpXA3wYEcsl1ZA9FyG718meWrVuphvZfR9m1XXgkq5LMX8UEfvW08dHZM+NuandlDTue0Avsn8HkCWO+a/kviO3vS1wR5rB2KC+uGztcEJhVl5LI6J3egF7gGwNxQjgvYjo3UDbEWTvOKdKGkD2rq8hd6Qx3gUmRsSiNNX8cESc1sTYP0y/V+a2ax/X/t9S+N3+QfHbJ9daFhEriu1QdiOjwcC+KTEYAVQUiWdFbnwViaG5x9sUdwDXAQPqqVMsNvj0OajvXBU7t4X912V5pLf25P5+EbFSq9YniGzWp75EdQZw4icBRAyStAXZTESdfUjqx6efM7V/MwEzIqJvHeN9kNu+BvhtRNyX+ruknjhtLfAaCrNWILKbJp1H9oK5FJgl6STIFr5J2qtIs67APGW3QT8jV74o7StmHLA38G1WvdubABwgaac0XmdJO5d2RJ/YT9mda9cjm75+EngWOFjSFsoWHZ4GPF5H+/yxbEz2grIwXS8/ohHjPwR8t/aBpE1pxPGmv8cCSQeloq/XE2Mx9wK/ov5Zo2KxFRoPHJdi3Ijszpy1nyLpIan2hfc0snOb15TzXMzfge+k5xeSdk4x5P0DqJD0nVxZfhFtY/rIewnYsva4JHWUtHsddbsBb6TtM+uoY2uREwqzViIingemkk2DnwF8S9JUsneBxxZpciHZi8bDwMxc+e3ABSryscb0zvcBshfjB1LZ22TvpEdJmkb2grvaItBmegYYSnZ76llk0/fzgP8FHiM73skRUdctw4cBf5P0WERMJVuTMINszcBTdbTJuxTYNC1KnAoc0oTjPZNsces0sjtj/qwR4wEQEYsi4vKI+KgpsRXpZzLZTNRzZH/rG9PzBLLLKWem+DYjWxOTb9uU81zMjcALwGRlH1H9IwWz2mmW4ziyxGWWpOfILg/9sLF9FPT3Edk6m8vTOZlCdvmvmEvILgs+AbzThOOyNcR3GzWzNSJNQw+OiKPKHIqZrQWeoTAzM7OSeYbCzMzMSuYZCjMzMyuZEwozMzMrmRMKMzMzK5kTCjMzMyuZEwozMzMr2f8HGRANN5QKg+8AAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.3843930757842561, pvalue=0.009989517499016335)\n", "Slope and P-value = PearsonRResult(statistic=-0.7246895149579092, pvalue=1.8287419848073125e-09)\n", "Slope and P-value = PearsonRResult(statistic=-0.6672501585592684, pvalue=1.1470315987843783e-10)\n", "Slope and P-value = PearsonRResult(statistic=-0.6606899734018742, pvalue=7.46722924864244e-14)\n", "Slope and P-value = PearsonRResult(statistic=-0.4366275816925966, pvalue=0.11853451692740817)\n", "Slope and P-value = PearsonRResult(statistic=-0.6286022957571435, pvalue=4.3723990812055566e-09)\n", "Slope and P-value = PearsonRResult(statistic=-0.3685660898767411, pvalue=0.003760235217488948)\n", "Slope and P-value = PearsonRResult(statistic=-0.6444331062562244, pvalue=0.002897921607091605)\n", "Slope and P-value = PearsonRResult(statistic=-0.5409756304137147, pvalue=1.3952184099046034e-05)\n", "Slope and P-value = PearsonRResult(statistic=-0.8825900062591617, pvalue=2.4954804958894247e-21)\n", "1 165\n", "0 34\n", "Name: SwineOnFarm, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.9402431896079531, pvalue=1.255769713554042e-23)\n", "Slope and P-value = PearsonRResult(statistic=-0.5740502981808991, pvalue=4.26878240621491e-10)\n", "Slope and P-value = PearsonRResult(statistic=0.30456450640907856, pvalue=0.0026921357509708957)\n", "Slope and P-value = PearsonRResult(statistic=-0.8481784379590729, pvalue=1.8636435758238944e-25)\n", "Slope and P-value = PearsonRResult(statistic=-0.3623091923950279, pvalue=0.08188259823650933)\n", "Slope and P-value = PearsonRResult(statistic=-0.9286076168859247, pvalue=5.528256981942282e-44)\n", "Slope and P-value = PearsonRResult(statistic=-0.9672673388548698, pvalue=4.846876083607354e-29)\n", "Slope and P-value = PearsonRResult(statistic=-0.9247939865772986, pvalue=1.6582649583320345e-20)\n", "Slope and P-value = PearsonRResult(statistic=-0.5682321740228441, pvalue=0.11042583705839493)\n", "Slope and P-value = PearsonRResult(statistic=-0.853562424070105, pvalue=6.201120128567748e-29)\n", "1 243\n", "0 70\n", "Name: SwineOnFarm, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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PearsonRResult(statistic=0.2644612350446808, pvalue=0.007840110962193884)\n", "Slope and P-value = PearsonRResult(statistic=-0.892496233636045, pvalue=1.1694639082969557e-35)\n", "1 148\n", "0 35\n", "Name: SwineOnFarm, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.14062122445600667, pvalue=0.162871967030552)\n", "Slope and P-value = PearsonRResult(statistic=-0.9304020210233026, pvalue=1.6590222447357067e-44)\n", "Slope and P-value = PearsonRResult(statistic=-0.8200242626506828, pvalue=1.0040723632920635e-12)\n", "Slope and P-value = PearsonRResult(statistic=-0.7772006896399152, pvalue=7.768821818883752e-12)\n", "Slope and P-value = PearsonRResult(statistic=-0.8792914394336929, pvalue=2.1730477485782426e-31)\n", "Slope and P-value = PearsonRResult(statistic=0.17234549688642786, pvalue=0.08641085291486883)\n", "Slope and P-value = PearsonRResult(statistic=-0.24315551109812544, pvalue=0.252246859942898)\n", "Slope and P-value = PearsonRResult(statistic=-0.2550894140817366, pvalue=0.010426757946256304)\n", "Slope and P-value = PearsonRResult(statistic=0.27592832821022706, pvalue=0.005456782997477805)\n", "Slope and P-value = PearsonRResult(statistic=-0.8907915056155481, pvalue=8.692487652976992e-29)\n", "1 150\n", "0 35\n", "Name: SwineOnFarm, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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PearsonRResult(statistic=-0.9385327935652069, pvalue=7.574205151786509e-37)\n", "Slope and P-value = PearsonRResult(statistic=-0.6388776499558724, pvalue=1.3829555566508959e-10)\n", "1 173\n", "0 35\n", "Name: SwineOnFarm, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.566028375560961, pvalue=8.402156010607473e-10)\n", "Slope and P-value = PearsonRResult(statistic=0.2283035529992907, pvalue=0.02233670975359497)\n", "Slope and P-value = PearsonRResult(statistic=0.4280775101895904, pvalue=2.8556621348283205e-05)\n", "Slope and P-value = PearsonRResult(statistic=-0.922247453343975, pvalue=3.0994432945099286e-42)\n", "Slope and P-value = PearsonRResult(statistic=-0.737949321155098, pvalue=1.978469637056473e-18)\n", "Slope and P-value = PearsonRResult(statistic=0.3111153131907817, pvalue=0.0016292693236831526)\n", "Slope and P-value = PearsonRResult(statistic=-0.8651847957302483, pvalue=0.00013370491519399828)\n", "Slope and P-value = PearsonRResult(statistic=-0.9777488718569874, pvalue=4.409691714218121e-43)\n", "Slope and P-value = PearsonRResult(statistic=-0.9008566987234802, pvalue=9.72014934530854e-35)\n", "Slope and P-value = PearsonRResult(statistic=0.6619145699219832, pvalue=6.473433683394386e-14)\n", "1 150\n", "0 45\n", "Name: SwineOnFarm, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.8660192442397887, pvalue=7.810149080601003e-29)\n", "Slope and P-value = PearsonRResult(statistic=0.7397160761780328, pvalue=7.413049493671803e-18)\n", "Slope and P-value = PearsonRResult(statistic=0.7878775506364536, pvalue=2.3628784804700743e-22)\n", "Slope and P-value = PearsonRResult(statistic=-0.08079472171346593, pvalue=0.42423423235112023)\n", "Slope and P-value = PearsonRResult(statistic=-0.8745835721797685, pvalue=1.4249357310354092e-32)\n", "Slope and P-value = PearsonRResult(statistic=0.8668710523941114, pvalue=2.186723720255869e-31)\n", "Slope and P-value = PearsonRResult(statistic=0.08510736382409953, pvalue=0.39984254373837136)\n", "Slope and P-value = PearsonRResult(statistic=-0.2776340518990638, pvalue=0.005398181530666588)\n", "Slope and P-value = PearsonRResult(statistic=0.28868563439716843, pvalue=0.0035814936117047466)\n", "Slope and P-value = PearsonRResult(statistic=-0.9122926933341751, pvalue=8.89469175061875e-40)\n" ] } ], "source": [ "sample = pd.merge(microbiome, poultry[['SampleID', 'SwineOnFarm','PastureTime','SampleType']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "#sample = sample[~sample.isin([np.nan, np.inf, -np.inf]).any(1)]\n", "sample = sample.drop(['Pathogen_Salmonella', 'new_Pathogen_Salmonella',\n", " 'Pathogen_Campy','new_Pathogen_Campy',\n", " 'Pathogen_Listeria','new_Pathogen_Listeria'],axis='columns')\n", "\n", "sample.SwineOnFarm.replace({'Y': 1,'N':0}, regex=True, inplace=True)\n", "\n", "\n", "feces1=sample[(sample.SampleType=='Feces') & (sample.PastureTime=='Start')]\n", "feces2=sample[(sample.SampleType=='Feces') & (sample.PastureTime=='Mid')]\n", "feces3=sample[(sample.SampleType=='Feces') & (sample.PastureTime=='End')]\n", "\n", "soil1=sample[(sample.SampleType=='Soil') & (sample.PastureTime=='Start')]\n", "soil2=sample[(sample.SampleType=='Soil') & (sample.PastureTime=='Mid')]\n", "soil3=sample[(sample.SampleType=='Soil') & (sample.PastureTime=='End')]\n", "\n", "ceca=sample[sample.SampleType=='Ceca']\n", "wcrp=sample[sample.SampleType=='WCR-P']\n", "wcrf=sample[sample.SampleType=='WCR-F']\n", "\n", "\n", "print('Feces_Start', feces1.shape)\n", "print('Feces_Mid', feces2.shape)\n", "print('Feces_End', feces3.shape,'\\n')\n", "\n", "print('Soil_Start', soil1.shape)\n", "print('Soil_Mid', soil2.shape)\n", "print('Soil_End', soil3.shape,'\\n')\n", "\n", "print('Ceca', ceca.shape)\n", "print('WCR-P', wcrp.shape)\n", "print('WCR-F', wcrf.shape,'\\n')\n", "\n", "\n", "sampletypes = [feces1,feces2,feces3, soil1,soil2,soil3,ceca, wcrp, wcrf]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES_START\", 1: \"FECES_MID\", 2:\"FECES_END\",\n", " 3: \"SOIL_START\", 4: \"SOIL_MID\", 5: \"SOIL_END\",\n", " 6:\"CECA\", 7: \"WCR-P\", 8: \"WCR-F\"\n", " }\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType', 'PastureTime','SwineOnFarm'],axis='columns'),sample.SwineOnFarm,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1] #multiclass\n", " \n", " try:\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " except ValueError:\n", " pass\n", " \n", " \n", " \n", " \n", "# mylist2.append([f\"SwineOnFarm_{sample_name[indexing]}\", rf_auc_normal])\n", " \n", " \n", " print(pd.value_counts(sample['SwineOnFarm']))\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim3_plot.max(), decimals=None, symbol=''))\n", " \n", " \n", " plt.title(f\"SwineOnFarm in {sample_name[indexing]} Model\")\n", " prelim3_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','SwineOnFarm','PastureTime','SampleType'],axis='columns').columns)\n", " prelim3_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", "\n", " xmax=prelim3_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " \n", " plt.show()\n", " \n", " prelim3_plot.nlargest(10).to_csv(\"prelim3.csv\")\n", " top10 = pd.read_csv('prelim3.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", "# plt.plot(pdp[1][0], pdp[0][0],'.')\n", "# plt.ylabel('Partial dependence'), plt.xlabel(top10[feature])\n", "# plt.show()\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"SwineOnFarm_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (19) GoatOnFarm" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Feces_Start (200, 878)\n", "Feces_Mid (313, 878)\n", "Feces_End (185, 878) \n", "\n", "Soil_Start (199, 878)\n", "Soil_Mid (313, 878)\n", "Soil_End (183, 878) \n", "\n", "Ceca (185, 878)\n", "WCR-P (208, 878)\n", "WCR-F (195, 878) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "0 130\n", "1 70\n", "Name: GoatsOnFarm, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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PearsonRResult(statistic=0.9218949750657088, pvalue=3.8352760297806485e-42)\n", "Slope and P-value = PearsonRResult(statistic=0.7082035840896921, pvalue=1.7147648837100713e-16)\n", "0 200\n", "1 113\n", "Name: GoatsOnFarm, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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PearsonRResult(statistic=0.8005981928959958, pvalue=1.590571812270451e-23)\n", "Slope and P-value = PearsonRResult(statistic=0.8458439662899737, pvalue=1.6976113849413138e-28)\n", "0 115\n", "1 70\n", "Name: GoatsOnFarm, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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PearsonRResult(statistic=0.36256955112473427, pvalue=0.0013899653285376636)\n", "Slope and P-value = PearsonRResult(statistic=0.7780064456017851, pvalue=1.6937870711089145e-21)\n", "0 129\n", "1 70\n", "Name: GoatsOnFarm, dtype: int64\n" ] }, { "data": { "image/png": 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"text/plain": [ "
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PearsonRResult(statistic=-0.5607593545198853, pvalue=1.2981286885148695e-09)\n", "Slope and P-value = PearsonRResult(statistic=0.7995826971938158, pvalue=3.4326434583973216e-21)\n", "0 200\n", "1 113\n", "Name: GoatsOnFarm, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.21389383407921092, pvalue=0.032610827454646996)\n", "Slope and P-value = PearsonRResult(statistic=0.4670756589562898, pvalue=9.643332937648834e-07)\n", "Slope and P-value = PearsonRResult(statistic=0.8707755154401983, pvalue=5.609145664569735e-32)\n", "Slope and P-value = PearsonRResult(statistic=-0.025923321382822377, pvalue=0.7979376882198205)\n", "Slope and P-value = PearsonRResult(statistic=0.919188259252002, pvalue=1.905596666592442e-41)\n", "Slope and P-value = PearsonRResult(statistic=-0.39154780822482593, pvalue=5.617738042526655e-05)\n", "Slope and P-value = PearsonRResult(statistic=-0.6979781039512223, pvalue=7.006045945710323e-16)\n", "Slope and P-value = PearsonRResult(statistic=0.6473578811169833, pvalue=3.390242723635072e-13)\n", "Slope and P-value = PearsonRResult(statistic=0.552548269186457, pvalue=2.519060047685005e-09)\n", "Slope and P-value = PearsonRResult(statistic=-0.31266048677668706, pvalue=0.0015397021939864098)\n", "0 113\n", "1 70\n", "Name: GoatsOnFarm, dtype: int64\n" ] }, { "data": { "image/png": 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i158BwNERMVFST6BHA8vn+npFRPxtqUSpAphfQj1mZmYN0qKnNoBRwJGS2khqBzT42zUkrUc2orFlRFRERAVwLllw8TLQQ9KGkloDP2hAlR2AD1IQcRCwVUp/GjhO0iap3Y6StqqtkjxrALm7Q04mO1aA9sCs1K9T8vI/DZyT2miVjm9uyp/zBPCjdK6QtFmuX2ZmZstDix6RiIgxkh4GJgLTgSqgoSsBjwGeiYj80YWHgD+R3enQF3gRmEW2ALFVPfXdAzwiqQqYALyW+viqpN8BT6a7Ib4kC1iWWYdQYD6ws6Sx6ZhOSOkXkwU604FqlgQKPwP6SzqTbMrknIh4UdLzkiYD/0rrJHYEXkzTLPOAH6b8DbbLZh2oWkHfiGZmZis3RdQ1wt/8JLWLiHnp7oWRQK+IGNfc/VqVVVZWRlVVVXN3w8zMWghJYyOi6A0PLXpEIumfvsSpDdlaBAcRZmZmLUSLDyQi4uT815JuZNnvfegMvFmQdl1E3LE8+2ZmZra6a/GBRKGIOLe5+2BmZmaZln7XhpmZmbVgDiTMzMysbA4kzMzMrGwOJMzMzKxsDiTMzMysbA4kzMzMrGwr3e2ftvxVz5xDRZ/HmrsbZmarnWkr4eMJPCJhZmZmZXMgYWZmZmVb6QMJSftIelnSBElTJPVN6T0k7duIentIerTEMtMkbVRum2ZmZiubVWGNxEDg+IiYKKkVsH1K70H2GO0XmqtjZmZmq7oWMSIh6WJJr0l6StJgSReWUHwTYBZARNRExKuSKoCzgZ+nkYr9JR2ZRi7GS/q3pE1T230l3SXpGUlvSvpxXt3tJN2f+naPMt+RNCyv74dIGlrkmH4haXL66Z2XfpqkSZImSrorpW0l6emU/rSkLVP6ppKGpbwTcyMstdQxQNJxee3MS787SRqZzsNkSfuXcG7NzMzq1OwjEpIqgWOB3cn6Mw4YW0IV1wKvS3oWeJzsUePTJN0CzIuIq1M7GwD7RERIOgv4JXBBqmNXYB9gXWC8pNwtC7sDOwPvAc+TPXX0GeBGSRtHxIfAGcBSTxmVtGdK/xYg4GVJI4AvgN8C+0XER5I6piJ/Be6MiIGSfgRcDxydfo+IiO+n0ZZ2knaupY7anAw8ERF/SHW0LZZJUi+gF0Cr9Taup0ozM7NMSxiR6A48FBELI2Iu8EgphSPiMqASeJLsQ/PxWrJuDjwhqRq4iCxAyMm1/xEwHNg7pY+OiBkR8TUwAaiIiADuAn4oaX2gG/CvIsc0LCLmR8Q8YCiwP/Bt4P7UDhHxccrfDRiUtu9K5Un5b055ayJiTh111GYMcEZaO7JLOsfLiIj+EVEZEZWt2naop0ozM7NMSwgk1NgKIuKtiLgZ+A6wm6QNi2S7AfhrROwC/ARok19FYZXp9+d5aTUsGcG5A/ghcBIwJCK+Kihf2zGpSFvF1JWntjq+Iv09JQlYCyAiRgIHADOBuySd1oD2zczMGqQlBBKjgCMltZHUDijp2zgkHZ4+OAE6k33gfwrMBdrnZe1A9mEKcHpBNUel9jckW6Q5pq42I+I9sumO3wEDimQZCRwtqa2kdYHvA88BTwPH5wKdvGmJF4AT0/YpZOeElP+clLeVpPXqqGMasGfueIDWaf9WwAcRcStwO7BHXcdmZmZWimZfIxERYyQ9DEwEpgNVwJwSqjgVuFbSArKr8lMiokbSI8D9ko4CzgP6AkMkzQReArbOq2M08BiwJfB/EfGepO3qafceYOOIeLXIMY2TNCDVC3BbRIwHkPQHYISkGmA80BM4H/i7pIuA3LoLgJ8B/SWdSRYgnRMRL9ZSx63AQ5JGkwUb81MdPYCLJH1JdheLRyTMzKzJKJvyb+ZOSO0iYp6ktmRX870iYtwKarsveYsySyj3V2B8RNy+XDrWjCorK6Oqqqq5u2FmZi2EpLERUVlsX7OPSCT9Je1Etm5h4IoKIsolaSzZFf8F9eU1MzNblbWIQCIiTs5/LelGslst83UG3ixIuy4i7qARIqJvGWX2rD+XmZnZqq9FBBKFIuLc5u6DmZmZ1a8l3LVhZmZmKykHEmZmZlY2BxJmZmZWNgcSZmZmVjYHEmZmZlY2BxJmZmZWNgcSZmZmVrYW+T0S1ryqZ86hos9jzd0NM7Plblq/kp4TaUV4RMLMzMzK5kDCzMzMyuZAohkp8ztJb0p6Q9JwSTvn7Z8mqVrSJEkjJG1VSz0DJE2VNCH9dE3pG0galsqPltRlBR2amZmtJhxINK9zgX2B3SJiO+AK4GFJbfLyHBQRuwLPAr+ro66LIqJr+pmQ0n4DTEjlTwOua+oDMDOz1ZsDiUaSdLGk1yQ9JWmwpAtLKP4r4LyIWAAQEU8CLwCnFMn7IrBZid3bCXg61f0aUCFp02IZJfWSVCWpqmbBnBKbMTOz1ZUDiUaQVAkcC+wOHANUllB2PWDdiHirYFcVsHORIocCD9ZR5R/SFMa1ktZOaRNTv5C0N7AVsHmxwhHRPyIqI6KyVdsODT0MMzNbzTmQaJzuwEMRsTAi5gKPNEGdAiLv9XBJHwAHA4NqKfNrYAdgL6Aj2UgHQD9gA0kTgPOA8cBXTdBHMzMzwIFEY6ncghHxGTBf0jcLdu0BvJr3+iCykYRXgMsAJD2RFlXeluqaFZnPgTuAvXNtRMQZEdGVbI3ExsDUcvtsZmZWyIFE44wCjpTURlI7oNRvNrkKuF7SOgCSDiYb5Vhq5CEiFgK9gdMkdYyI/5cWVZ6VynVKvwUcDUxOr9eXtFaq5ixgZApgzMzMmoS/2bIRImKMpIfJ1iJMJ1vfUMpKxRuADYBqSTXAf4GjUuBQ2NYsSYPJ7vT4v4Ld90jamGyEZAJwdkrfEbgz1f0qcGYJfTMzM6uXIqL+XFYrSe0iYp6ktsBIoFdEjGvufjVGZWVlVFVVNXc3zMyshZA0NiKK3lDgEYnG6y9pJ6ANMHBlDyLMzMxK4UCikSLi5PzXkm4E9ivI1hl4syDtuoi4Y3n2zczMbHlzINHEIuLc5u6DmZnZiuK7NszMzKxsDiTMzMysbA4kzMzMrGwOJMzMzKxsDiTMzMysbA4kzMzMrGwOJMzMzKxs/h4JW0b1zDlU9HmsubthZquIaf1KfZ6hrUw8ImFmZmZlcyBhZmZmZSsrkJBUI2mCpMmShqQnXza0bE9Jf61l3wv1lK2QdHLe60pJ1ze854vLTZO0UanlCuro3ZDjljSvMe3k1fNC+l0haXLa7iHp0aao38zMrBzljkgsjIiuEdEF+AI4O3+npFblVBoR+9aTpQJYHEhERFVEnF9OW02gN9DgAKqxGnBuzMzMVrimmNp4Dtg2XR0PlzQIqJbURtIdkqoljZd0UF6ZLSQ9Lul1SZfmEnNX78pclUY8qiWdkLL0A/ZPoyE/z78il9Qur71Jko4t5SAk7S3phdTXFyRtn9JbSbo6r97zJJ0PfAMYLml4yndSyjNZ0pUFdV8jaZykpyVtnNJ+LGmMpImSHsiNbkjaVNKwlD5R0r7556aO/veVdGHe68lp9GJdSY+luibnncvC8r0kVUmqqlkwp5RTZ2Zmq7FG3bUhaU3gMODxlLQ30CUipkq6ACAidpG0A/CkpO3y8wELgDGSHouIqryqjwG6ArsBG6U8I4E+wIURcURqv0demYuBORGxS9q3QYmH8xpwQER8Jelg4I/AsUAvYGtg97SvY0R8LOkXwEER8ZGkbwBXAnsCn6RjPToiHgTWBcZFxAWSLgEuBX4KDI2IW1NfLwfOBG4ArgdGRMT308hOuxKPo9ChwHsRcXhqq0OxTBHRH+gPsHanztHINs3MbDVR7ojEOpImAFXAO8DtKX10RExN292BuwAi4jVgOpALJJ6KiNkRsRAYmvLm6w4MjoiaiHgfGAHsVU+fDgZuzL2IiE9KPKYOwJC0/uBaYOe8em+JiK9SvR8XKbsX8GxEfJjy3QMckPZ9DdyXtu9mybF2kfScpGrglLz2vg3cnNqqiYjGDg9UAwdLulLS/k1Qn5mZ2WLljkgsjIiu+QmSAObnJ9VRvvCKt/B1XWVroyL1lOL/gOFpJKACeLaEekvpb66uAcDRETFRUk+gRwl1FPMVSweGbQAi4g1JewL/A1wh6cmIuKyRbZmZmQHL9/bPkWRX2qQpjS2B19O+QyR1lLQOcDTwfJGyJ6T1CRuTXd2PBuYC7Wtp70myKQNSm6VObXQAZqbtngX1np2mcZDUMaXn9+Vl4EBJG6XpiJPIRlEgO8fHpe2TgVFpuz0wS1Jr0nlKngbOSW21krReA/s/DdgjlduDbDqGNO2yICLuBq7O5TEzM2sKyzOQuAlolYbu7wN6RsTnad8osmmPCcADBesjAIYBk4CJwDPALyPivyntq7Rw8OcFZS4HNkgLCicCB1G3SZJmpJ8/A38iu2J/Hsi/6+Q2sumbSane3F0j/YF/SRoeEbOAXwPDU5/HRcRDKd98YGdJY8mmLXKjAReTBSBPka3PyPkZcFA6b2NZMuVRnweAjmnK6RzgjZS+CzA6pf+W7DyZmZk1CUV4XZ0trbKyMqqqCmM7MzNbXUkaGxGVxfb5my3NzMysbKv0Q7skvQysXZB8akRUN0d/zMzMVjWrdCAREd9q7j6YmZmtyjy1YWZmZmVzIGFmZmZlcyBhZmZmZXMgYWZmZmVzIGFmZmZlcyBhZmZmZXMgYWZmZmVbpb9HwspTPXMOFX0ea+5umNlKbFq/w5u7C7aCeETCzMzMyuZAwszMzMpWbyAhqUbShPR47iGS2ja0ckk9Jf21ln0v1FO2QtLJea8rJV3f0Lbzyv1IUrWkSekYjsrr2zdKra+Odo6WtFNT1WdmZrYyaMiIxMKI6BoRXYAvgLPzd0pqVU7DEbFvPVkqgMWBRERURcT5pbQhaXPgt0D3iNgV2AeYlHb3BIoGEmUe09GAAwkzM1utlDq18RywraQekoZLGgRUS2oj6Y505T9e0kF5ZbaQ9Lik1yVdmkuUNC/9lqSr0mhBtaQTUpZ+wP5pNOTnqc1HU5l2ee1NknRsLf3dBJgLzAOIiHkRMVXScUAlcE+qfx1J0yRdImkU8ANJ35X0oqRxaSSmXWp7mqQrJY1OP9tK2hf4HnBVqm8bSV0lvZT6N0zSBqn8tpL+LWliqnubOs4Bkn6Z0iZK6ldHHYvPT8rzV0k903Y/Sa+mvlxd7ERJ6iWpSlJVzYI59b8TzMzMKOGuDUlrAocBj6ekvYEu6YP5AoCI2EXSDsCTkrbLzwcsAMZIeiwiqvKqPgboCuwGbJTyjAT6ABdGxBGp/R55ZS4G5kTELmnfBrV0eyLwPjBV0tPA0Ih4JCLul/TTVH9VqgNgUUR0l7QRMBQ4OCLmS/oV8AvgslTvZxGxt6TTgL9ExBGSHgYejYj7U32TgPMiYoSky4BLgd7APUC/iBgmqQ1ZMFfbOehKNtLxrYhYIKljar9YHVsUOwGpzPeBHSIiJK1fLF9E9Af6A6zdqXPUcj7NzMyW0pARiXUkTQCqgHeA21P66IiYmra7A3cBRMRrwHQgF0g8FRGzI2Ih2Ydz94L6uwODI6ImIt4HRgB71dOng4Ebcy8i4pNimSKiBjgUOA54A7hWUt866r0v/d6HbJri+XTspwNb5eUbnPe7W2ElkjoA60fEiJQ0EDhAUntgs4gYlvq3KCIWUPs5OBi4I+UhIj6uo47afAYsAm6TdAxZQGdmZtYkGjIisTAiuuYnpKv3+flJdZQvvLotfF1X2dqoSD3FG48IYDQwWtJTwB1A31qy545JZAHQSbVVW8t2fWo71rrSG3q+vmLpwLANQER8JWlv4DvAicBPgW83qLdmZmb1aKrbP0cCpwCkKY0tgdfTvkMkdZS0Dtkw/fNFyp4gqZWkjYEDyD745wLta2nvSbIPRFKbRac2JH1D0h55SV3JRkuop/6XgP0kbZvqaZs3VQNwQt7vFwvri4g5wCeS9k/7TgVGRMRnwAxJR6d611Z2F0xt5+BJ4EcpD5I61lHHdGCn9LoDWeBAWtvRISL+STa10rWWYzYzMytZU32z5U3ALZKqya6Me0bE52nkYhTZtMe2wKCC9REAw8imByaSXX3/MiL+K2k28JWkicAAYHxemcuBGyVNBmqA35NNmxRqDVyt7DbPRcCHLLnrZEDq80IKpici4sO0UHGwpLVT8u/IpkcA1pb0Mlkglhu1uBe4VdL5ZFMpp6f62wJvA2ekfKcCf0vrJr4EflDbOQAel9QVqJL0BfBP4DfF6oiItyX9g+yulDfzzld74KG0lkLAz4ucp6XsslkHqvytdGZm1gDKRv6toSRNAyoj4qPm7svyUllZGVVVhfGemZmtriSNjYjKYvv8zZZmZmZWtlXmoV1pqmHtguRTI6K6KduJiIqmrM/MzGxltsoEEhHxrebug5mZ2erGUxtmZmZWNgcSZmZmVjYHEmZmZlY2BxJmZmZWNgcSZmZmVjYHEmZmZla2Veb2T2s61TPnUNHnsebuhpmtANP8dfjWSB6RMDMzs7I5kDAzM7OyOZBoIpIGSDoubXeUNF7SGbXkfSH97iHp0VryzCux/b6SLizsi5mZ2fLkQKKJSeoAPAH0j4g7Cva1AoiIfZujb2ZmZk3NgUQeSRdLek3SU5IG567wS9AO+BcwKCJuTnX2kDRc0iCgOqXljzasJ2mYpFcl3SJp8d9E0jWSxkl6WtLGKW0bSY9LGivpOUk71HNMl0gaI2mypP6SVOIxmZmZ1cqBRCKpEjgW2B04Bij63PV6/BkYFRHXFqTvDfw2InYqUmZv4AJgF2Cb1DbAusC4iNgDGAFcmtL7A+dFxJ7AhcBN9fTprxGxV0R0AdYBjiiWSVIvSVWSqmoWzKmnSjMzs4wDiSW6Aw9FxMKImAs8UkYdzwBHSdqkIH10REytpczoiHg7ImqAwakfAF8D96Xtu4HuktoB+wJDJE0A/gZ0qqdPB0l6WVI18G1g52KZIqJ/RFRGRGWrth3qqdLMzCzj75FYoimG/O8FRgH/lHRQCkgA5tdRJup5nZ++BvBpRHRtSGcktSEbsaiMiHcl9QXaNKSsmZlZQ3hEYolRwJGS2qQr/7K+pSUi/gI8DQyTtFYDiuwtaeu0NuKE1A/I/ja5Oy9OJpsy+QyYKukHAMrsVkfduaDho3RMvpPDzMyalAOJJCLGAA8DE4GhQBVQ1mKBiPgV8C5wF/Wf4xeBfsBkYCowLKXPB3aWNJZsSuKylH4KcKakicArwFF19ONT4FayRZ4PAmNKPxozM7PaKaK2kfTVj6R2ETFPUltgJNArIsY1d79WtMrKyqiqqmrubpiZWQshaWxEFL0JwWskltZf0k5kUwIDV8cgwszMrBQOJPJExMn5ryXdCOxXkK0z8GZB2nWFXz5lZma2OnAgUYeIOLe5+2BmZtaSebGlmZmZlc2BhJmZmZXNgYSZmZmVzYGEmZmZlc2BhJmZmZXNgYSZmZmVzYGEmZmZlc3fI2HLqJ45h4o+jzV3N8ysTNP6lfXMQbOyeETCzMzMyuZAwszMzMq2WgYSkvaR9LKkCZKmSOrb3H0qlaSzJZ3W3P0wM7PV2+q6RmIgcHxETJTUCti+sRVKahURNY3vWsPqjYhbmrotMzOzUq20IxKSLpb0mqSnJA2WdGEJxTcBZgFERE1EvJrq7CvpLknPSHpT0o9TuiRdJWmypGpJJ6T0HpKGSxoEVEtqI+mOlGe8pINSvp0ljU4jIJMkdU7pD0oaK+kVSb3yjm2epMskvQx0k3RaKjdR0l15fb0wbT8r6crUxhuS9k/pFZKekzQu/exbx/nsJalKUlXNgjklnEozM1udrZQjEpIqgWOB3cmOYRwwtoQqrgVel/Qs8DgwMCIWpX27AvsA6wLjJT0GdAO6ArsBGwFjJI1M+fcGukTEVEkXAETELpJ2AJ6UtB1wNtmjxu+RtBbQKpX9UUR8LGmdVOcDETE7tT05Ii6RtDPwW2C/iPhIUsdajmnNiNhb0v8AlwIHAx8Ah0TEohS8DAYqixWOiP5Af4C1O3WOhp9KMzNbna2sIxLdgYciYmFEzAUeKaVwRFxG9oH6JHAyWTCRk6v3I2A4WaDQHRicRi/eB0YAe6X8oyNial6/7kptvAZMB7YDXgR+I+lXwFYRsTDlP1/SROAlYAugc0qvAR5I298G7k/9ISI+ruWwhqbfY4GKtN0auFVSNTAE2KkBp8fMzKzBVsoRCUCNrSAi3gJulnQr8KGkDXO7CrPW0978+voVEYPSNMXhwBOSzgK+Jhs16BYRC9LoSJtUZFHeuggV6VMxn6ffNSz5u/4ceJ9sJGUNYFGRcmZmZmVbWUckRgFHpjUJ7cg+oBtM0uGSch/6nck+fD9Nr49K9W4I9ADGACOBEyS1krQxcAAwukjVI4FTUhvbAVuSTaF8E3g7Iq4HHiabPukAfJKCiB3IplOKeRo4Phfo1DG1UUwHYFZEfA2cypIpFTMzsyaxUo5IRMQYSQ8DE8mmD6qAUlYIngpcK2kB8BVwSkTUpNhiNPAYWRDwfxHxnqRhZOskJpKNDvwyIv6bAoB8NwG3pKmEr4CeEfF5Wpz5Q0lfAv8FLiMbyThb0iTgdbLpjWLH+oqkPwAjJNUA44GeDTzOm4AHJP2AbJpmfj35zczMSqKIlXNdnaR2ETFPUluykYBeETGukXX2BeZFxNVN0ceVVWVlZVRVVTV3N8zMrIWQNDYiii7WXylHJJL+knYiW1cwsLFBhJmZmZVupQ0kIuLk/NeSbgT2K8jWGXizIO26iLijljr7NlkHzczMVgMrbSBRKCLObe4+mJmZrW5W1rs2zMzMrAVwIGFmZmZlcyBhZmZmZXMgYWZmZmVzIGFmZmZlcyBhZmZmZXMgYWZmZmVbZb5HwppO9cw5VPR5rLm7YWZFTOtX0jMKzZY7j0iYmZlZ2RxImJmZWdlW+kBC0gBJUyVNSD/np/RpkjZqwnYqJE1uonpOrj9n0bIvNLZ9MzOzprSqrJG4KCLub+5ONFAFcDIwqKEFJLWKiJqI2He59crMzKwMLWJEQtLFkl6T9JSkwZIubOL6H5Q0VtIrknqltHMk/SkvT09JN6TtX0ianH5651W1pqSBkiZJul9S25T/EkljUv7+kpTSt5X0b0kTJY2TtA3QD9g/jZ78XFIrSVel8pMk/SSV7SFpuKRBQHVKm5e379G8vv9VUs+0PU3SHyW9KKlK0h6SnpD0lqSz6zhHvVL+qpoFcxp/0s3MbLXQ7IGEpErgWGB34Bigsoxqrsqb2tilyP4fRcSeqe7zJW0I3J/ayzkBuE/SnsAZwLeAfYAfS9o95dke6B8RuwKfAf+b0v8aEXtFRBdgHeCIlH4PcGNE7AbsC8wC+gDPRUTXiLgWOBOYExF7AXul9rZO5fcGfhsRO5V4Pt6NiG7Ac8AA4Lh0LJfVViAi+kdEZURUtmrbocTmzMxsddXsgQTQHXgoIhZGxFzgkTLquCh9MHeNiOoi+8+XNBF4CdgC6BwRHwJvS9onBRbbA8+n/gyLiPkRMQ8YCuyf6nk3Ip5P23envAAHSXpZUjXwbWBnSe2BzSJiGEBELIqIBUX69l3gNEkTgJeBDYHOad/oiJhaxvl4OP2uBl6OiLnpeBdJWr+M+szMzIpqCWsktFwrl3oABwPdImKBpGeBNmn3fcDxwGtkwUPkpiVqEYWvJbUBbgIqI+JdSX1T/Q09LgHnRcQTRfo9v5YyX7F0ENimYP/n6ffXedu51y3hb25mZquIljAiMQo4UlIbSe2Apv62lQ7AJymI2IFsiD9nKHA0cBJZUAEwEjhaUltJ6wLfJ5siANhSUre0fVLqe+5D/KPU/+MAIuIzYIakowEkrZ3WVMwF2uf14QngHEmtU77tUrt1mQ7slOrsAHynYafCzMysaTX71WlEjJH0MDCR7AOyCmjK1X6PA2dLmgS8Tja9kWv7E0mvAjtFxOiUNk7SAGB0ynZbRIyXVAFMAU6X9DfgTeDmFKDcSjaNMA0Yk9f2qcDfJF0GfAn8AJgEfJWmWgYA15HdyTEujYZ8SBbc1CqNfPwj1fUmML7002JmZtZ4iigcrW+GTkjtImJeumIfCfSKiHHN3a/VVWVlZVRVVTV3N8zMrIWQNDYiit4M0ewjEkl/STuRTRMMdBBhZma2cmgRgURELPVNj5JuBPYryNaZbBg/33URccfy7JuZmZnVrkUEEoUi4tzm7oOZmZnVr0UGEmZmtvL68ssvmTFjBosWLWrurliJ2rRpw+abb07r1q0bXMaBhJmZNakZM2bQvn17KioqqPureawliQhmz57NjBkz2HrrresvkLSE75EwM7NVyKJFi9hwww0dRKxkJLHhhhuWPJLkQMLMzJqcg4iVUzl/NwcSZmZmVjavkTAzs+Wqos9jTVrftH4Ne5LCsGHDOOaYY5gyZQo77LADAM8++yxXX301jz766OJ8PXv25IgjjuC4446jR48ezJo1izZt2rDWWmtx66230rVrVwDmzJnDeeedx/PPZ89u3G+//bjhhhvo0CF7YvIbb7xB7969eeONN2jdujW77LILN9xwA5tuumnZx/rxxx9zwgknMG3aNCoqKvjHP/7BBhtssEy+6667jltvvZWI4Mc//jG9e/cGYMiQIfTt25cpU6YwevRoKiuz75Sqrq7mmmuuYcCAAWX3LceBhC2jeuacJv+Hb2b1a+gHpDXM4MGD6d69O/feey99+/ZtcLl77rmHyspK7rjjDi666CKeeuopAM4880y6dOnCnXfeCcCll17KWWedxZAhQ1i0aBGHH344f/7znznyyCMBGD58OB9++GGjAol+/frxne98hz59+tCvXz/69evHlVdeuVSeyZMnc+uttzJ69GjWWmstDj30UA4//HA6d+5Mly5dGDp0KD/5yU+WKrPLLrswY8YM3nnnHbbccsuy+wee2jAzs1XQvHnzeP7557n99tu59957y6qjW7duzJw5E4D//Oc/jB07losvvnjx/ksuuYSqqireeustBg0aRLdu3RYHEQAHHXQQXbp0adRxPPTQQ5x++ukAnH766Tz44IPL5JkyZQr77LMPbdu2Zc011+TAAw9k2LBhAOy4445sv/32Res+8sgjyz43+RxImJnZKufBBx/k0EMPZbvttqNjx46MG1f6kxcef/xxjj76aABeffVVunbtSqtWrRbvb9WqFV27duWVV15h8uTJ7LnnnvXWOXfuXLp27Vr059VXX10m//vvv0+nTp0A6NSpEx988MEyebp06cLIkSOZPXs2CxYs4J///CfvvvtuvX2prKzkueeeqzdffTy1YWZmq5zBgwcvXidw4oknMnjwYPbYY49a70rITz/llFOYP38+NTU1iwOQiChatrb02rRv354JEyY0/EAaYMcdd+RXv/oVhxxyCO3atWO33XZjzTXr/3jfZJNNeO+99xrd/iofSKRHgh/IkkeTL4iIfYvkmwZURsRHZbTxLHBhRDTokZmSeqa2flpqWwX19AC+iIgXGlOPmdmqZPbs2TzzzDNMnjwZSdTU1CCJP/3pT2y44YZ88sknS+X/+OOP2WijjRa/vueee9htt93o06cP5557LkOHDmXnnXdm/PjxfP3116yxRjaY//XXXzNx4kR23HFHPvjgA0aMGFFv3+bOncv+++9fdN+gQYPYaaedlkrbdNNNmTVrFp06dWLWrFlssskmRcueeeaZnHnmmQD85je/YfPNN6+3L4sWLWKdddapN199VpepjYsiomv6WSaIWIn1AEo6HkmrfPBoZqu3+++/n9NOO43p06czbdo03n33XbbeemtGjRpF586dee+995gyZQoA06dPZ+LEiYvvzMhp3bo1l19+OS+99BJTpkxh2223Zffdd+fyyy9fnOfyyy9njz32YNttt+Xkk0/mhRde4LHHlixUf/zxx6murl6q3tyIRLGfwiAC4Hvf+x4DBw4EYODAgRx11FFFjzk35fHOO+8wdOhQTjrppHrP0xtvvNHoNRywkoxISLoYOAV4F/gIGBsRVzeyzg2BwcDGwGhAefseBLYge6z5dRHRP6XPA/4GHAR8ApwYER+mYj+QdBOwPnBmRDwnqQ1wM1AJfAX8IiKGp/xbSHoc2BoYFBG/r6ftQ4E/Aq3SOTgTOBuokfRD4DzgNeAWILcEt3dEPC+pL/ANoCKVXeppq6n+XkAvgFbrbVzSuTQzq8uKvhtl8ODB9OnTZ6m0Y489lkGDBrH//vtz9913c8YZZ7Bo0SJat27NbbfdtvgWznzrrLMOF1xwAVdffTW33347t99+O+eddx7bbrstEUG3bt24/fbbF+d99NFH6d27N71796Z169bsuuuuXHfddY06lj59+nD88cdz++23s+WWWzJkyBAA3nvvPc466yz++c9/Lj6+2bNn07p1a2688cbFt4gOGzaM8847jw8//JDDDz+crl278sQTTwDZXSWHH974v40iotGVLE+SKoHbgG5kgc844G8NDSSKTG28EhGnSLoe+CgiLpN0OPAosHFEfCSpY0R8LGkdYAxwYETMlhTADyPiHkmXAJtExE/T1MbYiLhA0v+QBQwHS7oA6BIRZ0jaAXgS2A44EbgC6AIsSG30jIiqYm2TjRyNAw6IiKl5efoC83LnQtIg4KaIGCVpS+CJiNgx5TsS6B4RC+s7Z2t36hydTv9LQ06vmTWhVeX2zylTprDjjjs2dzesDp9//jkHHnggo0aNWmY9RbG/n6SxEVFZrK6VYUSiO/BQ7gNQ0iNl1HFRRNxfkHYAcAxARDwmKX/S7HxJ30/bWwCdgdnA18B9Kf1uYGhemdz2WLIr/1zfb0htvCZpOlkgAfBURMxOxzQ05a2qpe2NgZERMTXV9XEtx3kwsFPewp/1JLVP2w83JIgwM7NV3zvvvEO/fv0atCizPitDILE8v7B9meGYtIDxYKBbRCxIow1tGlD+8/S7hiXnta6+F7YddbStYn0tYo1UdqmAIQUW8xtQ3szMVgOdO3emc+fOTVLXyrDYchRwpKQ2ktoBTTX2N5Js3QWSDgNy3znaAfgkfZDvAOyTV2YN4Li0fXLqW0Pb2I5s7cLrad8hkjqmKYyjgefraPtF4EBJW6e6Oqb0uUBuxAGyqZPFd4JI6lpP/8zMlouWPm1uxZXzd2vxIxIRMUbSw8BEYDrZ8P+cukst4ypJv8t7vTfwe2CwpHHACOCdtO9x4GxJk8g+9F/KKzcf2FnS2NSHE+pp9ybgFknVZIste0bE52mEYBRwF7At2WLLqpRvmbYj4sO0GHKopDWAD4BDgEeA+yUdRbbY8nzgxlR+TbJA5uxSThTALpt1oGoVmas1sxWvTZs2zJ49248SX8lEBLNnz6ZNm9oG4Ytr8YstASS1i4h5ktqSfTj2iojSv6as8f2YFxHtVnS7K1plZWVUVTXoKzHMzJbx5ZdfMmPGDBYtWtTcXbEStWnThs0335zWrVsvlb6yL7YE6C9pJ7L1AgObI4gwM7OGad26NVtvvXVzd8NWkJUikIiIpb73QNKNwH4F2ToDbxakXRcRdzRhP1b50QgzM7NSrBSBRKGIOLe5+2BmZmYrx10bZmZm1kKtFIstbcWSNJclt6nairER2deX24rjc77i+ZyveE11zreKiKLPT1gppzZsuXu9ttW5tnxIqvI5X7F8zlc8n/MVb0Wcc09tmJmZWdkcSJiZmVnZHEhYMf2buwOrIZ/zFc/nfMXzOV/xlvs592JLMzMzK5tHJMzMzKxsDiTMzMysbA4kbDFJh0p6XdJ/JPVp7v6siiRtIWm4pCmSXpH0s5TeV9JMSRPSz/80d19XJZKmSapO57YqpXWU9JSkN9PvDZq7n6sKSdvnvZcnSPpMUm+/z5uWpL9L+kDS5Ly0Wt/Xkn6d/n9/XdL/a7J+eI2EAUhqBbxB9njyGcAY4KSIeLVZO7aKkdQJ6BQR4yS1B8YCRwPHA/Mi4urm7N+qStI0oDIiPspL+xPwcUT0S4HzBhHxq+bq46oq/d8yE/gWcAZ+nzcZSQcA84A7I6JLSiv6vk4PvhwM7A18A/g3sF1E1DS2Hx6RsJy9gf9ExNsR8QVwL3BUM/dplRMRs3JPr42IucAUYLPm7dVq6yhgYNoeSBbQWdP7DvBWRExv7o6saiJiJPBxQXJt7+ujgHsj4vOImAr8h+z//UZzIGE5mwHv5r2egT/glitJFcDuwMsp6aeSJqXhSg+zN60AnpQ0VlKvlLZpRMyCLMADNmm23q3aTiS7Es7x+3z5qu19vdz+j3cgYTkqkuZ5r+VEUjvgAaB3RHwG3AxsA3QFZgHXNF/vVkn7RcQewGHAuWlI2JYzSWsB3wOGpCS/z5vPcvs/3oGE5cwAtsh7vTnwXjP1ZZUmqTVZEHFPRAwFiIj3I6ImIr4GbqWJhhwtExHvpd8fAMPIzu/7ac1Kbu3KB83Xw1XWYcC4iHgf/D5fQWp7Xy+3/+MdSFjOGKCzpK3TVcSJwMPN3KdVjiQBtwNTIuLPeemd8rJ9H5hcWNbKI2ndtLAVSesC3yU7vw8Dp6dspwMPNU8PV2knkTet4ff5ClHb+/ph4ERJa0vaGugMjG6KBn3Xhi2WbsX6C9AK+HtE/KF5e7TqkdQdeA6oBr5Oyb8h+w+3K9lQ4zTgJ7l5TmscSd8kG4WA7InHgyLiD5I2BP4BbAm8A/wgIgoXrlmZJLUlm5P/ZkTMSWl34fd5k5E0GOhB9qjw94FLgQep5X0t6bfAj4CvyKZV/9Uk/XAgYWZmZuXy1IaZmZmVzYGEmZmZlc2BhJmZmZXNgYSZmZmVzYGEmZmZlc2BhFkzkFSTnn44WdIjktavJ39fSRfWk+fo9GCe3OvLJB3cBH0dIOm4xtZTYpu90+2DLYakHdLfbLykbQr2TZP0XEHahNxTGSVVSrq+CfpQkf+kx4J9t+X//Zc3SZtKGiTp7fTV4y9K+v6Kat9aDgcSZs1jYUR0TU/s+xg4twnqPBpY/EESEZdExL+boN4VKj0tsjfQogIJsvP7UETsHhFvFdnfXtIWAJJ2zN8REVURcX5DG0rnoCQRcdaKelpv+mK1B4GREfHNiNiT7EvsNl/O7a65POu38jiQMGt+L5IeniNpG0mPpyu85yTtUJhZ0o8ljZE0UdIDktpK2pfsmQZXpSvhbXIjCZIOk/SPvPI9JD2Str+briTHSRqSngFSq3Tl/cdUpkrSHpKekPSWpLPz6h8paZikVyXdImmNtO8kSdVpJObKvHrnpRGUl4Hfkj3meLik4Wn/zam9VyT9vqA/v0/9r86dL0ntJN2R0iZJOrahxyupq6SXUrlhkjZIX9bWGzgr16ci/gGckLYLv9Gxh6RH6+lb/jnoJukX6TxNltQ7r501JQ1MZe/PjdxIelZSZQPO85Xp/fVvSXuncm9L+l7K00rSVek9NknST4oc67eBLyLillxCREyPiBvqqiOdh2dTv1+TdE8KSpC0p6QRqW9PaMnXPD+b3nMjgJ9JOlLSy8pGhv4tadNa/h62okSEf/zjnxX8A8xLv1uRPdDo0PT6aaBz2v4W8Eza7gtcmLY3zKvncuC8tD0AOC5v3wDgOLJvc3wHWDel3wz8kOzb8Ebmpf8KuKRIXxfXS/ZthOek7WuBSUB7YGPgg5TeA1gEfDMd31OpH99I/dg49ekZ4OhUJoDj89qcBmyU97pj3vl6Ftg1L1/u+P8XuC1tXwn8Ja/8BiUc7yTgwLR9Wa6e/L9BkTLTgO2AF9Lr8WSjQ5PzzsmjtfWt8BwAe5J9++m6QDvgFbInxVakfPulfH9nyfviWaCyAef5sLQ9DHgSaA3sBkxI6b2A36XttYEqYOuC4z0fuLaO93fROtJ5mEM2crEGWRDdPfXhBWDjVOYEsm/XzR3XTQV/y9yXKZ4FXNPc/55X9x8PE5k1j3UkTSD7YBgLPJWujvcFhqSLNMj+Ey7URdLlwPpkHzJP1NVQRHwl6XHgSEn3A4cDvwQOJPuwez61txbZf+z1yT2DpRpoFxFzgbmSFmnJWo/REfE2LP4a3+7Al8CzEfFhSr8HOIBsiLyG7EFmtTle2eO/1wQ6pX5PSvuGpt9jgWPS9sFkQ+25c/CJpCPqO15JHYD1I2JEShrIkidX1udj4BNJJwJTgAW15Fumb2kz/xx0B4ZFxPzUr6HA/mTn/t2IeD7lu5vsQ/3qvPr3ovbz/AXweMpXDXweEV9KqiZ7L0L2LJJdtWRdTAey5zJMre3AJd2Y+vxFROxVRx1fkL03ZqRyE1K7nwJdyP4dQBYw5n919n1525sD96URi7Xq6petGA4kzJrHwojomj64HiVbIzEA+DQiutZTdgDZFeZEST3JrvLqc19q42NgTETMTUPKT0XESSX2/fP0++u87dzr3P8phd+9HxR/jHHOooioKbZD2QOGLgT2SgHBAKBNkf7U5LWvIn0o93hLcR9wI9CzjjzF+gZLn4O6zlWxc1tYf22+jHQpT97fLyK+1pL1ByIb5akrQH0FOHZxByLOlbQR2chDrXVI6sHS75nc30zAKxHRrZb25udt3wD8OSIeTvX1raOftgJ4jYRZM4rsYUbnk31QLgSmSvoBZAvaJO1WpFh7YJayx5Gfkpc+N+0r5llgD+DHLLm6ewnYT9K2qb22krZr3BEttreyJ8muQTZMPQp4GThQ0kbKFhOeBIyopXz+saxH9kEyJ82HH9aA9p8Efpp7IWkDGnC86e/xiaT9U9KpdfSxmGHAn6h7lKhY3wqNBI5OfVyX7EmZubtCtpSU+8A9iezc5ivlPBfzBHBOen8habvUh3zPAG0knZOXlr84tiF15Hsd2Dh3XJJaS9q5lrwdgJlp+/Ra8tgK5EDCrJlFxHhgItlw9ynAmZImkl31HVWkyMVkHxZPAa/lpd8LXKQityemK91HyT6EH01pH5JdOQ+WNInsg3aZxZ1lehHoR/aY6Klkw/SzgF8Dw8mOd1xE1Pbo7v7AvyQNj4iJZGsOXiFbE/B8LWXyXQ5skBYbTgQOKuF4TydbtDqJ7EmVlzWgPQAiYm5EXBkRX5TStyL1jCMbeRpN9re+Lb1PIJs2OT31ryPZmpf8sqWc52JuA14Fxim71fRvFIxep1GNo8kClqmSRpNNA/2qoXUU1PcF2TqaK9M5mUA2zVdMX7Lpv+eAj0o4LltO/PRPM2tSabj5wog4opm7YmYrgEckzMzMrGwekTAzM7OyeUTCzMzMyuZAwszMzMrmQMLMzMzK5kDCzMzMyuZAwszMzMr2/wORNolQ/ON60wAAAABJRU5ErkJggg==\n", 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PearsonRResult(statistic=0.7171192695669366, pvalue=4.7797346181611607e-17)\n", "Slope and P-value = PearsonRResult(statistic=0.9001557414067205, pvalue=3.7791386013680906e-37)\n", "0 115\n", "1 70\n", "Name: GoatsOnFarm, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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PearsonRResult(statistic=-0.8370381822096711, pvalue=2.0636466168907706e-27)\n", "Slope and P-value = PearsonRResult(statistic=-0.769259119490206, pvalue=1.1551897194675605e-14)\n", "0 138\n", "1 70\n", "Name: GoatsOnFarm, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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PearsonRResult(statistic=0.4960752228302371, pvalue=6.013701969689047e-06)\n", "Slope and P-value = PearsonRResult(statistic=0.9232736821846534, pvalue=3.6459739715737063e-39)\n", "0 115\n", "1 80\n", "Name: GoatsOnFarm, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.35010344644372804, pvalue=0.00035599702237881954)\n", "Slope and P-value = PearsonRResult(statistic=-0.6110491283358206, pvalue=2.994381277690968e-11)\n", "Slope and P-value = PearsonRResult(statistic=0.4808776680933367, pvalue=4.106034517957184e-07)\n", "Slope and P-value = PearsonRResult(statistic=0.13750986312101063, pvalue=0.17247288856059373)\n", "Slope and P-value = PearsonRResult(statistic=-0.7879469996530382, pvalue=2.3294991060063847e-22)\n", "Slope and P-value = PearsonRResult(statistic=-0.7273349979395031, pvalue=1.0398994877520008e-17)\n", "Slope and P-value = PearsonRResult(statistic=0.7899952964491868, pvalue=1.5274987252254742e-22)\n", "Slope and P-value = PearsonRResult(statistic=0.9373766800827474, pvalue=1.1124102199953893e-46)\n", "Slope and P-value = PearsonRResult(statistic=-0.8910664218004232, pvalue=2.1564949069379314e-35)\n", "Slope and P-value = PearsonRResult(statistic=-0.6952652729016656, pvalue=6.399207504091613e-07)\n" ] } ], "source": [ "sample = pd.merge(microbiome, poultry[['SampleID', 'GoatsOnFarm','PastureTime','SampleType']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "#sample = sample[~sample.isin([np.nan, np.inf, -np.inf]).any(1)]\n", "sample = sample.drop(['Pathogen_Salmonella', 'new_Pathogen_Salmonella',\n", " 'Pathogen_Campy','new_Pathogen_Campy',\n", " 'Pathogen_Listeria','new_Pathogen_Listeria'],axis='columns')\n", "\n", "sample.GoatsOnFarm.replace({'Y': 1,'N':0}, regex=True, inplace=True)\n", "\n", "\n", "\n", "feces1=sample[(sample.SampleType=='Feces') & (sample.PastureTime=='Start')]\n", "feces2=sample[(sample.SampleType=='Feces') & (sample.PastureTime=='Mid')]\n", "feces3=sample[(sample.SampleType=='Feces') & (sample.PastureTime=='End')]\n", "\n", "soil1=sample[(sample.SampleType=='Soil') & (sample.PastureTime=='Start')]\n", "soil2=sample[(sample.SampleType=='Soil') & (sample.PastureTime=='Mid')]\n", "soil3=sample[(sample.SampleType=='Soil') & (sample.PastureTime=='End')]\n", "\n", "ceca=sample[sample.SampleType=='Ceca']\n", "wcrp=sample[sample.SampleType=='WCR-P']\n", "wcrf=sample[sample.SampleType=='WCR-F']\n", "\n", "\n", "print('Feces_Start', feces1.shape)\n", "print('Feces_Mid', feces2.shape)\n", "print('Feces_End', feces3.shape,'\\n')\n", "\n", "print('Soil_Start', soil1.shape)\n", "print('Soil_Mid', soil2.shape)\n", "print('Soil_End', soil3.shape,'\\n')\n", "\n", "print('Ceca', ceca.shape)\n", "print('WCR-P', wcrp.shape)\n", "print('WCR-F', wcrf.shape,'\\n')\n", "\n", "\n", "sampletypes = [feces1,feces2,feces3, soil1,soil2,soil3,ceca, wcrp, wcrf]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES_START\", 1: \"FECES_MID\", 2:\"FECES_END\",\n", " 3: \"SOIL_START\", 4: \"SOIL_MID\", 5: \"SOIL_END\",\n", " 6:\"CECA\", 7: \"WCR-P\", 8: \"WCR-F\"\n", " }\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType', 'PastureTime','GoatsOnFarm'],axis='columns'),sample.GoatsOnFarm,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1] #multiclass\n", " \n", " try:\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " except ValueError:\n", " pass\n", " \n", " \n", " \n", " \n", "# mylist2.append([f\"GoatsOnFarm_{sample_name[indexing]}\", rf_auc_normal])\n", " \n", " \n", " print(pd.value_counts(sample['GoatsOnFarm']))\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim3_plot.max(), decimals=None, symbol=''))\n", " \n", " \n", " plt.title(f\"GoatsOnFarm in {sample_name[indexing]} Model\")\n", " prelim3_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','GoatsOnFarm','PastureTime','SampleType'],axis='columns').columns)\n", " prelim3_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " \n", " xmax=prelim3_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " plt.legend()\n", "\n", " xmax=prelim3_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " \n", " plt.show()\n", " \n", " prelim3_plot.nlargest(10).to_csv(\"prelim3.csv\")\n", " top10 = pd.read_csv('prelim3.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", "# plt.plot(pdp[1][0], pdp[0][0],'.')\n", "# plt.ylabel('Partial dependence'), plt.xlabel(top10[feature])\n", "# plt.show()\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"GoatsOnFarm_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (20) SheepOnFarm" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Feces_Start (200, 878)\n", "Feces_Mid (313, 878)\n", "Feces_End (185, 878) \n", "\n", "Soil_Start (199, 878)\n", "Soil_Mid (313, 878)\n", "Soil_End (183, 878) \n", "\n", "Ceca (185, 878)\n", "WCR-P (208, 878)\n", "WCR-F (195, 878) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "1 100\n", "0 100\n", "Name: SheepOnFarm, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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PearsonRResult(statistic=-0.49175710280849283, pvalue=4.175721703854579e-07)\n", "Slope and P-value = PearsonRResult(statistic=0.4072226068200574, pvalue=0.0002613917919475575)\n", "0 168\n", "1 145\n", "Name: SheepOnFarm, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
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PearsonRResult(statistic=-0.4864482293230225, pvalue=2.878082748481181e-07)\n", "Slope and P-value = PearsonRResult(statistic=-0.8790126402782326, pvalue=2.7338244686671268e-33)\n", "0 100\n", "1 85\n", "Name: SheepOnFarm, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
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PearsonRResult(statistic=-0.29848220326007924, pvalue=0.009791709439894545)\n", "Slope and P-value = PearsonRResult(statistic=-0.44480120639649146, pvalue=1.5862083971213983e-05)\n", "1 100\n", "0 99\n", "Name: SheepOnFarm, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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PearsonRResult(statistic=0.702067267994714, pvalue=4.019124485351506e-16)\n", "Slope and P-value = PearsonRResult(statistic=-0.9578295939520495, pvalue=7.012806081518403e-55)\n", "0 168\n", "1 145\n", "Name: SheepOnFarm, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.9441314007852775, pvalue=4.880176743400455e-49)\n", "Slope and P-value = PearsonRResult(statistic=0.670311907632918, pvalue=4.770453620314702e-11)\n", "Slope and P-value = PearsonRResult(statistic=0.6344966845977534, pvalue=1.3616219305633649e-12)\n", "Slope and P-value = PearsonRResult(statistic=-0.22573304862171448, pvalue=0.023934373502174075)\n", "Slope and P-value = PearsonRResult(statistic=-0.07698895467262473, pvalue=0.4464533519266833)\n", "Slope and P-value = PearsonRResult(statistic=0.9705172691786912, pvalue=2.2971962058769044e-62)\n", "Slope and P-value = PearsonRResult(statistic=0.4386075844757233, pvalue=0.00010385664583150597)\n", "Slope and P-value = PearsonRResult(statistic=0.933679888592349, pvalue=1.6898419555378716e-45)\n", "Slope and P-value = PearsonRResult(statistic=0.7101067941451066, pvalue=1.3107684525423602e-16)\n", "Slope and P-value = PearsonRResult(statistic=0.9789105611842357, pvalue=2.0813997062921838e-69)\n", "0 100\n", "1 83\n", "Name: SheepOnFarm, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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PearsonRResult(statistic=0.38372595212427335, pvalue=0.0064928086845083425)\n", "Slope and P-value = PearsonRResult(statistic=-0.3590696100885199, pvalue=0.0013414776489302663)\n", "0 100\n", "1 85\n", "Name: SheepOnFarm, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.8655347475764016, pvalue=3.4494465590844973e-31)\n", "Slope and P-value = PearsonRResult(statistic=-0.944833600500182, pvalue=2.6707962857878022e-49)\n", "Slope and P-value = PearsonRResult(statistic=0.9620427516855854, pvalue=4.465110083337185e-57)\n", "Slope and P-value = PearsonRResult(statistic=-0.9502070009906834, pvalue=2.0050990936284336e-51)\n", "Slope and P-value = PearsonRResult(statistic=0.8292130014957452, pvalue=1.6820405899019047e-26)\n", "Slope and P-value = PearsonRResult(statistic=-0.3092164348560513, pvalue=0.09636930371109392)\n", "Slope and P-value = PearsonRResult(statistic=-0.1468710867828765, pvalue=0.16239384446389687)\n", "Slope and P-value = PearsonRResult(statistic=0.7483594878202365, pvalue=3.5885064230793457e-19)\n", "Slope and P-value = PearsonRResult(statistic=0.9380147193553937, pvalue=6.839983121793239e-47)\n", "Slope and P-value = PearsonRResult(statistic=-0.17133580254065303, pvalue=0.1867420392470338)\n", "1 108\n", "0 100\n", "Name: SheepOnFarm, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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PearsonRResult(statistic=0.42855921783222717, pvalue=8.654876362326273e-06)\n", "Slope and P-value = PearsonRResult(statistic=-0.8336205442056834, pvalue=5.228935580619487e-27)\n", "0 110\n", "1 85\n", "Name: SheepOnFarm, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.18782664631396495, pvalue=0.06129744575464229)\n", "Slope and P-value = PearsonRResult(statistic=0.8825852716577958, pvalue=6.572953877478361e-32)\n", "Slope and P-value = PearsonRResult(statistic=0.6786358156309498, pvalue=1.7062498765432428e-08)\n", "Slope and P-value = PearsonRResult(statistic=0.3244926979230052, pvalue=0.000988660278089046)\n", "Slope and P-value = PearsonRResult(statistic=0.7345184275297658, pvalue=3.818030737653399e-06)\n", "Slope and P-value = PearsonRResult(statistic=-0.8929070594568433, pvalue=9.793366694882493e-36)\n", "Slope and P-value = PearsonRResult(statistic=0.48674449416751847, pvalue=0.013607415398273339)\n", "Slope and P-value = PearsonRResult(statistic=0.37098386536185396, pvalue=0.0023478051414791206)\n", "Slope and P-value = PearsonRResult(statistic=-0.6565739139813123, pvalue=1.2009889223547327e-13)\n", "Slope and P-value = PearsonRResult(statistic=-0.4927591159268722, pvalue=1.9095937793074635e-07)\n" ] } ], "source": [ "sample = pd.merge(microbiome, poultry[['SampleID', 'SheepOnFarm','PastureTime','SampleType']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "#sample = sample[~sample.isin([np.nan, np.inf, -np.inf]).any(1)]\n", "sample = sample.drop(['Pathogen_Salmonella', 'new_Pathogen_Salmonella',\n", " 'Pathogen_Campy','new_Pathogen_Campy',\n", " 'Pathogen_Listeria','new_Pathogen_Listeria'],axis='columns')\n", "\n", "sample.SheepOnFarm.replace({'Y': 1,'N':0}, regex=True, inplace=True)\n", "\n", "\n", "\n", "feces1=sample[(sample.SampleType=='Feces') & (sample.PastureTime=='Start')]\n", "feces2=sample[(sample.SampleType=='Feces') & (sample.PastureTime=='Mid')]\n", "feces3=sample[(sample.SampleType=='Feces') & (sample.PastureTime=='End')]\n", "\n", "soil1=sample[(sample.SampleType=='Soil') & (sample.PastureTime=='Start')]\n", "soil2=sample[(sample.SampleType=='Soil') & (sample.PastureTime=='Mid')]\n", "soil3=sample[(sample.SampleType=='Soil') & (sample.PastureTime=='End')]\n", "\n", "ceca=sample[sample.SampleType=='Ceca']\n", "wcrp=sample[sample.SampleType=='WCR-P']\n", "wcrf=sample[sample.SampleType=='WCR-F']\n", "\n", "\n", "print('Feces_Start', feces1.shape)\n", "print('Feces_Mid', feces2.shape)\n", "print('Feces_End', feces3.shape,'\\n')\n", "\n", "print('Soil_Start', soil1.shape)\n", "print('Soil_Mid', soil2.shape)\n", "print('Soil_End', soil3.shape,'\\n')\n", "\n", "print('Ceca', ceca.shape)\n", "print('WCR-P', wcrp.shape)\n", "print('WCR-F', wcrf.shape,'\\n')\n", "\n", "\n", "sampletypes = [feces1,feces2,feces3, soil1,soil2,soil3,ceca, wcrp, wcrf]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES_START\", 1: \"FECES_MID\", 2:\"FECES_END\",\n", " 3: \"SOIL_START\", 4: \"SOIL_MID\", 5: \"SOIL_END\",\n", " 6:\"CECA\", 7: \"WCR-P\", 8: \"WCR-F\"\n", " }\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType', 'PastureTime','SheepOnFarm'],axis='columns'),sample.SheepOnFarm,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1] #multiclass\n", " \n", " try:\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " except ValueError:\n", " pass\n", " \n", " \n", " \n", " \n", "# mylist2.append([f\"SheepOnFarm_{sample_name[indexing]}\", rf_auc_normal])\n", " \n", " \n", " print(pd.value_counts(sample['SheepOnFarm']))\n", "\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim3_plot.max(), decimals=None, symbol=''))\n", " \n", " plt.title(f\"SheepOnFarm in {sample_name[indexing]} Model\")\n", " prelim3_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','SheepOnFarm','PastureTime','SampleType'],axis='columns').columns)\n", " prelim3_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", "\n", " xmax=prelim3_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " \n", " plt.show()\n", " \n", " prelim3_plot.nlargest(10).to_csv(\"prelim3.csv\")\n", " top10 = pd.read_csv('prelim3.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", "# plt.plot(pdp[1][0], pdp[0][0],'.')\n", "# plt.ylabel('Partial dependence'), plt.xlabel(top10[feature])\n", "# plt.show()\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"SheepOnFarm_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (21) WaterSource" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Feces_Start (200, 878)\n", "Feces_Mid (313, 878)\n", "Feces_End (185, 878) \n", "\n", "Soil_Start (199, 878)\n", "Soil_Mid (313, 878)\n", "Soil_End (183, 878) \n", "\n", "Ceca (185, 878)\n", "WCR-P (208, 878)\n", "WCR-F (195, 878) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "Well 100\n", "Public 90\n", "Rain 10\n", "Name: WaterSource1, dtype: int64\n" ] }, { "data": { "image/png": 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GupKWtM3HUgxVko6vo+4gSZWSKlcsXlDk4TAzsw3NBpdISKoATgT2AU4AKtaiu+OAqpznSyOiH/AocAlwRETsC1QCPwLGAQdI2jTVHwDcLmlb4ArgK0A5sL+k/hExBFgSEeURcZqk3VKbA9OIyArgtDriWwp8M8VwGPAbSSpUMSKGR0RFRFS06dil8UfCzMw2SBvi5Z/9gPsiYglAzYhAI10l6RLgfeDsnPLb098DgN2Bielze2Pg2YhYLulh4DhJdwHHAD8mSyDGR8T7KaZRwMHAvXnbPRzYD5ic+u0AvFdHnAJ+Lelg4HNgO2Ab4F9F7LOZmdkaNsREouA38ka6KCLuKlD+Sc42xkXEKQXq3A6cC3wITI6IhbWNEhQgYGRE/LSB9U8DtgL2i4hlkqqB9g1sa2ZmVq8N7tQGMIFsRKC9pE5kowJN7TngQEk18yc6Sto5rRsP7At8j1UjGM8Dh0jqluY8nAI8mdYtk9QuLT8GnCRp69RvV0lfqCOOLsB7KYk4DKirrpmZWaNtcCMSETFZ0v3AdGAO2fyFJp1dGBHvSxoIjJa0SSq+BHg1IlZIehAYCJyZ6s+T9FPgCbJRh79HxH2p3XBghqSpaZ7EJcBYSRsBy8hGN+bUEsoo4AFJlcA04OWGxL/ndl2oXA9+Tc3MzEpPEVHqGNY5SZ0iYpGkjmRXXgyKiKmljqulqKioiMrKylKHYWZmLYSkKRFR8OKEDW5EIhkuaXey+QIjnUSYmZkVZ4NMJCLi1Nznkq4HDsyr1gt4La/smoi4pTljMzMzW59skIlEvog4t9QxmJmZrY82xKs2zMzMrIk4kTAzM7OiOZEwMzOzojmRMDMzs6I5kTAzM7OiOZEwMzOzovnyT1tD1dsLKBvyUKnDMDPbYFWvR7cp8IiEmZmZFc2JhJmZmRXNiUQLIml7SfdJek3S65KukbSxpHJJX8+pN1TS4FLGamZmBk4kWgxJAu4B7o2IXsDOQCfgV0A58PXaWzd6W22aqi8zM9uwOZFoOb4CLK25KVhErAD+CzgHuBIYIGmapAGp/u6Sxkt6Q9IFNZ1I+o6kSanuDTVJg6RFki6T9DzQd53umZmZtVpOJFqOPYApuQUR8TFQDVwO3B4R5RFxe1q9K/A1oA/wC0ntJO0GDAAOjIhyYAVwWqq/KTAzIr4UERPyNy5pkKRKSZUrFi9o+r0zM7NWyZd/thwCohHlD0XEp8Cnkt4DtgEOB/YDJmdnSugAvJfqrwDurm3jETEcGA6wSfdehbZnZma2BicSLceLwIm5BZI2A3YgSwLyfZqzvILstRQwMiJ+WqD+0nS6xMzMrMn41EbL8RjQUdIZsHJC5G+AEcC7QOcG9nGSpK1TH10lfaF5wjUzM3Mi0WJERADfBL4l6TXgVWApcDHwBNnkytzJloX6eAm4BBgraQYwDuje7MGbmdkGS9nnl9kqFRUVUVlZWeowzMyshZA0JSIqCq3ziISZmZkVzYmEmZmZFc2JhJmZmRXNiYSZmZkVzYmEmZmZFc2JhJmZmRXNiYSZmZkVzYmEmZmZFc2JhJmZmRXNiYSZmZkVzXf/tDVUvb2AsiEPlToMM7NWr3rYMaUOYa15RMLMzMyK5kTCzMzMilZvIiFpRbp99UxJd0rq2NDOJQ2UdF0t656pp22ZpFNznldI+r+Gbjun3XclVUmakfbh+JzYtm1sf3Vsp7+k3ZuqPzMzs/VBQ0YklkREeUT0Bj4DfpC7UlKbYjYcEV+up0oZsDKRiIjKiLigMduQtD3wM6BfROwFHADMSKsHAgUTiSL3qT/gRMLMzDYojT218TTQU9Khkp6QdCtQJam9pFvSN/8XJB2W02YHSQ9LekXSL2oKJS1KfyXpqjRaUCVpQKoyDDgojYb8V9rmg6lNp5ztzZB0Yi3xbg0sBBYBRMSiiJgt6SSgAhiV+u8gqVrSzyVNAL4l6UhJz0qamkZiOqVtV0u6QtKk9Ogp6cvAN4CrUn87SSqX9FyKb4ykLVL7npIelTQ99b1THccAST9OZdMlDaujj5XHJ9W5TtLAtDxM0ksplqsLHShJgyRVSqpcsXhB/e8EMzMzGnHVhqS2wNHAw6moD9A7fTD/N0BE7ClpV2CspJ1z6wGLgcmSHoqIypyuTwDKgb2BbqnOU8AQYHBEHJu2f2hOm0uBBRGxZ1q3RS1hTwfeBWZLegy4JyIeiIi7JJ2X+q9MfQAsjYh+kroB9wBHRMQnkn4C/Ai4LPX7cUT0kXQG8PuIOFbS/cCDEXFX6m8GcH5EPCnpMuAXwIXAKGBYRIyR1J4smavtGJSTjXR8KSIWS+qatl+ojx0KHYDU5pvArhERkjYvVC8ihgPDATbp3itqOZ5mZmaraciIRAdJ04BK4E3g5lQ+KSJmp+V+wF8BIuJlYA5Qk0iMi4j5EbGE7MO5X17//YDREbEiIt4FngT2ryemI4Dra55ExEeFKkXECuAo4CTgVeB3kobW0e/t6e8BZKcpJqZ9PxP4Qk690Tl/++Z3IqkLsHlEPJmKRgIHS+oMbBcRY1J8SyNiMbUfgyOAW1IdIuLDOvqozcfAUuAmSSeQJXRmZmZNoiEjEksiojy3IH17/yS3qI72+d9u85/X1bY2KtBP4Y1HBDAJmCRpHHALMLSW6jX7JLIE6JTauq1luT617Wtd5Q09XstZPTFsDxARyyX1AQ4HTgbOA77SoGjNzMzq0VSXfz4FnAaQTmnsCLyS1n1VUldJHciG6ScWaDtAUhtJWwEHk33wLwQ617K9sWQfiKRtFjy1IWlbSfvmFJWTjZZQT//PAQdK6pn66ZhzqgZgQM7fZ/P7i4gFwEeSDkrrTgeejIiPgbmS+qd+N1F2FUxtx2As8N1UB0ld6+hjDrB7et6FLHEgze3oEhF/Jzu1Ul7LPpuZmTVaU/2y5R+AP0mqIvtmPDAiPk0jFxPITnv0BG7Nmx8BMIbs9MB0sm/fP46If0maDyyXNB0YAbyQ0+Zy4HpJM4EVwC/JTpvkawdcrewyz6XA+6y66mREinkJeacnIuL9NFFxtKRNUvElZKdHADaR9DxZIlYzanEbcKOkC8hOpZyZ+u8IvAGcleqdDtyQ5k0sA75V2zEAHpZUDlRK+gz4O3BxoT4i4g1Jd5BdlfJazvHqDNyX5lII+K8Cx8nMzKwoykb+raEkVQMVEfFBqWNpLhUVFVFZmZ/vmZnZhkrSlIioKLTOv2xpZmZmRWs1N+1Kpxo2ySs+PSKqmnI7EVHWlP2ZmZmtz1pNIhERXyp1DGZmZhsan9owMzOzojmRMDMzs6I5kTAzM7OiOZEwMzOzojmRMDMzs6I5kTAzM7OiOZEwMzOzorWa35GwplP19gLKhjxU6jDMzFq86mHHlDqEkvOIhJmZmRXNiYSZmZkVbb1NJCSNkDRb0jRJUyX1rb9Vg/qtltStCfq5uCniyenvmVrKR0g6qSm3ZWZm1lDrbSKRXBQR5cAQ4IaGNFBmXex3oxMJSW1qWxcRX167cMzMzJpeSRMJSZdKelnSOEmjJQ0usqungJ6SOkl6LI1QVEk6Pm2nTNIsSX8ApgI7SPqjpEpJL0r6ZV5/F0malB49Ux/HSXpe0guSHpW0TSrvJOmWtL0Zkk6UNAzokEZLRqV630n9TZN0Q03SIGmRpMvS3Uv7SvqRpJnpcWHOsVqU/krSdZJekvQQsHVOnf0kPSlpiqRHJHVP5Rek+jMk3VbLazEoHY/KFYsXFPkymJnZhqZkiYSkCuBEYB/gBKBiLbo7DqgClgLfjIh9gcOA30hSqrML8JeI2Cci5gA/i4gKYC/gEEl75fT3cUT0Aa4Dfp/KJgAHRMQ+wG3Aj1P5pcCCiNgzIvYCHo+IIcCSiCiPiNMk7QYMAA5MIygrgNNS+02BmenupUuAs4AvAQcA35O0T96+fjPty57A94AvA0hqB1wLnBQR+wF/Bn6V2gwB9knx/aDQAYyI4RFREREVbTp2qe04m5mZraaUl3/2A+6LiCUAkh4ooo+rJF0CvA+cDQj4taSDgc+B7YBtUt05EfFcTttvSxpEdgy6A7sDM9K60Tl/f5eWtwduT9/yNwZmp/IjgJNrOo2IjwrEeTiwHzA55TUdgPfSuhXA3Wm5HzAmIj4BkHQPcBDwQk5fBwOjI2IF8I6kx1P5LkBvYFzaRhtgXlo3Axgl6V7g3gLxmZmZFaWUiYTqr1KviyLirpUdSgOBrYD9ImKZpGqgfVr9SU69HsBgYP+I+EjSiJx6AFFg+VrgtxFxv6RDgaE5+5FbvxABIyPipwXWLU1JQU29hii0PQEvRkShSafHkCUg3wAulbRHRCxv4LbMzMxqVco5EhOA4yS1l9SJ7MNubXUB3ktJxGHAF2qptxlZYrEgzXU4Om/9gJy/z+b0/XZaPjOn7ljgvJonkrZIi8vS6QaAx4CTJG2d6nSVVCi2p4D+kjpK2pTsNMbTBeqcLKlNGh05LJW/AmxVc/WKpHaS9kgTS3eIiCfITsdsDnQqdFDMzMwaq2QjEhExWdL9wHRgDlAJrO0sv1HAA5IqgWnAy7Vse7qkF4AXgTeAiXlVNkmTHzcCTkllQ4E7Jb0NPAf0SOWXA9dLmkl2muKXwD3AcGCGpKlpnsQlwNj0wb4MODftd25cU9PoyKRUdFNE5J7WABgDfIVsTsirwJOp7WfpMtD/k9SF7LX9farzt1Qm4HcR8e+CR8/MzKyRFFHfqHwzblzqFBGLJHUk+6Y9KCKmliwgA6CioiIqKytLHYaZmbUQkqakCxTWUOp7bQyXtDvZ/ISRTiLMzMzWLyVNJCLi1Nznkq4HDsyr1gt4La/smoi4pTljMzMzs/qVekRiNRFxbqljMDMzs4Zb338i28zMzErIiYSZmZkVzYmEmZmZFc2JhJmZmRXNiYSZmZkVzYmEmZmZFc2JhJmZmRWtRf2OhLUMVW8voGzIQ6UOw8xakephTXFfRmuJPCJhZmZmRXMiYWZmZkXbIBIJSW0lfSDpf0sdi5mZWWuyQSQSwJHAK8C3Jak5NySpTXP2b2Zm1pKsF4mEpEslvSxpnKTRkgY3sotTgGuAN4EDcvqtlvRLSVMlVUnaNZX3kfSMpBfS311SeRtJV0maLGmGpO+n8kMlPSHpVqBKUntJt6Q+X5B0WE77q1P5DEnnp/LDU70qSX+WtEkq3z9tf7qkSZI619FHtaRuablC0vi0fIikaenxgqTOtRzjQZIqJVWuWLygkYfXzMw2VC3+qg1JFcCJwD5k8U4FpjSifQfgcOD7wOZkScWzOVU+iIh9Jf0HMBg4B3gZODgilks6Avh1iuFsYEFE7J8+7CdKGpv66QP0jojZkv4bICL2TMnJWEk7A2cBPYB9Ut9dJbUHRgCHR8Srkv4C/FDSH4DbgQERMVnSZsASYFB+H/UcgsHAuRExUVInYGmhShExHBgOsEn3XlHfcTUzM4P1Y0SiH3BfRCyJiIXAA41sfyzwREQsBu4Gvpl3+uGe9HcKUJaWuwB3SpoJ/A7YI5UfCZwhaRrwPLAl0CutmxQRs3Ni/itARLwMzAF2Bo4A/hQRy9O6D4FdgNkR8WpqOxI4OJXPi4jJqe7HqV2hPuoyEfitpAuAzWvamZmZNYX1IZFY2zkNpwBHSKomSxa2BA7LWf9p+ruCVSM0/0OWfPQGjgPa58RyfkSUp0ePiKgZkfikATELyP+235i6dZUvZ9XrWRMvETGMbJSlA/BczekbMzOzprA+JBITgOPSvINOQIN/1SSdDugH7BgRZRFRBpxLllzUpQvwdloemFP+CNlph3ap/50lbVqg/VPAaTV1gB3JJnuOBX4gqW1a15XsNEqZpJ6p7enAk6l8W0n7p7qdU7tCfQBUA/ul5RNzjsFOEVEVEVcAlYATCTMzazItfo5Emh9wPzCd7BRBJdDQ2YAnAI9HxKc5ZfcBV9ZMaKzFlcBIST8CHs8pv4ns9MfUdPXH+0D/Au3/APxJUhXZSMHAiPhU0k1kpzhmSFoG3BgR10k6i+xUSltgMtmpi88kDQCuTfM8lpCd1lijD+A64JfAzZIuJjvtUuPCNNlzBfAS8I+6DhjAntt1odK/QmdmZg2giJY/r05Sp4hYJKkj2bf9QRExtdRxtVYVFRVRWVlZ6jDMzKyFkDQlIioKrWvxIxLJcEm7k537H+kkwszMrGVYLxKJiDg197mk64ED86r1Al7LK7smIm5pztjMzMw2ZOtFIpEvIs4tdQxmZma2fly1YWZmZi2UEwkzMzMrmhMJMzMzK5oTCTMzMyuaEwkzMzMrmhMJMzMzK9p6efmnNa+qtxdQNuShUodhZq1EtX9yv1XziISZmZkVzYmEmZmZFa3VJhKSxkt6RdI30vMRkmZLmpYez9TTvkzSzCaIo0zSqfXXLNi2zhgb0c9OaZ8XNUV/ZmZmNVptIpGcFhH35zy/KCLK0+PL6yiGMqBRiYSkNgBNFWNEvB4R5U3Rl5mZWa4WnUhIulTSy5LGSRotaXAzbGNobr+SZkoqS0/bShopaYaku9JtzJH0c0mTU93hkpTKe0p6VNJ0SVMl7QQMAw5KIwL/JamNpKtS+xmSvp/aHirpCUm3AlWpbFHOugdzYrxO0sC0XC3p15KelVQpaV9Jj0h6XdIPmvp4mZmZ5WqxiYSkCuBEYB/gBKDgfdAb6aqcUxujGlB/F2B4ROwFfAz8Ryq/LiL2j4jeQAfg2FQ+Crg+IvYGvgzMA4YAT6dRkN8BZwMLImJ/YH/ge5J6pPZ9gJ9FxO6N3K+3IqIv8DQwAjgJOAC4rKEdSBqUEpHKFYsXNHLzZma2oWrJl3/2A+6LiCUAkh5ogj4vioi7GlH/rYiYmJb/BlwAXA0cJunHQEegK/CipPHAdhExBiAilqa48/s8EthL0knpeReyW6B/BkyKiNmN3iuoOX1TBXSKiIXAQklLJW0eEf+ur4OIGA4MB9ike68oIgYzM9sAteREYo1P4GaynNVHZtrnLOd/oIak9sAfgIqIeEvS0NSmofEKOD8iHlmtUDoU+KSIGAE+TX8/z1mued6SX2MzM1vPtdhTG8AE4DhJ7SV1AprrF02qgX0BJO0L9MhZt6Okvmn5lBRTzYf4BymukwAi4mNgrqT+qa9N0pyKhUDnnD4fAX4oqV2qt7OkTeuJcQ6we+qzC3B4MTtqZmbW1Frst9WImCzpfmA62QdpJbC2J++vknRJzvM+wN3AGZKmAZOBV3PWzwLOlHQD8Brwx4hYLOlGstMI1alNjdOBGyRdBiwDvgXMAJZLmk42f+Easis5pqZJmu8D/esKOo183JH6eg14obE7bmZm1hwU0XJPh0vqFBGL0jf7p4BBETG1gW3HA4MjorI5Y1yfSFoUEZ3qq1dRURGVlT5sZmaWkTQlIgpe9NCST20ADE8jBVOBuxuaRCQfAiNqfpBqQ1bzg1TAu6WOxczMWpcWe2oDICJW+yEnSdcDB+ZV60U23J/rmog4oTljW59ExOtAeanjMDOz1qdFJxL5IuLcUsdgZmZmq7T0UxtmZmbWgjmRMDMzs6I5kTAzM7OiOZEwMzOzojmRMDMzs6I5kTAzM7OiOZEwMzOzoq1XvyNh60bV2wsoG/JQqcMwsxaselhz3UfR1jcekTAzM7OiOZEwMzOzorXKRELSjyS9LKlK0nRJv5XUrp424yVVpOVqSd3S8jPp76GSHmyC2Molfb2IdttKumttt29mZtaUWl0iIekHwJHAARGxJ7A/8B7QoZj+IuLLTRgeZDfPalQiIaltRLwTESc1cSxmZmZrpUUmEpIuTSMK4ySNljS4Ec1/BvwwIv4NEBGfRcSwiPg49f1HSZWSXpT0ywbEsijn6WaSxkh6SdKfJG1UV5+S9pf0TBoVmSSpC3AZMEDSNEkDJG0q6c+SJkt6QdLxqe1ASXdKegAYK6lM0sycddflbOdBSYfWxCvpCklTJD0qqU8abXmjrluqSxqU9qFyxeIFDTvSZma2wWtxV22k0wsnAvuQxTcVmNLAtp2BThExu45qP4uIDyW1AR6TtFdEzGhgeH2A3YE5wMPACcBdhfoEXgZuBwZExGRJmwGLgZ8DFRFxXor518DjEfFdSZsDkyQ9mrbXF9gr9V3WwBg3BcZHxE8kjQEuB76a4h4J3F+oUUQMB4YDbNK9VzRwW2ZmtoFriSMS/YD7ImJJRCwEHmhEWwErPwQlfS1986+WVHOK4tuSpgIvAHuQfcA21KSIeCMiVgCjU6y19bkLMC8iJgNExMcRsbxAn0cCQyRNA8YD7YEd07pxEfFhI+ID+IwsyQGoAp6MiGVpuayRfZmZmdWpxY1IkCUDRYmIjyV9IqlHRMyOiEeAR9IkyY0l9QAGA/tHxEeSRpB9cDd4E/nP6+hTBeoXIuDEiHhltULpS8AntbRZzupJYO4+LIuImu1+DnwKEBGfS2qJr7eZma3HWuKIxATgOEntJXUCGvurJ/8L/DGdJkCSWPVBuxnZh/MCSdsARzey7z6SeqS5EQNSrLX1+TKwraT9Uxyd0wf5QqBzTp+PAOenOJG0TwPiqAbKJW0kaQeyUy5mZmbrXIv7hprmE9wPTCebi1AJNGb23x+BjsDzkj4FFgETgRciYoGkF4AXgTdSeWM8CwwD9gSeAsakb/pr9BkRn0kaAFwrqQOwBDgCeIJVpzL+F/gf4PfAjJRMVAPH1hPHRGA22emKmWTzSMzMzNY5rRoFbzkkdYqIRZI6kn1gD4oIf1iuIxUVFVFZWVnqMMzMrIWQNCUiKgqta3EjEslwSbuTnZIY6STCzMysZWqRiUREnJr7XNL1wIF51XoBr+WVXRMRtzRnbGZmZrZKi0wk8kXEuaWOwczMzNbUEq/aMDMzs/WEEwkzMzMrmhMJMzMzK5oTCTMzMyuaEwkzMzMrmhMJMzMzK5oTCTMzMyvaevE7ErZuVb29gLIhD5U6DLNWqXpYY+9DaNayeUTCzMzMiuZEwszMzIrmRGI9Iulbkl6U9LmkipzyLSU9IWmRpOvy2oyX9Iqkaemx9bqP3MzMWivPkVi/zAROAG7IK18KXAr0To98p0WE7wtuZmZNziMSjSTpUkkvSxonabSkwY1o21PSo5KmS5oqaadU/mNJVal8WG3tI2JWRLxSoPyTiJhAllAURdIgSZWSKlcsXlBsN2ZmtoHxiEQjpNMJJwL7kB27qcCURnQxChgWEWMktQc2knQ00B/4UkQsltS1icMGuEXSCuBu4PKIiPwKETEcGA6wSfdea6w3MzMrxCMSjdMPuC8ilkTEQuCBhjaU1BnYLiLGAETE0ohYDBwB3JKWiYgPmzjm0yJiT+Cg9Di9ifs3M7MNmBOJxlEztBXQbCMAEfF2+rsQuBXo01zbMjOzDY8TicaZABwnqb2kTkCDf1kmIj4G5krqDyBpE0kdgbHAd9MyTXlqQ1JbSd3ScjvgWLIJm2ZmZk3CiUQjRMRk4H5gOnAPUAk0Zmbi6cAFkmYAzwD/LyIeTn1WSpoG1Dp5U9I3Jc0F+gIPSXokZ1018FtgoKS5knYHNgEeSdubBrwN3NiIeM3MzOqkAvPurA6SOkXEojSC8BQwKCKmljquplRRURGVlb5a1MzMMpKmRERFoXW+aqPxhqdv++2Bka0tiTAzM2sMJxKNFBGn5j6XdD1wYF61XsBreWXXRMQtDdlGLX02uL2Zmdm64kRiLUXEuetDn2ZmZs3BiYSZmTWpZcuWMXfuXJYuLfrHdq1E2rdvz/bbb0+7du0a3MaJhJmZNam5c+fSuXNnysrKkNbm53dsXYoI5s+fz9y5c+nRo0eD2/nyTzMza1JLly5lyy23dBKxnpHElltu2eiRJCcSZmbW5JxErJ+Ked2cSJiZmVnRPEfCzMyaVdmQh5q0v+phDbs7wZgxYzjhhBOYNWsWu+66KwDjx4/n6quv5sEHH1xZb+DAgRx77LGcdNJJHHroocybN4/27duz8cYbc+ONN1JeXg7AggULOP/885k4cSIABx54INdeey1dunQB4NVXX+XCCy/k1VdfpV27duy5555ce+21bLPNNkXv64cffsiAAQOorq6mrKyMO+64gy222GKNetdccw033ngjEcH3vvc9LrzwQgDuvPNOhg4dyqxZs5g0aRIVFdlvSlVVVfGb3/yGESNGFB1bDScStoaqtxc0+T98sw1ZQz/4rGmNHj2afv36cdtttzF06NAGtxs1ahQVFRXccsstXHTRRYwbNw6As88+m969e/OXv/wFgF/84hecc8453HnnnSxdupRjjjmG3/72txx33HEAPPHEE7z//vtrlUgMGzaMww8/nCFDhjBs2DCGDRvGFVdcsVqdmTNncuONNzJp0iQ23nhjjjrqKI455hh69epF7969ueeee/j+97+/Wps999yTuXPn8uabb7LjjjsWHR/41IaZmbVCixYtYuLEidx8883cdtttRfXRt29f3n77bQD++c9/MmXKFC699NKV63/+859TWVnJ66+/zq233krfvn1XJhEAhx12GL17916r/bjvvvs488wzATjzzDO5995716gza9YsDjjgADp27Ejbtm055JBDGDNmDAC77bYbu+yyS8G+jzvuuKKPTS4nEmZm1urce++9HHXUUey888507dqVqVMbfzeDhx9+mP79+wPw0ksvUV5eTps2bVaub9OmDeXl5bz44ovMnDmT/fbbr94+Fy5cSHl5ecHHSy+9tEb9d999l+7duwPQvXt33nvvvTXq9O7dm6eeeor58+ezePFi/v73v/PWW2/VG0tFRQVPP/10vfXq41MbZmbW6owePXrlPIGTTz6Z0aNHs++++9Z6VUJu+WmnncYnn3zCihUrViYgEVGwbW3ltencuTPTpk1r+I40wG677cZPfvITvvrVr9KpUyf23ntv2rat/+N966235p133lnr7W/wiYSkEcAhZLcD/xw4NyKeLaKfgcDYiFj7V8XMzIo2f/58Hn/8cWbOnIkkVqxYgSSuvPJKttxySz766KPV6n/44Yd069Zt5fNRo0ax9957M2TIEM4991zuuece9thjD1544QU+//xzNtooG8z//PPPmT59OrvtthvvvfceTz75ZL2xLVy4kIMOOqjgultvvZXdd999tbJtttmGefPm0b17d+bNm8fWW29dsO3ZZ5/N2WefDcDFF1/M9ttvX28sS5cupUOHDvXWq49PbWQuiohyYAhwQ5F9DAS2baqAzMysOHfddRdnnHEGc+bMobq6mrfeeosePXowYcIEevXqxTvvvMOsWbMAmDNnDtOnT195ZUaNdu3acfnll/Pcc88xa9YsevbsyT777MPll1++ss7ll1/OvvvuS8+ePTn11FN55plneOihVRPVH374Yaqqqlbrt2ZEotAjP4kA+MY3vsHIkSMBGDlyJMcff3zBfa455fHmm29yzz33cMopp9R7nF599dW1nsMBrWREQtKlwGnAW8AHwJSIuLqIrp4CeqY+vwNcAGwMPA/8R0SskLSILNk4DPgIOJlsRKMCGCVpCdAX+DJwNdkxngz8MCI+lbQ/cA2wKfApcDiwDPhj6mM58KOIeEJSG+AK4GtAADdGxLW19HEiUBER56X4H0zbfxq4OfUdwJ8j4ncFjuEgYBBAm822KuLQmZkVtq6vWhk9ejRDhgxZrezEE0/k1ltv5aCDDuJvf/sbZ511FkuXLqVdu3bcdNNNKy/hzNWhQwf++7//m6uvvpqbb76Zm2++mfPPP5+ePXsSEfTt25ebb755Zd0HH3yQCy+8kAsvvJB27dqx1157cc0116zVvgwZMoRvf/vb3Hzzzey4447ceeedALzzzjucc845/P3vf1+5f/Pnz6ddu3Zcf/31Ky8RHTNmDOeffz7vv/8+xxxzDOXl5TzyyCNAdlXJMces/WujiFjrTkpJUgVwE9mHd1tgKnBDQxOJdGrjwYi4S9K3gMFkowtXAidExDJJfwCei4i/SArgOxExStLPga0j4jxJ44HBEVEpqT3ZbcQPj4hXJf0lxfUH4GVgQERMlrQZsBj4T6B3RJwlaVdgLLAzcBZwRKq/XFJXYFEtfXyHwonEQmBYRHw1lW8eEf+u65hs0r1XdD/z9w05fGbWABva5Z+zZs1it912K3UYVodPP/2UQw45hAkTJqwxn6LQ6ydpSkRUFOqrNZza6AfcFxFLImIh8EARfVwlaRrZN/Kzyb7h7wdMTuWHA19MdT8Hbk/Lf0vbz7cLMDsiXk3PRwIHp/J5ETEZICI+jojlqY+/prKXgTlkicQRwJ9SHSLiwzr6qM0bwBclXSvpKODjhh4UMzNrnd58802GDRvWoEmZ9WkNpzaa4gfdL4qIu1Z2KB0GjIyInzagbaEhndpiUhPUr62P5ayeGLYHiIiPJO1NdnrkXODbwHdr2Z6ZmW0AevXqRa9evZqkr9YwIjEBOE5Se0mdgKYYQ3wMOEnS1gCSukr6Qlq3EXBSWj41bR+yUwid0/LLQJmknun56cCTqXzbNMcBSZ0ltSWbm3FaKtsZ2BF4hewUxw9SHdKpjdr6qAbKJW0kaQegT1rfDdgoIu4GLgX2bYLjY2ZWp/X9tPmGqpjXbb0fkUjzBO4HppOdEqgku5Rzbfp8SdIlwFhJG5FNhjw39f8JsIekKWk7A1KzEcCfciZbngXcmT7kJ5OdovhM0gDgWkkdgCVkpy/+kNpWkY0sDEwTM28iO8UxQ9IyssmW19XSx0RgNlAFzCSbkwGwHXBL2g+AekdZ9tyuC5Ub2DldM2s67du3Z/78+b6V+HomIpg/fz7t27dvVLv1frIlgKROEbFIUkeyb/eDIqLxP2PWsG0tiohOzdF3S1FRURGVlZWlDsPM1lPLli1j7ty5LF26tNShWCO1b9+e7bffnnbt2q1WXtdky/V+RCIZLml3snkBI5sriTAzs/q1a9eOHj16lDoMW0daRSIREafmPpd0PXBgXrVeZJdk5romIm5p5LZa9WiEmZlZY7SKRCJfRJxb6hjMzMw2BK3hqg0zMzMrkVYx2dKalqSFZJef2rrTjezn3W3d8TFf93zM172mOuZfiIiC909olac2bK29UtvsXGsekip9zNctH/N1z8d83VsXx9ynNszMzKxoTiTMzMysaE4krJDhpQ5gA+Rjvu75mK97PubrXrMfc0+2NDMzs6J5RMLMzMyK5kTCzMzMiuZEwlaSdJSkVyT9U9KQUsfTGknaQdITkmZJelHSf6byoZLeljQtPb5e6lhbE0nVkqrSsa1MZV0ljZP0Wvq7RanjbC0k7ZLzXp4m6WNJF/p93rQk/VnSe5Jm5pTV+r6W9NP0//srkr7WZHF4joQBSGoDvAp8FZhLduvzUyLipZIG1spI6g50j4ipkjoDU4D+wLeBRRFxdSnja60kVQMVEfFBTtmVwIcRMSwlzltExE9KFWNrlf5veRv4EnAWfp83GUkHA4uAv0RE71RW8H2dbmw5GugDbAs8CuwcESvWNg6PSFiNPsA/I+KNiPgMuA04vsQxtToRMa/m7rQRsRCYBWxX2qg2WMcDI9PySLKEzpre4cDrETGn1IG0NhHxFPBhXnFt7+vjgdsi4tOImA38k+z//bXmRMJqbAe8lfN8Lv6Aa1aSyoB9gOdT0XmSZqThSg+zN60AxkqaImlQKtsmIuZBluABW5csutbtZLJvwjX8Pm9etb2vm+3/eCcSVkMFynzeq5lI6gTcDVwYER8DfwR2AsqBecBvShddq3RgROwLHA2cm4aErZlJ2hj4BnBnKvL7vHSa7f94JxJWYy6wQ87z7YF3ShRLqyapHVkSMSoi7gGIiHcjYkVEfA7cSBMNOVomIt5Jf98DxpAd33fTnJWauSvvlS7CVutoYGpEvAt+n68jtb2vm+3/eCcSVmMy0EtSj/Qt4mTg/hLH1OpIEnAzMCsifptT3j2n2jeBmfltrTiSNk0TW5G0KXAk2fG9HzgzVTsTuK80EbZqp5BzWsPv83Witvf1/cDJkjaR1APoBUxqig36qg1bKV2K9XugDfDniPhVaSNqfST1A54GqoDPU/HFZP/hlpMNNVYD3685z2lrR9IXyUYhILvj8a0R8StJWwJ3ADsCbwLfioj8iWtWJEkdyc7JfzEiFqSyv+L3eZORNBo4lOxW4e8CvwDupZb3taSfAd8FlpOdVv1Hk8ThRMLMzMyK5VMbZmZmVjQnEmZmZlY0JxJmZmZWNCcSZmZmVjQnEmZmZlY0JxJmJSBpRbr74UxJD0javJ76QyUNrqdO/3Rjnprnl0k6ogliHSHppLXtp5HbvDBdPthiSNo1vWYvSNopb121pKfzyqbV3JVRUoWk/2uCGMpy7/SYt+6m3Ne/uUnaRtKtkt5IPz3+rKRvrqvtW8vhRMKsNJZERHm6Y9+HwLlN0Gd/YOUHSUT8PCIebYJ+16l0t8gLgRaVSJAd3/siYp+IeL3A+s6SdgCQtFvuioiojIgLGrqhdAwaJSLOWVd3600/rHYv8FREfDEi9iP7Ebvtm3m7bZuzfyuOEwmz0nuWdPMcSTtJejh9w3ta0q75lSV9T9JkSdMl3S2po6Qvk93T4Kr0TXinmpEESUdLuiOn/aGSHkjLR6ZvklMl3ZnuAVKr9M3716lNpaR9JT0i6XVJP8jp/ylJYyS9JOlPkjZK606RVJVGYq7I6XdRGkF5HvgZ2W2On5D0RFr/x7S9FyX9Mi+eX6b4q2qOl6ROkm5JZTMkndjQ/ZVULum51G6MpC3Sj7VdCJxTE1MBdwAD0nL+LzoeKunBemLLPQZ9Jf0oHaeZki7M2U5bSSNT27tqRm4kjZdU0YDjfEV6fz0qqU9q94akb6Q6bSRdld5jMyR9v8C+fgX4LCL+VFMQEXMi4tq6+kjHYXyK+2VJo1JSgqT9JD2ZYntEq37meXx6zz0J/Kek4yQ9r2xk6FFJ29Tyeti6EhF++OHHOn4Ai9LfNmQ3NDoqPX8M6JWWvwQ8npaHAoPT8pY5/VwOnJ+WRwAn5awbAZxE9muObwKbpvI/At8h+zW8p3LKfwL8vECsK/sl+zXCH6bl3wEzgM7AVsB7qfxQYCnwxbR/41Ic26Y4tkoxPQ70T20C+HbONquBbjnPu+Ycr/HAXjn1avb/P4Cb0vIVwO9z2m/RiP2dARySli+r6Sf3NSjQphrYGXgmPX+BbHRoZs4xebC22PKPAbAf2a+fbgp0Al4ku1NsWap3YKr3Z1a9L8YDFQ04zken5THAWKAdsDcwLZUPAi5Jy5sAlUCPvP29APhdHe/vgn2k47CAbORiI7Ikul+K4Rlgq9RmANmv69bs1x/yXsuaH1M8B/hNqf89b+gPDxOZlUYHSdPIPhimAOPSt+MvA3emL2mQ/Secr7eky4HNyT5kHqlrQxGxXNLDwHGS7gKOAX4MHEL2YTcxbW9jsv/Y61NzD5YqoFNELAQWSlqqVXM9JkXEG7DyZ3z7AcuA8RHxfiofBRxMNkS+guxGZrX5trLbf7cFuqe4Z6R196S/U4AT0vIRZEPtNcfgI0nH1re/kroAm0fEk6loJKvuXFmfD4GPJJ0MzAIW11JvjdjSYu4x6AeMiYhPUlz3AAeRHfu3ImJiqvc3sg/1q3P635/aj/NnwMOpXhXwaUQsk1RF9l6E7F4ke2nVvJguZPdlmF3bjku6PsX8WUTsX0cfn5G9N+amdtPSdv8N9Cb7dwBZwpj709m35yxvD9yeRiw2risuWzecSJiVxpKIKE8fXA+SzZEYAfw7IsrraTuC7BvmdEkDyb7l1ef2tI0PgckRsTANKY+LiFMaGfun6e/nOcs1z2v+T8n/7f2g8G2MayyNiBWFVii7wdBgYP+UEIwA2heIZ0XO9lUghmL3tzFuB64HBtZRp1BssPoxqOtYFTq2+f3XZlmkr/LkvH4R8blWzT8Q2ShPXQnqi8CJKwOIOFdSN7KRh1r7kHQoq79nal4zAS9GRN9atvdJzvK1wG8j4v7U39A64rR1wHMkzEoospsZXUD2QbkEmC3pW5BNaJO0d4FmnYF5ym5HflpO+cK0rpDxwL7A91j17e454EBJPdP2Okraee32aKU+yu4kuxHZMPUE4HngEEndlE0mPAV4spb2ufuyGdkHyYJ0PvzoBmx/LHBezRNJW9CA/U2vx0eSDkpFp9cRYyFjgCupe5SoUGz5ngL6pxg3JbtTZs1VITtKqvnAPYXs2OZqzHEu5BHgh+n9haSdUwy5HgfaS/phTlnu5NiG9JHrFWCrmv2S1E7SHrXU7QK8nZbPrKWOrUNOJMxKLCJeAKaTDXefBpwtaTrZt77jCzS5lOzDYhzwck75bcBFKnB5Yvqm+yDZh/CDqex9sm/OoyXNIPugXWNyZ5GeBYaR3SZ6Ntkw/Tzgp8ATZPs7NSJqu3X3cOAfkp6IiOlkcw5eJJsTMLGWNrkuB7ZIkw2nA4c1Yn/PJJu0OoPsTpWXNWB7AETEwoi4IiI+a0xsBfqZSjbyNInstb4pvU8gO21yZoqvK9mcl9y2jTnOhdwEvARMVXap6Q3kjV6nUY3+ZAnLbEmTyE4D/aShfeT19xnZPJor0jGZRnaar5ChZKf/ngY+aMR+WTPx3T/NrEml4ebBEXFsiUMxs3XAIxJmZmZWNI9ImJmZWdE8ImFmZmZFcyJhZmZmRXMiYWZmZkVzImFmZmZFcyJhZmZmRfv/etB407Ziw2kAAAAASUVORK5CYII=\n", "text/plain": [ "
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PearsonRResult(statistic=0.6215944171061962, pvalue=5.15410341167977e-12)\n", "Slope and P-value = PearsonRResult(statistic=0.6998946208439549, pvalue=7.87198334053969e-12)\n", "Public 150\n", "Well 133\n", "Rain 30\n", "Name: WaterSource1, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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PearsonRResult(statistic=-0.47674426972250755, pvalue=5.323258991838428e-07)\n", "Slope and P-value = PearsonRResult(statistic=0.6727734459707836, pvalue=1.771098161771106e-14)\n", "Public 90\n", "Well 85\n", "Rain 10\n", "Name: WaterSource1, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.09274039405945121, pvalue=0.42242922526982135)\n", "Slope and P-value = PearsonRResult(statistic=-0.5993168630546766, pvalue=3.340463075043865e-07)\n", "Slope and P-value = PearsonRResult(statistic=0.2512643706569996, pvalue=0.029669134842947184)\n", "Slope and P-value = PearsonRResult(statistic=-0.8021105090953077, pvalue=1.1392337015803109e-23)\n", "Slope and P-value = PearsonRResult(statistic=-0.4328943661803393, pvalue=6.850260480999163e-06)\n", "Slope and P-value = PearsonRResult(statistic=-0.544688416623901, pvalue=0.0001591909227915533)\n", "Slope and P-value = PearsonRResult(statistic=-0.7541662238733743, pvalue=1.3348138268400473e-19)\n", "Slope and P-value = PearsonRResult(statistic=-0.017649486925341164, pvalue=0.8616379891198119)\n", "Slope and P-value = PearsonRResult(statistic=-0.21707416213217434, pvalue=0.19049760696735477)\n", "Slope and P-value = PearsonRResult(statistic=-0.5028269070289377, pvalue=9.754302584997555e-08)\n", "Well 99\n", "Public 90\n", "Rain 10\n", "Name: WaterSource1, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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PearsonRResult(statistic=0.4702212268907826, pvalue=0.0007452116345637246)\n", "Slope and P-value = PearsonRResult(statistic=0.8855068733189032, pvalue=2.152824988620976e-34)\n", "Public 149\n", "Well 134\n", "Rain 30\n", "Name: WaterSource1, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.819403281604296, pvalue=2.018010949688041e-25)\n", "Slope and P-value = PearsonRResult(statistic=0.5604181682206785, pvalue=5.31254981096015e-08)\n", "Slope and P-value = PearsonRResult(statistic=-0.8714079630766113, pvalue=4.4810178501348526e-32)\n", "Slope and P-value = PearsonRResult(statistic=-0.9556925779705203, pvalue=7.508726115939411e-54)\n", "Slope and P-value = PearsonRResult(statistic=-0.5271274833490522, pvalue=0.00019896257417499462)\n", "Slope and P-value = PearsonRResult(statistic=0.8863284261677593, pvalue=1.543962726823672e-34)\n", "Slope and P-value = PearsonRResult(statistic=-0.018626391781720864, pvalue=0.8540620461788405)\n", "Slope and P-value = PearsonRResult(statistic=0.8957353397507501, pvalue=2.8301079281520872e-36)\n", "Slope and P-value = PearsonRResult(statistic=0.8885588983294219, pvalue=6.180210687070599e-35)\n", "Slope and P-value = PearsonRResult(statistic=-0.34683791123521235, pvalue=0.0004075050719366655)\n", "Public 90\n", "Well 83\n", "Rain 10\n", "Name: WaterSource1, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
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PearsonRResult(statistic=-0.6114553159047377, pvalue=1.4065664675599e-11)\n", "Slope and P-value = PearsonRResult(statistic=0.6377880874920169, pvalue=9.598908782480125e-13)\n", "Public 90\n", "Well 85\n", "Rain 10\n", "Name: WaterSource1, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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PearsonRResult(statistic=0.6230089734295742, pvalue=4.467546852316793e-12)\n", "Slope and P-value = PearsonRResult(statistic=0.12104447179878494, pvalue=0.2302779535095771)\n", "Public 114\n", "Well 84\n", "Rain 10\n", "Name: WaterSource1, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.7137485743963935, pvalue=7.791649889713686e-17)\n", "Slope and P-value = PearsonRResult(statistic=0.9356273036690123, pvalue=4.11290611824948e-46)\n", "Slope and P-value = PearsonRResult(statistic=0.9411703151288926, pvalue=5.706118527947233e-48)\n", "Slope and P-value = PearsonRResult(statistic=-0.9427990207798436, pvalue=1.499665267524223e-48)\n", "Slope and P-value = PearsonRResult(statistic=0.5593130428770785, pvalue=2.9457567638364053e-05)\n", "Slope and P-value = PearsonRResult(statistic=0.7025930280617432, pvalue=3.7394238862168954e-16)\n", "Slope and P-value = PearsonRResult(statistic=-0.7334697393025099, pvalue=4.0240096784275774e-18)\n", "Slope and P-value = PearsonRResult(statistic=0.3143286209761871, pvalue=0.0014480391217880496)\n", "Slope and P-value = PearsonRResult(statistic=0.8759725557662821, pvalue=8.54851856150125e-33)\n", "Slope and P-value = PearsonRResult(statistic=-0.7309801024769005, pvalue=1.287233364835511e-17)\n", "Public 90\n", "Well 85\n", "Rain 20\n", "Name: WaterSource1, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.28372520721962113, pvalue=0.004228219145654299)\n", "Slope and P-value = PearsonRResult(statistic=0.8209785787956049, pvalue=1.368021984901431e-25)\n", "Slope and P-value = PearsonRResult(statistic=0.5370867168120693, pvalue=8.370377398133662e-09)\n", "Slope and P-value = PearsonRResult(statistic=-0.6498409197645785, pvalue=2.5723094773710264e-13)\n", "Slope and P-value = PearsonRResult(statistic=0.6751761018142117, pvalue=1.3196993792453435e-14)\n", "Slope and P-value = PearsonRResult(statistic=0.8229784360360255, pvalue=4.034428809653479e-16)\n", "Slope and P-value = PearsonRResult(statistic=0.5746211235569285, pvalue=4.0651317647558806e-10)\n", "Slope and P-value = PearsonRResult(statistic=-4.167352069083018e-05, pvalue=0.9996716739024085)\n", "Slope and P-value = PearsonRResult(statistic=-0.0010753583173162412, pvalue=0.99259423075171)\n", "Slope and P-value = PearsonRResult(statistic=-0.7062013613726914, pvalue=1.8581974428053256e-15)\n" ] } ], "source": [ "sample = pd.merge(microbiome, poultry[['SampleID', 'WaterSource1','PastureTime','SampleType']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "#sample = sample[~sample.isin([np.nan, np.inf, -np.inf]).any(1)]\n", "sample = sample.drop(['Pathogen_Salmonella', 'new_Pathogen_Salmonella',\n", " 'Pathogen_Campy','new_Pathogen_Campy',\n", " 'Pathogen_Listeria','new_Pathogen_Listeria'],axis='columns')\n", "\n", "\n", "feces1=sample[(sample.SampleType=='Feces') & (sample.PastureTime=='Start')]\n", "feces2=sample[(sample.SampleType=='Feces') & (sample.PastureTime=='Mid')]\n", "feces3=sample[(sample.SampleType=='Feces') & (sample.PastureTime=='End')]\n", "\n", "soil1=sample[(sample.SampleType=='Soil') & (sample.PastureTime=='Start')]\n", "soil2=sample[(sample.SampleType=='Soil') & (sample.PastureTime=='Mid')]\n", "soil3=sample[(sample.SampleType=='Soil') & (sample.PastureTime=='End')]\n", "\n", "ceca=sample[sample.SampleType=='Ceca']\n", "wcrp=sample[sample.SampleType=='WCR-P']\n", "wcrf=sample[sample.SampleType=='WCR-F']\n", "\n", "\n", "print('Feces_Start', feces1.shape)\n", "print('Feces_Mid', feces2.shape)\n", "print('Feces_End', feces3.shape,'\\n')\n", "\n", "print('Soil_Start', soil1.shape)\n", "print('Soil_Mid', soil2.shape)\n", "print('Soil_End', soil3.shape,'\\n')\n", "\n", "print('Ceca', ceca.shape)\n", "print('WCR-P', wcrp.shape)\n", "print('WCR-F', wcrf.shape,'\\n')\n", "\n", "\n", "sampletypes = [feces1,feces2,feces3, soil1,soil2,soil3,ceca, wcrp, wcrf]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES_START\", 1: \"FECES_MID\", 2:\"FECES_END\",\n", " 3: \"SOIL_START\", 4: \"SOIL_MID\", 5: \"SOIL_END\",\n", " 6:\"CECA\", 7: \"WCR-P\", 8: \"WCR-F\"\n", " }\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType', 'PastureTime','WaterSource1'],axis='columns'),sample.WaterSource1,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, :] #multiclass\n", " \n", " try:\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs, multi_class='ovo')\n", " except ValueError:\n", " pass\n", " \n", " \n", " \n", " \n", "# mylist2.append([f\"WaterSource1_{sample_name[indexing]}\", rf_auc_normal])\n", " \n", " \n", " print(pd.value_counts(sample['WaterSource1']))\n", "\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim3_plot.max(), decimals=None, symbol=''))\n", " \n", "\n", " plt.title(f\"WaterSource in {sample_name[indexing]} Model\")\n", " prelim3_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','WaterSource1','PastureTime','SampleType'],axis='columns').columns)\n", " prelim3_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", "\n", " xmax=prelim3_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " \n", " plt.show()\n", " \n", " prelim3_plot.nlargest(10).to_csv(\"prelim3.csv\")\n", " top10 = pd.read_csv('prelim3.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", "# plt.plot(pdp[1][0], pdp[0][0],'.')\n", "# plt.ylabel('Partial dependence'), plt.xlabel(top10[feature])\n", "# plt.show()\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " #mylist.append([f\"WaterSource1_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (22) FreqBirdHandling" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Feces_Start (200, 878)\n", "Feces_Mid (313, 878)\n", "Feces_End (185, 878) \n", "\n", "Soil_Start (199, 878)\n", "Soil_Mid (313, 878)\n", "Soil_End (183, 878) \n", "\n", "Ceca (185, 878)\n", "WCR-P (208, 878)\n", "WCR-F (195, 878) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "0 175\n", "1 25\n", "Name: FreqBirdHandling, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
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PearsonRResult(statistic=0.819475230209896, pvalue=0.003713684397100036)\n", "Slope and P-value = PearsonRResult(statistic=0.848729967368685, pvalue=1.1892171982326193e-10)\n", "0 273\n", "1 40\n", "Name: FreqBirdHandling, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
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PearsonRResult(statistic=0.6329793844732208, pvalue=1.5975041528995938e-12)\n", "Slope and P-value = PearsonRResult(statistic=0.854183564892624, pvalue=4.6834011038588305e-12)\n", "0 160\n", "1 25\n", "Name: FreqBirdHandling, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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PearsonRResult(statistic=0.913729402072527, pvalue=4.058645923959764e-27)\n", "Slope and P-value = PearsonRResult(statistic=0.20872634155246364, pvalue=0.20224348624707247)\n", "0 174\n", "1 25\n", "Name: FreqBirdHandling, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
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PearsonRResult(statistic=0.8104278099342673, pvalue=1.7238513295940474e-24)\n", "Slope and P-value = PearsonRResult(statistic=-0.6442802517136373, pvalue=4.75696010941151e-13)\n", "0 274\n", "1 39\n", "Name: FreqBirdHandling, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
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PearsonRResult(statistic=-0.5211311707150972, pvalue=2.7169544407694624e-08)\n", "Slope and P-value = PearsonRResult(statistic=0.925672629968374, pvalue=6.541583365366717e-13)\n", "0 158\n", "1 25\n", "Name: FreqBirdHandling, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.7621165732059824, pvalue=0.002459265887914961)\n", "Slope and P-value = PearsonRResult(statistic=0.7649552721037075, pvalue=3.076989626493987e-20)\n", "Slope and P-value = PearsonRResult(statistic=0.8223603469939671, pvalue=2.8086248560193778e-05)\n", "Slope and P-value = PearsonRResult(statistic=-0.3617789678343196, pvalue=0.00023379434560877077)\n", "Slope and P-value = PearsonRResult(statistic=0.2967725294637325, pvalue=0.0027151783486433307)\n", "Slope and P-value = PearsonRResult(statistic=0.6066110363381567, pvalue=2.2441531110212618e-11)\n", "Slope and P-value = PearsonRResult(statistic=-0.17234549688642778, pvalue=0.0864108529148686)\n", "Slope and P-value = PearsonRResult(statistic=-0.37093240829871676, pvalue=0.0001451897207698754)\n", "Slope and P-value = PearsonRResult(statistic=0.8607120823447175, pvalue=1.717485989794508e-30)\n", "Slope and P-value = PearsonRResult(statistic=0.8113284315170615, pvalue=1.3972533386316765e-24)\n", "0 160\n", "1 25\n", "Name: FreqBirdHandling, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
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PearsonRResult(statistic=0.3155449573384156, pvalue=0.002063289692691879)\n", "Slope and P-value = PearsonRResult(statistic=0.9472974206554616, pvalue=7.4074660747417966e-31)\n", "0 183\n", "1 25\n", "Name: FreqBirdHandling, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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PearsonRResult(statistic=0.7804198941342517, pvalue=0.0005969532301489787)\n", "Slope and P-value = PearsonRResult(statistic=0.8861518808600064, pvalue=9.60314493925407e-25)\n", "0 170\n", "1 25\n", "Name: FreqBirdHandling, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.8971077967689716, pvalue=1.4240920231748967e-17)\n", "Slope and P-value = PearsonRResult(statistic=-0.24804585519083777, pvalue=0.012835885816969796)\n", "Slope and P-value = PearsonRResult(statistic=0.835797426490817, pvalue=2.2507127664678919e-10)\n", "Slope and P-value = PearsonRResult(statistic=0.7469863601624886, pvalue=0.002140372606152406)\n", "Slope and P-value = PearsonRResult(statistic=0.9055750064619141, pvalue=3.9800471411080216e-19)\n", "Slope and P-value = PearsonRResult(statistic=0.7094297328010029, pvalue=2.3685284125969752e-05)\n", "Slope and P-value = PearsonRResult(statistic=0.8549322657846942, pvalue=0.003312369406317842)\n", "Slope and P-value = PearsonRResult(statistic=0.04725846517300731, pvalue=0.6405684872668017)\n", "Slope and P-value = PearsonRResult(statistic=0.735025574300675, pvalue=0.024056756495625782)\n", "Slope and P-value = PearsonRResult(statistic=-0.4764421854210827, pvalue=5.424507566681189e-07)\n" ] } ], "source": [ "sample = pd.merge(microbiome, poultry[['SampleID', 'FreqBirdHandling','PastureTime','SampleType']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "#sample = sample[~sample.isin([np.nan, np.inf, -np.inf]).any(1)]\n", "sample = sample.drop(['Pathogen_Salmonella', 'new_Pathogen_Salmonella',\n", " 'Pathogen_Campy','new_Pathogen_Campy',\n", " 'Pathogen_Listeria','new_Pathogen_Listeria'],axis='columns')\n", "\n", "sample.FreqBirdHandling.replace({'OIN': 0,'Daily':1}, regex=True, inplace=True)\n", "\n", "\n", "feces1=sample[(sample.SampleType=='Feces') & (sample.PastureTime=='Start')]\n", "feces2=sample[(sample.SampleType=='Feces') & (sample.PastureTime=='Mid')]\n", "feces3=sample[(sample.SampleType=='Feces') & (sample.PastureTime=='End')]\n", "\n", "soil1=sample[(sample.SampleType=='Soil') & (sample.PastureTime=='Start')]\n", "soil2=sample[(sample.SampleType=='Soil') & (sample.PastureTime=='Mid')]\n", "soil3=sample[(sample.SampleType=='Soil') & (sample.PastureTime=='End')]\n", "\n", "ceca=sample[sample.SampleType=='Ceca']\n", "wcrp=sample[sample.SampleType=='WCR-P']\n", "wcrf=sample[sample.SampleType=='WCR-F']\n", "\n", "\n", "print('Feces_Start', feces1.shape)\n", "print('Feces_Mid', feces2.shape)\n", "print('Feces_End', feces3.shape,'\\n')\n", "\n", "print('Soil_Start', soil1.shape)\n", "print('Soil_Mid', soil2.shape)\n", "print('Soil_End', soil3.shape,'\\n')\n", "\n", "print('Ceca', ceca.shape)\n", "print('WCR-P', wcrp.shape)\n", "print('WCR-F', wcrf.shape,'\\n')\n", "\n", "\n", "sampletypes = [feces1,feces2,feces3, soil1,soil2,soil3,ceca, wcrp, wcrf]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES_START\", 1: \"FECES_MID\", 2:\"FECES_END\",\n", " 3: \"SOIL_START\", 4: \"SOIL_MID\", 5: \"SOIL_END\",\n", " 6:\"CECA\", 7: \"WCR-P\", 8: \"WCR-F\"\n", " }\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType', 'PastureTime','FreqBirdHandling'],axis='columns'),sample.FreqBirdHandling,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1] #multiclass\n", " \n", " try:\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " except ValueError:\n", " pass\n", " \n", " \n", " \n", " \n", "# mylist2.append([f\"FreqBirdHandling_{sample_name[indexing]}\", rf_auc_normal])\n", " \n", " \n", " print(pd.value_counts(sample['FreqBirdHandling']))\n", "\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim3_plot.max(), decimals=None, symbol=''))\n", " \n", "\n", " plt.title(f\"FreqBirdHandling in {sample_name[indexing]} Model\")\n", " prelim3_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','FreqBirdHandling','PastureTime','SampleType'],axis='columns').columns)\n", " prelim3_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", "\n", " xmax=prelim3_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " \n", " plt.show()\n", " \n", " prelim3_plot.nlargest(10).to_csv(\"prelim3.csv\")\n", " top10 = pd.read_csv('prelim3.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", "# plt.plot(pdp[1][0], pdp[0][0],'.')\n", "# plt.ylabel('Partial dependence'), plt.xlabel(top10[feature])\n", "# plt.show()\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"FreqBirdHandling_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (23) AnyABXUse" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Feces_Start (200, 878)\n", "Feces_Mid (313, 878)\n", "Feces_End (185, 878) \n", "\n", "Soil_Start (199, 878)\n", "Soil_Mid (313, 878)\n", "Soil_End (183, 878) \n", "\n", "Ceca (185, 878)\n", "WCR-P (208, 878)\n", "WCR-F (195, 878) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "0 195\n", "1 5\n", "Name: AnyABXUse, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=nan, pvalue=nan)\n", "Slope and P-value = PearsonRResult(statistic=0.9536070081192449, pvalue=4.883526060376029e-45)\n", "Slope and P-value = PearsonRResult(statistic=0.8951593467161063, pvalue=0.01591116257068933)\n", "Slope and P-value = PearsonRResult(statistic=0.8876469152327139, pvalue=9.007409665347008e-35)\n", "Slope and P-value = PearsonRResult(statistic=-0.2140022263115148, pvalue=0.03252073083334577)\n", "Slope and P-value = PearsonRResult(statistic=0.853302617532169, pvalue=1.8054084734381068e-29)\n", "Slope and P-value = PearsonRResult(statistic=-0.28949031235064404, pvalue=0.0034853765061832664)\n", "Slope and P-value = PearsonRResult(statistic=0.36444313861540467, pvalue=0.00622904457504399)\n", "Slope and P-value = PearsonRResult(statistic=0.8774408000665938, pvalue=4.947764005998412e-33)\n", "Slope and P-value = PearsonRResult(statistic=0.9772316304230115, pvalue=1.415798231548754e-51)\n", "0 308\n", "1 5\n", "Name: AnyABXUse, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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PearsonRResult(statistic=-0.24251057814089036, pvalue=0.015056258604980369)\n", "Slope and P-value = PearsonRResult(statistic=0.6646454415572621, pvalue=4.6964848532936127e-14)\n", "0 180\n", "1 5\n", "Name: AnyABXUse, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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PearsonRResult(statistic=0.9361037887465863, pvalue=7.014548616219457e-24)\n", "Slope and P-value = PearsonRResult(statistic=0.6092929847507588, pvalue=0.00021457132215463952)\n", "0 194\n", "1 5\n", "Name: AnyABXUse, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.8860092601782965, pvalue=6.019007286804871e-16)\n", "Slope and P-value = PearsonRResult(statistic=0.8321782778463607, pvalue=1.139530618075125e-21)\n", "Slope and P-value = PearsonRResult(statistic=0.9443761776399396, pvalue=1.6649159570273457e-36)\n", "Slope and P-value = PearsonRResult(statistic=0.843229476007834, pvalue=1.0132354192953122e-25)\n", "Slope and P-value = PearsonRResult(statistic=0.8865358534906376, pvalue=7.437768668017198e-30)\n", "Slope and P-value = PearsonRResult(statistic=0.9943978967903481, pvalue=4.6987433585244717e-05)\n", "Slope and P-value = PearsonRResult(statistic=0.4810632751417967, pvalue=4.058117678534771e-07)\n", "Slope and P-value = PearsonRResult(statistic=0.8470289696689555, pvalue=1.198640059891161e-28)\n", "Slope and P-value = PearsonRResult(statistic=-0.2036385724143986, pvalue=0.04214057211467737)\n", "Slope and P-value = PearsonRResult(statistic=0.814165938618229, pvalue=8.847979853428002e-10)\n", "0 308\n", "1 5\n", "Name: AnyABXUse, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=nan, pvalue=nan)\n", "Slope and P-value = PearsonRResult(statistic=0.4998035050124653, pvalue=0.20723854510049966)\n", "Slope and P-value = PearsonRResult(statistic=0.8710451567998949, pvalue=2.5458907899964733e-06)\n", "Slope and P-value = PearsonRResult(statistic=0.5069947144694139, pvalue=0.011454342852797665)\n", "Slope and P-value = PearsonRResult(statistic=0.8362834071017428, pvalue=2.5386685705290557e-27)\n", "Slope and P-value = PearsonRResult(statistic=0.9606744031701121, pvalue=1.3264953564445338e-30)\n", "Slope and P-value = PearsonRResult(statistic=nan, pvalue=nan)\n", "Slope and P-value = PearsonRResult(statistic=nan, pvalue=nan)\n", "Slope and P-value = PearsonRResult(statistic=0.7204537358702897, pvalue=2.926941862756075e-17)\n", "Slope and P-value = PearsonRResult(statistic=0.7974824509221081, pvalue=1.1678017537928386e-11)\n", "0 178\n", "1 5\n", "Name: AnyABXUse, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.7235982210410313, pvalue=7.224341971299666e-14)\n", "Slope and P-value = PearsonRResult(statistic=0.990966469968438, pvalue=4.666465565391512e-10)\n", "Slope and P-value = PearsonRResult(statistic=0.2909440427936544, pvalue=0.0033175740288206228)\n", "Slope and P-value = PearsonRResult(statistic=nan, pvalue=nan)\n", "Slope and P-value = PearsonRResult(statistic=0.9162034100024004, pvalue=4.164453394957861e-06)\n", "Slope and P-value = PearsonRResult(statistic=0.7735126488846353, pvalue=4.0171343620952954e-21)\n", "Slope and P-value = PearsonRResult(statistic=-0.2954805084486747, pvalue=0.002839502734614327)\n", "Slope and P-value = PearsonRResult(statistic=0.959605565370101, pvalue=0.00015982822607751922)\n", "Slope and P-value = PearsonRResult(statistic=-0.5419680552738269, pvalue=5.766890424961642e-09)\n", "Slope and P-value = PearsonRResult(statistic=0.8405042573863722, pvalue=5.00315087946465e-10)\n", "0 180\n", "1 5\n", "Name: AnyABXUse, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.9478159734679571, pvalue=5.751755035572738e-44)\n", "Slope and P-value = PearsonRResult(statistic=0.5679124512241966, pvalue=7.178383497877778e-10)\n", "Slope and P-value = PearsonRResult(statistic=0.36315132764519864, pvalue=0.18337711148287508)\n", "Slope and P-value = PearsonRResult(statistic=0.6942517362466163, pvalue=0.19336766529858346)\n", "Slope and P-value = PearsonRResult(statistic=0.2121084867074725, pvalue=0.3693048385030693)\n", "Slope and P-value = PearsonRResult(statistic=0.5742896401272536, pvalue=0.08251578095022567)\n", "Slope and P-value = PearsonRResult(statistic=-0.5807922410375899, pvalue=2.382008038365338e-10)\n", "Slope and P-value = PearsonRResult(statistic=0.31144239274417734, pvalue=0.02188179980901392)\n", "Slope and P-value = PearsonRResult(statistic=0.7509013801312515, pvalue=0.14353508563016723)\n", "Slope and P-value = PearsonRResult(statistic=0.5219182198957617, pvalue=0.00010141725305371211)\n", "0 203\n", "1 5\n", "Name: AnyABXUse, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
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PearsonRResult(statistic=0.7764671423733632, pvalue=0.0006634734901039279)\n", "Slope and P-value = PearsonRResult(statistic=0.9134859524732594, pvalue=4.681051079055717e-40)\n", "0 190\n", "1 5\n", "Name: AnyABXUse, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.9253545178199877, pvalue=0.0001240364596609622)\n", "Slope and P-value = PearsonRResult(statistic=0.9864249820500557, pvalue=5.570193490574009e-10)\n", "Slope and P-value = PearsonRResult(statistic=1.0, pvalue=1.0)\n", "Slope and P-value = PearsonRResult(statistic=0.9184208977562974, pvalue=0.25892990096418783)\n", "Slope and P-value = PearsonRResult(statistic=0.9462638542599247, pvalue=9.483706374864446e-17)\n", "Slope and P-value = PearsonRResult(statistic=0.9907887797483219, pvalue=0.009211220251678087)\n", "Slope and P-value = PearsonRResult(statistic=0.15351554661157638, pvalue=0.6165669039984625)\n", "Slope and P-value = PearsonRResult(statistic=0.7854383472539574, pvalue=1.4869560390859054e-05)\n", "Slope and P-value = PearsonRResult(statistic=0.5744341300387796, pvalue=1.5925954338507498e-06)\n", "Slope and P-value = PearsonRResult(statistic=0.8488885135372911, pvalue=1.2290920313430919e-12)\n" ] } ], "source": [ "sample = pd.merge(microbiome, poultry[['SampleID', 'AnyABXUse','PastureTime','SampleType']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "#sample = sample[~sample.isin([np.nan, np.inf, -np.inf]).any(1)]\n", "sample = sample.drop(['Pathogen_Salmonella', 'new_Pathogen_Salmonella',\n", " 'Pathogen_Campy','new_Pathogen_Campy',\n", " 'Pathogen_Listeria','new_Pathogen_Listeria'],axis='columns')\n", "\n", "sample.AnyABXUse.replace({'Y': 1,'N':0}, regex=True, inplace=True)\n", "\n", "\n", "\n", "feces1=sample[(sample.SampleType=='Feces') & (sample.PastureTime=='Start')]\n", "feces2=sample[(sample.SampleType=='Feces') & (sample.PastureTime=='Mid')]\n", "feces3=sample[(sample.SampleType=='Feces') & (sample.PastureTime=='End')]\n", "\n", "soil1=sample[(sample.SampleType=='Soil') & (sample.PastureTime=='Start')]\n", "soil2=sample[(sample.SampleType=='Soil') & (sample.PastureTime=='Mid')]\n", "soil3=sample[(sample.SampleType=='Soil') & (sample.PastureTime=='End')]\n", "\n", "ceca=sample[sample.SampleType=='Ceca']\n", "wcrp=sample[sample.SampleType=='WCR-P']\n", "wcrf=sample[sample.SampleType=='WCR-F']\n", "\n", "\n", "print('Feces_Start', feces1.shape)\n", "print('Feces_Mid', feces2.shape)\n", "print('Feces_End', feces3.shape,'\\n')\n", "\n", "print('Soil_Start', soil1.shape)\n", "print('Soil_Mid', soil2.shape)\n", "print('Soil_End', soil3.shape,'\\n')\n", "\n", "print('Ceca', ceca.shape)\n", "print('WCR-P', wcrp.shape)\n", "print('WCR-F', wcrf.shape,'\\n')\n", "\n", "\n", "sampletypes = [feces1,feces2,feces3, soil1,soil2,soil3,ceca, wcrp, wcrf]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES_START\", 1: \"FECES_MID\", 2:\"FECES_END\",\n", " 3: \"SOIL_START\", 4: \"SOIL_MID\", 5: \"SOIL_END\",\n", " 6:\"CECA\", 7: \"WCR-P\", 8: \"WCR-F\"\n", " }\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType', 'PastureTime','AnyABXUse'],axis='columns'),sample.AnyABXUse,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1] #multiclass\n", " \n", " try:\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " except ValueError:\n", " pass\n", " \n", " \n", " \n", " \n", "# mylist2.append([f\"AnyABXUse_{sample_name[indexing]}\", rf_auc_normal])\n", " \n", " \n", " print(pd.value_counts(sample['AnyABXUse']))\n", "\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim3_plot.max(), decimals=None, symbol=''))\n", " \n", "\n", " plt.title(f\"AnyABXUse in {sample_name[indexing]} Model\")\n", " prelim3_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','AnyABXUse','PastureTime','SampleType'],axis='columns').columns)\n", " prelim3_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", "\n", " xmax=prelim3_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " \n", " plt.show()\n", " \n", " prelim3_plot.nlargest(10).to_csv(\"prelim3.csv\")\n", " top10 = pd.read_csv('prelim3.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", "# plt.plot(pdp[1][0], pdp[0][0],'.')\n", "# plt.ylabel('Partial dependence'), plt.xlabel(top10[feature])\n", "# plt.show()\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"AnyABXUse_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (24) AnimalSource" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Feces_Start (200, 878)\n", "Feces_Mid (313, 878)\n", "Soil_Start (199, 878)\n", "Soil_Mid (313, 878)\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "Broiler 185\n", "Cattle 5\n", "Layer 5\n", "Swine 5\n", "Name: AnimalSource, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.9125887905266666, pvalue=3.465307672357047e-05)\n", "Slope and P-value = PearsonRResult(statistic=-0.7738571088613393, pvalue=3.529209154675952e-06)\n", "Slope and P-value = PearsonRResult(statistic=-0.6301107058980286, pvalue=0.36988929410197136)\n", "Slope and P-value = PearsonRResult(statistic=-0.9552212200697744, pvalue=1.741572650080362e-18)\n", "Slope and P-value = PearsonRResult(statistic=-0.7782440223142271, pvalue=0.0017287593045530506)\n", "Slope and P-value = PearsonRResult(statistic=-0.9539115485470624, pvalue=1.4084913755994766e-14)\n", "Slope and P-value = PearsonRResult(statistic=-0.9403593210871495, pvalue=0.017327020014869063)\n", "Slope and P-value = PearsonRResult(statistic=-0.33942822680518964, pvalue=0.0005507637449807767)\n", "Slope and P-value = PearsonRResult(statistic=-0.5619459601073545, pvalue=0.008020333671497746)\n", "Slope and P-value = PearsonRResult(statistic=-0.7552855399957625, pvalue=0.01860958223260658)\n", "Broiler 183\n", "Layer 60\n", "Swine 40\n", "Cattle 30\n", "Name: AnimalSource, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.4960535268060431, pvalue=4.949504070754157e-07)\n", "Slope and P-value = PearsonRResult(statistic=-0.6821503776022971, pvalue=3.235648213086034e-12)\n", "Slope and P-value = PearsonRResult(statistic=-0.7575555905121488, pvalue=7.399076464586142e-20)\n", "Slope and P-value = PearsonRResult(statistic=-0.43713034775153214, pvalue=5.43399631650011e-06)\n", "Slope and P-value = PearsonRResult(statistic=-0.6955207443974238, pvalue=9.740234395202053e-16)\n", "Slope and P-value = PearsonRResult(statistic=-0.34719630996036266, pvalue=0.0004015345740432314)\n", "Slope and P-value = PearsonRResult(statistic=-0.40957363894088683, pvalue=0.024599348292717416)\n", "Slope and P-value = PearsonRResult(statistic=-0.9444570994879404, pvalue=3.69344606279791e-49)\n", "Slope and P-value = PearsonRResult(statistic=-0.9204251823708365, pvalue=9.224224139465145e-42)\n", "Slope and P-value = PearsonRResult(statistic=-0.5506603313187184, pvalue=2.8293139561200004e-05)\n", "Broiler 184\n", "Cattle 5\n", "Layer 5\n", "Swine 5\n", "Name: AnimalSource, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.5124833204037225, pvalue=0.0002713863582089685)\n", "Slope and P-value = PearsonRResult(statistic=-0.8126288100960378, pvalue=1.0296595567828951e-24)\n", "Slope and P-value = PearsonRResult(statistic=-0.9044839321216791, pvalue=2.906396895472932e-15)\n", "Slope and P-value = PearsonRResult(statistic=-0.38797134582816495, pvalue=0.12385069621774712)\n", "Slope and P-value = PearsonRResult(statistic=-0.5713099361946848, pvalue=0.02079225193706939)\n", "Slope and P-value = PearsonRResult(statistic=-0.7671371829018026, pvalue=1.3228474559954367e-20)\n", "Slope and P-value = PearsonRResult(statistic=-0.9655113267889666, pvalue=6.267744700470082e-48)\n", "Slope and P-value = PearsonRResult(statistic=-0.7368032391997478, pvalue=0.2631967608002522)\n", "Slope and P-value = PearsonRResult(statistic=-0.866685646211862, pvalue=2.481628567965191e-09)\n", "Slope and P-value = PearsonRResult(statistic=-0.7217118405830513, pvalue=2.428012592452415e-17)\n", "Broiler 183\n", "Layer 60\n", "Swine 40\n", "Cattle 30\n", "Name: AnimalSource, dtype: int64\n" ] }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.27447687220584216, pvalue=0.00571768344242577)\n", "Slope and P-value = PearsonRResult(statistic=-0.7891227549737919, pvalue=1.8293812675448094e-22)\n", "Slope and P-value = PearsonRResult(statistic=-0.8383830889134877, pvalue=1.422910317139614e-27)\n", "Slope and P-value = PearsonRResult(statistic=0.25654478034206996, pvalue=0.009981582376807269)\n", "Slope and P-value = PearsonRResult(statistic=-0.9172978917504737, pvalue=5.649326515258471e-41)\n", "Slope and P-value = PearsonRResult(statistic=-0.7095257725426416, pvalue=1.4231328024076367e-16)\n", "Slope and P-value = PearsonRResult(statistic=-0.29437564995089677, pvalue=0.026232356144653773)\n", "Slope and P-value = PearsonRResult(statistic=-0.8593518053393817, pvalue=5.251299215964457e-30)\n", "Slope and P-value = PearsonRResult(statistic=0.27428330783606353, pvalue=0.005753300922760536)\n", "Slope and P-value = PearsonRResult(statistic=0.2853567576107997, pvalue=0.004004834547874929)\n" ] } ], "source": [ "sample = pd.merge(microbiome, poultry[['SampleID', 'AnimalSource','PastureTime','SampleType']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "#sample = sample[~sample.isin([np.nan, np.inf, -np.inf]).any(1)]\n", "sample = sample.drop(['Pathogen_Salmonella', 'new_Pathogen_Salmonella',\n", " 'Pathogen_Campy','new_Pathogen_Campy',\n", " 'Pathogen_Listeria','new_Pathogen_Listeria'],axis='columns')\n", "\n", "\n", "feces1=sample[(sample.SampleType=='Feces') & (sample.PastureTime=='Start')]\n", "feces2=sample[(sample.SampleType=='Feces') & (sample.PastureTime=='Mid')]\n", "\n", "soil1=sample[(sample.SampleType=='Soil') & (sample.PastureTime=='Start')]\n", "soil2=sample[(sample.SampleType=='Soil') & (sample.PastureTime=='Mid')]\n", "soil3=sample[(sample.SampleType=='Soil') & (sample.PastureTime=='End')]\n", "\n", "ceca=sample[sample.SampleType=='Ceca']\n", "wcrp=sample[sample.SampleType=='WCR-P']\n", "wcrf=sample[sample.SampleType=='WCR-F']\n", "\n", "\n", "print('Feces_Start', feces1.shape)\n", "print('Feces_Mid', feces2.shape)\n", "\n", "print('Soil_Start', soil1.shape)\n", "print('Soil_Mid', soil2.shape)\n", "\n", "\n", "sampletypes = [feces1,feces2, soil1,soil2]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES_START\", 1: \"FECES_MID\",\n", " 2: \"SOIL_START\", 3: \"SOIL_MID\"}\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType', 'PastureTime','AnimalSource'],axis='columns'),sample.AnimalSource,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, :] #multiclass\n", " \n", " try:\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs, multi_class='ovo')\n", " except ValueError:\n", " pass\n", " \n", " \n", " \n", " \n", "# mylist2.append([f\"AnimalSource_{sample_name[indexing]}\", rf_auc_normal])\n", " \n", " \n", " print(pd.value_counts(sample['AnimalSource']))\n", "\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim3_plot.max(), decimals=None, symbol=''))\n", " \n", "\n", " plt.title(f\"AnimalSource in {sample_name[indexing]} Model\")\n", " prelim3_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','AnimalSource','PastureTime','SampleType'],axis='columns').columns)\n", " prelim3_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", "\n", " xmax=prelim3_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " \n", " plt.show()\n", " \n", " prelim3_plot.nlargest(10).to_csv(\"prelim3.csv\")\n", " top10 = pd.read_csv('prelim3.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", "# plt.plot(pdp[1][0], pdp[0][0],'.')\n", "# plt.ylabel('Partial dependence'), plt.xlabel(top10[feature])\n", "# plt.show()\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"AnimalSource_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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SampleIDFarmAvgNumBirdsAvgNumFlocksYearsFarmingEggSourceBroodBeddingBroodFeedBrGMOFreeBrSoyFree...MnMoNaNiPPbSSiZnEcoli
0E2-1E600414MMWSSSNN...173.7201.290642.0211.5633977.071.8591091.660863.766128.0391
1E2-2E600414MMWSSSNN...128.7631.000477.2421.1562696.481.739822.626534.30092.8691
2E2-3E600414MMWSSSNN...162.2491.278629.5401.3843385.392.3041068.750546.466136.0831
3E2-4E600414MMWSSSNN...155.9332.225602.2152.5752872.7013.079878.725242.178103.2001
4E2-5E600414MMWSSSNN...131.5561.0001473.7501.7302175.495.5221015.680157.706125.8681
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5 rows × 162 columns

\n", "
" ], "text/plain": [ " SampleID Farm AvgNumBirds AvgNumFlocks YearsFarming EggSource \\\n", "0 E2-1 E 600 4 14 MM \n", "1 E2-2 E 600 4 14 MM \n", "2 E2-3 E 600 4 14 MM \n", "3 E2-4 E 600 4 14 MM \n", "4 E2-5 E 600 4 14 MM \n", "\n", " BroodBedding BroodFeed BrGMOFree BrSoyFree ... Mn Mo Na \\\n", "0 WS SS N N ... 173.720 1.290 642.021 \n", "1 WS SS N N ... 128.763 1.000 477.242 \n", "2 WS SS N N ... 162.249 1.278 629.540 \n", "3 WS SS N N ... 155.933 2.225 602.215 \n", "4 WS SS N N ... 131.556 1.000 1473.750 \n", "\n", " Ni P Pb S Si Zn Ecoli \n", "0 1.563 3977.07 1.859 1091.660 863.766 128.039 1 \n", "1 1.156 2696.48 1.739 822.626 534.300 92.869 1 \n", "2 1.384 3385.39 2.304 1068.750 546.466 136.083 1 \n", "3 2.575 2872.70 13.079 878.725 242.178 103.200 1 \n", "4 1.730 2175.49 5.522 1015.680 157.706 125.868 1 \n", "\n", "[5 rows x 162 columns]" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "poultry[['pH', 'EC', 'Moisture', 'TotalC', 'TotalN', 'CNRatio', 'Al', 'B', 'Ca',\n", " 'Cd', 'Cr', 'Cu', 'Fe', 'K', 'Mg', 'Mn', 'Mo', 'Na', 'Ni', 'P', 'Pb',\n", " 'S', 'Si', 'Zn']] = poultry[['pH', 'EC', 'Moisture', 'TotalC', 'TotalN', 'CNRatio', 'Al', 'B', 'Ca',\n", " 'Cd', 'Cr', 'Cu', 'Fe', 'K', 'Mg', 'Mn', 'Mo', 'Na', 'Ni', 'P', 'Pb',\n", " 'S', 'Si', 'Zn']].apply(pd.to_numeric, errors='coerce', axis=1)\n", "poultry = poultry[poultry.SampleType.str.contains('Feces|Soil')]\n", "poultry.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Physicochemical Properties as Targets:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (25) pH" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Median: 6.55\n", "Total sample count: 1635 \n", "\n", "Distribution:\n", "1.0 819\n", "0.0 816\n", "Name: new_pH, dtype: int64\n", "SAMPLE DISTRIBUTION \n", "\n", "Feces (698, 877)\n", "Soil (695, 877) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "1.0 379\n", "0.0 319\n", "Name: new_pH, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
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PearsonRResult(statistic=0.8834887760719039, pvalue=4.819640434283367e-34)\n", "Slope and P-value = PearsonRResult(statistic=0.8801997725439292, pvalue=1.7370182008523998e-33)\n", "0.0 357\n", "1.0 338\n", "Name: new_pH, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.7205031933071562, pvalue=2.905573345176098e-17)\n", "Slope and P-value = PearsonRResult(statistic=-0.8582880185789165, pvalue=3.762765579306407e-30)\n", "Slope and P-value = PearsonRResult(statistic=-0.9379723585482185, pvalue=7.065569171069789e-47)\n", "Slope and P-value = PearsonRResult(statistic=-0.8309882427776001, pvalue=1.0549212618776464e-26)\n", "Slope and P-value = PearsonRResult(statistic=-0.6653343015894222, pvalue=4.329014369381396e-14)\n", "Slope and P-value = PearsonRResult(statistic=-0.9366127244544049, pvalue=1.978116663267444e-46)\n", "Slope and P-value = PearsonRResult(statistic=0.5807902391714809, pvalue=2.3824254014213324e-10)\n", "Slope and P-value = PearsonRResult(statistic=-0.8564818074364531, pvalue=6.686862494858254e-30)\n", "Slope and P-value = PearsonRResult(statistic=0.8813178215822433, pvalue=1.1281921216242491e-33)\n", "Slope and P-value = PearsonRResult(statistic=0.5741391798528875, pvalue=4.236440603189736e-10)\n" ] } ], "source": [ "print(\"Median:\",poultry.pH.median())\n", "\n", "poultry.loc[poultry['pH'] < poultry.pH.median(), 'new_pH'] = 0\n", "poultry.loc[poultry['pH'] >= poultry.pH.median(), 'new_pH'] = 1 \n", "print(\"Total sample count:\", poultry.pH.value_counts().sum(),\"\\n\")\n", "\n", "print(\"Distribution:\")\n", "print(poultry.new_pH.value_counts())\n", "\n", "sample = pd.merge(microbiome, poultry[['SampleID', 'new_pH','SampleType']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "sample = sample[~sample.isin([np.nan, np.inf, -np.inf]).any(1)]\n", "sample = sample.drop(['Pathogen_Salmonella', 'new_Pathogen_Salmonella',\n", " 'Pathogen_Campy','new_Pathogen_Campy',\n", " 'Pathogen_Listeria','new_Pathogen_Listeria'],axis='columns')\n", "\n", "feces=sample[sample.SampleType=='Feces']\n", "soil=sample[sample.SampleType=='Soil']\n", "\n", "print ('SAMPLE DISTRIBUTION \\n')\n", "\n", "print('Feces', feces.shape)\n", "print('Soil', soil.shape,'\\n')\n", "\n", "sampletypes = [feces, soil]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES\", 1: \"SOIL\"}\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType', 'new_pH'],axis='columns'),sample.new_pH,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1]\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " \n", "# mylist2.append([f\"pH_{sample_name[indexing]}\", rf_auc_normal])\n", " \n", " \n", " print(pd.value_counts(sample['new_pH']))\n", "\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim3_plot.max(), decimals=None, symbol=''))\n", " \n", "\n", " plt.title(f\"pH in {sample_name[indexing]} Model\")\n", " prelim3_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','new_pH','SampleType'],axis='columns').columns)\n", " prelim3_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", "\n", " xmax=prelim3_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " \n", " plt.show()\n", " \n", " prelim3_plot.nlargest(10).to_csv(\"prelim3.csv\")\n", " top10 = pd.read_csv('prelim3.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", "# plt.plot(pdp[1][0], pdp[0][0],'.')\n", "# plt.ylabel('Partial dependence'), plt.xlabel(top10[feature])\n", "# plt.show()\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"pH_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (26) EC" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Median: 879.0\n", "Total sample count: 1635 \n", "\n", "Distribution:\n", "1.0 818\n", "0.0 817\n", "Name: new_EC, dtype: int64\n", "SAMPLE DISTRIBUTION \n", "\n", "Feces (698, 877)\n", "Soil (695, 877) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "1.0 671\n", "0.0 27\n", "Name: new_EC, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.17234549688642775, pvalue=0.0864108529148686)\n", "Slope and P-value = PearsonRResult(statistic=-0.8821221429648494, pvalue=8.24864198072405e-34)\n", "Slope and P-value = PearsonRResult(statistic=-0.918430987843066, pvalue=0.00047089239170496)\n", "Slope and P-value = PearsonRResult(statistic=-0.23063712846992848, pvalue=0.020966253226360743)\n", "Slope and P-value = PearsonRResult(statistic=-0.36329130287493455, pvalue=0.00020319763103348327)\n", "Slope and P-value = PearsonRResult(statistic=-0.8986181849688231, pvalue=7.693109315358973e-37)\n", "Slope and P-value = PearsonRResult(statistic=-0.8984992482601877, pvalue=8.124104248884158e-37)\n", "Slope and P-value = PearsonRResult(statistic=-0.8889358024628964, pvalue=5.284159277238703e-35)\n", "Slope and P-value = PearsonRResult(statistic=-0.10058830741912313, pvalue=0.31936438634319375)\n", "Slope and P-value = PearsonRResult(statistic=0.04510812344277656, pvalue=0.6558594960648447)\n", "0.0 667\n", "1.0 28\n", "Name: new_EC, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.6654654644896629, pvalue=4.262271962549621e-14)\n", "Slope and P-value = PearsonRResult(statistic=0.9204460162243626, pvalue=9.111268608147409e-42)\n", "Slope and P-value = PearsonRResult(statistic=0.8828320862771567, pvalue=6.244738365839487e-34)\n", "Slope and P-value = PearsonRResult(statistic=0.9037346819982892, pvalue=5.908688944470797e-13)\n", "Slope and P-value = PearsonRResult(statistic=-0.14105340803059563, pvalue=0.1615703844012259)\n", "Slope and P-value = PearsonRResult(statistic=0.8476604529809286, pvalue=9.945308579942527e-29)\n", "Slope and P-value = PearsonRResult(statistic=0.866037105619462, pvalue=2.907915850158947e-31)\n", "Slope and P-value = PearsonRResult(statistic=0.6001061840667942, pvalue=4.151118744782436e-11)\n", "Slope and P-value = PearsonRResult(statistic=0.8459248078189762, pvalue=1.6579339500668836e-28)\n", "Slope and P-value = PearsonRResult(statistic=0.8632564297854083, pvalue=9.551040768503676e-14)\n" ] } ], "source": [ "print(\"Median:\",poultry.EC.median())\n", "\n", "poultry.loc[poultry['EC'] < poultry.EC.median(), 'new_EC'] = 0\n", "poultry.loc[poultry['EC'] >= poultry.EC.median(), 'new_EC'] = 1\n", "print(\"Total sample count:\", poultry.EC.value_counts().sum(),\"\\n\")\n", "\n", "print(\"Distribution:\")\n", "print(poultry.new_EC.value_counts())\n", "\n", "sample = pd.merge(microbiome, poultry[['SampleID', 'new_EC','SampleType']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "sample = sample[~sample.isin([np.nan, np.inf, -np.inf]).any(1)]\n", "sample = sample.drop(['Pathogen_Salmonella', 'new_Pathogen_Salmonella',\n", " 'Pathogen_Campy','new_Pathogen_Campy',\n", " 'Pathogen_Listeria','new_Pathogen_Listeria'],axis='columns')\n", "\n", "\n", "feces=sample[sample.SampleType=='Feces']\n", "soil=sample[sample.SampleType=='Soil']\n", "\n", "print ('SAMPLE DISTRIBUTION \\n')\n", "\n", "print('Feces', feces.shape)\n", "print('Soil', soil.shape,'\\n')\n", "\n", "sampletypes = [feces, soil]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES\", 1: \"SOIL\"}\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType', 'new_EC'],axis='columns'),sample.new_EC,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1]\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " \n", "# mylist2.append([f\"EC_{sample_name[indexing]}\", rf_auc_normal])\n", " \n", " \n", " print(pd.value_counts(sample['new_EC']))\n", "\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim3_plot.max(), decimals=None, symbol=''))\n", " \n", "\n", " plt.title(f\"EC in {sample_name[indexing]} Model\")\n", " prelim3_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','new_EC','SampleType'],axis='columns').columns)\n", " prelim3_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", "\n", " xmax=prelim3_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " \n", " plt.show()\n", " \n", " prelim3_plot.nlargest(10).to_csv(\"prelim3.csv\")\n", " top10 = pd.read_csv('prelim3.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", "# plt.plot(pdp[1][0], pdp[0][0],'.')\n", "# plt.ylabel('Partial dependence'), plt.xlabel(top10[feature])\n", "# plt.show()\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"EC_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (27) Moisture" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Median: 0.3699999999999999\n", "Total sample count: 1635 \n", "\n", "Distribution:\n", "1.0 820\n", "0.0 815\n", "Name: new_Moisture, dtype: int64\n", "SAMPLE DISTRIBUTION \n", "\n", "Feces (698, 877)\n", "Soil (695, 877) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "1.0 684\n", "0.0 14\n", "Name: new_Moisture, dtype: int64\n" ] }, { "data": { "image/png": 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/H0uvZZIWpOWBku5uifrNzMyaokMHAcDtwDfSEwhrDAGmRsSHEbFPfgFJXeuqMCJOjYjnWrKThfphZmZWbO0iCJA0StILkqZJmizprIaUi4j3yR4/PDgn+Xhgcqp3eXodKGm6pBuASkldJF0l6VlJd0v6m6QhKe8MSWseuSjpd5LmSnpQ0qYp7XuSZkuaL+m2miBE0uaS7kjp8yXtk9uPOrZ/dO42S1qQRg3Wk3RPqmuBpOMasl/MzMwaouhBQDrgHgPsDhwNFHzmcR0mkx34kbQlsCMwvUC+PYFfRsTOqZ0yYFfgVGDvWupeD5gbEQOAh4FfpfTbI+IrEbEb8DxwSkq/DHg4pQ8Anm3ktuQ7BHgjInaLiH7AvfkZJA2XVCGponrl0mY2Z2ZmpaToQQCwHzAlIj6IiGXAXY0sfzewn6T1gWOBWyOiukC+pyJiYU6bt0TE6oj4PwoHDQCrgZvS8vWpHEA/SY9IqgROBHZJ6V8H/gQQEdUR0dyjciUwSNJFkvYvVF9EjI2I8ogo79qjdzObMzOzUtIeggA1p3BEfEB2hnwUOZcCCljRAm1Gep0A/DAidgV+DXRvYn01PuGzn0V3gIh4CdiDLBi4UNK5zWzHzMxsjfYQBMwCBkvqLqkn0JQ7d0wGfgJsDjzRwDaPSXMDNgcG1pKvC9lEQ4ChqRxAL2CJpG5kIwE1HgR+ANkExDQ60RBVZJcPkDQA6JOWtwRWRsT1wCU1eczMzFpC0e8YGBGzJU0F5gOLgAqgscPo9wMTgXEREfVlBm4DDgIWAC8BT9bS5gpgF0lz0vqaiXmjUplFZGfpvVL6j4Cxkk4BqskCgscb2J+TJM0DZqc+QTZn4WJJq4GPU31mZmYtQg07ZrZyJ6SeEbE8zbKfCQyPiLlt1ObGwFPAvml+QIdVXl4eFRUVxe6GmZm1I5LmRETBSfdFHwlIxqYb9HQHJrZ2AJDcLWkDYG3gNx09ADAzM2usdhEERMTQ3PeSrgT2zcvWF3g5L+3SiBjfxDYHNqWcmZlZZ9EugoB8EXF6sftgZmbW2bWHXweYmZlZETgIMDMzK1EOAszMzEqUgwAzM7MS5SDAzMysRDkIMDMzK1EOAszMzEpUu7xPgDVN5eKllI28p9jdMGv3qsY05TllZp2PRwLMzMxKlIMAMzOzEtXuggBJa0l6R9KFRe7HMElXtEA9h0samZZHSzorLU+QNKS59ZuZmTVVuwsCgIOBF4FjJak1G5LUtTXrB4iIqRExprXbMTMza6wWDwIkjZL0gqRpkibXnPk2wgnApcCrwF459VZJ+rWkuZIqJe2U0veU9Jikp9PrF1N6V0kXS5ot6RlJ30/pAyVNl3QDUCmpu6Txqc6nJR2Y05dtJN0r6UVJv8rpy52S5kh6VtLwnPRDUv/mS3owpdU7opC2bZO0XC5pRlo+QNK89Pe0pF4Fyg6XVCGponrl0sbtaTMzK2kt+usASeXAMcDuqe65wJxGlF8XOAj4PrABWUDweE6WdyJigKT/Bs4CTgVeAL4WEZ9IGgT8b+rDKcDSiPiKpHWARyXdn+rZE+gXEQsl/RQgInZNgcX9knbMzQesBGZLuiciKoDvRsR7qb+zJd1GFlBdk/qyUNJGDd5xtTsLOD0iHpXUE1iVnyEixgJjAdbZom+0QJtmZlYiWnokYD9gSkR8EBHLgLsaWf4wYHpErARuA47KG7K/Pb3OAcrScm/gFkkLgD8Au6T0g4GTJM0DngQ2BvqmdU9FxMKcPl8HEBEvAIuAmiBgWkS8GxEfpLb3S+lnSpoPPAFsk+rdC5hZU29EvNfIbS/kUeD3ks4ENoiIT1qgTjMzM6Dlg4DmXsM/ARgkqYrsQL8xkDs8/2F6rebTUYzfkAUO/YDBQPecvpwREf3TX5+IqBkJWNHAPuefWYekgcAgYO+I2A14OrWpAvkb6hM+/Sxq+k+aS3AqsC7wRM0lEDMzs5bQ0kHALGBwus7eE2jwHTkkrU92pr1tRJRFRBlwOllgUJfewOK0PCwn/T7gB5K6pfp3lLRegfIzgRNr8gDbkk1MBPiGpI3SsP+RZGfmvYF/RcTKdFCumbfwOHCApD6prsZcDqgC9kjLx9QkSto+Iioj4iKgAnAQYGZmLaZFg4CImA1MBeaTDZ9XAA2drXY08FBEfJiTNgU4PF3Tr81vgQslPQrkXjq4FngOmJsuFfyZwnMgrgK6SqoEbgKG5fRhFtmlgnnAbWk+wL3AWpKeIRuFeCJt+9vAcOD2dKngpgZuN8CvgUslPUI2ylFjhKQFqb4PgL83ok4zM7M6KaJl55JJ6hkRyyX1IDvLHh4Rc1u0ESuovLw8Kioqit0NMzNrRyTNiYjyQuta49kBYyXtTHZte6IDADMzs/apxYOAiBia+17SlcC+edn6Ai/npV0aEeNbuj9mZmZWWKs/RTAiTm/tNszMzKzx2uNtg83MzKwNOAgwMzMrUQ4CzMzMSpSDADMzsxLlIMDMzKxEOQgwMzMrUQ4CzMzMSlSr3yfA2k7l4qWUjbyn2N0waxVVYxr8PDIzayCPBJiZmZUoBwFmZmYlqtWDAEkTJC2UNE/SfEkHtXabDSFptKSz0vJ5kgal5WvTA5CQtLyF2nosvZalxxojaaCku1uifjMzs6ZoqzkBZ0fErZIOBMaSPUCo3YiIc3OWT22F+vdp6TrNzMyaq0EjAZJGSXpB0jRJk2vOoJvgcWCrVOcwSVfktHG3pIFpebmkiyTNkfSApD0lzZD0iqTDc8rfKemuNNLwQ0k/kfS0pCckbZTybS/p3lTXI5J2KrB9EyQNScszJJXnrPudpLmSHpS0aUr7nqTZaWTjNkk9Uvrmku5I6fMl7VOzPfXs3zWjEun9gjRqsJ6ke1JdCyQdV6DscEkVkiqqVy5t4MdgZmbWgCAgHRCPAXYHjgbK6y5Rp0OAOxuQbz1gRkTsASwDzge+ARwFnJeTrx8wFNgTuABYGRG7kwUbJ6U8Y4EzUl1nAVc1or/rAXMjYgDwMPCrlH57RHwlInYDngdOSemXAQ+n9AHAs41oq5BDgDciYreI6Afcm58hIsZGRHlElHft0buZzZmZWSlpyOWA/YApEfEBgKS7mtDOxZJ+C2wG7NWA/B/x6QGvEvgwIj6WVAmU5eSbHhHLgGWSlgJ35ZT5sqSewD7ALZJqyqzTiH6vBm5Ky9cDt6flfpLOBzYAegL3pfSvk4KPiKgGmntqXglcIuki4O6IeKSZ9ZmZma3RkMsBqj9Lvc4GdgDOASamtE/y2u+es/xxRERaXg18CBARq/ls4PJhzvLqnPc1+boA/46I/jl/X2rGdtT0aQLww4jYFfh1Xt+bouC+iIiXgD3IgoELJZ1boKyZmVmTNCQImAUMltQ9nVk36Y4d6QB+KdBF0n8AVUB/SV0kbUM2pN+iIuJ9YKGkbwEos1sjqugCDEnLQ8n2BUAvYImkbsCJOfkfBH6Q2uoqaf0GtlNFdvkASQOAPml5S7JLHNcDl9TkMTMzawn1Xg6IiNmSpgLzgUVABU0c5o6ISMPoPwMGAQvJznIXAHObUmcDnAj8SdI5QDfgRrJtaYgVwC6S5pBtc83EvFHAk2T7o5IsKAD4ETBW0ilANVlA8HgD2rkNOEnSPGA28FJK35XsUspq4ONUn5mZWYvQp6PudWSSekbE8jQLfiYwPCJa66BtTVReXh4VFRXF7oaZmbUjkuZERMFJ/Q29T8DYdAOd7sBEBwBmZmYdX4OCgIgYmvte0pXAvnnZ+gIv56VdGhHjm949MzMzay1NumNgRJze0h0xMzOztuUHCJmZmZUoBwFmZmYlykGAmZlZiXIQYGZmVqIcBJiZmZUoBwFmZmYlykGAmZlZiWrSfQKsfapcvJSykfcUuxtmzVY1pknPKTOzRvJIgJmZWYlyEGBmZlaiSjYIkDRB0kJJ8yTNlbR3sfsEIOk8SYOK3Q8zM+v8Sn1OwNkRcaukg4E/A1/OXSmpa0RUt1VnUnvntlV7ZmZW2jr0SICkUZJekDRN0mRJZzWxqpnADqnOKknnSpoFfEvS9yTNljRf0m2SeqR8EyQNyenL8vS6haSZaYRhgaT9U/rBkh5Pow63SOpZS3tr6k3ps1M9YyWpwD4YLqlCUkX1yqVN3HwzMytFHTYIkFQOHAPsDhwNlDejusFAZc77VRGxX0TcCNweEV+JiN2A54FT6qlrKHBfRPQHdgPmSdoEOAcYFBEDgArgJ7W0l+uK1HY/YF3gsPzGImJsRJRHRHnXHr0bvsVmZlbyOvLlgP2AKRHxAYCku5pQx8WSzgHe5rMH95tylvtJOh/YAOgJ3FdPnbOBv0jqBtwZEfMkHQDsDDyaTubXBh6vpb1cB0r6GdAD2Ah4FmjKdpqZmX1ORw4CPjc03gRnR8StBdJX5CxPAI6MiPmShgEDU/onpJGUNEy/NkBEzJT0NeBQ4DpJFwP/AqZFxAm19GNFfoKk7sBVQHlEvCZpNNC9UVtnZmZWhw57OQCYBQyW1D1dX2+tu4v0ApakM/sTc9KrgD3S8hFANwBJ2wFvRcQ1wDhgAPAEsK+kmnkHPSTtWE+7NQf8d9L2Dakrs5mZWWN12JGAiJgtaSowH1hEdp29NWbGjQKeTG1UkgUFANcAUyQ9BTzIp2fzA4GzJX0MLAdOioi30yjCZEnrpHznAC/V1mhE/FvSNanNKrLLDHXadaveVPhOa2Zm1kCKiGL3ockk9YyI5WnG/kxgeETMLXa/iqW8vDwqKiqK3Q0zM2tHJM2JiIKT5zvsSEAyVtLOZEPnE0s5ADAzM2usDh0ERMTQ3PeSrgT2zcvWF3g5L+3SiBjfmn0zMzNr7zp0EJAvIk4vdh/MzMw6io786wAzMzNrBgcBZmZmJcpBgJmZWYlyEGBmZlaiHASYmZmVKAcBZmZmJapT/USw1FUuXkrZyHuK3Q0zqnz7arMOwSMBZmZmJcpBgJmZWYnqkEGApLUkvSPpwgbkHShpn5z3p0k6qRX6tLyl6zQzM2tNHTIIAA4GXgSOlaR68g4E1gQBEXF1RPy1FftmZmbWIRQlCJA0StILkqZJmizprEZWcQJwKfAqsFdOvYdImitpvqQHJZUBpwE/ljRP0v6SRte0J2mGpIskPSXpJUn7p/TuksZLqpT0tKQDU/owSVMk3SvpRUm/KrBtPVPbc1P5I1J6maTnJV0j6VlJ90taN63bPtU5R9IjknZK6d+StCBtz8xG7iMzM7M6tfmvAySVA8cAu6f25wJzGlF+XeAg4PvABmQBweOSNgWuAb4WEQslbRQR70m6GlgeEZek8gflVblWROwp6T+BXwGDgNMBImLXdEC+X9KOKf+eQD9gJTBb0j0RUZFT3yrgqIh4X9ImwBOSpqZ1fYETIuJ7km5O++F6YCxwWkS8LOmrwFXA14Fzgf+IiMWSNqhlfwwHhgN0XX/Thu5GMzOzoowE7AdMiYgPImIZcFcjyx8GTI+IlcBtwFGSupKNCMyMiIUAEfFeA+u7Pb3OAcpy+nhdqucFYBFQEwRMi4h3I+KDVHa/vPoE/K+kZ4AHgK2AzdO6hRExL7c9ST3JLlfcImke8Gdgi5TnUWCCpO8BXQt1PiLGRkR5RJR37dG7gZtsZmZWnPsE1HcNvz4nAPtKqkrvNwYOTPVGE+r7ML1W8+n+qKuP+W3kvz8R2BTYIyI+Tv3sntdWTXvrkgVi/46I/p9rKOK0NDJwKDBPUv+IeLfuzTEzM2uYYowEzAIGp+vuPckOcA0iaX2yM+9tI6IsIsrIhu5PAB4HDpDUJ+XdKBVbBvRqZB9nkh3MSZcBtiWbiAjwDUkbpcsSR5KdrefqDbyVAoADge3qaigi3gcWSvpWak+SdkvL20fEkxFxLvAOsE0jt8PMzKxWbR4ERMRsYCown2w4vQJY2sDiRwMPRUTuGfUU4HDgfbJr47dLmg/clNbfRXbJYF7NxL8GuAroKqky1TMsp81ZZJcK5gG35c0HAJgElEuqIAskXmhAeycCp6R+PwsckdIvTpMLF5AFJvMb2H8zM7N6KaIpI+jNbFTqGRHLJfUgO7gNj4i5bd6RRpI0DCiPiB8Wuy+FlJeXR0VFfkxiZmalTNKciCgvtK5Yzw4YK2lnsmvlEztCAGBmZtbZFCUIiIihue8lXQnsm5etL/ByXtqlETG+NftWl4iYAEwoVvtmZmYtqV08RTAiTi92H8zMzEpNR71tsJmZmTWTgwAzM7MS5SDAzMysRDkIMDMzK1EOAszMzEqUgwAzM7MS5SDAzMysRLWL+wRYy6hcvJSykfcUuxtW4qrGNPiZYGZWZB4JMDMzK1EOAszMzEpUUYMASXtJejI95vd5SaNT+kBJ+zSj3oGS7m5kmSpJmzS1TTMzs46m2HMCJgLHRsR8SV2BL6b0gcBy4LFidczMzKyza/ZIgKRRkl6QNE3SZElnNaL4ZsASgIiojojnJJUBpwE/TiME+0sanEYMnpb0gKTNU9ujJV0n6SFJL0v6Xk7dPSXdmvo2SZmDJN2R0/dvSLq9wDb9RNKC9DciJ/0kSc9Imi/pupS2naQHU/qDkrZN6ZtLuiPlnV8zslFLHRMkDclpZ3l63ULSzLQfFkjav0Bfh0uqkFRRvXJpI3a9mZmVumaNBEgqB44Bdk91zQXmNKKKPwAvSpoB3AtMjIgqSVcDyyPiktTOhsBeERGSTgV+Bvw01fFlYC9gPeBpSTXT43cHdgHeAB4le1TxQ8CVkjaNiLeB7wCfeTSxpD1S+lcBAU9Kehj4CPglsG9EvCNpo1TkCuCvETFR0neBy4Aj0+vDEXFUGuXoKWmXWuqozVDgvoi4INXRIz9DRIwFxgKss0XfqKc+MzOzNZo7ErAfMCUiPoiIZcBdjSkcEecB5cD9ZAe8e2vJujVwn6RK4Gyyg3uNmvbfAaYDe6b0pyLi9YhYDcwDyiIigOuA/5K0AbA38PcC23RHRKyIiOXA7cD+wNeBW1M7RMR7Kf/ewA1p+bpUnpT/TylvdUQsraOO2swGvpPmSuya9rGZmVmLaG4QoOZ2ICL+GRF/Ag4CdpO0cYFslwNXRMSuwPeB7rlV5FeZXj/MSavm01GP8cB/AScAt0TEJ3nla9smFWirkLry1FbHJ6TPQpKAtQEiYibwNWAxcJ2kkxrQvpmZWYM0NwiYBQyW1F1ST6BRdwmRdGg66AH0JTtY/xtYBvTKydqb7EAIcHJeNUek9jcmm1A4u642I+INsksE5wATCmSZCRwpqYek9YCjgEeAB4Fja4KUnKH8x4Dj0/KJZPuElP8HKW9XSevXUUcVsEfN9gDd0vrtgLci4hpgHDCgrm0zMzNrjGbNCYiI2ZKmAvOBRUAF0JjZad8G/iBpJdnZ8IkRUS3pLuBWSUcAZwCjgVskLQaeAPrk1PEUcA+wLfCbiHhD0o71tDsJ2DQiniuwTXMlTUj1AlwbEU8DSLoAeFhSNfA0MAw4E/iLpLOBmnkGAD8Cxko6hSy4+UFEPF5LHdcAUyQ9RRYorEh1DATOlvQx2a8lPBJgZmYtRtll8mZUIPWMiOWSepCdRQ+PiLkt0rv62x5NzgTCRpS7Ang6Isa1SseKpLy8PCoqKordDTMza0ckzYmI8kLrWuI+AWMl7Ux2nX5iWwUATSVpDtmZ9k/ry2tmZtaZNTsIiIihue8lXUn2c7xcfYGX89IujYjxNENEjG5CmT3qz2VmZtb5tfgdAyPi9Jau08zMzFqeHyBkZmZWohwEmJmZlSgHAWZmZiXKQYCZmVmJchBgZmZWohwEmJmZlSgHAWZmZiWqxe8TYMVTuXgpZSPvKXY3rJOqGtOo54OZWQfgkQAzM7MS5SDAzMysRHWqIEDSWpLekXRhC9d7uKSRaXmCpCEtUOcwSVs2v3dmZmZN06mCAOBg4EXgWEkqlEFS18ZWGhFTI2JMczuXZxjQqCBAkudwmJlZi2lXQYCkUZJekDRN0mRJZzWyihOAS4FXgb1y6q2SdK6kWcC3JB0s6XFJcyXdIqlnTr5fp/RKSTul9GGSrshpZ5CkRyS9JOmwlKcspc1Nf/vktP+zVN98SWPSSEI5MEnSPEnrStpD0sOS5ki6T9IWqewMSf8r6WHgRwX22XBJFZIqqlcubeTuMjOzUtZuziwllQPHALuT9WsuMKcR5dcFDgK+D2xAFhA8npNlVUTsJ2kT4HZgUESskPRz4CfAeSnfOxExQNJ/A2cBpxZorgw4ANgemC5pB+At4BsRsUpSX2AyUC7pm8CRwFcjYqWkjSLiPUk/BM6KiApJ3YDLgSMi4m1JxwEXAN9N7W0QEQcU2u6IGAuMBVhni77R0P1lZmbWboIAYD9gSkR8ACDprkaWPwyYng60twGjJP04IqrT+pvS617AzsCj6YrB2nw2WLg9vc4Bjq6lrZsjYjXwsqRXgJ2AhcAVkvoD1cCOKe8gYHxErASIiPcK1PdFoB8wLfWpK7AkZ/1NBcqYmZk1S3sKAgpew2+EE4B9JVWl9xsDBwIPpPcrctqZFhEn1FLPh+m1mtr3T/4ZdwA/Bt4EdiO7zLIqp736ztAFPBsRe9eyfkUt6WZmZk3WnuYEzAIGS+qertE3+M4kktYnG0nYNiLKIqIMOJ0sMMj3BFmwsEMq20PSjgXy1eVbkrpI2h74AtlkxN7AkjRC8G2ys3mA+4HvSuqR2tsopS8DeqXlF4FNJe2d8nSTtEsj+2RmZtYo7SYIiIjZwFRgPtmQfAXQ0JluRwMPRcSHOWlTgMMlrZPXzttkM/MnS3qGLCjYqZHdfRF4GPg7cFpErAKuAk6W9ATZpYAVqb1703ZVSJpHNs8AYAJwdUrrCgwBLpI0H5gHrJlYaGZm1hoU0X7mkknqGRHL01nzTGB4RMwtdr86ivLy8qioqCh2N8zMrB2RNCciyguta09zAgDGStoZ6A5MdABgZmbWetpVEBARQ3PfS7oS2DcvW1/g5by0SyNifGv2zczMrLNpV0FAvog4vdh9MDMz66zadRBgZmZt5+OPP+b1119n1apV9We2dqd79+5svfXWdOvWrcFlHASYmRkAr7/+Or169aKsrIxaHr9i7VRE8O677/L666/Tp0+fBpdrNz8RNDOz4lq1ahUbb7yxA4AOSBIbb7xxo0dxHASYmdkaDgA6rqZ8dg4CzMzMSpTnBJiZWUFlI+9p0fqqxjTsbvB33HEHRx99NM8//zw77ZTd0HXGjBlccskl3H333WvyDRs2jMMOO4whQ4YwcOBAlixZQvfu3Vl77bW55ppr6N+/PwBLly7ljDPO4NFHHwVg33335fLLL6d3794AvPTSS4wYMYKXXnqJbt26seuuu3L55Zez+eabN3lb33vvPY477jiqqqooKyvj5ptvZsMNN/xMnhdffJHjjjtuzftXXnmF8847jxEjRjBq1CimTJlCly5d2GyzzZgwYQJbbrkllZWV/O53v2PChAlN7lsuBwGdSOXipS3+j9ZKV0P/wzZraZMnT2a//fbjxhtvZPTo0Q0uN2nSJMrLyxk/fjxnn30206ZNA+CUU06hX79+/PWvfwXgV7/6Faeeeiq33HILq1at4tBDD+X3v/89gwcPBmD69Om8/fbbzQoCxowZw0EHHcTIkSMZM2YMY8aM4aKLLvpMni9+8YvMmzcPgOrqarbaaiuOOuooAM4++2x+85vfAHDZZZdx3nnncfXVV7Prrrvy+uuv8+qrr7Lttts2uX81fDnAzMzajeXLl/Poo48ybtw4brzxxibVsffee7N48WIA/vGPfzBnzhxGjRq1Zv25555LRUUF//znP7nhhhvYe++91wQAAAceeCD9+vVr1nZMmTKFk08+GYCTTz6ZO++8s878Dz74INtvvz3bbbcdAOuvv/6adStWrPjM9f7Bgwc3ed/kcxBgZmbtxp133skhhxzCjjvuyEYbbcTcuY2/e/y9997LkUceCcBzzz1H//796dq165r1Xbt2pX///jz77LMsWLCAPfbYo946ly1bRv/+/Qv+Pffcc5/L/+abb7LFFlsAsMUWW/DWW2/VWf+NN97ICSd89sG3v/zlL9lmm22YNGkS55133pr08vJyHnnkkXr73BC+HGBmZu3G5MmTGTFiBADHH388kydPZsCAAbXOfM9NP/HEE1mxYgXV1dVrgoeIKFi2tvTa9OrVa83QfUv76KOPmDp1KhdeeOFn0i+44AIuuOACLrzwQq644gp+/etfA7DZZpvxxhtvtEjb7X4kQNIESQslzUt/Z6b0KkmbNKG+gZLqfUxvandIU/qcV895kgal5RmSytNyk/pvZtZZvfvuuzz00EOceuqplJWVcfHFF3PTTTcREWy88cb861//+kz+9957j002+fS/0UmTJrFw4UKGDh3K6adnd53fZZddePrpp1m9evWafKtXr2b+/Pl86UtfYpdddmHOnDn19q2xIwGbb745S5YsAWDJkiVsttlmtdb997//nQEDBtQ6B2Ho0KHcdttta96vWrWKddddt94+N0S7DwKSsyOif/q7rJl1DQTqDQJaSkScGxEPtFV7ZmYd1a233spJJ53EokWLqKqq4rXXXqNPnz7MmjWLvn378sYbb/D8888DsGjRIubPn7/mFwA1unXrxvnnn88TTzzB888/zw477MDuu+/O+eefvybP+eefz4ABA9hhhx0YOnQojz32GPfc8+mk6nvvvZfKysrP1FszElDob+edd/7cthx++OFMnDgRgIkTJ3LEEUfUut2TJ0/+3KWAl1/+9Dl5U6dOXfMrCch+zdDcOQs12uRygKRRwInAa8A7wJyIuKQF6/8v4ExgbeBJ4L8jolrSIcD/Al1Tu6cApwHVqcwZwKvAX4BNgbeB70TEq6nqQZJ+BGwO/CQi7pZUBlwHrJfy/DAiHkv9+BnwbWA18PeIGClpAnB3RNxaS9/L0vp+6f1ZQM+IGJ1GPU4DPgGei4jjC5QfDgwH6Lr+po3ed2ZmtWnrX4hMnjyZkSNHfibtmGOO4YYbbmD//ffn+uuv5zvf+Q6rVq2iW7duXHvttWt+5pdr3XXX5ac//SmXXHIJ48aNY9y4cZxxxhnssMMORAR7770348aNW5P37rvvZsSIEYwYMYJu3brx5S9/mUsvvbRZ2zJy5EiOPfZYxo0bx7bbbsstt9wCwBtvvMGpp57K3/72NwBWrlzJtGnT+POf//y58i+++CJdunRhu+224+qrr16zbvr06Rx6aMt8NoqIFqmo1gay4e9rgb3Jgo65wJ8bGgSkg+gBwNKU9O2IqJRUBZSTHbx/CxwdER9Lugp4Avh7autrEbFQ0kYR8Z6k0cDymvYl3QXcGhETJX0XODwijkzt/j/gP4HtgenADmSjJ6sjYpWkvsDkiCiX9E1gFDAoIlbmtDeBFARImgGcFREVOf3vSe1BwBtAn4j4UNIGEfHvuvbVOlv0jS1O/mNDdqtZvfwTwdLz/PPP86UvfanY3bA6fPjhhxxwwAHMmjWLtdb6/Hl8oc9Q0pyIKC9UX1uMBOwHTImID1Jn7mpCHWfXdiYNHATsAcxOkzzWBd4C9gJmRsRCgIh4r5byewNHp+XryAKKGjdHxGrgZUmvADsBC4ErJPUHqoEdU95BwPiIWFlPe43xDDBJ0p3AnS1Qn5mZdWCvvvoqY8aMKRgANEVbBAGtfSNqARMj4hefSZQOB5oyzBG1LNe8/zHwJrAb2ahAzdMa1MT2PuGzczO65ywfCnwNOBwYJWmXiPikCW2YmVkn0LdvX/r27dti9bXFxMBZwGBJ3SX1JDuwtaQHgSGSNgOQtJGk7YDHgQMk9alJT/mXAb1yyj8G1FxrPzH1t8a3JHWRtD3wBeBFoDewJI0QfJtsvgHA/cB3JfXIa68+bwKbSdpY0jrAYal8F2CbiJgO/AzYgOzSgZlZq2ntS8TWepry2bX6SEBEzJY0FZgPLAIq+PT6fkvU/5ykc4D704HzY+D0iHgiTZq7PaW/BXwDuAu4VdIRZBMDzwT+Iuls0sTAnOpfBB4mmxh4WpoHcBVwm6Rvkc0TWJH6cW+6RFAh6SPgb8D/NKD/H0s6j2xC40LghbSqK3C9pN5kowx/qG9OwK5b9abC13HNrIm6d+/Ou+++68cJd0ARwbvvvkv37t3rz5yj1ScGAkjqGRHL01nyTGB4RDT+NlBWp/Ly8qioqCh2N8ysg/r44495/fXXG/1MemsfunfvztZbb023bt0+k17siYEAYyXtTHa9e6IDADOz9qdbt2706dOn2N2wNtQmQUBEDM19L+lKYN+8bH2Bl/PSLo2I8a3ZNzMzs1JVlGcHRMTpxWjXzMzMPtVRbhtsZmZmLaxNJgZa25C0jOwXDdZ2NiG7JbW1He/ztud93vZacp9vFxEF7yvvRwl3Li/WNgPUWoekCu/ztuV93va8z9teW+1zXw4wMzMrUQ4CzMzMSpSDgM5lbLE7UIK8z9ue93nb8z5ve22yzz0x0MzMrER5JMDMzKxEOQgwMzMrUQ4COglJh0h6UdI/JI0sdn86I0nbSJou6XlJz0r6UUofLWmxpHnp7z+L3dfORFKVpMq0bytS2kaSpkl6Ob1uWOx+dhaSvpjzXZ4n6X1JI/w9b1mS/iLpLUkLctJq/V5L+kX6//1FSf/RYv3wnICOT1JX4CWyRyW/DswGToiI54rasU5G0hbAFhExV1IvYA5wJHAssDwiLilm/zorSVVAeUS8k5P2W+C9iBiTgt4NI+LnxepjZ5X+b1kMfJXsMev+nrcQSV8DlgN/jYh+Ka3g9zo9gG8ysCewJfAAsGNEVDe3Hx4J6Bz2BP4REa9ExEfAjcARRe5TpxMRS2qegBkRy4Dnga2K26uSdQQwMS1PJAvGrOUdBPwzIhYVuyOdTUTMBN7LS67te30EcGNEfBgRC4F/kP2/32wOAjqHrYDXct6/jg9OrUpSGbA78GRK+qGkZ9IQn4emW1YA90uaI2l4Sts8IpZAFpwBmxWtd53b8WRnoDX8PW9dtX2vW+3/eAcBnYMKpPk6TyuR1BO4DRgREe8DfwK2B/oDS4DfFa93ndK+ETEA+CZwehpGtVYmaW3gcOCWlOTvefG02v/xDgI6h9eBbXLebw28UaS+dGqSupEFAJMi4naAiHgzIqojYjVwDS00TGeZiHgjvb4F3EG2f99MczRq5mq8VbwedlrfBOZGxJvg73kbqe173Wr/xzsI6BxmA30l9UnR+/HA1CL3qdORJGAc8HxE/D4nfYucbEcBC/LLWtNIWi9NwkTSesDBZPt3KnByynYyMKU4PezUTiDnUoC/522itu/1VOB4SetI6gP0BZ5qiQb964BOIv1c549AV+AvEXFBcXvU+UjaD3gEqARWp+T/IfvPsj/Z8FwV8P2a63rWPJK+QHb2D9lTT2+IiAskbQzcDGwLvAp8KyLyJ1lZE0nqQXYN+gsRsTSlXYe/5y1G0mRgINkjg98EfgXcSS3fa0m/BL4LfEJ2KfLvLdIPBwFmZmalyZcDzMzMSpSDADMzsxLlIMDMzKxEOQgwMzMrUQ4CzMzMSpSDALNGklSdnqK2QNJdkjaoJ/9oSWfVk+fI9JCQmvfnSRrUAn2dIGlIc+tpZJsj0k/M2g1JO6XP7GlJ2+etq5L0SF7avJqnu0kql3RZC/ShLPeJcXnrrs39/FubpM0l3SDplXQ75sclHdVW7Vv74SDArPE+iIj+6clf7wGnt0CdRwJrDgIRcW5EPNAC9bap9NS5EUC7CgLI9u+UiNg9Iv5ZYH0vSdsASPpS7oqIqIiIMxvaUNoHjRIRp7bVUz/TTa/uBGZGxBciYg+yG4xt3crtrtWa9VvTOAgwa57HSQ/ykLS9pHvTmdUjknbKzyzpe5JmS5ov6TZJPSTtQ3aP9ovTGej2NWfwkr4p6eac8gMl3ZWWD05ncHMl3ZKeaVCrdMb7v6lMhaQBku6T9E9Jp+XUP1PSHZKek3S1pC5p3QmSKtMIyEU59S5PIxdPAr8ke9TpdEnT0/o/pfaelfTrvP78OvW/smZ/SeopaXxKe0bSMQ3dXkn9JT2Ryt0hacN0I60RwKk1fSrgZuC4tJx/p7yBku6up2+5+2BvST9J+2mBpBE57awlaWIqe2vNiImkGZLKG7CfL0rfrwck7ZnKvSLp8JSnq6SL03fsGUnfL7CtXwc+ioiraxIiYlFEXF5XHWk/zEj9fkHSpBRQIGkPSQ+nvt2nT299OyN95x4GfiRpsKQnlY3IPCBp81o+D2srEeE///mvEX9kz1SH7O6MtwCHpPcPAn3T8leBh9LyaOCstLxxTj3nA2ek5QnAkJx1E4AhZHfJexVYL6X/CfgvsruMzcxJ/zlwboG+rqmX7C5vP0jLfwCeAXoBmwJvpfSBwCrgC2n7pqV+bJn6sWnq00PAkalMAMfmtFkFbJLzfqOc/TUD+HJOvprt/2/g2rR8EfDHnPIbNmJ7nwEOSMvn1dST+xkUKFMF7Ag8lt4/TTYqsyBnn9xdW9/y9wGwB9ldJdcDegLPkj1xsizl2zfl+wuffi9mAOUN2M/fTMt3APcD3YDdgHkpfThwTlpeB6gA+uRt75nAH+r4fhesI+2HpWQjBl3IAuD9Uh8eAzZNZY4ju2tpzXZdlfdZ1tyk7lTgd8X+91zqfx6eMWu8dSXNI/tPfQ4wLZ2V7gPckk6OIPsPNF8/SecDG5AdIO6rq6GI+ETSvcBgSbcChwI/Aw4gO1A9mtpbm+w/5frUPFOiEugZEcuAZZJW6dO5DU9FxCuw5tam+wEfAzMi4u2UPgn4GtmwcjXZQ5Vqc6yyRwCvBWyR+v1MWnd7ep0DHJ2WB5ENT9fsg39JOqy+7ZXUG9ggIh5OSRP59Al49XkP+Jek44HngZW15Ptc39Ji7j7YD7gjIlakft0O7E+271+LiEdTvuvJDsiX5NT/FWrfzx8B96Z8lcCHEfGxpEqy7yJkz1b4sj6dB9Kb7D7zC2vbcElXpj5/FBFfqaOOj8i+G6+ncvNSu/8G+pH9O4As2Mu9nfBNOctbAzelkYK16+qXtQ0HAWaN90FE9E8HnbvJ5gRMAP4dEf3rKTuB7MxuvqRhZGdX9bkptfEeMDsilqVh2GkRcUIj+/5hel2ds1zzvub/g/x7iQeFH2VaY1VEVBdaoexhJ2cBX0kH8wlA9wL9qc5pXwX60NTtbYybgCuBYXXkKdQ3+Ow+qGtfFdq3+fXX5uNIp9DkfH4RsVqfXm8X2ehKXcHls8AxazoQcbqkTcjO+GutQ9JAPvudqfnMBDwbEXvX0t6KnOXLgd9HxNRU3+g6+mltwHMCzJoosgernEl2kPsAWCjpW5BNvpK0W4FivYAlyh5JfGJO+rK0rpAZwADge3x6VvUEsK+kHVJ7PSTt2LwtWmNPZU+k7EI2tDsLeBI4QNImyia+nQA8XEv53G1Zn+wgsDRd//1mA9q/H/hhzRtJG9KA7U2fx78k7Z+Svl1HHwu5A/gtdY/OFOpbvpnAkamP65E9ca/m1wfbSqo5WJ5Atm9zNWY/F3If8IP0/ULSjqkPuR4Cukv6QU5a7kTOhtSR60Vg05rtktRN0i615O0NLE7LJ9eSx9qQgwCzZoiIp4H5ZEPEJwKnSJpPdrZ1RIEio8j+o58GvJCTfiNwtgr8hC2dYd5NdgC9O6W9TXbGOlnSM2QHyc9NRGyix4ExZI+KXUg2tL0E+AUwnWx750ZEbY/vHQv8XdL0iJhPdo39WbJr4I/WUibX+cCGaWLcfODARmzvyWQTLJ8he+LdeQ1oD4CIWBYRF0XER43pW4F65pKN+DxF9llfm74nkF1qODn1byOyOR65ZRuznwu5FngOmKvs54h/Jm/EN40mHEkWbCyU9BTZpZOfN7SOvPo+Ips3clHaJ/PILo0VMprsktkjwDuN2C5rJX6KoJmtkYZoz4qIw4rcFTNrAx4JMDMzK1EeCTAzMytRHgkwMzMrUQ4CzMzMSpSDADMzsxLlIMDMzKxEOQgwMzMrUf8fBia9FovtDzEAAAAASUVORK5CYII=\n", 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.9386523574344414, pvalue=4.235914868150194e-27)\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.5005505699974346, pvalue=1.1375814129231646e-07)\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=nan, pvalue=nan)\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.7269589305251687, pvalue=1.1012973721102649e-17)\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.7643115491157486, pvalue=2.2165354163384383e-20)\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.9502853101129947, pvalue=1.200078127424562e-34)\n" ] }, { "data": { "image/png": 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zCBiTlgMYkfY/kGxSMU8iZmY2iPr7ov4bpEEka4mIi3dR92jgyar1jcA7e+2zDDgN+Hmao2UcMCYiOiVdQ/aR5SvAwohYWOMc5wHfS8u3kyWtTWRJaI6/+jczG1z9ddR37GHdqrGtd5K6CrhW0lLgEWAJ0C2plSxBTAB+C3xf0lkRccurlUtXAN3AvLRpKrANOBxoBX4m6V8iYu0OQUkXABcAvPnNb96T9pmZWS99JpWI+M4e1r0RGFu1Pob0CKvqHJuBcyGbYRJYl34nAusioiuV3QEcS/atDJLOIfuy/4SI6ElUfw78c0RsBX4j6RdABdghqUTEXGAuQKVS6fNOzMzMdl+e6YTbJF0j6UeSftzzy1H3Q8CRkiakSb3OAO7sVffIqgm/ZgEPpESzAZgmaXhKNicAj6VjpgOXAKdExMtV1W0Ajk/TH48ApgGrcsRpZmYFydNRP4/sP+gTgM8DT5AljH6lzvQLycYOewy4LSJWSpotaXba7WhgpaRVZG+J/WU69kGyPpKHyR6LNZHuLoDrgIOAeyUtlXRD2n498DpgRYrv2xGxPEf7zMysIHrt6VEfO0idETFF0vL0Gi+SfhoRdT/+V6VSiY6OPe06MjNrLCkvVGqV5Rn7a2v6c5OkD5P1i4zpZ38zM2tQeZLKlyS9Hvhr4BvAwcCcUqMyM7O6lGfsr7vS4gvA+8oNx8zM6ll/Hz9+JiK+2tdHkDk+fjQzswbT353KY+lP92SbmVku/X38+P/S4ssR8f3qMkkfLTUqMzOrS3m+U6k1hLyHlTczs53016dyEvAhYLSkr1cVHUw25paZmdkO+utTeZqsP+UUoLNq++/wK8VmZlZDf30qyyStAD5YwOCSZmbWAPrtU4mIbcCoqkEfzczM+pTni/r1wC8k3Qm81LMxIr5WWlRmZlaX8iSVp9OviWx0YDMzs5ryDNPy+cEIxMzM6t8uk4qkNuAzwETggJ7tEXF8iXGZmVkdyjtJ1yp2c5IuMzNrPHmSyqiIuBHYGhE/jYjzyKbqNTMz24En6TIzs8J4ki4zMytMf2N/HQDMBv4DMBq4MSI8SZeZmfWpvz6V7wAV4BHgJODvBiUiMzOrW/09/mqPiLcBSLoRWDw4IZmZWb3q706lp4OeiPBQ92Zmtkv93am8XdLmtCzgwLQuICLi4NKjMzOzutLf0PfNgxmImZnVvzwfPw6YpOmSVktaI+nSGuWtkhZIWi5psaRJVWVzJK2UtELSreltNCRdLWlVOmaBpJFp+5mSllb9tks6psz2mZnZjkpLKpKagevJ3hxrB2ZKau+12+XA0oiYDJwNXJuOHQ1cDFQiYhLQDJyRjrkXmJSOeRy4DCAi5kXEMRFxDPBx4ImIWFpW+8zMbGdl3qlMBdZExNqI2ALMB2b02qcduA8gIlYB4yUdlspayPpxWoDhZF/yExELq14cWETtr/tnArcW2RgzM9u1MpPKaODJqvWNaVu1ZcBpAJKmAuOAMRHxFHANsAHYBLwQEQtrnOM84O4a2z9GH0lF0gWSOiR1dHV17UZzzMxsV8pMKqqxLXqtXwW0SloKXAQsAboltZLd1UwADgdGSDprh8qlK4BuslGUq7e/E3g5IlbUCioi5kZEJSIqbW1tu98qMzPrU56xvwZqIzC2an0M6RFWj4jYDJwLIEnAuvQ7EVgXEV2p7A7gWOCWtH4OcDJwQkT0TlRn4EdfZmZ7RZl3Kg8BR0qaIGkY2X/s76zeQdLIVAYwC3ggJZoNwDRJw1OyOQF4LB0zHbgEOCUiXu5VXxPwUbL+GzMzG2Sl3alERLekC4F7yN7euikiVkqancpvAI4Gbpa0DXgUOD+VPSjpduBhskdcS4C5qerrgP2Be7N8w6KImJ3K/gTYGBFry2qXmZn1TTs/PWoclUolOjo69nYYZmZ1RVJnRFRqlZX68aOZmTUWJxUzMyuMk4qZmRXGScXMzArjpGJmZoVxUjEzs8I4qZiZWWGcVMzMrDBOKmZmVhgnFTMzK4yTipmZFabMoe9tiOlc/zyL1j7LtCNGARSyPGVcayn1Nkp8RcY6ZVxrn9feLC8nFculc/3znPmtRWzp3k5Lk0Cie9ueLQ9raeLKkyfyhbtWFlpvo8RXZKzDWpqYN2uaE4vtMScVy2XR2mfZ0r2d7QFbtwUQBHu43L2du1dsKr7eRomvyFi7t7No7bNOKrbH3KdiuUw7YhTDWppoFuzXLPYrYrmliZMmvan4ehslviJjbWl69XGY2Z7wnYrlMmVcK/NmTSulH+CoNx601/sj6jW+ImP1XYoVwZN0eZIuM7Pd4km6zMxsUDipmJlZYZxUzMysME4qZmZWGCcVMzMrjJOKmZkVxknFzMwKU2pSkTRd0mpJayRdWqO8VdICScslLZY0qapsjqSVklZIulXSAWn71ZJWpWMWSBpZdcxkSf+Wjnuk5xgzMxscpSUVSc3A9cBJQDswU1J7r90uB5ZGxGTgbODadOxo4GKgEhGTgGbgjHTMvcCkdMzjwGXpmBbgFmB2REwEjgO2ltU+MzPbWZl3KlOBNRGxNiK2APOBGb32aQfuA4iIVcB4SYelshbgwJQshgNPp/0WRkR32mcRMCYtfxBYHhHL0n7PRsS2cppmZma1lJlURgNPVq1vTNuqLQNOA5A0FRgHjImIp4BrgA3AJuCFiFhY4xznAXen5bcCIekeSQ9L+kytoCRdIKlDUkdXV9cAm2ZmZrWUmVRUY1vvgcauAlolLQUuApYA3ZJaye5qJgCHAyMknbVD5dIVQDcwL21qAd4DnJn+/M+STtgpgIi5EVGJiEpbW9tA22ZmZjWUOUrxRmBs1foY0iOsHhGxGTgXQJKAdel3IrAuIrpS2R3AsWR9Jkg6BzgZOCFeGxFzI/DTiHgm7fMj4B2kx2tmZla+Mu9UHgKOlDRB0jCyjvY7q3eQNDKVAcwCHkiJZgMwTdLwlGxOAB5Lx0wHLgFOiYiXq6q7B5icjmkB3gs8WmL7zMysl9LuVCKiW9KFZP+xbwZuioiVkman8huAo4GbJW0jSwDnp7IHJd0OPEz2iGsJMDdVfR2wP3Bvlm9YFBGzI+J5SV8jS2YB/CgiflhW+8zMbGeeT8XzqZiZ7RbPp2JmZoPCScXMzArjpGJmZoVxUjEzs8I4qZiZWWHK/PjRzOpI5/rnWbT2WaYdMQqg8OUp41pLP0ejxFpkfFPGtdb4X8PAOamYGZ3rn+fMby1iS/d2WpoEEt3bilse1tLElSdP5At3rSztHI0Sa5HxDWtpYt6saYUmFicVM2PR2mfZ0r2d7QFbtwUQBAUud2/n7hWbyj1Ho8RaZHzd21m09tlCk4r7VMyMaUeMYlhLE82C/ZrFfkUvtzRx0qQ3lXuORom1yPhaml59HFYU36mYGVPGtTJv1rTS+wGOeuNBe70/YijEWmR8RfepeJgWD9NiZrZbPEyLmZkNCicVMzMrjJOKmZkVxknFzMwK46RiZmaFcVIxM7PCNPQrxZK6gPV7UMWhwDMFhbMvGurtA7dxqHAbB9e4iGirVdDQSWVPSero613toWCotw/cxqHCbdx3+PGXmZkVxknFzMwK46SyZ+bu7QBKNtTbB27jUOE27iPcp2JmZoXxnYqZmRXGScXMzArTUElF0nRJqyWtkXRpjXJJ+noqXy7pHbs6VtIhku6V9Mv0Z2tV2WVp/9WSTqzaPkXSI6ns65I0BNv4k7Rtafq9oR7bKGmUpPslvSjpul7nGRLXcRdtHCrX8QOSOtP16pR0fNUxpVzHfah9pV3DmiKiIX5AM/Ar4AhgGLAMaO+1z4eAuwEB04AHd3Us8FXg0rR8KfCVtNye9tsfmJCOb05li4F3pfPcDZw0BNv4E6AyBK7jCOA9wGzgul7nGSrXsb82DpXr+B+Bw9PyJOCpMq/jPta+Uq5hX79GulOZCqyJiLURsQWYD8zotc8M4ObILAJGSnrTLo6dAXwnLX8HOLVq+/yI+ENErAPWAFNTfQdHxL9FdsVvrjpmSLSxoLb0ZVDbGBEvRcTPgd9Xn2AoXce+2liywW7jkoh4Om1fCRwgaf8Sr+M+0b4C2rHbGimpjAaerFrfmLbl2ae/Yw+LiE0A6c+eW8v+6tq4izgGal9pY49vp9vtzxb4aGiw29hfHEPlOu7KULuOpwNLIuIPlHcd95X29SjjGtbUSEml1l9k7/ep+9onz7F5zzeQuvLaV9oIcGZEvA34T+n38V3Ulddgt3FP4hiofaWNMMSuo6SJwFeAT+1GHAOxr7QPyruGNTVSUtkIjK1aHwM8nXOf/o79dbpl7Xkk8pscdY3ZRRwDta+0kYh4Kv35O+C7FPdYbLDb2F8cQ+U69mkoXUdJY4AFwNkR8auqc5RxHfeV9pV5DWsru9NmX/kBLcBasg7lns6vib32+TA7dpwt3tWxwNXs2HH21bQ8kR07sdfyWif2Q6n+no7BDw2lNqa6Dk377AfcDsyuxzZW1fkJdu7EHhLXsa82DqXrCIxM+51eI5bCr+O+0r4yr2GfbS+z8n3tR/a2xeNkb1ZckbbN7vlLThf3+lT+CFVvTNQ6Nm0fBdwH/DL9eUhV2RVp/9VUvVECVIAVqew60sgGQ6WNZG8TdQLLyToNryUl1Dpt4xPAc8CLZP+K7HkTZyhdx53aOJSuI/C3wEvA0qrfG8q8jvtC+8q+hrV+HqbFzMwK00h9KmZmVjInFTMzK4yTipmZFcZJxczMCuOkYmZmhXFSMTOzwjip2JAmabykVyQtTetPFFx/m6Stkj6VY99TJbVXrX9B0vsLjme8pBVF1tnPuT4h6XNpeY6kDb2HzrfG46RijeBXEXFMSXV/FFgEzMyx76lkHxUCEBFXRsS/lBTXoIqIvweu3Ntx2N7npGKNpgtAUpOkb0paKekuST+S9JEB1DcT+GtgjKRXR6GVdHaaeGmZpP8t6VjgFODqNFrsWyT9U885JT0h6fOSHk4TLf1R2n6IpB+kuhZJmpy2fy7V++M0YdMneweW7lp+lup8OMWApOPSxE23S1olaV7PyLXKJqz6aZro6Z6qcaYulvRoimN+OsUrZF/gm72qZW8HYDaYIuKP0+JpwHjgbWTDWTwG3LQ7dUkaC7wxIhZLug34GPC1NFLsFcC7I+IZSYdExHOS7gTuiojb0/G9q3wmIt4h6S+AvwFmAZ8nG8b81DSb383AMWn/yWRjRo0Alkj6Ya/6fgN8ICJ+L+lI4FayIUkgm9RpItlAhb8A3i3pQeAbwIyI6JL0MeDLwHlk40xNiIg/SBqZ/i6/tzt/X9YYnFSsUb0H+H5EbAf+XdL9A6jjDOC2tDwfuBH4GnA8cHtEPAMQEc/lrO+O9GcnWdLrifP0VM+PlU39+/pU9n8j4hXglRT/VLIxn3rsB1wn6RhgG/DWqrLFEbERIPU3jQd+SzZr4L0p4TUDm9L+y4F5kn4A/CBne6wBOalYoypioqKZwGGSzkzrh6c7AjGwOTl6JlXaxmv/3+xvbo3e5+i9Pgf4NfB2skfd1TM7Vk/g1HM+ASsj4l01zvlh4E/IHuF9VtLEiOjuuynWqNynYo3q58DpqW/lMOC43TlY0lHAiIgYHRHjI2I88D/I7l7uA/5M0qi07yHpsN8BB+1mnA8AZ6Z6jiN7RLY5lc2QdEA6z3FkQ7hXez2wKd2NfZzszqM/q4E2Se9K59tP0kRJTcDYiLgf+AzZMOuv2812WINwUrFG9X/IhnhfAfwj8CDwwm4cP5NsQqTedc6MiJVkfRE/lbSM7JEYZI/I/pukJZLekvM8nwMqkpYDVwHnVJUtBn5I9vbZF+O1Ocp7fBM4R9IiskdfL/V3osjmQ/8I8JUU91LgWLJkdIukR4AlwN9HxG9zxm8NxkPf25AmaTxZ5/ikGmWvi4gX07/0F5N1rP/7YMc4EOn7kBcj4pq9HUsPSZ8gmxPkwr0di+09vlOxoW4b8Pqejx97uStt/xnZv/TrIqHsiyTNAS4DNu9qXxvafKdiVkXSArJpXKuNA9b32nZJRNwzOFGZ1Q8nFTMzK4wff5mZWWGcVMzMrDBOKmZmVhgnFTMzK8z/Bw3eoVC1Ew11AAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.7846023126502879, pvalue=4.594515919576695e-22)\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.782488950982933, pvalue=7.013462909046687e-22)\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.9576886470491779, pvalue=0.18585185274052032)\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.594526492011062, pvalue=0.12008681090300906)\n", "0.0 681\n", "1.0 14\n", "Name: new_Moisture, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.5082388540843145, pvalue=6.736706993313194e-08)\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.3477096790600104, pvalue=0.0003931225298530388)\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=nan, pvalue=nan)\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.9656143324339188, pvalue=3.6185296696233134e-13)\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=nan, pvalue=nan)\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.8089812882142068, pvalue=2.409937531244374e-24)\n" ] }, { "data": { "image/png": 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sL216VkS0VTjePluz5WGe3r2XvQHP7AkgCPpxfvdebty4Le8+ni+xDvX4hlOsQz2+4RTrUIlv917WbHl4YJNNRBzSx7ZnAO1pNBuSVgJzgXKymQssj4gA1kgaI2lcRGyLiFskTe6q8XRV9HbgxAarzwRW9DHuPpt11OGMahnBM7v3MjL9L2HPnv6bP6BlBHNeNY619+3Mto/nS6xDPb7hFOtQj284xTpU4jugZcQfb6v1lx6TTfpSPwuYEhGflTQRGNfgqQL1xgMPlJY7ePaqpbs646n2o9HjKN61c0+DdWdQJLKyb0raA1wDfC4luH517JEv5NvvnZX9PuorXnzIoN/PbYZYh3p8wynWoR7fcIp1qMTX33026uk7V9KlwF7gxIj4M0kvBG6KiNf0sN3bgDdFxHvT8juBGRHxoVKd7wOfj4ifpuUfAh+PiHVpeTJwfbnPpi6u9oj4cl35TIr+oVeXysZHxIOSDqFINv8eEcupI2kBsABg0qRJx95///3dnhszM9uXpHURUasvrzL0eWZEfBB4EiAiHgFGVdiuA5hYWp4AbO1Dnf1IagFOo/EQ7HnU3UKLiAfT56PAFRS3+PYTEUsiohYRtdbW1p7CMDOziqokm2dSZ38ASGqluNLpyVpgqqQpkkZRJIFVdXVWAfNVmAXsiogqt9BOAu6OiI5yoaQRwNsofhvUWdYiaWyaP4BihNvGCvswM7N+UiXZLAauA46Q9E/AT4H/29NGEbEbWASsBu4CroqITZIWSlqYqt0AbAHaKV5l8IHO7SWtAG4FXiGpQ9J7Ss3vd/WSvB7o6ByUkBwIrJa0AVgPPJj2ZWZmA6THPhsASa8E/goQ8MOIuCt3YIOtVqtFW1vWkdJmZk2nqz6bLkejSTqstLid0pWEpMMiYmf/hmhmZs2qu6HP6yj6aQRMAh5J82OA3wBTcgdnZmbNocs+m4iYEhFHUfS5vCUixkbE4RQd7NcOVIBmZjb8VRkg8JqIuKFzISJuBI7PF5KZmTWbKs9Ge0jSJ3n22WjvAB7OGpWZmTWVKlc2ZwKtFMOfr0vzZ+YMyszMmkuVB3HuBP52AGIxM7MmVeXKxszM7DlxsjEzs+ycbMzMLLvuniDwFdLDNxuJiA9nicjMzJpOdwME/GAwMzPrF10mm4j41kAGYmZmzavKa6FbgU8A04CDOssj4sSMcZmZWROpMkDg2xTvo5kCXAjcR/FiNDMzs0qqJJvDI2Ip8ExE/Dgi/gaYlTkuMzNrIlWejfZM+twm6a+BrcCEfCGZmVmzqZJsPifpUODvgK8Ao4Fzs0ZlZmZNpcqz0a5Ps7uAN+QNx8zMmlGXfTaSPp4+vyJpcf1UpXFJsyVtltQu6bwG65Xaa5e0QdIxpXXLJG2XtLFumyslrU/TfZLWp/LJkv5QWndZaZtjJd2R9rNYkqrEb2Zm/aO7K5u70mefftwpaSRwCXAy0AGslbQqIu4sVZsDTE3TTODS9AlwOfBVYHm53Yg4o7SPL1NccXW6NyKmNwjnUmABsAa4AZgN3NiX4zIzs97r7ked/5Fmn4iIq8vrJL2tQtszgPaI2JK2WQnMBcrJZi6wPCICWCNpjKRxEbEtIm6RNLmrxtPVyduBbn/vI2kcMDoibk3Ly4FTcbIxMxswVYY+n1+xrN544IHSckcq622drhwH/DYi7imVTZF0u6QfSzqutI+OPu7DzMz6QXcP4pwDnAKMr+ujGQ3srtB2o36R+gd7VqnTlTOBFaXlbcCkiHhY0rHAdyUd3Zt9SFpAcbuNSZMmVQzDzMx60l2fzVaK/pr/BawrlT9KtaHPHcDE0vKE1GZv6+xHUgtwGnBsZ1lEPAU8lebXSboXeHnaR/l3QV3uIyKWAEsAarVa1aRnZmY96K7P5pdpJNgb+/hQzrXAVElTgAeBecD/qauzCliU+nNmArsiYluFtk8C7o6IP94eS89w2xkReyQdRTHoYEtE7JT0qKRZwM+B+RS/FzIzswHSbZ9NROwBDpc0qrcNR8RuYBGwmmJk21URsUnSQkkLU7UbgC1AO/B14AOd20taAdwKvEJSh6T3lJqfx7630ABeD2yQ9EvgO8DCiNiZ1p0DfCPt5148OMDMbECpGAjWTQXpX4FjKK5CHu8sj4j/lze0wVWr1aKtza/0MTPrDUnrIqJWX17lcTVb0zQCOKS/AzMzs+ZX5XE1Fw5EIGZm1ryqvjzt48DR+OVpZmbWB1VfnnY3fnmamZn1kV+eZmZm2fnlaWZmlp1fnmZmZtl192y0g4CFwMsoHly5NCL88jQzM+u17vpsvgXUgDso3jvz5QGJyMzMmk53t9GmRcSrASQtBW4bmJDMzKzZdHdl0zkwoPM5Z2ZmZn3S3ZXN/5D0+zQv4OC0LCAiYnT26MzMrCl094qBkQMZiJmZNa8qP+o0MzN7TpxszMwsOycbMzPLzsnGzMyyc7IxM7PsnGzMzCy7rMlG0mxJmyW1SzqvwXpJWpzWb5B0TGndMknbJW2s2+ZKSevTdJ+k9an8ZEnrJN2RPk8sbXNziqNzuyMyHraZmdWp8tTnPpE0ErgEOBnoANZKWhURd5aqzQGmpmkmcGn6BLgc+CqwvNxuRJxR2seXgV1p8SHgLRGxVdKrgNUUDxDtdFZEtPXP0ZmZWW/kvLKZAbRHxJaIeBpYCcytqzMXWB6FNcAYSeMAIuIWYGdXjUsS8HZgRap/e0RsTas3AQdJOrBfj8jMzPokZ7IZDzxQWu5g3yuNqnW6chzw24i4p8G6twK3R8RTpbJvplton0qJyszMBkjOZNPoCz36UKcrZ5KuavZpUDoa+ALw/lLxWekJ1sel6Z2NGpS0QFKbpLYdO3ZUDMPMzHqSM9l0ABNLyxMoXind2zr7kdQCnAZcWVc+AbgOmB8R93aWR8SD6fNR4AqKW3z7iYglEVGLiFpra2tPYZiZWUU5k81aYKqkKZJGAfOAVXV1VgHz06i0WcCuiNhWoe2TgLsjoqOzQNIY4PvA+RHxs1J5i6Sxaf4A4M3ARszMbMBkSzbpHTiLKEaF3QVcFRGbJC2UtDBVuwHYArQDXwc+0Lm9pBXArcArJHVIek+p+XnsfwttEcUrrD9VN8T5QGC1pA3AeuDBtC8zMxsgiqjaRfL8UqvVoq3NI6XNzHpD0rqIqNWX+wkCZmaWnZONmZll52RjZmbZOdmYmVl2TjZmZpadk42ZmWXnZGNmZtk52ZiZWXZONmZmlp2TjZmZZedkY2Zm2TnZmJlZdk42ZmaWnZONmZll52RjZmbZOdmYmVl2TjZmZpadk42ZmWXnZGNmZtllTTaSZkvaLKld0nkN1kvS4rR+g6RjSuuWSdouaWPdNldKWp+m+yStL607P7W1WdKbSuXHSrojrVssSZkO2czMGsiWbCSNBC4B5gDTgDMlTaurNgeYmqYFwKWldZcDs+vbjYgzImJ6REwHrgGuTfubBswDjk7bfS3FQGp3QWlf+7VrZmb55LyymQG0R8SWiHgaWAnMraszF1gehTXAGEnjACLiFmBnV42nq5O3AytKba2MiKci4tdAOzAjtTc6Im6NiACWA6f221GamVmPciab8cADpeWOVNbbOl05DvhtRNzTQ1vj03xf9mFmZv0gZ7Jp1C8SfajTlTN59qqmu7Yq70PSAkltktp27NhRMQwzM+tJzmTTAUwsLU8Atvahzn4ktQCnAVdWaKsjzfe4j4hYEhG1iKi1trb2FIaZmVWUM9msBaZKmiJpFEXn/aq6OquA+WlU2ixgV0Rsq9D2ScDdEVG+PbYKmCfpQElTKAYC3Jbae1TSrNTPMx/43nM8NjMz64WWXA1HxG5Ji4DVwEhgWURskrQwrb8MuAE4haIz/wng7M7tJa0ATgDGSuoAPhMRS9Pqeex7C43U9lXAncBu4IMRsSetPodidNvBwI1pMjOzAaJigJbVq9Vq0dbWNthhmJkNK5LWRUStvtxPEDAzs+ycbMzMLDsnGzMzy87JxszMsnOyMTOz7JxszMwsOycbMzPLzsnGzMyyc7IxM7PsnGzMzCw7JxszM8vOycbMzLJzsjEzs+ycbMzMLDsnGzMzy87JxszMsnOyMTOz7Pymzi5I2gHc38fNxwIP9WM4w5XPQ8HnoeDzUGj283BkRLTWFzrZZCCprdFrUZ9vfB4KPg8Fn4fC8/U8+DaamZll52RjZmbZOdnksWSwAxgifB4KPg8Fn4fC8/I8uM/GzMyy85WNmZll52TTgKTZkjZLapd0XoP1krQ4rd8g6ZietpV0mKT/knRP+nxhad35qf5mSW/Kf4TVDOR5kHS4pB9JekzSVwfmCKsZ4PNwsqR1ku5InycOzFH2bIDPwwxJ69P0S0n/e2COsmcD/f2Q1k9K/zY+lvfoMooIT6UJGAncCxwFjAJ+CUyrq3MKcCMgYBbw8562BS4Czkvz5wFfSPPTUr0DgSlp+5HPw/Pwp8BfAguBrw728Q/iefgL4CVp/lXAg4N9DgbpPPwJ0JLmxwHbO5efT+eh1OY1wNXAxwb7HPR18pXN/mYA7RGxJSKeBlYCc+vqzAWWR2ENMEbSuB62nQt8K81/Czi1VL4yIp6KiF8D7amdwTag5yEiHo+InwJP5jyoPhjo83B7RGxN5ZuAgyQdmOnYemOgz8MTEbE7lR8EDJXO5YH+fkDSqcAWir8Pw5aTzf7GAw+UljtSWZU63W37oojYBpA+j+jF/gbDQJ+HoWowz8Nbgdsj4qk+R99/Bvw8SJopaRNwB7CwlHwG04CeB0l/CnwCuLCf4h80Tjb7U4Oy+v9VdVWnyrZ92d9gGOjzMFQNynmQdDTwBeD9VeoPgAE/DxHx84g4GngNcL6kg3qMMr+BPg8XAv8SEY9ViG1IaxnsAIagDmBiaXkCsLVinVHdbPtbSeMiYlu6pN7ei/0NhoE+D0PVgJ8HSROA64D5EXFvvxzFczdofx8i4i5Jj1P0YbU9p6N47gb6PMwETpd0ETAG2CvpyYgYUoNoKhnsTqOhNlEk4C0UnfWdnXhH19X5a/btALytp22BL7JvB+BFaf5o9h0gsIWhMUBgQM9Dqc13M7QGCAz034cxqd5bB/vYB/k8TOHZAQJHUnwpj32+nYe6di9gGA8QGPQAhuJEMZrkVxQjR/4hlS2kuG9M+kt0SVp/B1DrbttUfjjwQ+Ce9HlYad0/pPqbgTmDffyDeB7uA3YCj1H873Ba7mMcaucB+CTwOLC+NB0x2OdgEM7DOyk6xNcDvwBOHezjH6x/F6U6FzCMk42fIGBmZtl5gICZmWXnZGNmZtk52ZiZWXZONmZmlp2TjZmZZedkY2Zm2TnZ2LAjabKkP0han5bv68e2b5b0G0kqlX1XUrePC5E0RtIHSssnSLq+v+LqYp/vzvE6BkkvkfSd/txHOh+v7eN2l6f5M9Kj+bOeV8vDycaGq3sjYnqmtn8HvA6KJELxiPuejAE+0FOlgZberdKrf+cRsTUiTu/nUE4AepVsJO3zOK2IuBJ4bz/GZAPIycaawQ4ASSMkfU3SJknXS7pBUl++NFcC89L8acC15ZWS/l7S2vRirM6n8f4z8NL0sq8vprIXSPqOpLslfbvzaknSp9P2GyUtKZXfLOkLkm6T9CtJx6XygyR9U8UL1W6X9IZSOBMl/Wd6IddnUv3Jku6S9DWKX99PbBRz2lf5auwCSX+Xtt/Y3T5S/e+qeMHbJkkLSuWzJf1CxUvPfihpMsUv7M9N5+c4Sa2SrkkxrZX0ulIMSyTdBCwHngZ29e6Pz4akwX6EgSdPvZ2AycDGBuWnAzdQ/CfqxcAjwOm9bPtmiocfbqB42dVNaX+PpfVvBJZQPJJkBHA98Pr6mCj+J7+L4mGLI4Bbgb9M68qP6Pk34C2lfX85zZ8C/CDN/x3wzTT/SuA3FO94eTewjeJRJwcDG4FaimUvMKuHmP8C+HEpljuBSeVj6Wof5eMolR8OtFI8Rn9KXZ0LKD1qBbiidD4mAXeV6q0DDu7iz+cE4PrB/jvoqfeTn/pszeQvgasjYi/w35J+1Md29gA/Bc6g+NK7r9SF88Y03Z6WXwBMpUgA9W6LiA6A1L80ObX7Bkkfp3gb5WEUzwD7j7RN51XUulS/87i+AhARd0u6H3h5WvdfEfFw2se1qe53gfujeHFXlzFHxFJJR0h6CUWSeCQifpOuRMoa7aMN+LCefV3zxHQeWoFbongRIBGxs8F5ATgJmFY6r6MlHZLmV0XEH7rYzoYpJxtrJo3eF9JXKyke839Bg318PiL+dZ/C/b+gAcovPdsDtKh4J8vXKK4OHpB0AcVVSv02e3j232d3x1X/cMPO5cd7ijn5DsUV4YspjrnSPiSdQJEw/mdEPCHpZorjUIP6jYxI2+6TVFLyebzhFjasuc/GmslPgbemvpsXUdxy6aufAJ8HVtSVrwb+RtILACSNl3QE8ChwCD3rTCwPpTaq9CndApyV9vdyittOm9O6kyUdJulgilcJ/6zB9l3FDM/2T51OkXgaabSPQymuhJ6Q9EqKR+lDcbvweElT0r4OS+X15+cmYFHngqTpPZwDG+Z8ZWPN5Brgryj6D34F/Jw+di5HRABfalB+k6Q/A25N/wt/DHhHRNwr6WepY/1G4PtdtPs7SV+nePT8fcDaCuF8DbhM0h3AbuDdEfFU2v9PKfp9XgZcERFt9VdZXcUMbI+ITen21YORXkvcQKN93AEslLSBIvGtSfvakQYLXJtGwW0HTqa4TfgdSXOBDwEfBi5J27dQJNSFFc6FDVN+xYANO+nL9PqIeFWDdS+IiMckHQ7cBrwuIv57oGO0PNLtu49FxJsHORTrJd9Gs+FoD3Bo6nSvd30q/wnwWSea5iHpDIqrvEcGOxbrPV/ZWNOTdB3Fq3jLjgTuryv7RESsHpiozJ5fnGzMzCw730YzM7PsnGzMzCw7JxszM8vOycbMzLJzsjEzs+z+P4h65vAE5MF5AAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=nan, pvalue=nan)\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=nan, pvalue=nan)\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.25750056910309227, pvalue=0.009698369510590747)\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.36393955713136383, pvalue=0.00019754883221915882)\n" ] } ], "source": [ "print(\"Median:\",poultry.Moisture.median())\n", "\n", "poultry.loc[poultry['Moisture'] < poultry.Moisture.median(), 'new_Moisture'] = 0\n", "poultry.loc[poultry['Moisture'] >= poultry.Moisture.median(), 'new_Moisture'] = 1\n", "print(\"Total sample count:\", poultry.Moisture.value_counts().sum(),\"\\n\")\n", "\n", "print(\"Distribution:\")\n", "print(poultry.new_Moisture.value_counts())\n", "\n", "sample = pd.merge(microbiome, poultry[['SampleID', 'new_Moisture','SampleType']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "sample = sample[~sample.isin([np.nan, np.inf, -np.inf]).any(1)]\n", "sample = sample.drop(['Pathogen_Salmonella', 'new_Pathogen_Salmonella',\n", " 'Pathogen_Campy','new_Pathogen_Campy',\n", " 'Pathogen_Listeria','new_Pathogen_Listeria'],axis='columns')\n", "\n", "\n", "feces=sample[sample.SampleType=='Feces']\n", "soil=sample[sample.SampleType=='Soil']\n", "\n", "print ('SAMPLE DISTRIBUTION \\n')\n", "\n", "print('Feces', feces.shape)\n", "print('Soil', soil.shape,'\\n')\n", "\n", "sampletypes = [feces, soil]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES\", 1: \"SOIL\"}\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType', 'new_Moisture'],axis='columns'),sample.new_Moisture,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1]\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " \n", "# mylist2.append([f\"Moisture_{sample_name[indexing]}\", rf_auc_normal])\n", " \n", " \n", " print(pd.value_counts(sample['new_Moisture']))\n", "\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim3_plot.max(), decimals=None, symbol=''))\n", " \n", "\n", " plt.title(f\"Moisture in {sample_name[indexing]} Model\")\n", " prelim3_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','new_Moisture','SampleType'],axis='columns').columns)\n", " prelim3_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", "\n", " xmax=prelim3_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " \n", " plt.show()\n", " \n", " prelim3_plot.nlargest(10).to_csv(\"prelim3.csv\")\n", " top10 = pd.read_csv('prelim3.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", " plt.plot(pdp[1][0], pdp[0][0],'.')\n", " plt.ylabel('Partial dependence'), plt.xlabel(top10[feature])\n", " plt.show()\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"Moisture_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (28) TotalC" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Median: 7.909\n", "Total sample count: 1635 \n", "\n", "Distribution:\n", "1.0 818\n", "0.0 817\n", "Name: new_TotalC, dtype: int64\n", "SAMPLE DISTRIBUTION \n", "\n", "Feces (698, 877)\n", "Soil (695, 877) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "1.0 653\n", "0.0 45\n", "Name: new_TotalC, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.8463192311466851, pvalue=1.476965667995757e-28)\n", "Slope and P-value = PearsonRResult(statistic=-0.509859010801718, pvalue=0.0055776989377183325)\n", "Slope and P-value = PearsonRResult(statistic=-0.8990157456933221, pvalue=6.4085015304415925e-37)\n", "Slope and P-value = PearsonRResult(statistic=-0.8256290084422852, pvalue=4.2450241383090584e-26)\n", "Slope and P-value = PearsonRResult(statistic=-0.534041745025434, pvalue=0.040306527875281085)\n", "Slope and P-value = PearsonRResult(statistic=-0.6980907933589975, pvalue=6.900432502098448e-16)\n", "Slope and P-value = PearsonRResult(statistic=-0.7872714632366299, pvalue=3.2141074943468647e-11)\n", "Slope and P-value = PearsonRResult(statistic=0.17696420870187252, pvalue=0.07818382230785025)\n", "Slope and P-value = PearsonRResult(statistic=-0.10017888611497335, pvalue=0.3213481337496423)\n", "Slope and P-value = PearsonRResult(statistic=-0.586969349978595, pvalue=1.4908521602693953e-09)\n", "0.0 623\n", "1.0 72\n", "Name: new_TotalC, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.7273413002189351, pvalue=1.0388994265684542e-17)\n", "Slope and P-value = PearsonRResult(statistic=0.18525916719021743, pvalue=0.0649927413789278)\n", "Slope and P-value = PearsonRResult(statistic=0.32968799875875154, pvalue=0.0011040069960922016)\n", "Slope and P-value = PearsonRResult(statistic=0.9050228233830534, pvalue=3.6817801310792343e-38)\n", "Slope and P-value = PearsonRResult(statistic=0.7666116352339791, pvalue=1.4569394233325568e-20)\n", "Slope and P-value = PearsonRResult(statistic=0.5695923849773041, pvalue=0.0008241680980331139)\n", "Slope and P-value = PearsonRResult(statistic=-0.48034829170987353, pvalue=4.2456694117700555e-07)\n", "Slope and P-value = PearsonRResult(statistic=0.8740231609252483, pvalue=1.748160988128366e-32)\n", "Slope and P-value = PearsonRResult(statistic=0.2261675463645144, pvalue=0.02365765620973179)\n", "Slope and P-value = PearsonRResult(statistic=0.7700766736440343, pvalue=7.672441344525935e-21)\n" ] } ], "source": [ "print(\"Median:\",poultry.TotalC.median())\n", "\n", "poultry.loc[poultry['TotalC'] < poultry.TotalC.median(), 'new_TotalC'] = 0\n", "poultry.loc[poultry['TotalC'] >= poultry.TotalC.median(), 'new_TotalC'] = 1 \n", "print(\"Total sample count:\", poultry.TotalC.value_counts().sum(),\"\\n\")\n", "\n", "print(\"Distribution:\")\n", "print(poultry.new_TotalC.value_counts())\n", "\n", "sample = pd.merge(microbiome, poultry[['SampleID', 'new_TotalC','SampleType']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "sample = sample[~sample.isin([np.nan, np.inf, -np.inf]).any(1)]\n", "sample = sample.drop(['Pathogen_Salmonella', 'new_Pathogen_Salmonella',\n", " 'Pathogen_Campy','new_Pathogen_Campy',\n", " 'Pathogen_Listeria','new_Pathogen_Listeria'],axis='columns')\n", "\n", "\n", "feces=sample[sample.SampleType=='Feces']\n", "soil=sample[sample.SampleType=='Soil']\n", "\n", "print ('SAMPLE DISTRIBUTION \\n')\n", "\n", "print('Feces', feces.shape)\n", "print('Soil', soil.shape,'\\n')\n", "\n", "sampletypes = [feces, soil]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES\", 1: \"SOIL\"}\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType', 'new_TotalC'],axis='columns'),sample.new_TotalC,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1]\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " \n", "# mylist2.append([f\"TotalC_{sample_name[indexing]}\", rf_auc_normal])\n", " \n", " \n", " print(pd.value_counts(sample['new_TotalC']))\n", "\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim3_plot.max(), decimals=None, symbol=''))\n", " \n", "\n", " plt.title(f\"TotalC in {sample_name[indexing]} Model\")\n", " prelim3_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','new_TotalC','SampleType'],axis='columns').columns)\n", " prelim3_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", "\n", " xmax=prelim3_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " \n", " plt.show()\n", " \n", " prelim3_plot.nlargest(10).to_csv(\"prelim3.csv\")\n", " top10 = pd.read_csv('prelim3.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", "# plt.plot(pdp[1][0], pdp[0][0],'.')\n", "# plt.ylabel('Partial dependence'), plt.xlabel(top10[feature])\n", "# plt.show()\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"TotalC_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (29) TotalN" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Median: 0.535\n", "Total sample count: 1635 \n", "\n", "Distribution:\n", "1.0 818\n", "0.0 817\n", "Name: new_TotalN, dtype: int64\n", "SAMPLE DISTRIBUTION \n", "\n", "Feces (698, 877)\n", "Soil (695, 877) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "1.0 614\n", "0.0 84\n", "Name: new_TotalN, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.9004037245859327, pvalue=3.366148126674797e-37)\n", "Slope and P-value = PearsonRResult(statistic=-0.885149960286909, pvalue=2.485339680378704e-34)\n", "Slope and P-value = PearsonRResult(statistic=0.007225794926848764, pvalue=0.9431188766251841)\n", "Slope and P-value = PearsonRResult(statistic=-0.8132234910895044, pvalue=8.947841958795165e-25)\n", "Slope and P-value = PearsonRResult(statistic=-0.09409471538608631, pvalue=0.35175775850349933)\n", "Slope and P-value = PearsonRResult(statistic=-0.9154269998422115, pvalue=1.6148403451859994e-40)\n", "Slope and P-value = PearsonRResult(statistic=-0.9508609737291829, pvalue=1.0656959979891609e-51)\n", "Slope and P-value = PearsonRResult(statistic=-0.4791662791491223, pvalue=0.0002146990448290168)\n", "Slope and P-value = PearsonRResult(statistic=-0.8637868899165726, pvalue=1.5883078788832195e-22)\n", "Slope and P-value = PearsonRResult(statistic=-0.913209997307706, pvalue=5.434664804220873e-40)\n", "0.0 595\n", "1.0 100\n", "Name: new_TotalN, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.6778100492656981, pvalue=9.528502029301915e-15)\n", "Slope and P-value = PearsonRResult(statistic=0.8411913802285049, pvalue=6.474775329762904e-28)\n", "Slope and P-value = PearsonRResult(statistic=0.6141269358087931, pvalue=1.217253055589627e-10)\n", "Slope and P-value = PearsonRResult(statistic=0.5750355211628105, pvalue=3.923172163656228e-10)\n", "Slope and P-value = PearsonRResult(statistic=0.9377348192786747, pvalue=8.471989603249628e-47)\n", "Slope and P-value = PearsonRResult(statistic=0.8611668239797572, pvalue=1.4800652341837664e-30)\n", "Slope and P-value = PearsonRResult(statistic=0.7753764043196264, pvalue=1.2897799908068641e-15)\n", "Slope and P-value = PearsonRResult(statistic=0.9270454178140568, pvalue=1.5370650314185746e-43)\n", "Slope and P-value = PearsonRResult(statistic=0.9567905615215877, pvalue=2.2543817091683194e-54)\n", "Slope and P-value = PearsonRResult(statistic=0.0624743935907444, pvalue=0.5369096012114185)\n" ] } ], "source": [ "print(\"Median:\",poultry.TotalN.median())\n", "\n", "poultry.loc[poultry['TotalN'] < poultry.TotalN.median(), 'new_TotalN'] = 0\n", "poultry.loc[poultry['TotalN'] >= poultry.TotalN.median(), 'new_TotalN'] = 1 \n", "print(\"Total sample count:\", poultry.TotalN.value_counts().sum(),\"\\n\")\n", "\n", "print(\"Distribution:\")\n", "print(poultry.new_TotalN.value_counts())\n", "\n", "sample = pd.merge(microbiome, poultry[['SampleID', 'new_TotalN','SampleType']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "sample = sample[~sample.isin([np.nan, np.inf, -np.inf]).any(1)]\n", "sample = sample.drop(['Pathogen_Salmonella', 'new_Pathogen_Salmonella',\n", " 'Pathogen_Campy','new_Pathogen_Campy',\n", " 'Pathogen_Listeria','new_Pathogen_Listeria'],axis='columns')\n", "\n", "\n", "feces=sample[sample.SampleType=='Feces']\n", "soil=sample[sample.SampleType=='Soil']\n", "\n", "print ('SAMPLE DISTRIBUTION \\n')\n", "\n", "print('Feces', feces.shape)\n", "print('Soil', soil.shape,'\\n')\n", "\n", "sampletypes = [feces, soil]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES\", 1: \"SOIL\"}\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType', 'new_TotalN'],axis='columns'),sample.new_TotalN,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1]\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " \n", "# mylist2.append([f\"TotalN_{sample_name[indexing]}\", rf_auc_normal])\n", " \n", " \n", " print(pd.value_counts(sample['new_TotalN']))\n", "\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim3_plot.max(), decimals=None, symbol=''))\n", " \n", "\n", " plt.title(f\"TotalN in {sample_name[indexing]} Model\")\n", " prelim3_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','new_TotalN','SampleType'],axis='columns').columns)\n", " prelim3_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", "\n", " xmax=prelim3_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " \n", " plt.show()\n", " \n", " prelim3_plot.nlargest(10).to_csv(\"prelim3.csv\")\n", " top10 = pd.read_csv('prelim3.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", "# plt.plot(pdp[1][0], pdp[0][0],'.')\n", "# plt.ylabel('Partial dependence'), plt.xlabel(top10[feature])\n", "# plt.show()\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"TotalN_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (30) CNRatio" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Median: 12.16717791411043\n", "Total sample count: 1635 \n", "\n", "Distribution:\n", "1.0 818\n", "0.0 817\n", "Name: new_CNRatio, dtype: int64\n", "SAMPLE DISTRIBUTION \n", "\n", "Feces (698, 877)\n", "Soil (695, 877) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "0.0 373\n", "1.0 325\n", "Name: new_CNRatio, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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PearsonRResult(statistic=0.13206904641871864, pvalue=0.19025275998366434)\n", "Slope and P-value = PearsonRResult(statistic=-0.4496387436506372, pvalue=2.6921342794386987e-06)\n", "1.0 376\n", "0.0 319\n", "Name: new_CNRatio, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.9606243783980999, pvalue=2.604786868208449e-56)\n", "Slope and P-value = PearsonRResult(statistic=-0.8178885385212776, pvalue=2.922288679125266e-25)\n", "Slope and P-value = PearsonRResult(statistic=-0.6176289995773843, pvalue=7.665467693116988e-12)\n", "Slope and P-value = PearsonRResult(statistic=-0.8135958200369195, pvalue=8.192773279139387e-25)\n", "Slope and P-value = PearsonRResult(statistic=-0.7890169874895396, pvalue=1.8697058135338177e-22)\n", "Slope and P-value = PearsonRResult(statistic=0.4690009793034572, pvalue=8.579657911323533e-07)\n", "Slope and P-value = PearsonRResult(statistic=-0.8336923981869455, pvalue=5.1288321758780694e-27)\n", "Slope and P-value = PearsonRResult(statistic=-0.4122338054939732, pvalue=2.0295266481089827e-05)\n", "Slope and P-value = PearsonRResult(statistic=-0.9723820695744079, pvalue=9.773700557947102e-64)\n", "Slope and P-value = PearsonRResult(statistic=-0.9581111408040757, pvalue=5.084798459334926e-55)\n" ] } ], "source": [ "print(\"Median:\",poultry.CNRatio.median())\n", "\n", "poultry.loc[poultry['CNRatio'] < poultry.CNRatio.median(), 'new_CNRatio'] = 0\n", "poultry.loc[poultry['CNRatio'] >= poultry.CNRatio.median(), 'new_CNRatio'] = 1 \n", "print(\"Total sample count:\", poultry.CNRatio.value_counts().sum(),\"\\n\")\n", "\n", "print(\"Distribution:\")\n", "print(poultry.new_CNRatio.value_counts())\n", "\n", "sample = pd.merge(microbiome, poultry[['SampleID', 'new_CNRatio','SampleType']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "sample = sample[~sample.isin([np.nan, np.inf, -np.inf]).any(1)]\n", "sample = sample.drop(['Pathogen_Salmonella', 'new_Pathogen_Salmonella',\n", " 'Pathogen_Campy','new_Pathogen_Campy',\n", " 'Pathogen_Listeria','new_Pathogen_Listeria'],axis='columns')\n", "\n", "\n", "feces=sample[sample.SampleType=='Feces']\n", "soil=sample[sample.SampleType=='Soil']\n", "\n", "print ('SAMPLE DISTRIBUTION \\n')\n", "\n", "print('Feces', feces.shape)\n", "print('Soil', soil.shape,'\\n')\n", "\n", "sampletypes = [feces, soil]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES\", 1: \"SOIL\"}\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType', 'new_CNRatio'],axis='columns'),sample.new_CNRatio,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1]\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " \n", "# mylist2.append([f\"CNRatio_{sample_name[indexing]}\", rf_auc_normal])\n", " \n", " \n", " print(pd.value_counts(sample['new_CNRatio']))\n", "\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim3_plot.max(), decimals=None, symbol=''))\n", " \n", "\n", " plt.title(f\"CNRatio in {sample_name[indexing]} Model\")\n", " prelim3_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','new_CNRatio','SampleType'],axis='columns').columns)\n", " prelim3_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", "\n", " xmax=prelim3_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " \n", " plt.show()\n", " \n", " prelim3_plot.nlargest(10).to_csv(\"prelim3.csv\")\n", " top10 = pd.read_csv('prelim3.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", "# plt.plot(pdp[1][0], pdp[0][0],'.')\n", "# plt.ylabel('Partial dependence'), plt.xlabel(top10[feature])\n", "# plt.show()\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"CNRatio_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (31) Al" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Median: 654.682\n", "Total sample count: 818 \n", "\n", "Distribution:\n", "1.0 409\n", "0.0 409\n", "Name: new_Al, dtype: int64\n", "SAMPLE DISTRIBUTION \n", "\n", "Feces (698, 877)\n", "Soil (0, 877) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "0.0 369\n", "1.0 329\n", "Name: new_Al, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.7540801994548134, pvalue=1.3547846854985891e-19)\n", "Slope and P-value = PearsonRResult(statistic=0.7992958286478337, pvalue=2.1153359961182996e-23)\n", "Slope and P-value = PearsonRResult(statistic=0.9326496829695684, pvalue=3.507514660350263e-45)\n", "Slope and P-value = PearsonRResult(statistic=0.8693601946381534, pvalue=9.231754557265944e-32)\n", "Slope and P-value = PearsonRResult(statistic=0.6691431482628639, pvalue=2.7482000636896324e-14)\n", "Slope and P-value = PearsonRResult(statistic=0.7958848225561527, pvalue=4.4200319254534576e-23)\n", "Slope and P-value = PearsonRResult(statistic=0.5902125997514923, pvalue=1.0306329373555599e-10)\n", "Slope and P-value = PearsonRResult(statistic=-0.9593502008937256, pvalue=1.2028734278204504e-55)\n", "Slope and P-value = PearsonRResult(statistic=0.7490151158449072, pvalue=3.2137224424812283e-19)\n", "Slope and P-value = PearsonRResult(statistic=0.7949280084180457, pvalue=5.421411704149743e-23)\n" ] } ], "source": [ "print(\"Median:\",poultry.Al.median())\n", "\n", "poultry.loc[poultry['Al'] < poultry.Al.median(), 'new_Al'] = 0\n", "poultry.loc[poultry['Al'] >= poultry.Al.median(), 'new_Al'] = 1\n", "print(\"Total sample count:\", poultry.Al.value_counts().sum(),\"\\n\")\n", "\n", "print(\"Distribution:\")\n", "print(poultry.new_Al.value_counts())\n", "\n", "sample = pd.merge(microbiome, poultry[['SampleID', 'new_Al','SampleType']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "sample = sample[~sample.isin([np.nan, np.inf, -np.inf]).any(1)]\n", "sample = sample.drop(['Pathogen_Salmonella', 'new_Pathogen_Salmonella',\n", " 'Pathogen_Campy','new_Pathogen_Campy',\n", " 'Pathogen_Listeria','new_Pathogen_Listeria'],axis='columns')\n", "\n", "\n", "feces=sample[sample.SampleType=='Feces']\n", "soil=sample[sample.SampleType=='Soil']\n", "\n", "print ('SAMPLE DISTRIBUTION \\n')\n", "\n", "print('Feces', feces.shape)\n", "print('Soil', soil.shape,'\\n')\n", "\n", "sampletypes = [feces]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES\"}\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType', 'new_Al'],axis='columns'),sample.new_Al,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1]\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " \n", "# mylist2.append([f\"Al_{sample_name[indexing]}\", rf_auc_normal])\n", " \n", " \n", " print(pd.value_counts(sample['new_Al']))\n", "\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim3_plot.max(), decimals=None, symbol=''))\n", " \n", "\n", " plt.title(f\"Al in {sample_name[indexing]} Model\")\n", " prelim3_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','new_Al','SampleType'],axis='columns').columns)\n", " prelim3_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", "\n", " xmax=prelim3_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " \n", " plt.show()\n", " \n", " prelim3_plot.nlargest(10).to_csv(\"prelim3.csv\")\n", " top10 = pd.read_csv('prelim3.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", "# plt.plot(pdp[1][0], pdp[0][0],'.')\n", "# plt.ylabel('Partial dependence'), plt.xlabel(top10[feature])\n", "# plt.show()\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"Al_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (32) B" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Median: 6.348\n", "Total sample count: 818 \n", "\n", "Distribution:\n", "1.0 409\n", "0.0 409\n", "Name: new_B, dtype: int64\n", "SAMPLE DISTRIBUTION \n", "\n", "Feces (698, 877)\n", "Soil (0, 877) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "0.0 369\n", "1.0 329\n", "Name: new_B, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.6844457268187851, pvalue=4.131478641111002e-15)\n", "Slope and P-value = PearsonRResult(statistic=-0.5611919555923407, pvalue=1.252941010693974e-09)\n", "Slope and P-value = PearsonRResult(statistic=0.3857653755376765, pvalue=7.379208874701583e-05)\n", "Slope and P-value = PearsonRResult(statistic=0.9052867067103199, pvalue=3.2335106822697864e-38)\n", "Slope and P-value = PearsonRResult(statistic=-0.7048355358711954, pvalue=2.7442119395514935e-16)\n", "Slope and P-value = PearsonRResult(statistic=-0.8394073544148503, pvalue=1.0695939268448009e-27)\n", "Slope and P-value = PearsonRResult(statistic=-0.9649404501934001, pvalue=9.773015239293113e-59)\n", "Slope and P-value = PearsonRResult(statistic=-0.916879197071319, pvalue=7.161548000039466e-41)\n", "Slope and P-value = PearsonRResult(statistic=-0.8342066333917155, pvalue=4.465012085856488e-27)\n", "Slope and P-value = PearsonRResult(statistic=-0.5346285074590287, pvalue=2.191416158286816e-05)\n" ] } ], "source": [ "print(\"Median:\",poultry.B.median())\n", "\n", "poultry.loc[poultry['B'] < poultry.B.median(), 'new_B'] = 0\n", "poultry.loc[poultry['B'] >= poultry.B.median(), 'new_B'] = 1\n", "print(\"Total sample count:\", poultry.B.value_counts().sum(),\"\\n\")\n", "\n", "print(\"Distribution:\")\n", "print(poultry.new_B.value_counts())\n", "\n", "sample = pd.merge(microbiome, poultry[['SampleID', 'new_B','SampleType']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "sample = sample[~sample.isin([np.nan, np.inf, -np.inf]).any(1)]\n", "sample = sample.drop(['Pathogen_Salmonella', 'new_Pathogen_Salmonella',\n", " 'Pathogen_Campy','new_Pathogen_Campy',\n", " 'Pathogen_Listeria','new_Pathogen_Listeria'],axis='columns')\n", "\n", "\n", "feces=sample[sample.SampleType=='Feces']\n", "soil=sample[sample.SampleType=='Soil']\n", "\n", "print ('SAMPLE DISTRIBUTION \\n')\n", "\n", "print('Feces', feces.shape)\n", "print('Soil', soil.shape,'\\n')\n", "\n", "sampletypes = [feces]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES\"}\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType', 'new_B'],axis='columns'),sample.new_B,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1]\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " \n", "# mylist2.append([f\"B_{sample_name[indexing]}\", rf_auc_normal])\n", " \n", " \n", " print(pd.value_counts(sample['new_B']))\n", "\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim3_plot.max(), decimals=None, symbol=''))\n", " \n", "\n", " plt.title(f\"new_B in {sample_name[indexing]} Model\")\n", " prelim3_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','new_B','SampleType'],axis='columns').columns)\n", " prelim3_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", "\n", " xmax=prelim3_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " \n", " plt.show()\n", " \n", " prelim3_plot.nlargest(10).to_csv(\"prelim3.csv\")\n", " top10 = pd.read_csv('prelim3.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", "# plt.plot(pdp[1][0], pdp[0][0],'.')\n", "# plt.ylabel('Partial dependence'), plt.xlabel(top10[feature])\n", "# plt.show()\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"B_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (33) Ca" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Median: 3059.65\n", "Total sample count: 1635 \n", "\n", "Distribution:\n", "1.0 818\n", "0.0 817\n", "Name: new_Ca, dtype: int64\n", "SAMPLE DISTRIBUTION \n", "\n", "Feces (698, 877)\n", "Soil (695, 877) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "1.0 588\n", "0.0 110\n", "Name: new_Ca, dtype: int64\n" ] }, { "data": { "image/png": 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wOvAMcEDTjsLMzKy4jpw4jAeOklQhqQtQ1rd3SDpCHy8k6AksA/5TZh9dgN8Bd0XEexHxo9wsBZJ2B/5IljS8mWu3haT10vbGwH7AjHLGNjMza4oOuzgyIp6WdA8wlewTCNVAORf7TwV+JWkRsBQ4JSKWFVyUODolHWsBdwKX1FPvCqALcGvq99WIOBrYEbhKUpDNnFwZETUAks4Fvg98Gpgm6W8RMaCM4zIzM6uXIqK1Y2g1krqkTzasD4wj+3jlpNaOq6VVVVVFdXVjyzDMzKwjkTQxIlZa3N9hZxySYZJ2AiqAER0xaTAzMytHh04cIuLk/GNJ15CtF8jrCbxYUnZ1RNywOmMzMzNbE3XoxKFURJzV2jGYmZmtyTrypyrMzMysTE4czMzMrDAnDmZmZlaYEwczMzMrzImDmZmZFebEwczMzApz4mBmZmaF+XscjJo586gcfH9rh2FmrWTWkLLu8WcdnGcczMzMrDAnDmZmZlZY4cRB0jJJUyRNl3RruqNk0bb9Jf22nn2PN9K2UtLJucdVkn5TdOxcu1mSupfbrqSPQUWOW9KCVRkn18/j6XelpOlpu4+k+5qjfzMzs3KVM+OwOCJ6R0Qv4EPgzPxOSZ2aEkBEfLGRKpXAisQhIqoj4tymjNUMBgGFE6ZVVeDcmJmZtaimXqp4DNguvfsdLekmoEZShaQbJNVImizp4FybLSU9IGmGpJ/UFta+O1fmijSjUSOpb6oyBDggzXZ8O/+OW1KX3HjTJB1fzkFI2lvS4ynWxyV9PpV3knRlrt9zJJ0LfAYYLWl0qtcv1Zku6bKSvq+SNEnSI5I2S2XfkPS0pKmSbq+dvZC0uaQ7U/lUSV/Mn5sG4r9I0vm5x9PT7MQGku5PfU3Pnct824GSqiVVL1s0r5zTZmZmHVjZn6qQtDbwFeCBVLQ30CsiZkr6LkBE7CJpB+AhSdvn6wGLgKcl3R8R1bmujwN6A7sB3VOdccBg4PyIODKN3yfX5gJgXkTskvZtXObhPA8cGBFLJR0K/Bw4HhgIbA3snvZtEhHvSvoOcHBEvC3pM8BlwJ7Ae+lYj42Iu4ANgEkR8V1JFwI/Ac4G7oiIP6VYLwXOAIYCvwHGRsRX08xNlzKPo9ThwOsRcUQaq1tphYgYBgwDWLdHz1jF8czMrIMoZ8ZhPUlTgGrgVeC6VP5URMxM2/sDfwaIiOeBV4DaxOHhiHgnIhYDd6S6efsDoyJiWUS8AYwF9mokpkOBa2ofRMR7ZRwPQDfg1rR+4FfAzrl+/xARS1O/79bRdi9gTES8leqNBA5M+5YDN6ftv/DxsfaS9JikGuCU3HhfAn6fxloWEas6BVADHCrpMkkHNEN/ZmZmQHkzDosjone+QBLAwnxRA+1L39WWPm6obX1URz/luAQYnd7pVwJjyui3nHhr+xoOHBsRUyX1B/qU0UddlvLJ5K8CICJekLQn8F/ALyQ9FBEXr+JYZmZmzf5xzHFk76RJlyi2AmakfYdJ2kTSesCxwIQ62vZN6ws2I3v3/hQwH+haz3gPkV0CII1Z7qWKbsCctN2/pN8z02UZJG2SyvOxPAkcJKl7urzQj2yWBLLzekLaPhkYn7a7AnMldSadp+QR4FtprE6SNiwY/yxgj9RuD7LLK6TLKIsi4i/AlbV1zMzMVlVzJw6/Azqlqfibgf4R8UHaN57sMsYU4PaS9Q0AdwLTgKnAo8D3I+LfqWxpWuj37ZI2lwIbpwWAU4GDadg0SbPTzy+By8nekU8A8p8KuZbscsy01G/tpzqGAX+XNDoi5gI/AEanmCdFxN2p3kJgZ0kTyS5D1L7bv4As4XiYbH1FrfOAg9N5m8jHlzAaczuwSbqE9C3ghVS+C/BUKv8R2XkyMzNbZYrwuriOrqqqKqqrS/M4MzPryCRNjIiq0nJ/c6SZmZkV1u5uciXpSWDdkuJTI6KmNeIxMzNrT9pd4hAR+7R2DGZmZu2VL1WYmZlZYU4czMzMrDAnDmZmZlaYEwczMzMrzImDmZmZFebEwczMzApz4mBmZmaFtbvvcbDy1cyZR+Xg+1s7DDMDZg05orVDMGuQZxzMzMysMCcOZmZmVli7TRwkrS3pbUm/aO1YzMzM2ot2mzgAXwZmACdK0uocSFKn1dm/mZnZmmKNTRwkXSDpeUkPSxol6fwyu+gHXA28Cnwh1+8sST+VNElSjaQdUvnekh6XNDn9/nwq7yTpCklPS5om6ZupvI+k0ZJuAmokVUi6IfU5WdLBufZXpvJpks5J5YekejWSrpe0birfK40/VdJTkro20McsSd3TdpWkMWn7IElT0s9kSV3rOL8DJVVLql62aF6Zp9bMzDqqNfJTFZKqgOOB3clinARMLKP9esAhwDeBjciSiH/mqrwdEXtI+l/gfGAA8DxwYEQslXQo8PMUwxnAvIjYK724T5D0UOpnb6BXRMyU9F2AiNglJSMPSdoeOB3YGtg99b2JpApgOHBIRLwg6UbgW5J+B9wM9I2IpyVtCCwGBpb20cgpOB84KyImSOoCLCmtEBHDgGEA6/boGUXOq5mZ2Zo647A/cHdELI6I+cC9ZbY/EhgdEYuA24GvllxOuCP9nghUpu1uwK2SpgO/AnZO5V8Gvi5pCvAksCnQM+17KiJm5mL+M0BEPA+8AmwPHAr8ISKWpn3vAp8HZkbEC6ntCODAVD43Ip5Odd9P7erqoyETgF9KOhfYqLadmZnZqlpTE4dVXZPQDzhU0iyy5GBT4ODc/g/S72V8POtyCVmy0Qs4CqjIxXJORPROP1tHRO2Mw8ICMQsofUdfTt2Gypfy8d+wNl4iYgjZLMp6wBO1l2PMzMxW1ZqaOIwHjkrrBroAhb8RJU3v7w9sFRGVEVEJnEWWTDSkGzAnbffPlT9Idhmhc+p/e0kb1NF+HHBKbR1gK7LFmQ8BZ0paO+3bhOyySKWk7VLbU4GxqfwzkvZKdbumdnX1ATAL2DNtH587B9tGRE1EXAZUA04czMysWayRiUOaqr8HmEp2WaEaKLqC7zjg0Yj4IFd2N3B07QLEelwO/ELSBCB/WeNa4FlgUrqM8UfqXhvyO6CTpBqydQr9UwzXki3QnCZpKnByRCwhW/twa6q/nOxSxIdAX2Boqvsw2UzCSn2kMX8KXC3pMbLZk1qDJE1PdRcDf2/guM3MzApTxJq5Lk5Sl4hYIGl9snfzAyNiUmvH1R5VVVVFdXV1a4dhZmZrEEkTI6KqtHyN/FRFMkzSTmTvuEc4aTAzM2t9a2ziEBEn5x9LugbYr6RaT+DFkrKrI+KG1RmbmZlZR7XGJg6lIuKs1o7BzMyso1sjF0eamZnZmsmJg5mZmRXmxMHMzMwKc+JgZmZmhTlxMDMzs8KcOJiZmVlhThzMzMyssDbzPQ62+tTMmUfl4PtbOwyzVjVrSOF76Zl1aJ5xMDMzs8KcOJiZmVlhHSZxkLS2pLcl/aK1YyklaSNJ/5t7/BlJt7VmTGZmZnXpMIkD8GVgBnCiJK3OgSR1KrPJRsCKxCEiXo+IE5o1KDMzs2bQZhIHSRdIel7Sw5JGSTq/zC76AVcDrwJfyPU7S9JPJU2SVCNph1S+t6THJU1Ovz+fyjtJukLS05KmSfpmKu8jabSkm4AaSRWSbkh9TpZ0cKq3s6SnJE1J7XsCQ4BtU9kVkiolTU/1+0u6W9IDkmZI+kku9q/l+vpjiq2TpOGSpqexv13P+RwoqVpS9bJF88o8lWZm1lG1iU9VSKoCjgd2J4t5EjCxjPbrAYcA3yR7d98P+GeuytsRsUe6XHA+MAB4HjgwIpZKOhT4eYrhDGBeROwlaV1ggqSHUj97A70iYqak7wJExC4pGXlI0vbAmWS3/h4paR2gEzA4teud4q0sOYS9gV7AIuBpSfcDC4G+wH4R8ZGk3wGnAM8An42IXqmvjeo6JxExDBgGsG6PnlH0XJqZWcfWJhIHYH/g7ohYDCDp3jLbHwmMjohFkm4HLpD07YhYlvbfkX5PBI5L292AEWlGIIDOqfzLwK6STsjV6wl8CDwVETNzMQ8FiIjnJb0CbE+WsPxI0hbAHRHxYoErJw9HxDvp2O9IfS8F9iRLJADWA94E7gW2kTQUuB94qM4ezczMmqCtXKpY1TUJ/YBDJc0iSw42BQ7O7f8g/V7Gx8nUJWTJRi/gKKAiF8s5EdE7/WwdEbUvzgsbizkibgKOBhYDD0r6UoH4S2cEIvU/IhfH5yPiooh4D9gNGAOcBVxboH8zM7NC2kriMB44Kq0b6AIU/qYWSRuSvUPfKiIqI6KS7AW1XyNNuwFz0nb/XPmDwLckdU79by9pgzrajyO7dEC6RLEVMEPSNsDLEfEb4B5gV2A+0LWBWA6TtEm65HIsMAF4BDhB0qfSGJtI+pyk7sBaEXE7cAGwRyPHaWZmVlibuFQREU9LugeYCrwCVANFV/QdBzwaER/kyu4GLk9rFOpzOdmliu8Aj+bKrwUqgUnp0xlvkb2Yl/od8AdJNWSXFfpHxAeS+gJfk/QR8G/g4oh4V9KEtCDy78A1JX2NB/4MbAfcFBHVAJJ+TLZ2Yi3gI7KEaDFwQyoD+EEDxwjALp/tRrW/Nc/MzApQRNtYFyepS0QskLQ+2bv5gRExqbXjWt0k9QeqIuLs1TVGVVVVVFdXr67uzcysDZI0MSKqSsvbxIxDMkzSTmRrDUZ0hKTBzMxsTdNmEoeIODn/WNI1wH4l1XoCL5aUXR0RN6zO2FaniBgODG/lMMzMzIA2lDiUioizWjsGMzOzjqatfKrCzMzM1gBOHMzMzKwwJw5mZmZWmBMHMzMzK8yJg5mZmRXmxMHMzMwKa7Mfx7TmUzNnHpWD72/tMMxWi1n+OnWzZuUZBzMzMyvMiYOZmZkV1mjiIGmZpCmSpku6Nd1kqhBJ/SX9tp59jzfStlLSybnHVZJ+U3TsXLtZkmrSMUxprA9JvSX9V7njmJmZdQRFZhwWR0TviOgFfAicmd8pqVNTBo6ILzZSpRJYkThERHVEnNuUsYCD0zH0LtBHb6CsxEGS14qYmVmHUO6liseA7ST1kTRa0k1AjaQKSTekd/aTJR2ca7OlpAckzZD0k9pCSQvSb0m6Is1o1Ejqm6oMAQ5IswTfTmPel9p0yY03TdLx5R64pDGSLpP0lKQXJB0gaR3gYqBvGrevpA0kXS/p6XRsx6T2/dMMzL3AQ5I2kXRXiucJSbs2FKukfqlsuqTLcnEdLmmSpKmSHmmkjwW5didIGp62/zv1O1XSuHLPjZmZWX0Kv1NO76q/AjyQivYGekXETEnfBYiIXSTtQPZCun2+HrAIeFrS/RFRnev6OLJ3+bsB3VOdccBg4PyIODKN3yfX5gJgXkTskvZt3Ej4oyUtS9sjIuJXtccfEXunSxM/iYhDJV0IVEXE2anvnwOPRsT/SNoIeErSP1L7fYFdI+JdSUOByRFxrKQvATem41opVkmfAS4D9gTeS+frWGAC8CfgwHReN2ni8V4I/L+ImJNiXomkgcBAgE4bbtZId2ZmZpkiicN6kqak7ceA64AvAk9FxMxUvj8wFCAinpf0ClCbODwcEe8ASLoj1c0nDvsDoyJiGfCGpLHAXsD7DcR0KHBS7YOIeK+RYzg4It6uo/yO9Hsi2aWRunwZOFrS+elxBbBV2n44It7NHcfxKZ5HJW0qqVtdsUo6EBgTEW8BSBoJHAgsA8bVntdc3+Ue7wRguKRbcsf4CRExDBgGsG6PntFIf2ZmZkCxxGFxRPTOF0gCWJgvaqB96YtS6eOG2tZHdfTTFB+k38uo/1wIOD4iZnyiUNqHxs9BUHes9R1zfcdVX3m+rGJFYcSZKb4jgCmSetcmb2ZmZquiuT6OOQ44BSBdotgKqH2hPSxd/18POJbs3XBp276SOknajOyd91PAfKBrPeM9BJxd+6DA1H05Ssd9EDhHKVuStHs97fLnoA/wdkS8X0+sTwIHSequbHFpP2As8M9UvnWqW3upor7jfUPSjpLWAr6a279tRDwZERcCbwNblnsSzMzM6tJcicPvgE6SaoCbgf4RUftufjzwZ2AKcHvJ+gaAO4FpwFTgUeD7EfHvVLY0LfD7dkmbS4GNaxcAAgfTsNH6+OOYNzZWF9ipdnEkcAnQGZgmaXp6XJeLgCpJ08gWdp5WX6wRMRf4QRprKjApIu5Oly4GAnekujc3cryDgfvIztvcXCxX1C68JEtopjZyzGZmZoUowpe3O7qqqqqori7N58zMrCOTNDEiqkrL/c2RZmZmVli7+eIiSU8C65YUnxoRNa0Rj5mZWXvUbhKHiNintWMwMzNr73ypwszMzApz4mBmZmaFOXEwMzOzwpw4mJmZWWFOHMzMzKwwJw5mZmZWmBMHMzMzK6zdfI+DNV3NnHlUDr6/tcMwa1azhhzR2iGYtUuecTAzM7PCnDiYmZlZYU4c6iBpbUlvS/pFSfkPV9N4syR1r6P8TElfXx1jmpmZNYUTh7p9GZgBnChJufLVkjjUJyL+EBE3tuSYZmZmDWmXiYOkCyQ9L+lhSaMknV9mF/2Aq4FXgS+kPocA60maImlkKvuOpOnpZ1Aqq0xjj5A0TdJtktZP+w6RNFlSjaTrJeXv5vk9SU+ln+1S/YtqY5c0RlJV2u4uaVba7i/pLkn3Spop6ewU12RJT0japJ5zNFBStaTqZYvmlXl6zMyso2p3iUN6cT0e2B04Dqgqs/16wCHAfcAosiSCiBgMLI6I3hFxiqQ9gdOBfciSi29I2j1183lgWETsCrwP/K+kCmA40DcidiH7RMu3ckO/HxF7A78Ffl3mYfcCTgb2Bn4GLIqI3YF/AnVe6oiIYRFRFRFVndbvVuZwZmbWUbW7xAHYH7g7IhZHxHzg3jLbHwmMjohFwO3AVyV1qmecOyNiYUQsAO4ADkj7XouICWn7L6nu54GZEfFCKh8BHJjrb1Tu975lxjw6IuZHxFvAPD4+5hqgssy+zMzM6tUeEwc1XqVB/YBD06WAicCmwMFljhN1PG4srqhnu9ZSPv57VZTs+yC3vTz3eDn+rg4zM2tG7TFxGA8cJalCUheg8LfASNqQbHZgq4iojIhK4CzS5QrgI0md0/Y44FhJ60vaAPgq8Fjat5Wk2lmDfimm54HK2vULwKnA2NzwfXO//1lHeLOAPdP2CUWPyczMrDm1u3ejEfG0pHuAqcArQDXZ9H0RxwGPRkT+HfzdwOVpIeMwYJqkSWmdw3DgqVTv2oiYLKkSeA44TdIfgReB30fEEkmnA7dKWht4GvhDbpx1JT1Jlsz1Y2VXArdIOhV4tODxmJmZNStF1DUr3rZJ6hIRC9KnGcYBAyNiUguNXQncFxG9WmK85lBVVRXV1dWtHYaZma1BJE2MiJU+YNDuZhySYZJ2IlsLMKKlkgYzM7P2rl0mDhFxcv6xpGuA/Uqq9SS7jJB3dUTcsIpjzyL7eKSZmVm70y4Th1IRcVZrx2BmZtYetMdPVZiZmdlq4sTBzMzMCnPiYGZmZoU5cTAzM7PCnDiYmZlZYU4czMzMrDAnDmZmZlZYh/geB2tYzZx5VA6+v7XDMFth1pDC96YzsxbmGQczMzMrzImDmZmZFdbhEgdJwyXNlDRF0iRJ+zZSf0GZ/feRdF/aPlrS4Ny4JzQ98hX995f0mVXtx8zMrCk6XOKQfC8iegODgT+urkEi4p6IGNLM3fYHykocJHkti5mZNYs2mThIukDS85IeljRK0vlN7GocsJ2kLpIeSTMQNZKOqWPMFTMJ6fFvJfVP24eneMYDx+Xq9Jf021w3h0p6TNILko5MdSpT2aT088Vc+++neKZKGpJmLKqAkWnGZD1Je0oaK2mipAcl9Uhtx0j6uaSxwHl1HM9ASdWSqpctmtfE02dmZh1Nm3snKqkKOB7YnSz+ScDEJnZ3FFADLAG+GhHvS+oOPCHpnoiIAvFUAH8CvgT8C7i5geqVwEHAtsBoSdsBbwKHRcQSST2BUUCVpK8AxwL7RMQiSZtExLuSzgbOj4hqSZ2BocAxEfGWpL7Az4D/SeNtFBEH1RVIRAwDhgGs26Nno8dpZmYGbTBxAPYH7o6IxQCS7m1CH1dI+jHwFnAGIODnkg4ElgOfBTYH/l2grx2AmRHxYornL8DAeureEhHLgRclvVzbFvitpN7AMmD7VPdQ4IaIWAQQEe/W0d/ngV7Aw5IAOgFzc/sbSmLMzMzK1hYTBzVDH9+LiNtWdJhdctgM2DMiPpI0C6goabOUT17aye8v+o69tF4A3wbeAHZL/S+pDatAvwKeiYj6FnguLBiXmZlZIW1xjcN44ChJFZK6AM3xTTHdgDdT0nAw8Lk66rwC7CRpXUndgENS+fPA1pK2TY/7NTDOf0taK9XdBpiRxp6bZiJOJZs1AHgI+B9J6wNI2iSVzwe6pu0ZwGa1nwyR1FnSzmUduZmZWRna3IxDRDwt6R5gKtmLeTWwqqv7RgL3SqoGppAlA6XjvibpFmAa8CIwOZUvkTQQuF/S22SJTa96xpkBjCW7DHJmavs74HZJ/w2MJs0SRMQD6fJFtaQPgb8BPwSGA3+QtBjYFzgB+E1KZtYGfg08s0pnw8zMrB4qsP5vjSOpS0QsSO/GxwEDI2JSa8fVVlVVVUV1dXVrh2FmZmsQSRMjoqq0vM3NOCTDJO1Ets5ghJMGMzOzltEmE4eIODn/WNI1wH4l1XqSXVLIuzoiblidsZmZmbVnbTJxKBURZ7V2DGZmZh1Bu0gczMys9Xz00UfMnj2bJUuWNF7Z1jgVFRVsscUWdO7cuVB9Jw5mZrZKZs+eTdeuXamsrCR9GZ21ERHBO++8w+zZs9l6660LtWmL3+NgZmZrkCVLlrDppps6aWiDJLHpppuWNVvkxMHMzFaZk4a2q9y/nRMHMzMzK8xrHMzMrFlVDr6/WfubNaTYnQXuvPNOjjvuOJ577jl22GEHAMaMGcOVV17Jfffdt6Je//79OfLIIznhhBPo06cPc+fOpaKignXWWYc//elP9O7dG4B58+ZxzjnnMGHCBAD2228/hg4dSrdu3QB44YUXGDRoEC+88AKdO3dml112YejQoWy++eZNPtZ3332Xvn37MmvWLCorK7nlllvYeOONV6r3n//8hwEDBjB9+nQkcf3117PvvvsydepUzjzzTBYsWEBlZSUjR45kww03pKamhquuuorhw4c3ObZaThyMmjnzmv0fulm5ir44mNVn1KhR7L///vz1r3/loosuKtxu5MiRVFVVccMNN/C9732Phx9+GIAzzjiDXr16ceONNwLwk5/8hAEDBnDrrbeyZMkSjjjiCH75y19y1FFHATB69GjeeuutVUochgwZwiGHHMLgwYMZMmQIQ4YM4bLLLlup3nnnncfhhx/ObbfdxocffsiiRYsAGDBgAFdeeSUHHXQQ119/PVdccQWXXHIJu+yyC7Nnz+bVV19lq622anJ84EsVZmbWDixYsIAJEyZw3XXX8de//rVJfey7777MmTMHgH/9619MnDiRCy64YMX+Cy+8kOrqal566SVuuukm9t133xVJA8DBBx9Mr1713aqomLvvvpvTTjsNgNNOO4277rprpTrvv/8+48aN44wzzgBgnXXWYaONNgJgxowZHHjggQAcdthh3H777SvaHXXUUU0+N3lOHMzMrM276667OPzww9l+++3ZZJNNmDSp/DsRPPDAAxx77LEAPPvss/Tu3ZtOnTqt2N+pUyd69+7NM888w/Tp09lzzz0b7XP+/Pn07t27zp9nn312pfpvvPEGPXr0AKBHjx68+eabK9V5+eWX2WyzzTj99NPZfffdGTBgAAsXLgSgV69e3HPPPQDceuutvPbaayvaVVVV8dhjjxU/IfXwpQozM2vzRo0axaBBgwA46aSTGDVqFHvssUe9nxjIl59yyiksXLiQZcuWrUg4IqLOtvWV16dr165MmTKl+IEUsHTpUiZNmsTQoUPZZ599OO+88xgyZAiXXHIJ119/Peeeey4XX3wxRx99NOuss86Kdp/61Kd4/fXXV3l8Jw4tSNL5wABgKbAMuCoibpQ0BugBLE5VLwWeBG4EPg0sB4ZFxNWpn0uAY1L5m0D/iHhd0qbAbcBewPCIOLuljs3MrLW88847PProoysWCi5btgxJXH755Wy66aa89957n6j/7rvv0r179xWPR44cyW677cbgwYM566yzuOOOO9h5552ZPHkyy5cvZ621ssn55cuXM3XqVHbccUfefPNNxo4d22hs8+fP54ADDqhz30033cROO+30ibLNN9+cuXPn0qNHD+bOncunPvWpldptscUWbLHFFuyzzz4AnHDCCQwZMgSAHXbYgYceegjIFm/ef//H69eWLFnCeuut12jMjfGlihYi6UzgMGDviOgFHAjk09ZTIqJ3+rmNLLn4bkTsCHwBOCvdERTgiojYNSJ6A/cBF6byJcAFwPmr/4jMzNYMt912G1//+td55ZVXmDVrFq+99hpbb70148ePp2fPnrz++us899xzALzyyitMnTp1xScnanXu3JlLL72UJ554gueee47tttuO3XffnUsvvXRFnUsvvZQ99tiD7bbbjpNPPpnHH3/8Ey/MDzzwADU1NZ/ot3bGoa6f0qQB4Oijj2bEiBEAjBgxgmOOOWalOp/+9KfZcsstmTFjBgCPPPLIir5qL20sX76cSy+9lDPPPHNFuxdeeGGV12CAZxzKIukC4BTgNeBtYGJEXFmw+Q+BgyPifYCImAeMqK9yRMwF5qbt+ZKeAz4LPFvbR7IBEKneQmC8pO0KHMtAYCBApw03K3gIZmaNa+lPyIwaNYrBgwd/ouz444/npptu4oADDuAvf/kLp59+OkuWLKFz585ce+21Kz5Smbfeeuvx3e9+lyuvvJLrrruO6667jnPOOYftttuOiGDffffluuuuW1H3vvvuY9CgQQwaNIjOnTuz6667cvXVV6/SsQwePJgTTzyR6667jq222opbb70VgNdff50BAwbwt7/9DYChQ4dyyimn8OGHH7LNNttwww03rDgX11xzDQDHHXccp59++oq+R48ezRFHrPrfRhGxyp10BJKqgGuBfckSrknAH4skDpK6Aq9GxMofxs32j+GTlyoOiYh3cvsrgXFAr9qkQdLPgK8D88gSkrdy9fsDVUUvVazbo2f0OO3XRaqarTb+OGbb9dxzz7Hjjju2dhjWgA8++ICDDjqI8ePHs/baK88Z1PU3lDQxIqpK6/pSRXH7A3dHxOKImA/cW0ZbkWYFGpC/VJFPGroAtwOD8jMNEfGjiNgSGAl4LYOZmdXr1VdfZciQIXUmDeVy4lBck7+IPb3gL5S0TVkDSp3JkoaREXFHPdVuAo5vamxmZtb+9ezZkz59+jRLX04cihsPHCWpIs0ClDuv+gvgGkkbAkjaMK0zqJOyz/tcBzwXEb8s2dcz9/Bo4PkyYzEza1a+7N12lfu38+LIgiLiaUn3AFOBV4BqsvUFRf0e6AI8Lekj4CPgqgbq7wecCtRImpLKfhgRfwOGSPo82ccxXwFWLJuVNAvYEFhH0rHAlyNi5W8Zydnls92o9vVlM2uiiooK3nnnHd9auw2KCN555x0qKioKt/HiyDJI6hIRCyStT7ZYcWBElP/1ZGuYqqqqqK6ubu0wzKyN+uijj5g9ezZLlixp7VCsCSoqKthiiy3o3LnzJ8rrWxzpGYfyDEvfpVABjGgPSYOZ2arq3LkzW2+9dWuHYS3EiUMZIuLk/GNJ15BdUsjrCbxYUnZ1RNywOmMzMzNrCU4cVkFEnNXaMZiZmbUkf6rCzMzMCvPiSEPSfGBGa8fRwXQn+9pyazk+5y3P57zlNec5/1xErHRPAl+qMIAZda2ctdVHUrXPecvyOW95PuctryXOuS9VmJmZWWFOHMzMzKwwJw4GMKy1A+iAfM5bns95y/M5b3mr/Zx7caSZmZkV5hkHMzMzK8yJg5mZmRXmxKEDk3S4pBmS/iVpcGvH0x5J2lLSaEnPSXpG0nmp/CJJcyRNST//1dqxtieSZkmqSee2OpVtIulhSS+m3xu3dpzthaTP557LUyS9L2mQn+fNT9L1kt6UND1XVu9zW9IP0v/xMyT9v2aJwWscOiZJnYAXgMOA2cDTQL/GbsFt5ZHUA+gREZMkdQUmAscCJwILIuLK1oyvvUq3l6+KiLdzZZcD70bEkJQobxwR/9daMbZX6f+WOcA+wOn4ed6sJB0ILABujIheqazO53a6KeMoYG/gM8A/gO0jYtmqxOAZh45rb+BfEfFyRHwI/BU4ppVjanciYm7tXVQjYj7wHPDZ1o2qwzoGGJG2R5AlcNb8DgFeiohXWjuQ9igixgHvlhTX99w+BvhrRHwQETOBf5H9379KnDh0XJ8FXss9no1f0FYrSZXA7sCTqehsSdPS1KOnzZtXAA9JmihpYCrbPCLmQpbQAZ9qtejat5PI3uXW8vN89avvub1a/p934tBxqY4yX7daTSR1AW4HBkXE+8DvgW2B3sBc4KrWi65d2i8i9gC+ApyVpndtNZO0DnA0cGsq8vO8da2W/+edOHRcs4Etc4+3AF5vpVjaNUmdyZKGkRFxB0BEvBERyyJiOfAnmmH60D4WEa+n328Cd5Kd3zfSmpPatSdvtl6E7dZXgEkR8Qb4ed6C6ntur5b/5504dFxPAz0lbZ3eJZwE3NPKMbU7kgRcBzwXEb/MlffIVfsqML20rTWNpA3SQlQkbQB8mez83gOclqqdBtzdOhG2a/3IXabw87zF1Pfcvgc4SdK6krYGegJPrepg/lRFB5Y+GvVroBNwfUT8rHUjan8k7Q88BtQAy1PxD8n+g+1NNm04C/hm7TVKWzWStiGbZYDsDsA3RcTPJG0K3AJsBbwK/HdElC4ysyaStD7Z9fRtImJeKvszfp43K0mjgD5kt89+A/gJcBf1PLcl/Qj4H2Ap2aXSv69yDE4czMzMrChfqjAzM7PCnDiYmZlZYU4czMzMrDAnDmZmZlaYEwczMzMrzImDWQuQtCzdHXC6pHslbdRI/Ysknd9InWPTTWxqH18s6dBmiHW4pBNWtZ8yxxyUPs63xpC0Q/qbTZa0bcm+WZIeKymbUnvHQklVkn7TDDFU5u+CWLLv2vzff3WTtLmkmyS9nL7K+5+SvtpS49uaw4mDWctYHBG9093s3gXOaoY+jwVWvHBExIUR8Y9m6LdFpbspDgLWqMSB7PzeHRG7R8RLdezvKmlLAEk75ndERHVEnFt0oHQOyhIRA1rqbrbpi8zuAsZFxDYRsSfZl8ZtsZrHXXt19m9N48TBrOX9k3SjGUnbSnogvYN7TNIOpZUlfUPS05KmSrpd0vqSvkh2T4Ar0jvdbWtnCiR9RdItufZ9JN2btr+c3ilOknRruodGvdI765+nNtWS9pD0oKSXJJ2Z63+cpDslPSvpD5LWSvv6SapJMy2X5fpdkGZIngR+RHbL39GSRqf9v0/jPSPppyXx/DTFX1N7viR1kXRDKpsm6fiixyupt6QnUrs7JW2cvhxtEDCgNqY63AL0Tdul35jYR9J9jcSWPwf7SvpOOk/TJQ3KjbO2pBGp7W21MzOSxkiqKnCeL0vPr39I2ju1e1nS0alOJ0lXpOfYNEnfrONYvwR8GBF/qC2IiFciYmhDfaTzMCbF/bykkSkJQdKeksam2B7Ux1+ZPCY958YC50k6StKTymZ+/iFp83r+HtZSIsI//vHPav4BFqTfnchuAHR4evwI0DNt7wM8mrYvAs5P25vm+rkUOCdtDwdOyO0bDpxA9m2JrwIbpPLfA18j+6a5cbny/wMurCPWFf2Sfdvft9L2r4BpQFdgM+DNVN4HWAJsk47v4RTHZ1Icm6WYHgWOTW0CODE35iyge+7xJrnzNQbYNVev9vj/F7g2bV8G/DrXfuMyjncacFDavri2n/zfoI42s4DtgcfT48lksz/Tc+fkvvpiKz0HwJ5k3y66AdAFeIbsTqqVqd5+qd71fPy8GANUFTjPX0nbdwIPAZ2B3YApqXwg8OO0vS5QDWxdcrznAr9q4PldZx/pPMwjm5lYiyxp3j/F8DiwWWrTl+zba2uP63clf8vaLyscAFzV2v+eO/qPp4HMWsZ6kqaQvRBMBB5O736/CNya3oRB9p9uqV6SLgU2IntRebChgSJiqaQHgKMk3QYcAXwfOIjsxW1CGm8dsv/IG1N7D5MaoEtEzAfmS1qij9dqPBURL8OKr8TdH/gIGBMRb6XykcCBZFPey8hu/FWfE5XdDnttoEeKe1rad0f6PRE4Lm0fSjZ1XnsO3pN0ZGPHK6kbsFFEjE1FI/j4zo6NeRd4T9JJwHPAonrqrRRb2syfg/2BOyNiYYrrDuAAsnP/WkRMSPX+QvYifmWu/72o/zx/CDyQ6tUAH0TER5JqyJ6LkN3LY1d9vK6lG9k9DWbWd+CSrkkxfxgRezXQx4dkz43Zqd2UNO5/gF5k/w4gSxDzX0V9c257C+DmNCOxTkNxWctw4mDWMhZHRO/0QnUf2RqH4cB/IqJ3I22Hk72DnCqpP9m7uMbcnMZ4F3g6IuanKeKHI6JfmbF/kH4vz23XPq79P6T0u+uDum/pW2tJRCyra4eym/GcD+yVEoDhQEUd8SzLja86Ymjq8ZbjZuAaoH8DdeqKDT55Dho6V3Wd29L+6/NRpLfq5P5+EbFcH68fENksTkMJ6TPA8SsCiDhLUneymYV6+5DUh08+Z2r/ZgKeiYh96xlvYW57KPDLiLgn9XdRA3FaC/AaB7MWFNnNf84le2FcDMyU9N+QLUCTtFsdzboCc5XdnvuUXPn8tK8uY4A9gG/w8bu3J4D9JG2Xxltf0vardkQr7K3sTqtrkU07jweeBA6S1F3Z4r9+wNh62uePZUOyF4556Xr2VwqM/xBwdu0DSRtT4HjT3+M9SQekolMbiLEudwKX0/AsUF2xlRoHHJti3IDsTpK1n9rYSlLtC2w/snObV855rsuDwLfS8wtJ26cY8h4FKiR9K1eWX8xapI+8GcBmtcclqbOkneup2w2Yk7ZPq6eOtSAnDmYtLCImA1PJpq9PAc6QNJXsXd0xdTS5gOzF4WHg+Vz5X4HvqY6PC6Z3sveRvejel8reIntnPErSNLIX1pUWYzbRP4EhZLdNnkk27T4X+AEwmux4J0VEfbeyHgb8XdLoiJhKtmbgGbJr+hPqaZN3KbBxWhw4FTi4jOM9jWyR6TSyOzleXGA8ACJifkRcFhEflhNbHf1MIptZeorsb31tep5AdhnktBTfJmRrVvJtyznPdbkWeBaYpOyjn3+kZDY6zVocS5agzJT0FNllnf8r2kdJfx+SrYO5LJ2TKWSX7epyEdnlvMeAt8s4LltNfHdMM1slafr4/Ig4spVDMbMW4BkHMzMzK8wzDmZmZlaYZxzMzMysMCcOZmZmVpgTBzMzMyvMiYOZmZkV5sTBzMzMCvv/Er137wqdL7MAAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.6728001071143661, pvalue=1.187384416671372e-07)\n", "Slope and P-value = PearsonRResult(statistic=-0.8240371840816946, pvalue=6.360886812512942e-26)\n", "Slope and P-value = PearsonRResult(statistic=-0.2173739634749045, pvalue=0.2320538593809854)\n", "Slope and P-value = PearsonRResult(statistic=0.3335839724659984, pvalue=0.0006948718445965575)\n", "Slope and P-value = PearsonRResult(statistic=-0.917639444483682, pvalue=4.651157641050074e-41)\n", "Slope and P-value = PearsonRResult(statistic=-0.8878507073574428, pvalue=8.282575416144333e-35)\n", "Slope and P-value = PearsonRResult(statistic=0.13753631395868066, pvalue=0.1723895484313725)\n", "Slope and P-value = PearsonRResult(statistic=-0.8484797311266484, pvalue=7.917267501027589e-17)\n", "Slope and P-value = PearsonRResult(statistic=-0.7347272875729086, pvalue=3.301646249739726e-18)\n", "Slope and P-value = PearsonRResult(statistic=-0.7450645378673694, pvalue=6.2152112228363645e-19)\n", "0.0 575\n", "1.0 120\n", "Name: new_Ca, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.9285844039743143, pvalue=5.613850021226139e-44)\n", "Slope and P-value = PearsonRResult(statistic=-0.7033177434612232, pvalue=3.38463152831596e-16)\n", "Slope and P-value = PearsonRResult(statistic=0.9510168389098822, pvalue=9.154965464216905e-52)\n", "Slope and P-value = PearsonRResult(statistic=0.8218513592375778, pvalue=1.1011340770460757e-25)\n", "Slope and P-value = PearsonRResult(statistic=-0.5214352964583544, pvalue=2.6581561976822206e-08)\n", "Slope and P-value = PearsonRResult(statistic=0.9011698615072483, pvalue=2.34973681668848e-37)\n", "Slope and P-value = PearsonRResult(statistic=0.9766561494045636, pvalue=2.8598624086949748e-67)\n", "Slope and P-value = PearsonRResult(statistic=0.9352482015258496, pvalue=5.433868606227313e-46)\n", "Slope and P-value = PearsonRResult(statistic=0.8431242893660131, pvalue=3.732367251906713e-28)\n", "Slope and P-value = PearsonRResult(statistic=0.7955876624187881, pvalue=4.7099856653282875e-23)\n" ] } ], "source": [ "print(\"Median:\",poultry.Ca.median())\n", "\n", "poultry.loc[poultry['Ca'] < poultry.Ca.median(), 'new_Ca'] = 0\n", "poultry.loc[poultry['Ca'] >= poultry.Ca.median(), 'new_Ca'] = 1 \n", "print(\"Total sample count:\", poultry.Ca.value_counts().sum(),\"\\n\")\n", "\n", "print(\"Distribution:\")\n", "print(poultry.new_Ca.value_counts())\n", "\n", "sample = pd.merge(microbiome, poultry[['SampleID', 'new_Ca','SampleType']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "sample = sample[~sample.isin([np.nan, np.inf, -np.inf]).any(1)]\n", "sample = sample.drop(['Pathogen_Salmonella', 'new_Pathogen_Salmonella',\n", " 'Pathogen_Campy','new_Pathogen_Campy',\n", " 'Pathogen_Listeria','new_Pathogen_Listeria'],axis='columns')\n", "\n", "\n", "feces=sample[sample.SampleType=='Feces']\n", "soil=sample[sample.SampleType=='Soil']\n", "\n", "print ('SAMPLE DISTRIBUTION \\n')\n", "\n", "print('Feces', feces.shape)\n", "print('Soil', soil.shape,'\\n')\n", "\n", "sampletypes = [feces, soil]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES\", 1: \"SOIL\"}\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType', 'new_Ca'],axis='columns'),sample.new_Ca,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1]\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " \n", "# mylist2.append([f\"Ca_{sample_name[indexing]}\", rf_auc_normal])\n", " \n", " \n", " print(pd.value_counts(sample['new_Ca']))\n", "\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim3_plot.max(), decimals=None, symbol=''))\n", " \n", "\n", " plt.title(f\"new_Ca in {sample_name[indexing]} Model\")\n", " prelim3_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','new_Ca','SampleType'],axis='columns').columns)\n", " prelim3_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", "\n", " xmax=prelim3_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " \n", " plt.show()\n", " \n", " prelim3_plot.nlargest(10).to_csv(\"prelim3.csv\")\n", " top10 = pd.read_csv('prelim3.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", "# plt.plot(pdp[1][0], pdp[0][0],'.')\n", "# plt.ylabel('Partial dependence'), plt.xlabel(top10[feature])\n", "# plt.show()\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"Ca_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (34) Cd" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SAMPLE DISTRIBUTION \n", "\n", "Feces (698, 878)\n", "Soil (695, 878) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "1.0 534\n", "0.0 164\n", "Name: new_Cd, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.5596695022060355, pvalue=2.3835955396932226e-05)\n", "Slope and P-value = PearsonRResult(statistic=-0.7284595956204388, pvalue=8.754736293977213e-18)\n", "Slope and P-value = PearsonRResult(statistic=0.11697223114730235, pvalue=0.2464563183023815)\n", "Slope and P-value = PearsonRResult(statistic=-0.8739071236754482, pvalue=1.8235273366534033e-32)\n", "Slope and P-value = PearsonRResult(statistic=-0.9797714809687932, pvalue=2.7560799905061756e-70)\n", "Slope and P-value = PearsonRResult(statistic=-0.6510603533402575, pvalue=0.006301179606040712)\n", "Slope and P-value = PearsonRResult(statistic=0.1862414936805977, pvalue=0.06355809207215046)\n", "Slope and P-value = PearsonRResult(statistic=0.692143537515641, pvalue=1.523730001233405e-15)\n", "Slope and P-value = PearsonRResult(statistic=0.4592034619575206, pvalue=1.5437331803881141e-06)\n", "Slope and P-value = PearsonRResult(statistic=-0.6656188359164854, pvalue=4.185491253922431e-14)\n", "1.0 418\n", "0.0 277\n", "Name: new_Cd, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.8046709942053644, pvalue=6.431928356935773e-24)\n", "Slope and P-value = PearsonRResult(statistic=-0.8540360112200343, pvalue=1.4387205856265993e-29)\n", "Slope and P-value = PearsonRResult(statistic=-0.9014869283122929, pvalue=2.0231928927199286e-37)\n", "Slope and P-value = PearsonRResult(statistic=-0.8356133167431707, pvalue=3.048598214973836e-27)\n", "Slope and P-value = PearsonRResult(statistic=-0.8996591498225373, pvalue=4.760403160963416e-37)\n", "Slope and P-value = PearsonRResult(statistic=-0.9621083696730102, pvalue=4.108609191501135e-57)\n", "Slope and P-value = PearsonRResult(statistic=-0.7291815704290296, pvalue=7.835366806588072e-18)\n", "Slope and P-value = PearsonRResult(statistic=-0.7664598686086757, pvalue=1.4980628706055258e-20)\n", "Slope and P-value = PearsonRResult(statistic=-0.9179857339196703, pvalue=3.8157525111481047e-41)\n", "Slope and P-value = PearsonRResult(statistic=-0.9843605853773932, pvalue=1.028345725987208e-75)\n" ] } ], "source": [ "sample = pd.merge(microbiome, poultry[['SampleID', 'Cd','SampleType']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "sample = sample[~sample.isin([np.nan, np.inf, -np.inf]).any(1)]\n", "sample = sample.drop(['Pathogen_Salmonella', 'new_Pathogen_Salmonella',\n", " 'Pathogen_Campy','new_Pathogen_Campy',\n", " 'Pathogen_Listeria','new_Pathogen_Listeria'],axis='columns')\n", "\n", "feces=sample[sample.SampleType=='Feces']\n", "feces.loc[feces['Cd'] < feces.Cd.median(), 'new_Cd'] = 0\n", "feces.loc[feces['Cd'] >= feces.Cd.median(), 'new_Cd'] = 1 \n", "\n", "soil=sample[sample.SampleType=='Soil']\n", "soil.loc[soil['Cd'] < soil.Cd.median(), 'new_Cd'] = 0 \n", "soil.loc[soil['Cd'] >= soil.Cd.median(), 'new_Cd'] = 1 \n", "\n", "print ('SAMPLE DISTRIBUTION \\n')\n", "\n", "print('Feces', feces.shape)\n", "print('Soil', soil.shape,'\\n')\n", "\n", "sampletypes = [feces, soil]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES\", 1: \"SOIL\"}\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType', 'Cd', 'new_Cd'],axis='columns'),sample.new_Cd,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1]\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " \n", "# mylist2.append([f\"Cd_{sample_name[indexing]}\", rf_auc_normal])\n", " \n", " \n", " print(pd.value_counts(sample['new_Cd']))\n", "\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim3_plot.max(), decimals=None, symbol=''))\n", " \n", "\n", " plt.title(f\"new_Cd in {sample_name[indexing]} Model\")\n", " prelim3_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','new_Cd','Cd','SampleType'],axis='columns').columns)\n", " prelim3_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", "\n", " xmax=prelim3_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " \n", " plt.show()\n", " \n", " prelim3_plot.nlargest(10).to_csv(\"prelim3.csv\")\n", " top10 = pd.read_csv('prelim3.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", "# plt.plot(pdp[1][0], pdp[0][0],'.')\n", "# plt.ylabel('Partial dependence'), plt.xlabel(top10[feature])\n", "# plt.show()\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"Cd_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (35) Cr" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SAMPLE DISTRIBUTION \n", "\n", "Feces (698, 878)\n", "Soil (695, 878) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "0.0 349\n", "1.0 349\n", "Name: new_Cr, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.7763620372876439, pvalue=2.32868443822922e-21)\n", "Slope and P-value = PearsonRResult(statistic=0.4848327454075964, pvalue=3.192551984216054e-07)\n", "Slope and P-value = PearsonRResult(statistic=-0.8503872911990307, pvalue=4.3982519410078195e-29)\n", "Slope and P-value = PearsonRResult(statistic=-0.43155786426867826, pvalue=0.0008030017835502124)\n", "Slope and P-value = PearsonRResult(statistic=-0.8747500355122708, pvalue=1.3407271987306964e-32)\n", "Slope and P-value = PearsonRResult(statistic=0.724022555448065, pvalue=1.7179840413186912e-17)\n", "Slope and P-value = PearsonRResult(statistic=0.9395413772837926, pvalue=2.0913937842944957e-47)\n", "Slope and P-value = PearsonRResult(statistic=-0.012581951048725285, pvalue=0.9011232328742145)\n", "Slope and P-value = PearsonRResult(statistic=-0.4121515546899701, pvalue=2.0380336209299626e-05)\n", "Slope and P-value = PearsonRResult(statistic=-0.6417439911968138, pvalue=6.270538802163509e-13)\n", "1.0 354\n", "0.0 341\n", "Name: new_Cr, dtype: int64\n" ] }, { "data": { "image/png": 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vpe0PlHRC2mRrYFZE9AEeB+p6SeXTwNHA8cDNwKSI6AmsSuWfIGmwpHJJ5WtXLqtjd2Zm1lo19ohCP2B8RKyKiOXAffVo44L0IfwfwBGSvlCw7raC5R6SnpA0Hzgd2C+VHwKMS8s3VVaOiMeBvSTtBJwG3BURa9LqiRGxtKDtRyNiWUSsBhYCe+bEOhJ4LCLuI0saJkfEW6ndscAXU72PC2K/mew41cXfI+IjYD7QBngwlc8HSottEBGjIqIsIsradOhcx+7MzKy1auxEQQ3VUESsACaz4Yfq+wXLo4EfpjPrX7L+UgVA5DR7E1lScRZwQ067AB8ULK+lyNwOSYPIEohfVhbl9FlMsfjWsP7vU1Jl3QcAEfEx8FFEVG7/cbHYzMzM6quxE4WpwLGSSiR1JGdYvDbSxMKDgZdzqnQC3kjX6E8vKJ8GnJqWT6+yzWhgKEBEPLsRsR0ADAO+lT68AZ4GDktzGNqQjVo8ntZtAQxIywPJjlNVFcABafmk+sZmZma2MRo1UYiIGcAEYC5wN1AO1PUCeeUchXlkQ+t359S7kOzDeSLwfEH5fwJDJM0ANhhzj4h/kc1nKBxNqI8fAl2ASWlC43UR8QbwE2AS2f7Piojxqf77wH6SZpLNYfhVkTZ/CYyU9ATZKIaZmVmT0/pR60bqQOoYESvSXQhTgMERMatRO62lFNN8oE9EtJoZfmVlZVFeXt7cYZiZWQshaWZEFL3hoCm+cGlUGhGYRTZhsKUkCUeSjTxc2ZqSBDMzs7po9IlvETGw8LWkq8nuRCjUHXipStnIiNjYSwLVxfUI0LWx2jczM9scNPkM+YgY0tR9mpmZWf206Gc9mJmZWfNyomBmZma5nCiYmZlZLicKZmZmlsuJgpmZmeVyomBmZma5nCiYmZlZLj9psBWav2QZpcMfaO4wzMzqpGJEvZ8raBvBIwpmZmaWy4mCmZmZ5dpsEwVJoyUtSo99fl7SRQ3Y9t8kbSupVNKCBmhvV0l3puX+ku5Py4MkXbWx7ZuZmdXXZpsoJBdERC+gF3CmpG5VK0hqU9dGI+LrEfHvjY5ufXuvR8SAhmrPzMysobToREHShWk0YKKkcZKG1bOpkvT7/dRuhaRfSJoKfFPSVyQ9JWmWpDskdZR0lKTbC2LpL+m+gu13SKvaShojaZ6kOyV1SHV+IWmGpAWSRklSKt9L0iOS5qb+Pl2bkYk0QjKg4PWK9HsXSVPSyMkCSYfmbD9YUrmk8rUr/VRtMzOrnRabKEgqA04CegMnAmX1aOZySXOAxcCtEfFmwbrVEdEPeAT4OXBkRPQByoEfAROBz0vaOtU/BbitSB+fAUZFxP7Ae8APUvlVEXFgRPQA2gPHpPKxwNUR8TngC8Ab9divQgOBh9LIyeeAOcUqRcSoiCiLiLI2HTpvZJdmZtZatNhEAegHjI+IVRGxHLivHm1UXnr4D+AISV8oWFf5of95YF9gWkoqzgT2jIg1wIPAsZLaAkcD44v08VpETEvLN6e4AQ6X9LSk+cCXgP0kdQJ2i4h7ACJidUSsrMd+FZoBnCXpYqBnOlZmZmYNoiUnCmqohiJiBTCZ9R/ikC5DpH4mRkSv9LNvRHwnrbsNOJnsg35GzodwVH0tqQT4EzAgInoC15Jd/tiYfVpD+nulyxhbpn2bAnwRWALcJOmMjejDzMxsAy05UZhKdjZfIqkj2Rl9vaQRgYOBl4usng4cImmvVLeDpL3TuslAH+C7FL/sANBVUt+0fFqKu3JOxNsp9gEAEfEesFjSCamvrSrnNNRCBXBAWj4eaJfa2BN4MyKuBa5P8ZqZmTWIFpsoRMQMYAIwF7ibbO5AXWfhVc5RmAfMT+1U7ectYBAwTtI8ssRhn7RuLXA/cFT6XcxzZHdUzAO6ANekOyKuTX3eS3Z5oNK3gfNT/SfJLovUxrXAYZKeIUt6KkdE+gNzJM0mm9MxspbtmZmZ1UgRVUfOWw5JHSNiRTrrngIMjohZzR3Xpq6srCzKy8ubOwwzM2shJM2MiKI3DbT0Zz2MkrQv2VD+GCcJZmZmTatFJwoRMbDwtaSrgUOqVOsOvFSlbGRE3NCYsZmZmbUGLTpRqCoihjR3DGZmZq1Ji53MaGZmZs3PiYKZmZnlcqJgZmZmuZwomJmZWS4nCmZmZpbLiYKZmZnlcqJgZmZmuTap71GwhjF/yTJKhz/Q3GGYmW2gYkS9n/1njcgjCmZmZpbLiYKZmZnl2mQSBUltJb0t6X+rlE+WVJaW/yZp2wbqr1TSghrqDJJ0VQP0dZyk4Wn5YknD0vJoSQM2tn0zM7P62mQSBeArwAvAyZJUrEJEfD0i/t2kUTWAiJgQESOaOw4zM7OqmixRkHShpOclTZQ0rvKsuQ5OA0YC/wQ+n9NHhaQd0vIZkuZJmivpJkmdJC2S1C6t3ybVbydpL0mPpLqzJH26Srslkm6QNF/SbEmHF6zeQ9KDkl6QdFHBNvdKminpWUmDC8q/lvqYK+nRVFbjyESVfSuTNDktHyZpTvqZLalTzvaDJZVLKl+7cll1XZmZma3TJHc9pEsDJwG9U5+zgJl12L49cATwPWBbsqThqWrq7wf8DDgkIt6W1CUilqcP16OBe4FTgbsi4iNJY4EREXGPpBKyBGqngiaHAERET0n7AA9L2jutOwjoAawEZkh6ICLKgbMjYmmKfYaku1K71wJfjIhFkrrU9hhUYxgwJCKmSeoIrC5WKSJGAaMAttqlezRAv2Zm1go01YhCP2B8RKyKiOXAfXXc/hhgUkSsBO4CviGpTTX1vwTcGRFvA0TE0lR+HXBWWj4LuCGdge8WEfekuqtTP1Xjvymtfx54FahMFCZGxDsRsQq4O9UFOF/SXGA6sAfQnWwkZEpELKoS18aYBvxe0vnAthGxpgHaNDMzA5ouUSg6p6AOTgOOlFRBNhKxPXB4NfUFfOKsOSKmAaWSDgPaRMSCWsZWXZ2q/YSk/sCRQN+I+BwwGyjJi6uW1rD+71WyrrNsbsM5QHtgehrxMDMzaxBNlShMBY5N1/o7kg3/14qkbcjO0rtGRGlElJJdCjitms0eJZv0uH1qo3CI/0ZgHHADQES8ByyWdEKqu5WkDlXamwKcntbvDXQlm1gJ8GVJXdIlhhPIzvA7A+9GxMr0wV05p+Ip4DBJ3YrEVZMK4IC0fFJloaRPR8T8iLgUKAecKJiZWYNpkkQhImYAE4C5ZMPz5UBtZ9SdCDwWER8UlI0HjpO0VU5/zwK/AR5Pw/+/L1g9FtiOLFmo9G2ySwXzgCeB/6jS5J+ANpLmA7cBgwrimUp2WWIO2ZyHcuBBoG1q79dklx+IiLeAwcDdKa7bankMAH4JjJT0BLC2oHyopAWpvVXA3+vQppmZWbUU0TTz2iR1jIgV6Wx9CjA4ImY1SecbxjEAOD4ivt3UfbcUZWVlUV5e3txhmJlZCyFpZkSUFVvXlM96GCVpX7Lr62OaKUm4EjgK+HpT921mZrYparJEISIGFr6WdDVwSJVq3YGXqpSNjIgbGiiG8xqiHTMzs9ai2Z4eGRFDmqtvMzMzq51N6SuczczMrIk5UTAzM7NcThTMzMwslxMFMzMzy+VEwczMzHI5UTAzM7NcThTMzMwsV7N9j4I1n/lLllE6/IHmDsPMWrGKEbV+NqA1M48omJmZWS4nCmZmZpZrs0sUJI2WtEjSHElzJR1RsK5C0g4b0fZGbW9mZrap2ewSheSCiOgFDAX+3LyhmJmZbbpaZKIg6UJJz0uaKGmcpGH1bOopYLcqZedJmiVpvqR9Un9dJN0raZ6k6ZL2T+XbS3pY0mxJfwFUEOOPJC1IP0MLys9I7cyVdFMq21PSo6n8UUldU/nOku5JdedK+kI1bYyWNKCgnxXp9y6SpqQRlAWSDs05poMllUsqX7tyWT0Pp5mZtTYtLlGQVAacBPQGTgTKNqK5rwH3Vil7OyL6ANcAlQnIL4HZEbE/8FPgxlR+ETA1InoDE4DKD/gDgLOAg4HPA9+V1FvSfsDPgC9FxOeA/0ztXAXcmNofC1yRyq8AHk91+wDPVtNGnoHAQ2kE5XPAnGKVImJURJRFRFmbDp1raNLMzCzTEm+P7AeMj4hVAJLuq0cbl0u6DNiJ7IO80N3p90yyRKSyz5MAIuKxNJLQGfhiZZ2IeEDSuwX174mI91OMdwOHAgHcGRFvp22Wpvp9C/q6CbgsLX8JOCPVXQssk3RGTht5ZgB/ldQOuDci5tRQ38zMrNZa3IgCBcP7G+ECYC/g58CYKus+SL/Xsj5RKtZnVPldKC9G5dTPa7subawh/b0kCdgSICKmkCU0S4CbUqJhZmbWIFpiojAVOFZSiaSOQL2+lSMiPgZGAltI+moN1acApwNI6k92eeK9KuVHAdsV1D9BUgdJWwPfAJ4AHgVOlrR92qZLqv8kcGpaPj3tI6n+91PdNpK2qaaNCuCAtHw80C6t3xN4MyKuBa4nu4RhZmbWIFrcpYeImCFpAjAXeBUoB+o1+y4iQtIlwP8DHqqm6sXADZLmASuBM1P5L4FxkmYBjwP/TO3OkjQaeCbVuy4iZgNI+g3wuKS1wGxgEHA+2eWBC4C3yOY3QDb/YJSk75CNcHw/Ip7KaeNaYLykZ8iSifdTG/2BCyR9BKwgXcqoTs/dOlPub0UzM7NaUERtRsqblqSOEbFCUgeys/fBETGruePaXJSVlUV5eXlzh2FmZi2EpJkRUfTmgRY3opCMkrQvUAKMcZJgZmbWPFpkohARAwtfS7oaOKRKte7AS1XKRkbEDY0Zm5mZWWvSIhOFqiJiSHPHYGZm1hq1xLsezMzMrIVwomBmZma5nCiYmZlZLicKZmZmlsuJgpmZmeVyomBmZma5NonbI61hzV+yjNLhDzR3GGbWwlT4q92tCI8omJmZWS4nCmZmZparxScKkkZLWiRpTvo5P5VXSNqhAfsplbSggdoZWHPNots+ubH9m5mZNaRNZY7CBRFxZ3MHUUulwEDgltpuIKlNRKyNiC80WlRmZmb10CQjCpIulPS8pImSxkka1sDt3ytppqRnJQ1OZd+XdFlBnUGSrkzLP5K0IP0MLWiqraQxkuZJujM95hpJv5A0I9UfJUmpfC9Jj0iaK2mWpE8DI4BD0+jHf0lqI+nytP08Sd9L2/aXNEnSLcD8VLaiYN39BbFfJWlQWq6Q9D+SnpJULqmPpIckvSzp3IY8rmZmZo2eKEgqA04CegMnAkWfd12DywsuPfQssv7siDggtX2+pO2BO1N/lU4BbpN0AHAWcDDweeC7knqnOp8BRkXE/sB7wA9S+VURcWBE9ADaA8ek8rHA1RHxOeALwBvAcOCJiOgVEX8AvgMsi4gDgQNTf93S9gcBP4uIfet4PF6LiL7AE8BoYEDal1/lbSBpcEosyteuXFbH7szMrLVqihGFfsD4iFgVEcuB++rRxgXpg7dXRMwvsv58SXOB6cAeQPeIeAt4RdLnU+LwGWBaiueeiHg/IlYAdwOHpnZei4hpafnmVBfgcElPS5oPfAnYT1InYLeIuAcgIlZHxMoisX0FOEPSHOBpYHuyR2QDPBMRi+pxPCak3/OBpyNiedrf1ZK2LbZBRIyKiLKIKGvToXM9ujQzs9aoKeYoqFEbl/oDRwJ9I2KlpMlASVp9G3Ay8DxZchCVlw1yRNXXkkqAPwFlEfGapItT+7XdLwHnRcRDReJ+P2ebNWyYxJVUWf9B+v1xwXLl601l3omZmW0CmmJEYSpwrKQSSR2Bhv5Gj87AuylJ2IdsCL7S3cAJwGlkSQPAFOAESR0kbQ18g2wIH6CrpL5p+bQUe+WH9Nsp/gEAEfEesFjSCQCStkpzGpYDnQpieAj4vqR2qd7eqd/qvArsm9rsDBxRu0NhZmbWsBr97DMiZkiaAMwl+wAsBxryIvmDwLmS5gEvkF1+qOz7XUkLgX0j4plUNkvSaOCZVO26iJgtqRR4DjhT0l+Al4BrUgJyLdkwfwUwo6DvbwN/kfQr4CPgm8A8YE26FDIaGEl2J8SsNJrxFlnykiuNXNye2noJmF33w2JmZrbxFFF1tL0ROpE6RsSKdMY9BRgcEbMavWMrqqysLMrLy5s7DDMzayEkzYyIojcbNNX17FGS9iUbxh/jJMHMzGzT0CSJQkRs8E2Fkq4GDqlSrTvZMHuhkRFxQ2PGZmZmZvmaZYZ8RAxpjn7NzMysblr8sx7MzMys+ThRMDMzs1xOFMzMzCyXEwUzMzPL5UTBzMzMcjlRMDMzs1xOFMzMzCyXnzTYCs1fsozS4Q80dxhm1gJUjGjo5/TZ5sYjCmZmZpbLiYKZmZnlcqLQhCS1lfS2pP+tUn65pGfT73MlnVFDO8dJGt640ZqZmXmOQlP7CvACcLKkn8b6Z3x/D9gxIj6oTSMRMQGY0EgxmpmZreMRhTqQdKGk5yVNlDRO0rA6NnEaMBL4J/D51OYEYGvgaUmnSLq4sl1JkyX9UdKTkhZIOiiVD5J0VVo+VtLTkmZLekTSzjmxD5ZULql87cpl9TsAZmbW6jhRqCVJZcBJQG/gRKCsjtu3B44A7gfGkSUNRMRxwKqI6BURtxXZdOuI+ALwA+CvRdZPBT4fEb2BW4H/V6z/iBgVEWURUdamQ+e6hG5mZq2YE4Xa6weMj4hVEbEcuK+O2x8DTIqIlcBdwDcktanFduMAImIKsI2kbaus3x14SNJ84AJgvzrGZWZmlsuJQu1pI7c/DThSUgUwE9geOLwW20UNr68EroqInmRzHUo2Mk4zM7N1nCjU3lTgWEklkjoCtf6WEknbkI1IdI2I0ogoBYaQLj/U4JTURj9gWURUnWDQGViSls+sbUxmZma14bseaikiZqSJh3OBV4FyoLazAk8EHqtyV8N44DJJW9Ww7buSngS2Ac4usv5i4A5JS4DpQLdaxmRmZlYjrb9Dz2oiqWNErJDUAZgCDI6IWY3Y32RgWESUN2S7ZWVlUV7eoE2amdkmTNLMiCg6Sd8jCnUzStK+ZPMAxjRmkmBmZtYSOFGog4gYWPha0tXAIVWqdQdeqlI2MiJuqEd//eu6jZmZWUNyorARImJIc8dgZmbWmHzXg5mZmeVyomBmZma5nCiYmZlZLicKZmZmlsuJgpmZmeVyomBmZma5nCiYmZlZLn+PQis0f8kySoc/0NxhmFkVFSNq/aw5sybjEQUzMzPL5UTBzMzMcjV6oiDp85KeljRH0nOSLq7j9pMllaXlv0natg7bHidpeA11dpV0Z1oeJOmqusSX02Z/SV+ox3Zlkq7Y2P7NzMwaSlPMURgDnBwRcyW1AT5T34Yi4ut1rD8BmFBDndeBAfWNKUd/YAXwZG03kNQ2PU7az382M7MWo1YjCpIulPS8pImSxkkaVoc+dgLeAIiItRGxMLW5taS/Spohabak41N5e0m3Spon6TagfUEcFZJ2kFSa4rlO0gJJYyUdKWmapJckHZTqrxshkDRa0hWSnpT0iqQBqbxU0oKCePeQ9KCkFyRdVND3vZJmSnpW0uCC8q9JmiVprqRHJZUC5wL/lUZRDpW0o6S70r7OkHRI2vZiSaMkPQzcmEYi7i9YN6ygnwUp1lrte5G/4WBJ5ZLK165cVoc/n5mZtWY1jiikYf+TgN6p/ixgZh36+APwgqTJwIPAmIhYDfwMeCwizk6XE56R9AjwPWBlROwvaf/UXzF7Ad8EBgMzgIFAP+A44KfACUW22SXV2YdspOHOInUOAnoAK4EZkh5IZ/pnR8RSSe1T+V1kida1wBcjYpGkLqnOn4EVEfFbAEm3AH+IiKmSugIPAZ9N/R0A9IuIVZL6V3skN2LfI2IUMApgq126Ry37MTOzVq42lx76AeMjYhWApPvq0kFE/ErSWOArZB9op5ENzX8FOK7grLkE6Ap8EbgibTtP0rycphdFxPwU07PAoxERkuYDpTnb3BsRHwMLJe2cU2diRLyT2r2bbP/LgfMlfSPV2QPoDuwITImIRSnepTltHgnsK6ny9TaSOqXlCZXHtg7qs+9mZmZ1VptEQTVXqV5EvAxcI+la4C1J26d2T4qIFzboLPswrc0Z7wcFyx8XvP6Y/P0q3CZvv6r2HelM/0igb0SsTKMjJamN2sS6Rdp2g4Qg7ev7OdusYcNLQyUFy/XZdzMzszqrzRyFqcCxkkokdQTq9I0gko7W+lPp7sBa4N9kw+/nVa6T1DvVmQKcnsp6APvXpb8G8GVJXdIlhhOAaUBn4N2UJOwDfD7VfQo4TFK3FG+XVL4c6FTQ5sPADytfSOpVizgqgD6pfh+gWz33x8zMrN5qTBQiYgbZ9fy5wN1kw/B1mQ33bbI5CnOAm4DTI2It8GugHTAvTSb8dap/DdAxXXL4f8AzdeirIUxNcc4B7krzEx4E2qaYfg1MB4iIt8jmCdwtaS5wW2rjPuAblZMZgfOBsjRBcyHZZMea3AV0Scft+8CLDbR/ZmZmtaaImkfOJXWMiBWSOpCd8Q+OiLxJhtbClZWVRXm578I0M7OMpJkRUVZsXW2vZ4+StC/ZdfIxThLMzMxah1olChExsPC1pKuBQ6pU6w68VKVsZETcUP/wzMzMrDnVa4Z8RAxp6EDMzMys5fGtdGZmVicfffQRixcvZvXq1c0ditVRSUkJu+++O+3atav1Nk4UzMysThYvXkynTp0oLS1l/d3v1tJFBO+88w6LFy+mW7fa33Hvx0ybmVmdrF69mu23395JwiZGEttvv32dR4KcKJiZWZ05Sdg01efv5kTBzMzMcnmOgpmZbZTS4Q80aHsVI2r3pIB77rmHE088keeee4599tkHgMmTJ/Pb3/6W+++/f129QYMGccwxxzBgwAD69+/PG2+8QUlJCVtuuSXXXnstvXr1AmDZsmWcd955TJs2DYBDDjmEK6+8ks6dOwPw4osvMnToUF588UXatWtHz549ufLKK9l557xnDNZs6dKlnHLKKVRUVFBaWsrtt9/Odttt94l6f/jDH7juuuuQRM+ePbnhhhsoKckeAXTllVdy1VVX0bZtW44++mguu+wy5s+fz+9+9ztGjx5d79gqOVFoheYvWdbg/7DNWrvafrhZwxk3bhz9+vXj1ltv5eKLL671dmPHjqWsrIwbbriBCy64gIkTJwLwne98hx49enDjjTcCcNFFF3HOOedwxx13sHr1ao4++mh+//vfc+yxxwIwadIk3nrrrY1KFEaMGMERRxzB8OHDGTFiBCNGjODSSy/doM6SJUu44oorWLhwIe3bt+fkk0/m1ltvZdCgQUyaNInx48czb948ttpqK958800AevbsyeLFi/nnP/9J165d6x0f+NKDmZltglasWMG0adO4/vrrufXWW+vVRt++fVmyZAkA//jHP5g5cyYXXnjhuvW/+MUvKC8v5+WXX+aWW26hb9++65IEgMMPP5wePXps1H6MHz+eM888E4AzzzyTe++9t2i9NWvWsGrVKtasWcPKlSvZddddAbjmmmsYPnw4W221FQA77bTTum2OPfbYeh+bQk4UzMxsk3Pvvffyta99jb333psuXbowa1bdnyzw4IMPcsIJJwCwcOFCevXqRZs2bdatb9OmDb169eLZZ59lwYIFHHDAATW2uXz5cnr16lX0Z+HChZ+o/69//YtddtkFgF122WXdiECh3XbbjWHDhtG1a1d22WUXOnfuzFe+8hUguxzyxBNPcPDBB3PYYYcxY8aMdduVlZXxxBNP1OmYFONLD2ZmtskZN24cQ4cOBeDUU09l3Lhx9OnTJ3dWf2H56aefzvvvv8/atWvXJRgRUXTbvPI8nTp1Ys6cObXfkVp49913GT9+PIsWLWLbbbflm9/8JjfffDPf+ta3WLNmDe+++y7Tp09nxowZnHzyybzyyitIYqedduL111/f6P43yxEFSaMlLUqPeZ4lqW8Dt3+upDPS8mRJRZ+4Vcc2r0sP3kJShaQd0vKKjW3bzGxz8s477/DYY49xzjnnUFpayuWXX85tt91GRLD99tvz7rvvblB/6dKl7LDDDutejx07lkWLFjFw4ECGDMmeSLDffvsxe/ZsPv7443X1Pv74Y+bOnctnP/tZ9ttvP2bOnFljbHUdUdh555154403AHjjjTc2uHRQ6ZFHHqFbt27suOOOtGvXjhNPPJEnn3wSgN13350TTzwRSRx00EFsscUWvP3220D2fRft27evMeaabJaJQnJBRPQChgN/aciGI+LPEXFjA7d5TkR88l1kZmYbuPPOOznjjDN49dVXqaio4LXXXqNbt25MnTqV7t278/rrr/Pcc88B8OqrrzJ37tx1dzZUateuHZdccgnTp0/nueeeY6+99qJ3795ccskl6+pccskl9OnTh7322ouBAwfy5JNP8sAD6yeCP/jgg8yfP3+DditHFIr97Lvvvp/Yl+OOO44xY8YAMGbMGI4//vhP1OnatSvTp09n5cqVRASPPvoon/3sZwE44YQTeOyxx4DsMsSHH364Lil68cUXN3oOBbTgSw+SLgROB14D3gZmRsRv69HUFGAvSR2B8cB2QDvg5xExPvX1LeB8YEvgaeAHEbE2nc2PBI4BVgHHR8S/JF0MrCiI51uSrgC2Ac6OiGckHQT8EWiftj0rIl6Q1Aa4FPgqEMC1EXGlpMnAsIgozzke/dP6Y9Lrq4DyiBgtaQRwHLAGeDgihhXZfjAwGKDNNjvW4zCamRXX1Hd8jBs3juHDh29QdtJJJ3HLLbdw6KGHcvPNN3PWWWexevVq2rVrx3XXXbfuFsdC7du357//+7/57W9/y/XXX8/111/Peeedx1577UVE0LdvX66//vp1de+//36GDh3K0KFDadeuHfvvvz8jR47cqH0ZPnw4J598Mtdffz1du3bljjvuAOD111/nnHPO4W9/+xsHH3wwAwYMoE+fPrRt25bevXszePBgAM4++2zOPvtsevTowZZbbsmYMWPWXSqZNGkSRx+98X8bRcRGN9LQ0lD+dUBfsmRmFvCX2iYKkkYD90fEnZK+CQwjeyx2h4h4Lw3rTyd7NPY+wGXAiRHxkaQ/AdMj4kZJARwXEfdJugx4LyIuKUwU0gf8SxHxXUlfBP4UET0kbQOsjIg1ko4Evh8RJ0n6PnAkcEpa1yUilhYmCpIqgLKIeFvSiojomJcoABOAp4B9IiIkbRsR/67u+Gy1S/fY5cw/1uZQmlkttabbI5977rl1Z7TWMn3wwQccdthhTJ06lbZtNxwTKPb3kzQzIopeRm+pIwr9gPERsQpA0n31aONyST8H3gK+Awj4n/Rh/jGwG7AzcARwADAjZWHtgcpppx8Cld/aMRP4ck5f4wAiYoqkbSRtC3QCxkjqTjZyUPmoriOBP0fEmrTN0nrsW6H3gNXAdZIeKIjXzMxaqX/+85+MGDHiE0lCfbTURKEhvkT8goi4c12D0iBgR+CANHJQAZSkvsZExE+KtPFRrB9yWUv+8ao6LBPAr4FJEfENSaXA5MpQitSvjTVsOKekBCCNShxElvCcCvwQ+FI92jczs81E9+7d6d69e4O01VInM04FjpVUkuYWNMSYXmfgzZQkHA7smcofBQZI2glAUhdJe+Y1kuOUtG0/YFlELEv9LUnrBxXUfRg4V1Lbyv5q2cerwL6StpLUmSwxIB2fzhHxN2Ao0KuOsZuZ1VlLvGxtNavP361FjihExAxJE4C5ZB+Q5cCyjWx2LHCfpHJgDvB86mthukTxsKQtgI+AIanf2npX0pOkyYyp7DKySw8/Ah4rqHsdsDcwT9JHwLXAVTV1EBGvSbodmAe8BMxOqzoB4yVVjo78V01t9dytM+Wt6HqqmTWskpIS3nnnHT9qehMTEbzzzjvrnhFRWy1yMiNkZ8oRsUJSB7I7FwZHRN2/ess+oaysLMrLi95cYWZWo48++ojFixezevXq5g7F6qikpITdd9+ddu3abVC+KU5mBBiVvoCohGwOgZMEM7MWoF27dnTr1q25w7Am0mIThYgYWPha0tVktzgW6k42DF9oZETc0JixmZmZtRYtNlGoKiKGNHcMZmZmrU1LvevBzMzMWoAWO5nRGo+k5cALzR1HK7MD2VeRW9PxMW96PuZNr6GO+Z4RUfT7/TeZSw/WoF7Im91qjUNSuY950/Ixb3o+5k2vKY65Lz2YmZlZLicKZmZmlsuJQus0qrkDaIV8zJuej3nT8zFveo1+zD2Z0czMzHJ5RMHMzMxyOVEwMzOzXE4UWhFJX5P0gqR/SBre3PFsjiTtIWmSpOckPSvpP1P5xZKWSJqTfr7e3LFuTiRVSJqfjm15KusiaaKkl9Lv7Zo7zs2FpM8UvJfnSHpP0lC/zxuWpL9KelPSgoKy3Pe1pJ+k/99fkPTVBovDcxRaB0ltgBeBLwOLgRnAaRGxsFkD28xI2gXYJSJmSeoEzAROAE4GVkTEb5szvs2VpAqgLCLeLii7DFgaESNSYrxdRPy4uWLcXKX/W5YABwNn4fd5g5H0RWAFcGNE9EhlRd/X6SGK44CDgF2BR4C9I2LtxsbhEYXW4yDgHxHxSkR8CNwKHN/MMW12IuKNyiedRsRy4Dlgt+aNqtU6HhiTlseQJWzW8I4AXo6IV5s7kM1NREwBllYpzntfHw/cGhEfRMQi4B9k/+9vNCcKrcduwGsFrxfjD7BGJakU6A08nYp+KGleGk70MHjDCuBhSTMlDU5lO0fEG5AlcMBOzRbd5u1UsjPZSn6fN66893Wj/R/vRKH1UJEyX3dqJJI6AncBQyPiPeAa4NNAL+AN4HfNF91m6ZCI6AMcBQxJQ7bWyCRtCRwH3JGK/D5vPo32f7wThdZjMbBHwevdgdebKZbNmqR2ZEnC2Ii4GyAi/hURayPiY+BaGmhI0DIR8Xr6/SZwD9nx/VeaM1I5d+TN5otws3UUMCsi/gV+nzeRvPd1o/0f70Sh9ZgBdJfULZ0FnApMaOaYNjuSBFwPPBcRvy8o36Wg2jeABVW3tfqRtHWaOIqkrYGvkB3fCcCZqdqZwPjmiXCzdhoFlx38Pm8See/rCcCpkraS1A3oDjzTEB36rodWJN2q9EegDfDXiPhN80a0+ZHUD3gCmA98nIp/SvYfai+yocAK4HuV1xlt40j6FNkoAmRPxL0lIn4jaXvgdqAr8E/gmxFRdWKY1ZOkDmTXxD8VEctS2U34fd5gJI0D+pM9SvpfwEXAveS8ryX9DDgbWEN22fPvDRKHEwUzMzPL40sPZmZmlsuJgpmZmeVyomBmZma5nCiYmZlZLicKZmZmlsuJglkjkLQ2PT1vgaT7JG1bQ/2LJQ2roc4J6cEvla9/JenIBoh1tKQBG9tOHfscmm6vazEk7ZP+ZrMlfbrKugpJT1Qpm1P5VD9JZZKuaIAYSgufFFhl3XWFf//GJmlnSbdIeiV9NfZTkr7RVP1by+FEwaxxrIqIXumJb0uBIQ3Q5gnAug+KiPhFRDzSAO02qfS0waFAi0oUyI7v+IjoHREvF1nfSdIeAJI+W7giIsoj4vzadpSOQZ1ExDlN9bTX9MVh9wJTIuJTEXEA2Ze07d7I/bZtzPatfpwomDW+p0gPZ5H0aUkPpjO0JyTtU7WypO9KmiFprqS7JHWQ9AWy79S/PJ3JfrpyJEDSUZJuL9i+v6T70vJX0pngLEl3pGdQ5Epnzv+TtimX1EfSQ5JelnRuQftTJN0jaaGkP0vaIq07TdL8NJJyaUG7K9IIyNPAz8gegztJ0qS0/prU37OSflklnl+m+OdXHi9JHSXdkMrmSTqptvsrqZek6Wm7eyRtl76MbChwTmVMRdwOnJKWq34jYX9J99cQW+Ex6CvpR+k4LZA0tKCftpLGpG3vrBx5kTRZUlktjvOl6f31iKSD0navSDou1Wkj6fL0Hpsn6XtF9vVLwIcR8efKgoh4NSKurK6NdBwmp7iflzQ2JR1IOkDS4ym2h7T+a4gnp/fc48B/SjpW0tPKRnYekbRzzt/DmkpE+Mc//mngH2BF+t2G7IE5X0uvHwW6p+WDgcfS8sXAsLS8fUE7lwDnpeXRwICCdaOBAWTfRvhPYOtUfg3wLbJvc5tSUP5j4BdFYl3XLtm36X0/Lf8BmAd0AnYE3kzl/YHVwKfS/k1Mceya4tgxxfQYcELaJoCTC/qsAHYoeN2l4HhNBvYvqFe5/z8ArkvLlwJ/LNh+uzrs7zzgsLT8q8p2Cv8GRbapAPYGnkyvZ5ON7iwoOCb358VW9RgAB5B9e+fWQEfgWbInjZameoeken9l/ftiMlBWi+N8VFq+B3gYaAd8DpiTygcDP0/LWwHlQLcq+3s+8Idq3t9F20jHYRnZyMMWZElyvxTDk8COaZtTyL4dtnK//lTlb1n5ZYDnAL9r7n/Prf3HwzxmjaO9pDlk//HPBCams9svAHekkyzI/pOtqoekS4BtyT5EHqquo4hYI+lB4FhJdwJHA/8POIzsw2xa6m9Lsv+4a1L5DJD5QMeIWA4sl7Ra6+daPBMRr8C6r5ntB3wETI6It1L5WOCLZEPYa8kelJXnZGWPh24L7JLinpfW3Z1+zwROTMtHkg2FVx6DdyUdU9P+SuoMbBsRj6eiMax/8mFNlgLvSjoVeA5YmVPvE7GlxcJj0A+4JyLeT3HdDRxKduxfi4hpqd7NZB/avy1o/0Dyj/OHwIOp3nzgg4j4SNJ8svciZM/C2F/r56V0JnsuwKK8HZd0dYr5w4g4sJo2PiR7byxO281J/f4b6EH27wCyhLDwq51vK1jeHbgtjThsWV1c1jScKJg1jlUR0St9MN1PNkdhNPDviOhVw7ajyc4Q50oaRHaWVpPbUh9LgRkRsTwN+U6MiNPqGPsH6ffHBcuVryv/z6j63e9B8cfcVlodEWuLrVD2AJthwIHpA380UFIknrUF/atIDPXd37q4DbgaGFRNnWKxwYbHoLpjVezYVm0/z0eRTsUp+PtFxMdaf/1fZKM01SWgzwInrQsgYoikHchGDnLbkNSfDd8zlX8zAc9GRN+c/t4vWL4S+H1ETEjtXVxNnNYEPEfBrBFF9rCc88k+CFcBiyR9E7IJY5I+V2SzTsAbyh5XfXpB+fK0rpjJQB/gu6w/O5sOHCJpr9RfB0l7b9werXOQsieRbkE2jDwVeBo4TNIOyibrnQY8nrN94b5sQ/ZBsSxdjz6qFv0/DPyw8oWk7ajF/qa/x7uSDk1F364mxmLuAS6j+lGeYrFVNQU4IcW4NdmTFivvqugqqfID9TSyY1uoLse5mIeA76f3F5L2TjEUegwokfT9grLCyae1aaPQC8COlfslqZ2k/XLqdgaWpOUzc+pYE3KiYNbIImI2MJdsOPp04DuS5pKdtR1fZJMLyT4MJgLPF5TfClygIrfvpTPV+8k+ZO9PZW+RnfmOkzSP7IP0E5Mn6+kpYATZY4QXkQ2jvwH8BJhEtr+zIiLv0c6jgL9LmhQRc8mu+T9Ldk1+Ws42hS4BtkuT+eYCh9dhf88kmxQ6j+xJh7+qRX8ARMTyiLg0Ij6sS2xF2plFNnL0DNnf+rr0PoHsssaZKb4uZHNOCrety3Eu5jpgITBL2a2Yf6HK6HIalTiBLCFZJOkZsss0P65tG1Xa+5BsHsul6ZjMIbsMV8zFZJfnngDersN+WSPx0yPNrE7ScPCwiDimmUMxsybgEQUzMzPL5REFMzMzy+URBTMzM8vlRMHMzMxyOVEwMzOzXE4UzMzMLJcTBTMzM8v1/wHpYoQKqzw2bQAAAABJRU5ErkJggg==\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.7170476486578279, pvalue=4.82997750421113e-17)\n", "Slope and P-value = PearsonRResult(statistic=-0.9118327466338081, pvalue=1.1363994003298417e-39)\n", "Slope and P-value = PearsonRResult(statistic=-0.9441601432419979, pvalue=4.761966947226058e-49)\n", "Slope and P-value = PearsonRResult(statistic=-0.7222775934629712, pvalue=2.2315575546396368e-17)\n", "Slope and P-value = PearsonRResult(statistic=-0.8979606542410836, pvalue=1.0389949646345036e-36)\n", "Slope and P-value = PearsonRResult(statistic=0.8181350511405941, pvalue=2.752048621067934e-25)\n", "Slope and P-value = PearsonRResult(statistic=0.9140830061625127, pvalue=3.3832570106881754e-40)\n", "Slope and P-value = PearsonRResult(statistic=-0.8670742027578436, pvalue=2.0394467270321337e-31)\n", "Slope and P-value = PearsonRResult(statistic=0.8763189698606997, pvalue=7.518521888098681e-33)\n", "Slope and P-value = PearsonRResult(statistic=-0.8980161306544765, pvalue=1.0130630088292806e-36)\n" ] } ], "source": [ "sample = pd.merge(microbiome, poultry[['SampleID', 'Cr','SampleType']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "sample = sample[~sample.isin([np.nan, np.inf, -np.inf]).any(1)]\n", "sample = sample.drop(['Pathogen_Salmonella', 'new_Pathogen_Salmonella',\n", " 'Pathogen_Campy','new_Pathogen_Campy',\n", " 'Pathogen_Listeria','new_Pathogen_Listeria'],axis='columns')\n", "\n", "feces=sample[sample.SampleType=='Feces']\n", "feces.loc[feces['Cr'] < feces.Cr.median(), 'new_Cr'] = 0\n", "feces.loc[feces['Cr'] >= feces.Cr.median(), 'new_Cr'] = 1 \n", "\n", "soil=sample[sample.SampleType=='Soil']\n", "soil.loc[soil['Cr'] < soil.Cr.median(), 'new_Cr'] = 0 \n", "soil.loc[soil['Cr'] >= soil.Cr.median(), 'new_Cr'] = 1 \n", "\n", "print ('SAMPLE DISTRIBUTION \\n')\n", "\n", "print('Feces', feces.shape)\n", "print('Soil', soil.shape,'\\n')\n", "\n", "sampletypes = [feces, soil]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES\", 1: \"SOIL\"}\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType', 'Cr', 'new_Cr'],axis='columns'),sample.new_Cr,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1]\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " \n", "# mylist2.append([f\"Cr_{sample_name[indexing]}\", rf_auc_normal])\n", " \n", " \n", " print(pd.value_counts(sample['new_Cr']))\n", "\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim3_plot.max(), decimals=None, symbol=''))\n", " \n", "\n", " plt.title(f\"new_Cr in {sample_name[indexing]} Model\")\n", " prelim3_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','new_Cr','Cr','SampleType'],axis='columns').columns)\n", " prelim3_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", "\n", " xmax=prelim3_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " \n", " plt.show()\n", " \n", " prelim3_plot.nlargest(10).to_csv(\"prelim3.csv\")\n", " top10 = pd.read_csv('prelim3.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", "# plt.plot(pdp[1][0], pdp[0][0],'.')\n", "# plt.ylabel('Partial dependence'), plt.xlabel(top10[feature])\n", "# plt.show()\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"Cr_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (36) Cu" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Median: 1.8357\n", "Total sample count: 1635 \n", "\n", "Distribution:\n", "1.0 818\n", "0.0 817\n", "Name: new_Cu, dtype: int64\n", "SAMPLE DISTRIBUTION \n", "\n", "Feces (698, 877)\n", "Soil (695, 877) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "1.0 637\n", "0.0 61\n", "Name: new_Cu, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.5282784988604762, pvalue=8.041615562181448e-05)\n", "Slope and P-value = PearsonRResult(statistic=-0.7796662363085375, pvalue=1.224934503422717e-21)\n", "Slope and P-value = PearsonRResult(statistic=-0.9650290689117517, pvalue=8.651500270769072e-59)\n", "Slope and P-value = PearsonRResult(statistic=0.2177083509139808, pvalue=0.029564051293845494)\n", "Slope and P-value = PearsonRResult(statistic=-0.8607411157454752, pvalue=1.7012760839723357e-30)\n", "Slope and P-value = PearsonRResult(statistic=-0.877765816734481, pvalue=4.379525607392829e-33)\n", "Slope and P-value = PearsonRResult(statistic=-0.4679901003036635, pvalue=0.00021188430562651416)\n", "Slope and P-value = PearsonRResult(statistic=-0.628458440644768, pvalue=2.5582438024121297e-12)\n", "Slope and P-value = PearsonRResult(statistic=-0.7471272215070984, pvalue=4.411184690580484e-19)\n", "Slope and P-value = PearsonRResult(statistic=-0.6902840341695602, pvalue=5.2116306861854526e-15)\n", "0.0 644\n", "1.0 51\n", "Name: new_Cu, dtype: int64\n" ] }, { "data": { "image/png": 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Fvg9sSpa0PJ6rsioiDkx1zwSGRER5eg3wdkTsLem/gCHAmWm7fYADI2KlpHuBmyNitKTvAr8HBqR6pcDBwA7AZEk7Am8Ch0fEKkm9gTFAmaSvp+2+GBErJG0WEe9K+mFlXJI6A1cDx0TEW5IGApcB3039bRoRB1dzLAYBgwA6bbJ5XQ+hmZl1cB6ZabwDgfERsTIilgH31nP7I4HJEbECuAs4Np1SqnRHLdvfnX7PJEtMKk2IiJVpuS9wW1q+JcVc6a8RsToiXgReBnYGOgPXS5oPjAV2SXUPA25KsRIR7xaI5/PAbsBESXOAnwDb1GV/ImJkRJRFRFmnLt2r32MzM7Mcj8w0XsE5LvVwMtBP0sL0ugdwCPCP9PqDWrb/MP2uYN33s6btoprlytfnA28Ae5IlvKvSOhWoX5WApyOibzXra9sfMzOzevHITONNB46SVCKpK1DnOx5J2oRslGTbiCiNiFLgHLIEp5BlQLcGxPgYcFJaPjXFXOmbktaTtAPwObKJyN2BJRGxGvg2UDlS9BDwXUldUvybFYjreWBzSX1Tnc6Sdm1AzGZmZnXiZKaRImIGMAGYS3bKpxxYWsfNjwMejogPc2XjgaMlbVig/ijgj1UmANfFecAZkuaRJSf5ybfPA1OBvwM/iIhVwB+A0yQ9AexEGk2JiAfI9rU8nUKqnOi8Ji6yxOcE4HJJc4E5wJoJxGZmZk1NEbWdNbDaSOoaEcvTiMU0YFBEzGrtuIpZWVlZlJeXt3YYZmbWhkiaGRFlVcs9Z6ZpjJS0C1ACjHYiY2Zm1nKczDSBiDgl/1rStUC/KtV6Ay9WKbsqIm5qztjMzMzaOyczzSAizmntGMzMzDoKTwA2MzOzouZkxszMzIqakxkzMzMrak5mzMzMrKg5mTEzM7Oi5mTGzMzMipqTGTMzMytqvs+MtUnzX19K6dD7WzsMM7N1LBxR52cJWwvyyIyZmZkVNSczZmZmVtSczLQjkkZJel3Shul1T0kLWziG5en3VpLubMm+zcysY3Iy0/5UAN9tiY4kVTvnKiIWR8QJLRGHmZl1bE5m2hhJwyQ9J2mipDGShtSzid8B51dNNJS5QtICSfMlDcytuyiVzZU0IpWdJWlGKrtLUpdUPkrSbyRNBi6XtL2kx1Pdn+faLJW0ILf8iKRZ6eeAavZ9kKRySeUVK5bWc7fNzKyj8tVMbYikMuB4YC+y92YWMLOezbwGTAe+DdybKz8O6APsCfQEZkialsoGAF+MiBWSNkv1746I61NclwLfA65O63YCDouICkkTgOsi4mZJ1T0t/E3g8IhYJak3MAYoq1opIkYCIwE27NU76rnfZmbWQXlkpm05EBgfESsjYhnrJiP18QvgQtZ9fw8ExkRERUS8AUwF9gUOA26KiBUAEfFuqr9bGk2ZD5wK7Jpra2xEVKTlfmTJCcAt1cTTGbg+tTUW2KWB+2VmZvYpHplpW9QUjUTEPyXNAU6sQ9sCCo2CjAIGRMRcSacD/XPrPqjaZS0hnQ+8QTYqtB6wqpb6ZmZmdeaRmbZlOnCUpBJJXYHG3J3pMiA/32YaMFBSJ0mbA18CngIeAr6bmxNTeZqpG7BEUmeykZnqPAqclJarq9cdWBIRq8lOf3VqwP6YmZkV5GSmDYmIGcAEYC5wN1AONGgmbEQ8TTbnptI4YF5q+2Hgooj4d0Q8kPosT6M5lQnQMOBJYCLwXA1d/Qg4R9IMsqSlkD8Ap0l6gmy+TdWRHTMzswZThOdZtiWSukbE8jRSMg0YFBGzatuuvSkrK4vy8vLWDsPMzNoQSTMj4lMXkHjOTNszUtIuQAkwuiMmMmZmZvXhZKaNiYhT8q8lXUt2xVBeb+DFKmVXRcRNzRmbmZlZW+Rkpo2LiOru3WJmZmZ4ArCZmZkVOSczZmZmVtSczJiZmVlRczJjZmZmRc3JjJmZmRU1JzNmZmZW1JzMmJmZWVHzfWasTZr/+lJKh97f2mGYWRFbOKIxz+q1YuKRGTMzMytqTmbMzMysqLVIMiNpiKTnJC2QNFfSd5qo3SmSytLy3yRtWqDOcElDamlnQHq4Y1PEtFDSXbnXJ0galZaPljS0qftsCpJKJS1o7TjMzMzqq9mTGUk/AA4H9ouI3YAvAWrqfiLiGxHxXgM3HwA0ZWJRJmnXqoURMSEiRtTWp6R2M5epPe2LmZm1TbUmM5KGpVGViZLG1DbKUcD/Af8VEe8DRMTSiBid2r5Y0ow0YjNSklL5FEmXS3pK0guSDkrlG0m6XdI8SXcAG+XiXCipZ1r+saTnJf0D+Hyuzlmpv7mS7pLURdIBwNHAFZLmSNqhyohPT0kL0/KuKaY5KYbe1ezzlWm/qx7L0yVdU0Ofv5A0FfiRpEMlzZY0X9KfJW2Y2hgh6ZnU/5WpbDtJk1LZJEnbpvJv5kbDpuViGC/pgXSMfpoLsZOk6yU9LekhSRtVd9xS+ebp9Yz00y+VD0/v50PAzWnU5xFJs9LPAYUOmqRBksollVesWFrNoTUzM1tXjclM+kI/HtgLOA4oq0/jkroB3SLipWqqXBMR+6YRm42AI3Pr1o+I/YDBQOUX7tnAiojYA7gM2KdAn/sAJ+Vi3je3+u7U357As8D3IuIxYAJwYUT0qSFWgB8AV0VEH7Jjsaiaen8F9pa0Y6GVNfS5aUQcDFwLjAIGRsTuZFednS1pM+BYYNd0DC5N210D3JzKbgV+n8ovBr6a9vfoXAj7AacCfYBvViZuQG/g2ojYFXiP7L2HAsctlV8F/DYi9k11b8j1sQ9wTEScArwJHB4RewMDc/FVPS4jI6IsIso6deleqIqZmdmn1DYycyAwPiJWRsQy4N56ti8galh/iKQnJc0HvgzkT83cnX7PBErT8peAvwBExDxgXoE2DwLGRcSKNBo0IbdutzRCMJ/sy/xTp4Jq8Tjwf5L+B9guIlZWU68CuAL433q2f0f6/XnglYh4Ib0eTbbv7wOrgBskHQesSOv7Arel5VvI3jeAR4FRks4COuX6mRgR76T4787VfyUi5qTl/HGv7rgdBlwjaQ7Zcd4kJbAAE3LHpzNwfdp+LE17Ss/MzDq42pKZRs1tScnEB5I+96mGpRLgD8AJafTheqAkV+XD9LuCde+HU1NyVFudUcAPU38/q9Jf3iesPTZr6kTEbWQjHCuBByV9uYYYbiFLQLatQ7yVPki/Cx73iPiEbFTlLrI5Nw9U006k+j8AfgJ8FpgjqUd+fdX6rD3msO5xH0Xh47Ye0DeNLvWJiK1T0pvfF4DzgTeAPclGtDaoJm4zM7N6qy2ZmQ4cJalEUlegIXcg+iVwraRNACRtImkQa78Q305tn1CHtqaRjQwgaTdgj2rqHJvm13QDjsqt6wYskdS5sp1kWVpXaSFrT2GtiSslZS9HxO/JRiIK9Q9ARHwM/JbsNFkhVfvMew4ozZ2m+jYwNR2n7hHxt9Run7T+MbJTa6T9mp7i3SEinoyIi4G3yZIagMMlbZbmxAwgG8GpSXXH7SHgh5UvJPWhsO7AkohYnfalUzX1zMzM6q3GZCYiZpB9ac8lOx1RDtR3ZuZ1wGRghrJLf6eSzXt5j2w0Zj5wDzCjjm11lTQPuAh4qkDMs8hO18whG8F4JLd6GPAkMJEsYah0O3BhmnC7A9kE3rMlPQb0zNUbCCxIp1V2Bm6uJd4bqf4uy1X7zO/DKuAMYGw6NbMa+CNZUnFf2v+pZCMeAOcBZ6TybwM/SuVXpAnEC8iSvLmpfDrZyNEc4K6IKK9lP6o7bueRXbk1T9IzZHOKCvkDcJqkJ4CdWHfUxszMrFEUUfNZG0ldI2J5uoJlGjAoJQxWhCSdDpRFxA9rq9uaysrKory8thzLzMw6EkkzI+JTFyPV5R4gI5Xd3K0EGO1ExszMzNqSWpOZdGntOiRdC/SrUtwbeLFK2VURcVPDw7OmFhGjyCb0mpmZtQsNujtrRJzT1IGYmZmZNYQfNGlmZmZFzcmMmZmZFTUnM2ZmZlbUnMyYmZlZUXMyY2ZmZkXNyYyZmZkVNSczZmZmVtQadJ8Zs+Y2//WllA69v7XDMLMitHBEQ56JbMXMIzNmZmZW1JzMmJmZWVFzMtMEJO0v6UlJcyQ9K2l4Ku8v6YBGtNtf0n313GahpJ4N7dPMzKzYeM5M0xgNnBgRcyV1Aj6fyvsDy4HHWiswMzOz9s4jM4mkYZKekzRR0hhJQ+qx+RbAEoCIqIiIZySVAj8Azk8jNgdJOiqN4MyW9A9JW6a+h0u6RdLDkl6UdFau7a6S7kyx3arMoZLG5WI/XNLdBfbpvyUtSD+Dc+XfkTRP0lxJt6Sy7SRNSuWTJG2byreUNC7VnVs50lRNG6MknZDrZ3n63UvStHQcFkg6qJr3YJCkcknlFSuW1uPwm5lZR+aRGUBSGXA8sBfZMZkFzKxHE78Fnpc0BXgAGB0RCyX9EVgeEVemfj4D7B8RIelM4CLggtTGHsD+wMbAbEmVl/LsBewKLAYeBfoBDwPXSto8It4CzgBuqrJP+6TyLwICnpQ0FfgI+DHQLyLelrRZ2uQa4OaIGC3pu8DvgQHp99SIODaNOnWVtGs1bVTnFODBiLgstdGlUKWIGAmMBNiwV++opU0zMzPAIzOVDgTGR8TKiFgG3FufjSPiEqAMeIjsi/uBaqpuAzwoaT5wIVmSUqmy/7eBycB+qfypiFgUEauBOUBpRARwC/AtSZsCfYG/F9incRHxQUQsB+4GDgK+DNyZ+iEi3k31+wK3peVb0vak+teluhURsbSGNqozAzgjzSXaPR1jMzOzJuFkJqPGNhARL0XEdcChwJ6SehSodjVwTUTsDnwfKMk3UbXJ9PvDXFkFa0fTbgK+BZwMjI2IT6psX90+qUBfhdRUp7o2PiF9piQJ2AAgIqYBXwJeB26R9J069G9mZlYnTmYy04GjJJVI6grU645Lko5IX94AvcmSjveAZUC3XNXuZF/oAKdVaeaY1H8PsonDM2rqMyIWk516+gkwqkCVacAASV0kbQwcCzwCTAJOrEy2cqeIHgNOSsunkh0TUv2zU91OkjapoY2FwD6V+wN0Tuu3A96MiOuBG4G9a9o3MzOz+vCcGSAiZkiaAMwFXgXKgfrMQP028FtJK8hGJ06NiApJ9wJ3SjoGOBcYDoyV9DrwBLB9ro2ngPuBbYGfR8RiSTvV0u+twOYR8UyBfZolaVRqF+CGiJgNIOkyYKqkCmA2cDpwHvBnSRcClfNwAH4EjJT0PbIk7eyIeLyaNq4Hxkt6iizh+SC10R+4UNLHZFd31Toys/vW3Sn3XTzNzKwOlE2/MEldI2K5pC5koxqDImJWC/U9nNxE4Xpsdw0wOyJubJbAWlFZWVmUl5e3dhhmZtaGSJoZEWVVyz0ys9ZISbuQzWMZ3VKJTENJmkk28nFBbXXNzMzaMyczSUSckn8t6Vqyy6DzegMvVim7KiJuohEiYngDttmn9lpmZmbtn5OZakTEOa0dg5mZmdXOVzOZmZlZUXMyY2ZmZkXNyYyZmZkVNSczZmZmVtSczJiZmVlRczJjZmZmRc2XZlubNP/1pZQOvb+1wzCzNmyhH3liiUdmzMzMrKg5mTEzM7Oi5mTGzMzMipqTGTMzMytqngBsjSJpGHAq8C/gbWBmRFzZulGZmVlH4mTGGkxSGXA8sBfZZ2kWMLMR7Q0CBgF02mTzpgjRzMw6AJ9mssY4EBgfESsjYhlwb2Mai4iREVEWEWWdunRvmgjNzKzdczJjjaHWDsDMzMzJjDXGdOAoSSWSugK+g5WZmbU4z5mxBouIGZImAHOBV4FyYGnrRmVmZh2NIqK1Y7AiJqlrRCyX1AWYBgyKiFmNbbesrCzKy8sbH6CZmbUbkmZGRFnVco/MWGONlLQLUAKMbopExszMrD6czFijRMQp+deSrgX6VanWG3ixStlVEXFTc8ZmZmYdg5MZa1IRcU5rx2BmZh2Lr2YyMzOzouZkxszMzIqakxkzMzMrak5mzMzMrKg5mTEzM7Oi5mTGzMzMipqTGTMzMytqvs+MtUnzX19K6dD7WzsMM2ukhSP8/Flrfh6ZMTMzs6LmZMbMzMyKmpOZKiTtL+lJSXMkPStpeC31+0u6r5p1N6SHMDZlfNX2V892+kj6RgO220rSnY3t38zMrKl4zsynjQZOjIi5kjoBn29oQxFxZtOF1eT6AGXA3+q6gaT1I2IxcEJzBWVmZlZf7XJkRtIwSc9JmihpjKQh9dh8C2AJQERURMQzqc3hkm6R9LCkFyWdldumq6Q7U5+3SlLaZoqksrS8XNJlkuZKekLSlql8h/R6hqRLJC1P5ZJ0haQFkuZLGpjrbxNJ4yQ9I+mPktZL21wnqVzS05J+ljse+0p6LPX9lKTuwCXAwDQCNVDSxpL+nOKYLemYtO3pksZKuhd4SFKppAW5ddfk+rlPUv/c/l4uaaakf0jaLx2PlyUdXc37NijFX16xYmk93jIzM+vI2l0yk5KH44G9gOPIRh/q47fA8ylZ+L6kkty6PYAjgL7AxZK2SuV7AYOBXYDPAf0KtLsx8ERE7AlMAyqToauAqyJiX2Bxrv5xZKMnewKHAVdI6pXW7QdcAOwO7JDqAvw4IspSnAdL2kPSBsAdwI9S34cBHwAXA3dERJ+IuAP4MfBwiuOQ1N/Gqd2+wGkR8eXaD986+zslIvYBlgGXAocDx5IlUp8SESMjoiwiyjp16V6PrszMrCNrd8kMcCAwPiJWRsQy4N76bBwRl5AlQA8BpwAP5FZXtvs2MJksqQB4KiIWRcRqYA5QWqDpj4DKuS4zc3X6AmPT8m1V9mNMGh16A5gK7Jvr7+WIqADGpLoAJ0qaBcwGdiVLrj4PLImIGWn/3o+ITwrE9xVgqKQ5wBSgBNg2rZsYEe8W2KYmH7H22M0HpkbEx2m5tLqNzMzM6qs9zplRYxuIiJeA6yRdD7wlqUflqqpV0+8Pc2UVFD6uH0dE1FInr6b9+FQckrYHhgD7RsR/JI0iS0hUoH51/R0fEc+vUyh9kWwkp5BPWDchzo9i5fd3NekYRcRqSe3xc2dmZq2kPY7MTAeOklQiqSvZaaE6k3RE5ZwXoDdZ4vFeen1MarcH0B+Y0QTxPkF2WgzgpFz5NLI5LZ0kbQ58CXgqrdtP0vZprsxAsn3ehCzpWJrm43w91X0O2ErSvmn/uqVkYhnQLdffg8C5ufk+e9Uh9oVAH0nrSfosa0eqzMzMWky7+ws5ImZImgDMBV4FyoH6zCb9NvBbSSvIRh5OjYiK9B3/FHA/2emXn0fEYkk7NTLkwcBfJF2Q2q6MdRzZKai5ZCMrF0XEvyXtDDwOjCCbMzMNGJdGPGYDTwMvA48CRMRHafLw1ZI2AlaSzZuZzNrTSr8Efg78DpiXEpqFwJG1xP4o8ArZqaMFwKzGHAgzM7OG0NozAe2HpK4RsVxSF7Iv+0ER0agvWmX3m1keEVc2RYy5drsAKyMiJJ0EnBwRxzRlH8WorKwsysvLWzsMMzNrQyTNTBe6rKPdjcwkI5XdrK4EGN3YRKaZ7QNck0ZD3gO+27rhmJmZFZd2mcxExCn515Ku5dOXS/cGXqxSdlVE3FRNm8ObLMB1232E7PJrMzMza4B2mcxUFRHntHYMZmZm1jza49VMZmZm1oE4mTEzM7Oi5mTGzMzMipqTGTMzMytqTmbMzMysqDmZMTMzs6LmZMbMzMyKWoe4z4wVn/mvL6V06P2tHYaZFbBwRL2e32vW7DwyY2ZmZkXNyYyZmZkVNSczRULS+pLelvTLJmxzuKQhTdDOgPRgTzMzsxbnZKZ4fAV4HjgxPWH7UyR1atmQ1hgA1CuZkeT5WmZm1iSczLQQScMkPSdpoqQxDRgRORm4CngN2D/X7kJJF0uaDnxT0tckzZI0V9KkVGczSfdImifpCUl75NrdU9LDkl6UdFaq31XSpNTOfEnH5Pr7TmpnrqRbJB0AHA1cIWmOpB3SzwOSZkp6RNLOadtRkn4jaTJweYFjNEhSuaTyihVL63l4zMyso/Jfxy1AUhlwPLAX2TGfBcysx/YbAYcC3wc2JUtsHs9VWRURB0raPLX9pYh4RdJmaf3PgNkRMUDSl4GbgT5p3R5kydHGwGxJ9wNvAsdGxPuSegJPSJpANvryY6BfRLwtabOIeDetuy8i7kzxTgJ+EBEvSvoi8Afgy6m/nYDDIqKi6n5GxEhgJMCGvXpHXY+PmZl1bE5mWsaBwPiIWAkg6d56bn8kMDkiVki6Cxgm6fxcQnBH+r0/MC0iXgGIiHdz/R+fyh6W1ENS97SuMq6VacRkP+B+4BeSvgSsBrYGtiRLSO6MiLertL+GpK7AAcDY3NmwDXNVxhZKZMzMzBrKyUzLKDjHpR5OBvpJWphe9wAOAf6RXn+Q66fQiEah/qPK73z5qcDmwD4R8XHqt6SG9vPWA96LiD7VrP+gmnIzM7MG8ZyZljEdOEpSSRq5qPMdpyRtQjaysm1ElEZEKXAOWYJT1ePAwZK2T9tWnmaaRpagIKk/8HZEvJ/WHZPi6gH0B2YA3YE3UyJzCLBdqjuJbAJyjyrtLwO6AaR2X5H0zVRHkvas6/6amZnVl5OZFhARM4AJwFzgbqAcqOsM1+OAhyPiw1zZeOBoSfnTN0TEW8Ag4G5Jc1l7+mk4UCZpHjACOC232VNkp5WeAH4eEYuBW1P9crIk6LnU/tPAZcDU1P5vUhu3AxdKmi1ph7TN91Kdp4FjMDMzayaK8DzLliCpa0Qsl9SFbKRkUETMau242qqysrIoLy9v7TDMzKwNkTQzIsqqlnvOTMsZmW4sVwKMdiJjZmbWNJzMtJCIOCX/WtK1QL8q1XoDL1YpuyoibmrO2MzMzIqZk5lWEhHntHYMZmZm7YGTGTMza3c+/vhjFi1axKpVq1o7FGuAkpISttlmGzp37lyn+k5mzMys3Vm0aBHdunWjtLSUah5nZ21URPDOO++waNEitt9++zpt40uzzcys3Vm1ahU9evRwIlOEJNGjR496jao5mTEzs3bJiUzxqu9752TGzMzMiprnzJiZWbtXOvT+Jm1v4Yi6PZVm3LhxHHfccTz77LPsvPPOAEyZMoUrr7yS++67b029008/nSOPPJITTjiB/v37s2TJEkpKSthggw24/vrr6dOnDwBLly7l3HPP5dFHHwWgX79+XH311XTvnj07+IUXXmDw4MG88MILdO7cmd13352rr76aLbfcssH7+u677zJw4EAWLlxIaWkpf/3rX/nMZz6zTp3nn3+egQMHrnn98ssvc8kllzB48GCGDx/O9ddfz+abbw7AL37xC77xjW8wf/58fv3rXzNq1KgGx1bJyYy1SfNfX9rk//mYWePU9Qvc1hozZgwHHnggt99+O8OHD6/zdrfeeitlZWXcdNNNXHjhhUycOBGA733ve+y2227cfPPNAPz0pz/lzDPPZOzYsaxatYojjjiC3/zmNxx11FEATJ48mbfeeqtRycyIESM49NBDGTp0KCNGjGDEiBFcfvnl69T5/Oc/z5w5cwCoqKhg66235thjj12z/vzzz2fIkCHrbLP77ruzaNEiXnvtNbbddtsGxwc+zWRmZtYsli9fzqOPPsqNN97I7bff3qA2+vbty+uvvw7AP//5T2bOnMmwYcPWrL/44ospLy/npZde4rbbbqNv375rEhmAQw45hN12261R+zF+/HhOOy17pN9pp53GPffcU2P9SZMmscMOO7DddtvVWA/gqKOOavCxyXMyY2Zm1gzuuecevva1r7HTTjux2WabMWtW/Z9i88ADDzBgwAAAnnnmGfr06UOnTp3WrO/UqRN9+vTh6aefZsGCBeyzzz61trls2TL69OlT8OeZZ575VP033niDXr16AdCrVy/efPPNGtu//fbbOfnkk9cpu+aaa9hjjz347ne/y3/+85815WVlZTzyyCO1xlwbn2YyMzNrBmPGjGHw4MEAnHTSSYwZM4a999672it18uWnnnoqH3zwARUVFWuSoIgouG115dXp1q3bmlNCTe2jjz5iwoQJ/PKXv1xTdvbZZzNs2DAkMWzYMC644AL+/Oc/A7DFFluwePHiRvfrZKYISBoFHAwsBQT8d0RMaoF+BwMjI2JFLfWmAEMiwo+5NjMD3nnnHR5++GEWLFiAJCoqKpDEr371K3r06LHO6ARkk2x79uy55vWtt97KnnvuydChQznnnHO4++672XXXXZk9ezarV69mvfWyEyurV69m7ty5fOELX+DNN99k6tSptca2bNkyDjrooILrbrvtNnbZZZd1yrbcckuWLFlCr169WLJkCVtssUW1bf/9739n7733XmeOTn75rLPO4sgjj1zzetWqVWy00Ua1xlwbn2YqHhdGRB9gMPDHFupzMNClhfoyM2s37rzzTr7zne/w6quvsnDhQv71r3+x/fbbM336dHr37s3ixYt59tlnAXj11VeZO3fumiuWKnXu3JlLL72UJ554gmeffZYdd9yRvfbai0svvXRNnUsvvZS9996bHXfckVNOOYXHHnuM++9fe/HEAw88wPz589dpt3JkptBP1UQG4Oijj2b06NEAjB49mmOOOaba/R4zZsynTjEtWbJkzfK4cePWmcPzwgsvNHpOD3hkpsVIGgacCvwLeBuYGRFXNqCpx4GtU5slwHVAGfAJ2YjNZEm7AjcBG5AlrMdHxIuSvgWcl8qfBP4rIiokXQfsC2wE3BkRP5V0HrAVMFnS2xFxiKSvAD8DNgReAs6IiOVV9rNgHUkjgKNTnA9FxLrT2rNtBwGDADptsnkDDo2ZWWEtfSXWmDFjGDp06Dplxx9/PLfddhsHHXQQf/nLXzjjjDNYtWoVnTt35oYbblhzeXXeRhttxAUXXMCVV17JjTfeyI033si5557LjjvuSETQt29fbrzxxjV177vvPgYPHszgwYPp3Lkze+yxB1dddVWj9mXo0KGceOKJ3HjjjWy77baMHTsWgMWLF3PmmWfyt7/9DYAVK1YwceJE/vSnP62z/UUXXcScOXOQRGlp6TrrJ0+ezBFHNP69UUQ0uhGrmaQy4AagL1kCOQv4U12TmXSa6b6IuFPSAODEiDhF0gXAbhFxhqSdgYeAnYArgCci4lZJGwCdgFLgV8BxEfGxpD+kOjdL2iwi3pXUCZgEnBcR8yQtBMoi4m1JPYG7ga9HxAeS/gfYMCIuqTzNBCwsVAe4hiwJ2zkiQtKmEfFeTfu8Ya/e0eu039Xl8JhZCymmS7OfffZZvvCFL7R2GFaDDz/8kIMPPpjp06ez/vqfHlsp9B5KmhkRZVXremSmZRwIjI+IlQCS7m1AG1dI+hWwBbB/rt2rASLiOUmvkiUzjwM/lrQNcHcalTkU2AeYkSaKbQRUTkk/MY2KrA/0AnYB5lXpf/9U/mjafoPUT13qvA+sAm6QdD9wH2Zm1qG99tprjBgxomAiU19OZlpGUzwg5EKyUY/zgNFkiUnBdiPiNklPAkcAD0o6M9UdHRH/u05g0vZkoyr7RsR/0ihQSTX7MDEiTi6wrtY6kvYDDgVOAn4IfLmGdszMrJ3r3bs3vXv3bpK2PAG4ZUwHjpJUIqkrWZJRbxGxGrgKWE/SV4FpZPNwkLQTsC3wvKTPAS9HxO+BCcAeZKePTpC0Raq/maTtgE2AD4ClkrYEvp7rchnQLS0/AfSTtGPavkvqM69gnbTP3SPib2STivs0ZP/NzOrD0yiKV33fO4/MtICImCFpAjAXeBUoJ7vMuiFthaRLgYvIkqI/SppPNrH29Ij4UNJA4FuSPgb+DVyS5sT8BHhI0nrAx8A5EfGEpNnA08DLwKO57kYCf5e0JE0APh0YI2nDtP4nwAu52N6qps4yYHyasCzg/Nr2c/etu1NeROfnzaxtKSkp4Z133qFHjx5+enaRiQjeeecdSkoKnSQozBOAW4ikrumqni5kIyqDIqL+t4PsIMrKyqK83LetMbOG+fjjj1m0aBGrVq1q7VCsAUpKSthmm23o3LnzOuWeANz6RkrahWw+ymgnMmZmzadz585sv/32rR2GtRAnMy0kIk7Jv5Z0LdCvSrXewItVyq6KiJuaMzYzM7Ni5mSmlUTEOa0dg5mZWXvgq5nMzMysqHkCsLVJkpYBz7d2HB1MT7JHbVjL8TFveT7mLa8pj/l2EfGp5934NJO1Vc8XmrFuzUdSuY95y/Ixb3k+5i2vJY65TzOZmZlZUXMyY2ZmZkXNyYy1VSNbO4AOyMe85fmYtzwf85bX7MfcE4DNzMysqHlkxszMzIqakxkzMzMrak5mrM2R9DVJz0v6p6ShrR1PeyPps5ImS3pW0tOSfpTKh0t6XdKc9PON1o61vZG0UNL8dHzLU9lmkiZKejH9/kxrx9leSPp87vM8R9L7kgb7s960JP1Z0puSFuTKqv1cS/rf9P/785K+2iQxeM6MtSWSOgEvAIcDi4AZwMkR8UyrBtaOSOoF9IqIWZK6ATOBAcCJwPKIuLI142vPJC0EyiLi7VzZr4B3I2JESt4/ExH/01oxtlfp/5bXgS8CZ+DPepOR9CVgOXBzROyWygp+rtMDl8cA+wFbAf8AdoqIisbE4JEZa2v2A/4ZES9HxEfA7cAxrRxTuxIRSyqf2h4Ry4Bnga1bN6oO7RhgdFoeTZZYWtM7FHgpIl5t7UDam4iYBrxbpbi6z/UxwO0R8WFEvAL8k+z//UZxMmNtzdbAv3KvF+Ev2mYjqRTYC3gyFf1Q0rw0bOzTHU0vgIckzZQ0KJVtGRFLIEs0gS1aLbr27SSyEYFK/qw3r+o+183yf7yTGWtrVKDM50KbgaSuwF3A4Ih4H7gO2AHoAywBft160bVb/SJib+DrwDlpeN6amaQNgKOBsanIn/XW0yz/xzuZsbZmEfDZ3OttgMWtFEu7JakzWSJza0TcDRARb0RERUSsBq6nCYZ+bV0RsTj9fhMYR3aM30jzmCrnM73ZehG2W18HZkXEG+DPegup7nPdLP/HO5mxtmYG0FvS9umvqZOACa0cU7siScCNwLMR8Ztcea9ctWOBBVW3tYaTtHGacI2kjYGvkB3jCcBpqdppwPjWibBdO5ncKSZ/1ltEdZ/rCcBJkjaUtD3QG3iqsZ35aiZrc9Jlkr8DOgF/jojLWjei9kXSgcAjwHxgdSr+P7L/8PuQDfkuBL5fec7bGk/S58hGYwDWB26LiMsk9QD+CmwLvAZ8MyKqTqa0BpLUhWyOxuciYmkquwV/1puMpDFAf6An8AbwU+AeqvlcS/ox8F3gE7LT3H9vdAxOZszMzKyY+TSTmZmZFTUnM2ZmZlbUnMyYmZlZUXMyY2ZmZkXNyYyZmZkVNSczZh2YpIr01OAFku6VtGkt9YdLGlJLnQHpYXKVry+RdFgTxDpK0gmNbaeefQ5Ol/a2GZJ2Tu/ZbEk7VFm3UNIjVcrmVD7NWFKZpN83QQyl+SckV1l3Q/79b26StpR0m6SX02MiHpd0bEv1b22Dkxmzjm1lRPRJT7p9FzinCdocAKz5MouIiyPiH03QbotKT1keDLSpZIbs+I6PiL0i4qUC67tJ+iyApC/kV0REeUScV9eO0jGol4g4s6Wecp9uAHkPMC0iPhcR+5DdaHObZu53/eZs3+rPyYyZVXqc9MA3STtIeiD9pfuIpJ2rVpZ0lqQZkuZKuktSF0kHkD0D54o0IrBD5YiKpK9L+mtu+/6S7k3LX0l/Uc+SNDY9N6paaQTiF2mbckl7S3pQ0kuSfpBrf5qkcZKekfRHSeuldSdLmp9GpC7Ptbs8jSQ9CfwY2AqYLGlyWn9d6u9pST+rEs/PUvzzK4+XpK6Sbkpl8yQdX9f9ldRH0hNpu3GSPpNuKDkYOLMypgL+CgxMy1XvfNtf0n21xJY/Bn0l/Xc6TgskDc71s76k0WnbOytHsCRNkVRWh+N8efp8/UPSfmm7lyUdnep0knRF+ozNk/T9Avv6ZeCjiPhjZUFEvBoRV9fURjoOU1Lcz0m6NSVGSNpH0tQU24Nae0v+KekzNxX4kaSjJD2pbITsH5K2rOb9sJYQEf7xj3866A+wPP3uRPYQvq+l15OA3mn5i8DDaXk4MCQt98i1cylwbloeBZyQWzcKOIHsrrevARun8uuAb5HdNXRarvx/gIsLxLqmXbK7tp6dln8LzAO6AZsDb6by/sAq4HNp/yamOLZKcWyeYnoYGJC2CeDEXJ8LgZ6515vljtcUYI9cvcr9/y/ghrR8OfC73Pafqcf+zgMOTsuXVLaTfw8KbLMQ2Al4LL2eTTZKtiB3TO6rLraqxwDYh+xO0RsDXYGnyZ6yXprq9Uv1/szaz8UUoKwOx/nraXkc8BDQGdgTmJPKBwE/ScsbAuXA9lX29zzgtzV8vgu2kY7DUrIRnPXIEvkDUwyPAZunbQaS3YW8cr/+UOW9rLzx7JnAr1v733NH/vFQmVnHtpGkOWRfTjOBiWmU4ABgbPpjFbIvgqp2k3QpsCnZF92DNXUUEZ9IegA4StKdwBHARcDBZF+4j6b+NiD7cqlN5TO75gNdI2IZsEzSKq2d+/NURLwMa265fiDwMTAlIt5K5bcCXyI7XVFB9gDO6pwoaRDZl3OvFPe8tO7u9HsmcFxaPozstEflMfiPpCNr219J3YFNI2JqKhrN2ic+1+Zd4D+STgKeBVZUU+9TsaXF/DE4EBgXER+kuO4GDiI79v+KiEdTvb+QJRZX5trfl+qP80fAA6nefODDiPhY0nyyzyJkz67aQ2vnSXUne47PK9XtuKRrU8wfRcS+NbTxEdlnY1Habk7q9z1gN7J/B5AlrfnHHNyRW94GuCON3GxQU1zW/JzMmHVsKyOiT/ryvI9szswo4L2I6FPLtqPI/tKeK+l0sr92a3NH6uNdYEZELEvD+xMj4uR6xv5h+r06t1z5uvL/tqrPawlAVG9VRFQUWqHsoXhDgH1TUjIKKCkQT0WufxWIoaH7Wx93ANcCp9dQp1BssO4xqOlYFTq2VduvzseRhjTIvX8RsVpr56OIbLSrpiT5aeD4NQFEnCOpJ9kITLVtSOrPup+ZyvdMwNMR0bea/j7ILV8N/CYiJqT2htcQpzUzz5kxMyJ7AN95ZF/WK4FXJH0TskmWkvYssFk3YImkzsCpufJlaV0hU4C9gbNY+1fuE0A/STum/rpI2qlxe7TGfsqewL4e2SmD6cCTwMGSeiqb4HoyMLWa7fP7sgnZl9nSND/i63Xo/yHgh5UvJH2GOuxvej/+I+mgVPTtGmIsZBzwK2oeLSsUW1XTgAEpxo3JnjBdebXUtpIqv/RPJju2efU5zoU8CJydPl9I2inFkPcwUCLp7FxZfsJ2XdrIex7YvHK/JHWWtGs1dbsDr6fl06qpYy3EyYyZARARs4G5ZKceTgW+J2ku2V+/xxTYZBjZF9ZE4Llc+e3AhSpw6XD6i/8+skTgvlT2FtkIwhhJ88i+7D814biBHgdGAAvITgOMi+zpyP8LTCbb31kRMb6a7UcCf5c0OSLmks1BeZpsjsij1WyTdynwmTQBdi5wSD329zSyidTzyJ7wfEkd+gMgIpZFxOUR8VF9YivQziyyEbinyN7rG9LnBLJTWKel+DYjmwOV37Y+x7mQG4BngFnKLgP/E1XOJqTRnQFkSdMrkp4iOyX3P3Vto0p7H5HNq7o8HZM5ZKdcCxlOdir2EeDteuyXNQM/NdvM2qU09D8kIo5s5VDMrJl5ZMbMzMyKmkdmzMzMrKh5ZMbMzMyKmpMZMzMzK2pOZszMzKyoOZkxMzOzouZkxszMzIra/wfQq/HZAVLh7AAAAABJRU5ErkJggg==\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.7505902035615601, pvalue=2.462147627510429e-19)\n", "Slope and P-value = PearsonRResult(statistic=0.7926753541184566, pvalue=8.73106898444324e-23)\n", "Slope and P-value = PearsonRResult(statistic=-0.07255546582927284, pvalue=0.4731403251002099)\n", "Slope and P-value = PearsonRResult(statistic=0.8781156922635281, pvalue=3.839052204212688e-33)\n", "Slope and P-value = PearsonRResult(statistic=0.500102482084501, pvalue=1.1723896920057689e-07)\n", "Slope and P-value = PearsonRResult(statistic=0.7981526789215017, pvalue=2.712192407582218e-23)\n", "Slope and P-value = PearsonRResult(statistic=-0.6323160073672294, pvalue=1.712609708042097e-12)\n", "Slope and P-value = PearsonRResult(statistic=0.6778662368590872, pvalue=9.462181613106173e-15)\n", "Slope and P-value = PearsonRResult(statistic=0.8000069778141816, pvalue=1.7525040591102137e-14)\n", "Slope and P-value = PearsonRResult(statistic=0.9498151998237865, pvalue=1.8827166445775805e-30)\n" ] } ], "source": [ "print(\"Median:\",poultry.Cu.median())\n", "\n", "poultry.loc[poultry['Cu'] < poultry.Cu.median(), 'new_Cu'] = 0\n", "poultry.loc[poultry['Cu'] >= poultry.Cu.median(), 'new_Cu'] = 1 \n", "print(\"Total sample count:\", poultry.Cu.value_counts().sum(),\"\\n\")\n", "\n", "print(\"Distribution:\")\n", "print(poultry.new_Cu.value_counts())\n", "\n", "sample = pd.merge(microbiome, poultry[['SampleID', 'new_Cu','SampleType']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "sample = sample[~sample.isin([np.nan, np.inf, -np.inf]).any(1)]\n", "sample = sample.drop(['Pathogen_Salmonella', 'new_Pathogen_Salmonella',\n", " 'Pathogen_Campy','new_Pathogen_Campy',\n", " 'Pathogen_Listeria','new_Pathogen_Listeria'],axis='columns')\n", "\n", "\n", "feces=sample[sample.SampleType=='Feces']\n", "soil=sample[sample.SampleType=='Soil']\n", "\n", "print ('SAMPLE DISTRIBUTION \\n')\n", "\n", "print('Feces', feces.shape)\n", "print('Soil', soil.shape,'\\n')\n", "\n", "sampletypes = [feces, soil]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES\", 1: \"SOIL\"}\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType', 'new_Cu'],axis='columns'),sample.new_Cu,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1]\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " \n", "# mylist2.append([f\"Cu_{sample_name[indexing]}\", rf_auc_normal])\n", " \n", " \n", " print(pd.value_counts(sample['new_Cu']))\n", "\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim3_plot.max(), decimals=None, symbol=''))\n", " \n", "\n", " plt.title(f\"new_Cu in {sample_name[indexing]} Model\")\n", " prelim3_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','new_Cu','SampleType'],axis='columns').columns)\n", " prelim3_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", "\n", " xmax=prelim3_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " \n", " plt.show()\n", " \n", " prelim3_plot.nlargest(10).to_csv(\"prelim3.csv\")\n", " top10 = pd.read_csv('prelim3.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", "# plt.plot(pdp[1][0], pdp[0][0],'.')\n", "# plt.ylabel('Partial dependence'), plt.xlabel(top10[feature])\n", "# plt.show()\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"Cu_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (37) Fe" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Median: 76.9956\n", "Total sample count: 1635 \n", "\n", "Distribution:\n", "1.0 818\n", "0.0 817\n", "Name: new_Fe, dtype: int64\n", "SAMPLE DISTRIBUTION \n", "\n", "Feces (698, 877)\n", "Soil (695, 877) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "1.0 661\n", "0.0 37\n", "Name: new_Fe, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.6605981241172127, pvalue=7.547445060598971e-14)\n", "Slope and P-value = PearsonRResult(statistic=0.20564392944613377, pvalue=0.04011355374842798)\n", "Slope and P-value = PearsonRResult(statistic=-0.8523440567925575, pvalue=2.424603768606619e-29)\n", "Slope and P-value = PearsonRResult(statistic=-0.8077198004822099, pvalue=1.2564265551544187e-15)\n", "Slope and P-value = PearsonRResult(statistic=-0.8102026830544077, pvalue=1.8164653670427685e-24)\n", "Slope and P-value = PearsonRResult(statistic=-0.919998836395086, pvalue=1.186078357193709e-41)\n", "Slope and P-value = PearsonRResult(statistic=-0.5929506550483653, pvalue=8.037918088881985e-11)\n", "Slope and P-value = PearsonRResult(statistic=-0.7038210346756127, pvalue=8.753650617814742e-11)\n", "Slope and P-value = PearsonRResult(statistic=-0.8090389665618151, pvalue=1.618635145104388e-18)\n", "Slope and P-value = PearsonRResult(statistic=-0.6966765620795032, pvalue=8.3453215982254e-16)\n", "0.0 663\n", "1.0 32\n", "Name: new_Fe, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.7900335541026022, pvalue=1.350317713253267e-09)\n", "Slope and P-value = PearsonRResult(statistic=0.7831369300273462, pvalue=0.001544744690504916)\n", "Slope and P-value = PearsonRResult(statistic=0.7629030336532354, pvalue=2.8592393961421795e-20)\n", "Slope and P-value = PearsonRResult(statistic=0.5130417293575182, pvalue=4.824332662588495e-08)\n", "Slope and P-value = PearsonRResult(statistic=0.5555622828899976, pvalue=1.9790585750103496e-09)\n", "Slope and P-value = PearsonRResult(statistic=0.9789337761240406, pvalue=5.029637900460605e-41)\n", "Slope and P-value = PearsonRResult(statistic=0.7037317845436498, pvalue=3.19682427041662e-16)\n", "Slope and P-value = PearsonRResult(statistic=nan, pvalue=nan)\n", "Slope and P-value = PearsonRResult(statistic=0.5540793371066278, pvalue=0.061580480891643086)\n", "Slope and P-value = PearsonRResult(statistic=0.8671786172402132, pvalue=1.9675643544533827e-31)\n" ] } ], "source": [ "print(\"Median:\",poultry.Fe.median())\n", "\n", "poultry.loc[poultry['Fe'] < poultry.Fe.median(), 'new_Fe'] = 0\n", "poultry.loc[poultry['Fe'] >= poultry.Fe.median(), 'new_Fe'] = 1\n", "print(\"Total sample count:\", poultry.Fe.value_counts().sum(),\"\\n\")\n", "\n", "print(\"Distribution:\")\n", "print(poultry.new_Fe.value_counts())\n", "\n", "sample = pd.merge(microbiome, poultry[['SampleID', 'new_Fe','SampleType']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "sample = sample[~sample.isin([np.nan, np.inf, -np.inf]).any(1)]\n", "sample = sample.drop(['Pathogen_Salmonella', 'new_Pathogen_Salmonella',\n", " 'Pathogen_Campy','new_Pathogen_Campy',\n", " 'Pathogen_Listeria','new_Pathogen_Listeria'],axis='columns')\n", "\n", "\n", "feces=sample[sample.SampleType=='Feces']\n", "soil=sample[sample.SampleType=='Soil']\n", "\n", "print ('SAMPLE DISTRIBUTION \\n')\n", "\n", "print('Feces', feces.shape)\n", "print('Soil', soil.shape,'\\n')\n", "\n", "sampletypes = [feces, soil]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES\", 1: \"SOIL\"}\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType', 'new_Fe'],axis='columns'),sample.new_Fe,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1]\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " \n", "# mylist2.append([f\"Fe_{sample_name[indexing]}\", rf_auc_normal])\n", " \n", " \n", " print(pd.value_counts(sample['new_Fe']))\n", "\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim3_plot.max(), decimals=None, symbol=''))\n", " \n", "\n", " plt.title(f\"new_Fe in {sample_name[indexing]} Model\")\n", " prelim3_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','new_Fe','SampleType'],axis='columns').columns)\n", " prelim3_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", "\n", " xmax=prelim3_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " \n", " plt.show()\n", " \n", " prelim3_plot.nlargest(10).to_csv(\"prelim3.csv\")\n", " top10 = pd.read_csv('prelim3.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", "# plt.plot(pdp[1][0], pdp[0][0],'.')\n", "# plt.ylabel('Partial dependence'), plt.xlabel(top10[feature])\n", "# plt.show()\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"Fe_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (38) K" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Median: 1681.44\n", "Total sample count: 1635 \n", "\n", "Distribution:\n", "1.0 818\n", "0.0 817\n", "Name: new_K, dtype: int64\n", "SAMPLE DISTRIBUTION \n", "\n", "Feces (698, 877)\n", "Soil (695, 877) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "1.0 683\n", "0.0 15\n", "Name: new_K, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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pvalue=0.016106930205704424)\n", "Slope and P-value = PearsonRResult(statistic=-0.8955370542899163, pvalue=1.4861557221948798e-09)\n", "0.0 675\n", "1.0 20\n", "Name: new_K, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.7472451898436789, pvalue=0.0021285768790734825)\n", "Slope and P-value = PearsonRResult(statistic=0.9026673439526007, pvalue=1.1538790111495936e-37)\n", "Slope and P-value = PearsonRResult(statistic=0.906176235975958, pvalue=2.0815403085516408e-38)\n", "Slope and P-value = PearsonRResult(statistic=0.7777894410749295, pvalue=1.7667320195021957e-21)\n", "Slope and P-value = PearsonRResult(statistic=-0.22140517706878554, pvalue=0.02684480603112392)\n", "Slope and P-value = PearsonRResult(statistic=0.5383381528464861, pvalue=7.612168996235723e-09)\n", "Slope and P-value = PearsonRResult(statistic=0.8350168318874011, pvalue=3.585606869656317e-27)\n", "Slope and P-value = PearsonRResult(statistic=0.41810890694893194, pvalue=1.5010653259717659e-05)\n", "Slope and P-value = PearsonRResult(statistic=0.27439049234870194, pvalue=0.005733553804098077)\n", "Slope and P-value = PearsonRResult(statistic=0.7991251123129485, pvalue=2.1955497766957276e-23)\n" ] } ], "source": [ "print(\"Median:\",poultry.K.median())\n", "\n", "poultry.loc[poultry['K'] < poultry.K.median(), 'new_K'] = 0\n", "poultry.loc[poultry['K'] >= poultry.K.median(), 'new_K'] = 1 \n", "print(\"Total sample count:\", poultry.K.value_counts().sum(),\"\\n\")\n", "\n", "print(\"Distribution:\")\n", "print(poultry.new_K.value_counts())\n", "\n", "sample = pd.merge(microbiome, poultry[['SampleID', 'new_K','SampleType']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "sample = sample[~sample.isin([np.nan, np.inf, -np.inf]).any(1)]\n", "sample = sample.drop(['Pathogen_Salmonella', 'new_Pathogen_Salmonella',\n", " 'Pathogen_Campy','new_Pathogen_Campy',\n", " 'Pathogen_Listeria','new_Pathogen_Listeria'],axis='columns')\n", "\n", "\n", "feces=sample[sample.SampleType=='Feces']\n", "soil=sample[sample.SampleType=='Soil']\n", "\n", "print ('SAMPLE DISTRIBUTION \\n')\n", "\n", "print('Feces', feces.shape)\n", "print('Soil', soil.shape,'\\n')\n", "\n", "sampletypes = [feces, soil]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES\", 1: \"SOIL\"}\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType', 'new_K'],axis='columns'),sample.new_K,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1]\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " \n", "# mylist2.append([f\"K_{sample_name[indexing]}\", rf_auc_normal])\n", " \n", " \n", " print(pd.value_counts(sample['new_K']))\n", "\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim3_plot.max(), decimals=None, symbol=''))\n", " \n", "\n", " plt.title(f\"new_K in {sample_name[indexing]} Model\")\n", " prelim3_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','new_K','SampleType'],axis='columns').columns)\n", " prelim3_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", "\n", " xmax=prelim3_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " \n", " plt.show()\n", " \n", " prelim3_plot.nlargest(10).to_csv(\"prelim3.csv\")\n", " top10 = pd.read_csv('prelim3.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", "# plt.plot(pdp[1][0], pdp[0][0],'.')\n", "# plt.ylabel('Partial dependence'), plt.xlabel(top10[feature])\n", "# plt.show()\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"K_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (39) Mg" ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Median: 736.531\n", "Total sample count: 1635 \n", "\n", "Distribution:\n", "1.0 818\n", "0.0 817\n", "Name: new_Mg, dtype: int64\n", "SAMPLE DISTRIBUTION \n", "\n", "Feces (698, 877)\n", "Soil (695, 877) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "1.0 659\n", "0.0 39\n", "Name: new_Mg, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.7458684888642139, pvalue=5.439894990554715e-19)\n", "Slope and P-value = PearsonRResult(statistic=0.5642000298424473, pvalue=9.779655845446395e-10)\n", "Slope and P-value = PearsonRResult(statistic=-0.3330918668037707, pvalue=0.0007084586042018384)\n", "Slope and P-value = PearsonRResult(statistic=-0.6569678528684338, pvalue=1.14795630554079e-13)\n", "Slope and P-value = PearsonRResult(statistic=-0.8132244844163071, pvalue=8.945740020310126e-25)\n", "Slope and P-value = PearsonRResult(statistic=0.19667942027536792, pvalue=0.04984774693060688)\n", "Slope and P-value = PearsonRResult(statistic=-0.8873779550392704, pvalue=1.0059534281662552e-34)\n", "Slope and P-value = PearsonRResult(statistic=-0.8846138241591539, pvalue=0.1153861758408461)\n", "Slope and P-value = PearsonRResult(statistic=-0.6604731434252599, pvalue=7.657935943565908e-14)\n", "Slope and P-value = PearsonRResult(statistic=-0.838831838705606, pvalue=1.2559592376877127e-27)\n", "0.0 651\n", "1.0 44\n", "Name: new_Mg, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.8662273877349792, pvalue=1.315970689093655e-24)\n", "Slope and P-value = PearsonRResult(statistic=0.8526455247862406, pvalue=2.210354547294373e-29)\n", "Slope and P-value = PearsonRResult(statistic=0.9739371312435323, pvalue=5.927535116351216e-65)\n", "Slope and P-value = PearsonRResult(statistic=0.8587759676638864, pvalue=3.217020427259853e-30)\n", "Slope and P-value = PearsonRResult(statistic=0.4959628399490212, pvalue=2.77247348886645e-07)\n", "Slope and P-value = PearsonRResult(statistic=0.892134035503702, pvalue=5.552793041328531e-14)\n", "Slope and P-value = PearsonRResult(statistic=0.5494117935321224, pvalue=3.2298101048568514e-09)\n", "Slope and P-value = PearsonRResult(statistic=0.7618745915238251, pvalue=1.0588183571891846e-07)\n", "Slope and P-value = PearsonRResult(statistic=0.9127289876097808, pvalue=7.041424794718312e-40)\n", "Slope and P-value = PearsonRResult(statistic=0.8886630396744619, pvalue=8.32694303360204e-07)\n" ] } ], "source": [ "print(\"Median:\",poultry.Mg.median())\n", "\n", "poultry.loc[poultry['Mg'] < poultry.Mg.median(), 'new_Mg'] = 0\n", "poultry.loc[poultry['Mg'] >= poultry.Mg.median(), 'new_Mg'] = 1 \n", "print(\"Total sample count:\", poultry.Mg.value_counts().sum(),\"\\n\")\n", "\n", "print(\"Distribution:\")\n", "print(poultry.new_Mg.value_counts())\n", "\n", "sample = pd.merge(microbiome, poultry[['SampleID', 'new_Mg','SampleType']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "sample = sample[~sample.isin([np.nan, np.inf, -np.inf]).any(1)]\n", "sample = sample.drop(['Pathogen_Salmonella', 'new_Pathogen_Salmonella',\n", " 'Pathogen_Campy','new_Pathogen_Campy',\n", " 'Pathogen_Listeria','new_Pathogen_Listeria'],axis='columns')\n", "\n", "\n", "feces=sample[sample.SampleType=='Feces']\n", "soil=sample[sample.SampleType=='Soil']\n", "\n", "print ('SAMPLE DISTRIBUTION \\n')\n", "\n", "print('Feces', feces.shape)\n", "print('Soil', soil.shape,'\\n')\n", "\n", "sampletypes = [feces, soil]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES\", 1: \"SOIL\"}\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType', 'new_Mg'],axis='columns'),sample.new_Mg,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1]\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " \n", "# mylist2.append([f\"Mg_{sample_name[indexing]}\", rf_auc_normal])\n", " \n", " \n", " print(pd.value_counts(sample['new_Mg']))\n", "\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim3_plot.max(), decimals=None, symbol=''))\n", " \n", "\n", " plt.title(f\"new_Mg in {sample_name[indexing]} Model\")\n", " prelim3_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','new_Mg','SampleType'],axis='columns').columns)\n", " prelim3_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", "\n", " xmax=prelim3_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " \n", " plt.show()\n", " \n", " prelim3_plot.nlargest(10).to_csv(\"prelim3.csv\")\n", " top10 = pd.read_csv('prelim3.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", "# plt.plot(pdp[1][0], pdp[0][0],'.')\n", "# plt.ylabel('Partial dependence'), plt.xlabel(top10[feature])\n", "# plt.show()\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"Mg_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (40) Mn" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Median: 65.3722\n", "Total sample count: 1635 \n", "\n", "Distribution:\n", "1.0 818\n", "0.0 817\n", "Name: new_Mn, dtype: int64\n", "SAMPLE DISTRIBUTION \n", "\n", "Feces (698, 877)\n", "Soil (695, 877) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "1.0 520\n", "0.0 178\n", "Name: new_Mn, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.4122668300280428, pvalue=2.026120386472924e-05)\n", "Slope and P-value = PearsonRResult(statistic=-0.7907511933101844, pvalue=1.3056521187357378e-22)\n", "Slope and P-value = PearsonRResult(statistic=-0.5835863721524066, pvalue=1.863108143703895e-10)\n", "Slope and P-value = PearsonRResult(statistic=-0.02039126713265932, pvalue=0.8771024045423417)\n", "Slope and P-value = PearsonRResult(statistic=-0.9088174363367046, pvalue=5.482332686075771e-39)\n", "Slope and P-value = PearsonRResult(statistic=-0.9297627046399353, pvalue=2.55670964483969e-44)\n", "Slope and P-value = PearsonRResult(statistic=-0.8454008697612588, pvalue=1.9321018045000046e-28)\n", "Slope and P-value = PearsonRResult(statistic=0.6856466840389932, pvalue=3.543209565361506e-15)\n", "Slope and P-value = PearsonRResult(statistic=-0.9129243215961149, pvalue=6.339554153262373e-40)\n", "Slope and P-value = PearsonRResult(statistic=0.1699766741714945, pvalue=0.09088964637749081)\n", "0.0 505\n", "1.0 190\n", "Name: new_Mn, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.9687246206980461, pvalue=3.969398096816757e-61)\n", "Slope and P-value = PearsonRResult(statistic=0.9151800415441403, pvalue=1.8516108028924285e-40)\n", "Slope and P-value = PearsonRResult(statistic=0.9425465282488046, pvalue=1.849566976492622e-48)\n", "Slope and P-value = PearsonRResult(statistic=0.9689964986982558, pvalue=2.6046157786663258e-61)\n", "Slope and P-value = PearsonRResult(statistic=-0.4690784695406944, pvalue=8.539267348427107e-07)\n", "Slope and P-value = PearsonRResult(statistic=-0.6496061582248096, pvalue=2.6406290835087134e-13)\n", "Slope and P-value = PearsonRResult(statistic=0.9007260601453604, pvalue=2.894720177345458e-37)\n", "Slope and P-value = PearsonRResult(statistic=0.7525181006271205, pvalue=1.7722609502869158e-19)\n", "Slope and P-value = PearsonRResult(statistic=0.8181089765659861, pvalue=2.769588028364724e-25)\n", "Slope and P-value = PearsonRResult(statistic=-0.7215477432932307, pvalue=2.4880687615204565e-17)\n" ] } ], "source": [ "print(\"Median:\",poultry.Mn.median())\n", "\n", "poultry.loc[poultry['Mn'] < poultry.Mn.median(), 'new_Mn'] = 0\n", "poultry.loc[poultry['Mn'] >= poultry.Mn.median(), 'new_Mn'] = 1 \n", "print(\"Total sample count:\", poultry.Mn.value_counts().sum(),\"\\n\")\n", "\n", "print(\"Distribution:\")\n", "print(poultry.new_Mn.value_counts())\n", "\n", "sample = pd.merge(microbiome, poultry[['SampleID', 'new_Mn','SampleType']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "sample = sample[~sample.isin([np.nan, np.inf, -np.inf]).any(1)]\n", "sample = sample.drop(['Pathogen_Salmonella', 'new_Pathogen_Salmonella',\n", " 'Pathogen_Campy','new_Pathogen_Campy',\n", " 'Pathogen_Listeria','new_Pathogen_Listeria'],axis='columns')\n", "\n", "\n", "feces=sample[sample.SampleType=='Feces']\n", "soil=sample[sample.SampleType=='Soil']\n", "\n", "print ('SAMPLE DISTRIBUTION \\n')\n", "\n", "print('Feces', feces.shape)\n", "print('Soil', soil.shape,'\\n')\n", "\n", "sampletypes = [feces, soil]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES\", 1: \"SOIL\"}\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType', 'new_Mn'],axis='columns'),sample.new_Mn,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1]\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " \n", "# mylist2.append([f\"Mn_{sample_name[indexing]}\", rf_auc_normal])\n", " \n", " \n", " print(pd.value_counts(sample['new_Mn']))\n", "\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim3_plot.max(), decimals=None, symbol=''))\n", " \n", "\n", " plt.title(f\"new_Mn in {sample_name[indexing]} Model\")\n", " prelim3_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','new_Mn','SampleType'],axis='columns').columns)\n", " prelim3_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", "\n", " xmax=prelim3_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " \n", " plt.show()\n", " \n", " prelim3_plot.nlargest(10).to_csv(\"prelim3.csv\")\n", " top10 = pd.read_csv('prelim3.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", "# plt.plot(pdp[1][0], pdp[0][0],'.')\n", "# plt.ylabel('Partial dependence'), plt.xlabel(top10[feature])\n", "# plt.show()\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"Mn_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (41) Mo" ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SAMPLE DISTRIBUTION \n", "\n", "Feces (698, 878)\n", "Soil (695, 878) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "1.0 550\n", "0.0 148\n", "Name: new_Mo, dtype: int64\n" ] }, { 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.5090366059082619, pvalue=2.3911066296275688e-05)\n", "Slope and P-value = PearsonRResult(statistic=-0.8804337617539957, pvalue=1.5875787391155933e-33)\n", "Slope and P-value = PearsonRResult(statistic=0.7248634891841281, pvalue=1.5134501142972473e-17)\n", "Slope and P-value = PearsonRResult(statistic=0.5014105202213686, pvalue=1.0735175981917004e-07)\n", "Slope and P-value = PearsonRResult(statistic=-0.9610762030285328, pvalue=1.4958115712607821e-56)\n", "Slope and P-value = PearsonRResult(statistic=-0.7825154935112175, pvalue=6.976508746224396e-22)\n", "Slope and P-value = PearsonRResult(statistic=-0.7862243697100212, pvalue=3.3101316781864387e-22)\n", "Slope and P-value = PearsonRResult(statistic=0.32694908214109564, pvalue=0.0008997673469996459)\n", "Slope and P-value = PearsonRResult(statistic=-0.5465886311488034, pvalue=4.030755888398519e-09)\n", "Slope and P-value = PearsonRResult(statistic=0.4075579863861829, pvalue=2.5698778246006054e-05)\n", "1.0 485\n", "0.0 210\n", "Name: new_Mo, dtype: int64\n" ] }, { "data": { "image/png": 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SzsjF+E1JT0uaIulySZ1WsT9dJD2Ya//IJh0QMzOzRrSrZC+pBjgG2AM4Gij63N4G6nYFukbEK0W2VQEjgP4R0YvsWwrfzxV5OyL2BP4C5D9c7Az8F7AP8AtJnSV9FugP7BcRvYFlwImrCG0x8LXU/kHA7yR9YrZB0kBJtZJqly2cV+pum5mZta9kD/QFRkfEooiYD9zVhLoCooFtnwFei4iX0uuRZFP89W5PvycB1bn190TEhxHxNvAmsAVwMLAXMFHSlPT6043E9RtJ04AHgK1TOyuJiOERURMRNZ3W77aK5szMzFbW3r5n3+zz6xHxvqQPJH06Il5tYrsfpt/LWPmYfZhbrt8mYGRE/LTE0E4ENgP2ioglkmYAVSXWNTMza1R7G9k/ChwuqUpSF6Cpd7n4LXCZpA0BJG0oaSDwAlAtacdU7lvAw82M8UHgWEmbpz42kbTdKsp3A95Mif4gYFVlzczMmqxdjewjYqKkMcBUYCZQCzTlBPZfgC5kU+xLgCXA7yJisaRTgFskrQ1MBP7azBifk/Rz4H5Ja6U+BqV4i7keuEtSLTCF7IOHmZlZ2SiiodPYayZJXSJigaT1gQnAwIiY3NZxtaaampqora1t6zDMzGwNImlSRBS9cL1djeyT4ZJ2ITuvPbKjJXozM7OmanfJPiJOyL+WdBmwX0GxHsDLBesujohrWjI2MzOzNVG7S/aFImJQW8dgZma2JmtvV+ObmZlZEznZm5mZVTgnezMzswrnZG9mZlbhnOzNzMwqnJO9mZlZhXOyNzMzq3Dt/nv2HVHdnHlUD72nrcMwq3gzhjX1WVtmayaP7M3MzCqck72ZmVmFa/FkL2mEpNckTZH0gqRflKHNBU0sP0DSpQ1s+7ukjcoVg6RzJR3S1PbMzMxaSmudsz8zIm6VVAU8J+naiHgtX0BSp4hYVu6O0/PpGxQRXy1nfxFxdjnbMzMzW10ljewlnZVG5WMljZI0pJn9VaXfH6R2Z0g6W9KjwNclfVfSRElTJd2WnlmPpO0lPZG2/SoX13WSjsy9vl7SEWkkf4uku4D70+atJN0r6WVJF+TqzJDUXdJpafZhSpqJGJe2Hy+pTtJ0SecXHJffSZos6UFJm6V1IyQdm287LddIGp+Wz5E0UtL9qczRki5I/dwrqXORv8FASbWSapctnNfMw29mZh1Ro8leUg1wDLAHcDRQ04x+LpQ0BZgN3BgRb+a2LY6IvhFxI3B7ROwdEbsDzwPfSWUuBv4SEXsD/87VvRI4JcXZDdgX+Hva1gc4OSK+kF73BvoDvYD+kj6VDzAi/hoRvYG9U5y/l7QVcD7whVR/b0lHpSobAJMjYk/gYaCppyd2AA4FjgT+BoyLiF7AorR+JRExPCJqIqKm0/rdmtiVmZl1ZKWM7PsCoyNiUUTMB+5qRj9npkT6/4CDJe2b23ZTbrmnpEck1QEnArum9fsBo9LydfWFI+JhYEdJmwPHA7dFxNK0eWxEvJtr+8GImBcRi4HngO0aiPVi4KGIuIss8Y+PiLdSu9cDB6Ryy3Ox/43sODXFPyJiCVAHdALuTevrgOomtmVmZtagUpK9ytVZRCwAxrNyYvwgtzwC+GEa4f6Sj6f9AaKBZq8j+2BwCnBNA+0CfJhbXkaR6xUkDSD7EPDL+lUN9FlMsfiW8vExrirY9iFARCwHlkREff3lxWIzMzNrrlKS/aPA4ZKqJHWhyBRzqdLFcp8DXmmgSFdgbjpnfWJu/WPAN9LyiQV1RgCDASLi2dWIbS9gCPDNlIABngIOTOf0O5HNHjyctq0FHJuWTyA7ToVmAHul5WOaG5uZmdnqaDTZR8REYAwwFbgdqAWaeoVY/Tn7aWTT1Lc3UO4ssgQ7Fnght/6/gUGSJgIrnbCOiDfIzu/nR/XN8UNgE2BcukjvyoiYC/wUGEe2/5MjYnQq/wGwq6RJZOf0zy3S5i+BiyU9QjabYGZm1ur08ezxKgpJXSJiQbo6fgIwMCImt3h0JUgx1QF7RkSHuEy9pqYmamtr2zoMMzNbg0iaFBFFL6Iv9aY6w9PIfDLZRXBrSqI/hGwG4JKOkujNzMyaqqQLwSLihPxrSZeRXSGf1wN4uWDdxRGxutPrq4rrAWDblmrfzMysEjTrqu+IGFTuQMzMzKxl+EE4ZmZmFc7J3szMrMI52ZuZmVU4J3szM7MK52RvZmZW4ZzszczMKpyTvZmZWYXz09Xaobo586geek9bh2FWkhnDmv3sLDMrE4/szczMKpyTvZmZWYVrlWQvaYSk19KjY6dIOiOtnyGpexn7qZY0vUztnNB4yaJ1H1/d/s3MzMqpNc/ZnxkRt7Zif6ujGjgBuKHUCpI6RcSyiNi3xaIyMzNrhpJH9pLOkvSCpLGSRkkaUs5AJN0paZKkZyUNTOu+L+mCXJkBki5Jyz+SND39DM41tbakkZKmSbo1Pe8eSWdLmpjKD5ektH5HSQ9ImippsqQdgGHA/mkW4n8kdZJ0Yao/TdL3Ut1+ksZJugGoS+sW5LbdnYv9UkkD0vIMSb+R9ISkWkl7SrpP0iuSTmvg+AxMZWuXLfTTfM3MrHQlJXtJNcAxwB7A0UBNM/q6MDeN36vI9m9HxF6p7TMkbQrcmvqr1x+4SdJewCnA54DPA9+VtEcq8xlgeETsBrwP/CCtvzQi9o6InsB6wGFp/fXAZRGxO7AvMBcYCjwSEb0j4g/Ad4B5EbE3sHfqb/tUfx/gZxGxSxOPx6yI6AM8AowAjk37cm6xwhExPCJqIqKm0/rdmtiVmZl1ZKWO7PsCoyNiUUTMB+5qRl9npuTZOyLqimw/Q9JU4EngU0CPiHgLeFXS51Py/wzwWIrnjoj4ICIWALcD+6d2ZkXEY2n5b6kswEGSnpJUB3wB2FVSV2DriLgDICIWR8TCIrF9CThJ0hTgKWBToEfa9nREvNaM4zEm/a4DnoqI+Wl/F0vaqBntmZmZFVXqOXu1ZBCS+gGHAH0iYqGk8UBV2nwTcBzwAlmCj/op+AZE4WtJVcCfgZqImCXpnNR+qfsl4PSIuK9I3B80UGcpK3+YqirY/mH6vTy3XP/a9z8wM7OyKXVk/yhwuKQqSV2Act8loxvwXkr0O5NNZ9e7HTgKOJ4s8QNMAI6StL6kDYCvkU2HA2wrqU9aPj7FXp9o307xHwsQEe8DsyUdBSBp3XSOfz7QNRfDfcD3JXVO5XZK/a7KTGCX1GY34ODSDoWZmVl5lTSCjIiJksYAU8mSWC1QzqvE7gVOkzQNeJFsKr++7/ckPQfsEhFPp3WTJY0Ank7FroyIZyRVA88DJ0u6HHgZ+Ev6EHEF2ZT5DGBiru9vAZdLOhdYAnwdmAYsTacVRgAXk12hPznNKrxF9gGkQWkG4ebU1svAM00/LGZmZqtPEYWz3g0UlLpExII08p0ADIyIyS0anRVVU1MTtbW1bR2GmZmtQSRNioiiF9A35dzwcEm7kE2Jj3SiNzMzax9KTvYRsdId5SRdBuxXUKwH2ZR13sURcU3zwjMzM7PV1eyrviNiUDkDMTMzs5bhB+GYmZlVOCd7MzOzCudkb2ZmVuGc7M3MzCqck72ZmVmFc7I3MzOrcE72ZmZmFc5PV2uH6ubMo3roPW0dhnVwM4aV+3lYZtZSPLI3MzOrcE72ZmZmFc7JvgkkrS3pbUm/LVh/oaRn0+/TJJ3USDtHSBrastGamZllfM6+ab4EvAgcJ+n/4uPnA38P2CwiPiylkYgYA4xpoRjNzMxW0qFG9pLOkvSCpLGSRkka0sQmjgcuBv4FfD61OQbYAHhKUn9J59S3K2m8pD9KelzSdEn7pPUDJF2alg+X9JSkZyQ9IGmLBmIfKKlWUu2yhfOadwDMzKxD6jDJXlINcAywB3A0UNPE+usBBwN3A6PIEj8RcQSwKCJ6R8RNRapuEBH7Aj8Ari6y/VHg8xGxB3Aj8L/F+o+I4RFRExE1ndbv1pTQzcysg+swyR7oC4yOiEURMR+4q4n1DwPGRcRC4Dbga5I6lVBvFEBETAA2lLRRwfZtgPsk1QFnArs2MS4zM7NV6kjJXqtZ/3jgEEkzgEnApsBBJdSLRl5fAlwaEb3Izv1XrWacZmZmK+lIyf5R4HBJVZK6ACXfEUTShmQzA9tGRHVEVAODSFP5jeif2ugLzIuIwhPu3YA5afnkUmMyMzMrVYe5Gj8iJqaL6aYCM4FaoNQr3Y4GHiq42n40cIGkdRup+56kx4ENgW8X2X4OcIukOcCTwPaNBdNr627U+u5lZmZWIn387bHKJ6lLRCyQtD4wARgYEZNbsL/xwJCIqC1nuzU1NVFbW9YmzcysnZM0KSKKXnzeYUb2yXBJu5CdFx/ZkonezMxsTdGhkn1EnJB/LekyYL+CYj2AlwvWXRwR1zSjv35NrWNmZlZuHSrZF4qIQW0dg5mZWUvrSFfjm5mZdUhO9mZmZhXOyd7MzKzCOdmbmZlVOCd7MzOzCudkb2ZmVuE69Ffv2qu6OfOoHnpPW4dhHdgM367ZrF3xyN7MzKzCOdmbmZlVuDUq2Uv6vKSnJE2R9LykcxopP0DSpa0UnpmZWbu0pp2zHwkcFxFTJXUCPtOanUtaOyKWVmp/ZmbWMZV9ZC/pLEkvSBoraZSkIU2ovjkwFyAilkXEc6nNTSTdKWmapCcl7VbQZydJryqzkaTlkg5I2x6RtKOkfSQ9LumZ9PszafsASbdIugu4X1IXSQ9KmiypTtKRqdwGku6RNFXSdEn90/oZks6X9HT62TGt3y61My393jatHyHp95LGAec3FJeZmVm5lHVkL6kGOAbYI7U9GZjUhCb+ALyYngN/L9ljaBcDvwSeiYijJH0BuBboXV8pIpZJegnYBdg+9bm/pKeAbSLin5I2BA6IiKWSDgF+k2IF6APsFhHvSlob+FpEvC+pO/CkpDHAl4HXI+LQtK/dcnG/HxH7SDoJ+CNwGHApcG1EjJT0beBPwFGp/E7AISnuVcWVP7YDgYEAnTbcrAmH1MzMOrpyj+z7AqMjYlFEzAfuakrliDgXqAHuB04gS/j17V6XyjwEbFqQbAEeAQ5IP79NdfYGJqbt3YBbJE0n+1Cxa67u2Ih4Ny0L+I2kacADwNbAFkAdcEgaxe8fEfNy9UflfvdJy32AG9LydSmeerdExLIS4sofm+ERURMRNZ3WL9x1MzOzhpU72Wt1G4iIVyLiL8DBwO6SNm2g3Sh4/QiwP7AP8HdgI6AfMCFt/xUwLiJ6AocDVbm6H+SWTwQ2A/aKiN7AG0BVRLwE7EWW9H8r6ewGYimMq9j6fH+risvMzGy1lTvZPwocLqlKUhegSXfekHSopPrE3gNYBvyHLGGfmMr0A96OiPcLqj8F7AssT1P/U4DvkX0IgGwEPSctD1hFGN2ANyNiiaSDgO1Sv1sBCyPib8BFwJ65Ov1zv59Iy48D30jLJ5Idm4b6KyUuMzOzZinrOfuImJjOb08FZgK1wLxV11rJt4A/SFoILAVOTOe1zwGuSVPrC4GTi/T9oaRZwJNp1SPA8WQjcYALgJGSfgQ8tIoYrgfuklRL9oHhhbS+F3ChpOXAEuD7uTrrpusD1kp9ApwBXC3pTOAt4JQG+is1LjMzs2ZRREOzzs1sUOoSEQskrU82Ih8YEZPL2skaRNIMoCYi3m6tPmtqaqK2tra1ujMzs3ZA0qSIqCm2rSW+Zz9c0i5k555HVnKiNzMzaw/Knuwj4oT8a0mXAfsVFOsBvFyw7uKIuKbc8bS0iKhu6xjMzMxWpcXvoBcRg1q6DzMzM2vYGnVvfDMzMys/J3szM7MK52RvZmZW4ZzszczMKpyTvZmZWYVzsjczM6twTvZmZmYVrsW/Z2/lVzdnHtVD72nrMKwDmzGsSc+4MrM25pG9mZlZhXOyNzMzq3BlSfaSlkmaImm6pFvSE+9KrTtA0qUNbHu8kbrVkk7Iva6R9KfSI19Rb4akurQPdZKObGobqZ0jJA1Ny+dIGpKWR0g6tjltmpmZra5yjewXRUTviOgJfASclt8oqVNzGo2IfRspUg2sSPYRURsRZzSnL+CgiOgNHAs0+QND6n9MRAxrZv9mZmYtoiWm8R8BdpTUT9I4STcAdZKqJF2TRs7PSDooV+dTku6V9KKkX9SvlLQg/ZakC9PMQZ2k/qnIMGD/NCL/n9Tn3alOl1x/0yQdU2L8GwLv5WK4U9IkSc9KGphb/2VJkyVNlfRgWtfgLEWu3gxJ3dNyjaTxafnAtB9T0vHpWlBvoKRaSbXLFs4rcVfMzMzKfDW+pLWBrwD3plX7AD0j4jVJPwaIiF6Sdgbul7RTvhywEJgo6Z6IqM01fTTQG9gd6J7KTACGAkMi4rDUf79cnbOAeRHRK23buJHwx0kS8GnguNz6b0fEu5LWS/3eRvYh6QrggLRvm5RweBozBBgUEY9J6gIszm+MiOHAcIB1t+wRZejPzMw6iHKN7NeTNAWoBf4FXJXWPx0Rr6XlvsB1ABHxAjATqE/2YyPinYhYBNyeyub1BUZFxLKIeAN4GNi7kZgOAS6rfxER762iLGTT+D2BXsClKeECnCFpKvAk8CmgB/B5YEL9vkXEu420XYrHgN9LOgPYKCKWlqFNMzOzso3sF6Xz3Stkg2Q+yK9aRf3CkWrh61XVbYiKtNOoiHhF0hvALulCw0OAPhGxME25VzW37WQpH3/Iqsr1O0zSPcBXgSclHZI+FJmZma2W1vzq3QTgRIA0fb8t8GLa9kVJm6Sp8qPIRrmFdftL6iRpM+AA4GlgPtCV4u4Hflj/ooRp/PpymwPbk808dAPeS4l+Z7IRPcATwIGStk91mjKNPwPYKy2vuI5A0g4RURcR55PNkOzchDbNzMwa1JrJ/s9AJ0l1wE3AgIj4MG17lGyKfwpwW8H5eoA7gGnAVOAh4H8j4t9p3dJ0kdz/FNQ5D9g4XdQ3FTiIVRuXTkWMA4am0wX3AmtLmgb8imwqn4h4CxgI3J7avqkJx+GXwMWSHgGW5dYPzsW6CPhHE9o0MzNrkCJ8rVd7U1NTE7W1hZ+HzMysI5M0KSJqim3zHfTMzMwqXId6EI6kp4B1C1Z/KyLq2iIeMzOz1tChkn1EfK6tYzAzM2ttnsY3MzOrcE72ZmZmFc7J3szMrMI52ZuZmVU4J3szM7MK52RvZmZW4ZzszczMKlyH+p59paibM4/qofe0dRjWAc0Ydmhbh2BmzeCRvZmZWYVzsjczM6tw7SrZK/NzSS9LeknSOEm7lqntKyXtkpYXlKNNMzOzNUF7O2c/CNgX2D0iFkr6EjBG0q4RsThfUFKniFhWtJUiIuLUMsdqZma2Rmj1kb2ksyS9IGmspFGShjSh+k+A0yNiIUBE3A88DpyY2l4g6dz0dLufS7oj1+8XJd0u6QhJU9LPi5JeS9vHS6rJlf+1pKmSnpS0RVo3QtKxuTIL0u9+kh6WdHOacRgm6URJT0uqk7RDI/W3lDQhxTRd0v5FjttASbWSapctnNeEQ2ZmZh1dqyb7lEyPAfYAjgZqVl1jpbobAhtExCsFm2qB+qn8DYDp6el25wKflbRZ2nYKcE1EjImI3hHRG5gKXFSkuw2AJyNid2AC8N0SQtwd+G+gF/AtYKeI2Ae4Eji9kbonAPelmHYHphQWiIjhEVETETWd1u9WQjhmZmaZ1h7Z9wVGR8SiiJgP3FWGNgVEWl4G3AYQEQFcB3xT0kZAH+AfKypJ/wssiojLirT5EXB3Wp4EVJcQx8SImBsRHwKvAPen9XUl1J8InCLpHKBXOjZmZmZl0drJXs2tGBHvAx9I+nTBpj2B59Ly4oLz9NcA3wSOB26JiKUAkg4Gvg6c1kB3S9KHBcg+QNRf27CUdMwkCVgnV+fD3PLy3OvljdWPiAnAAcAc4DpJJzUQl5mZWZO1drJ/FDhcUpWkLkBT79BxIfAnSesBSDqEbLbghmKFI+J14HXg58CIVGc74M/AcRGxqIn9zwD2SstHAp3LUT/F9GZEXAFcRfYBxszMrCxa9Wr8iJgoaQzZufKZZOfbm3K12SXAxkCdpGXAv4EjG0na1wObRUT96H8AsClwRza45vWI+GqJ/V8BjJb0NPAg8EETYl9V/X7AmZKWAAsAj+zNzKxs9PFsdSt1KHWJiAWS1ie7+G1gRExuwf4uBZ6JiKtaqo/WVlNTE7W1tW0dhpmZrUEkTYqIohe+t8X37Ienm9dUASNbONFPIhs9/7il+jAzM1vTtXqyj4gT8q8lXQbsV1CsB/BywbqLI+KaJva1V+OlzMzMKlub30EvIga1dQxmZmaVrM2TvZmZta4lS5Ywe/ZsFi9e3HhhW+NUVVWxzTbb0Llz6V8Ic7I3M+tgZs+eTdeuXamuriZ9K8naiYjgnXfeYfbs2Wy//fYl12tXT70zM7PVt3jxYjbddFMn+nZIEptuummTZ2Wc7M3MOiAn+varOX87J3szM7MK53P2ZmYdXPXQe8ra3oxhpd0J/Y477uDoo4/m+eefZ+eddwZg/PjxXHTRRdx9990ryg0YMIDDDjuMY489ln79+jF37lyqqqpYZ511uOKKK+jduzcA8+bN4/TTT+exxx4DYL/99uOSSy6hW7fsSaEvvfQSgwcP5qWXXqJz58706tWLSy65hC222KLZ+/ruu+/Sv39/ZsyYQXV1NTfffDMbb7zxSmVefPFF+vfvv+L1q6++yrnnnsvgwYOZOnUqp512GgsWLKC6uprrr7+eDTfckLq6On73u98xYsSIZseW52TfDtXNmVf2f5xmDSn1P26zpho1ahR9+/blxhtv5Jxzzim53vXXX09NTQ3XXHMNZ555JmPHjgXgO9/5Dj179uTaa68F4Be/+AWnnnoqt9xyC4sXL+bQQw/l97//PYcffjgA48aN46233lqtZD9s2DAOPvhghg4dyrBhwxg2bBjnn3/+SmU+85nPMGXKFACWLVvG1ltvzde+9jUATj31VC666CIOPPBArr76ai688EJ+9atf0atXL2bPns2//vUvtt1222bHV8/T+GZm1uoWLFjAY489xlVXXcWNN97YrDb69OnDnDlzAPjnP//JpEmTOOuss1ZsP/vss6mtreWVV17hhhtuoE+fPisSPcBBBx1Ez549V2s/Ro8ezcknnwzAySefzJ133rnK8g8++CA77LAD2223HZCN+g844AAAvvjFL3LbbbetKHv44Yc3+9gUcrI3M7NWd+edd/LlL3+ZnXbaiU022YTJk5t+5/R7772Xo446CoDnnnuO3r1706lTpxXbO3XqRO/evXn22WeZPn06e+3V+E1V58+fT+/evYv+PPfcc58o/8Ybb7DlllsCsOWWW/Lmm2+usv0bb7yR448/fsXrnj17MmbMGABuueUWZs2atWJbTU0NjzzySKMxl8LT+GZm1upGjRrF4MGDAfjGN77BqFGj2HPPPRu80jy//sQTT+SDDz5g2bJlKz4kRETRug2tb0jXrl1XTLmX20cffcSYMWP47W9/u2Ld1VdfzRlnnMG5557LEUccwTrrrLNi2+abb87rr79elr7XyGQvaQRwINnjb5cDgyLiiTK2fxqwMCKulTQeGBIRq/UYOUlXAr+PiOckzQBqIuJtSQsiosvqR21mVhneeecdHnroIaZPn44kli1bhiQuuOACNt10U957772Vyr/77rt07959xevrr7+e3XffnaFDhzJo0CBuv/12dt11V5555hmWL1/OWmtlk9bLly9n6tSpfPazn+XNN9/k4YcfbjS2+fPns//++xfddsMNN7DLLrustG6LLbZg7ty5bLnllsydO5fNN9+8wbb/8Y9/sOeee650jcDOO+/M/fffD2QXEN5zz8fXYy1evJj11luv0ZhLsSZP458ZEb2BocDl5Ww4Iv4aEdeWuc1TI+KTczxmZraSW2+9lZNOOomZM2cyY8YMZs2axfbbb8+jjz5Kjx49eP3113n++ecBmDlzJlOnTl1xxX29zp07c9555/Hkk0/y/PPPs+OOO7LHHntw3nnnrShz3nnnseeee7Ljjjtywgkn8Pjjj6+UTO+9917q6upWard+ZF/spzDRAxxxxBGMHDkSgJEjR3LkkUc2uN+jRo1aaQofWDHtv3z5cs477zxOO+20Fdteeuml1b6moF6LjewlnQWcCMwC3gYmRcRFzWhqArCjpC7AaGBjoDPw84gYnfr6JnAGsA7wFPCDiFgmaQFwMXAYsAg4MiLekHQOsCAXzzcl/QnYEPh2RDwtaR/gj8B6qe4pEfGipE7A+cB/AQFcERGXNDZDIKlf2n5Yen0pUBsRIyQNA44AlgL3R8SQIvUHAgMBOm24WTMOo5lZca39jYtRo0YxdOjQldYdc8wx3HDDDey///787W9/45RTTmHx4sV07tyZK6+8csXX5/LWW289fvzjH3PRRRdx1VVXcdVVV3H66aez4447EhH06dOHq666akXZu+++m8GDBzN48GA6d+7MbrvtxsUXX7xa+zJ06FCOO+44rrrqKrbddltuueUWAF5//XVOPfVU/v73vwOwcOFCxo4dy+WXrzx2HTVqFJdddhkARx99NKeccsqKbePGjePQQ8vzt1FElKWhlRqVaoArgT5kHygmA5eXmuzTNP7dEXGrpK8DQ8geg7t+RLwvqTvwJNmjcHcGLgCOjoglkv4MPJmm6AM4IiLuknQB8H5EnJdP9ilJvxwR35V0APDniOgpaUOyqf6lkg4Bvh8Rx0j6PnAI0D9t2yQi3s0n+2LT+A0le2AM8ASwc0SEpI0i4j+rOj7rbtkjtjz5j6UcSrPV5q/eVZ7nn3+ez372s20dhq3Chx9+yIEHHsijjz7K2mt/clxe7G8oaVJE1BRrr6VG9n2B0RGxKAVwVzPauFDSz4G3gO8AAn6TEvJyYGtgC+BgYC9gYroIYz2g/nLIj4D6OzNMAr7YQF+jACJigqQNJW0EdAVGSupBNoKvf7zQIcBfI2JpqvNuM/Yt731gMXClpHty8ZqZWQf1r3/9i2HDhhVN9M3RUsm+HDddPjMibl3RoDQA2AzYK43gZwBVqa+REfHTIm0siY+nLpbR8P4WTm8E8CtgXER8TVI1ML4+lCLlS7GUla+RqAJIswP7kH1o+QbwQ+ALzWjfzMwqRI8ePejRo0fZ2mupC/QeBQ6XVJXOtZdjHrAb8GZK9AcB26X1DwLHStocQNImkrZrqJEG9E91+wLzImJe6m9O2j4gV/Z+4DRJa9f3V2IfM4FdJK0rqRtZcicdn24R8XdgMNC7ibGbmTVZS5zCtdbRnL9di4zsI2KipDHAVLIkV0v2NbrVcT1wl6RaYArwQurruTTdf7+ktYAlwKDUb6nek/Q46QK9tO4Csmn8HwEP5cpeCewETJO0BLgCuLSxDiJilqSbgWnAy8AzaVNXYLSk+lmK/2msrV5bd6PW51HNrJmqqqp45513/Jjbdqj+efZVVVVNqtciF+hBNmKNiAWS1ie7on5gRDT9Fkn2CTU1NVFbu1q3BTCzDmzJkiXMnj27yc9EtzVDVVUV22yzDZ07d15pfVtcoAcwXNIuZOemRzrRm5mtGTp37sz222/f1mFYK2qxZB8RJ+RfS7qM7OtzeT3IprTzLo6Ia1oqLjMzs46m1W6XGxGDWqsvMzMz+9iafLtcMzMzK4MWu0DPWo6k+cCLbR1HB9Od7LbP1np8zFufj3nrK+cx3y4iit5PfY186p016sWGrri0liGp1se8dfmYtz4f89bXWsfc0/hmZmYVzsnezMyswjnZt0/D2zqADsjHvPX5mLc+H/PW1yrH3BfomZmZVTiP7M3MzCqck72ZmVmFc7JvZyR9WdKLkv4paWhbx1OJJH1K0jhJz0t6VtJ/p/XnSJojaUr6+Wpbx1pJJM2QVJeObW1at4mksZJeTr83bus4K4Wkz+Tey1MkvS9psN/n5SXpaklvSpqeW9fg+1rST9P/7y9K+q+yxeFz9u2HpE7AS8AXgdnAROD4iHiuTQOrMJK2BLaMiMmSugKTgKOA44AFEXFRW8ZXqSTNAGoi4u3cuguAdyNiWPpwu3FE/KStYqxU6f+WOcDngFPw+7xsJB0ALACujYieaV3R93V6eNwoYB9gK+ABYKeIWLa6cXhk377sA/wzIl6NiI+AG4Ej2zimihMRc+uf0hgR84Hnga3bNqoO60hgZFoeSfahy8rvYOCViJjZ1oFUmoiYALxbsLqh9/WRwI0R8WFEvAb8k+z//dXmZN++bA3Myr2ejZNQi5JUDewBPJVW/VDStDQ15ynl8grgfkmTJA1M67aIiLmQfQgDNm+z6CrbN8hGlPX8Pm9ZDb2vW+z/eCf79kVF1vk8TAuR1AW4DRgcEe8DfwF2AHoDc4HftV10FWm/iNgT+AowKE1/WguTtA5wBHBLWuX3edtpsf/jnezbl9nAp3KvtwFeb6NYKpqkzmSJ/vqIuB0gIt6IiGURsRy4gjJNr1kmIl5Pv98E7iA7vm+kayjqr6V4s+0irFhfASZHxBvg93kraeh93WL/xzvZty8TgR6Stk+fxr8BjGnjmCqOJAFXAc9HxO9z67fMFfsaML2wrjWPpA3SxZBI2gD4EtnxHQOcnIqdDIxumwgr2vHkpvD9Pm8VDb2vxwDfkLSupO2BHsDT5ejQV+O3M+lrMH8EOgFXR8Sv2zaiyiOpL/AIUAcsT6v/j+w/xd5k02ozgO/Vn3ez1SPp02SjeciexnlDRPxa0qbAzcC2wL+Ar0dE4cVO1kyS1ic7R/zpiJiX1l2H3+dlI2kU0I/sUbZvAL8A7qSB97WknwHfBpaSnUL8R1nicLI3MzOrbJ7GNzMzq3BO9mZmZhXOyd7MzKzCOdmbmZlVOCd7MzOzCudkb9YAScvSU7+mS7pL0kaNlD9H0pBGyhyVHnZR//pcSYeUIdYRko5d3Xaa2Ofg9NWtNYakndPf7BlJOxRsmyHpkYJ1U+qfRiapRtKfyhBDdf4JZwXbrsz//VuapC0k3SDp1XQb4ickfa21+rc1h5O9WcMWRUTv9KSqd4FBZWjzKGDFf/YRcXZEPFCGdltVekraYGCNSvZkx3d0ROwREa8U2d5V0qcAJH02vyEiaiPijFI7SsegSSLi1NZ6SmW6OdSdwISI+HRE7EV2I65tWrjftVuyfWseJ3uz0jxBeiCFpB0k3ZtGSo9I2rmwsKTvSpooaaqk2yStL2lfsnuQX5hGlDvUj8glfUXSzbn6/STdlZa/lEZkkyXdku7Z36A0gv1NqlMraU9J90l6RdJpufYnSLpD0nOS/ipprbTteGXPlZ8u6fxcuwvSTMRTwM/IHsE5TtK4tP0vqb9nJf2yIJ5fpvjr6o+XpC6Srknrpkk6ptT9ldRb0pOp3h2SNk43nBoMnFofUxE3A/3TcuGd4/pJuruR2PLHoI+kH6XjNF3S4Fw/a0samereWj8DImm8pJoSjvP56f31gKR9Ur1XJR2RynSSdGF6j02T9L0i+/oF4KOI+Gv9ioiYGRGXrKqNdBzGp7hfkHR9+uCApL0kPZxiu08f3/J1fHrPPQz8t6TDJT2lbIblAUlbNPD3sNYSEf7xj3+K/JA90xuyuxXeAnw5vX4Q6JGWPwc8lJbPAYak5U1z7ZwHnJ6WRwDH5raNAI4lu2vcv4AN0vq/AN8ku+vWhNz6nwBnF4l1Rbtkdz37flr+AzAN6ApsBryZ1vcDFgOfTvs3NsWxVYpjsxTTQ8BRqU4Ax+X6nAF0z73eJHe8xgO75crV7/8PgCvT8vnAH3P1N27C/k4DDkzL59a3k/8bFKkzA9gJeDy9foZslmV67pjc3VBshccA2IvsLosbAF2AZ8mekFidyu2Xyl3Nx++L8UBNCcf5K2n5DuB+oDOwOzAlrR8I/DwtrwvUAtsX7O8ZwB9W8f4u2kY6DvPIZgDWIvug2zfF8DiwWarTn+wunvX79eeCv2X9TdtOBX7X1v+eO/qPp1vMGraepClk/3lPAsamUea+wC1psAPZf5SFeko6D9iILBHct6qOImKppHuBwyXdChwK/C9wIFlCeiz1tw7Zf76NqX9mQh3QJSLmA/MlLdbH1x48HRGvwopbevYFlgDjI+KttP564ACy6eBlZA8Hashxyh5NuzawZYp7Wtp2e/o9CTg6LR9CNq1cfwzek3RYY/srqRuwUUQ8nFaN5OMntjXmXeA9Sd8AngcWNlDuE7Glxfwx6AvcEREfpLhuB/YnO/azIuKxVO5vZIn3olz7e9Pwcf4IuDeVqwM+jIglkurI3ouQPTtgN318nUY3svuov9bQjku6LMX8UUTsvYo2PiJ7b8xO9aakfv8D9CT7dwDZh7r8bXRvyi1vA9yURv7rrCouax1O9mYNWxQRvVNyuZvsnP0I4D8R0buRuiPIRmpTJQ0gGy015qbUx7vAxIiYn6ZPx0bE8U2M/cP0e3luuf51/b/7wntlB8UfsVlvcUQsK7ZB2UM7hgB7p6Q9AqgqEs+yXP8qEkNz97cpbgIuAwasokyx2GDlY7CqY1Xs2Ba235AlkYbE5P5+EbFcH58PF9lsyao+RD4LHLMigIhBkrqTjeAbbENSP1Z+z9T/zQQ8GxF9Gujvg9zyJcDvI2JMau+cVcRprcDn7M0aEdkDQs4gS2aLgNckfR2yi6Ak7V6kWldgrrJH5Z6YWz8/bStmPLAn8F0+HiU9CewnacfU3/qSdlq9PVphH2VPUFyLbEr2UeAp4EBJ3ZVdgHY88HAD9fP7siHZf/bz0vnZr5TQ//3AD+tfSNqYEvY3/T3ek7R/WvWtVcRYzB3ABax6tqVYbIUmAEelGDcge0Jc/dX+20qqT4rHkx3bvKYc52LuA76f3l9I2inFkPcQUCXp+7l1+QsqS2kj70Vgs/r9ktRZ0q4NlO0GzEnLJzdQxlqRk71ZCSLiGWAq2dTuicB3JE0lGz0dWaTKWWT/oY8FXsitvxE4U0W+GpZGjHeTJcq707q3yEagoyRNI0uGn7ggsJmeAIaRPcL0NbIp6bnAT4FxZPs7OSIaeqzscOAfksZFxFSyc+DPkp2jfqyBOnnnARunC9SmAgc1YX9PJrvQcRrZE9rOLaE/ACJifkScHxEfNSW2Iu1MJpvBeZrsb31lep9Adorg5BTfJmTXYOTrNuU4F3Ml8BwwWdnX/C6nYKY2zQ4cRfah4jVJT5Od8vhJqW0UtPcR2XUd56djMoXslFYx55Cd6noEeLsJ+2UtxE+9M+uA0tTqkIg4rI1DMbNW4JG9mZlZhfPI3szMrMJ5ZG9mZlbhnOzNzMwqnJO9mZlZhXOyNzMzq3BO9mZmZhXu/wOObx24LmwUFQAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.8097005307689218, pvalue=2.0408554126458694e-24)\n", "Slope and P-value = PearsonRResult(statistic=-0.8994795132061041, pvalue=5.173440281006851e-37)\n", "Slope and P-value = PearsonRResult(statistic=-0.9325783839704631, pvalue=3.68776310020427e-45)\n", "Slope and P-value = PearsonRResult(statistic=-0.9474446684448811, pvalue=2.6435343199589023e-50)\n", "Slope and P-value = PearsonRResult(statistic=0.5303384985221766, pvalue=1.3875201746070448e-08)\n", "Slope and P-value = PearsonRResult(statistic=-0.8865154258486682, pvalue=1.4309353517264173e-34)\n", "Slope and P-value = PearsonRResult(statistic=0.28462458070431956, pvalue=0.004103742102775261)\n", "Slope and P-value = PearsonRResult(statistic=-0.9202424157955583, pvalue=1.0275650811741954e-41)\n", "Slope and P-value = PearsonRResult(statistic=-0.6205297333167127, pvalue=1.2910454374033643e-08)\n", "Slope and P-value = PearsonRResult(statistic=-0.9271847410879974, pvalue=1.4043919552196789e-43)\n" ] } ], "source": [ "sample = pd.merge(microbiome, poultry[['SampleID', 'Mo','SampleType']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "sample = sample[~sample.isin([np.nan, np.inf, -np.inf]).any(1)]\n", "sample = sample.drop(['Pathogen_Salmonella', 'new_Pathogen_Salmonella',\n", " 'Pathogen_Campy','new_Pathogen_Campy',\n", " 'Pathogen_Listeria','new_Pathogen_Listeria'],axis='columns')\n", "\n", "feces=sample[sample.SampleType=='Feces']\n", "feces.loc[feces['Mo'] < feces.Mo.median(), 'new_Mo'] = 0\n", "feces.loc[feces['Mo'] >= feces.Mo.median(), 'new_Mo'] = 1 \n", "\n", "soil=sample[sample.SampleType=='Soil']\n", "soil.loc[soil['Mo'] < soil.Mo.median(), 'new_Mo'] = 0 \n", "soil.loc[soil['Mo'] >= soil.Mo.median(), 'new_Mo'] = 1 \n", "\n", "print ('SAMPLE DISTRIBUTION \\n')\n", "\n", "print('Feces', feces.shape)\n", "print('Soil', soil.shape,'\\n')\n", "\n", "sampletypes = [feces, soil]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES\", 1: \"SOIL\"}\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType', 'Mo', 'new_Mo'],axis='columns'),sample.new_Mo,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1]\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " \n", "# mylist2.append([f\"Mo_{sample_name[indexing]}\", rf_auc_normal])\n", " \n", " \n", " print(pd.value_counts(sample['new_Mo']))\n", "\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim3_plot.max(), decimals=None, symbol=''))\n", " \n", "\n", " plt.title(f\"new_Mo in {sample_name[indexing]} Model\")\n", " prelim3_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','new_Mo','Mo','SampleType'],axis='columns').columns)\n", " prelim3_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", "\n", " xmax=prelim3_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " \n", " plt.show()\n", " \n", " prelim3_plot.nlargest(10).to_csv(\"prelim3.csv\")\n", " top10 = pd.read_csv('prelim3.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", "# plt.plot(pdp[1][0], pdp[0][0],'.')\n", "# plt.ylabel('Partial dependence'), plt.xlabel(top10[feature])\n", "# plt.show()\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"Mo_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (42) Na" ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Median: 138.332\n", "Total sample count: 1635 \n", "\n", "Distribution:\n", "1.0 818\n", "0.0 817\n", "Name: new_Na, dtype: int64\n", "SAMPLE DISTRIBUTION \n", "\n", "Feces (698, 877)\n", "Soil (695, 877) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "1.0 661\n", "0.0 37\n", "Name: new_Na, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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PearsonRResult(statistic=-0.9183103362495644, pvalue=3.1669343589458027e-41)\n", "Slope and P-value = PearsonRResult(statistic=-0.7369150200877572, pvalue=2.3338671373050126e-18)\n", "0.0 656\n", "1.0 39\n", "Name: new_Na, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.7183148164142851, pvalue=4.0122679641554785e-17)\n", "Slope and P-value = PearsonRResult(statistic=0.8314259599703943, pvalue=9.395062066144945e-27)\n", "Slope and P-value = PearsonRResult(statistic=-0.5233053148645296, pvalue=2.3224685666130754e-08)\n", "Slope and P-value = PearsonRResult(statistic=0.4822307422939156, pvalue=3.76868237100566e-07)\n", "Slope and P-value = PearsonRResult(statistic=0.4909342209840734, pvalue=2.1519279807369157e-07)\n", "Slope and P-value = PearsonRResult(statistic=0.8569992131792293, pvalue=5.675959385443983e-30)\n", "Slope and P-value = PearsonRResult(statistic=-0.3394396013339722, pvalue=0.0005505122196646068)\n", "Slope and P-value = PearsonRResult(statistic=0.9311911173491387, pvalue=9.672428615491643e-45)\n", "Slope and P-value = PearsonRResult(statistic=0.8959815832819616, pvalue=2.5359039260156526e-36)\n", "Slope and P-value = PearsonRResult(statistic=0.9373240291952762, pvalue=1.1576961704722749e-46)\n" ] } ], "source": [ "print(\"Median:\",poultry.Na.median())\n", "\n", "poultry.loc[poultry['Na'] < poultry.Na.median(), 'new_Na'] = 0\n", "poultry.loc[poultry['Na'] >= poultry.Na.median(), 'new_Na'] = 1 \n", "print(\"Total sample count:\", poultry.Na.value_counts().sum(),\"\\n\")\n", "\n", "print(\"Distribution:\")\n", "print(poultry.new_Na.value_counts())\n", "\n", "sample = pd.merge(microbiome, poultry[['SampleID', 'new_Na','SampleType']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "sample = sample[~sample.isin([np.nan, np.inf, -np.inf]).any(1)]\n", "sample = sample.drop(['Pathogen_Salmonella', 'new_Pathogen_Salmonella',\n", " 'Pathogen_Campy','new_Pathogen_Campy',\n", " 'Pathogen_Listeria','new_Pathogen_Listeria'],axis='columns')\n", "\n", "\n", "feces=sample[sample.SampleType=='Feces']\n", "soil=sample[sample.SampleType=='Soil']\n", "\n", "print ('SAMPLE DISTRIBUTION \\n')\n", "\n", "print('Feces', feces.shape)\n", "print('Soil', soil.shape,'\\n')\n", "\n", "sampletypes = [feces, soil]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES\", 1: \"SOIL\"}\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType', 'new_Na'],axis='columns'),sample.new_Na,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1]\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " \n", "# mylist2.append([f\"Na_{sample_name[indexing]}\", rf_auc_normal])\n", " \n", " \n", " print(pd.value_counts(sample['new_Na']))\n", "\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim3_plot.max(), decimals=None, symbol=''))\n", " \n", "\n", " plt.title(f\"new_Na in {sample_name[indexing]} Model\")\n", " prelim3_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','new_Na','SampleType'],axis='columns').columns)\n", " prelim3_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", "\n", " xmax=prelim3_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " \n", " plt.show()\n", " \n", " prelim3_plot.nlargest(10).to_csv(\"prelim3.csv\")\n", " top10 = pd.read_csv('prelim3.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", "# plt.plot(pdp[1][0], pdp[0][0],'.')\n", "# plt.ylabel('Partial dependence'), plt.xlabel(top10[feature])\n", "# plt.show()\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"Na_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (43) Ni" ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SAMPLE DISTRIBUTION \n", "\n", "Feces (698, 878)\n", "Soil (695, 878) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "1.0 664\n", "0.0 34\n", "Name: new_Ni, dtype: int64\n" ] }, { 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.5994916315634796, pvalue=4.396337261258647e-11)\n", "Slope and P-value = PearsonRResult(statistic=-0.579482556863616, pvalue=2.6706234872238277e-10)\n", "Slope and P-value = PearsonRResult(statistic=-0.9573599749446685, pvalue=1.1930780511631964e-54)\n", "Slope and P-value = PearsonRResult(statistic=0.14629724400068544, pvalue=0.14638869047150682)\n", "Slope and P-value = PearsonRResult(statistic=-0.7177617167082456, pvalue=4.351160129542608e-17)\n", "Slope and P-value = PearsonRResult(statistic=-0.8847616112737797, pvalue=2.9041736136902432e-34)\n", "Slope and P-value = PearsonRResult(statistic=0.09961506891383652, pvalue=0.32409290712264976)\n", "Slope and P-value = PearsonRResult(statistic=-0.7071721389098821, pvalue=1.9817537633867678e-16)\n", "Slope and P-value = PearsonRResult(statistic=-0.07611261023266554, pvalue=0.45166051662013057)\n", "Slope and P-value = PearsonRResult(statistic=-0.24010282115673082, pvalue=0.016121593259664377)\n", "1.0 348\n", "0.0 347\n", "Name: new_Ni, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.8916917336356781, pvalue=1.6518812333834524e-35)\n", "Slope and P-value = PearsonRResult(statistic=-0.8309436278507598, pvalue=1.0674337508661191e-26)\n", "Slope and P-value = PearsonRResult(statistic=-0.8822376362810296, pvalue=7.884472927398002e-34)\n", "Slope and P-value = PearsonRResult(statistic=-0.8648266495423753, pvalue=4.383146161261532e-31)\n", "Slope and P-value = PearsonRResult(statistic=-0.9235694695454568, pvalue=1.3815508251859685e-42)\n", "Slope and P-value = PearsonRResult(statistic=-0.9620704496124801, pvalue=4.3110797440776374e-57)\n", "Slope and P-value = PearsonRResult(statistic=-0.7911063770395304, pvalue=1.2125686675726386e-22)\n", "Slope and P-value = PearsonRResult(statistic=0.8152100282458121, pvalue=5.577540757554538e-25)\n", "Slope and P-value = PearsonRResult(statistic=-0.7567288739245785, pvalue=8.551941122778873e-20)\n", "Slope and P-value = PearsonRResult(statistic=-0.9656427853315975, pvalue=3.6871718118451785e-59)\n" ] } ], "source": [ "sample = pd.merge(microbiome, poultry[['SampleID', 'Ni','SampleType']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "sample = sample[~sample.isin([np.nan, np.inf, -np.inf]).any(1)]\n", "sample = sample.drop(['Pathogen_Salmonella', 'new_Pathogen_Salmonella',\n", " 'Pathogen_Campy','new_Pathogen_Campy',\n", " 'Pathogen_Listeria','new_Pathogen_Listeria'],axis='columns')\n", "\n", "feces=sample[sample.SampleType=='Feces']\n", "feces.loc[feces['Ni'] < feces.Ni.median(), 'new_Ni'] = 0\n", "feces.loc[feces['Ni'] >= feces.Ni.median(), 'new_Ni'] = 1 \n", "\n", "soil=sample[sample.SampleType=='Soil']\n", "soil.loc[soil['Ni'] < soil.Ni.median(), 'new_Ni'] = 0 \n", "soil.loc[soil['Ni'] >= soil.Ni.median(), 'new_Ni'] = 1 \n", "\n", "print ('SAMPLE DISTRIBUTION \\n')\n", "\n", "print('Feces', feces.shape)\n", "print('Soil', soil.shape,'\\n')\n", "\n", "sampletypes = [feces, soil]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES\", 1: \"SOIL\"}\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType', 'Ni', 'new_Ni'],axis='columns'),sample.new_Ni,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1]\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " \n", "# mylist2.append([f\"Ni_{sample_name[indexing]}\", rf_auc_normal])\n", " \n", " \n", " print(pd.value_counts(sample['new_Ni']))\n", "\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim3_plot.max(), decimals=None, symbol=''))\n", " \n", "\n", " plt.title(f\"new_Ni in {sample_name[indexing]} Model\")\n", " prelim3_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','new_Ni','Ni','SampleType'],axis='columns').columns)\n", " prelim3_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", "\n", " xmax=prelim3_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " \n", " plt.show()\n", " \n", " prelim3_plot.nlargest(10).to_csv(\"prelim3.csv\")\n", " top10 = pd.read_csv('prelim3.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", "# plt.plot(pdp[1][0], pdp[0][0],'.')\n", "# plt.ylabel('Partial dependence'), plt.xlabel(top10[feature])\n", "# plt.show()\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"Ni_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (44) P" ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Median: 1077.89\n", "Total sample count: 1635 \n", "\n", "Distribution:\n", "1.0 818\n", "0.0 817\n", "Name: new_P, dtype: int64\n", "SAMPLE DISTRIBUTION \n", "\n", "Feces (698, 877)\n", "Soil (695, 877) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "1.0 675\n", "0.0 23\n", "Name: new_P, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.8687266656792265, pvalue=1.151669846215342e-31)\n", "Slope and P-value = PearsonRResult(statistic=-0.4624836201829571, pvalue=0.019923411496864454)\n", "Slope and P-value = PearsonRResult(statistic=-0.8255356817055511, pvalue=4.347370070305326e-26)\n", "Slope and P-value = PearsonRResult(statistic=-0.9079593369255089, pvalue=1.3039366610723973e-18)\n", "Slope and P-value = PearsonRResult(statistic=nan, pvalue=nan)\n", "Slope and P-value = PearsonRResult(statistic=-0.9354364093387069, pvalue=4.733140138481022e-46)\n", "Slope and P-value = PearsonRResult(statistic=-0.8938187606690704, pvalue=6.588813477947816e-36)\n", "Slope and P-value = PearsonRResult(statistic=-0.8846539158695174, pvalue=3.032049804714778e-34)\n", "Slope and P-value = PearsonRResult(statistic=-0.780506644913245, pvalue=1.0384429136486837e-21)\n", "Slope and P-value = PearsonRResult(statistic=-0.576260294057289, pvalue=0.0001529687320776004)\n", "0.0 676\n", "1.0 19\n", "Name: new_P, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=nan, pvalue=nan)\n", "Slope and P-value = PearsonRResult(statistic=0.8450675147357776, pvalue=5.447783385338387e-13)\n", "Slope and P-value = PearsonRResult(statistic=nan, pvalue=nan)\n", "Slope and P-value = PearsonRResult(statistic=nan, pvalue=nan)\n", "Slope and P-value = PearsonRResult(statistic=0.5952436258580908, pvalue=8.148577376033168e-11)\n", "Slope and P-value = PearsonRResult(statistic=0.8177878482231421, pvalue=3.94496526180441e-21)\n", "Slope and P-value = PearsonRResult(statistic=0.6769417537111646, pvalue=0.3230582462888354)\n", "Slope and P-value = PearsonRResult(statistic=0.41109346094957977, pvalue=2.1504925496052994e-05)\n", "Slope and P-value = PearsonRResult(statistic=0.6994388839908363, pvalue=5.750745058556857e-16)\n", "Slope and P-value = PearsonRResult(statistic=0.2498604470985765, pvalue=0.012172924123570848)\n" ] } ], "source": [ "print(\"Median:\",poultry.P.median())\n", "\n", "poultry.loc[poultry['P'] < poultry.P.median(), 'new_P'] = 0\n", "poultry.loc[poultry['P'] >= poultry.P.median(), 'new_P'] = 1 \n", "print(\"Total sample count:\", poultry.P.value_counts().sum(),\"\\n\")\n", "\n", "print(\"Distribution:\")\n", "print(poultry.new_P.value_counts())\n", "\n", "sample = pd.merge(microbiome, poultry[['SampleID', 'new_P','SampleType']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "sample = sample[~sample.isin([np.nan, np.inf, -np.inf]).any(1)]\n", "sample = sample.drop(['Pathogen_Salmonella', 'new_Pathogen_Salmonella',\n", " 'Pathogen_Campy','new_Pathogen_Campy',\n", " 'Pathogen_Listeria','new_Pathogen_Listeria'],axis='columns')\n", "\n", "\n", "feces=sample[sample.SampleType=='Feces']\n", "soil=sample[sample.SampleType=='Soil']\n", "\n", "print ('SAMPLE DISTRIBUTION \\n')\n", "\n", "print('Feces', feces.shape)\n", "print('Soil', soil.shape,'\\n')\n", "\n", "sampletypes = [feces, soil]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES\", 1: \"SOIL\"}\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType', 'new_P'],axis='columns'),sample.new_P,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1]\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " \n", "# mylist2.append([f\"P_{sample_name[indexing]}\", rf_auc_normal])\n", " \n", " \n", " print(pd.value_counts(sample['new_P']))\n", "\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim3_plot.max(), decimals=None, symbol=''))\n", " \n", "\n", " plt.title(f\"new_P in {sample_name[indexing]} Model\")\n", " prelim3_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','new_P','SampleType'],axis='columns').columns)\n", " prelim3_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", "\n", " xmax=prelim3_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " \n", " plt.show()\n", " \n", " prelim3_plot.nlargest(10).to_csv(\"prelim3.csv\")\n", " top10 = pd.read_csv('prelim3.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", "# plt.plot(pdp[1][0], pdp[0][0],'.')\n", "# plt.ylabel('Partial dependence'), plt.xlabel(top10[feature])\n", "# plt.show()\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"P_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (45) Pb" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Median: 0.8343\n", "Total sample count: 1635 \n", "\n", "Distribution:\n", "1.0 818\n", "0.0 817\n", "Name: new_Pb, dtype: int64\n", "SAMPLE DISTRIBUTION \n", "\n", "Feces (698, 877)\n", "Soil (695, 877) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "1.0 538\n", "0.0 160\n", "Name: new_Pb, dtype: int64\n" ] }, { 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.0016285415861821062, pvalue=0.9871700297636924)\n", "Slope and P-value = PearsonRResult(statistic=-0.6387000270897136, pvalue=8.706237486819722e-13)\n", "Slope and P-value = PearsonRResult(statistic=-0.8341394907147377, pvalue=4.5466756282448066e-27)\n", "Slope and P-value = PearsonRResult(statistic=-0.6056357453733744, pvalue=2.4631288845095054e-11)\n", "Slope and P-value = PearsonRResult(statistic=0.04640134789440567, pvalue=0.6466458876123072)\n", "Slope and P-value = PearsonRResult(statistic=0.6454204703356801, pvalue=4.1978450307531063e-13)\n", "Slope and P-value = PearsonRResult(statistic=0.6368048301482241, pvalue=1.066033318906327e-12)\n", "Slope and P-value = PearsonRResult(statistic=-0.7048783050062776, pvalue=2.727987778316243e-16)\n", "Slope and P-value = PearsonRResult(statistic=-0.7877245825579002, pvalue=2.4380506461239135e-22)\n", "Slope and P-value = PearsonRResult(statistic=-0.4256082943984744, pvalue=1.012963624469639e-05)\n", "0.0 464\n", "1.0 231\n", "Name: new_Pb, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.07032427098312968, pvalue=0.48689069318805406)\n", "Slope and P-value = PearsonRResult(statistic=-0.9109775908072112, pvalue=1.785737161979399e-39)\n", "Slope and P-value = PearsonRResult(statistic=-0.904332658713487, pvalue=5.161169940982258e-38)\n", "Slope and P-value = PearsonRResult(statistic=-0.6282638625738632, pvalue=2.610170884096929e-12)\n", "Slope and P-value = PearsonRResult(statistic=-0.874480165573602, pvalue=1.479823796668199e-32)\n", "Slope and P-value = PearsonRResult(statistic=-0.6639986920298396, pvalue=5.0688461098630095e-14)\n", "Slope and P-value = PearsonRResult(statistic=-0.6380781924550188, pvalue=9.30575639830456e-13)\n", "Slope and P-value = PearsonRResult(statistic=-0.6476807352445054, pvalue=3.271164324136554e-13)\n", "Slope and P-value = PearsonRResult(statistic=-0.8766925092580031, pvalue=6.543651922424484e-33)\n", "Slope and P-value = PearsonRResult(statistic=-0.7873167181802573, pvalue=2.650048385821875e-22)\n" ] } ], "source": [ "print(\"Median:\",poultry.Pb.median())\n", "\n", "poultry.loc[poultry['Pb'] < poultry.Pb.median(), 'new_Pb'] = 0\n", "poultry.loc[poultry['Pb'] >= poultry.Pb.median(), 'new_Pb'] = 1 \n", "print(\"Total sample count:\", poultry.Pb.value_counts().sum(),\"\\n\")\n", "\n", "print(\"Distribution:\")\n", "print(poultry.new_Pb.value_counts())\n", "\n", "sample = pd.merge(microbiome, poultry[['SampleID', 'new_Pb','SampleType']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "sample = sample[~sample.isin([np.nan, np.inf, -np.inf]).any(1)]\n", "sample = sample.drop(['Pathogen_Salmonella', 'new_Pathogen_Salmonella',\n", " 'Pathogen_Campy','new_Pathogen_Campy',\n", " 'Pathogen_Listeria','new_Pathogen_Listeria'],axis='columns')\n", "\n", "\n", "feces=sample[sample.SampleType=='Feces']\n", "soil=sample[sample.SampleType=='Soil']\n", "\n", "print ('SAMPLE DISTRIBUTION \\n')\n", "\n", "print('Feces', feces.shape)\n", "print('Soil', soil.shape,'\\n')\n", "\n", "sampletypes = [feces, soil]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES\", 1: \"SOIL\"}\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType', 'new_Pb'],axis='columns'),sample.new_Pb,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1]\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " \n", "# mylist2.append([f\"Pb_{sample_name[indexing]}\", rf_auc_normal])\n", " \n", " \n", " print(pd.value_counts(sample['new_Pb']))\n", "\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim3_plot.max(), decimals=None, symbol=''))\n", " \n", "\n", " plt.title(f\"new_Pb in {sample_name[indexing]} Model\")\n", " prelim3_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','new_Pb','SampleType'],axis='columns').columns)\n", " prelim3_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", "\n", " xmax=prelim3_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " \n", " plt.show()\n", " \n", " prelim3_plot.nlargest(10).to_csv(\"prelim3.csv\")\n", " top10 = pd.read_csv('prelim3.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", "# plt.plot(pdp[1][0], pdp[0][0],'.')\n", "# plt.ylabel('Partial dependence'), plt.xlabel(top10[feature])\n", "# plt.show()\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"Pb_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (46) S" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Median: 955.479\n", "Total sample count: 818 \n", "\n", "Distribution:\n", "1.0 409\n", "0.0 409\n", "Name: new_S, dtype: int64\n", "SAMPLE DISTRIBUTION \n", "\n", "Feces (698, 877)\n", "Soil (0, 877) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "0.0 356\n", "1.0 342\n", "Name: new_S, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.6300552915638122, pvalue=2.1681558672993822e-12)\n", "Slope and P-value = PearsonRResult(statistic=-0.7447900073017681, pvalue=6.503783909477618e-19)\n", "Slope and P-value = PearsonRResult(statistic=-0.7152372766709939, pvalue=6.284559286013829e-17)\n", "Slope and P-value = PearsonRResult(statistic=0.07035816624578806, pvalue=0.4866802262478806)\n", "Slope and P-value = PearsonRResult(statistic=-0.7954098623815251, pvalue=4.8922457581090843e-23)\n", "Slope and P-value = PearsonRResult(statistic=-0.46376910167470853, pvalue=1.176735705663251e-06)\n", "Slope and P-value = PearsonRResult(statistic=-0.8052981214342942, pvalue=5.584360636280446e-24)\n", "Slope and P-value = PearsonRResult(statistic=-0.8238913121716691, pvalue=6.5997038493822e-26)\n", "Slope and P-value = PearsonRResult(statistic=0.18895534701418154, pvalue=0.05972806727517897)\n", "Slope and P-value = PearsonRResult(statistic=0.6144182433969502, pvalue=1.0528244712384391e-11)\n" ] } ], "source": [ "print(\"Median:\",poultry.S.median())\n", "\n", "poultry.loc[poultry['S'] < poultry.S.median(), 'new_S'] = 0\n", "poultry.loc[poultry['S'] >= poultry.S.median(), 'new_S'] = 1 \n", "print(\"Total sample count:\", poultry.S.value_counts().sum(),\"\\n\")\n", "\n", "print(\"Distribution:\")\n", "print(poultry.new_S.value_counts())\n", "\n", "sample = pd.merge(microbiome, poultry[['SampleID', 'new_S','SampleType']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "sample = sample[~sample.isin([np.nan, np.inf, -np.inf]).any(1)]\n", "sample = sample.drop(['Pathogen_Salmonella', 'new_Pathogen_Salmonella',\n", " 'Pathogen_Campy','new_Pathogen_Campy',\n", " 'Pathogen_Listeria','new_Pathogen_Listeria'],axis='columns')\n", "\n", "\n", "feces=sample[sample.SampleType=='Feces']\n", "soil=sample[sample.SampleType=='Soil']\n", "\n", "print ('SAMPLE DISTRIBUTION \\n')\n", "\n", "print('Feces', feces.shape)\n", "print('Soil', soil.shape,'\\n')\n", "\n", "sampletypes = [feces]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES\"}\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType', 'new_S'],axis='columns'),sample.new_S,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1]\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " \n", "# mylist2.append([f\"S_{sample_name[indexing]}\", rf_auc_normal])\n", " \n", " \n", " print(pd.value_counts(sample['new_S']))\n", "\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim3_plot.max(), decimals=None, symbol=''))\n", " \n", "\n", " plt.title(f\"new_S in {sample_name[indexing]} Model\")\n", " prelim3_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','new_S','SampleType'],axis='columns').columns)\n", " prelim3_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", "\n", " xmax=prelim3_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " \n", " plt.show()\n", " \n", " prelim3_plot.nlargest(10).to_csv(\"prelim3.csv\")\n", " top10 = pd.read_csv('prelim3.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", "# plt.plot(pdp[1][0], pdp[0][0],'.')\n", "# plt.ylabel('Partial dependence'), plt.xlabel(top10[feature])\n", "# plt.show()\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"S_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (47) Si" ] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Median: 291.454\n", "Total sample count: 818 \n", "\n", "Distribution:\n", "1.0 409\n", "0.0 409\n", "Name: new_Si, dtype: int64\n", "SAMPLE DISTRIBUTION \n", "\n", "Feces (698, 877)\n", "Soil (0, 877) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "1.0 373\n", "0.0 325\n", "Name: new_Si, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.6324824962145025, pvalue=1.682990840060094e-12)\n", "Slope and P-value = PearsonRResult(statistic=0.4207560038925194, pvalue=1.3078969876365313e-05)\n", "Slope and P-value = PearsonRResult(statistic=0.21620445129853236, pvalue=0.030735181877248084)\n", "Slope and P-value = PearsonRResult(statistic=-0.23612064623660312, pvalue=0.018026857906272356)\n", "Slope and P-value = PearsonRResult(statistic=0.9013482958821016, pvalue=2.1601142073592045e-37)\n", "Slope and P-value = PearsonRResult(statistic=0.39194949590459877, pvalue=5.5112635108845476e-05)\n", "Slope and P-value = PearsonRResult(statistic=0.9294607789891521, pvalue=3.131630967075519e-44)\n", "Slope and P-value = PearsonRResult(statistic=0.8969239571785388, pvalue=1.6618408885674435e-36)\n", "Slope and P-value = PearsonRResult(statistic=0.7171383606479882, pvalue=4.7664278683035823e-17)\n", "Slope and P-value = PearsonRResult(statistic=-0.7047697389142139, pvalue=2.7693544316323175e-16)\n" ] } ], "source": [ "print(\"Median:\",poultry.Si.median())\n", "\n", "poultry.loc[poultry['Si'] < poultry.Si.median(), 'new_Si'] = 0\n", "poultry.loc[poultry['Si'] >= poultry.Si.median(), 'new_Si'] = 1 \n", "print(\"Total sample count:\", poultry.Si.value_counts().sum(),\"\\n\")\n", "\n", "print(\"Distribution:\")\n", "print(poultry.new_Si.value_counts())\n", "\n", "sample = pd.merge(microbiome, poultry[['SampleID', 'new_Si','SampleType']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "sample = sample[~sample.isin([np.nan, np.inf, -np.inf]).any(1)]\n", "sample = sample.drop(['Pathogen_Salmonella', 'new_Pathogen_Salmonella',\n", " 'Pathogen_Campy','new_Pathogen_Campy',\n", " 'Pathogen_Listeria','new_Pathogen_Listeria'],axis='columns')\n", "\n", "\n", "feces=sample[sample.SampleType=='Feces']\n", "soil=sample[sample.SampleType=='Soil']\n", "\n", "print ('SAMPLE DISTRIBUTION \\n')\n", "\n", "print('Feces', feces.shape)\n", "print('Soil', soil.shape,'\\n')\n", "\n", "sampletypes = [feces]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES\"}\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType', 'new_Si'],axis='columns'),sample.new_Si,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1]\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " \n", " # mylist2.append([f\"Si_{sample_name[indexing]}\", rf_auc_normal])\n", " \n", " \n", " print(pd.value_counts(sample['new_Si']))\n", "\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim3_plot.max(), decimals=None, symbol=''))\n", " \n", "\n", " plt.title(f\"new_Si in {sample_name[indexing]} Model\")\n", " prelim3_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','new_Si','SampleType'],axis='columns').columns)\n", " prelim3_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", "\n", " xmax=prelim3_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " \n", " plt.show()\n", " \n", " prelim3_plot.nlargest(10).to_csv(\"prelim3.csv\")\n", " top10 = pd.read_csv('prelim3.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", "# plt.plot(pdp[1][0], pdp[0][0],'.')\n", "# plt.ylabel('Partial dependence'), plt.xlabel(top10[feature])\n", "# plt.show()\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"Si_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " indexing+=1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (48) Zn" ] }, { "cell_type": "code", "execution_count": 74, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Median: 27.1597\n", "Total sample count: 1635 \n", "\n", "Distribution:\n", "1.0 818\n", "0.0 817\n", "Name: new_Zn, dtype: int64\n", "SAMPLE DISTRIBUTION \n", "\n", "Feces (698, 877)\n", "Soil (695, 877) \n", "\n", "POULTRY CORRELATION WITH MICROBIOME IN.........\n", "\n", "1.0 598\n", "0.0 100\n", "Name: new_Zn, dtype: int64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=-0.5317323447247644, pvalue=1.0349601709019648e-05)\n", "Slope and P-value = PearsonRResult(statistic=-0.7717176759288926, pvalue=5.640687725471835e-21)\n", "Slope and P-value = PearsonRResult(statistic=-0.3335059914546781, pvalue=0.19081369713175728)\n", "Slope and P-value = PearsonRResult(statistic=-0.32470787491477, pvalue=0.08568556998367945)\n", "Slope and P-value = PearsonRResult(statistic=-0.8026576459906668, pvalue=1.1585886124289276e-11)\n", "Slope and P-value = PearsonRResult(statistic=-0.8475138395920835, pvalue=1.0386598304340093e-28)\n", "Slope and P-value = PearsonRResult(statistic=-0.2608824148790853, pvalue=0.14253903037657706)\n", "Slope and P-value = PearsonRResult(statistic=-0.9341649488557104, pvalue=1.1932947491897558e-45)\n", "Slope and P-value = PearsonRResult(statistic=-0.6200191884946085, pvalue=9.221236409835819e-11)\n", "Slope and P-value = PearsonRResult(statistic=-0.9111741281293257, pvalue=1.6102052525594898e-39)\n", "0.0 595\n", "1.0 100\n", "Name: new_Zn, dtype: int64\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Slope and P-value = PearsonRResult(statistic=0.9428041688131438, pvalue=1.4932519800540072e-48)\n", "Slope and P-value = PearsonRResult(statistic=0.7609056733770795, pvalue=4.090225561531654e-20)\n", "Slope and P-value = PearsonRResult(statistic=0.7445474034827992, pvalue=6.769603120303033e-19)\n", "Slope and P-value = PearsonRResult(statistic=0.811354667957133, pvalue=1.3887060546067058e-24)\n", "Slope and P-value = PearsonRResult(statistic=0.5546235528791733, pvalue=2.5772392200329126e-09)\n", "Slope and P-value = PearsonRResult(statistic=0.9253627163298989, pvalue=4.509538004488789e-43)\n", "Slope and P-value = PearsonRResult(statistic=0.51547123817697, pvalue=4.066574564553162e-08)\n", "Slope and P-value = PearsonRResult(statistic=0.8281443549799781, pvalue=2.221700340997389e-26)\n", "Slope and P-value = PearsonRResult(statistic=0.7605069711361316, pvalue=4.391421404100398e-20)\n", "Slope and P-value = PearsonRResult(statistic=0.8614807903927018, pvalue=1.3351709003169659e-30)\n" ] } ], "source": [ "print(\"Median:\",poultry.Zn.median())\n", "\n", "poultry.loc[poultry['Zn'] < poultry.Zn.median(), 'new_Zn'] = 0\n", "poultry.loc[poultry['Zn'] >= poultry.Zn.median(), 'new_Zn'] = 1 \n", "print(\"Total sample count:\", poultry.Zn.value_counts().sum(),\"\\n\")\n", "\n", "print(\"Distribution:\")\n", "print(poultry.new_Zn.value_counts())\n", "\n", "sample = pd.merge(microbiome, poultry[['SampleID', 'new_Zn','SampleType']])\n", "sample.loc[:, sample.isnull().any()].columns\n", "sample = sample[~sample.isin([np.nan, np.inf, -np.inf]).any(1)]\n", "sample = sample.drop(['Pathogen_Salmonella', 'new_Pathogen_Salmonella',\n", " 'Pathogen_Campy','new_Pathogen_Campy',\n", " 'Pathogen_Listeria','new_Pathogen_Listeria'],axis='columns')\n", "\n", "\n", "feces=sample[sample.SampleType=='Feces']\n", "soil=sample[sample.SampleType=='Soil']\n", "\n", "print ('SAMPLE DISTRIBUTION \\n')\n", "\n", "print('Feces', feces.shape)\n", "print('Soil', soil.shape,'\\n')\n", "\n", "sampletypes = [feces, soil]\n", "\n", "indexing=0\n", "\n", "sample_name = {0: \"FECES\", 1: \"SOIL\"}\n", "\n", "print (\"POULTRY CORRELATION WITH MICROBIOME IN.........\\n\")\n", "\n", "for item in sampletypes:\n", " sample = item\n", "\n", " #Split data\n", " X_train, X_test, y_train, y_test = train_test_split(sample.drop(['SampleID','SampleType', 'new_Zn'],axis='columns'),sample.new_Zn,test_size=0.3)\n", "\n", " #Models\n", " rf = RandomForestClassifier(n_estimators=100, random_state = 0)\n", "\n", " rf_score = cross_val_score(estimator=rf, X=X_train, y=y_train, cv=5)\n", "\n", " #RandomForest model\n", " rf.fit(X_train, y_train)\n", " y_pred = rf.predict(X_test)\n", "\n", " rf_probs = rf.predict_proba(X_test)\n", " rf_probs = rf_probs[:, 1]\n", " rf_auc_normal = roc_auc_score(y_test, rf_probs)\n", " \n", " \n", " \n", " print(pd.value_counts(sample['new_Zn']))\n", "\n", "\n", " fig = plt.figure(1, (7,4))\n", " ax = fig.add_subplot(1,1,1) \n", "\n", " ax.xaxis.set_major_formatter(mtick.PercentFormatter(xmax=prelim3_plot.max(), decimals=None, symbol=''))\n", " \n", "\n", " plt.title(f\"new_Zn in {sample_name[indexing]} Model\")\n", " prelim3_plot = pd.Series(rf.feature_importances_, index=sample.drop(['SampleID','new_Zn','SampleType'],axis='columns').columns)\n", " prelim3_plot.nlargest(10).plot(kind='barh',label='AUROC = %0.2f)' % rf_auc_normal).invert_yaxis()\n", " plt.xlabel('Relative Importance of Microbiome Genera')\n", " plt.legend()\n", "\n", " xmax=prelim3_plot.max()\n", " x=[0, 0.25*xmax, 0.5*xmax, 0.75*xmax, xmax]\n", " values=[0,25,50,75,100]\n", " plt.xticks(x,values)\n", " \n", " plt.show()\n", " \n", " prelim3_plot.nlargest(10).to_csv(\"prelim3.csv\")\n", " top10 = pd.read_csv('prelim3.csv',usecols=[0])\n", " top10 = top10.values.tolist()\n", " \n", "# mylist2.append([f\"Zn_{sample_name[indexing]}\", rf_auc_normal])\n", "# output2 = pd.DataFrame(mylist) \n", "# output2.to_csv('Microbiome as input vs Poultry AUC.csv', index = False, header=False) \n", " \n", " \n", " for feature in range(0, 10):\n", " pdp = partial_dependence(rf, X=X_train, features=top10[feature])\n", " #plt.plot(pdp[1][0], pdp[0][0],'.')\n", " #plt.ylabel('Partial dependence'), plt.xlabel(top10[feature])\n", " #plt.show()\n", " \n", " slope = sp.stats.pearsonr(pdp[1][0], pdp[0][0]) \n", " print(\"Slope and P-value =\", slope)\n", " \n", " \n", " mylist.append([f\"Zn_{sample_name[indexing]}\", str(top10[feature])[2:-2], slope[0], slope[1],rf_auc_normal])\n", " \n", " output = pd.DataFrame(mylist)\n", " \n", " output.to_csv('Microbiome Poultry Correlation Different Timepoints.csv', index = False, header=False)\n", " \n", "\n", " \n", " indexing+=1\n", " \n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (1) Pathogens and Probiotics Correlations at different timepoints" ] }, { "cell_type": "code", "execution_count": 75, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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SampleProbioticsCorrelationP-ValueAUC
27Salmonella_FECES_ENDStreptococcus0.9560964.840984e-540.803783
63Salmonella_CECAStreptococcus0.8545801.214831e-290.763333
81Salmonella_WCR-FBacillus0.8677281.628294e-310.707143
83Salmonella_WCR-FPropionibacterium0.7726572.503596e-050.707143
92Campylobacter_FECES_STARTLactobacillus0.9292333.647714e-440.861690
100Campylobacter_FECES_MIDLactobacillus0.9062022.055272e-380.920847
105Campylobacter_FECES_MIDBacillus-0.7853813.926800e-220.920847
109Campylobacter_FECES_MIDClostridium-0.7208312.767776e-170.920847
201Lab_Listeria_FECES_ENDStreptococcus0.7656831.726907e-200.715686
275BrGMOFree_FECES_STARTLactobacillus0.9247076.813867e-430.872354
\n", "
" ], "text/plain": [ " Sample Probiotics Correlation P-Value \\\n", "27 Salmonella_FECES_END Streptococcus 0.956096 4.840984e-54 \n", "63 Salmonella_CECA Streptococcus 0.854580 1.214831e-29 \n", "81 Salmonella_WCR-F Bacillus 0.867728 1.628294e-31 \n", "83 Salmonella_WCR-F Propionibacterium 0.772657 2.503596e-05 \n", "92 Campylobacter_FECES_START Lactobacillus 0.929233 3.647714e-44 \n", "100 Campylobacter_FECES_MID Lactobacillus 0.906202 2.055272e-38 \n", "105 Campylobacter_FECES_MID Bacillus -0.785381 3.926800e-22 \n", "109 Campylobacter_FECES_MID Clostridium -0.720831 2.767776e-17 \n", "201 Lab_Listeria_FECES_END Streptococcus 0.765683 1.726907e-20 \n", "275 BrGMOFree_FECES_START Lactobacillus 0.924707 6.813867e-43 \n", "\n", " AUC \n", "27 0.803783 \n", "63 0.763333 \n", "81 0.707143 \n", "83 0.707143 \n", "92 0.861690 \n", "100 0.920847 \n", "105 0.920847 \n", "109 0.920847 \n", "201 0.715686 \n", "275 0.872354 " ] }, "execution_count": 75, "metadata": {}, "output_type": "execute_result" } ], "source": [ "correlation = pd.read_csv(\"Microbiome Poultry Correlation Different Timepoints.csv\")\n", "\n", "cor_probiotics = correlation[correlation['Important_Feature'].isin(['Probiotic_Bacillus',\n", " 'Probiotic_Bifidobacterium',\n", " 'Probiotic_Clostridium',\n", " 'Probiotic_Enterococcus',\n", " 'Probiotic_Lactobacillus',\n", " 'Probiotic_Pediococcus',\n", " 'Probiotic_Propionibacterium',\n", " 'Probiotic_Streptococcus'])]\n", "\n", "#Select where targets are only pathogens (Salmonella, Campy, Listeria)\n", "#cor_probiotics = cor_probiotics[cor_probiotics['Target/SampleType'].str.contains('Lab_Campy|Lab_Salmonella|Lab_Listeria')]\n", "\n", "#Select where Correlation is > 0.7 or <-0.7 and P-value < 0.01\n", "cor_probiotics = cor_probiotics[((cor_probiotics.Correlation >= 0.7) & (cor_probiotics[\"P-Value\"] < 0.05) & (cor_probiotics.AUC >= 0.7)) | ((cor_probiotics.Correlation <= -0.7) & (cor_probiotics[\"P-Value\"] < 0.05) & (cor_probiotics.AUC >= 0.7))]\n", "\n", "#cor_probiotics = pd.read_csv(\"04b Probiotics vs Pathogens.csv\")\n", "cor_probiotics.head()\n", "cor_probiotics = cor_probiotics[[\"Target/SampleType\", \"Important_Feature\", \"Correlation\",\"P-Value\",\"AUC\"]]\n", "cor_probiotics.replace({'Probiotic_': ''}, regex=True, inplace=True)\n", "cor_probiotics.rename(columns={'Target/SampleType':'Sample', 'Important_Feature':'Probiotics'}, inplace = True)\n", "\n", "\n", "#cor_probiotics.to_csv('04c Probiotic Correlation.csv', index = False)\n", "cor_probiotics.head(10)\n" ] }, { "cell_type": "code", "execution_count": 76, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#Probiotics cor_probiotics (cor) with Lab pathogens (Labpath)\n", "cor_Labpath = cor_probiotics[cor_probiotics.Sample.str.contains('Campy')]\n", "\n", "#Probiotics cor_probiotics (cor) with Lab pathogens (Labpath) in FECES\n", "FECES = cor_Labpath[cor_Labpath['Sample'].str.contains('FECES')].copy()\n", "FECES.loc[len(FECES.index)] = ['Campylobacter_FECES_END',\"Bacillus\",0,0,0]\n", "FECES.replace({'_FECES': ''}, regex=True, inplace=True)\n", "FECES.rename(columns={'Sample':'Pathogens'}, inplace = True)\n", "FECES = pd.pivot_table(FECES,index=['Pathogens'],columns='Probiotics',values='Correlation',fill_value=0)\n", "\n", "\n", "#Plots layout\n", "plt.rcParams[\"figure.figsize\"] = [12, 6]\n", "plt.rcParams.update({'font.size': 15})\n", "\n", "plt.rcParams[\"figure.autolayout\"] = True\n", "fig, axs = plt.subplots(ncols=1)\n", "\n", "\n", "#Making the plots\n", "sns.heatmap(FECES,cmap=\"RdYlGn\", vmin=-1, vmax=1).set(title='FECES')\n", "\n", "#sns.heatmap(SOIL,cmap=\"RdYlGn\", vmin=-1, vmax=1, ax=axs[1]).set(title='SOIL')\n", "\n", "plt.suptitle('Common Probiotics Genera Correlation with Pathogens', y=1.05, fontsize=20)\n", "plt.savefig(\"Figure 2.jpg\")\n", "\n", "plt.show()\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 77, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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SamplePotential_ProbioticsCorrelationP-ValueAUC
1Salmonella_FECES_STARTg__Staphylococcus0.8930629.156170e-360.828993
2Salmonella_FECES_STARTg__Peptococcus0.8526783.961534e-230.828993
4Salmonella_FECES_STARTg__Dorea0.9108341.925928e-390.828993
5Salmonella_FECES_STARTg__cc_1150.9220954.021401e-400.828993
8Salmonella_FECES_STARTg__Rummeliibacillus0.8432123.639162e-280.828993
23Salmonella_FECES_ENDg__Oceanobacillus0.9653659.340277e-110.803783
28Salmonella_FECES_ENDg__Peptococcus0.7544781.264808e-190.803783
29Salmonella_FECES_ENDg__Faecalibacterium0.8036118.157129e-240.803783
33Salmonella_SOIL_STARTg__Pedobacter0.8120891.169132e-240.927273
35Salmonella_SOIL_STARTg__Peptoniphilus0.8289883.027356e-030.927273
\n", "
" ], "text/plain": [ " Sample Potential_Probiotics Correlation P-Value \\\n", "1 Salmonella_FECES_START g__Staphylococcus 0.893062 9.156170e-36 \n", "2 Salmonella_FECES_START g__Peptococcus 0.852678 3.961534e-23 \n", "4 Salmonella_FECES_START g__Dorea 0.910834 1.925928e-39 \n", "5 Salmonella_FECES_START g__cc_115 0.922095 4.021401e-40 \n", "8 Salmonella_FECES_START g__Rummeliibacillus 0.843212 3.639162e-28 \n", "23 Salmonella_FECES_END g__Oceanobacillus 0.965365 9.340277e-11 \n", "28 Salmonella_FECES_END g__Peptococcus 0.754478 1.264808e-19 \n", "29 Salmonella_FECES_END g__Faecalibacterium 0.803611 8.157129e-24 \n", "33 Salmonella_SOIL_START g__Pedobacter 0.812089 1.169132e-24 \n", "35 Salmonella_SOIL_START g__Peptoniphilus 0.828988 3.027356e-03 \n", "\n", " AUC \n", "1 0.828993 \n", "2 0.828993 \n", "4 0.828993 \n", "5 0.828993 \n", "8 0.828993 \n", "23 0.803783 \n", "28 0.803783 \n", "29 0.803783 \n", "33 0.927273 \n", "35 0.927273 " ] }, "execution_count": 77, "metadata": {}, "output_type": "execute_result" } ], "source": [ "correlation = pd.read_csv(\"Microbiome Poultry Correlation Different Timepoints.csv\")\n", "\n", "cor_nonprobiotics = correlation[~correlation['Important_Feature'].isin(['Probiotic_Bacillus',\n", " 'Probiotic_Bifidobacterium',\n", " 'Probiotic_Clostridium',\n", " 'Probiotic_Enterococcus',\n", " 'Probiotic_Lactobacillus',\n", " 'Probiotic_Pediococcus',\n", " 'Probiotic_Propionibacterium',\n", " 'Probiotic_Streptococcus'])]\n", "\n", "#Select where targets are only pathogens (Salmonella, Campy, Listeria)\n", "#cor_nonprobiotics = cor_nonprobiotics[cor_nonprobiotics['Target/SampleType'].str.contains('Lab_Campy|Lab_Salmonella|Lab_Listeria')]\n", "\n", "#Select where Correlation is > 0.7 or <-0.7 and P-value < 0.01\n", "cor_nonprobiotics = cor_nonprobiotics[((cor_nonprobiotics.Correlation >= 0.7) & (cor_nonprobiotics[\"P-Value\"] < 0.05) & (cor_nonprobiotics.AUC >= 0.7)) | ((cor_nonprobiotics.Correlation <= -0.7) & (cor_nonprobiotics[\"P-Value\"] < 0.05) & (cor_nonprobiotics.AUC >= 0.7))]\n", "\n", "#cor_nonprobiotics = pd.read_csv(\"04b Probiotics vs Pathogens.csv\")\n", "cor_nonprobiotics.head()\n", "cor_nonprobiotics = cor_nonprobiotics[[\"Target/SampleType\", \"Important_Feature\", \"Correlation\",\"P-Value\",\"AUC\"]]\n", "cor_nonprobiotics.replace({'Probiotic_': ''}, regex=True, inplace=True)\n", "cor_nonprobiotics.rename(columns={'Target/SampleType':'Sample', 'Important_Feature':'Potential_Probiotics'}, inplace = True)\n", "\n", "\n", "#cor_nonprobiotics.to_csv('04c Probiotic Correlation.csv', index = False)\n", "cor_nonprobiotics.head(10)\n" ] }, { "cell_type": "code", "execution_count": 78, "metadata": {}, "outputs": [ { "data": { "image/png": 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7Xdtmrt+4iJgp6enC4na+A9OaLG/0nnyT9PrtTOpLti/wmqRJwN4RcW+Lx7Ye5ETGqqB29WSZJuuXLZQru7+vR8QRA45qcOwHLACsHxGT61dI+i7ppK4bGl3lbud9WbrJNrV99fde1h/79haPXda0fF+qBiwiXiI3G5G0AumkagKwI6nG8KP97OIG0lXIFSStHBH3tBhv7bku2uSKZSfUjvHeiLi1xW2b1ZR05DMfEQ9KeoDUMXpdUsfsMtr5HPdX+9PX9+bmiPhAg/WlKE1ceyApWfpARDxaWL92ww1b1+nf35ZExKuSrgA+kYcn35B0Vf1F4G5JD5H6Vd5EanbWydqYbvgRKVFbI9cyvU7S0cxdK9tpA3m/a783S5MGE3idpNGkCyD1F4Q68h3oT66B+hXwK0ljSTWH25E6+r9T0juj/wEnrMeN6r+I2aCrNesZX1yR/5mvkx/Wj5RUq0JvdMWsNulYfyeW7eorhmbeBjxTPKHLBvsfWKv+Q7r6+z5JjZpY1OZEaDSC1TqSGv2+jM/3fTblou/PxBjSlfzaUJ41M/P6Uu9HRLxA6my+tKRGzYr62vbBiPgLqTPtPaTnu0Q/27xIGtIZ0pC6rRqKz/VgHKOTn/lj8v1+TT5fr5NUu6rdzue4ZZHmBbqddCK1eBu7WpJ0VfyqBknMwsxuElZvIL9JTb9rheUdeX2auCjfb0dq7nlR3bqLcwwfL5Ttz0Bei6HwNlKtUjGJGcXs/3VFs+jQ84iI50m1GMtJWrlBkUbfh9pnpFF8H6JwUbyD34HSIuKJiDg1Ij5L+sy8lfRZsmHOiYxVwemkkYu2z81Q6n2DND/FhTHnsLK1quw3F3cWaZjQy4EtJe3c6ICS3p2v4LSjaQx9mAIsLuk9hXi+SDoproyIeIXUBnlh4If16yS9lTSnyqukNuBFK5OGZq3fZnPSieu9pPenL3/O+/6apLcV1v2I1Pn+z4WrbQN5P2o1dkdLmqO5m6RR+QoxkpaS9MEG2y9Eukr8GnMOy9vM90lNpHaUdFjup9NIo6Z3R5Jek8MkrVJcKWleSe0mIMeTaqoOkLRWg2OMkjS+xX1OoXOf+cNIfaI+CvwxJ7XFGBeWtD9pUsx2P8cDdSip38NxTWJcTFJ/V6qfICVgq+fEpbbtPKSr0Es22GYqubN4C7FeSUr21pG0dSHOrUm1X3fTuH9Ep9RqWfYlNUkqJjKLkn5PZjH3sMPNDOT3YChMAVaW9Kbagjys9AGk0bkaeZo0al+nHEd6nX9Rf+FH0pLAD+rK1Pwx33+//ncyD+P8kybH6MR3oClJ80naUIUOOPn7UUueyvZftB7mpmXWdRExPSccJwOX5k57D5DmkdmI1LTiS4XNLiL1Ffhd7qMwHZgWEUfm9TuQ/gH+Prczv5Z0grY88B7SlZq1SScLA9VfDI0cTjp5u0JSbdz7NUhXuv5BGi+/SvYlnTR+NXcwv4TZ828sQhrV574G250HHCJpY9KJZ20emRnAF5sMBPC6iJgi6RukuQtuyq/Vk6REaG3ScLD7FDa7iNSk4NTcRvol4P6I6OsE9VjSa/954B5JZ+TjvInUsfg4UnOy5YBrJN1JulL5ICmZ2pTUROOIfKWzTxHxiKQNSUOSfgP4gqSLSc01ZpGabnyElAg+Qd0s7BFxV/6eHEfq3Hoe6QRzHtLJ2kdz7Kv2F0cf8T2dT15Py8/3ItKV1Vn5GGuTmpEUO+v35XA69JmPiBeV5rn5B2m0w80kXUBKjkeRPmcbkt6br9ZtOtDP8YBExHGSViedfP9X0vmk37TFSaMgrktKGnfvYx+zJB2RY/93/mzOS7pivnh+DusXtpku6Vrgo5L+Qvp8zATObNZUMCJC0hdI/TX+lo9zF/B2Uj+c50mjVfX5nW3TzaSLWWNJv6P1gyjUkpqxwA1N+ns1cgnpc/tTSe8iJXlExEGdCLgNh5EGXLlZadCQV0nf+dVIc9ls1mCbi4DtlAYcuZF04eSyiCgOvlDWL4GNSc06/5V/Lxck/X6OBQ6OiNcT14i4VNIxpOHqb6+LezPS9/kR0mtN3TZtfwf6sQBp7qMp+TN/P+l36eOkQQTOLNZ62TDV7WHTfBu+N2g8j0wf5dcknUA9Sbq6/QBpQsS5ZizP5fciNS16mcbDPy5Cms38RtI/x5dIQzWeQ/pBXqiu7IS8jwl9PJfJrcRAk+GASSe/15BOEKaR2vqv2ywGOjSPTJOyfT7vXGYMaSjU2qzw00gnPRs1KDue2UNur036R/Ncfq7/BNZssM1EmgyTSkpk/0k6CXmZdMJ6MM2H9PwJKSl4tfie9fU6kk6KLyX9U56RPyd/IfVNqL0G+5OS44dzLI/m93h7WhySmXRC+gVSx/eH8zFfIv0zPoM0BOrCTbZ9N2no2PtzHM+QmsgdDWxQKNvwM9jfe0/q83Nkfs9n5PfwLlKtxWcKZU/o7/NGi5/5Eq/fKHLSSppHYgbp6utdpOT0ww22GcMAPsdNjt/n+sLzPpuUlL5CuihzHXAQc8+PNNdvDOli416kEeJeytv/iTTPUsPXnZTMnUW6ij+r/vXtK25S4vIn0ue6NuLWn4G3t/idHUcfQ0D38Vqdkrc7p8G6/+R1P2+ybbPf5x2ZPW/YHP+P+vrcln1/G3yXSj3nXP4WUif/p0j/997d7HUlJRd/JXW2n1kfW3+vN83/D81P+v94W359nifVum3fx3fum6Tv2Muk5OXXpNqy50mjpnX8O9Ds/SJdwPkOaZLfB0i/AU+Sfmd2B+Zt5fPnW+/elD8QZmZmZmal5X42dwMnRcT23Y7HRh73kTEzMzOzpiQtUxxgQ2m+mMPzw9OGPCgz3EfGzMzMzPr2DdKAPJNJzQ6XIfVHW57UvOvkrkVmI5oTGTMzMzPrywWkCVg3InXYf43UpOwI4PBwPwXrEveRMTMzMzOznuM+MmZmZmZm1nOcyJiZmZmZWc9xImNmZmZmZj3HiYyZmZmZmfUcJzJmZmZmZtZznMiYmZmZmVnPcSJjZmZmZmY9x4mMmZmZmZn1HCcyZmZmZmbWc5zImJmZmZlZz3EiY2ZmZmZmPceJjJmZmZmZ9RwnMmZmZmZm1nOcyJiZmZmZWc9xImNmZmZmZj3HiYyZmZmZmfUcJzJmZmZmZtZznMiYmZmZmVnPcSJjZmZmZmY9x4mMmZmZmZn1HCcyZmZmZmbWc5zImJmZmZlZz3EiY2ZmZmZmPceJjJmZmZmZ9RwnMmZmZmZm1nOcyJiZmZmZWc9xImNmZmZmZj3HiYyZmZmZmfUcJzJmZmZmZtZznMiYmZmZmVnPcSJjZmZmZmY9x4mMmZmZmZn1HCcyZmZmZmbWc5zImJmZmZlZz3EiY2ZmZmZmPceJjJmZmZmZ9RwnMmZmZmZm1nOcyJiZmZmZWc9xImNmZmZmZj3HiYyZmZmZmfUcJzJmZmZmZtZznMiYmZmZmVnPcSJjZmZmZmY9x4mMmZmZmZn1HCcyZmZmZmbWc5zImJmZmZlZz3EiY2ZmZmZmPceJjJmZmZmZ9RwnMmZmZmZm1nOcyJiZmZmZWc9xImNmZmZmZj3HiYyZmZmZmfUcJzJmZmZmZtZznMiYmZmZmVnPcSJjZmZmZmY9x4mMmZmZmZn1HCcyZmZmZmbWc5zImJmZmZlZz3EiY2ZmZmZmPceJjJmZmZmZ9RwnMmZmZmZm1nOcyJiZWU+RNF5S9HH7UC7XV5mQtE6Dfa8h6QRJ/5P0kqQXJN0m6XBJq7YQw2uF/S4qaT9Jt0iaJmm6pPsknS5pl8F/1czMhp83dDsAMzOzAToRmNRg+b11f98CHNJk+//UP5C0PzAReAr4K3AnIOCdwLbAVyUtFhHPl4hhVt1+3whcD6wE/AM4DnglP/448HXg2CYxmplZE05kzMysV90UEX/up8zDJcogaWfgQOASYIuIeLaw/jvAAaTEptUYdgVWBr4REb9qcOzl+4vPzMzm5kTGzMxGNEnzAj8GpgPbFpMYgIh4Cdh3gIdYOd9f1GhlRDw0wP2amY1oTmTMzKxXLShpycKylwtNv+ZpUAYgIuLp/PdHgGWAP0XEkx2IAeCViHgu//3ffL+TpH0i4rUG5c3MrEWKiG7HYGZmVpqk8aQmYI38LSK2y+X6+gf3QkQsnMt9DTgC2DsiDu1ADADnRMSmuexiwL+AFYAngMtJfWauBK6KiFlN92JmZk25RsbMzHrVMcDJhWWPFR5fC+zXYNv6WpE35vvnGpQbSAwAr9fsRMRUSasDewNbAlvlG8AUSV+KiH8O4NhmZiOaExkzM+tV90TEhf2UeapEmVoCs8ggxUBusrYvsK+kJYC1gc8COwKnSXpvRNzb1z7MzGxOnkfGzMxGutvy/fuH4mAR8XREnB0Rnwd+CiwIbDcUxzYzG06cyJiZ2Uh3JalJ2mdybclQuibfLzfExzUz63lOZMzMbESLiFeA75Oalv1N0lxNzCTNL+kneXLLlkhaW9KYJqs/k+/vaHW/ZmYjnfvImJnZcLacpB2brLs6Iv4LEBHHSVqBNOnlvZL+SkouRgHvALYBxpKagtX7QB/7Pz0ipgOfIw29fA5wHfA0sASwCbB+Ps5xA32CZmYjlYdfNjOznlI39PG3I+KXfZTr7x/crhFxbGGbNYCvAesCywKzSPPAXAD8NiLuKcTQl5Uj4l5J7yL1gVkfWBFYEngZuBc4Azi0bs4ZMzMryYmMmZmZmdkIIeltwLeBDwHvAi6PiPEltlsUOJzUJHYUcDawZ93kwrVymwMHASsD/wMOjIi/de4ZzOY+MmZmZmZmI8c7SU1b7863sv4GjAd2ASYAawKn1xeQtA5wCqnGemPgHOBESRu1GXNDrpExMzMzMxshJI2KiFn5738AS/ZXIyNpbeAqYL2IuCwvW4s06fDHa/NpSTofmCciNqjbdhLwxohYp9PPxTUyZmZmZmYjRC2JadHGwOO1JCbv5zrgvrwOSfOR+gL+vbDtScDauWlaRzmRMTMzMzOzvqwK3NVg+Z15HcBbgXkalLuTlHOs0umgnMiYmZmZmVlfFgOmNVg+Na+j7r5Ybmphfcd4HhmzEoJLhk1nsnPH7N7tEDpmoYXU7RA6Zt2Hf9vtEGyYu+nte3Q7hI6ZOnXY/CSz4RNHdTuEjrh1teHz+XrvHXd19Z+L9vhQex/wo679ErBb3ZJjIuKYtvaZNIpLDZYXH6vJ8rY5kTEzMzMzGyZy0tKJxKXeVGCpBsvHMLsGZmrdsmIZaFyj0xY3LTMzMzMzqwiNUlu3QXIXs/vC1KvvO/Nf4NUG5VYlTS7cylDPpTiRMTMzMzOriIomMucCy+R5YlKc0hrASnkdEfEyaf6YbQrbbgtcHRHPdjooNy0zMzMzM6uIQUxG0v6lBUkTYgIsB7xR0tb58aSIeFHSvcClEfFFgIi4Os8R80dJ3yLVsPwcuKI2h0z2I2CypMNJk2Vukm+fHIzn4kTGzMzMzKwiBjuRAcYCJxeW1R6vCEwh5QijC2W2Aw4DjiO16job2LO+QERckZOig4A9SPPM7BAR/+xg/K9zImNmZmZmVhHS4CYyETGF2SOJNSszrsGyacBO+dbXtqeTamMGnfvImJmZmZlZz3GNjJmZmZlZRQxB07Jhw4mMmZmZmVlFOJEpz4mMmZmZmVlFOJEpz4mMmZmZmVlFOJEpz4mMmZmZmVlFOJEpz6OWmZmZmZlZz3GNjJmZmZlZRbhGpjwnMmZmZmZmFeFEpjwnMmZmZmZmFSE5kSnLiYyZmZmZWUW4RqY8JzJmZmZmZhXhRKY8j1pmZmZmZmY9xzUyZmZmZmYV4RqZ8pzImJmZmZlVhBOZ8pzImJmZmZlVhBOZ8pzImJmZmZlVhBOZ8pzImJmZmZlVhBOZ8jxqmZmZmZmZ9RzXyJiZmZmZVYRrZMpzImNmZmZmVhGSE5mynMiYmZmZmVWEa2TKcyJjZmZmZlYRTmTKcyJjZmZmZlYRTmTK86hlZmZmZmbWc1wjY2ZmZmZWEaNczVCaXyozMzMzs4oYLbV1K0PSapIukvSipEck/VDS6H62mSgpmty+W1fuhCZlVm3zpZmLa2TMzMzMzCpi9CD3kZG0GHAhcAewOfBW4BBSBcd+fWx6LHBeYdlngH2AcwvL7wJ2KiybMqCA++BExszMzMysIsrWqrRhd2ABYMuIeA64QNIbgYmSDs7L5hIRDwEP1S+T9APgroi4pVD8hYi4pvOhz8lNy8zMzMzMKmL0qPZuJWwMnF9IWE4iJTfrlY1T0uLAx4ETW3h6HeVExszMzMxs5FiV1PTrdRHxAPBiXlfW1sA8pCSoaDVJz0l6WdIVkkonSK1w0zIzMzMzs4oYgqZliwHTGiyfmteVtR1wU0TcXVh+M3AtqQ/OUsDepOZr60TEda2H25wTGTMzMzOzimg3kZG0G7Bb3aJjIuKYQrFotGmT5Y2OsSypGdo+xXUR8atC2XNISc33SIMDdIwTGTMzMzOzimh31LKctBQTl3pTgTENli9K45qaRj5LSnz+ViKelyRNAjYrue/SnMiYmZmZmVXE6EFvWcZdFPrCSFoBWIhC35k+bAdcEREPtnDcUrU9rXBnfzMzMzOzkeNc4BOSFqlbti3wEnBpfxtLGgd8iJKjlUlagDRS2o0tR9oP18iYmZmZmVXEYE+ICRwF7AmcKunnwErARODQ+iGZJd0LXBoRXyxsvx3wGvCP4o4lLQqcDfwZuBdYEvgmsBypOVpH9VyNjKQtJV0saVoe0u1uSQdJWrLbsbVC0mRJc30A+tlmvKSQ9K7BiqvuWGMlTcxZ95CQNC4/v0a3C+vKnZCXHd1gHzdIOqHu8YS6fcyS9KykWyUdLumtQ/TUzMzMzEoZLbV1609ETAU2BEYDZwEHAocBBxSKviGXKdoOuCginmyw7mXgSWA/YBKpr840YL2IuKHUC9CCnqqRkXQI8A3geNIL/hywGmmG0ncCW3QtuOFnLOkDPRmYMsTH/hZwZWHZsw3KTZD0w4h4uMQ+NyBVmS5M+qx8CdhV0tYRcW5b0ZqZmZl1yBDUyBARd5DOjfoqM67J8vf1sc0MYMt2YmtFzyQykjYD9gK+GBHH1a26VNIxwEbdiczKkLRARLxUsvh/IuKafsrcQaqu/DYpue3P9RExPf99oaSjSFWff5U0LiIaJUpmZmZmQ2oIOvsPG73UtOybpEl3jiuuiIiZtavqkn4m6d+Spkt6SNJfJC1TX17SFEm/lLSvpEdzc6NDlGwi6XZJz0s6XdJiddvVmnZtJOlsSS9IekDS7nVlPpWbMK1YOOaKefmnmz1BSRtIulbSDEmPS/qNpIUbFH1Ts+Pn/awt6UxJj+Qyt0j6XIPjvUXSiZKekvRibnK1Q25O9u9c7JJa06y67RaXdHSOcYakqyR9sLDvkLRXbsL1ZN3+OuUl4FBgN0ljW904Il4GvkYafnD7zoZmZmZmNjCjR6mt20jSE4mMpHmADwPnlSg+FvgJ8CnSlfqVgIslFdv4bQesBewEHEyq7TkU+BHwA1JztfWAnzY4xu+BW0lVZ+cCv5W0aV53HvAI8IXCNhNIbQYnNXmOq+VtnwK2IjXr2oEGHan6OT7AW0hNs3Yhjdl9CnC8pNdP2PPJ/9XAmqSmXJvl/a4APArUEp+vAGvnG5LmAy4EPk6qDflMfl4XFhPGvH5Z4P+ROpWVNUrSGwq3Rp/V3wAzSO9dyyLiLuAh0sgbZmZmZtZDeqVp2RLAfMAD/RWMiJ1rf+fk5WrSyepHgMvqis4AtomImcB5kjYnXaFfOSLuy9u/l5SQzFHjAZwbEd/Lf58vaSVSp6azI2Jm7mz+BUkHRkRIUt7PnyLitSah7w/cD3w6x4SkZ4C/SVo7Iq4uc/z8GpxU9xooP+/lgV2ZPVTeN0kTH60eEY/mZRfVbXdr/vOOQjOvHYF3Ae+MiHty2QuB/wB7k5KXmsciYtsmz7cvZzRYdiBpRI3XRcTzko4A9pL089x5rVUPAUsPYDszMzOzjivTYd+SnqiRqdPvRDqSNs5NnZ4lDQ33UF61SqHo5FrCkN0LTKklMXXLlpI0b2Hb0wqPTwVWr6v1OY5UKzI+P14/Pz6+j9DXAk4rxHRKfg7rtHJ8SYtJOkLS/cCr+bYbc74GGwDn1SUxZX2MNA74fbXakrz8UmCNQtlzWtx3zTdJNUX1t2Yz1P4q37dS41PPvxZmZmZWGYM9atlw0is1Mk+ThnN7c1+FJK0JnEk60f8Z8AQp+bkGmL9QfFrh8StNlgmYN/9d80Sh3BOk13JJ4PGI+J+kyaRma5fk++si4vY+wl8WeLx+Qa7deRpYvMHxmh4fOIHUXOpHpE7xzwF7AJvXbbMEcH0f8TSzZN73qw3W/bfw+PEGZcq4t+wQfRExVdJvgT3zqHatWo7ys9iamZmZDarRvVbN0EU98VJFxKukPh+f6KfoFqT+GttGxJm5SdRjgxBSsXP5WFLNyVN1y44FtpK0HKkvS1+1MZD6pcyx31zDsgTwTNnjS5qf1D/ogIg4MiIuzklB8b1+mpQ8teoZ4AbmrjFZk7mHv+63Bq1DDgEWAL7cykaS3kFqcnd1k/W7Kc1Lc8Mxx5zdfpRmZmZm/XCNTHk9kchkhwNrSCp2okfSKEmfJJ3MvhoR9SfQc43W1QHFE/YtgBsLzcJOJdXinER6nU+ib9cCWxQGJdiSVNNyRQvHn480edHLtZWSFgGKo6VdBHxCUrP+IbUaqGJN1kXA24AHIuKGwq3TI5OVEhFPAL8jdfpfoMw2edCCI0i1cA3fm4g4JiLWiIg1dttt00ZFzMzMzDrKo5aV1ytNy4iIsyQdCvxe0kdIHcKnA6uSOuNPIZ3MfkPS4aSZSj9M6pzeaRtL+jGpX8iWpBG86pttEREzJP2FNOrXiRExrZ99HgTcDJyem0otD/wcOL/Q0b/P40fEs5KuB/aX9BwwC9iXNKHkG+v2cRjweeDyvK8HgXcAC0XEwaSBFV4iDVrwLClBvAH4I+n1nizpl8D/SLVGa5E69x/Wz/Ms4+2SniosmxERt/SxzS9yXEvTuMncmpJeAhYkDVbwJWAcsLXnkDEzMzPrPT2TyABExN6SrgK+CvyVdPV9CqlfzC8j4jFJ+5BGH9uV1GRoU+DuDoeyC2lo52+Smlp9JSLObFDudFIiM9fcN0URcbukjUlDR59K6tdyIvCdARx/B1Ln+D+SmpAdSTqB/2rd8Z7MCeHBpNqu+YB7yMNN50RsV9Iw0JcC8wDKy9cHfkgaSWxpUh+d60jvQyf8ssGy/5JqghqKiIck/YH0vjdycb6fTvrMXAgcERHFfj1mZmZmXTPSmoe1Q3O2wrK+SBpP6rz/7oi4rUT5g4FtgRUjYtbgRmeDKbhk2HxRzh1THE28dy200PD5sV/34d92OwQb5m56+x7dDqFjpk4dNj/JbPjEUd0OoSNuXW34fL7ee8ddXf3nst25E9r6gJ+08QnD559jP3qqRqZXSHo7sBpppLADncSYmZmZWRmukSnPiczgOBr4IKmp1RFdjqUS8sSco/soMssJn5mZmY10I63DfjucyLQgIiZTYgLFiBg/6MH0nvVIzfKaORCYODShmJmZmVWTa2TKcyJjQ+VG0lwzzTwyVIGYmZmZWe9zImNDIiKeJ02kaWZmZmZNjO6lWR67zImMmZmZmVlFuGlZeU5kzMzMzMwqYrTzmNKcyJiZmZmZVcQo18iU5kTGzMzMzKwiXCNTnrsTmZmZmZlZz3GNjJmZmZlZRXg+zPKcyJiZmZmZVYSblpXnRMbMzMzMrCJGuUqmNCcyZmZmZmYV4RqZ8tzZ38zMzMysIkapvVsZklaTdJGkFyU9IumHkkb3s804SdHgdlKDsptL+rekGZLukLTtwF6NvrlGxszMzMxshJC0GHAhcAewOfBW4BBSBcd+JXbxLeDKusdPFfa/DnAK8BtgT2AT4ERJUyPin20/gTpOZMzMzMzMKmIImpbtDiwAbBkRzwEXSHojMFHSwXlZX/4TEdf0sf4HwGURsWd+fImkdwL7Ax1NZNy0zMzMzMysIkZJbd1K2Bg4v5CwnERKbtZrJ3ZJ8wHrA38vrDoJWFvSou3sv8iJjJmZmZlZRYxWe7cSVgXuql8QEQ8AL+Z1/Tle0kxJj0o6VNICdeveCsxT3D9wJynvWKVUhCW5aZmZmZmZWUUMwejLiwHTGiyfmtc18zLwa1LzsOeA8cA+pORl87p902D/UwvrO8KJjJmZmZlZRYwu1zysKUm7AbvVLTomIo4pFItGmzZZnjaIeBT4at2iyZIeB34j6X0RcUsf+1eT5W1xImNmZmZmNkzkpKWYuNSbCoxpsHxRGtfU9OUfpNHJPgDcwuyal+L+a49b3X+f3EfGzMzMzKwihmAembso9IWRtAKwEHP3belPFO7/C7xa3H9+PAu4u8X998mJjJmZmZlZRQxBZ/9zgU9IWqRu2bbAS8ClLYa7db6/ESAiXgYuAbYplNsWuDoinm1x/31y0zIzMzMzs4oYNfjVDEeRJqo8VdLPgZWAicCh9UMyS7oXuDQivpgfTwQWIU2G+RywLvBt4NSIuLVu/z8i9Z85HDidNCHmJsAnO/1EnMiYmZmZmVVEu539+xMRUyVtCBwJnEXqt3IYKZmp9wZgdN3ju4BvAbuQ5px5APgF8OPC/q+QtDVwELAHcB+wQ0R0dDLMWoBmZmZmZlYBQzD8MhFxB7BBP2XGFR6fRJrYssz+TyfVxgwq95ExMzMzM7Oe4xoZMzMzM7OKKNlh33AiY2ZmZmZWGUPRtGy4cCJjZmZmZlYRg93ZfzhxImNmZmZmVhGukSnPiYyZmZmZWUW4j0x5HrXMzMzMzMx6jmtkzMzMzMwqYpT7yJTmRMbMzMzMrCLctKw8JzJmZmZmZhXhGpnynMiYmZmZmVWEE5nynMiYmZmZmVWEE5nyPGqZmZmZmZn1HNfImJmZmZlVxCi5nqEsJzJmZmZmZhXhpmXlOZExMzMzM6sIJzLlOZExMzMzM6sIJzLlOZExMzMzM6uIUR6LqzS/UmZmZmZm1nNcI2NWwhUr7NHtEDpm42lHdTsEM+uCD/znt90OwYax99zhz1enuGlZeU5kzMzMzMwqwolMeU5kzMzMzMwqwvPIlOdExszMzMysIlwjU54TGTMzMzOzinAiU57rrszMzMzMrOe4RsbMzMzMrCJcI1Oea2TMzMzMzCpilEa1dStD0mqSLpL0oqRHJP1Q0uh+tllT0vGS7s3b/UfSAZLmL5Q7QVI0uK3axsvSkGtkzMzMzMwqYhSDWyMjaTHgQuAOYHPgrcAhpAqO/frYdNtc9ufAPcB7gB/l+60KZe8Cdiosm9Jm6HNxImNmZmZmVhFD0LRsd2ABYMuIeA64QNIbgYmSDs7LGvl5RDxZ93iypBnA0ZLeEhH31617ISKuGZzwZ3PTMjMzMzOzihiCpmUbA+cXEpaTSMnNes02KiQxNTfn+7Fln18nOZExMzMzMxs5ViU1/XpdRDwAvJjXteLDwCzgP4Xlq0l6TtLLkq6Q1DRBaoeblpmZmZmZVcQQNC1bDJjWYPnUvK4UScsA3wf+VKjduRm4ltQHZylgb1LztXUi4rqBBt2IExkzMzMzs4poN5GRtBuwW92iYyLimEKxaLRpk+WNjjEv8HdgOvDNOXYc8atC2XNISc33gM+U2X9ZTmTMzMzMzCqi7BDKzeSkpZi41JsKjGmwfFEa19TMQZKAPwLvBD4SEVP7ieclSZOAzfrbd6ucyJiZmZmZVcQQNC27i0JfGEkrAAtR6DvTxGGkYZs/HhFlyteUqu1pRUspn6StJH2x7vGKkq6SNE3SKZLGdDpAMzMzM7ORYhRq61bCucAnJC1St2xb4CXg0r42lPRd4GvAjhFxRZmDSVqANFLajWXKt6LVuqv9gDfWPf4/YEngZ8AHgB93KC4zMzMzM+u8o4CXgVMlfSz3qZkIHFrfaV/SvZJ+X/d4B+AnpGZlD0v6UN1tqVxmUUmXS/qSpA0lbQtcAiyXt+2oVpuWrQT8G1KgwEbAFhFxjqQHSAnNVzobopmZmZnZyDDYTcsiYqqkDYEjgbNI/WIOIyUz9d4AjK57vFG+n5Bv9XYCTiAlSE+SKj/GAjOAq4H1IuKGzjyDOQNsVa1923rATODC/Pgh0hBrZmZmZmY2AO129i8jIu4ANuinzLjC4wnMncAUt5kBbNledOW1msj8C/icpGuAXYBLIuLlvO7NwBOdDM7MzMzMbCQZgs7+w0aricz3SFVQXyCNG71R3brPkCa/MTMzMzOzAdAQ1MgMFy0lMhFxhaQ3A6sA/42IaXWrjwPu7WBsZmZmZmZmDbXcRyYinqfB8GkRMakjEZmZmZmZjVCjWh5UeORqOZGR9CZgU2B5YP7C6oiIfToRmJmZmZnZSOOmZeW1lMhI2gI4kTQU2xPAK4UiATiRMTMzMzMbgKEYtWy4aLVG5ifAP4EJEfHMIMRjZmZmZjZiyU3LSms1kVkB+JqTGDMzMzOzznONTHmtvlJXAW8fjEDMzMzMzMzKarVGZi/gL5KmAxcA04oFIuLFDsRlZmZmZjbiuGlZea0mMrfm++NJHfsbGT3wcMzMzMzMRi43LSuv1URmZ5onMGZmZmZm1gYPv1xeS4lMRJwwSHGYmZmZmY14nhCzvJYnxASQtBqwOmkUs+Mi4jFJbwMej4jnOxmgmZmZmdlI4RqZ8lqdEHNh4Dhga+DVvP15wGOkOWYeAL7V4RjNzMzMzMzm0GrKdyjwYWBDYBFAdesmAZ/sUFxmZmZmZiPOKI1q6zaStNq0bEvg6xFxiaTi6GT3A2/pTFhmZmZmZiOPPABwaa0mMgsATzdZtwgws71wzMzMzMxGrpFWq9KOVl+p64HPN1m3NXBVe+GYmZmZmY1cYlRbt5Gk1RqZ/YALJV0InEyaU2YTSd8kJTLrdjg+MzMzM7MRwzUy5bX0SkXEFaSO/vMBR5I6+x8IrAR8LCKu73iEZmZmZmZmBS3PIxMRVwIflbQAsBgwLSJe7HhkZmZmZmYjjOeRKW9AE2ICRMRLwEsdjMXMzMzMbEQbNcL6ubSj1Qkxj+tj9SzgOeAW4NSImN5GXK3EtCXwVeADpFHV7gf+DhweEU8NRQydIGky8FREbN3CNuOBS4B3R8RtgxPZ68caC3wZOCEipgzmseqOOQ64Lz9cJ9cG1q//AfBD4P6IGFfYZrOIODsvmwyslzd7DZgK/Bs4FTg2Il4ezOdhZmZmVpZrZMprtUbm3cAKwFjgceBJYClgaeAJ4FlSUvFjSRtGxN0djHUukg4BvgEcDxxGSqRWA3YH3glsMZjHH2HGAgcAk4EpQ3zs6cD2wJWF5dvmdWVcAnyP1C9sKWA88HPgi5I2iIhpHYnUzMzMrA3u7F9eq6/U/sA04IMRsWxEvCcilgU+REpivg28HXge+EUnAy2StBmwF7BrROwSEWdFxKUR8VtS7cwxg3l8a0/uY1XWWcDW9ZOwSno38A7g7JL7eCYiromIqyLijIj4JvBhYBVSEmxmZmbWdUMx/LKk1SRdJOlFSY9I+mGDye4bbbeopOMlTZX0rKS/SFqiQbnNJf1b0gxJd0jadgAvRb9aTWQOBg4ojk4WEdcBE4GfR8R9wM8Y/KGYvwncFBFzNXeLiJkRcS6ApJ/lF3K6pIfyC75MfXlJUyT9UtK+kh7Nb8whSjaRdLuk5yWdLmmxuu3GSwpJG0k6W9ILkh6QtHtdmU9JmiVpxcIxV8zLP93sCUraQNK1+UPwuKTfSFq4QdE3NTt+3s/aks7MH9QXJN0i6XMNjvcWSSdKeip/sG+VtENurvXvXOyS/JyjbrvFJR2dY5wh6SpJHyzsOyTtJelwSU/W7a+MM0gTrq5ft2w74Arg4Rb2M4eIuJU0+t7nJL1xoPsxMzMz6xX5XPZC0jQqm5Oa6e9NGom4P38jtWrZBZgArAmcXtj/OsAppNYwGwPnACdK2qgT8ddrtWnZ22jewf9FYFz++37SEM2DQtI8pKvph5QoPhb4CfAIqUnR3sDFkt4dETPrym0HXAfsBKwOHERK9NYFfkDqf3Mk8FNS07V6vwf+BPwfsCXwW0kP5T4a5+Vjf4GU7NVMIDXNm9TkOa6Wt70A2IrUpO9npKGuP9nC8QHeQmqWdRQwA/gIcLykWRFxYj7eWOBq0vv4LeBB4F35uI8CnwP+AnwFuKkuzvlIX4YxpBq5J4A9SPMNrRwRj9XF+W3gMuD/0VoS/QKp5mX7fCxI79cvSJ/JdlwA7EOqxZvc5r7MzMzM2jIETct2J53XbhkRzwEX5Au6EyUdnJfNRdLawCeA9SLisrzsYeBaSR+LiNo52g+AyyJiz/z4EknvJLXs+mcnn0iriczNwAGSrqs/QZW0LKn/xI150VtIJ++DZQlSovRAfwUjYufa37nK7GrgIdLJ/GV1RWcA2+Tk5jxJmwNfA1bOtUxIei8pISkmMudGxPfy3+dLWok0eejZETFT0gnAFyQdGBEhSXk/f4qI15qEvj8pIfx0LeGS9AzwN0lrR8TVZY6fX4OT6l4D5ee9PLArcGJe9U1gUWD1iHg0L7uobrtb8593RMQ1dcfekZTwvDMi7sllLwT+Q0oav11X9rGIGGjV4knA7yXtAbwPeDPwD2DfAe6v5qF8v3Sb+zEzMzNrW9nmYW3YGDi/kLCcROo7vB6pSX+z7R6vJTGQWmVJui+vuzBf4F4f2LOw7Umki+iLRsSzHXoeLb9SuwNvAqZIujI3tbqS1Pl7GdKVeHKZ33UqyD5EfwUkbZybOj1LGrGqduK6SqHo5EINzb3AlFoSU7dsKUnzFrY9rfD4VGB1zW5reBwpuRufH6+fHx/fR+hrAacVYjolP4d1Wjm+pMUkHSHpfuDVfNuNOV+DDYDz6pKYsj5GSmDvk/QGSbXk+FJgjULZc1rcd71JwGjSlYDtgIs6NCqdOrAPMzMzs44YpVFt3UpYFbirfkFEPEBqlbNqK9tld9Zt91Zgngbl7iTlHcXz77a0VCMTEbfmq/07k05SlwHuJjU5Oj7PLUNE/KyTQTbwNPAy6ap8U5LWBM4knej/jNTsKYBrgPkLxacVHr/SZJmAefPfNU8Uyj1Bem2XJGWu/1MaAngnUnvBnYDrIuL2PsJfljQy3Oty7c7TwOINjtf0+MAJpAEZfgTcQRrdbQ9Su8iaJYA5+j6VtGTe96sN1v238PjxBmVKiYiXJZ0O7AB8lFTj1AnL5fsBx2ZmZmbWKUMw/PJizH2OC2l6isUaLC+z3Up1ZWhQbmphfUe0PCFmTlZ+3ckgBhDDq7km6BP0fUK7BakfyrYREZA6tA9CSGMbPH4NqK8xOBb4naTvkvqx7N3PPh8t7jfXsCwBPFP2+JLmBz4FfDUijqrbV/Fb8jQpeWrVM8ANzK6Nq1ecn6XfGrR+nERqLvcqc9dCDdRGeX83FldI2o1Uc8W3xizNpxce06FDmpmZmTWmNs+WNGr2+Ut2TEQUR/NtdBQ1WT6Q7YqP1WR5W1pOZADyiFTrkGoGngEuzyOXDaXDgTMlfSEi/lCIbxTpBHUB4NVaEpPNNVpXB2wBnFt4fGOhWdippATwJFLV2kn07VpgC0nfq9vPlqT37Iqyx8+jnI2mLqmQtAjwaeb8MF0E7Clp6YhoVDtRq4Eq1mRdRHqtH4iIYs1Qp11Aal53VyfaV0p6D2nwgj9HxPPF9flLfwzA5Sus2tEvnpmZmdlgqD9/aWIqaZCmokVpXONSv91SDZaPqdtuat2yYhn62X/LWkpkJC0EnEwaNes10lX8JYDRks4jdZZ/sZMBNhMRZ0k6lNQB/COkIXqnk9ro7U7qt/M74BuSDid1XPowqXN6p20s6cekfiFbAh9nzmZbRMQMSbVRv04sMQHjQaTBFU6X9FtS5/yfkzpnXV0o2/T4EfGspOuB/SU9B8widZB/Fqgfcvgw4PPA5XlfD5LmaVkoIg4mDazwEmnQgmdJCeINwB9Jr/dkSb8E/kf6TKxF6tzfsTla8sAInx3g5otL+hApiVyC1E9pV1LTyL06E6GZmZlZm2JWe9v33/v3Lgp9YSStACxE4z4w9dt9tMHyVZk9BPN/SS1dViWdl9aXmUU67+qYgcwjszZpRvX582SY85M6X69NOtEeMhGxd45lZeCvpCv2e5NqCfaIiEmkoXW3IvWVWQ/YdBBC2YU0fO/pef9fiYgzG5Q7Pd/PNfdNUe4/szGpmdippMTmRGDrARx/B+A+UtLxK1Ktxh8Lx3uSNJLbzaTarrNJ1ZIP5PUzSCf+q5M+mNfXLV+f9NofSBpW71ek92Soa+n6sj5pxLpLSa//+0gJ3dolkkozMzOzoRGz2rv171zgE7mFTs22pAvWlzbe5PXtlsnzxAAgaQ1S/5hzIfVpJvUH36aw7bbA1Z0csQxAc7a66qew9Biwf4N2drX+BD+MiGXm3nJ4kjSe9Ga9OyJuK1H+YNIbuWJEu+m2DaXh1LRsnQd/2+0QzMzMKkus390RTV87v71zjjd8os/484SYdwC3kSohVgIOBQ6PiP3qyt0LXBoRX6xbdh5p5LFvkWpYfg48EREfrSuzDmluviNJF9k3yeU/GRFdnUdmUVKTo0YeZM6mSpZJejuwGqlD/IFOYszMzMysoUE+TYyIqZI2JCUaZ5H6rRzGnBO3Q8oTRheWbZfLHkdq2XU2hTljIuIKSVuTWhPtQWoVtEOnk5hagK34F7CHpPPqO9DnSRb3yOttbkcDHyQ1bzuiy7FUQv7MFL8c9WY54TMzM7MRZ9bgn/5ExB2kOQT7KjOuwbJppGlEdupn29OZ3aVi0LSayHyP1AbuLkmnkebeGEsaJWscqU/HiBERkynRpSoixg96ML1nPVKzvGYOZO4rA2ZmZmZmQOsTYl4s6f3A/qROPMuS5ju5FtgyZ3dmZdwIrNnH+keGKhAzMzOzynCDlNIGMiHmHaT2cWYDludtuaHbcZiZmZlVihOZ0gY0IaaZmZmZmQ0CJzKltZzI5FEItiRN0Fic5Z2IWKsDcZmZmZmZjTxD0Nl/uGgpkZE0kdQ/5l+k8adfGYSYzMzMzMxGJtfIlNZqjcwXgZ9FxPcGIxgzMzMzM7MyWk1kFgEuGoxAzMzMzMxGPNfIlDaqxfInAZ8cjEDMzMzMzEa8mNXebQTpt0ZG0iZ1Dy8EDpa0JHABMK1YPiImdSw6MzMzM7MRJGJmW9v3O1P7MFKmadnZQDDn6zIO+EKDsgGMbj8sMzMzM7MRyKOWlVYmkVlx0KMwMzMzM7MR1zysHf0mMhFxf+1vSW8GHo2IV4vlJL0BeFNnwzMzMzMzM5tbq5397wPe32Tde/N6MzMzMzMbCHf2L63V4Zf76j80P/ByG7GYmZmZmY1sIywZaUeZUcveA7yvbtEmklYtFJsf+Cxwd+dCMzMzMzMbYZzIlFamRmYL4ID8dwD7Nyl3H/ClTgRlZmZmZjYiedSy0sokMj8BfklqVvYcsAFwfaHMK40GADAzMzMzsxa4Rqa0MqOWvQrUkpRWBwcwMzMzMzPruFY7+wMgaXlgFVLfmDlExKR2gzIzMzMzG5FcI1NaS4mMpEWAvwMb1Rbl+6grNroDcZmZmZmZjTxOZEprtanYT4E3Ax8lJTFbAOOB35M6+3+ok8GZmZmZmY0os2a1dxtBWk1kNgF+DFybHz8SEZdFxG7AGcC3OxmcmZmZmdmI4gkxS2u1j8zSwIMRMVPSC8DidesmAad0LDIzMzMzs5FmhCUj7Wi1RuZBYMn89z3ApnXrPgjM6ERQZmZmZmZmfWm1RuYC4GPAacBhwB8krQ68DKwLHNLZ8MzMzMzMRpAR1s+lHaVqZCQtIGkr4AngKklLR8SfgK1InfynAl8F9h20SM3MzMzMhrtZ0d5tEEjaVdI9kmZIulHShiW2+ZKkCyQ9LulZSVdK2qhBuWhwu6ZMXP3WyEhaCbgQGFe3+DlJn42I00i1M2ZmZmZm1q6K1chI2g44CpgIXAHsBJwtac2IuK2PTb8PnAf8GngB2BE4T9JnIuLMQtlDgH/UPX6+TGxlmpYdDMwiDbl8I7Ai8Bvg6Py3mZmZmZl1QsUSGeBA4A8R8SMASZcC7ye1xNqxj+0+EBFP1T2+QNLKwDeBYiIzJSJK1cLUK9O0bG1gv4i4MiJmRMSdwJeAN0tattUDmpmZmZlZExVqWpZbZq0C/L22LCJmAScDG/e1bSGJqbkZGNup+MokMssC/yss+y9pQsxlOhWImZmZmZlVyqr5/q7C8juBxSUt1eL+1gbuaLB8oqTXJD0l6ThJizcoM5eyo5YNTs8hMzMzMzObrVpNyxbL99MKy6fWrX+yzI4k7UxqkrZ3YdUfgLPyftYAfgC8V9JaETGzr32WTWTOl/Rag+UXFZdHRMeqi8zMzMzMRpQ2ExlJuwG71S06JiKOqVu/KKnFVZ8ior4WplipoSbLm8W0OvB/wK8i4pLCcSbUPbxM0p3AJGAz4PS+9lsmkTmwTIBmZmZmZtamNvu55KTlmD6KbAP8rsSuxOyalzHAs3XrxuT7af3uJPWzOQe4iLlrYxo5D5gOfIB2E5mIcCJjZmZmZjYUBrlpWUQcCxxbsnitVmZV4P665asCz0REn83KJI0Fzs/bbtdfU7EcX0iCErU9pSbENDMzMzOzIVChUcsi4n/A3aRaHAAkjcqPz+1rW0kLk5qIAWwaES+WOaakTwILk6Z96VPZPjJmZmZmZjbyTAT+LGkKcCXwBWBlYIdaAUnrkZqObRgRl+bFpwLvASYAb5X01lr52pwxuT/PGsCFwFOk5mT7AdeRmqP1yYmMmZmZmVlVVGvUMiLixFy7sg9pRLHbSTUst9UVEzCa2YMAAHw83/+lwW5r5f5LSoy2At4IPAb8EfhBmWZoTmTMzMzMzKqiYokMQET8jj4GCIiIycyZxBARalx6jjIXkWpyBsSJjJmZmZlZRUS018+l3+xhGHEiY2ZmZmZWFRWskakqJzJmZmZmZlXhRKY0D79sZmZmZmY9xzUyZmZmZmZV0eG5YIYzJzJmZmZmZlXhpmWlOZExK2HJJbodgQ13t71rj26H0DELjpmn2yF0zCvTX+12CB2z6i2/7XYI1sBZC+/e7RA6YrPpR3U7hOHDiUxpTmTMzMzMzKrCTctKc2d/MzMzMzPrOa6RMTMzMzOrCjctK82JjJmZmZlZVTiRKc2JjJmZmZlZVbiPTGlOZMzMzMzMqsI1MqU5kTEzMzMzqwonMqV51DIzMzMzM+s5rpExMzMzM6sK95EpzYmMmZmZmVlVuGlZaU5kzMzMzMwqIma6RqYsJzJmZmZmZlXhpmWlOZExMzMzM6sK18iU5lHLzMzMzMys57hGxszMzMysIsJNy0pzImNmZmZmVhVuWlaaExkzMzMzs6qY6eGXy3IiY2ZmZmZWEW5aVp47+5uZmZmZVcXMaO82CCTtKukeSTMk3ShpwxLbTJQUDW6fLJSbT9Ihkp6Q9IKkcySNKxOXExkzMzMzM2tI0nbAUcAfgY2B24GzJb2rxObPAmsXblcXyhwBTAC+BWwNLAlcIGn+/nbupmVmZmZmZlVRvaZlBwJ/iIgfAUi6FHg/sC+wYz/bvhYR1zRbKWl54IvAzhHxx7zsVuC+vO9j+9q5a2TMzMzMzCoiZkZbt06StBKwCvD31+OLmAWcTKqdaddG+f7Uuv0/DFxRZv9OZMzMzMzMqmLWrPZunbVqvr+rsPxOYHFJS/Wz/RhJT0l6VdLNkrZssP+HImJ6g/2vSj+cyJiZmZmZVUW1Ovsvlu+nFZZPLaxv5F7gO8Bnga2AR4BTCsnMYg32Xdt/X/sG3EfGzMzMzKwy2h1+WdJuwG51i46JiGPq1i8KLNtvHBH1tTDFoNRkef32fy7EdRZwFbA/dU3JmuxDfe27xomMmZmZmdkwkZOWY/oosg3wuxK7ErNrXsaQRiCj7jE0rk1pFldIOhX4uaTRETEz739Mg+JjyuzbTcvMzMzMzKpikJuWRcSxEaH+brl4rVam2F9lVeCZiHhyAM+wPsi7gBUkLdRg/8V+OXNxImNmZmZmVhUV6iMTEf8D7ibV4gAgaVR+fG4r+5IkYAvgX7k2BuCf+X6LunJvAj5aZv9uWmZmZmZmVhHt9pEZBBOBP0uaAlwJfAFYGdihVkDSesBFwIYRcWledilwCqlmZSFgV+BDwGdq20XEQ5J+DxyeE50n8/HuB+boY9OIExkzMzMzs6qY2fEhlNsSESdKWhjYB/gBcDuwaUTcVldMwGhmDwIAadSyb5AGFpgF3AR8KiKKNS17Ai8AhwILApcC20fEjP5icyJjZmZmZlYRFayRISJ+Rx8DBETEZOZMYoiIL5bc98vAXvnWEveRMTMzMzOznuMaGTMzMzOzquj8pJbDlhMZMzMzM7OqqGDTsqpyImNmZmZmVhHhGpnSnMiYmZmZmVWFa2RKcyJjZmZmZlYVFRt+uco8apmZmZmZmfUc18iYmZmZmVVEFeeRqSonMmZmZmZmVeHO/qU5kTEzMzMzqwjXyJTnRMbMzMzMrCI8/HJ5XevsL2lLSRdLmibpZUl3SzpI0pLdimkgJE2W9I8WtxkvKSS9a7DiqjvWWEkTJY0b7GMVjvsuSadLelTSS5Luk3RSXj4uP//+buPyvpS3D0lva3Cs8YXtpkm6VtJn8vqJJY41eShfHzMzM7NGYla0dRtJulIjI+kQ4BvA8cBhwHPAasDuwDuBLboR1zA1FjgAmAxMGYoD5mTjGuA64KvAVGBlYBvgPcApwNp1m6wE/AX4CnBT3fJH8/3awLj893bAQU0O/Tngf8Di+binShoPHAucV1fua8AGzPk5e67cszMzMzOzKhjyREbSZsBewBcj4ri6VZdKOgbYaKhjsvIkLRARL/VTbCfgZWDjiHg5L7sYOFqSIiJIiU5tn9Pzn3dExDXMbXvgBeC2/HezRObWiLgt73My8CCwY0TsBjxUd7ytgZebHMvMzMysa2a5aVlp3Wha9k3gpkISA0BEzIyIcwEk/UzSvyVNl/SQpL9IWqa+vKQpkn4pad/chOlZSYfkpkibSLpd0vO5idNiddvVmiJtJOlsSS9IekDS7nVlPiVplqQVC8dcMS//dLMnKGmD3LRphqTHJf1G0sINir6p2fHzftaWdKakR3KZWyR9rsHx3iLpRElPSXpR0q2SdshNs/6di11Sa0ZVt93iko7OMc6QdJWkDxb2HZL2knS4pCfr9teXMcC0uiTmdTmJKU3SaFJNzpnAccBqkt7T33YR8SJwL7BCK8czMzMz6yY3LStvSBMZSfMAH2bOZj7NjAV+AnyK1AxtJeDifGJbbztgLVItwMGk2p5DgR8BPyA1V1sP+GmDY/weuBXYEjgX+K2kTfO684BHgC8UtpkAPAlMavIcV8vbPgVsRWrWtQPQqB9NX8cHeAtwJbALsBmpSdbxkravO95Y4GpgTeBbudzvSSfwj5KaW0FqtrV2viFpPuBC4OPAt4HP5Od1YTFhzOuXBf4fsGej511wE7CSpF/l16MdGwBLAyeRXsNXSbUyfZI0ClgeuK/N45uZmZkNmZg1q63bSDLUTcuWAOYDHuivYETsXPs7Jy9Xk5oHfQS4rK7oDGCbiJgJnCdpc1IfiJUj4r68/XtJCckcNR7AuRHxvfz3+ZJWAvYDzo6ImZJOAL4g6cCICEnK+/lTRLzWJPT9gfuBT+eYkPQM8DdJa0fE1WWOn1+Dk+peA+XnvTywK3BiXvVNYFFg9Yio9Sm5qG67W/OfxWZbOwLvAt4ZEffkshcC/wH2JiUvNY9FxLZNnm8jfyA1EdwT2DM//0nAryLihhb2AylpmQacFxGvSLoA2E7S9xrU7oyW9AZgMeA7wELAr1o8npmZmVnXeNSy8ro1alm/75CkjXNTp2eB15jdx2GVQtHJtYQhuxeYUkti6pYtJWnewranFR6fCqxeV+tzHKlWZHx+vH5+fHwfoa8FnFaI6ZT8HNZp5fiSFpN0hKT7STURrwK7MedrsAHpJP9RWvMx4EbgPklvyAkAwKXAGoWy57Sy44h4LSc+7yXVit0IfBa4WtKnyu4n1xptQXo9X8mLTyR1/P9Qg01uIb1GT5Bq5iZExH9aid3MzMysm9y0rLyhTmSeJnUCf3NfhSStSeoT8RCpOdPazD5xnb9QfFrh8StNlgkoJjJPNHj8BmBJgIj4H2m0r53y+p2A6yLi9j7CXxZ4vH5BTmqeJo2mVfr4wAnAtsAvSDUca5KSq/rXYAlmj+7ViiVJr+mrhdtOzN2v5HEGICJujYiDImIj4O05zmYd9RvZmNTfZpKkMZLGkN6Pl2ncvGw70mu0Falm6XhJbxpI7GZmZmZWbUOayETEq6Q+H5/op+gWpP4a20bEmblJ1GODENLYBo9fI/VvqTkW2ErScqS+LH3VxkA6WZ9jv7mGZQngmbLHlzQ/qX/QARFxZERcnJtlFd+zp0nJU6ueAW4gnfgXb8Xhr9tO7yNiCnAysGoLm9WSlZNJQzhPJY1ENh/w2Qb9pW6PiBsi4lRSX6EFSTVCAyJpN0k3SLrh709PG+huzMzMzEqLmdHWbSTpRtOyw4E1JBU70SNplKRPAgsArxb6QMw1WlcHFE/YtwBuLDQLO5VUo3MS6fU6ib5dC2xROMneklTTckULx58PGE2qfQBA0iJAcbS0i4BPSFq6STy1JlnFmqyLgLcBD+ST//pbmZHJmsoDEDSyMiVrd/Iob5uSmpKtX7jtRRoAYP1m20fEf0lJ6IQ+4ulTRBwTEWtExBqfXWLMQHZhZmZm1hI3LStvyOeRiYizJB0K/F7SR4AzgOmkK/W7kyZt/B3wDUmHA2eRRjrbcRDC2VjSj0n9QrYkjeC1eSHeGZJqkzWeGBHT+tnnQcDNwOmSfkvqnP9z4PxCR/8+jx8Rz0q6Hthf0nPALGBf4FngjXX7OAz4PHB53teDwDuAhSLiYNLACi+RBi14lpQg3gD8kfR6T5b0S9JEkkuQ+vg8FhGH9fM8+/KDPMDCX4E7SZ3utyTVknyr5D42J9Wo/Coirq1fIelK4PukGpsL+9jHwaSBEb5GGzUzZmZmZkNl1ghLRtrRlc7+EbE3qe/HyqST3QtII2VdBOwREZOAfUh9Hc4kDZ+8aeO9tWUX4APA6Xn/X4mIMxuUOz3fzzX3TVHuP7MxqZnYqaTE5kRg6wEcfwfS8MF/JI2+dUr+u/54T5JGcruZVNt1NmlAgAfy+hmkk/nVSQnT9XXL1ye99gcC/8zHWBm4rr/n2Y+/AP8lvafn5phXA7aPiENK7mN74J5iEpNjfxX4O7BlHhCgoYi4H/gz8GVJC7X2FMzMzMyGnpuWlacW5yccFiSNBy4B3l2bCb6f8geTEq8VI2JkDdBtANz5vlWHzRdl1Vt+2+0QrIHb3rVHt0PomAXHzNPtEDrmlemvdjuEjvF3v5rOWrg4M0Rv2mz6Ud0OoWPE+urm8R/ZfK22zjnedMZ1XY1/KA1507JeIuntpJqEPYADncSYmZmZmVWDE5m+HQ18kNS87Ygux1IJeWLO4mhh9WY54TMzMzMbmJHWYb8d3ZoQs6siYnJEqL9mZRExPiIWiIht6yZkHOnWY+65Z+pv+3cvNDMzM7PeVsU+MpJ2lXSPpBmSbpS0YYltpkiKJrdl68o1Wn9NmbhcI2OtupE010wzjwxVIGZmZmbDTcyqVsMWSdsBRwETSVOJ7AScLWnNfioFtiBNJ1Lvd8DMiChO5n4I8I+6x8+Xic2JjLUkIp4nTaRpZmZmZh1WwZHHDgT+EBE/ApB0KfB+0rQgTadHiYib6x9LWoY0Rcj3GxSfEhGlamHqjcimZWZmZmZmVVSlCTElrQSsQpr2IsWX+kKfTJpupBWfpdzk8qU5kTEzMzMzs0ZWzfd3FZbfCSwuaakW9rUdcHWe569ooqTXJD0l6ThJi5fZoZuWmZmZmZlVxKxqjVq2WL6fVlg+tW79k/3tRNJbgA8BX2+w+g/AWXk/awA/AN4raa2ImNnXfp3ImJmZmZlVRLt9ZCTtBuxWt+iYiDimbv2iwLJzbViMI6K+FqYYlJosb2Y7YBZ1TdTqjjOh7uFlku4EJgGbAaf3tVMnMmZmZmZmFdFuP5ectBzTR5FtSKOH9UfMrnkZAzxbt25Mvp9WMqztgEsi4vESZc8DpgMfoJ9Exn1kzMzMzMwqYrDnkYmIY/N8in3ecvFarcyqhd2sCjwTEWWalb0deB9wYqnnH1F7Ev0+GScyZmZmZmYVUaVRyyLif8DdpFocACSNyo/PLbmb7YFXgFPLFJb0SWBh0tyFfXLTMjMzMzMza2Yi8GdJU4ArgS8AKwM71ApIWg+4CNgwIi4tbL8tcG5ETCvuOPfnWQO4EHiK1JxsP+A64Jz+AnMiY2ZmZmZWEZ2uVWlXRJwoaWFgH9KIYrcDm0bEbXXFBIxm9iAAaaH0PlIztAOb7P6/pMRoK+CNwGPAH4Ef9DdiGTiRMTMzMzOrjHZHLRsMEfE7+hggICImU0hi8vJbGi2vW38RqSZnQJzImJmZmZlVRMXmkak0JzJmZmZmZhUxa1a3I+gdTmTMzMzMzCrCiUx5Hn7ZzMzMzMx6jmtkzMzMzMwqwjUy5TmRMTMzMzOrCPf1L8+JjJmZmZlZRbhGpjwnMmZmZmZmFeFEpjwnMmZmZmZmFeFEpjyPWmZmZmZmZj3HNTJmZmZmZhXhGpnynMiYmZmZmVWEE5nynMiYmZmZmVWEE5nynMiYmZmZmVWEE5ny3NnfzMzMzMx6jmtkzMzMzMwqwjUy5TmRMTMzMzOriIjodgg9w4mMmZmZmVlFuEamPCcyZmZmZmYV4USmPCcyZmZmZmYV4USmPI9aZmZmZmZmPcc1MmZmZmZmFeEamfKcyJiZmZmZVYQTmfKcyJiZmZmZVYQTmfKcyJiZmZmZVYQTmfLc2d/MzMzMrCJmRXu3TpO0raRTJT0qKSRNaGHbj0i6VtJLku6TtGeDMvNJOkTSE5JekHSOpHFl9u9ExszMzMzMmtkaGAec3cpGkt4GnA/cB3wKOBo4VNIuhaJHABOAb+VjLQlcIGn+/o7hpmVmZmZmZhVRwaZl20bELEkLA8UkpC/fBh4BdoyI14CLJb0ZOEDS7yMiJC0PfBHYOSL+CCDpVlLysyNwbF8HcI2MmZmZmVlFzJrV3q3TImKge90YODUnMTUnAcsD78qPN8r3p9Yd72Hgirx9n5zImJmZmZlVRNUSmYGQtBCwAnBXYdWd+X7VuvuHImJ6g3Kr0g83LTMzMzMzq4iqJCNtGpPvpxWWT833i9XdF8vUyi3WYPkcnMiYlfCOW+7SYB9D0m4RccxgH2co+Lm07t23FS9adZ7fl2ryc6mmoXoun57+n8E+hN+XHrND/Ketcw5JuwG71S06pv41k7QosGx/+4mITvxjajaOWvRTRn1s+zo3LTOrjt36L9Iz/Fyqyc+lmvxcqsnPpZqG03MZFBFxTESsUXcrJn7bkJpu9Xdrx7R8P6awfLHC+qkNytS2m9Zg+RycyJiZmZmZjRARcWxEqL9bm8d4AXiQufu51B7fVXe/Qu5TUyzXb42QExkzMzMzM+u0c4EtJI2uW7YtKcG5LT/+Z77folZA0puAj+bt++Q+MmbVMZza/Pq5VJOfSzX5uVSTn0s1Dafn0hMkrQasBtQmqFxD0nTgyYi4NJdZD7gI2LC2DPgF8DngT5J+B6wJfAnYIyICICIekvR74HBJAp4EJgL3A3/uN7a8HzMzMzMzszlImggc0GDVpRExPpcZD1wCrB8Rk+u2XQc4FHgP8BhwaEQcUdj/fMBPgf8HLAhcCnwlIu7rNzYnMmZmZmZm1mvcR8bMzMzMzHqOExkzMzMzM+s5TmTMKk7SKEkXS1q527EUSZpf0u8kfajbsZj1KkmrSvpMHqmnp0laTNL7cpt3q4jh9Bkzq+c+MmYVl4ctfBVYIyJu6nY8RZKeBzar79w3HElaAnhnRFzW7VjKyiPAfARYhdmjzbwuIn4z5EGNcJKOBiIids+PtyWNzDMamA58MiKu6mKIpUk6EJgvIvbNjzcAziB11n0U+ERE3N7FEEuTNBZYqNa5OH93diWN1HRRRJzVzfhaMZw+YzWSlqf579ikoY/IqsKJjFnF9UAicwZwS0Q0GtFk2JC0FfD3iBjdb+EKkLQ0aSjM1YAAapObvf6j30PPZV7gG6R5Bpaj8cnM2CEOa0Ak3Q98NyL+mh/fDVwDfAf4P2DxiNiwiyGWJule4KCIOCE/vpk0KtGBwI+BFyLi092LsDxJk4B7I2LP/PiHwPeAe4G3AbvUnmfVDbPP2CLA34GNaovyfc/9jtng8DwyZtauXwPH5ll5JwGPU/dPBiAi7uhGYCPcIcCzwAqkycc+SHpvdgQ+D3yqe6G17LekuQjOAC4GXuluOG0ZS3o/yM1F3wZsGRGPSToG+Fs3g2vRm4D/AUhaAXgv8KWIuE7SocDx3QyuRR8gz08iaRSwB/C9iDg41zx9Aziha9G1Zjh9xn4KvJk0OeIVpIsZU0m/YxsA23cvNKsCJzJm1q7z8v1e+VafxCg/ruwVM0n/K1l0wUENpPPWA75OauIDqQb+AeAn+UTtN8AnuhVci7YEvhERR3U7kA54Blg6//0x4LGIqM1wLSr8XWngeWDR/PcGwNSIuC4/nkFvfWcWBZ7Of68OLA78JT++GNi7G0EN0HD6jG0C7Adcmx8/EhHXA5dJOgT4NvDZbgVn3edExszatX63A2jTcsDNwOX9lFsF2HTww+mYMaRZl2dJeo50lbbmKmCfrkQ1MM8AD3Q7iA45F/hhbvr3HVKzmZp3AVO6EdQAXQrsK2kW8C1SjVnNKuRagR7xEKkZ5uWk2sq7IuLhvG5RUmLWK4bTZ2xp4MGImCnpBVKCWTMJOKU7YVlVOJExs7ZExKXdjqFNt5CuWH67r0K5j0wvJTL3Acvmv28nNc06Oz/ejJQc9IofAntLujQiXuh2MG3aGzgM2B24jDlny96C2TWcveCbwJ+Ak0jfo+/Xrfs86fn1iuOAgyV9jJTIfLdu3YeAO7sS1cAUP2P7163rtc/Yg8CS+e97SL/B5+fHH6S3EkwbBE5kzKqv1rlxga5G0Q9JHwTWIV0xewa4IiKu7XurSrgW2KZkWfVfpDLOIXWQ/TtwEHCGpIdIA0e8mR6qkYmIP0haDXhA0o3AtLmLxLZDH1nrIuJZYOcm6z46xOG0JddYbNBk9SfooZPMiPippIeBNYGvkRKbmsWBY7sS2AAMp88YcAGpedxppOTsD5JWB14G1iX1BbQRzKOWmVVcD4xathBwMvBJ4DVSO/MlSO2wzwO2iYgXuxdh3yQtCSwXEf/qdiyDSdKawGdICfEFEXFudyMqT9LewC9II2LdR4PO/hHRU00cc2K2OmkwhuNyR+y3AY9HxPPdjc6sGiQtCCwYEU/lx1sAW5N/x4CjI2JWF0O0LnMiY1ZxPZDI/BrYAdgNOCX3yRgFbAUcDfwlIr7WzRitt0l6ktTx+pvR4/+0JC1Mutq/Nel7/QZgzYi4SdLfgQci4lvdjLEsSddTGKGwKCLWGqJw2pITyz71yuiL+fvS3/vSE8OVm/XHTcvMrF1bAftExMm1BfkK2cmSFiP1b+iZRCZPfLkY6URgWkQ83c8mlZVnV98ZWIN05f8rEXFPniDv1ojolXb/As7u9SQmOxT4MLAhcCVzNr+aROo03xOJDKnvVfE9WRxYG3iJNI9Rr7iNfk7+6Z3Rvn5N4/dlA+CNwO+HPCKzQeJExszatSjNRyd6kPSPs9Ly1dh9gY2Zc1QcJD1DOsH8ea9ckQWQtAqp6cWiwI3AeGCRvPqjpA7Nn+9KcK07gZQwX9jlODphS+DrEXFJrm2tdz/wli7ENCARMaHR8lzrdCZpdLxe0ahp4uKkfmYbkYYy7wkRMbHRckki9Zl7bUgDalGZGqV6rl0a2ZzImFm7/gXsIem8+ivm+Z/mHnl9ZUnagDSa173AEaTRiaaSagHGAKuSBgO4XtKnImJydyJt2RGkIYs3A6YzZ7+SS4GfdyOoAXoI2EvShaQ5PaYV1kdE/HbIoxqYBZg9X0nRIsDMIYxlUETE9DzHx5H0SCf5PkZfPE3SQaS5Ss5uUqYnRERIOpY0UelPuh1PHxrVKJk15ETGzNr1PdK8BXdJOo00e/xY0jCf40i1HFV2MGmwggnNmi5J+jHpn/8vSc20esFHSQMtTGtw5f9xZg/N3AsOzffL03iUrAB6JZG5nlQT1mgI3K3prVqMvowhNdEcDi4BTu12EB2yEjBvt4PoS7MaJbNGnMiYWVsi4mJJ7yfNVbAN6QT5UdKwxlv2QHOsdwJ799X/Il/JPIGUsPWKGTQfsns55q7VqKyIGNXtGDpoP+DCXLt0MikJ20TSN0mJzLrdDK4VkjZpsHhe4B2kOWYuGdqIBs2n6KHvi6QvN1hce18+R/rcmQ0LTmTMukBSXycrs4DngP9ExMv58YHAI0MR20DkZGW7bscxQA8CHyE1t+rLR+itmcovAL6XT5in52WRBwD4Gqnfjw2xiLhC0obAz0hNr0T6fl8DfCwiru9mfC06m5SIFedXehU4A/jqkEc0QHnEuKJ5SU1LVybVPPeKIxsse5nURPM3pM9bZTV5L5qKiM8OVixWfR5+2awLJM1izjbAYu42wTNI7cv3iojKtpuXdDHw5Yi4q8G6VYCjIqLZpHldJ2lXUrOk40lXKu8iXX0N5uwjsxOwe0T0xIg/klYgjYpVm29hW1IH7HeSTtA+FBGPdS/C8ppc+Z9DRPRcYiZpAVLzq2lVnmupGUmNBiaYATzRayPMSWpUezSDdPJ/Wi9+vnpVk/eiqV6bQ8o6y4mMWRfkDua/J10VPxN4ElgK2BzYBNib1Azge8BhEfH9LoXar5yUfSgirmuwbg3gmoiodO2vpO2AicAqzJ1QCrgbmBgRJw1xaG3Jw1/vRRrqd0ngGdKQuIf20rDSdYl/8cr/6+9VRPTK0LhmZtYhTmTMukDSqaR5PCY2WDcRWD0iNst/T4iIcUMaYAvySeYHi01iJM0LfAPYMyKW70ZsrZK0IqkGptZJeSpwV0Tc172oWidpfuD/gN9HxDXdjqddTa7814bGnQDsVOXnKem4VspHxM6DFUunSRpLuvBSm6toi4i4XdLXgesi4uquBjhClKm1rNeLNUx5JMxlSTV+lR5C2oZOpa+Smg1jG5HaKjdyJbMnxLsM+O6QRNQCSQeQOvdDuip+Tfof09AvhiSoDsgJS08lLY1ExIxcy/SXbsfSCRFxf4PF9wM3S5pJqrn89NBG1ZJ3Fx6/mVQD+0S+jc23J0nPqydIWovUbPFJUh+z8cB8efWypARn664EV4Kkg1soHhGxz6AF075m/ZUaCXpncs9aknYA8D7SeeuawE2SfgdcGhF/7mJ41mVOZMy64xnSiVejCf4+ndcDLAg8O1RBtWAS8BTpn+YRwCHAlEKZV0i1GZcPbWiDQ9KSwGoRcVm3YynpYtIkf5O7HMdgu5nULLCyImLN2t+SNgMOJ9VcXFW3/CPAH4CDhjzAgTuMNDLZlsAoUj+ymuuAHboRVAu2aaFsAFVOZFbsdgCDQdLngeNIF2V+Q+rLWHM38EXAicwI5kTGrDsOBo6QNA44i7n7yHwtl1ufNO9EpeRmZNcDSHoeOCcinupuVINuPdKs2L1yJfPXwLGSFiIlno9T6P/TA0Nj9yk3X5xAGu67V/wM2K8+iQGIiCsl7U+aqPTMrkTWug8Am0fELM1dJfs0qZapsiJi2Jz8N6m1HA6+D/wiIr6b58OqT2RuZ3brBRuhnMiYdUFEHCnpYVKzsV+TvouvAbeQ5l45PRf9KXPOyF45EfGHbsdgDdUmXNwr3xqNktcTSZmk65l7EIZ5SROuLsKcNQFVtxLQbISyF0nPqVc8S7oA08hKpOTZhoCkBVsp30Oj5L2F1HyxkRnAG4cwFqsgJzJmXRIRpwGnSRpFOhl4MiJmFcr0RC2HpG2BXUmjfs1fXB8Rlb0yK+l/JYu2dKJQAcNpSNLbaTw8+cnA6RFx+9CHNGA3ARMlXRcRr9ckSXoTqYncjd0KbADOAA6UdDWz+/ZEbob5LeDUrkVWwjDrID+dub8jfemJixikubveT2oqW7QGcO/QhmNV40TGrMty8tKzVy4l7UBqw3wCsEH+exSpr8804I/diq2k5Uj9LPrry7MKsOngh9MZEdHfBJ89IyImdDuGDtoN+CcwRdKNzO7svzqpOdaOXYytVfuShvO+g9kJ2FHA20iDZuzfZLuqGE4d5HemtUSmV/weOEDS48DpeZnypLLfAX7YrcCsGjz8spm1RdLNwD9Ibf9fBdaIiJskLUJqEvCPiPhlN2Psi6RrgUcj4jP9lNsK+HuvzVci6YPAOqThip8BroiIa7sb1ciWh8femTT60jLAY6Q+Z8dHxEvdjK1VuZ/S/2PuuYr+GBEvdzO2/jQZ1rupYdwPpbJy36sjgd2BmaQL8K+SksqjI+IrXQzPKsCJjJm1RdJ0YNOImCzpVeDjETE5r9uCNKHnuC6G2CdJRwDbRMSy/ZTbCjg5IkYNTWTtyZ38TwY+Sep/9TSwBOkE4DzSc65sO3lJfwe+GxH/zX/3JSJi26GIy8yGnqS3Ah8j/YY9A1wcEXd3NyqrAjctM7N2PcvsuSMeBt7B7CF/RfrHU2U/JDVf6FNEnEJqMtcrDgbWBrYFTskjS40CtgKOJo2O9bU+tu+2pYB58t9jGZ7NZnpansNn7Yi4rsG61UkTYla2BlPSgrVkvkxn+Yon/teRJk++o8ngGHOIiLWGJrLOiIj/Av/tdhxWPU5kzKxdNwDvAc4nDRu7v6TXSKOt7Q9UuhlTHlChJwZVaNFWwD4RcXJtQe6PdbKkxUgJXGUTmYhYv+7v8V0MpW2SngA+ERE3S3qS/k8yKzs4RkFffUvmIdUEVtnzkmqJWJnO8pVNykgDYrxU9/ewSvwlrQIsT+PBZKo8CIMNMicyZtaun5KGyISUuLyFNHHZaFK7/926FFfH5GYN2wHbR8S7uh1PSYuSRvxp5EF6ZNjS3J/kVmDPiDivv/IV9WtmD+jxa3r4JFPSm5lziOj35/eo3vzAF0gd/qtsZ2Zf5e/pzvIRsVPd3xO6GEpHSVoN+BuwGo0T56oPwmCDzH1kzKzjJM0HzBcRz3U7loGStCypWdb2pGE+ZwIXRMSnuhpYSZKuIY2ItXnU/dDnzrNnAEtFxNrdiq8VuUZjx4j4Z7djGekkHQAcwOyT/ma1Mi8Bu0TEiUMSmL0uD7SycP3w3nXrlgWej4jpQx9Z6yRdTmpa+h3S6HhzzavmQRhGNicyZmZZbnK1NSl5WZfUJyaAXwCHRMSTXQyvJZI2AM4FpgCnkWoExgJbkK6obxwRl3QrvlZIOgxYJiK273YsnZJH+3o3s0eT+3dEVHryWwBJS5E+RyLVlH0u39d7BXig6qOW1ZP0XmC5Rs2U8nwzD0VE8XlWUh4c49mI2LXBuqOBRSNiu6GPrHV5MJntIuLsbsdi1eSmZWbWNklrkU6Ql6NxG+bPDnlQJeXRvTYnJS8bkdr230G66nweqXncpF5KYgAi4mJJ7yc199sGWBZ4lNRnacuIuKOb8bXoAeCzkm4AJpGSsvqrcBERv+1KZAMg6TvAd0nN+2o1Gs9K+klE/KJ7kfUvfw+eBJC0Imno8sonYCUcRppLqlF/izWBvUlDTPeCdUnDFTcyCeiZ7wqp6d9c/1PMapzImFlbJH0TOIR0cvk/GlT9V9zjwAKk9vy/BE6MiNsAJC3azcDalZOVnrjy2o9D8v2ywAcarA965ORM0jdI/cqOIrX9fxxYmtSM8aeSXo6II7oXYXkRcb+kN0jaljnnKrocODUiqt7Zv94HSHNhNXI18PUhjKVdiwLNRlibASw2hLG0a2/gYEk3RcT/uh2MVY8TGTNr197Ar4C9ojfbqorZV8WDHu7wW5Q7Zi+dHz4WEc06/1dar8zdU9JXgJ9FxPfrlv0HuEzSNGBPoCcSGUljgX+SRi2cQkrK1iY9x39J2qiHajJHAws1WbcQMO8QxtKue4BPkd6bok3orWGMf0qq6b9L0hRgWrFArw0lbZ3lRMbM2jUfcE6PJjGQ5iupNS37NvBdSXcCJ5KGlO4pkt5Aara0B7OTmNq6x0gjyv0sImZ2ITyDFYBmfZMmky4M9IpDSfNEfTAirq8tlLQmcEpe//+6FFuraiMsntZg3W6kYeZ7xf8BR0l6BTiB1KR0WdJIcl8h/Tb0itvyzawhd/Y3s7ZI+gWwUER8uduxtEvSGFJ/ku2A9ZhdU3MoqbP/Y10KrZQ8Itkk0gzY/wAuAB4iPY/lgE+Q5pc5v1dGX6vJV//3Jo0gtwKwRUTcLunrpIkXr+5qgCVJuhs4PSK+02DdwcBnImKVoY+sdZKeAb4aEX9tsO5zwP9FxOJDH1nrJK0LXAjcDPwBeIx08v954L3AxyPi8u5F2BpJ+5EuaNT3L5kB/CgimjWhM+s5TmTMrC15tvgjgVWAi5m76r+nOmLXSFqGlNBsB6xFGn75wojYuKuB9UHSjsDvgU9FxIVNynwcOBvYqdEJaBXlwSQuIHUyvxSYAKwZETdJ+hnwtojYuoshlibpq6SmY8eRks3aaHLbkJ7XnhHxm64F2AJJL5BGlDqrwbpPA3+NiIWHPrKBkTSe1JRpLVLyP4s0OMa+vZTE1OQ+fmuTas2eBq6OiGe7G9XASVqS1L/nmYh4utvxWDU4kTGztkj6GKkZySJNikRE9PSEZXl0ph1IJ23v7nY8zUg6hzRM7Jf6KXc0sHyv1MpIupKUxGxJGhL7FWCNnMhsCRweEW/uZoytkLQraVS8N5H6ZAl4BJgYEcd2M7ZWSLqI1LT0ExHxQt3yhUj9M16KiI91K76BkrQg6YR5akQ06zRvQyQPJjGRdLGs5m5g/4g4uStBWWU4kTGztuSmMlNIo/rcGxGvdjei9hQ6yD8eEQ90M55WSHoY+FpEnNpPuS1JzX6WG5rI2iPpJdLEnv+UNBp4ldmJzHqkpnKVH6JV0jykq/33kfotLM/sYbEf6rV+ZpLeR+rvE6TEpVa79AlScjY+Iv7VtQBHsNzM9COkk/9GQ+L3Sq3f9sBfSHNiFUf5+yTwuYg4qXsRWre5s7+ZtetNwJcj4s5uBzJQw6iD/BKkk+L+PJbL9opnSYMyNLIS6eSmF8wkNb/cJCIeAR7Mt54UEbdIWhn4FmmulfeQPn9HAYdGxFPdjK9VksYBO9L85L+y82HVk7Q0cBGwGrNr/GDOERl7IpEBvg8cExHFeXH+KOkoYD/AicwI5kTGzNp1IakzbMM+GVWXr1yeRd8d5A8EPkwa0rTK5iWdLPdnJmniz15xBnCgpKuB+/OyyG3mvwX0WQNVFRExS9I9FJLlXiRpftLoWL+PiH27HU+7JK1O6n/1ICmRuZU0H8s40u/BvV0LrnWHkJL/FUjP54OkZH9H0uAFVf8dq/c24JtN1p1C6ldmI5gTGTNr1xGkoT4XoHFn/9rEjFX1OWADYOMmHeSPq3WQl7RDD3SQ/5qk/mpllh2SSDpnX9IV5juAG/Oyo0gnOfcB+3cproH4PvBzSf+OiH93O5iBiogZkrYjNfsZDn5BOjHemdR08Yu56eKHSUOxH9zN4Fq0Hqmpb+13QLmJ7E/y4Cy/IV2g6QWPk0YqvKDBujXondpYGyTuI2NmbZE0q+5h8QdFVLyz/3DqIJ8njCv9ox4RKw5eNJ0laV7SnCQbAkuSZpC/CPhjRLzczdhaIel60lX+xYGHSSdic7xnvTLBn6QzgFsi4oBux9KuPJT09qS+PjOBdSLiqrxuZ9Jocu/rXoTlSXqe1Hzx8jzJ6o4RcXZetwFwRkQ0G5ylUiRNJDX7/RFzj/K3H/DTiDiwawFa17lGxszatX63A2jT+0hDFvfnfFJTmsqKiHHdjmGwRMQrpPepzHtVZcNpgr9fA8fmUcom0Tgpq3JtbL0AXomIkPQE8BbgqrzuQWDlrkXWuvuYXet6O6nW+ez8eDPSRYBe8UNSM9h9SU18a14CfpnX2wjmGhkzG9EkzQDW729CxdzE5OJeGB2rVbm5yYXAlyLinm7HUyRpJrB2RFzXYN3qpAkxK1vrN1wVamNhziSm8rWx9SRdDvwhIo6VdBqpf9znSEN9HwssHRHv6WaMZUn6KbBUROwiaWNSH7MnSE3m3gzsExG/7GaMrZK0GPAuZo/yd1tETO1uVFYFrpExs47JJ8SNRvup8lwMw7WDfCsEjKf5XEDdpj7WzQO8NlSBdEoeZGJ5Uofsf9XPw9JDer02tt4xpFoYgO+RmpjdlR+/APTEhKsAEfHdur/PlfQRYAvSb/MFEXFu14IboJy09NykpDb4nMiYWVvyCdl3gF2BZn0uqn5Vdjh2kO9peT6fcXWL3p9Hyqo3P/AFUlOaniHpy6T2/cuQajHWBG6SdCpwWUQc3sXwWnEbaZb1nm/aERF/qvv7TknvII1UOD9wTUQ80bXg2hQR1wPXdzuOsiRt0kr5iJg0WLFY9TmRMbN27Ulqv3ww8GPgIFLtxXak2o6fdC+0Uh4A1mmhrA2NnYADSCf6Afy2SbmXgF2GKqh2Sfo2qePyz0mTSV5ct3oyqcP54UMeWEl5Us8fA7sDCwGvSDoT+GpEPNnV4DooIqaTamV6lqSNSBOw1ppjXRsRjUb/qpqzmXP+m74E1b9QZoPIfWTMrC2SbiM1y/g1c864Poo0P8u/h8M8E8OZpNHUvXfdjgdA0lKk0YlEmtPjc/m+3ivAAz02atkU4DcRcXDxdZf0CeCvEVHZyUol7UdKME8gDYW9IvAlYHJEfKZ7kbVP0ljgGxRO/oEjIqJnhvmV9CbgNFJN3xP5NjbfbgC2iIiHuxdh3yS9pf9Ss0XE/f2XsuHKNTJm1q4VSUOwzpT0KjAGXp/87zekjrLDLpGpegf5Xpev7j8JIGlF4JGIeLW7UXXEMsyeC6doFg36mFXM54AfRcTro0XljvJnSFqoR/v6kPuRTCL1t7qANGfRWFLN09ckbRwRV3YxxFYcQ0rEXh9CGl5/jicCRwObdim2fjkxsVY4kTGzdj0NLJz/fgB4P7ObyywGLNCNoIZA1TvIDydrkzrF/6K4QtK3SLUyfx/yqAbmXtKEhRc1WLcu6QS6ylYiNYmrdxHp+zCONNxvLzqSlGBuVp+MSVqY1NTp/4APdCm2Vm0A7FyfxABExJWS9gV+152wypG0YCvlKz6YjA0yJzJm1q4rSU0YJgF/BSZKWpzU7OcrND5hs2qptUWvatL5XVLNXiMv5vW9ksgcDvxG0iukCf4Axkr6IrAXadCMKpsHKDbleyXfzzfEsXTSqsDWxRqliJgu6ZfAyd0Ja0AeJ/Uda+Ql4KkhjGUgptPCxL64j8yI5kTGzNo1kTTnAqSO/WOACaST4guAr3UjKGtJ7aSh2clPt72N5pNI3kkPTVaY5ylZDNif2RP8TSIlZBMj4q9dC6684ih/tUT465Lq+5JEROwzhHG14w5Ss79GlmX2UMy94CfADyXdGBEP1RZKWp7Uv+nHXYusnJ1pLZGxEcyd/c1swHI/kWWBZ/MoPyNGFTvID1TVn4ukJ4G96ofIrVv3eeCwKneQb0TSIqQmc0uSZlq/OiKe7W5U/cuDFZQ9cYiIWGkQw+mYPOHtn0hzyJweES9Lmo80/8qPgc9XuY+MpGKN5IeBpYCbmN3Z/wP576sj4rNDG6HZ4HAiY2YDJukNpKv4m0XEed2OZyhV/eS/FVV/LpJOBFYndV5+om75UsAVwE0RsX234mtFTrzOiYinG6xbHNg0Iv449JGNPDlBrj8JWojZgy1MZ3bfvxnACxExdgjDa4mkYr+lvkREbDBowZgNITctM7MBi4jXJN0PtNQ506xF+wDXAP+VdB5pWNxlgU8A00gTsvaK40k1MXMlMqQRAI8Hhl0iU9FR/n7NMGnCFBHrdzuGTpF0HTAhIu6QdD39vEcRsdbQRGZV5ETGzNr1c+D7ki4fThPilVD1DvLDRkQ8IOm9pM7w6wPvIyUC/0dqVlb1zsv1+prkbwnguaEKZIhVbpS/iJjY7RisoduZ3V/vdoZJsmmDw4mMmbVrI9LV8SmSbiSNmFP/jyciYtuuRDa4qt5BfljJSfJ3ux3HQEjaHNi8btEPcrOmevMDHwWuH7LAbNiQ9GXg5Ih4Mv/dp4j4zRCENSARsVPd3xO6GIr1ACcyZtauJYH/FB6b2WxjgXfXPX4rc4+Q9QrwT+CgoQpqpMsd5L8bEf9t0Fl+LhXvIH8kcANpEtkj+ykbQGUTmUYkCVieNJ/Uv3p14lXrPCcyZtaW4dQ2eziRtG4fq2eRmjD9JyJezo8PBB4ZitgGQtK2pDlWVmF2h+zXVbkjdkT8jjwJYe6U/eWIuLO7URlpVK958t9j6eEmTBExqtHfw0GuYdqPlPwHad6ymySdClwWEYd3MTzrMicyZtZRkuaJiFe7HYcxmTlPzMTcJ2ozJB1LGtr4QCpK0g7AccAJpFnLjwNGAZ8mdfbvmc7xTvyro/69iIjxXQzFmpD0beBHpL6YlwAX162eDGxPmmTWRignMmbWtjwHww+AdYAFJb0IXA78KCKu7mpwI9fHgN+TJls8k9TkZClSX41NgL2Bd5DmzZgOfL87YZZSO5n5GbAb8JuIuCnPxXIBaTLJniDpOGChRv3G8jDTL0TELkMfmfUySau1Uj4i7hisWDrsK8D+EXFwHia+3n9INbQ2gjmRMbO2SPo4cA7pn8ovSJ39lwa2BiZL+lREXNjFEEeqrwJ/aDAy0/mSJpKGN90szwU0gWonMisDV0bETEkzgTcCRMTzkn4OHAb8spsBtuDjpNHXGjkFOHQIYxlKlRvlbzh1kAduo1zTuFrNbDEpqKplgBubrJtFg2amNrI4kTGzdv2YdMV/m5hzht0fSjoF+Alp/ggbWhvRvEPvlcC38t+XUf3RwJ4F5st/P0yqSZqcH4s0bHGvWAp4psm6qaS+GsNRFUf5G04d5Idrk8V7gfWAixqsWxfolZolGyROZMysXe8GflBIYmqOAU4f2nAGbph1kH+G1IekURL5aWafTC9IShSq7AbgPcD5pKR5f0mvkUb62h+4touxtep+0glYsxOzh4Y2nJFrOHWQj4hLux3DIDkc+I2kV4B/5GVjJX2RVLO5a7cCs2pwImNm7ZpGGk62kbfl9b1iMsOkgzxwMHCEpHHAWczdR+Zrudz6VH/ukp8Cb8l/75///g2pecz1pH4zveIE4ABJT5Ca/k2XtDDweeA7pOTYrG2SPkjqt7g46cLFFRHRS0k/EXGspMVI3/vad2MSqV/cxIj4a9eCs0pQ44uoZmblSDoC+AKpU+Y/ImKGpPlJfWSOJJ2sfb2bMZYlaQPKd5A/LCKq3K8ESVuQmo29j3Th6jXgFuAnEXF6LrMk8EpE9NSM8pLmA+brwbhHkWoqdyYlyS8AC5GS5mOAPZrUbva03FH7VWCNiLip2/HA8O0gL2kh4GTgk6Tv/NOk5pejgfNIzYArO0CGpCOBv0bEVXXLFgE+THoezwBXR0TVa5JtCDiRMbO2SFoAOBbYLi+aDiyc/z4R2CUiZnQjtlbleQlubdBBntxBfvXcQX4iqbP8uCENcIDyyfNSwJMRMavb8bQjT4y3JPBUL5/wS3o7aSjpxUknmhdHxN3djWrwVDSRmUULHeQjoic6yEv6NbADqabylIiYlX8DtgKOBv4SEV/rax/dJOkZYFFSM8sTgRMj4l/djcqqyomMmXWEpFVJE5UtCzwKXB8Rd3U3qtZImg58ptEoa3l0ttMiYuFcc3NuRMw3105sUEjahDQp3urMrl26EfhxRJzTzdisfxVNZNZrpXyv9EOR9BhpyOJjGqzbDfhhRCwz9JGVk0dS/CTp4thmpAtjdwN/Bf42nBN+a537yJhZR+SkpacSlwaGUwf5YUPSl0h9Yi4Cvg48QRrda0vgTElfjoijuxhiyyQtT5oDY67hYyNi0tBHNPL0SmIyAIsCDzZZ9yB5+PKqiojXgLOBs3MT0s1ISc2+wERJN5OSmr9HhAfHGOFcI2NmLRvGbcu/ChxB+ifasIN8RPxa0iHAqhHxqa4FO4JIuh+YFBF7NFh3FLBJRLx56CNrXW7r/3fS8Ngwe36V1/8Z91ATptKj/OUmgfsDR0fEY0MS4ABI2hhYA1gBOCgiHsjP896IqOoIhXOQdA0p2d+8vvllfg/OAJaKiLW7Fd9A5b4/nwG2JX1/3kCaX6qlmjUbXpzImFnLhmvbchjeHeR7VW7yt0VEXNBg3etN/oY+stbljszrk4aNvQLYgjR/zI6kPjPbR0TVR5EDGv4ONBzlj9SHbq+ImDlUsbVK0tKkAT5WB6YAKwJrRsRNko4HZjRKpKuo1vSV9DxOI01SPJb0WRsHbBwRl3QrvnZJej+wO7AL9E7ib4PDTcvMbCCG6+RrRMRpwGl9dZCPiKe6EtzIdQlpUry5Epm8/LKhDacttb4+tWFwH8mJy2W5pu/bwGe7FVyLPkb5Uf6mA1Ue5e//SH0xViUlAK/UrbsQOKALMQ1IRFycT/b3B7Zhdr/Fa4Ete6WGvF7ug7ldvq1MSv6PJQ0GYCOYExkza9kwblv+upy8PN7tOEaqQvPFI4BjJS1BmmC11kdmC2Bj8pXZHrE08GBEzJT0AmnUsppJwCndCWtAvkoaXn1iYfn5dSP7bZY7b0+g2onMJ4EvRMS9eWCCeg8By3UhpgHLycp2/RasMEkrMjt5eRdpqPIzSQny+bkvjY1wTmTMrGNyLUajzsuVnbPAKus25m629KV8C2b3LYE0N0avNC95kDR8NMA9wKbA+fnxB0lNsXrFRqRBGBq5EvhW/vsyUnPNqmvW9G1J4KWhDKST8oSSbwOeiIj7ux1PfyR9k5S8rEGqGTsP+DFwVkT07Ptgg8OJjJm1JXcg/Q6pzf+KTYr1ykmmVcdwbb54AalJ1mnAYcAfJK0OvAysCxzSxdhaNZxG+bsc+Jqk+qG8a4n0zsDFQx9SeZI2BzYoTj4s6SekhHJ0fnwWsF3F5/Y6mNScdBfgVE98aX1xImNm7dqTNCzmwaSrZgeRrmxuB8wL/KR7oVmvGsbNF/chndgTEX/KAxlsDSxAaqrVS8NIHwwcIWkcTUb5y+XWB6o+gME+pMEXbiMlmQHsKuldpGZNH+pibGXsAcwxGpykrUm/zeeRas5WAX5I+s0+eKgDbMFyEfFEqxvlFgEX8v/bu/d4ueZzj+Ofb1ziXpegwSEojnJaGtfqUdSt1L3lHLzqUoqD04O4lKqkihRF63JQUaGlTkvcU7eUaA6pS+taVFFFSCokcUskec4fv9+cTCYzyZ59WWtm7+/79dqvmVlrzZpnz87OXs/8fr/ngSMi4i/dH5a1IlctM7MukfQMcCVwKVUN7/IflduBpyPilDJjNLOe0Zuq/ElaGxgKfIU0nWwyqXfR0Fa/MJb0BnBiRFxftW00sBmwWmVKlqTTga9HxOfLibTntGLTVet5TmTMrEvyguWvRsRYSdPz/TF5367AVRExsNQgre1ImgjsFBF/lDSJBZT7joiViomse0haD9iUVFHqTeCxiHih3Kg6b35V/qznSfqI9PsyNj9eCJgG3BYR/1Z13HakcuWfKifSnuNEpm/y1DIz66p3SGVLAV4DNmbOfPLlSFNmzJp1KXOqxl1Kx/oWtTxJywA/A/YB+pHKEi8FzJZ0M3BYq49c1OMqf6V7E1iLOaXItyAVXnmg5rh+NC5qYNZ2nMiYWVeNI32yfBdwPTBU0vKkajNHk6ZmmDUlIoZV3R9aYijd7TJSta9vkhYyfyxpMVJic0nef2CJ8fVZkvYllfRelfrVFzcrPKiOGw2cJukpUkJ5Bun/4FtrjtuU1CfHrFdwImNmXTWUOT0WzgaWJfWMWJxUoenYek8y66P2AI6rXsuQK0j9UtISwAWlRdaHSRpOqr74KPASczfEbAdnkJrDVooqBDAkIiZUDshTrw4h9WIy6xW8RsbMzFqOpP9p5viI2LenYulOkiaQpo/dWWffrsCIiPh08ZH1bXlN1oURcU7ZsXRWbjy6DenDpD9FxEs1+5cDtgPGR8TrhQfYw7xGpm/yiIyZNS33jvka8HJEPNvgmA1Ic7bvCH9iYs1bsewAesilwBBJY6qb++XRmCE0bjBpPesT4PGyg+iK3Om+Xk+fyv53gZuqt/WyksWVJrlel9mHOJExs874OmnB8obzOWYKcB3wHWBkEUFZ7xERvaYhpqTanh3rAH+XdC8wEVgJ2IHUPf6xgsOz5CfAYZLu7WMfvIg0irN0yXF0h8rP7aP5HmW9iqeWmVnTJD0IPLyg/jC5q/QXI2KbQgIza0GSXmni8IiItXosGGtI0vnAbsCDwHs1uyMiTi48qB7Wm6Zj9abvxTrOIzJm1hkbA2d14LgHSJXLzLpE0tKkhfLrUr+i1EmFB9VBEbFm2THY/Ek6APgvYDapHHbtYv8Ael0iY9bunMiYWWcsDEzvwHHTgUV6OBbr5XLH9XHAEsCSwCRgedK/w3dJ0xhbNpGxtjAcuBE4MiKmlR2MmXWMExkz64xXgC+QpmDMz2Dcs8C67kLS2pFvAB8AuwBPAvsB5+TbtpGLZWxF49ElL/gv3jLA1U5izNqLExkz64ybgBMk3RARb9U7QNKngeOBEYVGZr3RZsBhzBkFXDQiZgHXSxpAWqj9xbKCa4aklUlNYj9Lmq5UqbRUvWDViUzxbgK2xQ18zdqKExkz64zzSJXLHpd0FnA38BrpYmx1YCfgVNKUn/PLCtJ6jcWAqRExW9JkYJWqfc8Any8nrE75Men34p+AvwObkzqxHwh8E9i1vND6tLuB4fkDmDHMu9ifiLir6KAK4JLF1tacyJhZ0yJimqQvA5cDFzc4bBRwlKdqWDd4EVgj3/8jcKSku4BZwLeAN8sKrBO+TCpJXum4roh4DTg79/S4jPRBgBXrhnx7aP6qFcBCxYVTmJYuWSxp6/nsng1MBV6IiOn58TDa6/8D6yInMmbWKRExCdhH0urA1sCqedcbwNh8cWbWHX4FbETqS3Q66dPzqaQLl4WBg8sKrBOWBSbl0aWppB4yFf+LK2OVxZXlWtMDzD3tUjWPAT6WdBVwfEQMKyowaw1OZMysS3LC8ouOHNvLukhbQSLigqr7j0jaENiZNB1mTEQ8U1pwzXsFGJjvPwscANyRH+8GTC4jqL4uIv5WdgxW1/akdZZ3AbeRKhauSCrFvgtwArA+aSrz+8Bp5YRpZXFDTDMrjBuWWV8naTgwICIOk/RV4FZgIun3YnXg5IjwurKSSFqMtAarXjW554qPqGe1+v/Jkm4GnoqIoXX2DQUGR8Ru+f7BETGo0ACtdE5kzKwwrf5H01qTpP8EVomIU+rsOwd4IyIuKT6yrpO0CbAXaXTp3ogYXXJIfZKk1YArqb8+SUBERK9bI9Pq/ydLeh/YMyLuq7NvB2BURCwlaTtgdET0LzxIK5WnlpmZWav7DxpXv3sROBFo+URGUn9gCHBHRDwJEBGPkXrkWLmuA9YCjgFeAmaUG45lk4HdSVOSa+3OnKmYS5CqAVof40TGzMxa3Rqki8t6XgEGFRdK50XEdEmnAb8vOxabxybAARFxW9mB2FzOBX4qaRBwO/OukTk2H7ct8GgZAVq5nMiYmVmrexdYj1TBqNZ6pApm7WI8MBh4sOxAbC7PkT7Vb3u9qWRxRFwi6Q3gu8ClpOvWmcCfgL0j4pZ86Dl4FK1P8hoZMytMq8/HttYk6QrSNJIdI+Lpqu0bAvcAt0fEEWXF1wxJmwLXAz8hVWJ6m5pyshHxYQmh9WmSNgOuAI6JiHFlx9MVkmbTgZLFQKVk8ayiYuuKXPVyRXL58rLjsdbgRMbMCiNpYdKnZv/a7hcLVhxJy5NGMNYnNcScQCphvDHwDLBtRLxbXoQdly8yK+r+Ae6Ni8pbnaRFSc19DyP9HzVPI9+IWKl2WyvKC987WrL4wohwyWJrW55aZmZFauku0taaImJyHsk4iDQXfgXgr6QqU9fmKTLt4lAaJDBWqquAbwC/of0X+x8DjKxTsvjuqjLFu+UPlg7GvVesjXlExswK46llZtaKJE0DToyIy8uOpatcstj6kn5lB2BmZtYRkr4q6XRJV0paPW/bWtIqZcdmbW8S8FrZQXSTSsnielyy2HoVTy0zM7OWJmll0lz/wcCrwJrA5aQLz0NIC5ePKiu+ZknaDzgcWJf6HeTbYi1GL/MDYIiksRHxftnBdJFLFluf4UTGzMxa3cXAUsA/kxKZ6vUL9wFnlBBTp0jaH7gauAbYLt/vR/qk/D3g2rJi6+N2BdYBXpP0GOlnUS0iYr/Co+oElyy2vsSJjJmZtbqdgYMi4qW8zqra68CqJcTUWScCZwLDgW8Dl0XEE5KWBu4FXHq5HAOY03R1EdIIRtuKiFHAqPmVLI6If5QSnFk3ciJjZmbtoFGviwG0VxW8dYBxETFL0ixgGYCImCbpR8CFwPllBtgXRcS2ZcfQE3Ly8nbZcZj1FCcyZtYlvamLtLWsh4BjJd1Zta1ScvNQYEzxIXXaFKBSJeoNUj+PB/JjkUpLm5lZBziRMbOueoAOdJGWVOkiPayowKzXOBn4Pan55SjSv6/DJW0IbAhsUWJszXoM+BxwN6mAwfclzSStVfg+ML7E2PosSecu6JiIOKmIWMys49xHxsy6xF2krQiS1gaGAl8hTSebDNwPDI2Iv5QYWlMkbQGsERE3SloWGEn6PVmIVEHq3yPi5RJD7JMkvVJn83KkqX9TgHcjYq1iozKzBXEiY2ZdIulm4Kk6XaTJXaQH5y7SQ0kdpQcVGqBZC5G0AjA5qv74SuoP9I+IqeVFZvVI2hy4EjgyIh4uOx4zm5sbYppZV+1ImvZTzzhSrwKAscDAQiIyayGSFpF0rqSpwETgQ0k3SloRICKmO4lpTRExHjgPuKTsWMxsXl4jY2ZdVekifV+dfe4ibd1C0pbAt2jcRHKzwoPquJOB40i9Yx4nNfQ8AvgZsGdpUVlHvQOsV3YQZjYvJzJm1lXuIm09StIOpDVY9wNfAkYDiwNbkfrIPFhedB1yAHBmRPygskHSQ8CtkpaMiA/KC80AJC1RZ/OipPV9PwCeLTYiM+sIr5Exsy6TtBepi/RGzN1F+uxKF2lJA4AZnkJjzZL0MGma4snAJ8AmuYnkGqTqX2dHxLVlxjg/kqYD20fEQ1XbFgc+AP4lInyRXDJJs5m32iKkKoxvAHtGxOPFRmVmC+IRGTPrMneRth72WeB7pD5EASwJEBF/y0UkhgEtm8iQOsVPr9k2I9/2x1rBodQpG08a8ftDRHxSfEhmtiBOZMys27iLtPWQj4F+ERGSJgBrk5pkQmq4ulppkXXcsTn2CuXb70iq/p2JiDi5wLgMiIhrGu1Tsl9E3FhgSGbWAZ5aZmZmLU3SPcBtEXGJpJGkBpjHkEY1fgzMjIiWbYop6VXqT1uqJ9yvpHi5gtw/aspiL04qMHEcMCgiFiorPjOrzyMyZmbW6i4iVfqC1Fj1dtLaGEhTf/YqIaYOc++k1pQX+F8EHEia4jdN0g8j4nxJR5AW+a8I3AscXFacZtaYR2TMzKzlSNof+G1ETK6zT8BnSJXLno+IGbXH9AZ5zdl9wBER8Zey4+ltJA0njbZcDTwJrEEqiz0G2DvffjciXG3RrEU5kTEzs5YjaRawZUT8IT/uB7wK7BIRz5QZW1EkLURVlbay4+ltJL0IjIyIs6q27Uwq9X11RBxWWnBm1iH9yg7AzMysDtV5vBqpt4dZd1iDeXsQVR6PLDgWM+sEJzJmZmbWFy3CnDLYFZXHblJq1ga82N/MzMz6KpfFNmtjXiNjZmYtJ3da/yVQucgUcAJwHfP2KuqVF5leI9OzXBbbrP05kTEzs5bji0wnMmZmC+KpZWZm1nLcewWYM81p8VKjsLm4LLZZ6/BifzMz6zUk9ZM0RtI6ZcfSDSojUh+VGoXVErANsHTJcZj1eU5kzMysN/FFpplZH+FExszMzMzM2o4TGTMzMzMzaztOZMzMzMzMrO04kTEzMzMzs7bjRMbMzMys41wW26xFuI+MmZn1Ji19kSlp6/nsng1MBV6IiOn58TDgzSJisw5zWWyzFuFExszMepNWv8h8gDkxQkq8ouaYjyVdBRwfEcOKCszMrN04kTEzMyvO9sAI4C7gNmASsCKwB7ALcAKwPnAq8D5wWjlhmpm1PicyZmZmxTkGGBkRQ2u23y1pKHBwROwmaWHgYJzImJk15MX+ZmZmxdkR+H2DfeOAbfP9scDAQiIyM2tTTmTMzMyKMxnYvcG+3fN+gCWAKYVEZGbWpjy1zMzMrDjnAj+VNAi4nXnXyBybj9sWeLSMAM3M2oUTGTMzs4JExCWS3gC+C1xK+js8E/gTsHdE3JIPPQeYUUaMfZHLYpu1J0XUVn00MzNrHc1cZEoS8H3gioh4q5AAO0lSP9JozKSImF12PH2ZpNl0oCw2UCmLPauo2MysMScyZmbW0nyRaT1N0nZ0vCz2hRHhanJmLcCJjJmZtTRfZFpPk3Qz8FSdstjkstiDc1nsoaQS2YMKDdDM6nIiY2ZmLc0XmdbTJL0P7BkR99XZtwMwKiKWykn16IjoX3iQZjYPl182M7NW594r1tNcFtusDblqmZmZtbrKReY8n5bji0zrHi6LbdaGnMiYmVmr80Wm9SiXxTZrT14jY2ZmLU/SXqSLzI2Y+yLz7MpFpqQBwIyImFpOlNYbuCy2WftwImNmZm3DF5lmZlbhRMbMzMzMzNqOq5aZmZmZmVnbcSJjZmZmZmZtx4mMmVkfJmmopKj6elPSTZLWbuIcJ0napgdjXCnHOahm+zY55g2bPN8Dkn7TxPGvVr0/MyQ9L+l0SYs287oNzj0on/drCzhuqKR/NHnuRfPzNurMa5qZtTonMmZmNgXYMn8NIVUGu1/Skh18/knANj0SWbIScAYwqGb7E6SY/9qDr11xfX6tHYBf5Xj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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#Probiotics cor_nonprobiotics (cor) with Lab pathogens (Labpath)\n", "cor_Labpath = cor_nonprobiotics[cor_nonprobiotics.Sample.str.contains('Campy')]\n", "\n", "#Probiotics cor_nonprobiotics (cor) with Lab pathogens (Labpath) in FECES\n", "FECES = cor_Labpath[cor_Labpath['Sample'].str.contains('FECES')].copy()\n", "FECES.replace({'_FECES': ''}, regex=True, inplace=True)\n", "FECES.rename(columns={'Sample':'Pathogens'}, inplace = True)\n", "FECES = pd.pivot_table(FECES,index=['Pathogens'],columns='Potential_Probiotics',values='Correlation',fill_value=0)\n", "\n", "# #Probiotics cor_nonprobiotics (cor) with Lab pathogens (Labpath) in SOIL\n", "# SOIL = cor_Labpath[cor_Labpath['Sample'].str.contains('SOIL')].copy()\n", "# SOIL.replace({'_SOIL': ''}, regex=True, inplace=True)\n", "# SOIL.rename(columns={'Sample':'Pathogens'}, inplace = True)\n", "# SOIL = pd.pivot_table(SOIL,index=['Pathogens'],columns='None_Probiotics',values='Correlation',fill_value=0)\n", "\n", "#Plots layout\n", "plt.rcParams[\"figure.figsize\"] = [12, 8]\n", "plt.rcParams[\"figure.autolayout\"] = True\n", "#fig, axs = plt.subplots(ncols=2)\n", "\n", "plt.suptitle('Potential Probiotics Genera Correlation with Pathogens', y=1.05, fontsize=20)\n", "\n", "\n", "#Making the plots\n", "sns.heatmap(FECES,cmap=\"RdYlGn\", vmin=-1, vmax=1).set(title='FECES')\n", "plt.savefig(\"Figure 3.jpg\")\n", "\n", "\n", "plt.show()\n", "\n" ] }, { "cell_type": "code", "execution_count": 79, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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SamplePotential_ProbioticsCorrelationP-ValueAUC
4Salmonella_FECES_STARTg__Dorea0.9108341.925928e-390.828993
29Salmonella_FECES_ENDg__Faecalibacterium0.8036118.157129e-240.803783
90Campylobacter_FECES_STARTg__Parabacteroides-0.8041261.581932e-190.861690
94Campylobacter_FECES_STARTg__Solibacillus-0.9507361.203369e-510.861690
96Campylobacter_FECES_STARTg__Dorea-0.8196941.878680e-250.861690
98Campylobacter_FECES_STARTg__Faecalibacterium-0.7624803.085250e-200.861690
111Campylobacter_FECES_ENDg__Veillonella-0.8204621.303219e-210.792969
114Campylobacter_FECES_ENDg__Proteus-0.8387571.496051e-180.792969
117Campylobacter_FECES_ENDg__DA101-0.8904751.832927e-300.792969
119Campylobacter_FECES_ENDg__Caloramator-0.9352011.512185e-200.792969
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" ], "text/plain": [ " Sample Potential_Probiotics Correlation \\\n", "4 Salmonella_FECES_START g__Dorea 0.910834 \n", "29 Salmonella_FECES_END g__Faecalibacterium 0.803611 \n", "90 Campylobacter_FECES_START g__Parabacteroides -0.804126 \n", "94 Campylobacter_FECES_START g__Solibacillus -0.950736 \n", "96 Campylobacter_FECES_START g__Dorea -0.819694 \n", "98 Campylobacter_FECES_START g__Faecalibacterium -0.762480 \n", "111 Campylobacter_FECES_END g__Veillonella -0.820462 \n", "114 Campylobacter_FECES_END g__Proteus -0.838757 \n", "117 Campylobacter_FECES_END g__DA101 -0.890475 \n", "119 Campylobacter_FECES_END g__Caloramator -0.935201 \n", "\n", " P-Value AUC \n", "4 1.925928e-39 0.828993 \n", "29 8.157129e-24 0.803783 \n", "90 1.581932e-19 0.861690 \n", "94 1.203369e-51 0.861690 \n", "96 1.878680e-25 0.861690 \n", "98 3.085250e-20 0.861690 \n", "111 1.303219e-21 0.792969 \n", "114 1.496051e-18 0.792969 \n", "117 1.832927e-30 0.792969 \n", "119 1.512185e-20 0.792969 " ] }, "execution_count": 79, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#correlation = pd.read_csv(\"04c Correlations.csv\")\n", "\n", "cor_pot_probiotics = correlation[correlation['Important_Feature'].isin(['g__Caloramator', 'g__DA101', 'g__Dorea', 'g__Faecalibacterium','g__Parabacteroides',\n", " 'g__Proteus', 'g__Rumellibacillus', 'g__Solibacillus', 'g__Veillonella'])]\n", "\n", "#Select where targets are only pathogens (Salmonella, Campy, Listeria)\n", "#cor_pot_probiotics = cor_pot_probiotics[cor_pot_probiotics['Target/SampleType'].str.contains('Lab_Campy|Lab_Salmonella|Lab_Listeria')]\n", "\n", "#Select where Correlation is > 0.7 or <-0.7 and P-value < 0.01\n", "cor_pot_probiotics = cor_pot_probiotics[((cor_pot_probiotics.Correlation >= 0.7) & (cor_pot_probiotics[\"P-Value\"] < 0.05) & (cor_pot_probiotics.AUC >= 0.7)) | ((cor_pot_probiotics.Correlation <= -0.7) & (cor_pot_probiotics[\"P-Value\"] < 0.05) & (cor_pot_probiotics.AUC >= 0.7))]\n", "\n", "#cor_pot_probiotics = pd.read_csv(\"04b pot_probiotics vs Pathogens.csv\")\n", "cor_pot_probiotics.head()\n", "cor_pot_probiotics = cor_pot_probiotics[[\"Target/SampleType\", \"Important_Feature\", \"Correlation\",\"P-Value\",\"AUC\"]]\n", "cor_pot_probiotics.replace({'Probiotic_': ''}, regex=True, inplace=True)\n", "cor_pot_probiotics.rename(columns={'Target/SampleType':'Sample', 'Important_Feature':'Potential_Probiotics'}, inplace = True)\n", "\n", "\n", "cor_pot_probiotics.head(10)\n" ] }, { "cell_type": "code", "execution_count": 80, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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SampleProbioticsCorrelationP-ValueAUC
275BrGMOFree_FECES_STARTLactobacillus0.9247076.813867e-430.872354
289BrGMOFree_FECES_MIDEnterococcus-0.9358773.419274e-460.886171
364BrSoyFree_FECES_STARTStreptococcus0.9538255.426850e-530.956597
368BrSoyFree_FECES_STARTPediococcus0.9033471.674673e-210.956597
379BrSoyFree_FECES_MIDLactobacillus0.7061422.288398e-160.861789
457BrMedicated_FECES_STARTStreptococcus0.7866433.040345e-221.000000
613AvgAgeToPasture_FECES_MIDLactobacillus-0.9337731.581058e-450.909087
614AvgAgeToPasture_FECES_MIDBacillus0.8371312.011512e-270.909087
621AvgAgeToPasture_FECES_ENDEnterococcus0.8791032.641685e-330.909087
622AvgAgeToPasture_FECES_ENDBacillus0.8675261.745584e-310.909087
\n", "
" ], "text/plain": [ " Sample Probiotics Correlation P-Value \\\n", "275 BrGMOFree_FECES_START Lactobacillus 0.924707 6.813867e-43 \n", "289 BrGMOFree_FECES_MID Enterococcus -0.935877 3.419274e-46 \n", "364 BrSoyFree_FECES_START Streptococcus 0.953825 5.426850e-53 \n", "368 BrSoyFree_FECES_START Pediococcus 0.903347 1.674673e-21 \n", "379 BrSoyFree_FECES_MID Lactobacillus 0.706142 2.288398e-16 \n", "457 BrMedicated_FECES_START Streptococcus 0.786643 3.040345e-22 \n", "613 AvgAgeToPasture_FECES_MID Lactobacillus -0.933773 1.581058e-45 \n", "614 AvgAgeToPasture_FECES_MID Bacillus 0.837131 2.011512e-27 \n", "621 AvgAgeToPasture_FECES_END Enterococcus 0.879103 2.641685e-33 \n", "622 AvgAgeToPasture_FECES_END Bacillus 0.867526 1.745584e-31 \n", "\n", " AUC \n", "275 0.872354 \n", "289 0.886171 \n", "364 0.956597 \n", "368 0.956597 \n", "379 0.861789 \n", "457 1.000000 \n", "613 0.909087 \n", "614 0.909087 \n", "621 0.909087 \n", "622 0.909087 " ] }, "metadata": {}, "output_type": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#Probiotics correlation (cor) with Farm Practices (FarmPractice)\n", "cor_FarmPractice = cor_probiotics[cor_probiotics.Sample.str.contains('AvgNumBirds_|AvgNumFlocks_|YearsFarming_|EggSource_|BroodBedding_|BroodFeed_|BrGMOFree_|BrSoyFree_|BrMedicated_|AvgAgeToPasture_|PastureHousing_|FreqHousingMove_|AlwaysNewPasture_|PastureFeed_|PaGMOFree_|PaSoyFree_|PaMedicated_|LayersOnFarm_|CattleOnFarm_|SwineOnFarm_|GoatsOnFarm_|SheepOnFarm_|WaterSource_|FreqBirdHandling_|AnyABXUse_|LengthFeedRestrixProcess_|Seasons_|FlockAgeDays_|Breed_|FlockSize_|AnimalSource_|ProcessingType_|SkinOnOff_|ChillingMethod_|ScalderTempC_|RinseWaterSource_|RinseWaterChlor_|TransportTime_|StorageTempC_|StorageTimeD_')]\n", "cor_FarmPractice2 = cor_pot_probiotics[cor_pot_probiotics.Sample.str.contains('AvgNumBirds_|AvgNumFlocks_|YearsFarming_|EggSource_|BroodBedding_|BroodFeed_|BrGMOFree_|BrSoyFree_|BrMedicated_|AvgAgeToPasture_|PastureHousing_|FreqHousingMove_|AlwaysNewPasture_|PastureFeed_|PaGMOFree_|PaSoyFree_|PaMedicated_|LayersOnFarm_|CattleOnFarm_|SwineOnFarm_|GoatsOnFarm_|SheepOnFarm_|WaterSource_|FreqBirdHandling_|AnyABXUse_|LengthFeedRestrixProcess_|Seasons_|FlockAgeDays_|Breed_|FlockSize_|AnimalSource_|ProcessingType_|SkinOnOff_|ChillingMethod_|ScalderTempC_|RinseWaterSource_|RinseWaterChlor_|TransportTime_|StorageTempC_|StorageTimeD_')]\n", " \n", "display(cor_FarmPractice.head(10))\n", "\n", "#Probiotics correlation (cor) with Farm Practices (FarmPractice) in FECES\n", "FECES = cor_FarmPractice[cor_FarmPractice['Sample'].str.contains('FECES')].copy()\n", "FECES.replace({'_FECES': ''}, regex=True, inplace=True)\n", "FECES.rename(columns={'Sample':'Farm Practices'}, inplace = True)\n", "FECES = pd.pivot_table(FECES,index=['Farm Practices'],columns='Probiotics',values='Correlation',fill_value=0)\n", "\n", "#Probiotics correlation (cor) with Farm Practices (FarmPractice) in FECES\n", "FECES2 = cor_FarmPractice2[cor_FarmPractice2['Sample'].str.contains('FECES')].copy()\n", "FECES2.replace({'_FECES': ''}, regex=True, inplace=True)\n", "FECES2.rename(columns={'Sample':'Farm Practices'}, inplace = True)\n", "FECES2 = pd.pivot_table(FECES2,index=['Farm Practices'],columns='Potential_Probiotics',values='Correlation',fill_value=0)\n", "\n", "#Plots layout\n", "plt.rcParams[\"figure.figsize\"] = [18, 10]\n", "plt.rcParams[\"figure.autolayout\"] = True\n", "fig, axs = plt.subplots(ncols=2)\n", "\n", "plt.suptitle('Probiotics Correlation with Farm Practices in Feces', y=1.05, fontsize=20)\n", "\n", "#Making the plots\n", "sns.heatmap(FECES,cmap=\"RdYlGn\", vmin=-1, vmax=1, ax=axs[0]).set(title='FECES')\n", "sns.heatmap(FECES2,cmap=\"RdYlGn\", vmin=-1, vmax=1, ax=axs[1]).set(title='FECES')\n", "\n", "plt.savefig(\"Figure 4.jpg\")\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.6" } }, "nbformat": 4, "nbformat_minor": 4 }