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{
"cells": [
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "# Import modules and set options\n%matplotlib inline\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport pandas as pd\nimport numpy as np\nimport pymc3 as pm\nfrom itertools import product\n\nsns.set_context(\"notebook\")\n\nimport theano.tensor as tt\nfrom theano import shared\nfrom sklearn import metrics\n\nDATA_DIR = '../data/clean/'\nSEEDS = 20090425, 19700903",
"execution_count": 1,
"outputs": []
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Import bBAL data"
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "bbal_pathogens = pd.read_csv(DATA_DIR + 'bbal_pathogens.csv', index_col=0)\nbbal_pathogens.head()",
"execution_count": 2,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 2,
"data": {
"text/plain": " record_id day pathogen cfu_count\n1 1012 1.0 Staphylococcus aureus 6.0\n4 1041 1.0 Escherichia coli 6.0\n5 1043 1.0 Haemophilus influenza 6.0\n6 1050 1.0 Haemophilus influenza 6.0\n7 1060 1.0 Staphylococcus aureus 6.0",
"text/html": "<div>\n<style>\n .dataframe thead tr:only-child th {\n text-align: right;\n }\n\n .dataframe thead th {\n text-align: left;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>record_id</th>\n <th>day</th>\n <th>pathogen</th>\n <th>cfu_count</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>1</th>\n <td>1012</td>\n <td>1.0</td>\n <td>Staphylococcus aureus</td>\n <td>6.0</td>\n </tr>\n <tr>\n <th>4</th>\n <td>1041</td>\n <td>1.0</td>\n <td>Escherichia coli</td>\n <td>6.0</td>\n </tr>\n <tr>\n <th>5</th>\n <td>1043</td>\n <td>1.0</td>\n <td>Haemophilus influenza</td>\n <td>6.0</td>\n </tr>\n <tr>\n <th>6</th>\n <td>1050</td>\n <td>1.0</td>\n <td>Haemophilus influenza</td>\n <td>6.0</td>\n </tr>\n <tr>\n <th>7</th>\n <td>1060</td>\n <td>1.0</td>\n <td>Staphylococcus aureus</td>\n <td>6.0</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Aggregate bBAL data"
},
{
"metadata": {
"scrolled": false,
"trusted": true
},
"cell_type": "code",
"source": "bbal_agg = (bbal_pathogens.groupby(['record_id', 'day', 'pathogen'])[['cfu_count']].max())\nbbal_agg.head(10)",
"execution_count": 3,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 3,
"data": {
"text/plain": " cfu_count\nrecord_id day pathogen \n1001 10.0 Other 2.0\n1003 5.0 Klebsiella pneumoniae 3.0\n Other 2.0\n1007 3.0 Enterobacter cloacae 2.0\n Haemophilus influenza 6.0\n Other 2.0\n Streptococcus pneumonia 6.0\n1011 4.0 Staphylococcus aureus 6.0\n1012 1.0 Staphylococcus aureus 6.0\n1015 9.0 Other 2.0",
"text/html": "<div>\n<style>\n .dataframe thead tr:only-child th {\n text-align: right;\n }\n\n .dataframe thead th {\n text-align: left;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th></th>\n <th></th>\n <th>cfu_count</th>\n </tr>\n <tr>\n <th>record_id</th>\n <th>day</th>\n <th>pathogen</th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>1001</th>\n <th>10.0</th>\n <th>Other</th>\n <td>2.0</td>\n </tr>\n <tr>\n <th rowspan=\"2\" valign=\"top\">1003</th>\n <th rowspan=\"2\" valign=\"top\">5.0</th>\n <th>Klebsiella pneumoniae</th>\n <td>3.0</td>\n </tr>\n <tr>\n <th>Other</th>\n <td>2.0</td>\n </tr>\n <tr>\n <th rowspan=\"4\" valign=\"top\">1007</th>\n <th rowspan=\"4\" valign=\"top\">3.0</th>\n <th>Enterobacter cloacae</th>\n <td>2.0</td>\n </tr>\n <tr>\n <th>Haemophilus influenza</th>\n <td>6.0</td>\n </tr>\n <tr>\n <th>Other</th>\n <td>2.0</td>\n </tr>\n <tr>\n <th>Streptococcus pneumonia</th>\n <td>6.0</td>\n </tr>\n <tr>\n <th>1011</th>\n <th>4.0</th>\n <th>Staphylococcus aureus</th>\n <td>6.0</td>\n </tr>\n <tr>\n <th>1012</th>\n <th>1.0</th>\n <th>Staphylococcus aureus</th>\n <td>6.0</td>\n </tr>\n <tr>\n <th>1015</th>\n <th>9.0</th>\n <th>Other</th>\n <td>2.0</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Data by patient, day and pathogen"
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "pcr_path_lookup = {1: 'Staphylococcus aureus',\n2: 'Streptococcus pneumonia',\n3: 'Streptococcus Group B',\n4: 'Acetinobacter baumannii',\n5: 'Pseudomonas aeruginosa',\n6: 'Haemophilus influenza',\n7: 'Klebsiella pneumoniae',\n8: 'Escherichia coli',\n9: 'Enterobacter cloacae',\n10: 'Stenotrophomonas maltophilia',\n11: 'Enterobacter aerogenes',\n12: 'Serratia marcescens',\n13: 'Klebsiella oxytoca',\n14: 'Proteus mirabilis',\n15: 'Enterococcus faecalis',\n16: 'Enterococcus faecium',\n17: 'Candida albicans',\n18: 'Other'}",
"execution_count": 4,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "def fill_pathogens(x, labels, lookup, fill_with=1):\n return (x.dropna()\n .drop_duplicates(subset=['pathogen'])\n .set_index('pathogen')\n .reindex(list(lookup.values()))\n .reset_index()\n .assign(record_id=labels[0], day=labels[1])\n .fillna(fill_with))",
"execution_count": 5,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "bbal_groups = []\nfor labels, group in bbal_pathogens.groupby(['record_id', 'day']):\n recid, day = labels\n group_full = fill_pathogens(group, labels, pcr_path_lookup, fill_with=0)\n bbal_groups.append(group_full)",
"execution_count": 6,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "bbal_complete = pd.concat(bbal_groups).reset_index(drop=True)",
"execution_count": 7,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "bbal_agg = (bbal_complete.groupby(['record_id', 'day', 'pathogen'])[['cfu_count']].max())\nbbal_agg.head(10)",
"execution_count": 8,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 8,
"data": {
"text/plain": " cfu_count\nrecord_id day pathogen \n1001 10.0 Acetinobacter baumannii 0.0\n Candida albicans 0.0\n Enterobacter aerogenes 0.0\n Enterobacter cloacae 0.0\n Enterococcus faecalis 0.0\n Enterococcus faecium 0.0\n Escherichia coli 0.0\n Haemophilus influenza 0.0\n Klebsiella oxytoca 0.0\n Klebsiella pneumoniae 0.0",
"text/html": "<div>\n<style>\n .dataframe thead tr:only-child th {\n text-align: right;\n }\n\n .dataframe thead th {\n text-align: left;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th></th>\n <th></th>\n <th>cfu_count</th>\n </tr>\n <tr>\n <th>record_id</th>\n <th>day</th>\n <th>pathogen</th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th rowspan=\"10\" valign=\"top\">1001</th>\n <th rowspan=\"10\" valign=\"top\">10.0</th>\n <th>Acetinobacter baumannii</th>\n <td>0.0</td>\n </tr>\n <tr>\n <th>Candida albicans</th>\n <td>0.0</td>\n </tr>\n <tr>\n <th>Enterobacter aerogenes</th>\n <td>0.0</td>\n </tr>\n <tr>\n <th>Enterobacter cloacae</th>\n <td>0.0</td>\n </tr>\n <tr>\n <th>Enterococcus faecalis</th>\n <td>0.0</td>\n </tr>\n <tr>\n <th>Enterococcus faecium</th>\n <td>0.0</td>\n </tr>\n <tr>\n <th>Escherichia coli</th>\n <td>0.0</td>\n </tr>\n <tr>\n <th>Haemophilus influenza</th>\n <td>0.0</td>\n </tr>\n <tr>\n <th>Klebsiella oxytoca</th>\n <td>0.0</td>\n </tr>\n <tr>\n <th>Klebsiella pneumoniae</th>\n <td>0.0</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Import HME data"
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "hme_pathogens = pd.read_csv(DATA_DIR + 'hme_pathogens.csv', index_col=0)\nhme_pathogens.head()",
"execution_count": 9,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 9,
"data": {
"text/plain": " record_id day pathogen pcr_count\n3 1009 1.0 Staphylococcus aureus 66200000.0\n4 1012 1.0 Staphylococcus aureus 34100.0\n17 1027 1.0 Acetinobacter baumannii 671000.0\n21 1031 1.0 Serratia marcescens 15800000.0\n32 1045 1.0 Streptococcus pneumonia 897000.0",
"text/html": "<div>\n<style>\n .dataframe thead tr:only-child th {\n text-align: right;\n }\n\n .dataframe thead th {\n text-align: left;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>record_id</th>\n <th>day</th>\n <th>pathogen</th>\n <th>pcr_count</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>3</th>\n <td>1009</td>\n <td>1.0</td>\n <td>Staphylococcus aureus</td>\n <td>66200000.0</td>\n </tr>\n <tr>\n <th>4</th>\n <td>1012</td>\n <td>1.0</td>\n <td>Staphylococcus aureus</td>\n <td>34100.0</td>\n </tr>\n <tr>\n <th>17</th>\n <td>1027</td>\n <td>1.0</td>\n <td>Acetinobacter baumannii</td>\n <td>671000.0</td>\n </tr>\n <tr>\n <th>21</th>\n <td>1031</td>\n <td>1.0</td>\n <td>Serratia marcescens</td>\n <td>15800000.0</td>\n </tr>\n <tr>\n <th>32</th>\n <td>1045</td>\n <td>1.0</td>\n <td>Streptococcus pneumonia</td>\n <td>897000.0</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Aggregate HME data"
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "hme_groups = []\nfor labels, group in hme_pathogens.groupby(['record_id', 'day']):\n recid, day = labels\n group_full = fill_pathogens(group, labels, pcr_path_lookup)\n hme_groups.append(group_full)",
"execution_count": 10,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "hme_complete = pd.concat(hme_groups).reset_index(drop=True)",
"execution_count": 11,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "hme_complete.head()",
"execution_count": 12,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 12,
"data": {
"text/plain": " pathogen record_id day pcr_count\n0 Staphylococcus aureus 1002 5.0 37900.0\n1 Streptococcus pneumonia 1002 5.0 1.0\n2 Streptococcus Group B 1002 5.0 1.0\n3 Acetinobacter baumannii 1002 5.0 1.0\n4 Pseudomonas aeruginosa 1002 5.0 1.0",
"text/html": "<div>\n<style>\n .dataframe thead tr:only-child th {\n text-align: right;\n }\n\n .dataframe thead th {\n text-align: left;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>pathogen</th>\n <th>record_id</th>\n <th>day</th>\n <th>pcr_count</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>Staphylococcus aureus</td>\n <td>1002</td>\n <td>5.0</td>\n <td>37900.0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>Streptococcus pneumonia</td>\n <td>1002</td>\n <td>5.0</td>\n <td>1.0</td>\n </tr>\n <tr>\n <th>2</th>\n <td>Streptococcus Group B</td>\n <td>1002</td>\n <td>5.0</td>\n <td>1.0</td>\n </tr>\n <tr>\n <th>3</th>\n <td>Acetinobacter baumannii</td>\n <td>1002</td>\n <td>5.0</td>\n <td>1.0</td>\n </tr>\n <tr>\n <th>4</th>\n <td>Pseudomonas aeruginosa</td>\n <td>1002</td>\n <td>5.0</td>\n <td>1.0</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "hme_agg = (hme_complete.assign(log_count=np.log10(hme_complete.pcr_count).round(1))\n .drop('pcr_count', axis=1)\n .dropna()\n .groupby(['record_id', 'day', 'pathogen'])[['log_count']].max())\nhme_agg.head()",
"execution_count": 13,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 13,
"data": {
"text/plain": " log_count\nrecord_id day pathogen \n1002 5.0 Acetinobacter baumannii 0.0\n Candida albicans 0.0\n Enterobacter aerogenes 7.4\n Enterobacter cloacae 0.0\n Enterococcus faecalis 0.0",
"text/html": "<div>\n<style>\n .dataframe thead tr:only-child th {\n text-align: right;\n }\n\n .dataframe thead th {\n text-align: left;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th></th>\n <th></th>\n <th>log_count</th>\n </tr>\n <tr>\n <th>record_id</th>\n <th>day</th>\n <th>pathogen</th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th rowspan=\"5\" valign=\"top\">1002</th>\n <th rowspan=\"5\" valign=\"top\">5.0</th>\n <th>Acetinobacter baumannii</th>\n <td>0.0</td>\n </tr>\n <tr>\n <th>Candida albicans</th>\n <td>0.0</td>\n </tr>\n <tr>\n <th>Enterobacter aerogenes</th>\n <td>7.4</td>\n </tr>\n <tr>\n <th>Enterobacter cloacae</th>\n <td>0.0</td>\n </tr>\n <tr>\n <th>Enterococcus faecalis</th>\n <td>0.0</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "bbal_hme = (bbal_agg.join(hme_agg).fillna(0)\n .reset_index()\n .rename(columns={'log_count':'HME count',\n 'cfu_count':'bBAL count'}))",
"execution_count": 14,
"outputs": []
},
{
"metadata": {
"scrolled": true,
"trusted": true
},
"cell_type": "code",
"source": "bbal_hme.head()",
"execution_count": 15,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 15,
"data": {
"text/plain": " record_id day pathogen bBAL count HME count\n0 1001 10.0 Acetinobacter baumannii 0.0 0.0\n1 1001 10.0 Candida albicans 0.0 0.0\n2 1001 10.0 Enterobacter aerogenes 0.0 0.0\n3 1001 10.0 Enterobacter cloacae 0.0 0.0\n4 1001 10.0 Enterococcus faecalis 0.0 0.0",
"text/html": "<div>\n<style>\n .dataframe thead tr:only-child th {\n text-align: right;\n }\n\n .dataframe thead th {\n text-align: left;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>record_id</th>\n <th>day</th>\n <th>pathogen</th>\n <th>bBAL count</th>\n <th>HME count</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>1001</td>\n <td>10.0</td>\n <td>Acetinobacter baumannii</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>1001</td>\n <td>10.0</td>\n <td>Candida albicans</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>2</th>\n <td>1001</td>\n <td>10.0</td>\n <td>Enterobacter aerogenes</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>3</th>\n <td>1001</td>\n <td>10.0</td>\n <td>Enterobacter cloacae</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>4</th>\n <td>1001</td>\n <td>10.0</td>\n <td>Enterococcus faecalis</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "pathogen_list = bbal_hme.pathogen.unique()\nn_pathogens = pathogen_list.shape[0]\n\npathogen_encode = dict(zip(pathogen_list, range(n_pathogens)))\npathogen_decode = dict(zip(range(n_pathogens), pathogen_list))\n\nbbal_hme['pathogen_id'] = bbal_hme.pathogen.replace(pathogen_encode)",
"execution_count": 16,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "n_pathogens",
"execution_count": 17,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 17,
"data": {
"text/plain": "18"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "## Predict negative bBAL from HME"
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "bbal_hme['bbal_negative'] = bbal_hme['bBAL count'] < 3",
"execution_count": 18,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "bbal_hme.head()",
"execution_count": 19,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 19,
"data": {
"text/plain": " record_id day pathogen bBAL count HME count \\\n0 1001 10.0 Acetinobacter baumannii 0.0 0.0 \n1 1001 10.0 Candida albicans 0.0 0.0 \n2 1001 10.0 Enterobacter aerogenes 0.0 0.0 \n3 1001 10.0 Enterobacter cloacae 0.0 0.0 \n4 1001 10.0 Enterococcus faecalis 0.0 0.0 \n\n pathogen_id bbal_negative \n0 0 True \n1 1 True \n2 2 True \n3 3 True \n4 4 True ",
"text/html": "<div>\n<style>\n .dataframe thead tr:only-child th {\n text-align: right;\n }\n\n .dataframe thead th {\n text-align: left;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>record_id</th>\n <th>day</th>\n <th>pathogen</th>\n <th>bBAL count</th>\n <th>HME count</th>\n <th>pathogen_id</th>\n <th>bbal_negative</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>1001</td>\n <td>10.0</td>\n <td>Acetinobacter baumannii</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0</td>\n <td>True</td>\n </tr>\n <tr>\n <th>1</th>\n <td>1001</td>\n <td>10.0</td>\n <td>Candida albicans</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>1</td>\n <td>True</td>\n </tr>\n <tr>\n <th>2</th>\n <td>1001</td>\n <td>10.0</td>\n <td>Enterobacter aerogenes</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>2</td>\n <td>True</td>\n </tr>\n <tr>\n <th>3</th>\n <td>1001</td>\n <td>10.0</td>\n <td>Enterobacter cloacae</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>3</td>\n <td>True</td>\n </tr>\n <tr>\n <th>4</th>\n <td>1001</td>\n <td>10.0</td>\n <td>Enterococcus faecalis</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>4</td>\n <td>True</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "bbal_hme.shape",
"execution_count": 20,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 20,
"data": {
"text/plain": "(666, 7)"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "x_path = bbal_hme.pathogen_id.values\nx_hme = bbal_hme['HME count'].values\ny = bbal_hme.bbal_negative.astype(int).values\n\nwith pm.Model() as neg_pred_model:\n \n μ = pm.Normal('μ', 0, sd=10)\n σ = pm.HalfCauchy('σ', 2.5)\n θ_tilde = pm.Normal('θ_tilde', mu=0, sd=1, shape=n_pathogens)\n θ = pm.Deterministic('θ', μ + σ * θ_tilde)\n \n β = pm.Normal('β', 0, sd=10)\n π = pm.Deterministic('π', pm.math.invlogit(θ[x_path] + β*x_hme))\n \n pm.Bernoulli('likeihood', π, observed=y)",
"execution_count": 21,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "with neg_pred_model:\n \n neg_trace = pm.sample(3000, tune=2000, \n step_args = {'target_accept': 0.99}, \n njobs=2)",
"execution_count": 22,
"outputs": [
{
"output_type": "stream",
"text": "Auto-assigning NUTS sampler...\nInitializing NUTS using jitter+adapt_diag...\n 96%|█████████▋| 4820/5000 [00:19<00:00, 271.47it/s]/Users/fonnescj/Repos/pymc3/pymc3/step_methods/hmc/nuts.py:467: UserWarning: Chain 1 contains 3 diverging samples after tuning. If increasing `target_accept` does not help try to reparameterize.\n % (self._chain_id, n_diverging))\n100%|██████████| 5000/5000 [00:20<00:00, 246.62it/s]\n",
"name": "stderr"
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "invlogit = lambda x: 1 / (1 + np.exp(-x))",
"execution_count": 23,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "pm.forestplot(neg_trace, varnames=['θ'], ylabels=pathogen_list, transform=invlogit,\n xtitle='Probability of negative bBAL with no HME detected', vline=-1)",
"execution_count": 24,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 24,
"data": {
"text/plain": "<matplotlib.gridspec.GridSpec at 0x12ec57978>"
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": "<matplotlib.figure.Figure at 0x12ec39eb8>",
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Fevb08j17+n4urRY0mutBBLuVo5iQcjZwrmlmXy1SJisAnSMrAF1MxLlZSLlA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\nJOnzpLJLkiRJ+jz/H3tcv3JFKEl0AAAAAElFTkSuQmCC\n"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true,
"scrolled": true
},
"cell_type": "code",
"source": "pm.traceplot(neg_trace, varnames=['β', 'σ']);",
"execution_count": 25,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": "<matplotlib.figure.Figure at 0x12f5c1b70>",
"image/png": 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tgOp8maLjTkr5TIp16oH7hRBGtEzao1LKZyfKQIViNDi9Aa5/9COWbruNP5lX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\nS0MpWQ+z6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oEc92vIWZ/z952LycZPVbjsrG+pIGr1MOKqz/p86+IqYpXFU7E7bRYWVLnH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\noFEAAAAASUVORK5CYII=\n"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "def plot_pathogen(pathogen, trace, direction):\n\n fig, ax = plt.subplots()\n\n x_hme_pred = np.arange(0, 10)\n \n pathogen_ind = np.where(pathogen_list==pathogen)[0][0]\n\n p_pred = np.array([invlogit(trace['θ'][:, pathogen_ind] + trace['β']*x) for x in x_hme_pred])\n\n low, med, high = np.percentile(p_pred, [2.5, 50., 97.5], axis=1)\n\n ax.plot(x_hme_pred, med)\n ax.fill_between(x_hme_pred, low, high,\n color='k', alpha=0.35, zorder=5,\n label='95% posterior credible interval')\n \n y,x = bbal_hme.loc[bbal_hme.pathogen==pathogen, ['bbal_{}'.format(direction), 'HME count']].astype(int).values.T\n ax.plot(x, y + np.random.randn(len(y))*0.01, 'bo')\n \n ax.set_xlabel('Log-count of pathogen in HME');\n\n ax.set_ylabel('Probability of {} bBAL'.format(direction))\n ax.set_ylim(-0.1,1.1)\n\n ax.set_title(pathogen);",
"execution_count": 43,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "plot_pathogen('Haemophilus influenza', neg_trace, 'negative')",
"execution_count": 44,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": "<matplotlib.figure.Figure at 0x1312c3518>",
"image/png": 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G/hlfogB4G/ABVb1hget+F7hXVd/r334HcL2qfmDGOUXA8/jaPFKBu1R134JBizyBvwtu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\nS2lWTFbPmcUJzF6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xdS5H2wfZ0djN8fYhmrtHSE+KY3tVHturcq0qaoWbN0mo6pmlDMSY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\nRJIkSVLLn4GoTFPMB3sjAAAAAElFTkSuQmCC\n"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "plot_pathogen('Streptococcus pneumonia', neg_trace, 'negative')",
"execution_count": 45,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": "<matplotlib.figure.Figure at 0x12f5ee080>",
"image/png": 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ujwHfAb6rqpXDiiiGLEmYaIQ3cttI7sTW0d3DzhP1/N/RGqoa2gCYmJXCane+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\nYII=\n"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Here is a plot for a rarer species. Note the uncertainty:"
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "plot_pathogen('Proteus mirabilis', neg_trace, 'negative')",
"execution_count": 46,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": "<matplotlib.figure.Figure at 0x1305a1898>",
"image/png": 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},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "## Predict positive bBAL from positive HME"
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "bbal_hme['bbal_positive'] = bbal_hme['bBAL count'] >= 3",
"execution_count": 30,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "x_path = bbal_hme.pathogen_id.values\nx_hme = bbal_hme['HME count'].values\ny = bbal_hme.bbal_positive.astype(int).values\n\nwith pm.Model() as pos_pred_model:\n \n μ = pm.Normal('μ', 0, sd=10)\n σ = pm.HalfCauchy('σ', 2.5)\n θ_tilde = pm.Normal('θ_tilde', mu=0, sd=1, shape=n_pathogens)\n θ = pm.Deterministic('θ', μ + σ * θ_tilde)\n \n \n# ϕ = pm.Normal('ϕ', 0, sd=10)\n# τ = pm.HalfCauchy('τ', 2.5)\n# β_tilde = pm.Normal('β_tilde', mu=0, sd=1, shape=n_pathogens)\n# β = pm.Deterministic('β', ϕ + τ * β_tilde)\n\n β = pm.Normal('β', 0, sd=10)\n \n π_exp = pm.Deterministic('π_exp', pm.math.invlogit(θ + β))\n \n π = pm.Deterministic('π', pm.math.invlogit(θ[x_path] + β*x_hme))\n \n pm.Bernoulli('likeihood', π, observed=y)",
"execution_count": 31,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "with pos_pred_model:\n pos_trace = pm.sample(3000, tune=2000, init='ADVI',\n njobs=2,\n random_seeds=SEEDS)",
"execution_count": 32,
"outputs": [
{
"output_type": "stream",
"text": "Auto-assigning NUTS sampler...\nInitializing NUTS using advi...\nAverage Loss = 147.5: 8%|▊ | 16617/200000 [00:04<00:36, 4986.68it/s] \nConvergence archived at 16800\nInterrupted at 16,800 [8%]: Average Loss = 212.81\n100%|█████████▉| 4985/5000 [00:21<00:00, 294.94it/s]/Users/fonnescj/Repos/pymc3/pymc3/step_methods/hmc/nuts.py:467: UserWarning: Chain 0 contains 1 diverging samples after tuning. If increasing `target_accept` does not help try to reparameterize.\n % (self._chain_id, n_diverging))\n100%|██████████| 5000/5000 [00:21<00:00, 233.36it/s]\n",
"name": "stderr"
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "pm.forestplot(pos_trace, varnames=['θ'], ylabels=pathogen_list, transform=invlogit,\n xtitle='Probability of positive bBAL with no HME', vline=0)",
"execution_count": 33,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 33,
"data": {
"text/plain": "<matplotlib.gridspec.GridSpec at 0x140beb978>"
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": "<matplotlib.figure.Figure at 0x1409fb160>",
"image/png": 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8tW2r1O6P4GvlSoko55/LycCc9P4Q4H5gWGrHLsD7ilyzq4DzMtsfwrUwB6fz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\nyFKyZNs5c7yIMpESStQ9kbZ+Ve8AxrXjd1WW9kY5SJEPEhW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OjjK3L7puVFjBbWSO9JMy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\nVR25DCEAAAAASUVORK5CYII=\n"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "pm.traceplot(pos_trace, varnames=['β'])",
"execution_count": 34,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 34,
"data": {
"text/plain": "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x12ec74c18>,\n <matplotlib.axes._subplots.AxesSubplot object at 0x12ea5e6a0>]], dtype=object)"
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": "<matplotlib.figure.Figure at 0x12e9ba828>",
"image/png": 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4teISy+tLtXMNe05C5/H2QS1c66rVDRDwYvLa8FtKwN5F+cAWXAWx0TvxIYvx\neHyp1ocUXd019fgD5KXY2ursQh7YgQguIVn6m8Prx+n1s/XQIPPjCnXIUNXY2p7XaRmczfzw05yF\ncSGljCjl+lA5k9eGcfcbYE6uxj6/QzO6r1paqi1IUXwjWSnvQWfiZ+cPBCkdieZB7e8dZfnoEPmh\n9/1xn/egw0u5lBwM3RfVuvwfq6sHr6Vcu6czYPEMYe3cSzuxz7XVnehxPdQ/SnmBhbLS/Miy9mEn\npqCHWSX5CdvrCUoJXldWXpZsbUKDR1cuPs1+rK5egsElMeuEQDfZEAQRCvnT5+Q1vaIZtT2h6o8+\nF1QcE7OfdPmck022HqqisDEFIKUcBEoyjKkDXhVCbAPWo+VQTVGGrUIxORzqd/C95/ZQky/5cfAH\niM5NsPojcOH/O2xKo0+Ua05s5Dc3nUhASj7+h/U8u60LTvs0XPNbrav6H6+G1jE0AlQoxoEQ4kPA\nDuAV4H7gVeCnORVqOtDlN0kpGXB4IkpfONTK4w+wu9sW7R8zdAj69yXMJIcJf3VZfCNYvOlLSuu9\nBBLNmAsnvReNNlPg7Ig2AdcVe/CNoQy61d0TOVbANUzxwecoG4otid036iEQDMLAAUpsmds4dI24\nIjkuYfI8iefauTO28pxExhhqQgYiYVLeQDDGIKkY1EpECxFr24WNqTBZN2nVK/mDB7WCAHr2Pk9t\n7xuIoBdD6zoKXJ1aDtlkkCRUbXey3k+hEvuVg5vo7OpIqsiH2dVlw+4O0DYca3Hp7wx/IAgygNcf\nZH+G4gzxY1264gz5rtRNfOs7nqfYdoB8Zyc7mzsioa7BoGRg1BPxsPgCQba2RT1TRr/mzUgWfuZM\nkg/WrJPfGWdEvrG/L2nFSQlUDmygpvcNCkebQ5MJseg9mNV96zA6Otm/J13/JYnTG2DA4eVA32jk\n5nR6/XQMJT4XydjUOsT2lx9Oml+Z9CSSoBWrGJ8LLiYUMvJ3KGtKBklqmAXinjOfK0H+mVjlzyCE\nKAy/EUIUkcG7JaXcJqU8Xkq5Skq5Qkr57YkIqlBMNYGg5H/+uhWvz8vTsx7A0vqmlkN0xc/BcHS1\nv7lgWS0P3nIyeSYjdzy6iUffa4WV18K1D4DfBQ9/MDHZVaGYXL4CnAjsl1IeixYyflRZ8u1DLg71\nO+gYciUke3ce3Mmg0xfqDTNFeQVhSyy0fyGT5BPpPDip8l7S0d/VCkChQyvKYAhGlaSuUN5VoaOV\nPHeqMLMSFNQbAAAgAElEQVS4cEHdtdDvK0xnknC6qpZo5kJ139tUDG7B7Quyv8fO+pbBiEFr8jsQ\nUqadW9vXM74KYyMuL7aOfTHLZMh7ZdAp3fFFM0bjCzok+4z0q/1uht99hOBwa2iBvjlurDerdHgX\npr3PRHJYtGNluNcEyDSqZWtPP3Rs4mD/aIZcrLDAul3rrnu64iFCBimx7aVicDOu/a9xKFSye2vb\nEG8e6I8YGP2jnpiyF0nv7xDJPX2SPrtbCy1Mwp401SiNATdlwzs1r1fc87utPfQchZ+7OLNA75U0\nBD1U9b+H/kJ5/No9kU1zZD3ZTookK8jSOuhgW/swo/a4cx44gD8YTFp4pE/n6gxKicvn50DvKB7H\nMJV972IIepDEelFTMeLy4goYEia+p1N1y/ZQjwIvCSFuEkLcBLwIPDx1YikU0899rx9kY8sgD9f8\niVld/4L574NrfhfTkO9o4rQFlTz6qVMpL7Dw5Se3c8/aA8hlV2ll1f1u+NO1GUvuKhQTwC+l7CU0\neSel/CewKrciTS/hKmhOrx/2/zOiGIJWbWtHp439vaNMpCOJIckMeQIBH6JrS/KE/5Z1OELKUnVf\nyPOTVpzYlfowKmQAy0i0glu3TuGq6n8Xsz/Wo1E6vIvKgQ1A1LCo70ieKzMW8l1dbGodilz/+Bl+\nAwKZwqiyuWMVR38gyMH+UZy+9JUP9/eOsk/XYLlt0Jm0B0+eJ64Uexxlwztj3scov1LiPfA6B/pG\nI0VCTAEnJp+dqr53E5qmhr2VNb26whe63bkPvYOjK7Y3lJBBDDIu5E/3d3H7awQ8o/gC2d2zYaM6\nKGWM5ygc1hlTuj4D3cPa9QyX7o/Pgysabcbod2AIJIaJ7U9yjP4RJy2DTrZ3jNAxFNfTKa4YRKqC\nMIWONho6nqNiYGNMaXVtnTbJoGW7Re+B8DVJhV93bfW3qcPj17y+E+RQvwOnz8+QQ6vkOOT0RkLr\n7KOJRlwqD1nYyCoaPYSUcLDPwbDLy/rtuzA6o55QLRdzSBcBGHdtHR72947y7qEBEMaYdQlVKKeQ\nrDRFKeX3hBCdwJVon8+9UsqHplQyhWIa2dNt4yf/3Md3Cv7MabYXoP54uOERMGfX9+BIZWVjKX+9\n9TRu/u27/PCFvQw7fXzp4ssxXPsA/OWj8Kfr4BMvJcQtKxSTgEcIIYD9QojPAM1AdW5FmmKS9EtK\nh9Wlhc55/QGae2w0lhVQMMZDVgxuhlAoWzxhD09Hy14qGkqxeBNLuQdDld/0DDq9JPtGyBQ13ZDE\nGBpxRb1M4cIKZv8o5YNbtPDD+GMQm9w+FuxJZtEBLTcr9iBZ021zM+jw4vAEKLAYMw+QEpPfwaZW\naNDl8oS9Fpl67Ogr5QExhkuhowWLT/sMh5zeiMFY26OlJRb2vgdVsfllo3EGYuT6+ly0HdxNmUuf\n/6YVBNGzoWWQ8oJorpAh6EXK7O/SHrtbaxidJK+pqdcR06coE2Z3P968msh9KOJCxgodrRQ6NM9d\nR+NlMeuMzkQPaUHfllBZj3Ahl+i1ru5dh714QeT9oQEHlUV5KW/JfFc39HihbC4WzwBeSzkFzklo\nHxQy7BweP7u7bRTlmVgyK3XGjtcfxCDAZEztb/EHJbs6bXQY+mjozNzeoT9DdVGD9IOMFjPRex8T\nnj0AJMGg5gf1BoKRiQchg7jjDPWZWDYdKeWDUsrrpZTXKWNKcSTh9gX4zz9v5Rae4ubg01C1GD7y\nhJZwrWBBdRGP33Y6C6oLue/1g9z+yCacCy+Dy+7WOsT/8WqtX41CMbl8DS1X94vAVcA3gE9nGiSE\nuFgIsVcIcUAI8aUk6z8mhOgTQmwJvT6pW/dRIcT+0Oujk3gu2TGQvDy5Vi04URMzSE3Z7Rx2YnP7\nE3JSCkdbCI50saVtOENY4Pg9XE19o2l7GGUTrpOOVFW6khlTqYi/dvHvXen6JSXuDQMC4fcikoQU\nxhNWEuM9A+HZ/V2dIzH9wortB6jteY2anjciy+KNpLGgzzsKlxaH5Occ8DixuX3s7bZhCm0bnwcU\nMaoD3ojHqGw4mv9mDCYqz0NJjKGxkMyYAsZkTAGU9a2neGQfhX1b8Ph8NPWkzycMYwi4MbaOrRZO\n2HDV4/T40/YvA3DveIbqvndo6Hg+ku/o9AbCVRpSjhuOK60fbkId9hbuDr3Xh4g6fX4O9NpjQgi3\ndQyzrSNW9rRV/SaJkRStAfS4fUH29dg51O/gmW2ddI24YvqEGf1O3twR26NMTKNFlZWHSghRg9Zv\naoF+jJTy+imSS6GYNu56eierep/iy+ZHoaQRbv4bFFbmWqwZRX1ZPn+99XRufXgjz+/opm3IyW//\n7SZm2Xvg9R9q4X+3vACWsc6PKxQpeVtK6QJG0KrEZkQIYQR+BVwAtAPrhRBPSyl3xW36ZynlHXFj\nK4BvAmvQftE3hsZmp3VNITaPP05hisUdVpql1HoIlTZS2b8eq7sX255myDubus6XUo7PBr2nYl+P\njWOqizAZDHGKkE7m0PKi0SbKhrWE+pZ+B9ZsvDQ6vNnk2aRBShlplFrVp+V9xocgJStCkEwhDhPW\n0cqGd6fcJkEO3XFcvkBMIQt9wYMSm5ZHpfdE6Q2WZDS0P0tn/QVIQ2IlxLGowm5/IBI+Z02Rs1bo\naMFZ0EDvplfHsOdYkjW2TZbbNhWU2PdjMVjZ/uJejCK9ClzgaCXf2c1I6ZK022nItDlYALu6bSyu\nST1RG5Qy0rtLT4/djSzPQoQQrQOuSBhuquO0DTojn8OWtuGE9Xriw1jDJCuQoh/q8gXIVuyt7cPU\nRQzk5Hdt2DDv6BgGYUyYyLF6+rB6YicfDBOYLBor2XqongBqgZeBZ3UvheKw5q8b2hja+ATfNf8O\nmV+hGVOljbkWa0ZSUWjh4U+cwvVrGtnRYePKX77J1oWfhuNv0nq2PH3HjG66pzjsaBNC/FYIcUbm\nTSOcDByQUh6UUnqBx9C8W9lwEVo12sGQEfVPprt3YprZ1KY0FdFGHZoS7A/CgY3/wtaxJ1KNLWwr\nhL1Z40Wfv2Vz+xkY9abNCwrnA+V5BiM9c2wef8S4mU5aQ7KHc8B6koYRZUehozVSkazAmaI/jg59\nyFJ8aORkUmRvprbr1QQPkV6xTlcZLx6Z5l6sHNhA66AzsSBGlnQMu8bsXZoo+mIhYRs93TPR0P4s\n5UPbsXr6MAWyMPakzOp+0OfJxbOzM3U451h+Wu3u9B7BPrsnqVGbilTGWXVfYmEqfYXLsRjJeiMu\nk1dbvz7VhE5k22nMocrWoCqXUv67lPKBUOjfg1LKB6dUMoViitndZeMfTz3GLyy/RJitiJseh+rF\nuRZrRmMxGfjBNav42mVL6Rv1cP197/Ds3M/D7FNhxxPw1pFf1VoxbSwGtgA/E0LsE0J8RQiRabaj\nAdB3Om0PLYvnGiHENiHE40KI2WMcO2ORSIZdXpq2RosITFW4jkSyL00Vs1Qk8wZNNXbP+JX35P1+\nHEm2TM9Un3eedwBTIDE80qHrOZas6mEqDMHUxsZY9jNTiPWyjPGzkMknAfThe8agl2L7waTbZUs6\nIzOdgZtk67RrHUn60KXDOQbD2R13DgnNybMgU56gUVc0JFWFxTAzMYdqhxCiPvNmCsXhgc3t46cP\n/ZlfGe7GZBAYbngEGhIbYioSEULwybOO4XcfXYPJILj9sZ3cV3cXsqQBXv4W7JtYaJFCAVq/Qynl\nL6WUa4CrgUVA8kZLUZL9fMZrF88A86SUq9CiLsKTg9mMTTygEHcJIaQQQnZ2TjCJPFQ6eKLoe7ro\nPUuTSb/di38cRsJ4vRrjJaZowCQyGZ/T5DK5mmM2vb8OV8Y6x1A5uCnjNsaAC2OcJ6vQ0ZZi67Fj\n8jsxJjGYk5GupDyMoU9aiGQlz7MlWQhjJjKFTtb2rI20B8gUEpzpWkwmWXuogO1CiKeFEH8Jv6ZS\nMIViqvD6g3zn90/xXee3KBBeDNf+Dhacm2uxDjvOW1LLk58+g8byfL77+iA/Kvs60pQHT3wC+vfn\nWjzFEYAQwiCEuBz4FnAZ8IcMQ9qB2br3jUCMlSOlHJBShqc470frdZXV2GRIKe+SUgoppaivn+i8\no0wb9jOTiJ+Jnqlsap2aFLhUeSW5IpMSqogylnC3iRCfzzMRiu1NFI02Z7WtSOFRGw8TLSgyVRiC\n2d3vRv/UTCglI1uD6hHgv9ByqVQOleKwJRiUfO/PL3Nn9xeoFHa4/CewLNsUC0U8x84q5u+3n8Ga\nueXcs6+En1jv0Bp9PnoDuMbe5FOhCCOE+D80I+dzwN+BuVLKT2UYth5YJISYL4SwADcAT8ftt073\n9kogXFngReBCIUS5EKIcuDC0bNrwB5RSrBgf42mqrDhSmRyvjD8QTCibP2WM0Qg0ZWkolXesHYcw\n4yPbPlQqX0px2COl5Ad/W8eH995Jg2EA3zlfx7zmY7kW67CnqiiPP33qFL7y5A5+vglqCj7ATQN/\nhyc/BTf+eXpblSuOJAaBU6SUWcfNSCn9Qog70AwhI/CAlHKnEOLbwAYp5dPAZ4UQVwL+0DE+Fho7\nKIT4DppRBvBtKWWSTrZTR88UVTorCPXWUSgUimzw+oNs65g+I72h4/kxbV/dt26KJBk/2ZZNXwT8\nHmiQUs4XQpwAXCmlvGsqhVMoJotgUPKDpzdyydbPsMjQgXvNbVjP/u9ci3XEkGcycvd1q1hcW8Q3\nX7iWeZaDnLn/JVj7XTjva7kWT3EYIqX8f+Mc9xzwXNyyb+j+/jLw5RRjHwAeGM9xJ4OpKiBRPrR9\nSvarUCiOTKbTmJpKZmJRil8D/w+tHwholZeuSzdACDFbCPGqEGK3EGKnEOJzE5BToRg3vkCQL/5l\nPWdsvJPVhibcyz+E9dLvTu+TdhQghOA/zl7Ab24+mc/Lz9ESrIHXf4Tc9VSuRVMoDhOmv6S4QqFQ\nHKlMp5aXrUFVKqV8gVBgppQyCGTKVPMD/y2lXAqcCtwuhFg2bkkVinHg8ga49aH3OHvn13ifcTve\nBRdh/eA9KgxtCrlgWS0P3HYhX7N+BafMw/f4rcje7BtgKhRHLaqPm0KhUEwa6RocTzbZapUBIYSZ\nkEElhGggw1SalLJLSrkp9LcdLfH3sOrpoTi86R/1cNNv3+G8ph9yufFdArNPw3LDg2DMKtJVMQGW\n1pVw9x038kPrZ7AEnQw/cL0qUqFQZEIZVAqFQjFp+Kax7122BtU9wN+AKiHEXcAbwN3ZHkQIMQ84\nHnh3bOIpFONjS9swV/ziTc7uvI+PmP5FsHYFxo/8Gcz5uRbtqKG2xMq/3/Y/PGy6mnJ3Ky33fxiy\nLHWqUAghaoQQDwshXg+9XyWEuDXXck0lQ47pKeesUCgUisklK4NKSvkQ8H3gUaAA+KiU8tFsxgoh\nitDKrd8ppUzbYGNSGyQqjloefa+V63/zNpc4/sZnTX9HVhyD4ea/gbU016IdddSX5XP2rb/gHbGa\nuYNvsfWPX8i1SIrDh/uBN4Gy0Ps9wKdzJ87UM+JUBpVCoVAcjmSdSCKlfFNK+UUp5ReklG9kMyYU\nJvgE8Ccp5ZNZHGMSGyQqjjY8/gBfemIbX35yO9da3uIbpj9C0SzEzX+Doppci3fUMruqmPpP/Il2\najnu0G/Z9Pzvcy2S4vCgQUr5GyAAIKX0cqRXbVAhfwqFQnFYkm3Z9PUk6RQmpTw5zRgB/A7YLaX8\nv3FLqFBkQeewi9se3sjW9hE+VrWXbzru0TxSNz8J5fNyLd5Rz5zGRvZf80ecj1/Bsnc+z86K2Sw/\n5f25Fksxs4nJJhZClDG9RZumHTFJDTkVCoVCATXFedN2rGw9VP8DfD70+jqwC3g5w5gzgJuB84QQ\nW0KvS8ctqUKRgnVN/VzxizfZ2j7C/ywZ5JvuHyCMFvjwX6F2ea7FU4RYtPIUDp57DyYC1D3/MQ7u\n3ZZrkRQzmyeEEPcCxUKIjwEvkcMeUdODMqgUiokwVL4q1yIoZhCmaazonJWHSkr5mv69EOIltB+3\ndGPe5AifTVTkFiklv33jEN9/YQ8CuOccySWbvowI+uHGx2DOKbkWURHHinOuZdNgKyds+xbOR6+j\n99Z/UTOrMddiKWYgUsofCSE+gpZDdSnwcynlwzkWa4pRBtV0MlC5hsqBDYwWzaNotDnX4igmgYDR\nmmsRpo18sxEAl08Ve0rFTGzsG08JcMxkCqJQjAWn189nHt3M/z63m8pCC09fV8qlW25H+Bzwwfth\n0QW5FlGRghM++F9smPNxGulm5P6rcIwM5lokxQxFSvknKeWHpJTXH/nGFBRaju6WDiI0B1tRaBnz\n2KAwj3mMO7+WjsbLGC1S6syRw9g0aFvxoimSIzNmw8S0/eX1peN6VsaDs0B1PcrEeHKoDGjG1I+n\nSiiFIh0tAw7+448b2dNtZ83ccu69pJjKv3wA3MPwgd/Aig/mWkRFBk782I9Z//MeThp+lgP3XM78\n/3wRo7U412IpZgBCiB+mWy+lPGJLRZqNgvg2lAuqimgedBCYxn4q46HAbMQZmimXwoCQ468fYjHG\nzvXWFlvpsbvTjulquJCG9mfHdTw5ndPYihmFvXQxJfb9OTq6YKJe6bHWsemqez91XZkydhLxmwrH\nPCYbKgstDDi8U7Lv6WY8OVSfA1ZIKf93yqRSKFKwdm8vV/ziTfZ02/m30+byyDW1VD5xHTj74bL/\ng9U35lpERRYIg4HVtz/IuoJzWejZSesvrwSvM9diKWYGjgyvIxZzkl9kgyHRwJiJGGJm26feQNF7\nFryW8jGNDRosDFSumWyRckZn/UWRv6uKYpPwvZay+M0PM8Z2L022cbyqYQqvX0jUgGH6CifMtEyc\nqZZmOudKxpVDpVBMN1JK7lnbxN0v7cVsNPCja1dx3YIA/OEKsHfBRd+Fkz6RazEVY8BsNrPijkd5\n4/8+yFmj6+j8zQeo/48nIa8o16IpcoiU8lu5liFXJDOcCi0m5lcVsqtLa+MYMOZjDLgy7stnLsFW\nspjKgQ1jksFROJfhsuU0dDw3pnHpqC7Kw+MLYPPE+9+yRKcUufJnYS9eqHnAQp6FsSrR3bPORRoO\nr/BKiQGRpGuAs6Ah5lzmVRbSPxrtZyaFcVrkcxTOodDRGnkvEMhJyAmUoT1ly2QbkBZT4jPZWJ5P\n+1DmZzDfbEyb3xS5ayeg9Y/1GstxH2qKLJMpt3imz6LKatpLCNEnhOhN8uoTQvROtZCKoxun18/t\nj2ziRy/uZVaJlcdvPY3r5rrggUtgpBXO+xqcdnuuxVSMg5KCfI657TFeFSdTP/guw/deCk6VU6UA\nIUSxEOKHQogNQoj1QogfCCGO6LjQQkvsT7LVZMRkNGA1R5XigcoTstpXb+1ZuPNrxyzDcPmKcSk5\nesklIlJtbXZ5AQ3l+VmpfXMq8wGoKNLyQlz5dQDUllgpydOMBre1Fp+lNMGIumJV9r0r0xlTXkt5\n5LjTTYE5e+PHbyzIuE1n/YUTEScjXXWpW1+YsswPqi/VPvO5lZnPJxNBgwWEkZ7as5GhO7LEqn3W\nE8lXspUsjnlvKSynvGFxiq2jLJlVzIIqbYKwr/r0hIIZ1dNY0nuysRcvTFjmMxXFXKvhssxVlsfz\nsfjMJVlvW1YxfT1Is40j+DXwV+AC4ELgMeBuYA1w0tSIplBA94ib6+99m+e2d3Py/Aqe+cyZrDK2\nwO8vBnsnXPAdeN/ncy2mYgI0VJZSecuj/D34PsoGt+K+/2Kwd+daLEXueQCoBD4L3AlUABm7Qgsh\nLhZC7BVCHBBCfCnJ+v8SQuwSQmwTQvxLCDFXty6ga/Px9CSeS1ZUx4VrLa9PVBx845yB76l937jG\nZU2cYuQsnA1oxpDJYEhpUOWZokZEdZGVE+eUU2DWlGB78QJAU4YXz0qvRBmy1Mxc1mRGZnSs31TI\nYJzR6jWXxrxfM7ciq2ONlYn4c/Rhj8NlyxkqX4U0jL1QR7YMlR9H0Ki/X2Ov/8KaIuZWJBpJs8tj\nl9WX5bNmbgXVRYnV+fqqT8NoHkvVPk0Gv7kIV4FmFBtESM0d4ySB/nrWlMfee0IYWLB4RcqxPlMR\n3bPOxVi1kPJQ0QhvXrl2zULFU2aXF1AXMiZlBlU83fqivPSeVinix4rI5MRY0E9gzC4vwGsuSdiP\nz1wSMxnhKJqXcn/H1hZTYjUnhKgmo9BiYlVjGdVFeXQ0Xpa2kmNQFz4pEFhnH59x/5NFtgbV2VLK\n26WUW6WUW6SUnwUuk1K2SClbplJAxdHL9vYRrvrVm+zosPGhNbN5+BOnUNX3rhbm5xyEy38KZ3w2\n12IqJoFVc6oouP5e/uC/COvQXvz3nQ+9u3MtliK3LJVSfkJKuU5K+ZaU8lPAknQDhBBG4FfAJcAy\n4EYhxLK4zTYDa6SUq4DHAX0RDJeUcnXodeXknUq2SMoLolW7RAolcDyVyYKG2GpgiYrW+AgrdDXF\nVo6pGnu4bvwZhs95mc6YTH4dBMa45fn56b0cxXmmyPFOnJt93pU+1K6v+vSMW6cj32xkVWPUKO6r\nPi1rOdIRCBs3eSU4iuZFDNpUJDN2xoZm/sXfV2Gs5ljPKmgKbk1xHnWlmkKczhjoqTmLY4+Zj7vh\n1AlJaRDQX3Uyrvx6VtSXJt1moOIESqymGONe7wk2xvcyEoJ0n/NA1RoCpgKoif3qWbpoIV0NF+K1\nlFNbktlQLMoz4TcW4DenLghRmm9hWV0JJ8zJ7n5O99wvrokGABSYjSn3WVti5aLT17D41Mtw51VH\nlmsTIMmvS7xHtdhqZnFtMcbQREi8F9BtrYncWwuqi7AYDTSU5zOvMvW1qC22MlS+EoBVjWUcN7t0\nWpOosv1GrRdCVIXfhP7OjU9ccVTw/PYurrt3Hb12D1+9dCnfv2Ylli0Pwh+vBp9TK42+5pZci6mY\nRC5cUU/wou9zt+86TPZ25O8ugANjr0akOGJoifvdqQQOZhhzMnBASnlQSulFi6a4Sr+BlPJVKWW4\nAso7wIxqhBZWNpPRW3MGoFUmGyvxipSt5Nik25lTFMDorzoZiIZogeZRWzJLU+bKCixUFFpSem+S\nFSkMGvIS9Z2QnAVmE0vqSjEsvhCsUQNE6hS2FQ2aghwu6bx8+eqkxwYtfDKMQQgay5MbFOGZ+O5Z\n50ZFCpVSc+XX4c1Lr7jKJAql3kheVlcSkyvnzcvO25Uxj2iM5d6SewZSK58mg2BORRJlVndcvXEV\nbwTPKrGytL4YIQQNjfM4pqqIBdWxBni4AITXUo7fohnUfksprvxZFFhM4yrOUlWUh8daTcOqcxIM\nvLCB5S6oY3FtCaX5eo9eVH5v4eSqu/1VcYFdSZT+2mIreaH8rYHKkxIMDj0FFhOGFPsYE7pdlOSb\nk+4zhuJapEG7pj5TUegzS34fOguTf82GWyXEhxDai49JCCk1GQwcN7sspY00OzRJ4DdpBth0NvWF\n7A2qnwJbhRD3hjrXbwZ+MnViKY5WpJT86tUD3PanTRiE4L6b1/CpM+YgXvwq/ONOyCuBjz4Nq67L\ntaiKKeCWM+djP/lOPuO9A5/HjfzT9fDufWOvDas4Ehgl9ndnKzAUyqtKVVq9AWjTvW8PLUvFJ4Dn\nde+toZytd4QQH8hGSCHEXUIIKYSQnZ2d2QxJjZRJddrwIn2436zq6MxwvtmYoJwm7NpgZrBCC39Z\nXKvNRIeVGGdBVNkJK5X9VackzVWoLcnDHKeoxCteYeXfmhdVsGWSZ7ir7jz6Zl8cm2tRHTX0jqkq\n5ITF82BR8lwds9HAvMpC1qw+MSTc8qS5HWG5wyT27km86AGTzuAKlYCPGKXzzkrZ90oKA35T7Gdh\n1O0+ldcxE8Fsw/ey3H8yOTwh404kuR4VhXnUxHhVYj9PKQRi9skpj9dYXhAJ5aSwmopCS4LxnqwA\nRFhMkyHztUv2K1GYZ+TK4+ojyrbe8xlvYNXHTGZEjyWNWqiZJ68qtEZbp/doxcghjBw/uxzMViie\nRf3S01kyq4T6snwqCi2csnBW/Fkm7CNyqkIQMOVjLxm7V3p2RQELV5yS8VjxuPJnMUt3LbSQw/Tn\nHKa8tARpsmIvXhg5X0jiGauI6/0mBLXFVlY1lrGqsYzTF9XH3suWQijUrn+sZzr2fE6cW87Suuxz\nrCaTrAwqKWU4hGIHsBO4VEr566kUTHH04fEH+O+/bOVHL+6lvtTK47eezgUNPnjwCnjnV1B1LHzq\nFZibKeRCcbgihOAbVyzHufgDfMjzNUYNJfD85+GJT4JnNNfiKaaXXcB9QGfodT+ahypd+fRk2kJS\na1wIcRNaHvCPdIvnSCnXAB8GfiqEWJBJSCnlXVJKIaUU9fXZF0ZIsTcKzCbM5XNoKIt6guIVybmV\nhdQffwkDldGZ7tJ8M2X5ieFXnfUX0dFwCQCewgYayvIpsWrKua1kEQOVaxgqX4mhtB5byWLMRgPH\nzirGY63CXpzY8HYsBsFQ3dmRvwssxoRcJIQBaTTH5VrEWB8xm5sNBt1q7WOtKsqL5KkgBG6rpnQF\nRWw4WaHFRHWxFZDMqxpbT51oyF/o4MW1KavJBUwF9Mw6G1d+VGkuiA9tG4dRNVgR632L91iJJH+B\nZkDMryxkfhbnvLhMsGxWCaaIBRi31/L5kfemyDlE5bBkULYjVKXwthjHl/M1K034nDCYYu7ZArOJ\nRTVFCblcLLkM0zFnRt5KEa+4o1snQAgW1RQxv6ow4gkyGgQjpUsoLipmTrjIxrwzqV+wgmNnFWM2\nGjhrUbUW7rdIKxhiTpJfWJJn0oUEZnmvVMxPurh87ko6Gi/TLRGRXeqNnLrSqEfM33gqppolkF/G\nvMpCyvLNKQu1lFqj3zml+WZOX1zH4vfdQOGc1axqLGVeZSHL6kswIGLzFxtOgIYTY05vdkUBFqMB\nS+MJlFZUEcPcM+CYc2IWlZSUU1Ee6+GtqqhM6WmcasaSmdYMmKSUm6ZIFsVRTP+oh//440Y2tgyx\nehDtUZEAACAASURBVHYZ9/3bidS0/xMeukNr2LvsKrjyF2BNHgOtOHIwGgQ/v/F4bv6dlwtav82f\nK+5l7o7HoXs7XP8g1CzNtYiKaWCc5dPbAX3ySCOaMRaDEOL9wFfR8oMjNaallJ2h/w8KIdYCxwNN\n45BjfIS8OMfNLoeKE2C4FYaaYzaZX1XIsbOKwWDAnR+tYGUQgoU1RWxoia2Sqa9o11ieT50hbKgJ\nEIZIJcBZK8+nrXmQ46oL6bV7YvaxvL6E17zR4wRMVvA60wShaWsC5kIIjWtccxkmbz49bz8CEPHi\nhNWd4bIVQCeUNkLvrqR7XdlYSk9oXiWSmF4S64D05lUyXLYCt7WKWd1rQ9uGqgcWWjitphJjyFit\nK82nayS+/HVUAeurPh0hAxgDLsqHtuEonMPKhtjfIJ+5BLNPK2nvyauKVDc0hUKhpDCSZzJwwpzy\nqCfPXEhVoZ3WoKY0DlSuodjeRL60JT1v7RyiBkBtiZXOhCkFGRF/bmUhLQPaBiUWI8ZQeN+h/sR5\niOGyFZQN7wCgOl+CjN4vPnMRZp8dAH/pXKg/nqohHyOt2zlt1RL6fRaqRBk72voBgUHvuVx6JQW7\nngKShbHGKbnzzoSAN3Sv90TOJXJ/JfFuBg15GILafdpYXkC3La7xc3iMKdHYKs23oItc1TDnQzBa\n1n9lQzk18y4DQwDskZ1GtzdasJqNWM1GKguhttRK64CD1uIFZKWlWEtg9iksrhplqOMAnd3ajX3i\nnHLNAKxbjRh6k5HS5KG5XXUXgGFrVOaSRmBjysM5CmdjdfVGjPkT5pQzWHkC69sdzLdtoKFMu7+W\n15dgXlgFxhro2kaVaxiL2cwuk3bB4s2TpavW4N4zhLv+VEprtfu5tMDCyfNDho4QEc+ko2ge+e6e\n6ODiFKGUVcm9zPFYLHnMq7AyODSA21oDi87VrmuOyLZs+qVonqknQ+/XCCGemUrBFEcPe7vtfOBX\nb7GxZYgrj6vnsY8spOafn4M/3wR+D1zxM7juQWVMHUUU5pn4/S0nU9Uwn/MHv8C66g9B/1647xx4\n+x4IJvZjURxZCCHyhRAfF0J8NxzmlybUL8x6YJEQYr4QwgLcAMRU6xNCHA/cC1wppezVLS8XQuSF\n/q4CzkDzkuWGohpoTGw+u6qxLGPYTSrSBc7Wl+Vz5XH1VBblZcydcMw+BwCDtRSWpq7dofcMmPMK\nmV1ZxJJQtb5w1cHwbLKjaC6svDZOIYqVQy+XNOXDsZfC7PiQJm1fAVPUIyN1s/L68LuI0pcCb145\nHmsVs+YupaPhUrx55RwTCq1cPbuMkjwTUhiwlRzL8jnVLDzpIgIhxfO0BZWAZswJhCZ7US3MORWE\nYF5VEWeGtnHn1zJctjwh5G2kNHkNFr33MnqS0U/3uEbdb6WMflfGVI2sXMhAxQnadU+yjwQsRZrc\nK07huItvwVpURmN5AVaTgeKQB85cXIUnr4qByjUIkwXjgnNYM7cioqwnP5kToHgWlM0h/vMu1nv2\n4kTrqT0zoaFzMq+h0Fd/nHcm1Kep+qY7/3lVhRRW1ENZYnEPIYTWL3Hu6XCs5v0dV/Ptstnk1y2l\nvjx6r0aemaqFDC24Cnd+fHigRtBoAX35f0t6D+Rw+So8Cy8FIN+s5VxVVVRw0vJjWXDK5bDoAlh8\nMflLL8YUdy75unYO8V8N5uIqik+6keqG+UlDNqecomqtNUPDnJzriNl6qL6FVh79eQAp5YZMoRBC\niAeAy4FeKWXq+pKKo5pX9vTwmUc24/AGuPP8hXyueiPi3g+CaxDqVsPV90JN2sJeiiOU0nwzf/z4\nKdx4/zt8uO0qfrBsNdd334148cuw51n4wK+gfF6uxVRMHU8CQbRpV0+GbQGQUvqFEHcALwJG4AEp\n5U4hxLeBDVLKp9FC/IqAv4aUl9ZQRb+lwL1CiCDaZOP3pZTTbFCFvQyJBs38qkKOWVSdsDye1bPL\n2NI2nPlISY4RVubmVxUy7PQykiKwcs2COpqKrqWurhTSGHcLqgs1nyFElL+i1VfTOOKio03b+YqG\nUloHnbEDraXgHknqXQhz+cq6jE1sjls0H/tQH/0+M5EooCQz2LHNThONuOX1JRzsjw05LsozcUx1\nEf29YC9ZSP6qBvKB0xYYKbSYoCeayudffAnkGyEvVEWtZ6e2b4NWHKPX5mZ+dRFzLYXs6bLhDWhG\nkJD6iaOosm8IhTYWOtoiRkR5STGNwgXmgtiwzLxi8Gj3Q37IUzBQuQbqV+Pu7Uhz9cLXxoCQQar1\nLh2D7jMvrmN+tRdzYQNzZ5VxTM0H8AWCmgxFNbDkchg69P/bO+/4OKprj3/PNpXVqndbsmVbBveC\nbWzAhG4CAScPQkweCS9A3ksepCePEEJiknxCenspJJA8AgRIIAWTGIhNCdVgY3DH2MbCFu5NtiRb\n0mrv+2NG0kraqrLN5/v5jHZ2dlb7OzOzO/fce+453Tb3o+9cGpvpNYWU52fz1t7mbsv9bh+7yk+3\nQtXEwf7yM8AYZtV5ONK0ldbcKk6tzGdMmZeXn33dOobB59NXaXXG7Xo9nLVB6+Gjhwu6QmvzBxvi\na1NQA2yN/305hT0lRvo4VLurLoRTeztjE6vzOd7eyYjGnnNpJScJV6vJstftcBCipnRsVE6BPes4\nkVOBCXQA/edcRsKa39ncy75eJRIqp1LhqwJvkA1JmnMdc8ifMWZPn9jpaDe4e4CfA/fGL0vJdIwx\n3P38dr79+CY8Tgf3X+zkrG2fhhdXgNsLC+6A0/+r9w+3ctJR5PVw/w2ns+g3K7h54yjWTb6bbzjv\nxrH5H/DLefCem62izgOMvVdSmlpjTPTKkH0wxiwFlvbZ9rWg9ZAZDowxLwFT4v28IaW7IdC/wVHi\nzQJvcBa10P9iIJmt+ma887gcnD6mhGW7t4TcP8fjZHJNhNEd24yqghzwXgLtLeCytbuywGXomgYX\nMqvg2POhs63nPWCNZOzdyPwZU2g3rqh1p+rLfbirL6TYGIq3/BPa7Litit79u+W+bPY3tYf9P5Ea\nf1bK596Nt/KgzGrjyvI40OGhqKBP+ua8CktPbgmnlRZhjEH8J6DZgcOTA8dbKMhxczREw7DcLgZr\nKqdzoKkKp6+S2SMKqM6bD/vftOYaB1M5GbK81r107waa80aHLPic7XJCxSTL8QmObBt3PtOz9+Go\nDDPvaeQsPMV1jPGWgQhOh/QeqXBnW2HawQ5VhGM6ubqAFqePkjDpsfsVZbYdt5a8rsZ6+EyV0T67\nlwMfYb8hz8RdMo7a2V6cB96CwIGwu9VXFuMwHaw/ZAuoOR0ObLHmpAWJOlI4yRrBcluOU4k3i4Mt\nbfiyXZYD9a69b6AzLpl7Ks9hWsuK+GwrOwVKx+Nfu5uAMRzNH89pk3t+1qMdysPF0+icUNGrLTix\nppI9DQepriixtvtCj+IlmlgdqmMiUoH9NRORc4CIXWDGmOdEZPRgxCmZSbs/wK1/XcfDrzUyOe8Y\n9416nKJn/2a9OOEyWPBte/hfUawetD/91zyu//1K7l9/hHfGfZq7Lnsf2U/dBsu/Dmsegkt/CKPP\nTLZUZWhZLyJVxpjdyRaSMFweqyxEDB0EIkKOO74Op1AdtwsmVfbLdhb0ju61aTWFuEx8tYscAnhy\nrSV4e5/27hljS8lyO3rv4OgT1lY8BorHkAOECHjrR3bX/xOB4rGw+w2rAdqnk27e2BLWNHTSESZB\nY5HXPeAGdGGuh0I7VK4XlVOt0Q1vmS1RrMbv2PM45GrFv28LBf4G2rLL4Jjl1J5dX8rxjtxuh23u\nuHLa/KW9J+BXTesvwp1rOVXGgLeMOePzQxaKnVDls5yxnGLa9r+Mo9WKhg1kFeAYFTrhAWAdz7xw\nIxxBVE2D3Wui7pbtdpKd0/v6D5dwIxIncirIPb4rchRD5RTOzCntyVrpyqLY6+Ho8Q4kKGFDV4ZK\nCdfhkT8CjkYf7QuLCOUV1daF/U54hyqQU4Tz+P6e1PxON1T0LbPXnzPHldDeGegfKuyPaeC/m06X\nlxFFsXz7+iDCqZU+Nu4+yozTzqAgKIlML4rqoPVgyPcHkzd6BuN8vrCjm71wD0DvAInVoboFK9yv\nzp6oWw8koeihku4cbG7jk/evZn3DLr5T9E8+1P43ZNsJ68d2wR3aKFZCUuz18MANc/nUg6tZvmkf\nV7TWcNdHn6d65XfhtXvgnkssZ/yC26EkamI2JT24HXhFRN4AumecG2OuSp6kYaZmrjVXsDx6Iwms\nEVyr9k+nVaupsMZK3mLjECEQ5EU5HWL1wvtPdKfhdkYY6ZGg91b5siAvTAje+IvBdMKWZQBMGVHA\n6LGlYTMCluVlUVOc251prcwXqibSEFI6zjo2rjCfEzw3K2i9KNcTvgBwd4M73PGL0Ph3OEI7IbnF\nGGcnxwrGcyhvAu3NPSMIvmwXvq7Rr5rTuxMiRKNbhQh4SwnlEo8ozLGK1zocVm0hiSdfWYyU1sfk\nUIXiRHYpHD9IW054x63rWu+65o7nVrM7qxjKI9wPCkdR6u59TYw5ZTo07ws5DNXh9jGlyN8/bLRm\nDmz4a+wGhaOrOLPdodK//0Ns5y4+D19EQs+7jKOn4NIpVYgIjg0D612or/Axpiyv9+9N3x6ekafF\n9s+c7tiTU3ni6wQaDDF9a4wxr4jIucAZWGfyJWNM9CDtOBGRxcDXAaqqtG5wprF6x2Fuun8VZ7Qs\n4zfeRyg8ftDK8nL+12Dqov7dlooSRI7HyZ3XnMZtj27gwVd3cMlv1vODK7/CBdOvgSe/Apseg82P\nw6zrrVBAb0myJSuD416shBKrgfhiU9KVrDwrlXAwpfVWWE8YdledR4HHAfV2LakghyrX46S5rSdz\nmUOwUg83NXLcbyVXCJcaGiAv20424HAQMaVFVu+6S9nZOWRHcJJEhJm1kQvkDjnhnCksp7Ij6PnY\nsrzuosFhqZqGmE4Ot/UPnxsKjMPNuaeW8FZj0MZxF8Cx3SETJQwG//j3QUFPc9DtCkoUn7is02E/\nrMU7isNF5TTjg47OPm+x3jO/vpQt+5oZbacrP2tcKSf8ISb+BH+GO0QHQXX44tDtpZPwVbdD4aje\nLwzV1ARviZWIxtvfcfRlu8j3uJFOF+U+N+PGhr+/VeZnUTmmNOzrPcR4ckX6JaoYCJE6bzKBqA6V\niDiBFcaY7qQUw4UxZjGwGGDWrFlayTNDMMbw+5caeOLxv/Jrx71McW/HSI7V6D3zM1Gz0yhKFy6n\ng29/YDJTRhRw+2MbuOHeVdxwVh3/c+0TeN56DJYvhld/DWsehPlfgNM/EfqmqaQDHmPMTckWkXSc\n/WtL9UKcmDAhgmPK8ugMGDbuPooxxppflZUH5aeyoKiTEx2dEecizRlVhN9VGH9jKkKB11Qk2Kl8\nz/gypDq0M9VrPlVWHtSdjb9pEKFeIZg+spDVOw8zqTofX7Ybg8Oqg9U1JyYndP2rQeHKgqyenvwJ\nVfls6LZrGBrBIlYooi+6Mypi/fFnFSEd4ftVCnM9zB7dM6+vJG/oRz2Nww0lQ+vM9iNEiGKO28l5\np1ZAwxawyyPg639fO1g8k4Kjm8ktr7OTTUQjSjM7r8LqzAmqP4bHCzmRs2MOFdNrCtnTdKInfDfF\niepQGWM6ReSAiGQbY05E219Rgmlq7eAHDy9nztaf8JDLnsw45YPIBYuteiOKEiciwodPr2VGbSE3\nPrCau1/YzgtbD/DN95/N7BtfhVW/hX9915pftfJuawR08pU6App+rBCRKcaYddF3VbqZcBm77AlB\nThHqyr2U5WWxee8xqyFmE0vImMOd3ZMOOp7MWZ686PukErajJAiSHdphuXBiRchkB8VeD8XeSE5v\nfH3DBbluzj2lZ4RiT9W5uDuaEzoXJMftZExpHpubiKkgcMx0hYYCnHpJ+P1iOGSha4gllxyP9X0q\nyBmaJEnxurIncqs4kVtFuTNGZzLad9pXaZUmCA6bs9PEDxWRbBxV4mVUmOQkEamaCu+8NGBNAyXW\nQNktwHMi8gjQnTvUGPPLcG8QkQeBc4BSEWkEvm6M+e0gtCppxstv7mDTw9/gVv/fyHZ20F4xHc/7\nvp92vZdKajKhKp/HbjqLb/1jIw++upMP3vkyV8wcyZffex1l066G538Ir9wJf/k4rPgVXPQtnaOX\nXswBVonIZnrPoTq5fkC6eoP7FLANiyuLMRXFbNvfTEme1dAvyHVHrbkUkoKRcPyw9dn+GPpTx54H\nrYcSOm8hHAMOcQmTxCDXE7q5ND9cKvshipU7b/KoxIbd2RR7PcwrKeiVWXLQZEVztGMzdGRRDrke\nF7ubBi9pKKkqyGHGiCKqC9MkKsLEkAs9Bb7LcTNU6ezjJFaHyodV2Dd4FljE3ytjzNUDFaWkN8fb\n/Cz94y+Zt+3HzJNDNGeV0nnxN/FM13lSytDizXJxx79N5YOzarjtb+v58+pGntywh4/MG8V1Z36V\nstk3wFO3w/o/W4krTn2flbgixkrsSlL5TLIFpAS+CstRCTNyEorJIwqYUJU/+DkLIlZvL0BTY+R9\nAXKLrSUF6AwM0KVKhvcSAW/WMCSIsMnLctHc5u8eWUlFus5GkkoLxY63FIc7l9qSBDgggz0W4rCc\nqaSXG0n1kxofEb+pIvJDY8wXjDEfE5ELjTHLEiVMSU9WvPwcnmW3cEVgPe3iYu+0G6m45Csx9Ewp\nysCZWVvEkpvO4g+vvMPPntrKr57dxu9e2M7Vc2q59pz/pW7uf8OTt8Kbf4e3noDZN8DZ/6OJK1IY\nY8y/kq0hZQjjpJxS6eNQSztTRvaf8zPkE8DddkOxqzhtipMXhyPSlUY8MJR1DysmW/W3IiQ5GE7O\nri+juc0fcY7cmeNKOdTSHuN8m9Qh0c5VaV4WOw61Ul0YIexyzDnDL8RXZRXyLYhxtDoc9RfC0V0p\nUb8pxfovBkW0X5xzg9a/C6hDpYTk3T27efOBW3hP06O4JMCWwvnUXP0TKip0JEBJDE6H8NF5o7lq\nVg0Pr9rJnf96m3teauCelxqYPbqID552F5fPfo3sZ263QgHfeBDO/qJVQDpCBjAlOYhIAXAzMB3o\njqExxpyXNFEpRn62mwWTEtQoyi2G0fOHJynCMBBXQ00c7Kk8J3oCkHjw5MLYc6PvN0wUeT0URQnX\ny3Y7IzsJiaZ8AjTv6RkV7UOoc2oG4l0V1UF2lCyOQdQU55Kf7SY/Z/hGC2OiZKxVu2ywnRpZPqvg\nbgrgdDioK/EybnyY0Nk0ItrVIWHWFQWAIy0nePHhnzBv+885X46xyzWCwII7qJ+zMNnSlJOUbLeT\nj8wbzaI5tSxdt5uHVzXywtYDrGw4zFdd2bxn7C/5RMUzzNj+GxzLboOVd8F5t2niitTjd8BGYDxw\nG3Ad8FpSFZ3sxJCVLVWQOJssnS5v7yx+SuLJLYbJV4R8yfQJDxvUqYq13lEQBbnJDo+z6VsDKwMo\nycuC3CHszEgS0RyqLBGZgOVMBa8DYIzZOJzilNSlpc3P8qWPMHbN97iUbbRKNhsmfJaJ/3YLommq\nlRTA7XSwcPoIFk4fwc5Drfx5dSNPrN/Dss2HWcZ08vkeXy9YysKmf+D6y8fpfOGnOC9cbNV60YZV\nKjDOGHOFiCw0xjwoIn8B/pFsUUrm0RUeWDKUCRiUIaGuzMv+5jbqSr28tfdYsuUklFElXnYcamVC\nVeY5UcPOpA8k/COjOVS5wNKg58HrBhgz5IqUlOZIaztLn1xK3ZofspC1AGytuJiRV/2AScNdn0FR\nBkhNcS6fvWA8n71gPDsOtrJ8016e2byPWxsW8WP/uXzO9Qgf2PsC/OFKdvhmcnjuzZwy58KoaaWV\nYaXNfmwXkWLgMKC1FpQhp6Y4B4cDykPU9lGSS1VBDpdNrcbhEBoOtAL+kOnrM5Fir4fLp1V3Fy9W\n4mAo50PGSESHyhgzOkE6lBRn675mnnj6Geo3/S8fllcA2FE4h+LLvsW4sacnWZ2ixE5tSS7XnVXH\ndWfV0ebv5PUdR3hp2xl86c2VXLr/Ls47tpraZR9i5ZOnsrz0GvImLuCM+lKmjiw8aW7kKcJbtiP1\nALACOAK8kVxJSroQTxtURBhZlIbpoYeLgpFW0oLCUclWAtCdWGPmqEK27G3m1Cof2w+0JFlVYsho\nZ8qVDbklsZeESHGSPMNOSWXa/J08vXEvq57/B/P2/IGbnK+DwL78yfgu/Sa1p+jccCW9yXI5mTum\nhLljSuDC8bS0fYjXV/4T38qfMbvpZWYf+ipvPvdr7n/mAj7pOoepY0Yyv76U+fWl1JV6M/tml2SM\nMdfYqz8SkVeBQuDxJEpS0gj9Zg6Cwlor+UECiwnHQq7HxbSa9EiKosSASFITtww16lApvTDGsHrH\nEZauehNZ92cWBpbxXkcDOOFw8XR8F3yJ8gmX6hwTJSPxZrmYcdYlcNYlsHst7f/6EeM3P8a3HP/H\nV3iQR7fM47HN87g9MJGqQi9njy9lfn0ZZ4wtoTADJtWmIiJSCJQC240xnTHsfzHwU8AJ3G2M+U6f\n17OAe4HTgIPAh4wxDfZrtwDXA53Ap40xTw6hKYqSPqSYM9WXyvxsNu85ximV6ZHGX8l81KFS8HcG\nWNlwmKfWvcOx9U8wt+0FvuhYSY60E3A4aa5bQN65n6eodm6ypSpK4qiaimfRPVbdj9X3kfva/3H1\n0We4mmdochbx5InTeGrVFG5+dRItksvUkYX26FUZ02oKyHLp/KuBICL3A98zxqy1Q/7WAEeBUhG5\n1Rhzd4T3OoFfABcCjcBKEVnSJ4HS9cBhY8w4EVmEVRLkQyIyEVgETAKqgeUiMj4WJ05RlMRSmOvh\n0ilVuDQMu5vqwhx2HTmunXtJQh2qkxBjDG/tbebFLftpePM1shtfYmZgHZ9zrMUrbeCE43m1dM6+\nFueMfycvvyrZkhUlefgq4T1fgvmfh3degg1/oWDjo1zVupyrPMsJ4GSru54X99Sx6t16Hnm6noPO\nMiZW5zOjtpAZtUXMqClkZFGOhgjGxkxjzFp7/SPAJmPMRSIyEvg7ENahAuYAW40xbwOIyEPAQqz0\n610sBBbb648APxfrxCwEHjLGtAHbRWSr/f9eHhqzlISiX7WMR52p3pxWW8SEqvy4ilorQ4ce9ZOA\noyc6WL/zCNu2b6Wp4Q0CezYwpmMLlzk2UiZHrRuPE477RtE55QM4Jy0kp3qGhvUpSjAOJ9TNt5b3\nfh92rYatT+HY9hTjd73OeOebfMxpTfE54Chhzd5RbNhdyxMv1/JjM4rDWSOoryxgfIWPUyp91mOF\nL2rxzZOQE0HrZwF/BTDGNIpItCqeI4CdQc8bgb5Zc7r3Mcb4RaQJKLG3r+jz3qizpUVkMfB1gKoq\n7XxSFCU5OByizlQS0SOfKQQCNB3ez7u7dnJg9w5a920ncKgB59GdFLbvZoI0coY09+zvhNbsClpH\nX0Tu+HOgbj45RaOTpV5R0gunC2rmWMu5t0B7K+x6HRpfhZ0rKW1cyfktqznfsbr7LcfJYvOukWxq\nrGWzqeEpU8V2U0m7dwSjy/OpK/XaSx51pV5qi3PxuE7OHlgRqcZKk34OtrNiEy2vdaheoL5OWLh9\nYnlv/x2MWYw94jVr1qyo+yvDi9vpoKMzoCG3iqIklGF1qKJNDlYi0OnHtB6krWkfrUf2cuLIPtqP\n7qXj2H78x/YjrQdxnjhEdvthvJ1HyDdHKSBAQYh/FXAIR7JGsKdkHjk108gfNRWpnEJuUZ2OQinK\nUODJhdFnWksXzftg73rYsx72biBn7wam7X+T6YFtvd7a4Xexs7GMd3eWsI9CtplCVphCDpFPdl4h\n+QXFlBQVUV5WSnVpESOKfVQU5uFye8DpBocLHG5wZIzzdQdWevR24IWu+U8iMhfYEeW9jUBwQbyR\nwK4w+zSKiAsoAA7F+F4lxTn3lHIOt7ZTkONOthRFUU4ihs2hinFycFoSCBg6AgH8nQZ/p73uD9DR\nfpxOv59Ofxvt7R20tbfT1tZGW3s7/rYWAsePYk4cxbQdg7ZjSEcznrYjZLUfIrvjCLkdh/H6j+AL\nNJFnmnFgyCZyl+wR4+WI5LPLWUW7pwi8pbgLKskpq6OwehzFI+pxFIyg2KVhRYqSUPLKIe88GNtT\nXkD87XBwC+zbBIfehoNbcR/cRt2hbYw5vr7//2gD9tnL5sgfF8DR3UEiiD3eYneYdHechH5ugJa6\ni2g87+e0+wO0dwasR3+ANn+ANn8nU0cWUlfqjfswxIsx5mEReR6oxEpI0cUO4ONR3r4SqBeROuBd\nrCQTH+6zzxLgWqy5UVcCTxtjjIgsAR4QkR9hJaWoB14drD1KYsnxOMnxpHaGOkVRMo/hHKGKZXLw\nsPGJ+16j4WALxkDAGALGYKD7efCjMYaAAYP9GPR61/POgKEjYPB3BgiECOqolb08l/W5QWnuNMJh\nfOyXAt521NLiKuSEp5j2rGL82cWY3BJcvnLyiirwlVRRUlpJeVEeo90a2qAoaYHLAxWTrCUIAeg4\nbo1qNe+1l33Q3kxb61GONh2m5VgTx1tbaD3RRkd7Gx0d7fg7OnDSiZtOXOLv+V+A2NFqPY+9t9Nn\n+4pNfu5Y93xY6d98/+SEOFQAxpg9wJ4+26KOFtlzom4CnsSKjPidMWaDiHwDWGWMWQL8FrjPTjpx\nCMvpwt7vT1j3KD9wo2b4UxRFUWJBjBmekG8RuRK42Bhzg/38I8DpxpibIrxnMT3x8q3ApmERZ/U+\npkMoh+ocOtJBI6jOoUZ1Di2D1TnKGFM2VGJSDRHZD7wzyH+TLtfCYFAb059Mtw8y38ZMtw+GxsaY\n7lvD6VB9EFjQx6GaY4z51LB8YByIiDHGpPzkIdU5dKSDRlCdQ43qHFrSRWc6czIcY7Ux/cl0+yDz\nbcx0+yCxNg7nLGad4KsoiqIoiqIoSkYznA5V9+RgEfFgxakvGcbPUxRFURRFURRFSSjDlpQiZpaD\nQgAACHpJREFU3OTg4fq8OLk92QJiRHUOHemgEVTnUKM6h5Z00ZnOnAzHWG1MfzLdPsh8GzPdPkig\njcM2h0pRFEVRFEVRFCXTyZhKkIqiKIqiKIqiKIlGHSpFURRFURRFUZQBog6VoiiKoiiKoijKAFGH\nSlEURVEURVEUZYCoQ6UoiqIoiqIoijJAMtqhEpGLRWSziGwVkS+HeD1LRP5ov/6KiIxOQY1ni8hq\nEfGLyJWJ1hekI5rOz4vIRhFZKyJPicioFNX5CRFZJyJviMgLIjIxFXUG7XeliBgRmZVIfUGfH+14\n/oeI7LeP5xsickMq6rT3ucq+RjeIyAOJ1mhriHY8fxx0LN8SkSMpqrNWRJ4Rkdft7/wlydCZacT6\nu5DqiEhD0O/sKntbsYgsE5Et9mORvV1E5Ge2zWtFZGZy1YdGRH4nIvtEZH3QtrhtEpFr7f23iMi1\nybAlHGFsXCwi7wb9Ll0S9Notto2bRWRB0PaUvI5FpMb+3dpk3wc+Y2/PiPMYwb5MOofZIvKqiKyx\nbbzd3l4nVjt+i1jteo+9PWw7P5ztA8YYk5ELVu2rbcAYwAOsASb22ee/gTvt9UXAH1NQ42hgKnAv\ncGUKH8tzgVx7/ZOJPpZx6MwPWr8ceCIVddr7+YDngBXArFTUCfwH8PNEaxuAznrgdaDIfl6eijr7\n7P8prPp9KacT+A3wSXt9ItCQzGsgE5Z4r49UXoAGoLTPtu8BX7bXvwx8116/BHgcEGAu8Eqy9Yex\n6WxgJrB+oDYBxcDb9mORvV6UbNui2LgY+GKIfSfa12gWUGdfu85Uvo6BKmCmve4D3rLtyIjzGMG+\nTDqHAuTZ627gFfvc/AlYZG+/k577U8h2fjjbB6Mtk0eo5gBbjTFvG2PagYeAhX32WQj83l5/BDhf\nRCSVNBpjGowxa4FAAnX1JRadzxhjWu2nK4CRCdYIsek8GvTUCySjEFss1ybAN7F+6E8kUlwQsepM\nNrHo/DjwC2PMYQBjzL4Ea4T4j+fVwIMJUdabWHQaIN9eLwB2JVBfppIu37eBEny//T3w/qDt9xqL\nFUChiFQlQ2AkjDHPAYf6bI7XpgXAMmPMIfu3aBlw8fCrj40wNoZjIfCQMabNGLMd2Ip1DafsdWyM\n2W2MWW2vHwM2ASPIkPMYwb5wpOM5NMaYZvup214McB5WOx76n8NQ7fxwtg+YTHaoRgA7g5430v/C\n6t7HGOMHmoCShKjr8/k2oTSmAvHqvB6rVyfRxKRTRG4UkW1YzsqnE6QtmKg6RWQGUGOM+XsihfUh\n1vN+hR0O8YiI1CRGWi9i0TkeGC8iL4rIChFJxs0v5u+RWCGzdcDTCdDVl1h0LgauEZFGYCnWaJoy\nONLlfhALBviniLwmIv9pb6swxuwGq+EHlNvb09nueG1KV1tvsn/jf9cVDkea22iHfs3AGuHIuPPY\nxz7IoHMoIk4ReQPYh+XMbgOO2O146K03XDt/yG3MZIcq1EhT39GIWPYZTpL9+bESs04RuQaYBXx/\nWBWFJiadxphfGGPGAjcDXx12Vf2JqFNEHMCPgS8kTFFoYjmejwGjjTFTgeX09AQlklh0urDC/s7B\nGvm5W0QKh1lXX+L5vi8CHjHGdA6jnnDEovNq4B5jzEissJj77OtWGTjpcj+IhTONMTOB9wI3isjZ\nEfbNJLu7CGdTOtr6K2AsMB3YDfzQ3p62NopIHvBn4LN9olb67RpiW8rbGMK+jDqHxphOY8x0rEio\nOcCEULvZjwmzMZNvgI1AcG/5SPqHpXTvIyIurNCVWIe7h4JYNKYCMekUkQuAW4HLjTFtCdIWTLzH\n8yF6hoUTSTSdPmAy8KyINGDFBy+RxCemiHo8jTEHg871XcBpCdIWTKzf9UeNMR328P5mLAcrkcRz\nfS4iOeF+EJvO67Fi1jHGvAxkA6UJUZe5pMv9ICrGmF324z7gr1iNnr1doXz2Y1fYbTrbHa9NaWer\nMWav3YANYP3Gd4VFpaWNIuLGcjb+YIz5i705Y85jKPsy7Rx2YYw5AjyL1UYqtNvx0FtvuHb+kNuY\nyQ7VSqDezvzhwWqgLOmzzxKgKzvLlcDTxp6tlkIaU4GoOu0QtV9jOVPJmJ8CsekMbkRfCmxJoL4u\nIuo0xjQZY0qNMaONMaOx5qRdboxZlUo6ofvm08XlWDHbiSaW79HfsBKnICKlWCGAbydUZYzfdxE5\nBWui88sJ1tdFLDp3AOcDiMgELIdqf0JVZh7pcj+IiIh4RcTXtQ5cBKyn9/32WuBRe30J8FGxmAs0\ndYVfpQHx2vQkcJGIFNlhVxfZ21KWPr/xH8A6l2DZuMjOolaH1UH1Kil8HdtzZ34LbDLG/CjopYw4\nj+Hsy7BzWNYVXSIiOcAFWO2OZ7Da8dD/HIZq54ezfeCEy1aRCQtWKMpbWPGVt9rbvoHVOAWrEfAw\n1mS0V4ExKahxNpYn3QIcBDak6LFcDuwF3rCXJSmq86fABlvjM8CkVNTZZ99nSUKWvxiP5x328Vxj\nH89TU1SnAD8CNgLrsLMBpZpO+/li4DvJ0BfH8ZwIvGif9zeAi5KpN1OWUMc93RaszGBr7GVD0PVT\nAjyF1Yn1FFBsbxfgF7bN65L1WxeDXQ9ihUt12Pfk6wdiE3AdVptjK/CxZNsVg4332TasxWqEVgXt\nf6tt42bgval+HQNnYYV1raWnrXJJppzHCPZl0jmcipWxdy2WY/g1e/sYrHb8Vqx2fZa9PWw7P5zt\nA13E/qeKoiiKoiiKoihKnGRyyJ+iKIqiKIqiKMqwog6VoiiKoiiKoijKAFGHSlEURVEURVEUZYCo\nQ6UoiqIoiqIoijJA1KFSFEVRFEVRFEUZIOpQKYqiKIqiKIqiDBB1qBRFURRFURRFUQaIOlSKoiiK\noiiKoigD5P8Bzl8sVbxE8KUAAAAASUVORK5CYII=\n"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "plot_pathogen('Haemophilus influenza', pos_trace, 'positive')",
"execution_count": 48,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": "<matplotlib.figure.Figure at 0x12f6b60f0>",
"image/png": 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msYLBIIFAgNLSUiorK1m5cuWMPG8sSeJG4P0iEprUbwVwUET2AKjqLfEKzkyf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\nAElFTkSuQmCC\n"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "plot_pathogen('Streptococcus pneumonia', pos_trace, 'positive')",
"execution_count": 49,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": "<matplotlib.figure.Figure at 0x1305ab4a8>",
"image/png": 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W5aY7vBklXnNTrqqOAg+ISMHk7ao6lNTIjMkg4XCYiooKamtr8Xg8NmopQw2P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\nM0swGCQYDCIi+P1+8vLyTnr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},
"metadata": {}
}
]
}
],
"metadata": {
"kernelspec": {
"name": "python3",
"display_name": "Python 3",
"language": "python"
},
"language_info": {
"name": "python",
"version": "3.6.1",
"mimetype": "text/x-python",
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"pygments_lexer": "ipython3",
"nbconvert_exporter": "python",
"file_extension": ".py"
},
"gist": {
"id": "",
"data": {
"description": "DSMB Analyses.ipynb",
"public": true
}
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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