Mercurial > repos > rakesh4osdd > asist
view asist_dynamic.ipynb @ 7:fb83dfff5679 draft
Uploaded script
author | rakesh4osdd |
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date | Wed, 30 Jun 2021 05:57:47 +0000 |
parents | 3ea72fb2eaac |
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{ "cells": [ { "cell_type": "code", "execution_count": 1309, "id": "27cfc66f", "metadata": {}, "outputs": [], "source": [ "#ASIST program for phenotype based on Antibiotics profile\n", "# create a profile based on selected antibiotics only\n", "# rakesh4osdd@gmail.com, 14-June-2021" ] }, { "cell_type": "code", "execution_count": 1, "id": "75a352b7", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import sys\n", "import os\n", "from collections import Counter" ] }, { "cell_type": "code", "execution_count": 162, "id": "d66ec0d2", "metadata": {}, "outputs": [], "source": [ "#input_file=sys.argv[1]\n", "#output_file=sys.argv[2]\n", "input_file='test-data/strains_788_input_16k.csv'\n", "output_file='/mnt/d/PhD_Work/Tina_Work/ASIST_Galaxy/ASIST/strains_788_output_16k.csv'\n", "#input_file='/mnt/d/PhD_Work/Tina_Work/ASIST_Galaxy/ASIST/asist_example15.csv'\n", "#output_file='/mnt/d/PhD_Work/Tina_Work/ASIST_Galaxy/ASIST/asist_example15_output.csv'" ] }, { "cell_type": "code", "execution_count": 163, "id": "bf24c946", "metadata": {}, "outputs": [], "source": [ "# strain_profile to phenotype condition\n", "def s_phen(sus,res,intm,na,pb_sus):\n", " if (sus>0 and res==0 and na>=0):\n", " #print('Possible Susceptible')\n", " phen='Possible Susceptible'\n", " elif (sus>=0 and 3<=res<7 and na>=0 and pb_sus==0):\n", " #print('Possible MDR')\n", " phen='Possible MDR'\n", " elif (sus>=0 and 7<=res<9 and na>=0 and pb_sus==0):\n", " #print('Possible XDR')\n", " phen='Possible XDR'\n", " #special cases\n", " elif (sus>=1 and res>0 and na>=0 and pb_sus==1):\n", " #print('Possible XDR')\n", " phen='Possible XDR'\n", " #special cases\n", " elif (sus>0 and res==9 and na>=0):\n", " #print('Possible XDR')\n", " phen='Possible XDR'\n", " elif (sus==0 and res==9 and na>=0):\n", " #print('Possible TDR')\n", " phen='Possible TDR'\n", " else:\n", " #print('Strain could not be classified')\n", " phen='Strain could not be classified ('+ str(intm)+' | ' + str(na) +')'\n", " return(phen)\n", "\n", "#print(s_phen(1,9,0,0))" ] }, { "cell_type": "code", "execution_count": 164, "id": "8bad7d9d", "metadata": {}, "outputs": [], "source": [ "# define Antibiotic groups as per antibiotic of CLSI breakpoints MIC\n", "#Aminoglycoside\n", "cat1=['Amikacin','Tobramycin','Gentamycin','Netilmicin']\n", "#Beta-lactams- Carbapenems\n", "cat2=['Imipenem','Meropenam','Doripenem']\n", "#Fluoroquinolone\n", "cat3=['Ciprofloxacin','Levofloxacin']\n", "#Beta-lactam inhibitor\n", "cat4=['Piperacillin/tazobactam','Ticarcillin/clavulanicacid']\n", "#Cephalosporin\n", "cat5=['Cefotaxime','Ceftriaxone','Ceftazidime','Cefepime']\n", "#Sulfonamides\n", "cat6=['Trimethoprim/sulfamethoxazole']\n", "#Penicillins/beta-lactamase\n", "cat7=['Ampicillin/sulbactam']\n", "#Polymyxins\n", "cat8=['Colistin','Polymyxinb']\n", "#Tetracycline\n", "cat9=['Tetracycline','Doxicycline','Minocycline']\n", "\n", "def s_profiler(pd_series):\n", " #print(type(pd_series),'\\n', pd_series)\n", " #create a dictionary of dataframe series\n", " cats={'s1':cat1,'s2':cat2,'s3':cat3,'s4':cat4,'s5':cat5,'s6':cat6,'s7':cat7,'s8':cat8,'s9':cat9}\n", " # find the antibiotics name in input series\n", " for cat in cats:\n", " #print(cats[cat])\n", " cats[cat]=pd_series.filter(cats[cat])\n", " #print(cats[cat])\n", " #define res,sus,intm,na,pb_sus\n", " res=0\n", " sus=0\n", " intm=0\n", " na=0\n", " pb_sus=0\n", " # special case of 'Polymyxin b' for its value\n", " if 'Polymyxinb' in pd_series:\n", " ctp=cats['s8']['Polymyxinb'].strip().lower()\n", " if ctp == 'susceptible':\n", " pb_sus=1\n", " #print((ctp,p_sus))\n", " # check all categories\n", " for cat in cats:\n", " #ctp=cats['s8'].iloc[i:i+1].stack().value_counts().to_dict()\n", " #print(ctp)\n", " # Pandas series\n", " ct=cats[cat].value_counts().to_dict()\n", " #print(ct)\n", " # remove whitespace and convert to lowercase words\n", " ct = {k.strip().lower(): v for k, v in ct.items()}\n", " #print(ct)\n", " k=Counter(ct)\n", " #j=Counter(ct)+Counter(j)\n", " #print(j)\n", " # category wise marking\n", " if k['resistant']>=1:\n", " res=res+1\n", " if k['susceptible']>=1:\n", " sus=sus+1\n", " if k['intermediate']>=1:\n", " intm=intm+1\n", " if k['na']>=1:\n", " na=na+1\n", " #print(sus,res,intm,na,pb_sus)\n", " #print(s_phen(sus,res,intm,na,pb_sus))\n", " return(s_phen(sus,res,intm,na,pb_sus))" ] }, { "cell_type": "code", "execution_count": 165, "id": "7629fc10", "metadata": {}, "outputs": [], "source": [ "#input_file='input2.csv_table.csv'\n", "#output_file=input_file+'_output.txt'\n", "strain_profile=pd.read_csv(input_file, sep=',',na_filter=False,skipinitialspace = True)" ] }, { "cell_type": "code", "execution_count": 166, "id": "bed1abba", "metadata": {}, "outputs": [], "source": [ "old_strain_name=strain_profile.columns[0]\n", "new_strain_name=old_strain_name.capitalize().strip().replace(' ', '')" ] }, { "cell_type": "code", "execution_count": 167, "id": "a64b5022", "metadata": {}, "outputs": [], "source": [ "# make header capitalization, remove leading,lagging, and multiple whitespace for comparision\n", "strain_profile.columns=strain_profile.columns.str.capitalize().str.strip().str.replace('\\s+', '', regex=True)\n", "#print(strain_profile.columns)\n", "#strain_profile.head()\n", "#strain_profile.columns" ] }, { "cell_type": "code", "execution_count": 168, "id": "caac57d7", "metadata": {}, "outputs": [], "source": [ "# add new column in dataframe on second position\n", "strain_profile.insert(1, 'Strain phenotype','')\n", "#strain_profile.head()" ] }, { "cell_type": "code", "execution_count": 169, "id": "eb4b0c4d", "metadata": { "scrolled": true }, "outputs": [], "source": [ "strain_profile['Strain phenotype'] = strain_profile.apply(lambda x: (s_profiler(x)), axis=1)" ] }, { "cell_type": "code", "execution_count": 170, "id": "86441c0f", "metadata": {}, "outputs": [], "source": [ "#strain_profile.head()" ] }, { "cell_type": "code", "execution_count": 171, "id": "75698be5", "metadata": {}, "outputs": [], "source": [ "#rename headers for old name\n", "strain_profile=strain_profile.rename(columns = {new_strain_name:old_strain_name, 'Ticarcillin/clavulanicacid':'Ticarcillin/ clavulanic acid','Piperacillin/tazobactam':'Piperacillin/ tazobactam','Trimethoprim/sulfamethoxazole': 'Trimethoprim/ sulfamethoxazole','Ampicillin/sulbactam':'Ampicillin/ sulbactam', 'Polymyxinb': 'Polymyxin B'} )" ] }, { "cell_type": "code", "execution_count": 172, "id": "c14a13eb", "metadata": { "scrolled": true }, "outputs": [], "source": [ "#strain_profile.columns" ] }, { "cell_type": "code", "execution_count": 173, "id": "1b113050", "metadata": {}, "outputs": [], "source": [ "#strain_profile" ] }, { "cell_type": "code", "execution_count": 174, "id": "5ab72211", "metadata": {}, "outputs": [], "source": [ "strain_profile.to_csv(output_file,na_rep='NA',index=False)" ] }, { "cell_type": "code", "execution_count": 175, "id": "c17c84c4", "metadata": {}, "outputs": [], "source": [ "# Open a file with access mode 'a'\n", "with open(output_file, \"a\") as file_object:\n", " # Append 'hello' at the end of file\n", " file_object.write(\"Note: \\n1. 'MDR': Multidrug-resistant, 'XDR': Extensively drug-resistant, 'TDR':totally drug resistant, NA': Data Not Available.\\n2. 'Strain could not be classified' numbers follow the format as ('Number of antibiotics categories count as Intermediate' | 'Number of antibiotics categories count as NA')\")" ] }, { "cell_type": "code", "execution_count": null, "id": "7e8e1fa8", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.10" } }, "nbformat": 4, "nbformat_minor": 5 }