Mercurial > repos > rakesh4osdd > asist

annotate asist_dynamic.py @ 9:4f36d48d6090 draft default tip

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author rakesh4osdd
date Wed, 30 Jun 2021 07:42:51 +0000
parents 734777d3c253
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1 #!/usr/bin/env python
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2 # coding: utf-8
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4 # In[1309]:
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7 #ASIST program for phenotype based on Antibiotics profile
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8 # create a profile based on selected antibiotics only
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9 # rakesh4osdd@gmail.com, 14-June-2021
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12 # In[1]:
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c1a77856070c "planemo upload for repository https://github.com/rakesh4osdd/asist/tree/master commit f5b374bef15145c893ffdd3a7d2f2978d8052184-dirty"
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15 import pandas as pd
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16 import sys
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17 import os
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18 from collections import Counter
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21 # In[176]:
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24 input_file=sys.argv[1]
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25 output_file=sys.argv[2]
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26 #input_file='test-data/asist_input.csv'
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27 #output_file='test-data/asist_output.csv'
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30 # In[177]:
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33 # strain_profile to phenotype condition
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34 def s_phen(sus,res,intm,na,pb_sus):
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35 if (sus>0 and res==0 and na>=0):
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36 #print('Possible Susceptible')
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37 phen='Possible Susceptible'
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38 elif (sus>=0 and 3<=res<7 and na>=0 and pb_sus==0):
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39 #print('Possible MDR')
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40 phen='Possible MDR'
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41 elif (sus>=0 and 7<=res<9 and na>=0 and pb_sus==0):
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42 #print('Possible XDR')
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43 phen='Possible XDR'
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44 #special cases
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45 elif (sus>=1 and res>0 and na>=0 and pb_sus==1):
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46 #print('Possible XDR')
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47 phen='Possible XDR'
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48 #special cases
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49 elif (sus>0 and res==9 and na>=0):
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50 #print('Possible XDR')
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51 phen='Possible XDR'
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52 elif (sus==0 and res==9 and na>=0):
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53 #print('Possible TDR')
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54 phen='Possible TDR'
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55 else:
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56 #print('Strain could not be classified')
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57 phen='Strain could not be classified ('+ str(intm)+' | ' + str(na) +')'
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58 return(phen)
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60 #print(s_phen(1,9,0,0))
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62
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63 # In[178]:
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66 # define Antibiotic groups as per antibiotic of CLSI breakpoints MIC
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67 #Aminoglycoside
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68 cat1=['Amikacin','Tobramycin','Gentamycin','Netilmicin']
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69 #Beta-lactams- Carbapenems
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70 cat2=['Imipenem','Meropenam','Doripenem']
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71 #Fluoroquinolone
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72 cat3=['Ciprofloxacin','Levofloxacin']
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73 #Beta-lactam inhibitor
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74 cat4=['Piperacillin/tazobactam','Ticarcillin/clavulanicacid']
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75 #Cephalosporin
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76 cat5=['Cefotaxime','Ceftriaxone','Ceftazidime','Cefepime']
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77 #Sulfonamides
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78 cat6=['Trimethoprim/sulfamethoxazole']
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79 #Penicillins/beta-lactamase
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80 cat7=['Ampicillin/sulbactam']
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81 #Polymyxins
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82 cat8=['Colistin','Polymyxinb']
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83 #Tetracycline
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84 cat9=['Tetracycline','Doxicycline','Minocycline']
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85
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86 def s_profiler(pd_series):
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87 #print(type(pd_series),'\n', pd_series)
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88 #create a dictionary of dataframe series
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89 cats={'s1':cat1,'s2':cat2,'s3':cat3,'s4':cat4,'s5':cat5,'s6':cat6,'s7':cat7,'s8':cat8,'s9':cat9}
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90 # find the antibiotics name in input series
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91 for cat in cats:
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92 #print(cats[cat])
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93 cats[cat]=pd_series.filter(cats[cat])
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94 #print(cats[cat])
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95 #define res,sus,intm,na,pb_sus
0
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96 res=0
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97 sus=0
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98 intm=0
0
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99 na=0
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100 pb_sus=0
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101 # special case of 'Polymyxin b' for its value
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102 if 'Polymyxinb' in pd_series:
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103 ctp=cats['s8']['Polymyxinb'].strip().lower()
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104 if ctp == 'susceptible':
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105 pb_sus=1
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106 #print((ctp,p_sus))
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107 # check all categories
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108 for cat in cats:
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109 #ctp=cats['s8'].iloc[i:i+1].stack().value_counts().to_dict()
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110 #print(ctp)
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111 # Pandas series
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112 ct=cats[cat].value_counts().to_dict()
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113 #print(ct)
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114 # remove whitespace and convert to lowercase words
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115 ct = {k.strip().lower(): v for k, v in ct.items()}
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116 #print(ct)
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117 k=Counter(ct)
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118 #j=Counter(ct)+Counter(j)
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119 #print(j)
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120 # category wise marking
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121 if k['resistant']>=1:
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122 res=res+1
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123 if k['susceptible']>=1:
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124 sus=sus+1
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125 if k['intermediate']>=1:
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126 intm=intm+1
0
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127 if k['na']>=1:
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128 na=na+1
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129 #print(sus,res,intm,na,pb_sus)
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130 #print(s_phen(sus,res,intm,na,pb_sus))
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131 return(s_phen(sus,res,intm,na,pb_sus))
0
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132
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133
3
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134 # In[179]:
0
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135
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136
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137 #input_file='input2.csv_table.csv'
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138 #output_file=input_file+'_output.txt'
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139 strain_profile=pd.read_csv(input_file, sep=',',na_filter=False,skipinitialspace = True)
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140
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141
3
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142 # In[180]:
0
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143
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144
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145 old_strain_name=strain_profile.columns[0]
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146 new_strain_name=old_strain_name.capitalize().strip().replace(' ', '')
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147
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148
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149 # In[181]:
0
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150
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151
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152 # make header capitalization, remove leading,lagging, and multiple whitespace for comparision
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153 strain_profile.columns=strain_profile.columns.str.capitalize().str.strip().str.replace('\s+', '', regex=True)
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154 #print(strain_profile.columns)
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155 #strain_profile.head()
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156 #strain_profile.columns
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157
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158
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159 # In[182]:
0
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160
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161
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162 # add new column in dataframe on second position
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163 strain_profile.insert(1, 'Strain phenotype','')
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164 #strain_profile.head()
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165
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166
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167 # In[183]:
0
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168
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169
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170 strain_profile['Strain phenotype'] = strain_profile.apply(lambda x: (s_profiler(x)), axis=1)
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171
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172
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173 # In[184]:
0
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174
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175
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176 #strain_profile.head()
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177
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178
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179 # In[185]:
0
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180
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181
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182 #rename headers for old name
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183 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'} )
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184
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185
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186 # In[186]:
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187
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188
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189 #strain_profile.columns
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190
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192 # In[187]:
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195 #strain_profile
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198 # In[188]:
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201 strain_profile.to_csv(output_file,na_rep='NA',index=False)
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204 # In[189]:
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207 # Open a file with access mode 'a'
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208 with open(output_file, "a") as file_object:
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209 # Append 'hello' at the end of file
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210 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')")
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213 # In[ ]:
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