--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/asist_dynamic.py	Sat Jun 26 07:27:53 2021 +0000
@@ -0,0 +1,190 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[1309]:
+
+
+#ASIST program for phenotype based on Antibiotics profile
+# create a profile based on selected antibiotics only
+# rakesh4osdd@gmail.com, 14-June-2021
+
+
+# In[1403]:
+
+
+import pandas as pd
+import sys
+import os
+from collections import Counter
+
+
+# In[ ]:
+
+
+input_file=sys.argv[1]
+output_file=sys.argv[2]
+
+
+# In[1362]:
+
+
+# strain_profile to phenotype condition
+def s_phen(sus,res,na,pb_sus):
+    if (sus>0 and res==0 and na>=0):
+        #print('Possible Susceptible')
+        phen='Possible Susceptible'
+    elif (sus>=0 and 3<=res<7 and na>=0 and pb_sus==0):
+        #print('Possible MDR')
+        phen='Possible MDR'
+    elif (sus>=0 and 7<=res<9 and na>=0 and pb_sus==0):
+        #print('Possible XDR')
+        phen='Possible XDR'
+    #special cases
+    elif (sus>=1 and res>0 and na>=0 and pb_sus==1):
+        #print('Possible XDR')
+        phen='Possible XDR'
+    #special cases
+    elif (sus>0 and res==9 and na>=0):
+        #print('Possible XDR')
+        phen='Possible XDR'
+    elif (sus==0 and res==9 and na>=0):
+        #print('Possible TDR')
+        phen='Possible TDR'
+    else:
+        #print('Strain could not be classified')
+        phen='Strain could not be classified'
+    return(phen)
+
+#print(s_phen(1,9,0,0))
+
+
+# In[1363]:
+
+
+# define Antibiotic groups as per antibiotic of CLSI breakpoints MIC
+#Aminoglycoside
+cat1=['Amikacin','Tobramycin','Gentamycin','Netilmicin']
+#Beta-lactams- Carbapenems
+cat2=['Imipenem','Meropenam','Doripenem']
+#Fluoroquinolone
+cat3=['Ciprofloxacin','Levofloxacin']
+#Beta-lactam inhibitor
+cat4=['Piperacillin/tazobactam','Ticarcillin/clavulanicacid']
+#Cephalosporin
+cat5=['Cefotaxime','Ceftriaxone','Ceftazidime','Cefepime']
+#Sulfonamides
+cat6=['Trimethoprim/sulfamethoxazole']
+#Penicillins/beta-lactamase
+cat7=['Ampicillin/sulbactam']
+#Polymyxins
+cat8=['Colistin','Polymyxinb']
+#Tetracycline
+cat9=['Tetracycline','Doxicycline','Minocycline']
+
+def s_profiler(pd_series):
+    #print(type(pd_series),'\n', pd_series)
+    #create a dictionary of dataframe series
+    cats={'s1':cat1,'s2':cat2,'s3':cat3,'s4':cat4,'s5':cat5,'s6':cat6,'s7':cat7,'s8':cat8,'s9':cat9}
+    # find the antibiotics name in input series
+    for cat in cats:
+        #print(cats[cat])
+        cats[cat]=pd_series.filter(cats[cat])
+        #print(cats[cat])
+    #define res,sus,na,pb_sus
+    res=0
+    sus=0
+    na=0
+    pb_sus=0
+    # special case of 'Polymyxin b' for its value
+    if 'Polymyxinb' in pd_series:
+        ctp=cats['s8']['Polymyxinb'].strip().lower()
+        if ctp == 'susceptible':
+            pb_sus=1
+        #print((ctp,p_sus))
+    # check all categories
+    for cat in cats:
+        #ctp=cats['s8'].iloc[i:i+1].stack().value_counts().to_dict()
+        #print(ctp)
+        # Pandas series
+        ct=cats[cat].value_counts().to_dict()
+        #print(ct)
+        # remove whitespace and convert to lowercase words
+        ct =  {k.strip().lower(): v for k, v in ct.items()}
+        #print(ct)
+        k=Counter(ct)
+        #j=Counter(ct)+Counter(j)
+        #print(j)
+        # category wise marking
+        if k['resistant']>=1:
+            res=res+1
+        if k['susceptible']>=1:
+            sus=sus+1
+        if k['na']>=1:
+            na=na+1
+    #print(s_phen(sus,res,na,pb_sus))
+    return(s_phen(sus,res,na,pb_sus))
+
+
+# In[1397]:
+
+
+#input_file='input2.csv_table.csv'
+#output_file=input_file+'_output.txt'
+strain_profile=pd.read_csv(input_file, sep=',',na_filter=False,skipinitialspace = True)
+
+
+# In[1387]:
+
+
+old_strain_name=strain_profile.columns[0]
+new_strain_name=old_strain_name.capitalize().strip().replace(' ', '')
+
+
+# In[1388]:
+
+
+# make header capitalization, remove leading,lagging, and multiple whitespace for comparision
+strain_profile.columns=strain_profile.columns.str.capitalize().str.strip().str.replace('\s+', '', regex=True)
+#print(strain_profile.columns)
+#strain_profile.head()
+#strain_profile.columns
+
+
+# In[1389]:
+
+
+# add new column in dataframe on second position
+strain_profile.insert(1, 'Strain phenotype','')
+#strain_profile.head()
+
+
+# In[1390]:
+
+
+strain_profile['Strain phenotype'] = strain_profile.apply(lambda x: (s_profiler(x)), axis=1)
+
+
+# In[1391]:
+
+
+#strain_profile.head()
+
+
+# In[1392]:
+
+
+#rename headers for old name
+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'} )
+
+
+# In[1404]:
+
+
+#strain_profile
+
+
+# In[1394]:
+
+
+strain_profile.to_csv(output_file,na_rep='NA',index=False)
+