comparison asist_dynamic.py @ 3:734777d3c253 draft

Deleted selected files test

2:62afd73a0987

#ASIST program for phenotype based on Antibiotics profile
# create a profile based on selected antibiotics only
# [e-mail], 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')

    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']

    # 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()

        # 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)

3:734777d3c253

#ASIST program for phenotype based on Antibiotics profile
# create a profile based on selected antibiotics only
# [e-mail], 14-June-2021


# In[1]:


import pandas as pd
import sys
import os
from collections import Counter


# In[176]:


input_file=sys.argv[1]
output_file=sys.argv[2]
#input_file='test-data/asist_input.csv'
#output_file='test-data/asist_output.csv'


# In[177]:


# strain_profile to phenotype condition
def s_phen(sus,res,intm,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')

    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 ('+ str(intm)+' | ' + str(na) +')'
    return(phen)

#print(s_phen(1,9,0,0))


# In[178]:


# define Antibiotic groups as per antibiotic of CLSI breakpoints MIC
#Aminoglycoside
cat1=['Amikacin','Tobramycin','Gentamycin','Netilmicin']

    # 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,intm,na,pb_sus
    res=0
    sus=0
    intm=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()

        # category wise marking
        if k['resistant']>=1:
            res=res+1
        if k['susceptible']>=1:
            sus=sus+1
        if k['intermediate']>=1:
            intm=intm+1
        if k['na']>=1:
            na=na+1
    #print(sus,res,intm,na,pb_sus)
    #print(s_phen(sus,res,intm,na,pb_sus))
    return(s_phen(sus,res,intm,na,pb_sus))


# In[179]:


#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[180]:


old_strain_name=strain_profile.columns[0]
new_strain_name=old_strain_name.capitalize().strip().replace(' ', '')


# In[181]:


# 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[182]:


# add new column in dataframe on second position
strain_profile.insert(1, 'Strain phenotype','')
#strain_profile.head()


# In[183]:


strain_profile['Strain phenotype'] = strain_profile.apply(lambda x: (s_profiler(x)), axis=1)


# In[184]:


#strain_profile.head()


# In[185]:


#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[186]:


#strain_profile.columns


# In[187]:


#strain_profile


# In[188]:


strain_profile.to_csv(output_file,na_rep='NA',index=False)


# In[189]:


# Open a file with access mode 'a'
with open(output_file, "a") as file_object:
    # Append 'hello' at the end of file
    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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