old_contents: import sys
  
  
  
  if len(sys.argv)<3:
      print "This tool filters out discoSnp prediction having a minor allele frequency lower than a provided threshold for ALL datasets."
      print "python filter_out_using_MAF.py \".fa from discoSnp\" \"MAF threshold\""
      sys.exit()
  
  coherent_file=open(sys.argv[1],"r")
  maf_threshold = float(sys.argv[2])
  
  
  while True:
      #>SNP_higher_path_1384|P_1:30_A/G|high|nb_pol_1|left_unitig_length_108|right_unitig_length_1156|C1_0|C2_0|C3_0|C4_0|C5_183|Q1_0|Q2_0|Q3_0|Q4_0|Q5_70|G1_1/1:19724,2972,47|G2_1/1:21024,3168,50|G3_1/1:17124,2581,42|G4_1/1:19564,2948,47|G5_0/1:16063,1163,1575|rank_0.36839
      
      comment1 = coherent_file.readline()
      if not comment1: break
      splitted_comment1 = comment1.split("|")
      path1 = coherent_file.readline()
      comment2 = coherent_file.readline()
      splitted_comment2 = comment2.split("|")
      path2 = coherent_file.readline()
  
      coverage_high=[]
      coverage_low= []
      
      first_coverage_field_id=0
      while True:
          if splitted_comment1[first_coverage_field_id][0]=="C": break
          first_coverage_field_id+=1
      
      i=first_coverage_field_id
      while True:
          if splitted_comment1[i][0]!="C": break # no more a coverage
          coverage_high.append(int(splitted_comment1[i].split("_")[1]))
          coverage_low.append( int(splitted_comment2[i].split("_")[1]))
          i+=1
      # print comment1,
  #    print coverage_high
   #   print coverage_low
      to_output=False
      for i in range(len(coverage_high)):
          if coverage_high[i]==0 and coverage_low[i]==0: continue
          print (min(coverage_high[i],coverage_low[i]) / float(max(coverage_high[i],coverage_low[i])))
          if (min(coverage_high[i],coverage_low[i]) / float(max(coverage_high[i],coverage_low[i]))) >= maf_threshold:
              to_output=True
              break
      
      if to_output:
          print comment1,path1,comment2,path2,
      
        
              
          
     
  
  
new_contents: import sys
  
  
  
  if len(sys.argv)<3:
      print "This tool filters out discoSnp prediction having a minor allele frequency lower than a provided threshold for ALL datasets."
      print "python filter_out_using_MAF.py \".fa from discoSnp\" \"MAF threshold\""
      sys.exit()
  
  coherent_file=open(sys.argv[1],"r")
  maf_threshold = float(sys.argv[2])
  
  first_coverage_field_id=0
  while True:
      #>SNP_higher_path_1384|P_1:30_A/G|high|nb_pol_1|left_unitig_length_108|right_unitig_length_1156|C1_0|C2_0|C3_0|C4_0|C5_183|Q1_0|Q2_0|Q3_0|Q4_0|Q5_70|G1_1/1:19724,2972,47|G2_1/1:21024,3168,50|G3_1/1:17124,2581,42|G4_1/1:19564,2948,47|G5_0/1:16063,1163,1575|rank_0.36839
      
      comment1 = coherent_file.readline()
      if not comment1: break
      splitted_comment1 = comment1.split("|")
      path1 = coherent_file.readline()
      comment2 = coherent_file.readline()
      splitted_comment2 = comment2.split("|")
      path2 = coherent_file.readline()
  
      coverage_high=[]
      coverage_low= []
      
      if first_coverage_field_id==0:
          while True:
              if splitted_comment1[first_coverage_field_id][0]=="C": break
              first_coverage_field_id+=1
      
      i=first_coverage_field_id
      while True:
          if splitted_comment1[i][0]!="C": break # no more a coverage
          coverage_high.append(int(splitted_comment1[i].split("_")[1]))
          coverage_low.append( int(splitted_comment2[i].split("_")[1]))
          i+=1
      # print comment1,
  #    print coverage_high
   #   print coverage_low
      to_output=False
      for i in range(len(coverage_high)):
          if coverage_high[i]==0 and coverage_low[i]==0: continue
          print (min(coverage_high[i],coverage_low[i]) / float(max(coverage_high[i],coverage_low[i])))
          if (min(coverage_high[i],coverage_low[i]) / float(max(coverage_high[i],coverage_low[i]))) >= maf_threshold:
              to_output=True
              break
      
      if to_output:
          print comment1,path1,comment2,path2,
      
        
              
          
     
  
  
subject: debug filter