--- a/signature.py	Mon Feb 16 12:08:18 2015 +0100
+++ b/signature.py	Sun Mar 29 11:58:31 2015 -0400
@@ -6,86 +6,117 @@
 # 			  <13: R code>
 # version 2.0.0
 
-import sys, subprocess, argparse
+import sys
+import subprocess
+import argparse
 from smRtools import *
-from collections import defaultdict # test whether it is required
+from collections import defaultdict  # test whether it is required
+
 
 def Parser():
-  the_parser = argparse.ArgumentParser()
-  the_parser.add_argument('--input', action="store", type=str, help="input alignment file")
-  the_parser.add_argument('--inputFormat', action="store", type=str, choices=["tabular", "bam", "sam"], help="format of alignment file (tabular/bam/sam)")
-  the_parser.add_argument('--minquery', type=int, help="Minimum readsize of query reads (nt) - must be an integer")
-  the_parser.add_argument('--maxquery', type=int, help="Maximum readsize of query reads (nt) - must be an integer")
-  the_parser.add_argument('--mintarget', type=int, help="Minimum readsize of target reads (nt) - must be an integer")
-  the_parser.add_argument('--maxtarget', type=int, help="Maximum readsize of target reads (nt) - must be an integer")
-  the_parser.add_argument('--minscope', type=int, help="Minimum overlap analyzed (nt) - must be an integer")
-  the_parser.add_argument('--maxscope', type=int, help="Maximum overlap analyzed (nt) - must be an integer")
-  the_parser.add_argument('--outputOverlapDataframe', action="store", type=str, help="Overlap dataframe")
-  the_parser.add_argument('--referenceGenome', action='store', help="path to the bowtie-indexed or fasta reference")
-  the_parser.add_argument('--extract_index', action='store_true', help="specify if the reference is an indexed Bowtie reference")
-  the_parser.add_argument('--graph', action='store', choices=["global", "lattice"], help="small RNA signature is computed either globally or by item (global-lattice)")
-  the_parser.add_argument('--rcode', type=str, help="R code to be passed to the python script")
-  args = the_parser.parse_args()
-  return args
+    the_parser = argparse.ArgumentParser()
+    the_parser.add_argument(
+        '--input', action="store", type=str, help="input alignment file")
+    the_parser.add_argument('--inputFormat', action="store", type=str, choices=[
+                            "tabular", "bam", "sam"], help="format of alignment file (tabular/bam/sam)")
+    the_parser.add_argument(
+        '--minquery', type=int, help="Minimum readsize of query reads (nt) - must be an integer")
+    the_parser.add_argument(
+        '--maxquery', type=int, help="Maximum readsize of query reads (nt) - must be an integer")
+    the_parser.add_argument(
+        '--mintarget', type=int, help="Minimum readsize of target reads (nt) - must be an integer")
+    the_parser.add_argument(
+        '--maxtarget', type=int, help="Maximum readsize of target reads (nt) - must be an integer")
+    the_parser.add_argument(
+        '--minscope', type=int, help="Minimum overlap analyzed (nt) - must be an integer")
+    the_parser.add_argument(
+        '--maxscope', type=int, help="Maximum overlap analyzed (nt) - must be an integer")
+    the_parser.add_argument(
+        '--outputOverlapDataframe', action="store", type=str, help="Overlap dataframe")
+    the_parser.add_argument('--referenceGenome', action='store',
+                            help="path to the bowtie-indexed or fasta reference")
+    the_parser.add_argument('--extract_index', action='store_true',
+                            help="specify if the reference is an indexed Bowtie reference")
+    the_parser.add_argument('--graph', action='store', choices=[
+                            "global", "lattice"], help="small RNA signature is computed either globally or by item (global-lattice)")
+    the_parser.add_argument(
+        '--rcode', type=str, help="R code to be passed to the python script")
+    args = the_parser.parse_args()
+    return args
 
 args = Parser()
 
 if args.extract_index:
-  GenomeFormat = "bowtieIndex"
+    GenomeFormat = "bowtieIndex"
 else:
-  GenomeFormat = "fastaSource"
+    GenomeFormat = "fastaSource"
 
 if args.inputFormat == "tabular":
-  Genome = HandleSmRNAwindows (args.input, args.inputFormat, args.referenceGenome, GenomeFormat)
+    Genome = HandleSmRNAwindows(
+        args.input, args.inputFormat, args.referenceGenome, GenomeFormat)
 elif args.inputFormat == "sam":
-  Genome = HandleSmRNAwindows (args.input, args.inputFormat, args.referenceGenome, GenomeFormat)
+    Genome = HandleSmRNAwindows(
+        args.input, args.inputFormat, args.referenceGenome, GenomeFormat)
 else:
-  Genome = HandleSmRNAwindows (args.input, args.inputFormat, args.referenceGenome, GenomeFormat)
+    Genome = HandleSmRNAwindows(
+        args.input, args.inputFormat, args.referenceGenome, GenomeFormat)
 
 # replace objDic by Genome.instanceDict or... objDic = Genome.instanceDict
 objDic = Genome.instanceDict
 
 args.maxscope += 1
 
-general_frequency_table = dict ([(i,0) for i in range(args.minscope, args.maxscope)])
-general_percent_table = dict ([(i,0) for i in range(args.minscope, args.maxscope)])
-OUT = open (args.outputOverlapDataframe, "w")
+general_frequency_table = dict(
+    [(i, 0) for i in range(args.minscope, args.maxscope)])
+general_percent_table = dict(
+    [(i, 0) for i in range(args.minscope, args.maxscope)])
+OUT = open(args.outputOverlapDataframe, "w")
 
 if args.graph == "global":
-  ###### for normalized summing of local_percent_table(s)
-  readcount_dic = {}
-  Total_read_in_objDic = 0
-  for item in objDic:
-    readcount_dic[item] = objDic[item].readcount(args.minquery, args.maxquery)
-    Total_read_in_objDic += readcount_dic[item]
-  ######
-  for x in (objDic):
-    local_frequency_table = objDic[x].signature( args.minquery, args.maxquery, args.mintarget, args.maxtarget, range(args.minscope, args.maxscope) )
-    local_percent_table = objDic[x].hannon_signature( args.minquery, args.maxquery, args.mintarget, args.maxtarget, range(args.minscope, args.maxscope) )
-    try:
-      for overlap in local_frequency_table.keys():
-        general_frequency_table[overlap] = general_frequency_table.get(overlap, 0) + local_frequency_table[overlap]
-    except:
-      pass
-    try:
-      for overlap in local_percent_table.keys():
-        general_percent_table[overlap] = general_percent_table.get(overlap, 0) + (1./Total_read_in_objDic*readcount_dic[x]*local_percent_table[overlap])
-    except:
-      pass
-  print >> OUT, "overlap\tnum of pairs\tprobability"
-  for classe in sorted(general_frequency_table):
-    print >> OUT, "%i\t%i\t%f" % (classe, general_frequency_table[classe], general_percent_table[classe])
+    # for normalized summing of local_percent_table(s)
+    readcount_dic = {}
+    Total_read_in_objDic = 0
+    for item in objDic:
+        readcount_dic[item] = objDic[item].readcount(
+            args.minquery, args.maxquery)
+        Total_read_in_objDic += readcount_dic[item]
+    ######
+    for x in (objDic):
+        local_frequency_table = objDic[x].signature(
+            args.minquery, args.maxquery, args.mintarget, args.maxtarget, range(args.minscope, args.maxscope))
+        local_percent_table = objDic[x].hannon_signature(
+            args.minquery, args.maxquery, args.mintarget, args.maxtarget, range(args.minscope, args.maxscope))
+        try:
+            for overlap in local_frequency_table.keys():
+                general_frequency_table[overlap] = general_frequency_table.get(
+                    overlap, 0) + local_frequency_table[overlap]
+        except:
+            pass
+        try:
+            for overlap in local_percent_table.keys():
+                general_percent_table[overlap] = general_percent_table.get(
+                    overlap, 0) + (1. / Total_read_in_objDic * readcount_dic[x] * local_percent_table[overlap])
+        except:
+            pass
+    print >> OUT, "overlap\tnum of pairs\tprobability"
+    for classe in sorted(general_frequency_table):
+        print >> OUT, "%i\t%i\t%f" % (
+            classe, general_frequency_table[classe], general_percent_table[classe])
 
 else:
-  print >> OUT, "overlap\tnum of pairs\tprobability\titem"
-  for x in (objDic):
-    local_frequency_table = objDic[x].signature( args.minquery, args.maxquery, args.mintarget, args.maxtarget, range(args.minscope, args.maxscope) )
-    local_percent_table = objDic[x].hannon_signature( args.minquery, args.maxquery, args.mintarget, args.maxtarget, range(args.minscope, args.maxscope) )
-    for classe in range(args.minscope, args.maxscope):
-      print >> OUT, "%i\t%i\t%f\t%s" % (classe, local_frequency_table[classe], local_percent_table[classe], x)
+    print >> OUT, "overlap\tnum of pairs\tprobability\titem"
+    for x in (objDic):
+        local_frequency_table = objDic[x].signature(
+            args.minquery, args.maxquery, args.mintarget, args.maxtarget, range(args.minscope, args.maxscope))
+        local_percent_table = objDic[x].hannon_signature(
+            args.minquery, args.maxquery, args.mintarget, args.maxtarget, range(args.minscope, args.maxscope))
+        for classe in range(args.minscope, args.maxscope):
+            print >> OUT, "%i\t%i\t%f\t%s" % (
+                classe, local_frequency_table[classe], local_percent_table[classe], x)
 
 OUT.close()
 
-## Run the R script that is defined in the xml using the Rscript binary provided with R.
-R_command="Rscript "+ args.rcode
+# Run the R script that is defined in the xml using the Rscript binary
+# provided with R.
+R_command = "Rscript " + args.rcode
 process = subprocess.Popen(R_command.split())