--- a/imgtconvert.py	Mon Jul 07 09:54:42 2014 -0400
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,194 +0,0 @@
-import pandas as pd
-try:
-	pd.options.mode.chained_assignment = None  # default='warn'
-except:
-	pass
-import re
-import argparse
-import os
-
-def stop_err( msg, ret=1 ):
-    sys.stderr.write( msg )
-    sys.exit( ret )
-
-#docs.python.org/dev/library/argparse.html
-parser = argparse.ArgumentParser()
-parser.add_argument("--input", help="Input folder with files")
-parser.add_argument("--output", help="Output file")
-
-args = parser.parse_args()
-
-old_summary_columns = [u'Sequence ID', u'JUNCTION frame', u'V-GENE and allele', u'D-GENE and allele', u'J-GENE and allele', u'CDR1-IMGT length', u'CDR2-IMGT length', u'CDR3-IMGT length', u'Orientation']
-old_sequence_columns = [u'CDR1-IMGT', u'CDR2-IMGT', u'CDR3-IMGT']
-old_junction_columns = [u'JUNCTION']
-
-added_summary_columns = [u'Functionality', u'V-REGION identity %', u'V-REGION identity nt', u'D-REGION reading frame', u'AA JUNCTION', u'Functionality comment', u'Sequence']
-added_sequence_columns = [u'FR1-IMGT', u'FR2-IMGT', u'FR3-IMGT', u'CDR3-IMGT', u'JUNCTION', u'J-REGION', u'FR4-IMGT']
-added_junction_columns = [u"P3'V-nt nb", u'N1-REGION-nt nb', u"P5'D-nt nb", u"P3'D-nt nb", u'N2-REGION-nt nb', u"P5'J-nt nb", u"3'V-REGION trimmed-nt nb", u"5'D-REGION trimmed-nt nb", u"3'D-REGION trimmed-nt nb", u"5'J-REGION trimmed-nt nb"]
-
-inputFolder = args.input
-
-dirContents = os.listdir(inputFolder)
-if len(dirContents) == 1:
-    inputFolder = os.path.join(inputFolder, dirContents[0])
-    if os.path.isdir(inputFolder):
-        dirContents = os.listdir(inputFolder)
-files = sorted([os.path.join(inputFolder, f) for f in dirContents if os.path.isfile(os.path.join(inputFolder, f))])
-
-if len(files) % 3 is not 0:
-    stop_err("Files in zip not a multiple of 3, it should contain the all the 1_, 5_ and 6_ files for a sample")
-    import sys
-    sys.exit()
-
-triplets = []
-step = len(files) / 3
-for i in range(0, step):
-    triplets.append((files[i], files[i + step], files[i + step + step]))
-
-outFile = args.output
-
-#fSummary = pd.read_csv(triplets[0][0], sep="\t", low_memory=False)
-fSummary = pd.read_csv(triplets[0][0], sep="\t")
-#fSequence = pd.read_csv(triplets[0][1], sep="\t", low_memory=False)
-fSequence = pd.read_csv(triplets[0][1], sep="\t")
-#fJunction = pd.read_csv(triplets[0][2], sep="\t", low_memory=False)
-fJunction = pd.read_csv(triplets[0][2], sep="\t")
-tmp = fSummary[["Sequence ID", "JUNCTION frame", "V-GENE and allele", "D-GENE and allele", "J-GENE and allele"]]
-
-tmp["CDR1 Seq"] = fSequence["CDR1-IMGT"]
-tmp["CDR1 Length"] = fSummary["CDR1-IMGT length"]
-
-tmp["CDR2 Seq"] = fSequence["CDR2-IMGT"]
-tmp["CDR2 Length"] = fSummary["CDR2-IMGT length"]
-
-tmp["CDR3 Seq"] = fSequence["CDR3-IMGT"]
-tmp["CDR3 Length"] = fSummary["CDR3-IMGT length"]
-
-tmp["CDR3 Seq DNA"] = fJunction["JUNCTION"]
-tmp["CDR3 Length DNA"] = '1'
-tmp["Strand"] = fSummary["Orientation"]
-tmp["CDR3 Found How"] = 'a'
-
-for col in added_summary_columns:
-    tmp[col] = fSummary[col]
-
-for col in added_sequence_columns:
-    tmp[col] = fSequence[col]
-
-for col in added_junction_columns:
-    tmp[col] = fJunction[col]
-
-outFrame = tmp
-
-
-
-for triple in triplets[1:]:
-    fSummary = pd.read_csv(triple[0], sep="\t")
-    fSequence = pd.read_csv(triple[1], sep="\t")
-    fJunction = pd.read_csv(triple[2], sep="\t")
-
-    tmp = fSummary[["Sequence ID", "JUNCTION frame", "V-GENE and allele", "D-GENE and allele", "J-GENE and allele"]]
-
-    tmp["CDR1 Seq"] = fSequence["CDR1-IMGT"]
-    tmp["CDR1 Length"] = fSummary["CDR1-IMGT length"]
-
-    tmp["CDR2 Seq"] = fSequence["CDR2-IMGT"]
-    tmp["CDR2 Length"] = fSummary["CDR2-IMGT length"]
-
-    tmp["CDR3 Seq"] = fSequence["CDR3-IMGT"]
-    tmp["CDR3 Length"] = fSummary["CDR3-IMGT length"]
-
-    tmp["CDR3 Seq DNA"] = fJunction["JUNCTION"]
-    tmp["CDR3 Length DNA"] = '1'
-    tmp["Strand"] = fSummary["Orientation"]
-    tmp["CDR3 Found How"] = 'a'
-
-    for col in added_summary_columns:
-        tmp[col] = fSummary[col]
-
-    for col in added_sequence_columns:
-        tmp[col] = fSequence[col]
-
-    for col in added_junction_columns:
-        tmp[col] = fJunction[col]
-
-    outFrame = outFrame.append(tmp)
-
-
-outFrame.columns = [u'ID', u'VDJ Frame', u'Top V Gene', u'Top D Gene', u'Top J Gene', u'CDR1 Seq', u'CDR1 Length', u'CDR2 Seq', u'CDR2 Length', u'CDR3 Seq', u'CDR3 Length', u'CDR3 Seq DNA', u'CDR3 Length DNA', u'Strand', u'CDR3 Found How', u'Functionality', 'V-REGION identity %', 'V-REGION identity nt', 'D-REGION reading frame', 'AA JUNCTION', 'Functionality comment', 'Sequence', 'FR1-IMGT', 'FR2-IMGT', 'FR3-IMGT', 'CDR3-IMGT', 'JUNCTION', 'J-REGION', 'FR4-IMGT', 'P3V-nt nb', 'N1-REGION-nt nb', 'P5D-nt nb', 'P3D-nt nb', 'N2-REGION-nt nb', 'P5J-nt nb', '3V-REGION trimmed-nt nb', '5D-REGION trimmed-nt nb', '3D-REGION trimmed-nt nb', '5J-REGION trimmed-nt nb']
-
-"""
-IGHV[0-9]-[0-9ab]+-?[0-9]?D?
-TRBV[0-9]{1,2}-?[0-9]?-?[123]?
-IGKV[0-3]D?-[0-9]{1,2}
-IGLV[0-9]-[0-9]{1,2}
-TRAV[0-9]{1,2}(-[1-46])?(/DV[45678])?
-TRGV[234589]
-TRDV[1-3]
-
-IGHD[0-9]-[0-9ab]+
-TRBD[12]
-TRDD[1-3]
-
-IGHJ[1-6]
-TRBJ[12]-[1-7]
-IGKJ[1-5]
-IGLJ[12367]
-TRAJ[0-9]{1,2}
-TRGJP?[12]
-TRDJ[1-4]
-"""
-
-vPattern = [r"(IGHV[0-9]-[0-9ab]+-?[0-9]?D?)",
-						r"(TRBV[0-9]{1,2}-?[0-9]?-?[123]?)",
-						r"(IGKV[0-3]D?-[0-9]{1,2})",
-						r"(IGLV[0-9]-[0-9]{1,2})",
-						r"(TRAV[0-9]{1,2}(-[1-46])?(/DV[45678])?)",
-						r"(TRGV[234589])",
-						r"(TRDV[1-3])"]
-
-dPattern = [r"(IGHD[0-9]-[0-9ab]+)",
-						r"(TRBD[12])",
-						r"(TRDD[1-3])"]
-						
-jPattern = [r"(IGHJ[1-6])",
-						r"(TRBJ[12]-[1-7])",
-						r"(IGKJ[1-5])",
-						r"(IGLJ[12367])",
-						r"(TRAJ[0-9]{1,2})",
-						r"(TRGJP?[12])",
-						r"(TRDJ[1-4])"]
-
-vPattern = re.compile(r"|".join(vPattern))
-
-dPattern = re.compile(r"|".join(dPattern))
-
-jPattern = re.compile(r"|".join(jPattern))
-
-
-def filterGenes(s, pattern):
-    if type(s) is not str:
-        return "NA"
-    res = pattern.search(s)
-    if res:
-        return res.group(0)
-    return "NA"
-
-
-
-outFrame["Top V Gene"] = outFrame["Top V Gene"].apply(lambda x: filterGenes(x, vPattern))
-outFrame["Top D Gene"] = outFrame["Top D Gene"].apply(lambda x: filterGenes(x, dPattern))
-outFrame["Top J Gene"] = outFrame["Top J Gene"].apply(lambda x: filterGenes(x, jPattern))
-
-print outFrame
-
-tmp = outFrame["VDJ Frame"]
-tmp = tmp.replace("in-frame", "In-frame")
-tmp = tmp.replace("null", "Out-of-frame")
-tmp = tmp.replace("out-of-frame", "Out-of-frame")
-outFrame["VDJ Frame"] = tmp
-outFrame["CDR3 Length DNA"] = outFrame["CDR3 Seq DNA"].map(str).map(len)
-safeLength = lambda x: len(x) if type(x) == str else 0
-outFrame = outFrame[(outFrame["CDR3 Seq DNA"].map(safeLength) > 0) & (outFrame["Top V Gene"] != "NA") & (outFrame["Top J Gene"] != "NA")] #filter out weird rows?
-#outFrame = outFrame[(outFrame["CDR3 Seq DNA"].map(safeLength) > 0) & (outFrame["Top V Gene"] != "NA") & (outFrame["Top D Gene"] != "NA") & (outFrame["Top J Gene"] != "NA")] #filter out weird rows?
-outFrame.to_csv(outFile, sep="\t", index=False, index_label="index")