--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/sam2counts_galaxy_edger.py	Thu Jan 22 03:54:22 2015 -0500
@@ -0,0 +1,213 @@
+#!/usr/bin/python
+
+"""
+sam2count_galaxy_edger.py -- Take SAM files and output a table of counts with column
+names that are the filenames, and rowname that are the reference
+names.
+"""
+
+VERSION = 0.90
+
+import sys
+import csv
+from os import path
+try:
+    import pysam
+except ImportError:
+    sys.exit("pysam not installed; please install it\n")
+
+import argparse
+
+def SAM_file_to_counts(filename, sname, ftype="sam", extra=False, use_all_references=True):
+    """
+    Take SAM filename, and create a hash of mapped and unmapped reads;
+    keys are reference sequences, values are the counts of occurences.
+
+    Also, a hash of qualities (either 0 or >0) of mapped reads
+    is output, which is handy for diagnostics.
+    """
+    counts = dict()
+    unique = dict()
+    nonunique = dict()
+    mode = 'r'
+    if ftype == "bam":
+        mode = 'rb'
+    sf = pysam.Samfile(filename, mode)
+
+    if use_all_references:
+        # Make dictionary of all entries in header
+        try:
+            for sn in sf.header['SQ']:
+                if extra:
+                    unique[sn['SN']] = 0
+                    nonunique[sn['SN']] = 0
+                counts[sn['SN']] = 0
+        except KeyError:
+            print "Sample file of sample " + sname + " does not have header."
+
+    for read in sf:
+        if not read.is_unmapped:
+            id_name = sf.getrname(read.rname) if read.rname != -1 else 0
+
+            if not use_all_references and not counts.get(id_name, False):
+                ## Only make keys based on aligning reads, make empty hash
+                if extra:
+                    unique[id_name] = 0
+                    nonunique[id_name] = 0
+                ## initiate entry; even if not mapped, record 0 count
+                counts[id_name] = counts.get(id_name, 0)
+        
+            
+            counts[id_name] = counts.get(id_name, 0) + 1
+
+            if extra:
+                if read.mapq == 0:
+                    nonunique[id_name] = nonunique[id_name] + 1
+                else:
+                    unique[id_name] = unique[id_name] + 1
+
+    if extra:
+        return {'counts':counts, 'unique':unique, 'nonunique':nonunique}
+
+    return {'counts':counts}
+
+def collapsed_nested_count_dict(counts_dict, all_ids, order=None):
+    """
+    Takes a nested dictionary `counts_dict` and `all_ids`, which is
+    built with the `table_dict`. All files (first keys) in
+    `counts_dict` are made into columns with order specified by
+    `order`.
+
+    Output is a dictionary with keys that are the id's (genes or
+    transcripts), with values that are ordered counts. A header will
+    be created on the first row from the ordered columns (extracted
+    from filenames).
+    """
+    if order is None:
+        col_order = counts_dict.keys()
+    else:
+        col_order = order
+
+    collapsed_dict = dict()
+    for i, filename in enumerate(col_order):
+        for id_name in all_ids:
+            if not collapsed_dict.get(id_name, False):
+                collapsed_dict[id_name] = list()
+
+            # get counts and append
+            c = counts_dict[filename].get(id_name, 0)
+            collapsed_dict[id_name].append(c)
+    return {'table':collapsed_dict, 'header':col_order}
+
+
+def counts_to_file(table_dict, outfilename, delimiter=','):
+    """
+    A function for its side-effect of writing `table_dict` (which
+    contains a table and header), to `outfilename` with the specified
+    `delimiter`.
+    """
+    writer = csv.writer(open(outfilename, 'a'), delimiter=delimiter, lineterminator='\n')
+    table = table_dict['table']
+    header = table_dict['header']
+    
+    #header_row = True
+    for id_name, fields in table.items():
+        #if header_row:
+            #row = ['id'] + header
+            #writer.writerow(row)
+            #header_row = False
+
+        if id_name == 0:
+            continue
+        row = [id_name]
+        row.extend(fields)
+        writer.writerow(row)
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='parser for sam2counts')
+    parser.add_argument("-d", "--delimiter", help="the delimiter (default: tab)", default='\t')
+    parser.add_argument("-o", "--out-file", help="output filename (default: counts.txt)", default='counts.txt')
+    parser.add_argument("-u", "--extra-output", help="output extra information on non-unique and unique mappers (default: False)")
+    parser.add_argument("-r", "--use-all-references", dest="use_all_references",
+                      help="Use all the references from the SAM header (default: True)",
+                      default=True, action="store_false")
+    parser.add_argument("-f", "--extra-out-files", dest="extra_out_files",
+                      help="comma-delimited filenames of unique and non-unique output "
+                      "(default: unique.txt,nonunique.txt)",
+                      default='unique.txt,nonunique.txt')
+    parser.add_argument("-v", "--verbose", dest="verbose",
+                      help="enable verbose output")
+    parser.add_argument("--bam-file", help="bam file", nargs="+", action="append", required=True)
+    parser.add_argument("--group", help="group", nargs="+", action="append", required=True)
+    parser.add_argument("--treatment", help="treatment", nargs="+", action="append", required=True)
+    parser.add_argument("--sample-name", help="sample name", nargs="+", action="append", required=True)
+    parser.add_argument("--file-type", help="file type", nargs="+", action="append", required=True, choices=['sam','bam'])
+    args = parser.parse_args()
+
+    args.bam_file = [item for sublist in args.bam_file for item in sublist]
+    args.group = [item for sublist in args.group for item in sublist]
+    args.treatment = [item for sublist in args.treatment for item in sublist]
+    args.sample_name = [item for sublist in args.sample_name for item in sublist]
+    args.file_type = [item for sublist in args.file_type for item in sublist]
+    #print(args.sample_name)
+
+    if (len(args.sample_name) != len(set(args.sample_name))):
+        parser.error("Sample names must be unique.")
+
+    if not(len(args.bam_file) == len(args.group) and len(args.group) == len(args.treatment) and len(args.treatment) == len(args.sample_name) and len(args.sample_name) == len(args.file_type)):
+        parser.error("Number of total BAM files, groups, treatments, sample names, and types must be the same.")
+
+    file_counts = dict()
+    file_unique_counts = dict()
+    file_nonunique_counts = dict()
+    all_ids = list()
+
+    ## do a pre-run check that all files exist
+    for full_filename in args.bam_file:
+        if not path.exists(full_filename):
+            parser.error("file '%s' does not exist" % full_filename)
+
+    outf = open(args.out_file, "w")
+    outf.write("#")
+    for (g,t) in zip(args.group,args.treatment):
+        outf.write("\t" + g + ":" + t) 
+    outf.write("\n#Feature")
+    for s in args.sample_name:
+        outf.write("\t" + s)
+    outf.write("\n")
+    outf.close()
+
+    for (full_filename, sn, ft) in zip(args.bam_file, args.sample_name, args.file_type):
+        ## read in SAM file, extract counts, and unpack counts
+        tmp = SAM_file_to_counts(full_filename, sn, ftype=ft, extra=args.extra_output,
+                                 use_all_references=args.use_all_references)
+
+        if args.extra_output:
+            counts, unique, nonunique = tmp['counts'], tmp['unique'], tmp['nonunique']
+        else:
+            counts = tmp['counts']
+
+        ## save counts, and unique/non-unique counts
+        file_counts[sn] = counts
+
+        if args.extra_output:
+            file_unique_counts[sn] = unique
+            file_nonunique_counts[sn] = nonunique
+
+        ## add all ids encountered in this in this file
+        all_ids.extend(file_counts[sn].keys())
+
+    ## Uniquify all_ids, and then take the nested file_counts
+    ## dictionary, collapse, and write to file.
+    all_ids = set(all_ids)
+    table_dict = collapsed_nested_count_dict(file_counts, all_ids, order=args.sample_name)
+    counts_to_file(table_dict, args.out_file, delimiter=args.delimiter)
+
+    if args.extra_output:
+        unique_fn, nonunique_fn = args.extra_out_files.split(',')
+        unique_table_dict = collapsed_nested_count_dict(file_unique_counts, all_ids, order=files)
+        nonunique_table_dict = collapsed_nested_count_dict(file_nonunique_counts, all_ids, order=files)
+        
+        counts_to_file(unique_table_dict, unique_fn, delimiter=args.delimiter)
+        counts_to_file(nonunique_table_dict, nonunique_fn, delimiter=args.delimiter)
+