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1 #!/usr/bin/env python
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2 import sys
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3 import re
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4 from collections import defaultdict
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5 import argparse
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6
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7 parser = argparse.ArgumentParser()
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8 parser.add_argument("-i" ,"--input", type=argparse.FileType('r'), help="path to file CLUSTER_table.csv")
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9 parser.add_argument("-o" ,"--output", type=argparse.FileType('w'), help="output file name")
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10 parser.add_argument("-m", "--use_manual", action='store_true', default=False)
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11
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12 args = parser.parse_args()
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13
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14 column = 6 if args.use_manual else 4
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15
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16
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17 header = False
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18 clust_info = {}
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19 counts = defaultdict(lambda: 0)
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20 top_clusters = 0
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21 with open(args.input.name, 'r') as f:
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22 for l in f:
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23 parts = l.split()
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24 if re.match('.*Cluster.+Supercluster.+Size.+Size_adjusted.+Automatic_annotation.+TAREAN_annotation.+Final_annotation', l):
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25 print("header detected")
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26 header = True
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27 continue
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28 if header:
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29 classification = "Top_clusters\t" + "\t".join(parts[column].split("/")[1:]).replace('"','')
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30 counts[classification] += int(parts[3])
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31 top_clusters += int(parts[3])
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32
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33 elif len(parts) >= 2:
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34 clust_info[parts[0].replace('"', '')] = int(parts[1])
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35
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36 counts['Singlets'] = clust_info['Number_of_singlets']
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37 counts['Small_cluster'] = int(clust_info['Number_of_reads_in_clusters']) - top_clusters
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38
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39 with open(args.output.name, 'w') as fout:
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40 for cls_line, nreads in counts.items():
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41 fout.write(str(nreads) +"\t" + cls_line + "\n")
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42
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43
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44
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