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1 # -*- coding: utf-8 -*-
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2 """
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3 Created on Fri Jun 20 14:58:08 2014
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4
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5 @author: chmaramis
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6 """
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7
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8 from __future__ import division
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9 import numpy as np
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10 from pandas import *
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11 import functools as ft
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12 import sys
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13 import time
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14
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15 frm = lambda x,y: '{r}/{l}'.format(r=x,l=y)
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16
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17 def repertoireComputation(inp_name, fname):
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18
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19 df = DataFrame()
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20 df = read_csv(inp_name, sep='\t', index_col=0 )
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21 #tp = read_csv(inp_name, iterator=True, chunksize=5000,sep='\t', index_col=0 )
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22 #df = concat([chunk for chunk in tp])
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23
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24
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25 vgroup = df.groupby(['V-GENE'])
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26 vdi = vgroup.size()
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27 rep = DataFrame(list(vdi.index), columns=['V-GENE'])
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28 rep['Reads'] = vdi.values
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29 #rep['Reads/Total'] = ['{r}/{l}'.format(r=p , l = len(df)) for p in vdi.values]
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30 rep['Reads/Total'] = rep['Reads'].map(ft.partial(frm, y=len(df)))
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31 rep['Frequency %'] = (100*rep['Reads']/len(df)).map('{:.4f}'.format)
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32
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33 rep = rep.sort_values(by = ['Reads'] , ascending = False)
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34 rep.index = range(1,len(rep)+1)
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35
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36 su = rep[['V-GENE','Frequency %']].head(10)
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37 spl = fname.split('_')
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38 summdf = DataFrame([su['V-GENE'].values[0],su['Frequency %'].values[0]],
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39 index = ['Dominant V-GENE','Frequency'], columns = [spl[0]])
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40 summdf['%'] = ''
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41
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42 return (rep, su, summdf)
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43
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44
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45 if __name__ == '__main__':
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46
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47 start=time.time()
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48
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49 # Parse input arguments
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50 inp_name = sys.argv[1]
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51 outrep = sys.argv[2]
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52 summ_rep = sys.argv[3]
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53 summ_rep2 = sys.argv[4]
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54 fname = sys.argv[5]
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55
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56 # Execute basic function
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57 rep, su, summdf = repertoireComputation(inp_name, fname)
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58
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59 # Save output to CSV files
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60 if not rep.empty:
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61 rep.to_csv(outrep, sep = '\t')
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62 if not su.empty:
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63 su.to_csv(summ_rep, sep = '\t')
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64 if not summdf.empty:
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65 summdf.to_csv(summ_rep2, sep = '\t')
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66
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67 # Print execution time
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68 stop=time.time()
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69 print('Runtime:' + str(stop-start))
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