Mercurial > repos > devteam > weightedaverage

comparison WeightedAverage.py @ 0:9b7b4009f2c0 draft

Find changesets by keywords (author, files, the commit message), revision number or hash, or revset expression.
Imported from capsule None
author devteam
date Tue, 01 Apr 2014 10:48:52 -0400
parents
children 90611e86a998
comparison
equal deleted inserted replaced
-1:000000000000 0:9b7b4009f2c0
1 #!/usr/bin/env python
2 """
3 usage: %prog bed_file_1 bed_file_2 out_file
4 -1, --cols1=N,N,N,N: Columns for chr, start, end, strand in first file
5 -2, --cols2=N,N,N,N,N: Columns for chr, start, end, strand, name/value in second file
6 """
7
8 import collections
9 import sys
10 #import numpy
11 from galaxy import eggs
12 import pkg_resources
13 pkg_resources.require( "bx-python" )
14 from galaxy.tools.util.galaxyops import *
15 from bx.cookbook import doc_optparse
16
17
18 #export PYTHONPATH=~/galaxy/lib/
19 #running command python WeightedAverage.py interval_interpolate.bed value_interpolate.bed interpolate_result.bed
20
21 def stop_err(msg):
22 sys.stderr.write(msg)
23 sys.exit()
24
25
26 def FindRate(chromosome, start_stop, dictType):
27 OverlapList = []
28 for tempO in dictType[chromosome]:
29 DatabaseInterval = [tempO[0], tempO[1]]
30 Overlap = GetOverlap( start_stop, DatabaseInterval )
31 if Overlap > 0:
32 OverlapList.append([Overlap, tempO[2]])
33
34 if len(OverlapList) > 0:
35 SumRecomb = 0
36 SumOverlap = 0
37 for member in OverlapList:
38 SumRecomb += member[0]*member[1]
39 SumOverlap += member[0]
40 averageRate = SumRecomb/SumOverlap
41 return averageRate
42 else:
43 return 'NA'
44
45
46 def GetOverlap(a, b):
47 return min(a[1], b[1])-max(a[0], b[0])
48
49
50 options, args = doc_optparse.parse( __doc__ )
51
52 try:
53 chr_col_1, start_col_1, end_col_1, strand_col1 = parse_cols_arg( options.cols1 )
54 chr_col_2, start_col_2, end_col_2, strand_col2, name_col_2 = parse_cols_arg( options.cols2 )
55 input1, input2, input3 = args
56 except Exception, eee:
57 print eee
58 stop_err( "Data issue: click the pencil icon in the history item to correct the metadata attributes." )
59
60 fd2 = open(input2)
61 lines2 = fd2.readlines()
62 RecombChrDict = collections.defaultdict(list)
63
64 skipped = 0
65 for line in lines2:
66 temp = line.strip().split()
67 try:
68 assert float(temp[int(name_col_2)])
69 except:
70 skipped += 1
71 continue
72 tempIndex = [int(temp[int(start_col_2)]), int(temp[int(end_col_2)]), float(temp[int(name_col_2)])]
73 RecombChrDict[temp[int(chr_col_2)]].append(tempIndex)
74
75 print "Skipped %d features with invalid values" % (skipped)
76
77 fd1 = open(input1)
78 lines = fd1.readlines()
79 finalProduct = ''
80 for line in lines:
81 temp = line.strip().split('\t')
82 chromosome = temp[int(chr_col_1)]
83 start = int(temp[int(start_col_1)])
84 stop = int(temp[int(end_col_1)])
85 start_stop = [start, stop]
86 RecombRate = FindRate( chromosome, start_stop, RecombChrDict )
87 try:
88 RecombRate = "%.4f" % (float(RecombRate))
89 except:
90 RecombRate = RecombRate
91 finalProduct += line.strip()+'\t'+str(RecombRate)+'\n'
92 fdd = open(input3, 'w')
93 fdd.writelines(finalProduct)
94 fdd.close()