Mercurial > repos > nitrozyna > dm1_genotypying

comparison adams_tool.py @ 0:83458fec2479 draft

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author nitrozyna
date Sun, 31 Dec 2017 09:32:12 -0500
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-1:000000000000 0:83458fec2479
1
2 from __future__ import print_function
3 import sys
4 import numpy
5 import math
6 import random
7 import csv
8 import matplotlib.pyplot as plt
9 import pystache
10 import json
11 from sklearn import mixture
12
13 x = []
14 y = []
15
16 toolInput = sys.argv[1]
17 toolOutput = sys.argv[2]
18 toolWebsite = sys.argv[3]
19
20 with open(sys.argv[1], 'rb') as csvfile:
21 spamreader = csv.reader(csvfile, delimiter='\t')
22 for i, row in enumerate(spamreader):
23 if i != 0:
24 x.append(int(row[0]))
25 y.append(int(row[1]))
26
27 # you have to set this manually to weed out all the noise. Every bit of noise should be below it.
28 threshold = 20
29 rightLimit = 200
30
31 # unravelling histogram into samples.
32 samples = []
33 for no, value in enumerate([int(round(i)) for i in y]):
34 if value > threshold and no < rightLimit:
35 for _ in range(value):
36 samples.append(no)
37
38 # total number of reads
39 totalAmp = len(samples)
40
41 # reshaping numpy arrays to indicate that we pass a lot of samples, not a lot of features.
42 xArray = numpy.array(x).reshape(1, -1)
43 samplesArray = numpy.array(samples).reshape(-1, 1)
44
45 # learning a gaussian mixture model.
46 gmm2 = mixture.BayesianGaussianMixture(n_components=2).fit(samplesArray)
47
48 # getting the mean of each gaussian
49 means = [x[int(round(i[0]))] for i in gmm2.means_]
50
51 # rounding errors
52 roundErr = [i[0] - int(round(i[0])) for i in gmm2.means_]
53
54 # getting the coverage of each gaussian
55 weights = gmm2.weights_
56
57 sampleID = toolOutput + ".html"
58
59 with open(toolOutput, "w") as f:
60 print("sampleID", file=f, end="\t")
61 print("Al1", file=f, end="\t")
62 print("Al2", file=f, end="\t")
63 print("frac1", file=f, end="\t")
64 print("frac2", file=f, end="\t")
65 print(file=f)
66 print(sampleID, file=f, end="\t")
67 print(means[0], file=f, end="\t")
68 print(means[1], file=f, end="\t")
69 print(weights[0], file=f, end="\t")
70 print(weights[1], file=f, end="\t")
71
72 template_dir = {
73 "sampleID": sampleID,
74 "al1": means[0],
75 "al2": means[1],
76 "freq1": weights[0],
77 "freq2": weights[1],
78 "x": json.dumps(x),
79 "y": json.dumps(y)
80 }
81 with open(toolWebsite) as wt:
82 with open(sampleID, "w") as wr:
83 wr.write(pystache.render(wt.read(), template_dir))