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