Mercurial > repos > jay > pdaug_peptide_ngrams
annotate PDAUG_Fishers_Plot/PDAUG_Fishers_Plot.py @ 0:7557b48b2872 draft
"planemo upload for repository https://github.com/jaidevjoshi83/pdaug commit a9bd83f6a1afa6338cb6e4358b63ebff5bed155e"
author  jay 

date  Wed, 28 Oct 2020 02:10:12 +0000 
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0
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1 import matplotlib 
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2 matplotlib.use('Agg') 
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3 import os 
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4 import sys 
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5 sys.path.insert(0, os.path.abspath('..')) 
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6 
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7 import quantiprot 
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8 from quantiprot.utils.io import load_fasta_file 
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9 from quantiprot.utils.feature import Feature, FeatureSet 
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10 from quantiprot.metrics.aaindex import get_aa2volume, get_aa2hydropathy 
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11 from quantiprot.metrics.basic import average 
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12 
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13 from matplotlib import pyplot as plt 
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14 
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15 
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16 from math import log10, floor 
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17 import numpy as np 
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18 from matplotlib import pyplot as plt 
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19 from scipy.stats import fisher_exact 
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20 from quantiprot.utils.sequence import SequenceSet, compact 
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21 
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22 
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23 def _count_frame(data, frame_range, num_bins): 
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24 """ 
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25 Count instances in a 2D frame 
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26 
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27 The function discretizes the feature space into a grid of cells. 
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28 Then it counts the number of instances that fall into each cell. 
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29 An efficient method for counting instances is used. It performs parallel 
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30 logical comparisons of data instances to vectors that hold information on 
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31 grid lines. 
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32 
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33 Args: 
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34 data (numpy.matrix): a Nx2 data matrix 
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35 frame_range (numpy.matrix): a 2x2 matrix which defines feature ranges 
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36 num_bins (list): a pair defining the resolution of the 2D grid 
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37 Returns: 
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38 cell_counts (numpy.matrix): a matrix holding counts of instances in 
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39 each grid cell 
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40 bin_ranges (tuple): a pair of numpy matrices holding information on 
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41 bin(grid_cell) ranges 
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42 """ 
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43 grid_x = np.linspace(start=frame_range[0, 0], stop=frame_range[1, 0],\ 
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44 num=num_bins[0]+1, endpoint=True) 
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45 grid_y = np.linspace(start=frame_range[0, 1], stop=frame_range[1, 1],\ 
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46 num=num_bins[1]+1, endpoint=True) 
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47 # copy because we add ones in the next lines 
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48 bin_ranges = (np.copy(grid_x), np.copy(grid_y)) 
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49 
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50 
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51 #Count points in each grid cell 
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52 grid_x[1] += 1 # the last cell has to contain data at the border 
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53 grid_y[1] += 1 # the last cell has to contain data at the border 
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54 
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55 gte_x = np.matrix(data[:, 0] >= grid_x, dtype='float64') 
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56 lt_x = np.matrix(data[:, 0] < grid_x, dtype='float64') 
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57 gte_y = np.matrix(data[:, 1] >= grid_y, dtype='float64') 
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58 lt_y = np.matrix(data[:, 1] < grid_y, dtype='float64') 
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59 
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60 dif_x = gte_x  lt_x 
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61 dif_y = gte_y  lt_y 
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62 
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63 bins_x = dif_x.argmin(axis=1)  1 
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64 bins_y = dif_y.argmin(axis=1)  1 
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65 
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66 coords = np.concatenate((bins_x, bins_y), axis=1) 
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67 
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68 cell_counts = np.zeros(shape=(len(grid_x)1, len(grid_y)1)) 
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69 
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70 for i in range(coords.shape[0]): 
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71 cell_counts[coords[i, 0], coords[i, 1]] += 1 
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72 
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73 return cell_counts, bin_ranges 
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74 
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75 
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76 def local_fisher_2d(set1, set2, features=None, \ 
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77 windows_per_frame=10, overlap_factor=1, frame_range=None): 
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78 """ 
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79 Compare local and global distribution of samples from two populations 
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80 in the 2d feature space using the Fisher's exact test. 
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81 
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82 The function performs the Fisher Exact Test for comparing local and global 
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83 ratia of instance counts from two different populations. It uses the 
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84 '_count_frame' function to discretize the feature space and get instance 
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85 counts. Then it scans the 2d feature space with a sliding window and 
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86 performs the Fisher Exact test. 
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87 
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88 Args: 
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89 set1 (SequenceSet or numpy.matrix): the first set with at least 
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90 2 sequence features. 
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91 set2 (SequenceSet or numpy.matrix): the second set with at least 
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92 2 sequence features. 
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93 features (tuple or list): strings with feature names for running 
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94 the 2d Fisher test. If None then the first two features are 
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95 used. Relevant only if 'set1' or 'set2' are SequenceSets. 
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96 windows_per_frame (int): ratio between the whole feature space and 
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97 the sliding window (default 10). 
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98 overlap_factor (int):ratio between the size of a sliding window 
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99 and a discretization grid cell (default 1). 
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100 frame_range(numpy.matrix): 2x2 matrix with range of features 
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101 in both dimensions. 
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102 
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103 Returns final_res (dict): a dictionary including: 
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104 'odds_ratio' (numpy.matrix): a matrix of odds_ratios obtained 
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105 in each sliding window position. 
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106 'p_value' (numpy.matrix): a matrix containing Fisher test outcome 
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107 pvalues in each sliding window position. 
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108 'w_counts1' (numpy.matrix): a matrix with first population instance 
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109 counts in each sliding window position. 
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110 'w_counts2' (numpy.matrix): a matrix with second population instance 
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111 counts in each sliding window position. 
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112 'w_center_x' (numpy.matrix): matrix containing coordinates of window 
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113 centers in the X dimension. 
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114 'w_center_y' (numpy.matrix): matrix containing coordinates of window 
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115 centers in the Y dimension. 
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116 '_bin_ranges_x' (numpy.matrix): matrix containing bin(grid_cell) 
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117 ranges in the X dimension. 
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118 '_bin_ranges_y' (numpy.matrix): matrix containing bin(grid_cell) 
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119 ranges in the Y dimension. 
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120 """ 
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121 
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122 if isinstance(set1, SequenceSet): 
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123 mat1 = np.transpose(np.matrix(compact(set1, 
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124 features=features).columns())) 
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125 if isinstance(set2, SequenceSet): 
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126 mat2 = np.transpose(np.matrix(compact(set2, 
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127 features=features).columns())) 
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128 
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129 #Deal with window_per_frame and overlap_factor 
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130 #given either as a scalar or as a listlike 
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131 if not hasattr(windows_per_frame, "__len__"): 
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132 w_per_frame = (windows_per_frame, windows_per_frame) 
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133 else: 
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134 w_per_frame = (windows_per_frame[0], windows_per_frame[1]) 
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135 
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136 if not hasattr(overlap_factor, "__len__"): 
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137 w_size = (overlap_factor, overlap_factor) 
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138 else: 
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139 w_size = (overlap_factor[0], overlap_factor[1]) 
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140 
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141 num_bins = (w_per_frame[0]*w_size[0], w_per_frame[1]*w_size[1]) 
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142 
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143 if frame_range is None: 
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144 #Evaluate the range of features in both populations. 
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145 
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146 frame_range = np.concatenate((np.minimum(mat1.min(0), mat2.min(0)),\ 
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147 np.maximum(mat1.max(0), mat2.max(0)))) 
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148 
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149 margin_x = (frame_range[1, 0]  frame_range[0, 0])/w_per_frame[0] 
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150 margin_y = (frame_range[1, 1]  frame_range[0, 1])/w_per_frame[1] 
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151 
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152 frame_range[0, 0] = margin_x 
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153 frame_range[1, 0] += margin_x 
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154 
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155 frame_range[0, 1] = margin_y 
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156 frame_range[1, 1] += margin_y 
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157 
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158 #Discretize feature space into NxM grid, 
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159 #where N = w_per_frame[0]*w_size[0]. 
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160 # M = w_per_frame[1]*w_size[1]. 
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161 #count instances of population1 and population2 in each grid cell. 
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162 #both bin ranges are always the same because the frame range is common. 
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163 cell_counts1, bin_ranges = _count_frame(mat1, frame_range=frame_range,\ 
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164 num_bins=num_bins) 
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165 cell_counts2, _ = _count_frame(mat2, frame_range=frame_range,\ 
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166 num_bins=num_bins) 
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167 
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168 #Number of windows that fit in a single row/column of a frame 
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169 w_number = (cell_counts1.shape[0]w_size[0]+1, 
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170 cell_counts1.shape[1]w_size[1]+1) 
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171 
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172 #Initialize matrices holding counts at scanning window positions. 
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173 window_counts1 = np.zeros(shape=w_number) 
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174 window_counts2 = np.zeros(shape=w_number) 
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175 
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176 #Initialize matrices holding window coordinates 
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177 window_center_x = np.zeros(shape=w_number[0]) 
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178 window_center_y = np.zeros(shape=w_number[1]) 
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179 
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180 #Initialize matrices holding Fisher Exact test results 
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181 fisher_pv = np.ones(shape=w_number) 
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182 odds_ratio = np.ones(shape=w_number) 
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183 
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184 #Calculate population totals in the whole feature space 
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185 all1 = cell_counts1.sum() 
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186 all2 = cell_counts2.sum() 
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187 
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188 #Calculate window centers 
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189 for start_x in range(0, w_number[0]): 
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190 window_center_x[start_x] = (bin_ranges[0][start_x]+ \ 
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191 bin_ranges[0][start_x+w_size[0]])/2 
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192 for start_y in range(0, w_number[1]): 
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193 window_center_y[start_y] = (bin_ranges[1][start_y]+ \ 
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194 bin_ranges[1][start_y+w_size[1]])/2 
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195 
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196 #Scan the feature space with a step of 1 cell. 
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197 for start_x in range(0, w_number[0]): 
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198 
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199 for start_y in range(0, w_number[1]): 
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200 #Count instances of each population in the window 
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201 window_counts1[start_x, start_y] = \ 
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202 cell_counts1[start_x:(start_x+w_size[0]), \ 
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203 start_y:(start_y+w_size[1])].sum() 
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204 window_counts2[start_x, start_y] = \ 
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205 cell_counts2[start_x:(start_x+w_size[0]), \ 
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206 start_y:(start_y+w_size[1])].sum() 
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207 #Perform the Fisher Exact Test against 
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208 #h0: population ratio in the window the same as in the whole space. 
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209 odds_ratio[start_x, start_y], fisher_pv[start_x, start_y] =\ 
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210 fisher_exact([[all1, window_counts1[start_x, start_y]],\ 
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211 [all2, window_counts2[start_x, start_y]]]) 
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212 
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213 fisher_res = {'p_value':fisher_pv, 'odds_ratio':odds_ratio,\ 
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214 'w_counts1':window_counts1, 'w_counts2':window_counts2,\ 
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215 'w_center_x':window_center_x, 'w_center_y':window_center_y,\ 
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216 '_bin_ranges_x':bin_ranges[0], '_bin_ranges_y':bin_ranges[1]} 
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217 
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218 return fisher_res 
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219 
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220 
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221 def _plot_local_fisher_2d(fisher_res, xlabel="feat_1", ylabel="feat_2", 
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222 pop1_label="pop_1", pop2_label="pop_2", out_file_path=None, fig_width=8, fig_hight=8, fig_hspace=0.35, fig_wspace=0.25): 
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223 """ 
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224 Plot results of the local Fisher's extact test in the 2d space. 
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225 
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226 Args: 
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227 fisher_res (dict): output from 'fisher_local_2d'. 
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228 xlabel (str): name of the 1st feature to appear in the plots 
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229 (default: "feat_1") 
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230 ylabel (str): name of the 2nd feature to appear in the plots 
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231 (default: "feat_2") 
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232 pop1_label (str): name of the 1st population to appear in the plots 
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233 (default: "pop_1") 
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234 pop2_label (str): name of the 2nd population to appear in the plots 
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235 (default: "pop_2") 
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236 """ 
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237 fisher_or = fisher_res["odds_ratio"] 
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238 fisher_c1 = fisher_res["w_counts1"] 
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239 fisher_c2 = fisher_res["w_counts2"] 
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240 fisher_pv = fisher_res["p_value"] 
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241 
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242 for pos_x in range(len(fisher_or)): 
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243 for pos_y in range(len(fisher_or[0])): 
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244 if fisher_c1[pos_x][pos_y] == 0 and fisher_c2[pos_x][pos_y] == 0: 
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245 fisher_or[pos_x][pos_y] = np.nan 
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246 elif fisher_c1[pos_x][pos_y] == 0: 
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247 fisher_or[pos_x][pos_y] = np.inf 
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248 elif fisher_c2[pos_x][pos_y] == 0: 
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249 fisher_or[pos_x][pos_y] = np.inf 
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250 elif fisher_or[pos_x][pos_y] < 1: 
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251 fisher_or[pos_x][pos_y] = 1.0/fisher_or[pos_x][pos_y] 
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252 
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253 vmax_abs = np.nanmax(np.abs([x for x in np.array(fisher_or).flatten() 
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254 if x > np.inf and x < np.inf])) 
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255 
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256 for pos_x in range(len(fisher_or)): 
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257 for pos_y in range(len(fisher_or[0])): 
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258 if abs(fisher_or[pos_x][pos_y]) == np.inf: 
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259 fisher_or[pos_x][pos_y] = np.sign(fisher_or[pos_x][pos_y])*vmax_abs 
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260 
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261 ##### Extra Fig perimeters added ################################ 
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262 plt.figure(figsize=(fig_width, fig_hight)) # Figure size 
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263 plt.subplots_adjust(hspace = fig_hspace, wspace = fig_wspace) # space between the subplots. 
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264 ################################################################## 
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265 
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266 plt.subplot(221) 
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267 plt.pcolormesh(fisher_res["w_center_x"], fisher_res["w_center_y"], 
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268 np.ma.masked_invalid(fisher_c1).T, cmap="Reds") 
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269 plt.colorbar() 
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270 plt.xlabel(xlabel) 
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271 plt.ylabel(ylabel) 
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272 plt.title("Counts "+pop1_label) 
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273 
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274 plt.subplot(222) 
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275 plt.pcolormesh(fisher_res["w_center_x"], fisher_res["w_center_y"], 
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276 np.ma.masked_invalid(fisher_c2).T, cmap="Reds") 
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277 plt.colorbar() 
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278 plt.xlabel(xlabel) 
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279 plt.ylabel(ylabel) 
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280 plt.title("Counts "+pop2_label) 
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281 
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282 cmap = plt.get_cmap('RdBu') 
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283 cmap.set_bad(color='k', alpha=1.) 
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284 
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285 cbar_lo = 1.0/vmax_abs 
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286 cbar_lo_places = max(0, floor(log10(cbar_lo))+1) 
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287 cbar_hi = vmax_abs 
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288 cbar_hi_places = max(0, floor(log10(cbar_hi))+1) 
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289 
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290 plt.subplot(223) 
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291 plt.pcolormesh(fisher_res["w_center_x"], fisher_res["w_center_y"], 
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292 np.ma.masked_invalid(fisher_or).T, cmap=cmap, 
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293 vmin=vmax_abs, vmax=vmax_abs) 
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294 cbar = plt.colorbar(ticks=([vmax_abs, 0, vmax_abs])) 
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295 cbar.ax.set_yticklabels(['< '+str(round(cbar_lo, int(cbar_lo_places))), '1', 
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296 '> '+str(round(cbar_hi, int(cbar_hi_places)))]) 
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297 plt.xlabel(xlabel) 
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298 plt.ylabel(ylabel) 
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299 plt.title("Odds ratio") 
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300 
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301 plt.subplot(224) 
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302 plt.pcolormesh(fisher_res["w_center_x"], fisher_res["w_center_y"], 
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303 np.log10(np.ma.masked_invalid(fisher_pv)).T, cmap="RdGy") 
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304 plt.colorbar() 
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305 plt.xlabel(xlabel) 
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306 plt.ylabel(ylabel) 
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307 plt.title("Fisher test\npvalue (logarithm of 10)") 
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308 
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309 #Savefig function added with preserving default behavior 
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310 
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311 if out_file_path==None: 
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312 plt.show() 
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313 else: 
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314 plt.savefig(out_file_path,dpi=300) 
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315 
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316 
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317 def HTML_Gen(html): 
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318 
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319 out_html = open(html,'w') 
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320 part_1 = """ 
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321 
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322 <!DOCTYPE html> 
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323 <html lang="en"> 
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324 <head> 
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325 <title>Bootstrap Example</title> 
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326 <meta charset="utf8"> 
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327 <meta name="viewport" content="width=devicewidth, initialscale=1"> 
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328 <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.4.0/css/bootstrap.min.css"> 
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329 <script src="https://ajax.googleapis.com/ajax/libs/jquery/3.4.0/jquery.min.js"></script> 
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330 <script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.4.0/js/bootstrap.min.js"></script> 
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331 <body> 
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332 <style> 
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333 div.container_1 { 
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334 width:600px; 
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335 margin: auto; 
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336 paddingright: 10; 
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337 } 
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338 div.table { 
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339 width:600px; 
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340 margin: auto; 
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341 paddingright: 10; 
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342 } 
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343 </style> 
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344 </head> 
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345 <div class="jumbotron textcenter"> 
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346 <h1> Fisher's Plot </h1> 
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347 </div> 
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348 <div class="container"> 
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349 <div class="row"> 
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350 <div class="colsm4"> 
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351 <img src="1.png" alt="Smiley face" height="800" width="800"> 
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352 </div> 
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353 
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354 </div> 
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355 </div> 
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356 </body> 
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357 </html> 
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358 """ 
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359 out_html.write(part_1) 
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360 out_html.close() 
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361 # Load sets of amyloidogenic and nonamyloidogenic peptides: 
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362 
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363 def run(Fasta1, Fasta2, windows_per_frame, overlap_factor, xlabel, ylabel, pop1_label, pop2_label, htmlOutDir, htmlFname, Workdirpath): 
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364 
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365 if not os.path.exists(htmlOutDir): 
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366 os.makedirs(htmlOutDir) 
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367 
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368 amyload_pos_seq = load_fasta_file(Fasta1) 
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369 amyload_neg_seq = load_fasta_file(Fasta2) 
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370 
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371 # Calculate quantitive features: volume and hydropathy 
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372 mean_volume = Feature(get_aa2volume()).then(average) 
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373 mean_hydropathy = Feature(get_aa2hydropathy()).then(average) 
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374 
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375 fs = FeatureSet("volume'n'hydropathy") 
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376 fs.add(mean_volume) 
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377 fs.add(mean_hydropathy) 
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378 
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379 amyload_pos_conv_seq = fs(amyload_pos_seq) 
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380 amyload_neg_conv_seq = fs(amyload_neg_seq) 
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381 
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382 # Do local Fisher: 
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383 result = local_fisher_2d(amyload_pos_conv_seq, amyload_neg_conv_seq, 
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384 windows_per_frame=int(windows_per_frame), overlap_factor=int(overlap_factor)) 
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385 
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386 # Plot local Fisher: 
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387 _plot_local_fisher_2d(result, xlabel=xlabel, 
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388 ylabel=ylabel, 
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389 pop1_label=pop1_label, 
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390 pop2_label=pop2_label, 
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391 out_file_path =os.path.join(os.getcwd(), "out.png") 
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392 ) 
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393 
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394 
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395 # plt.savefig(os.path.join(Workdirpath, htmlOutDir, "1.png")) 
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396 
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397 HTML_Gen(os.path.join(Workdirpath, htmlOutDir, htmlFname)) 
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398 
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399 if __name__=="__main__": 
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400 
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401 
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402 import argparse 
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403 
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404 parser = argparse.ArgumentParser() 
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405 
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406 parser.add_argument("f1", "Fasta1", required=True, default=None, help="First fasta file ") 
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407 parser.add_argument("f2", "Fasta2", required=True, default=None, help="Second fasta file") 
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408 parser.add_argument("o", "overlap_factor", required=False, default=5, help="Overlap factor") 
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409 parser.add_argument("w", "windows_per_frame", required=False, default=5, help="Windows per frame") 
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410 parser.add_argument("x", "xlabel", required=True, default=None, help="X label") 
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411 parser.add_argument("y", "ylabel", required=True, default=None, help="Y label") 
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412 parser.add_argument("p1", "pop1_label", required=True, default=None, help="First population label") 
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413 parser.add_argument("p2", "pop2_label", required=True, default=None, help="Second population label") 
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414 parser.add_argument("htmlOutDir", required=False, default=os.path.join(os.getcwd(),'report_dir'), help="Path to html dir") 
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415 parser.add_argument("htmlFname", required=False, help="html output file", default="report.html") 
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416 parser.add_argument("Workdirpath", required=False, default=os.getcwd(), help="Path to output Working Directory") 
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417 args = parser.parse_args() 
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418 
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419 run(args.Fasta1, args.Fasta2, args.windows_per_frame, args.overlap_factor, args.xlabel, args.ylabel, args.pop1_label, args.pop2_label, args.htmlOutDir, args.htmlFname, args.Workdirpath) 
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420 