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1 #!/usr/bin/env python
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2
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3 import os,sys,matplotlib,zipfile,argparse,string
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4 matplotlib.use('Agg')
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5 from pylab import *
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6 from lefse import *
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7 import random as rand
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8
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9 colors = ['r','g','b','m','c']
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10
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11 def read_params(args):
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12 parser = argparse.ArgumentParser(description='Cladoplot')
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13 parser.add_argument('input_file_1', metavar='INPUT_FILE', type=str, help="dataset files")
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14 parser.add_argument('input_file_2', metavar='INPUT_FILE', type=str, help="LEfSe output file")
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15 parser.add_argument('output_file', metavar='OUTPUT_FILE', type=str, help="the file for the output (the zip file if an archive is required, the output directory otherwise)")
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16 parser.add_argument('--width',dest="width", type=float, default=10.0 )
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17 parser.add_argument('--height',dest="height", type=float, default=4.0)
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18 parser.add_argument('--top',dest="top", type=float, default=-1.0, help="set maximum y limit (-1.0 means automatic limit)")
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19 parser.add_argument('--bot',dest="bot", type=float, default=0.0, help="set minimum y limit (default 0.0, -1.0 means automatic limit)")
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20 parser.add_argument('--title_font_size',dest="title_font_size", type=str, default="14")
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21 parser.add_argument('--class_font_size',dest="class_font_size", type=str, default="14")
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22 parser.add_argument('--class_label_pos',dest="class_label_pos", type=str, choices=["up","down"], default="up")
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23 parser.add_argument('--subcl_mean',dest="subcl_mean", type=str, choices=["y","n"], default="y")
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24 parser.add_argument('--subcl_median',dest="subcl_median", type=str, choices=["y","n"], default="y")
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25 parser.add_argument('--font_size',dest="font_size", type=str, default="10")
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26 parser.add_argument('-n',dest="unused", metavar="flt", type=float, default=-1.0,help="unused")
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27 parser.add_argument('--format', dest="format", default="png", choices=["png","pdf","svg"], type=str, help="the format for the output file")
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28 parser.add_argument('-f', dest="f", default="diff", choices=["all","diff","one"], type=str, help="wheter to plot all features (all), only those differentially abundant according to LEfSe or only one (the one given with --feature_name) ")
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29 parser.add_argument('--feature_name', dest="feature_name", default="", type=str, help="The name of the feature to plot (levels separated by .) ")
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30 parser.add_argument('--feature_num', dest="feature_num", default="-1", type=int, help="The number of the feature to plot ")
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31 parser.add_argument('--archive', dest="archive", default="none", choices=["zip","none"], type=str, help="")
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32 parser.add_argument('--background_color',dest="back_color", type=str, choices=["k","w"], default="w", help="set the color of the background")
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33 parser.add_argument('--dpi',dest="dpi", type=int, default=72)
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34
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35 args = parser.parse_args()
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36
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37 return vars(args)
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38
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39 def read_data(file_data,file_feats,params):
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40 with open(file_feats, 'r') as features:
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41 feats_to_plot = [(f.split()[:-1],len(f.split()) == 5) for f in features.readlines()]
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42 if not feats_to_plot:
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43 print "No features to plot\n"
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44 sys.exit(0)
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45 feats,cls,class_sl,subclass_sl,class_hierarchy,params['norm_v'] = load_data(file_data, True)
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46 if params['feature_num'] > 0:
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47 params['feature_name'] = [line.split()[0] for line in open(params['input_file_2'])][params['feature_num']-1]
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48 features = {}
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49 for f in feats_to_plot:
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50 if params['f'] == "diff" and not f[1]: continue
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51 if params['f'] == "one" and f[0][0] != params['feature_name']: continue
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52 features[f[0][0]] = {'dim':float(f[0][1]), 'abundances':feats[f[0][0]], 'sig':f[1], 'cls':cls, 'class_sl':class_sl, 'subclass_sl':subclass_sl, 'class_hierarchy':class_hierarchy}
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53 if not features:
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54 print "No features to plot\n"
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55 sys.exit(0)
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56 return features
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57
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58 def plot(name,k_n,feat,params):
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59 fig = plt.figure(figsize=(params['width'], params['height']),edgecolor=params['fore_color'],facecolor=params['back_color'])
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60 ax = fig.add_subplot(111,axis_bgcolor=params['back_color'])
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61 subplots_adjust(bottom=0.15)
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62
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63 max_m = 0.0
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64 norm = 1.0 if float(params['norm_v']) < 0.0 else float(params['norm_v'])
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65 for v in feat['subclass_sl'].values():
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66 fr,to = v[0], v[1]
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67 median = numpy.mean(feat['abundances'][fr:to])
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68 if median > max_m: max_m = median
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69 max_m /= norm
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70 max_v = max_m*3 if max_m*3 < max(feat['abundances'])*1.1/norm else max(feat['abundances'])/norm
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71 min_v = max(0.0,min(feat['abundances'])*0.9/norm)
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72
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73 if params['top'] > 0.0: max_v = params['top']
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74 if params['bot'] >= 0.0: min_v = params['bot']
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75
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76 if max_v == 0.0: max_v = 0.0001
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77 if max_v == min_v: max_v = min_v*1.1
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78
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79 cl_sep = max(int(sum([vv[1]/norm - vv[0]/norm for vv in feat['class_sl'].values()])/150.0),1)
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80 seps = []
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81 xtics = []
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82 x2tics = []
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83 last_fr = 0.0
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84 for i,cl in enumerate(sorted(feat['class_hierarchy'].keys())):
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85 for j,subcl in enumerate(feat['class_hierarchy'][cl]):
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86 fr = feat['subclass_sl'][subcl][0]
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87 to = feat['subclass_sl'][subcl][1]
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88 val = feat['abundances'][fr:to]
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89 fr += cl_sep*i
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90 to += cl_sep*i
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91 pos = arange(fr,to)
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92 max_x = to
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93 col = colors[j%len(colors)]
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94 vv = [v1/norm for v1 in val]
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95 median = numpy.median(vv)
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96 mean = numpy.mean(vv)
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97 valv = [max(min(v/norm,max_v),min_v) for v in val]
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98 ax.bar(pos,valv, align='center', color=col, edgecolor=col, linewidth=0.1 )
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99 if params['subcl_median'] == 'y': ax.plot([fr,to-1],[median,median],"k--",linewidth=1,color=params['fore_color'])
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100 if params['subcl_mean'] == 'y': ax.plot([fr,to-1],[mean,mean],"-",linewidth=1,color=params['fore_color'])
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101 nna = subcl if subcl.count("_") == 0 or not subcl.startswith(cl) else "_".join(subcl.split(cl)[1:])
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102 if nna == "subcl" or nna == "_subcl": nna = " "
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103 xtics.append(((fr+(to-fr)/2),nna))
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104 seps.append(float(to))
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105 x2tics.append(((last_fr+(to-last_fr)/2),cl))
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106 last_fr = to + float(cl_sep)
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107 for s in seps[:-1]:
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108 ax.plot([s,s],[min_v,max_v],"-",linewidth=5,color=params['fore_color'])
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109 ax.set_title(k_n, size=params['title_font_size'])
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110 xticks([x[0] for x in xtics],[x[1] for x in xtics],rotation=-30, ha = 'left', fontsize=params['font_size'], color=params['fore_color'])
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111 yticks(fontsize=params['font_size'])
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112
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113 ylabel('Relative abundance')
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114 ax.set_ylim((min_v,max_v))
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115 a,b = ax.get_xlim()
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116 ax.set_xlim((0-float(last_fr)/float(b-a),max_x))
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117 ax.yaxis.grid(True)
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118
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119 def get_col_attr(x):
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120 return hasattr(x, 'set_color') and not hasattr(x, 'set_facecolor')
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121 def get_edgecol_attr(x):
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122 return hasattr(x, 'set_edgecolor')
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123
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124
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125 for o in fig.findobj(get_col_attr):
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126 o.set_color(params['fore_color'])
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127 for o in fig.findobj(get_edgecol_attr):
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128 if o.get_edgecolor() == params['back_color']:
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129 o.set_edgecolor(params['fore_color'])
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130
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131 for t in x2tics:
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132 m = ax.get_ylim()[1]*0.97 if params['class_label_pos']=='up' else 0.07
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133 plt.text(t[0],m, "class: "+t[1], ha ="center", size=params['class_font_size'], va="top", bbox = dict(boxstyle="round", ec='k', fc='y'))
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134
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135
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136 plt.savefig(name,format=params['format'],facecolor=params['back_color'],edgecolor=params['fore_color'],dpi=params['dpi'])
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137 plt.close()
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138 return name
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139
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140 if __name__ == '__main__':
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141 params = read_params(sys.argv)
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142 params['fore_color'] = 'w' if params['back_color'] == 'k' else 'k'
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143 features = read_data(params['input_file_1'],params['input_file_2'],params)
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144 if params['archive'] == "zip": file = zipfile.ZipFile(params['output_file'], "w")
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145 for k,f in features.items():
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146 print "Exporting ", k
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147 if params['archive'] == "zip":
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148 of = plot("/tmp/"+str(int(f['sig']))+"_"+"-".join(k.split("."))+"."+params['format'],k,f,params)
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149 file.write(of, os.path.basename(of), zipfile.ZIP_DEFLATED)
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150 else:
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151 if params['f'] == 'one': plot(params['output_file'],k,f,params)
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152 else: plot(params['output_file']+str(int(f['sig']))+"_"+"-".join(k.split("."))+"."+params['format'],k,f,params)
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153 if params['archive'] == "zip": file.close()
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