tomwalters@423: #!/usr/bin/python
tomwalters@423: """
tomwalters@423: plot_munged_results.py
tomwalters@423: 
tomwalters@423: """
tomwalters@423: 
tomwalters@423: import numpy as np
tomwalters@423: import pylab as p
tomwalters@423: import matplotlib.pyplot as plt
tomwalters@423: 
tomwalters@423: f=open("results_test_all.csv","r")
tomwalters@423: results = dict()
tomwalters@423: for line in f:
tomwalters@423:   if line[0] != "#":
tomwalters@423:     values = line.strip().split(",")
tomwalters@423:     results.setdefault(values[3],dict())
tomwalters@423:     results[values[3]].setdefault(values[0], dict())
tomwalters@423:     results[values[3]][values[0]].setdefault(values[1], dict())
tomwalters@423:     if values[2] == 'clean':
tomwalters@423:        snr = 40
tomwalters@423:     else:
tomwalters@423:        snr = int(values[2])
tomwalters@423:        results[values[3]][values[0]][values[1]][snr] = float(values[4])
tomwalters@423: #    results[values[3]].append((values[1],values[2],values[2],values[4]))
tomwalters@423: 
tomwalters@423: ax = plt.subplot(111)
tomwalters@423: 
tomwalters@423: train_set = 'inner'
tomwalters@423: lines = []
tomwalters@423: labels = []
tomwalters@423: for feature_type in ('mfcc', 'mfcc_vtln', 'aim'):
tomwalters@423:   for feature_subtype in results[train_set][feature_type].keys():
tomwalters@423:     this_line = results[train_set][feature_type][feature_subtype].items()
tomwalters@423:     this_line.sort(cmp=lambda x,y: x[0] - y[0])
tomwalters@423:     xs, ys = zip(*this_line)
tomwalters@423:     xs = list(xs)
tomwalters@423:     ys = list(ys)
tomwalters@423:     line, = ax.plot(xs,ys,'-o',linewidth=2)
tomwalters@423:     lines.append(line)
tomwalters@423:     labels.append(feature_type + "_" + feature_subtype)
tomwalters@423: p.legend(lines, labels, 'upper left', shadow=True)
tomwalters@423: p.xlabel('SNR/dB')
tomwalters@423: p.ylabel('Recognition performance %')
tomwalters@423: plt.show()