view experiments/scripts/cnbh-syllables/results_plotting/plot_munged_results.py @ 610:01986636257a

Second check-in of Alex Brandmeyer's C++ implementation of CARFAC. Addressed style issues and completed implementation of remaining functions. Still needs proper testing of the output stages against the MATLAB version, and runtime functions need improvements in efficiency.

#!/usr/bin/python
"""
plot_munged_results.py

"""

import matplotlib as mpl
mpl.use('PDF')
import numpy as np
import pylab as p
#import matplotlib.pyplot as plt

f=open("results_test_all.csv","r")
results = dict()
for line in f:
  if line[0] != "#":
    values = line.strip().split(",")
    results.setdefault(values[3],dict())
    results[values[3]].setdefault(values[0], dict())
    results[values[3]][values[0]].setdefault(values[1], dict())
    results[values[3]][values[0]][values[1]].setdefault(int(values[4]), dict())
    results[values[3]][values[0]][values[1]][int(values[4])].setdefault(int(values[5]), dict())
    results[values[3]][values[0]][values[1]][int(values[4])][int(values[5])].setdefault(int(values[6]), dict())
    if values[2] == 'clean':
       snr = 50
       results[values[3]][values[0]][values[1]][int(values[4])][int(values[5])][int(values[6])][snr] = float(values[7])
    else:
       snr = int(values[2])
       results[values[3]][values[0]][values[1]][int(values[4])][int(values[5])][int(values[6])][snr] = float(values[7])
#    results[values[3]].append((values[1],values[2],values[2],values[4]))

ax = mpl.pyplot.subplot(111)
train_set = 'inner'
for hmm_iterations in [2,3,15]:
  for hmm_states in [3,4]:
    for hmm_components in [3,4]:
      lines = []
      labels = []
      ax.cla()
      for feature_type in ('mfcc', 'mfcc_vtln', 'aim'):
        for feature_subtype in results[train_set][feature_type].keys():
          try:
            this_line = results[train_set][feature_type][feature_subtype][hmm_states][hmm_components][hmm_iterations].items()
            this_line.sort(cmp=lambda x,y: x[0] - y[0])
            xs, ys = zip(*this_line)
            xs = list(xs)
            ys = list(ys)
            line, = ax.plot(xs,ys,'-o',linewidth=2)
            lines.append(line)
            labels.append(feature_type + "_" + feature_subtype)
          except KeyError:
            print "Data not found"
      p.legend(lines, labels, 'upper left', shadow=True)
      p.xlabel('SNR/dB')
      p.ylabel('Recognition performance %')
      output_file = ("recognition_vs_snr_%diterations_%dstates_%d_components.pdf" % (hmm_iterations, hmm_states, hmm_components))
      p.savefig(output_file)