def plot_soutenance():
"""
Plot des PDFs des 4 attributs définis par Clément pour le ppt
de la soutenance.
"""
from options import MultiOptions
opt = MultiOptions()
opt.opdict['channels'] = ['Z']
#opt.opdict['feat_train'] = 'clement_train.csv'
#opt.opdict['feat_test'] = 'clement_test.csv'
opt.opdict['feat_list'] = ['AsDec','Dur','Ene','KRapp']
#opt.opdict['feat_log'] = ['AsDec','Dur','Ene','KRapp']
opt.do_tri()
opt.x = opt.xs[0]
opt.y = opt.ys[0]
opt.compute_pdfs()
gauss = opt.gaussians
fig = plt.figure(figsize=(12,2.5))
fig.set_facecolor('white')
for ifeat,feat in enumerate(sorted(gauss)):
ax = fig.add_subplot(1,4,ifeat+1)
ax.plot(gauss[feat]['vec'],gauss[feat]['VT'],ls='-',c='b',lw=2.)
ax.plot(gauss[feat]['vec'],gauss[feat]['EB'],ls='-',c='r',lw=2.)
ax.set_title(feat)
ax.xaxis.set_ticks_position('bottom')
ax.xaxis.set_ticklabels('')
ax.yaxis.set_ticks_position('left')
ax.yaxis.set_ticklabels('')
if ifeat == 0:
ax.legend(['VT','EB'],loc=1,prop={'size':10})
plt.savefig('/home/nadege/Dropbox/Soutenance/pdfs.png')
plt.show()
def plot_soutenance():
"""
Plot des PDFs des 4 attributs définis par Clément pour le ppt
de la soutenance.
"""
from options import MultiOptions
opt = MultiOptions()
opt.opdict['channels'] = ['Z']
#opt.opdict['feat_train'] = 'clement_train.csv'
#opt.opdict['feat_test'] = 'clement_test.csv'
opt.opdict['feat_list'] = ['AsDec', 'Dur', 'Ene', 'KRapp']
#opt.opdict['feat_log'] = ['AsDec','Dur','Ene','KRapp']
opt.do_tri()
opt.x = opt.xs[0]
opt.y = opt.ys[0]
opt.compute_pdfs()
gauss = opt.gaussians
fig = plt.figure(figsize=(12, 2.5))
fig.set_facecolor('white')
for ifeat, feat in enumerate(sorted(gauss)):
ax = fig.add_subplot(1, 4, ifeat + 1)
ax.plot(gauss[feat]['vec'], gauss[feat]['VT'], ls='-', c='b', lw=2.)
ax.plot(gauss[feat]['vec'], gauss[feat]['EB'], ls='-', c='r', lw=2.)
ax.set_title(feat)
ax.xaxis.set_ticks_position('bottom')
ax.xaxis.set_ticklabels('')
ax.yaxis.set_ticks_position('left')
ax.yaxis.set_ticklabels('')
if ifeat == 0:
ax.legend(['VT', 'EB'], loc=1, prop={'size': 10})
plt.savefig('/home/nadege/Dropbox/Soutenance/pdfs.png')
plt.show()
def compare_clement():
"""
Comparaison des attributs de Clément avec ceux que j'ai recalculés.
"""
from options import MultiOptions
opt = MultiOptions()
opt.opdict['channels'] = ['Z']
# Mes calculs
opt.opdict['feat_list'] = ['Dur', 'AsDec', 'RappMaxMean', 'Kurto', 'KRapp']
opt.opdict['feat_log'] = ['AsDec', 'RappMaxMean', 'Kurto']
#opt.opdict['feat_list'] = ['Ene']
#opt.opdict['feat_log'] = ['Ene']
opt.do_tri()
opt.x = opt.xs[0]
opt.y = opt.ys[0]
opt.x.columns = opt.opdict['feat_list']
opt.compute_pdfs()
my_gauss = opt.gaussians
if 'Kurto' in opt.opdict['feat_list'] and 'RappMaxMean' in opt.opdict[
'feat_list']:
fig = plt.figure()
fig.set_facecolor('white')
plt.plot(np.log(opt.x.Kurto), np.log(opt.x.RappMaxMean), 'ko')
plt.xlabel('Kurto')
plt.ylabel('RappMaxMean')
plt.show()
# Les calculs de Clément
#opt.opdict['feat_list'] = ['Dur','AsDec','RappMaxMean','Kurto','Ene']
opt.opdict['feat_log'] = []
opt.opdict['feat_train'] = 'clement_train.csv'
opt.opdict['feat_test'] = 'clement_test.csv'
opt.do_tri()
opt.x = opt.xs[0]
opt.y = opt.ys[0]
opt.compute_pdfs()
# Trait plein --> Clément
# Trait tireté --> moi
opt.plot_superposed_pdfs(my_gauss, save=False)
def compare_clement():
"""
Comparaison des attributs de Clément avec ceux que j'ai recalculés.
"""
from options import MultiOptions
opt = MultiOptions()
opt.opdict['channels'] = ['Z']
# Mes calculs
opt.opdict['feat_list'] = ['Dur','AsDec','RappMaxMean','Kurto','KRapp']
opt.opdict['feat_log'] = ['AsDec','RappMaxMean','Kurto']
#opt.opdict['feat_list'] = ['Ene']
#opt.opdict['feat_log'] = ['Ene']
opt.do_tri()
opt.x = opt.xs[0]
opt.y = opt.ys[0]
opt.x.columns = opt.opdict['feat_list']
opt.compute_pdfs()
my_gauss = opt.gaussians
if 'Kurto' in opt.opdict['feat_list'] and 'RappMaxMean' in opt.opdict['feat_list']:
fig = plt.figure()
fig.set_facecolor('white')
plt.plot(np.log(opt.x.Kurto),np.log(opt.x.RappMaxMean),'ko')
plt.xlabel('Kurto')
plt.ylabel('RappMaxMean')
plt.show()
# Les calculs de Clément
#opt.opdict['feat_list'] = ['Dur','AsDec','RappMaxMean','Kurto','Ene']
opt.opdict['feat_log'] = []
opt.opdict['feat_train'] = 'clement_train.csv'
opt.opdict['feat_test'] = 'clement_test.csv'
opt.do_tri()
opt.x = opt.xs[0]
opt.y = opt.ys[0]
opt.compute_pdfs()
# Trait plein --> Clément
# Trait tireté --> moi
opt.plot_superposed_pdfs(my_gauss,save=False)
def compare_lissage():
"""
Comparaison des kurtosis avec deux lissages différents.
"""
plot_envelopes()
from options import MultiOptions
opt = MultiOptions()
opt.opdict['channels'] = ['Z']
# Lissage sur des fenêtres de 0.5 s
opt.opdict['feat_list'] = ['Kurto']
opt.opdict['feat_log'] = ['Kurto']
opt.do_tri()
opt.x = opt.xs[0]
opt.y = opt.ys[0]
opt.x.columns = opt.opdict['feat_list']
opt.compute_pdfs()
gauss_stand = opt.gaussians
# Lissage sur des fenêtres de 1 s
opt.opdict['feat_train'] = '0610_Piton_trainset.csv'
opt.opdict['feat_test'] = '0610_Piton_testset.csv'
opt.do_tri()
opt.x = opt.xs[0]
opt.y = opt.ys[0]
opt.compute_pdfs()
gauss_1s = opt.gaussians
# Lissage sur des fenêtres de 5 s
opt.opdict['feat_train'] = '1809_Piton_trainset.csv'
opt.opdict['feat_test'] = '1809_Piton_testset.csv'
opt.do_tri()
opt.x = opt.xs[0]
opt.y = opt.ys[0]
opt.compute_pdfs()
gauss_5s = opt.gaussians
# Lissage sur des fenêtres de 10 s
opt.opdict['feat_train'] = '0510_Piton_trainset.csv'
opt.opdict['feat_test'] = '0510_Piton_testset.csv'
opt.do_tri()
opt.x = opt.xs[0]
opt.y = opt.ys[0]
opt.compute_pdfs()
gauss_10s = opt.gaussians
### PLOT OF SUPERPOSED PDFs ###
fig = plt.figure(figsize=(12,2.5))
fig.set_facecolor('white')
for feat in sorted(opt.gaussians):
maxi = int(np.max([gauss_stand[feat]['vec'],gauss_1s[feat]['vec'],gauss_5s[feat]['vec'],gauss_10s[feat]['vec']]))
ax1 = fig.add_subplot(141)
ax1.plot(gauss_stand[feat]['vec'],gauss_stand[feat]['VT'],ls='-',c='b',lw=2.,label='VT')
ax1.plot(gauss_stand[feat]['vec'],gauss_stand[feat]['EB'],ls='-',c='r',lw=2.,label='EB')
ax1.set_xlim([0,maxi])
ax1.set_xlabel(feat)
ax1.set_title('0.5 s')
ax1.legend(prop={'size':10})
ax2 = fig.add_subplot(142)
ax2.plot(gauss_1s[feat]['vec'],gauss_1s[feat]['VT'],ls='-',c='b',lw=2.)
ax2.plot(gauss_1s[feat]['vec'],gauss_1s[feat]['EB'],ls='-',c='r',lw=2.)
ax2.set_xlim([0,maxi])
ax2.set_xlabel(feat)
ax2.set_title('1 s')
ax2.set_yticklabels('')
ax3 = fig.add_subplot(143)
ax3.plot(gauss_5s[feat]['vec'],gauss_5s[feat]['VT'],ls='-',c='b',lw=2.)
ax3.plot(gauss_5s[feat]['vec'],gauss_5s[feat]['EB'],ls='-',c='r',lw=2.)
ax3.set_xlim([0,maxi])
ax3.set_xlabel(feat)
ax3.set_title('5 s')
ax3.set_yticklabels('')
ax4 = fig.add_subplot(144)
ax4.plot(gauss_10s[feat]['vec'],gauss_10s[feat]['VT'],ls='-',c='b',lw=2.)
ax4.plot(gauss_10s[feat]['vec'],gauss_10s[feat]['EB'],ls='-',c='r',lw=2.)
ax4.set_xlim([0,maxi])
ax4.set_xlabel(feat)
ax4.set_title('10 s')
ax4.set_yticklabels('')
#plt.savefig('%s/features/comp_%s.png'%(opt.opdict['outdir'],feat))
plt.show()
def compare_lissage():
"""
Comparaison des kurtosis avec deux lissages différents.
"""
plot_envelopes()
from options import MultiOptions
opt = MultiOptions()
opt.opdict['channels'] = ['Z']
# Lissage sur des fenêtres de 0.5 s
opt.opdict['feat_list'] = ['Kurto']
opt.opdict['feat_log'] = ['Kurto']
opt.do_tri()
opt.x = opt.xs[0]
opt.y = opt.ys[0]
opt.x.columns = opt.opdict['feat_list']
opt.compute_pdfs()
gauss_stand = opt.gaussians
# Lissage sur des fenêtres de 1 s
opt.opdict['feat_train'] = '0610_Piton_trainset.csv'
opt.opdict['feat_test'] = '0610_Piton_testset.csv'
opt.do_tri()
opt.x = opt.xs[0]
opt.y = opt.ys[0]
opt.compute_pdfs()
gauss_1s = opt.gaussians
# Lissage sur des fenêtres de 5 s
opt.opdict['feat_train'] = '1809_Piton_trainset.csv'
opt.opdict['feat_test'] = '1809_Piton_testset.csv'
opt.do_tri()
opt.x = opt.xs[0]
opt.y = opt.ys[0]
opt.compute_pdfs()
gauss_5s = opt.gaussians
# Lissage sur des fenêtres de 10 s
opt.opdict['feat_train'] = '0510_Piton_trainset.csv'
opt.opdict['feat_test'] = '0510_Piton_testset.csv'
opt.do_tri()
opt.x = opt.xs[0]
opt.y = opt.ys[0]
opt.compute_pdfs()
gauss_10s = opt.gaussians
### PLOT OF SUPERPOSED PDFs ###
fig = plt.figure(figsize=(12, 2.5))
fig.set_facecolor('white')
for feat in sorted(opt.gaussians):
maxi = int(
np.max([
gauss_stand[feat]['vec'], gauss_1s[feat]['vec'],
gauss_5s[feat]['vec'], gauss_10s[feat]['vec']
]))
ax1 = fig.add_subplot(141)
ax1.plot(gauss_stand[feat]['vec'],
gauss_stand[feat]['VT'],
ls='-',
c='b',
lw=2.,
label='VT')
ax1.plot(gauss_stand[feat]['vec'],
gauss_stand[feat]['EB'],
ls='-',
c='r',
lw=2.,
label='EB')
ax1.set_xlim([0, maxi])
ax1.set_xlabel(feat)
ax1.set_title('0.5 s')
ax1.legend(prop={'size': 10})
ax2 = fig.add_subplot(142)
ax2.plot(gauss_1s[feat]['vec'],
gauss_1s[feat]['VT'],
ls='-',
c='b',
lw=2.)
ax2.plot(gauss_1s[feat]['vec'],
gauss_1s[feat]['EB'],
ls='-',
c='r',
lw=2.)
ax2.set_xlim([0, maxi])
ax2.set_xlabel(feat)
ax2.set_title('1 s')
ax2.set_yticklabels('')
ax3 = fig.add_subplot(143)
ax3.plot(gauss_5s[feat]['vec'],
gauss_5s[feat]['VT'],
ls='-',
c='b',
lw=2.)
ax3.plot(gauss_5s[feat]['vec'],
gauss_5s[feat]['EB'],
ls='-',
c='r',
lw=2.)
ax3.set_xlim([0, maxi])
ax3.set_xlabel(feat)
ax3.set_title('5 s')
ax3.set_yticklabels('')
ax4 = fig.add_subplot(144)
ax4.plot(gauss_10s[feat]['vec'],
gauss_10s[feat]['VT'],
ls='-',
c='b',
lw=2.)
ax4.plot(gauss_10s[feat]['vec'],
gauss_10s[feat]['EB'],
ls='-',
c='r',
lw=2.)
ax4.set_xlim([0, maxi])
ax4.set_xlabel(feat)
ax4.set_title('10 s')
ax4.set_yticklabels('')
#plt.savefig('%s/features/comp_%s.png'%(opt.opdict['outdir'],feat))
plt.show()