def compare_pdfs_reclass():
"""
Affiche et compare les pdfs avant et après reclassification automatique.
"""
from results import AnalyseResults
opt = AnalyseResults()
opt.opdict['stations'] = ['IJEN']
opt.opdict['channels'] = ['Z']
opt.opdict['Types'] = ['Tremor', 'VulkanikB', '?']
opt.opdict['feat_list'] = [
'Centroid_time', 'Dur', 'Ene0-5', 'F_up', 'Growth', 'Kurto',
'RappMaxMean', 'RappMaxMeanTF', 'Skewness', 'TimeMaxSpec', 'Width'
]
for sta in opt.opdict['stations']:
for comp in opt.opdict['channels']:
opt.opdict[
'label_filename'] = '%s/Ijen_3class_all.csv' % opt.opdict[
'libdir']
opt.x, opt.y = opt.features_onesta(sta, comp)
opt.classname2number()
opt.compute_pdfs()
g1 = opt.gaussians
opt.opdict[
'label_filename'] = '%s/Ijen_3class_all_SVM.csv' % opt.opdict[
'libdir']
opt.x, opt.y = opt.features_onesta(sta, comp)
opt.classname2number()
opt.compute_pdfs()
g2 = opt.gaussians
c = ['r', 'b', 'g']
for feat in opt.opdict['feat_list']:
fig = plt.figure()
fig.set_facecolor('white')
for it, t in enumerate(opt.types):
plt.plot(g1[feat]['vec'],
g1[feat][t],
ls='-',
color=c[it],
label=t)
plt.plot(g2[feat]['vec'],
g2[feat][t],
ls='--',
color=c[it])
plt.title(feat)
plt.legend()
#plt.savefig('../results/Ijen/comp_BrutReclass_%s.png'%feat)
plt.show()
def compare_pdfs_reclass():
"""
Affiche et compare les pdfs avant et après reclassification automatique.
"""
from results import AnalyseResults
opt = AnalyseResults()
opt.opdict['stations'] = ['IJEN']
opt.opdict['channels'] = ['Z']
opt.opdict['Types'] = ['Tremor','VulkanikB','?']
opt.opdict['feat_list'] = ['Centroid_time','Dur','Ene0-5','F_up','Growth','Kurto','RappMaxMean','RappMaxMeanTF','Skewness','TimeMaxSpec','Width']
for sta in opt.opdict['stations']:
for comp in opt.opdict['channels']:
opt.opdict['label_filename'] = '%s/Ijen_3class_all.csv'%opt.opdict['libdir']
opt.x, opt.y = opt.features_onesta(sta,comp)
opt.classname2number()
opt.compute_pdfs()
g1 = opt.gaussians
opt.opdict['label_filename'] = '%s/Ijen_reclass_all.csv'%opt.opdict['libdir']
opt.x, opt.y = opt.features_onesta(sta,comp)
opt.classname2number()
opt.compute_pdfs()
g2 = opt.gaussians
c = ['r','b','g']
for feat in opt.opdict['feat_list']:
fig = plt.figure()
fig.set_facecolor('white')
for it,t in enumerate(opt.types):
plt.plot(g1[feat]['vec'],g1[feat][t],ls='-',color=c[it],label=t)
plt.plot(g2[feat]['vec'],g2[feat][t],ls='--',color=c[it])
plt.title(feat)
plt.legend()
#plt.savefig('../results/Ijen/comp_BrutReclass_%s.png'%feat)
plt.show()
