def stat_function_tst(conn, prefix='', OUTPUT_PATH=None, threshold=0.05):
fc = conn.hurst
tst = Parallel(n_jobs=3,
verbose=5)(delayed(ttest_group)(group, threshold, fc)
for group in groups)
if OUTPUT_PATH is None:
font = {'family': 'normal', 'size': 20}
changefont('font', **font)
gr = ['v', 'av', 'avn']
for i in range(3):
title = prefix + '_'.join(groups[i])
try:
img = conn.masker.inverse_transform(tst[i])
print title
plot_stat_map(img, cut_coords=(3, -63, 36))
plt.show()
except ValueError:
print "problem with tst " + title
changefont.func_defaults
else:
for i in range(3):
title = prefix + '_'.join(groups[i])
output_file = os.path.join(OUTPUT_PATH, title)
try:
img = conn.masker.inverse_transform(tst[i])
plot_stat_map(img,
cut_coords=(3, -63, 36),
output_file=output_file + '.pdf')
except ValueError:
print "problem with tst " + title
def stat_function_tst(conn, prefix='', OUTPUT_PATH=None, threshold=0.05):
fc = conn.hurst
tst = Parallel(n_jobs=3, verbose=5)(delayed(ttest_group)(group, threshold, fc)
for group in groups)
if OUTPUT_PATH is None:
font = {'family' : 'normal',
'size' : 20}
changefont('font', **font)
gr = ['v', 'av', 'avn']
for i in range(3):
title = prefix + '_'.join(groups[i])
try:
img = conn.masker.inverse_transform(tst[i])
print title
plot_stat_map(img, cut_coords=(3, -63, 36))
plt.show()
except ValueError:
print "problem with tst " + title
changefont.func_defaults
else:
for i in range(3):
title = prefix + '_'.join(groups[i])
output_file = os.path.join(OUTPUT_PATH, title)
try:
img = conn.masker.inverse_transform(tst[i])
plot_stat_map(img, cut_coords=(3, -63, 36), output_file=output_file + '.pdf')
except ValueError:
print "problem with tst " + title
def plot_syj_against_j(j1=2, j2=6, wtype=1, theoretical_Hurst=0.8, idx_simulation=0, OUTPUT_FILE=None):
idx = int(theoretical_Hurst * 10) - 1
if(idx < 0 or idx > 10):
idx = 7
simulation = np.cumsum(opas.get_simulation(), axis=-1)
dico = wtspecq_statlog3(simulation[idx,idx_simulation], 2, 1, np.array(2),
int(np.log2(simulation[idx,idx_simulation].shape[0])), 0, 0)
Elog = dico['Elogmuqj'][0]
Varlog = dico['Varlogmuqj'][0]
nj = dico['nj']
regression = regrespond_det2(Elog, Varlog, nj, j1, j2, wtype)
font = {'family' : 'normal',
'weight' : 'bold',
'size' : 22}
changefont('font', **font)
jmax = len(Elog)
j_indices = np.arange(0,jmax + 2)
fig = plt.plot(j_indices, j_indices * regression['Zeta'] + regression['aest'])
plt.text(j_indices.mean() - 2 * j_indices.var() / jmax, Elog.mean() + Elog.var() / jmax, r'Hurst Exponent = %.2f'%(regression['Zeta']/2))
j_indices = np.arange(0,jmax) + 1
plt.plot(j_indices, Elog, 'ro')
plt.xlabel('scale j')
plt.ylabel('log Sy(j,2)')
if not OUTPUT_FILE is None:
plt.savefig(OUTPUT_FILE)
plt.show()
def stat_function(conn, prefix='', OUTPUT_PATH=None):
fc = conn.hurst
a = Parallel(n_jobs=3, verbose=5)(delayed(classify_group)(group, fc)
for group in groups)
#tst = Parallel(n_jobs=3, verbose=5)(delayed(ttest_group)(group, .05, fc)
#for group in groups)
#save_stat({'a':a, 'tst': tst}, save_file=prefix)
##ost = Parallel(n_jobs=3, verbose=5)(delayed(ttest_onesample)(group, 0.05, fc)
##for group in ['v', 'av', 'avn'])
##mht = Parallel(n_jobs=3, verbose=5)(delayed(ttest_onesample_Hmean)(group, 0.05, fc)
##for group in ['v', 'av', 'avn'])
##mpt = Parallel(n_jobs=3, verbose=5)(delayed(mne_permutation_ttest)(group,0.05, fc, 1)
##for group in ['v', 'av', 'avn'])
##cot = Parallel(n_jobs=3, verbose=5)(delayed(ttest_onesample_coef)(np.reshape(coef['coef'], (coef['coef'].shape[0], coef['coef'].shape[-1])),
##0.05, fc)
##for coef in a)
#gr = ['v', 'av', 'avn']
if OUTPUT_PATH is None:
for i in range(3):
title = prefix + '_'.join(groups[i])
#try:
#img = conn.masker.inverse_transform(tst[i])
#plot_stat_map(img, cut_coords=(3, -63, 36), title=title)
#except ValueError:
#print "problem with tst " + title
##try:
##img = conn.masker.inverse_transform(cot[i])
##plot_stat_map(img, title='coef_map ' + title)
##except ValueError:
##print "problem with cot " + title
##title = prefix + gr[i]
##try:
##img = conn.masker.inverse_transform(ost[i])
##plot_stat_map(img, title='t-test H0 : H = 0.5 pvalue in -log10 scale groupe : ' + title)
##except ValueError:
##print "problem with ost " + title
##try:
##img = conn.masker.inverse_transform(mht[i])
##plot_stat_map(img, title='t-test H0 : H = 0.5 pvalue in -log10 scale groupe : ' + title)
##except ValueError:
##print "problem with mht " + title
##try:
##img = conn.masker.inverse_transform(mpt[i])
##plot_stat_map(img, title='t-test H0 : H = 0.5 pvalue in -log10 scale groupe : ' + title)
##except ValueError:
##print "problem with mpt " + title
else:
#for i in range(3):
#title = prefix + '_'.join(groups[i])
#output_file = os.path.join(OUTPUT_PATH, title)
#try:
#img = conn.masker.inverse_transform(tst[i])
#plot_stat_map(img, cut_coords=(3, -63, 36), title=title, output_file=output_file + '.pdf')
#except ValueError:
#print "problem with tst " + title
##try:
##img = conn.masker.inverse_transform(cot[i])
##plot_stat_map(img, title='coef_map ' + title, output_file=output_file + 'coef_map.pdf')
##except ValueError:
##print "problem with cot " + title
##title = prefix + gr[i]
##output_file = os.path.join(OUTPUT_PATH, title)
##try:
##img = conn.masker.inverse_transform(ost[i])
##plot_stat_map(img, title='t-test H0 : H = 0.5 pvalue in -log10 scale groupe : ' + title, output_file= output_file + '.pdf')
##except ValueError:
##print "problem with ost " + title
##try:
##img = conn.masker.inverse_transform(mht[i])
##plot_stat_map(img, title='t-test H0 : H = 0.5 pvalue in -log10 scale groupe : ' + title, output_file= output_file + 'meanH.pdf')
##except ValueError:
##print "problem with mht " + title
##try:
##img = conn.masker.inverse_transform(mpt[i])
##plot_stat_map(img, title='t-test H0 : H = 0.5 pvalue in -log10 scale groupe : ' + title, output_file= output_file + 'mnepermutH.pdf')
##except ValueError:
##print "problem with mpt " + title
font = {'family' : 'normal',
'size' : 12}
changefont('font', **font)
plt.figure()
box = plt.boxplot(map(lambda x: x['accuracy'], a))
#for i,line in enumerate(box['medians']):# get position data for median line
#x, y = line.get_xydata()[1] # top of median line
## overlay median value
#plt.text(x + 0.1, y - 0.02, '%.2f\n%.2e' % (np.mean(a[i]['accuracy']),
#np.var(a[i]['accuracy'])),
#horizontalalignment='center') # draw above, centered
plt.ylim(0.1,1)
plt.xticks([1,2,3], ['AV-V\n $\mu =$ %.2f\n $\sigma^2 = $%.2e' %(np.mean(a[0]['accuracy'],np.var(a[0]['accuracy'])))
, 'AV-AVn\n$\mu =$ %.2f\n $\sigma^2 = $%.2e' %(np.mean(a[1]['accuracy'],np.var(a[1]['accuracy'])))
, 'V-AVn\n$\mu =$ %.2f\n $\sigma^2 = $%.2e' %(np.mean(a[2]['accuracy'],np.var(a[2]['accuracy'])))])
plt.savefig(os.path.join(OUTPUT_PATH, prefix+'boxplot.pdf'))
changefont.func_defaults