def compute_hurst_and_stat(metric='dfa', regu='off', OUTPUT_PATH = '/volatile/hubert/beamer/test_hurst/', plot=False):
conn = Hurst_Estimator(metric=metric, mask=dataset.mask,smoothing_fwhm=0, regu=regu, n_jobs=5)
os.write(1,'fit\n')
fc = conn.fit(dataset.func1)
#conn.load_map(INPUT_PATH)
os.write(1,'save\n')
#stat_function_tst(conn, metric+' '+regu+' ', OUTPUT_PATH)
conn.save(save_path=OUTPUT_PATH)
if plot:
os.write(1,'plot\n')
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)
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 regu=='off':
OUTPUT_PATH = os.path.join(OUTPUT_PATH, metric)
else:
OUTPUT_PATH = os.path.join(OUTPUT_PATH, metric, regu)
for i in range(3):
title = '_'.join(groups[i])
output_file = os.path.join(OUTPUT_PATH, title)
img = conn.masker.inverse_transform(tst[i])
plot_stat_map(img, cut_coords=(3, -63, 36), title=title, output_file=output_file + '.pdf')
img = conn.masker.inverse_transform(cot[i])
plot_stat_map(img, title='coef_map ' + title, output_file=output_file + 'coef_map.pdf')
title = gr[i]
output_file = os.path.join(OUTPUT_PATH, title)
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')
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')
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')
plt.figure()
plt.boxplot(map(lambda x: x['accuracy'], a))
plt.savefig(os.path.join(OUTPUT_PATH, 'boxplot.pdf'))
def stat_computed_hurst(metric= ['wavelet', 'dfa', 'welch'], regu = ['off', 'tv', 'l2'], INPUT_PATH = '/volatile/hubert/beamer/test_hurst/', OUTPUT_PATH=None):
for met in metric:
for reg in regu:
conn = Hurst_Estimator(metric=met, mask=dataset.mask, regu=reg, n_jobs=5)
os.write(1,'load\n')
conn.load_map(INPUT_PATH)
fc = conn.hurst
os.write(1,'stat\n')
stat_function_tst(conn, met+' '+reg+' ', OUTPUT_PATH)
stat_ttest_function(conn, met+' '+reg+' ', OUTPUT_PATH)
plt.show()
def stat(metric='wavelet', regu='off', lbda = 1, OUTPUT_PATH = '/volatile/hubert/beamer/HEES0/'):
conn = Hurst_Estimator(metric=metric, lbda = lbda, mask=dataset.mask,smoothing_fwhm=0, regu=regu, n_jobs=5)
os.write(1,'load\n')
conn.load_map(INPUT_PATH = OUTPUT_PATH, save_file= 'hurstmap_metric_wavelet_regu_tv' +str(lbda))
os.write(1,'stat\n')
if regu=='off':
OUTPUT_PATH = os.path.join(OUTPUT_PATH, metric)
else:
OUTPUT_PATH = os.path.join(OUTPUT_PATH, metric, regu)
stat_function(conn, prefix = 'lbda' + str(lbda), OUTPUT_PATH=OUTPUT_PATH)
return conn
def diff_computed_hurst(metric='wavelet', regu='off', INPUT_PATH = '/volatile/hubert/beamer/test_hurst/', OUTPUT_PATH=''):
conn = Hurst_Estimator(metric=metric, mask=dataset.mask, regu=regu, n_jobs=5)
os.write(1,'load\n')
conn.load_map(INPUT_PATH)
fc = conn.hurst
os.write(1,'stat\n')
tst = ttest_group(['av', 'v'], .05, fc)
vmean_avmean = np.mean([fc[i] for i in dataset.group_indices['v']], axis=0) - np.mean([fc[i] for i in dataset.group_indices['av']], axis=0)
vmean_avmean[tst == 0] = 0
img = conn.masker.inverse_transform(vmean_avmean)
plot_stat_map(img)
plt.show()
def compute_hurst_and_stat(metric='wavelet', regu='off', lbda = 1, OUTPUT_PATH = '/volatile/hubert/beamer/HEES0/', plot=False):
conn = Hurst_Estimator(metric=metric, lbda = lbda, mask=dataset.mask,smoothing_fwhm=0, regu=regu, n_jobs=5)
os.write(1,'fit\n')
fc = conn.fit(dataset.func1)
#conn.load_map(INPUT_PATH = OUTPUT_PATH, save_file= 'hurstmap_metric_wavelet_regu_l2' +str(lbda))
fc = conn.hurst
os.write(1,'save\n')
#stat_function_tst(conn, metric+' '+regu+' ', OUTPUT_PATH)
conn.save(save_path=OUTPUT_PATH)
if plot:
os.write(1,'plot\n')
if regu=='off':
OUTPUT_PATH = os.path.join(OUTPUT_PATH, metric)
else:
OUTPUT_PATH = os.path.join(OUTPUT_PATH, metric, regu)
stat_function(conn, prefix = 'lbda' + str(lbda), OUTPUT_PATH=OUTPUT_PATH)
return conn
def stat_computed_hurst(metric=['wavelet', 'dfa', 'welch'],
regu=['off', 'tv', 'l2'],
INPUT_PATH='/volatile/hubert/beamer/test_hurst/',
OUTPUT_PATH=None):
for met in metric:
for reg in regu:
conn = Hurst_Estimator(metric=met,
mask=dataset.mask,
regu=reg,
n_jobs=5)
os.write(1, 'load\n')
conn.load_map(INPUT_PATH)
fc = conn.hurst
os.write(1, 'stat\n')
stat_function_tst(conn, met + ' ' + reg + ' ', OUTPUT_PATH)
stat_ttest_function(conn, met + ' ' + reg + ' ', OUTPUT_PATH)
plt.show()
def diff_computed_hurst(metric='wavelet',
regu='off',
INPUT_PATH='/volatile/hubert/beamer/test_hurst/',
OUTPUT_PATH=''):
conn = Hurst_Estimator(metric=metric,
mask=dataset.mask,
regu=regu,
n_jobs=5)
os.write(1, 'load\n')
conn.load_map(INPUT_PATH)
fc = conn.hurst
os.write(1, 'stat\n')
tst = ttest_group(['av', 'v'], .05, fc)
vmean_avmean = np.mean(
[fc[i] for i in dataset.group_indices['v']], axis=0) - np.mean(
[fc[i] for i in dataset.group_indices['av']], axis=0)
vmean_avmean[tst == 0] = 0
img = conn.masker.inverse_transform(vmean_avmean)
plot_stat_map(img)
plt.show()
def comparison(lbdatv = [0.5,1,2,3,4,5,10,15], lbdal2=[5,10,15,20,25,40], OUTPUT_PATH='/volatile/hubert/beamer/HEES0/'):
conn = Hurst_Estimator(mask=dataset.mask)
for lbda in lbdatv:
os.write(1,'tv' +str(lbda)+'\n')
conn.load_map(INPUT_PATH = OUTPUT_PATH, save_file= 'hurstmap_metric_wavelet_regu_tv' +str(lbda))
stat_function(conn, prefix = 'lbda' + str(lbda), OUTPUT_PATH=os.path.join(OUTPUT_PATH, 'wavelet', 'tv'))
#fc = conn.hurst[0]
#fc[fc==0.4] = 0
#img = conn.masker.inverse_transform(fc)
#lbda = '_'.join(str(lbda).split('.'))
#plot_stat_map(img, output_file=os.path.join(OUTPUT_PATH, 'wavelet', 'tv'+lbda))
for lbda in lbdal2:
os.write(1,'l2' +str(lbda)+'\n')
conn.load_map(INPUT_PATH = OUTPUT_PATH, save_file= 'hurstmap_metric_wavelet_regu_l2' +str(lbda))
stat_function(conn, prefix = 'lbda' + str(lbda), OUTPUT_PATH=os.path.join(OUTPUT_PATH, 'wavelet', 'l2'))
#fc = conn.hurst[0]
#fc[fc==0.4] = 0
#img = conn.masker.inverse_transform(fc)
#plot_stat_map(img, output_file=os.path.join(OUTPUT_PATH, 'wavelet', 'l2'+str(lbda)))
conn.load_map(INPUT_PATH = OUTPUT_PATH, save_file= 'hurstmap_metric_wavelet_regu_off')
stat_function(conn, prefix = 'regu_off', OUTPUT_PATH=os.path.join(OUTPUT_PATH, 'wavelet'))
#fc = conn.hurst[0]
#fc[fc==0.4] = 0
#img = conn.masker.inverse_transform(fc)
#plot_stat_map(img, output_file=os.path.join(OUTPUT_PATH, 'wavelet', 'regu_off'))
conn.load_map(INPUT_PATH = OUTPUT_PATH, save_file= 'hurstmap_metric_wavelet_regu_off_presmooth6')
stat_function(conn, prefix = 'presmooth', OUTPUT_PATH=os.path.join(OUTPUT_PATH, 'wavelet'))
#fc = conn.hurst[0]
#img = conn.masker.inverse_transform(fc)
#plot_stat_map(img, output_file=os.path.join(OUTPUT_PATH, 'wavelet', 'presmooth'))
plt.show()
def wavelet4_estimation(
INPUT_PATH='/volatile/hubert/beamer/test_hurst/wavelet4'):
conn = Hurst_Estimator(metric='wavelet',
mask=dataset.mask,
regu='tv',
nb_vanishmoment=4,
j1=3,
j2=7,
n_jobs=5)
conn.fit(dataset.func1)
conn.save(INPUT_PATH)
def compute_hurst_and_stat(metric='dfa',
regu='off',
OUTPUT_PATH='/volatile/hubert/beamer/test_hurst/',
plot=False):
conn = Hurst_Estimator(metric=metric,
mask=dataset.mask,
smoothing_fwhm=0,
regu=regu,
n_jobs=5)
os.write(1, 'fit\n')
fc = conn.fit(dataset.func1)
#conn.load_map(INPUT_PATH)
os.write(1, 'save\n')
#stat_function_tst(conn, metric+' '+regu+' ', OUTPUT_PATH)
conn.save(save_path=OUTPUT_PATH)
if plot:
os.write(1, 'plot\n')
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)
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 regu == 'off':
OUTPUT_PATH = os.path.join(OUTPUT_PATH, metric)
else:
OUTPUT_PATH = os.path.join(OUTPUT_PATH, metric, regu)
for i in range(3):
title = '_'.join(groups[i])
output_file = os.path.join(OUTPUT_PATH, title)
img = conn.masker.inverse_transform(tst[i])
plot_stat_map(img,
cut_coords=(3, -63, 36),
title=title,
output_file=output_file + '.pdf')
img = conn.masker.inverse_transform(cot[i])
plot_stat_map(img,
title='coef_map ' + title,
output_file=output_file + 'coef_map.pdf')
title = gr[i]
output_file = os.path.join(OUTPUT_PATH, title)
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')
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')
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')
plt.figure()
plt.boxplot(map(lambda x: x['accuracy'], a))
plt.savefig(os.path.join(OUTPUT_PATH, 'boxplot.pdf'))
def wavelet4_estimation(INPUT_PATH = '/volatile/hubert/beamer/test_hurst/wavelet4'):
conn = Hurst_Estimator(metric='wavelet', mask=dataset.mask, regu='tv', nb_vanishmoment=4, j1 =3, j2=7, n_jobs=5)
conn.fit(dataset.func1)
conn.save(INPUT_PATH)
