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 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()
