def compute_stability(subj, fix_vertex=True, reg=0):
data_len = np.hstack((np.r_[220:70:-20], 70))
# first, the complete pli
pli, labels, bands, selected_voxels = ve.compute_all_labels_pli(subj, reg=reg)
plis = [pli]
# then compute it for the chopped data
if not fix_vertex:
selected_voxels = None
for l in data_len:
pli, labels, bands, junk = ve.compute_all_labels_pli(subj, tmax=l, reg=reg, selected_voxels=selected_voxels)
plis.append(pli)
return plis, labels
def compute_stability(subj, fix_vertex=True, reg=0):
data_len = np.hstack((np.r_[220:70:-20], 70))
# first, the complete pli
pli, labels, bands, selected_voxels = ve.compute_all_labels_pli(subj,
reg=reg)
plis = [pli]
# then compute it for the chopped data
if not fix_vertex:
selected_voxels = None
for l in data_len:
pli, labels, bands, junk = ve.compute_all_labels_pli(
subj, tmax=l, reg=reg, selected_voxels=selected_voxels)
plis.append(pli)
return plis, labels
ax.grid(False)
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
if cbar:
cbar = fig.colorbar(mappable, ax=ax)
# def plot_many_pli():
data_len = [.0001, .001, .01, .1, 1, 10, 100, 1000, 10000]
band_names = ['delta', 'theta', 'alpha', 'beta', 'gamma']
subplot_inds = [2, 3, 4, 6, 7, 8, 10, 11, 12]
subj = 'CVKRVURL'
# first, the complete pli
pli, labels, bands = ve.compute_all_labels_pli(subj)
plis = [pli]
# then compute it for the chopped data
for l in data_len:
pli, labels, bands = ve.compute_all_labels_pli(subj, reg=l)
plis.append(pli)
# now that we're done with the heavy computation, let's plot every band
for plot_band, band_name in enumerate(band_names):
fig = pl.figure()
# figure out the color axis
vmin = np.inf
vmax = -np.inf
import virtual_electrode as ve
import numpy as np
import multiprocessing
import env
job_num = 6 #int(multiprocessing.cpu_count())
num_perms = 2
# Note that the voxels selected should stay the same because the pemrutation doesn't change the power, only the phase, but if we load it now we can speed it up later by forcing the voxels being chosen, and not having to do the power transform all the time
res = np.load(env.results + 'selected_voxels_NV.npz')
subj_voxels = res['subj_voxels'][()]
for subj, voxels in subj_voxels.iteritems():
print '==================================='
print '======= Subject ' + subj + ' ========='
print '==================================='
pli, labels, bands, junk = ve.compute_all_labels_pli(subj, rand_phase=num_perms, selected_voxels=voxels, job_num=job_num)
ax.grid(False)
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
if cbar:
cbar = fig.colorbar(mappable, ax=ax)
# def plot_many_pli():
data_len = [.0001, .001, .01, .1, 1, 10, 100, 1000, 10000]
band_names = ['delta', 'theta', 'alpha', 'beta', 'gamma']
subplot_inds = [2, 3, 4, 6, 7, 8, 10, 11, 12]
subj = 'CVKRVURL'
# first, the complete pli
pli, labels, bands = ve.compute_all_labels_pli(subj)
plis = [pli]
# then compute it for the chopped data
for l in data_len:
pli, labels, bands = ve.compute_all_labels_pli(subj, reg=l)
plis.append(pli)
# now that we're done with the heavy computation, let's plot every band
for plot_band, band_name in enumerate(band_names):
fig = pl.figure()
# figure out the color axis
vmin = np.inf
vmax = -np.inf
