subcortex2 = np.genfromtxt(
env.data + '/structural/thalamusR_SA_ADHD_10to21_MATCHscript.csv',
delimiter=',')
# removing first column and first row, because they're headers
subcortex2 = scipy.delete(subcortex2, 0, 1)
subcortex2 = scipy.delete(subcortex2, 0, 0)
# format it to be subjects x variables
subcortex2 = subcortex2.T
# selecting only a few vertices in the thalamus
# my_sub_vertices = [2310, 1574, 1692, 1262, 1350] # Philip's
# my_sub_vertices = range(0, subcortex.shape[1], 100) # every 100
# my_sub_vertices = range(subcortex.shape[1])
w = mne.read_w(env.fsl + '/mni/bem/cortex-3-rh.w')
my_cor_vertices = w['vertices']
# w = mne.read_w(env.fsl + '/mni/bem/thalamus-10-rh.w')
# my_sub_vertices = w['vertices']
# my_cor_vertices = range(0, cortex.shape[1], 20)
# my_sub_vertices = [2034, 950, 216, 52, 2276, 2893, 1386, 1922, 2187, 1831, 1828] # GS made it up by looking at anamoty, refer to Evernote for details. WRONG!
# my_sub_vertices = [1533, 1106, 225, 163, 2420, 2966, 1393, 1666, 1681, 1834, 2067] # GS made it up by looking at anamoty, refer to Evernote for details
my_sub_vertices = []
# in nice order from anterior to posterior in the cortex (cingulate is last)
label_names = [
'medialdorsal', 'va', 'vl', 'vp', 'lateraldorsal', 'lateralposterior',
'pulvinar', 'anteriornuclei'
]
label_names = ['medialdorsal', 'va', 'vl', 'vp', 'pulvinar', 'anteriornuclei']
def test_sensitivity_maps():
"""Test sensitivity map computation"""
fwd = mne.read_forward_solution(fwd_fname, surf_ori=True)
projs = None
proj_eog = read_proj(eog_fname)
decim = 6
for ch_type in ['eeg', 'grad', 'mag']:
w_lh = mne.read_w(sensmap_fname % (ch_type, 'lh'))
w_rh = mne.read_w(sensmap_fname % (ch_type, 'rh'))
w = np.r_[w_lh['data'], w_rh['data']]
stc = sensitivity_map(fwd, projs=projs, ch_type=ch_type,
mode='free', exclude='bads')
assert_array_almost_equal(stc.data.ravel(), w, decim)
assert_true(stc.subject == 'sample')
# let's just make sure the others run
if ch_type == 'grad':
# fixed
w_lh = mne.read_w(sensmap_fname % (ch_type, '2-lh'))
w_rh = mne.read_w(sensmap_fname % (ch_type, '2-rh'))
w = np.r_[w_lh['data'], w_rh['data']]
stc = sensitivity_map(fwd, projs=projs, mode='fixed',
ch_type=ch_type, exclude='bads')
assert_array_almost_equal(stc.data.ravel(), w, decim)
if ch_type == 'mag':
# ratio
w_lh = mne.read_w(sensmap_fname % (ch_type, '3-lh'))
w_rh = mne.read_w(sensmap_fname % (ch_type, '3-rh'))
w = np.r_[w_lh['data'], w_rh['data']]
stc = sensitivity_map(fwd, projs=projs, mode='ratio',
ch_type=ch_type, exclude='bads')
assert_array_almost_equal(stc.data.ravel(), w, decim)
if ch_type == 'eeg':
# radiality (4)
w_lh = mne.read_w(sensmap_fname % (ch_type, '4-lh'))
w_rh = mne.read_w(sensmap_fname % (ch_type, '4-rh'))
w = np.r_[w_lh['data'], w_rh['data']]
stc = sensitivity_map(fwd, projs=projs, mode='radiality',
ch_type=ch_type, exclude='bads')
# angle (5)
w_lh = mne.read_w(sensmap_fname % (ch_type, '5-lh'))
w_rh = mne.read_w(sensmap_fname % (ch_type, '5-rh'))
w = np.r_[w_lh['data'], w_rh['data']]
stc = sensitivity_map(fwd, projs=proj_eog, mode='angle',
ch_type=ch_type, exclude='bads')
assert_array_almost_equal(stc.data.ravel(), w, decim)
# remaining (6)
w_lh = mne.read_w(sensmap_fname % (ch_type, '6-lh'))
w_rh = mne.read_w(sensmap_fname % (ch_type, '6-rh'))
w = np.r_[w_lh['data'], w_rh['data']]
stc = sensitivity_map(fwd, projs=proj_eog, mode='remaining',
ch_type=ch_type, exclude='bads')
assert_array_almost_equal(stc.data.ravel(), w, decim)
# dampening (7)
w_lh = mne.read_w(sensmap_fname % (ch_type, '7-lh'))
w_rh = mne.read_w(sensmap_fname % (ch_type, '7-rh'))
w = np.r_[w_lh['data'], w_rh['data']]
stc = sensitivity_map(fwd, projs=proj_eog, mode='dampening',
ch_type=ch_type, exclude='bads')
assert_array_almost_equal(stc.data.ravel(), w, decim)
# format it to be subjects x variables
cortex2 = cortex2.T
subcortex2 = np.genfromtxt(env.data + '/structural/thalamusR_SA_ADHD_10to21_MATCHscript.csv', delimiter=',')
# removing first column and first row, because they're headers
subcortex2 = scipy.delete(subcortex2, 0, 1)
subcortex2 = scipy.delete(subcortex2, 0, 0)
# format it to be subjects x variables
subcortex2 = subcortex2.T
# selecting only a few vertices in the thalamus
# my_sub_vertices = [2310, 1574, 1692, 1262, 1350] # Philip's
# my_sub_vertices = range(0, subcortex.shape[1], 100) # every 100
# my_sub_vertices = range(subcortex.shape[1])
w = mne.read_w(env.fsl + '/mni/bem/cortex-3-rh.w')
my_cor_vertices = w['vertices']
# w = mne.read_w(env.fsl + '/mni/bem/thalamus-10-rh.w')
# my_sub_vertices = w['vertices']
# my_cor_vertices = range(0, cortex.shape[1], 20)
# my_sub_vertices = [2034, 950, 216, 52, 2276, 2893, 1386, 1922, 2187, 1831, 1828] # GS made it up by looking at anamoty, refer to Evernote for details. WRONG!
# my_sub_vertices = [1533, 1106, 225, 163, 2420, 2966, 1393, 1666, 1681, 1834, 2067] # GS made it up by looking at anamoty, refer to Evernote for details
my_sub_vertices = []
# in nice order from anterior to posterior in the cortex (cingulate is last)
label_names = ['medialdorsal', 'va', 'vl', 'vp', 'lateraldorsal',
'lateralposterior', 'pulvinar', 'anteriornuclei']
label_names = ['medialdorsal', 'va', 'vl', 'vp', 'pulvinar', 'anteriornuclei']
for l in label_names:
