except Exception as e:
print(e)
dist = np.nan
focality_score = np.nan
corr = np.nan
ori_error = np.nan
print(setting, dist, focality_score, corr, ori_error)
dists.append(dist)
focs.append(focality_score)
corrs.append(corr)
ori_errors.append(ori_error)
###############################################################################
# Save everything to a pandas dataframe
###############################################################################
df = pd.DataFrame(lcmv_settings,
columns=[
'reg', 'sensor_type', 'pick_ori', 'inversion',
'weight_norm', 'normalize_fwd', 'use_noise_cov',
'reduce_rank', 'project_pca'
])
df['dist'] = dists
df['focality'] = focs
df['corr'] = corrs
df['ori_error'] = ori_errors
df.to_csv(fname.lcmv_results(vertex=config.vertex, noise=config.noise))
print('OK!')
from tqdm import tqdm
import mne
import surfer
from mayavi import mlab
mlab.options.offscreen = True
from itertools import product
import config
from config import fname
fwd = mne.read_forward_solution(fname.fwd)
dfs = []
for vertex in tqdm(range(3765), total=3765):
try:
df = pd.read_csv(fname.lcmv_results(noise=config.noise, vertex=vertex))
df['vertex'] = vertex
df['noise'] = config.noise
dfs.append(df)
except Exception as e:
print(e)
lcmv = pd.concat(dfs, ignore_index=True)
lcmv['pick_ori'].fillna('none', inplace=True)
lcmv['weight_norm'].fillna('none', inplace=True)
regs = [0.05, 0.1]
sensor_types = ['joint', 'grad', 'mag']
pick_oris = ['none', 'normal', 'max-power']
weight_norms = ['unit-noise-gain', 'none']
use_noise_covs = [True, False]
depths = [True, False]
vsrc = fwd['src']
vertno = vsrc[0]['vertno']
# needs to be set for plot_vstc_sliced_old to work
if vsrc[0]['subject_his_id'] is None:
vsrc[0]['subject_his_id'] = 'sample'
###############################################################################
# Get data from csv files
###############################################################################
dfs = []
for vertex in tqdm(range(3756), total=3756):
try:
df = pd.read_csv(fname.lcmv_results(vertex=vertex, noise=0.1), index_col=0)
df['vertex'] = vertex
df['noise'] = config.noise
dfs.append(df)
except Exception as e:
print(e)
lcmv = pd.concat(dfs, ignore_index=True)
lcmv['pick_ori'].fillna('none', inplace=True)
lcmv['weight_norm'].fillna('none', inplace=True)
lcmv['ori_error'].fillna(-1, inplace=True)
def fix(x):
if x == np.nan:
return np.nan
elif x > 90:
return 180 - x