def compare_ieeg_freq(file_A, file_B, frequency, baseline, merge_method, measure,
output_dir):
"""
Parameters
----------
baseline : bool
if you want to substract baseline
merge_method : str
"dh2012"
measure : str
"dh2012_r2"
"""
ieeg_A = file_Core(file_A)
ieeg_B = file_Core(file_B)
with file_A.open('rb') as f:
dat_A = load(f)
with file_B.open('rb') as f:
dat_B = load(f)
if baseline:
dat_A, dat_B = correct_baseline(dat_A, dat_B, frequency)
hfa_A = merge(dat_A, merge_method, frequency)
hfa_B = merge(dat_B, merge_method, frequency)
if measure == 'diff':
ecog_stats = compute_diff(hfa_A, hfa_B)
elif measure == 'percent':
ecog_stats = compute_percent(hfa_A, hfa_B)
elif measure in ('zstat', 'dh2012_t'): # identical
ecog_stats = compute_zstat(hfa_A, hfa_B)
if measure == 'dh2012_t':
ecog_stats.data[0] *= -1 # opposite sign in dh2012's script
elif measure == 'dh2012_r2':
ecog_stats = calc_dh2012_values(hfa_A, hfa_B, measure)
# need to check pvalues
if True:
pvalues = calc_dh2012_values(hfa_A, hfa_B, 'dh2012_pv')
else:
pvalues = [NaN, ] * ecog_stats.number_of('chan')[0]
output = file_Core(
subject=ieeg_A.subject,
session=ieeg_A.session,
run=ieeg_A.run,
acquisition=ieeg_A.acquisition,
modality='compare',
extension='.tsv',
task=find_longest_match(ieeg_A.task, ieeg_B.task),
)
compare_file = output_dir / output.get_filename()
with compare_file.open('w') as f:
f.write('channel\tmeasure\tpvalue\n')
for i, chan in enumerate(ecog_stats.chan[0]):
f.write(f'{chan}\t{ecog_stats(trial=0, chan=chan)}\t{pvalues(trial=0, chan=chan)}\n')
return compare_file
def xmain(analysis_dir,
freesurfer_dir,
output_dir,
modality='compare',
surface='white',
surf_fwhm=0):
"""
map feat values on freesurfer surface',
Parameters
----------
analysis_dir : path
freesurfer_dir : path
output_dir : path
modality : str
"compare"
surface : str
"white", "pial"
surf_fwhm : float
FWHM
"""
p_all = []
surfs = []
for in_vol_file in find_in_bids(analysis_dir,
generator=True,
extension='.nii.gz',
modality=modality):
in_vol = file_Core(in_vol_file)
feat_path = find_in_bids(analysis_dir,
subject=in_vol.subject,
extension='.feat')
for hemi in ('lh', 'rh'):
p, out_surf = vol2surf(in_vol, feat_path, freesurfer_dir, hemi,
surface, surf_fwhm)
p_all.append(p)
surfs.append(out_surf)
# wait for all processes to run
[p.wait() for p in p_all]
[check_subprocess(p) for p in p_all]
[info['mri_nonan'].unlink() for info in surfs]
img_dir = output_dir / SURF_DIR
rmtree(img_dir, ignore_errors=True)
img_dir.mkdir(exist_ok=True, parents=True)
for one_surf in surfs:
plot_surf(img_dir, freesurfer_dir, one_surf, surface)
def import_df_regions(parameters):
regions_dir = name(parameters, 'brainregions_dir')
all_df = []
for tsv_file in regions_dir.glob('*_brainregions.tsv'):
bids = file_Core(tsv_file.name)
temp = read_csv(tsv_file, sep='\t')
temp['subject'] = bids.subject
temp['session'] = bids.session
temp['acquisition'] = bids.acquisition
all_df.append(temp)
regions = concat(all_df)
regions.drop(['x', 'y', 'z'], axis=1, inplace=True)
return regions
def _compute_summary(in_files, output_dir):
summary_file = output_dir / 'summary_per_subject.tsv'
with summary_file.open('w') as f:
f.write(f'subject\tsession\ttask\tacquisition\tsize_at_peak\tr2_at_peak\tslope_at_peak\tintercept_at_peak\tsize_at_concave\tr2_at_concave\tdiff_r2\n')
for corr_file in in_files:
corr_tsv = read_tsv(corr_file)
size_max, r2_max, slope, intercept = corr_tsv[argmax(corr_tsv['Rsquared'])]
deriv = gradient(gradient(corr_tsv['Rsquared']))
size_deriv, r2_deriv, *dummy = corr_tsv[argmin(deriv)]
file_info = file_Core(corr_file)
f.write(f'{file_info.subject}\t{file_info.session}\t{file_info.task}\t{file_info.acquisition}\t{size_max}\t{r2_max}\t{slope}\t{intercept}\t{size_deriv}\t{r2_deriv}\t{r2_max - r2_deriv}\n')
def import_df_ols(parameters):
"""Compute onset as well"""
TSV_DIR = name(parameters, 'ols_tsv')
all_ols = []
for tsv_file in TSV_DIR.glob('*.tsv'):
bids = file_Core(tsv_file.name)
ols = read_csv(tsv_file, sep='\t')
ols['subject'] = bids.subject
ols['session'] = bids.session
ols['run'] = bids.run
ols['acquisition'] = bids.acquisition
all_ols.append(ols)
ols = concat(
all_ols,
sort=False) # pandas throws a warning when data is not complete
return ols
def make_name(filename, event_type, ext='.html'):
"""Create name based on the data file name
Parameters
----------
filename : str
filename of the dataset of interest
event_type : str
event type used to identify the trials (one of 'cues', 'open', 'close',
'movements', 'extension', 'flexion')
ext : str
extension of the file
Returns
-------
str
file name specific to this filename and event type
"""
f = file_Core(filename)
if f.acquisition is None:
acq = ''
else:
acq = '_{f.acquisition}'
return f'{f.subject}_run-{f.run}{acq}_{event_type}{ext}'
from bidso import file_Core
subject = 'bert'
task_ieeg = file_Core(
subject=subject,
session='day02',
modality='ieeg',
task='motor',
run='1',
acquisition='clinical',
extension='.eeg',
)
task_fmri = file_Core(
subject=subject,
session='day01',
modality='bold',
task='motor',
run='1',
extension='.nii.gz',
)
task_anat = file_Core(
subject=subject,
session='day01',
acquisition='wholebrain',
modality='T1w',
extension='.nii.gz',
)
TEST_PATH = Path(__file__).resolve().parent
DATA_PATH = TEST_PATH / 'data'
BIDS_PATH = DATA_PATH / 'bids'
FREESURFER_PATH = DATA_PATH / 'freesurfer'
ANALYSIS_PATH = DATA_PATH / 'analysis'
ANALYSIS_PATH.mkdir(parents=True, exist_ok=True)
subject = 'delft'
task_ieeg = file_Core(
subject=subject,
session='UMCUECOGday01',
modality='ieeg',
task='motorHandLeft',
run='1',
acquisition='clinical',
extension='.eeg',
)
task_fmri = file_Core(
subject=subject,
session='UMCU3Tdaym13',
modality='bold',
task='motorHandLeft',
run='1',
extension='.nii.gz',
)
task_anat = file_Core(
subject=subject,
session='UMCU3Tdaym13',