def test_plot3d_surf_value_colorbar():
surf = Surf(surf_path)
v = Viz3()
v.add_surf(surf, values=surf.vert[:, 2], limits_c=(0, 50), colorbar=True)
v.save(VIZ_PATH / 'viz3_04_surf_colorbar.png')
v.close()
def test_plot3d_surf_color():
surf = Surf(surf_path)
v = Viz3()
v.add_surf(surf, color=(.5, .5, 0))
v.save(VIZ_PATH / 'viz3_02_surf_color.png')
v.close()
def test_plot3d_surf_value():
surf = Surf(surf_path)
v = Viz3()
v.add_surf(surf, values=surf.vert[:, 2], limits_c=(0, 50))
v.save(VIZ_PATH / 'viz3_03_surf_values.png')
v.close()
def test_plot3d_surf():
surf = Surf(surf_path)
v = Viz3()
v.add_surf(surf)
v.save(VIZ_PATH / 'viz3_01_surf.png')
v.close()
def test_plot3d_surf_chan_color_one_alpha():
surf = Surf(surf_path)
channels = Channels(chan_path)
v = Viz3()
v.add_chan(channels, color=(1, 0, 0), alpha=0.5)
v.add_surf(surf, alpha=.5)
v.save(VIZ_PATH / 'viz3_07_surf_chan_color_one_alpha.png')
v.close()
def test_plot3d_surf_chan():
surf = Surf(surf_path)
channels = Channels(chan_path)
v = Viz3()
v.add_chan(channels)
v.add_surf(surf, alpha=.8)
v.save(VIZ_PATH / 'viz3_05_surf_chan.png')
v.close()
def test_plot3d_surf_chan_color_everyother():
from numpy import tile
surf = Surf(surf_path)
channels = Channels(chan_path)
color_everyother = tile([[1, 0, 0], [0, 0, 1]], (14, 1))
v = Viz3()
v.add_chan(channels, color=color_everyother)
v.add_surf(surf, alpha=.5)
v.save(VIZ_PATH / 'viz3_08_surf_chan_color_everyother.png')
v.close()
def test_plot3d_surf_chan_values():
from numpy import arange
surf = Surf(surf_path)
channels = Channels(chan_path)
v = Viz3()
v.add_chan(channels, values=arange(channels.n_chan), colorbar=True)
v.add_surf(surf, alpha=.5)
v.save(VIZ_PATH / 'viz3_09_surf_chan_values.png')
v.close()
# reuse the limits_c
v.add_chan(channels, values=arange(channels.n_chan))
def test_source_morph():
surf = Surf(surf_path)
Morph(from_surf=surf)
def test_Surf_02():
Surf(str(surf_path))
def test_Surf_01():
Surf(surf_path)
def load(what, parameters, ieeg_file, opts=None):
"""
WHAT:
- 'continuous' returns: ChanTime, event_names, events_onsets
- 'data' returns: ChanTime, event_names
- 'events' returns: ndarray
- 'dataglove' returns: ndarray
- 'electrodes'
- 'freesurfer'
- 'pial' returns original freesurfer pial mesh
- 'surface' returns full mesh which should better reflect brain size
- 'aparc'
- 'aparc.a2009s'
- 'aparc.DKTatlas'
- 'BA_exvivo'
EVENT_TYPE:
- cues : all cues (to open and close)
- open : cues to open fingers
- close : cues to close fingers
- movements : all actual movements (from dataglove)
- extension : actual extension of all fingers
- flexion : actual flexion of all fingers
- realigned : realigned movement
"""
if opts in ('data', 'continuous'):
if opts is None:
raise ValueError('You need to specify event_type')
if opts not in ['cues', 'open', 'close', 'movements', 'extension', 'flexion', 'realigned']:
raise ValueError(f'"{opts}" is not one of the possible event types')
if what in ('data', 'continuous'):
if opts is None:
raise ValueError('You need to specify opts which is a dict with event_type, pre, post')
ieeg = Task(ieeg_file)
if what in ('continuous', 'data'):
events_tsv = load('events', parameters, ieeg_file, opts['event_type'])
events = events_tsv['trial_type']
onsets = events_tsv['onset']
if what == 'continuous':
data = read_data(parameters, ieeg_file, event_onsets=onsets, opts=opts, continuous=True)
return data, events, onsets
elif what == 'data':
data = read_data(parameters, ieeg_file, event_onsets=onsets, opts=opts, continuous=False)
return data, events
if what == 'electrodes':
pattern = f'sub-{ieeg.subject}_*_acq-{ieeg.acquisition}_electrodes.tsv'
folder = parameters['paths']['input']
elif what == 'events':
if opts in ('cues', 'open', 'close'):
pattern = f'sub-{ieeg.subject}_*_run-{ieeg.run}_events.tsv'
folder = parameters['paths']['input']
elif opts in ('movements', 'extension', 'flexion'):
pattern = f'sub-{ieeg.subject}_*_run-{ieeg.run}_dataglove.tsv'
folder = parameters['paths']['movements']
elif opts in ('realigned', ):
event_path = name(parameters, 'realign_tsv', ieeg_file)
pattern = event_path.name
folder = event_path.parent
elif what == 'dataglove':
pattern = f'sub-{ieeg.subject}_*_run-{ieeg.run}_recording-dataglove_physio.tsv.gz'
folder = parameters['paths']['input']
elif what in ['pial', 'freesurfer', ] + FS_LABELS:
pattern = 'sub-' + ieeg.subject
folder = parameters['paths']['freesurfer_subjects_dir']
elif what == 'surface':
elec = load('electrodes', parameters, ieeg_file)
right_or_left = (elec['x'] > 0).sum() / elec.shape[0]
if right_or_left > 0.5:
pattern = 'rh.pial'
else:
pattern = 'lh.pial'
folder = name(parameters, 'surface_dir', ieeg_file)
else:
raise ValueError(f'Unrecognize "{what}" selection')
found = list(folder.rglob(pattern))
if len(found) == 0:
raise FileNotFoundError(f'Could not find any file matching {pattern} in {folder}')
elif len(found) > 1:
raise ValueError('You need to specify more parameters')
filename = found[0]
if what == 'electrodes':
elec = Electrodes(filename)
return elec.electrodes.tsv[['name', 'x', 'y', 'z']]
elif what == 'events':
with filename.open() as f:
x = f.readline()
n_columns = x.count('\t') + 1
dtypes = [
('onset', 'float'),
('duration', 'float'),
('trial_type', 'U4096'),
]
if n_columns >= 4:
dtypes.insert(3, ('value', 'int'))
if n_columns == 5:
dtypes.insert(3, ('response_time', 'float')) # if -1, it means that we can reject trial
events = genfromtxt(filename, delimiter='\t', skip_header=1, dtype=dtypes)
if n_columns == 4:
x = empty(len(events), dtype='float')
x.fill(NaN)
events = append_fields(events, 'response_time', x, usemask=False)
return select_events(events, opts)
elif what == 'dataglove':
return read_physio(filename)
elif what == 'pial':
elec = load('electrodes', parameters, ieeg_file)
right_or_left = (elec['x'] > 0).sum() / elec.shape[0]
return read_surf(filename, right_or_left)
elif what == 'surface':
return Surf(filename)
elif what in FS_LABELS:
fs = load('freesurfer', parameters, ieeg_file)
pial = load('pial', parameters, ieeg_file)
hemi = pial.surf_file.stem
aparc_file = fs.dir / 'label' / f'{hemi}.{what}.annot'
region_values, region_ctab, region_names = read_annot(aparc_file)
out = {
'aparc': what,
'ras_shift': fs.surface_ras_shift,
'vert': pial.vert,
'regions': {
'values': region_values,
'names': [x.decode() for x in region_names],
'colors': read_brainregion_colors(region_names, region_ctab),
'colorscale': read_brainregion_colorscale(region_ctab),
}
}
return out
elif what == 'freesurfer':
return Freesurfer(filename)
from wonambi import Dataset
from wonambi.attr.chan import create_sphere_around_elec
from wonambi.attr import Freesurfer, Surf
from wonambi.source import Morph
from wonambi.utils import create_data
from .paths import (
fs_path,
surf_path,
eeglab_hdf5_1_file,
hdf5_file,
)
fs = Freesurfer(fs_path)
data = create_data()
surf = Surf(surf_path)
def test_import_chan():
with raises(ImportError):
create_sphere_around_elec(None, '')
def test_import_anat():
with raises(ImportError):
fs.surface_ras_shift
with raises(ImportError):
fs.read_label('')
with raises(ImportError):
def test_source_linear():
surf = Surf(surf_path)
channels = Channels(chan_path)
Linear(surf, channels)