def test_event(params):
"""Test that on_process event gets fired"""
handler = Mock()
params.on_process = handler
with patch('mnefun._mnefun.save_epochs') as func:
mnefun.do_processing(params, write_epochs=True, print_status=False)
handler.assert_called_with('Doing epoch EQ/DQ', func, func(), params)
def test_exits_with_warning_if_all_epochs_autorejected(params):
"""Test that save_epochs fails gracefully if no epochs remain."""
params.subject_indices = np.arange(1)
params.subjects = ['x']
params.run_names = ['%s']
params.analyses = ['x']
params.out_names = [[]]
params.out_numbers = [[]]
params.must_match = [[]]
os.makedirs(op.join(params.work_dir, params.subjects[0], 'sss_pca_fif'))
with patch('mnefun._epoching.read_raw_fif') as read_raw_fif, \
patch('mnefun._scoring.concatenate_events'), \
patch('mnefun._epoching._fix_raw_eog_cals'), \
patch('mnefun._scoring.get_event_fnames') as get_event_fnames, \
patch('mnefun._epoching._read_events') as _read_events, \
patch('mnefun._epoching.Epochs') as Epochs:
Raw = Mock()
Raw.info = {'sfreq': 1000, 'bads': []}
Raw._first_samps = [0]
Raw._last_samps = [1000]
read_raw_fif.return_value = Raw
get_event_fnames.return_value = ['foo']
_read_events.return_value = np.empty((0, 3), int)
epochs = Mock()
epochs.info = {'sfreq': 1000}
epochs.events = np.zeros((0, 3))
Epochs.return_value = epochs
with pytest.raises(ValueError, match='No valid'):
mnefun.do_processing(params, write_epochs=True, print_status=False)
def test_exits_with_warning_if_all_epochs_autorejected():
"""Test that save_epochs fails gracefully if no epochs remain."""
params = mnefun.Params()
setdefaults(params)
params.subject_indices = numpy.arange(1)
params.subjects = ['x']
params.run_names = ['%s']
params.analyses = ['x']
params.out_names = [[]]
params.out_numbers = [[]]
params.must_match = [[]]
with patch('mnefun._mnefun.inspect'):
with patch('mnefun._mnefun.os'):
with patch('mnefun._mnefun.Raw'):
with patch('mnefun._mnefun.concatenate_events'):
with patch('mnefun._mnefun._fix_raw_eog_cals'):
with patch('mnefun._mnefun.get_event_fnames'):
with patch('mnefun._mnefun.Epochs') as Epochs:
epochs = Mock()
epochs.info = {'sfreq': 1000}
epochs.events = numpy.zeros((0, 3))
Epochs.return_value = epochs
with assert_raises(ValueError) as cm:
mnefun.do_processing(params,
write_epochs=True,
print_status=False)
assert_in('No valid epochs', str(cm.exception))
def test_event():
"""Test that on_process event gets fired"""
handler = Mock()
params = mnefun.Params()
setdefaults(params)
params.on_process = handler
with patch('mnefun._mnefun.save_epochs') as func:
mnefun.do_processing(params,
write_epochs=True,
)
handler.assert_called_with('Doing epoch EQ/DQ', func, func(), params)
def test_event():
"""Test that on_process event gets fired"""
handler = Mock()
params = mnefun.Params()
setdefaults(params)
params.on_process = handler
with patch('mnefun._mnefun.save_epochs') as func:
mnefun.do_processing(
params,
write_epochs=True,
)
handler.assert_called_with('Doing epoch EQ/DQ', func, func(), params)
)
# Set what processing steps will execute
default = False
mnefun.do_processing(
params,
fetch_raw=default, # Fetch raw recording files from acquisition machine
do_score=default, # Do scoring to slice data into trials
# Before running SSS, make SUBJ/raw_fif/SUBJ_prebad.txt file with
# space-separated list of bad MEG channel numbers
push_raw=default, # Push raw files and SSS script to SSS workstation
do_sss=default, # Run SSS remotely (on sws) or locally with MNE
fetch_sss=default, # Fetch SSSed files from SSS workstation
do_ch_fix=default, # Fix channel ordering
# Before running SSP, examine SSS'ed files and make
# SUBJ/bads/bad_ch_SUBJ_post-sss.txt; usually, this should only contain EEG
# channels.
gen_ssp=default, # Generate SSP vectors
apply_ssp=default, # Apply SSP vectors and filtering
write_epochs=default, # Write epochs to disk
gen_covs=default, # Generate covariances
# Make SUBJ/trans/SUBJ-trans.fif using mne_analyze; needed for fwd calc.
gen_fwd=default, # Generate forward solutions (and source space)
gen_inv=default, # Generate inverses
gen_report=default, # Write mne report html of results to disk
print_status=default, # Print completeness status update
)
],
snr=[
dict(analysis='English',
name='EngGram',
inv='%s-80-sss-meg-free-inv.fif'),
dict(analysis='English',
name='EngUngram',
inv='%s-80-sss-meg-free-inv.fif'),
dict(analysis='Jab', name='JabGram', inv='%s-80-sss-meg-free-inv.fif'),
dict(analysis='Jab',
name='JabUngram',
inv='%s-80-sss-meg-free-inv.fif')
],
psd=False,
)
mnefun.do_processing(params,
fetch_raw=False,
push_raw=False,
do_sss=False,
fetch_sss=False,
do_ch_fix=False,
gen_ssp=False,
apply_ssp=False,
write_epochs=False,
gen_covs=False,
gen_fwd=False,
gen_inv=False,
gen_report=True,
print_status=True)
mnefun.do_processing(
params,
fetch_raw=False, # Fetch raw recording files from acquisition machine
do_score=False, # Do scoring to slice data into trials
# Before running SSS, make SUBJ/raw_fif/SUBJ_prebad.txt file with
# space-separated list of bad MEG channel numbers
push_raw=False, # Push raw files and SSS script to SSS workstation
do_sss=False, # Run SSS remotely (on sws) or locally with mne-python
fetch_sss=False, # Fetch SSSed files from SSS workstation
do_ch_fix=False, # Fix channel ordering
# Before running SSP, examine SSS'ed files and make
# SUBJ/bads/bad_ch_SUBJ_post-sss.txt; usually, this should only contain EEG
# channels.
gen_ssp=False, # Generate SSP vectors
apply_ssp=False, # Apply SSP vectors and filtering
plot_psd=False, # Plot raw data power spectra
write_epochs=True, # Write epochs to disk
gen_covs=True, # Generate covariances
# Make SUBJ/trans/SUBJ-trans.fif using mne_analyze; needed for fwd calc.
gen_fwd=False, # Generate forward solutions (and src space if needed)
gen_inv=False, # Generate inverses
gen_report=True, # Write mne report html of results to disk
print_status=False, # Print completeness status update
# params,
# fetch_raw=False,
# do_score=True, # True
# push_raw=False,
# do_sss=True, # True
# fetch_sss=False,
# do_ch_fix=True, # True
# gen_ssp=True, # True
# apply_ssp=True, # True
# write_epochs=True, # True
# plot_psd=False,
# gen_covs=False,
# gen_fwd=False,
# gen_inv=False,
# print_status=False,
# gen_report=True # true
)
# use_subjects = ['bad_302a'] # Expected 2 ERM projectors for channel type grad based on proj_nums but got 0 in /mnt/bakraid/larsoner/kam/badbaby/badbaby/data/bad_302a/sss_pca_fif/preproc_cont-proj.fif
# use_subjects = ['bad_310b'] # RuntimeError: Only 7/1527 good ECG epochs found
# use_subjects = ['bad_925b'] # ValueError: extended_proj[0] channel names (length 299) do not match the good MEG channel names (length 297)
# Re-run epoching/cov/report for all to make sure no events are missing.
continue_on_error = False
for subject in use_subjects:
params.subject_indices = [params.subjects.index(subject)]
default = False
try:
mnefun.do_processing(
params,
do_score=default,
do_sss=default,
do_ch_fix=default,
gen_ssp=default,
apply_ssp=default,
write_epochs=default,
gen_covs=True,
gen_report=True,
print_status=True,
)
except Exception:
if not continue_on_error:
raise
traceback.print_exc()
bad.append(subject)
else:
good.append(subject)
print(f"Successfully processed {len(good)}/{len(good) + len(bad)}, bad:\n{bad}")
[1] * len(params.in_numbers),
[101, 102, 103, 104, 105, 106, 107, 108,
201, 202, 203, 204, 205, 206, 207, 208]
]
params.must_match = [
[],
[],
]
# Set what will run
mnefun.do_processing(
params,
fetch_raw=False,
do_score=False, # True
push_raw=False,
do_sss=False, # True
fetch_sss=False,
do_ch_fix=False, # True
gen_ssp=False, # True
apply_ssp=False, # True
write_epochs=True,
plot_psd=False,
gen_covs=False,
gen_fwd=False,
gen_inv=False,
print_status=False,
gen_report=True
)
[1, -1, -1], # Combine all standards
[-1, 1, 2], # take deviants seperate
[1, 2, -1], # Combine each oddball stimulus separately
[1, -1, 2]
]
params.must_match = [
[],
[],
[1, 2],
[],
[]
]
# Set what will run
mnefun.do_processing(
params,
fetch_raw=True, # Fetch raw recording files from acq machine
do_score=False, # do scoring
push_raw=False, # Push raw files and SSS script to SSS workstation
do_sss=False, # Run SSS remotely
fetch_sss=False, # Fetch SSSed files
do_ch_fix=False, # Fix channel ordering
gen_ssp=False, # Generate SSP vectors
apply_ssp=False, # Apply SSP vectors and filtering
write_epochs=False, # Write epochs to disk
gen_covs=False, # Generate covariances
gen_fwd=False, # Generate forward solutions (and source space if needed)
gen_inv=False, # Generate inverses
gen_report=False,
print_status=True
)
inv='%s-80-sss-meg-free-inv.fif',
views=['lat', 'med'],
size=(800, 800)),
dict(analysis='Conditions',
name='speech',
times=times,
inv='%s-80-sss-meg-free-inv.fif',
views=['lat', 'med'],
size=(800, 800)),
],
psd=False,
)
default = False
mnefun.do_processing(
params,
fetch_raw=default,
do_score=default,
push_raw=default,
do_sss=default,
fetch_sss=default,
do_ch_fix=default,
gen_ssp=default,
apply_ssp=default,
write_epochs=default,
gen_covs=default,
gen_fwd=default,
gen_inv=default,
gen_report=True,
print_status=False,
)
params.in_numbers, # Leave events split the same way they were scored
]
params.must_match = [
[],
[0, 1], # only make the standard event counts match
]
# Set what will run
mnefun.do_processing(
params,
fetch_raw=True, # Fetch raw recording files from acq machine
do_score=True, # do scoring
# Make SUBJ/raw_fif/SUBJ_prebad.txt file with space-separated
# list of bad MEG channel numbers, needed for running SSS.
push_raw=True, # Push raw files and SSS script to SSS workstation
do_sss=True, # Run SSS remotely
fetch_sss=True, # Fetch SSSed files
do_ch_fix=True, # Fix channel ordering
# Examine SSS'ed files and make SUBJ/bads/bad_ch_SUBJ_post-sss.txt,
# usually only contains EEG channels, needed for preprocessing.
gen_ssp=True, # Generate SSP vectors
apply_ssp=True, # Apply SSP vectors and filtering
write_epochs=True, # Write epochs to disk
gen_covs=True, # Generate covariances
# Make SUBJ/trans/SUBJ-trans.fif file in mne_analyze, needed for fwd calc.
gen_fwd=True, # Generate forward solutions (and source space if needed)
gen_inv=True, # Generate inverses
gen_report=True, # Write mne report html of results to disk
print_status=True, # Print completeness status update
)
params.report_params['pre_fun'] = pre_fun
params.report_params['post_fun'] = post_fun
params.subject_indices = [0, 1]
# Set what processing steps will execute
default = False
mnefun.do_processing(
params,
fetch_raw=default, # Fetch raw recording files from acquisition machine
do_score=default, # Do scoring to slice data into trials
# Before running SSS, make SUBJ/raw_fif/SUBJ_prebad.txt file with
# space-separated list of bad MEG channel numbers
do_sss=default, # Run SSS locally with MNE
do_ch_fix=default, # Fix channel ordering
# Before running SSP, examine SSS'ed files and make
# SUBJ/bads/bad_ch_SUBJ_post-sss.txt; usually, this should only contain EEG
# channels.
gen_ssp=default, # Generate SSP vectors
apply_ssp=default, # Apply SSP vectors and filtering
write_epochs=default, # Write epochs to disk
gen_covs=default, # Generate covariances
# Make SUBJ/trans/SUBJ-trans.fif using mne_analyze; needed for fwd calc.
gen_fwd=default, # Generate forward solutions (and source space)
gen_inv=default, # Generate inverses
gen_report=default, # Write mne report html of results to disk
print_status=default, # Print completeness status update
)
] # Continuous (from ERM)
# Maxwell filter with mne-python
params.sss_type = 'python'
params.sss_regularize = 'svd' # mc-svd SSS
params.tsss_dur = 4.
params.int_order = 6
params.st_correlation = .9
params.trans_to = (0, 0, 0.03)
# Set what will execute
mnefun.do_processing(
params,
fetch_raw=False, # Fetch raw recording files from acq machine
do_score=False, # do scoring
push_raw=False, # Push raw files and SSS script to SSS workstation
do_sss=False, # Run SSS remotely
fetch_sss=False, # Fetch SSSed files
do_ch_fix=False, # Fix channel ordering
gen_ssp=False, # Generate SSP vectors
apply_ssp=False, # Apply SSP vectors and filtering
plot_psd=False, # Plot raw data power spectra
write_epochs=False, # Write epochs to disk
gen_covs=False, # Generate covariances
gen_fwd=False,
# Generate forward solutions (and source space if needed)
gen_inv=False, # Generate inverses
gen_report=False, # Write mne report html of results to disk
print_status=True # Print completeness status update
)
]
params.must_match = [
[],
[0, 1], # only make the standard event counts match
]
# Set what will run
mnefun.do_processing(
params,
fetch_raw=True, # Fetch raw recording files from acq machine
do_score=True, # do scoring
# Make SUBJ/raw_fif/SUBJ_prebad.txt file with space-separated
# list of bad MEG channel numbers, needed for running SSS.
push_raw=True, # Push raw files and SSS script to SSS workstation
do_sss=True, # Run SSS remote using Maxfilter or local with mne-python
fetch_sss=True, # Fetch SSSed files from SSS workstation
do_ch_fix=True, # Fix channel ordering
# Examine SSS'ed files and make SUBJ/bads/bad_ch_SUBJ_post-sss.txt,
# usually only contains EEG channels, needed for preprocessing.
gen_ssp=True, # Generate SSP vectors
apply_ssp=True, # Apply SSP vectors and filtering
plot_psd=True, # Plot raw data power spectra
write_epochs=True, # Write epochs to disk
gen_covs=True, # Generate covariances
# Make SUBJ/trans/SUBJ-trans.fif file in mne_analyze, needed for fwd calc.
gen_fwd=True, # Generate forward solutions (and source space if needed)
gen_inv=True, # Generate inverses
gen_report=True, # Write mne report html of results to disk
print_status=True, # Print completeness status update
)