def test_generate_stimfunction():
# Inputs for generate_stimfunction
onsets = [10, 30, 50, 70, 90]
event_durations = [6]
tr_duration = 2
duration = 100
# Create the time course for the signal to be generated
stimfunction = sim.generate_stimfunction(
onsets=onsets,
event_durations=event_durations,
total_time=duration,
)
assert stimfunction.shape[0] == duration * 100, "stimfunc incorrect length"
eventNumber = np.sum(event_durations * len(onsets)) * 100
assert np.sum(stimfunction) == eventNumber, "Event number"
# Create the signal function
signal_function = sim.convolve_hrf(
stimfunction=stimfunction,
tr_duration=tr_duration,
)
stim_dur = stimfunction.shape[0] / (tr_duration * 100)
assert signal_function.shape[0] == stim_dur, "The length did not change"
# Test
onsets = [0]
tr_duration = 1
event_durations = [1]
stimfunction = sim.generate_stimfunction(
onsets=onsets,
event_durations=event_durations,
total_time=duration,
)
signal_function = sim.convolve_hrf(
stimfunction=stimfunction,
tr_duration=tr_duration,
)
max_response = np.where(signal_function != 0)[0].max()
assert 25 < max_response <= 30, "HRF has the incorrect length"
assert np.sum(signal_function < 0) > 0, "No values below zero"
# Export a stimfunction
sim.export_3_column(
stimfunction,
'temp.txt',
)
# Load in the stimfunction
stimfunc_new = sim.generate_stimfunction(
onsets=None,
event_durations=None,
total_time=duration,
timing_file='temp.txt',
)
assert np.all(stimfunc_new == stimfunction), "Export/import failed"
# Break the timing precision of the generation
stimfunc_new = sim.generate_stimfunction(
onsets=None,
event_durations=None,
total_time=duration,
timing_file='temp.txt',
temporal_resolution=0.5,
)
assert stimfunc_new.sum() == 0, "Temporal resolution not working right"
# Set the duration to be too short so you should get an error
onsets = [10, 30, 50, 70, 90]
event_durations = [5]
with pytest.raises(ValueError):
sim.generate_stimfunction(
onsets=onsets,
event_durations=event_durations,
total_time=89,
)
# Clip the event offset
stimfunc_new = sim.generate_stimfunction(
onsets=onsets,
event_durations=event_durations,
total_time=95,
)
assert stimfunc_new[-1] == 1, 'Event offset was not clipped'
# Test exporting a group of participants to an epoch file
cond_a = sim.generate_stimfunction(
onsets=onsets,
event_durations=event_durations,
total_time=110,
)
cond_b = sim.generate_stimfunction(
onsets=[x + 5 for x in onsets],
event_durations=event_durations,
total_time=110,
)
stimfunction_group = [np.hstack((cond_a, cond_b))] * 2
sim.export_epoch_file(
stimfunction_group,
'temp.txt',
tr_duration,
)
# Check that convolve throws a warning when the shape is wrong
sim.convolve_hrf(
stimfunction=np.hstack((cond_a, cond_b)).T,
tr_duration=tr_duration,
temporal_resolution=1,
)
signal_B = sim.apply_signal(signal_func_B, ROI_B)
# Combine the two signal timecourses
signal = signal_A + signal_B
# spare the true noise.
#print('Generating noise')
#noise = sim.generate_noise(dimensions=dimensions,
# stimfunction_tr=np.zeros((numTRs, 1)),
# tr_duration=int(trDuration),
# template=template,
# mask=mask_cherry,
# noise_dict=noise_dict,
# temporal_proportion = 0.5)
brain = signal #+ noise
brain_nii = image.new_img_like(template_nii, brain, template_nii.affine)
out_path = os.path.join(dat_dir, '../simulated_data')
if os.path.isdir(out_path) is False:
os.makedirs(out_path, exist_ok=True)
brain_nii.to_filename(os.path.join(out_path, f"sub_{sid}_sim_dat.nii.gz"))
# write out the simulated epoch file
sim.export_epoch_file(
stimfunction=stimfunc_all,
filename=os.path.join(dat_dir, '../simulated_data/sim_epoch_file.npy'),
tr_duration=1.0,
temporal_resolution=1.0,
)
def test_generate_stimfunction():
# Inputs for generate_stimfunction
onsets = [10, 30, 50, 70, 90]
event_durations = [6]
tr_duration = 2
duration = 100
# Create the time course for the signal to be generated
stimfunction = sim.generate_stimfunction(onsets=onsets,
event_durations=event_durations,
total_time=duration,
)
assert stimfunction.shape[0] == duration * 100, "stimfunc incorrect length"
eventNumber = np.sum(event_durations * len(onsets)) * 100
assert np.sum(stimfunction) == eventNumber, "Event number"
# Create the signal function
signal_function = sim.convolve_hrf(stimfunction=stimfunction,
tr_duration=tr_duration,
)
stim_dur = stimfunction.shape[0] / (tr_duration * 100)
assert signal_function.shape[0] == stim_dur, "The length did not change"
# Test
onsets = [0]
tr_duration = 1
event_durations = [1]
stimfunction = sim.generate_stimfunction(onsets=onsets,
event_durations=event_durations,
total_time=duration,
)
signal_function = sim.convolve_hrf(stimfunction=stimfunction,
tr_duration=tr_duration,
)
max_response = np.where(signal_function != 0)[0].max()
assert 25 < max_response <= 30, "HRF has the incorrect length"
assert np.sum(signal_function < 0) > 0, "No values below zero"
# Export a stimfunction
sim.export_3_column(stimfunction,
'temp.txt',
)
# Load in the stimfunction
stimfunc_new = sim.generate_stimfunction(onsets=None,
event_durations=None,
total_time=duration,
timing_file='temp.txt',
)
assert np.all(stimfunc_new == stimfunction), "Export/import failed"
# Break the timing precision of the generation
stimfunc_new = sim.generate_stimfunction(onsets=None,
event_durations=None,
total_time=duration,
timing_file='temp.txt',
temporal_resolution=0.5,
)
assert stimfunc_new.sum() == 0, "Temporal resolution not working right"
# Set the duration to be too short so you should get an error
onsets = [10, 30, 50, 70, 90]
event_durations = [5]
with pytest.raises(ValueError):
sim.generate_stimfunction(onsets=onsets,
event_durations=event_durations,
total_time=89,
)
# Clip the event offset
stimfunc_new = sim.generate_stimfunction(onsets=onsets,
event_durations=event_durations,
total_time=95,
)
assert stimfunc_new[-1] == 1, 'Event offset was not clipped'
# Test exporting a group of participants to an epoch file
cond_a = sim.generate_stimfunction(onsets=onsets,
event_durations=event_durations,
total_time=110,
)
cond_b = sim.generate_stimfunction(onsets=[x + 5 for x in onsets],
event_durations=event_durations,
total_time=110,
)
stimfunction_group = [np.hstack((cond_a, cond_b))] * 2
sim.export_epoch_file(stimfunction_group,
'temp.txt',
tr_duration,
)
# Check that convolve throws a warning when the shape is wrong
sim.convolve_hrf(stimfunction=np.hstack((cond_a, cond_b)).T,
tr_duration=tr_duration,
temporal_resolution=1,
)