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"
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"
def test_apply_signal():
dimensions = np.array([10, 10, 10]) # What is the size of the brain
feature_size = [2]
feature_type = ['cube']
feature_coordinates = np.array([[5, 5, 5]])
signal_magnitude = [30]
# Generate a volume representing the location and quality of the signal
volume = sim.generate_signal(
dimensions=dimensions,
feature_coordinates=feature_coordinates,
feature_type=feature_type,
feature_size=feature_size,
signal_magnitude=signal_magnitude,
)
# 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,
)
signal_function = sim.convolve_hrf(
stimfunction=stimfunction,
tr_duration=tr_duration,
)
# Convolve the HRF with the stimulus sequence
signal = sim.apply_signal(
signal_function=signal_function,
volume_signal=volume,
)
assert signal.shape == (dimensions[0], dimensions[1], dimensions[2],
duration / tr_duration), "The output is the " \
"wrong size"
signal = sim.apply_signal(
signal_function=stimfunction,
volume_signal=volume,
)
assert np.any(signal == signal_magnitude), "The stimfunction is not binary"
def test_apply_signal():
dimensions = np.array([10, 10, 10]) # What is the size of the brain
feature_size = [2]
feature_type = ['cube']
feature_coordinates = np.array(
[[5, 5, 5]])
signal_magnitude = [30]
# Generate a volume representing the location and quality of the signal
volume = sim.generate_signal(dimensions=dimensions,
feature_coordinates=feature_coordinates,
feature_type=feature_type,
feature_size=feature_size,
signal_magnitude=signal_magnitude,
)
# 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,
)
signal_function = sim.convolve_hrf(stimfunction=stimfunction,
tr_duration=tr_duration,
)
# Convolve the HRF with the stimulus sequence
signal = sim.apply_signal(signal_function=signal_function,
volume_signal=volume,
)
assert signal.shape == (dimensions[0], dimensions[1], dimensions[2],
duration / tr_duration), "The output is the " \
"wrong size"
signal = sim.apply_signal(signal_function=stimfunction,
volume_signal=volume,
)
assert np.any(signal == signal_magnitude), "The stimfunction is not binary"
def test_generate_noise():
dimensions = np.array([10, 10, 10]) # What is the size of the brain
feature_size = [2]
feature_type = ['cube']
feature_coordinates = np.array(
[[5, 5, 5]])
signal_magnitude = [1]
# Generate a volume representing the location and quality of the signal
volume = sim.generate_signal(dimensions=dimensions,
feature_coordinates=feature_coordinates,
feature_type=feature_type,
feature_size=feature_size,
signal_magnitude=signal_magnitude,
)
# Inputs for generate_stimfunction
onsets = [10, 30, 50, 70, 90]
event_durations = [6]
tr_duration = 2
duration = 200
# Create the time course for the signal to be generated
stimfunction = sim.generate_stimfunction(onsets=onsets,
event_durations=event_durations,
total_time=duration,
)
signal_function = sim.convolve_hrf(stimfunction=stimfunction,
tr_duration=tr_duration,
)
# Convolve the HRF with the stimulus sequence
signal = sim.apply_signal(signal_function=signal_function,
volume_signal=volume,
)
# Generate the mask of the signal
mask, template = sim.mask_brain(signal, mask_threshold=0.1)
assert min(mask[mask > 0]) > 0.1, "Mask thresholding did not work"
assert len(np.unique(template) > 2), "Template creation did not work"
stimfunction_tr = stimfunction[::int(tr_duration * 100)]
# Create the noise volumes (using the default parameters)
noise = sim.generate_noise(dimensions=dimensions,
stimfunction_tr=stimfunction_tr,
tr_duration=tr_duration,
template=template,
mask=mask,
)
assert signal.shape == noise.shape, "The dimensions of signal and noise " \
"the same"
assert np.std(signal) < np.std(noise), "Noise was not created"
noise = sim.generate_noise(dimensions=dimensions,
stimfunction_tr=stimfunction_tr,
tr_duration=tr_duration,
template=template,
mask=mask,
noise_dict={'sfnr': 10000, 'snr': 10000},
)
system_noise = np.std(noise[mask > 0], 1).mean()
assert system_noise <= 0.1, "Noise strength could not be manipulated"
pattern_A = np.random.rand(voxels_A).reshape((voxels_A, 1))
pattern_B = np.random.rand(voxels_B).reshape((voxels_B, 1))
# Multiply each pattern by each voxel time course
# Noise was added to the design matrix, to make the correlation pattern noise, so FCMA could be challenging.
weights_A = np.tile(stimfunc_A, voxels_A) * pattern_A.T + np.random.normal(
0, 1, size=np.tile(stimfunc_A, voxels_A).shape)
weights_B = np.tile(stimfunc_B, voxels_B) * pattern_B.T + np.random.normal(
0, 1, size=np.tile(stimfunc_B, voxels_B).shape)
# Convolve the onsets with the HRF
# TR less than feature is not good, but b/c this is simulated data, can ignore this concer.
print('Creating signal time course')
signal_func_A = sim.convolve_hrf(
stimfunction=weights_A,
tr_duration=trDuration,
temporal_resolution=temporal_res,
scale_function=1,
)
signal_func_B = sim.convolve_hrf(
stimfunction=weights_B,
tr_duration=trDuration,
temporal_resolution=temporal_res,
scale_function=1,
)
# Multiply the signal by the signal change
signal_func_A = signal_func_A * signal_change #+ signal_func_B * signal_change
signal_func_B = signal_func_B * signal_change #+ signal_func_A * signal_change
# Combine the signal time course with the signal volume
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,
)
event_durations=event_durations,
total_time=duration,
temporal_resolution=temporal_res,
)
stimfunction_B = sim.generate_stimfunction(
onsets=onsets_B,
event_durations=event_durations,
total_time=duration,
temporal_resolution=temporal_res,
)
# Convolve the HRF with the stimulus sequence
signal_function_A = sim.convolve_hrf(
stimfunction=stimfunction_A,
tr_duration=tr_duration,
temporal_resolution=temporal_res,
)
signal_function_B = sim.convolve_hrf(
stimfunction=stimfunction_B,
tr_duration=tr_duration,
temporal_resolution=temporal_res,
)
# Multiply the HRF timecourse with the signal
signal_A = sim.apply_signal(
signal_function=signal_function_A,
volume_signal=volume_signal_A,
)
def generate_ROIs(ROI_file, stimfunc, noise, scale_percentage, data_dict):
# Create the signal in the ROI as specified.
print('Loading', ROI_file)
nii = nibabel.load(ROI_file)
ROI = nii.get_data()
# Find all the indices that contain signal
idx_list = np.where(ROI == 1)
idxs = np.zeros([len(idx_list[0]), 3])
for idx_counter in list(range(0, len(idx_list[0]))):
idxs[idx_counter, 0] = int(idx_list[0][idx_counter])
idxs[idx_counter, 1] = int(idx_list[1][idx_counter])
idxs[idx_counter, 2] = int(idx_list[2][idx_counter])
idxs = idxs.astype('int8')
# How many voxels per ROI
voxels = int(ROI.sum())
# Create a pattern of activity across the two voxels
if data_dict['multivariate_pattern'] is True:
pattern = np.random.rand(voxels).reshape((voxels, 1))
else: # Just make a univariate increase
pattern = np.tile(1, voxels).reshape((voxels, 1))
# Multiply each pattern by each voxel time course
weights = np.tile(stimfunc, voxels) * pattern.T
# Convolve the onsets with the HRF
temporal_res = 1 / data_dict['trDuration']
signal_func = sim.convolve_hrf(
stimfunction=weights,
tr_duration=data_dict['trDuration'],
temporal_resolution=temporal_res,
scale_function=1,
)
# Change the type of noise
noise = noise.astype('double')
# Create a noise function (same voxels for signal function as for noise)
noise_function = noise[idxs[:, 0], idxs[:, 1], idxs[:, 2], :].T
# Compute the signal magnitude for the data
sf_scaled = sim.compute_signal_change(
signal_function=signal_func,
noise_function=noise_function,
noise_dict=data_dict['noise_dict'],
magnitude=[scale_percentage],
method='PSC',
)
# Combine the signal time course with the signal volume
signal = sim.apply_signal(
sf_scaled,
ROI,
)
# Return signal needed
return signal
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,
)
def test_generate_noise():
dimensions = np.array([10, 10, 10]) # What is the size of the brain
feature_size = [2]
feature_type = ['cube']
feature_coordinates = np.array(
[[5, 5, 5]])
signal_magnitude = [1]
# Generate a volume representing the location and quality of the signal
volume = sim.generate_signal(dimensions=dimensions,
feature_coordinates=feature_coordinates,
feature_type=feature_type,
feature_size=feature_size,
signal_magnitude=signal_magnitude,
)
# Inputs for generate_stimfunction
onsets = [10, 30, 50, 70, 90]
event_durations = [6]
tr_duration = 2
duration = 200
# Create the time course for the signal to be generated
stimfunction = sim.generate_stimfunction(onsets=onsets,
event_durations=event_durations,
total_time=duration,
)
signal_function = sim.convolve_hrf(stimfunction=stimfunction,
tr_duration=tr_duration,
)
# Convolve the HRF with the stimulus sequence
signal = sim.apply_signal(signal_function=signal_function,
volume_signal=volume,
)
# Generate the mask of the signal
mask, template = sim.mask_brain(signal,
mask_self=None)
assert min(mask[mask > 0]) > 0.1, "Mask thresholding did not work"
assert len(np.unique(template) > 2), "Template creation did not work"
stimfunction_tr = stimfunction[::int(tr_duration * 100)]
# Create the noise volumes (using the default parameters)
noise = sim.generate_noise(dimensions=dimensions,
stimfunction_tr=stimfunction_tr,
tr_duration=tr_duration,
template=template,
mask=mask,
iterations=[1, 0],
)
assert signal.shape == noise.shape, "The dimensions of signal and noise " \
"the same"
noise_high = sim.generate_noise(dimensions=dimensions,
stimfunction_tr=stimfunction_tr,
tr_duration=tr_duration,
template=template,
mask=mask,
noise_dict={'sfnr': 50, 'snr': 25},
iterations=[1, 0],
)
noise_low = sim.generate_noise(dimensions=dimensions,
stimfunction_tr=stimfunction_tr,
tr_duration=tr_duration,
template=template,
mask=mask,
noise_dict={'sfnr': 100, 'snr': 25},
iterations=[1, 0],
)
system_high = np.std(noise_high[mask > 0], 1).mean()
system_low = np.std(noise_low[mask > 0], 1).mean()
assert system_low < system_high, "SFNR noise could not be manipulated"
# Check that you check for the appropriate template values
with pytest.raises(ValueError):
sim.generate_noise(dimensions=dimensions,
stimfunction_tr=stimfunction_tr,
tr_duration=tr_duration,
template=template * 2,
mask=mask,
noise_dict={},
)
# Check that iterations does what it should
sim.generate_noise(dimensions=dimensions,
stimfunction_tr=stimfunction_tr,
tr_duration=tr_duration,
template=template,
mask=mask,
noise_dict={},
iterations=[0, 0],
)
sim.generate_noise(dimensions=dimensions,
stimfunction_tr=stimfunction_tr,
tr_duration=tr_duration,
template=template,
mask=mask,
noise_dict={},
iterations=None,
)
# Test drift noise
trs = 1000
period = 100
drift = sim._generate_noise_temporal_drift(trs,
tr_duration,
'sine',
period,
)
# Check that the max frequency is the appropriate frequency
power = abs(np.fft.fft(drift))[1:trs // 2]
freq = np.linspace(1, trs // 2 - 1, trs // 2 - 1) / trs
period_freq = np.where(freq == 1 / (period // tr_duration))
max_freq = np.argmax(power)
assert period_freq == max_freq, 'Max frequency is not where it should be'
# Do the same but now with cosine basis functions, answer should be close
drift = sim._generate_noise_temporal_drift(trs,
tr_duration,
'discrete_cos',
period,
)
# Check that the appropriate frequency is peaky (may not be the max)
power = abs(np.fft.fft(drift))[1:trs // 2]
freq = np.linspace(1, trs // 2 - 1, trs // 2 - 1) / trs
period_freq = np.where(freq == 1 / (period // tr_duration))[0][0]
assert power[period_freq] > power[period_freq + 1], 'Power is low'
assert power[period_freq] > power[period_freq - 1], 'Power is low'
# Check it gives a warning if the duration is too short
drift = sim._generate_noise_temporal_drift(50,
tr_duration,
'discrete_cos',
period,
)
# Test physiological noise (using unrealistic parameters so that it's easy)
timepoints = list(np.linspace(0, (trs - 1) * tr_duration, trs))
resp_freq = 0.2
heart_freq = 1.17
phys = sim._generate_noise_temporal_phys(timepoints,
resp_freq,
heart_freq,
)
# Check that the max frequency is the appropriate frequency
power = abs(np.fft.fft(phys))[1:trs // 2]
freq = np.linspace(1, trs // 2 - 1, trs // 2 - 1) / (trs * tr_duration)
peaks = (power > (power.mean() + power.std())) # Where are the peaks
peak_freqs = freq[peaks]
assert np.any(resp_freq == peak_freqs), 'Resp frequency not found'
assert len(peak_freqs) == 2, 'Two peaks not found'
# Test task noise
sim._generate_noise_temporal_task(stimfunction_tr,
motion_noise='gaussian',
)
sim._generate_noise_temporal_task(stimfunction_tr,
motion_noise='rician',
)
# Test ARMA noise
with pytest.raises(ValueError):
noise_dict = {'fwhm': 4, 'auto_reg_rho': [1], 'ma_rho': [1, 1]}
sim._generate_noise_temporal_autoregression(stimfunction_tr,
noise_dict,
dimensions,
mask,
)
# Generate spatial noise
vol = sim._generate_noise_spatial(np.array([10, 10, 10, trs]))
assert len(vol.shape) == 3, 'Volume was not reshaped to ignore TRs'
# Switch some of the noise types on
noise_dict = dict(physiological_sigma=1, drift_sigma=1, task_sigma=1,
auto_reg_sigma=0)
sim.generate_noise(dimensions=dimensions,
stimfunction_tr=stimfunction_tr,
tr_duration=tr_duration,
template=template,
mask=mask,
noise_dict=noise_dict,
iterations=[0, 0],
)
def test_apply_signal():
dimensions = np.array([10, 10, 10]) # What is the size of the brain
feature_size = [2]
feature_type = ['cube']
feature_coordinates = np.array(
[[5, 5, 5]])
signal_magnitude = [30]
# Generate a volume representing the location and quality of the signal
volume = sim.generate_signal(dimensions=dimensions,
feature_coordinates=feature_coordinates,
feature_type=feature_type,
feature_size=feature_size,
signal_magnitude=signal_magnitude,
)
# 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,
)
signal_function = sim.convolve_hrf(stimfunction=stimfunction,
tr_duration=tr_duration,
)
# Check that you can compute signal change appropriately
# Preset a bunch of things
stimfunction_tr = stimfunction[::int(tr_duration * 100)]
mask, template = sim.mask_brain(dimensions, mask_self=False)
noise_dict = sim._noise_dict_update({})
noise = sim.generate_noise(dimensions=dimensions,
stimfunction_tr=stimfunction_tr,
tr_duration=tr_duration,
template=template,
mask=mask,
noise_dict=noise_dict,
iterations=[0, 0]
)
coords = feature_coordinates[0]
noise_function_a = noise[coords[0], coords[1], coords[2], :]
noise_function_a = noise_function_a.reshape(duration // tr_duration, 1)
noise_function_b = noise[coords[0] + 1, coords[1], coords[2], :]
noise_function_b = noise_function_b.reshape(duration // tr_duration, 1)
# Create the calibrated signal with PSC
method = 'PSC'
sig_a = sim.compute_signal_change(signal_function,
noise_function_a,
noise_dict,
[0.5],
method,
)
sig_b = sim.compute_signal_change(signal_function,
noise_function_a,
noise_dict,
[1.0],
method,
)
assert sig_b.max() / sig_a.max() == 2, 'PSC modulation failed'
# Create the calibrated signal with SFNR
method = 'SFNR'
sig_a = sim.compute_signal_change(signal_function,
noise_function_a,
noise_dict,
[0.5],
method,
)
scaled_a = sig_a / (noise_function_a.mean() / noise_dict['sfnr'])
sig_b = sim.compute_signal_change(signal_function,
noise_function_b,
noise_dict,
[1.0],
method,
)
scaled_b = sig_b / (noise_function_b.mean() / noise_dict['sfnr'])
assert scaled_b.max() / scaled_a.max() == 2, 'SFNR modulation failed'
# Create the calibrated signal with CNR_Amp/Noise-SD
method = 'CNR_Amp/Noise-SD'
sig_a = sim.compute_signal_change(signal_function,
noise_function_a,
noise_dict,
[0.5],
method,
)
scaled_a = sig_a / noise_function_a.std()
sig_b = sim.compute_signal_change(signal_function,
noise_function_b,
noise_dict,
[1.0],
method,
)
scaled_b = sig_b / noise_function_b.std()
assert scaled_b.max() / scaled_a.max() == 2, 'CNR_Amp modulation failed'
# Create the calibrated signal with CNR_Amp/Noise-Var_dB
method = 'CNR_Amp2/Noise-Var_dB'
sig_a = sim.compute_signal_change(signal_function,
noise_function_a,
noise_dict,
[0.5],
method,
)
scaled_a = np.log(sig_a.max() / noise_function_a.std())
sig_b = sim.compute_signal_change(signal_function,
noise_function_b,
noise_dict,
[1.0],
method,
)
scaled_b = np.log(sig_b.max() / noise_function_b.std())
assert np.round(scaled_b / scaled_a) == 2, 'CNR_Amp dB modulation failed'
# Create the calibrated signal with CNR_Signal-SD/Noise-SD
method = 'CNR_Signal-SD/Noise-SD'
sig_a = sim.compute_signal_change(signal_function,
noise_function_a,
noise_dict,
[0.5],
method,
)
scaled_a = sig_a.std() / noise_function_a.std()
sig_b = sim.compute_signal_change(signal_function,
noise_function_a,
noise_dict,
[1.0],
method,
)
scaled_b = sig_b.std() / noise_function_a.std()
assert (scaled_b / scaled_a) == 2, 'CNR signal modulation failed'
# Create the calibrated signal with CNR_Amp/Noise-Var_dB
method = 'CNR_Signal-Var/Noise-Var_dB'
sig_a = sim.compute_signal_change(signal_function,
noise_function_a,
noise_dict,
[0.5],
method,
)
scaled_a = np.log(sig_a.std() / noise_function_a.std())
sig_b = sim.compute_signal_change(signal_function,
noise_function_b,
noise_dict,
[1.0],
method,
)
scaled_b = np.log(sig_b.std() / noise_function_b.std())
assert np.round(scaled_b / scaled_a) == 2, 'CNR signal dB modulation ' \
'failed'
# Convolve the HRF with the stimulus sequence
signal = sim.apply_signal(signal_function=signal_function,
volume_signal=volume,
)
assert signal.shape == (dimensions[0], dimensions[1], dimensions[2],
duration / tr_duration), "The output is the " \
"wrong size"
signal = sim.apply_signal(signal_function=stimfunction,
volume_signal=volume,
)
assert np.any(signal == signal_magnitude), "The stimfunction is not binary"
# Check that there is an error if the number of signal voxels doesn't
# match the number of non zero brain voxels
with pytest.raises(IndexError):
sig_vox = (volume > 0).sum()
vox_pattern = np.tile(stimfunction, (1, sig_vox - 1))
sim.apply_signal(signal_function=vox_pattern,
volume_signal=volume,
)
def _generate_ROIs(ROI_file, stimfunc, noise, scale_percentage, data_dict):
"""Make signal activity for an ROI of data
Creates the specified evoked response time course, calibrated to the
expected signal change, for a given ROI
Parameters
----------
ROI_file : str
Path to the file of the ROI being loaded in
stimfunc : 1 dimensional array
Time course of evoked response. Output from
fmrisim.generate_stimfunction
noise : 4 dimensional array
Volume of noise generated from fmrisim.generate_noise. Although this
is needed as an input, this is only so that the percent signal change
can be calibrated. This is not combined with the signal generated.
scale_percentage : float
What is the percent signal change for the evoked response
data_dict : dict
A dictionary to specify the parameters used for making data,
specifying the following keys
numTRs - int - Specify the number of time points
multivariate_patterns - bool - Is the difference between conditions
univariate (0) or multivariate (1)
different_ROIs - bool - Are there different ROIs for each condition (
1) or is it in the same ROI (0). If it is the same ROI and you are
using univariate differences, the second condition will have a
smaller evoked response than the other.
event_duration - int - How long, in seconds, is each event
scale_percentage - float - What is the percent signal change
trDuration - float - How many seconds per volume
save_dicom - bool - Save to data as a dicom (1) or numpy (0)
save_realtime - bool - Do you want to save the data in real time (1)
or as fast as possible (0)?
isi - float - What is the time between each event (in seconds)
burn_in - int - How long before the first event (in seconds)
Returns
----------
signal : 4 dimensional array
Volume of signal in the specified ROI (noise has not yet been added)
"""
# Create the signal in the ROI as specified.
# Load in the template data (it may already be loaded if doing a test)
if isinstance(ROI_file, str):
logger.info('Loading', ROI_file)
nii = nibabel.load(ROI_file)
ROI = nii.get_data()
else:
ROI = ROI_file
# Find all the indices that contain signal
idx_list = np.where(ROI == 1)
idxs = np.zeros([len(idx_list[0]), 3])
for idx_counter in list(range(0, len(idx_list[0]))):
idxs[idx_counter, 0] = int(idx_list[0][idx_counter])
idxs[idx_counter, 1] = int(idx_list[1][idx_counter])
idxs[idx_counter, 2] = int(idx_list[2][idx_counter])
idxs = idxs.astype('int8')
# How many voxels per ROI
voxels = int(ROI.sum())
# Create a pattern of activity across the two voxels
if data_dict['multivariate_pattern'] is True:
pattern = np.random.rand(voxels).reshape((voxels, 1))
else: # Just make a univariate increase
pattern = np.tile(1, voxels).reshape((voxels, 1))
# Multiply each pattern by each voxel time course
weights = np.tile(stimfunc, voxels) * pattern.T
# Convolve the onsets with the HRF
temporal_res = 1 / data_dict['trDuration']
signal_func = sim.convolve_hrf(
stimfunction=weights,
tr_duration=data_dict['trDuration'],
temporal_resolution=temporal_res,
scale_function=1,
)
# Change the type of noise
noise = noise.astype('double')
# Create a noise function (same voxels for signal function as for noise)
noise_function = noise[idxs[:, 0], idxs[:, 1], idxs[:, 2], :].T
# Compute the signal magnitude for the data
sf_scaled = sim.compute_signal_change(
signal_function=signal_func,
noise_function=noise_function,
noise_dict=data_dict['noise_dict'],
magnitude=[scale_percentage],
method='PSC',
)
# Combine the signal time course with the signal volume
signal = sim.apply_signal(
sf_scaled,
ROI,
)
# Return signal needed
return signal
def test_generate_noise():
dimensions = np.array([10, 10, 10]) # What is the size of the brain
feature_size = [2]
feature_type = ['cube']
feature_coordinates = np.array(
[[5, 5, 5]])
signal_magnitude = [1]
# Generate a volume representing the location and quality of the signal
volume = sim.generate_signal(dimensions=dimensions,
feature_coordinates=feature_coordinates,
feature_type=feature_type,
feature_size=feature_size,
signal_magnitude=signal_magnitude,
)
# 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,
)
signal_function = sim.convolve_hrf(stimfunction=stimfunction,
tr_duration=tr_duration,
)
# Convolve the HRF with the stimulus sequence
signal = sim.apply_signal(signal_function=signal_function,
volume_signal=volume,
)
# Generate the mask of the signal
mask, template = sim.mask_brain(signal, mask_threshold=0.1)
assert min(mask[mask > 0]) > 0.1, "Mask thresholding did not work"
assert len(np.unique(template) > 2), "Template creation did not work"
stimfunction_tr = stimfunction[::int(tr_duration * 1000)]
# Create the noise volumes (using the default parameters)
noise = sim.generate_noise(dimensions=dimensions,
stimfunction_tr=stimfunction_tr,
tr_duration=tr_duration,
template=template,
mask=mask,
)
assert signal.shape == noise.shape, "The dimensions of signal and noise " \
"the same"
assert np.std(signal) < np.std(noise), "Noise was not created"
noise = sim.generate_noise(dimensions=dimensions,
stimfunction_tr=stimfunction_tr,
tr_duration=tr_duration,
template=template,
mask=mask,
noise_dict={'sfnr': 10000, 'snr': 10000},
)
system_noise = np.std(noise[mask > 0], 1).mean()
assert system_noise <= 0.1, "Noise strength could not be manipulated"
stimfunc).transpose() * signal_pattern[voxel_counter]
else:
# Add these elements together
temp = np.zeros((len(stimfunc), vector_size))
for voxel_counter in list(range(0, vector_size)):
temp[:, voxel_counter] = np.asarray(stimfunc).transpose() * \
signal_pattern[voxel_counter]
stimfunc_all += temp
# After you have gone through all the nodes, convolve the HRF and
# stimulation for each voxel
print('Convolving HRF')
signal_func = sim.convolve_hrf(stimfunction=stimfunc_all,
tr_duration=tr_duration,
temporal_resolution=temporal_res,
)
if save_signal_func == 1 and resample == 0 and run_counter == 0:
plt.plot(stimfunc_all[::int(temporal_res * tr_duration), 0])
plt.plot(signal_func[:,0])
plt.xlim((0, 200))
plt.ylim((-1, 5))
plt.savefig(signal_func_save)
# Convert the stim func into a binary vector of dim 1
stimfunc_binary = np.mean(np.abs(stimfunc_all)>0, 1) > 0
stimfunc_binary = stimfunc_binary[::int(tr_duration * temporal_res)]
# Bound, can happen if the duration is not rounded to a TR
stimfunc_binary = stimfunc_binary[0:signal_func.shape[0]]
def test_apply_signal():
dimensions = np.array([10, 10, 10]) # What is the size of the brain
feature_size = [2]
feature_type = ['cube']
feature_coordinates = np.array([[5, 5, 5]])
signal_magnitude = [30]
# Generate a volume representing the location and quality of the signal
volume = sim.generate_signal(
dimensions=dimensions,
feature_coordinates=feature_coordinates,
feature_type=feature_type,
feature_size=feature_size,
signal_magnitude=signal_magnitude,
)
# 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,
)
signal_function = sim.convolve_hrf(
stimfunction=stimfunction,
tr_duration=tr_duration,
)
# Check that you can compute signal change appropriately
# Preset a bunch of things
stimfunction_tr = stimfunction[::int(tr_duration * 100)]
mask, template = sim.mask_brain(dimensions, mask_self=False)
noise_dict = sim._noise_dict_update({})
noise = sim.generate_noise(dimensions=dimensions,
stimfunction_tr=stimfunction_tr,
tr_duration=tr_duration,
template=template,
mask=mask,
noise_dict=noise_dict,
iterations=[0, 0])
coords = feature_coordinates[0]
noise_function_a = noise[coords[0], coords[1], coords[2], :]
noise_function_a = noise_function_a.reshape(duration // tr_duration, 1)
noise_function_b = noise[coords[0] + 1, coords[1], coords[2], :]
noise_function_b = noise_function_b.reshape(duration // tr_duration, 1)
# Create the calibrated signal with PSC
method = 'PSC'
sig_a = sim.compute_signal_change(
signal_function,
noise_function_a,
noise_dict,
[0.5],
method,
)
sig_b = sim.compute_signal_change(
signal_function,
noise_function_a,
noise_dict,
[1.0],
method,
)
assert sig_b.max() / sig_a.max() == 2, 'PSC modulation failed'
# Create the calibrated signal with SFNR
method = 'SFNR'
sig_a = sim.compute_signal_change(
signal_function,
noise_function_a,
noise_dict,
[0.5],
method,
)
scaled_a = sig_a / (noise_function_a.mean() / noise_dict['sfnr'])
sig_b = sim.compute_signal_change(
signal_function,
noise_function_b,
noise_dict,
[1.0],
method,
)
scaled_b = sig_b / (noise_function_b.mean() / noise_dict['sfnr'])
assert scaled_b.max() / scaled_a.max() == 2, 'SFNR modulation failed'
# Create the calibrated signal with CNR_Amp/Noise-SD
method = 'CNR_Amp/Noise-SD'
sig_a = sim.compute_signal_change(
signal_function,
noise_function_a,
noise_dict,
[0.5],
method,
)
scaled_a = sig_a / noise_function_a.std()
sig_b = sim.compute_signal_change(
signal_function,
noise_function_b,
noise_dict,
[1.0],
method,
)
scaled_b = sig_b / noise_function_b.std()
assert scaled_b.max() / scaled_a.max() == 2, 'CNR_Amp modulation failed'
# Create the calibrated signal with CNR_Amp/Noise-Var_dB
method = 'CNR_Amp2/Noise-Var_dB'
sig_a = sim.compute_signal_change(
signal_function,
noise_function_a,
noise_dict,
[0.5],
method,
)
scaled_a = np.log(sig_a.max() / noise_function_a.std())
sig_b = sim.compute_signal_change(
signal_function,
noise_function_b,
noise_dict,
[1.0],
method,
)
scaled_b = np.log(sig_b.max() / noise_function_b.std())
assert np.round(scaled_b / scaled_a) == 2, 'CNR_Amp dB modulation failed'
# Create the calibrated signal with CNR_Signal-SD/Noise-SD
method = 'CNR_Signal-SD/Noise-SD'
sig_a = sim.compute_signal_change(
signal_function,
noise_function_a,
noise_dict,
[0.5],
method,
)
scaled_a = sig_a.std() / noise_function_a.std()
sig_b = sim.compute_signal_change(
signal_function,
noise_function_a,
noise_dict,
[1.0],
method,
)
scaled_b = sig_b.std() / noise_function_a.std()
assert (scaled_b / scaled_a) == 2, 'CNR signal modulation failed'
# Create the calibrated signal with CNR_Amp/Noise-Var_dB
method = 'CNR_Signal-Var/Noise-Var_dB'
sig_a = sim.compute_signal_change(
signal_function,
noise_function_a,
noise_dict,
[0.5],
method,
)
scaled_a = np.log(sig_a.std() / noise_function_a.std())
sig_b = sim.compute_signal_change(
signal_function,
noise_function_b,
noise_dict,
[1.0],
method,
)
scaled_b = np.log(sig_b.std() / noise_function_b.std())
assert np.round(scaled_b / scaled_a) == 2, 'CNR signal dB modulation ' \
'failed'
# Convolve the HRF with the stimulus sequence
signal = sim.apply_signal(
signal_function=signal_function,
volume_signal=volume,
)
assert signal.shape == (dimensions[0], dimensions[1], dimensions[2],
duration / tr_duration), "The output is the " \
"wrong size"
signal = sim.apply_signal(
signal_function=stimfunction,
volume_signal=volume,
)
assert np.any(signal == signal_magnitude), "The stimfunction is not binary"
# Check that there is an error if the number of signal voxels doesn't
# match the number of non zero brain voxels
with pytest.raises(IndexError):
sig_vox = (volume > 0).sum()
vox_pattern = np.tile(stimfunction, (1, sig_vox - 1))
sim.apply_signal(
signal_function=vox_pattern,
volume_signal=volume,
)
# Create the time course for the signal to be generated
stimfunction_A = sim.generate_stimfunction(onsets=onsets_A,
event_durations=event_durations,
total_time=duration,
temporal_resolution=temporal_res,
)
stimfunction_B = sim.generate_stimfunction(onsets=onsets_B,
event_durations=event_durations,
total_time=duration,
temporal_resolution=temporal_res,
)
# Convolve the HRF with the stimulus sequence
signal_function_A = sim.convolve_hrf(stimfunction=stimfunction_A,
tr_duration=tr_duration,
temporal_resolution=temporal_res,
)
signal_function_B = sim.convolve_hrf(stimfunction=stimfunction_B,
tr_duration=tr_duration,
temporal_resolution=temporal_res,
)
# Multiply the HRF timecourse with the signal
signal_A = sim.apply_signal(signal_function=signal_function_A,
volume_signal=volume_signal_A,
)
signal_B = sim.apply_signal(signal_function=signal_function_B,
volume_signal=volume_signal_B,
)
def test_generate_noise():
dimensions = np.array([10, 10, 10]) # What is the size of the brain
feature_size = [2]
feature_type = ['cube']
feature_coordinates = np.array([[5, 5, 5]])
signal_magnitude = [1]
# Generate a volume representing the location and quality of the signal
volume = sim.generate_signal(
dimensions=dimensions,
feature_coordinates=feature_coordinates,
feature_type=feature_type,
feature_size=feature_size,
signal_magnitude=signal_magnitude,
)
# Inputs for generate_stimfunction
onsets = [10, 30, 50, 70, 90]
event_durations = [6]
tr_duration = 2
duration = 200
# Create the time course for the signal to be generated
stimfunction = sim.generate_stimfunction(
onsets=onsets,
event_durations=event_durations,
total_time=duration,
)
signal_function = sim.convolve_hrf(
stimfunction=stimfunction,
tr_duration=tr_duration,
)
# Convolve the HRF with the stimulus sequence
signal = sim.apply_signal(
signal_function=signal_function,
volume_signal=volume,
)
# Generate the mask of the signal
mask, template = sim.mask_brain(signal, mask_self=None)
assert min(mask[mask > 0]) > 0.1, "Mask thresholding did not work"
assert len(np.unique(template) > 2), "Template creation did not work"
stimfunction_tr = stimfunction[::int(tr_duration * 100)]
# Create the noise volumes (using the default parameters)
noise = sim.generate_noise(
dimensions=dimensions,
stimfunction_tr=stimfunction_tr,
tr_duration=tr_duration,
template=template,
mask=mask,
iterations=[1, 0],
)
assert signal.shape == noise.shape, "The dimensions of signal and noise " \
"the same"
noise_high = sim.generate_noise(
dimensions=dimensions,
stimfunction_tr=stimfunction_tr,
tr_duration=tr_duration,
template=template,
mask=mask,
noise_dict={
'sfnr': 50,
'snr': 25
},
iterations=[1, 0],
)
noise_low = sim.generate_noise(
dimensions=dimensions,
stimfunction_tr=stimfunction_tr,
tr_duration=tr_duration,
template=template,
mask=mask,
noise_dict={
'sfnr': 100,
'snr': 25
},
iterations=[1, 0],
)
system_high = np.std(noise_high[mask > 0], 1).mean()
system_low = np.std(noise_low[mask > 0], 1).mean()
assert system_low < system_high, "SFNR noise could not be manipulated"
# Check that you check for the appropriate template values
with pytest.raises(ValueError):
sim.generate_noise(
dimensions=dimensions,
stimfunction_tr=stimfunction_tr,
tr_duration=tr_duration,
template=template * 2,
mask=mask,
noise_dict={},
)
# Check that iterations does what it should
sim.generate_noise(
dimensions=dimensions,
stimfunction_tr=stimfunction_tr,
tr_duration=tr_duration,
template=template,
mask=mask,
noise_dict={},
iterations=[0, 0],
)
sim.generate_noise(
dimensions=dimensions,
stimfunction_tr=stimfunction_tr,
tr_duration=tr_duration,
template=template,
mask=mask,
noise_dict={},
iterations=None,
)
# Test drift noise
trs = 1000
period = 100
drift = sim._generate_noise_temporal_drift(
trs,
tr_duration,
'sine',
period,
)
# Check that the max frequency is the appropriate frequency
power = abs(np.fft.fft(drift))[1:trs // 2]
freq = np.linspace(1, trs // 2 - 1, trs // 2 - 1) / trs
period_freq = np.where(freq == 1 / (period // tr_duration))
max_freq = np.argmax(power)
assert period_freq == max_freq, 'Max frequency is not where it should be'
# Do the same but now with cosine basis functions, answer should be close
drift = sim._generate_noise_temporal_drift(
trs,
tr_duration,
'discrete_cos',
period,
)
# Check that the appropriate frequency is peaky (may not be the max)
power = abs(np.fft.fft(drift))[1:trs // 2]
freq = np.linspace(1, trs // 2 - 1, trs // 2 - 1) / trs
period_freq = np.where(freq == 1 / (period // tr_duration))[0][0]
assert power[period_freq] > power[period_freq + 1], 'Power is low'
assert power[period_freq] > power[period_freq - 1], 'Power is low'
# Check it runs fine
drift = sim._generate_noise_temporal_drift(
50,
tr_duration,
'discrete_cos',
period,
)
# Check it runs fine
drift = sim._generate_noise_temporal_drift(
300,
tr_duration,
'cos_power_drop',
period,
)
# Check that when the TR is greater than the period it errors
with pytest.raises(ValueError):
sim._generate_noise_temporal_drift(30, 10, 'cos_power_drop', 5)
# Test physiological noise (using unrealistic parameters so that it's easy)
timepoints = list(np.linspace(0, (trs - 1) * tr_duration, trs))
resp_freq = 0.2
heart_freq = 1.17
phys = sim._generate_noise_temporal_phys(
timepoints,
resp_freq,
heart_freq,
)
# Check that the max frequency is the appropriate frequency
power = abs(np.fft.fft(phys))[1:trs // 2]
freq = np.linspace(1, trs // 2 - 1, trs // 2 - 1) / (trs * tr_duration)
peaks = (power > (power.mean() + power.std())) # Where are the peaks
peak_freqs = freq[peaks]
assert np.any(resp_freq == peak_freqs), 'Resp frequency not found'
assert len(peak_freqs) == 2, 'Two peaks not found'
# Test task noise
sim._generate_noise_temporal_task(
stimfunction_tr,
motion_noise='gaussian',
)
sim._generate_noise_temporal_task(
stimfunction_tr,
motion_noise='rician',
)
# Test ARMA noise
with pytest.raises(ValueError):
noise_dict = {'fwhm': 4, 'auto_reg_rho': [1], 'ma_rho': [1, 1]}
sim._generate_noise_temporal_autoregression(
stimfunction_tr,
noise_dict,
dimensions,
mask,
)
# Generate spatial noise
vol = sim._generate_noise_spatial(np.array([10, 10, 10, trs]))
assert len(vol.shape) == 3, 'Volume was not reshaped to ignore TRs'
# Switch some of the noise types on
noise_dict = dict(physiological_sigma=1,
drift_sigma=1,
task_sigma=1,
auto_reg_sigma=0)
sim.generate_noise(
dimensions=dimensions,
stimfunction_tr=stimfunction_tr,
tr_duration=tr_duration,
template=template,
mask=mask,
noise_dict=noise_dict,
iterations=[0, 0],
)
def generate_data(cfgFile):
cfg = loadConfigFile(cfgFile)
frame = inspect.currentframe()
moduleFile = typing.cast(str, frame.f_code.co_filename) # type: ignore
moduleDir = os.path.dirname(moduleFile)
cfgDate = parser.parse(cfg.session.date).strftime("%Y%m%d")
dataDir = os.path.join(
cfg.session.dataDir, "subject{}/day{}".format(cfg.session.subjectNum,
cfg.session.subjectDay))
imgDir = os.path.join(
cfg.session.imgDir, "{}.{}.{}".format(cfgDate, cfg.session.subjectName,
cfg.session.subjectName))
if os.path.exists(dataDir) and os.path.exists(imgDir):
print(
"output data and imgage directory already exist, skippig data generation"
)
return
runPatterns = [
'patternsdesign_1_20180101T000000.mat',
'patternsdesign_2_20180101T000000.mat',
'patternsdesign_3_20180101T000000.mat'
]
template_filename = os.path.join(moduleDir, 'sub_template.nii.gz')
noise_dict_filename = os.path.join(moduleDir, 'sub_noise_dict.txt')
roiA_filename = os.path.join(moduleDir, 'ROI_A.nii.gz')
roiB_filename = os.path.join(moduleDir, 'ROI_B.nii.gz')
output_file_pattern = '001_0000{}_000{}.mat'
if not os.path.exists(imgDir):
os.makedirs(imgDir)
if not os.path.exists(dataDir):
os.makedirs(dataDir)
print('Load data')
template_nii = nibabel.load(template_filename)
template = template_nii.get_data()
# dimsize = template_nii.header.get_zooms()
roiA_nii = nibabel.load(roiA_filename)
roiB_nii = nibabel.load(roiB_filename)
roiA = roiA_nii.get_data()
roiB = roiB_nii.get_data()
dimensions = np.array(template.shape[0:3]) # What is the size of the brain
print('Create mask')
# Generate the continuous mask from the voxels
mask, template = sim.mask_brain(
volume=template,
mask_self=True,
)
# Write out the mask as matlab
mask_uint8 = mask.astype(np.uint8)
maskfilename = os.path.join(
dataDir, 'mask_{}_{}.mat'.format(cfg.session.subjectNum,
cfg.session.subjectDay))
sio.savemat(maskfilename, {'mask': mask_uint8})
# Load the noise dictionary
with open(noise_dict_filename, 'r') as f:
noise_dict = f.read()
print('Loading ' + noise_dict_filename)
noise_dict = eval(noise_dict)
noise_dict['matched'] = 0
runNum = 1
scanNum = 0
for patfile in runPatterns:
fullPatfile = os.path.join(moduleDir, patfile)
# make dataDir run directory
runDir = os.path.join(dataDir, "run{}".format(runNum))
if not os.path.exists(runDir):
os.makedirs(runDir)
shutil.copy(fullPatfile, runDir)
runNum += 1
pat = sio.loadmat(fullPatfile)
scanNum += 1
# shifted labels are in regressor field
shiftedLabels = pat['patterns']['regressor'][0][0]
# non-shifted labels are in attCateg field and whether stimulus applied in the stim field
nsLabels = pat['patterns']['attCateg'][0][0] * pat['patterns']['stim'][
0][0]
labels_A = (nsLabels == 1).astype(int)
labels_B = (nsLabels == 2).astype(int)
# trialType = pat['patterns']['type'][0][0]
tr_duration = pat['TR'][0][0]
disdaqs = pat['disdaqs'][0][0]
begTrOffset = disdaqs // tr_duration
nTRs = pat['nTRs'][0][0]
# nTestTRs = np.count_nonzero(trialType == 2)
# Preset some of the parameters
total_trs = nTRs + begTrOffset # How many time points are there?
print('Generating data')
start = time.time()
noiseVols = sim.generate_noise(
dimensions=dimensions,
stimfunction_tr=np.zeros((total_trs, 1)),
tr_duration=int(tr_duration),
template=template,
mask=mask,
noise_dict=noise_dict,
)
print("Time: generate noise vols {} sec".format(time.time() - start))
nVoxelsA = int(roiA.sum())
nVoxelsB = int(roiB.sum())
# Multiply each pattern by each voxel time course
weights_A = np.tile(labels_A.reshape(-1, 1), nVoxelsA)
weights_B = np.tile(labels_B.reshape(-1, 1), nVoxelsB)
print('Creating signal time course')
signal_func_A = sim.convolve_hrf(
stimfunction=weights_A,
tr_duration=tr_duration,
temporal_resolution=(1 / tr_duration),
scale_function=1,
)
signal_func_B = sim.convolve_hrf(
stimfunction=weights_B,
tr_duration=tr_duration,
temporal_resolution=(1 / tr_duration),
scale_function=1,
)
max_activity = noise_dict['max_activity']
signal_change = 10 # .01 * max_activity
signal_func_A *= signal_change
signal_func_B *= signal_change
# Combine the signal time course with the signal volume
print('Creating signal volumes')
signal_A = sim.apply_signal(
signal_func_A,
roiA,
)
signal_B = sim.apply_signal(
signal_func_B,
roiB,
)
# Combine the two signal timecourses
signal = signal_A + signal_B
# testTrId = 0
numVols = noiseVols.shape[3]
for idx in range(numVols):
start = time.time()
brain = noiseVols[:, :, :, idx]
if idx >= begTrOffset:
# some initial scans are skipped as only instructions and not stimulus are shown
signalIdx = idx - begTrOffset
brain += signal[:, :, :, signalIdx]
# TODO: how to create a varying combined percentage of A and B signals
# if trialType[0][idx] == 1:
# # training TR, so create pure A or B signal
# if labels_A[idx] != 0:
# brain = brain + roiA
# elif labels_B[idx] != 0:
# brain = brain + roiB
# elif trialType[0][idx] == 2:
# # testing TR, so create a mixture of A and B signal
# testTrId += 1
# testPercent = testTrId / nTestTRs
# brain = brain + testPercent * roiA + (1-testPercent) * roiB
# Save the volume as a matlab file
filenum = idx + 1
filename = output_file_pattern.format(
str(scanNum).zfill(2),
str(filenum).zfill(3))
outputfile = os.path.join(imgDir, filename)
brain_float32 = brain.astype(np.float32)
sio.savemat(outputfile, {'vol': brain_float32})
print("Time: generate vol {}: {} sec".format(
filenum,
time.time() - start))
def toy_simulation(community_density=1,
added_isi=0,
rand=0,
signal_magnitude=1,
noise_type='coordinates',
noise_parameter=0,
restrict_overall_duration=0,
):
# Default these values
nodes = 15
runs = 5
vector_size = 2 # How many voxels did you make
all_pos = 1
ppt = '1' # What participant would you like to simulate
tr_duration = 2
event_durations = [1]
hrf_lag = 2
temporal_res = 100 # How many samples per second are there for timing files
# Select what the noise is applied to
noise_coordinates = 0 # How much noise are you adding to the coordinates
noise_timecourse = 0 # How many random noise are you adding to the timecourse
if noise_type == 'coordinates':
noise_coordinates = noise_parameter
elif noise_type == 'timecourse':
noise_timecourse = noise_parameter
# Load the timing information
timing_path = '../../community_structure/simulator_parameters/timing/'
#timing_path = '/Volumes/pniintel/ntb/TDA/Validation/code/community_structure/simulator_parameters/timing/'
onsets_runs = np.load(timing_path + 'sub-' + ppt + '.npy')
# Generate the graph structure (based on the ratio)
signal_coords = community_structure(1 - community_density,
)
# Add noise to these coordinates
noise = np.random.randn(np.prod(signal_coords.shape)).reshape(
signal_coords.shape) * noise_coordinates
signal_coords += noise
# Perform an orthonormal transformation of the data
if vector_size > signal_coords.shape[1]:
signal_coords = orthonormal_transform(vector_size,
signal_coords,
)
# Do you want these coordinates to be all positive? This means that
# these coordinates are represented as different magnitudes of
# activation
if all_pos == 1:
mins = np.abs(np.min(signal_coords, 0))
for voxel_counter in list(range(0, len(mins))):
signal_coords[:, voxel_counter] += mins[voxel_counter]
# Bound the value to have a max of 1 so that the signal magnitude is more interpretable
signal_coords /= np.max(signal_coords)
# Determine the size of the signal
signal_coords *= signal_magnitude
# Cycle through the runs and generate the data
node_brain = np.zeros([vector_size, nodes, runs], dtype='double') # Preset
for run_counter in list(range(1, runs + 1)):
# Pull out the onsets for this participant (reload it each time to deal with copying issues)
onsets_runs = np.load(timing_path + 'sub-' + ppt + '.npy')
onsets_run = onsets_runs[run_counter - 1]
# What is the original max duration of the onsets
max_duration_orig = np.max([np.max(onsets_run[x]) for x in range(onsets_run.size)])
max_duration_orig += 10 # Add some wiggle room
# Do you want to randomise the onsets (so that the events do not have a
# fixed order)
if rand == 1:
onsets_run = randomise_timing(onsets_runs[run_counter - 1],
)
# If you want to use different timing then take the order of the data
# and then create a new timecourse
onsets_run = extra_isi(onsets_run,
added_isi,
)
# If necessary, remove all the values greater than the max
if restrict_overall_duration == 1:
onsets_run = [onsets_run[x][onsets_run[x] < max_duration_orig] for x in range(
onsets_run.size)]
onsets_run = np.asarray(onsets_run)
# Determine how long the simulated time course is by finding the max of maxs
last_event = 0
for node_counter in range(len(onsets_run)):
if len(onsets_run[node_counter]) > 0 and onsets_run[node_counter].max() > last_event:
last_event = onsets_run[node_counter].max()
# How long should you model
duration = int(last_event + 10) # Add a decay buffer
# Preset brain size
brain_signal = np.zeros([2, int(duration / tr_duration)], dtype='double')
stimfunc_all = []
for node_counter in list(range(0, nodes)):
# Preset the signal
signal_pattern = np.ones(vector_size)
# Take the coordinates from the signal template
for coord_counter in list(range(0, signal_coords.shape[1])):
signal_pattern[coord_counter] = signal_coords[node_counter,
coord_counter]
onsets_node = onsets_run[node_counter]
# Only do it if there are onsets
if len(onsets_node) > 0:
# Create the time course for the signal to be generated
stimfunc = sim.generate_stimfunction(onsets=onsets_node,
event_durations=event_durations,
total_time=duration,
temporal_resolution=temporal_res,
)
# Aggregate the timecourse
if len(stimfunc_all) == 0:
stimfunc_all = np.zeros((len(stimfunc), vector_size))
for voxel_counter in list(range(0, vector_size)):
stimfunc_all[:, voxel_counter] = np.asarray(
stimfunc).transpose() * signal_coords[node_counter, voxel_counter]
else:
# Add these elements together
temp = np.zeros((len(stimfunc), vector_size))
for voxel_counter in list(range(0, vector_size)):
temp[:, voxel_counter] = np.asarray(stimfunc).transpose() * \
signal_coords[node_counter,
voxel_counter]
stimfunc_all += temp
# After you have gone through all the nodes, convolve the HRF and
# stimulation for each voxel
signal_func = sim.convolve_hrf(stimfunction=stimfunc_all,
tr_duration=tr_duration,
temporal_resolution=temporal_res,
)
# Multiply the convolved responses with their magnitudes (after being
# scaled)
for voxel_counter in list(range(0, vector_size)):
# Reset the range of the function to be appropriate for the
# stimfunc
signal_func[:, voxel_counter] *= stimfunc_all[:,
voxel_counter].max()
# Create the noise
noise = np.random.randn(np.prod(signal_func.shape)).reshape(
signal_func.shape) * noise_timecourse
# Combine the signal and the noise
brain = signal_func + noise
# Z score the data
#brain = zscore(brain.astype('float'), 0)
# Loop through the nodes
for node in list(range(0, nodes)):
node_trs = onsets_run[node]
if len(node_trs) > 0:
temp = np.zeros((vector_size, len(node_trs) - 1))
for tr_counter in list(range(1, len(node_trs))):
# When does it onset (first TR is zero so minus 1)
onset = int(np.round(node_trs[tr_counter] / tr_duration) +
hrf_lag)
# Add the TR if it is included when considering the hrf lag
if onset < brain.shape[0]:
temp[:, tr_counter - 1] = brain[onset, :]
# Average the TRs
node_brain[:, node, run_counter - 1] = np.mean(temp, 1)
# average the brains across runs
node_brain = np.mean(node_brain, 2)
return node_brain
