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