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 len(stimfunction) == duration * 1000, "stimfunction incorrect " \
"length"
eventNumber = np.sum(event_durations * len(onsets)) * 1000
assert np.sum(stimfunction) == eventNumber, "Event number"
# Create the signal function
signal_function = sim.double_gamma_hrf(
stimfunction=stimfunction,
tr_duration=tr_duration,
)
assert len(signal_function) == len(stimfunction) / (tr_duration * 1000), \
"The length did not change"
onsets = [10]
stimfunction = sim.generate_stimfunction(
onsets=onsets,
event_durations=event_durations,
total_time=duration,
)
signal_function = sim.double_gamma_hrf(
stimfunction=stimfunction,
tr_duration=tr_duration,
)
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.double_gamma_hrf(
stimfunction=stimfunction,
tr_duration=tr_duration,
)
# Convolve the HRF with the stimulus sequence
signal = sim.apply_signal(
signal_function=signal_function,
volume_static=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_static=volume,
)
assert np.any(signal == signal_magnitude), "The stimfunction is not binary"
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,
tr_duration=tr_duration,
)
assert len(stimfunction) == duration / tr_duration, "stimfunction incorrect " \
"length"
assert np.sum(stimfunction) == np.sum(event_durations * len(onsets)) / \
tr_duration, "Event number"
# Create the signal function
signal_function = sim.double_gamma_hrf(stimfunction=stimfunction,
)
assert len(signal_function) == len(stimfunction), "The length did not change"
onsets = [10]
stimfunction = sim.generate_stimfunction(onsets=onsets,
event_durations=event_durations,
total_time=duration,
tr_duration=tr_duration,
)
signal_function = sim.double_gamma_hrf(stimfunction=stimfunction,
)
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.double_gamma_hrf(stimfunction=stimfunction,
tr_duration=tr_duration,
)
# Convolve the HRF with the stimulus sequence
signal = sim.apply_signal(signal_function=signal_function,
volume_static=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_static=volume,
)
assert np.any(signal == signal_magnitude), "The stimfunction is not binary"
def test_generate_noise():
dimensions = np.array([64, 64, 36]) # What is the size of the brain
feature_size = [2]
feature_type = ['cube']
feature_coordinates = np.array([[32, 32, 18]])
signal_magnitude = [30]
# Generate a volume representing the location and quality of the signal
volume_static = 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,
tr_duration=tr_duration,
)
signal_function = sim.double_gamma_hrf(stimfunction=stimfunction, )
# Convolve the HRF with the stimulus sequence
signal = sim.apply_signal(
signal_function=signal_function,
volume_static=volume_static,
)
# Create the noise volumes (using the default parameters)
noise = sim.generate_noise(
dimensions=dimensions,
stimfunction=stimfunction,
tr_duration=tr_duration,
)
assert signal.shape == noise.shape, "The dimensions of signal " \
"and noise the same"
Z_noise = sim._generate_noise_temporal(stimfunction, tr_duration, 1)
noise = sim._generate_noise_temporal(stimfunction, tr_duration, 0)
assert np.std(Z_noise) < np.std(noise), "Z scoring is not working"
# Combine the signal and the noise
volume = signal + noise
assert np.std(signal) < np.std(noise), "Noise was not created"
noise = sim.generate_noise(dimensions=dimensions,
stimfunction=stimfunction,
tr_duration=tr_duration,
noise_strength=[0, 0, 0])
assert np.sum(noise) == 0, "Noise strength could not be manipulated"
assert np.std(noise) == 0, "Noise strength could not be manipulated"
plt.show()
# 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,
)
stimfunction_B = sim.generate_stimfunction(onsets=onsets_B,
event_durations=event_durations,
total_time=duration,
)
# Convolve the HRF with the stimulus sequence
signal_function_A = sim.double_gamma_hrf(stimfunction=stimfunction_A,
tr_duration=tr_duration,
)
signal_function_B = sim.double_gamma_hrf(stimfunction=stimfunction_B,
tr_duration=tr_duration,
)
# Multiply the HRF timecourse with the signal
signal_A = sim.apply_signal(signal_function=signal_function_A,
volume_static=volume_static_A,
)
signal_B = sim.apply_signal(signal_function=signal_function_B,
volume_static=volume_static_B,
)
feature_type=feature_type,
feature_size=feature_size,
signal_magnitude=signal_magnitude,
)
# Create the time course for the signal to be generated
stimfunction_cond = sim.generate_stimfunction(onsets=onsets[cond],
event_durations=
event_durations,
total_time=duration,
weights=weights[cond],
)
# Convolve the HRF with the stimulus sequence
signal_function = sim.double_gamma_hrf(stimfunction=stimfunction_cond,
tr_duration=tr_duration,
)
# Multiply the HRF timecourse with the signal
signal_cond = sim.apply_signal(signal_function=signal_function,
volume_static=volume_static,
)
# Concatenate all the signal and function files
if cond == 0:
stimfunction = stimfunction_cond
signal = signal_cond
else:
stimfunction = list(np.add(stimfunction, stimfunction_cond))
signal += signal_cond
def test_generate_noise():
dimensions = np.array([64, 64, 36]) # What is the size of the brain
feature_size = [2]
feature_type = ['cube']
feature_coordinates = np.array(
[[32, 32, 18]])
signal_magnitude = [30]
# Generate a volume representing the location and quality of the signal
volume_static = 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,
tr_duration=tr_duration,
)
signal_function = sim.double_gamma_hrf(stimfunction=stimfunction,
)
# Convolve the HRF with the stimulus sequence
signal = sim.apply_signal(signal_function=signal_function,
volume_static=volume_static,
)
# Create the noise volumes (using the default parameters)
noise = sim.generate_noise(dimensions=dimensions,
stimfunction=stimfunction,
tr_duration=tr_duration,
)
assert signal.shape == noise.shape, "The dimensions of signal " \
"and noise the same"
Z_noise = sim._generate_noise_temporal(stimfunction, tr_duration, 1)
noise = sim._generate_noise_temporal(stimfunction, tr_duration, 0)
assert np.std(Z_noise) < np.std(noise), "Z scoring is not working"
# Combine the signal and the noise
volume = signal + noise
assert np.std(signal) < np.std(noise), "Noise was not created"
noise = sim.generate_noise(dimensions=dimensions,
stimfunction=stimfunction,
tr_duration=tr_duration,
noise_strength=[0, 0, 0]
)
assert np.sum(noise) == 0, "Noise strength could not be manipulated"
assert np.std(noise) == 0, "Noise strength could not be manipulated"
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.double_gamma_hrf(
stimfunction=stimfunction,
tr_duration=tr_duration,
)
# Convolve the HRF with the stimulus sequence
signal = sim.apply_signal(
signal_function=signal_function,
volume_static=volume,
)
# Generate the mask of the signal
mask = sim.mask_brain(signal, mask_threshold=0.1)
assert min(mask[mask > 0]) > 0.1, "Mask thresholding 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,
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,
mask=mask,
noise_dict={
'temporal_noise': 0,
'sfnr': 10000
},
)
temporal_noise = np.std(noise[mask[:, :, :, 0] > 0], 1).mean()
assert temporal_noise <= 0.1, "Noise strength could not be manipulated"
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.double_gamma_hrf(
stimfunction=stimfunction_A,
tr_duration=tr_duration,
temporal_resolution=temporal_res,
)
signal_function_B = sim.double_gamma_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_static=volume_static_A,
)
# Visualize the signal that was generated for condition A
fig = plt.figure()
sim.plot_brain(fig, volume_static_A)
plt.show()
# 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, tr_duration=tr_duration
)
stimfunction_B = sim.generate_stimfunction(
onsets=onsets_B, event_durations=event_durations, total_time=duration, tr_duration=tr_duration
)
# Convolve the HRF with the stimulus sequence
signal_function_A = sim.double_gamma_hrf(stimfunction=stimfunction_A)
signal_function_B = sim.double_gamma_hrf(stimfunction=stimfunction_B)
# Multiply the HRF timecourse with the signal
signal_A = sim.apply_signal(signal_function=signal_function_A, volume_static=volume_static_A)
signal_B = sim.apply_signal(signal_function=signal_function_B, volume_static=volume_static_B)
# Combine the signals from the two conditions
signal = signal_A + signal_B
# Combine the stim functions
stimfunction = list(np.add(stimfunction_A, stimfunction_B))
# Create the noise volumes (using the default parameters
stimfunction_A = sim.generate_stimfunction(
onsets=onsets_A,
event_durations=event_durations,
total_time=duration,
tr_duration=tr_duration,
)
stimfunction_B = sim.generate_stimfunction(
onsets=onsets_B,
event_durations=event_durations,
total_time=duration,
tr_duration=tr_duration,
)
# Convolve the HRF with the stimulus sequence
signal_function_A = sim.double_gamma_hrf(stimfunction=stimfunction_A, )
signal_function_B = sim.double_gamma_hrf(stimfunction=stimfunction_B, )
# Multiply the HRF timecourse with the signal
signal_A = sim.apply_signal(
signal_function=signal_function_A,
volume_static=volume_static_A,
)
signal_B = sim.apply_signal(
signal_function=signal_function_B,
volume_static=volume_static_B,
)
# Combine the signals from the two conditions
# 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,
)
stimfunction_B = sim.generate_stimfunction(
onsets=onsets_B,
event_durations=event_durations,
total_time=duration,
)
# Convolve the HRF with the stimulus sequence
signal_function_A = sim.double_gamma_hrf(
stimfunction=stimfunction_A,
tr_duration=tr_duration,
)
signal_function_B = sim.double_gamma_hrf(
stimfunction=stimfunction_B,
tr_duration=tr_duration,
)
# Multiply the HRF timecourse with the signal
signal_A = sim.apply_signal(
signal_function=signal_function_A,
volume_static=volume_static_A,
)
signal_B = sim.apply_signal(
signal_function=signal_function_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 = 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.double_gamma_hrf(stimfunction=stimfunction,
tr_duration=tr_duration,
)
# Convolve the HRF with the stimulus sequence
signal = sim.apply_signal(signal_function=signal_function,
volume_static=volume,
)
# Generate the mask of the signal
mask = sim.mask_brain(signal, mask_threshold=0.1)
assert min(mask[mask > 0]) > 0.1, "Mask thresholding did not work"
# Create the noise volumes (using the default parameters)
noise = sim.generate_noise(dimensions=dimensions,
stimfunction=stimfunction,
tr_duration=tr_duration,
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=stimfunction,
tr_duration=tr_duration,
mask=mask,
noise_dict={'overall': 0},
)
assert np.sum(noise) == 0, "Noise strength could not be manipulated"
assert np.std(noise) == 0, "Noise strength could not be manipulated"
