def test_sample_condition_3():
""" Test the experimental condition sampling -- oversampling=10
"""
condition = ([1, 20, 36.5], [2, 2, 2], [1, 1, 1])
frame_times = np.linspace(0, 49, 50)
reg, rf = _sample_condition(condition, frame_times, oversampling=10,
min_onset=0)
assert_almost_equal(reg.sum(), 60.)
assert reg[10] == 1
assert reg[380] == 1
assert reg[210] == 1
assert np.sum(reg > 0) == 60
# check robustness to non-int oversampling
reg_, rf_ = _sample_condition(condition, frame_times, oversampling=10.,
min_onset=0)
assert_almost_equal(reg, reg_)
def test_sample_condition_1():
""" Test that the experimental condition is correctly sampled
"""
condition = ([1, 20, 36.5], [0, 0, 0], [1, 1, 1])
frame_times = np.linspace(0, 49, 50)
reg, rf = _sample_condition(condition, frame_times, oversampling=1,
min_onset=0)
assert reg.sum() == 3
assert reg[1] == 1
assert reg[20] == 1
assert reg[37] == 1
reg, rf = _sample_condition(condition, frame_times, oversampling=1)
assert reg.sum() == 3
assert reg[25] == 1
assert reg[44] == 1
assert reg[61] == 1
def test_sample_condition_5():
""" Test the experimental condition sampling -- negative onset
"""
condition = ([-10, 0, 36.5], [2, 2, 2], [1., -1., 5.])
frame_times = np.linspace(0, 49, 50)
reg, rf = _sample_condition(condition, frame_times, oversampling=1)
assert reg.sum() == 10
assert reg[14] == 1.
assert reg[24] == -1.
assert reg[61] == 5.
def test_sample_condition_7():
""" Test the experimental condition sampling -- different onsets, overlapping offsets
"""
condition = ([0, 10, 20], [11, 1, 1], [1., 1., 1.])
frame_times = np.linspace(0, 49, 50)
reg, rf = _sample_condition(condition, frame_times, oversampling=1)
assert reg.sum() == 13
assert reg[24] == 1.
assert reg[34] == 2.
assert reg[61] == 0.
def test_sample_condition_6():
""" Test the experimental condition sampling -- overalapping onsets, different durations
"""
condition = ([0, 0, 10], [1, 2, 1], [1., 1., 1.])
frame_times = np.linspace(0, 49, 50)
reg, rf = _sample_condition(condition, frame_times, oversampling=1)
assert reg.sum() == 4
assert reg[24] == 2.
assert reg[34] == 1.
assert reg[61] == 0.
def test_sample_condition_2():
""" Test the experimental condition sampling -- onset = 0
"""
condition = ([0, 20, 36.5], [2, 2, 2], [1, 1, 1])
frame_times = np.linspace(0, 49, 50)
reg, rf = _sample_condition(condition, frame_times, oversampling=1,
min_onset=- 10)
assert reg.sum() == 6
assert reg[10] == 1
assert reg[48] == 1
assert reg[31] == 1
def create_design_matrix(tr,
frame_times,
events,
hrf_model='kay',
hrf_idx=None):
""" Creates a design matrix based on a HRF from Kendrick Kay's set
or a default one from Nilearn. """
# This is to keep oversampling consistent across hrf_models
hrf_oversampling = 10
design_oversampling = tr / (0.1 / hrf_oversampling)
if hrf_model != 'kay': # just use Nilearn!
return make_first_level_design_matrix(frame_times,
events,
drift_model=None,
min_onset=0,
oversampling=design_oversampling,
hrf_model=hrf_model)
if hrf_model == 'kay':
if hrf_idx is None: # 20 different DMs (based on different HRFs)
to_iter = range(HRFS_HR.shape[1])
else: # use the supplied HRF idx (e.g., 5)
to_iter = [hrf_idx]
dms = [] # will store all design matrices
for hrf_idx in to_iter: # iterate across all HRFs
hrf = HRFS_HR[:, hrf_idx]
# scale HRF to have the same max as the glover HRF
# makes comparison easier
hrf /= (hrf.max() / 0.249007)
# Get info
trial_type, onset, duration, modulation = check_events(events)
# Pre-allocate design matrix; note: columns are alphabetically sorted
X = np.zeros((frame_times.size, np.unique(trial_type).size))
uniq_trial_types = np.unique(trial_type) # this is sorted
# Create separate regressor for each unique trial type
# Code copied from Nilearn glm module
for i, condition in enumerate(uniq_trial_types):
condition_mask = (trial_type == condition)
exp_condition = (onset[condition_mask],
duration[condition_mask],
modulation[condition_mask])
# Create high resolution regressor/frame times
hr_regressor, hr_frame_times = _sample_condition(
exp_condition, frame_times, design_oversampling, 0)
# Convolve with HRF and downsample
conv_reg = np.convolve(hr_regressor, hrf)[:hr_regressor.size]
# linear interpolation for now ...
f = interp1d(hr_frame_times, conv_reg)
X[:, i] = f(frame_times).T
# Note to self: do not scale such that max(X, axis=0) is 1, because you'll lose info
# about predictor variance!
dm = pd.DataFrame(X, columns=uniq_trial_types, index=frame_times)
dm['constant'] = 1 # and intercept/constant
dms.append(dm)
if len(dms) == 1:
# Just return single design matrix
dms = dms[0]
return dms
