removed unnecessary part_vec from make_dataset
@@ -36,11 +36,9 @@ def make_design(n_cond, n_part):
def make_dataset(model, theta, cond_vec, n_channel=30, n_sim=1,
- signal=1, noise=1, signal_cov_channel=None, noise_cov_channel=None,
- noise_cov_trial=None, part_vec=None, use_exact_signal=False,
- use_same_signal=False):
+ signal=1, noise=1, signal_cov_channel=None, noise_cov_channel=None,noise_cov_trial=None, use_exact_signal=False, use_same_signal=False):
"""
- Simulates a fMRI-style data set with a set of partitions
+ Simulates a fMRI-style data set
Args:
model (pyrsa.Model): the model from which to generate data
@@ -54,11 +52,9 @@ def make_dataset(model, theta, cond_vec, n_channel=30, n_sim=1,
(default = 1)
signal (float): Signal variance (multiplied by predicted G)
signal_cov_channel(numpy.ndarray): Covariance matrix of signal across channels
- noise (float) Noise variance
+ noise (float): Noise variance
noise_cov_channel(numpy.ndarray): Covariance matrix of noise (default = identity)
noise_cov_trial(numpy.ndarray): Covariance matrix of noise across trials
- part_vec (numpy.ndarray): optional partition vector if within-partition
- covariance is specified
use_exact_signal (bool): Makes the signal so that G is exactly as
specified (default: False)
use_same_signal (bool): Uses the same signal for all simulation
@@ -177,4 +173,5 @@ def make_signal(G, n_channel,make_exact=False, chol_channel=None):
lam = np.sqrt(lam)
chol_G = V * lam.reshape((1, V.shape[1]))
true_U = (chol_G @ true_U)
+ print('d')
return true_U
\ No newline at end of file
