def glm(image_ts, regressors):
'''
image_ts should be a matrix of the form (t x v) where t is the no. of timepoints
and v is the no. of voxels
regressors should be a matrix of the form (t x n) where t is the no. of timepoints
and n is the number of regressors
------------------
beta output is a serie of beta vector of the form (n x v).
'''
Y = image_ts
if len(regressors.shape) == 1:
X = np.expand_dims(regressors, axis=1)
else:
X = regressors
glm_dist = GeneralLinearModel(X)
glm_dist.transform(Y)
beta = glm_dist.get_beta()
return beta
def glm(image_ts, regressors):
'''
image_ts should be a matrix of the form (t x v) where t is the no. of timepoints
and v is the no. of voxels
regressors should be a matrix of the form (t x n) where t is the no. of timepoints
and n is the number of regressors
------------------
beta output is a serie of beta vector of the form (n x v).
'''
Y = image_ts
if len(regressors.shape) == 1:
X = np.expand_dims(regressors, axis=1)
else:
X = regressors
glm_dist = GeneralLinearModel(X)
glm_dist.transform(Y)
beta = glm_dist.get_beta()
return beta
def global_signal_regression(timeserie, regressor):
#Get timeseries data
Y = timeserie.data.T
X = np.expand_dims(regressor, axis=1)
glm_dist = GeneralLinearModel(X)
glm_dist.transform(Y)
beta_dist = glm_dist.get_beta()
r_signal = np.dot(X, beta_dist)
regressed_s = Y - r_signal
return regressed_s
def global_signal_regression(timeserie, regressor):
#Get timeseries data
Y = timeserie.data.T
X = np.expand_dims(regressor, axis=1)
glm_dist = GeneralLinearModel(X)
glm_dist.transform(Y)
beta_dist = glm_dist.get_beta()
r_signal = np.dot(X, beta_dist)
regressed_s = Y - r_signal
return regressed_s