def test_glm_mrss():
img = nib.load(project_path + \
'data/ds114/sub009/BOLD/task002_run001/ds114_sub009_t2r1.nii')
data = img.get_data()
convolved1 = np.loadtxt(project_path + \
'data/ds114/sub009/behav/task002_run001/ds114_sub009_t2r1_conv.txt')
X_matrix = np.ones((len(convolved1), 2))
X_matrix[:, 1] = convolved1
data_2d = np.reshape(data, (-1, data.shape[-1]))
B = npl.pinv(X_matrix).dot(data_2d.T)
B_4d = np.reshape(B.T, img.shape[:-1] + (-1, ))
test_B_4d = glm_beta(data, X_matrix)
# Pick a single voxel to check mrss functiom.
# Calculate actual fitted values, residuals, and MRSS of voxel.
fitted = X_matrix.dot(B_4d[12, 22, 10])
residuals = data[12, 22, 10] - fitted
MRSS = np.sum(residuals**
2) / (X_matrix.shape[0] - npl.matrix_rank(X_matrix))
# Calculate using glm_diagnostics function.
test_MRSS, test_fitted, test_residuals = glm_mrss(test_B_4d, X_matrix,
data)
assert_almost_equal(MRSS, test_MRSS[12, 22, 10])
assert_almost_equal(fitted, test_fitted[12, 22, 10])
assert_almost_equal(residuals, test_residuals[12, 22, 10])
def test_glm_beta():
# Read in the image data.
img = nib.load(project_path + \
'data/ds114/sub009/BOLD/task002_run001/ds114_sub009_t2r1.nii')
data = img.get_data()
# Read in the convolutions.
p = 2
convolved1 = np.loadtxt(project_path + \
'data/ds114/sub009/behav/task002_run001/ds114_sub009_t2r1_conv.txt')
# Create design matrix.
X_matrix = np.ones((len(convolved1), p))
X_matrix[:, 1] = convolved1
# Calculate betas, copied from the exercise.
data_2d = np.reshape(data, (-1, data.shape[-1]))
B = npl.pinv(X_matrix).dot(data_2d.T)
B_4d = np.reshape(B.T, img.shape[:-1] + (-1, ))
# Run function.
test_B_4d = glm_beta(data, X_matrix)
assert_almost_equal(B_4d, test_B_4d)
def test_glm_beta():
# Read in the image data.
img = nib.load(project_path + \
'data/ds114/sub009/BOLD/task002_run001/ds114_sub009_t2r1.nii')
data = img.get_data()
# Read in the convolutions.
p = 2
convolved1 = np.loadtxt(project_path + \
'data/ds114/sub009/behav/task002_run001/ds114_sub009_t2r1_conv.txt')
# Create design matrix.
X_matrix = np.ones((len(convolved1), p))
X_matrix[:, 1] = convolved1
# Calculate betas, copied from the exercise.
data_2d = np.reshape(data, (-1, data.shape[-1]))
B = npl.pinv(X_matrix).dot(data_2d.T)
B_4d = np.reshape(B.T, img.shape[:-1] + (-1,))
# Run function.
test_B_4d = glm_beta(data, X_matrix)
assert_almost_equal(B_4d, test_B_4d)
def test_glm_mrss():
img = nib.load(project_path + \
'data/ds114/sub009/BOLD/task002_run001/ds114_sub009_t2r1.nii')
data = img.get_data()
convolved1 = np.loadtxt(project_path + \
'data/ds114/sub009/behav/task002_run001/ds114_sub009_t2r1_conv.txt')
X_matrix = np.ones((len(convolved1), 2))
X_matrix[:, 1] = convolved1
data_2d = np.reshape(data, (-1, data.shape[-1]))
B = npl.pinv(X_matrix).dot(data_2d.T)
B_4d = np.reshape(B.T, img.shape[:-1] + (-1,))
test_B_4d = glm_beta(data, X_matrix)
# Pick a single voxel to check mrss functiom.
# Calculate actual fitted values, residuals, and MRSS of voxel.
fitted = X_matrix.dot(B_4d[12, 22, 10])
residuals = data[12, 22, 10] - fitted
MRSS = np.sum(residuals**2)/(X_matrix.shape[0] - npl.matrix_rank(X_matrix))
# Calculate using glm_diagnostics function.
test_MRSS, test_fitted, test_residuals = glm_mrss(test_B_4d, X_matrix, data)
assert_almost_equal(MRSS, test_MRSS[12, 22, 10])
assert_almost_equal(fitted, test_fitted[12, 22, 10])
assert_almost_equal(residuals, test_residuals[12, 22, 10])
