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Merge pull request #180 from berkeley-stat159/glm_test2
add glm test
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""" Tests for glm function in glm module | ||
This checks the glm function. | ||
Run at the tests directory with: | ||
nosetests code/utils/tests/test_glm.py | ||
""" | ||
# Loading modules. | ||
import numpy as np | ||
import numpy.linalg as npl | ||
import nibabel as nib | ||
import os | ||
import sys | ||
from numpy.testing import assert_almost_equal, assert_array_equal | ||
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# Add path to functions to the system path. | ||
sys.path.append(os.path.join(os.path.dirname(__file__), "../functions/")) | ||
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# Load our GLM functions. | ||
from glm import glm_beta, glm_mrss | ||
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def test_glm_beta(): | ||
# Read in the image data. | ||
img = nib.load('data/ds114/sub009/BOLD/task002_run001/ds114_sub009_t2r1.nii') | ||
data = img.get_data() | ||
# Read in the convolutions. | ||
p = 2 | ||
convolved1 = np.loadtxt('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 | ||
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# 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,)) | ||
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# Run function. | ||
test_B_4d = glm_beta(data, X_matrix) | ||
assert_almost_equal(B_4d, test_B_4d) | ||
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def test_glm_mrss(): | ||
img = nib.load('data/ds114/sub009/BOLD/task002_run001/ds114_sub009_t2r1.nii') | ||
data = img.get_data() | ||
convolved1 = np.loadtxt('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) | ||
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# 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)) | ||
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# 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]) | ||
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data/ds114/sub009/behav/task002_run001/ds114_sub009_t2r1_conv.txt
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