Merge pull request #187 from berkeley-stat159/Tests

Merge tests into master branch

code/utils/tests/test_glm.py

@@ -0,0 +1,71 @@
+""" 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
+
+
+# Add path to functions to the system path.
+sys.path.append(os.path.join(os.path.dirname(__file__), "../functions/"))
+
+
+# Load our GLM functions. 
+from glm import glm_beta, glm_mrss
+
+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
+
+    # 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('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)
+
+    # 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])
+
+
+
+
+

code/utils/tests/test_smoothing.py

@@ -0,0 +1,38 @@
+""" Tests for smoothvoxels in smooth module
+Run at the tests directory with:
+    nosetests code/utils/tests/test_smoothing.py
+"""
+
+import os
+import sys
+import numpy as np
+import itertools
+import scipy.ndimage
+from scipy.ndimage.filters import gaussian_filter
+import matplotlib.pyplot as plt
+import nibabel as nib
+from numpy.testing import assert_almost_equal
+from nose.tools import assert_not_equals
+
+
+# Add path to functions to the system path.
+sys.path.append(os.path.join(os.path.dirname(__file__), "../functions/"))
+
+# Load smoothing function.
+from smoothing import smoothing
+
+def test_smooth():
+    # Read in the image data.
+    img = nib.load('data/ds114/sub009/BOLD/task002_run001/ds114_sub009_t2r1.nii')
+    data = img.get_data()
+
+    # Run the smoothing function with sigma 0 at time 12
+    non_smoothed_data = smoothing(data, 0, 12)
+
+	# assert that data at time 12 and non_smoothed_data are equal since sigma = 0
+    assert_almost_equal(data[..., 12], non_smoothed_data)
+
+	# Run the smoothvoxels function with sigma 5 at time 100
+    smoothed_data = smoothing(data, 5, 100)
+	# assert that data at time 16 and smoothed_data are not equal
+    assert_not_equals(data[..., 100].all(), smoothed_data.all())

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