def test_02_convolve_random(self):
"""Convolve a random image with a large circular Gaussian kernel"""
np.random.seed(0)
image = np.random.uniform(size=(100, 100))
kernel = cpms.circular_gaussian_kernel(1, 10)
expected = scipy.ndimage.gaussian_filter(image, 1)
result = scipy.ndimage.convolve(image, kernel)
self.assertTrue(np.all(np.abs(result - expected) < 0.001))
def test_02_convolve_random(self):
"""Convolve a random image with a large circular Gaussian kernel"""
np.random.seed(0)
image = np.random.uniform(size=(100,100))
kernel = cpms.circular_gaussian_kernel(1, 10)
expected = scipy.ndimage.gaussian_filter(image, 1)
result = scipy.ndimage.convolve(image, kernel)
self.assertTrue(np.all(np.abs(result - expected) < .001))
def test_01_convolve_1(self):
"""The center of a large uniform image, convolved with a Gaussian should not change value"""
image = np.ones((100, 100))
kernel = cpms.circular_gaussian_kernel(1, 3)
result = scipy.ndimage.convolve(image, kernel)
self.assertTrue(np.all(np.abs(result[40:60, 40:60] - 1) < 0.00001))
def test_01_convolve_1(self):
"""The center of a large uniform image, convolved with a Gaussian should not change value"""
image = np.ones((100,100))
kernel = cpms.circular_gaussian_kernel(1, 3)
result = scipy.ndimage.convolve(image, kernel)
self.assertTrue(np.all(np.abs(result[40:60,40:60]-1) < .00001))
