def test_sigma2fwhm():
# Test from constant
assert_almost_equal(sigma2fwhm(1), 2.3548200)
assert_almost_equal(sigma2fwhm([1, 2, 3]), np.arange(1, 4) * 2.3548200)
assert_almost_equal(fwhm2sigma(2.3548200), 1)
assert_almost_equal(fwhm2sigma(np.arange(1, 4) * 2.3548200), [1, 2, 3])
# direct test fwhm2sigma and sigma2fwhm are inverses of each other
fwhm = np.arange(1.0, 5.0, 0.1)
sigma = np.arange(1.0, 5.0, 0.1)
assert_true(np.allclose(sigma2fwhm(fwhm2sigma(fwhm)), fwhm))
assert_true(np.allclose(fwhm2sigma(sigma2fwhm(sigma)), sigma))
def test_sigma2fwhm():
# Test from constant
assert_almost_equal(sigma2fwhm(1), 2.3548200)
assert_almost_equal(sigma2fwhm([1, 2, 3]), np.arange(1, 4) * 2.3548200)
assert_almost_equal(fwhm2sigma(2.3548200), 1)
assert_almost_equal(fwhm2sigma(np.arange(1, 4) * 2.3548200), [1, 2, 3])
# direct test fwhm2sigma and sigma2fwhm are inverses of each other
fwhm = np.arange(1.0, 5.0, 0.1)
sigma = np.arange(1.0, 5.0, 0.1)
assert np.allclose(sigma2fwhm(fwhm2sigma(fwhm)), fwhm)
assert np.allclose(fwhm2sigma(sigma2fwhm(sigma)), sigma)
def __init__(self, dimension_selection, slice_number, smoothing_sigma,
gauss_sigma, threshold):
# Image parameters
self.Number_of_images = 0
self.dimension_option = dimension_selection
self.slice = slice_number
#Smoothing parameters
self.fwhm = processing.sigma2fwhm(smoothing_sigma)
self.gauss = gauss_sigma
#Data parameters
self.threshold = threshold
#Data dimension options based on representation selection
self.reshape_parm = (0, 0)
if self.dimension_option == '2D':
dim_1 = 176
dim_2 = 208
self.image_dimension = dim_1 * dim_2
self.dim = [dim_1, dim_2]
elif self.dimension_option == '3D':
dim_1 = 176
dim_2 = 208
dim_3 = 176
self.image_dimension = dim_1 * dim_2 * dim_3
self.dim = [dim_1, dim_2, dim_3]
else:
self.dimension_option = '2D' # set a default option
dim_1 = 176
dim_2 = 208
self.image_dimension = dim_1 * dim_2
self.dim = [dim_1, dim_2]
