def test_centering_function():
# ni -> original shape of the data is (ni, ni)
# n_c -> the image center is (n_c, n_c)
# n -> after the centering function, the array has a shape (n, n)
for (ni, n_c, n) in [
(9, 5, 3),
(9, 5, 4),
]:
arr = np.zeros((ni, ni))
if n % 2 == 1:
arr[n_c - 1:n_c + 2, n_c - 1:n_c + 2] = 1
else:
arr[n_c - 1:n_c + 1, n_c - 1:n_c + 1] = 1
res = center_image(arr, (n_c, n_c), n)
# The print statements below can be commented after we fix the centering issue
# print('Original array')
# print(arr)
# print('Centered array')
# print(res)
assert_equal( is_symmetric(res), True,\
'Validating the centering function for ni={}, n_c={}, n={}'.format(ni, n_c, n))
def test_centering_function_shape():
# ni -> original shape
# n -> result of the centering function
for (ni, n) in [(20, 10), # crop image
(20, 11),
(4, 10), # pad image
(4, 11)]:
data = np.zeros((ni, ni))
res = center_image(data, (ni//2, ni//2), n)
assert_equal( res.shape, (n, n),
'Centering preserves shapes for ni={}, n={}'.format(ni, n))
def test_centering_function_shape():
# ni -> original shape
# n -> result of the centering function
for (ni, n) in [
(20, 10), # crop image
(20, 11),
(4, 10), # pad image
(4, 11)
]:
data = np.zeros((ni, ni))
res = center_image(data, (ni // 2, ni // 2), n)
assert_equal(
res.shape, (n, n),
'Centering preserves shapes for ni={}, n={}'.format(ni, n))
def test_centering_function():
# ni -> original shape of the data is (ni, ni)
# n_c -> the image center is (n_c, n_c)
# n -> after the centering function, the array has a shape (n, n)
for (ni, n_c, n) in [(9, 5, 3),
(9, 5, 4),
]:
arr = np.zeros((ni, ni))
if n % 2 == 1:
arr[n_c-1:n_c+2,n_c-1:n_c+2] = 1
else:
arr[n_c-1:n_c+1,n_c-1:n_c+1] = 1
res = center_image(arr, (n_c, n_c), n)
# The print statements below can be commented after we fix the centering issue
# print('Original array')
# print(arr)
# print('Centered array')
# print(res)
assert_equal( is_symmetric(res), True,\
'Validating the centering function for ni={}, n_c={}, n={}'.format(ni, n_c, n))
filename = 'data/Xenon_ATI_VMI_800_nm_649x519.tif'
# Name the output files
name = filename.split('.')[0].split('/')[1]
output_image = name + '_inverse_Abel_transform_HansenLaw.png'
output_text = name + '_speeds_HansenLaw.dat'
output_plot = name + '_comparison_HansenLaw.pdf'
# Step 1: Load an image file as a numpy array
print('Loading ' + filename)
#im = np.loadtxt(filename)
im = plt.imread(filename)
(rows, cols) = np.shape(im)
print('image size {:d}x{:d}'.format(rows, cols))
im = center_image(im, (340, 245), n=rows)
# Step 2: perform the Hansen & Law transform!
print('Performing Hansen and Law inverse Abel transform:')
recon = iabel_hansenlaw(im, verbose=True)
speeds, r = calculate_speeds(recon)
# save the transform in 8-bit format:
scipy.misc.imsave(output_image, recon)
# save the speed distribution
np.savetxt(output_text, speeds)
## Finally, let's plot the data
filename = 'data/Xenon_ATI_VMI_800_nm_649x519.tif'
# Name the output files
name = filename.split('.')[0].split('/')[1]
output_image = name + '_inverse_Abel_transform_HansenLaw.png'
output_text = name + '_speeds_HansenLaw.dat'
output_plot = name + '_comparison_HansenLaw.pdf'
# Step 1: Load an image file as a numpy array
print('Loading ' + filename)
#im = np.loadtxt(filename)
im = plt.imread(filename)
(rows,cols) = np.shape(im)
print ('image size {:d}x{:d}'.format(rows,cols))
im = center_image (im, (340,245), n=rows)
# Step 2: perform the Hansen & Law transform!
print('Performing Hansen and Law inverse Abel transform:')
recon = iabel_hansenlaw (im, verbose=True)
speeds, r = calculate_speeds(recon)
# save the transform in 8-bit format:
scipy.misc.imsave(output_image,recon)
# save the speed distribution
np.savetxt(output_text,speeds)
## Finally, let's plot the data
