def test_hansenlaw_shape():
n = 21
x = np.ones((n, n), dtype='float32')
recon = iabel_hansenlaw(x, verbose=False)
assert recon.shape == (n, n)
def test_hansenlaw_zeros():
n = 21
x = np.zeros((n, n), dtype='float32')
recon = iabel_hansenlaw(x, verbose=False)
assert_allclose(recon, 0)
def test_hansenlaw_zeros():
n = 21
x = np.zeros((n, n), dtype='float32')
recon = iabel_hansenlaw(x, verbose=False)
assert_allclose(recon, 0)
def test_hansenlaw_shape():
n = 21
x = np.ones((n, n), dtype='float32')
recon = iabel_hansenlaw(x, verbose=False)
assert recon.shape == (n, n)
def test_hansenlaw_zeros():
n = 21
x = np.zeros((n, n), dtype="float32")
recon = iabel_hansenlaw(x, calc_speeds=False, verbose=False)
assert_allclose(recon, 0)
def test_hansenlaw_shape():
n = 21
x = np.ones((n, n), dtype="float32")
recon = iabel_hansenlaw(x, calc_speeds=False, verbose=False)
assert recon.shape == (n, n)
def test_iabel_hansenlaw_gaussian():
"""Check iabel_hansenlaw with a Gaussian function"""
n = 1001 # better with a larger number of points
r_max = 501
ref = GaussianAnalytical(n, r_max, symmetric=True, sigma=200)
tr = np.tile(ref.abel[None, :], (n, 1)) # make a 2D array from 1D
recon = iabel_hansenlaw(tr, ref.dr)
recon1d = recon[n // 2 + n % 2] # centre row
ratio = absolute_ratio_benchmark(ref, recon1d)
assert_allclose(ratio, 1.0, rtol=1e-1, atol=0)
def test_iabel_hansenlaw_gaussian():
"""Check iabel_hansenlaw with a Gaussian function"""
n = 1001 # better with a larger number of points
r_max = 501
ref = GaussianAnalytical(n, r_max, symmetric=True, sigma=200)
tr = np.tile(ref.abel[None, :], (n, 1)) # make a 2D array from 1D
recon = iabel_hansenlaw(tr, ref.dr)
recon1d = recon[n//2 + n%2] # centre row
ratio = absolute_ratio_benchmark(ref, recon1d)
assert_allclose(ratio, 1.0, rtol=1e-1, atol=0)
def test_hansenlaw_gaussian():
"""Check a gaussian solution for HansenLaw"""
n = 51
r_max = 25
ref = GaussianAnalytical(n, r_max, symmetric=True, sigma=10)
tr = np.tile(ref.abel[None, :], (n, 1)) # make a 2D array from 1D
recon = iabel_hansenlaw(tr, calc_speeds=False, verbose=False)
recon1d = recon[n // 2 + n % 2]
ratio = absolute_ratio_benchmark(ref, recon1d)
# this only passes with a relative tolerance of 0.35, someone would
# need to look into it.
assert_allclose(ratio, 1.0, rtol=0.35, atol=0)
if cols % 2 == 0:
print("HL: even pixel width image, re-adjust image centre")
# re-center image based on horizontal and vertical slice profiles
# covering the radial range [300:400] pixels from the center
IM = find_image_center_by_slice(IM, radial_range=(300, 400))[0]
rows, cols = IM.shape # new image size
c2 = cols // 2 # half-image
print('image size {:d}x{:d}'.format(rows, cols))
# Step 2: perform the Hansen & Law transform!
print('Performing Hansen and Law inverse Abel transform:')
AIM = iabel_hansenlaw(IM,
dr=1,
use_quadrants=(True, True, True, True),
vertical_symmetry=False,
horizontal_symmetry=False,
verbose=True)
speeds, r = calculate_speeds(AIM)
# Set up some axes
fig = plt.figure(figsize=(15, 4))
ax1 = plt.subplot(131)
ax2 = plt.subplot(132)
ax3 = plt.subplot(133)
# Plot the raw data
im1 = ax1.imshow(IM, origin='lower', aspect='auto')
fig.colorbar(im1, ax=ax1, fraction=.1, shrink=0.9, pad=0.03)
ax1.set_xlabel('x (pixels)')
# Image center should be mid-pixel, i.e. odd number of colums
if cols%2 == 0:
print ("HL: even pixel width image, re-adjust image centre")
# re-center image based on horizontal and vertical slice profiles
# covering the radial range [300:400] pixels from the center
IM = find_image_center_by_slice (IM, radial_range=(300,400))[0]
rows,cols = IM.shape # new image size
c2 = cols//2 # half-image
print ('image size {:d}x{:d}'.format(rows,cols))
# Step 2: perform the Hansen & Law transform!
print('Performing Hansen and Law inverse Abel transform:')
AIM = iabel_hansenlaw(IM, dr=1, use_quadrants=(True,True,True,True),
vertical_symmetry=False,
horizontal_symmetry=False,
verbose=True)
speeds, r = calculate_speeds (AIM)
# Set up some axes
fig = plt.figure(figsize=(15,4))
ax1 = plt.subplot(131)
ax2 = plt.subplot(132)
ax3 = plt.subplot(133)
# Plot the raw data
im1 = ax1.imshow(IM,origin='lower',aspect='auto')
fig.colorbar(im1,ax=ax1,fraction=.1,shrink=0.9,pad=0.03)
ax1.set_xlabel('x (pixels)')
ax1.set_ylabel('y (pixels)')
