def run():
# download to this folder
current_folder = os.sep.join(__file__.split(os.sep)[:-1])
dpc_demo_data_path = os.path.join(current_folder, 'SOFC')
if not os.path.exists(dpc_demo_data_path):
zip_file_url = 'https://www.dropbox.com/s/963c4ymfmbjg5dm/SOFC.zip?dl=1'
import download
download.run()
# download_zip(zip_file_url, current_folder)
# 1. Set parameters
start_point = [1, 0]
first_image = 1
pixel_size = (55, 55)
focus_to_det = 1.46e6
scan_xstep = 0.1
scan_ystep = 0.1
scan_rows = 121
scan_cols = 121
energy = 19.5
roi = None
padding = 0
weighting = 1.
bad_pixels = None
solver = 'Nelder-Mead'
images = ImageSequence(dpc_demo_data_path + "/*.tif")
img_size = images[0].shape
ref_image = np.ones(img_size)
scale = True
negate = True
print('running dpc')
# 2. Use dpc.dpc_runner
phase, amplitude = dpc.dpc_runner(
ref_image, images, start_point, pixel_size, focus_to_det, scan_rows,
scan_cols, scan_xstep, scan_ystep, energy, padding, weighting, solver,
roi, bad_pixels, negate, scale)
# 3. Save intermediate and final results
print('saving dpc output to disk in --> %s' % current_folder)
scipy.misc.imsave(os.path.join(current_folder, 'phase.jpg'), phase)
np.savetxt(os.path.join(current_folder, 'phase.txt'), phase)
scipy.misc.imsave(os.path.join(current_folder, 'amplitude.jpg'), amplitude)
np.savetxt(os.path.join(current_folder, 'amplitude.txt'), amplitude)
def test_dpc_end_to_end():
"""
Integrated test for DPC based on dpc_runner.
"""
start_point = [1, 0]
pixel_size = (55, 55)
focus_to_det = 1.46e6
scan_rows = 2
scan_cols = 2
scan_xstep = 0.1
scan_ystep = 0.1
energy = 19.5
roi = None
padding = 0
weighting = 1
bad_pixels = None
solver = 'Nelder-Mead'
img_size = (40, 40)
scale = True
negate = True
num_imgs = scan_rows * scan_cols
ref_image = np.ones(img_size)
image_sequence = np.ones((num_imgs, img_size[0], img_size[1]))
# test the one-shot API
phase, amplitude = dpc.dpc_runner(ref_image, image_sequence, start_point,
pixel_size, focus_to_det, scan_rows,
scan_cols, scan_xstep, scan_ystep,
energy, padding, weighting, solver, roi,
bad_pixels, negate, scale)
# get the generator
gen = dpc.lazy_dpc(ref_image, image_sequence, start_point, scan_rows,
scan_cols, solver, roi, bad_pixels)
for partial_results in gen:
pass
phi, a = dpc.reconstruct_phase_from_partial_info(partial_results, energy,
scan_xstep, scan_ystep,
pixel_size[0],
focus_to_det, negate,
scale, padding, weighting)
assert_array_almost_equal(phi, np.zeros((scan_rows, scan_cols)))
assert_array_almost_equal(a, np.ones((scan_rows, scan_cols)))
# make sure we are getting the same results from the generator and the
# one-shot API. We better, since the one-shot API wraps the generator!
assert_array_almost_equal(phase, phi)
assert_array_almost_equal(amplitude, a)
# test to make sure I can do half of the image sequence
first_half_gen = dpc.lazy_dpc(ref_image, image_sequence[:num_imgs // 2],
start_point, scan_rows, scan_cols, solver,
roi, bad_pixels)
for first_half_partial_results in first_half_gen:
pass
second_half_gen = dpc.lazy_dpc(ref_image,
image_sequence[num_imgs // 2:],
start_point,
scan_rows,
scan_cols,
solver,
roi,
bad_pixels,
dpc_state=first_half_partial_results)
for second_half_partial_results in second_half_gen:
pass
phi_partial, a_partial = dpc.reconstruct_phase_from_partial_info(
second_half_partial_results, energy, scan_xstep, scan_ystep,
pixel_size[0], focus_to_det, negate, scale, padding, weighting)
assert_array_almost_equal(phi_partial, phi)
assert_array_almost_equal(a_partial, a)
def test_dpc_end_to_end():
"""
Integrated test for DPC based on dpc_runner.
"""
start_point = [1, 0]
pixel_size = (55, 55)
focus_to_det = 1.46e6
scan_rows = 2
scan_cols = 2
scan_xstep = 0.1
scan_ystep = 0.1
energy = 19.5
roi = None
padding = 0
weighting = 1
bad_pixels = None
solver = 'Nelder-Mead'
img_size = (40, 40)
scale = True
negate = True
num_imgs = scan_rows * scan_cols
ref_image = np.ones(img_size)
image_sequence = np.ones((num_imgs, img_size[0], img_size[1]))
# test the one-shot API
phase, amplitude = dpc.dpc_runner(
ref_image, image_sequence, start_point, pixel_size, focus_to_det,
scan_rows, scan_cols, scan_xstep, scan_ystep, energy, padding,
weighting, solver, roi, bad_pixels, negate, scale)
# get the generator
gen = dpc.lazy_dpc(ref_image, image_sequence, start_point, scan_rows,
scan_cols, solver, roi, bad_pixels)
for partial_results in gen:
pass
phi, a = dpc.reconstruct_phase_from_partial_info(
partial_results, energy, scan_xstep, scan_ystep, pixel_size[0],
focus_to_det, negate, scale, padding, weighting
)
assert_array_almost_equal(phi, np.zeros((scan_rows, scan_cols)))
assert_array_almost_equal(a, np.ones((scan_rows, scan_cols)))
# make sure we are getting the same results from the generator and the
# one-shot API. We better, since the one-shot API wraps the generator!
assert_array_almost_equal(phase, phi)
assert_array_almost_equal(amplitude, a)
# test to make sure I can do half of the image sequence
first_half_gen = dpc.lazy_dpc(ref_image, image_sequence[:num_imgs//2],
start_point, scan_rows, scan_cols, solver,
roi, bad_pixels)
for first_half_partial_results in first_half_gen:
pass
second_half_gen = dpc.lazy_dpc(ref_image, image_sequence[num_imgs//2:],
start_point, scan_rows, scan_cols, solver,
roi, bad_pixels,
dpc_state=first_half_partial_results)
for second_half_partial_results in second_half_gen:
pass
phi_partial, a_partial = dpc.reconstruct_phase_from_partial_info(
second_half_partial_results, energy, scan_xstep, scan_ystep,
pixel_size[0], focus_to_det, negate, scale, padding, weighting
)
assert_array_almost_equal(phi_partial, phi)
assert_array_almost_equal(a_partial, a)
